What’s in a Score? Differences in Consumers’ Credit Knowledge Using OLS and Quantile Regressions

Credit literacy depends, in part, on understanding credit reports and scores. The U.S. Government Accountability Office conducted a study in 2004 to assess consumers' knowledge of credit reports, credit scores, and the dispute resolution process. This study uses the Government Accountability Office data and estimates a series of ordinary least squares and quantile regressions to identify specific subgroups of the population that could benefit from more targeted consumer policies and financial education. The findings from this research have important implications for consumer educators, financial professionals, and policy makers, especially with respect to national strategies designed to improve consumers' financial well-being. ********** The Fair and Accurate Credit Transactions Act (FACT Act) was enacted in 2003 and amended the Fair Credit Reporting Act, a federal law that regulated, in part, who was permitted to access consumers' credit report information and how that information could be used. The FACT Act addressed ongoing concerns about inaccuracies in credit reports by providing consumers with new tools to enhance the accuracy, security, and reliability of their financial information. For example, the act expanded access to credit information by entitling consumers to one free credit report annually from each of the three major credit reporting agencies (CRAs) (Experian, Equifax, and TransUnion). The FACT Act also included a provision, which mandated that the U.S. Government Accountability Office (U.S. GAO) evaluate consumers knowledge and experience with credit reporting. As part of its response to the mandate, the GAO conducted a study in 2004 to assess consumers' knowledge of credit reports, credit scores, and the procedures needed to correct credit reporting errors (U.S. GAO 2005). This study extends the work of the GAO. Specifically, the GAO data are used to estimate ordinary least squares (OLS) and quantile regressions to determine how the impact of demographic factors varies across consumers with different levels of credit reporting knowledge. A priori one might think that consumers with widely varying credit knowledge scores would respond in very different ways to key determinants of financial behavior--differences that could be obscured by measuring only mean effects. Evidence from the data appears to support this hypothesis. Results from the standard OLS model identify, in general, demographic groups that are less credit knowledgeable than others. However, the findings from the quantile regressions identify more precisely specific demographic subgroups that are likely to benefit from more targeted financial education. While knowledge-based surveys continue to be widely used, it is important to acknowledge that financial education research is moving toward using more behavior-type measures (Lyons 2005; Lyons, Chang, and Scherpf 2007; Lyons et al. 2006). However, documenting changes in actual financial behavior is often difficult. Follow-up interviews with participants are needed but rarely occur because of time and resource constraints within organizations (Lyons 2005; Lyons, Chang, and Scherpf 2007; Lyons et al. 2006). Also, it is not always practical or appropriate to collect behavior information. The appropriateness of particular indicators depends on program objectives, delivery methods, and target populations. This study contributes to the literature by applying a fresh methodological approach to financial education research. Using quantile regression analysis, we show how valuable insight can be gained from a knowledge-based survey using data collected from a random sample of the U.S. population. Much of the existing research uses data collected from convenience samples such as participants of specific financial education programs. Thus, our study allows us to generalize our findings to the U.S. population as a whole. In particular, we demonstrate how quantile regression analysis can be a useful tool in identifying specific subgroups of U. …