Limited access to credit, possibly due to stereotyping and discrimination, is a longstanding concern for American Indian communities in the U.S.1 Previous research by Center for Indian Country Development (CICD) scholars Valentina Dimitrova-Grajzl, Peter Grajzl, Joseph Guse, and Richard Todd suggested that this concern might be valid, including for credit cards, which are a widespread and important channel of consumer credit. The authors used credit history data to show that consumers (of all races) who lived in federally recognized American Indian reservations were somewhat less likely to borrow on bank-issued credit cards (bank cards) than consumers who lived nearby but outside of these reservations.2 They also found evidence suggesting that the difference might be more closely associated with the percentage of American Indian and Alaska Native (AIAN) residents in the cardholder’s neighborhood than to reservation location per se.
In a new paper forthcoming in Comparative Economic Studies, these authors and Minneapolis Federal Reserve Bank Financial Analyst Michael Williams dig deeper into how access to bank card credit varies with location and other factors in communities in and around federally recognized American Indian reservations. Their new evidence finds no clear effect from residing inside versus near a reservation. However, it even more clearly associates reduced access to bank card credit with a high percentage of AIAN residents in the consumer’s neighborhood.
The authors focus on access to credit cards issued by commercial banks (e.g., not “retail” or store cards) in neighborhoods that are in or near federally recognized American Indian reservations in the contiguous 48 U.S. states.3 To focus on factors associated with lenders’ credit supply decisions rather than consumers’ demand for credit, the authors look only at consumers who are obtaining their first credit card (either first ever or first subsequent to a period without a bank-issued credit card). The key outcome measured is the credit limit the consumer obtains on their new card.
Using Census 2000 data on neighborhoods and anonymized individual credit history records from the Federal Reserve Bank of New York/Equifax Consumer Credit Panel (CCP), the authors apply an array of statistical techniques to associate the credit limit on the new card with neighborhood and personal factors. For consumers, the key factors include their:
- pre-existing4 Equifax Risk Score (an indicator of overall credit risk based on the consumer’s credit history),
- bankruptcy history,
- age, and
Location is measured in two ways—by the percentage of American Indian and Alaska Native (AIAN) residents in the consumer’s neighborhood and by whether the consumer lives inside a federally recognized reservation or in a neighborhood near but outside of a federally recognized reservation.5
The consumer’s own income is not included in the CCP. As a substitute, the researchers include variables that represent the distribution of household income in the neighborhood. Additional neighborhood variables include:
- the percentage of the population with certain education, marital status, employment, and housing characteristics, and
- the share of population that is black and Hispanic.
Year-by-year differences and factors specific to each reservation are also controlled for, and statistical techniques are used to mitigate the effects of sample selection bias (possibly arising from the fact that only certain types of consumers are first without a card and then obtain one).
One factor the authors do not control for is the individual’s race. That’s because racial information is not included in either individual credit card applications or in individual credit histories maintained by Equifax and other credit history reporting bureaus. In other words, the authors examine effects associated with the racial characteristics of the consumer’s neighborhood, but do not know and thus cannot analyze effects associated with the consumer’s race.
The following results consistently emerge, even when the authors check their results by repeating their analysis under a variety of statistical techniques:
- “Residing in an Indian Country neighborhood with a high share of [AIAN] residents is, after controlling for a wide range of factors, associated with statistically significantly lower awarded bankcard credit limits than is residing in an Indian Country neighborhood with a low share of [AIAN] residents.”6 For example, using the authors’ preferred statistical model, “consumers who never previously owned a bankcard and who reside in neighborhoods where all residents are [AIAN] are on average awarded a 23.6 percent lower total credit limit than consumers who reside in neighborhoods with no [AIAN] residents, all else equal.”
- As long as the share of AIAN residents is taken into account, living inside or outside of reservation boundaries has no statistically significant effect on the consumer’s credit limit.
- AIAN consumers are not an exception to the general rule that an individual's credit history (captured in this case by their Equifax Risk Score and recent history of bankruptcy) exerts an economically large and robustly statistically significant effect on their credit limit.
One potential yet uncertain implication of these finding is redlining—an illegal form of lender discrimination based on the characteristics of neighborhoods rather than individual characteristics. In particular, the study’s findings are consistent with the possibility that bank card lenders discriminate on the basis of the share of AIAN residents in the consumer’s neighborhood. In contrast, the findings are not consistent with discrimination based on reservation boundaries. This suggests that any legal or jurisdictional differences between residing inside versus outside of a reservation did not affect consumers’ credit card limits.
However, the authors stress that their “results should not be interpreted as conclusive evidence of discrimination.” They cite their lack of sufficient data to clearly determine why access to bank card credit is lower in American Indian neighborhoods. Future work to clarify this important policy question, they say, would require either experimental evidence or more detailed data, such as information about individual income (rather than the neighborhood income distribution) and the contractual terms of the credit relationship (such as the interest rate, fees, and other terms of debt repayment).
The finding that credit history factors known to affect access to bank card credit in the general population also apply in Indian Country supports efforts to improve consumers’ credit histories in these areas. The authors do not suggest that such efforts eliminate concerns about potential redlining. However, they find that even moderate improvements in an individual’s credit score are associated with large increases in credit limits.
In the authors’ preferred model, consumers with a moderately low Equifax Risk Score (in the second 20 percent, or quintile, of the distribution of scores) received credit limits 70 percent higher than consumers with very low scores (in the bottom 20 percent). In the extreme case, comparing consumers with very high Equifax Risk Scores (in the highest 20 percent) to consumers with very low scores (in the lowest 20 percent), credit card limits were on average 772 percent higher percent for the former. These effects are much higher than the effects associated with the share of AIAN residents in consumers’ neighborhoods, prompting the authors to suggest “that financial education and credit counseling…services often provided by community development financial institutions and other community service organizations in tribal communities are important for improving bankcard credit access and usage on and near reservations.”