L. Unit Harmonization Studies for the Annual Integrated Economic Survey

Attachment L
Department of Commerce
United States Census Bureau
OMB Information Collection Request
Annual Integrated Economic Survey
OMB Control Number 0607-1024
Unit Harmonization Studies for the Annual Integrated Economic Survey

Unit Harmonization Studies for the Annual
Integrated Economic Survey
Prepared for:
Lisa Donaldson, Chief, Economic Management Division
Prepared by:
Melissa A. Cidade, Economic Management Division
Hillary Steinberg, Data Collection and Methodology Research, Economic
Statistical Methods Division
Heidi St.Onge, Office of the Associate Director of Economic Programs

June 6, 2023

The Census Bureau has reviewed this data product to ensure appropriate access,
use, and disclosure avoidance protection of the confidential source data (Disclosure
Review Board (DRB) approval number: CBDRB-FY23-ESMD001-012).

Table of Contents
Contents
Executive Summary ...................................................................................................................................... 4 
Overview ....................................................................................................................................................... 4 
Research Questions ................................................................................................................................... 6 
Unit Harmonization ...................................................................................................................................... 7 
Surveying Businesses: The Unit Problem ................................................................................................. 7 
Data Accessibility ..................................................................................................................................... 8 
Methodological Overview ............................................................................................................................ 9 
Participant Overview ................................................................................................................................ 9 
Participant Recruitment ........................................................................................................................ 9 
Participant Characteristics................................................................................................................... 10 
Interview Procedures .............................................................................................................................. 10 
Round 1: Record Keeping Practices ....................................................................................................... 10 
Round 2: Data Accessibility ................................................................................................................... 12 
Findings and Recommendations ................................................................................................................. 16 
Finding #1: NAICS codes are not intuitive. ............................................................................................ 16 
Finding #2: Operating units impact level of detail of reporting. ............................................................. 17 
Finding #3: Companylevel data are the most accessible. ....................................................................... 18 
Finding #4: Granular data were less accessible, with participants struggling to report at the state and
industry levels. ........................................................................................................................................ 19 
Finding #5. Card sorting exercises were a useful method to understanding the practical accessibility of
a business’s data. ..................................................................................................................................... 21 
Methodological Limitations ........................................................................................................................ 23 
Recommendations for Next Steps ............................................................................................................... 23 
Bibliography ............................................................................................................................................... 23 
About the Data Collection Methodology and Research (DCMR) Branch .................................................. 25 
Appendix A ................................................................................................................................................. 26 
Appendix B ................................................................................................................................................. 32 

Table of Figures
Figure 1: Generic Chart of Accounts .......................................................................................................... 11 
Figure 2: Units and Definitions for Round 2 Interviewing .......................................................................... 12 
Figure 3: Color Coded Accessibility Scale ................................................................................................. 13 
Figure 4: Revenue Card Sort Screenshot .................................................................................................... 14 
Figure 5: Number of participants sorting "easily accessible" (green) by topic and unit ............................. 19 
Figure 6: Average (mean) accessibility of data by topic and unit ............................................................... 22 

Table of Tables
Table 1: Number of Industries and Establishments of Participating Companies by Round of Interviewing
 .................................................................................................................................................................... 10 
Table 2: Number of participants sorting as “accessible with minor effort” (yellow) or “easily accessible”
(green) by topic and unit ............................................................................................................................. 20 
Table 3: Participant descriptions of color-coded accessibility scale ........................................................... 21 

Executive Summary
The Economic Management Division (EMD) and the Data Collection Methodology and
Research Branch of the Economic Statistical Methods Division (ESMD) used qualitative
methodologies, specifically in-depth interviews and a card sort exercise, to better understand
how record keeping practices impact data accessibility and reporting in support of the
development of the Annual Integrated Economic Survey (AIES). The goal of this study is to
better unit harmonization as the survey is created.
In total, researchers conducted two rounds of interviewing. In Round 1, twenty-eight participants
answered questions about their record keeping practices and reporting behaviors. In Round 2, 30
participants first provided background information on the structure of the companies they
represent, and then completed a virtual card sort exercise using a four-point color coded scale to
indicate accessibility of data for their company for specific topics and units.
Finding #1: North American Industry Classification System (NAICS) codes are not
intuitive.
Recommendation: Specify a specific research agenda to troubleshoot solutions to align
meaning with NAICS codes. Allow respondents to signal or change NAICS codes for
establishments where appropriate.

Finding #2: Operating units impact level of detail of reporting.
Recommendation: Align the reporting to the level that companies are keeping
information based on operating units.
Finding #3: Company-level data are the most accessible.
Recommendation: Reflect the existing ways data are stored, including asking for information on
the company-level when possible.

Finding #4: Granular data were less accessible to locate, with participants struggling
to report at the state and industry levels.
Recommendation: Allow flexibility for respondents to input more granular data. Avoid asking
for information at the state-level or have this information sum from establishments for
businesses.
Finding #5: Card sorting exercises were a useful method to understanding the
practical accessibility of a business’s data.
Recommendation: We recommend the use of visualization and innovative methods to
operationalize complex topics such as data accessibility.

Overview
The U.S. Census Bureau enlisted the help of an expert panel through the National Academies of
Sciences, Engineering, and Medicine (NAS) to review the design, operations, and products of the

Census Bureau’s annual economic surveys. The goal of this NAS review was ambitious; the
panel was to “recommend short-term and longer-term agendas for systemic change that can
improve the relevance and accuracy of the data, reduce respondent burden, incorporate
alternative sources of data where appropriate, and streamline and standardize Census Bureau
processes and methods across surveys” (NAS 2018:6). During this review, this panel noted that
the “lack of integration prevents [the annual economic surveys] from being as useful, costeffective, and minimally burdensome on businesses as they could be” (9).
For the most part, the Census Bureau has used a sector‐driven approach to survey development.
While the US Census Bureau fields many annual economic data collection efforts, the integration
effort is limited to the following surveys:








Annual Survey of Manufactures (ASM)
Annual Retail Trade Survey (ARTS)
Annual Wholesale Trade Survey (AWTS)
Service Annual Survey (SAS)
Manufacturers' Unfilled Orders (M3UFO) Survey
Annual Capital Expenditures Survey (ACES)
Report of Organization/Company Organization Survey (COS)

One of the recommendations from the NAS panel is the implementation of an Annual Business
Survey System – which has evolved into the AIES, a streamlined, cross‐sector, integrated and
harmonized survey instrument designed to lower respondent burden while still achieving high
quality, timely data in the service of the American economy.
A major concern at the onset of the NAS study was the differing statistical units of operations
used across annual surveys. Most, but not all, surveys sampled at the enterprise or company, with
some focused on the establishment. The business register does not always reflect one or the other
accurately. This issue is highlighted early in the report: “The use of different reporting units for
different surveys is one of the challenges associated with harmonizing the annual economic
surveys, which is made even more challenging because of the dynamic nature of businesses,
which may add, close, or relocate establishments or may change ownership and organizational
structure” (13). The authors note the differences in complexity of organizations on the structural
level. Thus, an integral part of harmonization is understanding the accessibility of data on many
levels within companies.
Driving the development of the annual integrated survey has been a portfolio of research projects
to bring together disparate sources of data to one survey instrument. The findings presented in
this report represent formative research early in the process of integrating the existing surveys,
and are primarily focused on differences in unit of collection across these surveys. The purpose

of this study is to understand the record‐keeping practices of businesses, to develop a streamlined
instrument for the AIES.

