IDEAS home Printed from https://ideas.repec.org/p/nbr/nberte/0184.html
   My bibliography  Save this paper

Randomization as an Instrumental Variable

Author

Listed:
  • James J. Heckman

Abstract

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.

Suggested Citation

  • 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
    Note: LS
    as

    Download full text from publisher

    File URL: http://www.nber.org/papers/t0184.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Cockx, Bart & Bardoulat, Isabelle, 1999. "Vocational Training: Does it speed up the Transition Rate out of Unemployment ?," LIDAM Discussion Papers IRES 1999032, Université catholique de Louvain, Institut de Recherches Economiques et Sociales (IRES).
    2. James J. Heckman & Jeffrey A. Smith, 1995. "Assessing the Case for Social Experiments," Journal of Economic Perspectives, American Economic Association, vol. 9(2), pages 85-110, Spring.
    3. Pettersson Lidbom, Per, 2003. "Does the Size of the Legislature Affect the Size of Government? Evidence from a Natural Experiment," Research Papers in Economics 2003:18, Stockholm University, Department of Economics.
    4. Erich Battistin & Enrico Rettore, 2003. "Another look at the regression discontinuity design," CeMMAP working papers CWP01/03, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    5. Hidehiko Ichimura & Christopher R. Taber, 2000. "Direct Estimation of Policy Impacts," NBER Technical Working Papers 0254, National Bureau of Economic Research, Inc.
    6. Victor Aguirregabiria, 2006. "Another Look at the Identification of Dynamic Discrete Decision Processes: With an Application to Retirement Behavior," 2006 Meeting Papers 169, Society for Economic Dynamics.
    7. Ellison, Richard B. & Ellison, Adrian B. & Greaves, Stephen P. & Sampaio, Breno, 2017. "Electronic ticketing systems as a mechanism for travel behaviour change? Evidence from Sydney’s Opal card," Transportation Research Part A: Policy and Practice, Elsevier, vol. 99(C), pages 80-93.
    8. Dettmann, E. & Becker, C. & Schmeißer, C., 2011. "Distance functions for matching in small samples," Computational Statistics & Data Analysis, Elsevier, vol. 55(5), pages 1942-1960, May.
    9. James J. Heckman, 1991. "Randomization and Social Policy Evaluation Revisited," NBER Technical Working Papers 0107, National Bureau of Economic Research, Inc.
    10. Jeffrey Smith, 2000. "A Critical Survey of Empirical Methods for Evaluating Active Labor Market Policies," Swiss Journal of Economics and Statistics (SJES), Swiss Society of Economics and Statistics (SSES), vol. 136(III), pages 247-268, September.
    11. Rajeev Dehejia, 2013. "The Porous Dialectic: Experimental and Non-Experimental Methods in Development Economics," WIDER Working Paper Series wp-2013-011, World Institute for Development Economic Research (UNU-WIDER).
    12. Nobuhiko Fuwa & Asa Jose U. Sajise, 2009. "Exploring Environmental Services Incentive Policies for the Philippines Rice Sector: The Case of Intra-Species Agrobiodiversity Conservation," Natural Resource Management and Policy, in: Leslie Lipper & Takumi Sakuyama & Randy Stringer & David Zilberman (ed.), Payment for Environmental Services in Agricultural Landscapes, chapter 10, pages 221-238, Springer.
    13. Andres, Luis & Foster, Vivien & Guasch, Jose Luis, 2006. "The impact of privatization on the performance of the infrastructure sector : the case of electricity distribution in Latin American countries," Policy Research Working Paper Series 3936, The World Bank.
    14. Burt S. Barnow & Jeffrey Smith, 2015. "Employment and Training Programs," NBER Chapters, in: Economics of Means-Tested Transfer Programs in the United States, Volume 2, pages 127-234, National Bureau of Economic Research, Inc.
    15. Paul Ellickson & Sanjog Misra, 2012. "Enriching interactions: Incorporating outcome data into static discrete games," Quantitative Marketing and Economics (QME), Springer, vol. 10(1), pages 1-26, March.
    16. Jinho Bae & Chang-Jin Kim & Dong Kim, 2012. "The evolution of the monetary policy regimes in the U.S," Empirical Economics, Springer, vol. 43(2), pages 617-649, October.
    17. Marco Caliendo & Stefan Tübbicke, 2020. "New evidence on long-term effects of start-up subsidies: matching estimates and their robustness," Empirical Economics, Springer, vol. 59(4), pages 1605-1631, October.
    18. Mark B. Stewart, 2004. "The Impact of the Introduction of the U.K. Minimum Wage on the Employment Probabilities of Low-Wage Workers," Journal of the European Economic Association, MIT Press, vol. 2(1), pages 67-97, March.
    19. Rinku Murgai & Martin Ravallion & Dominique van de Walle, 2016. "Is Workfare Cost-effective against Poverty in a Poor Labor-Surplus Economy?," The World Bank Economic Review, World Bank, vol. 30(3), pages 413-445.
    20. Troske, Kenneth R. & Voicu, Alexandru, 2010. "Joint estimation of sequential labor force participation and fertility decisions using Markov chain Monte Carlo techniques," Labour Economics, Elsevier, vol. 17(1), pages 150-169, January.

    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

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nbr:nberte:0184. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: the person in charge (email available below). General contact details of provider: https://edirc.repec.org/data/nberrus.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.