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Patterns of Rainfall Insurance Participation In Rural India. Xavier Gine (World Bank) Robert Townsend (University of Chicago) James Vickery (NY Fed). World Bank Conference: Access to Finance March 15, 2007. Introduction.

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Patterns of rainfall insurance participation in rural india l.jpg
Patterns of Rainfall Insurance Participation In Rural India

Xavier Gine (World Bank)

Robert Townsend (University of Chicago)

James Vickery (NY Fed)

World Bank Conference: Access to Finance

March 15, 2007

Introduction l.jpg

  • ‘Index insurance’: pays out on realization of index correlated with household income / consumption.

    • eg. (i) rainfall at local rain gauge, (ii) area-level measure of crop yields, (iii) commodity price etc.

  • Key features:

    • Realization of index is exogenous to household.

    • Minimizes monitoring/screening costs. Makes small ‘micro-insurance’ contracts more cost-effective.

  • Recent growth in this type of insurance:

    • World Bank (2005) presents 10 case studies.

    • Indian National Agricultural Insurance Scheme (NAIS) example of index insurance, but poorly designed in some ways.

This paper l.jpg
This paper

Two goals:

  • Describe institutional features of an individual rainfall insurance scheme:

    • offered to rural households in Andhra Pradesh region of southern India.

  • Present some evidence on cross-sectional patterns in insurance takeup.

    • What are the barriers to trade? Limited exposure to rainfall risk? Transaction costs? Credit constraints? Limited cognition / understanding of product?

  • Product background l.jpg
    Product Background

    • Designed by ICICI Lombard, sold to farmers by BASIX, a microfinance institution (MFI).

    • Goal: Insure against deficient rainfall during primary monsoon season (~ June - September).

    • Rain gauges report daily rain at the mandal (county) level.

      • Payout promised <30 days of verification of rainfall data.

      • Survey villages average 10.6km (6.6 miles) from gauge.

    • Contract divides monsoon into three phases:

      • (i) sowing; (ii) podding; (iii) harvesting

      • Phase payout based on rainfall relative to trigger level. Includes payouts for excessive rain during harvest.

    Payouts are a collar option on rainfall l.jpg
    Payouts are a ‘collar’ option on rainfall


    • Total payout = sum of payouts across three phases.

    • Triggers chosen using crop model to minimize basis risk

    • Insurance premium based on actuarial value + 25% admin fee + tax.

    rainfall during phase

    2nd trigger

    (corresponds to crop failure)

    1st trigger

    Predictions about takeup patterns l.jpg
    Predictions About Takeup Patterns

    • Simple theoretical model of insurance participation under symmetric information. Willingness-to-pay for insurance is:

      • increasing in risk aversion

      • decreasing in basis risk (ie. imperfect correlation between insurance returns and consumption)

      • increasing in size of risk to be insured

    • Add a financial constraint to the model:

      • participation is decreasing in credit constraints / increasing in wealth.

    Predictions about takeup patterns ii l.jpg
    Predictions about Takeup Patterns II

    Other predictions outside formal model:

    • Product is new, and may not be well understood by farmers. Suggests insurance takeup may be:

      • higher for households who trust the insurance provider (BASIX), such as current customers.

      • higher for households with lower cost of understanding, experimenting with product:

        • younger, more educated households.

        • ‘early adopters’: members of local council, and self identified progressive households.

    • Informally, have in mind a model of limited cognition or limited information.

    Survey l.jpg

    • After 2004 monsoon: survey 1052 landowner households across 37 villages.

    • Subsample for this paper: 752 households in villages where BASIX insurance offered.

    • Stratified random sample. Three strata:

      • Purchased insurance.

      • Attended marketing meeting but did not purchase.

      • The remainder.

    • Choice based sampling:

      • Use sampling weights to produce consistent population parameter estimates in regressions.

    Slide10 l.jpg

    Weighted self-reports:

    “What are the major sources of risk faced by your household?

    Weighted self-reports:

    “If it does not rain, what do you do?”

    Slide11 l.jpg

    Reasons for purchasing insurance | meeting participation

    Reasons for not purchasing insurance | meeting participation

    Probit rhs variable 1 if bought insurance 0 otherwise13 l.jpg
    Probit. RHS variable = 1 if bought insurance, = 0 otherwise.

    Regression also includes village dummies, five other covariates. N=752

    Risk aversion interactions l.jpg
    Risk aversion interactions

    Inverse relationship between risk aversion, participation concentrated amongst households with less knowledge of insurance, insurance provider

    What have we learned l.jpg
    What have we learned?

    Evidence in this paper is a small, preliminary step forward in understanding ‘microinsurance’. Some lessons:

    • Participation rates lower amongst ‘vulnerable’ households (ie. poor, credit-constrained, not members of social networks etc.)

      • Morduch (2004): general equilibrium concerns; insurance purchasers ‘bid up’ prices of non-traded goods during drought, making poor worse off.

    • Social networks, familiarity with provider key determinant of insurance participation.

    • Simple practical suggestion for BASIX: provide payouts faster!

      • Household discount rates likely higher than for ICRISAT / BASIX.

    Future research directions l.jpg
    Future research directions

    • Effects on:

      • risk-taking behavior by households

      • existing informal risk-sharing arrangements amongst households

      • consumption smoothing

    • General equilibrium effects of insurance provision a la Morduch (2004).

    • Patterns of diffusion for new financial technologies.

       Research in progress: Field experiment, randomize insurance provision across households.