QSPP_supportingstatementB_2024

QSPP_supportingstatementB_2024.docx

Quarterly Survey of Public Pensions

OMB: 0607-0143

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U.S. Department of Commerce

U.S. Census Bureau

OMB Information Collection Request

Quarterly Survey of Public Pensions

OMB Control Number 0607-0143


Part B – Collection of Information Employing Statistical Methods


1. Universe and Respondent Selection


The 100 largest public retirement systems, as determined by their total cash and security holdings reported in the 2012 Census of Governments (CoG), account for about 88.4 percent of the total assets of all state and local government retirement systems. These 100 systems are the universe for the survey. After a census has been taken, a new universe of the largest 100 retirement systems is typically selected. The unit of analysis for the state component of the Annual Survey of Public Pensions was changed to the fund level following the 2012 CoG. The corresponding change to the local component of the Annual Survey of Public Pensions was not completed prior to the 2017 CoG, hindering the Census Bureau's ability to select a new sample for the Quarterly Survey of Public Pensions at the time of the 2017 CoG. Ongoing pensions frame updates will allow the program area to select a new panel for the Quarterly Survey of Public Pensions based on the 2022 CoG. The panel will be updated in 2024 based on the results of the 2022 Census of Governments.



2. Procedures for Collecting Information


The Quarterly Survey of Public Pensions requests information for electronic reporting via Centurion from the 100 largest public retirement systems, as determined by their total cash and security holdings. All weights are 1.0000, and the estimates are simple aggregations of reported and imputed data. On average, 70-72% of data are reported initially, with late responses pulling the response rate into the low 80% range by the time the revision period closes.


3. Methods to Maximize Response


Email reminders and follow-ups are conducted throughout the collection period to encourage respondents to report online and maximize response. Staff use prior response rates to determine which units report when, focusing follow-up efforts on the early reporting respondents in the early weeks and later reporting respondents in the later weeks. This approach allows them to meet response rate goals methodically and efficiently.


Staff research which public pensions publish financial data on the Internet and maintain a record of these systems. If these systems do not report by data collection closeout, staff compile data from the Internet for these particular units. Late reporting units submit data for prior quarters as well as revisions to prior quarter data. Revisions are made to the prior quarter data releases to incorporate these late receipts and revisions.







4. Tests of Procedures or Methods


The frequency, limited scope, flexibility, and continuity of this survey make it essentially self-testing. Conversations held with respondents and data providers identify areas of improvement that align content with existing industry practices.

Approximately 10 percent of the systems were contacted for formal cognitive testing during November and December 2018. The cognitive testing included a review of the new survey content, verification of concepts and verification of the time involved to complete the survey. This testing took place in person and over the telephone.


5. Contacts for Statistical Aspects and Data Collection


Questions relating to the statistical aspects of the survey:

Andreana Able, (301)763-0153, Chief, Public Sector Statistical Methods Branch, Economic Statistical Methods Division


Questions relating to the collection and analysis of the data:

Phillip Vidal, (301)763-1749, Chief, Pension Statistics Branch, Economy-Wide Statistics

Division


Attachments

  1. Quarterly Survey of Public Pensions-- Initial Request and Due Date Reminder Letters

  2. Existing Collection Instrument Screenshots

  3. Letter of Support from BEA




File Typeapplication/vnd.openxmlformats-officedocument.wordprocessingml.document
AuthorSamantha M Galindo (CENSUS/EWD FED)
File Modified0000-00-00
File Created2024-10-07

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