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The Labor Market Value of Higher Education: Now and in the Future

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Higher Education: Handbook of Theory and Research

Part of the book series: Higher Education: Handbook of Theory and Research ((HATR,volume 34))

Abstract

This chapter reviews the evidence on the labor market returns to investing in a college education for students in the U.S. First, we describe the primary method used to estimate the return on investment and catalog the main datasets used. We then summarize the available evidence on the returns to different awards and other types of investment. We show how earnings gains clearly vary with the incremental level and quality of postsecondary education. Completing a bachelor’s degree is associated with large gains in earnings amounting to at least one-quarter million dollars on average over the lifetime. Completing an associate degree is associated with sizeable and persistent earnings advantages over no college award. Completing a certificate can yield earnings gains, but these are variable and may only be temporary. Looking at field of study and college characteristics, we also find clear evidence of variability in returns. Finally, we consider the skills of college students need, both in their current occupations and in the future as robots and computers impact on what workers do. Overall, we find most college investments to have strong labor market pay-offs and that these pay-offs are unlikely to diminish in the near future.

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Notes

  1. 1.

    Early analyses justified the use of Mincerian OLS estimation on the grounds that positive and negative biases offset each other (Griliches, 1977). Gelbach (2016) demonstrates how the specific influence of each additional covariate may be misinterpreted. Here we are interested in the most valid estimate of the returns to education, not the measured influence of each covariate.

  2. 2.

    Other biases may be significant. For example, Webber (2016) identifies a sizeable influence of non-cognitive characteristics such as an individual’s locus of control and self-esteem. However, these characteristics may themselves be endogenous to either earnings (persons with high earnings may then report higher self-esteem) or education (those with degrees may report higher self-esteem; see de Araujo & Lagos, 2013).

  3. 3.

    For example, in their detailed analysis, Carneiro et al. (2011, p. 2779) conclude that “[s]ome marginal expansions of schooling produce marginal gains that are well below average returns. For other policies associated with other marginal expansions, the marginal gains are substantial.”

  4. 4.

    UI earnings data have low imputation, self-reporting, and nonresponse bias. However, UI data exclude independent contractors, military personnel, some federal personnel, and those working in the informal sector. Workers who migrate out of state are also excluded. Overall, UI coverage is reasonably high, with more than 90% of college enrollees having at least one wage record. For more information on the quality of these datasets, see Liu et al. (2015).

  5. 5.

    Vuolo, Mortimer, and Staff (2016) estimated the returns to college using the longitudinal Youth Development Survey. Although a small sample, the survey includes measures of biweekly earnings from 2005 to 2011, and earnings gains can be calculated relative to non-completers at either a two-year or four-year college. These estimates are very close to the consensus values for associate degrees. Adjusted to 2014 dollars, the returns over non-completers for associate degree holders are $2250 per quarter. Using fixed effects specifications for female welfare recipients in Colorado, Turner (2016) finds near-equivalent results. Adjusting for the types of associate degrees, the estimated earnings gain from completing an associate degree is $1840.

  6. 6.

    Using the National Longitudinal Survey of Youth (NLSY79), Agan (2014) separates out decision nodes based on college sector choices to yield eight different pathways. However, this method does not allow for non-completers to be linked to particular groups of completers such that ex ante returns could be calculated for programs. In most studies, dropouts are identified ex post and, as a combined group, are separated from all completers.

  7. 7.

    Alternatively, there may be a synergy effect: the combination and accumulation of credits within the award may represent a more valuable accumulation of skills than credits alone. For example, 60 random credits that do not correspond to an award are unlikely to be as valuable as 60 engineering credits that correspond to an associate degree in engineering.

  8. 8.

    Overall, the Ashenfelter dip is $200 to $500 in each of the 2–4 quarters before enrollment. For Virginia, the estimated dip is $480 per quarter in the 2 years before college enrollment (Jaggars & Xu, 2016, Table 2); for North Carolina, it is $370 for men ($210 for women) in the four quarters prior to enrollment (Liu, Belfield, & Trimble, 2015, Table 6). Finally, for Colorado welfare recipients, Turner (2016) estimates a decline in quarterly earnings by $900–$1400 (from $1700–$2200 down to $800) over one year prior to entry (with most of the decline in the quarter prior to enrollment). For California and Michigan, Bahr et al. (2015) and Bahr (2016) report an unspecified but significant Ashenfelter dip.

  9. 9.

    The differences in the results of these studies may be explained by empirical and methodological differences. The studies use different ways of identifying for-profit students. The studies also use different approaches to address the challenges of selection bias. Deming et al. (2012) use propensity score matching. Lang and Weinstein (2013) use a maximum likelihood sample selection model as well as propensity score matching. Chung (2012) directly addresses selection bias by estimating a multinomial logit of for-profit college choice, including variables for tuition prices, relative earnings, and distance to college. Cellini and Chaudhary (2012) use an individual fixed-effects estimation strategy.

  10. 10.

    Liu et al. (2015) also find evidence that longer periods at a transfer college are associated with higher earnings. Male (female) dropouts from four-year colleges have lifetime earnings gains of $52,000 ($73,000) over high school graduates. Dropouts from two-year colleges have earnings gains of $77,000 ($38,000).

  11. 11.

    As explained by Autor (2015): “Tasks that cannot be substituted by automation are complemented by it,” and “Productivity improvements in one set of tasks almost necessarily increase the economic value of the remaining tasks.” In other words, the worker and the robot are helping each other. As the robots become more sophisticated and the workers become more educated, firms adjust their task requirements in response to more skilled labor (Sasser Modestino, Shoag, & Balance, 2015). Importantly, skilled workers are now providing the social skills the machine cannot. So, Weinberger (2014) finds that demand for cognitive skills has only increased for persons with high endowments of social skills.

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Correspondence to Clive R. Belfield .

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Belfield, C.R., Bailey, T.R. (2019). The Labor Market Value of Higher Education: Now and in the Future. In: Paulsen, M.B., Perna, L.W. (eds) Higher Education: Handbook of Theory and Research. Higher Education: Handbook of Theory and Research, vol 34. Springer, Cham. https://doi.org/10.1007/978-3-030-03457-3_9

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  • DOI: https://doi.org/10.1007/978-3-030-03457-3_9

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