Paper to be presented at

DRUID15, Rome, June 15-17, 2015

(Coorganized with LUISS)

Swinging for the fences: How do top accelerators impact the trajectories

of new ventures?Sheryl Winston Smith

Temple UniversityFox School of Business, Dept. of Strategic Management

sheryl.winston.smith@temple.edu

Thomas J. HanniganTemple University

Fox School of Business, Department of Strategic Managementtuc70661@temple.edu

AbstractIncreasingly, entrepreneurs in search of critical early stage resources face an evolving paradigm: the rise of acceleratorsthat integrate small equity investments with an intensive, cohort-based mentoring experience. The emergence of theseaccelerators attracts substantial interest in the popular imagination; however scholars know little about their overallimpact. Specifically, in this paper we ask: What is the impact of receiving financing from a top accelerator, relative to thatfrom a top angel group, on the subsequent trajectory of the venture- i.e., being acquired, deciding to quit, or obtainingfollow-on funding from formal venture capitalists (VCs)? To answer this question, we bring to bear a novel,hand-collected dataset of n= 619 startups and their founders. We identify each cohort that has proceeded through twoof the most established accelerators?Y Combinator and Tech Stars?from the period 2005-2011 and construct amatched sample of startups that instead receive their first formal financing from top angel investor groups. We find thatparticipation in a top accelerator program increases the speed of exit. This occurs through two distinct channels:accelerators increase the likelihood of exit by acquisition as well as exit by quitting. We also find that participation in atop accelerator initially increases the speed of receiving follow-on funding from VC investors, particularly in the windowsurrounding the culminating ?Demo Day? presentations. However, in the longer term, participation in a top acceleratorrelative to a top angel group appears to decrease the speed?i.e. decelerate?the timing of follow-on funding from VCs.

Jelcodes:M13,O31

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Swinging for the fences: How do top accelerators impact the trajectories of new ventures?

“There’s so much luck involved with startups you increase your odds of success by swinging the bat multiple times.

Each time you do something that isn’t swinging the bat, you theoretically decrease your odds of success.”

(Harj Taggar, co-founder Auctomatic and partner in Y Combinator, quoted in Stross (2012))

1. Introduction

A long-standing question in the study of entrepreneurship and organizational growth has been: how

do different early resources shape a nascent venture? From an organizational perspective, startups are

resource constrained, yet inherently more malleable and open to advice than established organizations

(Fern et al., 2012, Stinchcombe, 1965). A key challenge faced by young startups is how to secure

sufficient financial and mentoring resources necessary to advance beyond the idea stage (Cassar, 2004,

Eisenhardt and Schoonhoven, 1990, Mollick, 2014), particularly after informal investors have contributed

initial financial support (Kotha and George, 2012, Mollick, 2014). Professional angel groups —with

formal screening mechanisms and investment criteria—have traditionally filled this gap with early-stage

seed capital (DeGennaro, 2012, Ibrahim, 2008, Kerr et al., 2011, Wiltbank and Boeker, 2007).

Increasingly however, entrepreneurs face a shift in the entrepreneurial ecosystem that opens up an

alternative model for formal equity backing and mentorship at a formative stage: the rise of seed

accelerators.

Accelerators have been heralded as a new model of intensified mentoring and equity investment that

facilitates launching a new venture efficiently. Top accelerators integrate small equity investments with

an intensive, cohort-based mentoring experience in a compressed time period (Andruss, 2013, Cohen and

Hochberg, 2014, Gruber et al., 2012). However, although anecdotes abound about the purported role and

success of top accelerators in helping entrepreneurs to “do more faster,” as a notable program proclaims

(Carr, 2012, O'Brien, 2012, Stross, 2012), scholars understand relatively little about how accelerators

might shape the trajectories of new startups relative to other early resources, such as angel investor

groups. In this paper, we ask: how might receiving early equity financing from a top accelerator impact

the trajectory of a new venture? To study more broadly the relationship between early entrepreneurial

financial and mentoring choices and venture outcomes, we compare facets and outcomes of receiving the

first formal outside equity finance from a top accelerator relative to that from a professional angel group.

We treat this problem in two steps: 1) the decision to enter a top accelerator instead of a top angel group;

and 2) the impact of this choice on the subsequent trajectory of the new venture through exit by

acquisition, exit by quitting, or receipt of follow-on formal venture capital (VC) investment.

We frame the expected differences in outcomes as arising from the differences in the structure

and incentives associated with top accelerators relative to top angel groups from the perspective of

entrepreneurial finance and organizational growth. The evolving paradigm of accelerators is important

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because it represents a key juncture for the entrepreneur, when decisions are being made about the

trajectory of the startup. Entrepreneurs face decisions about alternative paths as part of the inherent

evolution of nascent ventures (Arora and Nandkumar, 2009, Parker, 2006). Because accelerator or angel

group funding occurs at a very early stage—and because the nature of mentoring and group interaction in

top accelerators differs dramatically from that of top angel groups—these different sources of early

funding likely condition the subsequent choices of the startup. The entrepreneurial finance literature has

long recognized that the type of financing obtained by startups will influence decisions that entrepreneurs

face revolving around continuation, growth, and exit options (Chemmanur and Fulghieri, 1999, de

Bettignies, 2008, Winton and Yerramilli, 2008). On one hand, the entrepreneur may have exit options,

which may take the form of an attractive acquisition offer or an insight into quitting (Arora and

Nandkumar, 2011). Alternatively, the entrepreneur may attract follow-on funding from VCs. However,

this opportunity is double-edged, simultaneously enabling the growth potential of the company but also

curtailing the founders’ rights (de Bettignies, 2008, Winton and Yerramilli, 2008). Finally, the company

may simply plow forward without growth capital (Åstebro and Winter, 2012). For entrepreneurs, each

option carries distinct implications.

The relative paucity of scholarly attention paid to the longer-term impact of accelerators is partly

a function of the novelty of the phenomenon. The most established accelerators are starting to provide a

sufficient track record to identify distinct trajectories for the startups emerging from the accelerator

experience. We develop a novel dataset consisting of all startups funded by the two top accelerator

programs, Y Combinator and TechStars, over the time period 2005-2011. We create a comparable angel

group sample that covers 19 of the most active professional angel groups spanning a similar range of

industries and geographic locations over this time period. We track the full range of trajectories that each

startup might follow through June 2013: exit through acquisition; exit through quitting; continuation

through VC investment; or remaining alive without VC investment. Thus, we identify outcomes without

selecting on a given event (such as receipt of VC financing) having to occur. Creating a matched sample

controls for differences along observable dimensions, but does not alleviate selection biases that result

from preferences unobservable to the econometrician (Heckman and Vytlacil, 2007). In this paper, we

develop a novel instrument to mitigate selection bias. Specifically, we exploit the relationship between the

development of top accelerator programs and roots in “hacking” culture (Levy, 2010). We leverage the

affinity between of founders coming from an educational institution more heavily steeped in computer

science culture with the “hacking” ethos that underlies the accelerator model to estimate a two-stage

selection model to help account for selection bias that results if the founders or startups selecting into

each of these paths—either a top accelerator or a top angel group—differ systematically.

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We find that after accounting for founder selection into financing from a top accelerator, startups

going through a top accelerator experience significantly quicker exit outcomes through acquisition and

through quitting relative to those in angel groups. The timing of follow-on VC investment is more

nuanced: investment is accelerated in the period following “demo day” but is slower in the longer term

for startups going through the accelerators relative to angel groups.

Our contribution to the literature is two-fold. First, we make a substantial empirical contribution

to the literature on strategic entrepreneurship and entrepreneurial finance. To the best of our knowledge,

we provide the first large-scale, empirical analysis of the effect of accelerators on the full spectrum of

entrepreneurial outcomes: acquisition, quitting, and subsequent VC financing. In so doing, we provide an

empirical answer to the important question: which aspects of the entrepreneurial process do accelerators

accelerate? Second, we provide an important theoretical contribution to the literature on entrepreneurial

finance and the importance of early resources in shaping young firm trajectories. We elucidate a

theoretical underpinning for understanding why accelerators may accelerate exit outcomes— both

acquisition and quitting and why this relationship is more nuanced with respect to follow-on funding by

looking to the incentives and motivations of top tier accelerators relative to those of top tier angel groups.

