First Degree Price Discrimination Goes to School
利用公立大学研究生项目的录取数据，研究个性化定价对收入的影响，发现基于学生质量和居住地的定价可提高收入8.4%，对高校定价决策有参考价值。
Personalized pricing is widely discussed but seldom observed, making studies of its efficacy rare. Yet, first degree price discrimination is common in the pricing of higher education, and I use data on prices and the characteristics of students admitted to a professional graduate program at a public university to estimate a matriculation demand function. I then derive linear pricing functions that maximize revenue for a target number of students. By allowing these functions to depend on progressively richer sets of observables, I explore the effect of personalization of pricing on profit. Tailoring prices to a one‐dimensional measure of student quality would raise revenue by 2.2 per cent above the revenue with uniform pricing. Pricing based on both student quality and state residency raises revenue by 8.4 per cent, and further tailoring based on available observables raises prices 9.0 per cent above the maximum revenue under uniform pricing. Pricing that obeys current statutory tuition limits raises revenue less but still by just over half as much. I also infer the welfare weights that the pricing process implicitly attaches to student characteristics.