OCTOBER 2015
Kenya
Toward a National Crop and Livestock
Insurance Program
BACKGROUND REPORT
Kenya
Toward a National Crop and Livestock
Insurance Program
BACKGROUND REPORT
© 2015 International Bank for Reconstruction and Development /
International Development Association or
/// The World Bank ///
1818 H Street NW
Washington DC 20433
Telephone: 202-473-1000
Internet: www.worldbank.org
This work is a product of the staff of The World Bank with external
contributions. The findings, interpretations, and conclusions
expressed in this work do not necessarily reflect the views of The
World Bank, its Board of Executive Directors, or the governments
they represent.
The World Bank does not guarantee the accuracy of the data
included in this work. The boundaries, colors, denominations,
and other information shown on any map in this work do not
imply any judgment on the part of The World Bank concerning the
legal status of any territory or the endorsement or acceptance of
such boundaries.
Rights and Permissions
** **
The material in this work is subject to copyright. Because The World
Bank encourages dissemination of its knowledge, this work may be
reproduced, in whole or in part, for noncommercial purposes as long
as full attribution to this work is given.
Any queries on rights and licenses, including subsidiary rights,
should be addressed to the Office of the Publisher, The World Bank,
1818 H Street NW, Washington, DC 20433, USA; fax: 202-522-2422;
e-mail: pubrights@worldbank.org.
Photo Credits
** **
Photos specifically credited are done so under Creative Commons
Licenses. The licenses used are indicated through icons showcased
next to each image.
b 2.0 Attribution License
(http://creativecommons.org/licenses/by/2.0/legalcode)
bd Attribution No-Derivatives License
(http://creativecommons.org/licenses/by-nd/2.0/legalcode)
ba Attribution Share Alike License
(http://creativecommons.org/licenses/by-sa/2.0/legalcode)
If not indicated otherwise, photos used in this publication have been
sourced from the following locations with full rights:
World Bank Flickr Website
United Nations Flickr Website
All non-Creative Commons images in this publication require
permission for reuse.
K E N YA : A G R I C U LT U R E I N S U R A N C E S O L U T I O N S A P P R A I S A L
i
Table of Contents
05 Acknowledgments
06 Acronyms and Abbreviations
07 Introduction
09 Institutional Frameworks
09 Rationale for Public-Private Partnerships in Agricultural Insurance
11 Public And Private Sector Functions
23 Institutional Framework
25 Institutions
27 Livestock Insurance for Pastoralists Located in ASALs
in Northern Kenya
27 Context
29 Proposals For Large-Scale Livestock Insurance For Pastoralists Located In ASALs In
Northern Kenya
40 Fiscal Costing Assumptions And Scenarios
43 Welfare Impacts Of Index-Based Livestock Insurance In HSNP Countries
48 Crop Insurance
48 Context
50 Description of Potential Agricultural Insurance Programs for Crops
54 Fiscal Costing Assumptions and Scenarios
58 Welfare Impacts of Area Yield Insurance for Maize and Wheat in Kenya
65 Conclusion SECTION
i
ii K E N YA : A G R I C U LT U R E I N S U R A N C E S O L U T I O N S A P P R A I S A L
Table of Contents
ANNEXES
67 Annex A. Possible Options for Coinsurance Pools
in Kenya
70 Annex B.1. Index Based Livestock Insurance (IBLI)
Program
71 Annex B.2. Assumptions and Parameters for Fiscal
Costing Scenarios for Livestock
75 Annex B.3. Summary of Modeling and Simulations of
Welfare Analysis for Livestock
82 Annex C.1. Assumptions and Parameters for Fiscal
Costing Scenarios for Crops
85 Annex C.2. Summary of Modeling and Simulations of
Welfare Analysis for Crops
94 Bibliography
96 Endnotes
SECTION
i
K E N YA : A G R I C U LT U R E I N S U R A N C E S O L U T I O N S A P P R A I S A L
iii
Figures
11 Figure 1 — Toward an integrated private risk management and insurance framework for
different segments of Kenya’s crop and livestock producers
12 Figure 2 — Potential public sector roles for GOK in support of agricultural insurance
development in Kenya
31 Figure 3 — How government could support financial protection for different segments
of the population: Example of pastoralists in the four current HSNP counties (Mandera,
Marsabit, Turkana, and Wajir)
35 Figure 4 — Illustrative calculated pure loss cost rates for 12-month NDVI asset
protection cover at District and Division level
46 Figure 5 — Potential short-term impacts of livestock insurance on income available
for consumption
47 Figure 6 — Potential impacts of livestock insurance on herd accumulation
47 Figure 7 — Potential impacts of livestock insurance on probability of falling into
poverty trap
51 Figure 8 — Types of Agricultural Insurance Products
51 Figure 9 — Coverage Level and Insurance Payouts in AYII
55 Figure 10 — Estimated AYII Risk Premium Rates for Maize at District Level
55 Figure 11 — Estimated AYII Pure Premium Rates for Wheat at District Level
62 Figure 12 — Potential impacts of AYII on net income available for consumption
70 Figure 13 — Translating NDVI data into estimated livestock mortality and IBLI payouts
70 Figure 14 — IBLI seasonal sales periods, contract cover period and contract payout
dates
SECTION
i
iv K E N YA : A G R I C U LT U R E I N S U R A N C E S O L U T I O N S A P P R A I S A L
Tables
13 Table 1— Agricultural insurance data collected by GoK
34 Table 2— Comparison of Uruguayan pasture NDVI cover and proposed Kenyan
NDVI cover
40 Table 3— Proposed livestock safety net and insurance program for Kenya’s four HNSP
counties
41 Table 4— Fiscal costing projections for macro-level asset protection coverage
42 Table 5— Fiscal costing projections for top-up and nontargeted pastoralists options
56 Table 6— Variation of Premium Rates According to Different Coverage Levels
63 Table 9— Fiscal cost per household of achieving different policy goals in different
insurance scenarios
66 Table 10— Illustrative fiscal costing for agricultural insurance programs, 2016 and 2019
73 Table 12— Fiscal costing projections for macro-level asset protection coverage
74 Table 13— Fiscal costing projections for top-up and nontarget pastoralists options
81 Table 14— Summary statistics of pastoral households in four HSNP counties
83 Table 15— Yield, Area, and Premium Rate Data for Maize
85 Table 16— Yield, Area, and Premium Rate Data for Wheat
86 Table 17— Estimation of potential cost of additional data collection activities for AYII
90 Table 18— Summary statistics of maize and wheat growing households
92 Table 19— Summary of key impact indicators by contract variations
SECTION
i
K E N YA : A G R I C U LT U R E I N S U R A N C E S O L U T I O N S A P P R A I S A L
05
Acknowledgments
This technical report of the Agriculture Insurance The report benefited greatly from the data
Solutions Appraisal was led and prepared by Daniel and information provided by the Ministry of
Clarke (Disaster Risk Financing and Insurance Agriculture, Livestock and Fisheries; and special
Program, Finance and Markets Global Practice, World acknowledgements are extended to Kenneth
Bank Group) in collaboration with the Ministry of Ayuko (Director, State Department of Agriculture,
Agriculture, Livestock and Fisheries, Kenya, and Ministry of Agriculture, Livestock and Fisheries) and
with contributions from the following: Barry Maher Vincent Ngari (Deputy Director, State Department
(Disaster Risk Financing and Insurance Program, of Livestock, Ministry of Agriculture, Livestock
Finance and Markets Global Practice, World Bank and Fisheries). We also gratefully acknowledge the
Group); Felix Lung (Disaster Risk Financing and support, inputs, and feedback from Kenya’s National
Insurance Program, Finance and Markets Global Treasury, the National Drought Management
Practice, World Bank Group); Sarah Coll-Black Authority, Kimetrica, the UK Department for
(Labor and Social Protection Global Practice, International Development, and the Tegemeo
World Bank Group); Richard Carpenter, Sommarat Institute of Agricultural Policy and Development.
Chantarat, James Sinah, Andrea Stoppa, and Charles
Stutley (Consultants, World Bank Group); Andrew We gratefully acknowledge funding support from the
Mude (International Livestock Research Institute); Ministry of Foreign Affairs of the Netherlands and
and Michael Mbaka (Financial Sector Deepening U.S. Agency for International Development (USAID)
Kenya). Overall guidance was provided by Olivier through the World Bank’s Agricultural Insurance
Mahul (Program Manager, Disaster Risk Financing Development Program. The Agricultural Insurance
and Insurance Program, Finance and Markets Global Development Program is part of the World Bank–
Practice, World Bank Group) and Smita Wagh Global Facility for Disaster Reduction and Recovery
(Finance and Markets Global Practice, World Bank (GFDRR) Disaster Risk Financing and Insurance
Group). This nonlending technical assistance (NLTA) Program. Contributions of the International
report has been prepared as part of an NLTA to the Livestock Research Institute (ILRI) are supported by
Ministry of Agriculture, Livestock and Fisheries to the UK Department for International Development,
support the ministry in deciding whether to establish Australian AID, and the European Union, which fund
a large-scale public-private partnership in agricultural ILRI’s Index Based Livestock Insurance Agenda.
insurance. This report can be read in conjunction
with the accompanying World Bank policy note,
“Kenya: Toward a National Crop and Livestock
Insurance Program” (2015), and “Kenya: Agricultural
SECTION
Sector Risk Assessment Risk Prioritization” (2015),
a report by the World Bank Agricultural Risk
Management Team.
A
06 K E N YA : A G R I C U LT U R E I N S U R A N C E S O L U T I O N S A P P R A I S A L
Acronyms and
Abbreviations
AIDP Agriculture Insurance Development Program
AIM Agricultural Insurance Manager
ALRMP Arid Lands Resources Management Project
ARC Africa Risk Capacity
ASAL arid and semi-arid land
AYII area yield index insurance
CCE crop-cutting experiment
eMODIS enhanced Moderate Resolution Imaging Spectroradiometer
FSD Financial Sector Deepening
GFDRR Global Facility for Disaster Risk Reduction and Recovery
GIS geographic information system
GoK Government of Kenya
GPS Global Positioning System
HSNP Hunger Safety Net Program
IBLI Index Based Livestock Insurance
ILRI International Livestock Research Institute
IRA Insurance Regulatory Authority
MALF Ministry of Agriculture, Livestock and Fisheries
MPCI multi-peril crop insurance
NAIP National Agricultural Insurance Policy
NDV Normalized Difference Vegetation Index
NPCI named peril crop insurance
PPP public-private partnership
SACCO savings and credit cooperative
SDA State Department of Agriculture
SDL State Department of Livestock
TLU Tropical Livestock Unit
TSU technical support unit
SECTION USAID U.S. Agency for International Development
WII weather index insurance
A Currency: Kenyan shilling (K Sh)
K E N YA : A G R I C U LT U R E I N S U R A N C E S O L U T I O N S A P P R A I S A L
07
Introduction
This Agriculture Insurance Solutions
///
insurance, agriculture credit will remain insufficient
Appraisal proposes the technical concept of to fully meet the needs of farmers and herders.
a potential public-private partnership (PPP)
Moreover, international experience suggests
in agricultural insurance in Kenya, covering
///
that agricultural insurance programs will
both crop and livestock. It lays out the rationale
not scale up unless based on a balanced
///
for the proposal, offers an insurance PPP solution
partnership between the public and private
for the identified challenges, and makes a suggestion
sectors. In recent years, numerous private sector
for the required underlying institutional framework.
///
agricultural insurance pilots have been implemented
This technical report is meant to guide further policy
in Kenya with support from donor partners for
making and technical development processes and
index-based crop insurance. However, to date most
to form the basis for further discussion among all
of these programs have failed to reach significant
involved stakeholders.
scale. Overall, experience from other countries
In Kenya agriculture is risky, and that risk
///
suggests that both the government and the private
has large human and economic costs. ///
sector must play a role in developing the agriculture
Agriculture is key to the Kenyan economy, generating insurance market.2
approximately 30 percent of annual gross domestic
Recognizing the importance of its
product and approximately 50 percent of revenue
///
involvement, the government of Kenya
from exports. It is also an important source of
(GoK) is collaborating with the World Bank
employment: over 61 percent of the population has
to investigate how agriculture insurance can
jobs in agriculture.1 But agriculture in Kenya is a risky
help transform the agriculture sector and
activity, often unirrigated and highly vulnerable to the
promote food security, economic growth,
impacts of climate change.
and shared prosperity in the medium to
Despite the recognized need for a
///
long term. The collaboration, which is undertaken
///
commercially oriented, internationally with the Agriculture Insurance Development
competitive, and modern agricultural sector, Program (part of the World Bank Group’s Disaster
rural lending in Kenya is low. Agricultural lending
///
Risk Financing and Insurance Program), aims to
accounted for only 4.3 percent of total lending in understand how insurance could form part of a
Kenya in 2012 (Central Bank of Kenya 2012). A strategy to derisk agriculture value chains and more
large-scale agricultural insurance program would generally function within a broader risk management
support resilient, viable expansion of agriculture framework. The GoK has identified the agriculture
credit to farmers by removing agriculture risk from sector as a key area of focus under the Kenya
SECTION
the balance sheet of rural banks and cooperatives, Vision 2030 plan to promote Kenya’s transition
thereby making them more robust to agricultural to a middle-income country; and agricultural
shocks. Without adequate coverage of agricultural insurance is a stated priority of government, as
A
08 K E N YA : A G R I C U LT U R E I N S U R A N C E S O L U T I O N S A P P R A I S A L
reflected in the Second Medium Term Plan (MDP
2013). The government is exploring initiatives to
Box 1— Key International Lessons further derisk the agriculture sector value chain
for the Design and Implementation in order to facilitate better access to markets and
unlock access to credit, which would in turn allow
of Agricultural Insurance
farmers to purchase higher-yielding technology
• Agricultural insurance programs are challenging to (seeds, fertilizers, plant protection chemicals, etc.)
develop and successfully sustain.
and increase their incomes. These initiatives aim
• Carefully designed and well-implemented agricultural simultaneously to ensure food security in Kenya and
insurance programs can support a range of government transform the agriculture sector.
policy objectives, such as increased access to credit,
improved agricultural productivity, reduced vulnerability, This technical report investigates the
///
and social protection. institutional policy and design issues
associated with agriculture insurance PPP
• Agricultural insurance should be considered by
structures, as well as their fiscal cost and
government alongside other potential agricultural
risk management and social protection interventions, welfare benefit. Taking into account Kenya’s
///
since other interventions may offer higher benefit- current agriculture insurance policy and government
cost ratios or be a precondition for successful institutions, our analysis sought to identify sound
agricultural insurance. policies and institutional structures that would
• Agricultural insurance programs are more effective and unlock the innovative potential of the private sector
efficient when underwritten by the private insurance in agriculture insurance. For both crop and livestock,
sector and actively supported by government under we analyzed the current market to understand what
carefully designed PPPs. high-quality products could be developed in the
• Financial support to agricultural insurance programs can
short, medium, and long term to meet Kenya’s needs.
provide a faster, more cost-effective way of supporting
agricultural producers’ recovery from shocks than ad
hoc post-disaster relief.
• Cost sharing between government, donors, and farmers
may differ for different segments of the population
depending on policy priorities.
Sources: Mahul and Stutley 2010; World Bank–GFDRR
Disaster Risk Financing and Insurance Program 2014.
SECTION
A
K E N YA : A G R I C U LT U R E I N S U R A N C E S O L U T I O N S A P P R A I S A L
09
Institutional
Frameworks
index insurance for smallholder farmers on a retail
Rationale for Public-
basis at scale—has partly to do with the absence of
Private Partnerships in certain fundamental building blocks required by an
agricultural insurance market: (i) improved access to
Agricultural Insurance inputs, husbandry, and irrigation; (ii) reliable access
The agricultural insurance market in Kenya
///
to weather data; and (iii) a supportive regulatory
has failed to reach scale. With the exception of
///
framework (FSD 2013).
some small-scale pilots and niche retail activity, the
But international experience suggests that
private sector is currently not providing agricultural
///
the absence of these building blocks is not
crop and livestock insurance (GoK 2014a). This
the only reason why agricultural insurance
chapter considers possible causes for the agricultural
markets fail and pilots do not scale up.3 Most
insurance market’s failure in Kenya, the rationale
///
of the following reasons are applicable to Kenya:
for and benefits of a public-private partnership
(PPP) for developing agricultural insurance, and • Lack of agriculture data. As discussed
/// ///
the appropriate functions of the public and private below, there is very little reliable agricultural
sectors within a PPP. The final section suggests a data available in Kenya. This is a serious
vision for a PPP and makes recommendations for constraint on the development of agricultural
next steps. insurance products.
• Lack of capacity, especially for catastrophe
Weather index insurance (WII) has been
///
///
risk. Insurers do not have the capacity to cover
considered a potential solution for fostering a
///
catastrophe risk associated with drought, flood,
viable agricultural insurance market, but the
and other typical agricultural risks. Although
approach has achieved mixed results. Although ///
international reinsurance is available, it is
a number of small-scale agricultural insurance pilots
expensive, particularly where there is a lack of
have been commenced, only one, the UAP Syngenta
data.
program, has so far scaled up. This program,
currently in its fifth full year of implementation, • High distribution costs. Given that farms
/// ///
now insures more than 87,000 farmers annually tend to be small and spread over wide areas,
in Kenya (during long rains 2014) where crop WII agricultural insurance typically carries very high
SECTION
and crop-credit provision are automatically linked. distribution costs. These are exacerbated by the
The reason why WII has not scaled up in Kenya— lack of established branch or agent networks in
that is, why it has been difficult to establish viable rural areas.
01
10 K E N YA : A G R I C U LT U R E I N S U R A N C E S O L U T I O N S A P P R A I S A L
Photo Credit: Daniel Clarke
• High loss assessment costs. In relation to
/// /// Furthermore, flaws in the design of post-
///
traditional indemnity insurance, the costs of disaster relief mechanisms often result in
assessing losses are usually extremely high. This is the crowding out of insurance. If famers expect
///
especially true for small insured farm units, where post-disaster relief from government, development
the premium volume generated is usually very agencies, or nongovernmental organizations, they
have little incentive to purchase insurance.
low and insufficient to cover the costs of the loss
assessment. International experience suggests that
///
sustainable, scaled-up agricultural
• High development costs. Although index
/// ///
insurance programs are based on a strong
insurance lowers the transaction cost,
public-private partnership—one involving
it carries extremely high development
engagement, innovation, and action from
and other start-up costs. These start-up
both partners. The failure of the agricultural
///
costs cannot Expensive premiums. Small
insurance market in Kenya provides a clear
+ ./// ///
farmers are unwilling, and may be unable, to pay justification for intervention by the government
for commercially priced agricultural crop and of Kenya (GoK), but that intervention is further
livestock insurance. justified by the severe challenges that public sector–
only and private sector–only approaches face. These
• Poor understanding of insurance. Farmers’
range from inefficient delivery, distribution, and
/// ///
poor understanding of agricultural insurance
claims settlement in the case of the former, and
reduces demand and may lead to purchase of
underinvestment in necessary data in the case of the
inappropriate products.
latter. A strong partnership between the public and
• Lack of an enabling legal and regulatory
///
the private sectors will allow Kenya to build on each
framework. As discussed below, Kenya’s
///
sector’s comparative advantages.
Insurance Act does not support index insurance, 4
and a regulatory framework for microinsurance is
SECTION
still being developed.
01
K E N YA : A G R I C U LT U R E I N S U R A N C E S O L U T I O N S A P P R A I S A L
11
microinsurance or retail product, and most of these
Public And Private
programs are not achieving scale.
Sector Functions
Traditional indemnity-based multi-peril crop
///
Overview insurance (MPCI) is not well suited to the
risk transfer needs of subsistence farmers
Few functions belong exclusively to either
and pastoralists. It is therefore necessary to
///
the public sector or the private sector; rather,
///
identify other risk transfer solutions to meet these
most are shared. For example, both the public and
needs. In the short to medium term, potential
///
private sectors have separate functions in relation to
linkages between existing social safety net programs
data, marketing and outreach, and risk financing. The
and applications of macro-level index insurance
shared nature of the functions both strengthens the
programs could be explored as part of an integrated
arguments for a PPP framework and influences the
risk management framework (figure 1).
institutional framework’s design.
The Kenyan agricultural insurance market
The PPP framework for agricultural insurance
///
///
and wider risk management will need to is subject to market inefficiencies that the
support public and private sector institutions GoK can help to overcome through a number
in identifying, developing, and distributing of mechanisms. These include (i) collecting
///
the appropriate risk transfer solution to reliable agricultural insurance data, (ii) conducting
each segment of the farming population. ///
appropriate outreach to potential policyholders, (iii)
Currently, most traditional indemnity-based crop providing or supporting the risk financing of the
and livestock insurance in Kenya is targeted at small catastrophic layer of reinsurance, (iv) supporting
to medium-size commercial farmers and dairy cattle the design of appropriate insurance products, and
producers. On the other hand, index insurance is (v) establishing and implementing an enabling legal
being promoted by the donors as a small-scale farmer and regulatory environment.
Figure 1 — Toward an integrated private risk management and insurance
framework for different segments of Kenya’s crop and livestock producers
• Medium/large farm units
Commercial Crop and • Commercial dairy & beef herds
MPCI Livestock Producers • Mechanized production
(top 5% of Kenyan • High access to credit
Named
farmers) • High levels of input use
Peril
• Produce for sale
Index
• Smallholder farmers
Insurance
Semicommercial Crop • Smallholder
livestock producers
and Livestock Producers
• Some assets
(middle 20% of Kenyan
• Some access to credit
farmers)
• Part consumption/part sale
SECTION
Subsistence Farmers/ • Very small/ no land
01
Pastoralists • Very few assets
(bottom 75% of Kenyan • Subsistence farming
Social Safety-Net Programs: Macro-Level Crop
and Livestock Index Insurance farmers) • Nomadic Pastoralists
12 K E N YA : A G R I C U LT U R E I N S U R A N C E S O L U T I O N S A P P R A I S A L
The full participation of the private sector
///
Insurers expect to recover product development
is critical for the successful implementation costs over time through the premium paid. In
of an agriculture insurance program. The ///
the case of agricultural insurance, however, the
following are considered to be principally private high costs and the limited financial capacity of
sector functions: (i) product design and rating, policyholders make this recovery unrealistic.
(ii) risk acceptance and underwriting, (iii) decisions Therefore, although product development and
about risk retention and reinsurance strategies, (iv) technical support are private sector functions, the
supplementary data collection, (v) the marketing of support of government and development institutions
crop and livestock insurance products, and (vi) the (such as the International Livestock Research
distribution of these products. As indicated above,
Institute [ILRI], World Bank, UK Department for
many functions are shared by the private and public
International Development, and U.S. Agency for
sectors. The public sector plays a role in both risk
International Development [USAID]) is likely to be
financing and data collection; and although the
necessary, at least in the short to medium term.
private sector is responsible for product design and
rating, the government will have a strong interest Care will need to be taken to mitigate the risk
///
in the price of the product—and therefore in the of crowding out private sector innovation or
product’s rating—where it provides a subsidy. of subsidizing tasks that the private sector
is able to undertake. Once products have been
Product development and ongoing technical
///
///
support are costly. Given the actuarial and other
///
developed and demonstrated to be actuarially sound,
specialist expertise required to design and price insurers should be able to support their continued
new actuarially sound and sustainable agricultural development; and once agricultural insurance
insurance products, and to support their ongoing has reached scale, the premiums should be able
development, the costs are likely to impose a to support the costs of developing new products
significant entry barrier to commercial insurers. without public sector support.
Figure 2 — Potential public sector roles for GOK in support of agricultural
insurance development in Kenya
Data Outreach Risk Financing
Collect Link to social safety nets
Public Sector
Audit Link to Credit Reinsurance
Manage Premium Subsidies Promote Coinsurance
Pool
Finance Awareness Building
Financial Support
Support Product Enabling Environment
Design & Development
SECTION
Product Development & Institutional Framework
Pricing (Short Run)
Legal Framework
01
Technical Support for
Consumer Protection
Insurers (Long Run)
K E N YA : A G R I C U LT U R E I N S U R A N C E S O L U T I O N S A P P R A I S A L
13
Functions Of The Public Sector on an ongoing basis and informs risk management
strategies and systems, and it offers other benefits
Data Collection, Auditing, And Financing as well: (i) reliable data can be used for Ministry of
Agriculture, Livestock and Fisheries (MALF) policy
Effective insurance solutions require good-
decisions (about subsidies for fertilizer, water,
///
quality data; without such data, sustainable
seed, or irrigation); (ii) they can improve farm-
insurance markets are unlikely to develop.
level understanding of risks to empower farmers
///
For insurance purposes, data must be sufficient and
to undertake better risk management techniques
adequate to enable the design and rating of products;
(crowding in of good mitigation); and (iii) they
they must be relevant, so that products offer
reliable protection; they must be reliable enough can put a price on risk by (for example) informing
to be accepted by international reinsurers, whether farmers if they should stop growing a crop in a
through audit or otherwise; they must be timely, so given location.
that claims can be paid quickly; and they must be
The GoK intends to play an important role in
cost-effective.
///
collecting agricultural insurance data, both
for livestock and for crop insurance. Given
The different categories of risk and the
///
///
that the collection and management of most data for
different insurance schemes in Kenya require
different types of, and investments in, data. ///
agriculture insurance is expensive and nonrivalrous,5
For example, crop insurance and livestock insurance the function is usually more efficiently undertaken
require different types of data available from different through a monopoly. For example, it does not make
sources (such as ground-based data or remote- economic sense for every insurer to set up its own
sensing data, including satellite data, on agricultural weather stations in the same area to capture the
production or weather variables). Investment in same data. Thus the public sector has a natural
reliable data allows monitoring of risk dynamics role to play. In Kenya, as in many countries, the
Table 1— Agricultural insurance data collected by GoK
Data Type Public Institution in Charge of Collection
Kenya Meteorological Department under the
Meteorological data Ministry of Environment, Water and Natural
Resources
Kenya National Bureau of Statistics and Ministry
Time series crop production and yield data
of Agriculture, Livestock and Fisheries (MALF)
Crop and livestock damage data MALF
Arid Lands Resource Management Program
Further livestock statistics (ALRMP) and USAID’s Pastoral Risk Management
Project
SECTION
Source: Government of Kenya 2014a.
01
14 K E N YA : A G R I C U LT U R E I N S U R A N C E S O L U T I O N S A P P R A I S A L
collection of agricultural insurance data is largely Given the current lack of high-quality
///
coordinated by government agencies. This is true agricultural data, Kenya requires a strong
for all agricultural insurance data apart from claims audit function to ensure data quality and
data, which insurers collect themselves (see below). access to international reinsurance markets. ///
There may be other sources of data, but the main Agricultural shocks are covariate in nature, so it is
important to off-load some of this risk outside the
responsibility for collection lies with public sector
country through international reinsurance markets.
institutions (see table 1).
But reinsurers have high standards for the data
The GoK intends to investigate the benefits of
///
they use to develop and price insurance products,
and they charge significantly higher premiums if
outsourcing some parts of the data collection
they have concerns about how the data are audited.
to private providers. This approach has been tried
A transparent process for auditing insurance
///
in India, for example, where crop-cutting experiments
data will ensure the quality of the data and in
(CCEs) that support area yield indexes for insurance
turn allow local insurers to leverage international
are outsourced by several state governments to reinsurance markets.
private sector agents; it is too soon to judge whether
this approach will be successful, however (World Some of the concerns about data quality are
///
being addressed through a series of data
Bank 2011b). Outsourcing does not make the activity
collection guidelines developed by the GoK.
a private sector function but rather an outsourced
///
These guidelines are currently under review and
public sector function. This is an important
include the Kenya Agricultural Data Collection and
distinction, as ownership of the function suggests
Management Guideline, a complementary training
control; where public functions are outsourced, manual, and a list of standards and guidelines for
greater checks and balances will need to be built into food and agricultural data collection. However, much
the structure to protect the public sector interest. work remains to be done, including (i) implementing
the guidelines, (ii) providing for integrated databases
Most publicly collected agricultural insurance
///
of agricultural insurance data, and (iii) introducing
data are perhaps not of insurable quality. Data ///
clear protocols regarding access to and charges levied
are often incomplete, missing, or unavailable. The for use of agricultural insurance data.
