China: From rural to urban

economy
python
Examining Social and Economic Inequalities Amid China’s Urbanization
Published

Jun 12, 2026

Keywords

urbanization

Summary

The charts show the income distribution scale across cities in China and the evolution of income inequality between the rural and urban populations.

Code 
# Libraries
# =====================================================================
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
import matplotlib.ticker as ticker
import os

# Data (China) 
# =====================================================================
# Read wikipedia data
url = "https://en.wikipedia.org/wiki/List_of_prefecture-level_divisions_of_China_by_GDP"
tables = pd.read_html(url)
df = tables[0]
df.columns = ['region', '1', '2', '3', 'gdp', '4', 'gdpc', '5']
df['population'] = df['gdp'] / df['gdpc'] * 1000
df = df[['region', 'gdpc', 'population']]
df['region'] = df['region'].str.replace('*', '', regex=False)

data = pd.DataFrame({
    'region': ['Beijing', 'Shangai', 'Chongqing', 'Tianjin', 'Hong Kong', 'Macao'],
    'gdpc': [28294, 26747, 12350, 17727, 48800, 36909],
    'population': [21.8, 24.7, 32.0, 13.9, 7.5, 0.68],
})

df = pd.concat([df, data], ignore_index=True)

# Data Manipulation
# =====================================================================
# Order dataframe
df = df.sort_values(by=['gdpc'])

# Calculate 'left accrual widths'
df['population_cum'] = df['population'].cumsum()
df['left'] = df['population'].cumsum() - df['population']

# Pondered Gini Function
def gini(x, weights=None):
    if weights is None:
        weights = np.ones_like(x)
    count = np.multiply.outer(weights, weights)
    mad = np.abs(np.subtract.outer(x, x) * count).sum() / count.sum()
    rmad = mad / np.average(x, weights=weights)
    return 0.5 * rmad

# Calculate gini and median
gini_index = gini(df['gdpc'].values, df['population'].values)

# Calculate weighted median
df.sort_values('gdpc', inplace=True)
cumsum = df['population'].cumsum()
cutoff = df['population'].sum() / 2.0
median = df.loc[cumsum >= cutoff, 'gdpc'].iloc[0]

# Show dataframe, gini and median
print(df)
print(gini_index)
print(median)

# Data Visualization
# =====================================================================
# Seaborn figure style
sns.set(style="whitegrid")
fig, ax = plt.subplots(figsize=(12, 8))

# Create a palette
norm = plt.Normalize(df["gdpc"].min(), 25000)
colors = plt.cm.coolwarm_r(norm(df["gdpc"]))

# Create a Matplotlib plot
bars = plt.bar(df['left'], df['gdpc'], width=df['population'], 
        color=colors, alpha=1, align='edge', edgecolor='grey', linewidth=0.1)

# Title
fig.add_artist(plt.Line2D([0.08, 0.08], [0.90, 0.99], linewidth=6, color='#203764', solid_capstyle='butt'))
ax.text(0.02, 1.09, f'Regional GDP Distribution of China', fontsize=16, fontweight='bold', ha='left', transform=plt.gca().transAxes)
ax.text(0.02, 1.06, f'From rural to urban, the role of location in income inequality', fontsize=11, color='#262626', ha='left', transform=plt.gca().transAxes)
ax.text(0.02, 1.03, f'(GDP per capita in $US)', fontsize=9, color='#262626', ha='left', transform=plt.gca().transAxes)

# Configuration grid and labels
ax.set_xlim(0, df['population_cum'].max()) 
ax.set_ylim(0, df['gdpc'].max() * 1.093)
ax.set_xlabel('Cumulative Population (M)', fontsize=10, fontweight='bold')
ax.set_ylabel('GDP per capita ($USD)', fontsize=10, fontweight='bold')
ax.grid(axis='x')
ax.grid(axis='y', linestyle='--', linewidth=0.5, color='lightgray')
ax.tick_params(axis='x', labelsize=9)
ax.tick_params(axis='y', labelsize=9) 
ax.yaxis.set_major_formatter(ticker.FuncFormatter(lambda x, _: f'{int(x):,}'))
ax.xaxis.set_major_formatter(ticker.FuncFormatter(lambda x, _: f'{int(x):,}'))
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.axhline(y=median, color='red', linestyle='--', linewidth=0.5, zorder=0, alpha=0.4)
ax.text(75, median + 100, f"Median: {median:,.0f}$", color='darkred', fontweight='bold', fontsize=9, ha='center', va='bottom', zorder=2)

