[This paper demonstrates how a two or three component error structure can be used with seemingly unrelated regressions. Its application may be particularly useful with large panel data sets when the researcher wishes to estimate several equations simultaneously and believes that errors both between and within equations are correlated over time and across units. Relatively simple algorithms are presented for estimation of the error covariance matrix and generalized least squares coefficients.]

Although credit history scoring offers benefits to lenders and borrowers, failure to consider situational circumstances raises important statistical issues that may affect the ability of scoring systems to accurately quantify an individual’s credit risk. Evidence from a national sample of credit reporting agency records suggests that failure to consider measures of local economic circumstances and individual trigger events when developing credit history scores can diminish the potential effectiveness of such models. There are practical difficulties, however, associated with developing scoring models that incorporate situational data, arising largely because of inherent limitations of the credit reporting agency databases used to build scoring models.

Some have suggested that housing policy, embodied by the Community Reinvestment Act (CRA) and affordable housing goals of the government-sponsored enterprises (GSEs), caused the subprime crisis. We examine if these programs led to worse mortgage outcomes using two approaches. The first examines whether more activity by CRA-covered lenders, or more loan sales to the GSEs, was associated with worse outcomes. The second uses regression discontinuity to determine if outcomes were worse at the geographic thresholds used by each program. Our results suggest that neither program played a significant role in the subprime crisis.

This paper provides new empirical evidence on the relationship between personal commitments and the allocation of small business credit. The data suggest that personal commitments are important for firms seeking certain types of loans. Guarantees are more prevalent than collateral and organization type (corporate versus noncorporate status) appears to be particularly important in determining commitment use. No systematic relationship is observed between commitment use and owner wealth. Personal commitments appear to be substitutes for business collateral, at least for lines of credit, while personal collateral and personal guarantees do not seem to substitute for each other. Personal commitments have generally become more important to small business lending since the late 1980s.

This paper examines the association between consolidation and changes in levels of bank branching as measured by changes in the number of bank branches per capita. Using a specially-constructed data set, we address this issue as well as how this relationship varies with the type of consolidation and initial regulatory, competitive, and market conditions. We find that merges where merging institutions have branch networks which overlap within a ZIP code (within-ZIP merger) are strongly associated with a reduction in offices per capita in that ZIP code. This result is robust across time and holds in both rural and urban areas. The findings also suggest that, contrary to popularly held views, consolidation is not unambiguously negatively associated with changes in the number of banking offices per capita. Neither within-market-but-not-within-ZIP mergers nor out-of-market mergers consistently show such a relationship. We also find that the relationships between within-ZIP and within-market-but-not-within-ZIP mergers and changes in the number of bank offices per capita are more negative in low-income neighborhoods than in other neighborhoods. However, because most states now have unrestricted branching and because savings associations are less prevalent and financially healthier than in the past, these findings may not be indicative of future branching patterns.