Doctoral Dissertation Research: Comparative Regional Income Dynamics: Clustering, Scale, and Geocomputation

点击翻译按钮获取中文摘要

The relationship between regional economic growth and inequality is difficult to ascertain. In order to truly uncover the mechanisms involved in regional income dynamics, researchers must move past notions of correlation to address the more intricate issues of directionality and simultaneity. The spatial mechanisms involved in income convergence and inequality have only recently begun to take focus in the literature. A portion of this trend can be attributed to advances in spatial statistics and econometrics, which have helped to diffuse the knowledge of several inferential pitfalls associated with spatial effects in empirical data. The focus on space in economic growth and inequality is further apparent on an applied front, as evidence points to interesting socioeconomic constructs such as poverty traps and convergence clubs. This doctoral dissertation research project will examine these multifaceted phenomena using a number of different research approaches. The project will outline an exploratory geovisualization framework that is used to generate new insights into the functional relationship between regional growth and inequality in the presence of spatial dependence. A formal spatial econometric approach will be provided that addresses the role of intraregional spatial clustering and simultaneity in the evolution of regional incomes. This project also will examine regional economic change in a relative distribution framework in order to relax the assumptions often underlying the analysis if income data. Monte Carlo techniques will be used to evaluate the properties of the relative distribution subject to varying levels of spatial association. Several techniques will be employed to control for spatial effects thereby allowing for a decomposed and robust outlook on regional income dynamics.This research will contribute to the increased awareness of the importance of space in the dynamics of economic systems. Several new spatially explicit tools will be employed that further develop the relevance of geography and regional science in the analysis of economic growth and change. The spatial extensions to the relative distribution methods can be applied to the study of any distributional comparisons of areal data. In the field of demographics, this research could shed light into to spatial variations in race/ethnicity composition at various geographical scales. The relationship between the reference and comparison distribution can be seen as analogous to the control and treatment groups in an experimental design. As such, researchers from many social science fields could utilize this framework to analyze the effectiveness of various policies across spatial boundaries. Lastly, the methods designed in this project will be integrated in the Open Source geocomputational toolkit STARS (Space-Time Analysis of Regional Systems), which continues to provide the broader research community with alternative dynamic spatial analytic tools. Finally, this Doctoral Dissertation Research Improvement award will enable a promising graduate student to pursue the first major project of an independent research career.