The efficiency of dairy farms in Austria: Do natural conditions matter?

Transcription

1 The effcency of dary farms n Austra: Do natural condtons matter? K. M. ORTNER, L. KIRNER and J. HAMBRUSCH Introducton Dary farmng s a partcularly mportant enterprse wthn the Austran agrcultural sector. Some 50,000 farms (one thrd of all agrcultural enterprses) are producng and sellng mlk, and beef and mlk amounted to almost 30 % of the producton value of agrculture n Although dary farmng n Austra has experenced consderable structural change over many decades f.. the number of dary farms declned by 33 % from 995 to 2003 (KIRNER 2005) - dary farms n Austra are stll very small n sze compared to farms n many other countres. Furthermore, the Austran dary sector s characterzed by a hgh share of mountan farms (70 %) and organc farms (6 %). Structural change s expected to contnue. Although drect payments n Austra are slghtly hgher per hectare for small-scale farms and compensate for natural dsadvantages and/or envronmental servces, many farmers and ther famly are contemplatng to opt out of farmng or to earn more off-farm ncome. The alternatve s to try to stay compettve n a globalzng world. Ths wll be partcularly demandng as the EU s mplementng a reducton of prce support for dary products by almost 20 percent untl 2008, and the drect payments whch have been ntroduced to compensate for ncome loss are gong to be decoupled from producton. As a result, effcency wll be a key determnant of dary farm survval. The am of ths study s to explore the causes for dfferences n effcency of specalsed dary farms n Austra and to determne what can be done to ncrease t. In other words, we estmate the contrbuton of varous factors to the effcency of farms, ncludng natural condtons. Ths allows us to determne what potental there s to ncrease effcency as opposed to the contrbuton of factors over whch a farmer has no control. In partcular, we suspect that effcency correlates wth the degree of natural dsadvantage, farm sze, the farmng system (organc or conventonal) etc. In terms of methodology, Data Envelopment Analyses (DEA) has become a standard for the estmaton of techncal effcency scores. It offers a flexble approach wth consderable scope for the use of dverse types of data (real and monetary) and multple nputs and outputs. However, the statstcal propertes of DEA estmators have been gnored by prevous studes whch regressed effcency scores or transformatons of them on exogenous varables n a second stage. Snce SIMAR and WILSON (2005) examned these propertes and proposed DEA bootstrappng and truncated regresson methods for the frst and the second stage, respectvely, there appears to be no advantage n usng a stochastc parametrc model for the

2 estmaton of the producton fronter and devatons from t. On the other hand, t s mportant to hghlght the varous components or types of effcency and to evaluate them n terms of data avalablty and comparablty. The rest of the study s organsed as follows: Frst we present an overvew of the sample farms and ther characterstcs. Second, the components of effcency are presented theoretcally, startng wth ndcators from accountng to pure techncal effcency. Thrd, we present our methodology, and fourth, our estmaton results, n two stages, ncludng dscusson and comparson wth results of other studes. The summary wraps up the paper and concludes. 2 Revew of dary farm effcency studes Snce ts ntroducton n 978 DEA has been used n many ndustres to examne effcency levels. There have also been several applcatons of DEA to the dary sector n dfferent countres but no studes exst on Austran dary farmng. CLOUTIER and ROWLEY (993) compared the dstrbuton of effcency scores across Canadan dary farms between 988 and 989. Ther analyss was based on fve nputs (herd sze, labour, cultvated land, anmal feed, other nputs) and three outputs (total quantty of mlk, revenue from mlk sales, other revenue) of a sample of 87 farms whch ncreased ther effcency n 989. FRASER and CORDINA (999) found for a sample of 50 rrgated dary farms n Northern Australa that a sgnfcant number of farms were operatng effcently or were very close. If all farms would operate effcently, a reducton of water use by 6 % would have been possble. They recommended ncorporatng soco-economc data n further studes such as age or educatonal level of farmers. A number of studes focussed on the examnaton of scale effcences whle others generated effcency results by dfferent methods and analysed ther comparablty. For nstance FRASER and GRAHAM (2005) employed DEA to measure techncal effcency (TE) and scale effcency (SE) for a sample of 742 Australan dary farms. Ther results showed an average techncal effcency of 59 %, wth sgnfcant regonal dfferences reflectng dstnct levels of mlk marketng arrangements n the ndvdual states. Addtonal analyss showed the effects of an excluson of specfc nputs (e.g. rrgaton) on techncal effcency. The measurement of scale effcency of the New Zealand dary ndustry and the examnaton of relatonshps between farm sze and techncal effcency was analysed by JAFORULLAH and WHITEMAN (998). They appled DEA to a sample of 264 dary farms whose average techncal effcency turned out to be 89 %. Accordng to ther results more than half of the dary farms operated below optmal scale and consequently could ncrease techncal effcency by ncreasng farm sze. 2

