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The Banality of Price Fixing
Software-enabled price fixing schemes are pervasive in the economy, and are likely driving high prices. Why is it so hard to challenge these conspiracies under antitrust law?
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Hi, I’m Lee Hepner, an antitrust lawyer filling in for Matt Stoller. Today’s issue is about the pervasive software-enabled price fixing that is driving up the cost of everything from food to travel to rent, and the attempts to challenge it under antitrust law.
It’s a topic Matt has written about a lot, under the topic of unfair pricing. I wanted to take that conversation a step further and describe why - despite popular awareness of the problem - policymakers haven’t addressed it. The unenforceability of price fixing laws isn’t inevitable, and there are things that judges and regulators can do now to rid the monster from the machine and curb the pattern of spiraling prices.
So that’s what I wrote about below. But first, I wanted to highlight something we’ve been noticing in both the Google monopolization trial, and across the legal system. Secrecy. I wrote about the problem of secretive trials for The Hill. There’s a little-known body called the Judicial Conference that sets rules for courts, and I’m calling on them to create 21st-Century standards for making important trials accessible to all of us. This week, we’ve seen signs that Judge Amit Mehta, who has hidden broad swaths of the Google trial, is feeling the public pressure. He’s been taking a stronger position against Google’s efforts to seal testimony and exhibits from public view. That’s a welcome development.
The Banality of Price Fixing
The term “rainmaker” is appropriated from Native American culture for the English language to mean someone or something capable of producing money, often through artificial means. In 2006, rappers Fat Joe and Lil Wayne wrote a hit song about “making it rain,” a euphemism for throwing stacks of money into the air and watching it fall – like rain – onto its intended recipients. Not coincidentally, Rainmaker is also the name of a corporation that has, according to a federal lawsuit pending in Nevada, provided software algorithms to hotels on the Las Vegas Strip, enabling them to illegally fix prices. In other words, an artificial means of producing more money for oligopoly corporations.
Rainmaker may very well be a great name for attracting clients interested in making more money. But it’s a terrible name for avoiding allegations that your software is designed to maximize revenue by eliminating price competition and fleecing consumers. That’s the position Rainmaker finds itself in now, alongside popular Vegas Strip hotels Caesars, MGM, Treasure Island, and Wynn, facing claims that it has facilitated a price fixing conspiracy in clear violation of federal antitrust laws.
The Vegas hotels lawsuit describes how, prior to their use of Rainmaker, in a competitive and lawful market, hotels would price rooms independently based on their own assessment of how best to compete with other hotels. In a competitive market, empty rooms are lost revenue, so hotels had historically tried to fill rooms by granting discounts or comped rooms. In Vegas, where gaming revenue is significantly higher than room revenue, they can afford to do so. But in the eyes of the magic revenue generator Rainmaker, that’s money left on the table - a human error that Rainmaker is game to solve. The house always wins, as they say, but Rainmaker offered a tool for hotel casinos to win bigger.
In the past decade, a growing body of research has shown that algorithmic pricing - like the software offered by Rainmaker - produces extra high prices across industries. Matt has written about this over the years, and the halls of academia have seized on the political moment, too. Uber’s “surge pricing” is among the most-studied examples, if not also the most annoying for consumers who regularly use the service. In 2015, the New Yorker posited that “bot-driven price-fixing is more prevalent than the lack of prosecutions suggests.” A blockbuster report from ProPublica describes the use of landlord price fixing software by a company called RealPage which apparently helps increase rents. At a recent real estate conference in Nashville, when asked about the impact of YieldStar on 14.5% rent increases in some markets, RealPage VP Andrew Bowen responded, “I think it’s driving it, quite honestly.”
Economists have been slow to catch up with legal scholarship and the actual economy. For years economists have dismissed the prevalence of algorithmic price fixing as something that was unlikely to arise in real world conditions. But an economic analysis published this year confronts that earlier literature with an empirical analysis of gasoline prices in Germany. The study found that algorithmic price setting software increased margins by 9%, costing Germans as much as 500 million Euros per year since 2017. It marks a breakthrough in the prior consensus that algorithmic price fixing is the subject of mere hypotheticals, and it undergirds the emerging consensus that inflation must also be studied as a consequence of corporate profiteering.
