Papers Containing Tag(s): 'Survey of Income and Program Participation'
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Viewing papers 21 through 30 of 116
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Working PaperFinding Needles in Haystacks: Multiple-Imputation Record Linkage Using Machine Learning
November 2021
Working Paper Number:
CES-21-35
This paper considers the problem of record linkage between a household-level survey and an establishment-level frame in the absence of unique identifiers. Linkage between frames in this setting is challenging because the distribution of employment across establishments is highly skewed. To address these difficulties, this paper develops a probabilistic record linkage methodology that combines machine learning (ML) with multiple imputation (MI). This ML-MI methodology is applied to link survey respondents in the Health and Retirement Study to their workplaces in the Census Business Register. The linked data reveal new evidence that non-sampling errors in household survey data are correlated with respondents' workplace characteristics.View Full Paper PDF
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Working PaperMeasuring the Impact of COVID-19 on Businesses and People: Lessons from the Census Bureau's Experience
January 2021
Working Paper Number:
CES-21-02
We provide an overview of Census Bureau activities to enhance the consistency, timeliness, and relevance of our data products in response to the COVID-19 pandemic. We highlight new data products designed to provide timely and granular information on the pandemic's impact: the Small Business Pulse Survey, weekly Business Formation Statistics, the Household Pulse Survey, and Community Resilience Estimates. We describe pandemic-related content introduced to existing surveys such as the Annual Business Survey and the Current Population Survey. We discuss adaptations to ensure the continuity and consistency of existing data products such as principal economic indicators and the American Community Survey.View Full Paper PDF
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Working PaperAn Evaluation of the Gender Wage Gap Using Linked Survey and Administrative Data
November 2020
Working Paper Number:
CES-20-34
The narrowing of the gender wage gap has slowed in recent decades. However, current estimates show that, among full-time year-round workers, women earn approximately 18 to 20 percent less than men at the median. Women's human capital and labor force characteristics that drive wages increasingly resemble men's, so remaining differences in these characteristics explain less of the gender wage gap now than in the past. As these factors wane in importance, studies show that others like occupational and industrial segregation explain larger portions of the gender wage gap. However, a major limitation of these studies is that the large datasets required to analyze occupation and industry effectively lack measures of labor force experience. This study combines survey and administrative data to analyze and improve estimates of the gender wage gap within detailed occupations, while also accounting for gender differences in work experience. We find a gender wage gap of 18 percent among full-time, year-round workers across 316 detailed occupation categories. We show the wage gap varies significantly by occupation: while wages are at parity in some occupations, gaps are as large as 45 percent in others. More competitive and hazardous occupations, occupations that reward longer hours of work, and those that have a larger proportion of women workers have larger gender wage gaps. The models explain less of the wage gap in occupations with these attributes. Occupational characteristics shape the conditions under which men and women work and we show these characteristics can make for environments that are more or less conducive to gender parity in earnings.View Full Paper PDF
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Working PaperDetermination of the 2020 U.S. Citizen Voting Age Population (CVAP) Using Administrative Records and Statistical Methodology Technical Report
October 2020
Working Paper Number:
CES-20-33
This report documents the efforts of the Census Bureau's Citizen Voting-Age Population (CVAP) Internal Expert Panel (IEP) and Technical Working Group (TWG) toward the use of multiple data sources to produce block-level statistics on the citizen voting-age population for use in enforcing the Voting Rights Act. It describes the administrative, survey, and census data sources used, and the four approaches developed for combining these data to produce CVAP estimates. It also discusses other aspects of the estimation process, including how records were linked across the multiple data sources, and the measures taken to protect the confidentiality of the data.View Full Paper PDF
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Working PaperMale Earnings Volatility in LEHD before, during, and after the Great Recession
September 2020
Working Paper Number:
CES-20-31
This paper is part of a coordinated collection of papers on prime-age male earnings volatility. Each paper produces a similar set of statistics for the same reference population using a different primary data source. Our primary data source is the Census Bureau's Longitudinal Employer-Household Dynamics (LEHD) infrastructure files. Using LEHD data from 1998 to 2016, we create a well-defined population frame to facilitate accurate estimation of temporal changes comparable to designed longitudinal samples of people. We show that earnings volatility, excluding increases during recessions, has declined over the analysis period, a finding robust to various sensitivity analyses. Although we find volatility is declining, the effect is not homogeneous, particularly for workers with tenuous labor force attachment for whom volatility is increasing. These 'not stable' workers have earnings volatility approximately 30 times larger than stable workers, but more important for earnings volatility trends we observe a large increase in the share of stable employment from 60% in 1998 to 67% in 2016, which we show to largely be responsible for the decline in overall earnings volatility. To further emphasize the importance of not stable and/or low earning workers we also conduct comparisons with the PSID and show how changes over time in the share of workers at the bottom tail of the cross-sectional earnings distributions can produce either declining or increasing earnings volatility trends.View Full Paper PDF
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Working PaperTrends in Earnings Volatility using Linked Administrative and Survey Data
August 2020
Working Paper Number:
CES-20-24
