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) Theodor-Leser-Str Halle Germany Phone: , Fax: e-mal: bokusheva@amo.de Paper prepared for presentaton at the XI th Congress of the EAAE (European Assocaton of Agrcultural Economsts), The Future of Rural Europe n the Global Agr-Food System, Copenhagen, Denmark, August 24-27, 2005 Copyrght 2005 by Bokusheva and Hockmann. All rghts reserved. Readers may make verbatm copes of ths document for non-commercal purposes by any means, provded that ths copyrght notce appears on all such copes.

2 2 Summary Ths paper ams to contrbute to a better understandng of possble causes of consderable producton varably that charactersed Russan agrculture durng the last decade. The study nvestgates producton rsk and techncal neffcency as two sources that nfluence producton varably. Usng panel data from 1996 to 2001, an emprcal analyss of 443 large agrcultural enterprses from three regons n central, southern and Volga Russa s conducted. A producton functon specfcaton accountng for the effect of nputs on both rsk and techncal neffcency s found to descrbe producton technologes of Russan farms more approprately than the tradonal stochastc fronter formulaton. Key words: Producton rsk, Techncal effcency, Panel data, Russan agrculture JEL Classfcaton: D81, Q12 1. Introducton Durng transon perod, the development of Russan agrcultural producton has exhbed a remarkably ncoherent character. In general, producton declned over a consderable perod whle at the same tme a serous output varaton was observable 1. Numerous studes have been conducted n order to dentfy the possble causes of agrcultural producton declne n post-sovet Russa (Serova, 2000, Macours and Swnnen, 2000, Lefert, 2002, Bezlepkna and Oude Lansnk, 2003). Among others, deteroraton n terms of trade, the elmnaton of producer and consumer subsdes, a weak nstutonal envronment and undeveloped factor markets were revealed as determnants of producton slowdown. Addonally, Lefert (2002) specfes weather as an mportant factor of producton volatly n Russa; he states: weather s volatle n Russa, such that crop harvests can vary very substantally between years. However, so far the lerature has pad ltle attenton to hgh producton varably and s effects on the producton development n Russan agrculture. In recent years (from 1999 to 2002) Russan agrcultural producton has exhbed substantal growth, followed by a deceleraton n Many factors have posvely contrbuted to such a development. Brooks and Gardner (2004) refer to mproved macroeconomc stably, ncreased demand for domestc food after the fnancal crss n 1998, and an ncreased nterest n agrcultural nvestments from the sde of more entrepreneural producers. These factors must have nduced an ncrease n farm productvy through ther mpact on techncal change and techncal effcency. On the other hand, the lerature (Gadar, 2002) denotes favourable weather condons, partcularly n 1999 and 2000, as a determnant of the recent growth n agrcultural producton n Russa. However, weather has volatle effects on agrcultural producton and cannot have a long-term posve effect on farm productvy. Therefore, n these crcumstances s essental to dstngush between factors stablzng farm productvy n the long term and volatle weather effects that can posvely contrbute to producton growth only for short perods. In ths context the objectve of ths study s to analyze: whether Russan farms ncreased producton by enhancng ther productvy, and techncal effcency n partcular, n the later 1990s and the begnnng of the 2000s as well as to nvestgate the extent to whch the recent growth can be attrbuted to some reducton n producton rsk due to favourable weather condons n ths perod. There have been a number of emprcal nvestgatons concernng the development of agrcultural productvy n Russa. Osborne and Trueblood (2002a) found that multfactor productvy of corporate farms n Russa declned by 1.7 percent per year n the perod from 1993 to A smlar result for the same perod was reported by Vogt and Uvarovsky (2001). On the other hand, Lerman et al. (2003) estmated that multfactor productvy rose by 7.4 percent from 1992 to Ths contradcton of results of dfferent studes could be traced back to dfferences n producton condons n the ndvdual years of the consdered perods, especally the last years n these studes. One of the best years n the 1990s was 1997 a good weather and harvest year (Lefert, 2002), when gran producton reached 88.6 mllon tons (Goskomstat, 2002a). However, 1998 was a drought 1 Appendx A demonstrates the development of gran producton, sown areas and yelds from 1980 to A substantal declne of gran producton n the last decade was attrbuted to a gradual drop n sown gran areas, but gran yelds rose, on average, compared to the pre-reform perod. On the other hand, hgh yeld varably s remarkable n both perods: n the 1980s as well as durng transon (Appendx B).

