The Forum Newsletter | December 6, 2025

Published on Dec 05, 2025

Dear Reader,

In this issue of The Forum, we present analysis and commentary from experts across law, policy, and industry, examining the evolving relationship between technology, regulation, and market dynamics.

We invite you to read, reflect, and share The Forum with your colleagues. You can complete the short form on this page to subscribe.

This week’s Forum features Sencer Ecer, Ph.D., on the political economy behind the spread of algorithmic retail price discrimination. David O. Fisher examines how algorithmic pricing is reshaping antitrust enforcement, challenging long-standing assumptions. Tara Pincock makes the case for a tougher approach to holding price-fixers accountable. And Stacy Mitchell explores how Amazon’s expanding role in public purchasing highlights the risks of algorithmic pricing.

We hope you enjoy this issue!

— The Capitol Forum Team

In this Issue

Sencer Ecer, Ph.D.:  The Political Economy of Widespread Algorithmic Retail Price Discrimination

David O. Fisher:  New Technologies, Old Assumptions: Meeting the Antitrust Challenge of Algorithmic Pricing

Tara Pincock:  Can We Please Stop Letting Price-Fixers Off the Hook

Stacy Mitchell:  Amazon’s Growing Role in Public Purchasing Shows the Dangers of Algorithmic Pricing

The Political Economy of Widespread Algorithmic Retail Price Discrimination

By Sencer Ecer, Ph.D., Senior Vice President at Compass Lexecon in Washington, D.C.

***This article extends the version that was originally delivered to your inbox.

Traditional forms of price discrimination such as those employed by airlines and hotels through temporal pricing, student fares, senior discounts on specific days, coupons, loyalty programs, and quantity-based discounts by suppliers will appear relatively modest in scale and complexity compared to the forthcoming wave of advanced, data-driven price discrimination practices that are poised to transform both retail markets and upstream supply chains. Given the informational asymmetries increasingly favoring sellers enabled by surveillance-based pricing practices and their enhanced capacity to implement algorithmic pricing strategies that closely approximate each buyer’s maximum willingness to pay, and considering that consumers make decisions based on income and the relative (rather than absolute) prices of goods, significant shifts in consumer surplus, wealth distribution, purchasing power, and overall welfare are likely to occur. Although such price discrimination is not technically inflation since it does not stem from generalized price level increases, it may nonetheless contribute to or mimic inflationary effects in consumer perception, particularly when average prices rise in sectors that carry substantial weight in the cost of living, such as housing and healthcare. These developments are poised to generate political and economic repercussions of a magnitude not witnessed in contemporary market economies since the transformative ideological confrontations of the 20th century. Because the law remains largely silent with regard to price‑discrimination practices directed at end‑consumers, the impending surge of such practices may provoke reactive and hasty policy responses—such as price‑controls (e.g., rent control). Such reactionary measures could amplify the political upheaval likely to follow; accordingly, a thorough understanding of the political economy of price discrimination is essential to address both efficiency and distributional concerns that are expected to materialize imminently. Notably, the decision in National Retail Federation v. James (S.D.N.Y., Judge Jed Rakoff, Oct 8 2025) confirmed that New York State’s mandated disclosure requirement, “This Price Was Set By An Algorithm Using Your Personal Data” (or comparable language) is now operative.”

To characterize the forthcoming wave of price discrimination, I adopt a more refined and analytically precise definition than those typically found in contemporary economics textbooks. Existing definitions remain rooted in classical formulations by Pigou (1938) and Machlup (1955), which continue to dominate the discourse. While Anderson and Renault (2008) offer a valuable account of the definitional evolution, progress in reconceptualizing the framework has been comparatively slow.

The definition I propose centers exclusively on the consumer’s income and preferences, directly engaging the normative dimensions critical to the emerging political economy of price discrimination. Under this formulation, price discrimination is defined as the practice of selling an identical good or service to different buyers at different prices—effectively, at the same time and place—where the variation in price is not justified by any discernible differences in the cost of production, distribution, or transaction.

