The Evolving Economic Structure of Higher Education
Prepared for the Understanding Education in the United States: Its Legal and Social Implications Symposium held at the University of Chicago Law School on June 17 and 18, 2011. I am grateful to participants in that conference for helpful comments and to Gabrielle Holburt, Alice Hwang, Yuan Ji, Christine Ku, Jimmy Li, and Julie Wang for research assistance. I am also grateful to the University of Chicago Law Review for excellent editorial assistance.
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As an industry, higher education shows some striking anomalies. It is the only important industry in the United States that has been increasingly socialized in recent decades: the market share of public institutions grew, roughly, from 50 percent to 80 percent over the last half of the twentieth century. Now the industry is suddenly heading down a very different path, with the market share of proprietary institutions—which was negligible thirty-five years ago—presently in excess of 10 percent. Such contradictory developments bring into question the future economic organization of higher education and the forms of financing and regulation that might be appropriate to it. I’ll offer here some speculation in that direction, focusing just on several of the main structural features of the industry and, of necessity, addressing them at a very general level. And I’ll compare the evolution of higher education with the evolution of health care, seeking the reasons for both similarities and differences. The general conclusion I offer, perhaps unsurprising in the current political and economic climate, is that market forces are likely to play a much larger role in higher education in the future than they have in the past, and a larger role than they play in health care as well.
We thank Bruce Ackerman, Lucian Bebchuk, Robert Ellickson, Daniel Epps, Edward Fox, Jens Frankenreiter, Clayton Gillette, Brian Highsmith, Noah Kazis, Reinier Kraakman, Zachary Liscow, Jon Michaels, Mariana Pargendler, and David Schleicher, as well as those who provided feedback from presentations at Yale Law School and the annual meeting of the American Law and Economics Association. We also thank Josh Kaufman, Daniella Apodaca, Jonah Klausner, and the other editors of the University of Chicago Law Review for their excellent feedback on both substance and style.
We thank Bruce Ackerman, Lucian Bebchuk, Robert Ellickson, Daniel Epps, Edward Fox, Jens Frankenreiter, Clayton Gillette, Brian Highsmith, Noah Kazis, Reinier Kraakman, Zachary Liscow, Jon Michaels, Mariana Pargendler, and David Schleicher, as well as those who provided feedback from presentations at Yale Law School and the annual meeting of the American Law and Economics Association. We also thank Josh Kaufman, Daniella Apodaca, Jonah Klausner, and the other editors of the University of Chicago Law Review for their excellent feedback on both substance and style.
When one thinks of government, what comes to mind are familiar general-purpose entities like states, counties, cities, and townships. But more than half of the 90,000 governments in the United States are strikingly different: They are “special-purpose” governments that do one thing, such as supply water, fight fire, or pick up the trash. These entities remain understudied, and they present at least two puzzles. First, special-purpose governments are difficult to distinguish from entities that are typically regarded as business organizations—such as consumer cooperatives—and thus underscore the nebulous border between “public” and “private” enterprise. Where does that border lie? Second, special-purpose governments typically provide only one service, in sharp contrast to general-purpose governments. There is little in between the two poles—such as two-, three-, or four-purpose governments. Why? This Article answers those questions—and, in so doing, offers a new framework for thinking about special-purpose government.
We thank Lucian Bebchuk, Alon Brav, Ryan Bubb, Ed Cheng, Quinn Curtis, Elisabeth de Fontenay, Jared Ellias, Jill Fisch, Joe Grundfest, Cam Harvey, Scott Hirst, Colleen Honigsberg, Marcel Kahan, Louis Kaplow, Jonathan Klick, Brian Leiter, Saul Levmore, Dorothy Lund, John Morley, Mariana Pargendler, Elizabeth Pollman, Roberta Romano, Paolo Saguato, Holger Spamann, George Vojta, and Michael Weber for valuable suggestions and discussions. This Article has benefited from comments by workshop participants at Columbia Law School, George Mason University Antonin Scalia Law School, Georgetown University Law Center, Harvard Law School, Stanford Law School, UC Berkeley School of Law, the University of Chicago Law School, the University of Oxford Faculty of Law, the University of Pennsylvania Carey Law School, the University of Toronto Faculty of Law, the University of Virginia School of Law, and the Washington University School of Law, as well as at the American Law and Economics Association Annual Meeting, the Corporate & Securities Litigation Workshop, the Labex ReFi-NYU-SAFE/LawFin Law & Banking/Finance Conference, and the Utah Winter Deals Conference. Robertson gratefully acknowledges the support of the Douglas Clark and Ruth Ann McNeese Faculty Research Fund. Katy Beeson and Levi Haas provided exceptional research assistance. All errors are our own.
