Observing Acceleration

To appreciate what this book tries to do, the reader must first appreciate the importance of entrepreneurship to the global economy. The dynamic processes that allow promising entrepreneurs to turn ideas into successful companies also ensure the stream of new products and services that address previously unmet demands. The reader must also know that while these processes work well for certain entrepreneurs doing certain things in certain places, there are too many entrepreneurial dead spaces where local ecosystems are not identifying and supporting their most promising entrepreneurs. In these spaces, waves of impact-oriented entrepreneurs are not allowed to fulfill their dreams of building new companies. These opportunity costs multiply as we think about the potential employees and customers who will not have the chance to engage with these unbuilt companies, and multiply again as we think about the social and environmental contributions that they will not be allowed to make. To release the latent benefits that are trapped in entrepreneurial dead spaces, impact-oriented accelerators are springing up around the world. The importance of their collective mission—to find and support otherwise-marginalized impact-oriented entrepreneurs—motivates the various observations in this book, which attempt to observe and analyze the effects that accelerators are having on the ventures that participate in their programs.

To understand the work that accelerators are asked to do around the world, it is first necessary to understand the interplay between entrepreneurs and entrepreneurial ecosystems. When navigating the paths from promising ideas to successful companies, entrepreneurs rely on a host of individuals, organizations, and institutions to close various knowledge, network, and capital gaps. For certain entrepreneurs developing certain business ideas in certain places, the breadth and depth of the local ecosystems allow this to happen; not with absolute certainty, but with sufficient regularity to ensure a steady stream of new high-growth companies. Just think about the many white male entrepreneurs starting technology companies in Silicon Valley. For other entrepreneurs, like the founders of Togo’s first cashew processor or an Indian off-grid energy provider, these local ecosystems are much less robust. Therefore, a host of accelerators are setting up to target underserved and underestimated entrepreneurs who start impact-oriented ventures in these entrepreneurial dead spaces. We round out this chapter by using survey data collected by GALI researchers to explore this impressive global growth of accelerators. This sets the stage and provides context for the many observations that are presented throughout this book.

Chapters 1 and 2 provided the background for the analyses in this book. Chapter 4 provides our first analysis of the effects that accelerators have on the year-over-year growth of earned revenues, employees, and investment in a large sample of impact-oriented ventures. In between, this chapter describes the venture-level data, which were collected through an initiative called the Entrepreneurship Database Program (EDP) at Emory University. Working with scores of impact-oriented accelerators around the world, the EDP collects consistent data from thousands of entrepreneurs who apply to partner programs. Because these programs are impact oriented, virtually all of the applying ventures aspire to achieve specific social or environmental objectives. Beginning a year after each application window closes, the EDP collects annual follow-up information from program participants and from entrepreneurs whose ventures were rejected. This allows us to see how variables describing the commercial performance of participants and rejected ventures change during the post-application year. In the end, the EDP sample describes 5614 early-stage ventures. A total of 890 of these ventures were selected to participate in the program to which they applied.

Considerable time and money are being invested around the world in the belief that accelerators stimulate the growth of promising impact-oriented ventures. However, the current stock of evidence is inadequate to fully support this belief. The analyses in this chapter tackle this problem. While there are many ways that accelerators might justify the resources that are spent running them, a common set of expectations relates to their ability to stimulate short-term revenue, employment, and investment growth. The application and follow-up data introduced in Chap. 3 provide evidence of systematic short-term growth advantages for ventures that participate in accelerators compared to those that are rejected during the various selection processes. These accelerator effects are evident when looking at continuous variables measuring average year-over-year growth outcomes and categorical variables indicating positive versus negative growth. They are also evident among the very top-growing ventures in the sample. Even after accounting for the different starting points of participating and rejected ventures, accelerator program effects are still evident in the EDP data. This presents an optimistic first look at the effects of acceleration and a solid foundation for the analyses in the remainder of the book.

Chapter 4 analyzed the effects that accelerators have on the short-term growth of revenues, employment, and investment. The various observations consistently show that accelerator program participation influences these commercial and investment variables. However, the final presentation indicated that these effects are uneven across programs. Therefore, the general question of whether acceleration works gives way to more specific questions about which programs and program choices deliver more attractive ratios of accelerator benefits to program costs. This chapter begins by introducing a program-level variable called the net flow of funds (NFF). The NFF variable recognizes the four ways that funds flow into early-stage ventures; they are earned, invested, borrowed, or donated. For each program, the NFF measures the average of participants’ one-year growth in revenues plus investments minus the corresponding average for ventures that were rejected. We show that accelerators vary in the extent to which their NFF exceed the per-venture costs of running programs. After isolating the programs that more than cover their reported operating costs, we describe the program-level data that will be used in the more nuanced NFF analyses presented in Chaps. 6, 7, and 8.

Chapter 5 described three broad categories of work that must be completed before an accelerator can have any systematic effects on the NFF. The first two categories relate to building pipelines of entrepreneurs and then selecting the most promising ones into program cohorts. The third category relates to the specific interventions that ensure that participating entrepreneurs are able to identify and close their knowledge and network gaps so that they can effectively address their capital gaps. This chapter leverages information gleaned from the EDP program surveys to explore pipeline building. It begins by addressing and then setting aside the simple strategies of pumping up application numbers or looking for low-hanging fruit. Then, several specific observations indicate how differences in the number of applicant sources that are tapped, and in the extent of a program’s focus, help to explain which accelerators are more effective when it comes to driving new funding into early-stage ventures. At the end of the chapter, we pay special attention to programs that prefer to work with marginalized entrepreneurs, like women and minorities.

