Interest rate spread (lending rate minus deposit rate, %)

Source: worldbank.org, 01.09.2025

Year: 2024

Flag Country Value Value change, % Rank
Angola Angola 12 +19.5% 6
Albania Albania 4.58 -8.49% 47
Argentina Argentina 7.46 +731% 15
Armenia Armenia 5 +24.3% 46
Antigua & Barbuda Antigua & Barbuda 5.17 -1.66% 41
Azerbaijan Azerbaijan 6.08 +14.4% 27
Bangladesh Bangladesh 1.33 +26.8% 73
Bulgaria Bulgaria 4.29 -1.23% 50
Bahamas Bahamas 3.71 -0.118% 53
Bosnia & Herzegovina Bosnia & Herzegovina 2.84 -3.28% 64
Belarus Belarus 4.43 -13.3% 48
Belize Belize 6.35 -0.0894% 21
Brazil Brazil 32.5 +3.37% 1
Brunei Brunei 5.11 -3.69% 43
Bhutan Bhutan 6.04 -29.1% 28
Botswana Botswana 3.91 +3.14% 52
Switzerland Switzerland 2.05 +15.5% 71
China China 2.85 0% 63
Colombia Colombia 6.2 -20.7% 25
Cape Verde Cape Verde 6.12 -9.52% 26
Costa Rica Costa Rica 3.64 -9.42% 55
Czechia Czechia 2.68 +18% 65
Dominica Dominica 4.23 -1.68% 51
Dominican Republic Dominican Republic 5.48 +3.98% 36
Algeria Algeria 6.25 0% 23
Egypt Egypt 5.13 +0.489% 42
Fiji Fiji 2.94 -22% 61
Georgia Georgia 1.48 -23% 72
Grenada Grenada 5.93 -1.62% 30
Guatemala Guatemala 7.46 -3.78% 14
Guyana Guyana 7.6 0% 13
Hong Kong SAR China Hong Kong SAR China 5.21 -1.43% 39
Honduras Honduras 7.09 -16.2% 17
Hungary Hungary 2.95 +34.9% 60
Indonesia Indonesia 3.37 -21.5% 57
Jamaica Jamaica 5.76 -1.21% 32
Jordan Jordan 2.44 -27.6% 70
Kyrgyzstan Kyrgyzstan 18.1 +1.71% 2
St. Kitts & Nevis St. Kitts & Nevis 4.37 -6.46% 49
South Korea South Korea 1.25 -7.43% 74
Kuwait Kuwait 2.47 +1.55% 69
St. Lucia St. Lucia 5.08 -1.81% 44
Lesotho Lesotho 8.75 +1.16% 10
Macao SAR China Macao SAR China 5.36 -0.33% 37
Moldova Moldova 5.57 +3.82% 35
Maldives Maldives 7.83 +1.89% 12
Mexico Mexico 6.68 -2.11% 20
North Macedonia North Macedonia 3.44 -5.2% 56
Montenegro Montenegro 6.35 +4.45% 22
Mozambique Mozambique 13.5 -6.12% 5
Mauritius Mauritius 5.24 +6.44% 38
Malaysia Malaysia 2.64 -1.18% 66
Namibia Namibia 5.58 +0.831% 34
Nicaragua Nicaragua 8.99 +22.1% 9
Norway Norway 2.89 -0.886% 62
Qatar Qatar 0.977 -32.2% 75
Romania Romania 3.23 +11.9% 58
Rwanda Rwanda 5.69 -11.2% 33
Sierra Leone Sierra Leone 13.7 -1.45% 4
San Marino San Marino 3.2 -12% 59
South Sudan South Sudan 14.5 -10.4% 3
Suriname Suriname 5.18 -5.81% 40
Eswatini Eswatini 6.97 +9.06% 18
Seychelles Seychelles 7.42 -2.7% 16
Thailand Thailand 2.54 -12.9% 68
Timor-Leste Timor-Leste 10.1 -0.521% 8
Tonga Tonga 6.21 +0.246% 24
Trinidad & Tobago Trinidad & Tobago 6 -0.346% 29
Ukraine Ukraine 10.5 +7.24% 7
Uruguay Uruguay 2.61 -31.3% 67
Uzbekistan Uzbekistan 5.01 +21.3% 45
St. Vincent & Grenadines St. Vincent & Grenadines 5.85 -1.3% 31
Vanuatu Vanuatu 8.02 -6.1% 11
Samoa Samoa 6.84 +4.73% 19
South Africa South Africa 3.71 -23.6% 54 

                    
# 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.LNDP'

# 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.LNDP'

# 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))