Dynamic Assessment of Bertrand Oligopsony in the U.S. Cattle Procurement Market. In Bae Ji Researcher Korea Rural Economic Institute Seoul, Korea

Transcription

1 Dynamc Assessment of Bertrand Olgopsony n the U.S. Cattle Procurement Market In Bae J Researcher Korea Rural Economc Insttute Seoul, Korea Chann Chung Professor Oklahoma State Unversty Contact: Chann Chung Department of Agrcultural Economcs, 322 Ag Hall Oklahoma State Unversty Stllater, OK Phone: Fax: chann.chung@okstate.edu Selected Paper prepared for presentaton at the Agrcultural & Appled Economcs Assocaton s 2011 AAEA & NAREA Jont Annual Meetng, Pttsburgh, Pennsylvana, July 24-26, 2011 Copyrght 2011 by In Bae J and Chann Chung. 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 Abstract The ne emprcal ndustral organzaton approach th the Bertrand model s employed to measure the olgopsony market poer n the U.S. cattle procurement market. The assumpton of prce competton (Bertrand model) based on the nature of cattle producton such as cattle cycle and seasonalty s used and compared to quantty competton (Cournot model). The emprcal results sho that the olgopsony market poer exsts n the U.S. cattle procurement market. The cattle cycle and seasonalty affect the olgopsony market poer and the cattle cycle causes the change of market poer. Hoever, concentraton has a negatve effect on the olgopsony market poer. Keyords: cattle cycle, concentraton, market poer, NEIO, olgopsony, seasonalty

3 Dynamc Assessment of Bertrand Olgopsony n the U.S. Cattle Procurement Market Introducton Most studes usng the ne emprcal ndustral organzaton (NEIO) approach on the market poer of the U.S. beef packng ndustry focus on the market structure, such as horzontal concentraton and vertcal ntegraton, as sources of market poer rather than characterstcs of the market such as cattle cycle and seasonalty. Also, the studes generally use the Cournot model by assumng quantty competton n the cattle procurement market. Cattle producton fluctuates unpredctably based on eather condtons and economc envronment hle beef demand s relatvely stable and predctable. The fluctuaton of cattle producton leads to prce fluctuaton and these varatons could nfluence the barganng poston of producers and packers n the cattle procurement market (Cresp, Xa, and Jones 2010). Therefore, the perodc fluctuaton of the cattle producton, cattle cycle and seasonalty, could affect market poer n the cattle procurement market. In addton, ths fluctuaton n cattle supply could make packers compete th prce rather than quantty. In the long-run, packers make sgnfcant producton decsons based on quantty such as ncreasng factory sze, purchasng slaughter machnes or packng machnes, as ell as other nvestments. These decsons could lead to quantty competton. Hoever, n the short-run, cattle purchase could lead to prce competton (Lepore 2008). Under the condton of fxed plant capacty, f packers cattle amount s not guaranteed n the cattle market durng the short cattle supply then they ould aggressvely bd to obtan ther 1

4 optmal operaton level of cattle for beef producton. On the other hand, f there s enough cattle supply n the market, then packers mght try to loer the cattle prce under the margnal cost for cattle producton. Ths strategy for packers s consstent th the structural conduct performance (SCP) theory (Stegert, Azzam, and Brorsen 1993). Addtonally, the reason hy packers gradually ncrease the volume of alternatve marketng arrangements (captve supply) could be explaned by ths ve pont. Packers can reduce cattle supply rsk and prce competton by ncreasng captve supply durng the short cattle supply and they mght be tactly collusve to loer the cattle prce durng the excess cattle supply. From ths perspectve, the cattle cycle and seasonalty may affect prcng and market poer n the cattle market. It therefore stands to reason that the Bertrand model s more accurate than the Cournot model to measure the olgopsony market poer for the cattle procurement market. The obectve of ths paper s three fold. Frst, ths paper provdes a conceptual frameork for the Bertrand model to analyze olgopsony market poer and to compare the Bertrand model th the Cournot model. Second, ths paper estmates the overall market poer th the statc model and annual market poer changes th the dynamc model at natonal and to regonal levels. Fnally, the effects of concentraton, cattle cycle, and seasonalty on markdon for cattle prce are estmated to look nto ho they nfluence on market poer n the U.S. cattle procurement market. The emprcal results sho that olgopsony market poer exsts n the cattle procurement market and that olgopsony market poer s affected by cattle cycle and seasonalty and, fnally, that the varaton of market poer changes equvalently th the 2

