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DISEI - Università degli Studi di Firenze

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The findings, interpretations, and conclusions expressed in the working paper series are thoseof the authors alone. They do not represent the view of Dipartimento di Scienze perl’Economia e l’Impresa, Università degli Studi di Firenze

AN ASSESSMENT OF AGRO-FOOD FRAUDS IN

THE ITALIAN ECONOMY: A SAM-BASED APPROACH

B. Rocchi, D. Romano, A. Sadiddin, G. Stefani

Working Paper N. 23/2018

http://www.disei.unifi.it/

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BenedettoRocchia,DonatoRomanoa,AhmadSadiddina,GianlucaStefania,

AnAssessmentofAgro-foodFraudsintheItalianEconomy:ASAM-based

Approach

aDipartimentodiScienzeperl’Economiael’Impresa(DISEI),UniversitàdegliStudidiFirenze(UNIFI)

September,2018

Thisreportistheresultsoftheresearchactivitiesundertheresearchproject:AnalysisofFraudRisksintheAgro-FoodSector.

The findings, interpretations, and conclusions expressed in the working paper series are those of theauthors alone. They do not represent the view of Dipartimento di Scienze per l’Economia e l’Impresa,UniversitàdegliStudidiFirenze.

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Abstract

Buildingon the Italian input-output table,andusing structural informationonagro-foodproductionsub-sectors from several sources, a Social AccountingMatrix (SAM)model for the Italian economyhas beendeveloped, disaggregating the agro-food sector into agricultural production activities and ten differentagro-foodvaluechains (VCs).Then,usingprimarydatabytheCentral Inspectorate forQualityProtectionand Fraud Repression in Agro-Food Products (Ispettorato Centrale per la Qualità e la Repressioni delleFrodi,ICQRF)oftheItalianMinistryofAgriculture,weassessedtheeconomicsizeoffraudulentagro-foodoutput,estimatedthesizeoftheeconomydependingonfraudulentproduction,andsimulatedtheimpactsofagro-foodfraudsonthenationaleconomyintermsofGDP,employmentandincomedistribution.

Theanalysisshowsthatthewinevaluechainisthesub-sectormostexposedtofraudsaccountingfor88%of the total value of seized agro-food outputs. Second ranks olive oil value chain (6% of total seizures),whiletheotherVCsaccountsforonlytheremaining6%oftotalseizures.Theshareschangeslightlywhenthevaluesofirregularproductswereexpandedtothepopulationlevels.

TheresultsoftheSAMsimulationsshowsthattheshareofeconomydirectlyandindirectlylinkedtosupplyof irregular foodproducts accounts for 0.5%of total valueof output,while in termsof value added theshareofirregularfoodproductsrangesbetween0.1%and0.4%oftotalvalueadded.Thiscorrespondstoavalueof1.9billioneuro (consideringonly seizures) to13.9billioneuro (includingall irregularities)and isabletoactivateaupto156thousandlabourunitsintheworst-casescenario.Intermsofthesharerelativetotheagro-foodsector,thetotaloutput"driven"byirregularproductsismuchhigheraccountingfor3.2%ofoutputand5.8%ofemployment.

Resultsfromthecounterfactualanalysisshowsthatagro-foodfraudscausedalossesof1.8billioneurointermsof totaloutput, corresponding toabout20 thousandsof full time labourunits. Thenet impactonGDP ispositive thoughverysmall since theearnings feed rent-seekingactivities insteadof strengtheninglinkages with the rest of the economy. Household incomes are reduced by only 0.01%. However,considering that consumers build their own perceptions on the basis of a mix of quality and healthconsiderations,thepotentiallossesincasesoffoodscandalswouldbemuchmoretangible.

Theseresultsshowthatfightingagro-foodfraudsisjustifiedonefficiencyaswellasequityground.

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Acknowledgements

ThisworkingpaperisthesecondofaseriesoffourstemmingoutfromajointresearchprojectbetweentheDepartmentofEconomicsandManagement(DISEI)oftheUniversityofFlorence(UNIFI)andtheCouncilforAgricultural Research and Analysis of Agricultural Economics (CREA) on “Risk Analysis and Evaluation ofFraudsintheAgrofoodSystem”.

Theoverallobjectiveoftheprojectistoanalysetheagro-foodfraudevolutionoverthelastdecadeandtoassess its role in the Italian economy. In addition to this paper, readers are referred to the followingworkingpapersproducedunderthesameproject:Sadiddinetal.(2018a),Rocchietal.(2018),andSadiddinetal.(2018b).

The authors would like to thank the staff and management of the Central Inspectorate for QualityProtectionandFraudRepressioninAgro-FoodProducts(IspettoratoCentraleperlaQualitàelaRepressionidelle Frodi, ICQRF), namely Dr. Stefano Vaccari (ICQRF Director), Dr. Luca Veglia (Head of Risk AnalysisOffice),andDr.PlacidoIudicello(DataManager),forprovidingprimarydataonseizuresandirregularitiesaswellasprovidingfeedbacksonpreliminaryresults.

ImportantcontributionswerealsoprovidedbyDr.AmedeoDeFranceschi,HeadoftheAgro-foodUnitofthe Italian Forest Service (Nucleo Agroalimentare e Forestale del Corpo Forestale dello Stato), providingdataanddiscussingthemostimportantfraudmodalitiesinthewine,oliveoilanddairyvaluechains.

The authors would like to thank also Dr. Pietro Sandali, Director of the Italian Consortium of OliveProducers(ConsorzioOlivicoloItaliano,UNAPROL)forfruitfuldiscussionsonthefraudsintheoliveoilvaluechain, andDr. FrancescoDe Filippis andDr.DarioParenti forproviding the technical coefficientsof thewinevaluechain.

SpecialthanksalsototheInstituteofInternationalEducation/ScholarRescueFund(IIE-SRF)foritsfinancialsupport toDr. Ahmad Sadiddin, a Syrian refugee scholar:without the IIE-SRF support the Projectwouldnevermaterialize.

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Acronyms

COICOP Classificationofindividualconsumptionbypurpose(Classificazionedeiconsumeindividualiperfinalità)

CPA StatisticalClassificationofProductsbyActivity(Classificazionedeiprodottiperattività)

CREA CouncilforAgriculturalResearchandAgriculturalEconomicsAnalysis(Consiglioperlaricercainagricolturaeanalisidell’economiaagraria)

DISEI DepartmentofEconomicsandManagement(DipartimentodiScienzeperl’Economiael’Impresa)

FADN FarmAccountancyDataNetwork

ICQRF CentralInspectorateforQualityProtectionandFraudRepressioninAgro-FoodProducts(Ispettoratocentraledellatuteladellaqualitàedellarepressionefrodideiprodottiagroalimentari)

IRVAM InstituteforResearchandInformationfortheEvaluationofAgriculturalProduction(IstitutoperleRicercheeleInformazionidiMercatoeperlaValorizzazionedellaProduzioneAgricola)

ISMEA InstituteofServicesforAgriculturalFoodMarket(IstitutodiServiziperilMercatoAgricoloAlimentare)

ISTAT CentralInstituteofStatistics(IstitutoCentralediStatistica)

MIPAAF ItalianMinistryofAgriculture(Ministerodellepoliticheagricole,alimentarieforestali)

NACE NomenclaturegénéraledesActivitéséconomiquesdanslesCommunautésEuropéennes

SAM SocialAccountingMatrix

VC Valuechain

UNIFI UniversityofFlorence(UniversitàdegliStudidiFirenze)

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Tableofcontents

ABSTRACT.................................................................................................................................................II

ACKNOWLEDGEMENTS............................................................................................................................III

ACRONYMS.............................................................................................................................................IV

TABLEOFCONTENTS................................................................................................................................V

1.INTRODUCTION....................................................................................................................................2

2.LITERATUREREVIEW.............................................................................................................................3

3.IRREGULARFOODPRODUCTS:ESTIMATIONOFTHEIRVALUESBYVC...................................................53.1.THEROLEOFVARIOUSAGENCIESINTHEREPRESSIONOFAGRO-FOODFRAUDS......................................................53.2.ASSESSMENTOFTHEVALUEOFSEIZEDANDIRREGULARPRODUCTS:SAMPLEVS.POPULATION..................................6

3.2.1.OliveoilVC......................................................................................................................................73.2.2.WineVC..........................................................................................................................................83.2.3.OtherVCs......................................................................................................................................10

3.3.FROMSAMPLE-BASEDESTIMATIONSTOTHEPOPULATION................................................................................11

4.THEMODELUSEDTOASSESSTHEIMPACTOFAGRO-FOODFRAUDS...................................................134.1.ANINTRODUCTIONTOSOCIALACCOUNTINGMATRIX(SAM)..........................................................................134.2.SIMULATIONWITHINASAMFRAMEWORK...................................................................................................144.3.THESAMOFTHEITALIANECONOMYFORTHEANALYSISOFTHEAGRO-FOODSYSTEM..........................................16

4.3.1.Introduction..................................................................................................................................164.3.2.DisaggregationoftheFoodIndustryintheISTAT2009supply-usetable....................................164.3.3.DisaggregationofAgricultureintheISTAT2009supply-usetable..............................................184.3.4.InclusionofthedisaggregatedsupplyandusetableintheSAMofItalianeconomy..................20

5.THEECONOMICVALUEOFAGRO-FOODFRAUDS................................................................................215.1.THEOVERALLSHAREOFNON-AGRO-FOODFRAUDS........................................................................................215.2.THEIMPACTOFAGRO-FOODFRAUDS:ACOUNTERFACTUALANALYSIS................................................................22

6.CONCLUSIONS....................................................................................................................................26

REFERENCES............................................................................................................................................28

ANNEXES...................................................................................................................................................1

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1.Introduction

Foodfraudhasbeenconductedsincetheoldages,andevidencehasbeenfoundinthepast literatureofmost civilizations.However, the scale of food frauds has been rapidly growingmainly due to the rise ofmodernandcomplicatedfoodsupplychainsthathaveexpandingthescaleofthephenomenonmakingitsimpacts on the economy and society so important tomeasure. Food fraudsters do not complywith therulesregardingmanyaspectsofproductionandmarketing.Thismakesthemasourceofunfaircompetitiontotheregularproducers.Forexample,theydonotpaytaxes,theymayuseinformallabour,theymaynotcomplywith product safety regulations and theymay violate intellectual property rights, thus feeding alongchainofillegalactivities.

Ofcourse,notallfoodfraudstersgothroughthewholechainofillegalactivities;rather,theyareinvolvedat different degrees in such activities. But organised crime benefits from this chain by making use ofcommercechannelsopenedupbytrafficking,sotheycantakeadvantageofequallyprofitablebutfarlessriskyactivities suchas tradeofcounterfeitgoods.The flourishingof this trade ismadepossibledue toasignificant demand created by self-interested consumers who think they are getting a deal in buyingcounterfeitproductsthataremuchcheaperthangenuineproducts.Whenitcomestoagro-foodproducts,the complexity and the high levels of sophistication ofmany agro-food production processes thatmakemore difficult the chance to spot counterfeiting activities exacerbate the problem. For example, Censis(2012)estimatesthattheagro-foodsectorranksasthirdamongsectorsmostaffectedbycounterfeitinginItaly.

ConsiderableattentionhasbeenrecentlygiventothisphenomenonatthenationalaswellasEUlevels.InItaly,thereareindeedmanybodiesandagenciesinvolvedintheseactivitiesandeachonehasbuiltitsowndata management system. Among those bodies, the Ispettorato Centrale per la Tutela della Qualità eRepressioneFrodi (ICQRF) is theonly Italian inspectionbodythat isspecialized in therepressionofagro-food frauds and inmonitoring of relevant regulatory interventions. The ICQRF has a systematic controlsystemspanningacross thewholecountry,with inspectionactivitiesperformed inallvaluechainsof theagro-foodsectorfromagriculturalproductionthroughprocessinganddistributiontoretailing.

