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

Source: worldbank.org, 03.09.2025

Year: 2022

Flag Country Value Value change, % Rank
Afghanistan Afghanistan 1,652,411 -41.6% 47
Angola Angola 4,156,337 +11.6% 6
Albania Albania 497,342 -24.7% 104
Argentina Argentina 537,703 +2.52% 100
Armenia Armenia 217,818 -72.1% 125
Azerbaijan Azerbaijan 573,492 -43.3% 99
Burundi Burundi 1,994,178 -25.6% 35
Benin Benin 2,098,134 -14.5% 30
Burkina Faso Burkina Faso 2,336,953 -38.4% 26
Bangladesh Bangladesh 6,089,485 -23.7% 3
Bosnia & Herzegovina Bosnia & Herzegovina 237,408 -68.1% 122
Belarus Belarus 313,535 -21.2% 116
Belize Belize 237,078 -6.37% 123
Bolivia Bolivia 792,162 -6.15% 83
Brazil Brazil 852,095 -61.7% 77
Bhutan Bhutan 2,051,203 -33.8% 32
Botswana Botswana 845,258 -52.6% 80
Central African Republic Central African Republic 4,427,677 +9.56% 4
China China 3,785,202 -38.6% 7
Côte d’Ivoire Côte d’Ivoire 3,197,722 -7.69% 13
Cameroon Cameroon 1,807,039 -34.3% 41
Congo - Kinshasa Congo - Kinshasa 3,729,275 -22.2% 8
Congo - Brazzaville Congo - Brazzaville 1,993,620 -28% 36
Colombia Colombia 583,036 -52.1% 97
Comoros Comoros 531,231 -57.6% 101
Cape Verde Cape Verde 915,118 -24.9% 73
Costa Rica Costa Rica 357,787 -37.9% 115
Cuba Cuba 401,215 -5.53% 112
Djibouti Djibouti 836,239 +19% 81
Dominica Dominica 80,812 -43% 132
Dominican Republic Dominican Republic 499,120 -21.3% 103
Algeria Algeria 993,995 -26.6% 67
Ecuador Ecuador 465,947 -34.4% 109
Egypt Egypt 1,200,930 -32.3% 57
Eritrea Eritrea 927,036 -40.9% 72
Ethiopia Ethiopia 4,301,982 -14.5% 5
Fiji Fiji 231,480 -60.7% 124
Micronesia (Federated States of) Micronesia (Federated States of) 393,406 -2.53% 113
Gabon Gabon 2,760,095 -22.8% 16
Georgia Georgia 965,739 +52.6% 69
Ghana Ghana 2,154,570 -9.56% 28
Guinea Guinea 2,710,521 -19.6% 18
Gambia Gambia 764,613 -34.3% 84
Guinea-Bissau Guinea-Bissau 1,107,045 -11.1% 59
Equatorial Guinea Equatorial Guinea 1,460,927 -0.387% 52
Grenada Grenada 89,044 -4.97% 129
Guatemala Guatemala 901,028 -37.2% 74
Guyana Guyana 490,050 -47.5% 105
Honduras Honduras 736,199 -43% 86
Haiti Haiti 1,712,719 -22.9% 44
Indonesia Indonesia 2,550,534 -54.3% 22
India India 8,076,518 +6.12% 1
Iran Iran 974,012 -47.8% 68
Iraq Iraq 1,736,161 -40.9% 43
Jamaica Jamaica 290,229 -67.7% 119
Jordan Jordan 849,061 +13.7% 79
Kazakhstan Kazakhstan 303,118 -40.8% 117
Kenya Kenya 2,389,106 -27.4% 24
Kyrgyzstan Kyrgyzstan 1,067,912 -26.9% 63
Cambodia Cambodia 1,154,762 -50.6% 58
Kiribati Kiribati 448,934 -13.5% 110
Laos Laos 1,377,670 -70% 53
Lebanon Lebanon 949,762 -0.912% 70
Liberia Liberia 2,532,566 -24.7% 23
Libya Libya 586,806 -24.5% 96
St. Lucia St. Lucia 84,281 +15.8% 131
Sri Lanka Sri Lanka 1,736,979 -21.9% 42
Lesotho Lesotho 1,006,115 -22% 66