In fact, algorithmic price setting appears more ubiquitous than not. A simple internet search for price setting software produces dozens of companies, like PriceFX, Feedvisor360, and SmartPricing, each of which advertises its use of data-driven machine learning and algorithmic repricing to “bolster efficiency and profitability” for their clients. Several of these tools are aimed directly at people who sell on Amazon, which has become a hotbed for machine-driven dynamic pricing. The sheer availability of “price optimization” software, which according to their own advertisements is designed to raise prices on consumers, is an under-discussed factor in the ongoing conversation about inflation—or “greedflation.” Understanding this opportunistic corporate profiteering is among the barriers to reversing the trend.
Of course, the use of software to optimize pricing isn’t always, or even often, illegal. Comparing your rival’s public price to your own price is a perfectly reasonable and time-tested strategy, and that’s not any different in the era of big data. However, using software to privately collude with rivals on pricing or output, to allocate markets or set floors or ceilings on prices, is within the orbit of what antitrust laws are designed to address. And that’s for good reason - software-enabled price fixing is likely responsible for higher prices everywhere from the gas pump to the grocery store.
In case we need any more signposts, all the big consulting firms, led by McKinsey, are now encouraging the adoption of comprehensive ‘pricing tech,’ carefully sculpted to ensure it doesn’t hint at legal liability.
No Agreement, No Problem?
People generally understand that corporations are colluding. A 2022 survey found that 63% of voters, including majorities across political affiliation, believed that large corporations had taken advantage of the pandemic to unfairly raise prices and increase their own profits. A whopping 80% of voters said they want the government to “crack down on large corporations that raise prices unfairly.” And, of course, there are laws against this. In fact, horizontal price fixing—that is, price coordination between competitors—is among the limited categories of corporate conduct that are considered unlawful without having to prove much beyond the fact that there is price-fixing. (This is very different than most antitrust law, which requires lots of expert economic debate and is decided based on whether the judge finds it ‘reasonable.’)
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It flows from the mounting evidence of harm, the increased public awareness that people are being gouged by coordinated corporate market power, and legal theories developing in courtrooms across the country that these cases should be succeeding. But, in large part, they aren’t. Naturally, one should expect a higher rate of success in cases involving markets that are already highly concentrated, where few if any reasonable substitutes exist to discipline cartel-like pricing schemes. One might expect even greater success in markets characterized by inelastic demand, where consumers’ willingness and ability to pay are less affected by price increases. And one could expect success against companies like Rainmaker and YieldStar, which openly flaunt their ability to hike prices above competitive levels, squeezing consumers and enriching their investor clients.
We need to talk about is why, despite all of this, price fixing cases are so tough to bring. Since Matt wrote his article earlier this month, the judge overseeing the Rainmaker case threw it out, albeit with an opportunity for the plaintiffs to submit a revised complaint. In the RealPage rent-hiking case, plaintiffs are facing an almost identical attack on their initial pleadings, and it will be up to a federal court judge next month to either allow that matter to proceed or toss it out in the face of significant evidence of rent gouging. In another setback, an Illinois jury this week failed to hold poultry giant Sanderson Farms liable for conspiring to raise prices by sharing non-public information about boiler prices with third-party firm Agri Stats, which may have implications for a separate Department of Justice suit against Agri Stats in the pork, chicken, and turkey industries.
The roadblock in these cases, like so many other price fixing cases, is how to determine whether there’s an actual agreement among competitors to fix prices. Section 1 of the Sherman Act proscribes every “contract, combination… or conspiracy, in restrain of trade.” And while “conspiracy” might seem to invoke a broader level of speculation, it has conventionally been understood to mean a “meeting of the minds;” or, an agreement.
And that’s how colluding with software becomes legal in the minds of some judges. In a 13-page ruling dismissing the Rainmaker case, for instance, Chief Judge Miranda Du of the Nevada District Court, an Obama appointee, finds that the parties failed to allege with enough specificity that the hotel operators had actually agreed with each other to artificially inflate the prices of hotel rooms. Judge Du couldn’t determine, for instance, whether the hotel chains used the same exact pricing algorithm, even though they all entered into agreements with Rainmaker. Judge Du also dinged the complainants for not having proof that the hotel chains actually accept Rainmaker’s recommendations – never mind that Rainmaker itself advertises that its recommendations are accepted 90% of the time.
These are fatal misgivings, according to Judge Du. But it’s hard not to perceive Judge Du’s demands for specificity as requiring ultimate proof of the allegations at the infancy of the case, before plaintiffs can even get their foot in the door. This interpretation of the law of price fixing creates a Catch-22: Plaintiffs need to have more specific evidence of an unlawful conspiracy before they can begin the litigation discovery process to get that same evidence.
