Global North: Inequalities in GDP per Capita

economy
python
Absolute inequalities in GDP per capita between the Global North and the rest of the world’s regions.
Published

Nov 3, 2025

Keywords

wealth-income

Summary

Absolute inequalities in GDP per capita between the Global North and other regions of the world highlight the significant economic divide that persists globally. These disparities emphasize the concrete differences in wealth, living standards, and access to essential resources, underscoring the structural imbalance in global development.

Code 
# Libraries
# =====================================================================
import requests
import wbgapi as wb
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.ticker as ticker

# Data Extraction (Countries)
# =====================================================================
# Extract JSON and bring data to a dataframe
url = 'https://raw.githubusercontent.com/guillemmaya92/world_map/main/Dim_Country.json'
response = requests.get(url)
data = response.json()
df = pd.DataFrame(data)
df = pd.DataFrame.from_dict(data, orient='index').reset_index()
df_countries = df.rename(columns={'index': 'ISO3'})

# Data Extraction - WBD (1960-1980)
# ========================================================
# To use the built-in plotting method
indicator = ['NY.GDP.PCAP.KD', 'SP.POP.TOTL']
countries = df_countries['ISO3'].tolist()
data_range = ['1960', '2023']
data = wb.data.DataFrame(indicator, countries, data_range, numericTimeKeys=True, labels=False, columns='series').reset_index()
df_wb = data.rename(columns={
    'economy': 'ISO3',
    'time': 'year',
    'SP.POP.TOTL': 'pop',
    'NY.GDP.PCAP.KD': 'gdpc'
})

# Filter nulls and create total
df_wb = df_wb[~df_wb['gdpc'].isna()]
df_wb['gdpt'] = df_wb['gdpc'] * df_wb['pop']

# Data Manipulation
# =====================================================================
# Merge queries
df = df_wb.merge(df_countries, how='left', left_on='ISO3', right_on='ISO3')
df = df[['Analytical2', 'year', 'pop', 'gdpt']]
df = df.rename(columns={'Analytical2': 'group'})
df = df[df['group'].notna()]

# Summarizing Groups
dfg = df.copy()
dfg['group'] = np.where(dfg['group'] == 'Global North', 'Global North', 'Rest World')
dfg = dfg.groupby(['group', 'year'])[['pop', 'gdpt']].sum().reset_index()
dfg['gdpc'] = dfg['gdpt'] / dfg['pop']
dfg['gdpcdif'] = dfg['gdpc'] - dfg.groupby('group')['gdpc'].shift()

# Summarizing Analytical
df = df.groupby(['group', 'year'])[['pop', 'gdpt']].sum().reset_index()
df['gdpc'] = df['gdpt'] / df['pop']

# Porcentual
df['pop%'] = df['pop'] / df.groupby('year')['pop'].transform('sum')

df = df.sort_values(by=['pop%', 'year'], ascending=[False, True])

# Data Visualization
# =====================================================================
# Font Style
plt.rcParams.update({'font.family': 'sans-serif', 'font.sans-serif': ['Open Sans'], 'font.size': 10})

# Filter dataframes
df_1960 = df[df['year'] == 1960]
df_2023 = df[df['year'] == 2023]

# Values
rw60 = dfg.loc[(dfg['group'] == 'Rest World') & (dfg['year'] == 1960), 'gdpc'].values[0]
gn60 = dfg.loc[(dfg['group'] == 'Global North') & (dfg['year'] == 1960), 'gdpc'].values[0]
rw23 = dfg.loc[(dfg['group'] == 'Rest World') & (dfg['year'] == 2023), 'gdpc'].values[0]
gn23 = dfg.loc[(dfg['group'] == 'Global North') & (dfg['year'] == 2023), 'gdpc'].values[0]

# Colors
group_colors = {
    'East Asia & Pacific': '#4D93D9',
    'Eastern Europe & Central Asia': '#00B050',
    'Global North': '#808080',
    'Latin America and the Caribbean': '#C00000',
    'Middle East & North Africa': '#F1A983',
    'South Asia': '#FFC000',
    'Sub-Saharan Africa': '#7030A0'
}

