National Federation of Independent Businesses (NFIB) Award for Excellence in Research on the General Topic of Entrepreneurship
ENTREPRENEURIAL ACTIVITY AND ECONOMIC GROWTH
Zoltan
J. Acs, University of Baltimore
Catherine Armington, U.S. Bureau of the Census
Recent theories of economic growth have stressed the role of externalities in generating growth. Using data from the Census Bureau that tracks all employers in the whole U.S. private sector economy, we examine the impact of these externalities, as measured by entrepreneurial activity, on employment growth in Local Market Areas. We find that differences in levels of entrepreneurial activity, network externalities, diversity among geographically proximate industries, and the extent of human capital are positively associated with variation in growth rates, but the manufacturing sector appears to be an exception. This relationship is consistent with the theories of Porter, Jacobs, Hannan and Freeman, and Aldrich and Zimmer.
What is the relationship between economic growth and entrepreneurial activity—the process of creating a new organization (Reynolds et al. 2001)? This question is motivated by two further questions. First, what are the conditions, including economic, cultural and personal, that prompt the founding of new organizations? Second, what are the important economic and social outcomes of entrepreneurial activity? Little research has directly explored the outcomes of entrepreneurial activity (Schoonhoven and Romanelli, 2001).
Neoclassical growth theory had no mechanism to explain either technological change or entrepreneurial activity (Solow, 1956). Because scale economies operate at the plant level, in the traditional model, economic growth relied on capital investment in larger plants. However, capital accumulation can explain only a small amount of the variation in economic growth across regions. Recent theories of economic growth view externalities, as opposed to scale economies, as the primary engine of growth (Romer, 1986). Because externalities operate at the level of individual agents, the role of entrepreneurs, and the new organizations they create, may be important for growth. An important source of externalities is knowledge spillovers. The more people interact the greater the spillovers. These spillovers are important because they solve the technical problem in economic theory of reconciling increasing returns (which are generally needed to generate endogenous growth) with competitive markets. This suggests that if the domestic economy is endogenously growing and if we believe in competitive markets, then it almost follows that knowledge spillover feature in the economic landscape. When economists began looking for knowledge spillovers, cities presented the clearest examples of economic regions subject to local spillover benefits (Acs, 2002).
If knowledge spillovers are more important in the early stages of the industry life cycle (Utterback, 1994) and competition is more important than monopoly, the mechanics by which local competition is achieved should receive more attention. While there is general agreement amongst researchers that competition has a positive effect on later growth, the interpretations of this fact are less clear. One interpretation of these results is that competition foments intellectual growth. Alternatively, cities that are endogenously growing may have higher levels of entrepreneurial activity. Organization ecology supports the latter, suggesting that typically entrepreneurs enter the local economy through a new organization that involves some degree of local knowledge spillovers and benefits from local network externalities (Hannan and Freeman, 1989).1
The purpose of this paper is to examine empirically the question, “What is the relationship between knowledge externalities and future economic growth in a regional economy?” We do this in the context of a modified endogenous growth model with a particular emphasis on entrepreneurial activity and its role in promoting knowledge spillovers, thereby leading to economic growth. We expand on prior research in several ways. First, our approach is more comprehensive, including data for the whole private sector economy, rather than just selected industries. Second, our unit of analysis is not just cities, but entire local economic areas, which generally include a metropolitan area and the surrounding rural area from which it draws both employees and consumers. Third, we focus on the early stage of the product life cycle where competition is fiercer and technology is more fluid, measuring knowledge spillovers by new firm formation (Jovanovic, 2001).2
WHY DO LOCAL GROWTH RATES VARY?
The growth of cities and regions has many facets, and we focus on continuing the search for understanding why some areas persistently show much higher growth than others. Four variables have been theorized to impact regional growth rates. First, Minniti (2001) argues that when making decisions, individuals follow social cues and are influenced by what others have chosen, especially when facing ambiguous situations. Such influences may be describes as a non-pecuniary network externality, where one agent’s decision to adopt a certain behavior encourages further adoption. Second, several papers in the last decade have confirmed the connection between the internal level of human capital in an area and the more rapid growth of that area. Glaeser et al. (1995) documents this link between human capital and growth and employment. Third, in the debate between specialization and diversity, knowledge spillovers occur between firms in the same industry (Romer, 1986). Romer posits that knowledge accumulated and innovations produced by one firm tend to help other similar firms’ technologies, or improvement of products, processes, or marketing, without appropriate compensation. The Jacobs (1969) model of externalities stresses knowledge spillovers across industries. She posits that the crucial externality in local economic areas is cross-fertilization of ideas across different lines of business. Finally, several theories, including those of Porter (1990) and Jacobs (1969), suggest that local competition rather than monopoly promotes economic growth.
