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

Source: worldbank.org, 03.09.2025

Year: 2020

Flag Country Value Value change, % Rank
Angola Angola 4,283,396 +27.8% 23
Albania Albania 100,359 +261% 60
Argentina Argentina 2,938,815 -22.7% 31
Armenia Armenia 1,469,324 +65.9% 49
Azerbaijan Azerbaijan 3,114,701 +71.2% 27
Burundi Burundi 3,381,193 +773% 26
Burkina Faso Burkina Faso 2,071,196 +1,251% 40
Bangladesh Bangladesh 92.7 -100% 64
Bosnia & Herzegovina Bosnia & Herzegovina 1,699,276 +294% 45
Belarus Belarus 937,786 +20.9% 55
Central African Republic Central African Republic 7,932,675 +8,330% 7
China China 3,028,481 +10.8% 28
Côte d’Ivoire Côte d’Ivoire 5,139,531 +27.8% 19
Cameroon Cameroon 8,180,018 +515% 6
Congo - Kinshasa Congo - Kinshasa 22,885,248 -40.4% 2
Congo - Brazzaville Congo - Brazzaville 1,264,944 -58.5% 50
Costa Rica Costa Rica 2,534,597 +80.7% 37
Algeria Algeria 2,751,529 +1.23% 34
Ecuador Ecuador 1,897,138 -62.2% 44
Eritrea Eritrea 693,677 +233% 57
Georgia Georgia 1,992,677 +90.2% 43
Ghana Ghana 2,985,653 -28.1% 29
Guatemala Guatemala 4.24 -99.9% 65
Honduras Honduras 260,182 +420% 59
Indonesia Indonesia 3,983,991 +454% 24
India India 4,328,026 +52.4% 22
Iran Iran 4,375,984 -56.1% 21
Iraq Iraq 5,397,938 -50.2% 18
Jordan Jordan 2,837,502 +742,507% 32
Kazakhstan Kazakhstan 1,588,055 +14.3% 48
Kenya Kenya 54,303 -99.6% 61
Kyrgyzstan Kyrgyzstan 424,022 +15.2% 58
Lebanon Lebanon 12,295 -99.9% 63
Liberia Liberia 2,601,403 -53.3% 36
Sri Lanka Sri Lanka 1,650,122 +366% 46
Morocco Morocco 1,594,142 -29.7% 47
North Macedonia North Macedonia 962,572 +16% 53
Mali Mali 2,047,632 +48% 41
Myanmar (Burma) Myanmar (Burma) 35,556 -95.6% 62
Montenegro Montenegro 788,064 -39.8% 56
Mozambique Mozambique 2,009,150 -0.811% 42
Mauritania Mauritania 2,606,160 -52.8% 35
Malawi Malawi 7,734,375 +24.2% 8
Malaysia Malaysia 7,441,562 +141% 9
Niger Niger 10,081,395 +626% 4
Nigeria Nigeria 7,032,519 +130% 12
Nepal Nepal 2,340,597 -27.7% 39
Pakistan Pakistan 4,777,443 -20.9% 20
Panama Panama 5,735,801 -47.3% 15
Philippines Philippines 948,358 -16.3% 54
Rwanda Rwanda 6,706,995 -14% 13
Senegal Senegal 7,314,494 -23.8% 11
Somalia Somalia 2,473,190 -3.07% 38
Serbia Serbia 2,785,273 -13.8% 33
South Sudan South Sudan 36,014,023 -24.7% 1
Syria Syria 7,347,872 +17,061% 10
Chad Chad 10,241,665 -35.3% 3
Thailand Thailand 5,551,714 +209% 17
Tajikistan Tajikistan 1,183,025 +14.4% 52
Tunisia Tunisia 2,968,209 +17.6% 30
Ukraine Ukraine 5,829,981 +29% 14
Kosovo Kosovo 1,189,313 -25.1% 51
South Africa South Africa 9,917,268 -35.7% 5
Zambia Zambia 5,625,239 -5.29% 16
Zimbabwe Zimbabwe 3,445,615 +104% 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.UNCR.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.UNCR.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))