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Dillon, Amy

Community Development and Applied Economics

2006

MS

This study first investigates alternative indictors for measuring industrial diversification and then examines the impacts of industrial diversification in terms of labor force employment on the continuing prosperity and stability of local economies. Three indices appropriate for comparison over time and across space, the Gini Coefficient, Entropy Index, and Herfindahl-Hirshmann Index (HHI), are calculated with three different data replacement methods, including linear trend at point, series mean, and a weighted leftovers replacement. The HHI, calculated with the weighted leftovers replacement method, is found to be the most reliable indicator under conditions of imperfect information and therefore is used in this study. The study then examines the geographical distribution of industrial diversification in US counties from 1979 to 2000. A mixed analysis examines the effects of time, region, rural-urban continuum category, and most dominant industry on the level of industrial diversification. This analysis is followed by an ANOVA, using statistics robust under heterogeneous variances, to more specifically differentiate the effects observed in the mixed analysis. The effect of time suggests a small increase in diversification heading into the early 1990s, followed by a rapid increase in specialization. Regional results indicate the New England, East South Central and South Atlantic regions to be more specialized than other regions, possibly because of the predominance of local manufacturing. Results of rural-urban analyses primarily follow the theoretical relationship of increased specialization with decreased population, but with a couple notable exceptions. Large and mid-sized non-metro areas not adjacent to metro areas are just as industrially diverse as the largest metro areas, while large non-metro areas adjacent to metro areas are more specialized than even the smallest non-metro areas.
Finally, the investigation returns to the question of how industrial diversification affects the stability and prosperity of local economies. Linear regression models--using variations of employment and wage variables as indicators of economic vitality--are estimated to explore how industrial mix fits into the multitude of factors influencing the continued health of local economies. Unexpectedly, specialization corresponds to increased prosperity in terms of both employment and wages, while only wages link specialization to instability. In conclusion, all results are related to policy and planning implications to discuss how these trends might inform a larger perspective when making local or regional development decisions or planning future research along these lines.