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

Source: worldbank.org, 01.09.2025

Year: 2022

Flag Country Value Value change, % Rank
Afghanistan Afghanistan 9,410,000 +146% 11
Angola Angola 10,000 0% 87
Albania Albania 11,710,000 -15.9% 10
Argentina Argentina 680,000 +25.9% 58
Armenia Armenia 4,740,000 -8.67% 30
Azerbaijan Azerbaijan 360,000 -26.5% 68
Burundi Burundi 80,000 -27.3% 81
Benin Benin 0 -100% 88
Burkina Faso Burkina Faso 5,400,000 -60.4% 23
Bangladesh Bangladesh 25,190,001 +314% 3
Bosnia & Herzegovina Bosnia & Herzegovina 23,360,001 -11.7% 5
Belarus Belarus 1,680,000 -5.62% 44
Belize Belize 10,000 0% 87
Bolivia Bolivia 340,000 -15% 69
Brazil Brazil 2,710,000 -6.87% 34
Bhutan Bhutan 1,400,000 +119% 45
Botswana Botswana 10,000 0% 87
China China 6,740,000 +4.98% 19
Côte d’Ivoire Côte d’Ivoire 480,000 -58.6% 60
Cameroon Cameroon 330,000 -5.71% 70
Congo - Kinshasa Congo - Kinshasa 290,000 -29.3% 71
Colombia Colombia 2,100,000 -34.6% 39
Comoros Comoros 0 -100% 88
Costa Rica Costa Rica 220,000 -53.2% 74
Cuba Cuba 110,000 -21.4% 79
Dominica Dominica 20,000 +100% 86
Dominican Republic Dominican Republic 40,000 +100% 84
Algeria Algeria 460,000 +2.22% 62
Ecuador Ecuador 810,000 +65.3% 55
Egypt Egypt 4,050,000 +3.58% 31
Eritrea Eritrea 10,000 -66.7% 87
Ethiopia Ethiopia 13,440,000 -16.3% 6
Gabon Gabon 160,000 -15.8% 77
Georgia Georgia 5,970,000 -1.16% 22
Ghana Ghana 4,880,000 +63.2% 28
Gambia Gambia 170,000 +41.7% 76
Grenada Grenada 10,000 0% 87
Guatemala Guatemala 5,180,000 -7.33% 25
Honduras Honduras 110,000 -35.3% 79
Indonesia Indonesia 7,050,000 -15.9% 16
India India 12,050,000 +3.26% 8
Iran Iran 12,010,000 -34.9% 9
Iraq Iraq 1,020,000 -69.2% 49
Jamaica Jamaica 40,000 +300% 84
Jordan Jordan 4,810,000 -35.2% 29
Kazakhstan Kazakhstan 3,520,000 +3.23% 33
Kenya Kenya 1,770,000 -30% 41
Kyrgyzstan Kyrgyzstan 720,000 +1.41% 57
Cambodia Cambodia 60,000 -25% 82
Laos Laos 1,920,000 +84.6% 40
Lebanon Lebanon 5,020,000 -33.8% 27
Liberia Liberia 20,000 -33.3% 86
Libya Libya 6,770,000 +89.6% 17
St. Lucia St. Lucia 10,000 -50% 87
Sri Lanka Sri Lanka 570,000 -69.7% 59
Lesotho Lesotho 160,000 -30.4% 77
Morocco Morocco 980,000 0% 51
Moldova Moldova 6,760,000 +32% 18
Madagascar Madagascar 60,000 -40% 82
Mexico Mexico 5,060,000 +0.998% 26
North Macedonia North Macedonia 1,750,000 -46% 42
Mali Mali 2,120,000 +21.8% 38
Myanmar (Burma) Myanmar (Burma) 110,000 +57.1% 79
Montenegro Montenegro 910,000 -15% 53
Mongolia Mongolia 3,750,000 +5.04% 32
Mozambique Mozambique 7,440,000 -42.3% 15
Mauritius Mauritius 50,000 -16.7% 83
Malawi Malawi 100,000 +66.7% 80
Malaysia Malaysia 380,000 +81% 66
Namibia Namibia 40,000 -33.3% 84
Nigeria Nigeria 2,180,000 +39.7% 37
Nicaragua Nicaragua 900,000 +18.4% 54
Nepal Nepal 1,110,000 -51.7% 48
Pakistan Pakistan 6,510,000 +39.1% 20
Panama Panama 40,000 +33.3% 84
Peru Peru 1,700,000 +49.1% 43
Philippines Philippines 900,000 +21.6% 54
North Korea North Korea 10,000 0% 87
Paraguay Paraguay 430,000 +105% 64
Palestinian Territories Palestinian Territories 7,630,000 -2.3% 14
Rwanda Rwanda 410,000 +32.3% 65
Sudan Sudan 370,000 +5.71% 67
Senegal Senegal 470,000 -44% 61
Sierra Leone Sierra Leone 170,000 +183% 76
El Salvador El Salvador 440,000 -25.4% 63
Somalia Somalia 950,000 -9.52% 52
Serbia Serbia 54,970,001 +86.5% 2
South Sudan South Sudan 1,270,000 -47.1% 47
São Tomé & Príncipe São Tomé & Príncipe 10,000 87
Eswatini Eswatini 30,000 -50% 85
Syria Syria 9,320,000 -47.5% 12
Togo Togo 30,000 0% 85
Thailand Thailand 1,010,000 +20.2% 50
Tajikistan Tajikistan 190,000 -48.6% 75
Turkmenistan Turkmenistan 240,000 -17.2% 73
Timor-Leste Timor-Leste 10,000 -90% 87
Tunisia Tunisia 5,370,000 +60.3% 24
Turkey Turkey 24,459,999 -11.4% 4
Tanzania Tanzania 1,390,000 +11.2% 46
Uganda Uganda 12,140,000 -19.7% 7
Ukraine Ukraine 75,769,997 +270% 1
Uzbekistan Uzbekistan 2,310,000 +7.94% 36
St. Vincent & Grenadines St. Vincent & Grenadines 50,000 -68.7% 83
Venezuela Venezuela 340,000 -78.8% 69
Vietnam Vietnam 2,420,000 -10% 35
Vanuatu Vanuatu 130,000 -13.3% 78
Kosovo Kosovo 7,900,000 -28.8% 13
Yemen Yemen 6,300,000 -23.8% 21
South Africa South Africa 730,000 -5.19% 56
Zambia Zambia 110,000 -35.3% 79
Zimbabwe Zimbabwe 250,000 +56.3% 72 

                    
# 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.AUTL.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.AUTL.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))