The goal of this research was to investigate the value added from using worker flows to identify the spurious births and deaths of businesses. We identify four types of "at risk" businesses from ES202 using the successor/predecessor flag and mimic the same categories using UI wage record data. We use two critical decision rules in the analysis: a successor firm has to have at least 80% of employment coming from the donor firm and (in two of the four categories) at least 5 employees have to come from the donor firm. We examine the sensitivity of the categories based on the percentage definition, and find that the results stay very similar, with the exception of the identification of the pure successor. We examine the sensitivity based on the count threshold, and find that there are enormous differences, particularly with identifying spinoff businesses.
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Using Worker Flows in the Analysis of the Firm
August 2003
Working Paper Number:
tp-2003-09
This paper uses a novel approach to measure firm entry and exit, mergers and
acquisition. It uses information about the flows of clusters of workers across business
units to identify longitudinal linkage relationships in longitudinal business data. These
longitudinal relationships may be the result of either administrative or economic changes
and we explore both types of newly identified longitudinal relationships. In particular,
we develop a set of criteria based on worker flows to identify changes in firm
relationships ? such as mergers and acquisitions, administrative identifier changes and
outsourcing. We demonstrate how this new data infrastructure and this cluster flow
methodology can be used to better differentiate true firm entry/exit and simple changes in
administrative identifiers. We explore the role of outsourcing in a variety of ways but in
particular the outsourcing of workers to the temporary help industry. While the primary
focus is on developing the data infrastructure and the methodology to identify and
interpret these clustered flows of workers, we conclude the paper with an analysis of the
impact of these changes on the earnings of workers.
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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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Employer-to-Employer Flows in the United States: Estimates Using Linked Employer-Employee Data
September 2010
Working Paper Number:
CES-10-26
We use administrative data linking workers and firms to study employer-to-employer flows. After discussing how to identify such flows in quarterly data, we investigate their basic empirical patterns. We find that the pace of employer-to-employer flows is high, representing about 4 percent of employment and 30 percent of separations each quarter. The pace of employer-to-employer flows is highly procyclical, and varies systematically across worker, job and employer characteristics. Our findings regarding job tenure and earnings dynamics suggest that for those workers moving directly to new jobs, the new jobs are generally better jobs; however, this pattern is highly procyclical. There are rich patterns in terms of origin and destination of industries. We find somewhat surprisingly that more than half of the workers making employer-to-employer transitions switch even broadly-defined industries (NAICS supersectors).
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FIRM AGE AND SIZE IN THE LONGITUDINAL EMPLOYER-HOUSEHOLD DYNAMICS DATA
March 2014
Working Paper Number:
CES-14-16
The Census Bureau's Quarterly Workforce Dynamics (QWI) and OnTheMap now provide detailed workforce statistics by employer age and size. These data allow a first look at the demographics of workers at small and young businesses as well as detailed analysis of how hiring, turnover, job creation/destruction vary throughout a firm's lifespan. Both the QWI and OnTheMap are tabulated from the Longitudinal Employer-Household Dynamics (LEHD) linked employer-employee data. Firm age and size information was added to the LEHD data through integration of Business Dynamics Statistics (BDS) microdata into the LEHD jobs frame. This paper describes how these two new firm characteristics were added to the microdata and how they are tabulated in QWI and OnTheMap
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An Analysis of Key Differences in Micro Data: Results from the Business List Comparison Project
September 2008
Working Paper Number:
CES-08-28
The Bureau of Labor Statistics and the Bureau of the Census each maintain a business register, a universe of all U.S. business establishments and their characteristics, created from independent sources. Both registers serve critical functions such as supplying aggregate data inputs for certain national statistics generated by the Bureau of Economic Analysis. This paper examines key micro-level differences across these two business registers.
