Eric L. Talley (Columbia University – Law School; European Corporate Governance Institute (ECGI)) and Rhys Lester Talley (Columbia University – The Fu Foundation School of Engineering and Applied Science) have posted You’ve Got a FRAND in MLE: Coaxing Fair Prices from the Shadow of a Liability Rule on SSRN. Here is the abstract:
Licensing permeates modern markets. Yet negotiating licensing terms can be costly and cumbersome. Accordingly, several market structures have embraced (or are contemplating embracing) a ‘liability rule’ regime, in which a would-be licensee holds a real option: negotiate consensual terms with the asset’s owner; or appropriate the asset non-consensually, compensating the owner at an externally determined rate. That background rate is periodically calibrated by a third-party adjudicator (typically a regulator or court), whose typical mandate is to approximate the fair, reasonable, and non-discriminatory (‘FRAND’) terms that a willing buyer and willing seller (‘WBWS’) would hypothetically reach in the absence of a liability rule. While a liability-rule structure is theoretically attractive, the task of setting—and resetting—background rates has long vexed regulators and courts. In confronting this challenge, adjudicators often attempt to calibrate against rates observed in negotiated transactions executed prior to the new rate determination. But those deals were themselves typically struck in the ‘shadow’ of the preexisting background rate, which can distort both bargaining fallbacks and outcomes. Although courts and regulators readily acknowledge this circularity, they just as readily disregard it, citing the absence of a reliable method to correct for a liability rule’s shadow. This paper supplies that method. We develop a structural bargaining framework in which heterogeneous buyers and sellers are randomly matched and negotiate in the shadow of a prevailing background rate, generating a distribution of equilibrium transactions, both negotiated and non-consensual. We demonstrate that under weak conditions, the distribution of negotiated rates lies below the counterfactual WBWS counterpart—not just on average, but at every quantile. We then propose a correction, via a maximum likelihood estimation (‘MLE’) approach that recovers the pricing-relevant features and reliably simulates the WBWS counterfactual. Our methodology largely resolves the circularity problem, extends naturally to a variety of institutional features, and performs well in simulations.
Highly Recommended!
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