High-technology exports (% of manufactured exports)

The indicator 'High-technology exports (% of manufactured exports)' serves as a critical measure of a country's ability to compete in the global economy through advanced technology. This metric highlights the proportion of a nation's total manufactured exports that are categorized as high-technology goods. These goods typically include products from sectors such as aerospace, pharmaceuticals, electronics, and information technology. Understanding this percentage provides insights into a nation's technological prowess and economic development.

The importance of high-technology exports lies in their correlation with overall economic growth, innovation capabilities, and job creation. Countries that manage to increase their high-tech exports often see parallel growth in their GDP and employment in high-skill sectors. High-technology exports do not only impact economic performance but can also serve as a barometer for measurement against globalization trends. As economies become more interconnected, nations with higher percentages of high-tech exports are often better equipped to capitalize on global supply chains and leverage international partnerships.

This indicator does not operate in isolation; its relationships with other metrics, such as R&D expenditure, education levels, and the overall manufacturing output of a country, are significant. For instance, countries that invest heavily in research and development (R&D) often produce higher percentages of high-tech exports due to a robust environment that fosters innovation. Similarly, a highly educated workforce can drive better quality and more sophisticated manufacturing processes, which in turn enhances the high-technology sector's growth. Therefore, analyzing high-technology exports alongside R&D spending and educational attainment provides a more comprehensive view of a nation’s technological capabilities.

Several factors influence high-technology exports, including government policies, investment in education and R&D, access to capital, and infrastructure. A favorable policy environment, including tax incentives for tech companies and strong intellectual property protections, attracts investments into the high-tech sector. Furthermore, governments that prioritize STEM (Science, Technology, Engineering, and Mathematics) education create a skilled workforce that is essential for high-tech manufacturing. Infrastructure, including a reliable internet connection and logistics systems, also plays a significant role in supporting high-tech exports, as these products often require timely distribution and robust communication networks.

Strategies to enhance high-technology exports often involve comprehensive approaches that include fostering research ecosystems, investing in education, and encouraging public-private partnerships. For example, maintaining strong ties between universities and the tech industry can promote a flow of ideas and innovations that lead to the development of new, high-tech products. Governments can also provide incentives for companies that invest in sustainable practices, as global markets increasingly demand environmentally friendly technologies.

While the data for 2023 indicates a global median value of 6.4% for high-technology exports as a percentage of total manufactured exports, it reflects a world increasingly aware of the significance of high-tech industries. Top countries such as Hong Kong SAR China (72.32%), the Philippines (63.98%), Malaysia (59.42%), Singapore (56.14%), and Ireland (47.77%) showcase this trend effectively. These nations each boast substantial investments in technology infrastructures and strong educational frameworks, enabling them to export more technologically advanced products, thus driving their economies forward.

Contrarily, the bottom five areas, including Antigua & Barbuda, Belize, Macao SAR China, the Bahamas, and Seychelles with values as low as 0.0% to 0.04%, illustrate a stark contrast in the global landscape. These countries lag in high-tech exports possibly due to limited industrial capabilities, lower levels of investment in R&D, or insufficient educational systems catering to STEM fields. Such disparities raise concerns regarding global inequity in technology and its associated economic prospects.

Historically, the global values of high-technology exports indicate a gradual increase over the years, from a low of 19.84% in 2007 to the current value of 22.87% in 2023. This trend reflects a general shift in manufacturing towards higher value-added products as markets evolve. Peak values occurred during 2020 and 2022, which may correlate with increased global demand for technology-driven solutions during the COVID-19 pandemic, as countries relied more on technology for communication, healthcare, and remote working solutions.

However, this rise does not mean that all nations benefit equally. There are flaws in interpreting these percentages. High-tech exports do not always correlate with overall economic well-being or quality of life. For instance, a country can have a high percentage of high-tech exports while still facing domestic challenges such as unemployment, environmental degradation, or social inequality. Thus, a nuanced understanding of these figures is necessary, considering the broader socioeconomic context.

The road ahead for global economies will likely involve increasing emphasis on high-tech exports, pushing nations to innovate further and develop industries that can thrive amidst rapidly changing global dynamics. By fostering collaborative environments, investing in education and R&D, and implementing supportive policies, countries can enhance their high-technology export capabilities, thereby solidifying their place in the 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.MF.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.TECH.MF.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))