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Heterogeneity in the returns to higher education
C H A P T E R
6 Heterogeneity in the returns to higher education Ian Walker Department of Economics, Lancaster University Management School and IZA, Bonn, Germany
Introduction
forgiveness. There are three elements of subsidy inherent in the system: no debt is collected until earnings hit a threshold; the interest rate is, at least on average, below market rates; and after 30 years any unpaid debt is forgiven. The operation of this implies that courses which provide modest returns will attract larger subsidies than subjects that offer high returns. Personal debt arising from student loans has steadily risen in recent years in the UK (although less so in Scotland which has retained zero up-front fees), and in much of the English-speaking world.1 The most recent graduating college cohort in the UK (excluding Scotland) has incurred approximately £28,000 of student debt associated with their tuition fees, plus up to
There is considerable research on the returns to education. In recent ears some progress has been made in estimating the returns to particular types of education e in particular higher education (HE). Several contributions have attempted to estimate heterogeneity across higher education subjects (ie by major) and even by institution (HEI). This chapter reviews work on the returns to HE, and contributes to the UK strand of this literature. In the UK context, heterogeneity in returns is of particular interest because of the nature of the student loan system. This now takes the form of an income contingent loan with
The USA has always charged “college” fees and these fees have risen markedly in recent years especially for the most academically selective institutions (see, for example, Hoxby, 2019). UK HEI fees (excluding Scotland) have risen more recently. Australia and New Zealand also have income contingent loan systems although neither country experienced the Great Financial Crisis to the same degree as most other countries. Australia has a large threshold before repayment starts but no forgiveness. New Zealand has a modest repayment income threshold and has, very recently, abolished fees for some part of HE. Ireland abolished HE fees some time ago. No EU countries (with the exception of Hungary) have economically significant fees or charges for HE e indeed, some have extensive provision of grants to cover subsistence costs. See Barr et al. (2019) for further details of international comparisons and developments. 1
The Economics of Education, Second Edition https://doi.org/10.1016/B978-0-12-815391-8.00006-9
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6. Heterogeneity in the returns to higher education
£18,000 associated with their subsistence expenses (that arguably might have been incurred in the absence of attending university). In the US recent graduates average roughly US$30,000 of debt e although fees (or at least the sticker price) vary considerable across institutions, and the national total exceeds $1.4 trillion, a figure that some claim (FT April 9, 2017) represents an economic bubble which could have substantial negative effects for future generations. Particular concern has been expressed over US default rates (estimated to be 18%). An important difference between the US and elsewhere is that the US debt is typically mortgage style (known as time based repayment loans, TBRL) e the debt is repaid monthly at a constant rate until it is fully repaid, usually in 10 years. Most of the concern in the US is over former students being able to meet these payments. Elsewhere, loans are income contingent and collected via the tax system. In addition to concerns over the public finances and their macroeconomic implications,2 these numbers beg an important microeconomic question: is taking on substantial student loan debt to (possibly) obtain a college degree a sound financial investment? Although this is a simple question it has a complicated answer which depends on a variety of factors, such as the student’s major, the HEI attended, ability, probability of dropping out, among many others. This paper aims to outline the evidence around these factors. Thus, this paper is concerned with a range of issues surrounding the effects of, and funding of, Higher Education with a focus on the UK. HE in the UK is usually pursued from age 18, or soon thereafter, at over 150 Higher Education Institutions (HEIs), some very small and
specialized, which are collectively referred to as universities. Higher education participation rates are over 40% of the cohort and this has grown dramatically in the last three decades. The old funding model was that central government provided extensive direct funding to HEIs, there were no tuition fees, and students received maintenance grants to support themselves during studies (although these were subject to extensive means testing against parental incomes). Course fees in England (less so in Wales and Northern Ireland where the devolved administrations have pursued their freedom over spending to impose lower fees, and Scotland have chosen to have zero fees) have been dramatically increased (and public funding has almost been eliminated) since 2010. This was part of a post-recession austerity drive, but was accompanied by a comprehensive, sophisticated, and highly subsidized, student loan program that supports access, especially for low parental income students. Take-up of these loans is high and repayments are income contingent with the balance after 30 years being written off. As a result, demand for university is relatively tuition fee inelastic, and there is little evidence that fees have resulted in any fall in participationdeither overall or for low SES students (see Murphy et al., 2017), and this has not been the case in Scotland where low SES participation has fallen relative to higher SES participation. The UK offers a convenient laboratory for HE research since UG study is highly concentrated, throughout the duration of study, on a single major. Courses are usually specialized where a single narrow major is often pursued exclusively. Dropping-out is relatively scarce (around 8% across the sector). HEIs in England, (and
2 The ONS in the UK has recently recommended that the treatment of student debt in public finances should be changed. Previously, student debt did not appear in national accounts until the forgiveness point had been reached so, to date, no student debt has reached this maturity. Now, the debt is to be counted as part of national debt as it accrues. It remains to be seen whether money markets had already priced-in the anticipated debt.
II. Private and social returns to education
Introduction
Wales and Northern Ireland) offer undergraduate courses that are typically 3 years duration (Scotland has many 4 year courses), studied mostly on a full-time basis and mostly straight from senior high school.3 Although the UK is small, and so proximity to university is much higher than in many other countries, the majority of students move away from their parental homes to study HE, and most of those that do will form (or join) households elsewhere when they graduate and start work. Unlike the US, UK undergraduate professional courses such as law, medicine, and management are available across most HEIs. The UK HE sector is much less homogeneous that the HE sector in the US The UK equivalent of the US Ivy League is the so-called “Russell Group” (RG) of HEIs e 25 institutions including ancient ones such as Oxford and Cambridge, many of the top London institutions, as well as the major provincial “redbrick” HEIs (named for the appearance of their largely Victorian architecture). These are research intensive institutions, they are large, they offer a full range of subjects, and they attract students from all over the UK and many from outside the UK The “Old” non_Russell HEIs are often refereed to ask “Pre-92”, and comprise another 25 institutions that were founded before 1992 e the date when the former Polytechnics became able to grant their own degrees and could refer to themselves as universities. These are also full function HEIs but are not as focused on a research mission; many are old but not ancient and they include the HEIs that were founded in the 1960’s. The “New” HEIs are former Polytechnics and, together with newer entrants, comprise approximately one hundred institutions that vary considerably in size and range of functions. Some are small and specialized e for example on music or other performing/creative arts.
3
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HEIs are independent of government. Government has no control over the curriculum, and has, since 2014, exercised no control over student numbers across the sector, by course type, or by HEI. HEIs have also been free to set their fees within a range from £6000 to £9000 per annum (figures that have increased modestly since 2014 when they were set). While, the Minister for Universities has stated that he expected £9000 to be exceptional, in fact almost all institutions were immediately attracted to the £9000 focal point for almost all majors. Discounts on the sticker price are available but are usually both means tested and contingent on high school achievement. There is considerable obfuscation over the extent to which actual tuition costs vary by major differ considerable and firm figures are hard to find - it is thought that the costs of running most Arts, Social Science, and Management courses are below the £6000 lower bound for annual fees; while costs for most STEM subjects are likely to be well above the upper bound for fees; and those for medical, veterinary and dentistry courses are likely to be considerable higher than that (and these courses are invariably 5 years duration). The implication of the choice, by all universities, to engage in widespread cross-subsidization is that the large number of STEM (and the small number of medical/dental) students are being cross-subsidized by the larger number of low-cost students. The current government fees review has (so far) been largely silent on this and the regulator, the Office for Students, has not considered the fairness of a situation where low-cost students are subsidizing high cost ones who are, on average, going to enjoy higher levels of lifetime income. Indeed, the biggest cross-subsidies go from the successful upper tail of Arts students (who earn sufficient to not enjoy large subsidies) to the less successful lower tail STEM students
This has become more likely to be true since mature students were disadvantaged in the student loan scheme.
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6. Heterogeneity in the returns to higher education
who do badly in their studies and may not earn sufficient to make loan repayments early enough to completely repay their loans.4 In the meantime: institutions who have a small proportion of STEM and medical students can be extremely profitable; while others are experiencing financial difficulties in the face of competition for students from more prestigious institutions at the same time as English cohort sizes are falling; and political change makes the UK a less attractive destination for EU students and even for those from further afield.
The economic value of degrees HE costs and private returns are central to associated funding issues. The common perception is that costs are tuition fees and the benefits are the earnings differential for having a degree relative to not. However, this is only a partial view e the opportunity cost of studying for a degree is the earnings that one could have earned otherwise and such opportunity costs would probably considerably exceed the actual tuition fees for most English students, even at minimum wage. Moreover, the benefits to the student is not just the pecuniary effect of HE on the individual student’s earnings, but also includes any non-pecuniary benefits. Some of these are private benefits that are enjoyed immediatelye the consumption value of a stimulating and engaging course. Others will be discounted future benefits. For example, there is a well documented effect of education on health (although there is little evidence that directly relates to higher education in particular). We know almost nothing about these non-financial effects: but it seems likely that they might vary by subject studied (major) and the quality of teaching, and by student aptitudes and preferences which
will vary by prior teaching quality, and parental background. The potential for quantitatively significant non-pecuniary benefits to be an important driver of student course choice is suggested by the (albeit, limited) available evidence that we have from research on the determinants of student choices. However, it is difficult to put a lot of weight on the student choice literature for four reasons. First, the pecuniary returns occur in the future across the working lifecycle and these might be (heavily) discounted, while the opportunity costs of foregone earnings are borne early in the working life, giving rise to a net present value that might be lower than the raw earnings data might suggest. A weak financial private rate of return might be reinforced by credit market imperfections associated with debt incurred to finance human capital being inherently unsecured. It might be further weakened by the potential for young people to be time inconsistent in their decision-making. There is now an increasing volume of empirical research that points to young people being “hyperbolic” rather than “exponential” discounters leading them to make decisions that they ultimately regret. Secondly, the non-pecuniary benefits might be associated with the consumption value arising from the challenge of higher level learning, and the enjoyment of spending a few formative years in a safe environment surrounded by like-minded people. These are enjoyed in the moment and their value is therefore not affected by heavy discounting. The third argument, is that the benefits are uncertain and there is very little information that students can rely on to inform them. Higher education institutions will point to their successes amongst their graduate output and not to the long-left tail of their graduates’ earnings. The little quantitative data that is out there refers to the mean outcomes
4
Almost all medical/dental students are successful at university and almost invariably they pursue careers in medicine/dentistry.
