Small-sample properties of robust willingness-to-pay estimators

Abstract

The small-sample properties of robust, binary choice willingness-to-pay (WTP) estimators are analyzed. A Monte Carlo simulation compares the bias and mean-squared error of marginal and expected WTP estimates from probit, zero-centered $t_{\nu }$ (“robit”), logit, and a generalized Tukey lambda distribution called a “pregibit” regression under normal and nonnormal distributional assumptions. Robust binary choice estimators allow for variation in tail thickness (the robit and pregibit) or tail asymmetry (the pregibit). No previous studies have compared the performance of these WTP estimators. The findings will interest researchers who use contingent validation methods to estimate WTP for nonmarket or hypothetical goods.