Gender Bias And Its Implications On Workplace Equality: A Study Of Australian Taxation Office (ATO)

Gender Bias in the Workplace

Discuss about the Journal of Personality and Social Psychology.

Equality among human being is a very important aspect of human dignity. As such each every person should be treated with equal in every environment regardless of their color, gender or any other characteristic (McKinsey, 2010). Gender bias is a practice that should not still exist in this century. However, because of corrupt personal values, backward perceptions and patriarchal behavior, men and women still suffer. In the topic of equality, gender bias has always been at the center stage with much weight leaning on women discrimination. Studies have shown that women have been the main victims of discrimination at work places. The same studies have indicated that gender biasness in work places have come with dire consequences. It has come with social and financial consequences. Organizations with this kind of practice or lack gender diversity in their top management have been seen to suffer because of being less innovative, successful and profitable (McKinsey, 2010). On the contrary, organizations with females in their top management such as in board have been seen to perform better than organizations whose top management is purely made of males alone.

According to a research done by Stanford University Institute for gender research revealed that female workers suffered criticism from their male counterparts when it came to decision making (Major & McFarlin, 2012). The same research found that when women succeed at work places, other members usually do not believe that it is their individual effort. They therefore normally want to attribute the success to such factors as luck. Employers or hiring managers have been seen to show favoritism to males when it comes to employment. They believe that certain tasks can only be handled by men. Example of these tasks is technical jobs (Roxana, 2013).

Due to so many unanswered questions and gaps in gender discrimination topic, this research seeks to use data sample of 1000 employees sourced from Australian taxation Office (ATO) to find answers to the question. The data contains employees by gender, their earnings and profession. Another primary data was collected to support the research so as to answer the research question adequately. This data was collected by use of questionnaires which had its limitations. The major limitation of this method is that there were cases of non-responses.

Occupation Save Time On Research and Writing Hire a Pro to Write You a 100% Plagiarism-Free Paper. Get My Paper	Gender
	Male	Female	Grand Total
Clerical & Administrative worker	16	80	96
Community & Personal service workers	27	54	81
Consultants & Apprentices	36	45	81
Laborers	51	24	75
Machinery operators & Drivers	50	2	52
Managers	55	33	88
Not specified	94	83	177
Professionals	74	116	190
Sales workers	16	45	61
Technicians & Trade workers	85	14	99
Grand Total	504	496	1000

Occupation and gender graph sentation of how males and females are distributed across different occupations in Australian context. A general overview shows that there are more females than males in 5 occupations and there are also more males than females in 5 occupations. The occupations where females are more than males are in clerical and administrative, community and personal service, consultancy and apprentice, professionals and lastly sales workers. The professions where the number of males is higher than that of females include laborers, machinery operators and drivers, managers and technicians. In clerical and administrative jobs there were 80 females and only 16 males. In community and personal service, the females were 54 and while the males were 27. In consultants and apprentice, the females were 45 while the males were 36. In professionals the females were 116 while the males were 74. In sales the number of females was 45 while the number of males was 16. In occupations where the males were more than females, this is how the numbers were distributed. Among laborers there were 51 males and 24 females. Among machinery and operators management, there are 50 males and 2 females only. Among technicians and trade workers the number of males was 85 while that of females was 14.

	gender	Salary/wage
gender	1
Salary/wage	0.224259889	1

Proportion of Males and Females in Various Professions

Correlation values usually spans from -1 to 1. A value of 1 normally suggests a perfect correlation between any two variables being tested. The positive or negative sign gives the direction of the correlation. A zero value means there is no correlation between the variables. Now we can gauge the extent of correlation when the value is 0.22 as in the table above. Comparing this to 1, it can be said that there is a weak correlation between the two variables (salary and gender).

