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«CSAE Working Paper WPS/2014-34 Aspire* Marcel Fafchamps Simon Quinn Stanford University, BREAD and NBER University of Oxford March 2015 Abstract We ...»

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From Table 1 we see that the average age of contestants is 22. Only four individuals report an age outside the range allowed for contestants. The competition attracted mostly young men, but over one fifth of contestants are female. Unsurprisingly, most contestants are unmarried and fewer than 5% of them have children. Fewer than a quarter of the contestants have not (yet) completed high school. (This proportion is slightly higher in the endline survey, suggesting that some contestants over-reported their education level.) In the three study countries, 90% of contestants report speaking English at baseline.12 One quarter of all contestants has traveled abroad at least once in The proportion is 88% in the endline survey, so this proportion is probably about right. It reflects the fact that, in all three countries, English is used extensively as language of instruction in high school and university.

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their life, and almost 60% have parents who own a business. Half of the contestants are students at baseline; a little over a quarter are employed.

Contestants were asked what they would do if they win the prize. Of those who answer the question, 84% indicate that they would start or expand a business. Of those who say they would start or expand a business, the average percentage of their winnings that they plan to invest is 80%. Some 60% of contestants claim to have identified one or two partners, and 75% report they would invest some of the own funds into the business. Of course, these answers are partly wishful thinking, and contestants may have (mistakenly) believed that they could improve their chances of winning the prize by inflating their responses.

Data on the judging process is summarized in Table 2. Each committee of judges examined a nonoverlapping set of contestants. In Zambia, the target number of contestants per judging committee was set to six. In Ethiopia and Tanzania, the target was 12. The committees were instructed to rank contestants. This was done in several steps. First, each committee judge was asked to individually score each contestant in writing. Judges first scored each contestant on eight Likert scales going from 1 for ‘strongly agree’ to 5 for ‘strongly disagree’. Each score focuses on one aspect of the contestant written proposal and oral presentation. Scores given by individual judges are averaged for each judging committee. A low score is good, a high score is bad. There is considerable variation in scores across contestants, with score averages centered around the middle mark of 3.

The correlation across average scores for the eight Likert scale questions is high – around 0.8 on average, and never below 0.7.

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There is quite a bit of variation in the way each individual judge scores each contestant. We compute for each contestant the standard deviation of the scores given by individual judges. The average of this standard deviation across all contestants is between a low of 0.83 (for question 6) and a high of 0.94 (for question 5). This is a large value given that the lowest score is 1 and the highest is 5. This suggests that there is considerable variation in judges’ opinion regarding the value of the business ideas presented to them.

Next judges were asked to mark the growth potential of the contestant’s business idea on a scale from 0 to 100. Here high is good, low is bad. The average mark is 62%, suggesting that committee judges were on average only moderately impressed with the contestants’ performance. Judges were similar requested to rate on a scale from 0 to 100 their recommendation for others to invest in the contestant’s business. The average mark is 59%, with much variation either way. The correlation between the two marks is 0.7. There is also considerable variation in the marks given by different judges to the same contestant: the average standard deviation in marks is 19 for the first and 21 for the second.

Answers to the growth potential question are strongly correlated with answers to the eight Likert scale questions; as expected, applicants performing better on the Likert scale questions received higher scores on growth potential. The same is true for the investment recommendation question. When we regress the growth potential score on the eight Likert scores together (clustering by judging committee), we obtain an R2 of 0.67; all eight scores are individually significant, except

for financial viability and clarity of the written business plan (each of which is almost significant:

p = 0.131 and p = 0.154 respectively). When we regress the investment recommendation score in the same way, three Likert scores are individually significant: the clarity of the written business plan (p = 0.001), effectiveness of oral presentation (p = 0.013) and the applicant’s overall business sense (p = 0.086). The growth potential score and investment recommendation score have a

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positive correlation of 0.68.

After writing individual scores, each committee was asked to agree a common ranking of all the contestants who appeared in front of them. This common ranking was achieved through discussion among committee judges under the direction of a committee chair of their choosing. The best contestant is given rank 1, the second best receives rank 2, and so on. The competition winner selected by the judging committee is the contestant who was ranked #1. A high value of the rank variable thus implies low performance. As expected, committee rankings are positively correlated with the Likert scale questions – contestants who scored poorly on those questions were given a higher ranking (i.e., less favorable). committee rankings are also negatively correlated with the two 0 to 100 marks, as anticipated.





The written business plans that each contestant was asked to prepare were also independently ranked by individual judges not in a committee. These judges were drawn at random from the same population as the committee judges. The only difference is that they did not attend the oral presentation the contestant made, and they were unable to ask questions. These rankings were then averaged for each contestant. The correlation between committee rankings and averaged individual rankings is positive but low, at 0.23. Given how little a priori agreement there is among judges regarding contestants, there is a considerable element of chance in determining which contestant a committee ends up selecting as the winner. (This is directly consistent with Fafchamps and Woodruff (2014), who run a business plan competition in urban Ghana; the authors find that expert panels do reasonably well in predicting growth of microenterprises, but add very little explanatory power to a simple model with several key observable covariates.) In Table 3 we present descriptive statistics from the endline survey. The survey questionnaire was answered by the contestants themselves in face-to-face interviews with enumerators. The survey

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questionnaire is adapted to the fact that contestants need not be the head of their household and may not have control over their finances. Questions about employment, income, and time use are individual-specific and are all specifically aimed at testing the effect of winning the competition on business creation and self-employment.

Table 3 here.

The first part of Table 3 presents employment outcome variables. Self-employment and wage employment are dummies. Given the young age of respondents, it is perhaps heartening to note that the overwhelming majority had some form of employment by the time of the endline survey. (Of course, we should keep in mind that these percentages are not representative of all young people in the studied countries: our study population is, by construction, a self-selected group of overachievers.) Hours worked come from time budgets collected for the day preceding the survey.13 We also collected information on the minimum wage that respondents require before accepting a permanent employment position. There is considerable variation in this variable across the sample.

Conditional on being self-employed, information was collected on broad indicators of business performance, such as total sales or revenues, total costs, profits and number of permanent employees.

Not surprisingly, the average number of employees is small. We present two profit measures. The first one is monthly profits as reported by respondents. The second is calculated as the difference between total sales and total costs. As is common in these kind of data, self-reported profits far exceed calculated profits (e.g., Fafchamps, McKenzie, Quinn, and Woodruff (2012)). We suspect that self-reported profits over-estimate actual profits because some respondents do not understand the difference between profits and revenues. On the other hand, calculated profits are probably If the day preceding was a weekend, respondents were instead asked about the last weekday.

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underestimates because respondents often under-report sales. For this reason, we use both in our analysis.

Next we report income, expenditures and assets. All are presented in a US$ equivalent scale.

Earned income is the sum of reported profits and wage earnings. Because many respondents are not head of household, we do not attempt to measure household consumption expenditures. Rather, we focus on the respondent’s own expenditures, which we divide into three categories: expenditures made by the contestant for own consumption; expenditures made by others for the contestant’s consumption; and expenditures made by the contestant for someone else’s consumption. We focus on combined expenditures for mobile phones, food and drink, cigarettes and tobacco, clothing, and hair and beauty salons. These expenditure categories were selected because they are most relevant for young people, most of whom are living with their parents. We also collected information about durables such as appliances, electronics, and vehicles. Durables are measured at the level of the household, given that they are often shared between several individuals in the household. Information is also available about personal finances, notably outstanding debts, and the value of bank account and cash held. We note that there is considerable variation in income, expenditures, and assets across the sample.

To investigate whether the prize winnings were used to get married, we calculate the proportion of individuals in the sample who got married between the end of the competition and the endline survey. This proportion is very small, which is probably not entirely surprising given the relatively young age of our study population. The last section of Table 3 presents variables that were also collected at baseline. Since they have already been commented on when we discussed Table 1, they need not be discussed further.

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4 Testing strategy We wish to test whether winning the competition led to an increase in entrepreneurship among contestants. To this effect, we compare competition winners with close runners-up from each committee. Identification relies on the assumption that winners are no different from other highly ranked contestants who did not win the prize money. This assumption allows identification via a regression discontinuity design around the large transfer that winners receive. The estimating

equation is of the form:

y i = α + β · W i + εi, (1) where yi is an outcome variable of interest measured in the endline survey and Wi = 1 if individual i won the competition.

In Ethiopia and Tanzania, the set of observations is limited to competition winners and the two most highly ranked competitors in each committee. In Zambia, we use a slightly different counterfactual pool: we use respondents who were ranked first but who did not receive a prize, and respondents ranked second. Given endline attrition, this leaves us with 16 winners and 30 runners-up in Ethiopia, 16 winners and 31 runners-up in Tanzania, and 7 winners and 21 runners-up in Zambia.

To verify the robustness of our results, we also estimate regression (1) with controls for variables that are significantly unbalanced between winners and runners-up, and with judging committee fixed effects. We cluster in all cases by judging committee. Our main outcome variable of interest is whether winners are more likely to be self-employed in the endline survey than runners-up. We also investigate the effect on expected microenterprise profits.

Contestants probably have higher ability and determination than the general population. This is especially for those who do well in the competition, such as winners and runners-up. Consequently, they are more likely to be employed, either in self-employment or in a salaried position. A suffi

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Marcel Fafchamps & Simon Quinn Aspire

ciently large unobserved ability difference between winners and runners-up could thus potentially explain a higher likelihood of self-employment. But the same ability difference would also generate a higher likelihood of wage employment. As a placebo test to confirm our results, we verify that winners are not more likely to be employed in a salaried occupation. In our study country, obtaining a salaried job depends, like self-employment, on ability and determination but presumably does not require paying a large lumpsum.14 To check for robustness, we also use information from time budgets in the endline survey to conrm our findings regarding self-employment and wage employment. For the same reason, we check for income and wealth effects. In particular we investigate whether winners enjoy a higher ex post income and a higher consumption level, and we explore whether they are more likely to get married after winning the competition. The relative young age and dependent status of the participants, and the short interval between treatment and endline survey, nonetheless militate against finding a statistically significant effect.

4.1 Balance and attrition



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