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'Piggybacking credit' is a new practice that helps consumers improve their credit scores by paying to become 'authorized users' on established accounts. Authorized users are not liable for paying an account, but because of Regulation B (which implements the 1974 Equal Credit Opportunity Act), the account's history factors into their credit scores. As a result piggybacking can be used to manipulate the signal of creditworthiness that scores provide and may help borrowers obtain credit for which they would not have otherwise qualified. This article investigates the policy questions raised by piggybacking. First, we evaluate whether the credit history disparities that motivated these provisions of Regulation B have persisted since they were written. Then, we assess the potential for score improvement through piggybacking. Finally, we evaluate the likely score effects of allowing credit scoring models to exclude authorized-user accounts, the most widely proposed policy response to the emergence of piggybacking. [ABSTRACT FROM AUTHOR]

The widespread use of credit scoring in the underwriting and pricing of mortgage and consumer credit has raised concerns that the use of these scores may unfairly disadvantage minority populations. A specific concern has been that the independent variables that comprise these models may have a disparate impact on these demographic groups. By 'disparate impact' we mean that a variable's predictive power might arise not from its ability to predict future performance within any demographic group, but rather from acting as a surrogate for group membership. Using a unique source of data that combines a nationally representative sample of credit bureau records with demographic information from the Social Security Administration and a demographic information company, we examine the extent to which credit history scores may have such a disparate impact. Our examination yields no evidence of disparate impact by race (or ethnicity) or gender. However, we do find evidence of some limited disparate impact by age, in which the use of variables related to an individual's length of credit history appear to lower the credit scores of older individuals and increase them for the young. [ABSTRACT FROM AUTHOR]

The Fair and Accurate Credit Transaction Act of 2003 (the FACT Act) directed several federal agencies to conduct studies related to the credit reporting industry. A primary concern was the accuracy and fairness of the credit reporting and scoring systems. In this article, Federal Reserve System Board economists report on a study in which they examined various issues related to credit scoring, including how credit scoring has affected the availability and affordability of credit. In a responding commentary, Calvin Bradford notes flaws and deficiencies in the Federal Reserve System study. [ABSTRACT FROM AUTHOR]

Although credit scoring offers benefits to lenders and borrowers, its use raises important statistical issues that may affect the ability of scoring systems to accurately quantify an individual's credit risk. The evidence from a national sample of credit-bureau records suggests that concerns about omitted-variable bias may be justified, as local economic factors show significant correlations with credit scores. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Proposes orthogonality condition estimators for multiperiod probit models. Description of the models; Overview of multiperiod probit model; Problems with ML estimation; Orthogonality condition estimators for multiperiod probit models; Examination of model of female labor force participation.

If the research goal in applying classical discriminant analysis techniques is to test for differences in the general characteristics of groups, then standard tests from multivariate analysis of variance are appropriate. Statistically significant differences among group means and dispersions indicate that the groups arose from separate populations. If, however, the goal is to explore group structure in terms of group overlap and the locations of individual observations relative to each other and to the group means, then it can be shown that the significance tests provide incomplete and often misleading representations of the data. Similarly, in problems involving just more that just two groups, the null hypothesis can be rejected, when in fact only one of the several groups arose from a different population.

In this paper the authors present evidence on household saving in the U.S. based on the panel data from the 1983 and 1986 waves of the Survey of Consumer Finances. Saving is measured in these surveys as the change in wealth over the three-year period. Using a variety of models, we are able to explain only about 7 percent of the variation in the level of saving. Demographic factors appear to be modestly useful in explaining saving. However, one her is very clear from the patterns of correlation extracted so far; either the measurement error in the data is quite large, or idiosyncratic factors are very important in explaining saving behavior, or both. [ABSTRACT FROM AUTHOR]

Because wealth estimates from survey data have usually fallen substantially short of independent aggregate estimates, survey data have not been seen as adequate for assessing questions dependent on a good representation of the entire distribution of wealth, such as estimates of wealth concentration. The 1983 Survey of Consumer Finances (SCF), which contains a supplementary sample of very high income households drawn from a tax-file sample frame, is the first U.S. survey since the 1963 Survey of Financial Characteristics of Consumers that offers hope of accurately measuring the entire wealth distribution. In this paper, we discuss the design of the survey, the critical issue of proper weighting to merge the supplementary sample with an area probability sample, and the role of imputation. We show that the use of ordinary area probability samples alone leads to probable bias in the measurement of highly concentrated assets such as stocks and bonds. We compare the SCF data with aggregates derived from the flow-of-funds accounts of the Federal Reserve Board. While methodological issues cloud exact comparisons, it appears overall that the SCF estimates are at least as credible as other aggregate measurements. Finally, we use the data to assess the change in concentration of wealth from 1963 to 1983. We estimate that the concentration of wealth in terms of households did not change significantly over this period. [ABSTRACT FROM AUTHOR]