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

Source: worldbank.org, 03.09.2025

Year: 2022

Flag Country Value Value change, % Rank
Afghanistan Afghanistan 156,669,998 -26.3% 13
Angola Angola 5,470,000 -31.5% 81
Albania Albania -1,370,000 -13.3% 119
Argentina Argentina 2,170,000 -68.8% 103
Armenia Armenia -6,300,000 +268% 123
Azerbaijan Azerbaijan -24,389,999 +2.44% 126
Burundi Burundi 15,140,000 +35.4% 64
Benin Benin 5,230,000 -40.8% 86
Burkina Faso Burkina Faso 29,790,001 -53.3% 39
Bangladesh Bangladesh 2,283,020,020 +16.9% 2
Bosnia & Herzegovina Bosnia & Herzegovina -3,090,000 -193% 122
Belarus Belarus 570,000 +256% 112
Belize Belize 940,000 +42.4% 108
Bolivia Bolivia 26,750,000 +13.4% 45
Brazil Brazil 8,900,000 -96.4% 73
Bhutan Bhutan 42,389,999 +355% 33
Botswana Botswana 3,980,000 -54.9% 94
Central African Republic Central African Republic 7,080,000 +9.77% 75
China China -606,229,980 -24.3% 133
Côte d’Ivoire Côte d’Ivoire 140,940,002 +311% 16
Cameroon Cameroon 23,680,000 -30.4% 51
Congo - Kinshasa Congo - Kinshasa 21,959,999 -47.9% 54
Congo - Brazzaville Congo - Brazzaville 4,920,000 -44% 88
Colombia Colombia 142,289,993 +5,269% 15
Comoros Comoros 5,430,000 +443% 82
Cape Verde Cape Verde 4,160,000 -45.5% 93
Costa Rica Costa Rica -8,680,000 +46.4% 125
Cuba Cuba 4,380,000 -56.9% 92
Djibouti Djibouti 28,309,999 +140% 43
Dominica Dominica 5,280,000 +94.8% 85
Dominican Republic Dominican Republic 198,389,999 +55,008% 11
Algeria Algeria 1,510,000 +202% 106
Ecuador Ecuador 41,180,000 +241% 34
Egypt Egypt 295,220,001 +65.9% 10
Eritrea Eritrea 530,000 -77.4% 113
Ethiopia Ethiopia 57,200,001 -6.17% 29
Fiji Fiji 84,019,997 -30.7% 22
Micronesia (Federated States of) Micronesia (Federated States of) 15,630,000 +485% 62
Gabon Gabon 3,270,000 -46.8% 96
Georgia Georgia -2,730,000 -54.1% 121
Ghana Ghana 61,970,001 +95.2% 26
Guinea Guinea 24,309,999 +147% 50
Gambia Gambia 6,470,000 +9.85% 76
Guinea-Bissau Guinea-Bissau 3,870,000 -35.8% 95
Equatorial Guinea Equatorial Guinea 780,000 +550% 110
Grenada Grenada 10,000 -99.5% 116
Guatemala Guatemala -10,000 -100% 117
Guyana Guyana 80,000 -90.2% 115
Honduras Honduras 100,239,998 +526% 18
Haiti Haiti 5,800,000 -13.7% 79
Indonesia Indonesia -438,529,999 +39.6% 132
India India 2,970,639,893 +24.4% 1
Iran Iran 25,240,000 -40.6% 48
Iraq Iraq 597,090,027 +107% 5
Jamaica Jamaica 2,760,000 -204% 99
Jordan Jordan 85,860,001 +13.8% 21
Kazakhstan Kazakhstan -29,670,000 -10.7% 127
Kenya Kenya 185,720,001 -18.7% 12
Kyrgyzstan Kyrgyzstan 19,760,000 +56.6% 56
Cambodia Cambodia 485,750,000 +5.83% 6
Kiribati Kiribati 3,020,000 +120% 98
Laos Laos 60,520,000 +23.9% 27
Lebanon Lebanon 10,030,000 -2.81% 70
Liberia Liberia 5,980,000 -66.5% 78
Libya Libya 3,060,000 -79.2% 97
St. Lucia St. Lucia 9,010,000 +41% 72
Sri Lanka Sri Lanka 37,810,001 -232% 35
Lesotho Lesotho 4,500,000 +11.4% 91
