Drought Index Insurance versus Supplemental Irrigation: A Randomized Controlled Trial Experimental Evidence in Northern Ghana

Transcription

1 Drought Index Insurance versus Supplemental Irrgaton: A Randomzed Controlled Tral Expermental Evdence n Northern Ghana Francs H. Kemeze, Unversty of Ghana Abstract In ths paper, we assess va a three years randomzed controlled tral experment the effect of ndex nsurance on the demand for supplemental rrgaton among farmers n Ghana. We fnd a sgnfcant ncrease of the demand for supplemental rrgaton among nsured farmers compared to non-nsured farmers. On the one hand, ndex nsurance does not cover the actual loss and also suffers from the famous bass rsk problem, the ndemnty also appears very lttle to allow farmers to smooth ther consumpton durng drought tmes especally that the trgger of the nsurance s lkely to be assocated wth the rse of the prce of stable food n the local market. Besdes, the far premum s lkely to be hgh f the nsurance s to cover every lttle drought. On the other hand, whle supplemental rrgaton s very good at helpng farmers to overcome two to three weeks dry spells, t s usually not relable durng severe drought. Polcy that couples supplemental rrgaton wth drought ndex nsurance that trggers for severe drought may be at optmal beneft to rsk averse farmers as ths have the potental to reduce bass rsk and the cost of nsurance and rrgaton and to ncrease productvty. Key words: Index Insurance, Supplemental Irrgaton, Drought, Ghana 1

2 1. Introducton The vast majorty of smallholder farmers n sub-saharan Afrca (SSA) are dependent on ranfed agrculture for ther lvelhoods, and they are more often afflcted by the vagares of drought rsk (Elagb, 2014; Gautam, 2006; Hellmuth, Moorhead, Thomson, & Wllams, 2007; Shferaw et al., 2014). In fact, ranfed agrculture provdes about 95 percent of SSA 's food and feed (FAO, 2007) and t s the prncpal source of lvelhood for more than 70 percent of the populaton (Hellmuth et al., 2007). Therefore, for mllons of poor smallholder farmers, drought poses a major challenge that can crtcally restrct optons, lmt development and pull them nto poverty trap. Smallholder famers n Sub-Sahara Afrca (SSA) are ht hardest by drought shocks frst of all because of ther huge relablty on ranfall agrculture and second because the current drought mtgaton strateges adopted by smallholder farmers are not effectve n stablzng agrcultural producton durng a 2 to 3 weeks dry spells 1. Drought s especally pervasve n SSA due to the predomnance of ranfed rather than rrgated agrcultural practces as t s n developed countres (Shferaw et al., 2014). Severe food shortages attrbuted to drought affect mllons of vulnerable households, partcularly those whose lvelhoods depend drectly on agrculture. Smallholder farmers practcng ranfed agrculture n drought-prone areas are forced to adapt ther producton practces to reduce the adverse consequences of drought. These adaptatons can be costly n terms of sacrfced ncome, because they generally favor the choce of safer, but low-return actvtes (World Bank, 2005; Yesuf & Bluffstone, 2009). Rsk of drought prevents farmers from adoptng proftable technologes and practces that are perceved rsky, hence creatng a nexus that ncreases the cycle of vulnerablty and depletes the capablty to overcome hunger and poverty. Smallholder farmers often prepare for the possblty of drought shocks by engagng n conservatve rsk management strateges ex-ante at the cost of low productvty and proftablty gans. Therefore, even though drought may happen only once every fve years, the threat of drought s enough to block farmers nto poverty durng all years good or bad. The effects of drought are further accentuated by the loomng threat of clmate change that s projected to ncrease extreme events and drought frequences n many parts of SSA. Drought s lkely to get worse as IPCC (2007) predcted that the magntude and frequency of drought wll 1 Dry spells are prolonged perods (rangng from one week to several weeks) between ran events wthn the season. The mpact of dry spells on fnal yeld depends on whch crop development stage that s affected. 2

3 ncrease durng the 21st century due to clmate change. Ths wll ncrease the vulnerablty of smallholder farmers that depend on ranfed agrculture. Gven the underlned threats of drought on smallholder farmers lvelhoods n SSA, drought preparedness and adaptaton become a key prorty for any polcy ntended to help smallholder farmers. In developed countres rsk transfer approaches such as nsurance have played a role n mtgatng drought rsk but they have generally not been avalable n developng countres where nsurance markets are lmted and are not orented towards the poor. Recent advances n clmate scence help n the development of a new type of nsurance called weather ndex nsurance 2 that offers new opportuntes for managng drought rsk n areas where such servces were dffcult to delver due to hgh transacton costs related to poor nfrastructure and the classcal adverse selecton and moral hazard problems n provdng fnancal servces. Index nsurance s a type of nsurance that s lnked to an ndex, such as ranfall, temperature, humdty or crop yelds, rather than actual loss whch s dffcult to observe. Access to ths rsk transfer servces can help protect poor farmers aganst clmate varablty whle promotng the uptake of productvty enhancng technologes. Whle the potental benefts of ndex nsurance are great, ts mplementaton can be dffcult (Mranda, 1991). The results of most ndex nsurance plot programs however have been dsapponted, wth the demand dsappearng as soon as the subsdy s elmnated (Farrn & Mranda, 2015). Also, because of the more pronounced nfrastructural and technology gaps n developng countres, there s the dsadvantage that the payoff of the weather dervatve does not perfectly correlate to the actual shortfall n the underlyng exposure. Ths s called bass rsk. Bass rsk refers to the potental msmatch between the ndex trgger and actual on-farm losses. Besdes that, the true beneft of ndex nsurance at the smallholder farmers level s very puzzled as the nsurance does not replace the crop loss. And because of the systemc nature of the event, when t occurs t affects the whole communty, the local market ncluded. So, prce of staple food goes up and that reduces the value of the nsurance pay out and reduces the ablty of smallholder farmers to smooth ther consumpton. Index nsurance polces rarely ssue ndemnty payments due to hgh deductbles and low coverage levels. Investment n water management n ranfed agrculture s another sde of novel drought adaptaton strategy, partcularly n SSA where ranfed agrculture plays such an mportant economc role. Investments that reduce water-related rsks buld more reslent communtes better able to face ncreased occurrence of droughts, and dry spells under a changng clmate (Rockström et al., 2010). 2 Throughout ths study we wll focus our attenton on a specfy case of weather ndex nsurance called ranfall or drought ndex nsurance. 3

