Net official flows from UN agencies, IAEA (current US$)

Source: worldbank.org, 03.09.2025

Year: 2022

Flag Country Value Value change, % Rank
Afghanistan Afghanistan 93,968 -23.8% 103
Angola Angola 332,918 +138% 60
Albania Albania 272,983 +31.5% 76
Argentina Argentina 1,695,508 +121% 1
Armenia Armenia 258,447 -59% 81
Azerbaijan Azerbaijan 352,686 +840% 53
Burundi Burundi 610,082 +51.3% 27
Benin Benin 306,885 -27.6% 66
Burkina Faso Burkina Faso 398,536 -21.5% 50
Bangladesh Bangladesh 590,204 +6.47% 29
Bosnia & Herzegovina Bosnia & Herzegovina 194,507 -73% 93
Belarus Belarus 139,026 -58.1% 99
Belize Belize 187,146 -71.4% 94
Bolivia Bolivia 199,492 +104% 92
Brazil Brazil 956,436 +112% 7
Botswana Botswana 876,005 +69.3% 11
Central African Republic Central African Republic 285,356 -33.1% 73
China China 529,022 +2.59% 34
Côte d’Ivoire Côte d’Ivoire 715,165 +143% 15
Cameroon Cameroon 795,551 -17.5% 13
Congo - Kinshasa Congo - Kinshasa 578,189 +95.5% 30
Congo - Brazzaville Congo - Brazzaville 207,336 -52.8% 91
Colombia Colombia 286,294 -66.1% 71
Comoros Comoros 22,723 111
Costa Rica Costa Rica 338,971 +466% 57
Cuba Cuba 907,633 +50% 10
Djibouti Djibouti 275,076 -30.7% 75
Dominica Dominica 272,252 +151% 77
Dominican Republic Dominican Republic 90,129 +262% 105
Algeria Algeria 228,358 -41% 89
Ecuador Ecuador 1,164,300 +8.89% 4
Egypt Egypt 254,354 -28.4% 83
Eritrea Eritrea 513,820 +105% 36
Ethiopia Ethiopia 305,992 -20.3% 67
Fiji Fiji 233,927 +16% 87
Gabon Gabon 32,877 -92% 110
Georgia Georgia 442,653 -50.1% 45
Ghana Ghana 260,629 +34.3% 79
Guatemala Guatemala 241,383 +19.3% 85
Guyana Guyana 93,659 +565% 104
Honduras Honduras 342,085 +253% 56
Haiti Haiti 38,183 -86.2% 109
Indonesia Indonesia 307,877 +47.4% 65
Iran Iran 282,952 +4.37% 74
Iraq Iraq 425,842 +5.86% 46
Jamaica Jamaica 744,082 +104% 14
Jordan Jordan 567,927 -40.6% 31
Kazakhstan Kazakhstan 180,705 +22.6% 95
Kenya Kenya 911,714 +130% 9
Kyrgyzstan Kyrgyzstan 334,076 -41.8% 59
Cambodia Cambodia 995,724 +240% 5
Laos Laos 696,720 +143% 18
Lebanon Lebanon 683,965 +268% 20
Liberia Liberia 137,986 +84.9% 100
Libya Libya 73,684 -71% 106
Sri Lanka Sri Lanka 693,804 +168% 19
Lesotho Lesotho 346,936 -6.19% 55
Morocco Morocco 309,630 -15.3% 64
Moldova Moldova 364,074 -65.8% 52
Madagascar Madagascar 619,828 +17% 25
Mexico Mexico 391,740 -22.7% 51
Marshall Islands Marshall Islands 246,475 +55.2% 84
North Macedonia North Macedonia 504,034 -65.6% 39
Mali Mali 240,914 +4.88% 86
Myanmar (Burma) Myanmar (Burma) 558,173 -24.1% 33
Montenegro Montenegro 48,965 -76.2% 108
Mongolia Mongolia 602,806 -39.8% 28
Mozambique Mozambique 486,640 +25.9% 43
Mauritania Mauritania 311,897 -52.2% 63
Mauritius Mauritius 447,367 -42.9% 44
Malawi Malawi 681,509 +27.8% 21
Malaysia Malaysia 285,474 +19.9% 72
Namibia Namibia 324,866 -28.1% 61
Niger Niger 657,977 +50.8% 22
Nigeria Nigeria 405,082 +2.7% 48
Nicaragua Nicaragua 706,473 +137% 17
Nepal Nepal 494,105 +12.7% 41
Pakistan Pakistan 560,074 -36.9% 32
Panama Panama 300,857 +0.0709% 68
Peru Peru 866,130 +114% 12
Philippines Philippines 944,052 +788% 8
Palau Palau 95,475 -40.8% 102
Papua New Guinea Papua New Guinea 321,743 +2.08% 62
Paraguay Paraguay 287,450 -63.1% 70
Palestinian Territories Palestinian Territories 263,115 +11.1% 78
Rwanda Rwanda 174,847 -50.9% 96
Sudan Sudan 399,479 +36.8% 49
Senegal Senegal 517,116 -10.5% 35
Sierra Leone Sierra Leone 259,643 -62.1% 80
El Salvador El Salvador 1,423,088 +4,367% 3
Serbia Serbia 974,026 +117% 6
Eswatini Eswatini 215,129 +74% 90
Syria Syria 615,703 +467% 26
Chad Chad 510,631 +83.6% 37
Togo Togo 255,266 -16.7% 82
Thailand Thailand 632,120 +69.2% 24
Tajikistan Tajikistan 713,493 +0.725% 16
Turkmenistan Turkmenistan 13,054 112
Tunisia Tunisia 350,930 -55.3% 54
Turkey Turkey 154,671 +48.1% 97
Tanzania Tanzania 491,028 +16.3% 42
Uganda Uganda 645,251 +15.9% 23
Ukraine Ukraine 69,826 -85.8% 107
Uzbekistan Uzbekistan 1,439,638 +65.6% 2
St. Vincent & Grenadines St. Vincent & Grenadines 408,114 47
Venezuela Venezuela 295,519 -33.3% 69
Vietnam Vietnam 232,758 -48.2% 88
Vanuatu Vanuatu 338,462 +144% 58
Yemen Yemen 139,587 +879% 98
South Africa South Africa 494,756 +302% 40
Zambia Zambia 95,621 -60.2% 101
Zimbabwe Zimbabwe 506,743 +13.4% 38 

                    
# 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.NFL.IAEA.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 <- 'DT.NFL.IAEA.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))