Net ODA received per capita (current US$)

Source: worldbank.org, 01.09.2025

Year: 2022

Flag Country Value Value change, % Rank
Afghanistan Afghanistan 95.9 -18.2% 45
Angola Angola 2.74 -61.8% 126
Albania Albania 114 -49.6% 36
Argentina Argentina 8.46 +147% 116
Armenia Armenia 101 +85% 43
Azerbaijan Azerbaijan 4.65 +383% 121
Burundi Burundi 43.3 -7.73% 83
Benin Benin 61.4 -3.16% 70
Burkina Faso Burkina Faso 62 -15.4% 69
Bangladesh Bangladesh 30.7 +0.998% 101
Bosnia & Herzegovina Bosnia & Herzegovina 88.6 -49.1% 50
Belarus Belarus 1.77 -88.2% 130
Belize Belize 50.6 -74.1% 77
Bolivia Bolivia 27.7 -34.8% 103
Brazil Brazil 2.74 -48.4% 125
Bhutan Bhutan 249 +51.6% 17
Botswana Botswana 36.7 -8.63% 90
Central African Republic Central African Republic 134 +3.89% 32
China China -0.2 -49.9% 135
Côte d’Ivoire Côte d’Ivoire 65 +22.1% 66
Cameroon Cameroon 42.6 +0.531% 84
Congo - Kinshasa Congo - Kinshasa 31.7 -12.8% 98
Congo - Brazzaville Congo - Brazzaville 117 +239% 35
Colombia Colombia 36.4 -2.8% 92
Comoros Comoros 165 -12.4% 28
Cape Verde Cape Verde 159 -43.5% 30
Costa Rica Costa Rica 126 +703% 33
Cuba Cuba 12.4 -9.27% 113
Djibouti Djibouti 52 -66.8% 74
Dominica Dominica 891 -24.3% 7
Dominican Republic Dominican Republic 32.7 -18.9% 97
Algeria Algeria 4.73 -1.33% 120
Ecuador Ecuador 21.1 +24.1% 106
Egypt Egypt 51.7 -27.8% 76
Eritrea Eritrea 16 +22.3% 110
Ethiopia Ethiopia 39.3 +17.9% 87
Fiji Fiji 389 -41.4% 13
Micronesia (Federated States of) Micronesia (Federated States of) 1,341 +14.5% 6
Gabon Gabon 54.8 +32.2% 73
Georgia Georgia 101 -51.2% 42
Ghana Ghana 31.5 -17.2% 100
Guinea Guinea 35.5 -17.7% 94
Gambia Gambia 103 +7.47% 41
Guinea-Bissau Guinea-Bissau 70.6 -14% 62
Equatorial Guinea Equatorial Guinea 6.2 -14.6% 119
Grenada Grenada -801 -237% 136
Guatemala Guatemala 24.5 -15.8% 105
Guyana Guyana 242 +42.3% 19
Honduras Honduras 75.1 +25.1% 60
Haiti Haiti 77.4 -7.44% 58
Indonesia Indonesia 2.38 +5.09% 127
India India 1.99 -10.4% 129
Iran Iran 3.23 -5.81% 124
Iraq Iraq 35.6 -15.3% 93
Jamaica Jamaica 33.5 +62.3% 95
Jordan Jordan 176 -43.4% 25
Kazakhstan Kazakhstan 3.68 +26.4% 123
Kenya Kenya 48.9 -18.1% 78
Kyrgyzstan Kyrgyzstan 105 +58.4% 40
Cambodia Cambodia 89.9 +11.7% 49
Kiribati Kiribati 698 +23% 9
Laos Laos 72.5 -6.19% 61
Lebanon Lebanon 248 +1.45% 18
Liberia Liberia 90.2 -21.7% 48
Libya Libya 36.5 -24.1% 91
St. Lucia St. Lucia 187 -66.6% 24
Sri Lanka Sri Lanka 0.498 -92.8% 131
Lesotho Lesotho 67.1 -15.4% 64
Morocco Morocco 37.9 +45% 88
Moldova Moldova 350 +52.3% 15
