Procurement Center Franchise Model
Download
Report
Transcript Procurement Center Franchise Model
World Bank/ Agro Asemex
Comprehensive Management of
Agricultural Climate Risk
Rodney Lester, Cuidad de Queretaro
Mexico, October 9, 2008
The climate change debate can
take some bizarre turns
Skippy for dinner
Who We Are: How to make
Australians eat kangaroo.
Source: Garnaut Report, Ch 22 – recommending that 240 million
kangaroos replace 7 million beef cattle and 36 million sheep by 2020
to reduce methane emissions
A: The current big picture –
balanced thinking versus
dooms-dayers
There is a battle going on between an ‘end of the
world’ group and economic rationalists
My take1:
Climate change is occurring’
and there is a human effect, but
there is huge uncertainty in models
Kyoto, Stern etc are poor trade offs for current and next
few generations in an economic sense and are not
politically acceptable
The sensible debate is now about priorities and trade
offs,
and a key issue is the real shadow price of
atmospheric carbon ($2 to >$100)
1. Not necessarily WB policy
Copenhagen Consensus Priorities
www.copenhagenconsensus.com/Default.aspx?ID=788
B: The current ag. risk picture – not much
regional change in the short/ medium term
(except Africa?), and not much aggregate
change long term but
……..
Impact of CC is highly complex – Macaulay
Institute/ ISCI
Two marginal sites – Italy, Scotland – 2030 and 2090
Demonstrates highly complex nature of interactions – e.g
earlier cropping of grains but reduced biomass because
of shorter period to maturity
More heterogeneous rainfall distribution
Increased productivity for heat loving crops (tomatoes
etc) but increased water demand
Likely need to change rotation systems – and some
crops may become non viable
Increased yield variability
www.macaulay.ac.uk/LADS/papers/British_Council_report.pdf
IPCC 4 sees mixed CC medium term
impacts in Latin America
Source: IPCC 4, Synthesis Report
ODI also sees increased volatility
Not much change in average yields in the
short term (next 20 years) except Africa,
but
frequency and severity of extreme events is likely
to increase – mainly in areas already susceptible
Many tropical crops are already near threshold
temperatures
Extreme hydro- meteorological events will become
more frequent for most land areas over the longer
term
Source: ODI. A rough guide to climate change and agriculture, March 2007
But it is clear -the developing world is worst
affected in the long run
Summary Estimates for Impact of Global Warming on
World Agricultural Output Potential by 2080s (percent)
Without carbon
fertilization
With carbon
fertilization
World
Rich countries
Developing countries
Median
Africa
Asia
Middle East- North Africa
Latin America
-16
-6
-21
-26
-28
-19
-21
-24
-3
8
-9
-15
-17
-7
-9
-13
Mexico
-35
-26
Based on Cline, William, Global Warming and Agriculture: Impact Estimates by
Country, Table 7.1.
However there is great uncertainty in
projections
Results, selected countries
(percent change in agricultural productivity)
Ricardian
Crop Model
Argentina
Brazil
US
SW plains
India
China
S. Central
-4
-5.1
4.7
-11.1
-49.2
3.8
-18.8
-18.1
-28.7
-16.5
-59.0
-27.0
-12.6
-12.6
Based on Cline, William, Global Warming and Agriculture: Impact Estimates by
Country, Table 7.1.
Weighted Avg
w/oCF
w/CF
-11.1
2.2
-16.9
-4.4
-5.9
8.0
-35.1
-25.0
-38.1
-28.8
-7.2
6.8
-14.6
-1.8
Including price
Ref: FAO. Adaptation to Climate Change in Agriculture, Forestry and Fisheries, 2007
And models are incomplete
Factors not allowed for:
Positive:
Technology improvement (e.g. flood tolerant
rice, drought tolerant wheat and maize)
Changed income mixes
Negative: Increased weather volatility
Insect infestation
Loss of water table and glacial water (short
term increase for the latter)
Loss of species diversity
?:
Feedback loops (e.g. from land use change)
Scale effects – climate model v. farm systems
C: A comprehensive value chain
approach is required
Value chain management can reduce risk
Case Study of Maize Procurement Centers –
AP/ India
Federated Institutions with own savings and credit products
Procurement Centers
Physical infrastructure (i.e., small rented or own building, weighing
instruments, moisture meters, and sieving equipment)
Trading Platform (to be networked with Virtual Private Network).
