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

Source: worldbank.org, 03.09.2025

Year: 2022

Flag Country Value Value change, % Rank
Afghanistan Afghanistan 1,296,000,000 -12.5% 3
Angola Angola 60,150,002 +44.2% 67
Albania Albania 12,310,000 -2.3% 96
Argentina Argentina 2,860,000 -47.4% 115
Armenia Armenia 32,470,001 -13.9% 84
Azerbaijan Azerbaijan 12,150,000 -8.3% 97
Burundi Burundi 57,130,001 -7.2% 69
Benin Benin 116,160,004 -32.2% 49
Burkina Faso Burkina Faso 180,889,999 +0.00553% 32
Bangladesh Bangladesh 375,630,005 -15.4% 18
Bosnia & Herzegovina Bosnia & Herzegovina 46,599,998 -23.6% 75
Belarus Belarus 15,240,000 +77.6% 92
Belize Belize 5,990,000 +9.51% 103
Bolivia Bolivia 5,000,000 -40.4% 105
Brazil Brazil 45,590,000 -2.71% 76
Bhutan Bhutan 50,000 -95.9% 127
Botswana Botswana 51,869,999 -5.66% 72
Central African Republic Central African Republic 144,610,001 +18.4% 43
China China 7,250,000 -48.4% 100
Côte d’Ivoire Côte d’Ivoire 185,860,001 -1.23% 29
Cameroon Cameroon 162,000,000 +14.8% 36
Congo - Kinshasa Congo - Kinshasa 598,400,024 -33.4% 9
Congo - Brazzaville Congo - Brazzaville 12,030,000 -39.7% 98
Colombia Colombia 568,799,988 -15.5% 12
Comoros Comoros 980,000 +27.3% 119
Cape Verde Cape Verde 2,370,000 +51.9% 116
Costa Rica Costa Rica 37,080,002 +14.2% 81
Cuba Cuba 5,390,000 -32.7% 104
Djibouti Djibouti 20,940,001 -3.19% 88
Dominica Dominica 40,000 -96.7% 128
Dominican Republic Dominican Republic 71,129,997 -1.39% 61
Algeria Algeria 1,880,000 -26.3% 117
Ecuador Ecuador 74,730,003 +5.6% 58
Egypt Egypt 123,849,998 +165% 47
Ethiopia Ethiopia 1,437,010,010 +8.48% 2
Fiji Fiji 3,090,000 +19.8% 114
Micronesia (Federated States of) Micronesia (Federated States of) 113,160,004 +2.42% 50
Gabon Gabon 90,000 -96.3% 125
Georgia Georgia 59,610,001 -15.7% 68
Ghana Ghana 161,279,999 -33% 38
Guinea Guinea 37,930,000 -12.4% 80
Gambia Gambia 4,570,000 +65% 107
Guinea-Bissau Guinea-Bissau 4,380,000 -29.5% 109
Equatorial Guinea Equatorial Guinea 520,000 +92.6% 121
Guatemala Guatemala 222,339,996 -11.9% 26
Guyana Guyana 4,760,000 -19.2% 106
Honduras Honduras 138,820,007 -20.5% 44
Haiti Haiti 261,170,013 +1.14% 24
Indonesia Indonesia 105,610,001 -3.04% 52
India India 194,589,996 +47% 27
Iraq Iraq 282,429,993 -38.8% 23
Jamaica Jamaica 33,410,000 +36.5% 83
Jordan Jordan 323,790,009 -74.5% 19
Kazakhstan Kazakhstan 14,260,000 -6.98% 93
Kenya Kenya 590,770,020 -15.8% 10
Kyrgyzstan Kyrgyzstan 43,430,000 -9.24% 77
Cambodia Cambodia 109,190,002 +0.516% 51
Kiribati Kiribati 420,000 +90.9% 122
Laos Laos 90,010,002 +29.9% 54
Lebanon Lebanon 291,929,993 +7.79% 21
Liberia Liberia 119,709,999 -17.1% 48
Libya Libya 53,200,001 -32% 71
St. Lucia St. Lucia 410,000 +1,267% 123
Sri Lanka Sri Lanka 10,000,000 -81.7% 99
Lesotho Lesotho 71,900,002 -13.7% 60
Morocco Morocco 161,179,993 -12.4% 39
Moldova Moldova 53,330,002 +29.8% 70
