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

Source: worldbank.org, 03.09.2025

Year: 2022

Flag Country Value Value change, % Rank
Afghanistan Afghanistan 18,618,460 +118% 1
Angola Angola 3,144,162 +32.9% 41
Albania Albania 370,926 +2.24% 118
Argentina Argentina 604,457 +116% 101
Armenia Armenia 808,644 -27.7% 82
Azerbaijan Azerbaijan 633,190 -18.1% 98
Burundi Burundi 11,857,215 -21.6% 7
Benin Benin 4,839,183 -34.1% 29
Burkina Faso Burkina Faso 7,102,292 -4.97% 19
Bangladesh Bangladesh 5,939,234 -5.29% 26
Bosnia & Herzegovina Bosnia & Herzegovina 625,144 +19.2% 99
Belarus Belarus 561,319 +193% 105
Belize Belize 336,771 -11.9% 120
Bolivia Bolivia 944,318 +82.7% 75
Brazil Brazil 329,765 +5.9% 121
Bhutan Bhutan 815,213 -42.2% 80
Botswana Botswana 1,339,363 +158% 64
Central African Republic Central African Republic 7,797,839 -12.2% 13
China China 392,129 -4.84% 117
Côte d’Ivoire Côte d’Ivoire 1,278,157 -49.1% 66
Cameroon Cameroon 1,692,112 -41.4% 59
Congo - Kinshasa Congo - Kinshasa 12,170,304 -5.72% 6
Congo - Brazzaville Congo - Brazzaville 1,808,667 +53.5% 58
Colombia Colombia 536,717 -31.3% 106
Comoros Comoros 2,244,926 -38.5% 51
Cape Verde Cape Verde 2,344,297 +40.5% 49
Costa Rica Costa Rica 394,491 -30.5% 116
Cuba Cuba 755,639 +20.4% 90
Djibouti Djibouti 1,950,281 +82.8% 54
Dominica Dominica 22,893 -65.7% 127
Dominican Republic Dominican Republic 476,736 -20.4% 109
Algeria Algeria 907,252 +101% 78
Ecuador Ecuador 640,569 -12.1% 97
Egypt Egypt 1,213,300 +175% 71
Eritrea Eritrea 3,781,604 -40.1% 34
Ethiopia Ethiopia 13,925,881 +20.5% 2
Fiji Fiji 2,730,788 +86% 43
Micronesia (Federated States of) Micronesia (Federated States of) 39,880 +60.5% 125
Gabon Gabon 2,353,876 +110% 48
Georgia Georgia 880,505 -4.92% 79
Ghana Ghana 2,580,422 +20.4% 45
Guinea Guinea 7,930,337 -11.1% 12
Gambia Gambia 4,832,682 -7.43% 30
Guinea-Bissau Guinea-Bissau 4,502,315 -17% 31
Equatorial Guinea Equatorial Guinea 1,417,646 -31.2% 62
Grenada Grenada 42,561 +53.4% 124
Guatemala Guatemala 748,114 -17.6% 91
Guyana Guyana 452,669 -1.6% 110
Honduras Honduras 917,539 +28.5% 77
Haiti Haiti 5,670,918 +3.82% 27
Indonesia Indonesia 1,277,368 +112% 67
India India 2,133,058 -44.8% 52
Iran Iran 660,144 -26.1% 96
Iraq Iraq 798,395 -24.5% 84
Jamaica Jamaica 769,993 +102% 87
Jordan Jordan 666,727 -9.7% 95
Kazakhstan Kazakhstan 590,132 +77.9% 104
Kenya Kenya 2,443,938 -27.1% 47
Kyrgyzstan Kyrgyzstan 1,822,381 +24.9% 57
Cambodia Cambodia 4,339,808 +2.68% 32
Laos Laos 1,551,726 -7.81% 61
Lebanon Lebanon 1,034,334 -62.8% 74
Liberia Liberia 7,030,863 -14.7% 20
Libya Libya 1,101,519 +132% 73
