Evading Farm Support Reduction Via Efficient Input Use: The Case of Greek Cotton Growers

Transcription

1 Evadng Farm Support Reducton Va Effcent Input Use: The Case of Greek Cotton Growers Chrstos J. Pantzos (Dept of Economcs, Unversty of Patras, Greece) Stelos Rozaks (INRA, France & Dept of Economcs, Unversty of Crete, Greece) Vangels Tzouvelekas (Dept of Economcs, Unversty of Crete, Greece) Correspondng Author: Vangels Tzouvelekas Dept. of Economcs, School of Socal Scences Unversty of Crete, Unversty Campus Rethymno, Greece Tel.: ; Fax: , e-mal: The authors wsh to thank the partcpants of the 2 nd Hellenc Workshop of Productvty and Effcency Measurement (HE.W.P.E.M.), Patras, Greece, June 2003 for ther helpful comments and suggestons on an earler verson of ths paper. The usual caveats apply.

2 Evadng Farm Support Reductons Va Effcent Input Use: the Case of Greek Cotton Growers Abstract Utlzng a stochastc fronter approach, ths paper examnes the mportance that nput-orented techncal and scale effcency may have for Greek cotton farmers n the context of the current EU cotton polcy. To that end, a sample of cotton-growng farms n the representatve cotton -producng county of Kardtsa (central Greece) s emprcally analyzed. The results suggest that the farms examned exhbt decreasng returns to scale and they are both scale and techncally neffcent. Moreover, elmnaton of these neffcences could result n consderable gans; the cotton farmers examned could reduce producton costs by 46.0%, by becomng both techncally and scale effcent. Addtonally, we estmate that f cotton farms n the area examned were techncally and scale effcent the nterventon prce reductons (co-responsblty levy) mposed by the EU for excessve cotton producton would be smaller for all Greek cotton growers. 1. Introducton The measurement of techncal effcency of producton unts has become the focus of a rapdly expandng body of appled economc lterature. Recently, ths research has also been extended to the ntmately related ssue of scale effcency, that s, the devaton of a frm s ray-productvty from the maxmum attanable one. Naturally, the emprcal measurement of techncal and scale effcency s an attractve research theme on ts own, as a prmary objectve n producton economcs s the optmal resource utlzaton. However, there may be cases wheren achevement of techncal and scale effcency may also be advsable for addtonal reasons. One case may be polcy regmes wheren fnancal support s offered only for a pre-determned output level. Productve unts operatng under such regmes may fnd that pursung techncal and scale effcency may help them to not only utlze optmally ther resources but also to preserve the support they enjoy under the polcy regme. Agrcultural polces are a typcal example of regulated regmes wth provsons of maxmum output levels for whch farm support s avalable. Wthn the European Unon (EU), provsons referrng to celngs of output produced (or area cultvated) - 2 -

3 are ncluded n many EU Regulatons. As farm support (usually n the form of nterventon prces) naturally functons as a sgnal to ndvdual farmers to boost output, the actual way n whch such celngs are mposed becomes a crucal ssue, both for farmers and polcy makers n the EU member states. Greek cotton producton has been an llustratve case. The current EU polcy regme mposes a producton quota at the country level, for cotton-producng member states. Exceedng ths aggregate quota results n reductons of the nterventon cotton prce for all farmers. Ths mechansm however has faled to restran Greek cotton producton. Actng as prce takers, ndvdual producers conceved the demand for ther produce as perfectly elastc and arranged ther producton only accordng to the EU nterventon prce, not the aggregate producton quota. As a result, the Greek cotton producton boomed the last twenty years despte the quota whch n turn actvated reductons n cotton nterventon prces. Ths has been provokng loud protests by the Greek cotton growers the last fve years about shrnkng farm ncomes. The mplementaton of the EU cotton polcy regme, on a more ds-aggregated bass (.e., further allocatng the country-level producton quota down to the county or even to the farm level) could offer a natural settlement of ths problem (Karaganns and Pantzos, 2002). Ths paper offers an addtonal suggeston that could also help Greek farmers evade reductons n cotton nterventon prces: the mprovement of ther techncal and scale effcency. In the past, hgh, EU-guaranteed cotton prces have nfluenced the scale of producton n Greek cotton farms by encouragng farmers () to nvest n modern equpment and expand producton, () use excessvely water and chemcal fertlzer and, () dvert even margnal productvty land to cotton farmng. Under these crcumstances, pursung techncal and scale effcency may assst Greek cotton growers n two, complementary ways. Frst, t may yeld substantal cost savngs whch could compensate for the reduced nterventon prces caused by exceedngly hgh producton levels. Second, (and more mportantly) scale effcent producton may result n lower output levels whch n turn may mtgate or even elmnate reductons of the EU guaranteed cotton prces. In ths context, the objectve of ths paper s to estmate emprcally techncal and scale effcency levels as well as ther determnng factors of Greek cotton farms. To that end, recent methodologcal developments stochastc fronter modelng whch allow the measurement of techncal as well as scale effcency are utlzed on a representatve sample of Greek cotton farm operatons from the county of Kardtsa, - 3 -

