The Relationship between Entrepreneurs and Productivity

Transcription

1 Division of Economics A.J. Palumbo School of Business Administration and McAnulty College of Liberal Arts Duquesne University Pittsburgh, Pennsylvania LEARNING TO LOBBY? EDUCATION, INSTITUTIONS, AND ENTREPRENEURSHIP IN THE UNITED STATES Ashley Schmider Submitted to the Economics Faculty in partial fulfillment of the requirements for the degree of Bachelor of Science in Business Administration December 2012

2 Faculty Advisor Signature Page Matt Ryan, Ph.D. Assistant Professor of Economics Date 2

3 The purpose of this paper is to examine the relationship between education, institutional quality, and entrepreneurship. Following Baumol (1990) I specifically analyze entrepreneurship through the productive and unproductive activities of an entrepreneur. Expanding on prior empirical research to now include the effect education on productive and unproductive entrepreneurship, I use panel data for the U.S. from 1996 to 2009 to analyze this relationship. My results suggest quality institutions potentially increase both productive and unproductive entrepreneurship, thus shifting the focus of understanding the entrepreneur to its relationship with education. Analyzing the effect of high school and bachelor s degree graduation rates on productive and unproductive entrepreneurship reveals education may encourage entrepreneurs to lobbying the government, rather than create businesses. JEL classifications: H11, I21, P16 O31 Key words: government, political economy, schooling, entrepreneurship, innovation 3

4 Table of Contents I. Introduction 5 II. Institutions & Entrepreneurship 7 III. Education & Entrepreneurship...12 IV. Model...17 V. Data...20 VI. Results.23 VII. Conclusion.. 29 VIII. References.32 Appendix A: Other Unproductive Estimation Models. 34 4

5 I. Introduction Since Adam Smith wrote the Wealth of Nations, the roots and causes of economic growth concerned economists. The extensive research on economic growth began analyzing the implications and factors affecting growth due to the important role a growing economy plays in all realms of society, including people, jobs, and technological advancements. Economic growth is one of the most important factors used to understand differences in the quality of life and development of countries around the world as well as regional differences among states within countries. Furthermore, studies also focus on the factors affecting economic growth in order to provide recommendations and assistance to underdeveloped nations as well as those suffering from economic decline. In relation to the basic structure of an economy, a market s source of wealth and utility created comes from the trade activities between producers and consumers as well as the valuable resources involved in the transactions. The growth of a market depends on the amount and type of resources it has as well as its institutional structure. One valuable resource to an economy and its potential growth is the creative talents of an entrepreneur. Reynolds et al. (1999) and Zacharakis et al. (2000) empirically show that entrepreneurship accounts for one-third of the differences in economic growth among U.S. states as well as one-half of the differences between countries. In addition, Henderson (2002) empirically shows the impact of entrepreneurship, in the marketplace, on economic growth at the local level through increases in localized income and job creation. Overall, previous research concludes the creative talents of entrepreneurs in the private market are a major source of economic growth. Although previous research finds strong evidence of a relationship between entrepreneurship and economic growth in the market place, the research neglects to analyze the 5

6 idea that entrepreneurship does not always take place within the private market. Baumol (1990) introduces the idea of the unproductive entrepreneur where profit-seeking activities are performed outside the private market. Research on the role the unproductive entrepreneur plays in economic growth is lacking; however, Sobel (2008) combines his measures for productive and unproductive entrepreneurship across U.S. states and creates an index classification system, with negative index scores characterizing states with relatively more unproductive entrepreneurship, to examine the affect of overall entrepreneurship on growth. His analysis shows a clear positive relationship between the net entrepreneurial productivity score and median household income, concluding that unproductive entrepreneurship hinders economic growth. The distinction made in this analysis is important because it opens a new area of research on the unproductive entrepreneur. Forthcoming, I discuss more specific implications of the unproductive entrepreneur in the United States following Baumol (1990) and Sobel (2008). In addition, another previously analyzed determinant of economic growth is education. Just as entrepreneurship is positively related to growth, empirical analysis of education shows similar conclusions. Mankiw et al. (1992) and Mankiw (1995) empirically show that education accounts for 78 percent of the international differences in per capita income. Higher levels of educational attainment are linked to more economic growth due to the productivity increases of a more educated worker including affects on the rest of the labor force. The implications of the role entrepreneurship and education play in economic growth give way for a new area of research. Economists now turn towards understanding the determinants of entrepreneurship with the hope that finding ways to increase this factor will help to promote economic growth. Understanding the factoring affecting economic growth, particularly entrepreneurship and education, is very important, as in the current Great Recession 6

