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

67 Pages Posted: 2 Jan 2018

See all articles by David J. McKenzie

David J. McKenzie

World Bank - Development Research Group (DECRG); IZA Institute of Labor Economics

Dario Sansone

Georgetown University

Multiple version iconThere are 2 versions of this paper

Date Written: December 2017

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

JEL Classification: C53, L26, M13, O12

Suggested Citation

McKenzie, David John and Sansone, Dario, Man vs. Machine in Predicting Successful Entrepreneurs: Evidence from a Business Plan Competition in Nigeria (December 2017). CEPR Discussion Paper No. DP12523, Available at SSRN: https://ssrn.com/abstract=3095573

David John McKenzie (Contact Author)

World Bank - Development Research Group (DECRG) ( email )

1818 H. Street, N.W.
MSN3-311
Washington, DC 20433
United States

IZA Institute of Labor Economics ( email )

P.O. Box 7240
Bonn, D-53072
Germany

Dario Sansone

Georgetown University ( email )

Washington, DC 20057
United States

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