Merchandise imports by the reporting economy (current US$)

The indicator of merchandise imports by the reporting economy, measured in current US dollars, serves as a critical gauge of a nation's trade activities. This metric encompasses the total value of goods that an economy purchases from foreign countries over a specified period, usually measured annually. It reflects the strength of an economy's demand for foreign products and can reveal insights into consumption trends, economic health, and a country’s involvement in international trade.

The importance of tracking merchandise imports lies in its ability to provide comprehensive insights into the economic framework of a country. High import values can indicate a robust domestic demand and consumer spending capability, while also suggesting a dependency on foreign goods, which can be both a strength and a vulnerability. On the opposite end of the spectrum, low import levels might indicate a more self-reliant economy but could also signal issues such as low consumer confidence or economic stagnation.

This indicator is intricately connected with other crucial financial metrics, including Gross Domestic Product (GDP), balance of trade, and foreign exchange rates. A surge in imports can lead to a trade deficit—when a country imports more than it exports—which can subsequently affect the currency value. This is particularly evident in economies where consumer demand is high, compelling countries to rely heavily on foreign goods, as seen in the top five areas based on the latest available data from 2020.

In 2020, the United States recorded the highest merchandise imports at approximately \$2.34 trillion, followed closely by China at \$2.06 trillion. Germany, Japan, and the United Kingdom rounded out the top five, with imports valued at \$1.17 trillion, \$631 billion, and \$628 billion, respectively. Such immense import values underscore not only the size of these economies but also their significant roles in global trade networks. In contrast, the bottom five areas, including Tuvalu, Nauru, Kiribati, São Tomé and Príncipe, and American Samoa, reported imports that barely broke \$200 million, indicating they are either developing economies with limited access to global markets or isolated locales with minimal trade activities.

Examining global values of merchandise imports reveals a substantial increase over the decades. From just over \$106 billion in 1960 to nearly \$17.39 trillion in 2020, the trend depicts the rapid expansion of global trade. This growth corresponds with increased globalization, technological advancements, and the liberalization of trade policies throughout the world. However, this modern interdependence has also raised concerns regarding economic vulnerabilities, particularly in the face of global crises like the COVID-19 pandemic.

Several factors affect merchandise import levels, including economic policies, exchange rates, global demand, and geopolitical events. Currency strength is a critical aspect; for instance, a weaker local currency can make imports more expensive, thereby potentially reducing import volumes. Conversely, robust economic growth often leads to increased imports as consumers and businesses seek foreign products.

Strategies to optimize merchandise imports can vary greatly. For countries or economies seeking to enhance their import values responsibly, trade agreements can cultivate robust trade partnerships and reduce tariffs, easing the flow of goods. Furthermore, fostering domestic industries while encouraging foreign investments can create a balanced approach towards imports and exports, ensuring sustained economic growth.

However, a heavy reliance on imported goods can present significant flaws. Economies that prioritize imports may weaken domestic production capabilities, leading to potential job losses in local industries. Additionally, such dependency can create vulnerabilities due to market fluctuations and trade disputes, causing disruptions in supply chains. This scenario highlights the importance of developing a diversified economic base while strategically managing import levels.

In conclusion, merchandise imports by the reporting economy, as an indicator, provides critical insights into an economy’s health and its relationship with the global market. The data from 2020 vividly illustrates the disparities in global trade, showcasing the dominance of major economies alongside less active participants in international trade. Understanding this indicator allows nations to navigate their economic strategies better while balancing their reliance on foreign goods versus domestic production.

                    
# 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 = 'TM.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 <- 'TM.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))