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The Federal Trade Commission’s (FTC) recent complaint challenging the proposed merger of the supermarkets Kroger Co. and Albertsons Companies Inc. has important implications for antitrust enforcement in labor markets. Central to the FTC’s case is how it chooses to define the relevant markets, and particularly the commission’s focus on unionized grocery workers. The complaint alleges that the combined firm would dominate these markets, substantially lessening competition for unionized labor.

Read the full piece here.

For all my carping about this or that program or enforcement matter, it seems to me a very good thing that Congress passed—and President Joe Biden signed into law—the spending package that will keep much of the federal government up and running for Fiscal Year 2024 (see here for the news, and here and here for a couple of the consolidated appropriations bills just signed into law).

Read the full piece here.

The big news from the agencies may be the lawsuit filed today by the U.S. Justice Department (DOJ) and 16 states against Apple alleging monopoly maintenance in violation of Section 2 of the Sherman Act. It’s an 86-page complaint and it’s just out. I’ll write more about it next week.

Two quick observations: First, the complaint opens with an anecdote from 2010 that suggests lock-in (a hard case under antitrust law), but demonstrates nothing. Second, the anecdote is followed by a statement that “[o]ver many years, Apple has repeatedly responded to competitive threats… by making it harder or more expensive for its users and developers to leave than by making it more attractive for them to stay.” 

Read the full piece here.

ICLE Academic Affiliate Ivan Png produced a video with Yun Hou and Ivan Png  describing a benchmarking experiment they did with food-stalls hawkers in Singapore, which found that showing firm owners their relative performance increased exit among poor performers. The full video is embedded below.

In recent decades, various measures of “business dynamism”—such as new business entry rates and gross job or worker flows—have seen significant declines in the U.S. (figure 1, right panel). Over a similar time frame, there is evidence that an important measure of market power—the average markup—has risen significantly (figure 1, left panel; De Loecker, Eeckhout, and Unger 2020). A natural question is whether these patterns are related.

My last post provided an overview of my draft article The Cost of Justice at the Dawn of AI and explained the basic logic of Baumol’s cost disease for the practice of law. Just as in any other market, if the productivity of lawyers increases at a slower rate than the rest of the economy, legal services will become more expensive. And if a technology like artificial intelligence leads legal productivity to increase at a faster rate than the rest of the economy, then legal services will become cheaper.

It has been a while since my last post on the Volokh Conspiracy. In 2021, I became associate dean at George Washington and did not have time to write. Last year, I switched associate dean roles and my portfolio became smaller, so I was fortunate to have some time to return to scholarship and to complete several articles. I’ll begin my return to blogging by writing a series of posts offering shorter versions of the key arguments in a recently completed article that I have now submitted to law reviews, entitled The Cost of Justice at the Dawn of AI.

Abstract

Enrolling financiers is critical to new venture success. Building on the challenges of communicating novel and complex projects under Knightian uncertainty, we describe two approaches—the opportunity discovery path vis-à-vis the effectuation/bricolage path—entrepreneurs and financiers can use to resolve alternative sources of uncertainty sequentially rather than simultaneously.

Abstract

While modern mathematical models of settlement bargaining in litigation generally seek to identify perfect Bayesian Nash equilibria, previous computational models have lacked game theoretic foundations. This article illustrates how computational game theory can complement analytical models. It identifies equilibria by applying linear programming techniques to a discretized version of a cutting-edge model of settlement bargaining. This approach makes it straightforward to alter some assumptions in the model, including that the evidence about which the parties receive signals is irrelevant to the merits and that the party with a stronger case on the merits also has better information. The computational model can also toggle easily to explore cases involving liability rather than damages and can incorporate risk aversion. A drawback of the computational model is that bargaining games may have many equilibria, complicating assessments of whether changes in equilibria associated with parameter variations are causal.

Read at SSRN.