The information revolution and small business lending: the missing evidence

This paper provides empirical confirmation for Petersen and Rajan's (2002) widely accepted conjecture that information technology was the primary driver of the observed increase in small business borrower-lender distances in the United States in recent years. Using a different data source for small business loans, we show that annual increases in borrower-lender distances were slow and steady prior to 1993 (the end point in Petersen and Rajan's data) but accelerated rapidly after that. Importantly, we are able to assign at least half of this acceleration to the adoption of credit scoring technologies by the lending banks. Our tests also reveal strong statistical associations between lending distances and borrower characteristics, lender characteristics, market conditions, regulatory constraints, moral hazard incentives, and principal-agent incentives.

Development and Validation of Credit-Scoring Models

Accurate credit-granting decisions are crucial to the efficiency of the decentralized capital allocation mechanisms in modern market economies. Credit bureaus and many .nancial institutions have developed and used credit-scoring models to standardize and automate, to the extent possible, credit decisions. We build credit scoring models for bankcard markets using the Office of the Comptroller of the Currency, Risk Analysis Division (OCC/RAD) consumer credit database (CCDB). This unusu- ally rich data set allows us to evaluate a number of methods in common practice. We introduce, estimate, and validate our models, using both out-of-sample contempora- neous and future validation data sets. Model performance is compared using both separation and accuracy measures. A vendor-developed generic bureau-based score is also included in the model performance comparisons. Our results indicate that current industry practices, when carefully applied, can produce models that robustly rank-order potential borrowers both at the time of development and through the near future. However, these same methodologies are likely to fail when the the objective is to accurately estimate future rates of delinquency or probabilities of default for individual or groups of borrowers.

What "triggers" mortgage default?

This paper assesses the relative importance of two key drivers of mortgage default: negative equity and illiquidity. To do so, the authors combine loan-level mortgage data with detailed credit bureau information about the borrower's broader balance sheet. This gives them a direct way to measure illiquid borrowers: those with high credit card utilization rates. The authors find that both negative equity and illiquidity are significantly associated with mortgage default, with comparably sized marginal effects. Moreover, these two factors interact with each other: The effect of illiquidity on default generally increases with high combined loan-to-value ratios (CLTV), though it is significant even for low CLTV. County-level unemployment shocks are also associated with higher default risk (though less so than high utilization) and strongly interact with CLTV. In addition, having a second mortgage implies significantly higher default risk, particularly for borrowers who have a first-mortgage LTV approaching 100 percent.Mortgages ; Default (Finance)

Commercial lending distance and historically underserved areas

We study recent changes in the geographic distances between small businesses and their bank lenders, using a large random sample of loans guaranteed by the Small Business Administration. Consistent with extant research, we find that small borrower-lender distances generally increased between 1984 and 2001, with a rapid acceleration in distance beginning in the late-1990s. We also document a new phenomenon: a fundamental reordering of borrower-lender distance by the borrowers' neighborhood income and race characteristics. Historically, borrower-lender distance tended to be shorter than average for historically underserved (for example, low-income and minority) areas, but by 2000 borrowers in these areas tended to be farther away from their lenders on average. This structural change is coincident in time with the adoption of credit scoring models that rely on automated lending processes and quantitative information, and we find indirect evidence consistent with this link. Our findings suggest that there has been increased entry into local markets for small business loans and this should help allay fears that movement toward automated lending processes will reduce small businesses' access to credit in already underserved markets.

Small Business Lending and Social Capital: Are Rural Relationships Different?

We test whether rural versus urban location, and the amount of social capital present in those locations, influence the performance of Small Business Administration (SBA) 7(a) loans originated between 1984 and 2012. On average, we find that rural loans are about 11% less likely to default than urban loans, and that a standard deviation increase in social capital reduces default by about 5%. Surprisingly, these two effects are largely independent of each other, even though social capital is substantially higher in rural places than in urban places. Our findings advance the small business lending literature and offer insights for a more efficient allocation of SBA funds

Two Neuronal Nicotinic Acetylcholine Receptors, α4β4 and α7, Show Differential Agonist Binding Modes

Nicotinic acetylcholine receptors (nAChRs) are pentameric, neurotransmitter-gated ion channels responsible for rapid excitatory neurotransmission in the central and peripheral nervous systems, resulting in skeletal muscle tone and various cognitive effects in the brain. These complex proteins are activated by the endogenous neurotransmitter ACh as well as by nicotine and structurally related agonists. Activation and modulation of nAChRs has been implicated in the pathology of multiple neurological disorders, and as such, these proteins are established therapeutic targets. Here we use unnatural amino acid mutagenesis to examine the ligand binding mechanisms of two homologous neuronal nAChRs: the α4β4 and α7 receptors. Despite sequence identity among the residues that form the core of the agonist-binding site, we find that the α4β4 and α7 nAChRs employ different agonist-receptor binding interactions in this region. The α4β4 receptor utilizes a strong cation-π interaction to a conserved tryptophan (TrpB) of the receptor for both ACh and nicotine, and nicotine participates in a strong hydrogen bond with a backbone carbonyl contributed by TrpB. Interestingly, we find that the α7 receptor also employs a cation-π interaction for ligand recognition, but the site has moved to a different aromatic amino acid of the agonist-binding site depending on the agonist. ACh participates in a cation-π interaction with TyrA, whereas epibatidine participates in a cation-π interaction with TyrC2