221 S.P. Vahey Economics of Education Review 19 2000 219–227
Table 1 Incidence of skill mismatch
Attained Required
GRADE SOME
HIGH COLL
BACH POST
TOTAL Males n 5 569
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 n 5 424 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 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: lnY 5 PCa 1 REQb 1 OVERg 1 UNDERd 1 e
1 where Y denotes hourly earnings and PC a vector of per-
sonal characteristics including a constant. The vector REQ
contains one dummy variable for each required education level. The vectors OVER and UNDER contain
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-
5
Tsang and Levin 1985, Tsang et al. 1991, Sicherman 1991, Hersch 1991; 1995 and Sloane et al. 1996 present a
variety of evidence suggesting that overeducated workers exhi- bit higher turnover, less on the job training and are less satisfied
with their jobs than otherwise identical workers. Unfortunately, the NSCS data contain no information on these variables.
222 S.P. Vahey Economics of Education Review 19 2000 219–227
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 of Y 2.44
0.50 2.04
0.60 EXP
Experience in years 20.85
12.87 16.26
11.47 EXP2
EXP squared100 6.00
6.10 3.96
4.84 UNION
Union member 5 1; otherwise 5 0 0.51
0.50 0.38
0.48 SEX
Male 5 1; female 5 0 1.00
0.00 0.00
0.00 TEN
Years of tenure, present employer 10.02
9.04 6.17
6.64 TEN2
TEN squared100 1.82
2.83 0.82
1.67 BIL
Bilingual 5 1; otherwise 5 0 0.22
0.41 0.15
0.36 Industry
EXTR Extraction, construction 5 1; otherwise 5 0
0.09 0.29
0.03 0.16
MANUF Manufacturing 5 1; otherwise 5 0
0.31 0.46
0.11 0.31
DIST Distribution 5 1; otherwise 5 0
0.18 0.39
0.08 0.28
PUB Public services 5 1; otherwise 5 0
0.24 0.43
0.40 0.49
INFO Information services 5 1; otherwise 5 0
0.05 0.23
0.16 0.36
RET Retail, other services 5 1; otherwise 5 0
0.12 0.33
0.22 0.42
Occupation PROF
Professional 5 1; otherwise 5 0 0.18
0.39 0.13
0.34 SEMI
Semi-professional 5 1; otherwise 5 0 0.15
0.36 0.16
0.37 SUPER
Supervisory 5 1; otherwise 5 0 0.07
0.26 0.04
0.20 SKILL
Skilled trade 5 1; otherwise 5 0 0.25
0.43 0.23
0.42 SEMUN
Semi and unskilled 5 1; otherwise 5 0 0.34
0.48 0.43
0.50 Location
ATL Atlantic 5 1; otherwise 5 0
0.09 0.29
0.10 0.30
QUE Quebec 5 1; otherwise 5 0
0.35 0.48
0.28 0.45
ONT Ontario 5 1; otherwise 5 0
0.32 0.47
0.32 0.47
PRA Prairies 5 1; otherwise 5 0
0.13 0.33
0.21 0.41
BC British Columbia 5 1; otherwise 5 0
0.11 0.32
0.10 0.30
CITY Community 100 000 5 1; otherwise 5 0
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 GRADE 5 1; otherwise 5 0 0.12
0.32 0.07
0.25 ASOME
Attained SOME 5 1; otherwise 5 0 0.19
0.39 0.19
0.39 AHIGH
Attained HIGH 5 1; otherwise 5 0 0.18
0.39 0.19
0.39 ACOLL
Attained COLL 5 1; otherwise 5 0 0.30
0.46 0.38
0.49 ABACH
Attained BACH 5 1; otherwise 5 0 0.13
0.33 0.14
0.35 APOST
Attained POST 5 1; otherwise 5 0 0.08
0.28 0.04
0.19 REQ vector
RGRADE Required GRADE 5 1; otherwise 5 0
0.13 0.33
0.13 0.33
RSOME Required SOME 5 1; otherwise 5 0
0.15 0.36
0.13 0.33
RHIGH Required HIGH 5 1; otherwise 5 0
0.29 0.45
0.35 0.48
RCOLL Required COLL 5 1; otherwise 5 0
0.19 0.39
0.22 0.42
RBACH Required BACH 5 1; otherwise 5 0
0.17 0.37
0.16 0.37
RPOST Required POST 5 1; otherwise 5 0
0.07 0.26
0.02 0.14
Continued.
223 S.P. Vahey Economics of Education Review 19 2000 219–227
Table 2 Continued
Name Definition
Males Females
Education OVER vector
OGRADE Overed. req. GRADE 5 1; otherwise 5 0
0.07 0.25
0.08 0.27
OSOME Overed. req. SOME 5 1; otherwise 5 0
0.07 0.26
0.05 0.22
OHIGH Overed. req. HIGH 5 1; otherwise 5 0
0.11 0.31
0.16 0.37
OCOLL Overed. req. COLL 5 1; otherwise 5 0
0.02 0.13
0.02 0.14
OBACH Overed. req. BACH 5 1; otherwise 5 0
0.03 0.18
0.01 0.12
UNDER vector USOME
Undered. req. SOME 5 1; otherwise 5 0 0.03
0.16 0.01
0.12 UHIGH
Undered. req. HIGH 5 1; otherwise 5 0 0.10
0.30 0.06
0.24 UCOLL
Undered. req. COLL 5 1; otherwise 5 0 0.05
0.21 0.04
0.18 UBACH
Undered. req. BACH 5 1; otherwise 5 0 0.05
0.21 0.05
0.22 UPOST
Undered. req. POST 5 1; otherwise 5 0 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
