A Huge Democratic Divergence

economy
python
Where demographic weight does not match economic influence
Published
Nov 25, 2026
Keywords
divergence
Summary

The gap between a country’s share of the world’s population and its share of the global economy highlights a profound divergence. Some nations host large populations yet hold only a small fraction of global economic power, while others exert outsized influence relative to their demographic weight.
Code
# Libraries
# =====================================================================
import os
from io import BytesIO
import requests
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
from matplotlib.offsetbox import OffsetImage, AnnotationBbox
import matplotlib.ticker as mtick
import matplotlib.patches as mpatches
from matplotlib.legend_handler import HandlerPatch

# 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 (2024)
# ========================================================
#Parametro
parameters = ['LP', 'NGDPD']

# Create an empty list
records = []

# Iterar sobre cada parámetro
for parameter in parameters:
    # Request URL
    url = f"https://www.imf.org/external/datamapper/api/v1/{parameter}"
    response = requests.get(url)
    data = response.json()
    values = data.get('values', {})

    # Iterate over each country and year
    for country, years in values.get(parameter, {}).items():
        for year, value in years.items():
            records.append({
                'parameter': parameter,
                'iso3': country,
                'year': int(year),
                'value': float(value)
            })
    
# Create dataframe
df = pd.DataFrame(records)

# Pivot Parameter to columns and filter nulls
df = df.pivot(index=['iso3', 'year'], columns='parameter', values='value').reset_index()

# Add URSS 1980
urss = {'iso3': 'SUN', 'year': 1980, 'LP': 264, 'NGDPD': 354}
df = pd.concat([df, pd.DataFrame([urss])], ignore_index=True)

# Filter after 2024
df = df[df['year'].isin([1992, 2024])]
df = df.dropna(subset=['LP', 'NGDPD'])

# Data Manipulation
# =====================================================================
# Merge queries
df = df.merge(df_countries, how='left', left_on='iso3', right_on='ISO3')
df = df[(df['Country_Abr'].notna()) | (df['iso3'] == 'SUN')]
df = df[['iso3', 'Country_Abr', 'year', 'LP', 'NGDPD']]
df.columns = df.columns.str.lower()

# Add calculated fields
df['lp_percent'] = df['lp'] / df.groupby('year')['lp'].transform('sum')
df['ngdpd_percent'] = df['ngdpd'] / df.groupby('year')['ngdpd'].transform('sum')
df['gap'] = df['ngdpd_percent'] - df['lp_percent']

# Ordenar por gap
df = df.sort_values(by='gap', ascending=True)

# Filter countries
df = df[(df['iso3'].isin(['IND', 'NGA', 'PAK', 'IDN', 'BGD', 'CHN', 'USA', 'DEU', 'GBR', 'JPN', 'FRA']))]

# Assign colors
def get_color(row):
    if row['year'] == 2024:
        return '#C00000' if row['gap'] < 0 else '#153D64'
    else:  # 1992
        return '#FFCCCC' if row['gap'] < 0 else '#C0E6F5'

df['color'] = df.apply(get_color, axis=1)

# Data Visualization
# ==========================================
# Font and style
plt.rcParams.update({'font.family': 'sans-serif', 'font.sans-serif': ['Franklin Gothic'], 'font.size': 9})
sns.set(style="white", palette="muted")

# Create figure
fig, ax = plt.subplots(figsize=(8, 6))

# Configuración de posiciones
countries = df[df['year'] == 1992].sort_values('gap', ascending=True)['country_abr'].tolist()
y_pos = np.arange(len(countries))
bar_width = 0.4

# Plot bars for each year
for i, year in enumerate(sorted(df['year'].unique())):
    year_data = df[df['year']==year].set_index('country_abr').reindex(countries)
    offsets = y_pos - bar_width/2 + i*bar_width
    plt.barh(offsets, year_data['gap'], height=bar_width, color=year_data['color'], label=str(year))

# Labels for each column
years = sorted(df['year'].unique())
min_year, max_year = years[0], years[-1]

for year in years:
    year_data = df[df['year']==year].set_index('country_abr').reindex(countries)
    offsets = y_pos - bar_width/2 + (0 if year==min_year else bar_width)
    
    # Different color for min and max year
    color = '#595959' if year == min_year else '#0D0D0D'
    
    for y, val in zip(offsets, year_data['gap']):
        plt.text(val + (0.002 if val > 0 else -0.002), y,
                 f'{val:.1%}',
                 va='center',
                 ha='left' if val > 0 else 'right',
                 fontsize=7,
                 color=color)

