FUR XII 2006 at LUISS in Roma
12th International Conference on the Foundations and Applications of
Utility, Risk and Decision Theory
Insurance and Financing in Long-term
Agricultural Decision Making in Transition
Economies- The Case Of Croatia
Njavro, Ma, Van Asseldonk, Mb. and Meuwissen, M.b
a Faculty of Agriculture University of Zagreb
b Institute of Risk Management in Agriculture, Wageningen University
Content
1. Introduction
2. Rural Finance in
Croatia
Agricultural Lending
Crop Insurance
3. Objectives
4. Methodology
Stochastic simulation
SERF
Growth model
5. Risk Management
Model
6. Stochastic Inputs
7. Hail insurance
8. On-farm strategy- Hail
Nets
9. Leverage
10. Conclusions and
Recommendations
11. References
12. Contact details
2
Introduction [1]
•Market failures of agricultural risk sharing
instruments, such as crop insurance and
agricultural finance, in transition economies of
Central and Eastern Europe hamper efficient
risk management.
•Apart from hail insurance, other forms of
agricultural insurance products and hedging
instruments are only limited available.
•In combination with a constraint external fund
inflow investments are often postponed thereby
deteriorating farms’ competitiveness.
3
Introduction [2]
•In order to improve farms’ competitiveness in
the process of EU accession, policy makers’
have initiated incentive programs for
investments
in
agriculture
(so
called
“Operational Plans”).
•Plans aim to stimulate investments in order to
create a viable production sector.
•Impacts are questionable in the current state
of risk management markets!
4
Rural Finance in Croatia
Financial sector
•The financial sector in Croatia constantly grows and
develops in quality and quantity terms.
•Highly competitive and innovative market that has
already reached EU standards. Commercial banks
(mainly in foreign ownership) are the major players in
the financial sector.
•Their credit policies are mainly oriented to households
and the share of household loans in total bank
financing is 49.6%. Networks of the banks’ branches
bring financial products to almost every corner of
Croatia.
5
Rural Finance in Croatia- Agricultural
Lending [1]- Supply side
1. Agricultural policy reform- Model of capital
investment
• Step ahead from classical subsidized and directed
credit program.
• It is aimed at encouraging the development of
business relations between commercial banks and
farmers.
• The model can be described as the assigning of
irretrievable capital (25% out of total loan) from the
budget of the Republic of Croatia.
2. EU Pre- accession program- SAPARD
6
Rural Finance in Croatia- Agricultural
Lending [2]- Supply side
“Operational Plans”
To date, two programs have been launched, one for establishing
perennial crops and another for cattle production.
OPERA TIONAL PLAN
Purposes
Repayment period
Grace period
Interest rates
Max amount of a loan
Entitled to borrow
PERENNIA L CROPS
Investments in establishing
new
plantations,
buying
agricultural land, mac hinery
and equipment,
Olive
groves:
15
years
including grace period (5
years)
Investments
in
existing
orchards:10 years including
grace period (2 year)
Other investments: 12 years
including grace period (3
years)
4%
1,5 million kunas (natural
persons*)
3,5
million
kunas
(legal
persons**)
Family
farms,
trade
companies,
craftsmen,
cooperatives
Croatian National Bank- Exchange rate on 21.06.2006.
Euro: Kunas = 1: 7,254437
CA TTLE
Investments
in
buildings,
livestock,
equipment
and
agricultural land
12 years
period)
(including
grace
2 years
4%
3,5 million kunas
Family
farms,
companies, craftsmen
trade
7
Rural Finance in Croatia- Agricultural
Lending [3]- Demand side
The main constraints are:
• The
lack
of collateral
and
non-
functioning land market,
• Unfavorable farms’ structure,
• Lack of business history,
• Lack
of
clear
presentations
of
business ideas through business plans.
8
Rural Finance in Croatia - Crop
Insurance
•Agricultural
insurance (crop insurance
against unfavorable weather condition and
livestock insurance) is undeveloped in Croatia.
•Apart from hail insurance, other forms of
agricultural insurance products and hedging
instruments are of limited supply.
•Statistics show a low uptake of crop insurance
and a small land area covered by insurance.
