This paper empirically and theoretically studies the synergy between intangible capital and skilled labor and its relationship with increasing productivity dispersion across U.S. firms. Our empirical findings reveal that firms with higher intangible capital ratio and skill ratio achieve higher labor productivity. This relationship is further magnified by firm size, leading to increased productivity dispersion. To rationalize the reduced-form empirical evidence, we first outline a stylized model that explains the channels through which firms with higher intangible capital benefit from skilled labor. We then develop a firm-level general equilibrium model with non-homothetic constant elasticity of substitution production technology that integrates the complementarity between intangible capital and skilled labor, along with economies of scale. Our model elucidates how economies of scale enhance this complementarity within the firm-level production framework.
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%.
Under Review
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.
Under Review
We leverage the Reserve Bank of India’s 2006 Bank Authorization Policy as a quasi-natural experiment to study effects on credit markets, capital misallocation, and firm outcomes. We find asymmetric responses: private-sector bank branches expanded by 16.3% in underbanked districts relative to banked districts, while public-sector banks showed no systematic expansion. The resulting private-sector lending reduced the marginal revenue product of capital (MRPK) of ex-ante high-MRPK firms by about 60%, lowering capital misallocation. However, this decline did not raise firm sales or value added. We highlight the efficacy of financial reforms in alleviating misallocation under mixed-ownership banking environments in developing economies.
This paper investigates the role of artificial intelligence (AI) workers in shaping firm sales through trade channels. We provide novel empirical evidence on the association between the AI workforce and firm-level sales across the firm-size distribution, with a particular focus on domestic and foreign sales. Using a propensity score matching approach, we establish causal estimates of the impact of AI workers on firm sales. Our results show that the presence of AI workers leads to an approximately 18.6% increase in total sales. Furthermore, we find that this positive impact is even stronger for exporting firms, suggesting that AI can provide advantages in international markets. Examining the relationship across the firm-size distribution, we observe that the positive association between AI workers and sales becomes weaker as firm size and AI worker share increase, indicating potential diminishing returns to expanding the AI workforce beyond a certain point. Our event study analysis offers additional insights into trade channels and firm size heterogeneity, revealing that smaller firms benefit more from AI in domestic sales, while larger firms experience greater gains in foreign sales. To illustrate the underlying mechanisms behind our empirical findings, we develop an illustrative model that incorporates AI as a production input alongside production labor, with a focus on trade channels under firm heterogeneity. Our model delivers equilibrium characterizations consistent with our empirical insights, showing that lower AI adoption costs and reduced trade costs lead to increased AI adoption and higher productivity, particularly among exporting firms.
The rising importance of intangible capital presents a paradox: despite its critical role in innovation and productivity, aggregate U.S. productivity growth has slowed. This paper develops a firm-dynamics framework in which firms, depending on their reliance on intangible capital, face heterogeneous borrowing constraints—earnings-based and asset-based—that influence their ability to expand firm scope and productivity. We highlight a novel channel: firms more reliant on intangible capital are subject to earnings-based borrowing constraints, which tighten particularly during downturns, limiting their scope expansion and affecting firm cycle dynamics. This, in turn, contributes to the decline in aggregate productivity growth. We develop a model that incorporates firm-cycle behavior, including firm entry, where constraints shape investment and scope. This framework helps us understand how heterogeneous financial frictions influence firm scope and productivity through the lens of intangible capital.
Under Review
This paper examines how firms responded to a joint policy shock introduced by the 2017 U.S. Tax Cuts and Jobs Act (TCJA), which simultaneously replaced the progressive corporate tax schedule with a flat 21% rate and eliminated the deductibility of performance-based executive compensation under Section 162(m). We exploit cross-sectional variation in pre-reform reliance on performance-based pay and changes in marginal tax rates to show how ex-ante compensation structures shaped firm responses in innovation and intangible investment. We find that, relative to firms with lower pre-TCJA incentive-pay intensity, firms with higher exposure to ex-ante performance-based compensation increased R&D spending, patenting, and intangible investment after the reform—particularly when their marginal tax rates rose. These higher-exposure firms also reallocated performance-based pay away from tax-disfavored executives toward non-eligible executives. These effects are most pronounced in growth firms with high internal funding reliance. This pattern suggests a more complex relationship between executive pay design and intangible investment incentives under tax constraints.
This paper studies the impact of artificial intelligence (AI) adoption on workplace misconduct. We combine firm-level data on financials, corporate governance, and workplace misconduct with a measure of AI adoption based on employees’ AI-related skills. To identify causal effects, we exploit the 2015 release of Google TensorFlow as a plausibly exogenous shock within a difference-in-differences framework. We find that firms with higher pre-treatment AI intensity experience significant and persistent declines in workplace violations and penalty amounts after 2015. The effects operate primarily through productivity-enhancing complementarities, 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.