Farmers Responses to the Changes in Hungarian Agricultural Insurance System

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1 Farmers Responses to the Changes in Hungarian Agricultural Insurance System Anna Zubor-Nemes, Gábor Kemény, József Fogarasi, András Molnár EAAE Seminar: Prospects for agricultural insurance in Europe Wageningen, 3-4. October 2016.

2 Outline 1. 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?

3 Hungary why so important to tackle risks? 60 Wheat price deviation(%) from EU average Lower prices Corn price deviation (%) from EU average (DEU) Germany (FRA) France (NED) Netherlands (OST) Austria (BEL) Belgium (ITA) Italy (HUN) Hungary Source: Eurostat (DEU) Germany (FRA) France (NED) Netherlands (OST) Austria (BEL) Belgium (ITA) Italy (HUN) Hungary

4 Hungary why so important to tackle risks? Wheat yield deviation (%)s from MS's average Lower prices Greater crop yield variability Corn yield deviation(%) from MS's average (DEU) Germany (FRA) France (NED) Netherlands (OST) Austria (BEL) Belgium (ITA) Italy (HUN) Hungary Source: Eurostat (DEU) Germany (FRA) France (NED) Netherlands (OST) Austria (BEL) Belgium (ITA) Italy (HUN) Hungary

5 Hungary why so important to tackle risks? Fieldcrops (DEU) Germany (FRA) France (NED) Netherlands (OST) Austria (BEL) Belgium (ITA) Italy -60 (HUN) Hungary Granivores (DEU) Germany (FRA) France (NED) Netherlands (OST) Austria (BEL) Belgium (ITA) Italy (HUN) Hungary Lower prices Greater crop yield variability Greater income variability Dairy Source: EU FADN (DEU) Germany (FRA) France (NED) Netherlands (OST) Austria (BEL) Belgium (ITA) Italy (HUN) Hungary

6 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:

7 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

8 Evolution of risk management system in Hungary 1 st stage[ ]: 30 % flat rate subsidy to insurance premiums Problems: continuous mismatch between risks covered by insurances and damages caused by risks, only 30-40% penetration Water (flood, inland water and heavy rain) 18% Average proportion of damages caused by adverse climatic events Other 3% Hail and storm 21% Average proportion of risks covered by insurances Storm Other Frost 5% 3% 5% Frost (at winter and spring) 16% Drought 42% Hail 87%

9 Evolution of risk management system in Hungary 2 nd stage[ ]: 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 160,0 140,0 120,0 100,0 80,0 60,0 40,0 20,0 0,0 Claims and payments in the NMF Mitigation payments Compensation claims Rate of mitigation (right axis) 120% 100% 80% 60% 40% 20% 0%

10 Evolution of risk management system in Hungary 3 rd 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

11 Structure of ARMS Mitigation payments I. pillar II. pillar A, B, C insurance types regulated by the state (min. 55% : 40% : 30% rate of subsidy) Private insurance companies EAFRD + national source: 65% subsidy&35% own Insurance premium Compensation payments Farmers Compensation claims National Damage Mitigation Fund farmers + state aid (50% : 50%) Public damage requierements justification Crop losses caused by adverse c. events Information about the insurance contract and the compensation payments

12 Covered risks in ARMS Risks Hail, Storm, Fire Winter/spring frost Drought I. pillar >15% farm level, >30% crop level Heavy rain, flood Inland water II. pillar >30% crop level >50% crop level >50% crop level >40% crop level - Private add. i. >5% to <30% crop level

13 Plants Subsidy rate Covered risks Insurance options in the II. pillar A insurance B insurances C insurances Hail Optional Optional Sorm Optional Optional Fire Optional Optional Winter frost Optional Optional Compulsory Spring frost Optional Drought Optional Heavy rain Optional Flood Optional Inland water Maximum subsidy rate 65% 65% 65% Minimum subsidy rate 55% 40% 30% 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-request reduction 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 proportional reduction from 55/40/30%

14 Operation of ARMS Electronic notice of loss and claim declaration for farmers on a web application using mapping system 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