4 Supplemental rrgaton s one possble water management nvestment that can help overcomng the challenge of water defct of ranfed crops n sem-ard areas (Rockström et al., 2010; Boggess, Lynne, Jones, & Swaney, 1983; Foud & Erdlenbruch, 2012; Fox, Rockström, & Barron, 2005; Fox & Rockström, 2000; Owes & Hachum, 2012, 2006, 2009; Ya et al., 2012). Supplemental rrgaton s defned as the applcaton of addtonal water to otherwse ranfed crops, when ranfall fals to provde essental mosture for normal plant growth, to mprove and stablze productvty (Fox & Rockström, 2000; Owes & Hachum, 2006, 2012). Supplemental rrgaton (SI) s a smple but hghly effectve technology that allows farmers to plant and manage crops at the optmal tme, wthout beng at the mercy of unpredctable ranfall. All sources of water can be used for SI systems, ncludng runoff harvested water, surface water, underground water, treated ndustral waste water. The characterstcs of SI nclude the followng: (1) Water s appled to ranfed crops 3 whch s normally produced wthout rrgaton. (2) It s appled only when ranfall s nadequate, because ranfall s the prme source of water for ranfed crops. (3) The amount and tmng of SI are not meant to provde water stress-free condtons over the growng season, but to provde enough water durng the crtcal stages of crop growth to ensure optmal yeld n terms of yeld per unt of water (Owes, Hachum, & Jacob, 1999). Supplemental rrgaton contrbutes to smallholder farmers lvelhoods n three ways: (1) mproves yeld, (2) stablzes producton from year to year, and (3) provdes sutable condtons for economc use of hgher technology nputs. The crtcal mportance of SI les n ts capacty to brdge dry spells and thereby reduce rsks of drought n ranfed agrculture n SSA. By reducng rsk, SI provdes smallholder farmers wth the necessary ncentve for nvestments n mproved producton technologes. Many research studes conclude that n area characterzed by alternatng wet and dry seasons, addng small amounts of water durng the wet season can ncrease water productvty many-fold (Owes & Hachum, 2006; Rockström et al., 2010; Zhang et al., 2012). Ths potental of SI must be explored to make better use of the lmted resources avalable (Foud & Erdlenbruch, 2012). Desptes the underlyng contrbutons towards farmers lvelhood, SI s stll a rare nnovaton among smallholder farmers n SSA (Fox & Rockström, 2003). Whle both drought ndex nsurance and SI address the rsk of drought, they do so n very dfferent fashons. As such, smallholder farmers potentally vew drought ndex nsurance and SI as ether 3 Snce ranfall s the prncpal source of water for ranfed crops, supplemental rrgaton s appled only when ranfall fals to provde essental mosture for mproved and stable producton. Supplemental rrgaton s one possble way of overcomng the water defct of ranfed crops n sem-ard areas. 4

5 substtutable or complementary rsk management nstruments, dependng on varous factors such as farmers experence wth drought, whether the rsks are related to crop falure or to the addtonal costs of supplemental rrgaton durng dry spells or the structure of the nsurance contract. Ths paper makes use of the randomzed controlled trals experment to shed lght on the exstng debate whether drought ndex nsurance and supplemental rrgaton as two novel drought rsk management nstruments are substtute or complementary. 2. Weather Index Insurance and Supplemental Irrgaton: Prevous Studes Supplemental rrgaton seems to offer more beneft to farmers than drought ndex nsurance but despte that a lot of attenton have been gven to drought ndex nsurance compared to SI n developng countres. The queston s why s t so? Do farmers prefer most drought ndex nsurance compared to SI? The lterature does not adequately respond ths queston. Very few studes have looked at the nteracton between SI and drought ndex nsurance. Studes that jontly analyzed SI and drought ndex nsurance are: Foud and Erdlenbruch (2012) n France, Buchholz and Musshoff (2014) n Germany, Barham, Robnson, Rchardson, and Rster (2011), Dalton, Porter, and Wnslow (2004), Ln, Mullen, and Hoogenboom (2008) and Mafoua and Turvey (2003) n USA. Foud and Erdlenbruch (2012) n analyzng the way French farmers manage drought rsk found that SI serves as a self-nsurance to farmers. They further found that a farmers decson whether to rrgate (or not) depends on hs decson to purchase nsurance. The purchase of nsurance was found to decrease the probablty of adoptng rrgaton. Thus, the offered yeld nsurance, as they further concluded, may serve to decrease the amount of water used for rrgaton. Buchholz and Musshoff (2014) nvestgated the potental of drought ndex nsurance to cope wth the economc dsadvantages resultng from a reducton n water quotas and ncreased water prces. They dd that by comparng crop portfolos wthout and wth ndex nsurance and they found that the use of ndex nsurance offsets the loss n the farmers certanty equvalent resultng from moderate reductons n water quotas and water prce ncreases. They also found that ndex nsurance has the potental to substantally alter farm plans and the optmal rrgaton water demand. Barham et al. (2011) compared dscrete combnatons of multple-perl crop nsurance and varyng levels of rrgaton n a stochastc smulaton settng for a cotton farm n Texas. Ther fndngs shown that crop nsurance s partcularly benefcal at lower rrgaton levels. Dalton et al. (2004) evaluated the benefts of multple-perl crop nsurance and the nvestment n SI for potato producton n Mane. Usng a bophyscal smulaton model, the authors derved the 5

