Salaries And Occupations For Different Genders In Australia: An Analysis

Description of the Datasets

The article is based on the analysis of salaries and occupation of different gender. The data for the study has been provided by Australian Taxation Office (ATO) for a particular location in Australia.

The requirement of the study is to know about the relationship between salaries and occupation for different gender. So, consider the research question of the study as, whether there is a significance difference between the average salaries of different gender. To analysis a research question, the data has been collected for a particular location in Australia.

The dataset 1 has been provided by Australian Taxation Office (ATO) for a particular location in Australia which holds the taxation data. Thus, dataset 1 has been obtained from other source, so it can be consider as a secondary data.  The dataset 1 consists data of 1000 employees which have 4 variables that are Gender, Occupation code, salary/wage amount and the gift amount. The variable gender has divided into two categories as female and male, so it is a nominal level variable. The variable occupation code specifies the occupation of the employees which has divided into 10 categories, so it is a nominal level variable. The variable salary/wage amount indicates the salary of the employees, so it interval/ratio level variable. The gift amount indicates the gift or donation deductions, so it interval/ratio level variable.  The first five values of the dataset 1 is shown below;

Gender	Occ_code Save Time On Research and Writing Hire a Pro to Write You a 100% Plagiarism-Free Paper. Get My Paper	Sw_amt	Gift_amt
Male	9	31304	0
Female	0	0	27
Female	2	86934	0
Female	4	28649	144
Female	3	69620	0

The dataset 2 has been collected randomly by offline survey, the researcher asked questions to working people about their gender, position and the income amount.  Thus dataset 2 is a primary data.

The dataset covers 100 values which is greater than 30, so the results from the study will not be biased.  The dataset have 3 variables that are Gender, Occupation code, salary/wage amount. The variable gender has been divided into two categories as female and male, so it is a nominal level of measurement. The variable occupation code indicates the occupation of the employees which has been divided into 10 categories, so it is a nominal level of measurement. The variable salary/wage amount indicates the salary of the employees, so it interval/ratio level of measurement.

1. The bar graph for the relationship between variable Gender and the occupation is shown below:

The above bar plot indicates, Out of 1000, 77 male and 87 female employees were professionals, 76 male and 18 female employees were technicians ate trades workers, 97 male and 96 female employees not listed their occupation otherwise it is not specified, 25 male and 95 female were clerical and administrative workers, and 21 male and 32 female employees were sales workers.

1. The pie chart for the relationship between variable Gender and the salary/Wage amount is shown below:

The male earns is about 61% female earns is about 39% of the total salary.

1. The numerical summary for the relationship between variable Gender and the salary/Wage amount is shown below:

Row Labels	Average of Sw_amt	Sum of Sw_amt	Max of Sw_amt3	Count of Sw_amt2	StdDev of Sw_amt4
Female	33841.72	16176341	308183	478	33428.35
Male	48181.46	25150721	308183	522	46863.41
Grand total	41327.062	41327062	308183	1000	41596.55031

The maximum salary of a female employee is $308183 and the maximum salary of a male employee is $308183. The total salary of 478 female employees is $16176341 and the total salary of 522 male employees is $25150721. The average salary of 478 female employees is $33841.71 and the average salary of 522 male employees is $48181.46. The deviation in the salary from average salary of female employees is $33428.35 and in male employees is $46863.41.

1. The graphical summary for the relationship between variable Salary/Wage amount and Gift amount is shown below:

Data Collection and Limitations

The salary wage for female employees is 39.14% and the gift wage for the female employees is 16.12%. The salary wage for male employees is 60.86% and the gift wage for the male employees is 83.88%. So, male gets more gift amount and salary/wage amount than females.

1. The top 4 occupation based on median salary and the proportion of the gender is shown below:

Occupation description	Female	Male	Grand Total
Occupation not listed/ Occupation not specified	9.60%	9.70%	19.30%
Professionals	8.70%	7.70%	16.40%
Clerical and Administrative Workers	9.50%	2.50%	12.00%
Technicians and Trades Workers	1.80%	7.60%	9.40%

The Professionals get 16.40% of the total salary in which female earns 8.70% and male earns 7.70%. The Clerical and Administrative Workers get 12% of the total salary in which female earns 9.50% and male earn 2.50%. The Technicians and Trades Workers get 9.40% of the total salary in which female earn 1.80% and male earn 7.60%. The top salary employees does not listed or not specified their Occupation, and the top salary is 19.30% of the total salary in which female earns 9.60% and male earn 9.70%.

1. To test whether the proportion of machinery operators and drivers who are male is more than 80%, one sample Z-test will be used. The hypothesis of the test is given below:

The null hypothesis is . And, the alternative hypothesis.