3 GERBER and FRANKS (200) examned the scale effcency of farms n England and Wales. Ther results showed that napproprate farm szes contrbuted to neffcency. Dary farms wth herds between 70 and 60 cows operated on constant returns to scale whereas farms wth less or more cows showed dseconomes of scale. Furthermore, the average relatve techncal effcency of the farms was 87 %. BARNES and OGLETHORPE (2004) consdered techncal and cost effcency of 57 Scottsh dary farms over two years ( ). Ther results ndcate that large farms would beneft from reductons n scale whereas small to medum farms are recommended to ncrease farm sze n order to obtan hgher effcences. Usng data from the New Zealand dary ndustry for the year 993, JAFORULLAH and PREMACHANDRA (2003) compared the emprcal performance of three popular approaches to the estmaton of techncal effcency of producton: Corrected Ordnary Least Squares Regresson (COLS), Stochastc Fronter Analyss (SFA) and Data Envelopment Analyss (DEA). The results ndcate that estmates of techncal effcences of ndvdual dary farms were senstve to the choce of producton fronter estmaton method. Envronmental effcency measures were estmated by REINHARD (999) and REINHARD et al. (2000) for Dutch dary farms by applyng two dfferent methods: SFA and DEA. There are strengths and weaknesses of both methods whch came up wth dfferent mean techncal effcency scores (SFA 89 %, output orented DEA 78 %). Smlar comparsons were performed by JOHANSSON (2005) for a panel of Swedsh dary farms. She found that DEA measures for techncal and economc effcency were sgnfcantly hgher than the correspondng SFA measures. However, the allocatve effcency scores were hgher under the SFA approach whch mght be a result of the lower SFA techncal effcency ndces. She concluded that n case of analysng entre farms DEA s more approprate to use snce t does not requre assumptons on the parametrc forms. Average techncal and allocatve effcences were 0.77 and 0.57, respectvely, ndcatng that the major problem of the sample farms appears to be the nablty to allocate nputs n a cost mnmzng way, rather than the nablty to use the nputs n a techncally effcent way. MBAGA et al. (2003) appled DEA to augment the robustness of ther effcency scores for Quebec dary farms wth respect to the selecton of functonal forms (Leontef, Cobb-Douglas, translogarthmc functons) and the dstrbutons of the neffcency ndexes. 3

4 3 Methodology 3. Income and effcency The survval of a frm depends on ts ablty to remunerate ts factors of producton adequately. Farm ncome or proft (π ι ) s the remuneraton of a subset of the nputs used, whch usually ncludes self-employed labour (x q ) and propretor-owned captal nvested n frm n a certan perod, ncludng land (x q- ). For smplcty of exposton, these two sets of nputs wll be referred to as fxed nputs and varable nputs subsequently. Proft s defned as q p j= p s r π = x () j y j k= wth y j the quantty of output j produced by farm x p j r q p s the quantty of nput k used by farm the average prce receved by farm for output j the average prce pad by farm for nput k the number of outputs the number of nputs and the number of fxed nputs. If all nputs and outputs are valued at farm-specfc prces, the accountng dentty holds: q p j= j y j = p r k= x (2) An economc effcency ndcator can be defned as the rato between the value of all outputs (revenue) and all nputs (costs): I = q p j= j y j p r k = x / (3) wth p j p j and/or r r for some If the same (observed) prces were used n (2) and (3) for all nputs and outputs, ndex I would assume the same value for all frms due to (2). Effcency ndex (3) s thus a functon of the mputed (target) prces charged for some or all nputs and/or outputs. If only one prce, say r K, were held constant across frms (r K = r K ), I would smply mrror the varaton of ths prce across frms. Ths would ndcate how well a frm s buyng (sellng) the respectve nput (output). Snce we are nterested n a more comprehensve ndcator wth a focus on the relatonshp between nputs and outputs and less dependent on prces, the use of at least some common prces across frms n (3) s requred. 4

5 Indvdual prces can vary substantally by tme, locaton, marketng behavour etc. In order to elmnate the effect of ther varaton on the effcency score of a frm, common prces (p j =p j and r = r k ) can be used n (3) as objectve weghts for nputs and outputs,.e. by valung them at the same prces for all frms. Dong so yelds the usual defnton of economc (or cost) effcency (EE) where I = q p j= j y j / p r k= k x EE = I / max (I ) (4) Economc effcency measures effcency relatve to the best frm n the sample for whch EE =. Note that both EE and relatve economc effcences EE /EE j depend on the relatve prces used n (4), and, of course, on the frms n the sample. The measurement of quanttes s sometmes not easy because qualty dfferentals should be taken nto account. If prces are strongly related wth the qualty of the products purchased or sold by ndvdual frms, ths nformaton could be used to represent qualty dfferentals through a correspondng varaton n quantty, f.. by measurng quantty wth ts value; however, ths s equvalent to the use of ndvdual prces for each frm as n (3). In order to establsh the relatonshp between economc effcency and proft - the most famlar ndcator of economc performance of a frm assume that I n (3) s calculated usng the observed ndvdual prces for all outputs and varable nputs, as n (). Snce prces for fxed nputs of a partcular frm are not observed, they can be chosen arbtrarly as long as they meet the condton followng from () p r π = x (5) k = s+ Substtutng the ndvdual prces of () and (5) n (3) yelds j j j= = = c + π I = (6) p s p + + c π r k = q p x y r k= s+ x For I to vary, replace π n the denomnator wth target proft, usng common prces for fxed nputs: p r π = k x (7) k= s+ Ths yelds an economc effcency ndcator as a functon of proft 5