6 rsk management benefts of SI and crop nsurance over non-rrgated unnsured producton. The authors found that crop nsurance programs are neffcent at reducng producer exposure to weather-related producton rsk. They also found the rsk management benefts from SI to be scale and technology dependent. Increasng the scale of technology adopton ncreases the rsk management benefts of SI. Ln et al. (2008) assessed rrgaton strateges for maze producton n Georga n case of varyng water prces and the avalablty of a precptaton-based weather dervatve. Ther results revealed that the dervatve performs relatvely poorly n terms of ncreasng the estmated certanty equvalent revenues and has no mpact on the amount of rrgaton water used. Mafoua and Turvey (2003) provded a conceptual regresson model usng annual cross sectonal data from New Jersey. They demonstrated that precptaton-based weather dervatves may enable farmers to hedge aganst rrgaton costs n drought years. The lterature does not make t clear whether drought ndex nsurance and SI are substtute or complementarty rsk management tools. The frst lne of research shows that SI and drought ndex nsurance are substtute n the sense that drought ndex nsurance may be used to reduce the amount of water used. The second lne of research demonstrates the beneft of SI over drought ndex nsurance as drought ndex nsurance s neffcent at reducng farmers exposure to drought, performs poorly n terms of ncreasng farmers revenues and has no mpact on the amount of rrgaton water used. The last lne of lterature consdered drought ndex nsurance and SI as complementary rsk management tools because drought ndex nsurance can be used to offset the hgh cost of SI durng drought years. It appears therefore worthy to contrbute to ths nterestng lterature by assessng the mpact of drought ndex nsurance on the demand for SI n developng countres. 3. Methodology 3.1. Expermental Desgn In 2014, wth the support of USAID-BASIS fund a Randomzed Control Trals (RCT) study was undertaken by the Oho State Unversty and the Afrcan Center for Economc Transformaton, n 6

7 collaboraton wth the Unversty of Ghana, n order to nvestgate the mpact of drought ndex nsurance on the demand for SI among smallholder farmer n Northern Ghana 4. Followng a lst of farmers provded by the Rural Communty Banks (RCB), a rural nclusve fnancal servce provder n charge of all the three Northern regons of the study area, and based on a prelmnary feld vst wth ths nsttuton and ther farmers, a selecton of 279 farmer groups out of 791 groups was made. The 279 farmers groups were selected based on the followng fve crtera: 1- Farmer groups that have been n good standng wth the bank n terms of borrowng, potental groups that are qualfed to receve loans and groups that have been dened loan due to low regonal ranfall; 2- Farmers that belong to dstrcts located n low ranfall areas (between mm annually) snce the mpact of nsured loan s more lkely to be seen when ranfall s low; 3- Farmer groups whose prmary or secondary crop s maze snce maze s the prmary crop grown n the northern regons; 4- Farmers groups wth 7-15 members due to budget constrants and logstcs of mantanng smoother feld work; 5- Farmers that take out a loan of less than 10,000 GHC because farmer above ths range s probably not a smallholder farmer especally that t only requres 250 to 300 GHC on average to nvest n one acre of land (Karlan, Kutsoat, McMllan, & Udry, 2011). A baselne survey was then conducted n early 2015 n order to gather household s demographc and socoeconomcs characterstcs necessary to assure smlarty among potental assgned treatments and control groups. Durng the baselne survey an expermental fled survey was also undertaken to elct smallholder farmers WTP for SI. For the baselne survey, sx farmers were randomly selected from each of the 279 farmer groups usng a unform dstrbuton wth the ntent to ntervew the frst three farmers and the second three farmers as back up n case the frst three farmers were unavalable for the ntervew. A total of 777 farmers were ntervewed. Table 1 presents the composton of the sample sze by regon and gender. Table 1: Sample sze by regon and gender Regon Male Female Total Northern Upper West Upper East Total Many other outcomes varable were nvestgated ncludng: agrcultural loan defaults on lenders durng adverse systemc natural events; access to credt among smallholders farmers; nterest rates charged on agrcultural producton loans and uptake of mproved producton technologes among smallholder farmers. 7

8 Based on the nformaton collected from the baselne survey, the 279 farmer groups were randomly assgned nto three groups: 1- Control; smallholder farmers were offered conventonal loans, no drought ndex nsurance 2- Treatment 1; smallholder farmers were offered nsured loans where the farmers themselves were polcy holders and any payouts are made drectly to them 3-Treatment 2; smallholder farmers were offered nsured loans where the bank was the polcy holder and payouts were made to the bank and credted towards the outstandng debt of farmer groups. Randomzaton took place wthn two strata; regon and loan status of the farmers to ensure balance mpact across regon and loan status. Table 2 presents the prelmnary number of farmers wthn regon and treatment. Farmer groups were then nvted to apply for loans (control) or nsured loans (treatment 1 and 2). For both treatment 1 and 2, the nsurance premum was covered n full by the study. The nterventon took place durng the 2015 farmng season followed by a follow up survey of the same farmers who were ncluded n the baselne survey and we also repeated the WTP for SI experment. Table 2: Prelmnary farmers n treatment and control categores by Regons Treatment Northern Upper West Upper East Total Control Treatment Treatment Total Sample Sze, Power Calculatons and Mnmum Detectable Effect Choosng an approprate sample sze n expermental research matter as t ncreases the probablty to detect an effect, assumng there s a genune effect whch s to be detected. Ths s the so called power of the experment. It measures the ablty of a test to reject the null hypothess when t should be rejected. Duflo, Glennerster, and Kremer (2008) defne power of an experment as the probablty that, for a gven effect sze and a gven statstcal sgnfcance level, one can reject the hypothess of zero effect. The power of the experment s affected by the sample sze, the statstcal sgnfcant level and other desgn choces. The mnmum accepted level of power s consdered to be 80 percent, whch sgnfes that there s an eght n ten chance of detectng a dfference of the specfed effect sze (Bloom, 1995; Duflo et al., 2008). The statstcal sgnfcant level (p value) s the probablty of a type I error (that s the probablty that we reject the null hypothess when t s 8

