Charges for the use of intellectual property, receipts (BoP, current US$)

The indicator 'Charges for the use of intellectual property, receipts (BoP, current US$)' provides a critical insight into a country's economic performance in the context of globalization and innovation. This category encapsulates the financial transactions related to the use of intellectual property, which includes patents, copyrights, trademarks, and licenses, enabling countries to generate revenue from their intellectual assets. The balance of payments (BoP) reports this income, revealing how countries are effectively capitalizing on their creative and innovative capacities.

The importance of this indicator cannot be overstated. It reflects a nation's ability to innovate and protect its intellectual property while engaging in international trade. High charges for intellectual property usage often correlate with advanced economies that invest heavily in research and development, demonstrating the value of their innovations on a global scale. For instance, in 2023, the median value for this indicator stood at approximately 31.26 million US dollars, which underscores the overall importance of intellectual property revenue in global trade.

The top five countries that excel in generating revenue from the use of intellectual property are the United States, Netherlands, Japan, Germany, and the United Kingdom. Specifically, the United States leads by a significant margin with receipts totaling approximately 134.44 billion US dollars, indicating its substantial investment in technological and cultural innovation. The Netherlands and Japan follow with values of about 63.75 billion and 51.02 billion US dollars respectively. This trend signifies that these countries possess the means and structures to protect and monetize their intellectual property effectively, showcasing their competitive advantages in global markets.

In contrast, countries at the bottom of the scale, such as Ethiopia, Tajikistan, and Burundi, report significantly lower receipts, ranging from just a few hundred to a few thousand US dollars. This stark difference highlights the challenges faced by developing nations in nurturing and leveraging innovative capabilities due to limited resources, infrastructure, and investment in education and technology. For these nations, the journey toward improving their standings involves overcoming systemic barriers that inhibit intellectual property development and exploitation.

The relations to other economic indicators are also noteworthy. The charges for the use of intellectual property are often associated with GDP growth, trade balance, and foreign direct investment (FDI). A robust intellectual property sector can enhance a country's position in global value chains, leading to increased exports and a favorable trade balance. Furthermore, countries that excel in intellectual property receipts often attract more foreign investment, as companies seek to capitalize on a supportive technological and legal framework that protects intellectual assets.

Several factors influence a nation's performance in collecting charges for intellectual property. Firstly, the level of investment in research and development is a crucial determinant. Countries that allocate significant resources to R&D are more likely to produce innovative products and solutions, which can then be licensed or patented. Additionally, the effectiveness of a country's intellectual property laws and enforcement mechanisms plays a critical role. Strong legal frameworks encourage inventors and businesses to pursue intellectual property rights, fostering an environment conducive to creativity and innovation.

Strategies for enhancing this indicator often involve comprehensive policy adjustments aimed at improving education, promoting entrepreneurship, and fostering a culture of innovation. Governments can implement measures to support startups and small enterprises in navigating the complexities of intellectual property rights. They can also encourage international collaboration and partnerships that facilitate technology transfer and cross-border innovations. Furthermore, investing in human capital through education and training programs is essential for building a workforce capable of innovation.

Solutions to the challenges posited by low intellectual property revenues in some countries include developing partnerships with international organizations that can provide technical assistance and funding necessary to boost local innovation systems. Creating incentive structures that reward innovation and providing clear pathways for securing intellectual property rights can also help stimulate growth in this area.

Despite its significance, this indicator does have its flaws. Relying solely on monetary values does not account for the qualitative impacts of intellectual property. For example, a country might have high receipts from intellectual property, but the societal benefits, inclusive growth, and its role in sustainable development might not be reflected adequately. Additionally, disparities in reporting standards and the lack of harmonization in international intellectual property laws create challenges for accurate comparisons across countries.

Examining the historical data for this indicator reveals a steady increase in global receipts from the use of intellectual property over the decades. Starting at a mere 2.8 million US dollars in 1962, the figure has ballooned to approximately 486.59 billion US dollars by 2023. This escalation reflects the growing recognition of intellectual property as a valuable economic asset in an increasingly competitive and interconnected global market.

The trend also illustrates how emerging technologies, such as artificial intelligence and digital innovations, have revolutionized traditional industries and created new opportunities for intellectual property generation. Countries that embrace these technologies and adapt their legal frameworks to account for them are likely to see continued growth in this domain. As we move toward a future characterized by rapid technological advancements, the charges for the use of intellectual property will undoubtedly play a central role in shaping economic landscapes worldwide. Ultimately, fostering a culture of innovation and effectively managing intellectual assets will be crucial for countries seeking to enhance their economic trajectories and ensure sustained prosperity.

                    
# 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 = 'BX.GSR.ROYL.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 <- 'BX.GSR.ROYL.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))