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The role of gender
in the decision to cancel the
apprenticeship training contract
Bernard Trendle, Alexandra Winter and
Sophia Maalsen
Training and Skills Research Unit, DET
and Office for Women
Background
• Apprenticeship is an important pathway for
young people.
• The introduction of traineeships has provided
a new form of on-the-job training in female
dominated industries.
• This research presents an analysis of
cancellation rates from apprenticeship,
focussing on the role of gender.
• Techniques from duration modelling are used
to explore differences in completion rates.
Background
• While there are numerous studies of the issue
of drop-outs from education, studies of
apprenticeship cancellation are rare.
• Women have much lower levels of
participation in apprenticeship pathways.
• Over 80% of Queensland’s apprentice
commencements are male.
Background - General
• Completion of apprenticeships is influenced
by:
Age;
level of schooling;
Indigenous status;
Location;
level of qualification, and;
the type of employer.
Background - General
• Gender has also been identified as a
demographic variable influencing outcomes,
but these findings can be variable:
some find that women have significantly
higher completion rates
or, that women have lower completion
rates than males
or, that there is no real difference in noncompletion between males and females
Background - General
• Apprenticeship combines work and training.
– The training wage paid is an incentive for
employers to employ someone not yet
qualified.
• A 2008 survey found that, both completers
and non-completers were dissatisfied with the
training wage.
• In addition to low training wages, increased
knowledge of wages and conditions may
influence the completion decision of
apprentices.
Background – Impact of gender
• Key themes of impact of gender include:
attitudes towards “appropriate” work for
men and women;
social stereotypes; perceptions of
traditional trades and employer attitudes
about the “employability” of women.
• It has also been argued that the women are
required to adapt to the culture of the trade.
Background – Impact of gender
• Some analysis suggests that outcomes from
apprenticeship training are gendered. For
example:
VET study tends to benefit young men
(particularly Indigenous, and
regional/rural).
The financial outcomes from
apprenticeships have been found to be
lesser for females than males.
Background – Impact of gender
• Similarly, LSAY data has revealed that males who
complete apprenticeships experience the best
labour market outcomes of VET participants.
Full-time employment among former
apprentices is high, at more than 90.0%, and
their earnings are higher than those reported
by other VET participants.
Full-time employment outcomes were highest
for apprenticeships, followed by traineeships,
and non-apprenticeship courses.
Female apprenticeship completers,
experience less favourable outcomes (higher
part-time employment and unemployment,
lower wages) than their male counterparts.
Preliminary Analysis
• Analysis here makes use of data from the
DELTA database.
• Statistical tools from the field of Survival
analysis or Duration modelling are used.
• 2001 is the base year for the analysis.
• The base data unit is the training contract
Kaplan-Meier curve for male and female
apprenticeship completion
Demographics
a: Indigenouspersons, bygender
c: Personsfromanon-Englishspeakingbackground, bygender
b: Disabledpersons, bygender
Education
a: Year 9
b: Year 10
c: Year 11
d: Year 12
Occupation
a: ASCO41-44(Traditional trades)
c: ASCO49(Other trades)
b: ASCO45(Thefoodtrades)
Specific occupations
a: ASCO4512, Pastrycooks
c: ASCO4931, Hairdressers
b: ASCO4512, Cooks
Multivariate analysis of cancellation
• While the techniques conducted in the
preceding section are flexible and easy to
interpret, they have a number of limitations.
• Chief among these is the fact that univariate
techniques limit analysis to the stratification
by one variable at a time.
• A better approach is to use multivariate
techniques, and we estimate a model using
CPH.
