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

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  • Joshua Angrist

Abstract

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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Bibliographic Info

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. Angrist, J.D. & Imbens, G.W., 1992. "Average Causal Response with Variable Treatment Intensity," Papers, Tilburg - Center for Economic Research 9234, Tilburg - Center for Economic Research.
  2. 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.
  3. Steven D. Levitt, 1995. "Using Electoral Cycles in Police Hiring to Estimate the Effect of Policeon Crime," NBER Working Papers 4991, National Bureau of Economic Research, Inc.
  4. 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, American Economic Association, vol. 77(4), pages 513-30, September.
  5. Joshua Angrist & Alan Krueger, 1998. "Empirical Strategies in Labor Economics," Working papers, Massachusetts Institute of Technology (MIT), Department of Economics 98-7, Massachusetts Institute of Technology (MIT), Department of Economics.
  6. Justin McCrary, 2002. "Using Electoral Cycles in Police Hiring to Estimate the Effect of Police on Crime: Comment," American Economic Review, American Economic Association, American Economic Association, vol. 92(4), pages 1236-1243, September.
  7. Joshua Angrist & Alan B. Krueger, 2001. "Instrumental Variables and the Search for Identification: From Supply and Demand to Natural Experiments," NBER Working Papers 8456, National Bureau of Economic Research, Inc.
  8. 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.
  9. Abadie, Alberto, 2003. "Semiparametric instrumental variable estimation of treatment response models," Journal of Econometrics, Elsevier, Elsevier, vol. 113(2), pages 231-263, April.
  10. Angrist, Joshua D, 1990. "Lifetime Earnings and the Vietnam Era Draft Lottery: Evidence from Social Security Administrative Records," American Economic Review, American Economic Association, American Economic Association, vol. 80(3), pages 313-36, June.
  11. Alan B. Krueger, 1999. "Experimental Estimates Of Education Production Functions," The Quarterly Journal of Economics, MIT Press, MIT Press, vol. 114(2), pages 497-532, May.
  12. Joshua D. Angrist & Guido W. Imbens, 1995. "Identification and Estimation of Local Average Treatment Effects," NBER Technical Working Papers, National Bureau of Economic Research, Inc 0118, National Bureau of Economic Research, Inc.
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Cited by:
  1. Coviello, Decio & Mariniello, Mario, 2014. "Publicity requirements in public procurement: Evidence from a regression discontinuity design," Journal of Public Economics, Elsevier, Elsevier, vol. 109(C), pages 76-100.
  2. Johannes A. Skjeltorp & Elvira Sojli & Wing Wah Tham, 2013. "Identifying Cross-Sided Liquidity Externalities," Tinbergen Institute Discussion Papers 13-154/IV/DSF63, Tinbergen Institute.

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