Business, College of


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A DISSERTATION Presented to the Faculty of The Graduate College at the University of Nebraska In Partial Fulfillment of Requirements For the Degree of Doctor of Philosophy, Major: Economics, Under the Supervision of Professor James R. Schmidt. Lincoln, Nebraska: May 2011

Copyright 2011 Hoa Phu D. Tran


This study examines the linkage between industrial diversity and economic growth in the 48 contiguous states of the United States. The period of analysis is 1992 through 2009. Five diversity indices are considered and economic growth is measured as the growth rate of nonfarm earnings. Other variables thought to influence economic growth are included in the analysis. They are the growth rate of nonfarm employment, capital, and farm earnings. Tests for the endogeneity of variables are conducted and the need for instrumental variable estimation methods is demonstrated.

First, I consider multivariate model that relates nonfarm earnings growth to the diversity indices and the other variables noted above. The model includes regional fixed effects and time effects but does not allow for spatial dependence among states. The results show that diversity positively influences economic growth. Growth in nonfarm employment and capital are also found to be positively influence economic growth.

Second, I consider two spatial models that allow for a spatial lag and spatial autocorrelation effects among states. The first spatial model assumes a common spatial lag parameter for all states. The second spatial model allows the spatial lag parameter to be unique for each of eight regions within the United States. Two estimation methods are used, the generalized spatial two-state least squares estimator and an instrumental variables estimator along with a spatial heteroskedasticity and autocorrelation consistent matrix estimator.

The spatial lag parameter is small and statistically insignificant when the parameter is assumed to be the same across regions. However, when the spatial lag parameter is allowed to vary across regions, spatial effects among states are detected and are reasonably strong in some regions. Under both estimation methods for both spatial models, the results provide strong evidence that states with higher levels of diversity experience higher growth rates in nonfarm earnings. Nonfarm employment growth and capital growth are also significant influences upon the growth rate of nonfarm earnings.