Thedataof ICQRFmonitoring activitieswith all relevant information represents thebaseof informationthathasbeenusedtopresentanexploratoryanalysisoffraudsandcounterfeitingintheagro-foodsector,describinghowitdevelopedovertimeacrossmajoragro-foodsubsectors(cf.Sadiddinetal.,2018a).Thesame database is used in this paper to assess the economic size of agro-food frauds and simulate theimpacts of agro-food frauds on the national economy in terms of GDP, employment and incomedistributionusingaSAMmodelapproach.

Given theabove, the report isorganizedas follows.After reviewing the scantyexisting literatureon theimpacts of fraud in section 2, in section 3we provide an estimation of the value of irregular agro-foodproducts.Then,section4summarizestheSAMmodelusedintheassessmentofagro-foodfraudsimpacts,whilesection5isdevotedtothediscussionofsimulationresults.Thereportendswithaconcludingsectionsummarisingthemajorfindingsandtheirimplicationsforpolicymakersandpublicmanagers.

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2.LiteratureReview

Censis (2012) is the only source available that measured the impact of counterfeiting on the Italianeconomy1. Itprovidesanalysisofthe legalcontextandthe impacts ineconomicandfiscaltermsonGDP,employment, and government finances as a result of lost direct and indirect tax revenues. Besides theaggregateimpactofthephenomenon,thestudyfocusesonanumberofindustriesparticularlyaffectedbycounterfeitingsuchas leathergoods,cosmetics,anddesign,aswellasanexplorativeanalysisofdemandfactorsinsomeofthemostimportantcities.

Thestudystartswithachapteronthelegalandinstitutionalcontextsbyprovidingalistofthepublicbodiesandagenciesinvolvedinthefightagainstcounterfeitingwithsummaryoftheirdutiesandresponsibilities.The chapter also includesa reviewofnational legislationsagainst counterfeitingand the recent changesintroducedbyLawno.99/2009(knownastheDevelopmentLaw).Thenthereportpresentsthedefinitionsof several critical concepts, namely: intellectual property rights, patents for industrial inventions, utilitymodels,trademarks,designs,geographicalindications,counterfeiting,copyright,andpiracy.

Whenitcomestocounterfeiting,Censis(2012)adoptsthedefinitionoftheECRegulationno.1383of22July2003thatidentifiesascounterfeiting:

- goods,includingpackaging,bearingwithoutauthorisationatrademarkidenticaltothetrademarkvalidlyregisteredinrespectofthesametypeofgoods,orwhichcannotbedistinguishedinitsessentialaspectsfromsuchatrademark,andwhichtherebyinfringesthetrademark-holder’srights;

- anytrademarksymbol(includingalogo,label,brochure,etc.),evenifpresentedseparately,ifitisfoundinthesamesituationasdescribedabove;

- packagingmaterialsbearingthetrademarksofcounterfeitgoods,evenifpresentedseparately,foundinthesamesituationasdescribedabove.

Twosectionsofthechapterarethendevotedtothe innovations introducedbytheDevelopmentLawoncounterfeiting.ThefirstexplainsthechangestheLawmaderegardingthesanctionsandpenaltiesforbroadcategories of counterfeiting. The second explains the contribution of Directorate-General for the FightAgainst Counterfeiting, which has inherited the functions formerly performed by the Italian Patent andTrademarkOfficeandtheHighCommissiontoCombatCounterfeiting.

The last sectionof the chapter presents an explorative analysis of somedata on seizures available fromdifferent sources, namely: Tax Police (Guardia di Finanza), Customs Agency (Agenzia delle Dogane),National Police (Polizia di Stato), Local Polices (PolizieMunicipali), and activities ofCarabinieri.Here it isstated that data on seizures, although provide a proxy measurement of the phenomenon, should becarefullyanalysedastheycouldhelplearnandmonitorqualitativeandquantitativetrendsoncounterfeitgoods,theiroriginandtheroutestheyfollowandtheirfinaldestination.Forthispurpose,ItalythroughitsMinistryofEconomicDevelopmentismakingeffortstoharmonisethedatacomingfromvarioussourcesonseizures throughsettingup the IPERICO (IntellectualProperty-ElaboratedReportof the InvestigationandCounterfeiting)database.

Uptonow,however,harmonisationhasbeencompletedonlyfordatacomingfromtheTaxPoliceandtheCustoms Agency, while a number of inconsistencies within the data coming from Carabinieri, NationalPolice, and Local Polices prevented their integration into the other forces’ databases yet. Theseinconsistencies mainly concern the lack of distinction between seizures carried out for counterfeitingoffenses and those carried out for copyright violation offenses, which are aggregated at the relevantsource.Buttheyalsoconcernthelackofdataonthenumberofseizuresinrelationtothenumberofitemsseized(Riccioetal.,2014).

1Censis(2012)isanextensionofapreviousstudypublishedin2009,whichprovidedonlyanaggregateestimateofcounterfeiting,basedonamethodologydifferentfromtheoneusedinCensis(2012).

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The second chapter is devoted to themeasurement of economic and fiscal impact of counterfeiting onsomeItalianeconomicandfiscalindicators.ThescopeoftheanalysisislimitedtothetotalamountspentbyItalianconsumersonpurchasingcounterfeitgoodsandsoitexcludestheItaliancounterfeitgoodssoldabroadwhileitincludesproductsmanufacturedabroadbutsoldinItaly.Thestudyestimatestheeconomicimpactof counterfeitingusingan Input-OutputTable for the Italianeconomywhile relyingon secondarydata to estimate themarket value of counterfeit products. The lattermakes use of some sophisticatedstatistical techniques that exploits the relationships between the value of counterfeiting and someeconomicindicators(GDPandconsumption)atthelevelofOECDcountriesandthenapplyittotheItaliancontext,specificallyfocusingonthesectorsassumedtobemostlyaffectedbycounterfeiting.

Censis(2012)estimatestotalrevenuefromcounterfeitgoodstobeequalto7.1billioneurosin2008.Thisestimatefelltoabout6.9billioneuros inyear2010duetotherecession.This lastfigurewhencarriedtotheofficialmarket,meaningthatiflegitimateproductsoftheequivalentvaluehadbeensoldoverthesameperiod,13.7billioneurosofadditionaloutputwouldhavebeenproduced,5.5billioneurosofvalueaddedwouldhavebeengeneratedand110,000full-time jobswouldhavebeenguaranteed. In fiscal terms, thiswouldhavebroughtadditionalrevenuetothegovernmentthroughdirectandindirecttaxesequalto4.62billioneuros,whichisabout1.74%ofallgovernmenttaxrevenue2.Referringtothefoodanddrinkssector,theresultsshowthat it is thethirdtopsector intermsof impactofcounterfeitingaftertheaudio-visual,CDs,andDVDssectorandtheclothingandaccessoriessector.

The thirdchapterof thestudy isdevotedto theanalysisof threemainsectors,whichare leathergoods,cosmetics,anddesign. Inorder toperformtheanalysis,amixofmethodologieswasemployed includingbothofficeandfieldwork.Theanalysismadeuseofavailabledataontrendsinthesectorandtheseizuresmadebyvariousbodiesaswellassecondarydata/informationfrompreviousstudiesoncounterfeitingbyorganizations in the field or consumer associations. The analysis also relied, for the fieldwork, on semi-structuredopen-endedinterviewswithexpertsintherelevantsectors.Thelastchapterfocusesondemandanalysisbyuseoffocusgroupofspecialiststhroughdiscussions.

2Thefiscal impact isbasedfirstoncalculatingataxbaseforeach individual incomecategoryusingthedata fromI-OTableandfromothersource(Censis,2012,p.74,Table1,p.76.Table2).Thestudydemonstratesthatthetotalexpenditureoncounterfeitgoodsgeneratedalmost1.7billioneurosoflostrevenuetothegovernment37.3%ofwhichderivedfromdirecttaxationandtheremainderfromindirecttaxes.Inaddition,ifinducedproductioninothersectorsoftheeconomyisconsidered,almost3additionalbillioneurosarelostreachingatotalofalmost4.62billioneurosoflostgovernmentrevenue.Concerningfoodanddrinksector,itisplacedthirdinimportanceasthecasefortheeconomicimpact.

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3.Irregularfoodproducts:estimationoftheirvaluesbyVC

3.1. TheRoleofvariousagenciesintherepressionofagro-foodfraudsOther than the ICQRF, the following bodies are involved in the repression of agro-food frauds andcounterfeiting:CustomAgency(Agenziadelledogane),TaxPolice(GuardiadiFinanzia),Anti-Sophisticationand Health Units Carabinieri (NAS), Command of Agricultural and Food Policies Carabinieri (NAC), StateForestryBody(CFS), inadditiontoregularCarabinieri,nationalandlocalpolicebodies.Inthissection,wedescribetheavailabledata,bysourceandsector,andexplainhowweusedthemtoestimatethevaluesofirregularproductsatthenationallevelintheagro-foodVCs.

To the best of our knowledge, only the ICQRF performs systematic inspections based on representativesamples,while the inspection activities of the other institutions aremostlyad hoc andmainly basedoninformationonpotentialviolationsthatreachtherelevant institutions.Unfortunately,wecouldnothaveaccess to the detailed datasets of agencies other than that of the ICQRF. Therefore, we ought to relyexclusively on what is published in the IPERICO3 report on agro-food frauds (Riccio et al., 2014), whichprovidesanoverviewoftheirinspectionactivitieswithsummarytablesoftheirinspectionoutcomesofallinvolvedbodies.OnelimitationofIPERICO’sdataisthat,differentlyfromICQRF(2016),theyarefocusonlyon seizureswith no details on inspections samples and other types of irregularities that do not lead toseizures,whichaccordingtoICQRF(2016)representthevastmajorityofirregularities.

There is no clear-cutting allocation of responsibilities among the inspection agencies/bodies includingICQRF,exceptfortheCustomAgency,whichistheonlyonehavingthemandatetoactattheborders.Theotherbodiescaninspecttofightfraudsonthewholenationalterritoryaccordingtotheircapacities.Tothebestofourknowledge,thereisnostrictcoordinationintermsofinspectionplanningandsampling,whichleaves room foroverlappingof inspectiondomains.Moreover,examining thepublisheddataon seizuresreveals that not all of them have conducted seizures in all agro-food sub-sectors. For example, theCarabinieri,NationalandLocalPolicebodiesdidnotseizeoliveoilorwineover theperiodof2010-2012despitethehighimportanceofthesetwoVCs.

AsshowninTable3.1,theICQRF’sdatasetincludesalldetailsoninspections,reportsandtheirresultsandconsequences,whicharewellexplainedinSadiddinetal.(2018a).Variousunitsofmeasurementsareusedin this dataset according to the typeofdata. For example, inspections and irregularities areprovidedasnumbersforproductsandestablishments.However,seizures,whichformasub-setofirregularities,areinaddition expressed in terms of quantities and values,while quantities aremeasured by various units asweight(e.g.kg)orassize(e.g.litre).

Table 3.1. Avai lable data by source, year, type, detai l , method and unit of measurement and level of d isaggregation

Years ICQRF CustomAgency TaxPolice NAS

NAC,Carabinieri,National&LocalPolice

CFS

2007 X 2008 X X

2009 X X X

X

2010 X X X X X X

2011 X X X X X X

2012 X X X X X X

3 IPERICOstands for IntellectualProperty-ElaboratedReportof the InvestigationonCounterfeitingand isadatabasethatcollectandharmonizedataonfraudsfromallagencies/bodiesinvolvedinfraudsrepression.

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2013 X 2014 X 2015 X Levelofdetails Fulldatabase Seizures Seizures Seizures Seizures Fines

Methodofmeasurement Q&V Q Q Q&V Q V

Unitofmeasurement Litre&Euro kg kg kg&euro kg number

DisaggregationbyVC Yes Yes Yes Yes Yes No

Source:Authors’elaborationonICQRF(2016)andRiccioetal.(2014).

