Risk Management in Agriculture:
Assessing household level risk, Policy
layering and analytical challenges
Jesús Antón, OECD Trade and Agriculture
Annual Workshop of ERCA Research Network on SPAA. Ottawa 25 November 2011
Australia, New Zealand, Spain,
Canada and Netherlands
Farm level data analysis:
• Risk exposure & Optimal Strategies
OECD
Workshop on
Risk
Management
in Agriculture
(November 2010)
Aggregate Model Analysis of
Exogenous Risk :
AGLINK/COSIMO application
OECD Trade & Agriculture
(April 2011)
Thematic Review: country studies
Synthesis Report
1. OECD Work on Risk Management in Agriculture
2
Outline
• Why using Household level data...
–
...for risk assessment?
–
...and modelling response?
• Risk layering and good policy
• Analytical challenges
OECD Trade & Agriculture
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Why Using farm household level
data for risk analysis?
Aggregate ≠ Individual risk
Most farmers are exposed to higher yield risk than the
aggregate level
Percentage of farms exposed to higher yield variability than aggregate mean
UK
98
Italy
96
Estonia
96
Spain
78
Australia Canada
84
74
*Wheat for UK, Estonia, Australia and Canada Barley for Italy and Spain
Yield risk is higher than price risk… but not in all countries
Percentage of farms exposed to higher yield variability than price variability
UK
11
Italy
72
Estonia
68
Spain
91
OECD Trade & Agriculture
Australia Canada
51
7
5
Key role of interactions on Risk Assessment
Full attention to the interactions between risks,
strategies and policies
–
Correlations, diversification, government’s programmes
Farm income variance is reduced by ...
...price and yield changes
offsetting each other
Italy
United
Estonia Australia Kingdom
10%
0%
-10%
-20%
-30%
-40%
-50%
-60%
-70%
...output and input prices
offsetting each other
...diversification
Decomposition of the variance of income of arable crop lfarms. The reference level of risk is the calculated variance in the
counterfactual case of zero correlations, co-variances and diversification. Based on time series of individual farmers.
OECD Trade & Agriculture
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Endogenous pro-active risk management strategies:
Interesting results from Stochastic simulation model
Representative farmer maximize his expected utility of profits
~ 
~   )(1  )  degree of CRRA
(

U (~   ) 
 initial wealth
(1   )
n
 [( ~p
i 1
i
* q~i  ci ) * Li ]  LR * ( L   Li )  c~  g ( ~
pi , q~i ,  )
~
pi
uncertain output price of crop i
~
q
i
uncertain yield of crop i
ci cost adjustment factor for crop i
~ uncertain variable cost
c
LR revenue from livestock operation (applicable for only Australia)
Li area of land allocated to crop i
g transfer from government or benefit from risk market strategy
 level of coverage decided by farmer
Farmer’s welfare is computed as certainty equivalent of profit
CE  [(1   ) EU (~   )]1/(1  )  
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a) Are Minimum Intervention prices effective?
0%
-2%
-4%
-6%
-8%
-10%
-12%
-14%
-16%
-18%
MICRO SIMULATIONS RESULTS
Change in Income Variability CV
Expected price of barley
Current intervention level
Expected price of wheat
60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85
Intervention price (GBP per tonne)
2%
Diversification Index
• Reduce risk rather
than Manage risk
• Only effective for
very low Intervention
prices
• Beyond certain level,
it increases variability:
–
1%
0%
-1%
–
-2%
-3%
Expected price of barley
-4%
-5%
Expected price of wheat
Current intervention level
60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85
Less on-farm
diversification
less price-yield
correlation => less
natural hedging
Intervention price (GBP per tonne)
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b) Support to Insurance
Demand for crop yield insurance


Information databases and
sharing arrangements
Public-private partnership
• Hard to increase demand
Percentage transaction cost
• Start-up support
UK
10
8
6
4
2
0
0.0
• Insurance subsidies



