Evaluations of the EITC, including its antipoverty effectiveness, are based on simulated EITC benefits using either the Census Bureau’s tax module or from external tax simulators such as the National Bureau of Economic Research’s TAXSIM or Jon Bakija’s model. Each simulator utilizes model-based assumptions on who is and who is not eligible for the EITC, and conditional on eligibility, assumes that participation is 100 percent. However, recent evidence suggests that take-up of the EITC is considerably less than 100 percent, and thus claims regarding the impact of the program on measures of poverty may be overstated. We use data from the Current Population Survey Annual Social and Economic Supplement (CPS ASEC) linked to IRS tax data on the EITC to compare the distribution of EITC benefits from three tax simulation modules to administrative tax records. We find that significantly more actual EITC payments flow to childless tax units than predicted by the tax simulators, and to those whose family income places then well above official poverty thresholds. However, actual EITC payments appear to be target efficient at the individual tax unit level, whether correctly paid or not. We then compare the antipoverty impact of the EITC across the survey and administrative tax measures of EITC benefits. We find that in the full CPS ASEC the tax simulators overestimate the antipoverty effects of the EITC by about 1.8 million persons in a typical year. Restricting to a harmonized sample of filers, we find that the antipoverty estimates derived from the TAXSIM and Bakija models align more closely to actual EITC payments compared to the CPS, suggesting a discrepancy in assignment of tax filers between the tax simulators.
Measuring the economic status of low-income individuals and families is a central focus of poverty scholars and is at the fore of much public policy debate. The stakes are substantial as changes in poverty (and poverty thresholds) influence the scale and scope of redistributive tax and transfer programs at all levels of government. In the United States, official poverty statistics are derived from the Annual Social and Economic Supplement to the Current Population Survey, a nationally representative survey of about 90,000 households. The main source of household income comes from labor-market earnings, but as highlighted in the work of UKCPR Affiliates Charles Hokeyem, Christopher Bollinger, and James Ziliak (DP2014-05), published in the Journal of the American Statistical Association, a challenge to the proper measurement of income is survey non-response. Using a restricted-access dataset that links the CPS ASEC to Social Security's Detailed Earnings Records, they show that survey non-response is not random and leads to a systematic downward bias in the official poverty rate of about 10% in an average year. Follow-up research that is forthcoming in 2019 at the Journal of Political Economy (DP2015-02), and conducted jointly with Barry Hirsch at Georgia State University, shows that earnings non-response affects broader measures of the income distribution, such as earnings inequality and gender wage gaps.
This aim of this paper is to assess the economic status of rural people five decades after publication of President Johnson's National Commission on Rural Poverty report The People Left Behind. Using data from the Annual Social and Economic Supplement of the CPS, along with county data from the Regional Economic Information System, I focus on how changes in employment, wages, and the social safety net have influenced the evolution of poverty and inequality in rural and urban places. The evidence shows that large numbers of rural Americans are disengaged from the labor market, gains in human capital attainment have stagnated, and the retreat from marriage continues for the medium- and less-skilled individuals. However, the social safety net has been more effective in redistributing income within rural areas than in urban centers. Work, education, and marriage are the three main pathways out of poverty for most Americans, whether residing in urban or rural locales, and thus making progress against poverty and inequality faces major economic and demographic headwinds.
Refundable tax credits and food assistance are the largest transfer programs available to able-bodied working poor and near-poor families in the United States, and simultaneous participation in these programs has more than doubled since the early 2000s. To understand this growth, we construct a series of two-year panels from the 1981–2013 waves of the Current Population Survey Annual Social and Economic Supplement to estimate the effect of state labor-market conditions, federal and state transfer program policy choices, and household demographics governing joint participation in food and refundable tax credit programs. Overall, changing policy drives much of the increase in the simultaneous, biennial use of food assistance and refundable tax credits. This stands in stark contrast from the factors accounting for the growth in food assistance alone, where cyclical and structural labor market factors account for at least one-half of the growth, and demographics play a more prominent role. Moreover, since 2000, the business cycle factors as the leading determinant in biennial participation decisions in food programs and refundable tax credits, suggesting a recent strengthening in the relationship between economic conditions and transfer programs.
The aim of this paper is to assess the adequacy of the data infrastructure in the United States to meet future research and policy evaluation needs as it pertains to income, program participation, poverty, and financial vulnerability. I first discuss some major research themes that are likely to dominate policy and scientific discussions in the coming decade. This list includes research on the long-term consequences of income inequality and mobility, issues of transfer-program participation and intergenerational dependence, challenges with poverty measurement and poverty persistence, and material deprivation. I then summarize what information we currently collect in the U.S. that is used to address these issues, with particular focus on ten national panel datasets that cover these domains and continue to be fielded by the various federal agencies. Included in this section is a discussion of challenges posed by rising income nonresponse and underreporting in many panel surveys. I then conclude with a discussion of how the current panel surveys can be improved to address growing need for social science research on inequality, poverty, and material well being.