Datafromothersources/bodiesof inspectionsareallsummaries(Table3.1),buttheyalsodiffer inotherdetailsfromtheICQRF’sdata,andamongeachother.Forexample,datafromCustomAgency,TaxPolice,NAS,NAC,Carabinieri,NationalPoliceandLocalPoliceareprincipallyseizuresthataredisaggregatedbyVC,fromwhich,however,onlythoseofNASincludequantitiesandvalueswhiletheseizuresoftheothersareexpressed only in terms of quantities. In addition, Table 3.1 shows that the temporal coverage of theavailabledatadifferfromsourcetosource.WhilewecouldobtaindatafromtheICQRFtocovernineyears(2007-2015), the available data from Custom Agency, Tax Police and NAC covers 5, 4 and 3 yearsrespectively. In addition, the ICQRF system of records register in addition to seizures (in terms of bothquantities and values) the irregularities that do not lead to seizures, but are, however, frauds and theirimputedvaluesmustbeaccountedforwhenestimatingtheoverallvaluesofirregularproducts.

Moreover, the ICQRF (2016) includes information on the number of inspected establishments, which isnecessarytoexpandthevaluesofanysampletothepopulationlevelwhenjoinedwiththetotalnumberofactive establishments. Given the above, and accepting that the ICQRG inspections are based onrepresentativesamplescoveringtheentirenationaldomain,wecanignorethedataonseizuresconductedbytheotheragenciessincetheycanbeconsideredaspotentiallycoveredbytheICQRFinspections.Thisisavalidpropositionbecauseweareinterestednotonlyinestimatingtheactualvaluesofseizures/irregularproductsthatareactuallydetected,butalsoinassessingtheirvaluesatthepopulationlevel.Thelattercanbeperformedonresultsof the ICQRFdatausingapropermeasureofexpansionto thepopulation level,whileacceptingthattheICQRFdataarebasedonrepresentativesamples.

Since agro-food VCs differ in their technical and economic characteristics including those of theirmajorproducts, and given the time and resources available to this project, we follow an ad hoc plan for theestimationofthevaluesofirregularproducts.Theplanisbasedonconcentratingtheeffortsonoliveandwine VCs to achieve a reasonable level of precision in the calculations taking into account thetransformationsofvaluesalongeachofthetwoVCs.Thechoiceofthesetwochainsisdictatedbythefactthat theymake thebulkofagro-food irregularities (Sadiddinetal., 2018a) inaddition tobeing relativelysimplerthantheotherVCsaseachofthetwoincludesonlyalimitednumberofsimilarproducts.Thewaywe deal with the other VCs implies some simplifications that are driven by their high complexitiescomparedtothecasesofwineandoliveoil,whiletheyarenoticeablylessimportant.

3.2. Assessmentofthevalueofseizedandirregularproducts:samplevs.populationIn order to estimate the value of seized and irregular products, several data manipulations have beenundertakenonICQRF’sdataset,butcomplementedbysecondarydatafromliteratureandexpertsinsomecases. In this section, we describe the operations conducted in order to estimate the values of seizedproductsevaluatedattheconsumerlevel.Thelattermeansthatquantitiesofseizedproductsusedintheevaluation reflect quantities of final products, implying that all seized intermediary products should betransformedintotheirfinalproductsequivalents. Inthesametoken,pricesshouldbethoseprevailingattheconsumerlevelforeachfinalproduct.

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3.2.1. OliveoilVC

To obtain the technical coefficients of transformation of olive oil VC, we rely on ISMEA (2014), whichprovidesadetaileddescriptionof theoliveoilVC, fromwhichweadopt thenumbersonsupplyanduseshowninTable3.2.ThetablerevealsthatalongallphasesoftheVC,thetechnicalcoefficientsareall1:1becausethesumofsuppliesattheearlystagesoftheVCisequaltothesumofusesattheendoftheVC,andtherefore,therearenolossesofoliveoilalongthechain.

Table 3.2. Br ief supply-use table of the ol ive oi l VC (unit : 000 l i tres; reference year: 2011)

Item Supply Use

Imports 625 Nationalproductionofpressureoil 542

Nationalproductionofpomaceoil 31

Exports 402Domesticconsumption 611

HO.RE.CA 108CanningIndustry 77

Total 1,198 1,198

Source:ISMEA(2014).

Itisworthmentioningherethatweignoredthetechnicalcoefficientoftransformingolivesintooliveoilforthefollowingreasons.Firstofall,asthedataofICQRFreveal,therearenoseizuresofolivescarriedoutbytheICQRF,neitherbyanyother inspectionbody.Second,theICQRFreport,overtheperiodofnineyears(2007-2015),only twocasesof irregularitiesonolives,whichcanbeneglectedgiven that thenumberofirregularitiesoverthesameperiodapproachessixthousands.Thesefindings implythatnomanipulationsare necessary on the quantities of seized olive oil regardless of the VC level in which the seizure wasconducted, and the only necessary manipulation is to find a reasonable way of calculating an averageseizedquantity.Table3.2providesasummaryofallavailabledataonseizuresoftheICQRF.

Table 3.2. Ol ive oi l se izures by inspection body over t ime (000 l i tre)

Year ICQRF

2007 2,678.91

2008 322.74

2009 179.05

2010 79.68

2011 141.45

2012 678.35

2013 1,337.80

2014 1,709.30

2015 101.81

Average 803.23

Source:Authors’elaborationonICQRF(2016).

Giventheabove,andinordertocalculatethevaluesoftheseizures,itisenoughtomultiplythequantitiesbyanaveragerepresentativepriceattheconsumerlevel.Toreachthisprice,weusethedataoftheICQRF,which include detailed information on quantities and values at various levels of the VC. The price iscalculated as a weighted average of the quantities seized from production units that sell directly toconsumers.However,sinceseizureshavebeenenforcedindifferentyears,wedeflatetheirvaluesusingtheConsumer Price Index (CPI) with two different base years: 2009 and 2011. The latter is used only for

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comparisonbetweentheaveragepriceoftheseizedquantitieswiththepricecalculatedfromISMEA(2014)for2011,whiletheformerbaseyear,i.e.2009,isthereferenceyearofourSAM.ThecalculatedpricesarereportedinTable3.3,whichshowsthatthecalculatedaveragepricesaresimilarregardlessofsourceandbaseyear.Thisisbecause,fromonehand,inflationhasbeenlowinthelastfewyears;ontheotherhand,the price calculated by ISMEA (2014) in 2011 is noticeably close to the price calculated from the ICQRFdatabase, with the latter being higher, especially after deflation. This difference, that is tangible inproportional terms, might be explained, at least in part, by the reasonable assumption that frauds areusuallycommittedonmoreexpensiveproducts.

Table 3.3. Average prices of o l ive oi l at consumer level by year, source, and deflat ion basis

Item Price(Euro/litre)Differencewithrespecttoaveragepriceof

ISMEA(2011)

Averagepricewithoutdeflation(ICQRF) 3.52 9.6%Averagepricedeflatedto2009(ICQRF) 3.42 6.6%

Averagepricedeflatedto2011(ICQRF) 3.57 11.3%

Averagepricein2011(ISMEA) 3.20 0.0%

Source:Authors’elaborationonICQRF(2016),ISMEA(2014)andISTAT(2016).

Having estimated a reliable average price for the seizures, it is enough to multiply it with the seizedquantities toreachthevaluesofseizedproducts.TableXIV illustrates theresultsof thismultiplication inthe first column by inspection body. However, in order to estimate the values of all irregular products,includingthosethathavenotbeenseized,weneedaparameterofexpansion.TheonlywaywastousetheratioofthenumberofseizedproductstoirregularonescalculatedfromtheICQRFdatabase,andaveragedat15.61%overtheentireperiodof2007-2015.Assumingthat thesameratioapplies toother inspectionbodies,thesecondcolumninTable3.4includeswhatwouldbethevaluesofproductsdetectedirregular,includingbutnotconfinedtotheseizedones.

Table 3.4. Values of seized and irregular o l ive oi l : a sample based est imation (000 euros)

Agency Seizedoliveoil Irregularoliveoil Expansionsratio

ICQRF 2,748 17,306 15.61%


3.2.2. WineVC

Differentlyseizures intheoliveoilVC,seizuresenforced inthewineVC includeproductsotherthanfinalwine; therefore, it isnecessary touse technical coefficientsof transformation inorder toexpressall theseizedquantitiesintermsoffinalwinereadyforconsumers.DatafromICQRFshowstherethreetypesofseizedproducts:wines,musts,andgrapes.Table3.5.

Table 3.5.Quantit ies of seizures in the wine VC by product

Year Wine(litre) Must(litre) Grapes(Kg)2007 6,727,855 1,698,909 0

2008 19,687,960 9,736,393 150,2702009 5,951,702 1,893,024 0

2010 2,541,994 2,574,272 3302011 2,376,464 465,438 0

2012 4,986,305 14,434,455 0

2013 7,048,523 45,930,126 0

9

2014 1,875,047 3,287,634 24,2602015 34,397,280 20,761,140 333

Total 85,593,130 100,781,390 175,193


Thefirststepindatamanipulationistotransformtheseizedquantitiesofintermediaryproductsintofinalproduct equivalents, therefore, transform the quantities ofmusts and grapes reported in Table 3.5 intowine equivalents. To do this, we need technical coefficients of processing (transformation) which arereportedinTable3.6basedonthejudgementsoftwoexpertsinthewinesub-sector.

Table 3.6. Technical coeff ic ient of transformation in the wine VC

Source Grapestomust Musttowine GrapestoWine(Kgtolitre)

Expert1 0.80 0.90 0.72

Expert2 0.60 0.85 0.51

Average 0.70 0.88 0.61

Source:personalcommunication(Parenti&DeFilippis).

Table 3.7. Seizures in the wine VC by inspection body: a l l transformed into wine

equivalents

Year Wine Must Grapes Total2007 6,727,855 1,486,545 0 8,214,400

2008 19,687,960 8,519,344 92,040 28,299,3442009 5,951,702 1,656,396 0 7,608,098

2010 2,541,994 2,252,488 202 4,794,684

2011 2,376,464 407,258 0 2,783,7232012 4,986,305 12,630,148 0 17,616,453

2013 7,048,523 40,188,860 0 47,237,3842014 1,875,047 2,876,679 14,859 4,766,585

2015 34,397,280 18,165,997 204 52,563,481

Average 9,510,348 9,798,191 11,923 19,320,461


Table3.7includesthequantitiesofseizuresinthewineVCasiftheywereallfinalproduct(i.e.wine).ThenumbersareobtainedbymultiplyingthequantitiesofmustsandgrapesinTable3.5bytheircorrespondingtransformation coefficients intowine including the conversion ofmeasurement unit from kg to litre forgrapes,whilethoseofwineremainedunchanged.

Inordertocalculatethevaluesoftheseseizures,weneedmultiplytheaveragequantitiesbyanaveragerepresentativepriceattheconsumerlevel.Toreachthisprice,weusethedataoftheICQRF,whichincludedetailed information on quantities and values at various levels of the VC. The price is calculated as aweightedaverageofthequantitiesseizedfromproductionunitsthatselldirectlytoconsumers.However,since seizures have been enforced in different years, we deflate their values using the Consumer PriceIndex (CPI)with thebaseyears2009: the referenceyearofourSAM.The resultingaverageprice is2.02europerlitreoffinalwine.

Table 3.8. Values of seized and irregular products in the wine VC (000 euro)

Agency Seizedwine Irregularwine ExpansionratioICQRF 39,030.5 219,292 17.8%

10


Havingestimatedareliableaveragepricefortheseizures,itisenoughtomultiplyitwiththeaverageseizedquantityreportedinTable3.7bythispricetoreachthevaluesofseizedproducts,whichisshowninTable3.8.Moreover,inordertoestimatethevaluesofallirregularproducts,includingthosethathavenotbeenseized,weneedaparameterofexpansion.Asthecaseofoliveoil,weusetheratioofthenumberofseizedproductstoirregularonescalculatedfromtheICQRFdatabaseforwineVC,andaveragedat17.8%overtheentireperiodof2007-2015.

