Federal-State Joint Board on Universal Service, Procedures for Self Certifying as a Rural Carrier, CC Docket No. 96-45

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Federal-State Joint Board on Universal Service, Procedures for Self Certifying as a Rural Carrier, CC Docket No. 96-45

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Federal Communications Commission

FCC 99-304

Before the
FEDERAL COMMUNICATIONS COMMISSION
Washington, D.C. 20554

In the Matter of
Federal-State Joint Board on
Universal Service
Forward-Looking Mechanism
for High Cost Support for
Non-Rural LECs

)
)
)

CC Docket No. 96-45
)
)
)
)
)

CC Docket No. 97-160

TENTH REPORT AND ORDER
Adopted: October 21, 1999

Released: November 2, 1999

By the Commission: Commissioner Tristani issuing a separate statement; Commissioner
Furchtgott-Roth dissenting and issuing a statement.
TABLE OF CONTENTS
Paragraph Number
I. INTRODUCTION ..................................................................................................................... 1
II. PROCEDURAL HISTORY ..................................................................................................... 3
A.
Universal Service Order ........................................................................................ 3
B.
1997 Further Notice and the Input Value Development Process .......................... 5
C.
Platform Order and Second Recommended Decision............................................ 8
D.
Inputs Further Notice and Seventh Report and Order......................................... 10
III. ESTIMATING FORWARD-LOOKING ECONOMIC COST ............................................ 12
A.
Designing a Forward-Looking Wireline Local Telephone Network................... 12
B.
Synthesis Model.................................................................................................... 17
1.
Historical Background .............................................................................. 17
2.
Validation.................................................................................................. 21
C.
Selecting Forward-Looking Input Values............................................................. 29
IV. DETERMINING CUSTOMER LOCATIONS ..................................................................... 33

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Background ........................................................................................................... 33
Customer Location Data ....................................................................................... 36
1.
Geocode Data............................................................................................ 36
2.
Road Surrogate Customer Locations ........................................................ 40
3.
Methodology for Estimating the Number of Customer Locations .......... 48

V. OUTSIDE PLANT INPUT VALUES .................................................................................... 63
A.
Introduction........................................................................................................... 63
B.
Engineering Assumptions and Optimizing Routines............................................ 66
1.
Optimization ............................................................................................. 67
2.
T-1 Technology......................................................................................... 77
3.
Distance Calculations and Road Factor .................................................... 80
C.
Cable and Structure Costs..................................................................................... 83
1.
Background ............................................................................................... 83
2.
Nationwide Values.................................................................................... 90
3.
Preliminary Issues Cable Costs................................................................. 94
4.
Cost Per Foot of Cable............................................................................ 101
5.
Cable Fill Factors.................................................................................... 186
6.
Structure Costs ........................................................................................ 209
7.
Plant Mix................................................................................................. 227
D.
Structure Sharing ................................................................................................ 241
1.
Background ............................................................................................. 241
2.
Discussion ............................................................................................... 243
E.
Serving Area Interfaces .......................................................................................250
1.
Background ..............................................................................................250
2.
Discussion ................................................................................................253
F.
Digital Loop Carriers ...........................................................................................269
1.
Background ..............................................................................................269
2.
Discussion ................................................................................................274
VI. SWITCHING AND INTEROFFICE FACILITIES .............................................................286
A.
Introduction..........................................................................................................286
B.
Switch Costs ........................................................................................................290
1.
Background ..............................................................................................290
2.
Discussion ................................................................................................296
C.
Use of the Local Exchange Routing Guide (LERG) ...........................................320
D.
Other Switching and Interoffice Transport Inputs...............................................324
VII. EXPENSES .........................................................................................................................338
A.
Introduction..........................................................................................................338
B.
Plant-Specific Operations Expenses ....................................................................341
1.
Background ..............................................................................................341

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2.
Discussion ................................................................................................346
Common Support Services Expenses ..................................................................377
1.
Background ..............................................................................................377
2.
Discussion ................................................................................................382
GSF Investment ...................................................................................................409
1.
Background ..............................................................................................409
2.
Discussion ...............................................................................................411

VIII. CAPITAL COSTS .............................................................................................................419
A.
Depreciation.........................................................................................................419
1.
Background ..............................................................................................419
2.
Discussion ................................................................................................422
B.
Cost of Capital .....................................................................................................432
C.
Annual Charge Factors ........................................................................................436
IX.

PROPOSED MODIFICATION TO PROCEDURES FOR DISTINGUISHING
RURAL AND NON-RURAL COMPANIES..................................................................440
A.
Background ..........................................................................................................440
B.
Discussion ............................................................................................................443
1.
Annual Filing Requirement .....................................................................447
2.
Statutory Terms........................................................................................450
3.
Identification of Rural Telephone Companies.........................................458

X. PROCEDURAL MATTERS AND ORDERING CLAUSE..................................................460
A.
Final Regulatory Flexibility Analysis .................................................................460
B.
Paperwork Reduction Act Analysis .....................................................................472
C.
Ordering Clauses..................................................................................................473
Appendix A (Input Values)......................................................................................................... A-1
Appendix B (Methodology for Estimating Outside Plant Costs) ................................................B-1
Appendix C (Description of Methodology for Estimating Switching Costs)..............................C-1
Appendix D (Expenses) ............................................................................................................. D-1
Appendix E (Parties Filing Comments and Reply Comments) ...................................................E-1

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I. INTRODUCTION
1.
In the Telecommunications Act of 1996 (1996 Act),360 Congress directed this
Commission and the states to take the steps necessary to establish explicit support mechanisms
to ensure the delivery of affordable telecommunications service to all Americans. In response to
this directive, the Commission has taken action to put in place a universal service support system
that will be sustainable in an increasingly competitive marketplace. In the Universal Service
Order, the Commission adopted a plan for universal service support for rural, insular, and highcost areas to replace longstanding federal support to incumbent local telephone companies with
explicit, competitively neutral federal universal service support mechanisms.361 The
Commission adopted the recommendation of the Federal-State Joint Board on Universal Service
(Joint Board) that an eligible carrier's level of universal service support should be based upon the
forward-looking economic cost of constructing and operating the network facilities and functions
used to provide the services supported by the federal universal service support mechanisms.362
2.
In this Report and Order, we complete the selection of a model to estimate
forward-looking cost by selecting input values for the synthesis model we previously adopted.363
These input values include such things as the cost of switches, cables, and other network
components necessary to provide supported services, in addition to various capital cost
parameters. The forward-looking cost of providing supported services estimated by the model
will be used as part of the Commission's methodology to determine high-cost support for nonrural carriers beginning January 1, 2000. This methodology is established in a companion
360

Pub. L. No. 104-104, 110 Stat. 56. The 1996 Act amended the Communications Act of 1934, 47 U.S.C. §§
151 et. seq. (Act). Hereinafter, all citations to the Act will be to the relevant section of the United States Code
unless otherwise noted.
361

Federal-State Joint Board on Universal Service, Report and Order, CC Docket No. 96-45, 12 FCC Rcd 8776
(1997) (Universal Service Order), as corrected by Federal-State Joint Board on Universal Service, Errata, CC
Docket No. 96-45, FCC 97-157 (rel. June 4, 1997). See also Texas Office of Public Utility Counsel v. FCC and
USA, 183 F.3d 393 (5th Cir. 1999) (affirming in relevant part the Commission's decisions regarding implementation
of the high-cost support system).
362

Universal Service Order, 12 FCC Rcd at 8888, para. 199. The Commission also determined that high-cost
support for rural carriers should continue essentially unchanged and should not be based on forward-looking costs
until 2001, at the earliest. Universal Service Order, 12 FCC Rcd at 8889, para. 203. The Commission adopted the
Joint Board's recommendation to define "rural carriers" as those carriers that meet the statutory definition of a "rural
telephone company." Universal Service Order, 12 FCC Rcd at 8943, para. 310 (citing 47 U.S.C. § 153(37)).
363

Federal-State Joint Board on Universal Service, Fifth Report and Order, CC Docket Nos. 96-45, 97-160, 13
FCC Rcd 21323 (1998) (Platform Order).

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order.364
II. PROCEDURAL HISTORY
A.

Universal Service Order

3.
Prior to the 1996 Act, three explicit interstate universal service programs
provided assistance to small incumbent local exchange carriers (LECs) and LECs that served
rural and high-cost areas: high-cost loop support;365 dial equipment minutes (DEM) weighting;
and the Long-Term Support (LTS) program.366 Other mechanisms also have historically
contributed to maintaining affordable rates in rural areas, including support implicit in
geographic toll rate averaging, intrastate rates, and interstate access charges. Section 254 of the
Communications Act of 1934, as amended, directed the Commission to reform universal service
support mechanisms to ensure that they are compatible with the pro-competitive goals of the
1996 Act, and it required the Commission to institute a Joint Board on universal service and to
implement the recommendations from the Joint Board by May 8, 1997.367 After receiving the
recommendations of the Joint Board on November 7, 1996,368 the Commission adopted the
Universal Service Order on May 7, 1997.
4.
In the Universal Service Order, the Commission adopted a forward-looking
economic cost methodology to calculate support for non-rural carriers. Under this methodology,
a forward-looking economic cost mechanism selected by the Commission, in consultation with
the Joint Board, would be used to estimate non-rural carriers' forward-looking economic cost of
providing the supported services in high-cost areas.369
364

Federal-State Joint Board on Universal Service, Ninth Report and Order and Eighteenth Order on
Reconsideration, CC Docket No. 96-45, FCC 99-306 (adopted Oct. 21, 1999) (Methodology Order).
365

Although the existing high-cost loop fund has historically been known as the "Universal Service Fund," we
will avoid this terminology because of the confusion it may create with the new universal service support
mechanisms that the Commission has created pursuant to section 254 of the Communications Act.
366

The Commission's rules governing these programs are set forth at 47 C.F.R. §§ 36.601 et. seq. (high-cost
loop fund); 47 C.F.R. § 36.125(b) (DEM weighting); and 47 C.F.R. §§ 69.105, 69.502, 69.603(e), 69.612 (LTS).
367

47 U.S.C. § 254(a).

368

Federal-State Joint Board on Universal Service, First Recommended Decision, CC Docket No. 96-45, 12
FCC Rcd 87 (1996) (First Recommended Decision).
369

Universal Service Order, 12 FCC Rcd at 8890, para. 206. In the Universal Service Order, the Commission
concluded that the federal universal service support mechanism would support 25 percent of the difference between
the forward-looking economic cost of providing the supported service and a nationwide revenue benchmark. See
Universal Service Order, 12 FCC Rcd at 8888, para. 201. In response to issues raised by commenters and state
Joint Board members, the Commission referred back to the Joint Board questions related to how federal support

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1997 Further Notice and the Input Value Development Process

5.
In a July 18, 1997 Further Notice of Proposed Rulemaking, the Commission
established a multi-phase plan to develop a federal universal service support mechanism that
would send the correct signals for entry, investment, and innovation.370 The 1997 Further
Notice divided questions related to the cost models into "platform design" issues and "input
value" issues.371 The 1997 Further Notice subdivided each of the platform and input issues into
the following four topic groups: (1) customer location; (2) outside plant design; (3) switching
and interoffice; and (4) general support facilities (GSF) and expense issues.372
6.
After reviewing the comments received in response to the 1997 Further Notice,
the Common Carrier Bureau (Bureau) released two public notices to guide parties wishing to
submit cost models for consideration as the high-cost federal mechanism.373
7.
In addition to the 1997 Further Notice, the Bureau has solicited comment and
allowed interested parties the opportunity to participate in the development of the input values to
be used in the forward-looking cost model. On May 4, 1998, the Bureau released a Public
Notice to update the record on several input-related issues.374 The Bureau also issued data
should be determined. See Federal-State Joint Board on Universal Service, Order and Order on Reconsideration,
CC Docket No. 96-45, 13 FCC Rcd 13749 (1998) (Referral Order). See also Federal-State Joint Board on
Universal Service, Second Recommended Decision, CC Docket No. 96-45, 13 FCC Rcd 24744 (1998) (Second
Recommended Decision).
370

Generally, there is a platform component for each portion of the local exchange network being modeled.
Examples of platform design issues are the establishment of switch capacity limitations and the routing of feeder
and distribution cables. Examples of input values are the price of various network components, their associated
installation and placement costs, and capital cost parameters such as debt-equity ratios. See 1997 Further Notice, 12
FCC Rcd at 18516-18, paras. 17-18.
372

See generally 1997 Further Notice.

373

Guidance to Proponents of Cost Models in Universal Service Proceeding: Switching, Interoffice Trunking,
Signaling, and Local Tandem Investment, Public Notice, CC Docket Nos. 96-45, 97-160, DA 97-1912 (rel. Sept. 3,
1997) (Switching and Transport Public Notice); Guidance to Proponents of Cost Models in Universal Service
Proceeding: Customer Location and Outside Plant, Public Notice, CC Docket Nos. 96-45, 97-160, DA 97-2372
(rel. Nov. 13, 1997) (Customer Location & Outside Plant Public Notice).
374

Common Carrier Bureau Requests Further Comment On Selected Issues Regarding The Forward-Looking
Economic Cost Mechanism For Universal Service, Public Notice, CC Docket Nos. 96-45, 97-160, DA 98-848 (rel.

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requests designed to acquire information that could be useful in determining the final input
values,375 and conducted a series of public workshops designed to elicit further comment from
interested parties in selecting final input values.376 Finally, the Bureau conducted numerous ex
parte meetings with interested parties throughout this proceeding.377
C.

Platform Order and Second Recommended Decision

8.
In the Platform Order, released on October 28, 1998, the Commission adopted the
forward-looking cost model platform to be used in determining federal universal service highcost support for non-rural carriers.378 The model platform that the Commission adopted
combined elements from each of the three models under consideration in this proceeding: (1) the
BCPM, Version 3.0 (BCPM);379 (2) the HAI Model, Version 5.0a (HAI);380 and (3) the Hybrid
Cost Proxy Model, Version 2.5 (HCPM).381 In the Platform Order, the Commission also
specified several issues that would be addressed in the inputs stage of this proceeding. These
issues include: (1) the geocode data source to determine customer locations;382 (2) the road
May 4, 1998) (Inputs Public Notice).
375

Federal-State Joint Board on Universal Service, Order, CC Docket No. 96-45, 12 FCC Rcd 9803 (1997)
(1997 Data Request).
376

Common Carrier To Hold Three Workshops On Input Values To Be Used To Estimate Forward-Looking
Economic Costs For Purposes Of Universal Service Support, Public Notice, CC Docket Nos. 96-45, 97-160, DA
98-2406 (rel. Nov. 25, 1998) (Workshop Public Notice).
377

See, e.g., Letter from W. Scott Randolph, GTE, to Magalie Roman Salas, FCC, dated March 2, 1999; Letter
from Pete Sywenki, Sprint, to Magalie Roman Salas, FCC, dated, February 26, 1999; Letter from Chris Frentrup,
MCI, to Magalie Roman Salas, FCC, dated February 9, 1999.
378

See Platform Order.

379

Submission in CC Docket Nos. 96-45 and 97-160 by BellSouth Corporation, BellSouth Telecommunications,
Inc., U S WEST, Inc., and Sprint Local Telephone Company (BCPM proponents), dated Dec. 11, 1997 (BCPM
Dec. 11, 1997 submission).
380

Letter from Richard N. Clarke, AT&T, to Magalie Roman Salas, FCC, dated Dec. 11, 1997 (HAI Dec. 11,
1997 submission). HAI was submitted by AT&T and MCI (HAI sponsors). See also Letter from Richard Clarke,
AT&T, to Magalie Roman Salas, FCC, dated February 3, 1998 (HAI Feb. 3 submission).
381

HCPM was developed by Commission staff members William Sharkey, Mark Kennet, C. Anthony Bush,
Jeffrey Prisbrey, and Commission contractor Vaikunth Gupta of Panum Communications. Common Carrier Bureau
Announces Release of HCPM Version 2.0, Public Notice, DA 97-2712 (rel. Dec. 29, 1997). See also United States
Government Memo from W. Sharkey, FCC, to Magalie Roman Salas, FCC, dated Feb. 6, 1998.
382

Platform Order, 13 FCC Rcd at 21338, para. 34.

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surrogate method to determine the location of non-geocoded customer locations;383 and (3) the
use of the local exchange routing guide (LERG) to identify the existing host-remote switch
relationships.384
9.
On November 25, 1998, the Joint Board released the Second Recommended
Decision, in which it recommended that the Commission compute federal high-cost support for
non-rural carriers through a two-step process.385 First, the Joint Board recommended that the
Commission should estimate the total support amount necessary in those areas considered to
have high costs relative to other areas. Second, the Joint Board recommended that the
Commission should consider, in a consistent manner across all states, any particular state's
ability to support high-cost areas within the state.386 The Joint Board recommended that federal
support should be provided to the extent that the state would be unable to support its high-cost
areas through its own reasonable efforts.387 In addition, the Joint Board recommended that the
Commission continue to work with the Joint Board to select the input values to complete a
forward-looking cost model and to finalize the methodology for distributing federal high-cost
support.388
D.

Inputs Further Notice and Seventh Report and Order

10.
On May 28, 1999, the Commission released the Inputs Further Notice and the
Seventh Report and Order.389 In the Inputs Further Notice, we proposed and sought comment on
383

Platform Order, 13 FCC Rcd at 21341, para. 41.

384

Platform Order, 13 FCC Rcd at 21355, para. 76. The LERG is a database of switching information
maintained by Telcordia Technologies (formerly Bellcore) that includes the existing host-remote relationships.
385

Second Recommended Decision, 13 FCC Rcd at 24746, para. 5.

386

Second Recommended Decision, 13 FCC Rcd at 24746, para. 5.

387

Second Recommended Decision, 13 FCC Rcd at 24746-47, para. 5.

388

Second Recommended Decision, 13 FCC Rcd at 24757, para. 28.

389

Federal-State Joint Board on Universal Service, Forward-Looking Mechanism for High Cost Support for
Non-Rural LECs, Further Notice of Proposed Rulemaking, CC Docket Nos. 96-45, 97-160, FCC 99-120 (rel. May
28, 1999) (Inputs Further Notice); Federal-State Joint Board on Universal Service, Access Charge Reform, Seventh
Report and Order and Thirteenth Order on Reconsideration in CC Docket No. 96-45; Fourth Report and Order in
CC Docket No. 96-262; and Further Notice of Proposed Rulemaking, CC Docket Nos. 96-45, 96-262, 14 FCC Rcd
8078 (1999) (Seventh Report and Order). See also Common Carrier Bureau Releases Preliminary Results Using
Proposed Input Values In The Forward-Looking Cost Model For Universal Service, Public Notice, CC Docket Nos.
96-45, 97-160, DA 99-1165 (rel. June 16, 1999) (Preliminary Input Values Public Notice); Common Carrier
Bureau Releases Revised Spreadsheet For Estimating Universal Service Support Using Proposed Input Values In
The Forward-Looking Cost Model, Public Notice, CC Docket Nos. 96-45, 97-160, DA 99-1322 (rel. July 2, 1999).

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a complete set of input values for use in the model, such as the cost of switches, cables, and other
network components.390 For the most important inputs, we provided a detailed description of the
methodology that was used to arrive at the proposed values.391
11.
In the Seventh Report and Order, we adopted revisions to the federal support
mechanisms, in light of the Joint Board's recommendations, to permit rates to remain affordable
and reasonably comparable across the nation, consistent with the 1996 Act. To accomplish these
goals, we established and sought comment on a methodology for determining non-rural carriers'
support amounts, based on the forward-looking costs estimated using a national cost model, and
a national cost benchmark, that will begin on January 1, 2000.392
III. ESTIMATING FORWARD-LOOKING ECONOMIC COST
A.

Designing a Forward-Looking Wireline Local Telephone Network

12.
To understand the assumptions made in the mechanism, it is necessary to
understand the layout of the current wireline local telephone network.393 In general, a telephone
network must allow any customer to connect to any other customer. In order to accomplish this,
a telephone network must connect customer premises to a switching facility, ensure that
adequate capacity exists in that switching facility to process all customers' calls that are expected
to be made at peak periods, and then interconnect that switching facility with other switching
facilities to route calls to their destinations. A wire center is the location of a switching facility.
The wire center boundaries define the area in which all customers are connected to a given wire
center. The Universal Service Order required the models to use existing incumbent LEC wire
center locations in estimating forward-looking cost.394
390

See Inputs Further Notice at Appendix A.

391

See generally Inputs Further Notice.

392

See generally Seventh Report and Order.

393

We also note that technologies such as wireless services are likely to become more important over time in
providing universal service. We will continue to review suggestions for incorporating such technologies into the
forward-looking mechanism for future years. See, e.g., Letter from David L. Sieradzki, on behalf of Western
Wireless, to Magalie Roman Salas, FCC, dated January 26, 1999 (submitting the "Wireless Cost Model"). In
addition, we intend to initiate a proceeding in the near future to consider how changes in technologies and other
related factors should be accounted for in the model.
394

The Universal Service Order established ten criteria to ensure consistency in calculations of federal universal
service support. Universal Service Order, 12 FCC Rcd at 8913, para. 250. Criterion one requires that a model must
include incumbent LECs' wire centers as the center of the loop network and the outside plant should terminate at
incumbent LECs' current wire centers.

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13.
Within the boundaries of each wire center, the wires and other equipment that
connect the central office to the customers' premises are known as outside plant. Outside plant
can consist of either copper cable or a combination of optical fiber and copper cable, as well as
associated electronic equipment. Copper cable generally carries an analog signal that is
compatible with most customers' telephone equipment. The range of an analog signal over
copper is limited, however, so thicker, more expensive cables or loading coils must be used to
carry signals over greater distances. Optical fiber cable carries a digital signal that is
incompatible with most customers' telephone equipment, but the quality of a signal carried on
optical fiber cable is superior at greater distances when compared to a signal carried on copper
wire. Generally, when a neighborhood is located too far from the wire center to be served by
copper cables alone, an optical fiber cable will be deployed to a point within the neighborhood,
where a piece of electronic equipment will be placed that converts the digital light signal carried
on optical fiber cable to an analog, electrical signal that is compatible with customers'
telephones. This equipment is known as a digital loop carrier remote terminal, or DLC, which is
connected to a serving area interface (SAI). From the SAI, copper cables of varying gauge
extend to all of the customer premises in the neighborhood. Where the neighborhood is close
enough to the wire center to be served entirely on copper cables, copper trunks connect the wire
center to the SAI, and copper cables will then connect the SAI to the customers in the serving
area. The portion of the loop plant that connects the central office with the SAI or DLC is
known as the feeder plant, and the portion that runs from the DLC or SAI throughout the
neighborhood is known as the distribution plant.
14.
The model's estimate of the cost of serving the customers located within a given
wire center's boundaries includes the calculation of switch size, the lengths, gauge, and number
of copper and fiber cables, and the number of DLCs required. These factors depend, in turn, on
how many customers the wire center serves, where the customers are located within the wire
center boundaries, and how they are distributed within neighborhoods. Particularly in rural
areas, some customers may not be located in neighborhoods at all but, instead, may be scattered
throughout outlying areas. In general, the model divides the area served by the wire center into
smaller areas known as serving areas. For serving areas sufficiently close to the wire center,
copper feeder cable extends from the wire center to a SAI where it is cross-connected to copper
distribution cables. If the feeder is fiber, it extends to a DLC terminal in the serving area, which
converts optical digital signals to analog signals. Individual circuits from the DLC are crossconnected to copper distribution cables at the adjacent SAI.
15.
The model assumes that wire centers are interconnected with one another using
optical fiber networks known as Synchronous Optical Network (SONET) rings.395 The
395

SONET is a set of standards for optical (fiber optic) transmission. It was developed to meet the need for
transmission speeds above the T3 level (45 Mbps) and is generally considered the standard choice for transmission
devices used with broadband networks. BCPM Dec. 11 submission, Model Methodology at 68.

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infrastructure to interconnect the wire centers is known as the interoffice network, and the
carriage of traffic among wire centers is known as transport. In cases where a number of wire
centers with relatively few people within their boundaries are located in close proximity to one
another, it may be more economical to use the processor capacity of a single switch to supervise
the calls of the customers in the boundaries of all the wire centers. In that case, a full-capacity
switch (known as a host) is placed in one of the wire centers and less expensive, more limitedcapacity switches (known as remotes) are placed in the other wire centers. The remotes are then
connected to the host with interoffice facilities. Switches that are located in wire centers with
enough customers within their boundaries to merit their own full-capacity switches and that do
not serve as hosts to any other wire centers are called stand-alone switches.
16.
There are also a number of expenses and general support facilities (GSF) costs
associated with the design of a forward-looking wireline telephone network.396 GSF costs
include the investment related to vehicles, land, buildings, and general purpose computers.
Expenses include: plant-specific expenses, such as maintenance of facilities and equipment
expenses; plant non-specific expenses, such as engineering, network operations, and power
expenses; customer services expenses, such as marketing, billing, and directory listing expenses;
and corporate operations expenses, such as administration, human resources, legal, and
accounting expenses.397
B.

Synthesis Model
1.

Historical Background

17.
The synthesis model adopted in the Platform Order allows the user to estimate
the cost of building a telephone network to serve subscribers in their actual geographic locations,
to the extent these locations are known.398 To the extent that the actual geographic locations of
customers are not available, the Commission determined that the synthesis model should assume
that customers are located along roads.399
18.
Once the customer locations have been determined, the model employs a
clustering algorithm to group customers into serving areas in an efficient manner that takes into

396

See Platform Order, 13 FCC Rcd at 21357-61, paras. 81-91.

397

Platform Order, 13 FCC Rcd at 21357-58, para. 82.

398

Platform Order, 13 FCC Rcd at 21337, para. 33. See also discussion of customer location data, infra section

IV.
399

Platform Order, 13 FCC Rcd at 21340-41, para. 40. See also discussion of road surrogating method, infra.

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consideration relevant engineering constraints.400 After identifying efficient serving areas, the
model designs outside plant to the customer locations.401 In doing so, the model employs a
number of cost minimization principles designed to determine the most cost-effective technology
to be used under a variety of circumstances, such as varying terrain and density.402
19.
The Commission concluded that the federal universal service mechanism should
incorporate, with certain modifications, the HAI 5.0a switching and interoffice facilities module
to estimate the cost of switching and interoffice transport.403 The Commission noted that it
would consider adopting the LERG at the inputs stage of this proceeding to determine the
deployment of host and remote switches.404 In addition, the Commission adopted the HAI
platform module for calculating expenses and capital costs, such as depreciation.405
20.
The Commission noted that technical improvements to the cost model will
continue, both before implementation of the model for non-rural carriers and on an ongoing
basis, as necessary.406 The Commission therefore delegated to the Bureau the authority to make
changes or direct that changes be made to the model platform as necessary and appropriate to
ensure that the platform of the federal mechanism operates as described in the Platform
Order.407 As contemplated in the Platform Order, Commission staff and interested parties have
continued to review the model platform to ensure that it operates as intended. As a result, some
refinements have been made to the model platform adopted in the Platform Order.408 All
changes to the model platform are posted on the Commission's Web site.409
400

Platform Order, 13 FCC Rcd at 21342, para. 44.

401

Platform Order, 13 FCC Rcd at 21346, para. 55.

402

Platform Order, 13 FCC Rcd at 21348, para. 61.

403

Platform Order, 13 FCC Rcd at 21354-55, para. 75.

404

Platform Order, 13 FCC Rcd at 21355, para. 76.

405

Platform Order, 13 FCC Rcd at 21357, para. 81.

406

Platform Order, 13 FCC Rcd at 21329, para. 13.

407

Platform Order, 13 FCC Rcd at 21329, para. 13.

408

Common Carrier Bureau To Post On The Internet Modifications To The Forward-Looking Economic Cost
Model For Universal Service Support, Public Notice, CC Docket Nos. 96-45, 97-160, DA 98-2533 (rel. Dec. 15,
1998).
409

Model platform changes can be found at http://www.fcc.gov/ccb/apd/hcpm. Changes to the model are
detailed in the "History.doc" file. The model platform was not modified after June 2, 1999, in order to allow parties
an opportunity to evaluate the model platform, the proposed inputs to the model, and issues related to the

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Validation

21.
In the Universal Service Order, the Commission concluded that high-cost support
should be based on forward-looking costs.410 Since that time, the Commission has continued to
work to adopt a cost model that is reasonably accurate and verifiable.411 Although we have
remained confident in our ability to adopt a model, in the Inputs Further Notice, we sought
comment on how the Commission might determine support levels without using a model, "[i]n
the unlikely event that the model is not ready for timely implementation."412 A few commenters
offered concrete suggestions in response to this request, virtually all of which involved the use of
carriers' book costs in lieu of the model.413
22.
As an initial matter, we affirm the Commission's decision to base support
calculations on forward-looking costs. We have repeatedly articulated our reasons for believing
that forward-looking costs represent a superior method for determining support amounts. The
most significant of these is that forward-looking costs are the basis of economic decisions in a
competitive market, and therefore send the correct signals for entry and investment.
23.
Moreover, the Commission and its staff have undertaken a thorough review of the
model and its input values over the past six months. In so doing, the staff has coordinated
extensively with and received substantial input from the Joint Board staff and interested outside
parties. As a result of this examination of the model, we are convinced that it generates
reasonably accurate estimates of forward-looking costs and that the model is the best basis for
determining non-rural carriers' high-cost support in a competitive environment.
24.
After this review of the model, we find that none of the criticisms of the model
undermine our decision to use it for calculating non-rural carriers' high-cost support. For
example, some parties have observed that the model seems to generate unexpectedly high cost
estimates for certain states, such as Mississippi and Alabama.414 Because of the high levels of
methodology for determining high-cost support. After release of this Order, we will post a revised model platform
on the Commission's Web site, including the input values adopted herein.
410

Universal Service Order, 12 FCC Rcd at 8899-8900, paras. 224-226.

411

See, e.g., 1997 Further Notice; Inputs Public Notice; Workshop Public Notice; Inputs Further Notice.

412

Inputs Further Notice at para. 243.

413

See, e.g., Bell Atlantic Inputs Further Notice comments at 5-6, GTE Inputs Further Notice comments at 8991, USTA Inputs Further Notice comments at 4-5. But see US West Inputs Further Notice comments at 70-71.
414

Bell Atlantic Inputs Further Notice comments at 4.

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cost estimated in these states, they receive larger shares of forward-looking support than they
receive under the current mechanism, and also receive higher levels of support than some other
states (such as the sparsely populated Western and Midwestern states) that many parties
expected to lead the list of high-cost states. After further review, however, we have found
several factors that explain the model's results.
25.
We first sought to verify the model's results by determining whether the model
generated higher costs in areas where customers are more dispersed. Although there are other
relevant factors, most people agree that telephone plant costs tend to be highest when customers
are spread thinly over a large area. We used a "minimum spanning tree" measurement to
determine relative dispersion of customers.415 We found that the model's cost estimates were
highly correlated with dispersion of customers. This provides a preliminary, objective check on
the model's accuracy.
26.
This analysis does not, however, explain why the model estimates higher costs in
some states relative to others in a distribution that differs from carriers' book costs and from
some observers' expectations. In researching this issue, we discovered that significant
differences exist among the states in the territory served by larger carriers, which are typically
considered non-rural carriers under the Act.416 It is important to remember that the present
model runs only cover the territory served by non-rural carriers. The costs estimated by the
model will be significantly affected by the type of territory served by those carriers in the state
whose costs are being calculated, and to the extent that a rural territory is being served by a rural
carrier that is not receiving high-cost support under this mechanism, the cost of serving that
territory will not be reflected in the level of support for that state determined in this phase of the
proceeding. In general, we found that the states where the model estimated the highest costs
were those states in which the territory served by the non-rural carriers, which are typically
larger carriers, included more rural areas than in other states. We also found that some states
that are generally perceived as rural are served primarily by small carriers, so that the remaining
territory in the state, which would be served by the non-rural carrier, is less rural than the state as
a whole. For example, in Mississippi, the large incumbent LEC serves the vast majority of the
state's territory, including many very rural areas. By contrast, in Montana for example, the large
incumbent LEC serves less than a third of the state's territory, and its serving area includes all
but one of the largest cities in the state. Small rural carriers serve the most sparsely populated
rural areas in Montana. As a result, considering only the non-rural carriers' territory, Mississippi
appears to be a considerably more rural state than Montana. As discussed above, our analysis
showed that the model's cost estimates were highly correlated with dispersion -- that is, the areas
415

See C.A. Bush et al., The Hybrid Cost Proxy Model Customer Location and Loop Design Modules, Dec. 15,
1998 at 13-14 (HCPM Dec. 15, 1998 documentation).
416

See 47 U.S.C. § 153(37).

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with the most dispersed customers were estimated by the model to have the highest costs.
Although this results in relative cost estimates among states that differ from some people's
expectations, we believe that this may primarily reveal that those expectations were based on a
lack of information or incorrect premises about non-rural carriers' service territories.
27.
Moreover, our investigation revealed that most of the variations between carriers'
book cost levels and the model's estimated forward-looking costs can be explained by three
factors. The first is the percentage of business lines in the study area. Study areas with a lower
percentage of business lines tend to have lower book costs relative to forward-looking costs.
The second factor is the percentage of customers in rural areas. Study areas with a higher
percentage of rural customers also tend to have lower relative book costs. These two factors,
taken together, suggest that the book cost of the existing network is more likely to be below the
model's estimate of the cost of a forward-looking network in rural areas with fewer business
customers. This may suggest that these areas are served by networks of a different quality
standard than that assumed in the model, or that the networks in these areas have not been
upgraded or experienced much growth in some time and therefore are substantially depreciated
on carriers' books. The third factor is discrepancies in line counts between the data used in the
model and the most current carrier-reported data. We have taken steps to correct these
discrepancies in the line count data that we adopt in this Order.417
28.
We believe that the model, as used in the methodology we set out in the
companion Methodology Order, is the best way to generate non-rural carriers' support amounts
for the funding year beginning January 1, 2000. We also recognize, however, that the model
must evolve as technology and other conditions change. We therefore have committed to
initiating a proceeding to study how the model should be used in the future (e.g., how often
inputs data should be updated) and how the model itself should change to reflect changing
circumstances. We anticipate releasing a further notice of proposed rulemaking on these issues in
early 2000, and hope to reach significant decisions on these issues during the course of that year.
C.

Selecting Forward-Looking Input Values

29.
In the Universal Service Order, the Commission adopted ten criteria to be used in
determining the forward-looking economic cost of providing universal service in high-cost
areas.418 These criteria provide specific guidance for our selection of input values for use in the
synthesis model. Rather than reflecting existing incumbent LEC facilities, the technology
assumed in the model "must be the least-cost, most-efficient, and reasonable technology for

417

See infra para. 61.

418

Universal Service Order, 12 FCC Rcd at 8913-16, para. 250.

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providing the supported services that is currently being deployed."419 As noted below, existing
LEC plant in a particular area may not reflect forward-looking technology or design choices.420
Similarly, the input values we adopt in this Order are not intended to replicate any particular
company's embedded or book costs. Criterion three directs that "costs must not be the embedded
cost of the facilities, functions, or elements."421 Rather, the model "must be based upon an
examination of the current cost of purchasing facilities and equipment."422
30.
As discussed below, we generally adopt nationwide, rather than companyspecific, input values in the federal mechanism. In many cases, the only data for various inputs
on the record in this proceeding are embedded cost, company-specific data. We have used
various techniques to convert these data to forward-looking values. For example, we modify the
switching data to adjust for the effects of inflation and the cost changes unique to the purchase
and installation of digital switches.423 Where possible, we have tried to account for variations in
costs by objective means. For example, the model reflects differences in structure costs by using
different values for the type of plant, the density zone, and geological conditions. There may be
additional modifications we can make in the future to more accurately reflect variations in
forward-looking costs based on objective criteria. For example, we do not adjust our
maintenance expense estimates to reflect regional wage differences, as discussed below, because
we have not found and no party has suggested a specific data source or methodology that would
be useful in making such adjustments.424 We certainly remain open to considering data sources
in the future of the model proceeding that would permit us to vary these or other input values to
reflect differences in forward-looking cost that can be measured objectively.
31.
Although the BCPM sponsors have provided nationwide default values, they and
other LECs generally advocate company-specific input values. For purposes of determining
federal universal service support amounts, however, we believe that nationwide default values
generally are more appropriate than company-specific values. Under the new federal universal
service support mechanism, support is based on the estimated costs that an efficient carrier
would incur to provide the supported services, rather than on the specific carrier's book costs.
We also believe that it would be administratively unworkable to use company-specific values in
419

Universal Service Order, 12 FCC Rcd at 8913, para. 250 (criterion one).

420

See infra paras. 63, 351.

421

Universal Service Order, 12 FCC Rcd at 8913, para. 250 (criterion three).

422

Universal Service Order, 12 FCC Rcd at 8913, para. 250 (criterion three).

423

See infra para. 311.

424

See infra paras. 361-64.

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the federal nationwide model. Finally, we note that, for most inputs, we have no means of
adopting company-specific input values, except possibly by relying on embedded data for each
company. We make no finding as to whether nationwide values would be appropriate for
purposes other than determining federal universal service support.425
32.
For universal service purposes, we find that using nationwide averages is
appropriate. The Commission has not considered what type of input values, company-specific or
nationwide, nor what specific input values, would be appropriate for any other purposes. The
federal cost model was developed for the purpose of determining federal universal service
support, and it may not be appropriate to use nationwide values for other purposes, such as
determining prices for unbundled network elements. We caution parties from making any claims
in other proceedings based upon the input values we adopt in this Order.
IV. DETERMINING CUSTOMER LOCATIONS
A.

Background

33.
The determination of customer locations relative to the wire center heavily
influences a forward-looking cost model's design of outside plant facilities. This is because
assumptions about the locations of customers will determine the predicted loop length, which in
turn will have a large impact on the cost of service and the technologies employed by the
model.426 Each of the models under consideration in the Platform Order provided a
methodology for determining customer locations.427 The Bureau sought comment on these
proposals and solicited alternative proposals from interested parties for locating customers.428
34.
In the Platform Order, the Commission concluded that HAI's proposal to use
actual geocode data, to the extent that they are available, and BCPM's proposal to use road
network information to create "surrogate" customer locations where actual data are not available,
provided the most reasonable method for determining customer locations.429 The Commission
425

State commissions, for example, may find that it is not appropriate to use nationwide values in determining
state universal service support or prices for unbundled network elements and may choose instead to use statewide or
company-specific values.
426

See 1997 Further Notice, 12 FCC Rcd at 18535, para. 44.

427

Platform Order, 13 FCC Rcd at 21337, para. 31.

428

See, e.g., 1997 Further Notice, 12 FCC Rcd at 18535, para. 44; Inputs Public Notice at 3-4; Common Carrier
Bureau Seeks Comment On Model Platform Development, Public Notice, CC Docket Nos. 96-45, 97-160, DA 981587 (rel. Aug. 7, 1998) (Platform Public Notice) at 2-4.
429

Platform Order, 13 FCC Rcd at 21337, para. 31. The term "geocode data" refers to the identification of each
customer location by precise latitude and longitude coordinates. Surrogating methods, and customer location data

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concluded that "the source or sources of geocode data to use in determining customer location
will be decided at the inputs phase of this proceeding."430 The Commission also concluded that
"the selection of a precise algorithm for placing road surrogates pursuant to these conclusions
should be conducted in the inputs stage of this proceeding as part of the process of selecting a
geocode data set for the federal mechanism."431
35.
In the Inputs Further Notice, we tentatively concluded that no source of actual
geocode data had been made sufficiently available for review to be used in the model at that
time.432 Therefore, we tentatively concluded that a road surrogate algorithm would be used to
locate customers in the federal mechanism until a source of actual geocode data is selected by
the Commission. In doing so, we tentatively adopted the road surrogate algorithm proposed by
PNR Associates (PNR) to develop road surrogate customer locations.433
B.

Customer Location Data
1.

Geocode Data

36.
While we affirm our conclusion in the Platform Order that geocode data should
be used to locate customers in the federal mechanism, we conclude that no source of actual
geocode data has yet been made adequately accessible for public review. We conclude below
that we will use an algorithm based on the location of roads to create surrogate geocode data on
customer locations for the federal mechanism until a source of actual geocode data is identified
and selected by the Commission. We reiterate our expectation that a source of accurate and
verifiable actual geocode data will be identified in the future for use in the federal mechanism.434
37.
In the Platform Order, we concluded that a model is most likely to select the
least-cost, most-efficient outside plant design if it uses the most accurate data for locating
provided by the Census Bureau, constitute geocode data. For purposes of clarity, however, we will use the term
"geocode" data to refer only to actual precise latitude and longitude data, unless we specifically refer to the data as
"surrogate geocode" data.
430

Platform Order, 13 FCC Rcd at 21337-38, para. 34.

431

Platform Order, 13 FCC Rcd at 21340-41, para. 40.

432

Inputs Further Notice at paras. 25-28.

433

Inputs Further Notice at para. 29.

434

In the upcoming proceeding on future changes to the model, see supra note 34, we intend to consider
alternatives for obtaining customer location data.

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customers within wire centers, and that the most accurate data for locating customers within wire
centers are precise latitude and longitude coordinates for those customers' locations.435 We
noted that commenters generally support the use of accurate geocode data in the federal
mechanism where available.436 We further noted that the only actual geocode data in the record
were those prepared for HAI by PNR, but also noted that "our conclusion that the model should
use geocode data to the extent that they are available is not a determination of the accuracy or
reliability of any particular source of the data."437 Although commenters supported the use of
accurate geocode data, several commenters questioned whether the PNR geocode data were
adequately available for review by interested parties.438
38.
In the Universal Service Order, the Commission required that the "model and all
underlying data, formulae, computations, and software associated with the model must be
available to all interested parties for review and comment."439 In an effort to comply with this
requirement, the Commission has made significant efforts to encourage parties to submit
geocode data on the record in this proceeding.440 PNR took initial steps to comply with this
requirement in December 1998 by making available the "BIN" files441 derived from the
geocoded points to interested parties pursuant to the Protective Order.442 PNR also has
continued to provide access to the underlying geocode data at its facility in Pennsylvania.
Several commenters argue, however, that the availability of the BIN data alone is not sufficient
to comply with the requirements of criterion eight, particularly in light of the expense and
conditions imposed by PNR in obtaining access to the geocode point data.443 In addition, PNR
435

Platform Order, 13 FCC Rcd at 21337, para. 33.

436

Platform Order, 13 FCC Rcd at 21337-38, para. 34.

437

Platform Order, 13 FCC Rcd at 21338, para. 34.

438

Platform Order, 13 FCC Rcd at 21338, para. 34.

439

Universal Service Order, 12 FCC Rcd at 8915, para. 250 (criterion eight).

440

See Federal-State Joint Board on Universal Service, Protective Order, CC Docket Nos. 96-45, 97-160, 13
FCC Rcd 13910 (1998) (Protective Order). See also Inputs Public Notice at 3-4.
441

BIN files are the output of the clustering routine in the synthesis model platform derived from the actual
geocode customer locations and, as such, do not reveal the actual geocoded customer locations. The BIN files allow
users to run all aspects of the model except for the clustering. PNR has made the BIN files available to interested
parties for a fee of $25.00, pursuant to the terms of the Protective Order. See Letter from William M. Newman,
PNR, to Magalie Roman Salas, FCC, dated December 17, 1998 (PNR Dec. 17 ex parte).
442

See PNR Dec. 17 ex parte.

443

See, e.g., Bell Atlantic Petition for Reconsideration at 5-6; BellSouth Petition for Reconsideration at 3-4;
GTE Petition for Reconsideration at 21; Sprint Inputs Further Notice comments at 11.

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acknowledges that its geocode database relies on third-party data that PNR is not permitted to
disclose.444
39.
Consistent with our tentative conclusion in the Inputs Further Notice, we
conclude that interested parties have not had an adequate opportunity to review and comment on
the accuracy of the PNR actual geocode data set. The majority of commenters addressing this
issue support this conclusion.445 We note that a nationwide customer location database will, by
necessity, be voluminous, relying on a variety of underlying data sources. In light of the
concerns expressed by several commenters relating to the conditions and expense in obtaining
geocode data from PNR, we find that no source of actual geocode data has been made
sufficiently available for review. While PNR has made some effort to satisfy the requirements of
criterion eight, we prefer to adopt a data set that is more readily available for meaningful review.
In particular, we note that the geocode points are available only on-site at PNR's facilities,
making it difficult for parties to verify the accuracy of those points. We recognize, however, that
more comprehensive actual geocode data are likely to be available in the future, and we
encourage parties to continue development of an actual geocode data source that complies with
the criteria outlined in the Universal Service Order for use in the federal mechanism.446
2.

Road Surrogate Customer Locations

40.
We conclude that PNR's road surrogating algorithm should be used to develop
geocode customer locations for use in the federal universal service mechanism to determine
high-cost support for non-rural carriers beginning January 1, 2000. In the Platform Order, we
concluded that, in the absence of actual geocode customer location data, associating road
networks and customer locations provides the most reasonable approach for determining
customer locations.447
41.
As we noted in the Platform Order, "associating customers with the distribution
of roads is more likely to correlate to actual customer locations than uniformly distributing
customers throughout the Census Block, as HCPM proposes, or uniformly distributing customers
444

Letter to Thomas W. Mitchell, on behalf of GTE, from Charles A. White, PNR, dated April 29, 1999 (PNR
April 29 ex parte) at 1.
445

See, e.g., Ameritech Inputs Further Notice comments at 2; GTE Inputs Further Notice comments at 36-37;
SBC Inputs Further Notice comments at 4; Sprint Inputs Further Notice comments at 11.
446

We note that AT&T and MCI have suggested that the Commission condition receipt of universal service
funding on the provision of customer location data by the carrier. See AT&T/MCI Inputs Further Notice comments
at 5. We decline to adopt this suggestion at this time, but will consider this and other alternatives to obtaining
customer location data in the upcoming proceeding on future changes to the model.
447

Platform Order, 13 FCC Rcd at 21340-41, para. 40.

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along the Census Block boundary, as HAI proposes."448 We therefore concluded in the Platform
Order that the selection of a precise algorithm for placing road surrogates should be conducted
in the inputs stage of this proceeding.449 In the Inputs Further Notice, we tentatively adopted the
PNR road surrogate algorithm to determine customer locations.450
42.
Currently, there are two road surrogating algorithms on the record in this
proceeding - those proposed by PNR and Stopwatch Maps. On March 2, 1998, AT&T provided
a description of the road surrogate methodology developed by PNR for locating customers.451
On January 27, 1999, PNR made available for review by the Commission and interested parties,
pursuant to the terms of the Protective Order, the road surrogate point data for all states except
Alaska, Iowa, Virginia, Puerto Rico and eighty-four wire centers in various other states.452 On
February 22, 1999, PNR filed a more detailed description of its road surrogate algorithm.453
Consistent with the conditions set forth in the Inputs Further Notice, PNR has now made
available road surrogate data for all fifty states and Puerto Rico.454
43.
In general, the PNR road surrogate algorithm utilizes the Census Bureau's
Topologically Integrated Geographic Encoding and Referencing (TIGER) files, which contain
all the road segments in the United States.455 For each Census Block, PNR determines how
many customers and which roads are located within the Census Block.456 For each Census
448

Platform Order, 13 FCC Rcd at 21340-41, para. 40.

449

Platform Order, 13 FCC Rcd at 21341, para. 41.

450

Inputs Further Notice at para. 34.

451

Letter from Michael Liebermann, AT&T, to Magalie Roman Salas, FCC, dated March 2, 1998 (AT&T
March 2 ex parte).
452

Letter from William M. Newman, PNR, to Magalie Roman Salas, FCC, dated January 27, 1999 (PNR Jan. 27
ex parte). PNR has made available by mail to interested parties the road surrogate point data for a fee of $25.00,
pursuant to the terms of the Protective Order.
453

Letter from Charles A. White, PNR, to Magalie Roman Salas, FCC, dated February 22, 1999 (PNR Feb. 22
ex parte).
454

Letter from Charles A. White, PNR, to Magalie Roman Salas, FCC, dated July 29, 1999 (PNR July 29 ex

parte).
455

PNR Feb. 22 ex parte at 1. A road segment is a length of road between two intersections. The Census
Bureau classifies and numbers each of these road segments. PNR uses a slightly modified version of the Census
Bureau road classifications. Id. at 2
456

The PNR National Access Line Model is used to determine the number of residential and business customer
locations in a given wire center. See PNR Feb. 22 ex parte at 1.

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Block, PNR also develops a list of road segments. The total distance of the road segments within
the Census Block is then computed. Roads that are located entirely within the interior of the
Census Block are given twice the weight as roads on the boundary. This is because customers
are assumed to live on both sides of a road within the interior of the Census Block. In addition,
the PNR algorithm excludes certain road segments along which customers are not likely to
reside.457 For example, PNR excludes highway access ramps, alleys, and ferry crossings.458 The
total number of surrogate points is then divided by the computed road distance to determine the
spacing between surrogate points. Based on that distance, the surrogate customer locations are
uniformly distributed along the road segments.459 In order to ensure that its road surrogate data
set includes all currently served customers, PNR has made minor adjustments to its methodology
in some instances. For example, Census Blocks that are not assigned to any current wire center
have been assigned to the nearest known wire center, based on the "underpinned of the census
block in relation to the wire center's central office location."460
44.
Stopwatch Maps has compiled road surrogate customer location files for six states
suitable for use in the federal mechanism.461 We conclude, however, that until a more
comprehensive data set is made available, the Stopwatch data set will not comply with the
Universal Service Order's criterion that the underlying data are available for review by the
public. Only GTE endorses the use of the Stopwatch data set.462 In addition, we note that the
availability of customer locations for only six states is of limited utility in a nationwide model
designed to be implemented on January 1, 2000.
45.
AT&T and MCI contend that the exclusive use of a road surrogate algorithm to
locate customers produces a 2.7 percent upward bias in loop cost on average on a study area
basis when compared to a data set consisting of PNR actual geocode data, where available, and
surrogate locations where actual data are unavailable.463 AT&T and MCI argue that this occurs
457

PNR Feb. 22 ex parte at 2.

458

PNR Feb. 22 ex parte at 2.

459

PNR Feb. 22 ex parte at 2.

460

See PNR July 29 ex parte. PNR has also filled in the states and wire centers that were missing from earlier
versions of its road surrogate customer location data set.
461

See Letter from Pete Sywenki, Sprint, to Magalie Roman Salas, FCC, dated December 11, 1998 (Sprint Dec.
11 ex parte).
462

GTE Inputs Further Notice comments at 38.

463

AT&T/MCI Inputs Further Notice comments at 3. Because the PNR actual geocode data set does not
provide a complete data set of customer locations, AT&T and MCI compare a combination of actual and surrogate
data with the use of all surrogate data. The percentage of actual geocoded customer data varies in different areas.

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because the road surrogate methodology uniformly disperses customers along roads, failing to
take into consideration actual, uneven customer distributions that tend to cluster customer
locations more closely.464 AT&T and MCI therefore suggest a downward adjustment to produce
more accurate outside plant cost estimates.465 GTE disagrees and contends that, because the
PNR actual geocode data create serving areas that are too dense, it is not surprising that AT&T
and MCI have found that the use of road surrogate data produces costs that are slightly higher.466
GTE argues that there is no evidence to conclude, therefore, that a uniform dispersion of
customers is likely to overstate outside plant costs.467 Sprint contends that the decision to
optimize distribution plant in the model mitigates any concern that the road surrogate algorithm
overstates the amount of outside plant.468
46.
We agree with GTE and Sprint that there should be no downward adjustment in
cost to reflect the exclusive use of a road surrogate algorithm. In doing so, we note that,
although the Commission has gone to great lengths to identify a source of actual, nationwide
customer locations, no satisfactory data source has been identified. In fact, only one source of
such data, the PNR geocode data, has been placed on the record. As noted above, however, we
have rejected the PNR geocode data set at this time because it has not been made adequately
available for review. In the absence of a reliable source of actual customer locations by which to
compare the surrogate locations, it is impossible to substantiate AT&T and MCI's contention that
the road surrogate algorithm overstates the dispersion of customer locations in comparison to
actual locations.469 Although LECG has made comparisons between Ameritech geocode
locations and the PNR road surrogate locations, the validity of that comparison is dependent on
the accuracy of the geocode data used in that comparison.470 As Ameritech has not filed that
data on the record, we have no way of verifying the accuracy of its geocoded locations. In
464

AT&T/MCI Inputs Further Notice comments at 3 (contending that customers tend to cluster unevenly along
roads and even leave stretches unpopulated). See also Ameritech Inputs Further Notice comments at 5 (contending
that PNR surrogate locations tend to spread customers more evenly than when compared to Ameritech's geocoded
customer data).
465

AT&T/MCI Inputs Further Notice reply comments at 10.

466

GTE Inputs Further Notice reply comments at 4-5.

467

GTE Inputs Further Notice reply comments at 5.

468

Sprint Inputs Further Notice comments at 13.

469

As noted above, AT&T and MCI rely on the PNR actual geocode data that we have rejected for lack of a
meaningful verification process. In the absence of a verifiable, actual geocode data source, it is impossible to make
the type of comparison suggested by AT&T and MCI to determine the accuracy of the road surrogate algorithm.
470

Letter from Celia Nogales, Ameritech, to Magalie Roman Salas, FCC, dated July 14, 1999 (Ameritech July
14 ex parte). LECG is an economic consulting firm.

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addition, we note that Ameritech agrees that the PNR road surrogate "is a reasonable method for
locating customers in the absence of actual geocode data."471 Having no reliable evidence that
the PNR road surrogate algorithm systematically overstates customer dispersion, we conclude
that no downward adjustment to the outside plant cost estimate is required.
47.
We also disagree with Bell Atlantic's contention that road surrogate data is
inherently random and likely to misidentify high-cost areas.472 As noted in the Platform Order,
we believe that it is reasonable to assume that customers generally reside along roads and,
therefore, associating customers with the distribution of roadways is a reasonable method to
estimate customer locations. We note that PNR's methodology of excluding certain road
segments is consistent with the Commission's conclusion in the Platform Order that certain types
of roads and road segments should be excluded because they are unlikely to be associated with
customer locations.473 In addition, we note that PNR's reliance on the Census Bureau's TIGER
files ensures a degree of reliability and availability for review of much of the data underlying
PNR's road surrogate algorithm, in compliance with criterion eight of the Universal Service
Order.474 The PNR road surrogate algorithm is also generally supported by commenters
addressing this issue.475 While AT&T and MCI advocate the use of actual geocode data points,
AT&T and MCI endorse the PNR road surrogate algorithm to identify surrogate locations in the
absence of actual geocode data.476 We therefore affirm our tentative conclusion in the Inputs
Further Notice and adopt the PNR road surrogate algorithm and data set to determine customer
locations for use in the model beginning on January 1, 2000.
3.

Methodology for Estimating the Number of Customer Locations

48.
In addition to selecting a source of customer data, we also must select a
methodology for estimating the number of customer locations within the geographic region that
471

Ameritech Inputs Further Notice comments at 3.

472

Bell Atlantic Inputs Further Notice comments at 8. As noted, the decision to use a surrogating algorithm
based on roads was made by the Commission in the Platform Order. Our purpose in this Order is not to revisit that
decision but to select the road surrogate algorithm that will be used in the federal mechanism.
473

Platform Order, 13 FCC Rcd at 21341, para. 41.

474

We also note that PNR has made the road surrogate data points available to interested parties pursuant to the
provisions of the Protective Order in this proceeding. See PNR Jan. 27 ex parte; PNR Feb. 9 ex parte; PNR Feb. 22
ex parte.
475

See, e.g., Ameritech Inputs Further Notice at 3; AT&T/MCI Inputs Further Notice comments at 6-7; Sprint
Inputs Further Notice at 12.
476

AT&T/MCI Inputs Further Notice comments at 6-7.

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will be used in developing the customer location data. In addition, we must determine how
demand for service at each customer location should be estimated and how customer locations
should be allocated to each wire center. In the Inputs Further Notice, we tentatively concluded
that PNR's methodology for estimating the number of customer locations based on households
should be used for developing the customer location data.477 In addition, we also tentatively
concluded that we should use PNR's methodology for estimating the demand for service at each
location, and for allocating customer locations to wire centers.478 We now affirm these tentative
conclusions.
49.
In the Universal Service Order, the Commission concluded that a "model must
estimate the cost of providing service for all businesses and households within a geographic
region."479 The Commission has sought comment on the appropriate method for defining
"households," or residential locations, for the purpose of calculating the forward-looking cost of
providing supported services.480 Interested parties have proposed alternative methods to comply
with this requirement.481
50.
AT&T, MCI, and Ameritech support the methodology devised by PNR, which is
based upon the number of households in each Census Block, while BellSouth, GTE, SBC,
USTA, and US West propose that we use a methodology based upon the number of housing
units in each Census Block.482 A household is an occupied residence, while housing units
include all residences, whether occupied or not.483
477

Inputs Further Notice at para. 43.

478

Inputs Further Notice at para. 43.

479

Universal Service Order, 12 FCC Rcd at 8915, para. 250 (criterion 6).

480

Inputs Public Notice at 4-6. See also Inputs Further Notice at para. 46.

481

We note that the question of which residential and business locations should be included for purposes of
estimating the forward-looking cost of providing the supported services is distinct from the question of which lines
should be supported. See Universal Service Order, 12 FCC Rcd at 8829, paras. 95-96 (declining to adopt the Joint
Board's recommendation to restrict universal service high-cost support to primary residential lines and single-line
businesses).
482

Ameritech Inputs Further Notice comments at 6; AT&T/MCI Inputs Further Notice comments at 7-8;
BellSouth Inputs Further Notice comments at B-2; GTE Inputs Further Notice comments at 40; SBC Inputs Further
Notice comments at 6; USTA Inputs Further Notice comments at 2-3; US West Inputs Further Notice comments at
45-46.
483

These definitions reflect the Census Bureau's methodology for housing unit and household estimates. See
http://www.census.gov/population/methods/sthhmet.txt.

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51.
In the Inputs Further Notice, we tentatively adopted the use of the PNR National
Access Line Model, as proposed by AT&T and MCI, to estimate the number of customer
locations within Census Blocks and wire centers.484 The PNR National Access Line Model uses
a variety of information sources, including: survey information; the LERG; Business Location
Research (BLR) wire center boundaries; Dun & Bradstreet's business database; Metromail's
residential database; Claritas's demographic database; and U.S. Census Bureau estimates. PNR's
model uses these sources in a series of steps to estimate the number of residential and business
locations, and the number of access lines demanded at each location.485 The model makes these
estimates for each Census Block, and for each wire center in the United States.486 In addition,
each customer location is associated with a particular wire center.487 We conclude that PNR's
process for estimating the number of customer locations should be used for developing the
customer location data. We also conclude that we should use PNR's methodology for estimating
the demand for service at each location, and for allocating customer locations to wire centers.488
We believe that the PNR methodology is a reasonable method for determining the number of
customer locations to be served in calculating the cost of providing supported services.
52.
PNR's process for estimating the number of customer locations results in an
estimate of residential locations that is greater than or equal to the Census Bureau's estimate of
households, by Census Block Group, and its estimate is disaggregated to the Census Block level.
PNR's estimate of demand for both residential and business lines in each study area will also be
greater than or equal to the number of access lines in the Automated Reporting and Management
Information System (ARMIS) for that study area.
53.
The BCPM model relied on many of the same data sources as those used in PNR's
National Access Line Model. For example, BCPM 3.1 used wire center data obtained from BLR
and business line data obtained from PNR.489 In estimating the number of residential locations,
however, the BCPM model used Census Bureau data that include household and housing unit
484

HAI Dec. 11, 1997 submission, Model Description at 21.

485

See Inputs Further Notice at Appendix B.

486

HAI Dec. 11, 1997 submission, Model Description at 21.

487

Customer locations in unserved areas, as reflected by BLR wire center boundaries, are not associated with
particular wire centers. See Letter from Charles A. White, PNR, to Magalie Roman Salas, FCC, dated April 12,
1999. PNR has, however, taken steps to assign such customer locations to the nearest wire center. PNR July 29 ex
parte.
488

See Inputs Further Notice at Appendix B for a complete description of the PNR methodology for estimating
the number of customer locations.
489

BCPM April 30, 1998 documentation, Model Methodology at 26-27.

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counts from the 1990 Census, updated based upon 1995 Census Bureau statistics regarding
household growth by county. In addition, rather than attempting to estimate demand by location
at the Block level, the BCPM model builds two lines to every residential location and at least six
lines to every business.
54.
A number of commenters contend that the total cost estimated by the model
should include the cost of providing service to all possible customer locations, even if some
locations currently do not receive service.490 Some commenters further contend that, if total cost
is based on a smaller number of locations, support will not be sufficient to enable carriers to
meet their carrier-of-last-resort obligations. These commenters argue that basing the estimate of
residential locations on households instead of housing units will underestimate the cost of
building a network that can provide universal service.491 They therefore assert that residential
locations should be based on the number of housing units -- whether occupied or unoccupied.492
These commenters contend that only this approach reflects the obligation to provide service to
any residence that may request it in the future.493
55.
Some commenters also contend that the PNR National Access Line Model has not
been made adequately available for review.494 As noted above, the National Access Line Model
is a multi-step process used to develop customer location counts and demand and associate those
customer locations with Census Blocks and wire centers.495 As a result, PNR contends that the
National Access Line Model cannot be provided in a single, uniform format.496 The HAI
sponsors have provided a description of the National Access Line Model process in the HAI
490

BellSouth Inputs Further Notice at B-2; GTE Inputs Further Notice at 40; SBC Inputs Further Notice
comments at 6; USTA Inputs Further Notice comments at 2-3; US West Inputs Further Notice comments at 45-46.
491

BellSouth Inputs Further Notice comments at B-2; GTE Inputs Further Notice comments at 40; PRTC Inputs
Further Notice comments at 5.
492

See, e.g., BellSouth Inputs Further Notice at B-2; GTE Inputs Further Notice at 40; SBC Inputs Further
Notice comments at 6; USTA Inputs Further Notice comments at 2-3; US West Inputs Further Notice comments at
45-46.
493

See, e.g., BellSouth Inputs Further Notice comments at B-2; GTE Inputs Further Notice comments at 40; US
West Inputs Further Notice comments at 45-46.
494

Bell Atlantic Inputs Further Notice comments at 14-15; GTE Inputs Further Notice comments at 37-38;
Sprint Inputs Further Notice comments at 13-14; US West Inputs Further Notice reply comments at 12.
495

HAI has provided a complete description of the process by which PNR's National Access Line Model
develops customer counts. See HAI Dec. 11, 1997, Model Description at 21.
496

PNR Inputs Further Notice reply comments at 2.

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model documentation.497 PNR has made the National Access Line Model process available for
review through on-site examination and has provided more detailed explanation of the National
Access Line Model upon request from interested parties. PNR notes that several parties have
taken advantage of this opportunity.498 PNR also notes that the National Access Line Model
computer code is available for review on-site.499 PNR also has filed with the Commission the
complete output of the National Access Line Model process.500 In addition, Bell Atlantic and
Sprint argue that the National Access Line Model produces line counts that vary significantly
from actual line counts.501
56.
In adopting the PNR approach for developing customer location counts, we note
that the synthesis model currently calculates the average cost per line by dividing the total cost
of serving customer locations by the current number of lines. Because the current number of
lines is used in this average cost calculation, we agree with AT&T and MCI that the total cost
should be determined by using the current number of customer locations. As AT&T and MCI
note, "the key issue is the consistency of the numerator and denominator" in the average cost
calculation. According to AT&T and MCI, other proposed approaches result in inconsistency
because they use the highest possible cost in the numerator and divide by the lowest possible
number of lines in the denominator, and therefore result in larger than necessary support
levels.502 AT&T and MCI also assert that, in order to be consistent, housing units must be used
in the determination of total lines if they are used in the determination of total costs.503 MCI
points out that "[i]f used consistently in this manner, building to housing units as GTE proposes
is unlikely to make any difference in cost per line."504 Although SBC advocates the use of
housing units, it agrees that the number of lines resulting from this approach should also be used
in the denominator of any cost per line calculation to prevent the distortion noted by AT&T and
MCI.505 We agree with AT&T and MCI that, as long as there is consistency in the development
497

See HAI Dec. 11, 1997, Model Description at 21.

498

PNR Inputs Further Notice reply comments at 2.

499

PNR Inputs Further Notice reply comments at 2-3.

500

Letter from Charles White, PNR, to Magalie Roman Salas, FCC, dated October 6, 1999.

501

Bell Atlantic Inputs Further Notice comments at 14-15; Sprint Inputs Further Notice comments at 13-14.

502

AT&T and MCI ex parte, Dec. 23, 1997.

503

Letter from Chris Frentrup, MCI, to Magalie Roman Salas, FCC, dated March 5, 1999 (MCI March 5 ex

parte).
504

MCI March 5 ex parte (Issues 1 and 2).

505

SBC Inputs Further Notice comments at 6.

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of total lines and total cost, it makes little difference whether households or housing units are
used in determining cost per line. For the reasons discussed below, we believe that PNR's
methodology based on households is less complex and more consistent with a forward-looking
methodology than housing units.
57.
To the extent that the PNR methodology includes the cost of providing service to
all currently served households, we conclude that this is consistent with a forward-looking cost
model, which is designed to estimate the cost of serving current demand. As noted by AT&T
and MCI, adopting housing units as the standard would inflate the cost per line by using the
highest possible numerator (all occupied and unoccupied housing units) and dividing by the
lowest possible denominator (the number of customers with telephones).506
58.
If we were to calculate the cost of a network that would serve all potential
customers, it would not be consistent to calculate the cost per line by using current demand. In
other words, it would not be consistent to estimate the cost per line by dividing the total cost of
serving all potential customers by the number of lines currently served. The level and source of
future demand, however, is uncertain. Future demand might include not only demand from
currently unoccupied housing units, but also demand from new housing units, or potential
increases in demand from currently subscribing households. We also recognize that population
or demographic changes may cause future demand levels in some areas to decline. Given the
uncertainty of future demand, we noted in the Inputs Further Notice that we are concerned that
including such a highly speculative cost of future demand may not reflect forward-looking cost
and may perpetuate a system of implicit support. Ameritech and AT&T and MCI also note that
adopting the proposed conservative fill factors will ensure sufficient plant to deal with any
customer churn created as a result of temporarily vacant households.507
59.
In addition, we do not believe that including the cost of providing service to all
housing units would necessarily promote universal service to unserved customers. We note that
there is no guarantee that carriers would use any support derived from the cost of serving all
housing units to provide service to these customers. Many states permit carriers to charge
substantial line extension or construction fees for connecting customers in remote areas to their
network. If that fee is unaffordable to a particular customer, raising the carrier's support level by
including the costs of serving that customer in the model's calculations would have no effect on
whether the customer actually receives service. In fact, as long as the customer remains
unserved, the carrier would receive a windfall. We recognize that providing service to currently
unserved customers in such circumstances is an important universal service goal and the
Commission is addressing this issue more directly in another proceeding.508
506

AT&T and MCI ex parte, Dec. 23, 1997.

507

Ameritech Inputs Further Notice comments at 7; AT&T/MCI Inputs Further Notice comments at 8. See
infra section V for discussion of fill factors.
508

See Federal-State Joint Board on Universal Service: Promoting Deployment and Subscribership in

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60.
We also find that interested parties have been given a reasonable opportunity to
review and understand the National Access Line Model process for developing customer counts.
The HAI sponsors have documented the process by which the National Access Line Model
derives customer location counts and PNR has made itself available to respond to inquiries from
interested parties. The National Access Line Model is a commercially licensed product
developed by PNR, and we do not find it unreasonable for PNR to place some restriction on its
distribution to the public. In addition, we agree that the National Access Line Model is more
correctly characterized as a process consisting of several steps, and therefore we find no practical
alternative to on-site review. Even if it were possible for PNR to turn the National Access Line
Model over to the public in a single format, we believe that this would be of limited utility
without a detailed explanation of the entire process. We therefore conclude that PNR has made
reasonable efforts to ensure that interested parties understand the underlying process by which
the National Access Line Model develops customer counts and has made that process reasonably
available to interested parties. In addition, unlike the case with PNR's geocode data points,
PNR's road surrogate customer location points are available for review and comparison by
interested parties.
61.
In response to Bell Atlantic and Sprint's concern regarding the line counts
generated by the National Access Line Model, we note that the line count data proposed in the
Inputs Further Notice had been trued up by PNR to 1996 ARMIS line counts. We subsequently
have modified those data to reflect the most currently available ARMIS data. Accordingly, the
input values that we adopt in this Order will true up the line counts generated by the National
Access Line Model to 1998 ARMIS line counts. While the Commission has requested line count
data from the non-rural LECs,509 no party has suggested, and we have not been able to discern,
any feasible way of associating such data with wire centers used in the model. The Commission
intends to continue to review this issue in addressing future refinements to the forward-looking
cost model.
62.
In the Inputs Further Notice, we also noted that the accuracy of wire center
boundaries is important in estimating the number of customer locations.510 PNR currently uses
BLR wire center information to estimate wire center boundaries.511 As noted above, the BCPM
Unserved and Underserved Areas, Including Tribal and Insular Areas, Further Notice of Proposed Rulemaking, CC
Docket No. 96-45, FCC 99-204 (rel. Sept. 3, 1999) at paras. 120-121.
509

See Federal-State Joint Board on Universal Service, Forward-Looking Mechanism for High Cost Support for
Non-Rural LECs, Order, CC Docket Nos. 96-45, 97-160, DA 99-1406 (rel. July 19, 1999).
510

Inputs Further Notice at para. 47.

