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The usefulness of experiments

Author

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  • Jeffrey A. Smith

    (University of Wisconsin, and NBER, USA, and IZA, Germany)

Abstract

Non-experimental evaluations of programs compare individuals who choose to participate in a program to individuals who do not. Such comparisons run the risk of conflating non-random selection into the program with its causal effects. By randomly assigning individuals to participate in the program or not, experimental evaluations remove the potential for non-random selection to bias comparisons of participants and non-participants. In so doing, they provide compelling causal evidence of program effects. At the same time, experiments are not a panacea, and require careful design and interpretation.

Suggested Citation

  • Jeffrey A. Smith, 2018. "The usefulness of experiments," IZA World of Labor, Institute of Labor Economics (IZA), pages 436-436, May.
    • Handle: RePEc:iza:izawol:journl:y:2018:n:436
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    References listed on IDEAS

    as
    1. Markus Frölich & Michael Lechner & Heidi Steiger, 2003. "Statistically Assisted Programme Selection - International Experiences and Potential Benefits for Switzerland," Swiss Journal of Economics and Statistics (SJES), Swiss Society of Economics and Statistics (SSES), vol. 139(III), pages 311-331, September.
    2. Jeffrey Smith & Jeremy Lise & Shannon N. Seitz, 2003. "Equilibrium Policy Experiments And The Evaluation Of Social Programs," Working Paper 1012, Economics Department, Queen's University.
    3. Bruno Crépon & Esther Duflo & Marc Gurgand & Roland Rathelot & Philippe Zamora, 2013. "Do Labor Market Policies have Displacement Effects? Evidence from a Clustered Randomized Experiment," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 128(2), pages 531-580.
    4. 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.
    5. Gary Burtless & Larry L. Orr, 1986. "Are Classical Experiments Needed for Manpower Policy," Journal of Human Resources, University of Wisconsin Press, vol. 21(4), pages 606-639.
    6. Sianesi, Barbara, 2017. "Evidence of randomisation bias in a large-scale social experiment: The case of ERA," Journal of Econometrics, Elsevier, vol. 198(1), pages 41-64.
    7. Gary Burtless, 1995. "The Case for Randomized Field Trials in Economic and Policy Research," Journal of Economic Perspectives, American Economic Association, vol. 9(2), pages 63-84, Spring.
    8. James Heckman & Neil Hohmann & Jeffrey Smith & Michael Khoo, 2000. "Substitution and Dropout Bias in Social Experiments: A Study of an Influential Social Experiment," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 115(2), pages 651-694.
    9. James J. Heckman & Jeffrey A. Smith, 1999. "The Pre-Program Earnings Dip and the Determinants of Participation in a Social Program: Implications for Simple Program Evaluation Strategies," NBER Working Papers 6983, National Bureau of Economic Research, Inc.
    10. Dan A. Black & Jeffrey A. Smith & Mark C. Berger & Brett J. Noel, 2003. "Is the Threat of Reemployment Services More Effective Than the Services Themselves? Evidence from Random Assignment in the UI System," American Economic Review, American Economic Association, vol. 93(4), pages 1313-1327, September.
    11. Heckman, James J & Smith, Jeffrey A, 1999. "The Pre-programme Earnings Dip and the Determinants of Participation in a Social Programme. Implications for Simple Programme Evaluation Strategies," Economic Journal, Royal Economic Society, vol. 109(457), pages 313-348, July.
    12. Burt S. Barnow, 1987. "The Impact of CETA Programs on Earnings: A Review of the Literature," Journal of Human Resources, University of Wisconsin Press, vol. 22(2), pages 157-193.
    13. Djebbari, Habiba & Smith, Jeffrey, 2008. "Heterogeneous impacts in PROGRESA," Journal of Econometrics, Elsevier, vol. 145(1-2), pages 64-80, July.
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    More about this item

    Keywords

    experiment; random assignment; causality; evaluation;
    All these keywords.

    JEL classification:

    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C90 - Mathematical and Quantitative Methods - - Design of Experiments - - - General

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