A Profile of Financially At-Risk College Students

5 6 THE JOURNAL OF CONSUMER AFFAIRS ANGELA C. LYONS A Profile of Financially At-Risk College Students Using a random sample of college students, this study identities the factors that significautly affect the probability a college student is financially at risk for tnismanaging/misusing credit. Financially at-risk students are more likely to be financially independent, to receive need-based financial aid, lo hold $1000 or more in other debt, and to have acquired their credit card(s) by mail, at a retail store, and/or at a campus table. Students having difficulty making credit card payments are also more likely to be female, black, and/or Hispanic. Campus administrators and financial professionals can use this information to better allocate their resources and develop materials that specifically target those students who need them most. In recent years, the dramatic growth in credit card usage among college students has generated concern that students' credit card hehavior is putting them at greater risk for high debt levels and misuse and/or mismanagement of credit after graduation. Rising college costs and the recent economic slowdown have intensified these concerns. Several recent studies have attempted to determine whether college students are in fact incurring excessive amounts of credit card dehl (Allen and Jover 1997; Armstrong and Craven 1993; Baum and O'Malley 2003; The Education Resources Institute and the Institute for Higher Education Policy 1998; Hayhoe 2002; Hayhoe, Leach, and Turner 1999; Hayhoe, Leach, Turner. Bruin, and Lawrence 2000; Lyons and Andersen 2002; Joo, Grable. and Bagwell 2001; Staten and Barron 2002; U.S. General Accounting Office 2001; Xiao, Noring. and Anderson 1995). These studies examine students' use of credit including: credit card ownership, the amount of credit card deht incurred, the types of credit cards held, and students' attitudes towards credit usage. In general, these studies find that while the majority of college students now have credit cards, they appear to he using them responsibly and are not Angela C. Lyons is an Assistant Professor in the Department of Agricultural and Consumer Economics at the University of Illinois at Urbana-Champaign {anglyons@uiuc.edu). The author gratefully acknowledges ihe assistance and support of Orlo Austin, the Director of the Oftice of Financial Aid, and Patricia Andersen. The author also thanks Dorothy Bagwell. Celia Rae Hayhoe. Robert Weagley, and Tanse! Yilmazer for their invaluable comments and support. An earlier version of this paper was presented at the 2003 Annual Conference of the American Council on Consumer Interests. The Journal of Consumer Affairs, Vol. 38. No. 1. 2004 ISSN 0022-0078 Copyright 2004 by The American Council on Consumer Interests

SUMMER 2004 VOLUME 38, NUMBER 1 57 accumulating large amounts of debt. The findings from these studies indicate the following: approximately 70.0% of college students have at least one credit card; the vast majority obtain credit cards prior to college or during their freshman year; between 6.0% and 14.0% have four or more credit cards; over half of those with credit cards repay their balances in full each month; and only 14.0% to 16.0% report balances over $1000 while about 5.0% report balances over $3000. The findings from these studies suggest that growing concerns over rising credit card debt levels of college students are likely to be unwarranted. These studies, however, acknowledge that there are some college students who do have excessive amounts of debt. These students are at risk of not being able to repay their debts, either because of a lack of financial experience or a lack of funds. Unfortunately, these studies do not clearly identify who these students are. There may be identifiable groups on college campuses that are more at risk than others for experiencing financial strain. These at-risk groups may have a need for specific financial education programs to ensure that they are not at a financial disadvantage and are able to make informed financial decisions. What characteristics are associated with being financially at risk? Do these characteristics vary by the measure of risk used? This study addresses these issues and provides insight into what college administrators and financial professionals can do to help at-risk students better manage their finances and repay their debts after graduation. LITERATURE REVIEW As previously mentioned, several recent studies examine the behavior and attitudes of students with respect to credit card usage. Armstrong and Craven (1993) were among the first to examine college students' credit usage and payment practices and to identify race and sex as significant predictors of the number of credit cards held. With respect to gender, they found that women were more likely than men to own credit cards. Women, however, carried lower balances. They argued that women may have a better understanding of credit cards and their finances. With respect to race, they found that black students were less likely than white students to own credit cards. Xiao, Noring, and Anderson (1995) were among the first to focus on college students' attitudes towards credit card usage. They found that students with "favorable" attitudes towards credit cards were more likely to be male, living on campus, and majoring in consumer affairs. Students with

58 THE JOURNAL OF CONSUMER AFFAIRS favorable attitudes were also more likely to own credit cards and/or phone cards, use credit cards more frequently, and have credit cards that were cosigned by their parents. They concluded that effective credit education programs needed to find ways to change students' attitudes about credit so that they were more receptive to information on how to use credit responsibly. Joo, Grable, and Bagwell (2001) were also among those to examine the factors associated with students' attitudes towards credit cards as well as students' actual credit card usage. Using a sample of 250 students from a large university in a southwest state, they found the following: 70.7% of the students sampled had at least one credit card with more than 10.0% holding five or more cards; almost half of the students with credit cards (49.4%) paid their credit card bills in full each month; and 10.0% of those with credit cards made only the minimum payment each month. Positive attitudes towards credit cards were found among those students who owned a credit card(s), were white, were in a lower academic year, had parents who used credit cards, and had parents who did not have credit-related problems. Unlike Xiao, Noring, and Anderson (1995), they did not find gender, academic major, and living arrangements to be significant predictors of credit attitudes. A study by Hayhoe, Leach, and Turner (1999) used a 1997 sample of 426 college students from five state universities. Using this sample, they estimated an ordered logistic regression and identified the factors that affect a student's decision to hold 4 or more credit cards. Students with four or more credit cards were significantly more likely to be female, to have taken a personal finance course, to prepaie a list when shopping, and to have had money used as a reward in their family. They were also significantly more likely to feel an emotional "high" using credit cards and to think more about the consequences of using credit. They were less likely to borrow from friends or relatives. In the only longitudinal study to specifically examine college students and credit card usage, Hayhoe (2002) examines the credit usage of 120 college students from six universities in the spring of 1997 and then follows up with these students in 1999. The results from the follow-up show that, for the most part, students were able to repay their debts after graduation. Only one student had defaulted on his or her student loans and three were behind on rent/utility payments. Most students had decreased the number of credit cards they were holding. However, they increased the number of credit cards they were holding with an outstanding balance. Another recent study by Staten and Barron (2002) pooled a sample of 300,000 active accounts randomly selected from the portfolios of five of