3.2.3. OtherVCs

Inorder toestimate the valueof seizedand irregularproducts for all theotherVCs,weoptimally couldfollow steps similar to those taken forwineandoliveoil asexplainedabove.However, thehigh levelofcomplicationinalltheotherVCsentailinghighnumberoffinalproductsandintermediarytransformationshavepreventedusfromdoingsogiventhetimespanoftheproject.Therefore,weresorttoothersimplermechanisms.ThatistosaywejustdeflatedthevaluesoftheICQRFseizuresfortheotherVCsbeforetakingtheaveragesovertheentireperiodof2007-2015,with2009asabaseyearsinceitisthereferenceyearofourSAM.Ourchoiceisdictatedbylackoftimesincerepeatingthesameoperationsperformedoverwineandoliveoil requireplentyof time tobe invested forVCswhose importance in termsof fraudweight isrelatively small (a part of olive oil andwine, all the other 8 VCs include less than 40% of the values ofseizures).Inaddition,thepossibilitytorelyonpricesoffinalproductsintheotherVCswouldhavecausedthelossofsomeimportantdatasincemanyproductswereexclusivelyseizedatearlystagesoftheVCs.TheresultsarereportedinTable3.9.

Table 3.9. Values of seized and irregular products: sample-based est imation (000 euro)

Sector ValuesofSeizures(000euro) Expansionsratios

ValuesofIrregularities(000

euro)

Meat 40 3% 1,675

Fish 21 18% 161

OtherFood 203 10% 2,890

Horticultural 701 8% 11,462

DairyProducts 313 8% 5,233

CerealsandStarch 447 6% 10,921

AnimalFeed 77 7% 1,561

OtherDrinks 154 19% 1,126


However, values reported in Table 3.9 do not reflect the value of final products. This implies that theyshouldbehigher shouldweaccommodate themto theconsumer level.Oneway todo that couldbebyusingaratiothatcanbedrawnfromoliveoilandwineVCs,whichistheratioofvalueofseizedproductsaccommodated to theconsumer level to itscounterpartwithout thisaccommodation. Ifweassumethatthis ratioapplies toall theotherVCs,multiplying thevalues inTable3.9by this ratio (1.37)givesus thevaluesofeachVCattheconsumerlevel.Theresults(includingthoseofwineandoliveoilVCs)arereportedinTable3.10sothattheycanbeconsideredasifallseizedproductsarevaluedwithpricesoffinalproductsprevailingatretailers’level.

11

Table 3.10. Values of seizures and irregular products: sample-based est imation after

transforming the values of the other VCs to the consumer level (000 euro)

Sector Valuesof

Seizures(000euro)

ValuesofIrregularitiesICQRF

(000euro)

Meat 55 1,675

Fish 29 161

OliveOil 2,748 17,306

OtherFood 278 2,890

Horticultural 964 11,462

DairyProducts 430 5,233

CerealsandStarch 614 10,921

AnimalFeed 106 1,561

Wine 39,030 219,292

OtherDrinks 212 1,126


It isworthaddinghereacommentonthereliabilityoftheestimation.Wearehighlyconfidentaboutthereliabilityof the valuesof seizedproducts since this figurehasbeen calculatedbased completelyon theobserved. However, this confidence goes downwith the estimates of the irregular products values. Forexpanding the values of seizures to all irregular products,we assume that if all irregularities had led toseizures, theamountof seizedquantitywouldhave increased inproportion to the relationshipbetweenthenumberofirregularproductsandthenumberofseizures,whichhasnostrongevidence.However,asthis is the only parameter that can be drawn from the data, we have no other option to perform thisexpansion.

3.3. Fromsample-basedestimationstothepopulationResults inTables3.9and3.10stillreferonlytosample-basedestimatessincethesedataandcalculationsareconfinedtothesampleofestablishmentsthathavebeensubjecttoinspection.However,weneedtoexpandthesenumberstothepopulationlevelinordertoreachanestimateofagro-foodfraudeconomy.Therefore,weneedaparameterofexpandingthesample-basedcalculationstothepopulation level.Theonlyavailablewayofdoingsoisbyusingtheratiooftheinspectedestablishments(sample)tothenumberof active ones (population). On the population level, the most recent figures on total number ofestablishmentsinvolvedintheagro-foodVCsreferto2011andcouldbecalculatedfromthelastcensusonindustryandservices(ISTAT,2014).

UsingtheICQRFdatabase,weseecancalculatethe(sample)numberofinspectedestablishmentsperyearinallphasesoftheoliveoilVC.Wepreferredtousetheaverageoftheperiod2010-2012insteadofonlydataon2011toalleviateanybiascausedbyannualvariability.BydividingthevaluesofTables3.9and3.10over these ratios, we obtain Tables 3.11 and 3.12 that would reflect the values of seized and irregularproducts if all establishmentswere inspectedand ifourassumptionswerevalid.Weemphasize that thereliabilityofthisexpansiondependsonhowrepresentativethesamplingoftheICQRFis.

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Table 3.11. Values of seized and irregular products: populat ion level est imation (000 euro)

Sector Expansionparameter

(Inspected/Active)

ValuesofSeizures(000euro)

ValuesofIrregularproducts(000euro)

VCindustrialturn-over(millioneuro)

Proportionsofirregularproductsvalue/turnover

Meat 2.0% 2,039 85,121 18,821 0.45%

Fish 0.2% 13,961 107,049 1,387 7.72%

OliveOil 6.3% 43,577 274,433 3,000 9.15%

OtherFood 2.2% 9,135 130,298 43,456 0.30%

Horticultural 3.8% 18,618 304,352 8,087 3.76%

DairyProducts 4.8% 6,590 110,179 14,425 0.76%

CerealsandStarch 3.7% 12,135 296,530 8,110 3.66%

AnimalFeed 61.4% 125 2,544 5,700 0.04%

Wine 8.3% 470,740 2,644,843 10,600 24.95%

OtherDrinks 1.0% 15,894 115,818 8,800 1.32%


Table 3.12. Values of seizures and irregular products after transforming the values of the other VCs to the consumer level: populat ion level est imation (000 euro)

Sector Expansionparameter

(Inspected/Active)

ValuesofSeizures(000euro)

ValuesofIrregularitiesICQRF

(000euro)

VCindustrialturn-over(millioneuro)

Proportionsofirregularproductsvalue/turnover

Meat 2.0% 2,803 85,121 18,821 0.45%

Fish 0.2% 19,188 107,049 1,387 7.72%

OliveOil 6.3% 43,577 274,433 3,000 9.15%

OtherFood 2.2% 12,555 130,298 43,456 0.30%

Horticultural 3.8% 25,588 304,352 8,087 3.76%

DairyProducts 4.8% 9,056 110,179 14,425 0.76%

CerealsandStarch 3.7% 16,678 296,530 8,110 3.66%

AnimalFeed 61.4% 172 2,544 5,700 0.04%

Wine 8.3% 470,740 2,644,843 10,600 24.95%

OtherDrinks 1.0% 21,844 115,818 8,800 1.32%


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4.Themodelusedtoassesstheimpactofagro-foodfrauds

4.1. AnIntroductiontoSocialAccountingMatrix(SAM)Socialaccountingmatrix(SAM)isarepresentationmodelofthecircularflowwithinanexchangeeconomyinamatrixform.Differentfrominput-outputtables,whereonlyinterdependenciesbetweenindustriesinadisaggregated production account are represented, the SAM accounts for the interrelationships amongproduction activities, primary and secondary income distribution among factors and institutions, finalconsumptionsandcapitalformation.

Each row of the SAM shows the receipts for a specific sector while the corresponding column lists thesectorexpenditures.Thereareseveraltypesofaccountsintherowsofthematrix:a)productionactivities,b) factors of production, c) institutions’ current accounts, such as households (possibly furtherdisaggregatedbytype),firms,government,d)capitalformationaccount,ande)theaccountforexchangesoftheeconomywiththerestoftheworld.Thesamestructureholdsforthecolumnsofthematrix.

Asinadoubleentryaccountancysystem,thesumsofcorrespondingrowsandcolumntotalsmustbalance.The economic meaning of this balancing condition is that: a) costs must be equal to revenues in eachproductionsector;b)expendituresofeachinstitutionmustbeequaltoitsincome;c)totalsavingsmustbeequaltototalinvestmentsplusfinancialcapitalaccumulation.

SAMs have been first proposed as an accountancy framework to integrate input–output tables with acompleterepresentationofthedistributiveflowsintheeconomy.ThefirstareaofapplicationoftheSAMapproachwas the representation of developing economies for planning. Afterwards, the construction ofSAMsbecameawidespreadpracticebothforstatisticalandanalyticalpurposes.In1993,theSAMconceptwas integrated in the SystemofNational Accounts as a compact and consistent representation of flowsrecordedinthefullsystemofaccounts.

An interesting featureof theSAM is its flexibilityasanaccountingsystem.Differentblocksof thematrixcan be disaggregated according with the purpose of the analysis and data availability. This allowshighlightingpeculiar features of the economy that are relevant for thepurposeof the analysis,within acoherentmacroeconomicframework.ThisisthereasonwhySAM-basedmodelsareparticularlyinterestinginthestudyofspecificsectorsof theeconomywithinaneconomy-wideperspective.TheavailabilityofaSAMproperlydisaggregatedallows tocarryoutdetailedstructuralanalysesof interdependenciesamongthecomponentsoftheeconomyaswellascalibratingmodelstosimulatetheimpactscausedbyexogenousshocks(policydrivenorduetoexternalfactors).

WithreferencetotheItalianagro-foodsectorinItaly,theinput-outputtableandtheSAMapproachhavebeen already used to generate a longstanding stream of studies. Input-output tables with highlydisaggregated agriculture and food sectors have been produced by ISMEA at regular intervals (IRVAM,1987; ISMEA1997; ISMEA, 2009). Also ISTATproduced a versionof the input-output table of the Italianeconomyfor1992withtheagro-foodsectordisaggregatedinto10industries(ISTAT,2002).

ThisstudyfollowsthisapproachextendingittoobtainacompleteSAMrepresentationoftheItalianagro-foodsystem.TheISTATinput-outputtableoftheeconomyfor2009wasdisaggregatedtoinclude8groupsoffarmswithdifferentproductivespecialisationand10differentgroupsoffoodmanufacturingactivities.All theseactivitiesproducea setof11different commodities. Furthermore, thedisaggregated tablewasmerged intoa SAMof the Italianeconomypreviouslyestimatedby IRPET for the sameyear. Finally, thedisaggregatedSAMwasusedtosolvealinearmodeloftheItalianeconomywiththeaimofassessingtheeconomic impact of agro-food frauds. In the following section, a short description of the modellingapproachisprovided.

14

4.2. SimulationwithinaSAMframeworkThe first step inSAMmodelling is the identificationofendogenousandexogenousaccounts.Usually, forsmall economies, the government and the rest of theworld are consideredas exogenous, so themodeldoesnotexplainthebehaviourofthoseaccounts.Theprocessofcapitalformationcanbealsoconsideredasexogenouswhentheresearchquestiondoesnotfocusondynamicimpacts,asisthecaseinourstudy.Thereforethesethreeaccountswereconsideredasexogenous.

LetBbethematrixofexpenditurecoefficientsforendogenousaccounts,thatisthematrixofcoefficientsobtaineddividingeachsingleentryofthematrixbythecorrespondingcolumn/rowtotal.GiventhematrixB,themodelcanberepresentedinacompactformasasetofequationsrepresentingthebalanceoftheaccounts for the endogenous components (production activities, factors of production, households andfirms):

𝑌 = 𝐵𝑦 + 𝑥 (1)

wherex is thevectorofnominal incomeofendogenousaccounts (outputofproductionactivities, factorearningsandgrossincomeforinstitutions),andy isthevectorofexogenousinflowstowardsendogenousaccounts (foreign exchange, savings and capital formation, transactions between institutions and thegovernment).

