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

Source: worldbank.org, 03.09.2025

Year: 2022

Flag Country Value Value change, % Rank
Afghanistan Afghanistan 77,760,002 -3.01% 5
Angola Angola 5,490,000 -11.2% 46
Albania Albania 220,000 +83.3% 91
Argentina Argentina 180,000 +125% 92
Burundi Burundi 2,610,000 -17.1% 57
Benin Benin 840,000 +25.4% 71
Burkina Faso Burkina Faso 7,820,000 +0.644% 38
Bangladesh Bangladesh 14,770,000 -17.3% 28
Bosnia & Herzegovina Bosnia & Herzegovina 980,000 -75.4% 70
Belarus Belarus 510,000 +8.51% 77
Belize Belize 110,000 -31.2% 96
Bolivia Bolivia 2,200,000 +3.77% 59
Brazil Brazil 30,049,999 +17.2% 17
Botswana Botswana 50,000 0% 99
Central African Republic Central African Republic 7,220,000 -0.824% 41
China China 10,110,000 +13.9% 33
Côte d’Ivoire Côte d’Ivoire 250,000 +31.6% 89
Cameroon Cameroon 3,030,000 +1.68% 56
Congo - Kinshasa Congo - Kinshasa 28,170,000 -7.43% 20
Congo - Brazzaville Congo - Brazzaville 710,000 -21.1% 74
Colombia Colombia 56,910,000 -21.1% 9
Cuba Cuba 1,560,000 +50% 64
Djibouti Djibouti 160,000 -46.7% 93
Algeria Algeria 1,210,000 +4.31% 67
Ecuador Ecuador 17,070,000 +623% 27
Egypt Egypt 3,420,000 +16.3% 55
Ethiopia Ethiopia 90,400,002 +2.33% 2
Gabon Gabon 330,000 -98.1% 84
Georgia Georgia 4,080,000 -28.3% 51
Ghana Ghana 9,390,000 +56.5% 34
Guinea Guinea 150,000 -6.25% 94
Gambia Gambia 270,000 +12.5% 88
Guinea-Bissau Guinea-Bissau 70,000 -12.5% 97
Guatemala Guatemala 4,790,000 -9.62% 47
Guyana Guyana 4,650,000 +9.67% 49
Honduras Honduras 1,150,000 -10.2% 69
Haiti Haiti 7,540,000 +30.2% 39
Indonesia Indonesia 81,639,999 +169% 4
India India 11,120,000 +4.71% 32
Iran Iran 2,010,000 +7.49% 60
Iraq Iraq 19,059,999 -25.9% 25
Jordan Jordan 19,309,999 -13.4% 24
Kenya Kenya 11,740,000 +9.62% 30
Kyrgyzstan Kyrgyzstan 370,000 +5.71% 83
Cambodia Cambodia 4,740,000 +0.637% 48
Laos Laos 1,610,000 -23.7% 62
Lebanon Lebanon 44,820,000 -16.9% 13
Liberia Liberia 7,830,000 +149% 37
Libya Libya 4,040,000 +17.8% 52
Sri Lanka Sri Lanka 7,050,000 +1.44% 43
Lesotho Lesotho 280,000 +21.7% 87
Morocco Morocco 1,380,000 +66.3% 65
Moldova Moldova 30,790,001 +2,751% 16
Madagascar Madagascar 8,030,000 -10.8% 36
Mexico Mexico 310,000 86
North Macedonia North Macedonia 240,000 -66.2% 90
Mali Mali 25,040,001 -9.57% 22
Myanmar (Burma) Myanmar (Burma) 28,930,000 -9.59% 19
Montenegro Montenegro 130,000 -90.4% 95
Mongolia Mongolia 380,000 +31% 82
Mozambique Mozambique 51,310,001 -14.2% 10
Malawi Malawi 50,240,002 -6.32% 11
Namibia Namibia 60,000 -87.5% 98
Niger Niger 17,240,000 -1.71% 26
Nigeria Nigeria 21,410,000 +24.9% 23
Nicaragua Nicaragua 780,000 -55.9% 72
Nepal Nepal 33,849,998 -14.7% 15
Pakistan Pakistan 13,530,000 +14.4% 29
Peru Peru 8,040,000 -28.7% 35
Philippines Philippines 1,800,000 -6.74% 61
Papua New Guinea Papua New Guinea 1,600,000 +15.9% 63
North Korea North Korea 420,000 -10.6% 80
Palestinian Territories Palestinian Territories 69,440,002 -14% 7
Rwanda Rwanda 7,430,000 +20.8% 40
Sudan Sudan 26,920,000 -0.591% 21
Senegal Senegal 320,000 +68.4% 85
Sierra Leone Sierra Leone 1,220,000 -34.1% 66
El Salvador El Salvador 610,000 -10.3% 76
Somalia Somalia 66,769,997 +8.94% 8
Serbia Serbia 430,000 -88.9% 79
South Sudan South Sudan 70,300,003 -0.326% 6
Eswatini Eswatini 740,000 -20.4% 73
Syria Syria 86,489,998 -17% 3
Togo Togo 440,000 -21.4% 78
Thailand Thailand 640,000 -24.7% 75
Tajikistan Tajikistan 1,180,000 -48.2% 68
Timor-Leste Timor-Leste 410,000 +24.2% 81
Tunisia Tunisia 2,340,000 +1,070% 58
Turkey Turkey 4,020,000 -13.9% 53
Tanzania Tanzania 46,959,999 +2.76% 12
Uganda Uganda 39,759,998 +21.1% 14
Ukraine Ukraine 551,169,983 +2,879% 1
Uzbekistan Uzbekistan 20,000 0% 100
Venezuela Venezuela 7,170,000 -16.7% 42
Vietnam Vietnam 4,420,000 +20.4% 50
Kosovo Kosovo 3,620,000 -26.7% 54
Yemen Yemen 28,950,001 +15% 18
South Africa South Africa 5,530,000 +24.5% 45
Zambia Zambia 11,130,000 +102% 31
Zimbabwe Zimbabwe 6,040,000 +9.42% 44 

                    
# 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.NORL.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.NORL.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))