The Bargaining Strength of a Milk Marketing Cooperative

Transcription

1 The Barganng Strength of a Mlk Marketng Cooeratve Peeraon Prasertsr and Rchard L. Klmer As a result of economes of sze, food rocessors are generally large and few n number. These characterstcs ut rocessors at a barganng advantage over ndeendent farmers. Marketng cooeratves were establshed to counter the uneven barganng oston of ndvdual farmers. Ths artcle nvestgates the relatve barganng strength of one mlk marketng cooeratve and several flud mlk rocessors. The ash barganng model can be used to analyze the negotated rce n the Florda flud mlk market whch acts lke a blateral monooly. The mlk marketng cooeratves have barganed well wth the mlk marketng rocessors. The monthly barganng strength of the Southeast Dary Cooeratve, Inc. (SDC), exceeds the monthly barganng strength of the rocessors n all twelve months, rangng from a low of n January to a hgh of n Setember. The monthly average barganng strength across all months for SDC s Key Words: cooeratve, barganng, blateral monooly, dary, rocessors As a result of economes of sze, food rocessors are generally large and few n number (Durham and Sexton 1992). These characterstcs ut rocessors at a barganng advantage over ndeendent farmers. Marketng cooeratves were establshed to counter the uneven barganng oston of ndvdual farmers (Jesse et al. 1992). Ths artcle nvestgates the relatve barganng strength of one mlk marketng cooeratve and several flud mlk rocessors. The Southeast Dary Cooeratve, Inc. (SDC), sules farm mlk to several dary flud mlk rocessors n Florda. From January 1999 through May 2002, SDC was comosed of two cooeratves: Southeast Mlk, Inc. (SMI), and the Southeast Councl of Dary Farmers of Amerca, Inc. (DFA). SDC rovded all of the mlk to the Florda rocessors n 1999, 2000, 2001, and 2002, excet for August 2000 and 2001 when Maryland/ Vrgna roducers rovded some mlk (USDA varous ssues). In 2002, there were eght rocessors who owned twelve flud mlk rocessng lants n Florda (USDA varous ssues). The average annual amount at each lant was 199,552,230 Peeraon Prasertsr s a former graduate student and Rchard Klmer s Professor n the Food and Resource Economcs Deartment and the Insttute of Food and Agrcultural Scences at the Unversty of Florda n Ganesvlle, Florda. ounds of flud mlk for Class 1 use (USDA varous ssues). The market structure s a monooly on the suly sde (SDC) and olgosony on the rocessors sde. When barganng takes lace, the market envronment changes to one aroxmatng a blateral monooly. The barganng envronment s structured so that the rce s negotated monthly between SDC and one or two rocessors, wth the remanng rocessors accetng the rces negotated by the lead rocessor(s). Ths market structure was frst dentfed by Iskow and Sexton (1991) n the frut and vegetable markets and was labeled as an aroxmate blateral monooly by Sexton (1993, age 49). Equlbrum n a blateral monooly cannot be determned by tradtonal economc tools (suly and demand) (Koutsoyanns 1979). The sulyand-demand framework can defne only the barganng rce range that contans the soluton outcome (Helmberger and Chavas 1996). The recse rce s determned through barganng and wll be nvestgated n ths artcle. The frst secton of ths artcle descrbes the Florda farm mlk market. The next secton descrbes an axomatc barganng model found n the lterature and adats t to the Florda farm mlk market. An econometrc barganng rce model whch contans the barganng strength co- Agrcultural and Resource Economcs Revew 37/2 (October 2008) Coyrght 2008 ortheastern Agrcultural and Resource Economcs Assocaton

2 Prasertsr and Klmer The Barganng Strength of a Mlk Marketng Cooeratve 205 effcents s resented. Data for the varables and the econometrc estmates of barganng strength are analyzed. Fnally, a summary and conclusons for the artcle are rovded. Florda Farm Mlk Market Suly contracts between SDC and Florda rocessors are for a one-year erod and are renewed annually; however, the contract can be canceled by ether arty f a 60-days notce of cancellaton s gven. Each Thursday, rocessors order daly truckloads of mlk for each week, startng on Sunday and endng on Saturday. Processors can amend ther orders once they are laced. The Federal Mlk Marketng Order system has four classes of mlk. Class 1 mlk s farm mlk used for drnkng uroses. Class 2 mlk s farm mlk used for soft dary roducts, such