Firms competing against unregistered firms (% of firms)

Source: worldbank.org, 03.09.2025

Year: 2024

Flag Country Value Value change, % Rank
Angola Angola 52.5 +27.2% 13
Armenia Armenia 34.4 +19.2% 25
Azerbaijan Azerbaijan 16 -46.6% 41
Belgium Belgium 14.5 -24.4% 45
Benin Benin 58.2 -12.8% 9
Burkina Faso Burkina Faso 71.6 -4.42% 3
Bahrain Bahrain 26.1 34
Bhutan Bhutan 13.8 +91% 46
Canada Canada 17.4 39
China China 12.2 -78.9% 47
Cameroon Cameroon 75.4 -6.05% 1
Congo - Kinshasa Congo - Kinshasa 59 -24.7% 8
Congo - Brazzaville Congo - Brazzaville 59.2 -15% 7
Cape Verde Cape Verde 47 +5.47% 16
Cyprus Cyprus 26.2 -40.4% 32
Czechia Czechia 26.1 +6.4% 33
Ecuador Ecuador 67.7 +0.989% 5
Spain Spain 26.3 -15.1% 31
United Kingdom United Kingdom 24.3 35
Equatorial Guinea Equatorial Guinea 46.9 17
Ireland Ireland 33.4 -6.85% 27
Iceland Iceland 35.4 23
Israel Israel 31.8 +82.4% 30
Italy Italy 15.5 -30.1% 43
Jamaica Jamaica 73.4 +10.6% 2
Jordan Jordan 50.2 +3.38% 14
Kazakhstan Kazakhstan 39.7 +1.25% 21
South Korea South Korea 11 49
Laos Laos 49.5 +24.1% 15
Latvia Latvia 44 +17.7% 18
Moldova Moldova 36.5 -11% 22
Mali Mali 56.8 -28.5% 10
Malta Malta 35.3 +31.4% 24
Malaysia Malaysia 32.5 +9.67% 29
Namibia Namibia 56.7 +35.9% 11
Papua New Guinea Papua New Guinea 42.6 -41.1% 19
Senegal Senegal 55.2 -27.8% 12
Serbia Serbia 32.8 -34.1% 28
South Sudan South Sudan 15.2 -78.2% 44
Slovenia Slovenia 15.6 -13.9% 42
Sweden Sweden 17.7 -1.78% 38
Eswatini Eswatini 69.8 -5.04% 4
Tajikistan Tajikistan 11.3 -4.12% 48
Turkmenistan Turkmenistan 18.6 37
Tonga Tonga 7.21 -91.7% 50
Tunisia Tunisia 42.2 -30.6% 20
Turkey Turkey 34 -32.2% 26
Uruguay Uruguay 61.9 -8.86% 6
United States United States 22.4 36
Uzbekistan Uzbekistan 16.9 -21.5% 40 

                    
# 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 = 'IC.FRM.CMPU.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 <- 'IC.FRM.CMPU.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))