Papers Containing Tag(s): 'MAFID'
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Viewing papers 1 through 10 of 14
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Working PaperThe Design of Sampling Strata for the National Household Food Acquisition and Purchase Survey
February 2025
Working Paper Number:
CES-25-13
The National Household Food Acquisition and Purchase Survey (FoodAPS), sponsored by the United States Department of Agriculture's (USDA) Economic Research Service (ERS) and Food and Nutrition Service (FNS), examines the food purchasing behavior of various subgroups of the U.S. population. These subgroups include participants in the Supplemental Nutrition Assistance Program (SNAP) and the Special Supplemental Nutrition Program for Women, Infants, and Children (WIC), as well as households who are eligible for but don't participate in these programs. Participants in these social protection programs constitute small proportions of the U.S. population; obtaining an adequate number of such participants in a survey would be challenging absent stratified sampling to target SNAP and WIC participating households. This document describes how the U.S. Census Bureau (which is planning to conduct future versions of the FoodAPS survey on behalf of USDA) created sampling strata to flag the FoodAPS targeted subpopulations using machine learning applications in linked survey and administrative data. We describe the data, modeling techniques, and how well the sampling flags target low-income households and households receiving WIC and SNAP benefits. We additionally situate these efforts in the nascent literature on the use of big data and machine learning for the improvement of survey efficiency.View Full Paper PDF
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Working PaperNonresponse 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.View Full Paper PDF
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Working PaperEarnings Through the Stages: Using Tax Data to Test for Sources of Error in CPS ASEC Earnings and Inequality Measures
September 2024
Working Paper Number:
CES-24-52
In this paper, I explore the impact of generalized coverage error, item non-response bias, and measurement error on measures of earnings and earnings inequality in the CPS ASEC. I match addresses selected for the CPS ASEC to administrative data from 1040 tax returns. I then compare earnings statistics in the tax data for wage and salary earnings in samples corresponding to seven stages of the CPS ASEC survey production process. I also compare the statistics using the actual survey responses. The statistics I examine include mean earnings, the Gini coefficient, percentile earnings shares, and shares of the survey weight for a range of percentiles. I examine how the accuracy of the statistics calculated using the survey data is affected by including imputed responses for both those who did not respond to the full CPS ASEC and those who did not respond to the earnings question. I find that generalized coverage error and item nonresponse bias are dominated by measurement error, and that an important aspect of measurement error is households reporting no wage and salary earnings in the CPS ASEC when there are such earnings in the tax data. I find that the CPS ASEC sample misses earnings at the high end of the distribution from the initial selection stage and that the final survey weights exacerbate this.View Full Paper PDF
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Working PaperThe Impact of Parental Resources on Human Capital Investment and Labor Market Outcomes: Evidence from the Great Recession
June 2024
Working Paper Number:
CES-24-34
I study the impact of parents' financial resources during adolescence on postsecondary human capital investment and labor market outcomes, using house value changes during the Great Recession of 2007-2009 as a natural experiment. I use several restricted-access datasets from the U.S. Census Bureau to create a novel dataset that includes intergenerational linkages between children and their parents. This data allows me to exploit house value variation within labor markets, addressing the identification concern that local house values are related to local economic conditions. I find that the average decrease to parents' home values lead to persistent decreases in bachelor's degree attainment of 1.26%, earnings of 1.96%, and full-time employment of 1.32%. Children of parents suffering larger house value shocks are more likely to substitute into two-year degree programs, drop out of college, or be enrolled in a college program in their late 20s.View Full Paper PDF
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Working PaperThe 2010 Census Confidentiality Protections Failed, Here's How and Why
December 2023
Working Paper Number:
CES-23-63
Using only 34 published tables, we reconstruct five variables (census block, sex, age, race, and ethnicity) in the confidential 2010 Census person records. Using the 38-bin age variable tabulated at the census block level, at most 20.1% of reconstructed records can differ from their confidential source on even a single value for these five variables. Using only published data, an attacker can verify that all records in 70% of all census blocks (97 million people) are perfectly reconstructed. The tabular publications in Summary File 1 thus have prohibited disclosure risk similar to the unreleased confidential microdata. Reidentification studies confirm that an attacker can, within blocks with perfect reconstruction accuracy, correctly infer the actual census response on race and ethnicity for 3.4 million vulnerable population uniques (persons with nonmodal characteristics) with 95% accuracy, the