IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2506.03457.html
   My bibliography  Save this paper

Disentangling Barriers to Welfare Program Participation with Semiparametric and Mixed Effect Approaches

Author

Listed:
  • Lei Bill Wang
  • Sooa Ahn

Abstract

This paper examines why eligible households do not participate in welfare programs. Under the assumption that there exist some observed fully attentive groups, we model take-up as a two-stage process: attention followed by choice. We do so with two novel approaches. Drawing inspiration from the demand estimation for stochastically attentive consumers literature, Approach I is semiparametric with a nonparametric attention function and a parametric choice function. It uses fully attentive households to identify choice utility parameters and then uses the entire population to identify the attention probabilities. By augmenting Approach I with a random effect that simultaneously affects the attention and choice stages, Approach II allows household-level unobserved heterogeneity and dependence between attention and choice even after conditioning on observed covariates. Applied to NLSY panel data for WIC participation, both approaches consistently point to two empirical findings with regard to heterogeneous policy targeting. (1) As an infant ages towards 12 months and beyond, attention probability drops dramatically while choice probability steadily decreases. Finding (1) suggests that exit-prevention is the key for increasing the take-up rate because once a household exits the program when the infant ages close to 12 months old, it is unlikely to rejoin due to low attention. A value-increasing solution is predicted to be effective in promoting take-up by reducing exit probability. In contrast, an attention-raising solution is predicted to be ineffective. (2) Higher educated households are less attentive but more likely to enroll if attentive. Finding (2) suggests that running informational campaigns with parenting student groups at higher education institutions could be an effective strategy for boosting take-up.