We show that the earliest choice of equity finance—accelerator compared to angel groups—can have

important consequences for the next stages in performance of the youngest innovation-focused firms.

Taken together, we provide significant insights into an emerging paradigm for the earliest stages of

entrepreneurial finance.

2. Theory and Hypothesis Development

2.1. Institutional Background: Professional Angel Groups and Accelerators

The financial growth lifecycle of startups proceeds from informal, inside sources of growth

capital such as founders, family, and friends, to formal providers of outside financing, e.g., angel

investors and then venture capitalists as equity investors or banks as debt lenders (Aguilera et al., 2008,

Berger and Udell, 1998, Cassar, 2004, Robb and Robinson, 2012, Winston Smith, 2012).

Increasingly, the changing landscape of early-stage entrepreneurial finance points to the top

accelerators as well as top angel groups as premium sources of early outside equity. We provide an

overview of the structure and incentives of angel groups and accelerators below and then develop

expectations regarding the difference in impact on startup trajectories based on established theory and

evidence in the strategy and entrepreneurial finance literature.

2.1.1. Professional Angel Groups

Traditionally, angel investors fill the need for financing after the earliest support provided by

“family and friends”. Professional angel groups are comprised of high net worth individuals who are

accredited investors that co-invest in early stage ventures (DeGennaro and Dwyer, 2013, Kerr, Lerner and

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Schoar, 2011, Wiltbank and Boeker, 2007). A widely accepted definition of angel groups specifies the

criteria of 1) “net worth or accredited investor status of the group members”, and 2) “active participation

of angel group members in the investment of their own capital” (Preston, 2004). Professional angel

groups pool funds of members, which enables larger investments than individual angel investors might

make. However, angel groups are investing their “own” money, in contrast to VCs who raised funds

from investors (Preston, 2004). Investors receive financial returns upon exit, e.g. through an acquisition or

an initial public offering; this rarely occurs, however, without follow-on investment from VCs

(DeGennaro and Dwyer, 2013, Wiltbank and Boeker, 2007).

Investing practices of top angel groups are well-documented in the literature. Top angel groups

employ selection and application criteria and invest in only a small portion of the startups that pitch ideas

to them (Ibrahim, 2008). Angel groups carry out due diligence, invest with formal selection criteria, and

utilize term sheets similar to that of VCs (Kerr, Lerner and Schoar, 2011, Preston, 2004). The investment

criteria of angel groups focus heavily on the characteristics and experience of the entrepreneur and the

growth potential of the market (Novak, 2013, Sudek, 2007). Using a regression discontinuity analysis of

funded and unfunded startups that seek investment from two top angel groups, Tech Coast Angels and

CommonAngels, Kerr et al. (2011) find that financing by top angel groups increases survival and growth

relative to new firms that do not receive angel group financing, which they attribute to both financial

backing and mentorship.

2.1.2. Accelerators

Early-stage entrepreneurial accelerators have emerged as an alternative source of formal outside

equity finance and mentoring. The top accelerators--such as Y Combinator and Tech Stars --pursue high

levels of engagement with start-ups combined with relatively small levels of initial financial capital and

an intensive, typically cohort-based experience. From the perspective of the entrepreneur, the accelerator

provides financial capital, intensive mentoring, and a shared cohort experience as well as heightened

visibility for potential investors.

The top accelerators take a small equity stake in the startup in exchange for a fixed amount of

capital (e.g., Y Combinator currently takes a 7% equity stake for a $120,00 investment per team and

TechStars takes a 6-10% equity stake for up to $118,000 per team). While the specific investment has

fluctuated, these elite accelerators have been characterized by their fairly rigid adherence to roughly

equivalent investments in all startups in a given cohort.

Top accelerators are distinguished by their selection processes and organizational structure. The

hallmark of the top accelerators is the formal, highly competitive application and selection process.

Accelerators have a structured development program, with a pre-determined cohort and length of time,

e.g., three months, in the case of Y Combinator and Tech Stars. Accelerators also provide formalized

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mentoring and are intensively involved with each cohort (Cohen and Feld, 2011, Cohen and Bingham,

2013, Stross, 2012). These cohorts create a portfolio of companies who learn in tandem (Carr, 2012). A

hallmark of the top accelerators is culmination in a “demo day” in which the startups pitch to potential

investors. Preparation for the “demo day” launch shapes the startup experience from the first day in the

program (Carr, 2012, O’Brien, 2012).1

2.2. Expected differences in trajectories associated with accelerator financing relative to angel

group financing

Entrepreneurs face decisions about alternative paths as part of the inherent evolution of the

nascent ventures (Parker, 2006). These include important choices about continuation strategies such as

whether to accept viable acquisition offers, whether to quit , and the appropriateness of accepting VC

investors (Graham, 2003, Wasserman, 2012, Winton and Yerramilli, 2008). In the following section, we

elucidate how these features may result in a differential trajectory for startups in top accelerators relative

to those receiving equity financing from top angel groups.

2.2.1. Exit by acquisition

It is a tenet of entrepreneurial finance that investors—and entrepreneurs—reap returns through a

“successful” exit. This typically occurs through a financial or strategic acquisition, or more rarely,

through an initial public offering (Preston, 2004, Wiltbank and Boeker, 2007). At the same time, both

investors and entrepreneurs face potential dilemmas. For the entrepreneur, the choice is between

continuing to grow the company or deciding to exit (Wasserman, 2012). The paradox for early investors

is that early stage companies will have lower valuations (Wiltbank and Boeker, 2007). The overall

valuation of the company may increase with subsequent rounds of investment (e.g., from VCs). Thus,

there is a tension between lower returns in an early acquisition and the gamble of waiting for subsequent

higher valuation. We expect that accelerators are more likely to advise entrepreneurs to accept earlier

acquisition offers (rather than waiting for follow-on funding) relative to angel groups for two reasons.

First, angel groups receive returns when they cash out from the company. Angels receive higher

exit returns when a startup has greater revenue or customer traction, which might occur with subsequent

rounds of angel investment or through follow-on VC investors (further developing the startup and

increasing its potential valuation) than they would through an acquisition of a very early company. This

is summed up in a widely used saying among angel investors (Villalobos and Payne, 2007): “Lemons sour

quickly but plums take longer to ripen.”

1 The focus on “demo day” marks a clear distinction from related forms of early stage business incubation (i.e., business incubators) have existed for some time. Accelerators also are distinct from incubators along crucial dimensions including the formality of organization, the formation of cohorts, and the focus on a short, fixed period of time in the program (Amezcua et al., 2013, Smilor and Gill Jr., 1986).

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Second, angel groups—by nature of investing their own money in relatively few companies at a

time— look for an “acceptable” return on each company (Ibrahim, 2008, Wiltbank and Boeker, 2007).

Conversely, accelerators operate more like VC investors in that they seek an outsized return on just a few

companies in any given portfolio, while expecting that most companies will bring far lesser returns. This

greater tolerance for the entrepreneurs’ trade-off when faced with an acquisition offer is evidenced below:

“If you take a large amount of money from an investor, you usually give up this option [to sell

yourself when you're small for a few million, rather than take more funding and roll the dice

again]. But we realize (having been there) that an early offer from an acquirer can be very

tempting for a group of young hackers. So if you want to sell early, that's ok. We'd make more if

you went for an IPO, but we're not going to force anyone to do anything they don't want

to.”(YCombinator, 2013)

The net result is that for any given company, accelerators’ incentives are to focus on the entrepreneur

while angel groups’ incentives require a higher likelihood of return on the given company.

Given the relative incentives of accelerators and angel groups, we expect the following:

Hypothesis 1: Startups in entrepreneurial accelerators will exit through acquisition more quickly

relative to startups receiving their first formal financing from angel groups.

