Borrowers from commercial banks (per 1,000 adults)

Source: worldbank.org, 03.09.2025

Year: 2023

Flag Country Value Value change, % Rank
Albania Albania 150 +1.18% 49
United Arab Emirates United Arab Emirates 684 +9.34% 4
Argentina Argentina 395 +2.36% 17
Azerbaijan Azerbaijan 369 +7.7% 21
Bangladesh Bangladesh 91.7 +4.43% 56
Bulgaria Bulgaria 384 -0.289% 18
Bosnia & Herzegovina Bosnia & Herzegovina 338 +1.01% 26
Belize Belize 165 +9.68% 45
Bolivia Bolivia 80.1 -5.66% 57
Brazil Brazil 736 +0.511% 2
Botswana Botswana 177 +1.25% 43
Chile Chile 353 +1.38% 23
China China 641 +5.39% 5
Colombia Colombia 279 -0.92% 34
Cyprus Cyprus 323 +2% 29
Djibouti Djibouti 48.8 +6.57% 61
Dominican Republic Dominican Republic 237 +10.9% 37
Algeria Algeria 46.9 +11.8% 62
Ecuador Ecuador 144 +0.588% 51
Egypt Egypt 116 +4.6% 54
Spain Spain 466 +5.76% 12
Estonia Estonia 470 +0.0334% 11
Georgia Georgia 528 -3.44% 8
Guinea Guinea 18.2 +3.97% 68
Guatemala Guatemala 162 +3.85% 46
Honduras Honduras 117 +7.68% 53
Croatia Croatia 513 -2.22% 9
Hungary Hungary 414 -1.41% 14
Indonesia Indonesia 330 +7.91% 28
Italy Italy 584 +4.69% 6
Kenya Kenya 373 -13.1% 20
Kyrgyzstan Kyrgyzstan 197 +30% 40
Kuwait Kuwait 230 +2.69% 38
Laos Laos 20 -9.43% 67
Lebanon Lebanon 102 -20.6% 55
Liberia Liberia 12.8 +32.1% 69
Lesotho Lesotho 38.4 -0.794% 65
Lithuania Lithuania 363 +12.2% 22
Latvia Latvia 288 +0.333% 33
Moldova Moldova 184 +11.9% 41
Maldives Maldives 347 +6.35% 24
North Macedonia North Macedonia 381 -1.61% 19
Malta Malta 402 +6.64% 16
Montenegro Montenegro 309 +2.45% 31
Mozambique Mozambique 55 +4.52% 59
Mauritius Mauritius 261 +5.23% 35
Malaysia Malaysia 310 +1.53% 30
Pakistan Pakistan 21.8 -12.1% 66
Peru Peru 154 -2.97% 48
Poland Poland 460 +0.404% 13
Portugal Portugal 508 -1.49% 10
Paraguay Paraguay 343 +19.6% 25
Palestinian Territories Palestinian Territories 178 +7.91% 42
Romania Romania 242 +2.14% 36
Rwanda Rwanda 134 +53.2% 52
Saudi Arabia Saudi Arabia 171 +7.77% 44
Solomon Islands Solomon Islands 42.5 +2.51% 63
San Marino San Marino 413 -9% 15
Serbia Serbia 546 -2.92% 7
South Sudan South Sudan 1.24 +20.5% 70
Suriname Suriname 158 +2.25% 47
Thailand Thailand 333 -4.52% 27
Timor-Leste Timor-Leste 52.3 -1.02% 60
Turkey Turkey 985 +4.72% 1
Uganda Uganda 42 -3.36% 64
Uruguay Uruguay 732 +2.5% 3
Uzbekistan Uzbekistan 307 +20.6% 32
Samoa Samoa 149 +42.4% 50
Kosovo Kosovo 203 -9.85% 39
Zimbabwe Zimbabwe 74.5 +9.03% 58 

                    
# 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 = 'FB.CBK.BRWR.P3'

# 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 <- 'FB.CBK.BRWR.P3'

# 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))