5 cattle cycle. Hoever, concentraton has a negatve effect on market poer n the cattle procurement market. Concentraton, Cattle cycle, and Seasonalty The concentraton for cattle procurement as drastcally ncreased durng the 1980 s and has stayed at hgh levels after 1990 (USDA). Fgure 1 shos the concentraton change based on Herfndahl-Hrschman Index (HHI) for steer and hefer slaughter. The HHI as n 1980 but t as rapdly ncreased by n 1992, and has stayed around 0.2 untl recently. The Department of Justce consder the ndustry to be concentrated f the HHI s greater than Therefore, concentraton has been n the mddle of controversy for the market poer ssue n the cattle procurement market durng the last three decades. Cattle cycles are measured from one trough to the next. There are sx full cycles n cattle nventores snce 1928 and the average length of cattle cycles are about 10 years (Anderson, Robb, and Mntert 1996). Fgure 2 shos the cattle cycle based on cattle supply from 1980 to The ffth cycle began n 1979 and the sxth cycle began n The latest cycle began n 2004 and cattle supples sho an ncreasng trend. A seasonal pattern s a regularly repeatng cycle that s completed once every telve months. Fgure 2 shos the averaged monthly changes of the slaughtered cattle supply from 1980 to The slaughtered cattle quanttes are hgh from May to October hle those from November to Aprl are lo. These cattle cycle and seasonalty cycles are responsble for creatng prce fluctuaton n the cattle market. 3

6 Lterature reve Most studes usng NEIO approach use the Cournot model to analyze market poer for the U.S. beef packng ndustry because t s easy to parameterze the conectural elastcty, prce elastcty of demand, and prce elastcty of supply usng smultaneous equatons. Addtonally, those elastctes can be used to easly calculate market poer (Schroeter 1988; Azzam and Schroeter 1995; Azzam 1997; Sexton 2000; Lopez, Azzam and Espana 2002; Tostao and Chung 2005; Zheng and Vukna 2009). There are three studes that use the Bertrand olgopsony model for the cattle market (Koontz, Garca, and Hudson 1993; Koontz and Garca 1997; Ca, Stegert, and Koontz 2009). They develop the Regme-Stchng model based on the dynamc non-cooperatve game theoretc model. They fnd that the evdence of cooperatve/compettve conduct among the beef packers s present, but the conduct vares across markets. They attempt to determne hether the cooperatve conduct s present as evdence of market poer, but they don t estmate the conduct parameters n the cattle procurement market. To studes are concerned th cattle supply and cattle cycle. Stegert, Azzam, and Brorsen (1993) use the NEIO model assumng quantty competton to determne the effect of antcpated and unantcpated cattle supply on the departure of fed cattle prces from cattle s margnal value product. They fnd that packers follo an average processng cost (APC) prcng rule and that reducng concentraton s unlkely to affect change n cattle prces predcted by SCP based studes of the ndustry 1. They use antcpated and 1 Average processng cost (APC) prcng s that packers establsh a cattle bd prce by subtractng the average processng cost from the prce receved for carcasses or boxed beef hle the structure-conduct-performance (SCP) paradgm theory suggests that the markdon ll respond to supply changes, but n a drecton 4

7 unantcpated cattle supply to look nto packer s behavor, prmarly ho packers react to those cattle supples. The antcpated cattle supply s specfed by the cattle on feed, cattle placements, and the seasonal dummy varable. Therefore, the effects of cattle cycle and seasonalty are embedded n the antcpated cattle supply. Consequently, they do not explan the effects of cattle cycle and seasonalty. Cresp, Xa and Jones (2010) nvestgate the relatonshp beteen market poer and cattle cycle th a dynamc cattle producton decson model and a dynamc proft maxmzaton model based on the Cournot model. They provde a conceptual frameork for ho the cattle cycle and buyer markets are related. They fnd that a larger cattle stock leads to a loer fed cattle prce and the cattle stock s negatve effect on prce s magnfed by the degree of buyer market poer. They develop an equlbrum model that conssts of the dynamc cattle supply equaton for feeders and dynamc cattle demand for packers. Hoever, they assume that the market poer s determned by the number of packers rather than the packer s market conducts. Therefore, ths paper extends the tradtonal NEIO model n to ays. Frst, ths paper provdes ho the mplcatons of market poer from the Bertrand model dffer compared to the Cournot model through analytcal dervaton. Second, concentraton n the tradtonal ve as ell as cattle cycle and seasonalty for the nature of cattle market are ncluded n modelng the market conduct equaton n order to measure ther effects on market poer. opposte to that of APC prcng. That s, as antcpated supply declnes, packers bd more aggressve hle antcpated supply s abundant, bddng becomes less aggressvely and the markdon ncreases (Stegert, Azzam, and Brorsen 1993). 5

