ProblemS t a t e m e n t s
Agricultureis animportants e c to r inVietnameconomy.Agriculturea cc ounts f or18.2%ofthecountry'sgrossdomesticproductin2009.In2008,agriculturalexportaccount sfor12.3%oftotalexportvalueofthecountry.In
2009,theproportionoflaborforceinagriculture,forestry a n d fisherys e c t o r i s62.9%.Ruralpopula tion proportionwasaround71%(FAOSTAT,2010)and rurallaborforcemade up58.5
Rice is one of the most significant crops in agricultural production, ranking fifth globally in terms of production volume It accounts for 90% of food production and supports 80% of Vietnam's labor force In 2010, Vietnam's domestic rice production reached approximately 40 million tons, cultivated across 7.5 million hectares, yielding an average of over 5 tons per hectare The annual per capita rice consumption in Vietnam is notably high at 169 kg per person, contributing around 1,591 calories to the diet, with rice providing 60% of calorie intake in 2007.
Ricehasbeena Vietnam’sprincipala g r i c u l t u r a l e x p o r t andagreatsourc eofforeignexchange.Valueofexportedriceaccountsabout20%ofagricultural andforestryproducts.
Vietnamhasexportedriceto120countriesanditsshareinglobalmarketi s about20%,rankeda tthesecondintheworld(USDA,2011).In2010,Vietnamh a s exported6 8 8 mill iont o n s ofriceworth U S$ 3 2 3 b i l l i o n I n comparisonw i t h theyear2009,theq uantitywasi n c r e a s e d 1 5 4 percenta n d thevaluewasincreased21.2percent(GSO,2010).
Beingatropicalsubequatorialcountry,Vietnamhasfavorablenaturalconditionsforricec u l t i v a t i o n F a r m e r s c a n s o w , plant,c u l t i v a t e a n d h a r v e s t t h r e e c r o p s pery e a r Th ec o u n t r y h a s t w o e v e n a n d f l a t d e l t a s i r r i g a t e d b y i n t e r l a c e d s y s t e m r i v e r s broadlywithalluvialwaterflows.Humidclimateassociatedwithrainfallin allyearroundarefavorableforricecropping.Moreover,Vietnamwasoneofcradlesofwet
Vietnamese farmers face significant challenges as agricultural land for rice cultivation has decreased from 7.6 million hectares in 2000 to 7.4 million hectares in 2009 On average, approximately 73,300 hectares of agricultural land are repurposed each year, resulting in a total loss of 154,000 hectares over the last five years, equating to a 7.6% reduction in rice cultivation area This trend is expected to continue due to rising land demands for urbanization and other uses, with projections indicating that rice land will be reduced to 3.8 million hectares by 2020 Additionally, the population of Vietnam is anticipated to grow by 10%, reaching between 94 million and 98 million by 2020, further intensifying the pressure on agricultural land.
Onthe o t h e r h a n d , f a r m l a n d i s highlyf r a g m e n t e d a n d s e p a r a t e d a s are sulto f history.A v e r a g e l a n d s i z e belongedt o each h o u s e h o l d i s about1 he ctares t o 4hectares.Inthepast,governmentdeliveredlandforhouseholdswithegalitarianis minarea andkindofland.Landtenureis short(50yearsforperennialplantsand20yearsforannualcrops).Annualcroppingall ocatedagriculturallandforunderthe
Farmersarefacingchallengesofclimatechangecausinghighersealevel,deeper andlastingflood,moreintrusionofsaltywater ontheland.Unpredictableweather
2 inducesdroughts,floodsa n d frosta n d increasest h e risksofe p i d e m i c diseases. Climatechangesreducedl 3-
I.5%GDPofVietnam,an d agriculturei s themostsuffered sector.Vietnamhas3,26 0kmofcoastalongtheeastsidefromtheNorthtotheSouth.MekongRiverDeltaandRedRiverDel taaretwoagriculturalareasmostseverelyaffected b y climate changesandsealevelri se.Thesearetwomain r i c e cultivatingregionswith70%outputofthewholecountry(GS O,2010).Incomesoffarmersoftheseregionslargelycomefromrice production.Andriceproductionoftheser e g i o n s s e c u r e s f o r national f o o d a n d s u p p l i e s re ma rk a b l e g r a i n f o r internationalmarket.The one- meterriseofsealevelwillaffect12%areaand10%populationo f V i e t n a m , s u b m e r g e 5 , 0 0 0 k m ' o f R e d R i v e r D e l t a a n d 1 5 , 0 0 0 —
2millionhectaresofMekongRiverDeltaandthousandshectaresofCentralCoastalR egion.Riceyieldcanbereducedby1 0 % foreachof1'Cin c re a s e i n g l o b a l t e m p e r a t u r e I n h i g h e r t e m p e r a t u r e c o n d i t i o n , d e m a n d o f waterf o r cultivat ioni s hi gh e r a n d c u r r e n t i r r i g a t i o n s y s t e m w i l l b e o v e r l o a d e d (IPCC,
Waterinagricultureismostlyusedforirrigatingricefarm.About66,000millionin'isannuallyus edforriceproduction,accountsfor82%totalamountofwaterusednationalw i d e ( K
B R , 2009).It isestimatedt h a t, in2020agricultures t i l l needsalargeproportion ofwater, about72%
(KBR,2009).Waterisoneofmostimportantinputsfo r farming,e spe ci al ly r i c e p ro d u c t i o n B u t i r r i g a t i o n s y s t e m i n Vi etn am needstobedevelopedmorecompletely.
Inaddition,capitalforproductionis oneofthetroublesforfarmers.Farmershavemanyd ifficultieswhen accessingformalcredit.Manypeasantshavenothighvalueassetst o s e r v e ascollateralt o c o m m e r c i a l b a n k s , a n d they c a n o n l y b o r r o w 2 0 millionVNDforeachhect areofland.Proportionofcreditforagricultureandruralareasintotalcreditis ratherlow,22.8%(SBV,2009).Commercialbanks arelessinterested inagriculturalsector,becausethissectorinvolveswithhighriskandlow profitability.Anotherreasonisthatcommercial bankscannotbeabletocoverruralareas,monitorand retrieveloan.Inaddition,administrativecostsof lendinginthisareaarehigher.Soalargenumberoffarmersareborrowingfrominfor malcreditfundsathigherinterestrate.
Farmersalsoconcernabouttheincreasingcostsofproductiona n d inputssuchas fertilizer,pesticide,insecticideandfuel.AnnualfertilizerproductionofVietnamisabouton emillionoftons,andconsumptionamountsabove2.5 millionoftonsfortheperiodf r o m 2 0 0 4 -
2 0 0 7 ( F A O S T A T , 2 0 0 9 ) V i e t n a m e s e f a r m e r s a re h e a v i l y dependento n i m p o r t e d fertilizer,t h e i m p o r t e d v o l u m e a c c o u n t s f o r o v e r 4 0 % do mesticdemand.In2010aggregatesupplyofdomesticfertilizerwas2.59millionsoftons.M e a n w h i l e a g g r e g a t e d e m a n d w a s 7 7 m i l l i o n s o f t o n s e a c h y e a r I t i s estima tedthat,an nu a l e x p e n d i t u r e o n i mp o rt e d f e r t i l i z e r s i s about1 2 billiono f VND,withtotalquantityofover3milliontons(GSO,2010).
