What’s Working in Startup Acceleration
Insights from Fifteen Village Capital Programs
March 2016

VillageCapital
SOCIALENTERPRISEGOIZUETA

Acknowledgements
Authors

Peter W. Roberts, Academic Director
Social Enterprise @ Goizueta, Emory University
Saurabh Lall, Research Director
Aspen Network of Development Entrepreneurs
Ross Baird, Executive Director
Village Capital
Emily Eastman, Program Associate
Social Enterprise @ Goizueta, Emory University
Abigayle Davidson, Research Analyst
Aspen Network of Development Entrepreneurs
Amanda Jacobson, Latin America Manager
Village CapitalThis report would not have been possible without support
from the leadership and staff at Village Capital. In
addition to Ross and Amanda, we would like to single out
contributions by Victoria Fram, Brittney Riley, Dustin Shay,
Nasir Qadree, George Omedo, Kristen Moree, Varun Pawar,
Allyson Plosko and Whitney Muse. We would also like tothank the Village Capital entrepreneurs, mentors and otherprogram stakeholders who took the time for our interviews.
Your insights and contributions to this report are greatly
appreciated.
We also recognize that the Global Accelerator Learning
Initiative (GALI) has been made possible by its co-creatorsand founding sponsors, including the U.S. Global
Development Lab at the U.S. Agency for International
Development, Omidyar Network, The Lemelson Foundationand the Argidius Foundation. Additional support for GALI
has been provided by the Kauffman Foundation andStichting DOEN.

What’s Working in Startup
Acceleration
Insights from Fifteen Village Capital Programs
TABLE OF CONTENTS
Introduction and Research Motivation .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . 4

``Research methodology...........................................................................................................................6``About the programs in this report .....................................................................................................7
PART 1: The Early Impacts of Acceleration
Honing in on Program Performance Contrasts  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .10

``Honing in on three metrics .................................................................................................................10``Impacts of acceleration in year one:
Examining early program performance outcomes.......................................................................12``Explaining performance contrasts.....................................................................................................16
PART 2: Key Findings  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . 20``PREDICTION 1
Partner quality improves program performance..........................................................................21``PREDICTION 2Time spent on program-related activities lowers program performance............................. 22``PREDICTION 3
Quality of the applicant pool improves program performance................................................ 22``PREDICTION 4More advanced ventures benefit more from acceleration......................................................... 23``PREDICTION 5
Networking among cohort members improves program performance................................ 25``PREDICTION 6
Emphasis on financial acumen improves program performance.............................................26``PREDICTION 7
Mentor quality improves program performance...........................................................................27
Implications for Action  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .30Postscript  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .33Appendices  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .34

``Appendix 1. Fifteen Village Capital programs.................................................................................35``Appendix 2. Correlations among one-year changes.................................................................... 36``Appendix 3. Performance contrasts for fifteen programs..........................................................37``Appendix 4. Typology of program performance predictions ................................................... 38
Invitation to Join GALI .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .40

3

W H AT ’ S WO R K I N G I N S TA R T U P AC C E L E R AT I O NIntroduction

and Research Motivation
There is growing interest among public andprivate sector organizations in supporting
small and growing businesses as a catalyst
for economic development around theworld. This is stimulating a range of support
mechanisms for early stage entrepreneurs,
including incubators, angel investor networks,
training programs and, more recently,
accelerator programs.1
Accelerators, which emerged in 2005 with the launch of Y-Combinator, differ from otherentrepreneurial support programs in several ways:2``They tend to be limited in duration, typically offering roughly three months
of intense training and mentorship.

``They tend to select and work with cohorts of early stage entrepreneurs thatgo through the program as a single group.

``Finally, accelerators aim to facilitate greater connections with potential
investors so that entrepreneurs can raise the growth capital necessary toaccelerate their ventures.3 4
In the past decade, a wide array of acceleration models have emerged around the world, both indeveloped countries like the United States and the Netherlands and in emerging economies such
as India, South Africa, Kenya, Mexico, and Brazil. These programs receive support from a rangeof sources, including governments, foundations, corporations, and universities.5 For example, the1

Kempner, R. (2013). Incubators are popping up like wildflowers. Innovations, 8(3-4), 3-6.

2

Cohen, S. (2013). What do accelerators do? Insights from incubators and angels. Innovations, 8(3-4), 19-25.

3Cohen, S. & Hochberg, Y. V. (2014). Accelerating startups: The seed accelerator phenomenon. Available at SSRN 2418000.

4Miller, P. & Bound, K. (2011). The startup factories: The rise of accelerator programmes to support new technology ventures. NESTA.

5

Dempwolf, C. S., Auer, J. & D’Ippolito, M. (2014). Innovation accelerators: Defining characteristics among startup assistanceorganizations. SBAHQ-13-M-0197. US Small Business Administration.

I N S I G H T S F R O M F I F T E E N V I L L AG E C A P I TA L P R O G R A M S

4World Bank’s infoDev program provides grant funding for accelerators working in developing
countries. The Argidius Foundation also supports accelerator programs, including those run by
Agora Partnerships and Technoserve in Latin America. As an example of corporate support, bothcompanies and corporate foundations provide funding for the various cohorts of Points of Light
Civic Accelerator entrepreneurs. Lastly, Santa Clara University is a prime example of university
support, running the GSBI Accelerator through its Miller Center for Social Entrepreneurship.
These numerous acceleration programs, along with their cohort-based designs and relatively
short durations, provide settings that should be appealing for researchers. However, very few
published studies provide systematic insights about the impacts that accelerators are having on
the ability of early-stage ventures to grow revenues and employees and to attract outside
investment.6 Therefore, despite the existence of hundreds of programs around the world, we
currently know little about their effectiveness or how differences across programs and models
influence entrepreneur performance.
To address this shortcoming, Social Enterprise @ Goizueta at Emory University and the Aspen
Network of Development Entrepreneurs (ANDE) launched the Global Accelerator Learning Initiative(GALI) in collaboration with a consortium of public and private funders. GALI builds on the workof the Entrepreneurship Database Program at Emory, which has been working with accelerator
programs around the world to collect and analyze data describing the many entrepreneurs thatthey attract and support.
The Entrepreneurship Database Program works with a range of accelerators to collect systematic
data from every entrepreneur who applies. Then, roughly six months after the completion of eachprogram, participating and rejected entrepreneurs are re-surveyed to capture changes in venture
performance. When aggregated, these data:``Provide detailed information about thousands of entrepreneurs who
sought the services of accelerator programs;``Allow researchers to compare the performance of the two groups ofparticipating and rejected entrepreneurs to generate reliable insights aboutprogram effectiveness; and``Allow sector stakeholders to track changes in different variables todetermine whether and how participating in an accelerator program has an
impact on early-stage ventures.7
The first accelerator to work with the Entrepreneurship Database Program was Village Capital.
Village Capital is a seed-stage accelerator that runs a number of programs that focus on supporting
entrepreneurs in impact-oriented sectors such as agriculture, energy, education, financial inclusion,and health (see Figure 1 for an overview of the Village Capital model). They joined the Entrepreneurship
Database Program in 2013, seeking to better understand the impacts of their various programs.
By the end of 2015, application and follow-up data had been collected from fifteen different Village
Capital programs (see Appendix 1 for a list of these fifteen programs).

6

Kempner, R. & Roberts, P.W. Aren’t accelerators great? Maybe… WSJ Accelerators Blog. April 10, 2015.

7

For more information about our data, reports, publications and press coverage, visit www.entrepreneurdata.com or www.
andeglobal.org/accelerators.

