IDA resource allocation index (1=low to 6=high)

Source: worldbank.org, 01.09.2025

Year: 2024

Flag Country Value Value change, % Rank
Afghanistan Afghanistan 1.7 +2.51% 62
Burundi Burundi 3.07 0% 39
Benin Benin 3.93 +0.641% 4
Burkina Faso Burkina Faso 3.4 -0.73% 23
Bangladesh Bangladesh 3.11 -0.267% 38
Belize Belize 3.19 34
Bhutan Bhutan 3.78 +0.667% 9
Central African Republic Central African Republic 2.6 +0.322% 56
Côte d’Ivoire Côte d’Ivoire 3.9 +2.41% 5
Cameroon Cameroon 3.37 +1.25% 25
Congo - Kinshasa Congo - Kinshasa 3.13 -1.31% 36
Congo - Brazzaville Congo - Brazzaville 2.87 0% 47
Comoros Comoros 2.69 +0.937% 54
Cape Verde Cape Verde 3.88 0% 6
Djibouti Djibouti 3.13 0% 37
Dominica Dominica 3.51 -3.88% 20
Eritrea Eritrea 1.69 0% 63
Ethiopia Ethiopia 3.19 +1.32% 34
Fiji Fiji 3.57 +3.13% 16
Micronesia (Federated States of) Micronesia (Federated States of) 2.82 +1.5% 51
Ghana Ghana 3.41 0% 22
Guinea Guinea 3.33 -1.97% 26
Gambia Gambia 3.21 +2.39% 33
Guinea-Bissau Guinea-Bissau 2.63 +2.6% 55
Grenada Grenada 3.73 0% 12
Guyana Guyana 3.23 -2.03% 32
Honduras Honduras 3.31 +0.761% 27
Haiti Haiti 2.3 +3.76% 57
Kenya Kenya 3.85 +0.435% 7
Kyrgyzstan Kyrgyzstan 3.53 -1.17% 19
Cambodia Cambodia 3.55 -0.467% 18
Kiribati Kiribati 2.87 -2.27% 47
Laos Laos 3 +0.559% 43
Liberia Liberia 3.06 +2.23% 40
St. Lucia St. Lucia 3.73 +0.448% 11
Sri Lanka Sri Lanka 3.23 +3.2% 32
Lesotho Lesotho 3.27 0% 30
Madagascar Madagascar 3.28 +0.769% 29
Maldives Maldives 3.02 -2.95% 42
Marshall Islands Marshall Islands 2.69 +1.57% 54
Mali Mali 3.13 -2.6% 37
Myanmar (Burma) Myanmar (Burma) 1.94 -2.1% 59
Mozambique Mozambique 3.05 -2.66% 41
Mauritania Mauritania 3.58 +0.467% 15
Malawi Malawi 2.96 -2.2% 45
Niger Niger 3.15 -6.2% 35
Nigeria Nigeria 3.21 +0.522% 33
Nicaragua Nicaragua 3.05 -5.43% 41
Nepal Nepal 3.39 +0.743% 24
Pakistan Pakistan 3.31 0% 27
Papua New Guinea Papua New Guinea 2.85 0% 49
Rwanda Rwanda 4.16 +1.01% 1
Sudan Sudan 1.82 -9.17% 61
Senegal Senegal 3.62 -2.25% 13
Solomon Islands Solomon Islands 2.96 +1.43% 45
Sierra Leone Sierra Leone 3.07 -4.17% 39
Somalia Somalia 2.24 +0.749% 58
South Sudan South Sudan 1.63 -2.5% 64
São Tomé & Príncipe São Tomé & Príncipe 2.95 +0.855% 46
Suriname Suriname 2.79 52
Eswatini Eswatini 3.13 36
Chad Chad 2.75 +1.85% 53
Togo Togo 3.77 0% 10
Tajikistan Tajikistan 3.19 0% 34
Timor-Leste Timor-Leste 2.83 +1.49% 50
Tonga Tonga 3.48 +0.723% 21
Tuvalu Tuvalu 2.86 0% 48
Tanzania Tanzania 3.56 +1.43% 17
Uganda Uganda 3.51 -1.17% 20
Uzbekistan Uzbekistan 3.84 0% 8
St. Vincent & Grenadines St. Vincent & Grenadines 3.59 -1.82% 14
Vanuatu Vanuatu 3.24 -1.77% 31
Samoa Samoa 3.95 -0.211% 2
Kosovo Kosovo 3.94 +3.5% 3
Yemen Yemen 1.84 0% 60
Zambia Zambia 3.28 +1.29% 28
Zimbabwe Zimbabwe 2.98 0% 44 

                    
# 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 = 'IQ.CPA.IRAI.XQ'

# 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 <- 'IQ.CPA.IRAI.XQ'

# 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))