9 n fact true). Usually fve percent s used. The sample sze for our experment s at group level snce each of our farmers belongs to a farmer based organzaton (and more often are member of the same household). Ths s to reduce potental spllover effect. We then need to pck our sample sze such as to mnmze the effect sze taken nto consderaton power 80 percent and statstcal sgnfcant level fve percent. For ths purpose, t s useful to measure precson n terms of mnmum detectable effects (Bloom, 1995, 2006). A mnmum detectable effect s the smallest true treatment effect that a research desgn can detect wth confdence. As we ntend to test the hypothess whether drought ndex nsurance has effect on the demand for SI at farmer level, our sample sze s at ndvdual level. Our outcome varable s a bnary yes or no WTP. Followng Duflo et al. (2008), the mnmum detectable effect (MDE) for ths bnary outcome gven power (κ), sgnfcance level (α), sample sze (N) of dentcal sze (n), and porton of subjects allocated to treatment group (P) s gven by: 1 M N 2 1 MDE (1 ) (1) c p(1 p) N n where M j 2 = t α/2 + t 1 k, for a two sded test; C s the complance rate for the treatment; P s the proporton of treated sample; σ s the varance n the outcome varable; ρ s the ntraclass correlaton of the farmers wthn each group; π s the proporton of the study populaton that would have a value of one for the bnary varable n the absence of the program (Bloom, 1995). In order to calculate the MDE for our experment, we use the baselne survey to estmate the varance for the outcome varable. Table 3 presents the value of the parameters we used to estmate the MDE. Ths s agan another way of ensurng conservatve estmates for our MDEs. When we ncrease the number of farmer groups, our MDEs are lower. The results of the MDE estmatons are presented n Table 4. Table 3: Parameter assumptons for MDE calculatons Parameters Number of members per group (n) 3 Proporton of sample n treatment (P) 0.5 t α/ t 1 k 0.84 Power (k) 0.8 Sgnfcance level (α) 0.05 Share of treatment group actually treated (C) 0.5 9

10 Table 4: MDEs calculatons for bnary outcome varables Intra-class Correlaton WTP for Canal SI WTP for drp SI Sample group sze Sample group sze Sample group sze ( 1 ) The MDE s senstve to the sample sze. The MDE s lower when the sample sze s bgger. We choose our sample sze to be 279 groups (777 ndvdual farmers) roughly equally dvded among the groups Data We use the demand for SI outcomes to assess the mpact of ndex nsurance. The demand for supplemental rrgaton and socoeconomc characterstcs were collected before and after the nterventon. To elct the demand for SI, a Contngent Valuaton Method (CVM) s employed. Snce SI are not yet avalable n Ghana, the CVM method s convenent for ths study. Followng Arrow et al. (1993) recommendatons whch lead to maxmze the relablty of the CVM, we employed a sngle bounded dchotomous choce questons. Desgnng contngent valuaton questons n the form of hypothetcal referenda n whch respondents are told how much they would have to pay for each product before askng them to respond by a smple yes or no answer s used n ths study frst, to mrror the real world market where a prce s gven and the consumer chooses to purchase or not to purchase the product at the stated prce. Second, to avod bas nduced by askng follow up WTP questons as wth double bounded dchotomous choce questons (Arrow et al., 1993; Chantarat, Mude, & Barrett, 2009; Haab & McConnell, 2002). The seasonal subscrpton charged fee per acre s one of seven values whch were determned based on the estmated mean fee per acre of GHC 10

11 20.00, charged for smlar servces n Ghana, Burkna Faso, Kenya and Inda (Alhassan, Looms, Fraser, Daves, & Andales, 2013; Chandrasekaran, Devarajulu, & Kuppannan, 2009; Zongo et al., 2015). The seven bd values used n the study nclude the estmated mean fee wth sx addtonal values that are ±5%, ±15%, and ±25% of the estmated means fee (Bds: GHC 15, 17, 19, 20, 21, 23, & 25). Each of ths bd was randomly assgned to each respondent Study Area The socoeconomc characterstcs presented n ths study nclude: drought experence, rsk and tme preference, wealth and house structure, access to credt, access to extenson servces and dstance to market, household sze and dependency rato, dstance to nearby rver, age, gender, educaton, etc. We presented the holstc descrpton of varables used n ths study n appendx A. Ths study took place n the Northern Savannah zone of Ghana (Northern, Upper East and Upper West) to assess potental drought rsk management tools among smallholder farmers. The choce of the study area s based on ts agrcultural contrbuton to the entre country and the great threat of drought to agrculture, the man actvty n the zone. The Northern Savannah zone s the largest agrculture zone n Ghana. Most of the naton s supply of rce, mllet, sorghum, yam, tomatoes, cattle, sheep, goat and cotton are grown n Northern Savannah. Ths s because the Northern Savannah zone carry two-thrds of the naton s grassland. The Northern Savannah agro-ecologcal zone of Ghana s characterzed by un-modal ranfall of short duraton, hgh ncdence of droughts and excessve evapotranspraton 5 allowng only 4 to 5 months of farmng and 7 to 8 months of extended dry season. Yet agrculture n the zone s predomnantly ranfed wth less than 0.4 percent of the agrcultural land rrgated. As a result, droughts often mpact severely on smallholder farmers lvelhoods n the area (Detz, Ruben, & Verhagen, 2004; Laube, Awo, & Schraven, 2008; van de Gesen, Lebe, & Jung, 2010). The effects of drought on food producton n the area are greater than anywhere else n the country (EPA, 2007; MoFA, 2007). Ranfall varablty n the zone s exacerbated by clmate change, resultng n a rse n the frequency of droughts (Hesselberg & Yaro, 2006). Adaptaton polces wth regard to drought n ths regon have, however, been nsuffcent (EPA, 2012; Yaro, 2013). Fgure 1 presents the map of the study area. 5 Annual potental evapotranspraton s about 2000 mm n the north. 11

