Code
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import plotly.express as px Code
# Set style
sns.set_style("whitegrid")
plt.rcParams['figure.figsize'] = (12, 6)Code
# Load data
df_panel = pd.read_excel('Data/WBL2024-1-0-Historical-Panel-Data.xlsx',sheet_name='WBL Panel 2024')
df_mobility = pd.read_excel('Data/WBL2024-1-0-Historical-Panel-Data.xlsx',sheet_name='Mobility')
df_workplace = pd.read_excel('Data/WBL2024-1-0-Historical-Panel-Data.xlsx',sheet_name='Workplace')
df_pay = pd.read_excel('Data/WBL2024-1-0-Historical-Panel-Data.xlsx',sheet_name='Pay')
df_marriage = pd.read_excel('Data/WBL2024-1-0-Historical-Panel-Data.xlsx',sheet_name='Marriage')
df_parenthood = pd.read_excel('Data/WBL2024-1-0-Historical-Panel-Data.xlsx',sheet_name='Parenthood')
df_assets = pd.read_excel('Data/WBL2024-1-0-Historical-Panel-Data.xlsx',sheet_name='Assets')
df_pension = pd.read_excel('Data/WBL2024-1-0-Historical-Panel-Data.xlsx',sheet_name='Pension')
df_laws_legal_2024 = pd.read_excel('Data/WBL2024-2-0.xlsx',sheet_name='Legal2.0')
df_sfw_legal_2024 = pd.read_excel('Data/WBL2024-2-0.xlsx',sheet_name='SF2.0')
df_sfw_economy2024 = pd.read_excel('Data/WBL2024-2-0.xlsx',sheet_name='EO_ByEconomy')
df_crosssection = pd.read_csv('Data/cross_section_data_encoded.csv')Code
region = "Sub-Saharan Africa"Code
df_panel = df_panel[df_panel["Region"]== "Sub-Saharan Africa"]
df_mobility = df_mobility[df_mobility["Region"]== "Sub-Saharan Africa"]
df_workplace = df_workplace[df_workplace["Region"]== "Sub-Saharan Africa"]
df_pay = df_pay[df_pay["Region"]== "Sub-Saharan Africa"]
df_marriage = df_marriage[df_marriage["Region"]== "Sub-Saharan Africa"]
df_parenthood = df_parenthood[df_parenthood["Region"]== "Sub-Saharan Africa"]
df_assets = df_assets[df_assets["Region"]== "Sub-Saharan Africa"]
df_pension = df_pension[df_pension["Region"]== "Sub-Saharan Africa"]
df_laws_legal_2024 = df_laws_legal_2024[df_laws_legal_2024["Region"]== "Sub-Saharan Africa"]Code
#df_crosssection.columns
non_binary_cols = ['Economy', 'OVERALL']
binary_cols = [col for col in df_crosssection.columns if col not in non_binary_cols]
plt.figure(figsize=(12, 6))
means = df_crosssection[binary_cols].mean().sort_values(ascending=False)
means.plot(kind='bar')
plt.xlabel('Laws')
plt.ylabel('Proportion (% with feature)')
plt.title('Commonly Enforced Laws')
plt.xticks(rotation=45, ha='right')
plt.axhline(y=0.5, color='r', linestyle='--', alpha=0.5, label='50% threshold')
plt.legend()
plt.show()
Code
# Correlation Matrix for Women, Business and the Law 2.0 Data for 20244
numeric_cols = df_laws_legal_2024.select_dtypes(include=[np.number]).columns
correlation = df_laws_legal_2024[numeric_cols].corr()
plt.figure(figsize=(12,10))
sns.heatmap(correlation, annot=True, fmt='.2f', cmap='coolwarm',
center=0, square=True, linewidths=1)
plt.title('Correlation Matrix')
plt.tight_layout()
plt.show()
# Notes: 
Code
df_panel.head(5)| Economy | Economy Code | ISO Code | Region | Income Group | Report Year | WBL INDEX | MOBILITY | Can a woman choose where to live in the same way as a man? | Can a woman travel outside her home in the same way as a man? | ... | Do women and men have equal ownership rights to immovable property? | Do sons and daughters have equal rights to inherit assets from their parents? | Do male and female surviving spouses have equal rights to inherit assets? | Does the law grant spouses equal administrative authority over assets during marriage? | Does the law provide for the valuation of nonmonetary contributions? | PENSION | Is the age at which women and men can retire with full pension benefits the same? | Is the age at which women and men can retire with partial pension benefits the same? | Is the mandatory retirement age for women and men the same? | Are periods of absence due to childcare accounted for in pension benefits? | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 162 | Angola | AGO | AGO | Sub-Saharan Africa | Lower middle income | 1971 | 30.625 | 75 | No | Yes | ... | No | Yes | Yes | Yes | Yes | 25 | No | No | Yes | No |
