Skip to main content

Advertisement

Log in

Helping or Hindering? The Effects of Loans on Community College Student Persistence

  • Published:
Research in Higher Education Aims and scope Submit manuscript

Abstract

More community college students are taking out loans than ever before and their median debt levels are increasing. This trend is disconcerting because community college borrowers are overrepresented among loan defaulters and those who dropout without having earned a degree. While not without criticism, a growing number of community colleges are choosing not to participate in the federal student loan programs, citing a desire to protect their students from future financial hardships. This study used data from the Beginning Postsecondary Student (BPS:04/09) survey and propensity score matching techniques to examine the effects of loans on persistence for students enrolled in associate’s degree programs. Results indicated that borrowing during the 1st year had a positive effect on persistence at the end of year one, but had a negative effect on persistence measured three and 6 years after initial enrollment. As community college students assess their prospects for degree completion and the return on their financial investment in higher education, we hypothesize that borrowers are more likely to become dissatisfied with their investment decision than non-borrowers and choose to dropout rather than take on additional loan debt. Findings from this study suggest the need to carefully reconsider current policies and practices regarding loan use among community college students.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
€32.70 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (Finland)

Instant access to the full article PDF.

Similar content being viewed by others

Notes

  1. All unweighted n’s in this study are rounded to the nearest ten per NCES data security guidelines.

  2. At the 6 year time point, a small number of students in the sample had earned their bachelor’s degree from the community college. This reflects the fact that some community colleges now award their own bachelor’s degrees.

  3. The steps required for students to take out a federal loan are available at https://studentloans.gov/myDirectLoan/counselingInstructions.action.

References

  • Adelman, C. (2005). Moving into town—and moving on: The community college in the lives of traditional-age students. Washington, DC: U.S. Department of Education.

    Google Scholar 

  • Alon, S. (2005). Model mis-specification in assessing the impact of financial aid on academic outcomes. Research in Higher Education, 46(1), 109–125.

    Article  Google Scholar 

  • American Association of Community Colleges (2008). AACC statement regarding the Project on Student Loan Debt report on community college loan access. Retrieved from http://www.aacc.nche.edu/About/Positions/Pages/ps04162008.aspx.

  • Austin, P. C. (2011). An introduction to propensity score methods for reducing the effects of confounding in observational studies. Multivariate Behavioral Research, 46(3), 399–424.

    Article  Google Scholar 

  • Bahr, P. R. (2010). The bird’s eye view of community colleges: A behavioral typology of first-time students based on cluster analytic classification. Research in Higher Education, 51, 724–749.

    Article  Google Scholar 

  • Baime, D. S., & Mullin, C. M. (2011). Promoting educational opportunity: The Pell Grant program at community colleges (Policy Brief 2011-03PBL). Washington, DC: American Association of Community Colleges.

    Google Scholar 

  • Baum, S., Little, K., & Payea, K. (2011). Trends in community college education: Enrollment, prices, student aid, and debt levels. Washington, DC: College Board.

    Google Scholar 

  • Baum, S., Ma, J., & Payea, K. (2010). Education pays 2010: The benefits of higher education for individuals and society. Washington, DC: College Board.

    Google Scholar 

  • Bean, J. P. (1980). Dropouts and turnover: The synthesis and test of a causal model of student attrition. Research in Higher Education, 12(2), 155–187.

    Article  Google Scholar 

  • Bean, J. P., & Metzner, B. S. (1985). A conceptual model of nontraditional undergraduate student attrition. Review of Educational Research, 55(4), 485–540.

    Article  Google Scholar 

  • Bettinger, E. (2004). How financial aid affects persistence (NBER Working Paper No. 10242). Retrieved from http://www.nber.org/papers/w10242.

  • Board, College. (2010). The financial aid challenge: Successful practices that address the underutilization of financial in community colleges. Washington, DC: Author.

    Google Scholar 

  • Cellini, S. R. (2008). Causal inference and omitted variable bias in financial aid research: Assessing solutions. The Review of Higher Education, 31(3), 329–354.