8 The Model The to NEIO models are reveed n ths secton: the Bertrand model and the Cournot model. We analyze the nput market rather than output market, so e use monopsony or olgopsony as orkng terms nstead of monopoly or olgopoly. In the Bertrand olgopsony model, buyers choose the prce to pay for a unt of nput, hch affects the market supply. The Bertrand model predcts that a duopsony s enough to push prces up to the margnal cost level for nput producton. Ths suggests that duopsony ll result n perfect competton, commonly referred to n the economcs lterature as the Bertrand paradox. Hoever, n the Cournot olgopsony model, here buyers compete strategcally th ther quantty, buyers enoy postve profts as the resultng nput market prces do not exceed those of the margnal costs. By mposng some assumptons n the modelng, e ll demonstrate ho ths suggeston that duopsony ll result n perfect competton n the Bertrand model could be changed. Bertrand Model In ve of the ntended applcaton, e ll focus on olgopsony market poer n the cattle procurement market. Packers determne cattle prce as a strategy varable to maxmze ther proft from the cattle procurement market hle the holesale prce to sell ther output, boxed beef, to the retaler s assumed as a gven. The cattle supply s assumed as fxed n the short-run because feeders cannot ncrease ther cattle supply n the short-run. Feeders cannot determne the cattle supply, but they can sell ther cattle to the hghest bdder n the cattle market. 6

9 In the beef processng ndustry t s assumed that N packers convert a sngle farm nput, cattle, nto a fnal output, boxed beef, th fxed proportons technology beteen the farm nput and the output (Schroeter 1988; Azzam 1997). Converson of the farm nput nto output requres non-farm nputs that are purchased n compettve markets. Proft,, for the th frm (for 1, 2,, N ) s: (1) Max P q (, ( )) C ( q (, ( ), v), here P s the boxed beef holesale prce, s the cattle nput prce for th frm, s the cattle nput prce for th frm (for 1, 2,, N and ), q s the th frm s beef product or cattle nput, C (, v) s the processng cost for the th frm, and v s a vector of q prces of non-farm nputs. The frst order condton for proft maxmzaton s: q (2) ( ) q C q q q P 0. q Rearrangng the frst order condton and re-rtng n elastcty form yelds: (3) q q q ( P mc ), q here s the on prce elastcty of cattle purchase for the th frm, q q s the cross prce elastcty for the th frm th respect to the th frm s q 7

10 8 prce changes, s the prce conectural elastcty of th frm th respect to th frm s prce change, and q q C mc ), ( v s the th frm s margnal cost. If e assume that the effect of the th frm s prce change on the th frm s fed cattle purchase s smaller than the effect of ts on prce change, and frms are symmetrc (Ca, Stegert, and Koontz 2009), ths means q q q q and then e can rte the rato of on prce elastcty of cattle procurement to cross prce elastcty th respect to th frm s prce, as 1 q q q q R. That s, R, then the equaton (3) becomes: (4) mc R P 1, here. In equlbrum, all frms have the same value, R and, then the ndustry level margn equaton can be yeld (Appelbaum 1982; Schroeter 1988): (5) mc W R W P b 1 1, here W s the ndustral level cattle prce, s the ndustral level prce conectural elastcty, R s the ndustral level rato of on prce elastcty of cattle procurement to

11 cross prce elastcty, b s the on prce elastcty of cattle purchase, and mc s the ndustral level margnal cost. In equaton (5), the term n the left sde s the ndustral level margn for a unt of cattle, the frst term on the rght sde s markdon for a unt of cattle n the cattle procurement market, and the second term on the rght sde s the margnal cost for a unt of cattle n the beef processng ndustry. For analyzng the short-run olgopsony poer n the cattle procurement market e should look at the markdon term. The markdon s determned by three parameters: the prce conectural elastcty, the rato of on prce elastcty of cattle procurement to cross prce elastcty, and the on prce elastcty of cattle purchase. These three parameters sho the market partcpant s reactons about a frm s cattle prcng change. That s, the prce conectural elastcty shos the reacton of the rval s cattle prcng, the rato of on prce elastcty of cattle procurement to cross prce elastcty shos the relatve effect frms prce change on a frm s cattle purchase, and the on prce elastcty of cattle purchase reveals the decson of the feeder s cattle supply decson to choose ho they sell ther cattle to, correspondng to a frm s prcng change. The on prce elastcty of cattle purchase, b, s an nfnte n the tradtonal ve of the Bertrand model so that the market ll reflect perfect competton th no markdon n the ndustry. 2 In realty, hoever, there are some restrctons lke capacty constrants. If a sngle frm does not have the capacty to procure the hole cattle market then the prce 2 The Bertrand model rests on the follong assumptons: 1) There are at least to frms producng homogeneous (undfferentated) products; 2) Frms do not cooperate; 3) Frms compete by settng prces smultaneously; 4) Consumers buy everythng from a frm th a loer prce. If all frms charge the same prce, consumers randomly select among them (Bertrand 1883). The fourth assumpton represents that the on prce elastcty s nfnte. 9