MALF report (GoK 2014a)6 suggests various reasons
The discussion above suggests that
for this:
///
considerable investment is required in the
1. Data collection coverage is low. The Kenya collection, management, and audit of data. ///
Meteorological Department operates 92 synoptic To avoid wasted investment, it would be prudent to
agrometeorological automated weather stations undertake a preliminary analysis of the data available
across Kenya, but they are mainly located in the from public and private sector sources in Kenya. This
major towns in the central and southern regions; analysis could be used to:
total coverage of the country would require more 1. Produce a data gap analysis.
than 1,250 automatic weather stations.
2. Determine how to fill the data gap with
2. There has not been a farm-level census since 1999. agricultural insurance products whose
design requires minimum investment in the
3. MALF field extension officers are underfunded. data infrastructure.
SECTION
4. MALF data on crop production seem to be 3. Explore the extent to which data can be sourced
unavailable or not systematically maintained. externally as a substitute for local data (e.g., using
01 satellite or remote-sensing data).
K E N YA : A G R I C U LT U R E I N S U R A N C E S O L U T I O N S A P P R A I S A L
15
Outreach Photo Credit: Daniel Clarke
The GoK intends to provide general outreach
///
campaigns, it should ensure that associated products
support in relation to agricultural insurance are developed and offered in tandem.
with the objective of expanding market
The GoK will consider various ways
awareness. Achieving scale is fundamental to the
///
to support outreach for agricultural
///
sustainability of agricultural insurance programs
insurance products:
because it helps to spread the cost of providing
///
insurance among numerous policyholders. 1. Linking to rural lending. Rural banks and
/// ///
However, low levels of financial literacy in the target microfinance institutions have the potential to
market and poor understanding of the potential reach a large number of rural farmers in Kenya.
benefits of insurance often prevent programs from Linking agriculture insurance to rural credit
reaching scale. Although the marketing of specific may help to promote very broad outreach while
at the same time deepening access to financial
insurance products is a function of the private
services (i.e., both credit and insurance). The
sector, government can play a more general role
imposition of a legal obligation to purchase
aimed at building financial literacy among potential
insurance on taking agricultural credit can lead
policyholders and at helping them understand the
to poor incentives. However, banks may impose
types and potential benefits of agricultural insurance.
the requirement as part of the package they offer
The government should exercise caution
///
farmers, and this requirement can be supported
in this role, however. Experience has shown
///
by government.
that government consumer education and 2. Conducting financial literacy campaigns.
/// ///
marketing campaigns may be unsuccessful and even Unless potential policyholders have a basic
counterproductive if the insurance products are level of financial literacy, it will be difficult for
not available (for example, because insurers do not insurers to sell agricultural insurance products.
have the necessary distribution channels in place) With a greater degree of financial understanding,
SECTION
or if insurers are not trusted (for example, because farmers can better weigh the risks and benefits
of slow claims payment and low claims ratios). As of insurance products. It is expected that county
the government develops any financial awareness governments will play an essential role in this.
01
16 K E N YA : A G R I C U LT U R E I N S U R A N C E S O L U T I O N S A P P R A I S A L
3. Raising awareness of insurance. Beyond
/// /// Risk Financing
basic financial literacy, potential policyholders
Given the high costs for development,
need to understand the types and benefits of
///
distribution, claims assessment, and risk
agricultural insurance if they are to purchase it.
financing, agricultural insurance is unlikely
Raising awareness of insurance should be regarded to succeed without some public sector
as a shared role. Government may be better able subsidy. Development costs are an upfront charge;
///
than the private sector to utilize the media, but distribution costs can be mitigated through the
campaigns are unlikely to be effective unless the development and use of alternative distribution
private sector also plays a role, specifically by channels; and claims assessment costs can be
providing effective training to insurance agents and mitigated through product design. The cost of
by developing clear product documentation. financing the risk, however, is ongoing and must be
met on a year-to-year basis. An insurance product
4. Linking to the Hunger Safety Net Program
///
cannot be sustainable unless the risk financing costs
(HSNP). Linking livestock insurance to the HSNP
///
are fully met. It is therefore perhaps inevitable that
should help to increase outreach by targeting the public sector will have to provide support for risk
based on poverty data that was collected as part financing.
of the HSNP. This linkage will also serve to lower
Insurance offers governments an efficient
transaction costs by enabling more efficient
///
mechanism for providing financial support
collection of premiums and distribution of claims.
to vulnerable farmers and pastoralists in the
event of crop failure or significant livestock
Although the Insurance Regulatory Authority (IRA) is
losses. Well-designed insurance products are an
already engaged to a limited extent in raising public
///
efficient method of transferring extreme agricultural
awareness of insurance, the GoK and the county
risk. But agricultural insurance is unlikely to be
governments also have roles to play. The GoK’s role
purchased by vulnerable farmers and pastoralists.
in financial literacy and market awareness campaigns Thus governments may decide that purchasing
is to develop a strategy; the role of the country insurance on their behalf is more efficient than
governments is to lead implementation through the relying on other support mechanisms such as post-
devolution process. disaster relief.
Photo Credit: Daniel Clarke
SECTION
01
K E N YA : A G R I C U LT U R E I N S U R A N C E S O L U T I O N S A P P R A I S A L
17
Governments often provide support for the
/// This form of risk layering offers a number
///
financing of risk through direct premium of advantages: ///
subsidies with the objective of incentivizing
1. By covering the catastrophic layer of risk, the
insurers to enter the market and increasing
government reduces the premium paid by agricultural
the take-up of insurance product. However, this
producers, as the premium does not include the
///
approach has potential drawbacks, and there may be
price of the catastrophic risk.
better ways for government to reduce the premium
cost to farmers than direct premium subsidies, such 2. If the government decides to withdraw the
as through risk financing. subsidy, the nonsubsidized commercial layer can
still continue to be sold on a sustainable basis,
Providing public stop-loss reinsurance would
///
as the noncatastrophic risk is fully priced.
build on international experience that has The commercial layer will cover all but the
demonstrated the efficiencies gained by catastrophic risk.
splitting the risk into layers. For example, there
3. Significant efficiencies are obtained through the effects
///
are three layers of risk under the Mongolian livestock
of risk pooling at the national level, in both the
insurance scheme, which has now reached national
commercial layer and the catastrophic layer.
scale:
4. Government can optimize the cost of capital by
1. The first layer of risk (up to 6 percent livestock
managing the amount and type of reinsurance,
mortality), which covers the more frequent
or other types of risk transfer instruments, that it
low-impact events, is borne by the insured
purchases. Selectively transferring a portion of
livestock herders.
the catastrophic risk to the international market,
2. The second layer of risk (between 6 percent and and retaining the balance of the risk, lowers the
30 percent livestock mortality) is covered by government’s total costs; an indirect premium
commercial insurers, through a pool, for which the subsidy costs significantly less than direct
policyholders pay a fully priced rate.7 This is the premium subsidy.
noncatastrophic layer of risk.
This risk financing approach could be
///
3. The third layer of risk (over 30 percent livestock considered for crop insurance in the Kenyan
mortality) is covered by the government under context. Given the limited availability of data and
///
a stop loss agreement entered into with the need to develop affordable products for farmers,
commercial insurers. This is the catastrophic layer the government’s assumption of a role in risk
of risk. The government does not charge for the financing could have significant benefits for a crop
stop loss agreement. insurance program. In order to achieve the most
efficient pricing for the risk, the government intends
The commercial insurers reinsure part of their
to consider in the medium term a risk layering
liability under the commercial layer to the
approach— similar to that used in Mongolia—under
international reinsurance market, while the
which it provides support for the higher layers
government reinsures a portion of its risk under the
of risk.
catastrophic layer to the international reinsurance SECTION
market (Mahul and Skees 2007).
01
18 K E N YA : A G R I C U LT U R E I N S U R A N C E S O L U T I O N S A P P R A I S A L
For livestock insurance, where the objective
///
sound and sustainable agricultural insurance
is to reduce the vulnerability of pastoralists, products and to support their ongoing development,
direct premium subsidy may be necessary in the costs are likely to impose a significant entry
the short term. In the initial years, any livestock
///
barrier to commercial insurers. Insurers expect
insurance product should be based on high-quality to recover product development costs through
satellite data; these products would not be subject the premium, over time. However, in the case
to large increases in the premiums for poor data of agricultural insurance, the high costs and the
quality. In addition, as the primary objective of the
8 limited financial capacity of policyholders make this
livestock insurance program is to reduce vulnerability unrealistic. Thus although product development and
of households in Kenya’s arid and semi-arid lands technical support are private sector functions, the
(ASALs), the beneficiaries will be low-income support of government, together with development
households that would not be able to afford to pay institutions (such as ILRI and the World Bank), is
for the insurance. Thus the provision of premium likely to be necessary, at least in the short to medium
subsidies by the GoK could be considered a viable term.
option; the GoK intends to make sure that subsidies
Care will need to be taken to mitigate
are clearly targeted and options considered for their
///
the risks of crowding out private sector
gradual withdrawal over time. In particular, the
innovation or subsidizing tasks that the
GoK will carefully consider the challenges linked to
private sector is able, and would otherwise
maintaining the long-term financial stability of the
be willing, to undertake. Insurers should be able
insurance scheme and will consider devising a clear
///
to support the continued development of products
exit strategy or long-term financing before embarking
once they have been found to actuarially sound, and
on premium subsidies.
premiums should be able to support the costs of
The provision of agricultural insurance
///
developing new products once agricultural insurance
through a coinsurance arrangement is has reached scale.
recommended later in this chapter. Although
Setting And Implementing An Enabling Legal
///
the establishment of nonstatutory coinsurance pools
And Regulatory Environment
is a private sector function, the initial push for this
effort may need to come from the public sector. (See Efforts to establish an enabling environment
///
below for more details.) for insurance should take a number of general
considerations into account. Traditional
///
Support for the Design and Ongoing indemnity-based agricultural insurance should be
Development of Insurance Products regulated like any other line of insurance, although
special regulatory provisions may be required in
As stated above, there may be need for
relation to catastrophe risk. Recognizing that the
///
public sector support in product development
current Insurance Act and Regulations do not enable
and on-going technical support in the Kenya to comply with international standards, the
short to medium term, with the support of IRA has led the process to develop a new Insurance
Government together with development Bill and Insurance Regulations that would enable
institutions (such as ILRI and the World Bank). ///
substantial compliance with international standards.
SECTION
Product development and ongoing technical
///
For index insurance, an appropriate legal
///
support are costly. Given the actuarial and other framework needs to be established. Given that
01
///
///
expertise required to design and price new actuarially index insurance pays against an agreed-upon index
K E N YA : A G R I C U LT U R E I N S U R A N C E S O L U T I O N S A P P R A I S A L
19
rather than on the basis of actual losses, there is some issue consumer protection regulations that cover
question about whether index risk transfer products Poor-value products
can be properly classified as insurance at all. As the
• Lack of disclosure
current Insurance Act does not recognize index-
based insurance, the introduction of index insurance • Unfair contract terms
products carries both legal and regulatory risk. Under • Delays in insurance payments
the proposed new Insurance Bill, index risk transfer
products can be classified as insurance, subject to A number of countries have specific
///
certain general criteria. The bill also provides for agricultural insurance legislation. This///
supporting regulations concerning index insurance to legislation is not usually intended to cover regulatory
be issued by the IRA. The enactment of the bill and and supervisory issues, but rather to make statutory
issuance of regulations would significantly reduce the provision for a specific institutional framework
legal and regulatory risks associated with developing (such as a statutory coinsurance pool or statutory
new index insurance products. The government reinsurance arrangements) and to govern the
intends to expedite the legislative process. provision of subsidy. In relation to subsidy, the
legislation may obligate government to provide a
The primary responsibility for the
///
certain level of subsidy, to take the subsidy outside
implementation of the legal and regulatory the usual budgetary process, and/or to establish a
framework for insurance lies with the IRA. ///
framework or arrangements that govern the use of
Once the new Insurance Act has been enacted, the the subsidy and ensure it is not mischannelled or
IRA will need to issue appropriate regulations. We used inefficiently. This framework could include
recommend that the IRA consider including at least a body to make decisions relating to the subsidy,
the following in relation to index insurance: audit processes, etc. Whether such legislation is
1. Detailed criteria for determining whether an index required in Kenya will depend on the institutional
product can be classified as insurance framework that is eventually adopted and the level
and types of subsidy that are to be provided for in
2. Rules allowing for composite (i.e., index and
the long term. It is therefore too soon to make any
traditional) products and dual-trigger products
recommendations.
3. General requirements in relation to indexes aimed
at reducing basis risk Driving The Process For Change
4. Restrictions on persons to whom index insurance Considerable work is required to build the
///
may be sold (aimed at ensuring an appropriate necessary foundations for agricultural
insurable interest) insurance, to design and market appropriate
products, and to establish an appropriate
5. Key requirements for issues to be included in the
institutional framework. As discussed, these
policy document
///
tasks will require an effective PPP. Without the
6. Specific provisioning requirements active involvement of both the public and private
7. Consumer protection requirements sectors, it will not be possible to develop a mature,
scaled-up agricultural insurance market in Kenya.
Consumer protection is relevant to both
///
However, it is unlikely that the process will even
traditional and index insurance. Consumer
///
commence unless the GoK takes the initiative,
protection concerns are often exacerbated in rural encouraging insurers to engage and to collaborate, SECTION
settings, where farmers lack financial literacy and a for example, through a coinsurance pool. The GoK
full understanding of both the product’s details and intends therefore to mobilize and allocate adequate
its broader implications. We recommend that the IRA financial and human resources to lead this process.
01
20 K E N YA : A G R I C U LT U R E I N S U R A N C E S O L U T I O N S A P P R A I S A L
Functions Of The Private Sector may be an acceptable substitute or proxy for data
that are not available in Kenya (such as crop or
Data weather data). For example, Normalized Difference
Vegetation Index (NDVI) data are available from
Together with the public sector, private sector
the U.S. National Oceanic and Atmospheric
///
insurers play a primary role in the collection
Administration. Where data in the public domain
of some product-specific data. Although the
or available from commercial providers will enable
///
collection, management, and audit of aggregate
product design, it may be more efficient to use
weather and agricultural data are primarily public
these data than to establish systems for collecting,
sector functions, commercial insurers have functions
managing, and storing data in Kenya, even if the
related to collecting and storing product-specific
public sector contributes toward the cost. The
data, such as data relating to sales, distribution,
feasibility of using such data should form part of the
and claims. Moreover, international reinsurance
“data gap analysis” recommended above.
companies require a party other than the government
to be involved in either collecting or auditing data to
Outreach
ensure independence and transparency. This leaves a
key role for the private sector. Outreach and product marketing are
///
primarily private sector functions. As
The private sector could also cover some or
///
indicated, the public sector may have a role to play
///
all of the cost of collecting and managing
in raising financial literacy and general awareness
agriculture data. For example, an access fee could
of agricultural insurance, but outreach should be
///
be levied on all parties that wish to use the data. This
regarded as part of distribution, which is clearly a
approach has been adopted for Motor Third-Party
private sector function. Insurers sell insurance, and
Liability in Turkey: the government is responsible
even if public sector agencies are used as part of the
for the collection and management of data, while
distribution process, distribution remains a private
all insurance companies that wish to use the data to
sector function. Furthermore, the private sector
develop and price insurance products must pay an
may be better able than the public to (i) employ
(equal) access fee. What is important here is that
innovative distribution channels;9 (ii) leverage
the data are equally available to all users on the same
the significant outreach infrastructure in place;
terms, an arrangement that encourages competition.
and (iii) respond quickly to shifts in the market.
As the design and rating of agricultural
///
Most importantly, however, competition among
insurance products are also private sector private insurers can increase speed, scale, and the
functions, private sector insurers should effectiveness of outreach.
play a role in advising the GoK on their data
needs. That is, they should specify (i) the data they
///
Design And Development Of Agricultural
require; (ii) the form in which the data are required; Insurance And Related Tasks
and (iii) the quality of the data.
Insurers are responsible for the design
///
The private sector can play a key role in
///
and development of agricultural insurance
developing and providing commercially products, although they may receive
SECTION
available data. Data that are publicly available
///
financial and other public sector support
at no charge or from commercial providers, such in the short to medium term. Such support
01
///
as remote-sensing data (including satellite data), may be necessary in the early years when the costs
K E N YA : A G R I C U LT U R E I N S U R A N C E S O L U T I O N S A P P R A I S A L
21
would be unsupportable through the premiums.10
However, design and development remain private
sector functions. Insurers are required by the
regulatory regime in Kenya and elsewhere to take full
responsibility for the insurance products that they
sell, including the actuarial pricing of those products.
Specialized professional and technical skills and
experience are required to design, develop, and
price all insurance products, including agricultural
insurance products. Where insurers do not have the
resources in house, they are permitted to outsource
them, but insurers remain fully responsible for
all outsourced services, including those provided
by or through the public sector. The institutional
framework must be designed with this in mind.
Claims adjustment and settlement are also
///
private sector functions. The comparative
///
advantage of private insurers here is founded on their
(i) existing outreach channels; (ii) knowledge of the
clients (as they are responsible for distribution);
and (iii) greater ability to innovate. A good example
of private sector innovation is offered by India,
Photo Credit: Daniel Clarke
where cell phone technology is used to video record,
geotag, and upload the results of CCEs to a database,
important that agricultural insurance underwriters
allowing insurance companies to access the data in and loss adjusters receive the appropriate specialist
real time (World Bank 2011b). This mechanism has training. To ensure the long-term sustainability of
improved the quality of the CCE procedure—by the approach, and given the expertise of private
enabling insurance companies to witness the CCE insurers, this function should be taken on by the
being carried out, the video recording acts as an audit private sector. However, this is another area in which
mechanism—and it has also made the CCE procedure public financial and other support could be provided
more timely, which greatly speeds up the process of in the early years, particularly in relation to new and
payouts for area yield index insurance (AYII). A final technical areas, such as index insurance.
reason for the private sector’s comparative advantage
is that it is better suited to respond to the potential Risk Financing
complexity of claims adjustment processes.
Underwriting agricultural insurance products
///
Private insurers must properly train their
///
and financing the risk is a core private sector
insurance and distribution staff. Given the function. The insurance business involves the
SECTION
///
///
highly technical nature of insurance production, it acceptance of insurance risk and the financing of
is important that insurance staff have the required that risk. Although the public sector may have some
skills to carry out their tasks. It is particularly risk financing functions, the function primarily
01
22 K E N YA : A G R I C U LT U R E I N S U R A N C E S O L U T I O N S A P P R A I S A L
Photo Credit: Daniel Clarke
belongs to private sector insurers. Insurers are Insurers may reinsure their insurance risk
///
required by the legal and regulatory framework, and with national, regional, or international
the IRA, to take responsibility for the management reinsurers as a substitute for holding
and financing of their insurance risk. capital to support that risk. The negotiation
///
and conclusion of reinsurance contracts is part of
Through pooling and diversifying their
insurers’ risk management process. Thus even where
///
insurance risk, insurers are able to reduce the
the public sector offers risk financing support, for
price of the risk, which should result in lower
example in relation to catastrophe risk, insurers
premiums to policyholders. By underwriting
must decide whether that support is adequate to
///
through a coinsurance pool, as elaborated later in the
enable them to underwrite the products.
SECTION chapter, private insurers can significantly lower cost
to policyholders.
01
K E N YA : A G R I C U LT U R E I N S U R A N C E S O L U T I O N S A P P R A I S A L
23
• The institutional frameworks already operating in
Institutional Framework
other countries and the experiences and lessons
First Steps learned in those countries
• The applicability of international experience in the
Establishment Of A Coordinating Body
Kenyan context
Although significant work on the institutional
///
• The legal and regulatory implications, including
framework has already been undertaken, whether specific legislation or regulations will be
further work is required and important required
policy decisions must be made before the
framework can be finalized. One issue, for
///
The work of the task force will contribute toward
example, involves how much financial and other formulation of the National Agricultural Insurance
support the GoK is prepared to provide to agricultural Policy (NAIP), as recommended below.
insurance in the short, medium, and long term.
The considered views of stakeholders will need to National Agricultural Insurance Policy
be sought, including various GoK departments and (NAIP)
agencies, county governments, the IRA, and insurers.
The MALF report recommended that the GoK
The design of a firm and final institutional framework
///
expedite the policy process for formulating
at this stage would therefore be premature.
and finalizing the NAIP, which would serve
International experience demonstrates
///
as a guiding framework for developing the
that agricultural insurance is more likely to Kenyan agricultural insurance market (GoK
succeed under a PPP that is formalized within 2014a). The NAIP should address the following:
///
a well-designed institutional framework. ///
• The GoK’s objectives for agricultural insurance,
International experience has also demonstrated that including social objectives, such as preferential
the establishment of the institutional framework is promotion and support programs for agricultural
a necessary precondition for the design of specific insurance for small and marginal farmers
agricultural insurance products. One of the reasons
• Definition of the functions, roles, and obligations
why many donor-funded pilots fail to scale up is the
of each party to the PPP
lack of institutions to follow through once the donors
or development agencies have left. It is important, • Establishment of the institutions most suitable
therefore, to give priority to the institutional for delivering the functions the GoK wishes
framework, even ahead of product design. implemented
The government intends to establish
///
The GoK intends to make formulating the NAIP a
a Program Steering Committee with priority. Once finalized, the NAIP will provide the
representation from the GoK and the private blueprint for the institutional framework.
sector that will examine options for an
institutional framework and specifically The formulation of the NAIP will be
///
consider the following: ///
considered a process rather than a discrete
task that can be completed in the immediate
• The appropriate functions of the public and
future. The work undertaken by the task force
private sectors
///
should therefore feed into the development of the
SECTION
• The options for an institutional framework, NAIP, which should be regarded initially as a work in
building on those presented in the MALF report progress. As the work moves forward, the NAIP will
(GoK 2014a) be adjusted accordingly.
01
24 K E N YA : A G R I C U LT U R E I N S U R A N C E S O L U T I O N S A P P R A I S A L
Interim Framework could be given to establishing the iHub
as soon as possible within an existing
Considerable work is required on product
institution, such as MALF. The iHub’s function
///
development. This is likely to include an analysis
///
///
and work undertaken could then be transferred to
of the data required and available, the costs of
designing agricultural insurance products, the market another institution when the PPP is fully established
demand (including the willingness and ability of (or remain with MALF).
potential policyholders to pay for the insurance), and
the appetite of private sector insurers in Kenya and Coinsurance And Coinsurance Pools
national, regional, and international reinsurers to
As it is unlikely that a fully competitive
participate in agricultural insurance.
///
insurance market will be viable in Kenya, the
Existing institutions could be used on
///
task force should consider establishing a
an interim basis to commence the work.
coinsurance pool. A competitive Kenyan market
///
The functions could then be absorbed into the
///
is hampered by the high costs of designing and
institutional framework, once finalized. For example,
distributing agricultural insurance to small farmers.
the MALF report recommended the formation of a
national agricultural insurance Web-based data and Hence some form of cooperation between insurers
information iHub; this would link end-users, including is needed. Establishing a coinsurance pool would
agricultural risk managers, insurers, and MALF staff, meet this need and also enable the pooling of risk,
with the main institutions involved in agriculture and which should result in lower insurance premiums.
agricultural risk management and with their databases The concept of coinsurance is further elaborated on
(GoK 2014a). An iHub will be needed, whatever the
in Annex 1.
institutional framework eventually will be, and work
on it could start immediately. The MALF report There are many ways to structure a
///
specifically suggested that
coinsurance pool, each with different
“the starting point for the iHUB project would be features, advantages, and disadvantages.
to define exactly what minimum (priority) key The core principles are detailed in box 2. ///
data is required for agricultural insurance purposes
and to then check with . . . organisations what data
and information they currently hold in their own
databases, and the software formats of this data and Box 2— Core principles for a
time-series available and missing data. This would coInsurance pool
result in the production of a data and statistics
1. Insurers share the costs of certain core activities, such
catalogue covering the data held by each organisation
as product design and pricing.
(GoK 2014a, 199).”
2. Certain administrative costs are shared, such as
Defining the priority key data for agricultural
///
claims administration.
insurance could be used to undertake the
3. Other activities may be shared, depending on the pool
gap analysis recommended above. The detailed
///
design, including handling of distribution costs.
SECTION proposal is set out in GoK (2014a).
4. There is at least some risk pooling. This may include
Given that the data work is a foundation block
01
presenting a pooled portfolio of insurance to reinsurers,
///
for future product development, consideration enabling a lower reinsurance cost. Risk pooling should
reduce the cost of risk, which would then lower the cost
of the premiums.
K E N YA : A G R I C U LT U R E I N S U R A N C E S O L U T I O N S A P P R A I S A L
25
Institutions Photo Credit: Daniel Clarke
Given that public sector functions are Whether or not a separate entity is
///
established, certain core functions will need
///
spread between different GoK ministries, to be undertaken. These include
///
departments, and bodies, it is important
(i) coordinating the implementation of the PPP
to ensure that public policy on agricultural from a policy perspective;
insurance is effectively coordinated. The
(ii) conducting original risk assessment and risk
///
MALF report recommended that the GoK consider mapping studies on behalf of MALF;
establishing the Agricultural Risk Management (iii) coordinating the implementation of the NAIP
Agency to coordinate public policy and support the with the private sector insurers;
individual private sector companies that sign up for (iv) assisting private sector insurers in product
the PPP (GoK 2014a). A separate entity of this kind marketing and education programs for farmers,
including the allocation of subsidies;
would help to ensure that the GoK could effectively
carry out its functions in the PPP. The task force (v) providing data and statistics and general
assistance related to agricultural insurance
will consider this and other possible options for
products;
ensuring that the policy agenda is driven forward
(vi) conducting program research and development;
and that the PPP is implemented. If a separate entity and
is established, costs should be kept to a minimum, SECTION
(vii) coordinating donor technical assistance
meaning the entity will be small with a core staff programs for agricultural risk management and
of specialists. insurance in Kenya.
01
26 K E N YA : A G R I C U LT U R E I N S U R A N C E S O L U T I O N S A P P R A I S A L
The institutional framework will need to
///
cover monitoring, supervising, accounting,
and auditing for any public sector subsidy
provided as well as advising the GoK on the
size of the subsidy. The National Treasury will
///
have a key interest in this function, which could be
housed within it or within the coordinating unit.
The National Treasury clearly has strong experience
in public financial management, but it would be
necessary to ensure that the Ministry of Finance staff
also has, or has access to, the technical capacity to
undertake this function.
Given the high costs of technical tasks related
///
to agriculture insurance, the GoK should
consider establishing a technical support unit
(TSU) to house technical expertise centrally. ///
As already discussed, technical functions belong to
the private sector. Given their costs, however, there
is significant advantage in having insurers coordinate
and centralize these functions, and the GoK may
choose to support them. TSUs are typically present
in countries where some degree of competition exists
among private insurance providers or distributers. A
TSU can have a wide range of responsibilities, such as
(i) data analysis; (ii) insurance demand assessments;
(iii) product design and rating, including basis
risk analysis; (iv) design of operating systems and
procedures; (v) training for stakeholders; (vi)
awareness campaigns; (vii) analysis of any public
subsidies; and (viii) the development of catastrophe
risk models and other risk assessment tools.
However, the establishment of a TSU is not
///
the only option. For example, if the private insurers
///
went the route of a fully incorporated, capitalized,
and staffed pool insurance company, the TSU
would not be required, since its functions could be
performed by the managing underwriting unit of
the pool.
SECTION
01
K E N YA : A G R I C U LT U R E I N S U R A N C E S O L U T I O N S A P P R A I S A L
27
Livestock Insurance for
Pastoralists Located in
ASALs in Northern Kenya
severe droughts. Because they rely on livestock
Context
for their livelihoods, high livestock deaths can
Kenya’s Experience With have devastating effects, rendering many of these
Livestock Insurance households among the most vulnerable in Kenya.
The economic analysis presented in section 2.4
Kenya has a lengthy history of livestock
///
shows that without any form of livestock insurance
accident and mortality insurance for the protection, the poorest households (<5 Tropical
commercial livestock dairy sector, but until Livestock Units, or TLUs13) and vulnerable poor
recently the insurance market did not offer (5 - 10 TLUs) are very likely to lose all their livestock,
any cover to meet the risk transfer needs of and therefore their livelihoods, in severe drought
the many resource-poor pastoralists located events.
in the arid and semi-arid lands (ASALs) of
northern Kenya. Following the devastating drought
///
To help pastoralists manage drought risk and
///
losses in the livestock sector between 2008 and protect their animals, insurance solutions
2011—an estimated 9 percent of the national cattle were developed by the International
herd was lost, with total livestock losses valued at Livestock Research Institute (ILRI), together
K Sh 699 billion (GoK 2012)—the government has with its technical partners at Cornell
signaled its major commitment under the Second University and University of California–Davis. ///
Medium Term Plan (2013–2017) to provide funding The logistical challenges of working in the ASAL
regions suggested that an index-based insurance
for a national livestock insurance scheme (MDP
product would be appropriate. Developing the
2013).
