Net ODA received (% of GNI)

Source: worldbank.org, 01.09.2025

Year: 2022

Flag Country Value Value change, % Rank
Afghanistan Afghanistan 26.8 -18.1% 7
Angola Angola 0.101 -75% 113
Albania Albania 1.69 -52.6% 75
Argentina Argentina 0.0618 +89.9% 119
Armenia Armenia 1.58 +31.6% 82
Azerbaijan Azerbaijan 0.0642 +253% 118
Burundi Burundi 17.2 -21.3% 11
Benin Benin 4.88 +0.479% 45
Burkina Faso Burkina Faso 7.74 -9.82% 29
Bangladesh Bangladesh 1.09 -6.53% 89
Bosnia & Herzegovina Bosnia & Herzegovina 1.17 -51.4% 87
Belarus Belarus 0.0231 -88.9% 125
Belize Belize 0.751 -77.2% 94
Bolivia Bolivia 0.782 -39.3% 93
Brazil Brazil 0.0304 -55.8% 123
Bhutan Bhutan 7.08 +45.2% 33
Botswana Botswana 0.44 -14.3% 103
Central African Republic Central African Republic 27.2 +11% 6
China China -0.00155 -50.2% 129
Côte d’Ivoire Côte d’Ivoire 2.88 +28.8% 60
Cameroon Cameroon 2.72 +5.69% 62
Congo - Kinshasa Congo - Kinshasa 5.22 -22.9% 43
Congo - Brazzaville Congo - Brazzaville 5.02 +224% 44
Colombia Colombia 0.564 -7.07% 98
Comoros Comoros 10.7 -9.32% 21
Cape Verde Cape Verde 3.72 -48.2% 55
Costa Rica Costa Rica 0.995 +663% 90
Djibouti Djibouti 1.63 -69.1% 78
Dominica Dominica 9.51 -32.2% 24
Dominican Republic Dominican Republic 0.336 -32.4% 106
Algeria Algeria 0.0976 -16.9% 114
Ecuador Ecuador 0.329 +15.5% 107
Egypt Egypt 1.26 -34.5% 86
Ethiopia Ethiopia 3.9 +6.13% 53
Fiji Fiji 7.61 -49.4% 30
Micronesia (Federated States of) Micronesia (Federated States of) 32.4 +8.4% 5
Gabon Gabon 0.7 +21.2% 95
Georgia Georgia 1.62 -62.8% 79
Ghana Ghana 1.44 -10.4% 85
Guinea Guinea 2.55 -29.4% 65
Gambia Gambia 12.5 -0.619% 19
Guinea-Bissau Guinea-Bissau 8.04 -9.85% 27
Equatorial Guinea Equatorial Guinea 0.125 -10.3% 111
Grenada Grenada -8.17 -223% 130
Guatemala Guatemala 0.468 -23% 102
Guyana Guyana 2.23 +3.57% 69
Honduras Honduras 2.72 +13.4% 63
Haiti Haiti 4.39 -3.51% 47
Indonesia Indonesia 0.0516 -4.78% 120
India India 0.0858 -14.4% 116
Iran Iran 0.0735 -7.2% 117
Iraq Iraq 0.548 -37.2% 99
Jamaica Jamaica 0.568 +38.1% 97
Jordan Jordan 4.11 -45% 49
Kazakhstan Kazakhstan 0.0369 +10.9% 121
Kenya Kenya 2.35 -19.9% 68
Kyrgyzstan Kyrgyzstan 6.15 +17.3% 37
Cambodia Cambodia 4.02 +3.91% 51
Kiribati Kiribati 22.1 +35.5% 8
Laos Laos 3.83 +18% 54
Lebanon Lebanon 7.07 +7.38% 34
Liberia Liberia 12.9 -29.8% 18
Libya Libya 0.615 -38.2% 96
St. Lucia St. Lucia 1.56 -71.8% 84
Sri Lanka Sri Lanka 0.0153 -91.4% 126
Lesotho Lesotho 5.42 -14.9% 40
Morocco Morocco 1.1 +58.9% 88
Moldova Moldova 6.06 +42% 38
Madagascar Madagascar 6.69 -11.8% 35
Maldives Maldives 2.18 -8.56% 72
Mexico Mexico 0.0346 -21.3% 122
Marshall Islands Marshall Islands 48.2 +27.7% 3
North Macedonia North Macedonia 1.68 -33.5% 76
Mali Mali 5.55 -14.4% 39
Myanmar (Burma) Myanmar (Burma) 1.64 -28.9% 77
Montenegro Montenegro 1.62 -27.4% 80
Mongolia Mongolia 1.84 -15.1% 73
Mozambique Mozambique 14.7 +1.6% 15
Mauritania Mauritania 3.45 -27.1% 57
Mauritius Mauritius 0.536 -76.7% 100
Malawi Malawi 11.2 +13.7% 20
Malaysia Malaysia 0.00124 -68.7% 128
Namibia Namibia 2.71 +77% 64
Niger Niger 14.5 +21.6% 16
Nigeria Nigeria 0.957 +17.2% 91
Nicaragua Nicaragua 7.8 +39.4% 28
Nepal Nepal 2.92 -32.3% 59
Nauru Nauru 14.2 +11% 17
Pakistan Pakistan 0.498 -41.4% 101
Panama Panama 0.179 +24% 110
Peru Peru 0.362 +149% 105
Philippines Philippines 0.377 -5.92% 104
Palau Palau 21.5 +6.83% 9
Papua New Guinea Papua New Guinea 2.22 -54.1% 70
Paraguay Paraguay 0.238 -47.7% 109
Palestinian Territories Palestinian Territories 9.44 -5.04% 25
Rwanda Rwanda 8.26 -32.6% 26
Sudan Sudan 3.08 -73.5% 58
Senegal Senegal 5.4 +4.2% 41
Solomon Islands Solomon Islands 16 -6.58% 13
Sierra Leone Sierra Leone 7.42 -25.8% 31
El Salvador El Salvador 2.37 +204% 67
Somalia Somalia 19.1 -25.2% 10
Serbia Serbia 0.802 -4.22% 92
São Tomé & Príncipe São Tomé & Príncipe 9.64 -29.6% 23
Suriname Suriname 1.57 +48.3% 83
Eswatini Eswatini 2.21 -21.7% 71
Syria Syria 36.3 -48.2% 4
Chad Chad 3.93 -10.1% 52
Togo Togo 5.25 +25.2% 42
Thailand Thailand 0.115 +326% 112
Tajikistan Tajikistan 4.14 -19.8% 48
Turkmenistan Turkmenistan 0.0259 -55.1% 124
Timor-Leste Timor-Leste 7.31 -0.795% 32
Tonga Tonga 54.3 +163% 2
Tunisia Tunisia 2.76 +15.6% 61
Turkey Turkey 0.0891 -32% 115
Tuvalu Tuvalu 79.9 +80% 1
Tanzania Tanzania 3.58 -5.04% 56
Uganda Uganda 4.74 -26.7% 46
Ukraine Ukraine 16.9 +1,376% 12
Uzbekistan Uzbekistan 1.75 +16.8% 74
St. Vincent & Grenadines St. Vincent & Grenadines 1.58 -89.1% 81
Vietnam Vietnam 0.0124 -91.8% 127
Vanuatu Vanuatu 10.5 -29.4% 22
Samoa Samoa 15.5 +52.7% 14
Kosovo Kosovo 4.02 -14.9% 50
South Africa South Africa 0.258 +2.32% 108
Zambia Zambia 6.67 +23.8% 36
Zimbabwe Zimbabwe 2.44 -34% 66 

                    
# 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.GN.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.GN.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))