A disturbing trend that is witnessed globally is the lower average salaries witnessed for females in comparison to males. Such a trend if continued over the long time could lead to severe labour shortages as the participation level of females may drop owing to lack of adequate incentive to work. This difference in average salary across the gender is commonly referred to as gender gap and is reality in Australian workplace as well as highlighted through various reports. While the critics may criticise such claims citing difference in occupational representation of genders, report seem to indicate the presence of gender gap even in workplaces where the numerical strength is with females. Clearly, more research is required on the topic to highlight the presence and the key contributory reasons (Livsey, 2017). The given research study would also aim on the issue of gender gap along with exploring possible interconnections with occupation.
A unique dataset is provided in the form of Dataset 1. The sample size for this is 1000 observations. The source of the data is ATO or Australian Taxation Office and since this data has not been directly retrieved from the respondents or the selected sample, thus data would be regarded as secondary (Flick, 2015). The information contained in the dataset pertains to namely four aspects i.e. gender, occupation of taxpayer, underlying annual salary level coupled with gift amount tax deduction. The first two variables would be regarded as categorical variables that are measured through the use of nominal scale owing to absence of any natural order. Besides, the last two are quantitative variables since the corresponding data values are numerical in nature and belong to interval measurement scale (Hair et. al., 2015). The initial five cases from the dataset 1 that has been provided are illustrated as follows.
Yet another dataset that is utilised for completion of the task is dataset 2 which has not been provided and hence has been obtained directly from the respondents. The sample size of this data is 30 observations. This data is limited in variables as only two aspects have been collected namely the salary level and underlying gender of the taxpayer. The collection of data would enable further research to highlight if there is gender gap amongst the Australian population. However, the data collected has issues regarding presence of bias owing to non-probability sampling technique used coupled with a small sample size. Thus, the result obtained from the statistical analysis of this data would have limited utility and more emphasis would be accorded to dataset 1 to reach a meaningful conclusion (Eriksson and Kovalainen, 2015).
Section 2: Descriptive Statistics
The key reading from the graphical representation is that the representation of genders across occupations shows high variation. For instance the female representation in occupation with code 7 is 0% while in case of occupation with code 5, the representation of females is about 75%. Also, it is noticeable that males do not have lower than 20% representation in any of the occupations. Thus, it is quite surprising to find certain occupations where presence of women is almost absent. It needs to be explored as to why the representation of females in certain occupations is so low.
A clear reading from the above column chart is that for annual income levels below $ 50,000, females are in majority while the position reverses as soon as the annual salary becomes higher than $ 50,000. Further, as the annual salary levels witness an increase, the proportion of females tends to incrementally decline Thus, there is no denying on the basis of the sample dataset provided that the average salary levels of female employees is lower than the male counterparts. However, this does also raise incremental question as to whether this gender gap is on account of gender based discrimination or due to some other reason such as different proportion representation in different occupations considering that salary levels across occupations is different.
The above table contains information which further builds on the gender gap and the fact that the salary levels of females tends to be lower when compared to the other gender (i.e. males). The disparity is particularly stark at higher salary levels when the presence of female seems an aberration as there is high domination of males. To reach any conclusion based on the data provided, it needs to be ascertained as to the underlying reason for this gender gap is any discrimination or just unequal distribution of male and female in high paying occupations.
The scatter points do not form any definite pattern and seem to indicate random scattering thus highlighting that no signifciant relationship is visible between the taxpayer’s salary and deduction for gift. Another crucial evidence further extending support to the above conclusion is the coefficient of determiantion which has an almost zero value. This indicates the inability of the salary amount to explain any variation in the deduction amount as gift. Further, the underlying correlation of correlation would be practically zero (Eriksson and Kovalainen, 2015).
Section 3: Inferential Statistics
The above computation leads to the 95% confidence interval determination as (0.3544,0.5475). Hence, there is 95% likelihood that of all the people in Australia engaged in occupation code 1, the female proportion should lie between 0.3544 and 0.5475.
