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

Source: worldbank.org, 03.09.2025

Year: 2022

Flag Country Value Value change, % Rank
Afghanistan Afghanistan 1,998,200 +10.6% 5
Angola Angola 235,212 -16.2% 109
Albania Albania 516,769 -13.1% 75
Argentina Argentina 1,805,874 -9.9% 8
Armenia Armenia 848,519 -13.2% 45
Azerbaijan Azerbaijan 507,277 +36.2% 77
Burundi Burundi 422,481 -11.2% 82
Benin Benin 239,450 +65.6% 107
Burkina Faso Burkina Faso 446,541 -30.2% 81
Bangladesh Bangladesh 1,452,467 -33.3% 22
Bosnia & Herzegovina Bosnia & Herzegovina 394,455 +31.9% 85
Belarus Belarus 54,032 -60% 122
Belize Belize 366,660 -14.2% 90
Bolivia Bolivia 841,334 -44.4% 46
Brazil Brazil 1,759,161 -12.5% 10
Botswana Botswana 713,775 +37.1% 56
Central African Republic Central African Republic 328,788 -10.5% 95
China China 1,843,600 -12.1% 6
Côte d’Ivoire Côte d’Ivoire 1,242,919 -33.2% 29
Cameroon Cameroon 1,569,944 -14.8% 17
Congo - Kinshasa Congo - Kinshasa 1,184,737 -23.2% 34
Congo - Brazzaville Congo - Brazzaville 507,595 +264% 76
Colombia Colombia 897,342 -41% 42
Comoros Comoros 633,437 +130% 67
Cape Verde Cape Verde 668,522 +24.4% 64
Costa Rica Costa Rica 890,882 +1.55% 43
Cuba Cuba 368,745 +1,086% 89
Djibouti Djibouti 163,522 -21.7% 113
Dominica Dominica 78,944 -14.2% 119
Dominican Republic Dominican Republic 673,886 +109% 62
Algeria Algeria 767,398 +126% 50
Ecuador Ecuador 699,842 -29.8% 58
Egypt Egypt 1,149,294 -36.4% 35
Eritrea Eritrea 21,885 -96.1% 130
Ethiopia Ethiopia 1,238,367 -11.3% 31
Fiji Fiji 216,848 -69.9% 111
Gabon Gabon 372,450 -38% 88
Georgia Georgia 464,391 -28% 80
Ghana Ghana 301,269 -36.4% 100
Guinea Guinea 242,175 -13.8% 106
Gambia Gambia 468,393 +151% 79
Guinea-Bissau Guinea-Bissau 278,566 +187% 101
Equatorial Guinea Equatorial Guinea 202,448 -28.9% 112
Grenada Grenada 56,233 +17.4% 121
Guatemala Guatemala 1,199,650 +1.9% 33
Guyana Guyana 304,141 -0.478% 99
Honduras Honduras 605,524 -43.7% 71
Haiti Haiti 239,240 -1.71% 108
Indonesia Indonesia 1,821,191 -7.27% 7
India India 2,569,546 -0.709% 3
Iraq Iraq 638,958 -56.1% 66
Jamaica Jamaica 674,888 +15.3% 61
Jordan Jordan 850,518 -76.4% 44
Kazakhstan Kazakhstan 570,593 +12.2% 72
Kenya Kenya 720,565 -19.1% 55
Kyrgyzstan Kyrgyzstan 957,764 +43.5% 39
Cambodia Cambodia 1,229,313 +67.7% 32
Kiribati Kiribati 111,792 -45.4% 116
Laos Laos 755,106 -9.95% 51
Lebanon Lebanon 795,206 +2.47% 49
Liberia Liberia 119,015 -80.9% 114
Libya Libya 243,857 +187% 105
St. Lucia St. Lucia 374,957 +36.4% 86
Sri Lanka Sri Lanka 1,697,261 +16.8% 13
Lesotho Lesotho 657,725 +76.8% 65
Morocco Morocco 918,175 +8.67% 41
Moldova Moldova 1,427,236 +35.9% 24