Research Questions
Researchers conducted a record keeping study in the form of semi-structured interviews and a
card sort activity to understand data accessibility in support of the development of the AIES.
Throughout the research period, we were guided by a few key research concepts and questions.
First, we were interested in how businesses defined themselves, both internally and relative to
Census Bureau definitions. This included the business’ units of operation, industry, and other key
identifiers. We were also driven to understand how accessible data were at differing levels within
a company – that is, could respondents get the data to the level of granularity we were asking
with minimal effort and maximum accuracy? Finally, we asked about the burden – or resource
intensiveness – of pulling these data at various levels within the company. The research
questions for both rounds of interviewing, then, are:
1. Definitions: How do businesses define themselves relative to the Census Bureau
definitions?
2. Accessibility: How accessible are key data points at varying business units?
3. Burden: How resource intensive is gathering data at these varying business units?

Unit Harmonization
The NAS panel’s call for increased harmonization across surveys requires careful consideration
of the appropriate reporting unit (NAS 2018: 45). The so-called "unit problem” in establishment
surveys is not new, but it is growing increasingly complex as firms themselves grow in
complexity (Emm and Kale 2006). Not only are the business units within a given company prone
to change, the availability of economic data at these units is, for the most part, unknown. The
effect, then, is that Census Bureau surveys may ask for data at a business unit level where the
information is not tracked or is not easily aggregated.

Surveying Businesses: The Unit Problem
Central to the issue of data accessibility in establishment surveys is the “unit problem” – at
which business unit should the data be collected? But, what “unit” is appropriate, what is
available, and what is reported all provide different responses, depending on who is providing
the answer. To begin with, Sturm (2015: 59) provides a taxonomy of statistical units relevant to
establishment research comprised of three parts:
1. The reporting unit: This is the "unit providing information to the data collector", and is
often the top-level business unit, like firm or company.
2. The observation unit: This is the “unit about which information is provided/reported,”
and can be comprised of a group of segments within a firm, like establishments or lines
of business.
3. The statistical unit: This is the “unit a statistical output refers to”, and in some cases,
represents data that have been aggregated or otherwise manipulated before being made
available for researchers and others to review.
Within these groups, however, there are further gradations. In a seminal work on the “unit
problem,” van Delden et al. (2018) draws attention to the mismatches of identifying,
characterizing, and delineating statistical units, and call these errors “unit errors” (573). Using a
Total Survey Error (TSE) framework, the authors place unit error under errors of representation,
but note that because the unit of analysis permeates the entire research project, “it is necessary to
approach the unit problem from a more general perspective” (578) and integrate within the other
sources of error in the TSE. They delineate between the “administrative unit” – created outside
of the statistical system and used for administrative purposes, like tax reporting – and the
“statistical unit” – created within the statistical system and used for reporting out statistics,
noting that the “intrinsic relationships between statistical units are inferred and articulated in
terms of a classification or model of units” (574) like the NAICS or other classification systems.
This mismatch between the administrative unit and the statistical unit is, in their estimation,
based on a difference in epistemological lens, such that “the survey methodology approach and
the economic theory approach result in unit types that do not fully align with each other” (575);
while economists may want to know about one particular aspect of a business, operationalizing
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that aspect and providing an appropriate sampling scheme to reach those targets can be beyond
the abilities of the survey methodologist. Survey methodologists then construct a units model as
a means of arranging administrative records into a structure “suitable for surveys” (Smith and
Yung 2019).
Unit error can be challenging regardless of the scope of the intent of the research, but is
particularly detrimental when measuring firm growth or decline. Davidsson et al. (2006) argue
that the challenge of measuring the firm when trying to understand firm growth is paramount to
the project, arguing that a unit definition must be clear “before any meaningful discussion of
growth can take place” (43). They go on to describe a sort of business “Ship of Theseus” thought
experiment whereby “over time, ‘the firm’ is likely to change its activities, its assets, its
ownership, and its legal form” (42), begging the question of whether the originally sampled firm
still exists in a meaningful way. Providing an overview of the hierarchy of standardized business
units in place within the European Union, Struijs (2016: 9) notes that while the system is
“without a doubt a great achievement,” it cannot be a static system and must be revisited at
regular intervals to update with changing business dynamics. Representing Statistics Canada,
Jang (2016) echoes the mismatches between administrative units and statistical units noted in
van Delden, and describes the rigorous and resource-intensive methods used to harmonize units
from disparate sources.

Data Accessibility
Embedded within the decision of unit of collection is the accessibility of the requested data.
Discussing sources of measurement errors in establishment surveys, Bavdaz (2010: 35) finds that
a firm’s accounting system “typically reflects the organizational structure that supports business
activities” which may – or may not – include statistical reporting. In this case, the business
establishes the accounting system that most meets its needs, and the statistical reporting must be
retrofitted to that system. Van Delden et al. (2018) go one step further, noting that while “ideally,
the system of statistical units should mirror, as well as possible, business data availability” (575),
in fact, “the target statistical units often need to be ‘created’ [by the respondent], which
ultimately raises the issue of potential conceptualization error” (577). Gravem et al. (2011) found
that mismatches between survey questions and data availability were a leading cause of
perceived response burden among respondents.
Snijkers and Arentsen (2015) developed a four-point color coded scale as a reference for
respondents when assessing the accessibility of their data at various increments, in terms of both
time and organization. That work centered on combining two surveys collecting similar data
from large non-financial firms, and then adjudicating the level of measurement between these
two efforts. They hypothesize that “the more steps and the more sources involved in the response
process, and the deeper within the business information has to be retrieved, the higher the risks
of survey errors like measurement errors and item non-response.” This builds on Bavdaz (2010)
work on accessibility as an underlying concept of both burden and measurement error.
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Methodological Overview
Participant Overview
Participant Recruitment
For this research, we focused exclusively on recruiting participants representing “medium” sized
firms. These firms have historically lower response rates to the quinquennial flagship Economic
Census, but they also present a unique space for economic survey research.
Medium sized businesses fall in between the comparatively simplistic record-keeping of smaller
businesses and the complicated and highly structured record-keeping of larger businesses. Lavia
Lopez and Hiebl (2015: 105) investigated the management accounting systems at small and
medium enterprises and found that “as the information needs of the small and medium
enterprises increases, usage and implementation of management accounting system increases as
well, which is also related to the complexity of the organization,” suggesting that medium sized
businesses in particular have various reporting structures and are not monolithic. This is echoed
by Snijker and Jones (2013: 375) who find that medium sized businesses have a “structure and
accounting system more complex than small businesses and can vary from business to business”
which makes the response process for these enterprises more “diverse.” It is imperative, then,
that more research is focused on the structure and record keeping practices of medium sized
businesses, since they can vary in complexity and are a diverse subset of establishments.
For the first round of interviewing, we focused on companies that had experience with the
surveys that were candidates for integration on the AIES. We randomly selected medium-size
companies that were currently in sample for at least two in-scope annual surveys. We recruited
companies and conducted in-person interviews between August and November 2019.
For Round 2, we again selected a random sample of medium sized businesses currently in
sample for two or more annual surveys in-scope for integration. These interviews took place
during the COVID-19 global pandemic, in the winter of 2021. As such, we conducted all Round
2 interviews virtually using an approved videoconferencing platform.

