Industries as in a Network: Micro Evidence from Job Search (Draft; Slides)

(with Kailing Shen)

We consider the labor market linkages across industries based on the concept of cross-industry skills (CRISs) here. CRISs are skills that are productive beyond any single industry. CRISs connect industries through their impacts on workers' mobility hurdles across industries. In particular, we empirically estimate labor mobility network across industries (LMNInd) using online job board data and recently available machine learning algorithm. Based on the estimated LMNInd, vacancy-applicant skill match indices are then constructed and tested on individual job application outcomes. We further demonstrate how this estimated LMNInd can be used to predict the transmission of an exogenous shock on one industry to all the other industries – the "ripple effect". Our results show a one standard deviation increase in CRISs is associated with 0.51 percentage points increase in callback probabilities, which is equivalent to 1.16 times of the impacts of being more experienced than required. The results also suggest that the effect of CRISs is stronger for lower-paying jobs. Lastly, our aggregate level results suggest the so-called “ripple effect” can be non-linear and complicated due to the existence of CRISs and the specific configuration of LMNInd in an economy.

Unspecified Lending Arrangements: Evidence from Private Lending Markets

We investigate the lending and borrowing behaviors in private lending markets. This informal financial market is an integral but often overlooked segment of the financial system, particularly as it fits into the larger framework of shadow banking. Our analysis is based on an innovative dataset, derived from legal sentencing documents in China and processed via a state-of-the-art large language model. By investigating the fundamental loan characteristics, such as loan size/term/rate, we find a substantial proportion of loans in this market operate under unspecified lending arrangements, whereby around half of loans do not explicitly specify loan terms or rates. We then examine the role of loan-related characteristics in relation to the unspecified lending arrangements, such as relational lending, borrower’s creditworthiness, lender’s default risk management, and loan usage. We find heterogenous correlations between the unspecified lending arrangements and these loan-related characteristics. We further investigate the correlation between the unspecified lending arrangements and the fundamental loan characteristics and find that larger loans are often associated with loans with less unspecified lending arrangements.