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

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

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:

Product: Basic concept

• Basic product: weather security (rainfall excess)…

Payout (Rs.)

Triggervalue

ExitIndex

Price (premium)

Product: basic concept…

• Basic product: weather security (rainfall deficit)…

Payout (Rs.)

Triggervalue

ExitIndex

Price (premium)

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 20 Jul 21 – Sep 15 Sep 16 – Oct 15

Crop stage Sowing and germination Vegetative, reproductive and maturity

Harvest

Peril • Excessive rainfall • Excessive rainfall• Deficit rainfall

• Excessive rainfall

Index Maximum rainfall on any single day

Total cumulative rainfall Maximum rainfall on any single day

Final products: Dewas district

Security Cover period index strike Exit Payout 1 condition

Payout 2 condition Premium

incl of ST(Rs)

          (Rs 1000) (Rs 4000)  Security 1 Jun 25 – Jul 20 maximum rainfall on any

single day (mm)95 200 Index >

strikeIndex > exit

352Security 2 Jun 25 – Jul 20 maximum rainfall on any

single day (mm)120 200 Index >

strikeIndex > exit

265Security 3 Jul 21 – Sep 15 Total cumulative rainfall

(mm)280 130 Index <

strikeIndex < exit

265Security 4 Jul 21 – Sep 15 Total cumulative rainfall

(mm)340 130 Index <

strikeIndex < exit

352Security 5 Jul 21 – Sep 15 Total cumulative rainfall

(mm)635 960 Index >

strikeIndex > exit

352Security 6 Jul 21 – Sep 15 Total cumulative rainfall

(mm)700 960 Index >

strikeIndex > exit

265Security 7 Sept 16 – Oct 15 maximum rainfall on any

single day (mm)70 160 Index >

strikeIndex > exit

352Security 8 Sept 16 – Oct 15 maximum rainfall on any

single day (mm)85 160 Index >

strikeIndex > exit

265

Not implemented

triggered

Location and sample

• Product was marketed in 3 districts of Madhya Pradesh, India: Dewas, Bhopal, Ujjain

• Research focus: 30 landowning households per village

District Villages Households (total)

Households (sample)

%

Dewas 29 5356 881 16.4%

Bhopal 30 7264 904 12.4%

Ujjain (only last cover period)

13 2777 398 14.3%

Total 72 15397 2183 14.2%

Data: oversampled hhs with larger land holding and higher education

Treatment (Insurance)

Control (No insurance)

Total

Villages 72 38 110

Sample households (30 per village)

2183 1156 3339

Land holding (acres) In sample

8.4 8.9 8.6

Land holding (acres)Out of sample

3.5 3.7 3.6

Schooling head hh (yrs) In sample

5.4 5.7 5.5

Schooling head hh (yrs)Out of sample

4.3 4.2 4.3

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

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 villages Average distance

Existing weather station 44 10 Km

New weather station 28 5 Km

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

Research results, I

Treated Villages(all households)

Household sample

   # of Sales

Acres insured per sale Uptake

Acres insured per purchasing hh

Ujjain 115 0.5 2.5% (10/398) 0.9

Dewas 45 1.5 1.8% (16/881) 2.7

Bhopal 141 0.3 13.6% (123/904) 0.4

Total 301 0.6 6.8% (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

Research Results, IISummary Of Results: Dependent variable is whether household bought insurance or not  (1) (2) (3) (4) (5) (6) (7) (8)

  OLS IV OLS IV OLS IV OLS IV

Discount (ratio of price) 0.231*** 0.227*** 0.264*** 0.266***

(0.052) (0.052) (0.061) (0.061)

Distance to ref. Station -0.011* -0.011* -0.010* -0.010*

(0.006) (0.006) (0.006) (0.006)

Additional Training 0.049** 0.038 0.051** 0.050**

(0.024) (0.028) (0.024) (0.024)

District Fixed Effects Yes Yes Yes Yes Yes Yes Yes Yes

HH characteristic covariates No No No No Yes Yes Yes Yes

Observations 2,183 2,183 2,183 2,183 2,164 2,164 2,164 2,164

R-squared 0.124 0.061 0.099  0.134 0.005 0.072 0.082

Standard errors adjusted for clustering at village level are in parentheses; *** p<0.01, ** p<0.05, * p<0.1

Research ResultsSummary Of Results: Dependent variable is log of land insured  (1) (2) (3) (4) (5) (6) (7) (8)

  OLS IV OLS IV OLS IV OLS IV

Log of price of cheaper contract -0.582*** -0.566*** -0.594***

-0.596***

(0.133) (0.136) (0.137) (0.137)

Distance to ref. station -0.029 -0.028 -0.026 -0.027

(0.022) (0.022) (0.022) (0.022)

Additional training 0.167* 0.140 0.178* 0.180**

(0.091) (0.103) (0.090) (0.091)

District Fixed Effects Yes Yes Yes Yes Yes Yes Yes Yes

HH characteristic covariates No No No No Yes Yes Yes Yes

Observations 2,183 2,183 2,183 2,183 2,164 2,164 2,164 2,164

R-squared 0.100 0.043 0.110 0.023 0.052 0.094

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

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

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