Savings, Student Loan Debt, & Gainful Employment

Transcription

1 Savings, Student Loan Debt, & Gainful Employment Abed Rabbani 1 Department of Financial Planning, Housing, & Consumer Economics University of Georgia arabbani@uga.edu and Patryk Babiarz Department of Financial Planning, Housing, & Consumer Economics University of Georgia 1 Corresponding Author 1

2 Savings, Student Loan Debt, & Gainful Employment Abstract The U.S. Department of Education recently proposed a policy that requires certain educational programs to do a better job of preparing students for Gainful Employment or risk losing access to federal student aid. This study investigates the influence of student loan borrower characteristics, education, employment characteristics, and student loan information on the gainful employment measure proposed by the government. Particularly, this study intended to explore the influence of savings attitude on 'Gainful Employment'. Utilizing the 2010 Survey of Consumer Finances (SCF), this study found that saving attitude (spending less than the income) decreased debt-to-actual earnings ratio and increased the probability of being gainfully employed. Keywords: Gainful Employment, student loan, debt-to-earnings ratio JEL Classification: I00 Introduction Any public policy should be assessed for its outcomes in order to understand how implementation of a given policy impacts its target group. This assessment also applies to the case of student financial assistance policies and, more specifically, to the government-supported student loan programs. Education loans have been a major source of financial aid for higher education and are currently provided as subsidized or unsubsidized loans. Subsidized loans are given to students with financial needs, and the government pays the accrued interest while students are in school. In contrast to the subsidized loan, unsubsidized loans are available to all college students 2

3 regardless of whether the student has a financial need. Since these loans are offered without collateral or credit check and allow deferment options and extended repayment plans, education loans have been a readily accessible source of funds for college students. Not-for-profit public and private institutions of higher education have found it difficult to accommodate all students who seek college degrees. The for-profit colleges and universities (e.g. the University of Phoenix, Kaplan University, Argosy University, and many others) seek to fill this gap. Over the past several years, for-profit colleges and universities have come under increased scrutiny and criticism for poor record on employment placement that has made it difficult for students to pay off their college loans (Harkin, 2014). The debt load for students at for-profit schools is, on average, more than twice that of public schools (Figure 1). Although education loans allow prospective college students to finance their higher education, there is growing concern that students are burdened with excessive debt. Figure 1: Student Debt Load 15.00% 8.80% 7.20% 4.60% National Average For-Profit Public Private Source: Department of Education,

4 American Student Assistance (2014) provides important facts about the state of student loans in the United States. They reported that there is roughly $1 trillion in total outstanding student loan debt in the United States today. There are approximately 37 million student borrowers with outstanding student loans. Of them, 14%, or about 5.4 million borrowers, have at least one past due student loan account. Two out of five student loan borrowers, or 41%, are delinquent at some point in the first five years after entering repayment. As of October 2012, the average amount of student loan debt for the class of 2011 was $26,600, 5% % increase from approximately $25,350 in The percentage of undergraduate students who obtained federal loans increased from 23% in to 35% in Total borrowing for college reached $113.4 billion for the school year, up 24% from Approximately one in ten recipients of federal student loan becomes delinquent on his/her payments within three years of graduation (U.S. Department of Education, 2012). Student loan default is an undesirable consequence of the federal government s loan-based financial aid system because it costs borrowers, taxpayers, and colleges and universities extra time and money to manage student loan default risks (Hillman, 2014). Default on student loan is different than a default on other loans. Individuals can virtually become debt-free and/or obtain protection from creditors by filing personal bankruptcy. However, bankruptcy does not offer relieve from student debt. Once a borrower defaults on his or her federal student loan, the federal government can garnish a borrower s wages, seize borrowers tax refunds, impose collection costs, initiate litigation, and restrict borrowers from receiving other federal student aid or Social Security benefits (Loonin, 2006). Federal policymakers have become increasingly concerned about the borrowers who fail to repay their student loan debts. The individual and societal costs of defaulting on student loan 4

