Exports of goods and services (% of GDP)

The indicator "Exports of goods and services (% of GDP)" measures the proportion of a country's total production of goods and services that is sold to foreign markets. It serves as a critical metric for understanding a nation’s participation in the global economy and the extent to which it is integrated into international trade networks. This indicator not only reflects a country’s economic health but also provides insights into its competitiveness and potential for growth.

Understanding the importance of exports in the context of GDP is fundamental for policymakers and economists. A higher percentage indicates that a country is effectively capitalizing on global market opportunities, enhancing its GDP through trade. This can lead to job creation, increased investment in infrastructure, and a more innovative economy. Conversely, a lower percentage could signal a lack of competitiveness or may point to an economy that is overly reliant on domestic consumption, which could be problematic in times of national economic downturns.

The relations of this indicator with other economic indicators are manifold. For instance, there is typically a positive correlation between exports and employment rates. As firms expand to meet international demand, they may hire more workers, thereby reducing unemployment. Furthermore, exports can influence the balance of trade, which is crucial for a country’s currency valuation. A robust export sector can strengthen a nation's currency by increasing demand for it in the foreign exchange market.

Various factors can affect the exports of goods and services relative to GDP. Exchange rates play a significant role; a stronger currency can make exports more expensive and less competitive abroad. Additionally, trade policies, tariffs, and international relations can substantially impact export levels. Countries engaged in free trade agreements often see higher export percentages, as barriers to trade are lowered, allowing for more seamless international transactions.

Strategies to enhance export levels can include investing in trade facilitation measures, improving infrastructure, and providing incentives for businesses to explore foreign markets. For instance, governments can establish export promotion agencies that assist local businesses in understanding foreign market demands, regulatory requirements, and cultural considerations. Moreover, investing in research and development can provide businesses with the innovation necessary to produce goods that meet international standards, thereby improving export potential.

In order to sustain high export levels, countries might adopt several solutions. Diversification of export products and markets can protect economies from excessive reliance on a limited range of goods or unstable markets. Additionally, fostering partnerships with both private and public entities can encourage collaboration and resource sharing, leading to improved export strategies. It is also crucial to maintain and improve product quality to meet the demands of various international markets effectively.

Nonetheless, there are flaws and challenges inherent to relying heavily on exports as a driver of economic growth. Economies overly dependent on exports may be vulnerable to global economic shifts or downturns. The COVID-19 pandemic, for example, highlighted the risks associated with global supply chains, leading to substantial disruptions. If a country faces trade sanctions or hostility from trading partners, its export-driven economy could suffer significantly. Further, an overemphasis on exports might lead to neglect of domestic industries, creating imbalances that could instigate economic instability.

The latest 2023 data indicate that the average global export of goods and services stands at 29.32% of GDP. This figure is slightly lower than the previous year's median of 30.99%, which may suggest some fluctuations in global trade dynamics. The historical perspective highlights a trend of increasing export reliance, particularly since the early 2000s, where we can see a rising trajectory from about 23.51% in 2000 to close to 30% in the years preceding 2023.

Looking into top performing areas, Luxembourg, with 212.53% of GDP reliant on exports, showcases an economy that is heavily integrated into international finance and trade, benefiting from its status as a financial hub. Similarly, regions like Hong Kong SAR China and Singapore experience substantial export percentages due to their strategic geographical locations and developed infrastructure supporting global trade. On the opposite spectrum, Sudan's exports contributing only 1.11% of GDP further illustrates the challenges faced by economies that may be constrained by conflict, inadequate infrastructure, or political instability, hampering their ability to engage fruitfully in international markets.

In conclusion, while exports of goods and services as a percentage of GDP provide valuable insights into the economic health and competitiveness of a nation, it is vital to approach this indicator with a nuanced understanding of the underlying factors and broader economic context. Sustainable growth strategies that balance domestic needs with international market opportunities can lead to robust and resilient economies. The interplay amongst exports, economic policies, and global conditions will continue to shape the developments in this essential metric.

                    
# 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.ZS'

# 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.ZS'

# 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))