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NBER WORKING PAPER SERIES CLUSTERS OF ENTREPRENEURSHIP Edward L. Glaeser William R. Kerr Giacomo A.M. Ponzetto Working Paper 15377 http://www.nber.org/papers/w15377 NATIONAL BUREAU OF ECONOMIC RESEARCH 1050 Massachusetts Avenue Cambridge, MA 02138 September 2009 Comments are appreciated and can be sent to eglaeser@harvard.edu, wkerr@hbs.edu, and gponzetto@crei.cat. Kristina Tobio provided excellent research assistance. We thank Zoltan J. Acs, Jim Davis, Mercedes Delgado, Stuart Rosenthal, Will Strange, and participants of the Cities and Entrepreneurship conference for advice on this paper. This research is supported by Harvard Business School, the Kauffman Foundation, the National Science Foundation, and the Innovation Policy and the Economy Group. The research in this paper was conducted while the authors were Special Sworn Status researchers of the US Census Bureau at the Boston Census Research Data Center (BRDC). Support for this research from NSF grant (ITR-0427889) is gratefully acknowledged. Research results and conclusions expressed are our own and do not necessarily reflect the views of the Census Bureau or NSF. This paper has been screened to insure that no confidential data are revealed. Corresponding author: Rock Center 212, Harvard Business School, Boston, MA 02163; 617-496-7021; wkerr@hbs.edu. The views expressed herein are those of the author(s) and do not necessarily reflect the views of the National Bureau of Economic Research. © 2009 by Edward L. Glaeser, William R. Kerr, and Giacomo A.M. Ponzetto. All rights reserved. Short sections of text, not to exceed two paragraphs, may be quoted without explicit permission provided that full credit, including © notice, is given to the source.Clusters of Entrepreneurship Edward L. Glaeser, William R. Kerr, and Giacomo A.M. Ponzetto NBER Working Paper No. 15377 September 2009 JEL No. J00,J2,L0,L1,L2,L6,O3,R2 ABSTRACT Employment growth is strongly predicted by smaller average establishment size, both across cities and across industries within cities, but there is little consensus on why this relationship exists. Traditional economic explanations emphasize factors that reduce entry costs or raise entrepreneurial returns, thereby increasing net returns and attracting entrepreneurs. A second class of theories hypothesizes that some places are endowed with a greater supply of entrepreneurship. Evidence on sales per worker does not support the higher returns for entrepreneurship rationale. Our evidence suggests that entrepreneurship is higher when fixed costs are lower and when there are more entrepreneurial people. Edward L. Glaeser Department of Economics 315A Littauer Center Harvard University Cambridge, MA 02138 and NBER eglaeser@harvard.edu William R. Kerr Rock Center 212 Harvard Business School Boston, MA 02163 wkerr@hbs.edu Giacomo A.M. Ponzetto CREI - Universitat Pompeu Fabra C/ Ramon Trias Fargas, 25-27 08005 Barcelona Spain gponzetto@crei.cat1 Introduction Economic growth is highly correlated with an abundance of small, entrepreneurial Örms. Figure 1 shows that a 10% increase in the number of Örms per worker in 1977 at the city level correlates with a 9% increase in employment growth between 1977 and 2000. This relationship is even stronger looking across industries within cities. This relationship has been taken as evidence for competition spurring technological progress (Glaeser et al., 1992), product cycles where growth is faster at earlier stages (Miracky, 1993), and the importance of entrepreneurship for area success (Acs and Armington, 2006; Glaeser, 2007). Any of these interpretations is compatible with Figure 1ís correlation, however, and the only thing that we can be sure of is that entrepreneurial clusters exist in some areas but not in others. We begin by documenting systematically some basic facts about average establishment size and new employment growth through entrepreneurship. We analyze entry and industrial structures at both the region and city levels using the Longitudinal Business Database. Section 2 conÖrms that the strong correlation in Figure 1 holds true under stricter frameworks and when using simple spatial instruments for industrial structures. A 10% increase in average establishment size in 1992 associates with a 7% decline in subsequent employment growth due to new startups. Employment growth due to facility expansions also falls by almost 5%. We further document that these reductions come primarily through weaker employment growth in small entrants. What can explain these spatial di§erences? We Örst note that the connection between average establishment size and subsequent entrepreneurship is empirically stronger at the city-industry level than on either dimension individually. This suggests that simple theories emphasizing just industry-wide or city-wide forces are insu¢ cient. Theories must instead build upon particular city-industry traits or on endogenous spatial sorting and organizational forms due to interactions of city traits with industry traits. We consider three broad rationales. The Örst two theories emphasize spatial di§erences in net returns to entrepreneurship, while the last theory emphasizes spatial di§erences in the supply of entrepreneurs. The former theories are more common among economists. They assume that entrepreneurs choose locations and compete within a national market, so that the supply of entrepreneurship is constant over space. This frictionless setting would not hold for concrete manufacturing, of course, but would be a good starting point for many industries. Entrepreneurship is then evident where Örm proÖts are higher or where Öxed costs are lower, either of which increases