12 Fgure 1: Map of the study area 3.5. Statstcal Methods Logt models are often used to analyze expermental research wth bnary outcomes as t s the case of ths study. Pate and Hamlton (1992), Glens (2001), Hu (2003), Duch and Palmer (2004), Frey and Meer (2004), Gertler (2004), Evans and Schwab (1995) are amount the frst studes that employed logt model to estmate the mpact of an nterventon on bnary outcomes. However, novel studes have shown that n a RCT settng, Lnear Probablstc Model (LPM) may be approprate compared to the Logstc model (Deke, 2014). Logstc Model In ths study, we consdered n subjects ndexed by 1,..., n. We randomly assgned n subjects T to the treatment group and n C subjects to the control group, where and s a probablty for a subject to be assgned n the treatment group. Each subject has two responses: T Y f assgned to treatment, and wllng and 0 s not wllng. C Y f assgned to control. The responses are 1 or 0, where 1 s If subject s assgned to treatment (T ), then Y T (0,1 ) s observed and f subject s assgned to control Y C (0,1 ) s observed. Subject cannot be both n the control and the treatment group, as such, only one response s observed for each subject. T C T 12

13 We consdered X to be subject observable covarate, uncorrelated wth the nterventon T. The only stochastc element n the model setup s the randomzaton, condtonal on the nterventon varable T T 1 determnstc (Freedman, 2008)., nterventon, 0, otherwse, the observed response T C Y TY ( 1 T ) Y s We are concerned wth the effect of T on Y, whle controllng for covarates. Freedman (2008) suggested the use of latent (unobservable) random varable dstrbuton s gven by: U for each subject such as the logstc P ( U ) exp( ) /[1 exp( )], (2) Where and exp( ) e. The model assumes that T and X are exogenous, that are ndependent of U. The model assumes that Y 1 f 0 T X U 0 ; Otherwse T X Gven T and X, the condtonal probablty that Y 1s as follows: Y 0. ( 0 TT X X ) e P( Y 1). ( 0 TT X X ) (3) 1 e Followng the logstc transformaton, equaton (3) could be rewrtten as follow: WTP log t 0 Bd X bd X T D T D (4) Now regardng the nterpretaton of coeffcents n the model, consder the log odds when T 1, as well as the log odds T p(,1, X ) log 1 p(,1, X ) C p(,0, X ) log 1 p(,0, X ) Thus, T C T T C when T 0. From equaton (3) we have: X T of success 0 X (5) 0 X X (6) (7) for all, and X. 13

14 However, the logt model vews the responses Y as random as we saw above whch s not consstent n expermental research. Neyman (1923) has shown that n expermental data, Y s observed and determnstc. Freedman (2008) argued that n expermental data, randomzaton does not justfy the use of logt model. So the log odds estmators although already dffcult to nterpret can also be nconsstent n randomzed controlled trals experments. Lnear Probablstc Model Deke (2014) n a Mathematca Polcy Research bref suggested the Lnear Probablstc Model (LPM) as approprate compared to logstc model n calculatng the mpact of a bnary outcomes n a randomzed controlled tral. The LPM has the ultmate advantage that the estmators can be drectly nterpreted as the margnal effect of covarates on the bnary outcome. The man dsadvantage of the LPM n the textbooks s that the true relatonshp between a bnary outcome and a contnuous explanatory varable s nherently nonlnear. That s, the functonal form of the LPM s generally not correctly specfed, whch can lead to based estmates of some parameters of nterest. The reason s that the LPM assumes a constant margnal effect of covarate X for all values of X, but the margnal effect of X almost always vares wth respect to X. Ths msspecfcaton of the functonal form often leads to predcted probabltes that are less than 0 or greater than 1 (Deke, 2014). However, as Deke (2014) demonstrated, t turns out that the dsadvantage of the LPM hghlghted above does not apply to the context of randomzed controlled tral experment. The reason why LPM works well to estmate expermental mpacts s that the treatment status s a bnary varable, not a contnuous varable, whch would be subject to the potental bas descrbed above. Ths means that the functonal form concerns about LPM do not apply to estmatng mpacts under randomzed control tral, snce all that s requred s to estmate two prevalence rates; one for the treatment group and one for the control group (as opposed to estmatng a dfferent prevalence rate for every unque value of a contnuous varable). A second reason that the LPM provdes accurate estmates of expermental mpacts s that any other covarates ncluded n the mpact model are uncorrelated wth treatment status, whch means that the mpact estmate s unbased regardless of whether the correct functonal form s used to adjust for other (possbly contnuous) covarates. The LPM s smply the applcaton of ordnary least squares (OLS) to bnary outcomes nstead of contnuous outcomes as follows. WTP OLS 0 Bd X T D (8) bd X T D 14

15 In equatons (4) and(8) : WTP s the bnary response to the wllngness to pay queston for farmer ; Bd s the proposed prce of the SI to farmer ; X s the vector of household characterstcs; T s a vector of bnary varables representng whether the respondent was ssued drought ndex nsurance last season or not; T and X are parameters n equaton (8) representng mean margnal effects; D s the vector of dstrct dummes and s the error term. Followng the above dscussons, a lnear probablstc model and a logstc model are both used n ths study to nvestgate the mpact of drought ndex nsurance on a bnary hypothetcal demand of SI. 4. Results 4.1. Expermental Integrty: Balance Tests on Varables at Baselne The Table 5 presents the summary statstc and the balance test for the whole sample and for control and treatments groups wth smple mean comparson t-tests at baselne and Table 6 presents the balance test for the WTP varable at baselne wthout and wth complance. On average the respondents s 46 years old and 83 percent of them do not have any formel educaton (Table 7). Male represents 52 percent of the sample. The household s composed of predomnantly nactve members. The average household sze s about 11 members wth a dependency raton of 1.4. That s every actve member of the household s n charge of more than one nactve membes. On average about sx members of the household partcpate n agrcultural labour. Household on average earn GHC from agrculture compared to the average total ncome of GhC Households typcally obtan about 60 percent of ther ncome from agrculture (compared to less than sx percent on remttances). As shown n Table 8, nearly all (96 percent) are relant on ranfall for crop producton. Also 97 percent of household own lvestock. The average lvestock endowment measured by Tropcal Lvestock Unts (TLU) s 3.43 (Table 5). Landholdngs n Ghana are typcally small. Small farms predomnate throughout the country, although they tend to be larger on average n the savannah zones, wth land dstrbuton more skewed closer to the coast. Average langholdng n our sample s 6.20 acres (wth more than 62 percent holdng less than fve acres) whch s consderable hgher than the natonal average of 5.6 acres (Chamberln, 2008). Household s reached by extenson servce offcer about two tmes per season and the average walkng tme to the market s one hour. 15

