New Business Clustering 

in U.S. Counties, 1990-2006 

by 

Lawrence A. Plummer

University of Oklahoma

Price College of Business

& 

Wyckoff Consulting, LLC

for 

Under contract no. SBAHQ-08-Q-0023

Release Date: May 2010

 

This report was developed under a contract with the Small Business Administration, Office 

of Advocacy, and contains information and analysis that was reviewed and edited by 

officials of the Office of Advocacy. However, the final conclusions of the report do not 

necessarily reflect the views of the Office of Advocacy.

New Business Clustering 

in U.S. Counties, 1990-2006 

by 

Lawrence A. Plummer

University of Oklahoma

Price College of Business

& 

Wyckoff Consulting, LLC

for 

Under contract no. SBAHQ-08-Q-0023

Release Date: March 2010

 

This report was developed under a contract with the Small Business Administration, Office 

of Advocacy, and contains information and analysis that was reviewed and edited by 

officials of the Office of Advocacy. However, the final conclusions of the report do not 

necessarily reflect the views of the Office of Advocacy.

Contents 

EXECUTIVE SUMMARY

INTRODUCTION 1

LITERATURE REVIEW AND HYPOTHESES 2

A Model of Entrepreneurial Activity 4

The Hypotheses 5

RESEARCH DESIGN 7

Data Collection 7

Measuring the Rate of New Firm Births 8

Mapping the Location of New Firm Births 10

Calculation of the Independent Variables 11

Model Specification and Estimation 12

RESULTS 13

Where in America? 18

Why in America? 19

DISCUSSION 24

Insights for Policy 25

Insights for Practice 25

Insights for Research 26

CONCLUDING REMARKS 27

CITED WORKS 28

FIGURES 32

Percentile Maps 32

Circular Cartograms 38

LISA Cluster Maps 44
Executive Summary

The prescription for regions to position their economies strategically by supporting 

entrepreneurial activity in particular sectors is, of course, far from easy or simple to put into 

practice. In the absence of specific evidence, this paper maps county-level establishment birth data 

from 1990 to 2006 to explore where, and to what extent, new firms concentrate geographically. It

uses econometric methods to determine the county-level factors of new firm formation. Summary 

statistics reveal some interesting geographic aspects of entrepreneurship by county and industry. 

There were nearly 5 single-unit establishment births per 1,000 workers during the study period 

within a range of zero and 28.49 births per 1,000 workers. Given the dominance of retail trade in the 

U.S. economy, it is not surprising that this industry sector has the highest rate of new firm births 

(1.06 births per 1,000 workers), followed closely by the “local market” industries (1.03 births per 

1,000 workers). 

In raw counts, Los Angeles, Cook (Chicago), and New York counties have the highest levels 

of entrepreneurial activity. Rankings by the rate of firm births per 1,000 workers in each given 

sector suggest that the nation’s interior and northwestern counties—especially in states like 

Colorado, Utah, Washington, and others—had the highest levels of entrepreneurial activity per 

person during the study period. This finding is consistent with the level of westward migration over 

the last two decades. The mapping and spatial analysis indicate that entrepreneurial activity 

concentrates geographically. The entrepreneurial lows of the nation’s Rust Belt states and the 

entrepreneurial highs of the Rocky Mountain regions are evident. A pivotal determinant for this 

pattern appears to be changes in county population. The spatial analysis indicates several pockets of 

significant start-up activity, including manufacturing in the Pacific Northwest; retail trade and local 

market industries in the Rocky Mountain States; high technology industries in California, 

Massachusetts, and North Carolina; extractive industries in Texas, Oklahoma, and Wyoming; 

business services in New York, Washington, DC, and Florida; and distributive industries in the 

plains states.

The econometric findings in this study indicate that the high technology sector is a special 

case. The sector proves the exception in the relationship between the rate of new firm births and 

changes in population, the creation of new knowledge, and the educational attainment of the county 

population. Moreover, the findings indicate that the fascination of scholars and policymakers with 

places like Silicon Valley, Boston’s Route 128, and North Carolina’s Research Triangle is deserved. 

Equally interesting is the business services sector. In fact, the high correlation between the firm 

birth rates in these two sectors and the evidence that both rely on an educated workforce implies a 

close link. It may be that the formation of new business service firms occurs partly as a response to 

high technology entrepreneurial activity.

Among the central findings of this study is that the fascination of scholars and policymakers 

with high technology start-up activity is justified in the sense that the pattern and determinants 

thereof stand apart from the patterns in other sectors. In addition, the level of entrepreneurial 

activity in one county seems to reinforce the level in nearby counties. In short, aligning the 

development goals and activities of a group of neighboring counties may be a necessary condition 

for encouraging entrepreneurial activity in any given county. 
List of Tables

Table 1: Industry Classifications .......................................................................................................... 9

Table 2: Descriptive Statistics ............................................................................................................ 14

Table 3: County Rankings by Sector, Total Single-Establishment Births ......................................... 15

Table 4: County Rankings by Sector, Total Single-Establishment Births per Worker ...................... 17

Table 5: Correlations .......................................................................................................................... 21

Table 6: Log-Linear Fixed Effects Regression Analysis ................................................................... 22
List of Figures

Figure 1: Percentile Map of All Sectors ............................................................................................. 33

Figure 2: Percentile Map of Manufacturing Industries ...................................................................... 33

Figure 3: Percentile Map of Retail Trade Industries .......................................................................... 34

Figure 4: Percentile Map of Local Market Industries ........................................................................ 34

Figure 5: Percentile Map of High Tech Industries ............................................................................. 35

Figure 6: Percentile Map of Extractive Industries ............................................................................. 35

Figure 7: Percentile Map of Business Service Industries (1990-1998) .............................................. 36

Figure 8: Percentile Map of Business Service Industries (1999-2006) .............................................. 36

Figure 9: Percentile Map of Distributive Industries ........................................................................... 37

Figure 10: Cartogram of All Sectors .................................................................................................. 39

Figure 11: Cartogram of Manufacturing Industries ........................................................................... 39

Figure 12: Cartogram of Retail Trade Industries ............................................................................... 40

Figure 13: Cartogram of Local Market Industries ............................................................................. 40

Figure 14: Cartogram of High Tech Industries .................................................................................. 41

Figure 15: Cartogram of Extractive Industries ................................................................................... 41

Figure 16: Cartogram of Business Service Industries (1990-1998) ................................................... 42

Figure 17: Cartogram of Business Service Industries (1999-2006) ................................................... 42

Figure 18: Cartogram of Distributive Industries ................................................................................ 43

Figure 19: LISA Cluster Map of All Sectors (Moran’s I = 0.44, p < 0.01) ....................................... 45

Figure 20: LISA Cluster Map of Manufacturing Industries (Moran’s I = 0.40, p < 0.01) ................. 45

Figure 21: LISA Cluster Map of Retail Trade Industries (Moran’s I = 0.36, p < 0.01)..................... 46

Figure 22: LISA Cluster Map of Local Market Industries (Moran’s I = 0.47, p < 0.01) ................... 46

Figure 23: LISA Cluster Map of High Tech Industries (Moran’s I = 0.14, p < 0.01) ....................... 47

Figure 24: LISA Cluster Map of Extractive Industries (Moran’s I = 0.43, p < 0.01) ........................ 47

Figure 25: LISA Cluster Map of Business Services (1990-1998) (Moran’s I = 0.37, p < 0.01) ....... 48

Figure 26: LISA Cluster Map of Business Services (1999-2006) (Moran’s I = 0.40, p < 0.01) ....... 48

Figure 27: LISA Cluster Map of Distributive Industries (Moran’s I = 0.34, p < 0.01) ..................... 49

1 

Introduction

Entrepreneurship, especially new business creation, is both a cause and consequence of 

economic growth. Although the odds are against the survival of most new ventures, those that 

survive the early liabilities of the resource-limited start-up phase contribute to a chaotic, albeit 

generally positive, economic dynamic by ushering out obsolete practices and introducing new 

products and new business models. Several communities in the United States have wellestablished reputations for being especially hospitable to the creation and growth of new firms, 

and a few high technology regions—including Silicon Valley, Boston’s Route 128, and North 

Carolina’s Research Triangle—are a constant source of fascination for policymakers, business 

leaders, and academics around the world. Of these regions, Silicon Valley is of central interest as 

many communities seek to emulate its entrepreneurial successes and economic prosperity. 

Not every region can hope to be a hot spot of high technology entrepreneurial activity. It 

seems unlikely, for example, given the history of places like Silicon Valley, that a community 

can become the next high technology cluster without a solid research university. Moreover, from 

an economic welfare perspective, not every community should aspire to be the next high 

technology region, given that the national economy is more likely to benefit from entrepreneurial 

activity in a range of business sectors and product segments. Indeed, the premise of strategic 

positioning—in which competing businesses endeavor to distinguish themselves from their 

rivals—seems especially relevant in a regional context. That is, municipal or county planners, 

economic development authorities, and business leaders may find it advantageous to encourage 

the formation of new businesses in sectors or industries best suited to and sustained by extant 

local conditions, institutions, and resources. 

The prescription for regions to position their economies strategically by supporting 

entrepreneurial activity in particular sectors is, of course, far from easy or simple to put into 

practice. Indeed, such a strategy incurs the problems and very real dangers of “picking winners” 

and necessarily requires the formulation of industrial policies typically shunned in the United 

States. Such fundamental normative issues are beyond the scope of the current paper, which 

instead follows a far more positivist path by exploring the extent to which regions—in this case, 

the counties that constitute the lower 48 United States—specialize in entrepreneurial activity in 

particular sectors and the determinants of such specialization. In many respects, this study 

complements Armington and Acs’ (2002) study on regional variations in new firm formation, but 

also extends their work by focusing on individual counties rather than labor market areas (LMA) 

and analyzing the geographic patterns of entrepreneurial activity in the contiguous United States. 

Given its focus, this study has two objectives. First, it employs spatial data analysis tools 

to identify where, and to what extent, new firms concentrate geographically using county level 

establishment birth data from 1990 to 2006. The analysis tools provide a statistical test for the 

geographic clustering of entrepreneurial activity and allow this clustering to be shown in a series 

of maps. Second, the paper explores many of the regional determinants of new firm formation 

identified by Armington and Acs (2002), but at a county level of analysis using econometric 
2 

methods that account for the spatial autocorrelation of the data.

1

The paper is organized as follows. The next section briefly reviews the relevant literature 

on these topics and explicitly states the hypotheses to be tested. The third section describes the 

study’s research design including specifics on the data, the variable calculations, the empirical 

model, and the spatial data analysis methods used to assess the location of entrepreneurial 

activity in the United States. The fourth section reports the findings of the statistical and spatial 

analysis, including a summary and descriptive statistics of the data, maps indicating the locations 

of entrepreneurial activity by industry sector, and regression results for the hypothesis testing. 

The fifth section discusses some of the implications of findings for practice, policy, and future 

research. The final section offers a few concluding remarks. 

Indeed, where Armington and 

Acs’s (2002) data encompass 394 labor market areas (LMAs), the data for this study cover more 

than 3,000 counties, thus dramatically enhancing the statistical power and inference of the 

findings. 

