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

66 Pages Posted: 14 Dec 2017 Last revised: 27 Apr 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 12, 2017

Abstract

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.

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Educational Sciences, Educational Populations, Educational Policy and Planning - Textbook, Education for Development (superceded), Education For All

Suggested Citation

McKenzie, David John and Sansone, Dario, Man vs. Machine in Predicting Successful Entrepreneurs: Evidence from a Business Plan Competition in Nigeria (December 12, 2017). World Bank Policy Research Working Paper No. 8271, Available at SSRN: https://ssrn.com/abstract=3086928

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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