# Define flags
flag_urls = {
    'CHN': 'https://raw.githubusercontent.com/matahombres/CSS-Country-Flags-Rounded/master/flags/CN.png',
    'IND': 'https://raw.githubusercontent.com/matahombres/CSS-Country-Flags-Rounded/master/flags/IN.png',
    'IDN': 'https://raw.githubusercontent.com/matahombres/CSS-Country-Flags-Rounded/master/flags/ID.png',
    'NGA': 'https://raw.githubusercontent.com/matahombres/CSS-Country-Flags-Rounded/master/flags/NG.png',
    'PAK': 'https://raw.githubusercontent.com/matahombres/CSS-Country-Flags-Rounded/master/flags/PK.png',
    'BGD': 'https://raw.githubusercontent.com/matahombres/CSS-Country-Flags-Rounded/master/flags/BD.png',
    'USA': 'https://raw.githubusercontent.com/matahombres/CSS-Country-Flags-Rounded/master/flags/US.png',
    'GBR': 'https://raw.githubusercontent.com/matahombres/CSS-Country-Flags-Rounded/master/flags/GB.png',
    'JPN': 'https://raw.githubusercontent.com/matahombres/CSS-Country-Flags-Rounded/master/flags/JP.png',
    'FRA': 'https://raw.githubusercontent.com/matahombres/CSS-Country-Flags-Rounded/master/flags/FR.png',
    'DEU': 'https://raw.githubusercontent.com/matahombres/CSS-Country-Flags-Rounded/master/flags/DE.png',
}

# Load flags once
flags = {country: mpimg.imread(BytesIO(requests.get(url).content)) 
         for country, url in flag_urls.items()}

# Add flags
ax = plt.gca()
for y, ctry  in zip(y_pos, countries):
    iso = df.loc[df['country_abr'] == ctry, 'iso3'].iloc[0]
    if iso in flags:
        im = OffsetImage(flags[iso], zoom=0.025)
        ab = AnnotationBbox(im, (0, y), frameon=False, xycoords=('axes fraction','data'), box_alignment=(0,0.5))
        ax.add_artist(ab)

# Remove spines
for position, spine in ax.spines.items():
    if position != 'bottom':
        spine.set_visible(False)
    else:
        spine.set_color('#808080')
        spine.set_linewidth(1)

# Add title and subtitle
fig.add_artist(plt.Line2D([0.135, 0.135], [0.87, 0.97], linewidth=6, color='#203764', solid_capstyle='butt'))
plt.text(0.02, 1.12, f'A Huge Democratic Divergence', fontsize=16, fontweight='bold', ha='left', transform=plt.gca().transAxes)
plt.text(0.02, 1.08, f'Where demographic weight does not match economic influence', fontsize=11, color='#262626', ha='left', transform=plt.gca().transAxes)
plt.text(0.02, 1.045, f'(difference between economic share and population share)', fontsize=9, color='#262626', ha='left', transform=plt.gca().transAxes)

# Adjust axis
plt.xlim(-0.25, 0.25)
ax.xaxis.set_major_formatter(mtick.PercentFormatter(1.0))
plt.xticks(fontsize=8)
plt.yticks(y_pos, countries, fontsize=10)
plt.axvline(0, color='#808080', linewidth=0.5)

# Add label "Underrepresented" and "Overrepresented"
plt.text(0, -0.12, 'Under-represented',
    transform=ax.transAxes,
    fontsize=8, fontweight='bold', color='darkred', ha='left', va='center')
plt.text(0.85, -0.12, 'Over-represented',
    transform=ax.transAxes,
    fontsize=8, fontweight='bold', color='darkblue', va='center')

# Add Data Source
plt.text(0, -0.17, 'Data Source:', 
    transform=plt.gca().transAxes, 
    fontsize=8,
    fontweight='bold',
    color='gray')
space = " " * 23
plt.text(0, -0.17, space + 'IMF World Economic Outlook Database, 2024', 
    transform=plt.gca().transAxes, 
    fontsize=8,
    color='gray')

# Legend configuration
# Handler que divide el cuadrado en dos colores
class HandlerSplitSquare(HandlerPatch):
    def __init__(self, color_left, color_right, **kwargs):
        super().__init__(**kwargs)
        self.color_left = color_left
        self.color_right = color_right

    def create_artists(self, legend, orig_handle,
                       xdescent, ydescent, width, height, fontsize, trans):
        p1 = mpatches.Rectangle([xdescent, ydescent], width/2, height,
                                facecolor=self.color_left, transform=trans)
        p2 = mpatches.Rectangle([xdescent+width/2, ydescent], width/2, height,
                                facecolor=self.color_right, transform=trans)
        return [p1, p2]

# Handles ficticios
patch_min = mpatches.Rectangle((0,0),1,1, facecolor="none")
patch_max = mpatches.Rectangle((0,0),1,1, facecolor="none")

plt.legend(
    handles=[patch_min, patch_max],
    labels=[f"{min_year}", f"{max_year}"],
    handler_map={
        patch_min: HandlerSplitSquare("#C0E6F5", "#FFCCCC"),
        patch_max: HandlerSplitSquare("#153D64", "#C00000"),
    },
    loc='lower center',
    bbox_to_anchor=(0.5, -0.12),
    ncol=2,
    fontsize=8,
    frameon=False
)

# Adjust layout
plt.tight_layout()

# Save it...
download_folder = os.path.join(os.path.expanduser("~"), "Downloads")
filename = os.path.join(download_folder, f"FIG_IMG_GAP_GDP_Population.png")
plt.savefig(filename, dpi=300, bbox_inches='tight')

# Show :)
plt.show()
Economic-Population Gap
Difference between economic share and population share.