How to Study the Relationship Between Wealth Distribution and Health Inequality Using EDA

Exploratory Data Analysis (EDA) is a powerful approach for studying complex relationships like those between wealth distribution and health inequality. By systematically examining data patterns, trends, and anomalies, EDA helps uncover insights that can inform policy and research. Here’s a detailed guide on how to study the relationship between wealth distribution and health inequality using EDA:

1. Define the Scope and Gather Data

Before diving into analysis, clearly outline your research questions. For example:

• How does wealth distribution affect health outcomes?

• Are certain health inequalities more pronounced in specific wealth brackets?

• What are the key factors linking wealth and health disparities?

Collect datasets that include:

• Wealth distribution indicators: income, assets, Gini coefficient, wealth quintiles.

• Health inequality measures: life expectancy, infant mortality rate, prevalence of chronic diseases, access to healthcare.

• Demographic and socioeconomic variables: age, gender, education, geographic region.

Reliable sources include government statistics, World Bank, WHO, and academic datasets.

2. Data Cleaning and Preparation

Ensure your data is accurate and consistent:

• Handle missing values through imputation or exclusion.

• Normalize or standardize variables where necessary to allow comparison.

• Create derived variables, such as wealth quintiles or health disparity indices.

• Merge datasets using common identifiers (e.g., country, region, year).

3. Initial Univariate Analysis

Begin by exploring each variable individually to understand its distribution:

• Use histograms or density plots for continuous variables like income or life expectancy.

• Bar plots for categorical variables such as education level or health status categories.

• Summary statistics (mean, median, variance) help describe central tendencies and dispersion.

• Detect outliers that may skew results.

4. Bivariate Analysis

Study the pairwise relationships between wealth and health variables:

• Scatter plots of income vs. health outcomes can reveal correlations.

• Box plots to compare health metrics across wealth quintiles.

• Correlation coefficients (Pearson or Spearman) quantify strength and direction of associations.

• Cross-tabulations for categorical variables.

5. Multivariate Exploration

Health and wealth relationships are often influenced by multiple factors:

• Use heatmaps or pair plots to visualize correlations among several variables.

• Conduct Principal Component Analysis (PCA) to reduce dimensionality and identify key components linking wealth and health.

• Apply clustering techniques (k-means, hierarchical) to group populations based on wealth and health profiles.

6. Geographic and Temporal Analysis

Visualize spatial and time trends to add depth:

• Choropleth maps to show regional disparities in wealth and health.

• Time series plots to track changes over years or decades.

• Explore if health inequalities widen or narrow with changes in wealth distribution over time.

7. Identify Patterns and Hypotheses

Using EDA outputs:

• Identify patterns such as whether lower wealth groups consistently experience worse health outcomes.

• Detect thresholds or tipping points in wealth levels affecting health.

• Formulate hypotheses about causal pathways for further statistical testing.

8. Communicate Findings with Visualizations

Effective communication is key:

• Use clear, labeled plots and dashboards.

• Combine multiple visualizations (e.g., income distribution alongside health outcome charts) for holistic views.

• Include confidence intervals or error bars when applicable.

---

Example EDA Workflow

Suppose you have a dataset with individual income, life expectancy, and education level across several regions.

• Plot income distribution to understand wealth spread.

• Compare life expectancy by income quintile using box plots.

• Calculate correlation between income and life expectancy.

• Map average income and health outcomes by region.

• Use scatter plots with color coding by education level to see if education mediates wealth-health links.

---

By following this approach, EDA helps reveal the underlying dynamics between wealth distribution and health inequality, providing a foundation for more rigorous statistical modeling or policy formulation.