www.elsevier.com/locate/econedurev
The great Canadian training robbery: evidence on the returns
to educational mismatch
Shaun P. Vahey
*Faculty of Economics and Politics, University of Cambridge, Austin Robinson Building, Sidgwick Avenue, Cambridge CB3 9DD, UK
Received 30 October 1996; accepted 15 March 1998
Abstract
In this paper, I use data from the National Survey of Class Structure and Labour Process in Canada (NSCS) to estimate the returns to over and undereducation. I find that there are positive returns to overeducation for males in jobs that require a university bachelor’s degree; but for other levels of required education, the returns are insignificant. I also find evidence of lower pay for undereducated males in jobs with low education requirements. For females, the returns to over and undereducation are insignificant for all levels of required education. [I21, J31] 2000 Elsevier Science Ltd. All rights reserved.
1. Introduction
The mismatch between the skill requirements of jobs and the educational attainments of workers has long con-cerned social scientists. In “The Great Training Rob-bery”, Ivar Berg (1970) argued that overeducated work-ers are less productive than their counterparts because they find their jobs uninteresting and lack motivation.
A similar view was expressed by Freeman (1976, 1980) who coined the phrase “the overeducated Amer-ican”. He argued that the entry of the “baby-boom” gen-eration into the labour force in the 1970s caused an increased supply of highly educated workers. At the same time, the demand for these workers fell, forcing many of them into jobs with lower educational require-ments. Dooley (1986) identified similar demographic and demand-side changes in Canada.
Some researchers, including Kuttner (1983), Blue-stone and Harrison (1990) and Picot et al. (1990), have argued that the incidence of North American skill or edu-cational mismatch (hereafter, I use the terms
* E-mail: [email protected]
0272-7757/00/$ - see front matter2000 Elsevier Science Ltd. All rights reserved. PII: S 0 2 7 2 - 7 7 5 7 ( 9 8 ) 0 0 0 2 9 - 6
interchangeably) is increasing.1 Industrial restructuring
has caused a number of traditional, medium to high-skill jobs to disappear—the so-called “declining middle”— forcing many skilled workers into low-skill, service sec-tor jobs (Gunderson and Riddell, 1993).
No previous studies have examined the relationship between educational mismatch and wages in Canada. Using non-Canadian data, however, (among others) Dun-can and Hoffman (1981), Rumburger (1987), Hersch (1991), Sicherman (1991) and Kiker et al. (1997) esti-mated earnings (or wage) equations using the years of required schooling and educational mismatch as explana-tory variables. These researchers found that the earnings of (under) overeducated workers are (less) greater than their counterparts with exactly the required level of schooling. (Hereafter, I shall refer to workers who are not mismatched, but in jobs with the same educational
1The term “skill mismatch” is sometimes used to refer to
requirements as “otherwise identical”.) Assuming earn-ings reflect marginal productivity, their results do not support Berg’s (1970) hypothesis: overeducated workers earn more—not less—than otherwise identical workers. A number of studies have focused on whether the returns to skill mismatch are gender dependent. Frank (1978) argued that the limited geographic mobility of women causes a male–female differential in the returns to education. If relocation is a family-based decision, then it will be based upon the needs of the primary earner—usually the male. As a result, the secondary earner—the female—is geographically constrained in her job search. Duncan and Hoffman (1981), Rumburger (1987), Hartog and Oosterbeek (1988), Groot (1996) and Kiker et al. (1997) have examined male–female differ-ences in the returns to skill mismatch. These researchers found that their results were similar for the two sexes: (under) overeducated workers earn (less) more than otherwise identical workers. Using US data, however, Hersch (1991) found that for females, the returns to edu-cational mismatch were insignificant.
In this study, I use data from the National Survey of Class Structure and Labour Process in Canada (NSCS) to estimate the returns to educational mismatch. These are the only data available that contain self-report infor-mation on educational mismatch in Canada for either sex. For males, I find evidence of (negative) positive returns to (under) overeducation; but also find that the returns are sensitive to the level of required education. The results for the female sub-sample differ from those obtained using the full sample: the returns are insignifi-cant for all levels of required education. Hence, Berg’s (1970) hypothesis is rejected for both males and females: overeducated workers do not receive lower earnings than otherwise identical workers.
