veggielite – conjunction of agriculture, nutrition and health for ...

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SpencerMoore,SamikGhosh,SummerAllen,PKJoshi,SaibalRay,SrivardhiniJha,

DanielRoss&LauretteDubé

November2014–May2016

July20,2016

VEGGIELITE–CONJUNCTIONOFAGRICULTURE,NUTRITION

ANDHEALTHFORINCLUSIVEDEVELOPMENTOFWOMEN

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TableofContentsIndex Chapters PageNo.

Acknowledgements 4

Authors 5

AbbreviationsandTerminology 6-7

ExecutiveSummary 8-11

Chapter1 Introductionandreportoverview 13-21

Chapter2 eKutirSocialBusiness,VeggieKartecosystem,andVeggieLiteinterventionfromanoperationalperspective,eKutirfinancialviability

22-40

Chapter3 HouseholdandIndividualImpact

Background,EvaluationDesign

UrbanandRuralSampleDescription

Evaluationinstruments-Baseline/endlineinterventionevaluationquestionnaire,Evaluationmeasures,Analysismethods

eKutirOperationalData,AnalysisApproach

Evaluationfindingsforurbanandruralprimaryoutcome-totalandhome-grownvegetableconsumption;psychologicalmotivesandbeliefs,andsocialcapital/network

Findingsforruralsecondaryoutcome-farming,outcomes-vegetableproduction,priceandincome

Findingsforfoodinsecurity,Multi-DimensionalPovertyIndex,householdexpenditure

eKuitroperationalanalysisonruralsales

Summaryanddiscussion

41-78

Chapter4 ConvergentInnovationProcessAnalysis:Producers&Organizations,EcosystemNetworkFormation,andCompetitiveDynamics

Strategicresearchquestions

Methods

Findingsforstrategicresearchconvergence,networkformation,andcompetitivedynamics

79-116

Chapter5 EarlyPrototypeDevelopmentforBehavioralEconomicIncentivesInterventionandRelatedEcosystemExtension

Behaviouralincentivesintervention

Recommendation

117-121

Chapter6 Discussion,learningandlimitations 122-126

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References 127-129

Annexure#researchethics–IRB,studytools(attached) 130

Figuresandtables 131-132

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Acknowledgements

This study brings together a multi-disciplinary consortium to evaluate an

innovationatthenexusofagriculture,nutrition,andhealth.Thefindings

and analysis in the pages that follow is the contribution of the key

participants of the consortium, who shared their knowledge, data, and

insightsfortheprogramandthereport.

Theauthorswouldliketoacknowledgeandthankthesponsorsundertheaegis

of Grand Challenges India – Bill and Melinda Gates Foundation and BIRAC,

Department of Biotechnology, India – for the award, their support and

financing of the program. Their time, direction, and energy put in to this

programhasbeenhighlyusefulforus.

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AuthorsThe study report is authored by Dr. Spencer Moore [USC], Mr. Samik Ghosh

[IFPRI], Dr. Summer Allen [IFPRI], Dr. PK Joshi [IFPRI], Dr. Saibal Ray

[MCCHE],Dr.SrivardhiniK.Jha[IIMB&MCCHE],andDr.LauretteDube[MCCHE]

andincollaborationwithMr.DanielRoss[DAISA]andMr.K.C.MishraandMr.

SuvankarMishra,PrincipalInvestigatorsforeKutir.

AffiliationsandResearchCollaborationsMCCHE McGill Centre for Convergence of Health and Economics, McGill

University,Canada

USC ArnoldSchoolofPublicHealth,UniversityofSouthCarolina,USA

IFPRI InternationalFoodPolicyResearchInstitute,USAandIndia

IIMB IndianInstituteofManagementBangalore,India

DAISA DAISAEnterprises,LLC,USA

eKutir eKutirRuralManagementServicesPrivateLimited,India

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AbbreviationsandTerminologyA4NH AgricultureforNutritionandHealth

AE Agri-Entrepreneurs

ANOVA Analysisofvariance

ASHA AccreditedSocialHealthActivist

ASPH ArnoldSchoolofPublicHealth,USA

AWW AnganwadiWorker

BA BajajAllianz

BIHS BangladeshIntegratedHouseholdSurveyQuestionnaire(BIHS)

BIRAC BiotechnologyIndustryResearchAssistanceCouncil

CCDCentreforCollectiveDevelopment(anon-profit,non-governmental

organization)

CGIAR ConsultativeGroupforInternationalAgriculturalResearch,USA

CI ConvergentInnovation

DAISA DAISAEnterprisesLLC,USA;apartnerrepresentingWholesomeWave

DALY DisabilityAdjustedLifeYear

DBT DepartmentofBiotechnology

D-COR LocalagencyforfieldworkinBhubaneswar,Odisha

DVCP DoubleValueCouponProgram

Ekjut Not-for-profit

eKutir eKutirRuralManagementServices(P)Limited,Odisha(India)

F&V FruitsandVegetables

F2C FarmtoConsumer

FIG FarmerInterventionGroups

FSG FarmerSupportGroup

GCI GrandChallengesofIndia

GDP GrossDomesticProduct

GPS GlobalPositioningSystem

HH Household

HHID HouseholdIdentification

ICDS IntegratedChildDevelopmentServices

ICT InformationCommunicationTechnology

IFPRI InternationalFoodPolicyResearchInstitute,USA

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IRB InstitutionalReviewBoard

Mandi Localmarket

MCCHEMcGillCentrefortheConvergenceofHealthandEconomics,Canada

ME MicroEntrepreneur

MGM MGMAgrotechPrivateLimited

NCML NationalCollateralManagementServicesLimited

NGO Non-governmentalOrganization

Pratidhi eKutir-GCINutritionIncentiveProgram

PRSSP BangladeshPolicyResearchandStrategySupportProgram

PSU PrimarySampleUnit

SEED Localagencysupportedforfieldsurveyanddatamanagement

USC UniversityofSouthCarolina,USA

VE VeggieEntrepreneurs

VHND VillageHealthandNutritionDay

Wards Urbangeographicsettlements

WHO WorldHealthOrganization

WoS Whole-of-Society

WW WholesomeWave,USA

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ExecutiveSummary

eKutir is a social enterprise that leverages local micro-entrepreneurial

motivations and processes to solve smallholder farmers’ poverty through a

distribution network of digitally-trained entrepreneurs, market linkages,

technology, and data. One of its initiatives is to increase agricultural

productionand/ornutritionalintakeamongresource-poorgroups,particularly

womenandchildren,inbothruralandurbancommunitiesinIndia.InNovember

2014, Grand Challenges India awarded eKutir pilot funding for the project

“VeggieLite–ConjunctionofAgriculture,NutritionandHealthforInclusive

DevelopmentofWomen.”FundingwasprovidedbyBMGFandBIRAC-DBT(Government

ofIndia).

The VeggieLite/VeggieKart intervention had several important propositions

consistent with the goals of the Grand Challenges India Agriculture &

Nutrition program, including Nutrition Innovation, Agricultural Innovation,

andSocialInnovation:

• Toincreaseconsumptionofhealthyvegetablesforbothurbanconsumers

andruralproducers,particularlywomen;

• Toimprovelivelihoodsofparticipatingsmallholderfarmers;

• Todemonstrateaninnovationinagriculturalvaluechainthroughsocial

enterprise, able to deliver sustained benefits to both underserved

producers and consumers, and Convergent Innovation, a framework for

multi-stakeholderaction.

This report provides a description of the impact and outcomes of the

VeggieLiteconvergentinnovationpilotprojectpursuanttothesegoals.

Chapter one describes the background and development of the eKutir

entrepreneurialmodelanditsapplicationinaddressingthetwinchallenges

ofruralpovertyandpoormaternalandchildnutrition.Chaptertwopresents

eKutir Social Enterprise, VeggieKart Ecosystem, and VeggieLite Intervention

fromanOperationalPerspective.ThischapterfurtherelaborateseKutirasa

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social enterprise and its value and sustainability in delivering social

benefits. Chapter three presents the complex study design used to evaluate

theVeggieLitefieldprojectamongtheruralfarmingcommunitiesandthelow-

incomeurbanconsumers.Thedesignwasquasi-experimentalwithbaselineand

endlineassessments,aimedattheambitiousgoalofcapturingenvironmental

and household changes in vegetable production and consumption within a

limitedone-yearperiod.Thischapteralsopresentsthefieldresultsofthe

VeggieLite project in the rural and urban areas. Our report focuses on a

rangeofprocessandimpactindicatorsattheareaandhouseholdlevels,with

nutritionalimpactbeingmeasuredintermsofvegetableconsumption.Chapter

four provides the results of our study of the organizational dynamics and

inter-organizationalstructuresunderlyingandemergingduringthecourseof

the VeggieLite project. This chapter chronicles the creation of new

partnershipsandnetworksthatemergedinthedevelopmentandimplementation

of the VeggieLite program. Chapter five describes the behavioral incentive

mini-experiment designed to (i) improve the nutrition of pregnant and

lactatingmothersandtheirinfantsandpatientswithchronicconditionsand

(ii)fosterVeggieLitelinkstothemedicalandhealthcaresysteminOdisha.

Finally, chapter six summarizes overall discussion on successes, lessons

learnedandlimitations.

In summary, the VeggieLite project showed a number of successes in its

operationsandoutcomesparticularlyforfarmersandmicro-entrepreneurs,but

alsochallengesinincreasingtheconsumptionofvegetablesamongasampleof

consumersovertheprojectperiod.

eKutir successfully implemented the VeggieKart/VeggieLite intervention in

over five selected locations of rural and urban Odisha, engaging over 1350

farmersand90micro-entrepreneurs,morethandoublethenumbersprojectedin

theproposal.Roughly71%oftheruralVeggieLiteentrepreneurswerewomen,

and40%oftheurbanVeggieLiteentrepreneurswerewomenandoutofthetotal

farmers registered within this intervention women farmers constitute around

65℅.

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Intheurbanareas,eKutirexceededprogramobjectivesforthedeploymentof

micro-entrepreneur vendors, with VeggieLite sales robust and increasing

steadilyinthetargetedwardsovertheyear.Inourparticularsampleof

consumers,however,wedidnotseeanincreaseinvegetableintakefrompre-

topost-testing.Therewereanumberoffactorsthatmayhelptoexplainthis

null finding. For example, disruptions in the services or locations of

certain VeggieLite vendors or even the broader range of consumer options

available to urban residents, including other eKutir offerings such as

VeggieKart or VeggieMart, could have weakened the intervention’s effect.

Nevertheless, in our sample of urban residents, we did find that those who

reported purchasing mainly from VeggieLite vendors at posttest increased

theirvegetableintakeandconsumedmoredailyvegetableservingsthanthose

thatdidnot.

In rural Odisha, there was stronger evidence of VeggieLite’s nutritional,

agricultural and social impacts. First, the intervention showed a

significantincreaseinthefruitandvegetableconsumptionofthosefarmers

participating in the eKutir program. These increases in consumption were

driveninlargepartthroughincreasedfruitconsumption.Yet,inaperiod

inwhichallfarminggroupsshowedadeclineintheirdailyvegetableintake,

eKutir farmers experienced the least decline and showed at end line a

significantly higher level of vegetable consumption than the other groups.

Second,VeggieLite’sagriculturalimpactwasalsonoticeable.Atbaseline,

farmers joining eKutir produced and sold more vegetables than the other

farmer groups. At end line, eKutir farmers still produced and sold more

vegetablesthantheotherfarmergroups,generatingover30.6MINRinsales

forparticipatingfarmers.ThishighvolumeofsalesamongeKutirfarmersis

particularlyofnotesinceeKutirfarmerswereatendlineorthegroupthat

alsoconsumedthegreatestpercentageoftheirownvegetables,withroughly

28% going to their consumption. Finally, there was also evidence of

VeggieLite’ssocialimpact.Thekeytothesuccessofanyinnovationisits

wider social acceptance. Our social network analysis showed the broader

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acceptanceanddiffusionofeKutirprogrammingintothecommunity.Notonly

did the composition of the eKutir farmers’ networks change to include a

greaterpercentageoffarmersinthefarmerinterventiongroups(FIGs),but

so too did the composition of the non-eKutir farmers’ networks. In other

words,overtheone-yearinterventionperiod,regardlessofwhetherfarmers

participated directly in the intervention or not, all farmers in the

treatmentvillagescametodiscussfarmingmattersmoreandmorewithfarmers

intheFIGs.TheeKutirmodelthusseemstohaveincreaseditsacceptance

andbecomemorewidelydiffusedinthetargetedruralcommunities.

In the pilot period eKutir and partners learned many important lessons,

critical to success as a value chain social enterprise and nutrition

intervention. Increased numbers of eKutir-registered farmers are selling to

VeggieKartandtheirfarmingnetworkisgrowing.Intheruralareas,eKutir

farmersexhibitedahigherlevelofhomegrownconsumptionthanotherfarming

groups,suggestingthateKutirfarmersmayemployamoremixedproductionand

consumption strategy than other groups. In urban areas, the project found

that the availability of VeggieLite to certain consumers might be hampered

withtheclosingofspecificvendors,andthusgreaterattentionshouldfocus

more on developing client-vendor relationships and addressing consumer

loyaltyinthecomingageofmobilemarketing.

Organizationally,thestudyrevealedthatVeggieKart/VeggieLitehasbeenable

to create value for all the constituents in the system: farmers, micro-

entrepreneursandorganizationalpartners.Thenetworkstudyrevealedstrong,

collaborative ties among key eKutir business partners and strong vertical

ties among eKutir and its entrepreneurs. However, in order to capture the

value created and achieve economic profitability, VeggieKart needs to

increase its scale of operations and invest on consumer marketing and

behavioralchange.ICTcanplayacrucialroleinachievingthis.ICTisused

quite extensively on the supply side, especially between eKutir and agri

entrepreneurs.Itsusagecanbestrengthenedonthedemandsideaswell,to

supportrapidexpansion.Induecourse,asICTbridgesalltheactorsinthe

eKutir ecosystem, the information and collaboration networks can also be

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expected to become dense. In terms of organizational lessons, the network

mapping suggests the need to better connect agri-entrepreneurs and veggie-

entrepreneurstooneanotherandlocalcommunityhealthworkerssothatthey

might learn best practices from each other and naturally integrate

agriculture,nutrition,andhealthaims.

Our analysis showed that eKutir made significant progress towards self-

sustainability as an enterprise, nearing scale and efficiency for

profitability, and making it a strong deployment model of ongoing public

benefits. Over the project period eKutir average monthly sales grew 45%,

while operational efficiencies increased. eKutir’s value chain that reduces

intermediaries and delays in bringing crops to urban consumers shows

significantadvantagesofreducedwastageandincreasedvaluetothefarmers

over the traditional supply chain. eKutir social enterprise is poised to

scaleupoftheVeggieLitemodeltootherregionsofOdishaandIndia.The

micro-experimentstestinginnovativenutritionincentiveprogramshaveshown

enough promise in promoting nutrition that they have attracted a private

sectorpartner(ApolloHospitalSystems)andwillworkwiththeASHAworkers

forlargerpilotsmovingforward,majorinitiativestoaddressdemand.

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Chapter1:IntroductionThis document reports on the pilot project and associated research

“VeggieLite-ConjunctionofAgriculture,Nutrition,andHealthforInclusive

Development of Women, BIRAC/GCI/0013/01/13-AGN.” VeggieLite is a retail

channel in the broader VeggieKart business model and ecosystem that builds

uponthewell-establishedeKutirforprofitsocialenterprise.Theultimate

socialgoalistodesignandactivateaself-sustainingentrepreneurialmodel

to increase the availability, affordability, and consumption of fruits and

vegetables(F&V)targetedtolow-incomeruralandurbancommunitiesinlow-

and middle- income countries like India, while creating economic

opportunities for the poor as farmers, agri-, or retail- entrepreneurs,

throughoutthefarm-to-consumervaluechainandecosystem.Aspecialemphasis

is placed on progressively fostering inclusive development for women, in a

contextthatremainstoasignificantextentmen-dominated,beitathome,in

the farm, or in business. The pilot project unfolded in experimental and

controldistrictsinthestateofOdisha,IndiabetweenNovember2014andMay

2016, with endline surveys performed from March to April 2016. In this

chapter, we first review the project objectives in the funded proposal. We

then introduce the Convergent Innovation approach to evaluation in complex

systems,whichnotonlyevaluateimpactbutalsoinformstheadaptivedesign

ofaninterventionsuchasVeggieLitethattargetsbothbehavioralchangeand

ecosystem transformation. We also underscore the potential significance of

thepilotandresearchfindingsforgoingbeyondwhathasbeenpossiblethus

far in bringing solution at scale to nutrition, poverty alleviation, and

improvementinagricultureandeconomicoutcomesforsmallscalefarmingin

Odisha, the rest of India, and around the world. We conclude with some

informationontheorganizationofthisreport.

TheproposedpilotprojectobjectivesThepilotprojectgrantwasawardedtoIndianprincipalapplicant“eKutir”

and international partners; Wholesome Wave (now represented by DAISA) and

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McGillUniversityCenterfortheConvergenceofHealthandEconomics(MCCHE),

on behalf of other partners of the Convergent Innovation Coalition

(CGIAR/AG4NH,IFPRI,UniversityofSouthCarolina).Thepilotprojectaimed

to link the VeggieKart/VeggieLite initiative -- which at the time of the

proposal was engaged in the daily sourcing of fresh vegetables from 300

smallholder farmers across 3 districts in Odisha -- to the eKutir model of

micro-entrepreneurs.Thismodelfocusesonusingtechnologytofacilitateand

monitortransactionstoprovidedirectmarketlinkagesforsellingproduce.

The vegetables procured are distributed through our delivery points of

vegetable entrepreneurs established in thoroughfares of the capital city,

local slums, and in rural clusters of villages. The functional number of

vegetableentrepreneursatthattimewas25,with25percentofwomenacting

as vegetable entrepreneurs. The proposed strategy for the present pilot

projectwastoleveragethisearlypenetrationtoachievethefollowing:

1. Increase farmer base to 1,000 farmers for vegetable procurement and

incorporategenderinclusivityinfarmingpractices;

2. Increase the number of vegetable entrepreneurs from 23 to 80 through

thisproposedpilot,with40percentofthembeingwomen;

3. Increase acceptance of good agricultural practices primarily among

womenfarmers;

4. Increase accessibility and affordability of fresh, nutritious fruits

andvegetables;

5. Measure fruits and vegetable intake and its impact on dietary

consumptionofwomenandchildhealth.

Beyond increasing the nutritional intake of rural and urban consumers, the

broadimpactwouldalsoinclude:

1. Creatingruralemploymentthroughmicro-entrepreneurs;

2. Providingmicro-entrepreneurswithtechnology-enabledtoolsonatablet

orPC;

3. Utilizingtoolstoaddressspecificneedforthevegetablefarmer;

4. MonitoringandMeasuringsuccessandfailureonareal-timebasis;

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5. Promoting good agriculture practices among smallholder farmers for

vegetablecultivation;

6. Organizing the fragmented agriculture value chain through micro-

entrepreneursandtechnology;

7. Streamlining the un-organized groups of vegetable vendors, largely

women,todistributethevegetablesintheirlocalcommunitiesthrough

VeggieLite/VeggieKart;

8. Reaching out to woman of the household through woman vendors, to

distributegoodqualityandnutritiousvegetables;

9. Real-timedatamanagementallowingalltransactionstoberecordedand

shared.

Through VeggieKart/VeggieLite pilot, the detailed plans above established a

self-sustaining and enterprise-driven model for not only increasing

agricultureincomeandproductivity,butalsoprovidingacontinuouschainof

vegetable sourcing to improve nutritional consumption among consumers. The

targetwastoprocurevegetablesfrom1,000farmersanddistributeitthrough

150 vegetable entrepreneurs in rural and urban cluster by 2015/2016.

Henceforth, if proven efficacious, the VeggieKart/VeggieLite model will be

poisedforincreasedcapitalfrompublicandprivateinvestors,andscaling

throughout the region. A Convergent Innovation research framework described

inthenextsectionwasusedtoexamineboththedegreetowhichtheactual

operationaldeploymentoftheinterventionachievedtargetsfor(1)farmand

theVeggieLiteretailenterprisesand(2)thebehavioral/nutritionimpactin

termsofincreaseF&Vconsumptioninbothurbanandruralsample.

TheconvergentinnovationevaluationframeworkVeggieLiteinterventiontakesaholisticapproachtotheneeds,preferences,

and behaviours of individuals and households. Simultaneously, the

interventionalsotransformstheagro-ecological,social,andcommercialeco-

systemstomakenutritiouschoicesaccessible,affordable,andappealingin

aneconomicallysustainablemanner,evenforthepoor.Thecoreinnovationof

the project lies in this “integrative systemic approach” and the

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“convergence” between the impacts on agriculture, nutrition, and health.

Farming communities are empowered economically by assistance at the

production end in the form of cultivation and marketing support and are

helpedinimprovingthenutritionthroughincreasedawareness,affordability,

accessibility, and availability of vegetables and other food products

[through VeggieLite]. Urban poor are also ensured access to vegetables at

affordableprices[VeggieMarts,VeggieWheels,VeggieLite].eKutirthroughits

network of micro-entrepreneurs and vegetable entrepreneurs at the heart of

theVeggieKartinterventionlinksproducersandconsumersinanequitableand

efficientvaluechainthatempowerstheminawaythatothermethodsfailto

address.

The VeggieLite intervention has much in common with what has been called

ConvergentInnovation(CI;Dubeetal2014;Jhaetal2014;seeappendix),an

approachthatbridgestechnological,social,andinstitutionalinnovationto

placethehealthofpeople,planet,andeconomyasacoredriveroffarmand

foodbymulti-levelactors,targetingbothbehaviouralchangeandecosystem

transformation. CI has been advanced as a solution-oriented paradigm to

address the complex human problems at the nexus of agriculture, food and

healthbydefiningnewpathsofconvergencebetweeneconomicgrowthandhuman

development. In such complex context and nature of intervention, the

traditional impact evaluation methods that relies on randomized clinical

trial (RCT) or quasi experimental design does not suffice. Complex systems

haveanumberofdefiningcharacteristics,includingthepossibilityofnon-

linear change, the existence of emergent properties, and the importance of

feedbackmechanismsintheunfoldingofanintervention(Shiell,Hawe,Gold,

2008). These characteristics have implications for evaluation strategy and

design. Complex systems approaches recognize the fact that new structures,

institutions, and relationships may emerge from intervention activities,

requiring mixed-method designs and the measurement of intervention outcomes

atmultiplelevels(HammondandDube2012;Mooreetal2015).

ThedeploymentstrategyandimpactoutcomesofthepilotprojectVeggieLite

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thereforetookamulti-levelconvergentinnovationevaluationapproach(Fig.

1.1)thatusedmixedmethods(surveymethods,socialnetworkanalysis,value

chain analyses) to examine outcomes in terms of both behavioral change and

ecosystem transformation across multiple ecological levels – individual,

interpersonal,organizational,community,andsystems.

Figure1.1:Methods,LevelofAnalysisandKeymeasures

PotentialsocietalandscientificsignificanceTheagriculture-foodvaluechainisatthecoreofeconomicgrowthandhuman

development in developing countries and emerging economics like India.

Agriculture and food sectors contribute over 20% to India’s GDP and employ

morethan50%ofIndia’spopulation.Atthesametime,theagri-foodsectors

alsoshouldertheonusofsecuringthenutritionalsecurityforapopulation

that is simultaneously fighting under nutrition and over nutrition. These

challenges persist in spite of billions of dollars being allocated by the

Indiangovernmentandinternationalorganizationstoalleviateruralpoverty

Householdquestionnaireadministeredtoruralandurban

sample

Qualitativemethods

(Interviews,Focusgroups,ethnography)

SocialNetworkAnalysis(ego

network,wholenetwork)

Individuals(Farmers,Consumers)

Organizations(Micro-Enterprises,Input

Providers,Retailers)

Interventionprocess:Innovationsdeployed

#micro-entrepreneurs

#Veggie-entrepreneurs

#farmers/consumers

Individual:Totalvegetableconsumption,vegetableconsumptiondiversity

Interpersonal:Networksize,networkdiversity

Methods LevelsofAnalysis

KeyMeasures

InterpersonalNetworkDimensions

(NetworkSize,NetworkDiversity)

CommunityStructures

SystemsLevel-ValueChains

ValueChainand“SocialReturnonImpact”Analyses

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and to secure mother/child nutrition (The Hindu News, 2013). This is

particularly critical for in the horticultural sector where the lack of an

organized supply chain for fruits and vegetables in India has substantial

impact on both the farmer and the consumer, which are the first and last

nodes of the supply chain. Yet, it is estimated that yearly 2.7 million

deaths(4.9%)and26.7milliondisabilityadjustedlifeyears(DALYs;1.8%)

wereattributabletolowfruitandvegetableintakeglobally.Lowintakeis

particularlyprevalentinpoorpopulation.Forexample,Odisha,astatein

easternIndia,hasaMMRof222/100,000livebirths,roughly17%higherthan

the national average, and neo-natal mortality greater than 30 deaths/1000

livebirths,tenhigherthanthenationalaverage.1

Despite support from State Government and pro-farmer policies, the

unorganized disaggregated F&V supply chain leaks value in every step from

smallholder farmer to consumer with spoilage, graft, fees, and excess

middlemen.Thecurrentvegetablevaluechainisineffective,unreliable,and

involvesconsiderablewastage(25-30percent),withlowpricerealizationfor

the farmers and lack of fresh, quality vegetables for the consumers. As

farmers reduce the inclination towards vegetable cultivation, a dearth of

vegetables in the market causes prices to increase, which affects the

consumer’sbudget.Duetothis,theconsumerreducesthechoicesinvegetable

consumption and procures as per affordability. This challenge keeps the

farmers and consumers at the expense of the middlemen or unorganized value

chainsystemthatretainsmostofthevaluewhilecreatingwastage,lowvalue

forfarmers,consumers,andnutritionalinsecurity.