5

W H AT ’ S WO R K I N G I N S TA R T U P AC C E L E R AT I O NTHE BASIC VILLAGE CAPITAL MODEL figure 01 

1

2

3

DETERMINERECRUITWORKASSESS

to reﬁne businessmodels through: Customer interaction
 Hypothesis testing Mentor value-add
 Investor engagement

one another at theend of the program;
entrepreneurs thatare top-ranked bytheir peers receive
seed capital

the location andsector of program
and recruitorganizational
partners

ﬁfteenentrepreneurs
with uniquesolutions to major
problems

4

Ultimately, this report aims to provide accelerator program managers and funders with practicallessons and insights to consider for their own programs, based on the experiences and analysisof these fifteen Village Capital programs. Through this and other research efforts, we hope tostimulate more evidence-based learning and practice improvements in the entrepreneurial
acceleration sector.

Research methodology

The Village Capital data include application and follow-up information from hundreds ofentrepreneurs who applied to similar-but-different programs run in different sectors and regions
around the world. They present a unique opportunity to examine the performance of ventures
accelerated by a range of Village Capital programs and to compare their performance toentrepreneurs who applied to these same programs but were not selected.
In this report, these venture-level data are used to estimate the short-term impacts of the different
Village Capital programs in their ability to drive revenue, employee and investment growth. We
estimate each program-level impact by calculating the average year-over-year change in revenues,
full-time employees and investment levels (equity, debt and philanthropy) for entrepreneurs who
participated in a program and for those who applied but were not accepted.
These calculations allow us to sort the fifteen programs by degree of impact, from those with very
positive impacts to those with smaller — or even negative — impacts. With these program-level
performance contrasts in hand, we engaged a team of Village Capital program experts to brainstorm
potential explanations for these contrasts. These discussions yielded several predictions as towhy certain accelerator programs outperformed others. We put the most promising of thesepredictions in front of our research team to devise specific research strategies for addressing
each of them. In some cases, this called for a return to the application data. In other cases, it called
for information that was gleaned from a carefully-designed survey for program managers. In theremaining cases, it called for structured interviews with entrepreneurs, mentors and other program
stakeholders.

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6This combination of expert predictions and carefully-collected data provided several findings thatare relevant to the people responsible for designing and running accelerator programs; bothVillage Capital and other early-stage accelerator programs. We present these findings — related
to the importance of program partners, pipeline development, entrepreneur selection and program
design — in the main section of this report.
In the final section of this report, we provide guidance about the implications of these findings.
Here, we ask decision-makers within Village Capital to make sense of the findings and to determine
how to turn these insights into action.

About the programs in this report

A range of public and private institutions are excited about the potential that accelerators haveto transform early-stage ventures into world-changing companies. However, despite this enthusiasm,
rigorous research on the actual and potential effectiveness of entrepreneurial acceleration has
not kept pace with the proliferation of accelerator programs. There are literally hundreds ofaccelerators working in different sectors and regions around the world. Each has been working
with subtly different models, but all are trying to grow early-stage ventures into businesses withthe potential to scale. Despite these many pseudo-experiments with entrepreneurial acceleration,
few studies have been able to leverage the data — both quantitative and qualitative — that these
programs are generating to learn about what is and is not working.

Global Accelerator Learning Initiative

The Global Accelerator Learning Initiative (GALI) is a recently-launched collaboration between the
Aspen Network of Development Entrepreneurs (ANDE) and Social Enterprise @ Goizueta (SE@G)at Emory University. GALI is set up to explore and answer key questions about acceleration, suchas: Do acceleration programs contribute to revenue growth? Do they help early-stage ventures
attract investment? Do they work as well for developing-world entrepreneurs as they do for thosein the developed world?
This report begins to address this critical research gap by leveraging several years of commitment
on the part of three complementary organizations:

Aspen Network of Development Entrepreneurs

The Aspen Network of Development Entrepreneurs (ANDE) is a global network of organizations
that propel entrepreneurship in emerging markets. ANDE members provide critical financial,educational, and business support services to small and growing businesses (SGBs) based on theconviction that SGBs will create jobs, stimulate long-term economic growth, and produce
environmental and social benefits. Ultimately, ANDE believes that SGBs can help lift countries outof poverty. ANDE is a program of of the Aspen Institute, an educational and policy studies
organization.

Social Enterprise @ Goizueta

Social Enterprise @ Goizueta is a research center within Emory University that applies businessacumen and market-based solutions to achieve meaningful and enduring societal impacts. SE@G
is home to the Entrepreneurship Database Program. This program partners with a range ofaccelerator programs, integrating a core set of questions into their application processes. Then,the program follows up with every entrepreneur who applied to these programs, including those
who were not selected for acceleration. The comparative, longitudinal data that are collected andcollated allow for a deeper understanding of how different programs are affecting the growth ofearly-stage ventures.

7

W H AT ’ S WO R K I N G I N S TA R T U P AC C E L E R AT I O NVillage Capital

Village Capital is a peer-selected accelerator program with a global vision and a community focus.
Their early-stage entrepreneurs and investors thrive in communities driven by extensive training,
experienced mentorship, and data-driven research. Village Capital focuses on stimulating
collaboration among entrepreneurs within each of their programs. In many cases, cohorts focus
on a specific societal issue; honing in on specific sectors like agriculture, education, energy, financial
inclusion, and health. Focusing on specific sectors allows for peer-to-peer mentorship that may
not be possible with more general programs. This produces a true immersion of entrepreneursin each of the ventures. With this level of involvement, the entrepreneurs in each cohort are given
the opportunity to select which ventures will receive $50,000+ investments from Village Capitaland its partners at the conclusion of each program. This peer-selection model sets Village Capitalapart from other programs. In 2013, Village Capital’s peer-to-peer model won the Harvard Business
Review/McKinsey & Co.’s M-Prize Award for innovation.

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8

9W H AT ’ S WO R K I N G I N S TA R T U P AC C E L E R AT I O NPART 1:The Early Impacts of Acceleration:
Honing in on Program Performance Contrasts
Honing in on three metrics

The main challenge faced when studying the effectiveness of accelerator programs is that different
programs seek to accelerate different things. To get a sense for the full range of variables that
accelerators might focus on, consider Endeavor, which ‘works to catalyze long-term economic
growth by selecting, mentoring, and accelerating the best high-impact entrepreneurs worldwide.’A quick look at the impact dashboard for Endeavor shows the wide range of entrepreneurial
outcomes — related to human, financial, intellectual and social capital — that accelerators mightseek to promote, especially programs that work in developing countries and sectors.
Among the indicators tracked on Endeavor’s impact dashboard are those related to revenue,
employment and investment. Given the almost universal desire to promote commercially sustainable
and scalable businesses, and given the importance of these three variables in these businessprocesses, this report focuses on the ability of Village Capital programs to accelerate the growthof revenues, full-time employment and investment (equity, debt and philanthropic) (see Box 1, fordetails about the revenue, employment and investment data used in this report). These threevariables are clearly among those that Village Capital programs seek to accelerate and are also
relevant to most of the other accelerators in our data. The Village Capital website reports that“over the past five years, program graduates have reached 6 million customers, created over 7,000
jobs, and raised more than $110 million in follow-on capital.”
A second challenge arises from the fact that the performance of non-selected ventures also changes
over time. As such, it is not enough to show that accelerated ventures grew their revenues, employment
or investment levels. Rather, the analysis must show that participating entrepreneurs are growing
these variables faster on average than those who applied but were not accepted into programs.
A final challenge relates to the timeframe of the analysis. The underlying goal of accelerator
programs is to stimulate the long-term development of the most promising entrepreneurs. However,most programs do the bulk of their acceleration work within a short window during the earliest
stages of venture development. Therefore, the data that we currently have access to focus on the
short-term (i.e., current year) effects of acceleration. Therefore, we must caution readers to becareful in interpreting the long-term implications of acceleration based on these initial results.
While it is critical to better understand the effects of acceleration “in the moment,” it is entirely
plausible that some of the most important effects of accelerators like the Village Capital programsmight not be evident for several years.8
8

We will examine longer-term impacts in future studies as we continue to track the performance of participating and rejectedventures over time.

