Imprecision in the estimation of willingness to pay using subjective well-being data

Abstract: The subjective well-being (SWB) method has become a popular tool to estimate the willingness to pay for non-market goods. In this method, the willingness to pay measure contains the ratio of two coefficients (of the nonmarket good and consumption), which are both estimated in a regression on subjective well-being. Computing confidence intervals for such ratios turns out to be error-prone, in particular when the consumption coefficient is imprecisely estimated. In this paper, five different ways of computing the confidence intervals are compared: the delta, Fieller, parametric bootstrapping, and bootstrapping method, and a numerical integration of Hinkley\u2019s formula. Using a large number of simulated SWB data sets, confidence intervals and their coverage rates are computed for each method. The findings suggest that the delta method is accurate only if the consumption coefficient is estimated with very high precision. All other methods turn out to be more robust, with minor differences in accuracy