There has been disagreement in recent years' about the merits of empirical studies, pioneered by Ridker and Henning (R-H) in this Review,2 measuring the relationship between property values and air pollution within a metropolitan area. In these studies multiple regressions of property values on air pollution and other housing characteristics, and sometimes on income, are performed for crosssectional data within a metropolitan area. Unfortunately, the debate has centered on the prediction of changes in aggregate property values in response to changes in overall pollution levels. This has obscured the central issue, which is the measurement of the costs of pollution from the point of view of willingness-to-pay.3 Where the discussion has touched on this question, it has failed to recognize a straightforward argument by which the use of these empirical studies for cost-benefit analysis can be justified. Anderson and Crocker (A-C) and Polinsky and Shavell (P-S) both use the unrealistically strong assumption of identical tastes to argue that the regression can identify the parameters of a demand curve.4 Freeman (1971, p. 416) correctly points out that, in the real world, such a regression cannot isolate demand from supply elements; but he apparently does not realize how much information can be culled from the properties of the resulting equilibrium situation.5

The purpose of this paper is to demonstrate that the scheduling of activities by consumers can be explicitly modeled in theoretically satisfactory and empirically productive way. Even in the case of urban work trips, probably one of the most tightly constrained of everyday activities, schedule shifting is found to be of quantitative importance for the understanding of urban transportation systems. Considerable effort will be required to assess fully the implications of this type of behavior for such important areas of transportation analysis as demand studies, value-of-time measurement, policy simulation, and cost-benefit analysis. Meanwhile, it seems likely that this approach can be applied productively to other goods subject to peak demands.

In this paper, we investigate the complementary question of the optimal durability of highways. We find that in order to minimize discounted lifetime costs, typical urban interstate highways should be designed with thicker pavements lasting much longer between repavings. Furthermore, although existing roads have marginal pavement-wear costs that are quite high, optimal high-volume urban interstates would not. Thus the need for marginal-cost taxation, and the accompanying diversion of trucking industry revenues, would be virtually eliminated on a large portion of the nation's highway network if the highways were built to optimal standards. We begin by reformulating the standard model of optimal highway pricing and investment (see Winston, 1985, p. 78) to include highway durability as a long-run decision variable. The resulting pricing rule includes both a congestion charge related to scarce capacity, and a heavy-vehicle charge related to scarce durability. We derive expressions for marginal-cost user charges, optimal capacity, optimal durability, and long-run marginal pavement-wear cost. We then explore empirically those parts of the model related to durability.

Economists have been paying increasing attention to the study of situations in which csumers face a discrete rather than a continous set of choices.Such models are potentially very important in evaluating the impact of government programs upon consi.mterwelfare.But very little has been said in general regarding the tools of applied welfare economics in discrete choice situations.This paper shows how the conventional methods of applied welfare economics can be modified to handle such cases.It focuses on the cornputation of the excess burden of taxation, and the evaluation of gua].itychange.The results are applied to stochastic utility models, including the popular cases of prohit and logit analysis.Throughout, the ernp)-asis is on providing rigorous guidelines for carrying out applied work.

Optimal tolls, capacities, and service levels for highways can be determined jointly by way of an integrated peak-load pricing model. In this paper, such a model is developed and estimated with data for roads in the San Francisco Bay Area. The results suggest optimal peak user tolls of 2-7 cents per automobile mile on rural highways, 2-9 cents on suburban highways, and 6-35 cents on central city highways. Although our results are to some degree dependent on the interest rate, time value, and peak demand configuration assumed, one basic conclusion holds up under all alternative assumptions: current user charges are well below optimal peak tolls. However, our results also suggest considerably higher rush-hour speeds than currently prevail on Bay Area roads, and the lower travel time costs suggested by our analysis (relative to the current situation) should to some degree offset the corresponding higher user charges.

We apply recent econometric advances to study the distribution of commuters' preferences for speedy and reliable highway travel. Our analysis applies mixed logit to combined revealed and stated preference data on commuter choices of whether to pay a toll for congestion-free express travel. We find that motorists exhibit high values of travel time and reliability, and substantial heterogeneity in those values. We suggest that road pricing policies designed to cater to such varying preferences can improve efficiency and reduce the disparity of welfare impacts compared with recent pricing experiments.