Choosing College in the 2000s: An Updated Analysis Using the Conditional Logistic Choice Model

Abstract

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.

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Notes

  1. 1.

    For clarity and space in this section, I collapse the college enrollment decision into the single decision to attend. Application to college as an intermediate step in the enrollment process, modeled separately from attendance, is implied.

  2. 2.

    https://www.census.gov/geo/reference/gtc/gtc_bg.html

  3. 3.

    Approximately 12% of institutions were missing information on instructional expenditures in 2004, meaning that instructional expenditures per FTE student could not be computed. Due to the computational demands of fitting the conditional logit model to large data set, a multiple imputation procedure was not feasible. Rather than drop these institutions from the option set (and bias the results) I employed Buck’s method of conditional mean imputation (Little 1992; Buck 1960). The results of the conditional logit models obtained with these imputed values are similar to those obtained when the unadjusted fitted values are used.

  4. 4.

    Conditional logit equations were estimated using the asclogit command in the Stata 14 statistical package (StataCorp 2015). Analysis data sets, predicted probabilities, and logistic regression equations were all generated or fit using the R statistical package (Core Team 2016).

  5. 5.

    Full-time equivalent enrollment and various polynomial term were also included in the model but are not reported.

  6. 6.

    Application outcomes are reported in the second wave of the ELS survey. Final attendance outcomes are reported in the third wave. Because not all ELS students reported applications or began after the second wave collection (but within the two-year enrollment window), fewer students are included in the application model than in the unconditional attendance. Though applications could be logically imputed for those in the first model who attend but are missing application information, I have chosen only to include those students with complete application data in the second and subsequently third models.

  7. 7.

    As a test on robustness, I also fit a conditional logit choice model on attendance conditional on acceptance. Most of the multiple applicants (\(\sim\) 85%) were accepted to multiple schools and could be included in the model. Results were qualitatively similar to those for attendance conditional on application, and are available upon request.

  8. 8.

    These numbers consider the difference between the most-likely application school and the actual application school that was closest in the predicted rankings.

  9. 9.

    More detail about the procedure and accompanying figures can be found in the online appendix.

  10. 10.

    The unannounced shutdown of the Data Retrieval Tool from March to October 2017 due to concerns over data security make the future reliability and potential usefulness of this tool less certain.

References

  1. 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.

  2. 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.

  3. Allen, I. E., et al. (2016). Online Report Card: Tracking Online Education in the United States. Technical Report. Babson Survey Research Group.

  4. Avery, C., & Hoxby, C. M. (2004). Do and should financial aid packages affect students’ college choices? In C. M. Hoxby (Ed.), College choices: The economics of where to go, when to go, and how to pay for it (pp. 239–299). Chicago: University of Chicago Press.

    Google Scholar 

  5. 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.

  6. Baker, D. J., & Doyle, W. R. (2017). Impact of community college student debt levels on credit accumulation. The Annals of the American Academy of Political and Social Science, 671(1), 132–153. https://doi.org/10.1177/0002716217703043.

    Article  Google Scholar 

  7. 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 

  8. Baum, S., & Ma, J. (2012). Trends in college pricing., Trends in higher education series New York: The College Board.

    Google Scholar 

  9. 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 

  10. Bettinger, E. P., & Long, B. T. (2009). Addressing the needs of underprepared students in higher education: Does college remediation work? Journal of Human Resources, 44(3), 736–771.

    Article  Google Scholar 

  11. Bettinger, E. P., et al. (2012). The role of application assistance and information in college decisions: Results from the H&R block FAFSA experiment. The Quarterly Journal of Economics, 127(3), 1205–1242.

    Article  Google Scholar 

  12. 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.

  13. Betts, J. R., & McFarland, L. L. (1995). Safe port in a storm: The impact of labor market conditions on community college enrollments. The Journal of Human Resources, 30(4), 741–765.

    Article  Google Scholar 

  14. Black, D. A., & Smith, J. A. (2004). How robust is the evidence on the effects of college quality? Evidence from matching. Journal of Econometrics, 121(1), 99–124.

