学位(音乐)
价格歧视
人口统计学的
计算机科学
产品(数学)
计量经济学
有序概率单位
大数据
上诉
Probit模型
微观经济学
经济
数据挖掘
数学
物理
法学
人口学
社会学
几何学
声学
政治学
出处
期刊:RePEc: Research Papers in Economics - RePEc
日期:2014-01-01
被引量:8
摘要
Second and 3rd degree price discrimination (PD) receive far more attention than 1st degree PD, i.e. person-specific pricing, because the latter requires previously unobtainable information on individuals’ willingness to pay. I show modern web behavior data reasonably predict Netflix subscription, far outperforming data available in the past. I then present a model to estimate demand and simulate outcomes had 1st degree PD been implemented. The model is structural, derived from canonical theory models, but resembles an ordered Probit, allowing methods for handling massive datasets. Simulations show using demographics alone to tailor prices raises profits by 0.14%. Including web browsing data increases profits by much more, 1.4%, increasingly the appeal of tailored pricing, and resulting in some consumers paying twice as much as others do for the exact same product. There is an updated version of this paper. Personalized Discrimination Using Big Data, working paper #108.
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