Predicting Entrepreneurial Success is Hard: Evidence from a Business Plan Competition in Nigeria
Compares the absolute and relative performance of 3 approaches to predicting outcomes for entrants in a business plan competition
Abstract
We compare the absolute and relative performance of 3 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.
This is an output of the World Bank’s Strategic Research Program
Citation
David McKenzie, Dario Sansone, Predicting entrepreneurial success is hard: Evidence from a business plan competition in Nigeria, Journal of Development Economics, Volume 141, 2019,
Link
Predicting Entrepreneurial Success is Hard: Evidence from a Business Plan Competition in Nigeria