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

David McKenzie () and Dario Sansone ()

No 12523, CEPR Discussion Papers from C.E.P.R. Discussion Papers

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.

Keywords: business plans; entrepreneurship; Machine Learning; Nigeria (search for similar items in EconPapers)
JEL-codes: C53 L26 M13 O12 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-afr, nep-bec, nep-big, nep-ent and nep-for
Date: 2017-12
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