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

Source: worldbank.org, 01.09.2025

Year: 2022

Flag Country Value Value change, % Rank
Afghanistan Afghanistan 160,500,000 +41.8% 2
Angola Angola 440,000 -81.1% 95
Albania Albania 120,000 +1,100% 107
Argentina Argentina 1,460,000 -67% 80
Armenia Armenia 1,000,000 +11.1% 84
Azerbaijan Azerbaijan 940,000 -30.4% 86
Burundi Burundi 7,190,000 +32.2% 52
Benin Benin 17,850,000 0% 33
Burkina Faso Burkina Faso 49,759,998 +0.404% 17
Bangladesh Bangladesh 91,120,003 +5.94% 5
Bosnia & Herzegovina Bosnia & Herzegovina 170,000 +41.7% 103
Belarus Belarus 1,060,000 -30.3% 83
Belize Belize 870,000 -17.1% 88
Bolivia Bolivia 12,960,000 +14.5% 39
Brazil Brazil 1,810,000 +46% 77
Bhutan Bhutan 980,000 +104% 85
Botswana Botswana 190,000 -9.52% 102
Central African Republic Central African Republic 10,280,000 -22% 44
China China -10,450,000 -50% 116
Côte d’Ivoire Côte d’Ivoire 25,180,000 +148% 28
Cameroon Cameroon 11,940,000 -24.6% 41
Congo - Kinshasa Congo - Kinshasa 74,279,999 +7.76% 10
Congo - Brazzaville Congo - Brazzaville 3,090,000 +212% 70
Colombia Colombia 46,740,002 +25.7% 20
Comoros Comoros 150,000 -31.8% 105
Cape Verde Cape Verde 170,000 -15% 103
Costa Rica Costa Rica 900,000 -67% 87
Cuba Cuba 4,340,000 +37.3% 61
Djibouti Djibouti 230,000 -17.9% 99
Dominica Dominica 2,270,000 +62.1% 74
Dominican Republic Dominican Republic 340,000 +209% 97
Algeria Algeria -850,000 +118% 115
Ecuador Ecuador 11,420,000 +35.8% 42
Egypt Egypt 4,110,000 -72.6% 64
Eritrea Eritrea 110,000 -42.1% 108
Ethiopia Ethiopia 121,720,001 +22.9% 3
Fiji Fiji 80,000 -46.7% 110
Micronesia (Federated States of) Micronesia (Federated States of) 80,000 -20% 110
Gabon Gabon 310,000 -6.06% 98
Georgia Georgia 230,000 0% 99
Ghana Ghana 68,870,003 +30.7% 13
Guinea Guinea 3,260,000 -15.3% 68
Gambia Gambia 4,070,000 +1,257% 65
Guinea-Bissau Guinea-Bissau 870,000 +278% 88
Equatorial Guinea Equatorial Guinea 530,000 +194% 93
Grenada Grenada 1,500,000 +213% 79
Guatemala Guatemala 13,570,000 -17.5% 38
Guyana Guyana 4,970,000 +2.9% 59
Honduras Honduras 20,629,999 -9.64% 31
Haiti Haiti 84,349,998 +49.1% 7
Indonesia Indonesia 8,190,000 +15.2% 48
India India 2,110,000 -83.7% 76
Iran Iran 30,000 -66.7% 112
Iraq Iraq 44,060,001 -40.6% 22
Jamaica Jamaica 16,360,001 +152% 36
Jordan Jordan 58,560,001 -12.4% 16
Kazakhstan Kazakhstan 140,000 -17.6% 106
Kenya Kenya 39,419,998 +23% 24
Kyrgyzstan Kyrgyzstan 4,160,000 +187% 63
Cambodia Cambodia 2,900,000 +45% 72
Kiribati Kiribati 70,000 -41.7% 111
Laos Laos 800,000 +15.9% 90
Lebanon Lebanon 47,540,001 -30.4% 18
Liberia Liberia 5,600,000 +428% 58
Libya Libya 4,050,000 +10.7% 66
St. Lucia St. Lucia 1,790,000 +84.5% 78
Sri Lanka Sri Lanka 9,020,000 +201% 45
Lesotho Lesotho 170,000 -71.2% 103