The rest of the paper is organised as follows. In the following section, I discuss the incidence of educational mismatch in Canada. I then set out the empirical model and present the results. I draw some conclusions in the final section.
2. The incidence of educational mismatch in Canada
The data are taken from the NSCS, a cross-sectional survey that contains information on approximately 3000 respondents. This survey was carried out by Canada Facts, who conducted face to face interviews in 1982. Researchers at the Department of Sociology and Anthro-pology at Carleton University decoded the survey responses and transferred the information to tape.
I have excluded workers over 64 and under 18, and anyone with non-positive 1981 earnings or hours worked per week. After removing the self-employed and those
who did not work year round, the final sample is 993, of which 424 are female.2
The unique feature of this Canadian survey is that respondents were asked about both their attained edu-cation and the eduedu-cation requirements for the job. The following question was asked about educational attain-ments: “What is the highest level of education you have completed?”. The answers were categorised into six classes: grade school diploma or less (GRADE), some high school (SOME), completed high school (HIGH), college/vocational school (COLL), bachelor’s degree (BACH), and postgraduate or professional degree (POST). The question asked about required education was: “What type of formal schooling is now normally required for people who do your type of work?” Individ-uals are defined as (under) overeducated if their attained schooling is (less) greater than their required education. Since the second question inquired about schooling “now normally required”, arguably the resulting variable understates (overstates) the extent of overeducation (undereducation)–education requirements have generally increased with time.3
A drawback of this type of measure of skill mismatch is that it is (by definition) subjective. Workers who are dissatisfied with their jobs may misreport themselves as overqualified—introducing bias into the model. The main advantage of the self-report approach is that the measure is job specific. Requirements can differ greatly within occupations. For example, the schooling require-ments for a post as an economist can vary from an under-graduate degree for some private sector jobs, to a Ph.D. for research jobs. Other researchers have used occu-pation-based measures derived from either expert opi-nion of educational requirements (e.g. Alba-Ramı´rez, 1993) and/or deviations from average attainments (Verdugo and Verdugo, 1989). Kiker et al. (1997) and McGoldrick and Robst (1996) review the advantages and disadvantages of each measure in detail.4
2I have excluded seasonal workers on the grounds that their
earnings are influenced by (very) different factors—related to their labour market inflows and outflows. Mismatched workers could self-select into seasonal work, however, particularly if they are not rewarded by the market for their qualifications. Hence, the exclusion of workers who did not work year round could cause downward bias in the incidence of and the returns to educational mismatch.
3In contrast, the question asked of the Panel Study of Income
Dynamics respondents refers to schooling required “to get a job like yours?”. The question asked in Hersch’s (1991) survey in Eugene, Oregon enquired about schooling “needed to do a job like yours, not just be hired”. In both these cases the time frame is unclear.
4In most studies utilising self-report evidence, the
Table 1
Incidence of skill mismatch
Attained Required
GRADE SOME HIGH COLL BACH POST TOTAL
Males n5569
GRADE 6.0 2.6 2.8 0.5 0.0 0.0 12.0
SOME 3.3 5.6 6.9 2.3 0.5 0.0 18.6
HIGH 1.4 4.0 8.3 1.8 1.9 0.7 18.1
COLL 2.1 2.6 9.8 12.7 2.3 0.7 30.2
BACH 0.0 0.4 0.7 1.8 9.0 0.9 12.7
POST 0.0 0.0 0.4 0.0 3.2 4.9 8.4
Total 12.8 15.3 28.8 19.0 16.9 7.2 100.0
Females n5424
GRADE 4.7 1.4 0.5 0.0 0.0 0.0 6.6
SOME 5.4 6.1 5.4 1.4 0.5 0.0 18.9
HIGH 1.9 1.7 12.7 2.1 0.7 0.0 19.1
COLL 0.5 2.6 13.7 16.7 3.8 0.5 37.7
BACH 0.0 0.7 1.7 1.9 9.4 0.2 13.9
POST 0.0 0.0 0.7 0.2 1.4 1.4 3.8
Total 12.5 12.5 34.7 22.4 15.8 2.1 100.0
The incidence of educational mismatch in the sample is described in Table 1. There are a number of striking features about these data. First, educational mismatch is a common phenomenon; but, the incidence of overeduc-ation (males 30%, females 32%) is greater than the inci-dence of undereducation (males 24%, females 17%). These figures are similar to those based on self-report measures for the U.S. (McGoldrick and Robst, 1996) and Britain (Sloane et al., 1996). Second, attained schooling is generally within one education level of required schooling; the incidence of skill mismatch outside this interval is small. Third, for both sexes, the peak in required schooling is at the HIGH level, but the peak in attained education is at COLL. Fourth, the distributions of attained and required education are flatter for males; the job market is particularly thin for females in the upper tail. As a result, the estimates for well-educated females should be interpreted with some caution.
