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The Privacy-Protected Gridded Environmental Impacts Frame
December 2024
Working Paper Number:
CES-24-74
This paper introduces the Gridded Environmental Impacts Frame (Gridded EIF), a novel privacy-protected dataset derived from the U.S. Census Bureau's confidential Environmental Impacts Frame (EIF) microdata infrastructure. The EIF combines comprehensive administrative records and survey data on the U.S. population with high-resolution geospatial information on environmental hazards. While access to the EIF is restricted due to the confidential nature of the underlying data, the Gridded EIF offers a broader research community the opportunity to glean insights from the data while preserving confidentiality. We describe the data and privacy protection process, and offer guidance on appropriate usage, presenting practical applications.
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Financing, Ownership, and Performance: A Novel, Longitudinal Firm-Level Database
December 2024
Working Paper Number:
CES-24-73
The Census Bureau's Longitudinal Business Database (LBD) underpins many studies of firm-level behavior. It tracks longitudinally all employers in the nonfarm private sector but lacks information about business financing and owner characteristics. We address this shortcoming by linking LBD observations to firm-level data drawn from several large Census Bureau surveys. The resulting Longitudinal Employer, Owner, and Financing (LEOF) database contains more than 3 million observations at the firm-year level with information about start-up financing, current financing, owner demographics, ownership structure, profitability, and owner aspirations ' all linked to annual firm-level employment data since the firm hired its first employee. Using the LEOF database, we document trends in owner demographics and financing patterns and investigate how these business characteristics relate to firm-level employment outcomes.
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Nonresponse and Coverage Bias in the Household Pulse Survey: Evidence from Administrative Data
October 2024
Working Paper Number:
CES-24-60
The Household Pulse Survey (HPS) conducted by the U.S. Census Bureau is a unique survey that provided timely data on the effects of the COVID-19 Pandemic on American households and continues to provide data on other emergent social and economic issues. Because the survey has a response rate in the single digits and only has an online response mode, there are concerns about nonresponse and coverage bias. In this paper, we match administrative data from government agencies and third-party data to HPS respondents to examine how representative they are of the U.S. population. For comparison, we create a benchmark of American Community Survey (ACS) respondents and nonrespondents and include the ACS respondents as another point of reference. Overall, we find that the HPS is less representative of the U.S. population than the ACS. However, performance varies across administrative variables, and the existing weighting adjustments appear to greatly improve the representativeness of the HPS. Additionally, we look at household characteristics by their email domain to examine the effects on coverage from limiting email messages in 2023 to addresses from the contact frame with at least 90% deliverability rates, finding no clear change in the representativeness of the HPS afterwards.
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Incorporating Administrative Data in Survey Weights for the Basic Monthly Current Population Survey
January 2024
Working Paper Number:
CES-24-02
Response rates to the Current Population Survey (CPS) have declined over time, raising the potential for nonresponse bias in key population statistics. A potential solution is to leverage administrative data from government agencies and third-party data providers when constructing survey weights. In this paper, we take two approaches. First, we use administrative data to build a non-parametric nonresponse adjustment step while leaving the calibration to population estimates unchanged. Second, we use administratively linked data in the calibration process, matching income data from the Internal Return Service and state agencies, demographic data from the Social Security Administration and the decennial census, and industry data from the Census Bureau's Business Register to both responding and nonresponding households. We use the matched data in the household nonresponse adjustment of the CPS weighting algorithm, which changes the weights of respondents to account for differential nonresponse rates among subpopulations.
After running the experimental weighting algorithm, we compare estimates of the unemployment rate and labor force participation rate between the experimental weights and the production weights. Before March 2020, estimates of the labor force participation rates using the experimental weights are 0.2 percentage points higher than the original estimates, with minimal effect on unemployment rate. After March 2020, the new labor force participation rates are similar, but the unemployment rate is about 0.2 percentage points higher in some months during the height of COVID-related interviewing restrictions. These results are suggestive that if there is any nonresponse bias present in the CPS, the magnitude is comparable to the typical margin of error of the unemployment rate estimate. Additionally, the results are overall similar across demographic groups and states, as well as using alternative weighting methodology. Finally, we discuss how our estimates compare to those from earlier papers that calculate estimates of bias in key CPS labor force statistics.
This paper is for research purposes only. No changes to production are being implemented at this time.
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Outsourcing Dynamism
December 2023
Working Paper Number:
CES-23-64
This paper investigates the increasing importance of domestic outsourcing in U.S. manufacturing. Under domestic outsourcing, the agency is the employer of record for temporary workers, though they perform their tasks at the client business' premises. On a yearly basis, one in two manufacturing plants hires at least some of its workers through a temporary help agency. Furthermore, domestic outsourcing is becoming increasingly more important: the average share of revenue spent on such arrangements has gone up by 85 percent since 2006. We develop a methodology to transform reported expenses on temporary and leased workers into plant-level outsourced employment counts, using administrative data on the U.S. manufacturing sector. We find that domestic outsourcing is an important margin of adjustment that plants use to modify their workforce in response to productivity shocks. Plant-level outsourced employment adjusts more quickly and is twice as responsive as payroll employment. These micro implications have significant aggregate consequences. Without taking reallocations in outsourced employment into account, the measured pace at which jobs reallocate across workplaces is underestimated. On average, we omit the equivalent of 15 percent of payroll employment reallocations in each year. However, outsourced employment churns at a much higher rate compared to its payroll counterpart. Therefore, the omission of outsourced reallocations can rationalize 37 percent of the secular decline in the aggregate job reallocation rate. Lastly, the extent of mismeasurement varies with the business cycle; falling in downturns and increasing in upturns implying that the speed of economic recovery is underestimated.