the net returns to opening a new business. These spatial di§erences could be due to either exogenous or endogenous forces. To take Silicon Valley as an example, one story would suggest that Silicon Valleyís high rate of entrepreneurship over the past 30 years was due to abnormal returns in Californiaís computer sector as the industry took o§. These returns would need to have been greater than Californiaís and the 1computer industryís returns generally, perhaps descending from a technological breakthrough outside of the existing core for the industry (e.g., Duranton, 2007; Kerr, this issue). On the other hand, Saxenianís (1994) classic analysis of Silicon Valley noted its abundance of smaller, independent Örms relative to Bostonís Route 128 corridor. Following Chinitz (1961) and Jacobs (1970), Saxenian argued that these abundant small Örms themselves caused further entrepreneurship by lowering the e§ective cost of entry through the development of independent suppliers, venture capitalists, entrepreneurial culture, and so on. While distinct, both of these perspectives argue that spatial di§erences in net returns to entrepreneurship are responsible for the di§erences in entrepreneurship rates that we see empirically. An alternative class of theories, which Chinitz also highlighted, is that the supply of entrepreneurship di§ers across space. Heterogeneity in supply may reáect historical accident or relatively exogenous variables. William Shockleyís presence in Silicon Valley was partly due to historical accident (Shockleyís mother), and entrepreneurs can be attracted to Californiaís sunshine and proximity to Stanford independent of di§erences in net returns. Several empirical studies Önd entrepreneurs are more likely to be from their region of birth than wage workers, and that local entrepreneurs operate stronger businesses (e.g., Figueiredo et al., 2002; Michelacci and Silva, 2007). Immobile workers may possess traits that lend them to entrepreneurship (e.g., high human capital). Although quite di§erent internally, these theories broadly suggest that semi-permanent di§erences in entrepreneurial supply exist spatially. 1 While theories of the last kind are deserving of examination, they do not Öt easily into basic economic models that include both Örm formation and location choice. Section 3 presents just such a model that draws on Dixit and Stiglitz (1977). The baseline model illustrates the Örst class of theories that focus on the returns to entrepreneurship, as well as the di¢ culties of reconciling heterogeneity in entrepreneurial supply with the canonical framework of spatial economics. Two basic, intuitive results are that there will be more startups and smaller Örms in sectors or areas where the Öxed costs of production are lower or where the returns to entrepreneurship are higher. In the model, higher returns are due to more inelastic demand. A third result formalizes Chinitzís logic that entrepreneurship will be higher in places that have exogenously come to have more independent suppliers. Multiple equilibria are possible where some cities end up with a smaller number of vertically integrated Örms, like Pittsburgh, and others end up with a larger number of independent Örms. But, our model breaks with Chinitz by assuming a constant supply of entrepreneurs across space. While we assume that skilled workers play a disproportionately large role in entrepreneurship, we also require a spatial equilibrium that essentially eliminates heterogeneity in entrepreneurship supply. In a sense, the model and our subsequent empirical work show how far one can get without assuming that the supply of entrepreneurship di§ers across space (due to 1 These explanations are not mutually exclusive, especially in a dynamic setting. Areas that develop entrepreneurial clusters due to net returns may acquire attributes that promote a future supply of entrepreneurs independent of the factors. 2one or more of the potential theories). We operationalize this test by trying to explain away the average establishment size e§ect. Section 4 presents evidence on these hypotheses. Our Örst tests look at sales per worker among small Örms as a proxy for the returns to entrepreneurship. The strong relationship between initial industry structure and subsequent entry does not extend to entrepreneurial returns. While some entrepreneurial clusters are likely to be demand driven, the broader patterns suggest that higher gross returns do not account for the observed link between lower initial establishment size and subsequent entry prevalent in all sectors. We likewise conÖrm that di§erences in product cycles or region-industry age do not account for the patterns. These results are more compatible with views emphasizing lower Öxed costs or a greater supply of entrepreneurs. Our next two tests show that costs for entrepreneurs matter. Holding city-industry establishment size constant, subsequent employment growth is further aided by small establishments in other industries within the city. This result supports the view that having small independent suppliers and customers is beneÖcial for entrepreneurship (e.g., Glaeser and Kerr, 2009). We Önd a substantially weaker correlation between city-level establishment size and the facility growth of existing Örms, which further supports this interpretation. We also use labor intensity at the region-industry level to proxy for Öxed costs. We Önd a strong positive correlation between labor intensity and subsequent startup growth, which again supports the view that Öxed costs are important. However, while individually powerful, neither of these tests explains away much of the basic establishment size e§ect. We Önally test sorting hypotheses. The