Morocco Morocco 56,790,001 -61.6% 30
Moldova Moldova 28,690,001 +1,192% 42
Madagascar Madagascar 81,849,998 +96.4% 23
Maldives Maldives 15,920,000 -39.3% 61
Mexico Mexico 23,030,001 +352% 53
Marshall Islands Marshall Islands 11,670,000 +971% 69
North Macedonia North Macedonia -2,330,000 +13.1% 120
Mali Mali 5,610,000 -65.6% 80
Myanmar (Burma) Myanmar (Burma) 320,519,989 -20.7% 9
Montenegro Montenegro 610,000 +5.17% 111
Mongolia Mongolia 2,690,000 -85.9% 100
Mozambique Mozambique 104,239,998 +49.6% 17
Mauritania Mauritania 9,910,000 +21.9% 71
Mauritius Mauritius 6,050,000 -97.9% 77
Malawi Malawi 27,240,000 +38.5% 44
Malaysia Malaysia -66,910,004 -3.53% 129
Namibia Namibia 5,410,000 +35.2% 83
Niger Niger 12,810,000 -41.9% 67
Nigeria Nigeria 25,709,999 0% 46
Nicaragua Nicaragua 4,500,000 -73.8% 91
Nepal Nepal 146,740,005 +61% 14
Nauru Nauru 1,740,000 +52.6% 105
Pakistan Pakistan -153,050,003 -302% 130
Panama Panama 94,209,999 +42.7% 19
Peru Peru -31,820,000 +27.9% 128
Philippines Philippines 1,079,410,034 +47.3% 3
Palau Palau 18,629,999 +33.5% 59
Papua New Guinea Papua New Guinea 46,990,002 -86.4% 31
Paraguay Paraguay -7,590,000 -164% 124
Palestinian Territories Palestinian Territories 63,650,002 -30.3% 25
Rwanda Rwanda 67,480,003 -4.86% 24
Sudan Sudan 13,470,000 -72.5% 65
Senegal Senegal 92,000,000 +133% 20
Solomon Islands Solomon Islands 29,090,000 -43.2% 41
Sierra Leone Sierra Leone 24,690,001 +53.7% 49
El Salvador El Salvador 19,910,000 +39.3% 55
Somalia Somalia 30,450,001 +40.1% 37
Serbia Serbia 25,400,000 -58.7% 47
South Sudan South Sudan 19,549,999 -53.1% 57
São Tomé & Príncipe São Tomé & Príncipe 2,150,000 -43.9% 104
Suriname Suriname 210,000 +75% 114
Eswatini Eswatini 2,150,000 -61% 104
Syria Syria 44,900,002 -48.8% 32
Chad Chad 13,190,000 +39.1% 66
Togo Togo 5,400,000 +33.7% 84
Thailand Thailand 339,660,004 -633% 7
Tajikistan Tajikistan 29,959,999 -1.06% 38
Turkmenistan Turkmenistan -1,250,000 -203% 118
Timor-Leste Timor-Leste 19,510,000 -10.1% 58
Tonga Tonga 11,840,000 -13.7% 68
Tunisia Tunisia 15,540,000 +55.9% 63
Turkey Turkey 321,119,995 -869% 8
Tuvalu Tuvalu 2,550,000 -19.8% 101
Tanzania Tanzania 23,629,999 -43.4% 52
Uganda Uganda 30,530,001 -51.3% 36
Ukraine Ukraine 710,869,995 -4,499% 4
Uzbekistan Uzbekistan 59,290,001 -83% 28
St. Vincent & Grenadines St. Vincent & Grenadines 840,000 -75.9% 109
Venezuela Venezuela 4,720,000 -5.22% 89
Vietnam Vietnam -168,690,002 +3.37% 131
Vanuatu Vanuatu 1,120,000 -78.5% 107
Samoa Samoa 2,380,000 -72.2% 102
Kosovo Kosovo 4,620,000 +62.1% 90
Yemen Yemen 29,690,001 -55.5% 40
South Africa South Africa 5,070,000 -20.3% 87
Zambia Zambia 17,209,999 -35% 60
Zimbabwe Zimbabwe 8,690,000 -50.7% 74 

                    
# 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.JPNL.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.JPNL.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))