4 Thessaly. The emprcal results are used to estmate average cost savngs that could accrue to farmers f they were techncally and scale effcent. Then, utlzng goal programmng we attempt an estmaton of the output (cotton) reducton that could have been acheved n the broader area of our survey f farms were operatng under techncal and scale effcency. Ths fndng s fnally used to compute how much ths lower producton mght have mtgated the guaranteed prce reductons that cotton farmers have suffered n the perod examned. The rest of the paper s organzed as follows. A bref overvew of the Greek cotton sector s provded n the next secton. Methodology and the theoretcal model are developed n secton 3. Secton 4 dscusses the data and the estmaton results. Polcy mplcatons dervable from ths study are offered n secton 5. Secton 6 concludes. 2. The Greek Cotton Sector Tradtonally, cotton growng has been a promnent farmng actvty n Greece provdng the prmary nput to a major domestc processng ndustry (cotton gnners). Durng the last two decades the sector has shown an mpressvely rapd expanson. The acreage cultvated wth cotton almost doubled durng the 1980s reachng 240 thousand ha n 1991, from only 120 thousand ha n 1981 and kept expandng durng the 1990s reachng 430 thousand ha n The volume of cotton producton swelled accordng to the Greek Cotton Board from only 290 thousand tons n 1981 to about 1 mllon tons n Wthn the EU, Greece has thus become the largest cotton producer, accountng for about 70 percent of the total EU cotton producton. The sector s rapd enlargement has been manly the result of past, hgh supportmechansms of the EU cotton regme. Untl 1986, the EU cotton polcy was a typcal defcency payment scheme: the prce receved by cotton farmers was based on a target prce (hgher than the world prce), predetermned annually by EU authortes. Faced wth hgh fnancal costs however, the EU has replaced snce 1987 ths polcy regme wth an nterventon mechansm consstng of: () an nterventon prce, () an aggregate producton quota, called maxmum quantty guaranteed (MQG) whch s set at the country-level and, () a reducton n the nterventon prce, called coresponsblty levy whch s appled to all cotton farmers when the actual cotton producton of the country exceeded the pre-determned MQG

5 As a result of the ntal favorable CAP measures, cotton cultvaton became gradually the prmary farm actvty (and source of ncome) for a growng number of agrcultural households. Farmers dverted even margnal productvty land to cotton cultvaton; nvested n equpment (such as cotton harvesters, rrgaton systems, and water drllngs) and n general, they largely expanded ther scale of operaton. Naturally, negatve envronmental effects started to emerge as cotton ranks hgh n the lst of heavly pollutng crops; hgh levels of fertlzer resdues have been measured n cotton felds and the excessve use of rrgaton water appears to have reduced underground water supples to alarmng levels. In the wake of the latest reform n the CAP cotton regme, producton expanson s not anymore assocated wth correspondng ncreases n farm revenues. However, as the producton quota was mposed at the country-level, ndvdual cotton growers routnely gnored t and kept expandng ther own producton. Recently, cotton growers played a leadng role n loud farmer protests aganst the EU-mposed cotton producton quota clamng that t shrnks drastcally ther farm ncome n the face of ever ncreasng producton costs. 3. Methodologcal Framework Two measures of techncal effcency can be defned accordng to whether one adopts an output-expandng or an nput-conservng approach (Kumbhakar and Lovell, 2000, pp ). The frst one s the output-orented Debreu-type measure, defned as the rato of the observed to maxmum feasble output, gven the producton technology and the observed nput use. The second one s the nput-orented Shephard-type measure, defned as the rato of mnmum feasble to observed nput use, gven the producton technology and the level of output. Both measures of techncal effcency can be obtaned from the econometrc estmaton of the stochastc producton fronter (SPF) model suggested ndependently by Agner et al. (1977) and Meeusen and Van der Broeck (1977). In ths analytcal framework the agrcultural output s treated as a stochastc producton process of the followng general form: y ( x ; β) exp( ε ) = f and ε v u (1) - 5 -