Otherp ro b l e m i s t h e i n c r e a s e d m i g r a t i o n o f fa rm l a b o r t o c i t i e s t o e a rn h i g h e r incomesinindustrysector.Itreducesnotonlythequantitybutyoung,stron gandeducatedl a b o r forceofrurals e c t o r T h e r e wasaconsiderabled e c r e a s e i n ruralpopulationintotalfrom81%-
1988to70.4%in2009(GSO,2009).Besides,amajorpor tion offarmershavenotattendedinanye ducationlevel.Illiteracy limitsfarmerstotakeoverandapplynewtechnicalpracticesinfarming.
Apparently, resourcesallocatedtoriceproductionarebecomingscarcerandnaturalconditioni s notc o n v e n i e n t a s i t wasi n t h e p a s t T h i s f a c t raisesa questionf o rfarmersandpolicym a k e r s onhowtominimizer e s o u r c e s use d i n productionprocessa n d m a x i m i z e t h e r i c e q u a n t i t y I t h a s b e e n a n a t t e n t i o n o f agric ul tural economistsi n t h e w o r l d f o r a l o n g t i m e a g o M a x i m i z i n g e f f i c i e n c y w i l l h e l p prod ucerscomeclosertopotentialoutputlevelgivencurrenttechnologylevelandthesameinp utlevel.
ResearchO b j e c t i v e s
Objectiveso f thisres ea rc h a r e tofindt h e w a y t o i m p r o v e thee ffi c i e n c y o f ri cecultivating.B y t h a t w a y V i e t n a m e s e r i c e f a r m e r s a r e a l s o a b l e t o i n c r e a s e t h e output,exportandthenincreasetheirincome.Theanalysiswill (a) Findoutthecurrentleveloftechnicalefficiency ofricefarmsinVietnam, thefactorsinfluencingtechnicalefficiency.
(b) Givethegovernmentt o focusonessentialpolicies t o support Vietnam esefarmerst o i n c r e a s e e f f i c i e n c y b y i m p r o v i n g t e c h n i c a l
ResearchQuestionsandHypotheses
Thereweremanystudiesinelementseffectingontechnicalefficiencyindevelopingcountries.La ndstatus, creditapproach,i rr ig at io n methods, c h e m i c a l s usages, exten sions e r v i c e s a n d c h a r a c t e r i s t i c s o f h o u s e h o l d h e a d w e r e c o m m o n f a c t o r swhichhadbeenprovedtobesignificantrelatedto efficiencyleveloffarming(Hien,2003;Rios,2005;Tijani,2006;Singh,2007;
Kompas,2009,etc.).Inthisresearch,theau thor i s concerneda b o u t thee f f e c t s of l andp o l i c y , i r r i g a t i o n s i t u a t i o n a n d creditconditionandcharacteristicsofhous eholdheadontechnicalefficiency.Thepaperdoesnotexaminetheeffects ofinputusagesontechnicalefficiencyassomeauthord i d T h e re a s o n i s thei n p u t f a c t o r s a r e us e d a s i n d e p e n d e n t e l e m e n t s i n stochasticfrontierproductionfunc tion.Itnotshouldbeanalyzedagainintechnicalefficiencymodel.So, thisstudyfocusesonthreeproblemsandwillanswerthese
4ResearchMethodology
ThesisStructure
Thestructureoffollowingpartsin thispaperisasfollows:ChapterIILiteratureR eviewpresentsfindings offactorsinfluencingontechnicalefficiency, productionfrontier,researchmethodologiesfromprevioussimilarempiricalstud ies.ChapterIIIR e s e a r c h M e t h o d o l o g y p r e s e n t st h e m e t h o d o f e s t i m a t i n g s t o c h a s t i c f r o n t i e r productionf u n c t i o n a n d t e c h n i c a l e f f i c i e n c y m o d e l C h a p t e r I V D a t a A n a l y s i s
6 presentsm o d e l s p e c i f i c a t i o n , i n t r o d u c e s d a t a u s e d i n a n a l y s i s a n d d e f i n i t i o n o f variablesi n theempi ri ca l m o d e l , presents a n d discussesresu l ts o f dataanalysis.ChapterVConclusionandPolicyRecommendation.
Thisc h a p t e r e x p l a i n s c o n c e p t s o f t e c h n i c a l efficiency,p r o d u c t i o n frontier,stochasticp r o d u c t i o n frontier,a n d i n t r o d u c e s a p p r o a c h e s t o m e a s u r e t e c h n i c a l efficiency.Finally,theauthorsuggestsaconceptualframe workforthisstudypaper.
KeyConcepts
Technicale f f i c i e n c y ( T E ) measuresthepotentiali n c r e a s e i n outputgivena lev elo f inputsi n o u t p u t o r i e n t e d m a n n e r I n o t h e r w o r d s T E r e f e r s t o t h e s m a l l e s t s e t o f inputsn e e d e d t o p r o d u c e a g i v e n o u t p u t i n i n p u t o r i e n t e d m a n n e r ( F a r r e l l , 1957).TheT E c o n c e p t canb e a p p l i e d t o t h e a n a l y s i s o f m u l t i - o u t p u t ors i n g l e - o u t p u t dataset.Thete c hnic a l e f f i c i e n c y d o e s n o t r e f e r t o t h e a v e r a g e o u t p u t , b u t t h e p o s s i b l e maximumo u t p u t obtainablefromagivenbundle of inpu ts.Thetechnicale f f i c i e n c y ofaproducerc a n b e expresseda s t h e ratioofrealo u t p u t tothemaximump o t e n t i a l output.Italsodescribestheabili tyoffarmerstoapplygoods k i l l andknowledgei n production.
Figure2.Is h o w s twototalphysicalproductcurves ThoseareTPP1andTPP2.
Atanygivenlevel ofvariableinput.theTPP1alwayshasthe higheroutputthanTPP2becauseTPPIdisplaysthehighertechnicalefficiency(Ellis.1993)
Thefrontiershowsthebestperformance o b s e rv e d amongthefarms.Thefronti erproductiont u n c t i o n i s defineda s them a x i m u m p o s s i b l e o u t p u t t h a t a fa rmc a n producef r o n t a g i v e n l e v e l o f i n p u t s a n d t e c h n o l o g y ( K u m b h a k e r a n d L o v e l l 2000).
Figure2 2 s h o w s t h a t t h e o b s e r v e d input- outputv a l u e s a r e b e l o w t h e p r o d u c t i o n' frontier.W i t h t h e s a m e q u a n t i t y o f i n p u t s t h e o u t p u t v a l u e a t p o i n t B ( o n t h eproductionf r o n t i e r ) ishigherthan theoutputvalueatpointA.Thetechnical efficiencyistheratioofYtoY*(Battese,1991).
Figure2.3i s u s e d t o illustratet h e s t o c h a s t i c frontierproductionmod el Thehorizontalaxisdescribes thequantityofinputs,theverticalaxisdescribesquantityofoutputs.Considertwofirmsiand j Thevalue ofthestochasticfrontieroutput,Y
=exp(X.§)i s ontheproduction f r o n t i e r O u t p u t o f f i r m i i s Y,=e x p (X,.13+ v , ) abovethefrontier,becausetherandomerror‘v,’ispositive.Outputoffirmji s Y— exp( X I I + v , ) , i s b e l o w t h e f r o n t i e r be cause ther a n d o m error‘vi s n e g a t i v e(Coellieta1.,1998).