12 equals margnal cost result may not hold. Thus, the range of the on prce elastcty of cattle purchase ll be 0. Ths reflects that the Bertrand paradox can be fxed f b the on prce elastcty of cattle purchase s qute smaller than the nfnte. The on prce elastcty of cattle purchase s usually greater than the prce elastcty of cattle supply, Q W c (Anderson, Palma, and Kreder 2001). W Q For the market conduct n equaton (5), hen b, the market ll be perfect competton. When 0, the market ll be olgopsony or monopsony. Under ths b assumpton, 0 b, market conduct depends on the prce conectural elastcty,. 0 means Bertrand-Nash hen N 1 and pure monopsony hen N 1. 1 means cartel or symmetry among the frms and 0 R means olgopsony. 3 Cournot Model Alternatvely, n the Cournot model packers determne cattle quanttes as a strategy varable to procure the cattle from the cattle procurement market and feeders determne the cattle supply to the cattle procurement market. Fnally, cattle prce s determned n the market by cattle supply and demand. That s, the fxed supply assumpton s released by long-run decson makng for feeders and the feeders face a compettve market for sellng ther cattle to packers. Under the same assumpton of the fxed proportons technology, then th frm s proft s: 3 Koontz, Garca, and Hudson (1993) nterpret the value of 0 means packers dsplay non-cooperatve prcng and the value of 0 means packers have cooperatve prcng n the cattle procurement market. 10

13 (6) Max P W ( Q) q C ( q, v) q, here P s the beef holesale prce, W s the cattle prce n the market level, and q s the th frm s cattle or boxed beef quantty. The frst order condton for proft maxmzaton s: W Q C ( q, v) (7) P W q 0. q Q q q Rearrangng the frst order condton and re-rtng n elastcty form yelds: (8) P W W mc, c Q here q q Q, s the th frm s quantty conectural elastcty and Q W c s the W Q prce elastcty of cattle supply n the cattle procurement market. In equlbrum, all frms have the same value, then the ndustry level margn equaton can be rtten (Appelbaum 1982; Schroeter 1988): (9) P W W mc. c In the Cournot model, markdon conssts of the quantty conectural elastcty,, and the cattle supply elastcty,. The range of conectural elastcty s beteen 0 and 1. c The value 0 means perfect competton and 1 means pure monopsony, and other values mean varous degrees of olgopsony poer th hgher values of denotng greater olgopsony (Appelbaum 1982). The prce elastcty of cattle supply, c, s 11

14 consdered as 0 1 because prce elastcty of supply for agrcultural markets are usually nelastc and postve. c Emprcal procedures For econometrc estmaton, e assume a generalzed Leontef cost functon (Deert 1974; Schroeter 1988) for the beef processng ndustry: 2 (10) C ( Q, v ) Q k ( v k v 1 ) v, k here v and v are the nput prce of labor, captal, and materal. Then the ndustral level k margnal processng cost functon s gven by: (11) mc k 2 ( v v ) 1, by substtutng equaton (11) nto equaton (5), e obtan the ndustral level Bertrand margn equaton: 1 2 (12) P W W k ( vk v ) 1 eb, k 1 b R k k here 1 (1 R) b s the ndustry-de markdon n cattle prces from cattle s margnal value and e b s the error term for the Bertrand margn equaton respectvely. Hoever, the parameters n the ndustry-de markdon cannot be estmated because of lmtaton of frm level data. Consequently, e estmate the hole part of the ndustry-de markdon, M 1 1 R b, as follo: 12

15 2 (13) P W M W k ( v k v ) 1 eb. k To estmate the margn equatons (13), smultaneous equatons are needed such as three non-farm nput demand equatons. Non-farm nput demands are obtaned by applyng Shephard s lemma on the ndustry level processng cost functon represented by equaton (10) as: C( Q, v) v k (14) X Q k, v k v hch can be re-arranged as: X v k (15) k e, Q k v Q here materal, 1 2 X s the ndustry level derved non-farm nput demand for labor, captal, and X Q s the nverse of productvty for each non-farm nput, and e s the error term for the non-farm nput demand equaton respectvely. Fnally, to analyze the effect of concentraton, cattle cycle, and seasonalty on the cattle prce, e make the markdon as a functon of these varables. Ths specfcaton also allos the dynamc estmaton of the market conduct over tme. The ndustry-de markdon M s specfed as: (16) M 0 1H 2C 3D1 4D2 5D3, 1 2 here H s the Herfndahl-Hrschman Index, C s the cattle cycle, and D th 1, 2, 3 are seasonal dummy varables, and s are parameters. In order to generate cattle cycle varablty, the follong yearly cattle supply equaton s estmated: 13

