An "authorized user" is a person who is permitted by a revolving account holder to use an account without being legally liable for any charges incurred. The Federal Reserve's Regulation B, which implements the 1974 Equal Credit Opportunity Act, requires that information on spousal authorized user accounts be reported to the credit bureaus and considered when lenders evaluate credit history. Since creditors generally furnish to the credit bureaus information on all authorized user accounts, without indicating which are spouses and which are not, credit scoring modelers cannot distinguish spousal from non-spousal authorized user accounts. This effectively requires that all authorized user accounts receive similar treatment. Consequently, becoming an authorized user on an old account with a good payment history, may improve an individual's credit score, potentially increasing access to credit or reducing borrowing costs. As a result, the practice of "piggybacking credit" has developed. In a piggybacking arrangement, an individual pays a fee to be added as an authorized user on an account to "rent" the account's credit history. This paper provides the first comprehensive look at authorized user accounts in individual credit records and how their importance differs across demographic groups. Our analysis suggests that piggybacking credit can materially improve credit scores, particularly for individuals with thin or short credit histories. We also evaluate the effect that eliminating authorized user accounts from credit scoring models would have on individual credit scores. Our results suggest that removing this information has relatively little effect on credit scores, but may reduce model predictiveness.
4 Effects of Authorized User Accounts on Credit Scores and Access to Credit across Populations
To examine the contribution that the presence of authorized user accounts on an individual's credit record make to credit scores (in credit scoring models such as the FICO score that include them) and, consequently, access to credit for different populations, each of the credit characteristics that comprise the FRB base model was recalculated without the authorized user accounts. A new value for the FRB base score was computed based upon these recalculated credit characteristics. This new value represents the credit score that each individual would have received had their authorized user accounts not been in their credit record. The difference between each individual's FRB base score and this new value measures the contribution the authorized user accounts to each individual's credit score.
Table 3 provides the mean change in the FRB base score by demographic group resulting from the inclusion of authorized user accounts. A positive value indicates that an individual's score is higher when her authorized user accounts are factored into the calculation of the score than it would have been had these account not been reflected in her credit report. For individuals without authorized user accounts on their credit record, the score change will equal zero by definition. Therefore, the means presented in table 3 are calculated using only those individuals in each demographic group who have authorized user tradelines in their credit records. These changes are best interpreted as marginal contributions to an individual's credit score made by authorized user accounts and not as the changes that would result if credit scoring models were prohibited from using authorized user accounts, which is an exercise we conduct later in the paper.
Overall, the data suggest that people with authorized user accounts on their credit records experience an average score increase of 0.49 points and a median change of zero points because of the inclusion of this information in the score. Across different demographic groups, authorized user accounts appear to contribute very little to credit scores. The median score change for each of the demographic groups was zero, and the means, which were generally positive, were generally lower than 1. This suggests that none of the demographic groups examined appear to be substantially benefited or harmed, on average, by the authorized user tradelines in their credit records. For most demographic groups, a majority of individuals experienced score changes (positive or negative) of 2.5 points or less from the authorized user accounts on their credit record.
As discussed in the previous section, there are significant differences between married men and women in the number of authorized user tradelines in their credit records. These differences are reflected in their credit scores, as the increase in mean credit scores for married women (0.56 points) is larger than for married men (0.20). As with the other demographic groups, these differences are relatively small and suggest that the presence of authorized user tradelines can explain only slightly more than 10 percent of the average score difference between married women and men.21 These results suggest that the credit scores of married women are marginally higher than married men, even without contribution made by authorized user tradelines.
Besides demographic groups, there may be other subsets of the population that are more likely to be helped or harmed by the presence of authorized user accounts. In particular, the importance to an individual of authorized user accounts in the calculation of a credit score may differ substantially depending upon the contents of an individual's credit record. In particular, those individuals with fewer non-authorized user accounts on their file, shorter credit histories, or higher utilization rates on their non-authorized user tradelines may stand to gain (or lose) the most from the inclusion of authorized user accounts in the calculation of a credit score. In contrast, those individuals with a large number of accounts or who are currently delinquent on one or more accounts may be the least susceptible to benefit or harm from the inclusion of authorized user tradelines.
