Presentation by Ulrich Hess, Senior Advisor, GIZ, at the Scaling up agricultural adaptation through insurance conference, on the sidelines of SBSTA. https://ccafs.cgiar.org/scaling-agricultural-adaptation-through-insurance

1. Index-based agricultural insurance:
Where are we and
where are we going?
Ulrich Hess, Sr. Advisor, GIZ
SCALING UP AGRICULTURAL
ADAPTATION THROUGH INSURANCE
- Hilton Bonn, 14.05.2017 -

2. Why index-based insurance?
Farmers sacrifice 12-15% of
average income to reduce risk
with traditional risk management
Traditional risk strategies frequently
fail when disasters occur
STOP
of
development
efforts
COVARIATE RISKS: mostly self-insured
An example:
A farmer has to sell livestock to
survive a drought, but as he is not the
only one, animal prices go down.
Then, when he wants to recover, in
post-drought years, prices rocket.
12-15%

3. Extent of catastrophic losses due to natural disasters
World
7,000 natural natural disasters (1995-2015)
4.3 billion people affected
Damages: USD 2.3 trillion
Africa:
1,145 natural disasters (1995 - 2015)
308 million people affected (80% drought)
Damages: USD 17 billion.

4. Index-based Insurance can:
 reduce transaction costs & administration
 Avoid negative incentive problems
 Underwrite the costs of relief agencies
 Provide fast and reliable funding after a disaster
 Open up insuring anybody whose income is
correlated with the event
Lack of traditional
(indemnity based)
insurance market
Drought / rainfall
average area yield
• Inaffordable (high transaction costs)
• Poor data basis
• Adverse selection
• Moral hazard
• Poor regulatory framework
Reasons:
The innovation of index-based insurance (IBI)

5. Source: Hess, Hazell (2016): Innovations and emerging trends in Agricultural Insurance, GIZ publication.

6. Protection
In times of crisis, offer
timely, credible & fair
relief
Catalyze access to credit,
technology, new markets
 additional income
Promotion
1. affordable
2. cover most relevant risks
3. Minimal basis risk
Conceptual foundation of IBI
[1.] [2.]
WHAT IS THE
OBJECTIVE?

7. Levels of coverage
EXAMPLES
ARC, CCRIF, PIC
Zambia
NWK Agriservices
India
WBCIS
POLICYHOLDER
individuals or householdsMICRO LEVEL
risk aggregators
(e.g. agribusinesses, cooperatives)
MESO LEVEL
governmentsMACRO LEVEL

8. Challenges for Scaling up
?
?
“low” take-up due to famers´ cash constraints at the beginning
of the season; take-up is driven by trust & payout-experience
DEMAND
PROBLEM
difficulty to determine when an event has caused damage or
index measurements do not match actual losses
INDEX & BASIS
RISK PROBLEM
Difficulty of find adequate channels of distributionDISTRIBUTION
PROBLEM
scaling up versus sustainable marketsSUBSIDY
PROBLEM
disruption of traditional risk avoidance; costs of IBI will increaseCLIMATE
CHANGE
PROBLEM
rarely private insurers with IBI-products due to high initial
investment costs
FIRST MOVER
PROBLEM

9. Game-changer
REMOTE SENSING
• Better quality & accuracy of
rainfall estimates
• Higher availability & access
[1.]
IBI AS BUSINESS MODEL
DRIVER
• Input supplier
• Financial institutions
• Contract farming operators
[2.]
DIGITALIZATION
• More efficient and
effective insurance
process
[3.]
India IFMRZambia NWK
AgriServices
Paraguay/Zambia/India;
ERVO

10. [1.] Remote Sensing
Tirucherai
Inumboor
Zambia: Rainfall estimates TAMSAT, April 14th, 2017 India, Tanjavur district: Flood Depth Analysis

11. [2.] IBI as business model driver: Zambia
Insurance offered with credit for smallholder cotton farmers
• NWK AgriServices offers weather index insurance (drought & excessive rainfall)
and life insurance along with credit (for inputs) to its contract farmers
• Premium is pre-financed (high take-up increase, see Jack Willis, RCT evaluation
in Kenya)
• No subsidies involved
PRODUCT
• Over 2013-2016, 61,446 farmers have been insured
• In 2015/16: 52,000 farmers insured; (=75% take-up); 27,610 pay-outs
• higher levels of loan recovery & deliveries / lower side-selling for NWK
• Another cotton ginner (Alliance Ginneries) has adopted the product
RESULTS

12. [3.] Digitalization: India
RISK
ASSESS-
MENT
PRICING
ENROLL-
MENT
MONITORING SETTLEMENT
Big Data and
digital devices
Portfolio risk and
computer expert system
(cloud computing based)
PDA or online or
mobile
Geo-referenced
smart phone app
and remote sensing
Digital payments
or E-vouchers
Source: IFMR „BUSINESS INITIATIVE – DIGITAL WEALTH MANAGEMENT PLATFORM” 2017

13. Thank you!

14. References
 Hess / Hazell (2016): Innovations and Emerging Trends in Agricultural Insurance, GIZ. URL:
https://www.giz.de/fachexpertise/downloads/giz-2016-en-innovations_and_emerging_trends-
agricultural_insurance.pdf
 Vargas Hill / Torero (2009): Innovations in insuring the poor, International Food Policy Research Institute (IFPRI). URL:
http://ebrary.ifpri.org/cdm/ref/collection/p15738coll2/id/24325
 Willis / Casaburi (2015): Time vs. State in Insurance: Experimental Evidence from Contract Farming in Kenya, Harvard
University /Stanford University.
 Icons Credits: created by Noun Project contributors – creative commons license, https://thenounproject.com/
 Hazell/Hess (2017): Beyond hype: Another look at index-based agricultural insurance, in: Agriculture and Rural
Development in a Globalizing World, Ed. By Prabhu Pingali and Gershon Feder, Routledge

15. BACKUP: Poster
What is covered and what events are covered?
As weather conditions are different
in different areas, triggers vary
When and how does it pay?

16. BACKUP: Zambia
Insurance seems to incentivize farmers to repay their input credits (NWK 2013/14 data)

17. DEVELOP & PROMOTE SMART SUBSIDIES
EDUCATE FARMERS ABOUT THE VALUE OF INSURANCE
BUILDING WEATHER STATION INFRASTRUCTURE AND DATA SYSTEMS
PROVIDE ENABLING LEGAL AND REGULATORY FRAMEWORK
SUPPORT AGRO-METEOROLOGICAL RESEARCH & GOOD PRODUCT DESIGN
FACILITATE INITIAL INTERNATIONAL RISK POOLING OR ACCESS TO REINSURANCE
BACKUP: How to promote IBI