6 q p y = c + π π j j j= I = p s p c + r x + rk x k = k = s+ (8) Usng (8) t s easy to calculate the relatve economc effcency score from accountng data as the rato between revenue and target revenue. Note that by dsregardng varable nputs settng ther prces to zero, (8) becomes the rato of proft over target proft; ths s a popular ndcator of the relatve performance of farms n accountng. These two ndcators wll be calculated usng a prce of 9,050 for annual labour nput (as n the Green Reports) plus a remuneraton of 200 per ha of farmland. 3.2 Techncal effcency and stochastcs Techncal effcency refers to the relatonshp between nputs and outputs ndependently of all prces. For a frm to be economcally effcent t must use those partcular quanttes of nputs whch produce a gven value of outputs at mnmum cost. For ths to happen t must be techncally effcent,.e. t must operate at the fronter of the producton possblty (technology) set, defned as T = {( x, y) x can produce } y where 6 x R p and y + R q + Accordngly, economc and techncal effcency are related by EE = TE AE where AE = EE / TE s allocatve effcency. (9) Smlarly, as wll be explaned shortly, a dstncton between pure techncal effcency (PTE) and scale effcency (SE, TE = PTE SE) can be made, yeldng EE = PTE SE AE (0) The major objectve of ths paper s to determne how much TE depends on natural condtons and other attrbutes may enable a farm or farmer to attan a hgher level of TE. Data Envelopment Analyss (DEA) has been frequently used to calculate TE. It s a data orented, non-parametrc, determnstc approach for evaluatng the performance of a set of peer enttes called Decson Makng Unts (DMUs) whch convert multple nputs nto multple outputs (COOPER et al. 2004). The dstance δ0 of an ndvdual DMU, defned by ( x, y ) q+ p +, to the fronter of a producton possblty set T (9) s δ x y 0 = δ (, T ) = ( θ ( x, y)) sup{ δ ( ) T, δ > 0} δ x 0 0 R

7 δ0 s the FARRELL (957) measure of nput-orented techncal effcency. Input-orentaton s more approprate n a stuaton n whch the output of dary farms s restrcted by a mlk quota. Ths dstance can be calculated by the nput-orented lnear programmng model as orgnally presented n CHARNES et al. (978): θ = mn θ subject to n = n = x y j λ λ 0 λ θ x y j0 k 0 k =,..., p j =,..., q =,..., n max s subject to n = n r k= xkλ yλ = λ, s, k s r k s s s k j s j= + = = + y j0 x 0 k,, j j θ k 0 k =,..., p j =,..., q =,..., n () where θ s a scalar, l s a vector descrbng the contrbuton of the benchmark DMUs to the vrtual DMU on the fronter, x and y are nput and output quanttes respectvely, s - and s + are slacks (nput reducton) and surpluses (output ncrease) used to convert nequaltes to equvalent equatons. The value of θ represents the effcency score of the j-th DMU, where a value of less than one ndcates an neffcent DMU whch could mprove ts effcency by a proportonal reducton of all nputs and adopton of the best avalable technology. θ can then be used to detect nput slacks or output surpluses n a second step. The lnear program () assumes constant returns to scale (CRS), mplyng that a doublng of all nputs leads to a doublng of all outputs. If CRS prevals, farmers are able to lnearly scale the nputs and outputs wthout ncreasng or decreasng effcency. However, farms usually operate n an envronment wth varable returns to scale (VRS). Thus a doublng of all nputs may lead to a less than (or greater than) doublng of all outputs, f.. due to the law of dmnshng returns. BANKER et al. (984) addressed ths problem by mposng an addtonal convexty constrant to () n λ = = Ths removes the requrement n () that DMUs must be scale effcent (SE). The recprocal of s the value of the nput dstance functon and wll be denoted by d subsequently. Assumng that d depends lnearly on envronmental varables z, the second stage relatonshp can be specfed as: δ = z β + ε I (2) where z s a r-vector of attrbutes β s a r-vector of coeffcents ε s dstrbuted N(0,σ 2 ε ) wth left-truncaton at - z β for. 7