linkage between employment growth and small establishment size is deeper than simple industry-wide or city-wide forces like entrepreneurs generally being attracted to urban areas with lots of amenities. Instead, as our model suggests, we look at interactions between city-level characteristics and industry-level characteristics. For example, the model suggests that entrepreneurship will be higher and establishment size lower in high amenity places among industries with lower Öxed costs. The evidence supports several hypotheses suggested by the model, but controlling for di§erent forces again does little to explain away the small establishment size e§ect. Neither human capital characteristics of the area nor amenities can account for much of the observed e§ect. In summary, our results document the remarkable correlation between average initial establishment size and subsequent employment growth due to startups. The evidence does not support the view that this correlation descends from regional di§erences in demand for entrepreneurship. The data are more compatible with di§erences in entrepreneurship being due to cost factors, but our cost proxies still do not explain much of the establishment size e§ect. Our results are also compatible with the Chinitz view that some places just have a greater supply of entrepreneurs, although this supply must be something quite di§erent from the overall level of human capital. We hope that future work will focus on whether the small establishment size e§ect reáects entrepreneurship supply or heterogeneity in Öxed costs that we have been unable 3to capture empirically. 2 2 Clusters of Competition and Entrepreneurship We begin with a description of the Longitudinal Business Database (LBD). We then document a set of stylized facts about employment growth due to entrepreneurship. These descriptive pieces particularly focus on industry structure and labor intensity to guide and motivate the development of our model in Section 3. 2.1 LBD and US Entry Patterns The LBD provides annual observations for every private-sector establishment with payroll from 1976 onward. The Census Bureau data are an unparalleled laboratory for studying entrepreneurship rates and the life cycles of US Örms. Sourced from US tax records and Census Bureau surveys, the micro-records document the universe of establishments and Örms rather than a stratiÖed random sample or published aggregate tabulations. In addition, the LBD lists physical locations of establishments rather than locations of incorporation, circumventing issues related to higher legal incorporations in states like Delaware. Jarmin and Miranda (2002) describe the construction of the LBD. The comprehensive nature of the LBD facilitates complete characterizations of entrepreneurial activity by cities and industries, types of Örms, and establishment entry sizes. Each establishment is given a unique, time-invariant identiÖer that can be longitudinally tracked. This allows us to identify the year of entry for new startups or the opening of new plants by existing Örms. We deÖne entry as the Örst year in which an establishment has positive employment. We only consider the Örst entry for cases in which an establishment temporarily ceases operations (e.g., seasonal Örms, major plant retoolings) and later re-enters the LBD. Second, the LBD assigns a Örm identiÖer to each establishment that facilitates a linkage to other establishments in the LBD. This Örm hierarchy allows us to separate new startups from facility expansions by existing multi-unit Örms. Table 1 characterizes entry patterns from 1992 to 1999. The Örst column refers to all new establishment formations. The second column looks only at those establishments that are not part of an existing Örm in the database, which we deÖne as entrepreneurship. The Önal column 2 In a study of entrepreneurship in the manufacturing sector, Glaeser and Kerr (2009) found that the Chinitz e§ect was a very strong predictor of new Örm entry. The e§ect dominated other agglomeration interactions among Örms or local area traits. This paper seeks to measure this e§ect for other sectors and assess potential forces underlying the relationship. As such, this paper is also closely related and complementary to the work of Rosenthal and Strange (2009) using Dun and Bradstreet data. Beyond entrepreneurship, Drucker and Feser (2007) consider the productivity consequences of the Chinitz e§ect in the manufacturing sector, and Li and Yu (2009) provide evidence from China. Prior work on entry patterns using the Census Bureau data include Davis et al. (1996), Delgado et al. (2008, 2009), Dunne et al. (1989a, 1989b), Haltiwanger et al. (this issue), and Kerr and Nanda (2009a, 2009b). 4looks at new establishments that are part of an existing Örm, which we frequently refer to as facility expansions. Over the sample period, there were on average over 700,000 new establishments per annum, with 7.3 million employees. Single-unit startups account for 80% of new establishments but only 53% of new employment. Facility expansions are, on average, about 3.6 times larger than new startups. Table 1 documents the distribution of establishment entry sizes for these two types. Over 75% of new startups begin with Öve or fewer employees, versus fewer than half of entrants for expansion establishments of existing Örms. About 0.5% of independent startups begin with more than 100 workers, compared to 4% of expansion establishments. Across industries, startups are concentrated in services (39%), retail trade (23%), and construction (13%). Facility expansions are concentrated in retail trade (32%), services (30%), and Önance, insurance, and real estate (18%). The growing region of the South has the most new establishment formations, and regional patterns across the two classes of new establishments are quite similar. This uniformity, however, masks the agglomeration that frequently exists at the industry level. Well-known examples include the concentration of the automotive industry in Detroit, tobacco in Virginia and North Carolina, and high-tech entrepreneurship within regions like Silicon Valley and Bostonís Route 128. 