GDP, PPP (current international US$)

The indicator 'GDP, PPP (current international $)' represents the gross domestic product adjusted for purchasing power parity (PPP). It denotes the total value of all goods and services produced within a country's borders in a given year, while considering the relative cost of living and inflation rates. This provides a more accurate reflection of the economic stature of countries compared to nominal GDP, which does not account for price level differences between nations.

The importance of GDP at PPP lies in its ability to provide a more uniform basis for comparing economic productivity and standards of living across different nations. For example, even though some countries may have a lower nominal GDP, their GDP at PPP might reflect similar or even higher levels of economic productivity and potential consumer purchasing power due to lower cost structures. Therefore, it is a critical indicator for economists, policymakers, and businesses engaged in international markets.

Various relations exist between GDP at PPP and other economic indicators. It indirectly influences social factors such as income distribution, quality of life, and economic equality measures. For instance, a country with a higher GDP at PPP may showcase an improved standard of living, evidenced by higher consumption levels, access to health care, and educational facilities compared to countries with lower GDP at PPP values. The GDP at PPP also correlates with employment rates, as dynamic economies tend to create more job opportunities, leading to increased productivity.

Several factors affect GDP at PPP. Exchange rates play a significant role, as fluctuations can either inflate or deflate the apparent economic capacity of a nation. Economic policies regarding taxation, trade, and government spending also influence this metric. Additionally, demographic factors such as population size and growth rates impact GDP at PPP, as countries with larger populations may produce more goods and services if productivity is optimized. Furthermore, technological innovation can drive efficiency and productivity, which in turn can elevate GDP at PPP.

To enhance GDP at PPP, governments can implement a variety of strategies. These include investing in education to improve workforce skills, fostering innovation and technological advancements, and ensuring stable political and economic environments that encourage foreign investments. Moreover, addressing infrastructure issues can significantly improve productivity, thus positively affecting GDP at PPP. Additionally, engaging in international trade can broaden market access, allowing countries to leverage their competitive advantages to boost overall economic output.

While GDP at PPP is a useful indicator, it does have its flaws. It may obscure disparities in wealth and living conditions within countries, as the average per capita could hide significant intra-national inequalities. Additionally, the measure relies on purchasing power estimates that may not always be accurate, particularly in countries with volatile economies. Another issue lies in the interpretation of the data; GDP at PPP does not account for environmental sustainability and may overlook the shadow economies present in different regions, leading to potentially inflated assessments of economic health.

As of 2023, the median value of global GDP at PPP stands at approximately 100.65 trillion international dollars. The global economy has seen a remarkable rise since 1990 when the GDP at PPP was around 29.56 trillion dollars. This eight-fold increase within over three decades highlights a significant transformation in global economic dynamics. Countries like China, which leads with a staggering GDP at PPP of approximately 34.66 trillion dollars, and the United States, with around 27.72 trillion dollars, exemplify this growth. India is on a notable rising trajectory as well, with its GDP at PPP recorded at about 14.62 trillion dollars, indicating substantial economic development.

In contrast, the bottom five areas in terms of GDP at PPP clearly reflect the disparity present worldwide. Regions like Tuvalu with just over 60.38 million dollars, or Nauru at 164.25 million dollars, have limited economic activities that result in significantly lower GDP at PPP values. Such stark contrasts illustrate the varying levels of economic development and the challenges these smaller regions face in enhancing their economic performance.

The increase in world values from 29.56 trillion dollars in 1990 to approximately 184.11 trillion dollars in 2023 indicates a resilient, increasingly interconnected global economy. The steady annual growth along the years signifies advancements in productivity, globalization, and overall improvement in living standards in several regions.

In conclusion, GDP at PPP serves as a fundamental barometer for assessing and comparing the economic health of nations, yet it requires nuanced interpretation alongside other qualitative measures to provide a well-rounded view of global economic conditions and inequalities.

                    
# 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.MKTP.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.MKTP.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))