Evaluating the Impact of Public and Private Agricultural Extension on Farms Performance: A Non-neutral Stochastic Frontier Approach

Transcription

1 Evaluatng the Impact of Publc and Prvate Agrcultural Extenson on Farms Performance: A Non-neutral Stochastc Fronter Approach Arel Dnar (Rural Development Dept, World Bank, USA) Ganns Karaganns (Dept of Internatonal and European Economc and Poltcal Studes, Unversty of Macedona, GREECE) Vangels Tzouvelekas (Dept of Economcs, Unversty of Crete, GREECE) - 1 -

2 Evaluatng the Impact of Publc and Prvate Agrcultural Extenson on Farms Performance: A Non-Neutral Stochastc Fronter Approach 1. Introducton A composte role on output growth could been assgned to agrcultural extenson compared to R&D whch affect productvty only through techncal change (Alston and Pardey). In partcular, agrcultural extenson not only accelerates the dffuson process and the adopton of new technologes but also affects the utlzaton of these technologes by mprovng farmers know how. That s, apart from provdng nformaton about new farmng technques and hgh-yeldng varetes, extenson agents also assst farmers n the development of ther manageral sklls, facltatng a shft to more effcent producton. n ths sense t can narrow the gap between current and the potental productvty, gven the exstng set of technology and management alternatves. Thus, n n a contnuously changng economc envronment agrcultural extenson has the potental of enhancng the effcency of farm operatons and thus affectng the overall productvty. The lterature on the mpact of agrcultural extenson on farm productvty has followed two dfferent tracks. The early emprcal studes (e.g., Patrck and Kehrberg; Huffman) hnged on the estmaton of a producton functon, where extenson had been consdered as a separate factor of producton (for a revew of these studes see Brkhaeuser, Evenson and Feder). Ths producton-based approach assumes that farms are operatng at techncally full effcent levels and thus do not knowngly waste resources. If, however, they waste resources but are gnorant of dong so, ths s only due to lack of knowledge. Wthn ths approach the mpact of extenson on farms performance s evaluated through ts margnal product and n a sense captures ts drect effect on output. On the other hand, n the context of stochastc fronter models, the analyss of agrcultural extenson took a dfferent drecton. As the - -

3 assumpton of techncal effcency was relaxed, extenson was used as a factor explanng ndvdual techncal effcency levels rather than as an nput n the producton functon (e.g., Kalrajan; Kumbhakar, Ghosh and McGuckn; Bravo-Ureta and Evenson). Wthn ths approach, the mpact of extenson on farm product s ndrect as t s evaluated through the potental output gan due to elmnaton of techncal neffcency. Even though nformatve at that tme, both approaches can be crtczed as ncomplete snce t s ntutvely more appealng to nclude extenson both n the producton functon and n the neffcency effects functon. The man objectve of ths paper s to ntegrate both approaches nto a sngle framework usng the noton of non-neutral stochastc producton fronter proposed by Huang and Lu and Kalrajan and Obwona. The ntuton behnd the proposed formulaton s that dfferent levels and/or sources of extenson (.e., publc, prvate, or both) may nfluence output dfferently causng a dversty n nput productvtes as well as n the margnal rates of techncal substtuton among frms. Consequently, the effects of techncal neffcency on nput productvty may be greater on some nputs than on others mplyng that the estmated fronter would more accurately be modeled as a non-neutral shft of the tradtonal average producton functon. Moreover, each of the aforementoned approach can be retreved as a specal case wthn the proposed formulaton. That s, formal statstcal testng s used to check whether extenson should be ncluded only n the producton functon or n the neffcency effect functon, or n both. In the latter case, an emprcal evaluaton of both the drect and ndrect effects of agrcultural extenson can be obtaned. Unlke prevous studes, a clear dstncton s drawn between publc (non-fee) and prvate (pad) sources of extenson. Ths s essental consderng the sgnfcant changes that took place worldwde n the last twenty years concernng the structure of extenson systems and the nvolvement of publc and prvate sectors n fnancng and provdng extenson servces (Dancey; Dnar, 1989; 1996). 1 Dscrmnatng among dfferent farmers choces enables the dentfcaton of potental benefts that may arse from dfferent sources of extenson provson for ndvdual farm productvty (Dnar and Keynan). In that respect, t would be mportant from a polcy pont of vew to obtan an emprcal comparatve evaluaton of the ndvdual effects (drect and ndrect) that publc and prvate extenson servces may have on farm productvty. Indeed we attempt to document the hypothess of complementarty between publc and prvate extenson servces. The analyss s based on a sample of - 3 -

4 65 farms n Crete, Greece durng the perod and on a modfed translog producton fronter functon to account for possble zero entres as some farms n the sample were not exposed at all to any knd of extenson servces. The rest of the paper s organzed as follows. Secton presents the theoretcal framework and the emprcal specfcaton of the model. The data used n ths study are dscussed n Secton 3 and the emprcal results are analyzed n Secton 4. Secton 7 summarzes and concludes the paper.. Theoretcal Framework Let assume that the stochastc producton fronter model has the followng general form: ( X,EX ;α ) exp( ε ) Y = f (1) where, Y R ++ s the (Nx1) vector of farm output, X R+ s the (NxJ) matrx of the appled nputs n the producton process, EX R+ s the (Nx) vector of extenson vsts (publc and prvate) n the farm, f ( ) s the best practce producton fronter, α s the (Jx1) vector of the technology parameters, =1,,, n and j=1,,, J denote farms and appled nputs, respectvely, and ε s the (Nx1) vector of the composed error term consstng of two ndependent elements such that ε v u. The component v s a symmetrc..d. error term that represents random varaton n output due to factors not under farmers control (weather, dseases, etc.) as well as the effects of omtted explanatory varables, measurement errors, and statstcal nose. The component u s a non-negatve error term representng the stochastc shortfall of farms output from the producton fronter due to techncal neffcency. Thus, techncal neffcency s defned n an output-expandng manner ndcatng the maxmum amount by whch output can be ncreased gven the producton technology and observed nput usage. In the above general setup we can only measure the drect effect of extenson on agrcultural producton through ts ncluson n the producton fronter together wth physcal nputs. The dentfcaton of the ndrect effect requres knowledge of the mpact of extenson on ndvdual techncal neffcency. Ths can be done by adoptng Huang and Lu s generalzaton of the stochastc fronter model. In partcular, u n (1) can be replaced by a lnear functon of extenson and farm-specfc characterstcs that presumably affect ndvdual farmers performance. Specfcally, - 4 -