    Article  Google Scholar 

  • Center for Community College Student Engagement. (2012). A matter of degrees: Promising practices for community college student success (a first look). Austin, TX: The University of Texas at Austin, Community College Leadership Program.

    Google Scholar 

  • Chen, R. (2008). Financial aid and student dropout in higher education: A heterogeneous research approach. In J. C. Smart (Ed.), Higher education: Handbook of theory and research (Vol. 23, pp. 209–239). New York: Springer.

    Chapter  Google Scholar 

  • Cofer, J., & Somers, P. (2000). Within-year persistence of students at two-year colleges. Community College Journal of Research and Practice, 24, 785–807.

    Article  Google Scholar 

  • Cofer, J., & Somers, P. (2001). What influences persistence at two-year colleges? Community College Review, 29(3), 56–76.

    Article  Google Scholar 

  • Cohen, A. M., & Brawer, F. B. (2008). The American community college (5th ed.). San Francisco, CA: Jossey-Bass.

    Google Scholar 

  • Crosta, P. M. (2013). Intensity and attachment: How the chaotic enrollment patterns of community college students affect educational outcomes (CCRC Working Paper No. 60). New York: Community College Research Center.

  • Cunningham, A. F. & Santiago, D. A. (2008). Student aversion to borrowing: Who borrows and who doesn’t. Report by the Institute for Higher Education Policy and Excelencia in Education.

  • D’Agostino, R., & Rubin, D. B. (2000). Estimation and use of propensity scores with incomplete data. Journal of the American Statistical Association, 95, 749–759.

    Article  Google Scholar 

  • DesJardins, S. L. (2003). Event history methods: Conceptual issues and an application to student departure from college. Higher Education: Handbook of Theory and Research, 18, 421–471.

    Google Scholar 

  • Dowd, A. C. (2004). Income and financial aid effects on persistence and degree attainment in public colleges. Education Policy Analysis Archives, 12(21). Retrieved from http://epaa.asu.edu/ojs/article/view/176.

  • Dowd, A. C. (2008). Dynamic interactions and intersubjectivity: Challenges to causal modeling in studies of college student debt. Review of Educational Research, 78(2), 232–259.

    Article  Google Scholar 

  • Dowd, A. C., & Coury, T. (2006). The effect of loans on the persistence and attainment of community college students. Research in Higher Education, 47(1), 33–62.

    Article  Google Scholar 

  • Dynarski, M. (1994). Who defaults on student loans? Findings from the National Postsecondary Student Aid Study. Economics of Education Review, 13(1), 55–68.

    Article  Google Scholar 

  • Education Sector (2012). Degreeless in debt: What happens to borrowers who drop out. Washington, DC. Retrieved from http://www.educationsector.org/sites/default/files/publications/DegreelessDebt_CYCT_RELEASE.pdf.

  • Field, K., & Brainard, J. (2010). Government vastly undercounts defaults. The Chronicle of Higher Education, 56(40), A1–A17.

    Google Scholar 

  • Fox, J. (1997). Applied regression, linear models, and related methods. Thousand Oaks, CA: Sage.

    Google Scholar 

  • Gladieux, L. & Perna, L. (2005). Borrowers who drop out: A neglected aspect of the student loan trend. The National Center for Public Policy and Higher Education, Report #05-2. Washington, DC.

  • Hahs-Vaughn, D.L. (2006). Weighting omissions and best practices when using large-scale data in educational research (Professional File No. 101). Tallahassee, FL: Association for Institutional Research. ERIC Document Reproduction Service No. ED512237.

  • Hahs-Vaughn, D. L., & Onwuegbuzie, A. J. (2006). Estimating and using propensity score analysis with complex samples. Journal of Experimental Education, 75(1), 31–65.

    Article  Google Scholar 

  • Heller, D. (1997). Student prices response in higher education: An updated to Leslie and Brinkman. Journal of Higher Education, 23(1), 65–89.

    Google Scholar 

  • Hillman, N. W. (2013). Reforming repayment: Using income-related loans to reduce default. Paper prepared for the American Enterprise Institute for Public Policy Research. Retrieved from http://www.aei.org/files/2013/06/21/-kelly-hillmannick-conference_085349110877.pdf.

