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

Source: worldbank.org, 01.09.2025

Year: 2022

Flag Country Value Value change, % Rank
Afghanistan Afghanistan 19,680,000 -55.8% 60
Angola Angola 29,250,000 -1.52% 53
Albania Albania 88,550,003 +1,264% 30
Argentina Argentina 23,400,000 +74% 56
Armenia Armenia 154,350,006 +328% 11
Azerbaijan Azerbaijan 43,209,999 -1.88% 42
Burundi Burundi 10,900,000 +22.1% 71
Benin Benin 100,480,003 +18.6% 24
Burkina Faso Burkina Faso 134,210,007 +5.69% 16
Bangladesh Bangladesh 214,520,004 +22% 8
Bosnia & Herzegovina Bosnia & Herzegovina 3,400,000 +22.7% 89
Belarus Belarus 1,320,000 -1.49% 96
Belize Belize 10,000 -50% 113
Bolivia Bolivia 16,170,000 -89.3% 64
Brazil Brazil -55,709,999 -112% 125
Bhutan Bhutan 20,000 -50% 112
Botswana Botswana 2,270,000 +49.3% 92
Central African Republic Central African Republic 34,980,000 +14.6% 49
Côte d’Ivoire Côte d’Ivoire 588,190,002 +63.6% 1
Cameroon Cameroon 230,130,005 +69.5% 7
Congo - Kinshasa Congo - Kinshasa 93,050,003 +164% 28
Congo - Brazzaville Congo - Brazzaville 100,120,003 +105% 25
Colombia Colombia 148,270,004 -50.6% 12
Comoros Comoros 55,430,000 +19.6% 40
Cape Verde Cape Verde -510,000 -143% 116
Costa Rica Costa Rica 110,510,002 +1,877% 23
Cuba Cuba 23,280,001 +38.3% 57
Djibouti Djibouti 12,650,000 +26% 70
Dominica Dominica -1,940,000 +273% 118
Dominican Republic Dominican Republic -31,080,000 -112% 124
Algeria Algeria 129,619,995 +29.6% 18
Ecuador Ecuador 117,940,002 +192% 21
Egypt Egypt 248,179,993 +46.3% 6
Eritrea Eritrea 1,050,000 +64.1% 98
Ethiopia Ethiopia 92,000,000 +109% 29
Fiji Fiji 780,000 +21.9% 101
Gabon Gabon 112,430,000 +111% 22
Georgia Georgia 99,379,997 -49% 26
Ghana Ghana -7,910,000 -118% 121
Guinea Guinea 67,680,000 -11.9% 35
Gambia Gambia 4,610,000 +343% 85
Guinea-Bissau Guinea-Bissau 7,240,000 +215% 74
Equatorial Guinea Equatorial Guinea 2,260,000 -15.4% 93
Grenada Grenada 40,000 -20% 111
Guatemala Guatemala 1,510,000 -19.3% 94
Guyana Guyana 220,000 -71.1% 107
Honduras Honduras 5,000,000 +1,624% 84
Haiti Haiti 42,810,001 -39.7% 43
Indonesia Indonesia -123,669,998 -484% 126
India India 16,200,001 -225% 63
Iran Iran 14,880,000 +11.8% 66
Iraq Iraq 33,680,000 -15.3% 51
Jamaica Jamaica 2,890,000 -250% 91
Jordan Jordan 135,419,998 +117% 15
Kazakhstan Kazakhstan 3,970,000 +12.5% 86
Kenya Kenya 41,990,002 -42.6% 44
Kyrgyzstan Kyrgyzstan 980,000 -42.4% 99
Cambodia Cambodia 39,299,999 -64.8% 46
Laos Laos 14,730,000 +14.6% 67
Lebanon Lebanon 136,949,997 +48.3% 14
Liberia Liberia 6,840,000 +7.89% 77
Libya Libya 5,860,000 -13.1% 81
St. Lucia St. Lucia 730,000 -23.2% 102
Sri Lanka Sri Lanka 25,969,999 +87.5% 54
Lesotho Lesotho -10,000 -150% 115