Related, how does one prove the existence of an agreement that is, for all intents and purposes, being made by a machine based on nonpublic information provided by the hotel chains? This isn’t exactly the olden days, when executives sat in smoke-filled rooms and agreed to set price floors for the mutually assured preservation of their respective profits. On the other hand, it might be something like the automated version of traditional trade associations, where competitors have long gathered to share industry standards and business strategies that, at best, protect members of the public from nascent harms and, at worst, enhance profits through unfair and anticompetitive means. Pricing fixing laws have always applied to trade associations, despite Clinton Administration efforts to immunize healthcare systems from sharing sensitive price data. That safe harbor was repealed earlier this year, amid greater federal scrutiny of information exchanges among competitors.
A brief but relevant aside about Judge Du: In 2021, Judge Du made headlines for declaring unconstitutional a 90-year-old federal statute making it a crime for immigrants to return to the United States following deportation. Judge Du’s decision was a welcome surprise to advocates who had seen prior equal protection challenges to the statute fail. As one former Nevada federal public defender who had seen hundreds if not thousands of cases brought under the controversial statute put it: “This is a big deal. The issue was one that nobody thought you could win.” And yet, Judge Du found a way, declaring that “at no point [had] Congress confronted the racist, nativist roots” of the law.
It's not entirely fair to equivocate between disparate cases and bodies of law, each with their own wildly different fact patterns, distinct legal theories, and binding precedent. But the point is, judges have discretion, and the laws that are made by Congress are constantly being shaped, re-written, repealed, and reinforced by judges. And for decades, judges have been re-writing federal antitrust laws like the Sherman Act to make price fixing claims – like those against Rainmaker and YieldStar – nearly impossible to bring. If judges are equipped to rectify the injustices of century-old immigration laws, they should bring those same tools to bear to restore the intent of century-old antitrust laws.
One thing should be clear: allowing price fixing to occur through shared software rather than an explicit agreement is not just a technological change — it’s a legal change. Indeed, judges are simply rewriting the law to legalize price-fixing. When it’s extremely hard to bring a complaint because the burden is just too high for plaintiffs - as is happening in the Rainmaker case and potentially the RealPage litigation - judges have created a liability shield for corporations to collude. “Nobody thought you could win” is a devastating and avoidable sentiment in the face of clear and pervasive harm.
The moment calls for a judicial response, and a regulatory response if judges won’t apply the Sherman Act as intended. Judges, notoriously impervious to the thrusts of reality, can and should exercise discretion to avoid re-writing the law to provide immunity to price fixing, as long as it occurs by algorithm. This doesn’t mean defendants shouldn’t have an opportunity to defend themselves. Rather, it means defendants should have to defend themselves, instead of writing off complaints for lacking a degree of specificity that cannot be pled.
Should that prove inadequate, lawmakers at the federal and state level should enact legislation that better reflects the challenge of proving the existence of illegal agreements embedded in code. Over a year ago, my workplace, the American Economic Liberties Project, announced model legislation to solve for the Catch 22 problem that prevents plaintiffs from making it past the initial pleading stages. That legislation would, among other things, shift the burden of proof to defendants to argue that they are not, in fact, working together to fix prices. It would allow plaintiffs in the Rainmaker case to obtain information about how Rainmaker’s algorithm works and the types of data it uses to provide pricing “recommendations.” Absent access to that information, plaintiffs are reduced to perennial underdogs against quasi-cartels who may otherwise never have to defend themselves.
The issue may also be addressed through regulation of the types of data that third-party software companies are allowed to collect and process. Just as the federal antitrust agencies have historically established guardrails for trade associations, so too can they provide guardrails and outright prohibitions on the processing of sensitive data that can be used to facilitate intricate price fixing schemes.
In 2017, Acting Chair of the Federal Trade Commission Maureen K. Olhausen delivered a speech on the use of computer algorithms to automate decision-making by market participants. This is how she articulated the issue now confronting courts and policymakers across the country:
“Is it okay for a guy named Bob to collect confidential price strategy information from all the participants in a market, and then tell everybody how they should price? If it isn’t ok for a guy named Bob to do it, then it probably isn’t ok for an algorithm to do it either.”
The problem is clear, and the solutions are at our fingertips. Six years after Olhausen’s speech, as profits and prices remain high, the issue is more than ripe for addressing.
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