Papers Containing Keywords(s): 'survey'
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Viewing papers 1 through 10 of 161
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Working PaperEarnings Measurement Error, Nonresponse and Administrative Mismatch in the CPS
July 2025
Working Paper Number:
CES-25-48
Using the Current Population Survey Annual Social and Economic Supplement matched to Social Security Administration Detailed Earnings Records, we link observations across consecutive years to investigate a relationship between item nonresponse and measurement error in the earnings questions. Linking individuals across consecutive years allows us to observe switching from response to nonresponse and vice versa. We estimate OLS, IV, and finite mixture models that allow for various assumptions separately for men and women. We find that those who respond in both years of the survey exhibit less measurement error than those who respond in one year. Our findings suggest a trade-off between survey response and data quality that should be considered by survey designers, data collectors, and data users.View Full Paper PDF
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Working PaperThe Rural/Urban Volunteering Divide
June 2025
Working Paper Number:
CES-25-42
Are rural residents more likely to volunteer than those living in urban places? Although early sociological theory posited that rural residents were more likely to experience social bonds connecting them to their community, increasing their odds of volunteer engagement, empirical support is limited. Drawing upon the full population of rural and urban respondents to the United States Census Bureau's Current Population Survey (CPS) Volunteering Supplement (2002-2015), we found that rural respondents are more likely to report volunteering compared to urban respondents, although these differences are decreasing over time. Moreover, we found that propensities for rural and urban volunteerism vary based on differences in both individual and place-based characteristics; further, the size of these effects differ across rural and urban places. These findings have important implications for theory and empirical analysis.View Full Paper PDF
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Working PaperTapping Business and Household Surveys to Sharpen Our View of Work from Home
June 2025
Working Paper Number:
CES-25-36
Timely business-level measures of work from home (WFH) are scarce for the U.S. economy. We review prior survey-based efforts to quantify the incidence and character of WFH and describe new questions that we developed and fielded for the Business Trends and Outlook Survey (BTOS). Drawing on more than 150,000 firm-level responses to the BTOS, we obtain four main findings. First, nearly a third of businesses have employees who work from home, with tremendous variation across sectors. The share of businesses with WFH employees is nearly ten times larger in the Information sector than in Accommodation and Food Services. Second, employees work from home about 1 day per week, on average, and businesses expect similar WFH levels in five years. Third, feasibility aside, businesses' largest concern with WFH relates to productivity. Seven percent of businesses find that onsite work is more productive, while two percent find that WFH is more productive. Fourth, there is a low level of tracking and monitoring of WFH activities, with 70% of firms reporting they do not track employee days in the office and 75% reporting they do not monitor employees when they work from home. These lessons serve as a starting point for enhancing WFH-related content in the American Community Survey and other household surveys.View Full Paper PDF
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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 PaperU.S. Banks' Artificial Intelligence and Small Business Lending: Evidence from the Census Bureau's Annual Business Survey
February 2025
Working Paper Number:
CES-25-07
Utilizing confidential microdata from the Census Bureau's new technology survey (technology module of the Annual Business Survey), we shed light on U.S. banks' use of artificial intelligence (AI) and its effect on their small business lending. We find that the percentage of banks using AI increases from 14% in 2017 to 43% in 2019. Linking banks' AI use to their small business lending, we find that banks with greater AI usage lend significantly more to distant borrowers, about whom they have less soft information. Using an instrumental variable based on banks' proximity to AI vendors, we show that AI's effect is likely causal. In contrast, we do not find similar effects for cloud systems, other types of software, or hardware surveyed by Census, highlighting AI's uniqueness. Moreover, AI's effect on distant lending is more pronounced in poorer areas and areas with less bank presence. Last, we find that banks with greater AI usage experience lower default rates among distant borrowers and charge these borrowers lower interest rates, suggesting that AI helps banks identify creditworthy borrowers at loan origination. Overall, our evidence suggests that AI helps banks reduce information asymmetry with borrowers, thereby enabling them to extend credit over greater distances.View Full Paper PDF
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Working PaperPotential Bias When Using Administrative Data to Measure the Family Income of School-Aged Children
January 2025
Working Paper Number:
CES-25-03
Researchers and practitioners increasingly rely on administrative data sources to measure family income. However, administrative data sources are often incomplete in their coverage of the population, giving rise to potential bias in family income measures, particularly if coverage deficiencies are not well understood. We focus on the school-aged child population, due to its particular import to research and policy, and because of the unique challenges of linking children to family income information. We find that two of the most significant administrative sources of family income information that permit linking of children and parents'IRS Form 1040 and SNAP participation records'usefully complement each other, potentially reducing coverage bias when used together. In a case study considering how best to measure economic disadvantage rates in the public school student population, we demonstrate the sensitivity of family income statistics to assumptions about individuals who do not appear in administrative data sources.View Full Paper PDF
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Working PaperCTC and ACTC Participation Results and IRS-Census Match Methodology, Tax Year 2020
December 2024
Working Paper Number:
CES-24-76
The Child Tax Credit (CTC) and Additional Child Tax Credit (ACTC) offer assistance to help ease the financial burden of families with children. This paper provides taxpayer and dollar participation estimates for the CTC and ACTC covering tax year 2020. The estimates derive from an approach that relies on linking the 2021 Current Population Survey Annual Social and Economic Supplement (CPS ASEC) to IRS administrative data. This approach, called the Exact Match, uses survey data to identify CTC/ACTC eligible taxpayers and IRS administrative data to indicate which eligible taxpayers claimed and received the credit. Overall in tax year 2020, eligible taxpayers participated in the CTC and ACTC program at a rate of 93 percent while dollar participation was 91 percent.View Full Paper PDF
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Working PaperEITC Participation Results and IRS-Census Match Methodology, Tax Year 2021
December 2024
Working Paper Number:
CES-24-75
The Earned Income Tax Credit (EITC), enacted in 1975, offers a refundable tax credit to low income working families. This paper provides taxpayer and dollar participation estimates for the EITC covering tax year 2021. The estimates derive from an approach that relies on linking the 2022 Current Population Survey Annual Social and Economic Supplement (CPS ASEC) to IRS administrative data. This approach, called the Exact Match, uses survey data to identify EITC eligible taxpayers and IRS administrative data to indicate which eligible taxpayers claimed and received the credit. Overall in tax year 2021 eligible taxpayers participated in the EITC program at a rate of 78 percent while dollar participation was 81 percent.View Full Paper PDF
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Working PaperTip of the Iceberg: Tip Reporting at U.S. Restaurants, 2005-2018
November 2024
Working Paper Number:
CES-24-68
Tipping is a significant form of compensation for many restaurant jobs, but it is poorly measured and therefore not well understood. We combine several large administrative and survey datasets and document patterns in tip reporting that are consistent with systematic under-reporting of tip income. Our analysis indicates that although the vast majority of tipped workers do report earning some tips, the dollar value of tips is under-reported and is sensitive to reporting incentives. In total, we estimate that about eight billion in tips paid at full-service, single-location, restaurants were not captured in tax data annually over the period 2005-2018. Due to changes in payment methods and reporting incentives, tip reporting has increased over time. Our findings have implications for downstream measures dependent on accurate measures of compensation including poverty measurement among tipped restaurant workers.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
