Man vs. Machine in Predicting Successful Entrepreneurs : Evidence from a Business Plan Competition in Nigeria
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...
| Main Authors: | , |
|---|---|
| Language: | English |
| Published: |
World Bank, Washington, DC
2017
|
| Subjects: | |
| Online Access: | http://documents.worldbank.org/curated/en/968231513116778571/Man-vs-machine-in-predicting-successful-entrepreneurs-evidence-from-a-business-plan-competition-in-Nigeria http://hdl.handle.net/10986/29007 |
| Summary: | 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. |
|---|