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The Marginal Labor Supply Disincentives of Welfare: Evidence from Administrative Barriers to Participation

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  • Moffitt, Robert A.

    (Johns Hopkins University)

  • Zahn, Matthew V.

    (Johns Hopkins University)

Abstract

Existing research on the static effects of the manipulation of welfare program benefit parameters on labor supply has allowed only restrictive forms of heterogeneity in preferences. Yet preference heterogeneity implies that the marginal effects on labor supply of welfare expansions and contractions may differ in different time periods with different populations and which sweep out different portions of the distribution of preferences. A new examination of the heavily studied AFDC program uses variation in state-level administrative barriers to entering the program in the late 1980s and early 1990s to estimate the marginal labor supply effects of changes in program participation induced by that variation. The estimates are obtained from a theory-consistent reduced form model which allows for a nonparametric specification of how changes in welfare program participation affect labor supply on the margin. Estimates using a form of local instrumental variables show that the marginal treatment effects are quadratic, rising and then falling as participation rates rise (i.e., becoming more negative then less negative on hours of work). The average work disincentive is not large but that masks some margins where effects are close to zero and some which are sizable. Traditional IV which estimates a weighted average of marginal effects gives a misleading picture of marginal responses. A counterfactual exercise which applies the estimates to three historical reform periods in 1967, 1981, and 1996 when the program tax rate was significantly altered shows that marginal labor supply responses differed in each period because of differences in the level of participation in the period and the composition of who was on the program.