Literature Review and Hypotheses

Why do regions differ in their levels of entrepreneurial activity (Shane & Venkataraman, 

2000)? Evidence suggests that new firms, especially those in knowledge-intensive industries, 

tend to concentrate geographically, are more likely to fail as a result, and yet, in the case of the 

survivors, tend to outperform similar firms operating in more isolated locations (McCann and 

Folta, 2008). The question, of course, is why. Is it because some regions, in addition to 

possessing sound legal, capital, and physical infrastructures, offer more supportive cultures or 

effective leadership (Venkataraman, 2004)? Is it because the process of creative destruction 

operates principally on a local level (Pe'er & Vertinsky, 2008)? Is it because local socioeconomic 

conditions including the human capital of an area’s workforce (Armington & Acs, 2002), entry 

barriers, and the externalities found in highly concentrated areas (McCann & Folta, 2008) vary 

regionally? Perhaps it is all of these factors, but a core problem in most related studies is the 

presumption that new (as well as existing) firms actually cluster.

With this in mind, consider Cooper and Folta’s (2000) pivotal question: to what extent do 

start-ups concentrate in particular regions? Almost as a rule, most scholars, policymakers, and 

regional business leaders take as given the premise that start-ups cluster geographically, despite 

some evidence to the contrary. Dumais, Ellison, and Glaeser (2002), for example, decompose 

dynamic changes in industry clusters into plant entries, expansions, and closures by new and 

existing firms using metropolitan statistical area (MSA) data. They find that new firm plants

actually have a “de-agglomerating” effect in that these entries generally locate away from current 

geographic centers of industry. In other words, the clustering of new firms may be more apparent 

than real.

 

1 A spatial autocorrelation principle recognizes that data collected at any position will have a greater similarity to or 

influence on those locations within its immediate vicinity. In other words, the data are spatially dependent. 
3 

Of course, determining whether or not new firms cluster requires that a related question 

be addressed (Cooper and Folta, 2000: 351): “What are ways to identify clusters, to classify them 

and to measure differences across them?” Fortunately, empirical measures of spatial clustering 

are available for answering this question. Doh and Hahn (2008), for example, provide an 

excellent introduction to both global and local indicators of spatial autocorrelation that indicate 

the degree and location of clustering. Their application of these techniques to data on the 

geographic distribution of firms in China’s provinces (Chang & Park, 2005) is readily extended, 

as will be demonstrated in this paper, to analyzing the clustering of start-up activity. Likewise, 

Plummer (2010) offers a primer on the application of these techniques to the study of new firm 

formation. 

Another question posed by Cooper and Folta (2000) is “What are the processes that lead 

high technology firms to locate in clusters?” Again, posed more simply, why do new firms start 

where they do? What are the determinants of a particular region’s level of start-up activity? As 

can be imagined, a range of theories and studies suggest an array of explanations: 

• Proximity of a research university (Zucker, Darby, & Armstrong, 2002), 

• Availability of venture capital (Sorenson & Stuart, 2001), 

• Spatial distribution of entrepreneurial opportunities (Sorenson & Audia, 2000), 

• Availability of tangible and intangible resources (Ahuja & Katila, 2004; Russo, 2003), 

• Availability of labor (Acs & Armington, 2004a; Marchington, Carroll, & Boxall, 2003; 

Rumelt, 1987; Song, Almeida, & Wu, 2003), 

• Availability of knowledge (Acs & Armington, 2004b; Almeida, 1996; Almeida & Kogut, 

1999; Almeida & Phene, 2004; Chung & Alcácer, 2002; DeCarolis & Deeds, 1999; 

Gibbons, 2004; Phene & Almeida, 2003), 

• Munificence of local institutional environments (Audretsch, 2000; Baum & Oliver, 1996; 

Casson, 2003; Mugler, 2000; Storey, 2000), and 

• Quality of local supporting infrastructure (Lomi, 1995; Sorenson, 2003). 

Few studies have explored the determinants of new firm geographic concentration at the 

national level. A key exception, a study by Armington and Acs (2002), reports ordinary least

squares (OLS) estimates from a standard linear model using data from 394 labor market areas 

(LMAs) in the United States. with the number of firm births per worker as the dependent 

variable. Arguing that population growth contributes to demand externalities in labor markets, 

they find that the rate of population growth is positively associated with the rate of new firm 

births. In addition, as evidence of the importance of local entry barriers, they find that areas that 
4 

are home to larger businesses—indicated by local employment divided by the number of 

establishments—have lower start-up rates. Finally, consistent with the premise of start-ups 

relying on an educated and technically skilled workforce (as well as the skills of the 

entrepreneur), they find start-up rates are higher in areas where the share of the adult population 

holding at least a college degree is greater. 

As with any study, there is reason to examine Armington and Acs’ (2002) anew with 

updated theory and enhanced methodology. First, recent theoretical advancements have led to the 

knowledge spillover theory of entrepreneurship (Acs, Audretsch, Braunerhjelm, & Carlsson, 

2005; Audretsch, Keilbach, & Lehmann, 2006), in which the creation of new firms is offered as 

the missing link between new knowledge and economic growth. Aside from contributing to the 

enhancement of endogenous growth theory (Romer, 1986, 1987, 1990), the knowledge spillover 

theory of entrepreneurship incorporates the newest thinking and empirical evidence concerning 

the spatial dimension of economic activity (cf. Anselin, Varga, & Acs, 2000; Varga, 1998, 

2000). Second, from a research methodology standpoint, Armington and Acs’s (2002) analysis 

may be adversely affected by spatial dependence in their data, as well as the small sample size 

relative to what is possible with county-level data. For the remainder of this paper, regions are 

equated with counties. 

A Model of Entrepreneurial Activity

Entrepreneurship is the process by which enterprising individuals pursue and exploit 

profitable opportunities to introduce products, processes, and/or business models that are new to 

the market or that create new markets entirely (Shane & Venkataraman, 2000; Venkataraman, 

1997). At the heart of this process is the intersection of alert individuals and the market 

imperfections judged by these individuals to offer greater economic returns, if corrected, than 

other income streams available to them (e.g., working for an existing firm). While this process 

does not necessarily result in the formation of new firms, the difficulty existing firms have in

adapting and expanding to satisfy an economy’s growing needs suggests that new venture 

creation is the crucial contributor to a region’s growth and prosperity (Audretsch et al., 2006). 

From this point of view, the essence of an individual’s entrepreneurial choice is starting their 

own business versus offering their labor to an incumbent enterprise (Parker, 2004). Indeed, it can 

be assumed that those who choose to become entrepreneurs have judged that the returns from 

doing so will exceed the returns from earning a wage as the employee of an ongoing concern.

From a regional perspective, this entrepreneurial choice framework suggests that—

holding the characteristics of individuals constant—the level of new firm formation in a region

increases with the number of opportunities and decreases with the local burdens or barriers to the 

entrepreneurship process. Indeed, the knowledge spillover theory of entrepreneurship (Audretsch 

et al., 2006) suggests the following model of entrepreneurial activity for a given region or county 

i, 

Ei = (1/βi)(π*[gy, Aopp, θ] i – w i) (1)
5 

where π*[gy, Aopp, θ] is the returns expected from exploiting those opportunities arising from 

regional growth (gy) and investment in new knowledge (Aopp) not already exploited by 

incumbent firms (θ), w are wages to be earned by working for an incumbent firm, and β 

represents institutional barriers and resource constraints other than incumbent firms. 

The Hypotheses

The knowledge spillover model given by Equation 1 has two critical implications. First, 

the model implies that the spatial distribution of entrepreneurial activity in the economy is 

neither random nor uniform. That is, regional variations in the conditions that facilitate and 

impede entrepreneurial activity necessarily suggest regional variation in levels of entrepreneurial 

activity. In fact, the knowledge spillover model suggests that entrepreneurial activity 

concentrates across regions, especially if one thinks of regions in terms of contiguous 

administrative units like counties or census tracts. The reason is that any region (i.e., county, 

state, or census tract) is not a self-contained area of economic or entrepreneurial activity, as the 

relevant activities in one region spill over into another. More formally, the level of opportunities 

in a given county i feed the level of entrepreneurial activity both within the county and in the 

adjacent county j.

The regional concentration of entrepreneurial activity appears as a positive correlation in 

the levels of entrepreneurial activity across regions; that is, the level in one region or county is 

positively associated with the level in an adjacent area. This model is, of course, virtually a 

statement of fact in high technology clusters like Silicon Valley, given that the entrepreneurial 

progress in Santa Clara County has also boosted the economic well-being of the greater Bay 

Area. The model, however, has rarely been explored empirically and, thus, the following 

hypothesis will be tested:

Hypothesis 1

Second, the spillover model also suggests a number of factors that determine the level of 

entrepreneurial activity within the region. That is, an increase or decrease in the level of any one 

factor will lead to a corresponding change in the level of entrepreneurial activity. As mentioned, 

the model suggests four broad categories of factors that determine the level of entrepreneurial 

activity in the region. The first two sets of factors are the entrepreneurial opportunities that come 

from the economic growth of the region and the distinct contribution of new knowledge. That is, 

as the size of the region’s economy grows and the stock of knowledge in the region expands, so 

too should the number of new businesses expand. These first two groups of factors seem rather 

straightforward: 

: The level of entrepreneurial activity in a given county i is 

positively associated with the level of entrepreneurial activity in the set of 

neighboring counties J. 

Hypothesis 2: The level of entrepreneurial activity in a given county i 

increases with higher levels of economic growth within the county.
6 

Hypothesis 3

Third are the region’s entrepreneurial barriers that either limit the number of available 

opportunities (i.e., θ) or impede individuals’ willingness or ability to start new businesses (i.e., 

β). While these factors are difficult to observe, the number and size of existing firms in the 

region arguably capture the opportunity constraints. That is, the level of entrepreneurial activity 

in the region is determined, in part, by the number of opportunities (arising from either the 

region’s growth or knowledge investments) not exploited by existing firms (i.e., 1 – θ) 

(Audretsch et al., 2006). A greater number of existing firms suggests fewer opportunities left for 

individuals to exploit, but so does the presence of larger incumbent firms, given their presumably 

greater absorptive capacity.

: The level of entrepreneurial activity in a given county i 

increases with increases in the stock of knowledge within the county.

Hypothesis 4: The level of entrepreneurial activity in a given county i 

decreases with greater density of existing firms within the county.

Hypothesis 5

As part of the fourth and final set of factors, the level of self-employment in the region as 

well as the level of education of the region’s population may capture the willingness and/or 

ability of a region’s workforce to engage in entrepreneurial activity in the region. In particular, 

higher levels of self-employment would seem to indicate a more supportive environment for 

entrepreneurial activity, since such self-employment implies the proclivity of the region’s 

workforce to choose something other than wage-based employment. The population’s 

educational attainment may capture the ability of would-be entrepreneurs to overcome any 

entrepreneurial barriers and/or assess the future profit potential of a given opportunity. Thus, 

: The level of entrepreneurial activity in a given county i 

decreases as the average size of existing firms increases within the county. 

 

Hypothesis 6: The level of entrepreneurial activity in a given county i 

increases with higher levels of self-employment within the county. 

