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Abstract

Traditionally, it was not feasible for businesses to determine the maximum price the buyer was willing to pay, but with the availability of big data and the deployment of sophisticated algorithms, with a great degree of precision businesses can ascertain the maximum willingness price. Some forms of price discrimination are prohibited under the Robinson-Patman Act of Antitrust (1890), provided demographic characteristics such as race and gender are the determining factors. The problem with this interpretation is that sellers are not transparent about what factors are taken into consideration when determining price. Current laws are either limited in their interpretation or inadequate to properly respond to the potential for sellers to exploit the consumer through discrimination. In this paper, we present a common pricing strategy, behavioral-based price discrimination, broadly practiced in business, particularly retailers. In general, price discrimination occurs when instead of a set price, pricing for a product is determined by what the seller knows about the customer. This includes historical data indicating what the customer is willing to pay, combined with certain personal attributes. In this scenario, the same product may be offered at different prices to different individuals or market segments. What data points are considered when designing these sophisticated pricing schemes remains a mystery. Using a dataset containing transactions collected from 2500 households, we demonstrate price discrimination empirically by linking consumer spending to certain demographic characteristics. Additionally, we address the implication of price discrimination to the economic welfare of the consumer, to market competition, and to privacy.

Creative Commons License

Creative Commons Attribution-Noncommercial 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial 4.0 License

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