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32 are entirely in each highway class. Maintenance costs for vehicles. Experience shows that this should be done sep- routes that are in more than one highway class can then be arately for single-unit trucks and combinations (which split between highway classes based on lane-miles in each generally are more fuel efficient than single-unit trucks highway class and average maintenance costs per lane-mile at most registered weights). Consideration should be for the different highway classes. given to developing curves separately for two categories of fuels: gasoline-powered vehicles and all others; that The guidelines that accompany FHWA's State HCAS is, those powered by diesel and other special fuels. Model also include specific advice and options for the 2. The development of another carefully fitted curve for allocation of the following other categories of highway costs: average annual mileage per vehicle as a function of registered weights of vehicles. Experience shows that · Grading costs, this should also be done separately for single-unit · Residual allocators, trucks and combinations, which generally travel more · Width shares, annual miles than single-unit trucks, particularly at · General construction costs and transit costs, most higher registered weights. · Multi-highway system costs, · Other travel-related costs, Curves can be developed from VIUS data and have been · State police traffic management, and done using the 1992 TIUS in the default database that is part · Vehicle registration costs. of the FHWA State HCAS Model. Default data for much of the other data needed for revenue attribution can also be found there, along with guidelines on how these data can be ISSUES IN REVENUE ATTRIBUTION used in the model. Experience in preparing these default data suggests that it is important to focus primary attention on The revenue attribution process is a straightforward splitting data in the VIUS for each state rather than relying on national of revenue actually collected or projected to be collected for data. However, care is needed in doing this because individ- a future program period among the vehicle classes, sepa- ual state-level data are based on much smaller samples and rately for each highway-user tax or fee. Usually this is done tend to create much more erratic plots. The curve fitting often in a two or more step process; for example, by splitting fuel requires careful use of judgment and often requires supple- taxes into revenue by fuel type, then into light versus heavy mentary review and analysis of data for states that are simi- vehicles, and finally into the specific vehicle classes using lar in their economies and geography. historical or projected fuel economy by vehicle class based on data reported in the VIUS conducted every five years by To perform a sound revenue attribution process it is unnec- the U.S. Census Bureau. essary to deal with some related issues that have arisen in some states. For example, a tangential diversion from the basic A key part of accurately attributing revenue is to split analysis required for good practice in revenue attribution or vehicles in each class into full-fee paying vehicles and cost allocation is to confuse subsidies with costs and then allo- exempt-, partially-exempt, and special-fee paying vehicles, cate the amounts of a tax subsidy to the vehicle classes or any if any, based on vehicle registration data. Many states have other taxpayer. Economists often consider subsidies as costs all three non-full-fee-paying vehicle categories, and the num- in the sense one might think of the loss of revenue as an ber falling into each class varies by type of tax or fee. "opportunity cost" to the economy. However, a tax subsidy is by no means a cost attributable to vehicle classes in an HCAS In properly assessing the equity of a state's overall high- because the responsibility for extending the subsidy is that of way-user tax structure, only the full-fee-paying vehicles are a policy maker or legislator and not the highway user. generally included. It is important to recognize that the pro- portion of vehicles that are full-fee-paying varies widely by Another issue dealt with in at least two recent state vehicle class. In most, if not all states, these vehicles are a HCASs revolves around the issue of evasion. This is some- small minority of buses, but a majority for all other vehicle what related to the analysis required in the revenue attribu- classes. For these other vehicle classes, subsidized vehicles tion process in that an analysis of miles of travel within (mostly government owned) usually make up a larger portion a state by full-fee-paying vehicles could in theory be used of all vehicles for some of the lighter single-unit truck cate- to make rough estimates of evasion of diesel fuel taxes or gories. However, some subsidized vehicles are usually found weight-distance taxes. In turn, some states calculate revenue in all vehicle classes. that is not being collected and then effectively consider that lost tax revenue when determining the fair share that each Two analyses that are critical in the revenue attribution vehicle class should pay. For example, if payments from process are: heavy vehicles fall 10% short of cost responsibility but eva- sion is estimated at 10%, the argument has been advanced 1. The development of carefully fitted curves for fuel econ- that no adjustments to tax rates are necessary. Rather, those omy by fuel type as functions of registered weights of who are paying are currently meeting their cost responsibility