5 ( Z,EX ) ω u = g ;ζ + () where Z s the (NxM) matrx of the m=1,,, M farm-specfc characterstcs (.e., age, educaton, sze) that are assumed to affect techncal neffcency, ζ s the (Mx1) vector of the parameters to be estmated and, ω s an..d. random term defned by the truncaton of the normal dstrbuton such that ω [ g( Z,EX ;ζ )]. 3 Ths mples that the one-sded error term follows a truncated half-normal dstrbuton wth mean ( Z,EX ) µ = g ;ζ and constant varance. Under ths assumpton farm-specfc estmates of output-orented techncal effcency can be derved from the condtonal expectaton of u upon the observed value of ε. Gven the above specfcaton of the producton fronter t s possble to evaluate both drect and ndrect effects of agrcultural extenson on ndvdual farms output. That s, after substtutng () nto (1) and dfferentatng t wth respect to the extenson varable (publc or prvate), we get: E Y EX ( ) f ( X,EX ; α ) E{ g( Z,EX ; ζ )} = EX EX (3) where the frst-term on the RHS s the drect effect of extenson on farms output,.e., ts mpact on the productvty of conventonal nputs, whle the second-term s the ndrect effect of agrcultural extenson,.e., ts mpact on the effcent utlzaton of the avalable resources. If farmers are techncally effcent the ndrect effect s zero and the mpact of extenson on farms productvty s determned solely by the drect effect. However, the ndrect effect could also be zero n the presence of techncal neffcency as long as the later s ndependent of extenson servces. To provde quanttatve estmates of the drect and ndrect effects of (publc and prvate) extenson on farm output, (1) s modeled as a modfed translog,.e.,: ln y = α d = 1 1 j= 1 β SD 4 d 4 j= 1 l= 1 α ln x j d lj + j l= 1 δ ln x δ EX j + l 1 EX ( λ ) l 4 j= 1 k= 1 ( λ ) ( λ ) ( λ ) l 4 + v α 1 + jk u ln x l= 1 h= 1 j ln x lh k δ EX + β l Ar EX AR h + β At AT (4a) and - 5 -

6 9 = 9 0 m m l l lh l h m= 1 l= 1 m= 1 l= 1 ( ) ( ) ( ) ( ) λ λ λ λ u ζ + ζ z + ζ EX + ζ EX EX + ζ z EX + ω (4b) ml m l where, =1,,..., 65 are the number of farms n the sample, j,k=1,,..., 4 are the physcal nputs appled n farm producton, AR refers to an ardty ndex, AT to an alttude ndex, SD to sol dummes, l=1, are the publc (non-fee) and prvate (pad) extenson contacts, and m=1,,, 9 are the farm-specfc varables assumed to affect farmers techncal neffcency levels. In order to deal wth zero values and at the same tme obtan unbased estmates of the fronter functon parameters, publc and prvate extenson vsts are transformed usng the Box-Cox transformaton functon, ( ) λ λ.e., : EX ( EX 1) / λ. = After substtutng (4b) nto (4a) the resultng model can be estmated by a sngle-equaton estmaton procedure usng the maxmum lkelhood technque. Followng Battese and Broca, the varance parameters of the lkelhood functon can be estmated n terms of σ σ v + σ u and γ σ u σ. 4 The rato-parameter γ takes values between zero and one. The closer the estmated value of γ to one, the hgher the probablty that techncal neffcency s sgnfcant n explanng output varablty among sample partcpants. Gven however the transformaton of the extenson varables and the resultng non-lneartes, the fnal estmaton was carred out as n Gannakas, Tran and Tzouvelekas by usng a b-dmensonal grd search around the 0- range for the value of λ. The producton-based and the neffcency effect approaches on modelng the mpact of extenson on farm output can be retreved as specal cases of the general formulaton (1) and () by mposng the approprate parametrc restrctons on (4a) and (4b). In partcular, a strct verson of the former results f γ ϑ0 = ϑ = ϑ = ϑ = ϑ = 0 for all m, l, and h, whch mply that the systematc = m l h ml techncal neffcency effects are zero and consequently, each farm n the sample operates on the fronter. 5 These restrctons arse from Welch argument that snce extenson s consdered as the only source of knowledge for farmers, ts ncluson n the average producton functon makes the concept of techncal effcency somethng of a tautology. A less restrctve verson of the producton-based formulaton may be obtaned by assumng that ϑ = ϑ = ϑ = 0 for all m, l, and h, whch allows for l h ml techncal neffcency but ndependently of extenson. On the other hand, the - 6 -

