170 P. Christie, M. Shannon Economics of Education Review 20 2001 165–180
Table 4 Field of study by age and sex, 1991 census
Age groups 25–34
35–44 45–54
55–64
Women Educationrecreation
11.3 17.1
22.6 17.2
Fine and applied arts 6.9
5.0 4.1
4.7 Humanities
6.2 7.4
6.7 7.0
Social sciences 11.1
9.1 6.7
5.4 Business
18.8 14.3
12.5 12.8
Secretarial 16.7
16.2 16.5
20.7 Agriculturalbiology
4.2 3.4
3.0 3.7
Engineeringapplied 1.1
0.7 0.3
0.3 science
Engineeringapplied 4.9
3.5 2.4
1.9 science
technologytrades Nursing
9.5 15.2
19.6 20.9
Health not nursing 6.4
5.5 4.0
3.7 Mathphysics
2.8 2.4
1.5 1.6
All other 0.2
0.3 0.2
0.3 Number with post-
13 689 12 598 6831 2038
secondary: Women Men
Educationrecreation 3.6
6.9 9.0
5.5 Fine and applied arts
2.5 2.6
2.8 3.5
Humanities 3.8
5.1 5.6
5.9 Social sciences
9.0 9.7
7.8 5.6
Business 17.6
16.7 15.7
15.3 Secretarial
0.6 0.8
1.1 1.9
Agriculturalbiology 4.5
4.3 4.0
4.3 Engineeringapplied
7.3 7.6
7.7 8.8
science Engineeringapplied
42.4 38.3
38.3 41.5
science technologytrades
Nursing 0.6
0.8 0.5
0.5 Health not nursing
2.5 2.3
2.6 3.4
Mathphysics 5.5
4.9 4.8
3.5 All other
0.2 0.2
0.2 0.4
Number with post- 19 909 21 018 12 689 4862
secondary: Men Duncan segregation
46.7 47.9
51.8 53.6
index
with educational attainment, the index for field of study was highest for older workers and lowest for those aged
25–34. Calculation of the segregation index for occu- pation and industry revealed substantial differences in
the type of jobs held by men and women. In 1986 the index was 30.5 for occupation and 44.0 for industry.
These figures had fallen to 29.1 and 41.9 respectively by 1991.
3. Role of education in determining wage differences
Tables 1–4 illustrate significant gender differences in the level of educational attainment and the field of study
and often document substantial changes over time. What are the consequences of these findings for the gender gap
in earnings? The economics literature suggests that more education is associated with higher wages due to its
effect on skills or possibly because it reveals something about the person’s innate abilities. This prediction has
been verified by the vast majority of empirical studies. The field of study chosen gives access to different
opportunities and labour markets and consequently it too will influence the level of earnings obtained.
A first impression of the importance of educational differences to the earnings gap can be obtained by look-
ing at average wage and salary earnings figures by edu- cational attainment and field of study. Table 5 reports
average annual earnings and the female–male average earnings ratio by level of educational attainment. Note
that the 1996 Census data on earnings refers to 1995 and that from the 1986 Census to 1985. Moving down the
average earnings columns reveals the standard result — that earnings tend to rise with educational attainment. In
1990, women and men with less than grade 5 earned on average 17 633 and 27 877 respectively. This rises to
49 497 women and 63 546 men for those with PhDs. The earnings-education profile rises proportionally
faster for women than men. This produces the tendency for the size of the female–male earnings ratio to rise with
increased educational attainment. The ratio for the entire sample is 0.677. This ranges from 0.581 for those with
grades 5–8 up to 0.790 for those with university Master’s degrees. The patterns for 1985 are similar to 1990 across
educational levels.
The earnings ratio has risen from 1985 to 1990 at all levels of educational attainment except non-university,
post-secondary with a trades certificate and doctorate. Consistent with this rise in the earnings ratio with time
is the pattern of earnings ratios by age. The earnings ratio is 0.754 for 25–34 year olds and 0.618 for those
aged 45–54 in 1990. Similar differences by age exist by educational attainment level.
10
However, the positive relationship between age and earnings ratios may also
reflect differences in average labour market experience of men and women — a factor whose importance
increases with age.
