Exports of goods and services (current US$)

Exports of goods and services (current US$) is a critical economic indicator that measures the monetary value of goods and services sold by a country to the rest of the world during a specified period, reflecting a nation's economic health and trade competitiveness. This indicator is not only pivotal in gauging national economic performance but it also plays a significant role in assessing foreign exchange flows, establishing currency valuation, and determining a country’s balance of trade.

The importance of exports of goods and services lies in its direct correlation with a nation’s GDP, provided that exports are a significant component of the overall economic activity. A thriving export sector typically suggests that domestic industries are competitive on a global scale, leading to increased production, job creation, and overall economic growth. Furthermore, countries that successfully export more than they import tend to enjoy surplus balances, which can strengthen their currency and provide additional resources for investment in infrastructure, education, and technology.

This indicator is closely related to various other economic metrics, including GDP, trade balance, employment rates, and foreign direct investment (FDI). For instance, a rise in exports contributes positively to GDP growth, while a deficit in the trade balance may trigger devaluation of a currency, leading to inflationary pressures. Moreover, robust export activity often attracts foreign direct investment, as multinational companies are more likely to invest in markets that exhibit strong growth potential.

Several factors affect the exports of goods and services, including domestic production capabilities, global economic conditions, and international trade policies. For instance, an increase in production efficiency or adoption of innovative technologies can enhance the competitiveness of exporters. Conversely, global economic downturns or trade restrictions such as tariffs can adversely impact export levels. Additionally, exchange rates play a crucial role; a weaker domestic currency can make exports cheaper for foreign buyers, potentially boosting sales overseas.

Strategies to enhance exports may include investing in technology, improving supply chain efficiencies, and fostering trade relationships through negotiations and trade agreements. Countries may also focus on promoting diversification of their export products and expanding into emerging markets, thus reducing dependence on a narrow range of trading partners or products.

While the indicators of export performance are typically positive, it is essential to acknowledge potential flaws. An over-reliance on export markets can make an economy vulnerable to global economic fluctuations, resulting in significant volatility in domestic economic conditions. Furthermore, certain industries may experience adverse effects if their growth is prioritized at the expense of local consumption and industries.

In 2023, exports of goods and services reached approximately $31.13 trillion, indicating a slight decline from the previous year’s total of $31.54 trillion. The median export figure across various countries during this period stood at about $25.83 billion, illustrating considerable disparities in export volumes across different nations.

Among the leading exporters, China topped the list with exports amounting to over $3.5 trillion, followed by the United States at approximately $3.05 trillion, and Germany with around $1.96 trillion. Other notable exporters include the United Kingdom and France, with figures of about $1.07 trillion and $1.05 trillion, respectively. The dominance of these nations can be attributed to their diversified and advanced industrial bases, significant global market engagement, and robust trade agreements.

Conversely, the bottom five areas in terms of export indicators include Kiribati ($20.57 million), Nauru ($65.78 million), Marshall Islands ($100.93 million), Federated States of Micronesia ($125.79 million), and Burundi ($139.07 million). These figures highlight the challenges faced by smaller or less developed economies in competing within the global market, often stemming from poor infrastructure, limited production capabilities, and a narrowed economic base reliant on a few commodities or sectors.

Reviewing the historical trends of exports from 1970 to 2023, one can observe a remarkable increase from approximately $387.3 billion in 1970 to $31.13 trillion in 2023. This growth reveals the significant expansion of global trade and the increased interconnectedness of national economies. Despite fluctuations during economic crises—such as the drop observed in the 2008 financial crisis—the overall trend remains upward as globalization continues and the world becomes more integrated economically.

In conclusion, exports of goods and services (current US$) serve as a vital indicator of economic health, influencing a broad spectrum of financial outcomes for nations. Fostering robust export levels can drive growth, increase employment, and position countries favorably within the global economy. While pursuing export growth, policymakers must remain vigilant in addressing potential vulnerabilities, ensuring that domestic economic stability is maintained alongside their global trading aspirations.

                    
# 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 = 'NE.EXP.GNFS.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 <- 'NE.EXP.GNFS.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))