electricsheepafrica/africa-synth-insurance-penetration-africa-all

⚠️ Synthetic dataset — Parameterized from published SSA literature, not real observations. Not suitable for empirical analysis or policy inference.

Insurance Penetration in Africa

Synthetic dataset modeling insurance coverage, claims, and barriers across 15 Sub-Saharan African countries from 2018-2025.

Dataset Description

This dataset simulates individual-level insurance adoption, policy types, and claims experience. It captures the low insurance penetration rates across most of SSA (except South Africa) and the barriers that prevent broader adoption.

Key Statistics

Metric	Value
Total Records	15,000
Countries	15
Time Period	2018-2025
Insurance Coverage	~10% (varies by country)
South Africa Coverage	~42%
Avg Annual Premium	$50-200 USD
Claim Rejection Rate	~20%
Top Barrier	Affordability (45%)

Coverage by Scenario

• low_burden: 4,000 records
• moderate_burden: 5,000 records
• high_burden: 6,000 records

Column Descriptions

Column	Type	Description
country	string	One of 15 SSA countries
year	int	Year (2018-2025)
urban_rural	string	Urban or rural location
gender	string	Male or female
age	int	Age in years (18-75)
age_group	string	Age bracket
income_quintile	string	Income group (lowest to highest)
education	string	Education level
employment_status	string	Employment category
employer_provided_insurance	int	Has employer-provided insurance (0/1)
insurance_awareness	int	Insurance awareness score (1-10)
has_insurance	int	Has insurance coverage (0/1)
insurance_type	string	Type of insurance
provider	string	Insurance provider name
premium_annual_usd	float	Annual premium in USD
coverage_amount_usd	float	Coverage amount in USD
policy_term_years	int	Policy term in years
has_made_claim	int	Has made a claim (0/1)
claim_amount_usd	float	Claim amount in USD
claim_outcome	string	Claim outcome (approved_full/partial/rejected)
claim_payout_usd	float	Payout amount in USD
primary_barrier	string	Main barrier to insurance
satisfaction_score	int	Satisfaction score (1-10)
scenario	string	Burden scenario label

Usage Example

import pandas as pd

# Load the combined dataset
df = pd.read_csv("insurance_combined.csv")

# Analyze insurance types
types = df[df['has_insurance'] == 1]['insurance_type'].value_counts()

# Predict insurance adoption
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split

features = ['age', 'income_quintile', 'education', 'insurance_awareness', 'urban_rural']
X = pd.get_dummies(df[features], drop_first=True)
y = df['has_insurance']

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
model = RandomForestClassifier().fit(X_train, y_train)
print(f"Accuracy: {model.score(X_test, y_test):.2f}")

Research Sources

• Swiss Re Sigma Reports 2024
• World Health Organization health financing data 2024
• Insurance regulatory reports (AKI Kenya, NAICOM Nigeria)
• FSD Kenya insurance research 2024

Citation

@dataset{insurance_africa_2025,
  title={Insurance Penetration in Africa},
  year={2025},
  note={Synthetic dataset based on Swiss Re and WHO data}
}