Globalization Eras

economy
python
The pace of globalization slowed in the decade and a half since the global financial crisis, resulting in an era often referred to as “slowbalization
Published

Oct 9, 2025

Keywords

wealth-income

Summary

The chart illustrates the level of openness in global trade since the onset of globalization, with each period marked by unique economic conditions and distinct international relations.

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

# Extraction Data OURWORLDINDATA
# ============================================
# Get data
dfo = pd.read_csv("https://ourworldindata.org/grapher/globalization-over-5-centuries.csv?v=1&csvType=full&useColumnShortNames=true", storage_options = {'User-Agent': 'Our World In Data data fetch/1.0'})
dfo = dfo[dfo['Code'] == 'OWID_WRL']

# Rename columns
dfo = dfo.rename(columns={
    'Year': 'year',
    ' World trade based on Maddison (% of GDP) (Klasing and Milionis (2014)) ': 'trade_klasing',
    'World trade based on own estimates (% of GDP) (Klasing and Milionis (2014)) ': 'trade_klasing_madisson',
    'ne_trd_gnfs_zs': 'trade_penn',
    'trade_openness': 'trade_openness_index'
})

# Trade priority data
dfo = dfo[['year', 'trade_klasing', 'trade_klasing_madisson', 'trade_penn', 'trade_openness_index']]
dfo['trade'] = np.where(dfo['trade_openness_index'].notnull(), dfo['trade_openness_index'], 
                        np.where(dfo['trade_penn'].notnull(), dfo['trade_penn'], 
                        np.where(dfo['trade_klasing_madisson'].notnull(), dfo['trade_klasing_madisson'], 
                        dfo['trade_klasing'])))

# Prepare dfo
dfo = dfo[['year', 'trade']]
dfo = dfo[dfo['trade'].notnull()]
dfo = dfo.sort_values(by='year').reset_index(drop=True)

# Extraction Data 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()
dfc = pd.DataFrame(data)
dfc = pd.DataFrame.from_dict(data, orient='index').reset_index()
df_countries = dfc.rename(columns={'index': 'ISO3'})

# Extraction Data WORLDBANK
# ========================================================
# To use the built-in plotting method
indicator = ['NY.GDP.MKTP.CD', 'NE.EXP.GNFS.CD', 'NE.IMP.GNFS.CD']
countries = df_countries['ISO3'].tolist()
data_range = range(1970, 2024)
data = wb.data.DataFrame(indicator, 'WLD', data_range, numericTimeKeys=True, labels=False, columns='series').reset_index()
dfw = data.rename(columns={
    'time': 'year',
    'economy': 'iso',
    'NY.GDP.MKTP.CD': 'gdp',
    'NE.EXP.GNFS.CD': 'exports',
    'NE.IMP.GNFS.CD': 'imports'
})

dfw['comerce'] = dfw['imports'] + dfw['exports']
dfw = dfw[['year', 'gdp', 'comerce']].groupby('year', as_index=False).sum()
dfw['trade'] = dfw['comerce'] / dfw['gdp'] * 100
dfw = dfw[['year', 'trade']]

# Unify DF
# ========================================================
# Filter dataframes
dfo = dfo[dfo['year'] < 1970]
dfw = dfw[dfw['year'] > 1970]

# Filter dataframes
df = pd.concat([dfo, dfw], ignore_index=True)

# Divide dataframes
df_1870_1914 = df[(df['year'] >= 1870) & (df['year'] <= 1915)]
df_1915_1945 = df[(df['year'] >= 1915) & (df['year'] <= 1945)]
df_1945_1980 = df[(df['year'] >= 1945) & (df['year'] <= 1980)]
df_1980_2008 = df[(df['year'] >= 1980) & (df['year'] <= 2008)]
df_2008_2023 = df[(df['year'] >= 2008) & (df['year'] <= 2023)]

print(df)

# Visualization Data
# ============================================
# Font and style
plt.rcParams.update({'font.family': 'sans-serif', 'font.sans-serif': ['Open Sans'], 'font.size': 10})
sns.set(style="white", palette="muted")