16 (17) Q 1tme q 0, and the cattle cycle varable, C Q Qˆ, s calculated for each year. If equaton (16) substtutes nto equaton (13), then dynamc equaton can be rtten as: 2 (18) P W ( H C D D D W k v k v ) ( ) ed. We utlze to systems: the statc model and the dynamc model for three regons: Natonal, Nebraska, and Texas. Equatons (13) and (15) consttute a system of four equatons for the statc model and equatons (18) and (15) consttute a system of four equatons for the dynamc model. We use the generalzed method of moment (GMM) hch employs nstrumental varable estmators snce the system equatons have endogenety problems. GMM also provdes a consstent estmator hen heteroskedastcty and autocorrelaton are present (Breusch 1978; Godfrey 1978). The eghteen nstrumental varables ncluded n the equaton are Herfndahl-Hrschman Index (HHI) for the steer and hefer slaughter, 5 market steer eghted prce, Nebraska steer and hefer eghted prce, Texas steer and hefer eghted prce, labor prce, captal prce, materal prce, cattle on feed, cattle placement, cattle on marketng, dsappearance, cycle, seasonal dummy varables, tme, and squared tme. The ndustry-de markdon rates as market poer ndces and markdons n cattle prces from cattle s margnal value for each year are also estmated through GMM usng the MODEL Procedure n SAS 9.2. The frst null hypothess s that olgopsony market poer n the U.S. cattle procurement market equal zero. Reectng t suggests that packers exert olgopsony market poer n the U.S. cattle procurement market. The second null hypothess s that the k 14

17 concentraton, the cattle cycle, and the seasonalty have no effect on the olgopsony market poer. Reectng t suggests that the concentraton, the cattle cycle, and the seasonalty mght be used as a ay to prce under the margnal cost for cattle producton. Data In ths paper, e use monthly data seres for the U.S. cattle procurement market rangng from 1980 to As Natonal, Nebraska, and Texas cattle supples, the steer and hefer slaughter total lve eghts for Natonal level, for Nebraska regon, and Texas regon are from Lvestock Slaughter Annual Summary of Unted States Department of Agrculture (USDA). The holesale prce and the 5 market steer eghted prce data are from the beef value and prce spread monthly data sets from the USDA Economc Research Servce (ERS). The holesale prce s modfed by ncludng by-product value and by dvdng th 2.4 as converson factor to a unt of lve cattle (USDA). The steer and hefer eghted prces data for Nebraska and Texas are compled from USDA Agrcultural Marketng Servce (AMS) data. The producer prce ndex for farm products slaughter steer and hefer s from Bureau Labor Statstcs (BLS). The prce ndex and the productvty ndex of labor, captal, and materal for U.S. food and other ndustres are obtaned from the Maor Sector Multfactor Productvty Index Database of BLS. The cattle on feed, the cattle placement, the cattle on marketng, and dsappearance data are from the USDA Natonal Agrcultural Statstcs Servce and Red Meats Yearbook of USDA. The Herfndahl-Hrschman Index for the U.S. beef processng ndustry s the steer and hefer slaughter concentraton ndex compled from several annual reports from the Packers and Stockyards Statstcal Report 15

18 ( ). The defntons and descrptve statstcs of these varables are presented n table 1. Results The estmaton results of the statc model and the dynamc model for the Natonal level, Nebraska regon, and Texas regon by GMM are reported n table 2 and 3. All of the 10 parameter estmates n the statc model and most parameter estmates n the dynamc model for Natonal, Nebraska, and Texas are statstcally sgnfcant at the 5% sgnfcance level. Wth these estmaton results and th mean values of cattle prces and nput prces, the market poer parameters such as market poer (ndustry-de markdon rate), markdon, and margnal cost are calculated and summarzed n table 4. As the key parameter, the market poer estmates n the statc model are , , and for Natonal, Nebraska, and Texas respectvely hle , , and n the dynamc model. The market poer of Natonal s the bggest compared to the to regons and the market poer of Nebraska s greater than the market poer of Texas. The market poer from the dynamc model shos the same results th the statc model, but ther value s almost tce that of the statc models. The values of , , and for market poer n the statc model mean that there are about 3.66 percent, 1.99 percent, and 1.38 percent of markdons for the cattle prce respectvely. For the markdons and the margnal costs, n the statc model, the markdons are , , and and the margnal costs are , , and for Natonal, Nebraska, and Texas respectvely and they are all sgnfcant at the 5% 16