Madagascar Madagascar 175,660,004 +14.5% 33
Maldives Maldives 4,390,000 -23.9% 108
Mexico Mexico 166,500,000 +9.12% 35
Marshall Islands Marshall Islands 87,750,000 -0.555% 55
North Macedonia North Macedonia 17,480,000 +7.97% 91
Mali Mali 182,729,996 -17.7% 31
Myanmar (Burma) Myanmar (Burma) 161,690,002 +3.56% 37
Montenegro Montenegro 3,470,000 -33.8% 111
Mongolia Mongolia 51,410,000 +30.9% 73
Mozambique Mozambique 527,950,012 -8% 13
Mauritania Mauritania 23,719,999 +15.3% 87
Mauritius Mauritius 300,000 -68.7% 124
Malawi Malawi 300,279,999 +5.22% 20
Malaysia Malaysia 6,120,000 -62.6% 102
Namibia Namibia 77,370,003 -6.27% 57
Niger Niger 238,589,996 -8.73% 25
Nigeria Nigeria 772,349,976 -15.4% 6
Nicaragua Nicaragua 31,639,999 -21.5% 85
Nepal Nepal 129,029,999 -19.4% 45
Pakistan Pakistan 184,350,006 -22.1% 30
Panama Panama 36,590,000 +2.78% 82
Peru Peru 186,619,995 +9.62% 28
Philippines Philippines 147,660,004 -7.24% 42
Palau Palau 6,480,000 +46.9% 101
Papua New Guinea Papua New Guinea 18,000,000 +30% 90
North Korea North Korea 0 -100% 130
Paraguay Paraguay 13,870,000 -35.8% 95
Palestinian Territories Palestinian Territories 86,709,999 +87.9% 56
Rwanda Rwanda 170,380,005 +12.1% 34
Sudan Sudan 578,780,029 -21.2% 11
Senegal Senegal 152,850,006 -14.4% 41
Solomon Islands Solomon Islands 3,200,000 +63.3% 113
Sierra Leone Sierra Leone 41,910,000 -19.7% 78
El Salvador El Salvador 96,260,002 +4.52% 53
Somalia Somalia 519,289,978 +10.5% 15
Serbia Serbia 19,980,000 -42.9% 89
South Sudan South Sudan 949,820,007 +18.8% 5
São Tomé & Príncipe São Tomé & Príncipe 10,000 -66.7% 129
Suriname Suriname -30,000 -113% 131
Eswatini Eswatini 64,419,998 +8.76% 65
Syria Syria 649,809,998 -18.9% 7
Chad Chad 66,690,002 -40.3% 64
Togo Togo 14,110,000 +2.77% 94
Thailand Thailand 62,080,002 -3.77% 66
Tajikistan Tajikistan 48,700,001 +1.99% 74
Turkmenistan Turkmenistan 4,000,000 -12.3% 110
Timor-Leste Timor-Leste 28,889,999 -2.92% 86
Tonga Tonga 2,370,000 +130% 116
Tunisia Tunisia 72,180,000 -34% 59
Turkey Turkey 66,790,001 +4.07% 63
Tuvalu Tuvalu 80,000 -46.7% 126
Tanzania Tanzania 429,450,012 -15.3% 16
Uganda Uganda 615,969,971 -2.35% 8
Ukraine Ukraine 9,238,049,805 +2,932% 1
Uzbekistan Uzbekistan 39,959,999 +5.38% 79
St. Vincent & Grenadines St. Vincent & Grenadines 3,370,000 +153% 112
Venezuela Venezuela 123,889,999 +18.2% 46
Vietnam Vietnam 153,419,998 +7.43% 40
Vanuatu Vanuatu 1,470,000 -6.96% 118
Samoa Samoa 960,000 +23.1% 120
Kosovo Kosovo 68,730,003 +24.4% 62
Yemen Yemen 982,539,978 +9.1% 4
South Africa South Africa 527,479,980 -24.4% 14
Zambia Zambia 408,519,989 +2.14% 17
Zimbabwe Zimbabwe 289,190,002 -15.7% 22 

                    
# 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.USAL.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.USAL.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))