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

Source: worldbank.org, 01.09.2025

Year: 2022

Flag Country Value Value change, % Rank
Afghanistan Afghanistan 2,240,000 -25.8% 7
Angola Angola 20,000 0% 44
Albania Albania 100,000 -58.3% 37
Argentina Argentina 10,000 0% 45
Armenia Armenia 380,000 +15.2% 25
Azerbaijan Azerbaijan 30,000 43
Burkina Faso Burkina Faso 460,000 +64.3% 23
Bangladesh Bangladesh 230,000 0% 29
Bosnia & Herzegovina Bosnia & Herzegovina 3,400,000 -41.3% 5
Belarus Belarus 540,000 -58.8% 21
Bolivia Bolivia 10,000 0% 45
Brazil Brazil 110,000 +175% 36
China China 10,000 -75% 45
Cameroon Cameroon 0 -100% 46
Colombia Colombia 100,000 -56.5% 37
Cape Verde Cape Verde 40,000 -20% 42
Costa Rica Costa Rica 0 46
Cuba Cuba 230,000 -32.4% 29
Algeria Algeria 0 -100% 46
Ecuador Ecuador 20,000 0% 44
Egypt Egypt 50,000 -98.9% 41
Ethiopia Ethiopia 4,180,000 -6.07% 3
Gabon Gabon 20,000 +100% 44
Georgia Georgia 2,390,000 -48.9% 6
Ghana Ghana 610,000 -19.7% 20
Guinea Guinea 10,000 0% 45
Gambia Gambia 20,000 0% 44
Indonesia Indonesia 30,000 +200% 43
India India 130,000 -31.6% 35
Iran Iran 60,000 +500% 40
Iraq Iraq 980,000 -71.6% 16
Jordan Jordan 800,000 -55.8% 18
Kazakhstan Kazakhstan 20,000 0% 44
Kenya Kenya 240,000 -11.1% 28
Kyrgyzstan Kyrgyzstan 20,000 0% 44
Cambodia Cambodia 1,970,000 -3.43% 10
Laos Laos 20,000 -60% 44
Lebanon Lebanon 1,960,000 +83.2% 11
Libya Libya 1,460,000 +106% 14
Sri Lanka Sri Lanka 20,000 -50% 44
Morocco Morocco 250,000 -81.9% 27
Moldova Moldova 4,650,000 -19.4% 2
Mexico Mexico 10,000 0% 45
North Macedonia North Macedonia 150,000 +15.4% 33
Mali Mali 1,500,000 -21.1% 13
Myanmar (Burma) Myanmar (Burma) 1,010,000 -24.1% 15
Montenegro Montenegro 60,000 +100% 40
Mongolia Mongolia 100,000 -70.6% 37
Malawi Malawi 20,000 0% 44
Malaysia Malaysia 10,000 -66.7% 45
Namibia Namibia 40,000 -50% 42
Niger Niger 210,000 -84.8% 30
Nigeria Nigeria 70,000 -93.9% 39
Nicaragua Nicaragua 10,000 -75% 45
Nepal Nepal 190,000 +533% 32
Pakistan Pakistan 70,000 +250% 39
Peru Peru 50,000 -83.9% 41
Philippines Philippines 80,000 +14.3% 38
Paraguay Paraguay 20,000 +100% 44
Palestinian Territories Palestinian Territories 680,000 -19% 19
Rwanda Rwanda 20,000 +100% 44
Sudan Sudan 450,000 +95.7% 24
Senegal Senegal 610,000 -58.2% 20
Sierra Leone Sierra Leone 30,000 +200% 43
El Salvador El Salvador 30,000 +200% 43
Somalia Somalia 2,060,000 +4,020% 9
Serbia Serbia 480,000 0% 22
Syria Syria 1,650,000 -46.6% 12
Thailand Thailand 50,000 +66.7% 41
Tajikistan Tajikistan 0 -100% 46
Tunisia Tunisia 260,000 -69% 26
Turkey Turkey 2,080,000 -13% 8
Tanzania Tanzania 950,000 +1,088% 17
Uganda Uganda 40,000 +300% 42
Ukraine Ukraine 34,750,000 +752% 1
Uzbekistan Uzbekistan 30,000 -57.1% 43
Venezuela Venezuela 10,000 -85.7% 45
Vietnam Vietnam 200,000 -89.6% 31
Kosovo Kosovo 140,000 -22.2% 34
Yemen Yemen 450,000 +181% 24
South Africa South Africa 0 -100% 46
Zambia Zambia 4,050,000 +17.1% 4
Zimbabwe Zimbabwe 10,000 -66.7% 45 

                    
# 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.CZEL.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.CZEL.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))