5 debts are significant, so policymakers and students are questioning what can be done to prevent high levels of default (Hillman, 2014).To address some of these concerns, the U.S. Department of Education has proposed to amend the regulations on institutional eligibility under the Higher Education Act of 1965, as amended (HEA), and the Student Assistance General Provisions to establish measures for determining whether certain postsecondary educational programs prepare students for gainful employment in a recognized occupation, and the conditions under which these educational programs remain eligible under the Federal Student Aid programs authorized under title IV of the HEA (title IV, HEA programs) (Program Integrity: Gainful Employment, 2014). Here, Gainful Employment means that the graduate will be able to earn enough salary to pay back the loans that were obtained to pay for college. Although the regulations have been effective since July 1, 2012, failing programs will, at the earliest, lose eligibility in It is imperative that these regulations be examined and understood, because the relevant data in the calculations will largely involve students who have already left their programs. The continually increasing number and amount of student loans suggest that federal grants are doing very little to reduce the need for them. This failure implies that there is a need to (i) increase the amount of grant aid available, or (ii) make colleges more accountable, or (iii) find other strategies that align with the personal responsibility (e.g., saving) (Elliott and Nam, 2013). In the current economic climate, it is unlikely the federal government will opt to consider option 1. New Gainful Employment Rule is designed to follow option 2. Despite the significance of this regulation on future higher education landscape, there is little recent systematic inquiry into Gainful Employment. As for the option 3, there have been plethora researches on the effects of 5

6 savings on debt in general; however, little is known about its relationship with Gainful Employment. The overarching aim of this study is to contribute to the academic literature on student loan while also offering points of departure for ongoing public policy debates. The paper is organized as follows. First, it offers a brief discussion of key policy changes that impact the way Gainful Employment is calculated and defined; next, is a summary of key themes found in the existing student debt repayment literature as well as a theoretical framework that guides the analysis. The data and analysis section describes the multilevel model that was designed for this study, and the paper concludes with key findings and a brief discussion of policy implications. Gainful Employment Rule Gainful Employment In June 2011, the White House announced new steps to address growing concerns about burdensome student loan debt by requiring career colleges to do a better job of preparing students for Gainful Employment or risk losing access to taxpayer-funded federal student aid. On March 14, 2014, the U.S. Department of Education (DoED) released a Notice of Proposed Rulemaking on Gainful Employment (Program Integrity: Gainful Employment, 2014). The proposed regulations issued by the U.S. Department of Education are supposed to help strengthen students' options for higher education by giving all job training programs an opportunity to improve, while stopping the flow of federal funding to the lowest-performing ones that fail to do so. "Higher education should open up doors of opportunity, but students in these lowperforming programs often end up worse off than before they enrolled: saddled by debt and with few if any options for a career," said the U.S. Secretary of Education Arne Duncan (U.S. 6

7 Department of Education, 2011). "The proposed regulations address growing concerns about unaffordable levels of loan debt for students enrolled in these programs by targeting the lowestperforming programs while shining a light on best practices and giving all programs an opportunity to improve (U.S. Department of Education, 2011)." The proposed rule requires that to qualify for federal student aid most for-profit programs and certificate programs at non-profit and public institutions prepare students for Gainful Employment in a recognized occupation. Students at for-profit colleges represent only about 13 % of the total higher education population, but about 31 % of all student loans and nearly half of all loan defaults (U.S. Department of Education, 2011). Gainful Employment Measure Effective with the fiscal year ending September 30, 2012, every Gainful Employment program must pass at least one of the two tests. The first test uses the measure of Loan Repayment Rate (LRR) and requires that at least 35% of the program s graduates with outstanding student debt are repaying their loans according to schedule. The second test utilizes the measure of Debt-to-Earnings Ratio (DTE). There are two DTE measures, either of which should be satisfied to maintain eligibility: (i) Debt-to-Actual Earnings(DTAE): The median annual loan payment for program graduates does not exceed 12% of average graduate total annual earnings; (ii) Debt-to-Discretionary Earnings (DTDE): The median annual loan payment for program graduates does not exceed 30% of average graduate annual discretionary income. Literature Review and Conceptual Framework Research on topics such as consumer debt repayment, delinquency, and bankruptcy offers empirical evidence to explain the incidence of consumer insolvency (DeVaney and Lytton, 1995). Borrowing decisions require the consumer choosing both how much and when to repay 7