SUMMER 2004 VOLUME 38, NUMBER 1 59 the top 15 general-purpose credit card issuers in the U.S. The accounts were opened hetween mid-1998 and early 2000 and were active for a 12- month period during 2000-2001. The study compared hehavior across three types of credit card accounts: those opened through student marketing programs, those opened by young adults 18-24 through conventional marketing programs, and those opened hy adults 25 or older through conventional marketing programs. They found that accounts marketed to college students had smaller halances and lower credit limits than conventional accounts that were opened by young adults and older account holders. In addition, student accounts were more likely to be delinquent and to result in a charge-off compared to the other accounts. However, the dollar amounts on delinquent accounts and the actual amounts charged-off were substantially lower. According to the authors, the findings are consistent with issuers' statements that students establish accounts with relatively low credit limits with the expectation that the majority will learn how to manage a credit card and establish a good credit history. Overall, the previous studies have significantly contributed to the literature by laying a foundation for examining the credit card usage, attitudes towards credit, and fmancial knowledge and practices of college students. However, while these studies find that most college students use credit cards responsibly, there are some students whose credit card behavior puts them at greater risk for high debt levels and misuse of credit after graduation. Previous studies do not clearly identify who these students are. In addition, there are doubts about the randomness of the samples used in many of these studies. Most, if not all, use data that was not randomly collected and so the findings are based on select groups of students which calls into question the reported findings. This study picks up where recent literature has left off and uses a random sample of college students at the University of Illinois to identify the characteristics of financially at-risk students and provide insight into how college administrators and financial professionals can better address their financial education needs. The findings from this study are of particular importance given rising college costs and the recent economic slowdown, which have undoubtedly placed financial strain on many middle-to-low income students (Asinof and Chaker 2002; The Education Resources Institute and The Institute for Higher Education Policy 1998; Lyons 2003; Shenk 1997; Rohrke 2002; U.S. General Accounting Office 2001). In knowing which students are financially at risk, campus administrators and financial professionals can better allocate their resources and develop materials to specifically target those students who need them most.

60 THE JOURNAL OF CONSUMER AFFAIRS The remainder of this paper is structured as follows. The next section discusses the methodology and empirical framework used to identify financially at-risk college students. The third section describes the sample, and the fourth section presents the empirical findings. The final sections discuss and summarize the findings and explain in more detail the results of this study and why they are of particular importance and timeliness. METHODOLOGY Theoretical Framework Traditional life-cycle theory with budget constraints lends considerable insight into why students borrow to finance their college education. According to the theory, households are rational agents who form expectations about their future income and wealth holdings, borrowing against those expectations in order to smooth consumption over their life cycle. For college students, the level of debt incurred to finance their education in the current and future periods is the optimal level of debt needed to maintain their equilibrium position. Within this framework, some students may have every intention of repaying their debts when they are incurred. However, unforeseen events (i.e., an unexpected increase in tuition or living expenses, car repairs, or medical expenses) may place them in a position where they are no longer able to meet their debt obligations. In these cases, students are unable to repay their current debts. The end result for these students may be delinquency, and in the most extreme cases, bankruptcy. For each student that applies for a credit card, a level of creditworthiness, C(Wi), is established. A student's level of creditworthiness is based on a number of factors, W,, including the student's current income, expected future income, wealth holdings, past credit history, and other demographics. Credit card companies use this measure of creditworthiness to determine the amount of credit that will be extended to the student. Lender's reject a student's application when their level of creditworthiness falls below some minimum level, C(M^y), such that C(W,) < C(W,). In this case, the student's default risk is so large that the lender is unwilling to extend credit under any circumstances. Lenders extend credit when C{W^ ^ C(W,). However, the credit limit that is approved and the rate of interest and fees the student must pay depend entirely upon their level of creditworthiness, C{W,). Lenders will approve larger amounts of credit at lower rates of interest for students whose C{W^) is farther away from QW^. At the

