We study how expanding access to intellectual property (IP)-backed credit affects firm innovation and aggregate total factor productivity (TFP). Exploiting China's IP-backed financing pilot as a quasi-natural experiment, we use difference-indifferences estimates to show that the policy increases R&D participation, raises firm-level productivity, and reduces capital misallocation. To interpret these effects and quantify their aggregate implications, we develop a dynamic model of heterogeneous firms that borrow subject to collateral constraints backed by tangible assets and IP, and choose R&D investment and the accumulation of wealth. R&D raises future productivity and creates collateralizable IP. We estimate the model by the method of simulated moments, using the policy-induced shift in R&D participation to identify the change in IP pledgeability. The estimated model implies that a nationwide adoption of IP-backed financing would raise long-run TFP by 14%, with roughly two-thirds coming from static gains due to improved capital allocation and the remainder from dynamic gains driven by higher innovation that accumulate over time. Relative to a comparable expansion of lending backed by tangible collateral, IP-backed financing delivers larger productivity gains by easing constraints for high-productivity but low-wealth firms and by strengthening incentives to accumulate IP as collateral for future borrowing.

This paper studies the evolution of China's production and trade patterns during its integration into the global economy. We document and explain new facts concerning changes in production and exports at the industry and firm levels using microdata and a quantitative Ricardian and Heckscher–Ohlin model with heterogeneous firms. Counterfactual simulations reveal that capital deepening made China's production and exports more capital-intensive, although labor-biased productivity growth acted as a counterforce. Consistent with the data, our model demonstrates that China's trade openness peaked around the mid-2000s and fell until the 2020s, while the world's exposure to Chinese exports rose continuously.

Recessions are associated with sharp increases in turbulence that reshuffle firms’productivity rankings. To study the business cycle implications of turbulence shocks, we use Compustat data to construct a measure of turbulence based on the (inverse of) Spearman correlations of firms' productivity rankings between adjacent years. We document evidence that turbulence rises in recessions, reallocating labor and capital from high- to low-productivity firms and reducing aggregate TFP and the stock market value of firms. A real business cycle model with heterogeneous ffrms and ffnancial frictions can generate the observed macroeconomic and reallocation effects of turbulence. In the model, increased turbulence makes high-productivity ffrms less likely to remain productive, reducing their expected equity values and tightening their borrowing constraints relative to low-productivity firms. This leads to a reallocation that reduces aggregate TFP. Unlike uncertainty, turbulence changes both the conditional mean and the conditional variance of the firm productivity distribution, enabling a turbulence shock to generate a recession with synchronized declines in aggregate activities.

We document that monetary policy has asymmetric effects on investments by large and small firms in China. Large firms’ investment are highly responsive to monetary expansions, but less affected by monetary contractions. In contrast, small firms’ investments are less responsive to monetary expansions, but significantly affected by monetary contractions. We argue that this asymmetric responses of large and small firms stem from their differential access to credits in a two-tiered banking system. Large firms borrow from the big state-owned banks, which have a strong depositor base, whereas small firms borrow mainly from small banks which does not have a large depositor base and therefore rely heavily on the inter-bank market for financing their loans to small firms. We build a DSGE model with heterogeneous banks, heterogeneous firms, and an inter-bank market that is calibrated to the Chinese data. We show that the model’s quantitative predictions about the effects of monetary policy on large and small firms are consistent with the facts we documented.

We document that monetary policy has asymmetric effects on investments by large and small firms in China. Large firms’ investment are highly responsive to monetary expansions, but less affected by monetary contractions. In contrast, small firms’ investments are less responsive to monetary expansions, but significantly affected by monetary contractions. We argue that this asymmetric responses of large and small firms stem from their differential access to credits in a two-tiered banking system. Large firms borrow from the big state-owned banks, which have a strong depositor base, whereas small firms borrow mainly from small banks which does not have a large depositor base and therefore rely heavily on the inter-bank market for financing their loans to small firms. We build a DSGE model with heterogeneous banks, heterogeneous firms, and an inter-bank market that is calibrated to the Chinese data. We show that the model’s quantitative predictions about the effects of monetary policy on large and small firms are consistent with the facts we documented.