I N S I G H T S F R O M F I F T E E N V I L L AG E C A P I TA L P R O G R A M S

10BOX 01

Venture-Level Revenue,
Employment and Investment Data

Several core survey questions generated the revenue, employment and investment data used in thisreport. As part of the application for each Village Capital program, entrepreneurs were asked thefollowing questions. When responding, they were instructed to focus on the most recent calendar
year. Roughly six months after the close of each program, entrepreneurs — both participating andrejected — were asked the same questions again, this time focusing on the calendar year in which theprogram took place. By comparing responses from these application and follow-up surveys, we areable to track the year-over-year changes in revenues, employees and investment for both rejected
and participating entrepreneurs.
Using entrepreneurs who applied in 2013 as one example, the questions below are those asked
during application and in the follow-up survey (in parenthesis):

What was your venture’s
total earned revenue incalendar year 2012 (2013)?

How much did your ventureborrow from all sources incalendar year 2012 (2013)?

Not counting founders, on
December 31, 2012 (2013),
how many people worked
for your venture?

How much philanthropic
support did your venture
receive from all outside
sources in calendar year
2012 (2013)?

How much equity financing
did your venture obtainfrom all outside sources incalendar year 2012 (2013)?
The results in this report are based only on entrepreneurs who reported data at application timeand in the first wave of follow-up surveys. To ensure that our program comparisons were notoverly influenced by outlier observations, we examined the distribution of each of our main
variables (revenues, full-time employees, equity, debt and philanthropic investment) and set aside
those where reported values were clearly outside the typical range for each. For example,
entrepreneurs (and their ventures) were dropped if reported revenues were greater than $US
5,000,000 at application or $US 2,800,000 in the first follow up survey; or if prior-year equity was
greater than $US 1,500,000 at application or $US 1,200,000 in the first follow up survey. A totalof ten observations were set aside; seven rejected entrepreneurs and three participating
entrepreneurs.

11

W H AT ’ S WO R K I N G I N S TA R T U P AC C E L E R AT I O NImpacts of acceleration in year one:
Examining early program performance outcomes

Before looking at the programs individually, it is useful to examine all fifteen Village Capital programs
together. The information in Table 1 is based on application and follow-up surveys completed by
138 entrepreneurs who participated in one of the Village Capital programs in this report andanother 427 who applied but were not accepted.9

FIFTEEN VILLAGE CAPITAL PROGRAMS

 table 01 

REJECTEDENTREPRENEURSAVERAGEPARTICIPATINGENTREPRENEURSAVERAGE

1-YearRevenue Growth$7,934$11,329

1-Year
Employee Growth

0.95
employees

1.36
employees

1-YearInvestment Growth$6,274$54,236

Equity$2,570$24,588Debt$2,357$16,410

Philanthropy$1,347$13,238

427

138

Sample Size
Statistically significant difference at the p<.05 level:

YES

—

NO

Table 1 presents the average year-over-year change in reported revenues, full-time employees
and investment levels observed during the year in which programs were run. The first column
shows the average changes among the entrepreneurs who applied but were not accepted, while
the second column shows the average changes for entrepreneurs who participated in a Village
Capital program.
The first thing to note about Table 1 is that all of the cell values are positive. When we think about
the impacts of accelerator programs, we must account for the fact that the rejected pools ofentrepreneurs and ventures are also changing over time. Therefore, instead of simply looking at
the raw change in numbers for participating entrepreneurs, we must compare these changes tothe hold out group of rejected entrepreneurs.
In this respect, the second observation from Table 1 is that for each variable (revenue, employee
and investment growth), the reported average for participating entrepreneurs is greater thanthe corresponding average for the rejected pool. However, many of these differences are notstatistically significant.
9

The information in Table 1 is influenced by response rates. It is therefore important to consider whether survey respondents
are “representative” in the participating and rejected samples. A total of 138/178 (78%) of participating entrepreneurs provided
follow-up data and are therefore included in the sample. Supplementary analysis reveals that these respondents are notsignificantly different from non-respondents in terms of revenues, employees, equity, debt or philanthropy investment levels
reported at application. Among the rejected entrepreneurs, 427/1008 (43%) provided follow-up data. Here, respondents are
not significantly different in terms of revenues, employees, equity or debt at application. However, note that respondents weresignificantly higher than non-respondents in terms of philanthropy levels reported at application.

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12

BOX 02

Program Performance for all 28 Programs
As we consider generalizing the findings of this study to non-Village Capital accelerator programs,
it is important to see these overall Village Capital impacts in the context of those observed in thefull sample of programs that participated in the Entrepreneurship Database Program during the2013 to 2014 period.
Among all programs, including the fifteen Village Capital programs profiled in this study, we seea larger and significant impact on reported revenues. The 852 rejected entrepreneurs grew
revenues by an average of $1,359 while 335 participating entrepreneurs reported an averageincrease of $32,965. At the same time, the impact of acceleration on investment was more modest,with participating entrepreneurs growing total investment by $34,528, compared to $11,255 forrejected entrepreneurs.

REJECTEDENTREPRENEURSAVERAGEPARTICIPATINGENTREPRENEURSAVERAGE

1-Year Revenue Growth$1,359$32,965

1-Year Employee Growth

0.66

1.11

1-Year Investment Growth$11,255$34,528

Equity$4,951$14,792

Debt$2,436$7,520Philanthropy$3,369$12,216852

335

Sample Size

Statistically significant difference at the p<.05 level:

YES

—

NO

The most obvious short-term Village Capital impact is on investment levels. While the average
rejected entrepreneur increased new investment (equity, debt and philanthropy combined) by$6,274, the average program participant grew investment by $54,236. This more than eight-folddifference is statistically significant. Moreover, it is distributed widely across investment sources.
During their year of acceleration, Village Capital entrepreneurs grew equity investment by morethan nine-fold, new debt by almost seven-fold, and new philanthropic investment by almost tenfold relative to the entrepreneurs who applied but were not accepted.

13

W H AT ’ S WO R K I N G I N S TA R T U P AC C E L E R AT I O NThese numbers shed light on one very important question: are accelerator programs working?
Here, the answer seems to be “yes.” Across the board, accelerated ventures show higher growthin the variables that correlate with future success. These effects are especially prominent (andconsistent) when it comes to driving investment toward promising early-stage ventures.10

OUTLINE OF OUR RESEARCH PROCESS

 figure 02 

2 “MOST-POSITIVE” PROGRAMS

1
2
4 HIGH-PERFORMING
PROGRAMS

3
4
5
6

15

COMPARED
performance of
accepted versus
rejected entrepreneurs

7

VILLAGE CAPITALPROGRAMS

8
9
10
11

IDENTIFIED
the most positive andnegative-performing
programs based onthese diﬀerentials

FOLLOWEDthe same
process foreach metric:
 revenue
 employees
 investment

12

RESULT

4 LOW-PERFORMING
PROGRAMS

13
14
15
2 “MOST-NEGATIVE” PROGRAMSACCEPTEDREJECTED

10 This report does not aspire to provide the last word on the “does acceleration work” question. Rather, our goal is to begin
unpacking the acceleration model and to offer practical guidance to program managers and funders about more and lesspromising practices. Thus, it is beyond the scope of this report to determine whether the overall investment impact is dueto the “treatment” effect of actually participating in a specific program, or to Village Capital’s specific ability to locate andselect more promising entrepreneurs into their programs.

