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Revisions to the LEHD Establishment Imputation Procedure and Applications to Administrative Job Frame
September 2024
Working Paper Number:
CES-24-51
The Census Bureau is developing a 'job frame' to provide detailed job-level employment data across the U.S. through linked administrative records such as unemployment insurance and IRS W-2 filings. This working paper summarizes the research conducted by the job frame development team on modifying and extending the LEHD Unit-to-Worker (U2W) imputation procedure for the job frame prototype. It provides a conceptual overview of the U2W imputation method, highlighting key challenges and tradeoffs in its current application. The paper then presents four imputation methodologies and evaluates their performance in areas such as establishment assignment accuracy, establishment size matching, and job separation rates. The results show that all methodologies perform similarly in assigning workers to the correct establishment. Non-spell-based methodologies excel in matching establishment sizes, while spell-based methodologies perform better in accurately tracking separation rates.
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Revisions to the LEHD Establishment Imputation Procedure and Applications to Administrative Jobs Frame
September 2024
Working Paper Number:
CES-24-51
The Census Bureau is developing a 'jobs frame' to provide detailed job-level employment data across the U.S. through linked administrative records such as unemployment insurance and IRS W-2 filings. This working paper summarizes the research conducted by the jobs frame development team on modifying and extending the LEHD Unit-to-Worker (U2W) imputation procedure for the jobs frame prototype. It provides a conceptual overview of the U2W imputation method, highlighting key challenges and tradeoffs in its current application. The paper then presents four imputation methodologies and evaluates their performance in areas such as establishment assignment accuracy, establishment size matching, and job separation rates. The results show that all methodologies perform similarly in assigning workers to the correct establishment. Non-spell-based methodologies excel in matching establishment sizes, while spell-based methodologies perform better in accurately tracking separation rates.
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Expanding the Frontier of Economic Statistics Using Big Data: A Case Study of Regional Employment
July 2024
Working Paper Number:
CES-24-37
Big data offers potentially enormous benefits for improving economic measurement, but it also presents challenges (e.g., lack of representativeness and instability), implying that their value is not always clear. We propose a framework for quantifying the usefulness of these data sources for specific applications, relative to existing official sources. We specifically weigh the potential benefits of additional granularity and timeliness, while examining the accuracy associated with any new or improved estimates, relative to comparable accuracy produced in existing official statistics. We apply the methodology to employment estimates using data from a payroll processor, considering both the improvement of existing state-level estimates, but also the production of new, more timely, county-level estimates. We find that incorporating payroll data can improve existing state-level estimates by 11% based on out-of-sample mean absolute error, although the improvement is considerably higher for smaller state-industry cells. We also produce new county-level estimates that could provide more timely granular estimates than previously available. We develop a novel test to determine if these new county-level estimates have errors consistent with official series. Given the level of granularity, we cannot reject the hypothesis that the new county estimates have an accuracy in line with official measures, implying an expansion of the existing frontier. We demonstrate the practical importance of these experimental estimates by investigating a hypothetical application during the COVID-19 pandemic, a period in which more timely and granular information could have assisted in implementing effective policies. Relative to existing estimates, we find that the alternative payroll data series could help identify areas of the country where employment was lagging. Moreover, we also demonstrate the value of a more timely series.
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Payroll Tax Incidence: Evidence from Unemployment Insurance
June 2024
Working Paper Number:
CES-24-35
Economic models assume that payroll tax burdens fall fully on workers, but where does tax incidence fall when taxes are firm-specific and time-varying? Unemployment insurance in the United States has the key feature of varying both across employers and over time, creating the potential for labor demand responses if tax costs cannot be fully passed through to worker wages. Using state policy changes and administrative data of matched employer-employee job spells, I study how employment and earnings respond to unexpected payroll tax increases for highly exposed employers. I find significant drops in employment growth driven by lower hiring, and minimal evidence of passthrough to earnings. The negative employment effects are strongest for young workers and single-establishment firms.