To examine this possibility, table 3 also shows score changes for different groups based upon the contents of the each individual's credit record. The score changes suggest that some of the groups that were identified as being more likely to benefit from the inclusion of authorized user accounts experienced larger gains than other groups. In particular, those people with thin credit records (2 or fewer non-authorized user tradelines) or short credit histories (oldest non-authorized user account less than 24 months old) both experienced increases in credit scores of approximately 5 points on average because of authorized user accounts. This may reflect, in part, that these two groups have the highest share of individuals for whom authorized user accounts constitute at least half of the tradelines in their credit records (see table 1). In contrast there was a less consistent pattern of benefit or harm based upon past payment performance or utilization rates.22
On the whole, this analysis suggests that the inclusion of authorized user account information in computing an individual's credit score has only a modest effect. Furthermore, this inclusion does not appear to have a disproportionate impact on the members of any particular demographic group. However, there are some small subsets of the population (in particular, individuals with very thin or very short credit histories) for whom the inclusion of authorized user accounts has a relatively larger effect on scores. Consistent with our earlier findings about the relative quality of authorized user and non-authorized user tradelines, score changes from the inclusion of authorized user tradelines are generally positive.
5 The Effect of Piggybacking Credit on Credit Scores
The modest contribution that authorized user accounts appear to make, on average, to individual credit scores does not suggest that the potential change that can be achieved by buying authorized user status on an account is necessarily small. While authorized user accounts are generally higher-quality than non-authorized user accounts (in that they have lower utilization rates, older ages, and better payment histories) within most groups examined, the pattern of authorized user and non-authorized user accounts across groups is similar. For example, both the authorized user and non-authorized user accounts for young individuals (under age 30) are not as old on average, as the accounts of older individuals. Consequently, the estimates from the previous section may underestimate the gains in score that can be achieved from piggybacking on an even higher-quality account.
To evaluate the potential credit score boost that an individual might achieve by piggybacking on an additional high-quality account, we use the FRB base model to simulate the effect that the addition of an authorized user account would have on each individual's credit score. This process helps measure the magnitude that such changes are likely to have and helps identify which groups of individuals are the most likely to benefit from piggybacking credit.
The purpose of this exercise is to approximate the potential scope of the problem that piggybacking credit represents. If piggybacking credit has little or no impact on credit scores, or if the benefits are limited to a very small segment of the population, then the need for a remedy will be relatively smaller. In contrast, if piggybacking credit can increase credit scores substantially for a large share of the population, then the potential for harm from piggybacking credit may warrant a reconsideration of existing regulations, industry practices, or both to preserve the predictiveness of credit scoring models.
The simulation begins with the credit records of each of the 232,467 individuals in the 2003 sample and adds to their credit record an additional, authorized user credit card account. To provide an estimate of the maximum amount of benefit one could get by adding an authorized user tradeline to their record, we use values from the 90th percentile of the distributions for account age and credit limit (which translate into an account opening date of March 1987 and a credit limit of $15,000) and assume that the account has an unblemished payment history since opening.23 Additionally, we assume that the current balance on the account is $1. We simulate what each borrower's FRB base score would have been after the addition of this account and compare this to the original FRB base score. The increase in score that results from the addition of this simulated authorized user account provides an estimate of the benefits to a consumer of piggybacking credit.
Table 4 shows a breakdown of score changes by the demographic characteristics and credit record classifications used earlier. On average, the addition of this simulated account increases credit scores by 6.9 points. Those segments of the population that were identified earlier as being the most likely to benefit from an ability to purchase authorized user status, however, experienced much larger increases. The largest increase in score was experienced by individuals whose oldest tradeline was less than 2 years old. The addition of this simulated tradeline increased the credit scores for this group by an average of 22.4 points over the starting mean score of 37.9. As expected, individuals with thin credit files (2 or fewer non-authorized user tradelines) also experienced large increases in score, with their scores rising on average from 44.6 to 64.0.24
The importance of a change in credit score will depend crucially upon both the size of the change and the initial credit score. For example, a 20 point score increase might have a smaller effect for an already prime-quality borrower (who may already qualify for credit on the most favorable terms) than it would for a subprime borrower, who as a result might now appear to be near-prime or even prime-quality. To provide a better understanding of how credit score changes experienced after the addition of the simulated AU tradeline vary with the starting FRB base score level, figure 1 shows the mean change in score that resulted from the simulation against the beginning FRB base score.
As seen in that figure, there is substantial variation in the mean score change by credit score level. The smallest mean increases (and only decreases) are observed for individuals at the highest credit score levels. These are people who already are identified by the credit scoring model as having low default probabilities, such that the additional information provided by the simulated tradeline has little or no beneficial effect. The remainder of the population experienced larger increases on average, with a noticeable dip in the low 20s. The bottom of this dip occurs at a score of approximately 24, which roughly corresponds to the boundary between subprime and near-prime credit scores.25
An alternative method of evaluating the impact of the addition of the simulated tradeline is to look at threshold effects. In this case, we examine how many people with subprime-level credit scores experienced score increases that moved them into near-prime or even prime levels. The results of this analysis suggest that there is substantial potential for movement across these credit risk categories (table 5). More than one-quarter of the 56,000 subprime borrowers in our sample experienced credit score increases that moved their credit score into the near-prime range because of the simulated tradeline. Similarly, over one-third of near-prime borrowers had prime credit scores because of the simulated tradeline and 1.2 percent of these borrowers improved to super-prime credit scores.
