Listed domestic companies, total

Source: worldbank.org, 01.09.2025

Year: 2024

Flag Country Value Value change, % Rank
United Arab Emirates United Arab Emirates 154 +4.76% 33
Armenia Armenia 12 0% 61
Australia Australia 1,853 -3.19% 8
Austria Austria 68 -1.45% 46
Azerbaijan Azerbaijan 36 +56.5% 52
Bangladesh Bangladesh 691 +1.62% 12
Bulgaria Bulgaria 204 0% 29
Bahrain Bahrain 39 -2.5% 51
Belarus Belarus 12 -7.69% 61
Bermuda Bermuda 10 0% 62
Brazil Brazil 331 -4.06% 22
Canada Canada 4,226 -1.99% 2
Switzerland Switzerland 206 -3.29% 28
Chile Chile 437 +9.52% 17
China China 11,231 -1.03% 1
Colombia Colombia 60 -3.23% 47
Cyprus Cyprus 93 -1.06% 39
Czechia Czechia 25 0% 56
Germany Germany 435 -3.97% 18
Egypt Egypt 246 +2.07% 26
Spain Spain 277 -64.2% 25
Ghana Ghana 30 0% 54
Greece Greece 143 -5.92% 34
Hong Kong SAR China Hong Kong SAR China 2,449 +0.865% 7
Croatia Croatia 81 -10% 42
Hungary Hungary 68 +3.03% 46
India India 2,673 +12.8% 5
Iran Iran 594 +3.3% 13
Israel Israel 511 -0.195% 16
Jamaica Jamaica 101 +3.06% 38
Jordan Jordan 162 -2.99% 32
Japan Japan 3,971 +1.07% 4
Kazakhstan Kazakhstan 79 -13.2% 43
Kenya Kenya 59 0% 48
South Korea South Korea 2,599 +2.48% 6
Kuwait Kuwait 138 -4.83% 35
Sri Lanka Sri Lanka 284 -2.07% 23
Luxembourg Luxembourg 28 0% 55
Morocco Morocco 76 0% 44
Mexico Mexico 129 -1.53% 36
Malta Malta 34 0% 53
Mauritius Mauritius 93 0% 39
Malaysia Malaysia 1,031 +4.46% 9
Namibia Namibia 12 -7.69% 61
Nigeria Nigeria 169 -1.74% 31
New Zealand New Zealand 112 -5.88% 37
Pakistan Pakistan 530 +0.569% 15
Panama Panama 36 +5.88% 52
Peru Peru 176 +0.571% 30
Philippines Philippines 280 0% 24
Poland Poland 749 -0.399% 11
Palestinian Territories Palestinian Territories 48 -2.04% 50
Qatar Qatar 52 +1.96% 49
Romania Romania 83 0% 41
Rwanda Rwanda 5 0% 63
Saudi Arabia Saudi Arabia 352 +13.9% 21
Singapore Singapore 400 -2.91% 19
Slovenia Slovenia 18 -21.7% 60
Seychelles Seychelles 23 -4.17% 57
Thailand Thailand 860 +2.38% 10
Tunisia Tunisia 74 -5.13% 45
Turkey Turkey 551 +6.58% 14
Tanzania Tanzania 22 0% 58
United States United States 4,010 -7.11% 3
Uzbekistan Uzbekistan 89 -20.5% 40
Vietnam Vietnam 393 -0.254% 20
South Africa South Africa 220 -1.79% 27
Zambia Zambia 20 -9.09% 59 

                    
# 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.LDOM.NO'

# 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.LDOM.NO'

# 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))