Characteristics of Successful Entrepreneurial Pitches
Last registered on August 04, 2019


Trial Information
General Information
Characteristics of Successful Entrepreneurial Pitches
Initial registration date
October 30, 2018
Last updated
August 04, 2019 9:06 PM EDT
Primary Investigator
University of British Columbia
Other Primary Investigator(s)
PI Affiliation
Stanford Graduate School of Business
Additional Trial Information
Start date
End date
Secondary IDs
This study will look at how pitch characteristics impact their success.
External Link(s)
Registration Citation
Gornall, Will and Ilya Strebulaev. 2019. "Characteristics of Successful Entrepreneurial Pitches." AEA RCT Registry. August 04.
Former Citation
Gornall, Will, Will Gornall and Ilya Strebulaev. 2019. "Characteristics of Successful Entrepreneurial Pitches." AEA RCT Registry. August 04.
Experimental Details
This study will look at how pitch characteristics impact their success.
Intervention Start Date
Intervention End Date
Primary Outcomes
Primary Outcomes (end points)
1) Difference in reply rates to pitches sent by male entrepreneurs (WM+AM) and female entrepreneurs (WF + AF).
2) Difference in reply rates to pitches sent by white entrepreneurs (WM + WF) and Asian American entrepreneurs (AF + AM).
3-4) 1 and 2, but looking at "interested" replies which will be coded as detailed below.
Primary Outcomes (explanation)
Coding of interested replies will be done by RAs based on anonymized emails.

Interested replies are replies that suggest the sender is open to further contact. E.g.:
- replies that send contact information or request a meeting
- requests for more information
- actively forwarding of the email to someone who may be interested
- replies expressing interest in other ways

Non-interested replies are replies that are not interested and do not wish for further contact. E.g.:
- replies saying it is not a good fit due to industry
- replies saying the respondent is no longer investing
- replies expressing disinterest in other ways

All rates will be expressed as the # replies received / # emails sent in that condition that did not bounce.
Secondary Outcomes
Secondary Outcomes (end points)
The following secondary outcomes are all of interest. In many cases, they are looking at interaction terms and as such will

5-6) Primary outcomes 1 and 2, but looking at web page visits as a metric. (Note - this may not be possible as we may end up removing web page links due to spam filtering)
7-8) Interaction of race and gender terms in 1 and 3.
9-12) Homophiliy (difference in gender difference in rates between men and women and similarly for race). We do not expect power from the gender regressions due to the small sample of women.
13-14) Gender congruence (pitches will be rated on mechanical turk on probability founder is female, we will test if pitches that are more "feminine" show a different pattern of gender differences).
15-18) Regressions 1-4 but looking at the difference between VCs and angels. .
Secondary Outcomes (explanation)
Experimental Design
Experimental Design
This study will look at how pitch characteristics impact their success.
Experimental Design Details
Randomization Method
Randomization will be done in office by a computer.
Randomization Unit
Each investor will be matched to four industries. One of each of the four founder types (WM, WF, AM, AF) will be assigned to each of the pitches.
Was the treatment clustered?
Experiment Characteristics
Sample size: planned number of clusters
30,000 investors receiving 4 emails each.

Our initial sample will include contact information for approximately 30,000 investors. We will exclude recipients whose email addresses do not work. We estimate approximately 1/10th of the sample will be excluded in this manner.

It is unlikely that participants will find receiving four emails over a month surprising. However, it is possible that participants will detect that they are part of a study. We will stop emailing and exclude any participants who make reference to the existence of a research experiment or who mention specifics of other emails.
Sample size: planned number of observations
130,000 emails.
Sample size (or number of clusters) by treatment arms
~30,000 emails in the each of four conditions.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
We expect 10-20% of recipients will visit the web pages, 5-10% will reply, and 2.5-5% will send interested replies. Based on this, we will be able to detect a 0.4-0.5% change in open rates, a 0.3-0.4% change in reply rates, and a 0.2-0.3% change in interested reply rates. Those correspond to a 3-4%, 4-5%, and 5-8% change in the base rates, respectively. It is possible our response rates will be lower due to our messages being flagged as spam or other reasons. If so, our power will be correspondingly lower.
IRB Name
IRB Approval Date
IRB Approval Number
Post Trial Information
Study Withdrawal
Is the intervention completed?
Intervention Completion Date
November 16, 2018, 12:00 AM +00:00
Is data collection complete?
Data Collection Completion Date
December 20, 2018, 12:00 AM +00:00
Final Sample Size: Number of Clusters (Unit of Randomization)
28433 investors with working emails
Was attrition correlated with treatment status?
Final Sample Size: Total Number of Observations
79676 emails sent to working emails
Final Sample Size (or Number of Clusters) by Treatment Arms
39823 emails sent with female founders, 39853 emails sent with male founders, 39735 emails sent with white founders, 39941 emails sent with Asian founders
Data Publication
Data Publication
Is public data available?
Program Files
Program Files
Reports and Papers
Preliminary Reports
Relevant Papers

We study gender and race in high-impact entrepreneurship using a tightly controlled randomized field experiment. We sent out 80,000 pitch emails introducing promising but fictitious start-ups to 28,000 venture capitalists and angels. Each email was sent by a fictitious entrepreneur with a randomly selected gender (male or female) and race (Asian or White). Female entrepreneurs received a 9\% higher rate of interested replies than male entrepreneurs pitching identical projects and Asian entrepreneurs received a 6\% higher rate than their White counterparts. We find no indication that investors show bias against Asians or females when evaluating unsolicited pitch emails. Our results suggest that investors do not discriminate against female or Asian entrepreneurs when initially evaluating unsolicited pitches.
Gornall, Will and Strebulaev, Ilya A., Gender, Race, and Entrepreneurship: A Randomized Field Experiment on Venture Capitalists and Angels. Available at SSRN: or