Merchandise exports to high-income economies (% of total merchandise exports)

The indicator of merchandise exports to high-income economies as a percentage of total merchandise exports provides vital insights into the global trade dynamics of countries. This metric reflects the extent to which nations rely on wealthier markets for their export revenues. Analyzing this data is crucial because it not only indicates the health of an economy but also illustrates how interconnected global trade relationships are with high-income countries, which typically have higher purchasing powers.

In 2020, the world average for merchandise exports to high-income economies stood at 68.45%. This figure captures a significant aspect of trade economics, suggesting that most countries are heavily reliant on selling goods to more affluent nations. The median value of 58.82% provides a useful benchmark to discern how various regions are performing in comparison to the global stage.

Regions that scored highest in this indicator included Greenland (97.42%), French Polynesia (94.32%), Iceland (93.66%), Liberia (93.61%), and Cape Verde (93.46%). These figures highlight the overwhelming dependence of these territories on high-income economies. For example, Greenland's economy heavily relies on exports to affluent nations, primarily due to its limited domestic market and geographical constraints, which restrict its economic diversity and growth opportunities. In contrast, regions with the lowest percentages such as Kiribati (0.85%), Bhutan (1.54%), Gambia (3.5%), North Korea (3.97%), and Turkmenistan (4.51%) reveal a starkly different reality. These countries may have a more insular economic structure or lack the production capabilities to effectively engage with high-income economies. The low numbers point to potential challenges in accessing lucrative markets, thereby impairing their economic growth.

The importance of this indicator cannot be overstated. Nations that are more integrated into high-income economies tend to experience better economic stability, faster growth rates, and improved access to resources and technology. Conversely, countries with low export figures to wealthier markets may face economic isolation, resulting in slower development paths and a potential struggle to meet global standards in various sectors. Furthermore, sustained engagement with high-income partners can lead to the transfer of knowledge, investments, and innovations, which can considerably bolster a developing nation’s economy.

It is essential to consider that the reliance on high-income economies can also be a double-edged sword. Dependence on a limited range of markets exposes countries to risks, particularly in times of economic downturn, trade restrictions, or global shocks (such as the COVID-19 pandemic). In 2020, we saw that the merchandise export performance was severely impacted by disruptions in global trade, leading to cautious scrutiny of these flows moving forward.

Several factors can affect a country's merchandise exports to high-income economies, including trade policies, economic conditions, and the nature of domestic production. Countries with favorable trade agreements or direct access to large affluent markets can benefit considerably. Additionally, the presence of a competitive manufacturing base capable of producing goods aligned with high-income consumer preferences is pivotal. For example, nations that invest substantially in technology and skilled labor to boost their production capabilities find themselves in positions to enhance their export figures. On the other hand, countries that lack such infrastructures face hurdles in growing their export numbers to these markets.

To increase their exports to high-income economies, nations can adopt various strategies. First, investing in infrastructure and improving logistical capabilities can facilitate smoother and cheaper access to global markets. Countries should also focus on enhancing the quality of their goods, improving brand recognition, and ensuring competitive pricing to attract buyers from wealthier nations. Participation in international trade fairs and exhibitions can raise awareness and offer opportunities for smaller nations to showcase their products to larger audiences. Additionally, forging multinational partnerships with firms in high-income economies can help to unlock doors for export opportunities as these partnerships extend access to wider channels of distribution and marketing.

However, it is also necessary to recognize potential flaws in relying heavily on merchandise exports to high-income economies. This dependency can create an imbalance where domestic markets might suffer due to a focus on producing goods for export rather than catering to local demands. Such an approach may result in vulnerability during global economic fluctuations. Therefore, diversifying export markets while bolstering domestic production may be essential for long-term economic resilience.

In summary, the percentage of merchandise exports to high-income economies serves as a key economic indicator reflecting reliance on affluent markets and the broader global trade dynamics. It emphasizes the importance of understanding that while engaging with high-income countries can foster growth, it also necessitates strategies that ensure a balanced and stable economic environment. As nations continue to navigate through changing global landscapes, focusing on sustainable development alongside strategic trade partnerships will be essential in enhancing their economic prospects.

                    
# 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.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 <- 'TX.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))