Evaluation of Transition
from Education to work in
Australia
Student ID
University Name

Contents
 Aim
 Scope
 Insights
 Findings
 Discussion
 References

Aim
The main aim of this study is to describe the trends of transition of education to
work in Australia. This study will analyze the gender and age differences from
transition from education to work.

Scope
The main scope of this study is to evaluate the patterns of transition of
education to work in Australia among 15-74 aged person.
Women contribute approximately half of the Australian population. However, the
pay gap is 15.3% since past two decades.
Australian women are over-represented as part time workers than full time
workers which contributes to the pay gap.
On the other hand, Australian women outnumber high level of educational
qualification than men. This study will explore such patterns through descriptive
statistical analysis.

Education by age
0.0
1,000.0
2,000.0
3,000.0
4,000.0
5,000.0
6,000.0
7,000.0
8,000.0
Employed
full-time
Employed
part-time
Total employed Unemployed In labour force Not in labour force
Highest Educational Level by Employment Age 15-74 years
Males Females

Labor face by education institution
0 200 400 600 800 1000 1200
All other educational institutions/organisations
Gained a place but not enrolled in May
Higher education
Other institution/organisation
Secondary education
TAFE
Unable to gain placement on application
Sum of In labour force by Type of educational institution or organisation

Employment by education status
0.0
100.0
200.0
300.0
400.0
500.0
600.0
700.0
800.0
900.0
Education status of person aged 25-44
with no dependent children
Employed full-time Employed In the Labour Force Fully engaged
 Educated Labor forces are classified
into different age groups. The above
graph illustrates the total persons
aged between 25-44 years with no
dependent children. Most of the
bachelor degree holders are highly
engaged in the labor force than other
type of qualification such as
advanced studies, certificate
courses, 12th grade and 11th grade.
This shows that undergraduates
significantly play a part of Australian
labor force

Education to work among 25-44 aged
with children
0.0 200.0 400.0 600.0 800.01,000.0
1,200.0
1,400.0
Bachelor Degree or above
Advanced Diploma/Diploma
Certificate III/IV
Year 12 or equivalent
Year 11 or below
Transition from education to work
among Persons 25-44 age group with
children under 15 years
Fully engaged In the Labour Force Employed Employed full-time
 Most of the candidates work in
labor force instead of full type
employment. Bachelor degree
qualified persons are largely
engaged in daily labor force than
advanced or school certified
persons.

Qualification status by State
0.0
100.0
200.0
300.0
400.0
500.0
600.0
700.0
800.0
900.0
1,000.0
15–19 20–24 25–29 30–34 35–44 45–54 55–64 65–74
Non School qualification status of
Australians Aged 15-74 years
NSW Vic. Qld SA WA Tas. NT ACT
 The highest non-school qualified
persons belonged to age group 35-
44. Moreover, non-school qualified
persons largely live in New South
Wales and Victoria. Both states
belongs to South East Australian
region. However, the proportion of
non-school qualified person
increases with the age.

Citizenship status of non school goers in
Australia
2,325.8
1,968.5
1,614.7
544.8
717.7
176.5
65.4
145.0
1,458.6
1,203.7
657.4
187.1
492.8
53.5
35.4
64.8
0.0 500.0 1,000.0 1,500.0 2,000.0 2,500.0
NSW
Vic.
Qld
SA
WA
Tas.
NT
ACT
Citizenship status of non school goers in
Australia
Born overseas Born in Australia
 The above graph illustrates the
Australian people lacking literacy
based on citizenship status. Most of
the people lacking school
education was Australians. The
proportion of non-school goers was
found to be very less in Australian
Capital Territory.

Socioeconoic status
0.0
200.0
400.0
600.0
800.0
1,000.0
1,200.0
NSW Vic. Qld SA WA Tas. NT ACT
Socioeconomic Status of the Non-School
goers in Australia
Quintile 1 (lowest) Quintile 2 Quintile 3
Quintile 4 Quintile 5 (highest)
 The socioeconomic group possess large
impact on the non-school goers of Australia.
Highest percentage of quintile ranging from
lower to higher was recorded among NSW
state and Victoria State. Higher quintile was
recorded in Australian Capital Territory
among non-school goers than rest
socioeconomic groups. This infers that
irrespective of quintile status, NSW tops the
sheet with large count of non-school goers.
Highest socio economic group was consistent
with high non-school going status of NSW and
Victoria.

0.0 500.01,000.0
1,500.0
2,000.0
2,500.0
3,000.0
3,500.0
Postgraduate Degree
Advanced Diploma/Diploma
Certificate n.f.d.
Qualification and Work designation of
employed persons 15-75
Labourers
Machinery operators and drivers
Sales workers
Clerical and administrative workers
Community and personal service workers
Technicians and trades workers
Professionals
 Among the Australian residents aged 15-
75, most of the people lacking non- school
qualification work either as laborers and
clerical or administrative workers.
However, qualified non-school end up
being professionals. This shows the clear
demarcation between illiterate and
literate residents of Australia. Both
bachelor degree and post graduate degree
qualified residents also works as
professionals. Residents with advanced
degrees work as managers and trade
workers. Most of the non-school and
school goers also work as sales workers.

