The effects of private equity buyouts on employment, productivity, and job reallocation vary tremendously with macroeconomic and credit conditions, across private equity groups, and by type of buyout. We reach this conclusion by examining the most extensive database of U.S. buyouts ever compiled, encompassing thousands of buyout targets from 1980 to 2013 and millions of control firms. Employment shrinks 12% over two years after buyouts of publicly listed firms—on average, and relative to control firms—but expands 15% after buyouts of privately held firms. Postbuyout productivity gains at target firms are large on average and much larger yet for deals executed amid tight credit conditions. A postbuyout tightening of credit conditions or slowing of gross domestic product growth curtails employment growth and intrafirm job reallocation at target firms. We also show that buyout effects differ across the private equity groups that sponsor buyouts, and these differences persist over time at the group level. Rapid upscaling in deal flow at the group level brings lower employment growth at target firms. We relate these findings to theories of private equity that highlight agency problems at portfolio firms and within the private equity industry itself.

Based on a sufficient statistics approach, we show how the state of technology of European industries relative to the rest of the world can be empirically assessed in a way that is simple in terms of computation, parsimonious in terms of data requirements, but still comprehensive in terms of information. The lack of systematic cross-industry correlation between export specialization and technological advantage suggests that standard measures of revealed comparative advantage only imperfectly capture a country’s technological prowess due to the concurrent influences of factor prices, market size, markups, firm selection and market share reallocation. These findings offer policy insights relevant to the EU’s external competitiveness debate, echoing several recommendations from the Draghi report. Achieving export specialization in key sectors requires more than just technological superiority.

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 measure desired labour supply at the extensive (employment) margin in two representative surveys of the U.S. and German populations. We elicit reservation raises: the percent wage change that renders a given individual indifferent between employment and nonemployment. It is equal to her reservation wage divided by her actual, or potential, wage. The reservation raise distribution is the nonparametric aggregate labour supply curve. Locally, the curve exhibits large short-run elasticities above 3, consistent with business cycle evidence. For larger upward shifts, arc elasticities shrink towards 0.5, consistent with quasi-experimental evidence from tax holidays. Existing models fail to match this nonconstant, asymmetric curve.

Why do cities differ so much in productivity? A long literature has sought out systematic sources, such as inherent productivity advantages, market access, agglomeration forces, or sorting. We document that up to three quarters of the measured regional productivity dispersion is spurious, reflecting the “luck of the draw” of finite counts of idiosyncratically heterogeneous plants that happen to operate in a given location. The patterns are even more pronounced for new plants, hold for alternative productivity measures, and broadly extend to European countries. This large role for individual plants suggests a smaller role for places in driving regional differences.

Small and young businesses are essential for job creation, innovation, and economic growth. Even most of the superstar firms start their business life small and then grow over time. Small firms have less internal resources, which makes them more fragile and sensitive to macroeconomic conditions. This suggests the need for frequent and real-time monitoring of the small business sector’s health. Previously this was difficult due to a lack of appropriate data. This paper fills this important gap by developing a new Intuit QuickBooks Small Business Index that focuses on the smallest of small businesses with at most 9 workers in the US and the UK and at most 19 workers in Canada. The Index aggregates a sample of anonymous Quick- Books Online Payroll subscriber data (QBO Payroll sample) from 333,000 businesses in the US, 66,000 in Canada, and 25,000 in the UK. After comparing the QBO Payroll sample data to the official statistics, we remove the seasonal components and use a Flexible Least Squares method to calibrate the QBO Payroll sample data against official statistics. Finally, we use the estimated model and the QBO Payroll sample data to generate a near real-time index of economic activity. We show that the estimated model performs well both in-sample and out-of-sample. Additionally, we use this analysis for different regions and industries. Keywords:

We link USPTO patent data to U.S. Census Bureau administrative records on individuals and firms. The combined dataset provides us with a directory of patenting household inventors as well as a time-series directory of self-employed businesses tied to household innovations. We describe the characteristics of household inventors by race, age, gender and U.S. origin, as well as the types of patented innovations pursued by these inventors. Business data allows us to highlight how patents shape the early life-cycle dynamics of nonemployer businesses. We find household innovators are disproportionately U.S. born, white and their age distribution has thicker tails relative to business innovators. Data shows there is a deficit of female and black inventors. Household inventors tend to work in consumer product areas compared to traditional business patents. While patented household innovations do not have the same impact of business innovations their uniqueness and impact remains surprisingly high. Back of the envelope calculations suggest patented household innovations granted between 2000 and 2011 might generate $5.0B in revenue (2000 dollars). 

The U.S. Census Bureau in partnership with a team of external researchers developed a series of questions on the use of robotics in U.S. manufacturing establishments. The questions include: (1) capital expenditures for new and used industrial robotic equipment in 2018, (2) number of industrial robots in operation in 2018, and (3) number of industrial robots purchased in 2018. These questions are to be included in the 2018 Annual Survey of Manufactures. This paper documents the background and cognitive testing process used for the development of these questions.

This paper reports on the development and analysis of a newly constructed dataset on the early stages of business formation. The data are based on applications for Employer Identification Numbers (EINs) submitted in the United States, known as IRS Form SS-4 filings. The goal of the research is to develop high-frequency indicators of business formation at the national, state, and local levels. The analysis indicates that EIN applications provide forward-looking and very timely information on business formation. The signal of business formation provided by counts of applications is improved by using the characteristics of the applications to model the likelihood that applicants become employer businesses. The results also suggest that EIN applications are related to economic activity at the local level. For example, application activity is higher in counties that experienced higher employment growth since the end of the Great Recession, and application counts grew more rapidly in counties engaged in shale oil and gas extraction. Finally, the paper provides a description of new public-use dataset, the “Business Formation Statistics (BFS),” that contains new data series on business applications and formation. The initial release of the BFS shows that the number of business applications in the 3rd quarter of 2017 that have relatively high likelihood of becoming job creators is still far below pre-Great Recession levels.