II. Private and social returns to education
The economic value of degrees
and say nothing about differences associated with observable variation in the treatment (for example, across majors and across institutions which themselves exhibit a wide variation in student selectivity and in their own productivity) and in the treated (the students themselves are likely to vary in their degree of college readiness and their own non-cognitive skills and traits that might complement cognitive skills that a degree might offer, especially majors that are vocational in nature or offer high demand skills). In contrast, the costs are probably substantially less uncertain. In England (although not in the US) the sticker price is highly correlated with the actual price paid since discounts are smaller and less commonplace than in the US where alumni finance is often used to drive a large wedge between sticker and final prices. Given the large unexplained variance in private returns (i.e. wage rates) the role of attitudes to risk also play a role. Importantly, the income contingent nature of the loan scheme insures students against the financial risks they face surrounding the match component of returns e in particular, how well they will do in their chosen course. Moreover, the loan scheme provides the largest subsidies to human capital investments that offer the lowest mean return. This tips the balance further in favor of majors that offer low private financial returns, as well as being high risk (for example, creative arts degrees). The income contingent nature of fees, which might well have encouraged low SES students to access HE, might also have encouraged students to take lower return subjects than would previously the case. In addition, UK degrees are “classified” into “first class, upper second, lower second, third,
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and pass” and there are large labor market wage differentials associated with better quality outcomes.5 Since, within a course, there is relatively little variation in the quality of students on entry, the variation in quality on exit is largely due to the extent of individual engagement with studying. Students might now be less inclined to make the effort to excel in their studies because the highest loan subsidies are available to the lowest earning, and so weakest outcome, students. It would appear, from the size of wage differentials associated with degree class that there are large returns to student effort. Of course, the cost of effort is also endured in the moment so the hyperbolic nature of discounting will not affect this source of disutility. The literature on the returns to HE (which is referred to in the US literature as “the college premium”) is driven entirely by the “private benefits” of HE associated with the higher wage rates or earnings of graduates versus non-graduates. It ignores aspects of costs that could be very important, it ignores nonpecuniary private returns, and it ignores “social” costs and benefits. Moreover, it offers only a very narrow view on what is a very precise concept e the net present value calculation of private net (of tax, welfare, and loan subsidy) financial returns. Nonetheless, even this narrow view is itself very difficult to measure.
Estimating the college premium The ingredients of a measure of the college premium is the wages (or earnings) of graduates and of non-graduates. Such data is readily available in many countries e for example, a great deal of research has been done on the US Current
5
Walker and Zhu (2019) provide estimates from LFS data that differentials by degree class have proved, on average, to be stable in the face of the large HE expansion that occurred in the 80’s and 90’s even though, over this period grades themselves rose on average. This is surprising, since the expansion will have lowered the average quality of students entering the system. It would appear that the students and/or their teachers have increased their productivity and outcomes, in terms of markes skills, are actually better.
II. Private and social returns to education
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6. Heterogeneity in the returns to higher education
Population Survey (CPS) and the American Community Surveys (ACSs); while in the UK extensive use has been made of the Labor Force Survey (LFS). An early example of LFS UK research is Walker and Zhu (2008). This work looks at the returns to UG (and PG) and reports estimates of the UK “college premium” for young graduates across successive cohorts from large crosssection datasets for the UK pooled from 1994 to 2006da period when the higher education participation rate increased dramatically. The modest growth in relative labor demand over this period suggests that graduate supply considerably outstripped demand which ought to imply a fall in the college premium. However, the data showed no statistically significant fall for men (and a statistically insignificant rise for women). Quantile regression results revealed a fall in the premium only for men in the bottom quartile of the distribution of unobserved skills e a fall that was not statistically significant. This work was followed-up in Walker and Zhu
FIG. 6.1
(2013), a report to the Department of Business Innovation and Skills e then the government department responsible for universities. The additional data that was available for the BIS Report resulted in more precise estimates but they were not substantially different from those reported in the earlier paper. The most recent UK example is Blundell, Green, and Jin (2016) which is also focused on how the average college premium have changed across cohorts. HE participation rates have grown rapidly in recent years, especially the mid 80’s to mid ‘90s, but more slowly from the mid ‘60s before that. Fig. 6.1 reproduces their headline finding in Blundell et al. (2016) and underlines the puzzle e that the college premium does not, at face value, appear to respond to a large shift in supply of graduates. The figure shows the familiar fact that earnings of graduates rise strongly across the lifecycle relative to that of non-graduates. It also shows that this phenomenon has NOT changed across cohorts despite the huge increase in the supply of
The college premium (ratio of graduate to non-graduate earnings) by age across UK birth cohorts. Source: Blundell
et al. (2016).
II. Private and social returns to education
The economic value of degrees
graduates e the age-earnings profiles for each cohort group overlap considerably. In particular, the first three cohort groups were 18 before the big expansion and the last three groups were 18 after the expansion. Yet in their 30’s, where the lifecycles of these two groups overlap, the earnings for the pre and post expansion cohorts are very close to one another. Moreover, across the lifecycle the college premium suggested in this figure is, in present value terms, worth over £250 k. This is consistent with previous estimates of the college premium in the UK that, in simple specifications, report estimates between 25% and 35% where the control group is those that report having A-levels (as in the Walker/Zhu work), and between 35% and 45% where the control group is all those that did not attend HE (as in Blundell et al., 2016). In almost all cases, research finds that the returns to HE for men is lower than that for women - reflecting to poorer opportunities in the labor market for non-graduate women compare to non-graduate men. It is well known that OLS estimates, such as those in the Walker/Zhu and Blundell et al. work, may suffer from bias. In one direction, it may suffer from bias associated with omitted “ability”. This is traditionally thought of as biasing the college premium coefficient upwards. In the simplest version of the traditional ability bias story, earnings (w) and schooling are determined (C) by: w ¼ bC þ aA þ ε, and C ¼ gA þ z, where w is the (log) wage rate, C ¼ 1 if the individual has a “college” degree and 0 otherwise, A is unobserved “ability”, ε is uncorrelated with C and with A, and z is uncorrelated with ε. That is, z and w are correlated only through their joint dependence on A. However, A is unobservable so least squares estimates of b in w ¼ bC þ y, where y ¼ aA þ ε, will be biased such that plim (bOLS) ¼ b þ a (sAC/s2C) where s2C in the variance in C and sAC is the covariance between C and A - which is not recorded in the data because A is unobservable. If, as seems reasonable, g > 0 and sAS > 0,
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and if a > 0, as also seems reasonable, then bOLS > b. That is, OLS estimates of b capture the effects of both C and of any unobservables that are correlated with both C and w, such as A. The expansion of HE is likely to result in sAS falling since HE institutions would then be accepting individuals with lower unobserved skills, A. This results in a fall in the estimate of bOLS even if b were constant - that is, we would expect the anticipated fall in the OLS estimate of the college premium (bOLS), in response to the supply of college graduates, to appear to be even larger than the fall in the true effect (b). The only way to reconcile the rise in college graduate supply with the absence of a fall in the OLS estimate of the college premium is if a were also rising. Of course, a, the return to unobserved skill, may not be constant. Indeed, much of the existing literature suggests that a has been rising as well as b. Thus, available OLS estimates are consistent with the view that the return to unobserved skill has been rising in the UK. There is some suggestion in the literature that such ability, or “selection”, bias approximately cancels out the bias associated with measurement error in schooling. The latter is indubitably downwards, in data where education is selfreported and so prone to mis-measurement, because of attenuation. But there is a worry, in this context, that one or both of these sources of bias may be changing over time in ways that are hard to sign on both theoretical and statistical grounds. For example, work is becoming remunerated in more complicated ways and it seems likely that the traditional hourly pay recorded in our data is less good at capturing actual remuneration. Ultimately it is an empirical issue that will be difficult to resolve without additional administrative data. One approach to dealing with the problem of differential unobserved ability between graduates and non-graduates is to uncover some exogenous variation in the probability having a degree and use this to estimate the effect of a degree. It is unclear how one would be able to do
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6. Heterogeneity in the returns to higher education
that with the data that is currently available for the UK6 A second methodology is provided by identical twins e who are (arguably) identical in terms of those unobservables that affect earnings. The twins research compares the earnings within (identical) twin pairs and so differences out the (common) unobserved ability factor. Unfortunately, there is only one UK twins study (see Bonjour et al, 2003), which is based on a very small sample of female only twins. This work finds that the effects of (a year of) education is to raise wages by approximately 8% (which might, heroically, be extrapolated to approximately 25% for a three-year degree). Their figure of 8% is identical to what they obtain by applying least squares to the raw twin data, rather than looking at within-twin differences. This might suggest that the extent of ability bias may be modest. On the other hand since is uses differences in self-reported education then measurement error bias might be large and the true return might be larger than this. A third methodology is to attempt to control for a rich set of observable characteristics. Altonji, Elder, and Taber (2005) bases a method for backing out the selection bias on the ability bias argument above (and, 2017, has developed this test). This is relatively straightforward: estimate an earnings premium (Altoni et al. considered the premium to having a college diploma using the US NLSY dataset) either unconditionally or controlling only for basic demographic characteristics, and compare this estimate with the premium one estimates while controlling for ability and factors which might drive selection (test scores, mother’s education, etc.). The difference between the two earnings premia is an estimate of the degree of self-selection. Blundell et al. (2004) does something similar to this (as well as exploiting matching and instrumental variable methods) using the 1958 English birth cohort in the NCDS data. This study finds that 6
least squares provides a relatively tight bound on the earnings effect of a degree. Thus, there are some grounds, based on existing research, for thinking that least squares estimates are a reasonably good guide to the true causal effects of a first degree e at least at this broad level of computing a simple college premium. Indeed, the report by Belfield et al. (2018) employs the newly available Longitudinal Education Opportunities (LEO) dataset, formed from merging the schooling history of all children (NPD) with their HE records (HESA), and their income tax (HMRC) records. Unlike the LFS research where almost no educational attainment information is available apart from higher education, the idea in the LEO data is to exploit the “pretreatment” schooling cognitive achievements, as recorded by test scores, to better control for, at least, cognitive ability differences across individuals to attempt to drive out selection by ability into HE through reducing the covariance between higher educational attainment and the unobservables. Since the data is administrative it is likely that measurement error in the control variables is minimal so selection bias is the only source of bias that needs to be resolved. This LEO project report does this by exploring the role of control variables on the estimated effect of a bachelor degree on earnings. The LEO cohorts are relatively young and, as Fig. 6.1 suggests, the college premium is lower at young ages relative to higher ages. The definitive change in their estimates, reported below in Table 6.1, indeed occurs when prior attainment controls are added to the model e they find that then the return falls, for men, from 0.22 log points to 0.04, and for women from 0.46 to 0.23. In addition to adding cognitive skill controls, Belfield et al. (2018) also weight the data using estimated inverse probability (of having a degree) in its regression analysis. This IPWRA method is designed to ensure that the “treated”
See Kirkeboen et al. (2016).
