Eric Talley is the Isidor & Seville Sulzbacher Professor of Law at Columbia Law School. This post is based on a recent paper forthcoming in the University of Pennsylvania Law Review, authored by Prof. Talley; Jens Frankenreiter, Postdoctoral Fellow in Empirical Law and Economics at the Ira M. Millstein Center for Global Markets and Corporate Ownership at Columbia Law School; Cathy Hwang, Professor of Law at the University of Virginia School of Law; and Yaron Nili, Assistant Professor of Law at the University of Wisconsin-Madison Law School. Related research from the Program on Corporate Governance includes Learning and the Disappearing Association between Governance and Returns, by Lucian Bebchuk, Alma Cohen, and Charles C.Y. Wang (discussed on the Forum here); and What Matters in Corporate Governance? by Lucian Bebchuk, Alma Cohen, and Allen Ferrell.
In the iconic 1994 Tarantino film Pulp Fiction, Harvey Keitel makes a brief yet memorable appearance as Winston Wolfe (a.k.a., the “Cleaner”). His forte? Tidying up the inconvenient (and usually gruesome) messes perpetrated by others. Wolfe’s modus operandi was never pretty and rarely polite; but it was invariably effective.
Empirical corporate governance needs its own Winston Wolfe. Over the last thirty years, the field has risen in prominence by quantifying what was traditionally thought unquantifiable—text from state laws, federal regulations, and firm-level governance documents—to measure the quality of governance. Canonical studies have shown that countries with strong investor protections are more likely to have higher firm valuations, that more shareholder-friendly firms outperformed more management-friendly ones, and numerous other significant real-world predicted effects of governance on firm performance.
But beneath the field’s orderly veneer lurks an unsettling vulnerability: three decades of finance, economics, and legal studies in corporate governance have been built substantially on data sets with nearly unknown provenance.