2.2.2. Exit by quitting

Learning when to quit when an idea is not reaching fruition allows entrepreneurs to put their

human capital and financial capital to alternative use. Thus, while the literature often focuses on

“successful” outcomes such as follow-on rounds of funding or acquisition, exit by quitting can also be a

beneficial outcome from the perspective of the entrepreneur. For the entrepreneur, these decisions require

calculations on the part of the entrepreneur that take opportunity costs of alternate paths into account

(Arora and Nandkumar, 2011, Gimeno et al., 1997). This allows the entrepreneur the opportunity to

pursue alternatives, which may include starting a subsequent venture that is more likely to succeed.

Two features of the accelerator model stand to accentuate the likelihood of learning to quit. First,

the intensive mentoring experience draws on successful serial entrepreneurs who have often “failed” at

one or more startups and willingly share these lessons with founders (Cohen and Feld, 2011). The

importance of failing quickly is baked into the mentoring model. Individual entrepreneurs tend to be

overoptimistic about the prospects of success (Lowe and Ziedonis, 2006, Simon et al., 2000). As one of

the founders of TechStars backed startup Eventvue observed (Cohen and Feld, 2011): “We didn’t focus

on learning and failing fast until it was too late.” To this end, the founders of the accelerators encourage

insight into the value of quitting based on their prior experiences and broader perspective. As Brad Feld,

co-founder of TechStars, notes (Feld, 2013):

“I strongly believe that there are times you should call it quits on a business. Not everything

works. And — even after trying incredibly hard, and for a long period of time — failure is

sometimes the best option. An entrepreneur shouldn’t view their entrepreneur arc as being linked

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to a single company, and having a lifetime perspective around entrepreneurship helps put the

notion of failure into perspective.”

Second, peer effects rooted in the cohort-based experience of the accelerator model may further

facilitate learning to quit. The intensity of the cohort experience provides founders with a group of peers

going through a similar experience in the same time frame. For example, within the accelerator, each

cohort is seen as a “class” and entrepreneurs who go through a specific program are referred to as

“alumni” and a network develops amongst companies that have gone through the same accelerator

program in different cohorts (Cohen and Feld, 2011, Stross, 2012). This structure mirrors the formation of

cultural capital in the context of university or professional school social bonding and network formation

(Bourdieu, 1986). Recent studies suggest that the bonding ties from attending the same college at the

same time influence subsequent economic and financial decisions, such as investment decisions regarding

portfolio choice, to a greater extent than other aspects of college imprinting, including prestige (Massa

and Simonov, 2011).

Importantly, peer effects may be particularly salient in recognizing when ideas might fail. For

example, strong peer effects contribute to learning when to quit unsuccessful ventures, as found in the

Lerner and Malmendier (2013) study of cohorts of Harvard Business School graduates. Likewise, peer

effects more generally influence the perception of the viability of an entrepreneurial career option

(Kacperczyk, 2013, Stuart and Ding, 2006). Thus, the peer effects associated with accelerator

participation may enable entrepreneurs to more clearly and realistically evaluate the relative chance of

success and hence the value of quitting rather than continuing to burn through resources.

In sum, the mentoring model and the peer influence suggest Hypothesis 2:

Hypothesis 2: Startups in entrepreneurial accelerators will take less time to exit through quitting

relative to startups receiving their first formal financing from angel groups.

2.2.3. Follow-on funding from venture capitalists

Predictions about follow-on funding from VCs require understanding the motivations of founders

as well as the incentives of the earliest (i.e., accelerator or angel group) investors. Follow-on VC

financing can be expected to differ for startups coming from these initial funders for four reasons: i)

differing advice from mentors arising from the closer alignment of accelerators with the entrepreneurs’

incentives to defer early VC investment; ii) greater recognition of the down-side of VC investment; iii)

the potential for negative signaling that comes from direct competition for VC financing within a given

cohort; and iv) the time pressure of “Demo Day” compels VCs to bid more quickly on the top prospects.

We explain each of these below.

The incentives with respect to follow-on VC investment differ for angel groups and accelerators.

Angel groups require exit strategies for startups in which they invest that will result in an acceptable

return in the relatively near term (Wiltbank and Boeker, 2007, Wong et al., 2009). Contractually, angel

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groups write term sheets and clauses that facilitate later stage VC investors in cash flow and control rights

(DeGennaro, 2012, Ibrahim, 2008). In part, these clauses arise from the nature of angel group investing,

in which members are investing personal funds and receive their return when follow-on investors increase

the valuation of the company. For these reasons, angel group mentors are likely to advise startups to seek

and accept VC finance. On the other hand, the top accelerators invest from the accelerators’ fund, similar

to standard VC practice. From the perspective of the accelerator, the expected return from the outliers that

receive substantial follow-on investment essentially compensates for diminished or absent VC investment

in the rest of the portfolio of startups. Thus, accelerator mentors may advise startups to defer VC

financing.

Initially, founders may view obtaining follow-on funding (post-accelerator or angel round) as

tantamount to the “holy grail” (Stross, 2012). The literature suggests however, that more seasoned

entrepreneurs recognize that accepting VC financing carries a downside as well. Foremost, VC financing

requires giving up control rights (Kaplan and Stromberg, 2004). A substantial literature reinforces the

intuition that entrepreneurs seek to retain control rights when evaluating competing financing choices (de

Bettignies, 2008, Ibrahim, 2010, Winton and Yerramilli, 2008). In addition to the general concern of

ceding control rights, the decision to accept VC financing effectively limits subsequent options. Mentors

in the top accelerators more explicitly recognize these facets. Mentors acknowledge that VC fundraising

is a time-consuming process that impedes founders ability to devote full attention to developing the

product and idea behind the startup. As Paul Graham, founder of Y Combinator, notes (Graham, 2007):

“If you take VC money, you have to mean it, because the structure of VC deals precludes early

acquisitions.” In a similar vein, the founders of TechStars note (Cohen and Feld, 2011 ):

“Most companies come to TechStars with a goal of raising money. One of the first things we do is

make them take a step back and ask themselves “Do I need to raise money?” We're quite

emphatic that the answer can be “No.” ”

Finally, the signaling value from top angel groups and top accelerators may diverge. For young

startups, VC financing is costly to acquire and hard to obtain (Spence, 1973). Thus, at the earliest stages,

initial support, e.g., from a top angel group or accelerator, may serve as a signal of quality to follow-on

investors (Hsu, 2004). Follow-on VC investors develop familiarity with the top angel groups and become

more comfortable investing in startups receiving this initial backing (DeGennaro, 2012). The structure of

angel contracts is written to simplify follow-on VC investment, and companies receiving angel group

backing do not directly compete with one another for VC funds (DeGennaro, 2012). The signal value

will be different for companies coming out of top accelerators. On one hand, the selectivity of a top

accelerator potentially acts as a certification mechanism for follow-on VC investors (Alden, 2013, Rich,

2013). However, within the accelerator cohort, startups essentially vie against each other for funding.

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The distinctive nature of a culminating event, i.e. Demo Day, amplifies the differences between

top accelerators and top angel groups. For entrepreneurs, the accelerator model is built around the Demo

Day deadline. In the words of Paul Graham (Levy, 2011): "There are 77 days until Demo Day….After

that, anything you do will make you better - but it won't make you better on Demo Day.". The hype and

coverage that Demo Day compels investors to make quick decisions to invest or lose the opportunity. For

example, speaking of the “feeding frenzy”, one investor noted (Shih, 2012): “It’s like there are lots of

sharks and you have to be more edgy than them to invest. If you don’t, the big names will catch your

investment opportunity, your prey.”

Taken together, the logic above suggests Hypothesis 3a:

Hypothesis 3a: Startups in entrepreneurial accelerators receive follow-on VC financing more

quickly relative to startups receiving their first formal financing from angel groups in the short

term, i.e., in the window surrounding “Demo Day”.