SUMMER 2004 VOLUME 38. NUMBER 1 61 same time, lenders will charge higher rates of interest and extend smaller amounts of credit to students whose C(W/) is closer to or equal to C(Wi). The closer CiWj) is to C{Wj)., the more vulnerable the student is to unexpected financial distress and the more likely they are to niisuse/mismanage their credit and default. Misuse and/or mismanagement of credit further lowers a student's level of creditworthiness. Students with low levels of creditworthiness typically have lower credit scores because they hold large balances, are delinquent on their payments, have reached the limit on their cards, and/or are having difficulty paying their balances in full. Thus, these financially at-risk students have certain identifiable characteristics that make them more likely than others to accumulate high interest payments and large amounts of credit card debt. The next section presents the empirical models for being financially at risk and identifies the factors that are likely to contribute to misuse and/or mismanagement of credit. These factors also affect an individual's level of creditworthiness. The Empirical Model For the purposes of this study, students are identified as being financially at risk if they have one or more of the following four characteristics: 1) have credit card balances of $1000 or more, 2) are delinquent on their credit card payments by two months or more, 3) have reached the limit on their credit cards, and 4) only pay off their credit card balances some of the time or never. The measures of financial risk were constructed based on previous research which has consistently identified credit misuse and/or mismanagement according to these four characteristics (i.e., Baum and O'Malley 2003; The Education Resources Institute and The Institute for Higher Education Policy 1998; U.S. General Accounting Office 2001). These measures clearly identify students who are having difficulty managing their credit and/or repaying their credit card balances and allow for comparisons to more easily be made across studies. Each measure captures a slightly different aspect of financial risk the magnitude of how much is owed, the ability to make timely payments, future ability to borrow, and the ability to repay debts incurred. Students who are classified as financially at risk may have one or more of these characteristics. The next section identifies the students who fall into each of these categories. To identify the factors that affect the probability of being financially at risk, probit models are estimated for each of the four at-risk behaviors

62 THE JOURNAL OF CONSUMER AFFAIRS mentioned above. The relationship is assumed to be as follows for the first model: (1) Z)> - X,'^, -\- M, where D; = 1 if D,* ^ 1000 and 0 otherwise for i = {1 I}. D, is the discrete dependent variable that is equal to one if the /''' student holds credit card balances of $1000 or more and zero otherwise. D, is determined hy the continuous, latent variable D,*, the actual amount of credit card deht held hy the student. The factors that determine D,*, and thus D,, are represented hy the vector Xj. Included in X, are factors that are used to control for other types of credit usage and financial behavior including: whether the student is financially independent; whether the student receives needs-based financial assistance; whether the student has $ 1000 or more in other debt such as a car loan, mortgage, or other private loan; and how the student acquired their credit card(s) (by mail, at a bank/financial institution, at a retail store, and/or at a campus table). It is important to note that some of these factors may he endogenous. However, due to data limitations, it is not feasible to construct instruments to control for the possibility of endogeneity. Therefore, it is assumed that these values have been exogenously determined. In addition to the factors previously mentioned, the vector X, also controls for student demographics such as gender, ethnicity, marital status, hours worked per week, the population of the urban or rural area where the student attended their senior year of high school, whether they are a graduate student, whether they rent an apartment off campus, and whether they are likely to budget their money every month. Other variables in the vector X, are used to control for a student's attitudes towaids credit usage. Specifically, students were asked whether they believed it was all right to borrow money or use a credit card to pay for spring break or other vacations, to buy a car or make car payments, to pay for educational expenses, to pay for entertainment, and/or to use for shopping. The error terms, H,, are assumed to be distributed standard normally with mean zero and variance o", equal to one. The prohit method is used to find consistent estimates of equation (1). The probit method is also used to identify the factors that determine the probability that a student is 1) delinquent on their credit card payments hy two months or more, 2) reaches the limit on their credit cards, and 3) only pays off his or her credit card balances some of the time or never. In all three cases, the likelihood functions for each of the prohit models are estimated and consistent estimates of the regressors are obtained.

SUMMER 2004 VOLUME 38, NUMBER 1 63 SAMPLE The Office of Student Financial Aid (OSFA) conducted an online survey in the fall of 2001 to examine the credit usage of undergraduate and graduate students at the University of Illinois. A random sample of 2,650 students (approximately 7.0% of the total student population) was selected from the University's database to electronically receive the survey. The sample included undergraduate and graduate and professional students. The survey was posted on the web at the beginning of November. At this time, an email message with a formal description of the survey and a link to the survey's URL was sent to the random sample. Between November 9th and December 9th, 2001, the OSFA sent out a total of four mass e- mails to the random sample. Students who completed the survey were given the option to participate in a prize drawing for a $150 bookstore voucher. Five vouchers were awarded, the winners being randomly selected from a pool of students who chose to submit their email address to participate in the drawing. For a more detailed description of the survey methodology used to conduct this online study, see Lyons and Andersen (2002) and Lyons, Cude, Lawrence, and Gutter (2003). A total of 915 students responded to the survey (a response rate of 34.0%). However, of the 915 students who responded to the survey, only 835 were valid responses. Eighty student observations had to be dropped primarily due to missing information. Observations were also removed because a few students submitted their survey information more than once or submitted blank surveys. It should be noted that the sample used for this study includes both undergraduate students and graduate and professional students. Both groups are included in the sample primarily because of the small sample size and the small cell sizes that result if only undergraduate students are included. This may be viewed as a limitation of the study since there are some identifiable differences in the credit usage of undergraduate and graduate students. For example, graduate students are more likely to owe more credit card debt but less likely to mismanage/misuse credit. However, it can also be viewed as a contribution to the literature since very little work has focused on the credit usage of graduate students. The tables that follow provide an overview of the demographic and financial characteristics of the sample. Findings are presented for students with credit cai-ds, students with credit cards and no at-risk characteristics, and students with credit cards and at least one at-risk characteristic. Information is also presented according to the four at-risk characteristics: credit

