Generalized Multivariate Extreme Value Models for Explicit Route Choice Sets

This paper analyses a class of route choice models with closed-form probability expressions, namely, Generalized Multivariate Extreme Value (GMEV) models. A large group of these models emerge from different utility formulas that combine systematic utility and random error terms. Twelve models are captured in a single discrete choice framework. The additive utility formula leads to the known logit family, being multinomial, path-size, paired combinatorial and link-nested. For the multiplicative formulation only the multinomial and path-size weibit models have been identified; this study also identifies the paired combinatorial and link-nested variations, and generalizes the path-size variant. Furthermore, a new traveller's decision rule based on the multiplicative utility formula with a reference route is presented. Here the traveller chooses exclusively based on the differences between routes. This leads to four new GMEV models. We assess the models qualitatively based on a generic structure of route utility with random foreseen travel times, for which we empirically identify that the variance of utility should be different from thus far assumed for multinomial probit and logit-kernel models. The expected travellers' behaviour and model-behaviour under simple network changes are analysed. Furthermore, all models are estimated and validated on an illustrative network example with long distance and short distance origin-destination pairs. The new multiplicative models based on differences outperform the additive models in both tests

Information and the Demand for Supplemental Medicare Insurance

While the critical role of imperfect information has become axiomatic in explaining health care market failure, the theory is backed by little empirical evidence. In this paper we use a unique panel data set with explicit measures of information and an educational intervention to investigate the role of imperfect information about health insurance benefits on the demand for supplemental Medicare insurance. We estimate a structural discrete choice model of the demand for supplemental Medicare insurance that allows imperfect information to affect both the mean and the variance of the expected benefits distribution. The empirical specification is a structural panel multinomial probit with an unrestricted variance- covariance, including heteroskedasticity and random effects to control for unobserved heterogeneity. The model is computationally complex and is estimated by simulated maximum likelihood. The empirical results indicate that imperfect information affects the demand for supplemental Medicare insurance by increasing the variance of the expected benefits distribution rather than by systematically shifting the mean of the distribution. We find that the increase in variance due to imperfect information increases the probability of choosing not to purchase supplemental insurance by about 23%. We also found that controlling for unobserved heterogeneity is important. The goodness of fit increased by about 25% and the precision of the estimated effect of information on the variance of the expected benefits distribution improved dramatically.

Discrete Hours Labour Supply Modelling: Specification, Estimation and Simulation

The assumption behind discrete hours labour supply modelling is that utility-maximising individuals choose from a relatively small number of hours levels, rather than being able to vary hours worked continuously. Such models are becoming widely used in view of their substantial advantages, compared with a continuous hours approach, when estimating and their role in tax policy microsimulation. This paper provides an introduction to the basic analytics of discrete hours labour supply modelling. Special attention is given to model specification, maximum likelihood estimation and microsimulation of tax reforms. The analysis is at each stage illustrated by the use of numerical examples.Discrete hours labour supply, multinomial logit, maximum likelihood estimation, microsimulation

Location Decisions of the New Immigrants to the United States

This paper estimates a multinomial logit model of the location decisions of new immigrants to the United States. Data from the 5- percent Public Use Samples of the 1970 and 1980 Censuses of Population are used to study the geographic distribution of immigrants who arrived after 1965. The major findings are as follows: (1) In choosing both initial and subsequent locations, immigrants are considerably more geographically concentrated than native Americans who move to a new city. (2) All of the immigrant groups prefer to live in cities where their countrymen are already located, but this relationship is much weaker for the more educated immigrants. (3) There is ambiguous evidence on the question of whether immigrants learn about economic opportunities as they spend time in this country. On the one hand, with the exception of the Mexicans, distance from the home country has a much weaker negative impact on location choice as time in the U.S. elapses. On the other hand, the expected wage variable, which should have a larger positive effect over time, only did so for the Asians, and to some extent, the Central and South Americans (excluding Mexicans and Cubans). (4) Within each ethnic group, there are significant differences in the location choice behavior of the 1965-69 and 1975-79 immigrant cohorts. The results are consistent with an increase over time in the quality of Asian immigrants, and a decrease in the quality of Mexican, Cuban and European immigrants.

Methodological and empirical challenges in modelling residential location choices

The modelling of residential locations is a key element in land use and transport planning. There are significant empirical and methodological challenges inherent in such modelling, however, despite recent advances both in the availability of spatial datasets and in computational and choice modelling techniques. One of the most important of these challenges concerns spatial aggregation. The housing market is characterised by the fact that it offers spatially and functionally heterogeneous products; as a result, if residential alternatives are represented as aggregated spatial units (as in conventional residential location models), the variability of dwelling attributes is lost, which may limit the predictive ability and policy sensitivity of the model. This thesis presents a modelling framework for residential location choice that addresses three key challenges: (i) the development of models at the dwelling-unit level, (ii) the treatment of spatial structure effects in such dwelling-unit level models, and (iii) problems associated with estimation in such modelling frameworks in the absence of disaggregated dwelling unit supply data. The proposed framework is applied to the residential location choice context in London. Another important challenge in the modelling of residential locations is the choice set formation problem. Most models of residential location choices have been developed based on the assumption that households consider all available alternatives when they are making location choices. Due the high search costs associated with the housing market, however, and the limited capacity of households to process information, the validity of this assumption has been an on-going debate among researchers. There have been some attempts in the literature to incorporate the cognitive capacities of households within discrete choice models of residential location: for instance, by modelling households’ choice sets exogenously based on simplifying assumptions regarding their spatial search behaviour (e.g., an anchor-based search strategy) and their characteristics. By undertaking an empirical comparison of alternative models within the context of residential location choice in the Greater London area this thesis investigates the feasibility and practicality of applying deterministic choice set formation approaches to capture the underlying search process of households. The thesis also investigates the uncertainty of choice sets in residential location choice modelling and proposes a simplified probabilistic choice set formation approach to model choice sets and choices simultaneously. The dwelling-level modelling framework proposed in this research is practice-ready and can be used to estimate residential location choice models at the level of dwelling units without requiring independent and disaggregated dwelling supply data. The empirical comparison of alternative exogenous choice set formation approaches provides a guideline for modellers and land use planners to avoid inappropriate choice set formation approaches in practice. Finally, the proposed simplified choice set formation model can be applied to model the behaviour of households in online real estate environments.Open Acces