511

HAI Dec. 11, 1997 submission, Model Description at 21.

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model also uses BLR wire center boundaries, as does Stopwatch Maps in its road surrogate
customer location files.512 A few commenters support the use of BLR wire center boundaries,
noting widespread use by the model proponents.513 Others advocate the use of actual wire center
boundaries.514 These commenters acknowledge, however, that this information is generally
considered confidential and may not be released publicly by the incumbent LEC.515 We
conclude that the BLR wire center boundaries are the best available data that are open to
inspection and that they provide a reasonably reliable estimation of wire center boundaries. We
note that both the BCPM and HAI proponents have utilized the BLR wire center data in their
respective models. While use of actual wire center boundaries may be preferable, we agree that
such information is currently unavailable or proprietary. We therefore approve the use of the
BLR wire center boundaries in the current customer location data set.
V. OUTSIDE PLANT INPUT VALUES
A.

Introduction

63.
In this section, we consider inputs to the model related to outside plant. The
Universal Service Order's first criterion specifies that "[t]he technology assumed in the cost
study or model must be the least-cost, most efficient, and reasonable technology for providing
the supported services that is currently being deployed."516 Thus, while the model uses existing
incumbent LEC wire center locations in designing outside plant, it does not necessarily reflect
existing incumbent LEC loop plant.517 Indeed, as the Commission stated in the Platform Order,
"[e]xisting incumbent LEC plant is not likely to reflect forward-looking technology or design
choices."518 The Universal Service Order's third criterion specifies that "[o]nly long-run
forward-looking costs may be included."519 We select input values consistent with these criteria.
512

See Sprint Dec. 11, 1998 ex parte, attachment at 1.

513

AT&T/MCI Inputs Further Notice comments at 8; PNR Inputs Further Notice reply comments at 3.

514

PRTC Inputs Further Notice comments at 6; SBC Inputs Further Notice comments at 6.

515

SBC Inputs Further Notice comments at 6; PNR Inputs Further Notice reply comments at 3.

516

Universal Service Order, 12 FCC Rcd at 8913, para. 250.

517

Inputs Further Notice at para. 11; Universal Service Order, 12 FCC Rcd at 8913, para. 250.

518

Platform Order, 12 FCC Rcd at 21350, para. 66. "Instead, incumbent LECs' existing plant will tend to
reflect choices made at a time when different technology options existed or when the relative cost of equipment to
labor may have been different than it is today." Id.
519

Universal Service Order, 12 FCC Rcd at 8913, para. 250.

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64.
As the Commission noted in the Platform Order, outside plant, or loop plant,
constitutes the largest portion of total network investment, particularly in rural areas.520 Outside
plant investment includes the copper cables in the distribution plant and the copper and optical
fiber cables in the feeder plant that connect the customers' premises to the central office. Cable
costs include the material costs of the cable, as well as the costs of installing the cable.521
65.
Outside plant consists of a mix of aerial, underground, and buried cable.522 Aerial
cable is strung between poles above ground. Underground cable is placed underground within
conduits for added support and protection. Buried cable is placed underground but without any
conduit. A significant portion of outside plant investment consists of the poles, trenches,
conduits, and other structure that support or house the copper and fiber cables. In some cases,
electric utilities, cable companies, and other telecommunications providers share structure with
the LEC and, therefore, only a portion of the costs associated with that structure are borne by the
LEC. Outside plant investment also includes the cost of the SAIs and DLCs that connect the
feeder and distribution plant.
B.

Engineering Assumptions and Optimizing Routines

66.
As noted in the Inputs Further Notice, the model determines outside plant
investment based on certain cost minimization and engineering considerations that have
associated input values.523 In the Inputs Further Notice, we recognized that it was necessary to
examine certain input values related to the engineering assumptions and optimization routines in
the model that affect outside plant costs.524 Specifically, we tentatively concluded that: (1) the
optimization routine in the model should be fully activated; (2) the model should not use T-1
feeder technology; and (3) the model should use rectilinear distances and a "road factor" of
one.525
1.

Optimization

520

Platform Order, 13 FCC Rcd at 21335, para. 27.

521

As discussed below, cable installation costs for buried cable often are included with the structure costs.

522

The phrase "plant mix" refers to the ratio of outside plant that is aerial, underground, or buried in a network
or particular area.
523

See Inputs Further Notice at paras. 56-63.

524

Inputs Further Notice at para. 56.

525

Inputs Further Notice at paras. 58, 61-62.

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67.
When running the model, the user has the option of optimizing distribution plant
routing via a minimum spanning tree algorithm discussed in the model documentation.526 The
algorithm functions by first calculating distribution routing using an engineering rule of thumb
and then comparing the cost with the spanning tree result, choosing the routing that minimizes
annualized cost.527 The user has the option of not using the distribution optimization feature,
thereby saving a significant amount of computation time, but reporting network costs that may
be significantly higher than with the optimization. The user also has the option of using the
optimization feature only in the lowest density zones.
68.
In reaching our tentative conclusion that the model should be run with the
optimization routine fully activated in all density zones, we recognized that using full
optimization can substantially increase the model's run time.528 We noted that a preliminary
analysis of comparison runs with full optimization versus runs with no optimization indicated
that, for clusters with line density greater than 500, the rule of thumb algorithm results in the
same or lower cost for nearly all clusters.529 Accordingly, we sought comment on whether an
acceptable compromise to full optimization would be to set the optimization factor at "-p500," as
described in the model documentation.530
69.
We adopt our tentative conclusion that the model should be run with the
optimization routine fully activated in all density zones when the model is used to calculate the
forward-looking cost of providing the services supported by the federal mechanism. The first of
the ten criteria pronounced by the Commission to ensure consistency in calculations of federal
universal support specifies that "[t]he technology assumed in the cost study or model must be the
least-cost, most efficient, and reasonable technology for providing the supported services that is
currently being deployed."531 As we explained in the Inputs Further Notice, running the model
with the optimization routine fully activated complies with this requirement.532 In contrast,
526

The model uses a minimum spanning tree algorithm based on the Prim algorithm. The model always
optimizes feeder plant. See HCPM Dec. 15, 1998 documentation at 13.
527

HCPM Dec. 15, 1998 documentation at 11.

528

Inputs Further Notice at para. 58.

529

See Inputs Further Notice at para. 58 n. 135. Since, under full optimization, the model chooses the least cost
of the full optimization algorithm or the rule of thumb algorithm, a comparison run as described above can show
how well the full optimization performs as a function of density.
530

See HCPM Dec. 15, 1998 documentation at 30-31; see also Design History of HCPM, April 6, 1999 at
http://www.fcc.gov/ccb/apd/hcpm.
531

Universal Service Order, 12 FCC Rcd at 8913, para. 250.

532

Inputs Further Notice at para. 58.

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running the model with the optimization routine disabled may result in costs that are
significantly higher than with full optimization. The majority of commenters that address the
optimization issue support the use of full optimization.533 GTE opposes any implementation of
optimization.534
70.
We agree with AT&T and MCI and GTE that it is inappropriate to deviate from
full optimization merely to minimize computer run time.535 While the rule of thumb algorithm
generally results in costs that are approximately the same as the spanning tree algorithm for
dense clusters, for some dense clusters the spanning tree algorithm will result in lower costs. For
this reason, we believe that any choice in maximum density clusters in which the minimum
spanning tree algorithm is not applied may result in an arbitrary overestimate of costs for some
clusters. Accordingly, running the model with full optimization is consistent with ensuring that
the model uses the least-cost, most efficient, and reasonable distribution plant routings for
providing the supported services.
71.
As explained above, the model seeks to minimize costs by selecting the lower of
the cost estimates from the spanning tree algorithm and the rule of thumb algorithm. Both GTE
and US West challenge the selection of the routing that minimizes annualized cost on the basis
of a comparison between an engineering rule of thumb and the spanning tree result.536 US West
claims that use of the rule of thumb approach is inappropriate because combining it with the
spanning tree analytical approach to determine the amount of needed plant biases the results
downward and will produce inappropriately low results.537
72.

We find that US West's concerns are misplaced. Contrary to US West's

533

See e.g., AT&T and MCI Inputs Further Notice comments at 9-10; US West Inputs Further Notice
comments at 21; SBC Inputs Further Notice comments at 7. We note that SBC supports full optimization so long as
its application produces a significant difference in the results. As we explain, application of full optimization does
produce a significant difference in the results. Moreover, SBC states that the optimization routine offers "the most
cost effective design." Id.
534

GTE Inputs Further Notice comments at 33-35; GTE Inputs Further Notice reply comments at 9-11.

535

AT&T and MCI Inputs Further Notice comments at 10; GTE Inputs Further Notice comments at 33. We
note that although GTE opposes any implementation of optimization, GTE also specifically addressed whether the
compromise to full optimization on which we sought comment was acceptable.
536

US West Inputs Further Notice comments at 18-21; GTE Inputs Further Notice comments at 34-35.

537

US West contends that, because the optimization algorithm functions by choosing between the lowest value
produced by the rule of thumb or the spanning tree, the optimization algorithm retains those instances where the rule
of thumb underestimates the amount of plant needed while eliminating all estimates that exceed the more
analytically derived results, thereby biasing the results downward. In order to remedy this flaw, US West
recommends that the model be modified to consider only the minimum spanning tree results for distribution design.

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characterization, the rule of thumb used in the model is not an averaging methodology.538
Instead, it is a methodology that determines a sufficient amount of investment to serve each
customer in every cluster using a standardized approach to network design. This approach
connects every populated microgrid cell to the SAI using routes which are placed along the
vertical and horizontal boundaries of the microgrid cells constructed in the distribution
algorithm.539 The rule-of-thumb algorithm is somewhat similar in its functioning to the so-called
“pinetree” methodology proposed by both the early HAI and BCPM models for building feeder
plant. Thus, the rule of thumb provides an independent calculation of sufficient outside plant for
each cluster. The minimum spanning tree algorithm connects drop terminal points to the SAI
using a more sophisticated algorithm in which routes are not restricted to following the vertical
and horizontal boundaries of microgrid cells. The algorithm "chooses" a path independently of
the set route structure defined by the rule-of-thumb,but still connects all drop terminals to the
SAI. Since both the rule of thumb algorithm and the spanning tree algorithm use currently
available technologies and generate investments that are sufficient to provide supported services,
an approach which selects the minimum cost based on an evaluation of both of the algorithms is
fully consistent with cost minimization principles.540
73.
We also disagree with GTE's assertion that the optimization routine should be
disabled because it disproportionately affects lower density areas where universal service is
needed most.541 The task of the model is to estimate the cost of the least-cost, most-efficient
network that is sufficient to provide the supported services. Moreover, we note that the model
does not determine the level of high-cost support amounts. We have taken steps in our
companion order to ensure that sufficient support is provided for rural and high-cost areas.
538

See US West Inputs Further Notice comments at 18-21.

539

Because the optimization routine allows for the possibility of some, but not all possible junction nodes (also
called Steiner nodes), it is possible that the "rule of thumb" can provide a feasible lower cost result than the
optimization routine in certain cases. As explained in the model documentation, junction nodes can sometimes
reduce the cost of constructing a communications network. HCPM Dec.15, 1998 documentation at 14.
540

US West's recommendation that only the minimum spanning tree results be recognized would have us ignore
accepted practices in cost minimization. Because it is not possible in the general case to solve for the optimal
solution, it is accepted practice in cost minimization analysis to examine the results of various available alternative
cost minimization methodologies and choose the lowest cost result, provided that each alternative meets the
appropriate design standards. This is the same principle on which Branch and Bond algorithms work. See e.g.,
Mark S. Daskin, Network and Discrete Location: Models, Algorithms, and Applications (1995). In so doing, the
result that is chosen is the result that is closer to the least cost, while providing a sufficient amount of plant to
provide the supported services. For these reasons, the optimization algorithm employed in the model produces
results superior to those produced by the application of only a single cost minimization methodology.
541

GTE asserts that an analysis of GTE's service area in Oregon reveals that a majority of the cost impact occurs
when the spanning tree algorithm optimizes clusters with less than 100 lines per square mile. GTE Inputs Further
Notice reply comments at 11.

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74.
We also reject GTE's claim that the optimization routine does not work as
542
intended.
GTE bases this contention on the observation that in some instances when the
optimization factor is increased from -p100 to -p200 (i.e. going from density zones less than or
equal to 100 lines per square mile to density zones less than or equal to 200 lines per square
mile), both loop investment and universal service requirements increase. This, according to
GTE, would not happen if the optimization worked properly.543
75.
We disagree. Optimizing the distribution plant is not synonymous with
optimizing the entire network. Because the model's optimization routine optimizes distribution
and feeder sequentially, and the starting point for the optimization of feeder plant is the
distribution plant routing chosen, there are occasions when the optimal feeder plant will be more
costly than it would be if distribution plant and feeder plant had been optimized simultaneously.
In some cases, the lower distribution investment produced by the optimization routine may be
offset by higher feeder investment, resulting in higher total outside plant costs than produced by
the rule of thumb algorithm.544 Contrary to GTE's assertion, this phenomenon does not
demonstrate that the optimization works improperly. To the contrary, it demonstrates that
optimization occurs properly within the constraints of the model's design.
76.
Moreover, we conclude that such rare occurrences do not outweigh the benefits of
the optimization routine. The magnitude of the difference between the network cost produced by
the optimization routine in these instances and the rule of thumb algorithm is de minimis.
Furthermore, altering the model to optimize distribution investment and feeder investment
simultaneously would greatly add to the complexity of the model.

542

T-1 Technology

77.

A user of the model also has the option of using T-1 on copper technology as an

GTE Inputs Further Notice reply comments at 11.

543

GTE also claims that there are numerous cases where the optimization routine has resulted in increased costs
at the wire center level. GTE Input Further Notice comments at 34-35. Specifically, GTE contends that when the
optimization logic is applied to clusters with fewer than 100 lines per square mile for GTE's Florida serving area,
total monthly costs for eight wire centers were higher than without optimization.
544

This situation can occur because the minimum spanning tree algorithm may increase the distance of some
customers in a cluster from the serving area interface in order to achieve lower overall costs through more efficient
routing. In some cases, this increased distance might cause a cluster that fell within the maximum copper distance
constraint under the rule of thumb algorithm to exceed that constraint. The increased cost of serving the cluster
with the fiber feeder system could then increase total cost even though the optimization worked as intended in the
distribution portion of the model.

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alternative to analog copper feeder or fiber feeder in certain circumstances.545 T-1 is a
technology that allows digital signals to be transmitted on two pairs of copper wires at 1.544
Megabits per second (Mbps). If the T-1 option is enabled, the optimizing routines in the model
will choose the least cost feeder technology among three options: analog copper; T-1 on copper;
and fiber.546 For serving clusters with loop distances below the maximum copper loop length,
the model could choose among all three options; between 18,000 feet and the fiber crossover
point, which earlier versions of the model set at 24,000 feet, the model could choose between
fiber and T-1, and above the fiber crossover point, the model would always use fiber. In the HAI
model, T-1 technology is used to serve very small outlier clusters in locations where the copper
distribution cable would exceed 18,000 feet.
78.
In the Inputs Further Notice, we tentatively concluded that the T-1 option in the
model should not be used at this time.547 We noted that the only input values for T-1 costs on
the record were the HAI default values and tentatively found that, because the model and HAI
model use T-1 differently, it would be inappropriate to use the T-1 technology in the model
based on these input values.548 We also noted that the BCPM sponsors and other LECs
maintained that T-1 was not a forward-looking technology and therefore should not be used in
the model.549 Other sources indicated that advanced technologies, such as HDSL, could be used
to transmit information at T-1 or higher rates.550 We sought comment on this issue.551 We also
sought comment on the extent to which HDSL technology presently is being used to provide T-1
service.552
79.
We conclude that the T-1 option should not be employed in the current version of
the model. We agree with those commenters addressing this issue that traditional T-1 using

545

See Inputs Further Notice at para. 59.

546

HCPM Dec. 15, 1998 documentation at 10.

547

Inputs Further Notice at para. 61.

548

Inputs Further Notice at para. 61.

549

Inputs Further Notice at para. 59.

550

HDSL (high data rate digital subscriber line) transmits 1.544 Mbps or 2.048 Mbps in bandwidths ranging
from 80 kilohertz (kHz) to 240 kHz, rather than in a bandwidth of 1.5 megahertz (mHz) required for traditional T-1
services. See www.adsl.com/general_tutorial.
551

Inputs Further Notice at para. 60.

552

Inputs Further Notice at para. 60.

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repeaters at 6000 foot intervals is not a forward-looking technology.553 While HDSL and other
DSL variants are forward-looking technologies, we do not at this time have sufficient
information to determine appropriate input values for these technologies for use in the model.
We conclude, therefore, that use of T-1 in the optimization routine as an alternative to analog
copper or digital fiber feeder for certain loops under 24,000 feet is not appropriate at this time.554
Accordingly, the model will be run for universal service purposes with the T-1 option disabled.
3.

Distance Calculations and Road Factor

80.
In the distribution and feeder computations within the model, costs for cable and
structure are computed by multiplying the route distances by the cost per foot of the cable or the
structure facility, which depends on capacity and terrain factors. Distances between any two
points in the network are computed using either of two distance functions.555 The model allows
a separate road factor for each distance function, and every distance measurement made in the
model is multiplied by the designated factor. Road factors could be computed by comparing
average distances between geographic points along actual roads with distances computed using
either of the two distance functions. Given sufficient data, these factors could be computed at
highly disaggregated levels, such as the state, county, or individual wire center.
81.
In the Inputs Further Notice, we tentatively concluded that the model should use
rectilinear distance in calculating outside plant distances, rather than airline distance, because
553

See e.g., GTE Inputs Further Notice comments at 62; SBC Inputs Further Notice comments at 7; AT&T and
MCI Inputs Further Notice comments at 11; AT&T and MCI Inputs Further Notice reply comments at 12-13. We
note that, notwithstanding their support for the decision to not use T-1, AT&T and MCI encourage the Commission
to modify the model to use T-1 technology in the same manner as does the HAI model, i.e., as a distribution
alternative where, after using a fiber fed integrated digital loop carrier to link a main cluster of customer locations
with a serving wire center, outlying customer locations beyond 18,000 feet from the main cluster's center are served
by copper T-1 distribution loops. This recommendation, which would represent a platform change, will be
considered in the upcoming proceeding on future changes to the model.
554

SBC and GTE responded to our inquiry regarding the use and extent of advanced technologies to transmit
information at T-1 on higher rates. SBC maintains that it is not reasonable to expect that HDSL will be used on T-1
technology. SBC Inputs Further Notice comments at 7. SBC explains that HDSL is being considered primarily for
small pair gain (DLC) activation to meet specific customer needs or HI-CAP provisioning, and not for normal DLC
activation. GTE maintains that HDSL can be and is used to provide 1.544 Megabit per second data rates over
embedded copper plant, but its use is not an appropriate forward-looking technology. GTE Inputs Further Notice
comments at 62. GTE adds that predominant uses of HDSL are to provision "short fuse" 1.544 Mbps service
requests and extend the life of the embedded copper network. In sum, SBC and GTE assert that, even if augmented
by advanced technology such as HDSL, T-1 is still not a forward-looking technology.
555

A rectilinear measurement computes the distance between two points by constructing a rectangle with the
two points as opposite vertices and measuring the distance of two adjacent sides of the rectangle. The airline
distance is the length of the diagonal line that directly connects the two points.

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rectilinear distance more accurately reflects the routing of telephone plant along roads and other
rights of way.556 We also tentatively concluded that the road factor in the model, which reflects
the ratio between route distance and road distance, should be set equal to one.557 In addition, we
asked whether we should use airline miles with wire center specific road factors as an alternative
to rectilinear distance.558
82.
We reaffirm our tentative conclusion that the model should use rectilinear
distance rather than airline distance in calculating outside plant distances.559 As we noted in the
Inputs Further Notice, research suggests that, on average, rectilinear distance closely
approximates road distances.560 We agree with SBC that the calculation of outside plant
distances should reflect the closest approximation to actual route conditions and road distance.561
We also conclude that it would be inappropriate to use airline distance in the model without
simultaneously developing a process for determining accurate road factors (which would be
uniformly greater than or equal to 1 in this case). While the use of geographically disaggregated
road factors may merit further investigation, we note that the absence of such a data set on the
record at this time precludes our ability to adopt that approach.562 We therefore conclude that
the model should use a rectilinear distance metric with a road factor of one.
C.

Cable and Structure Costs
1.

Background

83.
The model uses several tables to calculate cable costs, based on the cost per foot
of cable, which may vary by cable size (i.e., gauge and pair size) and the type of plant (i.e.,
underground, buried, or aerial). There are four separate tables for copper distribution and feeder
556

Inputs Further Notice at para. 62.

557

Inputs Further Notice at para. 62.

558

Inputs Further Notice at para. 63.

559

As BellSouth attests, cable rarely follows a straight-line "as the crow flies" route. BellSouth Inputs Further
Notice comments, Attachment B at B-3.
560

Inputs Further Notice at para. 62 n. 142 citing Robert F. Love et al., Facilities Location Models and
Methods, Chapter 10 (1988).
561

SBC Inputs Further Notice comments at 7.

562

We make no finding as to whether using airline miles with geographically disaggregated road factors, if
available, would be a more appropriate method of calculating distances and intend to explore this issue further in the
future of the model proceeding.

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cable of two different gauges, and one table for fiber cable. The engineering assumptions and
optimizing routines in the model, in conjunction with the input values in the tables, determine
which type of cable is used.
84.
The model also uses structure cost tables that identify the per foot cost of loop
structure by type (aerial, buried, or underground), loop segment (distribution or feeder), and
terrain conditions (normal, soft rock, or hard rock) for each of the nine density zones.563
85.
After the model has grouped customer locations in clusters, it determines, based
on cost minimization and engineering considerations, the appropriate technology type for the
cluster and the correct size of cables in the distribution network. Every customer location is
connected to the closest SAI by copper cable. The copper cable used in the local loop typically
is either 24- or 26-gauge copper. Twenty-four gauge copper is thicker and, therefore, is expected
to be more expensive than 26-gauge copper. Twenty-four gauge copper also can carry signals
greater distances without degradation than 26-gauge copper and, therefore, is used in longer
loops. In the model, if the maximum distance from the customer to the SAI is less than or equal
to the copper gauge crossover point, then 26-gauge cable is used. Feeder cable is either copper
or fiber. Fiber is used for loops that exceed 18,000 feet, the maximum copper loop length
permitted in the model, as determined in the Platform Order.564 When fiber is more cost
effective, the model will use it to replace copper for loops that are shorter than 18,000 feet.
86.
In the 1997 Further Notice, the Commission sought comment on the input values
that the model should use for cable and installation costs.565 The Commission specifically
sought comment on the accuracy of the default values in the BCPM and HAI models and
encouraged companies to submit data to support their positions.566 The Commission tentatively
concluded that cable material and installation costs should be separately identified by both

563

The nine density zones (measured in terms of the number of lines per square mile) are as follows: (1) zero 4.99; (2) 5 - 99.99; (3) 100 - 199.99; (4) 200 - 649.99; (5) 650 - 849.99; (6) 850 - 2549.99; (7) 2550 - 4999.99; (8)
5000 - 9,999.99; (9) 10,000+.
564

Platform Order, 13 FCC Rcd at 21352-53, para. 70.

565

1997 Further Notice, 12 FCC Rcd at 18544.

566

1997 Further Notice, 12 FCC Rcd at 18544. The BCPM and HAI default values are the default input values
for the user-adjustable input values in the BCPM and HAI models, respectively. Although we had chosen a model
platform and were no longer considering adoption of the BCPM and HAI models, we continued to consider the
BCPM and HAI default input values for the inputs to be used in the model. As we explained in the Inputs Further
Notice, for some inputs, these were the only values on the record. Inputs Further Notice at para. 51 n. 125. We also
noted that although the BCPM model includes nationwide default values, the BCPM sponsors generally advocated
the use of company-specific values and, in some cases, proposed such values.

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density zone and terrain type.567 Because the Commission had received no documentation
confirming that feeder and distribution cable installation costs should differ, the Commission
tentatively concluded that the federal mechanism should adopt HAI's assumption that such costs
are identical.568
87.
The Commission also sought comment and adopted tentative findings and
conclusions relating to the cost of outside plant structure in the 1997 Further Notice.569 The
Commission directed the HAI and BCPM sponsors to justify fully their default values for their
mix of aerial, underground, and buried structure (i.e., plant mix) and sought comment on the
input values that will accurately reflect the impact of varying terrain conditions on costs.570 The
Commission noted that "recent installations of outside structure may more closely meet forwardlooking design criteria than do historical installations."571 The Commission found that an
efficient carrier will vary its plant mix according to the population density of an area and
tentatively concluded that the assignment of plant mix defined by the model should reflect both
terrain factors and line density zones.572
88.
In the Inputs Public Notice, the Bureau sought comment on the analysis of David
Gabel and Scott Kennedy of data from the Rural Utilities Service (RUS) regarding cable and
structure costs.573 On December 11, 1998, the Bureau held a public workshop designed to elicit
comment on the input values for materials costs.574 At the workshop, Dr. Gabel presented the
methodology used by the Commission staff to derive preliminary values for cable costs for nonrural LECs based on his earlier analysis of the RUS data.
89.
We sought to supplement the record with respect to cable and structure costs by
requesting additional data from LECs, including competitive LECs, in the form of a voluntary
567

1997 Further Notice, 12 FCC Rcd at 18544.

568

1997 Further Notice, 12 FCC Rcd at 18544.

569

1997 Further Notice, 12 FCC Rcd at 18541.

570

1997 Further Notice, 12 FCC Rcd at 18541.

571

1997 Further Notice, 12 FCC Rcd at 18541.

572

1997 Further Notice, 12 FCC Rcd at 18541.

573

Inputs Public Notice at 7. See David Gabel and Scott Kennedy, Estimating the Cost of Switching and Cables
Based on Publicly Available Data, National Regulatory Research Institute NRRI 98-09, April 1998, (NRRI Study).
Dr. Gabel and Mr. Kennedy are consultants for the Commission in this proceeding.
574

See Workshop Public Notice.

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survey of structure and cable costs.575 Ten companies eventually responded to the survey.576
2.

Nationwide Values

90.
As discussed in this section, we adopt nationwide average values for estimating
cable and structure costs in the model rather than company-specific values.577 In reaching this
conclusion, we reject the explicit or implicit assumption of most LEC commenters that
company-specific values, which reflect the costs of their embedded plant, are the best predictor
of the forward-looking cost of constructing the network investment predicted by the model. We
find that, consistent with the Universal Services Order's third criterion, the forward-looking cost
of constructing a plant should reflect costs that an efficient carrier would incur, not the
embedded cost of the facilities, functions, or elements of a carrier.578 We recognize that
variability in historic costs among companies is due to a variety of factors and does not simply
reflect how efficient or inefficient a firm is in providing the supported services. We reject
arguments of the LECs, however, that we should capture this variability by using companyspecific data rather than nationwide average values in the model. We find that using companyspecific data for federal universal service support purposes would be administratively
unmanageable and inappropriate. Moreover, we find that averages, rather than company-specific
data, are better predictors of the forward-looking costs that should be supported by the federal
high-cost mechanism. Furthermore, we note that we are not attempting to identify any particular
company's cost of providing the supported services. We are estimating the costs that an efficient
provider would incur in providing the supported services.
91.
AT&T and MCI agree that nationwide input values generally should be used for
the input values in the model.579 AT&T and MCI concur with our tentative conclusion that the
use of nationwide values is more consistent with the forward-looking nature of the high-cost
575

After numerous discussions with industry during development of the survey, we distributed a final version on
December 14, 1998, and requested responses by January 14, 1999.
576

BellSouth, Ameritech, Pacific Bell, Nevada Bell, Southwestern Bell, Sprint, GTE, Aliant, SNET, and AT&T
submitted data in response to the structure and cable cost survey. Several companies requested additional time to
complete and submit their data. After receiving and reviewing the data, staff found that, despite detailed survey
instructions, further discussions with a number of companies were required before we could assemble the data for
comparison and analysis. In a number of cases, respondents filed revised data or clarified the data they had
submitted.
577

See also supra paragraphs 29-32 and infra paragraph 348 for further discussion of the adoption of nationwide
average values for estimating costs and expenses in the model.
578

Universal Service Order, 12 FCC Rcd at 8913, para. 250.

579

AT&T and MCI Inputs Further Notice reply comments at 3.

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model because it mitigates the rewards to less efficient companies. Additionally, AT&T and
MCI maintain that developing separate inputs values on a state-specific, study-area specific, or
holding company-specific basis is not practicable. As AT&T and MCI contend, doing so would
be costly and administratively burdensome.
92.
While reliance on company-specific data may be appropriate in other contexts, we
find that for federal universal service support purposes it would be administratively
unmanageable and inappropriate. The incumbent LECs argue that virtually all model inputs
should be company-specific and reflect their individual costs, typically by state or by study
area.580 For example, GTE claims that the costs that an efficient carrier incurs to provide basic
service vary among states and even among geographic areas within a state.581 GTE asserts that
the only way for the model to generate accurate estimates, i.e., estimates that reflect these
differences, is to use company-specific inputs rather than nationwide input values. As parties in
this proceeding have noted, however, selecting inputs for use in the high-cost model is a
complex process. Selecting different values for each input for each of the fifty states, the District
of Columbia, and Puerto Rico, or for each of the 94 non-rural study areas, would increase the
Commission's administrative burden significantly. Unless we simply accept the data the
companies provide us at face value, we would have to engage in a lengthy process of verifying
the reasonableness of each company's data. For example, in a typical tariff investigation or state
rate case, regulators examine company data for one time high or low costs, pro forma
adjustments, and other exceptions and direct carriers to adjust their rates accordingly.
Scrutinizing company-specific data to identify such anomalies and to make the appropriate
adjustments to the company-proposed input values to ensure that they are reasonable would be
exceedingly time consuming and complicated given the number of inputs to the model.
93.
Where possible, we have tried to account for variations in costs by objective
means. As explained below, the model reflects differences in structure costs by using different
values for the type of plant, the density zone, and geological conditions. As discussed below, we
sought comment in the Inputs Further Notice on alternatives to nationwide plant mix values, but
the algorithms on the record produce biased results. We continue to believe that varying plant
mix by state, study area, or region of the country may more accurately reflect variations in
forward-looking costs and intend to seek further comment on this issue in the future of the model
proceeding.

580

See, e.g., Bell Atlantic Inputs Further Notice comments at 20-21; BellSouth Inputs Further Notice comments,
Attachment B at B-16, B-18; GTE Inputs Further Notice comments at 10-11; Ameritech Inputs Further Notice
comments at 8; Sprint Inputs Further Notice comments at 3-7.
581

GTE Inputs Further Notice comments at 10-11. See also BellSouth Inputs Further Notice comments,
Attachment A at A-5, A-8 - A-14.

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Preliminary Cable Cost Issues

94.
Use of 24-gauge and 26-gauge Copper. In the Inputs Further Notice, we
tentatively concluded that the model should use both 24-gauge and 26-gauge copper in all
available pair-sizes.582 We based our tentative conclusion on a preliminary analysis of the
results of the structure and cable cost survey, in which it appeared that a significant amount of
24-gauge copper cable in larger pair sizes currently is being deployed. We also noted that, while
HAI default values assume that all copper cable below 400 pairs in size is 24-gauge and all
copper cable of 400 pairs and larger is 26-gauge, the BCPM default values include separate costs
for 24- and 26-gauge copper of all sizes.583
95.
We conclude that the model should use both 24-gauge and 26-gauge copper in all
available pair sizes. No commenter refuted our observation that a significant amount of 24gauge copper cable in larger pair sizes currently is being deployed. Those commenters
addressing this issue concur with our tentative conclusion.584 SBC confirms our analysis of the
survey data and notes that it deploys 24-gauge cable in sizes from 25 to 2400 pairs.585 GTE
explains, and we agree, that the model should use both 24-gauge and 26-gauge copper in all
available pair sizes in order to stay within transmission guidelines when modeling 18 kilofoot
loops.586
96.
Distinguishing Feeder and Distribution Cable Costs. In the Inputs Further
Notice, we reaffirmed the Commission's tentative conclusion in the 1997 Further Notice that the
same input values should be used for copper cable whether it is used in feeder or in distribution
plant.587 We adopt this tentative conclusion. Those commenters addressing this issue agree with
our tentative conclusion.588 GTE contends that it is both unnecessary and inappropriate to have
582

Inputs Further Notice at para. 65.

583

Inputs Further Notice at para. 65 n. 145 citing HAI Inputs Portfolio at 20.

584

See e.g., AT&T and MCI Inputs Further Notice comments at 13; GTE Inputs Further Notice comments at
47-48; Sprint Inputs Further Notice comments at 17-18; SBC Inputs Further Notice comments at 7-8.
585

SBC Inputs Further Notice comments at 8.

586

GTE Inputs Further Notice comments at 47. GTE asserts that it believes that, even for 12 kilofoot loops, a
significant amount of 24-gauge cable will continue to be deployed in the network because of certain cost-saving
reasons related to its larger diameter.
587

Inputs Further Notice at para. 66.

588

See e.g., GTE Inputs Further Notice comments at 48; Sprint Inputs Further Notice comments at 18; SBC
Inputs Further Notice comments at 8.

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different costs for feeder and distribution cable material.589 GTE explains that, although
quantities of material and labor related to cable size may differ between feeder and distribution,
the unit costs for each remain the same.590 Similarly, Sprint agrees that the material cost of cable
is the same whether it is used for distribution or feeder.591 In sum, we find that the record
demonstrates that it is appropriate to use the same input values for copper cable whether it is
used in feeder or in distribution plant.
97.
Distinguishing Underground, Buried, and Aerial Installation Costs. In the Inputs
Further Notice, we also tentatively concluded that we should adopt separate input values for the
cost of aerial, underground, and buried cable.592 We reached this tentative conclusion on the
basis of our analysis of cable cost data supplied to us in response to data requests and through ex
parte presentations. We found considerable differences in the per foot cost of cable, depending
upon whether the cable was strung on poles, pulled through conduit, or buried.
98.
We conclude that separate input values for the cost of aerial, underground, and
buried cable should be adopted. Those commenters addressing this issue confirm our analysis of
the data, i.e., that there are differences, some significant, in placement costs for aerial,
underground, and buried cable.593 GTE explains that, from a material perspective, the cable may
have different protective sheathing, depending on construction applications.594 GTE adds that
labor costs also differ depending on the type of placement.595 Both SBC and Sprint identify the
cost of labor as varying significantly depending upon the type of placement.596 Based upon a
review of the record in this proceeding, we conclude that separate input values for the cost of
aerial, underground, and buried cable are, therefore, warranted.
589

GTE Inputs Further Notice comments at 47. See also SBC Inputs Further Notice comments at 8. SBC
contends that the same input values should be used as long as density values, which reflect costs differences in
varying degrees of urban and suburban construction, are properly reflected.
590

GTE Inputs Further Notice comments at 47.

591

Sprint Inputs Further Notice comments at 17. Sprint contends however that in actual practice, splicing costs
may be somewhat higher for distribution cable due to such factors as more frequent tapering of cable sizes and
branch splices, but this difference is not material for modeling purposes.
592

Inputs Further Notice at para. 68.

593

See e.g., GTE Inputs Further Notice comments at 48; SBC Inputs Further Notice comments at 8; Sprint
Inputs Further Notice comments at 18. See also AT&T and MCI Inputs Further Notice comments at 13.
594

GTE Inputs Further Notice comments at 48.

595

GTE Inputs Further Notice comments at 48.

596

SBC Inputs Further Notice comments at 8; Sprint Inputs Further Notice comments at 18.

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99.
Deployment of Digital Lines. We also conclude that two inputs, "pct_DS1" and
"pct_1sa", should be modified to provide more accurate deployment of digital lines in the
distribution plant. The model can deploy a portion of distribution plant on digital DS1 circuits
by specifying these two user adjustable inputs. The input "pct_DS1" determines the percentage
of switched business traffic carried on DS1 circuits, and the input "pct_1sa" determines the
percentage of special access lines carried on DS1 circuits. Previously, we used default values for
the inputs "pct_DS1" and "pct_1sa." We now adopt more accurate values for these inputs using
1998 line count data, following the methodology described below.
100. Initially the model determines the number of special access lines from a
"LineCount" table in the database "hcpm.mdb," which provides for each wire center the number
of residential lines, business lines, special access lines, public lines, and single business lines.597
The Commission required incumbent LECs to provide line counts for business switched and
non-switched access lines on a voice equivalent basis598 and on a facilities basis.599 Upon receipt
of those filings, we determined industry totals for each of the line count items requested.600 By
applying the model's engineering conventions to the totals, the model determines the percentage
of switched and non-switched lines provided as DS1-type service.601 Thus, using the channel
and facility counts submitted in response to the 1999 Data Request, it is possible to determine
the "pct_DS1" input value using the following formula: (1-pct_DS1)*channels +
pct_DS1*channels/12 = facilities.602 A similar calculation is performed to solve for the
"pct_1sa" input value. For both switched business and special access lines, the number of digital
lines is then determined by multiplying the respective line count by the input value "pct_DS1" or
"pct_1sa." Since 24 communications channels can be carried by two pairs of copper wires, the
number of copper cables required to carry digital traffic is computed by dividing the number of
digital channels by 12. These percentages are used to adjust the wire center cable requirements
by reducing the facilities needed to serve multi-line business and special access customers.
597

By model convention, business lines are reported as switched business lines.

598

For example, DS1 service provides 24 voice equivalent channels using two copper pairs.

599

See Federal-State Joint Board on Universal Service, Forward-Looking Mechanism for High Cost Support for
Non-Rural LECs, Order, CC Docket Nos. 96-45, 97-160, DA 99-1406 (rel. July 19, 1999) (1999 Data Request).
600

For these line count totals, we only use data from the responses that we found to be consistent with the
definitions prescribed in the 1999 Data Request. Submissions in which companies reported more facilities than
channels are inconsistent with those definitions and do not reflect current industry practice.
601

We note that only DS0 or DS1 service is provided under the model's conventions. The model does not allow
for the deployment of DS2 or DS3 services.
602

This equation is applied separately for switched and non-switched lines.

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Cost Per Foot of Cable
a.

Background

101. In the Inputs Further Notice, we tentatively concluded that we should use, with
certain modifications, the estimates in the NRRI Study, Estimating the Cost of Switching and
Cables Based on Publicly Available Data, for the per-foot cost of aerial, underground, and
buried 24-gauge copper cable.603 Concomitantly, we tentatively concluded that we should use,
with certain modifications, the estimates in the NRRI Study for the per-foot cost of aerial,
underground, and buried fiber cable.604
102. In reaching these conclusions, we rejected the default input values for cable costs
provided by both the HAI and BCPM sponsors which are based upon the opinions of their
respective experts, because they lacked additional support that would have enabled us to
substantiate those opinions.605 We also noted that we had received cable cost data from a
number of LECs, including data received in response to the structure and cable cost survey, and
were in the process of scrutinizing it.606
103. The HAI sponsors supported using the publicly available RUS data in the NRRI
Study to estimate cable costs and structure costs.607 In contrast, Sprint questioned the reliability
and suitability of these data, and urged us instead to use the cable cost data provided by
incumbent LECs.608 Sprint pointed out that the RUS data only reflect information from the two
lowest density zones.609 Sprint explained that because longer loops are used in sparsely
populated areas, lower-gauge copper often is used. We explained that Sprint had
mischaracterized the analysis of the RUS data in the NRRI Study. We noted for example, that
603

Inputs Further Notice at para. 72; See also Inputs Further Notice at 77, 82-83. As noted in paragraph 88
supra, this study provides a methodology for estimating cable and structure costs.
604

Inputs Further Notice at paras. 90, 92, 94.

605

Inputs Further Notice at para. 69.

606

Inputs Further Notice at para. 69.

607

See Inputs Further Notice at para. 71 n. 152 citing Letter from Chris Frentrup, MCI Worldcom, to Magalie
Roman Salas, FCC, dated Feb. 9, 1999 (MCI Feb. 9, 1999 ex parte).
608

See Inputs Further Notice at para. 71 n. 153 citing Letter from Pete Sywenki, Sprint, to Magalie Roman
Salas, FCC, dated Jan 29, 1999 (Sprint Jan. 29, 1999 ex parte).
609

See Inputs Further Notice at para 71 n. 154 citing Sprint Jan. 29, 1999 ex parte at 8-9.

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Sprint challenged the validity of the study because some of the observations have zero values for
labor or material, while failing to recognize that these values were excluded from Gabel and
Kennedy's regression analysis.610 Similarly, we found that Sprint's complaint that Gabel and
Kennedy do not analyze separately the components of total cable costs, labor and material,
overlooked the fact that Gabel and Kennedy's regression analysis is designed to explain the
variation in total costs.611
104. Moreover, in reaching our tentative conclusion to use the NRRI Study and the
underlying data from the two lowest density zones, i.e., rural areas, to estimate cable costs for
non-rural LECs, we noted that none of the parties proposed cable cost values that vary by density
zones. Nor did the models considered by the Commission have the capability of varying cable
costs by density zones.612
b.

Discussion

105. We affirm our tentative conclusion that we should use, with certain modifications
as described more fully below, the estimates in the NRRI Study for the per-foot cost of aerial,
underground, and buried 24-gauge copper cable and for the per-foot cost of aerial, underground,
and buried fiber cable. We conclude that, on balance, these estimates, as modified in the Inputs
Further Notice, and further adjusted herein, are the most reasonable estimates of the per-foot
cost of aerial, underground, and buried 24-gauge copper cable and fiber cable on the record
before us. In reaching this conclusion, we reject, for the reasons enumerated below, the
arguments of those commenters who contend that we should use company-specific data to
develop the inputs for the per-foot cost of cable to be used in the model.613
106. Company-specific data. As we discussed above, we have determined to use
nationwide average input values for estimating outside plant costs.614 In reaching this
conclusion, we determined that the use of company-specific inputs was inappropriate because of
the difficulty in verifying the reasonableness of each company's data, among other reasons. We
have examined cable cost and structure cost data received from a number of non-rural LECs, as
well as AT&T, in response to the structure and cable cost survey and through a series of ex parte
610

Inputs Further Notice at para. 73 n. 156 citing Sprint Jan. 29 ex parte, Attachment at 5.

611

Inputs Further Notice at para. 73 n. 157 citing Sprint Jan. 29 ex parte, Attachment at 7.

612

Inputs Further Notice at para. 73.

613

See e.g., Bell Atlantic Inputs Further Notice comments at 18; GTE Inputs Further Notice comments at 48;
BellSouth Inputs Further Notice comments, Attachment B at B-7 - B-11.
614

See supra paragraph 29-32 and 90-93.

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filings. In addition, we have examined additional company-specific data submitted by certain
parties with their comments. As discussed more fully below, we conclude that these data are not
sufficiently reliable to use to estimate the nationwide input values for cable costs or structure
costs to be used in the model.615
107. We conclude that the cable cost and structure cost data received in response to the
structure and cable cost survey, in the ex parte filings, and in the comments are not verifiable.
We find that with regard to the survey data, notwithstanding our request, most respondents did
not trace the costs submitted in response to the survey from dollar amounts set forth in contracts
by providing copies of these contracts and all of the interim calculations for a single project or a
randomly selected central office. With regard to the ex parte data and data submitted with the
comments, we find that, because most respondents did not document in sufficient detail the
methodology, calculations, assumptions, and other data used to develop the costs they submitted,
nor did they submit contracts or invoices setting forth in detail the cable and structure costs they
incurred, these data cannot be substantiated.616 Moreover, we note that the structure and cable
costs reported in the survey by some parties differ significantly from those reported by the same
parties in the ex parte filings. These differences are not explained, and render those sets of data
unreliable.
108. We find this lack of back-up information particularly unsettling given the
magnitude of certain of the costs reported. We agree with AT&T and MCI that the cable
installation costs submitted by the incumbent LECs appear to be high.617 We also agree with
AT&T and MCI that this is because the loading factors employed in calculating these costs
appear to be overstated. Because of the lack of back-up information to explain these loading
costs, however, there is no evidence on the record to controvert our initial assessment.
Accordingly, the level of these costs remains suspect.
109. Moreover, we find additional deficiencies beyond the critical lack of
substantiating data, impugning the reliability of the LEC survey data and the ex parte data we
have received. As discussed above, the task of the model is to calculate forward-looking costs of
615

The following discussion reaches conclusions with regard to the use of company-specific data in the
estimation of cable costs inputs. Such information was received initially, in conjunction with structure costs data, in
response to our survey on cable and structure costs. Because we find that the data for cable costs and structure costs
suffers from the same deficiencies, we also reach conclusions with regard to the use of such data in the estimation of
structure cost inputs.
616

In reaching this conclusion we also take note of AT&T and MCI's inability to link the incumbents LECs
actual contract costs and the data they submitted to the Commission. AT&T and MCI Inputs Further Notice
comments at 15 (Proprietary Version).
617

AT&T and MCI Input Further Notice comments at 15 (Proprietary Version).

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constructing a wireline local telephone network. To that end, the survey directed respondents to
submit cable and structure costs for growth projects for which expenditures were at least
$50,000.618 We believed that such projects would best reflect the costs that a LEC would incur
today to install cable if it were to construct a local telephone network using current technology.
In contrast, absent from the data would be costs associated with maintenance or projects of
smaller scale which do not represent the costs of installing cable during such construction using
current technology. Thus, the data would capture the economies of scale enjoyed on large
projects which, should result in lower cable costs on a per-foot basis. Notwithstanding the
survey directions, several of the respondents submitted data representing projects that were not
growth projects or projects for which expenditures were less than the $50,000 minimum we
established.
110. Conversely, some respondents included costs that should have been excluded
under the definitions employed in the survey. For example, some respondents included costs for
terminating structures, such as cross-connect boxes, in the cable costs they reported. Similarly,
some respondents reported underground structure costs on a "per duct foot" basis contrary to the
instructions set forth in the survey directing that such costs be reported on a "per foot" basis. We
find that these inconsistencies render the use of the survey data inappropriate.
111. In sum, we find that certain of the concerns we identified with regard to using
company-specific data, rather than nationwide average inputs for model inputs, have been borne
out in our review of the cable cost and structure cost data we have reviewed. Specifically, we
find that we are unable to verify the reasonableness of such data. Accordingly, we find that we
are unable to use the company-specific data we have received for the estimation of cable cost
and structure cost inputs for the model.
112. In reaching this conclusion, we reject the contention that the inability to link the
costs submitted in response to the cable and structure cost survey to contracts is irrelevant
because the survey request was not intended to create such a trail.619 This claim ignores the fact
that the reasonableness of the survey data was placed into question by the presence of data
received on the record that was inconsistent with the survey data. For this reason, as GTE
attests, we attempted to create such a trail by requesting contracts and other supporting data in an
effort to verify the reasonableness of the company-specific data received in response to the
survey as well as in ex parte filings.620
618

Inputs Further Notice, Appendix C, section III.C.

619

GTE Inputs Further Notice reply comments at 27.

620

As GTE explains in its comments, GTE submitted additional information as a follow-up to our original
request. GTE submitted such information in response to a request from the Bureau.

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113. Methodology. As we explained in the Inputs Further Notice, our tentative
decision to rely on the NRRI Study was predicated on our inability to substantiate the default
input values for cable costs and structure costs provided by the HAI and BCPM sponsors.621 For
that reason, we tentatively concluded, in the absence of more reliable evidence of cable and
structure costs for non-rural LECs, to use estimates in Gabel and Kennedy's analysis of RUS
data, subject to certain modifications, to estimate cable and structure costs for non-rural LECs.
As we explained, Gabel and Kennedy first developed a data base of raw data from contracts for
construction related to the extension of service into new areas, and reconstruction of existing
exchanges, by rural-LECs financed by the RUS. Gabel and Kennedy then performed regression
analyses, using data from the HAI model on line counts and rock, soil, and water conditions for
the geographic region in which each company in the database operates to estimate cable and
structure costs.622 Regression analysis is a standard method used to study the dependence of one
variable, the dependent variable, on one or more other variables, the explanatory variables. It is
used to predict or forecast the mean value of the dependent variable on the basis of known or
expected values of the explanatory variables.623
114. Those commenters advocating the use of company-specific data provide a litany
of alleged weaknesses and flaws in the NRRI Study, and the modifications we proposed, to
discredit its use to estimate the input values for cable costs and structure costs. In sum, they
argue that the overall approach we proposed is unsuitable for estimating the cable and structure
costs of non-rural LECs and generally leads to estimates which understate actual forwardlooking costs.624 As discussed below, we find the contentions in support of this claim
unpersuasive. Significantly, we note that these commenters provide no evidence that
substantiates the reasonableness of the company-specific cable costs and structure costs
submitted on the record to permit their use as an alternative in the estimation of cable and
structure cost inputs to be used in the model.625
115.

For similar reasons, we reject AT&T and MCI's recommendation that we rely on

621

Inputs Further Notice at paras. 69-74, 105. As noted above, we had received data in response to the cable
and structure cost survey and, at the time of the Inputs Further Notice, were in the process of scrutinizing it.
622

NRRI Study at 34-36.

623

For a discussion of regression analysis, See William H. Greene, Econometric Analysis (1990).

624

See e.g., GTE Inputs Further Notice comments at 13-33; Bell Atlantic Inputs Further Notice comments at 1519; BellSouth Inputs Further Notice comments, Attachment A at A-2 - A-5, Attachment B at B-1 - B-14; US West
Inputs Further Notice comments, Attachment A at 2-29; Sprint Inputs Further Notice comments at 5-7, 17-33.
625

As discussed in more detail below, we have relied on contract data in the estimation of input values for the
costs of DLCs and ex parte data in the estimation of input values for the costs of SAIs. As explained in paragraphs
253-254 and 274-275, such data is the only reliable data available on the record for the determination of such costs.

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the RUS data to develop cost estimates for the material cost of cable and then adopt "reasonable"
values for the costs of cable placing, splicing, and engineering based on the expert opinions
submitted by AT&T and MCI in this proceeding.626 We find that the expert opinions on which
AT&T and MCI's proposed methodology relies lack additional support that would permit us to
substantiate those opinions. Moreover, as discussed in more detail below, we reject AT&T and
MCI's contentions, often analogous to those raised by the non-rural LECs, that the approach we
proposed to estimate cable and structure costs is flawed in certain respects.
116. We reject the contentions of the commenters, either express or implied, that it is
inappropriate to employ the NRRI Study because the RUS data set on which it relies is not a
sufficiently reliable data source for structure and cable costs. We find that the RUS data set is a
reasonably reliable source of absolute cable costs and structure costs, and more reliable and
verifiable than the company-specific data we have reviewed. As explained in the NRRI Study,
and noted above, the RUS data reflect contract costs for construction related to the extension into
new areas, and reconstruction of existing exchanges, by rural LECs financed by the RUS.627
Thus, the RUS data reflect actual costs derived from contracts between LECs and vendors.
These costs are not estimates, but actual costs. Nor do they reflect only the opinions of outside
plant engineers. In sum, we conclude that these are verifiable data.
117. We also note that the RUS data reflect the costs from 171 contracts covering 57
companies operating in 27 states adjusted to 1997 dollars.628 These companies operate in areas
that have different terrain, weather, and density characteristics. This fact makes the RUS data
sample suitable for econometric analysis. Moreover, we find that, because the costs are for
construction that must abide by the engineering standards established by the RUS, these data are
consistent. We note also that the imposition of consistent engineering requirements mitigate the
impact of any inefficiencies or inferior technologies that may otherwise be reflected in the data.
118. Finally, as noted above, the RUS data reflect costs for additions to existing plant
or new construction. The use of such costs is consistent with the objective of the model to
identify the cost today of building an entire network using current technology.
119. In reaching our conclusion to use the NRRI Study and thus the underlying RUS
data, we have considered and rejected the contentions of the commenters that the RUS data set is
flawed thereby rendering use of the NRRI Study inappropriate. GTE claims that because certain
high-cost observations were removed from the RUS data, the NRRI Study's results are
unrepresentative of rural companies' costs, and are even less representative of non-rural
626

AT&T and MCI Inputs Further Notice comments at 15-16.

627

NRRI Study at 2.

628

NRRI Study at 2.

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companies' costs.629 We disagree. Gabel and Kennedy omitted data reflecting certain contracts
from the RUS data they used to develop cost estimates because estimates produced using the
data were inconsistent with the values of such estimates suggested by a priori reasoning or
evidence.630 For example, they excluded certain observations from the buried copper and
structure regression analysis because buried copper cable and structure estimates obtained from
this analysis would otherwise be higher in low density areas than in higher density areas. Such a
result is contrary to the information contained in the more than 1000 observations reflected in the
data from which Gabel and Kennedy developed their buried copper cable and structure
regression equation. Thus, removing the observations does not render the remaining data set less
representative of rural companies' costs or, as adjusted below, the estimates of the costs of nonrural companies. Moreover, we note that the evidence supplied on the record in this proceeding
demonstrates that structure costs increase as population density increases. Thus, we find that the
RUS data set is not flawed as GTE contends. We conclude that the removal of certain high cost
observations was reasonable.
120. We also disagree with GTE's and Bell Atlantic's assertion that the NRRI Study is
flawed because the RUS company contracts do not reflect actual unit costs for work performed,
but rather the total cost for a project.631 Both commenters claim that this alleged failure results
in unexplained variations in the RUS data which undermine the validity of the estimates
produced. Contrary to GTE's and Bell Atlantic's contention, the contracts from which Gabel and
Kennedy developed their data base for developing structure and cable costs do set forth per unit
costs for materials and per unit costs for specific labor tasks.632
121. We also disagree with AT&T and MCI's claim that the RUS data are defective
because they consist of primarily small cables.633 AT&T and MCI claim that 74 percent of the
RUS data are for cables of 50 pairs or less, and 95 percent are for cable sizes of 200 pairs or less.
As a result, AT&T and MCI contend that the RUS data are inaccurate, especially for cable sizes
above 200 pairs. We disagree with AT&T and MCI's analysis. We note that, for the buried
copper cable and structure regression equations we proposed and adopt, approximately 39
percent of the observations are for cable sizes of 50 pairs or less, and approximately 76 percent
are for 200 pairs or less. For the underground copper cable regression equation we proposed and
629

GTE Inputs Further Notice comments at 15-16.

630

NRRI Study at 37-40.

631

GTE Inputs Further Notice comments at 17-19; Bell Atlantic Inputs Further Notice comments at 16,
Attachment C at 9.
632

NRRI Study at 8-9 and 67-73.

633

AT&T and MCI Inputs Further Notice comments at 14.

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adopt, approximately 10 percent of the observations are for cable sizes of 50 pairs or less, and
approximately 33 percent are for 200 pairs or less. For the aerial copper cable regression
equation we proposed and adopt, approximately 40 percent of the observations are for cable
sizes of 50 pairs or less, and approximately 76 percent are for 200 pairs or less. Thus, the
proportion of the observations reflected in the copper cable cost estimates we adopt are
significantly greater for relatively large cables than what AT&T and MCI contend.
122. Finally, we reject the contention that it is inappropriate to use the NRRI Study
because the RUS data base is not designed for the purpose of developing input values for the
model.634 In the NRRI Study, Gabel and Kennedy explain that they began developing the data
base as an outgrowth of the Commission's January 1997 workshop on cost proxy models when it
became apparent that costs used as inputs in such models should be able to be validated by
regulatory commissions. For this reason, they prepared data that is in the public domain to
provide independent estimates of structure and cable costs.635
123. We also find unpersuasive the contention that there are econometric flaws in the
NRRI Study which render it unsuitable for developing input values.636 We disagree with the
contentions of several commenters that the structure cost and cable cost regression equations that
we develop from the RUS data are flawed because they are based on a relatively small number of
observations.637 As a general rule of thumb, in order to obtain reliable estimates for the intercept
and the slope coefficients in a regression equation, the number of observations on which the
regression is based should be at least 10 times the number of independent variables in the
regression equation.638 Ameritech claims that the sample size used to estimate the costs of
buried placement is too small because it contains only 26 observations in density zone one.639
Ameritech's criticism ignores the fact that we use a single regression equation to estimate buried
copper cable and structure costs for density zones one and two based on 1,131 observations
(1,105 in zone two and 26 in zone one). There are four independent variables in the buried
copper cable and structure regression equation, i.e., the variables that indicate the size of the
634

See e.g., Bell Atlantic Input Further Notice comments at 16, Attachment C at 9.

635

NRRI Study at 1-2.

636

See e.g., GTE Inputs Further Notice comments at 19-22; Bell Atlantic Inputs Further Notice comments at 1617, Attachment C at 13-14.
637

See e.g., GTE Inputs Further Notice comments at 15; Ameritech Inputs Further Notice comments at 26;
AT&T and MCI Inputs Further Notice comments at 14.
638

Richard W. Madsen and Melvin L. Moeschberger, Statistical Concepts with Applications to Business and
Economics, 490 (2nd Edition 1986).
639

Ameritech Inputs Further Notice comments at 16.

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cable, presence of a high water table, combined rock and soil type, and density zone. This
suggests that approximately 40 observations are needed to obtain reliable estimates for the
parameters in this regression equation. The total number of observations used to estimate this
regression equation, 1,131, readily exceeds the number suggested for estimating reliably this
regression equation. The number of observations for density zone one alone, 26, provides 65
percent of the suggested number of observations. Similarly, AT&T and MCI claim that the
sample size for underground cable is too small because it contains only 80 observations.640
There is one independent variable in the adopted underground copper cable equation, i.e., the
variable that indicates the size of the cable. Based on the rule of thumb noted above, 10
observations are needed to reliably estimate this regression equation. The number of
observations used to estimate the adopted underground copper cable regression equation, 81, is
more than eight times this suggested number.641 Moreover, we note that Ameritech does not
provide any evidence that suggest that a sample that has 26 observations in density zone 1
produces biased estimates of buried structure and cable costs for density zone one. Similarly
AT&T and MCI do not provide any evidence to support their allegation that a sample size of 80
observations produces biased estimates of underground copper cable costs. Finally, we note that
GTE contends that the regression results for aerial structure are undermined because the sample
size for poles is based only on 19 observations.642 While a sample of this size fails to satisfy the
general rule of thumb we noted above, we find that the estimates produced are reasonable. As
we pointed out in the Inputs Further Notice, the average material price reported in the NRRI
Study for a 40-foot, class four pole is $213.94. This is close to our calculations of the
unweighted average material cost for a 40-foot, class four pole, $213.97, and the weighted
average material cost, by line count, $228.22, based on data submitted in response to the 1997
Data Request. Moreover, we note that GTE does not provide any evidence that suggests that a
sample size of 19 poles for developing aerial structure costs produces biased estimates as GTE
seems to allege.
124. We also disagree with GTE's contention that the NRRI Study contains three
methodological errors that make its results unreliable. First, GTE asserts that the most serious of
these flaws is that the NRRI Study improperly averages ordinal or categorical data, i.e.,
qualitative values, for the costs of placing structure in different types of soil.643 Contrary to
640

AT&T and MCI Inputs Further Notice comments at 14.

641

The Inputs Further Notice indicated that 80 observations were used to estimate the proposed underground
copper cable costs. However, 81 observations were used to develop these proposed costs. Eighty one observations
are used to estimate the adopted underground copper cable costs.
642

GTE Inputs Further Notice comments at 15.

643

GTE Inputs Further Notice comments at 19-21. See also Bell Atlantic Inputs Further Notice comments at
16-17, Attachment C at 13-14.

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GTE's claim, the independent variables that indicate soil type, rock hardness, and the presence of
a high water table used in the regression equations for aerial and underground structure and
buried structure and cable costs in the NRRI Study and proposed in the Inputs Further Notice do
not reflect an incorrect averaging of ordinal data. The variables for soil, rock, and water indicate
the average soil, rock, and water conditions in the service areas of RUS companies. They are
based on averages of data obtained from the HAI database for the Census Block Groups in which
the RUS companies operate. In general, the magnitude of the t-statistics for the coefficients of
the independent variables for soil, rock, and water in the structure regression equations indicate
that these variables have a statistically significant impact on structure costs. The magnitude of
the F-statistic indicates that the independent variables in the structure regression equations,
including those that indicate water, rock, and soil type, jointly provide a statistically significant
explanation of the variation in structure costs. These statistical findings justify use of these
variables in the structure regression equations. We also note that HAI uses as cardinal values,
i.e., quantitative, not ordinal values, the soil and rock data from which the averages reflected in
the rock and soil variables in the NRRI Study are calculated. For example, HAI uses a multiplier
of between 1 and 4 to calculate the increase in placement cost attributable to the soil condition.
Moreover, and more importantly, we note that no commenter has demonstrated the degree of, or
even the direction of, any bias in the cost estimates derived in the NRRI Study or in the
regression equations proposed in the Inputs Further Notice as a result of the use of soil, water,
and rock variables based on averages of HAI data.
125. GTE also claims that the NRRI Study is flawed because it relies on the HAI
model's values relating to soil type which GTE claims were "made up."644 GTE contends that
this renders the variable relating to soil type judgmental and biased. We find GTE's concern
misplaced. As explained above, the econometric analyses of the data demonstrate a statistically
significant relationship between the geological variables developed from the HAI data and the
structure costs. Finally, we disagree with GTE's claim that the NRRI Study is flawed because of
a mismatch in the geographic coverage of the RUS data and the HAI model variables.645 GTE
does not provide any evidence showing that the alleged mismatch introduces an upward or
downward bias on the cost estimates obtained from the regression equations. Moreover, and
more importantly, the t-statistics for the coefficients of the variables that measure rock and soil
type generally indicate that these geological variables provide a statistically significant
explanation of variations in RUS companies' structure costs.
126. We also reject the claims that the derivation of the equations for 24-gauge buried
copper cable, buried structure, and buried fiber cable from the NRRI Study regression equations
for 24-gauge buried copper cable and structure and buried fiber cable and structure, respectively,
644

GTE Inputs Further Notice comments at 21.

645

GTE Inputs Further Notice comments at 22.

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is inappropriate.646 As we explained in the Inputs Further Notice, we modified the regression
equations in the NRRI Study for 24-gauge buried copper cable and structure and buried fiber
cable and structure, as modified by the Huber methodology described below, to estimate the cost
of 24-gauge buried copper cable, buried structure and buried fiber cable because the regression
equations for buried copper cable and structure and buried fiber cable and structure provide
estimates for labor and material costs for both buried cable and structure combined.647 In
layman's terms, we split the modified 24-gauge buried copper cable and structure regression
equation into two separate equations, one for 24-gauge buried copper cable and one for buried
structure costs. We also split the modified buried fiber cable and structure regression equation to
obtain an equation for buried fiber cable.648 We did this because the model requires a separate
input for labor and material costs for cable and a separate input for labor and material costs for
structure. In contrast, the RUS data and buried cable and structure regression equations
developed from these data, reflect labor and material costs for buried cable and structure
combined.
127. Significantly, the criticisms of our development of the 24-gauge buried copper
cable equation, buried structure equation and buried fiber cable equation in this manner ignore
the fact that reliable, alternative data for buried cable costs and buried structure costs is not
available on the record.649 Given that the model requires a separate input reflecting labor and
material costs for both copper and fiber cable and a separate input reflecting labor and material
costs for structure, and that the only reliable data on the record does not separate such costs
between cable and structure, we find it necessary to split the regression equation.
128. Contrary to the assertions of the commenters, either express or implied, the steps
we took to derive these equations were not arbitrary.650 We used a single buried structure
equation to estimate the cost for buried structure without distinguishing between the equation for
buried copper structure and the equation for buried fiber structure because the model does not
distinguish between buried copper structure costs and buried fiber structure costs. We find that
this is reasonable because the intercept and the coefficients for the variables that primarily
explain the variation in structure costs, i.e., the variables that indicate density zone, the combined
646

See e.g., GTE Inputs Further Notice comments at 52-53;

647

Inputs Further Notice at paras. 83, 113. See also Inputs Further Notice, Appendix D, sections I.C., III.C.

648

Inputs Further Notice at para. 94. See also Inputs Further Notice, Appendix D, section II.C.

649

Moreover, at least one LEC commenter states that it is not able to separate buried structure costs from total
buried plant costs. GTE Inputs Further Notice comments at 53. This inability may reflect the fact that under
current FCC accounting guidelines these costs are not identified separately.
650

See e.g., GTE Inputs Further Notice comments at 52; BellSouth Inputs Further Notice comments,
Attachment A at A-16.

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soil and rock type, and the presence of a high water table, in the combined regression equation
for buried fiber cable and structure are not statistically different from the intercept and the
coefficients for these variables in the combined regression equation for 24-gauge buried copper
cable and structure.651 We also find that it is reasonable to develop a separate structure equation
from the regression equation for the combined cost of 24-gauge buried copper cable and
structure rather than from the regression equation for the combined cost of buried fiber cable and
structure because the water and soil and rock type indicator variables in the regression equation
for the combined cost of 24-gauge buried copper cable and structure are statistically significant.
In contrast, these variables are not statistically significant in the buried fiber cable and structure
regression equation.652 In addition, we note that the number of observations used to estimate the
24-gauge buried copper cable and structure regression equation, 1,131, exceeds the number of
observations used to estimate the buried fiber cable and structure regression equation, 707
observations.
129. We note that we included in the separate buried cable equations the variable for
cable size and its coefficient reflected in the combined cable and structure regression equations.
We find that this is reasonable because the cable size variable and its coefficient explain the
variation in cable costs. We also note that we excluded from the separate buried cable equations
the independent variables in the combined cable and structure regression equations that indicate
density zone, the presence of a high water table, and the soil and rock type. We find that this is
reasonable because these variables and their coefficients explain primarily the variation in buried
structure costs. Conversely, we excluded from the separate buried structure equation the
variable for cable size and its coefficient reflected in the combined 24-gauge buried copper cable
and structure regression equation because this variable and its coefficient explain the variation in
cable costs.
130. We also included in the separate structure equation the variables and the
coefficients for the variables that indicate density zone, the combined soil and rock type, and the
presence of a high water table in the combined regression equation for 24-gauge buried copper
cable and structure. Again, we find this is reasonable because these independent variables and
coefficients primarily explain the variation in structure costs.
131.

Finally, because the estimated intercepts in the regression equations for the cost

651

That is, the values of the intercept and the coefficients for the variables that indicate density zone, the
combined soil and rock type, and the presence of a high water table in the combined regression equation for buried
fiber cable and structure lie within the 95 percent confidence interval surrounding the values of the intercept and the
coefficients for the respective variables in the combined regression equation for 24-gauge buried copper cable and
structure.
652

Nevertheless, the value of the F-statistic for the regression equation for the combined cost of buried fiber
cable and structure, 172.80, indicates that the regression equation is statistically significant.