Thesolutionofthesystem(1)mapsthevectorxofexogenouscomponentofthesystemtothevectoryoftotalsthroughthematrixMofSAMmultipliers:

𝑦 = (𝐼 − 𝐵)!!𝑥 = 𝑀𝑥 (2)

whereIistheidentitymatrix.

Equation(2)canbeusedasabasisforsimulationsasfollows:

𝑑𝑦 = 𝑀𝑑𝑥 (3)

wheredxisavectorofchangesinexogenousinjections,representingdifferentscenariostobeassessed.

AfirststepintheanalysisaimsatestimatingtheimpactoffinaldemandthatinthecurrentconfigurationoftheItalianagro-foodsystemissuppliedby irregularfoodproductions(includingbothseizedandirregularproducts).Inordertodothis,equation(3)wasusedwithdxbeingavectorincludingestimatesofthetotalofseizedandirregularfoodproductionextendedtothewholepopulation(seesection3.3fordetails).Thisexercise is quite straightforward and allows assessing the share of total economy directly and indirectlyrelying(viathecircularflow)onirregularfoodproductions.Weinterpretthetotalimpactofdemandmetbyirregularproductionasameasureoffragilityoftheagro-foodproductionsystem.Indeed,irregularfoodproductionbringsaboutaninherentriskoftrust lossbyfoodconsumersshouldafoodscandalandscarehappen:inthisevent,thelargertheshareoffinaldemandsuppliedbyirregularproductsthehighertheriskofadisruptionintheusualactivityofthesystem.

The adoption of the SAM frameworkmakes possible to estimate the impacts in terms of output, valueaddedandhouseholds'grossincome.Anassessmentoftheimpactintermsofemploymentispossibleaswell,simplypost-multiplyingthevectorofimpactsonoutputbyavectoroflabourrequirementcoefficientspereuroofoutputvalue.

Afurtheranalysiswascarriedoutadoptinga"counterfactual"framework.TheSAMrepresentstheactualflows in the economy, including both regular and irregular food production activities. The directexpenditure coefficients in the matrix B depict the average structure of intermediate consumption fordifferent subsectors of food industry, reflecting their composition in terms of regular and irregular

15

activities. However, it is reasonable to assume that the structure of costs in a production activity notcomplyingwithregulationsandstandardsisdifferentfromthatofacomplyingproductionunit.

Overall,weexpectthatanirregularand/orcounterfeitproductionwouldbeobtainedincreasingtheratiobetweenvalueaddedandintermediatecosts,andincreasingtheshareofprofitsintheprimarydistributionofvalueaddedto factors.Suchadifferentconfigurationofcostsdecreases thebackward linkagesof theirregular production activity, reducing also its ability to activate the economic system through industrialinterdependencies.Furthermore,theincreasedshareofdistributedprofits is likelytochangetheimpactsontheeconomyviatheincomedistribution–finalconsumptionexpenditurepath.

Acounterfactualanalysisoftheimpactofirregularactivitiesshouldcomparethetotalactivityoftheactualeconomy represented in the SAM,with that of a hypothetical economywhere all firms complywith allregulations.SupposeamatrixB*ofdirectcoefficientrepresentinga"fully-compliant"economyisavailable.ThecorrespondingmatrixM*ofSAMmultiplierscouldbecalculated.Usingequation(2)thetotalimpactofirregularproductionactivitiescanbecalculatedasfollows:

𝑐𝑓 = 𝑀 −𝑀∗ 𝑥 = 𝑦 − 𝑦∗ (4)

where,x is thevectorofactualexogenous inflowstowardstheendogenousaccounts,y* is thevectoroftotalsofendogenousaccountsthatwouldbeobservedshouldtheproductivesystembe“fully-compliant”,and cf estimates the impacts of irregular activities expressed as changes in the totals of endogenousaccounts.

Such a counterfactual analysis requires additional dataon thewaynon-compliancewith regulations andstandards affects the vector of costs of production activities in different food subsectors. LetAf be thematrixofirregularexpenditurecoefficientsandfthevectoroftotalvaluesofirregularproductions.TheusematrixZfoftotalcostsforirregularproductionscanbecalculatedasfollows:

𝑍! = 𝐴!𝑓 (5)

where the hat superscript indicates the diagonalization of vector f. Theusematrix for "fully-compliant"productionactivitiescanbeobtainedbythedifference:

𝑍! = 𝑍 − 𝑍! (6)

where,ZistheusematrixintheoriginalSAM.

Finally,matrixA*canbeobtainedfromZrbydividingelementsbycolumntotalsgivenbythetotalvalueofregularproducts:

𝐴∗ = 𝑍!(𝑦 − 𝑓)!!. (7)

Matrix B* representing the "fully-compliant" (i.e. counterfactual) economy is obtained substituting the"modified"matrixA*forthematrixofdirectexpenditurecoefficientsofproductionactivitiesinmatrixB.

Information on frauds is by definition difficult to obtain and, considering the high variety of productionprocesses in the food industry, getting the relevant information/datawould requireanextensive survey.However, this solution was prevented due to time and resource limitations. Therefore, we asked foodsectorexpertsandkeyinformantsworkinginorganizationsinchargeofinspectionsintheagro-foodsectortoprovidetheirbestguessontheexpenditurecoefficientsthatmaybecalculatedfromahypotheticalusematrixofonlyirregularproductionactivities.Afteraqualitativedescriptionofeachtypologyoffraud,theywereaskedtomodifythecoefficientsintheBmatrix(basedonactualSAMflows)tobetterrepresentthe

16

costvectorofeachirregularactivity.Theexercisewasactuallydevelopedonlywithreferencetoproductionofoliveoilandwinethatrepresentthelargestpartofseizedandirregularproductions.

4.3. TheSAMoftheItalianeconomyfortheanalysisoftheagro-foodsystem

4.3.1. Introduction

ISTAT provides both supply and use table for the Italian economy, which are consistent with Nationalaccounts. Current price tables are available from 1995 onwards and are based on the NACE rev 1classification for industries and CPA4 classification of products.We disaggregated the 2009 table, whichshowsdetailsfor59 industriesandcorrespondingproducts.Thetablepresentstheagro-foodcomponentoftheproductivesysteminanaggregatedway.Infact,bothagricultureandfoodindustryarerepresentedby a single production account. Furthermore, the commodity classification adopted in the ISTAT tableincludesonlytwoproducts,namely"agriculturalproducts"and"foodproducts".

In order to get a representation of the agro-food system that is most detailed as possible and in themeanwhile compatible with available statistical information, we proceeded according to the followingsteps:

- disaggregatetheagro-foodindustryinto11subsectors;

- disaggregate foodcommodity into11products,withaclassificationcorrespondentwiththe foodindustrydisaggregation;

- disaggregateagricultureinto8subsectorsproducingboththesingle"agriculturalcommodity"andthefoodproductsaccordingtothenewdisaggregation.

Finally,thedisaggregatedsupplyandusetableswereincludedwithintheSAMoftheItalianeconomythatwasconstructedbyIRPETforthesamereferenceyear.

4.3.2. DisaggregationoftheFoodIndustryintheISTAT2009supply-usetable

Theaimoftheworkwastodisaggregatetheoriginalfoodindustrybranch(column)andthecorrespondingproduct(row)into11moredetailedbranches/products,namely:

1. Meatandproductionofmeatproducts

2. Processingandpreservingoffish,crustaceansandmolluscs

3. Productionofoliveoil(virginandrefined)

4. Manufacture of other food products (vegetable oils and fats, sugar, bakery and farinaceousproducts)

5. Manufactureofprocessedvegetablesandfruitsproducts

6. Manufactureofdairyproducts

7. Manufactureofgrainmillproducts,starchesandstarchproducts

8. Manufactureofpreparedanimalfeed

9. Productionofwine

10. Manufactureofbeverages

11. Manufactureoftobaccoproducts

4StatisticalClassificationofProductsbyActivity

17

Withinthesupplyanduseframeworkthe"use"(or"make")providestheproductionaccountforthewholeeconomy disaggregated by groups of production units classified according to the nature of economicactivity (industries). Each column of the use table shows the value at purchasing prices of goods andservicesemployedbyeachgroup industry. So theoriginaloverall values composing theaggregated foodindustry column represent a set of row constraints for the estimation of the 69x11 matrix of thedisaggregated "use" account for the food industry (58 rows of non-food products plus 11 new rowsreplacingtheoriginalfoodindustryproduct).

Furtherinformationonthecellsofthematrixwascollectedfromthefollowingsources:

- EUROSTAT structural business statistics database which provides for each year the values for:production,valueadded,wagesandsalariesattheproperNACEdisaggregation.

- ISTAT surveyson industrialenterprisesaccounts (whichwehadavailableattheelementarydatalevel), a yearly business survey which as well as providing data on aggregated intermediateconsumption and labour input, is also a source of information for specific inputs such as energyconsumption,transportationandcommercialservices,legalservicesandsoon.

- ISMEA2003usematrixoftheItalianfoodsystemwhichprovidesinter-industryflowsfortheFoodIndustryatadisaggregatedlevelalthoughusingaclassificationnotcompletelyconsistentwiththeNACE one: for example, while it provides a separate industry for olive oil it aggregates othervegetableandanimaloilsandfatstotheotherfoodproductindustry.

Buildingontheabovementionedsourcesweproceededbytryingafirsttentativedisaggregationofeachcell of the original column into the 11 cells of the row of the 69x11 matrix. Since for aggregatedintermediateconsumptionwegot reliabledisaggregatedestimatesbyboth ISTATandespeciallyEurostatsources and also the row totalswere known from the original ISTAT table, itwas possible to use a RASprocedure (Bacharach, 1965) to get more accurate and consistent estimates for the intermediateconsumptionusesubmatrixoftheFoodIndustry.WagesweredisaggregatedbuildingonEurostatdata.Thecompositionofthefoodindustryintermsdifferentsub-industriesisrepresentedinFigure4.1.

Figure 4.1. Output and labour units in the food industry, I ta ly , 2009 - % values

Source:Thisstudy.

18

Themanufacture of other food products, including some of themost important sectors of Italian foodindustry, such as bakery, farinaceous products, sugar and vegetable oils and fats (excluding olive oil),represents almost 50%of employment and37%of theoutput value. TableA1 in theAppendix providesfiguresonthepercentagecompositionofintermediateconsumptionsinthefoodindustry.Thestructureofcosts is quite diversified across different sub-industries, reflecting the nature of different productionprocesses. Someof the componentsof the food industry showahigherdegreeofdecompositionof theproduction process, reflected by a higher share of purchases of semi-finished products from productionunitsincludedinthesameindustry(fishanddairyproducts,oliveoil,andbeverages).

Inthedisaggregatedinput-outputtable,thesinglerowforfoodindustry isreplacedbya11(commodity)row and by 69 (industry) column matrix with the inclusion of further columns for final uses, namelyhouseholdconsumption,grossinvestmentsandexports.Theestimatesofinterindustryflowsweremainlybasedondata from the ISMEAmatrix.Household consumptionwasdisaggregatedbuildingonestimatesfromthehouseholdbudgetsurvey,whichwasavailableatthemicrolevel.Weusedacorrespondencetableto reallocate consumption based on the COICOP5 classification to the corresponding voices of the CPAnomenclature.ExportbyindividualfoodindustriesweredirectlyobtainedbyexportstatisticsatFOBpricesbyCPAfromISTAT,whereasgrossinvestmentsweredisaggregatedonthebasisofthetotalsoftheothercomponents.

TableA2intheAppendixprovidesthemostimportantentriesofthesupplytableforthefoodindustry.Theoutputmix of subsectors is for the largest part concentrated in the characteristic produce of the sectoritself,withpercentagevaluesalmostalwaysbeyond90%.Anotableexceptionisrepresentedbytheoliveoil industry, where as relevant share of the output is represented by other vegetable oils. Indeed, sub-sector includes refinerieswhere theprocessingof raw (lampante) oliveoil is theprevalent activitiesbutusuallyprocessingalsoothervegetableoils.

