DYNAMIC PRICING ALGORITHMS, CONSUMER HARM, AND REGULATORY RESPONSE.

• Author: Mackay, Alexander

ABSTRACT

Pricing algorithms are rapidly transforming markets, from ride-sharing, to air travel, to online retail. Regulators and scholars have watched this development with a wary eye. Their focus so far has been on the potential for pricing algorithms to facilitate explicit and tacit collusion. This Article argues that the policy challenges pricing algorithms pose are far broader than collusive conduct. It demonstrates that algorithmic pricing can lead to higher prices for consumers in competitive markets and even in the absence of collusion. This consumer harm can be initiated by a single firm employing a superior pricing algorithm. Higher prices arise from the automated nature of algorithms, impacting any market where firms price algorithmically. Pricing algorithms that are already in widespread use may allow sellers to extract a massive amount of wealth from consumers. Because this consumer harm arises even when firms do not collude, antitrust taw cannot solve the problem. This Article looks to the history of pricing innovation in the early twentieth century' to show how government can respond when new pricing technologies and strategies disrupt markets. It argues for pricing regulation as a feasible solution to the challenges non-collusive algorithmic pricing poses, and it proposes interventions targeted at when and how firms set prices.

TABLE OF CONTENTS I. PRICING ALGORITHMS IN PRACTICE A. Function and Relevant Markets B. Algorithms and Antitrust: Current Approaches II. PRICING ALGORITHMS & COMPETITION: ECONOMIC THEORY A. Classic Oligopoly Models B. Pricing Algorithms Change the Competitive Game 1. Frequency 2. Commitment C. Empirical Evidence III. POLICY RESPONSES A. Antitrust & Pricing 1. Predatory' Pricing 2. Resale Price Maintenance 3. Price Discrimination: The Robinson-Patman Act B. Pricing Regulation 1. Disruptive Pricing Technologies: Price Displays and Discounting Strategies 2. Price Controls C. Regulating Algorithmic Pricing 1. Regulating Pricing Frequency 2. Prohibiting Reliance on Rivals' Prices 3. Selecting a Regulatory Approach 4. Innovation Effects CONCLUSION INTRODUCTION

Imagine you are a consumer shopping for over-the-counter allergy medicine online. A search for Allegra, a top brand, leads you to three popular e-commerce websites. One offers a fifteen-pack of Allegra for $17, the second charges $14 for the same pack, and the third asks for $13. (1) All other aspects of the offers being equal, you are of course likely to choose the $13 price. You are also likely to think your research paid off: you got the best deal available and saved some money. The price differences among the retailers might suggest that, by purchasing the lowest-priced offering, you are buying at the "competitive price." But how would you know? What if all three retailers are charging above the competitive price? If the retailers used pricing algorithms to set their prices, it is quite possible that this is exactly what happened. Despite the appearance of price competition, you, and every other purchaser of this medicine, paid a supracompetitive price. The retailers used their pricing algorithms to extract wealth from you and your fellow consumers and shift it to themselves.

Pricing algorithms are becoming an increasingly common feature of many markets. (2) Ride-sharing apps, (3) airlines, (4) and Amazon, (5) to name just a few examples, all rely on algorithms to set their prices dynamically. These algorithms are computerized formulas that determine prices automatically based on a set of data inputs. (6) Pertinent data might include competitors' prices, supply and demand conditions, day of the week, and even the personal characteristics of individual purchasers. (7) The advent of pricing algorithms initially seemed to offer the hope of near-perfect competition in online markets. Algorithms give firms the ability to react in real time to their rivals' prices, theoretically sharpening price competition. Combined with the enhanced pricing visibility online shopping offers consumers, pricing algorithms appeared poised to drive prices down to competitive levels. (8) But this has not happened in many markets.

Compared to traditional pricing methods, algorithms provide sellers with significant advantages. Algorithms can analyze much greater volumes of information in setting prices than human agents, lowering the cost of employing sophisticated pricing strategies. (9) And algorithms can react much more quickly to changing market conditions than human agents, allowing sellers to set the most advantageous prices more of the time. (10)

While pricing algorithms offer significant benefits to sellers, they also raise serious concerns about harm to consumers. In particular, scholars and policy makers worry that firms will employ pricing algorithms to raise prices. (11) Indeed, current scholarship on pricing algorithms' competitive impact has focused almost exclusively on enhanced risks of explicit (12) and tacit collusion, (13) which harm consumers by raising prices and reducing output.