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Participant Characteristics
From these recruitment efforts, we ultimately conducted 59 interviews across both rounds of
interviewing. See Table 1 for an overview of the number of industries and establishments of
participating companies by round of interviewing. In all, these companies represented over 70
NAICS six-digit business categories.
Table 1: Number of Industries and Establishments of Participating Companies by Round of Interviewing

Total Interviews
Number of Industries*
Three or fewer
Four or more

Phase 1
28

Phase 2
30

16
5

25
5

Number of establishments*
30 or fewer
9
19
31 or more
12
11
*
Numbers may not sum to total interviews because
of missing data.

Interview Procedures
Round 1: Record Keeping Practices
The first round of interviews was focused on the record keeping practices of medium sized
businesses. For this interviewing, researchers met participants in their offices, in person.
Interviewers asked participants to describe how their business was structured and how they
maintained their financial records relative to a generic chart of accounts. A chart of accounts is
an index of all the financial accounts in the general ledger of a company. It is an organizational
tool that provides a breakdown of all the financial transactions that a company conducted during
a specific accounting period, broken down into subcategories.
The interview guide was exploratory, and participants were told that the goal of the study was to
explore the link between financial records and company organizational and management
practices. First, using the mock chart of accounts presented in Figure 1, researchers asked
participants to compare and contrast how their business is structured and maintains its records.
Researchers probed participants on their chart of accounts relative to their company’s structure,
industries in which the company operates, and locations, as well as the types of software used to
maintain their chart of accounts.

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Figure 1: Generic Chart of Accounts

Once we had a better understanding of the company chart of accounts and record keeping
practices, we could then ask follow‐up questions about specifics within their chart of accounts.
Here, we were interested in mismatches between our understanding of how records are kept and
retrieved, and the questions respondents encountered on Census Bureau surveys. Specifically, we
explored companies’ reporting of five key variables:
1. Business segments by industry
2. Sales/receipts/revenues
3. Inventory
4. Expenses, focusing on payroll and employment
5. Capital expenditures
In some cases, interviewers showed participants specific questions and questionnaires from the
legacy annual surveys they had received and to which they had responded previously. We then
asked participants to explain their general response process, how they gathered data from
multiple sources, and whether they needed to manipulate the data in order to provide answers to
what they thought the questions were asking. Researchers probed apparent discrepancies
between participants’ reporting practices and the question’s intent.
Next, researchers asked participants to describe in their own words what their business does or
makes, and then to indicate the NAICS most appropriate for their business. NAICS is a
hierarchical taxonomy with nested values. Prompted with a list of high-level industrial sectors,
participants selected the one they most identified with and went on to describe their business
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activities in detail. They also selected their revenue-producing
goods or services from a detailed list used in collection of the
2017 Economic Census. Since sources of revenue are key
factors in classification for many industries, researchers were
then able to cross-check with the Census Bureau’s “official”
industry classification for the business, and detect likely or
potential mismatches, some of which were quite notable. See
Appendix A for the research protocol for the Round 1
interviews.

Round 2: Data Accessibility
Building on the first round of interviewing, the second round
of interviewing explored unit harmonization to focus on the
accessibility of data at various units within the company.
These interviews first established definitions and
equivalencies of response units (for example, what is the
company term that means the same as ‘establishment’?). We
began the semi-structured interviews by asking about the unit
mismatches. Participants were shown three units – company,
establishment, and line of business ‐ individually, with
corresponding definitions. They were then asked to “map”
themselves to these concepts, specific, “what is the word or
phrase that the business uses to mean the same thing?” See
Figure 2 for an overview of units and their definitions.

Figure 2: Units and Definitions for Round 2 
Interviewing

Company: A company or 
“enterprise” is comprised of all the 
establishments that operate under 
the ownership or control of a 
single organization. A company 
may be a business, service, or 
membership organization; consist 
of one or several establishments; 
and operate at one or several 
locations. It includes all subsidiary 
organizations, all establishments 
that are majority‐owned by the 
company or any subsidiary, and all 
the establishments that can be 
directed or managed by the 
company or any subsidiary. A 
company may have one or many 
establishments.  
Establishment: An establishment 
is a single physical location where 
business is conducted or where 
services or industrial operations 
are performed.  
Line of Business: A line of business 
is a general term which refers to a 
product or service, or a set of 
related products or services, that 
serve a particular customer 
transaction or business needs. 
Line of business refers to to an 
internal corporate business unit, 
and is sometimes referred to as a 
division. 

Then, we asked participants about the ways that their
companies are classified using the NAICS system. First, we
asked about their six-digit NAICS classifications, calling it
their ‘specific’ industry because that six‐digits is the most
specific classification in the Business Register.1 We then
asked about the four-digit NAICS classification, which was
less detailed. We called this their “general industry.” Interviewers walked participants through
each of the six-digit NAICS codes we could find for their company, asked for feedback or
impressions, and then did the same for the four-digit NAICS codes.

Finally, researchers extended a framework put forth by Snijkers and Arentsen (2015), who
developed a four‐point color coded scale as a reference for participants when assessing the
accessibility of their data at various increments, in terms of both time and organization. The
interviewers introduced participants to the four-point color coded scale ranging from green (very
accessible) to red (not at all accessible) to categorize data. This included specific topics (e.g.,

1

Note: there are instances where seven or eight digits further classify an establishment, but these are often industry
specific or otherwise niche to a particular set of businesses. For the purposes of this research, six-digit NAICS
classification was granular enough.
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revenue, expenses, and payroll) and specific levels (e.g., company-wide, by establishment, or at
the general and specific industry levels). This exercise was performed to understand how
accessible the company data is at each unit for specific topics. See Figure 3 for a screenshot of
the instructions we showed to participants.
Figure 3: Color Coded Accessibility Scale

Once the participant was comfortable with the scale, we then moved on to the card sort activity.
Note that the initial scale instructions remained at the top of the screen for each card sort so that
participants could reference the colors and their meaning. Figure 4 is an example question, the
revenue card sort. The general question was positioned at the top, followed by four color-coded
boxes, and six business units nested under the heading “Business Unit.”

