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Historical mean daily rainfall (4.18 mm vs 4.12 mm, t = -0.11) and historical coefficient of variation (0.868 vs 0.845, t = -0.16) were statistically indistinguishable between payout and non-payout villages, confirming that 2011 payouts were driven by exogenous weather shocks rather than baseline risk differences.
Unsubsidized insurance take-up is low (below 15%), but demand increases significantly to 60.2% when price subsidies reach 75%, demonstrating that spatial basis risk severely dampens demand at actuarially fair prices.
36 out of 120 Kebeles (30%) were excluded because dominant risks were non-drought perils (frost and flooding) lacking indices, and another 35 Kebeles were dropped due to incomplete historical station rainfall records required for reinsurance pricing.
Assigning an insurance contract to a distant rainfall station reduced mean willingness to pay by 29.45 Rupees (p < 0.01), cutting baseline policy valuation (68.4 Rupees) roughly in half.
Risk aversion is negatively correlated with stated willingness to pay (p < 0.05) and shows no positive effect on actual uptake (marginal effect = -0.00285, p > 0.10), confirming theoretical models where basis risk reverses standard expected utility predictions.
A 1 standard deviation increase in rainfall increases harvest labor demand by 3.3 days more for insured cultivators than uninsured cultivators (a 22% relative increase) [8]. A 10% increase in insured cultivators lowers drought wages by 33% at the 20th percentile of rainfall, but raises wages by 29% at the 80th percentile of rainfall [9].
A 1 standard deviation increase in rainfall per day increases harvest labor demand by 3.3 days more for insured cultivators compared to uninsured cultivators in the same village (a 22% increase in relative demand; t-stat = 2.39).
Insurance coverage leads cultivators to increase total agricultural investment expenditures by 18.6% and allocate 12.3% more land area to cash crops and high-yield variety seeds relative to uninsured control farmers.
Contract payouts triggered in 4 villages in Andhra Pradesh, dispensing tiered indemnity cash payouts of Rs. 300, Rs. 750, or Rs. 1,200 per unit purchased depending on the duration of monsoon delay.
Payouts ranged from Rs. 300 (15-day delay) to Rs. 1,200 (25-day delay) per unit; 14–15% of the sample households experienced qualified payouts across 4 Andhra Pradesh villages during the 2011 Kharif season.
Assigning an insurance policy to a distant weather station (introducing substantial basis risk) reduced farmer willingness-to-pay by Rs. 29.45 (p < 0.01), effectively halving consumer valuation from the baseline average bid of Rs. 68.40 despite identical actuarial expected payouts.
Linking payouts strictly to third-party automated rain gauges eliminated individual claims verification costs and completely removed moral hazard and adverse selection incentives.
Insured wage workers receiving insurance payouts had a labor participation rate 28.5 percentage points lower than uninsured peers at median rainfall shock [18], working 15.4 fewer days in the season [19]. A 10% increase in laborer insurance marketing raises village wages by 24.7% in payout states [20].
Experiencing a drought shock in the previous year negatively predicted ex-ante demand for insurance (p < 0.05) and reduced actual insurance purchase probability by 7.8 percentage points (marginal effect = -0.0781, p > 0.10).
36 out of 120 Kebeles (30%) were excluded from the study because rainfall deficit was not the primary hazard, as frost and flooding predominated and lacked suitable index data.
Insurance provision increased the proportion of farmers planting high-return cash crops by 6 percentage points (a 12% relative increase, p=0.041), generating a 27% increase in land area sown for cash crops [9, 10].