Halle Institute for Economic Research,Leibniz Association

We examine thousands of U.S. private equity (PE) buyouts from 1980 to 2013, a period that saw huge swings in credit market tightness and GDP growth. Our results show striking, systematic differences in the real-side effects of PE buyouts, depending on buyout type and external conditions. Employment at target firms shrinks 13% over two years in buyouts of publicly listed firms but expands 13% in buyouts of privately held firms, both relative to contemporaneous outcomes at control firms. Labor productivity rises 8% at targets over two years post buyout (again, relative to controls), with large gains for both public-to-private and private-to-private buyouts. Target productivity gains are larger yet for deals executed amidst tight credit conditions. A post-buyout widening of credit spreads or slowdown in GDP growth lowers employment growth at targets and sharply curtails productivity gains in public-to-private and divisional buyouts. Average earnings per worker fall by 1.7% at target firms after buyouts, largely erasing a pre-buyout wage premium relative to controls. Wage effects are also heterogeneous. In these and other respects, the economic effects of private equity vary greatly by buyout type and with external conditions.

We focus on listed non-financial companies from the largest European economies to explore the impact of rising stakeholders' attention towards climate risk on firms' probability to delist. An aggregate analysis shows that, despite growing awareness about the climate problem, enhanced scrutiny over more polluting firms and improved access to information about corporate sustainability over the last two decades, the share of carbon-intensive industries has not significantly declined in the listed sector relative to the whole economy. Also at the micro-level, firms in carbon-intensive sectors have not become more likely to delist, even in periods of enhanced stakeholders' concern about climate risk, potentially because they experienced a shift towards lower emissions over the same period. We confirm this result by means of a quasi-natural experiment centered around the enhanced disclosure requirements introduced by the EU Non-Financial Reporting Directive of 2014.

We use data from the Annual Survey of Manufactures to study the characteristics and geographic distribution of investments in robots across US manufacturing establishments. Robotics adoption and robot intensity (the number of robots per employee) cluster in “robot hubs.” Establishments that report having robotics are larger and have a larger production worker share, lower pay per worker, lower labor share, and higher capital expenditures, including higher IT capital expenditures. Notably, establishments are more likely to have robots if other establishments in the same core-based statistical area and industry also report having robotics, suggestive of agglomeration and peer effects.

We provide evidence for a psychological component of inflation concerns. Higher inflation concerns relate in a positive and significant way to respondents' reported levels of concerns about their financial situation. Results hold when controlling for income and financial constraints.

ABSTRACT In statistics, samples are drawn from a population in a data‐generating process (DGP). Standard errors measure the uncertainty in estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence‐generating process (EGP). We claim that EGP variation across researchers adds uncertainty—nonstandard errors (NSEs). We study NSEs by letting 164 teams test the same hypotheses on the same data. NSEs turn out to be sizable, but smaller for more reproducible or higher rated research. Adding peer‐review stages reduces NSEs. We further find that this type of uncertainty is underestimated by participants.