Many municipalities across the United States have turned to unit based pricing—also known as pay as you throw (PAYT)—as a vehicle for reducing municipal solid waste generation, increasing recycling, and promoting equity in paying for the service. In this paper, we reevaluate the standard...

This paper argues that the belief that discrete contingent valuation (CV) questions yield substantially larger estimates of the mean (and the median) willingness to pay (WTP) for nonmarket resources in comparison to open-ended CV questions is unfounded. Monte Carlo experiments estimate the...

A calibration strategy using ridge regression to generate more precise estimates for a particular parameter in a model is proposed. Formulae to compute the proposed ridge estimates from standard ordinary least squares (OLS) results are provided. The strategy is applied to recomputing marginal...

Innovations in the estimation of referendum type contingent valuation models have led to willingness-to-pay (WTP) measures inconsistent with consumer preferences and unbounded from above or below. We propose a set of criteria which guarantee a bounded measure of WTP. The criteria reject the...

This paper extends variation function theory by examining the effects of changes in prices, quality, and income on willingness to pay for quality change. Comparative static effects are found for both on-site users and nonusers of the resource. These results are used to interpret contingent...

We develop a temporal reliability test of the contingent valuation method. Separate random samples were administered the same telephone survey five years apart. In the retest, respondents have less favorable attitudes toward the environment. Given this result, a temporally reliable contingent...

We test for incentive incompatibility and starting-point bias to describe the effects of iterative valuation questions on willingness to pay. We compare double-, triple-, and multiplebounded models with data from two surveys with similar designs of the valuation questions. We find that incentive...