Hypothesis 7

In turn, since the spillover model in Equation 1 suggests that individuals choose to 

become entrepreneurs or accept wage-based work by comparing the earning potential of doing 

the former versus the latter, it follows that better wage opportunities will reduce the likelihood 

that someone will opt to start their own business. The stronger effect, however, may be observed 

with the loss of wage employment since the earned wage is necessarily zero. Thus, for 

unemployed members of the workforce, the opportunity cost of choosing entrepreneurship over 

wage employment is substantially lowered, if not nullified. In other words, the spillover model 

: The level of entrepreneurial activity in a given county i 

increases with higher levels of educational attainment within the county. 
7 

predicts a positive relationship between the region’s unemployment rate and the level of 

entrepreneurial activity. Thus, 

Hypothesis 8

Research Design

: The level of entrepreneurial activity in a given county i 

increases with higher unemployment within the county. 

The hypotheses established in the previous section predict changes in a county’s rates of 

entrepreneurial activity, given changes in the specified characteristics of that county. Thus, the 

hypotheses are tested using a panel dataset of repeated (i.e., time-series) observations of the 

relevant variables within each county in the continental United States. The panel data make it 

possible to report fixed effects regression estimates in which each coefficient captures the change 

in the dependent variable, given a unit change in the specific independent variable. Such 

longitudinal data are preferable to a purely cross-sectional dataset because both statistical power 

and inference are dramatically improved. That said, the repeated measures introduce a few 

complications to the analysis that limit both the length of the study period and the number of 

counties included in the analysis. 

Data Collection 

To begin, annual county-level business establishment birth and death (EBD) data 

covering every available year, nearly every industry, and every county in the 50 United States 

were obtained from the U.S. Census Bureau’s Business Information Tracking Series (BITS) file.

The EBD tabulations report single-unit, multi-unit, and total establishment births and deaths by 

four-digit SIC or five-digit NAICS code from 1990 to 2006 (Plummer & Headd, 2007). To 

facilitate data collection for the independent variables and the mapping of the locations of 

entrepreneurial activity, the EBD data are georeferenced to the 2000 Census county boundary 

definitions. Using the 2000 county definitions requires minor corrections to the data aggregations 

to accommodate changes in the Census Bureau’s data collection from Virginia’s independent 

cities and the redefined Miami-Dade County boundary in Florida. Using the 2000 boundaries 

omits Broomfield County in Colorado, which was created in 2001 from portions annexed from 

its four neighboring counties. As a result, the dataset includes 3,009 counties.2

Information for the independent variables originates from several public sources. The 

U.S. Census Bureau’s 2000 Gazetteer file and County Business Patterns database are the sources, 

respectively, for county land areas and the number of existing establishments. The Bureau of 

Economic Analysis regional accounts provide the information on population growth, income 

growth, and proprietors’ share of the labor force. The U.S. Patent and Trademark Office provides 

the number of patent grants per county and the Economic Research Service of the U.S. 

 

 

2 Alaska is omitted because of substantial changes in the composition of its counties and Hawaii is omitted because 

the spatial analysis methods used here require that the counties share a common border (i.e., the counties are not 

separated by water). 
8 

Department of Agriculture is the source of the college degree information. Finally, the Bureau of 

Labor Statistics Labor Local Area Unemployment Statistics (LAUS) file yields the labor force 

data for each county. Unfortunately, not all of the data used to calculate the independent 

variables are available for all 17 years of the EBD tabulations. The patent dataset used for the 

analysis, for example, is available for 1990 to 1999 only. Thus, all the maps showing the 

locations of entrepreneurial activity derive from data from the full 17-year period, while the 

regressions reported in Table 6 cover a shorter ten-year time period. 

Measuring the Rate of New Firm Births 

For this study, firm births are measured by the number of single-unit establishment births 

in the county. Since a single-unit establishment refers to the only physical location where a firm 

conducts its business or operations, a single-establishment birth is a reasonable proxy for the 

emergence of a new firm (Armington & Acs, 2002). In turn, the rate of new firm births in each 

county is calculated as the number of single-unit establishment births in year t divided by the 

number of workers (in thousands) in the labor force regardless of employment status in year t-1. 

This labor force method for calculating the rate of firm births, which is consistent with the 

Armington and Acs (2002) approach, facilitates comparisons of entrepreneurial activity across 

counties of varying sizes and captures the tendency of workers in the county to start new 

businesses (Audretsch & Fritsch, 1994). 

Given the expectation that the geographic distribution of firm births varies by industry, 

the county rate of new firm births per worker is calculated for seven industry sectors based on a 

modification of the definitions used by Armington and Acs (2002) as shown in Table 1. The 

principal difference from Armington and Acs’s (2002) study is the separation of high technology 

industries from the manufacturing sector based on Henderson’s (2003) industry definition. The 

central complication with the industry classifications is that the EBD data are indexed by 

Standard Industrial Classification (SIC) codes from 1990 to 1998 and North American Industy 

Classification System (NAICS) codes from 1999 to 2006. Fortunately, the SIC and NAICS 

sector definitions correspond well enough to add only a little noise to the data. The exception is 

the business service industries for which the SIC and NAICS codes poorly correspond. As a 

result, the industry disaggregation yields nine calculations of each county’s firm birth rates: the 

overall rate of firm births in all sectors; the new firm birth rates in the manufacturing, extractive, 

retail trade, local market, and high technology sectors; and the rate of firm births in business 

services from 1990 to 1999 and from 1999 to 2006. 
9 

Table 1: Industry Classifications 

Sector (Description) SIC 

Classification

NAICS 

Classification

Distributive (transportation, public utilities, wholesale trade) 4000-5199 48-49, 42 

Manufacturing (excluding high technology) 2000-3999 31-33 

Extractive (agricultural services and mining) 0700-1499 11, 21 

Retail Trade 5200-5999 44-45 

Local Market (construction, consumer, and financial 

services excluding business services)

1500-1799 

6000-8999 23, 52-53 

Business Services (1990-1998) (including engineering, 

accounting, research, and management services)

7300-7399 

8700-8799 

Business Services (1999-2006) 54 

High Technology (computers, electronic components, 

aircraft, and medical instruments)

357, 367, 

372, 384 

33411, 33331, 

33441, 33422, 

33431, 54171, 

33641, 33911, 

33451 
10 

Mapping the Location of New Firm Births 

Three types of maps are created to indicate the location of new firm births and to test 

Hypothesis 1. The first type is the percentile map, which categorizes the counties’ rates of new 

firm births into one of six percentile ranges (see Legend 1). Counties with new firm birth rates of 

less than 1 percent are shown in dark blue; those with increases of more than 99 percent are 

shown in red; four other percentile ranges between these poles are displayed in lighter shades of 

color. 

The second map is a circular cartogram map that resizes each county according to its 

rate of new firm births—that is, the higher the rate of new firm births, the larger the area of the 

circle in the cartogram (see Legend 2). The circular cartograms make it possible to compare the 

rates of new firm births across counties without the county’s size biasing the interpretation or 

comparison. In all of the circular cartograms, the nonoutlying observations are green, the upper 

outliers are shown in red, the lower outliers are blue, and zero observations are white or 

transparent. The outliers are observations with values well outside the interquartile range (the 

range between the top of the lower quartile and the bottom of the upper quartile). 

The third type of map is a LISA cluster map. LISA or “local indicators of spatial 

association” maps indicate the presence or absence of significant spatial clusters or outliers for 

each location. These maps require the calculation of two statistics—the global and local Moran’s 

I indicators for spatial autocorrelation. Calculating these statistics requires the construction of a 

spatial weights matrix, which specifies the spatial structure of the expected relationship across 

county boundaries (Plummer, 2010). In the present study, the spatial weights matrix (W) is a 

row-standardized “first-order, queen contiguity” matrix, meaning that each county’s 

neighborhood set includes all its adjacent (i.e., tangent and border-sharing) counties. Row 

standardization means that the original binary matrix—in which each element equals one when 

two adjacent counties are neighbors and zero otherwise—is transformed so that each row of the 

matrix sums to one. Row standardization allows for the calculation of the spatial lag (Wyj

Armed with the weights matrix, the global Moran’s I tests for the simple linear 

relationship between the given variable (y

), 

which is the value of y averaged across the set of neighboring counties J.

i) in county i and its spatial lag (Wyj

Using this information, the LISA cluster map indicates the locations where the 

statistically significant local Moran’s I statistics are either positive or negative (Anselin et al., 

2002). In fact, the LISA cluster map indicates the location of four types of spatial autocorrelation 

) (Anselin, 2001). 

A positive and statistically significant Moran’s I statistic indicates that values of y are spatially 

dependent, thus providing support for Hypothesis 1 (the level of entrepreneurial activity in a 

given county is positively associated with the level in neighboring counties) (Anselin & Bera, 

1998). Next, to identify the specific locations where the variable y is spatially correlated, the 

global Moran’s I statistic is decomposed to calculate a LISA statistic for each county (Anselin, 

1995; Anselin, Syabri, & Smirnov, 2002). As with the global indicator, a positive and 

statistically significant LISA means that the value of y observed in county i is similar to the 

spatially weighted average value of y in the set of neighboring counties J. 
11 

(see Legend 3). First, the “high-high” clusters—shown by the deep red color—are those counties 

with high values of y surrounded by counties with similarly high values of y. Second, the “lowlow” clusters—indicated by the deep blue color—are those counties with low values of y 

surrounded by counties with similarly low values of y. These high-high and low-low clusters 

indicate positive spatial autocorrelation. Third, the “low-high” clusters—indicated by the light

blue color—are those counties with low values of y surrounded by counties with high values of 

y. Finally, the “high-low” clusters—indicated by the light red color—are those counties with 

high values of y surrounded by counties with low values of y. These last two types of clusters are 

called spatial outliers and indicate negative spatial autocorrelation. 

In projecting the three sets of maps, the rates of new firm births are altered slightly to 

facilitate the spatial analysis. First, because only the cross-sectional spatial correlation is of 

interest in testing Hypothesis 1, the 16-year average of the new firm birth rate for each sector is 

mapped. Second, these average rates are “smoothed” to address the variance instability created 

by the denominator in the rate calculation (Anselin, Syabri, & Kho, 2006). Without the rate 

smoothing, the birth rates in counties with extremely small labor forces will be spurious outliers 

making any comparison of the raw rates misleading. Thus, the rates shown in the maps are 

stabilized by the Empirical Bayes (EB) smoother, which essentially “pulls” the outlier rates 

towards the overall mean with the rates from the counties with smaller workforces undergoing 

more shrinkage than counties with larger workforces. 

Calculation of the Independent Variables

Given the hypotheses established in the previous section, eight independent variables are 

defined for each county. To test Hypothesis 2, economic growth is captured by income growth, 

which is the annual change in per capita income from year t-1 to t divided by per capita income 

in year t-1. Since the county’s economy also grows with the number of consumers, economic 

growth is also measured by population growth, which is the annual change in population from 

year t-1 to t divided by total population in year t-1. To test Hypothesis 3, knowledge is the 

number of patent grants in year t divided by the number of business establishments in year t. As 

mentioned, the patent data are available from 1990 to 1999 only, which limits the study period 

for the regression analysis to this 11-year time period. 