Designation by Gender
 The above graph shows that female
qualified personnel outnumber the
male qualified personnel especially
as professional, technical and
trade staffs, community service
and managers. Male outnumbers
female in designations such as
machinery operators, laborers and
sales workers in Australia.

Descriptive statistics
Employed
full-time-M
Employed
part-time-
M
Total
employed-M
Unemployed-M In labour
force-M
Not in
labour
force-M
Employed
full-time-F
Employed
part-time-F
Total
employed-F
Unemployed-F In labour
force-F
Not in
labour
force-F
Mean 806.9538 170.1462 976.6538 73.10769 1049.685 371.5615 481.4077 383.6462 865.5077 64.34615 929.4923 518.9308
Standard Error 289.3919 52.34661 336.3173 22.32324 357.0066 114.7187 182.0287 125.4982 304.1935 20.11503 323.4886 157.0283
Median 516.6 84 585.9 44 617.6 227.4 395.7 191 575.2 34.9 611.8 346.7
Standard Deviation 1043.417 188.7384 1212.609 80.48757 1287.206 413.624 656.3139 452.4902 1096.785 72.52578 1166.355 566.1734
Sample Variance 1088720 35622.18 1470421 6478.249 1656898 171084.8 430748 204747.4 1202938 5259.989 1360383 320552.4
Kurtosis 6.98319 1.615881 6.174889 2.060466 5.925033 2.650631 7.648467 5.103998 6.876978 3.593491 6.737144 2.42409
Skewness 2.462048 1.592955 2.316761 1.67855 2.274798 1.731475 2.605145 2.154819 2.453872 1.910593 2.428384 1.754903
Range 3863.8 593.1 4458 261.8 4720.5 1399.3 2442.8 1647 4082.7 253.7 4339.1 1878.2
Minimum 43.4 6.1 47.4 3.2 48.9 9.3 18.3 11 32.2 2.7 33.8 8.5
Maximum 3907.2 599.2 4505.4 265 4769.4 1408.6 2461.1 1658 4114.9 256.4 4372.9 1886.7
Sum 10490.4 2211.9 12696.5 950.4 13645.9 4830.3 6258.3 4987.4 11251.6 836.5 12083.4 6746.1
Count 13 13 13 13 13 13 13 13 13 13 13 13
Confidence Level(95.0%) 630.5307 114.0535 732.7725 48.63815 777.8506 249.9505 396.6065 273.4371 662.7806 43.82689 704.8211 342.1352
 The mean employment status of
male is higher than female
 The mean total unemployment
status of female is higher than
male
 Most of the women are partially
employed

Predictive analysis – Female
unemployment
Using Rapidminer studio, Predictive regression
analysis was performed based on educational
qualification as key attributes
Coeffic
ients
Stand
ard
Error
t Stat P-
value
Lower
95%
Upper
95%
Lower
95.0%
Upper
95.0%
Intercept 0.25815
4
0.9426
94
0.2738
47
0.7903
83
-
1.8743
7
2.3906
77
-
1.8743
7
2.3906
77
Unemployed-F -
0.98757
0.0843
25
-
11.711
4
9.47E-
07
-
1.1783
2
-
0.7968
1
-
1.1783
2
-
0.7968
1
In labour
force-F
0.99925
1
0.0036
3
275.26
35
5.61E-
19
0.9910
39
1.0074
63
0.9910
39
1.0074
63
Not in labour
force-F
-9.9E-
09
0.0045
73
-2.2E-
06
0.9999
98
-
0.0103
5
0.0103
45
-
0.0103
5
0.0103
45
Not in labor force of female
is predicted to grow
0.0
500.0
1,000.0
1,500.0
2,000.0
2,500.0
3,000.0
3,500.0
4,000.0
4,500.0
0.0 500.0 1,000.01,500.02,000.0
Total
employed-F
Not in labour force-F
Not in labour force-F Line Fit
Plot
Total employed-F
Predicted Total
employed-F

Discussion
 The participation rate of youth in labor market experienced shift with the
focus on vocational and higher education programs. (ABS, 2015). However,
18% men are pursuing and 20% women are currently studying between people
aged 15-64 years in Australia. 69% people aged 20 – 64 shows non-school
qualification in Australia. However, 74% individuals among 15-74 aged appear
to be employed (ABS, 2016).

Conclusion
 Most of the work force are employed in labor work rather than professional
and managerial work. This shows that there is a need of policy changes and
diversification on employment patterns. Certificate course qualified persons
are not secured with suitable employment during 2011-2020. The growing
gender differences in the analysis shows that there is a differences in job
employment for women, while women are more qualified than men. This
shows that there is a need for policy implementation of gender biases at
education and workplace. The public employment services should focus on
equal opportunity and ensure equal work pay.

References
 ABS, 2013, Education and work, Australia – additional data cubes, May 2013,
Cat. no. 6227.0.55.003, ABS, Canberra.
 ABS, 2015, Labour force, Australia, ABS, Canberra. Australian Workforce and
Productivity Agency (AWPA) 2012, Future focus: Australia’s skills and
workforce development needs, AWPA, Canberra.
 Wyn, J, Cuervo, H, Smith, G & Woodman, D 2010, Young people negotiating
risk and opportunity: post-school transitions 2005–2009, Youth Research
Centre, University of Melbourne,

Thank You