Insurance Based Index Product (IBLI) involved two
Drought is the most pervasive hazard, natural
///
years of comprehensive research aimed at designing,
or otherwise, encountered by pastoralist developing, and implementing market-mediated
households in the ASAL regions; it can lead to index-based insurance products that livestock
widespread death of livestock and severely keepers—particularly in the drought-prone ASALs—
SECTION
deplete livestock assets for the affected could purchase to protect themselves from drought-
households. Many pastoralist households in related asset losses. The IBLI product is based on
02
///
the ASALs are now regularly hit by increasingly a satellite Normalized Difference Vegetative Index
28 K E N YA : A G R I C U LT U R E I N S U R A N C E S O L U T I O N S A P P R A I S A L
(NDVI)14 cumulative-season drought index, which State Department of Livestock
is combined with a predicted livestock mortality Interest in Large-Scale Drought
index to insure pastoralists against drought-related Insurance For Pastoralists in
deaths to their livestock (cattle, camels, sheep, and ASAL Regions
goats). It provides full-value animal cover to enable
the insured pastoralists to restock their herd after the As part of its plans to promote and
///
drought event. strengthen livestock insurance provision in
the ASALs, the government of Kenya (GoK)
The commercial sale of IBLI was launched in
///
has proposed creating a national livestock
Marsabit, northern Kenya, in January 2010 as insurance scheme under the Second Medium
a voluntary retail insurance product and was Term Plan. An indicative budget of K Sh 2,000
///
marketed to individual pastoralists. The IBLI
///
million–2,500 million over the fiscal years 2013/14
demand assessment studies identified affordability to 2017/18 was identified to support the national
as a constraint to uptake, and since launch in 2010 livestock insurance scheme (MDP 2013).
donor partners have financed premium subsidies
in the order of 40 percent of the full premium With this objective in mind, the GoK, through
///
costs. In 2010, UAP Insurance Company was the the State Department of Livestock (SDL)
underwriter, while Equity Insurance Agency was the within the Ministry of Agriculture, Livestock
insurance agent. Swiss Re provided reinsurance for and Fisheries (MALF), approached the World
the product. The IBLI program has gone through Bank’s Disaster Risk Financing and Insurance
various adjustments since it was launched, and Program in 2014 to ask for technical support
APA Insurance Company became the underwriter in developing a public private partnership
for Marsabit and Isiolo Counties in August 2012 (PPP) in livestock insurance to support
and August 2013 respectively. In Wajir County, a pastoralists. The MALF-World Bank team
///
Sharia-compliant version of IBLI is currently being partnered closely with ILRI and the Financial Sector
implemented by Takaful Insurance Company with Deepening (FSD) Kenya program to benefit from the
support from Mercy Corps. considerable practical experience those institutions
have in Kenya.
While the current program has driven
///
innovation in IBLI product development,
pricing, and distribution, several challenges
remain to be met to achieve large-scale
uptake. In 2010, when IBLI was first launched,
///
pastoralists bought nearly 2,000 policies to insure
about 6,000 TLUs (ILRI 2013). Since then, however,
the program has struggled to achieve scale and
sustainability in spite of making payouts to insured
pastoralists in response to droughts in 2011 and again
in 2012.
SECTION
02
K E N YA : A G R I C U LT U R E I N S U R A N C E S O L U T I O N S A P P R A I S A L
29
support any transition in the medium term toward
Proposals For Large-Scale
increasing contributions from targeted recipients
Livestock Insurance For and the pastoralist population at large.
Pastoralists Located In Under the plan, SDL would be assisted in
///
ASALs In Northern Kenya designing and implementing a macro-level
pasture drought index insurance program;
Livestock Insurance Options
starting in 2015, SDL would purchase the
insurance on behalf of approximately 71,000
Under the proposed plan, the SDL would
vulnerable pastoralists located in the four
///
collaborate with the National Drought
Hunger Safety Net Program (HSNP) counties
Management Agency (NDMA) to develop a
of Mandera, Marsabit, Turkana, and Wajir.
large-scale index-based livestock insurance
///
The SDL would insure itself at the national level,
program to cover pasture drought risk. In ///
with insurance payouts triggered by a satellite-based
order to quickly build a critical mass of covered
households, a macro-level product will form the index at a local/pastoralist level. The SDL would then
foundation of a sustainable livestock insurance channel payouts to pre-identified pastoralists on the
market. Grafted onto this will be the concrete triggering of the index, or underwriting insurer(s)
provision of “top-up” cover for targeted beneficiaries would provide payments to these pastoralists
who wish to expand their coverage, and a voluntary directly. An element of cost sharing between central
SECTION
purchase cover for all nontargeted pastoralists. This
15 and county governments will be explored, with the
structure is designed to gauge the level of untapped plan initially proposing that the entire cost of the
demand for voluntary livestock insurance and to compulsory coverage be paid by SDL.
02
30 K E N YA : A G R I C U LT U R E I N S U R A N C E S O L U T I O N S A P P R A I S A L
Start-up implementation of the SDL macro-
///
government subsidies for the top-up and voluntary
level pasture drought index insurance purchase options have tentatively been set at 50
program is recommended for the four HSNP percent and 25 percent, respectively, but these
counties because infrastructure systems may change; final percentages will be guided by
and procedures are already in place there. the program experience with partially subsidized
This infrastructure allows for (i) verifying voluntary contracts provided at the outset.
identification and registration of eligible
pastoralists who will be the beneficiaries Integrating Existing Social Protection
of this insurance; and (ii) delivering timely and Insurance Programs for
insurance payouts in kind or in cash to Pastoralists in the Target Counties
the individual beneficiaries. In the HSNP
///
Linkages Between Programs in the ASAL Region
counties, the NDMA Secretariat has partnered with
implementing nongovernmental organizations to The overall framework for the insurance
///
register 375,000 households and their dependents program should be mindful of various
and to classify them into four main income/ insurance products already being distributed
wealth status categories or poverty bands. The
and social protection measures currently
HSNP program is currently targeting the poorest
being developed in the four HSNP counties. ///
100,000 households under its regular program of
These include
bimonthly cash payments and plans to scale up this
program in times of extreme drought. The goal is to • The ILRI-developed IBLI contracts currently
complement the HSNP program by implementing being offered by APA Insurance and Takaful
the SDL insurance program with approximately Insurance Africa on a voluntary basis
71,000 vulnerable pastoralists who are just above • The HSNP protection
the poverty criteria for inclusion in the HSNP cash
transfer program (see below for further discussion). • The new SDL-led IBLI initiative for macro-level as
In addition, the HSNP payment system might be used well as top-up and voluntary coverage
as a way to distribute the payouts (complementary to
To avoid overlap between the three
///
the use of mobile payment options).
programs, the HSNP classification of
To supplement the macro-level pasture
///
households according to wealth/poverty
drought index product, the insurance status should be used to target each
program offers a top-up option for eligible insurance program to different poverty
pastoralists, plus voluntary policies that groups. The poorest 100,000 households would
///
would be sold to all pastoralists on an continue to be covered by the HSNP under the
individual basis. The macro-level product as
///
regular bimonthly cash transfer program, which is
described above would be supplemented by top-up 100 percent financed by the GoK and donors using
insurance policies to be purchased by pastoralists a variety of funding mechanisms, including Africa
on a voluntary basis; the costs of any such top-up Risk Capacity (ARC) index insurance payouts (see
cover would be shared between the GoK and the below for further discussion). Adopting the HSNP
pastoralists. This product would also be offered— poverty ranking, the macro-level pasture drought
SECTION
and partially subsidized—for voluntary, individual index insurance program—funded entirely by the
purchase by all pastoralists, independent of whether government—would apply to registered vulnerable
02 they are covered by the macro-level product. The pastoralists immediately above the HSNP’s target
K E N YA : A G R I C U LT U R E I N S U R A N C E S O L U T I O N S A P P R A I S A L
31
beneficiaries. Finally, relatively wealthier (though still IBLI pilot over the past five years, (ii) offer choice
low-income) pastoralist households could be covered to individual livestock producers not targeted by
by the top-up and voluntary SDL cover as well as SDL for its program, (iii) enable an assessment
ILRI’s IBLI product being marketed by APA Insurance to be made over the next two to three years of
and Takaful Insurance of Africa. This layering the voluntary demand for livestock insurance by
approach is illustrated in figure 3. individual pastoralists in the ASAL regions, and—
based on the assessment findings—(iv) allow SDL
The SDL macro-level product for the targeted
///
to decide whether to introduce its own top-up and
vulnerable category of pastoralists should be voluntary individual index-based livestock insurance
linked with the ILRI-developed IBLI product products and programs.
currently being offered on a voluntary basis
by APA Insurance and Takaful Insurance Possible Linkages with ARC
of Africa. This approach will ensure that all
In 2014, Kenya and four other African
///
///
pastoralists (not just those covered by the SDL countries—Mauritania, Mozambique, Niger,
program) can purchase livestock insurance. It will and Senegal—joined a new African drought
also allow the SDL and the GoK to do the following: index insurance facility under the ARC
(i) learn from the major technical design expertise initiative (ARC 2014).16 ARC is an initiative of the
///
and implementation experience gained under the Commission of the African Union’s Department of
Figure 3 — How government could support financial protection for different
segments of the population: Example of pastoralists in the four current HSNP
counties (Mandera, Marsabit, Turkana, and Wajir)
Income Level Livestock Safety Net Cost Share
and Insurance Program
Middle-income
100% of premium
and above Unsubsidized livestock insurance
covered by farmers
(>1 USD/day)
Premium cost
Low-income
Partially subsidized livestock insurance sharing at
(<1 USD/day)
50%/25%
Macro-level insurance program for
Ultra-poor Premium 100%
approximately 71,000 ultra-poor pastoralists
(<0.5 USD/day) subsidized by GoK
above HSNP poverty levels
Hunger Safety Net Program (HSNP), providing
Hardcore poor Premium 100%
scalable cash transfers to 100,000 hardcore
(<0.3 USD/day) subsidized by GoK
poor households
SECTION
Note: Classification is based on distribution of livestock holding size for Marsabit County, which may not be similar in other HSNP
counties
02
32 K E N YA : A G R I C U LT U R E I N S U R A N C E S O L U T I O N S A P P R A I S A L
Rural Economy and Agriculture and the World Food other circumstances would be too poor to
Program, It aims to create a pan-African-owned pool afford insurance premiums. The final decision
///
index insurance fund to underwrite catastrophic
on eligibility will involve technical input from the
weather events, initially to cover drought; in the
future, coverage could be expanded to include other SDL and other ministries. However, eligibility for
weather risks such as flood.17 The program is insured the public subsidy should be limited to vulnerable
by ARC Insurance Company Limited (ARC Ltd.), households that reside in the four pilot counties,
domiciled in Bermuda, and is reinsured by specialist
where vulnerable households are defined as a
international reinsurers of this class of business.
specific, not-yet determined number of households
In 2014, Kenya purchased protection from the
///
ranked just above the eligibility cut-off point for
ARC and determined that one of the primary
benefits under the HSNP.
purposes of the ARC program in Kenya would
be to support the scalability mechanism of For the macro-level index insurance program,
///
HSNP. Coverage has been purchased for both the
the GoK should purchase 100 percent of
///
long rains/long dry season and the short rains season
(maximum payout US$30 million each). The primary cover for 5 TLUs per eligible household. ///
use of the cover will be to lessen the fiscal burden to The number of households covered will depend
GoK of meeting the cost of scaling up the HSNP. on the resources the SDL has available to support
SDL could consider exploring possible links
///
the scheme. For illustrative purposes the team has
between the SDL macro-level insurance chosen annual to analyze three scenarios: budgets
coverage and the ARC program in Kenya. ///
of K Sh 100 million, K Sh 200 million, and K Sh 300
Given the clear complementarities between
million to analyze.
these programs, the synergies available should
be leveraged. For top-up coverage for eligible households,
///
Eligibility and Subsidy the amount of subsidy should be determined
by the SDL. An initial suggestion is that the GoK
///
The macro-level SDL product is intended to
provide a subsidy for 50 percent of the actuarially
///
provide insurance to pastoralists who, at this
stage, would not be able to afford commercial calculated commercial premium, with the other 50
premiums. The GoK therefore intends to provide
/// percent paid by pastoralists. This subsidy would be
a public subsidy for the product. Given the subsidy, subject to a cap of 5 TLUs per pastoralist.
eligibility criteria will be required to ensure that
targeting is in line with the government’s objective For voluntarily purchased individual
///
of supporting the most vulnerable pastoralist coverage, the GoK will need to decide
households. As eligibility and subsidy both affect
whether it will also be subsidized and what
product design, decisions are required on these issues
before the product design can be finalized. cap per pastoralist will apply. Over time, the
///
GoK may plan to reduce the size of public subsidies.
In principle, the macro-level program under
SECTION A 25 percent premium subsidy has been tentatively
///
which SDL would finance 100 percent of the
insurance premiums is targeted at poor and applied, subject to change going forward (see section
02 vulnerable pastoralist households who under 3.3).
K E N YA : A G R I C U LT U R E I N S U R A N C E S O L U T I O N S A P P R A I S A L
33
Design Options for SDL Macro-Level
Similar approaches have been implemented
///
Livestock Index Insurance Product in the following countries: ///
• Spain, United States, and Canada. In these
For implementation in calendar year 2015, a
/// ///
///
countries, the NDVI pasture drought index
macro-level livestock insurance product for
insurance programs operate as voluntary micro-
the SDL is proposed: its central objective is
level individual livestock producer programs. The
to effect timely cash payouts to vulnerable
cover period is defined as the normal pasture/
pastoralists at the onset of drought in order
grazing growing season (which usually coincides
to keep breeding stock alive. Under this scheme,
with the spring and summer rainy seasons of
///
the GoK would be the party entering the insurance maximum pasture and biomass production), and
contract. If it received a payout, it would in turn make the basis of the sum insured is usually calculated
payments to pastoralists as identified above. according to the nutritional requirements of the
livestock/costs of purchasing supplementary
For this macro-level cover, satellite data
livestock feeds in the event of loss of pasture
///
(NDVI) would be used to create a pasture
and grazing due to drought. Regular payouts are
drought index. This would enable the
made during the cover period for each month
development of an index that measures
(or time period as defined) that the NDVI policy
the onset of drought-related pasture and
is triggered. All three programs attract heavy
grazing degradation and that triggers early government premium subsidy support.
payouts (so that pastoralists can purchase
• Mexico. The federal and state governments
animal feeds to keep their core breeding
/// ///
purchase macro-level NDVI/pasture drought
animals alive). The advantage of this approach in
cover used in the events of catastrophic losses
///
comparison to the existing ILRI product (interim
in pasture and grazing to finance payouts to
period) is that payouts could be triggered earlier in
the many small, vulnerable livestock producers
the season, i.e., during the onset of drought—before
(owning <50 livestock units) who are eligible for
reduced pasture/grazing creates a disaster. Under state-funded natural disaster assistance under
this approach, pastoralists would not be forced into the CADENA program. One hundred percent of
untimely sales of livestock at very reduced prices, the premium cover is borne by federal and state
and their animals would be saved from starvation, governments together (80:20 ratio). Since the
disease, and ultimately death. Pastoralists would use program was introduced in 2006, it has been
the funds provided to preserve livestock (through massively scaled up such that in 2011, a total of
buying fodder, migrating, culling, etc.) rather than almost 60 million hectares of grazing lands were
having to replace it. This approach enables a faster insured in 21 states, and nearly 4 million head of
mobilization and increased effectiveness of the livestock were protected (World Bank 2013a).
emergency response.18 Among other important • Uruguay and Argentina. In 2011–2013, the
/// ///
welfare gains it offers, such an approach could World Bank assisted the governments of Uruguay
help facilitate income smoothing and reduce asset SECTION
and Argentina in designing NDVI/pasture drought
depletion; this protection of assets would increase macro-level products protecting livestock and
household resiliency to future shocks. issuing early payouts (World Bank 2012, 2013b).
02
34 K E N YA : A G R I C U LT U R E I N S U R A N C E S O L U T I O N S A P P R A I S A L
Table 2— Comparison of Uruguayan pasture NDVI cover and proposed Kenyan
NDVI cover
Product
Approach taken in Uruguay Considerations for Kenya
feature
NDVI/pasture index; 5km x 5km (2,500 ha). Required index and data will be based on the
existing NDVI database created and maintained
Index
by ILRI: NDVI/pasture index; 250m x 250m
(eMODIS).
Pasture growing season: 7 months (September- Still being explored. Northern Kenya has two
March). rainy seasons: long rains (March-June) and short
Cover period
rains (October-December); ideally both would be
covered.
Police Section (equivalent to a municipality). Still being explored. Preliminary discussions with
Homogeneous NDVI signature and individual ILRI indicate that index products could be more
Insured unit livestock herd data are registered at this effective at a scale smaller than the division level.
administrative level for foot-and-mouth disease
control purposes.
Beef cattle (breeding cows and heifers only). All households in the HSNP poverty census above
Insured interest Program has been designed to cover all registered the cut-off point for the regular program.
beef cattle herds in Uruguay.
Based on nutritional requirements of insured Still being explored. For example, more input
cattle during the insurance cover period, is being collected from livestock experts on
assuming animals are fed on supplementary feed nutritional requirements, etc.
Sum insured
rations that can be purchased locally.
Monthly payout frequency, because once pasture Different payout frequencies being explored
degradation is visible on NDVI, the insured cattle (monthly, every 3 months, seasonal basis); to be
Payout
are already suffering from starvation. determined based on pastoralists’ needs.
parameters
Sum paid out along gradual trigger with entry and Gradual and binary trigger options being
exit point. explored, i.e., paying out either in full or not at all,
for simplicity.
Source: World Bank 2013b
SECTION
Note: eMODIS = enhanced Moderate Resolution Imaging Spectroradiometer.
02
K E N YA : A G R I C U LT U R E I N S U R A N C E S O L U T I O N S A P P R A I S A L
35
Figure 4 — Illustrative calculated pure loss cost rates for 12-month NDVI
asset protection cover at District and Division level
SECTION
Source: World Bank
02
36 K E N YA : A G R I C U LT U R E I N S U R A N C E S O L U T I O N S A P P R A I S A L
The SDL macro-level NDVI product is
///
light on how early drought index insurance
currently under development, and a payouts could be used to support the protection
prototype contract has been designed that of breeding herds (e.g., by buying fodder and
will be presented to key stakeholders— animal feed supplements, migrating/transporting
including SDL and livestock producer animals to other grazing lands, controlled
associations in the ASAL regions—for review, destocking of animals, etc.).
refinement, and finalization. The product
• The most appropriate definition of the
///
draws on the lessons and experience of the Mexican,
///
insured unit. In determining whether the
Argentine, and Uruguayan livestock NDVI programs,
///
insured unit should be the department, division,
while taking the local Kenyan context into account.
or a smaller area with homogeneous grazing/
A comparison of the Uruguayan macro-level NDVI
rangeland conditions affected in a similar way by
cover and the proposed macro-level cover for SDL in
drought, it will be important to take into account
Kenya is presented in table 2.
the seasonal migration patterns of the pastoralists
An example of the outputs of the macro-
///
as they move their nonbreeding herds to their
level prototype NDVI pasture drought index dry season grazing lands, which may be outside
insurance cover is given in figure 4, which the defined insured geographic unit where they
shows the calculated pure loss costs (average normally reside. Setting the insured unit at a very
expected payouts) for an annual policy small localized level may invalidate the operation
for the four HSNP counties at district and of such an NDVI cover.
divisional levels. The calculated pure premium
19
• Ways to integrate any macro-index
///
///
rates are presented for the four HSNP counties
insurance program with NDMA and SDL
(and their divisions)—Turkana, Marsabit, Wajir, and
drought-response plans for the livestock
Mandera—and in general terms reflect the increasing
sector. Possible approaches include controlled
drought risk exposure in natural rangelands from
///
destocking programs, livestock watering, pasture
west to east as measured by eMODIS NDVI by month
and grazing conservation measures, government
for the 13-year period 2001 to 2013. Decisions that
emergency livestock feed programs (if these
will need to be taken with local stakeholders in due
currently exist), and veterinary support programs
course include the size of insured unit (county or
during times of drought. A key point is that under
division) and whether to market top-up or voluntary
a macro-level index insurance program aimed at
cover using differential premium rates in each
keeping animals alive, it will be important to avoid
insured unit.
suggesting to pastoralists that they do not need
To determine whether the proposed macro-
///
to destock their herd in times of acute drought, as
level NDVI pasture drought degradation they will receive insurance payouts.
cover, with its emphasis on early payouts to
• Presence of local public or private forage
///
keep breeding animals alive, is appropriate to
markets in times of drought in the ASALs. ///
the pastoralist production systems of Kenya’s
These markets are essential, and could be (i) a
ASALs, further research will be required into
GoK-SDL fodder supply program in place at the
the following key areas:
onset of drought; and/or (ii) a program under
///
SECTION
• Use of early payouts to protect breeding
///
which private traders are incentivized to truck
herds. Focus groups discussions with pastoralists fodder from Kenya’s surplus regions to drought-
02
///
in the target HSNP counties are planned to shed stricken regions.
K E N YA : A G R I C U LT U R E I N S U R A N C E S O L U T I O N S A P P R A I S A L
37
Registration and Distribution understand how the insurance product operates
Channels (how the triggers are based on satellite data, what
the trigger points for payout are, etc.). Households
SDL Macro-Level NDVI Program must understand their coverage in order to promote
the voluntary purchase market. Better understanding
Registering pastoralist households eligible for
will also encourage voluntary take-up by spreading
///
the GoK subsidy could be done automatically,
awareness of the product.
through the existing HSNP database; this
approach would be cost-effective and
save time, but it could also present some
complications: (i) Failure to explain the product
///
to beneficiaries could lead to poor awareness of the
benefits being provided and of claims procedures;
(ii) if the program is not well known in the region,
both political visibility and broader awareness of the
product would be reduced; (iii) without insurance
awareness creation, potential financial inclusion
gains would be lost; (iv) confirming beneficiaries’
inclusion in the program would be hard; and (v)
beneficiaries might never understand the benefit
being provided to them by the GoK.
It is preferable, therefore, to handle initial
///
registration in person and not automatically.
One option would be to enroll pastoralists
into the insurance program when Equity
Bank opens bank accounts with them and
they receive their bank cards. All pastoralists
///
registered under the HSNP poverty census are
scheduled to receive a bank account and a bank card
by end of the first quarter 2015. When pastoralists
receive their bank cards, they could also get an
explanation of the program, including benefits,
payments procedures, issuance of cards and pins,
confirmation of persons and identification details,
awareness/education, and consumer protection
issues.
Although more time-consuming and costly,
///
this method will support the development
SECTION
of a sustainable market. It has the key benefit
///
of ensuring that eligible households understand the
insurance coverage they are being given and, further,
02
38 K E N YA : A G R I C U LT U R E I N S U R A N C E S O L U T I O N S A P P R A I S A L
Top-up and Voluntary Purchase Products Legal and Regulatory Issues and the
Role of Insurers
There will be significant distribution
///
challenges beyond the subsidized cover, Both legal issues and regulatory issues
///
particularly as insurance will not be linked (beyond consumer protection) must be
to credit. The challenges would emanate from
///
considered during product design. The role of
///
potentially high operational costs associated with insurers and insurance intermediaries should also
the sales and service process, which to date have be considered. Ultimately, however, the regulatory
been significant in northern Kenya. The distribution framework is a matter for the Insurance Regulatory
should primarily be the responsibility of the private Authority (IRA), which should be kept informed as
sector. The top-up could potentially happen at the product is designed.
the time of registration, when beneficiaries of the
government-supported program could opt for The IRA regulates and supervises the
///
additional coverage. This approach could utilize the insurance sector under the current Insurance
proposed distribution channels, or the underwriter Act. However, if the new principles-based Insurance
///
could devise a cost-effective model for distribution Act under consideration is enacted, it will enable the
of this additional cover. Voluntary purchase could IRA to develop new regulations that will foster an
be done through developed or parallel network enabling environment for livestock insurance.
and infrastructure.
Contract Design (Macro-level Policy)
Ultimately, the registration and distribution
For the proposed SDL macro-level NDVI
///
process will be led by the private sector,
///
pasture drought index insurance policy,
though GoK support may be needed in
the insurance contract will be purchased
the short term. A private sector–led initiative
by GoK-SDL, not by individual pastoralists.
///
would demonstrate viable long-term business
///
The policy would be set up as a macro-level policy,
opportunities and hence be more sustainable. In
purchased by the GoK for the benefit of eligible
the long run, it would also provide an opportunity
pastoralists. Payment would be made either to the
for expanding more financial opportunities to the
GoK (which would then pay covered pastoralists) or
target communities, and the overall execution would
directly to the pastoralists. A point to note here is
cost less than if the initiative was government led.
that a beneficiary does not have an automatic right
The private sector–led process would undertake
to enforce a master policy against the insurer; this
fresh registration of beneficiaries and develop and
arrangement makes the program much easier to
manage payment infrastructure under contractual
implement. Enforcement rights are dependent on
arrangement with the government. The tendering
the terms of the policy.
process would be used to select the provider(s) or
consortium to offer registration and/or distribution Contract Design (Voluntary
processes. Purchase Product)
The voluntary purchase product will be
///
available to all pastoralists but will be
SECTION offered as a voluntary top-up to those
benefiting from the macro-level product. For
02
///
these pastoralists, the top-up should operate as an
K E N YA : A G R I C U LT U R E I N S U R A N C E S O L U T I O N S A P P R A I S A L
39
extension of the macro-level policy—that is, as an Role of Private Sector Insurers
extension of the same “master policy.” This would be
by far the most straightforward approach. An effective PPP requires the engagement
///
and willing participation of private sector
For all other pastoralists, the freely purchased
///
insurers at an early stage. ///
insurance policy would be more difficult to
fit within the constraints of a master policy. ///
The macro-level policy would need to be
///
However, issuing a series of individual policies would purchased from a local insurer or insurers. ///
add to the transaction cost. Under this scenario, one Under the Kenyan Insurance Act, insurance must
option would be to allow pastoralists to enforce the be purchased through the local market unless there
policy against the insurer, though this option has is insufficient capacity. If reinsurance is purchased,
possible cost implications, as the insurer might have it could be placed into the international market
to deal with pastoralists seeking payment on the basis after any compulsory cessions to national/regional
of losses even though the index had not triggered. reinsurers. The inability of the GoK to legally
This possibility would need to be factored into the purchase directly from the international market
premium as an additional risk (which would be very would almost certainly add some transaction cost.
difficult to cost). The product design includes a single master
///
policy plus a series of individual policies.
Form of Contract
///
Both the master policy and the individual policies
Even though eligible pastoralists will not
///
would need to be purchased from a local insurer or
contribute to the premium payable under insurers.
the macro-level policy issued to SDL, the
insurance contract/policy should be kept as
straightforward as possible. There are a number
///
of reasons for this:
• The contract/policy design will most likely become
a precedent.
• Eligible pastoralists will be paying for the
voluntary top-up.
• A similar contract form should be used for the
master policy and for the individual policies.
Other issues to consider will be the potential
///
use of electronic policy acceptance. This is not
///
an issue directly covered under the current legal and
regulatory framework, although there is precedent for
it in relation to other products.
SECTION
02
40 K E N YA : A G R I C U LT U R E I N S U R A N C E S O L U T I O N S A P P R A I S A L
Table 3— Proposed livestock safety net and insurance program for Kenya’s four
HNSP counties
Number of pastoralists
Government’s
Form of financial expected to be covered
Income level of contribution to cost
protection against across four counties over
beneficiary of premium or welfare
disasters next five years (out of
payments (%)
470,000 total)
Unsubsidized livestock Middle-income
0 0
insurance (US$1/day or more)
Partially subsidized livestock Low-income
15,000 (by 2019) 50, 25a
insurance (below US$1/day)
Ultra-poor
Macro-level insurance
(below national rural 71,000 100a
program
poverty line of US$0.5/day)
Hardcore poor
HSNP scalable cash transfers (below national food 100,000 100b
poverty line of US$0.3/day)
Note: The four HSNP counties are Mandera, Marsabit, Turkana, and Wajir
a. Contribution is from State Department of Livestock, based on annual assumed budget of K Sh 300 million per year; the two figures
refer to subsidies for the top-up and voluntary purchase options respectively,
b. Contribution is from National Drought Management Authority.
