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Identifying the average treatment effect in ordered treatment models without unconfoundedness

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  • Lewbel, Arthur
  • Yang, Thomas Tao

Abstract

We show identification of the Average Treatment Effect (ATE) when treatment is specified by ordered choice in cross section or panel models. Treatment is determined by location of a latent variable (containing a continuous instrument) relative to two or more thresholds. We place no functional form restrictions on latent errors and potential outcomes. Unconfoundedness of treatment does not hold and identification at infinity for the treated is not possible. Yet we still show nonparametric point identification and estimation of the ATE. We apply our model to reinvestigate the inverted-U relationship between competition and innovation, and find no inverted-U in US data.

Suggested Citation

  • Lewbel, Arthur & Yang, Thomas Tao, 2016. "Identifying the average treatment effect in ordered treatment models without unconfoundedness," Journal of Econometrics, Elsevier, vol. 195(1), pages 1-22.
  • Handle: RePEc:eee:econom:v:195:y:2016:i:1:p:1-22
    DOI: 10.1016/j.jeconom.2016.05.015
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    1. 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.
    2. Philippe Aghion & Nick Bloom & Richard Blundell & Rachel Griffith & Peter Howitt, 2005. "Competition and Innovation: an Inverted-U Relationship," The Quarterly Journal of Economics, Oxford University Press, vol. 120(2), pages 701-728.
    3. Meyer, Bruce D & Viscusi, W Kip & Durbin, David L, 1995. "Workers' Compensation and Injury Duration: Evidence from a Natural Experiment," American Economic Review, American Economic Association, vol. 85(3), pages 322-340, June.
    4. Yingying Dong & Arthur Lewbel & Thomas Tao Yang, 2012. "Comparing Features of Convenient Estimators for Binary Choice Models With Endogenous Regressors," Boston College Working Papers in Economics 789, Boston College Department of Economics, revised 15 May 2012.
    5. Philip J. Cook & George Tauchen, 1982. "The Effect of Liquor Taxes on Heavy Drinking," Bell Journal of Economics, The RAND Corporation, vol. 13(2), pages 379-390, Autumn.
    6. Marianne P. Bitler & Jonah B. Gelbach & Hilary W. Hoynes, 2006. "What Mean Impacts Miss: Distributional Effects of Welfare Reform Experiments," American Economic Review, American Economic Association, vol. 96(4), pages 988-1012, September.
    7. 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.
    8. Victor Chernozhukov & Ivan Fernandez-Val & Jinyong Hahn & Whitney K. Newey, 2008. "Identification and estimation of marginal effects in nonlinear panel models," CeMMAP working papers CWP25/08, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    9. Card, David & Krueger, Alan B, 1994. "Minimum Wages and Employment: A Case Study of the Fast-Food Industry in New Jersey and Pennsylvania," American Economic Review, American Economic Association, vol. 84(4), pages 772-793, September.
    10. David Card, 1990. "The Impact of the Mariel Boatlift on the Miami Labor Market," ILR Review, Cornell University, ILR School, vol. 43(2), pages 245-257, January.
    11. Robinson, Peter M, 1988. "Root- N-Consistent Semiparametric Regression," Econometrica, Econometric Society, vol. 56(4), pages 931-954, July.
    12. Arthur Lewbel & Yingying Dong & Thomas Tao Yang, 2012. "Viewpoint: Comparing features of convenient estimators for binary choice models with endogenous regressors," Canadian Journal of Economics, Canadian Economics Association, vol. 45(3), pages 809-829, August.
    13. James J. Heckman & Edward Vytlacil, 2005. "Structural Equations, Treatment Effects, and Econometric Policy Evaluation," Econometrica, Econometric Society, vol. 73(3), pages 669-738, May.
    14. Arthur Lewbel, 1998. "Semiparametric Latent Variable Model Estimation with Endogenous or Mismeasured Regressors," Econometrica, Econometric Society, vol. 66(1), pages 105-122, January.
    15. Ashenfelter, Orley C, 1978. "Estimating the Effect of Training Programs on Earnings," The Review of Economics and Statistics, MIT Press, vol. 60(1), pages 47-57, February.
    16. Aamir Rafique Hashmi, 2013. "Competition and Innovation: The Inverted-U Relationship Revisited," The Review of Economics and Statistics, MIT Press, vol. 95(5), pages 1653-1668, December.
