Abstract
In this chapter, we focus on the different ways in which economists measure the financial return on postsecondary education. We begin by providing some background on the work by economists on this topic, where studies of the return to postsecondary education grew out of the more general economic approach of cost-benefit analysis. We then explain how economists use aggregate-level data to measure the average return to postsecondary education, and demonstrate how the methods can be applied to different degree levels. Using data from 2011, we provide updated estimates of the return to earning an associate’s or a bachelor’s degree, as well as the average returns for all students who attend college as opposed to only graduates. In the Extension section of the chapter we discuss how economists use individual-level data to measure the financial benefits from college after controlling for observable student characteristics that may also affect earnings, and the emerging work on how to adjust these estimates for unobservable factors that can affect postsecondary decisions and earnings in labor markets. Finally, in the Policy Focus section we discuss policies relating to the use of return-on-education statistics to entice more students to go to college, and the extent to which students rely on loans to help finance their college education.
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Notes
- 1.
See U.S. Census Bureau, PINC-03. Educational Attainment–People 25 Years Old and Over, by Total Money Earnings in 2013, Work Experience in 2013, Age, Race, Hispanic Origin, and Sex (http://www.census.gov/hhes/www/cpstables/032014/perinc/pinc03_000.htm).
- 2.
- 3.
- 4.
See McMahon (2009) for more discussion of the justification used for the 10 % benchmark.
- 5.
- 6.
Interested readers are referred to the literature reviews on the rates of return to education conducted by Psacharopoulos (1973, 1981, 1985, 1994), and Psacharopoulos and Patrinos (2004) and international studies including Asadullah (2006), Denny and Harmon (2001), Menon (2008), Shafiq (2007), and Tilak (2007).
- 7.
- 8.
- 9.
- 10.
A comprehensive examination of these issues can be found in Walter McMahon’s book Higher Learning, Greater Good (2009).
- 11.
See Psacharopoulos and Patrinos (2004) and McMahon (2009). There is also a “shortcut method” that is sometimes used in rates of return studies, where the private internal rate of return is approximated by \( \left({\overline{I}}^g-{\overline{I}}^{na}\right)/4{\overline{I}}^{na} \) (Psacharopoulos, 1981). This is useful in situations where the earnings trajectories over time are flat and the researcher does not have enough data to apply the full method.
- 12.
Spreadsheet programs and advanced calculators typically have built-in routines that will calculate the internal rate of return. For example, the Excel formula “=IRR(cell1:cellN)” will find the rate of return represented by an array of expenditures and revenues in the range cell1 to cellN.
- 13.
As noted previously, this parameter is very difficult to estimate and is often omitted from rate of return studies for this reason. When combined with the added tax revenues from higher education, our choice of $8,000/student is consistent with estimates from McMahon (2009) of the public benefits from higher education.
- 14.
As with bachelor’s degrees, the earnings with an associate’s degree depend on the major chosen by the student. Associate-degree programs in fields such as nursing yield higher earnings than many other programs. The same is true, of course, for bachelor-degree programs. See Tuor and Backes-Gellner (2010) for more discussion.
- 15.
In some instances, a student with a bachelor’s degree may be accepted directly into a doctoral-degree program, and then receive a master’s degree during the completion of their doctoral degree.
- 16.
Direct costs may be offset in doctoral-degree programs when the student receives a teaching or research assistantship. Such assistantships may also cover a portion of the indirect costs incurred by the student if they receive a stipend from the institution.
- 17.
Keep in mind that economists usually calculate the internal rate of return assuming that the discount rate for time preference is 0 % above inflation because the return can then be directly compared to other investments. Accordingly, the values for the internal rate of return with a 3 % discount rate can be thought of as “adjusted rates of return” that are biased downward relative to other investments.
- 18.
For an excellent discussion of this issue, see Cohn and Geske (1986).
- 19.
- 20.
The challenge with household income is that it can be difficult to assign the income to one spouse and the education level of a single individual in the household.
- 21.
Webbink and Hartog (2004), however, found evidence that students can form reasonable expectations of their future income streams.
- 22.
For example, Monks (2000) found that college graduates’ earnings vary by students’ race, gender, ability, income, and years of work experience, and by various college characteristics such as institutional selectivity. Interested readers should also see Brewer, Eide, and Ehrenberg (1999), Carnoy (2010), and Dale and Krueger (2002).
- 23.
See Cohn and Geske (1986) for details.
- 24.
- 25.
Other limitations with these two studies are that they did not discount benefits, they used incomes for only employed individuals, they ignored taxes, and used average incomes for all workers and not younger workers.
- 26.
The private benefits in their calculations appear to use pre-tax incomes that would therefore include public as well as private benefits.
- 27.
- 28.
For example, McMahon (2009) used all revenues to measure societal support for higher education. Not only does this total include some revenues that were not used to support student instruction, it also double counts student net tuition payments.
- 29.
- 30.
- 31.
This assumption draws on the work by Arias and McMahon (2001) who showed that incomes for college graduates rise faster than incomes for non-college graduates.
