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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

    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Educational Sciences; Educational Populations; Educational Policy and Planning - Textbook; Education for Development (superceded); Education For All;

    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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