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Randomization as an Instrumental Variable

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This paper discusses how randomized social experiments operate as an instrumental variable. For two types of randomization schemes, the fundamental experimental estimation equations are derived from the principle that experiments equate bias in control and experimental samples. Using conventional econometric representations, we derive the orthogonality conditions for the fundamental estimation equations. Randomization is a multiple instrumental variable in the sense that one randomization defines the parameter of interest expressed as a function of multiple endogenous variables in the conventional usage of that term. It orthogonalizes the treatment variable simultaneously with respect to the other regressors in the model and the disturbance term for the conditional population. However, conventional `structural' parameters are not in general identified by the two types of randomization schemes widely used in practice.

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  • James J. Heckman, 1995. "Randomization as an Instrumental Variable," NBER Technical Working Papers 0184, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberte:0184
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    1. Angrist, J.D. & Imbens, G.W., 1991. "Sources of Identifying Information in Evaluation Models," Harvard Institute of Economic Research Working Papers 1568, Harvard - Institute of Economic Research.
    2. James Heckman & Jeffrey Smith & Christopher Taber, 1994. "Accounting for Dropouts in Evaluations of Social Experiments," NBER Technical Working Papers 0166, National Bureau of Economic Research, Inc.
    3. Heckman, James J. & Robb, Richard Jr., 1985. "Alternative methods for evaluating the impact of interventions : An overview," Journal of Econometrics, Elsevier, vol. 30(1-2), pages 239-267.
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    Cited by:

    1. Cecilia Elena Rouse, 1997. "Private School Vouchers and Student Achievement: An Evaluation of the Milwaukee Parental Choice Program," NBER Working Papers 5964, National Bureau of Economic Research, Inc.
    2. Kluve, Jochen & Lehmann, Hartmut & Schmidt, Christoph M., 1999. "Active Labor Market Policies in Poland: Human Capital Enhancement, Stigmatization, or Benefit Churning?," Journal of Comparative Economics, Elsevier, vol. 27(1), pages 61-89, March.
    3. A. Smith, Jeffrey & E. Todd, Petra, 2005. "Does matching overcome LaLonde's critique of nonexperimental estimators?," Journal of Econometrics, Elsevier, vol. 125(1-2), pages 305-353.
    4. Ahmad Reshad Osmani, 2021. "Conditional Cash Incentive and Use of Health Care Services: New Evidence from a Household Experiment," Journal of Family and Economic Issues, Springer, vol. 42(3), pages 518-532, September.

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    More about this item

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C93 - Mathematical and Quantitative Methods - - Design of Experiments - - - Field Experiments

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