Compounding this effect, once VCs invest in the perceived top candidates, absence of investment

becomes a potential negative signal for the remaining startups in a cohort. Indeed, beginning in early

2014 Y Combinator curtailed investment by its own partners (Grant, 2014) and limited participation by

VCs immediately after Demo Day to prevent negative signaling. As Y Combinator’s Sam Altman noted

(Altman, 2014):

“As YC has become a larger and larger part of the startup ecosystem, we had to deal with things

like signaling risk (e.g. a YC/VC investor not making a follow on investment in a company caused

some other investors to think the company may not be good) and information issues.”

Taken together, the arguments above suggest that startups backed by accelerators relative to top

angel groups can be expected to receive VC funding with different speed relative to angel groups. First,

these startups receive different advice from their mentors regarding the value and speed of VC financing.

Second, the dynamics of competing directly against other cohort companies can create negative signals

for those not at the “top of the class”.

Hypothesis 3b follows:

Hypothesis 3b: Startups in entrepreneurial accelerators will follow-on VC financing more slowly

relative to startups receiving their first formal financing from angel groups in the longer term.

3. Methods and Analyses

3.1. Empirical Setting and Sample

We focus on the two most established accelerators, Y Combinator (founded in 2005) and

TechStars (founded in 2006). They have well-documented, reproducible criteria, and are consistently

ranked as the top accelerators, allowing us to isolate effects in circumstances that represent the industry

standard (Geron, 2012; Gruber, 2011; Lennon, 2013). For entrepreneurs seeking seed-stage equity

finance, applying to a top angel group would be the closest alternative to applying to a top accelerator

(Kerr et al., 2011).

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We hand-collect a novel dataset of startups that received their first formal outside equity funding

from either a top accelerator or top angel group over the period 2005-2011. We hand collect our dataset

using data from accelerator and angel group websites, startup websites, Crunchbase, SeedDb, technology

blogs, and social media sources. We identify the full population of startups that were accepted into and

received financing from Y Combinator and TechStars over the period 2005-2011. The final sample

includes 25 cohorts over the 6 year period. We create a comparable angel group sample that covers 19 of

the most active professional angel groups spanning a similar range of industries and geographic locations

over this time period. We identified the top angel groups by number of deals using Thomson One’s

VentureXpert. We track exit and funding outcomes for all startups through the end of June 2013. The final

sample consists of n=389 accelerator-backed startups and n=230 angel group backed startups (total

sample of n=619). We further utilize the non-parametric Coarsened Exact Matching (CEM) approach to

derive a more stringent matched sample (Azoulay et al., 2010, Iacus et al., 2012).

3.2. Dependent Variables: Exit and Subsequent Funding Outcomes

We construct measures of the date at which each outcome occurs relative to firm founding and

relative to entry into the accelerator. TimeToExit measures the number of months from date of entry into

the accelerator or angel group to an exit through acquisition. TimeToQuit measures the number of months

from entry to exit through quitting. TimeToVCRound1, measures the number of months from entry until

the first round of VC investment is received.

3.3. Focal Independent Variable: Accelerator

Accelerator. Our focal independent variable is a dichotomous variable equal to 1 if the startup

receives financing from an accelerator and equal to 0 if the startup received its initial financing from a top

angel group.

3.4. Control variables

We include a number of control variables to capture other factors at the startup and founder level

that can be expected to influence timing of the various outcomes.

HighStatusEducation. The status of the founder’s education may signal the quality of the founder

and the peer effects associated with coming from a highly ranked institution (Hallen, 2008, Pollock et al.,

2010). The variable HighStatusEducation is a dichotomous variable equal to 1 if at least one founder

holds a degree from a high status institution. We determine the prestige of an educational institution from

the U.S. News Top 400 World University Rankings using the measure of academic reputation (U.S. News

Top 400 World University Rankings, 2012). We selected the top 13 U.S. schools as an initial group and

added several additional schools (see, e.g., Cohen, Frazzini, and Malloy (2010) and Kacperzyck (2013)).2

2 Consistent with the literature we include: Harvard, Princeton, Yale, Columbia, University of Pennsylvania, Cornell, Stanford, University of Chicago, UCLA, Berkeley, Stanford, University of

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StartupAgeAtEnter. We control for the age of the startup, relative to its founding date, when it

receives its first funding from either the accelerator or angel group.

HQ Location Dummies. The literature on the geography of innovation suggests that the

organization of firms within a cluster plays an important role in the output of that region (Saxenian,

1994). We include dummy variables for location. We also control for the proximity between the acceletor

and the startup headquarters location. LocationMatch captures the proximity issue, and is a dichotomous

variable equal to 1 if both the startup and accelerator or angel group headquarters share a city location.

CohortSize. We control for the size of the cohort in each accelerator group. It is possible that

portfolio firms of angel groups retain similar flows of advice, cooperation, competition, and resources.

Therefore, we coded those startups receiving funding from the same angel group within the same year as

being part of a unique cohort as well.

SingleFounder. We include a dummy variable equal to 1 if the startup is founded by a solo

entrepreneur.

Industry. We include dummy variables for industry level effects.

3.5. Empirical strategy

3.5.1. Selection into accelerator or angel group

As noted above, we designed our sample selection criteria explicitly to match startups on key

observable characteristics. This reflects the likelihood that startups at similar stages, in similar industries

and geographic locations could choose to apply either to a top accelerator or a top angel group for their

first outside equity finance. This is consistent with the advice given to entrepreneurs on technology and

investor blogs, question and answer forums, and directly from investors. Nonetheless, we take several

econometric approaches to guard against selection bias in our analysis. Selection bias would arise if the

pursuit of one path or the other is endogenous to the outcomes. First, in our main analyses, we employ a

two-stage Heckman correction model as generalized by Lee (Heckman, 1979, Lee, 1983) to address the

likelihood that there may be differences in preferences for entrepreneurs to choose to seek the first outside

financing from an accelerator rather than an angel group. Second, we carry out additional analyses for

robustness that utilize the non-parametric Coarsened Exact Matching (CEM) approach to derive a

balanced sample based on founder, program, and startup attributes such as age, location, and industry

focus (Azoulay, Graff Zivin and Wang, 2010, Iacus, King and Porro, 2012).

For the Heckman selection correction model, we first estimate a probit specification to predict the

likelihood of a startup entering an accelerator program relative to an angel group. We follow the standard

Heckman approach of calculating the inverse Mills ratio from the first-stage probit equation and including

it as a regressor in the second stage equation (Wooldridge, 2002). We develop two instruments—

Michigan, MIT, Oxford, Cambridge, Brown, Dartmouth, and Duke universities.

12

Computer_Science_Schools and Education_Match—that are likely to impact the decision to apply for,

and subsequent acceptance into, an accelerator program over that of an angel group, but should not have a

direct influence on the outcomes of interest. Details are given below.

We build our selection model by leveraging a culture within accelerators that leans towards a

“hacker” ethos. Broadly defined, a "hacker" can be thought of as “a technologist with a love for

computing and a "hack" is a clever technical solution arrived through a non-obvious means” (Coleman,

2010). Hackers often come from a computer science background, and use code to reimagine established

processes (Timanen, 2001).

Within the accelerator space, the managing partners are often hackers at heart. Y Combinator’s

Paul Graham got his start as a computer science major, while TechStars’ David Cohen is on the board of

advisors for the computer science department at the University of Colorado. The ethos of a hacker is

baked into accelerators’ conceptions of success. As Y Combinator’s Paul Graham notes (Newcomer,

2013): “…if you go look at the bios of successful founders this is invariably the case, they were all

hacking on computers at age 13”. At the same time, “hacking” is more than just a coding concept. For

example, Paul Graham also notes in his essay “Hackers and Painters” (Graham, 2003):

I've found that the best sources of ideas are not the other fields that have the word "computer" in

their names, but the other fields inhabited by makers. Painting has been a much richer source of

ideas than the theory of computation.

