Growth and Technological Leadership in US Industries: A Spatial Econometric Analysis at the State Level, 1963-1997

weight matrix is used in the estimation of the spatial process. Estimation results are
presented in Table 7 and summarized below.

[Table 7 about here]

In the “total” sector, the OLS estimation shows a positive Moran’s I of errors
significant at 1%. The Jarque-Bera test does not reject the assumption of normally
distributed errors, and homoskedasticity of the errors is not rejected by the Breusch-Pagan
test either. The LM tests indicate that the model should incorporate a spatially autoregressive
process. We therefore estimated the spatial error model with regimes using GM estimator
and allowing coefficients to vary across regimes. We distinguish two regimes, with high and
low initial GDP levels, as before. Results of the estimation show a significant and positive
effect of human capital in both poor and rich economies. The catch-up to the technology
leader and physical capital show a strong positive effect on the productivity growth of the
poor economies only. The productivity growth in the mining and FIRE sectors is mainly
dominated by the catch-up effect. In the construction sector, the effect of physical capital is
more prominent, with a stronger significance for the poor economies. Results are mixed
with the transportation/utilities sector. A strong and significant effect of catch-up is
observed for the poor economies while the spatial spillover effect and physical capital
dominate for rich economies. As far as the service and wholesale/retail sectors are
concerned, human capital dominates the productivity growth process for both rich and poor
economies. In addition, the catch-up and the physical capital are strongly significant for poor
economies. A strong and consistent catch-up effect is observed in the manufacturing sector.

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