16 Table 5: Expermental Integrty: Balance Tests on Varables at Baselne Whole Control Treatment 1 Treatment 2 t- Statstc Varables (1) (2) (3) (4) (2) # (3) (2) # (4) Age (12.73) (13.15) (12.01) (13.04) Male (0.50) (0.50) (0.50) (0.50) Household Sze (6.65) (5.73) (8.05) (5.86) Dependency rato * (2.73) (1.90) (2.49) (3.54) Total Income (100) * 1.01 (15.30) (16.12) (15.11) (14.58) Agrcultural Income (100) (8.86) (9.18) (9.19) (8.69) Remttance (100) ** 0.04 (2.23) (2.35) (2.17) (2.34) Savng (100) (0.41) (0.41) (0.40) (0.42) Loan Receved (100) (2.72) (2.89) (2.64) (2.64) TLU (3.47) (3.57) (3.38) (3.45) Dstance Nearest market (0.77) (0.70) (0.74) (0.87) Extenson_Servce (1.64) (1.71) (1.52) (1.69) Drought 2014, Dummy (0.50) (0.50) (0.50) (0.50) Years of drought experences (0.82) (0.81) (0.81) (0.86) Drought_Percepton (0.50) (0.50) (0.50) (0.50) Help durng Drought * 0.03 (2.39) (2.49) (2.14) (2.50) Rsk Preferences Rsk Averse * (0.49) (0.48) (0.50) (0.48) Rsk Neutral (0.34) (0.32) (0.36) (0.34) Rsk Lovng , (0.37) (0.38) (0,33) (0.39) Tme Preference (0.06) (0.05) (0.45) (0.07) Household Labor (4.01) (3.84) (4.87) (3.39) Plots (2.22) (2.40) (2.10) (2.15) Rceland (1.76) (1,36) (1.68) (2.16) Landholdng (6.24) (4.71) (8.00) (5.54) Observatons Note: Standard devaton n parentheses.*sgnfcant at 10 %; Sgnfcant at 5 %; Sgnfcant at 1 %. 16

17 On average 53 percent of farmers experenced drought the prevous croppng season and about 47 percent experenced at least three tmes and 91 percent experenced at least two tmes drought n the prevous fve croppng seasons. On average 53 percent of farmers beleve there wll be drought next comng croppng season. Farmers could call upon two to three members for help f there s drought. Table 6: Expermental Integrty: WTP at Baselne Whole sample Control Treatment 1 Treatment 2 t- Statstc Varables (1) (2) (3) (4) (2) # (3) (2) # (4) WTP Canal CSA (0.41) (0.4) (0.4) (0.4) WTP Drp CSA (0.39) (0.36) (0.41) (0.4) Observatons Wth Complance WTP Canal CSA (0.42) (0.42) (0.44) (0.40) WTP Drp CSA (0.4) (0.4) (0.42) (0.4) Observatons Note: Standard devaton n parentheses. *Sgnfcant at 10 percent; Sgnfcant at 5 percent; Sgnfcant at 1 percent. Table 7: Household level of educaton Level of Educaton Freq. Percent No educaton Prmary school Mddle school Hgh school/unversty Total Table 8: Access to rrgaton Access to Irrgaton Freq. Percent No Yes Total Impact of Drought Index Insurance on the Demand for Supplemental Irrgaton We report the results of the effect of drought ndex nsurance on supplemental rrgaton n three steps, frst, the relatonshp between demand of SI and treatments s presented va smple mean comparson. Second, we report the result of a smple LPM/logstc regresson of the demand of SI 17

18 wth treatment varables and bd value. Thrd, we analyze whether the mpact dffers when allowng for respondents heterogenety. Farmers who actually receved the treatment represent percent of farmers ntally assgned to the treatment. Treatment was contngent on the recevng of loans. Therefore, farmers ntally assgned to control and treatments who could not get credt from the banks are taken out of the analyss. Ths does not have any major mplcaton n the power of our analyss as we consdered 50 percent of complance n the calculaton of the sample sze. Besdes, as presented n the Table 9, complance s almost equally dstrbuted across groups. Table 9: Actual (Prelmnary) farmers nto treatment and control groups by Regons Treatment Northern Upper West Upper East Total Control 57 (103) 12 (27) 69 (131) 138 (261) Treatment 1 75 (96) 30(33) 78 (132) 183 (261) Treatment 2 66(99) 15(24) 90 (132) 171 (255) Total 198 (298) 57 (84) 237 (395) 407 (777) To test whether demand for SI was hgher or lower among treatments, we frst looked at smple mean outcomes post-nterventon. The randomzaton and the fact that the control and treatment samples are well balanced n the observed characterstcs mply that a smple comparson of mean outcomes post-nterventon wll lkely provde an unbased estmate of nterventon mpacts. However, we also control for other observed socoeconomc characterstcs n order to reduce dosyncratc varaton and to mprove the power of the estmates. Table 10 presents the results of a smple comparson of mean outcomes post-nterventon. We observe that the two treatment groups have hgher demand for SI compared to the control group. In treatment one (drought ndex nsurance wth farmer as polcy holder), the demand of SI ncreased by 7.8 percent for canal rrgaton and by 12.8 percent for drp rrgaton. In treatment two (drought ndex nsurance wth bank as polcy holder), the demand for SI ncreased by 13.5 percent for canal rrgaton and by 18.4 percent for drp rrgaton. The dfferences between control and treatments groups are all sgnfcant. Although treatment two appears to have greater effect than treatment one n both canal and drp rrgaton, ther dfferences are not sgnfcant. Also, we could not fnd any sgnfcant dfference of mpact between canal and drp rrgaton, except n the control group where the dfference s sgnfcant at fve percent level. 18