7 of the United States, when there is a downturn in the growth of an economy and policy makers turn to economists for recommendations to revive the market. In particular, institutional quality has been one of the main determinants in understanding different levels of entrepreneurship. For this study, I build from previous literature on the analysis of the effect of institutional quality and education on entrepreneurship to better understand these factors. II. Institutions and Entrepreneurship Baumol (1990) added a new dimension to the study of entrepreneurship that builds upon previous works, particularly that of Schumpeter and Kirzner, who researched the role this creative behavior has on society. Prior to Baumol (1990), Schumpeter viewed the role of the entrepreneur in the development of society as the creator of innovations or the carrying out of new combinations. His theory holds entrepreneurs responsible for fueling society s development from the new products that are created from innovations of new firms working beside the old. However, Schumpeter s list of entrepreneurial developments to society (including the introduction of a new good, method of production, market, supply source, or organization of an industry) neglect to encompass how society s entrepreneurs can also use their talents for rent seeking purposes that pose no wealth gain for society (Schumpeter, 1934, p.66). Baumol (1990) refers to an entrepreneur s use of talents for rent seeking purposes as unproductive entrepreneurial activities. Baumol s contribution to today s analysis of the entrepreneur encompasses not only the productive behavior that serves as a channel of wealth creation for society but also the unproductive nature in the form of rent seeking. Moving beyond Schumpeter s theory to include the unproductive activities of entrepreneurs makes sense because the goal of an entrepreneur is to use their creative minds to create wealth and prestige for themselves, with no focus on the benefit society will gain from the 7

8 creative actions (Baumol 1990). Baumol (1990) emphasizes that society should be less concerned about the supply of entrepreneurs and more concerned about the allocation of the current supply between productive and unproductive activities. Furthermore, Baumol (1990) proposes that the allocation between unproductive and productive entrepreneurial activities significantly depends on the rules of the game. The rules of the game refer to how the intuitions structure the payoffs and rewards for the two entrepreneurial activities. Societies with quality laws and legal procedures provide less incentives for entrepreneurs to utilize the public system for profit gains, as in litigation and tax evasion, and channel the creative talents of the entrepreneurs to productive activities. The work done by Baumol (1990) provides a valuable framework to understand the role of the entrepreneur in society as well as a means for society to better channel entrepreneurial talents towards productive activities through the restructuring of the legal institutions. In addition, relating differences in the rules of the game to differences in the allocation between productive and unproductive activities serves as a method of understanding local, regional, and even international differences in the level of entrepreneurial activities. Building from Baumol s (1990) seminal piece of literature on productive and unproductive entrepreneurship, many economists reexamined his theory to provide evidence for the important role intuitions play in channeling entrepreneurial talents to not only benefit the innovator, but society overall. In order to examine productive and unproductive entrepreneurship, it serves to understand the characteristics of quality institutions first. Clark and Lee (2006) theoretically examine the role of institutions by specifically looking at quality characteristics. The characteristics are based on the level of economic freedom, its enforcement in the market, as well as the role of politics, through lobbying and litigation, in disrupting the 8

9 enforcement of freedom in order to deter the entrance of new productive entrepreneurs. They argue that economic freedom, as in private property rights, held accountable through a disciplined market creates a tolerable environment for the entrepreneur. The key to the research done by Clark and Lee (2006) is that they not only encompass the importance of economic freedom, but also the enforcement of that freedom, when characterizing the environment needed for the growth of entrepreneurs. Freedom without responsibility is not freedom at all; it is incense, and is soon suppressed (Clark and Lee, 2006, p.4). As for the role of politics in entrepreneurship, the researchers argue that entrepreneurs threaten existing firms with innovative products and processes, and existing firms seek to restrict this type of competition from entering the market. In order to suppress the introduction of new entrepreneurs, existing firms turn to politics, in the form of lobbying, to restrict economic freedom and create barriers to entry. Politics threaten the ability of markets to protect and enforce economic freedom by allowing the legal structures to succumb to political pressures. Overall, Clark and Lee (2006) reveal that in order to create an environment that supports entrepreneurial growth, institutions must provide economic freedom as well as the enforcement of that freedom despite political pressures. Continuing with Clark and Lee (2006), Kreft and Sobel (2005) detail the areas of institutions that are significant in creating a fertile environmental for entrepreneurial growth. Kreft and Sobel (2005, p. 604) argue that: areas with institutions providing secure property rights, a fair and balanced judicial system, contract enforcement, and effective limits on government s ability to transfer wealth through taxation and regulation, creative individuals are more likely to engage in the creation of new wealth through productive market entrepreneurship. In areas without these institutions, creative individuals are more likely to engage in attempts to capture transfers of wealth through unproductive political entrepreneurship. 9