While Jacobs and Porter, assume that competition leads to economic growth, how competition operates and opportunities are explored is left unexplained. In other words, the dynamic process by which local competition is achieved remains a black box. In Porter, local competition accelerates imitation and improvements on the original innovator’s idea. This has two divergent effects. Although such competition reduces the returns to the innovator, it also increases pressure to innovate in order to remain competitive. Porter believes that the second effect is by far the more important. Porter’s model focuses on a set of factor conditions that he calls the “diamond,” which include, demand conditions, related and supporting industries, and firm strategy, structure and rivalry. Regions are most likely to succeed in industries or industry segments where the “diamond,” a term used to refer to the determinants of the system, is most favorable. The “‘diamond’” is a naturally reinforcing system in which new business formation is one of the key components of rivalry and competition.
Therefore, the Porter model suggests that intense rivalry result from entrepreneurial activity creating new competitors. This is a process linking spillovers to growth, and entrepreneurship may be the vital ingredient in this process by which externalities generate economic growth, both within and across sectors. No matter how richly endowed an economic environment is with intellectual, social, human and financial resources, some person has to organize resources to pursue market opportunities (Baumol, 1993). Firms create output (and jobs as a by-product), and entrepreneurs create firms. Framing the challenge this way sheds light on new firms entry and the entrepreneurs that start them, providing a new focus for addressing an old question—where does growth come from in local economies (Wennekers and Thurik, 1999; Hart, 2001).
Strictly speaking, the concept of entrepreneurship operates at the individual level. While requiring skills and other resources, essentially entrepreneurship has to do with people’s behavior. Entrepreneurial action, or the pursuit of opportunity, takes us from the individual to the firm level. A new firm, in which the entrepreneur has a controlling interest and strict protection of property rights, provides a vehicle transforming their personal skills and ambitions into actions. Underlying the start-up of each new firm is an entrepreneur who acquired the knowledge to recognize and pursue a good business opportunity.
Where do such opportunities come from? They come from the information and knowledge that accumulates in every local economy, and particularly in urban aggregations of economic and social activity. One of the key features of an urban economy is the partitioning of knowledge among individuals. Even if the total stock of knowledge were freely available, spatially and temporally unbounded, knowledge about the existence of any particular information would still be limited (Hayek, 1945). Because of asymmetric information, knowledge is not uniformly at everyone’s disposal, and no two individuals share the identical scope of knowledge or information about the economy. Thus, only a few people may know about a new invention, a particular scarcity, or resources lying follow. Such knowledge is typically idiosyncratic because it is acquired through each individual’s own channels, including jobs, social relationships, and daily life. It is this specific knowledge, frequently obtained through knowledge spillovers that may lead to profit-making opportunity.
However, many more opportunities are recognized than are actively pursued. Bringing new products and services into existence usually involves considerable risk. By definition, entrepreneurship requires making investments today without assurance of what the returns will be tomorrow. Despite the absence of current markets for future goods and services, and in spite of the moral hazard when dealing with investors, suppliers and customer markets for future goods and services, the fact is that many individuals do succeed in creating new businesses. The ability to overcome these barriers to entry varies among individuals, and such skill is not evenly distributed across economic areas.
Thus we propose a model where the growth of the local information base about new technology, products, and processes is dependent on the various information externalities present in this region. We estimate a model that explains regional employment growth as a function of the regional level of entrepreneurial activity, network externalities, economic specialization, and human capital:
economic growthsr = ƒ (entrepreneurial activitysr,
network externalities sr, specialization sr , human capital r).
MEASUREMENT OF EMPLOYMENT GROWTH RATE
Data and Measurement Units
This study utilizes a fairly new database that the Bureau of the Census has constructed for study of entry, survival, and growth in different types of businesses (Acs and Armington, 1998). The Longitudinal Establishment and Enterprise Microdata (LEEM) has multiple years of annual data for every U.S. private sector (non-farm) business with employees. The current LEEM file facilitates tracking employment, payroll, and firm affiliation and (employment) size for the more than eleven million establishments that existed at some time during 1989 through 1996.
The geographic unit of analysis used for this study is travel-to-work or Labor Market Areas (LMA’s). These are aggregations of all the 3,141 counties in the U.S. into 394 geographical regions that each contains a high proportion of residential-work location trips, as defined for 1990 by Tolbert and Sizer (1996) for the Department of Agriculture. Many of the 394 LMA’s cut across state boundaries, to better represent the functioning of local economic areas. Some adjacent smaller Commuting Zones have been grouped with adjacent areas so that all LMA’s had a minimum population of 100,000. Despite considerable differences across LMA’s in terms of area, population density, and total population, most of them are quite similar in their economic structures. Their percentage of workers in different economic sectors shows little variation for transportation, communications, wholesale and retail trade, consumer services, health, education, social services and government employees, which together account for 56 to 60 percent of all workers in each LMA (Reynolds, 1999).
