Global Analysis of Life Expectancy, Aging Populations, and Gender Equality

Overview

This repository contains the Jupyter Notebook (PopulationDemographics.ipynb) detailing a global analysis of population demographics using data from the World Development Indicators (World Bank), the UN Human Development Reports (Gender Inequality Index), and the WHO/UNESCO Water Supply, Sanitation and Hygiene (WASH) reports.

The primary goal of this analysis is to explore potential correlations between various demographic, economic, and social indicators.

Research Questions

The analysis focuses on answering the following questions for the year 2023:

1. How does life expectancy correlate with sanitation?

   Conclusion: Life Expectancy and Sanitation Coverage are highly correlated (r = 0.81).

2. Are countries with rapidly aging populations experiencing slower economic growth?

   Conclusion: There is no significant correlation between the Old Age Dependency Ratio and annual GDP Growth (r = -0.06).

3. Is there a connection between the Gender Inequality Index and life expectancy disparity between men and women?

   Conclusion: There is no strong correlation between the Gender Inequality Index (GII) and the Life Expectancy Disparity between females and males (r = -0.17).

Data Sources

The analysis utilizes three key public datasets:

1. World Development Indicators (WDI): Provides metrics like GDP growth, life expectancy, and old-age dependency ratio.

2. UN Human Development Reports (Gender Inequality Index): Provides the Gender Inequality Index (GII).

3. WHO/UNESCO Water Supply, Sanitation and Hygiene (WASH): Provides data on "At least basic" sanitation coverage.

Key Findings

The primary finding is the strong positive correlation (r = 0.81) between a country's basic sanitation coverage and its average life expectancy. This suggests that improvements in sanitation remain a significant factor in global public health and longevity.

Limitations

Users of this analysis should be aware of the following limitations:

• Data Timeliness: The primary analysis focuses solely on data from the year 2023 to ensure direct comparability across variables. This excludes decades of historical data that could reveal long-term trends or causal relationships.
• Missing Data Exclusions: Many countries were necessarily excluded from the correlation analysis if they lacked data for both variables of interest, which may introduce bias.
• Lack of Population Weighting: The current analysis treats all countries equally regardless of population size. Future work could incorporate population weighting to better reflect global impacts.

Author

Vidya Balachander

Email: vidyakbalachander@gmail.com

GitHub: vidyabalachander

LinkedIn: vidya-balachander