3 year n most gran producng regons of Russa, wh gran harvest amountng to only 47.9 mllon tons (Goskomstat, 2002a). Sotnkov (1998) and Sedk et al. (1999) were the frst to study 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 estmates employ the stochastc fronter approach. In addon, Sedk at al. (1999) carry out data envelopment analyss. The studes provde analogous results and show that techncal effcency declned from 1991 to A study by Osborne and Trueblood (2002b) analysed Russan crop producton between 1995 and 1998, and showed that the trends revealed n the earler studes have slowed down but have not been reversed. In contrast, the estmates of techncal effcency n 75 Russan regons obtaned by Vogt (2002) do not suggest serous changes n techncal effcency at the natonal level from 1993 to In addon, he found that the development of techncal effcency n dfferent regons does not have any common trend. Recently, several studes estmated techncal effcency usng farm level data. Bezlepkna and Oude Lansnk (2003) study techncal effcency of dary farms n the Moscow regon and consder ther development wh regard to capal structure and subsdsng programs 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 consstent 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 the cy can hardly be consdered representatve of Russan agrculture. 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 can be found wh regard to the objectves and background of the ndvdual studes. However, neher of the studes analysng the development of agrcultural producton n Russa took proper account of the presence of rsk and the farmers responses to, whereas s common knowledge that economc uns make ther decsons under uncertanty. The presence of rsk nfluences not only producton output but also producers behavour, prmarly wh regard to nput use. If rsk mgaton plays a prncpal role n decson-makng, then a farm's techncal effcency score 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 taken nto account. Thus, when 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 to 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 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 are estmated usng panel data (from 1996 to 2001) of 443 large agrcultural enterprses from three regons n central, southern and Volga Russa. Based on the estmaton results, two hypotheses wh respect to the study objectves wll be dscussed: - Agrcultural producton n Russa s subject to consderable producton rsk; besdes systemc weather effects, output varance depends on the ntensy of nput use. - Techncal neffcency (TI) enhances output varably n Russan agrculture; TI s farm specfc and can be explaned by nput use. 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 3

4 4 presents the specfcaton of the models used n the study. Data and estmaton results wh regard to the study objectves are dscussed n Secton 4. Conclusons are drawn n the fnal secton. 2. Theoretcal framework Ths study employs stochastc fronter analyss (SFA) 2, whch requres a parametrc representaton of the producton technology. In addon, consders output varably by a two-part error term. The dstrbutonal assumptons for both parts of the error term have to be mposed. Ths approach was poneered by Agner, Lovell and Schmdt (1977). The general notaton of the model s the followng: v y = 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 }: y TE = v f. (2) ( x ; α) e However, the conventonal 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 s the mplc assumpton that f any nput has a posve effect on output, then a posve effect of ths nput on output varably 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 more general stochastc specfcaton compared to the usual econometrc producton functon approach. 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) 3. Thus, E(y) = f(x), and V(y) = g 2 (x). Accordngly, the effect of nput changes has been separated nto two effects - the effect on mean and the effect on varance. Snce the varance of y s specfed as a functon of the producton nputs g(x ;β), the Just-Pope producton functon exhbs heteroscedastcy. 2 Emprcal studes on effcency usually utlze eher Data Envelopment Analyss (DEA) or 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, most non-parametrc programmng approaches to estmate producton fronter consder producton to be determnstc and do not regard the possbly of nosy data by assumpton. Devatons from the fronter are consdered as neffcency, though some authors have dealt wh ths ssue by ntroducng elements of statstcal analyss n the DEA models (Smar and Wlson, 1995; Gstach, 1996). Snce stochastc specfcaton of the producton fronter model perms takng nto account random shocks that affect producton but le outsde producer control, SFA s a more approprate approach for an envronment characterzed by consderable random effects. 3.e., ndependent and dentcally dstrbuted standard normal random varable.

5 5 The margnal producton rsk, defned as var( y ) = 2g( x; β) g j ( x; β) x j (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 () n multplcatve form. Here, 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 the 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 applyng them to panel data. In the followng, the subscrpts = 1,..,N and t = 1,,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( Dβ ) g( x ; β) v (Just and Pope wh SPR) (8) t t y = f ( x ; α) + exp( Dβ ) g( x ; β) v q( x ; γ) u (Kumbhakar wh SPR) (9) where, v s assumed to be..d. N(0,1),, and u s..d. N + (0,σ u 2 ). The functon g(x,β)v represents the dosyncratc component of producton rsk faced by selected farms. Systemc producton rsk s captured by a vector D, whch conssts of dummy varables for the ndvdual years (Greene, 2003). 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 sample. 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 ) ={exp(dβ t ) g (x)} 2. (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, 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) y f ( x ) ε wh exp( Dβ ) g( x ) where = = [ ν h( x ) u ] t h( x T t= 1 q( x ) ) =. exp( Dβ ) g( x ) t