More generally, such forms of price discrimination emerge under market conditions in which neither consumers nor producers would reasonably discern any substantive difference between the goods or services being exchanged, even if they were hypothetically interchanged. In operational terms, for the higher-paying consumer, the absence of a conventional or contextually accepted justification for the price differential such as the urgency of a last-minute airline ticket purchase renders the pricing practice more susceptible to perceptions of inequity or unfairness. It is precisely this perception that underpins the likely political backlash.

In the subsequent analysis, I focus primarily on the case of “perfect” price discrimination, wherein each individual is charged a price equal to their maximum willingness-to-pay (WTP). Nonetheless, the core arguments presented herein apply equally to group-based and market-segmented forms of price discrimination. Firms estimate an individual’s WTP based on their income and preferences, typically employing advanced pricing techniques such as surveillance-driven data collection and algorithmic pricing models.

It is essential to emphasize that price discrimination is feasible only in the presence of market power or other structural imperfections, and where secondary markets or arbitrage mechanisms are absent or ineffective—conditions that would otherwise erode the firm’s capacity to sustain discriminatory pricing strategies. Accordingly, the analysis concentrates on markets where these conditions hold—constituting, in effect, a substantial portion of the modern economy.

To render the issue more concrete: de gustibus non est disputandum—there is no disputing matters of taste—but this maxim becomes increasingly tenuous when applied to macro-level preference structures. A stable and substantial majority of individuals exhibit markedly stronger preferences for the attributes of their residence and neighborhood than for more discretionary goods such as morning coffee, surplus apparel, accommodations during travel, or leisure services at vacation resorts. Consequently, their willingness-to-pay for housing and related amenities tends to be systematically higher relative to their income.

It is therefore unsurprising that the first major case involving algorithmic pricing and alleged price collusion—United States v. RealPage—emerged within the housing sector. In effect, when the objective is to extract greater consumer surplus, whether through collusive conduct or through individualized price discrimination, firms naturally gravitate toward sectors where consumers’ preferences are inelastic and WTP is high.

Beyond housing, other sectors characterized by similarly strong preference intensity include health care, education, transportation, and energy. As consumers increasingly observe heightened expenditures in these domains many of which, notably housing-related services, have remained among the most persistent drivers of the current inflation, they are likely to respond through political and legal channels.

This raises a critical question: will political institutions and legislative bodies serve as the proverbial Dutch boy’s finger in the dike, stemming the rising tide of price discrimination and consumer welfare erosion?

Unfortunately, a growing disconnect is crystallizing between the increasing prevalence of retail‑level price discrimination and the extant legal frameworks governing such practices. It is relatively straightforward to identify unlawful price discrimination when it involves commercial buyers under the Robinson‑Patman Act (“harm to individual competitors or resellers”) or when it violates civil‑rights statutes, or constitutes conduct prohibited under Unfair, Deceptive, or Abusive Acts or Practices (UDAAP) provisions. However, there remains no clear legal prohibition against price discrimination at the retail‑consumer level.

Thus, as pricing practices become increasingly sophisticated in discerning individual preferences and inferring income—especially in the context of online commerce—they are likely to provoke both popular and political backlash, not to mention the ever‑present undercurrent of opposition to visible price discrimination such as congestion pricing (and yes, it effectively comes down to income and preferences). More broadly, the deep difficulty lies in uncovering the political‑economic philosophy underpinning opposition to price discrimination—and addressing it from the perspective of a market economy. In other words: although price discrimination is often characterized as unfair, a coherent normative foundation, which does not presently appear to exist, must be articulated if any effective policy or legal response to this perceived “unfairness” is to emerge—whether via legal doctrine, regulatory intervention, or broader institutional reform. Will it be? My educated guess is: not until the proponents of the market economy lay out theirs.

So, let us begin there.

Price discrimination provides an excellent opportunity to clarify the stance of contemporary economics on the political economy of pricing practices. Since the form of price discrimination I discuss here is confined to income and preferences, the demand side of economic analysis must take center stage; the supply‑side (cost‑based) price discrimination is comparatively straightforward.

