Using loan-level data on millions of used-car transactions across hundreds of lenders, we study the consumer response to exogenous variation
in credit terms. Borrowers offered shorter maturity decrease expenditures enough to offset 60% to 90% of the monthly payment increase. Most of this is driven
by shifting toward lower-quality cars, but affected borrowers offset 20% to 30% of a monthly payment shock by negotiating lower prices for equivalent cars.
Our results suggest that durable goods prices adjust to reflect credit terms even at the individual level, with one year of additional loan maturity
increasing a car's price by 2.8%.
Finance professionals commonly set prices based on the analysis of recently-closed, comparable transactions. Using data on syndicated loans, we exploit the lag
between loans' closing dates and their inclusion in a widely-used comparables database to identify the effect of past transactions on new transaction pricing. Comparables pricing
is an important determinant of individual loan spreads, but a failure to account for overlap in information across loans leads to pricing mistakes. Comparables used repeatedly are
overweighted as they develop redundant channels of influence on later transactions. Market conditions prevailing at the time a comparable was priced also unduly influence subsequent
We propose that, by financing their own product sales through captive finance subsidiaries, durable goods manufacturers commit to higher resale values for their
products in future periods. Using data on captive financing by the manufacturers of heavy equipment, we find that captive backed models have lower price depreciation. The
evidence is consistent with captive finance helping manufacturers commit to ex-post actions that support used machine prices. This, in turn, conveys higher pledgeability for
captive backed products, even for individual machines financed by banks. Although motivated as a rent seeking device, captive financing generates positive spillovers by relaxing
We explore the interaction of capital reallocation and entrepreneurship activities. Across a broad range of equipment types and industries, young firms
are the predominant buyers of vintage physical capital previously owned by older local firms. The pattern is strongest when financial constraints are most likely
to bind. We argue that this pattern drives a mutually-beneficial relationship between co-located young and old firms through local used capital markets. The
investment choices, growth, and job creation by start-ups depend on vintage capital supplied by older local firms. Meanwhile incumbents accelerate capital replacement
in the presence of young firms.
I study the effect of human capital on firms' leverage decisions in a structural dynamic model.
Firms produce using physical capital and labor. They pay a cost per employee they hire, thus investing in human capital.
In default a portion of this human capital investment is lost. The loss of human capital constitutes a significant cost of financial distress.
Labor intensive firms are more heavily exposed to this cost and respond by using less leverage. Thus the model predicts a decreasing relationship between leverage and labor intensity.
Consistent with this prediction, I show in the data that high labor intensity leads to significantly less use of debt. In the model a move from the lowest to the highest decile of labor
intensity is accompanied by a drop in leverage of 21 percentage points, very close to the 27 percentage point drop in the data. Overall, I argue that human capital has an important effect
on firm leverage and should receive more attention from capital structure researchers.