as ce cream and cottage cheese. Class 3 mlk s farm mlk used for hard cheeses. Class 4 mlk s farm mlk used for dry mlk and butter. On a monthly bass, the Federal Marketng Order system sets the mnmum rce ad by rocessors and manufacturers for farm mlk used n each class. Mlk revenues are ooled n each marketng order and farmers are ad the sum of the revenue from each class. The total revenue from all mlk classes s dvded by total farm mlk ounds ooled n the Federal Order, and ths fgure s then multled by the ounds of mlk from each farmer n order to arrve at the farmer s gross mlk recets before exenses are taken out (e.g., transortaton costs). Also, each farmer receves a Class 1 dfferental whch s added to the Class 1 rce comuted for each Federal Mlk Marketng Order. The Class 1 dfferental s dfferent n each order and s ntended to move mlk from surlus to defct regons f needed. A Generalzed ash Barganng Model for the Florda Farm Mlk Market Theoretc analyses of blateral barganng are categorzed nto statc axomatc and strategc aroaches (Krshna and Serrano 1996). The statc axomatc aroach was frst roosed by ash (1950). The ash axomatc aroach has been oular n emrcal work as t s smle to mlement and can be nterreted as a stable barganng conventon whch s mmune to a artcular argument resented by an arbtrary layer (Coles and Hldreth 2000, Muthoo 1999). The strategc aroach was ntally ntroduced by Rubnsten (1982), who suggested the alternatve-offer rocedure and the dea of frcton (.e., the value of tme) to the barganng rocess. Later, Bnmore, Rubnsten, and Wolnsky (1986) ntroduced the rsk of breakdown nto the Rubnsten alternatveoffer barganng model. The statc axomatc and strategc aroaches are closely related. The results from the Rubnsten strategc model and the strategc model wth the rsk of breakdown aroxmate the soluton suggested by ash s model when the resonse tme between the artes durng the game s small (Bnmore, Rubnsten, and Wolnsky 1986). Indeed, the ash barganng model s a secal case of more elaborate strategc models that converge to the ash model under certan assumtons (Burtraw 1993) (for more examles, see Bnmore 1987a, 1987b, 1987c). Furthermore, van Damme (1986) found that all soluton barganng concets wthn a large class (meta-game) lead to the axomatc ash barganng soluton. The SDC and flud mlk rocessors reach contract agreement through a monthly barganng rocess that aroxmates a blateral monooly. Ths barganng rocess s characterzed by usng the generalzed axomatc ash aroach. In order to account for an asymmetrc envronment, the generalzed ash barganng model s the orgnal ash barganng model wthout the symmetry axom (Roth 1979). Ths asymmetry can be used to determne the artes relatve barganng strengths n the negotaton. The generalzed ash barganng model (Muthoo 1999) for the rce negotaton between the SDC and a Florda rocessor can be shown as (1) 1 Max ( Uc U ) α α bc ( U Ub), where U c and U are SDC s ayoffs and the rocessor s ayoffs when a barganng agreement s reached and are a functon of the negotated rce ; U bc and U b are SDC s ayoff and the rocessor s ayoff when a barganng breakdown occurs and are a functon of the breakdown rce bc and b, resectvely; and α and (1 α) denote SDC s

3 206 October 2008 Agrcultural and Resource Economcs Revew and the rocessor s relatve barganng strength. Both artes have equal barganng strength f α equals 0.5. The dsagreement onts n the generalzed ash model are from the strategc barganng model (Bnmore, Rubnsten, and Wolnsky 1986, and Muthoo 1999) and are reresented by the breakdown onts (U bc, U b ). Assumng that the SDC and the rocessng lant are rsk-neutral, the ayoff functons for the SDC and rocessor are defned as (2) and (3) Uc c = ; U bc = bc c U r = ; U b = r b, where c denotes the total cost er hundredweght faced by the SDC and r reresents a rocessor s revenue mnus other costs assocated wth mlk rocessng er hundredweght of flud mlk. The generalzed ash barganng model can now be wrtten as (4) 1 Max [ c ( bc c)] α α [ r ( r b)]. Dfferentatng equaton (4) wth resect to and settng t equal to zero, the negotated rce from the strategc-based generalzed ash barganng model s (5) = (1 α )( ) +α ( ). Gven the barganng strength that SDC and the rocessor ossess, as well as ther resectve threat onts, the two organzatons negotate. The negotated rce and the threat ont rces bc and b make t ossble to econometrcally estmate the barganng