10 Clark and Lee (2006) and Kreft and Sobel (2005) provide a better understanding of the characteristics of quality intuitions as introduced in Baumol s theory as a key determinant of the allocation between productive and unproductive entrepreneurship. With this understanding of institutions, it serves best to examine Baumol s theory through previous empirical work done in this field. Kreft and Sobel (2005) were the first to examine the relationship between economic freedom and the productive entrepreneurial activities at the state level. They used the widely recognized Economic Freedom of North America index (EFNA) to measure the quality of institutions in each state as well as the annualized growth rate of sole proprietorships as the measure of productive entrepreneurship. Authors Nathan Ashby, Avilia Bueno, and Fred McMahon released this more specific index, ranking individual states and Canadian provinces based on their institutional quality. All independent variables were used for the base year 1996, while the dependent variable of the growth rate of sole proprietorships was analyzed for the next five years ( ) in order to encompass the subsequent growth related to an established institutional framework. Control variables are used to account for state level differences. Kreft and Sobel (2005) find significantly positive results at the one, five, and ten percent significance levels showing that states with the greatest economic freedom have the highest annualized growth rates in sole proprietorships. Campbell and Rogers (2007) also empirically examine the influence of quality intuitions on productive entrepreneurial activity by looking at net business formation at the state level. Following Kreft and Sobel (2005), they measure the quality of institutions using the Economic Freedom of North America index, however they use a different measure of entrepreneurial activities. Campbell and Rogers (2007) derive net new business formation from the difference of business births and deaths as a percentage of total businesses in each state. They argue that net 10

11 new business formation is a better measure of entrepreneurship in a state because it dynamically encompasses business formation and failure. Analyzing panel data on U.S. states from 1990 to 2001, Campbell and Rogers (2007) again provide evidence for Baumol s theory of productive entrepreneurship showing a significantly positive relationship between economic freedom and net new business formation. Hall and Sobel (2008) again revisit the analysis of the relationship between institutional quality and productive entrepreneurship. Throughout the literature there has been a trend (as seen in Kreft and Sobel [2005] and Campbell and Rogers [2007]) using the Economic Freedom of North America index as the main measurement of institutional quality. The trend to use this measure provides support for its reputation and quality in capturing the many dimensions of institutional quality. As seen in this literature, Hall and Sobel (2008) again use this index to measure the key variable of interest in their analysis. Choosing to measure productive entrepreneurial activities different than previous empirical works, Hall and Sobel (2008) restrict their analysis to a cross section view of U.S. states in order to use the newly created index, the Kauffman Index of Entrepreneurial Activity, by the Kauffman Foundation. Demographic control variables are used for education, ethnicity, and gender as well as socioeconomic variables. Cross sectional analysis on U.S. states for 2004 to 2005 confirm previous results found by Kreft and Sobel (2005) and Campbell and Rogers (2007), revealing a significantly positive relationship between institutional quality and productive entrepreneurship. Kreft and Sobel (2005), Campbell and Rogers (2007), and Hall and Sobel (2008) confirm through empirical analysis and various measures that quality intuitions positively affect productive entrepreneurial activities at the state level. Missing from these bodies of literature is empirical analysis on not only measures of productive entrepreneurship but also unproductive 11

12 entrepreneurship. Previous literature neglects to analyze both sides of the allocation of entrepreneurship until Sobel (2008), where he separately analyzes the impact of the quality of intuitions on productive and unproductive entrepreneurship. Following the trend of previous literature, he uses the Economic Freedom of North America index as the measure of institutional quality. By separately analyzing productive and unproductive entrepreneurship, Sobel (2008) uniquely has two measures of entrepreneurship. He measures productive entrepreneurship using venture capital investments per capita, patents per capita, the growth rate of self-employment activity, the establishment birth rate of all new firms, and the large establishment birth rate of new firms. As for unproductive entrepreneurship, the author measures the number of political, membership, and social services establishments in SIC code 8650, 8690, and 8390 as well as the Harris Poll index. Performing two cross-sectional analyses on the effect of intuitional quality on productive and unproductive entrepreneurship, Sobel (2008) finds confirming results of Baumol s theory. He finds a significantly positive relationship between intuitional quality and productive entrepreneurship, which is consistent with previous literature. As for unproductive entrepreneurship, he finds a negative relationship, revealing that increases in institutional quality decrease the amount of unproductive entrepreneurial activity. III. Education and Entrepreneurship Dating back to the work of Schumpeter (1934), understanding entrepreneurship in order to better encourage economic growth has continued to be a major topic in today s society. In order to promote productive entrepreneurship, as introduced by Baumol (1990), it is important to understand the factors that support that activity. Kreft and Sobel (2005), Campbell and Rogers (2007), Hall and Sobel (2008), and Sobel (2008) showed empirically the role quality 12

