Country

Indicator

Value type

Values present

Bank liquid reserves to bank assets ratio (%)

Source: worldbank.org, 03.09.2025

Year: 2024

Country	Value	Value change, %	Rank
Angola	52.4	-14.1%	8
Albania	14.3	-29.3%	66
United Arab Emirates	19.3	+6.08%	50
Argentina	24	-79.1%	35
Armenia	15.6	+0.112%	61
Antigua & Barbuda	12.4	-18.7%	70
Australia	8.34	-30.1%	85
Azerbaijan	22.4	-23.5%	39
Benin	4.4	-1.85%	108
Burkina Faso	6.95	+11.1%	94
Bangladesh	7.46	-0.734%	91
Bulgaria	21.7	-20.4%	44
Bosnia & Herzegovina	26.3	-6.49%	32
Belize	21.9	-11.4%	41
Bolivia	28.9	+11.4%	25
Brazil	21.7	-6.82%	43
Brunei	7.21	-26.6%	93
Bhutan	23.2	+38.2%	37
Botswana	4.85	-62%	107
Chile	8.25	-54.8%	86
Côte d’Ivoire	15.2	+31.9%	62
Colombia	4.89	-10.1%	106
Costa Rica	26.1	+8%	33
Czechia	51.3	+1.65%	9
Djibouti	7.61	-18%	89
Dominica	14.4	-30.6%	64
Denmark	3.71	-8.69%	111
Dominican Republic	33.5	-24.4%	20
Algeria	22.1	-14.1%	40
Ecuador	6.1	+20.4%	97
Egypt	30.1	-22%	24
Fiji	33.7	-0.437%	19
Georgia	10.7	-6.26%	76
Guinea-Bissau	9.86	+57.6%	79
Grenada	15.7	-16.9%	59
Guatemala	22.7	+3.01%	38
Guyana	31.7	+25.1%	21
Hong Kong SAR China	6.01	-4.99%	99
Honduras	19.4	-6.57%	49
Haiti	117	+22%	3
Hungary	31.2	-6.33%	22
Indonesia	20.3	-10.6%	47
Iraq	53.9	-40.1%	7
Iceland	6.88	+13.5%	95
Israel	26.4	-3.16%	31
Jamaica	19.7	+7.54%	48
Jordan	23.8	+8.82%	36
Japan	34.8	-5.26%	18
Kazakhstan	21.3	+8.23%	46
Kyrgyzstan	28.1	-0.502%	26
Cambodia	21.5	+6.17%	45
St. Kitts & Nevis	10.2	+11.2%	78
South Korea	3.57	-8.95%	112
Kuwait	8.56	-18.2%	84
Libya	214	-2.08%	1
St. Lucia	9.82	-12.2%	80
Lesotho	7.86	+79.7%	88
Macao SAR China	5.4	+4.86%	104
Morocco	4.06	+46.9%	109
Moldova	40.3	-27.3%	16
Madagascar	21.7	+30.5%	42
Maldives	28	-21.1%	27
Mexico	7.47	+4.46%	90
North Macedonia	26.9	-5.72%	28
Mali	7.21	+3.05%	92
Montenegro	18	-12.9%	55
Mozambique	107	+13.2%	4
Mauritius	12.5	+8.21%	69
Malaysia	5.76	-23.9%	101
Namibia	10.6	+34.8%	77
Niger	5.52	+27.8%	103
Nicaragua	36.8	-6%	17
Norway	1.05	+51.4%	114
Nepal	8.16	+18.9%	87
New Zealand	6.24	-24.1%	96
Pakistan	19	-13.8%	51
Panama	2.51	-7.37%	113
Philippines	18	-19.9%	54
Poland	24.9	+10.6%	34
Paraguay	18.2	-24.9%	53
Palestinian Territories	11.2	-1.55%	74
Qatar	5	-10.5%	105
Romania	18.3	-28.1%	52
Rwanda	15.7	+0.211%	60
Senegal	12	+10.9%	73
Solomon Islands	117	+10.4%	2
Sierra Leone	16.4	-6.51%	57
El Salvador	12.2	-1.92%	72
Somalia	4.04	-2.42%	110
Serbia	41.2	+4.5%	14
South Sudan	65.4	+14.1%	6
Suriname	49	-1.74%	10
Sweden	5.98	-19.1%	100
Eswatini	15.2	-2.06%	63
Seychelles	13.5	-13.9%	68
Togo	6.08	+15.2%	98
Thailand	16.3	-2.81%	58
Timor-Leste	11.2	-8.09%	75
Tonga	81.5	-7.26%	5
Trinidad & Tobago	12.4	-16.9%	71
Tunisia	9.69	+17.6%	81
Turkey	26.8	-0.949%	29
Tanzania	14.3	+8.4%	65
Uganda	17.9	-7.24%	56
Ukraine	46.6	-18.7%	12
Uruguay	30.9	+1.54%	23
United States	14.1	-14.4%	67
Uzbekistan	9.26	+6.26%	82
St. Vincent & Grenadines	26.8	-1.76%	30
Vanuatu	40.3	-16.5%	15
Samoa	46.8	+3.08%	11
Kosovo	8.84	-2.82%	83
South Africa	5.76	+34.3%	102
Zambia	43.1	+12.1%	13

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.RES.LIQU.AS.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.RES.LIQU.AS.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 liquid reserves to bank assets ratio (%)

Year: 2024

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