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The Startup Cartography Project: Measuring and mapping entrepreneurial ecosystems

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Abstract

This paper presents the Startup Cartography Project (SCP), which offers a new set of entrepreneurial ecosystem statistics for the United States from 1988 to 2016. The SCP combines state-level business registration records with a predictive analytics approach to estimate the probability of “extreme” growth (IPO or high-value acquisition) at or near the time of founding for the population of newly-registered firms. The results highlight the ability of predictive analytics to identify high-potential start-ups at founding (using a variety of different approaches and measures). The SCP then leverages estimates of entrepreneurial quality to develop four entrepreneurial ecosystem statistics, including the rate of start-up formation, average entrepreneurial quality, the quality-adjusted quantity of entrepreneurship, and the entrepreneurial ecosystem performance associated with a given start-up “cohort.” These statistics offer sharp insight into patterns of regional entrepreneurship, the correlation of quality (but not quantity) with subsequent regional economic growth and the evolution of entrepreneurial ecosystems over time. The SCP includes both a public-access dataset at the state, MSA, county, and zip code level, as well as an interactive map, the U.S. Startup Map, that allows academic and policy users to assess entrepreneurial ecosystems at an arbitrary level of granularity (from the level of states down to individual street addresses). The SCP and accompanying datasets may be found at: https://www.startupcartography.com/.