To test Hypotheses 4 and 5 regarding the influence of the county’s opportunity 

constraints, density is the number of business establishments in year t per county square mile and 

establishment size is the number of employed workers in the county in year t divided by the 

number of existing business establishments also in year t. To test Hypotheses 6 and 7 regarding 

the willingness and ability of the region’s workforce to engage in start-up activity, the county’s 

level of self-employment is measured by the share of proprietors, which is the number of 

business sole proprietors and partners in year t divided by the total labor force in year t. In turn, 

college degree is the percentage of adults older than 25 years of age holding at least a college 

degree. Because the college degree variable is available for 1990 and 2000 only, the observations 

for the intervening years are derived via linear interpolation. Finally, to test Hypothesis 8, the 
12 

unemployment rate is the number of unemployed workers in year t divided by the total labor 

force in year t. 

Model Specification and Estimation 

The regression model for testing Hypotheses 2 through 8 derives from the usual fixed effects 

(within subjects) model specification,

yist = Xitβ + μi + εit

where y

 (2)

it is the rate of new firm births in sector s at time t, Xit is a matrix of the independent 

variables, β is a vector of coefficients to be estimated, μi is panel-specific error term representing 

the unobserved effects peculiar to each county i, and εit

To account for the effect of outliers and to estimate the β coefficients as elasticities (i.e., 

the percentage change in the dependent variable given a 1 percent change in the independent 

variable), the model given by Equation 2 is transformed into log-linear form. That is, with the 

exception of the income growth and population growth variables, given their negative values, the 

variables in X

is the random error term. This 

specification controls for the effect of any unobserved variables specific to each county that are 

constant over time. Moreover, the coefficients are interpreted as the “internal response” of the 

dependent variable in the county to changes in the independent variables within the county. 

it and yit

ln(y

are transformed by the natural log. For all the dependent variables, as well 

as the knowledge variable—which included observations with zero values—the natural log 

transformation required the use of a started log. Thus, a small constant equal to one divided by 

the number of workers in the labor force was added to each observation before taking its natural 

log. As a result, the results reported in Table 6 are estimates of the following model,

ist) = α + β1ln(Establishment Size)it + β2(Income Growth)it + β3(Population 

Growth)it + β4ln(Share of Proprietors)it + β5ln(Density)it + β6ln(Knowledge)it + 

β7ln(Unemployment Rate)it + β8ln(College Degree)it + μi + εit

Estimating Equation 3 using the standard fixed effects estimator requires the assumption 

that the errors are uncorrelated within and across panels and homoskedastic. Of course, given the 

expectation that the county rates of new firms will exhibit spatial autocorrelation, the usual 

assumptions of the fixed effects model are likely to be violated. As a result, Equation 3 is 

estimated using the Driscoll-Kraay (Driscoll & Kraay, 1998; Hoechle, 2007) estimator that 

produces heteroskedastic and autocorrelation consistent estimates robust to general forms of 

spatial and temporal correlation. As a result, the covariance estimates account for any spatial 

autocorrelation across counties, any temporal autocorrelation within counties from year to year, 

and any violation of the homoskedasticity assumption (Driscoll & Kraay, 1998). Unlike other 

available panel estimators, the Driscoll-Kraay estimator is feasible even when, as in this case, the 

number of panels (N) is larger than the number of time periods (T). 

 

 (3)

The Driscoll-Kraay estimator is not without its limitations and it is reasonable to ensure 

that its use is justifiable. Thus, following the guidance of Hoechle (2007), three diagnostic 

procedures are completed to validate the results of the Driscoll-Kraay estimator. First, in addition 
13 

to the Moran’s I tests used to create the LISA cluster maps, a diagnostic test for cross-sectional 

spatial correlation in Equation 3 is applied to the standard fixed effects estimates to ensure that 

the Driscoll-Kraay estimator is justified. Pesaran’s cross-sectional dependence (CD) statistics 

tests the null that the residuals in Equation 3 are uncorrelated across counties. Second, diagnostic 

tests of the nulls of homoskedastic and serially uncorrelated residuals are applied to the standard 

fixed effects estimates. Finally, a modified Hausman test for fixed effects robust to general forms 

of spatial and temporal correlation is applied to the Driscoll-Kraay estimates. 

Results

Summary information is reported in Table 2. The table indicates that across all sectors 

there are nearly five single-unit establishment births per 1,000 workers during the study period 

within a range of zero and 28.49 births per 1,000 workers. Only 55 of the 3,009 counties in the 

dataset show zero single-establishment births in all sectors at least once during the study period. 

The table also summarizes the rates of new firm births in the seven industrial sectors. Given the 

dominance of retail trade in the U.S. economy, it is not surprising that this industry sector has the 

highest rate of new firm births (1.06 births per 1,000 workers), followed closely by the local 

market industries (1.03 births per 1,000 workers). By far the lowest rate of new firm births is in 

the high technology sector (0.01 births per 1,000 workers). This suggests, perhaps correctly, that 

new firms are rare in this sector, but it is also possible that the large difference relative to, say, 

the manufacturing sector is a function of the industry aggregations shown in Table 1. 

The bottom of Table 2 summarizes the independent variables, which are within the 

expected ranges with two exceptions. First, somewhat surprisingly, given that the majority of the 

nation’s 6 to 7 million business establishments have fewer than four employees, the average 

establishment size in the observed counties is a little more than 22 workers per place of business, 

within a range of 6.2 and 202.5 workers per place of business. A review of the variable, however, 

indicates the value is “real” in that is not the result of a miscalculation. Second, while the mean 

share of proprietors is consistent with the mean reported by Armington and Acs (2002), the 

maximum number of proprietors per worker in the labor force is greater than one. This probably 

reflects key differences in how the Bureau of Economic Analysis (BEA) counts the number of 

proprietors (the numerator) and the Bureau of Labor Statistics (BLS) counts the number of 

workers (the denominator).3

 

3 The BEA count of proprietors includes the total number of full-time and part-time sole proprietors and individual 

business partners. The BLS labor force measure, on the other hand, is the average annual number of employed 

workers and unemployed workers including those people working in their own business. More importantly, 

unemployed persons are those workers available for work and making “specific efforts” to find employment during 

the four-week reference period. It seems likely that the BEA measure slightly overstates the number of proprietors, 

while the BLS measure slightly understates the size of the labor force. 

Since the bias in the share of proprietors is small and constant 

across counties, the variable is unchanged prior to entering the regression model. 
14 

Tables 3 and 4 shed additional light on the comparative levels of entrepreneurial activity 

in U.S. counties. In particular, Table 3 is a list of the top 20 counties ranked by the total number 

of single-unit establishment births by given sector. In raw counts, it is clear that Los Angeles, 

Table 2: Descriptive Statistics

Variables Mean Std. Dev. Min Max

All Sectors

Single-Establishment Births per 1,000 Workers

4.71 2.05 0 28.49

Manufacturing 0.24 0.26 0 5.41

Retail Trade 1.06 0.69 0 14.99

Local Market 1.03 0.72 0 11.48

High Tech 0.01 0.03 0 2.16

Extractive 0.15 0.22 0 3.80

Business Services (1990-1998) 0.37 0.36 0 5.16

Business Services (1999-2006) 0.32 0.34 0 5.86

Distributive 0.50 0.39 0 7.52

Establishment Size

Independent Variables

22.69 7.82 6.23 202.50

Per Capita Income Growth 0.04 0.05 -0.41 0.84

Population Growth 0.01 0.02 -0.28 0.36

Share of Proprietors 0.26 0.12 0.06 1.48

Agglomeration Density 6.54 81.39 0.00 4601.60

Knowledge (per 1,000 workers)* 0.23 0.40 0 7.06

College Degree 15.22 7.39 3.84 59.83

Unemployment Rate 6.06 3.21 0.90 39.60

 

* Per 1,000 workers for this table only. Knowledge = patents per worker in all other tables.

Cook (Chicago), and New York counties are three regions with considerable levels of 

entrepreneurial activity. In fact, Los Angeles loses its top ranking only to Santa Clara County 

and Harris County (TX) in the high technology and extractive sectors, respectively. The rankings 

in Table 3 are consistent with the knowledge spillover model since it follows that communities 

with more people should have higher absolute levels of entrepreneurial activity. Of course, this 

means little comparatively, since such raw counts fail to capture the average tendency of the 

community’s residents to become entrepreneurs. 

With this in mind, Table 4 shows the top 20 counties ranked by the rate of firms births 

per 1,000 workers by sector. These rankings are qualitatively different from those in Table 3, in 
15 

that most large metropolitan areas are absent. Table 4 suggests that the nation’s interior and 

northwestern counties—especially in Colorado, Utah, and Washington states—have the highest 

levels of entrepreneurial activity per person during the study period. This is consistent with the 

knowledge spillover model, given the population growth in these western states over the last two 

Table 3: County Rankings by Sector, Total Single-Establishment Births

Rank All Sectors Local Market 

Industries

Retail Trade 

Industries

Manufacturing 

Industries

High Tech 

Industries

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

Los Angeles, CA

Cook, IL

New York, NY

Dade, FL

Orange, CA

Harris, TX

Maricopa, AZ

San Diego, CA

Broward, FL

Dallas, TX

King, WA

Nassau, NY, NY

Palm Beach, FL

Santa Clara, CA

Kings, NY

Queens, NY

Suffolk, NY

Clark, NV

Oakland, MI

Hennepin, MN

Los Angeles, CA

Cook, IL

Maricopa, AZ

Orange, CA

San Diego, CA

Dade, FL

Harris, TX

New York, NY

King, WA

Broward, FL

Dallas, TX

Palm Beach, FL

Suffolk, NY

Nassau, NY

Clark, NV

Queens, NY

Riverside, CA

Salt Lake, UT

Santa Clara, CA

Kings, NY

Los Angeles, CA

Cook, IL

New York, NY

Dade, FL

Harris, TX

Orange, CA

Maricopa, AZ

San Diego, CA

Kings, NY

Broward, FL

Queens, NY

Dallas, TX

King, WA

Nassau, NY

Suffolk, NY

Palm Beach, FL

Wayne, MI

Santa Clara, CA

Oakland, MI

Clark, NV

Los Angeles, CA

New York, NY

Orange, CA

Cook, IL

Harris, TX

San Diego

Maricopa, AZ

Dade, FL

Santa Clara, CA

Dallas, TX

King, WA

Kings, NY

Broward, FL

Queens, NY

Alameda, CA

San Bernardino, CA

Hennepin, MN

Suffolk, NY

Tarrant, TX

Oakland, MI

Santa Clara, CA

Los Angeles, CA

Orange, CA

San Diego, CA

Middlesex, MA

Maricopa

Alameda, CA

King, WA

Cook, IL

Harris, TX

Dallas, TX

Dade, FL

Hennepin, MN

Broward, FL

San Mateo, CA

Salt Lake, UT

Montgomery, MD

Boulder, CO

New York, NY

Travis, TX

Business Services 

(1999-2006)

