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Reverse matching for ex-ante policy evaluation

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  • George Planiteros

Abstract

The paper attacks the central policy evaluation question of forecasting the impact of interventions never previously experienced. It introduces treatment effects approach into a cognitive domain not currently spanned by its methodological arsenal. Existing causal effects bounding analysis is adjusted to the ex-ante program evaluation setting. A Monte Carlo experiment is conducted to test how severe the estimates of the proposed approach deviate from the "real" causal effect in the presence of selection and unobserved heterogeneity. The simulation shows that the approach is valid regarding the formulation of the counterfactual states given previous knowledge of the program rules and a sufficiently informative treatment probability. It also demonstrates that the width of the bounds are resilient to several deviations from the conditional independence assumption.

Suggested Citation

  • George Planiteros, 2022. "Reverse matching for ex-ante policy evaluation," DEOS Working Papers 2206, Athens University of Economics and Business.
    • Handle: RePEc:aue:wpaper:2206
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    References listed on IDEAS

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    1. Matzkin, Rosa L, 1992. "Nonparametric and Distribution-Free Estimation of the Binary Threshold Crossing and the Binary Choice Models," Econometrica, Econometric Society, vol. 60(2), pages 239-270, March.
    2. Petra E. Todd & Kenneth I. Wolpin, 2008. "Ex Ante Evaluation of Social Programs," Annals of Economics and Statistics, GENES, issue 91-92, pages 263-291.
    3. David A. Wise, 1992. "Topics in the Economics of Aging," NBER Books, National Bureau of Economic Research, Inc, number wise92-1, March.
    4. Charles F. Manski, 1989. "Anatomy of the Selection Problem," Journal of Human Resources, University of Wisconsin Press, vol. 24(3), pages 343-360.
    5. Joshua D. Angrist & Jörn-Steffen Pischke, 2009. "Mostly Harmless Econometrics: An Empiricist's Companion," Economics Books, Princeton University Press, edition 1, number 8769.
    6. Pedro Carneiro & Karsten T. Hansen & James J. Heckman, 2002. "Removing the Veil of Ignorance in Assessing the Distributional Impacts of Social Policies," NBER Working Papers 8840, National Bureau of Economic Research, Inc.
    7. Kenneth I. Wolpin & Petra E. Todd, 2006. "Assessing the Impact of a School Subsidy Program in Mexico: Using a Social Experiment to Validate a Dynamic Behavioral Model of Child Schooling and Fertility," American Economic Review, American Economic Association, vol. 96(5), pages 1384-1417, December.
    8. Jeffrey Smith & Jeremy Lise & Shannon N. Seitz, 2003. "Equilibrium Policy Experiments And The Evaluation Of Social Programs," Working Paper 1012, Economics Department, Queen's University.
    9. Matzkin, Rosa L., 1993. "Nonparametric identification and estimation of polychotomous choice models," Journal of Econometrics, Elsevier, vol. 58(1-2), pages 137-168, July.
    10. Guido W. Imbens & Whitney K. Newey, 2009. "Identification and Estimation of Triangular Simultaneous Equations Models Without Additivity," Econometrica, Econometric Society, vol. 77(5), pages 1481-1512, September.
    11. Rosa L. Matzkin, 2003. "Nonparametric Estimation of Nonadditive Random Functions," Econometrica, Econometric Society, vol. 71(5), pages 1339-1375, September.
    12. Angrist, Joshua D. & Krueger, Alan B., 1999. "Empirical strategies in labor economics," Handbook of Labor Economics, in: O. Ashenfelter & D. Card (ed.), Handbook of Labor Economics, edition 1, volume 3, chapter 23, pages 1277-1366, Elsevier.
    13. Susan Athey & Guido W. Imbens, 2017. "The State of Applied Econometrics: Causality and Policy Evaluation," Journal of Economic Perspectives, American Economic Association, vol. 31(2), pages 3-32, Spring.
    14. Robin L. Lumsdaine & James H. Stock & David A. Wise, 1992. "Three Models of Retirement: Computational Complexity versus Predictive Validity," NBER Chapters, in: Topics in the Economics of Aging, pages 21-60, National Bureau of Economic Research, Inc.
    15. Ben B. Hansen, 2008. "The prognostic analogue of the propensity score," Biometrika, Biometrika Trust, vol. 95(2), pages 481-488.
    16. Rosa L. Matzkin, 2008. "Identification in Nonparametric Simultaneous Equations Models," Econometrica, Econometric Society, vol. 76(5), pages 945-978, September.
    17. Orazio P. Attanasio & Costas Meghir & Ana Santiago, 2012. "Education Choices in Mexico: Using a Structural Model and a Randomized Experiment to Evaluate PROGRESA," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 79(1), pages 37-66.
    18. James J. Heckman, 2000. "Causal Parameters and Policy Analysis in Economics: A Twentieth Century Retrospective," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 115(1), pages 45-97.
    19. Robert A. Moffitt, 1979. "The Labor Supply Response in the Gary Experiment," Journal of Human Resources, University of Wisconsin Press, vol. 14(4), pages 477-487.
    20. Guido W. Imbens, 2004. "Nonparametric Estimation of Average Treatment Effects Under Exogeneity: A Review," The Review of Economics and Statistics, MIT Press, vol. 86(1), pages 4-29, February.
    21. Giovanni Cerulli, 2014. "ivtreatreg: A command for fitting binary treatment models with heterogeneous response to treatment and unobservable selection," Stata Journal, StataCorp LP, vol. 14(3), pages 453-480, September.
    22. Hidehiko Ichimura & Christopher Taber, 2002. "Semiparametric Reduced-Form Estimation of Tuition Subsidies," American Economic Review, American Economic Association, vol. 92(2), pages 286-292, May.
    23. repec:adr:anecst:y:2008:i:91-92:p:13 is not listed on IDEAS
    24. Lewbel, Arthur, 2000. "Semiparametric qualitative response model estimation with unknown heteroscedasticity or instrumental variables," Journal of Econometrics, Elsevier, vol. 97(1), pages 145-177, July.
    25. Daniel McFadden, 1977. "Quantitative Methods for Analyzing Travel Behaviour of Individuals: Some Recent Developments," Cowles Foundation Discussion Papers 474, Cowles Foundation for Research in Economics, Yale University.
    26. O. Ashenfelter & D. Card (ed.), 1999. "Handbook of Labor Economics," Handbook of Labor Economics, Elsevier, edition 1, volume 3, number 3.
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    More about this item

    Keywords

    Policy evaluation; forecasting; treatment e ects; hypothetical treatment group; bounding and sensitivity analysis;
    All these keywords.

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