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

Source: worldbank.org, 03.09.2025

Year: 2022

Flag Country Value Value change, % Rank
Afghanistan Afghanistan 93,169,998 -19.2% 2
Angola Angola 1,830,000 -10.3% 68
Albania Albania 13,730,000 -7.04% 34
Argentina Argentina 530,000 -44.8% 91
Armenia Armenia 7,520,000 +38.7% 45
Azerbaijan Azerbaijan 140,000 -84.6% 104
Burundi Burundi 820,000 -70.9% 83
Benin Benin 1,340,000 +0.752% 73
Burkina Faso Burkina Faso 47,189,999 -11.9% 11
Bangladesh Bangladesh 36,060,001 -33.4% 18
Bosnia & Herzegovina Bosnia & Herzegovina 20,639,999 -27.8% 27
Belarus Belarus 2,380,000 +66.4% 63
Belize Belize 40,000 0% 108
Bolivia Bolivia 19,610,001 -37.1% 28
Brazil Brazil 2,620,000 -23.6% 62
Bhutan Bhutan 400,000 -13% 94
Botswana Botswana 290,000 -51.7% 99
Central African Republic Central African Republic 11,690,000 -40% 37
China China 2,160,000 -19.7% 64
Côte d’Ivoire Côte d’Ivoire 1,290,000 -3.01% 75
Cameroon Cameroon 11,050,000 -16.8% 38
Congo - Kinshasa Congo - Kinshasa 85,889,999 -25.5% 3
Congo - Brazzaville Congo - Brazzaville 330,000 -55.4% 96
Colombia Colombia 34,980,000 -37.2% 20
Comoros Comoros 110,000 -26.7% 105
Cape Verde Cape Verde 60,000 -50% 107
Costa Rica Costa Rica 800,000 -26.6% 84
Cuba Cuba 3,240,000 -15% 57
Djibouti Djibouti 300,000 -65.9% 98
Dominican Republic Dominican Republic 190,000 -58.7% 103
Algeria Algeria 1,060,000 -67.4% 78
Ecuador Ecuador 740,000 -6.33% 86
Egypt Egypt 3,660,000 -37.6% 53
Eritrea Eritrea 640,000 -44.8% 89
Ethiopia Ethiopia 76,550,003 -10.3% 5
Fiji Fiji 210,000 -38.2% 101
Gabon Gabon 520,000 +478% 92
Georgia Georgia 17,639,999 -2.81% 30
Ghana Ghana 3,370,000 -22.5% 55
Guinea Guinea 870,000 -17.1% 81
Gambia Gambia 1,280,000 +14.3% 76
Guinea-Bissau Guinea-Bissau 750,000 -18.5% 85
Guatemala Guatemala 18,900,000 -51.4% 29
Guyana Guyana 310,000 +47.6% 97
Honduras Honduras 3,200,000 -3.03% 58
Haiti Haiti 8,250,000 +46.8% 44
Indonesia Indonesia 3,260,000 -49.2% 56
India India 7,430,000 -29.5% 46
Iran Iran 560,000 -66.7% 90
Iraq Iraq 29,700,001 -34.4% 25
Jamaica Jamaica 90,000 -30.8% 106
Jordan Jordan 5,880,000 -51% 49
Kazakhstan Kazakhstan 310,000 +343% 97
Kenya Kenya 46,950,001 -17.9% 12
Kyrgyzstan Kyrgyzstan 400,000 +60% 94
Cambodia Cambodia 14,470,000 -38.6% 32
Laos Laos 1,950,000 -4.88% 67
Lebanon Lebanon 10,580,000 -0.843% 39
Liberia Liberia 31,910,000 -29.1% 23
Libya Libya 6,210,000 +5.43% 48
Sri Lanka Sri Lanka 2,910,000 +11.5% 60
Lesotho Lesotho 90,000 -66.7% 106
Morocco Morocco 950,000 -24.6% 80
Moldova Moldova 35,279,999 +111% 19
Madagascar Madagascar 4,670,000 +0.43% 51
Mexico Mexico 690,000 -36.1% 87
North Macedonia North Macedonia 8,390,000 -5.62% 43
Mali Mali 36,700,001 -30.7% 17
Myanmar (Burma) Myanmar (Burma) 32,689,999 -21.6% 22
Montenegro Montenegro 1,800,000 +521% 70
Mongolia Mongolia 200,000 +42.9% 102
Mozambique Mozambique 70,970,001 -22.4% 7
Mauritania Mauritania 1,270,000 +30.9% 77
Mauritius Mauritius 0 -100% 111
Malawi Malawi 4,450,000 -13.1% 52
Malaysia Malaysia 860,000 +11.7% 82
Namibia Namibia 1,580,000 -29.5% 71
Niger Niger 10,180,000 -13.5% 40
Nigeria Nigeria 24,840,000 -16% 26
Nicaragua Nicaragua 20,000 -98.7% 109
Nepal Nepal 4,750,000 -15.6% 50
Pakistan Pakistan 12,070,000 +121% 36
Panama Panama 520,000 -13.3% 92
Peru Peru 1,510,000 -32.9% 72
Philippines Philippines 3,530,000 -56.1% 54
Papua New Guinea Papua New Guinea 10,000 0% 110
North Korea North Korea 660,000 -17.5% 88
Paraguay Paraguay 1,000,000 -41.2% 79
Palestinian Territories Palestinian Territories 44,770,000 -36.3% 14
Rwanda Rwanda 30,530,001 +5.75% 24
Sudan Sudan 45,490,002 -43.8% 13
Senegal Senegal 2,050,000 -5.96% 66
Solomon Islands Solomon Islands 40,000 -50% 108
Sierra Leone Sierra Leone 520,000 -50% 92
El Salvador El Salvador 1,820,000 -17.6% 69
Somalia Somalia 81,389,999 -25.9% 4
Serbia Serbia 13,150,000 -10.4% 35
South Sudan South Sudan 74,419,998 -13.1% 6
São Tomé & Príncipe São Tomé & Príncipe 20,000 -33.3% 109
Eswatini Eswatini 500,000 +6.38% 93
Syria Syria 58,630,001 -29.8% 10
Chad Chad 10,070,000 -13.3% 41
Togo Togo 2,060,000 -14.2% 65
Thailand Thailand 2,730,000 -16.5% 61
Tajikistan Tajikistan 370,000 -28.8% 95
Timor-Leste Timor-Leste 640,000 -31.2% 89
Tunisia Tunisia 1,310,000 -48.4% 74
Turkey Turkey 15,640,000 -24.6% 31
Tanzania Tanzania 59,509,998 -28.6% 8
Uganda Uganda 58,980,000 -26.3% 9
Ukraine Ukraine 206,000,000 +514% 1
Uzbekistan Uzbekistan 240,000 +50% 100
Venezuela Venezuela 8,940,000 -37% 42
Vietnam Vietnam 3,150,000 -13.2% 59
Vanuatu Vanuatu 3,150,000 +5,150% 59
Samoa Samoa 40,000 -66.7% 108
Kosovo Kosovo 13,920,000 -19.7% 33
Yemen Yemen 43,279,999 -15.6% 15
South Africa South Africa 6,500,000 -10.3% 47
Zambia Zambia 42,160,000 -31.7% 16
Zimbabwe Zimbabwe 32,930,000 -31.3% 21 

                    
# 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.SWEL.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.SWEL.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))