GNI per capita, Atlas method (current US$)

Gross National Income (GNI) per capita, measured using the Atlas method, is a vital indicator that provides insight into the economic performance and general well-being of residents within a country. GNI per capita reflects the average income earned per person in a given country, calculated by taking the total GNI and dividing it by the population. The Atlas method smooths exchange rate fluctuations, offering a more stable view of income levels in current US dollars, making it particularly useful for international comparisons.

The importance of GNI per capita lies in its ability to convey information about the economic health of countries. It serves as a proxy for wealth and living standards, influencing policy decisions, international aid, economic planning, and investment strategies. By examining GNI per capita, stakeholders can gauge economic vitality, identify trends that may indicate growth or recession, and assess the economic discrepancies between different nations.

GNI per capita is closely linked to other economic indicators, such as GDP per capita and Human Development Index (HDI). While GDP per capita measures the total economic output per person and is indicative of production capacity, GNI per capita provides insight into the income residents receive, including from overseas investments. The HDI, on the other hand, incorporates health and educational data, recognizing that income alone does not tell the full story of a nation's development or well-being. A high GNI per capita might coexist with low HDI if wealth is unevenly distributed, highlighting social inequalities within a country.

Several factors influence GNI per capita, including economic structure, employment rates, education levels, and external trade relationships. Countries with diversified economies, robust educational systems, and high employment rates typically show higher GNI figures. Conversely, regions suffering from political instability, corruption, or economic mismanagement often experience low GNI per capita. Unexpected global disruptions, such as the COVID-19 pandemic, can also drastically affect income levels, as evidenced by the economic downturn and recovery trajectories observed in various countries between 2020 and 2023.

To improve GNI per capita, nations may adopt several strategies, including investing in education to build a skilled workforce, fostering a favorable business environment that encourages investment, and pursuing trade partnerships that enhance market access. Infrastructure development also plays a crucial role, as improved transportation and communication networks can enhance productivity and facilitate economic activity. Additionally, policies aimed at reducing income inequality can create a more equitable environment that supports overall economic growth.

Despite its significance, GNI per capita is not without flaws. Primarily, it does not account for income distribution, meaning a high GNI per capita could mask significant income inequality, leading to misleading conclusions about a population's well-being. Additionally, the GNI figures may not accurately reflect the informal economy, which can be substantial in certain regions, especially in developing countries. This omission can skew the understanding of actual living standards and economic participation rates.

For context, the latest data indicates that the global median GNI per capita in 2023 is approximately $6,990. This median position underscores the existing economic disparity across different regions. For example, the top five regions with the highest GNI per capita include Bermuda at an astonishing $130,290, which is indicative of a highly developed, service-oriented economy. Norway, Switzerland, Luxembourg, and the United States follow, each with GNI per capita figures exceeding $80,000. Such figures reflect the long-standing stability and affluence in these economies, providing ample resources for health, education, and infrastructure.

Conversely, the bottom five regions present a stark contrast. Countries like Burundi, Afghanistan, Madagascar, the Central African Republic, and Mozambique have GNI per capita figures ranging from $220 to $540. These numbers indicate severe economic struggles, typically characterized by inadequate access to basic services, high poverty rates, and limited economic diversification. The gap between these nations and their more affluent counterparts underscores the pressing need for both local and international efforts to raise living standards and drive economic development.

Looking at the historical trajectory from 1962 to 2023 shows that the world's GNI per capita has significantly increased from approximately $492.24 to $13,179.37, illustrating a long-term trend of economic growth globally. However, the growth has not been uniform; while developed nations have steadily increased their figures, many developing and war-torn countries continue to lag years behind. The fluctuations seen in the data, such as the decline in 2020 due to the pandemic, serve as a reminder that global events can dramatically impact economic indicators.

In summary, GNI per capita, measured using the Atlas method, is an essential economic indicator that captures income levels while allowing for international comparisons. Although it has its limitations, understanding GNI per capita alongside other indicators can provide comprehensive insights into a country's economic health and general population well-being. With concerted efforts focused on investment, education, and infrastructure, nations can strive to improve their economic standings and elevate the living standards for all their residents.

                    
# 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.GNP.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.GNP.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))