Increasing uncertainty in the world economy has created challenges for regions to pursue development strategies to achieve economic growth. Globalization, increased marketing integration, and the advent of new technologies led to approaches from traditional industrial recruiting to less traditional approaches. Among these latter approaches is the increased importance of entrepreneurship for creating economic growth through establishment of new firms or growth from established firms. Exploring the characteristics of entrepreneurship and its contributions to the local economy can help develop a map for designing specific development policies for Appalachia.
The main objective of the study is to determine the relationship between regional growth and entrepreneurship. To examine the role of entrepreneurship in economic growth, this study used a regional economic growth model using a system of simultaneous equations. Data on 410 counties of Appalachia is employed where measures of entrepreneurial activity are constructed and regressed against measures of economic growth. The simultaneous equation model is used where the dynamics of population growth, employment growth, and per capita income growth is utilized to determine how regional factors affect patterns of growth. The focus is how entrepreneurial factors influence growth in population, employment, and per capita income. Entrepreneurship variables are constructed from proprietorship and firm births and deaths data. In addition, quality of human capital, agglomeration, poverty, infrastructure, natural amenities, government expenditures, crime, and taxes are used in estimating the models. The growth model is specified as a three- and a four-equation model regressed using ordinary least squares (OLS) and two-stage least squares (2-SLS) regressions. The three-equation growth model is empirically estimated using the methods of two-stage least squares (2-SLS) and ordinary least squares (OLS) regressions. Simultaneous equations are estimated using 2-SLS to account for the endogeneity issue in variables used as both dependent and explanatory variables. These variables include the measures of growth and the constructed entrepreneurship index in the four-equation model.
The results of estimating the change in population equation show that employment growth positively affects population growth. Considering entrepreneurship, firm births and population growth are positively related. In addition, firm death is found to negatively affect change in population. While population density and quality of infrastructure increase county population, percentage of families below poverty level, education, and the initial value of population have negative effects towards population growth. The empirical results in estimating the change in employment equation in both three and four-equation models indicate that growth in population is positively related with employment growth. Therefore, the study further supports the "jobs follow people and people follow jobs" theory. Results also show that employment growth and per capita income growth are positively related. Self-employment and firm births are found to have positive effects in determining increases in county employment. Firm death is found to negatively affect employment which further supports the theory on the role of entrepreneurship in increasing job creation. Crime rate is also found to reduce job creation. However, both estimation methods indicate negative relationships between natural amenities ranking and employment growth which is in contrast to the hypothesis. Furthermore, per capita taxes show positive effects in county employment growth. OLS results also show a positive effect of population density and negative effects of property taxes and the share of population 35 to 64 years old towards employment growth.
Empirical results in estimating the per capita income equation show that population growth negatively affects increases in per capita income. The initial value of per capita income is found to be positive in determining per capita income growth in all three estimations. Further, the estimation indicates a negative relationship between growth in firm deaths and per capita income growth. The OLS estimation revealed that increases in the number of self-employed and increases in per capita income are related. The lagged value of per capita income growth is positive in relation to per capita income growth in all three estimations. In addition, the hypothesis on the positive effects of education in increasing income is supported in all three estimations. While the results show positive relationships between the share of population 35 to 64 years old and per capita income growth, negative relationships exist between state road density and change in per capita income. The estimation of the entrepreneurship equation in the four-equation model shows significant relationships with all the other endogenous variables. However, a positive association is observed only between the employment growth and the growth in entrepreneurial activity.
The study recommends supporting the creation of an entrepreneurial environment to encourage entrepreneurial activity as a strategy to increase employment. Furthermore, supporting existing entrepreneurs and avoiding firm deaths may help in achieving economic growth.