The bulk of interventions that have catered to these issues have thus far

operatedinsilos,eachoneofthemworkingononeormoreissuesbutdoing

so in a disintegrated manner. The eKutir model aims at integrating the

ecosystem and making the communities evolve as active and responsible

1India.MinistryofStatisticsandProgrammeImplementation.MillenniumDevelopmentGoalsCountryReport,India2015.Availableathttp:mospo.nic.in/Mospi_New/upload/mdg_27feb15.pdf

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participants.Theapproachisuniqueinitsunderstandingofagricultureand

nutritionaspartofaholisticsystemthatempowerslow-incomeindividuals

aseconomicactorsinthissystem,especiallywomen.Bylinkingsmallholder

producers directly to vegetable entrepreneurs and urban consumers, the

project aims to building a food system that delivers better access to

economicopportunitiesandnutrition.Itrecognizesthefarmernotonlyasa

producer,butalsoasaconsumertherebyensuringthenutritionalsecurityof

theproduceralso.ThetheoryofchangebehindtheVeggieLiteintervention

isanchoredintoconvergenceofeffortsamongdifferentcollaboratorsinan

ecosystemthatspansocialandcommercialsectors.Italsoreducesthenumber

of intermediaries through the introduction of micro-entrepreneurial

structures and a novel distribution channel, bypassing the Mandi market

systemandthebrokersassociatedwiththesemarkets,reducinglossesdueto

poorhandling,fees,rents,interest,andevengraft.

Therefore, the present pilot project and associated research on VeggieLite

Intervention will set up a solid scientific and strategic foundation for

integrating approaches of demand-driven vegetable production, distribution,

wastereduction,andpriceoptimization,whichhavethepotentialtocreate

self-sustaininghumanandeconomicdevelopmentforthemostvulnerablerural

andurbancommunities.

TheresearchteamandthereportorganizationDr. Dubé, MCCHE’s founding chair and scientific director spearheaded the

research team. She cumulates training in nutrition, behavioral decision

making,marketing,finances,andcomplexitysciences.MCCHEhasactedasthe

internationalresearchpartneronbehalfofothermembersoftheconvergent

innovation coalition (CGIAR-AG4NH/IFPRI and the University of South

Carolina).Dr.P.K.Joshi,Dr.SummerAllenandMr.SamikGhoshfromIFPRI

haveelaboratedandassessedmanyinitiativesaimingtoincreaseagricultural

production and nutritional knowledge and intake in developing countries,

amongstothers.ParticularlyDr.AllenandMr.Ghoshhaveplayedakeyrole

in hiring the research team in Odisha that administered the surveys and

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collectedthedatafrombothfarmersandconsumers.Dr.Moore,Professorat

the University of South Carolina, is an expert in social networks and how

beingpartofacommunityhelpsprogramsthrive.Hehastakenaleadrolein

the quasi-experimental design study and diverse facets of individual,

household, and organizational assessment. The MCCHE team also include

management scholars Dr. Srivardhini Jha, at the time of the study post-

doctoral fellow at MCCHE supported by CGIAR-AG4NH/IFPRI and now a faculty

memberinstrategyandentrepreneurshipattheIndiaInstituteofManagement-

Bangalore, and Dr. Saibal Ray, a scholar in supply chain management and

analytical modeling from the Desautels Faculty of Management, McGill

University whom has brought highly valuable expertise in examining the

underlying competitive dynamics of the intervention. The research team has

worked in close collaboration with strategic consultant Mr. Ross who has

documented through close observation and field work with Veggie Kart/Light

and eKutir team the actual operational deployment of the various facets of

the intervention underlying ecosystem transformation. DAISA Enterprises

principals worked to develop the Wholesome Wave Healthy Food Commerce

Investments fund, a $3M initiative to capitalize and learn from a growing

class of enterprises in the US called “food hubs,” ecosystems much like

eKutir/Veggie Kart that are designed to maximize value for the smallholder

farmerstheyworkwithandcommunitystakeholders,aswellasfunctioningas

businesses. PI Suvankar Mishra and Krishna C. Mishra have also generously

provided key information and insights that have fueled research throughout

thewholeproject.

The remaining of the report is organized as follows: chapter 2 provides a

background on the eKutir social enterprises and describe the VeggieKart

business model and ecosystems, including but not limited to a value chain

mappingandcomparisonwithtraditionalmarkets.Chapter3reportsresearch

methods of the quasi-experiments and assess outcomes in terms of F&V

consumption and operational farming results. Chapter 4 reports on the

convergentinnovationprocessanalysisthatcombinesastrategicanalysisof

VeggieKart/VeggieLite intervention as a platform balancing both sides of

21

supplyanddemandoftheecosysteminthedirectionoftargetedoutcomeswith

afullnetworkanalysisoftheunderlyingsocialdynamicsinvolvedwiththe

VeggieKart/VeggieLite ecosystems. We also report key facets of analytical

model of the competitive dynamics underlying interaction of VeggieKart

ecosystems with traditional market actors. Chapter five describes the

behavioralincentivemini-experimenttoimprovenutritionofpregnantwomen,

lactatingmothersandinfantsandalsofosterVeggieLite’slinktothehealth

caresysteminOdisha.Finally,chaptersixsummarizesoveralldiscussionon

successes,lessonslearnedandlimitations.

22

Chapter2:eKutirSocialBusiness,VeggieKartEcosystem,andVeggieLiteInterventionfromanOperationalPerspective

Agri-entrepreneursprovideinputs,technicalassistance,andmarketlinkages

tosmallholderfarmerswhohavechosentoparticipateineKutiragricultural

programming. The Veggie Kart “Agri-Entrepreneurs” (AEs) provide inputs,

technicalassistance,marketlinkages,andhelpfuldemandinformationsothe

smallholder farmer can better plan their production and harvest, reducing

riskandincreasingvalue.Tofacilitatelocalizedinteractionswithfarmers,

agri-entrepreneurs organize eKutir farmers into groups of 15-25 members

calledFarmerInterventionGroups(FIGs),withpossiblymultiplegroupsper

village. As part of their activities, agri-entrepreneurs aggregate the

producecultivatedbythesegroupsatlocalaggregationpointsforinsertion

intoVeggieKartdistributionchannels.

TheVeggieKartDistributionChannel

VeggieKart purchases vegetables from eKutir farmers at local aggregation

points and transports them to a central warehouse, where quality control

checksareundertaken.Afterweighing,sorting,grading,packaging,andre-

weighing the vegetables, the produce is sent to the farmers’ markets

established in different parts of the capital city, Bhubaneswar. The

distributionchannelsofVeggieKartconsistofthefollowingstructures:

(1) Online:eCommerceplatformanddirectdoorstepdelivery;

(2) VeggieMart: converting existing small shops into farmers’ markets,

brandedbyVeggieKart;

(3) VeggieWheels: converting existing exploited vegetable vendors into

valuedvegetableentrepreneurswithpushcartsacrossthoroughfares

inthecity;

(4) VeggieLite: establishing vegetable entrepreneurs in low-income

communitiestosellvegetablesatreducedprices.

23

Through the diverse range of VeggieKart channels, farm-fresh food is made

accessibletoalltypesofconsumersataffordableprices.

As a for-profit social enterprise, and in order to build economic

sustainability to reach targeted impacts, eKutir must both be viable as a

businessaswelleffectiveindeliveringsocialbenefits.Thischapteroffers

abriefoverviewofeKutir’ssocialenterprisemodelandprovidesanin-depth

operational analysis of the VeggieKart business model and ecosystem, to

examine its differential positioning vis-à-vis traditional Mandi spot

markets.ThissetsthestageforapresentationofVeggieLiteinterventions

from a CI ecosystem perspective, leading to the anticipated economic

viabilityoftheenterprisesasseenthroughthelensesofeKutir/VeggieKart

managementandDAISAEnterprisesstrategicconsultant.

TheprocessofunderstandingtheeKutir/VeggieKartvaluechainstartedwith

interviewswitheKutirVeggieKartstaff.DAISAEnterprisesmappedthesteps

ofvegetablesupplychainforthetraditionalMandimarketsandcomparedto

eKutir’s VeggieKart procurement. DAISA gathered data from online market

postings, Mandi market visits, reviewing VeggieLite invoices and pricing

information, and conversing with farmers and venders, across 20 common

vegetables. We then compared data from pricing at various steps of these

supplychainsacrossseveraldaysandweeksin2015todeterminewherevalue

isbeingcapturedineachchain.

24

eKutirsocialenterprise

eKutir is a social enterprise that leverages a combination of local micro-

entrepreneurial motivations and processes, as well as modern digital

information and communication (ICTs) ecosystem platforms, to solve

smallholder farmers’ poverty through a distribution network of digitally-

trained entrepreneurs, market linkages, technology, and data. eKutir’s

missionistofindcatalytic,scalablesolutionstoglobalfarmingchallenges

andchangethewayagricultureandruraldevelopmentisviewedfromacharity

to a profitable enterprise perspective. eKutir has developed an

entrepreneurship model where digitally-trained micro-entrepreneurs support

groupsofsmallholderfarmersinfarmsoil-testing,fertilizers,seedsand

crop nutrient and pest management best practices recommendations, inputs

accessandmarketaccess.

Micro-entrepreneurs are selected, hired, and trained by eKutir to use

softwareapplicationstocapture,manage,andmonitoravarietyofdataand

transactions. These entrepreneurs are key actors in villages that have

received solid training in a multitude of components including

entrepreneurship and social business, as well as soil testing, supply of

vegetableseedswithpropercultivation,marketinglinkageswithbuyers,and

daily market price information literacy. Each micro-entrepreneur manages

between200smallholderfarmers,definedasfarmerswhoholdlessthanthree

acres of land. To facilitate localized interactions with farmers, micro-

entrepreneurs organize eKutir farmers into groups of 15-25 members called

FarmerInterventionGroups(FIGs)andregularlyorganizemeetingswiththem.

TheeKutirmodelservesasanengineforthecreationofmicro-entrepreneurs

inadiversityofsectors,includingveggie-entrepreneursplantingtheseed

for convergent innovation among rural villages. eKutir uses this model to

transform agriculture and nutrition linkages in rural communities, and

betweentheseareasandslums,aswellasotherpoorurbancommunitiesinthe

stateofOdisha,India.eKutiristhusworkingtoestablishanenterprise-

based, market-driven, agricultural production system that impacts the

25

nutritionalconsumptionpatternsofruralandurbanconsumersdirectly(Fig

2.1:VeggieKartvaluechain).

Figure2.1:eKutirPIEmodelforfarmersandVeggieKartvaluechain

Agri-entrepreneursprovideinputs,technicalassistance,andmarketlinkages

tosmallholderfarmerswhohavechosentoparticipateineKutiragricultural

programming. The Veggie Kart AEs provide inputs, technical assistance,

marketlinkages,andhelpfuldemandinformationsothesmallholderfarmercan

better plan their production and harvest, reducing risk, and increasing

value.Tofacilitatelocalizedinteractionswithfarmers,agri-entrepreneurs

organize eKutir farmers into groups of 15-25 members called FIGs, with

possibly multiple groups per village. As part of their activities, agri-

entrepreneurs aggregate the produce cultivated by these groups at local

aggregationpointsforinsertionintoVeggieKartdistributionchannels.

26

TheVeggieKartEcosystem

The VeggieKart ecosystems has been designed with the intent to deliver

comparable or better pricing (compared to Bhubaneswar neighborhood venders)

toconsumersforthecommonvegetablesthroughitsprimaryVeggieKartchannel

and VeggieLite channel. VeggieKart pays the farmers an average of

approximately 20% more for the vegetables they purchase than middlemen and

brokers associated with the traditional supply chain (correlating with the

farmersurveydata),withfluctuationsbasedonmarketandclimateissues.

The VeggieKart micro-entrepreneurs serve as retail outlets and distribution

channels to make fresh, healthy, and safe vegetable produce by eKutir and

othersmallholderfarmersaccessibletolow-incomeruralandurbanconsumers

at competitive prices. VeggieKart purchases vegetables from eKutir farmers

andothersourceswhenneededatlocalaggregationpointsandtransportsthem

toacentralwarehouse,wherequalitycontrolchecksareundertaken.After

weighing, sorting, grading, packaging, and re-weighing the vegetables, the

produceissenttothefarmers’marketsestablishedindifferentpartsofthe

capitalcity,Bhubaneswar.

The capacity of Veggie Kart to procure affordable vegetables to consumers

livinginruralandurbanslumsisbasedonitsabilitytore-organizethe

entiredistributionchain.Assuch,allunsoldvegetablesarecollectedfrom

smallholder farmers at the end of the day in major centralized warehouses

where they are inspected. They are then re-distributed to particular

distributioncentersinresourcepoorareasfromwheretheyarepickedupby

vegetablevendorsandre-soldatcheaperpricestoconsumers.Bycoordinating

theentirevegetablevalue-chain,eKutirensuresthattherearemoreeconomic

benefits for farmers who would not otherwise be able to sell their

vegetables, while simultaneously increasing the availability of vegetables

forconsumers.

TheVKecosystemispictoriallyrepresentedinthenexttwofigures.Figure

2.2isanorganizationalmapshowingthemainactorsintheecosystemandthe

27

mannerinwhichinformationismeanttoflowamongthem,informationsuchas

supply/demandofvegetables,advisory,markettrends,andsoon.eKutiris

the central node that manages this flow between the actors on the farming

side, the distribution side, and other organizational partners. Figure 2.3

shows the flow of material, such as produce, within the system. In

particular,thediagramhighlightshowthevegetablesflowfromthefarmers

totheconsumersthroughmultipledistributionchannels.

Figure2.2:eKutir/VKEcosystemInformationflow

RURALHOUSEHOLDS

URBANHOUSEHOLDS

OperationsExecutive-VeggieLiteUrban

VeggieLiteRural

Entrepreneur

KartManufacturerandDesigner

AgriculturalInputProviders

eKjut

Advisors-OrissaTechnical

UniversityandDr.Konde

ExternalStakeholders

Agriculturalsupportservices-soiltesting,weather

forecasting,insurance

Fieldexecutives(1)eKutiremployees(2)Swati(3)CCD

VeggieLiteUrbanEntrepreneur

AgriEntrepreneur

OperationsManager-Agriculture

CEO-VeggieHealth

Global

CEO-VeggieKartand

CentreofExcellenceforAgriculture

MarketingManager-VeggieLiteUrban

28

Legend-keyformaterialflow

Inputinflow

Vegetablesoutflow

Figure2.3:MaterialFlow

TheVeggieKartValuechainvs.TraditionalChainVegetablefarmingyield,priceandincomes(farmersandmicroentrepreneurs)The VeggieKart value chain seems to deliver comparable or better pricing

(compared to comparable venders) to consumers for the common vegetables

through its primary VeggieKart channel and VeggieLite channel. VeggieKart

paysthefarmersanapproximately20%moreforthevegetablestheypurchase

than middlemen and brokers associated with the traditional supply chain

eKutir

VeggieLiteRural

EntrepreneurVeggieKart

VeggieMartsVeggieWheels

AgriInput

Suppliers

Fieldexecutives(3)eKutiremployees(1)Swati

AgriEntrepreneurs

RuralHousehold

s

VeggieLiteUrban

Entrepreneur

Online&Instituti

onalBuyers

UrbanSlumHouseholds

29

(correlatingwiththefarmersurveydata),withfluctuationsbasedonmarket

andclimateissues.

Figure2.4-ExampleComparisonforBeans,IndrahanuMktvs.

VeggieKart

VeggieKartmakesupthisdifferencethroughverticalintegrationofitsvalue

chain.VeggieKartaggregatestheproductsfromthefarmersthroughitsagri-

entrepreneurs, owns trucks to bring the products to Bhubaneswar to its own

distributionfacilitywithitsownemployeesdoingwashingandpacking,then

sendstheproductsouttoVeggieKartvendorswhoarelicensedbyeKutir.The

eKutirVeggieKartsystemremovesseveralintermediariesinthesupplychain,

and maintains ownership of the products throughout. eKutir returns the

savingsofthisdisintermediationtothefarmers.

30

Figure2.5-ExampleComparisonforBrinjal,IndrahanuMktv

VeggieKart

Figure2.6:RidgeGourdValueChain,IndrahanuMktVVeggieKart

31

The following Value Chain Comparison diagram (figures 2.7 and 2.8) also

suggeststhattheVeggieKartintegratedvaluechainnotonlyallowsfarmers

to capture more portion of value from the end-product, but that the chain

itself retains more value. In the traditional supply chain, products are

lost to wastage as they sit without cold storage in villages and Mandi

markets, and there can be graft associated with the transactions in the

traditional supply chain where additional margins are captured away from

farmers, end-consumers, and worthy micro-entrepreneurs. While not fully

studied,eKutirseekstomanageitsintegratedvaluechaintominimizethese

losses,andhasstatedthatitreduceslossesdowntoaslowas9%,andis

strivingfor4%for2017.

32

Figure2.7:VeggieKartValueChainComparison

Figure2.8:Processflow–VeggieKart

Procurement(Dayone)

From7amto10:00am

-fromBaranga(Lot1–urban)

From1:00pmto5:00pm

-fromUnit1(Lot2–urban)

At5:00pm–7:00pm-fromrural

Sorting,GradingandPackagingFrom7:00amto3:00pm-Shift1From4:00pmto12:00am-Shift2

DemandCollection?+communicatetoMEs(Day

33

TheVeggieLiteInterventionTheeKutirteaminitiatedprojectimplementationinDecember2014.Thepilot

intervention took place in three stages. Stage one consisted of baseline

assessments of study participants (both case and comparison groups). Stage

two involved implementation of the micro-entrepreneurial and VeggieLite

intervention in the rural and urban areas. During this stage, eKutir

personnel and agents identified and recruited agricultural and vegetable

entrepreneurs, especially women, and engaged these entrepreneurs in social

enterprise activities. Micro-enterprise retail outlets and distribution

channelswereestablishedtoincreaseavailabilityandaccessibilityoffresh

and healthy produce. Stage three consisted of collecting endline or post-

intervention data from study participants. This project received a no-cost

extensionuntilJune2016sincetherewasadelayinthestart-upprocessand

logisticsarrangement.

TheinterventiontookplaceinthestateofOdisha.Ontheoperationsside,

thetotalnumberoffarmersengagedintheprojectasofMay2016was1,350

[almost double the target number proposed]. These farmers received

agricultural support products and services through the agri-entrepreneurs,

AmountCollection24hoursafterdeliveryforKarts,MartsandVeggieLite(Day2)CashPaymentforOnline(Day1)Flexibleforinstitutionalbuyers(basedontermsagreed

SupplyAt6amLot3-MartsandKartsAt10:30amLot1—OnlineAt4:00pmLot2-Marts,Karts,VeggieLiteOndemandforinstitutions

InventoryindentsandallocationAt9:30am

Lot1–OnlinecustomersAt3:30pm

Lot2–Marts,Karts,

VeggieLitesAt11:30pm

Lot3-MartsandKarts

34

who by now were well trained with farmer mobilization, procurement, and

marketing methodologies and ICT usage. On the marketing side, eKutir

establishedroughly90vegetableentrepreneursinthecityofBhubaneswarby

May2016.Thisfigureismorethan200%theprojectoriginaltarget.

Over the course of the project e-VeggieKart grew monthly sales from INR

1,814,102 in April 2015 to 2,576,578 in May 2016, 45% growth. VeggieKart

grew sales season over season, Rabi sales in 2015 exceeding Rabi sales in

2014 by nearly 400,000 INR, and nearly doubled sales in the second Zaid

season.

Figure2.9:TreatmenteKutirFarmer'sTotalSales

During the project period, according to DAISA interviews with VeggieKart

personnel and observations, VeggieKart improved its practices of planning

truckingroutesandvenderlocations,makingitsoperationsmoreefficient.

Itreducedspoilageofproductsfrom25%downtoapproximately9%according

to its internal records. VeggieKart supplemented its sales through the

rickshaw and VeggieLite channels with business-to-business VeggieMart

relationship,allowinghighervolumesales.AccordingtoeKutir’sExecutive

30,23,945

15,29,500

31,36,33033,99,670

29,82,145

-5,00,00010,00,00015,00,00020,00,00025,00,00030,00,00035,00,00040,00,000

Rabi-2014Season

Zaid-2015Season

Kharif-2015Season

Rabi-2015Season

Zaid-2016Season

TreatmenteKuarFarmer's"TotalSalesinINR"[5SeasonsSalesinINR]

TreatmenteKugrFarmer's"TotalSalesinINR"-5SeasonsSalesinINR

35

Director, VeggieKart has reached operational break-even, though some

administrationcostsarecoveredbyphilanthropicgrants.

Figure2.10:VeggieKartSalesProjectPeriod

2.11:TotalsalesforallVeggieLiteCentersinBhubaneswarUrban

0

20000

40000

60000

80000

100000

120000

TotalsalesforallVeggieLiteCentersinBhubaneswarUrbanSalesValue(inINR)

SalesValue(inINR)

36

Figure2.12:TotalWeeklySales(kgs)forVeggieLiteServicesbyMonth

inselectWards

VeggieLiteOperationsatRurallocation

OverthecourseoftheprojecteKutirhasestablishedstronglocalnetworkof

rural Micro-entrepreneurs (5) and VeggieLite Entrepreneurs (7) across 5

projectinterventionlocations.Thereare7VeggieLitecenterstoreachlow-

incomecommunities.Fig.2.13representsallVeggieLitelocations’cumulative

salesincreasesinpast15monthsduringprojectintervention.TheVeggieLite

centerfunctionsasanintegralpartofthevegetablesupplychain.Produce

aggregatedinruralareasissortedandgraded.Eachproductisgradedonthe

basisofitssize,texture,andshape.Thosedeemedofthebestqualityare

senttothewarehouse,whereasthosegradedaslesserqualitybutstillgood

aresoldatthe“ruralVeggieLite”locations.Unsoldproduceisreturnedona

dailybasistothewarehousefromthesellingpoints[cartsandmarts].These

items are then sold at the urban VeggieLite at discounted prices. By

coordinatingtheentirevegetablevalue-chain,eKutirensuresthatthereare

more economic benefits for farmers who could have not otherwise sold their

0

500

1000

1500

2000

2500

3000

3500

4000

4500

Mar

-15

Apr

-15

May

-15

Jun-

15

Jul-1

5

Aug

-15

Sep-

15

Oct

-15

Nov

-15

Dec

-15

Jan-

16

Feb-

16

Mar

-16

Apr

-16

Total Sales (kgs)

Ward 2

Ward 14

Ward 1

ClosureofoneVLvendor(W2)

37

vegetables and simultaneously increases the availability of vegetables for

consumers.

Figure2.13:AllVeggieLitelocationscumulativesalesinrural

locations

Below we discuss for each VeggieLite Entrepreneurs’ level operations and

sales information over the project period. Among all VeggieLite centers

Khandapada,JharsugudaandDaringbadihadshownstrongerandconsistentsales

sincebeginningofclusterformation.AngulVEcenterhasreferencecustomers

who also accessed VE center established inside campus of Jindal Steel and

PowersLimitedTownship.Acrossalllocationsitisevidentthatpreandpost

monsoon harvest was supplied to VE centers. Across all locations average

monthlysaleshadarangeofINR8000–30000foreachVEcenters.

42,09542,12542,156

42,18642,21742,248

42,27842,309

42,33942,370

42,40142,430

42,46142,491

42,522

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

AllVeggieLiteLocaaonscumulavesales-Sales(inINR)foreachmonthduringRuralintervenaon

38

Figure2.14:Jharsuguda(VeggieLiteCenter)

Figure2.15:Jharsuguda(VeggieLiteCenter)

Figure2.16:Paburia(VeggieLiteCenter) Figure2.17:Daringbadi(VeggieLiteCenter)

Figure2.18:Angul(VeggieLiteCenter) Figure2.19:Khandapada(VeggieLiteCenter)

-

10,000

20,000

30,000

1 2 3 4 5 6 7 8 9101112131415

Jharsuguda(VeggieLiteCenter)-ManojPatel(VE)

-

5,000

10,000

15,000

1 2 3 4 5 6 7 8 9101112131415

Paburia(VeggieLiteCenter)-GolapiNaik(VE)

Paburia(VeggieLiteCenter)

-

5,000

10,000

15,000

20,000

1 2 3 4 5 6 7 8 9101112131415

Angul(VeggieLiteCenter)-PadminiSahoo(VE)

Angul(VeggieLiteCenter)

-

5,000

10,000

15,000

1 2 3 4 5 6 7 8 9101112131415

Jharsuguda(VeggieLiteCenter)-RanjanKumarNayak(VE)

Jharsuguda(VeggieLiteCenter)

-

10,000

20,000

30,000

1 2 3 4 5 6 7 8 9101112131415

Daringbadi(VeggieLiteCenter)-NandiniDigal(VE)

Daringbadi(VeggieLiteCenter)

-

2,000

4,000

6,000

8,000

1 2 3 4 5 6 7 8 9101112131415

Khandpada(VeggieLiteCenter)-RitaNayak(VE)

Khandpada(VeggieLiteCenter)

39

Figure2.20:Khandapada(VeggieLiteCenter)

eKutirFinancialViabilityVegetable distribution is generally a competitive and low-margin business.

