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Nonresponse and Coverage Bias in the Household Pulse Survey: Evidence from Administrative Data
October 2024
Working Paper Number:
CES-24-60
The Household Pulse Survey (HPS) conducted by the U.S. Census Bureau is a unique survey that provided timely data on the effects of the COVID-19 Pandemic on American households and continues to provide data on other emergent social and economic issues. Because the survey has a response rate in the single digits and only has an online response mode, there are concerns about nonresponse and coverage bias. In this paper, we match administrative data from government agencies and third-party data to HPS respondents to examine how representative they are of the U.S. population. For comparison, we create a benchmark of American Community Survey (ACS) respondents and nonrespondents and include the ACS respondents as another point of reference. Overall, we find that the HPS is less representative of the U.S. population than the ACS. However, performance varies across administrative variables, and the existing weighting adjustments appear to greatly improve the representativeness of the HPS. Additionally, we look at household characteristics by their email domain to examine the effects on coverage from limiting email messages in 2023 to addresses from the contact frame with at least 90% deliverability rates, finding no clear change in the representativeness of the HPS afterwards.
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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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Introducing the Medical Expenditure Panel Survey-Insurance Component with Administrative Records (MEPS-ICAR): Description, Data Construction Methodology, and Quality Assessment
August 2022
Working Paper Number:
CES-22-29
This report introduces a new dataset, the Medical Expenditure Panel Survey-Insurance Component with Administrative Records (MEPS-ICAR), consisting of MEPS-IC survey data on establishments and their health insurance benefits packages linked to Decennial Census data and administrative tax records on MEPS-IC establishments' workforces. These data include new measures of the characteristics of MEPS-IC establishments' parent firms, employee turnover, the full distribution of MEPS-IC workers' personal and family incomes, the geographic locations where those workers live, and improved workforce demographic detail. Next, this report details the methods used for producing the MEPS-ICAR. Broadly, the linking process begins by matching establishments' parent firms to their workforces using identifiers appearing in tax records. The linking process concludes by matching establishments to their own workforces by identifying the subset of their parent firm's workforce that best matches the expected size, total payroll, and residential geographic distribution of the establishment's workforce. Finally, this report presents statistics characterizing the match rate and the MEPS-ICAR data itself. Key results include that match rates are consistently high (exceeding 90%) across nearly all data subgroups and that the matched data exhibit a reasonable distribution of employment, payroll, and worker commute distances relative to expectations and external benchmarks. Notably, employment measures derived from tax records, but not used in the match itself, correspond with high fidelity to the employment levels that establishments report in the MEPS-IC. Cumulatively, the construction of the MEPS-ICAR significantly expands the capabilities of the MEPS-IC and presents many opportunities for analysts.
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Nonemployer Statistics by Demographics (NES-D): Using Administrative and Census Records Data in Business Statistics
January 2019
Working Paper Number:
CES-19-01
The quinquennial Survey of Business Owners or SBO provided the only comprehensive source of information in the United States on employer and nonemployer businesses by the sex, race, ethnicity and veteran status of the business owners. The annual Nonemployer Statistics series (NES) provides establishment counts and receipts for nonemployers but contains no demographic information on the business owners. With the transition of the employer component of the SBO to the Annual Business Survey, the Nonemployer Statistics by Demographics series or NES-D represents the continuation of demographics estimates for nonemployer businesses. NES-D will leverage existing administrative and census records to assign demographic characteristics to the universe of approximately 24 million nonemployer businesses (as of 2015). Demographic characteristics include key demographics measured by the SBO (sex, race, Hispanic origin and veteran status) as well as other demographics (age, place of birth and citizenship status) collected but not imputed by the SBO if missing. A spectrum of administrative and census data sources will provide the nonemployer universe and demographics information. Specifically, the nonemployer universe originates in the Business Register; the Census Numident will provide sex, age, place of birth and citizenship status; race and Hispanic origin information will be obtained from multiple years of the decennial census and the American Community Survey; and the Department of Veteran Affairs will provide administrative records data on veteran status.
The use of blended data in this manner will make possible the production of NES-D, an annual series that will become the only source of detailed and comprehensive statistics on the scope, nature and activities of U.S. businesses with no paid employment by the demographic characteristics of the business owner. Using the 2015 vintage of nonemployers, initial results indicate that demographic information is available for the overwhelming majority of the universe of nonemployers. For instance, information on sex, age, place of birth and citizenship status is available for over 95 percent of the 24 million nonemployers while race and Hispanic origin are available for about 90 percent of them. These results exclude owners of C-corporations, which represent only 2 percent of nonemployer firms. Among other things, future work will entail imputation of missing demographics information (including that of C-corporations), testing the longitudinal consistency of the estimates, and expanding the set of characteristics beyond the demographics mentioned above. Without added respondent burden and at lower imputation rates and costs, NES-D will meet the needs of stakeholders as well as the economy as a whole by providing reliable estimates at a higher frequency (annual vs. every 5 years) and with a more timely dissemination schedule than the SBO.