Fiscal Costing Assumptions The optional top-up coverage for pastoralists
///
enrolled in the program will also be available
And Scenarios from the outset of the program. In addition,
pastoralists who are not part of the initial
The following assumptions underlie the
///
target group will also have the option
illustrative estimation of the potential fiscal
costs of the programs described above: ///
to purchase the NDVI-based insurance
coverage on a voluntary basis. The first layers
///
The macro-level NDVI-based index insurance
///
of both the top-up option and the coverage for
product for livestock asset protection will nontargeted pastoralists will be partially subsidized.
target the poor “vulnerabale pastoralists”
who are above the poorest 100,000 Table 3 shows the relationship of the SDL macro-
beneficiaries of the HSNP program. The ///
level automatic NDVI livestock insurance and the
program will be implemented in the four counties voluntary top-up cover at year 3 with the HSNP cash
SECTION transfer program. The figures used are indicative
covered by HSNP (Mandera, Marsabit, Turkana,
and Wajir) and is expected to cover free of charge and subject to change as the insurance program
02 approximately 71,000 pastoralists. is concretized.
K E N YA : A G R I C U LT U R E I N S U R A N C E S O L U T I O N S A P P R A I S A L
41
Table 4— Fiscal costing projections for macro-level asset protection coverage
SCENARIO KSh 100 million SCENARIO KSh 200 million SCENARIO KSh 300 million
(US$ 1.2 million) (US$ 2.3 million) (US$ 3.5 million)
Case A Case B Case A Case B Case A Case B
Budget available for
Macro-level asset
100,000,000 200,000,000 300,000,000
protection coverage
-K Sh
No. of pasotralists
eligible for livestock
11,592 33,636 24,204 69,636 36,815 105,636
asset protection
coverage
Estimated Financial Costs of the Macro-Level to approximately 12,000 to 106,000
Livestock NDVI Program pastoralists. Specifically, a budget of K Sh 100
///
million would cover 12,000 to 34,000 pastoralists,
Following specific indications from SDL, we
a budget of K Sh 200 million would cover 24,000 to
///
analyze here three public support scenarios
70,000 pastoralists, and a budget of K Sh 300 million
for the macro-level asset protection scheme:
would cover 37,000 to 106,000 pastoralists.
budgets of K Sh 100 million, K Sh 200 million,
and K Sh 300 million. The projected fiscal costs
///
To cover a reference target group of 37,000
///
of the programs are summarized in table 4 and table to 106,000 (mid-point of 71,000) pastoralists
5. For each budget scenario, two extreme cases are under the macro-level insurance option,
presented. Case A is structured by selecting, within under which SDL would be the insured and
a reasonable range of variation, the more costly responsible for payment of premium, fiscal
extremes of the key parameters (i.e., higher values resources of K Sh 300,000 (US$ 3.5 million)
per TLU insured, a higher number of TLUs per policy, or above would be required.20
///
and a higher insurance premium estimate). This
Estimated Fiscal Costs of the Top-Up and
defines a lower bound for the number of potential
Voluntary Livestock Insurance Programs
pastoralists to be covered with the reference budget
available. On the other extreme, Case B takes into A partially subsidized top-up option for
///
account the less expensive options, thus identifying covering an additional 5 TLUs will be offered,
the higher bound for the number of potential with a budget requirement ranging from
pastoralists to be covered. roughly K Sh 2 million in 2015 to K Sh 16
million in 2019. This scenario is estimated
///
Depending on the policy choices made assuming a progression over five years from 1,000
SECTION
///
and the parameters selected, budgetary to 10,000 pastoralists who voluntarily purchase the
support ranging from K Sh 100 million to coverage, together with tentative public support of
K Sh 300 million would provide coverage 50 percent of the premium cost.
02
42 K E N YA : A G R I C U LT U R E I N S U R A N C E S O L U T I O N S A P P R A I S A L
The other option, for all nontargeted
/// As next steps, SDL, MALF, and other
///
pastoralists, allows purchase of the NDVI key stakeholders will need to consider
asset protection coverage with tentative funding arrangements to cover the
public support of 25 percent for up to 10 TLUs costs of premiums as well as design and
insured; this generates an additional budget implementation for the macro-level NDVI
requirement of K Sh 5 million to K Sh 16 insurance program, and for the top-up and
million between 2015 and 2019. This projection
/// nontargeted pastoralist programs that carry
assumes that in the five-year interval, 1,000 to 5,000 premium subsidies. One option would be to use
///
nontargeted pastoralists purchase the coverage. the proposed National Livestock Insurance Fund
to finance the premiums and other program costs,
The two additional insurance schemes to
/// including those for registration of beneficiaries,
be implemented at year 3 of the program education and training programs, program design,
could in aggregate increase the budget and implementation and auditing.
requirements by roughly K Sh 6 million in
2015 and K Sh 31 million in 2019. ///
In summary, for the combination of (i) the
///
macro-level asset protection coverage, (ii)
the top-up option, and (iii) the expansion
to nontargeted pastoralists, the estimated
fiscal costs are estimated at around K Sh 306
million at program inception in 2015 and at
around K Sh 331 million by 2019. ///
Table 5— Fiscal costing projections for top-up and nontargeted
pastoralists options
2015 2016 2017 2018 2019
Cost of public support for top-up option
1.6 5.1 8.6 12.1 15.6
(million K Sh)
Cost of public support for nontargeted
pastoralists 4.7 8.6 11.7 14.1 15.6
(million K Sh)
Total cost for GoK
6.3 13.7 20.3 26.2 31.2
(million K Sh)
Total cost for GoK
0.1 0.2 0.2 0.3 0.4
SECTION (million US$ at 85 K Sh/US$)
02
K E N YA : A G R I C U LT U R E I N S U R A N C E S O L U T I O N S A P P R A I S A L
43
1999–2013, average livestock mortality rates were
Welfare Impacts Of Index-
9–18 percent per year. As recurrent droughts reduce
Based Livestock Insurance households’ average herd size, they lead to increasing
In HSNP Countries poverty and food insecurity in the region.
The four HSNP counties are among the
///
To cope with droughts, households in this
///
poorest counties in Kenya, with the majority region rely heavily on food aid, risk sharing
of the population depending heavily on within communities, and other emergency
livestock both for income and food. As ///
response and welfare programs, but they are
estimated by the HSNP household survey, the ultra- largely uninsured. Under the HSNP, one of the key
///
poverty rate in 2012 was 46.8 percent, with average government welfare programs, households receive
consumption expenditure per capita per month of cash transfers of approximately K Sh 3,500 every
1,746 K Sh.21 The share of livestock income in total
two months. HSNP impact evaluation results find
household economic income ranges from 25 percent
that the cash transfer has reduced poverty and that
to 80 percent, and the share is larger for the poorer
it was used as a safety net during the 2011 drought
quintiles. Livestock production is the key source
in the region. Other coping mechanisms appear
of livelihood in this region. Alternative productive
livelihood appears very limited, and is accessible very limited—for example, households are credit
only to the wealthiest. Alternative livelihoods for constrained and have limited access to financial
the poor majority are petty trading, casual labor, and services—leaving households uninsured against
small cropping. catastrophic herd losses from droughts.
Livestock holdings provide a good proxy for
///
We have conducted a detailed economic
///
welfare in this region, with the poor owning analysis of the likely impact of livestock
small herds but relying more heavily on insurance on four categories of pastoralist:
livestock than those who are better off. From
poorest, ultra-poor, poor, and nonpoor. We
///
///
the longitudinal monthly household survey by Arid
develop a dynamic model to explore the potential
Land Resource Management Project (ALRMP) in
varying welfare impacts on a representative pastoral
these counties, the average herd size owned by
household in each of the four wealth groups:
households was 10.4 TLUs during 1999–2013. On
the poorest with small herd, the ultra-poor with
average, households in the two poorest quintiles own
less than 5 TLUs, those in quintile 3, 4, and 5 own vulnerable herd, the poor with medium herd, and
respectively 5–10 TLUs, 11–20 TLUs, and more than the nonpoor with large herd (see annex B.3). In
20 TLUs. Livestock production systems could vary the economic model, a household earns income
across and within the four counties, with relatively each season from milk production and livestock
larger mobile pastoralists (i.e., those with larger herd off-take, which it uses for consumption and to
size) in the relatively low and arid lands of northern accumulate herd for the next season. Normally,
Marsabit, Mandera, and Wajir. the own herd grows each season, but it sometimes
Livestock production in this region is prone decreases in size due to livestock mortality arising
SECTION
///
to droughts that can cause catastrophic herd from various idiosyncratic factors (e.g., disease,
losses. Extreme droughts have occurred four times sickness, accidents, as well as the covariate
02
///
over the past 10 years. According to ALRMP data for catastrophic droughts).
44 K E N YA : A G R I C U LT U R E I N S U R A N C E S O L U T I O N S A P P R A I S A L
Catastrophic herd losses from droughts
///
could have immediate welfare effects by
reducing livestock income available for
consumption. While severe droughts can
immediately push the better-off poor and
nonpoor into poverty, they can likewise
push the ultra-poor and the poorest, whose
livestock income is extremely low, into
destitution. The black lines in figure 5 depict these
///
impacts. Specifically, a 1-in-4-year drought could
push livestock income of the poorest to the level
of destitution (K Sh 13/day/person). A 1-in-8-year
drought might also push income of the relatively
better-off households below the food poverty
line, and could bring the nonpoor households
into poverty.
We find that free provision of asset
///
protection livestock insurance could reduce
vulnerability but would not likely provide an
immediate exit from poverty. The long-dash
///
red lines in figure 5 depict these patterns. This result
is in contrast to the existing HSNP cash transfer
program, which transfers approximately K Sh 3,500
to the poorest eligible households every two months.
The analysis considers the likely impact of
///
As the green lines show, direct cash transfers could
asset protection livestock insurance. For the
///
potentially produce immediate poverty reduction
asset protection product, we assumed design features effects for some groups, e.g., the ultra-poor, who
similar to those of the IBLI asset replacement have been boosted up above the food poverty line in
some good years.
contract designed by ILRI, except that payouts were
made early as opposed to at the end of the season. By itself, direct cash transfer could still leave
///
The analysis assumes that monthly insurance payouts poor beneficiaries vulnerable to falling into
could allow effective early interventions that enable poverty in extreme years. Complementing
the insured pastoralist to keep insured livestock cash transfer with the free provision of
livestock insurance might provide a more
alive. Sensitivity analysis was also performed with
sustainable exit from poverty. Especially for
varying assumptions, and the key results did not vary
///
the ultra-poor, asset protection livestock insurance
significantly from our main assumption. Our analysis
coverage could immediately protect cash transfer
considers both short-term and long-term impacts
beneficiaries from falling into poverty in a 1-in-6 year
SECTION of this livestock insurance, along with the proposed extreme drought; see the long-dash and short-dash
forms of government support, on the four distinct green lines in figure 5. This is the intention of the
02 subsets of the population. current plan to scale up from the HSNP.
K E N YA : A G R I C U LT U R E I N S U R A N C E S O L U T I O N S A P P R A I S A L
45
The biggest impacts of livestock insurance
///
impacts of droughts vary across different herd
are expected to be realized in the longer groups, so will the potential impacts of livestock
term, as the insurance helps pastoralists insurance and related government support.
build up the herds over time and keep them
Livestock asset protection insurance that
at or above the viable size needed to stay
///
is designed to keep the core breeding stock
out of the poverty trap. Existing academic
///
alive during severe droughts could have
research (e.g., Lybbert et al. 2004; Barrett et al.
large long-term impacts, most notably by
2006; Chantarat et al. 2014) finds that a critical
protecting poor households with vulnerable
herd size of about 10–15 TLUs (see annex B.3) is
herds from a poverty trap. As shown in figure 6,
necessary to sustain a viable herd accumulation in
///
this region. Given limited productive nonlivestock free asset protection insurance and top-up coverage
livelihood options, and given the need for seasonal might provide enough cash for effective early
migration as adaptation to climate variability, intervention and allow households to save and grow
pastoral households in this region consume a good their viable herd, which otherwise could collapse.
portion out of their own herd each season (e.g., The overall impacts could be large when insurance
through direct slaughtering or off-taking for cash). coverage is offered with cash transfers, which could
This necessary consumption tends to slow down and also relax the required consumption out of the own
disrupt natural herd growth, especially for very small herd. Based on our simulation exercise with large
herds. Households with small herd sizes (below the numbers of replicated years, figure 7 further depicts
critical threshold) thus tend to deplete their herds the expected probability of falling into the poverty
over time. Furthermore, as poor households tend to trap (losing viable herd) five years after being hit
be credit constrained, they are unable to restock their by drought–induced livestock losses at different
herds up to the economically viable and sustainable magnitudes. It appears that free insurance and 50
levels. With a small, collapsing herd size and low percent subsidized top-up coverage could reduce
consumption, these households easily fall into the the probability of falling into the poverty trap by
poverty trap that researchers have found in this up to 60 percent. And if these schemes were to be
vulnerable pastoral region. combined with cash transfer, altogether they could
reduce the probability by up to 80 percent. This is
The existence of a viable herd threshold
in contrast to cash transfer alone, which could offer
///
size implies that catastrophic herd loss from
temporary poverty reduction while still leaving
drought could be irreversible, especially when
beneficiaries vulnerable to falling back into poverty
droughts have led to livestock losses below
in extreme drought years.
the viable level. Figure 6 provides an example of
///
common herd accumulation over time for different For better-off households with medium and
///
herd groups. It shows that especially for poor larger herds, livestock insurance could help
households with a vulnerable herd size around the to increase herds over time by stabilizing
viable threshold, a big 1-in-6-year herd loss could herd accumulation. With a typical insurance
///
push herd size down to a level that will not recover policy, the insured might need to sacrifice his average
without a restocking intervention. For small herds, income to pay for protection that reduced variability
extreme droughts could speed up herd collapse but did not increase productivity; but livestock
SECTION
and move households toward destitution. For large insurance could have a productivity improvement
herds, droughts could disrupt and slow down herd effect through stabilizing herd accumulation.
accumulation over time. Overall, just as the potential Figure 6 suggests that commercial asset protection
02
46 K E N YA : A G R I C U LT U R E I N S U R A N C E S O L U T I O N S A P P R A I S A L
insurance could be attractive to better-off households, Overall, these varying insurance impacts can
///
given that it could be less costly for the insurance be used to appropriately target public support
contract to disburse early payouts to keep livestock for livestock insurance. Support mechanisms
///
alive than to replace lost livestock, and given the targeted to ensure an effective safety net among
possible multiplier effects from protecting the critical the vulnerable group could be very cost-effective in
reducing poverty in the long run. As we see, poverty
breeding herd through herd accumulation.
increases (and herds decline) in this region over time
Livestock insurance might have the smallest
///
due to recurring droughts. A safety net intervention
long-term welfare effects on the poorest that can keep the vulnerable households from joining
(who own small and nonviable herds), since the ranks of the poor would allow the government
by itself livestock insurance is unlikely to help to concentrate its limited resources on bringing
the existing poor out of poverty. For the poorest, a
them to reach a viable herd size. Figure 6 and
///
combination of cash transfer and effective insurance
figure 7 suggest that combining direct cash transfers
could work to reduce vulnerability (and immediate
with free livestock insurance might help stabilize
poverty). But if the long-term goal is to move the
the herds of the poorest households and slow down
poorest households out of poverty through pastoral
their herd collapse in the short run (e.g., cash transfer
production, complementing livestock insurance with
could potentially relieve necessary consumption out interventions that promote restocking toward a viable
of owned herd), but the scheme might not alter the herd could be critical. For the larger herd groups,
probability of falling into the poverty trap for this promotion uptakes of the (potentially cost-effective)
small-herd group. commercial livestock insurance could be effective.22
Figure 5 — Potential short-term impacts of livestock insurance on income
available for consumption
SECTION
02
K E N YA : A G R I C U LT U R E I N S U R A N C E S O L U T I O N S A P P R A I S A L
47
Figure 6 — Potential impacts of livestock insurance on herd accumulation
Figure 7 — Potential impacts of livestock insurance on probability of falling into
poverty trap
SECTION
02
48 K E N YA : A G R I C U LT U R E I N S U R A N C E S O L U T I O N S A P P R A I S A L
Crop Insurance
more than 30 years until issues in performance
Context
and unsustainable financial losses led to its closure
Kenya’s Experience in Crop Insurance between 1977 and 1978.
As well documented in the recent Ministry of
///
Interest in agricultural crop and livestock
///
Agriculture, Livestock and Fisheries (MALF) insurance reemerged in the mid-2000s. Two ///
report (GoK 2014a), Kenya has a long tradition main routes were explored: (i) the development
of developing agricultural policy programs for of a Kenyan market crop insurance capability to
risk management purposes. ///
23 underwrite traditional indemnity-based multi-peril
crop insurance (MPCI) for medium- and large-scale
In Kenya, government support to agricultural
///
commercial farmers, and (ii) the introduction of
insurance dates back to 1942, with the index-based insurance as a potential retail product
formation of the Guaranteed Minimum Return to market to small and marginal crop and livestock
scheme. The objectives of this scheme were
///
producers (in situations where operating traditional
twofold: (i) to encourage food production to meet
indemnity-based crop and livestock insurance
Kenya’s basic food needs by providing seasonal crop
programs would be prohibitively expensive).
credit to farmers growing strategic food crops (such
as wheat and maize) through a system of guaranteed The reemergence of interest in agricultural
///
prices for output; and (ii) to provide these farmers insurance in Kenya began in 2006, when
with crop insurance in order to compensate them four local private insurance companies
for droughts, pests, diseases, and other natural perils came together to form a crop and livestock
(Sinah 2012; Kerer 2013). The system operated for insurance consortium or pool agreement
SECTION
03
K E N YA : A G R I C U LT U R E I N S U R A N C E S O L U T I O N S A P P R A I S A L
49
identified as Agricultural Insurance Manager to willing private sector players. An underlying
(AIM). The role of the AIM consortium was to design,
///
assumption is that agricultural insurance is nested
rate, and implement traditional indemnity-based within the broader context of integrated risk
crop and livestock insurance covers including MPCI. management systems (such as improved extension
The pool operated from 2008 to 2010, when it was for better asset protection, seed development, etc.).
disbanded. Since 2010, several of the companies have
continued to underwrite their own separate crop and The GoK proposes the development of a
///
livestock portfolios. dedicated public-private partnership (PPP)
in agricultural insurance and plans to invest
Kenya has subsequently witnessed an
///
resources in supporting it financially. From
increased interest in developing a crop
///
weather index insurance (WII) product. This ///
a program design point of view and for illustrative
effort is being led for the most part by the Syngenta purposes, the approach analyzed below is that of area
Foundation for Sustainable Agriculture and the yield index insurance (AYII) for maize and wheat
Financial Sector Deepening (FSD) Kenya program crops. AYII would be electively retailed through
through a public sector and donor-sponsored credit institutions via their lending operations for
initiative.24 agricultural inputs.
Government’s interest in a Once the PPP framework for crop insurance
///
new generation of agricultural has been implemented, appropriate solutions
insurance tools for other agricultural sectors could be
In order to reduce risk and promote growth
///
also developed. The State Department of
in the agricultural sector, the government of Agriculture (SDA) intends to extend future
Kenya (GoK) is now placing new emphasis on analyses to horticulture, coffee, and tea. ///
the development of insurance solutions for Maize and wheat have been selected as the sectors
agriculture. The GoK intends to foster a generation
///
to start with, given their relevance in terms of
of innovative and widespread insurance products food security (maize in particular), their major
by addressing the conditions that so far have been contribution to agricultural value added, and the
hampering their development.
availability of readily implementable insurance
GoK’s key assumption is that a well-
///
solutions.
structured agricultural insurance program,
As discussed in detail below, AYII seems the
with participation by both public and private
///
appropriate tool for reaching the operational
players, could unlock access to production
credit and stimulate investment in productive scale that would allow the GoK to meet its
inputs. It is now clear that, to be successful, an
///
policy objectives in the grains sector. The case ///
insurance scheme needs to reach a scale large enough of India, which has the largest insurance program in
to operate effectively, both in terms of the risk the world per number of farmers insured (34 million
transfer objectives and of the insurance industry’s farmers/20 percent of farmer households), offers an
commercial interests. International experience shows inspiring example of a PPP in agricultural insurance SECTION
that this scale is rarely achieved without the active developed in an emerging country.25
participation of government in building appropriate
institutions and in providing financial support
03
50 K E N YA : A G R I C U LT U R E I N S U R A N C E S O L U T I O N S A P P R A I S A L
Description of Potential
The more traditional NPCI products (e.g.,
///
those covering hail or frost) and MPCI
Agricultural Insurance products (covering all risks combined)
are already offered by several insurance
Programs for Crops companies in Kenya, but given the prevalent
operating conditions in Kenyan agriculture,
Rationale for Selecting AYII in an
they may not be suitable for large-scale
Agricultural Insurance PPP Framework application. Such products are probably better
///
suited for medium- and large-scale commercial
A PPP in agricultural insurance could make
agriculture than for small-scale subsistence farming,
///
use of any of four types of insurance products
and they require a strong network of loss adjusters.
for crops: multi-peril crop insurance, weather index
In addition, the moral hazard and adverse selection
///
insurance, area yield index insurance, and named
challenges they pose are difficult to manage.
peril crop insurance (NPCI) (see figure 8).
WII is an interesting innovation that has
In a mature agricultural insurance program
///
been extensively piloted in Kenya and is
///
these four different contract typologies would now starting to be retailed in niche markets. ///
not necessarily be alternative solutions, While not affected by moral hazard and adverse
but could be complementary in offering a selection, WII covers essentially weather perils
wide range of risk management tools to (mainly drought). Product design requires significant
select from. However, in the case of a nascent PPP
///
customized research and development activities that,
system, the GoK will need to concentrate efforts and together with its significant exposure to basis risk,
resources on the approach that best suits its policy limits the adoption of WII on a widespread scale.
objectives, leaving other approaches to develop as
GoK’s motivations for investing in a PPP
the system gains momentum. Figure 1 and figure 8
///
based on an AYII scheme seem to be
summarize the conditions in which the use of the
supported by the welfare impact analysis
different products is more appropriate.
presented in section 3.4. ///
The GoK has chosen to promote the
AYII Operating Modalities
///
development of an AYII program for maize
and wheat production. By definition, AYII is
///
The key feature of AYII is that it does not
///
based on an indexed approach, where the underlying indemnify crop yield losses at the individual
index is crop yield of a defined area called an insured field or grower level.26 Rather, an AYII product
unit. In AYII, the actual yield of the insured crop makes indemnity payments to growers
in the insurance unit is compared to the threshold according to yield loss or shortfall against an
yield. If the former is lower than the latter, all insured average area yield (the index) in a defined
farmers in the insurance unit are eligible for the same geographical area. An area yield index policy
///
rate of indemnity payout. establishes an “insured yield” that is expressed as a
percentage (termed the “coverage level”—see figure
AYII provides wide peril coverage if designed
///
9) of the historical average yield for selected crops in
appropriately: it is not affected by adverse the defined geographical area that forms the insured
selection and moral hazard, and it has a unit. Farmers whose fields are located within the
SECTION
standardized design that can lead to rapid insured unit may purchase optional coverage levels,
scalability. The main drawback of AYII is or insurers may offer only one coverage option in the
03 “basis risk” (explained below). /// insured unit.
K E N YA : A G R I C U LT U R E I N S U R A N C E S O L U T I O N S A P P R A I S A L
51
Figure 8 — Types of Agricultural Insurance Products
What are the various types of insurance products for agriculture?
Transaction Moral hazard and Claims settle-
What is it? costs adverse selection Basis risk ment
time
• Multi-peril crop insurance (MPCI) is
a traditional indemnity insurance
Multi-peril crop product against all perils
Farm High High Low Medium
insurance • Payouts are determined through
a farm-level loss assessment
process.
• Area yield index insurance is based
on average losses at the regional
Area-yield index level, rather than farm level.
Village
insurance Medium Low Medium Medium
• It is often based on crop cutting
experiments.
• Weather index insurance is based
on weather parameters (such
Weather index as rainfall, temperature, or soil
Village moisture) correlated with farm-
insurance level yields or revenue outcomes Low Low High Low
* Basis risk with index insurance arises when indices are imperfectly correlated with farmers’ losses. Some farmers with losses may
not receive payouts while some farmed without losses may receive payouts.
Source: Authors 2014
Figure 9 — Coverage Level and Insurance Payouts in AYII
2000
1800
Yield (kg per hectare)
Reference average yield
1600
1400
Coverage Level at 80% of average yield
1200
1000 Yield shortfall to be
compensated by insruance
800 payout
600
400
200
0
Crop season with no payment Crop season with payout
Sources: Authors 2014
SECTION
03
52 K E N YA : A G R I C U LT U R E I N S U R A N C E S O L U T I O N S A P P R A I S A L
The actual average yield for the insured
///
conducted on an individual farmer and field-by-field
crop is established by a statistical sample of basis, but rather according to a pre-agreed random
field measurement (usually involving crop sampling of crop yields on plots within the insurance
cuttings) in the insured unit, and an indemnity unit.
is paid by the amount that the actual average
yield falls short of the insured yield coverage The main drawback of AYII is basis risk, or
///
level purchased by each grower. ///
the potential difference between the insured
area yield outcome and the actual yields
The key advantages of the area yield
achieved by individual insured farmers within
///
approach are that moral hazard and adverse
selection are minimized, and the costs the insured area. Basis risk arises where an
///
of administering such a policy are much individual grower suffers severe crop yield losses due
reduced. The policy responds to yield loss at
///
to a localized peril (e.g., hail, or flooding by a nearby
a defined area level, and not at the level of the river) that does not have a large impact on the area-
individual farmer, so if the insured unit is large level average yield. In such cases the farmer who
enough, no farmer can influence the yield indemnity has incurred damage does not receive an indemnity.
payments—minimizing adverse selection and Basis risk may also arise where individual farmer
SECTION moral hazard. Administration costs are also greatly
crop production and yields are highly heterogeneous
reduced because there is no need for pre-inspections
(different) within the same department (that is,
03
on individual farms, and loss assessment is not
where an area-based approach invalid).
K E N YA : A G R I C U LT U R E I N S U R A N C E S O L U T I O N S A P P R A I S A L
53
Program Requirements Because data requirements for AYII are
///
very specific, they may go beyond what a
Developing a functional and effective AYII
///
traditional system of agricultural statistics
program for maize and wheat in Kenya requires. Specific public support will likely be
requires two key steps: (i) define homogeneous
///
needed to allow for additional CCE activity. ///
producing zones (the insured units) with high levels
of correlation between farmers of the same unit; and For an AYII program, the value of defining
///
(ii) generate an accountable, reliable, and statistically appropriate insurance units and of
accurate system of measuring actual average area developing a suitable data collection system
yields in the defined insured unit, and define the is paramount. We therefore suggest that a
basis for triggering payouts where actual yields fall specific multi-stakeholder study on how best
short of the insured yield(s). to organize the data collection system be
carried out. In addition to selected SDA officials,
///
As for any agricultural insurance program,
///
the team for such a study should also include staff
historical data for structuring and rating from the Kenya Bureau of Statistics, agricultural
AYII are fundamental. Ideally, for each of the research institutions, the insurance industry,
defined insured units, yield data for the past and any other interested party. The study would
15 years or more would be available. If such need to cover items such as (i) risk profile–based
data are not available, logistical and financial identification of insurance units; (ii) statistical
support to the insurance industry will be sampling methodology for identification of plots
critical. The GoK will likely need to help the private
///
for CCEs; (iii) number of CCEs per insurance
sector overcome challenges related to data in the unit; (iv) procedures, roles, and responsibilities
inception phase. As the program develops, the data for carrying out CCEs, with a potential view to
will be collected and compiled, thus generating the outsourcing the activities for which government
basis for a well-established and actuarially sound personnel may be overtasked; (v) training and
insurance program. accreditation for government and/or private sector
personnel to ensure consistency with international
The data for AYII are usually collected
reinsurer data collection standards; and (vi) reliable
///
through crop-cutting experiments (CCEs), in
auditing procedures to make sure that the national
which samples of crops are harvested, dried,
and international insurance community can have
and weighed, and yield values are inferred.
confidence in the quality of the data collected.27
///
This work is usually carried out by government
extension officers but could be outsourced to private Finally, those designing and implementing
///
entities if a great many CCEs are to be carried out. an AYII program for Kenyan agriculture
In this scenario, extension officers could play a key must take into account lessons learned
role in auditing the data collection activities. It is from similar programs in other countries,
worth noting that SDA is currently in the process and should also take advantage of the
of improving its data collection system in order latest developments in technology. Elements
///
to harmonize it with international and regional like real-time data transfer through mobile phone
standards. To this end, a dedicated set of guidelines connections, digital video recording, remote-sensing
was published in January 2014 (GoK 2014b). Efforts performance indicators, GIS (geographic information
by the GoK to update and improve the data collection system) mapping, GPS (Global Positioning System)
SECTION
system (partly in response to the devolution process georeferencing, etc. will increase the possibilities of
started in 2012) will certainly help in developing an assessing production losses in an efficient, effective,
AYII program. and transparent way.
