Date of Graduation


Document Type


Degree Type



Chambers College of Business and Economics



Committee Chair

Brian J Cushing


This dissertation is composed of three essays examining housing markets and economic growth, while carefully considering the role of space. The first chapter serves as an introduction and briefly discusses how each essay contributes to the literature. The second chapter estimates the relationship between residential property values and proximity to coal mines in Monongalia County, West Virginia. The study utilizes a spatial hedonic price model complemented with GIS techniques to estimate the marginal willingness to pay for properties surrounding coal mines. Study findings indicate that proximity to a coal mine translates negatively into property values. The third chapter is related to spatial county growth. Growth theories do not fully specify the prominent factors underlying the data-generating process for growth regressions. Introduction of space in growth regressions further complicates the estimations by adding uncertainty regarding the use of an appropriate spatial weight matrix and spatial regression specification. This study applies Markov Chain Monte Carlo model composition with the Bayesian model averaging methodology on a sample of U.S. counties, to deal with model uncertainty in spatial growth regressions. This study reports model averaged estimates to resolve the uncertainty pertaining to the determinants of U.S. county growth. The fourth chapter is related to the resource curse. An extensive literature has examined the presence and the possible causal mechanisms of a resource curse using a resource dependence indicator. However, a strand of the literature argues that switching from relative measures of resource abundance to absolute measures of resource abundance makes the resource curse disappear across countries. This study contributes to this strand of literature by examining whether coal abundance is a curse or a blessing for county economic growth, using both an absolute and a relative measure of resource abundance. Unlike previous research on resource curse, this study employs spatial county growth regressions to account for spatial dependence. Study findings suggest that introducing spatial dependence into growth regressions changes the results from non-spatial models drastically. When measured as a relative variable, coal dependence has a significant positive direct impact on own-county growth, and positive spillovers on related counties' growth. When measured as an absolute variable, coal abundance does not impact own-county growth, nor imposes spatial spillovers. Results imply that switching from non-spatial growth models to spatial growth models reverses the resource curse. Chapter 5 concludes and discusses areas of future research.