GDP per capita (current US$)

The indicator of GDP per capita (current US$) serves as a vital measurement of the economic health of a country by calculating the total gross domestic product divided by the population. It essentially provides a per-person economic output, offering insights into how prosperous a nation feels per capita. As of 2023, the global median value for GDP per capita stands at $7,820.23, showcasing a snapshot of wealth distribution and comparative prosperity among nations.

Understanding the importance of GDP per capita cannot be overstated. It acts as a critical barometer for living standards and economic well-being. In countries with high GDP per capita values, citizens typically experience better access to health care, education, and social services. Conversely, nations with lower GDP per capita often battle poverty, poor health care systems, and inadequate education. This indicator offers a simple yet powerful way to compare the economic performance of different countries and regions, and it influences strategic decisions in both public and private sectors globally.

GDP per capita is related to other vital economic indicators. For instance, there is a close relationship between GDP per capita and Human Development Index (HDI), as wealthier nations generally exhibit higher HDI scores, implying better health, education, and living conditions for their populations. Similarly, GDP per capita is often correlated with productivity metrics, as regions with higher productivity levels tend to produce more economic output per person. Furthermore, the connection between GDP per capita and poverty rates is significant; countries with higher GDP per capita levels typically experience lower poverty rates, illustrating the effectiveness of wealth distribution in enhancing life quality.

Several factors impact GDP per capita. Industrialization level plays a crucial role. Highly industrialized countries often possess advanced technologies and efficient production processes, leading to greater economic output per individual. Similarly, natural resources availability can influence a nation's GDP per capita; countries rich in resources like oil or minerals can generate significant income, thereby raising their economic output per person. Political stability and governance are also prominent factors; countries that maintain stability and effective rule of law are likely to attract foreign investment, fostering economic growth and subsequently increasing GDP per capita.

In considering strategies that can improve GDP per capita, several solutions come to mind. Investment in education and skills training is paramount; a well-educated workforce is more productive, which in turn can enhance economic output. Encouraging innovation and technology adoption can also drive economic growth, as businesses that leverage new technologies can produce more efficiently and competitively. Policymakers often focus on improving infrastructure, as efficient transportation and communication can reduce costs and enhance market access—crucial elements for enhancing productivity.

However, despite its widespread use, GDP per capita has its flaws. It does not account for income distribution within a country. Therefore, two nations with the same GDP per capita can provide vastly different standards of living for their citizens, especially if one has high levels of income inequality. Additionally, GDP per capita fails to consider the informal economy, which can also contribute significantly to a country's economic output. This oversight could misrepresent the actual economic conditions faced by individuals in different societies.

In 2023, the highest GDP per capita is observed in Monaco at approximately $256,580.52, showcasing an extraordinary level of wealth among its residents. This is followed closely by Luxembourg, Bermuda, Ireland, and Switzerland, reflecting their robust economies, favorable tax conditions, and well-developed financial services. These areas represent models for high economic performance, often attributed to their strategic policies focusing on attracting investment and fostering technology and innovation.

Conversely, at the lower end of the spectrum, we find countries such as Burundi ($193.01), Afghanistan ($415.71), and Yemen ($477.41), struggling with various socio-economic challenges. The central African nations like the Central African Republic and Madagascar also illustrate the dire circumstances in which many reside, with GDP per capita figures below $500. These stark contrasts highlight the global economic divide, as some countries flourish while others confront economic distress.

A historical perspective on GDP per capita shows remarkable growth over time. In 1960, the global GDP per capita was only $454.06, indicating a relatively undeveloped global economy. Fast forward to 2023, and the figure has soared to $13,169.60, demonstrating considerable progress, especially in the last few decades. Observing this growth, one can trace a timeline of increasing industrialization, technological advancements, and globalization influences that have collectively enhanced economic productivity worldwide.

GDP per capita remains a fundamental economic indicator, reflecting not just the wealth of individuals in a country, but also broader trends of economic development and disparity. By understanding its nuances, factors influencing it, and relationships to other economic indicators, we can develop more targeted strategies to enhance living standards globally. As we move forward, continuous analysis and application of this metric can help policymakers craft solutions that address not just the wealth itself, but the equitable distribution of resources that supports 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.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.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))