Gender Discrimination In Workplace: A Study Of Salary And Recruitment

Introduction and Research Question

Gender inequality has been a hot topic in the recent decades. Males and females have been said to be treated unfairly in different quotas. It is moral to treat people equally and judge them according to their abilities and not gender, race, region or even color. Despite the extent of civilization and education, the former have not been able to eliminate this archaic and backward practice among some people. Various researchers have found that the female gender has been the greatest victim of this vice (McKinsey, 2010). It has been found that organizations that have employed the female gender in top management positions have always shown success in their progress than those organizations that have purely males in the top management (McKinsey, 2010). The organizations with male managers only have been found to be having not only financial difficulties but also social problems.

A research conducted by Institute of gender and research at Stanford University found that male workers took credit away from female workers who had been found to be doing better in their areas of jurisdictions. Male counterparts would attribute this to luck and not their individual efforts (Major & McFarlin, 2012). In another dimension, some hiring managers have been found to paying attention to males than females when it comes to employment. They have the perception that male workers were more talented especially I technical assignments than females (Roxana, 2013).

It has not been clear why the problem of gender discrimination is still far from ending. There are many questions than answers as to why discrimination in terms of gender especially against females is still being practiced in some societies. Researches have been done but there still exist gaps. It is for this region that this research sought to unravel the reasons behind the discrimination and whether indeed females have suffered discrimination in work places in terms of salaries and recruitment. To answer the research question two data sets were used. One dataset containing a sample of 1000 workers was sourced from Australian taxation office. The data contained variables such as salaries and number of female and males in various occupations. Another data was collected by the research to aid the study answer the research question adequately. The data was collected through the use of questionnaires. The disadvantage of using questionnaire in data collection was found to be lack of honesty in some responses.

1. a) Gender Occupation cross-tabulation  

Occupation	Gender Save Time On Research and Writing Hire a Pro to Write You a 100% Plagiarism-Free Paper. Get My Paper
	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

Table 1

Types of Variables and Data

Graphical representation of Occupation distribution by gender

Figure 1

In order for the research to establish whether there were more males and females or vice versa, the research decided to analyze the distribution of both gender by occupation. This analysis sought to find the proportion of males and females in each profession to establish whether there are glaring disparities. The graphical analysis above shows that out of the 10 professions, the proportion of males was higher than that of females in 5 of them. The proportion of females was also high in the remaining 5 professions. For example the proportion of females was high in clerical and administrative jobs. They were 80 while the males were 16. Their proportion was also high in community and personal service jobs. Their number was 54 while the males were 27. The number of females was 45 and females were 16 among consultants and apprentices. Lastly, the number of females was also high among sales and professional workers. Their number was 116 and 45 respectively while that of their counterparts was 74 and 16 respectively. The occupations where the males were the majority were among laborers, machine operators, technicians and managers. Their number was 51, 50, 85 and 55 respectively. Their counterparts in those professions were 24,2,33, 83 and 14 respectively.

1. b) Scatterplot of Salary and gender.

Figure 2

1. Correlation

Table of relationship results

	gender	Salary/wage
gender	1
Salary/wage	0.224259889	1

Table 2

In order to establish whether there is a relationship between gender and salary, the research employed the Pearson correlation test to determine the same. The Pearson correlation coefficient runs from 0 to 1 where 1 can either be positive or negative. A value of zero indicates no relationship between variables. Correlation coefficient of 1 indicates a perfect strong positive correlation. Correlation coefficient of -1 indicates a perfect strong negative correlation. Having known that, the correlation coefficient between above which is 0.22 indicates a weak but positive correlation between gender and salary.

1. Graphical representation of linear relationship between amount of salary and gift amount

Figure 3

The scatterplot above was produced in order to determine whether there was a linear relationship between salaries and gift amount. And as can be observed from the chart above, there is little relationship between the two variables. The value of R-square is 0.000 indicating that gift amount is not responsible for any variation in salary.