In the US, even well established, efficient, and non-mission-oriented

vegetable distributors fight to obtain margins of under 4%. Success as a

food hub requires volume and efficiency, as well as creative strategies to

reach consumers directly. Depending on the business model, Wholesome Wave

analysisshowedthefoodhubsgenerallyneededthroughputof$1.25M-$1.75Mto

reach breakeven point, with food hubs focused on institutional and large

wholesale customers on the higher end of that scale, while food hubs with

innovative direct-to-consumer models generally can reach breakeven with

smallervolumes.Astrongfactorinsuccessforfoodhubsistheirability

totellthestoryoftheirdifferentiation(commitmenttofarmer)totheend-

consumer through strong branding and marketing materials shared with their

Business-to-Businesscustomers.ForIndia,themajorpersonnelandlogistics

costsaresignificantlylowerthantheUS,puttingthatbreakevenpointatan

estimated50%oftheUS.

If VeggieKart can maintain May monthly sales, roughly equivalent to US

$500,000, it can approximate the India breakeven benchmark. During the

projectperiod,accordingtoDAISAinterviewswithVeggieKartpersonneland

observations, VeggieKart improved its practices of planning trucking routes

-

5,000

10,000

15,000

1 2 3 4 5 6 7 8 9101112131415

Khandpada(VeggieLiteCenter)-AnupamaMohapatra(VE)

Khandpada(VeggieLiteCenter)

40

and vendor locations, making its operations more efficient. It reduced

spoilage of products from 25% down to approximately 9% according to its

internalrecords.VeggieKartsupplementeditssalesthroughtherickshawand

VeggieLite channels with business-to-business VeggieMart relationship,

allowinghighervolumesales.

41

Chapter3:HouseholdandIndividualImpactsBackgroundIt is estimated that 2.7 million deaths (4.9%) and 26.7 million disability

adjustedlifeyears(DALYs:1.8%)areattributabletolowfruitandvegetable

(F&V) intake globally (Hall et al., 2009). Low vegetable intake is

particularly prevalent among poor, low-income populations. The VeggieLite

intervention aimed to change these figures by transforming the systems and

environments in which vegetables are produced, marketed, and sold. While

VeggieLite targets systems-level change, we aim in this part of our

evaluation to assess whether the intervention has ultimately impacted

individual and household levels of vegetable consumption in both urban and

ruralareasofOdisha,India.

In the urban areas, our evaluation at the household and individual level

soughttotestthefollowingmainhypotheses:

1) UrbanconsumersexposedtotheVeggieLiteinterventionwouldhavea

greater increase in their average vegetable servings per day than

thosenotexposedtotheintervention;

2) UrbanconsumersexposedtotheVeggieLiteinterventionwouldhavean

increase in the amount and diversity of vegetables purchased

comparedtothoseconsumersnotexposedtoVeggieLite.

Secondarily, the evaluation examined whether urban consumers exposed to

VeggieLite exhibited changes in their daily fruit servings, expenditure

patterns, beliefs about vegetables, and perceptions of the accessibility,

availability, and affordability of vegetables. The evaluation also assessed

possible changes in the social networks and social participation of those

consumers exposed to VeggieLite but did not expect significant changes in

networkvariables.

Inruralareas,theevaluationsoughttotestthefollowingmainhypotheses:

42

1) eKutirfarminghouseholdshaveagreaterincreaseintheiraverage

dailyservingsoffruitsandvegetablesthannon-eKutirfarmersin

thesamevillageandandthecomparisonvillages;

2) eKutirfarminghouseholdshaveagreaterincreaseintheiraverage

daily servings of vegetables than non-eKutir farmers in the same

villageandandthecomparisonvillages;

3) eKutir farming households have an increase in the amount and

diversityofvegetablespurchasedcomparedtonon-eKutirfarmersin

thesamevillageandthecomparisonvillages;

4) eKutir farming households have a greater increase in their average

daily fruit servings than non-eKutir farmers in the same village and

thecomparisonvillages;

5) eKutir farming households have an increase in their income from

vegetablesalescomparedtonon-eKutirfarmersinthesamevillageand

thecomparisonvillages.

Secondarily, the rural evaluation component examined whether rural eKutir

farmersalsoshowedchangesintheirbeliefsaboutvegetables,perceptionsof

theaccessibility,etc.aswithurbaneKutirfarmers.Sincetheintervention

didtargetaspectsofsocialrelations,e.g.,theformationofFIGs,wedid

anticipatechangesinthecompositionoffarmers’socialnetworks.

EvaluationDesignandProcess

ToassessthehouseholdandindividualimpactoftheVeggieLiteintervention

onruralandurbancommunities,theprojectusedaquasi-experimentaldesign

with baseline and endline measures in both areas. Since the selection of

urban wards and rural villages had to be made in conjunction with eKutir

operational requirements, and consumers and farmers purchase or participate

in VeggieLite voluntarily, a quasi-experimental design was selected. The

baseline and endline measures were taken after a one-year period so as to

capture possible seasonal variations in the effects of the VeggieLite

intervention on urban and rural households. As shown in 3.1 two study

43

samples were created: (1) a rural sample of producers/consumers, which

consistedoffarmersandtheirhouseholdmembers,and(2)anurbansampleof

consumers.

Figure3.1Methodology–Quasi-experimentalevaluationdesign

In urban areas, two quasi-experimental groups were formed: (1) consumers

residing in urban wards in which VeggieLite vendors would be located and

operate and (2) consumers residing in urban wards that VeggieLite vendors

would not service during the intervention period. Wards from which these

consumersaresampledwerematchedonthebasisofward-levelsocio-economic

characteristics,therebyimprovingcomparability.Giventheformativenature

of the VeggieLite program, it was unknown to us what the proportion of

VeggieLiteusersintreatedwardswasandwhatthepotentialproportionmight

reachovertheinterventionperiod.

Intheruralareas,threequasi-experimentalgroupswereformed:(1)eKutir

farmersinvillageswhereeKutirdeliverservices(hereafterreferredtoas

eKutiror‘treatment’villages);(2)acomparisongroupoffarmersresiding

in the eKutir treatment villages; and (3) a comparison group of farmers in

non-eKutirtreatedvillages.Villagesfromwhichthesefarmersaredrawnwere

matched on the basis of village-level, socio-economic development

44

characteristics and population size. The three-group design was meant to

enable a series of comparative assessments on the effectiveness of the

VeggieLiteinterventionontheconsumptionandproductionpatternsofrural

farming households. The design was meant to enable contrasts between (1)

“directly-treated”eKutirfarmersand“indirectly-treated”farmerswhomight

receive intervention benefits by residing in an eKutir village, (2)

“directly-treated”eKutirfarmersand“non-treated”farmerswhoresided,and

(3) “indirectly-treated” farmers and “non-treated” farmers. Contrasting

thesethreegroupsenablesamorecompleteassessmentofpossiblespillover

benefits of eKutir activities and gradated impacts among those farmers in

eKutir-treatedvillages.

Data on fruit and vegetable consumption from the 2002-2003 World Health

Organization Survey (WHS) in India and the 2011 (NSSO) survey are used to

calculate a priori sample size numbers. The WHS estimates average daily

surveysoffruitsandvegetablesinIndiaat4.7(1.8SD)and2.7(1.8SD)in

urban and rural areas respectively. The NSSO provides estimates of an

averageof2.5(0.45SD)vegetableservingsperdayinurbanareasofOdisha

and 2.3 (0.43 SD) vegetable servings per day in rural areas of Odisha.

Separatesamplesizesarecalculatedfortheurbanandruralareas.Asample

sizeof174households,withanequaldistributionof87households,existin

both comparison and treatment wards. A sample size of 360 households is

calculatedfortheruralevaluationcomponent,withanequaldistributionof

120householdsacrossthethreequasi-experimentalconditions.Forurbanand

rural areas, sample sizes are calculated to detect a 20% increase in the

number of vegetable and fruit servings per day per person. In other words,

for those persons consuming at the WHO recommended minimum level of 5

servingsperday,a20%increaserepresentsanincreaseof1servingperday.

Assuming the larger standard deviations found in the WHS, an increase of 1

servingperpersonperdayimpliesaneffectsizeof0.5,witha99%greater

chanceofbeingdetectable.Aneffectsizeof0.15wouldbedetectableata

95% chance. Given the pilot nature of the research and data from the

national surveys, we assume in our calculations generally low levels of

45

clusteringamongurbanwardsandruralvillages.

SelectionofStudySites

UrbansamplesitesandhouseholdsStudysiteswereselectedinrelationtoeKutiroperationalrequirementsin

boththeurbanandruralareas.Urbaninterventionandevaluationsiteswere

identifiedbyeKutirunderthetechnicalguidanceofIFPRIandMCCHE.Urban

wards were identified and included as possible sites according to the

followingcriteria:(i)lowincomearea,(ii)highpopulationdensity,(iii)

potentialmarketandprofitability,and(iv)accessiblelocation.

Working with a local data collection agency, enumerators were trained and

given information about households to be recruited in the city of

Bhubaneswar.Twourbanareas-SikharchandiandMaitrivihar-wereidentified

asstudyareas.InSikharchandi,twowardswereselectedastreatmentsites:

Sikharchandi(Ward2)andSitanathBasti(Ward14).InMaitrivihar,twowards

Figure3.2:BhubaneswarWards

46

were selected as the comparison sites: ChirakhalToli (Ward 1) and Maitri

Vihar(Ward16).Thesewardsareshowninfigure3.2.

Intreatmentwards,householdsweredefinedaseligibleiftheywerewithina

onekilometercircularbufferzoneofapossibleVeggieLitecentrelocation.

Households were sampled systematically. The first household, which had the

nearestaccesstoapossibleVeggieLitecentre,wasrecruitedintothestudy

household samples. From that household, every tenth household in all

directionswasthenaskedtoparticipate.Ifthehouseholdrespondentrefused

to participate or did not answer, the eleventh household in distance was

selected.Householdswerenotscreenedorselectedonthebasisoftheiruse

orwillingnesstouseVeggieLiteservices.Inthecomparisonwards,asimilar

systematicsamplingprotocolwasused,withtheexceptionthatsamplingwas

initiated from an already existing vegetable vendor location. This vendor

was not affiliated with eKutir. All participating households were tagged

withgeo-coordinates(GPS)andassignedauniquehouseholdidentificationno.

(HHID) so that endline assessments could be conducted with the same

households.Figure3.3showsthelocationsofthehouseholdsparticipatingin

theurbanevaluation.

47

Figure3.3:GISmapforurbanwardssurveyed

RuralsamplesitesandhouseholdsRural sites were also selected in conjunction with eKutir operational

requirements. The following criteria were used to identify rural village

sites: (i) clear nutritional need, (ii) high farmer density, (iii)

procurementlogistics,and(iv)thepotentialhostingofaVeggieLitecenter.

Sixteentreatmentandsixteencomparisonvillageswereselectedandmatched

with another spatially proximate village with similar socio-demographic and

economic characteristics within the same Gram Panchayat. The selected

treatmentandcomparisonvillagesandhouseholdsweremappedandgeo-located

within their respective Gram Panchayats, Blocks and Districts. The rural

evaluation study was conducted in four districts: Kandhamal, Jharsuguda,

Angul, and Nayagarh. Initially, Dhenkanal district was selected but was

replaced by eKutir for Kandhamal. As shown in Figure 3.4, a total of 32

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n Road

Niladri Vihar Road

Xaviers Road

Newton Marg

Nalco Nagar Road

Sailashree Vihar Road

Vidya Marg

Maitri Vihar Road

Shiksha Marg

Mancheshwar Road

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Shakespeare Marg

!( Control Zone- Maitree Vihar Santala Basti

!( Treatment Zone- Sitanath Basti

!( Treatment Zone- Sikhar Chandi Slum

!( Control Zone- Chirakhal Toli

Veggikart_Bhubaneswar

L

0 690 1,380345 Meters

48

villages (16 intervention and 16 comparison villages) were selected from

thesefourdistricts.

Figure3.4:Rurallocations(map)

Withinvillages,thenumberofhouseholdsselectedwasproportionatetothe

villagepopulationsize.IneKutirtreatmentvillages,alistoffarmerswho

hadalreadyvoluntarilyregisteredtoreceiveeKutirserviceswasprovided.

The sampling frame for the eKutir farmers’ consisted of the list of those

namesarrangedalphabeticallyinascendingorder.ThefirsteKutirhousehold

was selected randomly. The next participating household was selected

systematicallyfromthisinitialhouseholdlocationandsoon,withtheaim

to cover the village geographically. Since a list was not available of the

non-eKutir registered farmers, non-eKutir farmers were systematically

selectedradiatingoutfromtheproximatelocationoftherandomlyselected

eKutirfarmer.Inthecomparisonvillages,thesurveyteamfirstvisitedthe

Government’s Anganwadi Centre (ICDS Units) and collected information about

the number of hamlets in the village from the AWW (ICDS staff). Comparison

households were equally divided among the hamlets. The first household,

whichwasengagedinfarming,locatedontherighthandsideofthehamlet

49

was selected. From that household, every tenth household on the same side

wasselecteduntiltherequiredsamplesizefortheareawasachieved.

Evaluationinstruments,measures,andanalysismethodsHouseholdquestionnairesStructured questionnaires were administered to participating households and

respondents. Urban consumer questionnaires consisted of five modules,

whereas rural producer questionnaires consisted of an additional module on

agriculturalproductionandcropping.Table3.1liststhedifferentmodules

and their sub-sections. Module A asked participants about basic household

information and demographics, including the literacy and the educational

attainment of each household member. Module B, which was included only in

the rural farmer questionnaire, asked farmers about their cropping and

productionpatterns,plotutilization,marketingandcreditinformation,and

householddecision-making.ModuleCcollectedinformationonhouseholdfruit

and vegetable consumption patterns and food security. For consumption,

households were asked to name the top five vegetables, fruits, and pulses

that they had consumed in the past seven days. Follow-up questions asked

about the quantity, source, and the amount purchased versus home grown.

Module D asked participants about their perceptions on the availability,

affordability, and accessibility of vegetables, their beliefs and motives

regardingvegetableconsumption,andtheimportanceplacedonvegetablesasa

partoftheirdiets.ModuleEcollectedinformationonsocialparticipation

and perceptions of local social cohesion. In the rural questionnaire, two

name generator questions were asked: one on participants’ farming advice

networks and the other on cooking and dietary advice. This information

allowedustoassessthecompositionoftheruralhouseholdnetworks.Module

F asked participants about non-household food expenditures and household

assets.

50

Table3.1:Questionnairemodules

LISTINGOFSURVEYMODULES–FarmerandConsumerQuestionnaires(seeAppendixforCompleteQuestionnaires+Informedconsent)CategoryofMeasure Sub-categoryofMeasure

RuralandUrbanHouseholdsModuleA:HOUSEHOLDINFORMATION A1:BasicHouseholdIdentification

A2:HouseholdDemographicInformationModuleC:FOODCONSUMPTIONINFORMATION

C1:F&VConsumptionC2:FoodSecurityQuestionnaireC3:FoodFrequencyQuestionnaire

ModuleD:F&VPSYCHOLOGICALBELIEFSANDMOTIVES

D1:F&VConsumptionReasonsD2:F&VImportanceandInitiativeAwareness

ModuleE:SOCIALCAPITALINFORMATION E1:GeneralizedandNeighborhoodTrustE2:Socialparticipation/involvementE3:Socialcohesion

ModuleF:HOUSEHOLDDEVELOPMENTANDPOVERTYINDICATORINFORMATION

F1:HouseholdNon-FoodExpendituresF2:Multi-DimensionalPovertyIndexF3:Dwellingcharacteristics

RuralHouseholdsOnlyModuleB:CROPPINGANDINPUTINFORMATION

B1:GeneralFarmInformationB2:CroppingPatternB3:ProductionInputsB4:LivestockB5:MarketingInformationB6:MarketLandscapeforSmallholdersB7:CreditInformationB8:HouseholdDecision-MakingB9:DynamicsofInteractionanddecisionmakingattheindividuallevel

ModuleE:ADDITIONALSOCIALCAPITAL/NETWORKINFORMATION

E4:NameGeneratorandInterpreterquestionsE5:NameGeneratorforFIGmembers

Thequestionnairewasadministeredatbaselineandre-administeredatendline

to participating households. Baseline questionnaires were administered in

theurbanwardsinMarch2015andintheruralvillagesinApril/May2015.

Endlinequestionnaireswereadministeredtourbanhouseholdsoneyearlater

in March 2016 and to rural households in April 2016. The survey team

maintained consistency in data collection and visited the same household

locationsatbaselineandendline.

51

Keyevaluationmeasures

VegetableandfruitconsumptionmeasuresTheWHOrecommendsaminimumoffiveservings,or400grams,offruitsand

vegetables per day, excluding potatoes and tubers: three servings of

vegetables and two servings of fruits per day. Since India’s dietary

guidelines include potatoes as vegetables, we considered potatoes as

vegetablesforourcalculation.Theaveragedailyservingsofvegetablesfor

each household was calculated by converting the total amount of vegetables

that was consumed over the last seven days into total grams per household.

Thetotalgramsperhouseholdwerethendividedbythehouseholdsizeforthe

averagegramsperperson.Toarriveataveragedailyservingsperperson,80

divided the average grams per person. The average daily servings of fruits

perpersonwerecalculatedinthesamefashion.Overallfruitandvegetable

consumptionisthesumofthevegetableandfruitservings.

The quantity of vegetables purchased is the total volume consumed by the

household without adjustment for household size. The number of different

vegetables consumed is the number of different types of vegetables that

households reported to consume. The questionnaire limits the number of

vegetables that could be named to five. For rural households, a fourth

measurerepresentingthepercentageofvegetablesconsumedthatwashomegrown

wasalsocalculated.

VegetableproductionandincomemeasuresFor the rural sample, household production measures were calculated. The

totalareadedicatedtovegetableproductionwasthesumofthecropacreage

dedicatedtogrowingdifferentvegetables.Vegetablesalesquantitywasthe

amountthathouseholdsproducedminustheamountthatwaseitherconsumedat

homeorgiftedorsoldininformalchannels.Householdincomegarneredfrom

vegetable production was the amount of vegetables sold multiplied by the

52

price.Inthequestionnaires,farmersreportedthepricethattheyreceived

fromthesaleofspecificvegetables.

BeliefsandperceptionsToassessbeliefsabouttheimportanceofvegetableconsumption,participants

were asked whether they believe vegetables are important for (i) improving

family health, (ii) good health and nutrition, (iii) your body, (iv) your

eyes and bones, (v) providing nutrients needed for the body, and (vi)

enhancingimmunityagainstdisease.Participantsrespondedusingafive-item

Likertscalefromnotimportant(1)toveryimportant(5).Summingthevalue

of the six responses created the “beliefs about vegetables” scale. Scores

couldthusrangefrom6to30.

In three separate questions, participants were also asked whether the (i)

affordability, (ii) availability, or (iii) accessibility of fruits and

vegetables impacted their decision to purchase fruits and vegetables.

Participantsrespondedonafive-pointLikertscalefromnotimportant(1)to

very important (5). The “perceptions” scale was constructed by summing the

threeitemstogether.Scorescouldrangefrom5to15.

Householdexpenditures(Urbanhouseholdsonly)Inurbanareas,participatinghouseholdswereaskedhowmuchtheyspentinan

averagemonthondifferentexpenditurecategories,including(i)fooditems

andbeverages,(ii)non-fooditems,and(iii)health.

Socialcohesion,socialnetworks,andparticipationTo assess social cohesion, participants were asked five items on whether

peopleintheirvillage(i)couldbetrusted,(ii)gotalongwitheachother,

(iii) were willing to help each other, (iv) could solve problems, and (v)

invited each other to family gatherings. Participants responded on a five-

point Likert scale from strongly agree (1) to strongly disagree (5). Items

53

werelaterreversecodedandsummedsothathigherscoresrepresentedhigher

perceptionsofsocialcohesion.Socialcohesionscorescouldrangefrom5to

25.Forsocialparticipation,respondentswereaskediftheyhadparticipated

in a village group or meeting in the past year. In urban wards, to assess

cognitivesocialcapital,participantswereaskediftheythoughtthatpeople

ingeneralcouldbetrustedornot.Inurbanwards,participantswereasked

howmanypeopletheydiscussedcookingordietarymatterswith,ingeneral,

overthepastsixmonths.Inruralvillages,namegeneratorquestionsasked

participants to name up to three people with whom they had discussed (i)

agriculturalandfarmingmattersinthepastthreemonthsand(ii)dietaryor

cooking matters in the last three months. Participants were then asked

informationaboutthosepersonswhomtheynamed,includingwhetherthey(i)

residedintheirvillage,(ii)werefarmers,and(iii)belongedtoeKutir’s

farmerinterventiongroups.

SocioeconomiccharacteristicsParticipantswereadministeredtenquestionitemsfromtheMulti-Dimensional

PovertyIndex.Usingtheresponsesfromthisindex,principalcomponentswas

used to construct a socioeconomic score for each household. Household

socioeconomic characteristics were considered a possible confounder in

certainanalyses.

eKutirOperationalDataTocomplimentthesamplesurveydata,eKutirprovidedsalesandoperational

data for VeggieLite vendors in urban wards and agricultural micro-

entrepreneursintheruralvillages.Intheurbanwards,salescouldnotbe

linkedtospecificsamplehouseholdsorrespondents.Assuch,theVeggieLite

vendorinformationintheurbanwardsservesmainlytosupplementourpicture

ofvegetablesalesinthoseareas.Intheruralvillages,micro-entrepreneur

datacouldbelinkedtotheresponsesofthoseeKutirfarmersselectedinto

thestudy.Thisallowedtheevaluationtocomparethefarmers’self-reported

production,priceandincomeinformationtoobjective,operationaldataand

54

assesswhethertheincomesofeKutirfarmerscomingfromvegetableproduction

objectivelyincreasedoverthepilotinterventionperiod.

AnalysisApproachTo assess the impact of the VeggieLite program, we elected to define

partipants’ exposure to the intervention in two ways. First, we used the

quasi-experimentalconditionsinwhichparticipantsfelltodefinewhethera

householdwasorwasnotexposedtotheintervention.Forexample,inurban

areas,thisrepresentedwhetheraparticipantresidedinatreatmentinstead

ofcomparisonward.Second,wealsoelectedtodefineexposureonthebasis

ofwhetherparticipantsengageddirectlyintheVeggieLiteprogrameitherby

purchasingvegetablesfromVeggieLitevendorsduringtheinterventionperiod

or participating in eKutir’s FIG groups. As shown in Tables (Table – 3.3,

3.4,3.5,3.7,3.8,3.11,3.12,3.13,3.14,3.15),separatesetsofanalyses

wereconductedforeachdefinitionofexposureforruralandurbansamples.

Foreachset,aseriesofthreestatisticaltestswereusedtoevaluatethe

nutritional,social,andeconomicimpactoftheVeggieLitemodelinruraland

urbanareasofOdisha.First,t-tests,ANOVA,andchi-squaretestswereused

toexaminewhetherthereweresignificantdifferencesatbaselineandendline

between treatment and comparison groups on the key measures. For these

analyses, we only examined the data for those respondents who replied at

baseline and endline to limit differences due to study attrition. Second,

variables representing the difference between endline and baseline (i.e.,

change) were constructed by subtracting baseline values from those at

endline.Positivevaluesthusreflectedanincreaseinthevariablefrom2015

to 2016. ANOVA and paired sample t-tests were then conducted using these

difference variables to assess whether there was a significant increase or

decrease in the treatment compared to the comparison group over the year.

Finally,regressionanalyseswereconductedwiththedifferencevariableson

vegetable consumption to examine whether treatment and comparison groups

differed significantly from one another after adjusting for possible

confounders,suchasthesocioeconomicanddecision-makingcharacteristicsof

the household. Statistical significance was marked at four p-value levels:

55

0.10, 0.05, 0.01, and 0.001. Analyses were conducted using Stata, version

14.

EvaluationFindingsThefollowingsectionprovidesthedescriptivestatisticsandtheevaluation

resultsofthepilotinterventionintheurbanandruralareas.

TheUrbanSampleFour Bhubaneswar wards were selected for the urban study: Ward 1

(ChirakhalToli),Ward2(Sikharchandi),Ward14(SitanathBasti),andWard16

(MaitriVihar).Asshownintable3.2,therewere218householdrespondents

recruited at baseline with 191 respondents participating again at endline.

Nine and eighteen respondents were lost to follow up in the treatment and

comparisonwardsrespectively.