13 intuitions play in promoting productive entrepreneurship. Although intuitions are a major factor in determining the allocation of entrepreneurship between productive and unproductive activities, merely studying the environment with this one factor ignores a crucial area of literature. With the government playing such a large role in the structure and rules of everyday life, including the business environment, another area that deserves attention is the role of education. In the United States, polices and reform for education is a major topic in politics due to its link to economic growth and the key to building a strong country. Throughout history, economists dating back to Adam Smith studied the role of education in society and economic growth. In The Wealth of Nations, Smith writes that history owes its success to the education and great views of their active and enterprising founders (Smith, 1909, p.345). Although the economies of the world have greatly changed since the era of Smith, the underlying factors that he posed as important remain topics of research today. The role of education in economic growth is a major topic of research due to the importance the government places on policies governing education. More recently, Smith (1997) builds on previous endogenous growth literature by broadening the role of knowledge creation. He devises a concept known as knowledge infrastructure that encompasses not only the stock of knowledge, but also how it is supported by institutions and organizations. Smith s idea of knowledge infrastructure expands the basic idea of education beyond merely knowledge, producing organizations, such as universities. This idea is very important when analyzing the role education plays in entrepreneurship and inevitably economic growth. The key component to the literature is how education supports innovation, which is a critical factor in entrepreneurship and growth. These theoretical views of education and knowledge creation give way for empirical research to further understand the role of education and entrepreneurship. 13

14 Previous empirical studies looked at various measures of education, including those that specifically account for years of schooling, while others aim to measure educational spillover effects. Much literature has focused on the spillover effects of education in the form of research and development of universities on the surrounding areas. Landry et al. (2006) and O Shea et al. (2005) describe the role of university research and development in firm creation as being a result of university spin-offs from research professors. Empirical analysis on this theory has suggested mixed results on the relationship between university research and development and entrepreneurship. Kirchhoff et al. (2007) analyzed the effect of university research and development on firm creation across the United States from 1990 to Using data from the Longitudinal Establishment and Enterprise Microdata (LEEM) on firm births in labor market areas (LMA) as well as university R&D expenditures from survey data collected by the National Science Foundation (NSF), the researchers empirically tested their hypothesis that new firms tend to develop in areas with high levels of university R&D. Controlling for other influential variables, their analysis suggests there is a significantly positive relationship between university R&D and firm births in LMA s. Woodward et al. (2006) found similar results that suggest the positive effect of university R&D on entrepreneurship when analyzed on U.S. counties from 1997 to Although significant, Woodward et al. (2006) points out the incredibly small magnitude of the coefficient that lacks a clear interpretation. In addition to Kirchhoff et al. (2007) and Woodward et al. (2006), Goldstein and Drucker (2006) find results that question similar studies. Goldstein and Drucker (2006) analyze the effect of university R&D on entrepreneurship in metropolitan statistical areas (MSA) from 1986 to Analysis of the entrepreneurship over all of the MSAs suggests a positive relationship with university R&D; however when the MSAs are 14

15 broken down based on size (i.e. small, medium, or large), university R&D has a significantly negative relationship with entrepreneurial activity in small MSAs. Supporting the negative relationship found in small MSAs by Goldstein and Drucker (2006), Kim et al. (2012) also finds university R&D to be negatively related to firm births under a panel analysis of states in the U.S. from 2000 to The differing results from the divided model provide ambiguity of the true role university research and develop plays in promoting entrepreneurship. Although much previous literature analyzes the role of education in entrepreneurship by measuring university R&D expenditures, research has also included the role of schooling, as measured by educational attainment. In particular, research has focused on the affect of a more educated population on the level of productive entrepreneurship in a particular area. Theory suggests that well-educated people are more capable of creating business ventures, however empirical analysis shows differing results. Acs and Armington (2002) use LMA data from the LEEM database for firm births and analysis of the effect of educational attainment levels on firm creation. Empirical analysis suggests a positive and significant relationship between share of college graduates and entrepreneurship; however when firm births are divided by industry, the affect of college graduates becomes insignificant and barely positive for the service and manufacturing industries. In addition, Acs and Armington (2002) find a significantly positive relationship between those without a high school diploma and firm births. Although this result is counterintuitive to the positive results of a college degree, the understanding of the effect of education on entrepreneurship still lacks definitive findings. Using similar data on firm births in MSAs from 1996 to 1997, Lee et al. (2004) finds similar results suggesting a higher educated population measured by the share of the population with a bachelor s degree is positively related to firm births across all industries. It is however, 15