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14

However, the main goal of this report is to dig deeper and learn from prominent performance
contrasts observed across the fifteen Village Capital programs (see Figure 2). Here, we leverage
the fact that some Village Capital programs do better than others when it comes to accelerating
early-stage ventures. To hone in on these program performance contrasts, we identified programsin the sample with the largest and smallest differentials between the averages observed forparticipating and rejected entrepreneurs. We isolated the “most positive” two programs and the“most negative” two programs for each of our three metrics — revenues, employees and investment
levels. Then, because the correlations between changes in revenues, employees and investment
are typically quite low (see Appendix 2), we ensured that the candidate programs also fared
reasonably well (or reasonable poorly) on the other two metrics.

HIGHEST VERSUS LOWEST-PERFORMING VILLAGE CAPITAL PROGRAMSHighest-Performing

 table 02 

Lowest-Performing

1-YEARREVENUEGROWTHDIFFERENCE

1-YEAREMPLOYEEGROWTHDIFFERENCE

1-YEARINVESTMENTGROWTHDIFFERENCEAPPLICATIONYEARCOUNTRY
TYPE

TECHFOCUSEDAgriculture &Cleantech: Louisville2013

Developed

Some
what

$73,882

1.09$84,528

FinTech Mexico

2014

Developing

Yes$108,777

1.42$21,398

Energy: Boulder &Houston (US)2014

Developed

No$18,109

0.81$141,888

EdTech:DC & Chicago (US)2014

Developed

Yes$114,667

3.28$97,478

Impact: Nairobi

2013

DevelopingNo$21,812-0.46$10,941

Health IT: Houston &Salt Lake City (US)2014

DevelopedYes-$343,658-2.88$55,689

Kenya: Innovations
for Agriculture

2014

Developing

Yes-$169,249

0.30$23,128

Last Mile:
Ahmedabad

2014

DevelopingNo-$4,700-2.27$21,626

PROGRAM

15

Table 2 presents the differences in accelerator program performance that we will emphasize inthe next section of the report (see Appendix 3 for the full range of program performance outcomes).
Among the four high-performing programs, the EdTech: DC & Chicago (US) program performed
well across all three metrics, while the Agriculture & Cleantech: Louisville and Energy: Boulder &Houston (US) programs performed well on investment growth and reasonably well on the othertwo metrics. Finally, the FinTech Mexico program performed well on revenue growth. At the otherend of the spectrum, the four low-performing programs included cases where the rejected
entrepreneur group actually out-performed the group of participating entrepreneurs. Revenue
growth differentials were negative for the Health IT: Houston & Salt Lake City (US) and Kenya:
Innovations for Agriculture programs, while employee growth differentials were negative for the
Health IT: Houston & Salt Lake City (US) and Last Mile: Ahmedabad programs.
With these observations in hand, we shift from answering the general question of whether
acceleration is working to the more focused task of explaining these performance differences. Thisis simplified by the fact that Village Capital programs share a number of consistent program elements.
For example, every program makes multiple $50K investments in the most promising ventures,with the investment decisions being made by participating entrepreneurs. These consistent program
elements should, all else equal, produce consistent performance outcomes across programs.
However, a number of factors that vary across programs might account for the observed performancedifferences. For example, Table 2 shows that three of the four high-performing programs were run
in developed countries, while three of the four low-performing programs were run in developing
countries (see Box 3 for more information on region and year effects).

Explaining performance contrasts

To get a handle on the full range of factors that might account for performance differences acrossprograms, we assembled a small group of Village Capital staff with experience on one or moreprograms and presented them with the starkest program performance differences. We asked
each of them to brainstorm all of the possible reasons why we might be seeing these performancedifferences.11 In this way, the predictions about accelerator effectiveness that we consider inthis report are based on the collective judgment of individuals with deep experiencerunning accelerator programs.
This brainstorming exercise generated a list of 133 potential reasons for accelerator program
performance differences. With these in hand, our research team organized and consolidated thevarious explanations into a concise typology of categories and sub-categories. The major categories
of explanations revealed by the program experts include: general, pipeline development, selection
and program design (including curriculum, mentorship, and investment). Another more generalcategory of explanations focuses on the program’s context; i.e., the country, sector and year of
the program. The final category of explanation focuses on the quality of the data used to produce
the observations (see Appendix 5 for the complete typology of factors).
This typology provided structure for analyzing how specific program elements might influence
accelerator program outcomes. To ensure that this report remains focused on the most promising
factors (as identified by our program experts), we settled on the following small set of potential
explanations that were raised most often during the brainstorming session (see Figure 3).
11 The specific instructions to the Village Capital team were … “For each of the three charts below, please think for a moment andthen fill in the blanks to suggest all plausible reasons why there may be differences between the programs that had positive
impacts on cohorts and those that had negative impacts. You do not have to believe every one of your listed reasons. We would
like to be exhaustive in our development of possible explanations. When you are finished, however, please indicate which of
the listed explanations you think are most plausible."

I N S I G H T S F R O M F I F T E E N V I L L AG E C A P I TA L P R O G R A M S

16

BOX 03

Year and Country Effects on
Village Capital Program Impacts

Programs from both application years and from developed and developing countries are foundin each of the high-performing and low-performing groups. However, when interpreting the (mostly
univariate) results presented in this report, it is important to remember that year and country
context clearly matter. For example, the overall Village Capital revenue impact was much higher
for programs that launched applications in 2013, while the investment impact was noticeably
higher in that latter year. At the same time, the Village Capital revenue impact is higher on averagefor programs run in developing countries, while its investment impact is greater in developedcountry programs.
APPLICATIONYEARREJECTEDENTREPRENEURSAVERAGEPARTICIPATINGENTREPRENEURSAVERAGEREJECTEDENTREPRENEURSAVERAGE

COUNTRY
TYPE

1-Year Revenue Growth

1-Year Revenue Growth
2013($11,947)$36,503

Developed$21,069$4,3642014$17,944$315

Developing($4,082)$19,381

1-Year Employee Growth

1-Year Employee Growth
2013

0.66

1.38

Developed

0.44

0.92

2014

1.1

1.35

Developing

1.42

1.88

1-Year Investment Growth

1-Year Investment Growth

17

PARTICIPATINGENTREPRENEURSAVERAGE2013($5,070)$31,163

Developed$10,091$83,534

2014$11,986$64,330Developing$2,783$20,360W H AT ’ S WO R K I N G I N S TA R T U P AC C E L E R AT I O N

PREDICTIONS EXAMINED IN THIS REPORT figure 03 

GENERAL FACTORS
Partner quality
Time spent on
program activities

PIPELINE
What factors might
explain thediﬀerences between
highest and lowestperforming
programs?

Brainstorm with
Village Capital
program experts –133 potential
reasons

Construct typology
of categories andidentify mostpopular predictions

Quality of applicant pool

SELECTION
What do selectors
emphasize?

PROGRAM DESIGN
Networking
Financials andaccounting training
Mentor quality

With these seven predictions in hand, we designed research strategies to assess each one. In
some cases, the prediction took us back to the detailed application data. To shed light on whether
differences in the quality of the applicant pool were consistent with program performanceoutcomes, we compared the applicant data across the high-performing and low-performing
programs. In other cases, validating a prediction required specific information gathered in surveys
of program managers. For example, to examine whether the quality of program partners influenced
the performance of programs, we asked program managers specific questions about the partners
involved in the high-performing and low-performing programs. Finally, some of the predictions
required more qualitative insights that come from structured interviews with program stakeholders.
To examine the relationship between mentor quality and program performance, we conductedinterviews with entrepreneurs and other program stakeholders from the high-performing andlow-performing programs.

I N S I G H T S F R O M F I F T E E N V I L L AG E C A P I TA L P R O G R A M S

18

19

W H AT ’ S WO R K I N G I N S TA R T U P AC C E L E R AT I O NPART 2:

Key Findings
In this section, we combine information from the applicationand program-level surveys along with interview data gatheredfrom entrepreneurs, mentors and other program stakeholders
to critically evaluate each of the seven predictions listed in theprevious section.