15 Operation of ARMS Insurance contracts Login to Compensation System (EK) Payment of mitigation contribution Determining the amount of compensation contribution ARDA Farmers Payments of compensation 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 Control of damage determination Insurance claim reporting Insurance compensation Client registration Customer register Common Master Data Management System Functional supervision and coordination of damage determination Register of activitiy area FADN data collection Regional government offices Damage claim notification Damage notification Professional supervision and coordination of damage determination Electronic notice of loss and subsidy claim declaration for farmers Market insurance Insurance contract data Interface Insurance compensation data Interface Agricultural risk management data base Statistical and reporting system Interface Interface Interface Interface Interface Interface HCA Damage determination workflow supporting software Determination of damage Damage determination control supporting ICT infrastructure and software Interface NFCSO Interface MRD RIAE IGCRS HMS GDWM Operation of the Hungarian FADN System Analysis of profit, cost and income data Agricultural Risk Management Systems Improved FADN (plot-level yield data collection) Operation of Remote Sensing Damage Assessment Ssystem Remote sensing damage assessment method Satellite images Remote sensing damage assessment software and data processing Basic meteorological data services for the determination of damage Extended Meteorological Data Collection System Meteorological data processing method Meteorological data storage ICT infrastructure General management of Agricultural Risk Management System (supervision, analysis and planning) Basic data services of inland water for the determination of damage Extended Defence Information System Main figures of the system: 77 thousand farmers 11 insurance companies and mutual funds 19 regional government offices 8 central offices and institutes

16 million EUR Successes of ARMS Change of agricultural insurance premium Sum of insurance fees From this: subsidised Increasing number of insured farmers Efficienter administration of a lot of new partners Precise control of compensation claims Complex databases making possibile developing new risk management tools

17 Insurance premium (million HUF) Number of contracts avg Subsidized: Number of contranct Without subsidy: Insurance premium (million HUF) Without subsidy: Number of contranct Subsidized: Insurance premium (million HUF)

18 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 all the country Installation of ground generators

19 Influencing factors of insurance use Dependent variable: Insured Age of manager 0,003 (0,001) Training of manager 0,088 (0,032) UAA 0,001 (0,000) Concentration -0,384 (0,087) Insurance last year 1,255 (0,029) Indebtness rate 0,508 (0,086) ROE 0,019 (0,022) period 0,018 (0,044) period -0,053 (0,038) period 0,128 (0,038) constant -0,974 (0,102) Pooled probit Pooled logit Probit (RE) Logit (RE) ** 0,004 (0,002) 0,150 (0,055) 0,001 (0,000) -0,653 (0,150) 2,063 (0,049) 0,853 (0,146) 0,033 (0,037) 0,029 (0,075) -0,098 (0,065) 0,214 (0,065) -1,620 (0,176) ** 0,002 (0,002) 0,003 (0,004) 0,155 0,262 (0,053) (0,091) 0,002 0,003 (0,000) (0,000) -0,461-0,766 (0,118) (-0,203) - - 0,608 (0,113) 0,025 (0,025) -0,074 (0,047) -0,098 (0,042) 0,112 (0,044) -1,461 (-0,118) rho 0,484 (0,016) 0,043 (0,043) 1,023 (0,193) -0,119 (-0,080) ** -0,172 (-0,071) ** 0,184 (0,076) -1,290 (0,268) 0,457 (0,017) Log-likelihood -3002, , ,24 Log pseudolikelihood -5291, ,87 ** ** Concentration: Calculated as the share of two major crops in the arable area. Factors: Managemet, production, financial status, last year insurance, stage of ARMS Main 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.

20 Efficiency analysis using DEA 1,0 0,9 0,8 0,7 0,6 0,5 0,4 0,3 0,2 0,1 0, Notes: Output oriented DEA Bootstraping to control overestimation FADN data Output variable: GVA Input variables: UAA, Labour, Capital, Inputs Total technical efficiency (TTE) Pure technical efficiency (PTE) Scale efficiency (SE)

21 Relationship between efficiency and insurance Age of manager Training of manager UAA 0,0000 (0,0000) Crop insurance premium Technical efficiency Pure technical efficiency Scale efficiency 0,0009 0,0010 0,0003 ** (0,0002) (0,0002) (0,0002) 0,0128 0,0135 0,0030 (0,0044) (0,0048) (0,0039) 0,0018 (0,0006) Investment rate 0,0000 (0,0000) Indebtness rate 0,1369 _cons rho (0,0092) 0,2918 (0,0101) 0,3813 (0,0114) 0,0002 (0,0000) ** * 0,0015 (0,0006) 0,0001 (0,0000) 0,1697 (0,0098) 0,3226 (0,0009) 0,42954 (0,0017) ** -0,0002 (0,0000) 0,0010 (0,0005) -0,0000 (0,0000) -0,0293 (0,0083) 0,8797 (0,0090) 0,3812 (0,0124) * 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. Log-likelihood 7400, , ,81

22 Thank you for your attention!

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