Our project "Women or We Men?" focuses on analyzing trends in indicators of women's empowerment, such as labor force participation and contraceptive use, across Asian countries. Our goal is to provide evidence-based insights into the challenges and opportunities for women's empowerment in diverse contexts.
Data Science Team
Understanding the importance of women's empowerment is crucial for societal progress.
"Shoot the vagina. No vagina, no use."
These were the words uttered by President Rodrigo Duterte in 2018. They highlight the deeply-rooted gender inequality in Philippine society, sparking a broader conversation about advancing gender equality and empowering women.
Despite laws protecting women's rights, genuine gender equality remains elusive.
We aim to answer these questions:
Using data science, we offer actionable insights to address gender inequality across Asian countries. Our statistical analysis will provide evidence-based guidance for policymakers, paving the way for targeted interventions aimed at empowering women.
The data will show no significant trends in women's labor force participation or correlation between labor force participation and contraceptive use.
The data will show significant trends and correlations, indicating progress and areas needing improvement in women's labor force participation and contraceptive use.
We used NumPy, Python, Matplotlib, Excel, and PSPP for our analysis.
Discover our data science process.
We sourced data from the World Bank's ‘World Development Indicators’ dataset, compiled from the ILO and the United Nations Population Division.
We handled missing data points through linear interpolation and ensured our dataset was consistent and focused on Asian countries.
Insights from our analysis.
Our findings reveal significant trends and correlations, indicating both progress and areas needing further intervention.
Our analysis provides evidence-based guidance for targeted policies and interventions to enhance women's empowerment across Asia.
Get to know the minds behind the project.
Data Analyst
Data Scientist
Data Engineer
Project Manager