Merchandise exports by the reporting economy (current US$)

The indicator 'Merchandise exports by the reporting economy (current US$)' reflects the total value of goods and commodities exported from a specific economy, measured in current US dollars. This indicator serves as a key measure of an economy's trade activity and overall economic health. By evaluating merchandise exports, economists and policymakers can assess the strength of international trade relationships and the competitiveness of domestic industries in global markets.

Understanding merchandise exports is crucial as it provides insight into a country's economic structure. A high level of exports usually indicates robust industrial production capacity, a well-established reputation for quality, or a competitive edge in specific sectors, such as technology or agriculture. Additionally, merchandise exports contribute to national income and employment, as increased trade activity typically leads to higher demand for domestic goods, stimulating business growth and job creation.

This indicator is also directly related to other economic indicators, such as Gross Domestic Product (GDP), balance of trade, and foreign exchange reserves. For instance, a high level of merchandise exports usually correlates with a positive balance of trade, which occurs when the value of exports exceeds the value of imports. Conversely, if imports surpass exports, it could lead to trade deficits, negatively impacting GDP and lowering foreign exchange reserves. Thus, monitoring merchandise exports is essential for understanding broader economic trends and potential vulnerabilities in an economy.

Several factors can affect merchandise exports, including global market demand, exchange rates, tariffs, trade agreements, and domestic production capabilities. For example, if a country's currency is strong relative to others, its exports can become more expensive for foreign buyers, potentially decreasing demand. Similarly, tariffs or trade barriers imposed by importing countries can hinder the flow of goods, adversely affecting export levels. Trade agreements can facilitate exports by eliminating tariffs, making it essential for countries to establish and maintain favorable relationships with trading partners.

Looking at the latest available data from 2020, the median value for merchandise exports in reporting economies was approximately $5.54 billion. This figure helps to set a benchmark, where economies below this median might struggle with international competitiveness, while those above it could be seen as more robust in global trade.

Among the top five economies for merchandise exports in 2020, China led with exports valued at $2.6 trillion, followed by the United States with $1.43 trillion, Germany at $1.38 trillion, the Netherlands at $674.68 billion, and Japan with $638.17 billion. This data indicates the dominance of large economies in international trade. China's vast industrial base and global supply chain networks emphasize its essential role in global commerce. The United States and Germany also benefit from advanced technologies and established brands that ensure strong demand for their exported goods.

In stark contrast, the bottom five areas for merchandise exports reveal the challenges faced by smaller economies. For instance, Palau with only about $2.89 million in exports showcases limited industrial capacity or market access. Countries like São Tomé & Príncipe and Tuvalu, with similarly low export figures, may struggle to diversify their economies beyond primary sectors like agriculture and fishing. These figures reflect not only a lack of competitive products on a global scale but also the difficulties these nations encounter in penetrating international markets.

It's essential to develop strategies to bolster merchandise exports for economies lagging in this area. Enhancements in infrastructure, investment in technology, and strategic planning to tap into emerging markets can potentially elevate their export levels. Diversifying product offerings, enhancing product quality, and identifying niche markets can help smaller economies leverage their unique resources. Additionally, favorable trade agreements and participation in regional trade blocs can also open new pathways for export growth.

However, reliance on merchandise exports comes with inherent risks. Fluctuations in global markets can dramatically affect export-driven economies in times of crisis. For instance, the COVID-19 pandemic severely disrupted global trade in 2020, resulting in a decline for many economies, and thus, businesses must remain agile and adaptable. Establishing a balanced economy that does not solely depend on exports will provide a buffer against external shocks. Investing in domestic markets and fostering consumption can create a more resilient economic environment.

In summary, while merchandise exports by the reporting economy constitute a vital economic indicator, understanding its complexities is equally important. The relationship between exports and other economic metrics, the factors influencing export capabilities, and the strategies for overcoming challenges are crucial components in the discourse surrounding international trade and economic empowerment. Moving forward, economies must adopt holistic approaches while embracing both global trade and local growth to ensure sustainable and advantageous positions in the ever-evolving international landscape.

                    
# 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 = 'TX.VAL.MRCH.WL.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 <- 'TX.VAL.MRCH.WL.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))