# Add text each region except Ávila and Segovia
for i, bar in enumerate(bars):
    region_name = df['region'].iloc[i]
    
    top_cities = [
        'Beijing', 'Shangai', 'Chongqing', 'Tianjin', 'Hong Kong',
        'Ordos', 'Suzhou, Jiangsu', 'Zhenjiang', 'Jieyang', 'Kashgar', 'Shangrao', 'Qujing',
        'Shenzhen', 'Guangzhou', 'Suzhou', 'Chengdu', 'Wuhan', 'Hangzhou', 'Nanjing',
        'Ningbo', 'Qingdao', 'Wuxi', 'Changsha', 'Zhengzhou', 'Fuzhou', 'Quanzhou',
        'Jinan', 'Dongguan', 'Foshan', "Xi'an", 'Dalian', 'Wenzhou', 'Shenyang',
        'Kunming', 'Baoding', 'Shijiazhuang', 'Linyi', 'Harbin', 'Nanyang',
        'Weifang', 'Handan', 'Changchun', 'Xuzhou', 'Ganzhou', 'Zhoukou', 'Nanning',
        'Heze', 'Fujian', 'Jining', 'Shaoyang', 'Hefei', 'Nantong', 'Shangqiu',
        'Tangshan', 'Hengyang', 'Cangzhou', 'Jinhua', 'Luoyang', 'Xingtai',
        'Zhanjiang', 'Zhumadian', 'Bijie'
    ]
    
    # Add labels
    if region_name in top_cities:
        x = bar.get_x() + bar.get_width() / 2
        y = bar.get_height()
        
        # Special position
        if region_name in ["Ordos", "Jinan", "Foshan", "Qingdao"]:
            x -= 5
            y += 1000
        else:
            y += 1000

        ax.text(
            x, y,
            region_name,
            ha='center', va='bottom', color='#363636', fontsize=7, rotation=90,
        )

# Add Year label 
ax.text(1, 1.12, f'2022',
             transform=plt.gca().transAxes,
             fontsize=22, ha='right', va='top',
             fontweight='bold', color='#D3D3D3')
    
# Add Data Source
ax.text(0, -0.1, 'Data Source: National Bureau of Statistics of China', 
            transform=plt.gca().transAxes, 
            fontsize=8, 
            color='gray')

# Show GINI Index
ax.text(
    0.09, 0.97, f"Gini Index: {gini_index:.2f}", 
    transform=ax.transAxes,
    fontsize=8.5,
    color='black',
    ha='right',
    va='top', 
    bbox=dict(boxstyle="round,pad=0.3", edgecolor='gray', facecolor='white')
)

# Add Gini Index
ax.text(0, -0.12, 'Notes: The Gini coefficient has been calculated using population weights for each region.', 
            transform=plt.gca().transAxes, 
            fontsize=8, 
            color='gray')

# Add label "poorest" and "richest"
ax.text(0, -0.065, 'Low Income',
             transform=ax.transAxes,
             fontsize=11, fontweight='bold', color='darkred', ha='left', va='center')
ax.text(0.915, -0.065, 'High Income',
             transform=ax.transAxes,
             fontsize=11, fontweight='bold', color='darkblue', va='center')

# Adjust layout
plt.tight_layout()

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

# Show :)
plt.show()

Code 
# Libraries
# =====================================================
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import os

