Country

Indicator

Value type

Values present

Monetary Sector credit to private sector (% GDP)

Source: worldbank.org, 03.09.2025

Year: 2024

Country	Value	Value change, %	Rank
Angola	8.08	-4.13%	124
Albania	31.4	+5%	85
United Arab Emirates	69.9	+4.08%	29
Argentina	13.2	+10.8%	117
Armenia	64.6	+16.5%	36
Antigua & Barbuda	38.3	+0.638%	68
Australia	130	+1.76%	5
Austria	83.8	-1.29%	20
Azerbaijan	24.5	+10.8%	98
Belgium	68.8	-1.71%	31
Benin	19	+2%	109
Burkina Faso	27	-14.5%	93
Bangladesh	35.8	-4.86%	75
Bulgaria	47.5	+5.1%	59
Bosnia & Herzegovina	46.2	+6.3%	63
Belize	37.6	-10.1%	69
Bolivia	65.5	-6.26%	35
Brazil	75.8	+6.7%	26
Brunei	36.3	+1.12%	72
Botswana	32.9	+9.55%	81
Chile	74.9	-6.21%	27
China	194	+2.4%	2
Côte d’Ivoire	22.7	+2%	103
Colombia	39.6	-5.04%	66
Costa Rica	51	+1.75%	52
Cyprus	58.7	-6.39%	41
Czechia	48.3	+0.686%	57
Djibouti	23.5	+8.08%	100
Dominica	39.4	-5.71%	67
Denmark	142	-2.84%	3
Dominican Republic	31	+3.71%	87
Algeria	19.4	+3.05%	108
Ecuador	54.6	+3.6%	47
Egypt	27.6	-5.77%	91
Spain	74.3	-5.08%	28
Estonia	61.1	+5.42%	38
Finland	91.7	-1.72%	17
Fiji	77.7	+2.89%	24
France	108	-4.62%	15
United Kingdom	114	-4.84%	13
Georgia	66.2	+4.47%	34
Guinea-Bissau	11.1	-11.6%	121
Greece	48.4	-1.5%	56
Grenada	54.1	+5.08%	49
Guatemala	36.3	-0.936%	73
Guyana	10.4	-18.7%	123
Hong Kong SAR China	231	-7.12%	1
Honduras	76.5	+3.35%	25
Croatia	47.1	-0.0513%	60
Haiti	4.11	-20.4%	126
Hungary	32.4	-1.04%	82
Indonesia	31.8	+1.59%	84
Ireland	25.1	-3.31%	97
Iraq	14	+11.2%	114
Iceland	90.3	+1.52%	18
Italy	60.6	-4.54%	39
Jamaica	49.9	+1.44%	54
Jordan	80.2	-2.03%	22
Japan	124	+0.452%	9
Kazakhstan	25.8	+6.85%	95
Kyrgyzstan	23.2	+16.1%	101
Cambodia	125	-4.2%	7
St. Kitts & Nevis	67	+8.74%	33
Kuwait	4.4	-4.15%	125
Libya	13.6	+13.7%	115
St. Lucia	50.7	+0.193%	53
Lesotho	25.6	+2.88%	96
Lithuania	36	+4.6%	74
Luxembourg	85.2	-9.82%	19
Latvia	29.7	+3.3%	89
Macao SAR China	128	-12.5%	6
Morocco	57	-2.31%	45
Moldova	24.3	+17.2%	99
Madagascar	16.6	-2.73%	112
Maldives	32	+0.706%	83
Mexico	26.6	+5.03%	94
North Macedonia	52	+4.32%	51
Mali	22.6	-3.39%	105
Malta	63.6	-2.73%	37
Montenegro	46.4	+7.84%	62
Mozambique	17.9	-3.41%	110
Mauritius	69.8	+0.0673%	30
Malaysia	116	-0.854%	11
Namibia	48.3	-3.34%	58
Niger	10.4	-6.19%	122
Nicaragua	29.1	+8.58%	90
Netherlands	81.5	-3.08%	21
Norway	110	+1.4%	14
Nepal	92.1	+0.605%	16
New Zealand	130	+0.0422%	4
Pakistan	11.4	-4.66%	120
Philippines	49.8	+3.11%	55
Poland	33.7	-2.91%	78
Portugal	78.4	-3.2%	23
Paraguay	57.5	+9.81%	44
Palestinian Territories	68.5	+24.5%	32
Qatar	120	+1.65%	10
Romania	22.7	-1.45%	102
Rwanda	22.6	+1.14%	104
Senegal	29.7	-5.49%	88
Solomon Islands	19.5	-1.71%	107
Sierra Leone	3.66	+13.2%	127
El Salvador	53.1	+1.31%	50
Somalia	0.000188	+2.3%	128
Serbia	33	-0.111%	80
Suriname	15.9	-4.41%	113
Slovakia	59.3	-3.29%	40
Slovenia	35.7	-2.04%	76
Sweden	124	-2.74%	8
Eswatini	21.5	+0.416%	106
Togo	27.2	-2.43%	92
Thailand	115	-3.18%	12
Timor-Leste	34.5	+45.7%	77
Trinidad & Tobago	46.9	+4.93%	61
Tunisia	58.3	-7.15%	42
Turkey	37	-14.7%	70
Tanzania	16.8	+2.25%	111
Uganda	12.6	-2.74%	118
Ukraine	13.4	-2.88%	116
Uruguay	31.1	+8.76%	86
United States	46.1	-6.08%	64
Uzbekistan	33.2	-3.37%	79
St. Vincent & Grenadines	36.7	-7.66%	71
Vanuatu	54.2	+2.22%	48
Samoa	41.3	-6.93%	65
Kosovo	55.9	+10.6%	46
South Africa	57.6	-0.046%	43
Zambia	12.5	-2.49%	119

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 = 'FM.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 <- 'FM.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

Monetary Sector credit to private sector (% GDP)

Year: 2024

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