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Instrumental Variables Methods in Experimental Criminological Research: What, Why, and How?

  • Joshua Angrist

Quantitative criminology focuses on straightforward causal questions that are ideally addressed with randomized experiments. In practice, however, traditional randomized trials are difficult to implement in the untidy world of criminal justice. Even when randomized trials are implemented, not everyone is treated as intended and some control subjects may obtain experimental services. Treatments may also be more complicated than a simple yes/no coding can capture. This paper argues that the instrumental variables methods (IV) used by economists to solve omitted variables bias problems in observational studies also solve the major statistical problems that arise in imperfect criminological experiments. In general, IV methods estimate the causal effect of treatment on subjects that are induced to comply with a treatment by virtue of the random assignment of intended treatment. The use of IV in criminology is illustrated through a re-analysis of the Minneapolis Domestic Violence Experiment.

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Paper provided by National Bureau of Economic Research, Inc in its series NBER Technical Working Papers with number 0314.

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Date of creation: Sep 2005
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Handle: RePEc:nbr:nberte:0314
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  1. Abadie, Alberto, 2003. "Semiparametric instrumental variable estimation of treatment response models," Journal of Econometrics, Elsevier, vol. 113(2), pages 231-263, April.
  2. Woodbury, Stephen A & Spiegelman, Robert G, 1987. "Bonuses to Workers and Employers to Reduce Unemployment: Randomized Trials in Illinois," American Economic Review, American Economic Association, vol. 77(4), pages 513-30, September.
  3. Joshua D. Angrist & Guido W. Imbens, 1995. "Average Causal Response with Variable Treatment Intensity," NBER Technical Working Papers 0127, National Bureau of Economic Research, Inc.
  4. Levitt, Steven D, 1997. "Using Electoral Cycles in Police Hiring to Estimate the Effect of Police on Crime," American Economic Review, American Economic Association, vol. 87(3), pages 270-90, June.
  5. Joshua D. Angrist & Alan B. Krueger, 2001. "Instrumental Variables and the Search for Identification: From Supply and Demand to Natural Experiments," Journal of Economic Perspectives, American Economic Association, vol. 15(4), pages 69-85, Fall.
  6. 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.
  7. Alan B. Krueger, 1997. "Experimental Estimates of Education Production Functions," NBER Working Papers 6051, National Bureau of Economic Research, Inc.
  8. Justin McCrary, 2002. "Using Electoral Cycles in Police Hiring to Estimate the Effect of Police on Crime: Comment," American Economic Review, American Economic Association, vol. 92(4), pages 1236-1243, September.
  9. 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.
  10. Joshua Angrist, 1989. "Lifetime Earnings and the Vietnam Era Draft Lottery: Evidence from Social Security Administrative Records," Working Papers 631, Princeton University, Department of Economics, Industrial Relations Section..
  11. Joshua D. Angrist & Victor Lavy, 2002. "The Effect of High School Matriculation Awards: Evidence from Randomized Trials," NBER Working Papers 9389, National Bureau of Economic Research, Inc.
  12. Joshua D. Angrist & Victor Lavy, 1997. "Using Maimonides' Rule to Estimate the Effect of Class Size on Student Achievement," NBER Working Papers 5888, National Bureau of Economic Research, Inc.
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