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Student response to tuition increase by academic majors: empirical grounds for a cost-related tuition policy

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Abstract

This study explored the responses of students in different academic majors to tuition increase, with a particular focus on the relationship between tuition increase, and future earnings and college expenditures. We analyzed effects of tuition increase on enrollment in six academic majors—Engineering, Physics, Biology, Mathematics, Business, and Education—where disciplinary enrollment data were available. The main findings are that students are elastic to tuition level in Physics, Biology, and Business, but not in Engineering, where the rate of return is the highest among the six majors and the college expenditure are the highest. The findings suggest that student enrollment in various academic majors is affected differentially by tuition. Further, the findings support a cost-related tuition policy, one designed to charge students higher tuition for higher-cost majors and lower tuition for lower-cost majors.

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Notes

  1. Johnson (1979), for instance, reported an interesting finding from his study of higher education institutions in the state of Washington, where the cost-related tuition concept was adopted in 1977. In his analysis of tuition and college expenditures, Johnson found that state universities, regional colleges and universities, and community colleges in Washington each charged students differently despite the similarities in college costs in 4-year and 2-year institutions. Johnson's finding implies that college pricing was not related to college expenditure, notwithstanding the state of Washington officially adopted cost-related tuition.

  2. Montmarquette and his colleagues (2002) developed a more complicated earnings model. They explained the construction of expected earnings as the perceived probability of degree completion, predicted earnings of degree completion, and expected earnings if students fail to complete. Among the three factors, the expected earnings was shown to have higher elasticity in college major choice than the probability of degree completion.

  3. The cost is the mean of the direct instructional expense per student credit hour in research, doctoral, and comprehensive colleges and universities. The college cost of Social Sciences is the averaged costs of Sociology, Political Science, and Economics; and the cost of Humanities is the average costs of History, English, and Philosophy.

  4. Indeed, research has also shown that the economic returns to higher-cost majors are higher than those to lower-cost majors. For example, engineers are known to have high earnings after graduation (Eide and Waehrer 1998), and the college costs for engineering majors are the highest among academic disciplines (Middaugh et al. 2003).

  5. In the IPEDS peer analysis system, we gathered data on 632 institutions by selecting sector of institution (FA2000HD), then by selecting “public 4-year or above” in the options.

  6. The log-linear model is a different functional form specified between the dependent variable and all independent variables from the more familiar linear form. In the linear form, each unit of a given independent variable is hypothesized to the same sized unit effect on the dependent variable. The log-linear model is a specification that hypothesizes that the effect of independent variables is non-linear; effects of independent variables are dependent on, for example, the level of the independent variable.

  7. CPI is provided by the Bureau of Labor Statistics. The yearly average CPI data is available from ftp://ftp.bls.gov/pub/special.requests/cpi/cpiai.txt

  8. Of course, students may transfer from colleges in which they were initially enrolled for other reasons, such as looking for a new major that their current college does not provide, family moves, scholarship opportunities, etc. These are variables that are unrelated to the institutional theories we are testing in this study.

  9. Less qualified students, especially, do not have options in choosing their colleges while highly qualified students do. Surprisingly, however, empirical results have shown that students’ tuition elasticity does not differ according to student quality (Hoeck and Weiler 1975).

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Authors and Affiliations

  1. Department of Education, Office 11-317, Seoul National University, Kwanak-gu, Shilim-dong, Seoul, 151-742, South Korea

    Jung Cheol Shin

  2. Educational Leadership and Policy Studies, Florida State University, Stone Building 113, Tallahassee, FL, 32306-4451, USA

    Sande Milton

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  1. Jung Cheol Shin
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  2. Sande Milton
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Correspondence to Jung Cheol Shin.

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Shin, J.C., Milton, S. Student response to tuition increase by academic majors: empirical grounds for a cost-related tuition policy. High Educ 55, 719–734 (2008). https://doi.org/10.1007/s10734-007-9085-1

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