Merchandise imports from high-income economies (% of total merchandise imports)

The indicator of 'Merchandise imports from high-income economies (% of total merchandise imports)' provides valuable insights into the economic and trade interactions between countries classified as high-income and other economies. This metric reflects the share of a country’s merchandise imports that comes from nations with higher income levels, categorically grouped by the World Bank based on gross national income (GNI) per capita. Understanding this indicator is crucial as it reveals dependencies in trade and can serve as an indicator of a nation’s position in the global economy.

In 2020, the global average of merchandise imports from high-income economies stood at 62.31%, a slight decline from previous years. The median value across various regions was 55.06%. This decline may signal shifting trade dynamics, influenced by various factors, including economic shocks, trade policies, and global events such as the COVID-19 pandemic. In this context, understanding changes in import patterns is key for policymakers aiming to navigate and stabilize their economies.

The top five areas with the highest percentages of merchandise imports from high-income economies include Greenland (97.94%), Guam (97.31%), Luxembourg (97.03%), San Marino (96.66%), and Bermuda (93.41%). These areas often reflect a heavy reliance on trade with wealthier nations, indicating that their local economies might be substantially dependent on imported goods or services from these high-income countries. This reliance may be due to limited domestic production capabilities or the strategic choice of sourcing high-quality goods that are typically produced in wealthier economies.

Conversely, the bottom five areas—North Korea (1.13%), Bhutan (6.07%), Lesotho (9.5%), Laos (9.7%), and Afghanistan (10.28%)—demonstrate a markedly different picture. Their low percentages suggest a more insular trade approach, either due to strict trade regulations, limited access to global markets, or an emphasis on self-sufficiency in local production. This stark contrast underscores how economic integration with high-income nations can be a critical driver for growth for some areas while others remain isolated by political or structural barriers.

The relationship between merchandise imports from high-income economies and various other economic indicators is profound. For instance, countries with high import shares from wealthier nations may experience greater foreign direct investment (FDI), technology transfers, and access to better goods and services. This forms a symbiotic relationship fueling economic growth and development. However, an over-dependence on high-income economies can also expose countries to vulnerabilities, especially in times of global economic downturns or geopolitical tensions.

Factors affecting the levels of merchandise imports from high-income economies include political relationships, trade agreements, currency fluctuations, and local production capabilities. For example, countries that are part of free trade agreements with high-income nations significantly benefit from reduced tariffs and trade barriers, leading to increased import levels. Key to understanding changes in this indicator is evaluating the interplay of these factors, as they can shift quickly and dramatically impact trade flow.

To address the complexities surrounding this indicator, countries can consider implementing strategies aimed at diversifying their sources of imports. This could involve developing trade relationships with emerging markets or enhancing local production capabilities to produce goods that are currently being imported from high-income economies. Such a multifaceted approach could help mitigate risks associated with over-dependence on specific economies while also supporting domestic industries.

Solutions to improve trade dynamics include enhancing local industries through investments in technology and skills training, which can help elevate domestic production. This self-reliance can reduce vulnerabilities tied to high-income imports. Additionally, establishing partnerships with other developing countries can facilitate access to alternative goods that are of comparable quality but sourced from a broader range of economies.

Despite its utility, analyzing merchandise imports from high-income economies does come with certain flaws. Data quality and availability can vary significantly between regions, influencing analysis accuracy. Behavioral responses to economic changes can also skew perceptions of the data if policymakers misinterpret trends or ineffectively communicate the importance of this indicator. Therefore, it's vital for industry stakeholders and policymakers to conduct comprehensive analyses and monitor changes in this sector consistently.

In summary, the indicator 'Merchandise imports from high-income economies (% of total merchandise imports)' serves as a critical tool for understanding trade dynamics globally. The values observed in 2020, such as the median of 55.06% and notable extremes in both high and low import areas, provide a nuanced view into global economic relations. As nations navigate the challenges of shifting trade patterns, balancing dependencies and promoting local economic resilience will be paramount in formulating effective trade strategies for the future.

                    
# 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.HI.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 <- 'TM.VAL.MRCH.HI.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))