A number of researchers (e.g. Sicherman, 1991; Groot, 1996) have noted that overeducation may be a short-run phenomenon. Entry level employees are often overqualified for their jobs, but go on to use their skills in later life. For the NSCS sample, overeducation is
asso-the respondents were asked about asso-the number of years in edu-cation. In others, for example Hersch (1991), the researchers converted the levels to years by making assumptions about the (average) equivalence between the two measures. The main advantage of using years is that the education variables are con-tinuous; the drawback is that, where a conversion factor is required, it is not job specific.
ciated predominately with younger workers. Approxi-mately 57% of males and 33% of females under 26 are overeducated.5
3. Empirical model
Consider the following earnings equation:
lnY5PCa 1REQb 1OVERg 1UNDERd 1 e (1)
whereYdenotes hourly earnings andPCa vector of per-sonal characteristics (including a constant). The vector REQ contains one dummy variable for each required education level. The vectorsOVERandUNDERcontain dummy variables for over and undereducation respect-ively; each variable corresponds to a specific required schooling level. It is, of course, possible to allow a dummy for each combination of attained and required education. Recall from Table 1, however, that required education is rarely more than one education level from attained education. As a result, such a model yields little additional insight.
A full list of the variables definitions, means and
stan-5Tsang and Levin (1985), Tsang et al. (1991), Sicherman
Table 2
Variable definitions, means and standard deviations
Name Definition Males Females
Mean SD Mean SD
Personal characteristics
ANY Annual income 27 782 14 333 16 086 9918
WY ANY per week 532.84 274.89 308.51 190.22
Y WY per hour usually worked 12.98 6.91 9.22 7.55
ln Y Natural log ofY 2.44 0.50 2.04 0.60
EXP Experience in years 20.85 12.87 16.26 11.47
EXP2 EXP squared/100 6.00 6.10 3.96 4.84
UNION Union member51; otherwise50 0.51 0.50 0.38 0.48
SEX Male51; female50 1.00 0.00 0.00 0.00
TEN Years of tenure, present employer 10.02 9.04 6.17 6.64
TEN2 TEN squared/100 1.82 2.83 0.82 1.67
BIL Bilingual51; otherwise50 0.22 0.41 0.15 0.36
Industry
EXTR Extraction, construction51; otherwise50 0.09 0.29 0.03 0.16
MANUF Manufacturing51; otherwise50 0.31 0.46 0.11 0.31
DIST Distribution51; otherwise50 0.18 0.39 0.08 0.28
PUB Public services51; otherwise50 0.24 0.43 0.40 0.49
INFO Information services51; otherwise50 0.05 0.23 0.16 0.36
RET Retail, other services51; otherwise50 0.12 0.33 0.22 0.42
Occupation
PROF Professional51; otherwise50 0.18 0.39 0.13 0.34
SEMI Semi-professional51; otherwise50 0.15 0.36 0.16 0.37
SUPER Supervisory51; otherwise50 0.07 0.26 0.04 0.20
SKILL Skilled trade51; otherwise50 0.25 0.43 0.23 0.42
SEMUN Semi and unskilled51; otherwise50 0.34 0.48 0.43 0.50
Location
ATL Atlantic51; otherwise50 0.09 0.29 0.10 0.30
QUE Quebec51; otherwise50 0.35 0.48 0.28 0.45
ONT Ontario51; otherwise50 0.32 0.47 0.32 0.47
PRA Prairies51; otherwise50 0.13 0.33 0.21 0.41
BC British Columbia51; otherwise50 0.11 0.32 0.10 0.30
CITY Community > 100 00051; otherwise50 0.56 0.50 0.61 0.49
Education
