farmers responses to the changes in hungarian agricultural ... · 2012 2013 2014 wheat price...

Farmers Responses to the Changes in Hungarian Agricultural Insurance System

EAAE Seminar: Prospects for agricultural insurance in Europe

Wageningen, 3-4. October 2016.

Anna Zubor-Nemes, Gábor Kemény, József Fogarasi, András Molnár

Outline1. Grounds of high risk exposure in Hungary

2. Evolution of national (crop)risk management system

3. What factors influence insurance use?

4. Does insurance use effect efficiency?

Hungary – why so important to tackle risks?

• Lower prices

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Wheat price deviation(%) from EU average

(DEU) Germany (FRA) France (NED) Netherlands(OST) Austria (BEL) Belgium (ITA) Italy(HUN) Hungary

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Corn price deviation (%) from EU average

(DEU) Germany (FRA) France (NED) Netherlands

(OST) Austria (BEL) Belgium (ITA) Italy

(HUN) HungarySource: Eurostat

Hungary – why so important to tackle risks?

• Lower prices

• Greater crop yield variability

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Wheat yield deviation (%)s from MS's average



(HUN) Hungary

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Corn yield deviation(%) from MS's average



(HUN) HungarySource: Eurostat

Hungary – why so important to tackle risks?• Lower prices

• Greater crop yield variability

• Greater income variability

Source: EU FADN

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(HUN) Hungary

Fieldcrops

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(DEU) Germany (FRA) France (NED) Netherlands(OST) Austria (BEL) Belgium (ITA) Italy(HUN) Hungary

Granivores

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(HUN) Hungary

Dairy

Combined probability of a single adverse event over (a) the baseline, (b) GISS-RCP8.5 and (c)

HadGEM-RCP8.5 scenarios with the size of the circle corresponding to the relative change

compared to the baseline.

Miroslav Trnka et al. J. R. Soc. Interface 2015;12:20150721

Possible risk management tools

• Price risk –> intervention (ebolished)• August of 2006: half of the EU’s intervention stock (8.1 million tons) is comprised of

Hungarian grain.

• Price risk –> use of futures• But the country and its stock market is too small.

• Production risk –> insurance&mitigation fund• Possible to implement in a tailored way at MS level

• Income risk –> Income Stabilisation Tool• Promissing opportunity, but almost impossible to implement…

Evolution of risk management system in Hungary

• 1st stage[1997-2003]: 30 % flat rate subsidy to insurance premiums

• Problems: continuous mismatch between risks covered by insurances and damages caused by risks, only 30-40% penetration

Hail and storm21%

Frost (at winter and

spring)16%

Drought42%

Water (flood, inland

water and heavy rain)

18%

Other3%

Average proportion of damages caused by adverse climatic events

Hail87%

Frost5%

Storm5%

Other3%

Average proportion of risks covered by insurances

• 2nd stage[2007-2011]: National Damage Mitigation Fund against all risks

• Problems: low penetration, very high compensation claims vs. low rate of damage mitigation, high administration burden, reduced number of insured farmers

0%

20%

40%

60%

80%

100%

120%

0,0

20,0

40,0

60,0

80,0

100,0

120,0

140,0

160,0

2007 2008 2009 2010 2011

Claims and payments in the NMF

Mitigation payments Compensation claims Rate of mitigation (right axis)


• 3rd stage[since 2012]: Agricultural Risk Management System (ARMS)

• Two pillars: I. pillar – Compulsory National Damage Mitigation Fund

• mitigation over 15% loss of at farm level AND

• 30% loss of crop level by all climatic risks

II. pillar – Voluntary insurance schemes with subsidy

• compensation over 30%/50% loss of crop level by 8 risk types

• Common risk definitions, common reference crop yields

• Interdependencies – only 50% of mitigation payments without insurance, the compensation payment is deductable from mitigation payments


Structure of ARMS

EAFRD + national source: 65% subsidy&35% own

National Damage Mitigation Fund –farmers + state aid

(50% : 50%)

Farmers

Private insurancecompanies

Public damagerequierements

justification

A, B, C insurance typesregulated by the state(min. 55% : 40% : 30%

rate of subsidy)

Insurancepremium

Crop lossescaused byadverse c.

events

Compensationpayments

Mitigationpayments

Compensationclaims

I. pillar

II. pillar

Information about the insurance contract and the compensation payments

Covered risks in ARMS

Risks Hail, Storm, Fire Winter/spring frost DroughtHeavy rain, flood

Inlandwater

I. pillar >15% farm level, >30% crop level

II. pillar >30% crop level >50% crop level >50% crop level >40% crop level -

Private add. i.

