ESTIMATING THE ROLE OF AGRICULTURAL TECHNOLOGIES IN IMPROVING RURAL HOUSEHOLD WELFARE: A CASE OF MASVINGO

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1 Russan Journal of Agrcultural and Soco-Economc Scences, 2(14) ESTIMATING THE ROLE OF AGRICULTURAL TECHNOLOGIES IN IMPROVING RURAL HOUSEHOLD WELFARE: A CASE OF MASVINGO Smon Munongo, Chtungo K. Shallone, Lecturers Great Zmbabwe Unversty, Zmbabwe E-mal: smonmunongo@gmal.com, sharone_mal@yahoo.com Phone: ABSTRACT To the majorty of Zmbabweans, agrculture s a strong opton for spurrng growth, overcomng poverty, and enhancng food securty. More than 90% of Zmbabwe s rural populace depend on agrculture for ther lvelhoods. Improvng the productvty, proftablty and sustanablty of smallholder farmng s therefore the man pathway out of poverty n usng agrculture for development (WDR, 2008). The adopton of new technologes by the rural communtes world over has been found to ncrease rural lvelhoods and food qualty. In rural Masvngo many technologes have been adopted n agrcultural producton, but the major queston has remaned, has ths mproved the households welfare or t has been nadequately utlzed and wrongly appled to an extent that no gan n welfare has been recorded? To estmate the mpact of technologcal adopton on household welfare n Masvngo cross-sectonal data of 2010 n 560 randomly selected households who responded to the questonnare was utlsed. Usng propensty score matchng (PSM) we found out that households that adopted new technologes had hgh consumpton expendtures and agrcultural ncome hence technology mproves household welfare n Masvngo provnce. KEY WORDS Household; Productvty; Technology; Welfare; Income; Propensty Score Matchng. To the majorty of Zmbabweans, agrculture s a strong opton for spurrng growth, overcomng poverty, and enhancng food securty. Escapng poverty traps n many developng countres lke Zmbabwe depends on the growth and development of the agrcultural sector (World Bank Report 2008). Improvng the productvty, proftablty and sustanablty of smallholder farmng s therefore the man pathway out of poverty n usng agrculture for development (World Development Report 2008). Agrcultural growth and development s not possble wthout yeld-enhancng technologcal optons because merely ncreasng the number of ndvduals on the area under cultvaton through land reform to meet the ncreasng food needs of growng populatons s not suffcent. Agrcultural technologes can help reduce poverty and mprove household welfare through drect and ndrect effects (Moyo et al. 2007). The drect effects of technology on poverty reducton nclude productvty gans and lower per unt costs of producton, whch can rase ncomes of producers that adopt technology. There are also a number of ndrect benefts from technology adopton: dependng on the elastcty of demand, outward shfts n supply can lower food prces; and ncreased productvty may stmulate the demand for labour (Asfaw 2010). The queston for Masvngo provnce remans, however, that, has technologcal adopton mproved the households welfare or t has been nadequately utlzed and wrongly appled to an extent that no gan n welfare has been recorded. Therefore, there has been a longstandng nterest n evaluatng the mpact of mproved technologes on food securty, poverty and welfare of rural communtes of Masvngo. Ths paper ams to contrbute to the lterature by provdng a mcro perspectve on the mpact of agrcultural technology n rural Masvngo households. Assessng the mpact of household technology adopton can assst wth settng prortes, provdng feedback to the government mechansaton programs, gudng government polcy makers and those nvolved n technology transfer to have a better understandng how technologcal adopton help n reducng poverty n rural farmng communtes. Most of the dstrcts of Masvngo are vulnerable to drought. Poverty, reflected n vulnerablty to food and ncome shocks, partcularly due to drought, s endemc n the 67

