arXiv 2303.012312023-03econ.THecon.EM

个体异质性下的消费者福利

Consumer Welfare Under Individual Heterogeneity

Charles Gauthier, Sebastiaan Maes, Raghav Malhotra

AI总结：

本文提出一种无偏好限制的非参数方法估计消费者福利分布，应用于美国家庭数据发现价格冲击下福利损失异质性显著，朴素测度会低估损失，且偏好异质性比非位似性对福利测量的贡献更大。

摘要：

我们提出了一种非参数方法，用于在不对个体偏好施加限制的情况下，从横截面数据估计消费者福利的分布。我们首先证明需求矩能够识别支出函数的曲率，随后利用这些矩近似货币度量的福利测度。该方法可捕捉价格变化行为响应中的偏好非位似性与异质性。我们将方法应用于美国家庭扫描数据，评估2020年12月至2021年的价格冲击对生活成本指数的影响。研究发现，人口群体内部及群体间的福利损失存在显著异质性；对大多数群体而言，朴素的消费者福利测度会显著低估福利损失。通过将行为响应分解为偏好非位似性与异质性带来的组成部分，我们发现两个因素对准确的福利测量均至关重要，其中异质性的贡献更为显著。

英文摘要：

We propose a nonparametric method for estimating the distribution of consumer welfare from cross-sectional data with no restrictions on individual preferences. First demonstrating that moments of demand identify the curvature of the expenditure function, we use these moments to approximate money-metric welfare measures. Our approach captures both nonhomotheticity and heterogeneity in preferences in the behavioral responses to price changes. We apply our method to US household scanner data to evaluate the impacts of the price shock between December 2020 and 2021 on the cost-of-living index. We document substantial heterogeneity in welfare losses within and across demographic groups. For most groups, a naive measure of consumer welfare would significantly underestimate the welfare loss. By decomposing the behavioral responses into the components arising from nonhomotheticity and heterogeneity in preferences, we find that both factors are essential for accurate welfare measurement, with heterogeneity contributing more substantially.

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