Researchers discuss how policymakers can help companies predict future government actions.
Accurate forecasting of government actions helps businesses minimize costs and operate efficiently. But as Yogi Berra said, it’s hard to make predictions, especially about the future. Companies often lack the information needed to predict regulatory and legislative changes.
In response, the government should help generate and share this information, conclude Jonathan S. Masur of the University of Chicago Law School and Jonathan Remy Nash of Emory University School of Law. In an article from Indiana Law ReviewMasur and Nash argue that policymakers should consider forcing regulated entities to predict the likelihood of future government action and to publicly disclose any information they generate from that prediction.
These “forcing prediction” regulations could help companies make more effective decisions by aggregating predictive information from other companies. Without mandatory disclosure rules, however, this information remains widely dispersed among regulated entities and is only available in bits and pieces.
Masur and Nash call this dispersion the “assembly problem”. When the government considers new regulation, each regulated entity develops its own slice of predictive information. As a result, these entities cannot easily combine their information into a complete whole. But if properly enforced, regulations forcing prediction could solve this problem, according to Masur and Nash.
For example, a 2010 guidance document issued by the US Securities and Exchange Commission directs public companies to disclose “MD&A” of financial risks related to future climate regulation. Therefore, a regulated company – known as a reporter – must predict whether it is “reasonably likely” that pending legislation or regulation will be enacted and, if so, disclose the likely impacts of that action on the financial situation of the company. In theory, then, as reporters divulge their predictions, other reporters could use this information to improve the accuracy of their own predictions.
Although the guidance document did not increase publicly available predictive information, Masur and Nash attribute this result to under-enforcement, manipulation by filers, and relatively small effects of climate change on filers’ operations. .
Nonetheless, Masur and Nash argue that policy makers should prefer regulations forcing prediction over other methods of promoting regulatory prediction, such as greater government transparency or the creation of property rights for predictive information.
Although Masur and Nash suggest that greater transparency would be the most effective way for government to improve private regulatory forecasting, they conclude that regulators could not implement effective transparency measures. They fear that decision-makers characterize their actions in a misleading way to avoid disclosing information. For example, a law requiring an agency to release transcripts of all official meetings would likely encourage the agency to communicate more often in informal settings. Even if rules prevent this “leakage,” Masur and Nash argue that greater transparency could reduce the effectiveness of legislation and regulation by preventing government actors from cooperating effectively on policy initiatives.
Masur and Nash also reject the idea that creating property rights for predictive information would improve regulatory forecasting. On the contrary, Masur and Nash assert that these rights are essentially unenforceable. Because companies use regulatory information to influence other corporate decisions, it is difficult to prove whether a company based a decision on information held by another company.
Market-based reforms also fail to promote the dissemination of regulatory information, conclude Masur and Nash. Some researchers argue that prediction markets – which pay participants based on achieving certain outcomes – can be successful in aggregating information. But Masur and Nash worry that the likelihood of government action is too abstract for investors to assess accurately, leaving prediction markets vulnerable to “thinness” – a lack of liquidity due to lack of participation – and manipulation by insiders.
Similarly, Masur and Nash reject regular capital markets as a viable means of aggregating predictive information. Although regular markets are very liquid and difficult to manipulate, they do not facilitate the sharing of predictive information. Unlike securities in predictive markets, the value of stocks in a regular market depends on many factors which may or may not include a company’s exposure to regulatory risk. Thus, companies cannot infer from stock prices any information about the likelihood of future regulation.
Without better options, Masur and Nash settle for regulation forcing the prediction.
Regulations forcing forecasts, however, could deteriorate the quality of government action, concede Masur and Nash. Because they require a company to disclose its forecast only when the likelihood of government action is reasonably high, regulations forcing the forecast encourage companies to lobby to reduce the likelihood of unwanted regulation. If lobbying costs less than producing and disclosing predictions, companies will use their resources to circumvent regulation rather than creating and sharing predictive information.
These incentives reward companies that thwart socially beneficial regulation for their own benefit. Policymakers must then consider whether the loss of such regulation outweighs the benefits of improved business predictions about future government action, Masur and Nash advise.