Australia
0.1
0.2
0.3 0.4 0.5 0.6 0.7
Proportion of land insured
0.8
0.9
1.0
Crowding out: Diversification index
Reduce diversification
Differentiate catastrophic and
marketable policies
Hard to deter ex post assistance
Australia
UK
0.0%
-2.0%
-4.0%
-6.0%
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
Proportion of land insured
OECD Trade & Agriculture
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c) Crowding out of BRM policies
Impacts of some BRM programs in a sample of Saskatchewan farms
Accounting
Change in CV
of Income
Simulated
Change in CV
of income
(without crowd out)
(with crowd out)
Change in
Change in
Diversification share of land
insured
Agri-Insurance
-23.5%
-0.5%
-4%
+33%
Agri-Stability
-33.4%
-9.0%
-18%
-16%
Agri-Invest
-0.7%
-1%
0%
Agri-Recovery
+2.5%
-8%
-0.2%
-4.9%
-18%
-16%
Lagged AgriStability
-1.4%
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d) AgriStability by tier
Certainty equivalent income
(change in CAD)
Overall
Contributing factors
change
Change in Change in
mean
variability
Total impact
Contribution of the coverage of
TIER 2
70-85% of reference
margin
TIER 3
up to 70% of reference
margin
TIER 4 negative
margin
CV of
income
(change in
percentage)
Change in
diversificati
on index
(Initial
= 100)
Minimum
income
(change
in CAD)
Change in
the share of
land insured
(change in
percentage
points)
3 769
3 728
40
-5.9
-31.9
8 224.2
-5.4
335
418
-83
2.6
-6.8
-4 887.7
-1.1
2 488
2 336
151
-8.6
-6.6
-2 804.7
-21.3
946
973
-27
0.1
-18.6
15 916.6
17.0
OECD Trade & Agriculture
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Improving AgriStability
• Simplify the delivery mechanism:
–
–
–
Eliminate the margin adjustments
Link payment directly to income tax declaration
Declaring tax in accrual basis, like other businesses
• Make an in-depth evaluation of the effectiveness and
targeting
–
–
Use available databases
Consider the following changes in policy:
•
•
•
Eliminate stabilization tier 2 and tier 4
Refocus on Tier 3, addressing the overlap with insurance
Increase participation fee and calculate actuarial premiums
OECD Trade & Agriculture
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e) Crowding-out effects
• Government compensation for non-catastrophic risks
off-sets pro-active risk management strategies:
–
–
Diversification: the main strategy
Market instruments
•
•
•
Contracts / integration
Futures: farmers benefit
Private insurance: asymmetric information
• Strongest crowding out from policies that address
normal business risk:
–
Price support, income stabilization, CCP
• Weakest crowding out: Fixed highly decoupled
payments
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Risk Layering and good policy
Governance of Catastrophic Risk
• OECD Governments will always be called on to
provide disaster assistance
–
–
Political pressure
Be prepared with some ex ante framework
•
•
•
•
•
Procedures
Delineation of responsibilities
Triggering criteria
Types or levels of assistance
Examples:
– New Zealand
• Crop insurance as a disaster assistance device
+
-
Farmers pay, transparent trigger, stable budget
Refrain from non-catastrophes, deter ex post assistance and
efficient administration
OECD Trade & Agriculture
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More government
involvement?
Good policy response
Catastrophic Risks
Marketable Risks
Normal Risks
Rare, high damage &
systemic
Middle range
Small damage but
frequent
On-farm strategy
On farm
strategies
- Diversification
-Saving
Market tools
Ex-ante
Policies
Ex-post
Policies
Probability
Market tools
- Forward contract
- Private insurance
Disaster Assistance
Policies
- Ex-ante/ Ex-post
payment
- Subsidized insurance
Higher income loss
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...to RM Policy Overcrowding in Canada...
Catastrophic Risks Marketable Risks
• Production and income
diversification
• Financial management
On farm
strategies
• Forward contracting
• Use of cooperatives
Market tools
Ex-ante Policies
Normal Risks
Public Insurance
(AgriInsurance)
Wheat Board
Tier 1: Saving acc.
AgriInvest
AgriStability countercyclical payment
Tier 4
Negative margin
Ex-post Policies
Tier 3
30-100% loss
Tier 2
15-30% loss
AgriRecovery
Ad Hoc disaster Aid
OECD Trade & Agriculture
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...plus supply management...
Catastrophic Risks Marketable Risks
• Production and income
diversification
• Financial management
On farm
strategies
• Forward contracting
• Use of cooperatives
Market tools
Ex-ante Policies
Ex-post Policies
Normal Risks
Supply Management
Arrangements
(dairy, eggs and poultry)
Tier 1: Saving acc.
AgriInvest
AgriRecovery
Ad Hoc disaster Aid
OECD Trade & Agriculture
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Main Policy Messages for Canada
a) Improve the definition of the boundaries
between programs and layers of risk: enhance
farmer’s pro-active strategies
b) Improve the delivery and target of AgriStability:
not effective for normal nor catastrophic risk
c) Develop an ex ante framework for catastrophic
risk covering AgriInsurance, AgriRecovery... and
avoiding proliferation of ad hoc
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Analytical Challenges and Areas for research on Risk
1. Use of micro panel data:
–
–
–
For farm risk assessment and inform the government and the
farmer
For simulation models to better understand the interactions
between policies and strategies
For estimating the value of risk for farmers (Why governments
tend to overestimate?) and the marginal impact of policies /
strategies
2. Economics of governance and institutions
–
–
–
How to make good information prominent?
Risks with externalities and co-funding
Boundaries and how to make most efficient institutions
emerge?
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OECD Trade and Agriculture
THANK YOU!
www.oecd.org/agriculture/policies/risk
Contact
[email protected]
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