8 To estmate β, we have to replace the δ n (2) wth ther estmates; these are serally correlated because every one of them s a functon of all observatons (x, y). Accordngly the error term n (2) s serally correlated, and correlated wth z due to the assumpton that there exsts a dependence between (x, y) and z. Snce the estmates of δ are always strctly based downward n fnte samples, t may be preferable to use bas-corrected estmates of δ n (2), as has been confrmed by SIMAR and WILSON (2005) n a Monte-Carlo experment. Bascorrected estmates are obtaned by the bootstrap method proposed by SIMAR and WILSON (2000) usng 000 replcatons. Bootstrappng yelds confdence ntervals for the estmated dstance scores and estmates of the bas for the DMUs. The parameters β n (2) are then estmated through maxmsaton of the lelhood functon. The software has been made avalable publcly by WILSON (2005) as a package of R. 4 Data 4. Sources The data for the present analyses orgnated from the voluntary Farm Accountng Data Network (FADN) whch comprses some 2400 farms and represents about two-thrds of all Austran farms. These data are used to prepare the annual report on the ncome of Austran farms (BMLFUW 2004). Some 550 of these farms specalze n dary farmng,.e. at least 75 % of ther gross margn orgnates from forage croppng and ther standard gross margn from mlk producton exceeds that from cattle fattenng. Snce the producton structure among these farms vares consderably, we appled the followng addtonal condtons to ensure a more homogeneous sample: - revenues from dversfcaton (e.g. drect marketng, accommodaton) must be less than 0 % of revenues - revenues from cash crops must be less than 0 %, and - more than 95 % of lvestock must be cattle. 222 dary farms fulflled these condtons and provded data for each of the years 200 through Table compares the average dary farm n the sample wth the average dary farm n Austra by some major characterstcs. The data refer to averages over three years. The sample farms are about twce as bg wth respect to mlk quotas, and there s a bgger share of mountan farms and organc farms n the sample. The sample covers a wde range of szes and feed bases (grassland vs. arable land); the bggest farm works about 66 ha of farmland. 8

9 Table : Comparson of dary farms n the sample and all dary farms n Austra (average of the years ) Varable Sample Mnmum Maxmum All dary farms Number ,07 Mountan farms (%) Organc farms (%) Mlk quota per farm (t) Utlzed agrcultural area (ha) Grassland n UAA (%) Labour (ALU) Source: BMLFUW, Invekos Data ; LBG ; own calculatons. 4.2 Inputs and outputs The problem of whch varables to use as nputs and outputs of farms can be solved on the bass of two crtera: Frst, all revenues and costs should be accounted for, n terms of ether physcal quanttes or monetary values, wth suffcently accurate data. Secondly, wth an ncreasng number of nputs and outputs chosen, the number of degrees of freedom declnes and the number of farms classfed as effcent goes up. Thus n order to ncrease the varablty of the effcency ndex, the rato of nputs and outputs relatve to the sze of the sample must be small. Accordngly, all producton nputs for mlk and all types of revenue of the farm were taken nto account by the fve nputs and two outputs selected, where the ndcators of output are: - quantty of mlk produced durng the year mnus mlk used as feed (ncludes mlk used for on-farm consumpton) (kg) - revenues accrung to the farm mnus revenue for mlk and drect payments ( ). The ndcators of nputs ncluded n the DEA model are: - expenses for anmal husbandry (e.g. feed, veternary) ( ) - expenses for machnery and energy (e.g. fuel, electrcty, repars, deprecaton) ( ) - other expenses (nsurance, taxes) ( ) - qualty adjusted farmland (ha) - heavy lvestock unts (HLU as a proxy for stables and related costs) - labour (unpad famly work unts FWU). 9

10 4.3 Farm attrbutes The effcency of the dary farms or the dstance of a partcular farm to the producton possblty fronter could depend on varous factors, n partcular on farm sze, the degree of natural dsadvantage, the farmng system and the sklls of the farmer. Wth ncreasng scale, labour, machnery, buldngs and land can be used more effectvely. Ths s expected to show up n changes of TE and SE as the sze of the farmng operaton ncreases. Sze may nduce a farmer to pay more effort to hs enterprse and thus to ncrease ts PTE. If on larger farms t s possble to acheve a hgher remuneraton for the factors of producton, ths would allow for the use of more up-to-date and/or specalsed machnery and equpment. Snce dfferences n the qualty of nputs and outputs are usually not accounted for by the numbers from whch effcency s estmated, these dfferences end up as varatons n effcency scores. These depend f.. on the qualty of labour and management (human captal), the qualty of the herd (yelds and fertlty), the qualty of the land (share of grassland, yelds, dstance, and steepness), and the farmng system (organc farms are restrcted to apply only a subset of the avalable technology). The Austran FADN ncludes data on farm attrbutes whch may explan some of the varatons n effcency between farms. They are summarzed n Table 2 for the sample of 222 dary farms. The frst group of varables refers to natural condtons, the second ndcates organc farms, the thrd group measures farm sze, the fourth government support, and the last sheds lght on the personal characterstcs of a farmer, hs/her famly and the mportance of off-farm actvtes. The hypothessed mpact of these varables on effcency scores wll become evdent durng the dscusson of the results of the analyss. Table 2: Farm and farmer s characterstcs of specalsed Austran dary farms whch may condton ther techncal effcency scores Attrbute unt mean stdev mn max mountan farm cadastre ponts number sea level m zone (3=flatland) level,97 0,63 3 organc farmng (=partcpaton) mlk quota kg gross mlk producton kg dary cows number standard gross margn drect payments off-farm actvty (0=full-tme farmng, 2=retred) level 0,2 0, vocatonal tranng level 2,42 0,7 4 household sze excl. retrees persons 5,93,96 2 household sze persons 6,94 2,48 2 mean of mountan farms = = on the job tranng, 2 = apprentceshp, 3 = vocatonal school, 4 = hgh school or unversty 0

