GDP (current US$)

The Gross Domestic Product (GDP) in current US dollars is a crucial economic indicator that measures the total monetary value of all goods and services produced within a country's borders in a specific time period, typically a year. It reflects the economic activity of a nation and serves as a comprehensive scorecard of the country’s economic health. When we discuss GDP in current US dollars, we are referring to the value at which these goods and services would be sold based on the exchange rate at that specific time, thus allowing for real-time assessment without being adjusted for inflation.

Understanding GDP is pivotal not only for economists but also for policymakers, business leaders, and investors. This metric provides insight into the size of an economy and its overall performance. High GDP figures often signal a booming economy with potential for job creation, increased consumer spending, and investment opportunities. Conversely, a declining or stagnant GDP can indicate economic troubles which may lead to increased unemployment and reduced business activities.

GDP does not operate in isolation; it is closely related to other economic indicators. For instance, unemployment rates are inversely related to GDP growth. When GDP grows, businesses tend to hire more, reducing unemployment rates. Additionally, inflation is another factor often analyzed alongside GDP. A growing GDP may also lead to inflationary pressures if the economy overheats and demand outstrips supply. It is essential to assess GDP alongside these and other indicators, such as the Consumer Price Index (CPI) and trade balances, to get a full picture of economic health.

Several factors can heavily influence GDP figures. Consumption patterns are crucial—when consumers feel confident, they spend more, propelling GDP. Investment from businesses in infrastructure, technology, and workforce training also enhances productivity, contributing positively to GDP. Government spending on services and public works can provide a substantial boost. On the flip side, factors such as economic recessions, changes in consumer confidence, and global economic trends can all negatively impact GDP.

To boost GDP, various strategies can be applied. Governments can implement fiscal policy measures, such as tax cuts or increased public spending, to stimulate economic growth. Additionally, investing in education and infrastructure leads to long-term productivity gains. From a business perspective, companies may look to innovate or expand their operations to capture market share and drive revenue growth. International trade agreements may also open new markets for goods and services, further enhancing GDP.

While GDP is an important measure, it is not without flaws. It does not account for the distribution of income among residents of a country, which means that increasing GDP could coincide with increasing inequality. Furthermore, GDP does not measure the informal economy or consider environmental factors; growth that leads to ecological degradation is not reflected in GDP metrics. Thus, while GDP provides a snapshot of economic activity, it should be analyzed alongside other measures of well-being and sustainability to grasp a complete understanding of a country’s economic health.

As of 2023, the global GDP sits at approximately $106.17 trillion. Observing the latest data, we see that the median GDP value among global economies is around $42.65 billion, highlighting significant disparities. The largest economies dominate the charts, with the United States leading at a staggering $27.72 trillion, followed by China at $17.79 trillion, Germany at $4.53 trillion, Japan at $4.20 trillion, and India at $3.57 trillion. These numbers encapsulate the economic prowess of these nations and reflect their significant roles in the global market.

On the other end of the spectrum, the five smallest economies—Tuvalu, Nauru, Marshall Islands, Kiribati, and Palau—further underscore the vast inequalities present in global economic structures, with GDP figures barely surpassing $62 million for Tuvalu. Such figures highlight the economic challenges these nations face and the need for targeted development strategies and international support to uplift these economies into more sustainable growth trajectories.

The historical progression of GDP from 1960 to 2023 reveals a remarkable upward trajectory, growing from just over $1.37 trillion to recent totals exceeding $106 trillion. This growth accentuates advancements in technology, globalization, and the interconnectedness of the world economy. Indeed, this historical context allows for a clear vision of how economies have evolved and adapted, facing challenges such as global recessions and pandemics, yet continuing to grow overall. Over decades, we have witnessed fluctuations, such as the stark decline during the 2008 financial crisis and the impacts of the COVID-19 pandemic in 2020, but recovery patterns show resilience in the global economic landscape.

In conclusion, GDP (current US$) is an essential indicator for understanding economic dynamics. While it provides a useful framework for assessing economic size and growth trends, it must be viewed critically, considering its limitations. Analyzing GDP in conjunction with other socio-economic factors can yield deeper insights into a country’s overall health and well-being, guiding policymakers towards more sustainable and equitable economic strategies.

                    
# 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.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.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))