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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.

Suggested Citation

  • Joshua Angrist, 2005. "Instrumental Variables Methods in Experimental Criminological Research: What, Why, and How?," NBER Technical Working Papers 0314, National Bureau of Economic Research, Inc.
  • Handle: RePEc:nbr:nberte:0314
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    References listed on IDEAS

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    1. 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.
    2. Alan B. Krueger, 1999. "Experimental Estimates of Education Production Functions," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 114(2), pages 497-532.
    3. Donald B. Rubin, 1977. "Assignment to Treatment Group on the Basis of a Covariate," Journal of Educational and Behavioral Statistics, , vol. 2(1), pages 1-26, March.
    4. Angrist, J.D. & Imbens, G.W., 1992. "Average causal response with variable treatment intensity," Discussion Paper 1992-34, Tilburg University, Center for Economic Research.
    5. 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.
    6. 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 513-530, September.
    7. Angrist, Joshua D, 1990. "Lifetime Earnings and the Vietnam Era Draft Lottery: Evidence from Social Security Administrative Records: Errata," American Economic Review, American Economic Association, vol. 80(5), pages 1284-1286, December.
    8. 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.
    9. Eva Lantos Rezmovic & Thomas J. Cook & L. Douglas Dobson, 1981. "Beyond Random Assignment," Evaluation Review, , vol. 5(1), pages 51-67, February.
    10. Howard S. Bloom, 1984. "Accounting for No-Shows in Experimental Evaluation Designs," Evaluation Review, , vol. 8(2), pages 225-246, April.
    11. Angrist, Joshua D, 1990. "Lifetime Earnings and the Vietnam Era Draft Lottery: Evidence from Social Security Administrative Records," American Economic Review, American Economic Association, vol. 80(3), pages 313-336, June.
    12. 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 270-290, June.
    13. 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.
    14. Abadie, Alberto, 2003. "Semiparametric instrumental variable estimation of treatment response models," Journal of Econometrics, Elsevier, vol. 113(2), pages 231-263, April.
    15. 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.
    16. 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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    Cited by:

    1. Wing Wah Tham & Elvira Sojli & Johannes A. Skjeltorp, 2018. "Cross-Sided Liquidity Externalities," Management Science, INFORMS, vol. 64(6), pages 2901-2929, June.
    2. Zahra Siddique, 2013. "Partially Identified Treatment Effects Under Imperfect Compliance: The Case of Domestic Violence," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 108(502), pages 504-513, June.
    3. Johannes A. Skjeltorp & Elvira Sojli & Wing Wah Tham, 2012. "Identifying cross-sided liquidity externalities," Working Paper 2012/20, Norges Bank.
    4. Organ, Paul R. & Ruda, Alex & Slemrod, Joel & Turk, Alex, 2022. "Incentive effects of the IRS’ passport certification and revocation process," Journal of Public Economics, Elsevier, vol. 208(C).
    5. Coviello, Decio & Mariniello, Mario, 2014. "Publicity requirements in public procurement: Evidence from a regression discontinuity design," Journal of Public Economics, Elsevier, vol. 109(C), pages 76-100.

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    More about this item

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models

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