Net bilateral aid flows from DAC donors, Switzerland (current US$)

Source: worldbank.org, 03.09.2025

Year: 2022

Flag Country Value Value change, % Rank
Afghanistan Afghanistan 34,330,002 -44.4% 11
Angola Angola 200,000 +81.8% 96
Albania Albania 24,900,000 -23.1% 23
Argentina Argentina 370,000 +23.3% 92
Armenia Armenia 7,360,000 +119% 58
Azerbaijan Azerbaijan 2,640,000 -45.3% 72
Burundi Burundi 11,720,000 +35.2% 48
Benin Benin 29,430,000 +2.65% 18
Burkina Faso Burkina Faso 41,369,999 -21.5% 3
Bangladesh Bangladesh 40,029,999 -4.94% 5
Bosnia & Herzegovina Bosnia & Herzegovina 20,360,001 -32.2% 30
Belarus Belarus 330,000 -26.7% 93
Bolivia Bolivia 16,600,000 -33.6% 37
Brazil Brazil 3,510,000 +19.4% 66
Bhutan Bhutan 580,000 +176% 88
Central African Republic Central African Republic 9,870,000 -10.2% 53
China China 2,990,000 +12.4% 67
Côte d’Ivoire Côte d’Ivoire 2,530,000 +352% 74
Cameroon Cameroon 8,930,000 -14.8% 54
Congo - Kinshasa Congo - Kinshasa 36,000,000 +1.69% 7
Congo - Brazzaville Congo - Brazzaville 370,000 +19.4% 92
Colombia Colombia 35,169,998 -7.86% 10
Cape Verde Cape Verde 10,000 -66.7% 104
Costa Rica Costa Rica 140,000 -30% 97
Cuba Cuba 10,690,000 -19.7% 51
Djibouti Djibouti 20,000 -90.9% 103
Dominica Dominica 20,000 -94.3% 103
Dominican Republic Dominican Republic 50,000 0% 101
Algeria Algeria 2,220,000 -4.31% 75
Ecuador Ecuador 1,070,000 +1.9% 83
Egypt Egypt 28,540,001 +5.98% 19
Eritrea Eritrea -100,000 -108% 106
Ethiopia Ethiopia 22,549,999 -11.1% 26
Fiji Fiji 80,000 -20% 99
Gabon Gabon 20,000 103
Georgia Georgia 11,030,000 +9.42% 50
Ghana Ghana 12,190,000 -40.9% 47
Guinea Guinea 1,470,000 -16% 79
Gambia Gambia 890,000 -14.4% 85
Guinea-Bissau Guinea-Bissau 590,000 +1.72% 87
Guatemala Guatemala 4,160,000 -11.3% 63
Honduras Honduras 16,420,000 -17.1% 38
Haiti Haiti 27,469,999 -22.1% 20
Indonesia Indonesia 15,790,000 -36.8% 40
India India 8,490,000 -35.3% 55
Iran Iran 2,530,000 +256% 74
Iraq Iraq 16,260,000 +8.33% 39
Jordan Jordan 17,559,999 -17.9% 33
Kazakhstan Kazakhstan 40,000 0% 102
Kenya Kenya 11,280,000 -16.4% 49
Kyrgyzstan Kyrgyzstan 21,440,001 -21% 28
Cambodia Cambodia 17,379,999 -11.9% 34
Laos Laos 12,960,000 -35.1% 44
Lebanon Lebanon 24,719,999 -5.18% 24
Liberia Liberia 60,000 +50% 100
Libya Libya 8,070,000 +0.248% 57
Sri Lanka Sri Lanka 3,710,000 -51.1% 65
Lesotho Lesotho 620,000 +47.6% 86
Morocco Morocco 2,570,000 -35.8% 73
Moldova Moldova 35,919,998 +136% 8
Madagascar Madagascar 4,120,000 -31.8% 64
Mexico Mexico 1,350,000 -4.93% 81
Marshall Islands Marshall Islands 90,000 -18.2% 98
North Macedonia North Macedonia 13,460,000 -34.6% 43
Mali Mali 40,869,999 -1.07% 4
Myanmar (Burma) Myanmar (Burma) 42,790,001 -6.88% 2
Mongolia Mongolia 7,310,000 -46.6% 59
Mozambique Mozambique 35,220,001 +6.53% 9
Mauritania Mauritania 380,000 -20.8% 91
Malawi Malawi 2,170,000 +229% 76
Malaysia Malaysia 20,000 103
Namibia Namibia 400,000 -9.09% 90
Niger Niger 34,200,001 +7.34% 12
Nigeria Nigeria 17,209,999 +10.6% 35
Nicaragua Nicaragua 13,650,000 -26.5% 42
Nepal Nepal 33,410,000 -27.5% 13
Nauru Nauru 220,000 +37.5% 95
Pakistan Pakistan 5,820,000 +637% 61
Panama Panama -30,000 -120% 105
Peru Peru 18,180,000 -21.8% 32
Philippines Philippines 1,490,000 -11.3% 78
North Korea North Korea 1,130,000 -79.8% 82
Paraguay Paraguay 420,000 -30% 89
Palestinian Territories Palestinian Territories 30,070,000 -14.5% 17
Rwanda Rwanda 8,140,000 +3.69% 56
Sudan Sudan 13,760,000 -5.17% 41
Senegal Senegal 5,170,000 -6.51% 62
Sierra Leone Sierra Leone 250,000 +733% 94
El Salvador El Salvador 2,890,000 +50.5% 69
Somalia Somalia 32,959,999 +27% 14
Serbia Serbia 26,320,000 +15.4% 22
South Sudan South Sudan 22,030,001 -12.8% 27
Eswatini Eswatini 2,090,000 +217% 77
Syria Syria 36,119,999 -5.07% 6
Chad Chad 30,719,999 +4.49% 16
Togo Togo 1,370,000 -18% 80
Thailand Thailand 980,000 -91.3% 84
Tajikistan Tajikistan 22,879,999 +8.44% 25
Tonga Tonga 50,000 +400% 101
Tunisia Tunisia 26,420,000 +16.3% 21
Turkey Turkey 2,750,000 -1.43% 71
Tanzania Tanzania 32,520,000 -1.57% 15
Uganda Uganda 2,870,000 -15.3% 70
Ukraine Ukraine 235,880,005 +415% 1
Uzbekistan Uzbekistan 6,100,000 -8.27% 60
Venezuela Venezuela 10,340,000 -17.5% 52
Vietnam Vietnam 12,520,000 -42.6% 45
Vanuatu Vanuatu 90,000 +50% 98
Samoa Samoa 90,000 98
Kosovo Kosovo 21,190,001 -1.12% 29
Yemen Yemen 16,629,999 -1.71% 36
South Africa South Africa 19,400,000 +44.6% 31
Zambia Zambia 2,940,000 +297% 68
Zimbabwe Zimbabwe 12,230,000 +5.07% 46 

                    
# 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 = 'DC.DAC.CHEL.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 <- 'DC.DAC.CHEL.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))