03
54 K E N YA : A G R I C U LT U R E I N S U R A N C E S O L U T I O N S A P P R A I S A L
above.31 Figure 10 and figure 11 illustrate the spatial
Fiscal Costing Assumptions
distribution of the estimated district-level premium
and Scenarios risk rates for AYII policies for maize and wheat,
respectively.
The objective of this section is to provide
///
indicative references on the potential fiscal It is very important to note that the pure
///
cost of developing an AYII scheme for maize premium rates presented in this report are
and wheat producers. In order to develop such
///
purely indicative and that the basic analysis
projections, it is necessary to estimate the potential carried out in this context aims only to
cost of insurance policies and to define the key highlight the diverse risk exposure of the
assumptions for potential policy choices and for the different areas of Kenya. The responsibility
expected uptake of the proposed insurance products. to perform appropriate actuarial analyses
for underwriting purposes lies with the
The cost of insurance is made up of several
insurance industry. In addition, we emphasize
///
key components, such as the cost of risk (in
///
that the maize data used in the analysis are
technical terms, the “pure risk premium”)
composed of annual yield values that do not account
and the charges required to cover data
for production performance in the individual long
collection, reinsurance fees, administration
and short rainfall seasons. This makes estimating
costs, tax, profits, and any other cost of doing
yield variability in the individual seasons more
business. Such charges are often estimated
difficult and increases the uncertainty in estimating
as a multiple of the cost of risk and, for the
the pure risk rate.
purpose of this analysis, we assume that
they will double the pure risk premium. An ///
Despite the limitations in the production data
///
approximated way of estimating the insurance available, it is still possible to identify rough
premium is indeed to start from the pure risk operational estimates of the fiscal costs
premium and scale it up by a comprehensive loading of an AYII program.32 However, for potential
///
factor defined as “premium multiple.” With the implementation activities, specific care should be
premium multiple set at 2, a pure risk premium rate taken in developing seasonal-based contracts.
of (for example) 6 percent in a particular area means
An essential assumption underlying this
that the final commercial premium rate in that area—
///
fiscal costing exercise is that the GoK will
the rate at which at which the policy will be sold—is
provide direct financial support to the AYII
12 percent.28
scheme.33 The first means for channeling
A preliminary assessment of the pure
///
public support will be to finance the cost of
risk premiums for both maize and wheat risk. The analysis assumes that the GoK will
was carried out on the basis of historical cover a 50 percent share of such costs. Risk ///
production records provided by SDA at financing support can be structured in many ways,
district level and assuming an 80 percent and for the purpose of this analysis we assume that
coverage level. As indicated in more detail in
///
29
it would come as a dedicated “risk financing fund”
annex C.1, the data were carefully analyzed, revised, covering part of reinsurance costs, or as premium
SECTION
and detrended.30 Pure premium rates were then subsidies.
determined on the basis of the historical payout
03 performance at the coverage level mentioned
K E N YA : A G R I C U LT U R E I N S U R A N C E S O L U T I O N S A P P R A I S A L
55
Public support for AYII will also entail
///
amount of subsidized insurance could be introduced.
providing resources to complement the data Differentiations could be also made between maize
collection activities needed for operating the and wheat production activities, given that the latter
insurance scheme. The current fiscal scenarios
///
are traditionally carried out in larger and more
assume that the GoK will cover the cost of the sustainable production units.
activities needed to complement the estimation
An important dimension to be defined for
process carried out by the public extension service,
///
determining the value insured per district
including costs for equipment, labor, management,
is the expected take-up rate of insurance
and auditing.34 However, more complex arrangements
products (identified, in insurance terms, as
can be envisioned in which the private stakeholders
the “degree of penetration”). As a tentative
also play relevant roles in supporting the data
reference, the fiscal scenarios have been
collection process.
developed by starting at 3 percent of
The current costing exercise does not
///
cultivated area at the beginning of the
distinguish between commercial and program in 2016, and reaching 15 percent for
subsistence farming, although different maize and 25 percent for wheat in 2023.35 The ///
supporting schemes could be envisioned for penetration rate is clearly difficult to predict, as it is
the two farming typologies. For example, in the
///
a function of many variables, some under the control
areas where agricultural production is carried out of the program and some not. These projections are
by smallholders at subsistence level, the program based on the assumption that AYII will be retailed in
could be operated in a more socially oriented connection with agricultural input credit operations
fashion by having higher levels of support (for that are currently accessed by less than 5 percent
example, near to full premium subsidy support). of farmers (see section 4.4). The availability of
At the same time, in more commercially oriented AYII should allow financial institutions to expand
production environments, specific limits to the their agricultural lending operations, generating
Figure 10 — Estimated AYII Risk Figure 11 — Estimated AYII Pure Premium
Premium Rates for Maize at District Level Rates for Wheat at District Level
SECTION
03
56 K E N YA : A G R I C U LT U R E I N S U R A N C E S O L U T I O N S A P P R A I S A L
a mutually reinforcing process that could lead to the program has reached significant scale
a progressive increment in the take-up of both (see table 7 for maize and table 8 for wheat,
insurance and credit. respectively). The bulk of the estimated fiscal
///
support—nearly 90 percent of resources provided by
Under the assumptions presented in the
the GoK—would be directed to maize production.
///
analysis, and excluding expenses related to
other support activities, the direct fiscal costs
to be borne by the GoK for supporting the
development of a national AYII program for
maize and wheat would total approximately
K Sh 140 million (US$ 1.6 million) at the start
of the program, and K Sh 740 million (US$
9 million) per year in 2021 assuming that
Box 3— Coverage Levels and Premium Rates
Table 6 provides an example of how the premium rates of hypothetical AYII contracts vary according to different coverage levels. In the
/// ///
simulations presented below, for a coverage level of 80 percent, the premium rate would be below the adopted 15 percent cap only for the district
of Uasin Gishu. In order to meet the 15 percent threshold, it would be necessary to reduce the coverage level to 70 percent for Kajiado, and to 50
percent for Machakos. These simple examples show the clear tradeoff between cost and coverage of AYII policies. This tradeoff is driven by the
underlying risk; and where risk proves excessive, insurance may not represent an economically viable proposition
Table 6 — Variation of Premium Rates According to Different Coverage Levels
Coverage Level
District 80% 70% 60% 50%
Machakos 31% 26% 20% 14%
Kajiado 17% 11% 7% 4%
U/Gishu 4% 3% 3% 2%
The case of Machakos suggests how combining seasonal production data in one annual observation may distort the perception of the risk profile
in the area. The extremely high premium rate estimated for Machakos is due to the inclusion of the March-May rainfall season, significantly drier
than the October-December one. Seasonal production data would provide different insurance premium rates for the two seasons, leading to
different risk management recommendations.
SECTION
03
K E N YA : A G R I C U LT U R E I N S U R A N C E S O L U T I O N S A P P R A I S A L
57
Table 7— Fiscal costing projections for AYII for maize, 2016–2023
2016 2017 2018 2019 2020 2021 2022 2023
Insurance Penetration (as a % of cultivated
3.0% 4.7% 6.4% 8.1% 9.9% 11.6% 13.3% 15.0%
area)
Penetration (hectares) 61,517 96,670 131,822 166,975 202,128 237,280 272,433 307,586
Premium volume (million KSh) 253 398 543 687 832 977 1,122 1,266
Projected public support as a share of
50% 50% 50% 50% 50% 50% 50% 50%
premium volume (%)
Cost of premium subsidy for GOK (million
127 199 272 344 416 489 561 633
KSh)
Additional costs for data collection / yield
0.2 0.6 1.2 1.7 2.3 2.9 3.5 3.8
estimation (million KSh)
Number of farmers covered (per season) 25,632 40,279 54,926 69,573 84,220 98,867 113,514 128,161
Total cost for GoK (million KSh) 127 200 273 345 418 491 564 637
Total cost for GoK (million USD at 85 KSh/
1.5 2.3 3.2 4.1 4.9 5.8 6.6 7.5
USD)
Table 8— Fiscal costing projections for AYII for wheat, 2016–2023
2016 2017 2018 2019 2020 2021 2022 2023
Insurance Penetration (as a % of cultivated
3.0% 6.1% 9.3% 12.4% 15.6% 18.7% 21.9% 25.0%
area)
Penetration (hectares) 3,819 7,820 11,821 15,822 19,823 23,824 27,825 31,827
Premium volume (million KSh) 24 49 74 98 123 148 173 198
Projected public support as a share of
50% 50% 50% 50% 50% 50% 50% 50%
premium volume (%)
Cost of premium subsidy for GOK (million
12 25 37 49 62 74 87 99
KSh)
Additional costs for data collection / yield
0.01 0.04 0.07 0.11 0.14 0.18 0.21 0.24
estimation (million KSh)
Number of farmers covered 1,273 2,607 3,940 5,274 6,608 7,941 9,275 10,609
Total cost for GoK (million KSh) 12 25 37 49 62 74 87 99
Total cost for GoK (million USD at 85 KSh/
0.1 0.3 0.4 0.6 0.7 0.9 1.0 1.2
USD)
SECTION
03
58 K E N YA : A G R I C U LT U R E I N S U R A N C E S O L U T I O N S A P P R A I S A L
Welfare Impacts of Area
2004), Kenya Integrated Household Budget
Survey data (2005)36, KARI (2009)37, and
Yield Insurance for Maize
Tegemeo Institute (2010) —show that 38
23–30 percent of maize and wheat farmers
and Wheat in Kenya
report using high-yielding technology and
Maize and wheat productions are crucial
///
hybrid seeds. And while almost 50 percent
///
for the livelihoods and food security of of farmers reported using some kind of
smallholder and medium-scale farmers in input credit, less than 5 percent of these
Kenya. Maize-growing areas span the country and
///
farmers reported obtaining credit from
can be classified into three production zones with formal financial institutions. Other sources of ///
distinct production systems and socioeconomic production credit include cooperatives, savings and
conditions. The low-potential zone occupies low- credit cooperatives (SACCOs), local traders, input
yielding and high-risk production in Eastern and suppliers, and other informal financial institutions.
Central Provinces, where the majority of farmers are Statistically, input loans have been relatively small,
poor smallholders (median farm size is 1.5 hectares) just enough to afford minimum input costs, and
who use subsistent production technology (we have been offered at interest rates of 8–19 percent
sometimes refer to this zone as the subsistent maize per year. Among other things, limited access to
zone). The medium-potential zone occupies the agricultural credit has thus served as a key supply-
relatively higher-yield, lower-risk regions of Nyanza side constraint to productive agricultural investment.
and Western Provinces, where farmers are slightly
Maize and wheat productions are
better off but still smallholders. The high-potential
///
significantly exposed to extreme production
zone occupies the high-yielding production regions
risk. We used detrended district-level yield
of Rift Valley Province, with relatively larger-scale
data for the 30-year period 1983–2012
farmers (with 2.5 hectares of land on average).
(obtained from the MALF report [GoK 2014a])
Maize production is one of the main livelihood
to determine how often maize production
bases and is mainly used for home consumption,
falls below 80 percent of the district average.
especially in the low- and medium-potential zones.
The analysis showed that these shortfalls
This is in contrast to the high-potential zone, where
are 1-in-3-year events in the low-potential
production is relatively more commercialized. Wheat
zone and 1-in-4- to 1-in-5-year events in the
production is concentrated in smaller regions of the
other two maize zones and wheat region.
Eastern and Rift Valley Provinces, is relatively more
///
The low-potential zone thus appears to have a larger
commercialized, and is practiced by relatively larger-
exposure to production risk than the other zones,
scale farmers (with 3.0 hectares of land on average).
with significant drops in production—below 50
(See annex C.2, table 18 for summary statistics of
percent of the district average—occurring once in
maize- and wheat-growing households.)
five years. While Kenyan farmers have established
Low investment in productive inputs and
///
various informal risk-sharing mechanisms that
limited access to production credit have allow unaffected farmers to help affected farmers
impeded efforts to improve productivity in reduce consumption shortfalls from shocks, these
SECTION
both maize and wheat productions in Kenya. /// mechanisms tend to be ineffective against extreme
Data from a variety of sources—Tegemeo production shocks, which generally affect whole
03 Institute’s household survey data (2000, communities.
K E N YA : A G R I C U LT U R E I N S U R A N C E S O L U T I O N S A P P R A I S A L
59
Uninsured production risk could have
///
The model is then calibrated using a combination of
significant welfare impacts on Kenyan maize 30 years of district production data from MALF and
and wheat farmers, not only by increasing detailed household survey data from the Tegemeo
their vulnerability but also by reinforcing Institute (2000, 2004)39 covering key maize-growing
both supply- and demand-side constraints areas of the country. Overall, maize yields vary
on smallholders’ adoption of productive significantly across the three production zones, with
inputs. Extreme production risk directly affects
///
the highest CV40 of 0.49 in the subsistent maize zone,
welfare by reducing the income/food available
following by 0.34 in the high-potential zone, 0.35 in
for consumption, especially among the poor
the wheat region, and 0.29 in the medium-potential
smallholders whose livelihoods rely extensively
zone. Input costs vary from 50 percent to 75 percent
on these crop productions. Exposure to extreme
of the expected crop revenues. For both crop
production risk could further reduce investment
productions, we thus assume that a farmer needs to
incentives, especially among risk-averse poor
farmers. Empirically, according to the household take an input loan at a median rate of 60 percent of
survey, farmers have always cited uninsured risks as expected revenue and at a cost of 17 percent interest
one of the key reasons for their underinvestment in per year.
production. At the same time, as agricultural loan
Net income available for consumption and
portfolios would also be exposed to large default
///
expected loan repayment rates vary greatly
risk following extreme production shortfalls, lenders
with frequency and severity of shocks in all
tend to limit the supply of agricultural credit or
offer credit at relatively high interest rates. Overall, zones. The black lines in figure 12 reflect annual
///
through the direct effect on vulnerability and the maize and wheat income after netting out input
indirect effect on productivity, exposure to uninsured loan repayment and thus the net income that
risk could increase the probability that Kenyan would be available for household consumption.
farmers will fall into poverty. Our simulations considered both price and yield
variability, and thus variations in net incomes reflect
Our empirical analysis reviews the
variations of both. As expected, the net incomes
///
significance of, and variations in, exposures
(realized in 1-in-2-year frequency) are very low—
and welfare impacts of covariate production
lower than the national food poverty line (at K Sh
risk on representative farmers in the key
988 per capita per month) in the subsistent and
maize and wheat production zones. We ///
develop a simple economic model to explore the medium-potential maize zones, and slightly above
potential welfare impacts on a representative farmer the food poverty line in the high-potential maize
in each of the three distinct maize zones and overall zone and in the wheat region.41
wheat production region (see annex C.2). In each
Net incomes available for consumption could
production zone, we assume that a representative
///
drop to or below zero at a frequency of once
farmer owns a farm of median size, produces with the
in three years in the subsistent zone and at
zone-specific production system, and realizes zone-
specific crop yields and variability. A representative a frequency of once in four years in others. ///
farmer is credit constrained and so needs to take In these cases, the farmer has no income left for
SECTION
an input loan at the beginning of the cropping year consumption and/or is unable to repay the full loan.
to purchase required minimum inputs. The loan is
repaid using crop income obtained after the harvest.
03
60 K E N YA : A G R I C U LT U R E I N S U R A N C E S O L U T I O N S A P P R A I S A L
and that is possible within a 15 percent maximum
commercial premium rate. Given differences in
yield variations, insurance coverage varies across
zones, with 50 percent, 85 percent, and 80 percent
in low-, medium-, and high-potential maize zones
respectively, and 75 percent in the wheat region. In
figure 12, net income available for consumption is
then plotted in red. As expected, AYII would reduce
net income in good years, as a farmer would need to
pay for the insurance premium, which is loaded at a
multiple of 2. But the AYII payout could stabilize net
income in bad years.
Especially for households that rely
///
extensively on crop production as their main
consumption, AYII that reduces variability in
crop production will also reduce households’
vulnerability to food insecurity. But when
///
an extreme shock—one with at least a 1-in-4-year
frequency—occurs, this high-coverage commercial
AYII generally does not guarantee enough income
for full repayment of input loans in any of the zones.
A 1-in-10-year production risk could further
There could several reasons for this: (i) commercial
///
force farmers in all production zones to
AYII is quite expensive; (ii) there is still basis risk
accumulate debt of up to 80 percent of
associated with AYII because it provides protection
their expected income each year. In reality,
///
only with respect to the district-level yield, not with
however, a farmer might not use all crop income to
respect to the individual yield; and (iii) there are
pay back a loan. To make this scenario more realistic,
other background risks due to uninsured variations
we computed the expected loan repayment rates
assuming that a farmer will try to pay back the loan in prices.
after meeting the necessary subsistent consumption
AYII could potentially increase the ability
///
at 30 percent of the food poverty line. As shown in
of farmers to pay back input loans in
annex C.2, table 19, the expected loan repayment
the bad years and so increase expected
rates in all zones could be reduced by extreme
loan repayment rates of rural lenders’
shocks.42
loan portfolio (see annex C.2, table 19).
/// ///
Area yield index insurance could potentially
///
Commercial AYII could stabilize loan
stabilize consumption in the years when repayment rates in bad years and so increase
extreme shocks affect the entire community. ///
expected loan repayment by as much as
SECTION
We first explore the potential of high-coverage 10 percent when the farmer faces 1-in-10-
AYII that pays out based on a district-level yield year production risk relative to the case
03 index at a coverage level specific to each zone, without AYII. ///
K E N YA : A G R I C U LT U R E I N S U R A N C E S O L U T I O N S A P P R A I S A L
61
Public support that reduces the commercial
///
(relative to the average cost) and the expected
premium rate could significantly improve the yield improvement (relative to the average yield)
welfare impacts of AYII on maize and wheat from a detailed maize production study by the
households. We show that a 50 percent Kenya Agricultural Research Institute (KARI) in
reduction in commercial premium (which 2009 and from a wheat production’s gross margin
allows the farmer to pay a fair premium rate), study of DASS (see annex C.2). Farmers who can
could potentially allow AYII to stabilize net afford to invest 126 percent and 138 percent more
income available for consumption above in productive inputs could improve maize yields by
zero even with the extreme 1-in-10-year risk as much as 196 percent in the high-potential maize
in all but the risky subsistent zone. Thus, for
///
zone and 182 percent in the medium-potential zone,
farmers who always use all crop income to repay respectively. Similar but less significant evidence
their loan, fair AYII could ensure full loan repayment is also found for wheat farmers, for whom 133
even in extremely bad years. Even if farmers meet percent more investment in productive input could
their subsistent consumption level before repaying enhance yield by up to 139 percent. The productivity
the loan, fair AYII could increase expected loan gain from increasing productive investment,
repayment by as much as 20 percent in extreme years however, could be limited in the subsistent maize
relative to the case without AYII. production zone by the zone’s low production
potential and scarce rainfall. Thus the extra cost of
If insurance could further unlock access to
///
expensive input appears to outweigh the additional
agricultural credit and enhance farmers’ yield improvement.
investment incentives, even commercial
AYII could potentially crowd in sustainable So while commercial AYII might be too
///
increases in productivity in line with a key expensive as a stand-alone insurance in this
recommendation of the Kenya Vision 2030. ///
setting, if it could unlock access to credit,
Various studies have documented positive effects it could potentially crowd in significant
of derisking agricultural production on productive improvement in income and reduce the
investment and credit demand—e.g., Cai et al. (2009) probability of falling into poverty for farmers
in China; Galarza and Carter (2011) in Peru. Existing in all zones except the low-potential zone.
agricultural programs in Kenya have also successfully As the green lines in figure 12 show, the
allowed banks to expand lending to farmers using crowding in effect of even the commercial
insurance as a prerequisite for loans and/or by AYII could improve expected net income
bundling insurance with credit directly. available for consumption by more than
double in the medium- and high-potential
Since the above analysis shows that AYII
///
zones and by about 65 percent in the wheat
can remove some production risk from region. The significant productivity gain from
rural lending institutions and thus increase expanded credit with commercialized AYII
expected loan repayment rates, we explore could further result in 67 percent and 30
the potential impacts of this effect by percent reductions in the probability of falling
allowing insured farmers to access larger into poverty for farmers in the high and
loans for investment in expensive but more medium zones, respectively. This crowding-
SECTION
///
productive inputs (hybrid seeds, fertilizer, in effect could be smaller for the better-off wheat
equipment). We used the crop- and zone- farmers, who already use relatively more expensive
03
///
specific evidence of expensive input cost markups inputs and achieve relatively higher productivity.
62 K E N YA : A G R I C U LT U R E I N S U R A N C E S O L U T I O N S A P P R A I S A L
The crowding in effect of AYII might be limited for double the production in high- and medium-
farmers in the low-potential zone, however. potential zones, and almost double the
By subsidizing AYII and using AYII to crowd
///
production in the wheat region. The program
in productive input loans, the government could potentially reduce farmers’ chance of
could further ensure sustainable and falling into poverty by 78 percent, 39 percent,
significant increases in productivity and thus and 29 percent in the high-potential zone,
in agricultural GDP, and in this way contribute
medium-potential zone, and wheat region,
toward achievement of the Kenya Vision
respectively (see annex C.2, table 19). These
2030. In turn, this approach could move
///
many small- and medium-scale farmers in poverty reduction effects come about as the AYII
some production regions out of poverty. and credit enhance farmer’s productivity, and as AYII
Subsidized AYII with extended productive acts as a safety net to protect yield shortfalls in bad
input loans could potentially more than
years.
Figure 12 — Potential impacts of AYII on net income available for consumption
SECTION
Note: The net income available for consumption depicted in the figure reflects crop income after any loan repayment. It does
not account for the potential that households might use some part of this for saving before consumption. Thus this should be
03 viewed as an upper bound of income that will be available for consumption.
K E N YA : A G R I C U LT U R E I N S U R A N C E S O L U T I O N S A P P R A I S A L
63
Table 9— Fiscal cost per household of achieving different policy goals in different
insurance scenarios
Public cost (KSh) per unit
Policy
Crop zone Free provision Subsidize AYII
objectives Subsidize AYII Cash transfer
of AYII + unlock credit
Low potential maize no effect no effect no effect 1,394
1% reduction Medium potential maize 27,453 no effect 118 1,169
in poverty High potential maize 4,425 no effect 420 1,832
Wheat 5,408 no effect 1,722 2,355
Low potential maize 761 3,774 no effect 802
1%
Medium potential maize 937 1,328 255 1,206
reduction in
High potential maize 3,540 4,963 739 2,917
vulnerability*
Wheat 4,804 5,235 1,679 4,175
Low potential maize 2.01 no effect no effect 1.00
1 KSh increase
Medium potential maize 2.02 no effect 0.10 1.00
in expected
High potential maize 2.01 no effect 0.08 1.00
income
Wheat 2.00 no effect 0.19 1.00
* Measured by probability of falling below zero net income available for consumption
Overall, the welfare impacts of AYII vary
///
improvement through increased productive input
across production zones with different use is low.
degrees of risk exposures, and AYII might
The welfare impacts of AYII could also vary
not be suitable as an intervention to improve
///
slightly across different insurable indexes
smallholders’ productivity in the subsistent
and coverage levels. Changing from a district-
maize production region. Given this region’s
///
level yield index to a division-level index, for which
///
low expected yield and large exposure to production
correlations of individual yields with the measured
risk, AYII with large coverage could be too expensive
area yield are potentially larger, could achieve a
to be useful for these farmers. But the coverage
larger reduction in net income variability (see
level (currently at 50 percent) affordable within a
annex C.2, table 19). The performance of AYII in
15 percent commercial premium could also be too
reducing income variability also declines as one
low to effectively insure net income and expected
moves from the high coverage, with 15 percent
loan repayment against extreme shocks. Even with
maximum premium rate, to the lower coverage level
50 percent premium reduction through public
affordable within 10 percent commercial premium.
supports, extreme production shock could still cause
This analysis assumes that there could be effective
serious shortfalls of consumption and expected loan
insurance demand even at the high commercial rate. SECTION
repayment. AYII is unlikely to unlock credit access
and so improve productivity for smallholders in this Government’s support to development of
03
///
subsistent zone, where the potential for productivity an AYII program could be a cost-effective
64 K E N YA : A G R I C U LT U R E I N S U R A N C E S O L U T I O N S A P P R A I S A L
strategy for meeting various policy would cost as little as K Sh 0.08 per household per
objectives. To identify the most cost-effective types
///
year for the same scheme to improve productivity
of support that can achieve different policy objectives and so increase household income by K Sh 1. And
targeted to different subsets of maize and wheat this is clearly cheaper than direct one-to-one cash
farmers, we compute the cost per household (in transfer. The combination of government subsidies
Kenyan shillings) per year of four types of support— for AYII and crowding in of input credit would not
explained below—to achieve three outcomes (see be an effective policy tool for smallholders in the
table 9): (i) 1 percent reduction in the poverty rate subsistent maize zone, however.
relative to the baseline without the program; (ii) 1
For a cost-effective tool to reduce
percent reduction in the vulnerability rate (measured
///
vulnerability of smallholders in the subsistent
by the probability of net income falling below zero);
maize zone, the government could freely
and (iii) an increase of K Sh 1 in net income available
provide AYII coverage as a social protection
for consumption when targeted to each of the
program. This could lead to a 1 percent reduction
maize and wheat areas . The four types of support
///
in vulnerability (i.e., the probability of household’s
include (i) free provision of AYII, (ii) 50 percent
net income falling to zero) and would cost the
subsidization of AYII, (iii) 50 percent subsidization
government about K Sh 761 per household per year.
of AYII and facilitation of access to input credit,
This is cheaper for government than providing only
and (iv) direct cash transfer program, the cost of
a 50 percent subsidy for AYII (which could cost
which we compare to the costs of the first three
K Sh 3,774 per household per year) and the direct
interventions. For the high-coverage AYII program
cash transfer (which could cost about K Sh 802 per
(at 15 percent maximum premium rate), the free
household per year). If the policy goal is to reduce
provision of AYII could cost government from K Sh
poverty, however, government’s support through
2,642 per household per year (in the subsistent maize
AYII would not be the appropriate policy tool
zone) to K Sh 34,448 per household per year (in the
relative to the direct cash transfer program.
wheat region). The cost is thus reduced by half when
the government subsidizes only 50 percent of AYII’s
premium cost.
The types of public support that reduce
///
AYII’s commercial premium and unlock
the agricultural credit market could be the
cost-effective tools that allow government
to reduce poverty and vulnerability and to
improve productivity among the median
farmers (smallholders) in the medium- and
high-potential maize zones and the wheat
region. In the medium-potential zone, it would
///
cost as little as K Sh 118 per household per year
for the government to reduce the poverty rate by
1 percent through subsidizing AYII and crowding
SECTION
in input credit access. This compares to K Sh 1,169
per household per year if the government tried
03 to achieve the same goal through cash transfer. It
K E N YA : A G R I C U LT U R E I N S U R A N C E S O L U T I O N S A P P R A I S A L
65
Conclusion
This report provides a detailed technical
/// For crop insurance, investments in data and
///
analysis of how the government of Kenya linkage to credit are key. Investments in data
///
(GoK) could develop agriculture insurance will be required to develop high-quality products
public-private partnerships (PPPs) to support that provide meaningful coverage to farmers, reduce
basis risk, and ensure that payments are made when
rural livelihoods and help to raise Kenya to
necessary. The welfare analysis has highlighted
middle-income status, as described in Kenya
the importance of linking insurance to credit as a
Vision 2030. This report, written with the guidance
way of empowering rural farmers to make capital
///
of the GoK, analyzes possible PPP structures and
investments on their farms, raising household
explores the options for developing crop and
income, and increasing the size of farms. The
livestock insurance programs in the short, medium, main costs to the GoK include developing the data
and long term. market infrastructure and some form of support for
financing the cost of risk.