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Figure 4: Revenue Card Sort Screenshot

Participants were instructed to click on each business unit and move it to the box that
corresponded with the accessibility of the requested data at that business unit. Researchers
prompted participants to “think aloud” as they moved the units to the corresponding box so that
we could capture their responses and ask follow-up questions about why they categorized the
data the way that they did. See Appendix B for the Round 2 interviewing protocol.
We used the card sort methodology to explore six topics included in the in-scope legacy surveys,
including:
 Revenue: What were the TOTAL sales, revenue, and other operating receipts for this
[business unit] in 2019?
 Capital Assets: What were the total capital assets for this [business unit] in 2019?
 Inventories: Did this [business unit] own inventories, regardless of where held, at the end
of 2019?
 Payroll: What was the annual payroll before deductions for this [business unit] in 2019?
 Expenses: What were the TOTAL operating expenses for this [business unit] in 2019?
 E-Commerce: What were the TOTAL e-commerce sales for this [business unit] in 2019?
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These questions represent the first attempts to harmonize content across the surveys. While we
did not ask cognitive processing questions to determine comprehension, we did ask about
applicability of each topic. We note that many participants – particularly those representing
companies classified in the services sector – found inventories to be out-of-scope for their
company, and so did not respond to that part of the card sort. Similarly, we found that many
participants struggled with the concept of e-commerce, including being unsure of what to include
or exclude in this category; after initial interviews, we ultimately dropped further investigation
into accessibility of e-Commerce data because of comprehension issues.2

2

For the remainder of this report, we will exclude e-commerce from accessibility findings. We strongly urge
additional research resources to be dedicated to further research into comprehension, accessibility, and reportability
of e-Commerce data.
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Findings and Recommendations
Finding #1: NAICS codes are not intuitive.
A major finding through both rounds of interviewing was the mismatch between how the Census
Bureau typically classifies a company and how the participant classified their company. Industry
classification was challenging and unnatural for participants, and companies can struggle to fit
within the NAICS classification system. It was also not salient to how businesses keep their
records and feels artificial for participants; that is, because NAICS is a standardized
classification system, and businesses often needed more or different details in their chart of
accounts, mapping records to the corresponding NAICS is challenging for some and impossible
for others.
As a standardized classification system, the NAICS taxonomy is imposed upon companies from
an external agency. Participants felt they were expected to align their companies accordingly in
order to report data for statistical purposes. At least seven companies in Round 1 interviewing
may have been misclassified or may not have understood Census Bureau distinctions among
classifications, particularly across levels of detail within the same sector. Participants also
indicated that Census surveys do not match internal reporting, and that they are uncomfortable
making decisions on how to manipulate their data to match our requests.
This finding held true for both rounds of interviewing. In Round 2, participants still struggled
with their NAICS classification, noting that the categories could be too broad or, conversely, not
encompassing enough to accurately describe the company as a whole. This part of the interview
was time consuming and difficult; participants had trouble understanding their NAICS
classification, and then struggled to think of how their business units might be related to their
NAICS classification. It seemed that the industry classification either worked or did not, with
few falling in between.
Some participants positively reacted to their general and specific industry codes. In these cases,
the NAICS we had on file made sense to the participant and fit how the participant saw their
company relative to the NAICS categorization scheme. Said one, “These [NAICS codes] are a
good fit. Most of what we do would fall under the first one [listed].” Another affirmed
classification, saying “[I] agree with the [given] NAICS.” A third echoed this sentiment, saying
“Specific industry: that's a perfect fit. General industry: that works as well, too.”
However, there were instances of mismatches, too. One participant familiar with NAICS codes
stated “I hate them. They have me in warehousing and say ‘You operate the warehouse’ but we
do not. [That’s] not what we do.” Another participant, who self-described as “somewhat
familiar” with NAICS codes, pointed out that each location is classified with one code, which
did not capture the breadth of economic activity; this participant noted that “one NAICS
wouldn't apply to one location” and that there is a “mix [of activities] within locations.” Another
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said the NAICS codes were not reflective of current operations, pointing out that the Census
Bureau’s assigned classifications are out of date. This participant noted that “all other codes are
coming from a long time ago, when [this company] was non-profit,” and that the business is
“focused entirely on [one industry] now.” Classifying a business is a critical component to
collecting data on that firm, both in terms of directing respondents to the appropriate survey
forms based on their classification and in terms of sampling, weighting, imputation, reporting
and other important data handling techniques.
Recommendations: The difference between how companies classify themselves and the
classifications that are assigned by the Census Bureau warrants additional investigation
into the NAICS taxonomy. While this investigation is outside of the scope of the current
program, it does inform recommendations for unit harmonization, mainly, that an
integrated instrument allow for respondents to verify and, if appropriate, update an
establishment’s assigned classification. At the very least, respondents to the AIES must
be given a way to signal that the assigned NAICS may be out of date or otherwise
inappropriate.

Finding #2: Operating units impact level of detail of reporting.
Businesses varied in their operating units, and this impacted the level and detail at which they
kept their records, which could negatively or positively impact response. For example, when
asked what he would report for his company, one participant explained, “We have different
reporting where we have to report by country or by the entire company.” Specifically, companies
tracked data according to operating units that make sense to the company. Further, companies
use disparate terminology to describe their various operating units. One participant explained
they would need to report data either on the company-level or “we would need to give you data
by store…I don't know why set up that way. The company used to be direct business only. As it
expanded into retail, we kept costs segregated.” Another participant told us, “I don't really think
we have divisions, so to speak.” Another participant said, “The way we do our reporting is by
country or by asset team, or a region. Not necessarily a physical location.” More like an
operational area.” Participants did not always know why data was stored in specific ways in their
records.
When asked about “establishments,” participants indicated that their company used a different
term – such as region, office, department, line of business, and business segment – or did not
track data by individual locations at all. One participant told us, “Things get a little tricky.
Accounting department is in this building, but also IT. Accounting is not just in this location, but
in multiple locations. Each manufacturing site is only one kind of manufacturing but may have
offices collated.” Another said of reporting by establishments, “But we don't really work this
way.”
We introduced this unit as a line of business early in testing. However, participants had trouble
with both this conceptualization and definition. When asked about what the company’s line of
business is, nearly every company gave a different answer, including “capability” “divisions”
“services” or “business unit.” More specific examples like “healthcare” or “operating segments”
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or “business streams” did not prove useful. Some participants said they know of no other term or
chose to skip the question. This shows that line of business was not a useful way to think about
company structure.
Recommendation: Minimize burden by allowing companies to report at the operating
units that make sense to them, especially at “rolled up” levels such as the industry or
company totals.

Finding #3: Companylevel data are the most accessible.
Throughout the interviewing, participants noted that consolidated financial records were a
mainstay for businesses and acted as an ‘anchor’ for most detailed information. These charts of
accounts varied in detail. However, we found that top-level consolidated financial reports were
nearly universal, and essential for how participants kept records. Consolidated totals were readily
accessible and accurate, and when a survey asked for disaggregated data, participants mentioned
checking those figures to the company-level total to ensure accuracy. Participants could
manipulate the data and break it down into a number of different categories for reporting, but it
was critical that they always rolled back up.
During the card sort exercise, we note that of all five of the topics that we tested – revenue,
capital assets, inventories, expenses, and payroll – participants were most likely to sort companylevel data as “accessible with no effort” (green). According to participants’ descriptions,
reporting company-level data involved the least amount of consultation with records and others
in the company, thereby representing the least burdensome data to report. One participant who
sorted company data as green said, “We have all of our sales/expenses for the entirety of our
company.” He clarified, “details are not readily available” on the level of locations. Another
participant explained that company data was easiest to pull because it is “part of our internal
reports.” One participant said, “It’s just more of an effort” when questions asked for data on
anything more specific than the company-level. “It’s not at our fingertips, because we report by
consolidated company.” See Figure 5 for an overview of the number of participants sorting as
“easily accessible” (green) by topic and unit.

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Figure 5: Number of participants sorting "easily accessible" (green) by topic and unit

Recommendation: Allow for survey questions to reflect existing financial records, but
especially consolidated financial records, when asking companies to report financial
information. Due to the accessibility of the data, we suggest that any question that can be
asked at a company-level be collected at that level, and that only those questions that
warrant additional granularity be asked at more specific units within the company. We
also suggest that where data are asked at both a company-level and at a disaggregated
level, that participants have a way of reconciling the total of the parts to the overall
company-level total.