4.3.3. DisaggregationofAgricultureintheISTAT2009supply-usetable

Thedisaggregationofagriculturewascarriedoutonthesupplyandusetableincludingthesub-sectorsoffood industry and the disaggregated classification of food products. Given the focus of the research onprocessed food, agriculture was disaggregated only as an industry; as a consequence, all 8 sub-sectorscomposingagricultureinthedisaggregatedsupplyandusetable,provideonlyone"agriculturalproducts"commodity.

ThedisaggregationofagricultureintheusetablewasbasedoninformationfromtheFarmBusinessSurvey(FBS)carriedoutbyISTATonasampleofrepresentativefarmsofItalianagriculture.Thesurveyisdesignedto support national accounts estimates for agriculture and includes information on the intermediateconsumptionsandlabourinputsoffarms.Dataweretakenfortheresearchatthemicrolevel.Thisallowedtoreclassifyobservationsandobtainestimatesofthecompositionofintermediateconsumptionbygroupsof farms with different specialisation. Farms were classified into 8 subsectors according to the Type ofFarmingclassificationadoptedattheEuropeanlevelwithintheFarmAccountancyDataNetwork6:

1. Specializedfieldcrops

2. Specializedhorticulture

3. SpecializedpermanentCrops

4. Specializedgrazinglivestock

5. Specializedgranivores

5Classificationofindividualconsumptionbypurpose.6FADNisasampleoffarmssurveyedtosupportdecisionsonagriculturalpolicyattheEuropeanlevel.TheGeneralTypeofFarmingclassificationusedintheanalysisisdefinedinthe2003/369EUregulation.TheFTclassificationisoneofthestratificationvariablesusedinthedesignoftheFBSsampleusedtodisaggregatetheproductionaccountforagricultureintheoriginalusetable.

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6. Mixedcropping

7. Mixedlivestock

8. Mixedcrops-livestock

Coherent with the approach followed for food industry, the 69x8 matrix of estimates of intermediateconsumptions for the subsectors of agriculture based on FBS datawas used to disaggregate by row thesingle entries of the Agriculture column of the original use matrix. A similar approach was used todisaggregateentries referred to factorsuse (wages foremployed labour,mixed incomeof self-employedlabour) as well as net indirect taxes on production. Table A3 in the Appendix provides figures on thecompositionof intermediateconsumptionsof the8 farming typesubsectorsofagriculture,aswellasontheirrelativeimportanceintermsofoutputvalueandnumberoflabourunits

Thedisaggregationofagricultureinthesupplytablewasbasedonadditionalinformationofproductssoldby different Types of Farming provided as Standard Results by the FarmAccountingData Systempublicdatabase7.TheinclusionofthisinformationwasnecessarytointegrateFBSinformationonthecompositionof agricultural output. Farms are typically multi-product production units. In recent years, a relevantincrease in the diversification of farm activity increased also the share of non-agricultural goods andservices(e.g.processedfoodortouristicservices)suppliedbyfarms.Furthermore,intheItalianagriculture,aconsiderableshareofwineandoliveoilproductionisproducedandtradedbyfarms.Inthedisaggregatedversionofthesupplyandusetable,the8subsectorscorrespondingtoTypesofFarmingsupplyagriculturalproducts as well as other commodities, including processed foods and restaurant and accommodationservices.TableA4providesfiguresonthemostimportantentriesofthesupplytableforthesubsectorsofItalian agriculture. Three faming types (permanent crops, mixed cropping and mixed livestock) show atypically lowershareofagriculturalproducts in theirproductmix.Asexpected, inspecializedpermanentcrops farms, more than 50% of output is represented by olive oil and wine, two products still largelyprocessedatthefarmlevel.Thecaseofwineandoliveoilispeculiarwithintheagro-foodsystem,withanalmostbalancedroleplayedbyagricultureandmanufacturinginthedomesticsupply.Table4.1showstheshareofdifferentindustriesrepresentedinthedisaggregatedSAMintheproductionofoliveoilandwine.Noticeably, in the case of olive oil a substantial share accrues to wholesale trade service units, usuallycarryingoutanactivityofblendingandpackagingofoilsforfinalconsumption.

Table 4.1. Share of different industr ies in the production of o l ive oi l and wine, I ta ly , 2009 – M€ % values

Oliveoil Wine

Specializedpermanentcrops 32.7 42.3Otheragriculture 9.4 3.7

Oliveoilindustry 16.5 0.0

Wineindustry 1.4 48.7Wholesaletradeservices 20.5 0.0

Otherindustries 19.5 5.2Source:Thisstudy.

Alsointhecaseofmixedcropsfarms,oliveoilrepresentsaconsiderableshareofoutput(12.6%,refertotable A4). In case of farms not featuring a prevalent groups of activities (mixed crops and farm), asubstantial share of output is represented by tourist services (restaurant and accommodation, notrepresentedinthetable).

7 Standard Results are estimates of average values by farm type. The Public FADN Database is accessible at the URLhttp://ec.europa.eu/agriculture/rica/index.cfm

20

4.3.4. InclusionofthedisaggregatedsupplyandusetableintheSAMofItalianeconomy

TheSocialAccountingMatrixof the Italianeconomyprovidedby IRPET for thereferenceyear includedasupply and use table essentially based on the ISTAT’s. The industry and commodity classifications arecoherent even though represented at a lower level of disaggregation. Furthermore, despite littlediscrepanciesduetothebalancingofthematrixaftertheinclusionofthe"social"(i.e.incomedistributionand final consumption) part of the table, the values of total output of industries and total supply ofcommodities are very close to those in the original supply and use ISTAT table. As a consequence, theinclusionofthedisaggregatedsupplyandusetablewasquitestraightforward,requiringonlyafewsimpleadaptations.

Accountsforindustriesandcommodities(exceptforagricultureandfoodindustryandproducts)werere-aggregatedwherenecessarytoharmonizewiththedisaggregationadoptedintheIRPETmatrix.Inthefinalversion, the supply and use part of the SAM includes 54 industries producing 64 commodities. In theoriginal IRPET SAM, institutions purchase bundles of goods and services corresponding to 23 finalconsumptionfunctions.Agricultureandfoodindustryselltheirproductstoconsumersthroughoutthefirsttwofunctionsreferredtoaspurchasesoffoodandbeverages.Asaconsequence, itwasnotnecessarytodisaggregate final consumptionaccording to thenewclassificationofcommodities in thesupplyandusetable.

The new accounts for agriculture and food industry sub-sectors were balanced adjusting the value ofdepreciation in the disaggregation of value added, while the accounts for new food commodities werebalancedadjustingvariationsinstocks.ThefinalSAMincludesatotalof183accounts,whicharenamely64commodities,54industries,12accountsforprimaryincomedistribution,23finalconsumptionsfunctions,18 accounts for current income use of institutions, 9 accounts to represent capital formation and 3accountsforflowswiththeRestoftheWorld.

21

5.Theeconomicvalueofagro-foodfrauds

5.1. Theoverallshareofnon-agro-foodfraudsFrauds and irregularity in the production of food estimated in the first part of this study represent arelevantshareofsectoractivity,rangingfrom0.5to3.3%ofturnoverdependingonestimates(respectivelybasedonthevalueofonlyseizedproductionorof the totalof irregularproductsdetectedby inspectionauthoritiesasclarifiedinsection3,table3.11e3.12).TheavailabilityofaSAM-basedmodeloftheItalianeconomy, with a breakdown of production activities suitable to represent in detail the structure of theagro-foodsector,allowsforamorein-depthanalysisoftheeconomicimpactoffrauds.

In section 4, data andmethods for impact analysis have been outlined. A first important result can beobtainedlookingattheshareofeconomydirectlyandindirectlylinkedtosupplyofirregularfoodproducts.Figures intable3.12havebeenusedaselementsofavectorofexogenousshocktobemultipliedbythematrixofSAMmultipliers.Thisvectorexpressesthefinaldemandforfoodthatonaverage, intheperiod2007-2015,hasbeensuppliedby irregularproductionunits (includingboth frauds that led to seizuresofproductsand/orproductssubjecttootheradministrativesanctions).Asaconsequence,theresultingfigurecomputedthroughequation(3)representsthevalueoftotaleconomyofagro-foodfraud,thustheshareofeconomicactivitydirectlyandindirectlyactivated(viathecircularflowintheeconomy)bydemandactuallymetbyirregularfoodproducts.Table5.1providessomeresultsfromthisfirstSAMbasedanalysis.

Table 5.1. Share of total economy act ivated by f inal demand for irregular food products.

Average 2007-2015 – Values expressed in euro 2009

Onlyseizedproducts AllirregularproductsTotalvalueofirregularfood(M€) 622 4693Shareoftotaloutput(M€) 1858 0.1% 13879 0.5%Shareoftotalemployment(000UL) 22 0.1% 156 0.6%Shareofvalueadded(M€) 795 0.1% 5828 0.4%

Shareofhouseholds'grossincome(M€) 715 0.0% 4754 0.3%

Source:Thisstudy.

Theresultingvaluesarenottrivial: incaseoftotal irregularproducts0.5%oftotalvalueofoutput,morethan 150,000 LUs. The numbers clearly show the importance of the agro-food frauds in the Italianeconomy. In the reference period, from 0.1 to 0.4% of value added (a proxy of GDP)was generated byirregularfoodproductionactivities.Thevalueofproductiondirectlyandindirectlyactivatedbythedemandfor irregularfoodamountedtoavaluerangingfrom1.9to13.9billionEuro,basedonthetwoestimates(conservative/only seizures vs. extended/including other irregularities) of the total value of irregularproducts. These figures corresponded to a share of total employment accounting up to 156 thousandlabourunitsintheextendedscenarioi.e.whenallirregularproductsareconsidered.

Theshareisevenmoreimportantwhenconsideringtheagro-foodsectors,wherethetotaloutput"driven"byirregularproductsaccountsfor3.2%oftotaloutputand5.8%oftotalemployment(datanotincludedinthetable).InFigure5.1,thedependenceoffoodindustryonthedemandsatisfiedbyirregularproductionis graphically represented. The "exposure" of different sectors to instability due to irregular productionmethods is variable among sectors but is particularly important (more than 25% when considering allirregular products) for thewine sector and the connected activity of specialized permanent crops farms(11.3%).Morethan5%of Italianagriculturedirectlyandindirectlydependsondemandfor irregularfoodproducts.

22

Figure 5.1. Share of the output of agro-food sectors act ivated by f inal demand for

irregular food products

Source:Thisstudy.

Theresultsgiveameasureofthe"exposure"oftheproductivesystemtopossibleproblemsandinstabilitylinkedtothepresenceofactivitiesnotcomplyingwithrulesandstandards.Infoodsector,wherethetrustofconsumersinthequalityofproductsisafundamentalfactordrivingthemarket,thepresenceoffraudsisa potential source of instability andmarket disruption, as largely observed in the past yearswhen foodscares and scandals caused large losses. Data in table 5.1 and Figure 5.2 show that the impactmay bespreadinthetotaleconomyinaconsiderableextentandthatsomesectorsaremoreatriskthanothers.

Aneconomicmeasureoftheexposureofthefoodsectortotherisksoffrauds isapolicyrelevantresult,thoughnotyetagenuinemeasureoftheimpactofagro-foodfraudsontheeconomy.Infact,eventhoughat riskof instability, the irregular foodproductionactivitiesgenerateanddistribute (illegal) incomesandactivatetheeconomythroughoutbackwardandforwardlinkages.However,thepresenceofsuchactivities,besidetheobviousadverseeffectonthevulnerabilityoftheagro-foodsectortofoodscandals/scares,mayhaveanegative impacton theeconomy inamore thoroughmeaning: in the followingsection theSAM-basedmodelwillbeusedtoquantifyitbymeansofacounterfactualanalysis.

