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

Source: worldbank.org, 03.09.2025

Year: 2022

Flag Country Value Value change, % Rank
Afghanistan Afghanistan 3,342,789,989 -10.5% 4
Angola Angola 91,550,002 -53.1% 98
Albania Albania 345,209,999 -47.8% 63
Argentina Argentina 215,970,001 +221% 76
Armenia Armenia 379,540,012 +56.4% 61
Azerbaijan Azerbaijan 75,880,000 +3.75% 105
Burundi Burundi 308,770,000 +7.26% 66
Benin Benin 461,760,006 -7.82% 53
Burkina Faso Burkina Faso 787,040,001 -12.1% 34
Bangladesh Bangladesh 3,737,030,028 +1.84% 3
Bosnia & Herzegovina Bosnia & Herzegovina 250,579,996 -54.5% 70
Belarus Belarus -11,370,000 -111% 134
Belize Belize 14,930,000 -17.9% 125
Bolivia Bolivia 239,089,999 -39.9% 74
Brazil Brazil 536,499,993 -50% 46
Bhutan Bhutan 60,559,999 +117% 111
Botswana Botswana 63,889,999 -18.2% 109
Central African Republic Central African Republic 391,069,999 -4.67% 59
China China -126,739,993 -62.4% 136
Côte d’Ivoire Côte d’Ivoire 1,140,000,006 +35.4% 25
Cameroon Cameroon 676,030,008 +3.81% 39
Congo - Kinshasa Congo - Kinshasa 1,538,180,027 -20.5% 12
Congo - Brazzaville Congo - Brazzaville 137,050,003 +40.9% 92
Colombia Colombia 1,682,439,985 -8.08% 8
Comoros Comoros 69,900,000 +28.5% 107
Cape Verde Cape Verde 29,659,999 -63.1% 118
Costa Rica Costa Rica 164,920,004 +196% 86
Cuba Cuba 89,420,000 -22.3% 99
Djibouti Djibouti 88,550,000 +28.6% 100
Dominica Dominica 13,070,000 +79% 126
Dominican Republic Dominican Republic 321,739,997 -22.8% 65
Algeria Algeria 196,189,995 +8.87% 80
Ecuador Ecuador 344,100,004 +29.9% 64
Egypt Egypt 799,289,993 -70.1% 32
Eritrea Eritrea 12,520,000 -24.5% 127
Ethiopia Ethiopia 2,929,650,009 +9.84% 5
Fiji Fiji 179,909,995 -56.9% 82
Micronesia (Federated States of) Micronesia (Federated States of) 134,670,004 +13.4% 93
Gabon Gabon 122,700,000 +41.3% 94
Georgia Georgia 471,669,992 -45.7% 52
Ghana Ghana 512,980,002 -37.2% 48
Guinea Guinea 253,760,004 +10.7% 69
Gambia Gambia 79,210,001 +4.84% 103
Guinea-Bissau Guinea-Bissau 61,660,000 +1.97% 110
Equatorial Guinea Equatorial Guinea 7,370,000 +4.24% 129
Grenada Grenada 2,770,000 -16.6% 133
Guatemala Guatemala 393,869,996 -14% 58
Guyana Guyana 17,840,000 -41.2% 123
Honduras Honduras 357,630,003 +7.51% 62
Haiti Haiti 510,650,013 -4.09% 50
Indonesia Indonesia 644,840,028 +15.8% 40
India India 3,971,499,876 +10.2% 2
Iran Iran 254,529,999 -0.613% 68
Iraq Iraq 1,554,080,033 -8.27% 10
Jamaica Jamaica 77,010,000 +53.4% 104
Jordan Jordan 1,268,769,993 -55.6% 18
Kazakhstan Kazakhstan 26,450,000 +246% 119
Kenya Kenya 1,356,290,019 -15.4% 16
Kyrgyzstan Kyrgyzstan 167,440,001 -25.4% 85
Cambodia Cambodia 1,020,759,998 -6.84% 26
Kiribati Kiribati 48,930,000 -17.9% 114
Laos Laos 396,159,998 -5.95% 57
Lebanon Lebanon 1,235,349,998 -2.06% 20
Liberia Liberia 244,949,999 -18.9% 72
Libya Libya 242,500,001 -23.2% 73
St. Lucia St. Lucia 20,590,000 +62% 122
Sri Lanka Sri Lanka 208,630,003 +9.64% 78
Lesotho Lesotho 92,690,001 -18.9% 97
Morocco Morocco 1,373,739,996 +34.8% 14