19 sgnfcance level. In the dynamc model, the markdons are , , and and the margnal costs are , , and for Natonal, Nebraska, and Texas respectvely and they are also all sgnfcant at the 5% sgnfcance level. The markdon value of and margnal cost of for Natonal n the statc model mean that the average markdon for the cattle prce s $ 2.65/ct and the margnal cost of cattle slaughter s $ 6.72/ct, respectvely. These results of market poer and markdon for Nebraska and Texas n the statc model are smlar to the results of Schroeter (1988) and Stegert, Azzam, and Brorsen (1993) hle the results for Natonal and dynamc model are slghtly bgger than ther results. The dynamc model shos the effect of concentraton, cattle cycle, and seasonalty. The parameters of concentraton, cattle cycle, and three seasonal varables for Natonal are , , , , and respectvely. They are all sgnfcant at the 5% sgnfcance level. Nebraska and Texas also sho smlar estmaton results. These results sgnfy that concentraton has a negatve mpact on the cattle prce markdon. That s, the markdon n cattle prce s decreased by ncreasng the concentraton. Ths result concdes th a maorty of results from smlar studes (Azzam 1997; Stegert, Azzam, and Brorsen 1993). The parameters of cattle cycle and seasonalty for May-July and Aug-Oct are postve hle the parameter of seasonalty for Feb-Apr s negatve. Ths result means that hen the cattle supply s greater than the normal trends (or expected supply), packers exercse more market poer n the cattle procurement market and vce versa. Ths result supports the ve of SCP that hen the cattle supply s nflated the packers tactly collude 17

20 to drop the cattle prce. When the cattle supply s short the packers bd aggressvely to get some amount of cattle n the cash market. The dynamc model allos market poer to change over tme, so e calculate the market poers for each year. Fgure 4 shos the changes of the market poer and cattle supply. The cattle supply fluctuates th an ncreasng trend hle market poer fluctuates th a decreasng trend. Ths decreasng trend of market poer s ncompatble th the tradtonal opnon that an ncrease of concentraton ll ncrease market poer. Hoever, the market poer for Natonal, Nebraska, and Texas have fluctuated along th the cattle cycle over the gven tme perod. Therefore, e can conclude that the cattle cycle causes the olgopsony market poer n the cattle procurement market and that the market poer has been fluctuatng and declnng over tme. Ths fndng also concdes th the prevous studes (Stegert, Azzam, and Brorsen 1993; Cresp, Xa, and Jones 2010). In summary, the frst null hypothess that olgopsony market poers n the U.S. cattle procurement market equal zero s reected n all regons and both statc and dynamc model. Therefore, e can conclude that packers exert an olgopsony market poer over the U.S. cattle procurement market. The second null hypotheses that the concentraton, the cattle cycle, and the seasonalty have no effect on the olgopsony market poer are all reected. That means the packers use the cattle supply and the seasonalty to ncrease ther margns by prcng cattle under the margnal cost of cattle producton. Hoever, the concentraton has a negatve effect on the olgopsony market poer. That s, the markdon decreases by ncreasng the concentraton for the sample perod. Ths result may support the 18

21 hypothess that cost effcency domnates the effect of market poer by ncreasng the concentraton. Conclusons There are many controversal ssues about market poer n the U.S. cattle procurement market. Many beleve that the maor beef processng companes attempt to merge and acqure the other companes as a vable strategy to ncrease ther market poers. Hoever, some studes ndcate that such consoldaton amongst packers leads to the ncrease of ther effcency n beef processng cost, rather than ncreasng the market poer. Therefore, ths study looks nto the beef packng ndustry from the perspectve that the market poer may not be from the concentraton, but from the characterstcs of cattle producton such as cattle cycle and seasonalty. If the market poer s caused by cattle supply, then the packers may compete th prce nstead of quantty. That s, follong the SCP theory, hen the cattle supply s short the packers mght bd aggressvely for procurng the cattle (lo markdon) and hen the cattle supply s enough the packers ll bd less aggressvely (hgh markdon). From ths ve pont, e use the Bertrand model that assumes prce competton n the market and e compare t to the Cournot model. In addton, the dynamc model th the tme varyng model s used to look nto the dynamc change of market conducts hch s caused by the concentraton, the cattle cycle, and the seasonalty for the U.S. cattle procurement market. Three regons: Natonal, Nebraska, and Texas, are estmated th the monthly data from 1980 to