Madagascar Madagascar 32.9 -8.69% 96
Maldives Maldives 227 +4.36% 21
Mexico Mexico 3.85 -12.8% 122
Marshall Islands Marshall Islands 3,502 +35.2% 2
North Macedonia North Macedonia 122 -33.6% 34
Mali Mali 51.9 -19.1% 75
Myanmar (Burma) Myanmar (Burma) 18.7 -33.9% 108
Montenegro Montenegro 165 -22.7% 27
Mongolia Mongolia 82.9 -0.576% 52
Mozambique Mozambique 78.4 +8.83% 57
Mauritania Mauritania 67.2 -25.9% 63
Mauritius Mauritius 60.4 -73.9% 71
Malawi Malawi 65.4 +11.3% 65
Malaysia Malaysia 0.141 -66.4% 134
Namibia Namibia 113 +73.3% 38
Niger Niger 80.4 +8.99% 54
Nigeria Nigeria 19.9 +23.3% 107
Nicaragua Nicaragua 167 +47.6% 26
Nepal Nepal 40.7 -25% 86
Nauru Nauru 3,007 +4.3% 4
Pakistan Pakistan 7.55 -38.1% 118
Panama Panama 29.6 +40.6% 102
Peru Peru 24.8 +171% 104
Philippines Philippines 14.1 -2.06% 112
Palau Palau 3,135 +12.8% 3
Papua New Guinea Papua New Guinea 64.8 -45.3% 67
North Korea North Korea 0.487 -36.5% 133
Paraguay Paraguay 14.3 -45.4% 111
Palestinian Territories Palestinian Territories 443 +1.03% 11
Rwanda Rwanda 78.9 -20.8% 56
Sudan Sudan 31.6 -60.3% 99
Senegal Senegal 82.3 +1.95% 53
Solomon Islands Solomon Islands 324 -6.78% 16
Sierra Leone Sierra Leone 63.2 -27.9% 68
El Salvador El Salvador 113 +231% 37
Somalia Somalia 109 -21.9% 39
Serbia Serbia 76.5 -1.91% 59
South Sudan South Sudan 188 -3.37% 23
São Tomé & Príncipe São Tomé & Príncipe 235 -27.8% 20
Suriname Suriname 87.9 +88.7% 51
Eswatini Eswatini 79.3 -23.5% 55
Syria Syria 369 -17.8% 14
Chad Chad 37.6 -8.2% 89
Togo Togo 47.3 +19.6% 80
Thailand Thailand 7.72 +318% 117
Tajikistan Tajikistan 57.8 +3.68% 72
Turkmenistan Turkmenistan 2.09 -48.4% 128
Timor-Leste Timor-Leste 163 -13.7% 29
Tonga Tonga 2,809 +162% 5
Tunisia Tunisia 100 +10.2% 44
Turkey Turkey 9.42 -25.2% 115
Tuvalu Tuvalu 6,361 +80.1% 1
Tanzania Tanzania 41.1 -1.28% 85
Uganda Uganda 44.7 -20% 82
Ukraine Ukraine 700 +1,300% 8
Uzbekistan Uzbekistan 45.6 +34.3% 81
St. Vincent & Grenadines St. Vincent & Grenadines 152 -87.6% 31
Venezuela Venezuela 9.65 -0.242% 114
Vietnam Vietnam 0.491 -90.8% 132
Vanuatu Vanuatu 399 -26.8% 12
Samoa Samoa 587 +48.5% 10
Kosovo Kosovo 216 -15% 22
Yemen Yemen 95.2 -9.15% 46
South Africa South Africa 16.5 -2.63% 109
Zambia Zambia 90.8 +63.7% 47
Zimbabwe Zimbabwe 48.7 -22% 79 

                    
# 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 = 'DT.ODA.ODAT.PC.ZS'

# 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 <- 'DT.ODA.ODAT.PC.ZS'

# 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))