THE DETERMINANTS OF FARM-LEVEL TECHNICAL EFFICIENCY AMONG ADOPTERS OF IMPROVED MAIZE PRODUCTION TECHNOLOGY IN WESTERN ETHIOPIA 1

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1 THE DETERMINANTS OF FARM-LEVEL TECHNICAL EFFICIENCY AMONG ADOPTERS OF IMPROVED MAIZE PRODUCTION TECHNOLOGY IN WESTERN ETHIOPIA AD Alene & RM Hassan Abstract A translog stochastc producton fronter was used to analyse the techncal effcency of small farmers usng mproved maze producton technology n Western Ethopa. The study estmated a mean techncal effcency for the entre sample of 76%, ndcatng that a sgnfcant potental for gans from effcency mprovement n maze producton remans to be exploted even among users of mproved technology. The study also revealed that farm sze, educaton, access to credt and tmely avalablty of modern nputs are mportant determnants of techncal effcency among maze producers n Ethopa. Polces and strateges that promote rural educaton, credt, tmely avalablty of nputs through better nfrastructure and markets wll be greatly nstrumental n realsng consderable gans n maze producton wth avalable farm resources through more effcent and approprate use of mproved technology.. INTRODUCTION The growng food gap n Ethopa has always been attrbuted to the poor performance of the agrcultural sector. In an effort to boost agrcultural productvty, polcy makers have placed substantal emphass on new technologes and ther adopton by farmers. Ths effort was coupled wth a polcy reform programme to reduce the taxaton of agrculture, to lberalse markets and to devalue the currency (Techane & Mulat, 999). The am was to attan food self-suffcency through ncreased use of mproved agrcultural producton technologes, and to allow prvate sector partcpaton and expanson of the extenson servces. The authors are grateful to the Department of Economcs, Adds Ababa Unversty, for provdng the data used n ths study. We also thank an anonymous revewer for comments on an earler verson of ths paper. Department of Agrcultural Economcs, Extenson and Rural Development, Unversty of Pretora, RSA.

2 Whlst varous ncentves have been used to nduce farmers to acheve a hgh rate of adopton of the chosen modern technologes (use of fertlzer, mproved seeds, and chemcal nputs), lttle has been acheved n terms of approprate applcaton and more effcent use of farmers lmted resources. Ths s manly attrbuted to the wrong hypothess that farmers may not be able to select approprate technologes but can nevertheless operate technology effcently when chosen for them. As a result, the feld-level performance of the new technologes has been low. The yeld levels of major cereal crops has remaned too low to justfy the substantal nvestments n the modern nputs used. Mulat (999) argued that cereal yeld ncreased by only 0.3 percent per annum between 990 and 997, and there s no ndcaton that yelds have sgnfcantly mproved snce 994, n spte of the sharp ncrease n the use of fertlzer and other nputs. In a dynamc technologcal and polcy envronment, t s beleved that farmers encounter consderable neffcences before realsng the ntended gans from technologcal progress. In other words, there s a lag between farmers attempt to adjust ther producton decsons to keep pace wth changes n the economc envronment and achevng the ultmate effcent use of ther resources. Al & Byerlee (99) ponted out that agrculture n much of the thrd world has experenced profound changes and can no longer be classfed as tradtonal. In ths new stuaton, the scope for neffcences n resource use s much greater and hence development strateges may need to be reexamned. New technologes demand a new set of sklls and knowledge f ther productvty-enhancng potentals are to be fully exploted. Devatons of farmers' practces from techncal recommendatons, coupled wth system constrants, wll ultmately lead to techncal neffcences. Knowledge of the extent of such neffcences and the underlyng farm-level as well as systemlevel constrants wll help gude polcy makers to ncrease agrcultural producton by enhancng techncal effcency n usng mproved technologes and farm resources. We are unaware of any research provdng nformaton on emprcal measures of the extent of farm-level neffcences and assocated determnants under mproved technology use n Ethopa. The purpose of ths study s, therefore, to quantfy farm-specfc techncal effcency and dentfy ts determnants among small-scale mproved maze producers adoptng mproved technologes n the Bako area of Western Ethopa. The selecton of maze s based on ts mportance, more than any other crop, n terms of producton, area coverage and better avalablty and utlsaton of mproved producton technologes (CSA, 997). The next secton gves a bref revew of prevous