22 The emprcal results sho that there exsts olgopsony market poer n the U.S. cattle procurement market. The olgopsony market poer s nfluenced by the cattle cycle and seasonalty. That s, the packers may tactly collude durng the excessve cattle supply perod, hle bdng to prce more aggressvely durng the short cattle supply perod. The varaton of market poer equvalently changes th the cattle cycle. Hoever, concentraton has a negatve effect on market poer n cattle procurement market. These results suggest that t s more mportant to make cattle supply stable and to contnue montorng the cattle procurement market to assure a compettve performance. Nevertheless, ths research s lmted n estmatng the Bertrand model derved from ths study because the parameters to be estmated requre frm level data. Therefore, addtonal research needs to develop a more sutable model to contnue ths study. 20

23 Reference Anderson, S.P., A.D. Palma, and B. Kreder Tax ncdence n Dfferentated Product Olgopoly. Journal of Publc Economcs 81: Anderson, D.P., J.G. Robb, and J. Mntert. August The Cattle Cycle. Managng for Today s Cattle Market and Beyond. U.S. Department of Agrculture and Cooperatng States Extenson Servces. Appelbaum, E The Estmaton of the Degree of Olgopoly Poer. Journal of Econometrcs 19: Azzam, A.M Measurng Market Poer and Cost-Effcency Effects of Industral Concentraton. The Journal of Industral Economcs 45(4): and E. Pagoulatos Testng Olgopolstc and Olgopsonstc Behavor: An Applcaton to the U.S. Meat-Packng Industry. Journal of Agrcultural Economcs 41: and J.R. Schroeter The Tradeoff beteen Olgopsony Poer and Cost Effcency n Horzontal Consoldaton: An Example from Beef Packng. Amercan Journal of Agrcultural Economcs 77-4: Breusch, T.S Testng for Autocorrelaton n Dynamc Lnear Model. Australan Economc Paper 17: Bertrand, J Book reve of theore mathematque de la rchesse socale and of recherches sur les prncples mathematques de la theore des rchesses. Journal de Savants 67: Ca, X., K.W. Stegert, and S.R. Koonts Olgopsony Poer: Evdence from the U.S. Beef Packng Industry. Paper presented at the Agrcultural and Appled Economcs Assocaton 2009 AAEA & ACCI Jont Annual Meetng n Mlaukee, Wsconsn, July Cresp, J.M. and T. Xa, and R. Jones Market Poer and the Cattle Cycle. Amercan Journal of Agrcultural Economcs. In press. Deert, W.E Applcatons of Dualty Theory. In M.D. Intrlgator and D.A. Kendrck (eds.). Fronters of Quanttatve Economcs Volume II (Amsterdam: North-Holland Publshng Company. 21

24 Godfrey, L.G Testng Aganst General Autoregressve and Movng Average Error Models When the Regressors Include Lagged Dependent Varables. Econometrca 46: Koontz, S.R. and P. Garca Meat-Packer Conduct n Fed Cattle Prcng: Multple-Market Olgopsony Poer. Journal of Agrcultural and Resource Economcs 21(1): , P. Garca, and M.A. Hudson Meatpacker Conduct n Fed Cattle Prcng: An Investgaton of Olgopsony Poer. Amercan Journal of Agrcultural Economcs 75: Lepore, J.J Cournot Outcomes under Bertrand-Edgeorth Copmetton th Demand Uncertanty. Techncal report, Calforna Polytechnc State Unversty, Orfalea College of Busness, CA, USA, Lopez, R.A., A.M. Azzam, and C.L. Espana Market Poer and/or Effcency: A Structural Approach. Reve of Industral Organzaton 20: Schroeter, J.R Estmatng the Degree of Market Poer n the Beef Packng Industry. The Reve of Economcs and Statstcs 70(1): Sexton, R.J Industralzaton and Consoldaton n the US Food Sector: Implcatons for Competton and Welfare. Amercan Journal of Agrcultural Economcs 82: Stegert, K.W., A. Azzam, and B.W. Brorsen Markdon Prcng and Cattle Supply n the Beef Packng Industry. Amercan Journal of Agrcultural Economcs 75: Tostao, E. and C. Chung Horzontal Consoldaton n the U.S. Food Processng Industry: Boon or Bane? Paper presented at the Southern Agrcultural Economcs Assocaton Annual Meetng n Lttle Rock, Arkansas, February 5-9. U.S. Department of Agrculture. Economc Research Servce. Meat Prce Spreads. Avalable at U.S. Department of Agrculture. Gran Inspecton, Packers and Stockyards Admnstraton. Packers and Stockyards Statstcal Reports. Avalable at pc=pub-stat 22