We distinguish six broad industry sectors for this study, to facilitate analysis of different industries’ sensitivities to factors affecting their growth, and to better control for aggregation effects in regions with different shares of weak industries—manufacturing, agriculture, and mining sectors. This expands both the scope, and the industrial detail beyond that of previous studies, most of which were limited to manufacturing. Industry codes are based on the most recently reported 4-digit SIC code for the original establishment in each firm. For most firms (single location firms) this is the only establishment. For most new multi-unit firms, the industry classification of the primary location is the same as that of their secondary locations. We use the most recently reported SIC code, rather than the first reported SIC, because the precision and accuracy of the codes tends to increase over time.
Variation in Growth of Local Economic Areas
It is evident from Table 1 that new firm start-ups play a far more important role in the economy than has previously been recognized. For the economy as a whole, over the five-year period, employment of establishments that started up after 1991 accounted for 26.3% of the mean employment over that period. The growth from expanding establishments that existed in 1991 was only 17.7%, and this increase was offset by the loss of 13.5% of their employment from shrinking establishments, and another 20.5% loss from the deaths of some of those 1991 establishments. This general pattern was robust for both types of establishments, although the single-unit firms had higher gross change rates, except for their lower loss rate from shrinkage. Each of the six sectors had similar patterns of gross employment change rates, with the notable exception of the very high rates of increase from both births in business services (43.6%) and expansions in business services (25.2%). The exceptionally low rate of increase from births in manufacturing (13.3%) supports Geroski’s (1995) earlier analysis that concluded that new firm births do not appear to play an important role in manufacturing.
Variables and Estimation Issues
The theory discussed earlier suggests that employment growth will be shaped by the role of externalities associated with knowledge spillovers in each economic area and sector. From the above discussion it should be clear that the major hypotheses concerning the regional variation in the employment growth rates are related to dynamic externalities, and one way to capture the extent of these spillovers is to look at the employment growth rate in regions. The literature suggests that higher employment growth rates should be associated with greater knowledge spillovers and increased entrepreneurial activity, which will lead to higher new firm formation rates. The detailed definitions of these explanatory (independent or exogenous) variables follow.
The firm entry rates include both new single unit firms (establishments, or locations) with less than 500 employees, and the primary locations of new multi-unit firms with less than 500 employees firm wide. Firm entry rates are calculated for each of the 394 LMAs, for each industry sector and for the total private sector (all-industry). The number of new firms in each LMA would tend to be proportional to the size of the LMA, so these numbers are standardized by dividing by the size of the local labor force (in thousands) in the central year. Labor force is preferred to population as a size measure, because it is a better measure of the number of potential entrepreneurs. Thus the entry rates represent the number of new firms per thousand of labor force in each LMA.
Average entry rates during the period from 1991 to 1996 were calculated from the average of the number of births in 1992, 1993, 1995, and 1996, divided by the labor force in 1993, in thousands, and standardized by the national average. The use of labor force size to standardize firm entry rates for the vastly different sizes of our economic areas has a particular theoretical appeal, based on the theory of entrepreneurial choice (Evans and Jovanovic, 1989). The entrepreneur starting a new business is assumed to live in the same LMA in which that new firm operates, and to have benefited from spillovers within that region. Regions with higher rates of entrepreneurial activity should have higher levels of employment growth, as suggested by Porter. These higher levels of firm entry would indicate higher levels of competition among existing firms, and higher levels of knowledge spillovers. The firm entry rates all should be positively related to the growth of local economies.
When making decisions, entrepreneurs follow social cues and are influenced by what others have chosen, especially when facing ambiguous situations. Economic Growth is likely to be proportional to the size of the local labor force (excluding children, retirees, and other non-participants) Kruger and Pischke (1997). These differences in the local entrepreneurial culture can be partially measured by the share of proprietors in the 1991 labor force. Proprietors are members of the labor force who are also business owners. This measure averaged 20.5 percent nationally, and varied from a low of 9.9 percent to a high of 44.8 percent across LMA’s in 1991. It includes both the self-employed who have no employees, and the owners of unincorporated businesses that have employees. Because of network externalities the share of proprietors should be positively related to the growth of local economies.
