with Jonathan Zhang
Abstract: Recipients of government transfers are economically disadvantaged, yet little is known about how their circumstances evolve leading up to and around program receipt. Using survey data and administrative health records, we establish three new stylized facts around enrollment in eight large safety net programs in the United States. First, market incomes decline prior to enrolling in almost all studied programs, while post-transfer incomes return to pre-enrollment levels within a year. Second, employment rates decline around program receipt and remain lower over a year later, with these patterns coinciding with increased disability and worse health. Third, spousal separations begin to increase prior to program enrollment, even for social insurance programs without mechanically related eligibility requirements. Taken together, these findings highlight the importance of considering persistent non-income shocks (e.g., to health and marital status) for all programs and suggest the need for future work to incorporate the insurance value of programs beyond their intended risk into welfare calculations.
with Kevin Corinth, Bruce Meyer, and Matthew Stadnicki
Abstract: The replacement of the Child Tax Credit (CTC) with a child allowance has been advocated by numerous policymakers and researchers. We estimate the anti-poverty, targeting, and labor supply effects of such a change by linking survey data with administrative tax and government program data which form part of the Comprehensive Income Dataset (CID). We focus on the provisions of the 2021 Build Back Better Act, which would have increased maximum benefit amounts to $3,000 or $3,600 per child (up from $2,000 per child) and made the full credit available to all low and middle-income families regardless of earnings or income. Initially ignoring any behavioral responses, we estimate that the replacement of the CTC would reduce child poverty by 34% and deep child poverty by 39%. The change to a child allowance would have a larger anti-poverty effect on children than any existing government program, though at a higher cost per child raised above the poverty line than any other means-tested program. Relatedly, the child allowance would allocate a smaller share of its total dollars to families at the bottom of the income distribution—as well as families with the lowest levels of long-term income, education, or health—than any existing means-tested program with the exception of housing assistance. We then simulate anti-poverty effects accounting for labor supply responses. By replacing the CTC (which contained substantial work incentives akin to the EITC) with a child allowance, the policy change would reduce the return to working at all by at least $2,000 per child for most workers with children. Relying on elasticity estimates consistent with mainstream simulation models and the academic literature, we estimate that this change in policy would lead 1.5 million workers (constituting 2.6% of all working parents) to exit the labor force. The decline in employment and the consequent earnings loss would mean that child poverty would only fall by at most 22% and deep child poverty would not fall at all with the policy change.
with Bruce Meyer and Brian Curran
Abstract: Geographic adjustments for local prices are embedded in many federal payments to states, localities, and individuals. Adjustments for geographic cost-of-living differences are also part of the Census Bureau’s Supplemental Poverty Measure and have been proposed for the Official Poverty Measure, yet academic work is divided as to whether or not geographic adjustments are justified. This paper proposes a rigorous approach to assess the desirability of geographic adjustments to poverty measures by examining how well they achieve a central objective of a poverty measure: identifying the least advantaged population. Specifically, we compare an exhaustive list of material well-being indicators of those classified as poor under the Supplemental Poverty Measure and the new Comprehensive Income Poverty Measure with and without a geographic adjustment. These well-being indicators are drawn from the Current Population Survey and the Survey of Income and Program Participation and include material hardships, appliances owned, home quality issues, food security, public services, health, education, assets, permanent income, and mortality. For nine of the ten domains of well-being indicators, we find that incorporating a geographic adjustment identifies a less deprived poor population. These results are broadly consistent across different poverty measures, various ways of implementing a geographic adjustment, and multiples of the poverty line.
with Bruce Meyer, Angela Wyse, Alexa Gruwaldt, and Carla Medalia
Abstract: Official poverty statistics and even the extreme poverty literature largely ignore people experiencing homelessness. In this paper, we examine the characteristics, labor market attachment, geographic mobility, earnings, and safety net utilization of this population in order to understand their economic well-being. This paper is the first to examine these outcomes at the national level using administrative data on income and government program receipt. It is part of the Comprehensive Income Dataset project, which combines household survey data with administrative records to improve estimates of income and related statistics. Specifically, we use restricted microdata from the 2010 Decennial Census, which enumerates both sheltered and unsheltered homeless people, the 2006-2016 American Community Survey (ACS), which surveys sheltered homeless people, and longitudinal shelter-use data from several major U.S. cities. We link these data to longitudinal administrative tax records as well as administrative data on the Supplemental Nutrition Assistance Program (SNAP), veterans’ benefits, Medicare, Medicaid, housing assistance, and mortality. Our approach benefits from large samples that offer a guide to national homelessness patterns and allow us to compare estimates between data sources, including the Department of Housing and Urban Development (HUD)’s point-in-time (PIT) counts. By shedding light on issues of data linkage and survey coverage among homeless people, this paper contributes to efforts to better incorporate this hard-to-survey population into income and poverty estimates.
