Who Benefits from Surge Pricing?

Juan-Camilo Castillo
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引用次数: 41

Abstract

In the last decade, new technologies have led to a boom in real-time pricing. I analyze the most salient example, surge pricing in ride hailing. Using data from Uber, I develop an empirical model of spatial equilibrium to measure the welfare effects of surge pricing. The model is composed of demand, supply, and a matching technology. It allows for temporal and spatial heterogeneity as well as randomness in supply and demand. I find that, relative to a counterfactual with uniform pricing, surge pricing increases total welfare by 1.59% of gross revenue. Welfare effects differ substantially across sides of the market: rider surplus increases by 5.25% of gross revenue, whereas driver surplus and platform profits decrease by 1.81% and 1.77% of gross revenue, respectively. Riders at all income levels benefit, while disparities in driver surplus are magnified.
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谁从动态定价中受益?
在过去的十年里,新技术带来了实时定价的繁荣。我分析了最突出的例子,叫车服务的动态定价。利用Uber的数据,我建立了一个空间均衡的实证模型来衡量高峰期定价的福利效应。该模型由需求、供给和匹配技术组成。它允许时间和空间的异质性以及供给和需求的随机性。我发现,相对于统一定价的反事实,动态定价使总福利增加了总收入的1.59%。市场两侧的福利效应差异很大:骑手剩余增加了总收入的5.25%,而司机剩余和平台利润分别减少了总收入的1.81%和1.77%。所有收入水平的乘客都受益,而司机盈余的差距被放大了。
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