This report is meant to guide the GoK in
///
key policy decisions based on the potential For livestock insurance, linking to the
///
fiscal cost and potential welfare benefits of Hunger Safety Net Program (HSNP) in the
developing an agriculture insurance PPP. ///
four northern counties is the key initial step. ///
Both the fiscal costing analysis, which estimates Building on the scalable component of the HSNP, the
the resources required to develop the PPP, and the analysis provides details of the costs and benefits of
a GoK-funded livestock insurance scheme that will
welfare analysis, which looks at how agriculture
reduce the vulnerability of low-income families, in
insurance could benefit farmers, are provided toward
addition to laying the foundation for a politically
that end. Leading the way for the African continent,
sustainable livestock insurance market. Initially,
these decisions will help to establish an appropriate
it is envisaged that a macro-level product will be
policy framework for an effective agriculture developed and given to vulnerable households. Over
insurance PPP in Kenya. time, top-up coverage will be made available to the
covered households, in addition to other households
On the institutional side, the report discusses
in the target counties.
///
key next steps toward developing a National
Agricultural Insurance Policy (NAIP), in Fiscally, the approach suggested in this
///
addition to the potential institutions to report would entail certain costs to the GoK
be established in support of the PPP’s through its involvement in the PPPs. These
public aspect. are outlined in table 10.
SECTION
///
///
04
66 K E N YA : A G R I C U LT U R E I N S U R A N C E S O L U T I O N S A P P R A I S A L
Table 10— Illustrative fiscal costing for agricultural insurance programs, 2016 and 2019
Annual fiscal cost Estimated number of Average cost per producer
Program Description (2016)
(K Sh millions) producers covered per year (K Sh)
Maize: area yield index insurance 127 25,632 5,000
Wheat: area yield index insurance 12 1,273 9,500
Pastoralists: satellite-based
livestock protection insurance 300 71,000 4,200
(fully subsidized)
Pastoralists: satellite-based
livestock protection insurance 14 5,250 2,600
(partially subsidized)
TOTAL 453 103,155
Annual fiscal cost Estimated number of Average cost per producer
Program Description (2019)
(K Sh millions) producers covered per year (K Sh)
Maize: area yield index insurance 345 69,573 5,000
Wheat: area yield index insurance 49 5,274 9,200
Pastoralists: satellite-based
livestock protection insurance 300 71,000 4,200
(fully subsidized)
Pastoralists: satellite-based
livestock protection insurance 31 15,000 2,100
(partially subsidized)
TOTAL 725 160,847
SECTION
04
K E N YA : A G R I C U LT U R E I N S U R A N C E S O L U T I O N S A P P R A I S A L
67
Annex A. Possible Options
If a coinsurance pool is established as an
///
insurer, the pool company underwrites the risks
for Coinsurance Pools
///
directly in its own right. A pool company that
underwrites risks must, of course, be licensed
in Kenya to write insurance business and must be fully
As discussed in chapter 1, it is unlikely that insurers capitalized as an insurer.
will be able to compete within a fully competitive
Other coinsurance pools, whether or not
market for agricultural insurance. The purpose of this
/// ///
established solely by contract or as a special
section is not to make detailed recommendations for
(noninsurer) company, usually share the following
a pool structure, but to demonstrate the variety of
features:
pool structures that could be considered.
1. Each insurer accepts a pre-agreed share in all the
Nonstatutory Coinsurance Pools
risks that are covered by the pool agreement.
Insurance pools can be statutory (i.e., established 2. All premiums are paid into the pool, less an
by specific legislation) or nonstatutory (i.e., not amount to cover expenses.
established by specific legislation).
3. The pool manager or administrator assesses and
Different structures are commonly used to establish settles claims.
nonstatutory insurance pools: 4. f there is an underwriting gain, the surplus
1. A coinsurance pool may be established by the (beyond any reserve retained in the pool) is paid
participating insurers as an insurer in its own to each insurer in accordance with its agreed
right, so that it is the pool itself that issues the share.
insurance contracts and assumes the risk on behalf 5. If there is an underwriting loss, the insurers
of the insurers. In this case, either the pool would contribute to the loss in accordance with their
sell its own insurance contracts or the insurers agreed share.
would sell insurance contracts as intermediaries
(i.e., agents) on the pool company’s behalf, the
risk being underwritten by the pool company.
2. The insurance contracts may be written by the
insurer pool members, on an individual basis, but
with the risk ceded to the pool. In this case, the
pool may be either (i) a special pool company
established by the insurers; or (ii) an arrangement
between the insurers whose terms are set out in a
pool agreement.
3. The insurance contracts may be written by a lead
insurer on behalf of the other insurers that are
members of the pool. Again, under this scenario,
the pool may be a special company established SECTION
by the insurers or an arrangement between the
insurers set out in a pool agreement.
05
Photo Credit: Neil Palmer (CIAT)
68 K E N YA : A G R I C U LT U R E I N S U R A N C E S O L U T I O N S A P P R A I S A L
Box 4— Benefits and limitations of coinsurance pool arrangements
Coinsurance pools offer these benefits:
They achieve economies of scale through operating as a single unit with shared (pooled) administration and operating functions. These lead
/// ///
to costs savings from (i) reduced staffing requirements (fixed costs); (ii) shared costs of product research and development, and actuarial services
including rating; and (iii) reduced costs of underwriting and claims control and loss adjustment.
There are cost advantages to companies when they purchase common account (pooled) reinsurance protection rather than trying to place
///
their own reinsurance program. The advantages arise from (i) a stronger negotiating position with reinsurers; (ii) larger and more balanced
///
portfolio and better spread of risk; (iii) reduced costs of reinsurance due to pooled risk exposure; and (iv) reduced transaction costs (reinsurance
brokerage, etc.).
There is no competition on rates in a soft market, and pools can maintain technically set rates. Most pools operate as the sole insurance
/// ///
provided or monopoly (as in Austria, Senegal, Spain, and Turkey, for example), and there is therefore no competition on pricing.
Pools are able to maintain underwriting and loss adjustment standards. Under a pool monopoly arrangement, the pool manager can ensure
/// ///
that common and high standards are maintained in the underwriting of crop and livestock insurance and in the adjusting of claims. Where
companies are competing against each other for standard crop insurance business, there is often a problem of varying loss adjustment standards
between companies.
Within a PPP, governments can more easily coordinate support to a pool than to individual insurers. Governments seeking to coordinate
/// ///
national agricultural insurance policy and planning and specific support functions (e.g., provision of premium subsidies, research and
development, education and training) can work more easily with a pool than with individual insurers, each of which may have very different
priorities for agricultural insurance.
Coinsurance pools have these limitations:
When a pool acts as the sole agricultural insurer, lack of competition in the market may result. This could (i) limit the range of products and
/// ///
services offered by the monopoly pool underwriter; (ii) restrict the range of perils insured; (iii) restrict the regions where agricultural insurance is
offered and/or the type of farmer insured; and (iv) lead to a lack of competitiveness in premium rates charged by the pool.
Source: Mahul and Stutley 2010.
If a pool is established solely through a contractual It is important to appreciate that where the insurers
arrangement, the “pool” is not a legal person and write their own insurance contracts and cede the
does not have the power to contract. The pool could risk to the pool, each participating insurer typically
not, therefore, write insurance contracts. accepts a pre-agreed share of all the risks ceded
to the pool, not just the risks that the insurer has
If the insurers enter into their own individual
written.
insurance contracts, the insurance business is
conducted under their individual licenses. The Management of a coinsurance pool, where
/// ///
capital of the participating insurers supports the the pool is incorporated as a (noninsurance)
SECTION
risk. The position may be rather more complicated company, involves the pool company acting as the
if the insurance contracts are underwritten by a lead pool manager or administrator. Where a special
05 insurer on behalf of the other insurers. pool company is not incorporated, the pool
K E N YA : A G R I C U LT U R E I N S U R A N C E S O L U T I O N S A P P R A I S A L
69
may be managed by a lead insurer; by a technical Benefits of an Agricultural Insurance Pool
management unit contracted or employed by, or on
All coinsurance pools offer benefits but also have
behalf of, the participating insurers; or by a third
limitations. These are summarized in box 4.
party such as a broker, another nonparticipating
insurer, or a reinsurer. The participating insurers International Precedents
typically share the management costs in accordance
with their proportionate risk share. If a Program Steering Committee is established to
address the institutional framework for agricultural
Statutory Coinsurance Pools
insurance, it could consider a number of precedents:
(i) the Turkish Agricultural Insurance Pool
Statutory insurance pools are often, but not
(TARSIM); (ii) the Spanish Agricultural Insurance
/// ///
necessarily, corporate bodies. Usually, statutory
Pool (AGROSEGURO); and (iii) the proposed
coinsurance pools are part of a national or regional Mongolian Index-Based Livestock Reinsurance
program and are established as part of a public- Company (which will have features of a pool and a
private partnership (PPP). Relevant legislation reinsurance company).
typically provides for the governance of the pool and
sets out the pool’s functions. The legislation may also The Turkish and Spanish pools are considered in
cover other matters, such as the provision of some more depth in the MALF report (GoK 2014a).
form of subsidy. Because they are established by
legislation, statutory pools take many forms and may
be structured very differently to a typical voluntary
pool.
The legislation may establish a coinsurance pool, but
not as a corporate body. For example, the pool may
be established as a contractual arrangement between
participating insurers. In this case, although the
legislation would set out the functions of the pool,
those functions would not usually include acting
as an insurer, since the pool is not a legal person.
Of course, the legislation may establish a corporate
body to act as manager of the pool, but not to write
insurance contracts.
The legislation establishing the pool would usually
provide the pool with exclusive rights in relation
to the business underwritten by the pool. This is
necessary to prevent nonpool insurers undermining
the pool by offering similar insurance products at a
lower, nonsustainable, price. SECTION
Statutory coinsurance pools sometimes operate as
hybrids, with some limited reinsurance functions.
05
70 K E N YA : A G R I C U LT U R E I N S U R A N C E S O L U T I O N S A P P R A I S A L
Annex B.1. Index Based Livestock Insurance (IBLI) Program
Figure 13 — Translating NDVI data into estimated livestock mortality and
IBLI payouts
Response
Insurance Predicted
NDVI data Function Contract Indenmity
purchased (measurement mortality (payout
(estimation payouts
(pricing precision options) precision index options)
option)
options)
Source: ILRI 2014.
Figure 14 — IBLI seasonal sales periods, contract cover period and contract
payout dates
1 year contract coverage
LRLD season coverage SRSD season coverage
Jan Feb Mar Apr Jun Jul Aug Sept Oct Nov Dec Jan Feb
Sale period for Period of NDVI observations for constructing LRLD mortality
LRLD index
Sale period for Period of NDVI observations for constructing
SRSD SRSD mortality index
Predicted LRLD mortality is announced. Indemnity
payment made if IBLI is triggered.
SECTION Predicted SRSD mortality is announced. Indemnity
payment made if IBLI is triggered.
06 Source: ILRI 2013
Note: LRLD = long rain /long dry season; SRSD = short rain / short dry season.
K E N YA : A G R I C U LT U R E I N S U R A N C E S O L U T I O N S A P P R A I S A L
71
Table 11— IBLI livestock insurance results, 2009-2012 (US$)
No. No. Tropical Total sum Premium Paid Avg. no. Avg. sum Avg. premium Avg. premium
Sales
Year contracts Livestock Units insured (TSI) by Herders insured TLUs insured TLUs rate per herder
period
sold insured (TLUs) (US$) (US$) per herder per herder (%) (US$)
Jan/Feb 2010 1,974 5,965 1,118,437 46,602 3 187.5 4.2 23.6
Jan/Feb 2011 595 1,229 230,437 9,033 2.1 187.5 3.9 15.2
Aug/Sept 2011 509 836 158,750 6,122 1.6 187.5 3.9 12
Aug/Sept 2012 219 413 77,437 3,150 1.9 187.5 4.1 144
Total 3,297 8,443 1,583,061 64,907 2.6 187.5 4.1 16.3
Source: ILRI 2013
Note: Exxchange rate: 1US$ = 1 KShs 0.80.
pastoralists to be covered on the basis of the given
Annex B.2. Assumptions
budget.
and Parameters for
The parameters presented in table 12 are based on
Fiscal Costing Scenarios the following assumptions and considerations:
for Livestock • Sum insured per TLU: Each TLU is valued at
/// //////
K Sh 7,000 in Case A, and at K Sh 5,000 in Case B.
1. Fiscal costing for the macro-level insurance
Values lower than K Sh 5,000 are not considered
///
coverage for asset protection ///
meaningful.
The fiscal costing scenarios for the macro-level • Number of TLUs insured per vulnerable
///
Normalized Difference Vegetation Index (NDVI)– pastoralist: The number of eligible TLUs has
///
based insurance coverage for livestock asset been set at seven in Case A and at five in Case B.
protection have been developed in order to calculate Five TLUs are considered the level below which
the number of pastoralists covered on the basis of the insurance coverage would not provide useful
budget references provided by the State Department support to pastoralists’ livelihoods.
of Livestock (SDL): K Sh 100 million, K Sh 200
• Sum insured per pastoralist: The reference
million, and K Sh 300 million.
/// ///
sum insured per pastoralist is obtained by
For each budget scenario, two extreme cases are multiplying the number of TLUs to be covered
presented (see table 12). Case A is structured by by the selected value of 1 TLU. The parameters
selecting, within a reasonable range of variation, selected in table 12 determine a range of sums
the more costly extremes of the key parameters insured between K Sh 49,000 (Case A) and K
(i.e., higher values per tropical livestock unit [TLU] Sh 25,000 (Case B). The difference between the
two extremes is significant because it highlights
insured, a higher number of TLUs per policy, and
how the policy choices that go into selecting
a higher insurance premium estimate). This will
the relevant parameters influence the support
define a lower bound for the number of pastoralists
provided to pastoralists. SECTION
to be covered for the reference budget figure. Case
B takes into account the less expensive options, • Premium rate: As the NDVI asset protection
06
/// ///
thus identifying the higher bound of the number of product is still in the design phase, actual
72 K E N YA : A G R I C U LT U R E I N S U R A N C E S O L U T I O N S A P P R A I S A L
estimates for the potential premium rates of • Cost of education and training: The cost
/// ///
the program are not available. Hence, while the for education and training has been set at K Sh
necessary elaborations are being carried out, the 5 million for Case A and at K Sh 4 million for
current average premium rates of the Index Based Case B.
Livestock Insurance (IBLI) products are used. The
• Cost of payout distribution: Given that
IBLI scheme allows pastoralists to select between
/// ///
the enrolled pastoralists will all be equipped
two trigger options. The average premium for
with bank accounts, costs for distributing the
the products with the lower trigger (hence the
payouts should be minimal (i.e., the cost of the
version that provides payouts more frequently)
bank transfer operation), and could be included
is 16.06 percent, while the average premium for
in the costs budgeted by insurance companies.
the higher trigger option is 9.24 percent. Hence,
However, raising awareness about program
approximating such figures, the premium rate for
payouts is important, so a lump sum of K Sh 2.0
Case A has been set at 15 percent and the premium
million for Case A and of K Sh 1.5 million for Case
rate for Case B at 10 percent.
B will likely be used for dedicated information
• Premium per pastoralist: The premium per
/// ///
campaigns on payout distribution.
pastoralist is obtained by applying the selected
• Cost of contract design and data
premium rate to the sum insured per pastoralist.
///
processing: Costs of K Sh 1 million have been
The premium amount for Case A is set at K Sh
///
assumed for handling NDVI data processing and
7,350 and for Case B at K Sh 2,500. Again, the
monitoring the contract.
spread between the two figures is quite significant,
and this has relevant implications for the cost of • Cost of auditing: An auditing cost of 1 percent
/// ///
the program. of the value of the program has been assumed.
• Cost of registration and enrollment per
///
2. Fiscal costing for the top-up and nontarget
///
pastoralist: The cost for registration and
///
purchases ///
enrollment has been set at K Sh 500 for Case A
As mentioned above, optional top-up coverage for
and at K Sh 250 for Case B. The target pastoralists
pastoralists enrolled in the program will be made
belong to the Hunger Safety Net Program
available in year 3 of program implementation.
framework, and they could therefore be registered
In addition, pastoralists who were not part of the
automatically without generating any specific
initial support program will also have the option
cost. However, the technical analysis presented in
to purchase the NDVI-based insurance coverage as
chapter 2 suggests that registration for households
a nontarget group of pastoralists. The first layers
eligible for GoK subsidy should not be automatic
of both the top-up option and the nontarget group
but rather done in person. In-person registration
coverage will be partially subsidized.
helps to create a sustainable market for livestock
insurance by ensuring that pastoralists understand The suggestion to make the top-up and nontarget
the details of how the scheme operates. In group coverage available at year 3 of program
addition, these activities could help spread implementation is motivated by the significant
awareness of the insurance product, which could challenges to be faced when moving beyond a
SECTION
both promote the purchase of the top-up option fully subsidized coverage scenario. In addition,
and encourage nontarget pastoralists to take up because the NDVI-based asset protection scheme
06 the insurance product. is still in the design phase, its performance will
K E N YA : A G R I C U LT U R E I N S U R A N C E S O L U T I O N S A P P R A I S A L
73
Table 12— Fiscal costing projections for macro-level asset protection coverage
Scenario: KS h 100 million Scenario: K Sh 200 million Scenario: K Sh 300 million
(US$ 1.2 million) (US$ 2.3 million) (US$ 3.5 million)
Case A Case B Case A Case B Case A Case B
Budget available for macro-level asset protection
100,000 100,000 200,000 200,000 300,000 300,000
coverage (K Sh)
Sum Insured per Tropical Livestock Unit (TLU) (K Sh) 7,000 5,000 7,000 5,000 7,000 5,000
No. of TLUs insured per vulnerable pastoralist 7 5 7 5 7 5
Sum Insured per pastoralist (K Sh) 49,000 25,000 49,000 25,000 49,000 25,000
Premium Rate {as a share of sum insured) 15.00% 10.00% 15.00% 10.00% 15.00% 10.00%
Premium per pastorarist (K Sh) 7,350 2,500 7,350 2,500 7,350 2,500
Cost of registration and enrollment per pastoralist
500 250 500 250 500 250
(K Sh)
Cost of education and training {lump sum) (K Sh) 5,000,000 4,000,000 5,000,000 4,000,000 5,000,000 4,000,000
Cost of payout distribution (lump sum) (K Sh) 2,000,000 1,500,000 2,000,000 1,500,000 2,000,000 1,500,000
Cost of contract design and data processing (lump
1,000,000 1,000,000 1,000,000 1,000,000 1,000,000 1,000,000
sum) (K Sh(
Cost of auditing (lump sum) (K Sh) 1,000,000 1,000,000 2,000,000 2,000,000 3,000,000 3,000,000
No of pastoralists eligble for livestock asset
11,592 33,636 24,204 69,636 36,815 105,636
protection coverage
need to be carefully accessed before it is launched rate of 12.5 percent (average of rates assumed for
on a semicommercial basis. Thus it remains to be the macro asset protection).
determined whether for these additional options the
• Number of vulnerable pastoralists to
GoK may support the asset protection structure, the
///
purchase top-up option: The assumed
IBLI product, or both. However, for the purposes
///
of this analysis, given that the values of selected take-up progression for the top-up option starts
parameters have been defined on the basis of the IBLI with 1,000 policies in year 1 and reaches 10,000
experience, the simulations would apply in any case. policies after five years of implementation.
The parameters presented in table 13 are based on the • Premium volume: The premium volume is
/// ///
following assumptions and considerations: obtained by multiplying the premium cost by the
number of pastoralists purchasing the coverage.
Top-Up Option
////// //////
• Projected public premium support: It is
/// ///
• Reference premium cost per pastoralist:
assumed that the government of Kenya (GoK) will
/// ///
The reference premium cost per pastoralist has
cover 50 percent of the cost of the coverage.
been obtained by assuming the average standard
conditions developed for the macro-level
Expansion to Nontarget Pastoralists SECTION
coverage. Hence, the K Sh 3,125 value derives
/// ///
from a sum insured per TLU of K Sh 5,000, five • Sum insured per TLU: In analogy with the top-
06
/// ///
additional TLUs to be covered, and a premium up option, the value of a TLU is set at K Sh 5,000.
74 K E N YA : A G R I C U LT U R E I N S U R A N C E S O L U T I O N S A P P R A I S A L
Table 13— Fiscal costing projections for top-up and nontarget pastoralists options
2015 2016 2017 2018 2019
Top-Up Option
Reference premium cost per pastoralist (K Sh) 3,125 3,125 3,125 3,125 3,125
No. of vulnerable pastoralists to purchase top-up option 3,000 3,250 5,500 7,750 10,000
Premium volume (K Sh) 3,125,000 10,156,250 17,187,500 24,218,750 31,250,000
Projected public premium support (%) 50% 50% 50% 50% 50%
Cost of public support for top-up option (K Sh) 1,562,500 5,078,125 8,593,750 12,109,375 15,625,000
Expansion to non-target pastoralists
Sum insured per Technical Livestock Unit (TLU) (K Sh) 5,000 5,000 5,000 5,000 5,000
Maximum no. of eligible TLUs per pastoralist (K Sh) 10 10 10 10 10
Values of additional sum insured per pastoralist (K Sh) 50,000 50,000 50,000 50,000 50,000
Premium Rate (as a share of sum insured) 12.5% 12.5% 12.5% 12.5% 12.5%
Premium per pastoralist (K Sh) 6,250 6,250 6,250 6,250 6,250
No of non-target pastoralists to purchase coverage 1,000 2,000 3,000 4,000 5,000
Premium volume 6,250,000 12,500,000 18,750,000 25,000,000 31,250,000
Projected public premium support (%) 25% 25% 25% 25% 25%
Projected public premium support (K Sh) 1,562,500 3,125,000 4,687,500 6,250,000 7,812,500
Costs for implementation as a share of premium support
200% 175% 150% 125% 100%
(%)
Costs for implementation (K Sh) 3,125,000 5,468,750 7,031,250 7,812,500 7,812,500
Cost of public support for non-target pastoralists (K Sh) 4,687,500 8,593,750 11,718,750 14,062,500 15,625,000
Total cost for GoK (million K Sh) 6.3 13.7 20.3 26.2 31.3
Total cost for GoK (million USD at 85 K Sh/USD) 0.1 0.2 0.2 0.3 0.4
• Maximum number of eligible TLUs per
///
pastoralist is obtained by multiplying the number
pastoralist: Pastoralists not belonging to the
///
of TLUs to be covered by the selected value
original target group will be able to purchase per TLU.
supported coverage for a maximum of 10 TLUs.
SECTION • Premium rate: Same as for top-up option.
/// ///
• Value of additional sum insured per
• Premium per pastoralist: Same as for top-up
06
///
/// ///
pastoralist: The reference sum insured per
option.
///
K E N YA : A G R I C U LT U R E I N S U R A N C E S O L U T I O N S A P P R A I S A L
75
• Number of nontarget pastoralists to
///
Annex B.3. Summary of
purchase coverage: The assumed take-up
Modeling and Simulations
///
progression for the nontarget group purchases
starts with 1,000 policies and reaches 5,000
of Welfare Analysis
policies after five years of implementation.
• Premium volume: Same as for top-up option.
/// ///
for Livestock
• Projected public premium support: It is
/// ///
A. A Dynamic Economic Model
/// ///
assumed that the GoK will cover 25 percent of the 1. Household consumption and
///
cost of the coverage. livestock accumulation: ///
• Projected public premium support: The
Consider a dynamic model of a representative
/// ///
projected public premium support is obtained
pastoral household, whose livelihood relies primarily
by applying the share of premium that will be
on livestock production. At the end of each season
supported by the GoK to the estimated premium
t=LRLD,SRSD where LRLD refers to long rain-long
volume.
dry season (March-September) and SRSD refers to
• Costs for implementation as a share of
///
short rain-short-dry season (October-February),
premium support: Implementation costs
///
this household earns and consumes from total
refer to extension, marketing, capacity building, income from milk production m(Ht ) out of their own
training, and infrastructure deployment. They are livestock Ht of which they can sell the milk at the
estimated by referring to the IBLI experience and on-going market price ptmm. The income available for
to the parameters that International Livestock consumption each period is thus ptmm(Ht ).
Research Institute (ILRI) researchers have
If milk production income is not enough for
developed for future projections.
consumption, household can also consume out of
The ratio of implementation costs to premium their own herd by off-taking (sale or slaughter) some
support cost is 5:2 in the short term, and nearly 1:1 of their livestock at the ongoing market price pth.
in the medium term. These are the references that Household can also use left over milk production
have been adopted for estimating these costs. income to invest more in its herd by buying livestock
at the ongoing market price.
• Costs for implementation: The actual costs
/// ///
for implementation are obtained by applying Household makes intertemporal decisions by
the assumed percentage share to the projected choosing optimal consumption and herd investment
premium support figures. each period to maximize their expected lifetime
utility function, of which they draw welfare gain
• Cost of public support for nontarget
from consumption as well as livestock.44 Let
///
pastoralists: The cost is the sum of the
represent the rate at which household discounts
///
projected premium support and the costs for
future. Let ot represent the net livestock off-take (the
implementation.
number of herd sold and slaughtered netting out
herd purchased) at the end of each season, we write
household’s intertemporal decision as
∞
SECTION
maxc E∑ βtu(ct, Ht ) subject to ct = ptmm(Ht )+ pthot
t (t=0)
H(t+1) = (1+bt+1-mt+1)(Ht -ot )
06
76 K E N YA : A G R I C U LT U R E I N S U R A N C E S O L U T I O N S A P P R A I S A L
At the end of each season, household herd—netting herd (e.g., selling off herd for consumption or
out net herd off- take—would be accumulated toward slaughtering herd), then it will try to accumulate
the next season herd. Herd can grow at natural livestock to maximize its herd size.
biological birth rate bt+1 and is also subjected to
mortality shock in that period mt+1 . The optimal herd off-take each season can thus be
written as
Droughts that could lead to catastrophic livestock
mortality could thus affect household herd, which And the optimal herd accumulation dynamic is thus
could have immediate effect on reducing current
milk production income and longer-term effect
on disrupting herd accumulation in the following Those with small herd will meet by off-taking out of
periods. own herd at the rate faster than the net herd growth.
2. Poverty trap and economically viable herd
///
Their herd thus tends to decline—instead of grow—
in arid and semi-arid land (ASAL) region ///
over time. The herd accumulation dynamic above
could thus imply the existence of an economically
With limited productive nonlivestock livelihood viable herd H* necessary to sustain seasonal herd
options and the need for seasonal migration as
growth each period:
adaptation to climate variability, pastoral households
in the ASAL region consume a good portion out of
their own herd each season (e.g., through direct
slaughtering or off-taking for cash). This necessary
Furthermore, as poor households tend to be to credit
consumption out of own herd each season tends
to slow down and disrupt natural herd growth, constrained, they have difficulty restocking their
especially for very small herds. Existing academic herds up to the economically viable and sustainable
research (e.g., Lybbert et al. 2004; Barrett et al. 2006; levels. So while we should expect household with
Chantarat et al 2014) has thus identified the existence Ht ≥H* to grow herd over time, those with Ht 1.
Livestock prices are also uncertain. During droughts
that could cause large livestock mortality, animals
tend to be weak, and together with lower demand in
the local market this could cause livestock price to
drop. We thus describe a joint relationship among
SECTION
ptm , m t, m(NDVIt ) in a joint multivariate normal
distribution with a correlation matrix capturing
meaningful correlations of these three series.
06
78 K E N YA : A G R I C U LT U R E I N S U R A N C E S O L U T I O N S A P P R A I S A L
While milk prices could also be uncertain, we observe other fixed administrative costs. Total premium per
relatively stable prices across different seasons in insured TLU is
each area. We thus assume that they are deterministic
ρi=xE(πp) where i= r , p.
at their mean level.