Table 6 reports average 1990 earnings by sex and field
10
By educational attainment level, the female–male earnings ratio for 25–34 and 45–54 year-olds were: 0.687 and 0.596 for
Grades 9–13, 0.727 and 0.592 for high school graduates, 0.735 and 0.608 for those with non-university post-secondary certifi-
cates and 0.810 and 0.704 for those with Bachelor’s degrees.
171 P.
Christie, M.
Shannon Economics
of Education
Review 20
2001 165–180
Table 5 Average wage and salary income by educational attainment of full-time, full-year workers
a
1985 1990
Women Men
Ratio
b
Women Men
Ratio
b
Less than grade 5 13,203
21,574 0.612
17,633 27,877
0.633 Grade 5–8
14,370 24,851
0.578 17,530
30,192 0.581
Grade 9–13 17,095
27,682 0.618
21,232 33,195
0.640 High school graduate: no further education
19,085 29,474
0.648 23,822
36,189 0.658
Trades certificate or diploma, no post-secondary 18,580
30,670 0.606
24,197 37,881
0.639 Some non-university post-secondary, no certificatediploma, not a high school graduate
18,806 28,943
0.650 23,645
34,663 0.682
Some non-university post-secondary, no certificatediploma, is a high school graduate 19,598
30,239 0.648
24,815 35,946
0.690 Non-university post-secondary, trades certificate
18,871 30,208
0.625 23,000
37,558 0.612
Non-university post-secondary, with certificatediploma 21,662
33,020 0.656
27,380 40,778
0.671 Some university, no degree, not a high school graduate
21,414 32,789
0.653 29,274
36,853 0.794
Some University, no degree, high school graduate 22,847
33,515 0.682
28,294 41,341
0.684 University, with trades certificate
21,644 33,741
0.641 26,737
41,029 0.652
University, with non-university certificatediploma 23,695
33,750 0.702
30,262 41,128
0.736 University graduate: other diploma or certificate below Bachelor’s
25,542 36,599
0.698 31,937
44,650 0.715
University graduate: Bachelor’s degree 28,937
40,077 0.722
36,440 49,262
0.740 University graduate: above Bachelor’s
30,882 42,101
0.734 40,975
52,872 0.775
University graduate: professional degree 34,209
54,414 0.629
50,859 66,499
0.765 University graduate: Master’s degree
35,250 45,089
0.782 44,904
56,862 0.790
University graduate: Doctorate 38,866
49,823 0.780
49,497 63,546
0.779 All
20,914 31,920
0.655 26,853
39,655 0.677
a
The number of observations in each group is reported in Table 1.
b
Women’s average wage and salary as a share of men’s average wage and salary.
172 P. Christie, M. Shannon Economics of Education Review 20 2001 165–180
Table 6 Average wage and salary income by field of study, 1990
a
Women Men
Ratio
b
All full-time, full-year workers
With post-secondary qualifications
Educationrecreation 35,497
45,788 0.775
Fine and applied arts 23,102
36,470 0.633
Humanities 34,518
42,276 0.816
Social sciences 34,244
49,307 0.695
Business 31,923
48,679 0.656
Secretarial 24,765
38,327 0.646
Agriculturebiology 27,534
36,810 0.748
Engineeringapplied science 39,412
56,056 0.703
Engineeringapplied science 28,217
39,360 0.717
technologytrades Nursing
30,905 33,831
0.914 Health non-nursing
34,208 52,028
0.658 Mathphysics
37,510 50,415
0.744 All other
25,727 32,595
0.789
No post-secondary 22,852
34,605 0.660
qualification
a
The number of observations for each field is reported in Table 3.
b
Women’s average wage and salary as a share of men’s average wage and salary.
of study for all post-secondary graduates. The top paying field for both sexes is engineering, followed, for men
by health other than nursing, mathphysics and social sciences and for women by, mathphysics then edu-
cation.
