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Can Business Owners Form Accurate Counterfactuals? Eliciting Treatment and Control Beliefs about Their Outcomes in the Alternat

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  • McKenzie, David

Abstract

A survey of participants in a large-scale business plan competition experiment, in which winners received an average of US$50,000 each, is used to elicit beliefs about what the outcomes would have been in the alternative treatment status. Participants are asked the percent chance they would be operating a firm, and the number of employees and monthly sales they would have, had their treatment status been reversed. The study finds the control group to have reasonably accurate expectations of the large treatment effect they would experience on the likelihood of operating a firm, although this may reflect the treatment effect being close to an upper bound. The control group dramatically overestimates how much winning would help them grow the size of their firm. The treatment group overestimates how much winning helps their chance of running a business, and also overestimates how much winning helps them grow their firms. In addition, these counterfactual expectations appear unable to generate accurate relative rankings of which groups of participants benefit most from treatment.

Suggested Citation

  • McKenzie, David, 2016. "Can Business Owners Form Accurate Counterfactuals? Eliciting Treatment and Control Beliefs about Their Outcomes in the Alternat," CEPR Discussion Papers 11280, C.E.P.R. Discussion Papers.
    • Handle: RePEc:cpr:ceprdp:11280
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    References listed on IDEAS

    as
    1. Charles F. Manski, 2004. "Measuring Expectations," Econometrica, Econometric Society, vol. 72(5), pages 1329-1376, September.
    2. Martin Ravallion, 2014. "Can We Trust Shoestring Evaluations?," The World Bank Economic Review, World Bank, vol. 28(3), pages 413-431.
    3. McKenzie, David & Gibson, John & Stillman, Steven, 2013. "A land of milk and honey with streets paved with gold: Do emigrants have over-optimistic expectations about incomes abroad?," Journal of Development Economics, Elsevier, vol. 102(C), pages 116-127.
    4. 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.
    5. David McKenzie, 2011. "How Can We Learn Whether Firm Policies Are Working in Africa? Challenges (and Solutions?) For Experiments and Structural Models -super-†," Journal of African Economies, Centre for the Study of African Economies, vol. 20(4), pages 600-625, August.
    6. Delavande, Adeline & Giné, Xavier & McKenzie, David, 2011. "Measuring subjective expectations in developing countries: A critical review and new evidence," Journal of Development Economics, Elsevier, vol. 94(2), pages 151-163, March.
    7. McKenzie, David, 2011. "How can we learn whether firm policies are working in africa ? challenges (and solutions?) for experiments and structural models," Policy Research Working Paper Series 5632, The World Bank.
    8. Randall A. Lewis & Justin M. Rao, 2015. "The Unfavorable Economics of Measuring the Returns to Advertising," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 130(4), pages 1941-1973.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Counterfactual elicitation; Subjective expectations; Randomized experiment; Business growth;
    All these keywords.

    JEL classification:

    • O12 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Microeconomic Analyses of Economic Development
    • D22 - Microeconomics - - Production and Organizations - - - Firm Behavior: Empirical Analysis
    • C80 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - General
    • 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
    • C93 - Mathematical and Quantitative Methods - - Design of Experiments - - - Field Experiments

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