Abstract

Purpose – Due to mounting fiscal pressures, the federal government as well as many state and municipal governments in the USA have had to re-examine their transportation policies. Tax increases and/or spending cuts which aim to trim budget deficits are preoccupations of most policy makers and legislative bodies nowadays. With regard to the task of building new or rehabilitating old bridges, highways, and toll gates, cost-benefit analysis and economic impact studies are often undertaken by various government entities to rank and prioritize spending in the hopes of maximizing fiscal efficiency and road usage benefits. Since most highway construction and maintenance expenditures are absorbed by state governments, it is mostly up to state policy makers to decide transportation priorities. Not much research to date has been conducted to evaluate the comparative efficiency of state road provision to commuters and shippers. Such research would be useful to a state government’s budgetary allocation, road planning, and spending plans. The paper aims to discuss these issues. Design/methodology/approach – This paper uses data envelopment analysis under both constant and variable returns-to-scale and then to explain variations in efficiency ratings by using Tobit regression analysis. Findings – The authors discovered that the greater the level of state resident income and/or the warmer the weather, the higher the road or mass transit provision efficiency on average. The authors also found that greater urbanization in a state had little to do with efficiencies with respect to road provision. Originality/value – This paper is one of the first to assess and evaluate the comparative efficiency of road provision across 50 states in the USA and then set a benchmark for utilizing state financial resources to improve road infrastructure. More importantly, this paper helps transportation planners and public policy makers better allocate their limited financial resources to public goods in time of budget cutbacks and shortfalls.

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