16 negatively related to firm births in the manufacturing industry. This analysis supports the findings of Acs and Armington (2002) when the firm births are divided by industry. Goldstein and Drucker (2006) provide further empirical evidence regarding educational attainment levels by looking at the affect of high school diploma, college degree, and more specifically science degrees on entrepreneurship. Using MSA data from 1986 to 2001, the analysis suggests the number of science degrees and the percentage of the population with a high school diploma to be significant and negatively related to entrepreneurship. However, the percentage of the population with a college degree is significantly positive. The differing results about educational attainment levels and productive entrepreneurship shows previous research is lacking clear results from a broad panel data analysis. Much of the literature described previously uses single-year educational measurements due to the limited amount of data available. Using the census as the source of data limits previous literature from measuring changes in education over time, which may result in mixed findings. In addition to the difficulty in finding continuous education data over time, previous literature also lacks analysis of the role of education in unproductive entrepreneurship. Empirical analysis of unproductive entrepreneurship by Sobel (2008) incorporates control variables for college education, however results are insignificant. The lack of literature on the effect of education on unproductive entrepreneurship, the main focus of my research analyzes this relationship. 16

17 IV. Model Figure 1. Representation of the Relationship between Entrepreneurship, Education, and Institutional Quality Figure 1 shows a graphical representation of the relationships between the three main variables of interest: entrepreneurship, institutional quality, and education. Previous literature empirically showed the important role the quality of governmental institutions plays in promoting productive entrepreneurship, while little research has been done on unproductive entrepreneurship. Similarly, research on the impact of schooling (educational attainment) on productive entrepreneurship found mixed results due to the varied relationship among different 17

18 sized regions and industries. The lack of research on the relationship between education and unproductive entrepreneurship serves as the motivation for current analysis discussed forthcoming. For the main model of this study, I combine the theoretical and empirical results from the literature on institutional quality and education to analyze the separate and joint effect of both of these variables on the allocation of entrepreneurship between productive and unproductive activities for the fifty United States from 1996 to Following the only empirical analysis of both productive and unproductive entrepreneurship in Sobel (2008), I hypothesize institutional quality, educational attainment and the joint affect of these variables will be positively correlated with productive entrepreneurship, while negatively correlated with unproductive entrepreneurship. Although I hypothesize education to be positively related to entrepreneurship, the theory behind the negative relationship found in previous literature poses some important questions to be answered about the role of education. In order to account for the joint affect of the educational and institutional environment for an entrepreneur, I use an interaction variable to represent the effect of education attainment given a state s institutional quality and vice versa. The model consists of two separate equations for productive and unproductive entrepreneurship. Control variables are used when applicable. Equations for the two-way fixed-effect panel estimation are as follows with a full explanation of the variable measurements in Table 1: 1 productive _ entrepreneurship s, t 1 1 IQ s, t 2 Edu s, t 3 IQ * Edu s, t B 4 C s, t s t s, t unproductive _ entrepreneurship s, t 2 5 IQ s, t 6 Edu s, t 7 IQ * Edu s, t B 8 C s, t s t s, t (1) (2) 1 The Hausman test confirms using a fixed-effects model over a random-effects one. 18

19 Table 1 describes the various measurements of the two dependent variables I use, productive_entrepreneurship and unproductive_entrepreneurship, in state s in year t. IQ x,t is the variable of institutional quality, measured by the all-government economic freedom index score for state s in year t. Edu s,t represents the two educational variable I interchangeably use in my equations. My two measurements of education are high school graduation rates as well six-year bachelor s degree graduation rates in state s in year t. IQ*Edu s,t is the interaction between the 19

20 institutional quality and education variable in state s in year t. C s,t is the control variable, median household income, I use in my regression in state s in year t. s is a vector of complete state-specific fixed effects. This variable controls for timeconsistent differences across states. t is a vector of complete year-specific fixed effects. This variable controls for characteristics that are common across states but differ across time. s,t is a random error term. V. Data The three variables of interest in my analysis (entrepreneurship, education, and institutional quality) are all variables that require proxy measurements due to the difficultly and unavailability of direct measurements. For that reason, I use variable measurements following previous literature to build on previous findings. I use panel data on the fifty United States from 1996 to Until this study, previous research was constrained to cross-sectional analyses due to the unavailability of data measurements, particularly unproductive entrepreneurship. Since then, data availability and quality increased enabling this study to better understand productive and unproductive entrepreneurship. I chose to begin my analysis with the year 1996 due to the availability of annual data from one of my main sources, the Census, beginning around that time. I extended my research to the year 2009 in order to provide analysis on the most current years of data available. Following the literature of Kreft and Sobel (2005), Campbell and Rogers (2007), Hall and Sobel (2008), and Sobel (2008), I use the widely recognized Economic Freedom of North America Index as my measure of institutional quality across U.S. states. The EFNA index ranges from 1 to 10, with 10 signifying complete economic freedom, and is comprised of three component parts: the size of government, takings and discriminatory taxation, and labor market 20