6 where f ( ) s approxmated by a translog producton functon,.e., n n n 1 ln y β + β ln x + β ln x ln x + ε t = 0 j jt j= 1 2 j= 1 k = 1 jk jt kt t (2) where y s the observed output produced by the th farm, x j s the quantty of the j th nput used by the th farm and, β s a vector of parameters to be estmated. The component v s a symmetrc d error term representng random varaton n output due to random exogenous factors, measurement errors, omtted explanatory varables, and statstcal nose. The component u s a non-negatve error term representng the stochastc shortfall of the th farm s output from ts producton fronter due to the exstence of output-orented techncal neffcency. Estmaton of nput-orented techncal effcency s possble wthn the above model specfcaton usng the approach suggested by Atknson and Cornwell (1994) and extended later on by Ray (1998) Renhard et al. (1999). Specfcally, all factors of producton are multpled by a scalar θ 0 such that the observed level of output s stll feasble. Thus the model n (1), assumng that u =0, can be wrtten as: y = f 0 ( θ x ; β) exp( v ) (3) Snce under weak monotoncty output-orented techncal effcency should mply - and must be mpled by - nput-orented techncal effcency, we can set the nput-orented specfcaton n (3) equal to the output orented specfcaton n (1). 0 Then solvng for θ farm- and tme-specfc estmates of nput-orented techncal effcency I ( ) TE can be obtaned. These estmates drectly ndcate cost savngs whch are possble through the elmnaton of nput-orented techncal neffcency (Kopp, I 1981, p. 490). Specfcally, 1 TE ndcate the reducton n total cost f nput-orented techncal neffcency s elmnated. 1 Both measures of techncal effcency are shown n Fgure 1. Let us assume that a farm uses x amount of nput x to produce y level of output (pont A). Obvously the farm n queston s techncal neffcent as t does not operate on t s producton fronter gven by f(x). The output-orented measure of ths techncal neffcency s - 6 -

7 defned as the rato of the observed to the maxmum attanable output (gven at pont D), that s x A xd. Analogously, an nput-orented measure of techncal neffcency s gven by the rato of optmum over the actual nput use, that s O x Ox. On the other hand, nput-orented scale effcency can be estmated n the context of the SPF model followng the approach suggested by Ray (1998). Specfcally, n the context of the translog producton fronter nput-orented scale neffcency can be calculated from: SE I = exp ( 1 E 2Bu ) 2B 2 (4) where, E s the local measure of the returns to scale for the th farm, u s the outputorented techncal neffcency of the th farm and, B s the sum of the j k β jk second-order parameters of the translog producton fronter n (2). If the matrx β jk s negatve defnte, then B<0. If the farms n the sample exhbt decreasng returns to scale, whch s probably the case of the Greek cotton sector, then makng them scale effcent would result to a reducton n ther total cotton produced. In order to compute that potental reducton n cotton produced and thus to approxmate the reducton n the co-responsblty levy as well as the possble reducton n total cost of producton we need to compute the nputoutput bundle that make the th farm scale effcent, from an nput conservaton perspectve., x~, y~, respectvely, In terms of Fgure 1 we need the combnatons ( x ) and ( ) 0 * where x = θ x and x~ = θ x. If all farms n the sample exhbt decreasng returns to scale then nputs must be scaled down n order to acheve the most productve scale sze, (pont C) where the ray average productvty of nputs s maxmzed. Otherwse nputs should be scaled up n order to attan maxmum scale effcency levels. If dmnshng returns to scale preval, then the nput reducton for the th cotton farm to be scale effcent can be estmated from: y 1 0 * ( θ θ ) x (5) - 7 -

8 0 Followng Ray (1998, pp ), θ (nput-orented techncal effcency) and * θ can be estmated n the context of the translog specfcaton as: θ 0 E = exp + E 2 B 2Bu and * 1 E θ = exp (6) B By computng the techncally and scale effcent nput levels ~ x from equaton (5) and nsertng them nto the ftted producton fronter we can compute the correspondng output level ~ y. Decreasng returns to scale at the actual pont A mply that ~ y s less than y. Thereafter the potental output reducton and the correspondng cost savngs when productve unts become scale effcent can be obtaned. These output reductons are used to compute the potental ncrease n farm s total profts that would arse from the correspondng declne n the co-responsblty levy. For dong so we generalze the fndngs obtaned from our sample survey to the whole county of Kardtsa. In order to do that each ndvdual farm n the sample has been weghted. These weght-coeffcents are defned through goal programmng n two stages. Frst, each sub regon relatve weght were determned, and then farms n each sub regon were weghted accordngly to represent all other farms of the sub regon. The model s specfed as follows: weght coeffcents, one for each farm was determned n such a way that devatons for regonal crop mx were kept mnmal. The dfference between maxmum and mnmum value of the weght coeffcents s a control varable to be mnmzed. The mathematcal form of the goal programmng model s as follows: MIN s.t. ( maxλ - mnλ) k λ s + d d = s ι k j + j R j j J d j ps R j j J d + j ps R j j J mn λ λ N λ max λ N - 8 -