Suggested Citation

  • Moffitt, Robert A. & Zahn, Matthew V., 2022. "The Marginal Labor Supply Disincentives of Welfare: Evidence from Administrative Barriers to Participation," IZA Discussion Papers 15046, Institute of Labor Economics (IZA).
  • Handle: RePEc:iza:izadps:dp15046
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    1. David S. Lee & Justin McCrary & Marcelo J. Moreira & Jack Porter, 2022. "Valid t-Ratio Inference for IV," American Economic Review, American Economic Association, vol. 112(10), pages 3260-3290, October.
    2. James J. Heckman & Vytlacil, Edward J., 2007. "Econometric Evaluation of Social Programs, Part II: Using the Marginal Treatment Effect to Organize Alternative Econometric Estimators to Evaluate Social Programs, and to Forecast their Effects in New," Handbook of Econometrics, in: J.J. Heckman & E.E. Leamer (ed.), Handbook of Econometrics, edition 1, volume 6, chapter 71, Elsevier.
    3. Pedro Carneiro & James J. Heckman & Edward Vytlacil, 2010. "Evaluating Marginal Policy Changes and the Average Effect of Treatment for Individuals at the Margin," Econometrica, Econometric Society, vol. 78(1), pages 377-394, January.
    4. Keane, Michael & Moffitt, Robert, 1998. "A Structural Model of Multiple Welfare Program Participation and Labor Supply," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(3), pages 553-589, August.
    5. Angrist, Joshua & Kolesár, Michal, 2024. "One instrument to rule them all: The bias and coverage of just-ID IV," Journal of Econometrics, Elsevier, vol. 240(2).
    6. Marc K. Chan & Robert Moffitt, 2018. "Welfare Reform and the Labor Market," Annual Review of Economics, Annual Reviews, vol. 10(1), pages 347-381, August.
    7. Pedro Carneiro & James J. Heckman & Edward J. Vytlacil, 2011. "Estimating Marginal Returns to Education," American Economic Review, American Economic Association, vol. 101(6), pages 2754-2781, October.
    8. Robert A. Moffitt, 2016. "Economics of Means-Tested Transfer Programs in the United States, Volume 1," NBER Books, National Bureau of Economic Research, Inc, number moff14-1.
    9. Nichols, Albert L & Zeckhauser, Richard J, 1982. "Targeting Transfers through Restrictions on Recipients," American Economic Review, American Economic Association, vol. 72(2), pages 372-377, May.
    10. Moffitt, Robert, 1983. "An Economic Model of Welfare Stigma," American Economic Review, American Economic Association, vol. 73(5), pages 1023-1035, December.
    11. Robert A. Moffitt, 2003. "The Temporary Assistance for Needy Families Program," NBER Chapters, in: Means-Tested Transfer Programs in the United States, pages 291-364, National Bureau of Economic Research, Inc.
    12. Beth Osborne Daponte & Seth Sanders & Lowell Taylor, 1999. "Why Do Low-Income Households not Use Food Stamps? Evidence from an Experiment," Journal of Human Resources, University of Wisconsin Press, vol. 34(3), pages 612-628.
    13. Thomas Cornelissen & Christian Dustmann & Anna Raute & Uta Schönberg, 2018. "Who Benefits from Universal Child Care? Estimating Marginal Returns to Early Child Care Attendance," Journal of Political Economy, University of Chicago Press, vol. 126(6), pages 2356-2409.
    14. Richard Blundell & Monica Costa Dias & Costas Meghir & Jonathan Shaw, 2016. "Female Labor Supply, Human Capital, and Welfare Reform," Econometrica, Econometric Society, vol. 84, pages 1705-1753, September.
    15. Michael Keane & Timothy Neal, 2021. "A New Perspective on Weak Instruments," Discussion Papers 2021-05a, School of Economics, The University of New South Wales.
    16. Joseph J. Doyle Jr., 2007. "Child Protection and Child Outcomes: Measuring the Effects of Foster Care," American Economic Review, American Economic Association, vol. 97(5), pages 1583-1610, December.
    17. Magne Mogstad & Andres Santos & Alexander Torgovitsky, 2018. "Using Instrumental Variables for Inference About Policy Relevant Treatment Parameters," Econometrica, Econometric Society, vol. 86(5), pages 1589-1619, September.
    18. Douglas Staiger & James H. Stock, 1997. "Instrumental Variables Regression with Weak Instruments," Econometrica, Econometric Society, vol. 65(3), pages 557-586, May.
    19. David S. Lee & Enrico Moretti & Matthew J. Butler, 2004. "Do Voters Affect or Elect Policies? Evidence from the U. S. House," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 119(3), pages 807-859.
    20. Michael P Keane & Sonya Krutikova & Timothy Neal, 2018. "The impact of child work on cognitive development: results from four Low to Middle Income countries," IFS Working Papers W18/29, Institute for Fiscal Studies.
    21. Lee, David S., 2008. "Randomized experiments from non-random selection in U.S. House elections," Journal of Econometrics, Elsevier, vol. 142(2), pages 675-697, February.
    22. Imbens, Guido W & Angrist, Joshua D, 1994. "Identification and Estimation of Local Average Treatment Effects," Econometrica, Econometric Society, vol. 62(2), pages 467-475, March.
    23. James J. Heckman & Sergio Urzua & Edward Vytlacil, 2006. "Understanding Instrumental Variables in Models with Essential Heterogeneity," The Review of Economics and Statistics, MIT Press, vol. 88(3), pages 389-432, August.
    24. Robert A. Moffitt, 2003. "Introduction to "Means-Tested Transfer Programs in the United States"," NBER Chapters, in: Means-Tested Transfer Programs in the United States, pages 1-14, National Bureau of Economic Research, Inc.