We document trends in earnings volatility separately by gender in combination with other characteristics such as race, educational attainment, and employment status using unique linked survey and administrative data for the tax years spanning 1995-2015. We also decompose the variance of trend volatility into within- and between-group contributions, as well as transitory and permanent shocks. Our results for continuously working men suggest that trend earnings volatility was stable over our period in both survey and tax data, though with a substantial countercyclical business-cycle component. Trend earnings volatility among women declined over the period in both survey and administrative data, but unlike for men, there was no change over the Great Recession. The variance decompositions indicate that nonresponders, low-educated, racial minorities, and part-year workers have the greatest group specific earnings volatility, but with the exception of part-year workers, they contribute least to the level and trend of volatility owing to their small share of the population. There is evidence of stable transitory volatility, but rising permanent volatility over the past two decades in male and female earnings.View Full Paper PDF
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Working PaperMaternal Labor Dynamics: Participation, Earnings, and Employer Changes
December 2019
Working Paper Number:
CES-19-33
This paper describes the labor dynamics of U.S. women after they have had their first and subsequent children. We build on the child penalty literature by showing the heterogeneity of the size and pattern of labor force participation and earnings losses by demographic characteristics of mothers and the characteristics of their employers. The analysis uses longitudinal administrative earnings data from the Longitudinal Employer-Household Dynamics database combined with the Survey of Income and Program Participation survey data to identify women, their fertility timing, and employment. We find that women experience a large and persistent decrease in earnings and labor force participation after having their first child. The penalty grows over time, driven by the birth of subsequent children. Non-white mothers, unmarried mothers, and mothers with more education are more likely to return to work following the birth of their first child. Conditional on returning to the labor force, women who change employers earn more after the birth of their first child than women who return to their pre-birth employers. The probability of returning to the pre-birth employer and industry is heterogeneous over both the demographics of mothers and the characteristics of their employers.View Full Paper PDF
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Working PaperA Task-based Approach to Constructing Occupational Categories with Implications for Empirical Research in Labor Economics
September 2019
Working Paper Number:
CES-19-27
Most applied research in labor economics that examines returns to worker skills or differences in earnings across subgroups of workers typically accounts for the role of occupations by controlling for occupational categories. Researchers often aggregate detailed occupations into categories based on the Standard Occupation Classification (SOC) coding scheme, which is based largely on narratives or qualitative measures of workers' tasks. Alternatively, we propose two quantitative task-based approaches to constructing occupational categories by using factor analysis with O*NET job descriptors that provide a rich set of continuous measures of job tasks across all occupations. We find that our task-based approach outperforms the SOC-based approach in terms of lower occupation distance measures. We show that our task-based approach provides an intuitive, nuanced interpretation for grouping occupations and permits quantitative assessments of similarities in task compositions across occupations. We also replicate a recent analysis and find that our task-based occupational categories explain more of the gender wage gap than the SOC-based approaches explain. Our study enhances the Federal Statistical System's understanding of the SOC codes, investigates ways to use third-party data to construct useful research variables that can potentially be added to Census Bureau data products to improve their quality and versatility, and sheds light on how the use of alternative occupational categories in economics research may lead to different empirical results and deeper understanding in the analysis of labor market outcomes.View Full Paper PDF
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Working PaperImmigrants' Earnings Growth and Return Migration from the U.S.: Examining their Determinants using Linked Survey and Administrative Data
March 2019
Working Paper Number:
CES-19-10
Using a novel panel data set of recent immigrants to the U.S. (2005-2007) from individual-level linked U.S. Census Bureau survey data and Internal Revenue Service (IRS) administrative records, we identify the determinants of return migration and earnings growth for this immigrant arrival cohort. We show that by 10 years after arrival almost 40 percent have return migrated. Our analysis examines these flows by educational attainment, country of birth, and English language ability separately for each gender. We show, for the first time, that return migrants experience downward earnings mobility over two to three years prior to their return migration. This finding suggests that economic shocks are closely related to emigration decisions; time-variant unobserved characteristics may be more important in determining out-migration than previously known. We also show that wage assimilation with native-born populations occurs fairly quickly; after 10 years there is strong convergence in earnings by several characteristics. Finally, we confirm that the use of stock-based panel data lead to estimates of slower earnings growth than is found using repeated cross-section data. However, we also show, using selection-correction methods in our panel data, that stock-based panel data may understate the rate of earnings growth for the initial immigrant arrival cohort when emigration is not accounted for.View Full Paper PDF
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Working PaperOptimal Probabilistic Record Linkage: Best Practice for Linking Employers in Survey and Administrative Data
March 2019
Working Paper Number:
CES-19-08
This paper illustrates an application of record linkage between a household-level survey and an establishment-level frame in the absence of unique identifiers. Linkage between frames in this setting is challenging because the distribution of employment across firms is highly asymmetric. To address these difficulties, this paper uses a supervised machine learning model to probabilistically link survey respondents in the Health and Retirement Study (HRS) with employers and establishments in the Census Business Register (BR) to create a new data source which we call the CenHRS. Multiple imputation is used to propagate uncertainty from the linkage step into subsequent analyses of the linked data. The linked data reveal new evidence that survey respondents' misreporting and selective nonresponse about employer characteristics are systematically correlated with wages.View Full Paper PDF