7 neffcency effect approach results f δ δ = δ = 0 for all l, h and j. Apparently, l = lh all prevous studes used a qute smpler formulaton where also ϑh = ϑ ml = 0 for all for all m, l, and h. Usng (3) and (4a), the drect effects of publc or prvate extenson on producton concde wth ther output elastctes. 6 lj That s, e l 4 λ ( λ ) λ 0. 5 δ lh EX l EX h δ lj EX l ln x j h= 1 j= 1 λ = δ ΕΧ + (5) l l whch also depends on conventonal nputs usage. The second-term on the RHS of (8) provdes the potental nteracton effect between publc and prvate extenson on farm productvty. 7 On the other hand, the ndrect effect of publc or prvate extenson on farms output through ts mpact on ndvdual techncal neffcency levels s obtaned by usng (3) and (4b) as: ln E ( u ε ) lnex = ξ ζ l EX λ l ( λ + ζ ) lh EX λ l EX h + ζ lm EX 9 m= 1 λ l z m (6) where ) 1 φ( ρ σ u ) φ( ρ ξ = 1 σ u, ρ = µ u ( ) σ and φ ( ) and Φ () are the Φ ( ρ σ u ) Φ ρ probablty and the cumulatve densty functons of the standard normal varable. Farm-specfc estmates of output-orented techncal neffcency are obtaned drectly from the estmated mean and varance of u I as follows (Battese and Broca): TE O = E { exp( u ) } ( µ ) [( ) 0 Φ µ σ o σ o Φ ( µ ) σ o +. σ ε = exp o 0 ] (7) 0 σ v µ σ u ε σ σ where µ =, σ = o and E s the expectaton operator. σ + σ σ + σ v u u u One can easly calculate from the above the correspondng varance and thus the standard errors of E exp( ) ε } by lnearzng (7) (Tsonas). Usng these standard { u errors we can statstcally examne by smple two-tal test whether there are dfferences n the predcted techncal neffcency between farms wth dfferent choce of extenson provson (Mester). v v - 7 -

8 5. Data Descrpton Data used n ths study are part of a broader survey on the structural characterstcs of the agrcultural sector n Crete fnanced by the Regonal Drectorate of Crete n the context of the Regonal Development Program (Lodaks). The sample conssts of 65 randomly selected farms located n the four major dstrcts of Crete, namely Chana, Rethymno, Heraklo and Lasth, durng the perod. The survey provdes detaled nformaton about producton patterns, nput use, average yelds, gross revenues, structural characterstcs and the number of vsts of both prvate and publc extenson agents to the farm. Descrptve statstcs are provded n Tables 1 and. 8 Durng the year of the survey, extenson servces n Crete were provded by 18 publc (non-fee) and 7 prvate (pad) agences. Publc extenson outlets consst of the 10 Regonal Offces of the Agrcultural Extenson Drectorate and 8 Agrcultural Expermental Statons located n the major rural areas of the sland (Table 1). Prvate outlets are of smaller capacty, exhbtng however, a wder dsperson around Crete. Ths s evdent from ther average dstance from the surveyed farms presented n the mddle panel of Table 1. Specfcally, average dstance of publc outlets from the surveyed farms s 39 km, whereas the correspondng value of prvate outlets s nearly four tme lower, 10 km. Publc outlets employ 67 persons, whle n prvate outlets the correspondng fgure s 314 persons. However, on the average extenson agents per prvate outlet are 4 persons, consderably lower than the correspondng value for publc outlets whch s 14 persons. Prvate extenson agents are more educated than ther publc counterparts as average educaton levels are 13.4 and 11.5 years, respectvely. However, the majorty of publc agents are very close to the retrement age (the average experence of publc extenson agents s 8.9 years, whle that of prvate extenson agents s only 14. years). Fnally, average offce hours per day avalable for farmers are 6.8 and 5.4 hrs/day, respectvely n prvate and publc outlets. Ths s due to the fact that prvate agences are usually open n the afternoon. Concernng farmers choce the data presented n the mddle panel of Table 1 reveal that 37.7 percent of the surveyed farms (100 farms) prefer exclusvely publc agences, 17.4 percent (46 farms) prvate agences, 33. percent (88 farms) both publc and prvate agences, whle the remanng 11.7 percent (31 farms) do not report any extenson vst durng the year of the survey

9 Concernng farmers perceptons of the qualty of the provded servces, 46.8 percent of the farms vsted by publc extenson agents perceve them as very good, 37.8 percent as good, and the remanng 15.4 percent as poor (lower panel of Table 1). At the same tme, only 4.5 percent of the farms vsted by prvate extenson agents regard the provded servces as poor, 41.0 percent as good and the other 54.5 percent, as very good. It seems therefore that despte the fact that the majorty of Cretan farmers prefer publc extenson for obtanng techncal nformaton, they rate the provded servces as less satsfactory than those provded by prvate extenson agents. Table presents some basc soco-economc and structural characterstcs of the surveyed farms by extenson source. From the data presented, t s evdent that large ntensve farms more specalzed n ther producton and wth hgher debts are vsted manly by prvate extenson agents. It s nterestng that average producton value s 406 /stremma for farms vsted by prvate extenson agents, 3 /stremma for those vsted by both publc and prvate extenson agents, 85 /stremma for those vsted by publc extenson agents and only 44 /stremma for those that do not use extenson at all. Furthermore, older farmers who are n general less educated than ther younger counterparts are vsted manly by publc extenson agents. The average age and educaton level of farmers usng publc extenson s 56 and 7.8 years, respectvely, whereas for farmers usng prvate extenson the correspondng values are 45 and 9. years. The possble correlaton between publc extenson and the level of subsdes can be explaned by the fact that publc extenson agents are responsble for the mplementaton of the varous CMOs n the context of CAP. Fnally, farms enjoyng more favorable envronmental condtons seems to prefer prvate extenson. Farms vsted by prvate extenson agents are manly located n sandy and lmestone sols, whch are generally more productve, wth hgher average annual temperature. The dependent varable n (4a) s the annual farm producton measured n euros. The aggregate nputs ncluded as explanatory varables are: (a) farms total land measured n stremmas (1 stremma equals 0.1 ha); (b) total labor, comprsng hred and famly (pad and unpad) labor, whch ncludes all farm actvtes related to farm producton, measured n hours; (c) other ntermedate nputs, consstng of pestcdes, fertlzers, fuel and electrc power, rrgaton taxes, and other mscellaneous expenses, measured n euros, and; (d) total captal nputs ncludng machnery and equpment, - 9 -

