PPG, IDA (DOD, current US$)

Source: worldbank.org, 03.09.2025

Year: 2023

Flag Country Value Value change, % Rank
Afghanistan Afghanistan 297,385,000 -1.99% 56
Angola Angola 340,001,000 -13.2% 55
Albania Albania 390,321,000 -9.8% 53
Armenia Armenia 735,143,000 -7.61% 37
Azerbaijan Azerbaijan 159,167,000 -8.21% 69
Burundi Burundi 118,000,000 -2.98% 76
Benin Benin 1,976,654,000 +27.1% 25
Burkina Faso Burkina Faso 2,417,008,000 +10.5% 20
Bangladesh Bangladesh 19,772,007,000 +8.48% 1
Bosnia & Herzegovina Bosnia & Herzegovina 639,280,000 -15.3% 41
Bolivia Bolivia 724,079,000 -0.121% 38
Bhutan Bhutan 438,353,000 +12.4% 50
Botswana Botswana 45,000 -66.7% 97
Central African Republic Central African Republic 124,222,000 -1.15% 75
China China 107,525,000 -60.1% 77
Côte d’Ivoire Côte d’Ivoire 4,031,392,000 +34.4% 13
Cameroon Cameroon 2,081,069,000 +11.3% 24
Congo - Kinshasa Congo - Kinshasa 2,996,855,000 +41% 18
Congo - Brazzaville Congo - Brazzaville 462,430,000 +17.8% 47
Comoros Comoros 45,359,000 +37.1% 84
Cape Verde Cape Verde 562,465,000 +10.5% 45
Djibouti Djibouti 273,621,000 +19.1% 58
Dominica Dominica 158,173,000 +30.3% 70
Ecuador Ecuador 0 -100% 98
Egypt Egypt 131,635,000 -31% 73
Eritrea Eritrea 419,255,000 +0.845% 52
Ethiopia Ethiopia 11,983,593,000 +6.89% 6
Fiji Fiji 296,486,000 +2.08% 57
Georgia Georgia 598,987,000 -14.9% 42
Ghana Ghana 5,393,107,000 +13.6% 9
Guinea Guinea 655,180,000 +6.29% 40
Gambia Gambia 128,488,000 +2.15% 74
Guinea-Bissau Guinea-Bissau 213,791,000 +14.3% 65
Grenada Grenada 235,826,000 +18.1% 62
Guyana Guyana 172,444,000 +46.8% 67
Honduras Honduras 970,213,000 +3.68% 31
Indonesia Indonesia 254,802,000 -42.1% 61
India India 17,111,892,000 -9.03% 3
Iraq Iraq 225,529,000 -6.06% 64
Jordan Jordan 187,680,000 +16.5% 66
Kenya Kenya 11,379,906,000 +8.66% 7
Kyrgyzstan Kyrgyzstan 720,807,000 +13.2% 39
Cambodia Cambodia 1,315,356,000 +40.5% 28
Laos Laos 835,672,000 +10.5% 33
Lebanon Lebanon 87,552,000 +1.81% 82
Liberia Liberia 827,972,000 +17.7% 34
St. Lucia St. Lucia 170,257,000 +10.6% 68
Sri Lanka Sri Lanka 3,394,103,000 +14.5% 15
Lesotho Lesotho 456,706,000 +7.02% 48
Morocco Morocco 294,000 -40.1% 94
Moldova Moldova 807,289,000 +3.18% 35
Madagascar Madagascar 2,387,771,000 +16.9% 21
Maldives Maldives 97,695,000 +2.63% 78
North Macedonia North Macedonia 140,642,000 -9.65% 72
Mali Mali 2,165,789,000 +6.53% 23
Myanmar (Burma) Myanmar (Burma) 1,565,920,000 -4.6% 26
Montenegro Montenegro 7,349,000 -41.8% 91
Mongolia Mongolia 779,641,000 +2.11% 36
Mozambique Mozambique 3,010,343,000 +0.895% 17
Mauritania Mauritania 424,024,000 +10.2% 51
Mauritius Mauritius 53,000 -81.2% 96
Malawi Malawi 1,549,633,000 +17.5% 27
Niger Niger 2,460,811,000 +6.53% 19
Nigeria Nigeria 15,126,588,000 +12% 4
Nicaragua Nicaragua 915,466,000 +3.37% 32
Nepal Nepal 4,367,464,000 +5.12% 11
Pakistan Pakistan 17,541,466,000 +10.2% 2
Philippines Philippines 11,213,000 -18.1% 89
Papua New Guinea Papua New Guinea 565,602,000 +3.81% 44
Paraguay Paraguay 868,000 -39.6% 92
Rwanda Rwanda 3,270,212,000 +21.3% 16
Sudan Sudan 258,971,000 -12% 60
Senegal Senegal 4,357,273,000 +19% 12
Solomon Islands Solomon Islands 78,991,000 +36.2% 83
Sierra Leone Sierra Leone 479,349,000 +2.85% 46
El Salvador El Salvador 720,000 -33.3% 93
Somalia Somalia 93,510,000 -10.6% 81
Serbia Serbia 31,074,000 -52.4% 87
São Tomé & Príncipe São Tomé & Príncipe 9,631,000 -2.31% 90
Eswatini Eswatini 149,000 -50% 95
Syria Syria 14,052,000 0% 88
Chad Chad 150,518,000 -3.13% 71
Togo Togo 575,065,000 +59.7% 43
Tajikistan Tajikistan 374,576,000 +1.61% 54
Timor-Leste Timor-Leste 41,807,000 +7.84% 85
Tonga Tonga 41,508,000 +1.82% 86
Tunisia Tunisia 0 -100% 98
Tanzania Tanzania 10,988,591,000 +19.1% 8
Uganda Uganda 4,675,099,000 +4.74% 10
Ukraine Ukraine 1,047,502,000 +81.3% 30
Uzbekistan Uzbekistan 3,603,152,000 +21.8% 14
St. Vincent & Grenadines St. Vincent & Grenadines 229,205,000 +6.96% 63
Vietnam Vietnam 12,347,560,000 -4.19% 5
Vanuatu Vanuatu 96,033,000 -2.22% 80
Samoa Samoa 96,336,000 -2.83% 79
Kosovo Kosovo 272,972,000 +16.9% 59
Yemen Yemen 1,154,693,000 -5.71% 29
Zambia Zambia 2,220,849,000 +8.49% 22
Zimbabwe Zimbabwe 442,730,000 +0.789% 49 

                    
# 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 = 'DT.DOD.MIDA.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 <- 'DT.DOD.MIDA.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))