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The economic value of degrees
TABLE 6.1
LEO estimate of the overall returns to HE at age 29. (1)
(2)
(3)
(4)
(5)
0.19***
0.25***
0.22***
0.04***
0.06***
(0.00)
(0.00)
(0.00)
(0.01)
(0.00)
No. of observations
2,183,120
2,183,120
2,183,120
2,183,120
2,183,120
No. of individuals
629,138
629,138
629,138
629,138
629,138
Women
0.44***
0.50***
0.46***
0.23***
0.23***
(0.00)
(0.00)
(0.00)
(0.00)
(0.00)
No. of observations
2,619,982
2,619,982
2,619,982
2,619,982
2,619,982
No. of individuals
731,200
731,200
731,200
731,200
731,200
Cohort/Age start controls
No
Yes
Yes
Yes
Yes
Background characteristics
No
No
Yes
Yes
Yes
Prior attainment
No
No
No
Yes
Yes
IPWRA weight
No
No
No
No
Yes
Men
Note: Table reports derived estimates of the overall impact of HE on annual earnings at age 29 based on the 2002e07 GCSE cohorts, conditional on at least five A*-C GCSEs and on being in sustained employment. Table sequentially adds age, background and prior attainment controls, and finally IPWRA weights. Estimates are in log points, which can be converted into percentage points using the transformation 100 * (ex e 1), where x is the log points estimate. Source: Belfield et al. (2018) Table 7.
observations with a degree who are a closer match to those that do not have a degree are giving greater weight in the regression. This is an additional method for attempting to control for differences between the treated and controls. While the method only controls for observed variables in all likelihood the observables that determine treatment are correlated with unobservable determinants so it should contribute to the reduction in selection bias. In the event, using IPWRA has no significant effect on the results for either men or women. One might argue that these estimates themselves fail to control for remaining unobservables and selection bias is not entirely removed. In particular, one might be concerned that there are important non-cognitive abilities that remain in the unobservables and these are correlated with C and with wjC. This idea is pursued in Buchmueller and Walker (2019) who use the
age 25 information on earnings (so even earlier than the LEO data), on whether individuals have a degree, and various non-cognitive skills that are recorded in the LSYPE dataset. Table 6.2 shows that adding non-cognitive abilities to the specification does reduce the estimated returns to college but by a much smaller extent than adding cognitive skills controls did in Table 6.1. The fall for men is just 0.002 log points and for women it is 0.005 e neither of which are statistically significant.
Student choice and heterogeneity in the returns to degrees All of the above does not allow for heterogeneity in the estimate of the college premium. Blundell, Dearden, and Sianesi (2005) was an early attempt to allow for heterogeneity in
II. Private and social returns to education
84 TABLE 6.2
6. Heterogeneity in the returns to higher education
LSYPE estimate of the returns to HE at age 25 and non-cognitive skills. OLS Pooled
Degree
Men
Women
(1)
(2)
(1)
(2)
(1)
(2)
0.115***
0.112***
0.101***
0.099***
0.132
0.127***
(0.016)
(0.016)
(0.024)
(0.024)
(0.022)
(0.022)
Locus of control
Conscientiousness
Self-esteem
0.023***
0.025***
0.021***
(0.005)
(0.007)
(0.006)
0.008
0.025
0.008
(0.011)
(0.016)
(0.015)
0.001
0.001
-0.003
(0.004)
(0.008)
(0.004)
R-sqr
0.074
0.083
0.071
0.084
0.067
0.076
N
4133
4133
1879
1879
2254
2254
Note: The dependent variable is log of gross hourly wage. Specification (1) does not control for non-cognitive skills, while specification (2) does. Non-cognitive skills included are: Locus of control, a proxy for conscientiousness, and a proxy for self-esteem where the higher the score the more non-cognitive skills the individual has. All specifications include the following additional controls: gender, ethnicity as well as regional dummies. OLS specifications further control for missing values in the sample for degree-observations as well as non-cognitive skill observations (see Section 3). PSM obtains the average treatment effect on treated. All observations are weighted by the most recent LSYPE sample weights. Source: Buchmueller and Walker (2019).
returns which contrasted estimates obtained using matching methods with those that relied on instrumental variables. Subsequent work by Walker and Zhu (2008, 2011, and 2013) explored heterogeneity by subject of degree at various levels of aggregation as the LFS data allowed. The 2008 and 2011 paper allowed only for differences by broad group of subjects (STEM, LEM (Law/Economics/Management), Social Studies, and Arts) while the 2013 report allowed for 30 majors. Since this work was based on LFS, unlike Belfield et al. 2018, there was no possibility of controlling for pre-treatment ability controls in any detail, leaving open the possibility of biased estimates. Moreover, the selection issue is now (much) more complicated since students can select into a wide variety of different majors across a wide range if institutions. Selection into each subject (and HEI) might be differently
correlated with unobservable determinants of wages there is no grounds for thinking that LFS research is able to come to a view about the ranking of returns across subject. Ignoring such reservations for the moment, such research does show that STEM and LEM are more strongly correlated with wages than Social Studies; and that Arts degrees have very little correlation with wages, relative to a control with no degree at all. The 2013 report gives a more nuanced view since the accumulation of LFS data meant that cells sizes at the level of 30 subjects were sufficient to provide more detail. For example, within LEM, Economics had by far the highest correlation with wages. Buchmueller and Walker (2019) also estimate the returns to degrees by subject groups (and by Russell Group HEIs compared to the rest) and compare results that include non-cognitive skill
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The economic value of degrees
TABLE 6.3
Returns to Degrees by Subject Group and HEI type. OLS All HEIs
(1) All subjects
STEM
Social sciences
(2)
IPWRA
Russell group (1)
(2)
Other HEI (1)
(2)
All HEIs (1)
(2)
Russell group (1)
(2)
Other HEI (1)
(2)
0.115*** 0.112*** 0.217*** 0.213*** 0.063*** 0.061**
0.093*** 0.087*** 0.170*** 0.152*** 0.038
0.019
(0.016)
(0.018)
(0.027)
(0.034)
0.147*** 0.143*** 0.264*** 0.260*** 0.104*** 0.100*** 0.111*** 0.103*** 0.192*** 0.179*** 0.087**
0.070*
(0.022)
(0.016)
(0.038)
(0.018)
(0.024)
(0.022)
(0.020)
(0.024)
(0.032)
(0.038)
(0.031)
(0.036)
0.144*** 0.142*** 0.228*** 0.221*** 0.110*** 0.111*** 0.112*** 0.106*** 0.175**
0.139*
0.076
0.060
(0.026)
(0.043)
(0.044)
(0.024)
(0.018)
(0.044)
(0.024)
(0.038)
(0.026)
(0.041)
Arts & 0.021 humanities
(0.022)
(0.026)
(0.026)
(0.044)
(0.030)
(0.030)
(0.026)
(0.028)
(0.060)
(0.064)
(0.039)
0.019
0.142*** 0.139*** 0.026
0.027
0.017
0.022
0.072
0.048
0.079* 0.087*
(0.024)
(0.042)
(0.026)
(0.027)
(0.028)
(0.049)
(0.048)
(0.039)
(0.042)
(0.026)
(0.039)
Note: The dependent variable is log of gross hourly wage. Specification (1) does not include non-cognitive skills, while specification (2) does. Noncognitive skills included are: Locus of control, a proxy for conscientiousness, and a proxy for self-esteem (where the higher the score the more noncognitive skills the individual has). All specifications include the following additional controls: gender, ethnicity as well as regional dummies. OLS specifications further control for missing values in the sample for degree-observations as well as non-cognitive skill observations (for reasoning see Section 3). IPWRA obtains the average treatment effect on treated. All observations are weighted by the most recent LSYPE sample weights.
control variables with results that do not. The IPWRA results are based on a multi-nominal logit model of the probabilities of attending a RG HEI versus the rest, and having a major in the STEM, Social Science and Arts & Humanities groups - based primarily on the individuals Alevel (senior high school) subjects studied and attainment in them. It uses the LSYPE cohort who report their earnings at age 25. Table 6.3 below presents the headline results by subject group. Controlling for non-cognitive skills makes little difference to the results (compare cols 1 and 2) except for IPWRA results for RG graduates where the inclusion of non-cognitive skills reduces returns by approximately 0.03 for each subject group. Using IPWRA compared to OLS reduces estimated returns by around 2pp. RG degrees command much higher wages than those from the other mission groups. STEM returns are higher than Social Science, and Arts
offer insignificantly positive returns for RG graduates and large negative returns (both relative to the omitted category of non-graduates). Recent US research by Altonji, Blom, and Meghir (2012) and Webber (2014) has focused on the ACS which has more detail on the nature of individual experiences of HE since it records detailed major. This work shows similar results as equivalent UK research. A notable advance over the UK LFS, and similar US, research is Britton, Dearden, Shephard, and Vignoles (2016) which uses SLC data merged to HMRC income tax records. SLC records major and HEI for a large majority of UK students. The ingenious feature in this paper was to use the average Alevel scores of students by course, obtained from HESA data, to provide, at least at the course level, a control for average student prior ability. Fig. 6.2 shows the highlights of the results e the dots near the horizontal axis are
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6. Heterogeneity in the returns to higher education
FIG. 6.2 Graduate earnings by subject, controlling for course level selectivity. Notes: Miss ¼ Miscellaneous. The box represents the interquartile range, with the median shown as he horizontal divider. The size of the blobs above the horizontal axis show relative cell sizes. Source: Britton et al. (2016).
