Australia, a country with total population of approximately 23,456,977 people (Australian Bureau of Statistics 2014) have shown to have an improving economy as the unemployment rate has fallen to 5.8% in March 2014. However, the number of full-time employment decreases by 22,100 whereas part-time employment increases by 48,200 (Australian Associated Press 2014). According to Denise Bradley review, he suggested funding an extra 330,000 graduates by 2020 to meet 40% population for tightening and strengthen the quality control of the future workforce (Slattery, L 2008). According to PC report, early school leavers are most likely to receive a lower pay compared to those of higher ...view middle of the document...
Besides that, Refer to table 2.3 and table 2.4 indicates that female is easier to be impacted by age and education level factor compared to male.
Where the previous research focus more on a single factor; therefore, in order to prevent possible issue on multicollinearity, other components that would most likely impact the allocation of wages are also included. Components like the respondents:-
Differs in experience. As the prejudices regarding to age can affect the individuals’ judgments towards certain situation (Acas 2006). It reflects the professionalism of the individual and possibility of getting a promotion.
Ability of speaking variety of languages
For individual entering the workforce in 2014 with a fluent second language is expected to have 10%-15% wage increase as the makes business trading easier (Chau, L 2014).
This is included because a healthy worker would mostly adopt a positive mindset and is more productive compared to one that is weak and sick. The productivity of a healthy worker can also leads to an increase in wages (Noelcke, L 2014).
Intelligence Quotient (IQ)
Workers with a high IQ most likely reflect the success in job training as they adopt faster and have a better job performance compared to a worker that has many years of job experience (Hambrick, D,Z & Slate, CC 2014).
3. Results and discussions:
Table 3.1 shows a relatively small significance F of 9.3748E-11 which is less than 0.05 and a large F-value, 42.90444396 that show the model is highly significant. Besides, the model shows a R2 of 0.0429515 that illustrating 4.3% of the variation in average wages is affected by the level of education. The Multiple R= 0.207247436, shows a positive relationship between the average wage earned and the independent variable, at this point, the education level.
Table 3.2 shows a relatively small significance F of 1.29742E-48 which is less than 0.05 and a large F-value, 240.16449209 that show the model is highly significant. Besides, the model illustrates a 19.68% of the variation in average wages is affected by the level of education. The Multiple R= 0.443655142, shows a positive relationship between the average wage earned and the independent variable, at this point, the education level.
The following multiple regressions, involves a variety of independent and dummy variables. Rows with missing data were removed for the construction of the regression model. The Dummy Educ variable was created out from Educ, stated that individuals with tertiary education level= 1 and otherwise= 0. Transformation (natural log) of average wages...