Finding #4: Granular data were less accessible, with participants struggling to
report at the state and industry levels.
There were varying levels of difficulty for accessing more granular data, but generally,
participants were more likely to say that data across topics were “easily accessible” (green) or
“accessible with minor effort” (yellow) at the establishment level than at the specific or general
industry levels. While 13 participants said that revenue data are “somewhat” or “very” accessible
at the establishment level, that number drops to 9 for the specific industry (six digit NAICS) and
the general industry (4 digit NAICS). This trend is repeated across each of the topics –
establishment is sorted as “accessible with minor effort” (yellow) or “easily accessible” (green)
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at higher rates than specific or general industry. One participant told us, “For office, there’s no
revenue generated by an office. We wouldn’t report that.” See Table 2 for an overview of the
number of participants sorting each topic as “accessible with minor effort” (yellow) or “easily
accessible” (green) by establishment, specific industry, and general industry. Of industry, a
participant told us, “We don't track it that way in our general ledger. I am not even sure I could
get it. It would be a guess.” One company told us about how it was difficult to report at lower
than the company-level if the question asked for data in a different way than how they stored it:
“Some of these are more difficult because we don't bring that much information into
consolidation system. From a transactional basis, the general ledger system, we don't pull it all
into the consolidation system. That’s what makes it hard to see. We have an old ERP, and it’s not
very friendly. We don't have consolidation structures to bring in transactional data. We bring in
month end balances. It would take extra steps.”
Table 2: Number of participants sorting as “accessible with minor effort” (yellow) or “easily accessible” (green) by topic and
unit

Specific
General
Establishment Industry Industry State
Revenue
13
9
9
9
Capital Assets
10
5
7
9
Inventories
8
4
6
7
Payroll
8
5
4
8
Expenses
8
6
4
6
Note: Total interviews is 30; not all participants sorted all topics
and all units.

Similarly, when we asked participants about data accessibility at the state-level, we noted that
while they may say that the data are “accessible with minor effort” (yellow) or “easily
accessible” (green), often this designation was given after explaining that state would be the sum
of data stored by location (establishment). There were exceptions: one participant said state-level
data would be easy only because all of their offices and headquarters were in one state. Some
were confused about how to consolidate the data on the state-level, such as a participant who
rated state reporting at orange and had to consider shipping destinations or sales by office. In this
way, while state can become accessible, it is not a typical unit at which businesses store their
records. One participant said, “My management reports are not by state. It would be more
effort.” Another explained that in general “our offices do not generate revenue; estimates could
be done, but it would not be definitively known.” Because of this, “For state, we could get our
data scientists to pull this but it would be a huge effort for the company. It requires significant
resources.”
Recommendation: Allowing for flexibility at the level (establishment or business) a
company reports can keep a holistic scope intact for the information collected. Do not ask
respondents to provide information on the state-level. If this is necessary, ensure there is
a feature to sum this data automatically.

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Finding #5. Card sorting exercises were a useful method to understanding the
practical accessibility of a business’s data.
Although not typically used in establishment research, participants were engaged in the card sort
interview. Interviewers noted that the card sort acted as a way of operationalizing the four-point
scale measuring accessibility, a complex construct, across units, another complex construct. The
interactive nature of the exercise (participants clicked-and-dragged the unit to match the level of
accessibility) kept participants engaged in the activity and provided a novel means of collecting
this exploratory information.
In early interviews in Round 2, we noted that some participants might be using different
tolerances for gauging “accessibility” of data. We modified the interviewing protocol slightly to
ask participants to describe their understanding of the four categories of accessibility. That way,
when we examined the interview data during analysis, we could note the level of effort that each
participant noted was “accessible” compared to “not accessible”. See Table 3 for examples of
participants’ descriptions of the four-point color coded accessibility scale.
Table 3: Participant descriptions of color-coded accessibility scale

Green
Easily
Accessible
“Green means
go. Green means
info is
available.”
“Can run a
report and get
information.”
“Green is
anything I pull
directly off of a
financial
statement that
I'm already
producing.”

Yellow
Orange
Accessible with Accessible with
minor effort
major effort
“I’d probably
“No one has any
have to reach out idea what we are
for help.”
looking for so
they need to dig.
“I would run a
If we don't know
new report for,
who to ask for it
but not have to
or know where
do a lot of
to get it but are
analysis and
pretty sure the
digging to find
data exist.”
[the data], or I
can modify an
“Orange would
existing report.” take more effort
- involving other
people or
creating
additional
reporting that we
don't normally
run.”

Red
Inaccessible
“Red is
inaccessible;
there's no way
for me to get that
information, and
it not tracked or
maintained.”
“Red is we just
can't pull it.”

At the same time, the card sort also allowed for compelling visualizations of the interview data
because of the standardized scale. Figure 6 displays the average (mean) accessibility score by
topic and unit as assigned by participants. In this case, we took each accessibility designation and
assigned a number value to indicate the accessibility, such that:
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Accessible with no effort (green) = 4
Accessible with minor effort (yellow) = 3
Accessible with major effort (orange) = 2
Inaccessible (red) = 1
Because these datapoints are representative of qualitative interpretation, we cannot run
significance testing to test the distribution of responses. But, we can see illustrative evidence of
difference in accessibility by unit and topic such that company-level data have the highest
average accessibility score across all topics compared to other units. From there, generally,
establishment outperforms both general and specific industry in mean accessibility across topics
generally, and specifically for inventories, payroll, and expenses. This is a quick and easy way to
communicate the complicated interplay of three concepts: accessibility, unit, and topic.
Figure 6: Average (mean) accessibility of data by topic and unit

Average (mean) accessibility of data by topic and unit
4
3.5
3
2.5
2
1.5
1
0.5
0
Revenue

Capital Assets
Company

Inventories

General industry

Specific industry

Payroll

Expenses

Establishment

Recommendation: We encourage the use of innovative methods that are often not
applied to data collection in establishment settings. Card sorts can be a useful tool in
establishment surveys. Visualization of qualitative data can have a powerful impact with
stakeholders.

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Methodological Limitations
While this testing provided insight into respondent record keeping and how data accessibility
impacts the scope of information provided, there are important limitations to this method that we
must consider.
The first methodological limitation is that we asked about broad categories in both rounds of
interviews, with the example chart of accounts and card sort exercises. We do not know the
burden of data accessibility for more specific questions at the level they will be asked on the
AIES.
Additionally, the testing involved some hypothetical thinking – we did not ask participants about
their specific behavior on a specific survey, but rather to describe for us how they generally
approach surveys, how accessible they believe data to be, and how they might go about reporting
requested data. Participants may be poor predictors of actual behavior based on hypothetical
parameters.

Recommendations for Next Steps
The testing in this report reflects the first step in the AIES unit harmonization by assessing data
accessibility and how record keeping impacts the way respondents provide response to survey
questions. Generally, we found that respondents keep most data at the company-level, but that
specific types of data are kept at various units depending on the business needs. We therefore
recommend that any instrument design be as flexible as possible to allow for variations in record
keeping to keep response burden as low as possible.
As a next step, the AIES team should consider independent response from the field in the form of
a pilot survey. This pilot survey could bring together the units and topics proposed for inclusion
in the AIES and could include additional research modalities like interviewing to further
understand the response processes that hinder or support economic survey response. A pilot
survey would bridge the gap between asking respondents about their response theoretically and
actually inducing realistic survey response. It could be a first step toward a unit harmonized and
content integrated survey.

