Evaluating the Success of President Johnson's War on Poverty: Revisiting the Historical Record Using a Full-Income Poverty Measure

Burkhauser, Richard V.; Corinth, Kevin; Elwell, James; Larrimore, Jeff

doi:10.2139/ssrn.3353906

Cited by 2 publications

(3 citation statements)

References 31 publications

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“…While there is an extensive literature that examines income and poverty measurement and trends (summarized in Ruggles 1990;Citro and Michael, 1995;Sullivan, 2012 andBurkhauser et al, 2019), none of these studies have addressed the long delay in the availability of nationally representative income data, and very few have used the data from the Monthly Current Population Survey (Monthly CPS). Bergmann and Coder (2010) However, this work has focused on median income (Green and Coder, 2020) and provided only very limited validation of its measures.…”

Section: Earlier Work On Timely Measures Of Income and Povertymentioning

confidence: 99%

“…There are many limitations of the official measure that numerous studies have noted, such as its adjusting thresholds over time using a price index that overstates inflation, its omission of taxes, tax credits, and in-kind benefits such as food stamps and housing subsidies, and its peculiar equivalence scale (Citro and Michael, 1995;Meyer and Sullivan 2012;Burkhauser et al 2019). These limitations are less relevant for the short-term changes in poverty that are the focus of this study as long as the errors do not change quickly over time.…”

Section: Measures Of Income Poverty and The Income Distributionmentioning

confidence: 99%

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Income and Poverty in the COVID-19 Pandemic

Han¹,

Meyer²,

Sullivan³

2020

Brookings Papers on Economic Activity

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This paper addresses the economic impact of the COVID-19 pandemic by providing timely and accurate information on the impact of the current pandemic on income and poverty to inform the targeting of resources to those most affected and assess the success of current efforts. We construct new measures of the income distribution and poverty with a lag of only a few weeks using high frequency data from the Basic Monthly Current Population Survey (CPS), which collects income information for a large, representative sample of U.S. families. Because the family income data for this project are rarely used, we validate this timely measure of income by comparing historical estimates that rely on these data to estimates from data on income and consumption that have been used much more broadly. Our results indicate that at the start of the pandemic, government policy effectively countered its effects on incomes, leading poverty to fall and low percentiles of income to rise across a range of demographic groups and geographies. Simulations that rely on the detailed CPS data and that closely match total government payments made show that the entire decline in poverty that we find can be accounted for by the rise in government assistance, including unemployment insurance benefits and the Economic Impact Payments. Our simulations further indicate that of those losing employment the vast majority received unemployment insurance, though this was less true early on in the pandemic and receipt was uneven across the states, with some states not reaching a large share of their out of work residents.

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Section: Earlier Work On Timely Measures Of Income and Povertymentioning

confidence: 99%

Section: Measures Of Income Poverty and The Income Distributionmentioning

confidence: 99%

Income and Poverty in the COVID-19 Pandemic

Han¹,

Meyer²,

Sullivan³

2020

Brookings Papers on Economic Activity

View full text Add to dashboard Cite

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“…The Census Bureau has a long history of calculating features of the income distribution after accounting for taxes (see, e.g., U.S. Census Bureau 1988Bureau , 1993. In recent years, research has proliferated on topics ranging from the progressivity of the tax and transfer system in the United States (Piketty and Saez 2007, Congressional Budget Office 2018, Saez and Zucman 2019, Splinter 2019 and poverty rates after accounting for taxes and transfers (Burkhauser et al 2019, Fox 2019, Meyer, Wu, and Medalia 2020 to the levels and trends in income inequality and distributional national accounts Zucman 2018, Auten andSplinter 2019).…”

Section: Introductionmentioning

confidence: 99%

The Accuracy of Tax Imputations: Estimating Tax Liabilities and Credits Using Linked Survey and Administrative Data

Meyer

Finley

et al. 2020

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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). * This paper was prepared for the NBER-CRIW book, "Measuring and Understanding the Distribution and Intra/Inter-Generational Mobility of Income and Wealth." Any opinions and conclusions expressed herein are those of the author(s) and do not necessarily represent the views of the U.S. Census Bureau, the Internal Revenue Service, any other agency of the federal government, or the NBER. All results were approved for release by the U.S. Census Bureau, authorization numbers CBDRB-FY20-ERD002-014 and CBDRB-FY20-ERD002-038. We thank Matthew Stadnicki, Brian Curran, Alexa Grunwaldt, and Angela Wyse for excellent research assistance; Katie Genadek for expertly handling lengthy disclosure requests; Jim Davis and Maggie Jones for helping us access TAXSIM on the Census servers; and Dan Feenberg, Tom Hertz, Jonathan Rothbaum, David Splinter,Alex Yuskavage, and participants at the NBER-CRIW and NTA Conferences for helpful comments and discussions. We also appreciate the financial support of the Alfred P. Sloan Foundation, the Russell Sage Foundation, the Charles Koch Foundation, and the Menard...

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Evaluating the Success of President Johnson's War on Poverty: Revisiting the Historical Record Using a Full-Income Poverty Measure

Cited by 2 publications

References 31 publications

Income and Poverty in the COVID-19 Pandemic

Income and Poverty in the COVID-19 Pandemic

The Accuracy of Tax Imputations: Estimating Tax Liabilities and Credits Using Linked Survey and Administrative Data

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