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LEHD Infrastructure S2014 files in the FSRDC
September 2018
Working Paper Number:
CES-18-27R
The Longitudinal Employer-Household Dynamics (LEHD) Program at the U.S. Census Bureau, with the support of several national research agencies, maintains a set of infrastructure files using administrative data provided by state agencies, enhanced with information from other administrative data sources, demographic and economic (business) surveys and censuses. The LEHD Infrastructure Files provide a detailed and comprehensive picture of workers, employers, and their interaction in the U.S. economy. This document describes the structure and content of the 2014 Snapshot of the LEHD Infrastructure files as they are made available in the Census Bureau's secure and restricted-access Research Data Center network. The document attempts to provide a comprehensive description of all researcher-accessible files, of their creation, and of any modifications made to the files to facilitate researcher access.
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Occupational Classifications: A Machine Learning Approach
August 2018
Working Paper Number:
CES-18-37
Characterizing the work that people do on their jobs is a longstanding and core issue in labor economics. Traditionally, classification has been done manually. If it were possible to combine new computational tools and administrative wage records to generate an automated crosswalk between job titles and occupations, millions of dollars could be saved in labor costs, data processing could be sped up, data could become more consistent, and it might be possible to generate, without a lag, current information about the changing occupational composition of the labor market. This paper examines the potential to assign occupations to job titles contained in administrative data using automated, machine-learning approaches. We use a new extraordinarily rich and detailed set of data on transactional HR records of large firms (universities) in a relatively narrowly defined industry (public institutions of higher education) to identify the potential for machine-learning approaches to classify occupations.
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Who Moves Up the Job Ladder?*
January 2017
Working Paper Number:
CES-17-63
In this paper, we use linked employer-employee data to study the reallocation of heterogeneous workers between heterogeneous firms. We build on recent evidence of a cyclical job ladder that reallocates workers from low productivity to high productivity firms through job-to-job moves. In this paper we turn to the question of who moves up this job ladder, and the implications for worker sorting across firms. Not surprisingly, we find that job-to-job moves reallocate younger workers disproportionately from less productive to more productive firms. More surprisingly, especially in the context of the recent literature on assortative matching with on-the-job search, we find that job-to-
job moves disproportionately reallocate less-educated workers up the job ladder. This finding holds even though we find that more educated workers are more likely to work with more productive firms. We find that while highly educated workers are less likely to match to low productivity firms, they are also less likely to separate from them, with less-educated workers both more likely to separate to a better employer in expansions and to be shaken off the ladder (separate to nonemployment) in contractions. Our findings underscore the cyclical role job-to-job moves play in matching workers to
better paying employers.
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Sorting Between and Within Industries: A Testable Model of Assortative Matching
January 2017
Working Paper Number:
CES-17-43
We test Shimer's (2005) theory of the sorting of workers between and within industrial sectors based on directed search with coordination frictions, deliberately maintaining its static general equilibrium framework. We fit the model to sector-specific wage, vacancy and output data, including publicly-available statistics that characterize the distribution of worker and employer wage heterogeneity across sectors. Our empirical method is general and can be applied to a broad class of assignment models. The results indicate that industries are the loci of sorting-more productive workers are employed in more productive industries. The evidence confirm that strong assortative matching can be present even when worker and employer components of wage heterogeneity are weakly correlated.
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Slow to Hire, Quick to Fire: Employment Dynamics with Asymmetric Responses to News
January 2017
Working Paper Number:
CES-17-15
Concave hiring rules imply that firms respond more to bad shocks than to good shocks. They provide a united explanation for several seemingly unrelated facts about employment growth in macro and micro data. In particular, they generate countercyclical movement in both aggregate conditional 'macro' volatility and cross-sectional 'micro' volatility as well as negative skewness in the cross section and in the time series at different level of aggregation. Concave establishment level responses of employment growth to TFP shocks estimated from Census data induce significant skewness, movements in volatility and amplification of bad aggregate shocks.