"Do Surveys Miss the Poor? Evidence from Administrative SNAP Records" (March 2021)
with Bruce Meyer
(email for draft)
Abstract: The ability of surveys to measure the circumstances of the poor hinges on whether or not surveys fully cover poor individuals. This paper is one of the first studies to examine the extent to which poor individuals – proxied for by recipients of the Supplemental Nutrition Assistance Program (SNAP) – are adequately covered in two major Census surveys: the American Community Survey (ACS) and the Current Population Survey Annual Social and Economic Supplement (CPS). We link survey individuals to SNAP administrative records from 21 states spanning 2006-2016. Overall, we find that 93.1% and 94.7% of SNAP recipients are represented in the ACS and CPS, respectively, during the interview months. However, coverage rates differ notably across characteristics in both surveys. Young children, Black individuals, those with irregular SNAP receipt, and those with less connection to others or work tend to be under-covered among SNAP recipients. Conversely, spouses in multiple-person cases and the elderly tend to be over-covered among SNAP recipients. These estimates can affect our understanding of a wide range of topics using survey data, such as the magnitude of poverty and the effects of means-tested government transfers.
Abstract: As one of the most important federal education programs, Title I accounts for approximately 30% of federal funding to all school districts. Yet, many studies have shown that Title I is associated with insignificant effects on student achievement. This paper tests one potential mechanism behind this effect - that receiving Title I funds causes state and local governments to offset funding they would otherwise have provided. I employ a regression discontinuity design that exploits discontinuities in the Title I funding formula. In particular, Title I grants are divided into four categories, and a school district is only eligible to receive a given subgrant if its poverty rate is above a given threshold. Evidence from the 2010 to 2015 school years suggests that receiving Title I grants leads to reductions in total expenditures, driven by crowding out of local revenues that becomes more pronounced in the years after gaining eligibility.
with Bruce Meyer, Grace Finley, Patrick Langetieg, Carla Medalia, Mark Payne, and Alan Plumley
In Measuring Distribution and Mobility of Income and Wealth (eds. R. Chetty, J. Friedman, J. Gornick, B. Johnson, & A. Kennickell), 459-498. NBER Book Series. (2022)
Abstract: This paper calculates accurate estimates of income and payroll taxes using a groundbreaking set of linked survey and administrative tax data that are part of the Comprehensive Income Dataset (CID). We compare our estimates to survey imputations produced by the Census Bureau and those generated using the TAXSIM calculator from the National Bureau of Economic Research. The administrative data include two sets of Internal Revenue Service (IRS) data: (1) a limited set of tax information for the population of individual income tax returns covering selected line items from Forms 1040, W-2, and 1099-R; and (2) an extensive set of population tax records processed by the IRS in 2011, covering nearly every line item on Form 1040 and most lines on a series of third-party information returns. We link these IRS records to the Current Population Survey Annual Social and Economic Supplement (CPS) for reference year 2010. We describe how we form tax units and estimate various types of tax liabilities and credits using these linked data, providing a roadmap for constructing accurate measures of taxes while preserving the survey family as the sharing unit for distributional analyses. We find that aggregate estimates of various tax components using the limited and extensive tax data estimates are close to each other and much closer to public IRS tabulations than either of the imputations using survey data alone. At the individual level, the absolute errors of survey-only imputations of federal income taxes and total taxes are on average 10% and 13%, respectively, of adjusted gross income. In contrast, the limited tax data imputations yield mean absolute errors for federal income taxes and total taxes that are about 2% and 3% of adjusted gross income, respectively. For the Earned Income Tax Credit, the limited tax data imputation is off by less than $20 on average for a typical family (compared to more than $500 using either of the survey-only imputations).
