A back-ofthe-envelope structural estimator of proprietary costs

This study develops a simple non-parametric structural estimator for the implied disclosure cost in the model of Verrecchia (1983). The estimator is a function of three characteristics of observed disclosures: (i) the minimum disclosure, (ii) the average disclosure surprise, and (iii) the frequency of disclosure. We also show that the estimator will correctly identify zero cost if withholding is driven by uncertainty about information endowment or if disclosures are not strategic. Applying the model to one-year ahead management earnings forecasts, we find positive costs for about two out of ten firms making at least one forecast and estimate, for the median firm in this group, the cost to be about two percent of lagged assets. A decomposition of the cost in terms of economic determinants offers some evidence that the cost captures proprietary competitive information. Litigation risk seems to provide some explanation why some firms exhibit zero costs. Several disclosure models postulate that proprietary costs may cause some firms to withhold information. However, proprietary costs cannot be directly observed because they take the form of either a counterfactual loss of value if the firm had disclosed, or vice-versa, a gain if the firm had withheld information. Identifying this counter-factual cost is thus problematic without further assumptions about disclosure behavior that identify what would have occurred if the disclosure had been different. In this paper, we propose a new identification method that relies on the classic disclosure equation of Verrecchia (1983) in order to identify which firms appear to bear disclosure costs and to quantify the disclosure cost, if any. This approach offers several benefits. First and foremost, it yields a straightforward measure that can be easily implemented in closed-form with publicly-available disclosure data. This simpl measure is, nevertheless, fully structural and derived from theory, thus making the assumptions that led to this inference explicit to researchers. Second, identification requires as few restrictions as possible on the data-generating process. In particular, we do not impose parametric distributional assumptions on the manager’s private information. Further, our estimation procedure consistently estimates the cost even if, in addition to the cost friction, the manager may be sometimes uninformed and unable ∗E. Cheynel is an assistant professor at Columbia University and M. Liuf-Watts is an assistant professor at the New York Institute of Technology. Contact author: Edwige Cheynel, Columbia Business School, Columbia University, 3022 Broadway, New York, NY 10027. Email address: ec2694@columbia.edu. We warmly thank Jeremy Bertomeu, Moritz Hieman, Ivan Marinovic, Korok Ray (discussant), and participants at the Stanford Summer Camp for helpful feedback. 1We only briefly delve into an empirical test as to whether such costs represent proprietary (i.e., competition-related) motives, what is typically referred to as the proprietary cost hypothesis. Testing this hypothesis persuasively is a challenge that goes beyond the scope of this paper and may be infeasible due to the limitations of common empirical proxies for competitive intensity.