    17. Heckman, James J. & Navarro, Salvador, 2007. "Dynamic discrete choice and dynamic treatment effects," Journal of Econometrics, Elsevier, vol. 136(2), pages 341-396, February.
    18. Bo E. Honore & Arthur Lewbel, 2002. "Semiparametric Binary Choice Panel Data Models Without Strictly Exogeneous Regressors," Econometrica, Econometric Society, vol. 70(5), pages 2053-2063, September.
    19. Marianne Bertrand, 2004. "From the Invisible Handshake to the Invisible Hand? How Import Competition Changes the Employment Relationship," Journal of Labor Economics, University of Chicago Press, vol. 22(4), pages 723-766, October.
    20. Heckman, James, 2013. "Sample selection bias as a specification error," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 31(3), pages 129-137.
    21. Victor Chernozhukov & Christian Hansen, 2005. "An IV Model of Quantile Treatment Effects," Econometrica, Econometric Society, vol. 73(1), pages 245-261, January.
    22. Guido W. Imbens & Jeffrey M. Wooldridge, 2009. "Recent Developments in the Econometrics of Program Evaluation," Journal of Economic Literature, American Economic Association, vol. 47(1), pages 5-86, March.
    23. Heckman, James J, 1978. "Dummy Endogenous Variables in a Simultaneous Equation System," Econometrica, Econometric Society, vol. 46(4), pages 931-959, July.
    24. Shakeeb Khan & Elie Tamer, 2010. "Irregular Identification, Support Conditions, and Inverse Weight Estimation," Econometrica, Econometric Society, vol. 78(6), pages 2021-2042, November.
    25. Yingying Dong & Arthur Lewbel, 2015. "A Simple Estimator for Binary Choice Models with Endogenous Regressors," Econometric Reviews, Taylor & Francis Journals, vol. 34(1-2), pages 82-105, February.
    26. 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.
    27. Ashenfelter, Orley & Card, David, 1985. "Using the Longitudinal Structure of Earnings to Estimate the Effect of Training Programs," The Review of Economics and Statistics, MIT Press, vol. 67(4), pages 648-660, November.
    28. Stephen Cecchetti & Enisse Kharroubi, 2012. "Reassessing the impact of finance on growth," BIS Working Papers 381, Bank for International Settlements.
    29. Brent R. Hickman & Timothy P. Hubbard, 2015. "Replacing Sample Trimming with Boundary Correction in Nonparametric Estimation of Firstā€Price Auctions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 30(5), pages 739-762, August.
    30. Lewbel, Arthur, 2007. "Endogenous selection or treatment model estimation," Journal of Econometrics, Elsevier, vol. 141(2), pages 777-806, December.
    31. 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.
    32. Card, David & Krueger, Alan B, 1993. "Trends in Relative Black-White Earnings Revisited," American Economic Review, American Economic Association, vol. 83(2), pages 85-91, May.
    33. James J. Heckman & Vytlacil, Edward J., 2007. "Econometric Evaluation of Social Programs, Part I: Causal Models, Structural Models and Econometric Policy Evaluation," Handbook of Econometrics,in: J.J. Heckman & E.E. Leamer (ed.), Handbook of Econometrics, edition 1, volume 6, chapter 70 Elsevier.
    34. A. D. Roy, 1951. "Some Thoughts On The Distribution Of Earnings," Oxford Economic Papers, Oxford University Press, vol. 3(2), pages 135-146.
    35. Philip J. Cook & George Tauchen, 1984. "The Effect of Minimum Drinking Age Legislation on Youthful Auto Fatalities, 1970-1977," The Journal of Legal Studies, University of Chicago Press, vol. 13(1), pages 169-190, January.
    36. repec:fth:prinin:310 is not listed on IDEAS
    37. 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.
    38. Ana L. Revenga, 1992. "Exporting Jobs?The Impact of Import Competition on Employment and Wages in U. S. Manufacturing," The Quarterly Journal of Economics, Oxford University Press, vol. 107(1), pages 255-284.
    39. Toru Kitagawa, 2009. "Identification region of the potential outcome distributions under instrument independence," CeMMAP working papers CWP30/09, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
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    More about this item

    Keywords

    Average treatment effect; Ordered choice model; Special regressor; Semiparametric; Competition and innovation; Identification;

    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

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