Stocks traded, total value (% of GDP)

Source: worldbank.org, 01.09.2025

Year: 2024

Flag Country Value Value change, % Rank
United Arab Emirates United Arab Emirates 22.4 +3.55% 17
Armenia Armenia 0.00822 -71.4% 68
Australia Australia 48.2 -7.14% 10
Austria Austria 5.79 +0.191% 27
Azerbaijan Azerbaijan 0.00962 +152% 66
Bangladesh Bangladesh 2.84 -6.77% 35
Bulgaria Bulgaria 0.163 -25.4% 57
Bahrain Bahrain 1.68 +50.3% 39
Belarus Belarus 0.0212 +248% 65
Bermuda Bermuda 0.125 -45.1% 59
Brazil Brazil 41.7 -23.7% 12
Botswana Botswana 0.459 -70.5% 50
Canada Canada 95.9 +3.2% 5
Switzerland Switzerland 76.8 -10.7% 7
Chile Chile 8.87 -2.91% 22
China China 186 +14% 2
Colombia Colombia 1.18 +6.14% 43
Cyprus Cyprus 0.422 +65.3% 51
Czechia Czechia 1.05 -22.9% 44
Germany Germany 19.8 -13.8% 18
Egypt Egypt 5.22 +4.01% 29
Spain Spain 16 -17.5% 19
Ghana Ghana 0.0948 +169% 61
Greece Greece 12.2 +1.94% 21
Hong Kong SAR China Hong Kong SAR China 736 +23.1% 1
Croatia Croatia 0.306 -12.8% 54
Hungary Hungary 3.31 +2.48% 34
Indonesia Indonesia 8.7 -15.1% 24
India India 85.6 +60% 6
Iran Iran 4.31 -96.2% 32
Israel Israel 8.6 -53.6% 25
Jamaica Jamaica 2.42 +68% 37
Jordan Jordan 2.83 -29.6% 36
Japan Japan 183 +21.3% 3
Kazakhstan Kazakhstan 0.158 -45.7% 58
Kenya Kenya 0.329 +7.56% 52
Kuwait Kuwait 29 +44.5% 16
Sri Lanka Sri Lanka 1.51 +23.6% 40
Luxembourg Luxembourg 0.042 -12.6% 64
Morocco Morocco 3.9 +66.4% 33
Mexico Mexico 5.91 -8.69% 26
Malta Malta 0.22 -24.2% 55
Mauritius Mauritius 1.7 -7.58% 38
Malaysia Malaysia 39.3 +48.8% 13
Namibia Namibia 0.169 -26.3% 56
Nigeria Nigeria 0.822 +83.1% 46
New Zealand New Zealand 4.83 +1.74% 30
Pakistan Pakistan 5.32 +105% 28
Panama Panama 0.491 -4.4% 49
Peru Peru 1.32 +390% 41
Philippines Philippines 4.73 -2.11% 31
Poland Poland 8.87 +3.15% 23
Palestinian Territories Palestinian Territories 1.2 -35.1% 42
Qatar Qatar 13.4 -17% 20
Romania Romania 0.788 -12.1% 47
Rwanda Rwanda 0.308 +697% 53
Saudi Arabia Saudi Arabia 38.3 +36% 14
Slovenia Slovenia 0.588 +29.7% 48
Seychelles Seychelles 0.121 -79.8% 60
Thailand Thailand 57.3 -13.7% 9
Tunisia Tunisia 1.03 -16.2% 45
Turkey Turkey 72.5 -25.7% 8
Tanzania Tanzania 0.0841 +86.6% 62
United States United States 146 +8.59% 4
Uzbekistan Uzbekistan 0.00887 -69% 67
Vietnam Vietnam 33.7 +7.15% 15
South Africa South Africa 48 -8.69% 11
Zambia Zambia 0.055 -28.2% 63 

                    
# Install missing packages
import sys
import subprocess

def install(package):
subprocess.check_call([sys.executable, "-m", "pip", "install", package])

# Required packages
for package in ['wbdata', 'country_converter']:
try:
__import__(package)
except ImportError:
install(package)

# Import libraries
import wbdata
import country_converter as coco
from datetime import datetime

# Define World Bank indicator code
dataset_code = 'CM.MKT.TRAD.GD.ZS'

# Download data from World Bank API
data = wbdata.get_dataframe({dataset_code: 'value'},
date=(datetime(1960, 1, 1), datetime.today()),
parse_dates=True,
keep_levels=True).reset_index()