The most rigorous framework for analyzing demand originates in neoclassical economics, which remains the dominant paradigm among expert witnesses and policy‑makers—particularly via the consumer‑welfare standard in antitrust. Thanks to its formal structure, neoclassical economics permits a critical and detailed interrogation of its own assumptions while still yielding meaningful conclusions. In this sense, its rigor does not preclude critique; rather, it enables it. Accordingly, I adopt the neoclassical framework as the basis for the analysis that follows.

To ground this analysis within neoclassical economics, it is important to begin with the basic construction of demand. Market demand for a good is understood as the aggregation of individual demands of consumers, each facing a set of choices among available goods and services. Embedded in this framework is a critical assumption underlying the consumer‑welfare standard in antitrust economics: individuals are modelled as self‑contained decision‑makers, capable of identifying and discerning the alternatives before them.

Moreover, these individuals are assumed to derive utility solely from their own consumption of goods and services—they are neither envious of others nor altruistic toward them in the economic sense. This assumption is foundational, especially to issues of pricing. That is, if each agent is concerned only with his or her own consumption bundle, then how much someone else pays or consumes is irrelevant to that agent’s own welfare. In other words, in its canonical analysis, neoclassical economics does not concern itself with interdependent preferences. Importantly, this assumption is not intended to characterize an idealized human being; rather, it serves as a model of the “average” individual, applicable to most routine and common decisions and behaviors. By adopting this simplification, the framework allows for the derivation of falsifiable predictions and the formulation of coherent, policy‑relevant conclusions. This is the methodological rationale and should be recognized as such.

A further core assumption completes the structure: individuals are rational, in the specific sense that their preferences over available choices are internally consistent (i.e., transitive and complete). In practice, this rationality postulate is typically paired with a behavioral preference‑maximizing assumption—that individuals select the most‑preferred alternative available to them. From these fundamentals, individual demand functions can be derived by imposing a budget constraint, reflecting the consumer’s income and the relative price of the good in question relative to the composite price of all other goods. One implication is that it is not just the income or preferences that determine an individual’s demand for a given good—the price of other goods also matters critically, hence the linkages between markets that constitute the skeleton for the “price mechanism.”

Taking one more step: these income levels and relative prices are themselves determined within the system (endogenously). Individuals supply labor and capital to firms under a production technology; firms, in turn, make profit‑maximizing decisions because their shareholders (who are themselves the individuals supplying the factors of production) are rational preference‑maximisers. Since firms are owned by the same individuals who supply factors of production, the model closes in a self‑contained general equilibrium, where both preferences and production technologies jointly determine market outcomes (recall the circular‑flow diagram in macroeconomics textbooks—a didactic precursor to general equilibrium analysis).

Now, we can proceed with the scenario of increased price discrimination and discuss its political economy more rigorously. With prevalent price discrimination, most existing consumers will start paying higher prices for those goods and services that they highly prefer and to which they can allocate sufficiently high income. Some existing consumers will no longer choose to buy. Some new consumers will be able to buy those same goods for the first time because the prices for them will be lower, owing to their lower preference for the product and their lower income. Up until this point, there seem to be no troubling social welfare issues, because the market expands, and exchanges (or their absence) remain voluntary. In fact, this is precisely the reason why laws on price discrimination are not necessarily prohibitive—at least at the retail level. More generally, the situation mirrors the familiar First Fundamental Welfare Theorem in economics (the formalization of Adam Smith’s “invisible hand”), which essentially holds that the more markets the better (within social norms) as long as prices are formed competitively (or nearly so) via the interaction of supply and demand. So far, the picture is more or less clear because we are dealing with one market and ignoring the inter‑market effects of the altered income allocation. The next stage, therefore, is to address such general‑equilibrium concerns.

