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

  • Jinyong Hahn
  • Petra Todd
  • Wilbert Van der Klaauw

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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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
Note: LS
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  1. Wolfgang Hardle & Oliver Linton, 1994. "Applied Nonparametric Methods," Cowles Foundation Discussion Papers 1069, Cowles Foundation for Research in Economics, Yale University.
  2. Donohue, John J, III & Heckman, James, 1991. "Continuous versus Episodic Change: The Impact of Civil Rights Policy on the Economic Status of Blacks," Journal of Economic Literature, American Economic Association, vol. 29(4), pages 1603-43, December.
  3. 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.
  4. 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.
  5. 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.
  6. 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.
  7. Joshua D. Angrist & Guido W. Imbens, 1995. "Identification and Estimation of Local Average Treatment Effects," NBER Technical Working Papers 0118, National Bureau of Economic Research, Inc.
  8. James Heckman & Hidehiko Ichimura & Jeffrey Smith & Petra Todd, 1998. "Characterizing Selection Bias Using Experimental Data," Econometrica, Econometric Society, vol. 66(5), pages 1017-1098, September.
  9. 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.
  10. 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.
  11. Brown, Charles & Medoff, James, 1989. "The Employer Size-Wage Effect," Journal of Political Economy, University of Chicago Press, vol. 97(5), pages 1027-59, October.
  12. James J. Heckman & Jeffrey A. Smith, 1998. "Evaluating the Welfare State," NBER Working Papers 6542, National Bureau of Economic Research, Inc.
  13. Heckman, James J, 1990. "Varieties of Selection Bias," American Economic Review, American Economic Association, vol. 80(2), pages 313-18, May.
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