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Bounding Program Benefits When Participation is Misreported

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  • Denni Tommasi

    ()

  • Lina Zhang

    ()

Abstract

In empirical research, measuring correctly the benefits of welfare interventions is incredibly relevant for policymakers as well as academic researchers. Unfortunately, the endogenous program participation is often misreported in survey data and standard instrumental variable techniques are not sufficient to point identify and consistently estimate the effects of interest. In this paper, we focus on the weighted average of local average treatment effects (LATE) and (i) derive a simple relationship between the causal and the identifiable parameter that can be recovered from the observed data, (ii) provide an instrumental variable method to partially identify the heterogeneous treatment effects, (iii) formalize a strategy to combine administrative data on the misclassification probabilities of treated individuals to further tighten the bounds. Finally, we use our method to reassess the benefits of participating to the 401(k) pension plan on savings.

Suggested Citation

  • Denni Tommasi & Lina Zhang, 2020. "Bounding Program Benefits When Participation is Misreported," Monash Econometrics and Business Statistics Working Papers 24/20, Monash University, Department of Econometrics and Business Statistics.
  • Handle: RePEc:msh:ebswps:2020-24
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    File URL: https://www.monash.edu/business/ebs/research/publications/ebs/wp24-2020.pdf
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    References listed on IDEAS

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    1. Aigner, Dennis J., 1973. "Regression with a binary independent variable subject to errors of observation," Journal of Econometrics, Elsevier, vol. 1(1), pages 49-59, March.
    2. Melvin Stephens & Takashi Unayama, 2019. "Estimating the Impacts of Program Benefits: Using Instrumental Variables with Underreported and Imputed Data," The Review of Economics and Statistics, MIT Press, vol. 101(3), pages 468-475, July.
    3. Alberto Abadie & Joshua Angrist & Guido Imbens, 2002. "Instrumental Variables Estimates of the Effect of Subsidized Training on the Quantiles of Trainee Earnings," Econometrica, Econometric Society, vol. 70(1), pages 91-117, January.
    4. Nguimkeu, Pierre & Denteh, Augustine & Tchernis, Rusty, 2019. "On the estimation of treatment effects with endogenous misreporting," Journal of Econometrics, Elsevier, vol. 208(2), pages 487-506.
    5. Frazis, Harley & Loewenstein, Mark A., 2003. "Estimating linear regressions with mismeasured, possibly endogenous, binary explanatory variables," Journal of Econometrics, Elsevier, vol. 117(1), pages 151-178, November.
    6. Takahide Yanagi, 2019. "Inference on local average treatment effects for misclassified treatment," Econometric Reviews, Taylor & Francis Journals, vol. 38(8), pages 938-960, September.
    7. Thomas J. Kane & Cecilia Elena Rouse & Douglas Staiger, 1999. "Estimating Returns to Schooling When Schooling is Misreported," NBER Working Papers 7235, National Bureau of Economic Research, Inc.
    8. Stephen Pudney & Monica Hernandez & Ruth Hancock, 2007. "The welfare cost of means-testing: pensioner participation in income support," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 22(3), pages 581-598.
    9. Bollinger, Christopher R., 1996. "Bounding mean regressions when a binary regressor is mismeasured," Journal of Econometrics, Elsevier, vol. 73(2), pages 387-399, August.
    10. Charles F. Manski & John V. Pepper, 2000. "Monotone Instrumental Variables, with an Application to the Returns to Schooling," Econometrica, Econometric Society, vol. 68(4), pages 997-1012, July.
    11. Rossella Calvi & Arthur Lewbel & Denni Tommasi, 2018. "LATE with Missing or Mismeasured Treatment," Boston College Working Papers in Economics 959, Boston College Department of Economics, revised 15 Mar 2021.
    12. Francis J. DiTraglia & Camilo García-Jimeno, 2017. "Mis-classified, Binary, Endogenous Regressors: Identification and Inference," NBER Working Papers 23814, National Bureau of Economic Research, Inc.
    13. Chalak, Karim, 2017. "Instrumental Variables Methods With Heterogeneity And Mismeasured Instruments," Econometric Theory, Cambridge University Press, vol. 33(1), pages 69-104, February.
    14. Sonja A. Swanson & Miguel A. Hernán & Matthew Miller & James M. Robins & Thomas S. Richardson, 2018. "Partial Identification of the Average Treatment Effect Using Instrumental Variables: Review of Methods for Binary Instruments, Treatments, and Outcomes," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 113(522), pages 933-947, April.
    15. Xuan Chen & Carlos A. Flores & Alfonso Flores-Lagunes, 2018. "Going beyond LATE: Bounding Average Treatment Effects of Job Corps Training," Journal of Human Resources, University of Wisconsin Press, vol. 53(4), pages 1050-1099.
    16. Tommasi, Denni, 2019. "Control of resources, bargaining power and the demand of food: Evidence from PROGRESA," Journal of Economic Behavior & Organization, Elsevier, vol. 161(C), pages 265-286.
    17. AIGNER, Dennis J., 1973. "Regression with a binary independent variable subject to errors of observation," LIDAM Reprints CORE 130, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    18. Thomas J. Kane & Cecilia Rouse & Douglas Staiger, 1999. "Estimating Returns to Schooling When Schooling is Misreported," Working Papers 798, Princeton University, Department of Economics, Industrial Relations Section..
    19. 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.
    20. Card, David, 2001. "Estimating the Return to Schooling: Progress on Some Persistent Econometric Problems," Econometrica, Econometric Society, vol. 69(5), pages 1127-1160, September.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    heterogenous treatment effects; causality; binary treatment; endogenous measurement error; discrete or multiple instruments; weighted average of LATEs; endogeneity; program evaluation;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C26 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Instrumental Variables (IV) Estimation
    • C35 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation

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