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

Source: worldbank.org, 03.09.2025

Year: 2022

Flag Country Value Value change, % Rank
Afghanistan Afghanistan 434,559,998 +67.9% 1
Angola Angola 410,000 -79.7% 95
Albania Albania 2,730,000 -43.4% 60
Argentina Argentina 2,030,000 -55.4% 64
Armenia Armenia 2,180,000 -62.3% 62
Azerbaijan Azerbaijan 1,870,000 -40.6% 67
Burundi Burundi 1,270,000 +15.5% 75
Benin Benin 70,000 +75% 113
Burkina Faso Burkina Faso 210,000 -25% 102
Bangladesh Bangladesh 67,400,002 -43.8% 10
Bosnia & Herzegovina Bosnia & Herzegovina 4,630,000 -29.7% 50
Belarus Belarus 1,470,000 -59.4% 71
Belize Belize 290,000 -81.6% 100
Bolivia Bolivia 1,010,000 -51.2% 79
Brazil Brazil 17,940,001 -53.8% 35
Bhutan Bhutan 180,000 -33.3% 105
Botswana Botswana 860,000 -73.5% 84
Central African Republic Central African Republic 12,820,000 -6.08% 37
China China 18,410,000 -74.1% 34
Côte d’Ivoire Côte d’Ivoire 3,220,000 -32.2% 55
Cameroon Cameroon 2,940,000 -73.4% 57
Congo - Kinshasa Congo - Kinshasa 57,790,001 -42.4% 13
Congo - Brazzaville Congo - Brazzaville 40,000 -60% 115
Colombia Colombia 62,880,001 +8.28% 11
Comoros Comoros 230,000 +15% 101
Cape Verde Cape Verde 360,000 +20% 97
Costa Rica Costa Rica 410,000 -63.7% 95
Cuba Cuba 690,000 -66% 88
Djibouti Djibouti 190,000 -51.3% 104
Dominica Dominica 960,000 -2.04% 80
Dominican Republic Dominican Republic 500,000 -58.7% 91
Algeria Algeria 3,100,000 -63.6% 56
Ecuador Ecuador 1,120,000 -22.8% 78
Egypt Egypt 10,870,000 -51.6% 38
Eritrea Eritrea 940,000 -58.2% 82
Ethiopia Ethiopia 111,110,001 -32.6% 5
Fiji Fiji 950,000 -46.3% 81
Georgia Georgia 5,000,000 -30.7% 48
Ghana Ghana 18,990,000 -44.8% 33
Guinea Guinea 150,000 -85.6% 108
Gambia Gambia 17,700,001 -28.5% 36
Guinea-Bissau Guinea-Bissau 30,000 -93% 116
Equatorial Guinea Equatorial Guinea 40,000 0% 115
Grenada Grenada 230,000 +76.9% 101
Guatemala Guatemala 1,580,000 -35% 69
Guyana Guyana 200,000 -89.9% 103
Honduras Honduras 170,000 -56.4% 106
Haiti Haiti 340,000 -74.8% 99
Indonesia Indonesia 29,660,000 -26.9% 27
India India 56,169,998 -55% 15
Iran Iran 350,000 -41.7% 98
Iraq Iraq 23,959,999 -65.6% 32
Jamaica Jamaica 4,880,000 -42.2% 49
Jordan Jordan 51,700,001 -39.8% 18
Kazakhstan Kazakhstan 2,100,000 -21.9% 63
Kenya Kenya 55,389,999 -44.1% 16
Kyrgyzstan Kyrgyzstan 3,590,000 -46% 54
Cambodia Cambodia 1,340,000 -52.1% 73
Laos Laos 720,000 -52% 86
Lebanon Lebanon 26,730,000 -66.5% 30
Liberia Liberia 4,520,000 +12.2% 51
Libya Libya 6,450,000 -63.3% 43
St. Lucia St. Lucia 140,000 -66.7% 109
Sri Lanka Sri Lanka 5,650,000 -52.2% 45
Lesotho Lesotho 450,000 -63.7% 93
Morocco Morocco 2,850,000 -70.6% 59