strength arameters by usng a restrcted regresson model where the relatve barganng strength arameters α and (1 α) sum to one. Emrcal Model Due to the weather condtons n Florda, there exst erods of mlk shortage and surlus. The bc b SDC s resonsble for balancng the suly and demand of flud mlk. In defct months, the SDC buys mlk from out-of-state roducers and morts t nto Florda for the Florda rocessors, whereas n surlus months the excess suly s shed out of Florda as Class III and Class IV mlk and s sold to butter, cheese, and/or owdered mlk manufacturers. In surlus months, the average rce receved by the SDC s usually less than the rce receved n the defct months (USDA varous ssues), whch ndcates the resence of seasonalty. In order to determne f the surlus and defct months n Florda have an mact on the monthly negotated barganng rce, monthly seasonalty s added to equaton (5) as (6) 12 1b (1 1) j b D j bc j = j 1 bc D j e j = 2 =α + α + α + [1 α α (1 α )] +, where s the rce receved by SDC from the rocessng lants for observaton ; b s the cost er hundredweght that the rocessng lants would ay for flud mlk f the barganng rocess wth SDC broke down for observaton ; D j s a bnary varable where D j equals 1 for the February (December) observaton when j = 2 (12), but zero otherwse, n order to ncororate seasonalty nto the model; bc s the mnmum rce flud mlk rocessors can ay SDC for farm mlk used n Class I roducts for observaton ; and ε s the error term for observaton. In order to ensure that the barganng strength arameters for SDC and the rocessors sum to one, the coeffcents assocated wth bc and bc D j are restrcted by other coeffcent values contaned n equaton (6). The cost er hundredweght that rocessng lants would ay for flud mlk f the barganng rocess wth SDC broke down ( b ) s assumed to be equal to the weghted average of the flud mlk rce (Class I lus over-order remum) n surlus areas (.e., Baltmore, Detrot, Kansas Cty, and Phladelha) lus the er hundredweght haulng costs from those areas to the Florda rocessors. bc s set by the Federal Mlk Marketng Order system, whch s admnstered by the U.S. De-

4 Prasertsr and Klmer The Barganng Strength of a Mlk Marketng Cooeratve 207 artment of Agrculture. 1 Processors cannot ay roducers less than the Class 1 rce. Furthermore, Florda has the hghest Class 1 rce n the naton. If SDC were to haul Florda mlk to rocessors outsde the state of Florda, the Class 1 rce receved would be lower than n Florda and transortaton costs would be more comared to Florda mlk delvered to Florda rocessors. on-florda rocessors are not lkely to ay a remum for Florda mlk because they can go north and fnd less exensve mlk, as the rce of mlk decreases as one travels north n the southeastern Unted States. Furthermore, f barganng breaks down and SDC s lookng for non-florda rocessors to buy ts mlk, the non-florda rocessors know that they have an advantage n the barganng rocess wth SDC. The rocessors, however, can ay more than the Class 1 rce. The SDC and the rocessors bargan for an overorder remum whch, when added to the Class 1 rce, equals the negotated rce. Data The U.S. Deartment of Agrculture and the Southeast Dary Cooeratve, Inc., rovded the data used n ths study. Frst, the data nclude the monthly rces ( ) SDC receved from the rocessng lants through the negotaton rocess, equalng the Class I flud mlk rce n Tama lus the over-order remum from the negotaton rocess. The monthly over-order remum s the dollars er hundredweght receved by SDC n excess of the Class I rce set by the Federal Mlk Marketng Order. The Class 1 rce ( bc ) s set by the Federal Mlk Marketng Order system, whch s admnstered by the U.S. Deartment of Agrculture. Processors cannot ay roducers less than the Class 1 rce; however, the rocessors can ay more than the Class 1 rce. The tme erod for all of the data sets was October 1998 to May 2002 (44 observatons). All mlk rces, remums, and other relevant costs are n dollars er hundredweght. 