Exploring Discrepancies between Market Exchanges Rates and Purchasing Power Parity

economy
Examines the differences between market exchange rates and PPP, highlighting how these discrepancies reflect economic imbalances and price level variations across countries.
Published
Feb 17, 2025
Keywords
Inequalities
Summary

A plot that shows the current Inequalities in capital productivity.
Code
# Libraries
# =====================================================================
import requests
import pandas as pd
import numpy as np
import requests
import plotly.graph_objects as go
import numpy as np

# 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 - IMF (1980-2030)
# =====================================================================
#Parametro
parameters = ['NGDPD', 'PPPGDP', 'LP']

# 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_imf = pd.DataFrame(records)

# Pivot Parameter to columns and filter nulls
df_imf = df_imf.pivot(index=['ISO3', 'Year'], columns='Parameter', values='Value').reset_index()

# Filter after 2024
df_imf = df_imf[df_imf['Year'] == 2024]

# Data Manipulation
# =====================================================================
# Concat and filter dataframes
df = df_imf.dropna(subset=['NGDPD', 'PPPGDP', 'LP'], how='any')

# Merge queries
df = df.merge(df_countries, how='left', left_on='ISO3', right_on='ISO3')
df = df[['Region', 'ISO2', 'Country', 'Cod_Currency', 'Year', 'NGDPD', 'PPPGDP', 'LP']]
df = df[df['Cod_Currency'].notna()]

# Calculate PPP
df = df.groupby(['Region', 'ISO2', 'Country', 'Cod_Currency', 'Year'])[['NGDPD', 'PPPGDP', 'LP']].sum()
df = df.reset_index()
df['PPP'] = df['NGDPD'] / df['PPPGDP']
df['NGDPDPC'] = df['NGDPD'] / df['LP']
df['PPPPC'] = df['PPPGDP'] / df['LP']

# Calculate Average Weight and Percent
df['AVG_Weight'] = df.groupby('Year')['NGDPDPC'].transform(lambda x: np.average(x, weights=df.loc[x.index, 'LP']))
df['Percent'] = df['NGDPD'] / df.groupby('Year')['NGDPD'].transform('sum')

# Filtering
df = df[df['NGDPDPC'] < 115 ]
df = df[df['PPP'] < 1.25]
df = df[~df['ISO2'].isin(['SZ', 'VA', 'NC', 'CI', 'MW', 'SS', 'MY'])]

# Data Visualization
# =====================================================================
fig = go.Figure()

# Tamaño de los marcadores
marker_size = np.sqrt(df["NGDPD"] / df["NGDPD"].max()) * 100 + 3
line_width  = np.sqrt(df["NGDPD"] / df["NGDPD"].max()) * 4 + 0.5

# Add scatter plot
fig.add_trace(go.Scatter(
    x=df["PPP"],
    y=df["NGDPDPC"],
    mode='markers',
    text=df["Country"],
    marker=dict(
        size=marker_size,
        color="rgba(0,0,0,0)",
        line=dict(
            width=line_width,
            color='black'
        )
    ),
    hovertemplate="<b>Country:</b> %{text}<br>" +
                  "<b>GDP per Capita:</b> $%{y:.2f}<br>" + 
                  "<b>PPP:</b> $%{x:.2f}<extra></extra>",
    showlegend=False
))

# Add flag images to scatterplot
for i, row in df.iterrows():
    country_iso = row["ISO2"]
    
    # Calculate image size
    image_size = marker_size[i] * 0.21

    # Add the flag image
    fig.add_layout_image(
        dict(
            source=f"https://raw.githubusercontent.com/matahombres/CSS-Country-Flags-Rounded/master/flags/{country_iso}.png",
            xref="x",
            yref="y",
            xanchor="center",
            yanchor="middle",
            x=row["PPP"],
            y=row["NGDPDPC"],
            sizex=image_size,
            sizey=image_size,
            sizing="contain",
            opacity=0.8,
            layer="above"
        )
    )
    
# Modeling a line trend
z = np.polyfit(df['PPP'], df['NGDPDPC'], 2, w=df['NGDPD'])
p = np.poly1d(z)
x_range = np.linspace(df['PPP'].min(), df['PPP'].max(), 100)
y_range = p(x_range)

# Add the line trend
fig.add_trace(go.Scatter(
    x=x_range,
    y=y_range,
    mode='lines',
    name='Trend Line',
    line=dict(color='darkred', width=0.5),
    showlegend=False
))

# Add red and green shapes
fig.add_shape(
    type="rect",
    xref="x", yref="paper",
    x0=0, x1=1,
    y0=0, y1=1,
    fillcolor="red",
    opacity=0.04,
    layer="below",
    line_width=0
)
fig.add_shape(
    type="rect",
    xref="x", yref="paper",
    x0=1, x1=1.2,
    y0=0, y1=1,
    fillcolor="green",
    opacity=0.04,
    layer="below",
    line_width=0
)

# Configuration plot
fig.update_layout(
    title="<b>Global Inequalities in GDP per Capita</b>",
    title_x=0.11,
    title_font=dict(size=16),
    annotations=[
        dict(
            text="Exploring Discrepancies between Market Exchanges Rates and Purchasing Power Parity",
            xref="paper",
            yref="paper",
            x=0,
            y=1.07,
            showarrow=False,
            font=dict(size=11)
        ),
        dict(
            text="<b>Data Source:</b> IMF World Economic Outlook Database, 2024",
            xref="paper",
            yref="paper",
            x=0,
            y=-0.13,
            showarrow=False,
            font=dict(size=10),
            align="left"
        ),
        dict(
            text=f"2024",
            xref="paper", 
            yref="paper",
            x=1, 
            y=1.1,
            showarrow=False,
            font=dict(size=22, color='lightgray', weight='bold'),
            align="right"
        )
    ],
    xaxis=dict(
        title="<b>GAP Between PPP and Market Exchange Rate</b>",
        range=[0, 1.2],
        tickvals=[i * 1.2 / 6 for i in range(7)],
        showline=True,
        linewidth=1,
        linecolor="black",
        gridcolor="#ebebeb"
    ),
    yaxis=dict(
        title="<b>GDP per Capita (US$)</b>",
        range=[0, 120],
        tickvals=[i * 120 / 6 for i in range(7)],
        ticktext=[f"{int(i * 120 / 6)}k" for i in range(7)],
        showline=True,
        linewidth=1,
        linecolor="black",
        gridcolor="#ebebeb"
    ),
    height=750,
    width=750,
    plot_bgcolor="white",
    paper_bgcolor="white"
)

# Add a custom legend
size_legend = ['Smaller', 'Middle', 'Bigger']
size_values = [5, 10, 20]

for label, size in zip(size_legend, size_values):
    fig.add_trace(go.Scatter(
        x=[None],
        y=[None],
        mode='markers',
        marker=dict(
            size=size,
            color="rgba(0,0,0,0)",
            line=dict(
                width=1,
                color='black'
            )
        ),
        legendgroup='size',
        showlegend=True,
        name=f'{label}'
    ))

fig.update_layout(
    legend=dict(
        title=dict(text='<b>   GDP Scale</b>'), 
        font=dict(size=11),
        x=0.025,
        y=0.95,
        xanchor='left',
        bgcolor='white',
        bordercolor='black',
        borderwidth=1
    )
)

# Show the plot!
fig.show()