for initial applications of November 25, 2011. The competition attracted 23,844 applications. A first stage basic scoring selected the top 6,000 applicants, who were offered a 4-day business plan training course. Those who attended the course were then asked to submit a detailed business plan, with 4,517 proposals received. These proposals were then scored by judges - in a process described below - with the highest scores used to select 2,400 semi-finalists. 1,200 among those were then chosen as winners and received grants averaging almost US$50,000 each. 480 of these winners were chosen on the basis of having the top scores nationwide or within their region, while 720 winners were randomly chosen from the remaining semi-finalists.

McKenzie (2017) uses this random allocation to measure the impact of winning this competition, and provides more details on this competition and selection process. His analysis found that being randomly selected as a competition winner resulted in greater firm entry from those with new ideas, higher survival of those with existing businesses, and higher sales, profits, and employment in their firms. It shows that the competition was able to attract firms that, on average, had high growth prospects. Our goal in this paper is to use this setting to see whether the judge scores, simple heuristic models, or machine learning approaches can identify ex ante which firms from among these applicants will be most likely to survive and grow.

# **2.2 Data**

Our sample consists of 2,506 firms that were tracked over time by McKenzie (2017). These comprise 475 non-experimental winners (the national and regional winners), 729 experimental winners, 1,103 semifinalists that are in the control group, and 199 firms that submitted business plans but did not score high enough for the semi-finals.<sup>3</sup> For each of these firms we have data from their initial online application, from the submission of their business plans, the score provided by judges on their business plans, short-term follow-up survey data on some ability and personality measures that are assumed to be stable over time, and then data from a three-year follow-up survey on key outcomes. We discuss each in turn, and provide a full description of the different variables in Appendix A.1.

The application form required individuals to provide personal background such as gender, age, education, and location, along with written answers to questions like “why do you want to be an entrepreneur?” and “how did you get your business idea?” We construct several simple measures of the quality of these written responses, such as the length of answer required, whether the application is written in all capital letters, and

> 3  McKenzie (2017) dropped 5 non-experimental winners who were disqualified by the competition, and includes 9 experimental winners who were disqualified. We drop 9 control group firms that were non-randomly chosen as winners. In addition, McKenzie (2017) attempted to survey 624 firms that were not selected for the business plan training. Since these firms did not submit business plans, they do not have business plan scores and are thus excluded from our analysis.

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