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2010 American Community Survey Match Study
July 2014
Working Paper Number:
carra-2014-03
Using administrative records data from federal government agencies and commercial sources, the 2010 ACS Match Study measures administrative records coverage of 2010 ACS addresses, persons, and persons at addresses at different levels of geography as well as by demographic characteristics and response mode. The 2010 ACS Match Study represents a continuation of the research undertaken in the 2010 Census Match Study, the first national-level evaluation of administrative records data coverage. Preliminary results indicate that administrative records provide substantial coverage for addresses and persons in the 2010 ACS (92.7 and 92.1 percent respectively), and less extensive though substantial coverage, for person-address pairs (74.3 percent). In addition, some variation in address, person and/or person-address coverage is found across demographic and response mode groups. This research informs future uses of administrative records in survey and decennial census operations to address the increasing costs of data collection and declining response rates.
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Estimating Record Linkage False Match Rate for the Person Identification Validation System
July 2014
Working Paper Number:
carra-2014-02
The Census Bureau Person Identification Validation System (PVS) assigns unique person identifiers to federal, commercial, census, and survey data to facilitate linkages across files. PVS uses probabilistic matching to assign a unique Census Bureau identifier for each person. This paper presents a method to measure the false match rate in PVS following the approach of Belin and Rubin (1995). The Belin and Rubin methodology requires truth data to estimate a mixture model. The parameters from the mixture model are used to obtain point estimates of the false match rate for each of the PVS search modules. The truth data requirement is satisfied by the unique access the Census Bureau has to high quality name, date of birth, address and Social Security (SSN) data. Truth data are quickly created for the Belin and Rubin model and do not involve a clerical review process. These truth data are used to create estimates for the Belin and Rubin parameters, making the approach more feasible. Both observed and modeled false match rates are computed for all search modules in federal administrative records data and commercial data.
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The Person Identification Validation System (PVS): Applying the Center for Administrative Records Research and Applications' (CARRA) Record Linkage Software
July 2014
Working Paper Number:
carra-2014-01
The Census Bureau's Person Identification Validation System (PVS) assigns unique person identifiers to federal, commercial, census, and survey data to facilitate linkages across and within files. PVS uses probabilistic matching to assign a unique Census Bureau identifier for each person. The PVS matches incoming files to reference files created with data from the Social Security Administration (SSA) Numerical Identification file, and SSA data with addresses obtained from federal files. This paper describes the PVS methodology from editing input data to creating the final file.
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Comparison of Survey, Federal, and Commercial Address Data Quality
June 2014
Working Paper Number:
carra-2014-06
This report summarizes matching of survey, commercial, and administrative records housing units to the Census Bureau Master Address File (MAF). We document overall MAF match rates in each data set and evaluate differences in match rates across a variety of housing characteristics. Results show that over 90 percent of records in survey data from the American Housing Survey (AHS) match to the MAF. Commercial data from CoreLogic matches at much lower rates, in part due to missing address information and poor match rates for multi-unit buildings. MAF match rates for administrative records from the Department of Housing and Urban Development are also high, and open the possibility of using this information in surveys such as the AHS.
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Firm Dynamics and Assortative Matching
May 2014
Working Paper Number:
CES-14-25
I study the relationship between firm growth and the characteristics of newly hired workers. Using Census microdata I obtain a novel empirical result: when a given firm grows faster it hires workers with higher past wages. These results suggest that productive, fast-growing firms tend to hire more productive workers, a form of positive assortative matching. This contrasts with prior research that has found negligible or negative sorting between workers and firms. I present evidence that this difference arises because previous studies have focused on cross-sectional comparisons across firms and industries, while my results condition on firm characteristics (e.g. size, industry, or firm fixed effects). Motivated by the empirical findings I develop a search model with heterogeneous workers and firms. The model is the first to study worker-firm sorting in an environment with worker heterogeneity, firm productivity shocks, multi-worker firms, and search frictions. Despite this richness the model is tractable, allowing me to characterize assortative matching, compositional dynamics and other properties analytically. I show that the model reproduces the positive firm growth-quality of hires correlation when worker and firm types are strong complements in production (i.e. the production function is strictly log-supermodular).