EA Attained education in levels 0.40 0.71 0.42 0.70
ER Required education in levels 0.35 0.70 0.21 0.50
AGRADE Attained GRADE51; otherwise50 0.12 0.32 0.07 0.25
ASOME Attained SOME51; otherwise50 0.19 0.39 0.19 0.39
AHIGH Attained HIGH51; otherwise50 0.18 0.39 0.19 0.39
ACOLL Attained COLL51; otherwise50 0.30 0.46 0.38 0.49
ABACH Attained BACH51; otherwise50 0.13 0.33 0.14 0.35
APOST Attained POST51; otherwise50 0.08 0.28 0.04 0.19
REQ vector
RGRADE Required GRADE51; otherwise50 0.13 0.33 0.13 0.33
RSOME Required SOME51; otherwise50 0.15 0.36 0.13 0.33
RHIGH Required HIGH51; otherwise50 0.29 0.45 0.35 0.48
RCOLL Required COLL51; otherwise50 0.19 0.39 0.22 0.42
RBACH Required BACH51; otherwise50 0.17 0.37 0.16 0.37
RPOST Required POST51; otherwise50 0.07 0.26 0.02 0.14
Table 2 Continued
Name Definition Males Females
Education
OVER vector
OGRADE Overed. & req. GRADE51; otherwise50 0.07 0.25 0.08 0.27
OSOME Overed. & req. SOME51; otherwise50 0.07 0.26 0.05 0.22
OHIGH Overed. & req. HIGH51; otherwise50 0.11 0.31 0.16 0.37
OCOLL Overed. & req. COLL51; otherwise50 0.02 0.13 0.02 0.14
OBACH Overed. & req. BACH51; otherwise50 0.03 0.18 0.01 0.12
UNDER vector
USOME Undered. & req. SOME51; otherwise50 0.03 0.16 0.01 0.12
UHIGH Undered. & req. HIGH51; otherwise50 0.10 0.30 0.06 0.24
UCOLL Undered. & req. COLL51; otherwise50 0.05 0.21 0.04 0.18
UBACH Undered. & req. BACH51; otherwise50 0.05 0.21 0.05 0.22
UPOST Undered. & req. POST51; otherwise50 0.02 0.15 0.01 0.08
dard deviations are given in Table 2. The dependent vari-able is generated as follows. Respondents were asked to estimate their personal income in 1981 and the number of hours worked per week. Removing those who did not work year round, I calculate the earnings per hour usu-ally worked.
If Berg’s proposition is correct, the coefficients on the overeducation dummies should be negative.
4. Results
The results from the OLS regressions are presented in Table 3. The first column includes only the personal characteristics of the respondents and the control vari-ables. The next column includes these and the education variables from Eq. (1). The third column includes the same variables but the sample is restricted to males; and the next column includes only females. The final column refers to single, urban-based females.
From the first column, it is apparent that the personal characteristic variables are generally significant at the 10% level (t-statistics in parentheses) and have the expected signs.6 The occupational and the industry
dummies are all significant. Employment in the retail and other services (RET) has a particularly strong negative impact on earnings; employment in the extraction and construction industries (EXTR) has an impact of similar magnitude, though the sign is reversed. The location
6The Breusch–Pagan–Godfrey test (Breusch and Pagan,
1979; Godfrey, 1978) indicates that the null hypothesis of homoskedasticty can be rejected at the 5% level. The reported standard errors are heteroskedasticity-consistent, calculated using White’s (1980) method.
dummies are insignificant, apart from those for living in British Columbia (BC) or living in a community with a population > 100 000 (CITY), which both have posi-tive coefficients.
