从离散选择中识别福利分布
Identifying the Distribution of Welfare from Discrete Choice
AI总结：
针对实证福利分析依赖参数假设、忽略未观测偏好异质性的问题，研究构建了离散选择下的福利分析框架，证明可通过非参数估计的选择概率推导福利分布，还得到了联合分布与社会福利的非参数结果，可用于评估价格变化的受益群体特征。
摘要：
实证福利分析通常对个体偏好施加严格的参数假设，且忽略未观测到的偏好异质性。我们提出了一个用于离散选择的个体与社会福利分析框架，可避免上述缺陷。首先，我们将Fleurbaey（2009）提出的一类个体福利测度适配到个体选择为离散的场景中。在允许不受限制的未观测偏好异质性的情况下，这些测度成为随机变量。随后我们证明，其分布可从选择概率中推导，而选择概率可通过横截面数据非参数估计得到。此外，我们还推导了福利与福利差异的联合分布、以及社会福利的非参数结果，其中前者是判断价格变化的受益者是否不成比例地来自初始富裕群体的重要工具。
英文摘要：
Empirical welfare analyses often impose stringent parametric assumptions on individuals' preferences and neglect unobserved preference heterogeneity. We develop a framework to conduct individual and social welfare analysis for discrete choice that does not suffer from these drawbacks. We first adapt the class of individual welfare measures introduced by Fleurbaey (2009) to settings where individual choice is discrete. Allowing for unrestricted, unobserved preference heterogeneity, these measures become random variables. We then demonstrate that their distribution can be derived from choice probabilities, which can be estimated nonparametrically from cross-sectional data. Additionally, we derive nonparametric results for the joint distribution of welfare and welfare differences, and for social welfare. The former is an important tool in determining whether the winners of a price change belong disproportionately to those groups who were initially well-off.
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