64 THE JOURNAL OF CONSUMER AFFAIRS card debt ^$1000, delinquent on credit card payments, reached the limit on their credit cards, and only paid off credit card balances some of the time or never. Of the 835 students who comprise the working sample, 78.8% report having at least one credit card. Of those with a credit card, 47.0% indicate that they engage in at least one of the four at-risk behaviors. About 44.0% of at-risk students report engaging in only one at-risk behavior (the majority not paying their bills in full each month), and 33.3% report engaging in two at-risk behaviors (primarily holding balances over $1,000 and not paying bills in full each month). Almost 16.0% of at-risk students have three of the four at-risk characteristics, and only 6.8% report all four characteristics. The first column in Table 1 provides general demographic information on the entire student sample used in this study regardless of at-risk status. Almost 55.0% of the students are female and 10.7% are married. With respect to ethnicity, 69.9% are white, 5.3% are black, 15.2% are Asian, and 5.1% are Hispanic. With respect to year in school, 20.0% are freshmen, 14.0% are sophomores, 16.5% are juniors, 22.9% are seniors, and 26.6% are graduate students. Over half of the students in the sample (55.0%) rent an apartment and 70.1 % budget their money every month. In addition, over half of the students (55.7%) are working either on or off campus. The majority of students in the sample (61.2%) lived in a city with a population of 20,000 or more during their senior year in high school. The sample of students used in this study is fairly representative of the campus population as a whole witb tbe exception that there appears to be an oversampling of female students. Campus statistics for 2002 indicate that 47.0% of the total student body is female, 6.9% is African-American, 13.1% is Asian, 5.8% is Latino, and 25.4% report being graduate students. The remaining columns in Table I focus on the demographics of students according to their financial risk status. The findings reveal that students with credit cards who exhibit at-risk behaviors are more likely than students with credit cards who do not exhibit at-risk behaviors to be female, married, black, Hispanic, and/or to be renting an apartment. They are also more likely than other students to be college seniors and graduate or professional students. Interestingly, students who are delinquent on their credit card payments by two months or more are more likely to be female. With respect to employment, students witb credit cards who exhibit at-risk behaviors are more likely to be working and to be working more hours per week than the entire sample of students. While the results are

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66 THE JOURNAL OF CONSUMER AFFAIRS somewhat mixed with respect to regional information, financially at-risk studentsaremorelikely to have lived in a city with a population of 100,000 or more during their senior year in high school. Table 2 summarizes tbe credit cbaracteristics of the entire sample and compares them to those who have been identified as financially at risk. As mentioned previously, 78.8% of the total number of students who were sampled report holding at least one credit card. About 15.0% report holding 4 or more credit cards. With respect to credit card balances, the first column of Table 2 shows that 15.7% of the students sampled indicate that they owe $1000 or more in credit card debt and 7.3% owe $3000 or more. The majority of students (69.6%) report paying off their balances in lull each month. However, 15.2% reached the borrowing limit on their cards almost every month, and 7.5% have been delinquent on their credit card payments by two months or more. With respect to their ability to obtain credit, 23.2% report that they have been rejected or turned down by a credit card company. These findings are consistent with the literature suggesting that the credit practices of students at the University of Illinois are similar to those of other college campuses. Compared to tbe entire sample, financially at-risk students are significantly more likely to hold 4 or more credit cards and to owe more than $3000 in credit card debt. Financially at-risk students are also much more likely to reach the borrowing limit on their credit cards, he delinquent on their payments, and/or be rejected for a credit card. They are less likely to be ahle to pay off their balances in full each month. These findings should not be surprising since many of these characteristics are being used to identify those at financial risk. Other characteristics that appear to play a key role in whether a student is financially at risk include whether or not the student is financially independent; receives some type of need-based financial assistance including need-based grants, financial aid loans, and/or federal work study; and/or owes $1000 or more in other debt including car loans, mortgages, and other personal loans. According to Table 2, students' attitudes towards using credit appear to contribute very little to a student's likelihood of mismanaging/misusing credit. However, a key characteristic that appears to determine financial risk is related to how students acquire their credit card{s). Financially at-risk students are more likely than the entire sample to acquire their credit card{s) through a mail application, at a retail store, and/or at a campus table. It is important to point out that in the survey students were asked to report all the ways in which they had acquired their credit card(s): in the

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68 THE JOURNAL OF CONSUMER AFEAIRS mail, at a retail store, at a financial institution/bank, at a campus table, over the phone, or completing an application online. Since students could select more than one choice, the percentages do not sum to 100%. Only the top four choices of how students acquired their credit card(s) are reported in the tables (in the mail, at a retail store, at a financial institution/bank, at a campus table). The other two categories (over the phone and completing an online application) are omitted from the tables. The percentages of students who indicated that they acquired their credit card(s) by completing an online application or over the phone were 10.3 and 5.2%. respectively. Overall, this initial investigation of the data provides insight into the factors that may explain why some college students are at greater financial risk than others. Those factors that are likely to be significant contributors to whether a student is financially at risk include gender, ethnicity, being a graduate student, being financially independent, receiving financial assistance, owing other debt, and whether the credit card(s) is acquired in the mail, at a retail store, or at a campus table. The next step is to see if the regression results support these preliminary findings. RESULTS The results from the probit models are presented in Table 3. Four probit models have heen estimated to determine the factors that significantly affect the probability a student 1) holds $1000 or more in credit card debt, 2) is delinquent on credit card payments by two months or more, 3) has reached the limit on his or her credit cards, and 4) only pays off credit card balances some of the time or never. For each model, students without credit cards and those with credit cards who are not classified as being financially at risk are grouped together and compared to those who are classified as being financially at risk. Researchers may be concerned that differences in the hehavior and characteristics of those without credit cards and those with credit cards may exist among those who are not financially at risk. Two-stage probit estimation models that correct for sample selection reveal that the characteristics of the two groups are very similar, and excluding students without credit cards from each model does not significantly affect the results. The appendix presents the probit estimates for only those students that reported having a credit cai"d. The two-stage estimates are available from the author upon request.