  • Hippensteel, D. G., St. John, E. P., & Starkey, J. B. (1996). Influence of tuition and student aid on within-year persistence by adults in two-year colleges. Community College Journal of Research and Practice, 20, 233–242.

    Article  Google Scholar 

  • Horn, L. & Berktold, J. (1998). Profile of undergraduates in U.S. postsecondary institutions: 19951996, with an essay on undergraduates who work. Washington, DC. U.S. Department of Education, National Center for Education Statistics.

  • Kennamer, M. A., Katsinas, S. G., & Schumacker, R. E. (2010). The moving target: Student financial aid and community college student retention. Journal of College Student Retention, 12(1), 87–103.

    Article  Google Scholar 

  • Kim, D. (2007). The effects of loans on students’ degree attainment: Differences by student and institutional characteristics. Harvard Educational Review, 77(1), 64–100.

    Article  Google Scholar 

  • King, J. (2002). Crucial choices: How students’ financial decisions affect their academic success. Washington, DC: American Council on Education.

    Google Scholar 

  • Leslie, L. L., & Brinkman, P. T. (1987). Student price response in higher education: The student demand studies. Journal of Higher Education, 58(2), 181–204.

    Article  Google Scholar 

  • Leuven, E. & Sianesi, B. (2003). PSMATCH2: Stata module to perform full Mahalanobis and propensity score matching, common support graphing, and covariate imbalance testing. Statistical Software Components S432001, Boston College Department of Economics, revised 19 July 2012.

  • Malcom, L. E. (2013). Student diversity in community colleges: Examining trends and understanding the challenges. In J. S. Levin & S. T. Kater (Eds.), Understanding Community Colleges (pp. 3–18). New York: Routledge.

    Google Scholar 

  • Malcom, L. E., & Dowd, A. C. (2012). The impact of undergraduate debt on the graduate school enrollment of STEM baccalaureates. Review of Higher Education, 35(2), 265–305.

    Article  Google Scholar 

  • McDonough, P. M. (1997). Choosing colleges: How social class and schools structure opportunity. Albany: State University of New York Press.

    Google Scholar 

  • McKinney, L., & Novak, H. (2013). The relationship between FAFSA filing and persistence among first-year community college students. Community College Review, 41(1), 63–85.

    Article  Google Scholar 

  • McKinney, L., & Roberts, T. (2012). The role of community college financial aid counselors in helping students understand and utilize financial aid. Community College Journal of Research and Practice, 36(10), 761–774.

    Article  Google Scholar 

  • McKinney, L., Roberts, T., & Shefman, P. (2013). Perspectives and experiences of financial aid counselors on community college students who borrow. Journal of Student Financial Aid, 43(1), 3–17.

    Google Scholar 

  • Meier, K. (2013). Community college mission in historical perspective. In J. S. Levin & S. T. Kater (Eds.), Understanding Community Colleges (pp. 3–18). New York: Routledge.

    Google Scholar 

  • Mendoza, P., Medez, J., & Malcolm, Z. (2009). Financial aid and persistence in community colleges: Assessing the effectiveness of federal and state financial aid programs in Oklahoma. Community College Review, 37(2), 112–135.

    Article  Google Scholar 

  • Mitra, N., Schnabel, F. R., Neuget, A. I., & Heitjan, D. F. (2001). Estimating the effect of an intensive surveillance program on stage of breast carcinoma at diagnosis. Cancer, 91(9), 1709–1715.

    Article  Google Scholar 

  • Mullin, C. M. (2013). Good, better, best: Examining outcomes in context. (Policy Brief-02 PB). Washington, DC: American Association of Community Colleges.

  • National Association of Student Financial Aid Administrators. (2013). Reimagining financial aid to improve student access and outcomes. Washington, DC: Author.

    Google Scholar 

  • Pascarella, E. T., & Terenzini, P. T. (2005). How college affects students: A third decade of research (Vol. 2). San Francisco, CA: Jossey-Bass Publishing.

