Min Qiang Zhao

Economic Valuation of Beach Erosion Control

In this study, we employ a choice-based conjoint survey design to elicit individual choices of beach erosion control programs that can potentially cause multiple effects on beach environment. Two empirical choice models, which incorporate individual heterogeneity, are used to analyze and compare the elicited individual choices of erosion control programs. Our results show that to a typical individual, both the positive and negative impacts of the programs affect his/her choices. We find that the economic benefit of an erosion control program to preserve a stretch of sand beach can be grossly exaggerated if potential negative impacts on the coastal environment from the same program are not considered. This study demonstrates feasible comparisons of beach erosion control programs that account for their multiple effects, as well as the demographics of program locations.

Valuing Environmental Resources through Demand for Related Commodities

We examine the revealed preference theory underlying the welfare analysis of public goods (e.g., environmental quality) by observing the consumption of related commodities. Inspired by Larson (1991) and Ebert (1998), and extended from Eom and Larson (2006), an empirical strategy is formulated, consistent with the theory of uniquely deriving use and nonuse values for a change in the public good. We show that the weak complementarity assumption and the Willig condition, the common preference assumptions used to support the revealed preference methods for non-market valuation, may be tested as parameter restrictions. A study of water quality valuation is presented to illustrate the proposed empirical strategy. Results show that the weak complementarity assumption and the Willig condition generally do not hold in the case study, and the consumer surplus derived from the indirect valuation method deviates largely from the exact welfare measures.

Model selection and misspecification in discrete choice welfare analysis

This study extends the work by Herriges and Kling (1997) to further evaluate the impact of discrete choice modelling techniques on welfare measures. Particularly, we evaluate the performance of the increasingly popular mixed logit model and the computational strategy for deriving discrete choice welfare measures. Our simulation results show that model misspecification can have profound effects on welfare measures. In general, the flexible mixed logit model performs relatively well in the presence of misspecification. However, when the nesting structure can be appropriately identified (via statistical tests and a priori knowledge/experience), the nested logit model provides more reliable welfare measures than the mixed logit model.