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Understanding the Quality of Alternative Citizenship Data Sources for the 2020 Census
August 2018
Working Paper Number:
CES-18-38R
This paper examines the quality of citizenship data in self-reported survey responses compared to administrative records and evaluates options for constructing an accurate count of resident U.S. citizens. Person-level discrepancies between survey-collected citizenship data and administrative records are more pervasive than previously reported in studies comparing survey and administrative data aggregates. Our results imply that survey-sourced citizenship data produce significantly lower estimates of the noncitizen share of the population than would be produced from currently available administrative records; both the survey-sourced and administrative data have shortcomings that could contribute to this difference. Our evidence is consistent with noncitizen respondents misreporting their own citizenship status and failing to report that of other household members. At the same time, currently available administrative records may miss some naturalizations and capture others with a delay. The evidence in this paper also suggests that adding a citizenship question to the 2020 Census would lead to lower self-response rates in households potentially containing noncitizens, resulting in higher fieldwork costs and a lower-quality population count.
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Foreign-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.
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The Opportunities and Challenges of Linked IRS Administrative and Census Survey Records in the Study of Migration
July 2018
Working Paper Number:
carra-2018-06
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 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 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 that are hard to reach with traditional Census 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 applications of our longitudinal IRS dataset to innovations in migration research in the United States.
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Reporting of Indian Health Service Coverage in the American Community Survey
May 2018
Working Paper Number:
carra-2018-04
Response error in surveys affects the quality of data which are relied on for numerous research and policy purposes. We use linked survey and administrative records data to examine reporting of a particular item in the American Community Survey (ACS) - health coverage among American Indians and Alaska Natives (AIANs) through the Indian Health Service (IHS). We compare responses to the IHS portion of the 2014 ACS health insurance question to whether or not individuals are in the 2014 IHS Patient Registration data. We evaluate the extent to which individuals misreport their IHS coverage in the ACS as well as the characteristics associated with misreporting. We also assess whether the ACS estimates of AIANs with IHS coverage represent an undercount. Our results will be of interest to researchers who rely on survey responses in general and specifically the ACS health insurance question. Moreover, our analysis contributes to the literature on using administrative records to measure components of survey error.
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Investigating the Use of Administrative Records in the Consumer Expenditure Survey
March 2018
Working Paper Number:
carra-2018-01
In this paper, we investigate the potential of applying administrative records income data to the Consumer Expenditure (CE) survey to inform measurement error properties of CE estimates, supplement respondent-collected data, and estimate the representativeness of the CE survey by income level. We match individual responses to Consumer Expenditure Quarterly Interview Survey data collected from July 2013 through December 2014 to IRS administrative data in order to analyze CE questions on wages, social security payroll deductions, self-employment income receipt and retirement income. We find that while wage amounts are largely in alignment between the CE and administrative records in the middle of the wage distribution, there is evidence that wages are over-reported to the CE at the bottom of the wage distribution and under-reported at the top of the wage distribution. We find mixed evidence for alignment between the CE and administrative records on questions covering payroll deductions and self-employment income receipt, but find substantial divergence between CE responses and administrative records when examining retirement income. In addition to the analysis using person-based linkages, we also match responding and non-responding CE sample units to the universe of IRS 1040 tax returns by address to examine non-response bias. We find that non-responding households are substantially richer than responding households, and that very high income households are less likely to respond to the CE.
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Longitudinal Environmental Inequality and Environmental Gentrification: Who Gains From Cleaner Air?
May 2017
Working Paper Number:
carra-2017-04
A vast empirical literature has convincingly shown that there is pervasive cross-sectional inequality in exposure to environmental hazards. However, less is known about how these inequalities have been evolving over time. I fill this gap by creating a new dataset, which combines satellite data on ground-level concentrations of fine particulate matter with linked administrative and survey data. This linked dataset allows me to measure individual pollution exposure for over 100 million individuals in each year between 2000 and 2014, a period of time has seen substantial improvements in average air quality. This rich dataset can then be used to analyze longitudinal dimensions of environmental inequality by examining the distribution of changes in individual pollution exposure that underlie these aggregate improvements. I confirm previous findings that cross-sectional environmental inequality has been on the decline, but I argue that this may miss longitudinal patterns in exposure that are consistent with environmental gentrification. I find that advantaged individuals at the beginning of the sample experience larger pollution exposure reductions than do initially disadvantaged individuals.
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