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The economic value of degrees
TABLE 6.4 IPWRA estimates of the college premium by subject group and HEI mission group controlling for course level selectivity. IPWRA average treatment effects Men (1)
Women (2)
Pooled (3)
OLS Men
Women
Pooled
(4)
(5)
(6)
New
0.075***
0.071**
0.007
0.007
0.010
0.003
Social science
(0.029)
(0.031)
(0.025)
(0.020)
(0.023)
(0.015)
New
0.010
0.050
0.040
0.155***
0.065***
0.106***
Arts & humanities
(0.036)
(0.035)
(0.030)
(0.025)
(0.024)
(0.017)
Old
0.161***
0.047
0.054*
0.038*
0.053*
0.001
STEM
(0.031)
(0.037)
(0.028)
(0.022)
(0.028)
(0.017)
Old
0.133***
0.036
0.045
0.085***
0.032
0.055***
Social science
(0.036)
(0.033)
(0.028)
(0.028)
(0.028)
(0.020)
Old
0.003
0.156***
0.077***
0.090***
0.121***
0.110***
Arts & humanities
(0.037)
(0.033)
(0.028)
(0.031)
(0.030)
(0.021)
RG
0.153***
0.039
0.102***
0.115***
0.071***
0.092***
STEM
(0.033)
(0.041)
(0.030)
(0.023)
(0.027)
(0.017)
RG
0.196***
0.049
0.108***
0.109***
0.070**
0.086***
Social science
(0.041)
(0.038)
(0.031)
(0.027)
(0.029)
(0.020)
RG
0.027
0.119***
0.065**
0.104***
0.072***
0.084***
Arts & humanities
(0.039)
(0.038)
(0.032)
(0.030)
(0.028)
(0.020) 0.131*** (0.009)
Female Selectivity Observations
3950
4083
2
R
8138
0.071*** (0.014)
0.123*** (0.014)
0.097*** (0.010)
4072
4089
8161
0.404
0.355
0.391
IPWRA stands for inverse probability weighted regression adjustment. Robust SE in parentheses, *p < 0.1, **p < 0.05, ***p < 0.01. The observations which are off common support are excluded from the treatment effect models. Other controls include age, age squared, nonwhite, dummies for decade of birth, survey years, and regions. PG and good degree dummies only enter the outcome (wage) equations but not the treatment (selection) equations in the IPWRA specifications. We do not report the coefficients for selectivity in the 1PWRA estimates because there is one coefficient for each potential outcome - too many to report. The same applies to the female dummy. Source: Walker and Zhu (2018).
proportional to cell sizes e Economics and Medicine have the highest median returns but are very small cells e each representing less than 2% of overall student numbers, while Maths/
Computing, Management, and the Creative Arts each represent more than 15% of overall male number; and Business, Creative Arts, and Education each represent more than 15% of
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6. Heterogeneity in the returns to higher education
FIG. 6.3
LEO Returns to UG Degree Courses - IPWRA estimates. Source: Belfield et al. (2018).
female students. The highest earnings are associated with Medicine and Economics students e subjects which are studied by a very small proportion of students; while Creative
Arts, in particular, offer earnings that, for females, are close to full-time minimum wage work, and little better for males, and yet large numbers of students study them.
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References
In the UK, recent work has exploited the more extensive HE information being collected in LFS datasets which, from 2005 onwards, contain the major of study, and contains both major studied and HEI attended, from 2011 onwards. The idea of controlling for selectivity at the course level was also pursued in Walker and Zhu (2018) using LFS data, exploiting both subject studies and HEI attended. The use of the LFS data allows a longer run of cohorts that did the SLC data in Britton et al. (2016), and this paper also adopted to IPWRA method to improve the comparability of treatment and controls. The wage effects of institution and subject types in Table 6.4 suggest that OLS substantially underestimates the effect of attending the more prestigious HEIs for men. The effects of Old and RG STEM are not significantly different from each other, but they are approximately 15% greater than New-STEM. Using OLS for men we find no significant effect of Old-STEM relative to New-STEM and a somewhat smaller effect of RG-STEM For women, Old and RG STEM are not significantly different than New-STEM. There appear to be no significant Arts and Humanities effects for men in RG or Old relative to New. Although, for men, RG-SocSci is large and significantly different from Old-SocSci, which in turn is significantly greater than NewSocSci. The same is true for OLS estimates although these are again considerably underestimated relative to IPWRA. The most definitive UK research is the recent work by Belfield et al. (2018) using the LEO data. This is based on administrative data on around 7 million students and so allowed much more granular analysis than the LFS does. This LEO work uses IPWRA estimation, much like the Walker and Zhu (2018) work. Fig. 6.3 visualizes the most disaggregated estimates which are ordered by returns from lowest on the left. These estimates are computed at age 29 e and early point in the lifecycle when we expect college premia to be low. The red dots are the point estimate course fixed effects and
the gray bars are confidence intervals. The height of the red dots show the returns relative to nongraduates. For men only 60% of the approximately 1200 courses for which estimation is possible (the HMRC impose a minimum cell size of 50 to preserve confidentiality) show positive returns and only 20% show significantly positive returns. For women approximately 50% are significantly positive. Subjects available for Cambridge (an elite UK HEI) are highlighted in yellow and some examples are labeled. Even elite HEIs offer Creative arts courses that yield negative returns.
Conclusion The evidence that the returns to higher education in both the UK and the US differs greatly by HEI and by subject is overwhelming. These findings have important implications for the implications of the effects of income contingent student loans that have, hitherto, gone unexplored. These differences are so large it is unclear what supports such differentials and why student choices do not respond to reduce them to more plausible levels. This is an important issue for further research.
Acknowledgments I am indebted to collaborators on the LEO project and to my long time collaborator in education research, Yu Zhu, whose joint work I rely on here.
References Altonji, J. G., Blom, E., & Meghir, C. (2012). Heterogeneity in human capital investments: High school curriculum, college major, and careers. Annual Review of Economics, 4, 185e223. Altonji, J. G., Elder, T. E., & Taber, C. R. (2005). Selection on observed and unobserved variables: Assessing the effectiveness of catholic schools. Journal of Political Economy, 113, 151e184. Barr, N., Chapman, B., Dearden, L., & Dynarski, S. (2018). Reflections on the US college loans system: Lessons from
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Australia and England. Centre for applied macroeconomic analysis, Working Paper 29. Crawford School of Public Policy, Australian National University. Barr, N., Chapman, B., Dearden, L., & Dynarski, S. (2019). The US college loans system: Lessons from Australia and England. Economics of Education Review, 71, 32e48. Belfield, C., Britton, J., Buscha, F., Dearden, L., Dickson, M., van der Erve, L., et al. (2018). The impact of undergraduate degrees on early-career earnings. Department of Education Research Report. https://assets.publishing.service.gov. uk/government/uploads/system/uploads/attachment data/file/759278/Theimpactofundergraduatedegreeson early-careerearnings.pdf. Blundell, R., Dearden, L., & Sianesi, B. (2004). Evaluating the impact of education on earnings in the uk: models, methods and results from the ncds. IFS Working Paper, WP03/20. Blundell, R., Dearden, L., & Sianesi, B. (2005). Evaluating the effect of education on earnings: Models, methods and results from the national child development survey. Journal of the Royal Statistical Society: Series A, 168, 473e512. Blundell, R. W., Green, D. A., & Jin, W. (2016). The UK wage premium puzzle: How did a large increase in university graduates leave the education premium unchanged?. IFS Working Paper 2016/1. Bonjour, D., Cherkas, L. F., Haskel, J. E., Hawkes, D. D., & Spector, T. D. (2003). Returns to Education: Evidence from U.K. Twins. American Economic Review, 93(5), 1799e1812. Britton, J., Dearden, L., Shephard, N., & Vignoles, A. (2016). How English domiciled graduate earnings vary with gender, institution attended, subject and socio-economic background. Technical report. Institute for Fiscal Studies WP1606.
Buchmueller, G., & Walker, I. (2019). Non-cognitive skills and the return to higher education. mimeo. Hoxby, C. (2019). The productivity of u.s. post-secondary institutions forthcoming. In C. Hoxby, & K. Strange (Eds.), Productivity in Higher Education. University of Chicago Press. Kirkeboen, L., Leuven, E., & Mogstad, M. (2016). Field of Study, Earnings, and Self-Selection. Quarterly Journal of Economics, 131(3), 1057e1111. Murphy, R., Scott-Clayton, J., & Wyness, G. (2017). Lessons from the end of free college in England. Brookings Economic Studies, Evidence Speaks Reports, 2, 13. Oster, E. (2019). Unobservable selection and coefficient stability: Theory and evidence. Journal of Business & Economic Statistics, 37(2), 187e204. Walker, I., & Zhu, Y. (2008). The college wage premium and the expansion of higher education in the UK. The Scandinavian Journal of Economics, 110, 695e709. Walker, I., & Zhu, Y. (2011). Differences by degree: Evidence of the net financial rates of return to undergraduate study for England and Wales. Economics of Education Review, 30, 1177e1186. Walker, I., & Zhu, Y. (2013). The impact of university degrees on the lifecycle of earnings: Some further analysis. Department for Business, Innovation and Skills Research Report. Walker, I., & Zhu, Y. (2018). University selectivity and the relative returns to higher education: Evidence from the UK. Labour Economics, 53, 230e249. Walker, I., & Zhu, Y. (2019). Wage differential by undergraduate degree class and grade inflation at college. mimeo. Webber, D. A. (2014). The lifetime earnings premia of different majors: Correcting for selection based on cognitive, noncognitive, and unobserved factors. Labour Economics, 28, 14e23.
