Spatial Hedonics and the Willingness to Pay for Residential Amenities

Housing rents may be influenced by characteristics of nearby properties, an effect captured by spatial autoregression in a hedonic rent equation. We investigate the implications of spatial autoregression for measuring the marginal welfare effects due to a change in a residential amenity such as air quality. We show that if spatial price interdependence arises from technological spillovers, such that utility depends directly on neighboring property values, then the welfare change is given by the reduced form of the autoregressive model, effectively applying a "spatial multiplier" to the relevant implicit price. If instead spatial interdependence arises from merely pecuniary spillovers, as is commonly supposed in motivating spatial autoregression, then no spatial multiplier on implicit prices is called for in computing welfare; but it is then especially important to use the autoregressive model to measure those implicit prices.Spatial autocorrelation; spatial lag; welfare; willingness to pay; hedonic price function

Improving Freight Efficiency with Load Matching Technology [Brief]

Project Number: 15-2.1eLoad-matching technology, or digital freight matching (DFM), helps an inefficient and often fragmented local trucking market by eliminating non-revenue-generating trips. The basic idea of the technology is to provide a real-time, GPS-based connection between shippers and carriers, somewhat similar to how Uber and Lyft connect drivers and passengers

Improving Freight Efficiency with Load Matching Technology

Project Number: 15-2.1eLoad-matching technology for truckers and shippers helps an inefficient and often fragmented local trucking market by eliminating non-revenue-generating trips. The basic idea of the technology is to provide a real-time, GPS-based connection between shippers and carriers, somewhat similar to how Uber and Lyft connect drivers and passengers

New Methods for Modeling and Estimating the Social Costs of Motor Vehicle Use

The body of this dissertation comprises two standalone essays, presented in two respective chapters.Chapter One develops estimates of how motorists value their travel-time savings and characterizes the degree of heterogeneity in these values by observable traits. These estimates are obtained by analyzing the choices that commuters make in a real market situation, where they are offered a free-flow alternative to congested travel. They are generated, however, in an empirical setting where several key observations are missing. To overcome this, Rubin’s Multiple Imputation Method is employed to produce consistent estimates and valid statistical inferences. These estimates are then compared to those produced in a “single imputation” scenario to illustrate the potential hazards of single imputation methods when multiple imputation methods are warranted. A preferred model suggests that the median commuter is willing to pay $30 to save an hour of travel time. However, taking observed heterogeneity into account, median estimates range from$7 to $65 according to varying, observable motorist characteristics.Chapter Two develops a theoretical framework for jointly modeling the marginal external accident and travel-delay costs of driving. The framework explicitly accounts for the optimal tradeoffs that motorists make between accident risk and risk-reducing effort. Accident and travel-delay externalities are decomposed into components that correspond to physical accident risk, efforts to offset this risk, and their effects on travel times. An empirical model is developed from this framework, suggesting that joint external costs are$1.80 per vehicle-mile and external accident costs are $0.80 per vehicle-mile during a typical peak-period commute. The analysis does not require observations on accident rates and illustrates how the commonly-adopted approach to modeling accident externalities tends to understate these costs

Defensive driving and the external costs of accidents and travel delays

A unified model of accident and travel-delay costs describes the role that defensive driving effort plays in balancing these costs, and the costs of effort itself. This motivates a simple method for jointly estimating risk, effort, and travel-delay externalities, which exploits ordinary travel-demand modeling to directly value the congestion that generates these costs. A unique empirical setting also allows for decomposing the joint externality into its travel-delay and accident-related components, with results suggesting that together risk and effort externalities are nearly on par with travel-delay externalities. It is also demonstrated that traditional value-of-time estimates substantially reflect risk and effort costs.

Heterogeneity in Commuters' "Value of Time" with Noisy Data: A Multiple Imputation Approach

We estimate how motorists value their time savings and characterize the degree of heterogeneity in these values by observable traits. We obtain these estimates by analyzing the choices that commuters make in a real market situation, where they are offered a free-flow alternative to congested travel. We do so, however, in an empirical setting where several key observations are missing. To overcome this, we apply Rubin's Multiple Imputation Method to generate consistent estimates and valid statistical inferences. We also compare these estimates to those produced in a "single imputation” scenario to illustrate the potential hazards of single imputation methods when multiple imputation methods are warranted. Our preferred model suggests that the median commuter is willing to pay $30 to save an hour of travel time. However, taking observed heterogeneity into account, median estimates range from$7 to $65 according to varying motorist characteristics

Estimating Commuters’ “Value of Time” with Noisy Data: a Multiple Imputation Approach

We estimate how motorists value their time savings and characterize the degree of heterogeneity in these values by observable traits. We obtain these estimates by analyzing the choices that commuters make in a real market situation, where they are offered a free-flow alternative to congested travel. We do so, however, in an empirical setting where several key observations are missing. To overcome this, we apply Rubin’s Multiple Imputation Method to generate consistent estimates and valid statistical inferences. We also compare these estimates to those produced in a "single imputation" scenario to illustrate the potential hazards of single imputation methods when multiple imputation methods are warranted. Our results show the importance of properly accounting for errors in the imputation process, and they also show that value of time savings varies greatly according to motorist characteristics

Estimating commuters' "value of time" with noisy data: a multiple imputation approach

We estimate how motorists value their time savings and characterize the degree of heterogeneity in these values by observable traits. We obtain these estimates by analyzing the choices that commuters make in a real market situation, where they are offered a free-flow alternative to congested travel. We do so, however, in an empirical setting where several key observations are missing. To overcome this, we apply Rubin's multiple imputation method to generate consistent estimates and valid statistical inferences. We also compare these estimates to those produced in a "single imputation" scenario to illustrate the potential hazards of single imputation methods when multiple imputation methods are warranted. Our results show the importance of properly accounting for errors in the imputation process, and they also show that value of time savings varies greatly according to motorist characteristics.