Alex Xi He

This paper argues that mergers that substantially increase firms' labor market power can weaken inventors' incentives to innovate by limiting the rents they capture from their innovations. We test this mechanism using linked employer-employee data from the U.S. Census Bureau. At both target and acquiring firms, inventors who experience large increases in concentration in already concentrated labor markets capture a smaller share of the surplus from their innovations and produce fewer patents, particularly high-quality ones; they also have lower earnings and become less likely to change jobs after the merger. Inventors at non-merging firms likewise experience declines in patenting and earnings when mergers increase concentration in their labor markets. Our results are robust to comparing inventors within the same firm across technological fields and to controlling for field-specific shocks, and we find no comparable effects around failed mergers. Taken together, our findings show that labor market power shapes inventors' incentives and productivity and constitutes an overlooked channel through which M&As affect innovation.

Using a new firm-level measure of AI investment based on AI-skilled employment, spanning machine learning through generative and agentic AI, we show that AI investments are associated with productivity growth in recent years, but not over the previous decade. We trace the productivity gains to the accumulation of organization capital that AI helps create: durable firm-specific knowledge acquired through learning-by-doing that enables more efficient production. We build a novel measure of organization capital based on workers' job descriptions and document that productivity gains are driven by AI-skilled jobs that build organization capital. Overall, our findings suggest that AI investment generates productivity growth by creating organization capital.

We provide direct evidence that firms' investments in new technologies affect the composition of firms' risk profiles. Leveraging comprehensive data on firm-level artificial intelligence (AI) investments, we document that firms that invest more in AI experience increases in their systematic risk, measured by market beta. This is unique to AI: robotics, IT, organizational capital, and R&D investments do not display similar effects. Our results are consistent with AI investments creating growth options: AI-investing firms become more growth-like, and the effect on market betas concentrates during market upswings and periods of increased news and attention around AI advances.

We provide evidence from the US and Denmark that CEOs with a business degree (“business managers”) reduce wages and the labor share (relative to non-business managers). Within five years of the appointment of a business manager, wages decline by 6% and the labor share by 5 percentage points in the US, and by 3% and 3 percentage points in Denmark. Firms appointing business managers are not on differential trends and do not enjoy higher output, investment, or employment growth thereafter. Using manager retirements and deaths and an IV strategy based on the composition of the board of directors at the time of new CEO appointments, we present additional evidence that these are causal effects. Exploiting exogenous export demand shocks, we establish that a key mechanism for these wage effects is changes in rent-sharing practices following the appointment of business managers and provide evidence suggesting that these effects are driven by business managers prioritizing shareholder value.

This paper uses a manager-firm-worker matched dataset covering the entire Danish population to identify manager effects on worker wages. We show that manager-specific wage premiums are almost as important as firm-specific wage premiums in explaining between-firm wage inequality. Managers who pay higher wages are more likely to be replaced through acquisitions. Manager-specific wage premiums are uncorrelated with productivity and seem to stem from managers’ traits and fairness views. Our results highlight the role of managers in understanding between-firm wage inequality and suggest that the managerial labor market tends to select low-paying managers and redistribute wealth from workers to shareholders.

How does artificial intelligence (AI) affect the volatility of firms' operational performance? We leverage detailed employer-employee data to link firm-level AI investments to the volatility of firm sales, earnings, and cash flows. Our results indicate that firms that invest more in AI experience reductions in the volatility of all three measures of operational performance compared to firms with lower AI investments. This finding highlights how adoption of technologies such as AI can benefit firms not only by increasing the first moment of their operational performance (e.g., raising sales) but also by reducing the second moment (lowering volatility).

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.

We study the causal effect of liquidity constraints on individual labor market outcomes by exploiting the 1992 mortgage reform in Denmark, which for the first time allowed homeowners to borrow against housing equity for nonhousing purposes. Following the reform, liquidity-constrained homeowners increased debt levels and had higher earnings growth and lower employment rates. The option to borrow against housing equity enabled liquidity-constrained individuals to move to high-wage jobs and invest in valuable human and physical capital. The results imply that relaxing household liquidity constraints during recessions can create better job matches, potentially increasing earnings and output in the longer run.

This article studies how federal funding affects the innovation outputs of university researchers. We link person-level research grants from 22 universities to patents, publications, and career outcomes from the U.S. Census Bureau. We focus on the effects of large, idiosyncratic, and temporary cuts to federal funding in a researcher’s preexisting narrow field of study. Using an event study design, we document that these negative federal funding shocks reduce high-tech entrepreneurship and publications but increase patenting. The lost publications tend to be higher quality and more basic, whereas the additional patents tend to be lower quality, less general, and more often privately assigned. These federal funding cuts lead to an increase in private funding, which partially compensates for the decline in federal funding. Together with evidence from industry-university contracts, the results suggest that federal funding cuts shift university research funding from federal to private sources and lead to innovation outputs that are less openly accessible and more often appropriated by corporate funders.

We study the shifts in U.S. firms’ workforce composition and organization associated with the use of AI technologies. To do so, we leverage a unique combination of worker resume and job postings datasets to measure firm-level AI investments and workforce composition variables, such as educational attainment, specialization, and hierarchy. We document that firms with higher initial shares of highly educated workers and STEM workers invest more in AI. As firms invest in AI, they tend to transition to more educated workforces, with higher shares of workers with undergraduate and graduate degrees, and more specialization in STEM fields and IT and analysis skills. Furthermore, AI investments are associated with a flattening of the firms’ hierarchical structure, with significant increases in the share of workers at the junior level and decreases in shares of workers in middle-management and senior roles. Overall, our results highlight that adoption of AI technologies are associated with significant reorganization of firms’ workforces.

Financial intermediaries may reduce welfare losses caused by hyperbolic discounting investors, who may liquidate their investment prematurely when the liquidation cost is low. In a competitive equilibrium, sophisticated investors are offered contracts with perfect commitment, and first best results are achieved; naïve investors are attracted by contracts that offer seemingly attractive returns in the long run but introduce discontinuous penalties for early withdrawal. If the investor types are private information, naïve investors withdraw early and cross-subsidize sophisticated investors. When a secondary market for long-term contracts opens for trading, financial intermediaries are compelled to offer contracts that have more flexible withdrawal options with linear schemes, and the welfare of naïve investors is improved. Arbitrage-free linear contracts allow for a unique term structure for interest rates that includes a premium for naïveté. Solvency requirements may limit competition for contracts and result in positive profits; banks that have capital are able to compete more aggressively, which improves investor welfare.