Our first instrument, Computer_Science_Schools, exploits the fact that the cohort type of

experience inherent in the top accelerators (and lacking in angel groups) may be relatively more attractive

to entrepreneurs coming from a background that involves familiarity with, and affinity for, the “hacking”

culture that underlies these accelerator cohorts (University of Colorado, 2014). The logic is that the

number of doctoral degrees in electrical and computer engineering by an institution reflects the relative

focus of that institution on computer science overall. We create a dummy variable equal to 1 if any of the

founders attended one of the top 30 producers of computer science doctoral graduates using National

Academy of Sciences data on research doctoral programs in the United States3.

We create a second instrument, Education_Match, to reflect the impact of the social environment

within a university. The literature has shown that social influence within a university environment

facilitates entry into entrepreneurship (Kacperczyk, 2013). Accelerators more closely mimic the

collaborative environment within a shared university experience and may represent an extension of that

experience. Education_Match is a dichotomous variable equal to 1 if any founders share the same

educational institution.

3 National Academies Press, A Data-Based Assessment of Research-Doctorate Programs in the United States. Data accessed at http://www.nap.edu/rdp/ on April 9, 2014. Schools were sorted by the number of doctoral degrees conferred in the area of “Electrical and Computer Engineering”. The top 30 schools were coded as Computer Science focused institutions, and the list included Stanford University, MIT, Purdue University, and Virginia Polytechnic Institute and State University..

13

Finally, we control for additional selection preferences that may also directly impact funding and

exit outcomes. Top accelerators have an expressed preference for startup teams rather than individuals.

The variable Single_Founder is a dummy variable that identifies firms with only a single founder. The

variable High_Status_Education reflects the possibility that angel groups look to signals of quality that

are similar to those of VC investors (Cohen and Feld, 2011, Stross, 2012). We include the variable

StartupAgeAtEnter, to capture the age of the startup at the time of entry into the accelerator or angel

group program and may reflect an earlier stage of the idea. Also, similar to VC investors, angel groups

make geographically close investment; thus startups in regions with substantial angel group presence

relatively more likely to seek angel group financing. We thus include regional dummy variables to take

this into account. All of these the above variables can be expected also to influence the outcomes and

thus are included in both first and second stage regressions.

3.5.2. Estimation of timing of outcomes

Given our focus on the timing to each outcome, a hazard rate model is most appropriate. We use

two specifications in line with our theorizing: the Cox proportional hazard model for exit outcomes and

the piecewise constant exponential model for follow-on funding.

First, we use a Cox proportional hazard model to estimate the timing of exit outcomes,

TimeToExit and TimeToQuit. The Cox proportional hazard model is a semiparametric model. This means

that all startups face the same baseline hazard rate of exit, !0(t). Proportional differences in startup-

specific hazard functions derive from the covariates, which enter multiplicatively in the model (Cox,

1972). The hazard function, !(t,xi), gives the instantaneous hazard of exit at time t, conditional on

survival through that time as a function of xi, the vector of the focal and control variables, !, the vector of

estimated coefficients, and !0(t), the baseline hazard common to all startups:

Second, we use a piecewise constant exponential model to estimate the timing of follow-on

investment, TimeToVCRound1 to better capture the distinct time periods expected for the follow-on VC

financing related to the short-term impact of “demo day” and the longer-term trajectory of financing

(Blossfeld et al., 2007, Burton et al., 2002). The piecewise constant exponential model is a semi-

parametric model that assumes that the hazard rate of an event occurring is proportional to a constant

baseline hazard; this baseline hazard is constant within a given time period, but is allowed to vary across

different time periods (Blossfeld, Golsch and Rohwe, 2007). Our choice of piecewise constant

exponential modeling for the funding outcomes is driven by this relative flexibility of specification over

constant proportional hazard models. It is consistent with the literature in estimating the effect of time-

period effects on the hazard rate of an event of interest (Bradley et al., 2011).

! t,x

i( ) = exp x

i"( ) #!0

t( )

14

Formally, the model can be specified in terms of the hazard of a given outcome for startup i at

time t, "(t), of a given event, relative to the hazard of the baseline event, "0. (In this case, the baseline

event is remaining alive without further funding or exit outcomes.) Each startup is characterized by a

vector of covariates, X, and the coefficient vector !. We include startup-level frailty effects, ", that

capture the shared likelihood of particular events (Blossfeld and Rohwer, 1995). Dummy variables are

included for each of the time periods above. The hazard rates !!!!! are equal to:

!! ! !"#!! if !!

!! ! !"#!! if !!

The final equation we estimate is thus the hazard of the startup experiencing a given outcome:

! ! ! !"#!!!!! ! !!!! ! !"!

In selecting specific time splits, we created hazard plots (Figure 1) to model changes in the shape

of outcome likelihood over time (Wooldridge, 2002). We establish short-term effects as occurring within

the first 120 days from entry. This takes into account the boot camp, demo day, and negotiation periods

subsequent to demo day interest. On the long-term horizon, we established a 550-day benchmark (~18

months), after which outcome likelihoods of both accelerator and angel group-backed firms flatten out.

We account for multiple events per startup in our specification by treating each potential exit or funding

event as the hazard of interest and other outcomes are treated as censored (Blossfeld, Golsch and Rohwe,

2007).

4. Results

4.1. Univariate statistics

Table 1 presents summary statistics of our full sample, and Table 2 displays the associated

correlation matrix. Appendix Table 2 breaks out the summary statistics for the accelerator-backed and

angel group-backed subsamples. The summary statistics in Table 1 strongly suggest that the trajectories

of startups that proceed through top accelerators differ from the trajectories of similar startups that instead

receive their first outside equity from top angel groups.

Figure 1 displays the hazard plots of each of the three outcomes of interest to this study: exit by

acquisition, exit by quitting, and receiving a first formal round of VC funding. This visual representation

of the likelihood of outcomes over time corroborates much of what we observe in the summary statistics.

Accelerator-backed firms are more likely to exit faster, while the VC predictions shift in the short and

long run.

4.2. Regression Results

4.2.1. First Stage Selection Model

Results from the first stage probit regression are provided in the notes of Table 3 and Table 4.

Briefly, both of our exogenous instruments, Computer_Science_Schools and Education_Match, strongly

15

predict entry into an accelerator. As noted earlier, the variable Computer_Science_Schools acts as a

proxy for unobservable preferences of founders to select into a “hacker friendly” environment or greater

familiarity with the accelerator model. As expected, Computer_Science_Schools is a positive and

significant predictor of entering an accelerator program (p<0.01). We also posited that accelerators should

be more attractive to founders with ties to the same educational institution as one another.

Education_Match captures the unobservable preference for the cohort type of environment that might

come from the tie between founders with close educational ties. This variable was also a positive and

significant predictor of selection into accelerator programs (p<0.01). Startups with a solo founder

(Single_Founder) were less likely to select into accelerators ( p<0.01). Our other first stage variables were

largely significant as expected. To give a flavor of the selection equation, the predicted probability of a

team coming from a computer science powerhouse choosing an accelerator over an angel group is 90.8%,

while the probability for a team without the computer science milieu and coming from different schools is

65.3%. Alternatively, the probability of a solo founder coming from a computer science environment

choosing an accelerator over an angel group is 69.9%.

4.2.2. Piecewise Exponential Regressions

Results of our piecewise constant exponential regressions are presented in Table 3. We display

results in order of each of the three entrepreneurial outcomes in question: exit by quitting, exit by

acquisition, and a first formal round of venture capital funding, respectively. For each outcome, results

are shown for the baseline model, and then for the two-stage selection mode. All results are given as

hazard ratios, with the sample sizes and requisite model diagnostics displayed at the bottom of each

column. Overall, the results in Table 3 provide strong support for our hypotheses regarding the

differential impact of accelerator backing relative to angel group backing on the timing of different new

venture trajectories.