Figure2.3:TheStochasticFrontierProductionFunctionSources:Coel li.etat(1998)
ApproachestoMeasureTechnicalEfficiency
Two primary methods are widely used to assess efficiency, with Data Envelopment Analysis (DEA) being a prominent non-parametric approach introduced by Charnes, Cooper, and Rhodes in 1978 This method does not require the imposition of a specific functional form for the production frontier or the assumption of a distribution pattern for disturbance terms Instead, it employs linear programming to define the frontier based on observed performance, allowing for the calculation of each producer's efficiency level relative to their distance from this frontier Discrepancies between actual output and the frontier are interpreted as technical inefficiencies, making the method sensitive to measurement inaccuracies, data heterogeneity, and outliers Additionally, hypothesis testing and confidence interval measures are not permissible within this framework (Horrace and Schmidt, 1996).
Thesecondapproach i s parametric.A i g n e r a n d C h u (1968)proposeds t o c h a s t i c frontierw i t h t h e i n f l u e n c e o f t h e r a n d o m c o m p o n e n t i n t h e m o d e l o f f a r m i n g Aigner,L o v e l l andS c h m i d t (1977)disaggregatedt h e disturbance erro r into datanoiseandtechnicali n e ff i c i e n c y Thisapproachis suitabletoan alyzeagriculturaldataw h i c h i s i n f l u e n c e d b y t h e m e a s u r e m e n t e r r o r s a n d t h e e f f e c t s o f r a n d o meffects,suchasweatherconditions,diseasesandsofort h(Coellietat.,1998).Moreover,stochasticfrontierapproachcan beusedtoconstruc ttheconfidenceintervalforp a r a m e t e r s andt o t e s t hypothesis.So,t h e e x i s t i n g t h e s i s appliesstochasticfrontierapproachtoanalysistechnicalefficiencyofricefar ming.
StochasticFrontier AnalysisF r a m e w o rk
Aigneretal(l977),MeeusenandBroeck(1977)suggestedthestochasticfrontierm odelfortheestimationoftechnicalefficiency.Thetechniqueassumesthatfarmscouldno treachtheefficiencyfro n t i e r becauseo f measuremente r r o r s statisticaln oise,anynon-systematicinfluenceandtechnicalefficiency.
Y,istheproduction ofthei-thfarm(i=1,2.3 n) X,isa(1xk)vectorofinputquantitiesused bythei-thfarm §isa(kxI)vectorofunknownparameterstobeestimated
=N(0,c 2 ) T his c o m p o n e n t i s r e p re s e n t i n g t h e e f f e c t s o f r a n d o m f a c t o r s ( e g , measurementerrorsinproduction,weather,industrialactions,etc.).The sefactors areoutofthecontrolofthefarm.
U,i s a n o n - n e g a t i v e technicalinefficiencyeffectst h a t a r e a s s u m e d to beindependentlydistributedamongthemselves( = N ( 0 , cr,')).Thedistributionalparam eters,U,andcr„'are inefficiencyindicators.U,indicatestheaverageleveloftechnicalinefficien cy.Andc„'showsthespreadoftheinefficiency.IfU,=0.itisimpliedthatproduction Iiesonthestochasticfrontier,thefarmobtainsitsmaximumattainableoutputgivenitslevel ofinput.IfU,>0,itisimplred thatproductionliesbelowthefrontier— indicationofinefficiency.Thisone-sidederrortermcanfolloweitherhalf- normal,e x p o n e n t i a l , orgammadis tribu tion ( A i g n e r , L o v e l l , and Schmi dt,1977:Greene,1980;MeeusenandBroeck.1977).
FollowingBattese andCoelli ( 19 95 ), thetechnicali n e f f i c i e n c y e f f e c t s , U,in equation(1)canbeexpressedas:
U,israndomvariable,definedbythetruncationofthenormaldistribution,withzero meanandvarianCéu' S U G h thatpointoftruncationisZ,6.
W;representsunobservablerandomvariables,whichareassumedtobeidenticallydistrib uted.T h e y a r e obtained b y t h e truncationo f then o r m a l d i s t r i b u t i o n w i t h meanzeroandunknownvariance‹i',suchthatU,isnon-negative.
Thetechnicalefficiency oft h e i-thsamplefarm,denotedbyTE,isgivenby:
Where i" f(Xj; §)exp(V;) i s thef a r m s p e c i f i c s t o c h a s t i c f r o n t i e r I f Y , i s e qua l t o Y ,
COLSwasproposedbyWinsten(1957)andGabrielsen(1975).Itisnot necessarytomakeassumptiononthedistributionoftechnicalinefficiency(U;).Firstly,OLSisusedt oestimateparametersoffrontierproductionmodel.Thenitincreasesinterceptinthemodeltohaveallre sidualsnegativewithatleastoneisnull.
MOLSw a s s u g g e s t e d b y R i c h m o n d ( 1 9 7 4 ) I t r e q u i r e s a s s u m p t i o n a b o u t distributiono f t e c h n i c a l inefficiencycomponent.T h i s m e t h o d d o e s n o t a d j u s t interceptofthestochasticfrontierproductionmodelbutthetechnica linefficiency component.Thetechnicalinefficiencyandtheresidualsofthemodelarechange dinoppositedirection.Reducingmeanvalueoftechnicalinefficiencywillshiftupthep roductionf r o n t i e r
8 0 ) werethefirstresearchersapplyingthismethod.Theproductionfrontierparameters(§)an dtechnicalinefficiency(U;)areestimatedsimultaneously.
COLSa n d MO L S o n l y a d j u s t i n t e r c e p t n o t t h e s l o p e s ofthes t o c h a s t i c f r o n t i e r model.TheCOLSandMOLSfrontiersareparallelwiththeOLSfrontier.They donotboundabovetheobservedvalueascloseaspossible(Porcelli, 2009).So,this
EmpiricalS t u d i e s
Theaveragetechnicale f f i c i e n c y o f Vietnameser i c e fa rmers in K o m p a s
A study conducted in 2002 revealed that the stochastic production frontier for 60 provinces from 1990 to 1999 had an efficiency level of 59.2%, based on 540 observations It was found that increasing farm size and the ratio of cultivated area ploughed by tractors could significantly enhance efficiency levels The coefficients for capital (horsepower), labor (working days), land (hectares), and material inputs (tons) in the stochastic production frontier were all positive However, small farm sizes and underdeveloped credit markets were identified as constraints to efficiency growth The author utilized a one-step maximum likelihood method to simultaneously estimate production and inefficiency models, explaining the impacts of various determinants on technical efficiency through the lens of technical inefficiency functions.
In 2003, a study estimated the technical efficiency of rice production in the Mekong Delta using a stochastic frontier analysis approach The model for technical inefficiency was estimated alongside the frontier model through a one-step maximum likelihood method, identifying key determinants of technical efficiency Analyzing three seasonal datasets, the study found an average technical efficiency of around 80%, with an average yield loss of approximately 700 kg/ha The stochastic frontier production function revealed that the quantity of seed, active nitrogen, and pesticide expenses negatively impacted rice yield, while the quantity of active phosphate and potassium, along with expenses for hired machinery, had positive effects Additionally, the technical inefficiency model indicated that education and market access positively influenced efficiency, while land size and variety dummies, as well as participation in Integrated Pest Management (IPM) practices, played significant roles in determining productivity.
15 technique(dummyforrowseedingtechniques),credit availability(totalborrowedamountf o r p r o d u c t i o n ) h a d n e g a t i v e s i g n s I t m e a n s t h a t t e c h n i c a l e f f i c i e n c y w a spositivelyaffectedbylandsize,variety ,IPMadoption,sowingtechniquetogetherwithavailabilityofcredit.