BOX 04

Average Cost of High- and
Low-Performing Programs

Before digging into our seven predictions, we calculated the average amount of money
spent on the high-performing versus low-performing programs. In our program surveys,
we asked each manager, “to the nearest $10,000, what is the total financial cost associatedwith running this program? Please include all living stipends paid to participating
entrepreneurs, but do not include any financial investments that you expect to make into
the ventures themselves.”
The four high-performing programs had an average spend of $140,321, which is morethan $60,000 more than the average for the four low-performing programs. Note that
this difference is not due to the fact that more low-performing programs were run indeveloping countries. In fact, the averages (across all fifteen programs) were $83,911 fordeveloped-country programs and $118,333 for developing-country programs.
AVERAGE PROGRAM COST

AVERAGE PROGRAM COST

HIGH-PERFORMING PROGRAMS

LOW-PERFORMING PROGRAMS$140,321$80,000I N S I G H T S F R O M F I F T E E N V I L L AG E C A P I TA L P R O G R A M S

20

SUPPORTEDPREDICTION 1
PARTNER QUALITY IMPROVES PROGRAM PERFORMANCE.

SUMMARY ASSESSMENT
Partner organizations were rated much higher in the high-performing programs. Relative
to those that worked on the low-performing programs, these organizations were describedas “engaged”; “putting entrepreneurs first”; and “contributing to program content.”
According to Village Capital records, 31 organizational partners were recruited to work across thefifteen programs. These included Accion Venture Lab (FinTech Mexico), VentureWell (Agriculture& Cleantech: Louisville) and Chilton Capital (Energy: Boulder & Houston (US)). In all cases, partners
were recruited to provide critical complementary resources for the different programs.
We asked three senior Village Capital leaders — each with broad experience across the fifteen
programs — to “give a quick and simple grade to each partner”; with a grade of 1 indicating below
average partner performance; 2 indicating average or expected partner performance; and 3
indicating above-average partner performance. These ratings were based on a “holistic assessmentof the quality of contributions to program effectiveness.” Partner grades were then averaged
across the three Village Capital leaders. Figure 4 shows that partner grades were much higher forthe ten partners that worked on high-performing programs; an average grade of 2.52, comparedto just 1.76 for the nine partners who worked on the low-performing programs.
We then asked these same three Village Capital leaders to “list up to five words that summarize
why a particular program partner was rated above average or below average.” The most common
partner descriptions for the high-performing program partners were “engaged” (four mentions
versus one for the low-performing programs); “put entrepreneurs first” (six versus zero); and“contributed to program content” (four versus zero). On the other hand, the most common partner
descriptions for the low-performing programs were “misfit” (three versus zero for the highperforming programs) and “disorganized” (three versus zero). In between, the partner descriptions
that were observed with similar frequency across performance groups were “contributed tocredibility” and “co-invested in ventures.”

PROGRAM PARTNER QUALITY

Low-Performing
Programs(9 Partners)

 figure 04 

1.76

High-Performing
Programs(10 Partners)

1.0
Below
Average

21

2.52

1.5

2.0
Average

2.5

3.0
Above
AverageW H AT ’ S WO R K I N G I N S TA R T U P AC C E L E R AT I O NSUPPORTEDPREDICTION 2
TIME SPENT ON PROGRAM-RELATED ACTIVITIES LOWERSPROGRAM PERFORMANCE.

SUMMARY ASSESSMENT
Rather than spending as much time as possible delivering program content, high-performing
programs tended to set aside more time for entrepreneurs to work on their own.
There are concerns among some accelerator program managers and supporters that the timespent “in the classroom” can be detrimental to the early-stage entrepreneur who is trying to launcha successful company. Although time spent in organized activities with other entrepreneurs andmentors can provide valuable insights that might pay off down the road, it might be beneficial to
give cohort entrepreneurs more time to work on their own (or with their own teams) to put a more
limited range of structured insights into practice.
In our program-level surveys, we asked the managers of the fifteen programs to “give us a roughidea of how a typical entrepreneur allocated his/her time.” The responses allowed program
managers to tell us roughly how much time was spent working on site versus remotely, and how
much time was spent working with other entrepreneurs, with mentors or on their own. When weaggregated the responses, we found that the percentage of time spent working on-site or remotelywith other entrepreneurs and/or mentors (versus working on their own) was 53% for the highperforming programs versus 83% for low-performing programs.
SUPPORTED
PREDICTION 3QUALITY OF THE APPLICANT POOL IMPROVES PROGRAMPERFORMANCE.

SUMMARY ASSESSMENT
The high-performing programs had smaller applicant pools on average. However, theirapplicants tended to have more intellectual property and more educational, entrepreneurialand senior management experiences.
To end up with the best cohorts of entrepreneurs, accelerator programs must attract promising
entrepreneurs into their applicant pools. Typically, this involves a lot of work in the months leading
up to the program, spreading the word about the program and its focus so that the most suitedand most promising entrepreneurs apply.
Our comparative analysis of the high-performing and low-performing Village Capital programs
reveal some interesting differences in program applicants (see Table 3). First, program success is
not related to the size of the applicant pools. On average, the low-performing programs selectedfrom almost 100 applicants, while high-performing programs considered roughly 75 applicants.
If — beyond some threshold level — the number of applicants does not influence program
performance, then we must turn our attention to the quality of those applicants. Looking througha range of variables that characterize Village Capital program applicants (all taken from our initial
application surveys), we find that the high-performing programs tended to attract ventures withI N S I G H T S F R O M F I F T E E N V I L L AG E C A P I TA L P R O G R A M S

22significantly more intellectual property — more patents, copyrights and trademarks. At the sametime, the entrepreneurs who applied to these high-performing programs had more college degrees
and more prior for-profit (but not nonprofit) founding experience. They were also more likely tohave at least one founder with CEO or executive director experience in another company.
These differences — especially those that relate to entrepreneurial credentials — are not particularly
surprising. However, they validate the importance of having more promising entrepreneurs in theapplicant pool, as opposed to larger numbers of applicants.

APPLICANT POOL CHARACTERISTICS

 table 03 
AVERAGEFOR HIGHPERFORMING
PROGRAMSAVERAGEFOR LOWPERFORMING
PROGRAMS

Total applicants

75.6

98.5

Percentage with patents

27.5%

21.9%

Percentage with copyrights

20.5%

14.0%

Percentage with trademarks

39.7%

27.0%

Percentage of teams with college degrees

56.2%

39.0%

Percentage of teams with prior For-profit
founding experience

68.9%

57.1%

Percentage of teams with prior Nonprofit
founding experience

25.8%

27.0%

Percentage of teams with CEO / ED experience

46.0%

31.4%

Statistically significant difference at the p<.05 level:

NO

YES

—

NOT SUPPORTEDPREDICTION 4
MORE ADVANCED VENTURES BENEFIT MORE FROMACCELERATION.

SUMMARY ASSESSMENT
Program selectors for the high-performing programs placed more emphasis on the quality
or promise of the underlying idea than the venture itself. This led them to select venturesthat were younger on average.
In our program surveys, we asked program managers “what were selectors asked to emphasize
when they made their selection decisions?” The respondents were asked to indicate how many
points (out of 100) they should allocate to the quality or promise of the idea, the founding team or
the enterprise itself. Responses from the high-performing and low-performing programs allocateda similar number of points (roughly 23 on average) to the quality of the team. However, highperforming programs allocated more points to the quality or promise of the idea (roughly 36 points

23

W H AT ’ S WO R K I N G I N S TA R T U P AC C E L E R AT I O N

I N S I G H T S F R O M F I F T E E N V I L L AG E C A P I TA L P R O G R A M S

24versus roughly 25 for the low-performing programs) and fewer points to the quality or promise of
the current enterprise (roughly 41 points compared to roughly 53 for the low-performing programs).
Consistent with this orientation, the average venture age for program participants was 2.47 years
for low-performing programs versus 1.73 years for high-performing programs.
LIMITED SUPPORTPREDICTION 5
NETWORKING AMONG COHORT MEMBERS IMPROVESPROGRAM PERFORMANCE.

