Production Risk and Technical Inefficiency in Russian Agriculture. Production Risk and Technical Inefficiency in Russian Agriculture

Transcription

1 Producton Rsk and Techncal Ineffcency n Russan Agrculture Raushan Bokusheva, Henrch Hockmann Instute of Agrcultural Development n Central and Eastern Europe (IAMO), Halle Germany Correspondng author: Raushan Bokusheva Instute of Agrcultural Development n Central and Eastern Europe (IAMO) Theodor-Leser-Str Halle Germany Phone: , Fax: , e-mal: bokusheva@amo.de Producton Rsk and Techncal Ineffcency n Russan Agrculture Summary Ths paper ams to contrbute to a better understandng of possble causes of the consderable producton volatly that has charactersed Russan agrculture durng the last decade. Usng panel data from 1995 to 2001, presents an emprcal analyss of producton rsk and techncal neffcency of 447 large agrcultural enterprses from three regons n Central, Southern and Volga Russa. Two sources of producton varably, producton rsk and techncal neffcency, are consdered. Key words: Producton rsk, Techncal effcency, Panel data, Russan agrculture JEL Classfcaton: D81, Q12

2 2 1. Introducton The development of Russan agrcultural producton durng the reform perod has been nconsstent n character. In general, producton declned over a consderable perod whle at the same tme serous output varatons were observable 1. Many studes have been conducted n order to reveal the possble causes of agrcultural producton declne n the post-sovet Russa (Sotnkov, 1998; Sedk at al., 1999; Vogt and Uvarovsky, 2001; Osborne and Trueblood, 2002; Bezlepkna and Lansnk, 2003). Among others, the deteroraton n terms of trade, elmnaton of producer and consumer subsdes, a weak nstutonal envronment and undeveloped nfrastructure, through ther mpact on techncal effcency, were revealed as mportant factors of producton declne. However, thus far the lerature has not pad much attenton to producton volatly n Russan agrculture. In recent years (from 1999 to 2002) Russan agrcultural producton has exhbed substantal growth followed by a deceleraton n In ths context s of great nterest to dentfy factors whch have contrbuted to such posve development n Russan agrculture. The central queston of ths study s: Have Russan farms mproved ther performance by ncreasng ther techncal effcency and productvy or mght ths growth be explaned by some reducton n producton rsk due to favourable weather condons n ths perod as some experts assert (Gadar, 2002)? Sotnkov (1998) and Sedk et al. (1999) were the frst who studed techncal effcency n Russan agrculture durng the reform era. In both studes, the authors estmate the magnudes of techncal effcency on the oblast level. The estmatons are conducted by employng the stochastc fronter approach. Furthermore, Sedk at al., (1999) carry out data envelopment analyss. The studes provde analogue results and show that techncal effcency declned from 1991 to Moreover, a study by Osborne and Trueblood (2002) concerns the effcency of Russan crop output n the successve perod, from 1995 to 1998, and shows that the trend revealed n the earler studes has slowed down but not been reversed. In contrast, the estmates of techncal effcency over 75 Russan regons obtaned by Vogt (2002) do not suggest serous changes n techncal effcency at the natonal level durng the perod from 1993 to However, the author found that the development of techncal effcency n dfferent regons does not have any common trend. Recently, several studes were conducted to estmate techncal effcency usng farm level data. Bezlepkna and Lansnk (2003) study techncal effcency of dary farms n the Moscow regon and consder s development wh regard to capal structure and subsdsng programs 1 In the Appendx, Fgure 1 demonstrates the development of gran producton from 1985 to 2003, whle Table 1 provdes addonal nformaton on yeld and yeld varably of the man agrcultural products n ths perod.

3 3 from 1996 to The study results show that even though techncal effcency decreases consderably n the year of fnancal crss, 1998, n general has a posve trend n the analysed perod. These results are n complance wh the fndngs by Stange and Lsssa (2004) who compare techncal effcency of farms n the same regon wh regard to ther specalzaton, sze and form of organzaton n the years 1993 and The results of both studes suggest an ncrease n techncal effcency of the consdered farms n recent years. However, the farms of the Moscow regon located near to the cy are rather less representatve for Russan agrculture and cannot represent the suaton n the other regons. In ths context, further nvestgaton s necessary to assess the current stage of techncal effcency development n Russan agrculture. The studes of techncal effcency of Russan agrcultural producers dffer wh respect to estmaton technques and subject of nvestgaton. Addonally, many partculares are found wh regard to the objectves and background of the ndvdual studes. However, neher of these studes consdered the producton development n Russa to be explaned by the presence of rsk and the farmers responses to. Ths, however, undermnes the fact that normally economc uns make ther decsons under condons of rsk. The presence of rsk not only nfluences producton output but also producers behavour, prmarly wh regard to nput use. If rsk mgaton plays a prncpal role n decson-makng, then techncal effcency may alter sgnfcantly. Therefore, techncal effcency assessed consderng a producer's response to uncertanty s not the same n a settng where no effect of rsk on nput-use decsons s concerned. Thus, n the case that uncertanty s pervasve, the theoretcal framework for studyng techncal effcency s to be extended wh respect to rsk and producers responses to rsk. In ths study producton rsk s assumed to be an mportant factor n Russan agrculture and to nfluence producton decsons of Russan farmers. Hence, the present study ams to estmate the magnudes of both techncal neffcency and producton rsk faced by agrcultural producers n Russa and therefore explan the pattern of Russan agrcultural producton development n the last decade. Two approaches are employed n the study: the Just and Pope model (1978), and a Kumbhakar extenson of ths model to ntroduce techncal effcency (Kumbhakar, 2002). The Just and Pope model allows to dstngush between the effects of nput use decsons on producton output and producton rsk (1978). Techncal effcency explaned by a complementary functon presents an addonal source of producton varably (Kumbhakar, 2002). Both models are extended to consder systemc producton rsk and estmated usng panel data (from 1995 to 2001) of 447 large agrcultural enterprses from three regons n

4 4 Central, Southern and Volga Russa. Based on the estmaton results three hypotheses wh respect to study objectves wll be dscussed: - Producton rsk s a sgnfcant factor n Russan agrculture. - Producton rsk ncludes a regonal systemc component. - Techncal neffcency n producton enhances the producton uncertanty of Russan agrculture. The paper s organzed as follows: Secton 2 outlnes the methodology appled to dstngush and assess two sources of producton varably: producton rsk and techncal neffcency. Secton 3 presents the specfcaton of the models used n the study. Estmaton results wh regard to the objectves of the study are dscussed n secton 4. Conclusons are drawn n the fnal secton. 2. Methodology The study employs a stochastc producton fronter approach. Emprcal studes on effcency usually utlze eher Data Envelopment Analyss (DEA) or Stochastc Fronter Analyss (SFA). DEA s a non-parametrc approach and employs lnear programmng to construct a pecewse-lnear, best-practce fronter for each economc un (Färe et al., 1985). No functonal form for the fronter s mposed on the data. However, ths technque consders producton to be determnstc. The stochastc parametrc approach, SFA, s founded on the tradonal stochastc specfcaton and takes nto account output uncertanty by means of a two-part error term (Agner et al.,1977). The dstrbuton assumptons for both part of the error term have to be mposed. The tradonal stochastc fronter producton model was frst proposed by Agner, Lovell and Schmdt (1977) and the general notfcaton of the model s the followng: y v = f ( x ; α) e TE, (1) where y s the output of producer ( I), x s a vector of nputs used by producer, α represents a vector of technology parameters, f(x ;α) s the producton fronter, and TE s the output-orented techncal effcency of producer. In addon, v represents a producerspecfc random component. Techncal effcency s defned as the rato of observed output to maxmum feasble output n a state of nature depcted by exp{v }: TE y = v. (2) f ( x ; α) e

5 5 Snce stochastc specfcaton of the producton fronter model perms takng nto account random shocks that affect producton but le outsde of producer control, SFA s a more approprate approach for an envronment characterzed by consderable random effects. However, the tradonal specfcaton of a stochastc producton functon has a feature whch may serously restrct s potental to depct producton technology approprately. An mportant dsadvantage of the tradonal multplcatve stochastc specfcaton of producton technology les n an mplc assumpton: f any nput has a posve effect on output, then a posve effect of ths nput on varably of output s also mposed. Just and Pope (1978) showed that the effects of nput on output should not be ted to the effects of nput on output varably a pror. Instead, they proposed a stochastc specfcaton whch has been more generally compared to the tradonal econometrc producton functon. Accordngly, the adequate producton functon specfcaton has to nclude two general functons: one whch specfes the effects of the nput on the mean of output and another whch specfes the effect of nput on the varance of the output: y = f ( x ; α) + g( x ; β) v, (3) where, f(x ; α) s the mean producton functon and g(x ; β) s the varance producton functon. Furthermore, α s a vector of the mean producton functon parameters, β s a vector of the varance producton functon parameters and v s a stochastc term assumed to be.. d. N(0,1). Thus, E(y) = f(x), and V(y) = g 2 (x). In ths manner, the effect of nput changes has been separated nto two effects - the effect on mean and the effect on varance. Snce varance of y s specfed as a functon of the producton nputs g(x ;β), the Just-Pope producton functon exhbs heteroscedastcy. The margnal producton rsk, defned as var( y ) = 2g( x; β) g x j j ( x; β) (4) can be posve as well as negatve, or zero, subject to the sgns of g(x ;β), and g j (x ;β), where the latter s the partal dervatve of g wh respect to nput j. Generally, there are 3 possbles for ntegratng techncal effcency nto the Just-Pope producton functon: () n addve form (Battesse at al., 1997). In ths case s attached to the varance producton functon, together wh the random term representng producton uncertanty: y = f x ; α ) + g( x ; β )( v u ); (5) ( j j j j

6 6 () n multplcatve form. Then techncal effcency s attached to the mean producton functon (Kumbhakar, 2002): y = f ( x ; α )(1 u ) + g( x ; β ) v, (6) j j j j In ths case an addonal assumpton: exp{-u}=1-u has to be ntroduced. () n the more flexble form suggested by Kumbhakar (2002), where an addonal functon q(x) for explanng techncal neffcency s ntroduced: y = f ( x ; α ) + g( x ; β ) v q( x ; γ ) u. (7) j j j j j j Equatons (5) and (6) are specal cases of (7). Dependng on the choce of q(x) functon the model n (7) can be reduced to (5) when q(x)=g(x) or to (6) when q(x)=f(x). 3. Model specfcaton In ths study two model specfcatons are consdered: the Just and Pope model (JP-model), and a Kumbkakars extenson of the model by consderng techncal effcency as provded by (7). These specfcatons are extended by ntroducng varables that account for a systemc part of producton rsk (SPR) and by applcaton them to panel data. In the followng the subscrpts and t denote the producer and the tme perod, respectvely. Defnng x = [x 1,...x J ] the producton functon can be wrten as y = f ( x ; α) + exp( β D ) g( x ; β) v (Just and Pope wh SPR) (8) t t t t y = f ( x ; α) + exp( β D ) g( x ; β) v q( x ; γ) u (Kumbhakar wh SPR) (9) where, v s assumed to be..d. N(0,1),.e., ndependent dentcally-dstrbuted standard normal random varables, and u s..d. N + (0,σ 2 u ),.e., ndependent dentcally-dstrbuted and half-normal. The functon g(x,β)v represents the dosyncratc component of producton rsk faced by selected farms. Systemc producton rsk s captured by matrx (D t ) whch consst of dummy varables for the ndvdual years (Hsao, 1986). Thus, β t can be vewed as a proxy for the systemc component of rsk, whch expresses a spatal effect of annual weather condons on producton varance for the entre group of the analysed farms. In the case of the model specfcaton wh TI, the mean producton functon and producton varance functon are defned at the fronter,.e., u =0. Thus, for both approaches E(y u=0 ) = f(x), V(y u=0 ) = g 2 (x). (10) A sngle-step maxmum lkelhood (ML) procedure was employed to estmate the parameters of the specfed models. Takng nto consderaton the dstrbutonal assumptons on ν und u,

7 7 the lkelhood functon of TN observatons s formulated as the product of the probably densy functons f(ε ) of TN sngle observatons and the Jacoban J of the undertaken transformaton (ε from y): L = N = 1 f (ε ) J, wh ε = [ ε 1,..., ε T ] and f ( ε ) = f ( ε ) (11) T t= 1 y f ( x ) ε wh exp( β D ) g( x ) where = = [ ν h( x ) u ] t t h( x q( x ) ) =. exp( β D ) g( x ) t t The probably densy functon of ε s f ( ε ) ε ( Φ σ uh x σ ) 1 ε exp 2 σ = 2 π σ (12) wh σ = 1+ h ( x ) σ u and Φ( ) beng the dstrbuton functon of the standard normal random varable (Kumbhakar, 2002). The Jacoban n our case s a TNxTN dagonal matrx 1 wh the elements. g( x ) Then the log-lkelhood functon to be estmated s ln( L) σ ln g( x ). (13) = = T N N N 2 N 1 2 ε = + Φ σ uh( x ) 1 ε const ln ln 2 t= σ 2 = 1 σ = 1 The maxmzaton of the log-lkelhood functon n (13) provdes the ML estmates of the parameters n f(x), g(x) and q(x), as well as of σ u (Greene, 2003). They can be used to calculate the techncal neffcency measures of ndvdual producers n a partcular year by employng the condonal dstrbuton of u, gven ε, whch were derved by Jondrow et al., (1982): [ u ε u) ] = σ µ / σ + { φ ( µ / σ ) / Φ( µ / )} { } E (14) ( σ 0 where 0 / σ 0 = { ε σ u h( )}/ σ µ x and { } σ x σ. 0 = σ u h ( ) / 4. Estmaton and Emprcal Results 4.1 Data and Estmaton The model s estmated usng balanced panel data of 447 large agrcultural enterprses from three Russan regons. 74 farms are located n Oroel (Central Russa), 180 farms n Krasnodar (South Russa) and 193 n Samara (Volga Russa). The data set covers the perod from 1995

8 8 to To be able to assess the dependence of producton on weather condons, crop producton s focussed on. All enterprses ncluded n the sample are large scale farms wh a crop area of more than 200 ha, whch extensvely grow gran for commercal use. On average, the sample represents between 22 and 45 per cent of the total crop area n the ndvdual regons. In the vew of experts, Krasnodar and Samara are regons wh a hgher exposure to natural hazards. Samara and Oroel belong to a small group of Russan regons that have recently been very actve n ntroducng Western producton technologes (Schüle and Zmmermann, 2002). Producton output s measured as annual farm revenues from crop producton plus the value of unsold gran (Y) 2. The mean output functon s a functon of labor (Labor), seed (Seed), fertlzer (Fertlzer), deprecaton (Capal), other costs (Suppl) 3 (usually cost of plant protecton) and tme (t) as an ndcator of techncal change. Producton rsk and techncal neffcency (TI) are functons of the same varables except t. The varable farm sze 4 (Sze) s ntroduced nto q(x,γ) to detect how techncal effcency vares wh the scale of agrcultural producton. In ths study output and all nputs were normalzed by the nput Land 5, therefore constant returns to scale are assumed. The data set was provded by Goskomstat - the Russan State Commtee of Statstcs. All monetary key data were adjusted to the year 2001 by the regonal prce ndces for agrcultural nputs and output. However, for fertlzer and capal these ndces were not obtanable. Two optons were avalable to adjust ths data: usng product-specfc prce ndces defned on the country level or usng regonal prce ndces defned for a wde range of products. The frst does not reflect regonal prce movements, whle the latter s unable to dentfy changes n regonal prce relatons snce the deflator represents a movement of average regonal prces. Snce the goal was to dentfy regonal producton patterns, the second opton was expected to cause a larger bas n the varables than the frst opton. Thus, monetary values were adjusted by product-specfc ndces. Unfortunately, dstngushng between seed produced on the farm and purchased seed was not possble. However, as many farms use seed produced themselves, was decded to employ the regonal agrcultural output prce ndex n ths case. Certanly for the farms, purchasng hgh qualy seed ths leads to some dstortons. 6 2 The value of unsold gran was reckoned as a dfference between a farm's annual gran producton and gran sales multpled by gran prces n the year Whereas other costs are calculated as the dfference of total producton costs of crop producton and costs of labor, seed, fertlzer, equpment and machne mantenance, and fuel. 4 Farm s crop land. 5 In order to avod problems wh multcollneary conductng estmatons. 6 Another mportant pont of the analyss: s assumed that all farms n the regons have the same prce rsk, although n our opnon ths may be rather restrctve.

9 9 A Cobb-Douglas specfcaton s used on mean producton and producton varance, as well as the techncal neffcency (TI) functon. Ths functonal form s hghly restrctve, however, other functonal forms such as translog and lnear-quadratc provded poor estmates. In the case of translog functon, many of the parameter s estmates were nsgnfcant. Moreover, monotoncy was not fulflled for the farms n Oroel, and quas-concavy was also not satsfed n general. The parameters α j, β j and γ j are elastces of the factor j n the mean, output rsk and TI functon, respectvely. Posve values of the coeffcents β j n the producton rsk functon mean that the correspondng factor ncreases producton varably, whereas negatve values sgnal that the factor s a rsk-decreasng one. Negatve sgns of the coeffcents γ j ndcate that a factor reduces techncal neffcency, otherwse a factor s TI ncreasng 7. The Just and Pope formulaton was estmated by addng tme effects (JP wh SPR) and then was adapted to take nto account techncal neffcency (Kumbhakar wh SPR). The parameter estmates are presented n Table Estmaton Results As the purpose of the study s to examne the effects of TI and producton rsk on agrcultural producton development, the dscusson of the parameter estmates s confned to a general extent. Table 1 shows that all coeffcents of the mean producton functon are posve and sgnfcant 8. For farms n the Oroel and Samara regons, labor and capal have the hghest proportonal contrbuton to the producton, whereas n Krasnodar, fertlzer and other producton costs exhb hgh output elastces as well. Accordng to the estmaton results, the producton technology n Krasnodar dffers from that n Oroel and Samara wh regard to labor elastcy. Lower Labor elastcy suggests a hgher elastcy of the Land n Krasnodar. Place Table 1 here The estmates for techncal change reveal that only one regon (Samara) had ncreasng producton possbles. The other two regons were experencng declnng productvy. Possble reasons for ths are nsuffcent replcaton of capal nput or lmed provson wh materal nputs due to lqudy problems. However, the estmate for techncal change n the Samara regon s rather hgh n the perod consdered. Consequently, makes estmaton results for ths regon less plausble. On the other hand, consderng the nal suaton n agrculture of ths regon n the frst part of the 1990s, as well as efforts of farms n Samara to 7 γ j =0 means that a factor s neutral wh regard to techncal neffcency. 8 All varables wh the nsgnfcant parameter estmates were excluded estmatng the model.

10 10 ntroduce modern producton technologes n the later 1990s may support such estmaton results. In the followng three hypotheses are dscussed: (1) Producton rsk s a sgnfcant factor n Russan agrculture (2) Producton rsk ncludes a regonal systemc component. (3) Techncal neffcency (TI) n producton enhances the producton uncertanty of Russan agrculture. (1) Parameter estmates of the rsk functon are hghly sgnfcant. Results of the lkelhoodrato test of the JP wh SPR model aganst the general specfcaton for all three regons support ths model formulaton. Ths mples that producton uncertanty s an mportant source of varably of agrcultural producton. In addon, accordng to the estmates, producton varably s very hgh and many of the nput coeffcents n the rsk functon have relatvely hgh values, frst of all n case of such fxed producton factors as labor and capal n Krasnodar. Ths suggests that farms wh a hgher fxed factor endowment face a hgher degree of producton uncertanty. More nsght can be ganed f the sze of enterprses s consdered. Large farms cannot be as flexble as ther small counterparts n regard to factor endowment, prmarly n the case of fxed factors, because once made, these knds of producton decsons (fxed factor acquson) have a long-term effect. Ths apples especally to Russa, where most farms stll use equpment and producton practces from the past. Moreover, wh regard to labor nput, most farms n Russa have retaned a protectve employment polcy that obstructs serous changes n ther producton patterns (Osborn and Trueblood, 2002). Accordngly, model estmates can serve as a bass for an emprcally-relevant concluson: current factor endowment of the farms analyzed n ths study s stll not adjusted to producton condons and must be fted to them n the future. One mportant advantage of the JP-approach s the possbly of dstngushng between an nput effect on mean output and s mpact on output varably,.e., rsk. In ths study, large dfferences among the sgn and the magnude of the parameter estmates n the mean and rsk functon could not be found, except for the nput Suppl : s coeffcents are posve and sgnfcant n the mean producton functon and negatve and also sgnfcant n the producton rsk functon of farms and n the Kumbhakar wh SPR specfcaton for farms n Samara. Ths ndcates that mean producton output s ncreased and producton rsk s reduced wh ncreased use of ths nput. Such results provde some confrmaton to the vew that pestcdes

11 11 are not a factor for ncreasng, but for stablzng agrcultural producton (Quggn and Chambers, 2003). (2) The parameter estmates of systemc rsk for all three regons are hghly sgnfcant. Ths suggests the presence of systemc rsk n these regons. In addon, the values of systemc rsk are rather large. These results mply that a consderable part of output varaton explaned by systemc rsk n the selected regons. The relatvely low values of systemc rsk n 2000 and 2001, compared to s seven-yearsaverage value for all three regons, confrm complance of the model estmates wh the actual clmatc condons n the perod consdered (Gadar, 2002). However, there may be a serous dentfcaton problem n the paper's estmates: the magnude of systemc rsk n Krasnodar and Oroel has declned mrrorng the recovery of the Russan economy snce Ths may suggest that the dummy varables n the producton rsk functon capture not only systemc rsk but also the effects of an mprovement of the macro-economc envronment. However, mproved economc condons should reveal ther mpact on the decson varables lke factor nput. Thus, the study assumes that they should be captured n the dosyncratc component of the rsk functon but not n the systemc rsk component. Moreover, the macroeconomc nfluence cannot be observed for the farms n Samara. Ths leads to the concluson that the dummy varable coeffcents reflect manly clmatc condons. (3) The lkelhood-rato tests show that the specfcaton of the model ncludng techncal neffcency s more approprate for two regons: Krasnodar and Samara,.e., techncal neffcency enhances the varably of agrcultural producton n these regons. The hypothess H0: No Ineffcency (q(x) = const., σu 2 = 0) was not rejected for the farms n the Oroel-regon. Thus, the presence of techncal neffcency for ths regon cannot be statstcally proven. The varance of output defned n the model wh TI as σ 2 = {exp(β t D t )g(x)} 2 + q(x) 2 σu 2 s explaned mostly by varance due to producton rsk - exp(β t D t )g(x) 2 : for almost all farms n the Krasnodar and Samara regons q(x) 2 σu 2 < exp(β t D t )g(x) 2,.e., accordng to model estmates, hgh agrcultural producton varably arses frst of all from producton rsk (Fgures 1 and 2). Place Fgures 1 and 2 here In the Samara regon techncal neffcency s explaned by capal nput and farm sze, whle n Krasnodar only farm sze has a strong sgnfcant effect on techncal neffcency. Farm sze has a negatve effect on TI, whch suggests that the large farms n these two regons have been

12 12 more effcent. However, capal nput ncreases techncal neffcency of the selected farms n Samara. Ths result s surprsng at frst glance because Samara was characterzed as a dynamc regon wh a relatvely hgh rate of techncal change (Table 1). However, the estmaton results may be explaned by lookng at the theoretcal mpact of machnery on factor nput and the development of nputs. As mechancal nnovaton, new machnes belong to labor savng technologes (Hayam and Ruttan 1985). Thus, they allow a substuton of labor by capal whout affectng output sgnfcantly. Ther adopton s, n general, a response to an ncrease n labor cost. Hgher capal ntensy calls for a decrease n labor ntensy. However, as mentoned above, the farms n Russan agrculture pursue a protectve employment polcy and avod sgnfcant labor releases. Thus, the decrease of techncal effcency results from the nsuffcent adjustment of factor endowment to the economc requrements. As represented n Fgures 3 and 4, techncal neffcency s rather moderate and the average rate does not sgnfcantly change along the consdered tme n both regons, where s presence was proven statstcally. Addonally, most parts of the farms n both regons have rather low techncal neffcency scores (about 84 per cent of the farms n Krasnodar have techncal neffcency lower than 0.2, whle n Samara 94 per cent of the selected farms do not exceed ths value,.e., there were not any serous dfferences n the effcency of most farms n the perod consdered. Place Fgures 4 and 5 here The concept of techncal effcency s based on comparng the enterprses whn the sample taken nto consderaton. Ths rather restrcts the expressveness of ths concept n the case of a rather homogeneous sample, as was the case n ths study 9. From ths pont of vew, 9 In ths study, we compared farms whch had to satsfy to followng crera: a crop area of more than 200 ha, revenue from crop producton more than 50 % of the whole farm revenue, gran area more than 40 % of total crop area and producton of gran for sale more than 50 % of the whole gran producton.

13 13 applyng the methodology employed to a group of rather heterogeneous farms 10 may allow more nsght nto how techncal neffcency nfluences the development of agrcultural producton n Russa. 5. Conclusons Ths study has focused on the estmaton of the magnudes of techncal neffcency and producton rsk faced by agrcultural producers n Russa. The study used Just and Pope model (1978) to estmate a producton functon consderng producton rsk, and s extenson, by ncorporatng techncal neffcency as specfed by Kumbhakar (2002) n the framework of cross-sectonal data. The models were extended by ntroducng a term to account for a systemc part of producton rsk and by applyng to panel data. By means of the panel data analyss of 447 farms n dfferent parts of Russa, results were obtaned whch support the hypothess that producton rsk s a major source of producton varably n Russan agrculture. Concurrently, the analyss demonstrates that there s only a weak response of the farms to producton rsk: most producton factors enhance farms' producton volatly. The study results do not support the hypothess that the recent growth of Russan agrcultural producton was nduced by an ncrease of farms techncal effcency. The farms selected for the study dd not remarkably mprove ther effcency durng the consdered perod. On the other hand, was found that the systemc component of rsk substantally nfluences the farms producton development. Its low estmated values for two successve years, 2000 and 2001, suggest that the producton ncrease n ths perod was related to the favourable weather condons. However, the possbly exsts that may comprse an effect of stablsaton n the Russan economy to some degree. Even though techncal effcency does not serously exaggerate agrcultural producton accordng to the study results, there s a potental to ncrease. Furthermore, s assumed 10 For example, to draw a sample of farms from dfferent regons.

14 14 that there was no farm n the samples whch has succeeded n shftng the respectve regonal producton fronter to the Western benchmark. Correspondngly, actual techncal neffcences of the farms may be hgher. However, as producton rsk plays a major role n the development of agrcultural producton at ths stage, Russan farms have to search for optons to mprove ther responses to natural hazards to whch they are exposed, frst of all wh respect to the ntroducton of nnovatve producton technologes and practces that can provde ncreased factor flexbly. References Battese, G.E., Rambald, A.N., Schmdt, P. (1997). A stochastc producton functon wh flexble rsk propertes. Journal of Productvy Analyss 8. pp Bezlepkna, I., Lansnk, A.O. (2003). Lqudy and productvy n Russan agrculture: Farm data evdence. Proceedngs of the 25th Internatonal Conference of Agrcultural Economsts (IAAE), pp Durban, South Afrca, August. Bezlepkna, I., Lansnk, A.O. (2003). Debts, subsdes and performance of Russan agrcultural enterprses. In Balmann, A., Lsssa A. (eds.) Studes on the Agrcultural and Food Sector n Central and Eastern Europe 20. Large Farm Management, pp Agrmeda. Quggn J., Chambers R. G. (2003). The State-contngent Approach to Modelng Envronmental Rsk Management. In Babcock B. A., Fraser R. W., Lekaks J.N. (eds) Rsk Management and the Envronment: Agrculture n Perspectve, pp Kluwer Academc Publshers. FAO Statstcal data base: Färe, R., Grosskopf, S., Lovell, C.A.K. (1985). The Measurement of Effcency of Producton. Boston: Kluwer-Njhoff. Gadar Y.T. (ed). (2002). Russan Economy n 2001 Trends and Outlooks. Issue 23. Instute for the Economy n Transon. Moscow. Goskomstat of Russa (2003). Agrculture n Russa Moscow, Russa: Goskomstat, State Statstcal Agency. Goskomstat of Russa ( ). Agrculture n Russa. Dverse annual edons, Moscow, Russa: Goskomstat, State Statstcal Agency.

15 15 Greene, W.H. (2003). Econometrc Analyss. Ffth edon. Upper Saddle Rver, Prentce Hall. Hayam, Y., Ruttan, V. W. (1985). Agrcultural Development. 2 nd. Baltmore and London. Hsao, Ch. (1986). Analyss of Panel Data. Econometrc Socety Monographs No. 11. Cambrdge Unversy Press. Jondrow, J., Lovell, C. A. K., Materov I.S., Schmdt, P. (1982). On the estmaton of techncal neffcency n the stochastc fronter producton functon model. Journal of Econometrcs 19, pp Just, R. E., Pope, R. D. (1978). Stochastc representaton of producton functons and econometrc mplcatons. Journal of Econometrcs 7, pp Kumbhakar, S. C. (2002). Specfcaton and estmaton of producton rsk, rsk preferences and techncal effcency. Amercan Journal of Agrcultural Economcs 84(1), pp Kumbhakar, S. C., Lovell, C. A. K. (2000). Stochastc Fronter Analyss. Cambrdge. Osborne S., Trueblood M.A., (2002). Agrcultural productvy and effcency n Russa and Ukrane: buldng on a decade of reform. Economc Research Servce, Uned States Department of Agrculture. Agrcultural Economc Report No Schüle, H. and Zmmermann J. (2002). Management nformaton systems (MIS) for large agrcultural enterprses n Russa. Proceedngs of the Internatonal Congress of Farm Management Assocaton (IFMA). Arnhem, The Netherlands, 7-12 July. Sedk, D., Trueblood, M., Arnade, C. (1999). Corporate farm performance n Russa, : An effcency analyss. Journal of Comparatve Economcs 27, pp Sotnkov, S. (1998). Evaluatng the effects of prce and trade lberalsaton on the techncal effcency of agrcultural producton n a transon economy: The case of Russa. European Revew of Agrcultural Economcs 25, pp Stange, H., Lsssa A. (2004). Russscher Agrarsektor m Aufschwung? Ene Analyse der technschen and Skaleneffzenz der Agrarunternehmen. IAMO Dscusson Paper No.52. Halle/Saale Vogt, P., Uvarovsky, V. (2001). Developments n productvy and effcency n Russa s agrculture: the transon perod. Quarterly Journal of Internatonal Agrculture 40, pp Vogt, P. (2002). Russa s agrculture n transon: A cross-sectoral comparson of productvy and effcency. Selected Paper. The Conference Success and Falures of Transon: Russan Agrculture between Fall and Resurrecton. Halle (Germany).

16 16 Table 1: Parameter estmates Varable JP wh SPR Krasnodar Oroel Samara Kumbhakar wh SPR JP wh SPR Kumbhakar wh SPR JP wh SPR Kumbhakar wh SPR α0 6.34*** 6.45*** 6.04*** 6.10*** 4.52*** 4.59*** αlabor 0.15*** 0.12*** 0.42*** 0.44*** 0,42*** 0,40*** Mean Producton Functon αcapal 0.07*** 0.07*** 0.10** 0.12** 0.03* 0.08*** αfertlz 0.12*** 0.10*** 0.07*** 0.07*** 0.02*** 0.01*** αseed ** 0.08** αsuppl ** αt -0.03*** -0.02*** -0.02* -0.02* 0.10*** 0.10*** βlabor 0.23*** 0.30*** 0.40*** 0.38*** 0.19*** 0.18*** βcapal 0.11*** 0.11*** βfertlz 0.04** 0.05** *** βseed 0.18*** 0.21*** *** ΒSuppl * *** Producton Rsk Functon β *** 5.28*** 5.38*** 5.37*** 3.80*** 3.78*** β *** 1.00*** 0.94*** 0.94*** 1.09*** 1.09*** β *** 0.98*** 0.95*** 0.95*** 1.13*** 1.13*** β *** 1.04*** 1.01*** 1.01*** 1.09*** 1.09*** β *** 0.95*** 0.98*** 0.98*** 1.09*** 1.09*** β *** 0.92*** 0.93*** 0.92*** 1.05*** 1.05*** β *** 0.91*** 0.91*** 0.91*** 1.06*** 1.06*** γlabor γcapal *** TI Functon γfertlz γseed γsuppl γsze -0.53*** ** σ u *** * Value of test statstcs (LR) *** - sgnfcant at the 1% level, ** - sgnfcant at the 5% level, * - sgnfcant at the 10% level 1 Suppl - other costs 2 σ u = γ0*σ u

17 17 Fgure 1: Rato of the varance nduced by TI to the total varance of selected farms n Krasnodar (average for the farms over ) 1,00 0,80 0,60 0,40 0,20 0, Varance nduced by TI Total varance Fgure 2: Rato of the varance nduced by TI to the total varance of selected farms n Samara (average for the farms over ) Varance nduced by TI Total varance

18 18 Fgure 3: Techncal neffcency of selected farms n Krasnodar ( ) (0.00 = 100 per cent effcency) Fgure 4: Techncal neffcency of selected farms n Samara ( ) (0.00 = 100 per cent effcency)

19 19 Appendx Fgure 1: Gran Producton n Russa, Mllon tone Cereals, total Wheat Table 1: Yeld and Yeld Varably of Man Russan Agrcultural Products n Product Average yeld, 0.1t/ha Standard devaton Coeffcent of varaton, % Cereals, total Wheat* Sugar Beet Sunflower Seed * - yeld per ha of crop area