# Extract year
data['year'] = data['date'].dt.year

# Convert country names to ISO codes using country_converter
cc = coco.CountryConverter()
data['iso2c'] = cc.convert(names=data['country'], to='ISO2', not_found=None)
data['iso3c'] = cc.convert(names=data['country'], to='ISO3', not_found=None)

# Filter out rows where ISO codes could not be matched — likely not real countries
data = data[data['iso2c'].notna() & data['iso3c'].notna()]

# Sort for calculation
data = data.sort_values(['iso3c', 'year'])

# Calculate YoY absolute and percent change
data['value_change'] = data.groupby('iso3c')['value'].diff()
data['value_change_percent'] = data.groupby('iso3c')['value'].pct_change() * 100

# Calculate ranks (higher GDP per capita = better rank)
data['rank'] = data.groupby('year')['value'].rank(ascending=False, method='dense')

# Calculate rank change from previous year
data['rank_change'] = data.groupby('iso3c')['rank'].diff()

# Select desired columns
final_df = data[['country', 'iso2c', 'iso3c', 'year', 'value',
'value_change', 'value_change_percent', 'rank', 'rank_change']].copy()

# Optional: Add labels as metadata (could be useful for export or UI)
column_labels = {
'country': 'Country name',
'iso2c': 'ISO 2-letter country code',
'iso3c': 'ISO 3-letter country code',
'year': 'Year',
'value': 'GDP per capita (current US$)',
'value_change': 'Year-over-Year change in value',
'value_change_percent': 'Year-over-Year percent change in value',
'rank': 'Country rank by GDP per capita (higher = richer)',
'rank_change': 'Change in rank from previous year'
}

# Display first few rows
print(final_df.head(10))

# Optional: Save to CSV
#final_df.to_csv("gdp_per_capita_cleaned.csv", index=False)
                    
                
                    
# Check and install required packages
required_packages <- c("WDI", "countrycode", "dplyr")

for (pkg in required_packages) {
  if (!requireNamespace(pkg, quietly = TRUE)) {
    install.packages(pkg)
  }
}

# Load the necessary libraries
library(WDI)
library(dplyr)
library(countrycode)

# Define the dataset code (World Bank indicator code)
dataset_code <- 'CM.MKT.TRAD.GD.ZS'

# Download data using WDI package
dat <- WDI(indicator = dataset_code)

# Filter only countries using 'is_country' from countrycode
# This uses iso2c to identify whether the entry is a recognized country
dat <- dat %>%
  filter(countrycode(iso2c, origin = 'iso2c', destination = 'country.name', warn = FALSE) %in%
           countrycode::codelist$country.name.en)

# Ensure dataset is ordered by country and year
dat <- dat %>%
  arrange(iso3c, year)

# Rename the dataset_code column to "value" for easier manipulation
dat <- dat %>%
  rename(value = !!dataset_code)

# Calculate year-over-year (YoY) change and percentage change
dat <- dat %>%
  group_by(iso3c) %>%
  mutate(
    value_change = value - lag(value),                              # Absolute change from previous year
    value_change_percent = 100 * (value - lag(value)) / lag(value) # Percent change from previous year
  ) %>%
  ungroup()

# Calculate rank by year (higher value => higher rank)
dat <- dat %>%
  group_by(year) %>%
  mutate(rank = dense_rank(desc(value))) %>% # Rank countries by descending value
  ungroup()

# Calculate rank change (positive = moved up, negative = moved down)
dat <- dat %>%
  group_by(iso3c) %>%
  mutate(rank_change = rank - lag(rank)) %>% # Change in rank compared to previous year
  ungroup()

# Select and reorder final columns
final_data <- dat %>%
  select(
    country,
    iso2c,
    iso3c,
    year,
    value,
    value_change,
    value_change_percent,
    rank,
    rank_change
  )

# Add labels (variable descriptions)
attr(final_data$country, "label") <- "Country name"
attr(final_data$iso2c, "label") <- "ISO 2-letter country code"
attr(final_data$iso3c, "label") <- "ISO 3-letter country code"
attr(final_data$year, "label") <- "Year"
attr(final_data$value, "label") <- "GDP per capita (current US$)"
attr(final_data$value_change, "label") <- "Year-over-Year change in value"
attr(final_data$value_change_percent, "label") <- "Year-over-Year percent change in value"
attr(final_data$rank, "label") <- "Country rank by GDP per capita (higher = richer)"
attr(final_data$rank_change, "label") <- "Change in rank from previous year"

# Print the first few rows of the final dataset
print(head(final_data, 10))