GNI, Atlas method (current US$)

The Gross National Income (GNI) measured using the Atlas method (current US$) serves as a significant economic indicator utilized to gauge the composition and growth of economies on a global scale. The Atlas method is designed to smooth out exchange rate fluctuations by employing a three-year average exchange rate, making it an effective tool for accurately assessing the incomes of nations without the volatility associated with annual currency shifts. This approach proves to be invaluable, particularly for developing countries, allowing for a more stable representation of their economic performance. In 2023, the GNI, calculated using this method, reached a staggering median value of approximately 43.61 billion dollars.

The importance of GNI cannot be understated; it serves as a direct reflection of a country's economic health and overall standard of living. Policymakers use this data to make informed decisions regarding fiscal and developmental policies, while investors evaluate GNI as part of their risk assessment when considering investments in a given nation. Further, GNI is also pivotal in international funding, with various financial institutions and aid organizations often basing assistance allocation on the income levels indicated by these figures.

When analyzing GNI, it is essential to understand its relationship with other key economic indicators. The correlation between GNI and Gross Domestic Product (GDP) is particularly noteworthy. While GDP focuses solely on the economic activity within a nation’s borders, GNI accounts for the income residents earn abroad and the remittances sent back home. A nation with high GNI compared to its GDP typically has significant overseas investments and remittance income, showcasing a broader economic engagement relative to its domestic economic activity.

Various factors influence GNI figures, including population growth, employment rates, productivity, levels of household consumption, and international trade relations. For instance, the latest data illustrates that the top five areas by GNI are the United States, China, Japan, Germany, and India, with the United States leading substantially with a GNI of approximately $26.94 trillion. This dominance is likely attributed to its expansive economy, robust technological sector, and global position as a leader in numerous industries.

China's GNI, around $18.88 trillion, reflects its rapid economic growth and increasing global trade integration, making it a formidable economic powerhouse. Japan and Germany, with GNI values of approximately $4.90 trillion and $4.56 trillion respectively, contribute significantly to the global economy, driven by technology and engineering sectors. India, at about $3.65 trillion, showcases how emerging markets are positioned to challenge traditional economic giants through population and consumption growth.

Conversely, the bottom five areas by GNI include Tuvalu, Palau, Nauru, Marshall Islands, and the Federated States of Micronesia, all of which have GNI figures indicating a much smaller economic footprint. For instance, Tuvalu's GNI is a mere $86 million, primarily reflecting its limited economic diversification and heavy reliance on a narrow base of income sources like fishing and some tourism, underscoring the vulnerabilities faced by smaller island nations.

Strategies to bolster GNI involve focusing on sustainable economic growth, enhancing productivity, and expanding international trade relationships. Countries can also improve their GNI through policies that encourage entrepreneurship, innovation, and foreign direct investment, emphasizing diversification to mitigate dependence on a single economic sector.

In terms of solutions, collaborative efforts in regional trade agreements, investments in emerging technologies, and building human capital through education are vital in enhancing GNI, particularly for developing countries. Moreover, addressing income disparity within nations can lead to an overall increase in GNI as more individuals partake in the nation's economic activities.

Despite its many strengths, the GNI indicator is not without flaws. For instance, it does not adequately account for income inequality within countries—two nations may have similar GNI figures but vastly different living standards when wealth is concentrated in the hands of a few. Moreover, issues such as tax avoidance by multinational corporations can distort GNI numbers, leading to inflated perceptions of economic strength in countries considered tax havens.

To summarize, while GNI, using the Atlas method, remains a crucial measure of economic performance on both national and global scales, it must be interpreted with an understanding of its complexities, limitations, and the distinct factors shaping income generation in different regions. The data from 1962 to 2023 demonstrates a notable upward trend in the global GNI, indicative of expanding economic activity and interconnectedness, with 2023 figures forecasted at approximately $106.25 trillion, signaling a resilient recovery and growth pathway following economic disruptions faced in the past few years. As economies continue to evolve, ongoing analysis and adaptation of strategies to enhance GNI will be paramount for prosperity and sustainability.

                    
# 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.ATLS.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.ATLS.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))