While there are only small differences across demographic groups, the credit record population segments that we have already highlighted exhibit much larger threshold effects. Amongst the thin file population, for example, 46.8 percent of subprime borrowers are moved into the near-prime segment and an additional 3.6 percent become prime borrowers because of the simulated tradeline. Threshold effects are notably smaller, however, for individuals with past delinquency. For example, only 7.9 percent of subprime borrowers with two or more delinquencies in the past 24 months experience score increases into the near-prime segment and only 3.4 percent of near-prime borrowers with 2 delinquencies receive prime scores.
These numbers appear to indicate that the practice of piggybacking credit can increase credit scores to an economically-significant extent, if the account to which a non-prime borrower is being added is of sufficiently high quality. Furthermore, it appears that a large fraction of borrowers - particularly borrowers with thin or short credit histories - can obtain substantially higher credit scores as a result of this practice. This suggests that the practice of piggybacking credit offers substantial potential to increase the credit scores of individuals added as authorized users on existing accounts and consequently to enhance their access to credit at lower costs..
7 Evaluating the Predictiveness of Authorized User Account Information
We next consider what impact including, or alternatively excluding, authorized user account information in credit scoring models will have on the model's ability to predictive future credit performance. Since authorized users are not liable for any debt incurred, performance on these accounts may not reflect the authorized user's creditworthiness and consequently may have little relationship to the performance of authorized users on their own (non-authorized user) accounts going forward. In such cases, including authorized user accounts in a credit score might reduce the predictiveness of a credit scoring model. However, if there is a close financial relationship between the account holder and the authorized user, in that the authorized user may rely on the account holder to provide financial support and to be a source of financial strength or may be the person who manages a household's finances, then both the account holder and the authorized user may have similar future loan performance. In this case, including authorized user accounts in the calculation of a credit score should increase the model's predictiveness.
To assess each model's predictiveness, we rely on credit performance on non-authorized user accounts that were opened in the six month period after the date for which the scores were calculated (July to December 2003). Credit performance is evaluated over the eighteen month performance window, running from July 2003 through December 2004 with performance on each account categorized as good, bad, or indeterminate using the same criteria described earlier in discussing model construction. All of an individual's accounts that exhibit performance are weighted equally with the sum of the weights summing to 1 for each individual. This is equivalent to measuring performance by selecting a random account from each credit record, which is a methodology commonly used in assessing credit scoring models.28
Using this performance measure, we calculate the credit scores produced by the FRB base model and by the re-estimated model without authorized user tradelines for the credit bureau records from June 2003. If authorized user accounts increase the predictiveness of a credit scoring model, we would expect the goodness-of-fit measures for the FRB base model to be higher than those for the re-estimated model. We rely on two commonly-used measures of goodness-of-fit to evaluate model predictiveness: the Kolmogorov-Smirnow ("KS") statistic and the divergence statistic.29
Both goodness-of-fit measures suggest that a credit scoring model that incorporates information on the authorized user accounts in a person's credit record has greater predictive power for new non-authorized user accounts than a model that does not consider this information. As shown in table 7, both the KS-statistic and the divergence statistic for the FRB base model (57.1 and 2.52, respectively) were higher than the values of these statistics for the versions of the FRB base model that was re-estimated without the information on each individual's authorized user accounts (57.0 and 2.51). However, both differences were small, suggesting that the increase in predictive power offered by authorized user accounts was marginal.
In addition to the overall change in score predictiveness, we can also evaluate how predictiveness is affected for specific subpopulations. Particularly interesting is the effect of including authorized user accounts in credit scoring models on predictiveness among individuals with short or thin credit histories based on their non-authorized user accounts. Since these are the individuals for whom non-authorized user accounts provide the least information, one might expect the additional information provided by authorized user accounts to be particularly effective at differentiating risk within these categories. Both goodness-of-fit measures suggest that the FRB base model has significantly greater predictiveness with the authorized user tradelines included than the model that excludes these tradelines. The improvements in fit for these populations are generally larger than for the other subpopulations examined.
Taken together, these results suggest that information provided by the authorized user accounts on an individual's credit record appear to provide additional information about the authorized user's future credit performance. The boost to credit score predictiveness, however, appears to be mild. Additionally, some of the decline in model predictiveness may be mitigated by altering the selection of credit characteristics included in the model. Nevertheless, authorized user account information does seem to add additional predictive power to the FRB base model.
