Graduation Year

2006

Document Type

Dissertation

Degree

Ph.D.

Degree Granting Department

Business Administration

Major Professor

Joseph S. DeSalvo, Ph.D.

Keywords

Transit subsidies, Highway subsidies, Spatial expansion, Census data, Costs

Abstract

This dissertation investigates transportation subsidies as sources of urban sprawl. Apart from tolls, motorists do not pay highway user-fees, but they do pay gasoline taxes. Gasoline tax revenues are insufficient to cover the U.S. highway costs. Government, therefore, uses general tax revenues to cover highway expenditures. Since users do not pay the full cost of their travel, they have an incentive to travel longer commuting distances. Highway subsidies are, therefore, a potential contributor to urban sprawl. A similar argument applies to public transit. To capture the effects of subsidized automobile and public transit travel, we ex-tend the standard urban spatial single-mode model (Brueckner, 1987) to incorporate public subsidies for both one and two modes. Comparative static analysis of both models produces empirically testable hypotheses. Our most important theoretical result is that transit subsidies are inversely related to urban sprawl while auto subsidies are directly related to urban sprawl. The empirical analysis focuses on tests of the two-mode model. For consistency with the monocentric assumption of our models, our sample consists of urbanized areas located within a single county and having only one central city. Spatial size of the urbanized area is the dependent variable. Following our theory, explanatory variables comprise the transit subsidy, the highway subsidy, number of households, agricultural land rent, mean household income, and fixed and variable costs for transit and auto. We find that the spatial size of the urbanized area shrinks with an increase in the transit subsidy. The effect of highway subsidies, however, is ambiguous. We apply both ordinary least squares and two-stage least square regression analyses, and the results are qualitatively the same for both methods of estimation.

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