Weather index insurance in a changing climate

Dr Joseph Daron1 and Dr David Stainforth2 1Met Office, Exeter, UK 2Grantham Research Institute, London School of Economics, UK

Our Common Future Under Climate Change Conference

Paris, 7-10 July 2015

Outline

1. Brief introduction to weather index insurance

2. The problem: implications of a changing

climate

3. Case study using Bayesian Networks applied to crop insurance in India

4. MSc thesis findings on weather index insurance as a climate change adaptation option in Malawi

Weather index insurance

“The indemnity is based on realisations of a specific weather parameter measured over a pre-specified period of time at a particular weather station” (World Bank, 2011) An alternative to claims-based insurance, where payouts and policies are designed according to a proxy for loss (e.g. rainfall).

Tier Payout (% of insured sum)

Tier 1 15

Tier 2 40

Tier 3 100

Skees et al. (2011) State of Knowledge Report: Market Development for Weather Index Insurance; Key Considerations for Sustainability and Scale Up

“Some of the current interest in, and funding for, index insurance has been rationalised by concerns about climate change.”

“Uncertainties surrounding expectations about climate change may lead to increases in the risk loads applied to weather insurance premiums.”

Helmuth et al. (2009) Index insurance and climate risk: Prospects for development and disaster management

“The challenge is to accommodate the added uncertainty due to climate change while keeping premiums affordable.”

(Part of) The problem

How can we combine multiple sources of climate data to improve risk estimates in a changing climate and avoid an over-reliance on historical observations to inform insurance decisions?

(Part of) The problem

A potential way forward?

Crop insurance in Kolhapur, India

Kolhapur district has a population of 3.9 million (2011). Agriculture is the primary source of income and rice is the main crop grown in the region; the main growing season is in the summer monsoon (June to September).

MicroEnsure ran a successful pilot weather index insurance scheme in 2009 with 5,000 farmers signing up within two days. The plan was to expand the scheme in 2010 to reach the potential market of 600,000 farmers.

Kolhapur

‘‘HighNoon’’ model domain for the climate model simulations.

Bayesian Networks (BNs)

BP

APABPBAP

||

Using Bayes Theorem:

Bayesian Network using observations only

Expected loss (%)

44 years of data 2.33% = 1 year

Tier Payout (% of insured sum)

Tier 1 15

Tier 2 40

Tier 3 100

Required premium increases to 53.4%

Options and weights can be adjusted to test sensitivities

“A Bayesian Network should be used as a ‘tool for

thinking’, not as an automatic answer provider.”

Cain (2001)

Implications for insurers

1. Climate change challenges conventional approaches to insurance decision making.

2. Pricing decisions based solely on historical data are likely to misrepresent the probability of exceeding thresholds associated with payouts.

3. Combining multiple sources of data (e.g. through using Bayesian Networks), including model data where possible, could lead to a more robust analysis of the climate risks.

4. Despite recent advances, climate models are still unable to provide reliable future climate projections at the scales needed to inform weather index insurance design.

Asimenye Nthakomwa-Chitika MSc thesis, University of Cape Town

Objectives:

• Determine the willingness and reasons for farmers to participate in weather index insurance.

• Understand the different roles of government, NGOs and the private sector in the implementation of weather based index insurance in Malawi.

• Understand different perspectives regarding weather index insurance as a means to reduce risks associated with climate variability and change.

Assessing the role of weather index insurance in climate change adaptation in Malawi

Asimenye Nthakomwa-Chitika MSc thesis, University of Cape Town

Assessing the role of weather index insurance in climate change adaptation in Malawi

Key Findings:

• Farmers primarily view weather index insurance as a means to secure loans. They do not typically associate it with reducing risks from climate variability and change.

• Most study participants had no knowledge about climate finance, nor the potential for such finance to be leveraged at the national and regional scale.

• In order to implement weather index insurance effectively, further capacity building and skills development is required across multiple levels.

Thanks for listening. Any questions? joseph.daron@metoffice.gov.uk