2.2 Industry Structure and Entrepreneurship Table 2 shows the basic fact that motivates this paper: the correlation between average establishment size and employment growth. We use both regions and metropolitan areas for spatial variation in this paper. While we prefer to analyze metropolitan areas, the city-level data become too thin for some of our variables when we use detailed industries. The dependent variable in the Örst three columns is the log employment growth in the region-industry due to new startups. The dependent variable for the second set of three columns is the log employment growth in the region-industry due to new facility expansions that are part of existing Örms. Panel A uses the log of average establishment size in the region-industry as the key independent variable. Panel B uses the HerÖndahl-Hirschman Index (HHI) in the region-industry as our measure of industrial concentration. Regressions include the initial periodís employment in the region as a control variable. For each industry, we exclude the region with the lowest level of initial employment. This excluded region-industry is employed in the instrumental variable speciÖcations. Crossing eight regions and 349 SIC3 industries yields 2,712 observations as not every region includes all industries. Estimations are unweighted and cluster standard errors by industry. The Örst regression, in the upper left hand corner of the table, shows that the elasticity of employment growth in startups to initial employments is 0.97. This suggests that, holding mean establishment size constant, the number of startups scales almost one-for-one with existing employment. The elasticity of birth employment with respect to average establishment size in the 5region-industry is -0.67. This relationship is both large and precisely estimated. It suggests that, holding initial employments constant, a 10% increase in average establishment size is associated with a 7% decline in the employment growth in new startups. These initial estimates control for region Öxed e§ects (FEs) but not for industry FEs. Column 2 includes industry FEs so that all of the variation is coming from regional di§erences within an industry. The coe¢ cient on average establishment size of -0.64 is remarkably close to that estimated in Column 1. In the third regression, we instrument for observed average establishment size using the mean establishment size in the excluded region by industry. This instrument strategy only exploits industry-level variation, so we cannot include industry FEs. The estimated elasticities are again quite similar. These instrumental speciÖcations suggest that the central relationship is not purely due to local feedback e§ects, where a high rate of growth in one particular region leads to an abundance of small Örms in that place. Likewise, the relationship is not due to measuring existing employment and average establishment size from the same data. Panel B of Table 2 considers the log HHI index of concentration within each region-industry. While the model in the next section suggests using average establishment size to model industrial structure, there is also a long tradition of empirically modeling industrial structure through HHI metrics. 3 The results using this technique are quite similar to Panel A. A 10% increase in region-industry concentration in 1992 is associated with a 4% decline in employment due to new startups over 1992-1999. The coe¢ cient on initial region-industry employment, however, is lower in this case. When not controlling for initial establishment size, there is a less than one-for-one relationship between initial employment and later growth through startups. Column 2 of Panel B again models industry FEs. The coe¢ cients are less stable than in the upper panel. The elasticity of startup employment to the HHI index continues to be negative and extremely signiÖcant, but it loses over 50% of its economic magnitude compared to the Örst column. Column 3 instruments using the concentration level in the omitted region. The results here are quite similar to those in the Örst column. Columns 4 to 6 of Table 2 consider employment growth from new facility expansions by multiunit Örms instead of new startups. These new establishments are not new entrepreneurship per se, but instead represent existing Örms opening new production facilities, sales o¢ ces, and similar operations. Nevertheless, formations of new establishments represent more discontinuous events than simple employment growth at existing plants. Again, there is a strong negative e§ect of mean establishment size in the region-industry and subsequent employment growth due to facility expansions. The e§ect, however, is weaker than in the startup regressions. The results are basically unchanged when we include industry FEs or in the instrumental variables regression. These conclusions are also mirrored in Panel Bís estimations using HHI concentration measures. 3 The appendix also reports estimations using the share of employees in a region-industry working in establishments with 20 employees or fewer. This modelling strategy delivers similar results to mean establishment size or HHI concentration. 