Bibliography
Abraham, Katharine G., Constance F. Citro, Glenn D. White, Nancy J. Kirkendall, National
Academies of Sciences, Engineering, and Medicine (U.S.), National Academies of
Sciences, Engineering, and Medicine (U.S.), and National Academies of Sciences,
Engineering, and Medicine (U.S.), eds. 2018. Reengineering the Census Bureau’s Annual
Economic Surveys. Washington, DC: The National Academies Press.
Bavdaž, Mojca. 2010. “Sources of Measurement Errors in Business Surveys.” Journal of Official
Statistics 26(1):25–42.
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Davidsson, Per, Frédéric Delmar, and Johan Wiklund. 2006. Entrepreneurship and the Growth
of Firms. Cheltenham, UK ; Northampton, MA: Edward Elgar.
Delden, Arnout van, Boris Lorenc, Peter Struijs, and Li-Chun Zhang. 2018. “Letter to the Editor:
On Statistical Unit Errors in Business Statistics.” Journal of Official Statistics 34(2):573–
80. doi: 10.2478/jos-2018-0027.
Emm, Emkaterina, and Jayant Kale. 2006. Efficiency Implications of Corporate Diversification:
Evidence from Micro Data. CES-06-26. U.S. Census Bureau.
Gravem, Dag, Gustav Haraldsen, and Tora Lofgren. 2011. “Response Burden Trends and
Consequences.” Pp. 221–36 in. Harleen, Netherlands: Statistics Netherlands.
Jang, Leon. 2016. “Resolving Differences in Statistical Units: Statistics Canada’s Experiences
with Using Administrative Data in Economic Programs.” in Proceedings of the Fifth
International Conference on Establishment Surveys. Geneva, Switzerland: American
Statistical Association.
Lavia López, Oro, and Martin R. W. Hiebl. 2015. “Management Accounting in Small and
Medium-Sized Enterprises: Current Knowledge and Avenues for Further Research.”
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Smith, Paul A., and Wesley Yung. 2019. “A Review and Evaluation of the Use of Longitudinal
Approaches in Business Surveys.” Longitudinal and Life Course Studies 10(4):491–511.
doi: 10.1332/175795919X15694142999134.
Snijkers, Ger. 2013. Designing and Conducting Business Surveys. Hoboken, New Jersey: Wiley.
Snijkers, Ger, and Aarenson. 2017. “Questionnaire Communication to Collect Financial Data
from Large Non-Financial Enterprises.” Southampton, UK.
Strujis, Peter. 2016. “The Desired Future System of Statistical Units from the Perspective of
Business Statistics.” in Proceedings of the Fifth International Conference on
Establishment Surveys. Geneva, Switzerland: American Statistical Association.
Sturm, Roland. 2015. “Revised Definitions for Statistical Units – Methodology, Application and
User Needs. The Main Conceptual Issues of the ‘Units Discussion’ of the Years 2009–
2014.”

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About the Data Collection Methodology and Research (DCMR)
Branch
The Data Collection Methodology and Research (DCMR) Branch in the EMD assists economic
survey program areas and other governmental agencies with research associated with the
behavioral aspects of survey response and data collection. The mission of DCMR is to improve
data quality in surveys while reducing survey nonresponse and respondent burden. This mission
is achieved by:
 Conducting expert reviews, cognitive pretesting, site visits and usability testing, along
with post-collection evaluation methods, to assess the effectiveness and efficiency of the
data collection instruments and associated materials;
 Conducting early-stage scoping interviews to assist with the development of survey
content (concepts, specifications, question wording and instructions, etc.) by getting early
feedback on it from respondents;
 Assisting program areas with the development and use of nonresponse reduction methods
and contact strategies;
 And conducting empirical research to help better understand behavioral aspects of survey
response, with the aim of identifying areas for further improvement as well as evaluating
the effectiveness of qualitative research.
For more information on how DCMR can assist your economic survey program area or agency,
please visit the DCMR intranet site or contact the branch chief, Amy Anderson Riemer.

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Appendix A
General Research Questions
 DEFINITION: How do companies define data items based upon their charts of accounts
and financial reporting requirements? Can we determine a harmonized definition that
aligns with company records?
For each topic/data item, compare the answers the companies provide to our current
questions when discussing:
• How our data items/topics are defined and the components of those items (e.g.
includes/excludes)
• The structure of the company
 UNIT: What data are available at what level? (e.g. establishment, company, industry,
state)
 TIMING: When are the data available? Are different data items available at different
times? If so, what and when?
 BURDEN: How readily available is the information we are asking? Are some items
easier? Harder? Why?
Review based on:
 How much manipulation of data in business records is involved in order to provide
data that meets Census Bureau requirements
 How many people or data sources are involved
 How much time it takes to gather the information
 In the ideal world, what would our survey look like according to our respondents?
Expected Length of Interview: 1 - 1½ hours
Materials Needed:






Consent forms.
Digital recorder.
Draft Survey Items (copies of relevant surveys and specific questions)
Bring list of industries (i.e. KAUs)
Information on Respondent’s answers to selected questions from in-scope surveys (these
can be screen shots or some other form of record of the participants’ responses). These
will be transported and secured by the researcher using the double envelope method, per
Census Bureau security requirements.

Introduction:
 Explain purpose of meeting:
o Feedback on how company records are kept to help inform content definition,
design of instrument, unit(s) of collection, and collection strategy for our surveys
o Assess the gap of availability of data between company record keeping, our
surveys, and the needs of our data users
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


If at any time a question seems odd to you, please let us know. We encourage all
feedback.
Before we start, I have a consent form that goes over the authority that we have to
conduct these interviews. There is also a piece in here where we ask for permission to
record this interview, which is strictly for our note taking purposes. These interviews will
only be heard by people directly involved in the development of the survey. Do we have
permission to record our conversation for research purposes?

ABOUT THE RESPONDENT
First, I would like to learn a bit about you and your role here at the company.


What is your job title?



What is your role relative to the company’s financial reporting needs?



Do you have a role in any external company reporting?
□
Yes
□
No
If yes, describe the role in external company reporting



What is your role in completing government surveys?



Do you work with anyone else in your company to get the data for government
surveys? Do you ask people in other parts of the company for data requested on
government surveys? E.g., payroll dept.? PROBES:
□
Yes
□
No
If yes:
o How are those other people involved? PROBES:
o How many people are involved?



•

Now I would like to learn a little about your company.
•

Can you give us a brief description of what your company does?

•

Can you tell me which industry(ies) your company operates in? *Show NAICS Code if
needed
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•

Can you give us a brief description of how your company is organized?

•

What is your fiscal year?

Next, we would like to get some background information on how your chart of accounts is
designed. Here is a generic example of a chart of accounts. For each of these categories, I
would like you to discuss how your company’s chart of accounts is set up.