10 measured n euros; (e) an ardty ndex (AR ) defned as the rato of the average annual temperature n the regon over the total annual precptaton (Stallngs); (f) the alttude of farms locaton (AT ) n meters; (g) three sol-dummes (SD d wth d=1,, 3) to dstngush sol qualty among farms (sandy, lmestone, marls); (h) publc and prvate extenson servces ( λ ) ( ) EX measured as the number of vsts to the farm. l Aggregaton over the varous components of the above output and nput categores (except for land nput) was conducted usng Dvsa ndces wth revenue and cost shares servng as weghts. Furthermore, to avod any problems assocated wth unts of measurement, all (but the dummy) varables were converted nto ndces wth the bass of normalzaton beng the representatve farm. The choce of the representatve farm was based on the smallest devaton of the varables (.e., output and nput levels) from the sample means. Concernng the varables ncluded n the neffcency effects model n (4b) these nclude: (a) farmers age measured n years; (b) farmers formal educaton measured n years of schoolng; (c) farms tenancy status measured as the share of leased or rented land to total farm s land; (d) farms debts measured n euros; (e) total amount of subsdes receved by farmers n the context of the CAP measured n euros; (f) the total off-farm ncome arsng from non-farm actvtes of the household, measured n euros; (g) the share of self-consumpton of total farm produce; (h) the degree of farms specalzaton measured as a Herfndhal ndex; 9 (g) a dummy varable ndcatng ntensve farmng operatons. The modfed translog stochastc fronter and neffcency effects models n (4a) and (4b) were jontly estmated usng the maxmum lkelhood methodsnce the employed data set was generated by an unknown technology, the regularty condtons, apart from symmetry, were assumed rather than mposed. 6. Emprcal Results ML Estmates The parameter estmates of the producton fronter and neffcency effects model along wth ther correspondng standard errors are reported n Table 3. The Box-Cox transformaton parameter, λ, that mnmzes the logarthm of the lkelhood functon, was found to be whch s statstcally sgnfcant at the 1 percent level. At the pont of approxmaton (.e., sample mean), the modfed translog producton fronter

11 s well-behaved satsfyng all regularty condtons, namely postve and dmnshng margnal products (the frst-order parameters are all between zero and one, whle the bordered Hessan matrx of the frst- and second-order partal dervatves s negatve sem-defnte). The varance parameters, σ and γ, are shown n the lower part of Table 3. The * true varance for the one-sded error term, σ, computed from these estmates, was * found to be and that of the statstcal nose, σ, (see footnote 9). The rato-parameter, γ, s postve and statstcally sgnfcant at the 1 percent level, ndcatng that the techncal neffcency s lkely to have an mportant effect n explanng output varablty among Cretan farms. Specfcally, the computed varance-rato, γ *, (see footnote 9) mples that 67.8 percent of the total varablty of output produced s due to techncal neffcency, whereas the remanng porton (.e., 1- γ * =0.3) s due to measurement errors, specfcaton bases and factors that are not ncorporated n the stochastc fronter and neffcency effects models. Several hypotheses concernng the specfcaton of the producton fronter model n (4a) and (4b) are examned usng the lkelhood rato test and the results are presented n Table 4. Frst, restrctve functonal forms such as the Cobb-Douglas specfcaton s rejected at the 5 percent level of sgnfcance usng lkelhood rato test (1 st hypothess n Table 4). In addton, the hypothess that the average response functon (.e., u =0) adequately represents the data set s rejected regardless of whether neffcency effects are present 10 ( nd hypothess n Table 4) or absent (3 rd hypothess n Table 4) n the producton functon model. Thus, the exstng degree of techncal neffcency s an mportant factor n explanng output varablty among farms n the sample. Moreover, the stochastc fronter model cannot be reduced ether to the Agner, Lovell and Schmdt half-normal or to the Stevenson truncated half-normal specfcaton as the respectve null hypotheses are rejected at the 5 percent level of sgnfcance (4 th and 5 th hypotheses n Table 4, respectvely). Fnally, the hypothess that the estmated coeffcents of the varables ncluded n the neffcency effects model n (4b) are jontly equal to zero s also rejected at the 5 percent level (6 th hypothess). u v

12 Hence, agrcultural extenson (publc or prvate) together wth the socoeconomc and structural characterstcs of Cretan farms are lkely to have an mportant role n determnng the economc performance of Cretan farms. An mportant queston that arses, however, s the nature of the effect of agrcultural extenson and ts dstncton between publc and prvate sources. Statstcal testng also presented n Table 4 examnes ths ssue. Frst, the hypothess that all the parameters assocated wth agrcultural extenson (publc and prvate) n both the producton fronter and neffcency effects model are jontly equal to zero s rejected at the 5 percent level of sgnfcance (7 th hypothess n Table 4). The LR-test statstc also rejects the hypotheses that agrcultural extenson (publc or prvate) affects ether drectly (8 th hypothess) or ndrectly (9 th hypothess) economc performance of farms. The same concluson can be drawn f the effect of agrcultural extenson s dscrmnated between publc and prvate sources. Specfcally, the jont drect and ndrect effect of ether publc or prvate agrcultural extenson servces on farm productvty s mportant and statstcally sgnfcant at the 5 percent level (10 th and 11 th hypotheses n Table 4, respectvely). Ths s also true when drect and ndrect effects are examned separately (hypotheses 1 through 15). Concernng the nature of these effects the relevant hypotheses testng presented n Table 5 suggests that these are non-neutral regardless of the source of extenson (hypotheses 16 through 1). Fnally, t seems that there exsts an mportant and statstcally sgnfcant nteracton between publc and prvate extenson servces concernng both ther drect and ndrect effect on farms productvty as the hypotheses through 4 n Table 4 are rejected at a sgnfcance level of 5 percent. Thus, statstcal testng emprcally valdates the assumpton made at the outset concernng the mpact of agrcultural extenson on farms productvty levels. The results suggest that publc and prvate extenson servces affect both drectly and ndrectly the productvty of Cretan farms through ther effect on the margnal productvtes of physcal nputs as well as on the attaned techncal neffcency levels. Producton Structure and Agrcultural Extenson - 1 -