This Article breaks new ground by identifying a distinct form of consumer harm that arises from the use of pricing algorithms in competitive markets, analyzing the legal ramifications of this algorithmic harm, and proposing policy responses. It builds on pioneering theoretical and empirical scholarship in economics by one of the authors (MacKay) and Zach Brown, which demonstrates that competition among pricing algorithms allows firms to charge consumers supracompetitive prices even in the absence of collusion. (14) These effects are driven by standard features of algorithms that are already in widespread use, including at the largest online retailers, such as Amazon (15) and Walmart.com. (16) Unlike algorithmic collusion, which requires some measure of coordination among firms to raise prices, the harms we identify can be initiated by a single firm employing a superior algorithm. Because it is likely to affect most markets where prices are set algorithmically, this threat to consumer well-being is in some respects more serious than that posed by explicit or tacit algorithmic collusion, which require more stringent market conditions to be successful. (17) The legal means to address algorithms' competitive price effects is the central focus of this Article.

Pricing algorithms facilitate supracompetitive pricing in competitive markets in two ways. First, they allow some firms to update prices faster than other firms. (18) For example, a firm with an advanced pricing algorithm might be able to reprice its goods every day or even multiple times per day, while a firm with a less sophisticated algorithm might be able to re-price only once a week. Typically, the firm with a faster algorithm will have a competitive advantage, as it will be able to undercut the price of a rival without a commensurate response. (19) The slower firm can perceive the ability of the faster firm to quickly reduce prices as a threat, limiting its incentives to compete on price. (20) The slower firm will charge a price above the competitive level, understanding that it will lose some customers to its faster rival. (21) The faster rival then chooses a price below its rival's price yet above the competitive level, taking share from the rival while also capturing supracompetitive margins. (22) The result of this asymmetric frequency is that both firms will charge above the competitive price and consumers will pay more for goods than they did before. (23)

A second way in which pricing algorithms lead to higher prices is through a commitment to pre-specified pricing strategies. (24) Algorithms typically encode in software a set of instructions to update prices, and this software is used to update prices many times before the instructions are changed. (25) In this way, the algorithm allows a Firm to commit to a pricing strategy in advance. Just as a faster algorithm provides a firm with a threat to undercut slower rivals, an algorithm that can autonomously observe and react to competitors' price changes gives a firm an advantage relative to one that lacks this technology. (26) When firms with superior technology commit to this strategy, firms with inferior technology know that their rivals can be relied on to undercut their prices. (27) In this asymmetric commitment scenario, as with asymmetric frequency, all firms will charge above the competitive price. (28) In both scenarios, higher prices can reduce output and total welfare in addition to generating consumer harm. (29)

While the firms in these scenarios are charging supracompetitive prices, it is important to emphasize that they are not colluding. (30) Collusion--explicit o r tacit--requires each firm to make short-run sacrifices for long-run gains. Antitrust enforcement against collusion is predicated on finding an agreement among firms to encourage such short-run sacrifices. (31) We focus instead on settings in which all firms act non-cooperatively to pursue their own rational self-interest; therefore, no agreement is necessary. Further, key characteristics distinguish collusive regimes from algorithmic competition. In a market subject to collusion, we would expect firms to charge similar prices and to engage in a reward-punishment regime to discipline price-cutters. (32) In such regimes, a single price cut is punished by an extended period of even more drastic price cuts by rivals, reducing the profits of all firms. (33) Neither similar prices nor reward-punishment schemes are necessary, or even expected, in the markets we describe. Notably--like in the allergy medicine example above--firms may be charging quite different prices, yet all prices are higher than what consumers would pay in a competitive market. (34) Perhaps the most significant difference between algorithmic collusion and the model we describe here is that a single firm can initiate a cycle of consumer harm simply by employing a superior pricing...