Many studies have measured industry specialization within an economic area with a simple measure of establishment density—the number of establishments per square mile of the area. This is more indicative of the extent of physical crowding of businesses, which is related to the probable relative costs of doing business there. Therefore, we introduce a new measure that captures the extent of both population density and the pre-existing supply of each industry’s establishments in a region. Industry intensity is measured as the number of establishments in the industry and region in 1991 divided by the region’s 1991 population, in comparison to the national average for that industry. In fact, this measure is almost identical to the specialization measure used by Glaeser et al. (1992). Industry intensity should be positively related to employment growth if specialization is important, as suggested by Porter.
We also assess the potential for positive externalities other than from knowledge spillovers. Firms often locate in an area because local demand is high there, and so they can sell their services or output without incurring much transportation or communication cost. Krugman (1991) models city growth based on local demand. The negative side of urbanization externalities is the potentially higher costs associated with crowding. As the number and size of establishments increase within an industry in a city, they cause increases in wages and rents, making it more expensive for further growth in that region. As a measure of these agglomeration effects we use establishment density, defined as the number of establishments per square mile in that industry in 1991, relative to the national average for the industry. If firms locate in cities or other areas with high concentrations of businesses, in order to benefit from the demand created by the other businesses, then higher establishment densities should be positively related to employment growth. Since we are using the relative levels of establishment density in each area, rather than absolute levels, there is no need to correct for changes in national industry presence or demand. Establishment density should be positively related to local growth rates if agglomerations are demand driven.
We include two measures of educational attainment in each region. The first is the share of adults with a high school degree, defined as the number of adults (population 25 years or older). Those adults without high school degrees are the principal supply of unskilled and semi-skilled labor for work in manufacturing branch plants and retail or unskilled service establishments, but higher levels of high school graduations indicate a generally higher level of human capital in the area. In 1990 73.0 percent of adults had high school degrees, nationally.
The second measure of educational attainment is the share of college graduates, defined as the number of adults with college degrees in 1990, divided by the total number of adults. This is a proxy measure of both the technical skills needed in the economy, and the skills needed to start and build a business. In 1990 an average of 15.9 percent of the adult population had a college degree. We expect that employment growth will be positively related to higher average levels of education, at both the high school and the college level (Glaeser et al., 1995).
To control for the size distribution of firms in each industry we include average local establishment size, measured for each industry sector and economic area by dividing the number of local employees by the number of local establishments in each sector. Mean establishment sizes vary nationally from 11 employees for the local market sector up to 55 for manufacturing. Regions that are dominated by large branch plants or firms are likely to be less competitive than those with many smaller establishments. The spatial division of labor within multi-site enterprises has resulted in some areas being dominated by externally owned branch plants performing routine assembly and production services or large-scale retail outlets.
The counts of firm births and numbers of establishments and employees were tabulated by LMA, industry sector, and year from the LEEM file the U.S. Bureau of the Census, Center for Economic Studies in suburban Washington DC. All other variables were tabulated from county-level data collected (often from other agencies) on a cd called ‘USA Counties 1998,’ by the U.S. Census Bureau. A data appendix is available from the authors.
Empirical Results
We estimate a regression model where the dependent variable is (compounded) average annual employment growth rates over the 5-year period of the early nineties. This is measured as the fifth root of the ratio of 1996 employment to 1991 employment in each LMA and sector: The annual average growth rate of each local economic area is defined as:
(2)Employment growth rate sr = (1996 empl sr / 1991 empl sr ) ** .2
where s stands for industrial sector and r stands for region. For all industries together the local growth rates varied from .988 (or –1.2% annual average change) to 1.080 (or 8.0% annual average change). The equations are estimated for 394 LMAs for all industries, as well as, for each of our six industry sectors.
All variables are used in the regressions in their standardized form, so that each is divided by its national mean and standard deviation. Thus each standardized variable measures how the area differs from the average. Standardizing their distribution over LMA’s so that each has a mean of zero and a standard deviation of 1 allows us to make direct comparisons of the estimated standardized beta coefficients for different industry sectors in Table 2. Each coefficient can then be interpreted as the share of the independent variable’s standard deviation that is reflected in the local deviation of the employment change rate from average rates.
There are four important results in the estimated model of local growth differences presented in Table 2. First, the coefficient for local competition, as measured by the new firm start up rate, is positive and statistically significant, as expected. This supports the theory of Porter (1990), that the firm entry rate is an important determinant of regional employment growth, and that growth is higher in areas with greater competition and lower barriers to entry.