13 Based on the modfed translog parameter estmates, reported n Table 3, we computed basc features of the producton structure, namely output elastctes and returns to scale. Average values of these estmates over farms are presented n Table 5 by extenson source. Frst, Cretan farms n the sample exhbt, on average, decreasng returns to scale regardless of ther choce of extenson servces. Ths fndng s consstent wth statstcal testng that rejects the constant returns to scale translog (.e., lnearly homogeneous producton technology) at any conventonal level of sgnfcance (see last hypothess n Table 4). The small sze of Cretan farms (5.1 ha on average) s probably responsble for the exstence of dmnshng returns. The lowest RTS value s observed for farms that are not vsted at all by extenson agents (0.7510) and the hghest for those farmers vsted only by prvate extenson agents (0.8431). Estmates of producton elastctes of physcal nputs usng Eq. (7) ndcate that land has contrbuted the most to Cretan farm producton, followed by labor, ntermedate nputs and captal. Ther magntude nonetheless vares wth extenson source. Farms vsted exclusvely by prvate extenson agents exhbt the hghest land and labour elastcty value (0.357 and , respectvely) and the lowest elastcty wth respect to captal (0.089). On the other hand, farms wth no extenson exhbt the hghest elastcty value wth respect to captal (0.1777) and ntermedate nputs (0.131) but the lowest land elastcty (0.3017). Favourable envronmental and physcal condtons postvely affect farm producton. The parameter estmate of the ardty ndex s negatve and statstcally sgnfcant at the 1 percent level, whle two out of the three sol dummes (S and S3) assocated wth better sol qualty (lmestones and marls) are postve and statstcally sgnfcant also at the 1 percent level. Fnally, the alttude of farms locaton does not seem to have any mpact on farm producton as the relevant parameter estmate s not statstcally sgnfcant at any conventonal level. The elastcty estmates wth respect to publc or prvate extenson servces computed usng Eq. (8), are also reported n Table 5. These estmates represent the drect effect of publc and prvate agrcultural extenson servces on farm productvty. The hghest elastcty value s observed for farms vsted exclusvely by prvate extenson agents (0.103) and the lowest for farms vsted by publc extenson agents (0.0409). These values mean that a 1 percent ncrease n vsts by publc (prvate) extenson agents would result ceters parbus n a percent ( percent)

14 ncrease n marketable output of Cretan farms. Usng the mean values reported n Table, ths means a 17.5 and 6.5 ncrease n farm producton per vst, respectvely. It s qute nterestng that the mpact of publc extenson servces on farm producton, as measured by the correspondng elastcty value, s hgher for farms vsted by both prvate and publc extenson agents (0.0554) compared wth farms vsted only by publc extenson. Nonetheless, prvate agrcultural extenson servces seem to have a larger drect effect on farm producton compared wth publc extenson servces. Techncal Effcency and Extenson Source The predcted farm-specfc output-orented techncal effcency measures for Cretan farms usng Eq. (5) are presented n Table 6 n the form of a frequency dstrbuton wthn a decle range. On average, the results reveal that Cretan farmers have not been successful n employng best-practce producton technology and achevng the maxmum possble output out of farmng. Specfcally, mean output-orented techncal effcency over farms s 67.5 percent and statstcally sgnfcant at the 1 percent level, ndcatng that a 3.5 percent ncrease n producton s feasble wth the current state of technology and unchanged nput use (last column of Table 6). Mean techncal effcency vares sgnfcantly among farms as t ranges from a mnmum of 33.5 percent to a maxmum of 99.9 percent. It s mportant that the majorty of the Cretan farms n the sample are faced wth severe techncal neffcency problems as only 1.1 percent of them acheved techncal effcency levels above 80 percent. Mnmal wdth ntervals of these techncal effcency estmates, computed usng Eq. (6), are also found to vary consderably. 11 On average the dfference between the lower and upper effcency nterval s 8. percent. The results also show a dfference between the attaned levels of output-orented techncal effcency and farmers choce of the source of extenson provson. The results presented n Table 6 suggest that farms takng advantage of both knd of extenson servces (publc and prvate) exhbt hgher average techncal effcency levels, whereas farms wth no extenson servces have the lowest mean value of techncal effcency. Specfcally, mean output-orented techncal effcency for farms vsted by both prvate and publc extenson agents s 76.6 percent, for farms vsted only by prvate

15 extenson agents t s 7.5 percent, for farms vsted only by publc extenson agents 66.7 percent and for farms that are not usng agrcultural extenson only 54. percent. These mean values of the predcted techncal effcences are all statstcally dfferent from zero. The mnmum value n Table 6 s observed for farms wth no extenson servces (33.5 percent) and for farms vsted only by publc agents (38.5 percent), whereas the hghest effcency scores are observed for farms enjoyng both knds of extenson (99.9 percent) and for farms vsted only by publc agents (99.8 percent). The correspondng computed mnmum wdth ntervals reveal that farms wth no extenson have the lowest range of the predcted mean techncal effcency (6.7 percent), whle farms vsted by prvate agents the hghest (9. percent). The conventonal t-test presented n Table 7 confrms the exstence of dfferences n the economc performance accordng to farmers choce of extenson provson source. Specfcally, the t-test suggests that mean techncal effcency values between the four sub-samples are statstcally sgnfcantly dfferent from one another at the 5 percent or better level, except for the case between farms vsted by prvate agents and farms vsted by both prvate and publc agents where the correspondng t-value s statstcally sgnfcant at the 10 percent level. The estmated 95 percent confdence ntervals of the dfference between the mean values of the predcted output-orented techncal effcency are also presented n Table 7. These estmates reveal also that there are mportant dfferences between mean output-orented techncal effcency for farms enjoyng any knd of extenson and farms that are not takng advantage of extenson servces (last three rows of Table 7). In accordance wth statstcal testng, the lowest confdence ntervals of the dfferences between the mean values are observed for farms vsted by both prvate and publc extenson agents and for farms vsted solely by prvate extenson agents. For the remanng two par-cases the dfferences range between nearly and 13.9 percent. Thus, there s sgnfcant varaton n output-orented techncal neffcency between farms wth dfferent choces concernng the source of extenson servces. Ths s an mportant fndng that should be taken nto account n the process of reform of the extenson provson system n Greece. The ndrect effect of agrcultural extenson (prvate and publc) on farms productvty.e., techncal neffcency levels, estmated usng Eq. (9) and presented

