Introduction

The gender pay gap has been an issue that has affected the economic growth, workplace equality and the overall social equity between the different genders. Currently we can see a 21.7% Gender pay gap in Australia. While much of the attention is given to numbers, we need the understand the underlying factors and address them appropriately.WGEA Data explorer. WGEA. (n.d.). https://www.wgea.gov.au/data-statistics/data-explorer

This presentation explores these aspects using data from the Workplace Gender Equality Agency or the WGEA in Australia.

Overview

The gender pay gap refers to the difference in the average earnings between Men and Women in the work force. This pay gap is mainly influenced by factors such as industry, occupation and employment type and status.Gender pay gap data. WGEA. (n.d.-a). https://www.wgea.gov.au/pay-and-gender/gender-pay-gap-data

Current statistics show us that on average women earn less than men.

wgea <- read_csv("C:/Users/vedan/OneDrive/Desktop/data vis/wgea.csv")
wgea

str(wgea)

## spc_tbl_ [138,264 × 15] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
##  $ reporting_year          : chr [1:138264] "2022-23" "2022-23" "2022-23" "2022-23" ...
##  $ primary_abn             : num [1:138264] 1.1e+10 1.1e+10 1.1e+10 1.1e+10 1.1e+10 ...
##  $ primary_employer_name   : chr [1:138264] "Morris; Mcmahon & Co Pty Ltd" "Morris; Mcmahon & Co Pty Ltd" "Morris; Mcmahon & Co Pty Ltd" "Morris; Mcmahon & Co Pty Ltd" ...
##  $ submission_group_size   : chr [1:138264] "< 250 employees" "< 250 employees" "< 250 employees" "< 250 employees" ...
##  $ primary_anzsic          : chr [1:138264] "2239" "2239" "2239" "2239" ...
##  $ primary_division_name   : chr [1:138264] "Manufacturing" "Manufacturing" "Manufacturing" "Manufacturing" ...
##  $ primary_subdivision_name: chr [1:138264] "Fabricated Metal Product Manufacturing" "Fabricated Metal Product Manufacturing" "Fabricated Metal Product Manufacturing" "Fabricated Metal Product Manufacturing" ...
##  $ primary_group_name      : chr [1:138264] "Metal Container Manufacturing" "Metal Container Manufacturing" "Metal Container Manufacturing" "Metal Container Manufacturing" ...
##  $ primary_class_name      : chr [1:138264] "Other Metal Container Manufacturing" "Other Metal Container Manufacturing" "Other Metal Container Manufacturing" "Other Metal Container Manufacturing" ...
##  $ manager_category        : chr [1:138264] "Manager" "Manager" "Manager" "Manager" ...
##  $ occupation              : chr [1:138264] "Other managers" "Other managers" "CEOs" "Senior managers" ...
##  $ employment_status       : chr [1:138264] "Full-time" "Full-time" "Full-time" "Full-time" ...
##  $ employment_type         : chr [1:138264] "Permanent" "Permanent" "Permanent" "Permanent" ...
##  $ gender                  : chr [1:138264] "Men" "Women" "Women" "Men" ...
##  $ n_employees             : num [1:138264] 3 1 1 3 1 3 1 2 12 1 ...
##  - attr(*, "spec")=
##   .. cols(
##   ..   reporting_year = col_character(),
##   ..   primary_abn = col_double(),
##   ..   primary_employer_name = col_character(),
##   ..   submission_group_size = col_character(),
##   ..   primary_anzsic = col_character(),
##   ..   primary_division_name = col_character(),
##   ..   primary_subdivision_name = col_character(),
##   ..   primary_group_name = col_character(),
##   ..   primary_class_name = col_character(),
##   ..   manager_category = col_character(),
##   ..   occupation = col_character(),
##   ..   employment_status = col_character(),
##   ..   employment_type = col_character(),
##   ..   gender = col_character(),
##   ..   n_employees = col_double()
##   .. )
##  - attr(*, "problems")=<externalptr>

Gender Distribution Across Management

The bar graph represents the number of women and men across different management levels.

Men are overrepresented in the manager roles while women are more likely to not be managers. This disparity can lead to a pay gap as managerial positions usually get higher salaries.

ggplot(wgea, aes(x = manager_category, fill = gender)) +
  geom_bar(position = "dodge") +
  labs(title="Gender Distribution across management",
       x= "Management", y="No of Employees")+
  scale_fill_manual(values =c("Men"="lightblue", "Women"="pink"))

Gender Distribution Across Employment type and status

# Gender Distribution Across Employment Types
ggplot(wgea, aes(x = employment_type, fill = gender)) +
  geom_bar(position = "dodge") +
  labs(title = "Gender Distribution Across Employment Types",
       x = "Employment Type", y = "Number of Employees") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))+
  scale_fill_manual(values =c("Men"="lightblue", "Women"="pink"))

ggplot(wgea, aes(x = employment_status, fill = gender)) +
  geom_bar(position = "dodge") +
  labs(title = "Gender Distribution Across Employment Statuses",
       x = "Employment Status", y = "Number of Employees") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))+
  scale_fill_manual(values =c("Men"="lightblue", "Women"="pink"))

Gender Distribution Across Occupations

ggplot(wgea, aes(x=occupation, fill=gender)) +
  geom_bar(position="dodge")+
  labs(title="Gender Representation across occupations",
       x="Occupation", y="Number of Employees") +
  theme(axis.text.x = element_text(angle=45, hjust=1))+
  scale_fill_manual(values =c("Men"="lightblue", "Women"="pink"))

Gender Distribution in the Manufacturing industry

manufacturing_data <- wgea %>% filter(primary_division_name == "Manufacturing")

ggplot(manufacturing_data, aes(x = primary_subdivision_name, fill = gender)) +
  geom_bar(position = "dodge") +
  labs(title = "Gender Distribution in the Manufacturing Industry",
       x = "Subdivision", y = "Number of Employees") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
  scale_fill_manual(values =c("Men"="lightblue", "Women"="pink"))

Factors contributing to the Pay Gap

• Women are expected to and more likely to take time off for caregiving responsibilities.
• Occupational segregration which is created in our social context leads men and women to have different jobs.
• Presence of subtle bias and discriminatory practices impact hiring, pay decisions and promoting.
• Women are more likely to work part time jobs which leads to lower pay.Orifici, A. (2019).

Steps to take

• Promoting gender diversity in leadership positions is extremely helpful.
• WGEA collects data from companies to display the Male to female statistics.
• To implement transparent pay scales and conduct regular pay audits.
• Mentorship and sponsorship programs to support women’s career advancement needs to be developed.
• Flexible work arrangements for caregivers needs to be encouraged.
• Provide incentives for companies to close the gender pay gap.
• Actively support initiatives that promote women’s participation in high paying industries.
• Equal pay legislations need to be reinforced to ensure compliances of the laws.Orifici, A. (2019).

Conclusion

• The gender pay gap requires addressing from a multifaceted approach which involves changes in policies, societal attitudes, and organizational practices. There needs to be continous efforts to make sure gender equality is present in the workforce at all times. Closing the gender pay gap will not only benefit women but also the broader economy and society.