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of buried cable and structure reflect the fixed cost for both buried cable and structure in density
zone one, we included in the separate equations for buried cable an intercept reflecting the fixed
cost of cable. Similarly, we included in the equation for buried structure an intercept reflecting
the fixed cost of structure in density zone one. Specifically, we allocated an estimate of the
portion of the combined fixed cable and structure costs that represents the fixed copper cable
costs reflected in the intercept in the 24-gauge buried copper cable and structure cost regression
equation to the intercept in the equation for 24-gauge buried copper cable. Correspondingly, we
allocated an estimate of the portion of fixed cable and structure cost that represents the fixed
costs of buried structure reflected in the intercept in the buried 24-gauge copper cable and
structure cost regression equation to the intercept in the equation for structure costs. We also
allocated to the intercept in the separate buried fiber cable equation the remaining portion of the
fixed costs reflected in the intercept in the combined buried fiber cable and structure regression
equation after subtracting from the value of this intercept the estimate for fixed structure costs in
density zone 1 in the separate buried structure equation. The sum of the particular values that we
adopt for the fixed cable cost in the separate 24-gauge copper cable equation, $.46, and the fixed
structure cost in density zone 1 in the separate structure equation, $.70, equals the 24 gauge
buried copper cable and structure fixed costs reflected in the intercept in the combined copper
cable and structure regression equation of $1.16. The sum of the particular values that we adopt
for the fixed cable cost in density zone 1 in the separate fiber cable equation, $.47, and the fixed
structure cost in the separate structure equation of $.70 equals the buried fiber cable and
structure fixed costs reflected in the intercept in the combined fiber cable and structure
regression equation, $1.17. We find that these values are reasonable. We note that $.46653 lies
between AT&T and MCI's estimate of the fixed cost for a 24-gauge buried copper cable of
$.12654 and the HAI default value for the installed cost of a 6-pair 24-gauge buried copper cable
of $.63.655 Moreover, we note that we could have used relatively higher or lower values for the
fixed structure and cable costs in the separate structure and cable equations. However, we note
that the sum of the fixed costs reflected in the buried structure cost estimates (excluding LEC
engineering costs) developed from the separate buried structure equation and the fixed costs
reflected in the buried cable cost estimates (excluding LEC engineering and splicing costs)
developed from the separate buried copper or fiber cable equation is not affected by the relative
653

This estimate of the fixed cost for a 24-gauge buried copper cable excludes fixed costs for structure, LEC
engineering, and splicing, but includes fixed costs for contractor engineering.
654

See AT&T and MCI Inputs Further Notice comments, Appendix A at A-7. The AT&T and MCI estimate of
the fixed cost for a 24-gauge buried copper cable excludes fixed costs for structure, splicing, and contractor and
LEC engineering.
655

See HAI Model, Release 5.0a, Model Description, Appendix B at 15. A 6-pair 24-gauge buried copper cable
is the smallest buried cable for which HAI has a default value. The HAI default value for the installed cost of a 6pair 24-gauge buried copper cable excludes fixed and variable costs for structure, but includes fixed and variable
costs for material, contractor and LEC engineering, and splicing. Fixed cable costs do not vary with cable size. A
large percentage of the installed cable cost for a small cable is a fixed cost.

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values that we use for the fixed cost in these separate equations.656
132. Finally, we note that GTE contends that the proposed equations for buried cable
and buried structure are questionable because the buried structure costs would not vary with the
presence of water.657 As discussed below, we have modified the regression equation for buried
copper cable and structure by adding the variable that indicates the presence of a high water
table. We obtain structure cost estimates used as input values by setting the coefficient for the
water indicator variable equal to zero. These structure cost estimates, therefore, assume that a
high water table is not present. The model adjusts these estimates to reflect the impact on these
costs of a high water table. GTE also claims that the proposed equations are questionable
because the costs for buried structure derived from the buried structure equation would not vary
with cable size. We reject this contention. GTE has not provided any evidence that
demonstrates that buried structure costs vary with cable size. To the contrary, GTE states that it
cannot produce such evidence because it is not able to separate actual costs of buried structure
from total costs of buried plant.
133. In sum, we find that the regression equations we proposed and tentatively adopted
in the Inputs Further Notice are an appropriate starting point for estimating cable costs and
structure costs for non-rural LECs for purposes of developing inputs for the model, particularly
given the absence of more reliable cable and structure cost data from any other source.658 We
find, however, that certain commenters' criticisms of the regression equations we proposed have
merit. We make the following adjustments to improve the regression equations consistent with
those criticisms.659
656

The sum of the fixed costs reflected in the buried structure cost estimates, including LEC engineering costs,
developed from the separate buried structure equation and the fixed costs reflected in the buried copper or fiber
cable cost estimates, including LEC engineering and splicing costs, developed from the separate buried cable
equation is affected slightly by the relative values used for the fixed cost in these separate equations. The relative
values used for these fixed costs affects slightly the sum of these fixed costs because a splicing loading of 9.4 or 4.7
percent is applied to the fixed cost reflected in the separate buried copper or fiber cable cost estimates (excluding
LEC engineering and splicing costs), while a loading of 10 percent for LEC engineering is applied to the fixed cost
reflected in the separate buried structure cost estimates (excluding LEC engineering costs).
657

GTE Inputs Further Notice comments at 52.

658

We note that the regression equations in the NRRI Study are a starting point because, as we explained in the
Inputs Further Notice, and discuss in more detail below, we proposed to modify the regression equations used to
estimate cable costs to capture the buying power of the non-rural LECs reflected in the price they pay for cable.
659

We set forth in Appendix B the regression equations that we adopt in this Order. We also set forth in
Appendix B the adjustments we make to those equations to reflect the buying power of large LECs, splicing costs,
LEC engineering costs, and to separate the buried cable and structure regression equations into separate equations
for buried cable and buried structure.

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134. First, we remove the independent variable that indicates whether two or more
cables are placed at the same location from the regression equations for 24-gauge aerial copper
cable, 24-gauge buried copper cable and structure, aerial fiber cable, and buried fiber cable and
structure.660 As a result, the regression equations we adopt do not have this variable as an
independent variable. We do not include this independent variable in any of the cable and
structure equations because the model does not use a different cable cost if the outside plant
portion of the network it builds requires more than one cable.
135. We also remove from the regression equation for 24-gauge underground copper
cable the variable that is the mathematical square of the number of copper cable pairs. We
remove this variable because its use results in negative values for the largest cable sizes, as some
parties point out.661 We note that none of the other proposed cable and structure regression
equations had this variable as an independent variable.
136. We add the variable that indicates the presence of a high water table to the
regression equations for buried copper cable and structure and underground structure costs.
With this change, all of the regression equations for structure costs adopted in this Order have
this variable as an independent variable.662 We include this variable in the structure equations
because the model applies a cost multiplier to all structure costs when the water table depth is
less than the critical water depth. To develop structure cost inputs, we set the value of the water
indicator variable equal to zero in the structure regression equations, thereby developing
structure costs that assume that there is no water in the geographic area where the structure is
installed. The multiplier in the model then adjusts these costs to reflect the impact on these costs
of a high water table when it determines that the water table depth is less than the critical water
depth.
137. We reduce the value of the intercept to $.46 from $.80 in the equation proposed in
the Inputs Further Notice for calculating the labor and material costs for buried copper cable
(excluding structure, LEC engineering, and splicing costs). We now estimate the buried 24gauge copper cable and structure regression equation after removing the multi-cable variable and
adding the water indicator variable. The value of the intercept in this regression equation of
660

See Bell Atlantic Inputs Further Notice comments, Attachment C at 25; Ameritech Inputs Further Notice
comments at 13-14; US West Inputs Further Notice comments, Attachment A at 9, 11.
661

See e.g., Ameritech Inputs Further Notice comments at 10-11; GTE Inputs Further Notice comments at 3031; Bell Atlantic Inputs Further Notice comments at 25-26; US West Inputs Further Notice comments, Attachment
A at 9.
662

See Bell Atlantic Inputs Further Notice comments, Attachment C at 25-26; Ameritech Inputs Further Notice
comments at 13; US West Inputs Further Notice comments, Attachment A at 9; GTE Inputs Further Notice
comments at 30-31.

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$1.16 is less than the intercept in the proposed regression equation of $1.51. As we did in the
Inputs Further Notice, we derive the buried copper cable equation from the regression equation
for 24-gauge buried copper cable and structure costs. The value of the intercept in the buried
copper cable and structure regression equation represents the fixed cost for both buried copper
cable and buried copper cable structure in density zone 1. We assume, as we did in the Inputs
Further Notice, that $.70 is the fixed cost for buried copper cable structure in density zone 1.
Accordingly, the fixed labor and material cost for buried copper cable is $1.16 minus $.70, or
$.46.
138. We also reduce the value of the intercept to $.47 from $.60 in the equation
proposed in the Inputs Further Notice for calculating the labor and material costs for buried fiber
cable (excluding structure, LEC engineering, and splicing costs). We now estimate the buried
fiber cable and structure regression equation after removing the multi-cable variable. The value
of the intercept in this regression equation, $1.17, is greater than the value of the intercept in the
proposed regression equation, $1.14. As we did in the Inputs Further Notice, we derive the
buried fiber cable equation from the regression equation for buried fiber cable and structure
costs. The value of the intercept in the buried fiber cable and structure regression equation
represents the fixed cost for both buried fiber cable and buried fiber cable structure in density
zone 1. We assume that $.70 is the fixed cost for buried fiber cable structure in density zone 1.
Accordingly, the fixed labor and material cost for buried fiber cable in density zone 1 is $1.17
minus $.70 or $.47
139. Huber Adjustment. In the Inputs Further Notice, we tentatively concluded that
one substantive change should be made to Gabel and Kennedy's analysis.663 As we explained,
we tentatively concluded that the regression equations in the NRRI Study should be modified
using the Huber regression technique664 to mitigate the influence of outliers in the RUS data.665
Statistical outliers are values that are much higher or lower than other data in the data set. The
Huber algorithm uses a standard statistical criterion to determine the most extreme outliers and
exclude those outliers. Thereafter, the Huber algorithm iteratively performs a regression, then
for each observation calculates an observation weight based on the absolute value of the
observation residual. Finally, the algorithm performs a weighted least squares regression using
663

Inputs Further Notice at para. 75.

664

We used Stata Statistical Software: Release 5 (Stata) to perform the calculations needed to estimate the
regression equations adopted in this Order for cable and structure costs. Stata has a robust regression methodology
that uses formulas developed by P.J. Huber, R.D. Cook, A.E. Beaton and J.W. Tukey. We used this methodology to
estimate the regression equations for cable and structure costs. We refer to this robust regression methodology as
the Huber methodology. See Stata Reference Manual, Release 5, Volume 3, P-Z, Stata Press, College Station, TX,
168-173.
665

Inputs Further Notice at para. 76.

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the calculated weights. This process is repeated until the values of the weights effectively stop
changing.666
140. We affirm our tentative conclusion to modify the regression equations in the
NRRI Study using the Huber methodology to develop input values for cable and structure costs.
The cable and structure cost inputs used in the model should reflect values that are typical for
cable and structure for a number of different density and terrain conditions. If they do not reflect
values that are typical, the model may substantially overestimate or underestimate the cost of
building a local telephone network. As discussed below, application of the Huber methodology
minimizes this risk, thereby producing estimates that are consistent with the goal of developing
cable and structure cost inputs that reflect values that are typical for cable and structure for
different density and terrain conditions.
141. The commenters attest to the fact that there are significant variances in the RUS
structure and cable cost data.667 We find that the presence of these outliers warrants the use of
the Huber methodology. By relying on the Huber methodology to identify and to exclude or
give less than full weight to these data outliers in the regressions, we decrease the likelihood that
the cost estimates produced reflect measurement error or data anomalies that may represent
unusual circumstances that do not reflect the typical case. We note that we are not readily able
to ascertain the specific circumstances that may explain why some data points are outliers
relative to more clustered data points because of the multivariate nature of the database. Such
occurrences are expected when dealing with such a database. Not only are there many
observations, but these observations reflect the circumstances surrounding the construction work
of many different contractors done for a large number of companies on different projects over a
number of years. We also note that the task of identifying structure cost outliers without using a
statistical approach such as Huber is especially difficult because these costs are a function of
different geological conditions and population densities. Given that it is not feasible, as a
practical matter, to determine why particular data points are outliers and our objective is to
develop typical cable and structure costs, we conclude that use of the Huber methodology is
appropriate.668
666

As noted in the Inputs Further Notice, we used the robust regression parameter estimates for cable, conduit,
and buried structure. The use of robust estimation did not improve the statistical properties of the estimators for
pole costs, so we tentatively concluded that the ordinary least squares technique is appropriate for pole costs. The
value of the F-statistic was not statistically significant at the five percent level. Inputs Further Notice at para. 76 n.
161.
667

See e.g., GTE Inputs Further Notice comments at 23-26; Bell Atlantic Inputs Further Notice comments at
17, Attachment C at 29-34; US West Inputs Further Notice comments, Attachment A at 11-13; BellSouth Inputs
Further Notice comments, Attachment A at A-17.
668

For example, for one to determine why the reported structure cost for a single project is an outlier, one would
have to interview the LEC engineers and contractors to verify the reported cost, identifying with precision whether
unusual circumstances surrounded the project thereby leading to atypical costs.

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142. We find the comments opposing application of the Huber methodology
unpersuasive. In the first instance, we reject the assertions of the commenters, either express or
implied, that the application of robust regression analysis is not the preferred method of dealing
with outliers in a regression.669 There is no preferred method. The use of robust regression
techniques is a matter of judgement for the estimator. As we explained above, the goal of our
analysis is to estimate values that are typical for cable and structure costs for different density
and terrain conditions. We determined that we should mitigate the effects of outliers occurring
in the data to ensure that the estimates we produce reflect typical costs. Noting that such outliers
have an undue influence on ordinary least squares regression estimates because the residual
associated with each outlier is squared in calculating the regression, we determined, in our expert
opinion, to employ the Huber methodology to diminish the destabilizing effects of these outliers.
Thus, while it can be argued that we could have produced a different estimate, the commenters
have not established that application of the Huber methodology produces an unreasonable
estimate.
143. Bell Atlantic and GTE assert that the probability distribution of the error term
must be symmetric about its mean and have fatter tails than in the normal distribution in order to
use the Huber methodology.670 We disagree. The Huber methodology in effect fits a line or a
plane to a set of data. The algebraic expression of this line or plane explains or predicts the
effects on a dependent variable, e.g., 24-gauge aerial copper cable cost, of changes in
independent variables, e.g., aerial copper cable size. It does this by assigning zero or less than
full weight to observations that have extremely high or extremely low values. The assignment of
weights to observations depends on the values of the observations. It does not depend on the
probability of observing these values. The error term to which Bell Atlantic and GTE refer is the
difference between the predicted or estimated values of the dependent variable and the observed
values of the dependent variable. Given that the error term is the difference between the
predicted and observed values of the dependent variable, and that the assignment of weights by
the Huber methodology does not depend on the probability of observing particular values of this
variable, this assignment of weights does not depend on the probability of observing particular
values of the error term. It, therefore, does not depend on whether the probability distribution of
the error term is symmetric about its mean and has fatter tails than in the normal distribution.
144.

Bell Atlantic also argues that the Huber methodology should not be used unless

669

See e.g., GTE Inputs Further Notice comments at 23-26; Bell Atlantic Inputs Further Notice comments at 17,
Attachment C at 29-34; US West Inputs Further Notice comments, Attachment A at 11-13; BellSouth Inputs
Further Notice comments, Attachment A at A-17.
670

See Bell Atlantic Inputs Further Notice comments at 17, Attachment C at 30, 31; See GTE Inputs Further
Notice comments at 25.

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there is evidence that outliers in the RUS data are erroneous.671 We disagree. We believe that
use of the Huber methodology with RUS data ensures that cost estimates reflect typical costs
regardless of whether there is evidence that outliers in the RUS data are erroneous. The RUS
data, as Bell Atlantic and other parties point out, have a number of high values and low values.672
These outliers may reflect unusual circumstances that are unlikely to occur in the future. The
Huber methodology dampens the effects of anomalistically high or low values that may reflect
unusual circumstances. Notwithstanding the dispersion in the RUS data, we believe that there
are relatively few errors in these data. As we explained, the RUS data are derived from
contracts. Gabel and Kennedy determined that the values reflected in the RUS data are within
one percent of the values set forth on the contracts.673 There are likely to be few errors in the
contracts themselves because these are binding agreements that involve substantial sums of
money between RUS companies and contractors. These parties have an obvious interest in
ensuring that these values are correctly reflected in these contracts. While we believe that errors
in these contracts are likely to be infrequent, outlier observations in the RUS data may reflect
large errors. The Huber methodology dampens the effects of outlier observations that may
reflect large errors.
145. We find that the estimates produced by applying the Huber methodology are
reasonable. As we explain more fully in Appendix B, the estimates resulting from application of
the Huber methodology reflect most of the information represented in nearly all of the cable and
structure cost observations in the RUS data. Approximately 80 percent of the cable and structure
observations are assigned a weight of at least 80 percent in each structure and regression
equation that we adopt. This large majority comprises closely clustered observations that clearly
represent typical costs. Conversely, approximately 20 percent of the cable and structure
observations are assigned a weight of less than .8 in each of these regression equations. This
small minority comprises observations that have extremely high and extremely low values that
do not represent typical costs. We also note that because the Huber methodology treats
symmetrically observations that have high or low values, it excludes or assigns less than full
weight to data outliers without regard to whether these are high or low cost observations.
146. Buying Power Adjustment. In the Inputs Further Notice, we tentatively
concluded that we should make three adjustments to the regression equations in the NRRI Study,
as modified by the Huber methodology described above, to estimate the cost of 24-gauge aerial
copper cable, 24-gauge underground copper cable, and 24-gauge buried copper cable.674 We
671

See Bell Atlantic Inputs Further Notice comments at 17.

672

Bell Atlantic Inputs Further Notice comments, Attachment C at 23, 24. See also GTE Inputs Further Notice
comments at 17, 18; AT&T Inputs Further Notice comments at 14.
673

NRRI Study at 34.

674

Inputs Further Notice at paras. 77-81; 82; 83-84.

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further tentatively concluded that these adjustments should be made in the estimation of the cost
of aerial fiber cable, buried fiber cable, and underground fiber cable.675 The first of these
adjustments was to adjust the equation to reflect the superior buying power that non-rural LECs
may have in comparison to the LECs represented in the RUS data. We noted that Gabel and
Kennedy determined that Bell Atlantic's material costs for aerial copper cable are approximately
15.2 percent less than these costs for the RUS companies based on data entered into the record in
a proceeding before the Maine Public Utilities Commission (the "Maine Commission).676
Similarly, Gabel and Kennedy determined that Bell Atlantic's material costs for aerial fiber cable
are approximately 33.8 percent less than these costs for the RUS companies.677 We also noted
that Gabel and Kennedy determined that Bell Atlantic's material costs for underground copper
cable are approximately 16.3 percent less than these costs for the RUS companies and 27.8
percent less for underground fiber cable. We tentatively concluded that these figures represent
reasonable estimates of the difference in the material costs that non-rural LECs pay in
comparison to those that the RUS companies pay for cable.678 Accordingly, to reflect this degree
of buying power in the copper cable cost estimates that we derived for non-rural LECs, we
proposed to reduce the regression coefficient for the number of copper pairs by 15.2 percent for
aerial copper cable, and 16.3 percent for 24-gauge underground copper cable.
147. We also proposed to reduce the regression coefficient for the number of fiber
strands by 33.8 percent for aerial fiber cable and 27.8 percent for underground fiber cable.679 As
we explained, this coefficient measures the incremental or additional cost associated with one
additional copper pair or fiber strand, as applicable, and therefore, largely reflects the material
cost of the cable. Because the NRRI Study did not include a recommendation for such an
adjustment for buried copper cable or buried fiber, we tentatively concluded we should reduce
the coefficient by 15.2 percent for buried copper cable and 27.8 percent for buried fiber cable.680
We explained that the level of these adjustments reflect the lower of the reductions used for
aerial and underground copper cable and aerial and underground fiber cable, respectively.
148. We adopt the tentative conclusion in the Inputs Further Notice and select buying
power adjustments of 15.2 percent, 16.3 percent and 15.2 percent for 24-gauge aerial copper
675

Inputs Further Notice at paras. 90-95.

676

Inputs Further Notice at para. 79 n. 163 citing NRRI Study at 47.

677

Inputs Further Notice at para. 91 n. 174 citing NRRI Study at 47.

678

Inputs Further Notice at paras. 79, 82.

679

Inputs Further Notice at paras. 91, 93.

680

Inputs Further Notice at paras. 84, 95.

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cable, 24-gauge underground copper cable, and 24-gauge buried copper cable, respectively.
Correspondingly, we adopt buying power adjustments of 33.8 percent, 27.8 percent, and 27.8
percent for aerial fiber cable, underground fiber cable, and buried fiber cable, respectively. We
find that, based on the record before us, the buying power adjustment is appropriate and the
levels of the adjustments we proposed for the categories of copper and fiber cable we identified
are reasonable.
149. As we explained in the Inputs Further Notice, the buying power adjustment is
intended to reflect the difference in the materials prices that non-rural LECs pay in comparison
to those that the RUS companies pay.681 Because non-rural LECs pay less for cable, a
downward adjustment to the estimates developed from data reflecting the costs of rural-LECs is
necessary to derive estimates representative of cable costs for non-rural LECs. The commenters
generally concede that such differences exist.682 There is, however, disagreement among the
commenters that an adjustment is necessary in this instance to reflect this difference.
150. Those commenters advocating the use of company-specific data oppose the
buying power adjustment as unnecessary. GTE and Sprint contend that the use of a more
representative data set, i.e., company-specific data, would account for any differences in buying
power.683 As we explained above, however, the RUS data are the most reliable data on the
record before us for estimating cable and structure costs. Because there is a difference in the
material costs that non-rural LECs pay in comparison to those that the RUS companies pay, a
downward adjustment to the RUS cable estimates is necessary to obtain representative cable cost
estimates for non-rural LECs.
151. We note that AT&T and MCI support the proposed adjustment for aerial and
underground copper and fiber cable.684 AT&T and MCI oppose, however, the use of the lower
of the reductions adopted for aerial and underground cable categories, for the buried cable
category. Although AT&T and MCI agree that an adjustment is appropriate for buried cable,
they contend that the buying power adjustment should be set at the higher figures of 16.3 percent
for buried copper cable and 33.8 percent for buried fiber cable, or at the very least, at the average
of the higher and lower values for aerial and underground cable. We disagree. We find that
AT&T and MCI offer no support to demonstrate why the higher values should be used. As
explained below, the levels of the adjustments we proposed and adopt are the most conservative
based on the available record evidence.
681

Inputs Further Notice at para. 79.

682

See e.g., SBC Inputs Further Notice comments at 8; Sprint Inputs Further Notice comments at 22; Sprint
Inputs Further Notice reply comments at 15; AT&T and MCI Inputs Further Notice comments at 21.
683

GTE Inputs Further Notice comments at 26-27; Sprint Inputs Further Notice reply comments at 14.

684

AT&T and MCI Inputs Further Notice comments at 21.

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152. Apart from opposing the buying power adjustment on the ground that as a general
matter the adjustment is unnecessary, those opposing the adjustment take issue with the
adjustment on methodological grounds. GTE contends that the adjustment cannot properly
convert RUS data into costs for non-rural carriers because the RUS data do not reflect the cost
structure of rural carriers.685 As we explained above, the assertion that the RUS data does not
reflect the cost structure of rural carriers is without merit. GTE also contends that the
application of the adjustment factors to the coefficients in the regression equations is contrary to
the fundamentals of sound economic analysis.686 The solution GTE recommends is that
additional observations for non-rural companies be added to the data set. This solution echoes
GTE's assertion that company-specific data should be used. Reliable observations for non-rural
LECs are not available, however, as explained above.
153. GTE also identifies what it considers flaws in the development of the buying
power adjustment.687 GTE argues that because the adjustment to the RUS data was developed
using only one larger company's data (Bell Atlantic's) reflecting costs for a single year, the
adjustment is not proper.688 We disagree for several reasons. First, we note that although we
specifically requested comment on this adjustment and its derivation in the Inputs Further
Notice,689 GTE and other parties challenging the use of Bell Atlantic's data have not provided
any alternative data for measuring the level of market power, despite their general agreement
that such market power exists.690 These parties failed to submit comparable verifiable data to
show that the buying power adjustment we proposed was inaccurate. Under these circumstances,
we cannot give credence to the unsupported claims that the Bell Atlantic data is not
representative.
154. Equally important, we have reason to conclude that the adjustment we adopt is a
conservative one. The buying power adjustment we proposed and adopt is based upon a
submission by Bell Atlantic to the Maine Commission in a proceeding to establish permanent
unbundled network element (UNE) rates.691 In that context, it was in Bell Atlantic's interests to
685

GTE Inputs Further Notice comments at 26.

686

GTE Inputs Further Notice comments at 27.

687

GTE Inputs Further Notice comments at 28.

688

GTE Inputs Further Notice comments at 28.

689

Inputs Further Notice at para. 79.

690

Such agreement is consistent with representations by parties in merger contexts that a merger will produce
costs savings.
691

NRRI Study at 47. See Inputs Further Notice at para. 79.

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submit the highest possible cost data in order to ensure that the UNE rates would give it ample
compensation. But in the context of the adjustment we consider here for buying power, a
relatively higher cost translates into a reduced adjustment because the greater the LEC costs, the
less the differential between LEC and rural carrier costs. Therefore, given the source of this
data, we conclude that it is likely to produce a conservative buying power adjustment, not an
excessive one. Nevertheless, in the proceeding on the future of the model, we intend to seek
further comment on the development of an appropriate buying power adjustment to reflect the
forward-looking costs of the competitive efficient firm. In sum, we find that GTE's criticisms
are not persuasive, and that the adjustment is a reasonable one, supported by the record.
155. GTE also asserts a litany of other concerns that, according to GTE, render the
buying power adjustment invalid.692 We find these concerns unpersuasive. GTE claims that the
adjustment is suspect because some RUS observations used in the determination of material
costs are not used in the regression.693 We disagree. As discussed above, we apply the Huber
methodology to RUS cable costs that reflect both labor and material costs.694 The observations
in the RUS database to which the Huber methodology assigns zero or less than full weight are
those with the highest and the lowest values. As described more fully below, a statistical
analysis demonstrates that this assignment of weights to these observations has little impact on
the level of material costs reflected in the cable cost estimates derived by using this
methodology. Therefore, material cost averages based on all of the RUS data are not likely to
vary significantly from material cost averages based on a subset of these data.
156. Specifically, with one exception, the value of the regression coefficient for the
variable representing the size of the cable in the cable cost regression equations derived by using
the Huber methodology lies inside the 95 percent confidence interval surrounding the value of
this coefficient in these regression equations in the NRRI Study obtained by using ordinary least
squares.695 The coefficient for the variable that represents cable size represents the additional
cost for an additional pair of cable and therefore represents cable material costs. The values of
the coefficient for the cable size variable obtained by using Huber and ordinary least squares are
based on a sample of RUS companies' cable costs drawn from a larger population of such costs.
The values of the coefficient obtained from this sample by using the Huber methodology and
692

GTE Inputs Further Notice comments at 28-29.

693

GTE Inputs Further Notice comments at 29.

694

See supra paras. 139-145.

695

We set forth in Appendix B a table that shows the value of this regression coefficient derived by using the
Huber methodology and the 95 percent confidence interval surrounding the value of this coefficient obtained by
using ordinary least squares. We also discuss in more detail the statistical evidence on the impact of the Huber
methodology on the level of the material costs reflected in the cable cost estimates.

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ordinary least squares are estimates of the true values of this coefficient theoretically obtained
from the population of cable costs by using these techniques. Generally speaking, a 95 percent
confidence interval associated with a coefficient estimate contains, with a probability of 95
percent, the true value of the coefficient.696 The fact that the value of the cable size coefficient
obtained by using the Huber methodology lies within an interval that contains with 95 percent
certainty the true value of the ordinary least squares cable size coefficient supports the
conclusion that the Huber methodology does not by its weighting methodology have a
statistically significant impact on the level of the material costs reflected in the cable cost
estimates derived by using this methodology.697
157. GTE also claims that some RUS observations appear to be from rescinded
contracts or contracts excluded from the NRRI Study per-foot cable cost calculation.698
However, GTE offers no evidence that this is the case. Finally, GTE claims that some RUS
observations are for technologies that may not be appropriate for a forward-looking cost
model.699 On the contrary, loading coils were excluded from the RUS data base. Thus, we find
that the RUS data do not reflect any non-forward-looking technologies.
158. GTE and Sprint each attempt to impugn the validity of the buying power
adjustment, claiming that there may be an incongruity between the data submitted to the Maine
Commission by Bell Atlantic and the RUS data.700 We find this claim unpersuasive. Both GTE
and Sprint assert that it is unknown whether the underlying data include such items as sales tax
or shipping costs and, if so, whether the level of these items is comparable between Maine and
the states included in the RUS data. Significantly, neither claim that such an incongruity exists
in fact, nor do they provide viable alternatives for the calculation of the adjustment. We note
696

As a general matter, 95 percent of the confidence intervals associated with different estimates of a given
coefficient derived from a large number of samples of a given population can be expected to contain the true value
of the coefficient.
697

The one exception is that the value of the cable size coefficient obtained by using the Huber methodology for
buried copper cable lies outside the 95 percent confidence interval associated with the cable size coefficient for
buried copper cable obtained in the NRRI Study using ordinary least squares. This suggests that the assignment of
weights by the Huber methodology does have a statistically significant impact on the level of the buried material
costs reflected in the buried cable cost estimates. We find that this does not lead to an unreasonable estimate for
buried cable costs. As we explained, application of the Huber methodology results in a better estimate of the
expected value or tendency of the material costs for the RUS companies. Moreover, as noted above, the level of the
buying power adjustment we adopt for buried copper cable is the most conservative estimate on the record before
us.
698

GTE Inputs Further Notice comments at 29.

699

GTE Inputs Further Notice comments at 29.

700

GTE Inputs Further Notice comments at 28-29; Sprint Inputs Further Notice comments at 22-23.

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that the RUS data reflect the same categories of costs as those reflected in the Bell Atlantic data.
More importantly, this data reflects the best available evidence on the record on which to base
the buying power adjustment.
159. BellSouth claims that the buying power adjustment is flawed because it does not
take into account the exclusion of RUS data resulting from the Huber adjustment.701 Bell
Atlantic makes a similar claim.702 Both parties argue that because the Huber methodology
excludes high cost data from the regression analysis, it is inappropriate to apply a discount which
essentially has the same effect. In sum, these commenters claim that we are adjusting for high
material costs twice. We disagree. This contention ignores the fact that the application of the
Huber methodology and the buying power adjustment are fundamentally different adjustments.
The Huber adjustment gives reduced weight to observations that are out of line with other data
provided by the RUS companies. The Huber adjustment provides coefficient estimates that can
be used to estimate the cost incurred by a typical RUS company. The adjustment is designed to
dampen the effect of outlying observations that otherwise would exhibit a strong influence on the
analysis. The large buying power adjustment, on the other hand, adjusts for the greater buying
power of the non-rural companies. None of the RUS companies have the buying power of, for
example, Bell Atlantic or GTE, and therefore have to pay more for material. The buying power
adjustment could only duplicate the Huber adjustment if some of the RUS companies have the
buying power of a company as large as Bell Atlantic. Because none of the firms in the RUS data
base are close to the size of Bell Atlantic, the commenters are incorrect when they assert that,
since the Huber methodology excludes high cost data from the regression analysis, it is
inappropriate to apply the buying power adjustment.
160. We also reject BellSouth's argument that, to determine the size of the buying
power adjustment, we should use a weighted average of the cable price differentials between
Bell Atlantic and the RUS companies that is based on the miles of cable installed, not the
number of observations, for each cable size.703 In the NRRI Study, this weighted average price
differential is determined by: (1) calculating the price differential between Bell Atlantic's
average cable price and the RUS companies' average cable price for each cable size; (2)
weighting the price differential for each cable size by the number of observations used to
calculate the RUS companies' average cable price; and (3) summing these weighted price
differentials.704 The average measures the central tendency of the data. In general, the average
more reliably measures this central tendency the larger the number of observations from which
701

BellSouth Inputs Further Notice comments, Attachment A at A-5, A-18.

702

Bell Atlantic Inputs Further Notice comments, Attachment C at 22-23, 27.

703

BellSouth Inputs Further Notice comments, Attachment A at A-18.

704

NRRI Study at 47 n. 47.

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this average is calculated. In the NRRI Study, the average cable prices calculated for the RUS
companies that reflect a relatively large number of observations are more reliable than those that
reflect relatively few observations. Accordingly, weighting the price differentials for each cable
size by the number of observations reflected in the average cable price calculated for the RUS
companies provides a weighted average that reliably measures the central tendency of the price.
In contrast, use of the miles of cable installed as weights to determine the average cable price
differentials could result in a less reliable measure of central tendency because price differentials
based on a small number of observations but reflecting a high percentage of cable miles
purchased would have a greater impact on the weighted average than price differentials based on
a large number of observations of cable purchase prices. Moreover, use of the number of miles
of cable installed as the weights would result in a weighted average price differential that reflects
RUS companies' relative use of different size cables. The RUS companies' relative use of
different size cables is irrelevant for use in a model used to calculate non-rural LECs' cost of
constructing a network.
161. We also reject Bell Atlantic's contention that the buying power adjustment is
flawed because it should have been applied to the material costs rather than the regression
coefficient of copper cable pairs or the number of fiber strands.705 Bell Atlantic has provided no
evidence that demonstrates that applying the discount to the coefficient is incorrect. It is an
elementary proposition of statistics that the result of applying the discount to the regression
coefficient is equal to applying the discount to the material costs.706 Significantly, Bell Atlantic
has not demonstrated that applying the discount to the regression coefficient does not produce
the same result as applying the discount to the material costs.
162. Finally, we disagree with Sprint that, because buying power equates to company
size, it is inappropriate to apply this adjustment uniformly to all carriers.707 We are estimating
the costs that an efficient provider would incur to provide the supported services.708 We are not
attempting to identify any particular company's cost of providing the supported services. We
find, therefore, that applying the buying power adjustment as we propose is appropriate for the
purpose of calculating universal service support.
163.
705

In sum, we find unpersuasive the criticisms of the buying power adjustment we

Bell Atlantic Inputs Further Notice comments, Attachment C at C-27.

706

E(aX) = aE(X) where "a" is the discount factor and X is the price of cable. See, e.g., Gerald Keller and Brian
Warrick, Statistics for Management Economics at 206 (Fourth Edition, Duxbury, 1997).
707

Sprint Inputs Further Notice comments at 22. See also Cincinnati Bell Inputs Further Notice comments at 3-

708

See supra at paragraph 29.

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proposed. We conclude that, based on the record before us, a downward adjustment to the
estimates developed from data reflecting the cable costs of rural LECs is necessary to derive
estimates representative of cable costs for non-rural LECs and that the levels we have proposed
for this adjustment are reasonable.
164. LEC Engineering. The second adjustment we proposed to the regression
equations used to estimate cable costs was to account for LEC engineering costs, which were not
included in the RUS data.709 As we noted, the BCM2 default values include a loading of five
percent for engineering.710 In contrast, the HAI sponsors claimed that engineering constitutes
approximately 15 percent of the cost of installing outside plant cables.711 This percentage
includes both contractor engineering and LEC engineering. The cost of contractor engineering
already is reflected in the RUS cable cost data. In the Inputs Further Notice, we tentatively
concluded that we should add a loading of 10 percent to the material and labor costs of cable (net
of LEC engineering and splicing costs) to approximate the cost of LEC engineering.712
165. We affirm our tentative conclusion to add a loading of 10 percent to the material
and labor for the cost of cable (net of LEC engineering and splicing costs) to approximate the
cost of LEC engineering. We find that, based on the record before us, the proposed LEC
engineering adjustment, as modified below, is appropriate. We also find that the level of the
adjustment we proposed is reasonable. We note that there is a general consensus among the
commenters that the proposed adjustment is necessary.713 We reject, however, the contentions of
those commenters that advocate that the level of the LEC adjustment be based on companyspecific data. As we explained above, we find such data to be unreliable. For similar reasons,
we reject the LEC engineering adjustment proposed by AT&T and MCI. As we explained,
AT&T and MCI's proposal is based on expert opinions which we find to be unsupported and,
therefore, unreliable.714 Accordingly, the level of the adjustment that we proposed, which, as we
709

See Inputs Further Notice at paras. 80, 91. It should be noted that the LEC Engineering Adjustment as well
as the Splicing Adjustment discussed infra in paragraphs 168-176 would be required in the estimation of costs for
rural LECs from the RUS data base because such costs were not reflected in the RUS data. These adjustments are
part of the process in developing estimates from the data.
710

Inputs Further Notice at para. 80.

711

Inputs Further Notice at para. 80.

712

Inputs Further Notice at paras. 80, 82, 84, 91, 93, 95.

713

See e.g., GTE Inputs Further Notice comments at 31-32; AT&T and MCI Inputs Further Notice comments at
16-18; BellSouth Inputs Further Notice comments, Attachment B at B-8 - B-9; BellSouth Inputs Further Notice
reply comments at 6-7; Sprint Inputs Further Notice comments at 24-25; Bell Atlantic Inputs Further Notice reply
comments, Attachment A at 1.
714

AT&T and MCI Inputs Further Notice comments at 16.

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explained in the Inputs Further Notice represents the mid-point between the HAI default loading
and the BCPM default loading, is the most reasonable value on the record before us.
166. Sprint contends that we should calculate the loadings for LEC engineering on a
flat dollar basis rather than on a fixed percentage of the labor and material costs of cable.715 We
find persuasive Sprint's contention that LEC engineering costs do not vary with the size of the
cable and therefore do not vary with the cost of the cable. Accordingly, we find it reasonable to
apply the loading for LEC engineering in the manner that Sprint recommends.
167. We also find that the commenters are correct that the loading for LEC engineering
should not reflect any adjustment for buying power because the buying power differential
between non-rural and rural LECs only relates to materials.716 We adjust our calculation
accordingly. Similarly, we also find it appropriate to include in the loading for LEC engineering
an allowance for LEC engineering associated with splicing.717 We find that this is appropriate
because the loading for LEC engineering is based on BCPM and HAI default values for this
loading that are expressed as a percentage of cable costs inclusive of engineering.718
168. Splicing Adjustment. The third adjustment to the regression equations that we
proposed in the Inputs Further Notice was to account for splicing costs, which also were not
included in the RUS data.719 As we explained, Gabel and Kennedy determined that the ratio of
715

Sprint Inputs Further Notice comments at 24.

716

See e.g., GTE Inputs Further Notice comments at 26-28; BellSouth Inputs Further Notice comments,
Attachment B at B-9.
717

AT&T and MCI develop equations for engineering costs that reflect engineering costs associated with
splicing. See AT&T and MCI Inputs Further Notice comments, Exhibit A at A-7.
718

We develop the flat cost-per-foot loading for LEC engineering for each type of cable by first estimating the
RUS companies' total cable cost inclusive of splicing and exclusive of LEC engineering costs based on: (1) the
regression equations we adopt in this Order; (2) the number of feet of cable that was placed pursuant to the contracts
from which the data used to develop these regression equation are derived; and (3) the loadings that we adopt in this
Order for splicing costs, 9.4 percent for copper cable and 4.7 percent for fiber cable. We then compute for each
type of cable the total LEC engineering cost based on the total cable cost inclusive of LEC splicing costs and the
loading that we adopt in this Order for LEC engineering, 10 percent. Finally, for each type of cable, we compute
the flat cost per foot loading for LEC engineering by dividing the total LEC engineering costs by the total number
of feet of cable placed pursuant to the RUS contracts.
Based on this methodology, we derive values for LEC engineering costs of $.19, $1.50, $.16, $.19, $.65,
and $.14 per foot for 24-gauge aerial copper cable costs, 24-gauge underground copper cable costs, 24-gauge buried
copper cable costs, aerial fiber cable costs, underground fiber cable costs, and buried fiber cable costs, respectively.
We add these LEC engineering costs to the cable cost estimates derived by using the Huber methodology.
719

See Inputs Further Notice at paras. 81, 91.

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splicing costs to copper cable costs (excluding splicing and LEC engineering costs) is 9.4
percent for RUS companies in the NRRI Study.720 Similarly, Gabel and Kennedy determined
that the ratio of splicing costs to fiber cable costs (excluding splicing and LEC engineering costs)
is 4.7 percent.721 Thus, we tentatively concluded that we should adopt a loading of 9.4 percent
for splicing costs for 24-gauge aerial copper cable, 24-gauge underground copper cable, and 24gauge buried copper cable.722 Correspondingly, we tentatively concluded that we should adopt a
loading of 4.7 percent for splicing costs for aerial fiber cable, underground fiber cable, and
buried fiber cable.723
169. We affirm these tentative conclusions. We find that, based on the record before
us, the splicing cost adjustment is appropriate and the levels of the adjustments proposed are
reasonable. In reaching this conclusion, we reject the claims of those commenters that advocate
the use of company-specific data to develop the splicing loadings.724 For the reasons enumerated
above, we find such data unreliable.
170. We disagree with GTE's claim that, because the splicing factor is based on the
RUS data, it is flawed.725 This contention echoes GTE's assertion that we should use companyspecific data. As we explained above, however, we conclude that such data are not reliable. We
also disagree with GTE's contention that an analysis of the source contract data shows that some
splicing costs are invalid.726 GTE is mistaken. The RUS cost data from which the regression
equations in the NRRI Study and in this Order are derived exclude splicing costs. Cable cost
estimates obtained by using this methodology and these data are net of LEC engineering and
splicing costs. We add to these cable cost estimates a loading factor for splicing that Gabel and
Kennedy developed separately using the RUS data in the NRRI Study without using the
regression analysis. In the NRRI Study, Gabel and Kennedy determined the ratio of splicing to
cable costs by comparing the cost for splicing and the cost for cable (exclusive of splicing and
LEC engineering costs) reflected in the contracts included in the RUS data base. Some of the
720

Inputs Further Notice at para. 81 n. 164 citing NRRI Study at 29.

721

Inputs Further Notice at para. 91 n. 176 citing NRRI Study at 29.

722

Inputs Further Notice at paras. 81, 82, 84.

723

Inputs Further Notice at paras. 91, 93, 95.

724

See e.g., GTE Inputs Further Notice comments at 32 and 50; Sprint Inputs Further Notice comments at 27;
BellSouth Inputs Further Notice comments, Attachment A at A-9 - A-11, and Attachment B at B-8; BellSouth
Inputs Further Notice reply comments at 6-7.
725

GTE Inputs Further Notice comments at 49.

726

GTE Inputs Further Notice comments at 49.

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splicing costs reflected in this database are relatively high and some are relatively low. None of
these high or low values is likely to influence significantly this ratio because it reflects a large
number of observations. Accordingly, we find it reasonable to apply the splicing ratios
developed in the NRRI Study to the cable cost estimates developed separately in this Order by
using the Huber methodology with the RUS data.
171. We also disagree with AT&T and MCI's contention that, rather than adopting the
proposed splicing loadings or the incumbent LEC's loading factors, we should adopt "reasonable
values for the costs of cable placing, splicing, and engineering based on the expert opinions
submitted in this proceeding."727 As discussed above, we find that these expert opinions are
unsupported, and therefore unreliable.
172. For the same reason, we also find unpersuasive AT&T and MCI's claim that the
loading of 9.4 percent for splicing copper cable is excessive.728 AT&T and MCI estimates that
splicing costs vary between 3.4 and 6.9 percent of cable investment in contrast to the proposed
rate of 9.4 percent. We find that these estimates, which rely on assumptions concerning the perhour cost of labor, the number of hours required to set up and close the splice, the number of
splices per hour, and the distance between splices, are unreliable. AT&T and MCI have
provided no evidence other than the unsupported opinions of their experts to substantiate these
data. In contrast, Bell Atlantic supports the use of the 9.4 percent loading indicating, that this
level is consistent with its own data.729
173. While Sprint agrees that a splicing loading is required in the NRRI regression,
Sprint recommends that a flat dollar "per pair per foot" cost additive should be employed rather
than the adjustment we proposed.730 We disagree. We find that Sprint's flat dollar "per pair per
foot" cost additive ignores the differences in set-up costs among different cable sizes. In
contrast, the percent loading for splicing costs we adopt herein implicitly recognizes such
differences because these loadings are applied to cable costs estimates (exclusive of splicing and
LEC engineering costs) derived from regression equations that have an intercept term that
provides a measure of the fixed cost of cable. Accordingly, we conclude that the percent loading
approach is more reasonable.
174.

Sprint also asserts that underground splicing costs are higher due to the need to

727

AT&T and MCI Inputs Further Notice comments at 16.

728

AT&T and MCI Inputs Further Notice comments at 16-18.

729

Bell Atlantic Inputs Further Notice reply comments, Attachment A at 1.

730

Sprint Inputs Further Notice comments at 25. We note that Sprint advocates the use of company-specific
data in the first instance.

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work in manholes.731 We agree. The dollar amounts associated with the fixed percentage
loadings adopted in this Order for underground copper and fiber cable are generally larger than
for aerial and buried copper cable and fiber cable. The dollar amounts that we adopt for splicing
are generally larger for underground cable because the costs that we develop from RUS data for
underground cable net of splicing and engineering costs are generally larger than the costs that
we develop for aerial and buried cable net of splicing and engineering costs. As a result, when
the fixed percentage is applied to these cable costs, the dollar amount for splicing is generally
larger for underground cable than for aerial and buried cable.732
175. We disagree with those commenters who argue that the splicing costs do not vary
with the cost of cable (net of splicing costs).733 We find that cable costs increase as the size of
the cable increases. Splicing costs increase as the size of the cable increases because larger
cables require more splicing than small cables. Therefore, splicing costs increase as the cost of
the cable increases.
176. Finally, we disagree with SBC's claim that the 14 percent splicing factor for fiber
cable is more appropriate than the 4.7 percent we proposed.734 We find that the 14 percent factor
SBC proposes is unsupported. SBC asserts that this factor is based on an average cost ratio from
an analysis using various lengths of underground fiber placement, including placing labor and
comparing it to associated splicing costs from current cost dockets. However, SBC has not
provided this analysis on the record.
177. 26-Gauge Copper Cable. In the Inputs Further Notice, we explained that,
because the NRRI Study did not provide estimates for 26-gauge copper cable, we must either use
another data source or find a method to derive these estimates from those for 24-gauge copper
cable.735 To that end, we tentatively concluded that we should derive cost estimates for 26-

731

Sprint Inputs Further Notice comments at 25.

732

There is one instance where the underground cable costs that we develop from RUS data (net of splicing and
engineering costs) are not the largest for a given cable size. For the largest fiber cable size, 288 pairs, the costs that
we develop for buried cable, $12.07 per foot, are greater than those for underground cable, $11.96 per foot.
However, the model is unlikely to frequently place the largest fiber cable size in the network it builds in high-cost
areas because most high-cost areas are in the lowest density zones where use of such a cable provides too much
capacity relative to demand.
733

See e.g., Sprint Inputs Further Notice reply comments at 16; GTE Inputs Further Notice reply comments 26-

27.
734

735

SBC Inputs Further Notice comments at 9.
Inputs Further Notice at para. 85.

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gauge cable by adjusting our estimates for 24-gauge cable.736 We proposed to estimate these
ratios using data on 26-gauge and 24-gauge cable costs submitted by Aliant and Sprint and the
BCPM default values for these costs.737 We noted, that while we would prefer to develop these
ratios based on data from more than these three sources, we tentatively concluded that these were
the best data available on the record for this purpose.
178. We affirm our tentative conclusion to derive cost estimates for 26-gauge cable by
adjusting our estimates for 24-gauge cable. As we explained in the Inputs Further Notice, we
agree with the BCPM sponsors that the cost of copper cable should not be estimated based solely
on the relative weight of the cable.738 Instead, we proposed to use the ordinary least squares
regression technique to estimate the ratio of the cost of 26-gauge to 24-gauge cable for each
plant type (i.e., aerial, underground, buried). We conclude that, based on the record before us,
this approach, adjusted as described more fully below, is reasonable.
179. Consistent with their position on estimating the costs of 24-gauge cable, many
commenters advocate that we use company-specific data to estimate the costs of 26-gauge
cable.739 As we explained above, we have determined that such data are not sufficiently reliable
to employ in the model.740 Accordingly, we reject the use of company-specific data to estimate
the costs of 26-gauge cable. We note that AT&T and MCI endorse the derivation of cost
estimates for 26-gauge cable from estimates for 24-gauge cable.741 Notwithstanding their
support of the general approach we proposed, AT&T and MCI oppose estimating the ratio of
costs of 26-gauge cable to 24-gauge cable using the cable costs submitted by Aliant and Sprint
and the BCPM default values. Instead, AT&T and MCI advocate the use of the relative weight
of copper to adjust the cost of the 24-gauge copper.742 AT&T and MCI claim that this approach
is the most logical because 26-gauge copper costs are directly proportional to the weight of the
metallic copper in the cable. We reject AT&T and MCI's recommended approach. We find that,
because AT&T and MCI have provided no evidence that the weight differential is approximately
736

Inputs Further Notice at para. 86.

737

We did not use the HAI default values in addition to these data to estimate these ratios because the HAI
defaults do not have separate values for 26-gauge and 24-gauge cable costs for each different cable size.
738

Inputs Further Notice at para. 86.

739

See e.g., BellSouth Inputs Further Notice comments at 6-7, Attachment B at B-8 - B-9; GTE Inputs Further
Notice comments at 48.
740

See supra paragraph 92.

741

AT&T and MCI Inputs Further Notice comments at 19-20.

742

AT&T and MCI Inputs Further Notice comments at 19-20.

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equal to the price differential, there is insufficient evidence on the record demonstrating the
reasonableness of this approach.
180. Many of those commenters advocating the use of company-specific data contend
that there are flaws in the methodology adopted herein to derive cost estimates for 26-gauge
cable by adjusting our estimates for 24-gauge cable. Bell Atlantic and GTE contend that our
methodology results in biased estimates due to statistical error.743 We agree and modify our
proposed methodology as explained below.
181. As we explained in Appendix D of the Inputs Further Notice, in order to derive
the 26-gauge copper cable costs, we first estimated the cost for 24-gauge copper cable for each
cable size from the RUS data using the Huber methodology.744 More specifically, we obtained
an estimate of the expected or mean value of the cost for 24-gauge copper cable (for given values
of the independent variables in the regression equation). We then obtained values for the ratio of
24-gauge copper cable to 26-gauge copper cable for each cable size using ex parte data obtained
from Aliant and Sprint and BCPM default values for the costs and employing ordinary least
squares regression analysis. As a result, we obtained an estimate of the expected value of the
ratio of 24-gauge copper cable to 26-gauge copper cable (for given values of the independent
variables in the regression equation). Finally, we multiplied the reciprocal of this ratio by the
cost of 24-gauge copper cable obtained by using the Huber methodology with RUS data to
obtain the proposed 26-gauge copper cable cost for each copper cable size. Bell Atlantic and
GTE contend, and we agree, that this is a biased estimate of the expected value of the cost for
26-gauge copper cable because the expected value of the ratio of two random variables, e.g., 26gauge copper cable cost and 24-gauge copper cable, does not equal the ratio of the expected
value of the first random variable to the expected value of the second random variable. We note
that the magnitude of the bias is larger as the difference grows between the expected value of the
ratio of 26-gauge copper cable cost to 24-gauge copper cable cost and the ratio of the expected
value of 26-gauge copper cable cost to the expected value of 24-gauge copper cable cost.
182. Accordingly, we modify the methodology tentatively adopted in the Inputs
Further Notice to derive estimates of 26-gauge copper cable costs from 24-gauge copper cable
costs that are not biased. As explained in more detail in Appendix B, in addition to estimating
the expected value of the cost for 24-gauge copper cable for each cable size using the RUS data,
we also estimate the expected value of the costs of 24-gauge and 26-gauge copper cable for each
cable size using the data submitted by Aliant and Sprint and the BCPM default values, as well as
data submitted by BellSouth,745 hereinafter identified in the aggregate as "the non-rural LEC
743

Bell Atlantic Inputs Further Notice comments, Attachment C at 26-27; GTE Inputs Further Notice comments
at 29-30.
744

Inputs Further Notice, Appendix D.

745

BellSouth Inputs Further Notice reply comments, Attachment A at A-22 - A-23.

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data." We divide the estimate of the expected value for 24-gauge copper cable cost derived from
the non-rural LEC data into the estimate of the expected value for 26-gauge copper cable cost
derived from these data for each cable size. The result is a ratio of an estimate of the expected
value for 26-gauge copper cable cost to an estimate of the expected value for 24-gauge cable cost
for each cable size. Finally, we multiply this ratio by the estimate of the expected value of the
cost for 24-gauge copper cable derived from the RUS data to obtain an estimate of the expected
value of the cost for 26-gauge copper cable for each cable size. We find that this adjustment
eliminates the bias identified by the commenters. We conclude, therefore, that these estimates
are reasonable and adopt them as inputs for 26-gauge copper cable costs.
183. We note that, in adopting these modifications, we find that it is reasonable to rely
on the non-rural LEC data for calculating the ratio of the cost for 24-gauge copper cable to that
for 26-gauge copper cable, but not for calculating the absolute cost for 24-gauge copper cable
and 26-gauge copper cable. As discussed above, we find that the non-rural LEC data are not a
reliable measure of absolute costs. Notwithstanding this finding, we conclude that it is
reasonable to use the non-rural LEC data to determine the relative value of the cost for 24-gauge
copper cable to that for 26-gauge copper cable. We find that it is reasonable to conclude that
each LEC used the same methodology to develop both 24-gauge and 26-gauge copper cable
costs. Accordingly, any bias in the costs for 24-gauge and 26-gauge copper cable that results
from using a given methodology is likely to be in the same direction and of a similar magnitude.
As a consequence, the estimate of the expected value of the cost for 26-gauge copper cable for
each cable size and the estimate of the expected value of the cost for 24-gauge copper cable
obtained from non-rural LEC data are likely to be biased by approximately the same factor. The
ratios of the estimates of these expected values are not likely to be affected significantly because
the bias in one estimate approximately cancels the bias in the other estimate when the ratio is
calculated.
184. GTE also contends that the proposed methodology systematically reduces the
amount of labor associated with placing cable.746 We conclude that the adjustments made in
response to GTE and Bell Atlantic's criticisms discussed above render this criticism irrelevant.
We find that no systematic bias will result because the ratio of the 24-gauge cost of copper cable
to the cost of 26-gauge copper cable represents the installed cost of 26-gauge copper cable
including all labor and materials divided by the installed cost of 24-gauge copper cable including
all labor and materials. Moreover, this ratio is applied to the installed cost of 24-gauge copper
cable which includes all labor and material costs.
185. BellSouth claims that neither the data used to develop the ordinary least squares
regression equation we employ in the Inputs Further Notice to estimate the cost of 26-gauge

746

GTE Inputs Further Notice comments at 48-50.

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copper cable or the computations used to derive that equation have been provided.747 BellSouth
contends that, as a result, it is not possible to confirm or contradict the discount value. We
disagree. Contrary to BellSouth's assertion, the data are available. As we explained, the
regression equation uses ex parte data submitted by Aliant and Sprint. These data are available
subject to the Commission's rules regarding the treatment of confidential material. We also note
that the BellSouth data we employ in the adjusted methodology we adopt herein are publicly
available. Moreover, the BCPM data are publicly available.
5.

Cable Fill Factors
a.

Background

186. As we explained in the Inputs Further Notice, in determining appropriate cable
sizes, network engineers include a certain amount of spare capacity to accommodate
administrative functions, such as testing and repair, and some expected amount of growth.748
The percentage of the total usable capacity of cable that is expected to be used to meet current
demand is referred to as the cable fill factor.749 If cable fill factors are set too high, the cable will
have insufficient capacity to accommodate small increases in demand or service outages. In
contrast, if cable fill factors are set too low, the network could have considerable excess
capacity. While carriers may choose to build excess capacity for a variety of reasons, it is
necessary to determine the appropriate cable fill factors for use in the federal mechanism. We
also explained that, if the fill factors are too low, the resulting excess capacity would increase the
model's cost estimates to levels higher than an efficient firm's costs, potentially resulting in
excessive universal service support payments. Accordingly, as discussed more fully below, we
tentatively selected the HAI defaults for distribution fill factors, the average of the HAI and
BCPM default values for copper feeder fill factors, and fiber fill factors of 100 percent.750
187. Variance Among Density Zones. As a preliminary matter, we noted that both the
HAI and BCPM sponsors provided default fill factors for copper cable that vary by density zone,
747

BellSouth Inputs Further Notice comments, Attachment A at A-19.

748

Inputs Further Notice at para. 96.

749

We note that the actual fill factor may be lower than the fill factor used to design the network (sometimes
referred to as administrative fill), because cable and fiber are available only in certain sizes. For example, assume a
neighborhood with 100 households has a current demand of 120 telephones. Dividing the 120-pair demand by an
80 percent administrative fill factor establishes a need for 150 pairs. Cable is not sold, however, in 150-pair units.
The company would purchase the smallest cable that is sufficient to provide 150 pairs, which is a 200 pair cable.
The fill factor that occurs and is measurable, known as the effective fill, would be the number of pairs needed to
meet demand, 120 pairs, divided by the number of pairs installed, 200 pairs, or 60 percent.
750

Inputs Further Notice at paras. 100, 101, 102.

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and that both agreed that fill factors should be lower in the lowest density zones.751 We
explained that the HAI sponsors claimed that an outside plant engineer is more interested in
providing a sufficient number of spares than in the ratio of working pairs to spares, so the
appropriate fill factor will vary with cable size.752 Because smaller cables are used in lower
density zones, HAI recommended that lower fill factors be used in the lowest density zones to
ensure there will be enough spares available. Similarly, the BCPM sponsors claimed that less
dense areas require lower fill ratios because the predominant plant type is buried and it is costly
to add additional capacity after installation.753 We tentatively agreed with the HAI and BCPM
sponsors that fill factors for copper cable should be lower in the lowest density zones, and
reflected this relationship in the fill factors that we proposed in the Inputs Further Notice.754
188. Distribution Fill Factors. We also noted in the Inputs Further Notice that the fill
factors proposed by the HAI sponsors for distribution cable were somewhat lower than for
copper feeder cable.755 In contrast, the BCPM default fill factors for distribution cable are set at
100 percent for all density zones.756 We explained that this difference is related to the
differences between certain assumptions that were made in the HAI and BCPM models. The
HAI proponents claimed that the level of spare capacity provided by their default values is
sufficient to meet current demand plus some amount of growth.757 This is consistent with the
HAI model's approach of designing plant to meet current demand, which on average is 1.2 lines
per household as defined by HAI. BCPM, on the other hand, designs outside plant with the
assumption that every residential location has two lines, which is more than current demand.
This reflects the practice of incumbent LECs to build enough distribution plant to meet not only
current demand, but also anticipated future demand because it is costly to add distribution plant
751

Inputs Further Notice at para. 97. As explained below, default values in BCPM 3.1 for distribution cable do
not vary by density zone.
752

Inputs Further Notice at para. 97 n. 187 citing HAI Dec. 11, 1997 submission, Inputs Portfolio at 39, 63.

753

Inputs Further Notice at para. 97 n. 188 citing BCPM 3.1 May 26, 1998 (Preliminary Edition) Loop Inputs
Documentation at 51.
754

Inputs Further Notice at para. 97.

755

Inputs Further Notice at para. 98 n. 189 citing HAI Dec. 11, 1997 submission, Inputs Portfolio at 39, 63.
HAI 5.0 default values range from 50 percent in the lowest density zone to 75 percent in the highest density zone
for distribution cable sizing fill factors, and range from 65 percent in the lowest density zone to 75 percent in the
highest density zone for copper feeder cable sizing fill factors.
756

Inputs Further Notice at para. 98 n. 190 citing BCPM Dec. 11, 1997 submission. We noted that earlier
versions of BCPM, however, had lower fill factors for distribution than for feeder. See, e.g., 1997 Further Notice at
para. 118. Default values in BCPM 3.1 range from 75 to 85 percent for feeder cable.
757

Inputs Further Notice at para. 98 n. 191 citing HAI Dec. 11, 1997 submission, Inputs Portfolio at 39, 63.

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at a later point in time.758
189.
We also noted that, in a meeting with Commission staff, Ameritech raised the
issue of whether industry practice is the appropriate guideline for determining fill factors to use
in estimating the forward-looking economic cost of providing the services supported by the
federal mechanism.759 Ameritech claimed that forward-looking fill factors should reflect enough
capacity to provide service for new customers for a few years until new facilities are built, and
should account for the excess capacity required for maintenance and testing, defective copper
pairs, and churn.760
190.
We tentatively concluded that the fill factors selected for use in the federal
mechanism generally should reflect current demand,761 and not reflect the industry practice of
building distribution plant to meet "ultimate" demand. We also tentatively selected the HAI
defaults for distribution fill factors and tentatively concluded that they reflect the appropriate fill
needed to meet current demand.762
191.
Feeder Fill Factors. In the Inputs Further Notice we explained that, in contrast to
distribution plant, feeder plant typically is designed to meet only current and short term capacity
needs.763 We noted that the BCPM copper feeder default fill factors are slightly higher than
HAI's, but both the HAI and BCPM default values appear to reflect current industry practice of
sizing feeder cable to meet current, rather than long term, demand.764 We tentatively selected
copper feeder fill factors that are the average of the HAI and BCPM default values because both
the HAI and BCPM default values assume that copper feeder fill reflects current demand.765
758

For example, in an ex parte meeting on March 24, 1999, Ameritech representatives said that Ameritech
designs distribution plant to meet "ultimate" demand and designs feeder plant that is "growable." See Letter from
Celia Nogales, Ameritech, to Magalie Roman Salas, FCC, dated March 25, 1999 (Ameritech March 25 ex parte).
759

Inputs Further Notice at para. 99.

760

Inputs Further Notice at para. 99 n. 194. Ameritech filed data, subject to the protective order in this
proceeding, showing how these considerations are used to calculate the actual and forward-looking fill factors in
Ameritech's territory. See Ameritech March 25 ex parte.
761

We define "current demand" to include a reasonable amount of excess capacity to accommodate short term
growth. Inputs Further Notice at para. 100 n. 195.
762

Inputs Further Notice at para. 100.

763

Inputs Further Notice at para. 101 citing Ameritech March 25 ex parte.

764

Inputs Further Notice at para. 101.

765

Inputs Further Notice at para. 101.

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192. Fiber Fill Factors. We also explained in the Inputs Further Notice that, because
of differences in technology, fiber fill factors typically are higher than copper feeder fill
factors.766 Standard fiber optic multiplexers operate on four fiber strands: primary optical
transmit, primary optical receive, redundant optical transmit, and redundant optical receive. In
determining appropriate fiber cable sizes, network engineers take into account this 100 percent
redundancy in determining whether excess capacity is needed that would warrant application of
a fill factor.767 Both the HAI and BCPM models use the standard practice of providing 100
percent redundancy for fiber and set the default fiber fill factors at 100 percent. Accordingly, we
tentatively concluded that the input value for fiber fill in the federal mechanism should be 100
percent.768
b.

Discussion

193. We affirm our tentative conclusion that fill factors for copper cable should be
lower in the lowest density zones. Significantly, those commenters addressing this issue agree
that lower density zones should utilize lower copper cable fill factor inputs.769 We also reject, at
the outset, certain assertions made by GTE and others, challenging the overall approach we
proposed and adopt herein for determining the appropriate cable fill factors to use in the federal
mechanism and reject GTE's assertions that the model is flawed.
194. We disagree with GTE's assertion that the use of generalized fill factors are not
proper inputs for a cost model that seeks to estimate the forward-looking costs of building a
network. GTE claims that the use of generalized fill factors disregards how actual distribution
plant is designed and that different levels of utilization are observed in different parts of the local
network.770 However, we find that GTE's concerns are misplaced. Contrary to GTE's
implication, generalized fill factors are an administrative input and are not the sole determinate
of the effective fill factor. As we explained in the Inputs Further Notice, the effective fill factor
will vary with the number of customer locations and the available discrete size of cable.771 Thus,
766

Inputs Further Notice at para. 102.

767

That is, fiber plant with a 100 percent fill factor has an actual utilization of 50 percent; whereas copper plant
with a 50 percent fill factor has an actual utilization of 50 percent.
768

Inputs Further Notice at para. 102.

769

Sprint Inputs Further Notice comments at 29; SBC Inputs Further Notice comments at 9; GTE Inputs
Further Notice comments at 54.
770

771

GTE Inputs Further Notice comments at 53.
Inputs Further Notice at para. 96 n. 135.

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the effective fill factor will reflect how distribution plant is designed and different levels of
utilization that are observed in different parts of the local network.
195. Similarly, we disagree with GTE's assertion that company-specific information
should be used to determine appropriate fill factor inputs.772 We note that the final effective fill
factors are the result of the input of the administrative fill factors and company-specific customer
location data. We also disagree with the contention that administrative fill factors must be
company-specific.773 The administrative fill factors are determined per engineering standards
and density zone conditions. These factors are independent of an individual company's
experience and measured effective fill factors. The administrative fill factors would be the same
for every efficient competitive firm.
196. We reject GTE's contention that the model should be modified to accept the
number of pairs per location to determine the required amount of distribution plant rather than
using fill factors.774 GTE claims that this is necessary because using fill factor inputs produces
anomalous results. GTE contends that the use of fill factors causes the number of implicit lines
per location to decrease as density increases, in contrast to what occurs in reality. There are,
according to GTE, always more business customers in higher density zones; therefore, the
number of lines that must be provisioned per location should increase as density increases.
197. We find that there is no need to modify the model to accept pairs per location
rather than fill factors, as GTE contends. The number of implicit lines per location does not
decrease in the model as GTE claims. On the contrary, the number of implicit lines per location
increases as a function of the number of business lines. The model will build to the level of
business demand. With business demand increasing as a function of density, the model
generates a higher number of lines per location as density increases. In sum, the anomaly that
GTE identifies does not exist. GTE's claim reflects a misunderstanding of the model's operation.
198. Finally, we disagree with GTE's assertion that there is an error in the way the
model calculates density zones that prevents correct application of zone-specific inputs.775 As
GTE explains, after the model has assigned customer locations to clusters, it constructs a
772

GTE Inputs Further Notice comments at 54. Ameritech contends that the nationwide fill factors proposed
by the Commission are reasonable estimates to use if company-specific or state-specific fill factors are not used.
Ameritech Inputs Further Notice comments at 20.
773

See e.g., GTE Inputs Further Notice comments at 54; BellSouth Inputs Further Notice comments,
Attachment B at B-12
774

GTE Inputs Further Notice comments at 54.

775

GTE Inputs Further Notice comments at 55.

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"convex hull" around all locations in the cluster. The model then calculates density as the lines
in the cluster divided by the area within the convex hull. GTE claims that the calculated
densities will be higher than those observed in the real world because the denominator excludes
all land not contained in the convex hull. While we agree with GTE's description of how the
model determines cluster density, we find GTE's claim that this methodology is erroneous to be
misplaced. In sum, GTE argues that the model employs a restricted definition of area which
causes the model to use excessively high utilization factors.776 In other words, the issue is
whether the model should recognize all of the area around a cluster. We conclude that it should
not. If the land outside the convex hull were included in the denominator, as GTE implies it
should, the denominator would recognize unoccupied areas where no customers reside. As a
result, the model would select density zone fill factors that are lower than needed to service the
customers in that cluster. There would be a downward bias in the model fill factors. Thus, there
is not an error in the way the model calculates density zones, as GTE contends. The model
generates density values that correspond to the way the population is dispersed. To do otherwise
would introduce a bias and distort the forward-looking cost estimates generated by the model.
199. Distribution Fill Factors. We also affirm our tentative conclusion that the fill
factors selected for use in the federal mechanism generally should reflect current demand and not
reflect the industry practice of building distribution plant to meet ultimate demand. As we
explained in the Inputs Further Notice, the fact that industry may build distribution plant
sufficient to meet demand for ten or twenty years does not necessarily suggest that these costs
should be supported today by the federal universal service support mechanism.777
200. We find unpersuasive GTE's assertion that the input values for distribution fill
factors should reflect ultimate demand.778 In concluding that the fill factors should reflect
current demand, we recognized that correctly forecasting ultimate demand is a speculative
exercise, especially because of rapid technological advances in telecommunications. For
example, we note that ultimate demand decreases substantially when computer modem users
switch from dedicated lines serving analog modems to digital subscriber lines where one pair of
copper wire provides the same function as a voice line and a separate dedicated line. Given this
uncertainty, we find that basing the fill factors on current demand rather than ultimate demand is
more reasonable because it is less likely to result in excess capacity, which would increase the
model's cost estimates to levels higher than an efficient firm's costs and could potentially result
in excessive universal service support payments.
201.