6 6 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 wh the elements 1. exp( Dβ t ) g( x ) Then, the log-lkelhood functon to be estmated s. ln( L) ln g( x ) = = (13) T N N N 2 N 1 2 ε = + Φ σ uh( x ) 1 ε const lnσ ln Dβ t 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 μ / x σ and σ = { σ h ( x )} σ. 0 σ = ε σ h( ) / where 0 { u } 4. Estmaton and Emprcal Results 0 u / 4.1 Data and Estmaton The model s estmated usng balanced panel data of 443 large agrcultural enterprses from three Russan regons. 70 farms are located n Oroel (central Russa), 180 farms n Krasnodar (southern Russa) and 193 n Samara (Volga Russa). The data set was provded by Goskomstat - the Russan State Commtee of Statstcs. The data set covers the perod from 1996 to To enable a more accurate assessment of the dependence of producton on weather condons, attenton s focused on crop producton. All enterprses ncluded n the sample are large-scale farms wh a crop area of more than 200 ha ntensvely growng gran for commercal use 4. On average, the structure of sowng area n the selected farms s as follows: 58.6 percent gran and legumes, 8.8 percent sunflower seed, 2.4 percent sugar beet, 0.3 percent potato and feld-grown vegetables; the remander refers to other crops 5. The sample represents between 22 and 45 percent 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 recently have 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) 6. The mean output functon s a functon of the area of sown land (Land), labor 4 Selectng farms was done by applyng the followng crera: sown area s to be more than 200 ha, crop revenue s to be more than 50 percent of the farm revenue and the rato of the gran sowng area to whole farm sowng area s to be hgher than. Addonally, farms wh very strong specalzaton on potato or vegetable producton, as well as seed breedng farms, were excluded from the sample. 5 The statstcal data used n the study does not provde a stronger dfferentaton of the sowng areas wh regard to ndvdual crops. 6 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 2001.

7 7 defned as the annual average number of employees nvolved n crop producton (Labor), the value of deprecaton, machnery mantenance and fuel costs n crop producton 7 as a proxy for capal (Capal), materals costs (Materals) 8 and tme (t and t 2 ) as an ndcator of techncal change 9. Techncal neffcency (TI) s a functon of the same varables. To enable a more precse assessment of varous nputs effects on producton rsk, some components of materals costs, such as seed costs (Seed), fertlzer costs (Fertlzer) and other producton costs (Suppl) 10, were consdered ndvdually n the producton rsk functon. The varables Land, Labor and Capal specfy ths functon as well 11. All monetary data were measured n 1,000 rubles and adjusted to the year 2001 by the regonal prce ndces for agrcultural nputs and output as they are provded by Goskomstat (Goskomstat, 2002a and b, 2003a and b). However, for fertlzer and capal, these ndces were not obtanable. Two optons exst for adjustng these data: usng the nput-specfc prce ndces defned on the country level, or usng the regonal prce ndex for aggregated agrcultural nput (whch s defned for a wde range of nputs). The frst opton dd not reflect regonal prce development, whle the second dd not account for changes n prce relatons. Snce the study ams to explore the effects of dfferent nput groups on producton output and s varably, the former opton was expected to cause a smaller bas than the latter. Therefore, n the case of fertlzer costs, ther values were adjusted by employng the fertlzer prce ndex at the country level. To deflate deprecaton, the country-level prce ndex for machnery n crop producton was appled. Mantenance and fuel costs were adjusted by the regonal ndex for aggregated agrcultural nput. Addonally, no dstncton between seed produced on the farm and purchased seed was possble. However, snce most of the farms use self-produced seed, the regonal agrcultural output prce ndex was employed. 12 The functonal forms whch are used for the mean producton functon are the Cobb-Douglas and translog specfcatons. Producton varance and techncal neffcency (TI) are assumed to follow the Cobb-Douglas form. The LR-test suggested a rejecton of the Cobb-Douglas form n favour of translog. However, snce the translog specfcaton dd not provde theoretcally consstent parameter estmates, constrants were ntroduced to fulfll monotoncy and the necessary condons of quasconcavy at the approxmaton pont 13. Both models (8) and (9) were estmated for the 443 farms from all 3 regons and for the farms from ndvdual regons,.e., 180 farms from Krasnodar, 70 from Oroel and 193 from Samara. The obtaned parameter estmates are presented n Table 1. The parameters α j, β j and γ j are elastces of the factor j n the mean, output varance 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 Estmaton Results Table 1 presents the coeffcent estmates for all 4 samples. Most parameter estmates are sgnfcant, wh the excepton of some cross-product varables, and the coeffcents of the rsk and 7 Snce we dd not have data on machnery mantenance and fuel costs separately, we decded to ntegrate both parts of these costs nto Capal varable. They have both defned the ntensy of machnery applcaton n Russan farms durng the transformaton perod. 8 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. 9 Because only cost data was avalable for most of the producton factors, no breakdown between factor qualy and quanty was possble. 10 Usually costs of plant protecton. Therefore, they could be consdered as a proxy for pestcdes and herbcdes use. 11 To reduce computng tme, all varables were normalzed by ther geometrcal means. 12 Certanly for the farms purchasng hgh qualy seed, ths leads to some dstortons. Furthermore, s assumed that all farms n the regons have the same prce rsk, although n our opnon ths may be rather restrctve. 13 For detals, see Morey (1986). 14 γ j =0 means that a factor s neutral wh regard to techncal neffcency.