19 Table 10: Mean Treatment Effect Control Treatment 1 Treatment 2 Impact (t-statstc) (1) (2) (3) (1) # (2) (1) # (3) (2) # (3) Canal SI * -13.5*** -5.7 Drp SI *** -18.4*** -5.6 Dff (t-statstc) 4.4** The observatons are percentage. *** p<0.01, ** p<0.05, * p<0.1 For the mpact wth covarates and based on the dscussons provded on the methods of estmaton of mpact n bnary outcomes, we frst estmated a Lnear Probablstc Model (LPM) of WTP for SI wth the outcome varable beng a dummy ndcatng whether the ndvdual was n one of the treatment groups or n the control group. The covarates are socoeconomc characterstcs measured durng the follow up survey whch took place just after the nterventon. The specfc varables ncluded n the model were those hghly correlated wth the dependent varables (dummy WTP) n the control group (Gertler, 2004). These nclude age, educaton, agrcultural ncome, the amount of loan receved, drought experence, rce land share, landownershp. Second we repeat the estmaton now usng the logstc regresson as t s commonly used n the lterature (Angeles et al., 2014; Frey & Meer, 2004b; Goetghebeur, Molenberghs, & Katz, 1998; Pregnancy & Grantees, 2014). We frst consdered only the WTP bd as the only covarate. Table 11 provdes the result of the treatments effect usng LPM specfcaton and Table 12 provdes the result of the logstc specfcaton. We found no strong dfference between the two LPM specfcaton and the logstc specfcaton except a neglgble dfference n the margnal effect of the coeffcents. Table 11: Treatment effect wth Bd WTP LPM Canal SI Drp SI (1) (2) (3) (4) (5) (6) WTP_Bd *** *** *** *** *** *** (0.007) (0.007) (0.007) (0.007) (0.007) (0.007) Insurance_Farmer 0.069* 0.118*** (0.043) (0.044) Insurance_Bank 0.121*** 0.170*** (0.042) (0.043) Insurance_Farmer_Bank (0.037) (0.037) Constant 1.867*** 1.787*** 1.646*** 1.812*** 1.792*** 1.526*** (0.135) (0.141) (0.127) (0.133) (0.142) (0.123) Observatons R-squared Log lkelhood

20 The logstc specfcaton tends to slghtly reduce the margnal effect of the treatment compared to the LPM specfcaton but the dfference s neglgble. For example, the effects of treatment two (Insurance_Bank) are 12.1 percent wth canal and 17 percent wth drp n the LPM model whle ts effects are 11.1 percent wth canal and 15.6 percent wth drp n the logstc specfcaton. Table 12: Treatment effect wth Bd WTP Logstc model Canal SI Drp SI (1) (2) (3) (4) (5) (6) WTP_Bd *** *** *** *** *** *** (0.005) (0.006) (0.006) (0.005) (0.006) (0.006) Insurance_Farmer 0.071* 0.118*** (0.040) (0.040) Insurance_Bank 0.111*** 0.156*** (0.038) (0.037) Insurance_Farmer_Bank (0.037) (0.037) Observatons Robust standard errors n parentheses, *** p<0.01, ** p<0.05, * p<0.1. Fnally, we added the other covarates as presented n Table 13 for LPM specfcaton and Table 14 for logstc specfcaton. We fnd smlar magntude effects of drought ndex nsurance on the WTP for SI as n Table 11 and Table 12. These results cohere wth the complementarty effect hypothess of the effect of drought ndex nsurance on SI. The complementarty nature of drought ndex nsurance and SI that we found can be comprehended n two ways. Frst, farmers wth drought ndex nsurance mght thnk of mplctly nsurng the cost of rrgaton. Drought ndex nsurance have been wdely studed n the past two decades n developng countres, yet ts take up s stll very poor wth demand dsappearng as soon as the subsdy s removed or the plot project s termnated. Farmers probably do not fnd drought ndex nsurance as stand-alone drought rsk management nstrument nterestng enough especally that drought ndex nsurance does not cover the actual loss and also the famous bass rsk problem. The ndemnty also appears very small to allow farmers to smooth ther consumpton durng drought tme especally that the trgger of the nsurance s lkely to be assocated wth the rse of the prce of stable food n the local market. Farmers usually have hgh ncentve to protect ther yeld compared to any other objectve. They do so sometme at the cost of becomng rratonal. Ths s because farmers heavly rely on ther own producton for consumpton and usually express very strong resstance to buy any stable foods that they thnk they should have had from ther farms. More mportantly, buyng stable foods n rural area s a shame because t mples lazness. Therefore, nsurance n ths case only provdes farmers wth the opportunty to afford SI whch s the drought rsk management tools that they really prefer because t helps ncrease ther producton. 20