16 n Table 8, can also provde some valuable nsghts for mportant polcy mplcatons. The hghest ndrect effects are observed for farms enjoyng both knds of extenson. Specfcally, the ndrect effect of publc extenson servces s and that of prvate extenson wth both values beng statstcally sgnfcant at the 5 percent level. On the other hand, the lowest ndrect effect s for farms enjoyng publc extenson servce (0.0059), whereas for farms vsted only by prvate extenson agents the correspondng value s somewhat hgher (0.0065). Regardng the decomposton of these effects, the results suggest that land tenancy (measured as the share of rented or leased land) and the off-farm ncome do not affect the ndrect effect of all knds of extenson as the correspondng estmates resulted n statstcally non-sgnfcant values. Subsdes seems to postvely affect the ndrect effect of only publc extenson servces. Snce the Regonal Agrcultural Extenson Drectorates have undertaken much of the admnstratve burden concernng the mplementaton of the CAP the result was rather expected. Formal educaton postvely affects the ndrect effect of agrcultural extenson. Ths s unform across farmers choce although for prvate extenson t s more n evdence. On the other hand, farmers age beng a proxy of hs experence affect postvely the ndrect effect of only publc extenson provson. Hence, t seems that young, more educated farmers prefer prvate extenson servces, whereas ther older counterparts prefer publc extenson servces. As the level of debts ncreases t seems that farmers swtch to prvate extenson agents to obtan the necessary techncal nformaton whle the specalzaton and the ntensty of farmng operatons postvely affect the ndrect effect of agrcultural extenson. Farms wth a hgh share of self-consumpton, whch are usually of small capacty, do not use extenson servces. Fnally, one strkng thng to note s that n contrast to the drect effect, publc extenson postvely affects the ndrect effect of prvate extenson and vce versa. The nteracton effects reported n the last two columns of Table 8 are both postve and statstcally sgnfcant at the 1 percent level. Fnally, the mpact of the structural and soco-economc characterstcs of the surveyed farms on ther techncal neffcency levels, computed usng Eq. (10), are shown n Table 9. Frst, farmers age and formal educaton are both mportant factors affectng techncal neffcency levels. Educaton strongly complements most of the nputs utlzed n the producton process, such as chemcal fertlzers, pestcdes, rrgaton and hgh-yeldng varetes. Its mportance s ndspensable: schoolng

17 enhances both the nformaton acquston process and the effcency n the use of the acqured nformaton. Ths s also evdent from the decomposton of the ndrect effect of extenson presented n Table 8 usng Eq. (9). Nevertheless, formal educaton may not be as mportant n performng an effcent functon as hands-on experence. Thus, educaton as a proxy of human captal may become less mportant the longer a farmer s nvolved n the same farmng actvty; experence obtaned through years and learnng-by-dong have been argued to be crtcal n determnng ndvdual performance level. However, as noted by Weersnk, Turvey and Godah, nexperenced (younger) farmers may tend to acqure knowledge about recent technologcal advances more easly than ther older counterparts. Ths reason s probably behnd the negatve and statstcally sgnfcant elastcty effect for farms usng only prvate extenson servces (characterzed by younger farmers). Farms debts postvely affect ndvdual techncal effcency levels only for farms usng exclusvely prvate extenson servces, whereas subsdes are sgnfcant only for farms usng publc or both knds of extenson servces. Farms wthout many off-farm actvtes (ncreased specalzaton) exhbt hgher techncal effcency values, whereas the ntensty of farmng operatons postvely affects only farms usng any knd of extenson servces. Fnally, the hgh share of self-consumpton s assocated wth low effcency scores, whereas off-farm ncome and land tenancy do not affect ndvdual techncal effcency (except for farms not usng extenson at all). 7. Conclusons and Polcy Implcatons Ths paper takes a fresh look at the way extenson affects farmers performance. Usng a detaled data set from Cretan agrculture, a non-neutral stochastc producton fronter model s developed. Specfcally, extenson mpacts are measured both va ther drect and ndrect effect on farms productvty. Havng separate observatons for several types of extenson choces (publc, prvate, publc and prvate, and non extenson), allows us to derve conclusons regardng the (drect and ndrect) mpact of each extenson type on Cretan agrculture. The results n ths paper, n agreement wth many fndngs of prevous work, shed lght on the nteracton between extenson, soco-economc and physcal characterstcs of farms. Some of the most mportant results n ths paper, that also have polcy mplcatons for other countres, are dscussed below

