Net acquisition of financial assets (% of GDP)

Source: worldbank.org, 01.09.2025

Year: 2023

Flag Country Value Value change, % Rank
Albania Albania 1.39 -277% 22
United Arab Emirates United Arab Emirates 1.52 +64.5% 20
Argentina Argentina 1.08 -39.6% 25
Armenia Armenia 1.13 -35.8% 24
Burkina Faso Burkina Faso -0.896 -47.8% 64
Bahamas Bahamas 3.5 -1,398% 7
Bosnia & Herzegovina Bosnia & Herzegovina -0.6 -306% 59
Belarus Belarus 0.254 -24.3% 40
Brazil Brazil -0.509 -226% 56
Botswana Botswana 0.353 -4.76% 37
Canada Canada 2.62 +3.8% 10
Switzerland Switzerland 1.66 +73.7% 17
Chile Chile -0.672 -119% 61
Côte d’Ivoire Côte d’Ivoire -0.451 -290% 54
Colombia Colombia 3.23 +1,782% 9
Costa Rica Costa Rica 3.36 +22.3% 8
Dominican Republic Dominican Republic -0.212 -156% 49
Spain Spain 1.54 -53.7% 19
Ethiopia Ethiopia 0.761 -1.11% 31
Fiji Fiji 0.27 -90.1% 39
United Kingdom United Kingdom 0.641 -155% 34
Georgia Georgia 0.816 -38.2% 30
Guinea-Bissau Guinea-Bissau -0.0424 -433% 46
Guatemala Guatemala -0.275 -40.2% 52
Iceland Iceland 1.54 +12.6% 18
Israel Israel -0.614 -29.6% 60
Kazakhstan Kazakhstan 1.19 -64.1% 23
Kenya Kenya 1.82 +6.55% 14
Kyrgyzstan Kyrgyzstan 4.85 +29.7% 3
Cambodia Cambodia -0.54 -126% 57
Kiribati Kiribati -4.38 -58.8% 66
South Korea South Korea 1.69 -19.9% 16
Sri Lanka Sri Lanka 0.854 -22.4% 29
Latvia Latvia 1.43 +394% 21
Macao SAR China Macao SAR China 2.19 -106% 11
Morocco Morocco 0.988 -217% 26
Moldova Moldova -0.181 -112% 48
Madagascar Madagascar 0.964 +217% 27
Mexico Mexico 0.234 -179% 41
North Macedonia North Macedonia 0.384 -159% 36
Mauritius Mauritius -0.216 -93.4% 50
Malaysia Malaysia 0.0757 +663% 43
Namibia Namibia 0.0152 -99.6% 44
Philippines Philippines -0.568 +51.9% 58
Papua New Guinea Papua New Guinea -0.489 +53.1% 55
Paraguay Paraguay 0.31 -58.5% 38
Russia Russia 3.93 +42.9% 6
Rwanda Rwanda 1.79 -69.2% 15
Saudi Arabia Saudi Arabia -0.273 -108% 51
Senegal Senegal 4.42 +42.7% 4
Singapore Singapore 14.8 +315% 1
El Salvador El Salvador -0.833 -3.46% 63
Somalia Somalia 0.00001 -17.4% 45
Togo Togo -0.822 -51.7% 62
Thailand Thailand -0.0738 -85.6% 47
Tajikistan Tajikistan -0.421 -173% 53
Tonga Tonga 6.17 -418% 2
Turkey Turkey 4.21 +7.22% 5
Tanzania Tanzania 0.545 -31.1% 35
Uganda Uganda 0.758 -195% 32
Ukraine Ukraine -1.07 -20.9% 65
Uruguay Uruguay 2.01 -721% 13
United States United States 0.728 +52.8% 33
Uzbekistan Uzbekistan 0.164 -105% 42
Vanuatu Vanuatu 0.887 -131% 28
Samoa Samoa 2.16 -50.5% 12 

                    
# 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 = 'GC.AST.TOTL.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 <- 'GC.AST.TOTL.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))