9 where, N are the total farms n the sample, j J s the crop group n the county (e.g. cereals), k J s the ndvdual crop wthn the crop group J, sk s the surface of crop k that belongs to the correspondng element j for farm, s the surface cultvated by crop group j n the county, p s the percentage of tolerated devaton from regonal crop mx, λ s the weght coeffcent for farm and, d j are the devatons from the crop group j. Frst, we select the elements to be compared n the sample and the populaton. They can be crops, groups of crops (cereals etc), or other farm data such as rrgated land per total useful surface n the farm. Each of these elements corresponds to an addtonal constrant. There s a compensaton possblty between the devaton tolerance and the dfference between maxmum and mnmum weghts. For nstance, f we am at a mnmal devaton of 1% of the observatons then the obtaned results wll dffer sgnfcantly as t wll be compelled to assgn hgher weghts to some farms and to almost gnore others wth a crop mx dfferent of the regonal crop mx. R s j 4. Data and Estmaton Data The data used n ths paper come from a questonnare survey of 172 cotton farms n the county of Kardtsa whch belongs to the regon of Thessaly (central Greece) for the croppng year. Thessaly s one of the major agrcultural regons of the country and hstorcally t has been a prme area for cotton farmng. Summary statstcs of the key-varables of the surveyed farms appear n Table 1. The varables nvolved n the analyss are measured as follows. In the producton fronter equaton (1) the dependent varable s the total annual cotton producton measured n klograms, whle the ndependent varables nclude: (a) total labor, that s, hred and famly (pad and unpad) labor related to cotton producton measured n hours; (b) farm land devoted to cotton cultvaton measured n stremmas (one stremma equals 0.1 ha); (c) total amount of seeds used n cotton producton measured n klograms and; (d) total value of purchased nputs (fertlzers and pestcdes) and captal (machnery etc) used n cotton cultvaton, measured n euros (EUR). In addton, we use a number of demographc and economc varables to explan the farm s techncal and scale neffcency. These nclude: (a) the age of the farmer - 9 -

10 measured n years; (b) the value of the farm s total assets (comprsng of the value of mechancal equpment, cultvated land and nfrastructure) measured n EUR; (c) the formal educaton of the farmer measured n years of schoolng; (d) the land fragmentaton measured as the number of plots cultvated wth cotton n each farm; and, (e) the specalzaton of the farm measured by means of a Herfndhall ndex (.e., as the sum of the squared output shares of cotton and all other farm products produced on the farm). Estmaton results The ML parameter estmates of the translog producton fronter (1) are lsted n Table 2. More than 2/3 of the estmated parameters n the producton fronter and all the estmated parameters n the neffcency model are found to be statstcally sgnfcant at least at the 5% level. The relatvely low value of the lkelhood functon s satsfactory for a cross-secton data settng, ndcatng a good ft of the data. Moreover, the estmated producton fronter satsfes all the regularty condtons, namely postve and dmnshng margnal productvtes, at the pont of approxmaton. Specfcally, monotoncty condtons are satsfed snce all the margnal products are postve, whle the determnants of the prncpal mnors of the bordered Hessan matrx alternate ther sgns ndcatng dmnshng margnal productvtes. The estmated varance-rato parameter, γ, s postve and statstcally sgnfcant at the 1% level. 2 ; ts value mples that 84.35% of output varablty s explaned by the correspondng dfferences n output-orented techncal neffcences of the cotton farms examned. Partal nput elastctes wth respect to output, and the returns to scale -RTS were computed for each farm n the sample. Ther average value and descrptve statstcs are reported n Table 3. Inspecton of the table reveals that ceters parbus, captal seems to have the largest mpact on cotton producton, followed by labor, acreage cultvated and seeds used. All farms n the sample exhbt decreasng RTS rangng from slghtly below one to 0.784; the average RTS s found to be Statstcal testng confrms the exstence of decreasng returns to scale at the 1% level of sgnfcance (the value of the respectve LR-test s wth 5 degrees of freedom)

