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
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
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Working Paper: Man vs. machine in predicting successful entrepreneurs: evidence from a business plan competition in Nigeria (2017)
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