St. Lucia St. Lucia 27,849 -37.5% 126
Sri Lanka Sri Lanka 921,476 +64.2% 76
Lesotho Lesotho 2,107,056 +39.4% 53
Morocco Morocco 706,746 +126% 92
Moldova Moldova 810,789 +35.7% 81
Madagascar Madagascar 9,263,237 -2.63% 11
Maldives Maldives 603,048 +8.31% 103
Mexico Mexico 513,984 +14.3% 108
North Macedonia North Macedonia 603,105 +6.36% 102
Mali Mali 9,672,414 +49.5% 9
Myanmar (Burma) Myanmar (Burma) 4,290,343 -22.2% 33
Montenegro Montenegro 433,203 -14.7% 112
Mongolia Mongolia 682,387 +56.9% 94
Mozambique Mozambique 11,386,345 +30.7% 8
Mauritania Mauritania 3,243,517 +14.3% 39
Mauritius Mauritius 1,573,064 +58.2% 60
Malawi Malawi 13,384,004 +11.1% 4
Malaysia Malaysia 325,336 -32% 122
Namibia Namibia 1,272,893 +2.38% 68
Niger Niger 9,517,628 -19% 10
Nigeria Nigeria 5,264,263 +40.9% 28
Nepal Nepal 6,597,065 -23.2% 23
Pakistan Pakistan 2,265,188 +18% 50
Panama Panama 404,296 +139% 115
Peru Peru 618,709 -21.2% 100
Philippines Philippines 1,827,484 +75.1% 56
Papua New Guinea Papua New Guinea 1,841,565 +142% 55
Paraguay Paraguay 535,046 -10.3% 107
Palestinian Territories Palestinian Territories 2,522,780 +2.56% 46
Rwanda Rwanda 6,025,703 -19.5% 25
Sudan Sudan 7,504,510 +116% 14
Senegal Senegal 3,538,061 -37.8% 37
Solomon Islands Solomon Islands 773,644 +7.06% 86
Sierra Leone Sierra Leone 6,284,543 -7.11% 24
El Salvador El Salvador 761,216 +48.1% 88
Somalia Somalia 13,637,190 +10.5% 3
Serbia Serbia 411,403 -31.2% 114
South Sudan South Sudan 13,112,888 +59.9% 5
São Tomé & Príncipe São Tomé & Príncipe 2,677,099 +106% 44
Suriname Suriname 346,276 +210% 119
Eswatini Eswatini 1,218,096 -12.8% 70
Syria Syria 3,755,453 +285% 35
Chad Chad 7,131,575 +82% 17
Togo Togo 7,121,179 +23.2% 18
Thailand Thailand 703,249 +10.4% 93
Tajikistan Tajikistan 2,917,002 +48.2% 42
Turkmenistan Turkmenistan 449,177 +154% 111
Timor-Leste Timor-Leste 1,104,103 -4.82% 72
Tunisia Tunisia 759,400 +197% 89
Turkey Turkey 412,464 -9.2% 113
Tanzania Tanzania 6,967,607 +3.79% 21
Uganda Uganda 7,339,811 -23.9% 16
Ukraine Ukraine 3,277,315 +434% 38
Uzbekistan Uzbekistan 1,243,265 -7.84% 69
St. Vincent & Grenadines St. Vincent & Grenadines 42,899 -64.5% 123
Venezuela Venezuela 1,285,445 +142% 65
Vietnam Vietnam 1,347,151 -37.2% 63
Samoa Samoa 801,288 +1.28% 83
Kosovo Kosovo 780,285 +141% 85
Yemen Yemen 7,419,036 +233% 15
South Africa South Africa 3,227,157 +179% 40
Zambia Zambia 3,624,143 +48.9% 36
Zimbabwe Zimbabwe 6,757,430 -11.4% 22 

                    
# 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.UNDP.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.UNDP.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))