Experimental Design Details
We will assess the main hypothesis using the regressions:
Y_ip=α_1 y_0p±α_2 F_ip±α_3 〖T1〗_ip ±〖 α〗_4 〖〖T1×F〗_ip±X_j±L_p±ε_ip〗_
Y_ip=〖β_1 y_0p± β〗_2 F_ip±β_3 〖T2〗_ip ±〖 β〗_4 〖〖T2×F〗_ip±X_j±L_p±ε_ip〗_
This is an ANCOVA regression, which we plan to use to increase our statistical power, following McKenzie (2012). Y0 is the baseline measurement of the dependent variable – before the program starts. i is the stage of measurement – the ranking variable over three time periods. We will measure the dependent variable three times during each program and pool the three measurements. (We will also measure the dependent variable at specific points in time, which will give us less statistical power, but will produce directional effects over time, which we also plan to analyze.)
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.
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.
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.
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).
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?
Gender. In the model, F is our gender variable – a female-led company. In our context, we define a female-led company as the founder that the investor interacts with identifies as female. We made this choice because other researchers have found the gender of the person pitching a startup to be meaningful to evaluations of that startup (e.g., Brooks at al. 2015, Kanze et al. 2018). Because our interventions focus on changing the organizational evaluation process after an interaction with a founder, we find this the most apt definition.
In practice, we know that many teams have multiple co-founders, who may be present during the program. To implement our definition, we have three measures, but the first is our preferred measure:
Binary: Was the female founder present in the interaction with the investor?
Binary: Does the company have a female founder on their founding team sheet for investors?
Scale (percentage): How much was the company represented by a female founder in interactions? We will note which founder speaks in each workshop during the program.
Intervention 1: For intervention 1 on prompting confident inquiry, we will have a sample size of at least 216 investor decisions on startups led by a female founder (within 720 total investor decisions on all startups). We will assess the effect of this treatment using a between subject design. Using 1 pre measure and 3 post measures in ANCOVA requires a sample size of 212 total decisions on females (across treatment and control) for a total power calculation of 0.85. We therefore estimate that we will see a significant positive effect of the treatment on the qualitative dependent variable primarily (but we will look for positive and significant effects on other DVs too). We expect a positive and significant sign on a4 on all DVs.
Intervention 2: For intervention 2 on evaluating using demonstrated competence, we will have the same sample size as intervention 1, with the same power calculations. We therefore estimate that we will see a significant positive effect of the treatment on the scale dependent variable primarily (but we will look for positive and significant effects on other DVs too). We expect a positive and significant sign on b4 on all DVs.
Intervention 3: TBD.
Control variables. In the equations where we do not use investor fixed effects, we will include potential control variables at the investor level including the gender of the investor (Greenberg & Mollick, 2018), and experience of the investor (Clingingsmith & Shane, 2018), which we will measure by a binary variable: having participated in another accelerator, and continuous variables: years of experience in the market.
In the equations where we do not use startup fixed effects, we will include control variables for the startups including:
Evidence of their underlying quality: whether they were accepted by Connect or waitlisted (binary); their average score in Connect due diligence (1-4 continuous)
Evidence of their maturity: whether they were in the most popular geographic market for the program from the finalist group i.e., Egypt for MENA (binary); number of founders (categorical); total employees (categorical); the log of funds raised (continuous).
We have additional hypotheses and data drawing from professional investors - attached.