II. Private and social returns to education
6 Heterogeneity in the returns to higher education Ian Walker Department of Economics, Lancaster University Management School and IZA, Bonn, Germany
Introduction
forgiveness. There are three elements of subsidy inherent in the system: no debt is collected until earnings hit a threshold; the interest rate is, at least on average, below market rates; and after 30 years any unpaid debt is forgiven. The operation of this implies that courses which provide modest returns will attract larger subsidies than subjects that offer high returns. Personal debt arising from student loans has steadily risen in recent years in the UK (although less so in Scotland which has retained zero up-front fees), and in much of the English-speaking world.1 The most recent graduating college cohort in the UK (excluding Scotland) has incurred approximately £28,000 of student debt associated with their tuition fees, plus up to
There is considerable research on the returns to education. In recent ears some progress has been made in estimating the returns to particular types of education e in particular higher education (HE). Several contributions have attempted to estimate heterogeneity across higher education subjects (ie by major) and even by institution (HEI). This chapter reviews work on the returns to HE, and contributes to the UK strand of this literature. In the UK context, heterogeneity in returns is of particular interest because of the nature of the student loan system. This now takes the form of an income contingent loan with
The USA has always charged “college” fees and these fees have risen markedly in recent years especially for the most academically selective institutions (see, for example, Hoxby, 2019). UK HEI fees (excluding Scotland) have risen more recently. Australia and New Zealand also have income contingent loan systems although neither country experienced the Great Financial Crisis to the same degree as most other countries. Australia has a large threshold before repayment starts but no forgiveness. New Zealand has a modest repayment income threshold and has, very recently, abolished fees for some part of HE. Ireland abolished HE fees some time ago. No EU countries (with the exception of Hungary) have economically significant fees or charges for HE e indeed, some have extensive provision of grants to cover subsistence costs. See Barr et al. (2019) for further details of international comparisons and developments. 1
The Economics of Education, Second Edition https://doi.org/10.1016/B978-0-12-815391-8.00006-9
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Copyright © 2020 Elsevier Ltd. All rights reserved.
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6. Heterogeneity in the returns to higher education
£18,000 associated with their subsistence expenses (that arguably might have been incurred in the absence of attending university). In the US recent graduates average roughly US$30,000 of debt e although fees (or at least the sticker price) vary considerable across institutions, and the national total exceeds $1.4 trillion, a figure that some claim (FT April 9, 2017) represents an economic bubble which could have substantial negative effects for future generations. Particular concern has been expressed over US default rates (estimated to be 18%). An important difference between the US and elsewhere is that the US debt is typically mortgage style (known as time based repayment loans, TBRL) e the debt is repaid monthly at a constant rate until it is fully repaid, usually in 10 years. Most of the concern in the US is over former students being able to meet these payments. Elsewhere, loans are income contingent and collected via the tax system. In addition to concerns over the public finances and their macroeconomic implications,2 these numbers beg an important microeconomic question: is taking on substantial student loan debt to (possibly) obtain a college degree a sound financial investment? Although this is a simple question it has a complicated answer which depends on a variety of factors, such as the student’s major, the HEI attended, ability, probability of dropping out, among many others. This paper aims to outline the evidence around these factors. Thus, this paper is concerned with a range of issues surrounding the effects of, and funding of, Higher Education with a focus on the UK. HE in the UK is usually pursued from age 18, or soon thereafter, at over 150 Higher Education Institutions (HEIs), some very small and
specialized, which are collectively referred to as universities. Higher education participation rates are over 40% of the cohort and this has grown dramatically in the last three decades. The old funding model was that central government provided extensive direct funding to HEIs, there were no tuition fees, and students received maintenance grants to support themselves during studies (although these were subject to extensive means testing against parental incomes). Course fees in England (less so in Wales and Northern Ireland where the devolved administrations have pursued their freedom over spending to impose lower fees, and Scotland have chosen to have zero fees) have been dramatically increased (and public funding has almost been eliminated) since 2010. This was part of a post-recession austerity drive, but was accompanied by a comprehensive, sophisticated, and highly subsidized, student loan program that supports access, especially for low parental income students. Take-up of these loans is high and repayments are income contingent with the balance after 30 years being written off. As a result, demand for university is relatively tuition fee inelastic, and there is little evidence that fees have resulted in any fall in participationdeither overall or for low SES students (see Murphy et al., 2017), and this has not been the case in Scotland where low SES participation has fallen relative to higher SES participation. The UK offers a convenient laboratory for HE research since UG study is highly concentrated, throughout the duration of study, on a single major. Courses are usually specialized where a single narrow major is often pursued exclusively. Dropping-out is relatively scarce (around 8% across the sector). HEIs in England, (and
2 The ONS in the UK has recently recommended that the treatment of student debt in public finances should be changed. Previously, student debt did not appear in national accounts until the forgiveness point had been reached so, to date, no student debt has reached this maturity. Now, the debt is to be counted as part of national debt as it accrues. It remains to be seen whether money markets had already priced-in the anticipated debt.
II. Private and social returns to education
Introduction
Wales and Northern Ireland) offer undergraduate courses that are typically 3 years duration (Scotland has many 4 year courses), studied mostly on a full-time basis and mostly straight from senior high school.3 Although the UK is small, and so proximity to university is much higher than in many other countries, the majority of students move away from their parental homes to study HE, and most of those that do will form (or join) households elsewhere when they graduate and start work. Unlike the US, UK undergraduate professional courses such as law, medicine, and management are available across most HEIs. The UK HE sector is much less homogeneous that the HE sector in the US The UK equivalent of the US Ivy League is the so-called “Russell Group” (RG) of HEIs e 25 institutions including ancient ones such as Oxford and Cambridge, many of the top London institutions, as well as the major provincial “redbrick” HEIs (named for the appearance of their largely Victorian architecture). These are research intensive institutions, they are large, they offer a full range of subjects, and they attract students from all over the UK and many from outside the UK The “Old” non_Russell HEIs are often refereed to ask “Pre-92”, and comprise another 25 institutions that were founded before 1992 e the date when the former Polytechnics became able to grant their own degrees and could refer to themselves as universities. These are also full function HEIs but are not as focused on a research mission; many are old but not ancient and they include the HEIs that were founded in the 1960’s. The “New” HEIs are former Polytechnics and, together with newer entrants, comprise approximately one hundred institutions that vary considerably in size and range of functions. Some are small and specialized e for example on music or other performing/creative arts.
3
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HEIs are independent of government. Government has no control over the curriculum, and has, since 2014, exercised no control over student numbers across the sector, by course type, or by HEI. HEIs have also been free to set their fees within a range from £6000 to £9000 per annum (figures that have increased modestly since 2014 when they were set). While, the Minister for Universities has stated that he expected £9000 to be exceptional, in fact almost all institutions were immediately attracted to the £9000 focal point for almost all majors. Discounts on the sticker price are available but are usually both means tested and contingent on high school achievement. There is considerable obfuscation over the extent to which actual tuition costs vary by major differ considerable and firm figures are hard to find - it is thought that the costs of running most Arts, Social Science, and Management courses are below the £6000 lower bound for annual fees; while costs for most STEM subjects are likely to be well above the upper bound for fees; and those for medical, veterinary and dentistry courses are likely to be considerable higher than that (and these courses are invariably 5 years duration). The implication of the choice, by all universities, to engage in widespread cross-subsidization is that the large number of STEM (and the small number of medical/dental) students are being cross-subsidized by the larger number of low-cost students. The current government fees review has (so far) been largely silent on this and the regulator, the Office for Students, has not considered the fairness of a situation where low-cost students are subsidizing high cost ones who are, on average, going to enjoy higher levels of lifetime income. Indeed, the biggest cross-subsidies go from the successful upper tail of Arts students (who earn sufficient to not enjoy large subsidies) to the less successful lower tail STEM students
This has become more likely to be true since mature students were disadvantaged in the student loan scheme.
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6. Heterogeneity in the returns to higher education
who do badly in their studies and may not earn sufficient to make loan repayments early enough to completely repay their loans.4 In the meantime: institutions who have a small proportion of STEM and medical students can be extremely profitable; while others are experiencing financial difficulties in the face of competition for students from more prestigious institutions at the same time as English cohort sizes are falling; and political change makes the UK a less attractive destination for EU students and even for those from further afield.
The economic value of degrees HE costs and private returns are central to associated funding issues. The common perception is that costs are tuition fees and the benefits are the earnings differential for having a degree relative to not. However, this is only a partial view e the opportunity cost of studying for a degree is the earnings that one could have earned otherwise and such opportunity costs would probably considerably exceed the actual tuition fees for most English students, even at minimum wage. Moreover, the benefits to the student is not just the pecuniary effect of HE on the individual student’s earnings, but also includes any non-pecuniary benefits. Some of these are private benefits that are enjoyed immediatelye the consumption value of a stimulating and engaging course. Others will be discounted future benefits. For example, there is a well documented effect of education on health (although there is little evidence that directly relates to higher education in particular). We know almost nothing about these non-financial effects: but it seems likely that they might vary by subject studied (major) and the quality of teaching, and by student aptitudes and preferences which
will vary by prior teaching quality, and parental background. The potential for quantitatively significant non-pecuniary benefits to be an important driver of student course choice is suggested by the (albeit, limited) available evidence that we have from research on the determinants of student choices. However, it is difficult to put a lot of weight on the student choice literature for four reasons. First, the pecuniary returns occur in the future across the working lifecycle and these might be (heavily) discounted, while the opportunity costs of foregone earnings are borne early in the working life, giving rise to a net present value that might be lower than the raw earnings data might suggest. A weak financial private rate of return might be reinforced by credit market imperfections associated with debt incurred to finance human capital being inherently unsecured. It might be further weakened by the potential for young people to be time inconsistent in their decision-making. There is now an increasing volume of empirical research that points to young people being “hyperbolic” rather than “exponential” discounters leading them to make decisions that they ultimately regret. Secondly, the non-pecuniary benefits might be associated with the consumption value arising from the challenge of higher level learning, and the enjoyment of spending a few formative years in a safe environment surrounded by like-minded people. These are enjoyed in the moment and their value is therefore not affected by heavy discounting. The third argument, is that the benefits are uncertain and there is very little information that students can rely on to inform them. Higher education institutions will point to their successes amongst their graduate output and not to the long-left tail of their graduates’ earnings. The little quantitative data that is out there refers to the mean outcomes
4
Almost all medical/dental students are successful at university and almost invariably they pursue careers in medicine/dentistry.