SUMMER 2004 VOLUME 38, NUMBER I 69 Model 1: Credit Card Balances of $1000 or More The first two columns in Table 3 present the marginal effects and standard errors from the prohit model for the prohability a student holds $1000 or more in credit card debt. As hypothesized, students who receive some TABLE 3 Prohit Models for Probability Student Is Financially At-Risk Groups of At-Risk Student; Credit card debt ^$1000 Delinquent on payments Variable ME SE ME SE Financially independent Receives financial aid Other debt >$1000 Female Black Asian Hispanic Graduate student Married Rents an aparlment Budgets money Works l-9hrs/wk Works 10-15 hrs/wk Works 16-20 hrs/wk Works >20 hrs/wk Town/city (pop 2,500-20.000) City (pop 20.000-99.999) City (pop 100,000 or more) Acquired card in tnail Acquired card at bank Acquired card at retail store Acquired card at campus table AH right to borrow for vacation All right to borrow for car All right to borrow for education Ail right to borrow for entertainment All right to borrow for shopping 0.0120 0.0463 0.0825-0.0282 0,1644-0.0343-0.0121 0.0582 0.0719 0.0652-0.0384 0.0416 0.0305 0.0671 0.0697-0.0512-0.0056 0.0226 0.0956 0.0025 0.1108 0.0581 0.0015-0.0370-0.0104-0.0214 0.0076 (0.0300) (0.0234)*' (0.0319)*** (0.0215) (0.0799)*** (0.0269) (0.0433) (0.0397)* (0.0476)* (0.0224)*** (0.0248)* (0.0370) (0.0333) (0.0506) (0.0470) * (0,0301)** (0.0301) (0.0296) (0.0229)*** (0.0262) (0.0352)*** (0.0403)* (0.0268) (0-0223)* (0.0355) (0.0305) (0,0316) 0.0544 0.0019 0.0369 0.0273 0.1528-0-0095 0-0964 -0.0394 0.0130 0.0416 0,0073-0,0.352-0.0207 0.0305-0.0167 0.0155 0-0036 0.0377 0.0293 0,0117 0,0215 0,0740 0,0043-0.0204-0.0222 0.0107-0.0196 (0-0235)*** (0-0132) (0.0199)** (0.0123)** (0.0716)*** (0,0170) (0.0541)*** (O.OL^S)"* (0.0239) (0.0139)*** (0,0141) (0.0122)** (0.0133) (0.0290) (0.0172) (0-0309) (0.0196) (0.0219)** (0.0148)** (0,0173) (0,0199) (0,0344)*** (0,0163) (0.0136) (0.0255) (0.0176) (0.0167) Observations No. of positive observations R' 835 131 0.2400 835 63 0-2222 Note: ME represents the marginal effects for the probit model. Standard errors for the marginal effects are indicated by ( ). Omitted categories include "not working" and living in a "rural area (pop <2,5OO)." *p < 0.10; **p < 0-05: ***p < 0.01

70 THE JOURNAL OF CONSUMER AFFAIRS TABLE 3 (continued) Probit Models for Prohahility Student Is Financially At-Ri.sk Groups ot At-Risk Student! Reached limit on credit cards Do not pay balance in full Variable MF SE MF SF Financially Independent Receives financial aid Oiher debt >$ 1000 Female Black Asian Hispanic Graduale student Married Rents an apartment Budgets money Works l-9hrs/wk Works 10-15 hrs/wk Works 16-20 hrs/wk Works > 20 hrs/wk Town/city (pop 2.500-20.000) City (pop 20.000-99.999) City (pop 100,000 or more) Acquired card in mail Acquired card al bank Acquired card ai retail store Acquired card al campus tahle All righl to borrow for vacation All right to borrow for car All righl to borrow for education All righl to borrow for entertainment All righl to borrow for shopping 0.0764 0.0446 0.1386 0.0017 0.0363 0.0314-0.0093-0.0923 0.0568 0.0341-0.0383-0.0222-0.0288 0.0252 0.0339-0.0456-0.0199-0.0399 0.0778 0.0586 0.0496 0.1232 0.0323-0.0532 0.0155-0.0240-0.0024 (0.0363)** (0.0256)* (0.0365)*** (0.0235) (0.0564) (0.0367) (0.0485) (0.0275)*** (0.0513) (0.0251) (0.0268) (0.0317) (0.0296) (0.04.53) (0.0476) (0.0347) (0.0308) (0.0281) (0.0249)*** (0.0335)* (0.0337) (0.0479)*** (0.0291) (0.0236)** (0.0346) (0.0335) (0.0348) 0.0984 0.1451 0.1191-0.0092 0.2624 ^ 0.0662 O.0719 0.0592 0.0026 0.1126-0.0565 0.0275 0.0743 0.1784 0-1043 0.0078 0.0753 0.0628 0.1049 0.0181 0.1377 0.1427-0.0322-0.0269 0.0459-0.0298 0.0390 (0.0485)** (0-0357)*** (0.0453)*** (0.0342) (0.0878)*** (0.0462) (0.0874) (0.0555) (0.0607) (0.0354)'** (0.0372) (0.0515) (0.0520) (0.0685)*** (0.0655)* (0.0650) (0.0520) (0.0471) (0.0346)*** (0.0439) (0.0461)*** (0.0587)*** (0.0411) (0.0362) (0.0510) (0.0524) (0-0528) Observations No. of positive observations R^ 835 127 0.1475 835 254 0.1887 Note: ME represents the marginal effects for the probii model. Standard errors for the marginal effects are indicated by ( ). Omitted categories include "not working" and living in a "rural area (pop <2.50O)."' *p < 0.10; **p < 0.05: ***p < 0.01 type of need-based financial aid and/or hold some type of other debt such as a car loan or mortgage are significantly more likely to hold $1000 or more in credit card debt. Specifically, receiving need-based financial aid increases a student's probability of owing $ 1000 or more in credit card debt