18 Frst, publc and prvate extenson are complementary, and not necessarly exclusve of each other. As could be seen n ths paper, farms that had access to both extenson outlets, demonstrated a hgher level of performance compared to all other sngle extenson outlets, and of course to the case of no extenson ceters parbus. As can be seen from the results, average number of annual vsts on farm s hghest for Prvate and Publc, followed by Prvate and concluded by Publc. What makes the combnaton of prvate and publc extenson that powerful? Our study measures extenson by only one varable, the number of vsts of the extenson agent to the farm. We have no nformaton on the nature of extenson servces provded by prvate and by publc outlets. In the case of Crete, t s the nature of the ssues addressed by publc and prvate agents, that together cover the entre spectrum of needs of farms. Ths ssue s of great mportance for polcy makers, and deserves further nvestgaton. Second, there s a knd of specalzaton n usng prvate and publc extenson outlets. The fndngs of the study suggest that lkely mpacts are to be realzed n certan farm types (e.g., captal ntensve, specalzaton level, etc). In addton to beng an nterestng emprcal fndng, there are also several mplcatons that could be of nterest to polcy makers. The recent wave of prvatzaton of extenson servces n developng countres exhbts mxed results (see Dnar, 1996). Dnar and Keynan also ndcate the selecton of adequate farmers to partcpate n prvate extenson projects n Ncaragua, and the fact that an napproprate selecton procedure caused farmers to dscontnue ther partcpaton. Therefore, the nature of the prvate extenson servces should be matched to the type of farms they are engaged wth. Thrd, an mportant fndng n the Cretan case s that farms wth a hgh share of self-consumpton, whch are usually of small capacty, do not use extenson servces. An analogue to developng world, would suggest that subsstence farms do not demand extenson servces of any type. It would be nterestng to check ths fndng aganst other studes n the developng world. However, nformaton s scarce. Most data on prvate extenson that exst, nclude pad extenson that s stll at a project level, thus beng funded or partally funded by external donors. Once the project termnates, the test would be the sustanablty of the system that was establshed

19 References Agner, D.J., C.A.K. Lovell and P. Schmdt. Formulaton and Estmaton of Stochastc Fronter Producton Functon Models. J. Econometrcs, 1977, 6: Battese, G.E. and S.S. Broca. Functonal Forms of Stochastc Fronter Producton Functons and Models for Techncal Ineffcency Effects: A Comparatve Study for Wheat Farmers n Pakstan. J. Prod. Anal., 1997, 8: Battese, G.E. and T.J. Coell. A Stochastc Fronter Producton Functon Incorporatng a Model for Techncal Ineffcency Effects. Workng Paper n Econometrcs and Appled Statstcs No 69, Department of Econometrcs, Unversty of New England, Armdale, Australa, Brkhaeuser, D., R.E. Evenson and G. Feder. The Economc Impact of Agrcultural Extenson. Econ. Dev. Cult. Change, 1991, 39: Bravo-Ureta, B.E. and R.E. Evenson. Effcency n Agrcultural Producton: The Case of Peasant Farmers n Eastern Paraguay. Agr. Econ., 1994, 10: Dancey, R.J. The Evoluton of Agrcultural Extenson n England and Wales, Presdental Address. J. Agr. Econ., 1993, 44: Dnar, A. Provson of and Request for Agrcultural Extenson Servces. Amer. J. Agr. Econ., 1989, 71: Dnar, A. Extenson Commercalzaton: How Much to Charge for Extenson Servces. Amer. J. Agr. Econ., 1996, 78: 1-1. Dnar, A. and G. Keynan. Economcs of Pad Extenson: Lessons from Experence n Ncaragua. Amer. J. Agr. Econ., 001, 83: Evenson, R.E. Estmated Economc Consequences of PIDAP I and PIDAP II Programs for Crop Producton. Yale Unversty Economc Center, Feder, G., R. Slade and L. Lau. The Impact of Agrcultural Extenson: The Tranng and Vstng System n Haryana. World Bank Staff Workng Paper no 76, World Bank, Washngton DC, Førsund, F.R. On the Calculaton of the Scale Elastcty n DEA Models. J. Prod. Anal., 1996, 7: Gannakas, K., K. Tran and V. Tzouvelekas. Effcency, Technologcal Change and Output Growth n Greek Olve-Growng Farms: A Box-Cox Approach. App. Econ., 000, 3: Gannakas, K., K. Tran and V. Tzouvelekas. On the Choce of Functonal Form n Stochastc Fronter Modelng. Emp. Econ., 00 (forthcomng). Greene, W.H. Econometrc Analyss. New York: Prentce Hall Inc,

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22 Table 1 An Overvew of Agrcultural Extenson Provson n Crete, Greece Publc Extenson: Mean Max Mn Stdev Outlets 18 Agents 67 Agents per Outlet Offce hrs per day Staff Experence (years) Staff Educaton (years) Prvate Extenson: Outlets 7 Agents 314 Agents per Outlet Offce hrs per day Staff Experence (years) Staff Educaton (years) Farmers Choce No of Farms 1 Publc Extenson 100 (37.7) No of vsts Dstance (km) Prvate Extenson 46 (17.4) No of vsts Dstance (km) Publc & Prvate Extenson 88 (33.) Publc Extenson No of vsts Dstance (km) Prvate Extenson No of vsts Dstance (km) No Extenson 31 (11.7) Farmers satsfacton wth: Very Good Good Poor Publc Extenson 1 88 (46.8) 71 (37.8) 9 (15.4) Prvate Extenson 1 73 (54.5) 55 (41.0) 6 (4.5) 1 n parentheses are the correspondng percentage shares. - -

23 Economc Data Table Summary Statstcs of the Varables Publc- Extenson Prvate- Extenson Farms wth: Publc & Prvate Extenson No Extenson Output ( ) 15,899 17,613 16,761 1,161 Land (stremmas) Value of Producton ( /stremma) Labour (hours) 1,836,31,089 1,70 Intermedate Inputs ( ),96,517,549,335 Captal ( ),860 4,338 3,059,5 Farm Characterstcs Age (years) Educaton (years) Tenancy (% of land owned) Debts ( ) 1,787 3,055, Subsdes ( ) 4,683,477 4,14 3,00 Off-Farm Income ( ) 4,131 4,114 4,38 3,933 Share of Producton Consumed Specalzaton (Herfndhal ndex) Alttude (meters) Ardty Index Farmng Intensty (dummy) Sol Type (% of Farm Land) Sandy Lmestone Marls

