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Can Business Owners Form Accurate Counterfactuals? Eliciting Treatment and Control Beliefs About Their Outcomes in the Alternative Treatment Status

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  • David McKenzie

Abstract

A survey of participants in a large-scale business plan competition experiment, in which winners received an average of U.S. $50,000 each, is used to elicit ex-post 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 their business surviving 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

  • David McKenzie, 2018. "Can Business Owners Form Accurate Counterfactuals? Eliciting Treatment and Control Beliefs About Their Outcomes in the Alternative Treatment Status," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 36(4), pages 714-722, October.
    • Handle: RePEc:taf:jnlbes:v:36:y:2018:i:4:p:714-722
    DOI: 10.1080/07350015.2017.1305276
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    Cited by:

    1. McKenzie, David & Sansone, Dario, 2019. "Predicting entrepreneurial success is hard: Evidence from a business plan competition in Nigeria," Journal of Development Economics, Elsevier, vol. 141(C).
    2. Martina Jakob, Konstantin Buechel, Daniel Steffen, Aymo Brunetti, 2023. "Participatory Teaching Improves Learning Outcomes: Evidence from a Field Experiment in Tanzania," Diskussionsschriften dp2310, Universitaet Bern, Departement Volkswirtschaft.
    3. McKenzie, David, 2020. "If it needs a power calculation, does it matter for poverty reduction?," World Development, Elsevier, vol. 127(C).
    4. Léo-Paul Dana & Edoardo Crocco & Francesca Culasso & Elisa Giacosa, 2023. "Business plan competitions and nascent entrepreneurs: a systematic literature review and research agenda," International Entrepreneurship and Management Journal, Springer, vol. 19(2), pages 863-895, June.
    5. Lori Beaman & Dean Karlan & Bram Thuysbaert & Christopher Udry, 2023. "Selection Into Credit Markets: Evidence From Agriculture in Mali," Econometrica, Econometric Society, vol. 91(5), pages 1595-1627, September.
    6. Jeffrey Smith, 2022. "Treatment Effect Heterogeneity," Evaluation Review, , vol. 46(5), pages 652-677, October.
    7. Tran, Chi Phuong & Pernia, Ronald A. & Nguyen-Thanh, Nhan, 2023. "Mess or match? How do academic perspectives meet the practitioner perspectives in terms of digital transformation?," Technological Forecasting and Social Change, Elsevier, vol. 191(C).
    8. Smith, Jeffrey A. & Whalley, Alexander & Wilcox, Nathaniel T., 2020. "Are Program Participants Good Evaluators?," IZA Discussion Papers 13584, Institute of Labor Economics (IZA).
    9. Oriana Bandiera & Robin Burgess & Erika Deserranno & Ricardo Morel & Imran Rasul & Munshi Sulaiman & Jack Thiemel, 2022. "Microfinance and Diversification," Economica, London School of Economics and Political Science, vol. 89(S1), pages 239-275, June.

    More about this item

    JEL classification:

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

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