Overview | Team | Plan | Reviewers | Program
Highlights
- Mixed-gender teams were at least twice as likely to be successful as all-male teams and all-female teams in all success categories.
- All-female teams slightly out-performed all-male teams, except in longevity, where they slightly under-performed.
- Entrepreneurs between the ages of 26 and 29 when starting their companies were 75% more likely to be successful at raising outside funds than other age groups.
- As entrepreneurs got older (measured by their age at company launch) men saw a slight decline in success measures. Women saw a far more dramatic decline — those starting their companies at the age of 37 of older were one-third as likely to be successful as the Men.
- The entrepreneurs formal educational backgrounds did not make much difference, excepting that entrepreneurs with doctorates being 75% more likely to be successful at raising funds. The least useful advanced degree in our study was the MBA.
Methodology
We looked at 773 companies that entered our program from 2008 to 2018, and identified the top 10% of those companies in four success measures — Funds Raised, Employees hired, Years in Business, Annual Revenue.
For revenue, we looked at the information both with and without adjustments for relative wealth of operating countries (e.g. $100,000 USD equivalent for a company based in, and selling to customers in, Myanmar is a different level of accomplishment from $100,000 USD in the USA.)
We have previously presented this data in actual currency / # hired, but that created problems with a handful of outlier companies that had been very successful. By using this method, the top companies are not distorting the rest of the data.
The most significant advantage, and our favorite, is that companies who re-entered our program more than once were far and away the most successful companies (502% over-representation in top revenue!). Alas, that was a fairly limited number of firms (20), so not large enough to be considered statistically significant.
We are looking at over- or under- representation in top 10% of listed success markers. A result of 100% would mean that the number of companies with that attribute (e.g. “Mixed gender leadership teams”) is the same percent in the top 10% of the success category listed as in the full set (all companies).
Details
Leadership Team Gender Makeup
(100% means equal distribution to overall set.)
| Expected Occurrence | Mixed Gender | All-Female | All-Male |
| Top Revenue | 185% | 92% | 81% |
| Top Revenue (Adjusted) | 216% | 87% | 73% |
| Top Funds Raised | 175% | 90% | 84% |
| Top Hires | 203% | 116% | 62% |
| Top Longevity | 160% | 79% | 95% |
What we learned: Mixed gender teams do better.
There were not enough non-binary individuals identified to be statistically significant.
Age of Company Lead (at time of entering our program)
(100% means equal distribution to overall set.)
| Expected Occurrence | 1…25 | 26…29 | 30…36 | 37…99 | Unknown |
| Top Revenue | 116% | 109% | 144% | 87% | 36% |
| Top Revenue (Adjusted) | 110% | 135% | 144% | 81% | 22% |
| Top Funds Raised | 97% | 178% | 104% | 111% | 0% |
| Top Hires | 129% | 108% | 152% | 72% | 31% |
| Top Longevity | 111% | 89% | 130% | 102% | 63% |
These age groups were chosen because they represent similar size samples of companies who entered our program during 2008-2018 for whom we have age data on their lead founder. The “Unknown” category is included here because it is a fairly large portion of the sample set, and explains why most of the known set score over 100%. We expect there is a self-selection bias here in regards to the poor performance, in that we can more easily obtain information about successful entrepreneurs.
Until 2013, many of our program participants found out about us through Net Impact, and were thus more likely to be recent MBA graduates, which might explain the heavy concentration of individuals between 26 and 29, which is a common set of ages to find in recent MBA graduates. That particular group was more successful at raising external funds.
| Age by Gender — Over/Under Representation of Women in Top Categories (100% means equal distribution to overall set.) |
| Category | Up to 25 | 26 to 29 | 30 to 36 | 37 and Up |
| Top Revenue | 90% | 84% | 72% | 42% |
| Top Funds Raised | 36% | 116% | 70% | 48% |
| Top Hires | 154% | 154% | 131% | 70% |
| Top Longevity | 62% | 135% | 68% | 71% |
| Top Revenue (Adjusted) | 98% | 71% | 88% | 42% |
Women who were in their 20s when they started their ventures did well compare to the men, particularly in the areas of employees hired. However, there was a significant drop-off, particularly in the over-36 group. We would like to think that this is because equitable resources are finally being made available to women entrepreneurs, but we are researching that further.
| Expected Occurrence | Masters+ | MBA | PHD | Legal | No Masters |
| Top Revenue | 114% | 94% | 125% | 125% | 84% |
| Top Revenue (Adjusted) | 116% | 104% | 125% | 125% | 81% |
| Top Funds Raised | 117% | 108% | 173% | 124% | 80% |
| Top Hires | 112% | 110% | 106% | 132% | 89% |
| Top Longevity | 119% | 122% | 130% | 108% | 78% |
Formal Education
The only significant outlier here is that founders with PHDs were over-represented in the set that raised more money. Not surprisingly, more than 85% of those top-raising PHD-led companies were in technical fields.
We also tracked founders with formal education in Accounting, Architecture, Medical, Policy, and Public Health, but none of those were sufficiently represented to be statistically significant.