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

Source: worldbank.org, 03.09.2025

Year: 2022

Flag Country Value Value change, % Rank
Afghanistan Afghanistan 11,180,000 +20% 20
Angola Angola -7,600,000 -141% 91
Albania Albania -360,000 -2.7% 77
Argentina Argentina -6,130,000 -174% 90
Azerbaijan Azerbaijan 20,000 +100% 72
Burundi Burundi 940,000 -47.8% 51
Benin Benin 400,000 +53.8% 62
Burkina Faso Burkina Faso 2,320,000 -47.6% 41
Bangladesh Bangladesh 11,150,000 +932% 21
Bosnia & Herzegovina Bosnia & Herzegovina -9,970,000 -20.9% 92
Belarus Belarus 10,000 0% 73
Belize Belize 30,000 71
Bolivia Bolivia 28,900,000 -31.9% 7
Brazil Brazil 5,200,000 -25% 33
Bhutan Bhutan 1,660,000 +16,500% 43
Central African Republic Central African Republic 1,030,000 +47.1% 50
China China -27,120,001 -3.97% 96
Côte d’Ivoire Côte d’Ivoire 1,130,000 -90.2% 49
Cameroon Cameroon -1,780,000 +79.8% 85
Congo - Kinshasa Congo - Kinshasa 10,690,000 +73.3% 24
Congo - Brazzaville Congo - Brazzaville 220,000 -79.2% 64
Colombia Colombia 72,330,002 -12.7% 2
Cape Verde Cape Verde -600,000 -190% 79
Costa Rica Costa Rica 14,260,000 +127% 17
Cuba Cuba 10,910,000 +69.7% 22
Djibouti Djibouti -130,000 -89.3% 74
Dominican Republic Dominican Republic 3,650,000 +135% 36
Algeria Algeria -1,430,000 -89.5% 84
Ecuador Ecuador 18,209,999 +97.7% 10
Egypt Egypt 9,910,000 -74.7% 26
Ethiopia Ethiopia 9,760,000 -18.4% 27
Gabon Gabon 60,000 -14.3% 69
Georgia Georgia 20,000 +100% 72
Ghana Ghana -4,670,000 +328% 88
Guinea Guinea 540,000 +86.2% 59
Gambia Gambia 450,000 +2.27% 60
Guinea-Bissau Guinea-Bissau 1,710,000 -58.7% 42
Equatorial Guinea Equatorial Guinea 2,580,000 -13.4% 40
Guatemala Guatemala 39,060,001 +11% 3
Guyana Guyana 540,000 +170% 59
Honduras Honduras 14,530,000 +47.4% 16
Haiti Haiti 8,710,000 -130% 29
Indonesia Indonesia -14,720,000 -1.41% 94
India India 5,990,000 -21.5% 31
Iran Iran 14,750,000 +2,358% 14
Iraq Iraq 390,000 -4.88% 63
Jamaica Jamaica 60,000 -91.7% 69
Jordan Jordan 3,610,000 +117% 37
Kazakhstan Kazakhstan 60,000 0% 69
Kenya Kenya -2,420,000 -1,713% 86
Cambodia Cambodia -5,900,000 -42.8% 89
Laos Laos 630,000 +3,050% 55
Lebanon Lebanon 8,890,000 +33.9% 28
Liberia Liberia 60,000 -14.3% 69
Libya Libya 440,000 +214% 61
St. Lucia St. Lucia 40,000 -42.9% 70
Sri Lanka Sri Lanka 40,000 -103% 70
Morocco Morocco 5,760,000 +49.6% 32
Moldova Moldova 2,670,000 +13,250% 39
Madagascar Madagascar 70,000 +133% 68
Mexico Mexico 12,610,000 +152% 19
North Macedonia North Macedonia -310,000 -16.2% 76
Mali Mali 16,100,000 -7.1% 11
Montenegro Montenegro -470,000 -11.3% 78
Mongolia Mongolia -650,000 -40.4% 80
Mozambique Mozambique 15,990,000 +1.07% 12
Mauritania Mauritania 4,200,000 -67.2% 34
Malawi Malawi 560,000 +3.7% 58
Malaysia Malaysia -230,000 0% 75
Namibia Namibia -1,050,000 -16.7% 82
Niger Niger 13,780,000 +27.2% 18
Nigeria Nigeria 30,530,001 +1,685% 5
Nicaragua Nicaragua 15,880,000 -47% 13
Nepal Nepal 90,000 -47.1% 67
Pakistan Pakistan 610,000 +1,933% 56
Panama Panama -1,370,000 -60.5% 83
Peru Peru 29,049,999 -4.94% 6
Philippines Philippines 1,620,000 -74.6% 44
Paraguay Paraguay 7,060,000 -64.9% 30
Palestinian Territories Palestinian Territories 31,629,999 -13.8% 4
Rwanda Rwanda 1,190,000 -88.3% 48
Sudan Sudan 10,730,000 -33.4% 23
Senegal Senegal 20,500,000 -15.4% 9
Sierra Leone Sierra Leone 400,000 -45.2% 62
El Salvador El Salvador 28,170,000 -15.9% 8
Somalia Somalia 640,000 +106% 54
Serbia Serbia 590,000 -18.1% 57
South Sudan South Sudan 1,340,000 +6.35% 47
São Tomé & Príncipe São Tomé & Príncipe 10,000 0% 73
Syria Syria 8,710,000 -15.5% 29
Chad Chad 3,730,000 -47.5% 35
Togo Togo 840,000 -27.6% 53
Thailand Thailand 140,000 +100% 65
Timor-Leste Timor-Leste 400,000 62
Tonga Tonga 20,000 -86.7% 72
Tunisia Tunisia -15,500,000 -9.14% 95
Turkey Turkey -11,100,000 -20.9% 93
Tanzania Tanzania 1,470,000 -5.16% 45
Uganda Uganda 14,640,000 -35.8% 15
Ukraine Ukraine 98,910,004 +141,200% 1
Uzbekistan Uzbekistan -710,000 -3,650% 81
Venezuela Venezuela 10,190,000 +37.9% 25
Vietnam Vietnam -3,240,000 -10.7% 87
Kosovo Kosovo 870,000 +8,600% 52
Yemen Yemen 1,410,000 +36.9% 46
South Africa South Africa 100,000 +42.9% 66
Zambia Zambia 3,080,000 -2.22% 38
Zimbabwe Zimbabwe 450,000 +246% 60 

                    
# 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.ESPL.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.ESPL.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))