How are ASSETS setup in your chart of accounts based on:
o your company’s organizational structure
o the industry(ies) that your company operates in
o level of detail for inventory within chart of accounts i.e. by location



How is INCOME setup in your chart of accounts based on:
o your company’s organizational structure
o the industry(ies) that your company operates in
o level of detail for trades operating in (i.e. wholesale/manufacturing)



How are EXPENSES setup in your chart of accounts based on:
o your company’s organizational structure
o the industry(ies) that your company operates in



Is information for these categories (assets, income, expenses, etc.) typically kept for
each physical location in your records?
□
Yes
□
No
If yes:
o Can you tell us more about why the company has decided to do this?





What types of reports do you create (i.e. payroll, sales, expenses, external
reporting)? How often (i.e. weekly, monthly)?



Can your system run reports by physical location?
o If so, what types of reports do you run?
o If not, what is the most detailed level can you run?
o Run reports at state-level? Geography?



What type of software is used to create/maintain your records?

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

How is the data captured to create your records for each data item? Each sales
transaction? Different department’s with-in company? Accountants/HR at individual
physical locations?



Where does the data captured to complete external reports reside?

Thank you for that background information, it is very helpful. Now I would like to present you
some questions asked in surveys you may have previously received (if necessary, utilize
information from respondents’ prior responses)
Sales/receipts/revenues
Show questions on forms. With your records in mind, I would like you to walk me through how
you would/did obtain information to answer this question.
How easy or difficult is it for you to answer this survey questions?
If difficult, why? Is it due to definitional differences? Definitions of the content/topic or
something else? E.g., industry, reporting unit? Timing? Access to data? Data sources?
If mismatch occurs, probe ease/difficulty of resolving based on respondent’s records. This
includes discussing the instructions and includes/excludes. Do they map industry to content or
map content to industry? How detailed is this information kept?
Inventory
Show questions on forms. With your records in mind, I would like you to walk me through how
you would/did obtain information to answer this question.
How easy or difficult is it for you to answer this survey questions? If retailer, would
removing the word “merchandise” change the way you answer the question.
If difficult, why? Is it due to definitional differences? Definitions of the content/topic or
something else? E.g., industry, reporting unit? Timing? Access to data? Data sources?
If mismatch occurs, probe ease/difficulty of resolving based on respondent’s records. This
includes discussing the instructions and includes/excludes. Do they map industry to content or
map content to industry? How detailed is this information kept?

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Expenses, including payroll and employment
Show questions on forms. With your records in mind, I would like you to walk me through how
you would/did obtain information to answer this question.
How easy or difficult is it for you to answer this survey questions?
If difficult, why? Is it due to definitional differences? Definitions of the content/topic or
something else? E.g., industry, reporting unit? Timing? Access to data? Data sources?
If mismatch occurs, probe ease/difficulty of resolving based on respondent’s records. This
includes discussing the instructions and includes/excludes. Do they map industry to content or
map content to industry? How detailed is this information kept?

Capital Expenditures
Show questions on forms. With your records in mind, I would like you to walk me through how
you would/did obtain information to answer this question.
How easy or difficult is it for you to answer this survey questions?
If difficult, why? Is it due to definitional differences? Definitions of the content/topic or
something else? E.g., industry, reporting unit? Timing? Access to data? Data sources?
If mismatch occurs, probe ease/difficulty of resolving based on respondent’s records. This
includes discussing the instructions and includes/excludes. Do they map industry to content or
map content to industry? How detailed is this information kept?
Business segments by industry (kind of business)
Show questions on forms. With your records in mind, I would like you to walk me through how
you would/did obtain information to answer this question.
How easy or difficult is it for you to answer this survey questions?
If difficult, why? Is it due to definitional differences? Definitions of the content/topic or
something else? E.g., industry, reporting unit? Timing? Access to data? Data sources?
If mismatch occurs, probe ease/difficulty of resolving based on respondent’s records. This
includes discussing the instructions and includes/excludes. Do they map industry to content or
map content to industry? How detailed is this information kept?

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

Lastly, we would like to hear if you have any suggestions on the way we structure,
organize, design, and/or deliver our surveys? (e.g., topics, industries, company
structure (e.g., departments), timing)? If topic based modules, all at one time or
staggered



In the ideal world, what would our survey look like?
(e.g., topics, industries, company structure (e.g., departments), timing)? If topic
based modules, all at one time or staggered



If you received an overall survey for your company, and it asked for each of the
categories we talked about at the start (assets, incomes, expenses...) for the overall
company and then for each industry you operate in, would that change the way you
are currently reporting and why?



Do you have any questions or concerns based upon what we have discussed today?

Thank you for your time.

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Appendix B
Cognitive Interviewing for the Content Harmonization
and Collection Unit Determination Instrument – Revised 11/18/20

Protocol
Hello; may I speak to [PIPED TEXT FOR CONTACT NAME]?
This is [Name]. I am a researcher with the Census Bureau's Economic Statistical Methods Division.
Thank you for your time today. We are looking to obtain feedback on how company records are kept to
help inform content definition, survey instrument design, units of collection, and collection strategy for
our surveys, as well as assess the gap of availability of data between company record keeping, our
surveys, and the needs of our data users.
I see that you have completed your consent form; thank you for that! Just to reiterate, this study is being
conducted under the authority of Title 13 of the United States Code. We plan to use your feedback to
inform changes to our surveys.
[If applicable:]
We have a few additional researchers listening in on our conversation today, though they will not be
participating. This staff is assigned to this project and are under the same requirements as I am with
regard to keeping your information and the information about your business confidential.
To make sure I'm capturing all of the important information, the consent form included information about
recording our session. I'm going to turn on the recorder now, and I will again ask for your consent to
proceed, just so that I have it on the tape. Do you give your consent to participate in
this research and be recorded?
Great - let's get started with some background information about your business.

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Respondent Background:

What is your job title?
What is your role in the business?
What kind of responsibilities do you have?
What is your role relative to the company's financial reporting needs?
Do you have a role in any external company reporting?

o Yes (1)
o No (2)
Display This Question: If Q = Yes

Describe your role in external company reporting.
What is your role in completing government surveys?
Do you work with anyone else in your company to get the data for government surveys?
Do you ask people in other parts of the company for data requested on government surveys?

o Yes (1)
o No (2)
Display This Question: If question = No

Can/do you query databases yourself to answer government surveys?
Do you need multiple databases or are your systems integrated, or something in between? In
what way?

Display This Question: If question = Yes

How are those other people involved?
How do you contact others who provide data to you for government surveys? Phone, email, some

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other way, some combination?
How many people are involved?

Here we will show the reporting history for this company, including:
Response status for 2017 Economic Census
Response status for Annual surveys
Response status for Quarterly surveys

Probes:
Do you remember completing the following surveys? What do you remember about them?
What are the major factors that encourage you to respond to Census Bureau surveys?

Could you tell me a little bit about your business?
What types of goods or services does this business provide?
Which industry(ies) does your company operate in?
Interviewer: Searchable NAICS documentation available here: Click here for NAICS codes
Currently, the Census Bureau has the following NAICS codes associated with this business:
<>
Q13 Briefly, can you describe how your company is organized?
What is your fiscal year?
What is the first month and day and the last month and day?
First month: ________________________________________________
First day: ________________________________________________
Last month: ________________________________________________
Last day: ________________________________________________

Chart of Accounts
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Next, I'd like to get some background information on how your chart of accounts is designed. I'm going
to ask you about categories commonly found on charts of accounts. I would like for you to discuss how
your company's chart of accounts is set up relative to these categories.

