Country

Indicator

Value type

Values present

Domestic credit to private sector by banks (% of GDP)

Source: worldbank.org, 03.09.2025

Year: 2024

Country	Value	Value change, %	Rank
Angola	7.95	-3.56%	129
Albania	31.3	+5.07%	90
United Arab Emirates	69.9	+4.08%	29
Argentina	13.2	+10.8%	122
Armenia	62.8	+17.3%	39
Antigua & Barbuda	38.3	+0.638%	72
Australia	130	+1.76%	5
Austria	83.5	-1.3%	20
Azerbaijan	24.5	+10.8%	103
Belgium	67.4	-1.45%	34
Benin	19	+1.98%	114
Burkina Faso	27	-14.5%	98
Bangladesh	35.7	-4.86%	79
Bulgaria	47.5	+5.11%	62
Bosnia & Herzegovina	46.2	+6.3%	66
Belize	37.4	-10.2%	73
Bolivia	65.5	-6.26%	37
Brazil	75.8	+6.7%	26
Brunei	36.3	+1.12%	76
Bhutan	53.2	-25.4%	52
Botswana	32.8	+9.57%	86
Chile	74.9	-6.21%	27
China	194	+2.4%	2
Côte d’Ivoire	22.7	+2.01%	108
Colombia	39.5	-5.05%	70
Costa Rica	51	+1.75%	55
Cyprus	58.6	-6.4%	43
Czechia	48.3	+0.685%	59
Djibouti	23.5	+8.08%	105
Dominica	39.4	-5.71%	71
Denmark	142	-2.84%	3
Dominican Republic	30.9	+3.75%	92
Algeria	19.4	+3.04%	112
Ecuador	54.6	+3.6%	49
Egypt	27.6	-5.77%	96
Spain	72.5	-5.14%	28
Estonia	61.1	+5.43%	40
Finland	90.5	-1.53%	17
Fiji	77.7	+2.89%	24
France	104	-4.34%	15
United Kingdom	114	-4.91%	13
Georgia	66.2	+4.47%	36
Guinea-Bissau	10.7	-12%	126
Greece	48.2	-1.49%	61
Grenada	54.1	+5.08%	50
Guatemala	36.3	-0.936%	77
Guyana	10.4	-18.7%	127
Hong Kong SAR China	231	-7.12%	1
Honduras	76.5	+3.35%	25
Croatia	47.1	-0.0588%	63
Haiti	3.96	-20.8%	132
Hungary	32.1	-0.971%	87
Indonesia	31.8	+1.6%	89
Ireland	25.1	-3.31%	102
Iraq	14	+11.2%	119
Iceland	90.3	+1.52%	18
Israel	69.7	+0.288%	31
Italy	59.5	-4.41%	41
Jamaica	49.7	+1.46%	58
Jordan	80.1	-2.03%	22
Japan	123	+0.725%	9
Kazakhstan	25.8	+6.83%	100
Kyrgyzstan	23	+16.3%	106
Cambodia	125	-4.22%	7
St. Kitts & Nevis	67	+8.74%	35
Kuwait	4.4	-4.15%	131
Libya	13.5	+13.4%	120
St. Lucia	50.7	+0.193%	56
Lesotho	25.1	+2.58%	101
Lithuania	36	+4.6%	78
Luxembourg	85.2	-9.82%	19
Latvia	29.7	+3.3%	94
Macao SAR China	128	-12.5%	6
Morocco	57	-2.3%	47
Moldova	24.2	+17.2%	104
Madagascar	16.4	-2.8%	117
Maldives	31.9	+0.689%	88
Mexico	26.6	+5.03%	99
North Macedonia	51.9	+4.32%	54
Mali	22.6	-3.42%	109
Malta	63.5	-2.73%	38
Montenegro	46.4	+7.84%	65
Mozambique	17.3	-3.62%	115
Mauritius	69.8	+0.0778%	30
Malaysia	116	-0.854%	11
Namibia	48.2	-3.35%	60
Niger	10.3	-6.23%	128
Nicaragua	29.1	+8.59%	95
Netherlands	81.5	-3.08%	21
Norway	110	+1.37%	14
Nepal	91.9	+0.622%	16
New Zealand	130	+0.0419%	4
Pakistan	11.3	-4.69%	125
Panama	68.3	-0.09%	33
Philippines	49.8	+3.11%	57
Poland	33.7	-2.91%	83
Portugal	78.3	-3.21%	23
Paraguay	57.5	+9.83%	46
Palestinian Territories	68.5	+24.5%	32
Qatar	120	+1.62%	10
Romania	22.7	-1.45%	107
Rwanda	22.5	+1.14%	110
Senegal	29.7	-5.51%	93
Solomon Islands	19.4	-1.75%	113
Sierra Leone	3.65	+13.2%	133
El Salvador	53.1	+1.31%	53
Somalia	0.000188	+2.35%	134
Serbia	33	-0.0927%	85
South Sudan	6.64	+28.3%	130
Suriname	15.9	-4.39%	118
Slovakia	59.3	-3.3%	42
Slovenia	35.7	-2.04%	80
Sweden	124	-2.74%	8
Eswatini	21.4	+0.467%	111
Seychelles	34.6	+9.11%	81
Togo	27.1	-2.49%	97
Thailand	115	-3.18%	12
Timor-Leste	34.4	+45.8%	82
Tonga	41.3	+0.683%	68
Trinidad & Tobago	46.9	+4.95%	64
Tunisia	58.2	-7.16%	44
Turkey	37	-14.7%	74
Tanzania	16.7	+2.33%	116
Uganda	12.6	-2.75%	123
Ukraine	13.4	-2.88%	121
Uruguay	31.1	+8.75%	91
United States	46.1	-6.08%	67
Uzbekistan	33.2	-3.37%	84
St. Vincent & Grenadines	36.7	-7.66%	75
Vanuatu	53.5	+2.12%	51
Samoa	41	-6.93%	69
Kosovo	55.9	+10.6%	48
South Africa	57.6	-0.0412%	45
Zambia	12.5	-2.52%	124

Python
R

                    
# 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 = 'FD.AST.PRVT.GD.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 <- 'FD.AST.PRVT.GD.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))

Compare

Domestic credit to private sector by banks (% of GDP)

Year: 2024

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