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Labor Reallocation, Employment, and Earnings: Vector Autoregression Evidence
January 2017
Working Paper Number:
CES-17-11R
Analysis of the labor market has given increasing attention to the reallocation of jobs across employers and workers across jobs. However, whether and how job reallocation and labor market 'churn' affects the health of the labor market remains an open question. In this paper, we present time series evidence for the U.S. 1993-2013 and consider the relationship between labor reallocation, employment, and earnings using a vector autoregression (VAR) framework. We find that an increase in labor market churn by 1 percentage point predicts that, in the next quarter, employment will increase by 100 to 560 thousand jobs, lowering the unemployment rate by 0.05 to 0.25 percentage points. Job destruction does not predict future changes in employment but a 1 percentage point increase in job destruction leads to an increase in future unemployment 0.14 to 0.42 percentage points. We find mixed results on the relationship between labor reallocation rates and earnings: we nd that, especially for earnings derived from administrative records data, a 1 percentage point increase to either job destruction or churn leads to increased earnings of less than 2 percent. Results vary substantially depending on the earnings measure we use, and so the evidence inconsistent on whether productivity-enhancing aspects of churn and job destruction provide earnings gains for workers in aggregate. Our findings on churn leading to increased employment and a lower unemployment rate are consistent with models of replacement hiring and vacancy chains.
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Locked In? The Enforceability of Covenants Not to Compete and the Careers of High-Tech Workers
January 2017
Working Paper Number:
CES-17-09
We examine how the enforceability of covenants not to compete (CNCs) affects employee mobility and wages of high-tech workers. We expect CNC enforceability to lengthen job spells and constrain mobility, but its impact on wages is ambiguous. Using a matched employer-employee dataset covering the universe of jobs in thirty U.S states, we find that higher CNC enforceability is associated with longer job spells (fewer jobs over time), and a greater chance of leaving the state for technology workers. Consistent with a 'lock-in' effect of CNCs, we find persistent wage-suppressing effects that last throughout a worker's job and employment history.
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Measuring the Effects of the Tipped Minimum Wage Using W-2 Data
June 2016
Working Paper Number:
carra-2016-03
While an extensive literature exists on the effects of federal and state minimum wages, the minimum wage received by tipped workers has received less attention. Researchers have found it difficult to capture the hourly wages of tipped workers and thus assess the economic effects of the tipped minimum wage. In this paper, I present a new measure of hourly wages for tipped servers (wait staff and bartenders) using linked W-2 and survey data. I estimate the effect of tipped minimum wages on the wages and hourly tips of servers, as well as server employment and hours worked. I find that higher mandatory tipped minimum wages increase that portion of wages paid by employers, but decrease tip income by a similar percentage. I also find evidence that employment increases over lower values of the tipped minimum wage and then decreases at higher values. These results are consistent with a monopsony model of server employment. The wide variance of tipped minimum wages compared to non-tipped minimums provide insight into monopsony effects that may not be discernible over a smaller range of minimum wage values.
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Hires and Separations in Equilibrium
January 2016
Working Paper Number:
CES-16-57
Hiring occurs primarily to fill vacant slots that occur when workers separate. Equivalently, separation occurs to move workers to better alternatives. A model of efficient separations yields several specific predictions. Labor market churn is most likely when mean wages are low and the variance in wages is high. Additionally, over the business cycle, churn decreases during recessions, with hires falling at the beginning of recessions and separations declining later to match hiring. Furthermore, the young disproportionately bear the brunt of employment declines. More generally, hires and separations are positively correlated over time as well as across industry and firm. These predictions are borne out in the LEHD microdata at the economy and firm level.
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Taking the Leap: The Determinants of Entrepreneurs Hiring their First Employee
January 2016
Working Paper Number:
CES-16-48
Job creation is one of the most important aspects of entrepreneurship, but we know relatively little about the hiring patterns and decisions of startups. Longitudinal data from the Integrated Longitudinal Business Database (iLBD), Kauffman Firm Survey (KFS), and the Growing America through Entrepreneurship (GATE) experiment are used to provide some of the first evidence in the literature on the determinants of taking the leap from a non-employer to employer firm among startups. Several interesting patterns emerge regarding the dynamics of non-employer startups hiring their first employee. Hiring rates among the universe of non-employer startups are very low, but increase when the population of non-employers is focused on more growth-oriented businesses such as incorporated and EIN businesses. If non-employer startups hire, the bulk of hiring occurs in the first few years of existence. After this point in time relatively few non-employer startups hire an employee. Focusing on more growth- and employment-oriented startups in the KFS, we find that Asian-owned and Hispanic-owned startups have higher rates of hiring their first employee than white-owned startups. Female-owned startups are roughly 10 percentage points less likely to hire their first employee by the first, second and seventh years after startup. The education level of the owner, however, is not found to be associated with the probability of hiring an employee. Among business characteristics, we find evidence that business assets and intellectual property are associated with hiring the first employee. Using data from the largest random experiment providing entrepreneurship training in the United States ever conducted, we do not find evidence that entrepreneurship training increases the likelihood that non-employers hire their first employee.
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