IDEAS home Printed from https://ideas.repec.org/p/iza/izadps/dp18483.html

Offsetting the Earnings Disincentive in Public Housing: Evidence from a Behaviorally Informed Field Intervention

Author

Listed:
  • Dykstra, Holly

    (University of Konstanz)

  • Fernández Guerrico, Sofía

    (University of Konstanz)

Abstract

Income-based rents in public housing create an earnings disincentive. We collaborate with a public housing authority to design a behaviorally informed program that returns part of the rent induced by higher earnings to residents. Importantly, the program automatically enrolled households and was explicitly designed to make the increased payoff to working salient. Using a difference-in-differences approach, we estimate that annual household-head earnings rise 17% ($1,370/year) and public assistance falls 7.5%, with impacts on both intensive and extensive margins. These results provide evidence that an in-work benefit designed for salience can offset the earnings disincentive and affect follow-through labor market behavior.

Suggested Citation

  • Dykstra, Holly & Fernández Guerrico, Sofía, 2026. "Offsetting the Earnings Disincentive in Public Housing: Evidence from a Behaviorally Informed Field Intervention," IZA Discussion Papers 18483, IZA Network @ LISER.
    • Handle: RePEc:iza:izadps:dp18483
    as

    Download full text from publisher

    File URL: https://docs.iza.org/dp18483.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Bruce D. Meyer & Dan T. Rosenbaum, 2001. "Welfare, the Earned Income Tax Credit, and the Labor Supply of Single Mothers," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 116(3), pages 1063-1114.
    2. Raj Chetty & Adam Looney & Kory Kroft, 2009. "Salience and Taxation: Theory and Evidence," American Economic Review, American Economic Association, vol. 99(4), pages 1145-1177, September.
    3. James G. MacKinnon & Matthew D. Webb, 2019. "Wild Bootstrap Randomization Inference for Few Treated Clusters," Advances in Econometrics, in: The Econometrics of Complex Survey Data, volume 39, pages 61-85, Emerald Group Publishing Limited.
    4. Dmitry Taubinsky & Alex Rees-Jones, 2018. "Attention Variation and Welfare: Theory and Evidence from a Tax Salience Experiment," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 85(4), pages 2462-2496.
    5. Murray, Michael P., 1980. "A reinterpretation of the traditional income-leisure model, with application to in-kind subsidy programs," Journal of Public Economics, Elsevier, vol. 14(1), pages 69-81, August.
    6. Raj Chetty, 2012. "Bounds on Elasticities With Optimization Frictions: A Synthesis of Micro and Macro Evidence on Labor Supply," Econometrica, Econometric Society, vol. 80(3), pages 969-1018, May.
    7. Raj Chetty & John N. Friedman & Emmanuel Saez, 2013. "Using Differences in Knowledge across Neighborhoods to Uncover the Impacts of the EITC on Earnings," American Economic Review, American Economic Association, vol. 103(7), pages 2683-2721, December.
    8. David Card & Dean R. Hyslop, 2005. "Estimating the Effects of a Time-Limited Earnings Subsidy for Welfare-Leavers," Econometrica, Econometric Society, vol. 73(6), pages 1723-1770, November.
    9. Esther Duflo & Emmanuel Saez, 2003. "The Role of Information and Social Interactions in Retirement Plan Decisions: Evidence from a Randomized Experiment," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 118(3), pages 815-842.
    10. Ning Zhang, 2025. "In-Kind Housing Transfers and Labor Supply: A Structural Approach," Journal of Labor Economics, University of Chicago Press, vol. 43(2), pages 585-633.
    11. Nada Eissa & Jeffrey B. Liebman, 1996. "Labor Supply Response to the Earned Income Tax Credit," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 111(2), pages 605-637.
    12. Robert Collinson & Ingrid Gould Ellen & Jens Ludwig, 2019. "Reforming Housing Assistance," The ANNALS of the American Academy of Political and Social Science, , vol. 686(1), pages 250-285, November.
    13. Marianne Bertrand & Esther Duflo & Sendhil Mullainathan, 2004. "How Much Should We Trust Differences-In-Differences Estimates?," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 119(1), pages 249-275.
    14. MacKinnon, James G. & Webb, Matthew D., 2020. "Randomization inference for difference-in-differences with few treated clusters," Journal of Econometrics, Elsevier, vol. 218(2), pages 435-450.
    15. Bruno Ferman & Cristine Pinto, 2019. "Inference in Differences-in-Differences with Few Treated Groups and Heteroskedasticity," The Review of Economics and Statistics, MIT Press, vol. 101(3), pages 452-467, July.