3 studes addressng ths aspect n Ethopan agrculture and presents the analytcal framework. Secton 3 descrbes sources of the data and emprcal procedures. Secton 4 dscusses the emprcal results, wth conclusons and polcy mplcatons dstlled n the last secton.. THE ANALYTICAL FRAMEWORK Early works on the effcency of Ethopan agrculture ranged from those that appled partal measures such as yeld per hectare and output per unt of labour to those that apply tradtonal response functons and programmng methods. Only recently have some attempts been made to measure farm effcency usng the new fronter approaches (see Assefa & Hedhues, 996; Getachew, 995; Getu et al., 998; Corppenstedt & Abb, 996; Mulat, 989; Seyoum et al., 998). However, these studes addressed the techncal effcency of food crop producton under tradtonal technology and lacked methodologcal rgour to the extent that they gnored the heterogenety of farmers n terms of the producton technologes used. Ths study attempts to nvestgate the techncal effcency of a sample of mproved maze producers n a stochastc fronter framework. A method of measurng the productve effcency of a farm relatve to other farms was frst suggested by Farrell (957) usng a producton fronter. A producton fronter s specfed to represent the maxmum output for a gven set of nputs and exstng producton technology. Falure to attan the fronter output mples the exstence of techncal neffcency. Farrell s proposed methodology was, however, determnstc, attrbutng all devatons from ths best practce level of producton to neffcency. Agner et al. (977) and Meeusen & Van den Broeck (977) ndependently proposed the stochastc fronter producton functon to account for the presence of measurement errors and other nose n the data, whch are beyond the control of frms. The stochastc fronter producton functon s gven by: ( ) ( ) Y = F X ; β exp V U () th Where Y s the quantty of agrcultural output of the farmer ( =,,3,..., N), X s a vector of the nput quanttes, β s a vector of parameters, and F ( X ; β ) s a sutable producton functon, V s a random error assocated wth random factors (e.g. measurement errors n producton, weather, luck, etc.). The random errors,v, =,,3,..., N, are assumed to be ndependently and dentcally dstrbuted as Ν (0, σ v ) random varables, ndependent of the U ' s, whch reflect techncal neffcency n producton and are assumed to be 3

4 ndependently and dentcally dstrbuted as half-normal, u ~ N(0, σ u). Jondrow et al. (98) suggested a technque for predctng frm-specfc techncal effcency usng the condtonal dstrbuton of U gven the total dsturbance ε as: ( ε ) E u σσ u v ƒ(.) ε γ = σ F(.) σ γ 0.5 () where ε = V U and f (.) and F(.) represent, respectvely, the densty and cumulatve dstrbuton functons and σ u and σ v are, respectvely, the standard errors of U and V. The maxmum lkelhood estmaton of equaton () yelds estmators for β and γ where σ u γ = σ σ = σ u + σ v. γ explans the total varaton of output from the fronter whch can be attrbuted to techncal neffcency and les between zero and one. v and Battese & Coell (995) proposed a model n whch the techncal neffcency effects n a stochastc producton fronter are a functon of other explanatory th varables. In ther model the techncal neffcency effect for the farmer, U, s obtaned by truncaton (at zero) of the normal dstrbuton wth mean, and varanceσ u, such that µ, µ =Ζ δ (3) where Ζ s a vector of farm-specfc explanatory varables, and δ s a vector of unknown coeffcents of the farm-specfc neffcency varables. In ths study, we apply the Battese & Coell (995) model to estmate the effcency scores and to dentfy the soco-economc and nsttutonal factors nfluencng techncal effcences of maze producers. 3. DATA AND EMPIRICAL PROCEDURES Detaled farm-level producton data for the 999/000 agrcultural year collected through ntensve year-round surveys by the Ethopan Rural Households Survey (ERHHS) were used for ths study. ERHHS s conducted by the Department of Economcs of the Adds Ababa Unversty, Ethopa n collaboraton wth USAID. Specfcally, ths study used the data collected from a sample of 60 farmers n the Bako area n Western Ethopa who produced maze usng fertlzer and mproved seed. 4

