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Man vs. machine in predicting successful entrepreneurs : evidence from a business plan competition in Nigeria


  • Mckenzie,David J.
  • Sansone,Dario
  • Mckenzie,David J.
  • Sansone,Dario


This paper compares 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. The paper compares these (out-of-sample) performances with those of three machine learning approaches. The results show 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) the models do twice as well as random selection in identifying firms in the top tail of performance.

Suggested Citation

  • Mckenzie,David J. & Sansone,Dario & Mckenzie,David J. & Sansone,Dario, 2017. "Man vs. machine in predicting successful entrepreneurs : evidence from a business plan competition in Nigeria," Policy Research Working Paper Series 8271, The World Bank.
  • Handle: RePEc:wbk:wbrwps:8271

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    Blog mentions

    As found by, 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. Woodruff, Christopher & De Mel, Suresh & McKenzie, David, 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,
    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,
    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


    Private Sector Economics; Private Sector Development Law; Marketing; Labor Markets; Gender and Development; Educational Sciences; Educational Populations; Educational Policy and Planning - Textbook; Education for Development (superceded); Education For All;
    All these keywords.

    JEL classification:

    • O12 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Microeconomic Analyses of Economic Development
    • 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


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