Developing good quality network of service providers with the
following skills:
•
•
•
•
•
•
Market Based Information: Micro planning for procurement & Market
Research Data
Quality Control: Moisture control, grading
Financial Management: Accounting & Book keeping
Logistics
Enterprise Management
Labor Management & Conflict Resolution
Procurement Centers in AP have raised
farmer incomes
1610 Procurement centers
81 Commodities
$100 Millions in turnover
Over 200,000 Paraprofessionals: Barefoot Botanists, Book keepers, Quality
Controllers
Partners include:
(i) AP Markfed (Largest Cooperative in Agricultural Products in the State)
(ii) ITC Ltd (Agribusiness Multinational)
(iii) Olam International (Largest Exporter of Cashews in the World)
(i) Agrotech Foods Ltd. (Multinational with dominant share in the refined oil
segment)
Products include: Turmeric, Lac, Maize, Neem, Red Gram, Sunflower
Seeds, Cashew, Groundnut, Organic Cotton
Source: Rao.K.P et al. South Asia Rural Livelihoods Series 1, Notes 2 and 4, World Bank
Procurement Center Franchise Model
For small and marginal farmers:
Enhanced Price (+10%)
Weighing Benefits (+5-10%)
Saving on Driage Loss (50% less than
middle men)
Saving on transportation (+ 15 – 20%)
Elimination of price uncertainty
Saving on one day wage labor
Cash Payment
Source: Rao.K.P et al. South Asia Rural Livelihoods Series 1, Notes 2 and 4, World Bank
Savings and credit have a role in inter-temporal
consumption smoothing – but not for relatively
infrequent severe systemic events
Informal credit – breaks down under
systemic risk and societal change
Microfinance – if available – but not useful
in extreme situations
Savings – if safe mechanisms are
available – but again not able to deal with
extreme situations
D: Insurance plays a key role for
production risk – but it’s a case of
horses for courses
Key Assumption for what follows
Price is set by international markets, or
otherwise independent of domestic yield
Limited direct public interventions in markets (e.g.
central purchasing boards).
Reasonably efficient value chain – storage,
transport, pricing methods etc
Limited misaligned incentives (e,g, corn ethanol
subsidies)
No systemic global weather correlations
Insurance seen as an important
mechanism by Rio, Kyoto and IPCC 4th
AR
Source: IPCC 4, Synthesis Report
Role of Insurance –Hazell et al – 1986
seminal book
Increase agricultural efficiency by reducing
ex ante risk
Smooth income ex post
Mechanism for social transfers – political
rather than effective approach – direct
transfers work better.
