Choosing College in the 2000s: An Updated Analysis Using the Conditional Logistic Choice Model
- 278 Downloads
In this paper I investigate the college enrollment decisions of a nationally representative cohort of students who first attended in the mid-2000s. I find that while cost, distance, and match continued to be important in the choice between colleges, characteristics of the most-likely college choice appear less important in the choice of whether to enroll at all when controlling for student characteristics and local labor market conditions. Subpopulation analyses on students with high SAT scores and students with low family income, two groups that remain the focus of many financial aid policies, indicate some differences in the way these particular students chose college. Extending prior work by modeling discrete steps in the enrollment decision process—application and enrollment conditional on application—I find choice characteristics were most significant in the application stage. These results support other research that shows students may self-select out of potentially better college matches due to lack of information about actual costs or limited geographic opportunity.
KeywordsCollege access College choice Conditional logistic choice model Geographic opportunity
I would like to thank Brent Evans, Laura Perna, and two anonymous reviewers for their insightful comments on previous iterations of this paper. I would also like to thank Will Doyle, Dale Ballou, Dominique Baker, and Richard Blissett for their thoughts and suggestions throughout. All errors and mistakes in interpretation remain my own.
- Allen, I. E., & Seaman, J. (2011). Going the Distance. Technical Report Babson Survey Research Group.http://www.babson.edu/Academics/centers/blank-center/global-research/Documents/going-the-distance.pdf.
- Allen, I. E., & Seaman, J. (2013). Changing Course: Ten Years of Tracking Online Education in the United States. Technical Report. Babson Survey Research Group. https://files.eric.ed.gov/fulltext/ED541571.pdf.
- Allen, I. E., et al. (2016). Online Report Card: Tracking Online Education in the United States. Technical Report. Babson Survey Research Group.Google Scholar
- Avery, C., et al. (2006). Cost should be no barrier: An evaluation of the first year of Harvard’s financial aid initiative. Working Paper 12029. National Bureau of Economic Research.Google Scholar
- Baum, S., Little, K., & Payea, K. (2011). Trends in community college education: Enrollment, prices, student aid, and debt levels. Trends in higher education series (p. 11b-3741). New York: College Board.Google Scholar
- Baum, S., & Ma, J. (2012). Trends in college pricing., Trends in higher education series New York: The College Board.Google Scholar
- Becker, G. S. (2009). Human capital: A theoretical and empirical analysis, with special reference to education (3rd ed.). Chicago: University of Chicago Press.Google Scholar
- Bettinger, E. P., et al. (2017). Virtual classrooms: How online college courses affect student success. American Economic Review, 107(9), 2855–2875. https://doi.org/10.1257/aer.20151193. http://www.aeaweb.org/articles?id=10.1257/aer.20151193.
- Bourdieu, P. (1977). Cultural reproduction and social reproduction. In J. Karabel & A. H. Halsey (Eds.), Power and ideology (p. 485). Oxford: Oxford University Press.Google Scholar
- Buck, S. F. (1960). A method of estimation of missing values in multivariate data suitable for use with an electronic computer. Journal of the Royal Statistical Society, Series B (Methodological), 22, 302–306.Google Scholar
- Cottom, T. M. (2017). Lower ed: The troubling rise of for-profit colleges in the new economy. New York: The New Press.Google Scholar
- de Souza Briggs, X., & Wilson, W. J. (2006). The geography of opportunity: Race and housing choice in metropolitan America., James A. Johnson Metro Series Washington, D.C: Brookings Institution Press.Google Scholar
- Deming, D., & Dynarski, S. (2009). Into college, out of poverty? Policies to increase the postsecondary attainment of the poor. Working Paper 15387. Cambridge, MA: National Bureau of Economic Research.Google Scholar
- Dynarski, S., & Scott-Clayton, J. (2013). Financial aid policy: Lessons from research. Working Paper 18710. National Bureau of Economic Research.Google Scholar
- Eagan, K., et al. (2016). The American Freshman: Fifty-year trends 1966–2015. Technical Report. Los Angeles: Higher Education Research Institute, UCLA.Google Scholar
- Greene, W. H. (2012). Econometric analysis (7th ed.). Boston, MA: Prentice Hall.Google Scholar
- Hillman, N., & Weichman, T. (2016). Education deserts: The continued significance of “place” in the twenty-first century. Viewpoints: Voices from the field. Washington, D.C.: American Council on Education.Google Scholar
- Hoxby, C., & Turner, S. (2013). Expanding college opportunities for high-achieving, low income students. In: Stanford Institute for Economic Policy Research Discussion Paper 12–014.Google Scholar
- Hoxby, C. M., & Avery, C. (2012). The missing “One-offs”: The hidden supply of high-achieving, low income students. Working Paper 18586. Cambridge, MA: National Bureau of Economic Research.Google Scholar
- Kane, T. J. (1995). Rising public college tuition and college entry: How well do public subsidies promote access to college? Working Paper 5164. Cambridge, MA: National Bureau of Economic Research.Google Scholar
- Kane, T. J. (1996). College cost, borrowing constraints and the timing of college entry. Eastern Economic Journal, 22(2), 181–194.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.Google Scholar
- Little, R. J. A. (1992). Regression with missing X’s: A review. Journal of the American Statistical Association, 87(420), 1227–1237.Google Scholar
- Ma, J., et al. (2016). Trends in college pricing., Trends in higher education series New York: The College Board.Google Scholar
- McFadden, D. (1973). Conditional logit analysis of qualitative choice behavior. In P. Zarembka (Ed.), Frontiers in econometrics (pp. 105–142). New York: Academic Press.Google Scholar
- National Center for Education Statistics. (2016). ELS: Education Longitudinal Study of 2002.Google Scholar
- Paulsen, M., & Smart, J. C. (2001). The finance of higher education: Theory, research, policy, and practice. New York: Algora Publishing.Google Scholar
- Perna, L. W. (2006). Studying college choice: A proposed conceptual model. In J. C. Smart (Ed.), Higher education: Handbook of theory and research (Vol. 21, pp. 99–157). Dordrecht: Springer.Google Scholar
- R Core Team. (2016). R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing.Google Scholar
- Rouse, C. E. (1995). Democratization or diversion? The effect of community colleges on educational attainment. Journal of Business & Economic Statistics, 13(2), 217–224.Google Scholar
- Snyder, T. D., de Brey, C., & Dillow, S. A. (2016). Digest of education statistics 2014. NCES 2016-006. Washington, D.C: National Center for Education Statistics, Institute of Education Sciences, U.S. Department of Education.Google Scholar
- Snyder, T. D., Dillow, S. A., & Hoffman, C. M. (2007). Digest of Education Statistics 2006. NCES 2007-017. Washington, D.C: National Center for Education Statistics, Institute of Education Sciences, U.S. Department of Education.Google Scholar
- Snyder, T. D., & Hoffman, C. M. (1995). Digest of Education Statistics 1995. NCES 95-029. Washington, D.C: National Center for Education Statistics, Institute of Education Sciences, U.S. Department of Education.Google Scholar
- StataCorp. (2015). Stata statistical software: Release 14. College Station, TX: StataCorp LP.Google Scholar
- The Delta Cost Project. (2016). The Delta Cost Project Database.Google Scholar