Research

This paper studies how the interaction between intangible capital and skilled labor shapes firm-level productivity. We proxy skilled labor using firm-level artificial intelligence (AI)-skilled workers and exploit the 2015 release of Google TensorFlow as a plausibly exogenous shock to AI effectiveness. Using a difference-in-differences framework, we show that firms with higher pre-shock AI exposure increase their intangible capital, and these increases translate into higher labor productivity. Moreover, productivity gains are concentrated in large firms, while smaller firms show little response. This heterogeneity implies that the interaction between intangible capital and AI workers contributes to rising productivity dispersion across firms.

This paper studies the impact of artificial intelligence (AI) adoption on workplace misconduct. Using regression analysis and quasi-natural experiment around the open-source launch of Google Brain's TensorFlow machine learning toolkit, we find that firms with higher AI intensity experience significant and persistent declines in workplace violations and penalty amounts. The effects operate primarily through productivity-enhancing complementarities and discretionary expenses increases, while labor-adjustment channels play no role. Benefits are concentrated among larger, intangible- and organizational-capital intensive firms, which highlights uneven gains from AI adoption and suggests that AI may widen compliance inequality among firms.

We study how artificial intelligence (AI) affects firms’ sales in the pre-ChatGPT era. Exploiting the 2015 release of Google TensorFlow as a plausibly exogenous shock to AI-worker effectiveness, we implement a difference-in-differences design that leverages firm-level variation in pre-shock AI exposure. We show that AI adoption generates large and persistent increases in total, domestic, and foreign sales, but only among large firms; smaller firms exhibit no responses. Mechanism evidence suggests that while AI exposure raises operating efficiency and productivity for large firms, firms with financial constraints and limited intangible capital are less able to translate AI into sales gains. 

This paper examines how internally generated intangible capital shapes merger patterns and post-merger performance in the U.S. banking sector. We construct a novel measure of intangible capital using granular regulatory expense data and quantify assortative matching between acquirers and targets. Employing a difference-in-differences design with propensity score matching, we causally show that higher assortative matching in intangible capital leads to significant improvements in post-merger bank performance. We complement the empirical analysis with a dynamic search-theoretic model of bank mergers, demonstrating that strategic complementarities in intangibles give rise to assortative matching equilibria. Our findings provide new insights into banking consolidation.

We study the effects of the Reserve Bank of India’s 2006 Bank Authorization Policy on credit allocation, capital productivity dispersion, and firm outcomes using a quasi-natural experiment. We show that private-sector bank branches increased by 16.3% in underbanked districts, while public-sector banks did not exhibit a comparable expansion. Private-sector lending increased disproportionately toward firms with high ex-ante marginal revenue product of capital (MRPK), leading to a decline of approximately 60% in their MRPK and a reduction in district-level MRPK dispersion. These findings emphasize the role of bank ownership in shaping MRPK dispersion by accounting for firm heterogeneity.

This paper examines how executive compensation influences firm investment, intangible capital, and innovation by exploiting the elimination of performance-based pay deductibility under the 2017 U.S. Tax Cuts and Jobs Act. Using difference-in-differences and event-study designs, we show that firms more exposed to the shock significantly reduce R\&D, intangible capital, capital expenditures, and patent applications, especially among growth-oriented and smaller firms. We trace these real effects to weaker incentives: high-exposure firms cut stock-based and non-equity incentive pay and experience declines in risk-taking incentives, and they shift toward safer financial policies with higher payouts, lower cash flow, and higher earnings per share.

We study how non-rival intangible capital interacts with borrowing structure and financial frictions to shape firm dynamics over business cycles. We show: (i) the positive and significant association between intangible-capital growth and labor productivity growth becomes smaller in recessions; (ii) the non-rivalry of intangible capital is evident such that intangible growth predicts faster sales growth and broader firm scope, yet this relationship declines in recessions; (iii) intangible-intensive firms carry less total and secured debt, and substitute toward earnings-based covenant (EBC) borrowing over asset-based covenant (ABC) borrowing; and (iv) intangible-intensive firms with EBC have tightening financially constraints in recessions, which mitigates the productivity payoff of non-rival intangibles. We rationalize these patterns in a general-equilibrium model in which firms draw EBC/ABC constraints at entry and intangibles are non-rival in the firm production technology. The model yields a credit-amplification mechanism with heterogeneous borrowing types, reconciling the productivity slowdown despite rising intangibles.

We construct a novel establishment-level dataset combining the nineteenth-century U.S. Census of Manufactures (1850–1880), patent records from CUSP, and population census microdata to trace entrepreneurs, their firms, and inventive activity. This is the first paper to provide systematic micro evidence on the returns to innovation during the Second Industrial Revolution. We examine both the intensive and extensive margin of the return on patenting. We find that patenting establishments were substantially larger by employing more labor, paying higher wages, holding more capital, and producing more output per worker than comparable non-patenters. They had higher survival ability and were less likely to exit their industry. They also operated in more distinct industries, indicating broader scope rather than mere specialization. Estimating historical establishment-level production functions, we find that innovation primarily raised their physical productivity (TFPQ) rather than price-cost markups. The effects are highly heterogeneous: exit reduction is largest for small establishments, TFPQ gains peak among mid-sized establishments, and markups increase only among the very largest establishments. However, women-owned establishments do not experience any observable and comparable returns. 

This study focuses on estimating the role of intangible capital on firms’ competitiveness. We use Lyft’s acquisition of Motivate, the biggest bike sharing company in the U.S. at the time, to evaluate the degree to which intangible capital affects the competition between Lyft and Uber. By acquiring Motivate, Lyft gained more consumer data as we interpret intangible capital, and bikes’ presence on the streets potentially helped Lyft build stronger brand salience. We estimate the effect of the acquisition on Lyft’s ridership by employing trip-level ride sharing data from New York City and using a difference-in-difference-in-differences model. We find that the acquisition helped Lyft increase its ridership by around 6%.