11 Techncal and Scale Effcency Scores Input-orented techncal and scale effcency scores of the farms examned (denoted as I TE and, respectvely) are lsted n Table 4, n the form of a frequency dstrbuton wthn a decle range. The table reveals that the cotton farms n the sample show consderable techncal but only slght scale neffcency. Ths mples that, prmarly Greek cotton growers have not been successful n utlzng optmally ther nput use under the exstng technology. More exactly, the computed nput-orented I techncal effcency TE has an average value of 74.69% mplyng that the farms examned could have produced the observed cotton quantty usng on the average, about 25% less nput quanttes wthn the current state of technology. Moreover, TE scores vary consderably across farms rangng from a mnmum of 55.2% to a maxmum of 94.6%. Of the 172 farms n the sample, only 65 (.e., less than 40% of the farms examned) acheved nput-orented techncal effcency above 80%. Ths means that the majorty of the sample partcpants face severe techncal neffcency problems. I SE Regardng scale effcency, the average nput-orented scale effcency SE I s found to be 98.5%, rangng from 89.75% up to 99.99%. In fact, all but one of the farms n the sample have SE I scores hgher than 90%. Ths mples that the average ray productvty of the cotton farms examned would devate from the maxmum attanable one by about 1.5%, f they operated under full techncal effcency (Fgure 1). Moreover, for ndvdual farms n the sample the devaton of ray productvty from the maxmum attanable one would range zero up to 10.25%. I Sources of Effcency Dfferentals To nvestgate the sources of nput-orented techncal and scale effcency of the farms examned we have regressed ther TE I and SE I scores aganst a set of relevant demographc and economc varables usng a two lmt-tobt model; the results are reported n Table 5. It must be noted that these parameter estmates are not subject to the same nterpretaton as conventonal regresson parameters; thus, we may only comment on the type of nfluence (postve or negatve) they mply for the TE I and SE I scores

12 A postve relatonshp s found between TE I and the farmer s age (and therefore experence). Ths s n accordance wth the noton that besdes educaton - hands-on experence obtaned through years and learnng-by-dong are crtcal factors n determnng ndvdual performance partcularly n crop producton 3. However, the mpact of age on the degree of techncal effcency needs not to be monotoncally ncreasng: that s, young cotton producers may well be expected to become more effcent over tme up to a pont where the relatonshp between age and effcency s leveled off; as they approach the retrement age effcency declnes. Ths noton of decreasng returns to human captal s captured by the negatve relatonshp found between TE I and the varable (Age) 2. A postve relatonshp s also found between the farmer s educaton and the TE I score of hs farm. Ths lends support to Welch s (1970) hypothess about the worker effect, that s, the noton that educaton s a strong complement wth most of the nputs utlzed n the producton process. Moreover, schoolng may enhance the nformaton acquston process and the effcency n the use of the acqured nformaton. On the other hand, farm sze (measured as the value of farm s total assets) appears no to have any sgnfcant nfluence on TE I scores. Lastly, TE I s found to be negatvely related to farm specalzaton (measured by means of a Herfndhal ndex). By constructon, ths ndex assumes hgher values for farms specalzng ether n cotton or n crops other than cotton. Thus the fndng mples that cotton farms whch also dversfy to other farmng actvtes appear to be more techncally effcent than hghly specalzed farms (ether n cotton or n other crops). Regardng farms hghly specalzng n cotton, ths fndng may reflect the fact that for farmers relyng almost exclusvely on cotton, actual producton volumes bascally shape ther farm ncome; thus they may tend to use nputs excessvely n ther effort to acheve as large produce as possble. Wth respect to farms hghly specalzng n other crops, ths fndng may be vewed as reflectng the massve entrance of farmers nto cotton cultvaton n recent years to take advantage of hgh support prces: producers lackng experence and sklls n cotton farmng and employng margnal productvty land have smply added cotton growng to ther actvtes. It s clear that such margnal cotton growers cannot be as techncally effcent as ther colleagues who nclude cotton producton among ther major farmng actvtes

