This dissertation uses a finite mixture logit (FML) model to investigate the heterogeneity of preferences of beach users for water quality at beaches in Southern California. The results are compared with conventional approaches based conditional logit (CL) and random parameters logit (RPL). The FML approach captures variation in preferences by modeling individual recreator choices using a mixture of several distinct preference groups, where group membership is a function of individual characteristic and seasonal variables. The FML parameter estimates are used to calculate welfare measures for improvements in beach quality through a reduction of water pollution. The FML segment specific welfare measures bound the traditional CL and RPL mean welfare estimates, and have the advantage of highlighting the distribution of the population sample's preferences. Analysis of beach recreation site choice data indicates the existence of four representative preference groups within the survey respondent sample. As a result, willingness to pay measures for improvements in water quality and other beach site attribute changes can be weighted across individuals to calculate the distribution of individual welfare measures.

One group of recreators is characterized as people who go to the beach and engage in water recreation with children. An interesting finding is that this group has a lower mean WTP for improving water quality than groups who go without children. This may well be an example of cognitive dissonance: parents find they go to the beach more often than others who don't have children, since that keeps the children occupied and happy, and they adapt their perception of the water quality to be consistent with their behavior.

Previous environmental and resource economic applications of the FML have been limited to applications with small choice sets (6) and group membership variables (4), This paper extends the FML model through the estimation of a large (51) choice set with 9 membership variables. This application is the first to incorporate seasonal variables into the group' membership function to capture seasonal heterogeneity.

Estimated welfare changes are calculated using the compensating variation measure for several hypothetical beach closure and water quality degradation scenarios. Estimation results indicate that the FML welfare estimates differ from those calculated using the traditional logit or RPL models. The FML model sheds light onto which subsets of beach recreators are likely to be impacted by different scenarios of resource change.

This paper uses a finite mixture logit (FML) model to investigate the heterogeneity of preferences of beach users for water quality at beaches in Southern California. The results are compared with conventional approaches based conditional logit (CL) and random parameters logit (RPL). The FML approach captures variation in preferences by modeling individual recreator preferences as a mixture of several distinct preference groups, where group membership is a function of individual characteristic and seasonal variables. The FML parameter estimates are used to calculate welfare measures for improvements in beach quality through a reduction of water pollution. These bound the traditional CL and RPL mean welfare estimates, and have the advantage of highlighting the distribution of the population sample's preferences. The data indicate the existence of four representative preference groups. As a result, willingness to pay measures for improvements in water quality can be weighted across individuals to calculate the distribution of individual welfare measures.

One of the groups is people who go to the beach with small children. An interesting finding is that these people have a lower mean WTP for improving water quality than people who go without a small child. This may well be an example of cognitive dissonance: parents find they go to the beach more often than others who don't have small children, since that keeps the children occupied and happy, and they adapt their perception of the water quality to be consistent with their behavior.

Previous environmental and resource economic applications of the FML have been limited to applications with small choice sets (6) and group membership variables (4). This paper extends the FML model through the estimation of a large (51) choice set with 9 membership variables. This application is the first to incorporate seasonal variables into the group membership function to capture seasonal heterogeneity.

This paper uses a finite mixture logit (FML) model to investigate the heterogeneity of preferences of beach users for water quality at beaches in Southern California. The results are compared with conventional approaches based conditional logit (CL) and random parameters logit (RPL). The FML approach captures variation in preferences by modeling individual recreator choices using a mixture of several distinct preference groups, where group membership is a function of individual characteristic and seasonal variables. The FML parameter estimates are used to calculate welfare measures for improvements in beach quality through a reduction of water pollution. The FML segment specific welfare measures bound the traditional CL and RPL mean welfare estimates, and have the advantage of highlighting the distribution of the population sample’s preferences. Analysis of beach recreation site choice data indicates the existence of four representative preference groups within the survey respondent sample. As a result, willingness to pay measures for improvements in water quality and other beach site attribute changes can be weighted across individuals to calculate the distribution of individual welfare measures. One group of recreators is characterized as people who go to the beach and engage in water recreation with children. An interesting finding is that this group has a lower meanWTP for improving water quality than groups who go without children. This may well be an example of cognitive dissonance: parents find they go to the beach more often than others who don’t have children, since that keeps the children occupied and happy, and they adapt their perception of the water quality to be consistent with their behavior.

Previous environmental and resource economic applications of the FML have been limited to applications with small choice sets (6) and group membership variables (4). This paper extends the FML model through the estimation of a large (51) choice set with 9 membership variables. This application is the first to incorporate seasonal variables into the group membership function to capture seasonal heterogeneity.

Estimated welfare changes are calculated using the compensating variation measure for several hypothetical beach closure and water quality degradation scenarios. Estimation results indicate that the FML welfare estimates differ from those calculated using the traditional logit or RPL models. The FML model sheds light onto which subsets of beach recreators are likely to be impacted by different scenarios of resource change.

The effect of expert opinion on demand for experience goods is difficult to quantify, as the relationship between purchases and reviews may be driven by product quality. Further, it is unclear whether a review-based demand effect is due to providing quality or existence information. Utilizing a retail field experiment to overcome these obstacles, we find a significant positive average consumer response to expert opinion labels for wine. Demand decreases for low scoring wines and demand increases for wines scoring average or higher. Results indicate that expert opinion labels transmit quality information as opposed to solely shelf visibility.

Correct (and Misleading) Arguments for Market-Based Pollution Control Policies

Faculty Profile: Larry Karp

Expert Opinion and the Demand for Wine

California Water Quality: The Role of Agriculture