Old-Age Dpendency Ratio

economy
python
The aging population inverts the population pyramid and puts strong pressure on economies and their pension systems.
Published
Dec 9, 2026
Keywords
population
Summary

The old age dependency refers to the increasing ratio of elderly people to the working-age population. As life expectancy rises and birth rates fall, fewer workers must support more retirees. This puts pressure on pension systems, healthcare services, and economic growth, making it a major challange for many economies.
Code
# Libraries
# ==============================
import os
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import requests
from matplotlib.offsetbox import OffsetImage, AnnotationBbox
from io import BytesIO

# Data Extraction
# ==============================
# Path file
path = r"https://github.com/guillemmaya92/Analytics/raw/refs/heads/master/Data/undata.parquet"

# Dataframe
df = pd.read_parquet(path)

# Filter countries
countries = ["WLD", "EUR", "ESP", "CHN", "IND", "JPN", "USA"]
df = df[df["iso3"].isin(countries)]

# Select columns and rename dependency
df = df[["variant", "country", "iso3", "year", "dependency3"]]
df = df.rename(columns={"dependency3": "dependency"})
df['dependency'] = df['dependency'] * 100

# Separate estimates and predictions
df_estimates = df[df["variant"] == "Estimates"]
df_prediction = df[df["year"] >= 2023]

# 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")

# Palette
countries = {
    "WLD": {"name": "World",  "color": "#262626"},
    "EUR": {"name": "Europe", "color": "#3C7D22"},
    "ESP": {"name": "Spain",  "color": "#ECB100"},
    "CHN": {"name": "China",  "color": "#C00000"},
    "IND": {"name": "India",  "color": "#E97132"},
    "JPN": {"name": "Japan",  "color": "#782170"},
    "USA": {"name": "USA",    "color": "#153D64"}
}

colors = {code: info["color"] for code, info in countries.items()}

# Create figure
fig, ax = plt.subplots(figsize=(10, 6))

# Estimates line
sns.lineplot(
    data=df_estimates,
    x="year",
    y="dependency",
    hue="iso3",
    palette=colors,
    legend=False,
    ax=ax
)

# Prediction line (dashed)
sns.lineplot(
    data=df_prediction,
    x="year",
    y="dependency",
    hue="iso3",
    palette=colors,
    linestyle='dotted',
    linewidth=0.9,
    legend=False,
    ax=ax
)

# Custom dashed style for predictions
n_prediction = df_prediction["iso3"].nunique()
for line in ax.lines[-n_prediction:]:
    line.set_linestyle((0, (1, 1.5))) 

# Add title and subtitle
fig.add_artist(plt.Line2D([0.08, 0.08], [0.87, 0.97], linewidth=6, color='#203764', solid_capstyle='butt'))
plt.text(0.02, 1.11, f'Old-Age Dependency Ratio', fontsize=16, fontweight='bold', ha='left', transform=plt.gca().transAxes)
plt.text(0.02, 1.06, f'Evolution by country and projection until 2100', fontsize=11, color='#262626', ha='left', transform=plt.gca().transAxes)
plt.text(0.02, 1.02, f'(population 65+ over population 25-64)', fontsize=9, color='#262626', ha='left', transform=plt.gca().transAxes)

# Axis x-axis limits and labels
ax.set_xlim(1950, 2100)
ax.set_xticks(range(1950, 2101, 10))
ax.set_xticklabels([str(tick) for tick in range(1950, 2101, 10)], fontsize=9)
ax.set_xlabel('') 

# Set y-axis limits and labels
ax.set_ylabel('Dependency rate (%)', fontsize=10)
ax.tick_params(axis='y', labelsize=9)
ax.grid(axis='x', linestyle=':', color='gray', alpha=0.7, linewidth=0.5)

# Remove spines and legend
ax = plt.gca()
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.spines['bottom'].set_linewidth(0.5)
ax.spines['left'].set_linewidth(0.5)

# Add flags
flag_urls = {
    'ESP': 'https://raw.githubusercontent.com/matahombres/CSS-Country-Flags-Rounded/master/flags/ES.png',
    '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',
    'JPN': 'https://raw.githubusercontent.com/matahombres/CSS-Country-Flags-Rounded/master/flags/JP.png',
    'USA': 'https://raw.githubusercontent.com/matahombres/CSS-Country-Flags-Rounded/master/flags/US.png',
    'EUR': 'https://raw.githubusercontent.com/guillemmaya92/circle_flags/refs/heads/gh-pages/flags/eu.png',
    'WLD': 'https://raw.githubusercontent.com/guillemmaya92/circle_flags/refs/heads/gh-pages/flags/xx.png'
}

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

for iso3 in df_prediction['iso3'].unique():
    if iso3 in flags:
        last_row = df_prediction[df_prediction['iso3'] == iso3].iloc[-1]
        x = last_row['year']
        y = last_row['dependency']
        
        # Adjust vertical position to avoid overlap
        if iso3 == 'IND':
            y += 1.5
        elif iso3 == 'USA':
            y -= 1.5
        elif iso3 == 'EUR':
            y += 1
        elif iso3 == 'ESP':
            y -= 1
        elif iso3 == 'JPN':
            y += 1
            
        # Add flag image
        img = flags[iso3]
        imagebox = OffsetImage(img, zoom=0.0225)
        ab = AnnotationBbox(imagebox, (x, y), frameon=False, box_alignment=(0,0.5), pad=0)
        ax.add_artist(ab)
        
        # Add country name and value
        if iso3 in countries:
            ax.text(
                x + 4,
                y,
                countries[iso3]["name"],
                va="center", ha="left",
                fontsize=8, color=countries[iso3]["color"]
            )
            
            # Specific offset for certain countries
            if iso3 == "USA":
                offset = 2
            elif iso3 == "EUR":
                offset = 1.5
            else:
                offset = 0.8

            # Add bold value with adjusted position
            ax.text(
                x + 4 + len(countries[iso3]["name"]) + offset,
                y,
                f"({y:.1f})",
                va="center", ha="left",
                fontsize=8, color=countries[iso3]["color"],
                fontweight="bold"
            )

# Custom Legend
for code, info in countries.items():
    ax.plot([], [], color=info["color"], label=info["name"], linestyle='-')
ax.legend(
    loc='lower center',
    bbox_to_anchor=(0.5, -0.12),
    ncol=len(countries),
    fontsize=8,
    frameon=False,
    handlelength=1,
    handleheight=1,
    borderpad=0.2,
    columnspacing=0.5
)

# Add Data Source
plt.text(0, -0.15, 'Data Source:', 
    transform=plt.gca().transAxes, 
    fontsize=8,
    fontweight='bold',
    color='gray')
space = " " * 23
plt.text(0, -0.15, space + 'United Nations, World Population Prospects 2024', 
    transform=plt.gca().transAxes, 
    fontsize=8,
    color='gray')
      
# Adjust layout
plt.tight_layout()

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

# Show :)
plt.show()