The inclusion of the education variables reveals that the returns to over and undereducation vary with the required level of education.7 Many of the educational
mismatch dummies are insignificant at the 10% level. However, the overeducation dummy at the BACH level of required education and the undereducation dummy at the HIGH level are significant. At the 15% significance level, the undereducation dummy at the SOME level is also significant. In these cases, overeducation is associa-ted with higher earnings and undereducation with lower earnings.8
Restricting the sample to males and females in turn reveals some startling differences between the sexes.9
First, the coefficients for the required schooling variables at the BACH and POST levels are much larger for females. Second, although the impacts of the educational mismatch variables for males are similar to those for the
7As with the previous equation, the Breusch–Pagan–Godfrey
test indicates that the errors are heteroskedastic and the reported standard errors are heteroskedasticity-consistent.
8The over and undereducation dummies are jointly
signifi-cant at the 5% significance level. The hypothesis that two dummy variables (one for overeducation, one for undereducation) capture the effects of educational mismatch, regardless of the level of required education, cannot be rejected at the 10% level.
9An F-test of the hypothesis that there is no difference
Table 3
Regression equations; dependent variable lnY
Variable All All Males Females Females
EXP 0.016 0.016 0.019 0.014 0.018
(3.404) (3.401) (3.140) (1.704) (1.124)
EXP2 —0.033 20.024 20.028 20.023 20.040
(23.423) (22.644) (22.491) (21.257) (21.080)
UNION 0.041 0.058 0.055 0.059 0.125
(1.177) (1.690) (1.354) (0.950) (1.018)
SEX 0.312 0.293
(9.071) (8.855)
TEN 0.021 0.021 0.017 0.022 0.006
(3.659) (3.731) (2.825) (2.147) (0.334)
TEN2 20.051 20.059 20.051 20.049 20.015
(22.415) (22.895) (22.345) (21.254) (20.256)
BIL 0.083 0.050 0.127 20.071 20.063
(1.892) (1.192) (2.666) (20.918) (20.431)
EXTR 0.198 0.184 0.155 0.262 0.171
(3.764) (3.689) (2.817) (1.503) (0.364)
DIST 0.091 0.042 20.004 0.147 0.120
(1.809) (0.869) (20.084) (1.236) (0.556)
PUB 0.079 20.011 20.009 20.021 20.021
(1.759) (20.251) (20.198) (20.216) (20.128)
INFO 0.108 0.018 0.076 20.025 0.194
(1.836) (0.322) (0.992) (20.227) (1.039)
RET 20.197 20.218 20.155 20.267 0.167
(23.782) (24.287) (22.442) (22.732) (0.873)
PROF 0.490 0.194 0.215 0.148 0.049
(9.412) (2.507) (2.447) (1.337) (0.228)
SEMI 0.452 0.263 0.229 0.282 0.180
(9.265) (4.576) (3.325) (3.020) (0.950)
SUPER 0.229 0.123 0.158 20.002 0.361
(4.087) (2.232) (2.629) (20.014) (1.093)
SKILL 0.195 0.114 0.130 0.070 0.088
(4.870) (2.815) (2.901) (0.996) (0.645)
ATL 20.075 20.049 20.083 0.007 20.049
(21.179) (20.791) (21.271) (0.075) (20.150)
QUE 20.027 0.002 20.076 0.110 0.183
(20.647) (0.053) (21.776) (1.568) (1.429)
PRA 0.009 0.009 20.025 0.045 0.179
(0.187) (0.195) (20.413) (0.624) (1.220)
BC 0.145 0.144 0.210 0.026 20.190
(2.862) (2.876) (3.175) (0.283) (21.302)
CITY 0.068 0.047 0.012 0.114
(2.091) (1.526) (0.363) (2.075)
RGRADE 20.358 20.406 20.305 20.391
(24.737) (24.868) (22.037) (21.082)
RSOME 20.136 20.148 20.106 20.254
(21.702) (21.587) (20.844) (20.968)
RCOLL 0.021 20.030 0.058 0.142
(0.384) (20.431) (0.574) (0.733)
RBACH 0.193 0.139 0.262 0.494
(2.181) (1.188) (1.989) (2.261)
RPOST 0.359 0.226 0.801 1.275
(3.665) (1.939) (3.424) (3.511)
OGRADE 20.037 0.051 20.152 0.212
(20.436) (0.492) (21.007) (0.621)
Table 3 Continued
Variable All All Males Females Females
OSOME 20.001 20.059 0.117 0.899
(20.008) (20.618) (0.777) (2.757)
OHIGH 0.017 20.012 0.042 20.056
(0.285) (20.168) (0.448) (20.359)
OCOLL 20.028 20.203 0.132 0.333
(20.271) (21.347) (0.730) (1.133)
OBACH 0.145 0.129 0.099 0.009
(2.139) (1.634) (0.446) (0.032)
USOME 20.181 20.259 20.048 0.317
(21.572) (22.369) (20.204) (0.628)
UHIGH 20.202 20.243 20.122 0.593
(23.170) (23.365) (20.950) (1.978)
UCOLL 20.047 20.106 0.093
(20.699) (21.302) (0.607)
UBACH 0.025 0.100 20.057 20.099
(0.277) (0.888) (20.400) (20.423)
