New business density (new registrations per 1,000 people ages 15-64)

The new business density, defined as the number of new registrations per 1,000 people aged 15-64, serves as a crucial indicator of entrepreneurship and economic vitality in different regions. This metric provides valuable insights into how conducive an area is for starting new enterprises and reflects both the health and dynamism of local economies. The latest data from 2022 offers a thorough overview of global trends and highlights significant disparities between regions.

In 2022, the median value for new business density was recorded at 2.82. This figure shows a slight uptick from prior years, suggesting a modest recovery and interest in entrepreneurship post-pandemic. However, it remains markedly lower than the peaks seen in earlier years, with the world average showing consistent growth from 2010 until 2020, when it reached a high of 3.41. This recent stabilization may indicate a variety of factors at play: economic uncertainty, regulatory challenges, and shifting consumer behavior, all of which can profoundly impact entrepreneurial activity.

The statistics for new business density vary dramatically across regions. The top five areas for new business density in 2022 included the Cayman Islands, with a staggering 228.69 new registrations per 1,000 people aged 15-64, followed by the Isle of Man at 31.53, and Estonia at 24.32. These figures suggest a remarkable entrepreneurial ecosystem in these regions, likely driven by favorable business regulations, tax policies, and a supportive environment for startups. In contrast, the bottom five areas revealed significantly lower densities; Liberia, at a dismal 0.02, accompanied by Bhutan, Madagascar, Iraq, and India, all indicated that these regions struggle with numerous hurdles that impede the establishment of new businesses.

The importance of new business density extends beyond mere statistics. High levels of new registrations typically translate to increased job creation, innovation, and economic growth. They signify that individuals are willing to take risks to start businesses, contributing to a cycle of entrepreneurial enthusiasm. However, low new business density may indicate deeper systemic issues such as bureaucratic hurdles, limited access to financing, or lack of entrepreneurial culture, which can stymie economic growth and exacerbate unemployment.

Several factors influence new business density, including economic conditions, government policy, and the overall ease of doing business. Regions with streamlined business registration processes, lower startup costs, and supportive legal frameworks tend to exhibit higher densities. For instance, nations offering favorable taxation, regulations that facilitate business ownership, and infrastructure supportive of business activities often enjoy a thriving entrepreneurial landscape. Conversely, areas with excessive regulatory barriers, high taxes, or political instability can severely limit new registrations.

Strategies to enhance new business density can involve both government interventions and community-driven initiatives. Policymakers may consider implementing tax incentives for startups, simplifying registration processes, or providing incubators and accelerators focused on nurturing new businesses. Education and training initiatives to equip potential entrepreneurs with the necessary skills can also bolster new business formation. Building a culture that celebrates entrepreneurship can further encourage individuals to pursue business ventures, creating an ecosystem that spreads innovation.

Despite the valuable insights it provides, the new business density is not without flaws. For instance, a high density does not automatically equate to sustainable businesses or economic growth. An influx of new registrations may merely reflect temporary trends or the proliferation of low-barrier businesses unable to survive the market's competitive nature. In addition, regions with high numbers of new registrations may still face challenges such as high rates of business failure. Therefore, it is essential to complement new business density data with other indicators, such as business survival rates and job creation metrics, to capture a fuller picture of economic health.

While the average global new business density has seen fluctuations, the continuous variation among regions underscores the complexities of entrepreneurship worldwide. The stark contrast between the top and bottom performers illustrates that while some areas boast vibrant and robust entrepreneurial ecosystems, others are mired in challenges that inhibit their potential. Understanding new business density not only helps gauge an economy's immediate state but also informs policymakers, investors, and educators as they aim to stimulate entrepreneurial growth. By fostering an inclusive environment that supports aspiring entrepreneurs, regions can leverage new business density as a critical component in their broader economic strategy.

                    
# 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 = 'IC.BUS.NDNS.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 <- 'IC.BUS.NDNS.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))