8 the loan (Navaro-Martinez et al., 2011). A relatively smaller payment allows the borrower to have more cash on hand to use for other purposes in the current month but, because of compounding interest rates, increases the future cost of the loan. A relatively larger amount leaves the consumer with less money available in the current month but reduces the future cost of the loan. Thus, the consumer must decide whether to repay now (decreasing current utility) or repay later (decreasing future utility). As such, debt repayment decision is a trade-off between the desires to reduce the current cost and minimize future, total cost over the life of the loan. According to the Human Capital Theory, the decision to invest in higher education is one that weighs the perceived costs and benefits accrued through the educational process. These outcomes are unknown in advance (Becker, 1993), and it is possible that students borrow more money than they can repay. Borrowers can only repay their debts in accordance with their budget constraints, so when a repayment plan falls beyond a budget line, a borrower may face the unintended consequence of delinquency/defaulting. These decisions are conditioned by various socioeconomic, demographic, and academic factors in addition to their financial needs while enrolled in college (Gross, Cekic, Hossler, and Hillman, 2009; Hillman, 2014). Gross et al. (2009) identified, reviewed, and summarized 41 studies of student loan default conducted between 1978 and They reported that research on student loan default has considered (a) the characteristics of students as they begin college (e.g., family income, race/ethnicity); (b) students college experiences (e.g., type of institution, field of study, educational outcomes); (c) students financial aid and the amount of debt they incur; and (d) students employment and income after college as well as their overall debt (including loans and other forms of consumer debt). 8

9 Godwin (1999) reviewed and summarized studies that have assessed four types of factors for their effects on households debt repayment or delinquency--households demographic and economic characteristics, their attitudes and previous behavior regarding debt, characteristics of their debt portfolios, and unexpected events that may have contributed to financial difficulty. The theoretical model of this study (Figure 2) relied on the conceptual model developed by Perna (2006). This model assumes that, consistent with human capital theory; students make decisions about higher education based on a comparison of the perceived costs and benefits. Also consistent with human capital theory, a student s academic preparation and achievement and access to financial resources are expected to influence the calculation of costs and benefits. The theoretical model assumes that four nested contextual layers shape students' college-enrollment decisions: the student and family context; the school and community context; the higher education context; and the broader social, economic, and policy context (Perna, 2006). For this study, we believe that students make decisions about loans within the student and family context, but that these decisions are shaped directly and indirectly by various forces outside of the family, particularly characteristics of the school a student attends and the broader social, economic, and policy context. In addition, we believe that the financial attitude of the student also affects these decisions. Data and Models Data The 2010 Survey of Consumer Finances (SCF) was used as the data set for this study. The SCF is a triennial survey designed primarily as an instrument for the study of U.S. household income, assets, liabilities, and demographics. The SCF was sponsored by the Federal Reserve Board in cooperation with the Department of the Treasury and conducted by the National Opinion Research Center (NORC) at the University of Chicago. 9

10 The unit of analysis used in the SCF was the household, and most information was collected from the household s primary economic unit. The primary economic unit was defined as the economically dominant person or couple within a household together with all persons financially dependent on that person or couple. The final public use version of the 2010 SCF consisted of 32,410 cases. However, this number was five times the actual amount of responses. This overstatement of cases was the result of multiple imputation techniques used by the Federal Reserve to adjust data for missing information. Thus, in reality, the data set consisted of five separate replicates each consisting of 6,482 cases. The use of the first replicate has precedence in the literature. In this research, the first-replicate dataset was used. The sample is additionally limited only to individuals who have an outstanding student loan. The number of observation used in the empirical analysis was 1,097. We did not use weights for descriptive analysis. In this study, factors affecting gainful employment test were explored. Here, debt to actual earnings ratio was used to measure Gainful Employment of a student borrower. If an individual s educational DTAE ratio is less than 12%, then the individual passes federally recognized Gainful Employment status. The policymakers motivation to use this threshold is that when the head of the household commits 12% or more of disposable income to education loan payment, usually little is left for meeting all other financial obligations. A binary Gainful Employment variable was also created from DTAE. If DTAE is less than or equal to 12%, then Gainful Employment =1 or 0 otherwise. 10

11 Figure 2: Conceptual Framework Demographics: -Age -Gender -Race/ethnicity Gainful Employment Measure: Debt to Actual Earnings Ratio Probability of Gainful Employment: -Gainfully Employed -Not Gainfully Employed Socio-economic status: -Has dependents Academic experience: - Number of education loan - Highest Degree Post-collegiate employment: -Occupation -Employment class Saving Attitude - Spent less than income - Otherwise Independent variables include SAVED, which is a dummy variable: 1=spent less than income, 0=all others; demographic variables: Age, Marital Status, Gender, Race, and Household size; education variable: Highest Degree (Associate-Bachelors, MS-MBA, PhD-MD-JD, and Other Degree), number of Education Loans (X 6693); employment variables: Occupation (Professional, Technical, Tradesperson, and Service), and Industry classification (Manufacturing and Non-manufacturing). Among these variables, Age, Household size, and the number of education loans were continuous variables. On the other hand, Marital Status, Gender, Race, Highest Degree, Occupation, and Industry classifications were categorical variables. 11

