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

A-Tier
Journal: Journal of Development Economics
Year: 2019
Volume: 141
Issue: C

Score contribution per author:

2.011 = (α=2.01 / 2 authors) × 2.0x A-tier

α: calibrated so average coauthorship-adjusted count equals average raw count

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.

Technical Details

RePEc Handle
repec:eee:deveco:v:141:y:2019:i:c:s0304387818305601
Journal Field
Development
Author Count
2
Added to Database
2026-01-26