Suggested Citation

  • Lei Bill Wang & Sooa Ahn, 2025. "Disentangling Barriers to Welfare Program Participation with Semiparametric and Mixed Effect Approaches," Papers 2506.03457, arXiv.org.
  • Handle: RePEc:arx:papers:2506.03457
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2506.03457
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Stefan Wager & Susan Athey, 2018. "Estimation and Inference of Heterogeneous Treatment Effects using Random Forests," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 113(523), pages 1228-1242, July.
    2. Victor Aguirregabiria & Jesus Carro, 2021. "Identification of Average Marginal Effects in Fixed Effects Dynamic Discrete Choice Models," Working Papers tecipa-701, University of Toronto, Department of Economics.
    3. Sun-Joo Cho & Sarah Brown-Schmidt & Woo-yeol Lee, 2018. "Autoregressive Generalized Linear Mixed Effect Models with Crossed Random Effects: An Application to Intensive Binary Time Series Eye-Tracking Data," Psychometrika, Springer;The Psychometric Society, vol. 83(3), pages 751-771, September.
    4. Levon Barseghyan & Francesca Molinari & Matthew Thirkettle, 2021. "Discrete Choice under Risk with Limited Consideration," American Economic Review, American Economic Association, vol. 111(6), pages 1972-2006, June.
    5. Christoph Wunder & Regina T. Riphahn, 2014. "The dynamics of welfare entry and exit amongst natives and immigrants," Oxford Economic Papers, Oxford University Press, vol. 66(2), pages 580-604.
    6. Jeffrey M. Wooldridge, 2005. "Simple solutions to the initial conditions problem in dynamic, nonlinear panel data models with unobserved heterogeneity," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 20(1), pages 39-54, January.
    7. Ali Hortaçsu & Seyed Ali Madanizadeh & Steven L. Puller, 2017. "Power to Choose? An Analysis of Consumer Inertia in the Residential Electricity Market," American Economic Journal: Economic Policy, American Economic Association, vol. 9(4), pages 192-226, November.
    8. Friedrichsen, Jana & König, Tobias & Schmacker, Renke, 2018. "Social image concerns and welfare take-up," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 168, pages 174-192.
    9. Janet Currie, 2004. "The Take Up of Social Benefits," NBER Working Papers 10488, National Bureau of Economic Research, Inc.
    10. Henrik Jacobsen Kleven & Wojciech Kopczuk, 2011. "Transfer Program Complexity and the Take-Up of Social Benefits," American Economic Journal: Economic Policy, American Economic Association, vol. 3(1), pages 54-90, February.
    11. Florian Heiss & Daniel McFadden & Joachim Winter & Amelie Wuppermann & Bo Zhou, 2021. "Inattention and Switching Costs as Sources of Inertia in Medicare Part D," American Economic Review, American Economic Association, vol. 111(9), pages 2737-2781, September.
    12. Gary Chamberlain, 2010. "Binary Response Models for Panel Data: Identification and Information," Econometrica, Econometric Society, vol. 78(1), pages 159-168, January.
    13. Nikhil Agarwal & Paulo J. Somaini, 2022. "Demand Analysis under Latent Choice Constraints," NBER Working Papers 29993, National Bureau of Economic Research, Inc.
    14. Crawford, Gregory S. & Griffith, Rachel & Iaria, Alessandro, 2021. "A survey of preference estimation with unobserved choice set heterogeneity," Journal of Econometrics, Elsevier, vol. 222(1), pages 4-43.
    15. Manudeep Bhuller & Christian N. Brinch & Sebastian Königs, 2017. "Time Aggregation and State Dependence in Welfare Receipt," Economic Journal, Royal Economic Society, vol. 127(604), pages 1833-1873, September.
    16. Xiaoxi Shen & Chang Jiang & Lyudmila Sakhanenko & Qing Lu, 2023. "Asymptotic properties of neural network sieve estimators," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 35(4), pages 839-868, October.
    17. Guilkey, David K. & Murphy, James L., 1993. "Estimation and testing in the random effects probit model," Journal of Econometrics, Elsevier, vol. 59(3), pages 301-317, October.
    18. Jason Abaluck & Abi Adams-Prassl, 2021. "What do Consumers Consider Before They Choose? Identification from Asymmetric Demand Responses," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 136(3), pages 1611-1663.
    19. Bo E. Honoré & Ekaterini Kyriazidou, 2000. "Panel Data Discrete Choice Models with Lagged Dependent Variables," Econometrica, Econometric Society, vol. 68(4), pages 839-874, July.
    20. Geòrgia Escaramís & Josep L. Carrasco & Carlos Ascaso, 2008. "Detection of Significant Disease Risks Using a Spatial Conditional Autoregressive Model," Biometrics, The International Biometric Society, vol. 64(4), pages 1043-1053, December.
    21. Manudeep Bhuller & Christian N. Brinch & Sebastian Königs, 2017. "Time Aggregation and State Dependence in Welfare Receipt," Economic Journal, Royal Economic Society, vol. 127(604), pages 1833-1873, September.
    22. Sarah Carpentier & Karel Neels & Karel Van den Bosch, 2017. "Do First- and Second-Generation Migrants Stay Longer in Social Assistance Than Natives in Belgium?," Journal of International Migration and Integration, Springer, vol. 18(4), pages 1167-1190, November.
    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. Kerstin Bruckmeier & Katrin Hohmeyer & Stefan Schwarz, 2018. "Welfare receipt misreporting in survey data and its consequences for state dependence estimates: new insights from linked administrative and survey data," Journal for Labour Market Research, Springer;Institute for Employment Research/ Institut für Arbeitsmarkt- und Berufsforschung (IAB), vol. 52(1), pages 1-21, December.
    2. Levon Barseghyan & Maura Coughlin & Francesca Molinari & Joshua C. Teitelbaum, 2021. "Heterogeneous Choice Sets and Preferences," Econometrica, Econometric Society, vol. 89(5), pages 2015-2048, September.
    3. Victor H. Aguiar & Maria Jose Boccardi & Nail Kashaev & Jeongbin Kim, 2023. "Random utility and limited consideration," Quantitative Economics, Econometric Society, vol. 14(1), pages 71-116, January.
    4. Kabir Dasgupta & Alexander Plum, 2023. "Human capital formation and changes in low pay persistence," Applied Economics, Taylor & Francis Journals, vol. 55(56), pages 6583-6604, December.
    5. Nikhil Agarwal & Paulo J. Somaini, 2022. "Demand Analysis under Latent Choice Constraints," NBER Working Papers 29993, National Bureau of Economic Research, Inc.
    6. Dahl, Gordon B. & Forbes, Silke J., 2023. "Doctor switching costs," Journal of Public Economics, Elsevier, vol. 221(C).
    7. Tue Gørgens & Dean Robert Hyslop, 2018. "The Specification of Dynamic Discrete-Time Two-State Panel Data Models," Econometrics, MDPI, vol. 7(1), pages 1-16, December.
    8. Christina Gravert, 2024. "From Intent to Inertia: Experimental Evidence from the Retail Electricity Market," CESifo Working Paper Series 11139, CESifo.
    9. Aguirregabiria, Victor & Gu, Jiaying & Luo, Yao, 2021. "Sufficient statistics for unobserved heterogeneity in structural dynamic logit models," Journal of Econometrics, Elsevier, vol. 223(2), pages 280-311.
    10. Manudeep Bhuller & Christian N. Brinch & Sebastian Königs, 2017. "Time Aggregation and State Dependence in Welfare Receipt," Economic Journal, Royal Economic Society, vol. 127(604), pages 1833-1873, September.
    11. repec:hal:spmain:info:hdl:2441/f6h8764enu2lskk9p2m9mgp8l is not listed on IDEAS
    12. Majid M. Al-Sadoon & Tong Li & M. Hashem Pesaran, 2017. "Exponential class of dynamic binary choice panel data models with fixed effects," Econometric Reviews, Taylor & Francis Journals, vol. 36(6-9), pages 898-927, October.
    13. Francesco Bartolucci & Claudia Pigini, 2017. "Granger causality in dynamic binary short panel data models," Working Papers 421, Universita' Politecnica delle Marche (I), Dipartimento di Scienze Economiche e Sociali.
    14. Geert Dhaene & Koen Jochmans, 2015. "Split-panel Jackknife Estimation of Fixed-effect Models," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 82(3), pages 991-1030.
    15. Michele Campolieti & Chris Riddell, 2019. "Interest Arbitration and the Narcotic Effect: Evidence from Three Decades of Collective Bargaining in Ontario," British Journal of Industrial Relations, London School of Economics, vol. 57(3), pages 421-452, September.
    16. Jõeveer, Karin & Kepp, Kaido, 2023. "What drives drivers? Switching, learning, and the impact of claims in car insurance," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 103(C).
    17. French, Declan, 2023. "Exploring household financial strain dynamics," International Review of Financial Analysis, Elsevier, vol. 86(C).
    18. Lucchetti, Riccardo & Pigini, Claudia, 2017. "DPB: Dynamic Panel Binary Data Models in gretl," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 79(i08).
    19. repec:spo:wpmain:info:hdl:2441/f6h8764enu2lskk9p2m9mgp8l is not listed on IDEAS
    20. Gualdani, Cristina & Sinha, Shruti, 2024. "Identification in discrete choice models with imperfect information," Journal of Econometrics, Elsevier, vol. 244(1).
    21. Aristodemou, Eleni, 2021. "Semiparametric identification in panel data discrete response models," Journal of Econometrics, Elsevier, vol. 220(2), pages 253-271.
    22. Sara Ayllón & Javier Valbuena & Alexander Plum, 2022. "Youth Unemployment and Stigmatization Over the Business Cycle in Europe," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 84(1), pages 103-129, February.

    More about this item

    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:arx:papers:2506.03457. 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: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    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.