C u M ’ g a rDistrict,D a k L ak P r o v i n c e , V i e t n a m i n 2004.First,technical e f f i c i e n c y w a s calculated using
DataEnvelopmentAnalysisapproach,thentobitregressionswereusedt o i d e n t i f y t h e f a c t o r s c o r r e l a t e d w i t h t e c h n i c a l a n d c o s t i n e f f i c i e n c y T h e findingin dicatedthatsmallfarmswerelessefficientthanlargefarmandthelargefarmshadthepo tentialstoincreasetheiroutputbyalmost35%.Forsmallfarms,highereducation appearstoreduceefficiency.Thepossiblereasonwasthateducationhadcreatedoppor tunitiesforoff-farmworkandtherebyreduceson-farmmanagementextent.Accesstocredit and securityofland tenurewerenotfoundtobesignificantfactorsinexplainingefficiencyinthesample.
Johansson(2005)analyzedtherelationbetweenthefarmsizea n d technicalefficie ncyofSwedishdairyfarms fortheperiod1 99 8 -
20 02 Maximumlikelihoodestimationm e t h o d w a s e m p l o y e d t o e s t i m a t e t h e s t o c h a s t i c f r o n t i e r p r o d u c t i o n functionforunbalanced- paneld a t a Theinputfactorsexaminedwerefodder,seed,fertilizer,c a p i t a l , l a b o r an denergy.T o t a l w o r k i n g h o u r o f family m e m b e r s a n d hiredworkersrepres entedforlabor.Energyconcludedtheoilandelectricity.Allinputfactorshadpositi vemarginalimpactsonproduction.ANOVAresultshowedthatthesmallfarmsw e r e mostefficientandthemediumfarmshadlowestefficiencyl e v e l
Tijani(2006)analyzedcrosssectionaldataof2002/2003ricefarmproductiono f OsunStatei n N i g e r i a Thep r o d u c t i o n frontierw a s d e s c r i b e d byat r a n s - l o g function.Therewasapossibilitytoincreasericeoutputby13.4%onaverage.The51
16 inefficiency.A r e a a n d f e r t i l i z e r w e r e i m p o r t a n t a n d p o s i t i v e l y r e l a t e d w i t h t h eoutputwhilethela bor wasnot.Thearea, fertilizera n d laborelasticit yo f outputar e0.32%,0.64% and0.17%,respectively.Theapplicationoftraditionalpreparations(dummyv a ri a b l e f o r frightenbirdso ff farm,notkillthem)andoff- farmincome (dummyv a r i a b l e ) w e r e importantf a c t o r s t o i n c r e a s e l e v e l o f e f f i c i e n c y
Unexpectedly,extensioncontactwithofficer(dummyvariable)hasnegativeimpactontechnicaleff iciency.Othervariables— familysize,age,educationandexperience(years)offarmerswerenotstatisticallysignifican t.Theconclusionwasbasedontechnicalinefficiencymodelwhichwasestimatedatt hesametimewithstochasticfrontierproductionfunctionbymaximumlikelihoodmethod.
Singh(2007)exploredthatsmal lsizefarmsaremoreefficient thanmediuma n dlargesizefarmsinwheatc u l t i v a t i o n i n Haryana, Indi a Thecross- sectionald a t a setincluded315observationsoftheseasonyear1998-
Alargepartoftechnicali n e f f i c i e n c y wa sattributedtothelowlevelofeducationoffa rmers,poorextensionservices,oldunbusiness- likeattitudeandgrossdistortioninthepriceofi n p u t s l i k e a g r o c h e m i c a l s , a n d l a b o r T h e e s t i m a t i o n m e t h o d w a s c o r r e c t e d ordinaryleastsquare(COLS).
Idiong(2007)estimatedtechnicalefficiencyof1 1 2 riceproducersofCrossRiverS tate,Nigeria.Technicalefficiencyrangedfrom49%to99%withmeanof77%.G ammah a d a valueo f 0 7 7 (i.e.technicali n e f f i c i e n c y e x p l a i n e d 7 7 % ofdi screpancyo f o b s e rv a t i o n o u t p u t a n d po t en t i a l f r o n t i e r ) S t o c h a s t i c p r o d u c t i o n frontierwasestimatedbymaximumlikelihoodmethod.Farmsizeinhectares,labor
, inman- days,capitalinputsinnaira(currencyunitofNigeria),fertilizerinkilogram,quantityofseedinkilogra mweretakenintotheproductionfunction.Theauthordidestimatet h e t e c h n i c a l e f f i c i e n c y m o d e l i n s t e a d o f technicali n e f f i c i e n c y m o d e l Independentv a r i a b l e i n technicale f f i c i e n c y fu n c t i o n werefarmsizeinhectares,yearsofschoolin g,ageoffarmersinyears,farmingexperienceinyears,household
17 size,membershipofcooperative/ farmerasso ci at ions (dummyvariable),extensioncontact(dummy),creditaccess (dummy),s e x (dummy).Farms i z e inhectaresappearedinbothtrans-logCobb- Douglasfunctionandtechnicalf u n c t i o n Labor,farmsizeandseedhadstatistical meaninginproductionfunctionwithpositivesign(+).Education,c o mmu n i c a t io n w i t h socialorganizations,creditaccessibilitysignificantlyaffectedefficiencylevel.
( 2 0 0 8 ) s u r v e y e d m a i z e f a r m e r s f r o m t w o d i s t r i c t s i n T a n z a n i a L o wlevelsofeducation,lackofextensionservices,limitedcapital,landfragmentation,an dunavailability a n d highinputpriceswerefoundtohaveanegativeeffe ct on technicalefficiency.Ina d d i t i o n , m a l e f a r m e r s w e r e m o r e e f f i c i e n t Specially,smallholderf a r m e r s using h a n d h o e a re foundto bemoree f f i c i e n t c o m p a r e d t othoseu s i n g tractoro r ox- plough.M o r e o v e r , f a r m e r s w h o u s e d a g r o c h e m i c a l s w e r elesse f f i c i e n t c o m p a r e d t o f a r m e r s w h o d i d n o t s p r a y t h e i r f a r m s L a n dfragmen tationwasmeasuredbythenumberoflandplots,andbythedistancefromcultivatedl a n d tofarmers
’h om e s D u m m y v a r i a b l e o f h a v i n g a n y c r e di t f o rmaterialswasr e p r e s e n t e d forc a p i t a l accessibility.Familylaborv a r i a b l e hadnegativecoefficienti nproductionmodel.Itwasassumedthatthere wasredundantoff a m i l y l a b o r D e t e r m i n a n t s oft e c h n i c a l efficiencyw e r e i n v e s t i g a t e d int h etechnicalinefficiencymodel.