11
Fine arts, agriculturebiology and nursing pay relatively poorly. The female–male earnings ratio is low-
est in secretarial, health other than nursing business roughly 0.65–0.66 and fineapplied arts 0.63. The
ratio is largest in nursing 0.91, the humanities 0.82 and education 0.78. These rankings are quite similar
across age groups. The earnings ratio rose in all fields except nursing and the humanities between 1985 and
1990. Ratios were also higher for younger than older groups in all fields of study.
It is possible to obtain a better idea of the importance of education in explaining the wage gap by recognising
that the average wage w equals a weighted average of the average wages for each educational grouping i w
i
where the weights, P
i
, are the share of the sample with level of educational attainment i. Letting superscripts m
and f denote male and female, the earnings gap can then be expressed as follows:
11
Focusing solely on those with university degrees, health becomes the top paying field and business becomes one of the
better paying fields.
w
m
2w
f
5
O
n i51
P
m i
w
m i
2
O
n i51
P
f i
w
f i
1 5
O
n i51
P
m i
2P
f i
w
f i
1
O
n i51
P
m i
w
m i
2w
f i
The second line decomposes the gender gap in average wages into a component attributable to differences in the
distribution of men and women across levels of edu- cation and due to differences in average wages for men
and women with equal educational attainment.
12
This exercise indicates that only a small part of the gap is
attributable to differences in educational attainment — eliminating gender differences in 1990 by making
women’s shares the same as men’s would raise the earn- ings ratio from 0.677 to 0.680 and close the absolute
gap by only 85. Apparently the gap is explained by differences in wages within educational groupings —
which will in part reflect gender differences in other earnings-related characteristics.
The apparent unimportance of educational attainment differences within a given year need not imply that
trends in the gender earnings gap are unaffected by rela- tive changes in attainment. To assess the effect of attain-
ment on trends in the earnings gap a simple set of projec- tions were performed. In the first set, it was assumed that
educational attainment levels of 25–34 year-olds in 1990 would eventually spread to older age groupings other
things being equal, i.e., taking average wages by attain- ment level and gender as given. This compositional
change would increase average earnings for men slightly to 39 764, while those for women would reach
27 660.
13
This creates a 700 fall in the size of the absolute earnings gap and a rise in the earnings ratio
from 0.677 to 0.696. A second, similar exercise can be used to project wages backward in time to a point where
educational patterns of workers aged 55–64 would apply to all workers age 25 and over. This exercise gives pre-
dicted average earnings of 24 512 for women and 37 738 for men and an earnings ratio of 0.650. These
simple projections suggest that the ultimate effect of ris- ing educational attainment on the gender gap is more
considerable than the role of differences in a particular year suggest.
A decomposition like that in Eq. 1 can also be used to identify the importance of post-secondary graduates’
field of study in explaining the size of the gender wage
12
The decomposition could also be performed by weighting the P
m
2P
f
terms by the average wage for women at each level of educational attainment. When this is done on our sample the
effect of eliminating differences in educational attainment is somewhat larger.
13
This assumes that all other earnings determining character- istics remain unchanged at 1991 levels.
173 P. Christie, M. Shannon Economics of Education Review 20 2001 165–180
gap. Eliminating gender differences in the field of study of post-secondary graduates would close the 1990 gap
by 455 and raise the earnings ratio from 0.677 to 0.689. This is a bigger effect than that produced by eliminating
differences in educational attainment.
14
Projecting future earnings on the assumption that the distribution of fields
for 25–34 year-old post-secondary graduates will spread to the older groups, suggests that the gap will fall slightly
to 12 428 and the earnings ratio would rise to 0.688 so that future changes in field will have less of an effect
than changes in level of attainment.
15
Projecting back- wards by applying the pattern of fields for 55–64 year
olds to all those 25 and over, gives a gap of 12 954 and an earnings ratio of 0.663 once again less than the effect
of the backward projection for attainment levels. Inter- estingly the projections suggest that, despite having a
larger impact than attainment differences on the 1990 gender earnings gap, changes in field of study have not
been and will not be as important as changes in attain- ment in producing changes in the earnings gap.
The projections suggest that past and future changes in educational attainment and field of study have indi-
vidually increased the earnings ratio by 0.046 from 0.650 to 0.696 and 0.025 respectively. To put the size
of these changes into perspective, consider that the earn- ings ratio rose by 0.047 over the 1970s and by 0.040 over
the 1980s.
16
The implied effects of gender differences in education and changes in educational outcomes are
not trivial. The data on earnings and the projections presented in
this section of the paper are very simple. They do not allow, for example, for the influence of characteristics
other than education on earnings and may incorrectly measure the earnings effects of education. This is
addressed in the following sections of the paper by esti- mation of earnings regressions.
4. Econometric analysis
