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5.4 Rising education standards
Based on the theory of human capital, the rising education level of the workforce may be an important contributing factor to productivity growth. This section reviews ABS data on the educational attainment of the workforce in individual service industries. These estimates relate to the highest educational attainment, regardless of whether the worker is using this education on the job or otherwise.
Table 5.5 presents data on the proportion of university graduates in the workforce of the 16 service industries examined.
|
1984 |
2003 |
Average 1984 to 2003 | |
|---|---|---|---|
|
% |
% |
% | |
|
Electricity and gas supply |
8.1 |
16.4 |
12.0 |
|
Water supply and sewerage |
9.0 |
36.5 |
21.5 |
|
Construction |
2.4 |
5.5 |
3.7 |
|
Wholesale trade |
5.6 |
13.2 |
9.0 |
|
Food retailing |
2.8 |
5.6 |
3.7 |
|
Household good retailing |
2.8 |
11.4 |
7.0 |
|
Motor vehicle retailing/service |
2.8 |
3.6 |
2.9 |
|
Accommodation and restaurants |
3.0 |
8.4 |
5.6 |
|
Road transport |
1.0 |
5.2 |
3.4 |
|
Air transport/travel |
9.8 |
14.0 |
11.3 |
|
Rail, water and other transport |
3.1 |
11.0 |
5.9 |
|
Storage and transport facilities |
4.8 |
13.2 |
10.0 |
|
Communications |
3.7 |
15.8 |
10.4 |
|
Finance |
7.3 |
40.1 |
20.5 |
|
Insurance |
8.8 |
29.8 |
16.4 |
|
Cultural and recreational |
10.6 |
25.9 |
16.5 |
Source: ABS Cat. No. 6227.0 and unpublished data at the two-digit ANZSIC level.
Notice from these figures that in some service industries (such as water supply, household good retailing, transport facilities, finance and insurance) the share of university graduates in the workforce more than tripled between 1984 and 2003. In most other service industries the proportion more than doubled. As indicated in NOIE (2004), a similar increase in the share of university graduates was also recorded in manufacturing and other sectors of the economy.
The figures in the third column represent the average share of university graduates between 1984 and 2003.1 This average ratio was used in the regressions and correlations. Like other static intensity indicators (such as ICT intensity or capital intensity) it remained unchanged between sub-periods in the context of multi-period statistical analyses.
Table 5.6 shows the share in the workforce of persons with post-school qualifications, either from universities or technical colleges. Evidently, this is another indicator of the education level of the workforce. It should be noted that many workers without post-school qualification have finished high school, whereas others with vocational qualifications did not complete high school. But what matters in the context of productivity analysis is not length of schooling by itself, but the fact that vocational staff received more technical training than those who did not pursue further training after high school. The purpose of the static education intensity indicators is to identify possible sectoral differences in predisposition to productivity growth due to differences in the level of schooling.
|
1984 |
2003 |
Average 1984–2003 | |
|---|---|---|---|
|
% |
% |
% | |
|
Electricity and gas supply |
60.9 |
70.3 |
66.5 |
|
Water supply and sewerage |
38.3 |
77.2 |
56.6 |
|
Construction |
54.1 |
59.8 |
56.9 |
|
Wholesale trade |
38.2 |
48.9 |
42.4 |
|
Food retailing |
29.7 |
26.0 |
24.9 |
|
Household good retailing |
29.7 |
37.9 |
32.1 |
|
Motor vehicle retailing/service |
29.7 |
53.4 |
43.8 |
|
Accommodation and restaurants |
30.0 |
40.6 |
34.2 |
|
Road transport |
30.5 |
39.3 |
32.9 |
|
Air transport/travel |
60.6 |
62.1 |
59.2 |
|
Rail, water and other transport |
36.9 |
49.2 |
41.8 |
|
Storage and transport facilities |
42.1 |
55.2 |
48.4 |
|
Communications |
37.9 |
51.9 |
44.4 |
|
Finance |
25.1 |
66.7 |
42.9 |
|
Insurance |
43.4 |
52.7 |
46.9 |
|
Cultural and recreational |
44.6 |
54.9 |
48.1 |
Source: ABS Cat. No. 6227.0 and unpublished data at the two digit ANZSIC level.
Using the data on the share of skilled persons in the workforce of individual industries, opened the way for constructing two explanatory variables based on changes over time:
- change in the share of university trained workers (in terms of annualised change between 1984 and 2003 and sub-periods): and
- change in the share of workers with post-school qualifications (in terms of annualised change between 1984 and 2003 and sub-periods).
Annualised changes between 1984 and 2003 are calculated by taking the difference between shares in 2003 and 1984 and dividing it by 19 (years). The same type of calculation is performed in the sub-periods. Tables D.2 and D.3 in the appendix show the data on changes in education levels used in the regressions and correlations.
Education variables are categorised as economic factors in this paper. However, as show in table E.1 in the appendix, they are moderately correlated with technological variables, that is, ICT intensity, capital intensity and OECD productivity growth rates. Education and technological variables are interrelated because the technological requirements of individual industries influence the demand for skilled labour. Yet, a better-educated labour force can by itself spur productivity growth, and this is the reason that education variables were used as a separate group of explanatory variables in the regressions.
1 The ABS supplied detailed education data at the two-digit ANZSIC level for three years—1984, 1994 and 2003. The time frame of these data extends slightly beyond the study period, which is from 1984–85 to 2001–02. Stretching the period of education variables from 17 to 19 years probably had little effect on the average ratio and annual change estimates.
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