How to Compare Demographic Trends Across Multiple African Countries Using World Bank Data

Comparing demographic trends across African countries is much more reliable when you use a consistent dataset and repeatable workflow. 



The World Bank’s World Development Indicators (WDI) provides standardized demographic variables that can be accessed directly in Google Colab, allowing you to build reproducible comparisons across countries like Kenya, Nigeria, Ethiopia, and South Africa.

This guide shows a practical workflow using Python in Colab.


1. Setup: Load World Bank Data in Google Colab

We will use the wbgapi library, which is the cleanest way to access World Bank indicators.

!pip install wbgapi pandas matplotlib seaborn -q

import wbgapi as wb
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

2. Select African countries for comparison

We define a focused comparison set:

countries = ["KEN", "NGA", "ETH", "ZAF", "EGY"]  # Kenya, Nigeria, Ethiopia, South Africa, Egypt

These represent different demographic stages across Africa.



3. Choose key demographic indicators

We will pull core demographic variables:

Indicator            World Bank Code
Total population            SP.POP.TOTL
Population growth rate            SP.POP.GROW
Fertility rate            SP.DYN.TFRT.IN
Urban population %            SP.URB.TOTL.IN.ZS
Life expectancy            SP.DYN.LE00.IN


4. Load data from World Bank API

Example: Fertility rate

fertility = wb.data.DataFrame(
    'SP.DYN.TFRT.IN',
    economy=countries,
    time=range(2000, 2024),
    columns='time'
)

Transpose for time-series structure:

fertility = fertility.T
fertility.index = fertility.index.astype(int)
fertility.head()


5. Visualize fertility trends across countries

plt.figure(figsize=(10,6))

for country in fertility.columns:
    plt.plot(fertility.index, fertility[country], label=country)

plt.title("Fertility Rate Trends Across African Countries")
plt.xlabel("Year")
plt.ylabel("Births per Woman")
plt.legend()
plt.show()

What this reveals:

  • Nigeria and Ethiopia show higher fertility persistence

  • Kenya shows faster fertility decline

  • South Africa is already near replacement-level fertility


6. Compare population growth rates

pop_growth = wb.data.DataFrame(
    'SP.POP.GROW',
    economy=countries,
    time=range(2000, 2024),
    columns='time'
).T

pop_growth.index = pop_growth.index.str.replace('YR', '').astype(int)
pop_growth.plot(figsize=(10,6))
plt.title("Population Growth Rate Across African Countries")
plt.xlabel("Year")
plt.ylabel("Annual % Growth")
plt.show()



7. Urbanization comparison (structural transformation)

urban = wb.data.DataFrame(
    'SP.URB.TOTL.IN.ZS',
    economy=countries,
    time=range(2000, 2024),
    columns='time'
).T

urban.index = urban.index.str.replace('YR', '').astype(int)
urban.plot(figsize=(10,6))
plt.title("Urban Population Share Across African Countries")
plt.xlabel("Year")
plt.ylabel("% Urban Population")
plt.show()



Interpretation:

Urbanization rises faster in Egypt and South Africa than in Ethiopia or Nigeria, signaling different economic transformation speeds.


8. Build a comparative “latest year snapshot”

Instead of time series, compare the most recent year:

latest = 2023

snapshot = pd.DataFrame({
    "Fertility": fertility.loc[latest],
    "Urbanization": urban.loc[latest],
    "Pop Growth": pop_growth.loc[latest]
})

snapshot


9. Visual comparison using heatmap

plt.figure(figsize=(8,5))
sns.heatmap(snapshot, annot=True, cmap="YlGnBu")
plt.title("Demographic Comparison of African Countries (Latest Year)")
plt.show()




10. Normalize trends for better comparison (indexing)

To compare trajectories, not levels:

fertility_index = fertility.div(fertility.iloc[0]).mul(100)

fertility_index.plot(figsize=(10,6))
plt.title("Indexed Fertility Decline (Base Year = 2000)")
plt.ylabel("Index (2000 = 100)")
plt.show()

Why this matters:

  • Removes structural level bias

  • Highlights speed of demographic transition

  • Makes countries directly comparable


11. Interpret results using demographic transition logic

From World Bank data patterns:

  • Nigeria, Ethiopia → early-to-mid transition (high fertility, fast population growth)

  • Kenya → faster fertility decline (accelerating transition)

  • South Africa, Egypt → later transition stage (lower fertility, higher urbanization)

This is more meaningful than comparing raw population sizes.


Using Google Colab and World Bank data, demographic comparison becomes a structured analytical workflow rather than a descriptive exercise.

The key value is not in plotting data—but in identifying:

  • Speed of fertility decline

  • Urbanization trajectories

  • Population momentum differences

  • Transition stages across countries

This framework is directly applicable to forecasting labor supply, education demand, and long-term economic structure across Africa.


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