Los Angeles, CA

Cook, IL

New York, NY

Orange, CA

Dade, FL

Maricopa, AZ

San Diego, CA

Harris, TX

Broward, FL

Santa Clara, CA

King, WA

Dallas, TX

Palm Beach, FL

Fairfax, VA

Fulton, GA

Nassau, NY

Clark, NV

Hennepin, MN

Middlesex, MA

Oakland, MI

Rank Extractive 

Industries

Distributive 

Industries

Business Services 

(1990-1998)

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

Harris, TX

Los Angeles, CA

Dallas, TX

Maricopa, AZ

King, WA

San Diego, CA

Palm Beach, FL

Oklahoma, OK

Suffolk, NY

Cook, IL

Orange, CA

Broward, FL

Tarrant, TX

Midland, TX

Riverside, CA

Tulsa, OK

Nassau, NY

Oakland, MI

Dade, FL

Clark, NV

Los Angeles, CA

New York, NY

Dade, FL

Cook, IL

Harris, TX

Orange, CA

Broward, FL

Maricopa, AZ

San Diego, CA

Dallas, TX

Queens, NY

Kings, NY

King, WA

Nassau, NY

Bergen, NJ

Suffolk, NY

Palm Beach, FL

Santa Clara, CA

Hennepin, MN

Alameda, CA

Los Angeles, CA

Cook, IL

New York, NY

Harris, TX

Orange, CA

Maricopa, AZ

Dallas, TX

Dade, FL

San Diego, CA

Broward, FL

King, WA

Santa Clara, CA

Hennepin, MN

Middlesex, MA

Nassau, NY

Fairfax, VA

Palm Beach, FL

Oakland, MI

Fulton, GA

Du Page, IL
16 
17 

Table 4: County Rankings by Sector, Total Single-Establishment Births per Worker

Rank All Sectors Local Market 

Industries

Retail Trade 

Industries

Manufacturing 

Industries

High Tech 

Industries

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

San Juan, CO

Ouray, CO

San Miguel, CO

Pitkin, CO

Summit, CO

Teton, WY

Archuleta, CO

Eagle, CO

Valley, ID

San Juan, WA

Grand, CO

Summit, UT

Gunnison, CO

Blaine, ID

Routt, CO

Sublette, WY

Fredericksb'g, VA

Mineral, CO

Teton, ID

Hinsdale, CO

San Miguel, CO

Valley, ID

Eagle, CO

Summit, CO

Archuleta, CO

Teton, ID

San Juan, WA

Ouray, CO

Pitkin, CO

Blaine, ID

Grand, CO

Routt, CO

Gunnison, CO

Teton, WY

Alpine, CA

Summit, UT

Mineral, CO

Washington, UT

Garfield, CO

Nantucket, MA

San Juan, CO

Ouray, CO

Mineral, CO

Hinsdale, CO

Archuleta, CO

Summit, CO

Grand, CO

San Miguel, CO

Pitkin, CO

Nantucket, MA

Lincoln, NM

Fredericksb'g, VA

Dukes, MA

Gunnison, CO

Emporia, VA

Valley, ID

San Juan, WA

Teton, WY

Keweenaw, MI

Blaine, ID

Sanders, MT

Carter, MO

Benewah, ID

Reynolds, MO

Webster, WV

Bradley, AR

Forest, WI

San Juan, CO

Wallowa, OR

Montgomery, AR

Meagher, MT

Granite, MT

Ripley, MO

Mineral, MT

Clearwater, ID

Winn, LA

Adams, ID

Boundary, ID

Lincoln, MT

Bonner, ID

Golden Valley, MT

Harding, NM

Los Alamos, NM

Santa Clara, CA

Custer, ID

Boulder, CO

San Juan, CO

Archuleta, CO

Jefferson, IA

Carson City, NV

Griggs, ND

Motley, TX

Douglas, NV

Charlottesville, VA

Kane, UT

Gallatin, MT

Deschutes, OR

Fairfax City, VA

Skamania, WA

Clay, KS

Rank Extractive 

Industries

Distributive 

Industries

Business Services 

(1990-1998)

Business Services 

(1999-2006)

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

Shackelford, TX

Kodiak Island, AK

Russell, KS

Sublette, WY

Ness, KS

Sitka, AK

Throckmorton, TX

Young, TX

Duchesne, UT

Barber, KS

Ochiltree, TX

McDowell, WV

Reagan, TX

Wrang.-Peter., AK

Valdez-Cord., AK

Johnson, WY

Uintah, UT

Stephens, TX

Midland, TX

Kingfisher, TX

Santa Cruz, AZ

Webb, TX

Maverick, TX

Magoffin, KY

Buchanan, VA

Thomas, NE

Valdez-Cord., AK

Lake & Pen., AK

New York, NY

Bristol Bay, AK

Wichita, KS

Mingo, WV

Atchison, MO

Ellis, OK

Lane, KS

Kingsbury, SD

Boundary, ID

Webster, WV

Jackson, CO

Letcher, KY

Fairfax City, VA

San Miguel, CO

Pitkin, CO

Summit, CO

Carson City, NV

Falls Church, VA

Teton, WY

New York, NY

Summit, UT

Jefferson, IA

Eagle, CO

Ouray, CO

San Juan, WA

Boulder, CO

Charlottesville, VA

Fulton, GA

Fredericksb'g, VA

Douglas, CO

Collier, FL

Blaine, ID

Fairfax City, VA

Pitkin, CO

Falls Church, VA

Carson City, NV

New York, NY

Jefferson, IA

Teton, WY

Summit, UT

Boulder, CO

Eagle, CO

Fredericksb'g, VA

Summit, CO

Ouray, CO

Fulton, GA

San Miguel, CO

Loudoun, VA

Gallatin, MT

Manassas C'y. VA

Sublette, WY

Charlottesville,VA
18 

decades. Colorado’s population, for example, grew nearly 44 percent from 1990 (3.3 million 

people) to 2006 (4.75 million people). 

Where in America?

Figures 1 through 27 show the location of entrepreneurial activity in the continental 

United States. Figures 1 through 9 are the percentile maps showing the distributions of the 

overall and sector-specific rates of new firms in six percentile ranges. Figures 10 through 18 are 

the circular cartograms in which the area of the county’s circle reflects the value of the firm birth 

rate, the red circles are the upper outliers, and the blue circles are the lower outliers. Finally, 

Figures 19 to 27 are the LISA cluster maps based on the global and local Moran’s I test for 

spatial autocorrelation. Indeed, each LISA cluster map reports the global Moran’s I statistic and 

its statistical significance. The Moran’s I estimates—all significant at the 1 percent level—range 

from a low of 0.14 for the high technology industries to a high of 0.47 for the local market 

sector. This indicates that, regardless of industry, the level of entrepreneurial activity in a given 

county is positively related to the level of entrepreneurial activity in its neighboring counties.

Thus, Hypothesis 1 is supported. 

Although these three sets of maps illustrate the location of entrepreneurial activity in the 

continental United States, they reveal different dimensions of this activity. In other words, each 

type of map has its unique interpretation. As an example, Figure 1 is the percentile map for the 

new firm birth rate in all sectors. Counties from light to dark blue are those in the three 

percentiles below the median value of the firm birth rate and counties colored yellow, orange, 

and red are those in the three percentiles above the median. Figure 1 clearly shows the highest 

rate of new firm births are in parts of New Mexico, Colorado, Wyoming, and Idaho. 

Interestingly, pockets of Maine, New Hampshire, Florida, and Michigan show rates of new firm 

births in the 90th to 99th

In turn, Figure 10 is the corresponding circular cartogram of the new firm birth rate for 

all sectors. The map shows a number of upper outliers in the western states, including the areas

in the upper percentile ranges shown in Figure 1. Figure 10 also shows outliers in several 

Northwest and Midwest states, down the eastern seaboard, and Texas. One lower outlier appears 

as a small dark blue circle in the state of Georgia. This lower outlier is Echols County, which 

added only 22 new firms from 1990 to 2006, making for an average new firm birth rate of 0.09 

new firms per 1,000 workers. Consistent with Figure 1, the circular cartogram in Figure 10 

indicates the highest rates of new firm births (i.e., the largest circles) in portions of Colorado and 

Wyoming. One interesting result from the circular cartogram in Figure 10 is New York County’s 

identification as an outlier, while the two counties with which it shares top rankings in Table 3—

Los Angeles and Cook Counties—are not.

percentile range. In contrast, a large area of counties with low rates of 

new firm births stretches from New York and Pennsylvania to the east to Illinois and Wisconsin 

to the west. 

Finally, Figure 19 is the corresponding LISA cluster map of the rate of new firm births 

across all sectors. The dark red counties in the cluster map are those with high levels of 
19 

entrepreneurial activity bordered by counties with similarly high levels of entrepreneurial 

activity. High-high clusters of entrepreneurial activity are evident around the District of 

Columbia, Florida, Missouri, central Texas, Oregon, Washington, Georgia, and the northern tip 

of Michigan’s lower peninsula. The largest and most conspicuous high-high cluster of 

entrepreneurial activity extends from Arizona and New Mexico to the south, through Colorado 

and eastern Utah, and into Idaho and Montana. In contrast, the dark blue counties show an 

extensive low-low cluster in the states that constitute the industrial “Rust Belt” from New York 

through Ohio. In turn, the pink shaded counties are “oases” of entrepreneurial activity. These 

pink-shaded high-low areas appear in the Texas Panhandle, Ohio, Pennsylvania, and North 

Carolina. Similarly, the counties shaded light blue—including Miami-Dade County and Prince 

Georges County in Maryland—indicate “low-high” clusters where the given county seems 

unable to match the level of entrepreneurial activity in its neighboring counties. 

In the case of the high technology industries, the percentile map in Figure 5 shows, 

interestingly enough, Santa Clara County at the heart of Silicon Valley and the counties of 

Boston’s Route 128 in the top percentile. Joining this group are Boulder County in Colorado as 

well as counties in Idaho, Montana, Texas, and other parts of California. The circular cartogram 

in Figure 14 yields a similar interpretation, but shows Golden Valley County in Montana with a 

high rate of high technology new firm births followed by Santa Clara County in California. 

Correspondingly, the LISA cluster map in Figure 23 clearly shows high-high clusters of hightech start-ups around Boston, the San Francisco Bay Area, and the Raleigh-Durham area of 

North Carolina, which is consistent with the conventional view of these regions as exemplars of 

high technology clusters. That said, the LISA cluster map also shows high-high clusters of high 

technology entrepreneurial activity is other regions of the United States including Boulder and 

Denver in Colorado, Minneapolis-St. Paul in Minnesota, the District of Columbia, Florida’s 

“Space Coast,” and large portions of the Pacific Northwest. In addition, the cluster map shows 

that Centre County in Pennsylvania is one of many oases of high technology entrepreneurial 

activity, no doubt due to the presence of Pennsylvania State University. 

Why in America?

Table 5 reports the correlations of the variables and Table 6 reports the results of the 

regression estimates. As noted earlier, the patent data to calculate the knowledge variable are

available only for 1990 to 1999. This makes it impossible to estimate a model with the rate of 

firm births in the business services sector from 1999 to 2006. Thus, the business services firm 

birth rate is for 1990 to 1998 only. 