8 8 neffcency functon. Addonally, LR-tests ndcate the Kumbhakar wh SPR model appears to be more approprate than the JP wh SPR model n all samples at an acceptable level of sgnfcance 15. Thus, the followng dscusson of estmaton results concentrates on ths approach. The results of model estmatons for the ndvdual regons show that the parameter estmates are que dfferent n the three regons. Therefore, a James/Welch-test (Severs, 1989) was conducted to prove the sgnfcance of dfferences between the producton functon parameter estmates n the consdered samples (H0: α jkrasnodar = α joroel = α jsamara ). The test showed that the three consdered regons have sgnfcantly 16 dfferent parameter estmates wh respect to land elastcy. Consequently, estmates of a common producton functon for all three regons appear to be ncorrect. For that reason, the results of the separate estmatons for the ndvdual regons are consdered more accurate and are dscussed n the followng. The estmates show that the elastces of scale are larger than those n Oroel and Samara, 1.22 and 1.17, respectvely; for Krasnodar, constant returns to scale are evdent. Hgh total elastcy of producton n Oroel s explaned prmarly by hgher land elastcy n ths regon compared to Krasnodar and Oroel, 6 aganst 0.30 and 0.34, respectvely. As Oroel belongs to the Russan regons wh the most productve sols, ths fndng comples wh the emprcal evdence. The greatest producton elastcy s observable n all samples wh respect to materals. Ther scores vary between 7 and Regardng capal, the hghest proportonal contrbuton to the producton s apparent n Samara 0.20 compared to 0.13 n Oroel and 9 n Krasnodar. The estmates of labor elastcy are comparable n all three regons, but relatvely low compared to the other partal producton elastces, although s estmate for the farms n Krasnodar s not sgnfcant. Ths ndcates that labor appears to be relatvely less productve. Ths result s n lne wh the conclusons of Osborne and Trueblood (2002b) as well as Lefert and Swnnen (2002) that labor s an excessve producton factor n Russan agrculture. The results suggest a posve mpact of techncal change n the consdered perod for two of the three regons Samara and Oroel (Fgure 1). Only n Krasnodar s the mpact of techncal change not dstnct - the parameter estmates for techncal change are not sgnfcantly dfferent from zero n ths regon. Wh regard to the bas of techncal change, there s evdence for land-savng and capal-usng technologcal change n the data. Dfferent studes (Osborne and Trueblood, 2002b, Bezlepkna and Landsnk, 2002) have found that farms n Russa overuse land and have explaned ths by low land shadow prces. In ths context, land savng can be seen as a posve development n Russan agrculture, as ths mght mply that the farms tend to put excessve land out of operaton. On the other hand, as the land market n Russa s developng rather slowly, ths land cannot be effectvely transferred to other economc agents. So the area of unused crop land grows n Russa Addonally, both models were compared wh tradonal specfcatons of the producton functon. However, the hypothess that the coeffcents of the rsk functon, as well as the functon explanng techncal effcency are not sgnfcantly dfferent from 0, was rejected wh 1 % sgnfcance level for all samples. 16 The null hypothess was rejected at the 1% level of sgnfcance wh respect to land. 17 Accordng to the Russan Statstcal Commtee, the area of sown land amounted to 79.6 Mo. ha n 2003, whch s approxmately 68 percent of s 1991 level (Goskomstat, 2004).

