High-technology exports (current US$)

High-technology exports, measured in current US dollars, represent a vital component of global trade dynamics. They encompass the export of goods and services in industries that rely heavily on advanced technologies, including electronics, aerospace, pharmaceuticals, and more. This indicator provides insights into a country’s technological capabilities, innovation levels, and economic competitiveness.

The importance of high-technology exports cannot be overstated. As economies around the world strive to transition from traditional manufacturing to knowledge-based industries, the ability to produce and export high-tech goods is often a measure of economic health and growth potential. Countries leading in high-tech exports are typically associated with strong research and development capabilities, skilled labor forces, and robust educational systems. Furthermore, high-tech exports contribute significantly to national GDP, fostering economic resilience and stability.

When considering the relationship between high-technology exports and other economic indicators, several connections emerge. For instance, high-tech exports are closely tied to a nation’s level of investment in research and development (R&D). Countries that allocate substantial funding to R&D, like Germany and the United States, often see a corresponding rise in high-tech exports. Furthermore, this indicator correlates with the Human Development Index (HDI), as nations with a higher HDI tend to have advanced technological capabilities and higher high-technology export volumes.

Several factors affect a country's high-technology exports, including government policies, trade agreements, and global economic conditions. For instance, supportive government policies that encourage innovation and provide tax incentives for research can significantly enhance a nation’s ability to export high-tech goods. In contrast, trade barriers, such as tariffs on technology-related imports and exports, can hinder growth in this sector. Additionally, global economic conditions, such as a recession or economic boom, can affect demand for high-tech products.

To enhance high-technology exports, countries can adopt various strategies. Promoting education and skills development in technology-related fields is crucial. By fostering a workforce that is well-versed in modern technologies, countries can improve their competitiveness in high-tech exports. Investment in infrastructure, such as internet access and transportation networks, also plays a significant role in facilitating trade. Moreover, collaboration between the government and private sectors can lead to advancements in technology that drive export capability.

However, there are several flaws associated with relying heavily on high-technology exports as an economic indicator. One major issue is the potential for overreliance on a narrow range of industries, which can be risky if global demand shifts. Furthermore, the rapid pace of technological change poses challenges for countries trying to keep up, leading to discrepancies in export capabilities between nations. Finally, high-tech exports might not fully reflect a country's economic health if significant portions of its economy are still reliant on low-tech industries.

Examining the latest data from 2023 provides further insights into high-technology exports. The median value of high-technology exports is approximately $192.8 billion, indicating that half of the countries surveyed either exceeded or fell short of this figure, offering a nuanced understanding of global trade dynamics. The top five areas leading in high-technology exports include China, with exports amounting to approximately $825 billion, followed by Hong Kong SAR China at $369 billion, Germany at $255 billion, the United States at $208 billion, and Singapore at $197 billion. These countries exhibit robust investment in technology, robust research capabilities, and substantial market demand for high-tech products.

Conversely, the bottom five areas reveal a stark contrast. Antigua & Barbuda, Belize, Macao SAR China, the Maldives, and Seychelles collectively demonstrate minimal high-technology exports. For instance, Antigua & Barbuda and Belize report zero high-technology exports, signaling a lack of industrialization in high-tech sectors. Similarly, the Maldives and Seychelles, with minuscule values of $646 and $711 respectively, indicate that their economies may rely more heavily on tourism or traditional industries, limiting their engagement in high-tech global trade.

Looking at global values, there has been significant fluctuation in high-technology exports over the years. This trend highlights both the dynamic nature of technological advancements and global trade. For instance, from 2011 to 2023, high-tech exports peaked in 2022 at around $3.72 trillion before witnessing a decline to approximately $3.42 trillion in 2023. Such fluctuations may reflect a range of issues, including market saturation, trade tensions, or shifts in consumer preferences towards emerging technologies.

In summary, high-technology exports represent a critical barometer of economic development and competitiveness in today's global landscape. The interplay between this indicator and various socio-economic factors underscore its importance for policymakers. By understanding and addressing the complexities surrounding high-technology exports, countries can better navigate the challenges of an increasingly technology-driven global economy.

                    
# 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.TECH.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.TECH.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))