Table 5 indicates that the firm birth rates in the retail trade, business services, and local 

market sectors are highly correlated with each other and with the overall firm birth rate. This 

result aside, all other correlations of firm birth rates across sectors are minimal. This may imply 

that the business services, retail trade, and local market birth rates are driven by similar county 

characteristics. Among the independent variables, the correlations are minimal. That said, the 

relatively high correlation between college degree and knowledge fits with the premise that new 
20 

knowledge is created by people with higher levels of educational attainment. Moreover, the 

relatively strong negative correlation between the unemployment rate and college degree 

suggests that the chances of being unemployed are lower where people have higher levels of 

educational attainment. 

Table 6 reports the regression estimates of Equation 3. In all cases, the statistical 

diagnostics validate the use of the Driscoll-Kraay fixed effects estimator. As mentioned, the 

estimates are limited to the first 10 years of the study period (i.e., 1990 to 1999) except in the 

case of the business services sector estimates, which are limited to 1990 to 1998 by the period of 

the rate calculation. This explains the difference in the number of observations and groups across 

the eight model estimates. The r-squared estimates range between 0.30 and 0.69 and the Fstatistics are significant at the 1 percent level. The very high F-statistic for the high technology 

sector estimates is troublesome given that only four of the nine coefficient estimates are 

statistically significant. This may indicate the influence of multicollinearity and suggests the 

results for this model should be accepted cautiously.4

 

4 Multicollinearity exists when two or more predictor variables in a multiple regression model are highly correlated. 
Table 5: Correlations

Variables 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

1 All Sectors 1

2 Manufacturing 0.33 1

3 Retail Trade 0.71 0.16 1

4 Local Market 0.76 0.15 0.39 1

5 High Tech 0.09 0.13 0.01 0.07 1

6 Extractive 0.32 0.03 0.16 0.15 -0.01 1

7 Business Services 0.53 0.06 0.19 0.44 0.10 0.10 1

8 Distributive 0.39 0.13 0.16 0.15 0.03 0.11 0.10 1

9 Establishment Size -0.53 -0.13 -0.38 -0.34 -0.03 -0.20 -0.28 -0.24 1

10 Per Capita Income Growth 0.01 0.01 0.00 0.01 -0.01 0.00 0.01 0.02 0.00 1

11 Population Growth 0.24 0.08 0.09 0.33 0.04 -0.01 0.28 0.00 0.15 -0.10 1

12 Share of Proprietors 0.26 0.05 0.28 0.13 0.00 0.19 -0.12 0.18 -0.25 0.00 -0.21 1

13 Agglomeration Density 0.07 0.03 0.02 0.02 0.01 -0.03 0.16 0.06 -0.05 0.00 -0.01 -0.05 1

14 Knowledge 0.02 -0.02 -0.08 0.07 0.06 -0.01 0.25 -0.04 -0.05 0.02 0.08 -0.11 0.05 1

15 College Degree 0.32 -0.04 0.09 0.33 0.09 0.05 0.60 0.00 -0.26 0.01 0.18 -0.13 0.16 0.41 1

16 Unemployment Rate -0.03 0.13 0.05 -0.08 -0.02 -0.03 -0.14 0.00 0.09 -0.03 -0.01 -0.04 0.00 -0.16 -0.34

21 
22 

Table 6: Log-Linear Fixed Effects Regression 

Determinants of New Firm Birth Rates (ln) by Sector

Analysis 

Variables

1

All

2

Man.

3

Ret.

4

Loc. 

5

High

6

Ext.

7

Bus.

8

Dis.

Establishment Size (ln)

Income Growth

Population Growth

Share of Proprietors (ln)

Density (ln)

Knowledge (ln)

Unemployment Rate (ln)

College Degree (ln)

Constant

-1.32 **

[-25.10]

0.16 **

[2.38]

2.22 **

[4.80]

-0.23 **

[-6.53]

0.40 **

[5.96]

-0.08 **

[-5.36]

0.08 **

[4.76]

-0.44 **

[-3.43]

5.72 **

[13.94]

-1.43 **

[-9.36]

0.41

[1.58]

3.25 *

[2.31]

-0.56 **

[-8.04]

0.35 *

[2.04]

-0.06

[-1.45]

0.31 **

[3.00]

-1.36 **

[-5.32]

4.07 **

[5.35]

-1.60 **

[-10.12]

0.44

[1.60]

4.41 *

[1.96]

-0.47 **

[-4.22]

0.29

[1.07]

-0.28 **

[-5.15]

0.24 **

[2.35]

-1.93 **

[-2.46]

6.94 **

[4.38]

-1.09 **

[-9.23]

0.08

[0.51]

2.29 **

[4.06]

0.04

[0.53]

0.69 **

[16.95]

-0.19 **

[-4.33]

0.05

[1.25]

-0.18

[-0.80]

2.36 **

[2.71]

0.02

[0.18]

-0.25

[-1.42]

-1.32 *

[-1.92]

-0.04

[-0.66]

-0.06

[-0.15]

0.56 *

[1.96]

-0.20

[-1.62]

0.33 **

[2.84]

-2.95 *

[-1.97]

-0.73 **

[-2.92]

1.10

[1.31]

6.90 **

[3.09]

0.05

[0.41]

-0.51

[-0.59]

-1.11 **

[-2.41]

0.52

[1.63]

-1.27

[-1.63]

-5.10 **

[-4.03]

-0.76 **

[-5.18]

0.06

[0.36]

1.48 **

[2.74]

0.30 **

[3.79]

1.22 **

[6.79]

-0.10 *

[-2.30]

-0.16 **

[-4.66]

0.40 *

[2.20]

-0.31

[-0.30]

-1.65 **

[-7.56]

0.60 *

[1.71]

3.34 **

[2.51]

-0.17 *

[-2.25]

0.54 *

[2.20]

-0.30 **

[-5.22]

0.17 *

[2.27]

-0.59

[-1.36]

3.10 **

[3.22]

Observations

Number of groups

R-squared

F-Statistic

22783

3009

0.69

2837

22783

3009

0.38

162.2

22783

3009

0.36

1813

22783

3009

0.45

596.1

22783

3009

0.48

3565

22783

3009

0.30

144.5

20331

2985

0.49

336.7

22783

3009

0.53

5665
23 

The income growth and population growth coefficients are tests of Hypothesis 2 

(entrepreneurial activity increases with economic growth). As shown in Table 6, the coefficient 

estimate for income growth is positive and statistically significant in only two of the eight 

models. In the overall estimates, the estimated coefficient in Model 1 suggests that the rate of 

new firm births across all sectors increases 2 percent when the per capita income growth 

increases by one-tenth of a unit (i.e., from 0.1 to 0.2 or from 0.8 to 0.9). Similarly, the coefficient 

estimate in Model 8 suggests that a similar increase in per capita incomes corresponds to a 6 

percent increase in the firm birth rate in the distributive sector. In contrast, the coefficient for 

population growth is statistically significant in all eight models and positive, except in Model 5. 

For the high technology sector, a tenth of a unit increase in population growth is associated with 

a 12 percent drop in that sector’s rate of firm births. By comparison, the results indicate that a 

0.10 increase in population growth corresponds to a 16 percent increase in business services birth 

rate and a 99 percent increase in the birth rate in extractive industries. In sum, the population 

growth estimates support Hypothesis 2 more strongly than do the estimates for income growth.

At odds with the prediction of Hypothesis 3, the knowledge coefficient estimates are 

negatively related to the rate of firm births except in the case of the high technology sector. In 

Model 1, for example, a 10 percent increase in the number of patents per worker in the county is 

associated with a 1 percent decrease in the firm birth rate across all sectors. An equal increase in 

knowledge also corresponds to a 1 percent drop in the business services birth rate and a 10 

percent drop in the extractive sector birth rate. In the high technology sector, however, a 10 

percent increase in the number of patents per worker is associated with a 5 percent increase in 

the sector’s new firm birth rate. As stated, the negative coefficient estimates for knowledge yield 

no support for Hypothesis 3 concerning the relationship between a county’s stock of knowledge 

and its rate of new firm formation. On the other hand, as will be discussed below, this finding 

does fit with a central assumption of the knowledge spillover model that new knowledge and 

economic growth stimulate quite different types of entrepreneurial opportunities. 

For Hypotheses 4 and 5, density and establishment size capture the opportunity 

constraints in the knowledge spillover models. In particular, the pool of opportunities available 

to would-be entrepreneurs to exploit is smaller if the region is home to a higher proportion of 

(large) incumbent businesses. The significant estimates for density, however, are positive; Model 

1, for example, indicates a 4 percent increase in the overall firm birth rate from a 10 percent 

increase in the number of existing establishments per square mile. The largest effect is in the 

business services sector, where a 10 percent increase in density corresponds to a 12 percent 

increase in the sector’s rate of firm births. In contrast, establishment size is negative in seven of 

the eight models. Indeed, Model 1 indicates that a 10 percent increase in average size of existing 

establishments corresponds to a 12 percent reduction in the overall rate of new firm births. The 

largest effect is seen in the distributive sector, where a 15 percent decrease in the birth rate 

corresponds to a 10 percent increase in establishment size. These results strongly support 

Hypothesis 5 (entrepreneurial activity decreases with increasing average firm size), but yield no 

support for Hypothesis 4 (entrepreneurial activity decreases with density of existing firms). 
24 

Hypotheses 6 and 7 concern the willingness and ability of workers in a county to engage 

in entrepreneurial activity. Indeed, the share of proprietors is used as an indicator of the county’s 

level of self-employment and its coefficient estimates are predicted to be positive. However, in 

all but one case, the significant coefficient estimate for this variable is negative. The exception is 

the business services sector where a 10 percent increase in the share of proprietors is associated 

with a 3 percent increase in the sector’s firm birth rate. In turn, college degree as an indicator of 

educational attainment is also predicted to be positively related to the rate of new firm births. 

Instead, the coefficients for this variable are negative in three of the five statistically significant 

estimates. The two exceptions are the high technology and business services sectors where a 10 

percent increase in the college degree is associated with a 3 percent and 4 percent increase in the 

respective sector birth rates. Thus, the support for Hypotheses 6 (entrepreneurial activity 

increases with self-employment) and 7 (entrepreneurial activity increases with educational 

attainment) is mixed.

Finally, the county’s unemployment rate is meant to capture the relationship between 

entrepreneurial activity and the earning potential of working for an existing firm. Hypothesis 8 

states that the relationship will be negative because poor wage opportunities should increase the 

likelihood that people will start new businesses. In Model 1, the coefficient estimate is negative 

and significant and suggests a 1 percent increase in the overall firm birth rate with a 10 percent 

increase in the county rate of unemployment. In the business services sector, however, the 

relationship is positive in that an equal decrease in the unemployment rate is associated with a 1 

percent decrease in the sector’s firm birth rate. In general, these results provide support for 

Hypothesis 8, but the negative relationship in the distributive sector is intriguing and worthy of 

discussion. 