4. NDVI index based livestock insurance
/// ///
As household needs to increase herd off-take to pay
for insurance premium when the cost is beyond the
Using objectively measured NDVI data to trigger milk production income, we can write the optimal
insurance payout, NDVI-based livestock insurance herd accumulation dynamics with asset protection
for the Hunger Safety Net Program (HSNP) counties insurance insuring Hp unit of herd as
could be of two forms:
(i) An asset replacement insurance. This
/// ///
form aims to compensate insured household where (πp)Hp reflects the amount of insured herd that
for livestock losses by making payout at the household could save using asset protection’s early
end of each season if m(NDVIt ) is above a indemnity payout. Thus (πp) reflects the effectiveness
predetermined strike level m*. Thus the seasonal of early intervention, made possible through early
indemnity payout per insured TLU is indemnity payout πp in keeping the insured herd that
π = max[m(NDVIt )-m*, 0] × p
r
survived from drought-related mortality.
where p is a replacement cost per TLU. The
If (πp)=m(NDVIt ), early intervention would be
product was already designed by ILRI and has
very effective in keeping all the insured herd
been on sale in two of the four HSNP counties.
that survived from drought-induced mortality.
(ii) An asset protection insurance. This form
////// ///
If (πp)=max[m(NDVIt )-m*,0], asset protection
aims to provide timely cash to allow insured contract would thus make equivalent payout to the
household to engage in actions (e.g., purchase comparable asset replacement contract. And if this
forage supplement or water, or migrate to better effective early intervention can be achieved with
forage/water sources) to save its livestock from comparable payout frequency and intensity, asset
the slow-onset drought. It makes payout as protection insurance would be cheaper and so more
early as possible at the end of every month in cost effective than the asset replacement counterpart.
the coverage season when monthly NDVI falls
Basis risk: Note that both forms of livestock insurance
below a predetermined strike level NDVI*. The
are written NDVI not actual mortality rate. Basis
seasonal payout is thus the sum of the monthly
risk – when indemnity payment deviates from or could
payout
not allow household to save their individual herd losses
– would exist. The value to farmers will thus depend on
the how closely individual herd mortality tracks that of
where exit is the minimum level of NDVI that
m(NDVIt ) especially for the case of asset replacement
will allow insured household to receive 100%
and so insurance will be valuable to pastoral household as
payout each month and c represents the cost to
r(mt, m(NDVIt ) ) -> 1.
keep animal alive each month.
5. Public supports
/// ///
Actuarial fair premium per insured TLU for these
SECTION
contracts is equal to the expected indemnity payout. We assume that public support could result in
Insurance company will however add some premium s% reduction in insurance premium rate (the free
06 multiple x > 1 to the commercial premium to cover provision of macro-level asset protection will have
K E N YA : A G R I C U LT U R E I N S U R A N C E S O L U T I O N S A P P R A I S A L
79
s=100%) and will cover up to a prespecified herd size. averaged livestock prices were obtained from
Total public cost per household i is thus 2005-2012 household survey data collected
by Arid Land Resource Management Project
S=sρHi
(ALRMP) in all four counties.
B. Calibrating Economic Model with
///
2. Milk price (K Sh/liter): ,ptm = 49
Actual Data ///
Mean inflation adjusted milk price obtained from
1999-2012 ALRMP household survey data in all
/// Livestock production ///
four counties.
1. Sublocation and division seasonal livestock 3. Milk production (liter/season): m(Ht )= % milking
mortality (%) and livestock price animal × averaged milk produced /TLU/ day × 180 ×
f(mt , m(NDVIt ),pth)~N(μm , μm(NDVI ) , σm , σm(NDVI ) ,σ p h ,r ) TLU =0.28 × 1 × 180 ×TLU
t t t t t
• Sublocation mortality mt: μ=0.11, σ=0.15 Parameters obtained from ILRI’s index-based
livestock insurance impact evaluation household
• NDVI-predicted division averaged livestock
survey in Marsabit, 2009-2012.
mortality m(NDVIt ): μ=0.11, σ=0.13
4. Natural herd growth rate (% per season): bt=0.2
• Division averaged TLU price (K Sh): μ=19,843,
Obtained from ILRI’s index-based livestock
σ=5,981
insurance impact evaluation household survey in
• Common correlation matrices for the Marsabit, 2009-2012.
three variables
5. Herd distribution (TLU): Ht obtained from HSNP
Livestock impact evaluation household survey 2009-2012 in
Sublocation Division Price
all the four counties.
Sublocation 1
Division 0.5 1 NDVI index based livestock insurance
/// ///
Livestock Price -0.4 -0.2 1
6. Index: Our analysis considered the impact of
asset replacement that triggers monthly payout
Our analysis was done on a representative based on monthly NDVI. Since the actual design
pastoral household at the sublocation level with of monthly trigger is still in progress, we assume
the assumption that perfect risk sharing exist at that this asset protection contract triggers payout
this level. Sublocation average yields were thus based on ILRI’s predicted livestock mortality
used to represent mortality of our representative index
household. 7. Coverage level: When predicted livestock
mortality index is above 15% similar to
Long-term mean and standard deviation of the
ILRI’s product
NDVI-predicted division averaged livestock
mortality were obtained from ILRI’s constructed 8. Sum insured (K Sh/TLU/season): c=4500
mortality indexes, which IRLI used to underwrite Drawing on discussion with some officials at the
its asset replacement contract. The series were Ministry of Agriculture, Livestock and Fisheries
constructed for each and every division in the and based on the recent droughts experience
SECTION
four HSNP counties from 1982-2013. Long-term in Wajir, Taita and Laikipia, we estimate that it
mean and standard deviation of the subdistrict- would cost 25 K Shs per day to keep 1 TLU alive
averaged seasonal mortality rates and division during drought.
06
80 K E N YA : A G R I C U LT U R E I N S U R A N C E S O L U T I O N S A P P R A I S A L
9. Pure premium rate = 9% per year means, standard deviations and correlation
matrices obtained above.
10. Premium multiple (% of fair rate): x=200%
This is a common rule of thumb in the industry 3. For each simulated year in each replicate, we
estimated key outcome variables for four levels
11. Effectiveness of asset protection in reducing
of starting herd sizes: 5 TLUs, 10 TLUs, 20 TLUs,
livestock mortality: πp=max[m(NDVIt )-15%,0]
and 40 TLUs.
We assume that monthly insurance payouts could
allow for effective early interventions, which 4. Finally, we calibrated our economic model using
would enable the insured pastoralist to perfectly empirical data and estimated 100 replicates of
avert all the predicted drought-related mortality 100-year series of key outcome variables of the
beyond 15% of insured livestock. representative households in the scenarios with
and without insurance and across government
12. Minimum subsistent consumption: c is
supports.
assumed at 30% of annual food poverty line of a
representative farming household with 4.7 adult
equivalent members (according to the HSNP
household survey data) calculated at national
food poverty line of rural regions at K Shs 988 per
month per adult equivalent.
13. Government supports represented as premium
reduction (%): s=100%, 50%, 25%
C. Simulations
/// ///
We took the following steps to simulate key outcome
indicators:
1. In order to describe the joint distributions of
the seasonal sublocation-averaged livestock
mortality rates, NDVI predicted division averaged
livestock mortality rates and division average
TLU prices, we first computed their long-term
means, standard deviations and correlation
matrices of the deviation of mortality rates from
their location-specific long-term means. These
statistics were calculated using variations over the
16 seasons from 2005 to 2012, when ALRMP and
ILRI’s index overlap.
2. We then simulated, 100 replicates of 100 years
series of these three levels of area yields assuming
that their joint distribution follows 3-variable
SECTION
truncated multivariate normal distribution with
06
K E N YA : A G R I C U LT U R E I N S U R A N C E S O L U T I O N S A P P R A I S A L
81
Table 14— Summary statistics of pastoral households in four HSNP counties
Socioeconomics * Mean SD Mandera Marsabit Turkana Wajir
Household member (adult equivalent) 4.7 1.8 4.7 4.5 4.5 5.275697
Monthly consumption expenditure/adult eq. 1746 789 2133 1363 1346 2202
Poverty headcount (2005 National poverty line) 47% 18% 72% 73% 20%
% with seasonal food shortage 60% 33% 45% 78% 71%
% receiving food aid 71% 69% 91% 51% 72%
Main Source of Income
Livestock production (rearing, sale of livestock/
47% 41% 54% 41% 53%
product)
Casual Labor 17% 29% 18% 3% 19%
Employment/Salary 2% 4% 3% 0% 1%
Business and trade 6% 3% 4% 7% 8%
Petty trade 18% 12% 5% 42% 13%
Remittances and gifts 8% 9% 14% 4% 5%
Statistics by income quartile Q1 Q2 Q3 Q4
% households who own livestock 89% 78% 89% 96% 93%
% engage in livestock production 51% 61% 59% 54% 31%
% share of livestock in total economic income 68% 63% 67% 74% 68%
Mean number of livestock owned by household (TLU) 10.5 14.9 5.1 9.0 11.4 14.8
Livestock production * * Mean SD Mandera Marsabit Turkana Wajir
Herd size 10.4 14.9 11.5 11.7 9.0 11.6
Herd composition
% Cattle 23% 19% 33% 36% 14% 42%
% Camel 18% 0% 44% 36% 30% 43%
% Smallstock 15% 0% 44% 28% 56% 15%
% Milking animal 28% 18%
Livestock mortality and price statistics
Sub-location TLU mortality (%) 0.15 0.18 0.18 0.09 0.14 0.12
S.D. 0.20 0.13 0.14 0.17
Division NDVI-predicted TLU mortality (%) 0.15 0.11 0.20 0.10 0.18 0.11
S.D. 0.09 0.14 0.08 0.11
Division averaged TLU price (KSh/TLU) 19,844 5,981 18,075 20,412 19129 24,448
Division averaged milk price (KSh/liter) 49 17 51 56 48 45 SECTION
* From Hunger Safety Net Programme (HSNP) Impact Evaluation 2009-2012 panel household survey in 4 counties.
* *From Arid Land Resource Management Project (ALRMP) Monthly Drought Monitoring Survey 1999-2012 in 4 counties
06
Milk and livestock prices are inflation adjusted using 2013 as base year
82 K E N YA : A G R I C U LT U R E I N S U R A N C E S O L U T I O N S A P P R A I S A L
Both the maize and wheat series presented data
Annex C.1. Assumptions
reporting issues (e.g., confusions between metric
and Parameters for Fiscal tons, kilograms, and bags), so data were revised and
corrected when compiling mistakes were evident.
Costing Scenarios for Crops
The assumptions adopted in the analysis are the
The fiscal analysis presented in section 3.3 is based
following:
on annual production data for maize and wheat at
the district level provided by the State Department • The reference figures for cultivated area are
of Agriculture. The administrative classification equivalent to the average of the latest five years
refers to the 73 “pre-2012” districts (see table 15 for available. For yields, reference is made to the
the list of districts). In such a data set the number average yield recorded in the period 2008–2012
of observations per district is quite heterogeneous, for maize, and in the period 2011–2012 for wheat
and there are many gaps. For maize, complete (data for the 2008–2010 wheat campaigns were
series ranging from 1983 to 2012 are available for mostly not available).
only 50 percent of the districts; in the remaining 50 • Yield data have been detrended with respect to a
percent of districts the series are shorter (as few trend reference composed of an average of linear,
as six observations in some instances). However, exponential, and moving average trends.
for 15 districts—accounting for over 50 percent
• The price at which maize and wheat production
of the maize cultivated area—the time series are
have been valued is K Sh 34/kg for maize and K Sh
acceptably long and start at the latest in the mid-
46/kg for wheat.
1990s. One significant limitation of the maize data
set is that it is composed of annual yield values, • The coverage level was set at 80 percent.
which make accounting for yield variability in the
• Progressively increasing insurance take-up has
biannual production areas impossible. While this
been projected, with a rate of 3 percent assumed
limitation would be more problematic in a potential
at the beginning of the program in 2016, and rates
implementation phase, from a fiscal analysis
of 15 percent for maize and 25 percent for wheat
perspective the data can still provide the basis for reached by 2023.
initial rough operational estimates of the fiscal costs.
• The number of farmers involved in the program
The data for wheat also shows many gaps; has been estimated by dividing the projected
unfortunately, data are missing for 2008 and 2009, cultivated area by the median farm size,
which were critical years for wheat production respectively 1.5 ha for maize and 3.0 ha for wheat.
(2009 in particular). The presence of these gaps in The reason for selecting the median, and not the
recent and sensitive years has a significant impact average, is linked to the possible introduction of
on the quality of the simulations. The wheat data caps in the number of hectares per farm insured
set is smaller than the maize data set, as 95 percent under the supported program. In addition, for
of cultivated area is concentrated in five districts maize, the biannual production pattern in 75
only (Meru Central, Laikipia, Narok, Nakuru, and percent of cultivated area has been accounted
Uasin Gishu). Given that wheat production in other for by dividing the estimated number of farmers
SECTION
districts is sparse and of low quality, the analysis has by 1.6, also considering that in the biannual
focused only on the five main production districts production regions cultivated area may be lower
07 (see table 16). in the less favorable season.
K E N YA : A G R I C U LT U R E I N S U R A N C E S O L U T I O N S A P P R A I S A L
83
Table 15— Yield, Area, and Premium Rate Data for Maize
Yield (kg/ha) Cultivated Area (ha) Pure Risk Capped Insurance
Province District
Avg ‘08-’12 Avg ‘08-’12 Premium Rate x District Premium Rate x District
1 Central Thika N.A. N.A. N.A. N.A.
2 Central Kiambu East 1,442 4,778 6.6% 13.2%
3 Central Kiambu West N.A. N.A. N.A. N.A.
4 Central Kirinyaga 1,108 20,671 8.4% 15.0%
5 Central Murang’a North 697 19,508 10.8% 15.0%
6 Central Murang’a South 1,443 30,085 8.6% 15.0%
7 Central Nyandarua North 2,038 8,892 6.3% 12.6%
8 Central Nyandarua South 2,152 2,171 1.8% 3.5%
9 Central Nyeri South 741 13,332 7.5% 14.9%
10 Central Nyeri North N.A. N.A. N.A. N.A.
11 Coast Taita Taveta 880 16,599 12.8% 15.0%
12 Coast Kwale 1,265 45,120 5.5% 10.9%
13 Coast T/River 1,484 8,893 6.7% 13.5%
14 Coast Mombasa 777 1,264 8.5% 15.0%
15 Coast Lamu 1,890 18,065 6.1% 12.2%
16 Coast Malindi 970 15,853 4.8% 9.5%
17 Coast Kilifi 885 52,811 4.9% 9.9%
18 Eastern Embu 1,390 19,722 6.4% 12.8%
19 Eastern Isiolo 543 736 1.8% 3.5%
20 Eastern Kitui 700 43,463 14.9% 15.0%
21 Eastern Machakos 713 140,485 15.6% 15.0%
22 Eastern Makueni 640 95,984 12.0% 15.0%
23 Eastern Marsabit N.A. N.A. N.A. N.A.
24 Eastern Mbeere 709 26,326 3.1% 6.2%
25 Eastern Meru central 1,739 37,104 10.0% 15.0%
26 Eastern Meru North 1,519 59,291 4.7% 9.3%
27 Eastern Meru South 1,405 15,262 7.8% 15.0%
28 Eastern Moyale 299 456 1.8% 3.5%
29 Eastern Mwingi 514 37,160 15.1% 15.0%
30 Eastern Tharaka 1,135 12,033 10.7% 15.0%
31 North Eastern Ijara 128 145 1.8% 3.5%
32 North Eastern Garrisa 580 356 1.8% 3.5%
33 North Eastern Wajir 222 785 1.8% 3.5%
34 North Eastern Mandera 329 1,385 18.9% 15.0%
35 Nairobi Nairobi N.A. N.A. N.A. N.A.
84 K E N YA : A G R I C U LT U R E I N S U R A N C E S O L U T I O N S A P P R A I S A L
Table 15— Yield, Area, and Premium Rate Data for Maize (continued)
Yield (kg/ha) Cultivated Area (ha) Pure Risk Capped Insurance
Province District
Avg ‘08-’12 Avg ‘08-’12 Premium Rate x District Premium Rate x District
36 Nyanza Bondo 1,134 19,921 13.5% 15.0%
37 Nyanza Gucha 2,240 17,894 4.4% 8.7%
38 Nyanza H/Bay 1,429 41,438 1.8% 3.5%
39 Nyanza Kisii 2,258 37,678 1.8% 3.5%
40 Nyanza Kisumu 1,394 18,140 1.9% 3.8%
41 Nyanza Kuria 2,277 13,533 2.9% 5.9%
42 Nyanza Migori 1,515 56,209 1.8% 3.5%
43 Nyanza Nyamira 2,019 61,632 1.8% 3.5%
44 Nyanza Nyando 1,502 9,243 1.8% 3.5%
45 Nyanza Rachuoyo 1,432 15,220 1.8% 3.5%
46 Nyanza Siaya 1,316 35,740 3.0% 6.0%
47 Nyanza Suba 1,263 7,569 2.2% 4.3%
48 Rift Valley Baringo 1,848 18,593 7.4% 14.7%
49 Rift Valley Bomet 1,938 34,234 8.4% 15.0%
50 Rift Valley Bureti 2,288 16,164 1.8% 3.5%
51 Rift Valley Kajiado 1,819 16,173 8.4% 15.0%
52 Rift Valley Keiyo Marakwet 1,696 39,364 9.4% 15.0%
53 Rift Valley Kericho 2,730 28,775 1.8% 3.5%
54 Rift Valley Koibatek 1,748 10,021 4.0% 8.1%
55 Rift Valley Laikipia 2,068 31,902 4.3% 8.6%
56 Rift Valley Marakwet 2,829 17,592 2.8% 5.5%
57 Rift Valley Nakuru 2,183 71,375 9.9% 15.0%
58 Rift Valley Nandi 2,768 77,603 1.8% 3.5%
59 Rift Valley Narok 1,849 38,884 12.3% 15.0%
60 Rift Valley Samburu 1,649 795 1.8% 3.5%
61 Rift Valley T/Mara 3,070 60,325 4.8% 9.5%
62 Rift Valley T/Nzoia 3,829 101,272 5.3% 10.6%
63 Rift Valley Turkana 1,319 1,631 3.7% 7.3%
64 Rift Valley U/Gishu 3,588 86,650 1.8% 3.5%
65 Rift Valley W/Pokot 2,068 23,416 4.7% 9.4%
66 Western Bungoma 2,529 79,872 1.8% 3.5%
67 Western Busia 1,219 30,416 1.8% 3.5%
68 Western Butere 1,899 4,366 1.8% 3.5%
69 Western Kakamenga 2,026 50,140 2.3% 4.5%
70 Western Lugari 2,921 19,359 1.8% 3.5%
71 Western Mt. Elgon 2,969 15,718 2.6% 5.2%
72 Western Teso 1,255 7,930 1.8% 3.5%
73 Western Vihiga 1,217 33,513 3.1% 6.3%
K E N YA : A G R I C U LT U R E I N S U R A N C E S O L U T I O N S A P P R A I S A L
85
Table 16 — Yield, Area, and Premium Rate Data for Wheat
Yield (kg/ha) Avg last Cultivated Area (ha) Pure Premium Capped Insurance
Province District
available 5 years Avg ‘11-’12 Rate x District Premium Rate x District
Eastern Meru central 2,264 16,078 12% 15%
Rift Valley Laikipia 2,178 5,468 3% 5%
Rift Valley Nakuru 2,850 26,111 6% 11%
Rift Valley Narok 2,681 49,982 10% 15%
Rift Valley U/Gishu 2,703 29,668 2% 3%
As mentioned in section 3.3, the fiscal scenarios by the extra CCEs should be 100 percent. In order to
include the provision of public support for additional account for equipment, management, and auditing
data collection activities that should complement costs, an approximated overhead of 50 percent has
the government of Kenya procedures for estimating been added to the cost of carrying out the CCEs.
production and area data operated at county level.
Although many different arrangements can be
envisioned, including the possibility of outsourcing Annex C.2. Summary of
some functions to the private sector, the present
analysis assumes that the additional crop-cutting
Modeling and Simulations of
experiments (CCEs) required would be carried out Welfare Analysis for Crops
by the public extension service.46 Hence, in terms of
costing, the government of Kenya will cover expenses A. A Simple Economic Model
for equipment, labor, management, and auditing. 1. Crop production
/// ///
The calculations that lead to the estimation of the
supplementary data collection costs are presented Consider a one period model in key crop regions with
many farmers. Each period, farmers’ crop production
in table 17. For simplicity, reference is made to an
yields yi kilograms per hectare of land and can enjoy
area of 10km x 10km (10,000 hectares), for which a
total income of yi pi K Shs per hectare of cultivated
hypothetical number of 10 additional CCEs would be
land, where yi is the crop price per kilogram.
foreseen. It is estimated that a team of two people
can carry out four CCEs per day, and that a man- At the beginning of each season, farmers are credit
day salary for such an activity could be set at K Sh constrained and so needs to take out loan L K Shs to
2,500. The cost of the supplementary CCE activity purchase inputs (e.g., seeds and fertilizer). Farmers
is obviously a function of the area to be covered. In then pay back the loan at the end of the harvest with
the beginning the CCEs can be carried out in the crop income.
areas where the AYII programs are piloted; but if the
2. Risk
programs are to expand significantly, all the areas
/// ///
should be surveyed and yield databases developed Both crop price and yield are uncertain. Crop price pi
SECTION
for them. This is why, despite the fact that the is assumed to follow a uniform distribution,
projected penetration of AYII in 2022 is 15 percent U(pi, ph). Crop production also faces various kinds of
for maize and 25 percent for wheat, the area covered risk including both farm-specific risk (e.g., disease
07
86 K E N YA : A G R I C U LT U R E I N S U R A N C E S O L U T I O N S A P P R A I S A L
or illness of farm labor) and covariate risk (e.g., ry ,y ̅ -> 1. On the other hand, when farm-specific
i
droughts and floods that tend to affect all farmers in shocks dominate the covariate ones, ry ,y ̅ will deviate
i
the area). With the presence of common covariate largely from one.
shocks, we should thus expect individual crop yields
3. Area yield index insurance (AYII)
to track average yields in their area to some extent.
/// ///
The contract is designed to protect farmers from
In order to understand this empirical relationship, we
covariate shocks that could affect all farmers in
describe joint distribution of individual yield yi and
_ the area and that are not effectively managed by
the average yield across all farmers in the area y with
existing ‘mutual risk sharing mechanisms’ within the
a bivariate normal distribution as
community. Specifically, AYII compensates insured
_
f(yi ,y)~N(μy , μy ̅ , σy , σ y ̅ , ry ,y ̅ ) farmer at an expected crop price p per kilogram
i i i _
when area averaged yield y falls below a prespecified
where μy ,μy represent
̅
long-term average levels of coverage level y*. Indemnity payout per insured
i
individual and area-averaged crop yields, σy , σ y hectare can thus be written as
i
̅
describe long-term standard deviations of the two
_
yield series and ry ,y ̅ represents correlations of the π=max[0,y*- y ]×p
i
two series observed in the empirical data. In the areas
where the coverage level is set as some percentage of
with large exposure to common covariate shocks, we
the expected area yield, i.e. y* = coverage × μy_ .
should expect individual yields to move together with
the area-averaged yield and thus Actuarial fair premium per insured hectare for this
contract is equal to the expected indemnity payout.
Insurance company will however add some premium
Table 17— Estimation of potential cost of multiple x > 1 to the commercial premium to cover
additional data collection activities for AYII other fixed, administrative costs. Total premium per
insured hectare can be written as
No of hectares in an area of 10km x 10km 10,000
Number of CCs per 10km x 10km area 10 ρ=xE(π) .
Number of people on a CC team 2 With AYII offering protection of income shortfall
Number of CCs carried out in a day by a CC team 4 from area yield variability, farmer’s insured crop
Number of man-days needed to cover each 10,000 ha 5 income per hectare can thus be yi pi+ π-ρ .
Man-day cost in Ksh 2500
Basis risk: Note that insurance is written on area
Cost of labor in KSh for each 10,000 ha 12500
yield, not individual yield. While this resolves
Maize: Reference total cultivated area 127,306
Wheat: Reference total cultivated area 127,306
2016 2017 2018 2019 2020 2021 2022 2023
Share of area covered by additional CCs 5% 15% 30% 45% 60% 75% 90% 100%
Overhead for equipment, management, auditing,
50% 50% 50% 50% 50% 50% 50% 50%
etc.
Maize: Additional costs of yield data collection
0.192 0.577 1.153 1.730 2.307 2.884 3.460 3.845
(million KSh)
Wheat: Additional costs of yield data collection
0.012 0.036 0.072 0.107 0.143 0.179 0.215 0.239
(million KSh)
K E N YA : A G R I C U LT U R E I N S U R A N C E S O L U T I O N S A P P R A I S A L
87
asymmetric information and reduces transaction reduce poverty rate by 1% (based on national food
cost, it also could limit the value of insurance to poverty line, 2005) relative to the baseline without
individual farmers because of basis risk, which occurs the program in these production zones. Direct cash
when indemnity payment deviates from individual transfer program to the poor was further used as
losses. The value to farmers will thus depend on counterfactual program for cost-benefit analysis. The
the how closely individual yields track that of area K Sh cost per farming household per year that can
average. AYII will be valuable to farmer as ry ,y -> 1. reduce poverty rate by 1% was computed as (poverty
i
gap x poverty line)/poverty rate.
4. Loan repayment
/// ///
6. Values of AYII
/// ///
Input credit is obtained at the interest rate r._ If
• Value to farmers AYII reduces vulnerability by
farmers always pay back their loans using crop
/// ///
providing buffer against sharp drop of net crop
income as much as possible, then net income
income available for consumption in the event of
available for consumption for farmer i _who
severe shocks
cultivates a median farm size Ai hectares of maize
will be • Value to lenders: Based on our assumption that
/// ///
farmers will try to pay back loan after meeting
Ci=(yi pi + π-ρ-(1+r)L)×Ai
required consumption, AYII thus will increase
Loan default is however possible and can be partial loan repayment rate on average. To make this
or total. While full repayment is an option, we more assumption more realistic, lenders can make
realistically assume that farmer will try to payback insurance a prerequisite for obtaining loan and/or
their loan as much as they can after meeting their link insurance with loan directly. With increasing
subsistent consumption c (set at 30% of food poverty loan repayment, lenders could eventually be
line).47 willing to extend more credit to farmers.
• Potential crowding in value of AYII through
Farmer ‘s loan repayment will be
///
credit market: In the medium term, insurance
///
LRi=max [(1+r)L,yi pi+ π-ρ-c ]×Ai could enhance agricultural productivity by
promoting smallholder farmers’ adoption of
5. Public supports
productive inputs (e.g., new technology, hybrid
/// ///
We assume that public support could result in s% seeds). This could be true when AYII relaxes
reduction in insurance premium rate and will cover demand-side constraint (i.e., enhancing farmer’s
the whole cultivated farm of representative farmer. investment incentives and credit demand when
Total public cost per farmer i is thus agricultural production is derisked) as well as
supply-side constraint (i.e., allowing lenders to
S=sρAi increase credit supply to farmers). Farmer i’s
net income available for consumption when AYII
Cost-benefit analysis of public support to agricultural
unlocks access to credit allowing him to afford
insurance program assumed that in the very first
more expensive but productive input with yield
years, development of insurance program would be
markup αy>1 per hectare and higher cost (and
possible only with public support and that one of the
larger loan size) relative to the current required SECTION
program’s key policy objectives was to reduce poverty
level with mark up of αL>1:
among smallholder farmers. We then computed K
Sh cost per farming household per year that could Cih=(αy (yi pi + π-ρ)-αL (1+r)L)×Ai
07
88 K E N YA : A G R I C U LT U R E I N S U R A N C E S O L U T I O N S A P P R A I S A L
B. Calibrating Economic Model with Country-level average maize producer prices
Actual Data (1991-2011) obtained from FAOSTAT and 2012-
2013 from Regional Agricultural Trade Intelligence
Crop production
Network (RATIN). Prices were inflation adjusted
/// ///
1. Sublocation, division and district yield (kg/ha): with 2013 as base year.
_
f(yi,y f(yi ,y)~N(μy , μy ̅ , σy , σ y ̅ , ry ,y ̅ )
i i i 3. Working capital loan (% of expected revenue):
with μsubl = μdiv = μdist = μ and σsubl = σdiv = σdist = σ _
Li=60%μy_ P
• Low-potential maize zone: μ=703, σ=347
From gross margin studies (KARI 2009, etc.),
• Medium-potential maize zone: μ=1426, σ=414 total input costs range from 50% to 75% of
• Low potential maize zone: μ=2892, σ=991 average crop revenue. This figure was also similar
to total working capital loan reported in Tegemeo
• Wheat region: μ=2505, σ=881
household survey. The median level is 60%.