Table3.2:Baselineandendlinesamplenumbersbyquasi-experimental

conditionandwardsite.

WardNo. Baseline Endline

TreatmentWard2 79 73Ward14 28 25

Total 107 98

ComparisonWard1 85 74Ward16 26 19

Total 111 93Total 218 191

Table 3.3 provides information on the socio-demographic characteristics of

the sample at baseline and endline stratified by experimental condition.

There was no significant difference between the treatment and comparison

groups at baseline but there was a difference noted at endline, with the

comparison groups having a larger household size. This suggests that

attrition was higher among households with smaller household size,

particularlyinthecomparisongroup.TheprimaryhouseholdlanguagewasOdia

withsimilarpercentagesinthetreatmentandcomparisongroups.Theaverage

56

ageofthemainrespondentofthehouseholdwas32yearsinthetreatmentand

26.8yearsinthecomparisonhouseholds.

Table3.3:Socio-demographiccharacteristicsofthesampleatbaseline

andendlinestratifiedbyexperimentalcondition

Baseline Endline

Treatment(n=107)

Comparison

(n=111)

Treatment(n=98)

Comparison(n=93)

Householdsize(persons)

4.7 4.3 5.1 4.7***

PrimaryHHLanguage(%)

Odiya 72.2 68.5 72.73 66.7Adivasi 1.0 12.6** 0.0 11.8Hindi 1.9 5.4 0.0 5.4Other 23.2* 3.6 27.3 16.1Householdrespondentone(R1)age(yrs.)

32.0 26.8 46.1 43.0

Topmainoccupations(HHR1)(%)

Non-earningoccupation 29.6 27.9 8.9 4.3WageLabour 17.6 17.1 26.3

40.9****p<0.10, **p<0.05, ***p<0.01, ****p<0.001 Values shown in parenthesis noted as Standard Error

Treatmentvs.ComparisonWardsi)ResultsfromtheANOVAanalysesareshownintable3.4.

VegetableConsumption:Atbaseline,theaveragedailyvegetableservings

was4.74and4.16forthetreatmentandcomparisongroupsrespectively.ANOVA

results indicated modest differences at baseline in the average daily

vegetableservingsbetweentreatmentandcomparisongroups.Oneyearlater,

theaveragedailyvegetableservingswere3.68and3.62.Endlinedifferences

were not significant. Results showed that there was a modestly greater

decline in the daily vegetable servings in the treatment versus the

57

comparison groups (F=3.05, p<0.10). At baseline, results showed that

treatment groups purchased a higher quantity of vegetables with modest

declineinthequantitypurchasedbetweenbaselineandendline.Therewereno

differences between treatment and comparison groups in the number of

differentvegetablespurchased.Therewerealsonodifferencesshownbetween

the treatment and comparison groups in their average daily servings of

fruits.

Perceptions,Beliefs,andExpenditures:Therewerenodifferencesbetween

treatment and comparison groups on their perceptions of the accessibility,

affordability, and availability of vegetables in the local environment.

Resultsshowedsignificantbaselinedifferencesintheimportanceplacedon

vegetablesbetweentreatmentandcomparisongroups(F=2.79,p<0.10),with

the treatment group placing greater importance on vegetables. These

differenceswerestarkeratendlinebuttherewasnosignificantchangeshown

over the year. At baseline, results showed that the treatment group had

lower percentage of spending on food than the comparison group (F=2.86,

p<0.10). Yet, endline results indicated that the treatment group spent a

higher percentage on food and that the increase over the year differed

significantlybetweenthegroups(F=9.16,p<0.01).Atbaselineandendline,

treatmentgroupshadhigherexpendituresthanthecomparisongroup.

Table3.4:ResultsfromtheANOVAanalyses

Baseline Levels Endline Levels Mean Change (Endline-Baseline)

Treatment Compari

son F-Stat

Treatment

Comparison F-Stat

Treatment

Comparison F-

Stat (n=98) (n=93) (n=98) (n=93) (n=98) (n=93) Fruits and Vegetables (servings/day)

5.38 4.79 3.67* 4.51 4.50 0.00 -0.88 -0.18 4.08

** (0.28) (0.20) (0.20) (0.21) (0.26) (0.22)

Vegetable (servings/day)

4.74 4.16 3.57* 3.68 3.62 0.08 -1.05 -0.54 3.05

* (0.25) (0.18) (0.16) (0.17) (0.19) (0.20)

Fruit (servings/day)

0.65 0.53 0.56 0.82 0.88 0.24 0.17 0.35 1.49 (0.09) (0.08) (0.09) (0.08) (11.00) (0.10)

58

Quantityofvegetablespurchased

11.86 9.598.16*** 9.95 8.88 2.87* -1.91 -0.72 2.68

*(0.63) (0.48) (0.49) 0.4 -0.48 (0.55)

Number of different vegetables purchased

4.72 4.78 0.44 4.86 4.76 2.41 0.13 -0.02 1.96

(0.07) (0.06)

(0.04) (0.05) (0.07) (0.08)

Perceptions of the environment

4.05 3.91 4.94* 3.87 3.80 1.42 -0.21 -0.09 1.49

(0.06) (0.05) (0.04) (0.04) (0.07) (0.06)

Importance of Vegetables

4.07 3.96 2.79* 4.03 3.81 14.19*

*** -0.03 -0.15 1.61

(0.05) (0.04) (0.04) (0.05) (0.07) (0.06)

Food Expenditures

0.45 0.49 2.86* 0.51 0.47 3.91** 0.06 (0.02) 9.16

*** (0.01) (0.01) (0.02) (0.01) (0.02) (0.02)

Health Expenditures

0.12 0.13 1.22 0.10 0.09 0.16 -0.02 -0.04 1.51 (0.01) (0.01) (0.10) (0.01) (0.01) (0.01)

Total Expenditures

7766.53 6651.83 4.68** 7909.58 6936.24 3.21* 143.05 284.41 0.08

(417.47 (293.18) (441.65) (306.68) (382.28) (308.58)

Social participation (% yes)

0.15 0.10 1.27 (Chi2)

0.38 0.42 0.35 (Chi2)

(0.03) (0.03) (0.05) (0.05)

Generalized Trust (% yes)

0.93 0.87 2.77 (Chi2)

0.77 0.78 0.11 (Chi2)

(0.02) (0.03) (0.04) (0.04) Cooking' matters network size

4.68 5.30 1.97 2.54 2.36 0.21 -2.14 -2.94 1.52

(0.31) (0.44) I0.29) (0.27) (0.42) (0.50)

*p<0.10,**p<0.05,***p<0.01,****p<0.001 ValuesshowninparenthesisnotedasStandardError

Social Characteristics: No differences in social participation,

generalizedtrust,andnetworksizewereobservedbetweenthetreatmentand

comparison groups. Regression results examining the difference between

endline and baseline vegetable consumption levels are shown in Table 3.5.

Unadjustedmodelsindicatethatthetreatmentgrouphadagreaterdecreasein

daily vegetable servings than the comparison group but this was no longer

significant after adjusting for the socioeconomic level of the households.

Analysesalsoshowedthatthetreatmentcomparedtothecomparisongrouphad

59

agreaterdecreaseinthequantityofvegetablespurchased(B=-1.30,p<0.10).

Finally, adjusted results showed that the treatment group had a modest

increaseinthenumberofdifferentvegetablespurchasedovertheyear(B=

0.22,p<0.10).

Table3.5:Regressionresultsexaminingthedifferencebetweenendline

andbaselinevegetableconsumptionlevels

Variable VegetableServingsperDay

QuantityofVegetablesPurchased

NumberofdifferentVegetablesPurchased

TreatmentHouseholds(n=98)

-0.52(0.30)*

-0.51(0.31)

-1.19(0.73)

-1.30(0.76)*

0.15(0.11)

0.22(0.11)*

Controlhouseholds(n=91) REF REF REF REF REF REF

Socioeconomic/Povertylevel -0.01

(0.10) 0.14(0.25) -0.08

(0.04)**

Constant -0.54(0.20)

-0.54(0.22)

-0.72(0.52)

-0.66(0.53)

-0.02(0.08)

-0.05(0.08)

*p<0.10, **p<0.05, ***p<0.01, ****p<0.001 Values shown in parenthesis noted as Standard Error VeggieLiteConsumersvs.Non-VeggieLiteConsumersTable 3.6 provides information on the main sources from which respondents

reported purchasing vegetables in the different seasons at baseline and

endline.ThehighestpercentagewasamongtheVillageMandiatbothbaseline

and endline. The following analyses spotlight the nutritional impact of

VeggieLite on those households that reported VeggieLite as being the main

sourceforpurchasingvegetablesovertheinterventionperiod(n=7).

60

Table3.6:Mainsourcesforpurchasingvegetablesatbaselineand

endlinestratifiedbyseason,percentage

Baseline(n=219)March-April2015

Endline(n=192)March-April2016

Z(%)

R(%)

K(%)

Z(%)

R(%)

K(%)

VeggieKart 5.0 4.6 4.6 0.5 1.4 1.4VeggieLite 15.1 16.0 15.1 3.2 1.4 1.8VillageMandi 34.3 34.3 37.0 61.6 62.1 61.6Door-to-door 17.8 18.3 18.7 11.4 12.3 12.3Shop 27.4 26.5 23.7 9.6 9.1 9.1Fellowvillagers 0.5 0.5 0.9 1.4 1.4 1.4

*LegendsforseasonZ=Zaid,R=Rabi,K=KharifNOTE:Treatment-levelcomparisonsaremadeseparetlywithinbaselineandendlinemeasures

Table 3.7 provides the ANOVA results from comparing those participants who

reportedusingVeggieLitetothosewhodidnotovertheinterventionperiod.

Analyses showed that those urban households that reported purchasing from

VeggieLite increased their daily vegetable servings over the intervention

year (F = 5.27, p<0.05). Table 3.8 provides the unadjusted and adjusted

regression estimates, which support the ANOVA results. Being a VeggieLite

consumer compared to a non-VeggieLite consumers was associated with an

increaseindailyvegetableservingsinunadjustedandadjustedmodels(B=

1.92,p<0.05).

Table3.7:ANOVAresultsfromcomparingthoseparticipantswhoreportedusingVeggieLitetoNon-VeggieLiteusersoverinterventionperiod[NextPage]

61

Baseline Levels Endline Levels Mean Change (Endline-Baseline)

VL Buyers

Non-VL F-Stat

VL Buyers

Non-VL F-

Stat

VL Buyers

Non-VL F-Stat

(n=7) (n=184) (n=7) (n=18

4) (n=7) (n=184)

Fruits and Vegetables (servings/day)

4.02 5.11 1.49 5.02 4.49 0.49 0.99 ‘-0.60 2.96*

(0.74) (0.18) (0.51) (0.15) (1.03) (0.18)

Vegetables (servings/day)

3.19 4.50 2.56 4.12 3.63 0.62 0.93 -0.86 5.27** (0.48) (0.16) (0.57) (0.11) (0.87) (0.14)

Quantityofvegetablespurchased

9.92 10.79 0.16 11 9.37 0.92 1.08 -1.42 1.65

(3.256) (0.40) (1.32) (0.33) (3.02) (0.36) Number of different vegetables purchased

4.43 4.77 1.95 4.86 4.81 0.09 0.43 0.04 1.73

(0.43) (0.04) (0.14) (0.03) (0.48) (0.05)

Fruit (servings/day)

0.84 0.58 0.70 0.90 0.85 0.02 0.06 0.25 0.28 (0.50) (0.06) (0.22) (0.06) (0.33) (0.07)

Perceptions of the environment

4.43 3.97 4.16** 3.95 3.83 0.58 -0.48 -0.14 1.90 (0.14) (0.04) (0.13) (0.03) (0.18) (0.05)

Importance of Vegetables

4.05 4.01 0.04 4.07 3.92 0.79 0.02 -0.09 0.22 (0.17) (0.03) (0.08) (0.03) (0.15) (0.05)

Food Expenditures

0.46 0.47 0.00 0.49 0.49 0.01 0.02 0.02 0.00 (0.07) (0.01) (0.01) (0.04) (0.08) (0.01)

Health Expenditures

0.12 0.12 0.01 0.12 0.09 0.89 0.00 -0.03 0.55 (0.01) (0.01) (0.03) (0.01) (0.02) (0.01)

Total Expenditures

5435.71

7291.79 1.81 8378.57 7399.7

8 0.45 2942.86 107.98 4.76**

(789.12)

(267.09)

(1426.86)

(278.52) (919.14) (250.6

3) Social participation (% yes)

0.14 0.12 0.02 0.71 0.39 3.04 (0.14) (0.02) (0.18) (0.04)

Generalized Trust (% yes)

0.86 0.91 0.26 0.86 0.77 0.28 (0.14) (0.02) (0.14) (0.03)

Cooking' matters network size

6.14 4.93 0.81 3.57 2.41 1.23 -2.57 -2.52 0.00

(2.10) (0.26) (0.81) (0.20) (2.19) (0.33) *p<0.10, **p<0.05, ***p<0.01, ****p<0.001

62

Table3.8:Unadjustedandadjustedregressionestimates

Variable

VegetableServingsperDay


NumberofdifferentVegetablesPurchased

Coefficient(SE)

Coefficient(SE)

Coefficient(SE)

Coefficient(SE)

Coefficient(SE)

Coefficient(SE)

VeggieLiteConsumer(@Endline)

1.80(0.78)**

1.92(0.79)** 2.50(1.95) 2.49(1.97) 0.39(0.29) 0.40(0.29)

-Non-VeggieLiteConsumer REF REF REF REF REF REF

Socioeconomic/Povertylevel -0.04

(0.10) 0.06(0.26) -0.09(0.04)

Ward1(control) 0.07(0.54) 0.63(1.34) 0.14(0.20)

Ward2(treatment) -0.48(0.56) -0.52

(1.38) 0.32(0.21)

Ward14(treatment) -0.57(0.64) -1.47

(1.59) 0.31(0.24)

Ward16(control)[Referent] REF REF REF

Constant -0.87(0.15)

-0.64(0.49)

-1.41(0.37)

-1.26(1.21) 0.04(0.06) -0.17

(0.18)*p<0.10, **p<0.05, ***p<0.01, ***p<0.001

eKutirOperationalDataonUrbanSalesFigure 3.5 shows the volume of sales within wards 1, 2, and 14 during the

pilot period. Despite the fact that most of our sample respondents did not

report VeggieLite as their main source for purchasing vegetables, eKutir

sales increased steadily in wards 1, 2, and 14. The data show a steady

increase in the purchasing of vegetables from VeggieLite vendors in three

wardsoverthepilotinterventionperiod.TherewerenoVeggieLitevendorsin

Ward16,whichiswhyWard16dataarenotshown.

63

Figure3.5:TotalWeeklySales(kgs)forVeggieLiteServicesbyMonth

inselectWards

ThedeclineshowninWard2islikelyattributabletotheclosureofoneof

theVeggieLitevendorsinSeptember2015.Thisvendorhadalongestablished

relationshipwithlocalresidentsandwasstrategicallysituatedatanentry

location for Ward 2 residents. Although another vendor opened in this same

period,thenewlyopenedvendorwassituateddeeperintheWard2area.

TheRuralSampleTable3.9providesthebaselineandendlinesamplesizesbyquasi-experimentalconditionanddistrictsite.Therewere

0

500

1000

1500

2000

2500

3000

3500

4000

4500

Mar

-15

Apr

-15

May

-15

Jun-

15

Jul-1

5

Aug

-15

Sep-

15

Oct

-15

Nov

-15

Dec

-15

Jan-

16

Feb-

16

Mar

-16

Apr

-16

Total Sales (kgs)

Ward 2

Ward 14

Ward 1

ClosureofoneVLvendor(W2)

64

Table3.9:BaselineandEndlinesamplesizesbyquasi-experimental

condition

BASELINE ENDLINE

District

eKutir Farmers

in Treatment

Villages

Non-eKutir Farmers in Treatment

Villages

Comparaison Farmers

(Non-eKutir Farmers in non-eKutir

Villages)

eKutir Farmer

s in Treatm

ent Village

s

Non-

eKutir Farmers

in Treatment

Villages

Comparaison Farmers

(Non-eKutir Farmers in non-eKutir

Villages)

Angul 20 25 25 20 25 25Jharsuguda 37 41 59 37 41 59Kandhamal 62 70 48 62 70 48Nayagarh 8 8 13 8 8 13

TOTAL 127 144 145 127 144 145

Table 3.10 provides information on the baseline household and socio-

demographic characteristics of the rural sample stratified by quasi-

experimental condition. eKutir farmers in the rural sample had an average

household size of 5.8 members with 2.5 members working on the farm. Non-

eKutir farmers in eKutir villages and those not in eKutir treated villages

hadsimilarhouseholdsizes.Inallthreegroups,theheadofhouseholdabout

agriculturalproductionweremadeamongeKutirfarmers,amongtotalhousehold

members, approx. 2.5 members (treatment eKutir farmers), 2.3 members

(treatment non-eKutir farmers) and 2.6 members (control households) were

engagedintofarmingactivitiesasamainsourceofoccupation.Themajority

ofdecisionsaboutagriculturalproductionweremadebythehouseholdhead,

but the majority of decisions related to household matters were made by

husband and wives together. The primary household language among eKutir

farmers and the non-eKutir farmers in the comparison villages was Odiya,

whereas the primary household language among non-eKutir farmers in the

treatment villages was Adivasi. Across the three quasi-experimental groups,

thepercentageofilliterateadultsrangedfrom15%to20%.

65

Table3.10:Baselinedataonhouseholdandsociodemographic

characteristics

Item

TreatmentVillages ComparisonVillages

eKutirFarmers Non-eKutirFarmers

Non-eKutirFarmers

(n=149) (n=148)Males 52% 52% 52%Females 48% 48% 48%Householdcharacteristics Householdsize 5.8 5 6#membersworkingonfarm

2.5 2.3 2.6

Decisionsaboutproduction

Headofhousehold 63.80% 55.70% 60.10%Both 29.20% 31.50% 27.00%Other 7.00% 12.80% 12.90%HHDecisionmaking Husbandandwifejointly 48% 42% 43%Mainfemaleorwife 4% 6% 3%Mainmaleorhusband 21% 22% 23%PrimaryHHLanguage Odiya 50.80% 35.60% 48.00%Sambalpur 10.80% 18.10% 25.00%Adivasi 29.20% 38.30% 21.60%Other 9.20% 8.00% 5.40%Educationlevel Illiterate 19% 20% 15%

eKutir,non-eKutir,andComparisonFarmersi)Table3.11A&BprovidestheANOVAresultsreportingthedifferences

amongthethreegroups:eKutirfarmersintreatmentvillages,non-eKutir

farmersintreatmentvillages,andnon-eKutirfarmersincomparisonvillages.

Vegetableandfruitconsumption:Overall,fruitandvegetableconsumption

increased from 2015 to 2016 in eKutir farmers when compared to non-eKutir

farmersintreatmentvillagesandcomparisonfarmers(F=2.88,p>0.05).At

66

baseline,theaveragedailyvegetableservingswere3.23,3.17,and2.98for

the eKutir farmer, non-eKutir farmers and the comparison farmers

respectively.Averagedailyservingsdecreasedinallthreegroupsoverthe

intervention with significant differences among groups at endline. eKutir

farmers had the highest daily vegetable servings and significantly higher

values than the comparison farmers. With regard to daily fruit servings,

analysesshowedthateKutirfarmerscomparedtootherfarmershadanincrease

intheirconsumptionoffruitsovertheyear(F=9.33,p<0.001).Atendline,

eKutir farmers had the highest average daily fruit servings (F = 7.83,

p<0.001). The comparison farmers reported consuming the most kilograms of

vegetables ANOVA results indicated modest differences at baseline in the

average daily vegetable servings between treatment and comparison groups.

Results showed a decline across all groups in the number of different

vegetables consumed, with eKutir farmers having the greater difference

between baseline and endline values (F = 2.78, p<0.05). All groups had a

declineintheirpercentageofhomegrownconsumption,althougheKutirfarmers

had the smallest decline over the year (F = 1.88, p<0.10). Nevertheless,

eKutirfarmersstillhadthehighestpercentageofhomegrownconsumptionat

endline(F=9.01,p<0.001).Withregardtodailyfruitservings,analyses

showedthateKutirfarmerscomparedtootherfarmershadanincreaseintheir

consumptionoffruitsovertheyear(F=9.33,p<0.001).Atendline,eKutir

farmershadthehighestaveragedailyfruitservings(F=7.83,p<0.001).

VegetableProduction:Comparedtotheothergroups,eKutirfarmershadthe

largest amount of acreage for vegetable farming at baseline (F = 5.97,

p<0.01) and endline (F = 3.16, p<0.001). At baseline and endline, eKutir

farmers had the highest the level of vegetable production compared to the

otherfarmergroups.

Perceptions,Beliefs,andExpenditures:Therewerenodifferencesbetween

rural farming group with regard to the importance that households place on

vegetables. Compared to non-eKutir farmers within treatment villages at

67

baseline and endline, eKutir farmers had the most favorable perspective on

theavailability,accessibility,andaffordability

SocialCharacteristics: Compared to non-eKutir farmers, the eKutir farmer

grouphadamorefavorableperceptionoflocalsocialcohesionatbaseline(F

=4.52,p<0.05)andendline(F=5.28,p<0.01).eKutirfarmersalsotended

tohaveahigherpercentageoffarmersandothervillagersintheirnetworks

thanthenon-eKutirfarmersintheirsamevillageandcomparisonfarmersin

othervillages.ThepercentageoffarmersineKutirfarmernetworksincreased

significantly from baseline to endline. Finally, the percentage of FIG

farmers in the networks of eKutir farmers also increased over the year,

suggesting the greater social clustering of eKutir farmers within farmer

networks.

Table3.11A:ANOVAresultsreportingthedifferencesamongthethree

groups

[BaselineandEndline]

Baseline Anova Endline Anova

n

EKutirFarmers

inTreatmen

tVillages

Non-

eKutirFarmers

inTreatme

ntVillage

s

ComparisonFarmers(Non-eKutir

Farmersinnon-eKutirVillages)

FStat

EKutirFarmers

inTreatmen

tVillages

Non-

eKutirFarmers

inTreatmen

tVillages

Comparison

Farmers(Non-eKutirFarmersinnon-eKutir

Villages)

FStat

(n=120) (n=141) (n=144) (n=120) (n=141) (n=144) Fruits&Vegetables(servings/day)

405 3.41 3.37 3.28 0.18 3.48^ 2.96^ 2.92^ 5.19*** (0.15) (0.15) (0.14) (0.16) (0.12) (0.12)

Fruit(servings/day)405 0.17 0.20 0.30 2.20 0.49^(0.

01) 0.24^ 0.24^ 7.83****

(0.03) (0.04) (0.05) (0.08) (0.03) (0.03)

Vegetable(servings/day) 405

3.23 3.17 2.98 0.96 2.98^ 2.72 2.69 1.99(0.14) (0.12) (0.12) (0.12) (0.10) (0.10)

QuantityofVegetablesConsumed(kgs)

405 8.59 8.18 9.21 2.38* 8.16' 7.33' 8.6' 4.18** (0.33) (0.34) (0.36) (0.320 (0.30) (0.34)

Numberofdifferentvegetablesconsumed(max.5)

4054.68 4.65 4.65 0.05 3.95^ 4.14 4.26^ 3.55**

(0.05) (0.07) (0.07) (0.09) (0.08) (0.07)

HomeGrownConsumption 36537.54 29.68 34.00 1.28 28.00^ 18.54' 15.76 9.01****

(3.45) (3.30) (3.64) (2.41) (2.09) (1.80)

68

Fruit(servings/day)405 0.17 0.20 0.30 2.20 0.49^(0.

01) 0.24^ 0.24^ 7.83****

(0.03) (0.04) (0.05) (0.08) (0.03) (0.03)

Vegetableproductionarea(Acre)

405 1.22^(n=405) 0.9' 0.64^' 5.97*** 1.72^'

n=372 1.3' 1.06^ 7.17****

372 (0.11) (0.11) (0.08) (0.15) (0.12) (0.09)

Vegetableproduction(Kg)

405 435.97^ 318.74 216.15^ 2.29 314.26' 152.04' 211.4 3.16**

372 (86.17) (75.19) (54.51) (65.68) (19.24) (42.39)

Vegetablesalesquantity(Kg)

372 408.3^ 283.04 180.79^ 2.61* 270.73' 129.02' 176.26 3.00*

372 (85.39) (73.39) (49.53) (60.08) (17.93) (36.83)

Incomefromvegetablesales(Rs.)