The calculation are provided in Excel, the proportion of male the Machinery operators and drivers is about 96%.  The obtained results are shown below:

Null Hypothesis            p =	0.8
Level of Significance	0.05
Number of Items of Interest	96
Sample Size	100
Sample Proportion	0.96
Standard Error	0.0400
Z Test Statistic	4.0000
Upper Critical Value	1.6449
p-Value	0.0000

The P-value of the test is less than 5% level of significance, so the null hypothesis of the test gets rejected. Thus, it can be concluded that the proportion of machinery operators and drivers who are male is more than 80%.

1. To test whether there is a difference in salary amount between gender, two-sample t-test will be used. The hypothesis of the test is given below:

The null hypothesis is.

And, the alternative hypothesis is.

The ratio between the male samples to female sample is greater than 1.5. So, the separate variance t-test will be used for analysis. The results of the test is shown below:

Hypothesized Difference	0
Level of Significance	0.05
Female Salary/Wage amount Sample
Sample Size	478
Sample Mean	33841.71757
Sample Standard Deviation	33428.3532
Male Salary/Wage amount Sample
Sample Size	522
Sample Mean	48181.45785
Sample Standard Deviation	46863.4081
Numerator of Degrees of Freedom	42837169748527.2000
Denominator of Degrees of Freedom	45432179135.9228
Total Degrees of Freedom	942.8817
Degrees of Freedom	942
Standard Error	2558.3219
Difference in Sample Means	-14339.7403
Separate-Variance t Test Statistic	-5.6051
p-Value	0.0000

The P-value of the test is less than 5% level of significance, so the null hypothesis of the test gets rejected. Thus, it can be concluded that salary of male and female differ significantly.

1. To test whether there is a significance difference between the average salary of male and female, two-sample t-test will be used. The hypothesis of the test is given below:

And, the alternative hypothesis is.

The ratio between the male samples to female sample is greater than 1.5. So, the separate variance t-test will be used for analysis. The results of the test is shown below:

Hypothesized Difference	0
Level of Significance	0.05
Female Salary
Sample Size	48
Sample Mean	42751.70833
Sample Standard Deviation	32813.9723
Male Salary
Sample Size	52
Sample Mean	51236.96154
Sample Standard Deviation	48208.2629
Degrees of Freedom	90
Standard Error	8193.0119
Difference in Sample Means	-8485.2532
Separate-Variance t Test Statistic	-1.0357
Two-Tail Test
Lower Critical Value	-1.9867
Upper Critical Value	1.9867
p-Value	0.3031

The P-value of the test is greater than 5% level of significance, so the null hypothesis of the test does not gets rejected. Thus, it can be concluded that there is not a significance difference between the average salary of male and female.

Conclusion

Out of 1000, 77 male and 87 female employees were professionals, 76 male and 18 female employees were technicians ate trades workers, 97 male and 96 female employees not listed their occupation otherwise it is not specified, 25 male and 95 female were clerical and administrative workers, and 21 male and 32 female employees were sales workers.

The male earns is about 61% female earns is about 39% of the total salary.

The maximum salary of a female employee is $308183 and the maximum salary of a male employee is $308183. The total salary of 478 female employees is $16176341 and the total salary of 522 male employees is $25150721. The average salary of 478 female employees is $33841.71 and the average salary of 522 male employees is $48181.46. The deviation in the salary from average salary of female employees is $33428.35 and in male employees is $46863.41.

The salary wage for female employees is 39.14% and the gift wage for the female employees is 16.12%. The salary wage for male employees is 60.86% and the gift wage for the male employees is 83.88%. So, male gets more gift amount and salary/wage amount than females.

The Professionals get 16.40% of the total salary in which female earns 8.70% and male earns 7.70%.

The Clerical and Administrative Workers get 12% of the total salary in which female earns 9.50% and male earn 2.50%.

The Technicians and Trades Workers get 9.40% of the total salary in which female earn 1.80% and male earn 7.60%.

The top salary employees does not listed or not specified their Occupation, and the top salary is 19.30% of the total salary in which female earns 9.60% and male earn 9.70%.

The proportion of machinery operators and drivers who are male is more than 80% in dataset 1

The mean salary of male and female differ significantly in dataset 1.

The average salary of male and female does not differ significantly in dataset 2.

The result of the study differs for dataset 1 and dataset 2, so the study may provide inaccurate results. Thus, the researcher should collect the data for a large sample to provide accurate results and Australian taxation Office should collect the data for all the locations, so that results for the study can be generalize for all the employees.