Does it makes sense – rational economic
model
Overall benefit –
OAD
Ultimate benefit to
farmer –
P1DO – P0AO
– Determinants
Elasticity of demand
Net price of insurance
Source; Hazell, P et, al. Crop Insurance for Agricultural Development, John Hopkins, 1986, Ch 7 (Siamwalla and Valdes)
It did not work with traditional indemnity
insurance
Subsidy necessary for all except hail, fire and
single crops (typically >50% total cost)
Moral hazard – farmer harvests insurance rather
than crops
Information asymmetry – farmer knows more
about risk than insurer
Administration – crop inspection, loss
assessment and opportunity cost of time to
payment of claim
Political capture
And pricing (expected loss plus expense
loading plus cost of capital) can be high for
other reasons
Trigger is hit too frequently – inefficient use of
insurance – credit and savings, (direct donor
support for sovereigns) may be better
instruments
High losses and uncertainty for infrequent
events pushes up capital charge and cost of
capital
The agriculture involved is non viable – a policy
issue not a market issue
In addition willingness to pay shows
complex patterns
Morocco (cereal) – willing to pay 12% to
20% above expected loss for contract –
but only for lower triggers in low variability
areas – MCarthy, N. IFPRI using WB study as input
Vietnam (litchi) – farmers overestimate
area yields, wealthier farmers more likely
to participate – education important –
Vandeveer, M. USDA, published in Agricultural Economics, 2001
However, subsidies are hard to justify in a
welfare economics framework
Economically inefficient per indifference
curves
May be justified:
To build institutions and infrastructure if
free rider problem exists
During initial learning curves of suppliers
and consumers
To support new technology – but
questionable
An ideal
Cost/ price nearer to expected loss
Simple objective claims triggers linked to
actual loss - reduced or no in field crop
and loss assessments
Simple pay out formulae
Rapid payout of claims
Correct risk level signaling
No direct long term subsidies
Initial WB studies (early 2000s) : rural bank exposures to
drought, emerging degree day market in the US, seed
funds from WB President and FSE VP
Mexico: For about 40% of the planted area, rainfall contracts could
reduce relative yield risk between 9% and 29%. More specifically,
in Durango the risk reduction in yields is 23%, in Jalisco 28.6%, in
Tamaulipas 9% and in Zacatecas 29%.
Morocco:
Two possible approaches the WB
now supports
Parametric – based on
objective weather or
related metric
Low cost, objective, but
Index – based on area
yields (e.g. cereals) or
animal census data.
Less basis risk,
but
–Basis risk
–Need tamper proof
measurement
infrastructure
–Still need to measure
area losses (sampling/
census)
–Claims payment can be
delayed
And the response depends on the client and
the hazard
Nature of
consumer
Type of hazard
Suitable insurance
instruments
Government – fiscal
management
Cities – EQ, Cyclone etc
Cat bonds, parametric
insurance (+DDO)
Rural – Drought, flood
Parametric insurance
(little basis risk)
Credit institution
Drought, flood, freeze
etc
Parametric insurance at
aggregate level (reduced
basis risk)
Individual farmer,
supplier, intermediary
As above
Index insurance if basis
risk too high. Multi
trigger parametric
insurance if correlations
are high enough.
Example of parametric payoff schedule
Reference: Skees, J. The Potential of Weather Insurance for Spurring a Green Revolution in Africa
Other issues
Links to credit
Appropriate insurance infrastructure – involve
the local industry or separate fund or
combination (PPP)?
Appropriate legal infrastructure – status of
parametric covers – insurance or derivatives?
Education of farmers/ herders – quality of
outreach services?
Payment in non cash/ non bank account
societies?
Role of government targets/ suasion (India and
South Africa)
Some additional questions will need to be
asked in the longer term CC scenario
How valid will past experience be for
pricing, benefit and trigger design?
Can these concerns be overcome by
actuarial techniques and physical
modeling – e.g credibility methods,
agronomic models etc
E: A taxonomy of initiatives
Case
Nature of client
Mechanism:
Instruments
Separate pool + employed
R/I/ market
insured/IFI
instrument/
combination
Mongolia – freeze
and drought
Herder
Market
Index Insurance [+
DDO]
India – AICI revenue
Cash farmer
Market
Index Insurance
India – AICA rainfall
Cash farmer
Market
Parametric
Insurance
Caribbean Islands
– hurricane,
volcano, EQ
Sovereign
Separate pool
Parametric R/I, Cat
Swap (IFI)
Ethiopia – drought
[food security]
Sovereign
Market
Parametric
Insurance
Malawi - rainfall
Sovereign
IFI
WB derivative
Some lessons learned over the last decade
Each situation is different – there are no silver bullets –
do a decent diagnostic first
Integrate into the full value chain of interventions
Infrastructure development will often be required in
advance
Educating the consumer will take time – but long term
subsidies are usually welfare reducing
The design will usually need tweaking with experience –
these projects often require above average levels of
supervision and can cover many years.
The Donors are now engaged
EC - €25 million – IFC administered TF for
ACP countries
Gates – Initial $1 million then $25 million WFP