Morocco Morocco 8,790,000 +50% 46
Moldova Moldova 3,470,000 +1,828% 67
Madagascar Madagascar 6,140,000 +27.4% 56
Maldives Maldives 140,000 -88.4% 106
Mexico Mexico 28,350,000 +498% 27
Marshall Islands Marshall Islands 730,000 +128% 92
North Macedonia North Macedonia 90,000 -18.2% 109
Mali Mali 71,449,997 -22.7% 12
Myanmar (Burma) Myanmar (Burma) 39,709,999 +97.4% 23
Montenegro Montenegro 90,000 -18.2% 109
Mongolia Mongolia 6,800,000 +36.8% 55
Mozambique Mozambique 83,370,003 +1.34% 8
Mauritania Mauritania 2,130,000 -31.9% 75
Mauritius Mauritius 140,000 -26.3% 106
Malawi Malawi 7,590,000 -27.9% 50
Malaysia Malaysia 760,000 +16.9% 91
Namibia Namibia 220,000 -4.35% 100
Niger Niger 15,910,000 -21.9% 37
Nigeria Nigeria 82,059,998 +35.9% 9
Nicaragua Nicaragua 8,010,000 -43.4% 49
Nepal Nepal 2,770,000 -64.9% 73
Nauru Nauru 80,000 -20% 110
Pakistan Pakistan 47,450,001 +59.7% 19
Panama Panama 820,000 +95.2% 89
Peru Peru 22,520,000 +19.8% 29
Philippines Philippines 16,459,999 +46.3% 35
Palau Palau 20,000 -80% 113
Papua New Guinea Papua New Guinea 140,000 +27.3% 106
Paraguay Paraguay 410,000 +128% 96
Palestinian Territories Palestinian Territories 35,660,000 -4.7% 26
Rwanda Rwanda 6,870,000 -68.8% 54
Sudan Sudan 45,599,998 -6.73% 21
Senegal Senegal 66,339,996 +6.18% 14
Solomon Islands Solomon Islands 80,000 -20% 110
Sierra Leone Sierra Leone 5,640,000 -5.21% 57
El Salvador El Salvador 7,500,000 +31.8% 51
Somalia Somalia 38,540,001 +4.3% 25
Serbia Serbia 140,000 +7.69% 106
South Sudan South Sudan 87,300,003 -19.9% 6
São Tomé & Príncipe São Tomé & Príncipe 190,000 +18.8% 102
Suriname Suriname 470,000 -49.5% 94
Eswatini Eswatini 3,250,000 +525% 69
Syria Syria 62,740,002 -9.83% 15
Chad Chad 12,280,000 +11.8% 40
Togo Togo 4,270,000 -4.04% 62
Thailand Thailand 470,000 +840% 94
Tajikistan Tajikistan 1,250,000 -19.4% 82
Turkmenistan Turkmenistan 80,000 +33.3% 110
Timor-Leste Timor-Leste 200,000 0% 101
Tonga Tonga 70,000 -41.7% 111
Tunisia Tunisia 4,910,000 -26.4% 60
Turkey Turkey 0 -100% 114
Tuvalu Tuvalu 70,000 -41.7% 111
Tanzania Tanzania 101,750,000 +108% 4
Uganda Uganda 22,299,999 -26.6% 30
Ukraine Ukraine 2,206,659,912 +5,730% 1
Uzbekistan Uzbekistan 2,920,000 +2,146% 71
St. Vincent & Grenadines St. Vincent & Grenadines 1,290,000 +40.2% 81
Venezuela Venezuela 18,940,001 +41.4% 32
Vietnam Vietnam 10,560,000 -11.8% 43
Vanuatu Vanuatu 160,000 -20% 104
Samoa Samoa 70,000 -41.7% 111
Kosovo Kosovo 120,000 +50% 107
Yemen Yemen 74,150,002 +14.8% 11
South Africa South Africa 7,100,000 -21.1% 53
Zambia Zambia 17,740,000 +122% 34
Zimbabwe Zimbabwe 8,410,000 -39.1% 47 

                    
# 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.CANL.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.CANL.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))