•Limited supply of agricultural insurance, but
premium subsidies (introduced in 2003)
positively influence supply.
9
Objectives
Microeconomic analysis of risk
sharing tools (insurance and
financial leverage) and their
effects in apple production
Potential actions in risk transfer
markets in the light of EU
accession will be suggested.
10
Methodology [1]- Stochastic simulation
The purpose of stochastic simulation in risk analysis is
to determine probability distributions of consequences
for alternative decisions to enable good and wellinformed choice (Hardaker et al., 2004, p.158).
“Particularly useful for problems that involve risks, are
dynamic and have discrete decision variables”…
“Given our need to study finance, legal and human
resources risks, simulation needs to be re-evaluated.”
(Musser and Patrick, 2002)
www.palisade.com11
Methodology [2a]- Stochastic
dominance
Stochastic Dominance – considers the
full range of the simulated distributions.
Quantitatively superior because every
point is used and compared one-to-one
with every point of another distribution.
www.simetar.com
12
Methodology [2b]- Stochastic efficiency with
respect to the function
Compares alternatives in terms of certainty
equivalents (CEs).
Form of utility function: negative exponential
Risk aversion coefficients : Richardson’s rule is
based on Meyer’s constant relative risk aversion
Risk aversion coefficient
1.0
0,1
0,01
0,001
0,0001
0,00001
Overall mean of random variable
if overall mean of random values is 0 to
10
if overall mean of random values is 10 to
100
if overall mean of random values is 100
to 1,000.00
if overall mean of random values is
1,000.00 to 10,000.00
if overall mean of random values is
10,000.00 to 100,000.00
if overall mean of random values is >
100,000.00
13
Methodology [2c]- Risk premium
• Risk Premium (RP) - calculate the risk premium
between each of the scenarios and a Base
scenario.
• Risk Premiums equal the difference between the
CE’s for the risky scenarios.
RPG to F = CEG – CEF
• Base scenario should be the current situation or
the scenario picked best by CE or stochastic
dominance
Richardson, J.W. (2005)
14
Growth model
Growth model (g) expresses the rate of growth of equity
capital as a function of the rate-of return on assets, the
interest rate on debt, the rates of taxation and consumption
g = ( r Pa – i Pd) (1- t)(1- c)
r = the average (stochastic) net rate of return on total assets over the
investment’s expected life
i = interest rates (4,5%)
t= the average rate of income taxation (20%)
c = the average rate of withdrawals for family consumption, dividends
and other non-business flows (0%)
Pa= the average ratio of assets to equity
Pb= the average ratio of debt to equity, the leverage ratio
σg= σr Pak
k = (1-c) (1-t)
(Barry et. al, 2000)
15
Risk Management Model in Apple
production
16
Risk Management Model
Investment in apple orchard (5 ha)
••••-
“Naive”
Hail insurance
Hail nets
Hail nets + hail insurance
17
Stochastic Inputs [1]
Climate risks
1
0,8
0,6
0,4
0,2
0
0
1
3
2
4
probability of occurance
frost
hail
Source: Croatian Hydro meteorological Institute
www.meteo.hr
18
Stochastic Inputs [2]
• Yields- CDF
1
0,9
0,8
Prob
0,7
0,6
0,5
0,4
0,3
0,2
0,1
0
0
10000
20000
30000
40000
50000
60000
kg/ha
Yield
1st class
Source: RM Model (@Risk simulation, 5000 iteration)
triangular distribution based on expert advice
and case study data
19
Stochastic Inputs [3]
Share of the first class apples (%) under different risks
1,00
0,90
0,80
0,70
Average
0,60
5th Percentile
0,50
25th Percentile
0,40
75th Percentile
0,30
95th Percentile
0,20
0,10
0,00
hail
frost
hail and frost
without climate risks
Source: RM Model (@Risk simulation, 5000 iteration)
triangular distribution based on expert advice
and case study data