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Place of Work and Place of Residence: Informal Hiring Networks and Labor Market Outcomes
October 2005
Working Paper Number:
CES-05-23
We use a novel dataset and research design to empirically detect the effect of social interactions among neighbors on labor market outcomes. Specifically, using Census data that characterize residential and employment locations down to the city block, we examine whether individuals residing in the same block are more likely to work together than those in nearby blocks. We find evidence of significant social interactions operating at the block level: residing on the same versus nearby blocks increases the probability of working together by over 33 percent. The results also indicate that this referral effect is stronger when individuals are similar in sociodemographic characteristics (e.g., both have children of similar ages) and when at least one individual is well attached to the labor market. These findings are robust across various specifications intended to address concerns related to sorting and reverse causation. Further, having determined the characteristics of a pair of individuals that lead to an especially strong referral effect, we provide evidence that the increased availability of neighborhood referrals has a significant impact on a wide range of labor market outcomes including employment and wages.
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The 1990 Decennial Employer-Employee Dataset
October 2002
Working Paper Number:
CES-02-23
We describe the construction and assessment of a new matched employer-employee data set, the 1990 Decennial Employer-Employee Dataset (1990 DEED). By using place of work name and address, we link workers from the 1990 Long Form Sample to their place of work in the 1990 Standard Statistical Establishment List. The resulting data set is much larger and more representative across regional and industry dimensions than previous matched data sets for the United States. The known strengths and limitations of the data set are discussed in detail.
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The Sensitivity of Economic Statistics to Coding Errors in Personal Identifiers
October 2002
Working Paper Number:
tp-2002-17
In this paper, we describe the sensitivity of small-cell flow statistics
to coding errors in the identity of the underlying entities. Specifically,
we present results based on a comparison of the U.S. Census Bureau's
Quarterly Workforce Indicators (QWI) before and after correcting for
such errors in SSN-based identifiers in the underlying individual wage
records. The correction used involves a novel application of existing
statistical matching techniques. It is found that even a very conservative
correction procedure has a sizable impact on the statistics. The
average bias ranges from 0.25 percent up to 15 percent for flow statistics,
and up to 5 percent for payroll aggregates.
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Modeling Labor Markets with Heterogeneous Agents and Matches
May 2002
Working Paper Number:
tp-2002-19
I present a matching model with heterogeneous workers, firms, and worker-fim
matches. The model generalizes the seminal Jovanovic (1979) model to the case of
heterogeneous agents. The equilibrium wage is linear in a person-specific component,
a firm-specific component, and a match specific component that varies with tenure.
Under certain conditions, the equilibrium wage takes a simpler structure where the
match specific component does not vary with tenure. I discuss fixed- and mixedeffect
methods for estimating wage models with this structure on longitudinal linked
employer-employee data. The fixed effect specification relies on restrictive identification
conditions, but is feasible for very large databases. The mixed model requires less
restrictive identification conditions, but is feasible only on relatively small databases.
Both the fixed and mixed models generate empirical person, firm, and match effects
with characteristics that are consistent with predictions from the matching model; the
mixed model moreso than the fixed model. Shortcomings of the fixed model appear to
be artifacts of the identification conditions.
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The Worker-Establishment Characteristics Database
June 1995
Working Paper Number:
CES-95-10
A data set combining information on the characteristics of both workers and their employers has long been a grail for labor economists. The reason for this interest is that while a number of theoretical models in labor economics stress the importance of employer-employee matching in determining labor market outcomes, almost all empirical work relies on either worker surveys with little information about employers or establishment surveys with little information about workers. The Worker-Establishment Characteristic Database (WECD) represents just such an employer-employee-matched database. Containing 199,557 manufacturing workers matched to 16,144 manufacturing establishments, the WECD is the largest worker-firm matched data set available for the U.S. This paper describes how this data set was constructed and assesses the usefulness of these data for economic research. In addition, I discuss some of the issues that can be addressed using employer-employee-matched data and plans for creating future versions of the WECD.
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