Startups face a trade-off between short-term profitability and long-term growth. Their cash flows are said to follow a so-called J-curve. The shape of the curve depends on investors’ financing capacity: their ability to sustain prolonged periods of negative cash flow. US venture capitalists are often believed to have greater financing capacity. We examine a large Swedish dataset with detailed cash flow information. Swedish startups backed by US venture capitalists experience deeper J-curves, with larger short-term losses and higher long-term sales, relative to those backed by non-US venture capitalists. These results are consistent with US venture capitalists having greater financing capacity: they can provide more funding directly and have better access to later-stage investors.

Private equity firms are often criticized for laying off workers, but the evidence on who loses their jobs and why is scarce. This paper argues that explanations for job polarization also explain layoffs after private equity buyouts. Buyouts reduce agency problems, which triggers automation and offshoring. Using rich employer-employee data, we show that buyouts generally do not affect unemployment incidence. However, unemployment incidence doubles for workers in less productive firms who perform routine or offshorable job tasks. Job polarization is also much more marked among workers affected by buyouts than for the economy at large.

Privatization of state‐owned enterprises is on the agenda across the globe. Using Swedish data covering two decades, we show that productivity gains and headcount reductions are associated with economic costs for incumbent workers. Workers experience income losses and higher unemployment, but half of the losses are covered by the social safety net. We also find small positive effects on entrepreneurship and cash holdings but no meaningful effects on other labor market, family, health, or household finance outcomes. Productivity improves when the CEO is replaced, and the gains outweigh workers' income declines by a factor of between two and six.

We develop a novel measure of job-worker allocation quality ( JAQ ) by exploiting employer-employee data with machine learning techniques. Based on our measure, the quality of job-worker matching correlates positively with individual labor earnings and firm productivity, as well as with market competition, non-family firm status, and employees’ human capital . Management plays a key role in job-worker matching: when managerial hirings and firings persistently raise management quality , the matching of rank-and-file workers to their jobs improves. JAQ can be constructed from any employer–employee data set including workers’ occupations, and used to explore research questions in corporate finance and organization economics.