Follow the Pipeline: Anticipatory Effects of Proposed Regulations

Abstract

Anecdotal evidence suggests that firms anticipate regulatory actions long before the proposed regulations are finalized. Applying a novel machine-learning algorithm to a new dataset, we provide the first large-sample evidence of substantial anticipatory effects. The granular data set tracks the entire rulemaking activity of all federal agencies since 1995. Out of 41,000 rule proposals, only two-thirds converted into a final rule, and they did so after spending two years on average in the rulemaking pipeline. We track the timeline of each proposed rule, assign proposed rules to firms based on a machine-learning algorithm, and derive a firm-level measure of exposure to the regulatory pipeline: the amount of rule proposals which are relevant to the firm. We find that firm-level exposure to the regulatory pipeline has significant anticipatory effects. Firms with greater exposure express more concerns about future political risk, increase their overhead costs, and see lower profits. To prepare for the anticipated regulatory changes, firms spend more on lobbying, build up cash reserves, and reduce capital investment. The effects are independent of the firm’s current regulatory burden and are driven by rule proposals that are more likely to convert into final rules. Financially constrained and small firms are especially responsive to the regulatory pipeline, which highlights the role of budget constraints and economies of scale. Our results are the first to consistently document anticipatory effects based on the entire body of potential federal regulations