9 9 Varable Mean Producton Functon Producton Rsk Functon TI Functon All 3 regons Krasnodar Oroel Samara JP wh SPR Kumbhakar wh SPR JP wh SPR Kumbhakar wh SPR JP wh SPR Kumbhakar wh SPR JP wh SPR Kumbhakar wh SPR αkrasnodar *** 1*** αoroel ** * αsamara 0.71*** 9*** *** αland 0.27*** 0.12*** 0.36*** 0.30*** 4*** 6*** 9*** 0.34*** αlabor 0.22*** 0.16*** 0.16** *** 0.11*** 0.14*** 0.10*** αcapal 0.18*** 0.22*** 0.12*** 9*** 0.19*** 0.13* 0.14*** 0.20*** αmaterals 8*** 0.60*** 0.50*** 7*** 2*** 0.52*** 0*** 0.53*** αt ** * -0.15*** -0.13*** αtt 1* 1*** -1** *** 3*** αland&t -1-0*** ** -8*** -5** -4* αlabor&t -2** -2*** 1 3** 2** 2*** -3*** -2*** αcapal&t 2*** 2*** 2** 2** 4** 5*** 3*** 2*** αmaterals&t 1 1*** 0 0 4*** 2 4*** 3*** αland^2 9*** 0.10*** * 0.20*** 0.18*** -0.11* -2 αlabor^2 0.11*** 0.11*** 8* 0.11*** 0.11*** 0.11*** 0.10*** 8*** αcapal^2 5*** 5*** 4*** 4*** 4** 5*** 5*** 5*** αmaterals^2 8*** 0.10*** 7** 9*** 0.17*** 0.16*** 0.12*** 0.15*** αland&labor -9*** -4*** * -8 αland&capal -2-3*** *** 0.10** αland&materals -4-8*** ** -0.30*** -0.27*** ** αlabor&capal -4*** -4*** -7** -8*** * αlabor&materals -5** -9*** -0.20*** -0.14*** -0.11** -9* 5* 3 αcap&materals -9*** -8*** *** -9*** -0.15*** -0.12*** βland 3*** 9*** 0.25*** *** 3 βlabor 0.14*** 0.24*** 0.15*** 3 2*** 0.39*** -7* -0.13*** βcapal 0.15*** 0.11*** 0.18*** 0.22*** 0.25*** 0.34*** 4-4*** βseed 0.17*** 0.13*** 0.23*** *** 0.15*** βfertlz 3*** 4*** 7*** 6*** 4*** 4*** 3*** 3*** βsuppl c 2*** *** 5*** 2** -1 βkrasnodar *** 0.67*** 0.51*** 0.37*** βkrasnodar *** 0.62*** 5*** 0.32*** βkrasnodar *** 0.59*** 3*** 0.32*** βkrasnodar *** 0.65*** 7*** 0*** βkrasnodar *** 0.54*** 0.39*** 0.30*** βkrasnodar *** 0.50*** 0.35*** 0.25*** βoroel96 2*** 0.38*** 3*** 0.33*** βoroel97 3*** 1*** 4*** 0*** βoroel98 7*** 4*** 8*** 7*** βoroel *** 0.33*** 0.36*** 0.36*** βoroel00 6*** 3*** 0.32*** 0.30*** βoroel01 6*** 3*** 0.36*** 0.33*** βsamara96 1*** 0.38*** 0.55*** 0.50*** βsamara97 4*** 2*** 0.62*** 0.60*** βsamara *** 0.27*** 0.35*** 0.29*** βsamara *** 0.30*** 2*** 0.36*** βsamara *** 0.27*** 4*** 0*** βsamara *** 0.26*** 2*** 0.36*** γland *** *** γlabor *** γcapal --- 1*** *** γmaterals *** *** *** *** γt --- 4*** * * *** σ u b Source: authors calculatons *** --- 2*** ** *** 76.31*** 81.75*** 11.16* 49.73*** Value of test statstcs (2logLR) a) *, ** and *** denote sgnfcantly dfferent from 0 at the 10%, 5%, and 1% level; b) σ u = γ 0*σ u; c) Suppl - other costs. Table 1. Parameter estmates. In Samara, the capal-usng mpact of techncal change s accompaned by labor-savng change. Ths ndcates that the farms n ths regon are lkely to release labor that becomes redundant. A dfferent process seems to occur on the farms from the Krasnodar and Oroel regons, where the capal usng mpact of techncal change does not cause labor savng. Under market condons, the oppose

10 10 would have been expected. Hayam and Ruttan (1985) argue that techncal change n agrculture s governed by relatve factor prces; those technologes, whch allow the greatest reducton of the scarcest (most costly) nput, are adopted. However, labor may not necessarly be consdered as a scarce factor n Russan agrculture. Durng Sovet tmes, the authores were concerned prmarly wh full employment (Macours and Swnnen, 2002). At that tme agrculture served as a buffer for labor whch was excessve n other sectors of the economy. Durng the transon process, most farms have pursued a protectve employment polcy, retanng surplus labor n agrculture 18. Ths argument suggests that labor nput s not governed by relatve prces; hdden unemployment remans a wdespread phenomenon n rural areas. Krasnodar and Oroel belong to the most mportant agraran regons n Russa; there, agrculture contrbuted 0.32 and 0.36 percent, respectvely, to the regonal GDP n 2001 (Goskomstat, 2003b). On the other hand, Samara s consdered as an ndustral regon (ndustry contrbutes approxmately 90 percent to the regonal GDP there). Respectvely, n 2001 the agrcultural sector employed 23.3, 21.4 and 7.6 percent (Goskomstat, 2002c) of the total labor force n these regons. Therefore, the results of the study seem to be n lne wh emprcal evdence, that the relatvely lmed employment opportunes outsde agrculture n Krasnodar and Oroel arguably cause dffcultes n releasng the redundant labor n farms; ths should be easer n Samara Krasnodar Oroel Samara Source: authors calculatons. Fgure 1. Annual technologcal change n the selected farms. In the followng, two hypotheses are dscussed: (1) Agrcultural producton n Russa s subject to consderable producton rsk; besdes systemc weather effects, output varance depends on the ntensy of nput use. (2) Techncal neffcency enhances the output varably n Russan agrculture; TI s farm specfc and can be explaned by nput use. (1) As mentoned before, to prove the hypothess on producton rsk, the tradonal specfcaton of the stochastc producton fronter was tested aganst the alternatves: JP wh SPR and Kumbhakar wh SPR models by means of the lkelhood-rato test. For all samples the null hypothess (H0: exp(dβ t )g(x)= 0) was rejected at the 1 % level of sgnfcance. In ths regard the data are consstent wh the hypothess that producton rsk presents an mportant source of varably of agrcultural producton n all selected regons. Prncpally, parameter estmates of the rsk functon are sgnfcantly dfferent from zero. Accordng to the estmates, producton varably s que hgh and many of the nput coeffcents n 18 Statstcal data shows that the number of employees n Russan agrculture sank from 9.7 n 1991 to 7.2 n 2001,.e., by 26 percent. However, as sown area dropped by 32 percent n the same perod, an ncrease n labor ntensy (man-to-land rato) was present n 2003 compared to the pre-reform perod (Goskomstat, 2004).