24 Table 3 Parameter Estmates of the Non-Neutral Translog Stochastc Producton Fronter and Ineffcency Effects Models Param. Estmate Std Error Param. Estmate Std Error Param. Estmate Std Error Stochastc Fronter Model α (0.081) * α AO (0.041) ** δ PbA (0.0304) ** α Α (0.0371) * α AC (0.0341) ** δ PbL (0.033) α L (0.0454) * α AA (0.033) δ PbO (0.046) ** α O (0.0411) * α LO 0.83 (0.0581) * δ PbC (0.049) * α C (0.0354) * α LC (0.0680) δ Pr (0.017) * β Ar (0.0343) * α LL (0.033) ** δ PrPr (0.0071) β At (0.0165) α OC (0.0653) δ PrA (0.048) * β S (0.0949) α OO (0.034) * δ PrL (0.0331) β S (0.0834) * α CC (0.041) ** δ PrO (0.0334) * β S (0.0479) * δ Pb (0.0033) * δ PrC (0.0359) * α AL (0.0609) δ PbPb (0.058) * δ PbPr (0.0171) ** Ineffcency Effects Model ζ (0.590) * ζ Age (0.001) * ζ PbAge (0.016) ** ζ PrAge (0.0048) * ζ Edu (0.0059) ** ζ PbEdu (0.0044) ** ζ PrEdu (0.00) * ζ Ten (0.0030) ** ζ PbTen (0.013) ζ PrTen (0.0006) ζ Dbt (0.001) * ζ PbDbt (0.008) ** ζ PrDbt (0.005) ** ζ Sub 0.00 (0.004) ζ PbSub (0.0060) ** ζ PrSub (0.0089) ζ Off (0.0000) ** ζ PbOff (0.008) ζ PrOff (0.011) ζ Sec (0.0115) ζ PbSec (0.001) * ζ PrSec (0.0051) ** ζ Spe (0.007) ** ζ PbSpe (0.004) * ζ PrSpe (0.0036) ** ζ Int (0.0008) ** ζ PbInt (0.0031) ζ PrInt (0.0031) ** ζ Pb (0.0091) * ζ Pr (0.0014) * ζ PbPr (0.0037) ** σ (0.038) * γ (0.0641) * λ (0.0414) * Ln(θ) A stands for area, L for labour, O for ntermedate nputs, C for captal, Ar for ardty ndex, At for alttude, S1-S3 for three sol dummes, Pb for publc-extenson vsts, Pr for prvate-extenson vsts, Age for farmers age, Edu for farmers educaton, Ten for the share of rented land, Dbt for farms debts, Sub for subsdes, Off for off-farm ncome and, Sec for self-consumpton level, Spe for farms specalzaton and Int for a dummy varable to dstngush ntensve farms. * ( ** ) ndcate sgnfcance at the 1 (5) percent level

25 Table 4 Model Specfcaton Tests Hypothess LR-test Crtcal Value (α=0.05) 1. δ lh = δ lj = α jk = χ = = 10.. γ = ζ 0 = ζ Pb = ζ Pr = ζ Pb Pr = χ γ = δ 0 = ζ m = ζ Pb = ζ Pr = ζ Pb Pr = ζ Pbm = ζ Pr m = 0 m 91.4 χ = ζ 0 = ζ m = ζ Pb = ζ Pr = ζ Pb Pr = ζ Pbm = ζ Pr m = 0 m 41.3 χ = ζ m = ζ Pb = ζ Pr = ζ Pb Pr = ζ Pbm = ζ Pr m = 0 m χ = ζ m = 0 m χ = 16 9 δ 7. ζ Pb Pb = δ = ζ PbPb Pr = δ = ζ Pbj Pb Pr = δ = ζ Pr Pbm = δ = ζ Pr Pr Pr m = δ = 0 Pr j = δ Pb Pr j,m 89.3 χ = ζ Pb = ζ Pr = ζ Pb Pr = ζ Pbm = ζ Pr m = 0 m 8.3 χ = =. 9. δ Pb = δ PbPb = δ Pbj = δ Pr = δ Pr Pr = δ Pr j = δ Pb Pr = 0 j χ δ Pb = δ PbPb = δ Pbj = δ Pb Pr = ζ Pb = ζ Pbm = ζ Pb Pr = 0 j, m χ = δ Pr = δ Pr Pr = δ Pr j = δ Pb Pr = ζ Pr = ζ Pr m = ζ Pb Pr = 0 j, m 7.35 χ = ζ Pb = ζ Pb Pr = ζ Pbm = 0 m χ = = δ Pb = δ PbPb = δ Pbj = δ Pb Pr = 0 j 3.34 χ ζ Pr = ζ Pb Pr = ζ Pr m = 0 m 41.3 χ = = δ Pr = δ Pr Pr = δ Pr j = δ Pb Pr = 0 j.54 χ δ Pbj = ζ Pbm = 0 j, m χ = δ Pr j = ζ Pr m = 0 j, m χ = δ Pbj = 0 j 1.14 χ = = 9. 9 = δ Pr j = 0 j χ ζ Pbm = 0 m 6.47 χ 9 1. ζ m 0 m 9.15 χ = 16 9 Pr = 9.. δ Pb Pr = ζ Pb Pr = χ = = 3. 1 = 3. 7 = ζ Pb Pr = χ δ Pb Pr = χ α j + δ l =1 and α jk + δ lh + δ lj = χ 07 Pb stands for publc-extenson vsts, Pr for prvate-extenson vsts, m= Age, Edu, Ten, Dbt, Sub, Off, Sec, Spe, Int and j = A, L, O, C. Note: When the null hypothess nvolves the restrcton of γ=0 then the LR-test statstc follows a mxed ch-squared dstrbuton, the crtcal values of whch are obtaned from Kodde and Palm (1986, table 1)