E vide nc e in Ag ric ulture : Risk C ra ig Mc Into sh (UC Sa n Die g o , AT AI) FAO ESA Se mina r T ue sd a y No ve mb e r 28, 2017
I. Q uic k Intro : AT AI a nd RC T s fo r Po lic y II. Risk a s c o nstra int to a g te c h a d o p tio n III. RC T e vid e nc e o n risk mitig a tio n IV. Ethio p ia c a se stud y with FAO V. C o nc lusio n
Mo tiva tio n Ag ric ultura l te c hno lo g ie s e xist tha t c a n • b o o st p ro d uc tivity • inc re a se p ro fits • fo rtify the fo o d sup p ly We ’ ve se e n a “ G re e n Re vo lutio n,” ye t a g ric ultura l p ro d uc tivity wa s no t tra nsfo rme d e ve rywhe re . • Whe n te c hno lo g y a d o p tio n fa ils -- Why? Wha t p o lic y le ve rs c a n he lp ? • Ho w c a n we imp ro ve sma llho ld e r fa rme rs’ p ro fits a nd we lfa re ? Da ta So urc e : Wo rld De ve lo p me nt Ind ic a to rs, FAO via the Wo rld Ba nk AT AI | EVIDENC E IN AG RIC ULT URE : RISK | 2
Q : Wha t he lp s a nd wha t hind e rs sma llho ld e r fa rme rs’ a doption o f te c hno lo g ie s a nd a c c e ss to ma rke ts? Whic h a p p ro a c he s impa c t fa rme r p ro fits a nd we lfa re ? A: ...we ll, le t’ s ta c kle this sc ie ntific a lly ➔ Re vie w a va ila b le e vid e nc e : id e ntify ke y re se a rc h ne e d s sinc e 2009 ➔ Mo b ilize re se a rc h ne two rks: “ c le a ring ho use ” ra the r tha n c o nsulta nt mo d e l, fund c o mp e titive ly-se le c te d , hig h-q ua lity ra nd o mize d e va lua tio ns ➔ Sha re find ing s: info rm re le va nt d e c isio nma king
Ra ndo mize d Co ntro lle d T ria ls (RCT s) E va lua tio n Pro g ra m E va lua tio n I mpa c t E va lua tio n RCT s: o ne type o f impa c t e va lua tio n RCT s J - PAL | WHY EVALUAT E ? 4
T ype s o f I mpa c t E va lua tio ns O the r me tho d s inc lud e : • Pre -p o st • Diffe re nc e in d iffe re nc e • Ma tc hing • Instrume nta l Va ria b le s • Re g re ssio n Disc o ntinuity he se no n-e xp e rime nta l me tho d s re ly o n b e ing a b le to “ mimic ” the T c o unte rfa c tua l und e r c e rta in a ssump tio ns Ra nd o miza tio n c a n “ c o nstruc t” a c o unte rfa c tua l with me a sura b le o utc o me s J - PAL | WHY EVALUAT E ? 5
Non-random assignment HQ Monthly inc ome , pe r c a pita 1457 1000 947 500 0 J - PAL | WHY EVALUAT E ? 6 T C
Ra ndo mize d e va lua tio ns pro vide a hig hly rig o ro us e stima te o f pro g ra m impa c t Be fo re the pro g ra m sta rts, e lig ib le individua ls a re ra ndo m ly a ssig ne d to two o r m o re g ro ups so tha t the y a re sta tistic a lly ide ntic a l b e fo re the pro g ra m . T wo g ro ups c o ntinue Inte rve ntio n to b e ide ntic a l, e xc e pt fo r tre a tme nt Po pula tio n is ra ndo mly Outc o me s fo r b o th split into 2 o r mo re g ro ups g ro ups a re me a sure d Any diffe re nc e s in o utc o me s b e twe e n Co mpa riso n the g ro ups c a n b e a ttrib ute d to the pro g ra m J - PAL | WHY EVALUAT E ? 7
Random assignment HQ Monthly inc ome , pe r c a pita 1242 1257 1000 500 0 J - PAL | WHY EVALUAT E ? 8 T C
RC T s fo r Po lic y Im pa c t re se a rc h imp o rta nt to id e ntify “ c a usa lity” • Le sso ns fo r p ro g ra m a nd p o lic y d e sig n • Sup p o rts re sults-b a se d ma na g e me nt o f inve stme nts s ha ve b e c o me a wid e ly use d me tho d o lo g y RC T • No t o nly a n a c a d e mic a p p ro a c h • Stro ng d e ma nd b y d e ve lo p me nt p a rtne rs (C G IAR, NARS, O ne Ac re Fund , ma tc hma king e xe rc ise s) s in e c o no m ic s he lp in p a rtic ula r und e rsta nd the ro le o f b e ha vio r a nd RC T institutio ns (a g ric ultura l syste ms) in p ro g ra m/ p o lic y o utc o me s. J - PAL | C EG A | AT 9 AI
Ine ffic ie nc ie s c o nstra ining a g te c h a d o p tio n 1. C re d it ma rke ts 2. Risk 3. Info rma tio n 4. Exte rna litie s 5. Inp ut a nd o utp ut ma rke ts 6. La b o r ma rke ts 7. La nd ma rke ts AT AI | EVIDENC E IN AG RIC ULT URE : RISK | 11
Pre vie w: risk Risk ma tte rs • Mo st inve stme nts in imp ro ve d inp uts inc re a se the fina nc ia l risks o f fa rming • Fa rme rs ma ke c o nse rva tive p ro d uc tio n d e c isio ns to se lf-insure So me p o te ntia l so lutio ns to risk: 1. Fina nc ia l instrume nts: We a the r Ind e x Insura nc e (WII) • Lo w d e ma nd fo r mic ro -insura nc e , in p a rtic ula r we a the r ind e x insura nc e 2. T e c hno lo g y tha t struc tura lly d e c re a se s risks • Risk-mitig a ting c ro p s, irrig a tio n: Pro mising e a rly re sults o n risk-mitig a ting c ro p s 3. C re d it p ro d uc ts with (e xp lic it o r imp lic it) limite d lia b ility in c a se o f we a the r sho c ks 4. Pub lic se c to r sa fe ty ne ts AT AI | EVIDENC E IN AG RIC ULT URE : RISK | 12
