A latent-class model of environmental preference groups is developed and estimated with only the answers to a set of attitudinal questions. Economists do not typically use this type of data in estimation. Group membership is latent/unobserved. The intent is to identify and characterize heterogeneity in the preferences for environmental amenities in terms of a small number of preference groups. The application is to preferences over the fishing characteristics of Green Bay. Anglers answered a number of attitudinal questions, including the importance of boat fees, species catch rates, and fish consumption advisories on site choice. The results suggest that Green Bay anglers separate into a small number of distinct classes with varying preferences and willingness to pay for a PCB-free Green Bay. The probability that an angler belongs to each class is estimated as function of observable characteristics of the individual. Estimation is with the expectation–maximization (E–M) algorithm, a technique new to environmental economics that can be used to do maximum-likelihood estimation with incomplete information. As explained, a latent-class model estimated with attitudinal data can be melded with a latent-class choice model. Copyright Springer 2006attitudinal data, E–M algorithm, latent-class attitudinal model, latent-class joint model,

Hurricane warnings are the primary sources of information that enable the public to assess the risk and develop responses to threats from hurricanes. These warnings have significantly reduced the number of hurricane-related fatalities in the last several decades. Further investment in the science and implementation of the warning system is a primary mission of the National Weather Service and its partners. It is important that the weather community understand the public's preferences and values for such investments; yet, there is little empirical information on the use of forecasts in evacuation decision making, the economic value of current forecasts, or the potential use or value for improvements in hurricane forecasts. Such information is needed to evaluate whether improved forecast provision and dissemination offer more benefit to society than alternative public investments.Fundamental aspects of households' perceptions of hurricane forecasts and warnings and their potential uses of and values for improved hurricane forecast information are examined. The study was designed in part to examine the viability of survey research methods for exploring evacuation decision making and for eliciting values for improved hurricane forecasts and warnings. First, aspects that affect households' stated likelihood of evacuation are explored, because informing such decisions is one of the primary purposes of hurricane forecasts and warnings. Then, stated-choice valuation methods are used to analyze choices between potential forecast-improvement programs and the accuracy of existing forecasts. From this, the willingness to pay (WTP) for improved forecasts is derived from survey respondents.

We assess the importance and robustness of cluster analysis and latent class analysis as methods to account for unobserved heterogeneity. We provide a critique and comparison of both methods in the context of measuring environmental attitudes and a contingent valuation study involving endangered species. We find strong evidence of robustness for these methods: group characterization and assignment of individuals to groups are similar between methods, and willingness-to-pay estimates are consistent. In addition, there are significant differences in willingness-to-pay across environmental attitudinal groups, and we find that accounting for unobservable heterogeneity provides a significantly better fitting model. Copyright Springer Science+Business Media, Inc. 2007Cluster analysis, Contingent valuation, Latent class analysis, New Ecological Paradigm, Unobservable heterogeneity, Willingness-to-pay,

ABSTRACT. Forests provide non-market goods and services that people are implicitly willing to pay for through hedonic housing and labor markets. But it is unclear if compensating differentials arise in these markets at the regional level. This empirical question is addressed in a study of Arizona and New Mexico. Hedonic regressions of housing prices and wages using census and geographic data show that forest area carries an implicit price of between $27 and $36 per square mile annually. Compensating differentials at the regional level suggest that care must be taken when applying the travel cost method to value regionally delineated characteristics. (JEL Q23, R14)

This study examines detailed data for faculty at a typical public research university in the United States between 1995 and 2004 to explore whether gender wage differentials can be explained by productivity differences. The level of detail�-�including the number of courses taught, enrollment, grant dollars, and number and impact of publications�-�largely eliminates the problem of unmeasured productivity, and the restriction to one firm eliminates unmeasured work conditions that confound investigations of wider labor markets. The authors find that direct productivity measures reduce the gender wage penalty to about 3 percent, only 1 percentage point lower than estimates from national studies of many institutions and with fewer productivity controls. The wage structure for women faculty differs markedly from the wage structure for men. Interpreted against the institutional features of wage setting for this population, the paper concludes that penalties for women arise at the department level.Academic labor markets, earnings differentials, gender wage gap, racial inequality, wage determination,