same precision as the confidential data achieve and far greater than statistical baselines. The flaw in the 2010 Census framework was the assumption that aggregation prevented accurate microdata reconstruction, justifying weaker disclosure limitation methods than were applied to 2010 Census public microdata. The framework used for 2020 Census publications defends against attacks that are based on reconstruction, as we also demonstrate here. Finally, we show that alternatives to the 2020 Census Disclosure Avoidance System with similar accuracy (enhanced swapping) also fail to protect confidentiality, and those that partially defend against reconstruction attacks (incomplete suppression implementations) destroy the primary statutory use case: data for redistricting all legislatures in the country in compliance with the 1965 Voting Rights Act.View Full Paper PDF
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Working PaperEstimating the U.S. Citizen Voting-Age Population (CVAP) Using Blended Survey Data, Administrative Record Data, and Modeling: Technical Report
April 2023
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.View Full Paper PDF
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Working PaperComparing the 2019 American Housing Survey to Contemporary Sources of Property Tax Records: Implications for Survey Efficiency and Quality
June 2022
Working Paper Number:
CES-22-22
Given rising nonresponse rates and concerns about respondent burden, government statistical agencies have been exploring ways to supplement household survey data collection with administrative records and other sources of third-party data. This paper evaluates the potential of property tax assessment records to improve housing surveys by comparing these records to responses from the 2019 American Housing Survey. Leveraging the U.S. Census Bureau's linkage infrastructure, we compute the fraction of AHS housing units that could be matched to a unique property parcel (coverage rate), as well as the extent to which survey and property tax data contain the same information (agreement rate). We analyze heterogeneity in coverage and agreement across states, housing characteristics, and 11 AHS items of interest to housing researchers. Our results suggest that partial replacement of AHS data with property data, targeted toward certain survey items or single-family detached homes, could reduce respondent burden without altering data quality. Further research into partial-replacement designs is needed and should proceed on an item-by-item basis. Our work can guide this research as well as those who wish to conduct independent research with property tax records that is representative of the U.S. housing stock.View Full Paper PDF
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Working PaperThe Use of Administrative Records and the American Community Survey to Study the Characteristics of Undercounted Young Children in the 2010 Census
May 2018
Working Paper Number:
carra-2018-05
Children under age five are historically one of the most difficult segments of the population to enumerate in the U.S. decennial census. The persistent undercount of young children is highest among Hispanics and racial minorities. In this study, we link 2010 Census data to administrative records from government and third party data sources, such as Medicaid enrollment data and tenant rental assistance program records from the Department of Housing and Urban Development, to identify differences between children reported and not reported in the 2010 Census. In addition, we link children in administrative records to the American Community Survey to identify various characteristics of households with children under age five who may have been missed in the last census. This research contributes to what is known about the demographic, socioeconomic, and household characteristics of young children undercounted by the census. Our research also informs the potential benefits of using administrative records and surveys to supplement the U.S. Census Bureau child population enumeration efforts in future decennial censuses.View Full Paper PDF
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Working PaperInvestigating 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.View Full Paper PDF
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Working PaperA Comparison of Training Modules for Administrative Records Use in Nonresponse Followup Operations: The 2010 Census and the American Community Survey
January 2017
Working Paper Number:
CES-17-47
While modeling work in preparation for the 2020 Census has shown that administrative records can be predictive of Nonresponse Followup (NRFU) enumeration outcomes, there is scope to examine the robustness of the models by using more recent training data. The models deployed for workload removal from the 2015 and 2016 Census Tests were based on associations of the 2010 Census with administrative records. Training the same models with more recent data from the American Community Survey (ACS) can identify any changes in parameter associations over time that might reduce the accuracy of model predictions. Furthermore, more recent training data would allow for the incorporation of new administrative record sources not available in 2010. However, differences in ACS methodology and the smaller sample size may limit its applicability. This paper replicates earlier results and examines model predictions based on the ACS in comparison with NRFU outcomes. The evaluation consists of a comparison of predicted counts and household compositions with actual 2015 NRFU outcomes. The main findings are an overall validation of the methodology using independent data.View Full Paper PDF