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

# Stackplot for each dataframe
plt.stackplot(df_1870_1914['year'], df_1870_1914['trade'], color='#084d95', alpha=1)
plt.stackplot(df_1915_1945['year'], df_1915_1945['trade'], color='#d92724', alpha=1)
plt.stackplot(df_1945_1980['year'], df_1945_1980['trade'], color='#084d95', alpha=1)
plt.stackplot(df_1980_2008['year'], df_1980_2008['trade'], color='#084d95', alpha=1)
plt.stackplot(df_2008_2023['year'], df_2008_2023['trade'], color='#d92724', alpha=1)

# Add vertical lines
plt.axvline(x=1915, color='white', linestyle='--', linewidth=0.75)
plt.axvline(x=1945, color='white', linestyle='--', linewidth=0.75)
plt.axvline(x=1980, color='white', linestyle='--', linewidth=0.75)
plt.axvline(x=2008, color='white', linestyle='--', linewidth=0.75)

# Add title and labels
plt.text(0, 1.08, f'Eras of globalization', fontsize=16, fontweight='bold', ha='left', transform=plt.gca().transAxes)
plt.text(0, 1.045, 'Trade openess slowed following the global financial crisis.', fontsize=11, color='#262626', ha='left', transform=plt.gca().transAxes)
plt.text(0, 1.01, '(sum of exports and imports as percent of GDP)', fontsize=9, fontweight='light', color='#262626', ha='left', transform=plt.gca().transAxes)
plt.xlim(1871, 2023)
plt.ylim(0, 65)
plt.xticks([1870, 1915, 1945, 1980, 2008])
plt.gca().yaxis.grid(True, linestyle='-', alpha=0.4)

# Delete spines
for spine in ["top", "left", "right"]:
    plt.gca().spines[spine].set_visible(False)

# Industrialization
plt.text(
    x=df_1870_1914['year'].mean(),
    y=3,
    s="1870-1914",
    fontsize=9, 
    color='white',
    ha='center',
    va='bottom'
)
plt.text(
    x=df_1870_1914['year'].mean(),
    y=1,
    s="Industrialization",
    fontsize=9, 
    fontweight='bold',
    color='white',
    ha='center',
    va='bottom'
)

# Wars and proteccionism
plt.text(
    x=df_1915_1945['year'].mean(),
    y=3,
    s="1915-1945",
    fontsize=9, 
    color='white',
    ha='center',
    va='bottom'
)
plt.text(
    x=df_1915_1945['year'].mean(),
    y=1,
    s="Wars, proteccionism",
    fontsize=9, 
    fontweight='bold',
    color='white',
    ha='center',
    va='bottom'
)

# Fixed exchanges rates
plt.text(
    x=df_1945_1980['year'].mean(),
    y=3,
    s="1945-1980",
    fontsize=9, 
    color='white',
    ha='center',
    va='bottom'
)
plt.text(
    x=df_1945_1980['year'].mean(),
    y=1,
    s="Fixed exchanges rates",
    fontsize=9, 
    fontweight='bold',
    color='white',
    ha='center',
    va='bottom'
)

# Liberization
plt.text(
    x=df_1980_2008['year'].mean(),
    y=3,
    s="1980-2008",
    fontsize=9, 
    color='white',
    ha='center',
    va='bottom'
)
plt.text(
    x=df_1980_2008['year'].mean(),
    y=1,
    s="Liberization",
    fontsize=9, 
    fontweight='bold',
    color='white',
    ha='center',
    va='bottom'
)

# Slowbalization
plt.text(
    x=df_2008_2023['year'].mean() +2,
    y=3,
    s="2008-current",
    fontsize=9, 
    color='white',
    ha='center',
    va='bottom',
    rotation=90
)
plt.text(
    x=df_2008_2023['year'].mean() +5,
    y=1,
    s="Slowbalization",
    fontsize=9, 
    fontweight='bold',
    color='white',
    ha='center',
    va='bottom',
    rotation=90
)

# Add Data Source
plt.text(0, -0.12, 'Data Source: PIIE, Jorda-Schularick-Taylor Macrohistory Database, Penn World Data (10.0), World Bank, and IMF staff calculations.', 
    transform=plt.gca().transAxes, 
    fontsize=8, 
    color='gray')

# Show plot!
plt.show()

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