II. Private and social returns to education
The economic value of degrees
and say nothing about differences associated with observable variation in the treatment (for example, across majors and across institutions which themselves exhibit a wide variation in student selectivity and in their own productivity) and in the treated (the students themselves are likely to vary in their degree of college readiness and their own non-cognitive skills and traits that might complement cognitive skills that a degree might offer, especially majors that are vocational in nature or offer high demand skills). In contrast, the costs are probably substantially less uncertain. In England (although not in the US) the sticker price is highly correlated with the actual price paid since discounts are smaller and less commonplace than in the US where alumni finance is often used to drive a large wedge between sticker and final prices. Given the large unexplained variance in private returns (i.e. wage rates) the role of attitudes to risk also play a role. Importantly, the income contingent nature of the loan scheme insures students against the financial risks they face surrounding the match component of returns e in particular, how well they will do in their chosen course. Moreover, the loan scheme provides the largest subsidies to human capital investments that offer the lowest mean return. This tips the balance further in favor of majors that offer low private financial returns, as well as being high risk (for example, creative arts degrees). The income contingent nature of fees, which might well have encouraged low SES students to access HE, might also have encouraged students to take lower return subjects than would previously the case. In addition, UK degrees are “classified” into “first class, upper second, lower second, third,
79
and pass” and there are large labor market wage differentials associated with better quality outcomes.5 Since, within a course, there is relatively little variation in the quality of students on entry, the variation in quality on exit is largely due to the extent of individual engagement with studying. Students might now be less inclined to make the effort to excel in their studies because the highest loan subsidies are available to the lowest earning, and so weakest outcome, students. It would appear, from the size of wage differentials associated with degree class that there are large returns to student effort. Of course, the cost of effort is also endured in the moment so the hyperbolic nature of discounting will not affect this source of disutility. The literature on the returns to HE (which is referred to in the US literature as “the college premium”) is driven entirely by the “private benefits” of HE associated with the higher wage rates or earnings of graduates versus non-graduates. It ignores aspects of costs that could be very important, it ignores nonpecuniary private returns, and it ignores “social” costs and benefits. Moreover, it offers only a very narrow view on what is a very precise concept e the net present value calculation of private net (of tax, welfare, and loan subsidy) financial returns. Nonetheless, even this narrow view is itself very difficult to measure.
Estimating the college premium The ingredients of a measure of the college premium is the wages (or earnings) of graduates and of non-graduates. Such data is readily available in many countries e for example, a great deal of research has been done on the US Current
5
Walker and Zhu (2019) provide estimates from LFS data that differentials by degree class have proved, on average, to be stable in the face of the large HE expansion that occurred in the 80’s and 90’s even though, over this period grades themselves rose on average. This is surprising, since the expansion will have lowered the average quality of students entering the system. It would appear that the students and/or their teachers have increased their productivity and outcomes, in terms of markes skills, are actually better.
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6. Heterogeneity in the returns to higher education
Population Survey (CPS) and the American Community Surveys (ACSs); while in the UK extensive use has been made of the Labor Force Survey (LFS). An early example of LFS UK research is Walker and Zhu (2008). This work looks at the returns to UG (and PG) and reports estimates of the UK “college premium” for young graduates across successive cohorts from large crosssection datasets for the UK pooled from 1994 to 2006da period when the higher education participation rate increased dramatically. The modest growth in relative labor demand over this period suggests that graduate supply considerably outstripped demand which ought to imply a fall in the college premium. However, the data showed no statistically significant fall for men (and a statistically insignificant rise for women). Quantile regression results revealed a fall in the premium only for men in the bottom quartile of the distribution of unobserved skills e a fall that was not statistically significant. This work was followed-up in Walker and Zhu
FIG. 6.1
(2013), a report to the Department of Business Innovation and Skills e then the government department responsible for universities. The additional data that was available for the BIS Report resulted in more precise estimates but they were not substantially different from those reported in the earlier paper. The most recent UK example is Blundell, Green, and Jin (2016) which is also focused on how the average college premium have changed across cohorts. HE participation rates have grown rapidly in recent years, especially the mid 80’s to mid ‘90s, but more slowly from the mid ‘60s before that. Fig. 6.1 reproduces their headline finding in Blundell et al. (2016) and underlines the puzzle e that the college premium does not, at face value, appear to respond to a large shift in supply of graduates. The figure shows the familiar fact that earnings of graduates rise strongly across the lifecycle relative to that of non-graduates. It also shows that this phenomenon has NOT changed across cohorts despite the huge increase in the supply of
The college premium (ratio of graduate to non-graduate earnings) by age across UK birth cohorts. Source: Blundell
et al. (2016).
II. Private and social returns to education
The economic value of degrees
graduates e the age-earnings profiles for each cohort group overlap considerably. In particular, the first three cohort groups were 18 before the big expansion and the last three groups were 18 after the expansion. Yet in their 30’s, where the lifecycles of these two groups overlap, the earnings for the pre and post expansion cohorts are very close to one another. Moreover, across the lifecycle the college premium suggested in this figure is, in present value terms, worth over £250 k. This is consistent with previous estimates of the college premium in the UK that, in simple specifications, report estimates between 25% and 35% where the control group is those that report having A-levels (as in the Walker/Zhu work), and between 35% and 45% where the control group is all those that did not attend HE (as in Blundell et al., 2016). In almost all cases, research finds that the returns to HE for men is lower than that for women - reflecting to poorer opportunities in the labor market for non-graduate women compare to non-graduate men. It is well known that OLS estimates, such as those in the Walker/Zhu and Blundell et al. work, may suffer from bias. In one direction, it may suffer from bias associated with omitted “ability”. This is traditionally thought of as biasing the college premium coefficient upwards. In the simplest version of the traditional ability bias story, earnings (w) and schooling are determined (C) by: w ¼ bC þ aA þ ε, and C ¼ gA þ z, where w is the (log) wage rate, C ¼ 1 if the individual has a “college” degree and 0 otherwise, A is unobserved “ability”, ε is uncorrelated with C and with A, and z is uncorrelated with ε. That is, z and w are correlated only through their joint dependence on A. However, A is unobservable so least squares estimates of b in w ¼ bC þ y, where y ¼ aA þ ε, will be biased such that plim (bOLS) ¼ b þ a (sAC/s2C) where s2C in the variance in C and sAC is the covariance between C and A - which is not recorded in the data because A is unobservable. If, as seems reasonable, g > 0 and sAS > 0,
81
and if a > 0, as also seems reasonable, then bOLS > b. That is, OLS estimates of b capture the effects of both C and of any unobservables that are correlated with both C and w, such as A. The expansion of HE is likely to result in sAS falling since HE institutions would then be accepting individuals with lower unobserved skills, A. This results in a fall in the estimate of bOLS even if b were constant - that is, we would expect the anticipated fall in the OLS estimate of the college premium (bOLS), in response to the supply of college graduates, to appear to be even larger than the fall in the true effect (b). The only way to reconcile the rise in college graduate supply with the absence of a fall in the OLS estimate of the college premium is if a were also rising. Of course, a, the return to unobserved skill, may not be constant. Indeed, much of the existing literature suggests that a has been rising as well as b. Thus, available OLS estimates are consistent with the view that the return to unobserved skill has been rising in the UK. There is some suggestion in the literature that such ability, or “selection”, bias approximately cancels out the bias associated with measurement error in schooling. The latter is indubitably downwards, in data where education is selfreported and so prone to mis-measurement, because of attenuation. But there is a worry, in this context, that one or both of these sources of bias may be changing over time in ways that are hard to sign on both theoretical and statistical grounds. For example, work is becoming remunerated in more complicated ways and it seems likely that the traditional hourly pay recorded in our data is less good at capturing actual remuneration. Ultimately it is an empirical issue that will be difficult to resolve without additional administrative data. One approach to dealing with the problem of differential unobserved ability between graduates and non-graduates is to uncover some exogenous variation in the probability having a degree and use this to estimate the effect of a degree. It is unclear how one would be able to do
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6. Heterogeneity in the returns to higher education
that with the data that is currently available for the UK6 A second methodology is provided by identical twins e who are (arguably) identical in terms of those unobservables that affect earnings. The twins research compares the earnings within (identical) twin pairs and so differences out the (common) unobserved ability factor. Unfortunately, there is only one UK twins study (see Bonjour et al, 2003), which is based on a very small sample of female only twins. This work finds that the effects of (a year of) education is to raise wages by approximately 8% (which might, heroically, be extrapolated to approximately 25% for a three-year degree). Their figure of 8% is identical to what they obtain by applying least squares to the raw twin data, rather than looking at within-twin differences. This might suggest that the extent of ability bias may be modest. On the other hand since is uses differences in self-reported education then measurement error bias might be large and the true return might be larger than this. A third methodology is to attempt to control for a rich set of observable characteristics. Altonji, Elder, and Taber (2005) bases a method for backing out the selection bias on the ability bias argument above (and, 2017, has developed this test). This is relatively straightforward: estimate an earnings premium (Altoni et al. considered the premium to having a college diploma using the US NLSY dataset) either unconditionally or controlling only for basic demographic characteristics, and compare this estimate with the premium one estimates while controlling for ability and factors which might drive selection (test scores, mother’s education, etc.). The difference between the two earnings premia is an estimate of the degree of self-selection. Blundell et al. (2004) does something similar to this (as well as exploiting matching and instrumental variable methods) using the 1958 English birth cohort in the NCDS data. This study finds that 6
least squares provides a relatively tight bound on the earnings effect of a degree. Thus, there are some grounds, based on existing research, for thinking that least squares estimates are a reasonably good guide to the true causal effects of a first degree e at least at this broad level of computing a simple college premium. Indeed, the report by Belfield et al. (2018) employs the newly available Longitudinal Education Opportunities (LEO) dataset, formed from merging the schooling history of all children (NPD) with their HE records (HESA), and their income tax (HMRC) records. Unlike the LFS research where almost no educational attainment information is available apart from higher education, the idea in the LEO data is to exploit the “pretreatment” schooling cognitive achievements, as recorded by test scores, to better control for, at least, cognitive ability differences across individuals to attempt to drive out selection by ability into HE through reducing the covariance between higher educational attainment and the unobservables. Since the data is administrative it is likely that measurement error in the control variables is minimal so selection bias is the only source of bias that needs to be resolved. This LEO project report does this by exploring the role of control variables on the estimated effect of a bachelor degree on earnings. The LEO cohorts are relatively young and, as Fig. 6.1 suggests, the college premium is lower at young ages relative to higher ages. The definitive change in their estimates, reported below in Table 6.1, indeed occurs when prior attainment controls are added to the model e they find that then the return falls, for men, from 0.22 log points to 0.04, and for women from 0.46 to 0.23. In addition to adding cognitive skill controls, Belfield et al. (2018) also weight the data using estimated inverse probability (of having a degree) in its regression analysis. This IPWRA method is designed to ensure that the “treated”
See Kirkeboen et al. (2016).