SUMMER 2004 VOLUME 38, NUMBER 1 71 by 4.6 percentage points while holding some type of other debt greater than or equal to $1000 increases a student's probability by 8.3 percentage points. Being financially independent does not appear to significantly affect the probability of holding a substantial credit card balance. Other factors that significantly increase a student's probability of holding $1000 or tnore in credit card debt include being black, being a graduate student, being married, renting an apartment, working more than 20 hours per week, and having acquired a credit card through the mail, at a retail store, or at a campus table. Of these, the factor that appears to have the largest effect is being black, which increases the probability of holding a substantial credit card balance by 16.4 percentage points. The findings also show that students who lived in a town/city with a population that was between 2,500 or 20,000 during their senior year in high school are less likely to hold substantial credit card balances than those who lived in a rural area with a population of less than 2,500. In addition, students who indicate that they budget their money every month are less likely to bold $1000 or more in credit card debt. However, the significance of these findings is fairly weak and the findings do not bold up across the other models. Model 2; Delinquent on Credit Card Payments The second set of columns in Table 3 present the marginal effects and standard errors from the probit model for the probability a student is delinquent on their credit card payments by two months or more. These students are perhaps the most at risk since they clearly have difficulty managing their credit and making payments. The findings from this model confirm many of those generated by the first model. Owing $1000 or more in other debt significantly increases the probability of being delinquent by two months or more by 3.7 percentage points. Being black also significantly increases tbe probability by 15.3 percentage points. In addition, renting an apartment and acquiring credit cards through a mail application or at a campus table also increase the probability of delinquency. Unlike the previous model, receiving financial aid does not significantly affect the probability of delinquency while being financially independent does by 5.4 percentage points. In addition, being a graduate student decreases the probability of delinquency whereas being female and Hispanic increase the probability by 2.7 and 9.6, respectively.

72 THE JOURNAL OE CONSUMER AEEAIRS Model 3: Reached Limit on Their Credit Cards Similar trends can be found in tbe data with respect to the probit model for the probability students have reached the limit on their credit cards. The third set of colutnns in Table 3 indicate that being financially independent, receiving financial aid, and owing $ 1000 or more in other debt increase the probability of maxing out credit cards by 7.6, 4.5, and 13.9, respectively. Acquiring a credit card in the mail, at a bank, or at a campus table also increase the probability a student maxes out their credit cards by 7.8, 5.9, and 12,3, respectively. Gender and ethnicity do not appear to play a significant role in this model. However, being a graduate student decreases the probability by 9.2 percentage points. This finding may be an indication that while graduate students have more debt, they may be more responsible about repayment. This finding may also refiect the fact tbat graduate students are able to acquire more credit cards with higher credit limits making them less likely to reach the borrowing limit on their cards than undergraduates. However, while more credit cards with higher limits may not put graduate students at risk for maxing out their credit cards, it does put them at greater risk for acquiring large balances, which was found in the first model. Model 4: Does Not Pay Credit Card Balances in Full The final set of columns in Table 3 show which factors affect a student's probability of only paying off their credit card balances some of the time or never. As in the previous models, being financially independent, receiving financial aid, and owing $1000 or more in other debt increase the probability of not paying balances in full each month. Being black, renting an apartment, and working 16 or more hours per week also increase the probability. As revealed by the other three models, the way in which students acquire their credit card(s) plays a key role in whether or not they are financially at risk. Acquiring a credit card in the mail, at a retail store, or at a campus table increases the probability a student only pays off his or her credit card balances some of the time or never. DISCUSSION Overall, the findings provide substantial insight into tbe characteristics of financially at-risk students and tbe reasons why some students are at greater risk for mismanaging and misusing credit. The findings from tbe

SUMMER 2004 VOLUME 38, NUMBER I 73 four models are fairly consistent in that those students who are having difficulty managing their credit card debt are having financial difficulties in general. As the results indicate, those who hold large credit card balances are more likely to hold other types of debt and receive need-based financial aid. They are also more likely to have little or no financial support from their parents (i.e., those who are financially independent and/or working 16 or more hours per week). These findings support concerns raised hy Zhou and Su (2000) who indicate that students with lower family incomes are likely to have higher student loan amounts and credit card debt than students from families with higher incomes. The findings suggest that the rising costs of obtaining a college education may be playing a key role in the rise of credit usage on college campuses (Asinof and Chaker 2002; The Education Resources Institute and The Institute for Higher Education Policy 1998; Lyons 2003; Shenk 1997; Rohrke 2002; U.S. General Accounting Office 2001). Lyons (2003) indicates that almost 50.0% of students receiving financial assistance do not feel that the amounts they are receiving are enough to cover college costs. Current levels of financial assistance may not be enough to cover the rising costs of college. Those students most in need of financial assistance may he forced to work more than 20 hours per week and to turn to other forms of horrowing such as credit cards to complete their college degree. In the end, those who are likely to be at greatest financial risk may be lowto-middle income students. All four models also indicate that how students acquire their credit cards has a significant effect on the amount of credit card debt incurred as well as a student's ahility to repay those balances. Financially at-risk students are more likely than other students to acquire their credit card(s) through a mail application, at a retail store, and/or at a campus table. These findings suggest that aggressive marketing practices by credit card companies to target college students (i.e., mass mailings, retail store discounts, and credit card representatives on campus) have likely contributed to the recent rise in credit card debt on college campuses putting some students at more financial risk than others. Finally, it is interesting to note that those groups who have traditionally had difficulty obtaining credit (i.e., women, minorities, and low-income individuals) are also more likely to be at financial risk. In particular, those students who are delinquent on their credit card payments are more likely to be female, black, and/or Hispanic. Black students are also significantly more likely to have credit card balances of $1000 or more and to not pay their balances in full, suggesting a general need for financial education.

