#!/usr/bin/env python3 """Generate synthetic African crop insurance payout dataset. Parameters derived from IBLI (Kenya/Ethiopia), WFP R4, ACRE Africa programs and published literature on index-based insurance in SSA. """ import csv import json import os import random from datetime import datetime import numpy as np # --- Country parameters (calibrated from IBLI/R4/ACRE field data) --- COUNTRIES = { "Kenya": { "regions": 8, "base_enrollment_rate": 0.12, "premium_rate": 0.055, "avg_premium_usd": 12, "drought_prob": 0.25, "ndvi_baseline": 0.48, "rainfall_baseline_mm": 650, }, "Ethiopia": { "regions": 10, "base_enrollment_rate": 0.08, "premium_rate": 0.045, "avg_premium_usd": 8, "drought_prob": 0.30, "ndvi_baseline": 0.42, "rainfall_baseline_mm": 580, }, "Tanzania": { "regions": 7, "base_enrollment_rate": 0.06, "premium_rate": 0.05, "avg_premium_usd": 10, "drought_prob": 0.20, "ndvi_baseline": 0.50, "rainfall_baseline_mm": 720, }, "Malawi": { "regions": 5, "base_enrollment_rate": 0.09, "premium_rate": 0.06, "avg_premium_usd": 7, "drought_prob": 0.22, "ndvi_baseline": 0.46, "rainfall_baseline_mm": 900, }, "Zambia": { "regions": 6, "base_enrollment_rate": 0.07, "premium_rate": 0.05, "avg_premium_usd": 9, "drought_prob": 0.18, "ndvi_baseline": 0.52, "rainfall_baseline_mm": 850, }, "Zimbabwe": { "regions": 5, "base_enrollment_rate": 0.05, "premium_rate": 0.06, "avg_premium_usd": 8, "drought_prob": 0.28, "ndvi_baseline": 0.44, "rainfall_baseline_mm": 600, }, "Mozambique": { "regions": 5, "base_enrollment_rate": 0.04, "premium_rate": 0.055, "avg_premium_usd": 6, "drought_prob": 0.22, "ndvi_baseline": 0.53, "rainfall_baseline_mm": 950, }, "Senegal": { "regions": 4, "base_enrollment_rate": 0.06, "premium_rate": 0.05, "avg_premium_usd": 11, "drought_prob": 0.35, "ndvi_baseline": 0.35, "rainfall_baseline_mm": 450, }, "Ghana": { "regions": 5, "base_enrollment_rate": 0.05, "premium_rate": 0.045, "avg_premium_usd": 10, "drought_prob": 0.15, "ndvi_baseline": 0.55, "rainfall_baseline_mm": 1100, }, "Nigeria": { "regions": 8, "base_enrollment_rate": 0.03, "premium_rate": 0.05, "avg_premium_usd": 9, "drought_prob": 0.20, "ndvi_baseline": 0.50, "rainfall_baseline_mm": 1050, }, "Rwanda": { "regions": 4, "base_enrollment_rate": 0.10, "premium_rate": 0.04, "avg_premium_usd": 7, "drought_prob": 0.12, "ndvi_baseline": 0.56, "rainfall_baseline_mm": 1200, }, "Uganda": { "regions": 5, "base_enrollment_rate": 0.07, "premium_rate": 0.05, "avg_premium_usd": 8, "drought_prob": 0.18, "ndvi_baseline": 0.52, "rainfall_baseline_mm": 1150, }, } CROP_TYPES = ["maize", "wheat", "sorghum", "rice", "cassava"] CROP_WEIGHTS = [0.35, 0.15, 0.20, 0.15, 0.15] INSURANCE_TYPES = ["index_based", "area_yield", "weather_derivative"] INSURANCE_WEIGHTS = [0.50, 0.30, 0.20] SEASONS = ["long_rains", "short_rains"] SCENARIOS = { "baseline": { "enrollment_mult": 1.0, "payout_mult": 1.0, "drought_shift": 0.0, "description": "Current-state parameters based on existing IBLI/R4/ACRE programs", }, "expanded_index_insurance": { "enrollment_mult": 2.5, "payout_mult": 1.1, "drought_shift": 0.0, "description": "Scaled enrollment via subsidy and mobile distribution (ACRE model)", }, "climate_shock": { "enrollment_mult": 1.1, "payout_mult": 1.0, "drought_shift": 0.15, "description": "Increased drought frequency reflecting IPCC AR6 projections for SSA", }, } # IBLI basis risk: Jensen et al. 2016 found ~69% residual risk exposure # and ~63% reduction in covariate risk from high-loss events BASIS_RISK_PARAMS = { "index_based": {"mean": 0.31, "std": 0.12}, "area_yield": {"mean": 0.22, "std": 0.10}, "weather_derivative": {"mean": 0.18, "std": 0.09}, } YEARS = list(range(2015, 2025)) # Crop-specific yield parameters (tonnes/ha) CROP_YIELD = { "maize": {"mean": 1.8, "std": 0.6}, "wheat": {"mean": 1.5, "std": 0.5}, "sorghum": {"mean": 0.9, "std": 0.4}, "rice": {"mean": 2.2, "std": 0.7}, "cassava": {"mean": 8.0, "std": 3.0}, } def generate_record(record_id, country, params, scenario_name, scenario, year, season): """Generate a single insurance record.""" rng = np.random.default_rng(seed=(record_id * 31 + hash(country) + year) % (2**31)) # Drought probability with scenario shift drought_prob = min(params["drought_prob"] + scenario["drought_shift"], 0.75) is_drought = rng.random() < drought_prob # NDVI anomaly (z-score). Negative during drought # IBLI uses cumulative NDVI deviation; payout triggers at 15th percentile if is_drought: ndvi_anomaly = round(rng.normal(-1.2, 0.5), 3) ndvi_anomaly = max(ndvi_anomaly, -3.0) else: ndvi_anomaly = round(rng.normal(0.1, 0.4), 3) ndvi_anomaly = max(min(ndvi_anomaly, 2.0), -0.5) # Rainfall deficit percentage if is_drought: rainfall_deficit = round(rng.uniform(25, 65), 1) else: rainfall_deficit = round(max(0, rng.normal(5, 15)), 1) # Yield loss percentage - correlated with NDVI anomaly and rainfall deficit base_yield_loss = max(0, -ndvi_anomaly * 15 + rainfall_deficit * 0.3 + rng.normal(0, 5)) yield_loss_pct = round(min(base_yield_loss, 90), 1) # Insurance type insurance_type = rng.choice(INSURANCE_TYPES, p=INSURANCE_WEIGHTS) # Crop type crop_type = rng.choice(CROP_TYPES, p=CROP_WEIGHTS) # Enrolled hectares (smallholder: 0.5-5 ha typical, ACRE data) enrolled_hectares = round(rng.lognormal(mean=1.2, sigma=0.7), 2) enrolled_hectares = max(0.2, min(enrolled_hectares, 20.0)) # Premium paid (ACRE: 5-25% of harvest value; R4: $7-12 avg) base_premium = params["avg_premium_usd"] premium_paid = round(base_premium * enrolled_hectares * rng.uniform(0.8, 1.3), 2) # Payout determination # Index-based: triggers on NDVI anomaly below ~15th percentile (z < -1.0) # Area-yield: triggers when yield_loss_pct > 20% # Weather derivative: triggers on rainfall_deficit > 30% if insurance_type == "index_based": payout_triggered = ndvi_anomaly < -1.0 elif insurance_type == "area_yield": payout_triggered = yield_loss_pct > 20.0 else: payout_triggered = rainfall_deficit > 30.0 # Apply scenario multiplier to slightly increase payout probability scenario_boost = 0.1 * (scenario["payout_mult"] - 1.0) if not payout_triggered and rng.random() < scenario_boost: payout_triggered = True # Payout amount # IBLI: payout proportional to severity above trigger # ACRE: payout ~ 60-90% of insured value when triggered if payout_triggered: severity = max(min(yield_loss_pct / 50.0, 1.0), 0.15) payout_ratio = round(max(rng.uniform(0.4, 0.9) * severity, 0.05), 3) insured_value = enrolled_hectares * CROP_YIELD[crop_type]["mean"] * 350 # USD/tonne payout_amount = round(max(insured_value * payout_ratio, 1.0), 2) else: payout_ratio = 0.0 payout_amount = 0.0 # Farmer count per record (represents a micro-insurance cohort) farmer_count = int(rng.lognormal(mean=4.5, sigma=1.0)) farmer_count = max(5, min(farmer_count, 500)) # Enrollment rate (scenario-adjusted) base_rate = params["base_enrollment_rate"] enrollment_rate = round( min(base_rate * scenario["enrollment_mult"] * rng.uniform(0.7, 1.3), 0.85), 4 ) # Lapse rate (IBLI: ~15-25% non-renewal; higher when no payout) if payout_triggered: lapse_rate = round(rng.uniform(0.05, 0.15), 4) else: lapse_rate = round(rng.uniform(0.15, 0.30), 4) # Basis risk score (0=perfect hedging, 1=no hedging) # Jensen et al. 2016: IBLI leaves ~69% residual risk br = BASIS_RISK_PARAMS[insurance_type] basis_risk_score = round(np.clip(rng.normal(br["mean"], br["std"]), 0.0, 1.0), 3) return { "record_id": record_id, "country": country, "year": year, "season": season, "crop_type": crop_type, "insurance_type": insurance_type, "scenario": scenario_name, "enrolled_hectares": enrolled_hectares, "premium_paid_usd": premium_paid, "ndvi_anomaly": ndvi_anomaly, "rainfall_deficit_pct": rainfall_deficit, "yield_loss_pct": yield_loss_pct, "payout_triggered": payout_triggered, "payout_amount_usd": payout_amount, "payout_ratio": payout_ratio, "farmer_count": farmer_count, "enrollment_rate": enrollment_rate, "lapse_rate": lapse_rate, "basis_risk_score": basis_risk_score, } def generate_dataset(): """Generate full dataset: 12 countries × 3 scenarios × 10000 records.""" all_records = [] record_id = 1 for scenario_name, scenario in SCENARIOS.items(): records_per_country = 10000 // len(COUNTRIES) remainder = 10000 % len(COUNTRIES) for i, (country, params) in enumerate(COUNTRIES.items()): n = records_per_country + (1 if i < remainder else 0) for _ in range(n): year = random.choice(YEARS) season = random.choice(SEASONS) rec = generate_record( record_id, country, params, scenario_name, scenario, year, season ) all_records.append(rec) record_id += 1 return all_records def write_csv(records, output_path): """Write records to CSV.""" fieldnames = list(records[0].keys()) with open(output_path, "w", newline="") as f: writer = csv.DictWriter(f, fieldnames=fieldnames) writer.writeheader() writer.writerows(records) def write_metadata(output_dir): """Write dataset metadata JSON.""" metadata = { "name": "african-crop-insurance-payouts", "description": ( "Synthetic dataset of agricultural index-insurance payouts across " "12 Sub-Saharan African countries. Parameters calibrated from IBLI " "(Kenya/Ethiopia), WFP R4, and ACRE Africa programs." ), "version": "1.0.0", "created": datetime.utcnow().isoformat() + "Z", "license": "CC-BY-4.0", "scenarios": { name: s["description"] for name, s in SCENARIOS.items() }, "countries": list(COUNTRIES.keys()), "crop_types": CROP_TYPES, "insurance_types": INSURANCE_TYPES, "seasons": SEASONS, "sources": [ "Jensen & Barrett (2016) AJAE - IBLI basis risk analysis", "WFP R4 Rural Resilience Initiative reports (2018-2023)", "ACRE Africa scaling data (GIIF 2016, IFC 2014)", "Ntukamazina et al. (2017) J. Agr. Rural Develop. Trop. Subtrop.", "IPCC AR6 WGII Chapter 9 - Africa climate projections", ], "variables": { "record_id": "Unique integer identifier", "country": "Country name", "year": "Calendar year (2015-2024)", "season": "Growing season (long_rains / short_rains)", "crop_type": "Insured crop (maize/wheat/sorghum/rice/cassava)", "insurance_type": "Product type (index_based/area_yield/weather_derivative)", "scenario": "Policy scenario (baseline/expanded_index_insurance/climate_shock)", "enrolled_hectares": "Hectares covered by the policy", "premium_paid_usd": "Premium amount in USD", "ndvi_anomaly": "NDVI z-score anomaly (negative = vegetation deficit)", "rainfall_deficit_pct": "Percentage below normal rainfall", "yield_loss_pct": "Estimated yield loss percentage", "payout_triggered": "Whether index triggered a payout (True/False)", "payout_amount_usd": "Payout disbursement in USD (0 if not triggered)", "payout_ratio": "Payout as fraction of insured value", "farmer_count": "Number of farmers in the micro-cohort", "enrollment_rate": "Regional enrollment rate (0-1)", "lapse_rate": "Policy non-renewal rate (0-1)", "basis_risk_score": "Residual unhedged risk (0=perfect, 1=none)", }, "parameter_sources": { "premium_rates": "IBLI: 5.5%/3.25% of insured value; R4: 10-15% farmer contribution", "payout_trigger": "IBLI: NDVI < 15th percentile; ACRE: yield < 80% of average", "basis_risk": "Jensen et al. 2016: IBLI reduces covariate risk by 63%, residual 69%", "enrollment": "R4: ~180K farmers (2020); ACRE: ~400K farmers (2015)", "sum_insured": "R4: ~$98/farmer; ACRE: $650/acre for bundled seed products", }, } with open(os.path.join(output_dir, "metadata.json"), "w") as f: json.dump(metadata, f, indent=2) if __name__ == "__main__": output_dir = os.path.join(os.path.dirname(__file__), "data") os.makedirs(output_dir, exist_ok=True) print("Generating 30,000 records (12 countries × 3 scenarios × ~10,000)...") records = generate_dataset() csv_path = os.path.join(output_dir, "african_crop_insurance_payouts.csv") write_csv(records, csv_path) print(f"Wrote {len(records)} records to {csv_path}") write_metadata(os.path.dirname(__file__)) print("Wrote metadata.json") # Print summary stats payouts = [r for r in records if r["payout_triggered"]] print(f"\nSummary:") print(f" Total records: {len(records)}") print(f" Payouts triggered: {len(payouts)} ({100*len(payouts)/len(records):.1f}%)") print(f" Countries: {len(COUNTRIES)}") print(f" Scenarios: {list(SCENARIOS.keys())}") if payouts: print(f" Avg payout: ${np.mean([r['payout_amount_usd'] for r in payouts]):.2f}") print(f" Avg basis risk: {np.mean([r['basis_risk_score'] for r in records]):.3f}")