21 freedom. In addition, the index provides an all-government index taking into account the federal, state, and local government, as well as a sub-national index. For my analysis, I use the all-government index of all fifty states within United States as well as the index for the labor market freedom component to better capture the differences in institutional quality among the states. I measure productive entrepreneurship following Sobel (2008) using measurements for total establishment birth rates and utility patents per 100,000 population. I use the total establishment birth rate for each state from the Statistics of U.S. Businesses from the U.S. Census. I measure the birth rate as the number of firm births, which are establishments that have zero employment in the first quarter of the initial year and positive employment in the first quarter of the subsequent year, as a percentage of the total employment at the start of the year. Total establishment birthrates are the primary measurement of productive entrepreneurship I use due to it capturing the true actions of a productive entrepreneur, which are essentially the introduction of new businesses in the private market. Gartner (1985, 1990) describes the primary function of the entrepreneur as the creation of new businesses, which measures people using their innovative and entrepreneurial talents in the private market to create wealth. Therefore using birthrates serves as an appropriate proxy measurement for productive entrepreneurship. In addition, I also use utility patents per 100,000 to measure productive entrepreneurship, which I obtained from the U.S. Patent and Trademark Office. Sobel (2008) also used venture capital investments per capita as a measure of productive entrepreneurship; I chose, however, not to include this measurement. The data range of venture capital investment across the states contained zero investment dollars, measuring zero productive entrepreneurship in a given year, which is not plausible. 21

22 As for unproductive entrepreneurship, I use a more indirect measurement due to the limited data availability and choices to capture this activity across the states. Following Sobel (2008), I use the number of establishments in a particular NAICS code to measure unproductive entrepreneurship. I use three measure of unproductive entrepreneurship combining the number of establishments in political, membership, and social services organizations. Although I follow the methodology used to measure unproductive entrepreneurship in Sobel (2008), my measurements are slightly different due to the change in industry groupings from the Standard Industrial Classification (SIC) system to the North American Industry Classification System (NAICS) in All of the establishment data is from the County Business Patterns section of the U.S. Census. I use total establishments in NAICS code and to measure political organizations, , , , and to measure membership organizations, and , , , and to measure social service organizations. These three organization classifications are used to proxy lobbying and rent-seeking activities of the unproductive entrepreneur. The next main variable of interest is that of education. Although previous literature all uses educational attainment variables as the measure of education, these measurements are proxies for quality of education, which is what I would like to directly measure. Education is an interesting variable because direct measurements of quality of knowledge aggregated at the state level are unavailable. Instead, measuring educational volume has been used consistently throughout previous literature, legitimizing the methodology in this study. I use high school graduation rates and bachelor s degree six-year graduation rates as my proxy measure for education. High school graduation rates are measured by the number of public high school graduates as a percentage of ninth graders four years prior. I collect this data from Tim 22

23 Mortenson, as part of the Post Secondary Education Opportunity organization. Six-year bachelor s degree graduation rates are from PEDS Graduation Rate Survey, as part of the National Center for Educational Statistics. The variable measures the percentage of first-time, full-time bachelor degree seeking students that obtains any formal award within six years. I chose to not use university research and development even though it was used as a proxy measurement for education in previous studies. I did this because the previous studies that used this measurement did not analyze entrepreneurship at the state level but rather metropolitan areas. For this theoretical reason, I felt that my state level analysis would inhibit the regional spillover effects of university R&D and would not serve a purpose in this study. Lastly I employ the control variable, median household income, to account for any changes among the states over the period from 1996 to Because my analysis uses panel data, it helps mitigate problems that arose in the past due to omitted variable bias in the crosssectional analyses. For the fourteen-year time span I analyze, demographic characteristics are generally fairly stable, thus using state-level fixed effects helps to control differences among the states. However, using median household income to control for economic differences is necessary due to evident changes in the business cycle from 1996 to VI. Results In order to present relevant findings on the factors affecting productive and unproductive entrepreneurship, I begin my results by attempting to imitate the cross-sectional regression results of Sobel (2008). Table 2 shows the cross-sectional results of the productive entrepreneurship regression estimation using the measurement of total establishment birth rate. The significantly positive relationship between institutional quality and productive 23

24 entrepreneurship confirms the results found in Sobel (2008) and lends reasoning that my data is similar to that used in previous literature. However, inconsistent results were found in the crosssectional analysis of unproductive entrepreneur. Insignificant and positive results for the effect of institutional quality on unproductive entrepreneurship show there are differences between the data in this study and that used by Sobel (2008). As mentioned prior, these differences could be due to the changes in industry classifications from SIC to NAICS. For that reason, the measurement of unproductive entrepreneurships does not exactly match previous literature. To correct for problems with non-stationarity found using the fisher test, I transform the all-government intuitional quality and median income variables using the first difference of the measurements. In addition, due to problems with heteroskedasticity and autocorrelation 2 when regressing productive entrepreneurship, measured by the total establishment birth rates, using the ordinary least squares estimation method is not the best model. Therefore, I use a panel corrected standard errors model to correct equation (1) for heteroskedasticity and autocorrelation 2 I test my data for serial correlation using the Wooldridge test (Wooldridge, 2002). 24