12 Empirical Model We estimated two models; the first one is a multiple regression model and the second one is a binary logistic regression model. For the first model, DTAE was used as dependent variable and the model specification is as follows: DTAE= f (SAVED, Education, Employment, Demographic).(i) For the second model, binary Gainful Employment was used as dependent variable and the model specification is as follows: logit(gainfully Employed) = f (SAVED, Education, Employment, Demographic)..(ii) The logistic relationship is expressed in the general form: Y = ln[p(y)/(1-p(y)] where P is the probability of being gainfully employed and ln[p(y)/(1-p(y)] is the natural log of the odds of gainfully employed (Y) relative to being not gainfully employed. Results Descriptive Statistics The descriptive statistics are shown in Table 1. Approximately 95% of the respondents were gainfully employed. The results indicated that the average debt-to-actual earning for gainfully employed respondents was 2%, whereas for non-gainfully employed respondents it was 40%. About half of the gainfully employed respondents had spent less than their income, whereas, one-third of the non-gainfully employed respondents had spent less than their income. Mean number of education loan for gainfully employed respondents was approximately 1.5, 12

13 whereas this number is more than 2 for non-gainfully employed respondents. Also, the descriptive statistics indicates that gainfully and non-gainfully employed respondents also differed in several demographic characteristics. Table 1: Descriptive Statistics Gainfully Employed (DTAE <0.12) Not Gainfully Employed (DTAE 0.12) Variable N Mean Std. Dev. N Mean Std. Dev. DTAE % 3% 56 40% 44% SAVED % 50% 56 32% 47% Highest Degree : ASandBS % 48% 56 43% 50% : MSandMBA % 30% 56 7% 26% : PhdMDJD % 22% 56 11% 31% : Other Degree % 6% 56 0% 0% Education Loan # Occupation : Professional % 49% 56 43% 50% : Technical % 46% 56 27% 45% : Tradesperson % 37% 56 13% 33% : Not Working % 36% 56 18% 39% Manufacturing % 36% 56 13% 33% Demographic Female % 43% 56 27% 45% Married % 48% 56 46% 50% Race : White % 46% 56 84% 37% : Black % 38% 56 11% 31% : Hispanic % 29% 56 4% 19% : Other race % 18% 56 2% 13% Age Household Size

14 Table 2: Regression results for Model 1 and Model 2 Model 1 Dep.Var: DTAE Model 2 Dep.Var: logit (Gainful Employment) Variable DF Parameter Estimate Std. Err. Parameter Estimate Std. Err. Odd Ratio Intercept SAVED * * Highest Degree : MSandMBA : PhdMDJD * : Other Degree Education Loan # * * Occupation : Professional : Technical : Tradesperson Manufacturing DEMOGRAPHIC Female * Married * * Race : White : Black : Hispanic Age * Household Size * * R F 4.16* LL Ratio (χ 2 ) * Model 1: Effect on Gainful Employment Measure (DTAE) In this model, gainful employment measure debt-to actual earnings (DTAE) was used as dependent variable. The model was significant and the results are presented in Table 2. Among the demographic variables, being female, being married, age and household size were negatively associated with the gainful employment measure. Race variables were not significantly 14

15 associated with this measure. None of the employment variables was significantly associated. Among the education variables, having PhD/MD/JD and the number of education loan were positively associated with gainful employment measure. When controlled for demographic, employment, and education variables, SAVED variable was negatively associated with gainful employment variables. Model 2: Effect on Gainful Employment Dummy In this model, the dependent variable was Gainful Employment dummy. A respondent is gainfully employed if DTAE of the respondent is less than or equal to 12%. The results are presented in Table 2. Among the demographic variables, being married, and household size was positively associated with the probability of being gainfully employed. Race variables have no effect. None of the employment variables was significantly associated with being gainfully employed. Among the education variables, only the number of education loan was negatively associated with the probability of being gainfully employed. When controlled for demographic, employment and education variables SAVED variable was positively associated with being gainfully employed. Discussion and Conclusion Saving attitude (spending less than the income) decreased gainful employment measure by 1.4%. This measure was calculated by debt-to-actual earnings. This saving attitude also increased the probability of being gainfully employed by about 111%. Elliot and Nam (2013) reported that parents college savings reduced the amount of college debt across all income levels with the exception of high-income students. 15

