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

Source: worldbank.org, 01.09.2025

Year: 2022

Flag Country Value Value change, % Rank
Afghanistan Afghanistan 407,059,998 -34.3% 9
Angola Angola 4,920,000 -3.72% 86
Albania Albania 50,369,999 -73.5% 48
Argentina Argentina 5,890,000 -49.3% 84
Armenia Armenia 126,550,003 +132% 22
Azerbaijan Azerbaijan -870,000 -91.8% 122
Burundi Burundi 25,530,001 -17.2% 62
Benin Benin 55,509,998 +0.144% 45
Burkina Faso Burkina Faso 66,269,997 -5.25% 37
Bangladesh Bangladesh 192,399,994 +43.7% 18
Bosnia & Herzegovina Bosnia & Herzegovina 5,770,000 -88.1% 85
Belarus Belarus 12,820,000 -14.8% 78
Belize Belize 20,000 -81.8% 117
Bolivia Bolivia 32,189,999 -14.6% 54
Brazil Brazil 424,149,994 +1,231% 6
Bhutan Bhutan 2,530,000 -4.89% 91
Botswana Botswana 1,210,000 -7.63% 101
Central African Republic Central African Republic 50,500,000 -27.1% 47
China China 413,769,989 +15% 7
Côte d’Ivoire Côte d’Ivoire 93,209,999 +0.507% 31
Cameroon Cameroon 126,220,001 -7.96% 23
Congo - Kinshasa Congo - Kinshasa 165,240,005 -13.6% 20
Congo - Brazzaville Congo - Brazzaville 480,000 -31.4% 107
Colombia Colombia 371,369,995 +29.6% 10
Comoros Comoros 40,000 +100% 116
Cape Verde Cape Verde 670,000 -38.5% 104
Costa Rica Costa Rica 1,930,000 -38.1% 94
Cuba Cuba 1,730,000 -11.7% 96
Djibouti Djibouti 180,000 0% 112
Dominica Dominica 150,000 +25% 113
Dominican Republic Dominican Republic 630,000 -76.3% 105
Algeria Algeria 20,049,999 -35.1% 70
Ecuador Ecuador 30,799,999 -28.1% 56
Egypt Egypt -119,970,001 +1,259% 126
Eritrea Eritrea 1,560,000 -59.4% 97
Ethiopia Ethiopia 252,520,004 +44.9% 15
Fiji Fiji 1,790,000 +34.6% 95
Micronesia (Federated States of) Micronesia (Federated States of) 0 -100% 119
Gabon Gabon 620,000 -20.5% 106
Georgia Georgia 14,310,000 -94.4% 75
Ghana Ghana 101,349,998 -61.4% 29
Guinea Guinea 11,800,000 -0.757% 80
Gambia Gambia 1,350,000 -37.2% 98
Guinea-Bissau Guinea-Bissau 180,000 -41.9% 112
Equatorial Guinea Equatorial Guinea 20,000 +100% 117
Grenada Grenada 400,000 -45.2% 109
Guatemala Guatemala 24,299,999 -8.02% 64
Guyana Guyana 1,210,000 +57.1% 101
Honduras Honduras 19,400,000 -2.32% 71
Haiti Haiti 5,980,000 -17.1% 83
Indonesia Indonesia 644,280,029 +206% 3
India India 235,169,998 -66.9% 17
Iran Iran 107,820,000 -2.99% 25
Iraq Iraq 347,390,015 -5.23% 12
Jamaica Jamaica -550,000 -26.7% 121
Jordan Jordan 271,929,993 -38% 14
Kazakhstan Kazakhstan 13,220,000 +245% 77
Kenya Kenya 74,949,997 -15.8% 35
Kyrgyzstan Kyrgyzstan 27,309,999 -24.8% 59
Cambodia Cambodia 51,419,998 -6.66% 46
Kiribati Kiribati 10,000 -96.6% 118
Laos Laos 28,900,000 -14% 58
Lebanon Lebanon 332,660,004 -6.08% 13
Liberia Liberia 25,450,001 -27.5% 63
Libya Libya 38,660,000 -24% 52
St. Lucia St. Lucia 20,000 0% 117
Sri Lanka Sri Lanka 15,560,000 -1.21% 74