25 (Hoechle 2007). I chose a panel corrected standard error model over generalized least squares due to having impure autocorrelation as well as more cross-sectional than time variables (50 states > 14 years). Unproductive entrepreneurship only suffers from heteroskedasticity, thus I employ robust standard errors to the fixed effect model. In addition, Table 3 presents the problem of multicollinearity between the entrepreneurship, institutional quality, education, interaction, and control variables. Performing the variance inflation factor test shows the interaction variables are highly correlated with the measure of institutional quality, as seen in the results for regressions (1) and (3). Regressions (2) and (4) show how eliminating the interaction variable solves the problem of multicollinearity. I conduct two regressions for both productive and unproductive entrepreneurship with and without the interaction variable. The results reveal the coefficient estimates remain the same, while variables become more significant, solving the problem of multicollinearity. Furthermore, Table 4 and 5 present the corrected panel regression results from equation (1) and (2) for productive and unproductive entrepreneurship using the all-government index of the EFNA for institutional quality as well as both measures of education (high school and bachelor s degree graduation rates). The results suggest institutional quality is significant and positively correlated to both productive and unproductive entrepreneurship, measured by the birthrate and lobbying1 (political organizations) variables. The regressions using utility patents 25

26 per capita as the measure of productive entrepreneurship revealed insignificant results, thus I confine my analysis of the relationship between intuitions, education, and productive entrepreneurship to using the birthrate measurement. In addition, I provide in Appendix A Tables 6 and 7 showing the results using unproductive entrepreneurship measurements lobbying2 and lobbying3 and both educational variables (high school and bachelor s degree graduation rates) 26

27 Although the results from my analysis differ from theory in Baumol (1990) and the empirical analysis in Sobel (2008), analyzing unproductive entrepreneurship in a panel model reveals different results. The positive relationship between unproductive entrepreneurship and institutional quality may be due to the fact that I analyze this relationship at the state level. When comparing the differences in institutional quality across states to those across countries, the differences are incomparable due to the large variance between institutions internationally. At the state level, institutional quality variation arise from differences in tax rates, for example, whereas internationally institutions differ substantially in their structure, concerning for example, trade and intellectual property rights. At the international level, there are large, clear determinants that separate quality intuitions from the rest, however, at the state level the differences may not be as distinct. Further interpretation of the positive relationship between unproductive entrepreneurship and institutional quality at the state level relates to my measurement of unproductive activity 27

28 capturing business establishments. In today s society, the government involvement in businesses across the country may appeal to all types of companies to look for rent-seeking options. Furthermore, quality institutions attract more business. Because of this relationship, the impact of quality institutions attracting more businesses may outweigh its impact on solely promoting more productive entrepreneurship. Institutional quality relating to increases in businesses overall is reason for my results showing positive relationships with both productive and unproductive entrepreneurship. Another interesting finding is the consistent significance of the education variable to be negatively related to productive entrepreneurship, while positively related to unproductive entrepreneurship. Confirming the mixed conclusions of previous literature about the role of education in entrepreneurship, the results suggest higher levels of educational attainment, measured by high school and six-year bachelor s degree graduation rates, promote unproductive entrepreneurship, while deterring productive entrepreneurship. The results reveal an interesting conclusion due to the attention society and particularly government polices place on strengthening education and encouraging young adults to graduate from high school and attend college. Interpreting the negative effect of education on productive entrepreneurship, the results indicate that the more schooling one receives, the less likely they are to start a business in the private market or become a productive entrepreneur. It is important to note, however, that education in this study as well as from the political perspective is measured by schooling rather than knowledge accumulation. There is a strict difference between these two measurements because schooling neglects to capture the true knowledge capabilities of a population. For example, a fourth-year undergraduate student technically has more schooling than Bill Gates, however, that does not necessarily correlate to being more knowledgeable than him, especially in 28

29 the area of business creation. For this reason the implications of these results suggest strong encouragement to continue schooling by the government will not necessarily promote entrepreneurship. As for the significantly positive relationship between education and unproductive entrepreneurship, this finding reveals interesting implications. Prior to this research, the understanding of unproductive entrepreneurship was limited to the conclusions found in Sobel (2008). Expanding the model to a panel analysis as well as adding the effect of education suggests different results. The link between education and unproductive entrepreneurship suggests formal education is promoting people to lobby the government. Interpreting this relationship may seem difficult, however, relating it to the negative relationship found with productive entrepreneurship sheds light on a counteracting force. As education levels increase, one expects to see increases in knowledge and ability. Furthermore, results in this study suggest the use of that gained knowledge may be allocated more towards unproductive activities, such as lobbying. Because formal education, such as college courses, gives educated people the tools to better understand the world around them, including the political arena, they may use that knowledge to manipulate politicians and the government through rent-seeking behavior. This interpretation may be suggested by the positive relationship between education and unproductive entrepreneurship. VII. Conclusion The research in this study examines empirically the role of education and intuitions in the allocation of a state s entrepreneurship between productive and unproductive activities. Expanding the research in unproductive entrepreneurship to better understand how the 29

