Random Utility Maximization Model Research Articles

Weekly Rhythm in Joint Time Expenditure for All At-Home and Out-of-Home Activities

This paper uses the Kuhn-Tucker demand system modeling technique to investigate the capacity of a typical week in capturing rhythms in activity-travel behavior. It considers all possible activity types within a weeklong modeling time frame. Complex interactions in time expenditure between at-home and out-of-home activities and among different out-of-home activities are captured by introducing behavioral elements in the model in terms of baseline preference, time translation, and satiation effects. The Kuhn-Tucker demand system model used in this paper is a random utility maximization model with the inherent assumption that every individual maximizes total utility in allocating time to the activities under consideration within the modeling time frame. Models are developed for each individual week of a 6-week travel diary drawn from the MobiDrive data set for Karlsruhe and Halle, Germany. Each model contains 83 variables and reveals behavioral details of complex activity-travel behavior. Based on the performances of the models in terms of fitting observed data and parameter values of specific variables, it is clear that a modeling time frame for a typical week is capable of capturing the rhythms of activity-travel behavior sufficiently. The paper concludes with the recommendation that the availability of activity diary data for a multiweek time period would further enhance understanding on this issue.

Income segregation and local progressive taxation: Empirical evidence from Switzerland

This study investigates spatial income segregation in fiscally decentralized urban areas. The theoretical part proposes the progressivity of local income taxes as a new theoretical explanation for income segregation. The empirical part studies how income tax differentials across municipalities affect the households' location decisions. I use data from the Swiss metropolitan area of Basel that contains tax information on all moving households in 1997. The location choice of the households is investigated within the framework of the random utility maximization model. Different econometric specifications of the error term structure, such as conditional logit, nested logit and multinomial probit, are compared. The empirical results show that rich households are significantly and substantially more likely to move to low-tax municipalities than poor households. This result holds after controlling for alternative explanations of segregation. Social interactions and distance from the central business district are established as other major factors for income segregation. Households in general tend to choose locations close to other households like themselves.

Income Segregation and Local Progressive Taxation: Empirical Evidence from Switzerland

This study investigates spatial segregation of the population in fiscally decentralized urban areas. The theoretical part proposes the progressivity of local income taxes as a new explanation for income segregation. The empirical part studies how income tax differentials across municipalities in the Swiss metropolitan area of Basel affect the households' location decisions. The (multinomial) location choice of households is investigated within the framework of the random utility maximization model (RUM). The theoretical model is used to identify the household preferences applied in the RUM. The empirical results show that rich households are significantly and substantially more likely to move to low-tax municipalities than poor households.

Estimating preferences for outdoor recreation:: a comparison of continuous and count data demand system frameworks

Continuous and count data demand system models have emerged as attractive alternatives to the discrete choice random utility maximization models (RUMs) that currently dominate the seasonal, multi-site recreation demand literature. This paper compares the frameworks conceptually and investigates their empirical performance with a common data set. Although the two modeling approaches employ substantially different behavioral and econometric assumptions, results from a recreation application based on the 1997 Iowa Wetlands Survey suggest that qualitatively similar policy inferences arise from the competing structures.

A random utility model for total maximum daily loads: Estimating the benefits of watershed‐based ambient water quality improvements

A random utility maximization (RUM) model is presented for the purpose of providing benefits estimates to aid in the implementation of the total maximum daily load (TMDL) process. The specific application is to North Carolina, for which the 1994 Environmental Protection Agency (EPA) National Demand for Water‐Based Recreation Survey is used to characterize the demand for water recreation in the state as a function of ambient water quality. The RUM choice set is defined to include all 8‐digit hydrological units or watersheds in the state, allowing benefits estimates to match the spatial aggregate of interest for the TMDL process. Water quality measures are linked to the choice set via the use of EPA's Index of Watershed Index database. A significant relationship between watershed‐level water quality and recreation trip taking behavior is found. Per trip and aggregate benefits estimates are presented for watershed, river basin, and statewide improvements in water quality.

Random utility and the preference for variety

In the literature of random utility maximization models, the preference for variety has been handled mainly by assumption of a stochastic choice parameter. The present model shows that a stochastic choice parameter can be determined in a model in which an agent is shown to have a deterministic preference for variety, as well as stable preferences for physical attributes of consumption choices.

A Non-Parametric Approach to the Estimation of Welfare Measures in Discrete Response Valuation Studies

ronmental goods. The idea of asking the individual to accept or reject a given price for an environmental good-rather than asking him/her to state his/her reservation price exactly-was introduced in the literature by Bishop and Heberlein (1979). Hanemann (1984) showed how this particular technique of preference revelation could be integrated into economic theory by using the Random Utility Maximization (RUM) model. A salient feature of the studies published to date is the use of a specific parametric statistical model to derive descriptive measures like mean or median Willingness to Pay (WTP) from the discrete response valuation experiment. The purpose of this article is to introduce a non-parametric estimator suitable for discrete response valuation experiments. There are two significant advantages of the approach suggested here; first, no distribution assumption is necessary and, second, it is very simple to use-in fact, the computations can be done on the back of an envelope. The paper is structured as follows: Section II outlines three different (parametric) approaches suggested in the literature, i.e., Bishop and Heberlein (1979), Hanemann (1984), and Cameron (1988). Section III introduces the non-parametric estimator and gives a brief description of the data used to illustrate the methods. Section IV compares the estimators, followed by concluding remarks.