2 Russan Journal of Agrcultural and Soco-Economc Scences, 2(14) provnce. The characterstcs of Masvngo Communal Areas are smlar to those of most communal areas of Zmbabwe. Its characterstcs nclude poor sols, whch cannot sustan reasonable crop returns wthout applcaton of fertlzer or manure hence mproved technology have a great potental to ncrease yeld. Drought has been occurrng frequently n the past decade; almost n three years out of every fve thus rrgatonal technology s very useful n the area to ncrease output. The land pressure n Masvngo s hgh and accompaned by a hgh populaton growth rate (Murwra 1995). Most of the agrcultural producton reles on ran, wth extremely low use of external nputs, partcularly among the poorest households, who also depend more on agrcultural ncome. Thus agrculture n Masvngo s manly ntensve farmng thus the success of t reles on maxmum land utlsaton whch requres mproved seed varetes, fertlzers and mechansaton. Grazng facltes are poor, leadng to low numbers of cattle, most of whch are n poor condton. The drect mpact s scarcty of draft power, wth less than 60% of the households ntervewed ownng cattle. The net result s low food producton, and exstence of hgh levels of malnourshment. Subsstence agrculture s the manstay of the household economy n the provnce. Other actvtes are tradng n clothng and food, sellng of agrcultural surpluses, crafts, pottery producton and pannng for gold. A sgnfcant porton of households n Masvngo also rely on cash remttances from famly members n urban areas and abroad thus ncreased agrcultural producton wll go a long way n mprovng household ncomes. Agrcultural technologcal adopton s mstakenly perceved as tractor mechanzaton. Agrcultural mechanzaton s defned as the use of any mechancal technology and ncreased power to agrculture. Ths ncludes the use of tractors, anmal-powered and human-powered mplements and tools such as jab planters, as well as rrgaton systems, food processng and related technologes, equpment and new seed varetes. Informaton on the economc mpact of mproved agrcultural technologes s needed to target nterventons effcently and equtably, and to justfy nvestment n such technologes. Ths paper assesses the mpact of mproved agrcultural technologes by constructng a counterfactual comparson group. In ths settng, a comparson of the outcome varable (total household ncome) s made between farmers who adopted technology (henceforth treated farmers) and ther counterparts wth smlar observable covarates (henceforth untreated farmers). ECONOMETRIC METHODOLOGY Estmaton of the welfare gan of adopton of agrcultural technologes based on non expermental observatons s not trval because of the need of fndng on counterfactual of nterventon. We cannot observe the welfare outcome for those farmers who adopted mproved technology had they not had adopted t. However, mproved technology s not randomly dstrbuted to the two groups of the households (adopters and non-adopters), but rather the households themselves decdng to adopt or not to adopt based on the nformaton they have. Therefore, adopters and non-adopters may be systematcally dfferent. Followng the leads of Asfaw (2010) two proxes are used to measure household welfare outcome n ths paper, namely crop ncome and household consumpton expendture. Thus we estmate two welfare outcome functons for adopters and another for non-adopters. The study wll employ non-parametrc technques, namely propensty score matchng (PSM), to overcome the econometrc problems and assess the robustness of our results. Browyn and Moffol (2005) noted that ths provdes a rgorous strategy of dentfyng statstcally robust control groups of non-partcpants. Though the deal evaluaton of a program necesstates the creaton of a treatment or control group t cannot be appled before the ntroducton of the program. Propensty Score Matchng (PSM) as frst propounded by Rosebaum and Rubn (1983) s a method that s used to measure the mpact of a program on the outcome of nterest. PSM s a method used to reduce selecton bas n the estmaton of treatment or program effects wth observatonal data sets. The methodology developed s used to assess a 68

3 Russan Journal of Agrcultural and Soco-Economc Scences, 2(14) counterfactual n a gven set of observatonal data just lke n any scentfc experment where the same sample can be used to assess the mpact on the outcome f the treatment was not admnstered. The effect of treatment evaluaton on polcy formulatons s drect because f an nterventon s successful t can be lnked to desrable socal programs or mprovements n exstng programs through revew. The am of adoptng such a process s to enable polcy makers attan the objectve or goal of nterventon. Accordng to Kasse et al (2010) the standard problem of treatment evaluaton nvolves the nference of a causal connecton between treatment and the ntended outcome. Thus gven a program we observe that: ( Y, X, D ) = 1,... N...(1), where the dependent varable or outcome of nterest sy, X s a vector of ndependent varables and D s a bnary varable ndcatng whether the ndvdual household s a technology adopter or not. The bnary varable takes the form: D { = 1fthehouseholdadoptedtechno log y =...(2) = 0otherwse It s the mpact of a hypothetcal change of D ony, holdng the vector X constant, that s of nterest. In ths case the outcome Y s compared to the treatment and non-treatment states. Snce no ndvdual household s smultaneously observed n both states we cannot use the ones who dd not receve the treatment n the sample as counterfactuals. The stuaton becomes that of mssng data set. The method of causal nference can be tackled by creatng a counterfactual. Therefore the queston we tackle when applyng PSM s to assess how the outcome of an average untreated ndvdual household would change f such a household dd not adopt new technology. The dea of measurng the effects of adopton or treatment requres constructng a measure that compares the average ncomes of the treated and non-treated groups. Rosebaum and Rubn (1983) defne a propensty score as a condton probablty of recevng a treatment gven pre-treatment characterstcs. They show that f the exposure to treatment or adopton of technology s random wthn the cells defned by the values of the propensty score. Therefore gven a populaton or sample of unts the propensty score or the condtonal probablty of recevng a treatment gven X s: ( x) Pr [ D = 1/ x] = E[ D x] p = /...(3) Once propensty scores are known we then can calculate the average effect of treatment on the treated (ATT) as follows: AAT = E = E[ Y1 Y0 / D = 1] ( E[ Y1 Y0 / D = 1, p( x) ]) ( E[ Y D = 1, p( x) ] E( Y )/ D = 0, p( x) / D 1 )...(4) = E 1 / 0 = In equaton 4 Y 1 assumes f the household adopted new technology Y 0 s a counterfactual f the same household dd not adopt technology. The hypothess requres two assumptons: the condtonal ndependence assumpton and the assumpton of unconfoundedness. 69

