Highway Infrastructure Investment and Regional Employment Growth: Dynamic Panel Regression Analysis
Piyapong Jiwattanakulpaisarn (),
Robert Noland (),
Daniel Graham () and
John Polak ()
ERSA conference papers from European Regional Science Association
A number of macro-level studies attempting to establish the statistical link between public investment in highway infrastructure and employment have applied econometric techniques to estimate the effect of highways while controlling for the effects associated with other factors. Unfortunately, direct use of empirical findings from these historic and recent studies, in shaping transport policy and supporting particular investment decisions, has been rather limited by mixed and inconclusive evidence in the literature. Apart from the common differences among these studies in scope and methodology, another possible reason for the contradictory evidence is that much of the previous work has generally suffered from several methodology drawbacks. In many studies, for instance, several important determinants of employment growth are omitted, and the choices of control variables included in the estimated equations generally are not based on theory. Those studies based solely on cross-sectional data also typically do not account for unobserved regional heterogeneity that may explain spatial differences in employment changes. Moreover, the possibility that the causal relationship between transportation investment and economic growth could work in both directions is generally ignored. This paper attempts to shed some light on this controversy by analysing the effect of highway investment on county-level employment in the State of North Carolina, United States. We derive a reduced from model of equilibrium employment that considers the effects of highways and other potential factors on the supply and demand for labour. Given the potential for lagged responses of the labour market to any exogenous shock, we assume a partial adjustment process for actual employment in our empirical model. A panel data set for 100 North Carolina counties from 1985 to 1997 is used in order to control for unobserved county and time specific effects using panel regression techniques. We also address the causality issue by the use of a two-stage least squares procedure with an instrumental variable. Our main results are that the employment effect of highway infrastructure depends critically on model specifications considered, and failure to account for the dynamics of employment adjustment could lead to an upward bias in the estimated effect of highways.
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