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Can predicting successful entrepreneurship go beyond “choose smart guys in their 30s”? Comparing machine learning and expert judge predictions

David McKenzie's picture

Business plan competitions have increasingly become one policy option used to identify and support high-growth potential businesses. For example, the World Bank has helped design and support these programs in a number of sub-Saharan African countries, including Côte d’Ivoire, Gabon, Guinea-Bissau, Kenya, Nigeria, Rwanda, Senegal, Somalia, South Sudan, Tanzania, and Uganda. These competitions often attract large numbers of applications, raising the question of how do you identify which business owners are most likely to succeed?

In a recent working paper, Dario Sansone and I compare three different approaches to answering this question, in the context of Nigeria’s YouWiN! program. Nigerians aged 18 to 40 could apply with either a new or existing business. The first year of this program attracted almost 24,000 applications, and the third year over 100,000 applications. After a preliminary screening and scoring, the top 6,000 were invited to a 4-day business plan training workshop, and then could submit business plans, with 1,200 winners each chosen to receive an average of US$50,000 each. We use data from the first year of this program, together with follow-up surveys over three years, to determine how well different approaches would do in predicting which entrants will have the most successful businesses.

How hard are they working?

Markus Goldstein's picture
I was at a conference a couple of years ago and a senior colleague, one who I deeply respect, summarized the conversation as: “our labor data are crap.”   I think he meant that we have a general problem when looking at labor productivity (for agriculture in this case) both in terms of the heroic recall of days and tasks we are asking survey respondents for, but also we aren’t doing a good job of measuring effort. 

Biased women in the I(C)T crowd

Markus Goldstein's picture
This post is coauthored with Alaka Holla

The rigorous evidence on vocational training programs is, at best, mixed.   For example, Markus recently blogged about some work looking at long term impacts of job training in the Dominican Republic.   In that paper, the authors find no impact on overall employment, but they do find a change in the quality of employment, with more folks having jobs with health insurance (for example). 

What makes bureaucracies work better? Lessons from the Nigerian Civil Service

Markus Goldstein's picture
Given Jed's post last week on thinking through performance incentives for health workers, and the fact that the World Bank is in the throes of a reform process itself, a fascinating new paper from Imran Rasul and Daniel Rogger on autonomy and performance based incentives in Nigeria gives us some other food for thought.   In a nutshell, Rasul and Rogger f