Firms using banks to finance working capital (% of firms)

Source: worldbank.org, 01.09.2025

Year: 2024

Flag Country Value Value change, % Rank
Angola Angola 3.9 -70.8% 50
Armenia Armenia 38.3 +7.29% 12
Azerbaijan Azerbaijan 30.1 +54.7% 23
Belgium Belgium 54.9 +1.36% 2
Benin Benin 41.4 +59.3% 10
Burkina Faso Burkina Faso 16.7 -49.6% 39
Bahrain Bahrain 27.6 28
Bhutan Bhutan 22.7 -46.1% 32
Canada Canada 29 24
China China 26.8 +21.2% 29
Cameroon Cameroon 32.8 +62% 19
Congo - Kinshasa Congo - Kinshasa 6.96 -20.4% 48
Congo - Brazzaville Congo - Brazzaville 12.8 +31.7% 44
Cape Verde Cape Verde 19.9 -60.1% 35
Cyprus Cyprus 32.9 -22.1% 18
Czechia Czechia 41.4 +41% 9
Ecuador Ecuador 45.4 +12.8% 6
Spain Spain 44.5 -19.4% 7
United Kingdom United Kingdom 22.4 33
Equatorial Guinea Equatorial Guinea 9.43 46
Ireland Ireland 17.9 -26.7% 37
Iceland Iceland 32.7 20
Israel Israel 54.1 +57.8% 3
Italy Italy 61.8 +122% 1
Jamaica Jamaica 34.1 -35.7% 17
Jordan Jordan 30.5 +52.3% 22
Kazakhstan Kazakhstan 13.9 +5.31% 40
South Korea South Korea 47.9 +16.2% 4
Laos Laos 31.5 +36.3% 21
Latvia Latvia 26.1 -1.41% 30
Moldova Moldova 35.7 +52.7% 14
Mali Mali 11.1 -78.5% 45
Malta Malta 42.3 -31.5% 8
Malaysia Malaysia 35.2 +9.66% 15
Namibia Namibia 28 +138% 27
Papua New Guinea Papua New Guinea 29 +26.5% 25
Senegal Senegal 19.3 -1.35% 36
Serbia Serbia 39.6 -22.5% 11
South Sudan South Sudan 37.7 +529% 13
Slovenia Slovenia 28.4 -31.7% 26
Sweden Sweden 21.1 -15.6% 34
Eswatini Eswatini 12.9 -59.6% 42
Tajikistan Tajikistan 13 +1.03% 41
Turkmenistan Turkmenistan 6.01 49
Tonga Tonga 8.12 +168% 47
Tunisia Tunisia 35 -16.1% 16
Turkey Turkey 23.9 -29.7% 31
Uruguay Uruguay 46.1 +15.7% 5
United States United States 17.7 38
Uzbekistan Uzbekistan 12.8 -46% 43 

                    
# 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.BKWC.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.BKWC.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))