The Current Population Survey Annual Social and Economic Supplement (ASEC) serves as the data source for official income, poverty, and inequality statistics in the United States. There is a concern that the rise in nonresponse to earnings questions could deteriorate data quality and distort estimates of these important metrics. We use a dataset of internal ASEC records matched to Social Security Detailed Earnings Records (DER) to study the impact of earnings nonresponse on estimates of poverty from 1997-2008. Our analysis does not treat the administrative data as the “truth”; instead, we rely on information from both administrative and survey data. We compare a “full response” poverty rate that assumes all ASEC respondents provided earnings data to the official poverty rate to gauge the nonresponse bias. On average, we find the nonresponse bias is about 1.0 percentage point.
The SNAP program cost one half of one percent, according to a 2013 estimate by Robert Moffitt. For that amount we get a 16 percent reduction in poverty (8 million fewer poor people) after an adjustment for underreporting, based on USDA Administrative data. Moreover we get a 41 percent cut in the poverty gap, which measures the depth of poverty and a 54 percent decline in the severity of poverty, when we add SNAP benefits to Census money incomes and recalculate the official poverty rate.
In 1964, President Lyndon Johnson went to Kentucky’s Martin County to declare war on poverty. The following year he signed the Appalachian Regional Development Act, creating a state-federal partnership to improve the region’s economic prospects through better job opportunities, greater human capital, and enhanced transportation. As the focal point of domestic antipoverty efforts, Appalachia took on special symbolic as well as economic importance. Nearly half a century later, what are the results? Appalachian Legacy provides the answers. Led by James P. Ziliak, prominent economists and demographers map out the region’s current status. They explore important questions such as: How has Appalachia fared since the signing of ARDA in 1965, and how does it now compare to the nation as a whole in key categories such as education, employment, and health. Was ARDA an effective place-based policy for ameliorating hardship in a troubled region, or is Appalachia still mired in a poverty trap? And what lessons can we draw from the Appalachian experience? In addition to providing important research to help analysts, policymakers, scholars, and regional experts discern what works in fighting poverty, Appalachian Legacy is an important contribution to the economic history of the eastern United States. (Published at Brookings Institution Press, 2012)
This paper examines the association between poverty and food insecurity among children using the official measure of poverty and the newsupplemental poverty measure of the Census Bureau based on a more inclusive definition of family resources and needs. Our objective is to study whether the association between food insecurity and poverty improves with a more comprehensive measure of income and needs. We find a strong and statistically significant association between income-to-needs ratio based on the official poverty metric and food insecurity among children—particularly very low food security among children. A more inclusive measure of income-to-needs-ratio, based on the supplemental poverty measurestrengthens the association. These findings remain robust in models using longitudinal data with person fixed effects.
To measure poverty, incomes must be made equivalent across households with different structures. In this paper, we use a very flexible ordered response model to analyze the relationship between income, demographic structure, and subjective assessments of financial wellbeing drawn from the 1991-2008 British Household Panel Survey. Our results suggest the existence of large-scale economies within marital/cohabiting couples, but substantial diseconomies from the addition of children or further adults. This pattern contrasts sharply with commonly-used equivalence scales and is consistent with explanations in terms of the capital requirements associated with additions to the core couple.
We examine differences in income within the U.S., and the regions of persistent poverty that have arisen, using a newly assembled dataset of counties that links historical 19th century Census data with contemporaneous data. The data, along with an augmented human capital growth model, permit us to identify the roles of contemporaneous differences in aggregate production technologies and factor endowments, in conjunction with the historical roles of institutions, culture, geography, and human capital. We allow for possible cross-county factor mobility via a correlated random effects GMM estimator that identifies simultaneously the coefficients on time varying and time-invariant determinants of income. We find evidence of significant regional differences in production technologies, but our decompositions of the poor/non-poor income gap suggests that at least three fourths of the gap is explained by differences in productive factors. Persistently poor counties are different (and poorer) primarily because they have lower levels of factors of production, not because they use the factors they have less efficiently. While much of the income difference is explained by contemporary factors, the contribution of historical levels of human capital is surprisingly large. The combined contribution of historical and contemporary human capital is striking: together, they explain nearly 60 percent of the overall income gap between the persistently poor and non-poor counties.