Morocco Morocco 379,410,004 +51.5% 3
Moldova Moldova 83,790,001 +2,117% 31
Madagascar Madagascar 79,389,999 -25.3% 32
Maldives Maldives -1,520,000 -1,269% 117
Mexico Mexico 286,010,010 -18.9% 5
North Macedonia North Macedonia 1,500,000 -1.32% 95
Mali Mali 70,510,002 -41.1% 34
Myanmar (Burma) Myanmar (Burma) 6,660,000 -18.3% 78
Montenegro Montenegro 5,070,000 +718% 83
Mongolia Mongolia 16,110,001 +490% 65
Mozambique Mozambique 6,110,000 -86.6% 80
Mauritania Mauritania 22,510,000 -300% 59
Mauritius Mauritius 25,420,000 -285% 55
Malawi Malawi 430,000 -85.5% 103
Malaysia Malaysia 3,790,000 -26% 87
Namibia Namibia -2,670,000 -37.6% 119
Niger Niger 122,070,000 -3.05% 20
Nigeria Nigeria 146,330,002 +24.3% 13
Nicaragua Nicaragua 1,210,000 -81.4% 97
Nepal Nepal 5,380,000 +360% 82
Pakistan Pakistan 14,650,000 -67.5% 68
Panama Panama -15,330,000 +76.6% 122
Peru Peru 18,549,999 -191% 62
Philippines Philippines -20,480,000 -106% 123
Palau Palau 0 -100% 114
Papua New Guinea Papua New Guinea 10,000 -87.5% 113
North Korea North Korea 280,000 -30% 106
Paraguay Paraguay -6,700,000 -122% 120
Palestinian Territories Palestinian Territories 72,930,000 +5.7% 33
Rwanda Rwanda 41,970,001 -41.3% 45
Sudan Sudan 31,440,001 -28.9% 52
Senegal Senegal 209,410,004 -14.9% 10
Solomon Islands Solomon Islands 0 114
Sierra Leone Sierra Leone 7,020,000 +325% 76
El Salvador El Salvador 18,900,000 -31% 61
Somalia Somalia 8,250,000 -60.4% 73
Serbia Serbia 64,940,002 -9.44% 36
South Sudan South Sudan 6,320,000 +23.7% 79
São Tomé & Príncipe São Tomé & Príncipe 290,000 -42% 105
Suriname Suriname 35,279,999 +5,245% 48
Eswatini Eswatini 170,000 +13.3% 110
Syria Syria 61,139,999 -3.44% 37
Chad Chad 98,489,998 +16% 27
Togo Togo 57,599,998 +31.9% 39
Thailand Thailand 34,099,998 +123% 50
Tajikistan Tajikistan 180,000 +5.88% 109
Turkmenistan Turkmenistan 390,000 -9.3% 104
Timor-Leste Timor-Leste 200,000 +11.1% 108
Tonga Tonga 860,000 +856% 100
Tunisia Tunisia 48,619,999 -66.6% 41
Turkey Turkey 128,750,000 +185% 19
Tanzania Tanzania 132,440,002 +230% 17
Uganda Uganda 36,750,000 -45.2% 47
Ukraine Ukraine 498,260,010 +1,867% 2
Uzbekistan Uzbekistan 213,619,995 +1,194% 9
St. Vincent & Grenadines St. Vincent & Grenadines 20,000 -88.2% 112
Venezuela Venezuela 9,940,000 +2.69% 72
Vietnam Vietnam 59,299,999 -52.1% 38
Vanuatu Vanuatu 3,640,000 -26.3% 88
Kosovo Kosovo 2,910,000 -20.9% 90
Yemen Yemen 13,240,000 +5.41% 69
South Africa South Africa 343,040,009 +58.8% 4
Zambia Zambia 22,590,000 +382% 58
Zimbabwe Zimbabwe 7,090,000 +24.8% 75 

                    
# 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.FRAL.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.FRAL.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))