405 5100.52^ 4603.25 3157.36^ 1.15 3555.47 2145.62 2318.76 1.63

372 (859.49) (1140.25) (771.86) (832.42) (399.63) (497.93)

Vegetableprices(Rs./Kg)

303 16.69 16.25 15.08 0.24 15.41 17.05 16.35 1.12

315 (1.51) (1.57) (1.85) (0.71) (0.76) (0.94)

ImportanceofVegetables

401 3.96 3.93 3.7 0.75 3.9 3.97 3.94 1.7

(0.06) (0.11) (0.25) (0.03) (0.03) (0.03) PerceptionsofEnvironment(Affordability,Access,Availability)

400 11.63' 11.02' 11.29 4.52** 11.18' 10.72' 11.15' 5.28***

(0.12) (0.15) (0.14) (0.12) (0.12) (0.10)

PerceptionsofSocialCohesion(Village)

405 0.03 0.06 0.09 1.57 0.06 0.00 -0.05 0.63

(0.07) (0.07) (0.06) (0.05) (0.08) (0.06)

NetworkComposition(%ofFarmersinNetwork)

405 0.63 0.65 0.58 1.5 0.93^' 0.88' 0.88^ 2.52*

(0.03) (0.03) (0.03) (0.01) (0.02) (0.02) NetworkComposition(%ofVillagersinNetwork)

393 0.7 0.77 0.75 1.43 0.96^' 0.91' 0.91 2.41*

(0.03) (0.03) (0.03) (0.01) (0.02) (0.02)

NetworkComposition(%ofFIG/eKutirFarmersinNetwork)

385 0.29' 0.16' 0.23 3.54** 0.54^' 0.23' 0.09^' 58.42****

349 (0.04) (0.03) (0.03) (0.04) (0.03) (0.02) *p<0.10,**p<0.05,***p<0.01,****p<0.001^signifcantcontrastp<0.05betweentreatment(eKutirfarmer)andcontrol;'signifcantcontrastp<0.05betweeneKutirfarmerandnon-eKutirfarmerinsamevillageValuesshowninparenthesisnotedasStandardError

69

Table3.11B:ANOVAresultsreportingthedifferencesamongthe

threegroups(MeanDifference[Endline–Baseline])

MeanDifference(Endline-Baseline) Anova PairwiseContrasts

n

EKutirFarmersinTreatmentVillages

Non-

eKutirFarmers

inTreatmen

tVillages

ComparisonFarmers

(Non-eKutirFarmersinnon-eKutirVillages)

FStat

Compare:Treatment–eKutir

FarmersVSComparison

Compare:eKutir

Farmersv.non-EKutir

Compare:Non-eKutir

v.Compari

son

(n=120) (n=141) (n=144) (n=120) (n=141) (n=144)

Fruits&Vegetables(servings/day)

405 0.07 -0.41 -0.36 2.88** 3.95** 4.73** 0.05 (0.16) (0.15) (0.14)

Fruit(servings/day)405 0.32 0.04 -0.06 9.33***

* 13.94**** 8.41*** 1.96

(0.08) (0.04) (0.06)

Vegetable(servings/day) 405-0.25 -0.45 -0.30 0.57 0.06 0.99 0.64

(0.13) (0.14) (0.12)

QuantityofVegetablesConsumed(kgs)

405 -0.42 -0.85 -0.61 0.32 0.12 0.68 0.22

(0.38) (0.35) (0.38) Numberofdifferentvegetablesconsumed(max.5)

405-0.73^ -0.51 -0.39^ 2.78* 5.86** 2.21 0.75

(0.10) (0.10) (0.10)

HomeGrownConsumption 365-9.54 -11.14 -18.24

1.88 3.12* 2.26 0.12(3.45) (3.15) (3.52)

Fruit(servings/day)405 0.32 0.04 -0.06 9.33***

* 13.94**** 8.41*** 1.96

(0.08) (0.04) (0.06)

Vegetableproductionarea(Acre)

405 0.49(n=372) 0.28 0.35

0.73 0.62 1.15 0.19372 (0.15) (0.12) (0.09)

Vegetableproduction(Kg)

405 -121.71 -166.7 -4.751.08 0.93 0.12 2.87*

372 (106.15) (75.26) (58.93)

Vegetablesalesquantity(Kg)

372 -137.57 -154.02 -4.531.14 1.33 0.02 2.76

372 (102.60) (73.08) (52.63)

Incomefromvegetablesales(Rs.)

405 -1587.91 -3046.85 -1233.280.74 0.06 0.71 1.33

372 (1166.19) (1274.37) (920.70)

Vegetableprices(Rs./Kg)

303 15.41 17.05 16.351.12 0.63 2.46 0.34

315 (0.71) (0.76) (0.94)

ImportanceofVegetables401 -0.06 0.05 0.24

0.82 1.39 0.69 0.54 (0.06) (0.11) (0.25)

PerceptionsofEnvironment(Affordability,Access,Availability)

400 -0.43 -0.29 -0.150.69 1.41 0.29 0.41

(0.18) (0.17) (0.15)

PerceptionsofSocialCohesion(Village)

405 0.04 -0.06 0.040.4 0 0.55 0.56

(0.08) (0.10) (0.09)

NetworkComposition(%ofFarmersinNetwork)

405 0.31 0.22 0.292.06 0.11 2.65 3.36*

(0.04) (0.03) (0.03)

70

NetworkComposition(%ofVillagersinNetwork)

393 0.26 0.13 0.163.21* 3.28* 6.59** 0.45

(0.04) (0.03) (0.04)NetworkComposition(%ofFIG/eKutirFarmersinNetwork)

385 0.27 0.06 -0.17 21.33**** 40.86**** 8.13*** 14.00**

**349 (0.06) (0.05) (0.04)*p<0.10,**p<0.05,***p<0.01,****p<0.001^signifcantcontrastp<0.05betweentreatment(eKutirfarmer)andcontrol;'signifcantcontrastp<0.05betweeneKutirfarmerandnon-eKutirfarmerinsamevillageValuesshowninparenthesisnotedasStandardError

ii) Regression results examining the differences in fruit and vegetable

consumption from baseline to endline are shown in table 3.12. Regression

results showed that eKutir farmers compared to comparison farmers had a

higherincreaseinoverallfruitandvegetableservings(B=0.43,p<0.05).No

significantincreaseordecreasewasshowninthedailyvegetableservingsof

eKutir farmers compared to the other groups. There was a modest decline in

the number of different vegetables purchased by eKutir farmers compared to

theotherfarminggroups.Therewasanincreaseindailyfruitservingsin

eKutirfarmerscomparedtonon-eKutirandcomparisonfarmers.

Table3.12:Regressionresultsexaminingthedifferencesinfruitand

vegetableconsumptionfrombaselinetoendline

Variable

VegetableServingsper

Day


Numberofdifferentveg

etablespurchased

FruitServingsperday

FruitandVegetable

Servingsperday

(n=405)

(n=338) (n=405) (n=33

8)(n=405)

(n=338) (n=405) (n=33

8)(n=405)

(n=338)

eKutirFarmers

0.04(0.19

)

0.01(0.20

)

0.19(0.53)

0.20(0.57

)

-0.34(0.14)**

-0.30(0.15)*

0.39****

(0.09)

0.42****

(0.10)

0.43**

(0.21)

0.43(0.23)*

non-eKutirFarmersinTreatmentVillages

-0.15(0.19

)

-0.09(0.20

)-0.24(0.51)

-0.02(0.57

)

-0.12(0.14

)

-0.12(0.15

)0.11

(0.09)

0.08(0.10

)

-0.04(0.83

)

-0.01(0.23

)

-ComparisonFarmers

REF REF REF REF REF REF REF REF REF REF

SocioeconomicIndex

0.04(0.06

)

0.13(0.47

)

-0.08(0.09

)

0.01(0.03

)

0.05(0.07

)HomeGrownConsumption

0.00(0.00

)

0.01(0.01)*

0.01(0.001)**

-0.001(0.00

0.002(0.002)

71

1)

HouseholdDecisions(Bothv.Sole)

-0.43(0.17)**

-0.94(0.48)*

-0.20(0.13

)

0.14(0.09)*

-0.29(0.19

)

Constant-0.28(0.12

)

-0.12(0.18

)

-0.61(0.36)

-0.47(0.54

)

-0.39(0.10

)

-0.45(0.14

)

-0.39(0.10)

-0.45(0.14

)

-0.36(0.14

)

-0.26(0.21

)*p<0.10, **p<0.05, ***p<0.01, ****p<0.001 Values shown in parenthesis noted as Standard Error

RoleofHomeGrownConsumption:Table3.13providesfurtherinformationon

home-grown consumption among farming households for each quasi-experimental

condition.Byendline,eKutirfarmerstendedtoconsumeahigherpercentage

oftheirownproduction(vegetables,fruits,pulsesoverall)thantheother

groups. Potatoes, tomatoes, onions, garlic and eggplant were the most

producedvegetables.

Table3.13:Home-grownconsumptionbyproducersatbaselinevs.

endline

Baseline Endline

Treatment:

eKutirFarmers(n=130)

Treatment:

Non-eKutirFarmers(n=149)

Control

(n=148

)

Treatment:

eKutirFarmers(n=127)

Treatment:Non-

eKutirFarmers

(n=144)

Control(n=145)

Averagehome-grownconsumptionofallproduce(vegetables,fruits,pulsesoverall)(%)

40.32*(3.47)

28.95*(3.16)

33.08*(3.55)

26.7*** 16.52***

14.61***

All vegetablesconsidered(in%)

35.68***

23.00*** 26.12***

30.78*** 19.32***

17.02***

Allpulsesconsidered(in%)

23.75* 13.6* 26.45* 22.54*** 7.67*** 14.14***

Allfruitsconsidered(in%)

22.0** 32.83** 37.27**

20.79* 15.56* 7.31*

ConsideringonlyPotatoes(%)

45.88 26.22 28.7 47.11*** 25*** 25.87***

Consideringonly 25.0 31.25 27.76 17.02 15.79 9.52

72

Banana(%)ConsideringonlyPigeonbean(%)

11.76* 10.53* 27.45* 6.89 1.61 7.56

ConsideringonlyMungbean(%)

45.31* 15.15* 24.24* 40.48 18.18 27.12

ConsideringonlyTomatoes(%)

- - - 17.0** 15.38** 5.74**

ConsideringonlyOnion(%)

- - - 33.33 19.77 18.6

ConsideringonlyOkra(%)

- - - 28.57 9.38 11.43

ConsideringonlyEggplant(%)

- - - 25.0 14.45 11.34

*p<0.05,**0<0.01,***p<0.001NOTE:Treatment-levelcomparisonsaremadeseparatelywithinbaselineandendlinemeasures.

Sincevegetableconsumptionathomeimpactstheamountavailabletosell,we

examinedwhetherthelevelofhomegrownconsumptionmightaltertheeffects

oftheinterventiononaveragevegetableservings.

eKutirOperationalDataonRuralSalesFarmers’ subjective reports of production, sales, and income may often

divergefromactualproduction,salesandincome.DatafromeKutir’srural

agricultural operations were used to examine the degree to which the

objective operational data corresponded to the farmers’ self-reported

information and whether the operational data might provide additional

insightsintotheeffectsoftheVeggieLiteinterventionontheagricultural

productionandrevenueofeKutirfarmers.Inparticular,weusedthesedata

toexamineif(i)therewereseasonaleffectstobefoundintheVeggieLite

intervention and (ii) there were differences between operational and self-

report farmer information. eKutir operational data were linked to the

responses of the eKutir farmers who participated in the rural evaluation.

Two sets of analyses were then conducted with these linked data. First,

paired-sample t-tests were used to compare agricultural production data at

baseline and endline in the Rabi and Zaid season. Endline data was not

availablefortheKharifseason.Additionalsubanalysesexaminedifthere

were certain villages in which intervention effects might be stronger.

73

Second,regressionanalyseswereusedtoexamineiftherewasanincreasein

different vegetable production variables over five seasons. Repeated

seasonalmeasuresoftheamountharvested,sold,andrevenuegeneratedwere

nested within each farmer for which we had data. Regression analyses were

stratifiedbywhetherthedatacamefromeKutiroperationsorfarmers’self

reports.

Table 3.14 shows the results from the paired sample t-tests. Results

indicated important seasonal differences in the effects of the VeggieLite

interventiononthevegetableproductivityandincomeofeKutirfarmers.In

Rabi, there were significant declines from 2015 to 2016 in the quantity of

vegetablesharvestedandsold.Thesedeclinesweremainlyinthevillagesof

Majhikia and Parmanpur. Yet, the prices and revenues that eKutir farmers

receivedincreasedintheRabi2016season.Priceandrevenueincreaseswere

generallysharedsimilarlyacrossthedifferenttreatmentvillages.InZaid,

therewasaconsistentincreasefromthe2015Zaidseasontothe2016Zaid

season.

Table 3.15 offers the results from the regression analyses. The self-

reported and operational data provide different pictures of the effects of

theVeggieLiteprogramoneKutirfarmersovertheinterventionperiod.Self-

reported information from the eKutir farmers suggests that there was a

decline in the amount that they produced and sold over the intervention

period, as well as the farmers’ revenues. In contrast, the eKutir

operationaldatashownoeffectoftimeontheamountofvegetablesharvested

and sold. The operational data also show that there was an increase in

revenue over the intervention period. The amount harvested, sold, and the

revenue generated among eKutir farmers was significantly less in Zaid

comparedtoKharif.

74

Table3.14:Resultsfromthepairedsamplet-tests

nRABI ZAID

VariableEndline Baseline

DifferenceEndline Baseline Differenc

e2016 2015 2016 2015

Quantityofthevegetablesharvested(kgs)

130

2218.08

2706.54

-488.46**

**2074.23 1133.8

5940.38*

***

(101.70)

(210.38) (130.74) (196.38) (85.20

)(209.47

)

Amountofvegetablessold(kgs)

130

2104.62 2571.2

1

-466.60**

**1970.52 1077.1

5893.37

(96.90)

(199.86)

(124.27) (186.56) (80.94)

(199.00)

Pricesreceived 13021.50 14.08 7.42**** 19.35 12.83 6.52***

(0.63) (0.52) (0.46) (0.84) (0.81) (0.76)

Revenues 130

26151.31

23261.12

2890.19**** 22939.58 11765.

38

11174.19****

(1720.65)

(2094.54) (807.02) (2140.81) (867.7

3)(2300.8

3)

*p<0.10, **p<0.05, ***p<0.01, ****p<0.001 Values shown in parenthesis noted as Standard Error

75

Table3.15:Resultsfromtheregressionanalyses

DiscussionAsaone-yearpilotinterventioninConvergentInnovation,VeggieLiteshowed

a number of successes in its impact on the diets, production, and social

lives of rural farmers and urban consumers. We focus in this section on

threemainareasofimpact:(1)nutrition,(2)economics,and(3)social.

Nutritional impact: To assess the nutritional impact of VeggieLite, the

evaluation focused primarily on changes in the vegetable and fruit

consumptionofthoseurbanhouseholdsandruralfarminghouseholdswhowere

exposed to VeggieLite services and providers. Among urban consumers, the

fieldstudyshowedmixedresultsinimprovingvegetableconsumptionlevels.

Basedonthequasi-experimentaldesign,consumersresidinginthetreatment

wards showed little change in their consumption patterns. There was a

SELF-REPORTED(n=507) OPERATIONAL(n=650)

Variable QuantityofHarvest

QuantityofSold Revenue QuantityofHarvest Quantity

ofSold Revenue

Time-58.15* -101.91**

-1796.86*

**75.32 71.13 2344.06****

(31.80) (49.97) (565.62) (59.23) (56.28) (607.53)

Season

Rabi39,73 224.15 1303.25 -85.03 82.27 1752.63

(116.94) (183.84) (2080.69) (219.61) (208.69) (2252.78)

Zaid-22.76

76.58

4811.61** -1018.62****

-967.48**

**-7945.17****

(134.29) (211.10) (2389.27) (219.61) (208.69) (2252.78)

Kharif REF REF REF REF REF REF

Constant

393.08 425.72 7141.62 2359.08 2242.37 17093.42

(102.35) (160.90) (1821.15) (259.28) (238.77) (2577.53)

76

general decline in vegetable consumption in the treatment and comparison

wards but consumers in treatment wards purchased a higher quantity of

vegetables at endline. The weak results from the quasi-experimental design

may be attributable to the tangling of consumers in the treatment and

comparisonwards,i.e.,consumersfromcomparisonwardscouldalsohavebeen

exposedtoVeggieLite.Furthermore,eKutiroperationaldecisionsledtothe

closing and relocating of certain vendors, which would have altered

consumer’sexposuretotheVeggieLiteservices.

Toaddressthismatter,theevaluationsoughttoexaminemorecloselythose

VeggieLite consumers that reported VeggieLite as their main source for

purchasing vegetables. While the number of respondents who reported

VeggieLiteastheirmainsourcewaslow,theevaluationsuggestedthatthose

consumers who did purchase from VeggieLite increased their daily vegetable

servings over the intervention period. This finding held in both the ANOVA

and regression analyses. eKutir operational data showed incremental and

stronggrowthovertheperiod.

ThenutritionalimpactofVeggieLiteintheruralareaswassharper.Compared

to both non-eKutir farmers in treatment villages and comparison farmers,

eKutirfarmersshowedanincreaseintheirdailyfruitandvegetableservings

over the intervention period. Daily vegetable servings decreased for all

farminggroupsovertheyearbuteKutirfarmer’smaintainedhigherlevelsof

daily vegetable servings at endline. The daily fruit servings of eKutir

farmerscomparedtonon-eKutirandcomparisonfarmersalsoincreasedoverthe

year. These findings were supported by both the ANOVA and regression

analyses. Yet, findings did also suggest that the number of different

vegetables that eKutir farmers purchased did decline over the intervention

period.Fortheinterventionperiod,theresultsdidnotsuggestsignificant

spillovereffectswithregardtoeKutirfarmerstransmittingbenefitstonon-

eKutirfarmersinthetreatmentvillagesbutthismaybeduetothefactthat

theevaluationwaslimitedtooneyear.

77

Economic impact: To assess the economic impact of VeggieLite on urban

consumers and rural farmers, the evaluation focused on the household

expenditures of urban households and the agricultural productivity and

revenue of rural farmers. Findings from the urban sample show little

indicationofeconomicbenefits.Giventhatmostparticipantsreportedthe

Village Mandi as the main source, this finding is not surprising. In the

ruralareas,theeffectsoftheinterventionagainseemclearer,particularly

InlightoftheeKutiroperationaldata.Whiletheself-reportdatasuggest

adeclineinquantityharvestedandsold,andinthefarmers’revenues,the

operational data show the opposite. The operational data indicated an

increaseintherevenueoftheeKutirfarmersovertime,eventhoughfarmers

hadlowerrevenuesinZaidcomparedtoKharif.UsingtheeKutiroperational

data,wefoundthateKutirfarmerstendedtounderreporttheiryield,price

and revenue information compared to their operational data. When examining

eKutir revenue and sales data separately for the seasons of Rabi and Zaid,

findingsshowsignificantincreasesinyield,pricesandrevenuesreceivedin

Zaid. eKutir data show also show an increase in the revenue of eKutir

farmersinRabi,althoughtherewasadeclineinthequantityofvegetables

harvestedandsoldinRabi.

Socialimpact:ToassessthesocialimpactoftheVeggieLiteintervention,

our evaluation used social network analysis to assess the degree to which

participants’socialnetworksandperceptionsofthesocialenvironmentmay

havechangedovertheyear.Whilethiswasnotacentralanalyticalfocus,

we viewed the intervention as possibly altering the way in which those

exposedtoVeggieLitesawtheirenvironmentormightinteractwithothersin

theircommunity.TherewasnosuggestionofasocialimpactofVeggieLitein

the urban sample. In the rural areas, there was however indication of a

social impact of the intervention on rural farmers. Among eKutir farmers,

theresultssuggestedasetofchangesinthenetworkcompositionofeKutir

farmers.First,thesocialnetworksofeKutirfarmerscametoconsistmore

heavily of other villagers in their agricultural and dietary networks.

Furthermore,thepercentageofFIGfarmerscomposingthenetworksofeKutir

78

farmers as well as the non-eKutir farmers in the treatment villages also

increasedovertheyear.Thislatterfindinghighlightsclearlythediffusion

andspreadoftheeKutirinnovationswithinthetreatmentvillages,andthe

promiseofVeggieLiteinspreadingmorewidelyintheruralareas.

Conclusion

The field evaluation indicates the VeggieLite intervention has produced

noticeable successes among rural farmers and villages. The rural quasi-

experimental groups were formed according to whether farmers participated

directly in the VeggieLite program or not. This may have given the rural

evaluationgreaterpowertodetectthedirectbenefitsofVeggieLiteonrural

farmers within the year. Detecting the effects of VeggieLite on urban

consumers was more challenging, particularly given the different consumer

optionsavailableandthegreaterdifficultyintrackingconsumerpurchasing

behaviorinurbanIndia.TheweakeffectsofVeggieLiteamongurbanconsumers

shouldthereforenotbeinterpretedasconclusive.Infact,VeggieLitesales

intheurbanwardssuggeststhatVeggieLitevendorsaredevelopingagrowing

consumerbase.Furtherresearchfocusingonawell-definedsetofVeggieLite

consumers compared to non-users would provide clearer evidence of the

nutritional,economicandsocialimpactofVeggieLiteinurbanareas.

79

Chapter 4: Convergent Innovation Process Analysis:Producers & Organizations, Ecosystem NetworkFormation,andCompetitiveDynamicsResearchquestionsThis chapter reports on the methods and results of a multi-component

convergent innovation evaluation analysis (Moore et al., 2015) designed to

uncoverthemechanismsinvolvedinbringingtogetherthevariousstakeholders

engagedinacollaborativeactiontargetingconvergentoutcomes(Dubeetal.,

2014) i.e., the simultaneous realization of improved agricultural and

economicreturnsforlow-incomeruralandurbancommunities.Weexaminedthe

eKutirsocialenterpriseVeggieKart(VK)systemanditscompetitivedynamics

with traditional commercial channels, with many middlemen operating in

informal spot markets. A team of management scholars in consumer behavior,

marketing,strategy,entrepreneurship,andcompletivedynamicmodeling(Drs.

Dube,Jha,andRay)joinedtheirexpertisewithDr.Moore’sinsocialnetwork

formation to collect field data in the months of December 2015 to January

2016. To collect primary data, specific criteria for identifying eligible

respondentswasusedandallactorsweresegmentedintovarioussub-groups.

The VK ecosystem has been pictorially represented in chapter 2 for the

ecosystem actors (Fig. 2.2, p.25), flow of material (Fig. 2.3, p.26) and

everydayoperationalprocessflow(Fig.2.9,p.31).Thesefiguresprovidea

comprehensiveoverviewoftheeKutir/VKsystemandsetthestageforfurther

analysis. The general expectation is that, vis-à-vis status quo with

traditionalMandisystems,aconvergentinnovationecosystemsuchaseKutir/

VKmicroenterprisemodelisgoodforbothfarmersandendconsumers,aswell

as for other actors involved. However, there has been very few scientific

studiesthatcombinethemanagementandsocialsciencestoexamineboththe

formation and outcomes of the ecosystem proper and the complex competitive

dynamics tied to its co-existence and co-evolution with the traditional

players.A3-componentstudywasthereforedesignedtoexaminethesecomplex

processes:(a)astrategicstudywithproducersandorganizations;(b)afull

80

social network analysis of the eKutir/ VK ecosystem formation; and (c) an

operationmanagementscienceanalyticalmodelofcompetitivedynamicsbetween

eKutir/ VK ecosystem and traditional commercial middlemen (Mandis and spot

market). Altogether, the 3 components of the CI process study aimed to

addressthefollowingresearchquestions:

- WhatarethemechanismsunderlyingeKutir/VKentrepreneurshipmodel?

- What processes drive supply and demand for farmers, consumers, and

other actors and foster engagement with the eKutir/ VK micro/

entrepreneurshipmodeland/orpartakeintheirecosystemevolution?

- How do networks form among various actors that need to be aligned in

ordertosupportthisentrepreneur-ledmodelastheecosystemevolves?

- Howdoes(Is/Why/When)anentrepreneurshipmodelcomparetotraditional

commercialchannels(Mandiandsportmarkets)intherisks,incentives,

andoutcomesitcreatesforfarmersandconsumersthatarepartofthe

eKutir/VKecosystem?Forthesewhooperateinalternativecompetitive

environment?Forthecommunityasawhole?Fortheruraleconomy?

- How should the incentives and governance be structured so that each

actorcreatesandderivesvalue?

- WhatisICT’sroleandwhatcanitdotofacilitatecomplexanddynamic

contexts?

The rest of this chapter recapitulates some of the key features of the

VeggieKart/VeggieLiteinterventionthatarerelevantforthethreecomponents

of the Convergent Innovation process analysis. We then present methods and

results of each CI analysis component to discuss how each component may

account for the results of the present pilot and provide insights for

transitiontoscaleoftheeKutir/VKentrepreneurshipmodel.