Significantly, we note that, contrary to GTE's inference, current demand as we

776

We note that GTE did not assert that this bias will increase structure costs.

777

Inputs Further Notice at para. 100.

778

GTE Inputs Further Notice comments at 55-56.

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define it includes an amount of excess capacity to accommodate short-term growth.779 We find
that GTE has not provided any evidence that demonstrates that the level of excess capacity to
accommodate short-term growth is unreasonable. Rather, GTE claims that, if distribution is not
built to reflect ultimate demand there will be delays in service and increased placement costs due
to the need to reinforce distribution plant in established neighborhoods on a regular basis.780
GTE also contends that telephone companies do not design distribution plant with the
expectation that it will require reinforcement because that is rarely the least-cost method of
placing plant.781 GTE also claims that, in a competitive environment, facilities-based
competitors would build plant to serve ultimate demand.782 We find, however, that these
unsupported claims do not demonstrate that reflecting ultimate demand in the fill factors more
closely represents the behavior of an efficient firm and will not result in the modeling of excess
capacity. Finally, we find that we did not misinterpret the meaning of building distribution plant
to serve "ultimate demand," as GTE asserts.783 Rather, we refused to engage in the highly
speculative activity of defining "ultimate demand." Moreover, we believe that universal service
support will be determined more accurately considering current demand, and not ultimate
demand. Although firms may have installed excess capacity, it does not follow that the cost of
this choice should be supported by the universal service support mechanism. As growth occurs,
however, we anticipate that the requirement for new capacity will be reflected in updates to the
model.784
202. Concomitantly, we adopt the proposed values for distribution fill factors. As we
explained in the Inputs Further Notice, the model designs outside plant to meet current demand
in the same manner as the HAI model.785 Accordingly, it is appropriate to choose fill factors that
are set at less than 100 percent. We conclude that, based on the record before us, the proposed
values reflect the appropriate fill factors needed to meet current demand.
203. There is divergence among the commenters with regard to the adoption of the
proposed values for the distribution fill factors. Sprint does not object to the use of the proposed
779

GTE Inputs Further Notice comments at 55-56.

780

GTE Inputs Further Notice comments at 55.

781

GTE Inputs Further Notice comments at 55.

782

GTE Inputs Further Notice comments at 55.

783

GTE Inputs Further Notice comments at 56.

784

We anticipate beginning a proceeding in the near future to determine how to incorporate changed
circumstances such as these into the modeling process.
785

Inputs Further Notice at para. 100.

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values, stating that "they appear to reasonably represent realistic, forward-looking practices."786
As noted above, Ameritech contends that the copper distribution and feeder fill factors are
reasonable estimates to use if company-specific or state-specific fill factors are not used.787 In
contrast, SBC disagrees with the HAI proponents' claim that the level of spare capacity provided
in the proposed values is sufficient to meet current demand plus some amount of growth.788
SBC, however, offers no controverting evidence demonstrating that the proposed values are
insufficient to meet current demand plus short-term growth. We find that the lone fact that SBC
disagrees is insufficient to controvert our conclusion that the proposed values reflect the
appropriate fill needed to meet current demand. BellSouth contends that the proposed values
will significantly understate distribution cable requirements.789 BellSouth submits instead
projected fill factors for its distribution copper, feeder copper, and fiber cables determined by
BellSouth network engineers. We find these estimates unsupported. Similarly, Bell Atlantic
contends that the proposed fill factors for feeder and distribution are too high and recommends
we adopt its proposed fill factors.790 We find these recommended fill factors unsupported. We,
therefore, select the proposed values for distribution fill factors.
204. We also disagree with AT&T and MCI's contention that the proposed values for
the distribution fill factors are too low. AT&T and MCI claim that distribution fill factors of 1.2
lines per household are more than adequate in a forward-looking cost study.791 We disagree.
We find that 1.2 lines per household are inadequate because they simply reflect the existing
provision of telephone service and are less than current demand as we define it herein.792
Moreover, AT&T and MCI's claim is belied by their own assertions. AT&T and MCI contend
that the "proposed conservative fill factors will ensure sufficient plant capacity to accommodate
potentially unaccounted service needs in the PNR data."793 AT&T and MCI also state that "[t]he
fill levels used in HAI provides more than enough spare capacity for service work, churn, and
786

Sprint Inputs Further Notice comments at 29.

787

Ameritech Inputs Further Notice comments at 20.

788

SBC Inputs Further Notice comments at 9.

789

BellSouth Inputs Further Notice comments, Attachment B at B-11.

790

Bell Atlantic Inputs Further Notice comments, Attachment D at 7 (Proprietary Version); Bell Atlantic Inputs
Further Notice reply comments, Attachment A at A-1.
791

AT&T and MCI Inputs Further Notice comments at 22-23.

792

See FCC, Common Carrier Bureau, Industry Analysis Division, Trends in Telephone Service at 20-6 (rel.
Sept. 1999).
793

AT&T and MCI Inputs Further Notice comments at 8.

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unforeseen spikes in demand.794 In sum, AT&T and MCI attest to the reasonableness of not only
use of the HAI default values for distribution plant, but also the use of the average of the HAI
and BCPM default values for copper feeder.
205. We also disagree with AT&T and MCI's claim that higher factors are appropriate
because the model's sizing algorithm produces effective fill factors that are lower than optimal
values.795 As we explained in the Inputs Further Notice, because cable and fiber are available
only in certain sizes, the effective fill factor may be lower than the administrative fill factor
adopted as an input.796 We find that AT&T and MCI's claim
ignores this fact.
206. Finally, we note that AT&T and MCI also claim that the factor should be higher
because universal service support does not include residential second lines or multiple business
lines. The Commission has never acted on the recommendation in the First Recommended
Decision that only primary residential lines should be supported.797 Moreover, we also note that
AT&T and MCI's claim ignores the sixth criterion, which requires that:
The Cost Study or model must estimate the cost of providing
service for all businesses and households. . . Such inclusion of
multi-line business services and multiple residential lines will
permit the cost study or model to reflect the economies of scale
associated with the provision of these services.798
In sum, we find AT&T and MCI's claim in this regard unpersuasive.
207. Feeder Fill Factors. We also affirm our tentative conclusion to adopt copper
feeder fill factors that are the average of the HAI and BCPM default values. The divergence
among the commenters noted above with regard to the use of the average of the HAI and BCPM
default values for the distribution fill factors is reflected in the comments regarding the proposed
feeder fill factors. Sprint finds that use of the average of the HAI and BCPM default values for
feeder fill factors is reasonable.799 Ameritech's conditional support was noted above. In
794

AT&T and MCI Inputs Further Notice reply comments at 20.

795

AT&T and MCI Inputs Further Notice comments at 22.

796

Inputs Further Notice at para. 96 n. 185.

797

See First Recommended Decision, 12 FCC Rcd at 91-92, 132-134, paras. 4, 89-92.

798

Universal Service Order, 12 FCC Rcd at 8915, para. 250.

799

Sprint Inputs Further Notice comments at 29.

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contrast, BellSouth contends that the average of the HAI and BCPM default values will
significantly understate copper feeder cable requirements.800 As noted above, BellSouth
advocates the use of projected fill factors for copper feeder determined by BellSouth network
engineers. Similarly, Bell Atlantic contends that the feeder fill factors are too high.801 We reject
the use of these fill projections for copper feeder for the reasons enumerated above. We also
reject, for the reasons enumerated above, AT&T and MCI's contention that feeder fill factors
based on the average of the HAI and BCPM default values are too low.
208. Fiber Fill Factors. Finally, we affirm our tentative conclusion that the input value
for fiber fill in the federal mechanism should be 100 percent. The majority of commenters
addressing this specific issue agree with our tentative conclusion.802 AT&T and MCI contend
that fiber feeder fill factors of 100 percent are appropriate because the allocation of four fibers
per integrated DLC site equates to an actual fill of 50 percent, since a redundant transmit and a
redundant receive fiber are included in the four fibers per site.803 AT&T and MCI explain that,
because fiber capacity can easily be upgraded, 100 percent fill factors applied to four fibers per
site are sufficient to meet unexpected increases in demand, to accommodate customer churn,
and, to handle maintenance issues. Similarly, SBC asserts that fiber fill factors of 100 percent
can be obtained because they are not currently subject to daily service order volatility and are
more easily administered.804 In contrast, BellSouth advocates that we employ projected fills
estimated by BellSouth engineers.805 As noted above, these estimates are unsupported and we
reject them accordingly. In sum, we find that the record demonstrates that it is appropriate to use
100 percent as the input value for fiber fill in the federal mechanism.
6.

Structure Costs
a.

800

Background

BellSouth Inputs Further Notice comments, Attachment B at B-11.

801

Bell Atlantic Inputs Further Notice comments, Attachment D at 7 (Proprietary Version); Bell Atlantic Inputs
Further Notice reply comments, Attachment A at A-1.
802

See e.g., AT&T and MCI Inputs Further Notice comments at 22; Sprint Inputs Further Notice comments at
30; GTE Inputs Further Notice comments at 56; SBC Inputs Further Notice comments at 9-10.
803

We note that GTE agrees with a fill factor of 100 percent for fiber as it relates to 100 percent redundancy
only if it provides fibers for redundant optical transmit and receive and does not equate to 100 percent fiber
utilization. We note that a fill factor of 100 percent for fiber does not equate to 100 percent fiber utilization.
804

SBC Inputs Further Notice comments at 9-10.

805

BellSouth Inputs Further Notice comments, Attachment B at B-9 - B-10.

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209.
Outside plant structure refers to the set of facilities that support, house, guide, or
otherwise protect distribution and feeder cable.806 We explained that aerial structure consists of
telephone poles and associated hardware such as anchors and guys. Buried structure consists of
trenches.807 Underground structure consists of trenches, conduit, manholes, and pullboxes.
Underground cable is placed underground within conduits for added support and protection.
Structure costs include the initial capital outlay for physical material associated with outside
plant structure, including manholes; conduit, trenches, poles, anchors and guys, and other
facilities; the capitalized cost for supplies, delivery, provisioning, right of way fees, taxes, and
any other capitalized costs directly attributable to these assets; and the capitalized cost for the
labor, engineering, and materials required to install these assets. For example, buried and
underground structure costs include capitalized labor, engineering, and material costs for such
activities as plowing or trenching, backfilling, boring cable, and cutting and restoring asphalt,
concrete, or sod, or any combination of such activities. Generally, the type of structure that is
placed will vary depending on the type of plant installed, i.e., the plant mix.
210. As noted above, the model uses structure cost tables that identify the per-foot cost
of structure by type (aerial, buried, or underground), loop segment (distribution or feeder), and
terrain conditions (normal, soft rock, or hard rock), for each of the nine density zones. For aerial
structure, the cost per foot that is entered in the model is calculated by dividing the total installed
cost per telephone pole by the distance between poles. We tentatively concluded that we should
use, with certain modifications, the estimates in the NRRI Study for the per-foot cost of aerial,
underground, and buried structure.808 We noted that, in general, these estimates are derived from
regression equations that measure the effect on these costs of density, water, soil, and rock
conditions.
211. In the Inputs Further Notice, we rejected the HAI and BCPM sponsors' default
input values for structure costs because they were based upon the opinions of their respective
experts and lacked supporting data that allowed us to substantiate these values.809 As noted
above, we have received other structure cost data from a number of LECs, as well as AT&T,
including data received in response to the structure and cable cost survey and data submitted in
ex parte filings.
212.

In the Inputs Further Notice, we tentatively decided to use the regression equation

806

Inputs Further Notice at para. 104.

807

When a plow is used to place buried cable, a separate trench is not required.

808

Inputs Further Notice at para. 106.

809

Inputs Further Notice at para. 105.

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for aerial structure in the NRRI Study as a starting point for aerial structure input values.810 We
proposed to use this equation to develop proposed input values for the labor and material cost for
a 40-foot, class-four telephone pole. We developed separate pole cost estimates for normal
bedrock, soft bedrock, and hard bedrock.811 The regression coefficients estimate the combined
cost of material and supplies. The NRRI Study reports that the average material price for a 40foot, class-four pole is $213.94.812 We noted that this estimate is very close to results obtained
from the data submitted in response to the 1997 Data Request.
213. We also tentatively concluded that we should add to these estimates the cost of
anchors, guys, and other materials that support the poles, because the RUS data from which this
regression equation was derived do not include these costs.813 As we noted, Gabel and Kennedy
used the RUS data to develop the following cost estimates for anchors, guys and other polerelated items: $32.98 in rural areas; $49.96 in suburban areas; and $60.47 in urban areas.814 We
tentatively concluded that these are reasonable estimates for the cost of anchors, guys, and other
pole-related items.815
214. We also explained, in the Inputs Further Notice, that in order to obtain proposed
input values that can be used in the model, it is necessary to convert the estimated pole costs into
per-foot costs for each of the nine density zones.816 For purposes of this computation, we
proposed to use, for density zones 1 and 2, the per-pole cost that we have estimated for rural
areas, based on the NRRI Study; for density zones 3 through 7, the per-pole cost for suburban
areas; and for density zones 8 and 9, the per-pole cost for urban areas. We then divided the
estimated cost of a pole by the estimated distance between poles. We proposed to use the
following values for the distance between poles: 250 feet for density zones 1 and 2; 200 feet for
zones 3 and 4; 175 feet for zones 5 and 6; and 150 feet for zones 7, 8, and 9. For the most part,
these values are consistent with both the HAI and BCPM defaults.
215.

We also tentatively concluded that we should adopt a methodology to estimate the

810

Inputs Further Notice at para. 107. This regression equation was set forth in Appendix D, section III.A of
the Inputs Further Notice.
811

See Inputs Further Notice, Appendix D, section III.A.

812

Inputs Further Notice at para. 107 n. 206 citing NRRI Study at 51, Table 2-11.

813

Inputs Further Notice at para. 108.

814

Inputs Further Notice at para. 108 n. 208 citing NRRI Study at 55, Table 2-14.

815

Inputs Further Notice at para. 108.

816

Inputs Further Notice at para. 110.

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cost of underground structure that is similar to the one we proposed for the cost of aerial
structure.817 We tentatively concluded that we should use the equation set forth in Appendix D
of the Inputs Further Notice as a starting point for this estimate.818 We proposed to use this
equation to develop proposed input values for the labor and material cost for underground cable
structure. We developed separate cost estimates for underground structure in normal bedrock,
soft bedrock, and hard bedrock for density zones 1 and 2.819
216. We also tentatively concluded that we should use the modified equation for
estimating the cost of 24-gauge buried copper cable and structure to estimate the cost of buried
structure.820 We determined that it is necessary to modify this equation because estimates
derived from it include labor and material costs for both buried cable and structure.821
217. Finally, we determined that, because the RUS data are from companies that
operate only in density zones 1 and 2, we were unable to develop estimates from the regression
equation for density zones 3 through 9 for underground and buried structure.822 We tentatively
concluded, therefore, that we should derive cost estimates for density zones 3 through 9 by
extrapolating from the estimates for density zone 2. We sought comment on alternative methods
for estimating structure costs for density zones 3 through 9.
b.

Discussion

218. We affirm our tentative conclusions to use the regression equation for aerial
structure in the NRRI Study as a starting point for the cost estimate for aerial structure; to use the
regression equation for underground structure in the Inputs Further Notice as a starting point for
the cost estimate for underground structure for density zones 1 and 2; and to use the regression
equation for the cost of 24-gauge buried copper cable and structure, as modified below, to

817

Inputs Further Notice at para. 111.

818

See Inputs Further Notice, Appendix D, section III.B. This regression equation is based on the RUS data,
but was developed after the publication of that report. The NRRI Study does not set forth a regression equation for
estimating the cost of underground structure.
819

This regression equation was developed using underground cost data for density zones 1 and 2. The variable
in this equation that represents the density zone of the geographic area in which the underground costs are incurred
is not statistically significant at any standard level of significance.
820

This equation is set forth in Appendix D, section III.C of the Inputs Further Notice.

821

See Inputs Further Notice, Appendix D, section III.C.

822

Inputs Further Notice at para. 112.

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estimate the cost of buried structure for density zones 1 and 2.823 Concomitantly, we affirm our
tentative conclusion to add to the estimates for aerial structure the costs of anchors, guys, and
other materials that support the poles. As we explained in the Inputs Further Notice, the RUS
data from which this regression equation was derived do not include these costs. We also adopt
the following values we proposed in the Inputs Further Notice for the distance between poles:
250 feet for density zones 1 and 2; 200 feet for zones 3 and 4; 175 feet for zones 5 and 6; and
150 feet for zones 7, 8, and 9.
219. As noted above, several commenters advocate that the input values we adopt for
structure costs reflect company-specific data. For the reasons enumerated above, we reject the
use of the company-specific data we have received to estimate the nationwide average input
values for structure costs to be used in the model.
220. Notwithstanding this conclusion, we find that it is unnecessary to extrapolate cost
estimates for underground and buried structure for density zones 3 through 9 as we proposed. At
the time of the Inputs Further Notice, we believed the extrapolated data were the best data
available to us at the time for density zones 3 through 9 although we noted our preference to use
data specific to those density zones.824 Upon further examination, we find that cost data, which
include values for density zones 3 through 9, submitted by various state commissions for use in
this proceeding are more reliable than the extrapolated data.825 Specifically, we reviewed
structure cost data from North Carolina, South Carolina, Indiana, Nebraska, New Mexico,
Montana, Minnesota, and Kentucky. These data reflect structure costs designed for use in the
HAI and BCPM models.826
221. The structure costs submitted by the state commissions have values for normal
rock, soft rock, and hard rock for density zones 3 through 9. We adopt as the buried and
underground structure cost input values for these density zones weighted average structure costs
823

See paragraphs 126-132 for a discussion of the development of the equation for buried structure.

824

Inputs Further Notice at para. 112.

825

In the Universal Service Order, we determined that states could submit their own cost studies to serve as the
basis for calculating federal universal service support in their states, if those studies met the criteria for forwardlooking economic cost determinations. In sum, we required that such cost studies must be based on forwardlooking economic cost principles and supported by publicly available data and computations. In order for the
Commission to accept a state cost study for these purposes, we also required that the study be the same cost study
that is used by the state to determine intrastate universal service support levels pursuant to 254(f) of the Act. See
Universal Service Order, 12 Fcc Rcd at 8912-16, paras. 248, 250-51. The Commission subsequently adopted the
Joint Board's recommendation to estimate forward-looking costs using a single national model. See Seventh Report
& Order, 14 FCC Rcd at 8103.
826

The RUS data underlying the NRRI Study reflect structure costs for density zones 1 and 2.

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developed from these data based on the number of access lines for the companies to which the
state decisions regarding the submitted structure costs apply. We find that these weighted
averages represent reasonable estimates for buried and underground structure costs in normal,
soft, and hard rock conditions for density zones 3 through 9.
222. Apart from the criticism of the extrapolation of structure costs for density zones 3
through 9 from the estimates for density zone 2,827 the comments we have received regarding
the values we proposed for structure costs vary as to the type of structure the commenters
address and vary as to the position they take on the reasonableness of the estimates.828 BellSouth
states that the values we adopt for aerial structures are "fairly representative of BellSouth's
values" but claims that, based on a comparison to its actual data, the values for underground and
buried structure are too low.829 Cincinnati Bell states that the values we adopt for underground
structure never vary from Cincinnati Bell's actual costs by more than 15 percent.830 Sprint
claims that our proposed cost of poles are understated but the costs of anchor and guys appear to
be reasonable.831 SBC claims that its actual weighted cost of a 40 foot pole is inconsistent with
the loaded cost from the NRRI Study. SBC asserts, however, that the NRRI-specified cost is
more closely aligned with SBC's anchor and guy costs.832 We find that, given this divergence of
positions, the support in the record for some of our proposed values, and lack of back-up data to
support the arguments opposing our proposals, on balance, the structure cost estimates we adopt
for aerial, underground, and buried structure for density zones 1 and 2 are reasonable.
Moreover, we find it is reasonable to use the values we adopt for density zones 3 through 9. As
we discussed above, these values reflect cost data for density zones 3 through 9 and have been
submitted to us by state commissions for use in this proceeding. These values are more reliable
than those derived through the extrapolation of data reflecting density zones 1 and 2, and for the
reasons discussed above, the company-specific data submitted on the record.
827

See GTE Inputs Further Notice comments at 53.

828

GTE contends that the model should rely on two sizes of poles in estimating aerial costs. GTE Inputs
Further Notice comments at 51. GTE also recommends that the calculation of the number of poles for a given
length of facility be modified. We find that there is insufficient evidence on the record at this time with regard to
the type of pole used in a particular density zone to make a determination as to GTE's first recommendation. We
may evaluate this, among other factors, and provide parties an opportunity to submit additional evidence on the
record in the upcoming proceeding on future changes to the model. We also find that GTE's second
recommendation represents a platform change which may be considered in the upcoming proceeding on future
changes to the model.
829

GTE Inputs Further Notice comments at 51.

830

Cincinnati Bell Inputs Further Notice comments at 4.

831

Sprint Inputs Further Notice comments at 30-31.

832

SBC Inputs Further Notice comments at 10.

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223. In reaching these conclusions, we note that AT&T and MCI advocate that we
adjust the regressions used to estimate structure costs to reflect the buying power of large nonrural LECs.833 We find that, because AT&T and MCI did not provide any data to support such a
determination, the record is insufficient to determine that such an adjustment is necessary. We
also reject AT&T and MCI's claim that the costs of underground structure are excessive because
they fail to exclude manhole costs from the costs of underground distribution.834 Contrary to
AT&T and MCI's assertion, we find that manhole costs are necessary to allow for splicing when
the length of the distribution cable exceeds minimum distance criteria adopted by the model.
224. Finally, we note, as described more fully above, that we have made adjustments
to certain of the regression equations in the Inputs Further Notice from which we estimate
structure costs in order to address certain of the criticisms reflected in the comments and
improve the regression equations accordingly.835
225. LEC Loading Adjustment. In the Inputs Further Notice, we tentatively concluded
that we should add a loading of ten percent to the material and labor cost (net of LEC
engineering) for aerial, underground, and buried structure because the cost of LEC engineering
was not reflected in the data from which Gabel and Kennedy derived their estimates.836 We find
that, based on the record before us, the LEC engineering adjustment is appropriate and the
proposed level of the adjustment is reasonable. In reaching this conclusion, we reject at the
outset the position of those commenters advocating that the adjustment be based on companyspecific data. As we explained above, we find such data are not the most reliable data on the
record.
226. As with the LEC adjustment proposed for cable costs discussed above, there is a
general consensus on the record among the commenters that an adjustment is necessary. We
find, therefore, that an adjustment to reflect the cost of LEC engineering is appropriate. Beyond
the general claim that we should adopt company-specific data, there is divergence among the
commenters regarding the appropriate level of this adjustment. GTE claims that the adjustment
should be greater than 10 percent based on a comparison to its data for buried plant.837 SBC
833

AT&T and MCI Inputs Further Notice comments at 23.

834

AT&T and MCI Inputs Further Notice comments at 24.

835

See supra at paragraphs 133-138.

836

Inputs Further Notice at paras. 109, 111, 114. We note that this adjustment is consistent with that made to
aerial, underground, and buried cable.
837

GTE Inputs Further Notice comments at 53.

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agrees that 10 percent is appropriate for aerial and buried structure but too low for underground
structure.838 SBC proposes a loading factor of 20 percent instead for underground structure.
Based on our review of the information, it is our judgement that the 10 percent adjustment is the
most reasonable value on the record before us to reflect the cost of LEC engineering.839
7.

Plant Mix
a.

Background

227. In the Inputs Further Notice, we explained that plant mix, i.e., the relative
proportions of different types of plant in any given area, plays a significant part in determining
total outside plant investment.840 This is because the costs of cable and outside plant structure
differ for aerial, buried, and underground cable and structure. The model provides three separate
plant mix tables, for distribution, copper feeder, and fiber feeder, which can accept different
plant mix percentages for each of the nine density zones.
228. Distribution Plant. In the Inputs Further Notice, we tentatively selected input
values for distribution plant mix that more closely reflected the assumptions underlying BCPM's
default values than HAI's default values.841 Specifically, we tentatively proposed input values,
for the lowest to the highest density zones, that range from zero percent to 90 percent for
underground plant; 60 to zero percent for buried plant; and 40 to ten percent for aerial plant. We
tentatively selected input values that more closely reflected the assumptions underlying the
BCPM default values because the model does not design outside plant that contains either riser
cable or block cable, so we did not believe it would be appropriate to assume that there is as high
a percentage of aerial plant in densely populated areas as the HAI default values assume.
Moreover, although our proposed plant mix values assumed somewhat less underground
838

SBC Inputs Further Notice comments at 10-11.

839

See supra paragraph 165.

840

Inputs Further Notice at para. 116.

841

In the Inputs Further Notice, we distinguished the BCPM default values for distribution plant from those
reflected in the HAI model. Inputs Further Notice at para. 47. As we explained, the BCPM default values for
distribution plant assume that there is no underground plant in the lowest density zone and the percentage increases
with each density zone to 90 percent underground distribution plant in the highest density zone. In contrast, the
HAI default values for distribution plant mix place no underground structure in the six lowest density zones and
assume that only 10 percent of the structure in the highest density zone is underground. The BCPM default values
assume there is no aerial plant in the highest density zone in normal and soft rock terrain, and 10 percent aerial plant
in hard rock terrain. In contrast, the HAI default values assume that there is significantly more aerial cable, 85
percent, in the highest density zone, but notes that this includes riser cable within multi-story buildings and "block
cable" attached to buildings, rather than to poles.

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structure in the lower density zones than the BCPM default values, we disagreed with HAI's
assumption that there is very little underground distribution plant and none in the six lowest
density zones.
229. Feeder Plant. We tentatively selected input values for feeder plant mix that
generally reflect the assumptions underlying the BCPM and HAI default plant mix percentages,
with certain modifications.842 We tentatively proposed input values, for the lowest to the highest
density zones, that range from five percent to 95 percent for underground plant; 50 to zero
percent for buried plant; and 45 to five percent for aerial plant. Based on our preliminary review
of the structure and cable survey data,843 the proposed values assume that there is no buried plant
in the highest density zone. In contrast to the BCPM defaults, the proposed values assume there
is some aerial plant in the three highest density zones. We tentatively found that it is reasonable
to assume that there is some aerial feeder plant in all density zones, as HAI does, particularly in
light of our assumption that there is no buried feeder in the highest density zone, where aerial
placement would be the only alternative to underground plant. Although the HAI sponsors had
proposed plant mix values that vary between copper feeder and fiber feeder, they offered no
convincing rationale for doing so. We tentatively concluded that, like the BCPM defaults, our
proposed plant mix ratios should not vary between copper feeder and fiber feeder.
230. Finally, we sought comment on alternatives to the nationwide plant mix input
values we tentatively adopted. As we explained, the Commission tentatively concluded, in the
1997 Further Notice, that plant mix ratios should vary with terrain as well as density zones.844
Because the model does not provide separate plant mix tables for different terrain conditions,
however, the nationwide plant mix values we proposed do not vary by terrain. We noted that
one method of varying plant mix by terrain would be to add separate plant mix tables, as there
are in BCPM, to the model. We observed that, while the BCPM model provides separate plant
mix tables, the BCPM default values reflect only slightly more aerial and less buried plant in
hard rock terrain than in normal and soft rock terrain. We suggested that another method of
varying plant mix would be to use company-specific or state-specific input values for plant mix,
as advocated by the BCPM sponsors and other LECs.
842

As we explained in the Inputs Further Notice, the default plant mix percentages for feeder plant are generally
similar in the BCPM and the HAI models. Inputs Further Notice at para. 120. Although the BCPM default values
vary between normal or soft rock terrain and hard rock terrain, as noted above, and the HAI default values differ
between copper and fiber feeder, the plant mix ratios across density zones are similar. For example, both the BCPM
default values and the HAI default values assume that there is only five or ten percent of underground feeder plant
in the lowest density zone. The HAI defaults assume there is somewhat more aerial feeder cable than the BCPM
defaults, except for fiber feeder cable in the four lowest density zones. The BCPM defaults assume there is no
aerial feeder plant in the three highest density zones, except in hard rock terrain. Despite these differences, the
relative amounts of aerial and buried plant across density zones are generally similar.
843

See Inputs Further Notice, Appendix C.

844

1997 Further Notice at para. 122.

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231. We also noted that, although we had generally chosen not to use study area
specific input values in the federal mechanism, and we recognized that historical plant mix ratios
may not reflect an efficient carrier's plant type choice today, historical plant mix might reflect
terrain conditions that will not change over time. We explained that our analysis of current
ARMIS data reveals a great deal of variability in plant mix ratios among the states. To that end,
we recognized that US West had proposed an algorithm in certain state proceedings for adjusting
plant mix to reflect its actual sheath miles as reported in ARMIS.845 We sought comment on a
modified version of this algorithm as an alternative to nationwide plant mix values.846
b.

Discussion

232. As explained above, although we tentatively chose to adopt nationwide plant mix
values, we presented and sought comment on an alternative algorithm based on sheath miles
reported in ARMIS to develop plant mix values. Consistent with that alternative, GTE asserts
that company-specific plant mix should be used instead of nationwide input values.847 Similarly,
Sprint contends that company-specific or state-specific plant mix values should be used.848 US
West asserts that the model should utilize study-area specific plant mix values that are available
in ARMIS as a starting point for plant mix inputs in the model.849
233. We find, however, as discussed more fully below, because companies do not
report aerial and buried route miles in ARMIS, that it is not possible to develop plant mix factors
845

Structure distance, also known as route distance, measures the distance of the pole line or the trench. Sheath
distance measures cable distance. If there is only one cable along a particular route then structure distance and
sheath distance are equal. When, however, there is more than one cable along a route, sheath distance will be a
multiple of the structure distance.
846

The proposed algorithm uses ARMIS 43-08 data on buried and aerial sheath distances and trench distances to
allocate model determined structure distance between aerial, buried, and underground structures. The first step is to
set the underground structure distance equal to the ARMIS trench distance and to allocate that distance among the
density zones on the basis of the nationwide plant mix defaults. Then an initial estimate of aerial plant is calculated
as the sum of the synthesis model structure distances by density zone multiplied by the nationwide aerial plant mix
defaults. A second estimate of aerial plant is calculated by multiplying structure distance less trench miles by the
aerial percentage of total ARMIS sheath miles. Then an adjustment ratio is calculated by dividing the second
estimate by the initial estimate. This adjustment ratio is then applied to each density zone to adjust the nationwide
default so that the final synthesis model plant mix reflects the study area specific plant mix. The buried plant mix
percentage is determined as a residual equal to one minus sum of the underground and aerial percentages.
847

GTE Inputs Further Notice comments at 58.

848

Sprint Inputs Further Notice comments at 34.

849

US West Inputs Further Notice comments at 32-36.

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directly from these data at this time. Moreover, we note that the record does not reflect
company-specific plant mix values for all companies, nor has any commenter presented a
methodology that recognizes the fact that plant mix varies across density zones and allocates it
accordingly. In sum, we conclude that neither company-specific nor ARMIS-derived data
represent reasonable alternatives to the use of nationwide inputs. We find, therefore, that the use
of nationwide inputs is the most reasonable approach in developing plant mix values on the
record before us.
234. US West claims that the plant mix algorithm we proposed places too much plant
in aerial. US West traces this flaw to several alleged errors in the plant mix algorithm.850 US
West claims that the algorithm erroneously double weights the model plant mix. This is not an
error as US West claims. Because the model results used in US West's analysis are based on the
low aerial distribution input, we find that the double weight should result in low levels of aerial
construction rather than high levels of aerial construction. US West also identifies several
formulaic errors.851 We find these errors attributable, however, to US West's lack of
understanding of how the proposed algorithm works.852 We agree, however, with US West that
the high aerial results do appear to be a function of incorrectly weighting aerial plant. We find
that this problem is a function of treating the aerial plant mix factor as a residual rather than
directly estimating an aerial factor. Given this flaw, we conclude that we should not adopt the
plant mix algorithm on which we sought comment.
235. As noted above, we sought comment on alternatives to nationwide plant mix input
values.
US West has proposed two algorithms. As explained below, we find that each of
these has its own biases and, therefore, that neither is a reasonable alternative to what we have
proposed. In brief, US West's first algorithm takes the geometric mean of the national default
and a structure ratio to determine the plant mix factor. It defines the structure ratio for
underground plant as the ratio of ARMIS trench miles to model route miles; for buried and aerial
plant the structure ratio is defined as the relative sheath miles of the structure type multiplied by
the model route miles less the ARMIS trench miles. We find that the final result of this
algorithm places too much underground structure because, for all but the lowest density zone, the
underground plant mix factor is significantly higher than the ARMIS ratio. The second
853

850

US West Inputs Further Notice comments, Attachment D.

851

US West Inputs Further Notice comments, Attachment D.

852

For example, the ARMIS buried ratio is not the ratio of model buried to the sum of model underground and
model aerial as US West claims, but rather the ratio of model buried to the sum of model buried and model aerial.
US West claims that the underground ratio is the ratio of ARMIS to model sheath miles. This is incorrect. It is the
ratio of ARMIS trench miles to model route miles.
853

Inputs Further Notice at para. 49.

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algorithm US West proposes starts with the relative share of ARMIS sheath miles for all three
structure types. It then establishes two series of fractions that sum to one. In the first series, the
fractions increase as the density zone increases. This series is applied to underground structure
and thus places more underground structure in the higher density zones. In the second series, the
fractions decrease as the density zones increase. This series is applied to aerial structure, with
the result that the percentage of aerial cable declines as density increases. For buried structure,
the ARMIS ratio is used for all density zones. We find that this algorithm is flawed because it
does not recognize the difference between sheath and route miles. As a consequence, the
algorithm produces a biased result. Specifically, it constructs too much underground cable. We
find that, until this problem is resolved, relying directly on ARMIS information leads to
unreasonable results.
236. Distribution Plant. We adopt the proposed input values for distribution plant mix
which are set forth in Appendix A. We conclude that these values for the lowest to the highest
density zones, which range from zero percent to 90 percent for underground plant; 60 to zero
percent for buried plant; and 40 to ten percent for aerial plant, are the most reasonable estimates
of distribution plant mix on the record before us.
237. There is divergence among the commenters with regard to the appropriateness of
the input values for the distribution plant mix proposed in the Inputs Further Notice. SBC
supports the proposed distribution plant mix, noting that it "closely aligns with the embedded
plant and future outside plant design."854 AT&T and MCI advocate the use of the HAI default
values for plant mix because, according to AT&T and MCI, they more properly reflect the use of
aerial and underground cable than the proposed distribution plant mix inputs.855 AT&T and
MCI claim that the proposed inputs reflect too much underground and too little aerial cable. As
we explained in the Inputs Further Notice, the model does not design outside plant that contains
either riser cable or block cable. Accordingly, use of the HAI default values, which assume a
high percentage of aerial plant in densely populated areas, would be inconsistent with the model
platform. AT&T and MCI ignore this fact.
238. In the Inputs Further Notice, we stated that we disagreed with HAI's assumption
that there is very little underground distribution plant and none in the six lowest density zones.856
In support of the HAI values for underground distribution plant, AT&T and MCI proffer the
distribution plant mix values for BellSouth, notably the only company to provide such data,
showing that its underground distribution plant mix value is very low. We find that, because we
are not adopting a company-specific algorithm, it is not necessary to address this issue. As noted
854

SBC Inputs Further Notice comments at 11.

855

AT&T and MCI Inputs Further Notice comments at 25.

856

Inputs Further Notice at para. 119.

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above, we will not adopt an alternative algorithm until the issue of underground structure
distances has been resolved. We adhere to employing a national value because we find that,
though it may not be exact for every company, it will be reasonable for all companies.
239. Feeder Plant. We also adopt the proposed input values for feeder plant mix which
are set forth in Appendix A. We conclude that these values for the lowest to the highest density
zones, which range from five percent to 95 percent for underground plant; 50 to zero percent for
buried plant; and 45 to five percent for aerial plant, are the most reasonable estimates of
distribution plant mix on the record before us. GTE's and Sprint's comments specifically address
the specific issue of feeder plant mix inputs. As noted above, both carriers advocate the use of
company-specific data for plant mix.857 We reject the use of such data for feeder plant mix for
the reasons we enumerate above.
240.
Finally, we affirm our tentative conclusion that the plant mix ratios should not
vary between copper feeder and fiber feeder. In reaching our tentative conclusion, we noted that,
although the HAI sponsors proposed plant mix values that vary between copper feeder and fiber
feeder, they have offered no convincing rationale for doing so. We find such support still
lacking. GTE claims that a distinction is necessary because the existing plant mix indicates that
the trend for more out-of-sight construction has already resulted in differing copper and fiber
feeder plant mixes.858 In contrast, SBC contends that plant mix ratios should not vary between
copper feeder and fiber feeder because existing structure is used whenever available for fiber and
copper placement so the mix ratio would not differ.859 We find neither of these claims to be
persuasive. Accordingly, we conclude that, given the absence of controverting evidence, it is
reasonable to assume that plant mix ratios should not vary between copper feeder and fiber
feeder in the model.
D.

Structure Sharing
1.

Background

241. Outside plant structures are generally shared by LECs, cable operators, electric
utilities, and others, including competitive access providers and interexchange carriers. To the
extent that several utilities place cables in common trenches, or on common poles, it is
appropriate to share the costs of these structures among the various users and assign a portion of
the cost of these structures to the telephone company.
857

GTE Inputs Further Notice comments at 58; Sprint Inputs Further Notice comments at 34.

858

GTE Inputs Further Notice comments at 59.

859

SBC Inputs Further Notice comments at 11.

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242. In the Inputs Further Notice, the Commission tentatively adopted structure
sharing values for aerial, buried, and underground structure.860 Several comments relating to
these values were filed in response to the Inputs Further Notice. Both the BCPM and HAI
models varied the percentage of costs they assume will be shared depending on the type of
structure (aerial, buried, or underground) and line density.861 Commenters differ significantly,
however, on their assumptions as to the extent of sharing and, therefore, the percentage of
structure costs that should be attributed to the telephone company in a forward-looking cost
model.862
2.

Discussion

243. We adopt the following structure sharing percentages that represent what we find
is a reasonable share of structure costs to be incurred by the telephone company. For aerial
structure, we assign 50 percent of structure cost in density zones 1-6 and 35 percent of the costs
in density zones 7-9 to the telephone company.863 For underground and buried structure, we
assign 100 percent of the cost in density zones 1-2, 85 percent of the cost in density zone 3, 65
percent of the cost in density zones 4-6, and 55 percent of the cost in density zones 7-9 to the
telephone company.864 In doing so, we adopt the sharing percentages we proposed in the Inputs
Further Notice, except for buried and underground structure sharing in density zones 1 and 2, as
explained below.
244. Commenters continue to diverge sharply in their assessment of structure
sharing.865 As noted by US West, "[s]ince forward-looking sharing percentages for replacement
of an entire network are not readily observable, there is room for reasonable analysts to differ on
860

Inputs Further Notice at para. 129.

861

See HAI Dec. 11, 1997 submission, Appendix B at 57; BCPM Jan. 31, 1997 submission, Attachment 9. The
BCPM sponsors assume that an efficient telephone company will benefit only marginally from sharing. The HAI
sponsors assume that utilities will engage in substantial sharing with telephone companies, and generally assigns
between 25% and 50% of the cost of shared facilities to the LEC.
862

See, e.g., AT&T/MCI Inputs Further Notice comments at 28-31; Bell Atlantic Inputs Further Notice
comments at 18; GTE Inputs Further Notice comments at 57; SBC Inputs Further Notice comments at 11.
863

The model uses nine density zones, ranging from the lowest density zone (1) to the highest density zone (9).
The nine density zones (measured in terms of the number of lines per square mile) are as follows: (1) zero - 4.99;
(2) 5 - 99.99; (3) 100 - 199.99; (4) 200 - 649.99; (5) 650 - 849.99; (6) 850 - 2549.99; (7) 2550 - 4999.99; (8) 5000 9,999.99; (9) 10,000+.
864

See Appendix A for a complete list of the input values that we adopt in this Order.

865

See, e.g., AT&T/MCI Inputs Further Notice comments at 28-31; Bell Atlantic Inputs Further Notice
comments at 18; GTE Inputs Further Notice comments at 57; SBC Inputs Further Notice comments at 11.

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the precise values for those inputs."866 While commenters engage in lengthy discourse on topics
such as whether the model should assume a "scorched node" approach in developing structure
sharing values, little substantive evidence that can be verified has been added to the debate.867
AT&T and MCI contend that the structure sharing percentages proposed in the Inputs Further
Notice assign too much of the cost to the incumbent LEC and fail to reflect the greater potential
for sharing in a forward-looking cost model.868 In contrast, several commenters contend that the
proposed values assign too little cost to the incumbent LEC and reflect unrealistic opportunities
for sharing.869 In support of this contention, some LEC commenters propose alternative values
that purport to reflect their existing structure sharing percentages, but fail to substantiate those
values. SBC, however, claims that the structure sharing percentages we propose reflect its
current practice and concurs with the structure sharing values that we adopt in this Order.870
245. More than with other input values, our determination of structure sharing
percentages requires a degree of predictive judgement. Even if we had accurate and verifiable
data with respect to the incumbent LECs' existing structure sharing percentages, we would still
need to decide whether or not those existing percentages were appropriate starting points for
determining the input values for the forward-looking cost model.871 AT&T and MCI argue that
past structure sharing percentages should be disregarded in predicting future structure sharing
866

US West Inputs Further Notice comments at 28.

867

In general, the "scorched node or utilities" debate concerns whether the model should assume that all utilities
are non-existent in developing structure sharing percentages. Commenters contend that if the model assumes that
everything is in place except for the telecommunications network, then the sharing percentages used in the model
should reflect fewer opportunities for sharing because it would not be possible to coordinate sharing with other
utilities in the development of a new network. In particular, opportunities for sharing of underground and buried
structure would be limited. See BellSouth Inputs Further Notice comments at 8-9; GTE Inputs Further Notice
comments at 18-21; US West Inputs Further Notice comments at 28-29. While this may provide an interesting
topic for academic debate, we do not believe it to be particularly useful or relevant in determining the structure
sharing values in this proceeding. We note that, as part of the logical argument that the entire telephone network is
to be rebuilt, it is also necessary to assume that the telephone industry will have at least the same opportunity to
share the cost of building plant that existed when the plant was first built. We also note that cable and electric
utilities continue to deploy service to new customers and replace existing technologies which provides an
opportunity for carriers to share structure.
868

AT&T/MCI Inputs Further Notice comments at 28.

869

See, e.g., BellSouth Inputs Further Notice comments, Attachment B at B-13; Sprint Inputs Further Notice
comments at 36-39; US West Inputs Further Notice comments at 29-32.
870

SBC Inputs Further Notice comments at 11.

871

In contrast, when developing inputs for tangible components of the network, we generally begin our analysis
with an estimation of the cost of today's technology at today's prices.

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opportunities. Incumbent LEC commenters argue that sharing in the future will be no more, and
may be less, than current practice.
246. In the Inputs Further Notice, we relied in part on the deliberations of a state
commission faced with making similar predictive judgment relating to structure sharing.872 The
Washington Utilities and Transportation Commission, conducted an examination of these issues
and adopted sharing percentages similar to those we proposed.873
247. In developing the structure sharing percentages adopted in this Order, we find the
sharing percentages proposed by the incumbent LECs to be, in some instances, overly
conservative. While we do not necessarily agree with AT&T and MCI as to the extent of
available structure sharing, we do agree that a forward-looking mechanism must estimate the
structure sharing opportunities available to a carrier operating in the most-efficient manner. As
discussed in more detail in this Order, the forward-looking practice of a carrier does not
necessarily equate to the historical practice of the carrier.874 Given the divergence of opinion on
this issue, and of AT&T and MCI's contention that further sharing opportunities will exist in the
future, we have made a reasonable predictive judgment, and also anticipate that this issue will be
revisited as part of the Commission's process to update the model in a future proceeding.
248. In the 1997 Further Notice, the Commission tentatively concluded that 100
percent of the cost of cable buried with a plow should be assigned to the telephone company.875
In the Inputs Further Notice, we sought comment on the possibility that some opportunities for
sharing existed for buried and underground structure in the least dense areas and proposed
assignment of 90 percent of the cost in density zones 1-2 to the telephone company.876 Several
commenters contend that there are minimal opportunities for sharing of buried and underground
structure, particularly in lower density areas.877 In addition, several commenters contend that, to
872

Inputs Further Notice at para. 130.

873

See Washington USF Proceeding, Tenth Supplemental Order, Docket No. UT-980311(a) at para. 108. See
also Washington Utilities and Transportation Commission, Eighth Supplemental Order, Docket No. UT-960369 at
paras. 73-76 (1998).
874

See Washington Utilities and Transportation Commission, Eighth Supplemental Order, Docket No. UT960369 (1998) at para. 73 (proposing a range of sharing values "which reflects the balance between maximum
achievable structure sharing and the amount of structure sharing achieved historically.").
875

1997 Further Notice, 12 FCC Rcd at 18547, para. 80.

876

Inputs Further Notice at paras. 129-132.

877

See, e.g., Bell Atlantic Inputs Further Notice comments at 19; BellSouth Inputs Further Notice, Attachment
B at B-14; GTE Inputs Further Notice comments at 56-57.

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the extent sharing is included in the RUS data, it is inappropriate to count that sharing again in
the calculation of structure cost.878 While we agree that structure sharing should not be double
counted, we note that the RUS data includes little or no sharing of underground or buried
structure in density zones 1-2.879 This does, however, support the contention of commenters that
there is, at most, minimal sharing of buried and underground structure in these density zones.880
We therefore modify our proposed input value in this instance and assign 100 percent of the cost
of buried and underground structure to the telephone company in density zones 1-2.
249. We believe that the structure sharing percentages that we adopt reflect a
reasonable percentage of the structure costs that should be assigned to the LEC. We note that
our conclusion reflects the general consensus among commenters that structure sharing varies by
structure type and density. While disagreeing on the extent of sharing, the majority of
commenters agree that sharing occurs most frequently with aerial structure and in higher density
zones.881 The sharing values that we adopt reflect these assumptions. SBC also concurs with
our proposed structure sharing values.882 In addition, as noted above, the Washington Utilities
and Transportation Commission has adopted structure sharing values that are similar to those
that we adopt.883 We also note that the sharing values that we adopt fall within the range of
default values originally proposed by the HAI and BCPM sponsors.
E.

Serving Area Interfaces
1.

Background

250. A serving area interface (SAI) is a centrally located piece of network equipment
that acts as a physical interface between a feeder cable connecting a wire center and
neighborhood distribution copper cables.884 The model includes appropriate investment for SAIs
878

Ameritech Inputs Further Notice comments at 12; Sprint Inputs Further Notice at 38; US West Inputs
Further Notice comments, Attachment A at 8.
879

NRRI Study at 30-31.

880

See GTE Inputs Further Notice comments at 57; Sprint Inputs Further Notice comments at 39 (noting that
the RUS data demonstrates that there are few sharing opportunities in rural areas).
881

See, e.g., HAI Dec. 11, 1997 submission, Appendix B at 57; BCPM Jan. 31, 1997 submission, Attachment 9;
Montana State Cost Study at 46-47.
882

SBC Inputs Further Notice comments at 11.

883

See Washington USF Proceeding, Docket No. UT-980311(a), Appendix D.

884

Generally, when a neighborhood is located near a wire center, copper feeder cable, using analog
transmission, is deployed to connect the wire center to the SAI. From the SAI, copper cables of varying gauge

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in all serving areas, whether served by copper or fiber feeder cable.
251. As we explained in the Inputs Further Notice, both the sponsors of BCPM and HAI
submitted default input values for indoor and outdoor SAI costs.885 In addition, Sprint submitted
cost estimates for a 7200 pair indoor SAI.886 Because the cost of an SAI depends on the cost of
its components, we tentatively concluded that, in the absence of contract data between the LECs
and suppliers, it was necessary to evaluate the cost of these components.887 We posted
preliminary ranges of SAI input values on the Commission's Web site to elicit comment and
empirical data from interested parties on the cost of SAIs.888 The Bureau also conducted a
workshop on December 11, 1998, to discuss the posted preliminary inputs.889 Accordingly, our
analysis began with a review of the data and justifications submitted by the HAI sponsors and
Sprint regarding the cost of the components that comprise a 7200 pair indoor SAI.890
Specifically, we reviewed the cost of the following SAI components for a 7200 pair indoor SAI:
building entrance splicing and distribution splicing; protectors; tie cables; placement of feeder
blocks; placement of cross-connect jumpers/punch down; and placement of distribution blocks.
Of these, we tentatively concluded that protector and splicing costs are the main drivers of SAI
costs, and cross-connect costs and feeder block and distribution block installation costs greatly
contribute to the difference in Sprint's and the HAI proponents' indoor SAI costs.891
extend to all of the customer premises in the neighborhood.
885

Inputs Further Notice at para. 134.

886

Inputs Further Notice at para. 134 n. 242 citing Indoor SAI Cost Analysis, submitted by Sprint - Local
Telecommunications Division, July 30, 1998.
887

Inputs Further Notice at para. 134.

888

Workshop Public Notice at 2. We used BCPM default inputs as the low end of the ranges for both indoor
and outdoor SAIs, and Sprint's cost estimates as the high end of the range for indoor SAIs. The high end of the
range for outdoor SAIs represented our analysis of state-approved SAI parameters. Our preliminary ranges for SAI
costs did not include HAI inputs because staff concluded that HAI had not included all of the materials and splicing
required to install this equipment.
889

See Common Carrier Bureau Releases Preliminary Common Input Values to Facilitate Selection of Final
Input Values for the Forward-Looking Cost Model for Universal Service, Public Notice, CC Docket Nos. 96-45, 97160, DA 99-295 (rel. Feb. 5, 1999) (Preliminary Input Values Public Notice); Workshop Public Notice. See also
Preliminary Input Values Handouts, dated December 11, 1998.
890

We noted that the BCPM defaults do not specify estimates for the cost of SAI components. Inputs Further
Notice at para. 134 n. 243.
891

Inputs Further Notice at para. 136. See Inputs Further Notice, Appendix D, section IV for a breakdown of
costs for each component calculated to derive the proposed cost of a 7200 pair DLC.

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252. In the Inputs Further Notice, we also proposed to determine the costs of the other
SAI sizes by extrapolating from the cost of the 7200 pair indoor SAI because we did not have
similar component-by-component data for other SAI sizes.892 We found that this appeared to be
a reasonable approach because of the linear relationship between splicing and protection costs,
which are the main drivers of cost, and the number of pairs in the SAI.893
2.

Discussion

253. We affirm our approach to derive the cost of an SAI on the basis of the cost of its
components and adopt a total cost of $21,708 for the 7200 pair indoor SAI. We find that there
remains an absence of contract data between the LECs and suppliers with regard to SAIs on the
record before us.894 Accordingly, we affirm, as discussed in more detail below, our tentative
conclusions with respect to the following issues: (1) the cost per pair for protector material; (2)
the appropriate splicing rate and corresponding labor rate; (3) the methodology employed in
cross-connecting in a SAI; and (4) the appropriate feederblock and distribution installation rate.
254. Based on the record before us, we conclude that $4 per pair is a reasonable
estimate of the cost for protected material. As we explained in the Inputs Further Notice, this
estimate is based on an analysis of ex parte submissions, which is the only evidence we have
available to evaluate the cost of SAI components.895 We also noted that Sprint has agreed that
$4 is a reasonable estimate of the cost.896 SBC and AT&T and MCI concur with our tentative
conclusion to adopt the $4 per pair cost.897 In sum, the record fully supports our conclusion that
892

Inputs Further Notice at para. 141.

893

As we explained in the Inputs Further Notice, we relied on HAI data to determine the relationship in cost
among the various sizes of SAI. Specifically, we developed a ratio of our proposed cost for a 7200 pair indoor SAI
to the cost proposed by HAI. We then proposed to apply this ratio, 2.25, to the values submitted by the HAI
sponsors for other sizes of indoor and outdoor SAIs. Applying this factor, we tentatively adopted the cost estimates
for indoor and outdoor SAIs. We proposed to use the HAI, rather than BCPM data, in this manner because BCPM
had not submitted estimates for all of the SAI sizes used in the model. We noted that using the BCPM data in this
way would result in roughly the same cost estimates for indoor and outdoor SAIs. Inputs Further Notice at para.
141.
894

BellSouth and Bell Atlantic submitted SAI costs in their comments. However, neither party provided any
support for these values which reflect total SAI costs. See BellSouth Inputs Further Notice comments at Exhibit 1;
Bell Atlantic Inputs Further Notice comments, Attachment D at 7.
895

Inputs Further Notice at para. 134-135.

896

See Letter from Pete Sywenki, Sprint, to Magalie Roman Salas, FCC, dated February 4, 1999 (Sprint Feb. 4,
1999 ex parte).
897

SBC Inputs Further Notice comments at 12. AT&T and MCI support the SAI costs tentatively adopted.

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$4 per pair is a reasonable estimate of the cost for protector material.
255. We also conclude that the record demonstrates that a splicing rate of 250 pairs is
reasonable, and adopt it accordingly. As we explained in the Inputs Further Notice, the HAI
sponsors proposed a splicing rate of 300 pairs per hour, while Sprint argued for a splicing rate of
100 pairs per hour.898 We believed that HAI's proposed rate was a reasonable splicing rate under
optimal conditions, and therefore, we tentatively concluded that Sprint's proposed rate was too
low.899 We noted that the HAI sponsors submitted a letter from AMP Corporation, a leading
manufacturer of wire connectors, in support of the HAI rate.900 We recognized, however, that
splicing under average conditions does not always offer the same achievable level of
productivity as suggested by the HAI sponsors. For example, splicing is not typically
accomplished under controlled lighting or on a worktable. Having accounted for such variables,
we proposed a splicing rate of 250 pairs per hour.
256. AT&T and MCI, the proponents of the 300 pairs per hour rate, support our
tentative conclusion.901 Sprint takes issue with the splicing rate we proposed.902 Sprint impugns
the evidence, appearing in the form of a letter from AMP Corporation on which we relied in part,
to determine a reasonable splicing rate.903 In sum, Sprint contends the letter represents an
"unsupported claim of someone trying to sell equipment."904 While Sprint is correct that the
proponent is an equipment manufacturer, neither Sprint nor any other commenter provided
evidence from any other equipment manufacturer to refute AMP.
AT&T and MCI Inputs Further Notice reply comments at 28.
898

Inputs Further Notice at para. 138 n. 250 citing Letter from Chris Frentrup, MCI WorldCom, to Magalie
Roman Salas, FCC, dated January 21, 1999; Letter from Kenneth T. Cartmell, U S West, dated February 8, 1999, to
Magalie Roman Salas, FCC; Letter from Pete Sywenki, Sprint, to Magalie Roman Salas, FCC dated February 4,
1999. On January 20, 1999, the sponsors of HAI provided a demonstration of splicing, in support of their splicing
rate.
899

Inputs Further Notice at para. 138.

900

Inputs Further Notice at para. 138 n. 251 citing attachment to letter from Chris Frentrup, MCI WorldCom, to
Magalie Roman Salas, FCC, dated January 21, 1999.
901

AT&T and MCI Inputs Further Notice reply comments at 29.

902

In its February 9 ex parte noted above, US West proposed a splicing rate of 150 pairs per hour, slightly
higher than Sprint's proposed rate.
903

Sprint Inputs Further Notice comments at 40. The letter from AMP Corporation was submitted by the HAI
sponsors. See Inputs Further Notice at para. 138 n. 251.
904

Sprint Inputs Further Notice comments at 40.

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257. Sprint also questions the fact that we did not utilize the data available from the
NRRI Study to determine the splicing rate.905 Sprint maintains that an analysis of that data
results in a splicing rate of 58.8 pairs per hour, substantially less than the 300 pairs per hour we
recognized as a ceiling in our analysis. We based our proposed splicing rate on an analysis of
such rates as they relate specifically to the installation of a complete and functional SAI. In
contrast, although the data to which Sprint refers is for modular splicing, it is not clear, nor does
Sprint claim, that such data specifically relates to the installation of SAIs. In sum, the validity of
this data as a measure in the derivation of splicing rates for SAI installation is not established on
the record. Sprint's critique ignores this fact. Accordingly, we reject the use of the data
available from the NRRI Study to determine the splicing rate.
258. We also conclude that the $60 per hour labor rate we proposed for splicing is
reasonable and adopt it accordingly. Those commenters addressing this specific issue agree.906
As we explained in the Inputs Further Notice, this rate, which equates with the prevalent labor
rate for mechanical apprentices, is well within the range of filings on the record.907
259. We also conclude that the model should assume that a "jumper" method will be
used half the time and a "punch down" method will be used the remainder of the time to crossconnect an SAI. A cross-connect is the physical wire in the SAI that connects the feeder and
distribution cable.
260. In the Inputs Further Notice, we tentatively concluded that neither the jumper
method nor the punch down method is used exclusively in SAIs.908 We reached this tentative
conclusion based on the conflicting assertions of Sprint and the HAI sponsors. We noted that,
Sprint asserted that the "jumper" method generally will be employed to cross-connect in a
SAI.909 In contrast, the HAI sponsors claimed that the "punch down" method is generally used
to cross-connect.910 We also noted that, in buildings with high churn rates, such as commercial
buildings, carriers may be more likely to use the jumper method. On the other hand, in
residential buildings, where changes in service are less likely, carriers may be more likely to use
the less expensive punch down method. Thus, we tentatively concluded that it appeared that
905

Sprint Inputs Further Notice comments at 40.

906

See e.g., SBC Inputs Further Notice comments at 12; AT&T and MCI Inputs Further Notice reply comments

at 28.
907

Inputs Further Notice at para. 138.

908

Inputs Further Notice at para. 139.

909

Inputs Further Notice at para. 139.

910

Inputs Further Notice at para. 139.

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both methods are commonly used, and that neither is used substantially more than the other.911
261. Based on the record before us, we affirm our tentative conclusion to assume that
the "jumper" method and the "punch down" method will be used an equal portion of the time.912
SBC challenges this conclusion, pointing out that it uses the "jumper" method in applications
involving hard lug or insulation displacement contact and that it is currently replacing existing
"punch down" interfaces.913 We conclude that SBC's sole claim is not sufficient to demonstrate
that the "jumper" method is used substantially more than the "punch down" method. We note
also that Sprint contends that the cross-connect proposed by AT&T and MCI is not an SAI, but a
building entrance terminal.914 We disagree. The design meets the SAI definition of providing an
interface between distribution and feeder facilities. In sum, we find that the record demonstrates
that it is reasonable for the model to assume that a "jumper" method will be used half the time
and a "punch down" method will be used the remainder of the time to cross-connect an SAI.
262. We also adopt a feeder block and distribution installation rate of 200 pairs per
hour. As we explained in the Inputs Further Notice, we derived this installation factor based on
a comparison of Sprint's proposed installation rate of 60 pairs per hour with HAI's proposed 400
pair per hour rate.915 We concluded that, because neither feeder block installation nor
distribution block installation is a complicated procedure, Sprint's rate of 60 pairs per hour is too
low. We also recognized that installation conditions are not always ideal. As we explained,
feeder block and distribution block installations are not typically accomplished under controlled
lighting or on a worktable. We proposed a rate of 200 pairs per hour to recognize these
variables.916
263. We note that our proposed feeder block and distribution block rates are
unchallenged. Significantly, SBC attests that this installation rate aligns with time-in-motion
studies performed in cross-connect building applications.917 We conclude, therefore, that our
proposed rate is reasonable, and adopt input values based upon it accordingly.
911

Inputs Further Notice at para. 139.

912

See Inputs Further Notice, Appendix D, section IV to see how this conclusion is used to determine proposed
costs for a 7200 pair SAI.
913

SBC Inputs Further Notice comments at 12.

914

Sprint Inputs Further Notice comments at 40-41.

915

Inputs Further Notice at para. 140.

916

See Inputs Further Notice, Appendix D, section IV to see how this value is used in the calculation of a 7200
pair SAI.
917

SBC Inputs Further Notice comments at 12.

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264. We also adopt the cost estimates for other size indoor and outdoor SAIs
tentatively adopted in the Inputs Further Notice.918 We conclude that, based on the record
before us, the derivation of the costs of the other SAI sizes from the cost of the 7200 pair indoor
SAI is reasonable.
265. GTE takes issue with the derivation of the costs of the other SAIs from the cost of
the 7200 pair indoor SAI.919 First, GTE contends that there is no need to extrapolate the costs of
other SAIs because the costs of individual SAI sizes and associated labor are readily available.920
We disagree. We concluded that it was necessary to extrapolate the costs of other SAI sizes
from the cost of a 7200 pair SAI because of the lack of component-by-component data for other
SAI sizes on the record. As noted above, we find the record still lacks such data. We also
disagree with GTE's contention that SAI costs are not subject to a linear relationship across all
sizes as we determined.921 We find GTE's contention, which relies on GTE's SAI estimates,
unpersuasive given the lack of substantiating data supporting these estimates.922 In sum, the
record demonstrates that the derivation of the costs of the other SAIs from the cost of the 7200
pair indoor SAI is reasonable.
266. US West contends that the costs of a SAI should be determined by the actual
cable sizes for the cables entering and leaving the SAI rather than the number of cable pairs
entering and leaving the interface.923 We agree. The model has been revised to calculate the
costs of an SAI on the basis of actual cable sizes for the cables entering and leaving the SAI.
267. US West raises an additional issue concerning the sizing of SAIs. US West notes
that some clusters created by the clustering module exceed the default line limit of 1800 lines
and gives as an example a specific cluster containing 7,900 lines.924 The largest SAI can
918

Inputs Further Notice at para. 141. These cost estimates are contained in Appendix A of the Inputs Further
Notice.
919

GTE Inputs Further Notice comments at 61.

920

GTE Inputs Further Notice comments at 61.

921

GTE Inputs Further Notice comments at 61.

922

We note that in contrast to GTE's claim, the SAI costs reflected in BellSouth's comments reflect linearity.

923

US West Inputs Further Notice comments at 15-16.

924

US West Inputs Further Notice comments at 14; US West Inputs Further Notice comments at 16; Letter
from Kenneth T. Cartmell, US West, to Magalie Roman Salas, FCC, dated September 24, 1999 (US West
September 24 ex parte) at 12.

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accommodate only 7200 lines, counting both feeder side and distribution side lines. Therefore,
US West contends that, in situations such as this, insufficient SAI plant is deployed by the
model. We agree with this analysis. There is no way to guarantee that the line limit of 1800
lines will not be exceeded for some clusters, even though modifications have been made to the
cluster algorithm to mitigate this possibility to the greatest possible extent. Therefore, in the
current version of the model, we modify the input table for SAI costs so as to allow for serving
areas (clusters) in which the capacity of feeder cable plus distribution cable meeting at the
interface may exceed 7200. We do this by allowing for line increments of 1800 up to a total line
capacity of 28,800. The values in the input table assume that, whenever more than 7200 lines
are required in an SAI, two or more standard SAIs are built, one with full capacity of 7200 and
the others with capacities equal to 1800, 3600, 5400 or 7200. The input values for each of the
multiply-placed SAIs are then summed.
268. A related issue is raised by US West with respect to drop terminal capacity in the
model.
In previous versions of the model, drop terminals were sized for residential housing
units and small business locations, with a maximum line capacity per drop location equal to 25
lines. For medium size and larger business locations with line demand greater than 25 lines, no
specific provision for additional drop terminal capacity was provided, except in situations in
which a single business accounted for all of the lines in a single cluster. Again, we agree with the
US West analysis of this issue. Accordingly, we have modified the input table for drop terminal
costs by adding additional line sizes equal to 50, 100, 200, 400, 600, 900, 1200, 1800, 2400,
3600, 5400, and 7200. At any location requiring a drop terminal with capacity exceeding 25
lines, the model will assume that the location will be served by an indoor SAI, and the cost of the
corresponding interface is equal to the corresponding value from the table for SAI costs.
925

Digital Loop Carriers
1.

Background

269. A digital loop carrier (DLC) is a piece of network equipment that converts an
optical digital signal carried on optical fiber cable to an analog, electrical signal that is carried on
copper cable and is compatible with customers' telephones.926 Because of the high cost of DLCs,
a single DLC is shared among a number of customers where possible. The model uses fiber
925

US West September 24 ex parte at 12.

926

Optical fiber cable carries a digital signal that is incompatible with most customers' telephone equipment, but
the quality of the signal degrades less with distance compared to a signal carried on copper wire. Generally, when a
neighborhood is located too far from the wire center to be served by copper cables alone, an optical fiber cable will
be deployed to a point within the neighborhood, where a DLC will be placed to convert incoming digital signals to
analog signals and outgoing analog signals to digital. From the DLC, copper cables of varying gauge extend to all
of the customer premises in the neighborhood.

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cable and DLCs whenever it calculates that this configuration is cheaper than using copper cable
or when the distance between the customer and the wire center exceeds the maximum copper
loop length. When using DLCs, the model determines the size and number of DLCs that should
be installed at a location, based on cost minimization and engineering constraints. In designing
outside plant, the model uses five different sizes of DLCs.927 In order to run the model, a user
must input the fixed and per-line cost for each of these DLC sizes. The total cost of a particular
DLC is determined by multiplying the number of lines connected to the DLC times the per-line
cost of the DLCs, and then adding the fixed cost of the DLC.
270. In the Inputs Further Notice, we tentatively concluded that we should estimate the
costs for DLCs based on an average of the contract data submitted on the record, adjusted for
cost changes over time.928 These contract data included data submitted to the Commission in
response to the 1997 Data Request,929 and in ex parte submissions following the December 11,
1998 workshop we sponsored, to estimate the costs of DLCs in the model.930 We found these
data to be the most reliable proffered at that time.931 We rejected use of the BCPM and HAI
default values because these values are based on the opinions of experts without data to enable
us to substantiate those opinions.932 Additionally, we rejected data submitted by the HAI
sponsors following the workshop.933 We found the data submitted by the HAI sponsors to be
significantly lower than the contract data on the record, and concluded that it would be
inappropriate to use the data submitted by the HAI sponsors, especially as no support was
provided to justify use of the data.934
927

The current version of the model supports a fifth DLC size in addition to those already supported. DLC
capacities currently supported are 2016, 1344, 672, 96, and 24 line facilities.
928

Inputs Further Notice at para. 144.

929

In response to the 1997 Data Request, Ameritech, Bell Atlantic (including NYNEX), BellSouth, SBC, US
West, GTE, Sprint, ATU, and PRTC originally submitted data to the Commission on DLC costs in 1997. Bell
South, US West and ATU resubmitted their data on the record of this proceeding subject to the Protective Order.
See Letter from William W. Jordan, BellSouth, to Magalie Roman Salas, FCC, dated March 15, 1999; Letter from
Robert B. McKenna, US West, to Magalie Roman Salas, FCC, dated March 8, 1999; Letter from Alane C. Weixel,
counsel for ATU, to Magalie Roman Salas, FCC, dated May 6, 1999 (ATU May 6, 1999 ex parte).
930

Letter from W. Scott Randolph, GTE, to Magalie Roman Salas, FCC, dated February 11, 1998; Letter from
Robert A. Mazer and Albert Shuldiner, Counsel for Aliant, to Magalie Roman Salas, FCC, dated February 8, 1998.
931

Inputs Further Notice at para. 143.

932

Inputs Further Notice at para. 143.

933

Inputs Further Notice at para. 144.

934

Inputs Further Notice at para. 144.

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271. In reaching our tentative conclusion to use the contract data, we noted that,
although we would have preferred to have a larger sampling of data, the contract data represent
the costs incurred by several of the largest non-rural carriers, as well as two of the smallest nonrural carriers.935 We noted that, throughout this proceeding, the Commission had repeatedly
requested cost data on DLCs, largely to no avail.936 Finally, we stated our belief that the data on
which we relied was the best data available on the record to determine the cost of DLCs.937
272. In the Inputs Further Notice, we also recognized that the cost of purchasing and
installing a DLC changes over time.938 We explained that such changes occur because of
improvements in the methods and components used to produce DLCs, changes in both capital
and labor costs, and changes in the functionality requirements of DLCs. Accordingly, we
tentatively concluded that it is appropriate to adjust the contract data, which represents the years
1995-1998, to reflect 1999 prices.939 We proposed a 2.6 percent annual reduction in both fixed
DLC cost and per-line DLC cost in order to capture changes in the cost of purchasing and
installing DLCs over time.940 We based this rate on the change in cost calculated for electronic
digital switches over a four year period. We noted our belief that the change in the cost of these
switches over time is a reasonable proxy for changes in DLC cost, because they are both types of
digital telecommunications equipment. We also noted that the 2.6 percent figure is a
conservative estimate, based on the change in cost of remote switches. Our analysis suggested
that the change in cost of host switches over the past four years is much higher. Finally, we
noted that use of the current consumer price index results in a similar figure over four years.941
The indexed amount is based on the effective date of the contracts.
935

Inputs Further Notice at para. 144.

936

Inputs Further Notice at para. 144. In addition to the data submitted in response to the 1997 Data Request,
and following the December 11, 1998, workshop, the Bureau requested further data on DLC costs in the 1997
Further Notice and in the Inputs Public Notice. See also Preliminary Input Values Public Notice.
937

Inputs Further Notice at para. 144. Only US West, BellSouth, and ATU presented their contract data from
the 1997 Data Request in a useable format Some of the data and comments that were submitted in response to the
1997 Data Request, but not re-filed on the record under the Protective Order, could not be used because the data
were either inadequate or presented in a format from which we could not extract relevant information. Inputs
Further Notice at para. 144 n. 262.
938

Inputs Further Notice at para. 145.

939

Inputs Further Notice at para. 146.

940

Inputs Further Notice at para. 146.

941

Inputs Further Notice at para. 146.

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273. Finally, we also sought comment on the extent, if any, to which we should
increase our proposed estimates for DLCs to reflect material handling and shipping costs.942 We
did this in response to comments submitted by ATU. It was unclear whether the DLC data
submitted by other parties included these costs. ATU suggested that these costs could represent
up to 10 percent of the material cost of a DLC.943
2.

Discussion

274. We adopt an average of the contract data submitted on the record, adjusted for
cost changes over time, as the cost estimates for DLCs. This decision is predicated on two
conclusions. The first is our determination that the contract data submitted to the Commission in
response to the 1997 Data Request, and in ex parte submissions following the December 11,
1998, workshop, remains the most reliable data on the record. Significantly, no additional
information has been proffered nor has any alternative method been proposed, on which to base
our estimate of DLC costs. The second is that we conclude that it is reasonable to reduce both
the fixed DLC cost and per-line DLC cost reflected in this data by a factor of 2.6 percent per
year in order to capture changes in the cost of purchasing and installing DLCs over time.
275. As we explained in the Inputs Further Notice, the contract data submitted to the
Commission in response to the 1997 Data Request, and in ex parte submissions following the
December 11, 1998, workshop, is the most reliable data because, not only is it the only data on
the record, but it reflects the actual costs incurred in purchasing DLCs.944 Moreover, although
we would have preferred a larger sample, the contract data is sufficiently representative of nonrural carriers because it reflects the costs incurred by several of the largest non-rural carriers, as
well as two of the smallest non-rural carriers.
276. GTE, Bell Atlantic and Sprint support the use of the contract data in estimating
the cost of DLCs.945 Only AT&T and MCI and SBC challenge the use of these data.946 SBC
942

Inputs Further Notice at para. 145.

943

ATU May 6, 1999 ex parte. ATU also suggested that costs for placement, installation, and testing should be
added to the DLC material costs it submitted. We note that these site preparation costs are already separately
accounted for in the model.
944

Inputs Further Notice at para. 143.

945

GTE Inputs Further Notice comments at 62; Bell Atlantic Inputs Further Notice comments, Attachment D at
8-9, Chart 12. Sprint attests to the reasonableness of the proposed inputs based on the contract data. Sprint Inputs
Further Notice comments at 41. Sprint explains that it demonstrated in a June 24, 1999 ex parte that the proposed
inputs are in line with Sprint's actual costs including material and handling.
946

AT&T and MCI Inputs Further Notice comments at 32-35 (Proprietary Version); SBC Inputs Further Notice
comments at 13.

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contends that the contract data is not the most reliable data on DLC costs because labor costs
associated with testing, turn-up, and delivery of derived facilities are not factored into the input
values.947 We disagree. The data we identify as "contract data" include these costs. As we
explained in the Inputs Further Notice and noted above, we sponsored a workshop on December
11, 1998, to further develop the record on DLC costs in this proceeding. During the workshop,
we presented a template of the components of a typical DLC to the attendees. The template
provided the respondents the opportunity to identify their contract costs with regard to each of
the components. In addition, we requested that the respondents identify, and thereby include,
other costs associated with DLC acquisition, including labor costs associated with testing, turnup, and delivery of the DLC. Using this opportunity to submit DLC cost data, GTE and Aliant
included such costs in their submissions. Sprint submitted similar data in a September 9, 1998
ex parte filing. These costs were identified and added to the analysis of US West's and
BellSouth's contract data. We derived these costs from ex parte filings made by these carriers in
this proceeding.
277. AT&T and MCI allege that the contract data overstates the actual costs of DLC
equipment and therefore, should not be adopted.948 AT&T and MCI instead advocate use of the
HAI default values.949 AT&T and MCI argue that the contract costs are not only unsupported
by any verifiable evidence but, more importantly, are refuted by the contract information from
which they were derived. In support, AT&T and MCI submit an analysis of the DLC cost
submissions of Bell Atlantic, BellSouth, and Sprint. In each instance, AT&T and MCI assert
that these data demonstrate DLC costs that are far below those proposed by the incumbent LECs
and the Commission and that are fully consistent with the HAI default values.
278. We disagree with AT&T and MCI's analysis. For example, AT&T and MCI
claim that information provided by Bell Atlantic shows that total DLC common equipment costs
for DLC systems capable of serving 672, 1344, and 2016 lines are similar to, and uniformly less
than, the corresponding HAI values.950 In reaching this conclusion, however, AT&T and MCI
omit the costs for line equipment. As Bell Atlantic points out, the cost of digital line carrier
equipment should include these costs, and we agree.951
279.

Similarly, AT&T and MCI assert that certain of Sprint's costs are significantly

947

SBC Inputs Further Notice comments at 13.

948

AT&T and MCI Inputs Further Notice comments at 32-35 (Proprietary Version)

949

AT&T and MCI Inputs Further Notice comments at 34.

950

AT&T and MCI Inputs Further Notice comments at 33-34 (Proprietary Version).

951

Bell Atlantic Inputs Further Notice reply comments 6-7.

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inflated and, once adjusted, are similar to and uniformly less than the corresponding HAI
values.952 We find, however, these adjustments to be unsupported. AT&T and MCI reduce the
supply expenses associated with Sprint's DLC costs, more than 66 percent, based on the
experience of AT&T and MCI's engineering team members.953 AT&T and MCI offer no
evidence, however, other than the opinions of their experts to substantiate this proposed
adjustment.
280. AT&T and MCI also contend that Sprint applies excessive mark-ups for sales
tax.
AT&T and MCI argue that, because Sprint operates its own logistics company, there is
no reason to apply sales tax to both supply expense and materials. We find that AT&T and MCI
offer no support to demonstrate that this results in an excessive mark-up for sales tax. We reach
the same conclusion with regard to AT&T and MCI's proposed reduction to Sprint's labor costs.
AT&T and MCI contend that Sprint's labor costs are inflated and propose reductions in such
costs through a reduction in the number of labor hours associated with DLC installation.955
AT&T and MCI provide no support for such a reduction and, therefore, we decline to reduce
Sprint's labor costs.956
954

281. Significantly, AT&T and MCI offer no evidence to controvert our tentative
conclusion that the HAI values they employ as a comparative benchmark, and advocate that we
adopt, are not more reliable than the contract data. We rejected the use of the HAI and the
BCPM default values because they are based on the opinions of experts without substantiating
data.957 Similarly, we rejected data submitted by the HAI sponsors following the December 11,
1998, workshop. We found that data to be significantly lower than the contract data on the
record, and concluded that it would be inappropriate to use because it also lacked support.958
AT&T and MCI have not provided any additional evidence to substantiate the HAI data.

952

AT&T and MCI Inputs Further Notice comments at 34.

953

AT&T and MCI Inputs Further Notice comments, Attachment B at B-4 (Proprietary Version)

954

AT&T and MCI Inputs Further Notice comments, Attachment B at B-4 (Proprietary Version).

955

AT&T and MCI Inputs Further Notice comments, Attachment B at B-4 (Proprietary Version).

956

AT&T and MCI also claim that Sprint fails to make use of forward-looking technology such as GR303capable hardware. AT&T and MCI Inputs Further Notice comments, Attachment B at B-4 (Proprietary Version).
Contrary to AT&T and MCI's assertion, the data supplied by Sprint and reflected in the contract data adopted herein
reflects the cost of GR303-capable hardware. See Sprint Sept. 9, 1998 ex parte.
957

Inputs Further Notice at para. 143.

958

Inputs Further Notice at para. 144.

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282. We also affirm our tentative conclusion that it is reasonable to reduce both the
fixed DLC costs and per-line DLC costs reflected in the contract data in order to capture changes
in the cost of purchasing and installing DLCs. As we explained in the Inputs Further Notice,
this reduction recognizes the fact that the cost of purchasing and installing a DLC diminishes
over time because of improvements in the methods and components used to produce DLCs,
changes in both capital and labor costs, and changes in the functionality requirements of
DLCs.959 The premise that overall DLC costs move downward over time is not disputed on the
record.
283. We also conclude that the 2.6 percent reduction we proposed in both the fixed
DLC costs and per-line DLC costs is appropriate. As we explained in the Inputs Further Notice,
this is a conservative estimate, based on the change in cost of remote switches, which is a
reasonable proxy for changes in DLC cost.960 More importantly, a comparison of data submitted
on the record by Sprint for the years 1997, 1998, and 1999 demonstrates that an overall reduction
of 2.6 percent is considerably less than Sprint's actual experience. An analysis undertaken by
staff produces an average reduction in DLC costs for Sprint of 9.2 percent per year. We note
that this estimate reflects both material and labor costs.
284. Only SBC and GTE specifically address the 2.6 percent reduction.961 SBC
supports the 2.6 percent reduction in fixed and per-line DLC costs as it applies to material costs
only. In contrast, GTE opposes the adjustment.962 GTE suggests that, as the inputs are adjusted
over time, the cost of current technology will be reflected in the revised data.963 GTE is correct
that the current cost of technology would be reflected in revised data. The adjustment we
proposed and adopt updates cost to current cost. Implicit in SBC's comment is the premise that
labor costs will not decrease over time. Although this may be a reasonable assumption, the 2.6
percent reduction we adopt is applied to the overall cost of a DLC. As we explained above, the
2.6 percent reduction is a conservative estimate compared to the actual reductions we have
observed in the Sprint data. As a result, we conclude that increases in labor will be offset by
reductions in other factors in the cost of DLCs.
285. Finally, as noted above, we sought comment on the extent, if any, to which we
should increase our proposed estimates for DLCs to reflect material handling and shipping costs
959

Inputs Further Notice at para. 146.

960

None of the commenters challenge the use of this proxy for estimating the change in DLC costs.

961

SBC Inputs Further Notice comments at 13; GTE Inputs Further Notice comments at 61-62.

962

GTE Inputs Further Notice comments at 61-62.

963

GTE Inputs Further Notice comments at 62.

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because it was unclear whether the DLC data submitted by other parties include these costs. On
further analysis, we note that material handling and shipping costs are reflected in the proposed
DLC estimates we adopt herein. Moreover, we conclude that it is appropriate to include these
costs in the cost estimates for DLCs. We note that no comments were filed opposing the
inclusion of such costs.
VI. SWITCHING AND INTEROFFICE FACILITIES
A.

Introduction

286. The central office switch provides the connection between a subscriber's local
loop and the outside world. Modern digital switches connect telephones, fax machines, and
computers to other subscribers on the public switched network.964 In order to accomplish this, a
telephone network must connect customer premises equipment to a switching facility, ensure that
adequate capacity exists in that switching facility to process calls, and interconnect the switching
facility with other switching facilities to route calls to their destination. A wire center is the
location of the switching facility and the wire center boundaries define the area in which all
customers are connected to a given wire center. The infrastructure to interconnect the wire
centers is known as the "interoffice" network, and the carriage of traffic between wire centers is
known as "transport."
287. In the Universal Service Order, the Commission stated that "[a]ny network
function or element, such as . . . switching, transport or signaling, necessary to provide supported
services must have an associated cost."965 In the 1997 Further Notice, the Commission sought
comment on issues that affect the input values relating to the forward-looking economic cost of
switching and interoffice transport.966 The Switching and Transport Public Notice established
several guidelines relating to switching, the design of the interoffice network, and interoffice
cost attributable to providing supported services.967 In the Platform Order, the Commission
964

The functions performed by the switch for local service include: line termination; line monitoring; usage call
processing, routing, and completion; interconnection to other carriers; billing and maintenance; and vertical services
and features. We note that not all of these functions are supported by universal service.
965

Universal Service Order, 12 FCC Rcd at 8913, para. 250 (criterion two).

966

1997 Further Notice, 12 FCC Rcd at 18560-66, paras. 121-38.

967

Switching and Transport Public Notice at 2-6. The Bureau guidelines established that: (1) the models permit
individual switches to be identified as host, remote, or stand-alone; (2) switching investment costs should be
separately estimated for host, remote, and stand-alone switches; (3) models should include switch capacity
constraints; (4) all of the line-side port costs and a percentage of usage costs should be assigned to the cost of
providing the supported service; and (5) models should accommodate an interoffice network that is capable of
connecting switches designated as hosts and remotes in a way that is compatible with capabilities of equipment and
technology that are available today and current engineering practices. Id.

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concluded that the federal mechanism should incorporate, with certain modifications, the HAI
5.0a switching and interoffice facilities module.968
288. Both HAI and BCPM sponsors have provided default input values for estimating
the forward-looking economic cost of switching and interoffice network.969 On December 1,
1998, the Bureau held a public workshop designed to elicit comment on the switching inputs
values to be used in the federal mechanism.970

968

Platform Order, 13 FCC Rcd at 21354, para. 75.

969

See Letter from Richard N. Clarke, AT&T, to Magalie Roman Salas, FCC, dated February 3, 1998 (HAI Feb.
3 submission) App. B; BCPM April 30, 1998 submission, Switch Model Inputs.
970

See Workshop Public Notice. The December 1, 1998 workshop addressed issues relating to switching and
expenses.

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289. In the Inputs Further Notice, we tentatively adopted input values associated with
switching and interoffice facilities, including values associated with the installation and purchase
of new switches.971 In addition, we tentatively adopted the Local Exchange Routing Guide
(LERG) database to identify host-remote switch relationships.972
B.

Switch Costs
1.

Background

290. In the Inputs Further Notice, we tentatively concluded that we should use
publicly available data on the cost of purchasing and installing switches that was compiled by
the Commission, in conjunction with the work of Gabel and Kennedy,973 and the Bureau of
Economic Analysis (BEA) of the U.S. Department of Commerce.974 This information was
gathered from depreciation reports filed by LECs at the Commission. In order to better estimate
the costs of small switches, we tentatively concluded in the Inputs Further Notice to augment the
depreciation data with data compiled by the Commission, in conjunction with Gabel and
Kennedy and the U.S. Department of Agriculture Rural Utility Service (RUS).975 This
information was gathered from reports made to RUS by LECs.
291. In order to make the RUS data comparable with the depreciation data, we
proposed a series of adjustments to the RUS data. The cost figures reported in the depreciation
information reflect the costs of purchasing and installing new switches. While the RUS cost data
also contain information on purchasing and installing new switches, they do not include: (1) the
cost associated with purchasing and installing the main distribution frame (MDF); (2) the cost
associated with purchasing and installing power equipment; (3) the cost of connecting each
remote switch to its respective host switch; and (4) LEC engineering costs.976 In order to make
the depreciation and RUS information comparable, we proposed in the Inputs Further Notice to
971

See Inputs Further Notice at paras. 147-91, App. A.

972

Inputs Further Notice at paras. 174-78.

973

See NRRI Study, supra note 214.

974

Inputs Further Notice at para. 152.

975

Inputs Further Notice at para. 162.

976

Letter from W. Scott Randolph, GTE, to Magalie Roman Salas, FCC dated December 18, 1998 (GTE Dec.
18 ex parte) at 5 and 6; NRRI Study at 97 and 102; Letter from Pete Sywenki, Sprint, to Magalie Roman Salas,
FCC, dated December 22, 1998 (Sprint Dec. 22 ex parte) at 13-21; Letter from Richard Clarke, AT&T, to Magalie
Roman Salas, FCC, dated January 7, 1999 (AT&T Jan. 7 ex parte) at 1.

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add estimates of these four components to the switch costs reported in the RUS information.977
292. In order to account for the cost of MDF omitted from the RUS information, we
tentatively concluded that $12 per line was a reasonable cost for purchasing and installing MDF
equipment.978 In order to account for the cost of power equipment omitted from the RUS
information, we tentatively concluded that the cost of purchasing and installing switches with 0999 lines should be increased by $12,000, the cost of purchasing and installing switches with
1,000-4,999 lines should be increased $40,000, and the cost of purchasing and installing
switches with 5,000-25,000 lines should be increased by $74,500.979 We tentatively concluded
that $27,598 should be added to the cost of each RUS remote switch in order to account for cost
of connecting the remote switch to the host switch, a cost omitted from the RUS information.980
We further proposed in the Inputs Further Notice that, in order to account for the LEC
engineering costs omitted in the RUS information, we should add, after making the above
adjustments for power, MDF, and remote connection costs, eight percent to the total cost of each
RUS switch.
293. In order to determine the reasonable forward-looking cost of switches, based on
the selected data set, we tentatively concluded in the Inputs Further Notice that we should
employ regression analysis.981 We tentatively concluded that the cost of a switch should be
estimated as a linear function of the number of lines connected to the switch and the type of
switch installed (i.e., host or remote).982
294. In order to capture changes in the cost of purchasing and installing switching
equipment over time, we tentatively concluded in the Inputs Further Notice that we should
modify the data to adjust for the effects of inflation, and explicitly incorporate variables in the
regression analysis that capture cost changes unique to the purchase and installation of digital
switches.983
977

Inputs Further Notice at paras. 157-161.

978

Inputs Further Notice at para. 158.

979

Inputs Further Notice at para. 159.

980

Inputs Further Notice at para. 160.

981

Inputs Further Notice at para. 163.

982

Inputs Further Notice at para. 164. In order to estimate the forward-looking cost of purchasing and installing
a switch, switch costs also are estimated as a function of the date of installation. By including information on
installation dates, the model produces forward-looking estimates that account for historical pricing trends.
983

Inputs Further Notice at para. 166.

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295. In the Inputs Further Notice, we tentatively concluded that in order to capture the
costs associated with the purchase and installation of new switches, and to exclude the costs
associated with upgrading switches, we should exclude switch cost data that contained costs
reported more than three years after installation. We tentatively concluded that this restriction
eliminates switch cost data that contain a significant amount of upgrade costs and, therefore, do
not solely represent the purchase and installation costs of new switches.984
2.

Discussion

296. Switch Cost Estimates. We adopt the fixed cost (in 1999 dollars) of a remote
switch as $161,800 and the fixed cost (in 1999 dollars) of both host and stand-alone switches as
$486,700. We adopt the additional cost per line (in 1999 dollars) for remote, host, and standalone switches as $87.985
297. For the reasons set forth below, we affirm our tentative conclusion to use the
publicly available data from LEC depreciation filings, and to supplement the depreciation data
with data from LEC reports to the RUS. We also affirm our tentative conclusion that we should
not rely on the BCPM and HAI default values, because these values are largely based on nonpublic information or opinions of their experts, without data that enable us adequately to
substantiate those opinions.
298. Switch Cost Data. The depreciation data contains for each switch reported: the
model designation of the switch; the year the switch was first installed; and the lines of capacity
and book-value cost of purchasing and installing each switch at the time the depreciation report
was filed with the Commission.986 The RUS data contains, for each switch reported: the switch
type (i.e., host or remote); the number of equipped lines; cost at installation; and year of
installation.987
299.
984

The sample that we use to estimate switch costs includes 1,085 observations. The

Inputs Further Notice at para. 170.

985

See Appendix C for regression results, and an explanation of how cost estimates are derived from these
results.
986

Until 1996, large incumbent LECs were required to file depreciation rate reports with the Commission
pursuant to 47 C.F.R. § 43.43. Prior to filing these reports, companies generally would submit depreciation rate
studies that included data for each digital switch in operation. See Appendix C for a further description of the data
set.
987

Many small telephone companies receive financial assistance from RUS, which requires these companies to
report the payments made for new switches. See Appendix C for a further description of the RUS data.

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sample contains 946 observations selected from the depreciation data, which provide information
on the costs of purchasing and installing switches gathered from 20 states. All observations in
the depreciation data set are for switches with 1,000 lines or more. In order to better estimate
the cost of small switches, we augmented the depreciation data set by adding data from RUS.
The RUS sample contains 139 observations which provide information from across the nation on
the costs of small switches purchased and installed by rural carriers. Over 80 percent of the
observations of switch costs in the RUS data set measure the costs for switches with 1,000 lines
of capacity or less. The combined sample represents purchases of both host and remote
switches, with information on 490 host switches and 595 remote switches, and covers switches
installed between 1989 and 1996. This set of data represents the most complete public
information available to the Commission on the costs of purchasing and installing new switches.
300. The depreciation data set proposed in the Inputs Further Notice excluded 26
observations that had been deemed to be outliers by the Bureau of Economic Analysis. Bell
Atlantic criticizes the Commission for excluding these outliers.988 The excluded observations
were not available in electronic form prior to the release of the Inputs Further Notice.
Subsequently, the Bureau obtained these outlying observations from the Bureau of Economic
Analysis and reinserted them into the data set used to derive the input values we adopt herein. In
addition, several commenters recommend that the depreciation data set also should include
switches with fewer than 1,000 lines of capacity. This information, however, is not available in
electronic format and, therefore, would be unduly burdensome to include.989
301. In response to the 1997 Data Request, the Commission received a second set of
information pertaining to 1,486 switches. Upon analysis, however, we have identified one or
more problems with most of the data submitted: missing switch costs; zero or negative
installation costs; zero or blank line counts; unidentifiable switches; or missing or inconsistent
Common Language Local Identification (CLLI) codes. After excluding these corrupted
observations, 302 observations remained. The remaining observations represented switches
purchased by only four companies. We affirm our tentative conclusion that the data set we use is
superior to the data set obtained from the data request, both in terms of the number of usable
observations and the number of companies represented in the data set.
302. Following the December 1, 1998, workshop, three companies voluntarily
submitted further data regarding the cost of purchasing and installing switches: BellSouth
provided data on switch investments for its entire operating region; Sprint provided similar data
for its operations in Nevada, Missouri, and Kansas; and GTE provided switch investment
988

Bell Atlantic Inputs Further Notice comments at 10 and 11.

989

The Bureau of Economic Analysis, in creating the electronic data set from depreciation filings, did not
include observations for switches with fewer than 1,000 lines.

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information for California.990 When consolidated, this information forms a data set of
approximately 300 observations representing the costs of new switches.991 As AT&T has noted,
however, the information submitted contains some inconsistencies.992 Considering these
inconsistencies, the limited number of companies represented, and the size of this voluntarily
submitted data set, we conclude that the data set we use is preferable.
303. BellSouth suggests that we merge either the information received in response to
the 1997 Data Request, the information from the voluntary submissions, or both, with the data
set we use.993 We reject this suggestion because there are significant inconsistencies between the
different data sets. For example, in its voluntary submission, GTE provides the amount of total
investment for each of its California switches at the time these switches were installed, but
reports associated line counts only for October 1998. This information is not consistent with the
data set used by the Commission, which contains aggregate investment and line counts measured
at the same point in time. Second, our analysis of the information provided in both the voluntary
submissions and the data request reveals, based on simple linear regression, inconsistencies
between these two data sets and the data set employed by the Commission.994 Our analysis
reveals that both alternative data sets contain information that is inconsistent with the comments
in this proceeding.995
990

BellSouth January 29, 1999 ex parte; Sprint February 5, 1999 ex parte; and GTE February 22, 1999 ex parte.

991

Some of the switch cost values provided in the voluntary submissions include the costs associated with
upgrading switching equipment. The voluntary information does not, however, contain information that would
allow us to identify the upgrade components associated with these additional costs. For example, post-installation
investments are not identified as investments in additional line capacity, additional software, and so forth. After
removing the information where new switch costs and the costs associated with post installation upgrades are
inextricably linked, using the process outlined in Appendix C, fewer than 300 observations remain.
992

AT&T points out that the data submitted by Sprint contains records that are either missing or inconsistent
with other records, records that are old or do not reflect equipment used exclusively to provide end office switching,
and records that contain ambiguous information. See AT&T Mar. 10, 1999 ex parte.
993

BellSouth Inputs Further Notice comments at B-14 and B-15.

994

A year-by-year analysis of the deprecation data and the RUS data reveals that the fixed cost of a host switch
is significantly more than the fixed cost of a remote switch. Our analysis examining the deprecation data reveals
that the difference is statistically significant and positive in four of the seven years covered by the Commission data
set. In 1995, there are only nine observations including only one host switch, and therefore, there is insufficient
data to draw any conclusion for 1995. In the other two remaining years, 1993 and 1994, the difference has a large
positive magnitude but is not statistically significant (the "t-statistics" for these years are 0.68 and 0.99). In contrast,
the fixed cost of host switches in the data from the 1997 Data Request do not differ statistically from the fixed costs
of remote switches, nor is there a large difference in the magnitudes of the estimated costs. Similarly, year-by-year
analysis of the voluntary data provided by the carriers does not reveal any systematic difference between host fixed
costs and remote fixed costs.
995

As noted in the previous footnote, the fixed cost of host switches exceeds the fixed cost of remote switches in

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304. Adjustments to the Data. As discussed above, in the Inputs Further Notice, we
proposed certain adjustments to the RUS data to account for the cost of MDF and power
equipment, which were omitted from the RUS information.996 Specifically, we proposed
increasing the cost of purchasing and installing switches by $12 per line for MDF and $12,000,
$40,000, or $74,500, depending upon switch size, for power costs. Commenters who address
this issue agree that the RUS data must be modified to account for the costs of MDF and power
to make the RUS data consistent with the depreciation data, which include these costs.997 Some
commenters who address these adjustments claim that we should use different values for MDF
and power costs, but provide little or no information we can use to verify their suggested
values.998 Sprint, for example, claims our power costs are too low and provides a breakdown of
power costs, but does not supply any data to support their higher proposed values for power
costs.999 AT&T and MCI claim our proposed power costs should be reduced because they are
substantially higher than those proposed by their experts.1000
305. We find that we need not attempt to resolve disagreement over the reasonableness
of our proposed values, in the absence of any additional information, because we adopt an
alternative methodology for estimating MDF and power costs. We find that we should adjust the
RUS data for MDF and power equipment costs in a way that is more consistent with the way in
which these costs are estimated in the depreciation data set. In the depreciation data, MDF and
power equipment costs are estimated as a percentage of the total cost of the switch, as are all
other components of the switch. Based on the estimates of Technology Futures, Inc., we find
the data set we have chosen. This is consistent with comments from this proceeding. See BellSouth Inputs Further
Notice comments at B-15; Sprint Inputs Further Notice comments at 46; and Letter from Richard Clarke, AT&T, to
Magalie Roman Salas, FCC, dated January 7, 1999 (AT&T Jan. 7 ex parte) at 1.
996

See supra para. 291.

997

See, e.g., AT&T/MCI Inputs Further Notice comments at 38; Sprint Inputs Further Notice comments at 44;
but cf. GTE Inputs Further Notice comments at 65. GTE appears to be confused about our use of the power
adjustment to make the RUS data comparable to the depreciation data and incorrectly assumes we only use the
depreciation data for switches with more than 25,000 lines.
998

SBC claims that our proposed $12 per line for MDF is too low and argues a more reasonable estimate is $30
per line. SBC Inputs Further Notice comments at 13. Sprint, AT&T and MCI, on the other hand, agree that $12
cost per line for MDF is reasonable. AT&T/MCI Inputs Further Notice comments at 38; Sprint Inputs Further
Notice comments at 44.
999

Sprint Inputs Further Notice comments at 44, attachment 7. GTE also claims its power investment is higher
than our proposed values, but offers no data to support this claim. GTE Inputs Further Notice comments at 66.
1000

AT&T/MCI Inputs Further Notice comments at 38.

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that these costs were eight percent of total cost.1001 Because we are adjusting the RUS data so
that they are comparable with the depreciation data, we find it is appropriate to use a comparable
method to estimate the portion of total costs attributable to MDF and power equipment.
Accordingly, in order to account for the cost of MDF and power equipment omitted from the
RUS information, we conclude that the cost of switches reported in the RUS data should be
increased by eight percent.
306. In the Inputs Further Notice, we tentatively concluded, based on an estimate
provided by Gabel and Kennedy, that $27,598 should be added to the cost of each remote switch
reported in the RUS data.1002 SBC recommends that remote termination costs should be added to
remote switch costs on a per-line basis, but provides no estimates of the per-line cost of remote
termination.1003 Sprint provides remote termination estimates of $22,636 for termination of
remote switches with less than 641 lines and $46,332 for termination of remote switches with
between 641 and 6,391 lines.1004 Using Sprint's methodology, the average cost of terminating a
RUS remote switch on a RUS host switch is $29,840.1005 Sprint's estimate is consistent in
magnitude with Gabel and Kennedy's estimate. Therefore, because Sprint's tiered estimates
captures differences between remote termination costs associated with remote switch size, we
adopt Sprint's estimates.
307. Based upon Gabel and Kennedy recommendations, derived from data analysis
undertaken by RUS, we conclude that the cost of switches reported in the RUS data should be
increased by eight percent in order to account for the cost of LEC engineering.1006 We
conclude, however, that this adjustment should not be added to the cost of power and MDF,
because these estimates already include the costs of LEC engineering.
1001

Lawrence K. Vanston, Ray L. Hodges, Adrian J. Poitras, Technology Futures, Inc., Transforming the Local
Exchange Network: Analyses and Forecast of Technology Change 149 (2d ed. 1997) (TFI Study). The
terminology used in the TFI study differs somewhat. What TFI calls "shell" is "the common equipment, such as
cabling and power equipment, that is not modular and lasts the life of the switch entity." TFI Study at 136. This
includes MDF and power investment.
1002

Inputs Further Notice at para. 160 (citing NRRI Study at 102-104).

1003

SBC Inputs Further Notice comments at 13.

1004

See Sprint Inputs Further Notice comments at 45. Sprint also provided an estimate of the cost of terminating
remote switches with over 6,390 lines. We note, however, that there are no remote switches in the RUS data with
over 6,390 lines.
1005

Sprint estimates the average cost of terminating its own remotes on its own host switches as $61,700. Its
tiered cost estimates indicate, however, that for remotes in the RUS data set, which do not include any remote
switches with over 6,390 lines, the average cost is $29,840. See Sprint Inputs Further Notice comments at 45.
1006

Id.

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308. Methodology. Consistent with our tentative conclusions in the Inputs Further
Notice, we employ regression analysis. In this Order, we also adopt our tentative conclusion to
use a linear function based on examination of the data and statistical evidence.
309. Sprint recommends using a non-linear function, such as the log-log function, to
take into account the declining marginal cost of a switch as the number of lines connected to it
increases.1007 We affirm our tentative conclusion that the linear function we adopt provides a
better fit with the data than the log-log function. A discussion of the effect of time and type of
switch on switch cost is presented below.
310. Based upon an analysis of the data and the record, we conclude that the fixed cost
(i.e., the base getting started cost of a switch, excluding costs associated with connecting lines to
the switch) of host switches and remote switches differ, but that the per-line variable cost (i.e.,
the costs associated with connecting additional lines to the switch) of host and remote switches
are approximately the same. This is consistent with statistical evidence1008 and the comments of
Sprint, BellSouth, and the HAI sponsors.1009
1007

Sprint Dec. 22 ex parte at 12. Sprint criticized the Commission's preliminary switch regression presented in
the December 1998 workshop based on the "R-squared" statistical goodness of fit criterion. After adjusting for
data transformations associated with moving to a log-log specification, however, the R-squared of a log-log
regression (0.56) suggested by Sprint is lower than the R-squared in the linear regression (0.73). Specifically, we
note that the R-squared measure resulting from a regression employing a log-log functional form is not directly
comparable to the R-squared measure from a linear regression. In order for the two measures to be comparable, the
R-squared measure computed from the log-log regression must be computed using observed and predicted cost
measures, not the logs of these measures. We also note that the log-log regression we employed is of the form:
Ln(Cost) = a1 + a2*Ln(Lines) + a3*Host + a4*Ln(Time) + a5*Ln(Lines)*Ln(Time) + a6*Host*Ln(Time) + e

where Ln(x) denotes the natural log of x. Because Sprint did not make these necessary adjustments, we believe that
its criticism of the use of a linear function is misplaced. For a discussion of the "R-squared" statistical goodness of
fit criterion and a discussion of log-log specifications, see William H. Greene, Econometric Analysis, 192-193 and
251 (1990).
1008

See General Wald Test for omitted variables in Ramu Ramanathan, Introductory Econometrics with
Applications 170 (1989).
1009

See Sprint Inputs Further Notice comments at 46. See also Letter from Richard Clarke, AT&T, to Magalie
Roman Salas, FCC, dated January 7, 1999 (AT&T Jan. 7 ex parte) at 1.
The primary difference between a host switch and remote switch is in the extent and complexity
of the `getting started equipment,' associated with each type of switch (e.g., switch central
processor functions, SS7 non-scaleable equipment, maintenance and testing, call recording for
billing purposes, etc.). Because most of these functions for lines terminating a remote switch are
performed at that switch's host, very little of this type of `getting started' equipment is required at
the remote. In contrast, the scaleable equipment used to terminate lines and trunks and to perform

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311. Accounting for Changes in Cost Over Time. We recognize that the cost of
purchasing and installing switching equipment changes over time. Such changes result, for
example, from improvements in the methods used to produce switching equipment, changes in
both capital and labor costs, and changes in the functional requirements that switches must meet
for basic dial tone service. In order to capture changes in the cost of purchasing and installing
switching equipment over time, we affirm our tentative conclusion in the Inputs Further Notice
to modify the data to adjust for the effects of inflation, and explicitly incorporate variables in the
regression analysis that capture cost changes unique to the purchase and installation of digital
switches.
312. To the extent that the general level of prices in the economy changes over time,
the purchasing power of a dollar, in terms of the volume of goods and services it can purchase,
will change. In order to account for such economy-wide inflationary effects, we multiply the
cost of purchasing and installing each switch in the data set by the gross-domestic-product chaintype price index1010 for 1997 and then divide by the gross-domestic-product chain-type price
index for the year in which the switch was installed, thereby converting all costs to 1997
values.1011
313. In order to account for cost changes unique to switching equipment, we enter time
terms directly into the regression equation.1012 US West agrees that the costs of the equipment,
such as switches and multiplexers, used to provide telecommunications services are declining,
basic call processing is essentially the same at the host and remote. In fact, the line units used by
Lucent 5E Remote Switching Modules are identical to those used by 5E host or stand-alone
switches. Similarly, the line cards used in Nortel DMS 100 host or stand-alone switches are the
same as those used in DMS 100 remotes, or in DMS 10 host or remote switches.
Id. BellSouth notes in its Inputs Further Notice comments that "BellSouth finds that the per line costs are slightly
different because hosts' lines also bear the costs of some umbilical trunking and control that is not provided at the
remotes. Still it is a reasonable simplification to allow host and remote per line costs to be the same." BellSouth
Inputs Further Notice comments at B-15.
1010

The gross-domestic-product chain-type price index, which tracks economy-wide inflation, is published
monthly by the Bureau of Economic Analysis of the U.S. Department of Commerce in the Survey of Current
Business.
1011

Switch costs are adjusted after estimation for both realized and expected inflation between 1997 and 1999.
See Appendix C for an explanation of these adjustments.
1012

Time was added to the regression in reciprocal form as an independent variable to measure fixed cost
changes unique to remote switches. Then, a time term was added in conjunction with the host identifier variable to
measure the fixed cost changes unique to host switches. A time term was also added in conjunction with the line
variable, in order to measure cost changes unique to line additions on switches.

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and that the per-unit cost of providing more services on average is declining.1013 Bell Atlantic
and GTE, however, contend that the cost of switches is not currently declining and therefore
pricing declines should not be expected to continue into the future.1014 As evidence, they cite
their own fixed-cost contracts. As AT&T notes, however, "[i]f Bell Atlantic in fact agreed to
switching contracts that ‘effectively froze prices on switching equipment,’ those prices would
reflect its idiosyncratic business judgement . . ."1015 GTE expresses concern that, under certain
specifications of time, the regression equation produces investments for remote switch "getting
started" costs that are negative and that such specifications overstate the decline in switch
costs.1016 As noted in the Inputs Further Notice, the HAI sponsors also caution that the large
percentage price declines in switch prices seen in recent years may not continue.1017 We affirm
our tentative conclusion that the reciprocal form of time in the regression equation satisfies these
concerns by yielding projections of switch purchase and installation costs that are positive yet
declining over time.1018
314. Ameritech and GTE advocate the use of the Turner Price Index to convert the
embedded cost information contained in the depreciation data to costs measured in current
dollars.1019 We note, however, that this index and the data underlying it are not on the public
record. We prefer to rely on public data when available. Moreover, we affirm our tentative
conclusion that it is not necessary to rely on this index to convert switch costs to current dollars.
Rather, as described in the preceding paragraph, we will account for cost changes over time
explicitly in the estimation process, rather than adopting a surrogate such as the Turner Price
Index.
315.

Treatment of Switch Upgrades. The book-value costs recorded in the depreciation

1013

US West Inputs Further Notice comments at 64-65.

1014

See Bell Atlantic Inputs Further Notice comments at 20, 21; GTE Inputs Further Notice Reply comments at

1015

AT&T/MCI Inputs Further Notice Reply comments at 35, n.54.

1016

GTE Dec. 18 ex parte at 4.

1017

See Inputs Further Notice at para. 168. See also AT&T Jan. 7 ex parte at 4.

32.

1018

Although the log specification of time proposed in the December 1, 1998, workshop yields similar results, it
produces investments for host switch "getting started" costs that become negative in 2000 and consequently
overstates pricing declines.
1019

See Ameritech Dec. 16, 1998 comments at 5; GTE Dec. 18, 1998 ex parte at 4. The Turner Price Index is an
index designed to measure the changing cost of telecommunications plant published semi-annually by AUS
consultants.

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data include both the cost of purchasing and installing new equipment and the cost associated
with installing and purchasing subsequent upgrades to the equipment over time. Upgrades costs
will be a larger fraction of reported book-value costs in instances where the book-value costs of
purchasing and installing switching equipment are reported well after the initial installation date
of the switch. We affirm our tentative conclusion that, in order to estimate the costs associated
with the purchase and installation of new switches, and to exclude the costs associated with
upgrading switches, we should remove from the data set those switches installed more than three
years prior to the reporting of their associated book-value costs.1020 We believe that this
restriction will eliminate switches whose book values contain a significant amount of upgrade
costs, and recognizes that, when ordering new switches, carriers typically order equipment
designed to meet short-run demand.
316. Bell Atlantic criticizes the Commission for excluding a large percentage of the
observations from the initial depreciation data set.1021 As noted in the preceding paragraph,
however, the observations that have been excluded do not accurately represent the price of a new
switch.
317. We reject the suggestions of Ameritech, Bell Atlantic, BellSouth, GTE, and
Sprint that the costs associated with purchasing and installing switching equipment upgrades
should be included in our cost estimates.1022 The model platform we adopted is intended to use
the most cost-effective, forward-looking technology available at a particular period in time. The
installation costs of switches estimated above reflect the most cost-effective forward-looking
technology for meeting industry performance requirements. Switches, augmented by upgrades,
may provide carriers the ability to provide supported services, but do so at greater costs.
Therefore, such augmented switches do not constitute cost-effective forward-looking
technology. In addition, as industry performance requirements change over time, so will the
costs of purchasing and installing new switches. The historical cost data employed in this
analysis reflect such changes over time, as do the time-trended cost estimates.
318. Additional Variables. Several parties contend that additional independent
variables should be included in our regression equation. Some of the recommended variables
include minutes of use, calls, digital line connections, vertical features, and regional, state, and

1020

Inputs Further Notice at para. 170.

1021

Bell Atlantic Inputs Further Notice comments at 12.

1022

Ameritech Dec. 16, 1998 comments at 4-5; GTE Dec. 18, 1998 ex parte at 4-5; Sprint Dec. 22, 1998 ex
parte at 5-7; GTE Inputs Further Notice comments at 68; Bell Atlantic Inputs Further Notice comments, Affidavit
of Harold Ware and Christian Michael Dippon at 9-13; Bell Atlantic Inputs Further Notice comments at 8-13;
BellSouth Inputs Further Notice comments at B-15 and B-16; Sprint Inputs Further Notice comments at 47 and 48.

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vendor-specific identifiers.1023 For the purposes of this analysis, our model specification is
limited to include information that is in both the RUS and depreciation data sets. Neither data
set includes information on minutes of use, calls, digital line connections, vertical features, or
differences between host and stand-alone switches. State and regional identifiers are not
included in the regression because we only have depreciation data on switches from 20 states.
Thus, we could not accurately estimate region-wide or state-wide differences in the cost of
switching. Our model specification also does not include vendor-specific variables, because the
model platform does not distinguish between different vendors' switches.1024
319. Switch Cost Estimates. A number of commenters criticize the switch cost
estimates contained in the Inputs Further Notice and suggest that they should be dismissed or
substantially revised. For example, Sprint suggests that we dismiss the results because the data
are collinear and the model is mis-specified.1025 Bell Atlantic and BellSouth suggest that the
Commission underestimates the cost of switches, while AT&T and MCI suggest that the
Commission overestimates the cost of switches.1026 The Commission's estimates, however, are
based upon the most complete, publicly-available information on the costs of purchasing and
installing new switches and therefore represent the Commission's best estimates of the cost of
host and remote switches. In the preceding paragraphs and in Appendix C, we have addressed
the specific objections that have been raised by parties with regard to the methodology, data set,
or other aspects of the approach we adopt to derive switch cost estimates, and for the reasons
given there, we reject those objections. We conclude that the remaining evidence provided as
grounds for dismissing or substantially revising these estimates is largely anecdotal or
unconfirmed and undocumented and does not lead us to believe that our estimates should be
altered. We conclude, therefore, that the switch cost estimates we adopt are the best estimates of
forward-looking cost.
C.

Use of the Local Exchange Routing Guide (LERG)
320.

In the Inputs Further Notice, we tentatively concluded that the Local Exchange

1023

GTE Dec. 18, 1998 ex parte at 5; Sprint Dec. 22, 1998 ex parte at 13; Ameritech Dec. 16, 1998 comments at
6; Bell Atlantic Inputs Further Notice comments, Affidavit of Harold Ware and Christian Michael Dippon at 17 and
18.
1024

Moreover, even if the model platform were changed, we do not believe that it would be appropriate to use
vendor-specific input values for switch costs. The model is intended to estimate the least-cost, most-efficient
technology being deployed, not the technology available from a particular vendor.
1025

In Appendix C, we discuss the issues of multicollinearity and mis-specification identified by Sprint in its
comments.
1026

AT&T/MCI Inputs Further Notice comments at 36; Bell Atlantic Inputs Further Notice comments at 10-11;
Sprint Inputs Further Notice comments at 46; BellSouth Inputs Further Notice comments at B-15.

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Routing Guide (LERG) database should be used to determine host-remote switch relationships in
the federal high-cost universal service support mechanism.1027 We now affirm that conclusion.
In the 1997 Further Notice, the Commission requested "engineering and cost data to demonstrate
the most cost-effective deployment of switches in general and host-remote switching
arrangements in particular."1028 In the Switching and Transport Public Notice, the Bureau
concluded that the model should permit individual switches to be identified as host, remote, or
stand-alone switches.1029 The Bureau noted that, although stand-alone switches are a standard
component of networks in many areas, current deployment patterns suggest that host-remote
arrangements are more cost-effective than stand-alone switches in certain cases.1030 No party has
placed on the record in this proceeding an algorithm that will determine whether a wire center
should house a stand-alone, host, or remote switch.1031 We therefore affirm our conclusion to
use the LERG to determine host-remote switch relationships.
321. In the Platform Order, we concluded that the federal mechanism should
incorporate, with certain modifications, the HAI 5.0a switching and interoffice facilities
module.1032 In its default mode, HAI assumes a blended configuration of switch technologies,
incorporating both hosts and remotes, to develop switching cost curves.1033 HAI also allows the
user the option of designating, in an input table, specific wire center locations that house host,
remote, and stand-alone switches. When the host-remote option is selected, switching curves
that correspond to host, remote, and stand-alone switches are used to determine the appropriate
switching investment. The LERG database could be used as a source to identify the host-remote
switch relationships. In the Platform Order, we stated that "[i]n the inputs stage of this
proceeding we will weigh the benefits and costs of using the LERG database to determine switch
1027

Inputs Further Notice at para. 174. The LERG is a database of switching information maintained by
Telecordia Technologies (formerly Bellcore) that includes the existing host-remote relationships. The HAI
proponents have placed on the record the portion of the LERG that identifies the host-remote relationships. Letter
from Chris Frentrup, MCI Worldcom, to Magalie Roman Salas, FCC, dated September 14, 1998 (MCI Sept. 14 ex
parte).
1028

1997 Further Notice, 12 FCC Rcd at 18560-61, para. 122.

1029

Switching and Transport Public Notice at 2. Switches can be designated as host, remote, or stand-alone
switches. Both a host and a stand-alone switch can provide a full complement of switching services without relying
on another switch. A remote switch relies on a host switch to supply a complete array of switching functions and to
interconnect with other switches.
1030

Switching and Transport Public Notice at 2-3.

1031

Platform Order, 13 FCC Rcd at 21355, para. 76.

1032

Platform Order, 13 FCC Rcd at 21354-55, para. 75.

1033

HAI Feb. 3, 1998 submission, Model Description at 58.

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type and will consider alternative approaches by which the selected model can incorporate the
efficiencies gained through the deployment of host-remote configurations."1034
322. The majority of commenters throughout this proceeding have supported the use of
the LERG database as a means of determining the deployment of host and remote switches.1035
These commenters contend that the use of the LERG to determine host-remote relationships will
incorporate the accumulated knowledge and efficiencies of many LECs and engineering experts
in deploying the existing switch configurations.1036 Sprint contends that there are many
intangible variables that can not be easily replicated in determining host-remote relationships.1037
Commenters also contend that an algorithm that realistically predicts this deployment pattern is
not feasible using publicly available data and would be unnecessarily "massive and complex."1038
AT&T and MCI argue, however, that use of the LERG to identify host-remote relationships may
reflect the use of embedded technology, pricing, and engineering practices.1039
323. We conclude that the LERG database is the best source set forth in this
proceeding to determine host-remote switch relationships in the federal high-cost universal
service support mechanism. As noted above, no algorithm has been placed on the record to
determine whether a wire center should house a stand-alone, host, or remote switch. In addition,
many commenters contend that development of such an algorithm independently would be
difficult using publicly available data.1040 While GTE suggests that the best source of hostremote relationships would be a file generated by each company, we note that no such
1034

Platform Order, 13 FCC Rcd at 21355, para. 76.

1035

See, e.g., BellSouth Inputs Further Notice reply comments at 17; Sprint Inputs Further Notice comments at
48. See also Aliant Switching and Transport Public Notice comments at 2; Bell Atlantic Switching and Transport
Public Notice reply comments at 2.
1036

Bell Atlantic Switching and Transport Public Notice reply comments, Attachment 1 at 2; BellSouth et al.
Switching and Transport Public Notice reply comments, Attachment 1 at 2-3.
1037

Sprint Inputs Further Notice comments at 48.

1038

See, e.g., AT&T/MCI Switching and Transport Public Notice comments at 6; BellSouth et al. Switching and
Transport Public Notice reply comments, Attachment 1 at 2.
1039

AT&T/MCI Inputs Further Notice comments at 44-45. Although AT&T and MCI oppose the use of the
LERG, they have taken steps to ensure that the LERG database is compatible with use in the switching module of
the synthesis model. See MCI Sept. 14 ex parte; Letter from Richard N. Clarke, AT&T, to Magalie Roman Salas,
FCC, dated September 16, 1998 (AT&T Sept. 16 ex parte).
1040

See, e.g., Ameritech Switching and Transport Public Notice comments at 3; AT&T/MCI Switching and
Transport Public Notice comments at 6; BellSouth et al. Switching and Transport Public Notice comments
Attachment 1 at 1-2; GTE Switching and Transport Public Notice at 11-12.

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information has been submitted in this proceeding.1041 In addition, GTE's proposal would
impose administrative burdens on carriers. We conclude that the use of the LERG to identify the
host-remote switch relationships is superior to HAI's averaging methodology which may not, for
example, accurately reflect the fact that remote switches are more likely to be located in rural
rather than urban areas. We therefore conclude that use of the LERG is the most feasible
alternative currently available to incorporate the efficiencies of host-remote relationships in the
federal high-cost universal service support mechanism.
D.

Other Switching and Interoffice Transport Inputs

324. General. In the Inputs Further Notice, we proposed several minor modifications
to the switching inputs to reflect the fact that the studies on which the Commission relied to
develop switch costs include all investments necessary to make a switch operational.1042 These
investments include telephone company engineering and installation, the main distribution frame
(MDF), the protector frame (often included in the MDF), and power costs.1043 To avoid double
counting these investments, both as part of the switch and as separate input values, the
commenters agree that the MDF/Protector investment per line and power input values should be
set at zero.1044 In addition, commenters agree that the Switch Installation Multiplier should be
set at 1.0.1045 We agree that including these investments both as part of the switch cost and as
separate investments would lead to double counting of these costs. We therefore adopt these
values.
325. Analog Line Offset. In the Inputs Further Notice, we tentatively concluded that
the "Analog Line Circuit Offset for Digital Lines" input should be set at zero.1046 We now affirm
that conclusion. AT&T and MCI contend that the switch investment in the model should be
adjusted downward to reflect the cost savings associated with terminating digital, rather than
analog, lines.1047 AT&T and MCI assert that this cost savings is due primarily to the elimination
1041

GTE Inputs Further Notice comments at 69.

1042

Inputs Further Notice at para. 178.

1043

AT&T Jan. 7 ex parte; Sprint Dec. 22 ex parte at 9.

1044

AT&T Inputs Further Notice comments at 40; GTE Dec. 18 ex parte at 5-6; Sprint Inputs Further Notice
comments at 49.
1045

See, e.g., AT&T Inputs Further Notice comments at 40; GTE Dec. 18 ex parte at 6; Sprint Inputs Further
Notice comments at 49.
1046

Inputs Further Notice at para. 179.

1047

AT&T/MCI Inputs Further Notice comments at 41-42. AT&T/MCI contend that the cost of terminating
digital lines is significantly less expensive than terminating analog lines.

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of a MDF and protector frame termination. AT&T and MCI further contend that the model
produces, on average, 40 percent digital lines, while the data used to determine switch costs
reflect the use of only approximately 18 percent digital lines.1048 In contrast, GTE contends that
the model may calculate more analog lines than carriers have historically placed due to the use of
an 18,000 feet maximum copper loop length.1049
326. AT&T and MCI suggest that the analog line offset input should reflect a $12
MDF and $18 switch port termination savings per line in switch investment for terminating
digital lines in the model.1050 Several commenters disagree and recommend setting the analog
line offset to zero.1051 Sprint contends that the analog line offset is inherent in the switching
curve in the model, thus making this input unnecessary and, therefore, justified only if the switch
cost curve is based on 100 percent of analog line cost.1052 Sprint argues that an unknown
mixture of analog and digital lines are taken into consideration in developing the switch
curve.1053
327. The record contains no basis on which to quantify savings beyond those taken
into consideration in developing the switch cost. We also note that the depreciation data used to
determine the switch costs reflect the use of digital lines. The switch investment value will
therefore reflect savings associated with digital lines. AT&T and MCI's proposed analog line
offset per line is based on assumptions that are neither supported by the record nor easily
verified. For example, it is not possible to determine from the depreciation data the percentage
of lines that are served by digital connections. It is therefore not possible to verify AT&T and
MCI's estimate of the digital line usage in the "historical" data. In the absence of more explicit
support of AT&T and MCI's position, we conclude that the Analog Line Circuit Offset for
Digital Lines should be set at zero.
328. Switch Capacity Constraints. In the Inputs Further Notice, we proposed to adopt
the HAI default switch capacity constraint inputs as proposed in the HAI 5.0a model
documentation.1054 We now adopt that proposal. The forward-looking cost mechanism contains
1048

AT&T/MCI Inputs Further Notice comments at 41.

1049

GTE Inputs Further Notice comments at 66.

1050

AT&T/MCI Inputs Further Notice comments at 42.

1051

BellSouth Inputs Further Notice comments at 16; GTE Inputs Further Notice comments at 66-67; Sprint
Inputs Further Notice comments at 49.
1052

Sprint Inputs Further Notice comments at 49.

1053

Sprint Dec. 22 ex parte at 12.

1054

HAI Feb. 3, 1998 submission, App. B at 38-39.

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switch capacity constraints based on the maximum line and traffic capabilities of the switch. In
their most recent filings on this issue, AT&T and MCI recommend increasing the switch line and
traffic capacity constraints above the HAI input default values for those inputs.1055 AT&T and
MCI contend that the default input values no longer reflect the use of the most current
technology.1056 For example, AT&T and MCI recommend that the maximum equipped line size
per switch should be increased from 80,000 to 100,000 lines.1057
329. We conclude that the original HAI switch capacity constraint default values are
reasonable for use in the federal mechanism. We note that Sprint, the only commenter to
respond to this issue, supports this conclusion.1058 We also note that the HAI model
documentation indicates that the 80,000 line assumption was based on a conservative estimate
"recognizing that planners will not typically assume the full capacity of the switch can be
used."1059 AT&T and MCI therefore originally supported the 80,000 line limitation as the
maximum equipped line size value with the knowledge that the full capacity of the switch may
be higher.1060
330. Switch Port Administrative Fill. In the Inputs Further Notice, we proposed a
switch port administrative fill factor of 94 percent.1061 We now adopt that proposed value. The
HAI model documentation defines the switch port administrative fill as "the percent of lines in a
switch that are assigned to subscribers compared to the total equipped lines in a switch."1062 HAI
assigns a switch port administrative fill factor of 98 percent in its default input values.1063 The
1055

AT&T Jan. 7 ex parte. The HAI proponents included the updated switch capacity constraints in a table
attached to the Jan. 7 ex parte.
1056

AT&T Jan. 7 ex parte.

1057

AT&T Jan. 7 ex parte.

1058

Sprint Inputs Further Notice comments at 49.

1059

See HAI Dec. 11 submission, Model Inputs at 80.

1060

In addition, we note that a decision to adopt the revised HAI values for maximum equipped lines per switch
would have only a minimal impact on the overall forward-looking cost estimation because fewer than 2 percent of
wire centers have more than 80,000 lines. A review of the data indicates that, of the 12,506 wire centers served by
non-rural LECs, only 189 (1.5 percent) have more than 80,000 lines and 57 (0.5 percent) have more than 100,000
lines. See HAI Feb. 3, 1998 model submission.
1061

Inputs Further Notice at para. 184.

1062

HAI Dec. 11, 1997 submission, Inputs Portfolio at 80.

1063

HAI Dec. 11, 1997 submission, Inputs Portfolio at 80.

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BCPM default value for the switch percent line fill is 88 percent.1064
331. Bell Atlantic contends that switches have significant unassigned capacity due to
the fact that equipment is installed at intervals to handle growth.1065 Sprint recommends an
average fill factor of 80 percent.1066 US West contends that its actual average fill factor is 78
percent.1067 AT&T and MCI contend that the switching module currently applies the fill factor
input against the entire switch when it should be applied only to the line port portion of the
switch.1068 AT&T and MCI therefore contend that, either the formula should be modified, or the
input needs to be adjusted upward so that the overall switching investment increase attributable
to line fill will be the same as if the formula were corrected.1069
332. We note that the switch port administrative fill factor of 94 percent has been
adopted in several state universal service proceedings and is supported by the Georgetown
Consulting Group, a consultant of BellSouth.1070 We also note that this value falls within the
range established by the HAI and BCPM default input values. The BCPM model documentation
established a switch line fill default value of 88 percent that included "allowances for growth
over an engineering time horizon of several years."1071 Sprint has provided no substantiated
evidence to support its revised value of 80 percent. US West's average fill factor of 78 percent is
based on data that include switches with unreasonably low fill factors.1072 Regarding AT&T and
1064

BCPM April 30, 1998 submission, Switch Model Inputs at 20-21. BCPM defines Switch Percent Line Fill
as the ratio between the number of working lines on the switch and the total number of lines for which the switch is
engineered.
1065

Bell Atlantic Inputs Further Notice comments at 8-9.

1066

Sprint Inputs Further Notice comments at 50.

1067

See Letter from Pete Sywenki, Sprint, to Magalie Roman Salas, FCC, dated Jan. 8, 1999 (attachment
includes US West switch data) (Sprint Jan. 8 ex parte).
1068

AT&T/MCI Inputs Further Notice comments at 43.

1069

AT&T/MCI Inputs Further Notice comments at 43.

1070

BellSouth Inputs Public Notice reply comments at Exhibit 2-13; Commonwealth of Kentucky, An Inquiry
Into Universal Service and Funding Fees, Administrative Case No. 360, App. F at 13; Louisiana Public Service
Commission, State Forward-Looking Cost Studies for Federal Universal Service Support (May 19, 1998)
(Louisiana Cost Study).
1071

BCPM April 30, 1998 submission, Switch Model Inputs at 20-21.

1072

For example, switches with installed lines of 65,001, 48,818, 11,520, 12,288, 74,039, 12,800, and 36,897
were listed as having, 1,1, 2, 10, 10, 21, and 26 working lines, respectively, or collectively, an average fill factor of
.027 percent. See Sprint Jan. 8 ex parte. Our analysis of the US West data indicated that, after eliminating the

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MCI's contention that the switching module currently applies the fill factor input against the
entire switch rather than the line port portion of the switch, we note that this occurs only when
the host-remote option is not utilized in the switch module. As noted above, we are using the
host-remote option and therefore no adjustment to the switch fill factor is required. We therefore
adopt a switch port administrative fill factor of 94 percent.
333. Trunking. In the Inputs Further Notice, we tentatively concluded that the switch
module should be modified to disable the computation that reduces the end office investment by
the difference in the interoffice trunks and the 6:1 line to trunk ratio. In addition, we tentatively
adopted the proposed input value of $100.00 for the trunk port investment.1073 We now affirm
these tentative conclusions and adopt this approach.
334. The HAI switching and interoffice module developed switching cost curves using
the Northern Business Information (NBI) publication, "U.S. Central Office Equipment Market:
1995 Database."1074 These investment figures were then reduced per line to remove trunk port
investment based on NBI's implicit line to trunk ratio of 6:1.1075 The actual number of trunks per
wire center is calculated in the transport calculation, and port investment for these trunks is then
added back into the switching investments.
335. Sprint notes that, under the HAI trunk investment approach, raising the per-trunk
investment leads to a decrease in the switch investment per line, "despite a reasonable and
expected increase" in the investment per line.1076 GTE also notes that the selection of the trunk
port input value creates a dilemma in that it is used to reduce the end office investment, as noted
above, and to develop a tandem switch investment.1077 GTE and Sprint recommend that the
switch module be modified by disabling the computation that reduces the end office investment
by the difference in the computed interoffice trunks and the 6:1 line to trunk ratio.1078 MCI
agrees that the trunk port calculation should be deactivated in the switching module.1079
observations with unreasonably low fill factors, the majority of US West switches had fill factors ranging from 88
percent to 98 percent.
1073

Inputs Further Notice at para. 187.

1074

HAI Dec. 11, 1997 submission, Model Description at 52.

1075

HAI Dec. 11, 1997 submission, Model Description at 53.

1076

Sprint Dec. 22 ex parte at 10.

1077

GTE Dec. 18 ex parte at 6.

1078

GTE Dec. 18 ex parte at 6; Sprint Inputs Further Notice comments at 50.

1079

Letter from Chris Frentrup, MCI Worldcom, to Magalie Roman Salas, FCC, dated Feb. 9, 1999 (MCI

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336. In the Inputs Further Notice, we agreed with commenters that the trunk port input
creates inconsistencies in reducing the end office investment.1080 Consistent with the
suggestions made by GTE and MCI, we conclude that the switch module should be modified to
disable the computation that reduces the end office investment by the difference in the computed
interoffice trunks and the 6:1 line to trunk ratio. Sprint, the only commenter to address this issue
in response to the Inputs Further Notice, agrees with our conclusion.1081
337. Because the trunk port input value is also used to determine the tandem switch
investment, we must determine the trunk port investment.1082 In the Inputs Further Notice, we
proposed an input value for trunk port investment per end of $100.00.1083 SBC and Sprint
contend that this value should be higher -- ranging from $150.00 to $200.00.1084 BellSouth has
filed information on the record that supports our proposed trunk port investment value.1085
BellSouth notes that the four states that have issued orders addressing the cost of the trunk port
for universal service1086 have chosen estimates of the cost of the trunk port that range from
$62.73 to $110.77.1087 We conclude that the record supports the adoption of a trunk port
investment per end of $100.00, as supported by the HAI default values. As noted above, this
value is consistent with the findings of several states and BellSouth. In addition, we note that
SBC and Sprint provide no data to support their higher proposed trunk port investment value.
We therefore adopt the HAI suggested input value of $100.00 for the trunk port investment, per
end.
VII. EXPENSES
Worldcom Feb. 9 ex parte) at 24.
1080

Inputs Further Notice at para. 190.

1081

Sprint Inputs Further Notice comments at 50.

1082

HAI defines this input as the "per trunk equivalent investment in switch trunk port at each end of a trunk."
HAI Dec. 11, 1997 submission, Appendix B (HM 5.0 Inputs, Assumptions, and Default Values) at 46.
1083

Inputs Further Notice at para. 191.

1084

SBC Inputs Further Notice comments at 14; Sprint Inputs Further Notice comments at 50.

1085

Letter from William W. Jordan, BellSouth, to Magalie Roman Salas, FCC, dated August 7, 1998,
Attachment to Question 1 at 5, 9, 13, 17 (dated July 15, 1998) (BellSouth Aug. 7 ex parte).
1086

BellSouth Aug. 7 ex parte, Attachment to Question 1 at 5, 9, 13, 17. The four states are Kentucky,
Louisiana, North Carolina, and South Carolina.
1087

BellSouth Aug. 7 ex parte, Attachment to Question 1 at 5, 9, 13, 17.

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Introduction

338. In this section, we consider the inputs to the model related to expenses and
general support facilities (GSF) investment. Consistent with the Universal Service Order's
seventh criterion, we select input values that result in a reasonable allocation of joint and
common costs for non-network-related costs, such as GSF, plant non-specific expenses,
corporate operations expenses, and customer services expenses. The Commission's methodology
for estimating these types of expenses is designed to "ensure that the forward-looking economic
cost [calculated by the model] does not include an unreasonable share of the joint and common
costs for non-supported services."1088 Consistent with the Universal Service Order's first and
third criteria, we also select input values for plant-specific operations expenses that reflect the
cost of maintaining a forward-looking network.1089
339. GSF costs include the investment and expenses related to vehicles, land,
buildings, and general purpose computers. Other expenses include: plant-specific operations
expenses,1090 plant non-specific expenses,1091 corporate operations expenses,1092 and customer
services expenses.1093 For purposes of this Order, costs associated with common support
services (often called overhead expenses) refer to plant non-specific expenses, corporate
operations expenses, and customer services expenses.
340. In the Platform Order, the Commission adopted HAI's algorithm for calculating
expenses and GSF costs, as modified to provide some additional flexibility in calculating
expenses offered by the BCPM sponsors.1094 With this added flexibility, the model allows the
user to estimate expenses as either a per-line amount or as a percentage of investment. We noted
that many of the questions regarding how best to calculate expenses would be resolved in the
1088

Universal Service Order, 12 FCC Rcd at 8915, para. 250, criterion 7; see also 47 U.S.C. § 254 (k).

1089

See Universal Service Order, 12 FCC Rcd at 8913, para. 250, criteria 1, 3; see also infra para. 351.

1090

Plant specific operations expenses (that are not associated with GSF) include the cost of maintaining
telecommunications plant and equipment. These network related expenses are not considered to be "joint and
common costs." In ARMIS accounts, plant-specific operations expenses include GSF expenses.
1091

Plant non-specific expenses include the costs of engineering, network operations, and power expenses.

1092

Corporate operations expenses include the costs of administration, human resources, legal, and accounting
expenses.
1093

Customer services expenses include the costs of marketing, billing, and directory listing expenses.

1094

Platform Order, 13 FCC Rcd at 21357, para. 81.

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input selection phase of this proceeding.1095 In the Inputs Further Notice, we tentatively
concluded that the input values for plant-specific operations expenses should be calculated as a
percentage of investment,1096 and that the input values for common support services expenses
should be estimated on a per-line basis.1097 In addition, we tentatively concluded that we should
adopt input values that reflect the average expenses that will be incurred by non-rural carriers,
rather than company-specific expense estimates.1098 As described below, we proposed
methodologies for calculating these expenses. In addition, we proposed a methodology for
estimating the GSF investment that should be allocated to the supported services.1099
B.

Plant-Specific Operations Expenses
1.

Background

341. Plant-specific operations expenses are the expense costs related to the
maintenance of specific kinds of telecommunications plant.1100 In the Inputs Further Notice, we
1095

Platform Order, 13 FCC Rcd at 21360, para. 87.

1096

Inputs Further Notice at para. 204.

1097

Inputs Further Notice at para. 213.

1098

Inputs Further Notice at paras. 198, 214.

1099

Inputs Further Notice at paras. 210-11.

1100

Plant-specific operations expenses correspond to the following ARMIS 43-03 report accounts:
6110 - Network Support Expense
6120 - General Support Expense
6210 - COE Switch
6212 - COE Digital Electronic Switch only
6220 - Operator Systems
6230 - COE Transmission
6231 - Radio Systems
6232 - COE Circuit - DDS
6232 - COE Circuit - Other than DDS
6310 - Information Origination/Termination
6311 - Station Apparatus (only)
6341 - Large PBX
6351 - Public Telephone
6362 - Other Terminal Equipment
6411 - Poles
6421.1 - Aerial Cable - Metallic (Copper)
6421.2 - Aerial Cable - Fiber
6422.1 - Underground Cable - Metallic (Copper)

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proposed a methodology for estimating expense-to-investment ratios consisting of four steps.1101
First, we obtained account-specific current cost to book cost (current-to-book) ratios for the
related investment accounts, for the years ending 1995 and 1996, from Ameritech, Bell Atlantic,
BellSouth, GTE, and SBC.1102 Second, we calculated two sets of composite current-to-book
ratios (year end 1995 and 1996) for each account based on composite current-to-book ratios for
each of the five companies.1103 Third, we applied these composite current-to-book ratios to the
year-end 1995 and 1996 investment account balances from the ARMIS 43-03 reports for all
ARMIS-filing companies and averaged the 1995 and 1996 adjusted balances for each
account.1104 Fourth, we calculated expense-to-investment ratios for each plant-specific
operations expense account by dividing the total 1996 account balance for all ARMIS-filing
companies by the current average investment calculated previously.1105 We tentatively
6422.2 - Underground Cable - Fiber
6423.1 - Buried Cable - Metallic (Copper)
6423.2 - Buried Cable - Fiber
6441 - Conduit Systems
1101

Inputs Further Notice at paras. 205-208.

