Exports of goods and services (annual % growth)

The indicator of "Exports of goods and services (annual % growth)" serves as a vital metric for assessing the performance of an economy's external sector. It represents the percentage change in the value of goods and services that a country exports over a specified period, typically on an annual basis. Understanding this growth rate is crucial as it directly ties to economic health, international competitiveness, and overall growth potential. A positive growth rate indicates that the country is effectively tapping into international markets, while a negative rate often signals trade deficits, economic downturns, or reduced competitiveness.

The importance of this indicator cannot be understated, as it plays a key role in determining not only the economic stability of a country but also its relationship with other global economies. A country that experiences strong export growth may see an influx of foreign currency, which can help strengthen its currency, stimulate domestic investment, and generate employment opportunities. Additionally, export growth can contribute significantly to the Gross Domestic Product (GDP), making it an essential component of a nation's economic strategy.

Exports of goods and services are intrinsically connected to various other economic indicators. For example, they have strong correlations with GDP growth—countries with robust export sectors generally enjoy higher GDP growth rates. There is also a linkage to trade balances; a growth in exports typically improves a nation's trade balance, reflecting a healthier economic state. Furthermore, this indicator can impact inflation rates, as increased demand for exports can lead to higher prices domestically.

Several key factors influence the growth rate of exports, ranging from external economic conditions to internal policies. Global demand, commodity prices, currency exchange rates, and international trade agreements all affect export performance. For instance, countries dependent on specific commodities for their exports may experience volatility due to fluctuating global market prices. Political stability and the regulatory environment also play significant roles; countries with favorable conditions and transparent trade policies are better positioned to attract foreign buyers.

To enhance export growth, countries can adopt several strategies. First, investing in infrastructure—such as transportation and logistics—can reduce costs and improve efficiency, making exports more competitive. Second, promoting innovation and value addition in production can help create higher-quality goods that stand out in the global market. Governments can also consider trade policies that facilitate easier access to foreign markets, such as reducing tariffs or entering into free trade agreements. Lastly, marketing and branding efforts that highlight a country’s unique products can bolster international recognition and demand.

Despite the benefits, relying too heavily on exports can expose economies to various vulnerabilities. For example, countries with small, undiversified economies may find themselves at risk during global downturns or fluctuations in demand for their primary exports. Such dependence can lead to economic instability if not managed correctly. Moreover, if exports hinder domestic development—by prioritizing foreign buyers over local consumers—long-term growth can be compromised.

Looking at the most recent data from 2023, the global median value for the growth of exports of goods and services stands at 2.24%. This figure reflects a moderate pace of expansion that indicates some recovery but remains below historical averages. Notably, some regions have experienced exceptional growth; for instance, Samoa has an astonishing export growth rate of 154.34%. This is likely fueled by specific niche markets or unique products attracting significant international demand. Similarly, Macao SAR, China, with a growth rate of 87.45%, and Palau at 76.19%, indicate strong performances that could be attributed to their tourism-related services or other specialized industries. At the opposite end of the spectrum, nations like Timor-Leste, with a staggering decline of -59.99%, and Sudan at -39.9%, reveal the stark challenges some economies face. Such percentages often arise from geopolitical issues, internal strife, or resource depletion that have devastating effects on their trade capabilities.

In observing the broader historical context, the export growth rates have shown considerable fluctuations over the decades. From a peak of 11.81% in 2000 to a notable drop in 2020 to -8.52%, these statistics reflect the dynamic nature of global trade influenced by multiple factors, including economic recessions, the COVID-19 pandemic, and subsequent recovery phases. The current rate of 0.86% in 2023 suggests a recovery phase following significant disruptions, although it indicates that many economies are still grappling with the remnants of recent global challenges, aiming to regain momentum in their export activities.

In conclusion, the annual percentage growth of exports of goods and services is a crucial economic indicator that offers intricate insights into a nation’s economic health and global positioning. It holds profound implications for GDP growth, trade balances, and currency stability. By understanding its multifaceted relationships with various economic indicators and the factors influencing its performance, policymakers can devise strategies to stimulate export growth while safeguarding against potential vulnerabilities. As nations navigate through fluctuating trends and challenges, fostering a robust export sector remains paramount for sustainable economic development.

                    
# 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.KD.ZG'

# 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.KD.ZG'

# 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))