Eliciting the Demand for Long Term Care Coverage: A Discrete Choice Modelling Analysis

We evaluate the demand for long term care (LTC) insurance prospects in a stated preference context, by means of the results of a choice experiment carried out on a representative sample of the Emilia-Romagna population. Choice modelling techniques have not been used yet for studying the demand for LTC services. In this paper these methods are first of all used in order to assess the relative importance of the characteristics which define some hypothetical insurance programmes and to elicit the willingness to pay for some LTC coverage prospects. Moreover, thanks to the application of a nested logit specification with partial degeneracy, we are able to model the determinants of the preference for status quo situations where no systematic cover for LTC exists. On the basis of this empirical model, we test for the effects of a series of socio-demographic variables as well as personal and household health state indicators

Airport, airline and access mode choice in the San Francisco Bay area

In this article, we present an analysis of air travel choice behaviour in the San Francisco Bay area. The analysis extends existing work by considering the simultaneous choice by passengers of a departure airport, an airline, and an access mode. The analysis shows that several factors, most notably flight frequency and in-vehicle access time, have a significant overall impact on the attractiveness of an airport, airline and access mode combination, while factors such as fare and aircraft size have a significant effect only in some of the population subgroups. The analysis highlights the need to use separate models for resident and non-resident travellers, and to segment the population by journey purpose. The analysis also shows that important gains can be made through the inclusion of airport-inertia variables, and through using a nonlinear specification for the marginal returns of increases in flight frequency. In terms of model structure, the results suggest that the use of the different possible two-level nested logit models leads to modest, yet significant gains in model fit over the corresponding multinomial logit models, which already exhibit very high levels of prediction performance