1102

Inputs Further Notice at para. 205. For each account or sub-account, a current-to-book ratio is developed by
first revaluing each type of equipment at its current replacement cost. The sum of these current costs is then divided
by the total, embedded cost account balance. The resulting current-to-book ratio will be greater than one if current
costs are rising relative to the historic costs and less than one if current costs are declining. The current-to-book
ratios submitted by Ameritech, Bell Atlantic, BellSouth, GTE, and SBC are proprietary information subject to
provisions in the Protective Order and therefore are not reproduced here. Although we would prefer to have data
from more companies, the other ARMIS-filing carriers informed us that they either no longer maintain this type of
information, or never used current-to-book ratios for accounting purposes.
1103

Inputs Further Notice at para. 206. For each study area of the five holding companies that provided
current-to-book ratios, we obtained year-end 1995 and 1996 investment balances from ARMIS for the plant
accounts consistent with the aforementioned plant-specific expense accounts. Study area-specific current-to-book
ratios for the two periods were multiplied by the 1995 and 1996 ARMIS investments in each account to derive the
forward-looking, "current," year-end 1995 and 1996 investment levels by account and by study area. The ARMIS
and current investments were then summed separately, by year and by account, for all study areas of the five
holding companies. The resulting total current investment (by year and by account for the sum of all study areas)
was then divided by the total ARMIS investment (by year and by account for the sum of all study areas) producing
two sets of composite current-to-book ratios (year end 1995 and 1996).
1104

Inputs Further Notice at para. 207. To calculate the expense-to-investment ratios for the plant-specific
operations expense accounts, we obtained total, year-end 1995 and 1996 investment account balances from the
ARMIS 43-03 reports for all ARMIS-filing companies. To make these embedded account balances forwardlooking, we next multiplied each investment account balance for each year by the current-to-book ratios for the
same year developed earlier. The resulting year-end 1995 and year-end 1996 "current" account balances were then
averaged by adding the two years together and dividing by two.
1105

Inputs Further Notice at para. 208. From the 1996 ARMIS 43-03 report, we obtained the 1996 balances for
each plant-specific operations expense account for all ARMIS-filing companies. The expense account balances

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concluded that these expense-to-investment ratios should be applied to the model-derived
investment balances to obtain forward-looking plant-specific operations expense estimates.
342. In the Inputs Further Notice, we proposed adopting input values that reflect the
average expenses that will be incurred by non-rural carriers, rather than a set of companyspecific maintenance expense estimates, for several reasons.1106 We stated that using nationwide
expense-to-investment ratios is consistent with the views of the states as reflected in the state
Joint Board staff recommendations.1107 In addition, our proposed methodology requires some
method of converting booked cost investment to current investment in order to estimate forwardlooking plant specific operations expenses based on present day replacement cost, rather than
historic, financial account balances. We noted that we have not been able to obtain current-costto-book-cost ratios for each non-rural ARMIS reporting firm, which would be necessary to
calculate company or study area specific expense-to-investment ratios.1108 We tentatively
concluded that averages are more consistent with the forward-looking nature of the high-cost
model because less efficient firms are not rewarded if they have higher than average costs. In
seeking comment on these proposals and tentative conclusions, we requested that parties
advocating the use of company-specific values or other alternatives to nationwide or regional
estimates identify the method and data readily available that could be used to estimate plantspecific expenses and indicate how their proposal is consistent with the goal of estimating
forward-looking costs.1109
343. In reaching our tentative conclusions, we recognized that parties have argued that
maintenance expenses vary widely by geographic area and type of plant, while others have
argued that plant-specific expenses are highly dependent on regional wage differences.1110 We
explained that the synthesis model takes into account the variance in maintenance cost by type of
plant installed because, as investment in a particular type of plant varies, the associated expense
cost also varies.1111 We noted that we had been unable to verify significant regional differences
among study areas or companies based solely on labor rate variations using the publicly
available ARMIS expense account data for plant-specific maintenance costs. Nonetheless, we
were divided by their respective average "current" investment to obtain expense-to-investment ratios.
1106

Inputs Further Notice at para. 198.

1107

See State Members' Report on the Use of Cost Proxy Models, March 26, 1997, at 22.

1108

Inputs Further Notice at para. 198.

1109

Inputs Further Notice at para. 198.

1110

Inputs Further Notice at para. 199.

1111

Inputs Further Notice at para. 199.

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sought comment on the degree to which regional wage rate differentials exist and are significant,
and asked parties to suggest independent data sources on variations of wage rates between
regions and a methodology that permits such distinctions without resorting to self-reported
information from companies.1112 In addition, we sought specific comment on a possible method
of estimating regional wage differences by using indexes calculated by the President's Pay
Agent.1113
344. We also tentatively concluded that we should not adopt different expense
estimates for small, medium, and large non-rural companies on a per-line basis.1114 We
explained that we had tested whether significant differences in maintenance expenses per line
could be discerned from segmenting companies into carriers serving less than 500,000 access
lines, carriers serving between 500,000 and 5,000,000 access lines, and carriers serving over
5,000,000 access lines.1115 Because we found no significant differences in the expense factor
per-line or per-investment estimates based on these criteria, we determined that economies of
scale should not be a factor in estimating plant-specific expenses.1116
345. Finally, we noted that we used data from 1995 and 1996 in the proposed
methodology and tentatively concluded that it is appropriate to adjust these data to account for
inflation and changes in productivity by obtaining revised 1997 current-to-book ratios from those
companies providing data.1117 In addition, we tentatively concluded that we should use the most
current ARMIS data available for the maintenance factor methodology. We sought comment on
using the most current data available in the final computation of expense estimates.1118
2.

Discussion

346. Consistent with our tentative conclusions, we adopt input values that reflect the
average expenses that will be incurred by non-rural carriers, rather than a set of company1112

Inputs Further Notice at para. 199.

1113

Inputs Further Notice at para. 200. These indexes are used to calculate locality pay differentials for federal
employees. See Report on Locality-based Comparability Payments for the General Schedule, Annual Report of the
President's Pay Agent, Appendix II, 1995.
1114

Inputs Further Notice at para. 201.

1115

Inputs Further Notice at para. 201.

1116

Inputs Further Notice at para. 201.

1117

Inputs Further Notice at para. 209.

1118

Inputs Further Notice at para. 209.

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specific maintenance expense estimates. We adopt our proposed four-step methodology for
estimating expense-to-investment ratios using revised current-to-book ratios and 1997 and 1998
ARMIS data. We clarify that the ARMIS investment and expense balances used to calculate the
expense-to-investment ratios in steps three and four should be based on the accounts for all nonrural ARMIS-filing companies. Although some rural companies file ARMIS reports, the
mechanism we adopt today will be used, beginning January 1, 2000, to determine high-cost
support only for non-rural carriers. We find, therefore, that it is appropriate to include only data
from the non-rural ARMIS-filing companies in calculating these expense-to-investment
ratios.1119
347. Current Data. Parties commenting on whether we should update our
methodology using more current ARMIS data agree that we should use the most currently
available data.1120 We obtained account-specific current-to-book ratios for the related plant
investment accounts, for the years ending 1997 and 1998, from Ameritech, Bell Atlantic,
BellSouth, GTE, and SBC.1121 Accordingly, we adopt input values using these updated currentto-book ratios and 1997 and 1998 ARMIS data to calculate the expense-to-investment ratios that
we use to obtain plant-specific operations expense estimates for use in the federal mechanism.
These input values and the non-proprietary data used to calculate the expense-to-investment
ratios are set forth in Appendix D.1122
348. Nationwide Estimates. As discussed in this section, we adopt nationwide average
values for estimating plant-specific operations expenses rather than company-specific values for
several reasons. We reject the explicit or implicit assumption of most LEC commenters that the
cost of maintaining incumbent LEC embedded plant is the best predictor of the forward-looking
cost of maintaining the network investment predicted by the model. We find that, consistent
with the Universal Service Order's criteria, forward-looking expenses should reflect the cost of
maintaining the least-cost, most-efficient, and reasonable technology being deployed today, not
the cost of maintaining the LECs' historic, embedded plant. We recognize that variability in
1119

Our proposed expense-to-investment ratios were based on ARMIS data for 91 study areas. The input values
we adopt herein are based on ARMIS data for 80 non-rural study areas. We note that there generally is little or no
difference between the expense ratios calculated using total ARMIS expense and investment accounts and non-rural
ARMIS expense and investment. Where there are differences, the ratios based on non-rural data are higher for all
categories except network support and general support.
1120

See, e.g., GTE Inputs Further Notice comments at 76; Sprint Inputs Further Notice comments at 59.

1121

Due to the manner in which SBC develops current-to-book ratios for each year (average beginning and endof-year current investment divided by average beginning and end-of-year embedded investment) year-end 1998
current-to-book ratios are not available for SBC. Therefore, we applied year-end 1997 current-to-book ratios to
both SBC's year-end 1997 and year-end 1998 investment in developing 1998 expense-to-investment ratios.
1122

See Appendix D at D-4.

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historic expenses among companies is due to a variety of factors and does not simply reflect how
efficient or inefficient a firm is in providing the supported services. We reject arguments of the
LECs, however, that we should capture this variability by using company-specific data in the
model. We find that using company-specific data for federal universal service support purposes
would be administratively unmanageable and inappropriate. Moreover, we find that averages,
rather than company-specific data, are better predictors of the forward-looking costs that should
be supported by the federal high-cost mechanism. In addition, we find that using nationwide
averages will reward efficient companies and provide the proper incentives to inefficient
companies to become more efficient over time, and that this reward system will drive the
national average toward the cost that the competitive firm could achieve. Accordingly, we
affirm our tentative conclusion that we should adopt nationwide average input values for plantspecific operations expenses.
349. AT&T and MCI agree with our tentative conclusion that we should adopt input
values that reflect the average expenses incurred by non-rural carriers, rather than companyspecific expenses. They argue that the universal service support mechanism should be based on
the costs that an efficient carrier could achieve, not on what any individual carriers has
achieved.1123 In contrast, incumbent LEC commenters argue that we should use companyspecific values.1124
350. BellSouth, for example, contends that the approach suggested by AT&T and MCI
conflicts with the third criterion for a cost proxy model, which states that "[t]he study or model,
however, must be based upon an examination of the current cost of purchasing facilities and
equipment . . .."1125 BellSouth argues that the "only logical starting point for estimating forwardlooking expenses is the current actual expenses of the ILECs."1126 We agree that we should start
with current actual expenses, as we do, in estimating forward-looking maintenance expenses.
We do not agree with the inferences made by the incumbent LEC commenters, however, that our
input values should more closely match their current maintenance expenses.
351. BellSouth's reliance on criterion three fails to quote the first part of that criterion,
which states:

1123

AT&T/MCI Inputs Further Notice comments at 45.

1124

See, e.g., Bell Atlantic Inputs Further Notice comments at 20-21; BellSouth Inputs Further Notice
comments at B-16, B-18; GTE Inputs Further Notice comments at 75-76.
1125

See BellSouth Inputs Further Notice reply comments at 17 (citing Universal Service Order, 12 FCC Rcd at
8913, para. 250, criterion three).
1126

BellSouth Inputs Further Notice reply comments at 17-18.

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Only long-run forward-looking economic cost may be included.
The long-run period must be a period long enough that all costs
may be treated as variable and avoidable. The costs must not be
the embedded cost of facilities, functions, or elements.1127
Thus, the model's forward-looking expense estimates should not reflect the cost of maintaining
the incumbent LEC's embedded plant. The Universal Service Order's first criterion specifies
that "[t]he technology assumed in the cost study or model must be the least-cost, most efficient,
and reasonable technology for providing the supported services that is currently being
deployed."1128 As we explained in the Inputs Further Notice, while the synthesis model uses
existing incumbent LEC wire center locations in designing outside plant, it does not necessarily
reflect existing incumbent LEC loop plant.1129 Indeed, as the Commission stated in the Platform
Order, "[e]xisting incumbent LEC plant is not likely to reflect forward-looking technology or
design choices."1130 Thus, for example, the model may design outside plant with more fiber and
DLCs and less copper cable than has been deployed historically in an incumbent LEC's network.
We find that the forward-looking maintenance expenses also should reflect changes in
technology.
352.
GTE argues that expense-to-investment ratios should not be developed as
national averages, because no national average can reflect the composition of each company's
market demographics and plant.1131 GTE argues further that costs vary by geographic area and
that this variability reflects operating difficulties due to terrain, remoteness, cost of labor, and
other relevant factors.1132 GTE contends that "[u]sing national average operating expenses will
either understate or overstate the forward-looking costs of providing universal service for each
carrier, depending on the variability of each company to the average."1133 GTE claims that the
use of the national average penalizes efficient companies that operate in high-cost areas.1134
1127

Universal Service Order, 12 FCC Rcd at 8913, para. 250 (criterion three).

1128

Universal Service Order, 12 FCC Rcd at 8913, para. 250.

1129

Inputs Further Notice at para. 50.

1130

GTE Inputs Further Notice comments at 76.

1132

GTE Inputs Further Notice comments at 73.

1133

GTE Inputs Further Notice comments at 72.

1134

GTE Inputs Further Notice comments at 73.

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353. Similarly, Sprint contends that the use of nationwide estimated data does not
accurately depict the realities of operating in Sprint's service territories.1135 Sprint claims that
the national averages are far below Sprint's actual costs, because the Commission's methodology
for estimating plant-specific expense inputs is heavily weighted toward the Bell companies'
urban operating territories.1136 According to Sprint, the Bell companies have a much higher
access line density than Sprint, and the expense data from such companies with a higher density
of customers will result in expense levels that are much lower than the expense levels
experienced by smaller carriers.1137 AT&T and MCI respond by showing that a particular small
carrier, serving a lower density area than Sprint, has plant-specific expenses that, on a per-line
basis, are less than half of Sprint's expenses.1138 AT&T and MCI claim that "the most significant
driver of cost differences between carriers in the ARMIS study area data is efficiency."1139 Like
other LECs, SBC argues that the costs for LECs vary dramatically, based on various factors
including size, operating territories, vendor contracts, relationships with other utility providers
and the willingness to accept risk.1140 SBC asserts that "[t]hese differences are not in all
instances attributable to inefficient operations."1141
354. We agree with SBC that not all variations in costs among carriers are due to
inefficiency. Although we believe that some cost differences are attributable to efficiency, we
are not convinced by AT&T and MCI's example that Sprint is less efficient than the small carrier
they identify. Sprint could have higher maintenance costs because it provides higher quality
service. But we also are not convinced by Sprint's argument that maintenance expenses
necessarily are inversely proportional to density. Sprint provides no evidence linking higher
maintenance costs with lower density zones, and we can imagine situations where there are
maintenance costs in densely populated urban areas that are not faced by carriers in low density
areas. For example, busy streets may need to be closed and traffic re-routed, or work may need
to be performed at night and workers compensated with overtime pay.
355.

We cannot determine from the ARMIS data how much of the differences among

1135

Sprint Inputs Further Notice comments at 51.

1136

Sprint Inputs Further Notice comments at 51.

1137

Sprint Inputs Further Notice comments at 51-52.

1138

AT&T/MCI Inputs Further Notice reply comments at 38 n.58.

1139

AT&T/MCI Inputs Further Notice reply comments at 38 n.58.

1140

SBC Inputs Further Notice comments at 4.

1141

SBC Inputs Further Notice comments at 4.

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companies are attributable to inefficiency and how much can be explained by regional
differences or other factors. BellSouth's consultant concedes that there is nothing in the ARMIS
expense account data that would enable the Commission to identify significant regional
differences.1142 GTE concedes that it may be difficult to analyze some data because companies
have not been required to maintain a sufficient level of detail in their publicly available financial
records.1143 GTE's proposed solution for reflecting variations among states is simply to use
company-specific data.1144 Indeed, none of the LECs propose a specific alternative to using selfreported information from companies.1145 For example, SBC argues we should use companyspecific expenses provided pursuant to the Protective Order to develop company-specific costs,
because these are the costs that will be incurred by the providers of universal service.1146
356. While reliance on company-specific data may be appropriate in other contexts, we
find that, for federal universal service support purposes, it would be administratively
unmanageable and inappropriate. The incumbent LECs argue that virtually all model inputs
should be company-specific and reflect their individual costs, typically by state or by study
area.1147 As parties in this proceeding have noted, selecting inputs for use in the high-cost model
is a complex process.1148 Selecting different values for each input for each of the fifty states, the
District of Columbia, and Puerto Rico, or for each of the 94 non-rural study areas, would
increase the Commission's administrative burden significantly.1149 Unless we simply accept the
data the companies provide us at face value, we would have to engage in a lengthy process of
verifying the reasonableness of each company's data. For example, in a typical tariff
1142

BellSouth Inputs Further Notice comments, Attachment A at A-13. (comments of Georgetown Consulting
Group, Inc.).
1143

GTE Inputs Further Notice comments at 73.

1144

GTE Inputs Further Notice comments at 73.

1145

In its reply comments, Sprint argues that inputs should vary by company size and region, but does not
provide a specific methodology for doing so. See Sprint Inputs Further Notice reply comments at 3-4.
1146

SBC Inputs Further Notice comments at 14-15.

1147

See, e.g., Bell Atlantic Inputs Further Notice comments at 20-21; BellSouth Inputs Further Notice
comments, Attachment B at B-16, B-18; GTE Inputs Further Notice comments at 75-76.
1148

See, e.g., AT&T/MCI Inputs Further Notice reply comments at 3-7.

1149

There are 94 non-rural study areas. As noted above, the expense-to-investment ratios were calculated using
ARMIS data for 80 non-rural study areas. There are more non-rural study areas than there are non-rural study areas
for which we have ARMIS data because some non-rural companies do not file ARMIS data (Roseville, North State,
and Contel of Minnesota) and some ARMIS-filing companies file consolidated data for combined study areas
(Puerto Rico, some GTE companies). See supra note 756.

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investigation or state rate case, regulators examine company data for one-time high or low costs,
pro forma adjustments, and other exceptions and direct carriers to adjust their rates accordingly.
Scrutinizing company-specific data to identify such anomalies and to make the appropriate
adjustments to the company-proposed input values would be exceedingly time consuming and
complicated given the number of inputs to the model.1150 We recognize that such anomalies
invariably exist in the ARMIS data, but we find that, by using averages, high and low values will
cancel each other out.
357. Where possible, we have tried to account for variations in cost by objective
means. As we stated in the Inputs Further Notice, we believe that expenses vary by the type of
plant installed.1151 The model takes this variance into account because, as investment in a
particular type of plant varies, the associated expense cost also varies. The model reflects
differences in structure costs by using different values for the type of plant, the density zone, and
soil conditions.
358. As discussed above, we cannot determine from the ARMIS data how much of the
differences among companies are attributable to inefficiency and how much can be explained by
regional differences or other factors. To the extent that some cost differences are attributable to
inefficiency, using nationwide averages will reward efficient companies and provide the proper
incentives to inefficient companies to become more efficient over time. We find that it is
reasonable to use nationwide input values for maintenance expenses because they provide an
objective measure of forward-looking expenses. In addition, we find that using nationwide
averages in consistent with our forward-looking economic cost methodology, which is designed
to send the correct signals for entry, investment, and innovation.
359. Bell Atlantic contends that using nationwide averages for plant specific expenses,
rather than ARMIS data disaggregated to the study area level, defeats the purpose of a proxy
model because it averages high-cost states with low-cost states.1152 Bell Atlantic argues that we
should use the most specific data inputs that are available, whether region-wide, company
specific, or study-area specific.1153 Conceding that data are not always available at fine levels of
disaggregation, Bell Atlantic contends there is no reason to throw out data that more accurately

1150

As discussed below, when the Commission has had the opportunity to scrutinize carriers' company-specific
costs, as with the local number portability tariffs, we use company-specific input values in the model. See infra at
para. 408.
1151

Inputs Further Notice at para. 199.

1152

Bell Atlantic Inputs Further Notice comments at 20.

1153

Bell Atlantic Inputs Further Notice comments at 20.

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identify the costs in each area.1154 Bell Atlantic argues that, even if the Commission does not
have current-to-book ratios for all of the ARMIS study areas, it could use average current-tobook ratios and apply them to company-specific ARMIS data.1155
360. Contrary to Bell Atlantic's contention, we do not find that using nationwide
average input values in the federal high-cost mechanism is inconsistent with the purpose of using
a cost model. In addition to the administrative difficulties outlined above, we find that
nationwide values are generally more appropriate than company-specific input values for use in
the federal high-cost model. In using the high-cost model to estimate costs, we are trying to
establish a national benchmark for purposes of determining support amounts. The model
assumes, for example, that all customers will receive a certain quality of service whether or not
carriers actually are providing that quality of service.1156 Because differences in service quality
can cause different maintenance expense levels, by assuming a consistent nationwide quality of
service, we control for variations in company-specific maintenance expenses due to variations in
quality of service. Clearly, we are not attempting to identify any particular company's cost of
providing the supported services. We are, as AT&T and MCI suggest,1157 estimating the costs an
efficient provider would incur in providing the supported services. We are not attempting to
replicate past expenses, but to predict what support amounts will be sufficient in the future.
Because high-cost support is portable, a competitive eligible telecommunications carrier, rather
than the incumbent LEC, may be the recipient of the support. We find that using nationwide
averages is a better predictor of the forward-looking costs that should be supported by the federal
high-cost mechanism than any particular company's costs.1158
361. Estimating regional wage differences. We do not adjust our nationwide input
values for plant-specific operations expenses to reflect regional wage differences. Most LEC
commenters advocate the use of company-specific data to reflect variations in wage rates.1159
GTE, for example, claims that regional wage rate differentials are reflected in the company1154

Bell Atlantic Inputs Further Notice comments at 20.

1155

Bell Atlantic Inputs Further Notice comments at 20.

1156

In contrast, if we were determining the rates a carrier could charge for a particular service, the quality of
service the carrier actually was providing could be a relevant factor.
1157

See supra para. 349; AT&T/MCI Inputs Further Notice comments at 45.

1158

As noted above, the Commission has not considered what type of input values, company-specific or
nationwide, nor what specific input values, would be appropriate for any other purposes and caution parties from
making any claims in other proceedings based upon the input values we adopt in this Order. See supra para. 32.
1159

See, e.g., Bell Atlantic Inputs Further Notice comments at 20; GTE Inputs Further Notice comments at 7475; Sprint Inputs Further Notice comments at 54.

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specific data available from ARMIS.1160 GTE complains that our proposed input values suggest
there is no difference in labor and benefits costs between a company operating in Los Angeles
and one operating in Iowa.1161 As discussed above, the publicly available ARMIS expense
account data for plant-specific maintenance expenses do not provide enough detail to permit us
to verify significant regional differences among study areas or companies based solely on labor
rate variations.1162 For the reasons discussed above, we find that we should not use companyspecific ARMIS data to estimate these expenses, but instead use input values that reflect
nationwide averages.1163
362. Although they would prefer that we use company-specific data, some LEC
commenters suggest that the wage differential indexes used by the President's Pay Agent, on
which we sought comment, would be an appropriate method of disaggregating wage-related
ARMIS expense data.1164 GTE, on the other hand, contends that these indexes are not relevant
to the telecommunications industry, because they are designed for a specific labor sector, that is,
federal employees.1165 GTE claims that there are numerous publicly available sources of labor
statistics and that, if we adopt an index factor, it should be specific to the telecommunications
industry.1166
363. We agree with GTE that, if we were to use an index to adjust our input values for
regional wage differences, it would be preferable to use an index specific to the
telecommunications industry. We looked at other publicly available sources of labor statistics,
however, and were unable to find a data source that could be adapted easily for making
meaningful adjustments to the model input values for regional wage differences. Specifically,
we looked at U.S. Department of Labor, Bureau of Labor Statistics (BLS) information on wage
rate differentials for communications workers comparing different regions of the country.1167
1160

GTE Inputs Further Notice comments at 74-75.

1161

GTE Inputs Further Notice comments at 74-75.

1162

See supra para. 355.

1163

See supra para. 356.

1164

Bell Atlantic Inputs Further Notice comments at 21; Sprint Inputs Further Notice comments at 54.

1165

GTE Inputs Further Notice comments at 75.

1166

GTE Inputs Further Notice comments at 75.

1167

See Bureau of Labor Statistics, Employment Cost Trends, Employment Cost Index, June 1999, at
http://www.bls.gov/news.releases/eci.toc.htm. In particular, we looked at the following tables: Table 4,
Compensation (not seasonally adjusted), Employment Cost Index for total compensation, private industry workers,
by bargaining status, region and area; Table 5, Wages and Salaries (not seasonally adjusted), Employment Cost

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The Employment Cost Indexes calculated by BLS identify changes in compensation costs for
communications workers as compared to other industry and occupational groups. In a number of
the indexes, communications is not broken out separately, but is included with other serviceproducing industries: transportation, communication, and public utilities; wholesale and retail
trade; insurance, and real estate; and service industries. In making regional comparisons, the
Employment Cost Indexes divide the nation into four regions: northeast, south, midwest, and
west. There also are separate indexes comparing metropolitan areas to other areas.
364. We find that the regions used in the BLS data are too large to make any
significant improvement over our use of nationwide average numbers. For example, Wyoming
is in the same region as California, but we have no reason to believe that wages in those two
states are more comparable than wages rates in California and Iowa. That is, there is no simple
way to use the BLS data to make the type of regional wage adjustments suggested by GTE. We
note that no party has suggested a specific data source or methodology that would be useful in
making such adjustments. Accordingly, we decline to adopt a method for adjusting our
nationwide input values for plant-specific operations expenses to reflect regional wage
differences.
365. Methodology. As discussed in this section, we adopt our proposed methodology
for calculating expense-to-investment ratios to estimate plant-specific operations expenses. We
reject arguments of some LEC commenters that this methodology inappropriately reduces these
expense estimates.
366. Several LEC commenters generally support our methodology for calculating
expense-to-investment ratios to estimate plant-specific operations expenses, although, as
discussed above, only if we use company-specific input values. For example, GTE agrees with
our tentative conclusion that input values for each plant-specific operations expense account can
be calculated as the ratio of booked expense to current investment, but only if this calculation is
performed on a company-specific basis.1168 BellSouth states that "[t]he methodology proposed
by the Commission for plant-specific expenses is very similar to the methodology employed by
BellSouth."1169
367. Other LEC commenters object to our use of current-to-book ratios to convert
historic account values to current cost. Although their arguments differ somewhat, they
Index for wages and salaries only, civilian, and state and local government workers, by industry and occupational
group; and Table 7, Wages and Salaries (not seasonally adjusted) Employment Cost Index for wages and salaries
only, private industry workers, by bargaining status, regional and area.
1168

GTE Inputs Further Notice comments at 72, 75-76.

1169

BellSouth Inputs Further Notice comments, Attachment B at B-16.

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essentially claim that the effect of our methodology is to reduce forward-looking maintenance
expenses and that this is inappropriate because the input values are lower than their current
maintenance expenses.1170 AT&T and MCI counter that, if there is any problem with our
maintenance expense ratios, it is that they reflect the servicing of too much embedded plant,
which has higher maintenance costs, and too little forward-looking plant, which has lower
maintenance costs.1171
368. US West asserts that, while in theory it is correct to adjust expense-to-investment
ratios using current-to-book ratios, in practice there is a problem because the current-to-book
ratio is based on reproduction costs and the model estimates replacement costs.1172 US West
defines reproduction cost as the cost of reproducing the existing plant using today’s prices and
replacement cost as the cost of replacing the existing plant with equipment that harnesses new
technologies and is priced at today’s prices.1173 US West claims that our methodology actually
increases the mismatch between historic and forward-looking investment levels because the
reproduction costs are not the same as the replacement costs.1174 We agree that reproduction
costs are not the same as replacement costs because the mix of equipment and technology will
differ, but we disagree with US West's characterization of this as a mismatch.
369. US West estimates that applying current-to-to book ratios to existing investment
would generate reproduction costs that are 141 percent higher than historic costs.1175 US West
claims that, in contrast, forward-looking models generally show that the cost of replacing those
facilities would be slightly less than historic costs, if new technologies were deployed. US
West's claim that our methodology results in a mismatch because of these cost differences,
however, is wrong. Rather, the differences between reproduction costs and replacement costs
merely show that the mix of technologies has changed. The hypothetical example US West uses
to illustrate its argument fails to account for changes in technology. The following hypothetical
1170

See SBC Inputs Further Notice comments at 14-18; Sprint Inputs Further Notice comments at 55-59; US
West Inputs Further Notice comments at 21-26.
1171

AT&T/MCI Inputs Further Notice reply comments at 38.

1172

US West Inputs Further Notice comments at 23-24.

1173

US West Inputs Further Notice comments at 23-24.

1174

US West Inputs Further Notice comments at 23-24.

1175

US West Inputs Further Notice comments at 24-25. US West indicates that it used the Telephone Plant
Index (TPI) to derive the 141 percent figure. US West implies, therefore, that the TPI is a reproduction cost index.
This raises questions with respect to how a reproduction index deals with old technology that cannot be purchased
today at any price. Without detailed knowledge about the TPI, we cannot say whether it reflects only reproduction
costs or may also reflect replacement costs when new technology has replaced old technology.

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example illustrates how changes in the mix of technology will change maintenance expenses.1176
If historic investment on a company's books consists of 100 miles of copper plant, at a cost of
$10 per mile, and 10 miles of fiber plant, at a cost of $1 per mile, then the historic cost is $1010.
If current maintenance costs are $10 for the copper plant and $0.10 for the fiber plant, the total
maintenance expense is $10.10. If the price of copper increases to $15 per mile and the price of
fiber decreases to 80 cents per mile, then the reproduction costs would increase to $1508. If the
forward-looking model designs a network with 60 miles of copper and 50 miles of fiber, the
resulting replacement cost is $940.1177 Using our methodology, we use the current-to-book
ratios of 1.5 ($15/$10) and .8 (80 cents divided by $1) to revalue the copper and fiber
investment, respectively, at current prices, and the resulting maintenance expense for the
forward-looking plant would be $6.58 rather than $10.10.1178 This does not result in a mismatch.
In our hypothetical example, the maintenance costs for fiber were substantially less on a permile basis than they were for copper. Thus, we would expect the forward-looking plant with
considerably more fiber and less copper to have lower maintenance costs than the current plant,
which has more copper. Because the mix of plant changes, the Commission should not, as US
West suggests, simply adjust book investment to current dollars to derive maintenance expenses
for the forward-looking plant estimated by the model.
370. Sprint argues that we should simply divide the current year's actual expense for
each account by the average plant balance associated with that expense.1179 Sprint claims that,
when this ratio is applied to the investment calculated by the model, forward-looking expense
reductions occur in two ways: (1) the investment base is lower due to the assumed economies of
scale in reconstructing the forward-looking network all at one time; and (2) greater use of fiber
in the forward-looking network reduces maintenance costs because less maintenance is required
of fiber than of the copper in embedded networks.1180 Sprint claims that reducing maintenance
for a current-to-book ratio as well as for technological factors constitutes a "double-dip" in

1176

The values used in this example are hypothetical and do not represent actual input values.

1177

Our hypothetical example reflects US West’s contention that reproduction costs are significantly higher than
replacement costs and that replacement costs are only slightly lower than historic costs.
1178

To revalue the copper investment, we multiply $1000 by 1.5 (=$1500); then to calculate the expense-toinvestment ratio, we divide current maintenance expenses for copper by the adjusted copper investment ($10/$1500
= .0067). Similarly, to revalue the fiber investment, we multiply $10 by .8(=$8); then to calculate the expense-toinvestment ratio, we divide current maintenance expenses for fiber by the adjusted fiber investment ($.10/8=.0125).
Finally, we apply these adjusted expense-to-investment ratios to the forward-looking plant to derive the forwardlooking maintenance expenses: $900 x .0067 ($6.03) + $40 x .0125(.50) = $6.58.
1179

Sprint Inputs Further Notice comments at 55.

1180

Sprint Inputs Further Notice comments at 55.

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maintenance expense reduction.1181
371. Sprint's claim that our methodology constitutes a "double dip" in reducing
maintenance expenses is misleading because the effect of using current-to-book ratios depends
upon whether current costs have risen or fallen relative to historic costs. Current-to-book ratios
are used to restate a company's historic investment account balances, which reflect investment
decisions made over many years, in present day replacement costs. Thus, if current costs are
higher than historic costs for a particular investment account, the current-to-book ratio will be
greater than one, and the expense-to-investment ratio for that account will decrease when the
investment (the denominator in the ratio) is adjusted to current replacement costs.1182 Sprint
calls this double dipping because copper costs have risen and the model uses less copper plant
than that which is reflected on Sprint's books. If current costs are lower than historic cost,
however, the current-to-book ratio will be less than one and the adjusted expense-to-investment
ratio for that account will increase when the investment (the denominator in the ratio) is adjusted
to current replacement costs. Fiber cable and digital switching costs, for example, have fallen
relative to historic costs. Sprint essentially is arguing that our methodology is wrong because it
understates Sprint's historical costs. The input values we select are not intended to replicate a
particular company's historic costs, for the reasons discussed above.1183
372. SBC disputes our assumption that the model takes into account variations in the
type of plant installed because, as investment in a particular type of plant varies, so do the
associated expense costs.1184 SBC argues that expenses do not vary simply because investment
varies.1185 Nonetheless, SBC believes that developing a ratio of expense to investment and
applying it to forward-looking investments is a reasonable basis for identifying forward-looking
plant specific expenses.1186 SBC complains that our methodology is inconsistent, however,
because it has defined two completely different sets of forward-looking investments: one based
on historical ARMIS investments adjusted to current amounts; and another derived on a bottomup basis employing the cost model.1187 Until we reconcile these "inconsistencies," SBC
1181

Sprint Inputs Further Notice comments at 55.

1182

For example, if a pole cost $200 to install in 1980, and $400 today, the current-to-book ratio is $400/$200
= 2.0. If the maintenance expense associated with the pole is $20, the expense-to-investment ratio on the books is
$20/$200 = .10; and the expense-to-investment ratio adjusted by the current-to-book ratio is $20/$400 = .05.
1183

See supra para. 351.

1184

SBC Inputs Further Notice comments at 15.

1185

SBC Inputs Further Notice comments at 15.

1186

SBC Inputs Further Notice comments at 15.

1187

SBC Inputs Further Notice comments at 16.

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recommends that we use unadjusted historical investment amounts in developing plant specific
expense factors, because they are closer to SBC's historical plant specific expenses.1188
373. Although they characterize the issue somewhat differently, US West, Sprint, and
SBC essentially argue that our methodology is wrong because it understates their historical
costs. AT&T and MCI counter that a forward-looking network often will result in lower costs
than an embedded network and that the trend in the industry has been to develop equipment and
practices to minimize maintenance expense.1189 AT&T and MCI claim that, if there is any
problem with our maintenance expense ratios, it is that they reflect the servicing of too much
embedded plant, which has higher maintenance costs, and too little forward-looking plant, which
has lower maintenance costs.1190 AT&T and MCI further claim that, if our analysis had been
based exclusively on financial information that reflected equipment consistent with the mostefficient forward-looking practices, the maintenance expenses would have been lower.1191
374. None of the commenters provide a compelling reason why we should not use
current-to-book ratios to adjust historic investment to current costs. SBC in fact suggests that
the Commission consider using the Telephone Plant Index (TPI) in future years to convert
expense estimates to current values.1192 SBC appears to be confusing the effect of measuring
inputs in current dollars, which it recognizes is reasonable, and the end result of the calculation,
which includes the impact of measuring all inputs in current dollars, changes in the mix of
inputs, the impact of least-cost optimal design used by the model, and the model's engineering
criteria. The relationship between maintenance costs and investment in the Commission's
methodology is related to all of these factors.
375. Sprint also claims that our methodology understates maintenance costs, because it
assumes new plant and the average maintenance rate will be higher than the rate in an asset's first
year.1193 AT&T and MCI dispute Sprint's claim that maintenance costs per unit of plant increase
over time.1194 Sprint provides an example which purports to show that an asset with a ten year
1188

SBC Inputs Further Notice comments at 16-17, Attachment A (comparing Southwestern Bell/Texas costs of
5.96 percent of related investments to the Commission's proposed 3.08 percent of related investment).
1189

AT&T/MCI Inputs Further Notice reply comments at 38.

1190

AT&T/MCI Inputs Further Notice reply comments at 38.

1191

AT&T/MCI Inputs Further Notice reply comments at 38.

1192

SBC Inputs Further Notice comments at 15.

1193

Sprint Inputs Further Notice comments at 55.

1194

AT&T/MCI Inputs Further Notice reply comments at 38.

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life, a ten percent maintenance fee in the first year, and annual costs increasing annually at three
percent, would result in an average maintenance rate of 11.55 percent.1195 Sprint's example,
however, does not consistently apply our methodology. Sprint's example fails to apply the
current-to-book ratio to the total and average plant in service estimates used in the example.
When the current-to-book ratio is applied to the total and average plant in service estimates, the
resulting maintenance rate is ten percent for all years.
376. BellSouth argues that the investment calculated by the model is unrealistically
low because sharing assigned to the telephone company is unrealistically low and fill factors are
unrealistically high.1196 BellSouth argues that, because it has shared in cost of trenching, this
does not mean the maintenance cost for buried cable would be less, and in fact, the costs may be
higher.1197 BellSouth apparently is confused about the Commission's methodology, because the
sharing percentages apply only to the costs of structure, not the costs of the cable.
C.

Common Support Services Expenses
1.

Background

377. Common support services expenses include corporate operations expenses,
customer service expenses, and plant non-specific expenses. Corporate operations expenses are
those costs associated with general administrative, executive planning, human resources, legal,
and accounting expenses for total company operations. Customer services expenses include
marketing, billing, operator services, directory listing, and directory assistance costs.1198 Plant
1195

Sprint Inputs Further Notice comments at 55-57, Attachment 10a.

1196

BellSouth Inputs Further Notice comments, Attachment B at B-19.

1197

BellSouth Inputs Further Notice comments, Attachment B at B-16.

1198

Corporate operations and customer service expenses include the following ARMIS accounts and their
subaccounts:
6610 - Marketing Total
6611 - Product Management
6612 - Sales
6613 - Product Advertising
6620 - Service Expense Total
6621 - Call Completion (Operator Service Expense)
6622 - Number Services (Directory Publishing Expense)
6623 - Customer Services
6710 - Executive and Planning Total
6711 - Executive
6712 - Planning
6720 - General and Administrative

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non-specific expenses are common network operations and maintenance types of expenses,
including engineering, network operations, power, and testing expenses, that are considered
general or administrative overhead to plant operations.1199
378. In the Inputs Further Notice, we proposed a methodology using regression
analysis to estimate common support services expenses on a per-line basis. We noted that,
unlike plant-specific expenses, common support services expenses are costs that cannot readily
be associated with any particular maintenance expense or investment account.1200 In the
regression methodology, we used publicly available 1996 ARMIS expense data1201 and minutes
of use information from NECA,1202 by study area, to estimate the portion of these company-wide
expenses that should be supported by the federal high-cost mechanism.1203 Specifically, we used
the average of the estimates from two specifications that estimated total expenses per line as a
function of the percentage of switched lines, the percentage of special lines, and toll minutes per
line, either in combination (Specification 1) or separated between intrastate and interstate toll
minutes (Specification 2).1204 The specifications were designed to separate the portion of
6721 - Accounting and Finance
6722 - External Relations
6723 - Human Resources
6724 - Information Management
6725 - Legal
6726 - Procurement
6727 - Research and Development
6728 - Other General and Administrative
1199

Plant non-specific expenses include the following ARMIS expense accounts:
6510 - Other Property Plant and Equipment Expense
6530 - Network Operations

1200

Inputs Further Notice at para. 213.

1201

Data was taken from 1996 ARMIS 43-01, Subject to Separations (Column F) for Accounts 6610, 6620,
6710 and 6720. Data was taken from 1996 ARMIS 43-03, Subject to Separations (Column M) for Accounts 6510
and 6530. Line counts were taken from 1996 ARMIS 43-08, Table III, Total Switched Lines (Column DJ) and
Total Access Lines (Column DM).
1202

Dial Equipment Minutes of Use (DEMs) for 1996 were taken from NECA and are available on the
Commission's Web site at http://www.fcc.gov/Bureaus/Common_Carrier/Reports/FCC-State_Link/neca.html.
1203

Inputs Further Notice at para. 217.

1204

See Inputs Further Notice at para. 218-19. Specification 1 used the following regression equation:
Expense/Total Lines = β1 (Switched Lines/Total Lines)+ β2 (Special Lines/Total Lines)+ β3 (Toll Minutes/Total
Lines). Specification 2 used the following equation: Expense/Total Lines = β1 (Switched Lines/Total Lines)+ β2
(Special Lines/Total Lines)+ β3 (State Toll Minutes/Total Lines)+ β4 (Interstate Toll Minutes/Total Lines).

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expenses attributable to special access lines and toll usage, which are not supported by the
federal high-cost mechanism, from the portion of expenses attributable to switched lines and
local usage, which are supported.
379. As with plant-specific operations expenses, we tentatively concluded that input
values for corporate operations, customer service, and plant non-specific expenses should be
estimated on a nationwide basis, rather than a more disaggregated basis.1205 In reaching this
tentative conclusion, we recognized that parties have argued that these types of expenses may
vary as a result of company-specific plant configurations, geographic and labor demographic
variables, one-time exogenous costs, and non-recurring adjustments such as re-engineering
expenses.1206 We observed that we had not been able to distinguish significant differences in
regional wage differentials for administrative services based solely on ARMIS expense data for
these accounts.1207 Moreover, costs associated with corporate overhead and customer service
accounts are not directly linked to a specific company's investment levels. We tentatively
concluded that these types of administrative and service expenses are less dependent on carrier
physical plant or geographic differentials than on factors that also correlate to company size
(number of lines) and demand (minutes of use).1208
380. After estimating common support services expenses using the regression
methodology, we made certain adjustments to remove additional portions of those expenses
attributable to services that are not supported by the federal universal service support
mechanism. The expenses we removed were associated with services that could be identified
and estimated from ARMIS expense data.1209 We tentatively concluded that 95.6 percent of
marketing expenses should be attributed to non-supported services, based on an Economics and
Technology, Inc. (ETI) analysis.1210 In addition, we adjusted the estimates for non-supported
service costs related to coin operations and collection, published directory, access billing,
interexchange carrier office operation, and service order processing.1211 We noted that nonrecurring expenses for corporate operations can be significant and that our estimates should be
1205

See Inputs Further Notice at para. 214.

1206

Inputs Further Notice at para. 215.

1207

Inputs Further Notice at para. 215.

1208

Inputs Further Notice at para. 215.

1209

Inputs Further Notice at para. 223.

1210

Inputs Further Notice at para. 224.

1211

Inputs Further Notice at para. 225.

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adjusted to account for these one-time charges.1212 We explained, however, that we had been
unable to find an objective public data source or discern a systematic method for excluding these
costs from the ARMIS expense data used in the regression methodology.1213 We sought
comment on how to identify, estimate, and remove these one-time non-recurring expenses.1214
381. We also adjusted our estimates for common support services expenses by
converting the values, which were based on 1996 ARMIS data, to 1999 values.1215 Specifically,
we reduced the estimated expenses by a 6.0 percent productivity factor for each year (1997 and
1998) and added an inflation factor based on the fixed weighted Gross Domestic Product Price
Index (GDP-PI) for 1997 (2.1120 percent) and for 1998 (2.1429 percent).1216 That is, we
proposed a net reduction of 3.888 percent for 1997 and 3.8571 percent for 1998, and sought
comment on this method for converting expenses to 1999 values.1217
2.

Discussion

382. Consistent with our tentative conclusions, we adopt input values that estimate the
average common support services expenses that will be incurred by non-rural carriers on a perline basis, rather than a set of company-specific common support services expenses.1218 We
affirm our tentative conclusion that input values for corporate operations, customer service, and
1212

Inputs Further Notice at para. 220-222.

1213

Inputs Further Notice at para. 221.

1214

Inputs Further Notice at para. 222.

1215

Inputs Further Notice at para. 226.

1216

Inputs Further Notice at para. 226.

1217

Inputs Further Notice at para. 226.

1218

Aggregate ARMIS Accounts

Expense Input Values

6510 Other Property, Plant, and Equipment
6530 Network Operations
6610 Marketing
6620 Service Expense/Customer Operations
6700 Executive, Planning, General, and Administrative

$ (0.05)
1.48

Total Common Support Services Expenses Per Line, Per Month

0.09
3.62
2.18
$ 7.32

Rather than using the $7.32 directly as an input value, the model uses this amount, annualized and adjusted for
uncollectibles, or $92.46316, which appears in cell C33 of the per line tab of the wire center expense module.

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plant non-specific expenses should be estimated on a nationwide basis, rather than a more
disaggregated basis. As noted above, we find that for universal service purposes nationwide
averages are more appropriate than company-specific values.1219 We conclude that we should
use Specification 1 of our proposed regression methodology to estimate expenses for ARMIS
accounts 6510 (Other Property, Plant, and Equipment); 6530 (Network Operations); 6620
(Service Expense/Customer Operations); and 6700 (Executive, Planning, General, and
Administrative).1220 As discussed below, we use an alternative methodology to estimate
expenses for ARMIS account 6610 (Marketing).1221 We conclude that we should use 1998
ARMIS data in both methodologies, and an estimate of 1998 Dial Equipment Minutes of Use
(DEMs) in the regression equation, to calculate these input values. We clarify that the ARMIS
data we use to calculate these estimates are based on ARMIS accounts for all non-rural ARMISfiling companies. We find that it is appropriate to include only data from the non-rural ARMISfiling companies in calculating the expense per line for common support services expenses.1222
383. Current Data and Use of Productivity Factor. The input values we adopt in this
Order are explained more fully in Appendix D, which contains a summary of the per-line, permonth input values for plant non-specific expenses, corporate operations expenses, and customer
services expenses, including regression results, calculations, and certain adjustments made to the
data based on the methodologies described below.1223 Because we used 1996 ARMIS data in our
regression methodology to estimate our proposed input values for common support services
expenses, we proposed a method of converting those estimates to 1999 values.1224 Specifically,
we proposed using a productivity factor of 6.0 percent for the years 1997 and 1998 to reduce the
estimated input values.1225 We further proposed adjusting the expense data for those years with
an inflation factor based on the Gross Domestic Product Price Index (GDP-PI) in order to bring

1219

See supra para. 348.

1220

Specifically, we adopt estimates using results solely from the Specification 1 regression equation:
Expense/Total Lines = β1 (Switched Lines/Total Lines) + β2 (Special Lines/Total Lines) + β3 (Toll Minutes/Total
Lines) rather than an average of results from two model specifications, as proposed. See Inputs Further Notice at
para. 218.
1221

See infra paras. 403-407.

1222

As noted above, although some rural companies file ARMIS reports, the mechanism we adopt today will be
used, beginning January 1, 2000, to determine high-cost support for non-rural carriers. See supra para. 346.
1223

See Appendix D at D-5.

1224

Inputs Further Notice at para. 226

1225

Inputs Further Notice at para. 226

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the input values up to current expenditure levels.1226
384. AT&T and MCI claim that the 6.0 productivity factor is too low,1227 while most
LEC commenters contend that it is too high.1228 Sprint argues that expenses should not be
adjusted for a productivity or an inflation factor and that we should use 1998 data.1229 GTE
argues that no productivity adjustments are necessary, if we use current, company-specific
ARMIS data to develop input values.1230 Although we generally decline to adopt companyspecific input values for common support services expenses, we agree that using the most
currently available ARMIS data (1998) obviates the need to adjust our estimates for either
productivity gains or an inflation factor at this time. We believe, however, that there should be
an incentive for increased productive efficiency among carriers receiving high-cost universal
service support. Accordingly, we believe that a reasonable productivity measure or some other
type of efficiency incentive to decrease costs associated with common support services expenses
should be incorporated into the universal service high-cost support mechanism in the future. We
intend to address this issue in the proceeding on the future of the model.
385.
The input values we adopt in this Order are estimates of the portion of companywide expenses that should be supported by the federal high-cost mechanism.1231 We derive the
estimates using standard economic analysis and forecasting methods. The analysis relies on
publicly available 1998 ARMIS expense data and the most current minutes of use information
from NECA. This data is organized by study area. The estimate of 1998 DEMs is based on a
calculated growth rate of 1997 to 1996 DEMs reported by NECA.1232 As a result of deleting
rural ARMIS-filing companies and including company study area changes since 1996, pooling of
1226

Inputs Further Notice at para. 226

1227

See AT&T/MCI Inputs Further Notice comments at 46-47.

1228

See e.g., Aliant Inputs Further Notice comments at 2-3; Bell Atlantic Inputs Further Notice comments at
22; BellSouth Inputs Further Notice comments at B-21-B-23; USTA Inputs Further Notice comments at 2.
1229

Sprint Inputs Further Notice comments at 60, 68.

1230

GTE Inputs Further Notice comments at 88.

1231

Data were taken from 1998 ARMIS 43-03, Total Regulated (Column I) for Accounts 6610, 6620, 6710,
6720, 6510, and 6530. Line counts were taken from 1998 ARMIS 43-08, Table III, Total Switched Lines (Column
DJ) and Total Access Lines (Column DM).
1232

Dial Equipment Minutes of Use (DEMS) for 1996 and 1997 were taken from NECA, available on the
Commission's web site at http://www.fcc.gov/Bureaus/Common_Carrier/Reports/FCC-State_Link/neca.html.
Estimated 1998 DEMs were calculated by multiplying the number of 1997 DEMs for each study area by the ratio of
1997 DEMs to 1996 DEMs for that study area. Actual 1998 DEMs classified by local, interstate and intrastate toll
minutes needed for use as variables in the regression analysis are not currently available from NECA.

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the 1998 data sets provides expense, minutes of use, and line count data for 80 study areas.1233
This is in comparison to the 91 study areas resulting from pooling the 1996 data described in the
Inputs Further Notice.1234
386. Some parties object to our using data at the study area level, because they claim
that ARMIS-filing companies report data in two distinct ways. Ameritech and US West argue
that parent companies generally assign a significant portion of plant non-specific and customer
operations expenses across their operating companies on the basis of an allocation
mechanism.1235 As a result, they claim that a simple regression on the study area observations
will produce coefficients that reflect a blend of two relationships: the cost-based relationship
and the allocation-based relationship, of which only the former is appropriate to measure.1236
They argue further that it is necessary to model the allocation method explicitly, to net out the
latter data, or to aggregate the data to the parent company level. Although we acknowledge that
our accounting rules provide carriers with some flexibility, we expect that the allocation
mechanism used by the parent company represents underlying cost differences among its study
areas.1237 We find that it is reasonable to assume that the companies use allocation mechanisms
that are based on cost relationships to allocate costs among their study areas. Accordingly, we
find that it is reasonable to use ARMIS data at the study area level in the regression
methodology.
387. Regression Methodology. As described in the Inputs Further Notice, we adopt
standard multi-variate regression analysis to determine the portion of corporate operations
expenses, customer services expenses, and plant non-specific expenses attributable to the
services that should be supported by the federal high-cost mechanism.1238 We adopt an equation
(Specification 1) which estimates total expenses per line as a function of the percentage of
switched lines, the percentage of special lines, and toll minutes per line.1239 We use this
1233

See Appendix D at D-1.

1234

Inputs Further Notice at para. 217.

1235

See Ameritech Inputs Further Notice comments at 28; US West Inputs Further Notice comments,
Attachment A at 27.
1236

See Ameritech Inputs Further Notice comments at 28; US West Inputs Further Notice comments at
Attachment A, 27.
1237

To the extent a particular company believes that its ARMIS filings do not represent cost differences among
its study areas, we would be interested in receiving more detailed information.
1238

Standard multi-variate regression analysis uses ordinary least squares with more than one variable.

1239

Expense/Total Lines = β1 (Switched Lines/Total Lines) + β2 (Special Lines/Total Lines) + β3 (Toll
Minutes/Total Lines).

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regression methodology to estimate the expenses attributable to universal service for the
following accounts:
Other Property, Plant, and Equipment (6510);
Network Operations (6530);
Service Expense/Customer Operations (6620); and
Executive, Planning, General and Administrative (6700).
We adopt this specification, rather than an average of the two specification estimates suggested
in the Inputs Further Notice, to separate the portion of expenses that could be estimated as
attributable to special access lines and toll usage, which are not supported by the federal highcost mechanism, from switched lines and local usage.1240 As explained below, we use an
adjusted weighted average of study areas to estimate the support expense attributable to Account
6610, Marketing.
388. Several parties contend that our regression analysis is flawed.1241 Sprint, for
example, claims that we have exaggerated the significance of our statistical findings beyond a
level justified by the regression result; and have made the often-committed error of interpreting
our regression results in a way that implies causality.1242 US West argues that, although there is
a causal relationship between the level of expenses and the variables we use in the regression,
the coefficient of determination or R2 is fairly low, which implies that the causal relationship
only explains a small portion of the total costs.1243 GTE claims that our regression is misspecified because it utilizes only the mix of output as explanatory variables, and excludes
important variables related to differences in input prices and production functions.1244 Because
of this mis-specification and the omitted variables, GTE also claims that our equations have a
low predictive ability, as measured by the R2s.1245
389.

We disagree with commenters who claim that there is little explanatory value in

1240

See US West Inputs Further Notice comments, Attachment A at 22 (claiming it is inappropriate to average
the two specifications).
1241

See, e.g., Ameritech Inputs Further Notice comments at 25-28; GTE Inputs Further Notice comments at 7982; Sprint Inputs Further Notice comments at 61-65; US West Inputs Further Notice comments at 53-57,
Attachment A at 20-27.
1242

Sprint Inputs Further Notice comments at 61.

1243

US West Inputs Further Notice comments at 55.

1244

GTE Inputs Further Notice comments at 81.

1245

GTE Inputs Further Notice comments at 81.

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our regression analysis.1246 In accounts 6620, 6700, 6530 the regressions explain a high degree
of the variability in the expense variables.1247 Only account 6510 (Other Property, Plant, and
Equipment) has a low R2, which is not surprising given the reported data in this account. Based
on the 1998 ARMIS data, the resulting regression coefficient for this expense category is
negative due to the numerous negative expenses reported by carriers in 1998. Because the
ARMIS reports represent actual 1998 expenses incurred by the non-rural telecommunications
companies within their various study areas, we find that it is appropriate to include this negative
expense in our calculations. We note, however, that inclusion of this account in our calculations
represents less than one percent of the total expense input for common support services
expenses.1248
390. We believe that our regressions represent a cost-causative relationship, and that
common support services expenses are a function of the number of total lines served, plus the
volume of minutes. Because in the long run, all costs are variable, we disagree with commenters
who suggest that our methodology is flawed because we do not include an intercept term in our
regression equation to represent fixed or start-up costs.1249 As discussed above, the model is
intended to estimate long-run forward-looking cost over a time period long enough so that all
costs may be treated as variable and avoidable.1250 Moreover, the federal high-cost mechanism
calculates support on a per-line basis, which is distributed to eligible carriers based upon the
number of lines they serve. We would not provide support to carriers with no lines. Nor would
we vary support, which is portable, between an incumbent and a competitive eligible
telecommunications carrier, based on differences in their fixed or start-up costs. We explicitly
assume, therefore, that if a company has zero lines and zero minutes, it should have zero
expenses. Thus, we have no constant or fixed cost in our regressions. We also believe that these
expenses are driven by the number of channels, not the number of physical lines.

1246

According to our calculations using the 1998 data, the R2s for the four regressions are:
Account:
R2:

6620
0.96

6700
0.92

6510
0.20

6530
0.95

We note that the commenters' analysis was based on the 1996 ARMIS data.
1247

As we discuss below, we no longer use the regression for the 6610 account.

1248

We calculate an expense input value of -$0.05 for Account 6510 (Other Property, Plant, and Equipment) and
a total expense input value of $7.32 for total common support services expenses, per line, per month.
1249

See, e.g., Sprint Inputs Further Notice comments at 62-64 & n.15.

1250

See supra para. 351.

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391. That is, our assumptions imply that expenses are a linear function of lines and
minutes.1251 We next need to separate out the common support services expenses related to
special access lines and toll minutes, because these services are not supported by the federal
high-cost mechanism. Therefore, we split the lines variable into switched and special access
lines, and we split the minutes variable into local and toll minutes. In this modified equation,
expenses are a function of switched lines, plus special access lines, plus local minutes, plus toll
minutes.1252 We believe that changes in local minutes, however, should not cause changes in
common support services expenses that are not already reflected in the expenses associated with
switched lines. We find that it is reasonable to assume that local calls do not increase these
overheard costs in the same way that toll minutes do. For example, in most jurisdictions local
calls are a flat-rated service and additional local calling requires no additional information on the
customer's bill. With toll calling, however, even subscribers that have some kind of a calling
plan receive detailed information about those calls. It is reasonable to assume that adding an
additional line on a subscriber's bill for a toll call causes overhead costs that are not caused by
local calls. Moreover, toll calling outside a carrier's serving area involves the costs associated
with completing that call on another carrier's network. As discussed below, we tested our
assumption that local calls do not affect costs in the same way that toll calls do by running the
regressions to include local minutes. Based on theory and our analysis, we decided to drop the
local minutes variable, so that expenses are a function of switched lines, plus special access
lines, plus toll minutes.1253 Because we are calculating a per-line expense estimate, we divide all
the variables by the total number of lines to derive our final equation: expenses divided by total
lines equals the percentage of switched lines, plus the percentage of special lines, plus toll
minutes divided by total lines.1254
392.
US West claims that our regressions may not be based on appropriate costcausative relationships, because we count special access lines by channels and not by physical
pairs.1255 The ARMIS data used in the regressions count special lines as channels. That is,
1251

Expenses = β1 Lines + β2 DEMS + ε.

1252

Expenses = β1 Switched Lines + β2 Special Lines + β3 Local DEMS + β4 Toll DEMs + ε.

1253

Expenses = β1 Switched Lines + β2 Special Lines + β3 Toll DEMs + ε.

1254

Expenses/Total Lines = β1 (Switched Lines/Total Lines) + β2 (Special Lines/Total Lines) + β3 (Toll
DEMs/Total Lines) + ε'.
1255

US West Inputs Further Notice comments at Attachment A, 21. US West also claims that our regression
analysis estimates a common support per minute of access of $0.02, which does not include any of the capital or
maintenance costs associated with the switching investment used to provide access. Because the traffic sensitive
common costs associated with access services alone exceeds the current access charge rate of approximately $.01 to
$.02 per minute, US West claims that are analysis shows that access charges are priced below costs. US West
Inputs Further Notice comments at 56-57. The coefficient for toll is an estimate of the increase in expenses due to
an increase in 1000 toll minutes. Summing across all accounts and dividing by 1000, according to our calculations

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special access lines are counted as DS0 equivalents: a DS1 has 24 channels, and a DS3 has 672
channels. US West contends that it is far from clear how this method of counting special access
lines reflects how these services cause expenses, because it is clear that DS1s and DS3s are not
priced as if they cause 24 and 672 times the amount of expenses as a narrowband line.1256
393. The fact that DS1s and DS3s are priced differently in the current marketplace
does not imply that it is improper to count lines as channels. US West's suggested alternative,
counting special lines as physical pairs, would assume that a residential customer with two lines
causes the same amount of overhead expenses as a special access customer with one DS1 line.
To the contrary, we find that it is reasonable to assume that more overhead expenses are devoted
to winning and keeping the DS1 customer than the residential customer. Further, we expect that
more overhead expenses are related to customers using higher capacity services than those using
lower capacity services. Accordingly, we find that it is reasonable to use channel counts in our
regression equations.1257
394. Some commenters also criticized our regression analysis on the grounds that
variables are highly correlated and that the predicted coefficients are not stable.1258 In particular,
US West claims that the confidence intervals and standard errors are large and that a dividingthe-sample experiment leads to drastically different results.1259 While these commenters are
correct that the correlation values are high for the raw variables, the values are not high once the
variables under consideration are adjusted by dividing by total lines.1260 We find that the
correlation values are all very reasonable. We note, in particular, the -1 correlation between
switched lines and special lines. The fact that switched lines plus special lines equals one is the
reason the regression cannot be run with a separate constant. We note that our parameterization
has switched lines, special lines, and toll minutes as explanatory variables. We have chosen not
an estimate of the expense cost per toll minute is equal to $ 0.0006331807.
1256

US West Inputs Further Notice comments, Attachment A at 21.

1257

We note that we also count switched business lines as channels in our regression equations.

1258

See, e.g., Ameritech Inputs Further Notice comments at 27-28; GTE Inputs Further Notice comments at 7980; US West Inputs Further Notice comments, Attachment A at 21-22.
1259

US West Inputs Further Notice comments at 53-57, Attachment A at 20-27.

1260

The correlation matrix for the variables under consideration is:

switched
special
toll
local

switched
1.00
-1.00
0.54
0.06

special
-1.00
1.00
-0.54
-0.06

toll
0.54
-0.54
1.00
-0.13

170

local
0.06
-0.06
-0.13
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to include local minutes in our regressions for theoretical reasons. So, the key correlation values
are the correlations of toll minutes with special lines and with switched lines. We find that those
values are reasonable.
395. Several commenters suggested that we use local minutes as an explanatory
variable.1261 Despite our tentative conclusion that our regressions should not include local
minutes as a variable, in response to these comments, we re-ran each of the regressions with
local minutes per line as an additional variable. In three of the four regressions, the coefficient
for local minutes was not significant at the five percent level, and for account 6700, its sign was
the opposite of what was expected.1262 The resulting difference in the estimated expenses
attributable to supported services was very small in magnitude as well. If we used the local
minutes variable in our parameterization, after summing across all expense accounts, our perline, per-month estimate for a switched line would be approximately $0.01 more.1263 Given our
belief that local minutes should not influence these expenses, the lack of significance in the
coefficients, and the overall lack of impact when the variable was consistently included in the
regressions, we conclude that we should not include local DEMs per line in our specifications.
396. Except for the inclusion of local minutes as a variable, no commenters have
suggested a better parameterization or methodology for using the ARMIS data to estimate
expense inputs for these accounts. Further, no commenters have suggested an alternative
publicly available data set to use for our estimation of expense input values. We acknowledge
that there is substantial variation in the underlying expense data taken from the ARMIS reports.
Common support services expenses often contain charges unrelated to the specified relationships
in the regression equation. For example, there are many one-time expenses and non-recurring
1261

See Appendix D at D-6.

1263

The table below shows the cost per switched line without local minutes in the equation (nloc), with local
minutes in the equation and an average number of local minutes for each line (wloc), and the difference between the
two in dollars.
lm6620
lm6700
lm6510
lm6530

nloc
3.39
2.47
-0.05
1.41

wloc
3.62
2.18
-0.05
1.48

diff
-0.24*
0.30
0.00
-0.07

We note that the 6620 account is the one regression where local minutes variable is significant. In the other cases it
is not.

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charges associated with these accounts. We have tried to limit the effect of this problem by
making adjustments to the expense data, as discussed below. Given the data limitations and the
parameterization we have chosen, we find that the estimated coefficients are the best estimate of
the applicable expenses, regardless of the resulting standard errors.
397. Removal of One-Time Expenses. In the Inputs Further Notice, we discussed our
efforts to adjust estimates of common support services expenses to account for one-time and
non-recurring expenses.1264 We sought comment on the need for information about and
estimates of various types of exogenous costs and common support service expenses that are
recovered through non-recurring charges and tariffs. These expenses include specific one-time
charges for the cost of mergers or acquisitions and process re-engineering, and network and
interexchange carrier connection, disconnection, and re-connection (i.e., churn) costs.
398. In the Inputs Further Notice, we tentatively concluded that we should not use an
analysis submitted by AT&T and MCI to estimate one-time and non-recurring expenses for
corporate and network operations expenses.1265 This analysis averaged five years (1993-1997)
of data from Security and Exchange Commission (SEC) 10-K and 10-Q filings for all tier one
companies to identify and calculate a percentage estimate of corporate and network operations
expenses classified as one-time and non-recurring charges associated with these types of
activities. Our tentative conclusion not to rely on the AT&T and MCI analysis to make these
adjustments was based on the fact that we were using 1996 ARMIS data to estimate the expense
inputs. Because the SEC reports do not indicate whether the one-time expenses were actually
made solely during a specific year indicated, we tentatively concluded that we could not use the
analysis' five year average or the actual 1996 SEC figures to make adjustments to the 1996
ARMIS data. In the Inputs Further Notice, we noted however that the AT&T and MCI analysis
indicates that one-time expenses for corporate and network operations can be significant.1266 We
sought comment on how to identify and estimate one-time and non-recurring expenses
associated with these common support services.
399. AT&T and MCI disagree with our tentative decision to reject their one-time cost
estimates and argue that it is better to estimate one-time costs through use of the SEC reports,
although these reports may imperfectly establish the precise date of the occurrence, than to fail to
exclude these costs at all.1267 Although some LEC commenters may agree that we should adjust
our estimates to exclude one-time and non-recurring expenses, they provide no data or
1264

Inputs Further Notice at paras. 220-225.

1265

Inputs Further Notice at para. 221.

1266

Inputs Further Notice at para. 221.

1267

AT&T/MCI Inputs Further Notice comments at 45-46.

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methodology to accomplish this, other than suggesting that we should get this information from
the companies.1268 GTE claims that unless companies implement specific tracking mechanisms,
these data are not generally or easily identified after the fact.1269
400. We now reconsider our tentative conclusion not to use the analysis submitted by
AT&T and MCI to adjust our network and corporate operations expense estimates to account for
one-time and non-recurring expenses. We do so for a number of reasons. First, we received no
additional information on publicly available data sources or other reasonable methods to estimate
these one-time and non-recurring costs at this time. Second, the problems associated with
determining the actual costs of 1996 one-time expenses based on the SEC reports are obviated
because we are using 1998 expense data to estimate the forward-looking input values. We find
that using the estimated average of one-time costs over the five preceding years (1993-1997) to
adjust 1998 data is a reasonable method to determine the impact of costs related to mergers and
acquisitions and work force restructuring. Further, we believe any adjustments for one-time
costs based on the AT&T and MCI analysis may be biased downward after comparing the
number of companies involved in these types of activities in 1998 and 1999 to those in 19931997.1270 Accordingly, we adjust downward estimated expenses in account 6530 (Network
Operations) by 2.6 percent and in account 6700 (Executive, Planning, General, and
Administrative) by 20 percent.
401. Removal of Non-Supported Expenses. In the Inputs Further Notice, we also
discussed our efforts to adjust marketing and other customer service expenses to account for
recurring expenses that are not related to services supported by the federal high-cost
mechanism.1271 The non-supported expenses we attempted to identify include vertical features
1268

SBC does not believe one-time and non-recurring costs are significant, but agrees that they should be
excluded to the extent they are significant. SBC suggests we could either base our inputs on company data that does
not include these costs or base the inputs on data from years where it is known that no one-time or non-recurring
activities occurred. SBC Inputs Further Notice comments at 20. Sprint suggests that current information with
respect to one-time corporate operations expenses should be supplied by the companies on an annual basis. Sprint
Inputs Further Notice comments at 65. GTE, on the other hand, agrees with our tentative conclusion and argues
that we should not attempt to adjust our input values for one-time, non-recurring, and non-supported costs. GTE
argues that, if we do so, we should also adjust our estimates to account for certain cost increases due to regulatory
requirements, and other factors. If any adjustments are made, GTE claims that company-specific cost adjustments
would have to be requested from each company annually. GTE Inputs Further Notice comments at 82.
1269

GTE Inputs Further Notice comments at 82.

1270

The following companies have either filed notice with the Commission or have indicated in the press that
they were or are actively engaged in merger discussions and activity: Bell Atlantic, GTE, US WEST, Ameritech,
SBC, Frontier, Puerto Rico Telephone, Cincinnati Bell, Aliant Communications, and Sprint.
1271

Inputs Further Notice at para. 221, 223-225.

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expenses, billing and collection expenses not related to supported services, operational support
systems and other expenses associated with providing unbundled network elements and
wholesale services to competitive local exchange carriers. We proposed adjustments to extract
non-supported service costs related to marketing, which is discussed separately below,1272coin
operations, published directory, access billing, interexchange carrier office operation, and
service order processing.1273 Specifically, we made percentage reductions to the regression
coefficient results for specific expense accounts based on a time trend analysis of average
ARMIS 43-04 expense data for five years (1993-1997).
402. Some commenters argue that our proposed methodology removes non-supported
services twice because these expenses were already taken out by the regression when expenses
are subdivided among switched lines, special lines, and toll minutes.1274 Although we agree, as
discussed below, that our methodology double counted the marketing expenses associated with
special access lines, we do not agree with the theory that combining a percentage reduction with
the regression methodology invariably removes expenses twice. For example, vertical features
associated with switched lines such as call waiting are not supported, but the expenses associated
with call waiting are not removed using the regression analysis. If we had the data to separately
identify and remove vertical features expenses from switched lines, we believe that it would be
appropriate to do so and to continue using the regression analysis to separate the remaining
expenses. Nonetheless, upon further analysis, we find that we should not adopt our proposed
method of removing these non-supported recurring expenses. We find that this method is not
sufficient to adequately identify non-supported common support service expenses due to
differences in account classifications from the ARMIS 43-03 and ARMIS 43-04 reports.
Therefore, we do not utilize the time trend analysis or take reductions for these non-supported
expenses in the input values at this time. We recognize that this causes an overstatement of in
our estimate of the expenses attributable to supported services in account 6620 (Service Expense
and Customer Operations). Unlike the case with marketing, however, we do not have an
alternative source of information on which to base a methodology for removing the nonsupported expenses in this account. We plan to seek comment on a verifiable and systematic
method to identify and remove these costs in the proceeding on the future of the model.
403. Marketing. As explained in the Inputs Further Notice, we made an adjustment to
the Account 6610 (Marketing)1275 regression coefficient based on an analysis made by
1272

See infra paras. 403-407.

