Net bilateral aid flows from DAC donors, Belgium (current US$)

Source: worldbank.org, 01.09.2025

Year: 2022

Flag Country Value Value change, % Rank
Afghanistan Afghanistan 3,580,000 +36.1% 33
Angola Angola 420,000 +20% 53
Albania Albania 40,000 0% 69
Argentina Argentina 0 73
Armenia Armenia 150,000 -80.5% 62
Burundi Burundi 26,590,000 -26.9% 6
Benin Benin 27,709,999 +1.54% 5
Burkina Faso Burkina Faso 35,910,000 -8.83% 4
Bangladesh Bangladesh 940,000 -22.3% 46
Bosnia & Herzegovina Bosnia & Herzegovina 160,000 0% 61
Belarus Belarus 330,000 +37.5% 55
Bolivia Bolivia 3,870,000 -43.6% 32
Brazil Brazil 850,000 -42.6% 48
China China 120,000 -72.7% 64
Côte d’Ivoire Côte d’Ivoire 200,000 -41.2% 60
Cameroon Cameroon 2,190,000 -17% 39
Congo - Kinshasa Congo - Kinshasa 103,849,998 -8.11% 1
Congo - Brazzaville Congo - Brazzaville 10,000 -98.9% 72
Colombia Colombia 890,000 -48.9% 47
Cuba Cuba 820,000 -81.7% 49
Dominican Republic Dominican Republic 0 -100% 73
Algeria Algeria 20,000 -83.3% 71
Ecuador Ecuador 4,360,000 -40.6% 28
Egypt Egypt 3,920,000 +288% 31
Ethiopia Ethiopia 4,180,000 -60.7% 29
Georgia Georgia 0 -100% 73
Ghana Ghana 4,560,000 +347% 26
Guinea Guinea 17,020,000 +36.3% 14
Gambia Gambia 110,000 +83.3% 65
Guatemala Guatemala 2,070,000 -12.7% 40
Honduras Honduras 2,230,000 +101% 38
Haiti Haiti 3,340,000 -41.2% 35
Indonesia Indonesia 1,280,000 -56.8% 43
India India 650,000 -59.6% 51
Iran Iran 60,000 -72.7% 68
Iraq Iraq 4,040,000 -21.4% 30
Jordan Jordan 1,130,000 -53.7% 45
Kenya Kenya 15,240,000 +17.1% 15
Cambodia Cambodia 3,390,000 -18.1% 34
Kiribati Kiribati 210,000 59
Lebanon Lebanon 6,840,000 -31.5% 22
Liberia Liberia 0 -100% 73
Sri Lanka Sri Lanka 260,000 -36.6% 57
Lesotho Lesotho 0 -100% 73
Morocco Morocco 9,820,000 -42.1% 19
Moldova Moldova 0 -100% 73
Madagascar Madagascar 1,580,000 -66.1% 41
Mexico Mexico 2,290,000 +3,717% 37
Mali Mali 18,129,999 -16.2% 11
Myanmar (Burma) Myanmar (Burma) 0 -100% 73
Mozambique Mozambique 20,129,999 +23.1% 9
Mauritania Mauritania 770,000 +83.3% 50
Malawi Malawi 7,160,000 +149% 20
Niger Niger 25,389,999 -26.6% 7
Nigeria Nigeria 7,030,000 +18.4% 21
Nicaragua Nicaragua 410,000 -81.3% 54
Nepal Nepal 250,000 +31.6% 58
Pakistan Pakistan 90,000 +50% 66
Panama Panama 10,000 -97.6% 72
Peru Peru 4,630,000 -44.1% 24
Philippines Philippines 3,180,000 -42.1% 36
Paraguay Paraguay 0 -100% 73
Palestinian Territories Palestinian Territories 18,100,000 -23.7% 12
Rwanda Rwanda 42,029,999 -19.7% 3
Sudan Sudan 10,520,000 +105,100% 17
Senegal Senegal 19,139,999 +13.3% 10
Solomon Islands Solomon Islands 60,000 -40% 68
El Salvador El Salvador 1,290,000 -43.2% 42
Somalia Somalia 0 -100% 73
Suriname Suriname 90,000 -50% 66
Syria Syria 17,610,001 -23.2% 13
Chad Chad 70,000 -98.3% 67
Togo Togo 440,000 -38% 52
Thailand Thailand 30,000 +50% 70
Tunisia Tunisia 130,000 -78% 63
Turkey Turkey 11,650,000 -11.1% 16
Tanzania Tanzania 10,090,000 -31.9% 18
Uganda Uganda 21,430,000 -56.9% 8
Ukraine Ukraine 63,060,001 +1,525% 2
Venezuela Venezuela 10,000 72
Vietnam Vietnam 4,620,000 -43.4% 25
Yemen Yemen 5,260,000 -23.2% 23
South Africa South Africa 4,540,000 -60.9% 27
Zambia Zambia 1,200,000 -20% 44
Zimbabwe Zimbabwe 320,000 -60% 56 

                    
# 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 = 'DC.DAC.BELL.CD'

# 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 <- 'DC.DAC.BELL.CD'

# 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))