Andre Diegmann, Halle Institute for Economic Research
Nicolas Serrano-Velarde, Università Bocconi
Abstract: This paper investigates a unique policy designed to maintain employment during the privatization of East German firms after the fall of the Iron Curtain. The policy required new owners of the firms to commit to employment targets, with penalties for non-compliance. Using a dynamic model, we highlight three channels through which employment targets impact firms: distorted employment decisions, increased productivity, and higher exit rates. Our empirical analysis, using a novel dataset and instrumental variable approach, confirms these findings. We estimate a 22\% points higher annual employment growth rate, a 14\% points higher annual productivity growth, and a 3.6\% points higher probability of exit for firms with binding employment targets. Our calibrated model further demonstrates that without these targets, aggregate employment would have been 15\% lower after 10 years. Additionally, an alternative policy of productivity investment subsidies proved costly and less effective in the short term.
Discussant: Maddalena Ronchi, Northwestern University
Abstract: We study the implications of ownership type in infrastructure privatization, focusing on global airports over a 25-year period. Surprisingly, privatization in general does not improve airport performance, though foreign firms appear good at selection. However, private equity ownership leads to higher volume, efficiency, and quality. The disparities across ownership types are related to local state capacity, competition, and the owner’s ability to invest capital and negotiate with airlines. Overall, private equity’s high-powered incentives and access to capital seem to add value, while the essential and salient nature of airports may lead to decent performance under government ownership.
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.
