CPIA economic management cluster average (1=low to 6=high)

Source: worldbank.org, 01.09.2025

Year: 2024

Flag Country Value Value change, % Rank
Afghanistan Afghanistan 1.33 +14.3% 19
Burundi Burundi 3 +5.88% 10
Benin Benin 4.17 0% 3
Burkina Faso Burkina Faso 3.67 +4.76% 6
Bangladesh Bangladesh 3.5 +5% 7
Belize Belize 2.83 11
Bhutan Bhutan 3.67 0% 6
Central African Republic Central African Republic 3 -5.26% 10
Côte d’Ivoire Côte d’Ivoire 4.17 +4.17% 3
Cameroon Cameroon 3.83 +4.55% 5
Congo - Kinshasa Congo - Kinshasa 3.67 -4.35% 6
Congo - Brazzaville Congo - Brazzaville 3 0% 10
Comoros Comoros 2.67 0% 12
Cape Verde Cape Verde 3.5 0% 7
Djibouti Djibouti 2.83 0% 11
Dominica Dominica 3.83 0% 5
Eritrea Eritrea 1.5 0% 18
Ethiopia Ethiopia 3 +5.88% 10
Fiji Fiji 3.33 +5.26% 8
Micronesia (Federated States of) Micronesia (Federated States of) 2.83 +6.25% 11
Ghana Ghana 2.83 0% 11
Guinea Guinea 3.67 -4.35% 6
Gambia Gambia 3.17 +18.7% 9
Guinea-Bissau Guinea-Bissau 3 +5.88% 10
Grenada Grenada 4 0% 4
Guyana Guyana 3.5 0% 7
Honduras Honduras 3.5 0% 7
Haiti Haiti 2.67 +23.1% 12
Kenya Kenya 4.17 +4.17% 3
Kyrgyzstan Kyrgyzstan 4 0% 4
Cambodia Cambodia 4 -4% 4
Kiribati Kiribati 2.5 -6.25% 13
Laos Laos 2.67 +6.67% 12
Liberia Liberia 3.33 0% 8
St. Lucia St. Lucia 3.67 +4.76% 6
Sri Lanka Sri Lanka 2.5 +25% 13
Lesotho Lesotho 3.17 0% 9
Madagascar Madagascar 3.67 0% 6
Maldives Maldives 1.67 -9.09% 17
Marshall Islands Marshall Islands 2.5 0% 13
Mali Mali 3.83 0% 5
Myanmar (Burma) Myanmar (Burma) 1.83 -8.33% 16
Mozambique Mozambique 2.83 -10.5% 11
Mauritania Mauritania 4 +4.35% 4
Malawi Malawi 2 -7.69% 15
Niger Niger 3.33 -9.09% 8
Nigeria Nigeria 3.67 +4.76% 6
Nicaragua Nicaragua 3.83 -4.17% 5
Nepal Nepal 3.33 0% 8
Pakistan Pakistan 3.17 0% 9
Papua New Guinea Papua New Guinea 2.67 0% 12
Rwanda Rwanda 4 0% 4
Sudan Sudan 1.83 0% 16
Senegal Senegal 3.33 -9.09% 8
Solomon Islands Solomon Islands 3.17 +5.56% 9
Sierra Leone Sierra Leone 2.83 -5.56% 11
Somalia Somalia 2.5 +7.14% 13
South Sudan South Sudan 1.5 -10% 18
São Tomé & Príncipe São Tomé & Príncipe 2.33 0% 14
Suriname Suriname 3 10
Eswatini Eswatini 3.17 9
Chad Chad 3.17 0% 9
Togo Togo 3.83 0% 5
Tajikistan Tajikistan 3.33 0% 8
Timor-Leste Timor-Leste 2.83 0% 11
Tonga Tonga 3.33 0% 8
Tuvalu Tuvalu 2.67 0% 12
Tanzania Tanzania 4.17 0% 3
Uganda Uganda 3.83 0% 5
Uzbekistan Uzbekistan 4.17 0% 3
St. Vincent & Grenadines St. Vincent & Grenadines 3.67 0% 6
Vanuatu Vanuatu 3.33 -9.09% 8
Samoa Samoa 4.33 +4% 2
Kosovo Kosovo 4.5 +8% 1
Yemen Yemen 1.67 0% 17
Zambia Zambia 3 +5.88% 10
Zimbabwe Zimbabwe 2.5 0% 13 

                    
# 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.ECON.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.ECON.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))