In order to establish the relationship between salary and gift amount, the research employed the use of a scatterplot. In this case the gift amount became the independent variable while salary was the dependent variable. It can be seen from the chart above that the regression line is almost horizontal indicating that there is very insignificant effect of the independent variable (gift amount) on the dependent variable (salary). The value of R-square is 0.0002. This means that gift amount explains only 0.02% of the variation that occurs in salary.

managers
Mean	83416.7841
Standard Error	5971.92793	female	33
Median	72401.5	total	88
Mode	0	proportion	0.375

Technicians & Trade workers
		female	14
Mean	69624.40404	total	99
Standard Error	4447.829874	proportion	0.14
Median	64886
Mode	#N/A

Table 4

Professional
Mean	69771.03158
Standard Error	3843.825377		female	116
Median	62108		total	190
Mode	308183		proportion	0.61

Clerical & Administrative worker
Mean	46762.51
Standard Error	4163.464		female	80
Median	41605		total	96
Mode	#N/A		proportion	0.83

The research also sought to find out the best paying professions. Therefore in order to find a more valid value, the measure of central tendency, median was used. This was used instead of the mean since it is always not affected by extreme values. The top 4 paying professions according to median salaries are as shown from table3 to table 6. The proportion of females in those four top professions was also established. It can be observed that the proportion of females in clerical and administrative jobs was 0.83. In profession, the proportion of female was 0.61 while among the technicians and trade workers, the proportion was 0.14.

From the calculations above, the value of Z is 1.69. In order to make a decision, the Z-value computed is compared with the Z-tabulated. For 95% confidence level, the Z-value tabulated is 1.65. This means that the z-tabulated is less than the z-computed. This means that the null hypothesis is not rejected. Hence, the population of females who are machinery operators and drivers is 0.8.

From the calculations above, the p-value is 0.00. In order to make a decision, the p-value computed is compared with the alpha value. For 95% confidence level, the alpha value is 0.00. This means that the null hypothesis is rejected. Hence, there is a significant difference in the salary amount between males and females.

Relationship between Gender and Salary

In another analysis, the data that I collected which also sought to give answers to the research topic, indicated that the proportion of female workers was lower than the proportion of male workers. This scenario is explained by the fact that many hiring managers are largely patriarchal and believe that some jobs or that generally males better than females at work. The results of the analysis are as shown in table 9 below.

Current salary level ($)	Gender
	Male	Female	Grand Total
25,000 ≥	10	2	12
26,000 – 36,000	2		2
37,000 – 47,000		2	2
48,000 – 58,000		2	2
48,000-58,000	1		1
58,000 ≤	1		1
Grand Total	14	6	20

The number of males enjoyed preference from employers thus explaining why the number of males in many of professions is the majority. The second data results shown in figure four are a manifestation of this. The females are 30% while the males constitute to 70% of the total workers.

It can be observed from the table and figure above that the percentage of females in the work place is lower than the percentage of males. The percentage of females is 30% while that of males is 70%,

Conclusion

From the analysis conducted in the previous section, the research has been able study the results and come up with various conclusions which verge to answer the main question of the research. The findings indicated that the proportion of the males and females in the professions in this research varied in each. On the same context, it was also found that the males did not dominate all the professions in terms of numbers. There are jobs where the number of males were more than the females and jobs where the females were more than the males. The professions where the number of males is higher than that of females include laborers, machinery operators and drivers, managers and technicians. In clerical and administrative jobs there were 80 females and only 16 males. In community and personal service, the females were 54 and while the males were 27. In consultants and apprentice, the females were 45 while the males were 36. In professionals the females were 116 while the males were 74. In sales the number of females was 45 while the number of males was 16. In occupations where the males were more than females, this is how the numbers were distributed. Among laborers there were 51 males and 24 females. Among machinery and operators, there are 50 males and 2 females only. Among technicians and trade workers the number of males was 85 while that of females was 14. The research also found out that there were differences in salaries of males and females but this was insignificant.

References

Major, B., & McFarlin, D. B. (2012). Overworked and underpaid: On the Nature of Gender Differences in Personal Entitlement. Journal of Personality and Social Psychology, 47(6), 44-56.

McKinsey, C. (2010). Women Matter:Gender Diversity; A Corporate Performance Driver.

Roxana , B. (2013). Women in the workplace: A research round up.