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

Source: worldbank.org, 03.09.2025

Year: 2022

Flag Country Value Value change, % Rank
Afghanistan Afghanistan 19,309,999 -26.4% 11
Angola Angola 5,230,000 +17.4% 41
Albania Albania 748,000 -8% 117
Argentina Argentina 893,000 -42.6% 100
Armenia Armenia 953,000 -18.4% 95
Azerbaijan Azerbaijan 917,000 -29% 98
Burundi Burundi 9,244,000 -1.04% 28
Benin Benin 6,996,000 -12.6% 32
Burkina Faso Burkina Faso 14,279,000 -22.5% 17
Bangladesh Bangladesh 15,132,000 -14.5% 15
Bosnia & Herzegovina Bosnia & Herzegovina 889,000 +8.15% 101
Belarus Belarus 757,000 -14.9% 115
Belize Belize 785,000 -10.7% 112
Bolivia Bolivia 1,280,000 -32.9% 77
Brazil Brazil 1,346,000 +0.373% 73
Bhutan Bhutan 872,000 -14.6% 104
Botswana Botswana 893,000 -16.5% 100
Central African Republic Central African Republic 6,623,000 -23.1% 35
China China 5,166,000 -22.7% 42
Côte d’Ivoire Côte d’Ivoire 12,337,000 0% 22
Cameroon Cameroon 10,477,000 -22% 26
Congo - Kinshasa Congo - Kinshasa 60,172,001 +5.68% 1
Congo - Brazzaville Congo - Brazzaville 1,581,000 -7.65% 65
Colombia Colombia 1,565,000 +43.2% 66
Comoros Comoros 1,104,000 -23.7% 87
Cape Verde Cape Verde 1,324,000 +70.2% 75
Costa Rica Costa Rica 985,000 -27.1% 92
Cuba Cuba 800,000 -7.51% 109
Djibouti Djibouti 1,433,000 +40.6% 72
Dominican Republic Dominican Republic 795,000 -5.24% 110
Algeria Algeria 1,484,000 -11.9% 70
Ecuador Ecuador 905,000 +7.35% 99
Egypt Egypt 3,623,000 -19.5% 49
Eritrea Eritrea 2,683,000 -14% 54
Ethiopia Ethiopia 35,950,001 +1.76% 5
Fiji Fiji 11,181,000 +1.33% 25
Gabon Gabon 873,000 -2.13% 103
Georgia Georgia 793,000 -5.37% 111
Ghana Ghana 8,236,000 -21% 30
Guinea Guinea 11,775,000 -11.5% 24
Gambia Gambia 2,081,000 +7.05% 58
Guinea-Bissau Guinea-Bissau 1,782,000 -16.1% 62
Equatorial Guinea Equatorial Guinea 750,000 +9.81% 116
Guatemala Guatemala 1,240,000 -36.9% 78
Guyana Guyana 1,733,000 +19.8% 63
Honduras Honduras 978,000 -51.2% 93
Haiti Haiti 6,881,000 +2.53% 33
Indonesia Indonesia 4,089,000 -17% 46
India India 54,098,000 +10.7% 2
Iran Iran 1,473,000 +30.8% 71
Iraq Iraq 1,823,000 -24.8% 60
Jamaica Jamaica 1,023,000 +28.5% 90
Jordan Jordan 2,288,000 -12.3% 56
Kazakhstan Kazakhstan 882,000 -39.2% 102
Kenya Kenya 12,447,000 -3.44% 20
Kyrgyzstan Kyrgyzstan 1,543,000 -0.194% 68
Cambodia Cambodia 3,626,000 -16.2% 48
Laos Laos 1,785,000 -13% 61
Lebanon Lebanon 4,282,000 -0.557% 45
Liberia Liberia 4,582,000 -7.3% 44
Libya Libya 1,132,000 +10% 84
Sri Lanka Sri Lanka 517,000 -46.1% 119
Lesotho Lesotho 1,185,000 -41.3% 81
Morocco Morocco 1,684,000 -20.5% 64
Moldova Moldova 7,109,000 +486% 31
Madagascar Madagascar 13,901,000 +16% 19
Maldives Maldives 1,214,000 +34% 79
Mexico Mexico 1,142,000 -9.72% 83
North Macedonia North Macedonia 762,000 +3.81% 113
Mali Mali 20,018,999 +5% 9
Myanmar (Burma) Myanmar (Burma) 14,177,000 +4.06% 18
Montenegro Montenegro 758,000 -12.3% 114
Mongolia Mongolia 817,000 -25.3% 106
Mozambique Mozambique 21,947,001 -5.83% 7
Mauritania Mauritania 3,631,000 +21.5% 47
Malawi Malawi 12,428,000 +6.32% 21
Malaysia Malaysia 1,114,000 +25.6% 85
Namibia Namibia 812,000 -12.7% 107
Niger Niger 22,500,000 -9.82% 6
Nigeria Nigeria 50,859,001 +8.45% 3
Nicaragua Nicaragua 1,111,000 -1.24% 86
Nepal Nepal 6,715,000 +0.494% 34
Pakistan Pakistan 37,191,002 -0.684% 4
Panama Panama 807,000 -6.92% 108
Peru Peru 1,000,000 -9.67% 91
Philippines Philippines 5,105,000 +30.9% 43
Papua New Guinea Papua New Guinea 2,079,000 +16.7% 59
North Korea North Korea 1,522,000 -26.7% 69
Paraguay Paraguay 926,000 -13.2% 96
Palestinian Territories Palestinian Territories 5,633,000 -11.9% 39
Rwanda Rwanda 6,423,000 +0.109% 36
Sudan Sudan 12,300,000 +15.9% 23
Senegal Senegal 5,800,000 -16.6% 38
Sierra Leone Sierra Leone 10,017,000 -16.9% 27
El Salvador El Salvador 1,344,000 -1.25% 74
Somalia Somalia 16,226,999 -2.81% 14
Serbia Serbia 920,000 -6.03% 97
South Sudan South Sudan 14,365,000 -14% 16
São Tomé & Príncipe São Tomé & Príncipe 696,000 -29.9% 118
Eswatini Eswatini 1,051,000 -2.5% 89
Syria Syria 2,812,000 -17.9% 53
Chad Chad 20,701,000 +5.09% 8
Togo Togo 5,583,000 +0.794% 40
Thailand Thailand 1,189,000 +24.1% 80
Tajikistan Tajikistan 3,341,000 -483% 51
Turkmenistan Turkmenistan 854,000 +1.18% 105
Timor-Leste Timor-Leste 1,177,000 +12.4% 82
Tunisia Tunisia 967,000 +2.98% 94
Turkey Turkey 2,168,000 +74.1% 57
Tanzania Tanzania 19,209,999 +7.64% 12
Uganda Uganda 19,481,001 +8.66% 10
Ukraine Ukraine 1,298,000 +0.698% 76
Uzbekistan Uzbekistan 3,001,000 -18.2% 52
Venezuela Venezuela 2,589,000 -19.3% 55
Vietnam Vietnam 3,621,000 -7.75% 50
Kosovo Kosovo 1,067,000 +16.2% 88
Yemen Yemen 17,691,999 +106% 13
South Africa South Africa 1,554,000 -19.6% 67
Zambia Zambia 8,751,000 +19.6% 29
Zimbabwe Zimbabwe 6,035,000 -22.6% 37 

                    
# 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.UNCF.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.UNCF.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))