5.2. Theimpactofagro-foodfrauds:acounterfactualanalysisHowmuchthepresenceofirregularproductionactivitiesaffectstheoverallactivityleveloftheeconomy?Inotherwords,whatcouldbethelevelofactivityoftheeconomicsystemsifallproductionactivitieswerecarriedoutcomplyingwithallrulesandstandards?Toanswerthisquestion,acounterfactualapproachtotheanalysis isneeded.The resultspresented rightbelowareobtainedusing themethodologyoutline insection4.2.

The construction of matrix B* of expenditure coefficients representing a hypothetical food sectorcomposedonlybyregularproductionactivitiesfollowedspecificapproachesforthewineandtheoliveoilindustries,twosub-sectorsthatarestrategicforItalianfoodsystemandlargelyaffectedbyfrauds.Inthecaseofwineandoliveoilthevectorofdirectexpenditureswascorrectedwiththehelpofexpertsandkeyinformants, taking also into account the different possible configurations of the process in differenttypologiesofproductionunits (for instance farm-basedor industrialproductionactivities).Detailson thehypothesesmadeareprovidedinthereportdedicatedtothetwosub-sectors.

23

Regardingtheotherfoodsub-sectors, inordertocarryoutacounterfactualanalysisofthe impactofthetotalamountoffraudsweassumedahomogeneous(andsimplified)hypothesisontheimpactoffraudsonthestructureofcosts.Moreprecisely,weassumedthat theadoptionof irregularpractices inproductionand trade was able to triple the value of output at the same cost. Consequently, all coefficientsrepresenting the composition of intermediate consumptions were divided by 3. Only the cost ofprofessionalandlegalserviceswasassumedtomaintainthesameshareonthetotalvalueofproductionofthe"averages"activitiesrepresentedintheSAM.

Suchprocedure isquite simplistic and implies strongassumptions. Twoelements,however, suggest thattheanalysis couldanywayproduceauseful lookon theeconomicsofagro-food frauds in Italy.First, thevalueofirregularproductsotherthanwineandoliveoilrepresentsasmallshareofthetotal(27%).Second,thepresenceoffraudsinallsub-sectorsoftheagro-foodsystem,is likelytomultiplytheimpactofsingleproductions,duetointer-industryinterdependencywithinthefoodsystem(firstofallbetweenagricultureandfoodproduction).Evenarawdescriptionoftheeffectsoffraudsonproductioncostsislikelytobetterapproximateresultstotheactualvalue.Inanycase,acounterfactualanalysisoftheimpactoffraudsinthewine andoliveoil sector considered is caseby case in the sub-sectors’ specific reports. In table 5.2, theimpact of frauds in the agro-food sector on thewhole Italian economy is presented by a few summaryfigures.

Table 5.2. Impact of irregular production act iv it ies, average 2007-2015 – Values expressed

in €2009

Typeofimpact

Onlyseizedproducts Allirregularproducts

Absolutevalue %impact Absolutevalue %impact

Impactontotaloutput(M€) -139 0.00% -1827 -0.06%

Impactontotalemployment(000LU) -2 -0.01% -20 -0.08%

Impactonvalueadded(M€) 6 0.00% 87 0.01%

Impactonhouseholds'grossincome(M€) -16 0.00% -174 -0.01%

Source:Thisstudy.

Changesinthevalueofoutputarenegative.Thepresenceoffraudsinfoodproductiondecreasesthetotallevelofactivity intheeconomy.Thetableprovidestwodifferentestimatesobtainedconsidering(1)onlyproductionsubject(whendetected)toseizures;(2)allirregularproduction.Theoverallestimatedimpactson total output are tangible: up to 1.8 billion € of total output in all sectors of Italian economy arepotentially lost, corresponding to about 20 thousands of full time labour units. In times ofunderemployment,thisisastrikingfigure.Theincomedistributedtohouseholdsisdecreasedtoo(upto-0.01%).Interestingly,thenetimpactonvalueaddedispositivethoughsmall.Theextra-profitsproducedbyfraudulent activities counterbalance the reduction due to the loss of productive activities, but forwardlinkages in their use are not able to activate the economy (throughout final demand) and generateemploymentandincome.Thelargestpartsoftheseprofitsarelikelytobesavedinnon-productiveassetsand/or transferred outside the economic systems. Overall results provide a fairly clear evidence of theexistenceofrentsfromfraudsnegativelyaffectingtheviabilityoftheeconomicsystem.

Looking at the agro-food system, the figures expressed in relative terms are higher as expected. Thepercentageimpactonoutputisfarmoreimportantinagriculturethaninthefoodindustry(Figure5.2).Theloss of qualification in irregular food production reduces the value of agricultural input used per unit ofprocessedfood.LookingatFigure5.2,wecanseethatthepresenceoffraudsnegativelyaffectstheoutputofagricultureupto1%.Theimpactonfoodindustryislower(varyingfrom-0.03%-%-0.33%)eventhoughhigherthantheaveragelossintheeconomy.

24

Figure 5.2. Impact on total output in the agro-food sectors, average 2007-2015 - % of

total output

Source:Thisstudy.

Table5.3showsthebreakdownbyindustryoftheimpactsestimatedbasedonthevalueoftotalirregularproduction. Overall, the agro-food sector loses more than 13,700 labour units, with the largest partconcentratedinagriculture,wheretheproductivityoflabouristypicallylowerbutalsowherethelossesintermsofoutputarehigher.Theimpactisdifferentiatedamongdifferentproductionactivities,rangingfrom0.17%inthe"Otherfood"sectorto1.28%inthe"Fishproducts"industry.Interestingly,thewinesectorisone of the most impacted by frauds, (output-1.06%, employment -218 LU). The datum should be readtogetherwiththatofspecializedpermanentcropsfarms,producingarelevantshareofwineandoliveoil.Thiscomponentofagricultureloses1.0%ofitspotentialoutputandmorethan5,300LUforthepresenceoffraudsintheagro-foodsystem.

Table 5.3. Impact on output and employment in the agro-food sectors, average 2007-2015 - % of total output and total labour units

Sub-sectorAllirregularproducts

%totaloutput LU

Meatandproductionofmeatproducts -0.16% -102Processingandpreservingoffish,crustaceansandmolluscs -1.28% -74Productionofoliveoil -0.47% -15Manufactureofotherfoodproducts -0.17% -368Manufactureofprocessedvegetablesandfruitsproducts -0.22% -63Manufactureofdairyproducts -0.37% -192Manufactureofgrainmillproducts,starchesandstarchproducts -0.74% -72Manufactureofpreparedanimalfeed -0.78% -90Productionofwine -1.06% -218Manufactureofotherbeverages -0.25% -77Specializedfieldscrop -1.03% -2681Specializedpermanentcrops -1.00% -5335Totalagriculture -1.00% -12507

Totalfoodindustry -0.33% -1271

Source:Thisstudy.

-1.20%

-1.00%

-0.80%

-0.60%

-0.40%

-0.20%

0.00%agriculture foodindustry

seized irregular

25

Afurtherinterestingresultconcernsthedistributivefeaturesofimpacts.Table5.2showsthatfraudsintheagro-food sector decreases the gross income earned by households by about 187M€. In Figure 5.3 theimpactisdisaggregatedamongdecilesofequivalentper-capitaincomeandexpressedinpercentageterms.The impact seems to show a slight "progressive" profile, affecting more the income of highest deciles.Considering that thedifference in average incomebetween the twoodds of income groups is far largerthan the differences in impacts, the changes in the relative position of groups in income distributiongeneratedbyfraudsarelikelytobesmall.Further,aswillbeshownintheanalysisdedicatedtowineandoliveoil,thedistributiveimpactisfairlydifferentiatedamongdifferentfoodproduction.Fraudsinwineandolive oil sub-sectors show "regressive" distributive impacts, affecting poorest families in a larger extentthanrichestones.Thisisprobablyduetostrongerlinkoftheseproductionswithagriculturalactivities.

Afurtherinterestingresultconcernsthedistributivefeaturesofimpacts.Dataintable5.2showsthatthepresenceof frauds intheagro-foodsectorcoulddecreasethegross incomeearnedbyhouseholdsabout174M€.Infigure5.3suchanimpact isdisaggregatedamongdecilesofequivalentper-capita incomeandexpressed inpercentageterms.The impactseemstoshowaslight"progressive"profile,advantagingthelowest deciles and negatively affecting more the income of higher deciles. A relevant exception isrepresented by the highest decile, whose income is not affected. Consider also that the difference ofaverage income between the two odds of distribution is far larger than the estimated differences inimpacts.Thechangesintherelativepositionofgroupsinincomedistributiongeneratedbyfraudsarelikelytobesmall.

Figure 5.3. Impact on households' gross income, average 2007-2015 - % values by income

deci les

Source:Thisstudy.

-0.03% -0.02% -0.01% 0.00% 0.01% 0.02% 0.03%

1°

2°

3°

4°

5°

6°

7°

8°

9°

10°

Incomede

ciles

26

6.Conclusions

In this paper,we used data from the ICQRFmonitoring activities (Sadiddin et al., 2018a) to assess theeconomicsizeoffraudulentagro-foodoutput,estimatedthesizeoftheeconomydependingonfraudulentproduction, and simulated the impacts of agro-food frauds on the national economy in terms of GDP,employmentandincomedistributionwithinaSAMframework.

Theanalysisshowsthatthewinevaluechainisthesub-sectormostexposedtofraudsaccountingfor88%of the total value of seized agro-food outputs. Second ranks olive oil value chain (6% of total seizures),whiletheotherVCsaccountsforonlytheremaining6%oftotalseizures.Theshareschangeslightlywhenallirregularproducts(i.e.seizuresplusotherirregularities)areconsidered.

Thesharesofvariousvaluechainschangeabitmoreafterexpandingthesamplevaluestothepopulationlevel.ThewineVCisstillranksfirstbutwithalowershare,namely76%(whenonlyseizuresareconsidered)and65%(whenallirregularproductsareaccountedfor).ThesharesofoliveoilVCslightlyincreasedupto7% inboth cases,while amorenoticeable increases areobserved for theotherVCs, especiallywhenallirregularproductsareconsidered.Forexample, fishVC thataccountedonly for0.1%at thesample levelincreasedupto3%atthepopulationlevel.

Afirstimportantresultoftheanalysisistheestimationoftheeconomicsizeoftheagro-foodfrauds.TheresultsoftheSAMsimulations8showsthattheshareofeconomydirectlyandindirectlylinkedtosupplyofirregularfoodproductsaccountsfor0.5%oftotalvalueofoutput,whileintermsofvalueaddedtheshareofirregularfoodproductsrangesbetween0.1%and0.4%oftotalvalueadded.Thiscorrespondstoavalueof1.9billioneuro(consideringonlyseizures)to13.9billioneuro(includingallirregularities)andisabletoactivateupto156thousand labourunits intheworst-casescenario. Intermsofthesharerelativetotheagro-food sector, the total output "driven" by irregular products ismuch higher accounting for 3.2% ofoutputand5.8%ofemployment.

The heavy dependence of some value chains on the demandmet by irregular production makes themvulnerable to shocks as theones deriving from food scandals/scares, especially if they refer to productsfeaturingrelativelylargepriceelasticities.Wineseemstobethemostfragilevaluechainconsideringthatroughly25%ofitsdemandismetbyirregularproduction.Thistranslates,throughbackwardlinkages,intoa11%ofthedemandforproductsofpermanentcrops.Overall,forthewholeagriculturalsectortheshareofdemandmetbyirregularproductisaround5%,asignificantlowerfigurethoughstillnotnegligible.