11 11 the rsk functon have comparatvely large values, frst of all n the case of labor and capal n Oroel, and capal and fertlzer n Krasnodar. The parameters of the systemc component of producton rsk are hghly sgnfcant for all three regons. In addon, the estmated values of ths component of producton rsk are relatvely large, whch mples that a consderable porton of the output varaton can be attrbuted to systemc rsk n the selected regons. Two dfferent patterns can be dstngushed among the consdered regons. Frst, systemc rsk, and accordngly, yeld varably ncreases n those years when weather condons are unfavourable for crop producton. Second, the systemc rsk component s low n adverse weather years, and conversely, enhances output varably n good weather years. In Krasnodar and Oroel, a negatve effect of the assessed values of systemc rsk has been found. Fgure 2 demonstrates gran yelds development n the selected farms wh respect to systemc rsk 19. The farms n these regons had ther hghest yelds n the years 2000 and Accordngly, for these years, systemc rsk estmates show that reached s lowest values 0.30 and 0.25 n Krasnodar, and 0.30 and 0.33 n Oroel, respectvely (Table 1). In Krasnodar and Oroel, weather condons tend to be relatvely favourable for crop producton; consequently, expected gran yelds are rather hgh. So, adverse weather condons n ndvdual years appear to be caught by hgher systemc rsk values. For Samara, the oppose holds true. Ths regon belongs to the so-called rsky crop growng regons n Russa (Sheltkov et al., 2001), where the prevalng clmatc and natural producton condons are rather harsh. Ths s reflected n que low expected yelds n these regons. Hgh output varably s caused there by favourable, rather than adverse weather condons. Therefore, n Samara, another pattern appears to be present hgh parameter estmates of the systemc rsk component pont to favourable weather condons n the ndvdual years and correlate posvely wh the gran yelds, as can be seen from Fgure 10. Accordngly, the lowest value of systemc rsk s assessed for 1998, when a severe drought caused wdespread crop falure n Volga Russa. The hghest value of systemc rsk s assessed for 1997, whch was a good crop year. Gran yelds Systemc rsk Krasnodar Orel Samara * - trends are assessed by applyng the logarhmc functonal form. Source: authors calculatons. Fgure 2. Systemc rsk (estmated) and gran yelds n the selected farms*. 19 To evaluate the estmates of the systemc rsk component, only gran yelds were taken nto consderaton n ths paper. However, s necessary to keep n mnd that there are also other crops whch contrbute to the farm output.

