Papers Containing Keywords(s): 'linkage'
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Viewing papers 1 through 10 of 13
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Working PaperNon-Random Assignment of Individual Identifiers and Selection into Linked Data: Implications for Research
January 2026
Working Paper Number:
CES-26-06
The U.S. Census Bureau's Person Identification Validation System facilitates anonymous linkages between survey and administrative records by assigning Protected Identification Keys (PIKs) to person records. While PIK assignment is generally accurate, some person records are not successfully assigned a PIK, which can lead to sample selection bias in analyses of linked data. Using the American Community Survey (ACS) and the Current Population Survey Annual Social and Economic Supplement (CPS ASEC) between 2005 and 2022, we corroborate and extend existing findings on the drivers of PIK assignment, showing that the rate of PIK assignment varies widely across socio-demographic subgroups. Using earnings as a test case, we then show that limiting a survey sample of wage earners to person records with PIKs or successful linkages to W-2 wage records tends to overestimate self-reported wage earnings, on average, indicative of linkage-induced selection bias. In a validation exercise, we demonstrate that reweighting methods, such as inverse probability weighting or entropy balancing, can mitigate this bias.View Full Paper PDF
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Working PaperGradient Boosting to Address Statistical Problems Arising from Non-Linkage of Census Bureau Datasets
June 2024
Working Paper Number:
CES-24-27
This article introduces the twangRDC package, which contains functions to address non-linkage in US Census Bureau datasets. The Census Bureau's Person Identification Validation System facilitates data linkage by assigning unique person identifiers to federal, third party, decennial census, and survey data. Not all records in these datasets can be linked to the reference file and as such not all records will be assigned an identifier. This article is a tutorial for using the twangRDC to generate nonresponse weights to account for non-linkage of person records across US Census Bureau datasets.View Full Paper PDF
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Working PaperWhere Are Your Parents? Exploring Potential Bias in Administrative Records on Children
March 2024
Working Paper Number:
CES-24-18
This paper examines potential bias in the Census Household Composition Key's (CHCK) probabilistic parent-child linkages. By linking CHCK data to the American Community Survey (ACS), we reveal disparities in parent-child linkages among specific demographic groups and find that characteristics of children that can and cannot be linked to the CHCK vary considerably from the larger population. In particular, we find that children from low-income, less educated households and of Hispanic origin are less likely to be linked to a mother or a father in the CHCK. We also highlight some data considerations when using the CHCK.View Full Paper PDF
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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 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
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Working PaperSqueezing More Out of Your Data: Business Record Linkage with Python
November 2018
Working Paper Number:
CES-18-46
Integrating data from different sources has become a fundamental component of modern data analytics. Record linkage methods represent an important class of tools for accomplishing such integration. In the absence of common disambiguated identifiers, researchers often must resort to ''fuzzy" matching, which allows imprecision in the characteristics used to identify common entities across dfferent datasets. While the record linkage literature has identified numerous individually useful fuzzy matching techniques, there is little consensus on a way to integrate those techniques within a single framework. To this end, we introduce the Multiple Algorithm Matching for Better Analytics (MAMBA), an easy-to-use, flexible, scalable, and transparent software platform for business record linkage applications using Census microdata. MAMBA leverages multiple string comparators to assess the similarity of records using a machine learning algorithm to disambiguate matches. This software represents a transparent tool for researchers seeking to link external business data to the Census Business Register files.View Full Paper PDF
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Working PaperFile Matching with Faulty Continuous Matching Variables
January 2017
Working Paper Number:
CES-17-45
We present LFCMV, a Bayesian file linking methodology designed to link records using continuous matching variables in situations where we do not expect values of these matching variables to agree exactly across matched pairs. The method involves a linking model for the distance between the matching variables of records in one file and the matching variables of their linked records in the second. This linking model is conditional on a vector indicating the links. We specify a mixture model for the distance component of the linking model, as this latent structure allows the distance between matching variables in linked pairs to vary across types of linked pairs. Finally, we specify a model for the linking vector. We describe the Gibbs sampling algorithm for sampling from the posterior distribution of this linkage model and use artificial data to illustrate model performance. We also introduce a linking application using public survey information and data from the U.S. Census of Manufactures and use LFCMV to link the records.View Full Paper PDF
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Working PaperPlaying with Matches: An Assessment of Accuracy in Linked Historical Data
June 2016
Working Paper Number:
carra-2016-05
This paper evaluates linkage quality achieved by various record linkage techniques used in historical demography. I create benchmark, or truth, data by linking the 2005 Current Population Survey Annual Social and Economic Supplement to the Social Security Administration's Numeric Identification System by Social Security Number. By comparing simulated linkages to the benchmark data, I examine the value added (in terms of number and quality of links) from incorporating text-string comparators, adjusting age, and using a probabilistic matching algorithm. I find that text-string comparators and probabilistic approaches are useful for increasing the linkage rate, but use of text-string comparators may decrease accuracy in some cases. Overall, probabilistic matching offers the best balance between linkage rates and accuracy.View Full Paper PDF
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Working PaperAssessing Coverage and Quality of the 2007 Prototype Census Kidlink Database
September 2015
Working Paper Number:
carra-2015-07
The Census Bureau is conducting research to expand the use of administrative records data in censuses and surveys to decrease respondent burden and reduce costs while improving data quality. Much of this research (e.g., Rastogi and O''Hara (2012), Luque and Bhaskar (2014)) hinges on the ability to integrate multiple data sources by linking individuals across files. One of the Census Bureau's record linkage methodologies for data integration is the Person Identification Validation System or PVS. PVS assigns anonymous and unique IDs (Protected Identification Keys or PIKs) that serve as linkage keys across files. Prior research showed that integrating 'known associates' information into PVS's reference files could potentially enhance PVS's PIK assignment rates. The term 'known associates' refers to people that are likely to be associated with each other because of a known common link (such as family relationships or people sharing a common address), and thus, to be observed together in different files. One of the results from this prior research was the creation of the 2007 Census Kidlink file, a child-level file linking a child's Social Security Number (SSN) record to the SSN of those identified as the child's parents. In this paper, we examine to what extent the 2007 Census Kidlink methodology was able to link parents SSNs to children SSN records, and also evaluate the quality of those links. We find that in approximately 80 percent of cases, at least one parent was linked to the child's record. Younger children and noncitizens have a higher percentage of cases where neither parent could be linked to the child. Using 2007 tax data as a benchmark, our quality evaluation results indicate that in at least 90 percent of the cases, the parent-child link agreed with those found in the tax data. Based on our findings, we propose improvements to the 2007 Kidlink methodology to increase child-parent links, and discuss how the creation of the file could be operationalized moving forward.View Full Paper PDF
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Working PaperPerson Matching in Historical Files using the Census Bureau's Person Validation System
September 2014
Working Paper Number:
carra-2014-11
The recent release of the 1940 Census manuscripts enables the creation of longitudinal data spanning the whole of the twentieth century. Linked historical and contemporary data would allow unprecedented analyses of the causes and consequences of health, demographic, and economic change. The Census Bureau is uniquely equipped to provide high quality linkages of person records across datasets. This paper summarizes the linkage techniques employed by the Census Bureau and discusses utilization of these techniques to append protected identification keys to the 1940 Census.View Full Paper PDF