Simetar© graphical tools
20
Stochastic Inputs [4]
Prices
Average Distribution
@Risk funkcija
Price I class-fall
4,40
Normal
=RiskNormal(4.40, 0.50)
Price I class-spring
5,54
Normal
=RiskNormal(5.54, 0.49)
Price- second class
1
Normal
=RiskNormal(1, 0.25)
Source: based on Market information system in
Agriculture dana (weekly data)
www.mps.hr/tisup
21
Hail insurance
Insured sum: between 50,000.00 and 100,000.00 kunas per
hectare (@RiskSimtable function)
Damages- normal(50%, 20%)
Deductibles
0
5
10
15
Premium rate* (quantity + quality
insurance
0,19
0,15
0,11
0,09
* 5th premium class (on scale from 1 to 15)
Source: Croatia osiguranje d.d., Insurance Company
Premium subsidy: 25% (state level)
No of combination= 24
22
Hail Nets
Investment
(kunas)
1
954,077.25
AREA UNDER NET (HA)
2
3
4
1,034,077.25 1,114,077.25 1,194,077.25
5
1,274,077.25
No of combination= 5
23
Selected Risk Management Strategies
Descriptive statistics
• Net present Value
Name
HailInsurance I *
HailInsurance II **
HailNets 4 ha
HailNets 5ha
Naive
Insurance (1 ha)+nets(4ha)
Mean***
246.543,94
237.053,27
480.565,84
519.974,58
250.751,41
449.988,35
Std Dev
480.184,55
478.697,33
473.588,57
472.179,26
527.479,85
470.717,64
CV
194,77
201,94
98,55
90,81
210,36
104,61
* Insured sum: 50.000,00 kunas, deductibles 15%
** Insured sum: 50.000,00 kunas, deductibles 15%
*** Discount rate= 4%
24
Selected Risk Management Strategies
• SERF analysis and Risk Premium
Stochastic Efficiency with Respect to A Function (SERF) Under a
Neg. Exponential Utility Function
800.000,00
600.000,00
400.000,00
200.000,00
0,00
-200.000,00
0
2E-06 4E-06 6E-06 8E-06 1E-05 1E-05 1E-05 2E-05
Neg. Exponential Utility Weighted Risk Premiums Relative to
Naive
-400.000,00
-600.000,00
600.000,00
-800.000,00
-1.000.000,00
500.000,00
-1.200.000,00
400.000,00
-1.400.000,00
ra
300.000,00
HailInsurance I
HailInsurance II
HailNets4
HailNets5
Naive
Insurance200.000,00
(1 ha)+nets(4ha)
100.000,00
-
0
2E-06 4E-06 6E-06 8E-06 1E-05 1E-05 1E-05 2E-05
-100.000,00
ra
25
HailInsurance I
HailInsurance II
HailNets4
HailNets5
Naive
Insurance (1 ha)+nets(4ha)
Financial Leverage
Scenario
I
II
III
Sources of funds
External funds to 90% of
total investment
30% of own capital
Own capital
Risk Management strategies taken in consideration:
1. hail insurance I,
2. hail net 4 ha, and
3. Insurance (1ha) + hail net (4 ha)
26
Growth model
Scenario I growth rate
st.dev.
Scenario II growth rate
st.dev.
Scenario III growth rate
st.dev.
Naive
-4,74%
0,30%
-5,19%
0,39%
-0,08%
0,11%
Hail insurance
0,48%
1,47%
0,01%
0,21%
0,00%
0,32%
Hail insurance
(1ha)+ nets (4ha)
4,21%
0,18%
4,01%
0,13%
2,14%
0,05%
Hailnets4
2,98%
0,15%
2,52%
0,09%
1,01%
0,03%
Source: Simulation model (5000 iteration)
27
Growth Model- “Naive”
PDF
-50,00%
-40,00%
-30,00%
-20,00%
Scenario I
-10,00%
growth
Scenario II
0,00%
10,00%
20,00%
Scenario III
Source: Simulation model (5000 iteration), Simetar© graphical tools
28
Growth Model- hail insurance
PDF
-4,00% -3,00% -2,00% -1,00% 0,00%
1,00%
PDF
2,00%
3,00%
4,00%
5,00%
-0,02000%
Scenario I
-0,01000%
0,00000%
0,01000%
0,02000%
0,03000%
Scenario II
PDF
-0,002000%
-0,001500%
-0,001000%
-0,000500%
0,000000%
0,000500%
0,001000%
Scenario III
Source: Simulation model (5000 iteration), Simetar© graphical tools
29
Growth Model- hail insurance (1 ha) and
hail nets (4 ha)
PDF
-10,00%
-5,00%
0,00%
5,00%
10,00%
15,00%
20,00%
growth
Scenario I
Scenario II
Scenario III
Source: Simulation model (5000 iteration), Simetar© graphical tools
30
Growth Model- hail nets (4 ha)
PDF
-10,00%
-5,00%
0,00%
5,00%
10,00%
15,00%
growth
Scenario I
Scenario II
Scenario III
Source: Simulation model (5000 iteration), Simetar© graphical tools
31
Conclusions
•“Naïve” strategy, the most common, represents all its weakness.