    16. Dean Karlan & Margaret McConnell & Sendhil Mullainathan & Jonathan Zinman, 2016. "Getting to the Top of Mind: How Reminders Increase Saving," Management Science, INFORMS, vol. 62(12), pages 3393-3411, December.
    17. Daniel Waldinger, 2021. "Targeting In-Kind Transfers through Market Design: A Revealed Preference Analysis of Public Housing Allocation," American Economic Review, American Economic Association, vol. 111(8), pages 2660-2696, August.
    18. Amy Finkelstein & Matthew J Notowidigdo, 2019. "Take-Up and Targeting: Experimental Evidence from SNAP," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 134(3), pages 1505-1556.
    19. Elizabeth Linos & Allen Prohofsky & Aparna Ramesh & Jesse Rothstein & Matthew Unrath, 2022. "Can Nudges Increase Take-Up of the EITC? Evidence from Multiple Field Experiments," American Economic Journal: Economic Policy, American Economic Association, vol. 14(4), pages 432-452, November.
    20. James G. MacKinnon & Matthew D. Webb, 2018. "The wild bootstrap for few (treated) clusters," Econometrics Journal, Royal Economic Society, vol. 21(2), pages 114-135, June.
    21. A. Colin Cameron & Jonah B. Gelbach & Douglas L. Miller, 2008. "Bootstrap-Based Improvements for Inference with Clustered Errors," The Review of Economics and Statistics, MIT Press, vol. 90(3), pages 414-427, August.
    22. Kahneman, Daniel & Knetsch, Jack L & Thaler, Richard H, 1990. "Experimental Tests of the Endowment Effect and the Coase Theorem," Journal of Political Economy, University of Chicago Press, vol. 98(6), pages 1325-1348, December.
    23. MacKinnon, James G. & Nielsen, Morten Ørregaard & Webb, Matthew D., 2023. "Cluster-robust inference: A guide to empirical practice," Journal of Econometrics, Elsevier, vol. 232(2), pages 272-299.
    24. Leonesio, Michael V, 1988. "In-Kind Transfers and Work Incentives," Journal of Labor Economics, University of Chicago Press, vol. 6(4), pages 515-529, October.
    25. Kleven, Henrik, 2024. "The EITC and the extensive margin: A reappraisal," Journal of Public Economics, Elsevier, vol. 236(C).
    26. Carina Neisser, 2021. "The Elasticity of Taxable Income: A Meta-Regression Analysis [The top 1% in international and historical perspective]," The Economic Journal, Royal Economic Society, vol. 131(640), pages 3365-3391.
    27. Eissa, Nada & Hoynes, Hilary Williamson, 2004. "Taxes and the labor market participation of married couples: the earned income tax credit," Journal of Public Economics, Elsevier, vol. 88(9-10), pages 1931-1958, August.
    28. Brian A. Jacob & Jens Ludwig, 2012. "The Effects of Housing Assistance on Labor Supply: Evidence from a Voucher Lottery," American Economic Review, American Economic Association, vol. 102(1), pages 272-304, February.
    29. Gruber, Jon & Saez, Emmanuel, 2002. "The elasticity of taxable income: evidence and implications," Journal of Public Economics, Elsevier, vol. 84(1), pages 1-32, April.
    30. Jachimowicz, Jon M. & Duncan, Shannon & Weber, Elke U. & Johnson, Eric J., 2019. "When and why defaults influence decisions: a meta-analysis of default effects," Behavioural Public Policy, Cambridge University Press, vol. 3(2), pages 159-186, November.
    31. Luis Alvarez & Bruno Ferman & Kaspar Wuthrich, 2025. "Inference with few treated units," Papers 2504.19841, arXiv.org, revised May 2026.
    32. Emmanuel Saez & Joel Slemrod & Seth H. Giertz, 2012. "The Elasticity of Taxable Income with Respect to Marginal Tax Rates: A Critical Review," Journal of Economic Literature, American Economic Association, vol. 50(1), pages 3-50, March.
    33. Damon Jones, 2010. "Information, Preferences, and Public Benefit Participation: Experimental Evidence from the Advance EITC and 401(k) Savings," American Economic Journal: Applied Economics, American Economic Association, vol. 2(2), pages 147-163, April.
    34. Raj Chetty & John N. Friedman & Søren Leth-Petersen & Torben Heien Nielsen & Tore Olsen, 2014. "Active vs. Passive Decisions and Crowd-Out in Retirement Savings Accounts: Evidence from Denmark," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 129(3), pages 1141-1219.
    35. Eric P. Bettinger & Bridget Terry Long & Philip Oreopoulos & Lisa Sanbonmatsu, 2012. "The Role of Application Assistance and Information in College Decisions: Results from the H&R Block Fafsa Experiment," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 127(3), pages 1205-1242.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Kleven, Henrik, 2024. "The EITC and the extensive margin: A reappraisal," Journal of Public Economics, Elsevier, vol. 236(C).
    2. Nyman, Pär & Aggeborn, Linuz & Ahlskog, Rafael, 2023. "Filling in the blanks: How does information about the Swedish EITC affect labour supply?," Labour Economics, Elsevier, vol. 85(C).