Madagascar Madagascar 1,490,220 +42.2% 20
Maldives Maldives 307,296 +59.2% 98
Mexico Mexico 1,730,714 -23% 12
Marshall Islands Marshall Islands 22,500 -59.7% 129
North Macedonia North Macedonia 490,869 +3.43% 78
Mali Mali 322,462 +82.1% 97
Myanmar (Burma) Myanmar (Burma) 1,438,014 -23.2% 23
Montenegro Montenegro 249,870 -49% 104
Mongolia Mongolia 815,019 -16.5% 48
Mozambique Mozambique 373,548 -30.7% 87
Mauritania Mauritania 336,479 -20.5% 93
Mauritius Mauritius 259,781 -48% 103
Malawi Malawi 686,740 +102% 60
Malaysia Malaysia 1,765,071 +131% 9
Namibia Namibia 1,329,062 +66.9% 27
Niger Niger 341,960 -22.2% 92
Nigeria Nigeria 1,490,899 +2.43% 19
Nicaragua Nicaragua 43,497 -82.3% 123
Nepal Nepal 1,588,575 +13.4% 16
Pakistan Pakistan 1,626,066 -2.82% 15
Panama Panama 555,373 -16.1% 73
Peru Peru 1,516,032 -29.3% 18
Philippines Philippines 2,291,356 +1.03% 4
Palau Palau 20,013 -46.3% 131
Papua New Guinea Papua New Guinea 72,356 -79.1% 120
Paraguay Paraguay 1,690,329 -12% 14
Palestinian Territories Palestinian Territories 1,369,160 -27.4% 26
Rwanda Rwanda 397,520 +104% 84
Sudan Sudan 624,987 -5.56% 69
Senegal Senegal 1,240,066 -24.2% 30
Solomon Islands Solomon Islands 33,520 +37.9% 125
Sierra Leone Sierra Leone 523,808 +65% 74
El Salvador El Salvador 617,350 -36.8% 70
Somalia Somalia 734,881 -25.3% 53
Serbia Serbia 260,788 -20.4% 102
South Sudan South Sudan 332,037 -37.9% 94
São Tomé & Príncipe São Tomé & Príncipe 322,816 -59.3% 96
Suriname Suriname 702,743 +456% 57
Eswatini Eswatini 750,523 -18% 52
Syria Syria 112,304 -83.1% 115
Chad Chad 108,846 -16.6% 117
Togo Togo 343,343 +32.2% 91
Thailand Thailand 923,021 +12.8% 40
Tajikistan Tajikistan 669,271 +6.74% 63
Turkmenistan Turkmenistan 219,890 +59.9% 110
Timor-Leste Timor-Leste 963,744 -12.5% 37
Tonga Tonga 24,728 -75.1% 127
Tunisia Tunisia 957,786 +26.3% 38
Turkey Turkey 415,556 -22.5% 83
Tuvalu Tuvalu 95,504 -59.8% 118
Tanzania Tanzania 1,749,680 +92.2% 11
Uganda Uganda 722,878 +28.2% 54
Ukraine Ukraine 1,259,685 +8.96% 28
Uzbekistan Uzbekistan 1,462,864 +75.4% 21
St. Vincent & Grenadines St. Vincent & Grenadines 30,966 -48.3% 126
Venezuela Venezuela 625,395 +4,367% 68
Vietnam Vietnam 2,634,650 +6.51% 2
Vanuatu Vanuatu 39,831 -22.7% 124
Samoa Samoa 698,831 +117% 59
Kosovo Kosovo 23,341 -69.1% 128
Yemen Yemen 3,198,457 +628% 1
South Africa South Africa 1,061,254 -47.6% 36
Zambia Zambia 824,061 -30.4% 47
Zimbabwe Zimbabwe 1,370,673 -40.9% 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.ILOG.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.ILOG.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))