Under Review
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
The banking industry has seen significant growth in mergers and acquisitions (M&A) and intangible assets over the last decades. This paper investigates how the accumulation of intangible assets influences bank M&A strategies. We first reveal three key empirical findings: (i) the intangible asset ratio in the banking industry has increased five-fold over the last thirty years, (ii) there is strong assortative matching in M&A transactions, with acquirer banks tending to merge with target banks that share similar characteristics, such as size, loans, net interest income, and intangible assets, and (iii) considering the cyclicality of bank M&A activity and assortative matching, this matching appears to be a general phenomenon rather than a time-specific pattern. Next, we conduct a causal analysis using a difference-in-differences framework to estimate the effect of bank M&As on performance through the channel of intangible asset synergies. We find that M&A activity has a positive causal impact on bank loan growth and operating efficiency gains, particularly for transactions with higher intangible asset synergies. Further, we employ a search model to ground our empirical evidence and outline the conditions under which assortative matching occurs pre-merger and how intangible asset synergies lead to efficiency gains post-merger.
Under Review
We leverage the Reserve Bank of India’s 2006 Bank Authorization Policy as a quasi-natural experiment to causally examine the policy impact through the bank credit channel on capital misallocation at the district level. Using a difference-in-differences approach, the policy increases private-sector bank (PVB) branches by an average of 3.9 and expands net credit growth in industrial loans by 1.9 percentage points in underbanked districts. The underlying mechanism shows that the boost in PVB lending to ex-ante high MRPK industrial firms experienced a 60% reduction in MRPK after the policy relative to low MRPK firms located in underbanked districts. With the additional credit, these high-MRPK firms expand their capital base disproportionately to sales growth, resulting in a decline in capital misallocation in underbanked districts. We find nil policy effects on public-sector banks. Our evidence highlights the efficacy of financial reforms in developing economies and shows bank ownership characteristics are an important factor for policymakers.
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.