Introduction

The gender wage gap remains a contentious topic, with many factors in play. Breaking through barriers of gender inequality will improve the world economy and the individual lives of women. Understanding the data behind this gender inequality will help to ensure women around the world empowered to achieve their full economic potential. This presentation will focus on OECD countries. By utilising data visualisation techniques, we will examine gender wage gap trends over time, the distribution of the wage gap across regions, and correlations between the gender wage gap and popular measures of inequality. This presentation aims to discover patterns and provide insights about the gender wage gap.

How has the gender wage gap changed?

• Overall trend: slow decrease.
• Asia-Pacific - Japan and South Korea: biggest reductions, highest initial wage gaps.
• Western Europe and other states - Australia and Canada: slight decreases, moderate initial wage gaps.
• Eastern Europe - Bulgaria and Hungary: increases, small initial wage gaps.

What is the gender wage gap like across different regions?

• Eastern Europe and Asia-Pacific regions: small amount of data points (>3).
• Western Europe and other states and Latin America and Caribbean: similar ranges with somewhat different distributions.
• Eastern Europe: medium median at 13% Wage Gap percentage.
• Asia-Pacific: highest median of the regions.

Are there measures that can be used to predict the gender wage gap?

• The predictors Gini coefficient (GINI), Human Development Index (HDI), Women’s Empowerment Index (WEP) and Global Gender Pay Parity (GPP) were used to test their correlations to the Gender Wage Gap percentage (Wage.Gap).
• The most recent figures available were used.
• These measures are typically used to highlight possible inequalities within countries.
• The first row shows the correlation between the wage gap and these measures.
• The Gini coefficient was the only predictor for 2023 that had a low-to-moderate negative association to the gender wage gap
• This association was not statistically significant (p-value > 0.05).
• This means that, as the Gini coefficient increases, the gender wage gap tends to decrease but this relationship is not sound enough to be meaningful.

• We can re-test for GDP per Capita and population

• GDP per Capita has a low positive association to wage gap and population has an almost moderate association to wage gap.
• This suggests that as they increase, the wage gap increases as well. However, these relationships are not meaningful (p-value > 0.05).

Conclusion and Recommendations

• Overall, the gender wage gap has improved in recent years for OECD countries. The Asian-Pacific countries Japan and South Korea experienced the sharpest decrease compared to other regions. Australia and Canada and saw small improvements over the period. Additionally, the wage gap for Bulgaria and Hungary located in Eastern Europe, had worsened gender wage gaps. The OECD nations that initially had higher wage gaps experienced greater outcomes.
• The measures that are usually cited related to inequalities did not provide a meaningful relationship to predict the gender wage gap. However, the Gini coefficient showed the highest negative correlation to the gender wage gap.
• Among the factors tested, aiming at reducing the Gini coefficient should be focused on. This will hopefully help to improve the wage gap, further empowering women across the world.

References to the Data sources

• OECD. (2024). Gender wage gap [Data set]. https://data-explorer.oecd.org/vis?pg=0&bp=true&snb=71&tm=gender&df[ds]=dsDisseminateFinalDMZ&df[id]=DSD_EARNINGS%40GENDER_WAGE_GAP&df[ag]=OECD.ELS.SAE&df[vs]=1.0&dq=……_T&pd=2005%2C&to[TIME_PERIOD]=false

• UN Women. (2023). The paths to equal: Twin indices on women’s empowerment and gender equality. https://www.unwomen.org/sites/default/files/2023-07/paths-equal-twin-indices-women-empowerment.pdf

• United Nations Development Programme. (n.d.). Human Development Index (HDI) [Data set]. https://hdr.undp.org/data-center/human-development-index#/indicies/HDI

• The World Bank Group. (2024). Population, total (SP.POP.TOTL) [Data set]. https://databank.worldbank.org/reports.aspx?source=2&series=SP.POP.TOTL&country=

• The World Bank Group. (n.d.). GDP per capita (current US$) [Data set]. https://data.worldbank.org/indicator/NY.GDP.PCAP.CD

• The World Bank Group. (n.d.). Gini index (World Bank estimate) [Data set]. https://genderdata.worldbank.org/en/indicator/si-pov-gini#idRelatedIndicators