Country

Indicator

Value type

Values present

Bank capital to assets ratio (%)

Source: worldbank.org, 01.09.2025

Year: 2023

Country	Value	Value change, %	Rank
Albania	8.69	+1.12%	52
United Arab Emirates	11.5	+1.2%	25
Argentina	18.3	+24.3%	3
Antigua & Barbuda	10.1	+8.14%	36
Austria	8.2	+3.53%	62
Azerbaijan	8.18	+1.34%	63
Belgium	5.72	-4.11%	93
Bangladesh	6.55	-2.35%	83
Bulgaria	10	+1.3%	39
Bosnia & Herzegovina	10.1	+2.15%	37
Belarus	12.7	-12.1%	16
Brazil	8.87	+1.23%	50
Botswana	8.69	-2.76%	53
Central African Republic	11.1	-20.7%	26
Canada	4.75	-2.55%	100
Switzerland	7.78	-1.26%	69
Chile	7.75	+5.59%	70
Cameroon	6.65	-0.947%	82
Congo - Kinshasa	5.89	+6.95%	91
Congo - Brazzaville	8.86	-21.1%	51
Colombia	10.7	-0.743%	30
Costa Rica	9.88	+1.97%	41
Cyprus	7.68	+11.8%	71
Czechia	6.33	-5.85%	84
Germany	7.29	+4.18%	75
Denmark	4.9	+0.487%	98
Dominican Republic	47.9	+19.8%	1
Ecuador	10.8	-3.54%	27
Spain	5.66	+2.48%	94
Estonia	9.34	-1.94%	48
Finland	5.8	+1.01%	92
France	5.47	+2.23%	95
Gabon	8.59	-5.21%	57
United Kingdom	6.08	-5.8%	87
Georgia	15.2	+17.2%	7
Ghana	5.23	-32.5%	96
Equatorial Guinea	-6.4	-656%	103
Greece	7.79	+12.5%	68
Guatemala	7.24	+12.8%	76
Hong Kong SAR China	7.81	+1.24%	67
Croatia	9.33	-6.17%	49
Hungary	8.6	-3.94%	56
Indonesia	13.4	+0.549%	11
India	6.08	-21.6%	88
Ireland	10	-5.2%	40
Iraq	11.8	+7.19%	22
Iceland	13.7	+1.73%	10
Israel	7.03	+6.86%	79
Jordan	10.1	+3.93%	38
Kazakhstan	12.8	+14.8%	15
Kenya	10.4	-8.73%	32
Kyrgyzstan	12.5	+10.6%	17
Cambodia	15.2	+1.91%	6
St. Kitts & Nevis	9.81	+5.69%	42
Kuwait	12.1	-4.62%	19
St. Lucia	8.06	+13.6%	64
Lesotho	9.54	-23.5%	45
Luxembourg	8.51	+4.57%	59
Latvia	9.52	+0.949%	46
Macao SAR China	3.84	+9.46%	101
Moldova	13.1	-3.22%	12
Madagascar	7.06	+12.1%	78
Maldives	19.1	+6.47%	2
Mexico	10.2	+4.63%	34
North Macedonia	11.6	+2.82%	24
Malta	7.93	+9.2%	66
Montenegro	8.56	+7.84%	58
Mauritius	9.75	+8.31%	43
Malawi	8.23	-8.48%	61
Malaysia	7.64	+0.961%	72
Namibia	10.8	+11.2%	28
Nicaragua	7.95	+0.696%	65
Netherlands	6.13	+2.65%	86
Norway	7.52	-0.56%	74
Nepal	6.78	-7.25%	81
Pakistan	4.79	-2.97%	99
Panama	12.1	+2.33%	20
Peru	11.7	+13.6%	23
Philippines	9.42	+6.13%	47
Papua New Guinea	12.3	+35.1%	18
Poland	6.32	-3.35%	85
Paraguay	8.6	-3.7%	55
Saudi Arabia	13.7	-1.92%	9
Solomon Islands	13.9	+1.06%	8
El Salvador	8.47	-3.62%	60
San Marino	5.94	+11.4%	89
Slovakia	7.59	+4.4%	73
Slovenia	9.6	+5.8%	44
Sweden	5.92	+4.79%	90
Eswatini	12.9	+2.53%	14
Chad	-1.04	-126%	102
Thailand	10.6	+2.94%	31
Tonga	15.6	+2.2%	4
Trinidad & Tobago	11.9	+3.08%	21
Turkey	7.2	-12.3%	77
Uganda	15.5	+3.72%	5
Ukraine	4.98	-14.5%	97
Uruguay	10.1	+5.71%	35
United States	8.68	+1.22%	54
Uzbekistan	13	+6.93%	13
St. Vincent & Grenadines	6.87	+1.93%	80
Samoa	10.7	-12.7%	29
Kosovo	10.4	+10.3%	33

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 = 'FB.BNK.CAPA.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 <- 'FB.BNK.CAPA.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

Bank capital to assets ratio (%)

Year: 2023

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