We study the interaction of search and application approval in credit markets. We combine a unique dataset, which details search behavior for a large sample of mortgage borrowers, with loan application and rejection decisions. Our data reveal substantial dispersion in mortgage rates and search intensity, conditional on observables. However, in contrast to predictions of standard search models, we find a novel non-monotonic relationship between search and realized prices: borrowers, who search a lot, obtain more expensive mortgages than borrowers’ with less frequent search. The evidence suggests that this occurs because lenders screen borrowers’ creditworthiness, rejecting unworthy borrowers, which differentiates consumer credit markets from other search markets. Based on these insights, we build a model that combines search and screening in presence of asymmetric information. Risky borrowers internalize the probability that their application is rejected, and behave as if they had higher search costs. The model rationalizes the relationship between search, interest rates, defaults, and application rejections, and highlights the tight link between credit standards and pricing. We estimate the parameters of the model and study several counterfactuals. The model suggests that “overpayment” may be a poor proxy for consumer unsophistication since it partly represents rational search in presence of rejections. Moreover, the development of improved screening technologies from AI and big data (i.e., fintech lending) could endogenously lead to more severe adverse selection in credit markets. Finally, place based policies, such as the Community Reinvestment Act, may affect equilibrium prices through endogenous search responses rather than increased credit risk.

We reconsider the classic problem of recovering exogenous variation from an endogenous regressor. Two-stage least squares recovers exogenous variation through presuming the existence of an instrumental variable. We rely instead on the assumption that the regressor is a mixture of exogenous and endogenous observations–say as the result of temporary natural experiments. With this assumption, we propose an alternative two-stage method based on nonparametrically estimating a mixture model to recover a subset of the exogenous observations. We demonstrate that our method recovers exogenous observations in simulation and can be used to find pricing experiments hidden in grocery store scanner data.

We propose a framework for estimation of spillovers between funding costs of individual banks. The estimation proceeds in three steps: First, using data from liquidity auctions of the European Central Bank, we estimate the funding costs in a given week for each individual bank. In the second step, we apply the adaptive elastic net (a LASSO type estimator) to this panel to estimate the financial network. Finally, using the estimated network we propose new measures of the systemicness and vulnerability of each bank. Our measure of systemicness has quite a natural interpretation, since it can roughly be viewed as the total externality a bank would impose on the funding costs of all other banks in the system. We estimate that most of the banks have fairly weak links and, therefore, if one were to suffer an adverse shock there would likely be a rather limited effect on the other ones. On the other hand, there are a few banks that are quite central: an increase in their funding costs would result in a very significant increase (up to 95 bp per 100 bp shock) in the funding costs of the other banks. Our vulnerability scores estimated using data from 2007-2008 are positively correlated with the probability of a bank being bailed out later.

Auction theory has ambiguous implications regarding the relative efficiency of three formats of multiunit auctions: uniform-price, discriminatory-price, and Vickrey auctions. We empirically evaluate the performance of these three auction formats using the bid-level data of the Federal Reserve’s purchase auctions of agency mortgage-backed securities (MBS) from June 1, 2014 through November 17, 2014. We estimate marginal cost curves for all dealers, at each auction, based on structural models of the multiunit discriminatory-price auction. Our preliminary results suggest that neither uniform price nor Vickrey auctions outperform discriminatory-price auctions in terms of thetotal expenditure. However, they do outperform it in terms of efficiency, with efficiencygains around 0.74% of the surplus that dealers extract. We caution that our empiricalestimation and analysis involve technical assumptions made about the specific auctionmechanism the Federal Reserve uses and how auction participants perceive the auctionmechanism, both of which may be distinct from practice and may alter the conclusionssubstantively.

Using data on Government of Canada securities auctions, this paper shows that in countries where direct access to primary debt issuance is restricted to government securities dealers, “order-flow” information is potentially the key source of private information for these security dealers. “Order-flow” information is revealed to a security dealer through his interactions with customers, who can place bids in the auctions only through the security dealer. Since each dealer interacts with a different set of customers, they, in effect, see different portions of the market demand and supply curves, leading to differing private inferences of where the equilibrium price might lie. We document several pieces of evidence consistent with the strategic response of dealers and customers to the presence of valuable information in their interactions.