# Get Data (NBS and WID)
# =====================================================
data = {
    "year": list(range(1980, 2024)),
    "urban_population": [
        19140, 20171, 21480, 22274, 24017, 25094, 26366, 27674, 28661, 29540, 30195, 31203, 32175,
        33173, 34169, 35174, 37304, 39449, 41608, 43748, 45906, 48064, 50212, 52376, 54283, 56212,
        58288, 60633, 62403, 64512, 66978, 69927, 72175, 74502, 76738, 79302, 81924, 84343, 86433,
        88426, 90220, 91425, 92071, 93267
    ],
    "rural_population": [
        79565, 79901, 80174, 80734, 80340, 80757, 81141, 81626, 82365, 83164, 84138, 84620, 84996,
        85344, 85681, 85947, 85085, 84177, 83153, 82038, 80837, 79563, 78241, 76851, 75705, 74544,
        73160, 71496, 70399, 68938, 67113, 64989, 63747, 62224, 60908, 59024, 57308, 55668, 54108,
        52582, 50992, 49835, 49104, 47700
    ],
    "urban_consumption": [
        490, 517, 504, 547, 621, 750, 847, 953, 1200, 1345, 1404, 1623, 2017, 2676, 3671, 4810,
        5437, 5705, 5977, 6429, 7083, 7409, 7826, 8166, 8942, 9900, 10820, 12582, 14147, 15161,
        17119, 19853, 21563, 23386, 25264, 27039, 29324, 31454, 33700, 35841, 34823, 39205, 40066,
        43797
    ],
    "rural_consumption": [
        178, 202, 227, 252, 280, 346, 385, 427, 506, 588, 627, 661, 701, 822, 1073, 1344, 1655, 1768,
        1778, 1793, 1917, 2032, 2157, 2292, 2521, 2784, 3066, 3538, 3981, 4295, 4782, 5880, 6573,
        7397, 8365, 9409, 10609, 12145, 13985, 15460, 16209, 18720, 19929, 21953
    ],
    "gini_pre": [
        0.38, 0.39, 0.39, 0.39, 0.4, 0.4, 0.42, 0.42, 0.43, 0.44, 0.43, 0.45, 0.46, 0.48, 0.48,
        0.48, 0.47, 0.47, 0.47, 0.48, 0.5, 0.51, 0.53, 0.54, 0.55, 0.56, 0.56, 0.56, 0.56, 0.56,
        0.57, 0.56, 0.55, 0.56, 0.55, 0.56, 0.55, 0.56, 0.56, 0.56, 0.56, 0.57, 0.57, 0.57
    ],
    "gini_pos": [
        0.37678, 0.38164, 0.38878, 0.38653, 0.39165, 0.39926, 0.41379, 0.41783, 0.42156, 0.43046,
        0.4264, 0.44225, 0.45711, 0.47219, 0.47823, 0.47116, 0.46628, 0.46659, 0.46798, 0.4751,
        0.49029, 0.49467, 0.52002, 0.52932, 0.53136, 0.54142, 0.53733, 0.53991, 0.53665, 0.53439,
        0.53852, 0.53215, 0.51823, 0.5256, 0.51737, 0.51879, 0.51648, 0.52329, 0.51837, 0.5174,
        0.52289, 0.52394, 0.52394, 0.52394
    ],
   "gini_urb": [
        0.2415, 0.243, 0.2424, 0.2463, 0.2606, 0.2886, 0.2711, 0.2576, 0.267, 0.2738,
        0.2709, 0.2643, 0.2848, 0.3021, 0.3103, 0.3074, 0.3151, 0.3239, 0.3316, 0.3372,
        0.3456, 0.3589, 0.4127, 0.4245, 0.4383, 0.4464, 0.4492, 0.452, 0.456, 0.4516,
        0.4569, 0.4802, 0.4412, 0.474, 0.4466, 0.4474,
        None, None, None, None, None, None, None, None
    ],
    "gini_rur": [
        0.3329, 0.3422, 0.349, 0.3542, 0.3584, 0.3619, 0.3707, 0.3774, 0.3827, 0.387,
        0.3907, 0.4043, 0.4128, 0.4201, 0.4262, 0.4275, 0.43, 0.4315, 0.4267, 0.4311,
        0.4531, 0.4608, 0.4665, 0.4677, 0.4601, 0.4886, 0.483, 0.4921, 0.4903, 0.4963,
        0.5239, 0.5292, 0.5259, 0.524, 0.5232, 0.524, 
        None, None, None, None, None, None, None, None
    ]
}

df = pd.DataFrame(data)
df['var_consumption'] = df['urban_consumption'] / df['rural_consumption']
df['var_population'] = df['urban_population'] / df['rural_population']
df['gini'] = df['gini_pos']
df = df[['year', 'gini', 'var_consumption', 'var_population', 'gini_urb', 'gini_rur']]

