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Man vs. Machine in Predicting Successful Entrepreneurs: Evidence from a Business Plan Competition in Nigeria

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  • McKenzie, David J.
  • Sansone, Dario

Abstract

We compare the relative performance of man and machine in being able to predict outcomes for entrants in a business plan competition in Nigeria. The first human predictions are business plan scores from judges, and the second are simple ad-hoc prediction models used by researchers. We compare these (out-of-sample) performances to those of three machine learning approaches. We find that i) business plan scores from judges are uncorrelated with business survival, employment, sales, or profits three years later; ii) a few key characteristics of entrepreneurs such as gender, age, ability, and business sector do have some predictive power for future outcomes; iii) modern machine learning methods do not offer noticeable improvements; iv) the overall predictive power of all approaches is very low, highlighting the fundamental difficulty of picking winners; and v) our models can do twice as well as random selection in identifying firms in the top tail of performance.

Suggested Citation

  • McKenzie, David J. & Sansone, Dario, 2017. "Man vs. Machine in Predicting Successful Entrepreneurs: Evidence from a Business Plan Competition in Nigeria," CEPR Discussion Papers 12523, C.E.P.R. Discussion Papers.
  • Handle: RePEc:cpr:ceprdp:12523
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    Blog mentions

    As found by EconAcademics.org, the blog aggregator for Economics research:
    1. Can predicting successful entrepreneurship go beyond “choose smart guys in their 30s”? Comparing machine learning and expert judge predictions
      by David McKenzie in Development Impact on 2018-01-22 12:29:00

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    Cited by:

    1. González-Uribe, Juanita & Reyes, Santiago, 2021. "Identifying and boosting “Gazelles”: Evidence from business accelerators," Journal of Financial Economics, Elsevier, vol. 139(1), pages 260-287.
    2. Fernando Vega-Redondo & Paolo Pin & Diego Ubfal & Cristiana Benedetti-Fasil & Charles Brummitt & Gaia Rubera & Dirk Hovy & Tommaso Fornaciari, 2019. "Peer Networks and Entrepreneurship: a Pan-African RCT," Working Papers 648, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    3. Alex Coad & Stjepan Srhoj, 2020. "Catching Gazelles with a Lasso: Big data techniques for the prediction of high-growth firms," Small Business Economics, Springer, vol. 55(3), pages 541-565, October.
    4. Gonzalez-Uribe, Juanita & Reyes, Santiago, 2021. "Identifying and boosting “gazelles”: evidence from business accelerators," LSE Research Online Documents on Economics 103145, London School of Economics and Political Science, LSE Library.
    5. De Mel, Suresh & McKenzie, David J. & Woodruff, Christopher, 2019. "Micro-equity for Microenterprises," CEPR Discussion Papers 13698, C.E.P.R. Discussion Papers.
    6. Erin L. Scott & Pian Shu & Roman M. Lubynsky, 2020. "Entrepreneurial Uncertainty and Expert Evaluation: An Empirical Analysis," Management Science, INFORMS, vol. 66(3), pages 1278-1299, March.
    7. Falco J. Bargagli-Stoffi & Jan Niederreiter & Massimo Riccaboni, 2020. "Supervised learning for the prediction of firm dynamics," Papers 2009.06413, arXiv.org.
    8. Grover,Arti Goswami & Imbruno,Michele, 2020. "Using Experimental Evidence to Inform Firm Support Programs in Developing Countries," Policy Research Working Paper Series 9461, The World Bank.
    9. Dafei Yin & Jing Li & Gaosheng Wu, 2021. "Solving the Data Sparsity Problem in Predicting the Success of the Startups with Machine Learning Methods," Papers 2112.07985, arXiv.org.
    10. Henrik Hansen & John Rand & Finn Tarp & Neda Trifkovic, 2021. "On the Link Between Managerial Attributes and Firm Access to Formal Credit in Myanmar," The European Journal of Development Research, Palgrave Macmillan;European Association of Development Research and Training Institutes (EADI), vol. 33(6), pages 1768-1794, December.

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    More about this item

    Keywords

    business plans; entrepreneurship; Machine Learning; Nigeria;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • L26 - Industrial Organization - - Firm Objectives, Organization, and Behavior - - - Entrepreneurship
    • M13 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Administration - - - New Firms; Startups
    • O12 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Microeconomic Analyses of Economic Development

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