25 U.S. Department of Labor. Bureau of Labor Statstcs. Avalable at gov/ Zheng, X. and T. Vukna Do Alternatve Marketng Arrangements Increase Pork Packers Market Poer? Amercan ournal of Agrcultural Economcs 91(1):

26 Table 1. Descrptve Statstcs of Varables Used n the Emprcal Estmaton ( , N=360) Varable Symbol Mean S. D. Mnmum Maxmum Natonal margn (cattle prce based) ($/ct) Nebraska margn (cattle prce based) ($/ct) Texas margn (cattle prce based) ($/ct) Wholesale prce of boxed beef ($/ct) Natonal cattle prce (5 market steer prce) ($/ct) Nebraska cattle prce (steer and hefer eghted prce) ($/ct) Texas cattle prce (steer and hefer eghted prce) ($/ct) Herfndahl Hrschman ndex for steer and hefer slaughter Natonal steer and hefer slaughter eght (bl./lbs) Nebraska steer and hefer slaughter eght (bl./lbs) Natonal steer and hefer slaughter eght (bl./lbs) Labor productvty (2000=100) P W P W P W P W W W H Q Q Q Q X l Prce of labor (2000=100) Captal productvty (2000=100) Prce of captal (2000=100) Materal productvty (2000=100) v l Q X c v c Q X m Prce of materal (2000=100) PPI for farm products slaughter steers and hefers (2000=100) vm PPI Cycle (bl./lbs) C

27 Table 2. Estmates of the Parameters for the Bertrand and Cournot Models for the Natonal, Nebraska, and Texas regons cattle procurement market by GMM Parameter Natoanl Nebraska Texas M (0.0035) (0.0030) (0.0030) ll (0.0646) (0.0030) (0.0563) cc (0.0196) (0.0122) (0.0083) mm (0.0881) (0.0783) (0.0716) lc (0.0090) (0.0096) (0.0093) cm (0.0247) (0.0154) (0.0144) ml (0.0681) (0.0673) (0.0610) l (0.0113) (0.0071) (0.0023) c (0.0094) (0.0045) (0.0014) m (0.1031) (0.0069) (0.0023) Notes: All parameters are sgnfcant at the 5% sgnfcance level. Parentheses are approxmate standard errors. 25

28 Table 3. Estmates of the Parameters for the Dynamc Model for the Natonal, Nebraska, and Texas regons cattle procurement market by GMM Parameter Natonal Nebraska Texas (0.0048)** (0.0068)** (0.0057)** (0.0193)** (0.0266)** (0.0231)** (0.0005)** (0.0005)** (0.0005)** (0.0021)** (0.0023)** (0.0023)* (0.0024)** (0.0026)** (0.0024)** (0.0026)** (0.0027)* (0.0026)** ll (0.0899)** (0.1371)** (0.1079)** cc (0.0520)** (0.0505)** (0.0396)** mm (0.0736)** (0.0993)** (0.0786)** lc (0.0311)** (0.0434)** (0.0331)** cm (0.0182)** (0.0116) (0.0069)** ml (0.0595)** (0.0971)** (0.0765)** l (0.0191) (0.0158)** (0.0044)** c (0.0146)** (0.0083)** (0.0026)** m (0.0147)** (0.0116)** (0.0036) Notes: * sgnfcant at the 10% sgnfcance level. ** sgnfcant at the 5% sgnfcance level. Parentheses are approxmate standard errors. 26

29 Table 4. Olgopsony Market Poer, Markdon, and Margnal Cost for the U.S. Cattle Procurement Market Market Poer Statc Model Dynamc Model Natonal Nebraska Texas Natonal Nebraska Texas Market Poer (%) (0.0035) (0.0030) (0.0030) (0.0030) (0.0036) Markdon ($/ct) (0.2525) (0.2188) (0.2159) (0.2200) (0.2578) Margnal Cost ($/ct) (0.1965) (0.1700) (0.1682) (0.1763) (0.1954) Notes: Parentheses are approxmate standard errors. All estmates are statstcal sgnfcant at the 5% sgnfcance level (0.0035) (0.2575) (0.2091) 27

30 Fgure 1. The Concentraton (HHI for Steer and Hefer) Change for U.S. Cattle Procurement Market from 1980 to

31 Fgure 2. The Cattle Supply for the U.S. Cattle Procurement Market from 1980 to

32 Fgure 3. The Average Monthly Changes of Slaughtered Cattle for the U.S. Cattle Procurement Market from 1980 to

33 Fgure 4. The Changes of Market Poer and Cattle Supply for the U.S. Cattle Procurement Market from 1980 to