We examine the structure of preferences for mitigating impacts of global climate change that will not occur during the lifetimes of most who are alive today. Because no market data exist for such distant markets, a statedpreference approach is used. The analysis is based on the random-parameters logit model, and the results indicate substantial heterogeneity in respondent preferences, that mean willingness to pay is a significant and increasing function of the scope of the impact, and, provocatively, that respondents have the same preferences over the two very different time horizons that we consider.

In many surveys, multiple observations on the dependent variable are collected from a given respondent. The resulting pooled data set is likely to be censored and to exhibit cross-sectional heterogeneity. We propose a model that addresses both issues by allowing regression coefficients to vary randomly across respondents and by using the Geweke-Hajivassiliou-Keane simulator and Halton sequences to estimate high-order probabilities. We show how this framework can be usefully applied to the estimation of power outage costs to firms using data from a recent survey conducted by a U.S. utility. Our results strongly reject the hypotheses of parameter constancy and cross-sectional homogeneity.