Ho w d o e s risk c o nstra in a d o p tio n? • Ag ric ulture is inhe re ntly risky a c tivity – We a the r a nd d ise a se risks a re a g g re g a te , a ffe c ting a ll fa rme rs in g e o g ra p hic a re a • Fa rme rs ma y lo se la rg e p o rtio n o f ha rve st to e xtre me we a the r e ve nt • Witho ut a ny wa y to mitig a te o r insure risks, inve stme nt in c ro p s o r te c hno lo g ie s a p p e a rs to b e a n unsa fe g a mb le – Hig he r-va lue c ro p s ma y a lso b e mo re se nsitive to we a the r • Exa c e rb a te d b y risk a ve rsio n a nd a mb ig uity a ve rsio n AT AI | EVIDENC E IN AG RIC ULT URE : RISK | 13
Pro te c ting fa rme rs thro ug h fo rma l insura nc e • Ag ric ultura l insura nc e to he d g e risk ub iq uito us in d e ve lo p e d c o untrie s – La rg e numb e r o f sma ll fa rme rs, p o o r re g ula to ry e nviro nme nts ma ke mo st tra d itio na l p ro d uc ts ill-suite d to sma llho ld e rs • We a the r ind e x insura nc e a s inno va tio n to insure sma llho ld e rs – Pa yo uts ma d e o n o b se rva b le va ria b le (e .g . ra infa ll) – Avo id s so me d isa d va nta g e s o f c o nve ntio na l insura nc e : le ng thy c la ims p ro c e ss, a d ve rse se le c tio n, mo ra l ha za rd – But ha s b a sis risk: o ffic ia l o b se rva tio n d o e s no t a c c ura te ly p re d ic t fa rme rs’ lo sse s AT AI | EVIDENC E IN AG RIC ULT URE : RISK | 14
Stylize d ind e x insura nc e p a yo ut sc he d ule Ma x Pa yo ut Pa yo ut Pa yo ut inc re a se s with ra infa ll d e fic it Ra infa ll (mm) 15
Ma jo r dra wb a c k to inde x insura nc e : b a sis risk 16
A d e c a d e o f e xp e rime nta tio n o n we a the r ind e x insura nc e • 10 ra nd o mize d e va lua tio ns in va rio us c o nte xts – Ind ia , Ethio p ia , G ha na , Ma la wi – Diffe re nc e s in c ro p s insure d , c o nd itio ns tha t trig g e re d p a yo ut, e tc . – Effe c ts o f d isc o unts, o the r e nc o ura g e me nts to p urc ha se insura nc e – Effe c ts o n p ro d uc tio n d e c isio ns J-PAL 2016 17
De ma nd wa s lo w a t ma rke t pric e s b ut inc re a se d with la rg e disc o unts Ka rla n e t a l 2013; Mo b a ra k & Ro se nzw e ig 2012; “ Ma ke it Ra in” 18
I nsure d fa rme rs to o k mo re risks o n the ir fa rms • Whe n g ive n sub sid ize d insura nc e , fa rme rs to o k o n g re a te r p ro d uc tio n risks – And hra Pra d e sh: Fe we r sub siste nc e c ro p s, mo re c a sh c ro p s – G ha na : Mo re la nd p la nte d to ma ize , g re a te r fe rtilize r use – T a mil Na d u: Shift fro m d ro ug ht-to le ra nt va rie tie s to hig h-yie ld va rie tie s – C hina : Insura nc e fo r so ws c a use d fa rme rs to mo ve into this risky b ut hig hly p ro fita b le c ro p – Me xic o (C ADENA): insure d fa rme rs p la nt mo re the ye a r a fte r a sho c k tha n no n-insure d fa rme rs – Ke nya (IBLI): insura nc e he lp s p a sto ra lists a vo id d e c a p ita lizing live sto c k in re sp o nse to d ro ug ht C a i e t a l. 2015; C a i 2013; C o le e t a l 2014; Ka rla n e t a l. 2013; Mo b a ra k & Ro se nzwe ig 2014; d e Ja nvry e t a l 2016; Ja nze n & C a rte r 2013 19
C a n we via b ly imp ro ve d e ma nd ? • Ma rke ting & T ra ining ? • Pric e Sub sid ie s? • Inte rlinking with C re d it? AT AI | EVIDENC E IN AG RIC ULT URE : RISK | 20
Va ria tio ns o n tra ining , ma rke ting , a nd pro duc t de sig n ha d mo de st e ffe c ts o n ta ke -up • Re la tive ly lo w ta ke -up with vid e o a nd flye r ma rke ting • Fina nc ia l lite ra c y tra ining – Inc re a se d ta ke -up – No t c o st-e ffe c tive • T rust a nd e xp e rie ntia l le a rning – Mixe d re sults o n e nd o rse me nts – O b se rving p a yo uts o ve r time inc re a se d ta ke -up (c o nve rse a lso true ) • G ro up risk-sha ring – So me e vid e nc e tha t p re se nc e o f info rma l risk-sha ring ne two rks inc re a se d d e ma nd C o le e t a l 2014; De rc o n e t a l, 2014; G a ura v e t a l 2011; Ka rla n e t a l 2014; Mo b a ra k & Ro se nzwe ig 2012 21
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