| 163 | Angola | AGO | AGO | Sub-Saharan Africa | Lower middle income | 1972 | 30.625 | 75 | No | Yes | ... | No | Yes | Yes | Yes | Yes | 25 | No | No | Yes | No |
| 164 | Angola | AGO | AGO | Sub-Saharan Africa | Lower middle income | 1973 | 30.625 | 75 | No | Yes | ... | No | Yes | Yes | Yes | Yes | 25 | No | No | Yes | No |
| 165 | Angola | AGO | AGO | Sub-Saharan Africa | Lower middle income | 1974 | 30.625 | 75 | No | Yes | ... | No | Yes | Yes | Yes | Yes | 25 | No | No | Yes | No |
| 166 | Angola | AGO | AGO | Sub-Saharan Africa | Lower middle income | 1975 | 30.625 | 75 | No | Yes | ... | No | Yes | Yes | Yes | Yes | 25 | No | No | Yes | No |
5 rows × 55 columns
Code
result = pd.merge(df_panel, df_mobility, on='Economy Code', how='inner')Code
result.head()| Economy_x | Economy Code | ISO Code_x | Region_x | Income Group_x | Report Year_x | WBL INDEX | MOBILITY | Can a woman choose where to live in the same way as a man?_x | Can a woman travel outside her home in the same way as a man?_x | ... | Legal Basis | Can a woman travel outside her home in the same way as a man?_y | Score.1 | Legal Basis.1 | Can a woman apply for a passport in the same way as a man?_y | Score.2 | Legal Basis.2 | Can a woman travel outside the country in the same way as a man?_y | Score.3 | Legal Basis.3 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Angola | AGO | AGO | Sub-Saharan Africa | Lower middle income | 1971 | 30.625 | 75 | No | Yes | ... | Decreto-Law No.47344 of 1966 (Civil Code), Art... | Yes | 1 | No restrictions could be located | Yes | 1 | No restrictions could be located | Yes | 1 | No restrictions could be located |
| 1 | Angola | AGO | AGO | Sub-Saharan Africa | Lower middle income | 1971 | 30.625 | 75 | No | Yes | ... | Civil Code, 1672 | Yes | 1 | No restrictions could be located | Yes | 1 | No restrictions could be located | Yes | 1 | No restrictions could be located |
| 2 | Angola | AGO | AGO | Sub-Saharan Africa | Lower middle income | 1971 | 30.625 | 75 | No | Yes | ... | Civil Code, 1672 | Yes | 1 | No restrictions could be located | Yes | 1 | No restrictions could be located | Yes | 1 | No restrictions could be located |
| 3 | Angola | AGO | AGO | Sub-Saharan Africa | Lower middle income | 1971 | 30.625 | 75 | No | Yes | ... | Civil Code, 1672 | Yes | 1 | No restrictions could be located | Yes | 1 | No restrictions could be located | Yes | 1 | No restrictions could be located |
| 4 | Angola | AGO | AGO | Sub-Saharan Africa | Lower middle income | 1971 | 30.625 | 75 | No | Yes | ... | Civil Code, 1672 | Yes | 1 | No restrictions could be located | Yes | 1 | No restrictions could be located | Yes | 1 | No restrictions could be located |
5 rows × 73 columns
Code
question_cols = [col for col in result.columns if '?' in col and col.endswith('_x')]
result['num_yes_answers'] = result[question_cols].apply(lambda row: (row == 'Yes').sum(), axis=1)
# Calculate compliance percentage
total_questions = len(question_cols)
result['Increase of Laws'] = (result['num_yes_answers'] / total_questions) * 100
plt.figure(figsize=(12, 6))
yearly_avg = result.groupby('Report Year_x')['compliance_percentage'].mean()
yearly_avg.plot(kind='line', marker='o', linewidth=2, markersize=8, color='steelblue')
plt.xlabel('Year', fontsize=12)
plt.ylabel('Average Compliance Percentage (%)', fontsize=12)
plt.title('Sub-Saharan Africa Gender Laws Over Time', fontsize=14, fontweight='bold')
plt.grid(alpha=0.3)
plt.tight_layout()
plt.show()