1 There was a concern about bc and b havng endogenety roblems. bc s set by formula and s not random. b s calculated by formula and s assumed to be non-random. As a recauton, nstrumental varables were tred but the results of the analyss dd not change. Results The regresson results (Table 1) were estmated usng nonlnear least squares, 2 whch gves the same estmates of the arameters as the maxmum lkelhood estmator under the assumton of normally dstrbuted dsturbances. The nonlnear least squares estmator s asymtotcally normal, asymtotcally effcent, and asymtotcally consstent (Greene 2000). Equaton (6) was estmated n ts restrcted and unrestrcted forms (Table 1) wthout the monthly sloe shfters to test the hyothess that barganng arameters α and (1 α) sum to one [H o : α b + (1 α b ) = 1 (restrcted model); H a : α b + (1 α b ) 1 (unrestrcted model)]. We faled to reject the null hyothess as the lkelhood rato test was nsgnfcant at the 0.01 ercent level. Ths ndcates that we faled to reject the ash barganng strength theoretcal restrcton. The coeffcents from the unrestrcted model sum to The R 2 s are for the unrestrcted model and for the restrcted model. Ths nformaton suorts the ash barganng assumton that the dfferences n barganed outcomes are attrbutable to varaton n outsde otons. The restrcted equaton (6) was estmated wth and wthout the monthly sloe shfters to determne f relatve barganng ower vares seasonally (Table 1). We faled to reject the alternatve hyothess (the model wth the monthly sloe shfters) as the lkelhood rato test was sgnfcant at the 0.10 ercent level (H o : α 2 =... = α 12 = 0; -α 2 =... = -α 12 = 0; H a : at least one α j s not equal to zero, or at least one -α j s not equal to zero). Usng functons of the coeffcents from the restrcted model wth sloe shfters [equaton (6)], the relatve barganng strengths for SDC and the rocessors were calculated for each month (Table 2). t tests were run to determne f the relatve barganng strengths were dfferent from 0.5 [H o : ( α b + α j ) 0.5 = 0 ; H a : ( α b + α j ) 0.5 0], the ont at whch SDC and the rocessors have 2 The LSQ routne n TSP verson 4.4B was used. Seral correlaton was corrected usng a subroutne called formar1. The frst observaton was retaned.

5 208 October 2008 Agrcultural and Resource Economcs Revew Table 1. Regresson Results Unrestrcted Model Restrcted Models Varable Coef. Std. Err. Coef. Std. Err. Coef. Std. Err. b *** *** *** b D ** b D b D b D b D b D b D b D b D b D b D ** bc *** *** bc D ** bc D bc D bc D bc D bc D bc D bc D bc D bc D bc D * Rho a *** *** *** Log lkelhood R Adjusted R Observatons a Seral correlaton was corrected usng a subroutne called formar1 whch gves maxmum lkelhood estmates usng LSQ n TSP verson 4.4B. The frst observaton s retaned. otes: *** coeffcents are sgnfcantly dfferent from zero at the 0.01 level, ** at the 0.05 level, and * at the 0.1 level. equal barganng strength. All coeffcents were statstcally dfferent from 0.5 at the.01 level, excet those for January and ovember, whch were sgnfcantly dfferent from 0.5 at the 0.05 level (Table 2). Ths ndcates that SDC and the rocessors have unequal barganng strength. The average monthly barganng strength for SDC and the rocessors was statstcally dfferent from 0.5 at the 0.01 level. There are two reasons why the SDC has a majorty of the barganng ower. Frst, rocessors are more matent than SDC because the rocessors have buyers who need a contnuous suly of dary roducts. SDC rovdes the rocessors

6 Prasertsr and Klmer The Barganng Strength of a Mlk Marketng Cooeratve 209 Table 2. The Monthly Barganng Strength for SDC and Processor (October 1998 through May 2002) SDC Barganng Strength Processor Barganng Strength January ** ** February *** *** March *** *** Arl *** *** May *** *** June *** *** July *** *** August *** *** Setember *** *** October *** *** ovember ** ** December *** *** Average *** *** otes: *** and ** coeffcents are sgnfcantly dfferent from 0.5 at the 0.01 and 0.05 levels. wth all of the mlk the rocessors need (a full suly contract) and the rocessors have come to exect that relable servce from SDC. Second, there s a rsk of breakdown n negotatons because alternatves do exst for the SDC to sell mlk to and the rocessors to buy mlk from. If a breakdown does occur, there are at least three consequences that would affect the rocessors. Frst, Florda rocessors refer fresh mlk as oosed to mlk that has been n a bulk tank for several days. The older mlk s before beng bottled, the qucker bottled mlk wll sol. If barganng breaks down, mlk wll not be as fresh because t wll be hauled from greater dstances than mlk roduced n Florda. Second, f rocessors dd not have access to Florda-roduced mlk, Florda flud mlk rocessors could urchase mlk only from sulers outsde the northern border of Florda. The Florda rocessors would not be able to go east or south