13 The same factors appear to have analogous relatonshps wth the scale effcency scores of the farms examned. More exactly, the farmer s age and (age) 2 have the same types of relatonshp wth SE I scores (as they have wth TE I scores) and they may be gven smlar nterpretatons. The same holds true for the farmer s educaton. Fnally, Total assets and specalzaton do not seem to affect sgnfcantly SE I scores. Potental cost savngs and co responsblty levy reductons The average TE I score of 74.7%% mples that (on the average) the observed cotton output levels could have been produced wth 25.3% less producton costs wthout alterng producton technology. Table 6 reports the potental cost reductons from elmnatng nput-orented techncal and scale neffcency from the cotton farms examned 4. Our calculatons ndcate that these cotton farms would be able to reduce ther actual costs by 46% f they became techncally and scale effcent. In partcular, the farms examned would reduce ther cost by 25.3% f they became techncally effcent and by another 20.7% f, n addton, they became scale effcent. In absolute terms, these potental cost savngs would be on the average, EUR 117/stremma. Reducng therefore techncal neffcency could substantally mprove the economc vablty of cotton farms. Conclusons on the level of cotton producton can be made when farms become techncally and scale effcent; n order to project to the county level reduced cotton producton level by farm an dea of the representatve power of each sample farm s necessary. For ths purpose the goal programmng method presented n secton 3 has been used The survey ncluded 172 farms that belong to 20 communes n Kardtsa plan. Frst these communes have been selected to represent plan and sem-mountanous areas of the regon out of 83 communes n total that are classfed to sx homogeneous sub regons accordng to farm characterstcs such as average sze of farms, labor force avalable, rrgated land percentage and crop mx. Then a number of farms has been selected randomly from wthn each sub-regon. Fnally 172 farms have been surveyed that represent a total farm number about stuated n the plan and sem-mountan communes of Kardtsa, that s each sample farm represents about 100 farms. As polcy recommendatons wll be extracted from the analyss and extrapolatons would be made attemptng to generalze conclusons, the necessty of a fner weghng of sample farms became apparent. For ths purpose goal programmng s used so that results can

14 be projected to the regonal level wth the mnmal devatons from observed crop mx aggregates. Utlzng goal programmng we were able to determne weghts for each sample farm. In average each sample farm represents 70 real farms of the county, some of them represent 500 (maxmum weghts) whle others only 10 (mnmum). The detaled pcture of the weghts per farm s presented n Fgure 3. Our calculatons ndcate that n the perod , cotton output would be reduced by 55, metrc tons (MT) n the county of Kardtsa, f all ts cotton producng farms were both techncally and scale effcent. The potental reducton of the co-responsblty levy (faced by all Greek cotton growers) s computed n Table 7. In , actual cotton producton n Greece was 1,045,488 MT; the country s producton quota (MQG) was set at 782,000 MT; the target prce was drachmas/kg; and, the co-responsblty levy was 49.8 drachmas/kg (Karaganns and Pantzos, 2002). In percentage terms, ths co-responsblty levy represents a 15.01% reducton of the target prce. The actual Greek producton exceed the pre-determned MQG by 38.8% n ; t would have exceeded the MQG by 31.72%, f the Kardtsa county cotton growers were techncally and scale effcent. Snce the actual exceedng of the MQG by 38.8% resulted n 15% drop of the nterventon prce, smple algebrac calculatons suggest that f the Kardtsa county cotton growers were techncally and scale effcent, the reducton of the nterventon prce (the co-responsblty levy) would have been drachmas/kg n the perod 1997/98. In other words, f only the Kardtsa cotton growers were both techncally and scale effcent, the fnancal penalty (.e., the coresponsblty levy) for all Greek cotton growers would have been smaller by 9.1 drachmas/kg. 5. Polcy mplcatons Our analyss ndcates that the benefts the Common Agrcultural Polcy offered to Greek cotton producers have come at a rather heavy opportunty-cost. Specfcally, the satsfactory farm ncome that the EU cotton regme secured n the past to cotton growers (va admnstratve prces, set well above world levels) allowed them to dsregard effcency consderatons n the way they apply ther producton technology. Nowadays however, the resultng effcency dstorton s becomng a crtcal factor for Greek (and other EU) cotton growers, for at least two reasons. Frst, the EU tself has

15 taken a course of gradual reducton of ts expensve farm programs; thus, ways to acheve cost savngs are becomng ncreasngly mportant for the economc vablty of farmng operatons. Second, further lberalzaton of the world agrcultural markets wll only ntensfy competton, thus makng effcency a major determnant for the survval of cotton producng countres. The consderable techncal and scale neffcency n Greek cotton farmng becomes also mportant n the lght of the atttude Greek cotton farmers have been takng aganst the CAP cotton regme: as already mentoned, they have repeatedly protested clamng that ther revenues are severely reduced by the current EU regme (outlned n secton 2). Ths study ndcates however that nstead of blndly demandng hgher prces to secure ther ncome, cotton farmers could acheve a smlar result va cost savngs stemmng from the reducton of ther techncal and scale neffcency. 5 Thus, the prmary polcy suggeston dervable from our study calls for Greek polcy planners to complement the current EU cotton regme wth structural polces whch explctly nduce cotton farmers to mprove the techncal and scale effcency of ther operatons. Such measures can effectvely address the dffcultes assocated wth the prospects facng Greek cotton growers, outlned above. Frst, they can mplctly nduce Greek cotton farmers not to exceed (or at least exceed by less) the EU mposed, cotton producton quota (MQG), thus mantanng the level of EU support, currently avalable to them, and ther farm revenue. Second, (and perhaps, more mportantly) such measures may prepare Greek cotton growers to cope wth future support reductons n lght of future CAP reforms and ncreasngly ntegrated, world agrcultural markets. Specfc measures of such complementary structural polces may nclude: () measures to mprove the ablty of cotton farmers to apply effcently the exstng technology e.g., measures desgned to mprove educaton, nformaton acquston, and learnng-by-dong processes, () ncentves to farmers to adjust the excessve scale of ther cotton operatons; and, () measures to favor balanced cotton farmng operatons by dscouragng occasonal or margnal cotton growers but also farmers whch base ther farm ncome exclusvely on cotton producton. 6. Concludng Remarks Productve unts operatng wth decreasng returns to scale under polcy regmes wheren fnancal support s offered only for a pre-determned output level may fnd

