The Legal Salience of Taxation
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Before an injury becomes a legal dispute, the injury must be named, a party must be blamed, and a right against that injury must be claimed. What motivates people to do these things and use legal institutions to seek redress? I provide a partial answer to this question, using a unique dataset to identify the effect that the salience of a tax—that is, its psychological prominence—has on whether a taxpayer will use legal means to lighten the tax’s burden. I term this effect its “legal salience.” I find that reducing property tax salience makes homeowners less likely to appeal their property-value assessments, making it more likely that homeowners will remain overassessed and overtaxed. These overtaxed homeowners never perceive—are never able to “name”—their injury and consequently never obtain the relief to which they might be entitled. Moreover, I show that the selective use of appeals caused by legal salience shifts the tax burden to racial minorities, immigrants, and working families with children. Scholars and lawmakers operate as if only substantive law drives the distribution of a tax burden. But I show that legal salience is one of a number of factors that also affects the tax distribution by motivating only certain individuals to seek tax relief, and I argue that tax laws should be evaluated after taking into account the effects of legal salience.
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.