Project
PP434 Automated Data Visualization Course, 2024
Candidate Number: 39052
Despite significant progress, a gender gap persists in the labor market.
While many barriers still exit for most women,
it is important to examine this social issue in the context of educated women.
This article explores the gender gap among women
who have already overcome numerous barriers to attain education and experience.
The focus is on their representation in executive roles, such as CEOs,
and in public institutions, as well as on the persistent gender wage gap.
Is education enough to secure leadership positions?
Is there a lack of qualified women to fill top leadership roles?
And, perhaps most crucially, does the gender gap significantly correlate with economic growth?
Among adults with advanced education, labor force participation rates are expected to be equal for women and men. However, a gender gap persists in most countries, including high-income economies.
When examining the relationship with GDP, a negative correlation emerges. Smaller participation gaps are associated with higher GDP per capita.
In OECD countries, women hold less than 50% of executive roles (including managerial and parliamentary positions), even in high-income economies. The gender gap is smaller in parliamentary roles in some countries, possibly due to laws regarding gender quotas.
A closer look at CEO roles shows that most enterprises are led by male CEOs. In most countries, the proportion of enterprises with women as CEOs is less than 25%. However, this disparity is less pronounced in small enterprises but more significant in large companies.
It is reasonable to expect men and women in the same leadership roles to receive similar salaries. However, the gender wage gap reveals substantial disparities. In high-income countries like Austria, female managers earn around 36% less than their male counterparts.
When examining the potential causes of this disparity,
the data on gender parity in tertiary education enrollment tells a different story.
Surprisingly, the share of women in advanced education has exceeded that of men
in many economies for decades.
For instance, the gender parity in tertiary education was achieved, on average,
in Europe and Central Asia by 1980.
Despite advances, a gender gap persists in leadership roles and wages. While women have surpassed men in tertiary education enrollment, societal and organizational barriers continue to limit their representation in executive positions and influence their earnings.
The data was obtained from various sources: the World Bank, the International Labor Organization (ILO), the United Nations, and public datasets from GitHub accounts. To ensure automation and replicability, I used the World Bank's API (via the public package "wbgapi") and the ILO's API. For the United Nations data, due to the absence of an API, I downloaded the necessary indicators in raw CSV format. Similarly, for the World Bank's Enterprise Survey data, I downloaded the aggregate indicators in raw format.
The indicators used at the country level include, for example:
> Labor force participation rate, ILO (id = EAP_DWAP_SEX_AGE_EDU_RT_A)
> Monthly Wage ($), ILO (id = EAR_4MTH_SEX_OCU_CUR_NB_A)
> GDP per capita ($), World Bank (id = NY.GDP.PCAP.KD)
> Parity index, tertiary education, World Bank (id = SE.ENR.TERT.FM.ZS)
> Women in CEO positions, Enterprise Surveys (downloaded manually)
> Women in executive positions, United Nations (downloaded manually)
Additional data:
> List of OECD countries, public data from a github account
> GeoJSON files for maps, public data from a github account
> Key country characteristics, World Bank
To see how this data was imported and the specific URLs, refers to the Google Colab file (here), and for details on how the graphs were created, visit the GitHub repository (here).
One of the main challenges was collecting data from various sources with different formats. For instance, the data from the World Bank differed from that of the ILO. Additionally, some key characteristics relevant to my analysis were not available directly via API. To address this, I collected supplementary data, such as the list of OECD countries, from a GitHub account and merged it with the main dataset. Furthermore, some data were not accessible via API, so I downloaded CSV files, which were raw (including titles or footnotes), and required cleaning.
Even the data imported through the API were often aggregated, requiring further cleaning and formatting before they could be used in the Vega Lite Editor to create graphs in JSON format. For example, some datasets included unnecessary categories (rows) or columns, were in wide-format instead of long-format, or required the calculation of additional indicators for analysis. The Google Colab file demonstrates the data cleaning and analysis process.
The final challenge involved creating complex, multi-layered, interactive graphs in the Vega Lite Editor. To overcome this, I recreated previous graphs and tested each layer separately to diagnose and resolve the issues.