Morocco Morocco 944,912 -4.98% 71
Moldova Moldova 278,917 -52.9% 121
Madagascar Madagascar 1,902,094 +9.03% 37
Maldives Maldives 1,697,685 -37.1% 45
Mexico Mexico 693,393 -42.5% 89
Marshall Islands Marshall Islands 87,663 -47.6% 130
North Macedonia North Macedonia 686,309 -14.1% 90
Mali Mali 1,846,107 -29.9% 39
Myanmar (Burma) Myanmar (Burma) 3,000,237 -8.68% 14
Montenegro Montenegro 70,708 -63.9% 133
Mongolia Mongolia 1,048,324 -68.8% 64
Mozambique Mozambique 2,658,667 -4.03% 19
Mauritania Mauritania 1,080,204 -39.6% 60
Mauritius Mauritius 482,260 -49.9% 106
Malawi Malawi 1,077,898 -19.1% 61
Malaysia Malaysia 530,423 -43.2% 102
Namibia Namibia 1,285,926 -24.9% 55
Niger Niger 1,243,183 -50.5% 56
Nigeria Nigeria 3,375,315 -62.6% 11
Nicaragua Nicaragua 730,558 -46.5% 87
Nepal Nepal 3,230,965 -30.1% 12
Pakistan Pakistan 1,667,372 -36.6% 46
Panama Panama 469,858 -60.5% 108
Peru Peru 614,661 -37.7% 94
Philippines Philippines 822,828 -67.6% 82
Palau Palau 51,830 -30.1% 134
Papua New Guinea Papua New Guinea 1,358,137 -33.6% 54
North Korea North Korea 667,334 -8.31% 92
Paraguay Paraguay 611,075 -57.7% 95
Palestinian Territories Palestinian Territories 1,075,851 -18.1% 62
Rwanda Rwanda 1,830,447 -18% 40
Sudan Sudan 2,576,380 -3.67% 20
Senegal Senegal 2,018,004 +15.3% 34
Solomon Islands Solomon Islands 713,804 -38.8% 88
Sierra Leone Sierra Leone 2,821,768 -14.8% 15
El Salvador El Salvador 412,654 -42.2% 111
Somalia Somalia 1,585,734 -57.9% 49
Serbia Serbia 1,016,328 +43.6% 65
South Sudan South Sudan 6,278,494 -6.49% 2
São Tomé & Príncipe São Tomé & Príncipe 855,914 -20.8% 76
Suriname Suriname 279,191 -40.4% 120
Eswatini Eswatini 851,207 -34.4% 78
Syria Syria 1,868,763 -15.3% 38
Chad Chad 2,041,232 -25.5% 33
Togo Togo 2,141,686 -35.4% 29
Thailand Thailand 2,574,708 -4.37% 21
Tajikistan Tajikistan 297,526 -59.8% 118
Turkmenistan Turkmenistan 200,387 -56.7% 126
Timor-Leste Timor-Leste 1,513,913 -51.3% 50
Tonga Tonga 576,997 +31% 98
Tunisia Tunisia 650,969 -24.6% 93
Turkey Turkey 482,212 -65.2% 107
Tuvalu Tuvalu 37,379 -63% 135
Tanzania Tanzania 2,740,146 +5.7% 17
Uganda Uganda 3,531,886 -37.7% 10
Ukraine Ukraine 1,635,536 -15.7% 48
Uzbekistan Uzbekistan 876,854 -30.8% 75
St. Vincent & Grenadines St. Vincent & Grenadines 89,835 -70% 128
Venezuela Venezuela 390,492 -71.7% 114
Vietnam Vietnam 1,490,146 -62.4% 51
Vanuatu Vanuatu 668,207 -34.4% 91
Samoa Samoa 740,058 -31% 85
Kosovo Kosovo 91,781 -71.1% 127
Yemen Yemen 3,689,304 -47.3% 9
South Africa South Africa 2,087,824 -22% 31
Zambia Zambia 2,191,610 +19.8% 27
Zimbabwe Zimbabwe 2,388,014 -42.5% 25 

                    
# 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.WHOL.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.WHOL.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))