    Article  Google Scholar 

  15. Black, D. A., & Smith, J. A. (2006). Estimating the returns to college quality with multiple proxies for quality. Journal of Labor Economics, 24(3), 701–728.

    Article  Google Scholar 

  16. 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 

  17. Bowen, W. G. (2013). Higher education in the digital age. Princeton, NJ: Princeton University Press.

    Google Scholar 

  18. 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.

    Article  Google Scholar 

  19. Card, D. (1999). The causal effect of education on earnings. Handbook of Labor Economics, 3, 1801–1863.

    Article  Google Scholar 

  20. Cheng, S., & Long, S. J. (2007). Testing for IIA in the multinomial logit model. Sociological Methods & Research, 35(4), 583–600.

    Article  Google Scholar 

  21. Coleman, J. S. (1988). Social capital in the creation of human capital. American Journal of Sociology, 94, S95–S120.

    Article  Google Scholar 

  22. Cottom, T. M. (2017). Lower ed: The troubling rise of for-profit colleges in the new economy. New York: The New Press.

    Google Scholar 

  23. 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 

  24. 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.

  25. Deming, D. J., Goldin, C., & Katz, L. F. (2012). The for-profit postsecondary school sector: Nimble critters or agile predators? The Journal of Economic Perspectives, 26(1), 139–164.

    Article  Google Scholar 

  26. Doyle, W. R., & Skinner, B. T. (2016). Estimating the education-earnings equation using geographic variation. Economics of Education Review, 53, 254–267.

    Article  Google Scholar 

  27. Doyle, W. R., & Skinner, B. T. (2017). Does postsecondary education result in civic benefits? The Journal of Higher Education. https://doi.org/10.1080/00221546.2017.1291258.

    Article  Google Scholar 

  28. Drewes, T., & Michael, C. (2006). How do students choose a university? An analysis of applications to universities in Ontario, Canada. Research in Higher Education, 47(7), 781–800.

    Article  Google Scholar 

  29. Dynarski, S. (2002). The behavioral and distributional implications of aid for college. American Economic Review, 92(2), 279–285.

    Article  Google Scholar 

  30. Dynarski, S., & Scott-Clayton, J. (2013). Financial aid policy: Lessons from research. Working Paper 18710. National Bureau of Economic Research.

  31. Eagan, K., et al. (2016). The American Freshman: Fifty-year trends 1966–2015. Technical Report. Los Angeles: Higher Education Research Institute, UCLA.

  32. Eide, E., Brewer, D. J., & Ehrenberg, R. G. (1998). Does it pay to attend an elite private college? Evidence on the effects of undergraduate college quality on graduate school attendance. Economics of Education Review, 17(4), 371–376.

    Article  Google Scholar 

  33. Fuller, W. C., Manski, C. F., & Wise, D. A. (1982). New evidence on the economic determinants of postsecondary schooling choices. The Journal of Human Resources, 17(4), 477–498.

    Article  Google Scholar 

  34. Greene, W. H. (2012). Econometric analysis (7th ed.). Boston, MA: Prentice Hall.

    Google Scholar 

  35. Grubesic, T. H. (2008). Zip codes and spatial analysis: Problems and prospects. Socio-Economic Planning Sciences, 42(2), 129–149.

    Article  Google Scholar 

  36. Hart, C. M., Friedmann, E., & Hill, M. (2016). Online course-taking and student outcomes in California community colleges. Education Finance and Policy, 13(1), 42–71.

    Article  Google Scholar 

  37. 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.

  38. 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.

  39. 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.

  40. Huntington-Klein, N., Cowan, J., & Goldhaber, D. (2017). Selection into online community college courses and their effects on persistence. Research in Higher Education, 58(3), 244–269. https://doi.org/10.1007/s11162-016-9425-z. https://doi.org/10.1007/s11162-016-9425-z.

  41. Jepsen, C., & Montgomery, M. (2009). Miles to go before I learn: The effect of travel distance on the mature person’s choice of a community college. Journal of Urban Economics, 65(1), 64–73.

    Article  Google Scholar 

  42. 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.