5 For the nvestgaton of the farm-specfc techncal effcences of mproved maze producers, the followng translog stochastc fronter producton functon was estmated: lny = β + β ln( X ) + β ln( X )ln( X ) + V U 0 k k kj k j k= k= j= (4) th where Y denotes total maze output of the farmer n kg and Xk, k = j =,,3,4, are the four nput varables ncluded (Land measured as total area planted to maze n hectare; labour, for total pre-harvest famly labour, exchange labour, and hred labour used n man-days; fertlzer, as the total quantty of fertlzer used n kg; and oxen, the number of oxen avalable to the household3. The V ' s are the random varables assocated wth dsturbances n producton, and the U ' s are non-negatve random th varables assocated wth techncal neffcency of the farmer and are obtaned by truncaton (at zero) of the normal dstrbuton wth mean, µ, and varanceσ, such that: u 9 = 0 + m= µ δ δ X m (5) where δ s a vector of the parameters of the neffcency model to be estmated, and the X m ' s, m =,,,9, are the farm-specfc soco-economc varables as well as the nsttutonal factors hypotheszed to nfluence effcency of resource use under mproved maze technology n Western Ethopa. These are: farm sze measured as land planted to maze n hectares; age of the household head n years; extenson vsts pad to the farmer; dstance of the nearest product/nput market from home n mnutes and fve dchotomous (0-) dummy varables accountng for credt for modern nputs, educaton/lteracy of the head of household, tmely avalablty of nputs (0-), plot ownershp (0-) based on whether the maze plot was allocated by local admnstraton and thus belonged to the farmer, and plot qualty (0-) based on whether the maze plot was perceved as fertle by the farmer. In the translog fronter, the elastcty of the mean output wth respect to land s also a functon of the techncal neffcency effects because the model for the techncal neffcency effects s a functon of land, as specfed n equaton (5). In general, the elastctes of mean output wth respect to each of the nputs are defned by: 5

6 k ln EY ( ) µ = βk + βkkln Xk + βkjln Xkj θ, k =,,3,4. ln Xk j= k Xk (6) where µ s defned by equaton (5) and θ s defned by µ µ φ σ φ = σ σ θ σ µ µ Φ Φ σ σ σ (7) where φ and Φ represent the densty and dstrbuton functons of the standard normal random varable, respectvely. The last term n equaton (6) drops out for all varables except land as t also appears n the neffcency effects model. Sgnfcance tests on the estmated elastctes are easly conducted by recoverng ther standard errors from the standard errors of the estmated parameters of the translog fronter producton functon and the averages of the logs of nputs (see Greene, 000, for dervatons). Moreover, tests of hypotheses nvolvng the parameters of the stochastc fronter and neffcency model are conducted usng the generalsed Lkelhood Rato (LR) statstc, λ, defned by: λ = - ln [L(H0)/L(H)] (8) where L(H0) s the value of the lkelhood functon for the fronter model, n whch the parameter restrctons specfed by the null hypothess,h0, are mposed; and L(H) s the value of the lkelhood functon for the general fronter model. If the null hypothess s true, then λ has approxmately a ch-square (or mxed ch-square) dstrbuton wth degrees of freedom equal to the dfference between the number of parameters estmated under the null and alternatve hypotheses (Coell & Batesse, 996). 4. EMPIRICAL RESULTS 4. Parameter estmates and tests of hypotheses The maxmum-lkelhood estmates of the parameters of the translog stochastc fronter producton functon specfed n equaton (4) and the neffcency model specfed n equaton (5) were obtaned usng the computer program FRONTIER 4. (Coell, 994). These results, together wth the output elastctes of nputs, are presented n Table. 6

7 The elastctes of mean output were estmated at the means of the nput varables and the explanatory varables n the neffcency model usng equatons (6) and (7). The output elastctes of land, fertlzer and oxen are postve and sgnfcant as expected. Table : Maxmum-lkelhood estmates of the parameters of the translog stochastc fronter and neffcency model for maze producers n Western Ethopa a,b Varable Parameter ML estmates Elastctes Stochastc fronter Intercept β *** (4.3) - ln (land) β 0.63 (.58) 0.54 **(.3) ln (labour) β -.5 (-.9) -0.09* (.9) ln (fertlzer) β * (.87) 0.4**(.05) ln (oxen) β (-.8) 0.4*(.87) ½ [ln(land)] β 0.0*** (4.67) ½ [ln(labour)] β 0.90* (.85) ½ [ln(fertlzer)] β (0.) ½ [ln(oxen)] β ** (.0) ln (land)*ln (labour) β (-.4) ln (land)*ln (fertlzer) β (.43) ln (land)*ln (oxen) β ***(-5.39) ln (labour)*ln (fertlzer) β * (-.98) ln (labour)*ln (oxen) β 4 0.8(.) ln (fertlzer)* ln (oxen) β (-0.3) Ineffcency model Intercept δ (0.3) Farm sze δ -0.05** (-.66) Age δ -0.0 (-.38) Credt δ 3 -.7* (-.76) Educaton δ *** (-3.75) Extenson δ (0.08) Tmely avalablty of nputs δ 6 -.5** (.37) Plot ownershp δ 7-0.5* (-.67) Market dstance δ (.44) Plot qualty δ (-.) γ 0.98*** (60) σ v 0.33*** (5.3) Log lkelhood 3.60 a ***, sgnfcant at 0.0 level; **, sgnfcant at 0.05 level; *, sgnfcant at 0. level. b The numbers n parentheses represent asymptotc t-ratos, correct to two sgnfcant dgts. As shown n Table, these estmates are 0.54, 0.4, and 0.4 for land, fertlzer and oxen respectvely. The results confrm that these are crtcal 7

