Market capitalization of listed domestic companies (current US$)

Source: worldbank.org, 01.09.2025

Year: 2024

Flag Country Value Value change, % Rank
United Arab Emirates United Arab Emirates 1,054,162,330,000 +6.03% 12
Armenia Armenia 4,847,050,000 +1,632% 60
Australia Australia 1,737,106,310,000 -2.89% 10
Austria Austria 120,781,830,000 -12.9% 28
Azerbaijan Azerbaijan 1,635,320,000 +3.68% 66
Bangladesh Bangladesh 87,941,640,000 -21.2% 30
Bulgaria Bulgaria 8,413,470,000 +7.31% 54
Bahrain Bahrain 20,407,640,000 -0.964% 45
Belarus Belarus 6,011,190,000 +75.6% 59
Bermuda Bermuda 1,927,960,000 +906% 65
Brazil Brazil 658,911,870,000 -33.5% 15
Canada Canada 3,374,477,170,000 +8.88% 6
Switzerland Switzerland 1,971,411,690,000 -3.58% 9
Chile Chile 262,010,510,000 -9.04% 22
China China 11,755,757,950,000 +7.5% 2
Colombia Colombia 72,637,650,000 -8.29% 34
Cyprus Cyprus 10,928,480,000 +5.28% 52
Czechia Czechia 34,621,120,000 -0.116% 42
Germany Germany 2,044,248,710,000 -6.14% 8
Egypt Egypt 42,595,320,000 -23.3% 39
Spain Spain 753,993,840,000 -2.02% 14
Ghana Ghana 7,575,240,000 +22% 57
Greece Greece 83,199,050,000 +2.31% 31
Hong Kong SAR China Hong Kong SAR China 4,549,720,760,000 +14.5% 5
Croatia Croatia 27,816,780,000 +9.71% 43
Hungary Hungary 41,542,380,000 +6.91% 41
India India 5,131,397,430,000 +18.2% 4
Iran Iran 171,583,580,000 -91.7% 26
Israel Israel 330,881,920,000 +25.1% 21
Jamaica Jamaica 12,429,190,000 +2.17% 50
Jordan Jordan 24,902,520,000 +4.23% 44
Japan Japan 6,310,681,430,000 +2.63% 3
Kazakhstan Kazakhstan 62,818,040,000 +6.33% 35
Kenya Kenya 15,001,870,000 +63.2% 49
South Korea South Korea 1,557,487,560,000 -20.9% 11
Kuwait Kuwait 141,480,520,000 +7.89% 27
Sri Lanka Sri Lanka 19,501,340,000 +48.6% 46
Luxembourg Luxembourg 41,861,930,000 -22.2% 40
Morocco Morocco 74,459,640,000 +17.3% 33
Mexico Mexico 417,606,000,000 -27.6% 19
Malta Malta 4,287,740,000 -16.8% 61
Mauritius Mauritius 9,124,920,000 +7.34% 53
Malaysia Malaysia 449,470,390,000 +18.9% 18
Namibia Namibia 2,480,220,000 +4.19% 64
Nigeria Nigeria 54,400,940,000 +17.1% 36
New Zealand New Zealand 92,866,710,000 -3.84% 29
Pakistan Pakistan 52,073,620,000 +61.4% 37
Panama Panama 18,288,000,000 -10.2% 48
Peru Peru 82,195,770,000 -3.26% 32
Philippines Philippines 251,828,420,000 +6.5% 23
Poland Poland 197,361,480,000 -6.75% 25
Palestinian Territories Palestinian Territories 4,080,060,000 -11.8% 62
Qatar Qatar 170,320,000 -99.9% 68
Romania Romania 47,413,490,000 -0.943% 38
Rwanda Rwanda 2,754,760,000 -6.07% 63
Saudi Arabia Saudi Arabia 2,727,001,390,000 -9.56% 7
Singapore Singapore 637,630,270,000 +4.82% 16
Slovenia Slovenia 11,439,970,000 +13.2% 51
Seychelles Seychelles 811,060,000 +23.8% 67
Thailand Thailand 519,672,000,000 +0.0138% 17
Tunisia Tunisia 8,302,070,000 +4.4% 55
Turkey Turkey 378,961,230,000 +12.3% 20
Tanzania Tanzania 7,319,600,000 +26.2% 58
United States United States 62,185,685,320,000 +27% 1
Uzbekistan Uzbekistan 18,799,570,000 +37% 47
Vietnam Vietnam 204,615,880,000 +8.94% 24
South Africa South Africa 985,697,370,000 -3.91% 13
Zambia Zambia 7,765,130,000 +125% 56 

                    
# 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.LCAP.CD'

# 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.LCAP.CD'

# 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))