12 12 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, serous dfferences among the parameter estmates n the mean and rsk functon could be found n the case of labor and capal n Samara. The coeffcents on these varables are negatve and sgnfcant for the Samara regon farms. Ths ndcates that producton rsk s reduced wh ncreased use of ths nput. These estmates can be attrbuted to the fact that farms n Samara have been very actve n the last decade n ntroducng mnmum-tllage technology 20, whch allows to save sol mosture and hence reduce yeld varably caused by drought. Wh regard to labor, to the best knowledge of the authors, s rskreducng effect has not been dscussed n the lerature so far. In the framework of ths study, labor mght be regarded as a proxy of farm sze. Should ths be the case, large farms turn out to manage producton rsk more effectvely than ther small counterparts. However, accordng to the model estmates for two other regons, most of the consdered factors have a rsk-ncreasng effect on agrcultural output there. Addonally, the estmaton results do not confrm the vew that pestcdes are a factor of stablzng rather than ncreasng agrcultural producton (Quggn and Chambers, 2003) ndeed, the parameter estmates are mostly nsgnfcant wh regard to ths factor. Ths contradcts, however, to the estmates of the model wh a Cobb- Douglass specfcaton of the mean output functon where a hghly sgnfcant rsk-reducng effect of plant protecton was found 21. In general, the analyss demonstrates that there s only a weak response of the farms to producton rsk: most producton factors enhance farms' producton volatly. Therefore, the model estmates can serve to draw an emprcally-relevant concluson: current factor endowment of Russan farms s not adjusted to producton condons and should be adjusted to them n the future. (2) The lkelhood-rato tests leads to a rejecton of hypothess H0: no neffcency,.e., q(x) = const., and σu 2 = 0, at the 1 % sgnfcance level wh respect to the model estmatons for farms n Krasnodar and Samara. Ths suggests that the specfcaton of the model ncludng techncal neffcency s more approprate,.e., techncal neffcency enhances the varably of agrcultural producton n these regons. The hypothess H0 cannot be rejected for the farms for Oroel only at the 10 % sgnfcance level, however 22. The varance of output defned n the model wh TI (σ 2 = {exp(dβ t )g(x)} 2 + q(x) 2 σu 2 ) s explaned mostly by varance due to producton rsk. For most farms n all regons q(x) 2 σu 2 < {exp(dβ t )g(x)} 2,.e., accordng to model estmates, producton rsk contrbutes consderably to the volatly of agrcultural producton (Fgures 3 to 6). The model estmates show that only materals have an effect sgnfcantly dfferent from zero on the techncal effcency of the farms n all consdered regons. Even though, accordng to the parameter estmates of the mean producton functon, ths factor s ceters parbus most productve n all three regons, varable nputs seem to be used neffcently n most farms the parameters estmates have posve values,.e., the farms are neffcent wh regard to ths factor. Ths fndng s n lne wh the results of Osborne and Truebood (2002a) as well as Bezlepkna and Oude Lansnk (2003) who argue that Russan farms use too much varable nput. Unlke the other two regons, n Samara, most of the parameter estmates n the TI functon are sgnfcant. Accordng to the model estmates, techncal effcency decreases wh ncreased use of capal n ths regon. Ths result apparently ndcates the overuse of capal and hence s n lne wh fndngs of Osborne and Truebood (2002a) who argue that techncal and allocatve neffcency n Russan agrculture can be explaned by machnery ntensve farmng practces nhered from the Sovet era. Addonally, the use of old machnery can nduce effcency losses because of hgher mantenance costs. On the other hand, unjustfed hgh capal use can result from nvestments n new 20 Accordng to nformaton from the regonal government, n 2004 mnmum tllage was ntroduced on 560,000 ha,.e., 27 percent of the total sown area, n the Samara regon (Samara government offcal se, 2005). 21 The estmaton results of the model wh the Cobb-Douglas specfcaton of the mean producton functon are avalable from the authors upon request. 22 Lower sgnfcance of the estmates s possbly caused by a lower estmaton effcency due to relatvely small number of observatons from Oroel 420 observatons aganst 1,158 and 1,080 from Samara and Krasnodar, respectvely.

13 13 machnery as well. In ths regard, two optons are avalable for Russan farms at the moment: nvestng n eher hghly productve Western, or relatvely old-fashoned domestc equpment. Ths mples that nvestng farms ntroduce capal-ntensve and labor-savng technologes. At the same tme, due to nstutonal restrctons, there s hardly a labor release on farms 23. Ths nduces neffcences wh respect to both capal and labor use n ths regon. Land has a posve effect on techncal effcency n Samara - large farms appear to be more effcent n ths regon Varance by TI Varance by PR Varance by TI Varance by PR Fgure 3. Sample 1 (all 443 farms) Fgure 4. Sample 2 (183 farms from Krasnodar) Varance by TI Varance by PR Varance by TI Varance by PR Fgure 5. Sample 3 (70 farms from Oroel) Source: authors calculatons. Fgure 6. Sample 4 (193 farms from Samara) Fgures 3 6. Rato of the varance nduced by TI and by producton rsk (PR) n the selected farms (average for the farms over ). The development of farms' techncal effcency over the consdered perod s presented n Fgures 7 to10. The estmates suggest that techncal effcency ncreased over the consdered perod n both regons, Oreol and Krasnodar, whle n Samara no mprovement of farms' techncal effcency took place from 1996 to In order to understand the development of techncal effcency n the transon process, both the nstutonal and technologcal aspects have to be taken nto account. The nstutonal aspects fnd ther expresson n the U-curve effect (Havrylyshyn et al., 1998) suggestng that adaptng to market coordnaton causes hgh transacton costs, as agents have to learn how to act n the new envronment. Apparently, ths nally nduces an effcency declne. Learnng and formng the new ncentves lead to a reducton of transacton costs durng transon, as well as mproved performance. In ths context, ncreasng effcency can be regarded as an ndcator of the adjustment of economc agents to the requrements of the new envronment. On the other hand, due to the dffuson of nnovatons, a technologcal effect can nduce an outward shft of the producton fronter. However, for the enterprses whch fal to adopt nnovatve 23 The data used n ths study shows that the annual average number of workers n crop producton declned n the Samara regon farms, from 81 to 70 per farm, on average (.e., by 14 percent) n the perod. However, gven the relatvely low real wages n Russan agrculture, ths was apparently not suffcent to prevent neffcences wh respect to capal use.