Successful price discrimination will inevitably increase production and extract more income in markets for goods that have priority preference (“priority markets”)—such as housing, including rents—over eating out or other discretionary consumer goods. Individual incomes and wealth will start shifting away from other goods and services, thereby putting downward price pressure on those markets. Expansion in priority markets will prompt a reallocation of inputs and resources toward them. Individuals will begin to feel utility decline due to potentially decreased consumption and even loss of the benefits of variety in consumption (when they reduce or cease consumption of other goods) which they might otherwise have enjoyed (due to convex preferences). Furthermore, income distribution will deteriorate and begin tilting toward shareholders and employees in the priority industries. This process produces several dynamics between consumers in priority and discretionary markets vis‑à‑vis incomes derived from wages and profits in both types of markets. The clearest transfer is that existing consumers in discretionary markets will reduce their consumption and transfer the remaining wage and profit income to the producers (shareholders) in priority markets. The average consumer surplus obtained from non‑priority items (such as morning coffee, streaming services, new clothing, leisure travel—“small markets”) diminishes. Concentration in “essential” goods—which exists primarily due to structural, regulatory and economic entry barriers—will be reinforced. This is particularly true in sectors such as housing, healthcare, utilities and core food‑supply.

Consumer discontent resulting from diminished variety and a deterioration of income distribution (benefitting primarily shareholders and employees in the priority sectors) will manifest in voting preferences, which in turn will shape legislative priorities. More urgently, the executive branch—faced with panic—will quite likely begin imposing, at a minimum, price‑controls, which will never enhance allocative efficiency but may provide some short‑term relief for distributional concerns.

Therefore, the action plan to remedy this immense challenge must be multifaceted: economic scholars should examine the welfare effects of price discrimination taking income into account in a general‑equilibrium (rather than merely partial‑equilibrium) setting (there is little literature on this). Legal scholars must elucidate the coherence of conclusions drawn from the economics literature in terms of legal philosophy. Legislative bodies should expedite the enactment of laws informed by the findings of economic and legal scholars—not merely the whims of public sentiment. And the executive branch should refrain from hasty price‑control interventions and instead pursue efficiency‑neutral distributional measures.

New Technologies, Old Assumptions: Meeting the Antitrust Challenge of Algorithmic Pricing

By David O. Fisher, Senior Counsel at the American Antitrust Institute

As algorithmic pricing transforms our markets, the antitrust laws are being tested in new ways. Empirical evidence demonstrates that pricing algorithms facilitate price coordination, resulting in the textbook anticompetitive harms of higher prices and lower output. But they do so through complex and novel mechanisms which allow for tacit collusion in ways that antitrust law has come to treat as unlikely. Faced with this reality, antitrust law must cast off old assumptions about tacit cartel agreements. To face the threat that algorithmic pricing poses to competition, courts must rehabilitate tacit agreement as a theory of Section 1 liability and enjoin the shared use of algorithmic pricing tools that raise market prices above the competitive level.

Although tacit agreements to fix prices are in principle illegal under Section 1 of the Sherman Act, they have become increasingly difficult to prove because of steadily heightening evidentiary standards. Lulled into a false sense of security by Chicago School thinking, courts have implicitly accepted that oligopolies are more likely to engage in interdependent pricing than to collude and that cartel agreements are by nature unstable. But contemporary economic evidence demonstrates that market concentration is associated with not only higher prices but also long-lasting, durable cartels. Pricing algorithms can make cartels even more durable by using novel methods which allow for stable agreements among disparate actors. With machine learning, they even “learn” to adopt joint profit maximization strategies and to collude on their own. These capabilities further undermine our old assumptions about tacit cartel agreements, reducing the likelihood of false positives and raising the costs of false negatives. This should prompt us to question the burdens we put on plaintiffs to prove a tacit agreement.

As part of the project of rehabilitating the theory of tacit agreement, we should also be willing to enjoin the use of algorithmic pricing that results in sustained supracompetitive prices. Long before the advent of algorithmic pricing, Judge Richard Posner and others argued that oligopoly pricing is properly understood as an illegal agreement under Section 1: one oligopolist’s price increase is an implicit offer, which the others implicitly accept by matching. Recognizing the potential for mutual benefit, the parties perform by collectively maintaining supracompetitive prices. But according to former AAG Donald Turner and others, there is no effective legal remedy for oligopoly pricing. Judges are not equipped to set and regulate “reasonable” market prices, and they cannot order firms not to react to their competitors’ prices. Courts have tenuously resolved this debate by inferring a tacit agreement only when defendants engage in communication, information sharing, or other conduct that can be enjoined. In cases of algorithmic collusion, courts can enjoin the use of shared pricing algorithms that facilitate oligopoly pricing. Viewed in this light, the secret to addressing algorithmic collusion may be as simple as a well-crafted injunction.