16 that pursung techncal and scale effcency s a deceve factor for the support level they enjoy. The Greek cotton producton under the EU cotton regme s an llustratve case. Ths paper examnes the mportance that nput-orented techncal and scale effcency may have for the support Greek cotton farmers receve, n the context of the current EU cotton polcy. For ths purpose we estmated econometrcally the techncal and scale effcency levels of a sample of cotton growers n the representatve, cottonproducng county of Kardtsa, n Thessaly-central Greece, for the perod 1997/98. In addton, by utlzng goal programmng we computed the cotton output of Kardtsa county, f all cotton farmng operatons n the county were both techncally and scale effcent. Our emprcal fndngs suggest that, n general, the cotton farms examned are techncally and scale neffcent. The 1980s hgh support polces of the EU appear to have consderably contrbuted to the neffcences observed. Our analyss ndcates that elmnaton of these neffcences could result n consderable gans; the cotton farmers examned could reduce producton costs by 46.0%, by becomng both techncally and scale effcent. Addtonally, we estmate that f cotton farms n the area examned were techncally and scale effcent the nterventon prce reductons (co-responsblty levy) mposed by the EU for excessve cotton producton would be smaller for all Greek cotton growers. Polcy recommendatons dervable from our study suggest that Greek polcy planners should complement the current EU cotton regme wth structural polces whch explctly nduce cotton farmers to mprove the techncal and scale effcency of ther operatons. Such measures can effectvely mtgate the amount by whch Greek cotton farmers exceed the EU mposed, cotton producton quota (MQG), thus mantanng the level of EU support, currently avalable to them, and ther farm revenue. Second, (and perhaps, more mportantly) such measures may prepare Greek cotton growers to cope wth future support reductons n lght of future CAP reforms and ncreasngly ntegrated, world agrcultural markets

17 References Agner, D.J., Lovell, C.A.K. and P. Schmdt (1977). Formulaton and estmaton of stochastc fronter producton functon models. Journal of Econometrcs 6, Atknson, S.E. and C. Cornwell (1994). Estmaton of output and nput techncal effcency usng a flexble functonal form and panel data. Internatonal Economc Revew 35, Färe, R. and C.A.K. Lovell (1978). Measurng the techncal effcency of producton. Journal of Economc Theory 19, Greene, W.H. (1999). Fronter producton functons n Handbook of appled econometrcs, Volume II: Mcroeconomcs, M.H. Pesaran and P. Schmdt (eds), Oxford: Blackwell Publshers. Karaganns G., and C. J. Pantzos (2002). To comply or not to comply wth polcy regulatons-the case of Greek cotton growers: A Note. Journal of Agrcultural Economcs 53, Kopp, R.J. (1981). The measurement of productve effcency: A reconsderaton. Quarterly Journal of Economcs 96, Kumbhakar S.C. and C.A.K. Lovell (2000). Stochastc Fronter Analyss, Cambrdge Unversty Press. Meeusen, W. and J. Van der Broeck (1977). Effcency estmaton from Cobb-Douglas producton functons wth composed error. Internatonal Economc Revew 18, Ray, S.C. (1998). Measurng scale effcency from a translog producton functon. Journal of Productvty Analyss 11, Renhard, S., Lovell, C.A.K. and G.J. Thjssen (1999). Econometrc estmaton of techncal and envronmental effcency: An applcaton to Dutch dary farms. Amercan Journal of Agrcultural Economcs 81, Weersnk, A., Turvey, C.G. and A. Godah (1990). Decomposton measures of techncal effcency for Ontaro dary farms. Canadan Journal of Agrcultural Economcs 38, Welch, F. (1970). Educaton n producton. Journal of Poltcal Economy 8,