21 Also, there s a strong correlaton between the cost of rrgaton or the dsrupton of rrgaton and the lkelhood of nsurance beng trggered, as severe drought ncreases both the cost of rrgaton, the dsrupton of allocaton of water for SI and the probablty of nsurance payout. Table 13: Treatment effect wth covarate LPM Canal SI Drp SI (1) (2) (3) (4) (5) (6) WTP_Bd *** *** *** *** *** *** (0.007) (0.007) (0.006) (0.007) (0.007) (0.006) Age (0.002) (0.002) (0.002) (0.002) (0.002) (0.002) Educaton 0.038* 0.044** *** 0.042* (0.021) (0.020) (0.015) (0.020) (0.023) (0.017) Agrcultural Income 0.008*** 0.006** 0.007*** 0.006*** *** (0.002) (0.003) (0.002) (0.002) (0.003) (0.002) Loan Receved *** *** (0.007) (0.007) (0.006) (0.007) (0.007) (0.006) Remttance (0.010) (0.009) (0.008) (0.010) (0.009) (0.007) Drought Last Season 0.127*** * 0.130*** (0.041) (0.042) (0.037) (0.042) (0.043) (0.037) Nearer Rver (0.045) (0.049) (0.045) (0.047) (0.049) (0.044) Extenson Servces 0.036** * (0.014) (0.012) (0.011) (0.014) (0.013) (0.011) Landowned 0.005** ** (0.002) (0.005) (0.002) (0.002) (0.005) (0.003) Share Rce Land ** (0.009) (0.011) (0.007) (0.010) (0.011) (0.007) Insurance_Farmer 0.088** 0.136*** (0.042) (0.043) Insurance_Bank 0.119*** 0.167*** (0.042) (0.043) Insurance_Farmer_Bank (0.036) (0.037) Canal Asked 1st (0.041) (0.041) (0.036) Drp Asked 1st (0.043) (0.042) (0.037) Constant 1.628*** 1.635*** 1.402*** 1.586*** 1.627*** 1.253*** (0.157) (0.164) (0.149) (0.160) (0.167) (0.141) Observatons R-squared Log lkelhood Mafoua and Turvey (2003) found smlar result. The authors employed an economc model to analyze the tradeoff between the loss n revenues from unrrgated crops durng drought and the cost of rrgaton to preserve yelds n perods of drought and smulate drought ndex nsurance n both scenaros. They then came to the concluson that ranfall nsurance can be used to nsure 21

22 aganst the cost of rrgaton. Second, also very close to the frst pont, farmers may be ncreasng ther demand for SI just because they got the nsurance for free. The nsurance was provded to farmers for free as ths s a plot study. Therefore, any ratonal farmer would take advantage of ths free premum to convert nsurance to SI whch offer a more satsfactory opton: that of securng hs producton. The results of ths analyss may be dfferent f one was to consder the cost mplcaton of drought ndex nsurance. Table 14 Treatment effect wth covarate Logstc model Canal SI Drp SI (1) (2) (3) (4) (5) (6) WTP_Bd *** *** *** *** *** *** (0.005) (0.006) (0.005) (0.005) (0.006) (0.005) Age * (0.002) (0.002) (0.001) (0.002) (0.002) (0.001) Educaton ** (0.037) (0.034) (0.024) (0.046) (0.034) (0.026) Agrcultural Income 0.007*** 0.005* 0.007*** 0.005** *** (0.003) (0.003) (0.002) (0.003) (0.003) (0.002) Loan Receved *** *** (0.007) (0.007) (0.008) (0.007) (0.007) (0.008) Remttance (0.010) (0.009) (0.009) (0.010) (0.009) (0.010) Drought Last Season 0.095** * 0.106*** (0.039) (0.040) (0.034) (0.040) (0.041) (0.035) Nearer Rver (0.040) (0.045) (0.043) (0.045) (0.048) (0.047) Extenson Servces 0.033** * (0.014) (0.012) (0.010) (0.013) (0.012) (0.010) Landowned (0.002) (0.005) (0.005) (0.002) (0.005) (0.002) Share Rce Land (0.010) (0.013) (0.015) (0.011) (0.014) (0.014) Insurance_Farmer 0.088** 0.136*** (0.038) (0.038) Insurance_Bank 0.109*** 0.157*** (0.037) (0.037) Insurance_Farmer_Bank (0.036) (0.035) Canal Asked 1st (0.038) (0.039) (0.034) Drp Asked 1st (0.041) (0.040) (0.036) Observatons Robust standard errors n parentheses, *** p<0.01, ** p<0.05, * p<0.1. It appears therefore that t s worth nvestng n SI technology because t mproves the overall profle of the proft dstrbutons of farmers. But the only nconvenence s the rsk of no allocaton of water and the hgh cost of alternatve solutons (.e. underground pumpng water for supplemental 22

23 rrgaton) nvolved durng severe drought year due to the water supply vulnerablty to drought (reducton of runoff and hgh evaporaton rate). Therefore coupled SI for short dry spell wth drought ndex nsurance for long dry spell or severe drought mght help reduce the hgh cost of rrgaton durng severe drought and reduce the cost of nsurance and bass rsk that undermne the take up of nsurance n developng countres Robustness consderaton: Panel Condtonal Logt/Fxed Effects Logt Models For robustness consderatons, we also estmated the panel condtonal fxed effect logt models. In the expermental research, unmeasured dfferences between subjects are controlled for va random assgnment to treatment and control groups. However, Allson (2009) noted that when we have panel data (the same subjects measured at two or more ponts n tme) another alternatve presents tself: we can also use the subjects as ther own controls. Wth bnary dependent varables, ths can be done va the use of condtonal logt/fxed effects logt models. We ran a condtonal logt wth the treatment group usng both the baselne data (before the nterventon) and the follow up data (after the treatment). But we arrved at the same concluson. Farmers after the treatment ncreased ther demand of SI showng a complementary effect between drought ndex nsurance and SI. The only dfference wth the results from the RCT s the magntude of the mpacts, as n the condtonal logt regresson farmers are compared to ther baselne stuaton (control) and n the RCT treatment farmers are rather compared to the control group. The results of the condtonal logt are presented n appendx B. 5. Conclusons Rural and poor smallholder farmers n Sub-Sahara Afrca (SSA) are very senstve to adverse ncome shocks. Drought consttutes the greater adverse ncome shocks n SSA. For mllons of poor smallholder farmers, drought crtcally restrcts optons, lmt development and pull them nto poverty trap. In order to mnmze the negatve threat of drought, households encounter the challenges of selectng the most optmum combnaton of nstruments, from those avalable to them. There s then a need to analyze possble drought rsk management tools that wll allow smallholder farmers to make optmal and productve decsons. To address the ssue, we dentfy drought ndex nsurance and supplemental rrgaton as two potental drought rsk management tools that can help n renforcng farmer s decsons to adopt new productve agrcultural technology n drought prone areas. Index nsurance and supplemental rrgaton have the potental to hedge drought rsk and as such they offer rsk averse farmers the opportunty to nvest more on ther farms. Ths study assess va 23