The subjective well-being (SWB) method has become a popular tool to estimate the willingness to pay (WTP) for non-market goods using widely available well-being data. In this method, the WTP measure contains the ratio of two coefficients (of the non-market good and consumption), which are both estimated in a regression on SWB. Computing confidence intervals for such ratios turns out to be error-prone, in particular when the consumption coefficient is imprecisely estimated. Even though this problem is known, many studies either do not report imprecision in the final estimate, or use inadequate methods. This paper compares five different methods to compute confidence intervals for normal ratio distributions: the delta, Fieller, parametric bootstrapping, and bootstrapping method, and a numerical integration of Hinkley’s formula. In a simulation, a large number of emulated SWB data sets are generated to calculate confidence intervals for WTP and the corresponding coverage rates with each method. The findings suggest that the delta method is the least accurate and not robust to lowering the statistical power or changing correlations between the estimators. All other methods are fairly accurate, robust, and can be recommended for use.

主观幸福感（SWB）方法已成为使用广泛可用的幸福感数据来估计非市场商品的支付意愿（WTP）的流行工具。在这种方法中，WTP 度量包含两个系数（非市场商品和消费）的比率，这两个系数都是在 SWB 回归中估计的。计算此类比率的置信区间很容易出错，特别是当消耗系数估计不精确时。尽管这个问题是已知的，但许多研究要么没有报告最终估计的不精确性，要么使用的方法不充分。本文比较了计算正态比率分布置信区间的五种不同方法：Delta、Fieller、参数自举法、自举法以及欣克利公式的数值积分。在模拟中，会生成大量模拟 SWB 数据集，以计算 WTP 的置信区间以及每种方法的相应覆盖率。研究结果表明，Delta 方法最不准确，并且对于降低统计功效或改变估计量之间的相关性而言不稳健。所有其他方法都相当准确、稳健，可以推荐使用。