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Predicting entrepreneurial success is hard: Evidence from a business plan competition in Nigeria

David McKenzie () and Dario Sansone ()

Journal of Development Economics, 2019, vol. 141, issue C

Abstract: We compare the absolute and relative performance of three approaches to predicting outcomes for entrants in a business plan competition in Nigeria: Business plan scores from judges, simple ad-hoc prediction models used by researchers, and 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 competition winners.

Keywords: Entrepreneurship; Machine learning; Business plans; Nigeria (search for similar items in EconPapers)
JEL-codes: C53 L26 M13 (search for similar items in EconPapers)
Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:eee:deveco:v:141:y:2019:i:c:s0304387818305601

DOI: 10.1016/j.jdeveco.2019.07.002

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