  43. Kane, T. J. (1996). College cost, borrowing constraints and the timing of college entry. Eastern Economic Journal, 22(2), 181–194.

    Google Scholar 

  44. 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 

  45. Little, R. J. A. (1992). Regression with missing X’s: A review. Journal of the American Statistical Association, 87(420), 1227–1237.

    Google Scholar 

  46. Long, B. T. (2004). How have college decisions changed over time? An application of the conditional logistic choice model. Journal of Econometrics, 121, 271–296.

    Article  Google Scholar 

  47. Long, M. C. (2010). Changes in the returns to education and college quality. Economics of Education Review, 29(3), 338–347.

    Article  Google Scholar 

  48. Lovenheim, M. F., & Reynolds, C. L. (2011). Changes in postsecondary choices by ability and income: Evidence from the National Longitudinal Surveys of Youth. Journal of Human Capital, 5(1), 70–109.

    Article  Google Scholar 

  49. Ma, J., et al. (2016). Trends in college pricing., Trends in higher education series New York: The College Board.

    Google Scholar 

  50. Manski, C. F., & Wise, D. A. (1983). College choice in America. Cambridge, MA: Harvard University Press.

    Google Scholar 

  51. 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 

  52. National Center for Education Statistics. (2016). ELS: Education Longitudinal Study of 2002.

  53. Niu, S. X., & Tienda, M. (2008). Choosing colleges: Identifying and modeling choice sets. Social Science Research, 37(2), 416–433.

    Article  Google Scholar 

  54. Paulsen, M., & Smart, J. C. (2001). The finance of higher education: Theory, research, policy, and practice. New York: Algora Publishing.

    Google Scholar 

  55. 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 

  56. R Core Team. (2016). R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing.

  57. 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 

  58. 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 

  59. 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 

  60. 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 

  61. StataCorp. (2015). Stata statistical software: Release 14. College Station, TX: StataCorp LP.

  62. Tate, W. F. (2008). Geography of opportunity: Poverty, place, and educational outcomes. Educational Researcher, 37(7), 397–411.

    Article  Google Scholar 

  63. The Delta Cost Project. (2016). The Delta Cost Project Database.

  64. Toutkoushian, R. K., & Paulsen, M. (2016). Economics of higher education: Background, concepts, and applications. Dordrecht: Springer.

    Google Scholar 

  65. Turner, S. E. (2004). Going to college and finishing college: Explaining different educational outcomes. In M. C. Hoxby (Ed.), College choices: The economics of where to go, when to go, and how to pay for it (pp. 13–62). Chicago: University of Chicago Press.

    Google Scholar 

  66. Umbach, P. D. (2007). How effective are they? Exploring the impact of contingent faculty on undergraduate education. The Review of Higher Education, 30(2), 91–123.

    Article  Google Scholar 

  67. Xu, D., & Jaggars, S. S. (2011). The effectiveness of distance education across virginia’s community colleges: Evidence from introductory college-level math and english courses. Educational Evaluation and Policy Analysis, 33(3), 360–377.

    Article  Google Scholar 

  68. Xu, D., & Jaggars, S. S. (2013). The impact of online learning on students’ course outcomes: Evidence from a large community and technical college system. Economics of Education Review, 37, 46–57.

    Article  Google Scholar 

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Acknowledgements

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.

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Correspondence to Benjamin T. Skinner.

Appendix

Appendix

In this study, characteristics of the most-likely college (or nearest public two-year institution) were not consistently predictive of whether a student would enroll in college within two-years of high school graduation. In particular, the parameter on distance, which was negatively correlated with the choice between colleges and negatively signed across logistic models, was only statistically significant at the 5% level in two of eight models. Student-specific characteristics such as SAT percentile, family income, parental education, and gender as well as local unemployment rates were more consistently predictive of enrollment.