• Common correlation matrixes for all zones
4. Yield and cost markup rates with respect to
Sublocation Division District high-cost, more productive input invested (%
Sublocation 1
of expected yield and cost): αy , αL vary across
Division 0.85 1
crops and production zones. They were estimated
District 0.75 -0.81 1
from the ratio of yields and costs of high versus
low input crop productions of three to five
Our analysis was done on a representative farmer representative small-scaled farmers with less
at the sublocation level with the assumption than 4 hectares of land in some key growing
that perfect income risk sharing exists at the
provinces in each zone. Maize data were derived
sublocation level. Sublocation average yields
from KARI (2009)’s Assessment of Costs of Maize
were thus used to represent yields of our
Production, Marketing and Processing in Kenya:
representative farmer.
A Maize Grain-Maize Meal Value Chain Analysis.
Mean and standard deviations obtained from Wheat data were obtained from DASS’s (2010)
detrended annual district yields are from the gross margin analysis.
Ministry of Agriculture, Livestock and Fisheries
from 1983 to 2013.48 Correlation metric is from Crop zone Studied county Markup (% of average)
2-year Tegemeo panel household survey in 2000 Production
Yield cost
and 2004. Survey covers 15-60 representative
Low-
households in representative sub-locations, potential Machakos,
Eastern 233% 308%
maize
locations, divisions and districts in all production
Med- Kirinyaga,
zones. Sample size varies by relative populations. potential Central 182% 138%
maize
Districts selected for analysis in all zones are those
Laguri, Western 200% 123%
High-
with large maize and wheat-growing areas with potential
maize Narok, Rift 193% 129%
available data in both Tegemeo’s household survey Valley
and district-level yield data.49 Mean 196% 126%
Narok, Rift 139% 132%
SECTION 2. Producer price (K Sh/kg): pi~U(pl , ph ) Wheat Valley
Nakuru, Rift 139% 133%
• Maize: pl =22.7, pl =45.5 Valley
07 • Wheat: pl =34.9, pl =58.1
Mean 139% 133%
K E N YA : A G R I C U LT U R E I N S U R A N C E S O L U T I O N S A P P R A I S A L
89
C. Simulations
/// ///
5. Farm size (hectare): We took the following steps to simulate key outcome
Ailow = Aimedium = 1.5 , Aihigh = 2.5 , Aiwheat = 3 indicators and zone-specific longitudinal series of
representative and area yields and prices from their
This was obtained from Tegemeo
joint distribution:
household survey.
1. Using the two-year Tegemeo household data, we
Area yield index insurance (AYII)
/// ///
constructed two years of annual area-average
_
6. Indexes: y, we constructed both division and yields at sublocation, division and district level by
district average yields, as the goal was also to averaging individual yields across households in
evaluate the AYII with these two different indexes. each area in each year.
7. Premium multiple (% of fair rate): x =200%. 2. In order to describe the zone-specific joint
This is a common rule of thumb in the industry. distributions of the three levels of yields, we
computed zone-specific means, standard
8. Coverage level: deviations and correlation matrices of sub-
location, division and district yields. These
High coverage Low coverage statistics were calculated using variations over
Zone (15% maximum (10% maximum
rate) rate) the two years and across respective area yields
Coverage Fair Coverage Fair within each zone. 50
premium premium
3. The relatively short temporal coverage
Low-
potential
maize
50% 7.30% 30% 4.50% of household data could have resulted in
underestimation of temporal variations of these
Medium-
potential
maize
85% 6.60% 75% 4.20% series. We thus complemented the data with
High-
longitudinal detrended district-level yield data
potential 80% 7.40% 65% 4.20%
maize and computed zone-specific moments. While
Wheat 75% 6.60% 65% 4.50%
means of these three levels of area yields were
comparable within each zone, standard deviations
were a lot smaller in the two-year data. Means and
9. Interest rate on working capital loan (% per year): standard deviations of these sublocation, division,
r=17% and district yield series in each zone were then
Weighted average commercial bank lending assumed to be similar to that estimated from the
rate as of April 2014 obtained from Financial 1983–2013 district yield series.
Sector Deepening. 4. For each production zone, we simulated 100
_
10. Minimum subsistent consumption: c is replicates of 100-year series of these three
assumed at 30% of annual food poverty line of a levels of area yields assuming that their joint
representative farming household with five adult distribution followed 3-variable multivariate
equivalent members (statistics from Tegemeo normal distribution, with zone-specific means
survey) calculated at national food poverty line and standard deviations obtained from 1983–2013
of rural regions at K Shs 988 per month per district yield data and correlation matrixes
adult equivalent. obtained from the variations within the two-year SECTION
household data.
11. Public supports represented as premium reduction
(%): s=50%
07
90 K E N YA : A G R I C U LT U R E I N S U R A N C E S O L U T I O N S A P P R A I S A L
Table 18— Summary statistics of maize and wheat growing households
Mean Median SD Min Max
Household member (adult equivalent) 5.2 5.1 1.9 0.6 13.2
Poverty headcount (national rural poverty line (2005)) 41.5%
Yield and price statistics*
Maize in low potential zone (kg/ha) 703 671 347 18 2,085
Maize in medium potential zone (kg/ha) 1,426 1,342 414 159 3,165
Maize in high potential zone (kg/ha) 2,892 2,790 991 397 5,325
Wheat (kg/ha) 2,505 2,617 881 26 4,581
Aggregated maize price (Ksh/kg) 34 34 7 23 45
Aggregated wheat price (Ksh/kg) 46 45 8 35 58
Maize producing households
Cultivated land size (ha) 2.5 1.5 4.5 0.1 110.0
Low potential zone 2.2 1.5 2.9 0.1 21.0
Medium potential zone 1.7 1.5 1.6 0.1 11.5
High potential zone 2.9 2.5 6.0 0.1 110.0
% households who own land 52% 67% 48% 0% 100%
% with two croping seasons a year 42% 39% 38% 0% 100%
% use purchased hybrid seed 23% 19% 33% 0% 100%
% households with maize sale 18% 0% 27% 0% 100%
Low potential zone 8% 0% 18% 0% 100%
Medium potential zone 7% 0% 17% 0% 100%
High potential zone 28% 20% 31% 0% 100%
% maize income from total econ income 29% 23% 20% 3% 100%
Low potential zone 70% 70% 10% 50% 100%
Medium potential zone 28% 31% 20% 3% 100%
High potential zone 28% 23% 20% 4% 100%
5. For each simulated year in each replicate, we 6. Finally, we calibrated our economic model using
also randomly drew one price realization from empirical data and estimated 100 replicates of
a uniform distribution specified with 10-year 100-year series of key outcome variables for
minimum and maximum national aggregate,
the representative farmer in each zone in the
inflation-adjusted price observed empirically from
scenarios with and without AYII and across
1991 to 2013.
contract variations.
SECTION
07
K E N YA : A G R I C U LT U R E I N S U R A N C E S O L U T I O N S A P P R A I S A L
91
(continued)
Wheat producing households
Cultivated land size (ha) 7.6 3.0 23.6 0.0 240.0
% households who own land 60% 100% 48% 0% 100%
% with two croping seasons a year 0% 0% 0% 0% 0%
% use purchased hybrid seed 26%
% households with wheat sale 78% 90% 30% 0% 100%
% wheat income from total econ income 29% 23% 20% 0% 86%
Credit access*
% households with input credit 46% 39% 14% 0% 100%
Purpose of credit
Fertilizer 81% 79% 21% 0% 100%
Seed 9% 9% 41% 0% 100%
Other equipments 10% 3% 43% 0% 100%
Credit source
AFC 1% 1% 6% 0% 14%
Commecial banks 1% 1% 3% 0% 8%
Cooperatives/Saccos 25% 39% 21% 0% 100%
Local trader/companies 10% 10% 14% 0% 100%
NGOs/MFIs 1% 1% 5% 0% 11%
Money lenders 2% 1% 32% 0% 100%
Friend/relatives, ROSCAs, etc. 6% 6% 26% 0% 100%
Lending rates
Commercial banks (5-yr statistics) 16% 15% 2% 14% 20%
* From 30 years detrended district-level yield data from 1983-2012 obtained from the Ministry of Agriculture
Inflation adjusted aggregated producer pricesFAOSTAT and Regional Agricultural Trade Intellegence Network
** Household data from 2000, 2004 household survey of Tegemeo Agricultural Monitoring and Policy Analysis Project
National rural poverty line is 1,562 KSh/capita/month
*** Data from Kenya Integrated Household Expenditure Survey 2005
****Monthly FSD data on commercial bank’s weighted average lending rates from 2005-2014
SECTION
07
92 K E N YA : A G R I C U LT U R E I N S U R A N C E S O L U T I O N S A P P R A I S A L
Table 19— Summary of key impact indicators by contract variations
District-level yield index
High coverage Low coverage
Commercial AYII 50% subsidized Commercial AYII 50% subsidized
Commercial 50% subsidized Commercial 50%
Impact indicators No insurance w/ increased w/ increased w/ increased w/ increased
AYII AYII AYII subsidized AYII
investment investment investment investment
Maize - Low potential zone
Coverage = 50%, fair rate = 7.3% Coverage = 30%, fair rate = 4.5%
Net income available for
10,721 9,408 10,721 10,234 10,721
consumption per year (Ksh). - - - -
(18,940) (17,713) (17,713) (18,383) (18,383)
Std.Dev in parenthesis
Probability of falling into
100% 100% 100% - - 100% 100% - -
poverty*
Loan repayment (%) after min.
59% 56% 59% - - 58% 59% - -
consumption
Maize - Medium potential zone
Coverage = 85%, fair rate = 6.6% Coverage = 75%, fair rate = 4.2%
Net income available for
21,723 17,647 21,723 54,342 61,753 19,447 21,723 57,615 61,753
consumption per year (Ksh).
(25,840) (23,250) (23,250) (41,500) (41,500) (24,075) (24,075) (43,210) (43210)
Std.Dev in parenthesis
Probability of falling into
90% 95% 90% 60% 55% 90% 90% 60% 55%
poverty
Loan repayment (%) after min.
84% 82% 86% 95% 97% 83% 85% 96% 97%
consumption
Maize - High potential zone
Coverage = 80%, fair rate = 7.4% Coverage = 65%, fair rate = 4.2%
Net income available for
73,582 58,965 73,582 237,515 266,227 66,847 73,582 252,999 266,227
consumption per year (Ksh).
(99,641) (85,534) (85,534) (168,014) (168,014) (92,757) (92,757) (180,753) (180,753)
Std.Dev in parenthesis
Probability of falling into
45% 50% 45% 15% 10% 50% 50% 15% 10%
poverty
Loan repayment (%) after min.
91% 90% 93% 98% 99% 91% 92% 98% 98%
consumption
Wheat
Coverage = 75%, fair rate = 6.6% Coverage = 65%, fair rate = 4.5%
Net income available for
104,466 87,274 104,466 169,047 194,750 94,179 104,466 179,372 194,750
consumption per year (Ksh).
(136,714) (117,954) (117,954) (176,345) (176,345) (125,404) (125,404) (185,782) (185,782)
Std.Dev in parenthesis
Probability of falling into
35% 40% 35% 30% 25% 40% 35% 30% 25%
poverty (%)
Loan repayment (%) after min.
92% 92% 94% 95% 96% 92% 94% 95% 96%
consumption
K E N YA : A G R I C U LT U R E I N S U R A N C E S O L U T I O N S A P P R A I S A L
93
(continued)
Division-level yield index (with reduced basis risk)
High coverage Low coverage
Commercial AYII 50% subsidized Commercial AYII 50% subsidized
Commercial 50% subsidized Commercial 50% subsidized
Impact indicators No insurance w/ increased w/ increased w/ increased w/ increased
AYII AYII AYII AYII
investment investment investment investment
Maize - Low potential zone
Coverage = 50%, fair rate = 7.3% Coverage = 30%, fair rate = 4.5%
Net income available for
10,721 9,402 10,717 10,227 10,717
consumption per year (Ksh). - - - -
(18,940) (16,483) (16,483) (17,289) (17,289)
Std.Dev in parenthesis
Probability of falling into
100% 100% 100% - - 100% 100% - -
poverty*
Loan repayment (%) after min.
59% 55% 58% - - 57% 58% - -
consumption
Maize - Medium potential zone
Coverage = 85%, fair rate = 6.6% Coverage = 75%, fair rate = 4.2%
Net income available for
21,723 17,652 21,733 54,346 61,766 19,450 21,733 57,615 61,766
consumption per year (Ksh).
(25,840) (21,765) (21,765) (39,530) (39,530) (23,075) (23,075) (41,151) (41,151)
Std.Dev in parenthesis
Probability of falling into
90% 95% 90% 60% 55% 95% 90% 60% 55%
poverty
Loan repayment (%) after min.
84% 82% 86% 95% 97% 83% 85% 96% 97%
consumption
Maize - High potential zone
Coverage = 80%, fair rate = 7.4% Coverage = 65%, fair rate = 4.2%
Net income available for
73,582 58,952 73,579 237,492 266,224 66,837 73,579 252,980 266,224
consumption per year (Ksh).
(99,641) (84,314) (84,314) (154,653) (154,653) (90,487) (90,487) (178,876) (178,876)
Std.Dev in parenthesis
Probability of falling into
45% 50% 45% 10% 5% 50% 50% 15% 10%
poverty
Loan repayment (%) after min.
91% 91% 94% 98% 99% 92% 93% 99% 99%
consumption
Wheat
Coverage = 75%, fair rate = 6.6% Coverage = 65%, fair rate = 4.5%
Net income available for
104,466 87,271 104,462 169,044 194,745 94,185 104,462 179,380 194,745
consumption per year (Ksh).
(136,714) (111,404) (111,404) (173,571) (173,571) (123,509) (123,509) (183,904) (183,904)
Std.Dev in parenthesis
Probability of falling into
35% 40% 35% 30% 25% 40% 35% 30% 25%
poverty (%)
Loan repayment (%) after min.
92% 93% 95% 96% 97% 93% 94% 96% 96%
consumption
* At national food poverty line (2005) at KSh 988 per month per adult equivalent. For a representative household of 5 equivalent adults, food poverty
line is calculated at 988*12*5 = Ksh 59,280 per year.
94 K E N YA : A G R I C U LT U R E I N S U R A N C E S O L U T I O N S A P P R A I S A L
Bibliography
Barrett, C. B., M. Carter, M. Ikegami. 2012. “Poverty Traps and Social ———. 2014a. __Kenya: Situation Analysis for a National Agricul-
Protection.” Working paper, Cornell University, University of tural Insurance Policy (NAIP)__. Nairobi: Government of Kenya,
California–Davis, and International Livestock Research Institute. Ministry of Agriculture, Livestock and Fisheries.
Barrett, C. B., P. P. Marenya, J. McPeak, B. Minten, F. Murithi, W. ———. 2014b. __Kenya Agricultural Data Collection and Man-
Oluoch-Kosura, F. Place, J. C. Randrianarisoa, J. Rasambainari- agement Guideline__. Nairobi: Government of Kenya, State
vo, and J. Wangila. 2006. “Welfare Dynamics in Rural Kenya and Department of Agriculture.
Madagascar.” __Journal of Development Studies__ 42 (2): 248–77.
ILRI (International Livestock Research Institute). 2013. __Index
Cai, H., Y. Chen, H. Fang, and L. Zhou. 2009. “Microinsurance, Based Livestock Insurance (IBLI) in Northern Kenya: Project
Trust and Economic Development: Evidence from a Randomized Document__. January 2013 revision. Nairobi: ILRI.
Natural Field Experiment.” NBER Working Papers 15396, National
Bureau of Economic Research, Cambridge, MA. KARI (Kenya Agricultural Research Institute). 2009. “Assessment of
Costs of Maize Production, Marketing and Processing in Kenya:
Central Bank of Kenya. 2012. __Annual Report__. Nairobi: Central A Maize Grain-Maize Meal Value Chain Analysis.” KARI, Nairobi.
Bank of Kenya.
Kerer, J. 2013. __Background Paper on the Situation of Agricultural
Chantarat, S., A. G. Mude, C. B. Barrett, and C. G. Turvey. 2014. Insurance with Reference to International Best Practices__.
“Welfare Impacts of Index Insurance in the Presence of a Poverty Nairobi: Adaptation to Climate Change and Insurance (ACCI),
Trap.” Working paper, Cornell University and Australian National German Agency for International Cooperation (GIZ), and Minis-
University. try of Agriculture, Livestock and Fisheries (MALF).
Clarke, D., and R. Vargas-Hill. 2013. “Cost-Benefit Analysis of the Lybbert, T., and C. B. Barrett, S. Desta, and D. L. Coppock. 2004.
African Risk Capacity Facility.” IFPRI Discussion Paper 01292, “Stochastic Wealth Dynamics and Risk Management among a
International Food Policy Research Institute, Washington, DC. Poor Population.” __Economic Journal__ 114, (498): 750–77.
FSD (Financial Sector Deepening). 2013. __Review of FSD’s In- Mahul, O., and J. Skees. 2007. “Managing Agricultural Risk at the
dex-Based Weather Insurance Initiatives__. Nairobi: FSD. Country Level: The Case of Index-Based Livestock Insurance in
Mongolia.” Policy Research Working Paper 4325, World Bank,
Galarza, F., and M. Carter. 2011. “Risk Preferences and Demand for Washington, DC.
Insurance in Peru: A Field Experiment.” Working Paper 11-08,
Departamento de Economía, Universidad del Pacífico, Lima; Mahul, O., and C. Stutley. 2010. __Government Support to Agricul-
revised January 2011. tural Insurance__. Washington, DC: World Bank.
GoK (Government of Kenya) 2006. Kenya Integrated Household MDP (Ministry of Devolution and Planning). 2013 Second Medium
Budget Survey 2006/06. Ministry of Planning and National Devel- Term Plan, 2013-17. Government of Kenya, Ministry of Devolu-
opment. Nairobi. tion and Planning, Nairobi.)
SECTION
———. 2012. __Kenya Post Disaster Needs Assessment, Drought Smith, A., H. Smit, and D. Chamberlain. 2011. __Beyond Sales: New
08
2008–2011__. Nairobi: Government of Kenya. Frontiers in Microinsurance Distribution: Lessons for the Next
Wave of Microinsurance Distribution Innovation__. Geneva:
K E N YA : A G R I C U LT U R E I N S U R A N C E S O L U T I O N S A P P R A I S A L
95
International Labour Organization. Producers in South West Buenos Aires Province: Feasibility
Study, Final Report__. World Bank, Washington, DC.
Sinah, J. 2012. __Index-Based Weather Insurance: International and
Kenyan Experiences__. Nairobi: Adaptation to Climate Change ———. 2013a. __Mexico: Agriculture Insurance Market Review__.
and Insurance (ACCI), German Agency for International Cooper- World Bank, Washington, DC.
ation (GIZ), and Ministry of Agriculture, Livestock and Fisheries
(MALF). ———. 2013b. __Uruguay: NDVI Pasture Index-based Insurance
for Livestock Producers in Uruguay—Feasibility Study, Final
Woodard, J. 2012. “A Spatial Econometric Analysis of Loss Experi- Report__. World Bank, Washington, DC.
ence in the U.S. Crop Insurance Program.” __Journal of Risk and
Insurance__ 79 (1): 261–85. ———. 2014. __Financial Protection against Natural Disasters__.
Global Facility for Disaster Reduction and Recovery (GFDRR).
World Bank 2011a. “Burkina Faso—Risk Management in the Cotton World Bank, Washington, DC.
Sector—Index Insurance Feasibility Study—Draft Report.” Agri-
cultural Risk Management Team, World Bank, Washington, DC.
———. 2011b. __Enhancing Crop Insurance in India__. Washington,
DC: World Bank.
———. 2011c. “India Crop Insurance Non-Lending Technical Assis-
tance: Summary of Policy Suggestions.” World Bank, Washington,
DC.
———. 2012. __NDVI Pasture Index-based Insurance for Livestock
SECTION
96 K E N YA : A G R I C U LT U R E I N S U R A N C E S O L U T I O N S A P P R A I S A L
Endnotes
1
World Development Indicators, World Bank, Washington, DC 14
NDVI is a very good indicator of pasture growth, grazing quality,
(accessed November 3, 2014), http://data.worldbank.org/data-catalog/ and the impact of drought on pasture degradation over time.
world-development-indicators.
15
The main differences between a micro-level individual pastoralist
2
See Mahul and Stutley (2010) for a comprehensive review of pasture/grazing-drought NDVI index insurance program and a
government support to agricultural insurance. macro-level program are these: (i) under the micro-level program,
individual pastoralists purchase their own policy and are the
3
See in particular Kerer (2013). insured for their declared number of animals (TLUs), while under
the macro-level program the insured is government (or another
4
Support for index insurance is included in a proposed bill for a new appointed entity), which purchases a single policy on behalf of
Insurance Act. a defined target audience of pastoralist households (termed the
beneficiaries); (ii) under a macro-level policy, premium payments
5
Nonrivalrous goods are those that may be consumed by many at the
are usually fully covered by the insured (government), and the
same time at no additional cost (e.g., national defense or a piece of
beneficiaries do not contribute at all toward the costs of insurance
scientific knowledge). premiums; and (iii) under the macro-level policy, the beneficiaries
have no legal rights to make any claim against the policy, as they are
6
This report, issued by the GoK, was based on data supplied by
not deemed to be insured.
MALF and was developed with technical assistance from the German
Agency for International Cooperation (Deutsche Gesellschaft für 16
See ARC, “First Risk Pool,” http://www.africanriskcapacity.org/
Internationale Zusammenarbeit, or GIZ). countries/risk-pool-1.
7
The fully rated price includes the full price of the risk and an 17
This initiative is being supported by the Rockefeller Foundation,
administrative loading to cover the ongoing costs of the insurers, the UK Department for International Development, the Global
although not the development costs. Facility for Disaster Reduction and Recovery, and the International
Fund for Agricultural Development.
8
Reinsurance companies add potentially large “data uncertainty”
increases to insurance premiums if they have concerns about the
18
According to Clarke and Hill (2013), compared with an emergency
data quality, thus significantly increasing the cost for farmers. assistance baseline in which cash or food is provided seven to
nine months after harvest, an early payout when combined with
9
A good example is the Kilimo Salama scheme in Kenya, which is improved contingency planning will lead to substantial speed, cost,
supported by the Global Index Insurance Facility and which uses and targeting gains. Speed benefits could be as large as a nine-
mobile phones as a point-of-sale device Other distribution channels month improvement.
include cash-based retailers, utility companies, or third-party bill
19
Under its old constitution, Kenya comprised eight provinces,
payment providers. For a discussion of innovative distribution
each headed by a provincial commissioner. The provinces (mikoa
channels, see Smith, Smit, and Chamberlain 2011.
in Swahili) were subdivided into districts (wilaya). There were 69
10
See the discussion above. districts at the 1999 census. Districts were then subdivided into 497
divisions (taarafa). The divisions were further subdivided into 2,427
11
Possible options for coinsurance pools are set out in annex A. locations (kata) and 6,612 sublocations (kata ndogo). Under the
Constitution of 2010, the districts became counties (there are now
12
Countries that have TSUs include Brazil, Chile, France, Ghana, 46) and the divisions became subcounties (there are 290).
SECTION Italy, Mexico, Poland, the Russian Federation and Spain.
20
A full description of the assumptions and the parameters adopted
08
13
An adult cow or 10 goats are equal to 1 TLU; a camel is equal to in the fiscal costing scenarios for the livestock insurance options is
1.43 TLUs. presented in annex B.2.
K E N YA : A G R I C U LT U R E I N S U R A N C E S O L U T I O N S A P P R A I S A L
97
21
The ultra-poverty rate is based on a national rural poverty line level is de facto reduced to lower levels (see box 3 for a more
equivalent to US$0.5/day. detailed discussion).
22
These results are parallel to findings in Chantarat et al. (2014) and 33
See section 3.4 for more detailed argumentation on the need for
Barrett et al. (2012). public support.
23
For further discussion of Kenya’s experience with agricultural 34
A detailed description of how these costs have been estimated is
insurance, see GoK (2014a), which is the source of the information presented in annex C.1.
presented in this section.
35
A higher take-up rate has been assumed for wheat since farming
24
GoK (2014a) details the different schemes and product approaches units are generally larger than for maize, and the value chain is
and thoroughly explains why these faltered or were inappropriate.
generally more integrated with the financial environment.
Some of the key challenges were the lack of entrepreneurship of
the insurance companies involved, and potentially the costs of 36
Data underlying GoK 2006
the products.
37
http://www.kalro.org/
25
For more details on the National Agriculture Insurance Scheme of
India and its modifications see, World Bank (2011b). 38
http://www.tegemeo.org/
26
The material presented in this section has been adapted from 39
http://www.tegemeo.org/
World Bank (2011a).
40
The CV, or coefficient of variation, is the standard deviation
27
Along these lines, some of the suggested topics to be covered divided by the mean and expressed as a ratio or percentage variation
in the revision analysis of India’s National Agriculture Insurance around mean.
Scheme were the following: (i) establishment of a standardized
national manual on CCEs; (ii) systematic training and certification 41
Since maize and wheat households would potentially earn
of loss adjusters; (iii) commission of randomized, independent, high- income from other sources of livelihood, poverty measures based
quality CCE audits to be conducted alongside the standard CCEs; on household crop income relative to either the national food
(iv) standardized statistical approach to handle outlier yields in the poverty line (K Sh 988 per capita per month, according to the Kenya
calculation of the area yield; and (v) implementation of an auditing Integrated Household Budget Survey [KIHBS 2005]) or the national
system, such as video recording, satellite imagery, and/or additional rural poverty line (K Sh 1,562 per capita per month, according to
CCEs on plots adjacent to the official CCE plots (World Bank 2011c). KIHBS [2005]), would reflect only the upper bound of poverty
incidence in the region. Since maize income constitutes the majority
28
In insurance transactions it is customary to refer to “premium
of economic income of those households in the low-potential areas,
rates” where the cost of the policies is expressed as a share of the
poverty measures for this group could well reflect their actual
value insured.
poverty incidence.
29
The coverage level determines the cases in which a payout is
42
We note that our model assumes away the potential that farmers
triggered; e.g., any time the recorded yield level in a specific area falls
below 80 percent of the reference average yield, a payout is issued. can save in a good year and draw on their saving to consume and
See figure 9 for a graphical representation of the role of the coverage pay back loans in a bad year. Our results for the expected loan
level. repayment rate should thus be interpreted as the lower bound of the
potential rate.
30
Annex C.1 also presents the district breakdown adopted in the
analysis.
43
This annex draws in part on GoK 2014a.
31
In technical terms this process is defined as a historical 44
This reflects the reality of the pastoral households in the regions,
burn analysis. where livestock also provides intrinsic value beyond just serving as
store of wealth.
32
The selected coverage level (80 percent) generates premium
rates that for some districts would be excessive and not sustainable. 45
See Woodard et al. 2012 for detail.
Hence in order to generate more realistic projections, commercial
premium rates were capped at a maximum of 15 percent. In a
46
This leads to a conservative cost estimate, in particular if SECTION
potential implementation phase, it will be important to assess the compared to situations in which CCEs would have to be outsourced
08
tradeoff between the cost of the policies and their actual coverage to a private entity and extension officers would mainly have an
capacity. In districts where the capping is binding, the coverage auditing function.
98 K E N YA : A G R I C U LT U R E I N S U R A N C E S O L U T I O N S A P P R A I S A L
47
This should capture the important feature from reality that farmers
will prefer to satisfy their basic needs before relaying any loan.
48
Longitudinal district yield data were detrended assuming an
average combination of linear, exponential, and moving average
trend (Stutley’s method). We also used estimated trends in the
longitudinal data to detrend the two-year yield data.
49
Districts considered in low-potential maize zone include Kitui,
Machakos, and Makueni in Eastern Province and Muranga, Kirinyaga,
and Nyeri in Central Province. Districts in medium-potential maize
zone include Kisumu, Siaya, and Nyamira in Nyanza Province; Vihiga
and Busia in Western Province; and Meru in Eastern Province.
Districts considered in high-potential maize zone are Nakuru, Trans
Mara, Trans Nzoia, and Uasin Gishu in Rift Valley Province and
Bungoma and Kakamenga in Western Province.
50
Because our temporal coverage was limited and could results
in underestimation of actual temporal variations, we decided to
exploit spatial variations of the area yield within each zone as well
as with the assumption that variations in area yields within each
homogenous zones could represent variations of yield realizations
over time in that zone.
SECTION
08
The World Bank
/// ///
1818 H Street NW
Washington DC 20433
Telephone: 202-473-1000
Internet: www.worldbank.org