# 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 and axis
fig, ax1 = plt.subplots(figsize=(8, 6))

# Axis 1 DISPARITY
ax1.set_ylabel('Gini coefficient', fontsize=10)
line1, = ax1.plot(df['year'], df['gini'], color='#C00000', linewidth=2)
ax1.tick_params(axis='y')
ax1.set_xlim(1980, 2024)
ax1.set_ylim(0, 0.6)
ax1.tick_params(axis='x', labelsize=9)
ax1.tick_params(axis='y', labelsize=8)

# Axis 2 GINI
ax2 = ax1.twinx()
ax2.set_ylabel('Urban-rural ratio', fontsize=10)
line2, = ax2.plot(df['year'], df['var_consumption'], color='#215C98', linewidth=2)
line3, = ax2.plot(df['year'], df['var_population'], color='#282828', linewidth=1, linestyle=":")
ax2.tick_params(axis='y')
ax2.set_ylim(0, 4)
ax2.tick_params(axis='y', labelsize=8)

# Title and grid
plt.text(0.02, 1.13, f'Inequality Trends in China', fontsize=16, fontweight='bold', ha='left', transform=plt.gca().transAxes)
plt.text(0.02, 1.08, f'Urban-Rural Consumption Ratio and Gini Coefficient since 1980', fontsize=11, color="#3A3A3A", ha='left', transform=plt.gca().transAxes)
ax1.grid(axis='y', linestyle='-', alpha=0.5)

# Remove spines
for ax in (ax1, ax2):
    for spine_name, spine in ax.spines.items():
        if spine_name == 'bottom':
            spine.set_visible(True)
            spine.set_linewidth(0.5)
        else:
            spine.set_visible(False)

# Legend at bottom center
plt.plot([], [], color='#C00000', label='Gini coefficient')
plt.plot([], [], color='#215C98', label='Consumption ratio')
plt.plot([], [], color='#282828', label='Population ratio', linestyle=':')
plt.legend(
    loc='lower center',
    bbox_to_anchor=(0.5, -0.15),
    ncol=3,
    fontsize=8,
    frameon=False,
    handlelength=1,
    handleheight=1,
    borderpad=0.2,
    columnspacing=0.5
)

# Add Data Source
plt.text(0, -0.18, 'Data Source:', 
    transform=plt.gca().transAxes, 
    fontsize=8,
    fontweight='bold',
    color='gray')
space = " " * 23
plt.text(0, -0.18, space + 'National Bureau of Statistics of China (NBS), World Inequality Database (WID)', 
    transform=plt.gca().transAxes, 
    fontsize=8,
    color='gray')

# Add Notes
plt.text(0, -0.21, 'Ratio:', 
    transform=plt.gca().transAxes, 
    fontsize=8,
    fontweight='bold',
    color='gray')
space = " " * 11
plt.text(0, -0.21, space + 'Urban-Rural Ratio measures the relative size between urban and rural for population and consumption', 
    transform=plt.gca().transAxes, 
    fontsize=8,
    color='gray')

# Add Notes
plt.text(0, -0.24, 'Gini:', 
    transform=plt.gca().transAxes, 
    fontsize=8,
    fontweight='bold',
    color='gray')
space = " " * 9
plt.text(0, -0.24, space + 'Gini coefficient is calculated using post-tax national income to measure income inequality', 
    transform=plt.gca().transAxes, 
    fontsize=8,
    color='gray')

# Adjust
plt.tight_layout()

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

# Show it :)
plt.show()

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