The recovery rate of capital determines lenders’ credit supply, and in equilibrium, affects the demand and total credit amounts. Recent rising intangibles in the US may reduce recovery. I use CRSP/Compustat database to find that firms and industries with higher asset tangibility, a proxy for the recovery rate, issue more debts and have lower distance to default. To understand the aggregates, I build a canonical quantitative general equilibrium heterogeneous firm model and estimate the recovery rate by matching investment and debt covariance, average spread, and average default rate. The simulated method of moments (SMM) estimate of the recovery rate is 74%. The counterfactuals reveal that declines in the recovery rate reduce aggregate output, credit, and welfare by constraining capital accumulation. Tackling intangibles by a broader notion of capital, I estimate a recovery rate of 46% with the same model structure, implying that rising intangibles could cause nontrivial output and welfare losses due to financial frictions. 


Liquidation Value of Intangibles and Aggregate Efficiency

Intangible capital has grown in importance as the US economy has evolved towards service-based and technology-based industries. Intangible capital spending is a type of capital expenditure that is not negligible compared to physical capital investment. Drawing on CRSP/Compustat merged dataset of US public firms, I evaluate financial positions of firms with high and low asset tangibility. The key finding of my empirical exercise is that industries and firms with lower average asset tangibility have lower average debt-to-sales ratios and higher average value of distance-to-default both in the long run and short run. To study the aggregate implications of rising intangibility, I extend the canonical discrete-time firm investment model with risky debt by incorporating firms’ decisions about intangible investment and liquidation value of intangible capital in my pricing function of risky debts, and combine it into the general equilibrium framework. If the model parameters are externally calibrated to values in the literature, welfare and macro TFP increase when intangibles are liquidatable.

Intangible Investment, Financial Heterogeneity and Monetary Policy

This paper examines how firms with different leverage levels react differently to a monetary policy shock in intangible investments in microdata. I use quarterly Compustat data spanning 1995-2014 and calculate intangible capital as the sum of knowledge and organization capital. I interpret R&D spending by firms as an investment in knowledge capital and interpret a constant fraction of SG&A spending as an investment in organization capital. The perpetual inventory method is used to calculate the replacement costs. I average the high-frequency monetary policy shocks from the macroeconomics literature to estimate the quarterly monetary shock. Following a positive monetary policy shock, firms with higher leverage invest less in knowledge capital and organization capital. The differential response of organization investment is generally persistent, while the differential response of knowledge investment comes with a lag and lasts for only two quarters following the shocks. These reduced-form micro-level findings imply a firm's intangible investment decisions are subject to capital adjustment costs and financing frictions.

Immigration and Entrepreneurship

I demonstrate that immigration has a causal effect on local entrepreneurship in US counties. I use the exogenous variation in ancestry composition taken from Burchardi et al. (2020) as an instrumental variable to predict the total number of migrants flowing into each US county from 1990 to 2010. First, I find a strong and significant causal impact of immigration on the number of new business registrants per person. A one standard deviation increase in the number of migrants increases the change of new start-ups by 10% relative to its mean. Second, I find a significant causal impact of immigration on the quality of start-ups. A one standard deviation increase in local immigration increases the probability of a start-up achieving growth by 9% relative to the mean decline. Taking into account both the quantity and quality of start-ups, I find a significant causal impact of immigration on the expected number of start-ups with growth per person. A one standard deviation increase in the number of migrants increases the change in the number of start-ups achieving growth by 39% relative to its mean.