11 5 Emprcal results and dscusson 5. Economc performance of specalsed dary farms The economc performance of the 222 farms n the sample s usually measured by ther a) proft and b) proft per self-employed famly work unt. Whether the remuneraton of labour and captal through proft on a partcular farm s adequate can be judged aganst a benchmark whch each farmer sets for hm- or herself. The general benchmark of 9,054 per FWU per year (on average for ) has been used for publc evaluaton of farm ncome data n the respectve annual Green Reports (BMLFUW). Addng a target prce of 200 per ha for the servces of captal, we calculate a target proft π for labour and quasfxed captal and compare t wth the observed proft of the farm π n Fgure. More than two thrds of the farms n the sample dd not acheve the target proft whch s shown as the break-even lne n Fgure. Fgure : Proft vs. target proft of specalsed Austran dary farms, average of proft (ncludng drect payments) per farm target proft Some of the farms may be proftable only because some of ther nvestments may be so old as to command no deprecaton any more,.e. ther book value may be zero. In order to elmnate the bas ntroduced by ndvdual prces for the servces of buldngs, we replaced them wth the medan prce per lvestock unt (255 ) to calculate an adjusted value of the nputs used. Fgure 2 shows that only n 6 out of 222 farms revenues and drect payments exceeded the costs of the nputs employed. As drect payments are gong to be decoupled from

12 producton, there wll be no ncentve to use them for the remuneraton of farm nputs, and the pressure to qut farmng wll ncrease. Fgure 2: Revenues vs. value of nputs of specalsed Austran dary farms, average of revenues ncludng and excludng drect payments economcally effcent farm value of nputs ( ) excludes ncludes drect payments Also from Fgure 2 t s obvous that the economcally effcent farm n the sample accordng to (8) s rather small; t uses some 43,000 of nputs to produce some 54,000 of outputs. A lne through the orgn and ths pont would ndcate the economcally effcent fronter. It s subject to the partcular crcumstances and marketng condtons under whch ths farm operates; f.. on the partcular mlk prce at whch ths farm s able to sell. Mlk prces of the varous farms ranged from 27 to 60 Cents per kg, wth a medan of 33 Cents. Prce varablty adds substantally to the volatlty and unrelablty of ths EE and the correspondng AE ndcator. 5.2 Techncal effcency The recprocals of effcency scores and ther 95 % confdence ntervals were estmated under the assumpton of both constant and varable returns to scale technology, usng average data over three years of the two output and fve nput varables mentoned above, wth 000 bootstrap replcatons each. Scale effcency was obtaned from the orgnal estmates of TE () and PTE n order to meet the condton that SE for all. A summary of the results s presented n Table 3. 2

13 < 0,4 0,4 - < 0,5 0,5 - < 0,6 0,6 - < 0,7 0,7 - < 0,8 0,8 - < 0,9 0,9 - <,0,0 Table 3: Effcency of specalsed Austran dary farms, mean medan share of (00 %) effcent farms TE (%) PTE (%) SE (%) Fgure 3 compares the dstrbuton of the varous effcency scores by percentles. Wth respect to TE, the hghest number of farms s n the bracket between 70 and 80 %. All but 8 farms score less than 90 % on PTE. Whle most SE -s are close to, there s an extraordnary farm whose SE s less than 0.4 (not shown n Fgure 4). Fgure 3: Dstrbuton of effcency scores Number of farms < < < < < < <.0.0 Techncal effcency Pure techncal effcency Scale effcency On average, TE of the specalsed Austran dary farms analysed was 72.3 %. If t were possble to move all farms to the CRS producton fronter estmated from the sample farms, almost 28 % of the current nputs nto dary farmng could be saved, wthout changng output. 25 % of the farms were scale effcent, but scale neffcency contrbuted 6.4 % to techncal neffcency. As demonstrated by Fgure 4, scale effcency was attaned by small and large farms, but small farms were less lely to attan t. Note that sze does not cause scale effcency to ncrease. The estmates of TE n Fgure 5 show that the bas n the TE scores calculated by () s substantal and should not be neglected, as has been done n most prevous studes usng DEA. In contrast to the uncorrected estmates (), the dstance to the bas-corrected fronter s postve for all farms,.e. none of the farms touches the fronter. The relablty of the estmates of TE whch are close to or determne the fronter s lower, as expected. Snce there are no TE scores equal to, no degrees of freedom are lost for the estmaton of secondstage effects whch explan why certan farms are farther from the fronter than others. 3

