Women Business and the Law Index Score (scale 1-100)

The Women Business and the Law Index Score is a crucial metric designed to measure the legal rights and opportunities afforded to women in the business sphere across different countries. Ranging from 1 to 100, this index is instrumental in assessing the progress of women's economic empowerment and indicates the level of legal equality in the workplace, as well as access to resources for female entrepreneurs. The index includes various factors such as the legal rights related to property ownership, employment, entrepreneurship, and sexual harassment protection. The latest update, in 2023, shows a significant median value of 81.25, highlighting the ongoing advancements towards gender equality in business frameworks globally.

The importance of the Women Business and the Law Index cannot be overstated. It provides crucial insights not only into women’s rights but also into the overall economic climate of countries. A higher index score implies not just a legal framework that supports women but also indicates a potential for greater economic development. Research has demonstrated that gender equality stimulates economic growth, improves labor productivity, and fosters innovation. Countries that score higher tend to have more robust economies, as they leverage the talents and resources of their entire population, not just a fraction.

The relation of the Women Business and the Law Index to other indicators is profound. It correlates with various indices measuring human capital development, economic growth, and poverty reduction. For instance, the Global Gender Gap Report indicates how close countries are to gender parity across multiple domains, and the Women Business and the Law Index offers a deeper dive into the specific legal frameworks affecting this parity. Furthermore, countries with high scores in this index often have better rankings in World Bank Ease of Doing Business Index, as supportive legal frameworks make it easier and safer for women to engage in business activities.

Several factors affect the Women Business and the Law Index. Cultural and societal norms play a significant role; in cultures where patriarchal values dominate, legal frameworks may not be as supportive of women's rights. Government policies, political stability, and economic conditions also critically influence the index score. Additionally, the level of advocacy and activism by women’s rights organizations can pressure governments to implement reforms that improve women's economic participation. For example, in countries like Belgium, Canada, Denmark, France, and Germany, which boast perfect scores of 100.0, there has been a history of strong advocacy for women's rights combined with robust legal frameworks that ensure equality in business.

To improve the Women Business and the Law Index, various strategies can be employed. First, public awareness campaigns can educate citizens about women’s rights and the importance of gender equality in the workplace. Second, updating legal frameworks to eliminate discrimination against women and to establish clear protections can help in elevating index scores. Additionally, mentorship programs and access to financial resources for women entrepreneurs can significantly enhance economic opportunities for women. Finally, collaboration between governments, NGOs, and the private sector is essential in creating environments that foster equity in business.

Nonetheless, the current index does have its flaws. For example, while the numbers indicate success in several regions, they may not fully encapsulate the nuances of women's experiences in business. Legal rights on paper do not necessarily translate to real-world equality. Implementation gaps—where laws exist but are not enforced effectively—can create discrepancies between the index score and the actual environment for women in business. Moreover, the index may overlook areas such as cultural barriers that prevent women from fully utilizing their legal rights, which can hinder true progress toward equality.

The latest statistics show a steady improvement from the historical world values, with the index scoring 77.86 in 2023, reflecting a significant rise from earlier years, especially since the early 2000s when it hovered around 60.65. This increasing trend in the Women Business and the Law Index over decades illustrates a growing global recognition of the importance of women's rights. However, the significant gap between the top and bottom performers, like the Palestinian Territories at 26.25 and Yemen at 26.88, highlights the continued challenges that many regions face.

In conclusion, the Women Business and the Law Index Score serves as an instrument for evaluating the legal environment for women in business worldwide. Its implications are profound, influencing economic development and social justice. As we observe both remarkable achievements in countries leading in gender equality and persistent challenges in others, it is evident that the journey towards true equality in business is ongoing. The reported scores encourage continuous reflection and action to ensure that women around the globe have equal access to opportunities and protections in the business landscape.

                    
# 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 = 'SG.LAW.INDX'

# 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 <- 'SG.LAW.INDX'

# 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))