30 educational and institutional environment, in a particular state, affects the entrepreneur suggests noteworthy conclusions about the current situation in the United States. My findings regarding education shed light on an important topic, not only in growth theory, but also in presidential election debates. As seen in the most recent campaigns of Mitt Romney and President Obama, reforming educational policies and planning for the future is a concern in the United States. As noted previously, economists, such as Mankiw, support the critical role of education in economic growth and the relevance of these findings are seen in the constant focus to reform and better educational policies in our nation s capital and across the country. However, my findings suggest education, given the quality of the intuitional environment, may deter entrepreneurship, an important determinant of growth. The key to this finding is how education, in general, with regard to schooling may not be as effective as previously thought in promoting economic prosperity. The results in this study suggests our country s constant encouragement to pursue education and to get a college degree may not translate to more people using those skills to become entrepreneurs and start businesses. Instead, formal schooling may promote people to lobby the government through unproductive behavior. The relationship between education and productive and unproductive entrepreneurship proposes education policies may not prepare students well enough or with the right skills to become entrepreneurs in the private market. Theoretical research elucidates this point, arguing formal education deters students from becoming entrepreneurs due to psychological factors when starting a career. In particular, higher levels of education are said to discourage entrepreneurship by gearing students more for the corporate world. In addition, with the difficulty in finding a permanent position in today s job market, students have a take-a-job mindset when starting their professional careers. Studies 30

31 also link education to stifling creativity and the ability of students to become entrepreneurs (Chamard [1989], Kourilsky [1995], Plaschka and Welsch [1990], and Timmons [1994]). Building from these conclusions, I argue that education is not about the more schooling you have but rather the types of skills you learn. Instead of politicians claiming their home state ranks in the top five for high school graduation rates, their focus should be on the types of programs offered in the education system. In recent years, colleges and universities developed specific entrepreneurship programs to better equip students with the skills needed to become entrepreneurs when they enter the real world in the private market. Entrepreneurship programs are evidence of progress in this area, but are only the beginning of needed additions across all universities in the United States. The research in this study regarding the role of education and intuitions in productive and unproductive entrepreneurship suggests there is a need for change in the current educational system if the goal is to promote productive entrepreneurship, and decrease unproductive activities. 31

32 VIII. References Armington, C. and Z.J. Acs The Determinants of Regional Variation in New Firm Formation. Regional Studies. 36: Baumol, William J "Entrepreneurship: Productive, Unproductive, and Destructive." Journal of Political Economy. 98(5): Chamard, J Public Education: Its Effect on Entrepreneurial Characteristics. Journal of Small Business and Entrepreneurship. 6(2): Clark, J.R., Dwight R. Lee, and Russell S. Sobel Freedom, Barriers to Entry, Entrepreneurship, and Economic Progress. Rev Austrian Econ. 20: Gartner, W. B A Conceptual Framework for Describing the Phenomenon of New Venture Creation. Academy of Management Review. 10(4): Gartner, W. B What are We Talking about When We Talk about Entrepreneurship? Journal of Business Venturing, 5: Goldstein, H. and J. Drucker The Economic Development Impacts of Universities on Regions: Do Size and Distance Matter? Economic Development Quarterly. 20: Hall, Joshua, and Russell S. Sobel "Institutions, Entrepreneurship, and Regional Differences in Economic Growth." Southern Journal of Entrepreneurship. 1(1): Henderson, J Building the Rural Economy with High-Growth Entrepreneurs. Federal Reserve Bank of Kansas City Economic Review. 87(3): Hoechle, Daniel Robust Standard Error for Panel Regressions with Cross- Sectional Dependence. The Stata Journal. 7(3): Kim, Younghwan, Wonjoon Kim, and Taeyong Yang The Effect of the Triple Helix System and Habitat on Regional Entrepreneurship: Empirical Evidence from the U.S. Research Policy. 41: Kirchhoff, B.A., S.L. Newbert, I. Hasan, and C. Armington The Influence of University R&D on New Business Formations and Employment Growth. Entrepreneurship Theory and Practice. 31: Kourilsky, M.L. (1995). Entrepreneurship Education: Opportunity in Search of Curriculum. Business Education Forum, October. Kreft, Steven F., and Russell S. Sobel "Public Policy, Entrepreneurship, and Economic Freedom." Cato Journal. 25(3):