Swinging for the Fences: How Do Top Accelerators Impact the Trajectories of New Ventures? Sheryl Winston Smith Temple University Fox School of Business, Dept

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 University Fox School of Business, Dept. of Strategic Management [email protected]

Thomas J. Hannigan Temple University Fox School of Business, Department of Strategic Management [email protected]

Abstract Increasingly, entrepreneurs in search of critical early stage resources face an evolving paradigm: the rise of accelerators that integrate small equity investments with an intensive, cohort-based mentoring experience. The emergence of these accelerators attracts substantial interest in the popular imagination; however scholars know little about their overall impact. Specifically, in this paper we ask: What is the impact of receiving financing from a top accelerator, relative to that from a top angel group, on the subsequent trajectory of the venture- i.e., being acquired, deciding to quit, or obtaining follow-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 two of the most established accelerators?Y Combinator and Tech Stars?from the period 2005-2011 and construct a matched sample of startups that instead receive their first formal financing from top angel investor groups. We find that participation 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 a top accelerator initially increases the speed of receiving follow-on funding from VC investors, particularly in the window surrounding the culminating ?Demo Day? presentations. However, in the longer term, participation in a top accelerator relative to a top angel group appears to decrease the speed?i.e. decelerate?the timing of follow-on funding from VCs.

Jelcodes:M13,O31 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

1 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.

2 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

3 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

4 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).

5 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

6 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

7 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.

8 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).

9 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

10 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.

11 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..

12 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:

! t,x = exp x " #! t ( i ) ( i ) 0 ( ) 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 semiparametric 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).

13 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

14 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).

15 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

16 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

17 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

18 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.

19 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

20 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 Industry (Music, Gaming, t -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 Media) Industry (Social, Location, u -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 Mobile Apps) 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

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 Two Stage Two Stage Two Stage Base Base Base Selection Selection 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

22 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 Two Stage Two Stage Two Stage Base Base Base Selection Selection 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

aHQ_Location_Foreign dropped from model due to matching restrictions

23 Figure 1. Kaplan-Meier Hazard Plots

24 References

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