- 32.
Mincer’s use of the natural log of earnings as the dependent variable has since become the most commonly-accepted way to specify earnings equations, and has been used in countless studies. Its use has been justified on the grounds that the distribution of earnings is often skewed to the right and the log transform helps to normalize the dependent variable. In addition, the functional form is appealing in applications where salaries are compounded over time, such as when workers receive a common percentage increase in salary. The discussion in this section, however, would apply equally in situations where actual salary and not the log of salary is used as the dependent variable in the earnings equation.
- 33.
Other degree levels could also be used as the reference category for this purpose.
- 34.
- 35.
Another variation on the earnings equations shown here is to use a “spline function” where variables are added to the model to capture years of education above specific threshold values (such as 12 or 16 years). More discussion on the incorporation of risk into rate of return studies can be found in Christiansen, Joensen, and Nielsen (2007), and Hussey and Swinton (2011).
- 36.
- 37.
- 38.
- 39.
Harmon and Walker (1995) provide a summary of the issues surrounding this type of ability bias in return to education studies.
- 40.
Examples of studies using an instrumental variable approach to estimate returns to education include Card (1993), Angrist and Krueger (1995, 2001), and Heckman and Vytlacil (1998). Readers who are interested in the methodological issues on this topic should see Griliches (1977), Heckman, Lochner, and Todd (2008), Dale and Krueger (2002), and Card (1995).
- 41.
See, for example, Ashenfelter and Krueger (1994).
- 42.
Data were taken from the Digest of Education Statistics 2013, Table 331.95. Additional analysis of trends in student borrowing can be found in Woo (2013).
- 43.
- 44.
The data were obtained from the National Postsecondary Student Aid Study (NPSAS) for the 2011–2012 academic year. We would like to thank Manuel Gonzalez Canché from the University of Georgia for compiling the statistics shown in this table.
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Glossary
Glossary
Symbol | Definition |
|---|---|
Subscript j | Student |
Subscript t | Time |
P | Price of college (tuition + fees) |
F | Financial aid per student (grants and scholarships) |
txna, txg, txng | Income tax rates for not attend college, graduate, and not graduate |
W | Proportion of foregone income earned while in college |
Ina, Ig, Ing | Incomes if not attend college, graduate college, and not graduate |
i | Annual rate of inflation |
z | Annual discount rate for time preference of money |
δ | Internal rate of return to college |
T1 | Years in college |
T2 | Years until retirement |
T | Lifetime |
\( {\uppi}_{\mathrm{t}}^{\mathrm{r}} \) | Probability of enrolling in college in year t |
\( {\uppi}^{\mathrm{g}} \) | Probability of graduating college |
rp | Rate of growth of future costs and benefits of college |
Gt | Public costs of college per year (e.g., state appropriations) |
\( {\mathrm{E}}_{\mathrm{t}}^{\mathrm{g}} \) | Public benefits (positive externalities) per year beyond the tax revenues created by students who graduate from college |
C(pri)t | Annual private costs of college |
C(pri)g | Cumulative private costs of graduating college |
C(pri)a | Cumulative private costs of attending college |
B(pri)g t | Annual private benefits of graduating college |
B(pri)a t | Annual private benefits of attending college |
B(pri)g | Cumulative private benefits of graduating college |
B(pri)a | Cumulative private benefits of attending college |
NPV(pri)g | Private net present value of graduating college |
NPV(pri)a | Private net present value of attending college |
NPV(soc)g | Social net present value of graduating college |
NPV(soc)a | Social net present value of attending college |
Ratio(pri)g | Ratio of private benefits to costs of graduating college |
Ratio(pri)a | Ratio of private benefits to costs of attending college |
Ratio(soc)g | Ratio of social benefits to costs of graduating college |
Ratio(soc)a | Ratio of social benefits to costs of attending college |
IROR(pri)g or δ(pri)g | Private internal rate of return of graduating college |
IROR(pri)a or δ(pri)a | Private internal rate of return of attending college |
IROR(soc)g or δ(soc)g | Social internal rate of return of graduating college |
IROR(soc)a or δ(soc)a | Social internal rate of return of attending college |
ED | Set of variables used to represent educational attainment |
X | Set of variables used to represent observable characteristics of students that may influence earnings in the labor market |
W | Set of variables used to represent unobservable characteristics of students that may influence earnings in the labor market |
lnI | Natural logarithm of income |
YrsED | Years of education completed |
AA, BA, MA, PHD | Dummy variables for terminal (last) degree earned |
γ(gamma) | Average percentage differences in predicted earnings between two students with different levels of education, controlling for other student characteristics |
α | Average percentage differences in predicted earnings between two students with different levels of ability, gender or other characteristics controlling for levels of education |
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Toutkoushian, R.K., Paulsen, M.B. (2016). Private and Social Returns to Higher Education. In: Economics of Higher Education. Springer, Dordrecht. https://doi.org/10.1007/978-94-017-7506-9_4
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