Moldova Moldova 699,710,011 +55.9% 38
Madagascar Madagascar 563,100,003 +8.63% 44
Maldives Maldives 25,790,000 -35.3% 120
Mexico Mexico 460,490,014 -13.4% 54
Marshall Islands Marshall Islands 110,280,000 +11.4% 96
North Macedonia North Macedonia 138,040,000 -59% 90
Mali Mali 810,329,996 -22.5% 31
Myanmar (Burma) Myanmar (Burma) 941,799,985 -15.1% 28
Montenegro Montenegro 11,810,000 -90.9% 128
Mongolia Mongolia 223,940,003 +33.2% 75
Mozambique Mozambique 1,360,210,015 -4.84% 15
Mauritania Mauritania 137,449,999 +9.98% 91
Mauritius Mauritius 44,670,000 -84.2% 116
Malawi Malawi 626,640,003 -9.44% 41
Malaysia Malaysia -27,700,004 +170% 135
Namibia Namibia 164,590,002 +5.46% 87
Niger Niger 963,320,007 -2.04% 27
Nigeria Nigeria 1,541,599,981 -10.9% 11
Nicaragua Nicaragua 156,260,000 -29.9% 89
Nepal Nepal 556,850,003 -18% 45
Nauru Nauru 24,870,001 +0.161% 121
Pakistan Pakistan 483,480,001 -46.4% 51
Panama Panama 117,859,999 +12.1% 95
Peru Peru 785,019,987 +199% 35
Philippines Philippines 1,534,340,035 -3.18% 13
Palau Palau 50,250,000 +105% 113
Papua New Guinea Papua New Guinea 610,000,003 -41.5% 42
North Korea North Korea 5,000,000 -66.6% 132
Paraguay Paraguay 73,069,999 -51% 106
Palestinian Territories Palestinian Territories 1,187,070,011 -2.37% 23
Rwanda Rwanda 583,920,011 -19.6% 43
Sudan Sudan 1,216,540,031 -21.4% 21
Senegal Senegal 792,640,002 -14% 33
Solomon Islands Solomon Islands 176,790,002 -21.5% 84
Sierra Leone Sierra Leone 207,210,000 -23.9% 79
El Salvador El Salvador 209,680,002 -2.04% 77
Somalia Somalia 1,300,089,975 -36.9% 17
Serbia Serbia 733,629,993 +10.1% 36
South Sudan South Sudan 1,606,030,012 -0.763% 9
São Tomé & Príncipe São Tomé & Príncipe 45,359,999 +87.7% 115
Suriname Suriname 39,729,999 +349% 117
Eswatini Eswatini 87,729,998 -11.5% 101
Syria Syria 2,318,489,986 -17.7% 6
Chad Chad 412,260,002 +1.46% 56
Togo Togo 163,709,996 -11% 88
Thailand Thailand 512,230,003 +454% 49
Tajikistan Tajikistan 178,119,997 -22.4% 83
Turkmenistan Turkmenistan 7,080,000 -48.3% 130
Timor-Leste Timor-Leste 182,350,003 -19% 81
Tonga Tonga 67,970,000 +9.22% 108
Tunisia Tunisia 1,191,329,991 +13.8% 22
Turkey Turkey 814,789,993 -24.8% 30
Tuvalu Tuvalu 16,830,000 -28% 124
Tanzania Tanzania 1,173,720,010 -7.87% 24
Uganda Uganda 1,263,259,965 -17.7% 19
Ukraine Ukraine 28,424,739,935 +1,252% 1
Uzbekistan Uzbekistan 423,119,995 -21.3% 55
St. Vincent & Grenadines St. Vincent & Grenadines 6,070,000 -34.9% 131
Venezuela Venezuela 249,849,998 +2.85% 71
Vietnam Vietnam 389,369,995 -23.2% 60
Vanuatu Vanuatu 86,920,000 -37.2% 102
Samoa Samoa 58,369,999 -20.5% 112
Kosovo Kosovo 294,690,001 -20% 67
Yemen Yemen 2,110,089,958 +4.84% 7
South Africa South Africa 834,699,990 +5.85% 29
Zambia Zambia 719,929,988 +3.1% 37
Zimbabwe Zimbabwe 524,580,002 -18.6% 47 

                    
# 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.TOTL.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.TOTL.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))