Al-Hassan(2008)observedof732(252irrigatorsand480non- irrigators)ricefarmsinN o r t h e r n G h a n a , andgatheredi n f o r m a t i o n a b o u t 200 5/06croppingyear.Applyingtranscendentall o g a r i t h m i c productionfrontier herealizedthatthemaindeterminantsoftechnicalefficiencyin thestudiedareawereeducation,extension
, contact,ageand familysize.Therewasnosignificantdifferenceinmeantechnicalefficienciesfornon- irrigators,andirrigators.Theauthorapply two- stepproceduretop r e d i c t frontierm o d e l andd e t e r m i n a n t s o f t e c h n i c a l e f f i c i e n c y l e v e l , u s i n g maximumlikelihoodestimationmethodatfirststepandordinar yleastsquare(OLS) atsecondstep.Theauthorpredictedthe log- lineartechnicalefficiencymodelwiththedependentvariablewaslogarithmoftechnicalefficiency
Onojaeta1(2008)foundthatfarmcredit,farmsiz e, chemicalfertilizer quanti ty applied,laborandseedlingplantedsignificantlyinfluencedoutputofcassavafarmsinKogiS tate,Nigeriasignificantly Datainfieldsurveyof174farmersfromtwoagricultural areasselectedrandomlywasfittheCobb-
C o e f f i c i e n t s o f f a m i l y l a b o r a n d credits i z e h a d p o s i t i v e s i g n s w h i l e t h o s e o f h i r e d l a b o r , q u a n t i t y o f i n o r g a n i c fertilizer,quantityo f seed,lan ds i z e hadnega tiv es i g n s Yearsoffarmingexperience,n u m b e r o f y e a r s o f f o r m a l e d u c a t i o n , n u m b e r o f m e e t i n g s w i t h extensionagents andhouseholdsizeintheinefficiencymodel werenotstatisticallysignificant.Ma g n i t u d e o f variablese f f e c t technicale f f i c i e n c y v a ri e d a c r o s s differentag r ic ul tu r al z o n e s.
The study analyzed three datasets: provincial data from 540 observations between 1990 and 1999, survey data from 388 farms in 2004, and the VHLSS 2004 dataset encompassing information from 3,865 rice farms It revealed that farmers with better education or proper certifications exhibited higher efficiency in their farming operations Key factors contributing to farm efficiency included larger and less fragmented farms, improved irrigation, a higher capital-to-land ratio, secure land tenure, and access to agricultural extension services The research employed a one-step maximum likelihood method to simultaneously predict production functions and technical efficiency The technical efficiency model for the third dataset was regressed on logarithmic independent variables, and average farm sizes in each province were analyzed to assess the impact of land fragmentation on efficiency.
(1).Binaryvariablefor provincesinRedRiverDeltaandMekongRiverDeltar e p r e s e n t e d t o s o i l q u a l i t y T h e p r o p o r t i o n o f l a n d p l o u g h e d b y t r a c t o r s , numberoftractors,and numberofthreshingmachineswereregressors,too.Capitalvariable inproductionfunctionofdataset(1)wasmeasuredinhorsepowerbutthat19 ofdata set(2)itwasmeasuredinhours.Numberoflandplotswasaproxyvariableforl a n d f r a g m e n t a t i o n i n e f f i c i e n c y r e g r e s s i o n C h e m i c a l c o m p o s i t i o n o f f a r m rankedindecreasingorderfrom1to6wasusedtodescribesoil quality.Irrigationwasd e f i n e d b y d i f f i c u l t y o f watering a n d d r a i n a g e t h e f i e l d , r a n k e d 1-
4decreasingly.Educationofdecisionmakershadfourcategoriesforprimary,second,high- schoolandhighereducation.Education inefficiencymodelbuiltfromdataset
(3)wasaddedtwocategories:vocationaltrainingandcollege/ university.Proportionoftitledlandareaovertotalwasusedtoexpressdegreeofcertificatedland. Qualityofsoilismeasuredbytheratiooflandbetweentwobestkindsandthetotal.T hekindoflandisassessedonthebaseoflandtaxapplied.
Khai(2009)studiedtechnicalefficiency,allocationefficiencyandeconomicef ficiencyo f s o y a - b e a n p r o d u c t i o n inC a n T h o a n d A n G i a n g p r o v i n c e s w i t h primary dataof113familiesin2004.OLSand stochasticfrontiermodelwereusedtoregresstrans- logCobbDouglasmodel.Stochasticfrontiermodelwasestimatedbymaximuml i k e l i h o o d method.Hypothesistestingconcludedthatinefficiencyeffectwassignifica nt.Sousingtechnicalefficiencyscoresderivedfrom stochasticmodelintobit modelto investigatefactors impactonefficiency.
Especially,policyvariablesw e r e a l s o p ut i n t o th ee f f i c i e n c y m o d e l P o l i c y i s dummy v a r i a b l e o f recognitionv a l u e oneofanyprogramssu c h ascredit,sho rteducation,materials-productspolicy.
In a 2010 survey conducted by Khan involving 150 rice farmers from Boro and Annan in Jamalpur District, Bangladesh, it was found that larger farm sizes and effective irrigation positively influenced Boro production Additionally, farmers with higher education levels demonstrated greater efficiency compared to their less educated counterparts In the Annan region, factors such as farm size, pesticide use, and the cost of power tillers also had a positive impact on production The study revealed that the mean technical efficiency for Boro and Aman farmers was 95.38% and 90.79%, respectively, indicating a need for government investment in research to develop new high-yield rice varieties The analysis focused on technical inefficiency rather than technical efficiency.
Amoretat (2010) utilized a stochastic production frontier approach in the Cobb-Douglas form to assess technical efficiency in vegetable, cereal, and fruit-tree farming The study analyzed cross-sectional data from a national survey conducted in 2006, which included 218 irrigated farmers across 11 regions in Tunisia Parameters of the frontier production function and the technical inefficiency model were estimated simultaneously using maximum likelihood methods The technical inefficiency model, based on a half-normal specification, revealed that age, land ownership (as a dummy variable), and traditional irrigation methods (with a value of 1 for traditional techniques) had significantly negative impacts on inefficiency levels Additionally, education (measured as a dummy variable for illiterate farmers) was positively correlated with inefficiency The findings indicated that cereal and fruit-tree cultivation was more efficient compared to vegetable farming Furthermore, all inputs—such as farmyard manure, labor, mechanization, and water—except for farm size positively and significantly influenced output in the frontier function.
Kocetat(2011)applieddataenvelopmentapproachtoanalyzetechnicalefficiencyofseco ndcropmaizeinTurkey.Thentheyusedefficiencys cores attainedatthefirstto determineimportantelementsaffectonthescoresbytobitregression.Dataofthisstu dywascollectioninformationo f thegrowingseason2004-
2005.Technicalefficiencyint h e inputorientedmethodwas88%,s u g g e s t i n g thatproducerscoulduseless 12%inputsonaveragebutstillhavethesameoutputlevel.Theageandformaleducationof farmerwerenotabletoexplainefficiencyscores,evena t 1 0 % criticall e v e l T h e a u t h o r a l s o r e v i e w e d e f f i c i e n c y r e s e a r c h e s a n d
concludedthateducationdid nothavestrong relationshipwithefficiencyindevelopingco un t ri e s T he numbero f irrigationm e t h o d s w a s i n s i g n i f i c a n t a t 5%level.Theareawaspositivelysignificantat5%.
Author Data Variables Results Short- comings
Smallf a r m s i z e constrainede f f i c i e n c ygrowth,ratioof landcultivatedb y t r a c t o rimpactedonefficien cypositively
Analysis atprovin celeveln otfarmle vel
(2003) Cross-section LandIPM,sowingtechnique,size, variety, credit access, education, market access.
Technicale f f i c i e n c y waspositivelyaffectedbyl ands i z e , v a r i e t y , IPMadoption,sowing techniquetogetherwit ha v a i l a b i l i t y o f credit.E d u c a t i o n a ndmarketa c c e s s were negativerelatedtoeffic iency.
(2005) Cross- section,surve y2 0 9coffe efarmers
Smallsize,education,r atiooft i t l e d l a nd,cre dit,irrigationpipelength ,
Smallfarmswereless efficientthanlargefar m.Forsmallfarms,high er educationappearst o red uceefficiency.Accesst ocredita n d s e c u r i t y o f landtenurewerenotf oundtobesignificantfac tors.