1273

See Inputs Further Notice at para. 225.

1274

See, e.g., GTE Inputs Further Notice comments at 84; Sprint Inputs Further Notice comments at 67.

1275

Account 6610 Marketing consists of three sub-accounts: 6611 Product Management, 6612 Sales, and 6613
Advertising.

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Economics and Technology, Inc. (ETI).1276 The ETI analysis offered a method for
disaggregating product management, sales, and advertising expenses for basic (residential)
telephone service from total marketing costs. Based on information from the New England
Telephone Cost Study, ETI attributed an average of 95.6 percent of company marketing costs to
non-supported customers or activities, such as vertical and new services. Relying on this
analysis, we reduced the input estimate to reflect 4.4 percent of marketing expenses determined
by the regression. In the Inputs Further Notice, we tentatively concluded that this was the most
accurate method on the record for apportioning marketing expenses between supported and nonsupported services.1277
404. We agree with commenters that, in making this adjustment to the post-regression
analysis input estimate, we incorrectly estimated marketing expenses because reductions were
taken twice for special access lines.1278 We agree with the commenters that any adjustments to
exclude expenses based on the type of service should be made from total relevant marketing
expenses rather than the regression results. Therefore, we do not use the regression methodology
to estimate marketing expenses. Instead, using the 1998 ARMIS data, we adjust the total
weighted average of relevant expenses for all study areas.
405. Commenters also point out that the adjustment figure of 4.4 percent based on the
ETI Study as initially reported was determined under the assumption that only expenses
attributable to residential local service would be supported.1279 Further, the ETI estimate of costs
associated with the marketing of supported services was calculated by taking a percentage of
expenses only from Account 6611, Product Management. Specifically, the ETI estimate did not
include any relevant expenses from Account 6613, Product Advertising. As noted in the Inputs
Further Notice, funding support for marketing is to be based on those expenses associated with
advertising. Section 214 of the Communications Act requires eligible telecommunications
carriers to advertise the availability of residential local exchange and universal service supported
services.1280 Moreover, we note that under the current high cost loop support mechanism,
carriers receive no support for marketing.1281
1276

Inputs Further Notice at para. 224.

1277

Inputs Further Notice at para. 225.

1278

See, e.g., Sprint Inputs Further Notice comments at 65-66 (arguing direct reduction of total company
marketing expenses for only ETI factor is an acceptable method); Ameritech Inputs Further Notice comments at
29; US West Inputs Further Notice comments, Attachment at 27-28.
1279

See,, e.g., Ameritech Inputs Further Notice comments at 29; GTE Inputs Further Notice comments at 83;
US West Inputs Further Notice comments, Attachment at 28.
1280

47 U.S.C. § 214(e)(1)(B).

1281

See, e.g., NECA, Universal Service Fund 1999 Submission of 1998 Study Results, Oct.1, 1999 at Tab 2.

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406. We received further documentation and an alternative analysis from ETI which
included an estimate for advertising expenditures.1282 The revised analysis included proportional
allocations of advertising costs based on the percentage of lines estimated for primary line
residential service and single-line business service. ETI also used line count source material
from the Preliminary Statistics of Common Carriers 1998 rather than relying on 1996 data used
in its original analysis.
407. Based on the new information provided and the lack of any reasonable alternative
presented by the commenters, we calculate an input estimate of supported advertising expenses
using the ETI study and 1998 ARMIS expenses.1283 By adding a proportional allocation for
multi-line business advertising expenses to the ETI alternative analysis (which only included an
estimate representing primary line and single line business advertising costs), we conclude that
34.4 percent of Account 6613, Product Advertising, would be the most appropriate expense
amount for the advertising of universal service.1284 Because the additional data provided by ETI
allowed for the calculation and estimate of supported and non-supported advertising
expenditures, we did not allocate costs associated with product management or sales. As
previously mentioned, these marketing activities are not specifically required for support under
Section 214 of the Communications Act and currently receive no high cost loop support. Taking
34.84 percent of total 1998 advertising expenses for the 80 non-rural high cost study areas and
dividing by total lines per month, the average per line per month input value for advertising
support is $0.09. This level of advertising expenses represents 5.82 percent of total 1998
marketing costs for non-rural carriers.
408. Local Number Portability. There is an additional input value that we estimate
separately from our consideration of other expense input values. Specifically, the synthesis
model has a user-adjustable input for the per-line costs associated with local number portability
(LNP). In the Inputs Further Notice, we proposed a per-line monthly LNP cost of $0.39, based
The data collection instructions identify the accounts that are included in calculating high-cost loop support.
Accounts 6610 (Total Marketing), 6611(Product Management), 6612(Sales), and 6613(Advertising) do not appear
in the list of accounts included in calculating high-cost loop support.
1282

See Susan Baldwin, An Alternative Analysis of Marketing Expenses Related to Calculation of USF Support.
This paper supplements the earlier ETI study: Susan M. Baldwin, Lee L. Selwyn, Economics and Technology,
Inc. Converging on a Cost Proxy Model Primary Line Basic Residential Service, August 1996.
1283

See Appendix D at D-7 for analysis. For further information regarding formulas and calculations, see the
spreadsheet posted on the Commission's Web site.
1284

Although the statute requires advertising of the supported services, as noted above, we do not find that this
requires advertising of secondary lines to consumers already receiving the supported services.

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on a weighted average of the LNP rates filed by the LECs available at that time.1285 AT&T and
MCI point out that the Commission suspended and investigated some of those rates, and that the
rates we approved are generally lower than the rates we used to estimate our LNP input
value.1286 They argue that we should use the line-weighted nationwide average of approved LNP
rates, which they estimate currently is $.032.1287 GTE claims that there is no justification for
using the nationwide average LNP rate, as suggested by AT&T and MCI, because the approved
LNP rates provide the best representation of each company's LNP costs.1288 We agree with GTE
and in this instance depart from our general practice of using nationwide input values in the
federal universal service support mechanism. Because the Commission has investigated and
approved LNP rates for most LECs, we find that it is appropriate to use the company-specific
input values listed in Appendix D.1289 For those carriers that have not yet filed an LNP tariff, we
will use the line-weighted nationwide average of approved LNP rates.
D.

GSF Investment
1.

Background

409. GSF investment includes buildings, motor vehicles, and general purpose
computers. The synthesis model platform uses a three-step algorithm to estimate GSF
investment. First, for each study area, the model calculates a GSF investment ratio for each GSF
account by dividing the ARMIS investment for the account by the ARMIS total plant in service
(TPIS) less GSF investment. The values proposed in the Inputs Further Notice used 1996
ARMIS data in this step.1290 Second, the model calculates a preliminary estimate for GSF
investment for each account by multiplying the model's estimate of TPIS by the GSF investment
ratios developed in step one.1291 Third, the model reduces the preliminary GSF investment
estimates for each account by multiplying these estimates by one of two factors.1292
1285

See Inputs Further Notice at Appendix A, A-31.

1286

AT&T/MCI Inputs Further Notice comments at 47.

1287

AT&T/MCI Inputs Further Notice comments at 47.

1288

GTE Inputs Further Notice reply comments at 32.

1289

See Appendix D at D-8.

1290

In the synthesis model, ARMIS data for each non-rural study area are contained in the "1996 Actuals" tab of
the expense modules.
1291

As calculated by the model, TPIS excludes GSF investment.

1292

The synthesis model platform incorporates HAI's expense and GSF module. See Platform Order, 13 FCC
Rcd at 21361, para. 91.

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410. In the Inputs Further Notice, we tentatively concluded that the model's
preliminary estimates of GSF investment should be reduced in the third step of the algorithm,
because only a portion of GSF investment is related to the cost of providing the services
supported by the federal mechanism, but that we should not use the same factors as those used in
the HAI model.1293 We noted that the HAI sponsors used one factor for some accounts and a
different factor for others, but had not explained why either particular factor should be used.1294
Rather than using two different factors, we proposed using a factor that reflects the percentage of
customer operations, network operations, and corporate operations used to provide the supported
services. Specifically, we proposed calculating preliminary GSF investment on a study area
specific basis (steps one and two), and then multiplying these estimates by a nationwide
allocation factor derived from the regression methodology that we used to estimate the portion of
common support services expenses attributable to switched lines and local usage.1295
2.

Discussion

411. We conclude that the model's preliminary estimates of GSF investment should be
reduced in the third step of the algorithm, because we find that only a portion of GSF investment
is related to the cost of providing the services supported by the federal mechanism. In response
to certain comments, however, we modify our proposed allocation factor, as discussed below.
Although we reject commenters' arguments that the preliminary GSF investment should not be
reduced at all, we agree that we should not exclude facility-related maintenance expenses in our
proposed allocation factor. In addition, we modify our method of calculating the denominator of
our allocation factor so that both the numerator and denominator are simple averages. Finally,
we clarify that the ARMIS TPIS used in the first step of the algorithm excludes ARMIS GSF
investment.

1293

Inputs Further Notice at 211.

1294

The HAI model used the following two factors to reduce the preliminary GSF investment estimates: (1)
one minus the Total Operations General Support Allocator (Total Operations Allocator) or (2) the Office Worker
General Support Allocator (Office Worker Allocator). Each of these allocators is a fraction. The Total Operations
Allocator is the ratio of the sum of customer operations expenses and corporate operations expenses to total
operating expenses. The Office Worker Allocator is the ratio of the sum of corporate operations expenses and
network operations expenses to the sum of customer operations expenses, corporate operations expenses and
network operations expenses. The Total Operations Allocator is applied to the Motor Vehicles, Garage Work
Equipment, and Other Work Equipment accounts. The Office Worker Allocator is applied to the Furniture, Office
Equipment, Buildings and General Purpose Computer accounts. See HAI Dec. 11, 1997 submission.
1295

The proposed ratio was the sum of customer operations expenses, network operations expenses, and
corporate operations expenses attributable to the supported services, to the sum of those expenses calculated on a
total regulated basis.

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412. Reduction of Preliminary GSF Estimate. Several LEC commenters argue that the
preliminary GSF investment should not be reduced by an allocator in the third step of the
algorithm.1296 SBC contends that the factor we use to reduce our preliminary GSF investment
estimates substantially underestimates the GSF amounts related to the supported services.1297
SBC claims that the ratios used to estimate the preliminary GSF investment already provides a
reasonable basis for allocating GSF to supported services, because the GSF ratio (derived from
the ARMIS accounts) is only applied to investment identified by the model as associated with
supported services.1298 BellSouth also claims that the TPIS calculated by the model is the
investment necessary to provide the supported services and that no further reductions in the
preliminary GSF investment estimate are appropriate.1299 Sprint similarly claims that by
applying a book GSF ratio to the forward-looking plant necessary to provide supported services,
the modeled GSF plant also has been converted to a forward-looking level necessary to provide
the supported services. Sprint contends that applying an additional allocator is not necessary and
has the effect of reducing GSF plant twice.1300
413. We disagree with SBC's contention that only a portion of GSF is assigned to
supported services in deriving our preliminary estimates of GSF investment.1301 To the contrary,
the GSF ratio is applied to all model investment, which includes the investment required to
provide both supported and non-supported services. As discussed above, the model estimates
the cost of providing services for all businesses and households within a geographic region,
including the provision of special access, private lines, and toll services.1302 Because these
services are not supported by the federal high-cost mechanism, the preliminary GSF investment
estimate must be adjusted to reflect the portion of GSF investment attributable to the supported
1296

See, e.g., BellSouth Inputs Further Notice comments at Attachment B, B-21; SBC Inputs Further Notice
comments at 17; Sprint Inputs Further Notice comments at 59-60. US West also claims generally that our multistep process results in a significant reduction in costs "assumed to be recoverable." US West Inputs Further Notice
comments at 47.
1297

SBC Inputs Further Notice comments at 17.

1298

SBC Inputs Further Notice comments at 17.

1299

BellSouth Inputs Further Notice comments at Attachment B, B-21.

1300

Sprint Inputs Further Notice comments at 59-60. Sprint also claims that we used a mathematically incorrect
method to compute the GSF ratio by including ARMIS GSF investment in the denominator and then applying that
to TPIS investment as calculated by the model, which does not include GSF investment. We clarify below, that the
ARMIS GSF investment used in the denominator also excludes GSF investment, and we thus calculate the ratio as
Sprint suggests: ARMIS GSF plant divided by ARMIS TPIS less ARMIS GSF plant. See infra para. 417.
1301

See SBC Inputs Further Notice comments at 17.

1302

See supra paras. 49, 391.

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services. Thus, BellSouth's assertion that the TPIS calculated by the model is the investment
necessary to provide the supported services is wrong. For the same reasons, we reject Sprint's
argument that, by applying the book GSF ratio, the modeled GSF plant has somehow been
converted to a forward-looking level necessary to provide the supported services. On the
contrary, the conversion estimates the amount of GSF investment attributable to all services,
supported and non-supported. The second reduction is required to estimate the amount of GSF
investment that should be supported by the federal universal service support mechanism.
414. Allocation Factor. Assuming that we use an allocator to reduce preliminary GSF
investment, several commenters criticize the particular allocator that we proposed in the Inputs
Further Notice. For example, GTE questions why we used only expenses for customer
operations, network operations, and corporate operations in the allocation calculation and
excluded plant-specific expenses.1303 GTE argues that plant-specific operations also use GSF
investments and should be counted in the calculation. SBC also argues that GSF investment
supports all aspects of a LEC's operations, and contends that it makes no sense to exclude
facility-related maintenance expenses in our proposed allocation factor.1304 We agree that
expenses for plant-specific operations expenses should be included in our calculation of the
nationwide allocation factor derived from the regression methodology. Accordingly, the
allocation factor we adopt to estimate GSF investment includes plant-specific operations
expenses.1305
415. GTE also contends that the forward-looking way to calculate a GSF investment
ratio is to convert all ARMIS investments to current values using current-to-book ratios, before
calculating an adjusted ARMIS GSF to TPIS investment ratio.1306 Although we concede there is
some logic to GTE's argument that we should convert ARMIS GSF investments to current values
by using current-to-book ratios, we note that this would require a change in the model platform.
As we explain above, the model platform uses a three-step algorithm to estimate GSF
investment.1307 Although we can easily change the input value for the factor used in step three,
1303

GTE Inputs Further Notice comments at 77. Although GTE agrees that we should not base a reduction to
the preliminary GSF investment on the same factors used in the HAI model, GTE claims our proposed methodology
has several problems.
1304

SBC Inputs Further Notice comments at 18.

1305

Due to equations embedded in the HAI expense module, the total operations general support allocator is set
equal to one minus the office worker general support allocator. That is, because one factor is one minus the other in
the HAI expense module, to use the same allocation factor for all GSF investment, we must enter one minus the
factor in some instances. See Appendix D at D-9.
1306

GTE Inputs Further Notice comments at 77.

1307

See supra para. 409

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we could not adjust the ARMIS data by applying a current-to-book factor without modifying the
model platform.1308 Proposals to change the model platform are properly addressed in response
to pending petitions for reconsideration of the Platform Order or the proceeding on the future of
the model.
416. Finally, GTE claims that our estimation of the universal service portion of the
GSF investment is flawed because our regression methodology uses a wrong specification and
incorrectly excludes expenses.1309 GTE also claims that the calculation allocator itself is flawed
because the numerator is a simple average of expenses derived from the regression results, but
the denominator is a weighted average of the total expenses developed from ARMIS data.1310
GTE argues that the type of average in the numerator and denominator should match.1311 While
we do not agree that our regression methodology is flawed, we find that GTE has pointed out an
inconsistency in our GSF methodology. Specifically, we agree that we should use the same type
of average in both the numerator and denominator of our allocation factor. As a result, we use
the simple average of total expenses in the denominator of the allocation factor we adopt for
estimating the portion of GSF attributable to supported services.1312
417. Clarification. BellSouth claims that the algorithm used to estimate GSF
investment contains an error in consistency. BellSouth suggests that in step one we should
determine the ratio of ARMIS-based GSF investment to the ARMIS-based TPIS less GSF
investment.1313 In step two, this ratio is multiplied by the TPIS investment determined by the
model, which excludes GSF. We clarify that the model calculates GSF investment as BellSouth
suggests it should. That is, the model uses ARMIS-based TPIS less GSF investment.1314 US
1308

We also do not at this time consider Bell Atlantic's suggestion that we develop GSF investments on some
other basis, such as an activity based approach, rather than as a ratio of investment. See Bell Atlantic Inputs Further
Notice comments at 21. Such an approach also would require changes to the model platform.
1309

GTE Inputs Further Notice comments at 77-78.

1310

GTE Inputs Further Notice comments at 78.

1311

GTE Inputs Further Notice comments at 78.

1312

Specifically, the GSF allocator is the ratio of universal service expenses to total company expenses.
Universal service expenses are determined by the following: switched lines to total lines times loop maintenance
plus switched lines to total lines times circuit maintenance plus local DEMs to total DEMs times switch
maintenance plus $7.32, which is the per-line, per month amount for the common support services expenses
attributed to the supported services, as discussed above. See supra note 855. Total company expenses are the sum
of loop maintenance, circuit, switch maintenance, and the total corporate overhead. This allocator is .6769.
1313

1314

BellSouth Inputs Further Notice comments at Attachment B, B-20.
This can be verified by examining the formulas in the " 96 Actuals" tab of the expense modules.

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West claims that in the second step of the algorithm the synthesis model includes only fifty
percent of the building investment and no land investment.1315 The synthesis model incorporates
the HAI switching and expense modules and calculates the investment related to wire center
buildings and land in the switching module. So, US West is mistaken that fifty percent of the
building and land investment is eliminated, because this investment is added back in calculating
switching costs.1316
418. For the reasons stated above, we adopt input values for GSF investment that
reflect the portion of GSF investment attributable to the cost of providing the services supported
by the federal mechanism. Specifically, we calculate preliminary GSF investment on a study
area specific basis, using 1998 ARMIS data, and then multiply these estimates by a nationwide
allocation factor derived from the regression methodology that we used to estimate the portion of
common support services expenses attributable to switched lines and local usage and the portion
of plant-specific operations expenses attributable to the supported services.1317 The allocation
factor is the sum of plant specific operations expenses, customer operations expenses, network
operations expenses, and corporate operations expenses attributable to the supported services,
divided by the sum of those expenses calculated on a total regulated basis.
VIII. CAPITAL COSTS
A.

Depreciation
1.

Background

419. We now consider the inputs related to the calculation of depreciation expenses.
The model uses "adjusted projection lives" to recover the current costs of the assets.1318 Under
this approach, the annual depreciation charges associated with an asset are computed by dividing
the asset's current cost by its adjusted projection life.1319 A shorter life will increase the annual
1315

US West Inputs Further Notice comments at 48.

1316

To the extent that not all of the land investment is included in the synthesis model logic, such a change
would require a change to the model platform.
1317

See Appendix D at D-9.

1318

1997 Further Notice, 12 FCC Rcd at 18570, para. 149. The projection life of an asset is the asset's expected
service life at installation, reflecting not only the physical life of the equipment, but also the obsolescence associated
with the replacement of older equipment with equipment that uses new technologies and forecasts of future
replacements. The adjusted projection life of an asset is its projection life adjusted by its future net salvage value.
Future net salvage is the percentage of the asset's value that the owner expects to obtain when selling the asset at the
end of its useful life. Id.
1319

Depreciation charges are computed in this manner for the first year. In subsequent years, depreciation
charges are computed using reserve.

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depreciation expense.
420. In the Universal Service Order, the Commission concluded that "economic lives
and future net salvage percentages used in calculating depreciation expense should be within the
FCC-authorized range" and use currently authorized depreciation lives.1320 In the 1997 Further
Notice, the Commission tentatively concluded that it should adopt depreciation expenses that
reflect a weighted average of the rates authorized for carriers that are required to submit their
rates to us.1321 The Commission also sought comment on whether adjusted projected asset lives
should reflect the lives of facilities and equipment dedicated to providing only the services
supported by universal service or whether the asset lives should reflect a decision to replace
existing plant with plant that can provide broadband services.1322 The May 4 Public Notice
requested further information on these issues.1323
421. In the Inputs Further Notice, we tentatively adopted a method of depreciation that
should be used in the model, i.e., how depreciation allowances should be allocated over the life
of an asset.1324 Because the Commission's depreciation accounting rules require the use of
straight-line equal-life-group depreciation, rather than a more accelerated depreciation method,
we tentatively concluded that this method, which is used for all Commission-proposed
depreciation, is also appropriate for use in the high-cost support mechanism.1325
2.

Discussion

1320

Universal Service Order, 12 FCC Rcd at 8913-14, para. 250 (criterion 5).

1321

1997 Further Notice, 12 FCC Rcd at 18571, para. 152.

1322

Id.

1323

See Inputs Public Notice.

1324

Inputs Further Notice at para. 231.

1325

47 C.F.R. § 32.2000(g). The equal-life-group procedure subdivides all units of a plant account installed in a
particular year (a "vintage") into groups in which all units are expected to have the same life span. Each group is
depreciated using the straight-line method, which spreads depreciation costs equally over the life of the group. See
Amendment of Part 31 (Uniform System of Accounts for Class A and B Companies) so as to Permit Depreciable
Property to be Placed in Groups Comprised of Units with Expected Equal Life for Depreciation Under the
Straight-Line Method, Report and Order, 83 FCC2d 267 (1980), recon., 87 FCC2d 916 (1981), supplemental
opinion, 87 FCC2d 1112 (1981) [Straight-Line Equal-Life-Group Report and Order]. Thus, the annual depreciation
of a single vintage of a plant account equals the sum of the depreciation amounts of all surviving life-groups from
that vintage. For a discussion of the equal-life-group method of depreciation, see Bryan Clopton, "Equal Life
Group Depreciation Rates", in National Ass'n of Regulatory Utility Commissioners, Public Utility Depreciation
Practices at 165-186 (August 1996) [Public Utility Depreciation Practices].

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Method of Depreciation

422. For the reasons explained below, we adopt a straight-line equal-life-group method
of depreciation. Further, we select curve shapes to be used to distribute equal-life groups in each
plant account.1326
423. Most commenters support our tentative conclusion to use the straight-line equallife-group method of depreciation.1327 Ameritech argues, however, that the Commission's
adoption of a straight-line depreciation method in other contexts need not limit us to that method
for use in this model, and that "the method of depreciation for a specific study area needs to be
consistent with any study that underlie [sic] the development of economic lives or net
salvage."1328 Although Ameritech may correctly assert that there is no requirement that we adopt
a method of depreciation simply because it is the method previously adopted by the Commission
in another context, we believe that the Commission's adoption, in other proceedings, of the
straight-line equal-life-group method reflects the well-considered conclusion that this method of
depreciation is best-suited to determining the economic costs of providing local service. The
straight-line equal-life-group depreciation method is also consistent with our method of
developing economic lives and net salvage for the same plant accounts. Because the
Commission consistently uses a straight-line equal-life-group depreciation method in all other
Commission-proposed depreciation, and in light of the general support received in favor of
straight-line equal-life-group depreciation, we conclude that straight-line equal-life-group
depreciation is appropriate for use in the high-cost support mechanism.1329
424. In using the straight-line equal-life-group method of depreciation in other
contexts, the Commission has acknowledged that the method necessarily requires the selection
of a curve shape for the distribution of the equal-life groups.1330 The HAI model assumed a
1326

A curve shape is the result of actuarial analysis which determines the probable frequency of plant mortality
for a particular plant account from the time the plant vintage is placed in service to the end of life of the final
surviving plant of that vintage. In the equal-life-group methodology, curve shapes are used to determine the number
of units of a plant account in each equal-life group. See generally Public Utility Depreciation Practices at 111-129.
The adopted curve shapes for each plant account are shown in the table attached in Appendix A at A-30.
1327

See AT&T/MCI Inputs Further Notice comments at 47-48; BellSouth Inputs Further Notice comments,
Appendix B at B-26; GTE Inputs Further Notice comments at 85; Sprint Inputs Further Notice comments at 75;
AT&T/MCI Inputs Further Notice reply comments at 41.
1328

Ameritech Inputs Further Notice comments at 30.

1329

We note, furthermore, in response to the comments of AT&T/MCI, that we intend to follow our standard
practice of accounting for the impact of deferred taxes. See, e.g., 47 C.F.R. § 65.830(a)(1).
1330

See, e.g., Straight-Line Equal-Life-Group Report and Order.

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single curve shape for all plant accounts.1331 Because the curve shapes are not easily averaged
across all categories, however, we believe that use of the single HAI curve shape will unduly
distort the model input values. We, therefore, determine that separate curve shapes should be
adopted for each plant account category. Actuaries have developed generic, standardized curve
shapes, called Gompertz-Makeham (GM) standard curves, that describe generalized mortality
patterns. GM standard curve shapes are recognizable to many knowledgeable parties concerned
with depreciation methods and are normally more immediately meaningful to them than
nonstandard curve shapes, which are identified by the values for three variables.1332 For
convenience purposes, GM standard curves are often substituted for nonstandard curves. USTA
has developed nonstandard curve shapes for most plant accounts based on mortality data
provided by its members, using the same methodology approved in other Commission
proceedings.1333 For the remaining plant accounts, the Commission has developed composite
curves, also nonstandard, utilizing data from available depreciation studies. Because the GM
standard curves are recognizable and convenient to parties interpreting the data inputs in the
high-cost model, and because the standardized curves will not vary significantly from the
nonstandardized curves, we conclude that GM standard curves will be more useful in the highcost inputs model than nonstandard curves. For each plant category, therefore, we adopt the GM
standard curve shape nearest that developed by USTA or the Commission.1334
b.

Depreciation Lives and Future Net Salvage Percentages

425. We adopt the tentative conclusion of the Inputs Further Notice that we should use
HAI's input values with respect to depreciation lives and future net salvage percentages. As
explained below, we reject the objections by some commenters that the HAI input values are not
1331

Letter from Chris Frentrup, MCI WorldCom, to Magalie Roman Salas, FCC, dated July 16, 1999
(AT&T/MCI July 15 ex parte).
1332

The variables describing a nonstandard curve shape are not usually meaningful in and of themselves. There
are an infinite number of curves that the variables could describe and the variables themselves offer no insight into
the shape of the curve until they have been used to actually plot the curve they describe. Until that has been done the
depreciation consequences of a particular set of variables are unknown. The GM standard curves are a set of
thirteen generalized curves that may stand in place of the infinite number of possible nonstandard curves. Because
of the small, finite number of GM standard curves, a person familiar with depreciation practices will recognize the
depreciation consequences of a particular identified GM standard curve. For a detailed discussion of GM standard
curves, formerly known as Bell standard curves, see American Telegraph & Telephone, Engineering Economy 34565 (1977).
1333

See Public Utility Depreciation Practices at 120-26.

1334

See Public Utility Depreciation Practices at 123-25 for a discussion regarding the method for matching
generalized curves to observed life table values. The adopted curve shapes for each plant account category are
shown in the table attached in Appendix A at A-30..

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appropriate for determining depreciation rates in a competitive environment.
426. In estimating depreciation expenses, the model uses the projected lives and future
net salvage percentages for the asset accounts in Part 32 of the Commission's rules.1335
Traditionally, the projected lives and future net salvage values used in setting a carrier's rates
have been determined in a triennial review process involving the state commission, the
Commission, and the carrier. In order to simplify this process, the Commission has prescribed
ranges of acceptable values for projected lives and future net salvage percentages.1336 The
Commission's prescribed ranges reflect the weighted average asset life for regulated
telecommunications providers. These ranges are treated as safe harbors, such that carriers that
incorporate values within the ranges into their depreciation filings will not be challenged by the
Commission. Carriers that submit life and salvage values outside of the prescribed range must
justify their submissions with additional documentation and support.1337 Commission-authorized
depreciation lives are not only estimates of the physical lives of assets, but also reflect the impact
of technological obsolescence and forecasts of equipment replacement. We believe that this
process of combining statistical analysis of historical information with forecasts of equipment
replacement generates forward-looking projected lives that are reasonable estimates of economic
lives and, therefore, are appropriate measures of depreciation.
427. We disagree with comments by incumbent LECs that the Commission's
prescribed ranges are not appropriate for determining depreciation rates in a competitive
environment.1338 These parties argue that rapid changes in technology and competition in local
telecommunications markets will diminish asset lives significantly below the Commission's
prescribed range by causing existing equipment to become obsolete more quickly.1339 We agree
1335

See 47 C.F.R. § 32.2000(j)

1336

See 47 C.F.R. § 32.2000(g)(iii).

1337

The Commission has proposed streamlining the depreciation prescription process by, inter alia, expanding
the prescribed range for the digital switching plant account and eliminating salvage from the depreciation process.
See 1998 Biennial Regulatory Review -- Review of Depreciation Requirements for Incumbent Local Exchange
Carriers, Notice of Proposed Rulemaking, CC Docket No. 98-137, 13 FCC Rcd 20542 (1998).
1338

Ameritech Inputs Further Notice comments at 31-32; Bell Atlantic Inputs Further Notice comments at 7, 2324, and Attachment B, at 7-10; BellSouth Inputs Further Notice comments, Appendix B at B–23-B–26; Cincinnati
Bell Telephone Inputs Further Notice comments at 5; GTE Inputs Further Notice comments at 85-86; SBC Inputs
Further Notice comments at 21-23; Sprint Inputs Further Notice comments at 76-79; see also Aliant June 1, 1998
comments at 3-4; Ameritech June 1, 1998 comments at 4; BCPM June 1, 1998 comments at 11-13; GTE June 1,
1998 comments at 15-16; Southwestern June 1, 1998 comments at 9-10.
1339

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with GSA, AT&T and MCI that there is no evidence to support the claim that increased
competition or advances in technology require the use of shorter depreciation lives in the model
than are currently prescribed by the Commission.1340 The Commission's prescribed lives are not
based solely on the engineered life of an asset, but also consider the impacts of technological
change and obsolescence. We note that the depreciation values we adopt are generally at the
lower end of the prescribed range. We also find compelling the data presented in GSA's
comments showing that, although the average depreciation rate for an incumbent LEC's Total
Plant in Service is approximately seven percent, incumbent LECs are retiring plant at a four
percent rate.1341 This difference has allowed depreciation reserves to increase so that the
depreciation reserve-ratio is currently greater than 50 percent.1342 We conclude that the
existence of this difference implies that the prescribed lives are shorter than the engineered lives
of these assets. In addition, this difference provides a buffer against technological change and
competitive risk for the immediate future. We, therefore, conclude that the Commission's
prescribed ranges are appropriate to determine depreciation rates for use in the federal high-cost
mechanism.
428. We also decline to adopt the values for projected lives and net salvage
percentages submitted by several incumbent LEC commenters. These commenters propose
adoption of default values for projected lives and salvage based LEC industry date surveys1343 or
on similar values currently used by LECs for financial reporting purposes.1344 The LEC industry
data survey's projected lives generally fall outside of the Commission's prescribed ranges.1345
This is significant because the values that fall outside of the prescribed ranges represent accounts
that reflect the overwhelming majority of plant investment, thus potentially triggering a dramatic
1998 comments at 4.
1340

AT&T/MCI Inputs Further Notice comments at 47-48; GSA Inputs Further Notice comments at 5-6,
Attachment 1; see also HAI June 1, 1998 comments at 13.
1341

GSA Inputs Further Notice comments at 5-6, Attachment 1.

1342

Id.

1343

See, e.g., Ameritech Inputs Further Notice comments at 31-32 (recommending adoption of values endorsed
by Technology Futures, Inc.).
1344

See, e.g., Bell Atlantic Inputs Further Notice comments at 24; GTE Inputs Further Notice comments at 86;
SBC Inputs Further Notice comments at 22-23.
1345

The eight categories in which BCPM's values fall outside required ranges for projected lives were: Digital
Circuit Equipment; Digital Switching; Aerial Cable-Metallic; Aerial Cable-Non-Metallic; Underground CableMetallic; Underground Cable-Non-Metallic; Buried Cable-Metallic; and Buried Cable-Non-Metallic. The two
categories in which BCPM's values fall outside required ranges for net salvage percentage were Digital Circuit
Equipment and Poles.

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distortion of the estimated cost of providing the supported services. Moreover, these
commenters assert that technological advances and competition will have the effect of displacing
current technologies, but offer no specific evidence that this displacement will occur at greater
rates than the forward-looking Commission-authorized depreciation lives take into account. The
record is particularly silent regarding the displacement of technologies associated with the
provision of services supported by the federal high-cost mechanism. We do not believe that the
LEC industry data survey's projected lives have been adequately supported by the record in this
proceeding to justify their adoption.
429. We also agree with GSA's comments that the projected-life values currently used
by LECs for financial reporting purposes are inappropriate for use in the model.1346 In addition,
the commenters proposing these values have not explained why the values used for financial
reporting purposes would also reflect economic depreciation. The depreciation values used in
the LECs' financial reporting are intended to protect investors by preferring a conservative
understatement of net assets, partially achieving this goal by erring on the side of overdepreciation. These preferences are not compatible with the accurate estimation of the cost of
providing services that are supported by the federal high-cost mechanism. We, therefore, decline
to adopt the projected life values used by LECs for financial reporting purposes.
430. In the 1997 Further Notice, the Commission tentatively concluded that it should
adopt depreciation expenses that reflect a weighted average of the rates authorized for carriers
that are required to submit their rates to us.1347 The values submitted by the HAI sponsors
essentially reflect such a weighted average. The HAI values represent the weighted average
depreciation lives and net salvage percentages from 76 study areas.1348 According to the HAI
sponsors, these depreciation lives and salvage values reflect the experience of the incumbent
LEC in each of these study areas in retiring plant and its projected plans for future
retirements.1349
431. In the Inputs Further Notice, we tentatively concluded that HAI's values represent
the best forward-looking estimates of depreciation lives and net salvage percentages.1350
Generally, these values fall within the ranges prescribed by the Commission for projected lives
and net salvage percentages. Although the HAI values for four account categories fall outside of
1346

GSA Inputs Further Notice reply comments at 5.

1347

1997 Further Notice, 12 FCC Rcd at 18571, para. 152.

1348

HAI June 1, 1998 comments at 10.

1349

Id.

1350

The proposed values for these inputs are listed in Appendix A.

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the Commission's prescribed ranges,1351 these values still reflect the weighted average of
projected lives and net salvage percentages that were approved by the Commission and,
therefore, are consistent with the approach proposed in the 1997 Further Notice. As noted
above, the fact that an approved value falls outside of the prescribed range simply means that the
carrier proposing the value was required to provide additional justification to the Commission
for this value. We are satisfied that HAI calculated its proposed rates using the proper
underlying depreciation factors and that HAI's documentation supports the selection of these
values. We, therefore, adopt HAI's values for estimating the depreciation lives and net salvage
percentages.
B.

Cost of Capital

432. We now adopt the conclusions that we tentatively reached in the Inputs Further
Notice regarding the cost of capital. For the reasons discussed below, we do not find that any
commenter has provided a compelling argument for altering the current federal rate of return of
11.25 percent, absent the adoption of a different rate of return by the Commission in a rate
prescription order.
433. The cost of capital represents the annual percentage rate of return1352 that a
company's debt-holders and equity holders require as compensation for providing the debt and
equity capital that a company uses to finance its assets.1353 In the Universal Service Order, the
Commission concluded that the current federal rate of return of 11.25 percent is a reasonable rate
of return by which to determine forward-looking costs.1354
434. GSA, AT&T and MCI comment that the cost of capital for incumbent LECs is
well below 11.25 percent.1355 Bell Atlantic advocates a cost of capital rate in the range of 12.75
1351

HAI's lives and salvage values fall within the Commission's prescribed ranges with the exception of values
for four accounts: Digital Circuit Equipment; Garage Work Equipment; Operator Systems; and Poles.
1352

Rate of return is the percentage which a telephone carrier is authorized to earn on its rate base. For example,
if the rate of return is 11.25% and the rate base is $1 million, the carrier is authorized to earn $112,500.
1353

See Local Exchange Carriers' Rates, Terms, and Conditions for Expanded Interconnection Through
Physical Collocation for Special Access and Switched Transport, Second Report and Order, CC Docket No. 93316212, FCC Rcd 18370, 18765 (1997).
1354

Universal Service Order, 12 FCC Rcd at 8913, para. 250.

1355

AT&T/MCI Inputs Further Notice comments at 50-51 (arguing that forward-looking cost of capital is
approximately 8.5-9.0 percent, but endorsing HAI value of 10.01 percent); GSA Inputs Further Notice comments at
6-7 (noting that GSA had recommended 9.5 percent in rate of return proceeding); AT&T/MCI Inputs Further
Notice reply comments at 41 (arguing that true forward-looking cost of capital is 8.64 percent); see also HAI June 1,
1998 comments at 13.

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to 13.15 percent.1356 GTE and USTA dispute the lower cost of capital asserted by AT&T and
MCI and GSA.1357 All commenters addressing this issue agreed that, if a different rate of return
is adopted in a rate prescription order, that value should be adopted in the model.1358
435. We find that the commenters proposing an adjustment to the cost of capital have
failed to make an adequate showing to justify rates that differ from the current 11.25 percent
federal rate of return. We conclude, therefore, that the current rate is reasonable for determining
the cost of providing services supported by the federal high-cost mechanism. If the Commission,
in a rate prescription order, adopts a different rate of return, we conclude the federal mechanism
should use the more recently determined rate of return.
C.

Annual Charge Factors

436. We also now adopt our tentative conclusion in the Inputs Further Notice to use
HAI's annual charge factor methodology. As explained below, we find this appropriate because
the synthesis model uses a modified version of HAI's expense module.
437. Incumbent LECs develop cost factors, called "annual charge factors," to
determine the dollar amount of recurring costs associated with acquiring and using particular
pieces of investment for a period of one year. Incumbent LECs develop these annual charge
factors for each category of investment required. The annual charge factor is the sum of
depreciation, cost of capital, adjustments to include taxes on equity, and maintenance costs.
438. To develop annual charge factors, the BCPM proponents proposed a model with
user-adjustable inputs to calculate the depreciation and cost of capital rates for each account.1359
The BCPM proponents stated that this account-by-account process was designed to recognize
that all of the major accounts have, among other things, differing economic lives and salvage
values that lead to distinct capital costs.1360 HAI's model is also user adjustable and reflects the
1356

Bell Atlantic Inputs Further Notice comments at 23.

1357

USTA Inputs Further Notice reply comments at 3-4; GTE Inputs Further Notice reply comments at 34-35;
see also BCPM Dec. 11 submission (advocating an 11.36 percent cost of capital).
1358

See, e.g., Ameritech Inputs Further Notice comments at 33-34; AT&T/MCI Inputs Further Notice
comments at 50-51 (but advocating adoption of different rate for model if rate prescription proceeding will not be
concluded prior to January 1, 2000 implementation of model); GSA Inputs Further Notice comments at 6-7; USTA
reply comments at 3.
1359

BCPM Dec. 11 submission at 80.

1360

Id. BCPM's model includes all of the methodologies that are in practice today, including: Deferred taxes;
Mid-year, Beginning Year, and End Year placing conventions; Gompertz-Makeham Survival Curves; Future Net
Salvage Values; Equal Life Group Methods; and others. The model also incorporates separate Cost of Debt and

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sum for the three inputs: depreciation, cost of capital, and maintenance costs.1361 In the Inputs
Further Notice, the Commission tentatively adopted HAI's annual charge factor methodology,
and invited comment on this tentative decision.1362 GTE argues that the annual charge factors
should be company specific, in order to make the cost calculations in the optimization phase and
the expense module comparable.1363 We do not believe it would be appropriate to adopt GTE's
proposal of using company-specific annual charges, because we are adopting nationwide
averages for all other inputs, including those that make up the annual charge. Adopting
company-specific annual charges would therefore result in likely inconsistencies between
various related inputs and in the model as a whole. AT&T and MCI support the use of the HAI
annual charge factor methodology.1364
439. Because the synthesis model uses HAI's expense module, with modifications, we
adopt HAI's annual charge factor methodology, utilizing the capital cost and expense inputs
adopted above.1365 We believe that HAI's annual charge factor methodology is consistent with
other inputs used in the model adopted by the Commission, and is, therefore, easier to implement
and yields more reasonable results.
IX.

PROPOSED MODIFICATION TO PROCEDURES FOR DISTINGUISHING
RURAL AND NON-RURAL COMPANIES

Background

440. In the Universal Service Order, the Commission determined that rural and nonrural carriers will receive federal universal service support determined by separate mechanisms
until at least January 1, 2001.1366 The Commission stated that it would define rural carriers as
those carriers that meet the statutory definition of a rural telephone company in section 153(37)
of the Communications Act.1367 Under this definition, a "local exchange carrier operating entity"
Equity rates, along with the Debt to Equity ratio. Id.
1361

HAI Dec. 11 submission at 41.

1362

Inputs Further Notice at para. 242.

1363

GTE Inputs Further Notice comments at 87.

1364

AT&T/MCI Inputs Further Notice comments at 51.

1365

The expense module contains the expense values including plant-specific maintenance ratios and the
algorithms that determine monthly cost per-line, given the results of all other modules.
1366

Universal Service Order, 12 FCC Rcd at 8927, para. 273.

1367

See 47 U.S.C. § 153(37); Universal Service Order, 12 FCC Rcd at 8944, para. 310.

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is deemed a "rural telephone company" to the extent that such entity-(A) provides common carrier service to any local exchange carrier study
area that does not include either-(i) any incorporated place of 10,000 inhabitants or more, or any
part thereof, based on the most recently available population statistics of
the Bureau of the Census; or
(ii) any territory, incorporated or unincorporated, included in an
urbanized area, as defined by the Bureau of the Census as of August 10,
1993;
(B) provides telephone exchange service, including exchange access, to
fewer than 50,000 access lines;
(C) provides telephone exchange service to any local exchange carrier
study area with fewer than 100,000 access lines; or
(D) has less than 15 percent of its access lines in communities of more
than 50,000 on the date of enactment of the Telecommunications Act of 1996.
441. In addition, the Commission determined that LECs should self-certify their status
as a rural company each year to the Commission and their state commission.1368 On September
23, 1997, the Bureau released a Public Notice requiring carriers seeking to be classified as rural
telephone companies to file a letter with the Commission by April 30 of each year certifying that
they meet the statutory definition.1369 The Self-Certification Public Notice requires a LEC
certifying as a rural carrier to explain how it meets at least one of the four criteria set forth in the
statutory definition.1370 On June 22, 1998, the Accounting Policy Division (Division) released a
Public Notice with a list of the approximately 1,400 carriers that had certified as rural carriers as
of April 30, 1998.1371 On March 16, 1999, the Bureau released a Public Notice revising the
annual deadline for LECs seeking to be classified as rural carriers to July 1 of each year. In the
Inputs Further Notice, the Commission extended the July 1, 1999, recertification filing deadline
to October 15, 1999.1372 On September 27, 1999, the Division released a Public Notice further
extending the deadline to December 1, 1999, in consideration of the possibility that certain
1368

Universal Service Order, 12 FCC Rcd at 8943-44, para. 310.

1369

Self-Certification as a Rural Telephone Company, Public Notice, CC Docket No. 96-45, DA 97-1748 (rel.
Sept. 23, 1997) (Self-Certification Public Notice).
1370

See 47 U.S.C. § 153(37).

1371

Commission Acknowledges Receipt of Letters Self-Certifying LECs as Rural Telephone Companies, Public
Notice, CC Docket No. 96-45, DA 98-1205 (rel. June 22, 1998).
1372

Inputs Further Notice at para. 255.

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carriers might not be required to file the certification letter in light of the action we take in this
Order.1373
442. Because a vast majority of the carriers certifying as rural telephone companies
serve fewer than 100,000 access lines, we tentatively concluded in the Inputs Further Notice that
we should adopt new filing requirements for carriers filing rural self-certification letters.1374 We
proposed that carriers who serve fewer than 100,000 access lines should not have to file the
annual rural certification letter unless their status has changed since their last filing.1375 We also
sought comment on certain terms relevant to the definition of a rural telephone company in
section 153(37) of the Act.1376 In addition, we sought comment on whether the Commission
should reconsider its use of section 153(37) to distinguish rural telephone companies from nonrural telephone companies.1377
B.

Discussion

443.
Consistent with our tentative conclusion in the Inputs Further Notice, we
eliminate the annual filing requirements for carriers serving fewer than 100,000 access lines that
have self-certified as rural, unless changes occur in their status as rural carriers. In addition, we
will require carriers serving study areas with more than 100,000 access lines to file rural selfcertifications that are consistent with the statutory interpretation discussed below. Thereafter,
such carriers also will be required to file only in the event of a change in their status.
444. As discussed below, we interpret "local exchange operating company" in section
153(37) of the Act to refer to the legal entity that provides local exchange service. In addition,
we interpret "communities of more than 50,000" in that section to refer to legally incorporated
1373

Common Carrier Bureau Extends Rural Carrier Recertification Filing Deadline, Public Notice, CC Docket
No. 96-45, DA 99-1948 (rel. September 27, 1999).
1374

Inputs Further Notice at para. 246.

1375

Id. The National Exchange Carrier Association, Inc. (NECA) has requested that the Commission eliminate
the annual rural certification process.
NECA states that the majority of carriers
that meet the rural definition are small LECs
with limited resources, whose status is not
likely to change. Letter from Richard A.
Askoff, NECA to Irene Flannery, FCC,
dated April 9, 1999.

1376

Inputs Further Notice at paras. 251-53.

1377

Id. at para. 254.

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localities, consolidated cities, and census-designated places with populations of more than
50,000 according to Census Bureau statistics.
445. With respect to our request for comment on whether we should reconsider our use
of section 153(37) to distinguish rural telephone companies from non-rural companies, we
conclude below that we should not use an alternative definition of rural telephone company to
determine which companies are subject to the rural or non-rural high-cost support mechanisms.
446. Because of settled expectations in this ongoing proceeding, the Commission will
accept a carrier's current rural self-certification for purposes of calculating support based on that
status for calendar year 2000. We will require carriers serving study areas with more than
100,000 access lines to certify their rural status by July 1, 2000, for purposes of receiving
support beginning January 1, 2001.
1.

Annual Filing Requirement

447. Carriers serving study areas with fewer than 100,000 access lines. We adopt the
proposed change in the annual self-certification requirement for rural carriers and will require
that carriers serving fewer than 100,000 access lines file a rural self-certification letter only if
their status has changed since their last filing. All commenters addressing this issue urge the
Commission to eliminate annual filing requirements.1378 We believe that this is a better
approach because the overwhelming majority of the companies that filed rural certification
letters qualified as rural telephone companies under the 50,000- or 100,000-line thresholds
identified in the statute. Access line counts can be verified easily with publicly available data.
Further, this relaxation in filing requirements will lessen the burden on rural carriers. We
estimate that this change will eliminate the filing requirement for approximately 1,380 of the
carriers that filed in 1998, many of which are small businesses on which even limited regulatory
requirements may be unduly burdensome. We, therefore, conclude that carriers serving study
areas with fewer than 100,000 access lines that already have certified their rural status need not
re-certify for purposes of receiving support beginning January 1, 2000, and need only file
thereafter if their status changes. As explained below, we must determine the status for carriers
serving study areas with more than 100,000 access lines.
1378

ALLTEL Further Inputs Notice comments at 2; Alaska Telephone Association Further Inputs Notice
comments at 2; Bentleyville Telephone Company Further Inputs Notice comments at 1; CenturyTel Further Inputs
Notice comments at 7; Citizens Utilities Further Inputs Notice comments at 6; GTE Further Inputs Notice
comments at 91; GVNW Consulting (GVNW) Further Inputs Notice comments at 2; Matanuska Telephone
Association (MTA) Further Inputs Notice comments at 1; NECA Further Inputs Notice comments at 2; Rural
Telephone Coalition (RTC) Further Inputs Notice comments at 8; Skyline Telephone Membership Corporation
Further Inputs Notice comments at 1, 3; South Slope Cooperative Telephone Company Further Inputs Notice
comments at 1-2; TXU Communications Telephone Company (TXU) Further Inputs Notice comments at 6; USTA
Further Inputs Notice comments at 6; Virgin Islands Telephone Corporation (Vitelco) Further Inputs Notice
comments at 7; Yukon Telephone Company Further Inputs Notice comments at 1.

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448. We believe, as GTE suggests, that carriers generally (although not uniformly)
have filed for rural status in this proceeding on a study area basis. Indeed, the synthesis model
that has been posted on the Commission's Web site -- allowing carriers to determine how the
Commission has been treating them throughout this proceeding -- estimates cost on a study area
basis.1379 Not all carriers, however, have uniformly filed for rural status on a study area basis, as
we noted in the Inputs Further Notice, resulting in inconsistencies that must be resolved in order
to assure equitable treatment of all carriers. These inconsistencies will be addressed below.
449. Carriers serving study areas with more than 100,000 access lines. For purposes of
calculating high cost support using the model for the year 2000, we will continue to treat carriers
as rural if they have previously self-certified as rural carriers. We will then require rural carriers
serving study areas with more than 100,000 access lines to file certification letters by July 1,
2000, for their year 2001 status. Commenters that address the issue broadly support recertification requirements that require these carriers to re-certify only if their status has changed,
rather than require them to re-certify each year.1380 Finding that the relaxed re-certification
requirements will reduce administrative burdens for carriers subject to rural certification and for
the Commission, we conclude that certified rural carriers with more than 100,000 access lines
need only re-certify their status if it changes. Therefore, in 2001 and subsequent years, a carrier
serving study areas with more than 100,000 access lines and claiming rural status will be
required to file only if its status changes.
2.

Statutory Terms

450. As noted in the Inputs Further Notice, carriers' line counts are readily available to
the Commission, but information about service territories and communities served are not. As a
result, the Commission can easily determine whether a carrier satisfies criteria (B) or (C) of the
rural telephone company definition,1381 because these criteria are based on information that can
be verified easily with publicly available data -- the number of access lines served by a carrier.
1379

The model also estimates cost on a wire center basis. Also, we note that PRTC and Anchorage Telephone
Utility previously had been excluded from the non-rural model runs because of the unavailability of data for Puerto
Rico and Alaska, but those companies have participated in the proceeding on the presumption that were non-rural.
The formerly unavailable data is now available, and has been incorporated into the model posted on the
Commission's web site. See Letter from Charles A. White, PNR, to Magalie Roman Salas, FCC, dated July 29,
1999 (PNR July 29 ex parte).
1380

See, e.g., ALLTEL Inputs Further Notice comments at 2; CenturyTel Inputs Further Notice comments at 7;
GVNW Inputs Further Notice Comments at 2; MTA Inputs Further Notice Comments at 1; NECA Inputs Further
Notice comments at 2; TXU Inputs Further Notice comments at 6; USTA Inputs Further Notice comments at 6;
TXU Inputs Further Notice reply comments at 4.
1381

47 U.S.C. § 153(37)(B), (C).

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In contrast, criteria (A) and (D) require additional information and analysis to verify a carrier's
self-certification as a rural company.1382 Specifically, under criterion (A), a carrier is rural if its
study area does not include "any incorporated place of 10,000 inhabitants or more" or "any
territory . . . in an urbanized area," based upon Census Bureau statistics and definitions.1383
Under criterion (D), a carrier is rural if it had "less than 15 percent of its access lines in
communities of more than 50,000 on the date of enactment of the [1996 Act]."1384
451. We conclude that criterion (A), by referencing Census Bureau sources, can be
applied consistently without further interpretation by the Commission. We will require,
however, that carriers self-certifying as rural telephone companies pursuant to criterion (A)
include with their self-certification letter a description of the study areas in which they provide
service and the basis for their assertion that they meet the requirements of criterion (A).
452. In the Inputs Further Notice, we sought comment on the meaning of the term
"local exchange operating entity." Commenters have offered three different interpretations of
this term. Many commenters suggest that we should interpret the term as applying at the study
area level.1385 Although in most cases an operating entity will provide service to only one study
area within a state, that is not always the case. As a result, the study area approach could mean
classifying a carrier at an organizational level smaller than the actual legal entity responsible for
the provision of the local exchange services (e.g., a "division" of a company). In contrast,
AT&T and MCI argue that the term should mean the holding company within a state whose
affiliates provide the local exchange services.1386 The third interpretation has been proposed by
RTC and Citizens Utilities, who argue that the most natural understanding of "local exchange
operating entity" is the legal entity responsible for the provision of local exchange services,
regardless of whether that entity serves a single or multiple study areas.1387 We conclude that
this interpretation is the most reasonable one.
1382

Most carriers asserting rural status under criteria (A) or (D) also claim rural status under the access line
thresholds in criteria (B) or (C). In those cases, the Commission does not need additional information to verify the
carrier's rural status.
1383

47 U.S.C. § 153(37)(A).

1384

47 U.S.C. § 153(37)(D).

1385

CenturyTel Further Inputs Notice comments at 3-4; Commonwealth Telephone Company (Commonwealth)
Further Inputs Notice comments at 4-5; GTE Further Inputs Notice comments at 92-93; USTA Further Inputs
Notice comments at 7; USTA Further Inputs Notice reply comments at 4-5; Vitelco Further Inputs Notice
comments at 8; Vitelco Further Inputs Notice reply comments at 1-4.
1386

AT&T/MCI Inputs Further Notice comments at 42.

1387

Citizens Utilities Further Inputs Notice comments at 3-5; RTC Further Inputs Notice comments at 9-11;
RTC Further Inputs Notice reply comments at 2; TXU Further Inputs Notice reply comments.

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453. We believe that it is most logical to classify the carrier at the actual corporate
level through which it offers its local exchange services. As RTC and Citizens Utilities point
out, it is that entity that has legal responsibility for the provision of the local exchange
services.1388 The holding company interpretation proposed by MCI and AT&T seems to rest
upon the concern that study area designations will be manipulated and, as a result, carriers will
inappropriately be eligible for support as rural carriers, when they should not be.1389 We do not
believe that the potential for manipulation of the federal universal service support mechanism by
rural carriers poses the threat that AT&T and MCI suggest; to the contrary, the study area waiver
process provides the Commission with oversight over the creation, division, and combination of
study areas.1390
454. On the other hand, if a carrier should be operating within multiple study areas, we
see no basis for interpreting the term "local exchange operating entity" in a manner that would
ignore the legal entity responsible for the provision of services by designating a subunit of the
legal entity as the local exchange operating entity for a particular study area. Rather, it is more
reasonable to have the term local exchange operating entity be synonymous with the corporate
entity bearing legal responsibility for the services provided.1391
455. Although we adopt Citizen Utilities' interpretation of "local exchange operating
entity," we reject its proposed interpretation of criterion (C). Citizens Utilities proposes that a
local exchange carrier operating entity be considered a rural carrier for each of its study areas,
regardless of whether those study areas have fewer than 100,000 access lines, if any single study
area in which it operates contains fewer than 100,000 access lines.1392 Under this interpretation,
which only Citizens Utilities supports, an incumbent LEC offering service to a significant
portion of a state, including major urban areas, could be certified as a rural carrier for all study
areas that it serves within the state if it merely has one outlying study area with less than 100,000
access lines. We find this interpretation to be inconsistent with the statutory language that an
entity is an rural telephone company only "to the extent" that it serves a study area with fewer
1388

Citizens Utilities Further Inputs Notice comments at 3-5; RTC Further Inputs Notice comments at 9-11;
RTC Further Inputs Notice reply comments at 2; TXU Further Inputs Notice reply comments.
1389

See AT&T/MCI Inputs Further Notice comments at 42.

1390

Study areas have been "frozen" since November 15, 1984, except where a waiver has been obtained. 47
C.F.R. § 36 (App.) (defining "study area").
1391

We further note that it appears that some carriers with multiple study areas within a state will have a separate
corporate entity for each study area. As a result, for these carriers there would be little practical difference between
the first interpretation and the one that we adopt.
1392

Citizens Utilities Further Inputs Notice comments at 6.

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than 100,000 lines. Essentially, Citizens Utilities' interpretation would read that limiting
language out of section 153(37). The effect of such a reading would be to permit some of the
largest LECs in the nation to claim rural status for all of their study areas if they happen to serve
a rural study area within in the state. Such an interpretation would undermine not only the
Commission's universal service support mechanisms, but also the fundamental procompetitive
policies underlying the 1996 Act.1393 We do not believe that this could be what Congress
intended when it specified that carriers would be deemed rural telephone companies "to the
extent" that they satisfied the various criteria, including criterion (C) pertaining to serving study
areas with less than 100,000 access lines. Accordingly, consistent with the language of the
statutory provision, its purpose, and its context in the Act, we adopt the interpretation that a LEC
may be properly considered a rural carrier with respect to those study areas to which its
operating company provides service to fewer than 100,000 access lines. In contrast, a LEC will
be deemed a non-rural carrier for study areas serving more than 100,000 access lines unless it
satisfies one of the other criteria under section 153(37).
456. We also sought comment in the Inputs Further Notice regarding the proper
interpretation of "communities of more than 50,000." GTE offers an interpretation of this phrase
based on the definition of "rural area" in section 54.5 of the Commission's rules.1394 GTE
calculates its percentages of rural and non-rural lines by determining whether each of its wire
centers is associated with a metropolitan statistical area (MSA). The lines in each wire center
associated with an MSA are considered to be urban, unless the wire center has rural pockets, as
defined by the most recent Goldsmith Modification.1395 The approach suggested by GTE in its
comments has merit because it prevents rural treatment of a suburban area adjacent to a censusdesignated place. At this time, however, there is no information on the record to indicate that
this circumstance presents a serious problem in our determination of a carrier's status as a rural
or non-rural company. Other commenters addressing the issue support the definition of
1393

For example, if a carrier with more than one study area could claim that it was rural because one of its study
areas served less than 100,000 lines, it could, under Citizen Utilities' definition of criterion (C), also claim that it
was exempt from the obligations of 251(c) throughout its service territory.
1394

GTE Further Inputs Notice comments at 94-96. Section 54.5 provides the following definition of rural area:
A "rural area" is a non-metropolitan county or county equivalent, as defined in the Office of
Management and Budget's (OMB) Revised Standards for Defining Metropolitan Areas in the
1990s and identifiable from the most recent Metropolitan Statistical Area (MSA) list released by
OMB, or any contiguous non-urban Census Tract or Block Numbered Area within an MSA-listed
metropolitan county identified in the most recent Goldsmith Modification published by the Office
of Rural Health Policy of the U.S. Department of Health and Human Services.

47 C.F.R. § 54.5.
1395

See 47 C.F.R. § 54.5.

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"communities of more than 50,000" by using Census Bureau statistics for legally incorporated
localities, consolidated cities, and census-designated places,1396 and some specifically reject the
use of the Commission's definition in section 54.5 because of the added complication of its
use.1397
457. Because GTE's approach is more complicated and difficult to administer and
because the consequences of the approach would reach only a few, if any, rural carriers' study
areas, we decline to adopt GTE's interpretation of "communities of more than 50,000." Instead,
we now adopt the use of Census Bureau statistics for legally incorporated localities,
consolidated, cities, and census-designated places for identifying communities of more than
50,000, as Census Bureau statistics are widely available and may be consistently applied by the
Commission. We further require that, when a carrier files for rural certification under criterion
(D), it must include in its certifying letter a list of all communities of more than 50,000 to which
it provides service, the population of those communities, the number of access lines serving
those communities, and the total number of access lines the carrier serves.
3.

Identification of Rural Telephone Companies

458. States apply the definition of rural telephone company in determining whether a
rural telephone company is entitled to an exemption under section 251(f)(1) of the Act and in
determining, under section 214(e)(2) of the Act, whether to designate more than one carrier as an
eligible telecommunications carrier in an area served by a rural telephone company.1398
Although the Commission used the rural telephone company definition to distinguish between
rural and non-rural carriers for purposes of calculating universal service support, there is no
statutory requirement that it do so. The Commission adopted the Joint Board's recommendation
to allow rural carriers to receive support based on embedded costs for at least three years,
because, as compared to large LECs, rural carriers generally serve fewer subscribers, serve more
sparsely populated areas, and do not generally benefit as much from economies of scale and
scope.1399 The Commission also noted that, for many rural carriers, universal service support
provides a large share of the carriers' revenues, and thus, any sudden change in the support
mechanisms may disproportionately affect rural carriers' operations.1400
1396

CenturyTel Further Inputs Notice comments at 7; Citizens Utilities Further Inputs Notice comments at 7-8;
Commonwealth Further Inputs Notice comments at 5.
1397

Citizens Utilities Further Inputs Notice comments at 8; Commonwealth Further Inputs Notice comments at

1398

47 U.S.C. §§ 214(e)(2), 251(f)(1).

1399

Universal Service Order, 12 FCC Rcd at 8936, para. 294.

1400

Universal Service Order, 12 FCC Rcd at 8936, para. 294.

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459. In the Inputs Further Notice, we sought comment on whether to reconsider the
means of distinguishing rural and non-rural carriers. Commenters generally oppose any
reconsideration of our decision to use the definition of rural telephone company to distinguish
between rural and non-rural carriers for the purpose of evaluating universal service support on
the grounds that changing the definition at this time could disrupt the settled expectations that
they have developed.1401 We agree that we should not change our reliance on the statutory
definition of rural telephone company to distinguish between rural and non-rural carriers for
universal service purposes. Accordingly, we will leave in place the Commission's decision to
use the definition of rural telephone company in section 153(37) of the Communications Act to
distinguish rural telephone companies from non-rural ones.
X. PROCEDURAL MATTERS AND ORDERING CLAUSE
A.

Final Regulatory Flexibility Analysis

460. As required by the Regulatory Flexibility Act (RFA),1402 an Initial Regulatory
Flexibility Analysis (IRFA) was incorporated in the Inputs Further Notice.1403 The Commission
sought written public comment on the proposals in the Inputs Further Notice, including
comments on the IRFA. The Final Regulatory Flexibility Analysis (FRFA) in this Order
conforms to the RFA, as amended.1404
461. Need for and Objectives of This Order. In the Universal Service Order, the
Commission adopted a plan for universal service support for rural, insular, and high-cost areas to
replace longstanding federal subsidies to incumbent local telephone companies with explicit,
competitively neutral federal universal service mechanisms.1405 In doing so, the Commission
adopted the recommendation of the Joint Board that an eligible carrier's support should be based
upon the forward-looking economic cost of constructing and operating the networks facilities
and functions used to provide the services supported by the federal universal service mechanism.
1401

CenturyTel Further Inputs Notice comments at 6; Commonwealth Further Inputs Notice comments at 2;
Citizens Utilities Further Inputs Notice comments at 3; GTE Further Inputs Notice comments at 96-98; RTC
Further Inputs Notice comments at 15; RTC Further Inputs Notice reply comments at 2; TXU Further Inputs Notice
reply comments at 4; USTA Further Inputs Notice comments at 7.
1402

See 5 U.S.C. § 603. The RFA, see 5 U.S.C. § 601 et seq., has been amended by the Contract with America
Advancement Act of 1996, Pub. L. No. 104-121, 110 Stat. 847 (1996) (CWAAA). Title II of the CWAAA is the
Small Business Regulatory Enforcement Fairness Act of 1996 (SBREFA).
1403

Inputs Further Notice at paras. 257-271.

1404

See 5 U.S.C. § 604.

1405

Universal Service Order, 12 FCC Rcd. 8776.

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462. In the Universal Service Order, the Commission also determined that rural and
non-rural carriers will receive federal universal service support determined by separate
mechanisms until at least January 1, 2001.1406 The Commission stated that it would define rural
carriers as those carriers that meet the statutory definition of a rural telephone company in
section 153(37) of the Communications Act.1407 We have found that carriers self-certifying as
rural have not always applied section 153(37) uniformly.1408 In section IX of this Order, we
clarify our interpretation of section 153(37). We also address the possibility that our annual selfcertification requirements may be modified or eliminated in order to reduce the reporting burden
on filing entities.
463. Our plan to adopt a mechanism to estimate forward-looking costs for larger, nonrural carriers has proceeded in two stages. On October 28, 1998, the Commission completed the
first stage of this proceeding: the selection of the model platform. The platform encompasses
the aspects of the model that are essentially fixed, primarily assumptions about the design of the
network and network engineering. In this Order, we complete the second stage of this
proceeding, by selecting input values for the cost model, such as the cost of cables, switches and
other network components, in addition to various capital cost parameters.
464. Summary and Analysis of the Significant Issues Raised by Public Comments in
Response to the IRFA. No comments were received specifically in response to the IRFA. We
received several comments, however, addressing concerns that may affect small entities. These
comments universally supported our proposal, adopted in this Order,1409 to reduce the burden of
carriers self-certifying as rural by eliminating the annual filing requirement.
465. Description and Estimate of the Number of Small Entities to which the Order will
Apply. The RFA generally defines "small entity" as having the same meaning as the term "small
business," "small organization," and "small government jurisdiction."1410 In addition, the term
"small business" has the same meaning as the term "small business concern" under the Small
Business Act, unless the Commission has developed one or more definitions that are appropriate
to its activities.1411 Under the Small Business Act, a "small business concern" is one that: (1) is
1406

Id. at 8927, para. 273.

1407

See 47 U.S.C. § 153(37); Universal Service Order, 12 FCC Rcd at 8944, para. 310.

1408

See Inputs Further Notice at para. 249.

1409

See section IX, above.

1410

5 U.S.C. § 601(6).

1411

5 U.S.C. § 601(3) (incorporating by reference the definition of "small business concern" in 5 U.S.C. § 632).

201

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independently owned and operated; (2) is not dominant in its field of operation; and (3) meets
any additional criteria established by the SBA.1412 The SBA has defined a small business for
Standard Industrial Classification (SIC) category 4813 (Telephone Communications, Except
Radiotelephone) to be small entities when they have no more than 1,500 employees.1413
466. We have included small incumbent LECs in this present RFA analysis. As noted
above, a "small business" under the RFA is one that, inter alia, meets the pertinent small
business size standard (e.g., a telephone communications business having 1,500 or fewer
employees), and "is not dominant in its field of operation."1414 The SBA's Office of Advocacy
contends that, for RFA purposes, small incumbent LECs are not dominant in their field of
operation because any such dominance is not "national" in scope.1415 We have therefore included
small incumbent LECs in this RFA analysis, although we emphasize that this RFA action has no
effect on Commission analyses and determinations in other, non-RFA contexts.
467. Local Exchange Carriers. Neither the Commission nor SBA has developed a
definition of small providers specifically directed toward LECs. The closest applicable
definition under SBA rules is for telephone communications companies other than
radiotelephone (wireless) companies. The most reliable source of information regarding the
number of LECs nationwide of which we are aware appears to be the data that we collect
annually in connection with the Telecommunications Relay Service (TRS).1416 According to our
most recent data, 1,410 companies reported that they were engaged in the provision of local
Pursuant to 5 U.S.C. § 601(3), the statutory definition of a small business applies "unless an agency after
consultation with the Office of Advocacy of the Small Business Administration and after opportunity for public
comment, establishes one or more definitions of such term which are appropriate to the activities of the agency and
publishes such definition in the Federal Register."
1412

15 U.S.C. § 632. See, e.g., Brown Transport Truckload, Inc. v. Southern Wipers, Inc., 176 B.R. 82 (N.D.
Ga. 1994).
1413

13 C.F.R. § 121.201.

1414

5 U.S.C. § 601(3).

1415

Letter from Jere W. Glover, Chief Counsel for Advocacy, SBA, to William E. Kennard, Chairman, FCC
(May 27, 1999). The Small Business Act contains a definition of "small business concern," which the RFA
incorporates into its own definition of "small business." See 15 U.S.C. § 632(a) (Small Business Act); 5 U.S.C. §
601(3) (RFA). SBA regulations interpret "small business concern" to include the concept of dominance on a
national basis. 13 C.F.R. § 121.102(b). Since 1996, out of an abundance of caution, the Commission has included
small incumbent LECs in its regulatory flexibility analyses. Implementation of the Local Competition Provisions of
the Telecommunications Act of 1996, CC Docket, 96-98, First Report and Order, 11 FCC Rcd 15499, 16144-45
(1996).
1416

See 47 C.F.R. § 64.601 et seq.