Abstract: This paper is the first to examine changes in poverty over time using a comprehensive set of linked survey and administrative data, implementing recommendations of the Interagency Technical Working Group on Evaluating Alternative Measures of Poverty. Using the Comprehensive Income Dataset (CID), we correct for measurement error in survey-reported incomes, focusing on single parent families from 1995 to 2016. Our preferred estimates indicate that single parent family poverty declined by 62% over time, while it fell by only 45% using survey data alone. Moreover, survey-reported deep poverty among single parent families increased over time, while it fell using the CID.
Recent research suggests that rates of extreme poverty, commonly defined as living on less than $2/person/day, are high and rising in the United States. We re-examine the rate of extreme poverty by linking 2011 data from the Survey of Income and Program Participation and Current Population Survey, the sources of recent extreme poverty estimates, to administrative tax and program data. Of the 3.6 million non-homeless households with survey-reported cash income below $2/person/day, we find that more than 90% are not in extreme poverty once we include in-kind transfers, replace survey reports of earnings and transfer receipt with administrative records, and account for the ownership of substantial assets. More than half of all misclassified households have incomes from the administrative data above the poverty line, and several of the largest misclassified groups appear to be at least middle class based on measures of material well-being. In contrast, the households kept from extreme poverty by in-kind transfers appear to be among the most materially deprived Americans. Nearly 80% of all misclassified households are initially categorized as extreme poor due to errors or omissions in reports of cash income. Of the households remaining in extreme poverty, 90% consist of a single individual. An implication of the low recent extreme poverty rate is that it cannot be substantially higher now due to welfare reform, as many commentators have claimed.
Education Finance and Policy, 15(1): 104-135. (2020)
Schools often have to decide between extending the length of the school year or the school day. This paper examines the effects of changes in the distribution of instructional time on eighth-grade student achievement through a methodological framework that disaggregates total yearly instructional time into separate inputs for days per year and hours per day. This study's dataset brings together nearly 900,000 student observations across eighty countries and four quadrennial testing cycles of the Trends in International Mathematics and Science Study (TIMSS) Assessments (1995–2007). I find that the positive effects of instructional time on student achievement are driven largely by the length of the school day and not by the length of the school year, with diminishing marginal returns to the former. Socioeconomically underprivileged students are most likely to realize gains from a longer school day. Furthermore, isolating the amount of instructional time spent on TIMSS-tested subjects from the rest of the school day reveals spillover effects from time spent in non-tested subjects that are especially meaningful for underprivileged students. In contrast, the effects of time spent in tested subjects are more homogeneous across student groups.
with Carla Medalia, Bruce Meyer, and Amy O'Hara
International Journal of Population Data Science, 4(1): 1-8. (2019)
Winner of the Administrative Data Research Facilities (ADRF) Network 2018 Annual Conference Best Paper Award
Income is one of the most important measures of well-being, but it is notoriously difficult to measure accurately. In the United States, income data are available from surveys, tax records, and government programs, but each of these sources has important strengths and major limitations when used alone. We link multiple data sources to develop the Comprehensive Income Dataset (CID), a prototype for a restricted micro-level dataset that combines the demographic detail of survey data with the accuracy of administrative measures. By incorporating information on nearly all taxable income, tax credits, and cash and in-kind government transfers, the CID surpasses previous efforts to provide an accurate and comprehensive measure of income for the population of United States individuals, families, and households. We also evaluate the accuracy of different income sources and imputation methods. While still in development, we envision the CID enhancing Census Bureau surveys and statistics by investigating measurement error, improving imputation methods, and augmenting surveys with the best possible estimates of income. It can also be used for policy related research, such as forecasting and simulating changes in programs and taxes. Finally, the CID has substantial advantages over other sources to analyze numerous research topics, including poverty, inequality, mobility, and the distributional consequences of government transfers and taxes.
"A Note on the Reporting of Unemployment Insurance and Unemployment in Survey and Administrative Sources" (Aug 2021)
with Bruce Meyer, Matthew Stadnicki, and Patrick Langetieg
(email for draft - under IRS review)
Pre-Doctoral Research and Policy Reports
with Christine Mulhern, Richard Spies, and Matthew Staiger