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Military Service and Immigrants' Integration: Evidence From the Vietnam Draft Lotteries
June 2026
Working Paper Number:
CES-26-35
Seminal theories in political science argue that military service is a critical driver of minority integration. However, a major obstacle bedeviling the study of military service is self-selection: individuals who are better integrated may be more likely to join the military in the first place. We address the selection problem by examining the effects of military conscription during the Vietnam War using an instrumental variables approach. Conscription during 1970--72 was decided on the basis of national draft lotteries that assigned draft numbers based on an individual's date of birth. Using the draft lottery instrument, we find no evidence of a causal effect of military service on a range of integration outcomes from the 2000 decennial census. At least for the Vietnam era, the link between service and long-term integration is largely driven by self-selection, which points to important scope conditions for the integrationist view.
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Comparison of Child Reporting in the American Community Survey and Federal Income Tax Returns Based on California Birth Records
September 2024
Working Paper Number:
CES-24-55
This paper takes advantage of administrative records from California, a state with a large child population and a significant historical undercount of children in Census Bureau data, dependent information in the Internal Revenue Service (IRS) Form 1040 records, and the American Community Survey to characterize undercounted children and compare child reporting. While IRS Form 1040 records offer potential utility for adjusting child undercounting in Census Bureau surveys, this analysis finds overlapping reporting issues among various demographic and economic groups. Specifically, older children, those of Non-Hispanic Black mothers and Hispanic mothers, children or parents with lower English proficiency, children whose mothers did not complete high school, and families with lower income-to-poverty ratio were less frequently reported in IRS 1040 records than other groups. Therefore, using IRS 1040 dependent records may have limitations for accurately representing populations with characteristics associated with the undercount of children in surveys.
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Citizenship Question Effects on Household Survey Response
June 2024
Working Paper Number:
CES-24-31
Several small-sample studies have predicted that a citizenship question in the 2020 Census would cause a large drop in self-response rates. In contrast, minimal effects were found in Poehler et al.'s (2020) analysis of the 2019 Census Test randomized controlled trial (RCT). We reconcile these findings by analyzing associations between characteristics about the addresses in the 2019 Census Test and their response behavior by linking to independently constructed administrative data. We find significant heterogeneity in sensitivity to the citizenship question among households containing Hispanics, naturalized citizens, and noncitizens. Response drops the most for households containing noncitizens ineligible for a Social Security number (SSN). It falls more for households with Latin American-born immigrants than those with immigrants from other countries. Response drops less for households with U.S.-born Hispanics than households with noncitizens from Latin America. Reductions in responsiveness occur not only through lower unit self-response rates, but also by increased household roster omissions and internet break-offs. The inclusion of a citizenship question increases the undercount of households with noncitizens. Households with noncitizens also have much higher citizenship question item nonresponse rates than those only containing citizens. The use of tract-level characteristics and significant heterogeneity among Hispanics, the foreign-born, and noncitizens help explain why the effects found by Poehler et al. were so small. Linking administrative microdata with the RCT data expands what we can learn from the RCT.
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Producing 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.
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Noncitizen Coverage and Its Effects on U.S. Population Statistics
August 2023
Working Paper Number:
CES-23-42
We produce population estimates with the same reference date, April 1, 2020, as the 2020 Census of Population and Housing by combining 31 types of administrative record (AR) and third-party sources, including several new to the Census Bureau with a focus on noncitizens. Our AR census national population estimate is higher than other Census Bureau official estimates: 1.8% greater than the 2020 Demographic Analysis high estimate, 3.0% more than the 2020 Census count, and 3.6% higher than the vintage-2020 Population Estimates Program estimate. Our analysis suggests that inclusion of more noncitizens, especially those with unknown legal status, explains the higher AR census estimate. About 19.8% of AR census noncitizens have addresses that cannot be linked to an address in the 2020 Census collection universe, compared to 5.7% of citizens, raising the possibility that the 2020 Census did not collect data for a significant fraction of noncitizens residing in the United States under the residency criteria used for the census. We show differences in estimates by age, sex, Hispanic origin, geography, and socioeconomic characteristics symptomatic of the differences in noncitizen coverage.