    Google Scholar 

  • Project on Student Debt (2008). Denied: Community college students lack access to affordable loans. Berkeley, CA: The Institute for College Access & Success.

  • Project on Student Debt (2009). Getting with the program: Community college students need access to federal loans. Berkeley, CA: The Institute for College Access & Success.

  • Project on Student Debt (2011). Still denied: How community colleges shortchange students by not offering federal loans. Berkeley, CA: The Institute for College Access & Success.

  • Rosenbaum, P. R., & Rubin, D. B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70(1), 41–55.

    Article  Google Scholar 

  • Rubin, D. B. (1997). Estimating causal effects from large data sets using propensity scores. Annals of Internal Medicine, 127, 757–763.

    Article  Google Scholar 

  • Sauerbrei, W., Meier-Hirmer, C., Benner, C., & Royston, P. (2006). Multivariable regression model building by using fractional polynomials: Description of SAS, STATA and R programs. Computational Statistics & Data Analysis, 50(12), 3464–3485.

    Article  Google Scholar 

  • Sianesi, B. (2004). An evaluation of the active labour market programmes in Sweden. The Review of Economics and Statistics, 86(1), 133–155.

    Article  Google Scholar 

  • Spady, W. G. (1970). Dropouts from higher education: An interdisciplinary review and synthesis. Interchange, 1, 64–85.

    Article  Google Scholar 

  • St. John, E. P., & Starkey, J. B. (1994). The influence of costs on persistence by traditional college-age students in community colleges. Community College Journal of Research and Practice, 18, 201–213.

    Article  Google Scholar 

  • Starobin, S. S., Hagedorn, L. S., Purnamasari, A., & Chen, Y. (2013). Examining financial literacy among transfer and nontransfer students: Predicting financial well-being and academic success at a four-year university. Community College Journal of Research and Practice, 37, 216–225.

    Article  Google Scholar 

  • Steele, P., & Baum, S. (2009). How much are college students borrowing?. Washington, DC: College Board.

    Google Scholar 

  • The Institute for College Access & Success (2009). Quick facts about financial aid and community colleges, 2007–2008. Berkeley, CA: The Institute for College Access & Success.

  • The Institute for College Access & Success (2012). Making loans work: How community colleges support responsible borrowing. Berkeley, CA: The Institute for College Access & Success.

  • Thomas, S. L., & Heck, R. H. (2001). Analysis of large-scale secondary data in higher education research: Potential perils associated with complex sampling designs. Research in Higher Education, 42, 517–540.

    Article  Google Scholar 

  • Tinto, V. (1975). Dropout from higher education: A theoretical synthesis of recent research. Review of Educational Research, 45(1), 89–125.

    Article  Google Scholar 

  • Vargas, J. H. (2004). College knowledge: Addressing information barriers to college. Boston, MA: The Education Resources Institute.

    Google Scholar 

  • Zanutto, E. L. (2006). A comparison of propensity score and linear regression analysis of complex survey data. Journal of Data Science, 4, 67–91.

    Google Scholar 

Download references

Acknowledgments

An early version of this paper was presented at the 2013 Association for Institutional Research (AIR) Forum in Long Beach, California. The authors would like to thank participants of the Institute for Higher Education Law and Governance (IHELG) 2012 Finance Roundtable and two anonymous reviewers for their very helpful comments on earlier drafts of this paper. Any omissions or errors are our own. This study is based upon work supported by the Association for Institutional Research, the National Center for Education Statistics, the National Science Foundation, and the National Postsecondary Education Cooperative under the Association for Institutional ResearchGrant Number (#RG12-54).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Lyle McKinney.

Appendix

Appendix

See Fig. 1 and Table 4.

Fig. 1
figure 1

Region of common support for propensity score analysis

Table 4 Balance check for propensity score stratification

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

McKinney, L., Burridge, A.B. Helping or Hindering? The Effects of Loans on Community College Student Persistence. Res High Educ 56, 299–324 (2015). https://doi.org/10.1007/s11162-014-9349-4

Download citation

  • Received:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11162-014-9349-4

Keywords

Navigation