Date of Award

Winter 1998

Project Type


Program or Major


Degree Name

Doctor of Philosophy

First Advisor

Bruce Elmslie


This dissertation is concerned with testing economic growth theory using data from US States. Work on endogenous growth has recently been extended to determine the rate of technological change across economies where the incentive to innovate is linked to economic rewards. These models of endogenous innovation are on the cutting edge of theoretical advances in economic growth.

I extend the endogenous innovation literature to study the consequences of knowledge spillovers and the different industrial concentrations that clearly exist across states. This extension of the theory suggests it is reasonable to expect rates of innovation to differ across states if knowledge spillovers across economies are not significant, even though states are similar in most respects.

Two important empirical anomalies existing in the area of economic growth are addressed. First, the most basic model of economic growth suggests that convergence in labor productivity should occur at a rate higher than the rate actually observed. This could be due to an omitted variable in the empirical specification, or it could be due to theoretical problems with neoclassical production theory raised a number of decades ago during the Cambridge Capital Controversies.

A re-estimation of the rate of convergence after accounting for potential differences in rates of technological advance across states is provided. Using data for the period 1972 to 1996 it is found that differing rates of technological advance are important in explaining inter state differences in productivity growth. The exclusion of such a measure biases the estimate of convergence in the expected direction, but it cannot account for the slow speed of convergence.

A prediction of scale effects in innovation as suggested by the endogenous innovation approach is tested. While evidence of absolute scale effects are not found, evidence that the density of economic activity is important for determining the rate of innovation is strongly supported. This finding suggests that scale effects in innovation have an important spatial component and are likely to be related to what are known as agglomeration effects in the urban and regional economics literature. A synthesis of these approaches provides an important direction for future research.