1. Smallholder access to weather securities: demand and impact on consumption and production decisions Tirtha Chatterjee, Isaac Manuel, Ashutosh Shekhar Centre for Insurance and Risk Management, CIRM-IFMR Ruth V. Hill, Peter Ouzounov, Miguel Robles International Food Policy Research Institute - IFPRI Netherlands – April 2012

2. Research problem: Smallholders in developing countries are exposed to weather shocks. Weather shocks have large impact on output In the absence of efficient mechanisms to transfer/share risks then impact on welfare –Negative impact on investment decisions –Volatile income and consumption Smallholders have none or very limited acces to weather insurance markets Weather index-based products is an effort to provide access to smallholder to weather insurance markets Uptake on existing weather index-based products is low

3. Research problem: We propose a new approach in providing weather index–based insurance products –Multiple weather securities that pay a fixed amount as opposed to a unique policy –Weather securities are simple and flexible We run a pilot project to provide weather securities and understand demand (uptake) and impact on consumption and production decisions In this presentation: What is the impact of three interventions on weather securities uptake? (preliminary results) –Price discounts –Insurance literacy training –Distance to weather station (basis risk) Research questions:

4. Product: Basic concept Basic product: weather security (rainfall excess)… Payout (Rs.) Trigger value Exit Index Price (premium)

5. Product: basic concept… Basic product: weather security (rainfall deficit)… Payout (Rs.) Trigger value Exit Index Price (premium)

6. Product: multiple securities We identify 3 cover periods: For each cover period we have multiple (at least two) products: –Different trigger values –Different prices –Same payouts Farmers are free to choose among different products! Cover period Jun 25 – Jul 20Jul 21 – Sep 15Sep 16 – Oct 15 Crop stageSowing and germinationVegetative, reproductive and maturity Harvest PerilExcessive rainfall Deficit rainfall Excessive rainfall IndexMaximum rainfall on any single day Total cumulative rainfallMaximum rainfall on any single day

7. Final products: Dewas district SecurityCover periodindexstrikeExitPayout 1 condition Payout 2 condition Premium incl of ST(Rs) (Rs 1000)(Rs 4000) Security 1Jun 25 – Jul 20maximum rainfall on any single day (mm) 95200Index > strike Index > exit 352 Security 2Jun 25 – Jul 20maximum rainfall on any single day (mm) 120200Index > strike Index > exit 265 Security 3Jul 21 – Sep 15Total cumulative rainfall (mm) 280130Index < strike Index < exit 265 Security 4Jul 21 – Sep 15Total cumulative rainfall (mm) 340130Index < strike Index < exit 352 Security 5Jul 21 – Sep 15Total cumulative rainfall (mm) 635960Index > strike Index > exit 352 Security 6Jul 21 – Sep 15Total cumulative rainfall (mm) 700960Index > strike Index > exit 265 Security 7Sept 16 – Oct 15maximum rainfall on any single day (mm) 70160Index > strike Index > exit 352 Security 8Sept 16 – Oct 15maximum rainfall on any single day (mm) 85160Index > strike Index > exit 265 Not implemented triggered

8. Location and sample Product was marketed in 3 districts of Madhya Pradesh, India: Dewas, Bhopal, Ujjain Research focus: 30 landowning households per village DistrictVillagesHouseholds (total) Households (sample) % Dewas29535688116.4% Bhopal30726490412.4% Ujjain (only last cover period) 13277739814.3% Total7215397218314.2%

9. Data: oversampled hhs with larger land holding and higher education Treatment (Insurance) Control (No insurance) Total Villages7238110 Sample households (30 per village) 218311563339 Land holding (acres) In sample 8.48.98.6 Land holding (acres) Out of sample 3.53.73.6 Schooling head hh (yrs) In sample 5.45.75.5 Schooling head hh (yrs) Out of sample 4.34.24.3

10. Data Average 8.6 acres of land, 90% sown with soy Over last 10 years, 15% experienced flood and 40% experienced drought 35% trust private insurance schemes Low knowledge of insurance (1/2 correct) 26% believe closest weather station is a good measure of rain for their field