•Insurance protects from shortfalls, stabilize financial indicators and
liquidity. Premium subsidy made a great influence on the results.
Higher subsides (>25%) enables greater coverage in terms of
insured sum and lower on no deductibles.
•Hail nets positively influences yields, ratio of extra quality fruits
and possibility for storing which has been only indirectly
considered.
•In combination with insurance, hail net is dominant strategy.
•The main problem with insurance is number of perils covered.
Perils, like frost usually remain uncovered. Active protection
against frost is possible however with additional investment and
possible higher exposure to financial risk.
32
Conclusions
• Sources of external financing and interest rates seem not
to be a problem at the moment. Nevertheless, access to
agricultural credits is still constrained.
• Hail insurance stabilizes income enabling positive
risk/return for borrowers and for lenders.
• In order to accelerate growth, higher leverage is
necessary. Results showed that efficient risk management
strategies (hail insurance and hail nets) enables higher
financial leverage without significant influence on farm
risk position.
• Outreach of agricultural credits is influenced by the
collateral that borrowers can offer. A deficiency in or an
absence of collateral influences use of own financial
sources
slowing
down
business
development,
specialization, modernization and rural development in all.
• Policy makers need to have it in mind in creation in
conduction EU pre-accession programs!
33
References
1.
2.
3.
4.
5.
6.
7.
8.
Barry P.J., Ellinger P.N., Hopkin J.A., Baker C.B.(2000): Financial
Management in Agriculture, Interstate Publishers, Inc., Danville, Illinois, USA
Hardaker, J.B., Huirne, R.B., Anderson, J.R., Lien, G. (2004): Coping with
risk in agriculture. CABI Publishing, London, UK.
Just, R.E. i Pope, R.D. (edit.) (2002): A Comprehensive Assessment of the
Role of Risk in U.S. Agriculture, Kluwer Academic Publishers, USA
Palisade Corporation (2004): Guide to Using @RISK, Risk Analysis and
Simulation Add-In for Microsoft® Excel, Version 4.5, USA
Rejda, G.E. (2005): Principles of Risk Management and Insurance, Addison
Wesley, London, UK
Republic of Croatia, Ministry of Agriculture, Forestry and Water Management
of the Republic of Croatia (2004). Operativni plan podizanja trajnih nasada.,
Zagreb, Croatia. http://www.mps.hr/pdf/publikacije/op_prog_trajni_nasadi.pdf
Richardson, J.W., Schumann, K., Feldman, P. (2004): Simetar©, simulation
& econometrics to analyze-Users manual. Simetar Inc. USA, p 46.
van Asseldonk, M.A.P.M., Meuwissen, M.P.M., Huirne, R.B.M.(2001.):
Stochastic Simulation of Catastrophic Hail and Windstorm Indemnities in the
Dutch Greenhouse Sector, Risk Analysis, Vol 21. no.4, p. 761-769
34
Correspondence address
Mario Njavro, PhD
Faculty of Agriculture University of Zageb
Svetosimunska c. 25
10000 Zagreb, Croatia
tel: +385 1 23 93 762
fax: +385 1 23 93 745
mnjavro@agr.hr
http://agririsk.agr.hr or
http://www.agr.hr/cro/curriculum/njavro_mario.htm
35