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Social, Economic, Spatial, and Commuting Patterns of Self-Employed Jobholders
April 2007
Working Paper Number:
tp-2007-03
A significant number of employees within the United States identify themselves as selfemployed,
and they are distinct from the larger group identified as private jobholders. While
socioeconomic and spatial information on these individuals is readily available in standard
datasets, such as the 2000 Decennial Census Long Form, it is possible to gain further information
on their wage earnings by using data from administrative wage records. This study takes
advantage of firm-based data from Unemployment Insurance administrative wage records linked
with the Census Bureau's household-based data in order to examine self-employed jobholders -
both as a whole and as subgroups defined according to their earned wage status - by their
demographic characteristics as well as their economic, commuting, and spatial location
outcomes. Additionally, this report evaluates whether self-employed jobholders and the defined
subgroups should be included explicitly in future labor-workforce analyses and transportation
modeling. The analyses in this report use the sample of self-employed workers who lived in Los
Angeles County, California.
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The Promise and Potential of Linked Employer-Employee Data for Entrepreneurship Research
September 2015
Working Paper Number:
CES-15-29
In this paper, we highlight the potential for linked employer-employee data to be used in entrepreneurship research, describing new data on business start-ups, their founders and early employees, and providing examples of how they can be used in entrepreneurship research. Linked employer-employee data provides a unique perspective on new business creation by combining information on the business, workforce, and individual. By combining data on both workers and firms, linked data can investigate many questions that owner-level or firm-level data cannot easily answer alone - such as composition of the workforce at start-ups and their role in explaining business dynamics, the flow of workers across new and established firms, and the employment paths of the business owners themselves.
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Social, Economic, Spatial, and Commuting Patterns of Informal Jobholders
April 2007
Working Paper Number:
tp-2007-02
A significant number of employees within the United States can be considered "informal" or
"off-the-books" workers. These workers, who by definition do not appear in administrative wage
records, are distinct from the larger group of private jobholders who do appear in administrative
records. However, while socioeconomic and spatial information on these individuals is readily
available in standard datasets, such as the 2000 Decennial Census Long Form, it is not possible
to identify the informal workers by only using such data because of the lack of accurate, formal
wage records. This study takes advantage of firm-based data that originates in Unemployment
Insurance administrative wage records linked with the Census Bureau's household-based data in
order to examine informal jobholders by their demographic characteristics as well as their
economic, commuting, and spatial location outcomes. In addition this report evaluates whether
informal jobholders should be included explicitly in future labor-workforce analyses and
transportation modeling. The analyses in this report use the sample of workers who lived in Los
Angeles County, California.
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Matching State Business Registration Records
to Census Business Data
January 2020
Working Paper Number:
CES-20-03
We describe our methodology and results from matching state Business Registration Records (BRR) to Census business data. We use data from Massachusetts and California to develop methods and preliminary results that could be used to guide matching data for additional states. We obtain matches to Census business records for 45% of the Massachusetts BRR records and 40% of the California BRR records. We find higher match rates for incorporated businesses and businesses with higher startup-quality scores as assigned in Guzman and Stern (2018). Clerical reviews show that using relatively strict matching on address is important for match accuracy, while results are less sensitive to name matching strictness. Among matched BRR records, the modal timing of the first match to the BR is in the year in which the BRR record was filed. We use two sets of software to identify matches: SAS DQ Match and a machine-learning algorithm described in Cuffe and Goldschlag (2018). We find preliminary evidence that while the ML-based method yields more match results, SAS DQ tends to result in higher accuracy rates. To conclude, we provide suggestions on how to proceed with matching other states' data in light of our findings using these two states.
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The Contribution Of Establishment Births And Deaths To Employment Growth
April 1998
Working Paper Number:
CES-98-05
The purpose of this paper is to examine how establishment births and deaths contribute to job creation, job destruction, and net employment growth at different frequencies of measurement. The longitudinal data are constructed from quarterly unemployment insurance microdata, and are essentially a census of establishments in all industries. Defining establishment births and deaths turns out to be an exercise in how to use cross-sectional administrative data for longitudinal research purposes. The analysis of job flows indicates that the frame is relatively small but certainly non-trivial, whereas births and deaths account for roughly half of all jobs created and destroyed on a triennial time frame. Net Employment Growth
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