Farmsize Smallfarms were moste ffi c i e n t a n d th emediumf a r m s h a dl owest efficiencylevel.
Only concentr atedonfa rm size
Application of traditionalpreparations,off -farm income,extensionc o n t a c t w i t h officer,famil ysize,age,e d u c a t i o n andexperienceoffarmers
Theapplicationof traditional preparationsa n d o f f - farmincomeincreasedlev elofefficiency.Extens ioncontactwithofficer reducedefficiency.
Education, extension services, price of inputs,businessattitUde
All factors had significanteffectson efficiency.
COLSis notsuitab leforeffi cienc yanalysis.
(2007) Cross-section Farm size,education, age, experience, household size, membership ofcooperative/farmer
Education, communicationw i t h socialo r g a n i z a t i o n s ,creditaccessibi litysignificantlya f f e c t e d
23 associations,e x t e n s i o ncontact,creditaccess ,sex efficiencyl e v e l
Education,extension services, credit,distancef r o m h o m e t o landplot,gender ,handhoe,a g r o -
(2008) Cross- section Education, experience, services extension, familysize
All variables were insignificant intechnicali n e f f i c i e Al-
Cross- section,surv ey732ricefar ms
Education, extension contact, gender, ageandfamilysize.
All variables significant influenced onefficiency.
Averagef a r m s i z e , proportionoflandwithtrac tor,no.ofp l o t s , educat ion,irrigated,ratioo f l a n d w i t h u s erights, extensionservices
Lessfragmented, largerl a n d s i z e ,exte nsions e r v i c e s , land useright,greatercap italf o r c u l t i v a t e d land increasedefficiency.
Area, experience, dummy of policyrecognition,
Age,landproperty, education,dummyfortr aditionalirrigationtech nique
Age,landownership, traditionali r r i g a t i o nreducedi n e f f i c i e n c y ;educationhadoppo siteimpacts.
(2010) Cross- section Age, education, experience Educationr e d u c e d inefficiency.Higheffi ciencyproposedresearc hnewr i c e
(2011) Cross- section Age,education,no.of irrigationa p p l i c a t i o n s ,no.o f p e s t i c i d e appl ication
All variables were insignificant at even 10%
RESEARCH METHODOLOGY FOR TECHNICALEFFICIENCYATTHEFAMRSLEVEL
DataSource
This research utilizes data from the Vietnam Household Living Standard Survey (VHLSS) conducted from 2007 to 2008, along with rice production data provided online by the General Statistics Office of Vietnam The VHLSS is a comprehensive dataset collected biennially, supported financially and technically by the World Bank, offering extensive information on the population and households It encompasses details from 9,189 households, including 4,691 involved in rice production, serving as the primary data source for the technical efficiency analysis in this study.
ModelsSpecification and VariablesD e fi ni tio n
Applyingformula(I)inthesection 2 3 I,thelogarithmmo d e l o t ’ thestochasti cfrontierfunctioniswrittenasfollows:
Ln(Y,) §s*1n(BSEED,)+§,*ln(FERT,)+07*ln(INSECT,)+§*ln(HERB,)+§*ln(FUEL,)
Thevariablesi n stochasticfrontierpr oduc ti on f u n c t i o n aredefinedi n thefol l o wi ng
INSECT Expenditure on buying insecticide
Riceoutput(Y).Riceoutputistotalamountofallvarietiesofriceharvestedb y eachhouseholdintheyear.Thevarietiesofriceincludewinter-springordinaryrice,summer- autumnordinaryrice,autumn- winterordinaryrice,glutinousrice,ordinaryrice,ordinaryriceonburnt-overland,specialrice.
Familylabor(FLA).Familylaboris measuredbytotalhoursthatall membersin familyworkingfortheirfamily.
Thelaborhoursoffamily memberistheproductoftheaveragenumberofworkingmonthsintheyearmultipliedbyth eaveragenumberofworkingdaysinamonth,multipliedb ytheaveragenumberofwo rkinghourperday.Foreveryhousehold,familylaboristhesummationoftotalho ursintheyearofallmemberswhotakepartintheproduction.
Area( A R E A ) Area i s thesumofsquaredm e t e r o f alll a n d parcelsb e l o n g e d t o householdwhichareunderricecultivationofallvarietiesintheperiodoftwel vemonths.
Familyseed(FSEED).Familyseedmeasuresthequantityofpaddythatthefarm harvestedi n thepreviousyearandkeptitforthesowingintheyear2008.
Out-sourceds e e d (BSEED).Some householdsu s e s e ed whichwasboughtf r o m - themarketo r institutionsf o r sowing.Thisvariablem e a s u r e s th eexpenditur eo n buyingseed.
Fertilizer(F ER T) Thisvariableistotalamountofactiveingredientsasnitrogen ,phosphate,potassium,NPK,andotherfertilizers.
Insecticides(INSECT).Thisistheexpenditureonbuyingtheusedinsecticides.Herbicides(H ERB).Thisistheexpenditureonbuyingtheusedherbicides.
Fuel(FUEL).Thisistheexpenditureonfuel,energy, gas,lubricant, electricityand soforth.
Hiredcattle(HIRED_CATTLE).Thisvariableismeasured byexpenditurethatafarmpaidforrentingoxenorbuffaloestodrawplough.
Capital(CAPITAL).Capitalisthetotaloriginalvalue(thevalueatbuyingdate)ofsomecommo nmeansofproductionsuchastractor,waterpumpingmachine,ploughmachine,p l u c k i n g m a c h i n e , a n d c a t t l e B e c a u s e t h e d a t a s e t d o e s n o t s t a t e t h e , capacityofcattleandmachine,theanalysisusesthemonetaryvalue topresentforcapitalassets.
Int w o r e s e a r c h e s o f K o m p a s i n 2 0 0 2 a n d 2 0 0 9 , a l l c o e f f i c i e n t s ofs t o c h a s t i c frontie rproductionfunctionarepositive.Inthisresearch,theauthoralso assumedthatallinputsstillhavepositivecoefficients.
Thes t o c h a s t i c f r o n t i e r p r o d u c t i o n functionint h i s r e s e a r c h ise s t i m a t e d by maximuml i k e l i h o o d m ethod.
Maximizinglog-likelihoodfunction aboveistoestimate§,cr²andy.Then,crgand vºanbecalculatedbytheformulac r ² c p r 2 ²and
Jondroweta1.(1982)proposedthedistributionofU,conditionalons,asN(di'²) toestimatevalueoftechnicalinefficiency—U,andtechnicalefficiency—TE,:
(.)and ›(.)=Thest a n d a rd n o r m a l d e n s i t y f u n c t i o n andthestandard no rmaldistributionfunctionevaluatedat(st/ri)
• whethert h e d a t a s e t i s c o m p a t i b l e w i t h s t o c h a s t i c f r o n t i e r a n a l y s i s m e t h o d AccordingtoBatteseandC o r r a (1997)thevariancer a t i o p a r a m e t e r y whichr e l a t e sthevariabilityofU,tototalvariancecanbecalculatedasfollows: y c›’/n’wh ere a’——(a +n,‘")
Thevalueofyrangesfrom0to1.Whenitapproaches1,therandomcomponentoftheineffici encyeffectscontributesagreatparttothevariationofobservedoutputsacrossfarms(Coel liandBattese,1996).Ifthevalueofyequalszero,thedifferencebetweenyields(outputs) offarmsisentirelyduetostatisticalnoise.Ontheotherhand,a valueo f onewoul di n d i c a t e thatthed i f f e r e n c e i s attributedt o technicalinefficiency( B a t t e s e a n d C o rra , 1 9 7 7 ; Coelli,1 9 9 5 ) Parameterycandet ermi ne whether astochasticfrontier modeliswarrantedasopposedtoasimpleproductionfunction.Therejectionofthenullhyp othesis,H:y=0,impliestheexistenceofastochasticproductionf r o n t i e r
Then u l l h y p o t h e s i s i m p l i e s t h a t t h e d a t a s e t i s n o t c o m p a t i b l e w i t h s t o c h a s t i c frontiermodel.Thealternativehypothesisi m p l Iesthatthedata setisappropriatewithstochasticfrontiermodel.