74 THE JOURNAL OF CONSUMER AFFAIRS Women and Hispanics do not appear to be at risk with respect to the amounts of credit card debt held and being overextended. However, their inability to make payments on time suggests that these groups may need information on the factors that affect an individual's credit rating as well as the importance of building a good credit history. The bottom line is that these findings indicate a need for financial education programs that specifically target financially at-risk groups such as low-to-middle income students, women, and minorities to ensure that they are not at a financial disadvantage. What types of financial services do financially at-risk students find useful? On the Office of Student Financial Aid (OSFA) survey, students were asked about their need for campus financial services. Specifically, they were asked if they would use the following financial services if made available on campus: pamphlets and informational handouts on money management and credit card debt; self-help online information on money management and credit card debt; seminars/workshops on money management and credit card debt; and counseling services concerning money management and credit card debt. Table 4 presents the findings for all students, students with credit cards and no al-risk behaviors, and students with credit cards and at least one at-risk behavior. (Note that students could select more than one financial service so the percentages do not sum to 100%.) Three findings are of particular interest. First, at-risk students are more likely than non at-risk students to use financial services, and they prefer to access financial information online. Table 4 shows that financially at-risk students ranked online information on money management as their first choice, online information on credit card debt as their second choice, and materials on money management as their third choice. However, these findings may be due to sample selection bias since online methods were used to collect the data. Second, regardless of the mode of delivery, at-risk students prefer to receive information on money management rather than credit card debt. This finding may be due to the wording of the survey questions. Perhaps, if the survey had asked students about "credit card management" rather than "credit card debt," students would have indicated that they would be equally likely to use services related to both credit card debt and money management. Third, and perhaps most importantly, at-risk students who report being delinquent on their credit card payments by two months or more appear to be more likely than other at-risk groups to use some type of financial service if offered by the University. Overall, these findings begin to provide

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76 THE JOURNAL OF CONSUMER AFFAIRS insight into the types of services and programs that college campuses and other organizations may want to offer to assist students who are financially at risk. CONCLUSIONS Using data from a random sample of college students at the University of Illinois, this study has identified and characterized those students at greatest financial risk for mismanaging/misusing credit. Prohit models were estimated to determine which factors significantly affect the prohability a student 1) holds $ 1000 or more in credit card deht, 2) is delinquent on credit card payments by two months or more, 3) has reached the credit limit on his or her credit cards, and 4) only pays off credit card balances some of the time or never. The results indicate that financially at-risk students are more likely than others to be financially independent, to receive need-based financial assistance, to hold $1000 or more in other types of debt, and to have acquired their credit card(s) by mail, at a retail store, and/or at a campus table. Students who are having difficulty making credit card payments are more likely to be female, black, and/or Hispanic. Black students are also significantly more likely to have credit card balances of $ 1000 or more and to not pay their balances in full each month. Also, at-risk students are more likely than non at-risk students to prefer receiving financial information online and in the form of pamphlets and informational handouts rather than seminars, workshops, and/or counseling services. These findings have clear implications for educational outreach and policy on college campuses nationally. Financially at-risk groups (i.e., students from low-to-middle income families, financially independent students, and minorities) are likely to have specific financial education needs with respect to programs and services. Campus administrators and financial professionals can use the findings from this study to begin identifying which students are most likely to have specific financial education needs. In knowing which students are financially at risk, campus administrators and financial professionals can better allocate their resources and develop programs and services to specifically target those students most at risk. These resources can better help students build financial knowledge, make informed financial decisions, use financial services responsibly, and development a sense of financial independence. Given the rising costs of a college education and the recent economic slowdown, it has become even more important that financially at-risk stu-

SUMMER 2004 VOLUME 38, NUMBER I 77 dents receive the appropriate financial interventions so they are not at a financial disadvantage when they graduate. Targeted financial education programs are likely to result in fewer payment problems, defaults, and charge-offs after graduation. In addition to identifying the characteristics associated with financial risk, this study also provides insight into how financial strain may be affecting campus retention rates and which students are likely to be affected. This study points out that students who owe larger balances on their credit cards are significantly likely to borrow more in financial aid. Thus, those students most in need of financial assistance may be forced to turn to other forms of borrowing such as credit cards to complete their college degree. Campus administrators and financial professionals can use this information to lobby policy makers and lending agencies to improve the current financial aid system so that it better meets the needs of those students most affected by the rising costs of a college education. In the end, this study provides significant insight into which students may be financially at risk and how campus administrators and financial professionals can better help these students manage their credit and avoid future misuse of credit down the road. However, the research is far from complete and three areas in particular need further investigation. First, research is needed to investigate the ability of recent college graduates to manage their credit card burdens after graduation, especially given the rise in the number of bankruptcies for those under 25 years of age (U.S. General Accounting Office 2001; Asinof and Chaker 2002; Rohrke 2002). Longitudinal studies are needed to examine the long-run implications of debt accumulation and the role that credit card debt plays in the postcollege lives of students, especially their ability to repay their debts. Future research is also needed to examine the dynamics between all types of debt, in particular the causal relationship between credit card debt and financial aid loans. Lyons (2003) is already investigating whether students are charging more on their credit cards, because financial aid is not enough to cover rising college costs or because they want to consume more and maintain a certain standard of living. However, research in this area is still in the investigative stages and more detailed analysis needs to be conducted before our understanding is complete. Finally, research is needed to examine the long-term effects that financial education programs and services have on the ability of financially atrisk students to manage their credit cards and repay their debts. Programs and services that specifically target financially at-risk students do not currently exist, and thus we are unahle to show whether financial education is

