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The Changing Safety Net for Low-Income Parents and Their Children: Structural or Cyclical Changes in Income Support Policy?

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Demography

Abstract

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.

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Notes

  1. A recent study by Heflin et al. (2015) highlighted that although 35 states in 2014 had reportedly removed liquid asset tests, 28 of them still listed the test in their prescreening web-based tools for potential SNAP eligibility, which could have the effect of deterring some from applying.

  2. Online Resource 1 details sample construction and characteristics of our CPS data set, including matching procedures. It also contains numerous specification checks on the baseline estimates presented in the main text.

  3. The focus on federal EITC is informative for understanding state EITC participation and eligibility as well given that, by construction, almost all state EITC programs allocate refunds using federal rules and as a fixed proportion of federal EITC received (Johnson and Williams 2011).

  4. With the 1994 survey, the CPS also asked about country of birth. Because the variable is available for only two-thirds of the sample period, we do not include it in our main analyses. We do, however, report the counterfactual simulations for 2000–2012 inclusive of nativity status in Tables S9 and S10 (Online Resource 1), with no substantive change in results reported in this article.

  5. Because most research has found little evidence that marriage or fertility responds endogenously to the generosity of welfare benefits (Bitler et al. 2004; Hoynes 1997; Lopoo and Raissan 2014; Moffitt et al. 1998), the family size–specific EITC and SNAP parameters are treated as exogenous in the model.

  6. The data on the state economic and policy environment are obtained from the University of Kentucky Center for Poverty Research, and the SNAP policy variables come from the Economic Research Service in the U.S. Department of Agriculture (http://www.ers.usda.gov/data-products/snap-policy-database.aspx#.UhQQ-ZLVC3I). All income and spending data are deflated by the 2010 Personal Consumption Expenditure Deflator (http://www.whitehouse.gov/sites/default/files/docs/erp2013/ERP2013_Appendix_B.pdf).

  7. See http://www.bls.gov/lau/lauov.htm for details on construction of state unemployment rates.

  8. For the minimum wage, we use the maximum of the state and federal minimum in each state and year; for the median wage, we compute the average annual hourly wage across the two years for each matched individual in the CPS and then compute the median in each state and year. We use the personal consumption expenditure deflator with 2010 base year to adjust for inflation.

  9. A Wald test of the null hypothesis that the three unemployment rate coefficients are jointly zero is rejected at the p < .001 level.

  10. Extending the notation in Eq. (1), for any given continuous regressor z k , the elasticity of participation in program y k (SNAP, EITC, and ACTC) equals \( {\delta}_k\left(\frac{\overline{z_k}}{\overline{y_k}}\right) \), where \( \overline{z_k} \) and \( \overline{y_k} \) reflect mean values of the policy variable and dependent variable, respectively.

  11. Outreach spending has an unexpected negative sign. Ziliak (2015b) found a similar result in the study of cross-sectional SNAP participation, attributing this to the federal response to SNAP during the Great Recession as the coefficient is the expected positive sign if the sample period stops in 2006.

  12. Wald tests of the joint hypothesis that the three unemployment rate coefficients are 0 (zero) is rejected at the 0.07, 0.10, 0.002, and 0.08 level for all, low-income, low-skilled, and single-mother families, respectively.

  13. In an independent analysis conducted concurrent to this initial draft of this paper, Bitler et al. (2014) used annual cross-sections of IRS Statistics of Income data and found that single-year EITC participation was acyclical for single-mother families and countercyclical among married-couple families.

  14. The Wald test of the three coefficients on unemployment being jointly 0 is rejected at p < .001 level for all families, low-income, and low skilled; and at p < .034 for single mothers.

  15. A limitation of the counterfactual simulation is that the observable and unobservable characteristics of the examined subgroups could, themselves, change over time.

  16. Our baseline models require that the head of household remain the same across the two survey waves, which could depress the influence of demographic factors in our simulations. As a robustness check, we reestimated the models in Tables 1 and 3 that relaxed the constant headship requirement. We report the counterfactual simulations in Tables S7 and S8 (Online Resource 1). As shown in those tables, the results are little changed from the baseline models.

  17. The forecasted spending on the EITC and ACTC was before the ARRA expansions of the ACTC, which were set to expire in 2017, were extended indefinitely as part of the federal budget agreement reached in December 2015 (Rachidi 2015).

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Acknowledgments

We thank the Smith Richardson Foundation for providing financial support. We also thank Susan Mayer, Dan Puskin, Darrick Hamilton, and Robin Lumsdaine, as well as participants at the 2013 APPAM conference, 2014 ASSA meetings, the 2014 Building Human Capital Conference, and seminar participants at Washington and Lee University for helpful feedback on earlier versions. We thank Robert Hartley for timely and valuable research assistance. Finally, this project was completed while Hardy served as the 2017–2018 Okun-Model fellow in Economic Studies at The Brookings Institution, and we acknowledge their support.

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  1. Department of Public Administration and Policy, American University, Kerwin Hall, Washington, DC, 20016-8070, USA

    Bradley Hardy

  2. La Follette School of Public Affairs, University of Wisconsin–Madison, Madison, WI, USA

    Timothy Smeeding

  3. Center for Poverty Research, Department of Economics, University of Kentucky, Lexington, KY, USA

    James P. Ziliak

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Hardy, B., Smeeding, T. & Ziliak, J.P. The Changing Safety Net for Low-Income Parents and Their Children: Structural or Cyclical Changes in Income Support Policy?. Demography 55, 189–221 (2018). https://doi.org/10.1007/s13524-017-0642-7

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