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Last Published May 18, 2021 09:41 AM June 14, 2021 04:20 PM
Primary Outcomes (End Points) Dependent Variable: The dependent variable is the propensity to invest in a startup . Dependent Variable: The dependent variable is propensity to invest in a startup.
Primary Outcomes (Explanation) Dependent Variable: The dependent variable is Yijp – the propensity to invest in a startup by i – the investor, j – the startup, and p – the paired program. We will measure the dependent variable using two methods. We will build our model around the scales and the qualitative proportion measurements. Method 1 and 2 are what we used to build our model: Binary: Before meeting each company, each professional investor will be asked “Do you want to meet this company?” [Definitely want to meet, Would meet if there’s time, No thank you]. The first two options will be treated as binary responses. First, 1 = definitely want to meet and 0 = other. Second 1 = meet / other, 0 = No thank you. Qualitative: Each professional investor will be asked what additional information they need from the investor. We will assess the proportion of promotion vs. prevention-focused questions (Kanze et al. 2018). Performance-reward bias: Normalized scale (Male normalized qualitative proportion – Female normalized qualitative proportion) – see Castilla (2008). Intuitively, if you get the same scale rank, what is the difference between the qualitative score by the gender of the entrepreneur? Dependent Variable: The dependent variable is Yp – the propensity to invest in a startup by the paired program. The four paired programs will take place in four geographic regions and include entrepreneurs from across those regions: Sub-Saharan Africa, India, MENA and Latin America. This will result in a total sample of eight programs - four treatment programs and four control programs – with one treatment and one control program in each location. We use fixed effects for the paired program in all regressions. (We will only include fixed effects for the investor in pooled regressions when we join up the sample with the professional investors.) We will measure the dependent variable using 4 methods. For the first treatment – prompting consistent enquiry – our primary dependent variable will be qualitative, following Kanze et al. (2018). For the second treatment – evaluating demonstrated competence – our primary dependent variable will be scales, inspired by Clingingsmith and Shane’s (2018) dependent variable. 1. Scales: Each trainee investor will evaluate each startup on a scale. The baseline evaluation takes place on a 6-point scale. Thereafter, evaluators will use a 24-point scale (control group) and a 32-point scale (treatment group). All scale evaluations are normalized by the program using a z-score. 2. Binary: Each trainee investor will know that the top 2 rated startups will receive investment. Therefore, each trainee investor will carefully consider who they place in the top 2. 3. Qualitative: Each trainee investor will be asked what additional information they need from the startup. Trainee investors will also ask for additional information in conversations, and combined, this will form a secondary dependent variable. All responses will be coded as “promotion-focused” or “prevention-focused”. We will assess the proportion of promotion vs. prevention-focused questions (Kanze et al. 2018). 4. Performance-reward bias: Normalized scale (Male normalized qualitative proportion – Female normalized qualitative proportion) – see Castilla (2008). Intuitively, if you get the same scale rank, what is the difference between the qualitative score by the gender of the entrepreneur?
Randomization Unit individual. Planned Number of Clusters Each trainee investor is a cluster and will make at least 9 decisions. Each professional investor is a cluster and will make at least 3 decisions. Planned Number of Observations At least 1,500 individual investor decisions, from at least 200 individual investors. Sample size (or number of clusters) by treatment arms * For treatment 1 and treatment 2, I will have a sample size of at least 1,500 individual investor decisions, and 456 on female founders. These stem from at least 200 individual investors. Power calculation: Minimum Detectable Effect Size for Main Outcomes The minimum detectable effect size for the ANCOVA calculations is 0.225. individual investor Planned Number of Clusters Each trainee investor is a cluster and will make at least 9 decisions. Each professional investor is a cluster and will make at least 3 decisions. Planned Number of Observations At least 1,500 individual investor decisions, from at least 200 individual investors. Sample size (or number of clusters) by treatment arms * For treatment 1 and treatment 2, I will have a sample size of at least 1,500 individual investor decisions, and 456 on female founders. These stem from at least 200 individual investors. Power calculation: Minimum Detectable Effect Size for Main Outcomes The minimum detectable effect size for the ANCOVA calculations is 0.225.