We looked at 773 companies that entered our program from 2008 to 2018, and identified the top 10% of those companies in five success measures — Funds Raised, Employees hired, Years in Business, Annual Revenue, and Annual Revenue (Adjusted) which accounted for differences in relative wealth at the country level (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 information probably isn’t so useful to other firms. But, in case you are interested…
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).
Leadership Team Gender Makeup
|Expected Occurrence||Mixed Gender||All-Female||All-Male|
|Top Revenue (Adjusted)||216%||87%||73%|
|Top Funds Raised||175%||90%||84%|
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)
|Top Revenue (Adjusted)||110%||135%||144%||81%||22%|
|Top Funds Raised||97%||178%||104%||111%||0%|
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 Funds Raised||36%||116%||70%||48%|
|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 (Adjusted)||116%||104%||125%||125%||81%|
|Top Funds Raised||117%||108%||173%||124%||80%|
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.