These results are robust for five of our six industry sectors, with the exception of manufacturing, where it was insignificant. This exception supports the prior findings of industrial organization economists that entry is not important for employment growth in manufacturing (Geroski, 1995). Much research in both industrial organization, labor economics and regional science has been based only on analysis of data from the manufacturing sector, and these results have been frequently generalized to the whole economy. It appears that that generalization was not valid in this case.
Our findings of positive relationships between firm entry and local economic growth rate differences are inconsistent with Fritsch (1994) who found no relationship between firm births and employment growth in Germany, but they are consistent with Reynolds (1999) who found a similar relationship. Certain aspects of our results are consistent with Audretsch and Fritsch (2002), and with Glaeser et al. (1992), who found the impact of competition on growth stronger outside of manufacturing than in manufacturing. Our findings on whether local competition or monopoly promotes growth are unambiguously supportive of competition and consistent with the theories of Jacobs and Porter.
Second, the negative and statistically significant coefficient on industry intensity suggests that increased geographic specialization lead to less growth, rather than greater growth. These results are again robust for all industries sectors with the exception of manufacturing, where the coefficient is positive but not significant. This suggests that specialization does not lead to higher levels of technological externalities or other knowledge spillovers that promote growth. In this, they are consistent with the findings of Glaeser et al. (1992), and Feldman and Audretsch (1999). They also support Jacobs theory over those of Porter.
Third, the negative and statistically significant coefficients on establishment density suggest that when other factors are the same, employment growth will be greater in regions that have less physical crowding in their industry. Thus, by this measure of the number of establishments per square mile the agglomeration effect on growth seems to be negative for Labor Market Areas. This is in contrast with the findings of Glaeser et al., (1992) who used growth in other industries in each area as an indication of the size of the agglomeration effect, or urbanization effect, and found a positive relationship of growth with that. Indeed, it contrasts with much of the theoretical literature on agglomerations (Krugman, 1991). Perhaps these older studies’ inability to adequately measure the impact of differences in the level of competition resulted in the agglomeration variables serving as proxies for competition instead.
Fourth, the coefficient on the share of proprietors is positive and statistically significant for the economy as a whole, suggesting that network externalities play an important role in economic growth. However, this relationship did not hold up for most of the industrial sectors, probably because sector-specific data were not available for share of entrepreneurs. These results are consistent with Minniti (2001).
As expected, the greater the proportion of the area’s adults with a high school degree, the higher the growth rate. These results are consistent with Glaeser et al. (1995) that the general level of education in a region is important for development. However, after all of the other exogenous variables are taken into account, the additional impact of higher proportions of college graduates was negative but insignificant. These human capital variables were weaker and inconsistent for the various industry sectors. When the all-industry regression was run without the college graduate measure, the results were virtually unchanged. Both of these human capital variables were dropped and this had no substantial impact on the estimated parameters for the remaining variables either. Therefore, the results are robust with respect to the inclusion or exclusion of the human capital variables.
Recent theories of economic growth view externalities and other knowledge spillovers as the primary engine in generating growth. We examined the impact of these externalities on regional employment growth from an entrepreneurial perspective by examining the relationship of entrepreneurial activity to local employment growth rates. Since higher rates of entrepreneurial activity in an industry sector and region imply lower barriers to entry and greater local competition, this analysis can be interpreted as an investigation of the impact of local competition on local economic growth. Using data on 394 labor market areas and six industrial sectors, we found that higher rates of entrepreneurial activity were very strongly associated with faster growth of local economies.
Industrial specialization has a negative effect on local employment growth, after controlling for agglomeration effects, network externalities, and differences in educational attainment. These results are consistent with the theories of Porter that stress the role of business formation in promoting rivalry and competition.
Our analysis suggests that new organizations play an important role in taking advantage of knowledge externalities within a region, and that entrepreneurship may be the vehicle by which these spillovers contribute to economic growth (Freeman and Hannan, 1989).
This research was initiated and supported by the Kauffman Center for Entrepreneurial Leadership at the Ewing Marion Kauffman Foundation, as the first step of a larger project to analyze the causes of regional differences in new firm formation rates in the United States. The research was carried out at the Center for Economic Studies (CES), U. S. Bureau of the Census Washington D. C. under the title, “U. S. Geographical Diversity in Business Entry Rates.” Research results and conclusions expressed are those of the authors and do not necessarily indicate concurrence by the Bureau of the Census or the Center for Economic Studies. We would like to thank David Bruce Audretsch and Attila Varga, and seminar participants at the University of Maryland at College Park for valuable comments. All errors and omissions are our responsibility.
CONTACT: Zoltan J. Acs, Merrick School of Business, University of Baltimore, Baltimore, MD 21201; (T) 410/837-5012; (F) 410/837-5722; zacs@ubmail.ubalt.edu
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