>5% to <30% crop level - - - -

Insurance options in the II. pillarA insurance B insurances C insurances

Co

vere

dri

sks

Hail

Compulsory

Optional OptionalSorm Optional OptionalFire Optional Optional

Winter frost Optional OptionalSpring frost Optional

Drought OptionalHeavy rain Optional

Flood OptionalInland water

Sub

sid

yra

te

Maximum subsidy rate

65% 65% 65%

Minimum subsidy rate

55% 40% 30%

Pla

nts Insurable

plants

14 most important

plants (corn, wheat, apple,

etc.)

76 important plants (mainly

fruits and vegetables)

All plants

• Fixed sum of subsidies

• By case of over-requestreduction of subsidy rate• First by C from 65 to 30%

• Second by B from 65 to 40%

• Third by A from 65 to 55%

• Thereafter proportionalreduction from 55/40/30%

Operation of ARMS• Electronic notice of loss and

claim declaration for farmers ona web application using mappingsystem

• Claim adjustment support and substitution with meteorological, remote sensing and water management indices

• Site inspection verification by electronic and GIS technologies

• Electronic notice of official decisions about the damage mitigation and the insurance premium subsidy

Operation of ARMS

Main figures of the system:• 77 thousand farmers

• 11 insurance companies and mutual funds

• 19 regional governmentoffices

• 8 central offices and institutes

ARDA

Market insurance

MRD

RIAE IGCRS HMS GDWM

Regional government offices

Insurance contracts

Insurance claim reporting

Insurance compensation

Client registration

Damage notification

Damage claim notification

Insurance

contract

data

Insurance

compensation

data

Common Master Data

Management System

Customer

register

Register of

activitiy area

Farmers

InterfaceElectronic notice of loss and

subsidy claim declaration for

farmers

Interface

Statistical

and

reporting

system

Agricultural risk

management data

base

Control of

damage

determination

FADN data collection

Professional supervision

and coordination of damage

determination

Functional supervision and

coordination of damage

determination

Interface Interface Interface Interface Interface Interface

Improved FADN

(plot-level yield data

collection)

Agricultural Risk

Management

Systems

Analysis of profit, cost

and income dataRemote sensing

damage assessment

method

Operation of Remote

Sensing Damage

Assessment Ssystem

Satellite images

Remote sensing

damage assessment

software and data

processing

Basic meteorological data

services for the

determination of damage

Extended Meteorological

Data Collection SystemDamage determination workflow

supporting software

Meteorological data

processing method

Meteorological data

storage ICT

infrastructure

Operation of Damage Mitigation

Fund, management of

compensation contributions,

determination, payment and

accounting of compensation

or/and insurance subsidies

Improved

compensation and

subsidy management

system

Improved claim

management

system Determination of damage

Damage determination control

supporting ICT infrastructure

and software

Operation of the

Hungarian FADN System

Payment of

mitigation

contribution

Basic data services of

inland water for the

determination of damage

Extended Defence

Information System

Interface

General management of

Agricultural Risk Management

System (supervision, analysis

and planning)

Login to

Compensation

System (EK)

Determining the

amount of

compensation

contribution

Payments of

compensation

Interface

HCA NFCSO

Successes of ARMS

0

5

10

15

20

25

30

35

40

45

2007 2008 2009 2010 2011 2012 2013 2014 2015

mill

ion

EU

R

Change of agricultural insurance premium

Sum of insurance fees From this: subsidised

• Increasing number of insured farmers

• Efficienter administrationof a lot of new partners

• Precise control of compensation claims

• Complex databasesmaking possibiledeveloping new riskmanagement tools

0

1 000

2 000

3 000

4 000

5 000

6 000

7 000

8 000

0

5 000

10 000

15 000

20 000

25 000

2006-2010 avg. 2012 2013 2014

Num

ber

of

contr

acts

Insu

rance

pre

miu

m (

mil

lion H

UF

)

Subsidized: Number of contranct Without subsidy: Number of contranct

Without subsidy: Insurance premium (million HUF) Subsidized: Insurance premium (million HUF)

Future plans in ARMS

• Establishment of III. pillar – Income Stabilisation Tool• Target groups: dairy, pig and poultry farmers – they could not be members of

pillar I. and II.