UPOST 0.040 0.122 20.263
(0.355) (0.965) (20.721)
Constant 1.534 1.700 2.046 1.642 1.608
(25.080) (25.570) (23.670) (11.550) (7.135)
N 993 993 569 424 98
R¯2 0.363 0.418 0.385 0.304 0.264
Note:t-statistics in parentheses.
full sample,10for females all these terms are
insignifi-cant.11
One interpretation of the difference in the returns to educational mismatch by gender is that Frank’s (1978) hypothesis holds.12Female job search is geographically
constrained and, as a result, competition within the lab-our market is insufficient to generate the usual returns to educational mismatch. In order to test this theory, I
10For males, the over and undereducation dummies are
jointly significant at the 5% level; but the hypothesis that two dummy variables capture educational mismatch, regardless of required education, cannot be rejected at the 10% level. For females, the educational mismatch dummies are jointly insig-nificant at the 10% level.
11Recall from Table 1 that for high educational requirements,
the numbers of mismatched females are smaller than those of mismatched males. This contributes to the imprecision of the estimates.
12It is often argued that unions reduce the variance in their
members’ earnings. Hence, differences in union coverage could account for the differences in the returns to educational mis-match by gender. However, the hypothesis that there is no dif-ference between union and non-union workers cannot be rejected for either males or females at the 10% level. In both cases, the results for both union and non-union workers are similar to those obtained using all workers.
restrict the sample to females that are unmarried and urban-based. The returns to skill mismatch, shown in the final column, are larger than for all female workers. However, only two mismatch variables are significantly different from zero at the 10% level, OSOME and UHIGH; and the coefficient on the latter implies that undereducation raises wages relative to otherwise ident-ical workers. These results are difficult to reconcile with Frank’s theory.
Using Oaxaca’s (1973) decomposition, Table 4 shows the contributions of the explanatory variables to the male–female earnings gap. I decompose the gap into the difference in the sample means multiplied by the esti-mated male coefficient (first column) and the difference
Table 4
Male–female earnings decomposition
Characteristics Returns
PC 0.092 0.397
REQ 0.011 20.069
OVER 0.002 20.008
UNDER 20.015 20.006
in the coefficients multiplied by the female mean (second column). The first component shows the difference due to male–female characteristics; the second, the return to these characteristics. The latter is sometimes attributed to discrimination. The largest proportion of the gap is explained by the returns to personal characteristics. The overeducation and undereducation variables have rela-tively small impacts; and their combined effect is nega-tive. The majority of the earnings gap accounted for by the educational mismatch variables is due to the teristics themselves, rather than the returns to the charac-teristics.
5. Conclusions
Ivar Berg (1970) argued that overeducated workers are less productive than otherwise identical workers because they lack interest and motivation. In this paper, I have used Canadian data from the NSCS to estimate the returns to educational mismatch. Since overeducated workers do not receive lower earnings than otherwise identical workers, the Canadian evidence does not sup-port Berg’s (1970) claim. I have also shown that the returns to skill mismatch are sensitive to both the edu-cational requirements of the job and the gender of the worker. For males, there is evidence of positive returns to overeducation if the job requires a university bachelor degree; but, the returns are insignificant for other required education levels. I have found some evidence of lower pay for undereducated males: they are penalised in jobs with low education requirements. Remarkably, I have found no evidence of significant returns to either over or undereducation for females.