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The economic value of degrees
TABLE 6.1
LEO estimate of the overall returns to HE at age 29. (1)
(2)
(3)
(4)
(5)
0.19***
0.25***
0.22***
0.04***
0.06***
(0.00)
(0.00)
(0.00)
(0.01)
(0.00)
No. of observations
2,183,120
2,183,120
2,183,120
2,183,120
2,183,120
No. of individuals
629,138
629,138
629,138
629,138
629,138
Women
0.44***
0.50***
0.46***
0.23***
0.23***
(0.00)
(0.00)
(0.00)
(0.00)
(0.00)
No. of observations
2,619,982
2,619,982
2,619,982
2,619,982
2,619,982
No. of individuals
731,200
731,200
731,200
731,200
731,200
Cohort/Age start controls
No
Yes
Yes
Yes
Yes
Background characteristics
No
No
Yes
Yes
Yes
Prior attainment
No
No
No
Yes
Yes
IPWRA weight
No
No
No
No
Yes
Men
Note: Table reports derived estimates of the overall impact of HE on annual earnings at age 29 based on the 2002e07 GCSE cohorts, conditional on at least five A*-C GCSEs and on being in sustained employment. Table sequentially adds age, background and prior attainment controls, and finally IPWRA weights. Estimates are in log points, which can be converted into percentage points using the transformation 100 * (ex e 1), where x is the log points estimate. Source: Belfield et al. (2018) Table 7.
observations with a degree who are a closer match to those that do not have a degree are giving greater weight in the regression. This is an additional method for attempting to control for differences between the treated and controls. While the method only controls for observed variables in all likelihood the observables that determine treatment are correlated with unobservable determinants so it should contribute to the reduction in selection bias. In the event, using IPWRA has no significant effect on the results for either men or women. One might argue that these estimates themselves fail to control for remaining unobservables and selection bias is not entirely removed. In particular, one might be concerned that there are important non-cognitive abilities that remain in the unobservables and these are correlated with C and with wjC. This idea is pursued in Buchmueller and Walker (2019) who use the
age 25 information on earnings (so even earlier than the LEO data), on whether individuals have a degree, and various non-cognitive skills that are recorded in the LSYPE dataset. Table 6.2 shows that adding non-cognitive abilities to the specification does reduce the estimated returns to college but by a much smaller extent than adding cognitive skills controls did in Table 6.1. The fall for men is just 0.002 log points and for women it is 0.005 e neither of which are statistically significant.
Student choice and heterogeneity in the returns to degrees All of the above does not allow for heterogeneity in the estimate of the college premium. Blundell, Dearden, and Sianesi (2005) was an early attempt to allow for heterogeneity in
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84 TABLE 6.2
6. Heterogeneity in the returns to higher education
LSYPE estimate of the returns to HE at age 25 and non-cognitive skills. OLS Pooled
Degree
Men
Women
(1)
(2)
(1)
(2)
(1)
(2)
0.115***
0.112***
0.101***
0.099***
0.132
0.127***
(0.016)
(0.016)
(0.024)
(0.024)
(0.022)
(0.022)
Locus of control
Conscientiousness
Self-esteem
0.023***
0.025***
0.021***
(0.005)
(0.007)
(0.006)
0.008
0.025
0.008
(0.011)
(0.016)
(0.015)
0.001
0.001
-0.003
(0.004)
(0.008)
(0.004)
R-sqr
0.074
0.083
0.071
0.084
0.067
0.076
N
4133
4133
1879
1879
2254
2254
Note: The dependent variable is log of gross hourly wage. Specification (1) does not control for non-cognitive skills, while specification (2) does. Non-cognitive skills included are: Locus of control, a proxy for conscientiousness, and a proxy for self-esteem where the higher the score the more non-cognitive skills the individual has. All specifications include the following additional controls: gender, ethnicity as well as regional dummies. OLS specifications further control for missing values in the sample for degree-observations as well as non-cognitive skill observations (see Section 3). PSM obtains the average treatment effect on treated. All observations are weighted by the most recent LSYPE sample weights. Source: Buchmueller and Walker (2019).
returns which contrasted estimates obtained using matching methods with those that relied on instrumental variables. Subsequent work by Walker and Zhu (2008, 2011, and 2013) explored heterogeneity by subject of degree at various levels of aggregation as the LFS data allowed. The 2008 and 2011 paper allowed only for differences by broad group of subjects (STEM, LEM (Law/Economics/Management), Social Studies, and Arts) while the 2013 report allowed for 30 majors. Since this work was based on LFS, unlike Belfield et al. 2018, there was no possibility of controlling for pre-treatment ability controls in any detail, leaving open the possibility of biased estimates. Moreover, the selection issue is now (much) more complicated since students can select into a wide variety of different majors across a wide range if institutions. Selection into each subject (and HEI) might be differently
correlated with unobservable determinants of wages there is no grounds for thinking that LFS research is able to come to a view about the ranking of returns across subject. Ignoring such reservations for the moment, such research does show that STEM and LEM are more strongly correlated with wages than Social Studies; and that Arts degrees have very little correlation with wages, relative to a control with no degree at all. The 2013 report gives a more nuanced view since the accumulation of LFS data meant that cells sizes at the level of 30 subjects were sufficient to provide more detail. For example, within LEM, Economics had by far the highest correlation with wages. Buchmueller and Walker (2019) also estimate the returns to degrees by subject groups (and by Russell Group HEIs compared to the rest) and compare results that include non-cognitive skill
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The economic value of degrees
TABLE 6.3
Returns to Degrees by Subject Group and HEI type. OLS All HEIs
(1) All subjects
STEM
Social sciences
(2)
IPWRA
Russell group (1)
(2)
Other HEI (1)
(2)
All HEIs (1)
(2)
Russell group (1)
(2)
Other HEI (1)
(2)
0.115*** 0.112*** 0.217*** 0.213*** 0.063*** 0.061**
0.093*** 0.087*** 0.170*** 0.152*** 0.038
0.019
(0.016)
(0.018)
(0.027)
(0.034)
0.147*** 0.143*** 0.264*** 0.260*** 0.104*** 0.100*** 0.111*** 0.103*** 0.192*** 0.179*** 0.087**
0.070*
(0.022)
(0.016)
(0.038)
(0.018)
(0.024)
(0.022)
(0.020)
(0.024)
(0.032)
(0.038)
(0.031)
(0.036)
0.144*** 0.142*** 0.228*** 0.221*** 0.110*** 0.111*** 0.112*** 0.106*** 0.175**
0.139*
0.076
0.060
(0.026)
(0.043)
(0.044)
(0.024)
(0.018)
(0.044)
(0.024)
(0.038)
(0.026)
(0.041)
Arts & 0.021 humanities
(0.022)
(0.026)
(0.026)
(0.044)
(0.030)
(0.030)
(0.026)
(0.028)
(0.060)
(0.064)
(0.039)
0.019
0.142*** 0.139*** 0.026
0.027
0.017
0.022
0.072
0.048
0.079* 0.087*
(0.024)
(0.042)
(0.026)
(0.027)
(0.028)
(0.049)
(0.048)
(0.039)
(0.042)
(0.026)
(0.039)
Note: The dependent variable is log of gross hourly wage. Specification (1) does not include non-cognitive skills, while specification (2) does. Noncognitive skills included are: Locus of control, a proxy for conscientiousness, and a proxy for self-esteem (where the higher the score the more noncognitive skills the individual has). All specifications include the following additional controls: gender, ethnicity as well as regional dummies. OLS specifications further control for missing values in the sample for degree-observations as well as non-cognitive skill observations (for reasoning see Section 3). IPWRA obtains the average treatment effect on treated. All observations are weighted by the most recent LSYPE sample weights.
control variables with results that do not. The IPWRA results are based on a multi-nominal logit model of the probabilities of attending a RG HEI versus the rest, and having a major in the STEM, Social Science and Arts & Humanities groups - based primarily on the individuals Alevel (senior high school) subjects studied and attainment in them. It uses the LSYPE cohort who report their earnings at age 25. Table 6.3 below presents the headline results by subject group. Controlling for non-cognitive skills makes little difference to the results (compare cols 1 and 2) except for IPWRA results for RG graduates where the inclusion of non-cognitive skills reduces returns by approximately 0.03 for each subject group. Using IPWRA compared to OLS reduces estimated returns by around 2pp. RG degrees command much higher wages than those from the other mission groups. STEM returns are higher than Social Science, and Arts
offer insignificantly positive returns for RG graduates and large negative returns (both relative to the omitted category of non-graduates). Recent US research by Altonji, Blom, and Meghir (2012) and Webber (2014) has focused on the ACS which has more detail on the nature of individual experiences of HE since it records detailed major. This work shows similar results as equivalent UK research. A notable advance over the UK LFS, and similar US, research is Britton, Dearden, Shephard, and Vignoles (2016) which uses SLC data merged to HMRC income tax records. SLC records major and HEI for a large majority of UK students. The ingenious feature in this paper was to use the average Alevel scores of students by course, obtained from HESA data, to provide, at least at the course level, a control for average student prior ability. Fig. 6.2 shows the highlights of the results e the dots near the horizontal axis are
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86
6. Heterogeneity in the returns to higher education
FIG. 6.2 Graduate earnings by subject, controlling for course level selectivity. Notes: Miss ¼ Miscellaneous. The box represents the interquartile range, with the median shown as he horizontal divider. The size of the blobs above the horizontal axis show relative cell sizes. Source: Britton et al. (2016).
