Firms formally registered when operations started (% of firms)

Source: worldbank.org, 03.09.2025

Year: 2024

Flag Country Value Value change, % Rank
Angola Angola 72.5 +15.7% 44
Armenia Armenia 96.4 -0.45% 6
Azerbaijan Azerbaijan 82.6 -14.9% 37
Belgium Belgium 88.3 -7.84% 33
Benin Benin 86.6 -4.46% 34
Burkina Faso Burkina Faso 43.9 -43.6% 50
Bahrain Bahrain 93.4 19
Bhutan Bhutan 97.7 +2.62% 1
Canada Canada 89.9 29
China China 96 +0.241% 9
Cameroon Cameroon 74 -8.78% 43
Congo - Kinshasa Congo - Kinshasa 56 -18.8% 48
Congo - Brazzaville Congo - Brazzaville 61.5 -27% 47
Cape Verde Cape Verde 88.6 +9.05% 32
Cyprus Cyprus 94.3 +3.15% 15
Czechia Czechia 95.1 -2.64% 12
Ecuador Ecuador 75.8 -14.3% 42
Spain Spain 94.6 -2.08% 14
United Kingdom United Kingdom 89.1 30
Equatorial Guinea Equatorial Guinea 63.2 46
Ireland Ireland 82.3 -0.312% 38
Iceland Iceland 81.2 40
Israel Israel 80.8 -11% 41
Italy Italy 92.3 -3.09% 22
Jamaica Jamaica 91.7 +1.83% 24
Jordan Jordan 91.4 -5.82% 26
Kazakhstan Kazakhstan 96.6 -0.727% 4
South Korea South Korea 95.3 11
Laos Laos 65.4 -15.7% 45
Latvia Latvia 94.1 -5.38% 17
Moldova Moldova 96.2 +0.129% 7
Mali Mali 54 -37.6% 49
Malta Malta 91.7 +0.649% 23
Malaysia Malaysia 96 +3.85% 8
Namibia Namibia 85.3 +2.11% 36
Papua New Guinea Papua New Guinea 89 -6.32% 31
Senegal Senegal 90.5 +3.06% 27
Serbia Serbia 96.9 -1.93% 3
South Sudan South Sudan 81.8 +2.34% 39
Slovenia Slovenia 96.6 -3.21% 5
Sweden Sweden 85.5 -9.98% 35
Eswatini Eswatini 90.3 -3.58% 28
Tajikistan Tajikistan 91.6 +5.76% 25
Turkmenistan Turkmenistan 93 21
Tonga Tonga 95.3 +1.9% 10
Tunisia Tunisia 93.4 -2.7% 20
Turkey Turkey 97.1 +1.43% 2
Uruguay Uruguay 95 +0.295% 13
United States United States 93.5 18
Uzbekistan Uzbekistan 94.2 -5.06% 16 

                    
# 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.FREG.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.FREG.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))