2distribution.Kh an etal(2010)s h o w e d parairieteroflikelihoodratiotestasfollowing: i\=-2log[L(Hg)/L(H)]=-2{log[L(Ht)]—log[L(Hull
Thenullhypothesisstatest h a t allcoefficientsofs t o c h a s t i c frontierfunctions imultaneouslye q u a l tozero.Itimpliesthatallfactorsinthemodeldonotaffecttheoutput A l t e r n a t i v e h y p o t h e s i s s t a t e s t h a t a t l e a s t o n e c o e f f i c i e n t s o f s t o c h a s t i cfrontierf u n c t i o n isd i f f e r e n t fromz e r o I t i m p l i e s t h a t a t l e a s t o n e f a c t o r i n t h emodelaffectstheoutput.Waldtestisemployedtotestthishypothesis.
Thenullhypothesisstatesthateachcoefficientofstochasticfrontiermodelequalstoz e r o I t i m p l i e s t h a t t h e f a c t o r d o e s n o t s i g n i f i c a n t l y a f f e c t t h e o u t p u t T h e alternativeh y p o t h e s i s s t a t e s thatea ch c o e f f i c i e n t o f s t o c h a s t i c f r o n t i e r m o d e l i s differentfromzero.Itimpliesthatthefactorsignificantlyaffec tsthe output.T-testandWaldtestemployedtotestthesehypotheses.
Thenullhypothesisst a t e s thatsumofallcoefficientsinstochasticfrontierprodu ctionf u n c t i o n equalto1 Itimpliesthatthel e v e l ofincreasingin outputisdiffer entfro m lev el o f increasingi n allinputfactors Thealternative h y p o t h e s i s statesthatsumofcoefficientsinstochasticfrontierproductionfunctionisdifferentfrom1.Iti mpliesthatthelevelofincreasinginoutputisdifferentfromthelevelofincreasinginallinputfactors.Wal dtestisemployedtotestthishypothesis.
Efficiencymodel— w'hichusetechnicale]’[iciencys’cores'asJcpendentvariableori ne ff i ci en cymodel— whichusetechnicalinefficiencylcvc•las’‹1cpendc•ntvariahlccanb e u s e d t o i nv e s t ig a t e t h e e ffic ie nc y d e t e r m i n a n t s I n the p r e s e n t s t u d y theauthoremploystechnicaleffi ciencymodelunderthefollowingform:
TEI00j' o+ i ‘tilTléLUR+ fi‘ C E R RATIO;+ 6 3 * A V E ; + 4‘MCIR;
+ 5*NA_IRj+i 5 , * M N _ l R j+J 7 ‘ PPRO;+ g*SEXj+ g‘AGE;+ j 9 * S C H ;
+i‘EXPERIENCE,+d*CRE,+ºi*RRD,+6*MRD,+W,
Thedefinitionsofvariablesi n technicalef fi ci en cy modelarepresentedin t hefollowingt a b l e s :
CONCEPT VARIABLE DEFINITION UNIT EXPECTED
Landuseright timeLUR Average time from registering land use rights year +
CERRATIO Averager a t i o o f l a n d having long-term certificateoflandusing
Fragmentation AVE Averageareao f l a n d used,e q u a l l a n d a r e a dividedb y n u mb e r o f parcels in'/parcel +
Irrigation MCI R Proportion of land wateredb y machinesint otallandarea
NAIR Proportion of land watered by commonirrigationsyste
MNl R Proportion of land wateredbyhand
Finance CRE Dummy variable of accessingc r e d i t
PPRO Dummy variable of joining agriculturalpromotedpro
SEX Gender of household head
SCH Schooling year of householdhead
Region RRD Dummy variable of region-RedRiverDelta
MRD Dummy variable of region-
Technicalefficiency l e v e l offarmsisderivedfro m theformula(6)inthesection 3.2.1.2,b a s e d o n t h e e s t i m a t e d p a r a m e t e r s estimatedi n t h e s t o c h a s t i c f r o n t i e r productionmodel.
TheoriginaldatasetofVHLSS2008containedrecordsoftheyearthatfamilieshadregisteredl a n d userightsforeachof theirparcels.The timeoflandusingiscountedfromtheregisteringyearto2008.Eachfam ilycanhold anumber ofparcelsoflandusedtocultivatingrice.So,thisvariableisdefinedastheaveragetimeoflandusing ofallcultivatedparcels.
• Thefarmswhichhadregisteredlanduserightsforlonger timemaybemoresettleinlifeandproductionactivity.So,itisassumedthattechnicaleff iciencyincreaseswithtimeoflandusing.It means thatcoefficientofthisvariableisexpectedtobepositiveinthetechnicalefficiencymodel.
Asmentionedabove,someparcelshadnotbeenregistered.Itisnotaccurateiftheresearc herd e f i n e s adummyvariabletorepresentl a nd userightconcept.So,this
Landuserighthelpsfarmersfeelassuredabout thelandsecurityandcanimproveefficiency.So,i n c r e a s i n g r a t i o o f t i l t e d l a n d i s e x p e c t e d t o i n c r e a s e t e c h n i c a l efficiency.Itmeansthatcoefficientofthis variableispredictedtobepositive.
Thisistheaveragesizeofallcultivatedlandparcelsbelongedt o eachfarm.Itis calculatedbydividingsumofall landareaoverthenumberofparcels.Itexpressesthe fragmentationofagriculturallandinruralarea.Theresearcherdoesnotusethenumberoflan dparcelstoanalyzethefragmentationbecausenumberofparcelsmaybeincreasewiththetotalarea.
Iti s usually a s s u m e d t h a t h i g h l a n d f r a g m e n t a t i o n r e d u c e s t e c h n i c a l e f f i c i e n c y Predictably,a v e r a g e s i z e o f l a n d p a r c e l have positivecoefficientint e c h n i c a l efficiencyf u n c t i o n
Irrigation(NA_IR,MC_IR,MN_IR)
Families can cultivate on various land parcels, each requiring different irrigation methods such as machines, natural flows, or manual watering Some parcels may rely solely on rainfall Each irrigation method varies in speed and quantity, leading to distinct technical efficiency scores based on the proportion of land area irrigated by different techniques These scores are calculated using the area units rather than the number of parcels, with the total area of parcels watered by each method divided by the overall area of all parcels.
41 cultivated.Theseproportionsa r e informofunit,valuefrom0to1 Theauthordoesno tm u l t i p l i e d t h e s e u n i t s b y 100toc o n v e r t t h e s e n u m b e r s i n t o p e r c e n t a g e scale,sinceU,— technicalinefficiencyvariableisalsomeasuredinformofunitandvaluesint h e r a n g e o f 0 a n d 1 , t oo.T h i s i s m o r e c o n v e n i e n t f o r i n t e r p r e t i n g t h e d a t a analysisresults.