14 Fgure 4: Dstrbuton of scale effcency of Austran dary farms by farm sze Scale effcency by mlk quota,00 0,90 0,80 0,70 0,60 0, mlk quota (t per farm) Fgure 5: Estmates and dstrbuton of techncal effcency scores for 222 Austran dary farms TE score estmates and confdence ntervals,00 0,90 0,80 0,70 0,60 uncorrected 0,50 0,40 0,30 upper bound bas-corrected lower bound percent of farms 5.3 Impact of attrbutes on effcency scores The maxmum lelhood estmaton of the mpact of varous farm and farmer s attrbutes was hampered by the problem that convergence of the estmator s not ensured. The problem s exacerbated by hgh correlatons between the attrbutes whch makes t dffcult for the algo- 4

15 rthm to separate the contrbuton of the ndvdual attrbutes to effcency. In fact t was mpossble to obtan parameter estmates for more than 2 condtonng varables smultaneously. However, the estmaton of the mpact of changes of ndvdual attrbutes on the bascorrected dstances from the CRS and VRS fronters was possble. The resultng coeffcents capture not only the mpact the respectve varable but also the mpacts of other varables whch correlate wth t, dependng on the sze of the correlaton. In order to get a feelng for the sze of the omtted-varable bas n these coeffcents, the attrbutes can be represented by ndependent factors or conversely factors can be represented by certan attrbutes. Results of the factor analyss n Table 4 show that farm sze s most ndependently represented by the standard gross margn and the mlk quota. As the sze goes up, drect payments go up, too, and the farmer s less prone to pursue off-farm actvtes. Wth regard to natural condtons, the level of dsadvantage s most ndependently represented by mountan farm class and mountan farm cadastre ponts; sea level and drect payments are postvely correlated wth t. Household sze appears to be also qute ndependent of other attrbutes, except of age, because younger farmers tend to have more household members. Table 4: Factors representng attrbutes of farms and farm managers factor name Factor Factor 2 Factor 3 Factor 4 Factor 5 farm sze level of dsadvantage household sze sea level technology standard gross margn mlk quota drect payments off-farm earnngs mountan farm class mountan farm ponts household sze 0.99 household sze excl. retrees sea level zone share of grassland (%) organc educaton level vocatonal tranng level age of farm manager proporton of varance represented Wth the correlatons gven n Table 4 n mnd t s possble to nterpret the estmated mpact of ndvdual varables gven n Table 5 correctly. Table 5 shows the estmated parameters β of model (2), ther relablty (gven by t-statstcs), and the correspondng effects of certan changes n attrbutes on average effcency levels; these effects must not be added. Coeff- 5

16 cents whose t-statstc was less than were assumed to be zero. Snce the dstances to the CRS fronter d TE (= /TE ) nclude the effects of both scale and pure techncal neffcency, they are larger than d PTE (= /PTE ) and depend much more on attrbutes whch measure farm sze. The mlk quota s a case n pont; t turned out to be the most sgnfcant condtonng varable on both TE and PTE, but t affects TE almost 4 tmes as much. The dfference s due to SE whch ncreases wth the quota on average (as shown n Fgure 4). Table 5: Estmated effects of condtonng varables on the dstance to the CRS and VRS fronter effect of Attrbute TE PTE on d TE t c on d PTE t v 00 mountan farm cadastre ponts m sea level level Zone organc farmng ,37 0, , t mlk quota t gross mlk producton dary cow standard gross margn drect payments level off-farm ncome level vocatonal tranng person household sze excl. retrees person household sze t-statstc = b/s(b); the estmated coeffcent (b) s sgnfcantly dfferent from zero wth a probablty of 95 % f t > 2 and a probablty of 67 % f t >. evaluated at the average d TE =.4337, d PTE = Startng wth natural condtons, the degree of dsadvantage as measured by mountan farm cadastre ponts (MFCP) has a sgnfcant effect on how far a farm would be expected to be located from the producton possblty fronter. Mountan farms n the sample are characterzed by up to 328 MFCP. A farm wth 00 ponts s lely to need 8 percentage ponts more nputs for the same level of producton than a non-mountan farm. Half of that s due to PTE. Sea level s a less approprate ndcator for natural dsadvantage and correlated wth MFCP and zone (see Table 4); both TE and PTE decrease wth t, but PTE not sgnfcantly. Farmng n flat and hlly areas (zone ) ncreases techncal effcency by 5.6 percentage ponts over farmng n mddle hgh areas (zone 2) and.2 percentage ponts over farmng n alpne regons (zone 3). Organc farmng nvolves a loss of 3.7 percentage ponts of TE. Ths estmated effect s not sgnfcantly dfferent from zero but plausble snce organc farms have a restrcted producton possblty set at ther dsposal. Standard gross margn and mlk quota are farly ndependent ndcators of farm sze (see Table 4). In addton, we used gross mlk producton and the number of dary cows as alter- 6

17 natve ndcators for sze. All sze attrbutes pont to sgnfcant mpacts on TE. Wth ncreasng quota and mlk producton, farms become not only more scale effcent but also more purely techncal effcent, ths may be partly due to a hgher vocatonal tranng level for farmers wth a larger quota. Hgher amounts of drect payments are correlated wth farm sze, the degree of natural dsadvantage and/or organc farmng. But hgh drect payments can be also obtaned for nvestments whch result n hgher effcency. Snce the effect of the former varables domnates, the combned effect s that TE goes down as drect payments go up. The attrbute off-farm ncome dstngushes between full-tme and part-tme farmers. As a result of beng more economcally dependent on farmng, full-tme farmers are expected to produce more effcently. In fact, part-tme farmng reduces TE on average (not statstcally sgnfcant) but part-tme farmers operate at a 6 percentage ponts hgher level of PTE than fulltme farmers. The dfference must be due to scale neffcency whch s possble as the sze of ther farms s rather small. The vocatonal tranng level s postvely correlated wth TE; ths may be due to ts correlaton wth farm sze. The effect of household sze depends on there beng a retred farmer n the household or not; retrees appear to decrease TE. 6 Summary and concluson Dary farms n Austra are qute small relatve to ther compettors n the EU. Wth an average mlk quota of 50 t, they are about half the sze of dary farms n Bavara and Swtzerland. In addton they face substantal natural dsadvantages as 70 % of them are mountan farms. The Common Agrcultural Polcy s compensatng some of the ncome loss due to ths dsadvantage through drect payments. But the pressure to become economcally more effcent or qut mlk producton s ncreasng for two reasons: Mlk prces could go down as a consequence of a decrease n nterventon prces for dary products whch started n 2004, and drect payments are gong to be completely decoupled from mlk producton n The optons for a farmer to ncrease effcency are lmted by the natural condtons under whch he/she operates. But the rest represents an opportunty whch could be exploted. The current study explored the relatonshp between proft and economc effcency ndcators, estmated the level of economc and techncal effcences (TE) of 222 specalsed dary farms n and nvestgated to what extent natural and other condtons mpact on TE and pure TE (PTE). The DEA bootstrappng approach of SIMAR AND WILSON (2000) produced estmates of the confdence ntervals of the TE and PTE scores and the bas of DEA estmates. The method 7

18 of SIMAR AND WILSON (2005) was used to estmate the mpact of farm and farmer s attrbutes on the dstance to the effcent fronters of the producton possblty sets at constant and varable returns to scale. Very few of the farms n the sample covered ther costs (whch nclude a far remuneraton of labour and land) wth revenue and drect payments. The average TE of the sample farms was 72.3 %;.e. f all farms were able to apply the best avalable technology, almost 28 % of nputs could be saved. But what s avalable for some farms s unattanable for others. F.. a farm wth 00 mountan farm cadastre ponts (MFCP) wll need some 8 percentage ponts more nputs than a non-mountan farm. MFCP was found to be the most sgnfcant varable ndcatng natural condtons mpact on TE. There are other attrbutes whch are postvely related to TE, most mportantly farm sze. Mlk quota s the most sgnfcant varable, but mlk producton and the number of dary cows are attrbutes whch foster effcency. Also vocatonal tranng (possbly provded by extenson servces) s lely to ncrease TE. Consderng that 75 % of the farms nvestgated operate at less than optmal scale, there s ample opportunty to ncrease TE. Unfortunately, t was mpossble to estmate the margnal effects of the attrbutes on effcency because convergence of the ML-estmator s not ensured and was not acheved for more than 2 attrbutes wth the current sample. Therefore the coeffcents had to be estmated for every varable n turn; accordngly they are subject to omtted-varable bas. The sze of the bas can be nferred from the results of a factor analyss of the attrbutes by the experenced reader. It seems to be small for some attrbutes, as ponted out n the dscusson of the results. 7 References BARNES, A. and OGLETHORPE, D. (2004): Scale Effcences and the Md-Term Revew: An Analyss of Scottsh Dary Farmng, Conference paper presented at the 78th Agrcultural Economcs Socety, 2-4 Aprl 2004, Imperal College, London. BMLFUW (Bundesmnsterum für Land- und Forstwrtschaft, Umwelt und Wasserwrtschaft) (2004): Grüner Bercht (Green Report) Wen. CHARNES, A., COOPER, W.W. and RHODES, E. (978): Measurng the Effcency of Decson Makng Unts, European Journal of Operatonal Research, 2, CLOUTIER, L.M. and ROWLEY, R. (993): Relatve techncal effcency: Data Envelopment Analyss and Quebec s dary farms, Canadan Journal of Agrcultural Economcs 4, COOPER, W.W., SEIFORD, L.M. and J. ZHU, J. (2004): Handbook on Data Envelopment Analyss Sprnger, Kluwer Academc Publshers, Boston. 8