4 Russan Journal of Agrcultural and Soco-Economc Scences, 2(14) The frst assumpton states that condtonal on X the outcomes are ndependent of treatment. In other words, partcpaton n the adopton of technology does not depend on the outcome. Mathematcally the representaton states that the nterventon outcomes are orthogonal of treatment condtonal on the covarates gven as follows: Y0,, Y1 D / X...(5) The unconfoundedness assumpton, whch n some cases s referred to as balancng condton s necessary f we are to dentfy some populaton measures of mpact (Rosenbaum and Rubn 1983), gven the overlap or matchng assumpton n 3 the assumpton n 5 ensures that for each of the vector X, there exst both treated and non-treated cases. The propensty score measure can be computed gven the data ( D, X ) through a logstc regresson. Our X shows the selecton crtera: Educatonal background of household head; Income; Closeness to the chef s compound; Gender and age of household head; Total area of land utlsed; Whether the land s under rrgaton. Thus for the unconfoundedness assumpton t states that gven the propensty score: D X / p x ( )...(6 ) Equaton 6 states that for ndvduals wth the same propensty score, the adopton of technology s orthogonal or random, thus wth the balancng condton, the condtonal p x : ndependence assumpton gven X mples condtonal ndependence gven ( ) Y Y D / X Y, Y D / 0, Based on the above set of assumptons the PSM technque employs predcted probablty of group membershp that s treatment versus non-treatment group based on observed predctors usually obtaned from a logstc regresson to create a counterfactual group. Usng calculated propensty scores as defned n 3 s not enough to estmate average treatment effects of an nterventon (Deheja et al 2002). The reason s that the propensty score s usually a contnuous varable and the probablty of observng two unts wth the same propensty score s n prncple not possble. The propensty score allows the dentfcaton of farmers of smlar covarates. The man purpose of propensty score s, gven a treated farmer, to fnd an untreated farmer wth smlar characterstcs. Accordngly, the dfference n the outcome varable wll be attrbuted to the treatment, and s denoted the average treatment effect. There are obvously some contentous ssues, manly the overlap and the unconfoundedness assumptons. p( x) SURVEY DESIGN, DATA AND DESCRIPTIVE STATISTICS The data used for ths study s from a prmary data collecton n fve dstrcts of Masvngo. These dstrcts are Chredz, Zaka, Masvngo rural, Chv and Gutu whch have smlar weather condtons. Durng ths survey, dscussons were held wth dfferent stakeholders ncludng farmers, traders and extenson staff workng drectly wth farmers. We dd a random samplng of farm households from each dstrct and 560 famles responded to the questonnare. The survey collected valuable nformaton on several factors ncludng household composton and characterstcs, land and non-land farm assets, lvestock ownershp, household membershp n dfferent rural nsttutons, varetes and area planted, costs of 70

