The Efficiency-Equity Trade-Off in Algorithmic Pricing: Can Market Power Change The Outcome?

Abstract

This paper evaluates the economic and welfare implications of algorithmic personalized pricing. While advanced data collection and machine learning allow firms to approximate first-degree price discrimination, this study analyzes the resulting welfare effects and comes to the conclusion that they are heavily mediated by market structure and firm power. By comparing the "monopoly-like" algorithmic pricing strategies Uber partakes in with the highly competitive, elastic constraints faced by an oligopoly like American Airlines, this paper demonstrates the extent to which market power determines the effects of personalized pricing. In highly concentrated markets, as seen in our Uber case study, personalized pricing reduces consumer surplus, deepens information asymmetry, and exacerbates economic inequality. On the other hand, firms with competitive constraints, as seen with the industrial oligopoly American Airlines, frequently rely on segmented second- and third-degree price discrimination, such as discount seat allocation, which expands output and minimizes deadweight loss. As a result, the firm shifts closer to allocative efficiency, reflecting benefits for society. Ultimately, this paper discusses the fundamental efficiency-equity trade-off in welfare economics. It concludes that while personalized pricing can expand total welfare and the volume of surplus through the absorption of deadweight loss, the inevitable costs (privacy erosion, economic inequality, digitally illiterate consumers ending up worse off, etc.) outweigh the total sum of efficiency gains(total welfare), resulting in weakened overall social welfare.

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