Parcelirrigatedbyanymannerisexpectedtohavepositiveimpactontechnicalefficienc y.So,coefficientsofvariablesNAI R , MCI R andMNI R arepredictedtobepositive. Accesstocredit(CRE)
Thisisadummyvariableusedtoappreciatetheaccessibilityoffarmerstofinancialsources.Ifthe familytookanykindofcreditintheyear2008theobservedvariablehasthev a lu e o f 1 Oth erwiset h e o b s e r v e d v a r i a b l e h a s thev a l u e o f 0.Lacko f capitalw o u l d c a u s e d i f fi c u l t i e s i n f a r m m a n a g e m e n t a n d re d u c e t e c h n i c a lefficiency.A c c e s s i b i l i t y o f creditf u n d s ma ye n c o u r a g e t e c h n i c a l e f f i c i e n c y o f r i c e prod uction.Therefore,the coefficientofvariableCREisexpectedtohave positivesign.
Ift h e householdattendedanypromotionp rograms fora g r i c u l t u r a l area,th eobservedv a r i a b l e h a s thev a l u e o f 1 Otherwiset h e v a r i a b l e h a s thev a l u e o f 0.Purposeso f agriculturalp r o m o t i o n p r o g r a m a r e toaidfarmerstoimprov e
Dummyvariableofhouseholdheads’gendertakesI-valueformaleand0- valueforfemale.Manye m p i r i c a l s t u d i e s s h o w e d t h a t f e m a l e h ou s eh o ld h e a d w e r e m o r e42 efficientt h a n t h e i r m a l e c o u n t e r p a r t s ( I d i o n g , 2 0 0 7 ; M s u y a , 2 0 0
Theempiricalstudiesshowthat thereisnoconsistentevidenceofsignofvariable householdheads’ageintechnicalinefficiency mo d e l Agehadpositiveeffect ontechnicale f f i c i e n c y (A l -
Thevariabler e c o r d s t h e n u m b e r o f yeart h a t h o u s e h o l d h e a d h a d a t t e n d e d g e n e r a leducation.Educationwasprovedtoincreaseefficiencyoffarmers(Idi ong,2007;Khan,2 0 1 0 ) I t i s usualt o d e s i r e t h a t f a r m e r s w h i c h a t t e n d e d e d u c a t i o n l o n g e r wouldh a v e h i g h e r e f f i c i e n c y , a n d S C H hasposi tiv e c o e f f i c i e n t i n thete chni ca lefficiencyf u n c t i o n Yearofexperienceofhouseholdh e a d ( E X P E R I E N C E )
Traditionally,longerexperienceisthoughttoincreaseefficiencyof production(Khai,2009).So,thevariableispredicted tohavepositivecoefficient.
RedRiverDeltaandMekongRiverDeltaaretwomainricegranariesofVietnam.Thep urposeofintroducingtwodummyvariablesfortheseregionsi s tofindoutwhet herthereisdifferenceoftechnicalefficiencybetweentheseareaswithothers.Thisdumm yvariablecanberepresentativef o r customandhabitinproductiono f eachr e g i o n , t h e q u a l i t y o f s o i l , o r k i n d s o f c l i ma t e a n d m a n y u n k n o w n characteristics ofeachregionsetc.
Thesetworegionshavel arge advantageinriceproduction,co mp a re d with ot herregionsinVietnam.So,itispresumed tohavepositivecoefficientsinefficiencymodel.
Theefficiencym o d e l i s estimatedb y o rd i n a ry l e a s t s qu a re methodp r o p o s e d b y Gauss(Gujarati,2003).Themethodestimatescoefficientsofindependentvariablesbym inimizingsumofsquaresofdifferencebetweenestimatedvaluesandobservedvalueofdependentv ariable.
Thenullh y p o t h e s i s s t a t e s thata l l c o e f f i c i e n t s o f technicale f f i c i e n c y m o d e l a r esimultaneouslye q u a l tozero.Itmeansthatallfactorsinthemodeldo not affectthetechnicale f f i c i e n c y l e v e l Thealternativeh yp ot h e si s s t a t e s thatth erei s atl e a s t o n e factorstatisticallysignific ant F- testi s employedtotestthishypothesis.
Thenullhypothesisstatesthatthecoefficientisnotstatisticallysignificant.Itmeansthatt h e f a c t o r d o e s n o t s i g n i f i c a n t l y a f f e c t t h e t e c h n i c a l e f f i c i e n c y level T h e alternativehypothesisstatesthatthe coefficientisstatisticallysignificant.Itmeansthatthefactorsignificantlyaffectsthete chnicalin e ffi c i e n c y level.TheT-testandWaldtestareemployedtotestthishypothesis.
Variable Obs Mean Std.Dev Min Max
Thisc h a p t e r p r e s e n t s r e s u l t s o f d a t a a n a l y s i s o f s t o c h a s t i c f r o n t i e r p r o d u c t i o n , function,technicale f f i c i e n c y m o d e l a n d statisticssummaryo f variablesi n thesemodels.T h i s p a r t a l s o d i s c u s s e s a n d c o m p a r e s t h e r e s u l t o f d a t a a n a l y s i s f r o m VHLSS2008withotherempiricalstudies.
ResultsofDataAnalysis
4.1.1StochasticF r on ti er ProductionF u n c t i o n
TheTable4.1showsthedescriptivestatistics ofvariablesobtainedfrom VHLSS20 08infrontiermodel.
5 0 9 tons/ hectares.T he amountofpaddyusedfo r seedingwas283kg,accountingfor45
Laborh o u r o f f a m i l y m e m b e r s i n they e a r w a s 3 , 0 7 4 h o u r s E x p e n d i t u r e o n l a b o rwas1 , 4 0 6 , 0 0 0 VNDforeachhouseholda year.Thiscostseachhouseh old3 , 8 5 2 VNDtohirelaboraday.Almostfarmersworkontheirownland.
Spendingonhiringmachinesissohighincomparisonwithoriginalvalueofcapital(1.175millio nVNDcomparesto7.065millionVND).Hiringcattleraisesrentingcostmorethan40
0,000VND/ year.Expenditureonfuel(539,000VND)isequallytoexpenditureonhiringcattlean disaroundhalfofspendingonhiringmachines.Expenditureo n i n s e c t i c i d e s ( 6 5
6 , 0 0 0 V N D / y e a r ) i s highert h a n t w o t i me sexpenditureonherbicides( 2 4 4 , 0 0 0 VND/year).
Theref ore, multi- collinearproblemi s notaseriousprobleminestimatingtheproductionfunction.
Initially.a l l i n t e r e s t e d v a r i a b l e s a r e u s e d t o e s t i m a t e t he F r o n t i e r p r o d u c t i o n function.Theestimationresultispresented intheTable4.2
InHLA 0.005 0.001 4.0 0.00 lnAREA 0.837 0.006 132.8 0.00 lnFSEED —0.004 0.001 -2.7 0.01 lnBSEED 0.014 0.004 3.1 0.00 lnFERT 0.091 0.004 25.0 0.00 lnINSECT 0.028 0.002 11.9 0.00 lnHERB 0.010 0.002 5.0 0.00 lnFUEL 0.009 0.002 5.6 0.00 lnHIRED_MACIONE 0.015 0.001 10.9 0.00 lnJORED_CATTLE 0.007 0.001 4.4 0.00 lnCAPITAL -0.002 0.001 -2.0 0.05
Thechi-squared( a t thebottomo f AppendixB ) likelihood- ratiot e s t i s veryl a r g e Itrejectsnullhypothesis—He: