The second approach is to compare the participation rates of treatment and control and use Lee (2009) bounds to adjust for differential attrition. The participation rate in the semi‐finals was 63.2 percent for the treatment group, and 58.7 percent for the control group. The difference of 4.5 percent is not statistically significant (p=0.39, or 0.37 after controlling for strata fixed effects). Nevertheless, we test sensitivity to this difference in attrition rates by dropping the top or bottom eight (4.5% of 174) scores from the treatment group. The next two columns of Appendix Table 6.2 then show the Lee upper and lower bounds respectively are 0.41 and 0.18. Since Table 1 and appendix Figure 1 shows that the differential attrition is not coming from the tails of the baseline investment readiness score distribution, we think it highly unlikely that it would be coming from either tail of the follow‐up distribution.

As a final robustness check, we show in the last two columns of Appendix Table 6.2 that our results are not sensitive to how we aggregate the different sub‐scores. Column 5 aggregates the five sub‐scores using equal weights instead of the different weights in our main specification, while Column 6 also includes the presentation score. We see the estimated effects of 0.277 and 0.293 are very similar in sign, significance, and magnitude to those using the unequal weights.

Taken together, these results show that the impact of treatment on the investment readiness score is unlikely to be driven by differential participation patterns in the semi‐finals between the treatment and control groups, nor by the weighting, and so our finding that the investment readiness program has improved investment readiness is robust.

|**Appendix Table 6.2: Robustness of Impact on Investment Readiness to Attrition and to how scores are weighted**|
|---|

||Score|Imputed<br>Score|Lee<br>Upper|Lee<br>Lower|5 components<br>Equally|6 components<br>weighted|
|---|---|---|---|---|---|---|
|Assigned to Treatment|0.284**|0.193***|0.408***|0.176|0.277**|0.293**|
||(0.126)|(0.065)|(0.119)|(0.130)|(0.123)|(0.124)|
|Sample Size|211|343|203|203|211|211|
|Control Mean|2.908|2.865|2.908|2.908|2.950|2.966|
|Control Std. Dev|0.903|0.750|0.903|0.903|0.884|0.894|

Notes: Robust standard errors in parentheses. Regressions control for randomization strata.

*, **, *** indicate significance at the 10,5, and 1 percent levels respectively

Score is the investment readiness score in the semi‐finals. Imputed score imputes missing scores based on regressing the score for the control group on baseline team, traction, market readiness, product technology, country, and having an outside investor and using predicted score for missing observations. Lee upper and Lee lower bounds trim the bottom 8 and top 8 scores respectively from the treatment group to adjust for higher attrition in the control group. Equally weighted scores weight the five (team, technology, traction, market and progress) or six (also presentation) sub‐scores equally.

# **Appendix 7: Impact on Web Traffic and Being Included on AngelList**

Our pre‐analysis plan also noted that we would consider several measures of web traffic and web presence that have been used by other researchers (e.g. Kerr et al. (2014), Gonzales‐Uribe and Leatherbee (2015)), but which may be less appropriate for firms in a less developed market: whether or not the firm is included in AngelList, a popular web platform for fundraising, startup jobs and investing allowing startups to raise capital from angel investors; and the global web‐traffic rankings of the company’s webpage as collected by Alexa and SimilarWeb. We see no significant impacts on any of these measures.

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