Papers Containing Keywords(s): 'linked census'
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Authors
Lars Vilhuber - 4
John M. Abowd - 3
Viewing papers 1 through 10 of 12
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Working PaperThe Census Historical Environmental Impacts Frame
October 2024
Working Paper Number:
CES-24-66
The Census Bureau's Environmental Impacts Frame (EIF) is a microdata infrastructure that combines individual-level information on residence, demographics, and economic characteristics with environmental amenities and hazards from 1999 through the present day. To better understand the long-run consequences and intergenerational effects of exposure to a changing environment, we expand the EIF by extending it backward to 1940. The Historical Environmental Impacts Frame (HEIF) combines the Census Bureau's historical administrative data, publicly available 1940 address information from the 1940 Decennial Census, and historical environmental data. This paper discusses the creation of the HEIF as well as the unique challenges that arise with using the Census Bureau's historical administrative data.View Full Paper PDF
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Working PaperProducing U.S. Population Statistics Using Multiple Administrative Sources
November 2023
Working Paper Number:
CES-23-58
We identify several challenges encountered when constructing U.S. administrative record-based (AR-based) population estimates for 2020. Though the AR estimates are higher than the 2020 Census at the national level, they are over 15 percent lower in 5 percent of counties, suggesting that locational accuracy can be improved. Other challenges include how to achieve comprehensive coverage, maintain consistent coverage across time, filter out nonresidents and people not alive on the reference date, uncover missing links across person and address records, and predict demographic characteristics when multiple ones are reported or when they are missing. We discuss several ways of addressing these issues, e.g., building in redundancy with more sources, linking children to their parents' addresses, and conducting additional record linkage for people without Social Security Numbers and for addresses not initially linked to the Census Bureau's Master Address File. We discuss modeling to predict lower levels of geography for people lacking those geocodes, the probability that a person is a U.S. resident on the reference date, the probability that an address is the person's residence on the reference date, and the probability a person is in each demographic characteristic category. Regression results illustrate how many of these challenges and solutions affect the AR county population estimates.View Full Paper PDF
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Working PaperCoverage of Children in the American Community Survey Based on California Birth Records
September 2023
Working Paper Number:
CES-23-46
The U.S. Census Bureau's American Community Survey (ACS) collects information on individuals and households. The ACS provides survey-based estimates of children drawn from a sample of the U.S. population. However, survey responses may not match administrative records, such as birth records. Birth records should provide a complete account of all births, along with child-parent relationships and demographic characteristics. California is a state that has both a large population of children and a high undercount for young children. This paper uses California as a case study to examine differences between reported versus unreported children in the ACS based on state birth records. Child reporting rates were lower for more recent data years, younger children, for Black and Hispanic mothers, and for more complex households. Child reporting rates were higher for more educated mothers and for households above the poverty line. Using mother's race and Hispanic ethnicity from the birth records combined with poverty indices from the ACS, this analysis also finds that child reporting does not uniformly vary with poverty status across all race and ethnicity groups. This research builds support for the utility of state birth records in analyzing the undercount of children.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 PaperLEHD 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.View Full Paper PDF
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Working PaperUsing Linked Data to Investigate True Intergenerational Change: Three Generations Over Seven Decades
August 2018
Working Paper Number:
carra-2018-09
It is widely thought that immigrants and their families undergo profound cultural and socioeconomic changes as a consequence of coming into contact with U.S. society, but the way this occurs remains unclear and controversial due in large part to data limitations. In this paper, we provide proof of concept for analyses using linked data that allow us to compare outcomes across more 'exact' family generations. Specifically, we are able to follow immigrant parents and their children and grandchildren across seven decades using census and survey data from 1940 to 2014. We describe the data and linkage methodology, evaluate the representativeness of the linked sample, test a method for adjusting for biases that arise from non-representative linkages, and describe the size, diversity, and socioeconomic characteristics of the linked sample. We demonstrate that large sample sizes of linked data will likely permit us to compare several national origin groups across multiple generations.View Full Paper PDF
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Working PaperForeign-Born and Native-Born Migration in the U.S.: Evidence from IRS Administrative and Census Survey Records
July 2018
Working Paper Number:
carra-2018-07
This paper details efforts to link administrative records from the Internal Revenue Service (IRS) to American Community Survey (ACS) and 2010 Census microdata for the study of migration among foreign-born and native-born populations in the United States. Specifically, we (1) document our linkage strategy and methodology for inferring migration in IRS records; (2) model selection into and survival across IRS records to determine suitability for research applications; and (3) gauge the efficacy of the IRS records by demonstrating how they can be used to validate and potentially improve migration responses for native-born and foreign-born respondents in ACS microdata. Our results show little evidence of selection or survival bias in the IRS records, suggesting broad generalizability to the nation as a whole. Moreover, we find that the combined IRS 1040, 1099, and W2 records may provide important information on populations, such as the foreign-born, that may be difficult to reach with traditional Census Bureau surveys. Finally, while preliminary, the results of our comparison of IRS and ACS migration responses shows that IRS records may be useful in improving ACS migration measurement for respondents whose migration response is proxy, allocated, or imputed. Taking these results together, we discuss the potential application of our longitudinal IRS dataset to innovations in migration research on both the native-born and foreign-born populations of the United States.View Full Paper PDF
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Working PaperDisclosure Limitation and Confidentiality Protection in Linked Data
January 2018
Working Paper Number:
CES-18-07
Confidentiality protection for linked administrative data is a combination of access modalities and statistical disclosure limitation. We review traditional statistical disclosure limitation methods and newer methods based on synthetic data, input noise infusion and formal privacy. We discuss how these methods are integrated with access modalities by providing three detailed examples. The first example is the linkages in the Health and Retirement Study to Social Security Administration data. The second example is the linkage of the Survey of Income and Program Participation to administrative data from the Internal Revenue Service and the Social Security Administration. The third example is the Longitudinal Employer-Household Dynamics data, which links state unemployment insurance records for workers and firms to a wide variety of censuses and surveys at the U.S. Census Bureau. For examples, we discuss access modalities, disclosure limitation methods, the effectiveness of those methods, and the resulting analytical validity. The final sections discuss recent advances in access modalities for linked administrative data.View Full Paper PDF
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Working PaperLEHD Infrastructure files in the Census RDC - Overview
June 2014
Working Paper Number:
CES-14-26
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 2011 Snapshot of the LEHD Infrastructure files as they are made available in the Census Bureaus 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 modifcations made to the files to facilitate researcher access.View Full Paper PDF
