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 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.

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.

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. 

This paper studies the impact of the dynamic process of credit reallocation on aggregate innovative activities. To draw out theoretical predictions, we build a model with financial and matching frictions and investigate the consequences of lenders' credit reallocation decisions on borrowers' innovation choices. We show that an intensification of the credit reallocation process improves the matching between lenders and innovative firms but, overall, it disrupts innovation activities. Using a novel data set on bank balance sheets and the number of patents in Italian local markets (provinces) during a period of great economic growth and tighter banking regulation, we construct measures of credit reallocation and examine their effect on innovation. Consistent with the predictions of the model, we find that an increase in credit reallocation depresses innovative activity while aggregate credit growth helps to expand it.

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.

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.