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Urban-Biased Growth: A Macroeconomic Analysis
June 2024
Working Paper Number:
CES-24-33
After 1980, larger US cities experienced substantially faster wage growth than smaller ones. We show that this urban bias mainly reflected wage growth at large Business Services firms. These firms stand out through their high per-worker expenditure on information technology and disproportionate presence in big cities. We introduce a spatial model of investment-specific technical change that can rationalize these patterns. Using the model as an accounting framework, we find that the observed decline in the investment price of information technology capital explains most urban-biased growth by raising the profits of large Business Services firms in big cities.
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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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Same-Sex Couples and the Child Earnings Penalty
May 2023
Working Paper Number:
CES-23-25
Existing work has shown that the entry of a child into a household results in a large and sustained increase in the earnings gap between male and female partners in opposite-sex couples. Potential reasons for this include work-life preferences, comparative advantage over earnings, and gender norms. We expand this analysis of the child penalty to examine earnings of individuals in same sex couples in the U.S. around the time their first child enters the household. Using linked survey and administrative data and event-study methodology, we confirm earlier work finding a child penalty for women in opposite-sex couples. We find this is true even when the female partner is the primary earner pre-parenthood, lending support to the importance of gender norms in opposite-sex couples. By contrast, in both female and male same-sex couples, earnings changes associated with child entry differ by the relative pre-parenthood earnings of the partners: secondary earners see an increase in earnings, while on average the earnings of primary and equal earners remain relatively constant. While this finding seems supportive of a norm related to equality within same-sex couples, transition analysis suggests a more complicated story.
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LEHD Snapshot Documentation, Release S2021_R2022Q4
November 2022
Working Paper Number:
CES-22-51
The Longitudinal Employer-Household Dynamics (LEHD) data at the U.S. Census Bureau is a quarterly database of linked employer-employee data covering over 95% of employment in the United States. These data are used to produce a number of public-use tabulations and tools, including the Quarterly Workforce Indicators (QWI), LEHD Origin-Destination Employment Statistics (LODES), Job-to-Job Flows (J2J), and Post-Secondary Employment Outcomes (PSEO) data products. Researchers on approved projects may also access the underlying LEHD microdata directly, in the form of the LEHD Snapshot restricted-use data product. This document provides a detailed overview of the LEHD Snapshot as of release S2021_R2022Q4, including user guidance, variable codebooks, and an overview of the approvals needed to obtain access. Updates to the documentation for this and future snapshot releases will be made available in HTML format on the LEHD website.
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Opening the Black Box: Task and Skill Mix and Productivity Dispersion
September 2022
Authors:
John Haltiwanger,
Lucia Foster,
Cheryl Grim,
Zoltan Wolf,
Cindy Cunningham,
Sabrina Wulff Pabilonia,
Jay Stewart,
Cody Tuttle,
G. Jacob Blackwood,
Matthew Dey,
Rachel Nesbit
Working Paper Number:
CES-22-44
An important gap in most empirical studies of establishment-level productivity is the limited information about workers' characteristics and their tasks. Skill-adjusted labor input measures have been shown to be important for aggregate productivity measurement. Moreover, the theoretical literature on differences in production technologies across businesses increasingly emphasizes the task content of production. Our ultimate objective is to open this black box of tasks and skills at the establishment-level by combining establishment-level data on occupations from the Bureau of Labor Statistics (BLS) with a restricted-access establishment-level productivity dataset created by the BLS-Census Bureau Collaborative Micro-productivity Project. We take a first step toward this objective by exploring the conceptual, specification, and measurement issues to be confronted. We provide suggestive empirical analysis of the relationship between within-industry dispersion in productivity and tasks and skills. We find that within-industry productivity dispersion is strongly positively related to within-industry task/skill dispersion.
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Trade Liberalization and Labor-Market Outcomes: Evidence from US Matched Employer-Employee Data
September 2022
Working Paper Number:
CES-22-42
We use matched employer-employee data to examine outcomes among workers initially employed within and outside manufacturing after trade liberalization with China. We find that exposure to this shock operates predominantly through workers' counties (versus industries), that larger own industry and downstream exposure typically reduce relative earnings, and that greater upstream exposure often raises them. The latter is particularly important outside manufacturing: while we find substantial and persistent predicted declines in relative earnings among manufacturing workers, those outside manufacturing are generally predicted to experience relative earnings gains. Investigation of employment reactions indicates they account for a small share of the earnings effect.
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