8 nputs n maze producton. Unexpectedly, the output elastcty of labour turned out to be negatve but not sgnfcant at less than 0 percent probablty level. An examnaton of the labour data revealed that there was actually excess utlsaton of labour on most maze farms as ndcated by the average labour use of 45 man-days per hectare as opposed to the 0 mandays average labour use per hectare of mproved maze wthn the SG 000 Project (see Seyoum et al., 998). The choce of the emprcal fronter producton functon was made based on the generalsed LR test specfed n equaton (8) wthout havng to mpose any functonal form a pror. Further, other tests of the hypothess nvolvng the parameters of the fronter and neffcency model are conducted usng the same procedure. The test results are presented n Table. Table : Generalsed LR tests of hypotheses nvolvng the parameters of the stochastc fronter and neffcency model for mproved maze producers n Western Ethopa Null Hypothess Test statstc ( λ ) χ - Crtcal value Decson H 0 : β kj = Reject H 0 H 0 : γ = * Reject H 0 H 0 : γ= δ 0 =δ =δ =... =δ 9 = * Reject H 0 H 0 : δ = δ =... = δ 9 = Reject H 0 * The (mxed)χ crtcal values for the hypotheses nvolvng γ =0 are obtaned from Kodde & Palm (986).. The functonal form of the stochastc fronter was determned by testng the adequacy of the Cobb-Douglas model aganst the more flexble translog model. As shown n Table, the frst null hypothess specfyng that the Cobb-Douglas model s an approprate representaton of the data, gven the specfcatons of the translog, was hghly rejected ndcatng that the Cobb- Douglas s not actually approprate. Therefore, the translog stochastc fronter model was used n ths study. The second null hypothess, H0: γ =0, specfes that the techncal neffcency effects are not stochastc. Alternatvely, t tests whether any fronter model s approprate at all as opposed to a tradtonal response functon. As ths s clearly rejected, the test results confrm that the tradtonal response functon s not an adequate representaton for maze producton n Western Ethopa, gven the specfcatons of the translog stochastc fronter and neffcency model. Furthermore, the thrd null hypothess, H0: γ=δ 0 =δ =δ =... =δ 9 =0, specfes that both the parameters of the 8

9 stochastc fronter and the neffcency model are jontly zero such that the neffcency effects are absent from the model. The results show that ths s agan strongly rejected ndcatng that the stochastc fronter model wth the neffcency effects s the preferred model. The last null hypothess, H0: δ = δ =... = δ9=0, specfes that the explanatory varables n the model for the techncal neffcency effects have zero coeffcents. Ths null hypothess s also strongly rejected mplyng that, taken together, the explanatory varables have a sgnfcant mpact on the neffcency effects. 4.. Determnants of techncal effcency The estmates of the coeffcents for the neffcency varables are of partcular nterest n ths study. The estmate of the varance parameter, γ, s sgnfcantly dfferent from zero, whch mples that the neffcency effects are sgnfcant n determnng the level and varablty of maze output of farmers n Western Ethopa. The tradtonal (average) producton functon wth no techncal neffcency effects s thus not an adequate representaton of the data. As shown n Table, the coeffcents for farm sze, credt, educaton, and tmely avalablty of nputs, are negatve and sgnfcant, suggestng that they sgnfcantly and negatvely nfluence neffcency. The negatve nfluence of farm sze on neffcency ndcates that those farmers who operate relatvely large maze plots are less neffcent n maze producton under mproved technology. These farmers are more lkely to explot the potental of mproved varetes and thus make effcent use of ther exstng farm resources by allocatng more land to mproved maze. As expected, access to nput credt, educaton, and tmely avalablty of nputs had neffcency-reducng effect. Assefa (995) also obtaned a postve and sgnfcant mpact of educaton, tmely nput supply, and credt on techncal effcency of crop producton n central Ethopa. The rest of the varables, ncludng extenson, plot qualty, plot ownershp, and age turned out to be negatve and nsgnfcant. Market dstance was postve but nsgnfcant and ths may ndcate that relatvely remote farmers are more lkely to experence neffcency arsng from poor access to nputs and lack of markets for maze. Plot ownershp has turned out to have an neffcency-reducng but nsgnfcant effect. Ths drecton of nfluence s n agreement wth Gavan & Ehu (999) who, based on ther study n Ars regon of Ethopa, found that producton effcency on owned plots was hgher than that on contracted plots even though they attrbuted ths varaton to plot qualty 9