SUMMARY ASSESSMENT
Descriptions of cohort dynamics were mainly positive in both high and low-performing
programs. While the differences were modest, participants in high-performing programs
described the cohorts as being more partnership-oriented and as having more peer-topeer involvement. Participants in low-performing programs did not describe a lack ofpeer-to-peer involvement in their cohorts but emphasized individual qualities, such as
creativity and innovation.
Cohort dynamics were addressed in interviews with entrepreneurs, mentors, and other program
informants. We received thirteen responses from the high-performing programs and eleven fromthe low-performing programs, asking each to list three adjectives that best describe the dynamics
of their cohort, including how entrepreneurs worked together and got along, and to provide
specific examples to support those descriptions.
While the adjectives used to describe cohort dynamics were largely positive among both high andlow-performing programs, we did observe some minor trends:
Participants in high-performing programs described the entrepreneurs as collegial and supportivewith emphasis on action and shared opportunity. They used adjectives such as “collaborative,”“productive,” and “sincere” and shared examples such as “everyone took comments and feedback
well and used them to improve” and “it was a great environment to put it all on the table; avery transparent environment that nurtured a sense of sharing and vulnerability.” Thissuggests entrepreneurs within high-performing cohorts focused more on partnerships andpeer-to-peer involvement.
Alternatively, low-performing cohorts seemed to place more emphasis on innovation and creativity
when describing the entrepreneurs. Adjectives such as “innovative,” “passionate,” and“knowledgeable” were common, citing examples such as “entrepreneurs all wanted to change
the world — always high intensity” and “people knew their sector and shared perspective on how
to sell to specific targets.” This suggests the low-performing programs may have been moreindividualistic and self-sufficient.
Based on these differences, it appears that networking and collaboration within cohorts was more
predominate among the high-performing programs, while innovation within ventures was moreof the focus in low-performing programs. However, as participants from low-performing programs
did not describe their cohorts as not collaborative or not partnership-orientated, these differences
should be considered initial insights rather than definitive findings.

25

W H AT ’ S WO R K I N G I N S TA R T U P AC C E L E R AT I O NNOT SUPPORTEDPREDICTION 6
EMPHASIS ON FINANCIAL ACUMEN IMPROVESPROGRAM PERFORMANCE.

SUMMARY ASSESSMENT
The high-performing programs spent less time working on finance, accounting, and formal
business plan development and more time on presentation and communication skills,
networking, and organization structure and design.
With the heavy emphasis among accelerators on getting early-stage ventures more investmentready, it makes sense to equate higher program performance with a heavier emphasis on financeand accounting. However, when we asked program managers about the emphasis placed on
various topics, the differences between the high-performing and low-performing programs were
somewhat surprising (see Figure 5). On average, the low-performing programs spent relatively
more time on accounting, business plan development and finance. The high-performing programs
spent more time on the softer skills, including networking, organization structure and design, andpresentation and communication skills.
While these differences speak to the direction that accelerator programs might go when refining
deliverables to entrepreneurs, we must be wary about misinterpreting the importance of accounting,business plan development and financial acumen for early-stage entrepreneurs. In this respect,future research must look at the interplay between selection, program content and mentorshipin the acceleration of early-stage entrepreneurs and ventures. For example, it is possible that theentrepreneurial credentials favored during selection (college degrees, prior for-profit
entrepreneurship experience and prior CEO / Executive Director experience) bring these criticalskills into the more successful cohorts.

PERCENT OF EMPHASIS PLACED ON DIFFERENT PROGRAM TOPICS■ High-Performing Program Average figure 05 

■ Low-Performing Program Average
24%

20%
18%

18%

15%

10%

14%
11%

9%

8%
5%

4%

Accounting

11%

Business
Plan
Development

Finance

Human
Relations

8%
5%

9%

8%

5%

Legal

Marketing

Networking

I N S I G H T S F R O M F I F T E E N V I L L AG E C A P I TA L P R O G R A M S

Organization Presentation &Structure & Communication
DesignSkills26

MIXED SUPPORTPREDICTION 7
MENTOR QUALITY IMPROVES PROGRAM PERFORMANCE.

SUMMARY ASSESSMENT
High-performing programs connected entrepreneurs with a larger number of mentors.
However, this did not translate into more time spent with mentors overall. While all
programs tended to use similar individuals as mentors, there is some evidence that
program alumni are not very effective mentors and that including potential customers
as mentors is a good idea.
On average, high-performing Village Capital programs recruited 54 male mentors and 22 female
mentors. On the other hand, the average low-performing program recruited only 29 male and
13 female mentors. Thus, while the gender composition of mentor pools was similar across thetwo groups of programs, the high-performing programs exposed entrepreneurs to a larger
group of mentors.
But who were these mentors? According to our program-level surveys, all Village Capital programs
recruited experienced investors, business practitioners and entrepreneurs to serve as mentors.
It is therefore difficult to provide concrete insights about how mentor backgrounds influenced
program performance. However, it is interesting to note that three of the four low-performing
programs used program alumni as mentors, while none of the high-performing programs did so.
On the other hand, two high-performing programs recruited potential customers to serve as
mentors, while none of the low-performing programs did so.
Notwithstanding differences in the size of mentor pools, program performance was not related
to the total amount of time spent working with these mentors. In fact, the low-performing programs
reported that entrepreneurs spent roughly 34 percent of their program time working (on site andremotely) with mentors. Entrepreneurs in the high-performing programs spent less than 12 percentof their time working with mentors.
Of course, these high-level observations do not provide insights about the more subtle evaluationsof mentors by participating entrepreneurs. We therefore raised questions about mentor quality
in structured interviews with entrepreneurs and other program stakeholders. We asked eachindividual to list three adjectives that best describe their program’s mentors as a whole and toprovide specific examples to support those descriptions. The adjectives used to describe mentors
were mainly positive in both the high- and low-performing programs, with terms like “engaged”
and “helpful” commonly expressed.

27

W H AT ’ S WO R K I N G I N S TA R T U P AC C E L E R AT I O NBOX 05

An Additional Role for Mentors?

In six of the fifteen programs considered in this study, individual mentors were specifically named
during the promotion of the program; in recruitment flyers, emails or on program websites. It isinteresting to note that half of the high-performing programs adopted this practice, compared toonly one of the four low-performing programs. This suggests an additional benefit to having theright mentors — attracting more promising entrepreneurs into the application process.
MENTOR NAMES MENTIONED IN PROMOTION

Low-Performing
Programs

25%

High-Performing
Programs

I N S I G H T S F R O M F I F T E E N V I L L AG E C A P I TA L P R O G R A M S

50%

28

29

W H AT ’ S WO R K I N G I N S TA R T U P AC C E L E R AT I O NImplications for Action
We close this report by respecting the division of labor between
generating data-driven insights and determining what to do withthem. Because Ross Baird and his team are in the best position
to know which findings are most compelling and how Village
Capital plans to react to them, we will share his reflections as themain recommendations from our report.