Hypothesis 1 laid forward the possibility that accelerator-backed startups were more likely to exit

via acquisition than were angel group-funded firms. The results from the piecewise constant exponential

analysis in Table 3 (Columns 1 and 2) demonstrate that Hypothesis 1 receives strong support. In our

baseline model (Column 1) the accelerator effect up to the 120-day benchmark is positive and significant

(18.916, p<0.05), suggesting that accelerators increase the hazard of acquisition in the short run. Over the

longer 550-day benchmark, the accelerator effect intensifies, increasing in magnitude and statistical

significance (38.095, p<0.01). These results are corroborated in our two-stage selection model at both the

120-day (18.693, p<0.05) and 550-day (36.835, p<0.01) time levels. The finding that accelerators speed

up the time to acquisition suggests that incentives may indeed be more closely aligned with the

entrepreneur, sticking with a few high level successes, rather than a more stable return (Ibrahim, 2008).

16

Hypothesis 2 posited that startups proceeding through top accelerators would take less time to

exit through acquisition relative to startups with angel group backing. The results (Table 3, Columns 3

and 4) provide compelling support for this hypothesis. Up to the 120-day period, accelerators exert a

positive and significant effect (18.771, p<0.01) on the timing to exit by acquisition. This effect carries

over to the 550-day benchmark (23.870, p<0.01), indicating an influence that transitions in magnitude,

but remains positive. Our results are virtually identical in the two-stage selection model, with the

accelerator effect positive and significant in both the 120-day (18.532, p<0.01) and 550-day (23.313,

p<0.01) periods. Of note, the accelerator effect at the 120-day benchmark is roughly similar between the

exit by quitting and exit by acquisition outcomes. We had elucidated two mechanisms behind the

hypothesized role of accelerators in speeding up the time to exit via quitting: mentoring and cohorts. First,

our results show key role of mentoring appears to extend beyond the boot camp period, with the ethos of

learning from failure remaining intact as startups face key operational decisions and continue to interact

with the entrepreneurial ecosystem. Second, the close connections between firms within a cohort suggest

a role for peer effects when deciding to shut a business; our results support this finding.

Hypotheses 3a and 3b proposed that the time to receiving the first round of formal VC investment

would occur more quickly in the short term (Hypothesis 3a) but would take a longer time to achieve in the

longer term (Hypothesis 3b). The results in Table 3 (Columns 5 and 6) provide support for hypothesis

3a. Accelerator-backed firms are more likely to receive VC funding faster in the 120-day period (2.200,

p<0.05). We can interpret this in the following manner. A given accelerator backed startup after 120

days (but before 550 days) from entry into the accelerator, faces a 2.2 times greater hazard of receiving

VC investment in this period than a similar angel group backed startup 120 to 550 days after the initial

angel group investment. This effect is statistically significant (p<0.05). This result is stronger in our two-

stage selection model (2.272, p<0.01), with a higher level of statistical significance. On the other hand,

the results in Table 3 (Columns 5 and 6) do not provide unequivocal support for Hypothesis 3b. After

550 days beyond starting with either the accelerator or angel group funds, the relative hazard of receiving

VC investment for the accelerator backed startup decreases relative to that of the angel group backed

startup, with a hazard ratio of 0.490 that is statistically indistinguishable from zero. While the hazard plots

in Figure 1 demonstrate a drop-off in time for accelerator-backed firms to receive VC funding, the results

in Table 3 (Columns 5 and 6) are negative, but not statistically significant. Finally, the net effect/overall

hazard of receiving VC investment remains lower for startups going through an accelerator rather than a

top angel group.

Two crucial mechanisms may be at play when evaluating the short and long term effects of

accelerator backing on VC funding. First, the “demo day” effect presents a strong short-term option for

startups passing through accelerator programs. The presence of VCs at a showcase event in which

17

accelerators certify the quality of portfolio firms is a key signaling process that may quicken deal flow.

We had hypothesized that those firms that develop over the longer term would be encouraged to take their

time and continue to receive guidance from accelerators. This effect was not statistically significant in our

results, however.

4.3. Robustness: Coarsened Exact Matching

In order to ensure the robustness of our selection methodology, we address potential bias on

observable characteristics. For this process, we use Coarsened Exact Matching (CEM) to balance the

treatment and control groups in our sample. CEM is a non-parametric approach that is well-suited to

facilitating causal inference from observational data by creating a balanced sample of treated and control

group observations based on a priori specification of degree of desired matching (Blackwell et al., 2009,

Iacus, King and Porro, 2012). Increasingly, CEM is viewed as an advantageous method for matching

samples without imposing undue balance restrictions and has been applied to observational data in the

management and political science arenas (Azoulay, Graff Zivin and Wang, 2010, Singh and Agrawal,

2011, Younge et al., 2012). We used the CEM process to assess the rigidity of our core matching

variables. The ultimate matching of samples in a smaller overall number of observations ultimately

provides weights in which the “better” match is regressed to add robustness to results.

After balancing, the final sample consists of n=470 matched observations (summary statistics

available from authors). As suggested by Azoulay et al. (2010) the selection of covariates ought to center

on a relatively small group. We focus on the key variables of industry, age of the startup, location of the

startup, and education of the founders.

In a t-test of means of our matching criteria of geography and industry, there were no significant

differences in several characteristics such as the industries of Payment/Commerce, Web Business, and

Media/Music/Gaming, and the locations of California, and the northeastern and southern United States.

Significant differences did exist between the accelerator and angel-backed startups in other characteristics

such as Industry: Social, Location, & Mobile Apps, Industry: Other as well as locations in U.S. West and

Midwest, and cities outside of the United States. Founder education was not found to have a significant

difference between the samples, but accelerator-backed firms were younger on average. The CEM

weighting procedures take industry and location differences into account.

For the purposes of robustness, we used CEM as a standalone matching procedure and also in the

selection model. This was an effort to account for both the observable and unobservable differences

between the samples. In the selection model, we used the CEM weights in the first stage matching in the

first stage as well as the main model. The results are presented by outcome in Table 4, and are broken out

by the CEM models, followed by the CEM and two stage selection models. The results for exit by

18

acquisition are displayed first (Columns 1-2), followed by exit by quitting (Columns 3-4) and VC round 1

(Columns 5-6).

Overall, our results are strengthened in Table 4, with stronger overall hazard ratios, and

hypotheses 1, 2, and 3a increasing support. Most of the results are qualitatively similar to what we

observed in the baseline and two stage selection models.

5. Conclusion and Discussion

In this paper, we identify the entrepreneurial accelerator as emerging type source of very early

stage entrepreneurial finance. At the outset, we asked: What is the impact of receiving financing from a

top accelerator on subsequent outcomes-i.e., being acquired, deciding to quit, or obtaining follow-on

funding from formal venture capitalists (VCs)? We bring to bear unique data and find that accelerators

contribute to substantial differences in timing of each of these outcomes relative to startups that receive

formal angel group financing. Specifically, we find that participation in a top accelerator program

increases the speed of exit through multiple channels: accelerators increase the likelihood of exit by

acquisition as well as exit by quitting. Second, we find that accelerator participation increases the timing

follow-on financing from formal venture capitalists, a key audience in the “demo day” event at the end of

the accelerator formal program.

Overall, we demonstrate a potentially important role for top accelerators in shaping the trajectory

of startups through in the earliest stages of the entrepreneurial landscape. Our contribution to the literature

is several-fold. We examine the full population of startups that have gone through the top two

accelerators and follow them through to their final outcome (at the end of our sample period in June

2013). Likewise, our angel sample is matched based on characteristics at time of funding, and are

followed through the same range of outcomes over the same period of time. To our knowledge, this is the

first comprehensive study of a large sample of startups from first round of formal accelerator finance

through current outcomes that is not censored on outcomes, such as receipt of VC backing. We thus

provide invaluable evidence of a significant and growing phenomenon.

To be clear, there are a number of accelerators, many of which are trying to emulate the relatively

senior models of Y Combinator and TechStars (e.g, 500 Startups, Dreamit Ventures, etc. to name just a

few). However, scholars and practitioners alike have lacked sufficient data on the actual outcomes of

even the more established accelerators. In this paper, we provide compelling evidence that the top

accelerators have demonstrably distinct impacts on a multitude of entrepreneurial trajectories.