14 14 technques, the dstance to the best domestc practce ncreases, mplyng an ncrease n techncal neffcency due to the technologcal effect Fgure 7: all 443 farms Fgure 8: 183 farms from Krasnodar Fgure 9: 70 farms from Oroel Source: authors calculatons. Fgure 10: 193 farms from Samara Fgure Techncal neffcency of selected farms ( ) ( = 100 percent effcency). In accordance wh the U-curve effect, the results of techncal effcency estmates for the farms n Krasnodar and Oroel are n lne wh the fndngs of the earler studes on techncal effcency of Russan farms, whch reported a declne of techncal effcency n the perod (Sotnkov, 1998; Sedk et al., 1999) as well as a slowdown n ths declne n the subsequent perod (Osborne and Trueblood, 2002b). Arguably, the results of ths study present evdence suggestng the begnnng of the second,.e., upward, part of the U-curve n these regons. At the same tme, n Krasnodar and Oreol, only a margnal mpact of technologcal change on the producton fronter was dentfed. Ths mples that the adopton of new technologes plays only a mnor role n the adjustment process n these regons. It can be expected that n ths suaton the Ucurve effect domnates, wh farms becomng more effcent, though under a constant technology. In contrast, the technologcal effect has arguably been prevalng n the case of Samara, where farms have been very actve n ntroducng new technologes that have enabled them to shft the producton fronter outwards 24. The U-curve effect mght have been also present n Samara, but the development of neffcency scores suggests that the technologcal effect has been domnant n ths regon. Thus, the study results demonstrate that the regonal dfferences n the development of techncal effcency revealed by Vogt (2002) seem to be ongong. 24 One consequence of ths nterpretaton s that the rank of the farms n Samara wh regard to techncal effcency scores n the ndvdual years s subject to sgnfcant changes. Ths s exactly what can be found n the results: many farms whch were very effcent at the begnnng of the consdered perod acheved only a medum level of effcency n subsequent years. Many farms whch were rather neffcent n 1996 moved nto the effcent group. Moreover, once they have reached ths group, ther poson becomes relatvely stable.

15 15 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 the 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 crosssectonal 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 analyzng panel data of 443 farms from dfferent parts of Russa, results were obtaned whch suggest that techncal neffcency enhances the varably of agrcultural producton n Russa. Moreover, accordng to the model estmates, producton rsk consderably contrbutes to the volatly of Russan agrcultural producton. For most farms, output varably s explaned manly by producton rsk. Ths ndcates that when nvestgatng agrcultural producton development n Russa, more attenton should be pad to the presence of producton rsk and related farmer behavor. In partcular, concernng the studes on techncal effcency of Russan farms, neglectng the effect of rsk on producton output and farmers' response to rsk may cause ncorrect estmatons of techncal effcency. The estmates ndcate that there are sgnfcant dfferences n producton technologes n the three nvestgated regons. Ths holds not only for the producton elastces but also for the mpact of technologcal change. Whle n Oreol and Krasnodar, a shft of the producton fronter has hardly been observed, Samara has experenced more dynamc development whch has apparently sgnfcantly enhanced s producton possbles. Accordng to the study results, n Oreol and Krasnodar, farm effcency ncreased sgnfcantly between 1996 and However, the farms n Samara have been less successful n adjustng to the best regonal practce n the consdered perod. In ths regard, the study results show that the development of techncal neffcency can be related to the effect of technologcal change. Under constant technology s reasonable to assume that frms learn from past experence and, thus, are on a path towards the best producton practce. On the other hand, the dffuson of nnovaton shfts the producton possbly set outward. Thus, dfferent farms can defne the producton fronter n subsequent years. Should the technologcal effect preval, for the enterprses whch fal to adopt nnovatve technques, the dstance to the best domestc practce ncreases, mplyng a declne n techncal effcency. Ths seems to be the case for the farms n Samara. Addonally, the analyss demonstrates that there s only a weak response of the farms to producton rsk: most producton factors enhance farms' producton volatly. Ths mples that current factor endowment of Russan farms s not adjusted to producton condons. At the same tme, as producton rsk plays an mportant role n the development of agrcultural producton at ths stage, farms have to search for optons to mprove ther responses to producton rsks, prmarly wh respect to the ntroducton of modern producton technologes and practces that can reduce output volatly and enable a more flexble factor use subject to the state of nature. Fnally, further research s needed to analyse the farmers' response to producton rsk. If agrcultural producers do not exhb rskadjustng behavour, the reasons for ths have to be analysed. In ths regard, would be necessary to model farmers' rsk preferences and estmate s mpact on nput use explcly.

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