• Reforming of „pillar IV.” – subsidised credits for damaged farmers• Connection of occurence of damages and crediting of farmers

• Establishment of „pillar 0.” – the hail protection system covering allthe country• Installation of ground generators

Influencing factors of insurance useDependent variable: „Insured”

Pooled probit Pooled logit Probit (RE) Logit (RE)

Age of manager 0,003

(0,001)

** 0,004

(0,002)

** 0,002

(0,002)

0,003

(0,004)

Training of manager 0,088

(0,032)

*** 0,150

(0,055)

*** 0,155

(0,053)

*** 0,262

(0,091)

***

UAA 0,001

(0,000)

*** 0,001

(0,000)

*** 0,002

(0,000)

*** 0,003

(0,000)

***

Concentration -0,384

(0,087)

*** -0,653

(0,150)

*** -0,461

(0,118)

*** -0,766

(-0,203)

***

Insurance last year 1,255

(0,029)

*** 2,063

(0,049)

***- -

Indebtness rate 0,508

(0,086)

*** 0,853

(0,146)

*** 0,608

(0,113)

*** 0,043

(0,043)

***

ROE 0,019

(0,022)

0,033

(0,037)

0,025

(0,025)

1,023

(0,193)

2007-2008 period 0,018

(0,044)

0,029

(0,075)

-0,074

(0,047)

-0,119

(-0,080)

2009-2011 period -0,053

(0,038)

-0,098

(0,065)

-0,098

(0,042)

** -0,172

(-0,071)

**

2012-2014 period 0,128

(0,038)

*** 0,214

(0,065)

*** 0,112

(0,044)

** 0,184

(0,076)

**

constant -0,974

(0,102)

*** -1,620

(0,176)

*** -1,461

(-0,118)

*** -1,290

(0,268)

***

rho 0,484

(0,016)

0,457

(0,017)

Log-likelihood -3002,55 -6588,12 -6588,24

Log pseudolikelihood-5291,62 -5290,87

Concentration:Calculated as the share of two major crops in the arable area.Factors: Managemet, production, financial status, last year insurance, stage of ARMSMain results:• Concentration turned out to be

negative• Farms the size (UAA) affects

positively the use of crop insurance in a robust way

• Indebtedness rate and the use of insurance are also positively correlated. This can be explained by the fact that in most loans requires the presence of insurance.

• Two-pillar ARMS started from 2012 led to positive significant effect on crop insurance use.

Efficiency analysis using DEA

0,0

0,1

0,2

0,3

0,4

0,5

0,6

0,7

0,8

0,9

1,0

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

Total technical efficiency (TTE) Pure technical efficiency (PTE) Scale efficiency (SE)

Notes:• Output oriented DEA• Bootstraping to control

overestimation• FADN data• Output variable: GVA• Input variables: UAA,

Labour, Capital, Inputs

Relationship between efficiency and insuranceTechnical efficiency

Pure technical

efficiencyScale efficiency

Age of manager0,0009

(0,0002)

*** 0,0010

(0,0002)

*** 0,0003

(0,0002)

**

Training of manager0,0128

(0,0044)

*** 0,0135

(0,0048)

*** 0,0030

(0,0039)

UAA 0,0000

(0,0000)

***

0,0002

(0,0000)

*** -0,0002

(0,0000)

***

Crop insurance

premium

0,0018

(0,0006)

**

* 0,0015

(0,0006)

**

0,0010

(0,0005)

*

Investment rate 0,0000

(0,0000)

***

0,0001

(0,0000)

*** -0,0000

(0,0000)

***

Indebtness rate 0,1369

(0,0092)

***

0,1697

(0,0098)

*** -0,0293

(0,0083)

***

_cons0,2918

(0,0101)

*** 0,3226

(0,0009)

*** 0,8797

(0,0090)

***

rho

0,3813

(0,0114)0,42954

(0,0017)

0,3812

(0,0124)

Log-likelihood 7400,70 6750,22 8444,81

• Results are in line with Latruffe et al. (2004)• Farms with large area (UAA) experience

smaller scale efficiency compared to the smaller ones. This coincide with Latruffe et al. (2012) results, who found that 55 percent of Hungarian crop farms could increase their efficiency by reducing their size. Moreover, the investment and indebtedness rates are also decrease the scale efficiency.

Thank you for your attention!

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