Moldova Moldova 1,790,000 -42.6% 68
Madagascar Madagascar 5,670,000 -35% 44
Maldives Maldives 2,010,000 -10.3% 65
Mexico Mexico 8,280,000 -70.4% 41
North Macedonia North Macedonia 2,880,000 -48.4% 58
Mali Mali 10,040,000 -13.9% 39
Myanmar (Burma) Myanmar (Burma) 57,669,998 -36.8% 14
Montenegro Montenegro 380,000 -85.4% 96
Mongolia Mongolia 1,180,000 -52.4% 77
Mozambique Mozambique 60,400,002 +17.1% 12
Mauritania Mauritania 120,000 -83.3% 110
Mauritius Mauritius 690,000 -43.9% 88
Malawi Malawi 39,049,999 -32.9% 23
Malaysia Malaysia 5,280,000 -64.9% 47
Namibia Namibia 910,000 -54.3% 83
Niger Niger 440,000 -70.5% 94
Nigeria Nigeria 135,740,005 -29.6% 3
Nicaragua Nicaragua 490,000 -14% 92
Nepal Nepal 45,910,000 -43.7% 20
Nauru Nauru 30,000 +50% 116
Pakistan Pakistan 71,309,998 -59.4% 9
Panama Panama 840,000 -29.4% 85
Peru Peru 2,320,000 -78.8% 61
Philippines Philippines 9,970,000 -47% 40
Papua New Guinea Papua New Guinea 700,000 -57.6% 87
Paraguay Paraguay 570,000 -50% 90
Palestinian Territories Palestinian Territories 27,770,000 -51.1% 29
Rwanda Rwanda 31,629,999 -34.2% 26
Sudan Sudan 36,639,999 -71.7% 25
Senegal Senegal 1,360,000 -65.8% 72
Solomon Islands Solomon Islands 670,000 -18.3% 89
Sierra Leone Sierra Leone 41,150,002 -40.2% 21
El Salvador El Salvador 70,000 -92.7% 113
Somalia Somalia 122,980,003 -11.2% 4
Serbia Serbia 1,260,000 -69.1% 76
South Sudan South Sudan 93,820,000 -29.1% 7
São Tomé & Príncipe São Tomé & Príncipe 80,000 112
Eswatini Eswatini 160,000 -70.9% 107
Syria Syria 78,180,000 -37.6% 8
Chad Chad 230,000 -86.1% 101
Togo Togo 10,000 0% 117
Thailand Thailand 4,180,000 -67.9% 52
Tajikistan Tajikistan 1,480,000 -42% 70
Turkmenistan Turkmenistan 200,000 -60.8% 103
Timor-Leste Timor-Leste 50,000 -77.3% 114
Tonga Tonga 150,000 -21.1% 108
Tunisia Tunisia 5,570,000 -59.4% 46
Turkey Turkey 37,040,001 -28.9% 24
Tuvalu Tuvalu 10,000 -66.7% 117
Tanzania Tanzania 41,090,000 -50.7% 22
Uganda Uganda 55,070,000 -37.8% 17
Ukraine Ukraine 421,399,994 +882% 2
Uzbekistan Uzbekistan 1,930,000 -42.7% 66
St. Vincent & Grenadines St. Vincent & Grenadines 200,000 -82.1% 103
Venezuela Venezuela 1,280,000 -63.1% 74
Vietnam Vietnam 8,240,000 -43.2% 42
Vanuatu Vanuatu -60,000 -106% 118
Samoa Samoa 100,000 -89% 111
Kosovo Kosovo 3,810,000 -48.2% 53
Yemen Yemen 95,349,998 -39.4% 6
South Africa South Africa 28,309,999 -79.9% 28
Zambia Zambia 24,010,000 -37.5% 31
Zimbabwe Zimbabwe 46,610,001 -33.5% 19 

                    
# 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.GBRL.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.GBRL.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))