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

Source: worldbank.org, 03.09.2025

Year: 2022

Flag Country Value Value change, % Rank
Afghanistan Afghanistan 97,089,996 +0.0825% 2
Angola Angola 700,000 -82.3% 72
Albania Albania 14,980,000 +10.2% 22
Argentina Argentina 78,040,001 -5,482% 3
Armenia Armenia 220,000 -4.35% 86
Azerbaijan Azerbaijan 700,000 +37.3% 72
Burundi Burundi 2,290,000 -45.1% 58
Benin Benin 3,730,000 -32.2% 46
Burkina Faso Burkina Faso 23,090,000 +30.3% 13
Bangladesh Bangladesh 9,900,000 +342% 30
Bosnia & Herzegovina Bosnia & Herzegovina 2,480,000 -50.2% 55
Belarus Belarus 440,000 +57.1% 81
Belize Belize 0 99
Bolivia Bolivia 4,830,000 +17.8% 41
Brazil Brazil 12,540,000 -2.34% 25
Bhutan Bhutan 140,000 +180% 91
Central African Republic Central African Republic 4,640,000 -40.6% 42
China China -33,790,001 +54.8% 103
Côte d’Ivoire Côte d’Ivoire 3,040,000 -30% 50
Cameroon Cameroon 8,730,000 +15.2% 32
Congo - Kinshasa Congo - Kinshasa 7,830,000 -35.4% 33
Congo - Brazzaville Congo - Brazzaville 310,000 -62.7% 83
Colombia Colombia 14,860,000 +292% 23
Comoros Comoros 30,000 +50% 97
Cape Verde Cape Verde 740,000 -55.4% 70
Costa Rica Costa Rica 500,000 +525% 79
Cuba Cuba 3,370,000 -78.7% 49
Djibouti Djibouti 600,000 +25% 77
Dominica Dominica 0 -100% 99
Dominican Republic Dominican Republic 40,000 -50% 96
Algeria Algeria 1,450,000 +3.57% 63
Ecuador Ecuador 2,080,000 -35.2% 59
Egypt Egypt 9,110,000 +29% 31
Eritrea Eritrea 5,000,000 +207% 40
Ethiopia Ethiopia 10,590,000 -80% 27
Fiji Fiji 0 -100% 99
Micronesia (Federated States of) Micronesia (Federated States of) 190,000 -32.1% 88
Gabon Gabon 90,000 -25% 93
Georgia Georgia 640,000 +237% 75
Ghana Ghana 2,010,000 +71.8% 60
Guinea Guinea 2,550,000 +58.4% 53
Gambia Gambia 310,000 -65.9% 83
Guinea-Bissau Guinea-Bissau 1,430,000 +151% 64
Guatemala Guatemala 180,000 -75.3% 89
Guyana Guyana -180,000 -14.3% 100
Honduras Honduras -520,000 -843% 101
Haiti Haiti 680,000 -71.3% 74
Indonesia Indonesia 690,000 -8% 73
India India 20,670,000 +10.1% 18
Iran Iran 23,490,000 +496% 12
Iraq Iraq 20,150,000 +65.2% 19
Jamaica Jamaica 60,000 0% 94
Jordan Jordan 21,459,999 -64.8% 17
Kazakhstan Kazakhstan 1,630,000 +552% 62
Kenya Kenya 21,500,000 +76.5% 16
Kyrgyzstan Kyrgyzstan 250,000 +400% 85
Cambodia Cambodia 7,190,000 +2,379% 35
Laos Laos 120,000 -7.69% 92
Lebanon Lebanon 57,720,001 +98.8% 4
Liberia Liberia 200,000 87
Libya Libya 39,020,000 +78.3% 7
St. Lucia St. Lucia 1,140,000 +208% 65
Sri Lanka Sri Lanka 1,820,000 +2.25% 61
Lesotho Lesotho 470,000 +236% 80
Morocco Morocco -3,020,000 -5.03% 102
Moldova Moldova 21,830,000 +4,098% 15
Madagascar Madagascar 2,390,000 -20.3% 57
Maldives Maldives 320,000 +167% 82
Mexico Mexico 620,000 +40.9% 76
North Macedonia North Macedonia 280,000 +155% 84
Mali Mali 12,740,000 +43% 24
Myanmar (Burma) Myanmar (Burma) 4,450,000 -27.4% 43
Montenegro Montenegro 280,000 +460% 84
Mongolia Mongolia 160,000 +100% 90
Mozambique Mozambique 41,439,999 +80.8% 6
Mauritania Mauritania 1,010,000 -18.5% 67
Mauritius Mauritius 10,000 0% 98
Malawi Malawi 2,530,000 +51.5% 54
Malaysia Malaysia 50,000 +150% 95
Namibia Namibia 30,000 +50% 97
Niger Niger 22,910,000 +0.97% 14
Nigeria Nigeria 5,950,000 +45.1% 38
Nicaragua Nicaragua 90,000 -96.8% 93
Nepal Nepal 890,000 -32.6% 69
Pakistan Pakistan 17,930,000 +298% 20
Panama Panama 0 -100% 99
Peru Peru 3,390,000 +14.1% 48
Philippines Philippines 710,000 -16.5% 71
Palau Palau 560,000 +64.7% 78
Papua New Guinea Papua New Guinea 220,000 +57.1% 86
North Korea North Korea 30,000 +50% 97
Paraguay Paraguay 310,000 -47.5% 83
Palestinian Territories Palestinian Territories 31,570,000 -9.44% 10
Rwanda Rwanda 3,490,000 -56.2% 47
Sudan Sudan 38,020,000 +18.4% 8
Senegal Senegal 10,300,000 -67.9% 29
Sierra Leone Sierra Leone 5,980,000 +199% 37
El Salvador El Salvador 6,630,000 +155% 36
Somalia Somalia 17,879,999 -96.7% 21
Serbia Serbia 500,000 +19% 79
South Sudan South Sudan 12,530,000 +74% 26
São Tomé & Príncipe São Tomé & Príncipe 280,000 +155% 84
Eswatini Eswatini 60,000 -110% 94
Syria Syria 31,680,000 +139% 9
Chad Chad 2,950,000 -44.8% 51
Togo Togo 980,000 -50.3% 68
Thailand Thailand 190,000 -36.7% 88
Tajikistan Tajikistan 30,000 97
Turkmenistan Turkmenistan 10,000 0% 98
Timor-Leste Timor-Leste 0 -100% 99
Tunisia Tunisia 55,189,999 +3.33% 5
Turkey Turkey 29,980,000 +22.4% 11
Tanzania Tanzania 10,560,000 +34% 28
Uganda Uganda 7,250,000 +11.9% 34
Ukraine Ukraine 360,470,001 +22,429% 1
Uzbekistan Uzbekistan 600,000 +757% 77
Venezuela Venezuela 4,200,000 +1,135% 44
Vietnam Vietnam 5,150,000 -13.7% 39
Vanuatu Vanuatu 160,000 -48.4% 90
Kosovo Kosovo 2,410,000 +316% 56
Yemen Yemen 50,000 -90.6% 95
South Africa South Africa 1,020,000 -20.3% 66
Zambia Zambia 4,020,000 +483% 45
Zimbabwe Zimbabwe 2,670,000 +1,114% 52 

                    
# 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.ITAL.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.ITAL.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))