Important as the phenomenon may turn out to be, our contribution to the literature extends

beyond the descriptive. We provide careful theoretical predictions about the relationship between the type

of earliest formal financing—accelerator or angel group—and the subsequent trajectory. We also build

on recent papers that focus on the importance of learning to fail quickly. Finally, we contribute to a rich

19

and vast literature on the importance of early financial and human capital resources on new venture

performance.

It is clear in this study that there is greater depth to the accelerator story. Isolating specific

mechanisms within the accelerator environment may yield further insight into which aspects of the

organizational form are both novel and significant. This nascent literature has begun to explore learning

within accelerator environments, for instance (Cohen and Bingham, 2013). Even more valuable would be

the parsing of mentoring and cohort effects within accelerator programs. For instance, the university-style

cohort system may yield unique peer effects that influence entrepreneurial decisions (Lerner and

Malmendier, 2013).

Our study, of course, is not without its limitations. Foremost, we have intentionally studied two of

the most well known and longest established accelerators (and thus compared them to established angel

groups). However, our study does not include the many other accelerators that are in existence. Our

results suggest that top accelerators influence the trajectory and outcomes of the entrepreneurs and

startups whom they mentor/select to work with. We cannot comment on the role of less established or

lesser-ranked accelerators; instead, we leave that to future research.

20

Table 1. Summary Statistics of Full Sample

Variable Name Mean S.D. Min Max

Time to Outcomes (Months)

TimeToExitByAcquisition) 37.16 22.87 5 102

TimeToExitByQuitting 24.36 20.19 2 105

TimeToVCRound1 21.25 16.00 2 89

Focal Variable

Accelerator 0.63 0.48 0 1

Founder & Startup Controls

High_Status_Education 0.48 0.50 0 1

Single_Founder 0.29 0.45 0 1

Computer_Science_Schools 0.84 0.96 0 10

Education_Match 0.27 0.44 0 1

Startup_Age_At_Enter (Months) 11.74 14.96 0 92

Cohort_Size 16.30 12.39 1 42

Location Controls

HQ_Location_Silicon_Valley 0.49 0.50 0 1

HQ_Location_Boston 0.17 0.38 0 1

HQ_Location_Foreign 0.03 0.18 0 1

HQ_Location_California 0.52 0.50 0 1

HQ_Location_West 0.14 0.35 0 1

HQ_Location_Northeast 0.23 0.42 0 1

HQ_Location_Midwest 0.05 0.22 0 1

HQ_Location_South 0.03 0.16 0 1

Location_Match 0.73 0.45 0 1

Industry Controls

Industry (Music, Gaming, Media) 0.13 0.34 0 1

Industry (Social, Location, Mobile Apps) 0.26 0.44 0 1

Industry (Payment/Commerce) 0.17 0.37 0 1

Industry (Web Business) 0.17 0.38 0 1

Industry (Underlying Tech) 0.18 0.38 0 1

Industry (Other) 0.09 0.28 0 1

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Table 2. Correlation Matrix of Full Sample

Variable Name a b c d e f g h i j k l m n o p q r s t u v w x y Time to Outcomes (Months)

a TimeToExitByAcquisition) 1.00

b TimeToExitByQuitting 0.00 1.00

c TimeToVCRound1 0.00 0.00 1.00

Focal Variable

d Accelerator -0.64 -0.52 -0.54 1.00

Founder & Startup Controls

e High_Status_Education -0.07 0.13 0.04 0.06 1.00

f Single_Founder 0.24 0.24 0.22 -0.44 -0.20 1.00

g Computer_Science_Schools -0.13 0.01 -0.12 0.21 0.35 -0.30 1.00

h Education_Match -0.21 -0.12 -0.24 0.35 0.14 -0.38 0.36 1.00

i Startup_Age_At_Enter (Months) 0.69 0.78 0.86 -0.57 -0.12 0.29 -0.17 -0.24 1.00

j Cohort_Size -0.52 -0.30 -0.36 0.61 0.11 -0.33 0.23 0.22 -0.37 1.00

Location Controls

k HQ_Location_Silicon_Valley -0.39 -0.11 -0.05 0.29 0.14 -0.17 0.17 0.11 -0.20 0.56 1.00

l HQ_Location_Boston 0.20 0.05 0.06 0.00 0.03 0.01 0.00 0.03 -0.03 -0.20 -0.44 1.00

m HQ_Location_Foreign 0.18 -0.06 -0.04 0.10 -0.08 -0.06 -0.04 0.10 -0.05 0.09 0.08 0.01 1.00

n HQ_Location_California -0.19 0.12 0.00 0.05 0.15 -0.08 0.19 0.07 -0.07 0.38 0.46 -0.20 -0.19 1.00

o HQ_Location_West -0.15 -0.02 -0.13 0.08 -0.16 0.02 -0.07 -0.05 0.00 -0.20 -0.23 -0.17 -0.07 -0.42 1.00

p HQ_Location_Northeast 0.16 -0.08 0.05 -0.06 0.05 0.07 -0.13 -0.03 0.02 -0.22 -0.30 0.43 -0.09 -0.56 -0.22 1.00

q HQ_Location_Midwest 0.15 0.02 0.09 -0.18 -0.03 0.02 -0.01 -0.06 0.10 -0.15 -0.12 -0.06 -0.04 -0.24 -0.09 -0.12 1.00

r HQ_Location_South 0.24 0.00 0.08 -0.05 -0.10 0.07 -0.03 -0.06 0.08 -0.08 -0.05 -0.08 -0.03 -0.18 -0.07 -0.09 -0.04 1.00

s Location_Match 0.03 0.09 0.02 -0.14 0.05 0.05 -0.05 -0.05 0.03 0.00 0.00 -0.19 -0.30 -0.30 0.01 -0.13 -0.11 -0.10 1.00

Industry Controls

t Industry (Music, Gaming,

Media) -0.04 0.09 -0.01 0.02 -0.00 -0.03 -0.03 -0.03 0.03 -0.03 -0.02 0.08 -0.04 0.00 -0.01 0.02 -0.00 0.02 -0.05 1.00

u Industry (Social, Location,

Mobile Apps) -0.19 -0.09 -0.09 0.10 -0.02 -0.03 0.02 0.01 -0.12 0.07 0.06 -0.05 -0.01 0.07 -0.00 -0.02 -0.07 -0.06 0.02 -0.23 1.00

v Industry (Payment/Commerce) -0.10 -0.24 -0.03 0.05 0.07 -0.07 0.08 0.01 -0.06 0.13 0.10 -0.11 0.04 0.04 -0.01 -0.06 0.06 -0.07 -0.00 -0.18 -0.27 1.00

w Industry (Web Business) 0.09 -0.05 0.01 -0.00 0.10 0.02 -0.05 -0.00 -0.01 -0.09 -0.10 0.05 0.04 -0.14 0.08 0.03 0.03 0.05 -0.08 -0.18 -0.27 -0.20 1.00

x Industry (Underlying Tech) 0.21 0.05 0.06 -0.06 0.03 0.06 0.01 0.01 0.06 -0.10 -0.08 0.10 -0.06 -0.07 -0.04 0.11 0.01 0.08 0.08 -0.18 -0.28 -0.21 -0.21 1.00

y Industry (Other) 0.07 0.34 0.10 -0.15 0.03 0.06 -0.06 -0.00 0.15 0.01 0.06 -0.06 0.04 0.11 -0.04 -0.10 -0.02 -0.02 0.03 -0.12 -0.18 -0.14 -0.14 -0.14 1.00

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Table 3. Piecewise exponential model predicting entrepreneurial outcomes: split at 120 & 550 days

after accelerator/angel group funding

(Origin Date: Startup First Round of Funding)

Exit By Acquisition Exit By Quitting VC Round 1

Base

Two Stage

Selection Base

Two Stage

Selection Base

Two Stage

Selection

Variables (1) (2) (3) (4) (5) (6)

Accelerator 0.030** 0.031** 0.446 0.44 0.333** 0.283***

(-2.47) (-2.42) (-0.83) (-0.85) (-2.53) (-2.85)

