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Evaluating the Effect of an Antidiscrimination Law Using a Regression-Discontinuity Design

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  • Jinyong Hahn
  • Petra Todd
  • Wilbert Van der Klaauw

Abstract

The regression discontinuity (RD) data design is a quasi-experimental design with the defining characteristic that the probability of receiving treatment changes discontinuously as a function of one or more individual characteristics. This data design occasionally arises in economic and other applications but is only infrequently exploited in evaluating the effects of a treatment. We consider the problem of identification and estimation of treatment effects under a RD data design. We offer an interpretation of the IV or so-called Wald estimator as a regression discontinuity estimator. We propose nonparametric estimators of treatment effects and present their asymptotic distribution theory. Then we apply the estimation method to evaluate the effect of EEOC-coverage on minority employment in small U.S. firms.

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Bibliographic Info

Paper provided by National Bureau of Economic Research, Inc in its series NBER Working Papers with number 7131.

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Date of creation: May 1999
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Handle: RePEc:nbr:nberwo:7131

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  1. James J. Heckman & Brook S. Payner, 1989. "Determining the Impact of Federal Antidiscrimination Policy on the Economic Status of Blacks: A Study of South Carolina," NBER Working Papers 2854, National Bureau of Economic Research, Inc.
  2. Hardle, Wolfgang & Linton, Oliver, 1986. "Applied nonparametric methods," Handbook of Econometrics, in: R. F. Engle & D. McFadden (ed.), Handbook of Econometrics, edition 1, volume 4, chapter 38, pages 2295-2339 Elsevier.
  3. William J. Carrington & Kristin McCue & Brooks Pierce, 2000. "Using Establishment Size to Measure the Impact of Title VII and Affirmative Action," Journal of Human Resources, University of Wisconsin Press, vol. 35(3), pages 503-523.
  4. John J. Donohue III & James Heckman, 1991. "Continuous Versus Episodic Change: The Impact of Civil Rights Policy on the Economic Status of Blacks," NBER Working Papers 3894, National Bureau of Economic Research, Inc.
  5. James Heckman & Hidehiko Ichimura & Jeffrey Smith & Petra Todd, 1998. "Characterizing Selection Bias Using Experimental Data," NBER Working Papers 6699, National Bureau of Economic Research, Inc.
  6. H. J. Holzer, . "Employer hiring decisions and antidiscrimination policy," Institute for Research on Poverty Discussion Papers 1085-96, University of Wisconsin Institute for Research on Poverty.
  7. Joshua D. Angrist & Victor Lavy, 1999. "Using Maimonides' Rule To Estimate The Effect Of Class Size On Scholastic Achievement," The Quarterly Journal of Economics, MIT Press, vol. 114(2), pages 533-575, May.
  8. Charles Brown & James L. Medoff, 1989. "The Employer Size-Wage Effect," NBER Working Papers 2870, National Bureau of Economic Research, Inc.
  9. Heckman, James J, 1990. "Varieties of Selection Bias," American Economic Review, American Economic Association, vol. 80(2), pages 313-18, May.
  10. J.D. Angrist & Guido W. Imbens & D.B. Rubin, 1993. "Identification of Causal Effects Using Instrumental Variables," NBER Technical Working Papers 0136, National Bureau of Economic Research, Inc.
  11. Smith, James P & Welch, Finis R, 1989. "Black Economic Progress after Myrdal," Journal of Economic Literature, American Economic Association, vol. 27(2), pages 519-64, June.
  12. James J. Heckman & Jeffrey A. Smith, 1998. "Evaluating the Welfare State," NBER Working Papers 6542, National Bureau of Economic Research, Inc.
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