    25. James P. Ziliak, 2007. "Making Work Pay: Changes in Effective Tax Rates and Guarantees in U.S. Transfer Programs, 1983–2002," Journal of Human Resources, University of Wisconsin Press, vol. 42(3).
    26. Joshua D. Angrist & Alan B. Keueger, 1991. "Does Compulsory School Attendance Affect Schooling and Earnings?," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 106(4), pages 979-1014.
    27. Heckman, James J. & Robb, Richard Jr., 1985. "Alternative methods for evaluating the impact of interventions : An overview," Journal of Econometrics, Elsevier, vol. 30(1-2), pages 239-267.
    28. Anthony Bald & Eric Chyn & Justine Hastings & Margarita Machelett, 2022. "The Causal Impact of Removing Children from Abusive and Neglectful Homes," Journal of Political Economy, University of Chicago Press, vol. 130(7), pages 1919-1962.
    29. Edward Vytlacil, 2002. "Independence, Monotonicity, and Latent Index Models: An Equivalence Result," Econometrica, Econometric Society, vol. 70(1), pages 331-341, January.
    30. Moffitt, Robert, 1992. "Incentive Effects of the U.S. Welfare System: A Review," Journal of Economic Literature, American Economic Association, vol. 30(1), pages 1-61, March.
    31. Marc K. Chan, 2013. "A Dynamic Model of Welfare Reform," Econometrica, Econometric Society, vol. 81(3), pages 941-1001, May.
    32. Amanda Kowalski, 2016. "Doing more when you're running LATE: Applying marginal treatment effect methods to examine treatment effect heterogeneity in experiments," Artefactual Field Experiments 00560, The Field Experiments Website.
    33. James J. Heckman & Edward Vytlacil, 2005. "Structural Equations, Treatment Effects, and Econometric Policy Evaluation," Econometrica, Econometric Society, vol. 73(3), pages 669-738, May.
    34. Nicole Maestas & Kathleen J. Mullen & Alexander Strand, 2013. "Does Disability Insurance Receipt Discourage Work? Using Examiner Assignment to Estimate Causal Effects of SSDI Receipt," American Economic Review, American Economic Association, vol. 103(5), pages 1797-1829, August.
    35. Robert A. Moffitt, 2003. "Means-Tested Transfer Programs in the United States," NBER Books, National Bureau of Economic Research, Inc, number moff03-1.
    36. Magne Mogstad & Alexander Torgovitsky & Christopher R. Walters, 2021. "The Causal Interpretation of Two-Stage Least Squares with Multiple Instrumental Variables," American Economic Review, American Economic Association, vol. 111(11), pages 3663-3698, November.
    37. Chen, Xiaohong, 2007. "Large Sample Sieve Estimation of Semi-Nonparametric Models," Handbook of Econometrics, in: J.J. Heckman & E.E. Leamer (ed.), Handbook of Econometrics, edition 1, volume 6, chapter 76, Elsevier.
    38. Joshua D. Angrist & Kathryn Graddy & Guido W. Imbens, 2000. "The Interpretation of Instrumental Variables Estimators in Simultaneous Equations Models with an Application to the Demand for Fish," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 67(3), pages 499-527.
    39. Moffitt, Robert A. (ed.), 2016. "Economics of Means-Tested Transfer Programs in the United States, Volume I," National Bureau of Economic Research Books, University of Chicago Press, number 9780226370477, August.
    40. J.J. Heckman & E.E. Leamer (ed.), 2007. "Handbook of Econometrics," Handbook of Econometrics, Elsevier, edition 1, volume 6, number 6b.
    41. Christian N. Brinch & Magne Mogstad & Matthew Wiswall, 2017. "Beyond LATE with a Discrete Instrument," Journal of Political Economy, University of Chicago Press, vol. 125(4), pages 985-1039.
    42. Hsiao,Cheng & Morimune,Kimio & Powell,James L. (ed.), 2001. "Nonlinear Statistical Modeling," Cambridge Books, Cambridge University Press, number 9780521662468, September.
    43. J.J. Heckman & E.E. Leamer (ed.), 2007. "Handbook of Econometrics," Handbook of Econometrics, Elsevier, edition 1, volume 6, number 6a.
    44. Amanda E. Kowalski, 2016. "Doing More When You're Running LATE: Applying Marginal Treatment Effect Methods to Examine Treatment Effect Heterogeneity in Experiments for the Young and Privately Insured"," Cowles Foundation Discussion Papers 2045, Cowles Foundation for Research in Economics, Yale University.
    45. Bjorklund, Anders & Moffitt, Robert, 1987. "The Estimation of Wage Gains and Welfare Gains in Self-selection," The Review of Economics and Statistics, MIT Press, vol. 69(1), pages 42-49, February.
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    2. Silvia Moler‐Zapata & Richard Grieve & Anirban Basu & Stephen O’Neill, 2023. "How does a local instrumental variable method perform across settings with instruments of differing strengths? A simulation study and an evaluation of emergency surgery," Health Economics, John Wiley & Sons, Ltd., vol. 32(9), pages 2113-2126, September.

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    More about this item

    Keywords

    welfare; labor supply; marginal treatment effects;
    All these keywords.

    JEL classification:

    • I3 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty
    • J2 - Labor and Demographic Economics - - Demand and Supply of Labor
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models

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