The second best time

to plant a tree is now. – Chinese proverb

The most difficult part about understanding what works in entrepreneurship is how long it takes
to see true results. Seed funds, incubators, accelerators, and other organizations supporting new
ventures are deploying millions of dollars around the world – and we’ve got very little information
on what is and is not working. To get useful data that inform these questions requires time andeffort. Moreover, the best time to plant this tree, according to our Chinese proverb, was 20 years ago.
We do not have a twenty-year track record at Village Capital. However, for several years, we havebeen working with Social Enterprise @ Goizueta to understand what’s working in our various
programs. We have run over 50 programs over the past seven years. Thanks to the Global
Accelerator Learning Initiative, we are now able to take a systematic look at fifteen of those
programs using solid data.
This report focuses on fifteen programs over the past several years globally, and with the help of
GALI, we have evaluated the differences in (a) how we operated the program, and (b) what difference
these changes made. In doing so, we looked at variables most closely related to improved outcomesfor companies, specifically (1) increased revenue growth; (2) increased job creation; (3) increasein funds raised. Are there differences in programming that get more resources to entrepreneurs
looking to grow, improve their businesses, and ultimately create more jobs?
In looking at the findings in this report, my team sees seven key insights for individuals looking todevelop excellent programs that support the most promising entrepreneurs:

I N S I G H T S F R O M F I F T E E N V I L L AG E C A P I TA L P R O G R A M S

30

have better results with ventures that have some initial revenues, but need
1 Accelerators
to “speed up” investment. Because we run time-bound, limited-engagement programs, we
generate better results for ventures that are based on great ideas and a limited track record, butneed to accelerate the flow of investment funds. If it is true that entrepreneurship is a relay race,
acceleration programs like ours may be better for this critical second lap of the race.
need program partners who will roll up their sleeves. We’ve partnered with a lot of
2 We
organizations and are now getting a better sense of what kinds of partners are better for ourentrepreneurs. Partners that contribute to curriculum and play a meaningful role in programming
tend to yield better outcomes. On the other hand, while they are really attractive, partners who
simply add brand value (many of which are corporations) do not figure strongly into positive
program outcomes.
the applicant pool, focus on quality not quantity. Many programs — ourselves included — use
3 Forquantity of applications as a proxy for program value. “We had 300 applications and picked justfifteen — we are more selective than Harvard!” These findings tell us that the expected quality of
applicants (not surprisingly) has a stronger relationship with good program outcomes. What was
somewhat surprising is that the quantity of applicants actually had an inverse relationship with
program outcomes. This really makes us think about the amount of time (and bragging) we spend
trying to drive those application numbers upward.
is more’ when it comes to program content. Programs where entrepreneurs spend a lot
4 ‘Lessof time in the classroom — listening to guest speakers or to us teaching — have inferior outcomes.
The more remote work we allow, the better. Accelerators would do well to design content assomething that entrepreneurs do remotely as they work on their businesses, and save the on-sitetime for building valuable relationships.
need to focus more on building entrepreneurial networks and less on delivering
5 Programs
content. Many promising entrepreneurs don’t have access to resources because they don’t havethe right networks. They didn’t go to the right schools, or don’t speak the same language as
investors (in emerging markets, quite literally). The program time that is most closely related tobetter program outcomes was time that focused on overcoming these networking andcommunication challenges. Because many of our best entrepreneurs don’t come from wellconnected backgrounds, networking sessions are literally levelling the playing field.

31

W H AT ’ S WO R K I N G I N S TA R T U P AC C E L E R AT I O Nunderstanding financials is clearly necessary for investment readiness, we should
6 Whilenot be building more content or classes around finance and accounting. This is a provocative
finding that makes us think about how we accelerate a necessary set of skills without taking ourentrepreneurs to school.
you’re an entrepreneur, don’t take accelerators at their word when they say “we provide
7 Ifmentorship”—ask
who those mentors are and what they will be doing. We know thatmentorship is critically important to what we do! In this respect, our programs do better when
we expose our entrepreneurs to a broader set of mentors. However, we are not seeing the
expected relationships between time spent with mentors and program outcomes. If I had to placea bet on where this research will go in the future, I would say that who we recruit is extremely
important, and that entrepreneurs will want to know how we plan to expand their networks during
our programs.
These are our initial reactions to the findings presented in this report. As the companies we haveworked with continue to mature and grow, and as we continue to modify our programming into
the future, we look forward to more opportunities to reflect on the data we are generating toensure that our entrepreneurs are best served by the time they spend with us.
Ross Baird, Executive Director, Village Capital

I N S I G H T S F R O M F I F T E E N V I L L AG E C A P I TA L P R O G R A M S

32

Postscript
This report leverages two years of collaboration between GALI and Village Capital. Between 2013
and 2015, this partnership collected and collated data from hundreds of entrepreneurs who
applied to fifteen different Village Capital programs. With these data in hand, we deployed ablended research methodology to assess seven specific predictions about the drivers of accelerator
program performance that were developed by a team of program experts.
Some of our findings are equivocal. For instance, while we still believe that subtle differences incohort dynamics will influence program performance, we were unable to provide reliable insights
about their specific effects. In this respect, this report should be considered the first in a streamof research that uses quantitative and qualitative evidence to test specific ideas about the driversof accelerator program effectiveness.
Some of our findings are interesting, although not completely surprising. For example, it seems
sensible that programs that attract entrepreneurs with superior educational, entrepreneurial andprofessional credentials will deliver better program-level performance.
The remaining findings are the ones that lead to changes in how program managers think about
designing and executing accelerators. For example:`` Program performance depends less on the size of applicant pools andmore on their composition;`` Programs that spend less time on finance, accounting and business plandevelopment perform better than the others;`` Programs that allow more time for entrepreneurs to work on their own tend
to experience better performance; and`` Organizational partners that are willing to engage with entrepreneurs andto work on program content are more valuable than those that simply
contribute to the program’s brand or credibility.
It is clear from the reactions from Ross Baird (in the previous section) that these latter findings
are specifically relevant to future Village Capital programs. It is our hope — and belief — that they
are also relevant to other programs that work in similar sectors and regions using similar program
methodologies.
However, it is also clear that this report merely scratches the surface when it comes to generating
specific and defensible insights that move the needle on our understanding of acceleration
practices. In the course of completing this report, it became obvious to the research team that:`` There are many more research questions and variables than those coveredin this report (see Appendix 4);`` There are many other accelerator program models that are quite different
from the Village Capital model; and`` There are many (sector and country) contexts that will influence the kindsof observations presented in this report.
In this respect, the GALI team encourages (and stands ready to support) others who are prepared
to see this report as the first among many that will use our expanding dataset to examine specificcause-effect relationships that lie behind effective accelerator program decision-making.

33

W H AT ’ S WO R K I N G I N S TA R T U P AC C E L E R AT I O NAppendices

APPENDIX 1.

Fifteen Village Capital programs
PROGRAM NAMEYEARHOST COUNTRY

FOCUS

PARTICIPANTS

EXAMPLE VENTURES

Agriculture &Cleantech: Louisville

2013

United States

Agriculture

13

Aisle Won, BlocPower,
Future Tech Farm

Agriculture:
Louisville (US)2014

United States

Agriculture

10

FIn Gourmet, Growcentia,
IUNU

EdTech:DC & Chicago (US)2014

United States

Education

11

Mathalicious, MPOWER,
Pear Deck

Edupreneurs:
India

2013

India

Education

14

MangoSense Pvt Ltd,RMinds Education, Sage
School

Edupreneurs:
South Africa

2014South Africa

Education

11

Learning Horizon, Lekki
Peninsula Affordable
Schools, Mea Opus/
Systemic

Energy: Boulder &Houston (US)2014

United States

Energy &Water

10e-Chromic Technologies,
OptiEnz Sensors, PAX Pure

FinTech Mexico

2014

MexicoFinancial
Inclusion

12

BillPocket, Chapulin,
ComproPago

FinTech: Salt Lake
City/Bay Area (US)2014

United States

Financial
Inclusion

11

WiseBanyan, eMoneyPool,
MPOWER

Health IT:
Boston (US)2014

United States

Health12

Kohana, TruClinic, Rimidi
Diabetes

Health IT: Houston &Salt Lake City (US)2014

United States

Health11

VerbalCare, 1DocWay,
Healarium, Inc.