Discussion

The findings of this study are intriguing on a number of levels with implications for 

practitioners, policymakers, and researchers. First, the clearest support for any of the hypotheses 

is for the first: the spatial analysis clearly indicates that entrepreneurial activity concentrates 

geographically. Indeed, perhaps the most conspicuous observation is the entrepreneurial lows of 

the nation’s Rust Belt states and the entrepreneurial highs of the Rocky Mountain regions during 

the 17-year period of the study (see Figure 19). A pivotal determinant for this pattern appears to 

be changes in county population. Indeed, the distribution of economic activity in Figure 19 

corresponds to changes in county populations during a period in which many Rust Belt county 

populations shrank and many western state county populations grew (Perry & Mackun, 2001). 

Second, the findings in this study indicate that the entrepreneurial activity in the high 

technology sector is a special case. As shown in Table 6, the sector proved the exception in the 

relationship new firm birth rates have to changes in population, the creation of new knowledge, 

and the educational attainment of the county population. The results in Figure 23 indicate that the 

fascination of scholars and policymakers with places like Silicon Valley, Boston’s Route 128, 
25 

and North Carolina’s Research Triangle is deserved. Perhaps more interestingly, much the same 

may be said for the business services sector. The high correlation between the firm birth rates in 

these sectors and the evidence that both rely on an educated workforce imply a close link. In 

other words, it may be that the formation of new business services firms occurs partly as a 

response to high technology entrepreneurial activity. 

Insights for Policy

The ramifications of this study’s findings are particularly relevant for economic 

development authorities, which increasingly favor supporting local entrepreneurship over 

“smokestack chasing” (Quello & Toft, 2006). Indeed, several broad recommendations are 

implied from the findings. First, counties with access to an educated workforce and a local 

research and development (R&D) infrastructure favor entrepreneurial activity in no other sector 

than high technology. This may explain why the community around Penn State University in 

Centre County in Pennsylvania is an oasis of high technology entrepreneurial activity. It may 

also explain why some regions even with top research universities still find it difficult to 

encourage high technology start-up activity. The experience of South Carolina’s Upstate 

Alliance may be a case in point. Although home to a top public research university and the center 

of a vibrant automotive cluster, the area’s comparatively low levels of educational attainment 

may explain the struggle to attract and encourage entrepreneurial activity in targeted segments.5

Second, the findings suggest that one aspect of the SC Upstate Alliance’s strategy is quite 

correct. The broad swaths of dark red and dark blue on the LISA cluster maps suggest that the 

spatial scale of the entrepreneurship process is larger than the land area of the average county. In 

other words, the level of entrepreneurial activity in one county seems to reinforce the level in 

nearby counties. According to the LISA cluster maps, it is rare for counties—those shown in 

pink—to achieve high levels of entrepreneurial activity when surrounded by counties with low 

levels of such activity. Therefore, aligning the goals and actions of a group of neighboring 

counties may be a necessary condition for encouraging entrepreneurial activity in any given 

region. In fact, the results suggest that the formation of county-level development alliances 

should ignore arbitrary boundaries, such as state borders, to avoid excluding counties crucial to 

the success of any shared development program.

Insights for Practice

Although the analysis is at the county level, the findings here suggest a number of factors 

entrepreneurs might consider in choosing a location for their new ventures. To be clear, the 

analysis is silent on the post-start-up ramifications of the location decision in that neither the 

prospects for survival nor the potential for growth is explored. This study only assesses the 

extent to which counties are supportive of new venture creation. Therefore, the results in Table 6 

 

5 The Upstate Alliance is an economic development initiative for the ten-county upstate region of South Carolina. 

Their targeted segments include automotive, distribution and logistics, plastics, life sciences, and advanced 

materials. 
26 

should not be interpreted as suggesting that entrepreneurs considering a high technology venture 

move to regions prone to entrepreneurial activity in the same sector. Instead, the results simply 

imply that entrepreneurs factor into their decision to start a venture the human capital needs of 

the business relative to what the local labor market can provide. 

First, if a college-educated workforce is required for the venture’s success, the 

entrepreneur must think carefully about how to attract and retain such a workforce. Consider the 

case of Analytical Graphics, founded in 1989 in Malvern, Pennsylvania, to produce and sell 

commercial off-the-shelf software for spacecraft and national security applications. To attract 

and retain a workforce of aerospace, electrical, and software engineers—a comparatively limited 

workforce pool in the suburbs of Philadelphia—the company offers unusual benefits. Among 

these is a professional kitchen serving breakfast, lunch, and dinner on a daily basis; a fitness 

room and laundry room; and arrangement for mundane services like dry cleaning, flower 

delivery, shoeshines, and oil changes. In return, the company reports that its employee turnover 

is 3 percent, compared with the industry average of over 20 percent (Von Bergen, 2004). 

Second, entrepreneurs looking to launch new ventures in sectors other than business 

services should weigh carefully the level of self-employment in the county. Per the results in 

Table 6, the rate of new venture creation in the manufacturing, retail trade, and distributive 

sectors is lower when the level of self-employment goes up. While this may be the result of 

would-be entrepreneurs opting for a sole proprietorship or partnership over an incorporated firm 

with employees, it may also suggest that where self-employment is higher, the pool of available 

workers to staff new ventures is lower—perhaps too low for the start-up to proceed. This premise 

is further supported by the generally positive relationship between firm birth rates and the 

unemployment rate, as well as the negative—albeit negligible—correlation between the share of 

proprietors and the unemployment rate shown in Table 5. In sum, as is often taught in college 

entrepreneurship courses, the results imply that entrepreneurs must ensure that the resources 

needed for the new venture are locally available.

Insights for Research 

The insights offered for policy and practice hinge crucially on the validity of the spatial 

analysis and econometric results. As with most—if not all—empirical studies, there is reason to 

find fault with the present study. Indeed, despite the care to correctly specify and estimate the 

empirical model given in Equation 3 by accounting for the spatial and serial correlation and 

heteroskedasticity of the residuals, there is room for concern and caution. As a case in point, 

consider the evidence in support of Hypothesis 8 that higher unemployment should lead to higher 

firm birth rates in the county. The exception in the unemployment relationship is the business 

services sector. Here, as unemployment goes up, the number of start-ups in the sector goes down. 

This makes sense in that business services firms are exploiting opportunities to provide other 

businesses with accounting, legal, consulting, and other services. Indeed, if higher 

unemployment is an indicator of the distress of local businesses, then it follows that any 

opportunities in the business services sector are fewer as a result.
27 

From a theoretical standpoint, however, this interpretation may indicate that the variables 

in Equation 3 poorly capture the elements of the knowledge spillover model, such as the source 

of opportunities, the constraints on these opportunities, the ability and willingness to engage in 

entrepreneurship, and the quality of local wage and employment conditions. In addition, other 

important determinants of entrepreneurial activity may be missing entirely from the estimated 

models, or the method of calculating and transforming the variables may be problematic. In 

short, as with the analysis by Armington and Acs (2002), the results here should be viewed with 

caution and interpreted judiciously. In addition, although within-county fixed effects estimates 

are reported, the relationships between the dependent and independent variables are not 

necessarily causal.6 As an example, while spikes in a county’s patenting activity are likely to be

associated with higher high technology firm birth rates, a host of intervening conditions may 

prevent this result. In sum, there is ample room for further analysis. 

Conclusion 

This study set out to explore the extent to which regions within the United States 

specialize in entrepreneurial activity in particular sectors and the determinants of such 

specialization. The spatial analysis indicates several pockets of significant start-up activity, 

including manufacturing in the Pacific-Northwest; retail trade and local market industries in the 

Rocky Mountain States; high technology industries in California, Massachusetts, and North 

Carolina; extractive industries in Texas, Oklahoma, and Wyoming; business services in New 

York, Washington, DC, and Florida; and distributive industries in the plains states. The 

fascination of scholars and policymakers with high technology start-up activity is justified in that 

the pattern and determinants of such activity stand apart from the patterns in other sectors. 

Finally, the implications of the findings for policy, practice, and future research were discussed. 

Of course, further analysis using more homogenous industry definitions is possible and 

warranted. As in the case of South Carolina’s Upstate Alliance, efforts to spur start-up activity in 

fields as different as life sciences and advanced materials may depend on factors unique to each 

industry. Likewise, further analysis of the spatial outliers—the “oases” of entrepreneurial activity 

shown in pink on all the LISA cluster maps—is required. One immediately wonders what these 

counties do or possess in the way of resources that sets them apart from their neighbors. It may 

be that these oases of entrepreneurial activity hold the key to a complete understanding of where 

and why enterprises start up in America.

 

6 A fixed effects model represents the observed quantities in explanatory variables that are all treated as if those 

quantities were nonrandom. In contrast, in random effects and mixed models, all or some of the explanatory 

variables are treated as if they arise from random causes.
28 

Cited Works

Acs, Z. J. & Armington, C. 2004a. The Impact of Geographic Differences in Human Capital on 

Service Firm Formation Rates. Journal of Urban Economics

Acs, Z. J. & Armington, C. 2004b. Employment Growth and Entrepreneurial Activity in Cities. 

, 56(2): 244-277. 

Regional Studies

Acs, Z. J., Audretsch, D. B., Braunerhjelm, P., & Carlsson, B. 2005. The Knowledge Spillover 

Theory of Entrepreneurship,

, 38(8): 911-927. 

Max Planck Institute

Ahuja, G. & Katila, R. 2004. Where Do Resources Come From? The Role of Idiosyncratic 

Situations. 

. Jena, Germany: Max Planck Institute of 

Economics. 

Strategic Management Journal

Almeida, P. 1996. Knowledge Sourcing by Foreign Multinationals: Patent Citation Analysis in 

the U.S. Semiconductor Industry. 

, 25(8/9): 887-107. 

Strategic Management Journal

Almeida, P. & Kogut, B. 1999. Localization of Knowledge and the Mobility of Engineers in 

Regional Networks. 

, 17(Special Issue): 155-165. 

Management Science

Almeida, P. & Phene, A. 2004. Subsidiaries and Knowledge Creation: The Influence of the 

MNC and Host Country on Innovation. 

, 45(7): 905-917. 

Strategic Management Journal

Anselin, L. 1995. Local Indicators of Spatial Association - LISA. 

, 25(8/9): 847-864. 

Geographical Analysis

Anselin, L. & Bera, A. K. 1998. Spatial Dependence in Linear Regression Models with an 

Introduction to Spatial Econometrics. In A. Ullah & D. Giles (Eds.), 

, 27(2): 

93-115. 

Handbook of Applied 

Economic Statistics

Anselin, L., Varga, A., & Acs, Z. J. 2000. Geographical Spillovers and University Research: A 

Spatial Econometric Perspective. 

: 237-246. New York: Marcel Dekker. 

Growth & Change

Anselin, L. 2001. Spatial Econometrics. In B. Baltagi (Ed.), 

, 31(4): 501-515. 

A Companion to Theoretical 

Econometrics

Anselin, L., Syabri, I., & Smirnov, O. 2002. Visualizing Multivariate Spatial Correlation with 

Dynamically Linked Windows, 

: 310-330. Oxford: Basil Blackwell.

Spatial Analysis Laboratory, University of Illinois -

Urbana/Champaign

Anselin, L., Syabri, I., & Kho, Y. 2006. Geoda: An Introduction to Spatial Data Analysis. 