Lesotho Lesotho 800,000 -27.3% 103
Morocco Morocco 409,489,990 +103% 8
Moldova Moldova 75,519,997 +377% 34
Madagascar Madagascar 63,400,002 -24.5% 39
Maldives Maldives 470,000 +27% 108
Mexico Mexico -87,019,997 +168% 125
Marshall Islands Marshall Islands 3,860,000 +147% 88
North Macedonia North Macedonia 14,260,000 +109% 76
Mali Mali 89,220,001 -24.6% 32
Myanmar (Burma) Myanmar (Burma) 23,469,999 -42.4% 65
Montenegro Montenegro 1,340,000 +570% 99
Mongolia Mongolia 18,660,000 +2.98% 72
Mozambique Mozambique 80,910,004 -17.2% 33
Mauritania Mauritania 23,219,999 +5.4% 66
Mauritius Mauritius 3,980,000 +1.27% 87
Malawi Malawi 73,430,000 -21.3% 36
Malaysia Malaysia 12,330,000 -7.36% 79
Namibia Namibia 65,889,999 +12.7% 38
Niger Niger 174,990,005 +13.4% 19
Nigeria Nigeria 106,370,003 -27.1% 26
Nicaragua Nicaragua 3,680,000 -52.8% 89
Nepal Nepal 60,930,000 +12.1% 42
Pakistan Pakistan 61,430,000 -58.3% 41
Panama Panama 920,000 -3.16% 102
Peru Peru 491,959,991 -21,868% 4
Philippines Philippines 27,110,001 +53.1% 60
Palau Palau 40,000 -20% 116
Papua New Guinea Papua New Guinea 2,180,000 -7.63% 92
North Korea North Korea 2,080,000 +5.05% 93
Paraguay Paraguay 3,430,000 -7.8% 90
Palestinian Territories Palestinian Territories 244,990,005 -16.4% 16
Rwanda Rwanda 62,490,002 -38% 40
Sudan Sudan 109,010,002 -34.9% 24
Senegal Senegal 50,360,001 -57.6% 49
Solomon Islands Solomon Islands 250,000 -41.9% 110
Sierra Leone Sierra Leone 18,170,000 -18.9% 73
El Salvador El Salvador -5,160,000 +51,500% 123
Somalia Somalia 133,429,993 -21.9% 21
Serbia Serbia 30,180,000 -30.7% 57
South Sudan South Sudan 98,000,000 -28.5% 30
Suriname Suriname 60,000 -57.1% 114
Eswatini Eswatini -370,000 -276% 120
Syria Syria 664,849,976 -0.826% 2
Chad Chad 49,290,001 +31.1% 50
Togo Togo 43,099,998 -36.2% 51
Thailand Thailand 20,969,999 +7.98% 69
Tajikistan Tajikistan 22,139,999 +72.3% 67
Turkmenistan Turkmenistan 1,240,000 -12.7% 100
Timor-Leste Timor-Leste 6,780,000 +12.3% 82
Tonga Tonga 50,000 0% 115
Tunisia Tunisia 354,940,002 -7.4% 11
Turkey Turkey 101,379,997 -29.7% 28
Tanzania Tanzania 35,240,002 -43.5% 53
Uganda Uganda 59,369,999 -28.1% 43
Ukraine Ukraine 2,021,290,039 +804% 1
Uzbekistan Uzbekistan 26,090,000 +37.6% 61
St. Vincent & Grenadines St. Vincent & Grenadines 40,000 +100% 116
Venezuela Venezuela 10,250,000 -57.5% 81
Vietnam Vietnam 101,760,002 -17.8% 27
Vanuatu Vanuatu 20,000 -66.7% 117
Samoa Samoa 190,000 +26.7% 111
Kosovo Kosovo 21,500,000 -29.7% 68
Yemen Yemen 460,109,985 +15.7% 5
South Africa South Africa -31,549,999 +161% 124
Zambia Zambia 56,709,999 +19.4% 44
Zimbabwe Zimbabwe 31,350,000 -28.8% 55 

                    
# 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.DEUL.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.DEUL.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))