We study the use and economic impact of AI technologies. We propose a new measure of firm-level AI investments using employee resumes. Our measure reveals a stark increase in AI investments across sectors. AI-investing firms experience higher growth in sales, employment, and market valuations. This growth comes primarily through increased product innovation. Our results are robust to instrumenting AI investments using firms' exposure to universities' supply of AI graduates. AI-powered growth concentrates among larger firms and is associated with higher industry concentration. Our results highlight that new technologies like AI can contribute to growth and superstar firms through product innovation

Privately issued money often bears default risk, which creates transaction frictions when used as a medium of exchange. The late 19th century US provides a unique context to evaluate the real effects of supplying a new type of money that is safe from default. We measure the local change in “monetary” transaction frictions with a market access approach derived from general equilibrium trade theory. Consistent with theories hypothesizing that lowering transaction frictions benefits the traded and inputs-intensive sectors, we find an increase in traded goods production, in the share of manufacturing output and employment, and in innovation

We show how uncertainty shapes corporate asset allocation, composition, and productivity using data from the shipping industry. Firms curtail both ship acquisitions and disposals when uncertainty increases, primarily through cuts in new ship orders and ship demolitions — decisions that are costlier to reverse vis-à-vis secondary market transactions. Uncertainty also prompts firms to concentrate their fleets into narrower, less productive portfolios. We corroborate our findings using the 2009–2011 spike in Somali pirate attacks as an uncertainty shock to shipping activity. Uncertainty hampers “creative destruction,” slowing both the adoption of innovation embodied in new capital and the disposal of old capital

We propose no-arbitrage term structure models with volatility factors that follow GARCH processes. The models’ tractability is similar to canonical affine term structure models, but they fit yield volatility much better, especially for long-maturity yields. This improvement does not come at the expense of a deterioration in yield fit. Because of the improved volatility fit, the model performs substantially better in pricing Treasury futures options. We conclude that the specification of the volatility factors is critical. Modeling volatility as a function of (lagged) squared innovations to factors improves on models where volatility is a linear function of the factors

An important strand of research in macro-finance investigates which factors impede enterprise investment, and what is their aggregate economic cost. In this paper, we make two contributions to this literature. The first contribution is methodological: we introduce a novel framework to calibrate macroeconomic models with firm-level distortions using enterprise survey micro-data. The core of our innovation is to explicitly model the firms' decisions to report in the survey the distortions they face. Our second contribution is to apply our method across seven countries to characterize the distribution of these distortions and estimate the gross domestic product (GDP) loss induced by distortionary red tape. Our estimates are based on a dynamic general equilibrium model with heterogeneous firms whose capital investment decisions are distorted by red tape. We find that the aggregate cost of red tape varies widely across the countries in our dataset, with an average cost of 0.8% of annual GDP. Our framework opens up a new range of applications for enterprise surveys in macro-financial modeling and policy analysis