. Urbana, Il. 

Geographical Analysis

Armington, C. & Acs, Z. J. 2002. The Determinants of Regional Variation in New Firm 

Formation. 

, 38(1): 5-22. 

Regional Studies, 36(1): 33-45. 
29 

Audretsch, D. B. & Fritsch, M. 1994. On the Measurement of Entry Rates. Empirica

Audretsch, D. B. 2000. Entrepreneurship in Germany. In D. L. Sexton & H. Landstrom (Eds.), 

, 21(1): 105-

113. 

Handbook of Entrepreneurship

Audretsch, D. B., Keilbach, M. C., & Lehmann, E. E. 2006. 

: 107-127. Oxford: Blackwell Publishers. 

Entrepreneurship and Economic 

Growth

Baum, J. A. C. & Oliver, C. 1996. Toward an Institutional Ecology of Organizational Founding. 

. Oxford: Oxford University Press. 

Academy of Management Journal

Casson, M. C. 2003. Entrepreneurship, Business Culture and the Theory of the Firm. In Z. J. Acs 

& D. B. Audretsch (Eds.), 

, 39(5): 1378-1427. 

Handbook of Entrepreneurship Research

Chang, S.-J. & Park, S. 2005. Types of Firms Generating Network Externalities in MNC's CoLocation Decisions. 

: 223-246. Boston: Kluwer. 

Strategic Management Journal

Chung, W. & Alcácer, J. 2002. Knowledge Seeking and Location Choice of Foreign Direct 

Investment in the United States. 

, 26(7): 595-615. 

Management Science

Cooper, A. C. & Folta, T. B. 2000. Entrepreneurship in High-Technology Clusters. In D. L. 

Sexton & H. Landstrom (Eds.), 

, 48(12): 1534-1554. 

Handbook of Entrepreneurship

DeCarolis, D. M. & Deeds, D. L. 1999. The Impact of Stocks and Flows of Organizational 

Knowledge on Firm Performance: An Empirical Investigation of the Biotechnology Industry. 

: 348-367. Malden, MA: 

Blackwell.

Strategic Management Journal

Doh, J. P. & Hahn, E. D. 2008. Using Spatial Methods in Strategy Research. 

, 20(10): 953-968. 

Organizational 

Research Methods

Driscoll, J. C. & Kraay, A. C. 1998. Consistent Covariance Matrix Estimation with Spatially 

Dependent Panel Data. 

, 11(4): 659-681. 

Review of Economics and Statistics

Dumais, G., Ellison, G., & Glaeser, E. L. 2002. Geographic Concentration as a Dynamic 

Process. 

, 80(4): 549-560. 

Review of Economics and Statistics

Gibbons, D. E. 2004. Network Structure and Innovation Ambiguity Effects on Diffusion in 

Dynamic Organizational Fields. 

, 84(2): 193-204. 

Academy of Management Journal

Henderson, J. V. 2003. Marshall's Scale Economies. 

, 47(6): 938-951. 

Journal of Urban Economics

Hoechle, D. 2007. Robust Standard Errors for Panel Regressions with Cross-Sectional 

Dependence. 

, 53(1): 1-28. 

Stata Journal

Lomi, A. 1995. The Population Ecology of Organizational Founding: Location Dependence and 

Unobserved Heterogeneity. 

, 7(3): 281-312. 

Administrative Science Quarterly, 40(1): 111-144. 
30 

Marchington, M., Carroll, M., & Boxall, P. 2003. Labour Scarcity and the Survival of Small 

Firms: A Resource-Based View of the Road Haulage Industry. Human Resource Management 

Journal

McCann, B. T. & Folta, T. B. 2008. Location Matters: Where We Have Been and Where We 

Might Go in Agglomeration Research. 

, 13(4): 5-22. 

Journal of Management

Mugler, J. 2000. The Climate for Entrepreneurship in European Countries in Transition. In D. L. 

Sexton & H. Landstrom (Eds.), 

, 34(3): 532-565. 

Handbook of Entrepreneurship

Parker, S. C. 2004. 

: 150-175. Oxford: Blackwell 

Publishers. 

The Economics of Self-Employment and Entrepreneurship

Pe'er, A. & Vertinsky, I. 2008. Firm Exits as a Determinant of New Entry: Is There Evidence of 

Local Creative Destruction? 

. Cambridge: 

Cambridge University Press.

Journal of Business Venturing

Perry, M. J. & Mackun, P. 2001. Population Change and Distribution, 

, 23(3): 280-306. 

Census 2000 Brief, US 

Census Bureau

Phene, A. & Almeida, P. 2003. How Do Firms Evolve? The Patterns of Technological Evolution 

of Semiconductor Subsidiaries. 

. Washington, DC. 

International Business Review

Plummer, L. A. & Headd, B. 2007. Rural and Urban Establishment Births and Deaths Using the 

Us Census Bureau's Business Information Tracking Series, 

, 12(3): 349-367. 

Small Business Administration, 

Office of Advocacy

Plummer, L. A. 2010. Spatial Dependence in Entrepreneurship Research: Challenges and 

Methods. 

. Washington, DC. 

Organizational Research Methods

Quello, S. & Toft, G. 2006. Economic Gardening: Next Generation Applications for a Balanced 

Portfolio Approach to Economic Growth, 

, 13(1): 146-175. 

The Small Business Economy: A Report to the 

President

Romer, P. M. 1986. Increasing Returns and Long Run Growth. 

: 157-190. Washington, DC: Small Business Administration, Office of Advocacy.

Journal of Political Economy

Romer, P. M. 1987. Growth Based on Increasing Returns Due to Specialization. 

, 

94(5): 1002-1037. 

American 

Economic Review

Romer, P. M. 1990. Endogenous Technological Change. 

, 77(2): 56-72. 

Journal of Political Economy

Rumelt, R. P. 1987. Theory, Strategy, and Entrepreneurship. In D. J. Teece (Ed.), 

, 98(5): 

S71-S102. 

The 

Competitive Challenge

Russo, M. V. 2003. The Emergence of Sustainable Industries: Building on Natural Capital. 

: 137-158. New York: Harper & Row.

Strategic Management Journal, 24(4): 317-331. 
31 

Shane, S. & Venkataraman, S. 2000. The Promise of Entrepreneurship as a Field of Research. 

Academy of Management Review

Song, J., Almeida, P., & Wu, G. 2003. Learning-by-Hiring: When Is Mobility More Likely to 

Facilitate Interfirm Knowledge Transfer? 

, 25(1): 217-226. 

Management Science

Sorenson, O. & Audia, P. G. 2000. The Social Structure of Entrepreneurial Activity: Geographic 

Concentration of Footwear Production in the United States, 1940-1989. 

, 49(4): 351-365. 

American Journal of 

Sociology

Sorenson, O. & Stuart, T. E. 2001. Syndication Networks and the Spatial Distribution of Venture 

Capital Investments. 

, 106(2): 424-461. 

American Journal of Sociology

Sorenson, O. 2003. Social Networks and Industrial Geography. 

, 106(6): 1546-1588. 

Journal of Evolutionary 

Economics

Storey, D. 2000. Six Steps to Heaven: Evaluating the Impact of Public Policies to Support Small 

Business in Developed Economies. In D. L. Sexton & H. Landstrom (Eds.), 

, 13(5): 513-527. 

Handbook of 

Entrepreneurship

Varga, A. 1998. 

: 177-193. Oxford: Blackwell Publishers. 

University Research and Regional Innovation: A Spatial Econometric Analysis 

of Academic Technology Transfer

Varga, A. 2000. Local Academic Knowledge Transfers and the Concentration of Economic 

Activity. 

. Boston: Kluwer. 

Journal of Regional Science

Venkataraman, S. 1997. The Distinctive Domain of Entrepreneurship Research. In J. A. Katz 

(Ed.), 

, 40: 289. 

Advances in Entrepreneurship, Firm Emergence and Growth

Venkataraman, S. 2004. Regional Transformation through Technological Entrepreneurship. 

, Vol. Volume 3: 119-138. 

Greenwich, CT: JAI Press.

Journal of Business Venturing

Von Bergen, J. M. 2004. Happiness Equals Productivity, 

, 19(1): 153-167. 

Philadelphia Inquirer

Zucker, L. G., Darby, M. R., & Armstrong, J. S. 2002. Commercializing Knowledge: University 

Science, Knowledge Capture, and Firm Performance in Biotechnology. 

: D1. Philadelphia. 

Management Science, 

48(1): 138-153. 
32 

Figures

(All figures are color images.)

Percentile Maps

Legend 1: Percentile Maps

< 1 % 

1% - 10% 

10% - 50% 

50% - 90% 

90% - 99% 

>99% 
33 

Figure 1: Percentile Map of All Sectors

Figure 2: Percentile Map of Manufacturing Industries
34 

Figure 3: Percentile Map of Retail Trade Industries

Figure 4: Percentile Map of Local Market Industries
35 

Figure 5: Percentile Map of High Tech Industries

Figure 6: Percentile Map of Extractive Industries
36 

Figure 7: Percentile Map of Business Service Industries (1990-1998) 

Figure 8: Percentile Map of Business Service Industries (1999-2006) 
37 

Figure 9: Percentile Map of Distributive Industries 
38 

Circular Cartograms 

Legend 2: Circular Cartogram Maps 

Non-Outlier Observations

Lower-Outlier Observations

Upper-Outlier Observations

Zero-Value Observations
39 

Figure 10: Cartogram of All Sectors

Figure 11: Cartogram of Manufacturing Industries
40 

Figure 12: Cartogram of Retail Trade Industries

Figure 13: Cartogram of Local Market Industries
41 

Figure 14: Cartogram of High Tech Industries

Figure 15: Cartogram of Extractive Industries
42 

Figure 16: Cartogram of Business Service Industries (1990-1998) 

Figure 17: Cartogram of Business Service Industries (1999-2006) 
43 

Figure 18: Cartogram of Distributive Industries
44 

LISA Cluster Maps

Legend 3: LISA Cluster Maps

Not Significant

High- High 

Low- Low

Low- High 

High-Low
45 

Figure 19: LISA Cluster Map of All Sectors (Moran’s I = 0.44, p < 0.01) 

Figure 20: LISA Cluster Map of Manufacturing Industries (Moran’s I = 0.40, p < 0.01) 
46 

Figure 21: LISA Cluster Map of Retail Trade Industries (Moran’s I = 0.36, p < 0.01) 

Figure 22: LISA Cluster Map of Local Market Industries (Moran’s I = 0.47, p < 0.01) 
47 

Figure 23: LISA Cluster Map of High Tech Industries (Moran’s I = 0.14, p < 0.01) 

Figure 24: LISA Cluster Map of Extractive Industries (Moran’s I = 0.43, p < 0.01) 
48 

Figure 25: LISA Cluster Map of Business Services (1990-1998) (Moran’s I = 0.37, p < 0.01) 

Figure 26: LISA Cluster Map of Business Services (1999-2006) (Moran’s I = 0.40, p < 0.01) 
49 

Figure 27: LISA Cluster Map of Distributive Industries (Moran’s I = 0.34, p < 0.01)