RecapoftheVeggieKart/VeggieLiteintervention

From a management perspectives, VeggieKart/VeggieLite is a venture by for-

profit social enterprise eKutir that focuses on providing innovative

products,services,andsustainablemodelstotheBottomofthePyramid(BoP)

81

market through the effective use of ICT (Information and Communication

Technologies)andadistributionnetworkoflast-mileentrepreneurs(Jhaet

al.,2016).VKaimstoreducetheaccesscostsforfarmersandconsumersby

using “micro-entrepreneurs,” which help marginal farmers sell their produce

and marginal retailers to reach end consumers (through handcarts and other

distributionchannels)inpoorruralandurbancommunitiesinOdisha.

eKutirruralmicro-entrepreneurs(calledagrientrepreneurorAEforshort)

operateatthelocal(villageorblock)level,workinacatchmentareaof15

kilometers,andcatertoamaximumof500-1000farmers.Recruitedandtrained

byeKutir,AEssupportvegetable-growingfarmersbyprovidingqualityinputs

andfarmingadvisorythroughamicro-entrepreneur,theAEisalocalperson,

well acquainted with the local context and the people. AEs also act as

“aggregators” of the produce to feed the different distribution systems in

theVKvaluechain.Onaverage,VKreachesoutto50-100farmers(oncritical

mass) per village, 250-300 farmers (on critical mass) per panchayat, in a

catchment area of at least 1,500 households in a block. To facilitate

localizedinteractionswithfarmers,micro-entrepreneursorganizeVKfarmers

into groups of 15-25 members called Farmer Intervention Groups (FIGs).

Depending on the number of VK farmers in a village, there may be multiple

FIGspervillage.Themicro-entrepreneursmaintainanattendanceregisterfor

each meeting that the farmers at the FIG meeting sign, thereby providing

informationaboutfarmers’levelsofparticipationandpossibleinteractions

withotherFIGmembers.InanygivenVKvillage,therearefarmerswhoare

linkedwithVKmicro-entrepreneursandotherswhoarenot.AEsworksclosely

witheKutir’sfieldexecutive’sand/orthelocalNGOstosupportthefarmers

through the crop cycle and procure their produce for the VeggieKart

distribution system. The AEs expect to make a tidy profit by facilitating

transactionsbetweeneKutir/inputprovidersandthefarmers.

TheVKMicro-Retailers(calledretailentrepreneurs[Res]forshort)arethe

local vegetable vendors who are converted into an organized chain of

vegetable entrepreneurs, earning a livelihood by selling the vegetables

82

sourced by the different VeggieKart distribution channels. These

entrepreneurs operate in small stores (Veggie Mart) or are stationed with

pushcarts on thorough fares (Veggie Wheel), both in geographically defined

urbanenvironmentonlyandcateringtothewalk-inconsumers.Anothersegment

of these vegetable entrepreneurs is part of the VeggieLite channel and

operatesinbothrural(VKvillages)andurbanareastodistributeandsell

the vegetables to resource-poor communities. The present intervention has

focusedonVeggieLitewhiledeploymentthroughtheotherchannelsoccurredin

parallel as part of the broader operational deployment. The different

channelscatertodifferenteconomicstrataofsociety.Theonlineandlarge

retailoutletscatertothehigh-incomeurbanpopulation.VeggieMarts(small

groceryshopsthatalsosellvegetables)andVeggieWheels(cartvendorswho

move through crowded thoroughfares) cater to the middle-class urban

population.VeggieLiteisachannelexclusivelyforurbanslumdwellersand

theruralpoor.

OthereKutir/VKecosystemactors:eKutir/VKhasalsopartneredwithlocal

andregionalgrassrootNGOs,agri-inputorganizations(e.g.seed,fertilizer

companies), agri-experts, cart manufacturers, cold storage suppliers, rural

banks, and other actors in the agriculture-food-ecosystem to both promote

demandontheconsumersideandtoimproveproductivityandefficiencyofthe

value chain on the supply chain. ICT enabled ecosystem: eKutir/ VK has

leveraged an Information and Communication Technology (ICT) platform to

enable seamless collaboration and coordination between the aforementioned

entitiesineKutir’secosystem(Jhaetal.,2016).

Direct interactions between farmers and consumers with no or minimal

intermediariesaregainingconsiderableinterestinboththedevelopingand

developed world (Dube et al., 2014). While intermediaries deliver critical

services to rural producers, they are also often exploitative and their

removalcanresultinlargeefficiencygains.However,researchontheITC

intervention in the soy value chain and market (Besley and Burgess, 2000)

showsthatitrequiresseriousinvestmenttobypassintermediariesbutthat

83

itispossible,andcanbebeneficial,forbothfarmersandfinalconsumers.

TheimmediatebenefittoITCLimitedofthisinterventionwastheimprovement

in quality of soybeans procured, from the creation of a direct marketing

channel, and a reduction in its transaction costs. The results in this

earlierstudy,muchinlinewiththetrendinoperationaldatainthepresent

intervention, suggest that there can be net welfare gains to farmers

resultingfromaredistributionofsurplusawayfromtraderstothefarmers.

Strategicstudywithproducersandorganizations

Datawasgatheredfromfarmers,microentrepreneurs,andrepresentativesfrom

the various organizational constituents in the ecosystem – eKutir and

organizations partnering with eKutir. This would not only allow us to

increase the internal validity of the study but reconcile and synthesize

multiple perspectives to give a fuller picture of the process. An embedded

case study design (Yin, 2009) was clearly suitable for studying such a

system.Thisdesigninvolvedanalyzingtheoverallsystembyanalyzingeach

individualcomponentofthesystem.Thebroadpropositionguidingthisstudy

was based on the theoretical exposition of convergent innovation (Dube et

al.,2014;Jhaetal.,2016)whichstatesthatjointrealizationofeconomic

and human development outcomes requires an ecosystem of actors spanning

societal sectors (individuals, firms, NGOs) to engage in collaborative

action,witheachactorcreatingandcapturingvalueinthesystem.

A concept-stakeholder grid (table 4.1) was constructed to guide the data

collection.Theconceptsareshownontherowsandtherespondentsareshown

on the column. Each cell indicates the sub-concepts those were studied for

theparticularrespondentcategoryandthepotentialsourcesthatcouldshed

lightonthatconcept.

Conceptsofinterestsincludethefollowing:

- Strategy, which captures key activities/ innovations, why they were

undertakenandwhatenabledthem;

84

- Structures and processes, which capture the new structures and

processesputinplacetofacilitateinteraction,coordination,andthe

natureofrelationshipbetweenthevariousentitiesintheecosystem;

- Technology, which captures the nature of technology deployed, its

perceptionanduse;

- Impact,whichcapturesboththetangibleandintangibleimpact.

Foreachoftheactors,wegathereddataaspertheconcept-stakeholdergrid.

Wegathereddatafrommultiplesourcestoensureinternalvalidityincluding:

interviews, whole network analysis, focus groups, operating guides/manuals

andotherinternaldocuments.

Table4.1:Concept-stakeholdergrid

eKutir MicroEntrepreneurs(AgriEntrepreneurandVeggieEntrepreneur)

Farmers OrganizationalPartners

Strategy Sub-concepts:-Keyactivitiesundertaken,theirrationaleandenablers-BusinessmodelSources:-Interviews-PricingandrevenuesharingmodelLocation:ateKutir–HeadOffice,DistributionCenter,

Sub-concepts:-Keyactivitiesundertaken,theirrationaleandenablers-InvestmentSources:-Interviews-PricingandrevenuesharingmodelLocation:atRuralVeggieLitelocationsandUrbanVeggieLitecenters

Sub-concepts:-Keyactivitiesundertaken,theirrationaleandenablersSources:-FocusgroupsLocation:atVillagesparticipatingwitheKuutirprogrammes

Sub-concepts:-Motivationforpartnership-KeyactivitiesundertakenSources:-Interviews-Presentations,whitepapersLocation:Agri-inputcompanies,Seed&Fertilizercompanies,

85

Procurementpoints

Retailmarket,cartmanufacturingcenters

StructuresandProcesses

Sub-concepts:-Org.structuretosupporttheinitiative-Interfaceroles-Newactorsinductedintothesystem-RelationshipbetweeneKutir,MEsandotherentitiesintheecosystem-Governance/Coordinationmechanisms-ConflictSources:-Interviews-Wholenetworkanalysis-Operatingguides

Sub-concepts:-RelationshipbetweenMEsandotheractorsSources:-Interviews-Operatingguides-Wholenetworkanalysis

Sub-concepts:-RelationshipbetweenfarmersandotheractorsSources:-Focusgroups

Sub-concepts:-RelationshipwitheKutirandotheractorsinthesystemSources:-Interviews-Wholenetworkanalysis

Technology

Sub-concepts:-Toolsdeployed-Technologyfeatures-TechnologyacceptanceSources:-Interviews-Productoperatingguides-Tool

Sub-concepts:-Technologyuse-ChallengesfacedSources:-Interviews-Productoperatingguides

Sub-concepts:-PerceptionoftechnologySources:-Focusgroups

Sub-concepts:-Roleoftechnologyinpartnership-UseoftechnologySources:-Interviews

86

brochures Impact Sub-concepts:

-Financialimpact-Reputationwiththefarmingcommunity,entrepreneursandconsumersSources:-Interviews-Current/projectedP&LstatementofeKutir

Sub-concepts:-Financialimpact-Credibility,statusinthecommunitySources:-Interviews

Sub-concepts:-Empowerment-Informalinstitutions-Overallwell-beingSources:-Focusgroups

Sub-concepts:-BenefitsofpartnershipSources:-Interviews

Participants were identified applying mixed type random and purposive

sampling based on certain inclusion and exclusion criteria. For GCI eKutir

staff,inclusioncriteriawasdefinedasthosethathaveworkedinthefield

foratleast1yearandpossessknowledgeofdemandandsupplydynamicsin

business operation or field management. Adhoc staff that were not based in

theheadquarters(HQ)and,therefore,didnotunderstandtheprojectframe,

were not included. For other organizational representatives, the inclusion

criteriawasdefinedashavingcollaboratedwiththeGCIproject/eKutirfor

at least 1 year and longer, participated at organized events, extended

support, and developed business connections. Organizational partners that

werenotdirectlyconnectedanddidnothavearegularbusinessrelationship

witheKutirwereexcludedfromtheanalysis.

Thevarioustypesofrespondentsfromwhomdatawascollected,thenumberof

respondentsofeachtype,andthemodeofdatacollectionareoutlinedinthe

intable4.2.Thefollowingsectionfurtherelaboratestheselectioncriteria

of respondents. We adhered to confidentiality norms during data recording,

translation, and transcription. We utilized an electronic IC-recorder and

87

storedallvoicerecordsandwrittennotesinapasswordprotectedcomputer;

thesefilesareauthorizedtobeusedonlybythecoreresearchteam.

Table4.2:Typesofrespondents

Actors/Respondents

Participants Interviews FocusGroupDiscussions

WholeNetworkData

eKutir eKutirorganizationstaffsinvolved with GCI Projectandoperationteam

6 - 6

OtherOrganizations

OtherorganizationswithinnetworkofGCIprojectandeKutir e.g. Cartdesigners, Agri inputproviders, supportservice, partners forimplementations, businessinstitutions, supermarkets,marts

7 - 11

Micro-Entrepreneurs(Rural)

Rural micro-entrepreneursconnected with farmers atvillagelevel

5 - 5

Veggie-Entrepreneurs(Rural)

Veggie-entrepreneursconnectedwithfarmersandconsumersatvillagelevel

5 - 5

Veggie-Entrepreneurs(Urban)

Veggie-entrepreneursconnected with consumersat urban slums andresidencies

5 - 10

Farmers Village level farmers ofrural households acrossprojectlocations

- 5 -

PRATIDHIbeneficiary

Pregnant women andlactating mothers whoaccessed to Pratidhiprogram and availed

2 - -

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nutrition incentivecoupons

We recruited a local agency for language interpretation, data translation,

and transcript preparations. In every phase of data collection, a local

language interpreter accompanied a researcher. For better quality data, we

haveusedmultiplelanguages(English,HindiandOdia)duringinterviewsas

perthecomfortoftherespondent.Forwholenetworkgrid-datacollection,we

havecollecteddatausingpenandpaperfirstandthencodedinpre-designed

locked Ms-Excel sheet. During this process researchers have travelled all

projectlocations–blocksanddistrictsandurbanproximitiesandcollected

requiredsamplesize.Adetailedaccountofeachtypeofdatacollectionis

below.

Interviews: This is a 15 month long project (extended until May 2016).

During this period, eKutir recruited new entrepreneurs, created vegetable

producingclusters,andlinkedthemtomarkets.Asthisprocessunfolded,we

interviewed eKutir staffs (senior management, executives, and field

coordinators), other organization partners, micro-entrepreneurs2, veggie-

entrepreneurs (urban and rural), pregnant women availed “Pratidhi” coupons

(seenextchapter).Theinterviewswereconductedduringmonths11-13asthe

project had reached a steady state. Location: at eKutir – head office,

distributioncenter,andprocurementpoints.

Theinterviewswerebasedonsemi-structuredquestionnairesdevelopedonthe

basis of the concept-stakeholder grid presented above. The questionnaires

were customized based on the respondent and the interview timing. Some

questions were common across respondents to increase internal validity of

data.Theinterviewswithmicro-entrepreneurshappenedtowardstheendofthe

project,whentheywereabletorecounttheirjourneythroughtheprojectand

articulate the benefits/ challenges. Similarly, the interviews with

2eKutir-VeggieKarthasmainly2typesofMicroEntrepreneurs–AgriEntrepreneurs(AE)andVeggie-Entrepreneurs(VE).

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organizationalpartnerswerealsoconductedtowardstheendoftheproject,

whentheywereabletoprovideacompleteaccountoftheirassociationwith

eKutirandhowithasevolved.Weinterviewedonerepresentativefromeachof

the partnering organizations. These organizations included agri-input

companies, retailers, cart manufacturers, and technical support

organizations.

Focus groups: Focus groups were conducted with the farmer’s community to

understand the impact of the project on their lives. One focus group

conducted with the farmers of each AE interviewed i.e., 5 focus group

discussions across 5 locations. Each focus group had an average of 10-20

farmers participate. Inclusion criteria centered on having direct contact

withAEsandVEs,externalinputproviders,andparticipationintheFarmer

InterventionGroup.ThosewhoweremembersoftheFarmerInterventionGroup

forlessthan6months,hadnoregularcontactwithAEsorVEs,andwerenot

registeredfarmerswitheKutirwereexcludedfromthedata.

Operating guides and other internal documents: Finally, manuals,

checklists, operating guides, presentations, and other literatures were

reviewedateKutirforanalysis.Thishasprovidedusefulinsightsintothe

strategyandsupportingmechanismsofeKutir.

AnalysisAfter the data was collected, we grouped it based on respondents. For

instance,alltheAEinterviewtranscriptswereputtogetherinasub-folder.

WefirstlookedattheinterviewdatafromeKutir’srespondentsbecausethis

wouldgiveusanoverviewoftheentireVeggieKartoperation.Fromthis,we

createdseveralfiguresthatcapturetheVeggieKartsystem,theroleplayed

by the various actors, and the nature of interaction between them. Having

developedthisclarity,wewentontoisolatethekeyprocessesinvolvedin

buildingthissystem.Wedevelopedaninitialprocessframeworkbasedonthe

interviewswitheKutirpersonnel.Wethenanalyzedthedatafromotheractors

andprogressivelyrefinedtheframework,ensuringitsinternalvalidity.By

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constantly iterating between data and analysis, we arrived at the final

framework.

ResultsIn this strategic analysis, we found VeggieKart to operate as platform

business that links farmers and vegetable retailers/ consumers i.e., it

servestwodistinctsetsofusersbyconnectingthem.Platformsaredefined

asproductsandservicesthatbringtogethergroupsofusersintwo-sidedor

multi-sided networks (Eisenmann et al., 2006). In two-sided platform

businesses,thetwousergroupsareattractedtooneanotheri.e.,thereisa

significant network effect. The more users there are in one network, the

highertheabilitytoattractusersintheothernetwork.Thisissoincase

ofVeggieKartaswell.IfVeggieKartcanmobilizealargenumberoffarmers,

itcanattractandservemoreretailers.Similarly,ifitcansignupmore

retailers/consumers,itcanleveragethemarketlinkagetoexpanditsfarmer

network. In such platform businesses, the challenge is to develop both

networkssimultaneouslyandtobeabletobalancethetwo.

Ouranalysisofthequalitativedataisolatestheprocessthroughwhichthe

farmernetworkandtheconsumernetworkaredeveloped.Thefarmernetworkis

the supply side of the equation while the consumer network represents the

demand side. Figure 4.1 below captures the processes playing out on the

supplyanddemandside.

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Figure4.1:Processnetworkonsupplyanddemandside

Weseefromtheabovefigurethatthesupplysidedevelopmentgoesthrougha

three-layered process of cluster formation, legitimacy building, and

procurement. Similarly, the development of the demand side also happens

throughathree-layeredprocess:channeldevelopment,orderfulfillment,and

consumermarketing.Whentheseprocessesfunctionseamlessly,theysetupa

virtuouscyclethatrapidlyexpandsthesupplyanddemandnetworks,whichin

turn creates a vibrant and balanced platform. We will elaborate on each of

theseprocessesbelow.

Supply-side(FarmerNetworkbuilding)ProcessesClusterformationThe supply side network building begins with the process of ‘cluster

formation.’ClusterformationinvolveseKutiridentifyingavegetable-growing

regionandrecruitingalocalpersontobeanagri-entrepreneur(AE).Local

NGOs(e.g.CCD,Swati)mightbeveryhelpfulinidentifyingtheAE.TheAEis

usually a progressive farmer who is well connected to the local community.

The AE is trained by eKutir on the various technology-based solutions for

farmers (soil testing, seed selection, farmer database etc.) and becomes

eKutir’srepresentativeintheregion.TheAEleverageshislocalconnections

and his technology training to sign up farmers into the VeggieKart system.

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(S)he also aggregates farmers into smaller groups called ‘farmer support

groups’orFSGsandfacilitatesregularmeetingofthesegroups.Thegoalof

the FSG meetings is to enable free exchange of information, plan the crop

cycle, discuss best practices, and jointly solve farming issues. The

mobilizationoffarmersandthesubsequentformationofFSGscreatesawell-

knitclusterthatiscoordinatedbytheAE.Further,theFSGstructureallows

theAEtoeffectivelyreachouttoallthefarmersinhis/hercluster.The

key activities and actors in this process are captured in the table below.

The five clusters created for this pilot have about 200 farmers each, and

each cluster has an AE acting as the orchestrator. The qualitative data

indicatesthatFSGshavebeenformedandFSGmeetingsarebeingheld.Onan

ongoing basis, the key indicators of a vibrant cluster are the number of

farmersenrolled,thenumber/frequencyofFSGmeetings,andtheattendance

atthemeetings.Thisfirstprocessofclusterformationcreatestheplatform

toengagewiththeruralcommunity.ItisevidentthattheAEplaysapivotal

roleinthis.ThestatureoftheAEinthecommunityandabilitytomobilize

people play a crucial role in the effectiveness of this process. Table 4.3

belowrepresentsthesupplysideprocess.

LegitimacyBuildingAs the cluster falls into place, the next key process is ‘legitimacy

building’withtheruralcommunity.Technologyplaysacrucialroleinmaking

thishappen.TheAEcanleveragetech-enabledservicessuchassoiltesting,

seedselection,pestmanagementandotherstodecreasethecostincurredby

the farmers as well as improve their yield (Jha et al., 2016). Further,

eKutir’spartnershipwithvariousactorsintheecosystemenablestheAEto

createvalueforthefarmers.Forinstance,partnershipwithinputproviders

(seed,fertilizercompanies)allowstheAEtosourcehighqualityinputsto

thefarmersatreasonableprices.Similarly,theAEcanleveragetie-upswith

agri experts to provide timely resolution to farmers’ problems through the

cropcycle.

As the AE creates value for the farmers, there is a higher level of

legitimacy/trustthatthefarmershavefortheAEandeKutir.Thislegitimacy

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buildingprocessisnecessarybecausetransactionsinruralcommunitiesare

oftenhingedonlegitimacyandtrust.AsoneofourAErespondentsmentioned,

farmerstendtobuyinputsandselltheirproducetolocaltraderswithwhom

they have a long-standing relationship. This relationship is based on

familiarityandtrustthatstemsfrommultipletransactionsovertheyears.

If eKutir is to get a foothold in these rural communities, it needs to be

perceivedasatrustedadvisorandallybythefarmers.

Fromthefarmerfocusgroupdiscussions,itisevidentthatthislegitimacy

for eKutir is beginning to take shape. Several farmers mentioned that they

aregratefulfortheadviceandhelptheyhavereceivedfromeKutirandit

has helped them lower costs and increase yield and diversify. They also

indicated their indebtedness and preference to sell to eKutir’s VeggieKart

(asopposedtolocalmarket/traders)ifprocurementweretohappenatsimilar

prices. The large sample survey also reveals that the production of

vegetables among eKutir farmers has shown a marginal increase, which is

likelytoenhanceeKutir’slegitimacyintheruralcommunities.

Table4.3:Supply-sideprocess[NextPage]

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The success of the legitimacy building process depends critically on the

partnershipseKutirisabletoforge,bothwithinputsuppliers,agriexperts

andotheractorsintheecosystem.Themorecomprehensivethefarmersupport

ecosystemandtheresponsivenessoftheecosystemtofarmerneeds,thehigher

thelegitimacyofeKutir/AE.Theleveloflegitimacyandtrustinthecluster

maybeassessedbasedonthenumberoffarmersprocuringinputsthrutheAE

andseekingadviceforcrop-relatedissues.

Process Activities Keyactors Measurestotrackprogress

Clusterformation

• AErecruitment

• Mobilizationoffarmers

• GroupingoffarmersintoFSGs

• ConductingFSGmeetings

• AE

• Farmers

• Numberoffarmersmobilized

• Frequency/NumberofFSGmeetings

• AttendanceatFSGmeetings

Legitimacybuilding

• TechnologytrainingforAE

• Qualityagriinputs

• Agriadvisory

• AE

• Farmers

• Inputproviders

• Agriexperts

• NumberoffarmerswhoprocuredinputsthruAE

• Numberoffarmerswhoreceivedadvisory

• Yield

Procurement

• Demandforecasting

• Sortingandgrading

• Timelypayment

• AE

• Farmers

• %ofproduceprocured

• Numberoffarmersfromwhomproducewasprocured

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Once eKutir has established itself as a legitimate player in the rural

communities, the next process is to leverage the farmer network for

‘procurement’ into VeggieKart. Procurement involves communicating demand

information, collecting the produce from the farmers and completing the

transactionwithpromptpayment.

Procurement not only feeds the VeggieKart business but it also sets up a

virtuouscyclewithintheruralfarmingcommunities.Themarketlinkageand

the resulting word-of-mouth will incentivize more farmers into the eKutir

system.

ThelargesamplesurveyindicatesthateKutirfarmersarekeentoaccessthe

marketsthroughtheVeggieKartvaluechain.However,currentlyVeggieKartis

procuring from a very small percentage of the farmers it is engaging with.

Though each cluster has about 200 farmers, totaling to 1000 farmers,

procurementisbeingdonefromfewerthan100farmers.Thisisunderscoredby

thefactthatseveralfarmersexpressedaninterestinsellingtoeKutirbut

lamented that they were not able to do so due to limited and selective

procurement. This could be because the business is still in the process of

rampingup.However,regularandextensiveprocurementfromtheclustersis

necessary for eKutir to recover its investments as well as strengthen its

farmernetwork.

On an ongoing basis, the effectiveness of the procurement process may be

measured by how many farmers in the network are touched by the procurement

processandthepercentageoftheirproduceprocuredbyVeggieKart.

Insummary,thesupplysidehasthreelayeredprocesses–clusterformation,

legitimacybuildingandprocurement.Thesecanbevisualizedasbuildingon

top of one another in a pyramid structure, as depicted in figure 2.2. The

flatterthetopofthepyramid,themoreeffectivethesupplysideprocess.

Inotherwords,ifeKutirisabletoprocurefrommostofthefarmersinthe

cluster,itiscapturingthevalueitiscreatingforthem.Ifnot,thevalue

created by mobilizing the farmers and linking them with various ecosystem

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actors still benefits the farmers but does not contribute towards the

VeggieKart’sbusiness.

Table4.4inthenextpagerepresentstheprocurementprocess.

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Table4.4:Procurementprocess

Process Activities Keyactors Measurestotrackprogress

Channeldevelopment

• VErecruitment

• Retailersignup

• VE

• Retailers

• Cartproviders

• NumberofVErecruited

• Numberofretailers

Orderfulfilment

• Ordercapture

• Vegetabledelivery

• VE

• Retailers

• Timelinessofdelivery

• Qualityperception

• Costperception

• Repeatorders

Consumermarketing

• VeggieKartbrandawareness

• Associationwithhealth/nutrition

• eKutir • Brandrecallamongcustomers

• Brandassociation

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Demand-Side(Consumernetworkbuilding)ProcessChannelDevelopmentMirroringthesupplyside,athree-layeredprocessunfoldsonthedemandside

as well. The base process is that of ‘Channel development’. This involves

either opening new retail outlets or partnering with existing retailers to

carryVeggieKartproducts.

VeggieKart has multiple channels, both online and brick-and-mortar. The

brick-and-mortar channels come in different forms, catering to different

economic strata of society. These channels are depicted in figure 2.3. The

focus of this study is the VeggieLite centers, which are small-scale

vegetableshops,runbyamicro-entrepreneurcalled‘VeggieLiteEntrepreneur’

(VEforshort).