The above computation leads to the 95% confidence interval determination as (0.4815,0.6322). Hence, there is 95% likelihood that of all the people in Australia engaged in occupation code 2, the female proportion should lie between 0.4815 and 0.6322
The above computation leads to the 95% confidence interval determination as (0.0426, 0.1636). Hence, there is 95% likelihood that of all the people in Australia engaged in occupation code 3, the female proportion should lie between 0.0426 and 0.1636.
The above computation leads to the 95% confidence interval determination as (0.000, 0.000). Hence, there is 95% likelihood that of all the people in Australia engaged in occupation code 7, the female proportion should be 0%.
The computation of the intervals and their interpretation clearly highlight that in occupation code 3 and 7, there is under-representation of females. The most curious case is that of occupation code 7 where there is not even a single female from amongst a sample of 1000 people. Further probe is required in order to understand for such skewed gender distribution observed in some of the occupations such as 7.
For the given hypothesis test, the suitable test statistics will be a z and the test would be right tailed test. Owing to the preference for p value approach, using excel as an enabling tool, p value computation has been carried out below.
The above result hints at p value being 0.0002. If this is compared with the assumed level of significance (5%), then it is apparent that the level of significance is found to be greater. This in turn highlights the presence of evidence to cause rejection of null hypothesis and acceptance of alternative hypothesis (Hillier, 2016). Hence, the given sample data provides support to the claim that more than 80% of the employees in occupation code 7 are males.
For the given hypothesis test, the suitable test statistics will be a t and the test would be two tailed test. The t test is preferred to z test as the underlying standard deviation of the variable population is not given. Owing to the preference for p value approach, using excel as an enabling tool, p value computation has been carried out below.
The above result hints at p value being 0.000. If this is compared with the assumed level of significance (5%), then it is apparent that the level of significance is found to be greater. This in turn highlights the presence of evidence to cause rejection of null hypothesis and acceptance of alternative hypothesis (Flick, 2015). Hence, the given sample data provides support to the claim of presence of gender gap in the workforce in Australia.
For the given hypothesis test, the suitable test statistics will be a t and the test would be two tailed test. The t test is preferred to z test as the underlying standard deviation of the variable population is not given. Owing to the preference for p value approach, using excel as an enabling tool, p value computation has been carried out below.
The above result hints at p value being 0.364. If this is compared with the assumed level of significance (5%), then it is apparent that the level of significance is found to be lower. This in turn highlights the absence of evidence to cause rejection of null hypothesis and acceptance of alternative hypothesis (Hair et. al, 2015). Hence, the given sample data does not provide support to the claim of presence of gender gap in the workforce in Australia.
Conclusion
The hypothesis regarding gender gap persisting in Australia has received support from Dataset 1 but not so from Dataset 2. However, this is not because the gender gap does not exist but on account of the dataset 2 being unrepresentative of the underlying population. The representation of different gender in occupations is quite variable and no uniform trends are visible. However, there are certain occupations where the females are essentially absent and further research in this regards is necessary. Further, the precise contributory reasons for existence of gender gap also need to be explored in wake of the results obtained.
There is ample scope for further research which would focus essentially on two aspects. One would be on finding reasons as to why female representation in some occupations is so low. The other would be on comparing the average salary levels of male and females across occupation to see if there is a significant different or not which would provide a more concrete evidence with regards to presence of gender gap in Australia.
References
Eriksson, P. and Kovalainen, A. (2015) Quantitative methods in business research 3rd ed. London: Sage Publications.
Flick, U. (2015) Introducing research methodology: A beginner’s guide to doing a research project. 4th ed. New York: Sage Publications.
Hair, J. F., Wolfinbarger, M., Money, A. H., Samouel, P., and Page, M. J. (2015) Essentials of business research methods. 2nd ed. New York: Routledge.
Hillier, F. (2016) Introduction to Operations Research 6th ed. New York: McGraw Hill Publications.
Livsey, A (2017) Australia’s gender pay gap: why do women still earn less than men? Retrieved from https://www.theguardian.com/australia-news/datablog/2017/oct/18/australia-gender-pay-gap-why-do-women-still-earn-less-than-men
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