19 DYSON, R.G., ALLEN, R., CAMANHO A.S., PODINOVSKI, V.V., SARRICO, C.S., SHALE, E.A. (200): Ptfalls and protocols n DEA. European Journal of Operatonal Research 32, FARRELL, M. J. (957): The measurement of productve effcency. Journal of the Royal Statstcal Socety, Seres A 20, FRASER, I. and CORDINA, G. (999): An applcaton of data envelopment analyss to rrgated dary farms n Northern Vctora, Australa. Agrcultural Systems 59, FRASER, I. and GRAHAM, M. (2005): Effcency Measurement of Australan Dary Farms: Natonal and Regonal Performance, School of Agrculture and Food Systems, GERBER, J. and FRANKS, J. (200): Techncal effcency and benchmarkng n dary enterprses, Journal of farm management, Vol. 0, No. 2, JAFORULLAH, M. and PREMACHANDRA, E. (2003): Senstvty of techncal effcency estmates to estmaton approaches: An nvestgaton usng New Zealand dary ndustry data. Economcs Dscusson Papers No. 306, Unversty of Otago JAFORULLAH, M. and WHITEMAN, J. (998): Scale Effcency n the New Zealand Dary Industry: A Non-parametrc Approach, General Paper No. G-29, Centre of Polcy studes, Monash Unversty. http// JOHANSSON, H. (2005): Techncal, allocatve and economc effcency n Swedsh dary farms: the Data Envelopment Analyss versus the Stochastc Fronter Approach. Poster background paper prepared for presentaton at the XI th Internatonal Congress of the European Assocaton of Agrcultural Economsts (EAAE), Copenhagen, Denmark, August KIRNER, L. (2005): Strukturwandel n der österrechschen Mlchvehhaltung Veränderungen von 995 bs Der fortschrttlche Landwrt, Nr. 2/2005, MBAGA, M.D., ROMAIN,R., LARUE, B., LEBEL,L. (2003): Assessng techncal effcency of Quebec dary farms, Canadan Journal of Agrcultural Economcs 5, REINHARD, S. (999): Econometrc analyss of economc and envronmental effcency of Dutch dary farms, Ph.D. Thess, Wagenngen Agrcultural Unversty. REINHARD, S., LOVELL, C.A.K., THIJSSEN, G.J. (2000): Envronmental effcency wth multple envronmentally detrmental varables; estmated wth SFA and DEA, European Journal of Operatonal Research 2, SIMAR, L., WILSON, P.W. (2000): A general methodology for bootstrappng n nonparametrc fronter models. Journal of Appled Statstcs 27/6, SIMAR, L., WILSON, P.W. (2005): Estmaton and nference n two-stage, sem-parametrc models of producton processes. Insttut de statstque, UCL, B-348 Louvan-la-Neuve. Forthcomng n Journal of Econometrcs. WILSON, P.W. (2005): Fronter Effcency Analyss wth R. FEAR 0.93 user s gude. 9

20 8 Annex Fgure 6 llustrates the relatonshp between CRS and VRS for one nput and one output. Under CRS the best-practce fronter s a sngle ray emanatng from the orgn. To be on the techncal effcency fronter a frm would have to be operatng at optmal scale. Under VRS, the lnear program determnes a convex, pece-wse lnear fronter as best practce where dfferent nput to output combnatons can be effcent, dependng on the sze of the operaton. From three dfferent farms (A, B, C) only farm B s operatng at optmal sze as t touches the CRS-ray. Farm A les on the VRS-fronter but not on the CRS-fronter and thus represents only pure techncal effcency but not scale effcency. Experencng decreasng returns to scale a reducton n sze would mprove scale effcency of farm A. Farm C s nether techncal nor scale effcent. Overall neffcency of farm C can be decomposed nto techncal neffcency and scale neffcency. Farm C could ncrease ts scale effcency by movng up on the VRS-fronter and ts techncal effcency by reducng ts nputs. Fgure 6: Relatonshp between neffcences and returns to scale y CRS 0 C Scale B C C Pure techncal A VRS 0C ' Techncal Effcency ( TE ) = 0C 0C ' ' Pure TechncalEffcency ( PTE ) = 0C TE 0C ' Scale Effcency = = PTE 0C ' ' neffcency x Lmtatons of DEA concern the mplct assumpton that all dfferences n performances of dfferent farms are caused by neffcences (e.g. errors n measurement could be nterpreted as neffcences). In addton, snce effcency s measured relatve to other DMUs, these should be comparable n terms of producton technologes, nput requrements and output mx. In the current study ths s ensured by the selecton of a homogeneous sample of farms and the excluson of less specalsed enterprses. In general the number of effcent farms ncreases the more nputs and outputs are dstngushed and the fewer farms are compared wthn the sample. DYSON et al. (200) suggest that to acheve a reasonable level of dscrmnaton, the number of DMUs should be at least twce the product of the number of nputs and outputs. 20