# Define ticks
xticks = np.linspace(0, 50000, 5)

# Create figure and suplots
fig, axes = plt.subplots(2, 1, figsize=(12, 5))

# First plot (1960)
axes[0].scatter(df_1960['gdpc'], df_1960['year'], s=df_1960['pop%'] * 12000, alpha=0.4, c=df_1960['group'].map(group_colors), edgecolors='w')
axes[0].set_yticks([1960])
axes[0].set_xlim(0, 50000)
axes[0].set_xticks(xticks)
axes[0].xaxis.set_major_formatter(ticker.FuncFormatter(lambda x, pos: f'{x:,.0f}'))
axes[0].axvline(x=rw60, color='darkred', linewidth=0.5, linestyle='--', label=f'GDPC Global North 1960: {rw60}')
axes[0].axvline(x=gn60, color='darkred', linewidth=0.5, linestyle='--', label=f'GDPC Global North 1960: {gn60}')
axes[0].text(rw60 +2500, 1960 + 70, f'{rw60:,.0f}$', color='darkred', fontsize=8, 
             va='bottom', ha='center', rotation=0)
axes[0].text(gn60 +1800, 1960 + 70, f'{gn60:,.0f}$', color='darkred', fontsize=8, 
             va='bottom', ha='center', rotation=0)
axes[0].text((gn60-rw60)/2+rw60, 1960 + 50, f'GAP \n{gn60-rw60:,.0f}$', color='#373737', fontsize=8, fontweight='bold', 
             va='bottom', ha='center', rotation=0)
axes[0].text((gn60-rw60)/2+rw60, 1960 + 25, f'+{(gn60-rw60)/rw60*100:,.0f}%', color='#12330b', fontsize=8, 
             va='bottom', ha='center', rotation=0)

# Second plot (2023)
axes[1].scatter(df_2023['gdpc'], df_2023['year'], s=df_2023['pop%'] * 12000, alpha=0.4, c=df_2023['group'].map(group_colors), edgecolors='w')
axes[1].set_xlabel('GDP per capita ($US constant 2015)', fontsize=10, fontweight='bold')
axes[1].set_yticks([2023])
axes[1].set_xlim(0, 50000) 
axes[1].set_xticks(xticks)
axes[1].xaxis.set_major_formatter(ticker.FuncFormatter(lambda x, pos: f'{x:,.0f}'))
axes[1].axvline(x=rw23, color='darkred', linewidth=0.5, linestyle='--', label=f'GDPC Global North 1960: {rw23}')
axes[1].axvline(x=gn23, color='darkred', linewidth=0.5, linestyle='--', label=f'GDPC Global North 1960: {gn23}')
axes[1].text(rw23 +1800, 2023 + 70, f'{rw23:,.0f}$', color='darkred', fontsize=8, 
             va='bottom', ha='center', rotation=0)
axes[1].text(gn23 +1800, 2023 + 70, f'{gn23:,.0f}$', color='darkred', fontsize=8, 
             va='bottom', ha='center', rotation=0)
axes[1].text((gn23-rw23)/2+rw23, 2023 + 50, f'GAP \n{gn23-rw23:,.0f}$', color='#373737', fontsize=8, fontweight='bold', 
             va='bottom', ha='center', rotation=0)
axes[1].text((gn23-rw23)/2+rw23, 2023 + 25, f'+{(gn23-rw23)/rw23*100:,.0f}%', color='#12330b', fontsize=8, 
             va='bottom', ha='center', rotation=0)

# Configuration
yticklabels_1960 = axes[0].get_yticklabels()
yticklabels_1960[0].set_fontweight('bold')
yticklabels_1960[0].set_fontsize(14)
yticklabels_1960[0].set_color('darkgrey')
axes[0].set_yticklabels(yticklabels_1960)

yticklabels_2023 = axes[1].get_yticklabels()
yticklabels_2023[0].set_fontweight('bold')
yticklabels_2023[0].set_fontsize(14)
yticklabels_2023[0].set_color('darkgrey')
axes[1].set_yticklabels(yticklabels_2023)

# Grid and labels
axes[0].text(0, 1.25, 'Global Inequalities in GDP per Capita', fontsize=13, fontweight='bold', ha='left', transform=axes[0].transAxes)
axes[0].text(0, 1.1, 'Evolution by region from 1960 to 2023', fontsize=9, color='#262626', ha='left', transform=axes[0].transAxes)

# Add custom legend
handles = [plt.Line2D([0], [0], marker='o', color='w', markerfacecolor=color, markersize=10) 
           for color in group_colors.values()]

labels = list(group_colors.keys())

legend = axes[0].legend(handles, labels, title="Regions", bbox_to_anchor=(1.05, 0.5), loc='upper left', 
                        frameon=False, fontsize='8', title_fontsize='10')

plt.setp(legend.get_title(), fontweight='bold')

# Add Data Source
axes[1].text(0, -0.5, 'Data Source: World Bank.', 
    transform=plt.gca().transAxes, 
    fontsize=8, 
    color='gray')

# Add Notes
axes[1].text(0, -0.6, 'Notes: The size corresponds to the percentage of the total population.', 
    transform=plt.gca().transAxes, 
    fontsize=8, 
    color='gray')

# Add Global North
axes[1].text(0, -0.7, 'Global North: US, European Union, Canada, Australia, New Zealand, Russia, Switzerland, Iceland, Israel, Greenland, Norway, Japan', 
    transform=plt.gca().transAxes, 
    fontsize=8, 
    color='gray')

# Adjusting plot...
plt.tight_layout()

plt.savefig("C:/Users/guill/Downloads/FIG_WORLDBANK_Global_North.png", dpi=300, bbox_inches='tight') 

# Print it!
plt.show()

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