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

Source: worldbank.org, 03.09.2025

Year: 2022

Flag Country Value Value change, % Rank
Afghanistan Afghanistan 5,990,000 -21.3% 19
Albania Albania 130,000 -7.14% 64
Argentina Argentina 20,000 +100% 71
Burundi Burundi 2,620,000 +4.8% 33
Benin Benin 30,000 +50% 70
Burkina Faso Burkina Faso 1,120,000 -14.5% 41
Bangladesh Bangladesh 2,670,000 +3.89% 32
Belarus Belarus 210,000 +950% 61
Bolivia Bolivia 100,000 -9.09% 65
Brazil Brazil 900,000 +91.5% 44
Central African Republic Central African Republic 4,970,000 -20.2% 22
China China 50,000 0% 68
Côte d’Ivoire Côte d’Ivoire 40,000 +100% 69
Cameroon Cameroon 740,000 -26% 48
Congo - Kinshasa Congo - Kinshasa 7,870,000 +0.255% 14
Congo - Brazzaville Congo - Brazzaville 20,000 0% 71
Colombia Colombia 2,920,000 +2.46% 30
Cape Verde Cape Verde 0 72
Dominica Dominica 0 -100% 72
Ecuador Ecuador 160,000 +433% 63
Egypt Egypt 30,000 0% 70
Eritrea Eritrea 1,280,000 -11.1% 40
Ethiopia Ethiopia 43,320,000 -9.69% 2
Fiji Fiji 40,000 +33.3% 69
Georgia Georgia 310,000 -31.1% 56
Ghana Ghana 250,000 -51% 59
Guinea Guinea 410,000 -2.38% 55
Gambia Gambia 0 -100% 72
Guinea-Bissau Guinea-Bissau 300,000 -3.23% 57
Guatemala Guatemala 1,860,000 +14.8% 36
Honduras Honduras 1,650,000 +20.4% 37
Haiti Haiti 2,990,000 +24.6% 28
Indonesia Indonesia 100,000 +25% 65
India India 1,110,000 -28.4% 42
Iraq Iraq 1,320,000 -30.2% 39
Jamaica Jamaica 0 72
Jordan Jordan 3,020,000 -13% 27
Kenya Kenya 12,020,000 +15.9% 10
Cambodia Cambodia 1,060,000 -25.4% 43
Laos Laos 790,000 -38.8% 46
Lebanon Lebanon 5,970,000 +29.2% 20
Liberia Liberia 6,900,000 -0.719% 16
Libya Libya 90,000 +28.6% 66
Sri Lanka Sri Lanka 30,000 0% 70
Lesotho Lesotho 90,000 -35.7% 66
Moldova Moldova 5,360,000 +8,833% 21
Madagascar Madagascar 180,000 -76.3% 62
Mexico Mexico 60,000 -68.4% 67
Mali Mali 820,000 -26.8% 45
Myanmar (Burma) Myanmar (Burma) 3,150,000 +10.1% 26
Mongolia Mongolia 20,000 +100% 71
Mozambique Mozambique 28,709,999 -10.4% 3
Mauritania Mauritania 420,000 +5% 54
Malawi Malawi 23,700,001 +15% 6
Namibia Namibia 20,000 -60% 71
Niger Niger 4,430,000 +16% 24
Nigeria Nigeria 2,370,000 -20.2% 35
Nicaragua Nicaragua 1,530,000 +11.7% 38
Nepal Nepal 760,000 -28.3% 47
Pakistan Pakistan 530,000 -11.7% 52
Peru Peru 450,000 -29.7% 53
Philippines Philippines 690,000 +32.7% 49
Papua New Guinea Papua New Guinea 40,000 +300% 69
Paraguay Paraguay 20,000 -33.3% 71
Palestinian Territories Palestinian Territories 9,490,000 -25.6% 13
Rwanda Rwanda 2,860,000 +20.7% 31
Sudan Sudan 9,780,000 +53.1% 12
Senegal Senegal 610,000 +15.1% 50
Sierra Leone Sierra Leone 19,299,999 +0.626% 7
Somalia Somalia 17,350,000 +103% 8
South Sudan South Sudan 13,820,000 +19.9% 9
Syria Syria 7,040,000 -25.3% 15
Chad Chad 2,380,000 -7.75% 34
Togo Togo 20,000 0% 71
Thailand Thailand 230,000 -17.9% 60
Timor-Leste Timor-Leste 0 72
Tonga Tonga 260,000 58
Turkey Turkey 2,930,000 -14.1% 29
Tanzania Tanzania 26,290,001 -11.8% 4
Uganda Uganda 25,610,001 -11% 5
Ukraine Ukraine 55,599,998 +12,830% 1
Venezuela Venezuela 540,000 -8.47% 51
Vietnam Vietnam 4,630,000 +7.93% 23
Kosovo Kosovo 250,000 -3.85% 59
Yemen Yemen 6,730,000 -11.1% 17
South Africa South Africa 4,260,000 -25.1% 25
Zambia Zambia 6,440,000 +9.52% 18
Zimbabwe Zimbabwe 11,950,000 +10.9% 11 

                    
# 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.IRLL.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.IRLL.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))