Although pipeline development is an important first step for accelerators, entrepreneur selection is perhaps the most controversial aspect of their work. From the most practical perspective, selecting promising but overlooked entrepreneurs is a cornerstone of any successful accelerator. Most program managers believe that the best way to ensure maximum return on their human, social, and financial capital investments is to ensure that these resources are directed toward ventures with the best chance of delivering and scaling impact. Therefore, they invest an immense amount of thought and effort selecting the most promising entrepreneurs into their cohorts. On the other hand, accelerators that end up delivering excellent results are often accused of simply cherry-picking entrepreneurs and ventures that would have succeeded any way. This accusation matters when presenting evidence to potential funders and supporters. If left unchallenged, the push to interpret accelerator effects solely through the lens of randomized or pseudo-randomized experimentation discounts this critical aspect of accelerator work. Rather than considering selection as a problematic confound in assessments of program efficacy, this chapter frames it as one of three core domains of work and examines several of the choices made by high-NFF and low-NFF programs to see which of them produce better accelerator outcomes.

Although building pipelines and selecting promising entrepreneurs are critical antecedents to a successful accelerator program, most of the work that accelerators do relates to designing and implementing the actual accelerator experience. This experience, which is constrained to unfold within a fixed time window, must identify and close the various knowledge, network, and capital gaps that sit between promising ideas and successful companies. This chapter elaborates this knowledge-networks-capital framework to show how various program choices influence the effects that accelerators have on participating entrepreneurs. By definition, the major design features are determined by the model itself. All accelerators run cohorts of entrepreneurs through fixed-duration programs that emphasize investment readiness. However, they differ in the myriad of smaller programmatic choices that they make. The EDP data show how few of the differences relating to curriculum structure, design, and delivery help distinguish high-NFF programs from their low-NFF counterparts. However, important differences appear when attention shifts to how entrepreneurs spend their time during the program, and how investment is channeled into the more promising ventures.

If it is important to accelerate impact-oriented ventures in places where economies are developed and per capita incomes are high, then it is doubly important to do so in emerging markets. However, the increased need for positive entrepreneurial outcomes in emerging markets is met with amplified shortcomings in these local ecosystems. A large number of the programs identified in Chap. 3 attempt to meet these needs by accelerating early-stage ventures that operate in countries across Africa, Latin America, and Asia. However, it is not yet clear whether the accelerator model, which originated in well-developed entrepreneurial ecosystems, also works in less-developed economies. After using the World Bank classification to organize the EDP ventures into those operating in high-income countries versus emerging markets, the first analysis shows that this latter group actually comprises two sub-groups: those seeking acceleration services in their home countries and those applying to programs run in other countries. The next observations show how these two groups of emerging-market ventures and entrepreneurs differ from their high-income-country counterparts. The chapter closes by showing that the effects of acceleration are still evident, but are somewhat muted, among the two groups of emerging-market ventures.

In earlier chapters, EDP data showed the effects that impact-oriented accelerators have on the short-term commercial performance of participating ventures. The focus tightened in Chap. 9 to look at differences between emerging markets and high-income countries, highlighting the specific challenges associated with accelerating early-stage ventures in less robust ecosystems. This chapter shifts focus again to home in on a group of entrepreneurs whose potential and performance tend to be under-appreciated: women. More specifically, EDP data are used to illuminate the issues that women entrepreneurs face when it comes to attracting outside equity investment. Roughly half of the ventures in the sample report at least one woman among their top three founders, most on mixed-gender founding teams. A look at outside equity investment across three groups of ventures—founded by men, mixed-gender teams, and women—reveals a troubling cascade that favors men. The ensuing analyses show that this cascade is not fully explained by obvious venture or entrepreneur differences across the three groups. The chapter closes by showing that accelerators may not be as helpful as many would hope when it comes to addressing inequities faced by women entrepreneurs.

This book presents a range of observations about impact-oriented accelerators and the entrepreneurs and ventures that they attract and sometimes work with. Relying on data collected through the many programs that partnered with the EDP from 2013 to 2017, these observations provide consistent evidence that accelerators are influencing short-term commercial performance. It should be heartening for supporters of these programs to see that, on average, one dollar spent on acceleration leads to more than one dollar of incremental funding for participating ventures. The average effects are even more impressive when we consider that accelerators are implicated in positive growth outcomes for the most promising entrepreneurs, and in the negative growth outcomes experienced by participants that are (arguably) less promising. However, these accelerator effects are not equal across the board. When we zoom in to provide specific insights on these program-to-program differences, the EDP data become more equivocal. With a few exceptions, it is difficult to identify specific program choices that correspond systematically with better accelerator outcomes. The one thing that we can say with some degree of certainty is that accelerators do not currently work as well for certain emerging-market ventures, or for ventures launched by all-women founding teams. Thus, while accelerators appear to be a promising intervention for stimulating ‘entrepreneurial dead spaces,’ more work must be done to ensure they maximize their potential. Much of this work will be done by future program managers—innovating with donors, mentors, and investors—as they continue to adapt their offerings to bolster the quality of support offered to still-neglected entrepreneurs. However, we close the book by calling for additional heavy lifting by researchers, who must bolster collective learning efforts so that we lose less of the potential inherent in the world’s impact-oriented entrepreneurs.