Algorithms and AI Can Make Hiring More Diverse
The cost is likely minimal to achieve a fairer outcome.
Algorithms and AI Can Make Hiring More DiverseChris Gash
Teva Pharmaceuticals, one of the world’s biggest makers of generic medicines, has spent the better part of a decade fighting off suspicions and then allegations of price-fixing. In 2014, Connecticut’s attorney general began investigating suspected price-fixing by Teva and its competitors in a probe that initially centered on just a few drugs but expanded to more than 100. Prices for these had risen sharply—often 50 percent or more—after the alleged collusion began in the wake of Teva hiring a key marketing executive. This led to lawsuits filed by dozens of states as well as the US Department of Justice. In January, Teva agreed to pay $420 million to settle related shareholder litigation.
But prosecutors, along with patients and shareholders, were not the only ones to notice the price hikes. Other drug manufacturers saw an opportunity to win market share by undercutting the cartel pricing, according to research by Northwestern’s Amanda Starc and Chicago Booth’s Thomas Wollmann. As soon as the prices on certain drugs started to rise, the research demonstrates, applications to the Food and Drug Administration to make these drugs shot up too, by 30–40 percent.
The findings suggest that market dynamics can play an important role in undermining long-running collusion. Moreover, given the speed with which companies react, new market entrants can do a faster job of reducing prices for customers than can antitrust lawsuits. Starc and Wollmann point out that the inflated drug prices persisted well after the Connecticut investigation began, and even as other US states joined the case, as public scrutiny rose, and as Teva’s stock price sank. In contrast, when a new manufacturer started producing one of the cartel’s drugs, that drug’s price tag dropped swiftly.
The stumbling block is regulatory delays. “The approval process [for a single drug] can take years and cost millions,” the researchers write. Specifically, for an average drug in the Teva case, the FDA approval process took several years and cost $3.2 million, the researchers find. As a counterfactual experiment, they modeled reducing those costs by $400,000–$800,000 per application, and find this would have translated to $142 million–$347 million in savings for customers (or their insurers—often Medicare or Medicaid). If approval times were one to two years shorter, consumers would have saved between $600 million and $1.5 billion in total.
Governments might guard against the negative effects of health-care consolidation not just by increasing antitrust actions, but also by making it easier for competitors to enter particular markets. Yet reducing barriers to entry might have its own set of drawbacks, warn Starc and Wollmann: “Lower fees may draw resources away from other oversight activities, while quicker approvals may require additional staff (or . . . result in lax in enforcement).”
Moreover, in the Teva case, some price-fixed drugs were not profitable enough to lure new competitors to market. For these and others like them, regulators would have to speed up approval or drop drug-application fees dramatically if they wanted to rely on market forces to disrupt cartels. Absent that, customers and taxpayers will have to hope their losses are eventually stemmed by lawsuits and compensation is awarded in court.
When generic-drug maker Teva Pharmaceuticals’ alleged collusion drove up prices, competitors noticed and entered the markets.
Amanda Starc and Thomas Wollmann, “Does Entry Remedy Collusion? Evidence from the Generic Prescription Drug Cartel,” Working paper, March 2022.
The cost is likely minimal to achieve a fairer outcome.
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