or west of Florda (there s water on three sdes of Florda) to urchase mlk. Ths ncreases the transortaton cost for mortng mlk nto Florda and could lead to Florda rocessors ayng hgher rces for mlk. Thrd, when Florda rocessors look for mlk north of the Florda border, they fnd that the entre southeastern Unted States s defct n mlk. Surlus mlk s located many mles from Florda, whch affects the age of mlk before t can be bottled. The SDC has a majorty of the barganng strength, although t vares from month to month. The barganng strength of SDC ranges from a hgh of n Setember to a low of n January, for a monthly average of (Table 2). The months above the monthly average barganng strength for SDC are February, Arl, July, August, Setember, and December, wth October beng aroxmately average. July, August, Setember, October, and December are defct months; however, February and Arl are surlus months. Thus, barganng strength for SDC s hgher n most defct months (ovember s a defct month) and some surlus months, whch means that hgh barganng strength levels do not haen exclusvely durng surlus months. Summary and Conclusons In a blateral monooly, the tools of suly and demand cannot be used to analyze the negotated rce ( ). The ash barganng model can be used to analyze the negotated rce n the Florda flud mlk market, whch acts lke a blateral monooly. The restrcted model wth monthly sloe shfters accounts for 95 ercent of the varaton n the negotated rce. The barganng strength of SDC and the rocessors sums to one, as hyotheszed. Based on the study erod, the mlk marketng cooeratves have barganed well wth the mlk marketng rocessors. The monthly barganng strength of SDC exceeds the monthly barganng strength of the rocessors n all twelve months, rangng from a low of n January to a hgh of n Setember. The monthly average barganng strength across all months for SDC s In concluson, the threat onts for the rocessor and SDC defne the rce range wthn whch the negotated rce can be found. The rce range s set by the Mlk Marketng Order system. The negotated rce s determned by the relatve barganng strength of SDC and the rocessors. References Bnmore, K. 1987a. ash Barganng and Incomlete Informaton. In K. Bnmore and P. Dasguta, eds., The Economcs of Barganng. Cambrdge, MA: Basl Blackwell.

7 210 October 2008 Agrcultural and Resource Economcs Revew. 1987b. ash Barganng Theory II. In K. Bnmore and P. Dasguta, eds., The Economcs of Barganng. Cambrdge, MA: Basl Blackwell c. Perfect Equlbra n Barganng Models. In K. Bnmore and P. Dasguta, eds., The Economcs of Barganng. Cambrdge, MA: Basl Blackwell. Bnmore, K., A. Rubnsten, and A. Wolnsky The ash Barganng Soluton n Economc Modellng. RAD Journal of Economcs 17(2): Burtraw, D Barganng wth osy Delegaton. RAD Journal of Economcs 24(1): Coles, M., and A.K.G. Hldreth Wage Barganng, Inventores, and Unon Legslaton. Revew of Economc Studes 67(2): Durham, C.A., and R.J. Sexton Olgosony Potental n Agrcultural: Resdual Suly Estmaton n Calforna s Processng Tomato Market. Amercan Journal of Agrcultural Economcs 74(4): Greene, W.H Econometrc Analyss. Uer Saddle Rver, J: Prentce Hall, Inc. Helmberger, P., and J. Chavas The Economcs of Agrcultural Prces. ew York: Prentce Hall, Inc. Iskow, J., and R.J. Sexton Barganng Assocatons n Grower-Processor Markets for Fruts and Vegetables. Research Reort o. 104, ACS, U.S. Deartment of Agrculture. Jesse, E.V., A.C. Johnson, B.W Maron, and A.C. Manchester Interretng and Enforcng Secton 2 of the Caer- Volstead Act. Amercan Journal of Agrcultural Economcs 64(3): Koutsoyanns, A Modern Mcroeconomcs (2nd edton). London: Macmllan Press Ltd. Krshna, V., and R. Serrano Multlateral Barganng. Revew of Economc Studes 63(1): Muthoo, A Barganng Theory wth Alcatons. Cambrdge, UK: Cambrdge Unversty Press. ash, J.F The Barganng Problem. Econometrca 18(2): Roth, A.E Axomatc Models of Barganng. Berln and ew York: Srnger. Rubnsten, A Perfect Equlbrum n a Barganng Model. Econometrca 50(1): Sexton, R.J oncooeratve Game Theory: A Revew wth Potental Alcatons to Agrcultural Markets. Food Marketng Polcy Center Research Reort o. 22, Unversty of Connectcut. U.S. Deartment of Agrculture. Varous ssues. Florda Marketng Area Federal Order o. 6 Annual Statstcs. Agrcultural Marketng Servce, Dary Programs. Avalable at htt:// (accessed ovember 1, 2007). van Damme, E.E.C The ash Barganng Soluton Is Otmal. Journal of Economc Theory 38(1):