    3. Michaël Sicsic, 2023. "L’élasticité de l’offre de travail et des revenus dans la littérature. Une analyse comparative des méthodes et des résultats sur données microéconomiques," Revue de l'OFCE, Presses de Sciences-Po, vol. 0(4), pages 5-40.
    4. Louis Kaplow, 2024. "Optimal Income Taxation," Journal of Economic Literature, American Economic Association, vol. 62(2), pages 637-738, June.
    5. Robert McClelland & Shannon Mok, 2012. "A Review of Recent Research on Labor Supply Elasticities: Working Paper 2012-12," Working Papers 43675, Congressional Budget Office.
    6. Lundberg, Jacob & Norell, John, 2018. "Taxes, benefits and labour force participation: A survey of the quasi-experimental literature," Ratio Working Papers 313, The Ratio Institute.
    7. Roth, Jonathan & Sant’Anna, Pedro H.C. & Bilinski, Alyssa & Poe, John, 2023. "What’s trending in difference-in-differences? A synthesis of the recent econometrics literature," Journal of Econometrics, Elsevier, vol. 235(2), pages 2218-2244.
    8. Kumar, Anil & Liang, Che-Yuan, 2020. "Estimating taxable income responses with elasticity heterogeneity," Journal of Public Economics, Elsevier, vol. 188(C).
    9. Todd Morris & Benoit Dostie, 2023. "Graying and staying on the job: The welfare implications of employment protection for older workers," Cahiers de recherche / Working Papers 15, Institut sur la retraite et l'épargne / Retirement and Savings Institute.
    10. MacKinnon, James G. & Nielsen, Morten Ørregaard & Webb, Matthew D., 2023. "Cluster-robust inference: A guide to empirical practice," Journal of Econometrics, Elsevier, vol. 232(2), pages 272-299.
    11. Raj Chetty, 2012. "Bounds on Elasticities With Optimization Frictions: A Synthesis of Micro and Macro Evidence on Labor Supply," Econometrica, Econometric Society, vol. 80(3), pages 969-1018, May.
    12. Raj Chetty & Adam Guren & Day Manoli & Andrea Weber, 2013. "Does Indivisible Labor Explain the Difference between Micro and Macro Elasticities? A Meta-Analysis of Extensive Margin Elasticities," NBER Macroeconomics Annual, University of Chicago Press, vol. 27(1), pages 1-56.
    13. Slavko Bezeredi & Marko Ledić & Ivica Rubil & Ivica Urban, 2019. "Making work pay in Croatia: An ex-ante evaluation of two in-work benefits using miCROmod," International Journal of Microsimulation, International Microsimulation Association, vol. 12(3), pages 28-61.
    14. Jakobsen, Katrine Marie & Søgaard, Jakob Egholt, 2022. "Identifying behavioral responses to tax reforms: New insights and a new approach," Journal of Public Economics, Elsevier, vol. 212(C).
    15. James G. MacKinnon & Matthew D. Webb, 2020. "When and How to Deal with Clustered Errors in Regression Models," Working Paper 1421, Economics Department, Queen's University.
    16. Kosonen, Tuomas & Matikka, Tuomas, 2020. "Discrete Labor Supply: Empirical Evidence and Implications," Working Papers 132, VATT Institute for Economic Research.
    17. Louis Kaplow, 2022. "Optimal Income Taxation," NBER Working Papers 30199, National Bureau of Economic Research, Inc.
    18. Alexander M. Gelber & Damon Jones & Daniel W. Sacks, 2013. "Earnings Adjustment Frictions: Evidence From Social Security Earnings Test," Working Papers 13-50, Center for Economic Studies, U.S. Census Bureau.
    19. Alexander M. Gelber & Damon Jones & Daniel W. Sacks, 2013. "Estimating Earnings Adjustment Frictions: Method and Evidence from the Social Security Earnings Test," NBER Working Papers 19491, National Bureau of Economic Research, Inc.
    20. Matthew D. Webb, 2023. "Reworking wild bootstrap‐based inference for clustered errors," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 56(3), pages 839-858, August.

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    JEL classification:

    • D91 - Microeconomics - - Micro-Based Behavioral Economics - - - Role and Effects of Psychological, Emotional, Social, and Cognitive Factors on Decision Making
    • I38 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - Government Programs; Provision and Effects of Welfare Programs
    • J22 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Time Allocation and Labor Supply
    • R38 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Real Estate Markets, Spatial Production Analysis, and Firm Location - - - Government Policy

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:iza:izadps:dp18483. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Mark Fallak (email available below). General contact details of provider: https://edirc.repec.org/data/izaaalu.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.