Impact: Amsterdam

2013

Netherlands

Impact10

SolarSwing Energy,
GreenGraffiti, Susteq

Impact: Nairobi

2013

Kenya

Impact14

Dinero Limited, ZanaAfrica
Group, Baobantu Naturals

Kenya: Innovations
for Agriculture

2014

Kenya

Agriculture

10

Develatech Ltd, Esoko,
Mi-Crop

Last Mile:
Ahmedabad

2014

India

Impact13

MicroX Labs, edustbin,
Krishi Star

Tech4Impact:
Ahmedabad

2013

India

Impact8Doctor On Call,
GreenNerds Solutions,
Aakar Innovations

35

W H AT ’ S WO R K I N G I N S TA R T U P AC C E L E R AT I O NAPPENDIX 2.

Correlations among one-year changes
It is important to recognize that the factors that accelerate one of our variables of interest might have different
effects on the other variables. This is clear in the following correlations among one-year growth outcomes in thefull sample of ventures that applied to a Village Capital program and in the subset of ventures that participated.
While there is a modest positive correlation between one-year growth in revenues and employees, the correlation
between investment growth (the variable in which we see the biggest difference between selected and rejected)
and the other two growth variables is roughly zero.
ALL VILLAGE CAPITAL APPLICANTS:REVENUEGROWTHEMPLOYEEGROWTH

Revenue Growth

1.00

Employee Growth

0.21

1.00

Investment Growth

0.03

0.11

INVESTMENTGROWTH

1.00

VILLAGE CAPITAL PARTICIPATING VENTURES:REVENUEGROWTHEMPLOYEEGROWTH

Revenue Growth

1.00

Employee Growth

0.55

1.00

Investment Growth

0.01

0.04

INVESTMENTGROWTH

1.00

I N S I G H T S F R O M F I F T E E N V I L L AG E C A P I TA L P R O G R A M S

36

APPENDIX 3.

Performance contrasts for fifteen programs
COUNTRY
TYPE*

TECHFOCUSEDREVENUE
CHANGEREJECTEDREVENUE
CHANGEPARTICIPATEDFTEMPLOYEESCHANGEREJECTEDFT EMPLOYEESCHANGEPARTICIPATEDDIFFERENCEINVESTMENTCHANGEREJECTEDINVESTMENTCHANGEPARTICIPATEDDIFFERENCEGROUP

PROGRAM

APP
YEAR

—

Edupreneurs: India

2013

DevelopingNo$2,892$6,244$3,352

0.48

2.44

1.97

$10,358$55,111$44,753

High

Energy: Boulder &Houston (US)2014

DevelopedNo-$13,407$4,702$18,109

0.19

1.00

0.81

$12,712$154,600$141,888

Low

Last Mile:
Ahmedabad

2014

DevelopingNo$12,733$8,033-$4,700

2.17

-0.10

-2.27

$16,295$37,922$21,626

Low

Kenya: Innovations
for Agriculture

2014

Developing

Yes$78,193-$91,055-$169,249

3.45

3.75

0.30

-$17,590$5,538$23,128

Low

Impact: Nairobi

2013

DevelopingNo$14,549$36,361$21,812

1.76

1.30

-0.46

$2,043$12,984$10,941­—

Impact: Amsterdam

2013

Developed

No$259,576$87,183-$172,392

-0.56

1.33

1.89

-$20,328$14,083$34,411—

Agriculture:
Louisville (US)2014

Developed

No$20,533$32,275$11,742

0.41

1.33

0.92

-$5,492$44,667$50,158—

Health IT:
Boston (US)2014

Developed Somewhat-$11,063-$2,329$8,734

0.48

0.18

-0.30

$19,179$93,455$74,276High

EdTech: DC &Chicago (US)2014

Developed

Yes$31,644$146,312$114,667

0.72

4.00

3.28

$32,967$130,444$97,478—

FinTech: Salt Lake
City/Bay Area (US)2014

Developed

Yes$34,996$77,483$42,488

0.59

1.00

0.41

$27,676$70,750$43,074

Low

Health IT: Houston &Salt Lake City (US)2014

Developed

Yes$9,211-$334,447-$343,658

0.68

-2.20

-2.88

-$4,789$50,900$55,689—

Edupreneurs: South
Africa2014

Developing Somewhat-$20,904$41,949$62,852

0.00

2.92

2.92

$1,129$4,771$3,642—Tech4Impact:
Ahmedabad

2013

Developing-$117,565-$13,210$104,355

0.29

0.14

-0.15

-$11,313-$17,786-$6,473

High

Agriculture &Cleantech: Louisville2013

Developed Somewhat-$5,614$68,268$73,882

0.31

1.40

1.09

-$12,228$72,300$84,528High

FinTech Mexico

2014

Developing$23,444$132,221$108,777

1.08

2.50

1.42

$18,720$40,118$21,398

Yes

Yes

DIFFERENCE*B
 ased on the World Bank Country Classification. Countries designated as High-Income (with per capita GNI > $12,736)
are classified as Developed, with all others classified as Developing.
I N S I G H T S F R O M F I F T E E N V I L L AG E C A P I TA L P R O G R A M S

37

APPENDIX 4.

Typology of program performance predictions
PREDICTION (TIMES MENTIONED BY VILLAGE CAPITAL PROGRAM EXPERTS)
GENERAL

Sector Focus

The degree of sector focus affects program performance (4)Program Partner Quality

Partner quality improves program performance (4)Program Staff Quality

The quality of program staff improves program performance (4)Program as Distraction

Time spent on program-related activities lowers program performance (5)PIPELINE DEVELOPMENT

Recruiting Team Quality

The quality of the recruiting team improves program performance (5)Applicant Quality

The quality of the applicant pool improves program performance (5)SELECTION

Quality of Process

The quality of the selection process improves program performance (7)Participant Quality

The average promise of selected ventures improves program performance (4)Stage of Development

More advanced companies benefit more from acceleration (6)PROGRAM DESIGN

Cohort Dynamics

Cohort diversity affects program performance (2)Cohort size affects program performance (1)Networking among cohort members improves program performance (5)Curriculum

Emphasis on financial acumen improves program performance (2)Emphasis on organization and hiring improves program performance (2)Mentorship

Mentor quality improves program performance (6)External Connections

Programs with better external connections have better performance (4)Programs with better customer connections have better performance (4)Program Design –Investment

38

Programs with better access to investors have better performance (7)W H AT ’ S WO R K I N G I N S TA R T U P AC C E L E R AT I O NAPPENDIX 4. (CONTINUED)

Typology of program performance predictions
PREDICTION (TIMES MENTIONED BY VILLAGE CAPITAL PROGRAM EXPERTS)DATA / MEASUREMENT ISSUES

Results are driven by outliers (2)Results are driven by response rates (1)Some data are misreported (2)Program benefits will be more obvious in year two (4)YEAR EFFECTS

Program performance varies from year to year (1)SECTOR EFFECTS

Program performance varies systematically across sectors (26)

REGION EFFECTS

Program performance varies systematically across regions (9)OTHER COMMENTS

Revenue, employment and investment performance outcomes areinter-related (8)I N S I G H T S F R O M F I F T E E N V I L L AG E C A P I TA L P R O G R A M S

39

Invitation to Join GALI
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Photos generously provided by:
9 (bottom), 10, 15, 19, 20, 24 (top), 29 (bottom), 30, 32, 34, 41 (top), Back Cover John-Michael Maas/Darby Communications
Cover (top), 3 (top), 4, 8, 9 (top), 17, 28, 31, 36, 39, 41 (bottom) Village Capital40

W H AT ’ S WO R K I N G I N S TA R T U P AC C E L E R AT I O NThe views expressed in this document reflect the personal opinions of the authors
and are entirely the authors’ own. They do not necessarily reflect the opinions of the
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