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The economic value of degrees
TABLE 6.4 IPWRA estimates of the college premium by subject group and HEI mission group controlling for course level selectivity. IPWRA average treatment effects Men (1)
Women (2)
Pooled (3)
OLS Men
Women
Pooled
(4)
(5)
(6)
New
0.075***
0.071**
0.007
0.007
0.010
0.003
Social science
(0.029)
(0.031)
(0.025)
(0.020)
(0.023)
(0.015)
New
0.010
0.050
0.040
0.155***
0.065***
0.106***
Arts & humanities
(0.036)
(0.035)
(0.030)
(0.025)
(0.024)
(0.017)
Old
0.161***
0.047
0.054*
0.038*
0.053*
0.001
STEM
(0.031)
(0.037)
(0.028)
(0.022)
(0.028)
(0.017)
Old
0.133***
0.036
0.045
0.085***
0.032
0.055***
Social science
(0.036)
(0.033)
(0.028)
(0.028)
(0.028)
(0.020)
Old
0.003
0.156***
0.077***
0.090***
0.121***
0.110***
Arts & humanities
(0.037)
(0.033)
(0.028)
(0.031)
(0.030)
(0.021)
RG
0.153***
0.039
0.102***
0.115***
0.071***
0.092***
STEM
(0.033)
(0.041)
(0.030)
(0.023)
(0.027)
(0.017)
RG
0.196***
0.049
0.108***
0.109***
0.070**
0.086***
Social science
(0.041)
(0.038)
(0.031)
(0.027)
(0.029)
(0.020)
RG
0.027
0.119***
0.065**
0.104***
0.072***
0.084***
Arts & humanities
(0.039)
(0.038)
(0.032)
(0.030)
(0.028)
(0.020) 0.131*** (0.009)
Female Selectivity Observations
3950
4083
2
R
8138
0.071*** (0.014)
0.123*** (0.014)
0.097*** (0.010)
4072
4089
8161
0.404
0.355
0.391
IPWRA stands for inverse probability weighted regression adjustment. Robust SE in parentheses, *p < 0.1, **p < 0.05, ***p < 0.01. The observations which are off common support are excluded from the treatment effect models. Other controls include age, age squared, nonwhite, dummies for decade of birth, survey years, and regions. PG and good degree dummies only enter the outcome (wage) equations but not the treatment (selection) equations in the IPWRA specifications. We do not report the coefficients for selectivity in the 1PWRA estimates because there is one coefficient for each potential outcome - too many to report. The same applies to the female dummy. Source: Walker and Zhu (2018).
proportional to cell sizes e Economics and Medicine have the highest median returns but are very small cells e each representing less than 2% of overall student numbers, while Maths/
Computing, Management, and the Creative Arts each represent more than 15% of overall male number; and Business, Creative Arts, and Education each represent more than 15% of
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88
6. Heterogeneity in the returns to higher education
FIG. 6.3
LEO Returns to UG Degree Courses - IPWRA estimates. Source: Belfield et al. (2018).
female students. The highest earnings are associated with Medicine and Economics students e subjects which are studied by a very small proportion of students; while Creative
Arts, in particular, offer earnings that, for females, are close to full-time minimum wage work, and little better for males, and yet large numbers of students study them.
II. Private and social returns to education
89
References
In the UK, recent work has exploited the more extensive HE information being collected in LFS datasets which, from 2005 onwards, contain the major of study, and contains both major studied and HEI attended, from 2011 onwards. The idea of controlling for selectivity at the course level was also pursued in Walker and Zhu (2018) using LFS data, exploiting both subject studies and HEI attended. The use of the LFS data allows a longer run of cohorts that did the SLC data in Britton et al. (2016), and this paper also adopted to IPWRA method to improve the comparability of treatment and controls. The wage effects of institution and subject types in Table 6.4 suggest that OLS substantially underestimates the effect of attending the more prestigious HEIs for men. The effects of Old and RG STEM are not significantly different from each other, but they are approximately 15% greater than New-STEM. Using OLS for men we find no significant effect of Old-STEM relative to New-STEM and a somewhat smaller effect of RG-STEM For women, Old and RG STEM are not significantly different than New-STEM. There appear to be no significant Arts and Humanities effects for men in RG or Old relative to New. Although, for men, RG-SocSci is large and significantly different from Old-SocSci, which in turn is significantly greater than NewSocSci. The same is true for OLS estimates although these are again considerably underestimated relative to IPWRA. The most definitive UK research is the recent work by Belfield et al. (2018) using the LEO data. This is based on administrative data on around 7 million students and so allowed much more granular analysis than the LFS does. This LEO work uses IPWRA estimation, much like the Walker and Zhu (2018) work. Fig. 6.3 visualizes the most disaggregated estimates which are ordered by returns from lowest on the left. These estimates are computed at age 29 e and early point in the lifecycle when we expect college premia to be low. The red dots are the point estimate course fixed effects and
the gray bars are confidence intervals. The height of the red dots show the returns relative to nongraduates. For men only 60% of the approximately 1200 courses for which estimation is possible (the HMRC impose a minimum cell size of 50 to preserve confidentiality) show positive returns and only 20% show significantly positive returns. For women approximately 50% are significantly positive. Subjects available for Cambridge (an elite UK HEI) are highlighted in yellow and some examples are labeled. Even elite HEIs offer Creative arts courses that yield negative returns.
Conclusion The evidence that the returns to higher education in both the UK and the US differs greatly by HEI and by subject is overwhelming. These findings have important implications for the implications of the effects of income contingent student loans that have, hitherto, gone unexplored. These differences are so large it is unclear what supports such differentials and why student choices do not respond to reduce them to more plausible levels. This is an important issue for further research.
Acknowledgments I am indebted to collaborators on the LEO project and to my long time collaborator in education research, Yu Zhu, whose joint work I rely on here.
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Australia and England. Centre for applied macroeconomic analysis, Working Paper 29. Crawford School of Public Policy, Australian National University. Barr, N., Chapman, B., Dearden, L., & Dynarski, S. (2019). The US college loans system: Lessons from Australia and England. Economics of Education Review, 71, 32e48. Belfield, C., Britton, J., Buscha, F., Dearden, L., Dickson, M., van der Erve, L., et al. (2018). The impact of undergraduate degrees on early-career earnings. Department of Education Research Report. https://assets.publishing.service.gov. uk/government/uploads/system/uploads/attachment data/file/759278/Theimpactofundergraduatedegreeson early-careerearnings.pdf. Blundell, R., Dearden, L., & Sianesi, B. (2004). Evaluating the impact of education on earnings in the uk: models, methods and results from the ncds. IFS Working Paper, WP03/20. Blundell, R., Dearden, L., & Sianesi, B. (2005). Evaluating the effect of education on earnings: Models, methods and results from the national child development survey. Journal of the Royal Statistical Society: Series A, 168, 473e512. Blundell, R. W., Green, D. A., & Jin, W. (2016). The UK wage premium puzzle: How did a large increase in university graduates leave the education premium unchanged?. IFS Working Paper 2016/1. Bonjour, D., Cherkas, L. F., Haskel, J. E., Hawkes, D. D., & Spector, T. D. (2003). Returns to Education: Evidence from U.K. Twins. American Economic Review, 93(5), 1799e1812. Britton, J., Dearden, L., Shephard, N., & Vignoles, A. (2016). How English domiciled graduate earnings vary with gender, institution attended, subject and socio-economic background. Technical report. Institute for Fiscal Studies WP1606.
Buchmueller, G., & Walker, I. (2019). Non-cognitive skills and the return to higher education. mimeo. Hoxby, C. (2019). The productivity of u.s. post-secondary institutions forthcoming. In C. Hoxby, & K. Strange (Eds.), Productivity in Higher Education. University of Chicago Press. Kirkeboen, L., Leuven, E., & Mogstad, M. (2016). Field of Study, Earnings, and Self-Selection. Quarterly Journal of Economics, 131(3), 1057e1111. Murphy, R., Scott-Clayton, J., & Wyness, G. (2017). Lessons from the end of free college in England. Brookings Economic Studies, Evidence Speaks Reports, 2, 13. Oster, E. (2019). Unobservable selection and coefficient stability: Theory and evidence. Journal of Business & Economic Statistics, 37(2), 187e204. Walker, I., & Zhu, Y. (2008). The college wage premium and the expansion of higher education in the UK. The Scandinavian Journal of Economics, 110, 695e709. Walker, I., & Zhu, Y. (2011). Differences by degree: Evidence of the net financial rates of return to undergraduate study for England and Wales. Economics of Education Review, 30, 1177e1186. Walker, I., & Zhu, Y. (2013). The impact of university degrees on the lifecycle of earnings: Some further analysis. Department for Business, Innovation and Skills Research Report. Walker, I., & Zhu, Y. (2018). University selectivity and the relative returns to higher education: Evidence from the UK. Labour Economics, 53, 230e249. Walker, I., & Zhu, Y. (2019). Wage differential by undergraduate degree class and grade inflation at college. mimeo. Webber, D. A. (2014). The lifetime earnings premia of different majors: Correcting for selection based on cognitive, noncognitive, and unobserved factors. Labour Economics, 28, 14e23.
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