GDP per capita growth (annual %)

The indicator of GDP per capita growth (annual %) is a critical measure used to assess the economic health of a country or region. It reflects the annual percentage change in GDP per capita, which is derived by dividing the gross domestic product (GDP) by the total population. By evaluating this indicator, one can ascertain how much individual economic output is growing over time, offering insights into the standard of living and overall economic well-being of the population. In 2023, the global median value for GDP per capita growth stands at 1.92%, suggesting modest economic growth across various regions.

Understanding the importance of GDP per capita growth is essential for various stakeholders, including policymakers, economists, and international organizations. A higher GDP per capita growth rate typically correlates with improved living standards, better access to healthcare and education, and overall quality of life for citizens. On the opposite end, stagnant or declining growth rates might indicate economic difficulties, leading to heightened poverty levels and reduced public services. When closely monitored, GDP per capita growth can act as a barometer for future investment opportunities, economic strategies, and social momentum.

This indicator is often related to several other key economic indicators, such as unemployment rates, inflation rates, and income distribution. For instance, rising GDP per capita growth usually coincides with declining unemployment numbers, as businesses expand and create more job opportunities. Furthermore, a closer inspection of income distribution reveals that while GDP per capita might rise, its growth may not be equally experienced among different income groups, leading to inequality. Therefore, observing GDP per capita growth alongside other metrics can provide a more comprehensive view of economic conditions.

Several factors can affect GDP per capita growth. Economic policies and political stability play dominant roles, as uncertainty and poor governance can deter investment and stifle growth. Additionally, external factors such as global economic trends, changes in commodity prices, and trade relationships significantly influence a country's GDP performance. For example, the top-performing regions in 2023, like Macao SAR China at 74.67% and Guyana at 33.04%, likely benefit from unique economic conditions, such as booming industries or favorable trade agreements, contributing to their exceptional growth rates.

Conversely, regions facing economic distress, such as Timor-Leste (-19.0%), Sudan (-14.3%), and Kuwait (-8.88%), experience negative GDP per capita growth. These declines can stem from various issues, including political instability, conflict, economic mismanagement, and global market shifts affecting local economies. The fluctuation between the top and bottom performers underscores the disparity in economic resilience and growth strategies across diverse contexts.

To enhance GDP per capita growth, countries can implement several strategies. Investing in infrastructure boosts productivity by improving logistics and transportation, while education and workforce development programs empower individuals with skills necessary for high-demand jobs. Moreover, governments can foster innovation and entrepreneurship by providing access to capital, reducing regulatory barriers, and encouraging the development of new technologies. Involving public-private partnerships can create synergies that enhance economic potential.

Despite the positive implications of GDP per capita growth, the indicator does present some flaws. It does not account for income inequality; thus, a country might report increased per capita GDP while the wealth remains concentrated within a small population segment. Moreover, it overlooks environmental factors, failing to consider the sustainable use of resources in generating economic growth. Relying solely on this metric without contextualizing it can lead policymakers to adopt strategies that may not be beneficial for the long-term economic health of a nation.

Examining historical data further aids in understanding the trajectory of GDP per capita growth. Between 1961 and recent years, the world has seen fluctuations from as high as 4.37% in 1964 to a dramatic downturn of -3.86% in 2020, likely a consequence of the COVID-19 pandemic. Such volatility reflects the interconnectedness of global economies. In recent years, the recovery is palpable, as indicated by the growth rates of 5.48% in 2021 and 2.35% in 2022. The 1.92% growth in 2023 signals continued recovery, though at a slow pace, amidst global uncertainties.

In summary, GDP per capita growth (annual %) serves as a pivotal indicator of economic performance and individual prosperity across nations. Monitoring this metric, alongside related indicators, is crucial for understanding the broader economic landscape and implementing effective policies. By appreciating the complexities surrounding GDP per capita growth, stakeholders can strategize ways to overcome barriers, reverse negative trends, and enhance economic potential for a more equitable and prosperous future.

                    
# 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.KD.ZG'

# 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.KD.ZG'

# 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))