8 Conclusions and Caveats
Regulation B, which implements the Equal Credit Opportunity Act (ECOA), contains several requirements about the treatment of spousal authorized user accounts. Among the requirements are that creditors who report information to credit bureaus must report the information in a manner that reflects the participation of both spouses and that information on spousal authorized user accounts must be considered, when available, in a evaluating credit history to assess creditworthiness.
Our analysis of authorized user tradelines in a random sample of credit records suggests that over one-third of individuals have one or more authorized user accounts. The characteristics of authorized user accounts are generally superior to non-authorized user accounts in that they tend to be older, have lower utilization levels, and less evidence of past delinquency. The usage of authorized user account status appears to differ across demographic groups with minorities, single individuals, and the young being less likely to have authorized user accounts than the rest of the population.
Married women, whose treatment was a central factor motivating the provisions of Reg. B, are more likely to have authorized user accounts on their credit records than are married men. The greater frequency with which married women are authorized users, however, does not appear to come at the expense of being an account holder. The credit records of married women also had the same or slightly more non-authorized user tradelines than the records of married men. This suggests that the concerns about a lack of credit history for married women as a result of accounts being reported to the credit bureaus in the husband's name that motivated the Reg. B provisions relating to spousal authorized user accounts may be less relevant today, possibly reflecting the success of ECOA in equalizing credit opportunity and credit reporting for married women.
Based on the analyses documented in this paper, authorized user accounts appear to provide only a modest boost on average to individual credit scores in a scoring model that incorporates authorized user account information. Despite the differences in usage patterns across groups, there is little evidence that authorized user accounts contribute meaningfully to score differences across demographic groups. While authorized user accounts increased the credit scores of married women more than married men, this difference explains only about 10 percent of the score difference between married women and men. For those individuals with thin or short credit histories, however, the incorporation of authorized user tradeline information may offer an economically-meaningful boost to scores.
Despite the minor differences observed in scores as a result of authorized user accounts, our results suggest that the practice of piggybacking credit can have a large effect on the scores of some individuals. Particularly for individuals with short or thin credit histories, the addition of a high-quality authorized user account can significantly improve an individual's credit score. In a substantial fraction of cases, these improvements are economically meaningful in that an individual with a subprime credit score can be moved to near-prime levels, or someone with a near-prime score can become prime. If an authorized user account is added in advance of a major credit transaction, such as a new mortgage, the individual may be able to access credit for which he would not have otherwise qualified or to obtain credit with much lower borrowing costs.
The potential distortions in credit scores that piggybacking credit may introduce suggest that a reconsideration of existing regulations, industry practices, or both may be warranted to preserve the predictiveness of credit scoring models. Our results suggest that eliminating authorized user accounts from a credit scoring model has only a modest effect on score differences across groups. Nevertheless, these same results suggest that the predictiveness of the credit scoring model without authorized user tradelines is somewhat diminished from that of the model that incorporates authorized user account information. This suggests that authorized user accounts provide useful information and there may be some downside to excluding this information from credit scoring models.
There are several caveats that go along with this analysis. First, the analysis here utilizes the FRB base model, instead of a commercially available credit scoring model. While we would have preferred to use one or more of the models actually used in credit underwriting, these models were not available and without the estimation sample used (along with demographic information on at least marital status) would not have been sufficient. Nevertheless, the FRB base model has been shown to produce very similar results to commercially available models (Board of Governors, 2007) and we believe that the results obtained from this model are broadly applicable.
Second, our analysis has examined the potential score improvement that an individual can achieve by piggybacking on a high quality account. The characteristics of the account used in our simulation were selected to provide, essentially, a reasonable upper bound on the benefit that an individual could achieve by buying authorized user status. The actual accounts available for piggybacking may result in smaller score improvements or even score declines (particularly if the account eventually becomes delinquent).
Third, the analysis presented in this paper has looked at a single possible response to piggybacking credit. Other possible responses, such as continuing to incorporate authorized user account information in credit scoring models but in a manner that is distinct from non-authorized-user accounts, have not been evaluated. This may be a useful area for further research to identify effective methods to minimize the potential harm from piggybacking credit while continuing to incorporate the predictive information provided by authorized user accounts.
Finally, our analysis has made no attempt to document the extent to which individuals are engaging in piggybacking credit. Because of the way data is reported to and stored by the credit bureaus, identifying such conduct is difficult if not impossible. Regardless of how often the practice is being used, however, we have shown that piggybacking credit has the potential to artificially improve credit scores at least for specific segments of the population.
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