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This paper outlines the development of an entrepreneurial culture in the US Capitol region and the formation of a regional industrial cluster. The conditions that the literature associates with entrepreneurship lag rather than lead the development of the cluster. Supportive social capital, venture capital and entrepreneurial support services, as well as actively engaged research universities, are conditions that reflect the successful establishment of an entrepreneurial culture, built by the actions of pioneering entrepreneurs who often adapted to constructive crisis. Copyright 2001 by Oxford University Press.
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Assessing the state of American entrepreneurship requires not simply counting the quantity but also the initial quality of new ventures. Combining comprehensive business registries and predictive analytics, we present estimates of entrepreneurial quantity and quality from 1988 to 2014. Rather than a secular pattern of declining business dynamism, our quality-adjusted measures follow a cyclical pattern sensitive to economic and capital market conditions. Consistent with the role of investment cycles as a driver of high-growth entrepreneurship, our results highlight the role of economic and institutional conditions as a driver of both initial entrepreneurial quality and the scaling of new ventures over time. (JEL G24, G32, L25, L26, M13)
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Prior work has established that the financing environment can impact firm strategy. We argue that this influence can shape the earliest strategic choices of a new venture by creating a potential trade-off between two objectives: rapid growth and reaping the benefits of a positive reputation (glory). We leverage a simple reputation-building strategic choice—naming the firm after the founder (eponymy)—that is associated with superior profitability. Next, we argue via a formal model that the availability of/dependence on external financing can explain why high-growth firms are rarely eponymous. We find empirical support for the model’s predictions using a large data set of 1 million European firms. Eponymous firms grow considerably more slowly than similarly profitable firms. Moreover, eponymy varies in accordance with the firm’s financing environment in a pattern consistent with our model. We discuss implications for the literature on new-venture strategy. This paper was accepted by Bruno Cassiman, business strategy.
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Using data on the entire population of businesses registered in the states of California and Massachusetts between 1995 and 2011, we decompose the well-established gender gap in entrepreneurship. We show that female-led ventures are 63 percentage points less likely than male-led ventures to obtain external funding (i.e., venture capital). The most significant portion of the gap (65 percent)stems from gender differences in initial startup orientation, with women being less likely to found ventures that signal growth potential to external investors. However, the residual gap is as much as 35 percent and much of this disparity likely reflects investors’ gendered preferences. Consistent with theories of statistical discrimination, the residual gap diminishes significantly when stronger signals of growth are available to investors for comparable female- and male-led ventures or when focal investors appear to be more sophisticated. Finally, conditional on the reception of external funds (i.e., venture capital), women and men are equally likely to achieve exit outcomes, through IPOs or acquisitions.
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We demonstrate that eponymy—firms being named after their owners—is linked to superior firm performance, but is relatively uncommon (about 19 percent of firms in our data). We propose an explanation based on eponymy creating an association between the entrepreneur and her firm that increases the reputational benefits/costs of successful/unsuccessful outcomes. We develop a corresponding signaling model, which further predicts that these effects will be stronger for entrepreneurs with rarer names. We find support for the model's predictions using a unique panel dataset consisting of over 1.8 million firms.
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Beyond aspiring to become the “next” Silicon Valley, how can a region leverage innovation-driven entrepreneurship for economic and social progress? Given the poor performance of government support for entrepreneurship, should the job be left to the private sector? Moving beyond the traditional
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We compare 20 years of data from SDC’s Mergers and Acquisitions database with a hand-collected database for the same period. Our analysis provides evidence on the completeness and accuracy of SDC data across time, informing the decisions of researchers contemplating the use of SDC merger and acquisition data in their studies. We find that SDC’s database has poor coverage from 1978 to 1980, moderate coverage through the early 80’s, and fairly good coverage from 1984 onward. We also investigate areas where the reported acquisitions details differ between SDC and our hand-collected data. Both datasets contain errors, although hand-collection in general provides more accurate observations. Errors are more likely to occur with smaller, high book-to-market acquirers when the announcement response to the acquirer and target is weak. Our analysis also highlights many of the subtleties that researchers should consider when investigating mergers and acquisitions.
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Measures of entrepreneurship, such as average establishment size and the prevalence of start-ups, correlate strongly with employment growth across and within metropolitan areas, but the endogeneity of these measures bedevils interpretation. Chinitz (1961) hypothesized that coal mines near Pittsburgh led that city to specialization in industries, like steel, with significant scale economies and that those big firms led to a dearth of entrepreneurial human capital across several generations. We test this idea by looking at the spatial location of past mines across the United States: proximity to historical mining deposits is associated with bigger firms and fewer start-ups in the middle of the 20th century. We use mines as an instrument for our entrepreneurship measures and find a persistent link between entrepreneurship and city employment growth; this connection works primarily through lower employment growth of start-ups in cities that are closer to mines. These effects hold in cold and warm regions alike and in industries that are not directly related to mining, such as trade, finance and services. We use quantile instrumental variable regression techniques and identify mostly homogeneous effects throughout the conditional city growth distribution.
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This paper describes recent efforts at CES to create a new longitudinal research dataset: the Longitudinal Business Database (LBD). The LBD is a major improvement over existing longitudinal establishment datasets. Unlike the LRD, which covers only manufacturing, the LBD covers nearly all the non-farm private economy, as well as some public sector activities. Also, research using the LRD found problems with broken longitudinal linkages that lead to spurious establishment births and deaths (Dunne 1992). We supplemented the longitudinal numeric identifiers assigned by the Census Bureau with name and address matching to repair broken linkages.
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We study rare events data, binary dependent variables with dozens to thousands of times fewer ones (events, such as wars, vetoes, cases of political activism, or epidemiological infections) than zeros (“nonevents”). In many literatures, these variables have proven difficult to explain and predict, a problem that seems to have at least two sources. First, popular statistical procedures, such as logistic regression, can sharply underestimate the probability of rare events. We recommend corrections that outperform existing methods and change the estimates of absolute and relative risks by as much as some estimated effects reported in the literature. Second, commonly used data collection strategies are grossly inefficient for rare events data. The fear of collecting data with too few events has led to data collections with huge numbers of observations but relatively few, and poorly measured, explanatory variables, such as in international conflict data with more than a quarter-million dyads, only a few of which are at war. As it turns out, more efficient sampling designs exist for making valid inferences, such as sampling all available events (e.g., wars) and a tiny fraction of nonevents (peace). This enables scholars to save as much as 99% of their (nonfixed) data collection costs or to collect much more meaningful explanatory variables. We provide methods that link these two results, enabling both types of corrections to work simultaneously, and software that implements the methods developed.
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