78 THE JOURNAL OF CONSUMER AFFAIRS effective at changing these students' behavior both before and after graduation. The bottom line is that more longitudinal research is needed to address the long-run consequences of credit card usage and the impact of financial education on the financial well-being of college students, especially those who are financially at risk. APPENDIX PROBIT RESULTS FOR STUDENTS WITH CREDIT CARDS Groups of Al Risk Student; Credit card debt ^$1000 Delinquent on payments Variable ME SE ME SE Financially independent Receives financial aid Other debi >$1000 Female Black Asian Hispanic Graduate student Married Rents an apartment Budgets money Works 1-9 hrs/wk Works lo-l? hrs/wk Works 16-20 hrs/wk Works >20 hrs/wk Town/city {pop 2.500-20,000) City (pop 20.000-99.999) City (pop 100.000 or more) Acquired card in maii Acquired card at bank Acquired card at retail store Acquired card at campus table All right lo borrow for vacation All right to borrow for car All right lo borrow for education All right to borrow for entertainmenl All right to borrow for shopping 0.0194 0.0712 0.1090-0.0422 0.2160-0.0547 0.0078 0.0759 0.0814 0.0834-0.0546 0.0586 0.0313 0.0637 0-0897 -0.0791-0.0216 0.0131 0.0727-0.0383 0.1236 0-0481 0.0142-0.0378-0-0063-0-0467 0.0152 (0,0424) (0.0324)** (0.0405)*** (0.0298) (0.1013)*** (0.0367) (0.0689) (0.0505) (0.0579) (0.0299)*** (0.0342)* (0.0517) (0.0434) (0.0616) (0.0590)* (0.0410) (0.0403) (0.0395) (0.0296)** (0.0326) (0.0397)*** (0.0463) (0.0356) (0.0311) (0.0490) (0.0428) (0.0438) 0.0750 O0043 0.0489 0.0377 0.2111-0.0144 0.1631-0.0595 0.0103 0-0484 -O0078-0.0505 -O3230 0-0302 -O0238 0.0213-0.0018 O0429 0.0106 -O0054 0.0213 O0705 O0046 -O0233 -O0297 O0063 -O0247 (0.0315)*** (0.0190) (0.0260)** (0.0177)** (0.0976)*** (0.0238) (0.0783)*** (0.0211)** (0.0309) (0.0176)*** (0.0197) (0.0177)** (0.0189) (0,0364) (0.0248) (0.0422) (0.0261) (0.0285)* (0.0190) (0.0218) (0.02-34) (0.0374)** (0.0220) (0.0194) (0 360) (0.0238) (0.0240) Observations R- 658 0.1929 658 01926

SUMMER 2004 VOLUME 38, NUMBER I 79 PROBIT RESULTS FOR STUDENTS WITH CREDIT CARDS (continued) Groups of At Risk Students Reached limit on credit cards Do not pay balance in full Variable ME SB ME SE Financially independent Receives linancial aid Other debt ^$] 000 Female Black Asian Hispanic Graduale student Married Rents an apartment Budgets money Works l-9hrs/wk Works 10-15 hrs/wk Works 16-20 hrs/wk Works >20 hrs/wk Town/city (pop 2.500-20.000) City (pop 20.000-99.999) City (pop loo.wx) or more) Acquired card in mail Acquired card at bank Acquired card at retail store Acquired card at campus table All right to borrow for vacation All right to borrow for car All right to borrow for education All right to borrow for entertainment All right to borrow for shopping 0.1271 0.0625 0.1810 0.0045 0.0873 0.0314 0.0167-0.1384 0.0381 0.0180-0.0518-0.0358-0.0501 0.0152 0.0343-0.0648-0.0387-0.0657 0.0191 0.0118 0.0392 0.1081 0.0424-0.0490 0.0096 - O.{)541-0.0019 (0.0477)*** (O.O338J* (0.0445)*** (0.0314) (0.0852) (0.0456) (0.0723) (0.0378)*** (0.0581) (0.0331) (0.0351) (0.0427) (0.0378) (0.0544) (0.0579) (0.0458) (0.0401) (0.0367)* (0.0320) (0.0380) (0.0379) (0.0515)** (0.0368) (0.0314) (0.0483) (0.0438) (0.0459) 0.1581 0.2083 0.1463-0.0211 0.3755-0.1046 0.1781 0.0456-0.0400 0.1022-0.0744 0.0286 0.0637 0.1669 0.1163-0.0047 0.0563 0.0347-0.0493 -O.llll 0.1071 0.0803-0.0469 0.0141 0.0443-0.0930 0.0559 (0.0597)*** (0.043!)*** (0.0516)*** (0.0426) (0.0952)*** (0.0551)* (0.1107)* (0.0643) (0.0686) (0.0441)** (0.0460) (0.0641) (0.0610) (0.0755)** (0.0723)* (0.0785) (0.0613) (0.0558) (0.0450) (0.0480)** (0.0490)** (0.0610) (0.0485) (0.0459) (0.0676) (0.0633) (0.0645) Observations R= 658 0,1202 658 O.t695 Note: ME represents the marginal effects for the probit model. Standard errors for the marginal effects are indicated by ( ). Omitted categories include "not working" and living in a "rural area (pop <2.5OO)." *p < 0.10; **p < 0.05; ***p < 0.01

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