Identifying a Model of Screening with Multidimensional Consumer Heterogeneity

Gaurab Aryal
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引用次数: 1

Abstract

In this paper, I study the nonparametric identification of a model of price discrimination with multidimensional consumer heterogeneity from disaggregated data on consumers' choices and characteristics. In particular, I consider the screening problem faced studied by Rochet and Chone (1998) where a seller of a product with multiple (and continuous) characteristics who only knows the joint density of consumer 'taste' and the production cost and chooses a product 'line' -- endogenous product characteristics. I determine the data features and additional conditions that are sufficient to identify the joint density of consumer heterogeneity, the cost function, and the utility functions that are common across consumers. If the product characteristics enter the utility function linearly, data from only one market is enough for identification, but if they enter nonlinearly we need data from at least two markets, or over two periods, with exogenous differences in costs. I also derive all testable restrictions imposed by the model on the data, i.e., the empirical content of the model, and also explore identification when prices are mismeasured and a product characteristic is missing.
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识别一个具有多维消费者异质性的筛选模型
本文从消费者选择和特征的分类数据出发,研究了具有多维消费者异质性的价格歧视模型的非参数识别问题。特别是,我考虑了Rochet和Chone(1998)所研究的筛选问题,其中具有多个(连续)特征的产品的卖方只知道消费者“口味”和生产成本的联合密度,并选择了产品“线”-内生产品特征。我确定了数据特征和附加条件,这些特征和附加条件足以确定消费者异质性的联合密度、成本函数和消费者之间共有的效用函数。如果产品特性线性地进入效用函数,那么仅来自一个市场的数据就足以进行识别,但如果它们非线性地进入,我们需要来自至少两个市场或两个时期的数据,并且成本存在外生差异。我还推导了模型对数据施加的所有可测试的限制,即模型的经验内容,并探讨了在价格测量错误和产品特征缺失时的识别。
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