In the discussion section of the paper, I note that while these results suggest that distance was less important for students when choosing whether to attend college, it may also be the case that correlation between model covariates masks underlying realities about college access. If students are not sorted randomly in relation to postsecondary options, but instead are clustered based on observable characteristics such as family income, parental education level, and race/ethnicity, then it may be that controlling for these characteristics in a regression framework offers misleading insights into the relationship between distance and enrollment. To investigate whether distances to the closest public postsecondary institution are related to the likelihood of college enrollment, I proposed a thought experiment and method for carrying it out using synthetic data. I describe the procedure and results below.

I first use U.S. Census data to construct a population of synthetic students, each one representing a single census tract in the lower 48 states and created using its population characteristics. Specifically, synthetic students represent the “modal” person from the tract in that their characteristics are assigned using modal values of tract demographics. For example, in a tract in which women outnumber men, the student becomes female; in tracts where the plurality of residents have some college education, the student is assigned a parental education level of some college, and so on. Because Census data do not give SAT scores, I generate four students for each tract, each with a different SAT percentile—30th, 50th, 70th, or 90th—representing different levels of college readiness. As with the ELS cohort students, I first predict each synthetic student’s most-likely college to attend and then, using the characteristics of this college as well as those of the student, the likelihood that they will enroll in college. I generate four sets of predictions in total, one for each SAT percentile.

Figure 1 shows the heterogeneity in these predictions across the distribution of SAT score percentiles. Each map, one for each level of SAT percentile, shades census tracts according to the probability that its “modal” simulated student will enroll in college within two-years of high school graduation. Each tract is coded blue for predicted probabilities above than 0.5—more likely than not to attend within two-years of earning a high school diploma—and red for those below. Darker shades show predictions that are statistically significant (\(p < 0.05\)). The maps in Fig. 1 show variation in the likelihood of enrollment, despite the fact that all synthetic students within each simulation are equally college ready in terms of their SAT scores. Simulation models suggest that even for synthetic students with SAT scores in the 90th percentile (bottom right map in Fig. 1), that is, those most likely to benefit from attending college and earning a degree, the odds of enrollment change depending on location.

Fig. 1
figure1

Visualization of predicted probability that a synthetic modal student at the census tract level will enroll in college within two-years of earning high school diploma/GED based on characteristics of his/her most-likely college choice. Shades of blue represent predicted probabilities greater than 0.5 (more likely than not to attend); shades of red represent predicted probabilities less than 0.5 (less than likely to attend). In both cases, shades are darker when statistically significant. Because Census data used to create modal students does not contain SAT scores, each synthetic student was given multiple scores across the distribution of possible scores. Each map shows results for synthetic students given SAT scores at the stated point in the distribution: 30th, 50th, 70th, and 90th

When considering only those census tracts with probabilities of enrollment that are statistically significant, I find statistically significant differences in population characteristics between the bottom and top quartiles of predicted probability—those in which the synthetic student is least and most likely to enroll, respectively. Tracts in the bottom quartile of probable enrollment tend to have more persons of color, lower average educational attainment, and lower median income than those in the top quartile. Computing the distance to the nearest public, two-year, and public two-year institution for each census tract centroid, I find that tracts that produced non-enrollees in the simulations tend to be farther removed from the nearest institution. Figure 2 shows the differences in median distance to nearest college between tracts with non-enrollees and those with enrollees. Looking at the upper right-hand facet in which synthetic students were given the 50th percentile of SAT scores, the median distance to the nearest public two-year institution for non-enrollee tracts is around 12 miles. For enrollee tracts, the median distance is less than five miles. Results are similar across the range of SAT percentiles.

Fig. 2
figure2

Median distances in miles to nearest public, two-year, and public two-year for census tracts that produced modal synthetic students likely (Prob > 0.5) and unlikely (Prob < 0.5) to enroll within two years of high school graduation across distribution of SAT percentiles. Only those tracts with statistically significant positive or negative predictions (\(p < 0.05\)) are included

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Skinner, B.T. Choosing College in the 2000s: An Updated Analysis Using the Conditional Logistic Choice Model. Res High Educ 60, 153–183 (2019). https://doi.org/10.1007/s11162-018-9507-1

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Keywords

  • College access
  • College choice
  • Conditional logistic choice model
  • Geographic opportunity