VeggieLitecentersaresetuptoservethelowereconomicstrataofsociety,

both in urban and rural areas. eKutir has setup more than 30 VeggieLite

centers.LiketheAE,theVEisalsoalocalperson,familiarwiththelocal

context. Often, the VE already has a small grocery shop and takes on

VeggieKart products to diversify. The VeggieLite centers receive vegetables

thataresortedoutofotherchannels(becauseoftheirsmallsize,oddshape

etc.). Traditionally, such vegetables would be discarded even though their

nutritivevalueisintact.ByopeningVeggieLitecentersthatcansellthese

vegetables to the low-income strata, significant amount of wastage in the

valuechainisprevented.

However, the VeggieLite channel is a low-volume channel i.e., each VE

procuresasmallquantityofvegetables(500-800Rsworthperday)andsells

itforasmallprofit.Whathe/sheisunabletoselliseitherconsumedby

the VE or discarded. Since the volume of procurement by VE is low, eKutir

willneedtoopenupalotofVeggieLitecentersiftheyaretosaturatean

area and bring about a tangible difference in vegetable consumption. This

aspect is underscored by the somewhat inconclusive results of the large

sample survey. While we find that the treatment households buying from

VeggieLitecentershaveincreasedvegetableconsumption(probablyduetothe

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largevarietyofvegetablesavailabletoVLcenters),wealsofindthatthe

overallvegetableconsumptionhasremainedrelativelyflatacrosscontroland

treatmentgroups.ThiscouldbeattributedtothesmallnumberofVeggieLite

unitsandtheabsenceofsaturationinthetreatmentarea.

Opening up tens and hundreds of VeggieLite centers can be an operations-

intensive activity. The key activities and measures to track the channel

developmentprocessarecapturedintable4.6.

OrderfulfillmentWiththevariouschannelsandretailpointsforvegetabledeliveryinplace,

thenextprocessthatkicksinis‘Orderfulfillment’.Asthelabelsuggests,

itissimplytheprocessofaggregatingthedemandfromthevariouschannels

andfulfillingthedemandinatimelyandefficientmanner.

ForVeggieLite,thedemandinformationisgatheredonadailybasisoverthe

phone. An eKutir staff member calls the VE and writes down the requirement

forthefollowingday.ThisisthenaggregatedandpassedontotheAEswho

procure the vegetables from the farmers. Some vegetables that are not

availablefromthefarmingclustersareprocuredfromthelocalmarket.The

vegetablesaretransportedtothedistributioncenterwheretheyaregraded,

sorted, cleaned and packed before they are dispatched to various channels.

The online and large institutional buyers get the best quality vegetables.

The lower quality vegetables are sent to the other channels. VeggieLite

centersareatthebottomofthischainandgetvegetablesthatweresorted

outofotherchannelsand/orreturnedbytheinstitutionalbuyers.Figure2.8

captureshowthevegetablesflowthroughoutdifferentchannelsinVeggieKart

operation.

The success of the order fulfillment process depends on whether it is

creatingvalueforthecustomersontheparametersthataremostimportantto

them.Thekeyparametersarecost,qualityandconvenienceandtheyvaryby

channel.Forinstance,wefoundthatforVEs,costandconveniencemattered

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the most and they were willing to compromise on quality. Whether the

procurementpricewascomparabletothepriceinthelocalmarketandwhether

the vegetables were delivered on time were important factors. In fact,

several VEs expressed that the quality of vegetables was very ordinary and

the cost was the same as in the local market. But, the fact that the

vegetablesweregettingdeliveredtotheirdoorstep(i.e.,convenience)made

VeggieKartattractive.Perhaps,fortheonlinechannel,qualitywouldbethe

keyconsideration.

Successfulorderfulfillmentiskeyforbuildingaloyalcustomerbase.Itis

important to note that the VEs and retailers have multiple sources for

procurement and the only way VeggieKart can recover the cost of channel

development is by securing repeat orders. Therefore, successful order

fulfillmentiskey.

ConsumerMarketingConsumer marketing is the third of the three processes on the demand side.

ThisprocessaimstocreateastrongbrandforVeggieKartwithendcustomers.

While the first two processes focus on direct customers (retailers), this

processes aims to reach out to the end consumers. However, the first two

processes need to be in place in order to service the demand created by

consumermarketing.Infact,thelargesamplesurveyresultssuggestthatthe

consumption of vegetables has increased in households that procure from

VeggieLite centers. This could be due to a larger variety of vegetables,

lower prices or a combination thereof. Therefore, consumer-marketing

activity,whichmaydriveupthefootfallintoVeggieLitecenters,islikely

tohaveanimpactontheend-consumervegetableconsumption.

VeggieKartisonlystartingtoexplorethisprocess.Anattemptwasmadein

the form of a pilot project called ‘Pratidhi’, which aimed to sensitize

pregnantmothersonnutritionalrequirementsandprovideavegetablebasket

tohelpthemachieveanutritionallybalanceddiet.But,consumermarketing

is still very limited, except for the online channel, which reaches out to

thehigh-incomeurbanconsumers.

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ThisprocesswillinvolvecreatingawarenessabouttheVeggieKartbrandand

associatingitwithcertainattributessuchashealth/nutrition.Thisshould

lead to an increased demand for VeggieKart products, which will in turn

utilizethecapacityofthevariousretailchannelsandprovideimpetusfor

furtherexpansion.

In summary, the demand side or consumer side comprises three processes -

channel development, order fulfillment and consumer marketing. Here again,

eachprocessbuildsontheother.Throughthefirsttwoprocesses,VeggieKart

seekstocreateaninfrastructurethatcreatesvalueforretailers(VEsand

others).Thethirdprocess,consumermarketing,enablesVeggieKarttocapture

the value from this retailer network and further expand it by building a

brand.Hereagain,similartothesupplyside,theprocessesmovesfromvalue

creationtovaluecapture.Themoretheresourcesspentonconsumermarketing

(flatter pyramid), the higher the value capture. Of course, this can only

happenifthefoundationalprocessesareinplace.

ICT

ICTcanplayacrucialroleinstrengtheningtheconnectionsbetweenvarious

actorsintheeKutirecosystem.Asofnow,ICTisusedquiteextensivelyon

thesupplyside,especiallybetweeneKutirandtheAgrientrepreneurs.But,

mostotherinteractionsaremanual.AsthischangesandICTbridgesallthe

actors,theinformationandcollaboration(regularmeeting)networkscanalso

beexpectedtobecomedense.

NetworkMappingofEcosystemFormationTo assess the early formation of the VeggieKart/VeggieLite ecosystem, a

network mapping exercise was undertaken. The exercise was intended to

identify the structural features, including gaps, characterizing the

relationship among the various actors in the VeggieKart system. Interviews

were conducted with those actors who were involved (through regular

interaction) in the day-to-day business operations of VeggieKart. The

participants included eKutir executives, VeggieLite Distribution center

representatives, the agri- and veggie-entrepreneurs, and a range of other

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organizational partners, such as cart manufacturers, input providers, MGM,

retailoutlets,governmentagencies.Inundertakingthenetworkanalysis,we

did not presume that there was an ideal network structure that the eKutir

ecosystem should assume. Instead, our aim was to map and document the

structure of the eKutir network at this early stage of its development so

that eKutir might use this knowledge to address important gaps in

connectivitywithinthenetworkandassessovertimehowthenetworkevolved

throughfuturescale-upactivities.

To gather these data, each partner was asked about their awareness and

exchange of information with the other partners in the ecosystem. In

addition,eachwasaskedhowimportantonascaleof1-5theythoughtthat

informationwastotheiractivitiesandlevelofcollaborationwiththeother

partners. Using this information, three different network dimensions were

mapped: (1) knowledge and awareness of network members; (2) information

sharing–a)sendingandb)receivinginformation);and(3)holdingmeetings.

Social connections within the network can be bi- or uni-directional,

dependingonwhetherthepartnersidentifytheirconnectiontotheotherina

similar manner. Each node was color coded to represent its specific role

within the eKutir ecosystem: (1) red – eKutir officials; (2) green – rural

micro-entrepreneurs;(3)blue–ruralveggie-entrepreneurs;(4)grey–urban

veggie-entrepreneurs;(5)pink–ASHAworkers;and(6)yellow–othertypes

of actors. Furthermore, this information was used to calculate the overall

density and level of centralization of the eKutir network. Density is

defined as the number of ties in the network divided by the number of

possiblenetworkties.Densityisthusaproportionthatrangesfrom0-1.A

densityof1.0impliesthateachactorisconnectedtoalltheotheractors

inthenetwork.Networkcentralizationisameasureofhowcentralthemost

central node(s) are in relation to all the other network members.

Centralizednetworksarethoseinwhichoneortwoactorshavethemajority

ofconnectionsinthenetwork.Lesscentralizednetworksarethoseinwhich

connectionsanddecision-makingismorediffusedthroughoutthesystem.The

values for each of these measures can vary depending on which network

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dimension is being examined. Table 4.5 provides the network density and

centralizationvaluesforeachnetworkdimension.

Table4.5:Densityandcentralizationvaluescharacterizingeach

networkdimension

NetworkDimension Density Centralization

(undirected)

Knowledge/Awareness

dimension

0.15^ 0.79

InformationSending 0.12 0.86

InformationReception 0.12 0.86

HoldingMeetings 0.08+ 0.84

Figures 4.2-4.5 illustrate the eKutir ecosystem along different network

dimensions and capacities. Figure 4.2 maps the network’s “knowledge and

awarenessof”(KA)eachoftheactorswithintheecosystem.Ofthedifferent

network dimensions, the KA network is not surprisingly the most dense and

leastcentralized.Nevertheless,thenetworkdensityis0.15andonenotes

thepresenceofacore-peripherystructure.Inotherwords,eKutirofficers

and key personnel are well-connected to each other and the other partners

(i.e., core) but the other actors in the ecosystem (e.g., the micro-

entrepreneurs)areconnectedmainlytothecoreactorsandnotothernetwork

members (i.e., the periphery). This core-periphery structure comes into

sharperreliefasthenetworkdimensionsbegintorepresentahigherorderof

exchanges among partners, such as information and other resources. As

mentioned earlier, we do not presume that there is an ideal structure to

whicheKutirshouldnecessarilyassume,particularlyintheseearlystages.

Nevertheless, it may be important as eKutir seeks to scale up and out to

other locations that it establishes greater inter-connectivity among the

variousentrepreneurialclassessothatknow-howandbestpracticesmightbe

shared more directly and broadly among these key network members. In

addition, as eKutir begins to integrate the health sector into its

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programming,greaterattentionmayneedtobegiventoconnectingtherural

entrepreneurs to local ASHA workers. Moreover, as the eKutir ecosystem

evolves, future analyses can link changes in eKutir’s network to various

impactandoutcomemeasurestoassesswhethercertainnetworkstructuresare

more effective for eKutir than others in achieving its social enterprise

goals

Figure4.2:eKutirEcosystem:Knowledgeandawarenessdimension

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Figure4.3:eKutirEcosystem:Sendinginformationdimension

Figure4.4:eKutirEcosystem-Informationreceptionnetwork

Figure4.5:RegularMeetingsNetwork

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Competitivedynamics(seeappendixfordetailedmodelsandresults)

Analyticalmodelstocapturethecompetitivedynamicsinsupply/valuechains

and markets have evolved from a long research tradition that has examined

competitive dynamics in industrial value chains and markets. VK aims to

reduce the access cost for the farmers (Fv) and for the consumers (Cv) by

using “micro-entrepreneurs and in this context the model will consider the

welfare of all stakeholders within the VK ecosystem (i.e., farmers,

VeggieKart and Consumer), as well as their competitive dynamics in

interaction with traditional commercial middlemen (Mandis). The research

questionsspecificallyaddressedinthecompetitivedynamicsanalysisforthe

VKenterpriseinthepresentanalyticalmodelare:

- How does a new ecosystem like VK compete with the traditional Mandi

channel and affect the farmers (prices received) as well as end

consumers (prices paid)? This will address how much to pay to the

farmers?;Howmuchamountofvegetabletocollect?;Howmuchtocharge

toendconsumers?;Howmuchstocktosell?;Howmanychannelstouse?;

How many micro-aggregators and micro-retailers to “hire” and their

coverageareas?;Howwillthetraditionalvaluechain(Mandi)respond

tothisbusinessmodelinnovation?;WhatadditionalservicesshouldVK

provide?; How should it charge for the additional service?; Is a

business innovation like VK good good for the farmers? Good for the

consumers?;WhoextractsthebenefitsfromaninnovationlikeVKinthe

valuechain?Morespecifically,

o How do the following factors shape the above dynamics for VK:

(a)Levelofyielduncertainty;(b)Costsofrecruitingmicro-

aggregatorstoprocurefarmer’sproduce;(3)Costsofselecting,

training and equipping micro-retailers for selling to the end

consumers?

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ModelFrameworkInordertoevaluatetheimpactofVKontraditionalagri-foodsupplychains,

we develop a dual- channel supply chain model, where supply of producers

(i.e.,farmers)anddemandofconsumersaremet.Seefigure4.6forthehigh-

level representation of the dual-channel supply chain model considered in

thispaper.

Thefirstchannelcharacterizestraditional(Mandi)middlemenwhoseroleis

to collect goods from the farmers and sell them to the consumers. As

mentioned in introduction, farmers and consumers incur various costs to

accesstothistraditionalMandichannelduetoitsgeographicallimitations,

and lack of logistics and operational infrastructure. Later on, we will

normalize the total access costs of the farmers and consumers to this

traditionalchannelto1inordertobenchmarktheimpactofVK.Also,dueto

thefactthattheMandichannelishighlyfragmentedandconsistsofmany

smallplayers,itislogicaltoassumethattheplayersinMandichannelare

ofatomicnatureandhencedonotinfluencethepricingdecisionsrealizedat

thefarmerandconsumersidesofchannel.Wedenotetheunitpriceatwhich

the Mandi collects supplies of farmers by wM, and the unit price at which

collectedgoodsaresoldtotheconsumersattheMandibypM.Whenwepresent

thedetailsofsupplyanddemandfunctionsoffarmersandconsumers,wewill

derive pM and wM so that the supply and demand clear in every contingency

state. Note that this derivation procedure does essentially capture the

fragmentednatureofMandichannel.

Figure4.6:Dual-channelmodelconsideredinthepaper

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The second channel between farmers and consumers is meditated by VK which

presentstwokeydifferencescomparedtoMandichannel.Inordertocapture

the first one, we define two key notations: fV and cV. The former (resp.,

latter) denotes the access cost of farmers (resp., consumers) to VK, and

takesvaluesbetween0and1.Duetotheabovenormalization,onecanthink

of VK getting closer to the farmers (resp., consumers) as fV (resp., cV)

decreasesfrom1to0.TheseconddifferencebetweenVKandMandichannelis

related to price formation. As opposed to price-taker nature of Mandi

channel,VKcanexertpricingpowertobothfarmersandconsumersduetoits

proximitytobothmarkets(ascapturedbyless-than-onefVandcV).Similar

toMandi,wedefinetwounitpricesforVK:oneforfarmersdenotedbywVand

oneforconsumersdenotedbypV.Also,sinceVKsetsthesepricesbeforethe

uncertainty realizes, the total supply collected by VK and total demand

satisfied by VK do not necessarily match in every contingency state (as

opposedtoMandichannel).

Next,inordertofocusontheoperationalrisksduetoweather,technology

andperishablenatureofthegoods(suchasfreshfruits,vegetables,etc.),

weconsideravailabilityriskatthesupplyside.Morespecifically,letSbe

thetotalamountoffreshgoodsthatwillbeavailabletothefarmersafter

supplyrisksrealize.Inordertoensuretractability,weassumethatScan

beeitherSH=1+aorSL=1−awithequalprobabilities(representing

highandlowsupplystates,respectively),where

a∈[0,1].Notethatameasuresthedegreeofexogenousrisksfacedbythe

farmers.

Thetimingofdecisionsandeventsinourmodelisillustratedinfigure4.7.

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Figure4.7:Timingofevents

First,VKsetstheunitwholesalepricewVtopaytothefamersandtheunit

retail price pv to charge to the end consumers. Each farmer then decides

whethertosupplydirectlytotheVKorsellviaMandi.Forthosewhochoose

VK,theyselltheirgoodsatunitwholesalepricewV.Duetolossinharvest,

logistics,weatherandperishmentetc.,uncertaintyaboutsupplyfacedbythe

farmersisrealized:thetotalsupplyrealizesatvalueSL=1−aorSH=1

+a.ThespotmarketMandithenclearstheproductsofthefarmerswhochoose

Mandi channel with the consumers, i.e., the fair spot market price wM is

determined by market clearing, DM = SM . VK receives supply SV from the

farmerswhochoosethischannelandsellsthemdirectlytotheconsumersat

pV.

ThemodeldevelopsdemandandsupplyfunctionsforMandiandVKchannelsand

searchfromequilibriumunderdifferentconditionsthroughaprocesscalled

Walrasian tatonnement, where buying and selling prices adjust continuously

upwardanddownwardfashionsasmoremiddlemenenterintoorleavefromthe

market.

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Discussionandinsights

Wehavelaidoutthekeyprocessesdrivingtheformationandoperationofthe

VeggieKartecosystem.WehavealsodiscussedwhereeKutirstandswithrespect

totheseprocesses.Tosummarize:

- eKutirhasbeenabletoformruralclustersbyrecruitingfiveAEs,who

havemobilizedabout1000farmers,200ineachcluster;

- eKutirhaspartneredwithseveralorganizations–NGOs,input-providers

and agri-experts – to provide comprehensive support to the farmers

throughthecropcycle.ThefarmersvieweKutirasalegitimate/trusted

partner and are grateful for the support provided through the crop

cycle;

- Procurement has headroom, with VeggieKart currently procuring from a

smallpercentageoffarmerstheyreachoutto.Thiscouldbeduetoa

combination of reasons – the sheer volume of demand (or lack of it),

mismatch between demand and supply in terms of vegetable varieties

(e.g.: demand is for capsicum but clusters don’t grow them), and

variation in prices at the local Mandis (VeggieKart procures where

priceislowest);

- On the demand side, eKutir has developed several channels. Snapshots

below:

Table4.6:Demandsideprocess

Channel Dailysales Grossprofitmargin

foreKutir

Marts 2300-2400kg 30%-40%

Karts(Wheels) 350kg-400kg 30%-40%

VeggieLite 200kg-250kg (-30%)ofprocurement

price

Institutions 480kg-600kg 20%-25%

Online 120kg-150kg 30%-40%

Total 3450kg-3800kg

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VeggieLite, the channel focused on serving the rural and urban low-

incomecommunities,isalow-volume,negative-marginchannel.However,

the negative margin from VeggieLite is offset by the fact that these

vegetables would otherwise have to be discarded. Therefore, this

channelistobeviewedasonethatcanreducewastageasopposedto

onethatcangiveprofits.Fromthatperspective,theybringefficiency

tothevaluechainoverall.However,thesupplytotheVeggieLite-Rural

centersismanagedbythelocalAE,whomanytimeshastoprocuresome

varieties of vegetables for the VE from the local market. This seems

likeanoverheadfortheAE,whoseprimaryfunctionistosupportthe

farmers;

- Intermsoforderfulfillment,thelargeretailersaresatisfiedwith

thequalityofthevegetablesandtimelinessoftheorderfulfillment.

The VEs value the convenience of delivery to VeggieLite centers.

However, they do not perceive any advantage in terms of price or

quality. The value VeggieKart offers is different for different

channels;

- Consumermarketingisintheinitialphasesandneedstoberampedup.

Inessence,onthesupplyside,VeggieKartiscreatingvalueforthefarmers

andiswellpositionedtomonetizeonthegoodwillcreated.However,atthe

presentjuncture,thevalueisnotbeingfullycapturedduetolowlevelsof

procurement. On the demand side, eKutir is in the process of

building/expanding the retailer network and fulfilling the demand of the

variouschannels.Thediversesetofchannelsandtheiruniquerequirements

make this a tough terrain. Currently, the scale seems to be tilted towards

the supply side (as shown in figure 4.1) and more demand needs to be

generatedtobalanceitout.

InsightsfortransitiontoscaleBasedontheseobservations,wehavethefollowingrecommendationsforeKutir

toconsiderastheyscaleuptheirbusiness.

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1. Focus on demand generation: The procurement levels have been

graduallyincreasingsincetheformationoftheclustersbutthereis

scopetofurtherenhanceprocurementlevelsfromtheexistingclusters.

This will also enable eKutir to recover the investments made into

creatingfarmerclusters.Thiscanbeaccomplishedusingacombination

ofthefollowingstrategies:

a. Invest more into consumer marketing to create VeggieKart brand

awareness and consumer pull. This will automatically result in

higherdemandfromexistingretailoutletsaswellasreducethe

effort involved in creating new outlets. Capacity building of

Agri-Entrepreneurs and Veggie-Entrepreneurs on product marketing

will add value in business growth and expansion. Appropriate

brand promotion and communication strategy needs to be adhered

withoperationchannelsacrossconsumersegment.

b. A second strategy could be to position VeggieKart as a

differentiatedbrand.Asitstandstoday,VeggieKartisageneric

provider of a whole range of vegetables, competing head-to-head

with several providers. Given VeggieKart’s linkage to farming

clusters, it could consider positioning itself as a provider of

exotic seasonal vegetables or provider of organic vegetables

i.e.,positionasanicheplayer.Suchapositioningwillallow

VeggieKart to leverage its farmer connect and differentiate

itself from its competitors by supplying vegetables that are

otherwise not easily available. During our interviews, farmers

indicatedawillingnesstoundertakefarmingofnewvarietiesof

vegetables (e.g. mushrooms). The focus on a limited basket of

vegetables might help increase the levels of procurement and

simplify logistics. This however involves significant

repositioningbutshouldbeevaluatedbeforescalingup.

2. Streamline the channels: VeggieKart has developed several channels,

both online and office. Offline channels include VeggieMart,

VeggieWheels and VeggieLite. As brought forward by our analysis, the

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VeggieLiteisalow-volumechannel,witheachVEprocuringvegetables

worthjustafewhundredrupeesperday.Whilethischannelservesto

minimize the wastage in the value chain, it would be beneficial to

increase the volume of procurement of each VeggieLite channel.

Otherwise,astheoperationscalesup,thenumberofVeggieLitecenters

that would need to be opened would pose a logistical/distribution

challenge.

One possibility is to consider having fewer channels, in particular

foldingVeggieLiteintoVeggieWheels.VeggieWheelsaremobileandhence

morelikelytoreachalargernumberofconsumers.Thismeans,theVE

can procure more vegetables from VeggieKart. Further, more low-income

consumerscanbereachedwithfewernumberofVeggieWheelssincethey

aremobile.eKutirhasstartedtoexperimentalongtheselineswiththe

VeggieLiteExpress.Thisneedstobefurtherexplored.

3. HybridmodelforAgri-Entrepreneur:TheAE,asthenamesuggests,is

envisaged to provide a range of services to the farmers and get a

commission on each transaction facilitated. He can earn money from

sourcing inputs (seeds, fertilizer etc.) to the farmers or by

facilitatingprocurementbyVeggieKart.Ifheisabletodeliverboth

theseservices,hewillhaveasteadystreamofrevenueandcansustain

asamicro-entrepreneur.However,intheabsenceofactiveprocurement

fromtheclusters,theviabilityofthemicro-entrepreneurshipmodelis

low. The AE might not have enough incentive to continue, which might

leadtoattrition.

ThisproblemcanbeaddressedbyadoptingahybridmodelwheretheAE

ispaidafixedsalaryduringtheinitialclusterdevelopmentprocess

and is transitioned to a micro-entrepreneurship model only when

procurementhasreachedalevelwherehe/shecanearnatleastasmuch

as his fixed salary through commissions. eKutir is already

experimentingwiththismodel.However,itisbesttoformalizethisin

theAErecruitmentprotocol.

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4. Use of ICT: eKutir has developed an ICT platform to support

VeggieKart.Thisplatformisbeingusedeffectivelyonthesupplyside

to capture farmer/crop data and provide various services (soil test,

seedselectionetc.).However,theuseofICTisverylimitedwhenit

comes to demand aggregation, forecasting and interfacing with the

various retail channels. eKutir staff calling them on a daily basis

capturestheVEdemand.Thiscanbecomeabottleneckduringscaleup.

Therefore, one of the important infrastructural elements to have in

placebeforescalingupisanICTbackbonetomanagethedemandsideof

thebusiness.

The project will be sustained from revenues from sales of produce, and

incentivefundingfrompublicandprivatepartnersinthehealthcaresector.

ThisprojectistestingandbuildingpartnershipswithOdishagovernmentas

well as other state governments, the federal ASHA program, and private

insurancecompanyBajajAllianz.

Due to its extensive distribution network of vetted and trained micro-

entrepreneurs,eKutirisanattractivepartnerformanycorporationstoreach

dispersed markets in both urban and rural areas of eastern India. eKutir

willcontinuetomakemutually-beneficialpartnershipstosustainthishigh-

impactproject.

Though still improving, VeggieKart has now established strong distribution

chainfromsmallholderfarmertoconsumer,andisreadytotaketheexpansion

steps required for sustainability and scaling. The project will take

learningsaboutthebestvenderpartnerships,andwillexpanddistributionto

anadditional50newbrandedretailpoints.VeggieKarthasfoundthatthe

VeggieMart (B2B) channel is beginning to offer the greatest volumes and

profitability,aswellasopportunitiesforscaling.Stillcontinuingahigh

proportion of micro-entrepreneur VeggieMart and VeggieWheels in underserved

neighborhoods, this project will help build out the partnership with chain

retail partners Big Bazaar, Home Shop, and Reliance Fresh in strategic

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locations.Theexpansionofthisvendernetworkthroughtheprojectshould

getVeggieKarttoabreakeventhroughput(4.5MIR).