One explanation for the differences in returns by gen-der is that females are more geographically constrained in their job search by family considerations. As a result, they do not obtain the same returns to education as males (Frank, 1978). However, I have found that (in general) unmarried, urban-based females—who are less tied by families—do not earn greater absolute returns to edu-cational mismatch than other females. This finding is inconsistent with the constrained search theory.
Acknowledgements
I thank Paul Beaudry, Jean-Michel Cousineau, John Cragg, Ron Giammarino, David Green, Ken Hendricks, Craig Riddell and two anonymous referees for helpful comments. All calculations were performed using the econometrics package SHAZAM (White, 1978).
References
Alba-Ramı´rez, A. (1993). Mismatch in the Spanish labor mar-ket—overeducation? Journal of Human Resources, 28, 259–278.
Berg, I. (1970).Education and Jobs: The Great Training Rob-bery. Boston: Beacon Press.
Bluestone, B., & Harrison, B. (1990). Wage polarisation in the US and the “flexibility” debate.Cambridge Journal of Eco-nomics,14, 351–373.
Dooley, M.D. (1986). The overeducated Canadian?Canadian Journal of Economics,19, 142–159.
Breusch, T. S., & Pagan, A. R. (1979). A simple test for het-erskedasticity and random coefficient variation. Econo-metrica,47, 1287–1294.
Duncan, G., & Hoffman, S. D. (1981). The incidence and wage effects of overeducation.Economics of Education Review, 1, 75–86.
Frank, R. (1978). Why women earn less: the theory and esti-mation of differential overqualification.American Economic Review,68, 360–373.
Freeman, R. (1976).The Overeducated American. New York: Academic Press.
Freeman, R. (1980). The facts about the declining value of col-lege.Journal of Human Resources,15, 124–142. Godfrey, L. G. (1978). Testing for multiplicative
heteroskedas-ticity.Journal of Econometrics,8, 227–236.
Groot, W. (1996). The incidence of and returns to overeduc-ation in the UK.Applied Economics,28, 1345–1350. Gunderson, M., & Riddell, W.C. (1993).Labour Market
Eco-nomics. Toronto: McGraw–Hill Ryerson.
Hartog, J. (1986). Allocation and the earnings function. Empiri-cal Economics,11, 97–110.
Hartog, J., & Oosterbeek, H. (1988). Education, allocation and earnings in the Netherlands. Economics of Education Review,7, 185–194.
Hersch, J. (1991). Education match and job match. Review of Economics and Statistics,73, 140–144.
Kiker, B. F., Santos, M. C., & De Oliveira, M. (1997). Overed-ucation and underedOvered-ucation: evidence for Portugal. Econom-ics of Education Review,16, 111–125.
Kuttner, R. (1983). The declining middle.Atlantic Monthly,7, 60–72.
McGoldrick, K., & Robst, J. (1996). Gender differences in overeducation: a test of the theory of differential overqual-ification.American Economic Review,86, 280–284. Oaxaca, R. (1973). Male–female wage differentials in urban
labor markets. International Economic Review, 14, 693– 709.
Picot, G., Myles, J., & Wannell, T. (1990).Good Jobs/Bad Jobs and the Declining Middle, 1967–1986. Ottawa Analytical Studies Branch: Statistics Canada.
Rumburger, R. W. (1987). The impact of surplus schooling on productivity and earnings.Journal of Human Resources,22, 1–50.
Sicherman, N. (1991). Overeducation in the labor market. Jour-nal of Labor Economics,9, 101–122.
Tsang, M. C., & Levin, H. (1985). The economics of overeduc-ation.Economics of Education Review,4, 93–104. Tsang, M. C., Rumburger, R. W., & Levin, H. M. (1991). The
impact of surplus schooling on worker productivity. Indus-trial Relations,4, 629–643.
Verdugo, P. R., & Verdugo, N. T. (1989). The impact of surplus schooling on earnings—some additional results.Journal of Human Resources,4, 629–643.
