Three essays on self-employment

This dissertation is a collection of three essays, each of which examines different aspects of self-employment. The different analyses use different sources of data and various econometric methods to test issues related to US entrepreneurship. In the first essay, I use NSCG (National Survey of College Graduates) dataset in order to estimate the impact of human capital on the self-employment status of individuals. To capture this effect, I use Probit and Logit techniques separately on men and women and find out that individuals who majored in medicine, law, business, architecture, psychology, fine arts and agricultural sciences are more likely to be self-employed. I do find evidence that women, Blacks and Hispanics have the smallest interest to run their own business. One important finding is that people with degrees in engineering, education and science choose mostly to be wage-employed. The second essay examines the effect of human capital on self-employment earnings using the same dataset obtained for the first one. To estimate the coefficients on income levels, I take advantage of three different econometric methods, namely OLS, Heckit and matching estimator. Regression results on men show that having higher education brings more success in terms of self-employment earnings. Evidence shows that men do better when they are self-employed whereas women are better off when they are wage workers. The most lucrative majors for the male entrepreneurs are architecture, math, physics, chemistry and most fields of engineering. Women entrepreneurs who enjoy higher earnings are mostly majored in architecture, medicine, law, psychology and counseling. However, no particular education level is found to contribute to female entrepreneurial earnings. Being Asian or in the middle-age category increases the likelihood of having higher self-employment incomes. Marriage is found to considerably contribute to male entrepreneurial earnings whereas it adversely affects female self-employment income. In the final essay, I look at the effect population density has on US local self-employment rates. The motivation is the existence of a huge variation of population densities and a considerable deviation of self-employment rates across US states and counties. My hypothesis, which stems from the theory of division of labor, is that self-employment rate declines with a greater fraction of the population. Data for this analysis are taken from Integrated Public Use Microdata Series (IPUMS) in year 2000. I employ Probit and Logit models as the empirical strategy, separately on men and women. Results show that a negative and statistically significant impact of population density is present for both sexes. Hence, people who are located in less crowded areas are more likely to be self-employed. Considering the relatively low availability of good-matching jobs in smaller places, self-employment can be explained as a failure of the size of the market place. This effect is found to be larger for people like physicians and lawyers who are in more specialized occupations, indicating a greater negative impact of the size of the location on the self-employment status of people with higher skills