World Development Indicators (2022): A Quick Exploratory Analysis

Author

Oliver Xie

Published

February 25, 2026

1 Overview

This report analyzes a 2022 cross-section of the World Development Indicators (WDI) dataset.
We focus on three indicators:

GDP per capita (current US$)
Life expectancy (years)
Unemployment rate (% of total labor force)

The dataset comes from the World Bank’s WDI program (World Bank 2022). We also reference the classic “Preston curve” relationship between income and health outcomes (Preston 1975).

2 Data loading

import pandas as pd
import numpy as np

df = pd.read_csv("wdi.csv")
df.columns = [c.strip().lower() for c in df.columns]

# Keep only the columns we use in this report
cols = ["country", "gdp_per_capita", "life_expectancy", "unemployment_rate", "total_population"]
d = df[cols].copy()

# Basic cleaning: remove rows missing key values for core analysis
d_core = d.dropna(subset=["gdp_per_capita", "life_expectancy", "unemployment_rate"]).copy()

d.shape, d_core.shape

((217, 5), (179, 5))

3 Exploratory Data Analysis

3.1 GDP per capita

d["gdp_per_capita"].describe()

count       203.000000
mean      20345.707649
std       31308.942225
min         259.025031
25%        2570.563284
50%        7587.588173
75%       25982.630050
max      240862.182448
Name: gdp_per_capita, dtype: float64

Countries vary dramatically in GDP per capita, which motivates using logs.

3.2 Life expectancy

d["life_expectancy"].describe()

count    209.000000
mean      72.416519
std        7.713322
min       52.997000
25%       66.782000
50%       73.514634
75%       78.475000
max       85.377000
Name: life_expectancy, dtype: float64

Life expectancy shows less dispersion than GDP but still meaningful variation.

3.3 Unemployment rate

d["unemployment_rate"].describe()

count    186.000000
mean       7.268661
std        5.827726
min        0.130000
25%        3.500750
50%        5.537500
75%        9.455250
max       37.852000
Name: unemployment_rate, dtype: float64

Unemployment rates show substantial dispersion.

4 Visualizations

4.1 GDP per capita vs Life expectancy

import matplotlib.pyplot as plt

x = d_core["gdp_per_capita"]
y = d_core["life_expectancy"]

fig, ax = plt.subplots()
ax.scatter(x, y)
ax.set_xscale("log")
ax.set_title("GDP per Capita vs Life Expectancy (2022)")
ax.set_xlabel("GDP per capita (current US$, log scale)")
ax.set_ylabel("Life expectancy (years)")
plt.show()

Figure: GDP per capita and life expectancy (log scale).
Source: World Bank WDI (2022).

Figure 1

4.2 Top 10 Countries by GDP per Capita

top10 = d.dropna(subset=["gdp_per_capita"]).sort_values("gdp_per_capita", ascending=False).head(10)
top10[["country", "gdp_per_capita"]]

	country	gdp_per_capita
130	Monaco	240862.182448
114	Liechtenstein	187267.132381
116	Luxembourg	125006.021815
21	Bermuda	123091.123516
147	Norway	108798.451166
93	Ireland	103983.291336
36	Cayman Islands	96073.763595
188	Switzerland	93259.905718
171	Singapore	88428.702423
159	Qatar	87480.419712

fig, ax = plt.subplots()
ax.bar(top10["country"], top10["gdp_per_capita"])
ax.set_title("Top 10 Countries by GDP per Capita (2022)")
ax.set_xlabel("Country")
ax.set_ylabel("GDP per capita (current US$)")
ax.tick_params(axis="x", rotation=60)
plt.show()

Figure: Top 10 countries by GDP per capita.
Source: World Bank WDI (2022).

Figure 2

5 Summary Table

summary = d_core[["gdp_per_capita", "life_expectancy", "unemployment_rate"]].describe().T
summary = summary[["count", "mean", "std", "min", "50%", "max"]]
summary

	count	mean	std	min	50%	max
gdp_per_capita	179.0	17358.165812	23532.137393	259.025031	6810.114041	125006.021815
life_expectancy	179.0	72.165990	7.860414	52.997000	73.444000	85.377000
unemployment_rate	179.0	7.246855	5.866449	0.130000	5.504000	37.852000

Table 1

Table: Descriptive statistics for selected indicators.

6 Conclusion

GDP per capita is strongly associated with life expectancy (see Figure 1).
The income distribution is highly skewed (see Figure 2).
Key statistics are summarized in Table 1.

7 References

Preston, Samuel H. 1975. “The Changing Relation Between Mortality and Level of Economic Development.” Population Studies 29 (2): 231–48.

World Bank. 2022. “World Development Indicators.” World Bank Open Data.