Lending interest rate (%)

Source: worldbank.org, 01.09.2025

Year: 2024

Flag Country Value Value change, % Rank
Angola Angola 19.4 +14.6% 10
Albania Albania 6.44 +1.44% 56
Argentina Argentina 61.7 -35.7% 1
Armenia Armenia 13.1 +6.17% 18
Antigua & Barbuda Antigua & Barbuda 6.53 -2.82% 54
Azerbaijan Azerbaijan 14.7 +5.06% 17
Bangladesh Bangladesh 9.85 +30.2% 34
Bulgaria Bulgaria 4.61 +3.93% 73
Bahamas Bahamas 4.25 0% 76
Bosnia & Herzegovina Bosnia & Herzegovina 4.45 +8.24% 74
Belarus Belarus 9.99 +0.295% 32
Belize Belize 8.42 -0.238% 44
Brazil Brazil 40.2 -7.74% 2
Brunei Brunei 5.5 0% 65
Bhutan Bhutan 10.5 -14.1% 31
Botswana Botswana 6.26 -7.1% 58
Switzerland Switzerland 2.98 +3.91% 78
China China 4.35 0% 75
Colombia Colombia 16.4 -22.1% 11
Cape Verde Cape Verde 7.79 +3.49% 47
Costa Rica Costa Rica 7.33 -19.4% 50
Czechia Czechia 4.71 +0.662% 71
Dominica Dominica 5.83 -1.73% 63
Dominican Republic Dominican Republic 15.3 +5.67% 14
Algeria Algeria 8 0% 46
Egypt Egypt 24.3 +36.8% 4
Fiji Fiji 4.61 -7.19% 72
Georgia Georgia 12 -10.9% 21
Grenada Grenada 6.89 -0.623% 52
Guatemala Guatemala 12.4 +3.62% 20
Guyana Guyana 8.38 0% 45
Hong Kong SAR China Hong Kong SAR China 5.74 -0.542% 64
Honduras Honduras 16 +11.1% 13
Hungary Hungary 9.13 -40.4% 37
Indonesia Indonesia 8.8 -1.47% 39
Iceland Iceland 12.5 +8.03% 19
Italy Italy 5.25 +7.36% 68
Jamaica Jamaica 12 +2.96% 22
Jordan Jordan 8.48 -5.34% 42
Kyrgyzstan Kyrgyzstan 19.8 +3.55% 8
St. Kitts & Nevis St. Kitts & Nevis 6.46 -3.08% 55
South Korea South Korea 4.73 -8.91% 70
Kuwait Kuwait 5.17 +4.94% 69
St. Lucia St. Lucia 6.29 -1.82% 57
Lesotho Lesotho 11.2 +1.13% 27
Macao SAR China Macao SAR China 6.02 +0.131% 61
Moldova Moldova 8.91 -27.8% 38
Maldives Maldives 11.6 -1.12% 24
Mexico Mexico 11.2 -3.18% 26
North Macedonia North Macedonia 5.85 +7.54% 62
Montenegro Montenegro 6.6 +3.97% 53
Mozambique Mozambique 21.7 -8.33% 6
Mauritius Mauritius 9.38 -1.32% 36
Malawi Malawi 37.1 +10.5% 3
Malaysia Malaysia 5.28 -0.652% 67
Namibia Namibia 11 +0.273% 28
Nicaragua Nicaragua 11.2 +18.5% 25
Norway Norway 6.15 +15.8% 60
Qatar Qatar 6.18 -4.17% 59
Romania Romania 8.76 -6% 40
Rwanda Rwanda 16 -0.254% 12
Sierra Leone Sierra Leone 20.4 +4.07% 7
San Marino San Marino 5.49 -2.51% 66
South Sudan South Sudan 14.7 -10.3% 16
Suriname Suriname 14.8 +4.39% 15
Eswatini Eswatini 10.9 +0.385% 29
Seychelles Seychelles 9.81 +2.92% 35
Thailand Thailand 4.17 -2.72% 77
Timor-Leste Timor-Leste 10.6 -1.09% 30
Tonga Tonga 7.76 +0.332% 48
Trinidad & Tobago Trinidad & Tobago 7.5 -0.277% 49
Ukraine Ukraine 19.6 -11.4% 9
Uruguay Uruguay 9.91 -16.7% 33
Uzbekistan Uzbekistan 23.1 +4.44% 5
St. Vincent & Grenadines St. Vincent & Grenadines 7.1 -0.939% 51
Vanuatu Vanuatu 8.64 -3.33% 41
Samoa Samoa 8.43 +1.33% 43
South Africa South Africa 11.6 +1.09% 23 

                    
# 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 = 'FR.INR.LEND'

# 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 <- 'FR.INR.LEND'

# 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))