16 The student DTAE is a critical component of one s overall debt-to-earning (DTE) ratio that includes all other debt items as well. While DTE ratio isn't one of the five critical factors that calculate credit score, it has a significant impact on one s ability to get credit. Lenders scrutinize DTE ratio because it's an indicator of one s ability to repay debts. If one s DTE ratio is low, then she is likely to repay debts. If one s DTE ratio is high, then she may be overwhelmed by debt and unable to pay back new debt obligations. The standard rule of thumb is that one s DTE ratio should be less than 36 % as lenders require that borrowers have a DTE ratio no higher than 40 % in order to qualify for a mortgage. A DTE ratio as high as 36 % puts a consumer at risk of paying higher interest rates, or being denied altogether. One additional credit increased DTAE ratio by 1.2%. This relationship is also reflected in the model 2, where one additional credit decreased the probability of being gainfully employed by 36%. Having a PhD, MD, or JD have increased DTAE ratios significantly by about 9% compared to the respondents holding Associates and Bachelor s degree. However, post graduate degree did not affect the probability of Gainful Employment. Females had lower DTAE ratio than males but did not affect the probability of Gainful Employment. Married households have lower DTAE ratio than their single counterpart. Being married increased the likelihood of Gainful Employment by 123%. Age has a negative effect on DTAE ratio. One more year of age reduced the ratio by 0.075% but did not affect the probability of Gainful Employment. One additional family member in the household decreased the gainful employment measure by 0.57% and thus increased the probability of being gainfully employed by 25%. 16

17 There are significant differences between respondents who spend less than the income than who do not on both gainful employment measure and probability of being gainful employed. One of the main weaknesses of this study is the potential for selection bias. The sample of this study was limited to individuals with outstanding educational debt. Despite the limitations, it can be concluded that policies must help consumers accumulate savings to pay college costs and reduce reliance on loans. One recommendation is to implement programs that can provide students with initial deposits, savings matches, incentives, and the opportunity for third parties to make deposits in these accounts. Reference American Student Assistance (2014). Student loan debt statistics. Retrieved from Becker, G. S. (1993). Human capital: a theoretical and empirical analysis, with special reference to education. Chicago, University of Chicago Press. DeVaney, S. A., & Lytton, R. H. (1995). Household insolvency: a review of household debt repayment, delinquency, and bankruptcy. Financial Services Review, 4(2), 137. Elliott, W., & Nam, I. (2013). Reducing student loan debt through parents college savings (CSD Working Paper 13-07). St. Louis, MO: Washington University, Center for Social Development. Godwin, D. D. (1999). Predictors of households' debt repayment difficulties. Financial Counseling and Planning, 10(1),

18 Gross, J. P.., Cekic, O., Hossler, D., & Hillman, N. (2009). What matters in student loan default: a review of the research literature. Journal of Student Financial Aid, 39(1), Harkin, T (2014). For-profit college Investigation. Retrieved from Hillman, N. (2014). College on credit: A multilevel analysis of student loan default. The Review of Higher Education, (2), 169. Loonin, D. (2006). No way out: Student loans, financial distress, and the need for policy reform. National Consumer Law Center, Washington, D.C. Navarro-Martinez, D., Salisbury, L., Lemon, K., Stewart, N., Matthews, W., & Harris, A. (2011). Minimum Required Payment and Supplemental Information Disclosure Effects on Consumer Debt Repayment Decisions. Journal Of Marketing Research (JMR),48S60-S77. doi: /jmkr.48.spl.s60 Perna, L. W. (2006a). Studying college choice: A proposed conceptual model. In J. C. Smart (Ed.), Higher education: Handbook of theory and research (Vol. XXI, pp ). Springer. U.S. Department of Education (2014). Program Integrity: Gainful Employment; Proposed Rule, 79 Federal Register, No. 57 (March 25, 2014) (to be codified at 34 CFR Parts 600 and 668). U.S. Department of Education (2014). Negotiated Rulemaking Gainful Employment: Draft regulatory language. Retrieved from 18

19 U.S. Department of Education (2012). National student loan default rates. Retrieved from U.S. Department of Education (2011). Obama administration announces new steps to protect students from ineffective career college programs. Retrieved from 19