Let's start with Expenses:
How are expenses set up in your chart of accounts based on:




your company's organizational structure?
the industry(ies) that your company operates within?
the level of detail for inventory within chart of accounts with regard to:
o Product lines?
o Location (establishment)?
o Kind of activity (KAU), e.g., manufacturing plants, retail stores, etc.?

Next, let's look at REVENUE:
How is income set up in your chart of accounts based on:




your company's organizational structure?
the industry(ies) that your company operates within?
the level of detail for inventory within chart of accounts with regard to:
o Product lines?
o Location (establishment)?
o Kind of activity (KAU), e.g., manufacturing plants, retail stores, etc.?

Finally, let's look at ASSETS:
How are assets set up in your chart of accounts based on:


your company's organizational structure?



the industry(ies) that your company operates within?



the level of detail for inventory within chart of accounts with regard to:
o Product lines?
o Location (establishment)?
o Kind of activity (KAU), e.g., manufacturing plants, retail stores, etc.?

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Ok, now I'm going to have you do an activity we call "card sorting." Card sorting is a method of
categorizing information based on similar attributes or uses. This may be the first time you've done an
exercise like this, so I thought we could start with a practice.
[Instruct respondent to click on the URL you emailed to them]
Here, you'll see four categories - very far, far, close, very close - followed by five American cities. Please
categorize each of the cities based on your location right now. And, while you are working through this,
please think aloud - that is, let me know what you're doing and, more importantly, what you're doing.

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Alright! Thank you!
Advance to the next screen, and let's try the first card sort using a Census Bureau question.
For this, first, you'll see a question based on a Census Bureau survey. Then, I'd like you to 'sort' how
accessible or inaccessible the answer to that question at various levels of organization at your business
into four categories:


GREEN: Easily accessible: The information is easily and readily available



YELLOW: Accessible with minor effort: The information is available at a central location, but
not in the group accounts, which requires more effort.



ORANGE: Accessible with major effort: The information is available but decentralized (general
ledger level), which requires considerable effort to acquire.



RED: Inaccessible: The information is not available.

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Probes:
[If report Red: inaccessible for any item] You reported that this data is inaccessible (Red) by [xxx level of
organization], can you tell me what you would do in the case where this data was requested at that level?

How easy or difficult was it for you to think through the accessibility of this information?

If difficult, why? Some anticipated examples might be: definitional differences, timing, access to data,
data sources, industry, and reporting unit.

If difficult, how easy or difficult would it be to resolve these issues? What definitions, instructions, or
other language do you think you might need on a survey to accurately report your data?

Ok, on to the next question. Remember to think aloud as you organize the different levels of your
business.

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Probes:
[If report Red: inaccessible for any item] You reported that this data is inaccessible (Red) by [xxx level of
organization], can you tell me what you would do in the case where this data was requested at that level?

How easy or difficult was it for you to think through the accessibility of this information?

If difficult, why? Some anticipated examples might be: definitional differences, timing, access to data,
data sources, industry, and reporting unit.

If difficult, how easy or difficult would it be to resolve these issues? What definitions, instructions, or
other language do you think you might need on a survey to accurately report your data?

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Let's turn our attention to expenses now.

Probes:
[If report Red: inaccessible for any item] You reported that this data is inaccessible (Red) by [xxx level of
organization], can you tell me what you would do in the case where this data was requested at that level?

How easy or difficult was it for you to think through the accessibility of this information?

If difficult, why? Some anticipated examples might be: definitional differences, timing, access to data,
data sources, industry, and reporting unit.

If difficult, how easy or difficult would it be to resolve these issues? What definitions, instructions, or
other language do you think you might need on a survey to accurately report your data?

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Q31
And next, payroll.

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Probes:
[If report Red: inaccessible for any item] You reported that this data is inaccessible (Red) by [xxx level of
organization], can you tell me what you would do in the case where this data was requested at that level?

How easy or difficult was it for you to think through the accessibility of this information?

If difficult, why? Some anticipated examples might be: definitional differences, timing, access to data,
data sources, industry, and reporting unit.

If difficult, how easy or difficult would it be to resolve these issues? What definitions, instructions, or
other language do you think you might need on a survey to accurately report your data?

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Question: What were the employer's 2019 annual costs for each of the following at each business unit:


Health Insurance? - Insurance premiums for hospitals, medical plans, and single service plans
such as dental, vision, and prescription drug plans.
 Defined benefit pension plans? - Costs for both qualified and nonqualified defined benefit
pension plans. Plans that specify the benefit to be paid to employees upon retirement, generally
either a specific amount or a percentage of compensation. Employer contributions are based on
actuarial computations that include an employee's compensation and years of service and are not
allocated to specific accounts maintained for employees.
 Defined contribution plans? - Costs for defined contribution plans. Pension plans that define the
employer contributions to a separate account provided for each employee. The employee
"benefit" at retirement depends on the amount contributed and the results of the account's activity.
 Payroll taxes, employer-paid insurance premiums, and other employer-paid benefits?

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Probes:
[If report Red: inaccessible for any item] You reported that this data is inaccessible (Red) by [xxx level of
organization], can you tell me what you would do in the case where this data was requested at that level?

How easy or difficult was it for you to think through the accessibility of this information?

If difficult, why? Some anticipated examples might be: definitional differences, timing, access to data,
data sources, industry, and reporting unit.

If difficult, how easy or difficult would it be to resolve these issues? What definitions, instructions, or
other language do you think you might need on a survey to accurately report your data?
And next, inventory.

Probes:
[If report Red: inaccessible for any item] You reported that this data is inaccessible (Red) by [xxx level of
organization], can you tell me what you would do in the case where this data was requested at that level?
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How easy or difficult was it for you to think through the accessibility of this information?

If difficult, why? Some anticipated examples might be: definitional differences, timing, access to data,
data sources, industry, and reporting unit.

If difficult, how easy or difficult would it be to resolve these issues? What definitions, instructions, or
other language do you think you might need on a survey to accurately report your data?
Q37
And now finally, capital assets.

Probes:
[If report Red: inaccessible for any item] You reported that this data is inaccessible (Red) by [xxx level of
organization], can you tell me what you would do in the case where this data was requested at that level?

How easy or difficult was it for you to think through the accessibility of this information?

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If difficult, why? Some anticipated examples might be: definitional differences, timing, access to data,
data sources, industry, and reporting unit.

If difficult, how easy or difficult would it be to resolve these issues? What definitions, instructions, or
other language do you think you might need on a survey to accurately report your data?
Lastly, I would like to hear any suggestions you have on the way we structure, organize, design, and/or
deliver our surveys? Examples include: topics, industries, company structure (e.g., departments), timing.

In the ideal world, what would our survey look like? Examples include: topics, industries, company
structure (e.g., departments), timing.
If you received an overall survey for your company, and it asked for each of the topics we have talked
about today - assets, incomes, and expenses - for the overall company and then for each industry you
operate in, would that change the way you are currently reporting your company data? How and why?

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Any other questions, comments, or suggestions? [Capture here].

Thank you so much for your time and attention!

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