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Full Report of the Comparisons of Administrative Record Rosters to Census Self-Responses and NRFU Household Member Responses
March 2023
Working Paper Number:
CES-23-08
One of the U.S. Census Bureau's innovations in the 2020 U.S. Census was the use of administrative records (AR) to create household rosters for enumerating some addresses when a self response was not available but high-quality ARs were. The goal was to reduce the cost of fieldwork during the Nonresponse Followup operation (NRFU). The original plan had NRFU beginning in mid-May and continuing through late July 2020. However, the COVID-19 pandemic forced the delay of NRFU and caused the Internal Revenue Service to postpone the income tax filing deadline, resulting in an interruption in the delivery of ARs to the U.S. Census Bureau. The delays were not anticipated when U.S. Census Bureau staff conducted the research on AR enumeration with the 2010 Census data in preparation for the 2020 Census or during the fine tuning of plans for using ARs during the 2018 End-to-End Census Test. These circumstances raised questions about whether the quality of the AR household rosters was high enough for use in enumeration. To aid in investigating the concern about the quality of the AR rosters, our analyses compared AR rosters to self-response rosters and NRFU household member responses at addresses where both ARs and a self-response were available.
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Is Affirmative Action in Employment Still Effective in the 21st Century?
November 2022
Working Paper Number:
CES-22-54
We study Executive Order 11246, an employment-based affirmative action policy tar geted at firms holding contracts with the federal government. We find this policy to be in effective in the 21st century, contrary to the positive effects found in the late 1900s (Miller, 2017). Our novel dataset combines data on federal contract acquisition and enforcement with US linked employer-employee Census data 2000'2014. We employ an event study around firms' acquiring a contract, based on Miller (2017), and find the policy had no ef fect on employment shares or on hiring, for any minority group. Next, we isolate the impact of the affirmative action plan, which is EO 11246's preeminent requirement that applies to firms with contracts over $50,000. Leveraging variation from this threshold in an event study and regression discontinuity design, we find similarly null effects. Last, we show that even randomized audits are not effective, suggesting weak enforcement. Our results highlight the importance of the recent budget increase for the enforcement agency, as well as recent policies enacted to improve compliance
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Comparing the 2019 American Housing Survey to Contemporary Sources of Property Tax Records: Implications for Survey Efficiency and Quality
June 2022
Working Paper Number:
CES-22-22
Given rising nonresponse rates and concerns about respondent burden, government statistical agencies have been exploring ways to supplement household survey data collection with administrative records and other sources of third-party data. This paper evaluates the potential of property tax assessment records to improve housing surveys by comparing these records to responses from the 2019 American Housing Survey. Leveraging the U.S. Census Bureau's linkage infrastructure, we compute the fraction of AHS housing units that could be matched to a unique property parcel (coverage rate), as well as the extent to which survey and property tax data contain the same information (agreement rate). We analyze heterogeneity in coverage and agreement across states, housing characteristics, and 11 AHS items of interest to housing researchers. Our results suggest that partial replacement of AHS data with property data, targeted toward certain survey items or single-family detached homes, could reduce respondent burden without altering data quality. Further research into partial-replacement designs is needed and should proceed on an item-by-item basis. Our work can guide this research as well as those who wish to conduct independent research with property tax records that is representative of the U.S. housing stock.
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Small Business Pulse Survey Estimates by Owner Characteristics and Rural/Urban Designation
September 2021
Working Paper Number:
CES-21-24
In response to requests from policymakers for additional context for Small Business Pulse Survey (SBPS) measures of the impact of COVID-19 on small businesses, we researched developing estimates by owner characteristics and rural/urban locations. Leveraging geographic coding on the Business Register, we create estimates of the effect of the pandemic on small businesses by urban and rural designations. A more challenging exercise entails linking micro-level data from the SBPS with ownership data from the Annual Business Survey (ABS) to create estimates of the effect of the pandemic on small businesses by owner race, sex, ethnicity, and veteran status. Given important differences in survey design and concerns about nonresponse bias, we face significant challenges in producing estimates for owner demographics. We discuss our attempts to meet these challenges and provide discussion about caution that must be used in interpreting the results. The estimates produced for this paper are available for download. Reflecting the Census Bureau's commitment to scientific inquiry and transparency, the micro data from the SBPS will be available to qualified researchers on approved projects in the Federal Statistical Research Data Center network.
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Redesigning the Longitudinal Business Database
May 2021
Working Paper Number:
CES-21-08
In this paper we describe the U.S. Census Bureau's redesign and production implementation of the Longitudinal Business Database (LBD) first introduced by Jarmin and Miranda (2002). The LBD is used to create the Business Dynamics Statistics (BDS), tabulations describing the entry, exit, expansion, and contraction of businesses. The new LBD and BDS also incorporate information formerly provided by the Statistics of U.S. Businesses program, which produced similar year-to-year measures of employment and establishment flows. We describe in detail how the LBD is created from curation of the input administrative data, longitudinal matching, retiming of economic census-year births and deaths, creation of vintage consistent industry codes and noise factors, and the creation and cleaning of each year of LBD data. This documentation is intended to facilitate the proper use and understanding of the data by both researchers with approved projects accessing the LBD microdata and those using the BDS tabulations.
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