11. Exogenous (randomized) treatments 1.Insurance literacy training –Basic training (2 hours)72 (all) villages –Intensive training (4 hours)35 villages 2.Three new randomly placed reference weather stations –2 in Dewas: 16/29 villages –1 in Bhopal: 12/30 villages No. of villagesAverage distance Existing weather station4410 Km New weather station285 Km

12. Exogenous (randomized) treatments 3.Allocation of price discount vouchers In Dewas and Bhopal (59 villages) –Random selection at household level: –5 hhs x [ Rs. 45, Rs. 90, Rs 135, Rs 180 ] –10 hhs x No discount –Only sample households received discounts In Ujjain (13 villages) –Random selection at village level (all hhs receive vouchers) –2 villages x [ Rs. 30, Rs 60, Rs 90, Rs 120 ] –5 villages x No discount

13. Research results, I Treated Villages (all households) Household sample # of Sales Acres insured per saleUptake Acres insured per purchasing hh Ujjain 1150.52.5% (10/398)0.9 Dewas 451.51.8% (16/881)2.7 Bhopal 1410.313.6% (123/904)0.4 Total 3010.66.8% (149/2183)0.6 Overall uptake 6.8% On average, they insured less than an acre and much less than their total soy land ownership There are important differences between districts

14. Research Results, II Summary Of Results: Dependent variable is whether household bought insurance or not (1)(2)(3)(4)(5)(6)(7)(8) OLSIVOLSIVOLSIVOLSIV Discount (ratio of price) 0.231***0.227***0.264***0.266*** (0.052) (0.061) Distance to ref. Station -0.011* -0.010* (0.006) Additional Training 0.049**0.0380.051**0.050** (0.024)(0.028)(0.024) District Fixed Effects Yes HH characteristic covariates No Yes Observations 2,183 2,164 R-squared 0.1240.0610.099 0.1340.0050.0720.082 Standard errors adjusted for clustering at village level are in parentheses; *** p<0.01, ** p<0.05, * p<0.1

15. Research Results Summary Of Results: Dependent variable is log of land insured (1)(2)(3)(4)(5)(6)(7)(8) OLSIVOLSIVOLSIVOLSIV Log of price of cheaper contract -0.582***-0.566***-0.594*** - 0.596*** (0.133)(0.136)(0.137) Distance to ref. station -0.029-0.028-0.026-0.027 (0.022) Additional training 0.167*0.1400.178*0.180** (0.091)(0.103)(0.090)(0.091) District Fixed Effects Yes HH characteristic covariates No Yes Observations 2,183 2,164 R-squared 0.1000.0430.1100.0230.0520.094 Standard errors adjusted for clustering at village level are in parentheses; *** p<0.01, ** p<0.05, * p<0.1 Distance to weather station has no effect quantity bought, but only on whether household buys or not

16. Implications policy and practice Cost and Benefit (uptake) analysis of interventions ILT Cost per ‐ person $10.40 -> + 5% points take-up Cost of Increasing take ‐ up rates by 10% points = $20.80 per-person New weather stations Cost per-person $6.67 -> + 5% points take ‐ up Cost of Increasing take ‐ up rates by 10% points = $13.34 per-person Price discounts To increase take ‐ up rates by 10% points a discount of 115 Rs ($2.30) per policy is needed. In Bhopal and Dewas the amount spent on discounts per-person who was offered a discount was $0.2 -> increase in take-up by 10% points Price discounts is the most cost effective intervention

17. Discussion Marketing efforts are key! We have casual evidence that take-up differences across districts is related to marketing efforts by insurance company –Second round implementation will pay more attention to incentives to insurance agents –Research pilots need to encourage permanent presence among treatment group Ideal study is on impact on consumption and production decisions (welfare) –We requiere higher take-up rates What’s the ideal demand analysis of multiple products? –System of demand equations –Again we need higher take-up rates