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

The indicator 'Charges for the use of intellectual property, payments (BoP, current US$)' encompasses the financial transactions associated with acquiring the rights to use intellectual property, which includes patents, copyrights, trademarks, and other forms of intellectual assets. This indicator provides vital insights into how economies interact in terms of knowledge and technology transfer, allowing countries to track the flows of payments related to intellectual property. The importance of this indicator transcends mere economic quantification; it signals the level of innovation and competitiveness within nations, influencing both domestic and international market dynamics.

Understanding the significance of this indicator involves its relations to other economic metrics. For instance, countries that show high payments for intellectual property rights often correlate with advanced technological sectors and strong research and development (R&D) capabilities. This is valid for nations such as Ireland, the United States, and the Netherlands, which feature prominently among the top contributors. Furthermore, high values can signal a sophisticated export structure focused on innovations, featuring goods and services that are protected by robust intellectual property laws.

Data from 2023 reveals a median value of approximately 155 million US dollars for intellectual property payments. Leading the list are countries like Ireland, with colossal payments of over 153 billion USD, showcasing its role as a global hub for tech and pharmaceutical industries where intellectual property is paramount. Following Ireland are the United States at 47 billion, the Netherlands at 44 billion, and China at nearly 43 billion. These figures illustrate not just economic strength but also the strategic positioning of these nations in maintaining and leveraging their intellectual assets internationally.

Conversely, the bottom end of the spectrum illustrates a stark reality for many nations. Countries like Bhutan, Tajikistan, and Rwanda have reported payments in the thousands or low hundreds of thousands USD. These figures point to limited engagement with global intellectual markets, often reflective of domestic economies that are still developing, where local innovation and intellectual property systems may need strengthening. Their lower values call for policy interventions to enhance national capabilities in creating and protecting intellectual assets.

The historical data reveals a dramatic increase in global payments for intellectual property over the decades, from less than 7 million USD in 1960 to an astounding 586 billion in 2023. This growth reflects increasing globalization, as knowledge economies flourished, leading to the rise of digital products, biotechnology, and new media. The evolution of global commerce has pushed countries to behave similarly to work collectively to establish frameworks that protect intellectual property. As innovations proliferate, especially in technology and pharmaceuticals, so does the throughfare of payments for these vital economic assets.

Several factors drive the charges for the use of intellectual property. For countries at the top, robust legal frameworks, investment in R&D, and market demand for innovative products play critical roles. In countries like Ireland and the United States, venture capital funding and a culture of entrepreneurship further stimulate innovation, contributing to higher intellectual property values. Conversely, countries with weaker legal institutions may struggle to retrieve royalties owed for intellectual property or lack the infrastructure to support R&D activities, resulting in less significant payments.

Strategies to enhance the flow of payments for intellectual property often focus on fostering innovation ecosystems—establishing partnerships between universities and industry, refining legal protection for intellectual property, and promoting an entrepreneurial culture. Solutions might include developing national IP strategies that align with global standards, strengthening IP enforcement mechanisms, cultivating talent, and advancing collaboration in research across institutions and borders. Such measures can elevate a country's standing in the global intellectual property landscape and encourage foreign direct investments in technology-rich sectors.

However, inherent flaws exist within the framework of intellectual property payments. There is often a growing tendency for developed nations to dominate these payments, creating an imbalance where developing countries may find themselves in disadvantaged positions, perpetuating global inequalities. Furthermore, unclear legal definitions and boundaries regarding intellectual property can lead to disputes over ownership and rights, deterring both local and international investments. Maintaining equitable access to technology for all nations remains a challenge. The digital divide can exacerbate disparities, compelling developing nations to either rely excessively on foreign intellectual property or create insufficient domestic innovations.

In conclusion, the charges for the use of intellectual property reflect vital economic interactions that influence global and local economies. As nations continue to traverse the intricate landscape of innovation and technology, understanding and analyzing this indicator will remain crucial for policymakers aiming to harness the economic potential of intellectual assets while addressing disparities among differing economies.

                    
# 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 = 'BM.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 <- 'BM.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))