Carlson School of Management
A large amount of literature in economics, finance, and accounting has studied conditions under which firm insiders (e.g. managers) will voluntarily disclose private information concerning the firm's performance in the absence of a mandatory disclosure policy. The general theoretical result is that in the absence of frictions, all firms will voluntarily disclose their private information to separate themselves from firms with even worse news, e.g. the standard unravelling argument in Milgrom (1982).
The aim of this project is to study a particular friction that may prevent all firms from disclosing their information. The idea is that managers are probabilistically endowed with information (Dye, 1985). Hence investors who observe a firm that does not disclose information are uncertain whether the news is bad, or whether the firm simply does not have the information to begin with. This project will build a tractable structural model of the firm's decision to disclose or withhold information based on Dye (1985). The model allows estimation of key parameters of interest (e.g. the probability the manager is informed) by matching empirical data moment analogues to their theoretical counterparts. The computing challenge is that the researchers are maximizing a complicated log likelihood function and bootstrapping the standard errors. The sample consists of 2,500 firms, and the resulting necessary computing runtime requires the use of supercomputing resources.