I investigate the influence of unemployment insurance (UI) benefits on the cost of bank loans by exploiting changes in state unemployment insurance laws as a source of variation in labor unemployment costs. The evidence shows that the cost of bank loans is significantly lower for firms headquartered in states with more generous UI programs. The relation between UI benefits and the cost of bank loans is more pronounced for firms that face higher unionization rates, are more labor intensive, or have a higher layoff propensity. These results are robust to controlling for loan characteristics, macroeconomic conditions, and borrower characteristics and using fixed effect regressions. I also find further evidence of a causal relation between UI benefits and the cost of bank loans through a difference-in-differences (DID) approach. Overall, the results suggest that the credit market evaluates workers' unemployment costs while approving and pricing loan contracts.

We investigate the influences of local product market competition on the cost of private debt. Our evidence suggests that the cost of bank loans is significantly higher for firms headquartered in states with greater local product market competition measured by the Herfindahl-Hirschman Index for resident industries. To establish causality, we examine the recognition of the Inevitable Disclosure Doctrine and firm relocations to identify exogenous shocks to local product market competition. We find that the cost of bank loans is lower for firms facing less intense local product market competition after the adoption of IDD and higher for firms relocated to states with more competitive product markets. The results imply that banks value the characteristics of a firm's local product market when approving loan contracts.

Despite the widespread use of graphs in empirical research, little is known about readers’ ability to process the statistical information they are meant to convey (“visual inference”). We study visual inference in the context of regression discontinuity (RD) designs by measuring how accurately readers identify discontinuities in graphs produced from data-generating processes calibrated on 11 published papers from leading economics journals. First, we assess the effects of different graphical representation methods on visual inference using randomized experiments. We find that bin widths and fit lines have the largest effects on whether participants correctly perceive the presence or absence of a discontinuity. Our experimental results allow us to make evidence-based recommendations to practitioners, and we suggest using small bins with no fit lines as a starting point to construct RD graphs. Second, we compare visual inference on graphs constructed using our preferred method with widely used econometric inference procedures. We find that visual inference achieves similar or lower type I error (false positive) rates and complements econometric inference.