5 Russan Journal of Agrcultural and Soco-Economc Scences, 2(14) producton, yeld data for dfferent crop types, ndcators of access to nfrastructure and rrgaton facltes, household market partcpaton, household ncome sources and major consumpton expenses. Table 1 below shows descrptve statstcs of the respondents, the data shows that 53% of the households adopted new technology where adopton n the study s defned as use of new seed varetes, new machnery and fertlzers durng the farmng season From table 1 the survey also showed that those who adopted new technology on average had better educatonal background, were closer to the market and had agrcultural extenson servces close to ther farmng plots. Table 1. Descrpton, unts, and statstcs for varables ncluded n the study Varable Adopters Non-adopters (N=301) (N=259) Average net crop ncome (n USD) Total household ncome (n USD) Average dstance to vllage market (n km) Dstance to extenson servces (n km) Average household head age (n years) Average household head educaton (n years of schoolng) 11 6 Average ncome from off-farm actvtes (n USD) Access to rrgaton 30 0 The technologcal adopters we also found to have on average less ncome from nonfarmng actvtes than the non-adopters. Ths fndng s because those who are educated have better knowledge on the mportance of adoptng new technologes. Farmers close to the extenson servces get full encouragement to adopt new technologes from the experts hence the fndng that those close to extenson servces adopted new technology better than those who lved far from the servces. RESULTS AND DISCUSSION The logt estmates of the adopton propensty equaton are presented n table 2. The 2 logt model has a McFadden pseudo R value of and correctly predcts 88 percent of adopters and 67 percent of non-adopters. Most of the varables are statstcally sgnfcantly assocated wth adopton of mproved agrcultural technology. Farm sze, occupaton, and educaton are postvely assocated wth adopton. Table 2. Logt estmates of the propensty to adopt agrcultural technology Varables Coeffcent Robust std. error Ln (farm sze) 0.353*** Ln (dstance to vllage market) -0.20*** Dstance to man market -0.33** Dstance to extenson worker -0.47*** Age ** Educaton 040*** Occupaton 0.54** Off-farm ncome Constant *** Summary statstcs Pseudo R-squared Model ch-square Log lkelhood rato Non-adopters correctly predcted 67% Adopters correctly predcted 88% Number of observatons

6 Russan Journal of Agrcultural and Soco-Economc Scences, 2(14) After estmatng the propensty scores for the adopters and non-adopter group we check the common support condton. We fnd that there s consderable overlap n common support. Based on Table 2, among adopters, the predcted propensty score ranges from to , wth a mean of 0.576, whle among non-adopters, t ranges from to , wth a mean of Thus, the common support assumpton s satsfed n the regon of [0.0139, ], wth only a loss of 16 (0.2 percent) observatons from adopters. 2 2 The pseudo- R s 18, 7%. Ths low pseudo- R suggests that the proposed specfcaton of the propensty score s farly successful n terms of balancng the dstrbuton of covarates between the two groups. Table 3. Impact of adopton on crop ncome and consumpton expendture and Rosenbaum senstvty analyss results Mean outcome varables Matchng Algorthms Outcomes ATT (USD) Adopters Non-adopters 1 Crop ncome (3.21)*** NNM Consumpton expendture (3.15)*** 2 Crop ncome (4.01)*** NNM Consumpton expendture (2.14)*** Crop ncome (3.42)*** KBM Consumpton expendture (1.97)*** NNM 1 = sngle nearest neghbour matchng wth replacement, common support, and callper (0.06). NNM 2 = fve nearest neghbour matchng wth replacement, common support, and callper (0.06). KBM= kernel based matchng wth band wdth 0.06, common support, and callper (0.06). Note: ***, **, * s sgnfcant at 1%, 5%, and 10%, respectvely. Table 3 reports the estmates of the average adopton effects estmated by NNM and KBM methods. As a senstvty analyss, the table reports estmates based on the sngle and fve nearest neghbours, and kernel estmator wth one bandwdths. All the analyses were based on mplementaton of common support and callper, so that the dstrbutons of adopters and non-adopters were located n the same doman. As suggested by Rosenbaum and Rubn (1985), we used a callper sze of one-quarter of the standard devaton of the propensty scores. The outcome varables are the net value of crop ncome per hectare and consumpton expendture. Although the two matchng algorthms based on the logt model produced dfferent quanttatve results, the qualtatve fndngs are smlar. The results ndcate that adopton of mproved agrcultural technologes have a postve and sgnfcant effect on crop ncome and consumpton expendture. The ncrease n crop ncome ranges from USD 243 to USD 432 per hectare. Ths s the average dfference n crop ncome of smlar pars of households that belong to dfferent technologcal status (adopters and non-adopters). The ncrease n crop ncome helps adopters to ncrease ther consumpton expendture and thus mprove welfare. Adopton has also mpact on ncreasng the consumpton expendture wth both matchng algorthms technques showng that adopters have hgher average consumpton expendtures. The results show that adopton of technology ncreases ncome and consumpton expendture to households thus ncreasng welfare and reducng poverty. These conclusons are consstent wth recent fndngs by other authors (Kasse et al 2010, Asfaw et al 2010 and Cunguara 2010). REFERENCES [1] Davd, C.C. & Otsuka, K. (1994). Modern rce technology and ncome dstrbuton n Asa, Lynne Rener Publshers, Boulder, CO, USA. [2] De Janvry, A. & Sadoulet, E. (2001). World poverty and the role of agrcultural technology: drect and ndrect effects. Journal of Development Studes, 38 (4), pp,

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