GDP per capita, PPP (current international US$)

The Gross Domestic Product (GDP) per capita, measured in Purchasing Power Parity (PPP) using current international dollars, serves as an important economic indicator that reflects the average economic output per person in a given area, adjusted for price level differences across countries. This indicator provides a more accurate representation of living standards than nominal GDP per capita as it takes into account the relative cost of living and inflation rates. In 2023, the global median value for GDP per capita, PPP, stands at approximately $19,581.34. This figure paints a picture not only of economic productivity but also of potential access to goods and services.

The significance of GDP per capita, PPP cannot be overstated; it is vital in understanding economic health and human welfare. Policymakers often use this statistic to make comparisons between countries, informing development strategies and investment opportunities. Higher GDP per capita, PPP values typically correlate with higher living standards and better access to education and healthcare. For instance, in 2023, the top five areas showcasing GDP per capita, PPP include Singapore ($141,553.47), Luxembourg ($139,105.96), Qatar ($128,918.55), Ireland ($124,578.18), and Macao SAR China ($116,491.07). These nations exhibit incredibly high figures that directly relate to their economic policies, workforce education, and technological advancements.

Conversely, the bottom five areas reveal alarming statistics that reflect poverty and economic hardship: Burundi ($919.91), the Central African Republic ($1,259.82), Somalia ($1,556.52), Congo - Kinshasa ($1,615.75), and Mozambique ($1,677.68). The stark contrast between these figures and those of wealthier regions underlines the global disparity in economic development. Such discrepancies can fuel migration claims, social unrest, and calls for international aid as nations grapple with inequality.

GDP per capita, PPP also relates closely to other economic and social indicators. For instance, it often correlates with levels of education, health outcomes, and overall life satisfaction. Countries with higher GDP per capita frequently enjoy better health care systems, lower infant mortality rates, and higher life expectancy. Furthermore, it encourages investment in infrastructure, technology, and innovation, setting off a virtuous cycle of improvement. However, this relationship isn’t perfect. While GDP per capita, PPP can indicate progress, it might mask underlying issues like income inequality and regional disparities within high-performing countries.

Several factors can affect a nation's GDP per capita, PPP, including government policies, natural resources, education, and infrastructure development. Effective governance and stable political environments often contribute positively to economic outcomes. Countries rich in natural resources may experience booms that temporarily inflate their GDP per capita, but such growth can be unsustainable if not managed properly. On the other hand, nations with strong education systems and robust infrastructures can create a conducive environment for long-term economic growth.

To improve GDP per capita, PPP in lower-performing areas, several strategies can be employed. These include investing in education and workforce training, improving infrastructure, fostering entrepreneurship, and enhancing access to technology. Additionally, foreign aid and investment can play significant roles in bolstering the economies of developing nations. Substantial investment in healthcare can also yield dividends, as healthier populations are more productive and engaged in the workforce.

Nonetheless, the GDP per capita, PPP as an indicator isn’t without flaws. It does not measure income distribution, so a high GDP per capita can exist without equitable wealth distribution, misleading the interpretation of economic well-being. Furthermore, this measure may overlook non-market transactions and informal economies, particularly in developing nations where subsistence farming and barter systems are prevalent. While GDP per capita, PPP remains a crucial tool for economic analysis, it should be utilized alongside other social and economic indicators to offer a comprehensive view of a country’s prosperity.

Historically, the trend of GDP per capita, PPP reveals consistent growth over the years, from $5,578.13 in 1990 to $22,836.96 in 2023, illustrating global economic progress. This upward trajectory, especially notable after crises, indicates resilience and recovery in the global economy. Data shows fluctuations in growth rates influenced by global events like the 2008 financial crisis and the COVID-19 pandemic, affecting even wealthier nations. The significant increase from $17,725.79 in 2020 to over $22,000 in 2023 suggests a robust recovery phase.

In summary, GDP per capita, PPP is a vital economic indicator that sheds light on individual prosperity and overall economic health. While it offers a lens into living standards and economic performance, it needs to be balanced with other factors to gain a well-rounded view of a nation’s well-being. As we navigate a world of economic disparities, understanding the nuances of GDP per capita, PPP can facilitate more informed policy-making and targeted strategies for sustainable development.

                    
# 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 = 'NY.GDP.PCAP.PP.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 <- 'NY.GDP.PCAP.PP.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))