Accelerator x 120 Days 18.916** 18.693** 18.771*** 18.532*** 2.200** 2.272***

-2.44 -2.43 -3.55 -3.53 -2.5 -2.61

Accelerator x 500 Days 38.095*** 36.835*** 23.870*** 23.313*** 0.49 0.506

-2.99 -2.95 -3.63 -3.59 (-1.21) (-1.16)

High_Status_Education 0.92 0.966 0.653 0.674 0.858 0.928

(-0.17) (-0.07) (-0.92) (-0.84) (-0.59) (-0.29)

Startup_Age_At_Enter 0.999* 1 0.996*** 0.997* 0.999*** 1

(-1.88) (-0.02) (-4.60) (-1.66) (-3.97) (-0.15)

HQ_Location_Silicon_Valley 1.377 1.494 0.933 0.969 0.180*** 0.174***

-0.5 -0.61 (-0.11) (-0.05) (-4.84) (-4.95)

HQ_Location_Boston 0.323 0.34 0.612 0.636 0.230*** 0.224***

(-1.54) (-1.45) (-0.68) (-0.62) (-3.90) (-4.01)

HQ_Location_Foreigna 0.010*** 0.008*** 0.155 0.141 0.333 0.294*

(-2.96) (-2.98) (-1.12) (-1.17) (-1.48) (-1.67)

Location_Match 0.216** 0.187** 0.392* 0.396* 0.801 0.789

(-2.42) (-2.50) (-1.66) (-1.65) (-0.73) (-0.79)

Single_Founder 0.167*** 0.255* 1.486 1.991 0.276*** 0.456*

(-2.91) (-1.69) -0.63 -0.69 (-4.06) (-1.81)

Cohort_Size 0.956 0.955 0.928*** 0.929*** 0.975 0.979

(-1.61) (-1.64) (-2.84) (-2.78) (-1.59) (-1.32)

120 Days From First Funding 0.751 0.756 0.55 0.556 2.836*** 2.746***

(-0.45) (-0.44) (-0.93) (-0.91) -4.19 -4.07

500 Days From First Funding 3.180* 3.264* 0.741 0.753 4.160*** 3.968***

-1.72 -1.74 (-0.43) (-0.41) -3.05 -2.95

Inverse Mills Ratio N/A 0.439 N/A 0.631 N/A 0.437*

(-0.81) (-0.38) (-1.68)

Industry Y Y Y Y Y Y

Observations 2,395 2,395 2,401 2,401 1,442 1,442

!! 1277.42 1269.62 1185.19 1187.95 2210.24 2212.16

d.f. 18 19 18 19 18 19

log pseudolikelihood -487.6 -487.6 -467.6 -467.6 -1084 -1084

Robust z-statistics in parentheses

*** p<0.01, ** p<0.05, * p<0.10

i) a: Heckman first stage selection equation (Standard Errors in parenthesis): Accelerator = 0.998*** (-7.60) Startup_Age_At_Enter +

1.151*** (2.70) Computer_Science_ Schools + 2.168 ***(5.97) Education_Match + 0.757*** (-2.93) High_Status_Education +

0.429*** (-10.18) Single_Founder + Location Controls + Industry Controls

ii) b: Heckman first stage selection equation and CEM weights (Standard Errors in parenthesis): Accelerator = 0.998 ***(-6.27)

Startup_Age_At_Enter + 1.176*** (3.71) Computer_Science_ Schools + 2.335 ***(6.69) Education_Match + 0.707 ***(-2.87)

High_Status_Education + 0.430*** (-8.70) Single_Founder +Location Controls + Industry Controls

aHQ_Location_Foreign dropped from model due to matching restrictions

23

Table 4. Piecewise exponential model predicting entrepreneurial outcomes, Coarsened Exact

Matching (CEM) models: split at 120 & 550 days after accelerator/angel group funding

(Origin Date: Startup First Round of Funding)

Exit By Acquisition Exit By Quitting VC Round 1

Base

Two Stage

Selection Base

Two Stage

Selection Base

Two Stage

Selection

Variables (1) (2) (3) (4) (5) (6)

Accelerator 0.014*** 0.014*** 0.039*** 0.039*** 0.348*** 0.348***

(-4.24) (-4.23) (-3.91) (-3.90) (-3.70) (-3.64)

Accelerator x 120 Days 283.269*** 283.299*** 110.414*** 110.446*** 3.258*** 3.235***

-4.7 -4.7 -5.2 -5.19 -3.56 -3.54

Accelerator x 500 Days 68.124*** 67.493*** 57.550*** 57.556*** 1.391 1.416

-3.91 -3.89 -4.43 -4.43 -0.56 -0.59

High_Status_Education 0.466** 0.510** 0.470** 0.469** 0.491*** 0.500***

(-2.48) (-1.96) (-2.40) (-2.31) (-3.19) (-3.16)

Startup_Age_At_Enter 0.996*** 0.997*** 0.995*** 0.995*** 0.998*** 0.998***

(-5.47) (-3.43) (-5.53) (-4.48) (-4.46) (-2.58)

HQ_Location_Silicon_Valley 0.807 0.834 0.758 0.758 0.423*** 0.428***

(-0.69) (-0.58) (-1.03) (-1.00) (-3.90) (-3.84)

HQ_Location_Boston 0.457** 0.441** 0.636 0.635 0.365*** 0.358***

(-2.09) (-2.09) (-1.30) (-1.28) (-4.18) (-4.18)

HQ_Location_Foreigna 0.225 - 0.000*** - 0.183 -

(-1.34) (-32.32) (-1.61)

Location_Match 0.227*** 0.235*** 0.256*** 0.256*** 0.355*** 0.355***

(-5.87) (-5.81) (-5.14) (-5.20) (-5.59) (-5.69)

Single_Founder 0.386*** 0.494 0.607* 0.603 0.511*** 0.537**

(-3.14) (-1.63) (-1.94) (-1.17) (-3.36) (-2.13)

Cohort_Size 0.940*** 0.939*** 0.926*** 0.926*** 0.949*** 0.949***

(-2.94) (-2.94) (-3.77) (-3.79) (-4.23) (-4.16)

120 Days From First Funding 0.031*** 0.031*** 0.072*** 0.072*** 0.447*** 0.447***

(-5.37) (-5.39) (-5.00) (-4.99) (-3.02) (-3.03)

500 Days From First Funding 0.115*** 0.114*** 0.039*** 0.039*** 0.088*** 0.088***

(-5.84) (-5.81) (-6.43) (-6.43) (-5.68) (-5.59)

Inverse Mills Ratio N/A 0.66 N/A 1.012 N/A 0.928

(-0.53) -0.01 (-0.16)

Industry Y Y Y Y Y Y

Observations 1,827 1,811 1,832 1,816 1,085 1,072

!! 4072.76 4032.9 7795.53 4191.35 6490.8 6451.11

d.f. 18 18 18 18 18 18

log pseudolikelihood -452.3 -452.3 -421.1 -421.1 -929.7 -929.7

Robust z-statistics in parentheses

*** p<0.01, ** p<0.05, * p<0.10

i) a: Heckman first stage selection equation (Standard Errors in parenthesis): Accelerator = 0.998*** (-7.60) Startup_Age_At_Enter +

1.151*** (2.70) Computer_Science_ Schools + 2.168 ***(5.97) Education_Match + 0.757*** (-2.93) High_Status_Education +

0.429*** (-10.18) Single_Founder + Location Controls + Industry Controls

ii) b: Heckman first stage selection equation and CEM weights (Standard Errors in parenthesis): Accelerator = 0.998 ***(-6.27)

Startup_Age_At_Enter + 1.176*** (3.71) Computer_Science_ Schools + 2.335 ***(6.69) Education_Match + 0.707 ***(-2.87)

High_Status_Education + 0.430*** (-8.70) Single_Founder +Location Controls + Industry Controls

aHQ_Location_Foreign dropped from model due to matching restrictions

24

Figure 1. Kaplan-Meier Hazard Plots

25

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