10 dfferences and not to a lower ntensty of use of nputs lke fertlzer and mproved seeds. Contrary to these, however, Corppenstedt & Abb (996) found that sharecroppers are more techncally effcent than owner cultvators n three regons of Ethopa. The results are, therefore, mxed suggestng a need for further nvestgaton. 4.. Techncal effcency estmates The frequency dstrbuton of the techncal effcency estmates obtaned s gven n Table 3. Predcted techncal effcences ranged between 7% and 98%. The results show that 36% of the sample maze producers have techncal effcences greater than 90%, operatng close to the technology fronter. However, about 47% of the sample maze producers have techncal effcency levels below 80% whle the mean techncal effcency of the entre sample was estmated at 76% ndcatng substantal neffcences n maze producton under mproved technology. Table 3: Frequency dstrbuton of techncal effcency estmates for a sample of maze producers n Western Ethopa Level (%) Number of farms % farms Cumulatve % < Mean =76 Mnmum =7 Maxmum =98 Ths suggests that by operatng at full techncal effcency levels, maze producers can ncrease ther producton by an average of 4% wth ther avalable farm resources and mproved technology. The study confrmed that maze producton under new technology nvolves consderable neffcences n Western Ethopa. It s argued that new and mproved technologes can brng about neffcency of producton and contnue untl such tme that farmers acqure enough techncal knowledge and are well ntegrated nto the nput and product markets through better nformaton and nfrastructure, credt and extenson servces (Ghatak & Ingersent, 984). For nstance, Xu & Jeffrey (998) obtaned sgnfcantly lower techncal, allocatve, and economc effcency ndces for hybrd rce producton n Chna as compared wth conventonal rce producton across all the three regons studed. On the other hand, Sngh et al. (000) obtaned 0

11 lower techncal, allocatve and economc effcency for newly establshed Indan dary processng plants after lberalsaton of the dary ndustry compared to the old plants as they needed tme to reach full operaton, the rght choce of products and other manageral sklls requred for hgher performance. 5. CONCLUSION AND POLICY IMPLICATIONS In ths study, the techncal effcency levels of maze producers n Western Ethopa were estmated and the nfluence of factors determnng techncal effcency were measured usng a stochastc fronter producton functon framework. The mean techncal effcency of the sample maze producers was estmated at 76% ndcatng that there are substantal neffcences n maze producton under mproved technology n Western Ethopa. By operatng at full techncal effcency levels, maze producers can ncrease producton by an average of 4% wth the gven nputs and currently avalable technology. An examnaton of the relatonshp between effcency and varous socoeconomc and nsttutonal attrbutes revealed that farm sze, educaton, provson of nput credt, and tmely avalablty of crtcal nputs lke fertlzer, mproved seed and chemcals are mportant factors postvely nfluencng the techncal effcency of maze producers. The results suggest that any attempt to mprove the productve effcency of farmers must gve due attenton to rural educaton, provson of credt for crtcal nputs lke fertlzer, mproved seeds, and chemcals, and tmely supply of modern nputs. Polces and strateges that promote rural educaton, credt, tmely avalablty of nputs through better nfrastructure and markets wll be greatly nstrumental n realsng consderable gans n maze producton wth avalable farm resources through more effcent and approprate use of mproved technology. NOTES. The use of sngle-equaton model n equaton () s justfed by assumng that farmers maxmse expected rather than actual profts as s commonly done n studes of ths type (Zellner, Kmenta & Dreze, 966; Kopp & Smth, 980).. The Cobb-Douglas fronter producton functon was hghly rejected by the data, gven the specfcatons of the translog stochastc fronter producton functon.

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