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exchange service as incumbents.1417 Although it seems certain that some of these carriers are not
independently owned and operated, or have more than 1,500 employees, we are unable at this
time to estimate with greater precision the number of LECs that would qualify as small business
concerns under SBA's definition. Consequently, we estimate that there are fewer than 1,410
small entity LECs that may be affected by this Order. We also note that, with the exception of
our clarification of the definition of rural carrier under section 153(37) and the modification of
reporting requirements, the rules adopted by this Order apply only to larger, non-rural LECs.
468. Description of Projected Reporting, Recordkeeping, and Other Compliance
Requirements. This Order imposes no new reporting, recordkeeping, or other compliance
requirements. As discussed more fully in section IX, above, this Order immediately eliminates
the requirement that carriers serving study areas with fewer than 100,000 access lines must
annually file letters certifying themselves as rural carriers in order to remain in the rural carrier
universal service support mechanism. Further, this Order eliminates, after the July 1, 2000,
filing deadline, the requirement that rural carriers serving study areas with more than 100,000
access lines must file annual self-certification letters. All rural carriers must, however, notify the
Commission in the event of a change in rural status.
469. The overall effect of this Order will be to reduce reporting, recordkeeping, and
other compliance requirements for small entities.1418 This benefit will apply to all carriers
deemed rural under section 153(37), regardless of whether they are a small or large entity.
Carriers serving study areas with fewer than 100,000 access lines--which are more likely to be
small entities than those serving study areas with more than 100,000 access lines--will be most
immediately benefited, as no further filings will be required of them unless and until their rural
status changes. The largest carriers will generally be non-rural and not affected by this change in
reporting. To the extent that large and small entities are treated differently, therefore, small
entities will not carry a disproportionately high cost of compliance.
470. Steps Taken to Minimize Significant Economic Impact on Small Entities and
Significant Alternatives Considered. As noted above and discussed more fully in section IX,
with respect to reporting requirements affecting small entities, we eliminate the burden of an
annual filing requirement for rural carriers. For carriers serving study areas with fewer than
100,000 access lines, this change is effective immediately. Rural carriers serving study areas
with more than 100,000 access lines will be required to file a self-certification letter by July 1,
2000, but will not be required to refile additional annual certifications unless their status
changes. These changes have at their heart consideration of the resources of small entities, and
will reduce, if not eliminate, the costs of compliance for small entities. The alternative to this
1417

FCC, Carrier Locator: Interstate Service Providers, at Figure 1 (Jan. 1999).

1418

See para. 447, supra.

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approach would have been to require additional unnecessary self-certification letters from the
vast majority of filing carriers, even though the data supporting those self-certifications are
easily verified by publicly available documentation.1419 The other changes to Commission rules
that we adopt in this Order affect only larger, non-rural LECs, and should have no direct affect
on small entities.
471. Report to Congress. The Commission will send a copy of this Order, including
this FRFA, in a report to be sent to Congress pursuant to the Small Business Regulatory
Enforcement Fairness Act of 1996.1420 In addition, the Commission will send a copy of the this
Order, including FRFA, to the Chief Counsel for Advocacy of the Small Business
Administration. A copy of this Order and FRFA (or summaries thereof) will also be published in
the Federal Register.1421
B.

Paperwork Reduction Act Analysis

472. The decision herein has been analyzed with respect to the Paperwork Reduction
Act of 1995, Pub. L. 104-13, and has been approved in accordance with the provisions of that
Act. On August 4, 1999, the Office of Management and Budget approved the proposed
requirements contained in the Inputs Further Notice under OMB control number 3060-0793.
C.

Ordering Clauses

473. IT IS ORDERED, pursuant to Sections 1, 4(i) and (j), 201-209, 218-222, 254, and
403 of the Communications Act, as amended, 47 U.S.C. §§ 151, 154(i), 154(j), 201-209, 218222, 254, and 403 that this Report and Order IS HEREBY ADOPTED.
474. IT IS FURTHER ORDERED that the Commission's Office of Public Affairs,
Reference Operations Division, SHALL SEND a copy of this Report and Order, including the
Final Regulatory Flexibility Analysis, to the Chief Counsel for Advocacy of the Small Business
Administration.
FEDERAL COMMUNICATIONS COMMISSION
1419

See para. 447, supra.

1420

See 5 U.S.C. § 801(a)(1)(A).

1421

See 5 U.S.C. § 604(b).

204

Federal Communications Commission

Magalie Roman Salas
Secretary

205

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Separate Statement of
Commissioner Gloria Tristani
Re:

Federal-State Joint Board on Universal Service; Ninth Report & Order and
Seventeenth Order on Reconsideration. CC Docket No. 96-45
Federal-State Joint Board on Universal Service; Forward-Looking Mechanism
for High Cost Support for Non-Rural LECs. CC Docket Nos. 96-45 & 97-160.

In adopting these Orders, the Commission has taken an important step towards
fulfilling its mandate under the 1996 Act to ensure that all Americans have access to
telecommunications and information services. The new high-cost mechanism, together
with the selected inputs, establishes a specific, predictable, and sufficient mechanism to
preserve and advance universal service. I believe that the mechanism will provide
sufficient resources to the states to ensure reasonable comparability of rates among states.
Moreover, I am pleased that the Commission will be ready to provide forward-looking
support to non-rural carriers based on this mechanism, effective January 1, 2000.
I commend my fellow Joint Board members, the Joint Board staff, and the
Common Carrier Bureau for their outstanding cooperation in developing the model and
model inputs. I likewise commend the outside parties who worked with the Joint Board
and the Bureau throughout this process. I look forward to continued cooperation as we
confront the other pieces of universal service reform, including adjusting interstate access
charges to account for explicit support, selecting an appropriate methodology for rural
carriers serving high cost areas, and addressing the needs of unserved and underserved
areas.

Federal Communications Commission

FCC 99-304

DISSENTING STATEMENT OF COMMISSION FURCHTGOTT-ROTH
Re:
Federal-State Joint Board on Universal Service, Ninth Report & Order and
Eighteenth Order on Reconsideration, CC Docket No. 96-45; Federal-State Joint Board
on Universal Service, Forward-Looking Mechanism for High Cost Support for NonRural LECs, Tenth Report and Order, CC Docket Nos. 96-45, 97-160.
In the companion orders that it releases today, the Commission finalizes its
implementation of a computer model that it will use to determine the total cost of
providing service to every resident in the country. It plans to use this model to distribute
universal service support among “non-rural carriers,” the term that is used to describe the
large telephone companies that serve rural areas. As I have said at earlier stages in this
proceeding, this Commission’s approach to universal service is fundamentally at odds
with the Telecommunications Act generally and specifically with its express directive
that the Commission “preserve and advance” universal service. Moreover, its adoption
of this unwieldy model is inconsistent with the Act’s mandate that universal service
support be “specific” and “predictable.” Finally, as a consequence of the Commission’s
action today, consumers will now pay higher bills for dubious subsidies to large
companies. I therefore dissent from these orders.
The Orders Are Inconsistent With Congress’s Objective of Preserving
Universal Service Support for Rural Carriers. By way of background, four years ago,
universal service was a $2 billion per year program targeted mostly at small, rural
telephone companies. Today, as a result of the Commission’s unwarranted interference
in the existing universal service system and the new programs that it has dreamed up, the
program costs taxpayers more than $5 billion a year.
I believe that this proceeding illustrates, yet again, that this Commission has its
universal service priorities entirely backward. Section 254 of the Telecommunications
Act of 1996 was drafted with rural carriers in mind. The primary objective of that
provision was to ensure that rural carriers continued to receive sufficient funding to
enable them to provide local service at rates comparable to those in urban areas. In light
of this objective, the Commission should have turned first to the matter of preserving
rural universal service. Instead, the Commission has squandered a tremendous amount of
its employees’ time and taxpayers’ money coming up with an entirely new approach to
universal service. And the matter of universal service support for rural carriers has been
this Commission’s very last priority.
I am relieved to see that the Commission has in these orders taken steps to ensure
that funding for rural carriers will not decrease – at least in the near term. I have little
confidence, however, that rural carriers can count on this promise for long. This
Commission has so substantially increased universal service funding for other, less

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FCC 99-304

essential programs that, if and when it finally turns to addressing the issue of rural
universal service support, I question whether there will be any money left for rural
telephone companies.
The Commission’s Model Is Unwieldy, Easily Manipulated, and Will Require
Constant Maintenance. Not only does the Commission have its universal service
priorities wrong, but also the model on which it relies is inconsistent with the
Telecommunications Act’s requirement that universal service support be “specific” and
“predictable.” The model is an immensely complicated computer program that requires
around 180 hours – more than one week – to run. Since issuing an October 1998 NPRM
in which it proposed this model, the Commission has made numerous changes to the
model platform, and each change has required interested parties to go back to their
computers and spend days testing the model. Only in the last few weeks has the
Commission decided on final input values. In my view, it is unclear whether interested
parties have even had the opportunity meaningfully to comment on a final version of the
model, as the Administrative Procedure Act requires.
The model is also completely dependent on hundreds of assumptions about the
local exchange markets and costs. The bottom line is that, simply by making different
assumptions about local exchange networks, or by picking different input values for
costs, the Commission is able to push the end result in whatever direction it chooses. I
do not believe that a system that can be manipulated in this way will generate the
“specific” and “predictable” universal service support that the 1996 Act requires. In
addition, the fact that the Commission has found it necessary to tinker with this model so
extensively reflects its fundamental lack of confidence in its model.
The model is also going to be enormously time-consuming and expensive to
maintain. Each time technology or prices change, the Commission’s staff will be
required to adjust the model. I am opposed to wasting resources on this effort.
The Commission’s Approach to Universal Service Means that Consumers
Will Pay More. As a final matter, I want to point out what the Commission’s current
approach to high-cost universal service will mean for consumers. According to the
model, carriers in a few states (primarily Mississippi and Alabama) should receive
significantly more funding than they currently do, and the Commission plans to increase
subsidies for carriers in these states. But the model also says that carriers in many other
states should receive less universal service funding than they now do. The Commission,
however, does not plan to follow the model’s guidance with respect to these carriers.
Instead, because it committed to Congress in April 1998 that universal service support
would not decrease for any state, the Commission plans to continue distributing current
levels of universal service support to carriers in all states.
The result of this so-called “hold harmless” requirement is that all carriers will
receive as much or more universal service funding as they did before the issuance of

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FCC 99-304

these two orders. In other words, the bill for high-cost universal service support will go
up, and consumers’ phone bills are going to increase correspondingly. I predict that these
will be only the first of several increases that consumers can expect to see in the
upcoming months as a result of this Commission's misguided universal service policies.

APPENDIX A
Input Values
Part 2: Switching and Expense Modules

A-10

A-11

A-12

A-13

A-14

A-15

A-16

A-17

A-18

A-19

*Values for Host/Remote Assignment are company-specific and are intentionally left
blank in this file. The Host/Remote Assignments are contained in the model DB folder,
HM50 file, Lerg.host.remote table.

A-20

A-21

A-22

A-23

A-24

A-25

A-26

APPENDIX A
Input Values
Part 3: Capital Costs

Appendix A
Part 3: Capital Costs

A-27

GrUpROR
16.01%

Account
2112
2115
2116
2121
2122
2123.1
2123.2
2124
2212
2220
2232.2
2351
2411
2421-m
2421-nm
2422-m
2422-nm
2423-m
2423-nm
2426-m
2426-nm
2441

USOA Category
Motor Vehicles
Garage Work Equipment
Other Work Equipment
Buildings
Furniture
Office Support Equipment
Company Comm Equipment
Computers
Digital Switching
Operator Systems
Digital Circuit Equipment
Public Telephone
NID, SAI and Drop
Poles
Aerial Cable - Metallic
Aerial Cable - Non-Metallic
Underground - Metallic
Underground - Non-Metallic
Buried - Metallic
Buried - Non-Metallic
Intrabuilding - Metallic
Intrabuilding - Non-Metallic
Conduit Systems

Appendix A
Part 3: Capital Costs

DefTax:
Adjusted Rgltry
Economic Net Salvage Projection Deprec
Life
Percent
Life Method
8.24
0.1038 9.194376 elg
12.22
-0.0558 11.57416 elg
13.04
0.0169 13.26416 elg
46.93
0.0164 47.71248 elg
15.92
0.0402 16.58679 elg
10.78
0.0412 11.24322 elg
7.4
0.0252 7.591301 elg
6.12
0.0229 6.263433 elg
16.17
0.0157 16.42792 elg
9.41
-0.0041 9.371577 elg
10.24
-0.0062 10.1769 elg
7.6
0.0512 8.010118 elg
19 elg
30.25
-0.8998 15.92273 elg
20.61
-0.2303 16.75201 elg
26.14
-0.1753 22.24113 elg
25
-0.1797 21.19183 elg
26.45
-0.1458 23.08431 elg
21.57
-0.0839 19.90036 elg
25.91
-0.0691 24.23534 elg
18.18
-0.1569 15.71441 elg
26.11
-0.1043 23.64394 elg
56.19
-0.0995 51.10505 elg

A-28

TRUE

Capital Annual Charge Factors
(RegDeprec/TaxDeprec)

IRS
Deprec
Category SL/SL
2 0.202106
3 0.182903
2 0.174079
6 0.150941
3 0.163143
3 0.185005
2 0.222954
2 0.249096
2 0.163532
2
0.2003
2 0.192912
2 0.216563
5 0.158367
5 0.164836
5 0.162748
5 0.154376
5 0.155438
5 0.153648
5 0.157033
5 0.152816
5 0.165416
5 0.153224
5 0.151339

SL/Accel
0.182596
0.165529
0.149475
0.134557
0.140292
0.168117
0.206413
0.235937
0.136172
0.18049
0.171924
0.199226
0.150531
0.160569
0.15741
0.143657
0.145575
0.142287
0.14832
0.140631
0.161426
0.141453
0.128939

ELG/ELG
0.202419
0.181323
0.168837
0.150121
0.158946
0.183124
0.223643
0.249314
0.16217
0.200098
0.191334
0.216442
0.155576
0.161979
0.161353
0.152517
0.152417
0.150465
0.155345
0.150839
0.185288
0.167453
0.150529

ELG/Accel
0.181483
0.164976
0.1494
0.133878
0.140065
0.167662
0.20537
0.235185
0.135036
0.179483
0.171178
0.198331
0.150771
0.161015
0.157454
0.143524
0.145762
0.142432
0.148254
0.140448
0.156518
0.137457
0.127937

Selected
KACF
0.181483
0.164976
0.1494
0.133878
0.140065
0.167662
0.20537
0.235185
0.135036
0.179483
0.171178
0.198331
0.150771
0.161015
0.157454
0.143524
0.145762
0.142432
0.148254
0.140448
0.156518
0.137457
0.127937

Account

USOA Category

2112
2115
2116
2121
2122
2123.1
2123.2
2124
2212
2220
2232.2
2351

Adjusted
Economic Net Salvag Projection IRS
Lives
Percent
Lives
Deprec
(years)
Category

Motor Vehicles
Garage Work Equipment
Other Work Equipment
Buildings
Furniture
Office Support Equipment
Company Comm Equipme
Computers
Digital Switching
Operator Systems
Digital Circuit Equipment
Public Telephone
NID, SAI and Drop
2411 Poles
2421-m
Aerial Cable - Metallic
2421-nm Aerial Cable - Non-Metallic
2422-m
Underground - Metallic
2422-nm Underground - Non-Metall
2423-m
Buried - Metallic
2423-nm Buried - Non-Metallic
2426-m
Intrabuilding - Metallic
2426-nm Intrabuilding - Non-Metallic
2441 Conduit Systems

Appendix A
Part 3: Capital Costs

8.24
12.22
13.04
46.93
15.92
10.78
7.4
6.12
16.17
9.41
10.24
7.6

0.1038
-0.0558
0.0169
0.0164
0.0402
0.0412
0.0252
0.0229
0.0157
-0.0041
-0.0062
0.0512

30.25
20.61
26.14
25
26.45
21.57
25.91
18.18
26.11
56.19

-0.8998
-0.2303
-0.1753
-0.1797
-0.1458
-0.0839
-0.0691
-0.1569
-0.1043
-0.0995

A-29

9.194376
11.57416
13.26416
47.71248
16.58679
11.24322
7.591301
6.263433
16.42792
9.371577
10.1769
8.010118
19
15.92273
16.75201
22.24113
21.19183
23.08431
19.90036
24.23534
15.71441
23.64394
51.10505

Regulatory
Deprec
ELG Curve Parameters
Method
(SL/ELG) c
Log g
Log s
2
3
2
6
3
3
2
2
2
2
2
2
5
5
5
5
5
5
5
5
5
5
5

elg
elg
elg
elg
elg
elg
elg
elg
elg
elg
elg
elg
elg
elg
elg
elg
elg
elg
elg
elg
elg
elg
elg

1.262139
1.027665
0
1.499068
1.019541
1.020103
1.262139
1.027665
1.133397
1.133397
1.020103
1.102494
1.027665
1.020103
1.133397
1.133397
1.027665
1.027665
1.133397
1.133397
0.96
0.96
1.499068

-0.0396
-5.710313
0
-0.00491
-4.48722
-8.97444
-0.0396
-5.710313
-0.217455
-0.217455
-8.97444
-0.3341
-5.710313
-8.97444
-0.217455
-0.217455
-5.710313
-5.710313
-0.217455
-0.217455
-0.194886
-0.194886
-0.00491

0.008554
0.145524
-0.043441
0.001774
0.058591
0.163161
0.008554
0.145524
0.023969
0.023969
0.163161
0.024012
0.145524
0.163161
0.023969
0.023969
0.145524
0.145524
0.023969
0.023969
-0.011252
-0.011252
0.001774

Account
2112
2115
2116
2121
2122
2123.1
2123.2
2124
2212
2220
2232.2
2351
2411
2421-m
2421-nm
2422-m
2422-nm
2423-m
2423-nm
2441

GM Std.
Curve Shape

USOA Category

GM 3.5
GM 1.5
GM 0.0
GM 4.5
GM 0.5
GM 1.0
GM 3.5
GM 1.5
GM 2.5
GM 3.5
GM 1.0
GM 2.0
GM 1.5
GM 1.0
GM 2.5
GM 2.5
GM 1.5
GM 1.5
GM 2.5
GM 2.5
GM 4.5

Motor Vehicles
Garage Work Equipment
Other Work Equipment
Buildings
Furniture
Office Support Equipment
Company Comm Equipment
Computers
Digital Switching
Operator Systems
Digital Circuit Equipment
Public Telephone
NID, SAI and Drop
Poles
Aerial Cable-Metallic
Aerial Cable-Non-Metallic
Underground-Metallic
Underground-Non-Metallic
Buried-Metallic
Buried-Non-Metallic
Conduit Systems

Appendix A
Part 3: Capital Costs

A-30

ELG Curve
Parameters
c

Log g

Log s

1.2621388
1.0276647
0
1.4990682
1.0195406
1.0201029
1.2621388
1.0276647
1.1333974
1.2621388
1.0201029
1.102494
1.0276647
1.0201029
1.1333974
1.1333974
1.0276647
1.0276647
1.1333974
1.1333974
1.4990682

-3.96000900E+02
-5.71031270E+00
0.00000000E+00
-4.91004630E-03
-4.48721900E+00
-8.97443950E+00
-3.9600900E+02
-5.71031270E+00
-2.17455120E-01
-3.96000900E+02
-8.97443950E+00
-3.34100410E-01
-5.71031270E+00
-8.97443950E+00
-2.17455120E-01
-2.17455120E-01
-5.71031270E+00
-5.71031270E+00
-2.17455120E-01
-2.17455120E-01
-4.91004630E-03

8.5537158E-03
1.45524080E-01
-4.34411150E-02
1.77395090E-03
5.8590832E-02
1.6316108E-01
8.5537158E-03
1.45524080E-01
2.39688400E-02
8.5537158E-03
1.6316108E-01
2.40118790E-02
1.45524080E-01
1.6316108E-01
2.39688400E-02
2.39688400E-02
1.45524080E-01
1.45524080E-01
2.39688400E-02
2.39688400E-02
1.77395090E-03

Actuals for 1996 ($000s)
Investments
Plant-Specific Operations Expenses
TPIS - General Support
2111 Land
0.000001
2112 Motor Vehi 0.000001
2113 Aircraft
0.000001
2114 Special Pur 0.000001
2115 Garage Wo 0.000001
2116 Other Work 0.000001
2121 Buildings
0.000001
2122 Furniture
0.000001
2123 Office Equi 0.000001
2124 General Pu 0.000001
2110 Total Land 0.00001

Expenses Calculated Factor

TPIS - Central Office Switching
2211 Analog Ele
0
2212 Digital Elec
0

2.76E-08
2.76E-08
2.76E-08
2.76E-08
2.76E-08
2.76E-08
9.06E-08
9.06E-08
9.06E-08
9.06E-08
5.28E-07

0.0276
0.0276
0.0276
0.0276
0.0276
0.0276
0.0906
0.0906
0.0906
0.0906
0.0528

0 0.000001
0 0.000001

2210 Total Centr

0 0.000001

2220 Operator S

0 0.000001

TPIS - Central Office Transmission
2231 Satellite & Earth Station Facilities
2231 Other Radio Facilities
2231 Radio Systems
2232 Circuit Equ
0
2230 Total Centr
0

0 0.000001
0 0.000001

TPIS - Information Orig/Term
2311 Station App
2321 Customer P
2341 Large Priva
2351 Public Tele
2362 Other Term
2310 Total Inform

0
0
0
0
0
0

TPIS - Cable & Wire Facilities
2411 Poles
0
2421 Aerial Cabl
0
2422 Undergroun
0
0
2423 Buried Cab
2424 Submarine Cable
2425 Deep Sea Cable
2426 Intrabuilding Network Cable
2431 Aerial Wire
2441 Conduit Sy
0
2410 Total Cable
0
240 Total TPIS

710 Total Corpo

720 Total Opera 5.38E-07
note: does not include dep/amort

Appendix A
Part 3: Capital Costs

Land & Bldg Exp Appli

0.0558 NET CO Sw

0.02 alternative

0.0276
0.0276
0.0276
0.0276
0.0276
0.0276
0.0906
0.0906
0.0906
0.0906
0

0.0558
0.0558

0.0204
0.0171
0.013
0.02
0.02

0.000001
0.000001
0.000001
0.000001
0.000001
0.000001

0
0
0
0

0.000001
0.000001
0.000001
0.000001
0.000001
0.000001
0.000001
0.000001
0 0.000001
0 0.000001

Alternative Cable
Maintenance Factors
Fiber
Copper
0.0073
0.0669
0.0084
0.021
0.0061
0.0446

0.0219
0.0669
0.021
0.0446

0.0058

5.28E-07 0.000001

1
1

A-31

0
0.3231 Total Operations General Support Allocator
0.6769 "Office Worker" General Support Alllocator

APPENDIX B
METHODOLOGY FOR ESTIMATING OUTSIDE PLANT COSTS
I.

Introduction

1.
Section II in this appendix explains in specific detail the regression equations and the
adjustments to these equations for estimating the input values adopted in this Order for structure and cable
costs. These regression equations and these adjustments are set forth in this appendix on the following
tables: Table I., labeled "Regression Equations Derived From RUS Data For Estimating Cable And
Structure Costs;" Table II., labeled "Adjustments To Regression Equations Derived From RUS Data For
Estimating Cable And Structure Costs;" and Table III., labeled "Regression Equations Derived From
Non-Rural LEC Data For Estimating Cable Costs."
2.
Section III illustrates use of the Huber methodology to derive reasonable estimates for
24-gauge aerial copper cable costs.1 This illustration uses the diagram in this appendix labeled "Scatter
Diagram Of 24-Gauge Aerial Copper Cable Cost And Size With The Huber Regression Line." This
diagram shows RUS cable cost observations for 24-gauge aerial copper cable and the regression line fit to
these observations by using the Huber methodology. It also uses the frequency distribution in this
appendix set forth on Table IV., labeled "Frequency Distribution Of Huber Weights For 24-gauge Aerial
Copper Cable Cost." This frequency distribution shows the number of aerial copper cable observations to
which the Huber methodology assigns particular weights.
3.
Section IV demonstrates that the Huber methodology generally does not have a
statistically significant impact on the level of the material costs reflected in the cable cost estimates
adopted in this Order. This finding provides support for the large LEC buying power adjustment
reflected in these estimates. This finding is supported by the statistical information set forth in this
appendix on Table V., labeled "Analysis Of Coefficient For Cable Size Variable In The Huber Regression
Equations."
II.

Regression Equations For Estimating Outside Plant Structure Costs
A.
Regression Equations Derived From RUS Data For Estimating Cable
And Structure Costs

4.
Table I, labeled "Regression Equations Derived From RUS Data For Estimating Cable
And Structure Costs," sets forth the regression equations adopted in this Order for estimating the cost of:
1

We used Stata Statistical Software: Release 5 (Stata) to perform the calculations needed to estimate the
regression equations adopted in this Order for cable and structure costs. Stata has a robust regression methodology
that uses formulas developed by P. J. Huber, R. D. Cook, A. E. Beaton and J. W. Tukey. We used this methodology
to estimate the regression equations for cable and structure costs. We refer to this regression methodology as the
Huber methodology. See StataCorp., Stata Reference Manual, Release 5, vol. 3, P-Z, 168-173 (College Station,
TX: Stata Press, 1997).

B-1

(1) 24-gauge aerial copper cable; (2) 24-gauge underground copper cable; (3) 24-gauge buried copper
cable and structure; (4) aerial fiber cable; (5) underground fiber cable; (6) buried fiber cable and structure;
(7) poles; and (8) underground structure. These regression equations, other than the equations for poles
and underground structure, are developed by revising the regression equations for cable and structure
costs developed by Gabel and Kennedy in the NRRI Study.2 The regression equations adopted in this
Order, other than the equation for poles, are estimated by using the Huber methodology with RUS data.
The regression equations in the NRRI Study3 are developed by using ordinary least squares (OLS) with
RUS data.4 The regression equation for poles adopted in this Order is the regression equation for poles in
the NRRI Study. The regression equation adopted in this Order for poles is not estimated by using the
Huber methodology because the Huber regression for poles is not statistically significant at the five
percent level.
5.
Column A identifies, by type of cost, the regression equations adopted in this Order. Set
forth in columns B, D, F, H, J, L, and N are the intercepts and the slope coefficients reflected in these
regression equations. The coefficients set forth in these columns for these regression equations are for the
variables that indicate the size of a cable,5 density zone,6 soil surface texture,7 bedrock type,8 combined
2

There is no regression equation for underground structure in the NRRI Study. The regression equation for
underground structure adopted in this Order was developed after the NRRI Study was published.
3

These regression equations are set forth in the NRRI Study at 58, Table 2-16 (24-gauge aerial copper cable
cost); 60, Table 2-19 (24-gauge underground copper cable cost); 41, Table 2-7 (24-gauge buried copper cable and
structure cost); 59, Table 2-18 (aerial fiber cable cost); 61, Table 2-20 (underground fiber cable cost); 49, Table 210 (buried fiber cable and structure cost); 52, Table 2-12 (pole cost).
4

None of the regression equations adopted in this Order has a variable that indicates the presence of a second
cable at the same location. The regression equations in the NRRI Study, other than the equation for poles, have a
variable that indicates the presence of a second cable at the same location. The regression equations adopted in this
Order for poles, underground structure, buried copper cable and structure, and buried fiber cable and structure have
a variable that indicates the presence of a high water table. The regression equation in the NRRI Study for poles
and buried fiber cable and structure have a variable that indicates the presence of a high water table. The regression
equation in the NRRI Study for buried copper cable and structure does not have this variable.
5

The cable size variable is used in the regression equations for estimating the cost of 24-gauge aerial copper
cable, 24-gauge underground copper cable, 24-gauge buried copper cable and structure, aerial fiber cable,
underground fiber cable, and buried fiber cable and structure. It has values that equal the number of copper cable
pairs in the 24-gauge copper cable regression equations and the number of fiber cable strands in the fiber cable
regression equations.
6

The density zone variable is used in the regression equations for 24-gauge buried copper cable and structure
cost and buried fiber cable and structure cost. It has a value of 1 if a buried cable is installed in density zone 2; 0 if
a buried cable is installed in density zone 1.
7

The variable that indicates soil surface texture is used in the regression equation for pole cost. It has values that
range from 0 for normal soil, to 1 for soft soil, to 3 for hard soil. See NRRI Study at 16 and 46, Table 2-8.
8

The variable that indicates bedrock type is used in the regression equation for pole cost. It has values that range
from 0 for normal rock, to 1 for soft rock, to 2 for hard rock. These bedrock types are at a depth of 48 inches. See

B-2

bedrock and soil type,9 and the presence of a high water table.10 Columns C, E, G, I, K, M, and O display
the t-statistics used to measure the statistical significance of these intercepts and coefficients. Column P
displays the F-statistics used to measure the statistical significance of these regression equations. Column
O displays the number of observations in the data used to estimate these equations.
6.
The coefficients for the variable that indicates the size of the cable in the regression
equations for 24-gauge copper cable cost and fiber cable cost do not reflect the adjustments adopted in
this Order for large LEC buying power. The intercepts and the coefficients in these equations do not
reflect splicing and LEC engineering costs because these costs are not reflected in the RUS data from
which these equations are derived. The intercepts and the coefficients for the water, soil, and bedrock
indicator variables in the regression equations for structure costs do not reflect LEC engineering costs
because these costs are not reflected in the RUS data from which these equations are derived. The
intercept and the coefficients for the water, soil, and bedrock indicator variables in the regression equation
for pole costs do not reflect costs for anchors, guys, and other pole-related items because these costs are
not reflected in the RUS data from which this equation is derived.
B.
Adjustments To Regression Equations Derived From RUS Data For
Estimating Cable And Structure Costs
7.
Table II, labeled "Adjustments To Regression Equations Derived From RUS Data For
Estimating Cable And Structure Costs," sets forth adjustments to the regression equations adopted in this
Order for estimating costs for 24-gauge copper cable, fiber cable, and structure. The equations that reflect
these adjustments, i.e., the adjusted equations, are used for estimating the cost of: (1) 24-gauge aerial
copper cable; (2) 24-gauge underground copper cable; (3) 24-gauge buried copper cable; (4) aerial fiber
cable; (5) underground fiber cable; (6) buried fiber cable; (7) aerial structure; (8) underground structure;
and (9) buried structure.
8.

Column A identifies, by type of cost, the adjusted equations used to derive the cable and

NRRI Study at 16 and 44, Table 2-8.
9

The combined bedrock and soil type variable is used in the regression equations for 24-gauge buried copper
cable and structure cost, buried fiber cable and structure cost, and underground structure cost. It is the sum of
separate variables for surface soil texture and bedrock type at a depth of 36 inches. See NRRI Study at 45, Table 28. The value of the variable that indicates surface soil texture ranges from 0 for normal soil, to 1 for soft soil, to 3
for hard soil. See NRRI Study at 16 and 46, Table 2-8. The value of the variable that indicates bedrock type ranges
from 0 for normal rock, to 1 for soft rock, to 2 for hard rock at a depth of 36 inches. See NRRI Study at 16 and 44,
Table 2-8. Accordingly, the value of the variable for the combined bedrock and soil type indicator ranges from 0
where there are normal surface soil texture and normal bedrock at a depth of 36 inches to 5 where there are hard
surface soil texture and hard bedrock at a depth of 36 inches.
10

The variable that indicates the presence of a high water table is used in the regression equations for 24-gauge
buried copper cable and structure cost, buried fiber cable and structure cost, pole cost, and underground structure
cost. It has values that range from 0 for the absence of a high water table, to 1 for the presence of a high water
table. This variable assumes that a high water table has a depth of five feet or fewer. See NRRI Study at 12, 16 and
46, Table 2-8.

B-3

structure costs adopted as input values in this Order.
9.
Column B displays the intercepts in the adjusted equations. In the adjusted equations for
the cost of aerial and underground 24-gauge copper cable, fiber cable, and structure, the intercepts are
those in the regression equations for these costs. The intercepts in the adjusted equations for 24-gauge
buried copper cable and buried fiber cable represent the fixed cost of buried copper cable and the fixed
cost of buried fiber cable, respectively. The intercepts in the regression equations for 24-gauge buried
copper cable and structure and buried fiber cable and structure represent the fixed cost of buried copper
cable and structure and the fixed cost of buried fiber cable and structure, respectively, in density zone 1.
The fixed cost of 24-gauge buried copper cable used as the intercept in the adjusted equation for 24-gauge
buried copper cable, approximately $.46 per foot, is derived by subtracting from the intercept in the
regression equation for 24-gauge buried copper cable and structure, approximately $1.16 per foot, the
value of the fixed cost for buried structure in density zone 1 adopted in this Order, $.70 per foot. The
fixed cost of fiber cable used as the intercept in the adjusted equation for fiber cable, approximately $.47
per foot, is derived by subtracting from the intercept in the regression equation for buried fiber cable and
structure, approximately $1.17 per foot, the $.70 per foot fixed cost adopted for buried structure in density
zone 1. The intercept in the adjusted equation for buried structure represents the fixed cost of buried
structure in density zone 1. The fixed cost of buried structure in density zone 1 used as the intercept in
the adjusted equation for buried structure is the $.70 per foot fixed cost adopted for buried structure in
density zone 1.
10.
Column C displays the coefficients for the cable size variable in the adjusted 24-gauge
copper and fiber cable equations. In the adjusted equations for the cost of aerial and underground 24gauge copper cable and fiber cable, the coefficients for the cable size variable are those for this variable in
the regression equations for these costs. In the adjusted 24-gauge copper cable equation, the coefficient
for the cable size variable is the coefficient for this variable in the 24-gauge buried cable and structure
regression equation. In the adjusted 24-gauge fiber cable equation, the coefficient for the cable size
variable is the coefficient for this variable in the buried fiber cable and structure regression equation.
11.
Column D displays the large LEC buying power adjustment factors. These factors are
applied to the coefficients for the cable size variable in the adjusted copper and fiber cable equations.
Column E displays the values of the coefficients for these cable size variables in these equations, as
adjusted to reflect large LEC buying power.
12.
Columns F, G, and H display the coefficients for the density zone, bedrock indicator, and
combined soil and bedrock indicator variables in the adjusted structure equations. In the adjusted
equations for the cost of aerial and underground structure, these coefficients are those for these variables
in the regression equations for these costs. In the adjusted buried structure equation, these coefficients are
those for these variables in the 24-gauge buried copper cable and structure regression equation. The
coefficients for the water and soil indicator variables in the structure regression equations are not reflected
in the adjusted equations because the value for these variables is set equal to zero to estimate structure
costs used as input values.
13.
Column I displays the loading factors used to reflect splicing costs in the cable cost
estimates for 24-gauge copper cable and fiber cable.

B-4

14.
Column J displays the loading factor used to reflect LEC engineering costs in the
structure cost estimates.
15.
Column K displays the flat dollar loading used to reflect LEC engineering costs in the
cable cost estimates for 24-gauge copper cable and fiber cable.
16.
Column L displays the adjusted equations used to estimate costs for aerial, underground,
and buried 24-gauge copper and fiber cable, buried and underground structure, and poles.
17.
Columns M-O display adjustments to the adjusted pole equation. These adjustments add
to the cost of poles the costs for anchors, guys, and other pole-related items, including LEC engineering
costs associated with these additional items, and convert per pole costs, inclusive of costs for anchors,
guys, and other pole-related items, i.e., aerial structure costs, to per foot costs. Column M displays the
costs for anchors, guys, and other pole-related items for density zones 1 and 2 ($32.98 per pole), density
zones 3-7 ($49.96 per pole), and density zones 8 and 9 ($60.47 per pole).11 Column N displays the
loading factor used to reflect LEC engineering costs in the costs for anchors, guys, and other pole-related
items. Column O displays the distance between poles used to calculate aerial structure cost per foot for
density zones 1 and 2 (250 feet per pole), density zones 3 and 4 (200 feet per pole), density zones 5 and 6
(175 feet per pole), and density zones 7-9 (150 feet per pole).
18.
Column P displays the adjusted equation used to estimate aerial structure cost per foot,
including poles, anchors, guys, and other pole-related items.
19.
We illustrate how the adjusted equations are used to develop the input values adopted in
this Order by calculating the cost for a 100-pair 24-gauge aerial copper cable. Column L sets forth the
adjusted equation used to develop the input values adopted in this Order for 24-gauge aerial copper cable.
The adjusted equation set forth in column L for 24-gauge aerial copper cable is as follows:12
A1 = (B1 + (E1)(# of Prs.))(1 + I1) + K1
where:
A1 = 24-gauge aerial copper cable cost per foot;
B1 = the intercept for 24-gauge aerial copper cable in dollars per foot;
E1 = the coefficient, adjusted for buying power, in dollars per pair per foot, for the variable
that represents the number of 24-gauge aerial copper cable pairs;
I1 = the splicing loading for 24-gauge aerial copper cable expressed as a percentage;
11

These costs for anchors, guys, and other pole-related items are based on the costs for these items in rural,
suburban, and urban areas derived by Gabel and Kennedy in the NRRI Study. See NRRI Study at 51, Table 2-11.
12

Set forth on Table II in specific columns and on specific rows are the values for the intercepts, coefficients
(including the adjusted coefficients for the cable size variable), splicing loadings, and LEC engineering loadings
reflected in the adjusted equations used to estimate structure and cable costs. The specific column is identified by a
letter. The specific row is identified by a number. B1, for example, refers to the value set forth in column B on row
1.

B-5

K1 = the LEC engineering loading for 24-gauge aerial copper cable in dollars per foot.
20.
By substituting into the above equation for 24-gauge aerial copper cable the values from
Table II for the intercept, adjusted coefficient for the cable size variable, splicing loading, and LEC
engineering loading, and the number of cable pairs in this example, 100, we obtain the following estimate
for the cost of a 100-pair 24-gauge aerial copper cable:
A1 = (1.014907 + (.008329)(100))(1 + .094) + .19
= (1.014907 + .8329)(1.094) + .19
= (1.847807)(1.094) + .19
= 2.021501 + .19
= $2.21 per foot.
We adopt this estimate as the input in the model for the cost of a 100-pair 24-gauge aerial copper cable.
C.
Regression Equations Derived From Non-Rural LEC Data For Estimating
Cable Costs
21.
We adopt in this Order a methodology to derive estimates of 26-gauge copper cable costs
from 24-gauge copper cable costs. We first estimate by using the Huber methodology with RUS data the
cost for 24-gauge copper cable for each cable size.13 We then obtain by using the Huber methodology
with certain non-rural LEC data estimates of the cost for 24-gauge copper cable and 26-gauge copper
cable for each cable size.14 We next divide the 24-gauge copper cable cost estimate derived from the nonrural LEC data into the estimate for 26-gauge copper cable cost derived from these data for each cable
size. The result is a ratio of 26-gauge copper cable cost to 24-gauge copper cable cost for each cable
size.15 Finally, we multiply this ratio by the estimate of the cost for 24-gauge copper cable derived from
the RUS data to obtain the cost for 26-gauge copper cable for each cable size.16 We adopt these estimates
as inputs for 26-gauge copper cable costs in the SM.
22.
Table III, labeled "Regression Equations Derived From Non-Rural LEC Data For
Estimating Cable Costs," sets forth regression equations derived from the non-rural LEC data for: (1) 2413

More technically, we obtain from these RUS data an estimate of the expected value of the cost for 24-gauge
copper cable for each cable size.
14

More technically, we obtain from these non-rural LEC data estimates of the expected value of the cost for 24gauge copper cable and 26-gauge copper cable for each cable size.
15

More technically, we obtain from these non-rural LEC data a ratio of an estimate of the expected value for 26gauge copper cable cost to an estimate of the expected value for 24-gauge cable cost for each cable size.
16

More technically, we obtain an estimate of the expected value for 26-gauge copper cable cost.

B-6

gauge aerial copper cable; (2) 24-gauge underground copper cable; (3) 24-gauge buried copper cable; (4)
26-gauge aerial copper cable; (5) 26-gauge underground copper cable; and (6) 26-gauge buried copper
cable. We use these regression equations to develop the ratios of 26-gauge copper cable costs to 24gauge copper cable costs used to derive the cost for 26-gauge copper cable. Column A identifies these
regression equations by type of copper cable cost. Set forth in columns B and D are the intercepts and the
slope coefficients reflected in these regression equations. Columns C and E display the t-statistics used to
measure the statistical significance of these intercepts and coefficients. Column F displays the F-statistics
used to measure the statistical significance of these regression equations. Column G displays the number
of observations in the data used to estimate these equations. Column H shows the regression equations
derived from the non-rural LEC data for estimating costs for 24-gauge and 26-gauge copper cable.
23.
These regression equations are derived from ex parte data on 24-gauge and 26-gauge
copper cable costs submitted by Sprint and Aliant, data on these cable costs submitted by BellSouth with
its comments,17 and the BCPM default values for these cable costs. These regression equations are
developed by using the Huber methodology. Using the Huber methodology with non-rural LEC data to
estimate cable costs for 24- and 26-gauge copper cable costs is consistent with use of this methodology to
estimate 24-gauge copper cable costs from the RUS data. The regression equations derived from nonrural LEC data use the number of copper cable pairs as the sole independent variable. Using the number
of copper cable pairs as the sole independent variable in these regression equations is consistent with
using this variable as the sole independent variable in the regression equations for 24-gauge copper cable
costs estimated from the RUS data.
24.
In this Order, we find it reasonable to rely on the non-rural LEC data for calculating the
ratio of the cost for 24-gauge copper cable to that for 26-gauge copper cable but not for calculating the
absolute cost for 24-gauge copper cable and 26-gauge copper cable.18 As discussed in this Order, we find
that the non-rural LEC data is not a reliable measure of absolute costs. Notwithstanding this finding, we
conclude that it is reasonable to use the non-rural LEC data to determine the relative value of the cost for
24-gauge copper cable to that for 26-gauge copper cable. We find that it is reasonable to conclude that
each LEC used the same methodology to develop both 24-gauge and 26-gauge copper cable costs.
Accordingly, any bias in the costs for 24-gauge and 26-gauge copper cable that results from using a given
17

See BellSouth Inputs Further Notice comments, Exhibit 1. BellSouth submitted separate copper cable costs
for nine study areas. We calculate the weighted average of these copper cable costs for each cable size based on the
number of access lines in each study area. We include this weighted average cable cost for BellSouth for each cable
size in the non-rural LEC data from which we derive 24-gauge and 26-gauge copper cable costs. By using a
weighted average, the regression equations derived from the non-rural LEC data do not reflect a disproportionate
number of observations for BellSouth compared to the number of observations for the other non-rural LECs for
which costs are reflected in these data. The cable costs reflected in the data for these other LECs are either
company-wide costs or an average for multiple study areas. In either case, there is a single observation for each of
these companies for a given cable size for 24-gauge and 26-gauge copper cable cost. By reflecting the weighted
average cost for BellSouth in the data, there is only one observation for BellSouth for a given cable size for 24gauge and 26-gauge copper cable cost.
18

We discuss the rationale for using non-rural LEC data to calculate relative copper cable costs, but not absolute
copper cable costs, in this Order, section V.C.4.b.

B-7

methodology is likely to be in the same direction and of a similar magnitude. As a consequence, cost
estimates for 24-gauge and 26-gauge copper cable for each cable size developed from non-rural LEC data
by using the Huber methodology are likely to be biased by approximately the same factor. The ratios of
these estimates are not likely to be affected significantly because the bias in one estimate approximately
cancels the bias in the other estimate when the ratio is calculated.
25.
We illustrate how we calculate the costs that we adopt in this Order for 26-gauge copper
cable by calculating the cost for a 100-pair 26-gauge aerial copper cable. As explained above, we derive
a ratio of 26-gauge copper cable cost to 24-gauge copper cable cost from non-rural LEC data to obtain
costs for 26-gauge copper cable. To calculate this ratio for a 100-pair aerial copper cable, we estimate
separately from non-rural LEC data the cost for a 100-pair 24-gauge aerial copper cable and a 100-pair
26-gauge aerial copper cable. We first estimate the numerator of this ratio, i.e., the cost for a 100-pair 24gauge aerial copper cable. Column H shows the regression equation derived from non-rural LEC data for
estimating the cost for 24-gauge aerial copper cable. The regression equation set forth in column H for
24-gauge aerial copper cable is as follows:
A1 = B1 + (D1)(# of Pairs)
where:
A1 = 24-gauge aerial copper cable cost per foot;
B1 = the intercept for 24-gauge aerial copper cable in dollars per foot;
D1 = the coefficient in dollars per pair per foot for the variable that represents the number of
24-gauge aerial copper cable pairs.
26.
By substituting into the above equation for 24-gauge aerial copper cable the values from
Table III for the intercept and the coefficient for the cable size variable, and the number of cable pairs in
this example, 100, we obtain the following result for the cost of a 100-pair 24-gauge aerial copper cable:
A1 = 2.1548 + (.012393)(100)
= 2.1548 + 1.2393
= $3.39 per foot.
27.
We next estimate the denominator for the ratio of 26-gauge aerial copper cable cost to
24-gauge aerial copper cable cost for a 100-pair aerial copper cable, i.e., the 26-gauge aerial copper cable
cost for a 100-pair cable. Column H shows the regression equation derived from non-rural LEC data for
estimating the cost for 26-gauge aerial copper cable. The regression equation set forth in column H for
26-gauge aerial copper cable is as follows:
A4 = B4 + (D4)(# of Pairs)
where:
A4 = 26-gauge aerial copper cable cost per foot;
B4 = the intercept for 26-gauge aerial copper cable in dollars per foot;

B-8

D4 = the coefficient in dollars per pair per foot for the variable that represents the number of
26-gauge aerial copper cable pairs.
28.
By substituting into the above equation for 26-gauge aerial copper cable the values from
Table III for the intercept and the coefficient for the cable size variable, and the number of cable pairs in
this example, 100, we obtain the following result for the cost of a 100-pair 26-gauge aerial copper cable:
A4 = 2.385108 + (.008721)(100)
= 2.385108 + .8721
= $3.26 per foot.
29.
We next calculate the ratio of 26-gauge copper cable cost to 24-gauge copper cable cost
for a 100-pair cable. The ratio of 26-gauge copper cable cost to 24-gauge copper cable cost for a 100-pair
cable is .96 ($3.26 per foot divided by $3.39 per foot).
30.
Finally, we multiply this ratio by the estimate of the 24-gauge copper cable cost for a
100-pair cable derived from the RUS data, $2.21 per foot, to obtain the cost for a 100-pair 26-gauge
copper cable, $2.12 per foot. We adopt this estimate as the input in the SM for the cost of a 100-pair 26gauge aerial copper cable.
III.

Huber Methodology

31.
We find in this Order that it is reasonable to use the Huber methodology to develop input
values for cable and structure costs. The structure and cable cost inputs used in the SM should reflect
those that are typical for cable and structure for a number of different density and terrain conditions.
Otherwise, the model may substantially overestimate or underestimate the cost of building a network.
The Huber methodology produces estimates of costs that are typical for cable and structure by assigning
zero or less than full weight to cable and structure cost observations that have extremely high or
extremely low values. At the same time, it assigns full or nearly full weight to closely clustered cable and
structure cost observations.
32.
Use of the Huber methodology to derive reasonable estimates from RUS data is
illustrated for aerial copper cable cost on the diagram labeled "Scatter Diagram Of 24-Gauge Aerial
Copper Cable Cost And Size With The Huber Regression Line" and on the frequency distribution set
forth on Table IV, labeled "Frequency Distribution Of Huber Weights For 24-Gauge Aerial Copper Cable
Cost." The scatter diagram shows RUS cable cost data points representing combinations of aerial copper
cable costs (measured on the vertical axis in dollars per foot) and cable size (measured on the horizontal
axis by number of pairs). It also shows the regression line that the Huber methodology fits to these data
points. The algebraic expression of this line explains or predicts the effects on aerial copper cable costs
of changes in cable size.19 The observations to which Huber assigns a weight that is less than .47 are
19

The algebraic expression of the regression line for 24-gauge aerial copper cable estimated from RUS data by
using the Huber methodology is as follows:

B-9

identified with an “o”; those to which it assigns a weight that is greater than .47 are identified with an “*”.
The frequency distribution shows the number of aerial copper cable observations to which the Huber
methodology assigns particular weights.
33.
The scatter diagram and the frequency distribution demonstrate that the aerial copper
cable estimates derived by using the Huber methodology with RUS data reflect most of the information
contained in nearly all of the observations. As depicted on the scatter diagram, the majority of the aerial
copper cable observations are clustered closely around the regression line. These are the observations to
which Huber assigns the greatest weight when fitting the regression line to the data. As the frequency
distribution shows, approximately 82 percent of the aerial copper cable observations is assigned a weight
of at least .8. This large majority of closely clustered observations clearly represents typical cable costs.
The minority of the aerial copper cable observations lies a considerable distance from the regression line.
These are the observations to which Huber assigns the least weight when fitting the regression line to the
data. As the frequency distribution shows, approximately 18 percent of the observations is assigned a
weight of at less than .8. This small minority of observations comprises extremely high and extremely
low values that do not represent typical cable costs. The scatter diagram also shows that some of the
observations that receive a relatively small weight lie a substantial distance above the regression line
while others that receive such weight lie a substantial distance below this line. This demonstrates that the
Huber methodology excludes or assigns less than full weight to data outliers without regard to whether
these are high or low cost observations.
IV.

Analysis Of Coefficient For Cable Size Variable In The Huber Regression
Equations

34.
In this Order, we derive equations to estimate the non-rural LECs' labor and material cost
for cable. We derive these equations by: (1) deriving regression equations by using the Huber
methodology with RUS cable cost data that reflect labor and material costs; and (2) adjusting downward
the coefficient for the variable that represents cable size in these regression equations to reflect the buying
power of large LECs in comparison to RUS companies. The coefficient for the variable that represents
cable size represents the additional cost for an additional pair of cable and therefore represents cable
material costs. The adjustment to this coefficient is based on the difference between the average cable
material prices that Bell Atlantic and the RUS companies pay for different cable sizes. The RUS
companies' average cable material prices are calculated by using unweighted RUS data. Conversely, the
Huber methodology used to estimate the regression equations assigns zero or less than full weight to data
points that have extremely high or extremely low values. Below we demonstrate that the Huber
methodology generally does not have a statistically significant impact on the level of material costs
reflected in the cable cost estimates. That is, in general, there is not a statistically significant difference
24-gauge aerial copper cable cost per foot = 1.014907 + (.009822)(number of pairs).
In this regression equation, 24-gauge aerial copper cable cost is the dependent variable for which a value is
measured along the vertical axis. The number of pairs is the independent variable for which a value is measured
along the horizontal axis. The value 1.014907 is the intercept of the regression line. It is the point at which the
regression line hits the vertical axis. It measures the fixed cost for 24-gauge aerial copper cable. The value .009822
is the slope coefficient of the regression line. It is the slope of the regression line. It measures the additional cost
for one additional pair of 24-gauge aerial copper cable.

B-10

between the value of the coefficient for the cable size variable in the regression equations estimated by
using the Huber methodology and the value of this coefficient in the regression equations developed in
the NRRI Study by using OLS. Accordingly, the buying power adjustment for material is based on
averages of RUS companies' cable material prices calculated by using unweighted RUS data.
35.
Table V, labeled "Analysis Of Coefficient For Cable Size Variable In The Huber
Regression Equations," displays the values of the coefficient for the cable size variable in the regression
equations estimated from RUS data by using the Huber methodology in this Order and the 95 percent
confidence interval surrounding the value of this coefficient in these equations in the NRRI Study
estimated from these data by using OLS. Except for 24-gauge buried copper cable, the value of the this
coefficient estimated by using the Huber methodology lies inside the 95 percent confidence interval
surrounding the value of this coefficient in these equations in the NRRI Study estimated from these data
by using OLS. That is, except for 24-gauge buried copper cable, the value of the cable size coefficient
estimated by using the Huber methodology lies within an interval that contains with 95 percent certainty
the true value of the OLS cable size coefficient.20 This statistical evidence supports a finding that the
Huber methodology does not have a statistically significant impact on the level of the material costs
reflected in the cable cost estimates derived by using this methodology.21 The cable size coefficient
obtained by using the Huber methodology for buried copper cable lies outside the 95 percent confidence
interval associated with the cable size coefficient obtained by using OLS for buried copper cable. This
supports a finding that the Huber methodology does have a statistically significant impact on the level of
the material costs reflected in the buried copper cable cost estimates.22
20

Strictly speaking, over a large number of different samples, 95 percent of the confidence intervals associated
with different OLS estimates of the cable size coefficient are expected to contain the true value of the OLS cable
size coefficient.
21

In this Order, we affirm the tentative decision in the Inputs Further Notice to use conservatively the lower of
the buying power adjustments for aerial and underground copper cable material costs as the adjustment for buried
copper cable material costs because the Huber methodology does have a statistically significant impact on the
buried copper cable material costs reflected in the buried copper cable cost estimates. See this Order, section
V.C.4.b.
22

The specifications for the copper and fiber cable regression equations in the NRRI Study differ slightly from
the copper and fiber cable regression equations adopted in this Order. The difference in the specifications does not
alter the statistical conclusions regarding the impact of the Huber methodology on the level of cable material costs
reflected in the cable cost estimates. We estimated by using OLS copper and fiber cable regression equations for
which the specifications matched identically those for the copper and fiber cable regression equations estimated by
using the Huber methodology. Again, with one exception, the cable size coefficient in the regression equations
estimated by using the Huber methodology lies inside the 95 percent confidence interval associated with the cable
size coefficient in the regression equations with the identical specifications estimated by using OLS. The one
exception is that the value of the cable size coefficient in the buried copper cable and structure regression equation
estimated by using the Huber methodology lies outside the 95 percent confidence interval associated with the cable
size coefficient in the buried copper cable and structure regression equation with the identical specification
estimated by using OLS. Again, we conclude that the Huber methodology does not have a statistically significant
impact on the level of the cable material costs reflected in the cable cost estimates other than the buried cable cost
estimates.

B-11

B-12

Scatter Diagram Of 24-Gauge Aerial Copper Cable
Cost And Size With The Huber Regression Line

o Huber < .47
20
0
0

cost per foot

0
0
**

10
0
0

0
0

0
0 0 0 **
*
**** *** ** ****
****** *** ** *
*** * *
*

**
**
***
*

0
**
**
***
**

**
**
**
**
**
*

0
**
**
**
*

0
0
0*
**
**
**

500

1000
number of pairs

B-15

0
*
0
0

1500

2000

APPENDIX C
DESCRIPTION OF METHODOLOGY FOR ESTIMATING SWITCHING COSTS
1.
Switch Cost Data. The depreciation rate reports filed by LECs contain
information on Bell Operating Companies' (BOCs') digital switches that were reported as
installed between 1983 and 1995 in the states specified, with certain exceptions. A small
number of switches associated with apparent inconsistencies in the studies were not included in
the set. In particular, for several locations in California, switches that were at the same location,
but had different capacities, types, and year of installation, were reported as having the same perline costs. These anomalies were judged to be the results of averaging by the respondent, and
the switches in these locations were excluded from the data set. The following switches are also
excluded from the data set: (1) switches for which there were no lines of capacity, such as those
functioning solely as tandem switches; and (2) switches with fewer than 1,000 lines of capacity.
2.
The sample was restricted to the period following the divestiture of AT&T, and to
those switch types that could clearly be identified as either host or remote switches. These
included the DMS-100, DMS-100 remote, DMS-10, 5ESS, 5ESS remote, and EWSD switch
types. In total these restrictions removed about 500 observations from a data set of nearly 3,600
observations. Thus, after exclusions, the data set compiled by the Commission in conjunction
with Gabel and Kennedy and the Bureau of Economic Analysis (BEA) of the Department of
Commerce consisted of approximately 3,100 switches. In order to estimate the costs associated
with the purchase and installation of new switches, and exclude the costs associated with
upgrading switches, we removed those switches installed more than three years prior to the
reporting of their associated book-value costs. The three-year restriction resulted in the removal
of nearly 70% of observations, which do measure the cost of new switches. The depreciation
data included in the data set selected by the Commission includes the remaining 946
observations.
The reports made to RUS by rural telephone companies contain information on
the 181 digital switches installed in 1995 and 1996. To increase the reliability of analysis using
these data, we removed the following observations from the data set: (1) observations
containing information on switching equipment classified as upgrades to existing equipment and
(2) observations containing information on switches reported as having no attached lines. These
exclusions result in the removal of 42 observations. The RUS data included in the data set we
select includes the remaining 139 observations.
3.

4.
Combined, the data set we employ includes 1,085 observations, 946 from the
depreciation information and 139 from the RUS information. The RUS information includes a
variable identifying switches as either hosts or remotes. The depreciation information does not.
Therefore, an additional variable uniquely identifying switches as host switches or remote
switches was added to the data set. Where data classifications were deemed unreasonable,
switch types were reclassified. For example, switches identified as DMS-100 and 5ESS

C-1

switches which terminated less than 2,000 customers and cost in the neighborhood of $500,000
were reclassified as remote switches. These classifications identified approximately 55% of the
switches included in the combined data set as remotes.
5.

Regression Formulation. The regression employed is of the form:

Cost =a1 + a2*Lines + a3*Host + a4*(1/Time) + a5*Lines*(1/Time) + a6*Host*(1/Time) + e

where time takes on the value of 1 in 1985, 2 in 1986...15 in 1999. Regression results, including
estimated coefficient values (in 1997 dollars), are:
Cost = 11,110 + 10.32*Lines - 402,400*Host + 2,205,000*(1/Time) + 1,121*Lines*(1/Time) + 1,080,000*Host*(1/Time)
(105,100) (41.52)

(635,700)

(970,500)

(352.6)

(4,757,000)

Robust (heteroscedasticity adjusted) standard errors in parenthesis. Regression R-squared = 0.73.

Estimates, identified using the regression equation, for the fixed cost of host and remote switches
and for the per-line cost of all switches (in 1997 dollars) are, respectively:
Host Fixed Cost = a 1+ a3+ a4*(1/Time) + a6*(1/Time)
Remote Fixed Cost = a1+ a4*(1/Time)
Per-line Cost = a2 + a5*(1/Time)

In estimating switch costs for 1999, the regression results (with time defined as 15) were
converted into 1999 values using actual inflation between 1997 and 1998 and projected inflation
between 1998 and 1999. Estimates for 1999, in 1999 dollars, identified using the regression
equation, for the fixed cost of host and remote switches and for the per-line cost of all switches
are, respectively:
Host Fixed cost =(1+inflation1998)*(1+inflation1999)*(a 1+ a3 + a4*(1/15) + a6*(1/15))
Remote Fixed Cost =(1+inflation1998)*(1+inflation1999)*(a1+ a4*(1/15))
Per-line Cost = (1+inflation1998)*(1+inflation1999)*(a2 + a5*(1/15))

The inflation rate for 1998 is measured by the gross-domestic-product chain-type price index as
published monthly by the Bureau of Economic Analysis of the U.S. Department of Commerce in
the Survey of Current Business. The projected inflation rate for 1999 is reported in The
Economic and Budget Outlook: An Update, published by the Congressional Budget Office on
July 1, 1999. Inserting these inflation rates, the fixed cost of a host switch, the fixed cost of a
remote switch, and the per-line cost for host or remote switches (in 1999 dollars) are,
respectively:
Host Fixed cost = (1.01)*(1.013)*(a 1+ a3 + a4*(1/15) + a6*(1/15))
Remote Fixed Cost = (1.01)*(1.013)*(a1+ a4*(1/15))
Per-line Cost = (1.01)*(1.013)*(a2 + a5*(1/15))

Inserting the coefficients from the regression analysis, the fixed cost of a host switch, the fixed

C-2

cost of a remote switch, and the per-line cost for host or remote switches (in 1999 dollars) are,
respectively:
Host Fixed cost = (1.01)*(1.013)*(11,110 - 402,400 + 2,205,000*(1/15) + 1,080,000*(1/15) )= 486,700
Remote Fixed Cost = (1.01)*(1.013)*(11,110 + 2,205,000*(1/15) ) = 161,800
Per-line Cost = (1.01)*(1.013)*(10.32 + 1,121*(1/15)) = 87

In response to the Inputs Further Notice, Sprint contends the following:1

Sprint conducted regression analysis on the two data sets (depreciation and RUS)
individually and arrived at the following conclusions:
1. No RUS variables are significant (5% level of significance).
2. Only the ‘lines*1/time’ variable in the depreciation data set is significant
(5% level of significance).
3. Severe multicollinearity was found in the proposed regression equation
(VIF>55).
Based upon this evidence Sprint suggests that the data in the Commission’s proposed data set or
the proposed regression equation appears to be "severely tainted" and recommends “dismissing
all conclusions suggested as a result of this tainted data set and mis-specified regression model.”
7.
We reject Sprint’s argument. While we acknowledge that there is collinearity
amongst the explanatory variables, we note that this is typically the case in multiple regression
models. Anderson, Sweeney, and Williams note that “…most independent variables in a
multiple regression problem are correlated to some degree with one another.”2 Similarly Fomby,
Hill, and Johnson note, “[f]requently in nonexperimental situations, some explanatory variables
exhibit little variation, or the variation they do exhibit is systematically related to variation in
other explanatory variables.”3
Muliticollinearity does not as Sprint implies indicate that the regression model is mis-specified.4
1

Sprint Input Further Notice Comments Attachment 6.

See David Anderson, Dennis Sweeney, and Thomas Williams (1996), Statistics for Business and Economics,
Sixth Edition at page 597.
3

See Thomas Fomby, R. Carter Hill, and Stanley Johnson (1988), Advanced Econometric Methods at page 283.

See William Green (1990), Econometric Analysis at 278 (noting that “the case of near collinearity or high
intercorrelation among the variables is … a statistical problem. The difficulty in estimation is not one of
identification but of precision.”), or Fomby, Hill, and Johnson at 284 (noting that “the primary statistical
consequence of multicollinearity is that one or more of the estimated coefficients of the linear model may have large

C-3

Therefore, the issues of mis-specification and mutlticollinearity are independent, and Sprint
provides no evidence that the regression model is mis-specified.
8.
Even with multicollinearity the least squares estimate is the minimum variance
linear unbiased estimator, its standard error is correct, and the conventional confidence interval
and hypothesis tests are valid.5 The least squares estimates and hence forecasts based upon them
are also best linear unbiased estimates and maximum likelihood estimates and hence are
unbiased, efficient, and consistent.6 Furthermore Ramanthan notes that “Multicollinearity may
not affect the forecasting performance of a model and may possibly even improve it.”7
9.
Sprint also raises concerns in their comments regarding the lack of statistical
significance of individual parameters in our estimates.8 However as Golberger notes, while
muliticollinearity may make the estimates of individual parameters less precise, it may “facilitate
the precise estimation of particular combinations of elements.”9 For example Sprint expresses
concern that the lines variable "by itself" should be more significant. Staff analysis indicates,
however, that jointly the variables Lines and Lines/Time are statistically significant, indicating
that switches increase significantly in cost when additional lines are purchased at installation..
Therefore, one would be in error to conclude that, based upon individual “t-statistics,” switch
costs do not vary with line size.

standard errors.”)
5

See Arthur Goldberger (1991), A Course in Econometrics at 246.

See Ramu Ramanthan (1989), Introductory Econometrics at 232.

See Ramu Ramanthan (1989), Introductory Econometrics at 233.

See Sprint Inputs Further Notice Comments at 44.

See Arthur Goldberger (1991), A Course in Econometrics at 250.

C-4

APPENDIX D
DESCRIPTION OF METHODOLOGY FOR ESTIMATING EXPENSES
1.
Data Sources used in Regression Analysis. The use of multiple variables in the
estimation process required that various data sources be used to determine the common support
service expense model inputs. Because the reporting requirements and number of company
study areas were different among the reports used, it was necessary to reconcile the data for 1998
expenses, access lines, and dial equipment minutes, as described below.
1998 Expenses
Data Source: ARMIS, 43-03 Report, "Total Regulated" Column.
Study Areas (SAs) reconciled:
Total SAs from ARMIS 43-03 Report:
Less:

125

SAs combined to agree with access line data in ARMIS 43-08:
Study Area(s) Combined with
MSID
PNID
COCA
GTCA
COTX
GTTX
PRCC
PRSA
COIL
GTIL
COIN
GTIN
COMO
GTMO
CONC
GTNC

(8)

SAs removed (not in NECA Tier I reporting):
GTGO

(1)

SAs removed (certified rural):
(GTAR, COAZ, GNCA, ALGC, COIA, COSI, GTIA,
GTID, GLIL, GLIN, UTIN, COKY, GLMI, COCM,
COEM, UTMO, ALNC, GTNE, UTNJ, CONM,
GTNM, CONV, CTRH, CTUP, CTWC, ALWR,
UTNW, ALPA, COPA, COQS, UTPA, COSC, UTTX,
COVA, GTVA, COWA)

(36)

SAs used in analysis:

Access Lines
Data Source: ARMIS, 43-08 Report, Table III,
Column (dj) "Total Switched Access Lines", and
Column (dm) "Total Access Lines (Switched and Special)"
Study Areas (SAs) reconciled:
Total SAs from ARMIS 43-08 Report::
116
Less:
SAs combined to agree with ARMIS expense data and
NECA usage data:
Study Area(s) Combined with
NYNY (Conn) NYNY (New York)
CBTC (IN & KY)
CBTC (Ohio)
LTNE (IA & KS)
LTNE (Nebraska)
UTIM (VA)
UTIM (Tenn)
PRCC
PRPR

(7)

SAs removed (not in NECA Tier I reporting):
(COTM, CWTC)

(2)

SAs removed (certified rural)
(27)
[GTSW(Ar & Nm), GTGC(Az & Nv), GTNW (Ca & Id),
ALGC, GTMD(Ia & Ne), GTSO(Il & Va), COSO (Mi & In),
UTIN, UTMO(Ia, Ks & Mo), ALNC, UTNJ, COWW, CTNY,
ALWR, UTNW(Or &Wa), ALPA, UTPA, UTTX]
SAs used in analysis:

Dial Equipment Minutes
Data Source: NECA filed statistics on network usage by carrier
Study Areas reconciled:
Total SAs per NECA data filing:
Less:

131

SAs combined to agree with access line data:
Study Area(s) Combined with:
COCA
GTCA
MSID
PNID
CBTC (KY)
CBTC (Ohio)
PRCC
PRPR
COTX
GTTX
COIL
GTIL
COIN
GTIN
COMO
GTMO
CONC
GTNC
UTIN(Va)
UTIN(Tn)

(10)

SAs removed (not in ARMIS reporting):
(4)
(GA Alltel Telecom, Micronesian Tel, GTE No. Inc. - MN,
Citizens Utilities DBA Citizens of Tennessee)
SAs removed (certified rural)
(37)
[GTSW(Ar), COAZ, GNCA, COIA, COSI, GTIA,
GTNW (Id), ALGC, GTSO(Il & Va), COKY, COCM,
COEM, COSO (Mi & In), UTIN, UTMO, GTNE,
CONM, GTNM, CONV, CTUP, CTWC, CTRH, ALNC,
UTNJ, ALWR, ALPA, COPA, COQS, UTPA, COSC,
UTNW(Or &Wa), UTTX, COVA, COWA]
SAs used in analysis:

Local Number Portability
(cents per month)
Ameritech

$0.28

BA/NYNEX

$0.23

BellSouth

$0.35

Pacific Bell

$0.34

Southwestern

$0.33

US West

$0.43

GTE

$0.36

Sprint LTCs

$0.48

Cincinnati Bell

$0.34

Nationwide Line-weighted Avg.

$0.32

D-8

General Support Facilities
Investment Calculation

Office

Loop

Worker

TOTAL

Main.

GSA

Circuit

Local DEM

Switch

TOTAL

Main.

+ Total DEM

Main.

=
Loop Main. + Circuit Main. + Switch Main. + Total Corp.

.6769

.8225(2.99) + .8225(.45) + .7438(1.38) +7.32
2.99 + .45 + 1.38 + 11.69

Office Worker GSA = .6769
Total Operation GSA = 1 - .6769

GSA
SW
TOTAL
Main.
Corp.

.3231

= General Support Allocation
= Switched Lines
= Total Lines
= Maintenance
= Expenses Related to Part 32 Accounts 6510, 6530, 6600, 6700
D-9

+ USF Corp.

File Type	application/pdf
File Title	Microsoft Word - 16017
Author	edocs
File Modified	2006-01-25
File Created	2002-03-22