Multivariate analysis of cancellation
Table 10: Cox proportional hazard model of cancellation with gender
interaction terms
coef
exp(coef)
se(coef)
age
0.006
1.006
0.004
atsi
0.259
1.295
0.089
disability
0.092
1.097
0.066
language
0.482
1.619
0.131
year10
-0.230
0.795
0.079
year11
-0.336
0.714
0.086
year12
-0.552
0.576
0.079
asco41
-0.400
0.670
0.110
asco42
-0.517
0.597
0.061
asco43
0.038
1.039
0.129
asco44
-0.109
0.897
0.091
asco49
-0.615
0.541
0.072
empgto
0.064
1.066
0.042
trainrto
0.191
1.211
0.044
trainclosed
1.476
4.377
0.062
trainpublic
-0.819
0.441
0.135
lincome
-3.667
0.026
0.514
Rsquare = 0.169
Likelihood ratio test = 1472 on 34 df, p = 0
Wald test = 1640 on 34 df, p = 0
Score (logrank) test = 1869 on 34 df, p = 0
Gender interaction terms
coef
exp(coef)
p
-0.029
0.971
0.000
-0.247
0.781
0.250
-0.092
0.912
0.610
-1.155
0.315
0.001
0.304
1.355
0.140
0.516
1.675
0.021
0.457
1.580
0.027
0.011
1.011
0.980
0.204
1.226
0.490
-0.839
0.432
0.240
-0.003
0.997
0.990
0.122
1.130
0.270
-0.073
0.929
0.520
-0.131
0.877
0.150
-0.449
0.638
0.000
-0.595
0.551
0.420
0.137
1.147
0.180
Reasons for cancellation
T raining Employment Recognit ion
Part y is Not Locat able - S61
T raining Recognit ion Council
T raining Cont ract Approval Revoked
Employer Has Financial Difficult y
Apprent ice Has Relocat ed
Incompat ibilit y
T raining Cont ract Not Approved
St at e T raining Council Decision
No Off-t he-Job T raining Available
Ot her or Unknown Reason
Part y is Not Locat able
Personal or Medical Reasons
Inadequat e Wage
Apprent ice Has Anot her Job
Apprent ice Misconduct
Employer Closure or Sale of Business
Difficult ies Off-t he-Job
Difficult ies On-t he-Job
Employer Cannot Provide T raining
Mut ual Consent
Deceased
0.0
Female
Male
10.0
20.0
% Contribution by reason
30.0
Reasons for cancellation
Figure 16a: Age profile of individuals who cancelled for Personal or medical reasons
Figure 16b: Highest year of schooling of individuals who cancelled for Personal or medical reasons
60.0
40.0
Male
Male
30.0
25.0
20.0
15.0
10.0
Female
50.0
Female
%of commencements
% of commencements
35.0
40.0
30.0
20.0
10.0
5.0
0.0
0.0
15 - 17
18 - 19
20 - 24
25 - 34
Age cohort
35 - 44
45 +
year 9
70.0
%of commencements
60.0
Female
50.0
40.0
30.0
20.0
10.0
0.0
ASCO41
ASCO42
ASCO43
ASCO44
Age cohort
year 11
Highest year of schooling
Figure 16c: ASCO second division occupation of employment for individuals who cancelled for Personal or medical reasons
Male
year 10
ASCO45
ASCO49
year 12
Key issues
• Higher non-completion by females for
personal or medical reasons.
• Higher cancellation rates for women with
higher levels of education.
• Mature age women are more likely to
complete an apprenticeship.
• Public sector training appears to be linked
with higher completion rates for females.
Implications




Policy to minimise the differences in the rate
of completion across gender has several
options.
Firstly, wages and conditions vary across
occupation, and some trades have an
expected lifetime income below not acquiring
a post-school qualification.
These low wages imply a relatively low
opportunity cost in cancellation.
Encouraging female entry into higher paying
trades appears to support apprenticeship
completion rates.
Implications
• Research suggests that social stereotyping,
perceptions and a workplace culture that has
deeply entrenched norms and values can act
as barriers to women entering trades.
• Such research may identify cultural or
workplace issues associated with the
traditional trade model.
• Therefore, policy may need to seek to effect
longer term cultural change.