The antitrust laws are flexible enough to address the threats that algorithmic pricing poses to competition. But if we want to use them effectively, we must revisit old assumptions about tacit cartel agreements, and we must apply the law in a way that accounts for the realities of this new technology. Only time will tell if we are up to the task.

The American Antitrust Institute (AAI) is an independent, nonprofit organization devoted to promoting competition that protects consumers, businesses, and society. We serve the public through research, education, and advocacy on the benefits of competition and the use of antitrust enforcement as a vital component of national and international competition policy.

Can We Please Stop Letting Price-Fixers Off the Hook

By Tara Pincock, Policy Counsel at Open Markets Institute

Let’s begin with a basic principle under the antitrust laws: Businesses are required to make pricing decisions independently. Coordinating with competitors or using a third-party intermediary to fix prices is against the law. Likewise, sharing nonpublic information with competitors is not allowed because doing so is against the law. But the DOJ’s proposed settlement with software developer RealPage makes it abundantly clear that even when large corporations are caught with their hand in the cookie jar, they will face few consequences on the federal level.

In their amended complaint, the DOJ and ten states accused RealPage and seven landlords of conspiring to increase rental prices by using “nonpublic, material, and granular rental data” from competitors, which was fed into RealPage’s algorithm. The algorithm then spat out pricing recommendations that pushed rents higher and higher across the country. Rental prices went up approximately 35% between 2019 and 2024. According to one analysis, renters paid an additional $3.8 billion in 2023 alone. While there are likely multiple factors responsible for the increase, at least some of the blame must be laid at the conspirators’ feet. But despite the massive public interest in lowering the cost of living, the DOJ’s settlement does little to address it.

Pursuant to the settlement, RealPage will pay no fines and pay no restitution to the millions of renters who saw their rent go through the roof. Two of the seven landlords have previously settled with the DOJ with similar terms. To be clear, RealPage and the two landlords have disclaimed any wrongdoing and maintain that their business practices are legal. Even so, RealPage must change its business practice to stop feeding competitors’ nonpublic, competitively sensitive information into its algorithm.

On one hand, this settlement is a win because it could help restore competition in the rental market. If landlords can no longer use a third party (i.e., RealPage) to coordinate prices, they might have to start competing with each other. Theoretically, they could compete for renters by offering better prices than the competition.

On the other hand, this settlement will have little to no deterrent effect on the next software company that engages in the similar conduct. As I’ve written before, people aren’t dissuaded from committing criminal acts by threats of punishment but instead are deterred by the likelihood of getting caught. The more likely you are to get caught, the less likely you are to commit the crime. Nevertheless, there must be consequences if you are caught, and the punishment must sting. Keep in mind that corporations can face up to $100 million in fines if they are found criminally liable for participating in a price-fixing conspiracy. Individuals can face up to 10 years in prison and $1 million in fines.

But here, in a conspiracy that spread across the country and allegedly cost renters billions per year, three of the eight named defendants were let off the hook with less than a slap on the wrist. They are only required to stop doing what they shouldn’t have been doing in the first place.

All is not lost, however. The settlement is between the DOJ and RealPage only. It is highly likely that the ten plaintiff states in this case are currently in settlement discussions with RealPage and the remaining defendant landlords. The states have the opportunity to send a clear and strong message that this type of conduct will not be tolerated and those who are caught breaking the law will face painful consequences. Perhaps they should demand partial disgorgement of the ill-gotten gains. That would make the next guy think twice before entering into a price-fixing conspiracy.