18 Table 1 Summary Statstcs of the Varables Varable Mean StDev Mn Max Output (kgs) 22,736 17,139 2,000 94,500 Labor (hours) 1,728 1, ,574 Captal (EUR) 3,655 1, ,354 Seeds (kgs) ,698 Land (stremmas) Specalzaton (%) Age (years) Educaton (years) Total Assets (EUR) 6,046 1,670 2,036 26,

19 Table 2 Parameter Estmates of the Translog Stochastc Producton Fronter for Greek Cotton Farms Parameter Estmate Std Error Constant (0.0612) ** Labour (0.1039) * Captal (0.1244) ** Seeds (0.0545) * Area (0.0454) * Labor x Captal (0.0930) Labor x Seeds (0.0920) ** Labor x Area (0.0883) ** Labor x Labor (0.0266) ** Captal x Seeds (0.1608) * Captal x Area (0.1448) Captal x Captal (0.0526) * Seeds x Area (0.0912) ** Seeds x Seeds (0.1268) Area x Area (0.1211) σ (0.0354) * γ (0.0502) * Ln(θ) *(**) ndcate sgnfcance at the 1 (5)% level

20 Table 3 Producton Elastctes and Returns to Scale of Greek Cotton Farms Mean StDev Mn Max Labor Captal Seeds Area RTS Table 4 Frequency Dstrbuton of Input-Orented Techncal and Scale Effcency Effcency (%) I TE < I SE > N Mean Mnmum Maxmum

21 Table 5 Tobt Regresson of Input-Orented Techncal and Scale Effcences on Specfc Farm Characterstcs Varable Techncal Effcency Scale Effcency Estmate Std Error Estmate Std Error Farmers Age (years) (0.4125) * (0.3145) * Farmers Age-squared (years) (0.0044) * (0.0017) * Total Assets ( ) (0.0014) (0.0015) Farmers Educaton (years) (0.0125) * (0.1458) * Specalzaton (Herfndhal ndex) (0.2036) * (0.3025) McFadden R Table 6 Potental Cost Savngs for Cotton Farms In Euros In Euros/Stremma Actual Cost of Producton 14, Total Cost Savngs 6, Due to Elmnaton of: (46.0) Techncal Ineffcency 3, (25.3) Scale Ineffcency 2, (20.7) One stremma equals 0.1 ha. Numbers n parentheses are the correspondng percentage values

22 Table 7 Potental Co-Responsblty Levy Reducton for all Greek Cotton Growers. Perod: Actual data Estmates f Kardtsa cotton growers were tech. and scale effcent Actual cotton producton 1,085,488 MT 1,030, MT (-5.1%) Producton quota (MQG) 782,000 MT 782,000 MT Co-responsblty levy 49.8 drs/kg drs/kg (-18.27%)

23 Y Fgure 1 Measurement of Techncal and Scale Effcency y B D A f(x) y~ C O ~ x x x x

24 Fgure 2 Muncpaltes n the Survey Fgure 3 Farm Weght Dstrbuton for the 172 Sample Farms f1 f12 f23 f34 f45 f56 f67 f78 f89 f100 f111 f122 f133 f144 f155 f

25 Endnotes 1 Ths relatonshp between nput-orented techncal effcency and total cost of producton s not vald for output-orented techncal effcency measures except n cases where lnear homogenety (constant returns to scale) n all nputs holds (Färe and Lovell, 1978). 2 It should be noted here that γ s not equal to the rato of the varance of the techncal neffcency effects to the resdual varance. Ths s because the varance of u s 2 2 equal to [( π ) π] σ not. The relatve contrbuton of the neffcency effects to 2 u σ u * { [{( ) } ( )]} the total varance term s equal to γ = γ γ + 1 γ π π 2 (Greene, 1999, p. 101). 3 However, Weersnk et al., (1990) argued that nexperenced farmers tend to acqure more easly knowledge about recent technologcal advances than ther older counterparts. ( ) 4 I These estmates are obtaned by multplyng total average cost by 1 TE. 5 In a dfferent analytcal framework Karaganns and Pantzos (2002) show that full complance wth (rather than consstent volaton of) the country-level producton quota mposed by the current EU cotton regme would make Greek cotton farmers better-off. The emprcal results of the present study lend addtonal support to the vew that Greek cotton farmers can mantan ther farm ncome by fully abdng to producton controls and reducng producton costs va effcency mprovements rather than persstently demandng ever hgher, admnstratve prces