Further insights in accounting for the present results and preparing

transition to scale come from the competitive dynamics analytical model.

The model inform on different course of actions under different

competitive dynamics for addressing five types of risks Market,

Business,Regulatory,Financial,Operational)

1. Market Risks – Vegetables have great fluctuations of price due to

supply and climate factors, and enterprises face competition from

formal and informal venders. VeggieKart’s market risk has been

minimized by diversifying into different distribution channels with

both Business-to-Business relationships and direct consumer

relationships, and reducing volatility with longer-term relationships

withsuppliers.

2. Business Risks – Inadequate training and circumstantial situations at

the vegetable entrepreneur’s front. This is being tracked through

properselection,training,andtimelymonitoring.

3. Regulatory Risks – Within Odisha, the Department of Horticulture and

theHeadofStatesupportedthelaunchofVeggieKartandhavefavored

and created a feasible environment for the growth of VeggieKart.

OutsideOdisha,VeggieKartneedstoadheretotheAgricultureProducers

MarketingCommittee’spoliciesandtreaditsscaleaccordingly.

4. Financial Risks - Delayed contractual payments from the institutional

buyersandensuringallvegetableentrepreneursdocashsettlementona

daily basis needs to be tightened to control the inflow of cash and

outflowofsupply.

5. Operational Risks – VeggieKart needs to ensure 100% procurement from

the farmers, further optimize its supply chain, and model its state-

levelexpansion,andcontrolgraftand

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In sum, for scalability, VeggieKart will have to increase sales volume

whilemaintainingleanoperations.Whiletherecouldbemultiplestrategies,

DAISAhasmadethefollowingrecommendations:

- Focus on increase procurement from existing eKutir farmers, and

existingvillages.Furthertrainingofagri-entrepreneursonoutreach

andcommunicationsskillstobuildfurthercontactonVeggieKartmarket

needs.

- Improve marketing at VeggieKart and VeggieLite locations. Train

vendersonproductdisplay,havemorebrandedpoint-of-salepromotional

materials, utilize nutrition incentives and promotions more

aggressively, have most successful venders share strategies with each

other.VeggieKartshouldhaveacentralmarketdepartmenttaskedwith

promotions,training,andsupportingallvendersmorepro-actively

- Maximize revenue and mitigate risks from the established network and

logistics systems, explore aggregation of products including

vegetables,fruits,pulses,grains,andmore.

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Chapter5:EarlyPrototypeDevelopmentforBehavioralEconomic Incentives Intervention and RelatedEcosystemExtension

ThischapterreportsonconcurrentworkdonebyeKutirandDAISAEnterprises

inpreparationfortransitiontoscaleinearlyprototypedevelopmentforan

improvementtothepresentVeggieLiteintervention.Thekeyareaitfocuses

on is improving the behavioral change component by bringing in methods

anchored in behavioral economics and the integration of this effort with

existing community and health ecosystems to ensure affordable and self-

sustainingdeliveryoutsideofformalcommercialchannels.Wefirstintroduce

thesuccessfulUSAinterventiontheIndianprojectprototypebuildsuponand

thenwereportonlessonslearnedforfuturesteps.

Behavioralincentivesintervention

eKutir and partners set out to assess if it was possible to adapt the US-

originatedDoubleValueCouponProgram(DVCP)totheIndiancontextthrougha

limitedfeasibilitytest.TheDVCPwaspioneeredintheUnitedStatesbythe

nonprofit organization Wholesome Wave and eventually grew to encompass 26

states, subsequently becoming part of the federal Farm Bill with a $100M

allocation called the Food Insecurity Nutrition Incentive Program. This

program provides an incentive voucher for low-income participants to use

their Supplemental Nutrition Assistance Program benefits, an existing

governmental scheme in which benefits can also be used for purchases of

unhealthy foods, instead for vegetables purchased directly from a local

farmer.Theprogramgeneratesadditionalsalesforsmallholderfarmerswhile

improvingaffordabilityofhealthyfoodsforthosemostinneed.

The “SNAP Healthy Food Incentives Cluster Evaluation Final Report 2013,”

whichreviewedtheincentiveprogramsimplementedat518marketsthroughout

thecountry,foundthat“Thelargemajority(morethanthree-fourths)ofSNAP

recipients reported that they have increased their purchase of produce

118

because of the incentives and that SNAP incentives were a strong factor in

their decision to shop at a farmers’ market.” More than 75% reported

increasesinconsumptionoffruitsandvegetables.

The eKutir adaptation version of the DVCP named Pratidhi was applied in 3

villages, namely Dorapida, Jalangia and Mallikpada of Tikabali block in

KandhamaldistrictofOdisha.Aseriesofnutritioneducationprogramswere

held over a three-month period from 25th July 2015 to 25th October 2015 for

either pregnant or lactating woman (i.e. until 6 months post-pregnancy).

Couponswereissuedtotheparticipantsprovidingdiscountsforvegetablesat

the VeggieLite center in the village. The participants were provided two

couponsofvalueofINR50eachweek,whichtheycouldredeematthenearest

VeggieLiteoutletbycontributingasumofINR50fromtheirsidetoreceive

vegetable baskets worth INR 100. The program was supported by village

Accredited Social Health Activists(ASHAs) who further used the program

opportunity to promote additional existing village health services. During

theprogram,ASHAandICDSworkershelpedeKutiroperationteamtoidentify

participantsandalsoprovidedthemcounselingonhealthandnutrition.

Figure5.1:Processflowfor“Pratidhi”programme

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Out of the 23 program participants, 16 consistently participated in both

nutrition education sessions and redeemed their coupons at the VeggieLite

centerduringthe3-monthpilot.Intermsofoutcomes,forconsumptionthere

wasanimprovementintheconsumptionintakeoftheregularparticipantsdue

to easy availability of the vegetables (two times a week) at discounted

rates.Also,someparticipantsbegantoconsumeitemstheydidnotregularly

consume before the intervention. In terms of regularity of participation,

overthethree-monthperiodtherewasadeclineinthenumberofparticipants

coming to the VeggieLite center for purchasing the discounted vegetables.

Therefore,asurveywasconductedtounderstandthereasonsforthedecline

anditwasdiscoveredthatmostoftheparticipantshadfinanciallyunstable

conditions, and some just did not want to travel to the VeggieLite centre,

despite the close proximity. Out of all the 23 participants, 16 (67%)

regularlyaccessedtheprogramandhaveavailedtheprogrambenefitsforthe

whole period while the rest have been irregular due to the either of the

aforementionedreasons.Finally,althoughthemedicalparametersdependona

lotoffactorsotherthantheconsumptionofvegetables,theself-reportof

regular participants suggest that there have been improvements in their

hemoglobinlevelsandbodyweights(around3kg).

Thus this limited test provided some indications of viability for India: a

highpercentageofwomeninthetargetpopulationconsistentlyparticipated

in trainings and utilized the coupons; the VeggieLite vendors redeemed the

couponseffectively;theprogramsuccessfullyengagedASHAworkers;andthe

program showed potential to leverage further public and private support.

Insightsforimprovementsalsoemergedinthedesignoftheprogram:

- Introductionoffreedomofchoice--thepilothadpredefinedbaskets,

which were seen as a limiting factor in meeting the choices of

participantsandbeneficiaries.Thusforscale-upofthepilot,itis

learnt that the option of choosing vegetables should be left to the

customeritself;

- Definingapricerangeforincentives--duringthepilotitwasseen

thatsomefamilieswerenotabletoaffordanexpenseofINR50twicea

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weekandtherebyforscale-up,possiblyamatchingcontributioncoupon

systemcanbefollowedwithnominimumcap.Also,consideringthatthe

contributionfromtheorganizationcannotbeindefinite,therecanbea

maximumcapofcontributingINR50(50%ofINR100)pertransactionper

customer,i.e.,ifthetransactionamountofthecustomerexceedsINR

100,thecompensationwouldnotbemorethanINR50.

Recommendations of the DVCP have been shared with the Women and Child

DevelopmentDepartment,alongwithadetailedproposalonincludingvegetable

baskets as a part of the ongoing Mamata Scheme in Odisha. Under Mamata

Scheme, monetary benefits are provided to pregnant and lactating women to

encourage healthy lifestyles and sufficient nutritional intake. eKutir also

presentedthisideatotheDepartmentofHealthandFamilyWelfare(Govtof

Orissa)tointegratenutrition-trainingprogramswiththeNationalProgramme

forPreventionandControlofCancer,Diabetes,CardiovascularDiseasesand

Stroke(NPCDCS).Currently,underNPCDCSthehealthinfrastructureisbeing

transformed and programmes aimed at creating awareness about good dietary

habits for preventing non-communicable diseases like hypertension and

cardiovascular diseases are being introduced. With Pratidhi initiative

successfully tested in the pilot phase, we have introduced another social

marketing initiative at the nexus of agriculture-nutrition-health. Called

AaharX,thisinitiativewillbepilotedinBhubaneswarandHyderabadduring

thetransition-to-scalephase.ApolloHospitalsandApolloSugarClinichave

agreedtopartnerwitheKutirtorunthepilotsandsubsequentlycollaborate

forpan-IndiaexpansionofAaharX.

Both Pratidhi and AaharX could potentially operate as social marketing

strategies at the nexus of agriculture-nutrition-health. This combined

innovation will allow consumers to remain nutritionally secure and enable

farmers to obtain fair and equitable return for their product. The

sensitization sessions for educating community members on the benefits of

healthy nutrition started in June 2015 in partnership with Ekjut India (a

not-for-profit). This six-month program worked in tandem with eKutir’s

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marketingandoutreacheffortsincommunitiesusingcollateralpartnersand

theVeggieLiteentrepreneurs.

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Chapter6:DiscussionandLimitations

Overthecourseofthisproject,eKutirhascontinuedbuildingcapacityfor

leveraged local micro-entrepreneurial motivations and processes to solve

smallholder farmers’ poverty through a distribution network of digitally

trained entrepreneurs, market linkages, technology, and data. eKutir has

established further linkages between vulnerable populations and local food

systems.ThisprojectwasimplementedoverfiveselectedlocationsinOdisha

including urban and rural locations, with most of the project components

being operational in nature, going above target in engaging 1,350 farmers.

Fromtheindividualandhouseholdleveltothesystemslevel,theevaluation

componentsusedmixedmethodstoidentifyandmeasurebehavioralandsocial

changesoccurringwithintheone-yearinterventionperiod.Asdescribedin

previous chapters, this involved the use of a quasi-experimental, pre- and

post-testdesigninbothurbanandruralsettings,(2)aprocessstudythat

included a network analysis, and (3) value chain analysis and modeling.

Among rural eKutir households, the evaluation showed multiple impacts on

thoseruralfarmersparticipatingintheVeggieLiteintervention.

123

Even though the pilot intervention was limited to one year, the evaluation

showedanincreaseinthefruitandvegetableintakeofeKutirfarmers,which

seemstohavebeendrivenbyincreasedfruitintake.Inaperiodinwhich

all farming groups showed declines in their daily vegetable intake, eKutir

farmers exhibited at endline a significantly higher level of vegetable

consumptionthantheothergroups.VeggieLite’sagriculturalimpactwasalso

noticeable.Atbaselineandendline,eKutirfarmersproducedandsoldmore

vegetablesthantheotherfarmergroups,generatingover30.6MINRinsales

forparticipatingfarmersovertheintervention.Thishighvolumeofsales

among eKutir farmers is particularly of note since eKutir farmers consumed

the greatest percentage of their own vegetables, with roughly 28% going to

their consumption. Finally, there was also evidence of VeggieLite’s social

impact. The key to the success of any innovation is its wider social

acceptance. Our social network analysis showed the broader acceptance and

diffusion of eKutir programming into the community. Not only did the

composition of the eKutir farmers’ networks change to include a greater

percentage of farmers in the farmer intervention groups (FIGs), but so too

did the composition of the non-eKutir farmers’ networks. In other words,

over the one-year intervention period, regardless of whether farmers

participated directly in the intervention or not, all farmers in the

treatmentvillagescametodiscussfarmingmattersmoreandmorewithfarmers

intheFIGs.TheeKutirmodelthusseemstohaveincreaseditsacceptance

andbecomemorewidelydiffusedinthetargetedruralcommunities.

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Intheurbanareas,eKutirexceededprogramobjectivesforthedeploymentof

micro-entrepreneur vendors, with VeggieLite sales robust and increasing

steadilyinthetargetedwardsovertheyear.Inourparticularsampleof

consumers,however,wedidnotseeanincreaseinvegetableintakefrompre-

topost-testing.Therewereanumberoffactorsthatmayhelptoexplainthis

null finding. For example, disruptions in the services or locations of

certain VeggieLite vendors or even the broader range of consumer options

available to urban residents, including other eKutir offerings such as

VeggieKart or VeggieMart, could have weakened the intervention’s effect.

Nevertheless, in our sample of urban residents, we did find that those who

reported purchasing mainly from VeggieLite vendors at posttest increased

theirvegetableintakeandconsumedmoredailyvegetableservingsthanthose

that did not. VeggieLite program showed little effects on vegetable

consumptioninoursampleofurbanconsumers;thismayhavebeenduetothe

sample itself since VeggieLite showed robust and growing sales over the

intervention period. It is noteworthy that the very small number of urban

respondentswhoreportedprocuringtheirfruitsandvegetablesprimarilyfrom

Veggie-Litehadmorepositivechangeintheirconsumptionthatthosewhodid

not. This suggests that one of the key areas for improvement in moving

forwardistoreinforcethebehavioralchangestrategy.Thiscanpossiblybe

donebymorefocusedandpreciselydesignedinterventionthatusebehavioral

economics principles such as the Pratidhi intervention that has been the

object of prototype development in anticipation of transition-to-scale. It

couldalsobedonebyamorecomprehensivestrategytoenrollthenetworksof

NGOsthatsupportotherlivelihooddimensionsinthelocalcommunities.The

presentdeploymenthasfocusedprimarilyonthefarmingandfooddistribution

andscale.

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The organizational study underscored the value being created by eKutir for

micro-entrepreneurs and farmers. The inter-organizational network analysis

revealedthestructureoftheeKutirVeggieLitenetworkintheearlystages

of its formation. While dense communication ties characterize the network

core,furtherattentionmayneedtobegiventodevelopingthetiesbetween

theperipheralmembers,e.g.betweenentrepreneurs,aseKutirexpands.The

growing scale of operations and increased spending on marketing and

behavioralchangewouldallowalsoeKutirtobettercapturethevaluebeing

createdthroughchangesinthebroaderecosystem.Itisalsonoteworthythat

in spite of strategic efforts to favor women in the role of farmers and

micro-entrepreneurs, the achievement remains modest in this regard. While

the difficulty of changing well-entrenched cultural and social norms like

men’spredominanceintheseareasmustbeacknowledged,itcouldbepossible

in moving forward to bring to bear in the intervention design more of

ethnographicknowledgeanddesignapproaches.

Limitations

Limitations to the pilot study itself and the evaluation strategy must be

acknowledged.First,theVeggieLitepilotinterventionwaslimitedtoaone-

year period. This limited the capacity of the evaluation to detect more

preciselyseasonalvariationsintheimpactofVeggieLiteonurbanorrural

households. eKutir operational data showed significant seasonal variations

intheyieldandsalesofeKutirfarmers.Yet,giventhepre-andpost-test

natureoftheevaluationdesign,thestudycouldonlydetectchangesattwo

time points, and not track consumption or production changes on a seasonal

basis.Inaddition,thepre-andpost-designlimitedthestudy’sabilityto

capture real-time changes in the consumption patterns of urban residents.

While the pre-and post-design served well for pilot evaluation purposes,

futureevaluationdesignsshouldaccountforseasonaleffectsandtherefore

trackconsumerbehavioronamoreregularbasis.

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Second,theVeggieLiteprogramrepresentsacomplexinterventionintoreal-

lifesocialsettings.eKutiroperationalchoicesmaysometimesconflictwith

research evaluation goals. For example, the closure of certain VeggieLite

entrepreneursduetolossesincurredinthelastsixmonthsmeantthatother

delivery channels and activities came to serve certain urban residents.

VeggieWheel, which is a mobile van carrying vegetables in the urban wards,

replacedtheVeggieLiteincertainareas,indicatingthestrategicshiftfrom

thefixedplace&fixedtimemarketingsystemtoamobilemarketingsystem.

WhilethisimpactedVeggieLitesales,overallvegetablesalesincreased.More

generally,becauseVeggieLiteisacomplexintervention,itseffectsarenot

necessarilyconstrainedtospecificgroupsorsettings.

127

ReferencesAddyN.A.,PoirierA.,BlouinC.,DragerN.,andDubé,L.(2014).Wholeof‐

Society Approach for Public Health Policymaking: a Case Study of

Polycentric Governance from Quebec, Canada. Annals of the New York

AcademyofSciences,1331:216-229.

AroraN.K.,PillaiR.,DasguptaR.,andRaniGargP.(2014).Whole-of-society

monitoring framework for sugar, salt and fat consumption and

communicable diseases. Annals of the New York Academy of Sciences,

1331:157-173.

BesleyT.andBurgessR.(2000).LandReform,PovertyReduction,andGrowth:

EvidencefromIndia.TheQuarterlyJournalofEconomics,May:389-430.

DubéL.,AddyN.A.,BlouinC.,andDragerN.(2014).FromPolicyCoherenceto

21st Century Convergence: A Whole-of-Society Paradigm of Human and

EconomicDevelopment.AnnalsoftheNewYorkAcademyofSciences,1331:

201-215.

DubéL.,JhaS.K.,FaberA.,StrubenJ.,LondonT.,MohapatraA.,DragerN.,

Lannon C., Joshi P.K. and McDermott J. (2014). Convergent Innovation

for Sustainable Economic Growth and Affordable Universal Health Care:

Innovating the Way We Innovate. Annals of the New York Academy of

Sciences,1331:119-141.doi:10.1111/nyas.12548.

Dubé L., Pingali P., Webb P. (2012). Paths of Convergence for Agriculture,

Health, and Wealth. Proceedings of the National Academy of Sciences,

109(31):12294-12301.

DubéL.,WebbP.,AroraN.K.,andPingaliP.(2014).Agriculture,Health,and

Wealth Convergence: Bridging Traditional Food Systems and Modern

Agribusiness Solutions. Annals of the New York Academy of Sciences,

1331(2014):1-14.

Eisenmann T.R., Parker G.G., van Alstyn, M.W. (2006), Strategic Planning -

StrategiesforTwo-SidedMarkets.HarvardBusinessReview,October2006

Issue.

FAO2011.Evaluationofcertaincontaminantsinfood:Seventy-secondreport

of the Joint FAO/WHO Expert Committee on Food Additives (2011).

128

EditedbyJackC.Ng.Seventy-secondreportoftheJointFAO/WHOExpert

CommitteeonFoodAdditives,16-25February2010,Rome,Italy.

Hall J.N., Moore S., Harper S.B., Lynch J.W. (2009). Global variability in

fruitandvegetableconsumption.AmJPrevMed.2009May;36(5):402-

409.

Hammond R.A., Dubé L. (2012). A Systems Science Perspective and

Transdisciplinary Models for Food and Nutrition Security. Proceedings

oftheNationalAcademyofSciences,109(31):12356-12363.

Jha S.K., McDermott J., Bacon G., Lannon C., Joshi P.K., Dubé L. (2014).

Convergent Innovation for Affordable Nutrition, Health, and Health

Care: The Global Pulse Roadmap. Annals of the New York Academy of

Sciences,1331:142-156.

Jha S.K., Pinsonneault A., and Dube L. (2016). The evolution of an ICT

platform-enabled Ecosystem for poverty alleviation: The case of

eKutir. MIS Quarterly, Special Issue: ICT and Social Challenges, MIS

Quarterly,Vol.40No.2,pp.431-445.

NSSO2014.HouseholdConsumptionofvariousgoodsandservicesinIndia.68th

Round,20ll-12.

Shiell A., Hawe P., and Gold L. (2008). Complex interventions or complex

systems? Implications for health economic evaluation. BMJ : British

Medical Journal, 336(7656), 1281–1283.

http://doi.org/10.1136/bmj.39569.510521.AD

Spencer M., Jha S.K., Mishra S., Ross D., Allen S., and Dube L. (2015).

InnovationinEvaluationtoInformPolicyConvergence:ComplexSystems

Approach to Assess Entrepreneurship-driven Intervention in

eKutir’sVeggieKart, pp. 197-208, in Evaluations for Sustainable

Development: Experiences and Learning, New Delhi: Daya Publishing

House,2015.

The Hindu Website: World Bank promises big push to poverty alleviation

schemesinIndia.2013.TheHinduNews.March14th.Availableonline

at:http://www.thehindu.com/news/national/worldbank-promises-big-push-

to-poverty-alleviation-schemes-in-india/article4506331.ece

UN2009.TheMilleniumDevelopmentGoalsReport2009.

129

WEF2010.TheGlobalCompetitivenessReport.2010–2011.

Yin, Robert (2009). Case Study Research: Design and Methods. Sage

Publications.

130

Annexure:ResearchEthics–IRB,Studytoolsattached.

Figures:Figure1.1:Methods,LevelofAnalysisandKeymeasuresFigure2.1:eKutirPIEmodelforFarmersandVeggieKartvaluechainFigure2.2:EKutir/VKecosystemInformationflowFigure2.3:MaterialFlowFigure2.4-ExampleComparisonforBeans,IndrahanuMktvVeggieKartFigure2.5-ExampleComparisonforBrinjal,IndrahanuMktvVeggieKartFigure2.6:RidgeGourdValueChain,IndrahanuMktVVeggieKartFigure2.7:VeggieKartValueChaincomparisonFigure2.8:Processflow–VeggieKartFigure2.9:TreatmenteKutirFarmer'sTotalSalesFigure2.10:VeggieKartSalesProjectPeriodFigure2.11:TotalsalesforallVeggieLiteCentersinBhubaneswarUrbanFigure2.12:TotalWeeklySales(kgs)forVeggieLiteServicesbyMonthinselectWardsFigure2.13:AllVeggieLitelocationscumulativesalesinrurallocationsFigure2.14:Jharsuguda(VeggieLiteCenter) Figure2.15:Jharsuguda(VeggieLiteCenter) Figure2.16:Paburia(VeggieLiteCenter) Figure2.17:Daringbadi(VeggieLiteCenter)Figure2.18:Angul(VeggieLiteCenter) Figure2.19:Khandpada(VeggieLiteCenter)Figure2.20:Khandpada(VeggieLiteCenter)Figure3.1:Methodology–Quasi-experimentalevaluationdesignFigure3.2:BhubaneswarWardsFigure3.3:GISmapforurbanwardssurveyedFigure3.4:Rurallocations(map)Figure4.1:ProcessnetworkonsupplyanddemandsideFigure4.2:eKutirEcosystem:KnowledgeandawarenessdimensionFigure4.3:eKutirEcosystem:SendinginformationdimensionFigure4.4:eKutirEcosystem-InformationreceptionnetworkFigure4.5:RegularMeetingsNetworkFigure4.6:Dual-channelmodelconsideredinthepaperFigure4.7:Timingofevents.Figure5.1:Processflowfor“Pratidhi”programme

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Tables:Table3.1:QuestionnairemodulesTable3.2:Baselineandendlinesamplenumbersbyquasi-experimentalconditionandwardsite.Table3.3:Socio-demographiccharacteristicsofthesampleatbaselineandendlinestratifiedbyexperimentalconditionTable3.4:ResultsfromtheANOVAanalysesTable3.5:RegressionresultsexaminingthedifferencebetweenendlineandbaselinevegetableconsumptionlevelsTable3.6:Mainsourcesforpurchasingvegetablesatbaselineandendlinestratifiedbyseason,percentage.Table3.7:ANOVAresultsfromcomparingthoseparticipantswhoreportedusingVeggieLitetoNon-VeggieLitenotovertheinterventionperiodTable:3.8:UnadjustedandadjustedregressionestimatesTable3.9:BaselineandEndlinesamplesizezbyquasi-experimentalconditionTable3.10:BaselinedataonhouseholdandsociodemographiccharacteristicsTable3.11A:ANOVAresultsreportingthedifferencesamongthethreegroups[BaselineandEndline]Table3.11B:ANOVAresultsreportingthedifferencesamongthethreegroups[MeanDifference(Endline–Baseline)]Table3.12:RegressionresultsexaminingthedifferencesinfruitandvegetableconsumptionfrombaselinetoendlineTable3.13:Home-grownconsumptionbyproducersatbaselinevs.endlineTable3.14:Resultsfromthepairedsamplet-testsTable3.15:ResultsfromtheregressionanalysesTable4.1:Concept-stakeholdergridTable4.2:TypesofrespondentsTable4.3:SupplysideprocessTable4.4:ProcurementprocessTable4.5:DensityandcentralizationvaluescharacterizingeachnetworkdimensionTable4.6:Demandsideproc

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