62.3 Variations by Sector Figures 2a and 2b document estimations of the relationship between establishment entry rates and initial region-industry structure by sector. The underlying regressions, which are reported in the appendix, include region and industry FEs and control for log initial employment in region-industry. The squares document the point estimates, and the lines provide conÖdence bands of two standard errors. Negative coe¢ cients again associate greater entry over 1992-1999 with smaller average establishment size by region-industry in 1992. Figure 2a shows that the average establishment size e§ect is present for startups in all sectors to at least a 10% conÖdence level. The elasticity is largest and most precisely estimated for manufacturing at greater than -0.8; the elasticity estimate for Önance, insurance, and real estate is the weakest but still has a point estimate of -0.2. On the other hand, Figure 2b shows the average establishment e§ect is only present for facility expansions in manufacturing, mining, and construction. This relative concentration in manufacturing is striking, as this sector was the subject of the original Chinitz study and much of the subsequent research. The di§erence in levels between Figures 2a and 2b also speaks to concentration among startupsó in every sector, the average establishment size e§ect is largest for new entrepreneurs. 4 2.4 Entry Size Distribution Table 3 quantiÖes how these e§ects di§er across establishment entry sizes. Table 1 shows that most new establishments are quite small, while others have more than 100 workers. We separate out the employment growth due to new startups into groupings with 1-5, 6-20, 21-100, and 101+ workers in their Örst year of observation. Panel A again considers average Örm size, while Panel B uses the HHI concentration measure. These estimations only include region FEs, and the appendix reports similar patterns when industry FEs are also modelled. A clear pattern exists across the entry size distribution. Larger average establishment size and greater industrial concentration retard entrepreneurship the most among the smallest Örms. For example, a 10% increase in mean establishment size is associated with a 12% reduction in new employment growth due to startups with Öve workers or fewer. The same increase in average establishment size is associated, however, with a 1% reduction in new employment growth due to entering Örms with more than 100 employees. The patterns across the columns show steady declines in elasticities as the size of new establishments increases. The impact for new Örms with 6-20 workers is only slightly smaller than the impact for the smallest Örms, while the elasticity for entrants with 21-100 employees is 50% smaller. Larger establishments and greater concentration are associated with a decrease in the number of smaller startups, but not a decrease in the number of larger startups. 4We have separately conÖrmed that none of the results for new startups reported in this paper depend upon the construction sector, where startups are over-represented in Table 1. 73 Theoretical Model This section presents a formal treatment of entrepreneurship and industrial concentration. We explore a range of di§erent explanations for the empirical observation that startup activity has a strong negative correlation with the size of existing Örms. Our goal is to produce additional testable implications of these explanations. We develop a simple model based on monopolistic competition following the classic approach of Dixit and Stiglitz (1977). Entrepreneurs create Örms that earn proÖts by selling imperfectly substitutable goods that are produced with increasing returns to scale. The startup costs of entrepreneurship are Önanced through perfectly competitive capital markets, and no contractual frictions prevent Örms from pledging their future proÖts to Önanciers. Each company operates over an inÖnite horizon and faces a constant risk of being driven out of business by an exogenous shock, such as obsolescence of its product or the death of an entrepreneur whose individual skills are indispensable for the operation of the Örm. These simple dynamics generate a stationary equilibrium, so that we can focus on the number and size of Örms and on the level of entrepreneurial activity in the steady state. The baseline model enables us to look at the role of amenities, Öxed costs, and proÖtability in explaining Örm creation. Several of its empirical predictions are very general: for instance, essentially any model would predict that an exogenous increase in proÖtability should result in an endogenous increase in activity. An advantage of our approach is that di§erent elements can easily be considered within a single standard framework. We also extend the model to address multiple human capital levels and to allow for vertical integration. 3.1 Baseline Model Consider a closed economy with a perfectly inelastic factor supply. There are I cities characterized by their exogenous endowments of real estate Ki and by their amenity levels ai such that ai > ai+1 for all i. There is a continuum of industries g 2 [0; G], each of which produces a continuum of di§erentiated varieties. Consumers have identical homothetic preferences deÖned over the amenities a of their city of residence, the amount of real estate K that they consume for housing, and their consumption qg () of each variety in each industry. SpeciÖcally, we assume constant elasticity of substitution  (g) > 1 across varieties in each sector and an overall Cobb-Douglas utility function U = log a +  log K + (1 ) Z G 0 (g) log " Z n(g) 0 qg () (g)1 (g) d # (g) (g)1 dg, (1) with budget shares for consumption expenditures  2 [0; 1) and (g) > 0 such that R G 0 (g) dg = 1. n (g) denotes the equilibrium number of Örms in each industry. 8