Demonstration and Evaluation of Structural Benefit Transfer and Bayesian Benefit Transfer for Valuing Welfare Impacts of Saltwater Beach Quality ChangesEPA Grant Number: R833461
Title: Demonstration and Evaluation of Structural Benefit Transfer and Bayesian Benefit Transfer for Valuing Welfare Impacts of Saltwater Beach Quality Changes
Investigators: Poulos, Christine , Phaneuf, Daniel J. , Van Houtven, George L. , Parsons, George
Current Investigators: Poulos, Christine , Phaneuf, Daniel J. , Van Houtven, George L. , Parsons, George , Massey, Matt
Institution: Desert Research Institute , University of Delaware
EPA Project Officer: Hahn, Intaek
Project Period: September 1, 2007 through August 31, 2010
Project Amount: $199,111
RFA: Methodological Advances in Benefit Transfer Methods (2006) RFA Text | Recipients Lists
Research Category: Environmental Justice
To overcome some of the observed shortcomings in existing benefit transfer (BT) methods, economists have developed other methods, including structural benefit transfer (SBT) (e.g., preference calibration and structural meta-analysis) and Bayesian BT methods. Both methods involve data combination, where results from previous studies are systematically combined with each other or with data from the policy site of interest to generate benefit estimates. The methods apply different strategies for combining data; however, due mainly to their novelty and technical requirements they have been applied infrequently for policy analysis.
This study uses currently available information—prior studies of saltwater beach use, as well as data from the Coastal Module of the National Survey of Recreation and the Environment (NSRE)—to both demonstrate how these two methods can be operationalized and to evaluate their performance.
SBT and Bayesian BT will be employed to measure the economic losses due to beach closures and erosion in North Carolina’s Southern Banks region (south of Wilmington). The SBT method links nonmarket valuation information from different types of studies by deriving equations for the benefit measures from a single preference model so that each benefit measure is a function of the same set of preference parameters. This proposed SBT application will combine three types of data: (1) prior estimates of Marshallian consumer surplus losses based on completed single-site travel cost studies; (2) prior estimates of compensating variation based on completed multiple-site travel cost studies; and (3) micro data on beach use from the NSRE. These data will be combined in a multiple sample generalized method of moments estimator to estimate preference parameters. The main advantage of this method is that it requires that benefit estimates be consistent with the underlying utility model’s income and preference constraints.
Bayesian BT will use existing studies to develop priors on the distributions of site choice model regression coefficients for the Southern Banks. North Carolina NSRE data will be used to estimate a new multiple-site travel cost model that will be used to update the prior distributions using Markov Chain Monte Carlo simulation. The posterior distributions will be used to specify a benefit function for estimating economic losses.
The BT results will be evaluated by comparing them with findings from a 2003 study of beach use in the Southern Banks region.
The main methodological contribution lies in the demonstration and evaluation of these two BT methods, which are not in common use, in a new application (valuing beach closures). Another important contribution is the use of the NSRE data for rigorous nonmarket valuation applications. These national data are readily available but underutilized, and research that demonstrates methods for using the data in rigorous nonmarket valuation will increase its value for policy analysis.
The empirical contribution includes estimating welfare losses due to beach closures and erosion. This study is intended to form the basis of follow-on studies to estimate saltwater recreation losses in previously unstudied portions of the coast by either relying on NSRE data or small-scale primary data collection.