1. a) Table of 4 highest median salaries

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

Table 3

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

Table 5

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

Table 6

Analysis was also done to get the best 4 paying profession and the proportion of females in those professions. As can be seen from the tables above, it was established that the top paying profession according to median salary was managerial profession where the median salary was 72,401.5. This is followed by technicians who earn 64,886 then professions who earn 62,108. The low earners among the top 4 were clerks and administrators who earn 41,605 dollars.

1. b) Machinery operators & drivers female proportion

Gender Inequality – A Major Concern

Hypothesis testing

H₀: ? = 80%

Versus

H₀: ? > 80%

As can be observed from the computations above, the value of Z is 1.69. At 95% confidence, the value of Z from the normal tables is 1.65. Comparing these two Z values, it is found that Z-computed is greater than the Z-value tabulated. Therefore the null hypothesis is accepted and the conclusion is that the proportion of female machinery operators is 80%.

1. c) T-test for difference in mean salary between male and females.

Independent paired sample t-test is used in this test because the variables are two.  

Hypothesis

H₀: Female salary = male salary.  

Versus

H₁: There is a significant difference male and female salary.  

Results 

The t-test conducted above to test the claim that female and male salaries are equal gave a p-value of 0.00. Given the alpha value (0.05) is greater than the p-value calculated; the research is guided to reject the null hypothesis. It is therefore concluded that there is a significant difference between male and female salary.

1. d) This research also involved another data set other than the one that was taken from Australian taxation office. The results from this data were also used to answer the same research question. The data entailed female and male workers. Their salary category was also among the variables. These were the main variables that the research was interested in.  The results obtained are in the cross-tabulation and graph 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

Figure 4

The analysis of the workers data reveals that the proportion of female workers was 0.33 while the proportion of male workers was 0.67. The results also show that the proportion of females in categories with high salaries is small compared to their proportion in low salary categories. This confirms that there is some kind of preference when it comes to hiring of employees. Males seemed to be favored by employers than their female counterparts. The same scenario also plays out when it comes to remuneration between males and females.

Descriptive statistics for number of years worked of males and females before promotion

The table above shows summary statistics of the number of years worked before promotion for males and females. It can be observed that the mean number of years worked by males before they get a promotion is 3.4 years while that of females is 6.2 years. The mode number of years for males is 3 while the mode number of years for females is 6. This indicates that female employees work longer than male employees before they can get a promotion at work.

Test for equality in mean number of years before promotion

Independent paired sample t-test is used in this test because the variables are two.  

Hypothesis

H₀: Female years = male years.  

Versus

H₁: There is a significant difference male and female number of years before promotion. 

The t-test conducted above to test the claim that female and male employees’ number of working years before promotion is equal gave a p-value of 0.00. Given the alpha value (0.05) is greater than the p-value calculated; the research is guided to reject the null hypothesis. It is therefore concluded that there is a significant difference between male and female number of working years before promotion.

Conclusion

This research study has come up with various findings that has enabled it make various inferences which has enabled the research to answer its main question. It was hypothesized earlier that the female gender had been discriminated in work places when it came to salaries and also employment. The first data showed that there was some balance between males and females when it came to proportions in each profession.  Out of the 10 professions, the proportion of males was higher than that of females in 5 of them. The proportion of females was also high in the remaining 5 professions. For example the proportion of females was high in clerical and administrative jobs. They were 80 while the males were 16. Their proportion was also high in community and personal service jobs. Their number was 54 while the males were 27. The number of females was 45 and females were 16 among consultants and apprentices. Lastly, the number of females was also high among sales and professional workers. Their number was 116 and 45 respectively while that of their counterparts was 74 and 16 respectively. The occupations where the males were the majority were among laborers, machine operators, technicians and managers. Their number was 51, 50, 85 and 55 respectively. Their counterparts in those professions were 24,2,33, 83 and 14 respectively. However, the second data found that there was inequality in employment where the proportion of females was much lower than the proportion of males. To add on, the number of years that male employees worked before they were awarded a promotion were less compared to the number of years worked by female employees before being awarded with a promotion. Since this research concentrated on the employee side, it is not enough to make conclusions with finality. Employers’ side of the story should also be incorporated in such a research in order to make valid conclusions. This means that there is still a gap in this research, therefore this research study recommend further research on employers so as to adequately answer the research question.

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.