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

Source: worldbank.org, 01.09.2025

Year: 2022

Flag Country Value Value change, % Rank
Afghanistan Afghanistan 66,940,002 +32.4% 8
Albania Albania 10,000 0% 72
Argentina Argentina 50,000 -16.7% 68
Burundi Burundi 1,640,000 +24.2% 44
Burkina Faso Burkina Faso 60,000 +20% 67
Bangladesh Bangladesh 84,970,001 -8.69% 4
Bosnia & Herzegovina Bosnia & Herzegovina 30,000 +50% 70
Bolivia Bolivia 40,000 0% 69
Brazil Brazil 90,000 -25% 64
Bhutan Bhutan 2,770,000 +11.7% 41
Botswana Botswana 70,000 -78.8% 66
Central African Republic Central African Republic 3,260,000 +12.4% 38
China China 260,000 +23.8% 59
Côte d’Ivoire Côte d’Ivoire 40,000 -20% 69
Cameroon Cameroon 210,000 -63.8% 60
Congo - Kinshasa Congo - Kinshasa 5,600,000 +121% 31
Colombia Colombia 30,000 -50% 70
Comoros Comoros 50,000 +150% 68
Cape Verde Cape Verde 10,000 -50% 72
Costa Rica Costa Rica 20,000 +100% 71
Cuba Cuba 10,000 0% 72
Djibouti Djibouti 10,000 -66.7% 72
Dominican Republic Dominican Republic 10,000 0% 72
Algeria Algeria 0 73
Ecuador Ecuador 30,000 0% 70
Egypt Egypt 160,000 +167% 61
Ethiopia Ethiopia 3,210,000 -62.2% 40
Fiji Fiji 57,439,999 -70.3% 12
Micronesia (Federated States of) Micronesia (Federated States of) 5,240,000 +33% 32
Ghana Ghana 690,000 -58.9% 55
Guinea Guinea 20,000 -60% 71
Guinea-Bissau Guinea-Bissau 20,000 0% 71
Grenada Grenada 30,000 +50% 70
Guatemala Guatemala 10,000 0% 72
Guyana Guyana 10,000 0% 72
Honduras Honduras 20,000 +100% 71
Indonesia Indonesia 206,779,999 -36.9% 2
India India 6,800,000 -66.7% 28
Iran Iran 3,400,000 +3,300% 37
Iraq Iraq 5,890,000 -63% 29
Jamaica Jamaica 30,000 +50% 70
Jordan Jordan 3,240,000 -78.8% 39
Kenya Kenya 7,050,000 +22.6% 27
Kyrgyzstan Kyrgyzstan 30,000 0% 70
Cambodia Cambodia 69,290,001 -11.4% 7
Kiribati Kiribati 26,910,000 -9% 19
Laos Laos 36,459,999 -11.1% 17
Lebanon Lebanon 10,580,000 -2.76% 25
St. Lucia St. Lucia 10,000 0% 72
Sri Lanka Sri Lanka 50,549,999 +152% 13
Lesotho Lesotho 70,000 -22.2% 66
Morocco Morocco 30,000 +200% 70
Moldova Moldova 20,000 +100% 71
Madagascar Madagascar 10,000 -95.5% 72
Maldives Maldives 1,610,000 +6.62% 45
Mexico Mexico 80,000 -27.3% 65
Marshall Islands Marshall Islands 3,900,000 +45.5% 34
North Macedonia North Macedonia 20,000 +100% 71
Mali Mali 3,750,000 +18,650% 35
Myanmar (Burma) Myanmar (Burma) 66,180,000 -3.02% 9
Montenegro Montenegro 10,000 0% 72
Mongolia Mongolia 5,600,000 +16.9% 31
Mozambique Mozambique 1,000,000 -13% 52
Mauritius Mauritius 310,000 +10.7% 58
Malawi Malawi 910,000 -50% 54
Malaysia Malaysia 1,820,000 +11% 43
Namibia Namibia 430,000 +514% 56
Niger Niger 70,000 -73.1% 66
Nigeria Nigeria 1,450,000 -39.8% 46
Nicaragua Nicaragua 30,000 -40% 70
Nepal Nepal 19,080,000 -25.4% 22
Nauru Nauru 20,290,001 0% 21
Pakistan Pakistan 16,639,999 +53.8% 23
Panama Panama 10,000 0% 72
Peru Peru 160,000 -20% 61
Philippines Philippines 76,809,998 +17.8% 5
Palau Palau 23,350,000 +404% 20
Papua New Guinea Papua New Guinea 489,720,001 -16.5% 1
Paraguay Paraguay 20,000 -33.3% 71
Palestinian Territories Palestinian Territories 32,369,999 +77.3% 18
Rwanda Rwanda 1,390,000 -50.4% 49
Sudan Sudan 1,140,000 +500% 51
Senegal Senegal 10,000 -50% 72
Solomon Islands Solomon Islands 115,120,003 -16.4% 3
Sierra Leone Sierra Leone 10,000 0% 72
El Salvador El Salvador 10,000 0% 72
Somalia Somalia 1,410,000 +114% 48
Serbia Serbia 20,000 0% 71
South Sudan South Sudan 5,720,000 +209% 30
São Tomé & Príncipe São Tomé & Príncipe 20,000 71
Eswatini Eswatini 70,000 -46.2% 66
Syria Syria 2,640,000 -13.7% 42
Chad Chad 10,000 -66.7% 72
Thailand Thailand 12,900,000 +90.3% 24
Tajikistan Tajikistan 400,000 +3,900% 57
Turkmenistan Turkmenistan 30,000 0% 70
Timor-Leste Timor-Leste 72,230,003 -30.6% 6
Tonga Tonga 43,570,000 +38.8% 15
Tunisia Tunisia 20,000 +100% 71
Turkey Turkey 50,000 -28.6% 68
Tuvalu Tuvalu 8,340,000 -26.3% 26
Tanzania Tanzania 1,230,000 -49% 50
Uganda Uganda 4,350,000 +39% 33
Ukraine Ukraine 46,029,999 +65,657% 14
Uzbekistan Uzbekistan 100,000 +400% 63
St. Vincent & Grenadines St. Vincent & Grenadines 50,000 0% 68
Vietnam Vietnam 65,199,997 -25.9% 10
Vanuatu Vanuatu 61,799,999 -31.2% 11
Samoa Samoa 37,529,999 +7.26% 16
Kosovo Kosovo 130,000 +1,200% 62
Yemen Yemen 3,460,000 -53.9% 36
South Africa South Africa 1,420,000 -3.4% 47
Zambia Zambia 940,000 -64.7% 53
Zimbabwe Zimbabwe 3,260,000 +11.6% 38 

                    
# 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.AUSL.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.AUSL.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))