We thank Lucian Bebchuk, Alon Brav, Ryan Bubb, Ed Cheng, Quinn Curtis, Elisabeth de Fontenay, Jared Ellias, Jill Fisch, Joe Grundfest, Cam Harvey, Scott Hirst, Colleen Honigsberg, Marcel Kahan, Louis Kaplow, Jonathan Klick, Brian Leiter, Saul Levmore, Dorothy Lund, John Morley, Mariana Pargendler, Elizabeth Pollman, Roberta Romano, Paolo Saguato, Holger Spamann, George Vojta, and Michael Weber for valuable suggestions and discussions. This Article has benefited from comments by workshop participants at Columbia Law School, George Mason University Antonin Scalia Law School, Georgetown University Law Center, Harvard Law School, Stanford Law School, UC Berkeley School of Law, the University of Chicago Law School, the University of Oxford Faculty of Law, the University of Pennsylvania Carey Law School, the University of Toronto Faculty of Law, the University of Virginia School of Law, and the Washington University School of Law, as well as at the American Law and Economics Association Annual Meeting, the Corporate & Securities Litigation Workshop, the Labex ReFi-NYU-SAFE/LawFin Law & Banking/Finance Conference, and the Utah Winter Deals Conference. Robertson gratefully acknowledges the support of the Douglas Clark and Ruth Ann McNeese Faculty Research Fund. Katy Beeson and Levi Haas provided exceptional research assistance. All errors are our own.
We thank Lucian Bebchuk, Alon Brav, Ryan Bubb, Ed Cheng, Quinn Curtis, Elisabeth de Fontenay, Jared Ellias, Jill Fisch, Joe Grundfest, Cam Harvey, Scott Hirst, Colleen Honigsberg, Marcel Kahan, Louis Kaplow, Jonathan Klick, Brian Leiter, Saul Levmore, Dorothy Lund, John Morley, Mariana Pargendler, Elizabeth Pollman, Roberta Romano, Paolo Saguato, Holger Spamann, George Vojta, and Michael Weber for valuable suggestions and discussions. This Article has benefited from comments by workshop participants at Columbia Law School, George Mason University Antonin Scalia Law School, Georgetown University Law Center, Harvard Law School, Stanford Law School, UC Berkeley School of Law, the University of Chicago Law School, the University of Oxford Faculty of Law, the University of Pennsylvania Carey Law School, the University of Toronto Faculty of Law, the University of Virginia School of Law, and the Washington University School of Law, as well as at the American Law and Economics Association Annual Meeting, the Corporate & Securities Litigation Workshop, the Labex ReFi-NYU-SAFE/LawFin Law & Banking/Finance Conference, and the Utah Winter Deals Conference. Robertson gratefully acknowledges the support of the Douglas Clark and Ruth Ann McNeese Faculty Research Fund. Katy Beeson and Levi Haas provided exceptional research assistance. All errors are our own.
For years, academic experts have championed the widespread adoption of the “Fama-French” factors in legal settings. Factor models are commonly used to perform valuations, performance evaluation and event studies across a wide variety of contexts, many of which rely on data provided by Professor Kenneth French. Yet these data are beset by a problem that the experts themselves did not understand: In a companion article, we document widespread retroactive changes to French’s factor data. These changes are the result of discretionary changes to the construction of the factors and materially affect a broad range of estimates. In this Article, we show how these retroactive changes can have enormous impacts in precisely the settings in which experts have pressed for their use. We provide examples of valuations, performance analysis, and event studies in which the retroactive changes have a large—and even dispositive—effect on an expert’s conclusions.
She graduated from Tel-Aviv University and Harvard Law School. Named as one of the most cited legal scholars in the United States, and specifically the most cited scholar in employment law and one of the most cited in law and technology, she is influential in her field. Professor Lobel has served on President Obama’s policy team on innovation and labor market competition, has advised the Federal Trade Commission (FTC), and has published multiple books to critical acclaim. Her latest book, The Equality Machine, is an Economist Best Book of the Year.
This Essay argues for the development of more robust—and balanced—law that focuses not only on the risks, but also the potential, that AI brings. In turn, it argues that there is a need to develop a framework for laws and policies that incentivize and, at times, mandate transitions to AI-based automation. Automation rights—the right to demand and the duty to deploy AI-based technology when it outperforms human-based action—should become part of the legal landscape. A rational analysis of the costs and benefits of AI deployment would suggest that certain high-stakes circumstances compel automation because of the high costs and risks of not adopting the best available technologies. Inevitably, the rapid advancements in machine learning will mean that law soon must embrace AI; accelerate deployment; and, under certain circumstances, prohibit human intervention as a matter of fairness, welfare, and justice.