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Estimating the U.S. Citizen Voting-Age Population (CVAP) Using Blended Survey Data, Administrative Record Data, and Modeling: Technical Report
April 2023
Authors:
J. David Brown,
Danielle H. Sandler,
Lawrence Warren,
Moises Yi,
Misty L. Heggeness,
Joseph L. Schafer,
Matthew Spence,
Marta Murray-Close,
Carl Lieberman,
Genevieve Denoeux,
Lauren Medina
Working Paper Number:
CES-23-21
This report develops a method using administrative records (AR) to fill in responses for nonresponding American Community Survey (ACS) housing units rather than adjusting survey weights to account for selection of a subset of nonresponding housing units for follow-up interviews and for nonresponse bias. The method also inserts AR and modeling in place of edits and imputations for ACS survey citizenship item nonresponses. We produce Citizen Voting-Age Population (CVAP) tabulations using this enhanced CVAP method and compare them to published estimates. The enhanced CVAP method produces a 0.74 percentage point lower citizen share, and it is 3.05 percentage points lower for voting-age Hispanics. The latter result can be partly explained by omissions of voting-age Hispanic noncitizens with unknown legal status from ACS household responses. Weight adjustments may be less effective at addressing nonresponse bias under those conditions.
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Full Report of the Comparisons of Administrative Record Rosters to Census Self-Responses and NRFU Household Member Responses
March 2023
Working Paper Number:
CES-23-08
One of the U.S. Census Bureau's innovations in the 2020 U.S. Census was the use of administrative records (AR) to create household rosters for enumerating some addresses when a self response was not available but high-quality ARs were. The goal was to reduce the cost of fieldwork during the Nonresponse Followup operation (NRFU). The original plan had NRFU beginning in mid-May and continuing through late July 2020. However, the COVID-19 pandemic forced the delay of NRFU and caused the Internal Revenue Service to postpone the income tax filing deadline, resulting in an interruption in the delivery of ARs to the U.S. Census Bureau. The delays were not anticipated when U.S. Census Bureau staff conducted the research on AR enumeration with the 2010 Census data in preparation for the 2020 Census or during the fine tuning of plans for using ARs during the 2018 End-to-End Census Test. These circumstances raised questions about whether the quality of the AR household rosters was high enough for use in enumeration. To aid in investigating the concern about the quality of the AR rosters, our analyses compared AR rosters to self-response rosters and NRFU household member responses at addresses where both ARs and a self-response were available.
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Determination of the 2020 U.S. Citizen Voting Age Population (CVAP) Using Administrative Records and Statistical Methodology Technical Report
October 2020
Authors:
John M. Abowd,
J. David Brown,
Lawrence Warren,
Moises Yi,
Misty L. Heggeness,
William R. Bell,
Michael B. Hawes,
Andrew Keller,
Vincent T. Mule Jr.,
Joseph L. Schafer,
Matthew Spence
Working Paper Number:
CES-20-33
This report documents the efforts of the Census Bureau's Citizen Voting-Age Population (CVAP) Internal Expert Panel (IEP) and Technical Working Group (TWG) toward the use of multiple data sources to produce block-level statistics on the citizen voting-age population for use in enforcing the Voting Rights Act. It describes the administrative, survey, and census data sources used, and the four approaches developed for combining these data to produce CVAP estimates. It also discusses other aspects of the estimation process, including how records were linked across the multiple data sources, and the measures taken to protect the confidentiality of the data.
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Developing a Residence Candidate File for Use With Employer-Employee Matched Data
January 2017
Working Paper Number:
CES-17-40
This paper describes the Longitudinal Employer-Household Dynamics (LEHD) program's ongoing efforts to use administrative records in a predictive model that describes residence locations for workers. This project was motivated by the discontinuation of a residence file produced elsewhere at the U.S. Census Bureau. The goal of the Residence Candidate File (RCF) process is to provide the LEHD Infrastructure Files with residence information that maintains currency with the changing state of administrative sources and represents uncertainty in location as a probability distribution. The discontinued file provided only a single residence per person/year, even when contributing administrative data may have contained multiple residences. This paper describes the motivation for the project, our methodology, the administrative data sources, the model estimation and validation results, and the file specifications. We find that the best prediction of the person-place model provides similar, but superior, accuracy compared with previous methods and performs well for workers in the LEHD jobs frame. We outline possibilities for further improvement in sources and modeling as well as recommendations on how to use the preference weights in downstream processing.
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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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