Resultsfromthecounterfactualanalysisshowsthatagro-foodfraudscausedalossesof1.8billioneurointermsof totaloutput, corresponding toabout20 thousandsof full time labourunits. Thenet impactonGDP ispositive thoughverysmall since theearnings feed rent-seekingactivities insteadof strengtheninglinkageswiththerestoftheeconomy.Theextra-profitsproducedbyfraudulentactivitiescounterbalancethe reduction due to the loss of productive activities, but forward linkages in their use are not able toactivatetheeconomy(throughoutfinaldemand)andgenerateemploymentandincome.Thelargestpartsof theseprofits are likely tobe saved innon-productive assets and/or transferredoutside theeconomicsystems. Overall results provide a fairly clear evidence of the existence of rents from frauds negativelyaffecting theviabilityof theeconomic system. Lookingat theagro-food system, the figuresexpressed inrelativetermsarehigherasexpected.Thepercentageimpactonoutputisfarmoreimportantinagriculturethaninthefoodindustry.Fraudsnegativelyaffecttheoutputofagricultureupto1%.Theimpactonfoodindustry is lower (varying from -0.03% - % - 0.33%) even though higher than the average loss in theeconomy.

8Disaggregated impact evaluationof frauds committed inwine andoliveoil sub-sectors is provided inRocchi et al. (2018) andSadiddinetal.(2018b)respectively.

27

Householdincomesarereducedbyonly0.01%.However,inthefoodsector,wherethetrustofconsumersinthequalityofproductsandhealthconcernsarefundamentalfactorsdrivingthemarket,thepresenceoffrauds is apotential sourceof instabilityandmarketdisruptionashasbeen largelyobserved in thepastyearswhenfoodscaresandscandalscausedlargelosses.

Inconclusion,agro-foodfraudsdeceiveconsumersandcreateunfaircompetitiontocompliantproducersandagents.Theresultsofthispapershowthatfightingagro-foodfraudsisjustifiedbothonefficiencyandequitygrounds.

28

References

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Riccio, P., Felici, G., Alisi, G. & Caira, E. (2014). La Lotta alla Contraffazione in Italia nel Settore Agro-alimentare 2009-2012, Direzione Generale per la Lotta alla Contraffazione – Ufficio Italiano Brevetti eMarchi, Ministero dello Sviluppo Economico, Roma, p. 129. Retrieved from:<http://www.uibm.gov.it/iperico/home/Rapporto%20IPERICO-%20Lotta%20alla%20contraffazione%20nel%20settore%20agroalimentare_definitivo.pdf>. [February 11,2016].

Rocchi,B.,Sadiddin,A.,Romano,D.,Stefani,G.,Zucaro,R.,Manganiello,V.(2018).FraudsintheItalianWineSubsector:AvaluechainanalysisandSAM-basedevaluationofeconomicimpacts.WorkingPaperno….DipartimentodiScienzeperl’Economiael’Impresa.UniversitàdegliStudidiFirenze.

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Sadiddin,A.,Romano,D.,Zucaro,R.,&Manganiello,V.,(2018a).FraudsintheItalianAgro-FoodSector:AnIntroduction.Working Paper no… . Dipartimento di Scienze per l’Economia e l’Impresa.Università degliStudidiFirenze.

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1

Annexes

Table A1 - Composit ion of intermediate consumption, labour units and total output in the food industry, I ta ly , 2009 – Column %

values

Commodities/Industries

Productionofmeatmeatproducts

Processingandpreservingoffish,crustaceansandmolluscs

Productionofoliveoil(virginandrefined)

Manufactureofotherfoodproducts(vegetableoilsandfats,sugar,bakeryandfarinaceousproducts)

Manufactureofprocessedvegetablesandfruitsproducts

Manufactureofdairyproducts

Manufactureofgrainmillproducts,starchesandstarchproducts

Manufactureofpreparedanimalfeed

Productionofwine

Manufactureofotherbeverages

Manufactureoftobaccoproducts

Agriculturalproducts 36.6% 1.4% 35.2% 10.5% 29.2% 26.0% 49.9% 19.7% 21.9% 2.6% 19.2%

Meatandmeatproducts 12.6% 0.3% 0.4% 5.2% 2.0% 1.4% 0.2% 5.1% 0.0% 0.0% 0.0%

Processedfish,crustaceansandmolluscs0.3% 50.4% 0.0% 0.6% 1.3% 0.0% 0.0% 1.9% 0.0% 0.0% 0.0%

Oliveoil,virginandrefined 3.8% 4.4% 11.3% 8.3% 6.9% 2.8% 5.1% 1.9% 2.2% 0.4% 0.0%

Otherfoodproducts 1.4% 6.7% 1.1% 4.7% 12.5% 2.5% 0.8% 12.2% 0.8% 4.5% 0.0%

Processedfruitsandvegetables 0.1% 0.3% 0.9% 3.5% 4.4% 1.4% 0.4% 0.5% 0.7% 2.4% 0.0%

Dairyproducts 0.3% 0.0% 0.3% 2.5% 1.4% 33.4% 0.2% 3.9% 0.0% 0.0% 0.0%Grain mill products, starches and starchproducts 0.2% 0.7% 0.7% 11.9% 0.3% 0.2% 3.1% 10.2% 0.1% 0.5% 0.0%

Preparedanimalfeeds 0.9% 0.8% 0.0% 0.0% 0.0% 0.4% 1.0% 5.9% 0.1% 0.1% 0.0%

Wine 6.8% 0.3% 0.1% 0.0% 5.5% 4.9% 9.3% 3.7% 6.9% 11.1% 0.0%

Otherbeverages 0.0% 0.3% 0.0% 0.1% 0.3% 0.4% 0.0% 0.1% 0.7% 16.7% 0.0%

Processedtobaccoproducts 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 6.0%

Othergoods 8.0% 9.4% 17.1% 11.8% 10.6% 4.8% 6.0% 10.4% 22.6% 13.7% 26.6%

Tradeandtransportationservices 17.8% 12.9% 12.6% 18.5% 17.2% 17.1% 16.6% 19.6% 19.6% 23.9% 13.4%

Financialservices 1.6% 1.3% 3.8% 3.0% 1.3% 0.5% 0.7% 0.8% 6.9% 2.1% 1.0%

Otherservices 9.6% 10.8% 16.7% 19.4% 7.0% 4.1% 6.7% 4.2% 17.5% 21.7% 33.8%

LabourUnits(000) 62 5 3 213 28 51 9 11 18 30 2

Output(M€) 20358 1844 2553 43389 9126 15767 4465 4661 6295 8697 419

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Source:Thisstudy.

Table A2 - Composit ion of intermediate consumptions, labour units and total output in agriculture, I ta ly , 2009 – Column % values

Commodities/Industries Specializedfieldcrops

Specializedhorticulture

Specializedpermanentcrops

Specializedgrazinglivestock

Specializedgranivores

Mixedcropping

Mixedlivestock

Mixedcrops-livestock

Agriculturalproducts 31.5% 31.2% 11.6% 17.7% 26.2% 28.2% 26.7% 62.2%

Meatandmeatproducts 0.1% 0.0% 0.5% 0.1% 0.0% 0.5% 0.8% 0.1%

Processedfish,crustaceansandmolluscs 0.8% 0.1% 2.5% 0.5% 0.1% 2.6% 4.2% 0.5%

Oliveoil,virginandrefined 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

Otherfoodproducts 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

Processedfruitsandvegetables 0.0% 0.0% 0.1% 0.0% 0.0% 0.1% 0.1% 0.0%

Dairyproducts 1.5% 0.3% 4.8% 1.0% 0.2% 5.1% 8.0% 0.9%

Grainmillproducts,starchesandstarchproducts 0.3% 0.1% 1.1% 0.2% 0.1% 1.2% 1.9% 0.2%

Preparedanimalfeeds 1.9% 0.0% 0.3% 37.9% 46.1% 0.6% 21.7% 20.2%

Wine 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

Otherbeverages 0.1% 0.0% 0.2% 0.0% 0.0% 0.2% 0.3% 0.0%

Processedtobaccoproducts 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

Othergoods 40.0% 27.6% 46.5% 26.1% 19.3% 37.3% 23.4% 10.1%

Tradeandtransportationservices 9.9% 34.1% 19.7% 2.4% 2.8% 9.2% 0.5% 2.2%

Financialservices 8.5% 2.2% 5.6% 4.4% 2.0% 5.0% 3.8% 1.2%

Otherservices 5.5% 4.4% 7.3% 9.6% 3.2% 9.9% 8.6% 2.4%

LabourUnits(000) 258 94 485 185 21 97 6 48

Output(M€) 9894 5419 12813 9678 3837 2641 189 1665

Source:thisstudy.

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Table A3 - Output composit ion in the food industry, I ta ly , 2009 – Row % values

Industries/CommoditiesMeatandmeatproducts

Fish,crustaceansandmolluscs

Olive oil,virgin andrefined

Other foodproducts

Processedfruits andvegetables

Dairyproducts

Grain millproducts,starchesand starchproducts

Preparedanimalfeeds

WineOtherbeverages

Processedtobaccoproducts

Meatandproductionofmeatproducts 92.4% 0.0% 0.0% 1.3% 0.0% 0.1% 0.0% 3.8% 0.0% 0.0% 0.0%Processing and preserving of fish, crustaceans andmolluscs 1.1% 91.4% 0.0% 4.8% 0.3% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

Productionofoliveoil 0.0% 0.0% 50.9% 46.3% 0.1% 0.0% 0.0% 0.0% 0.2% 0.0% 0.0%

Manufactureofotherfoodproducts 0.8% 0.2% 1.0% 87.4% 1.9% 2.1% 2.1% 0.7% 0.1% 0.9% 0.0%

Manufactureofprocessedvegetablesandfruitsproducts 0.1% 0.0% 0.8% 7.8% 86.9% 0.0% 0.7% 0.0% 0.3% 0.8% 0.0%

Manufactureofdairyproducts 2.2% 0.0% 0.0% 3.6% 0.4% 91.3% 0.0% 0.0% 0.0% 0.1% 0.0%Manufacture of grainmill products, starches and starchproducts 0.0% 0.0% 0.0% 0.9% 0.0% 0.0% 91.1% 4.6% 0.0% 0.0% 0.0%

Manufactureofpreparedanimalfeed 1.2% 0.0% 0.0% 2.0% 4.8% 0.0% 4.2% 84.9% 0.0% 0.0% 0.0%

Productionofwine 0.0% 0.0% 1.7% 0.1% 0.7% 0.0% 0.0% 0.0% 89.2% 5.5% 0.0%

Manufactureofotherbeverages 0.0% 0.0% 0.0% 0.6% 0.4% 0.2% 0.0% 0.0% 2.8% 92.3% 0.0%

Manufactureoftobaccoproducts 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 97.5%Source:Thisstudy.

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Table A4 - Output composit ion in agr iculture, I ta ly , 2009 – Row % values

Industries/Commodities Agriculturalproducts

Meatandmeatproducts

Fish,crustaceansandmolluscs

Olive oil,virginandrefined

Otherfoodproducts

Processedfruits andvegetables

Dairyproducts

Grain millproducts,starchesand starchproducts

Preparedanimalfeeds

Wine Otherbeverages

Specializedfieldcrops 95.2% 0.0% 0.0% 2.2% 0.2% 0.0% 0.0% 0.0% 0.0% 0.7% 0.0%

Specializedhorticulture 95.5% 0.0% 0.0% 0.5% 1.5% 0.3% 0.0% 0.2% 0.2% 0.6% 0.4%

Specializedpermanentcrops 37.9% 0.0% 0.0% 20.1% 0.2% 0.0% 0.0% 0.0% 0.0% 38.1% 0.1%

Specializedgrazinglivestock 92.4% 0.2% 0.1% 0.7% 0.5% 0.1% 0.6% 0.1% 0.1% 0.3% 0.1%

Specializedgranivores 98.4% 0.0% 0.0% 0.1% 0.0% 0.0% 0.1% 0.0% 0.0% 0.7% 0.0%

Mixedcropping 65.6% 0.0% 0.0% 12.6% 0.1% 0.0% 0.1% 0.0% 0.0% 7.7% 0.0%

Mixedlivestock 69.3% 0.1% 0.0% 0.7% 0.3% 0.1% 0.2% 0.0% 0.0% 0.3% 0.1%

Mixedcrops-livestock 87.0% 0.2% 0.1% 5.0% 0.0% 0.0% 0.7% 0.0% 0.0% 4.8% 0.0%

Source:Thisstudy.

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