Amazon’s Growing Role in Public Purchasing Shows the Dangers of Algorithmic Pricing

By Stacy Mitchell, Co-Executive Director at the Institute for Local Self-Reliance

Amazon is quietly engineering a profound shift in how cities and school districts buy routine supplies. The company has persuaded thousands of local governments to abandon competitive bidding and fixed-price contracts — the core safeguards of public-sector purchasing. In their place, agencies have signed agreements with Amazon Business, the company’s corporate- and government-facing platform, that specify dynamic pricing. As one agreement puts it, “This contract will not need to be amended when prices fluctuate.”As a result, cities and school districts that rely on Amazon pay wildly varying prices for the same items, even on the same day. On January 10, 2023, the Pittsburgh Schools bought two cases of Kleenex for $57.99 each; that same day the Denver Schools paid just $36.91 for a single case. On September 15 that year, two Iowa City, Iowa, school employees each ordered identical cartons of FritoLay snacks. One paid $26.22, the other $34.31.

My colleagues and I uncovered thousands of similar examples as part of an investigation into Amazon’s growing role in public purchasing. We analyzed transaction records from 128 cities, counties, and school districts, reviewed contract documents, and interviewed public officials and vendors. The findings are detailed in a new report, Turning Public Money into Amazon’s Profits: The Hidden Cost of Ceding Government Procurement to a Monopoly Gatekeeper.

We estimate that local governments spent $2.2 billion with Amazon Business in 2023 — nearly quadruple what they spent (after adjusting for inflation) in 2016.

For the vast majority of this spending, what governments pay on Amazon Business is determined by whatever Amazon’s algorithms decide to charge at the moment a public employee clicks “buy.” Because these prices are constantly shifting and Amazon provides no transparency into how they are set, public agencies cannot readily discern when they’re being overcharged.

While it’s impossible to know how much Amazon’s pricing systems may be inflating costs, the evidence suggests the impact could be substantial. We found that the Denver Public Schools, which spent $5.7 million with Amazon in 2023, could have saved 17 percent — roughly $1 million — had the district consistently received the lowest prices observed in our dataset.

How has Amazon persuaded local officials to sign up for opaque and fluctuating pricing? Its pitch is that its marketplace provides all the competition needed. “Amazon Business makes it easy for public sector customers to find the price competition they need — in a marketplace that shows offers from multiple sellers,” the company tells governments. Many officials have accepted this idea.

But while Amazon’s platform might seem to have the hallmarks of genuine competition, it is, in fact, only a semblance of it.  Its seller fees — which average 45 percent of each sale in the U.S. — impose high costs that sellers must pass along. And its own pricing algorithms, along with the automated repricing tools used by many third-party sellers, are built to maximize profits.

Recent academic work shows that multiple pricing algorithms in an online market can interact in ways that drive prices up rather than down, especially when one firm dominates the market. With the ability to adjust at lightning speed, a firm like Amazon can instantly undercut any competitor that lowers its price, teaching rivals that discounting will not win them additional market share. Sellers learn to keep prices elevated.

The contrast with established procurement norms could not be more stark. Traditionally, local governments solicit competitive bids. To win a contract, suppliers compete to offer the lowest prices they can in exchange for a multi-year commitment. This process not only locks in low prices and appropriate volume discounts but also provides transparency: Bid documents are public records, allowing anyone to see how a contract was awarded and at what prices.

As Amazon’s reach grows, the small and mid-sized businesses that have long supplied schools, cities, and other public institutions are disappearing, along with the local jobs and tax revenue they create. Over the last decade, the number of independent suppliers of office products and janitorial goods has fallen from 1,300 to 900. Contrary to common assumptions, we found that these suppliers often beat Amazon on price and delivery speed. As their numbers shrink, Amazon’s leverage over schools and cities will only intensify.

Two policy steps are essential. First, cities must restore competition to their procurement systems and deliberately seek out local suppliers, who are a crucial source of market diversity in an increasingly consolidated landscape.

Second, states must ban dynamic pricing in public procurement and require all contracts signed by local governments and state agencies to specify fixed prices. Without such safeguards, public institutions will become increasingly beholden on an opaque, algorithmic pricing regime designed to benefit a single dominant company at the expense of taxpayers.