An Analyss of he Banana Impor marke n he U.S. Cha-Hsen Su Ph.D. Suden Deparmen of Agrculural Economcs Texas A&M Unversy CSu@ag.amu.edu Arun Ishdor Asssan Professor Deparmen of Agrculural Economcs Texas A&M Unversy AIshdor@amu.edu Davd J. Leaham Professor Deparmen of Agrculural Economcs Texas A&M Unversy d-leaham@amu.edu Seleced paper presened for presenaon a he Souhern Agrculural Economcs Assocaon Annual Meeng, Corpus Chrs, Texas, February 5-8, 2011 Copyrgh 2011 by Cha-Hsen Su, Arun Ishdor, and Davd J. Leaham. All rghs reserved. Readers may make verbam copes of hs documen for non-commercal purposes by any means, provded ha hs copyrgh noce appears on all such copes.
1. Inroducon Accordng o he sascs of Uned Saes Deparmen of Agrculure (USDA) and Uned Saes Inernaonal Trade Commsson (USITC), banana s he number one fresh fru consumed n he Uned Saes. Is share s over 25% of he yearly quany of fresh fru consumpon per capa, and even exceeded he sum of annual consumpon of all crus fru snce 1989. The volume of banana mpors ncreased seadly unl peaked n 1999, and snce hen, has flucuaed beween 3,800 o 4,100 housand ons annually. The value of banana mpors has flucuaed; however, he value has ncreased connuously snce 2004. Because of he geographc locaon of he Uned Saes, all he producon of bananas s n Hawa on 1,200 o 1,500 acres of land, and he rao of hs producon o domesc consumpon of bananas s nconsequenal. In oher words, he Amercan consumpon of bananas mosly depends on mpors. Moreover, n erms of he mpor quany of fresh frus, bananas are he larges saple n he Uned Saes, whch s he bgges mpor counry of bananas n he world wh an approxmae 3,977.9 housand ons n 2008 and whose average share n global banana ne mpor durng 2003 o 2008 s abou 25.69%. The share of banana mpors n he European Unon (EU) s more han ha of he Uned Saes and accouns for abou 30.94% share n he perod, however, s made up of 27 counres and has abou 1.66 mes he populaon of Uned Saes. Global banana expors are hghly concenraed n sx counres: Ecuador, Cosa Rca, Colomba, Phlppnes, Guaemala, and Panama. Along wh whea, rce, or corn; bananas are a sgnfcan saple commody for hese developng counres. Neverheless, because of he consderaon of ransporaon coss, me, he delcae and pershable properes n banana dsrbuon, and dvergng mpor polces n he consumng counres, he U.S. banana mpor orgnaes almos enrely from Lan Amercan counres near he equaor, wh mpors from oher pars of he world consdered neglgble. Colomba, Cosa Rca, Ecuador, Guaemala, and Honduras are he larges provders of fresh bananas o he Uned Saes. These equaoral counres ogeher supply over 95% of oal U.S. fresh banana mpors, whch makes up abou 40% of he fresh fru quany shpped by hem o he Uned Saes n 2008. Furhermore, he percenage of banana expor value o oal expor value (banana quany exporng o U.S. o oal expor quany of bananas) n Colomba, Cosa Rca, Ecuador, Guaemala, and Honduras are 1.77% (25.07%), 7.57% (46.78%), 9.29% (17.57%), 4.36% (87.75%), 6.39% (83.29%), respecvely. These show ha he U.S. banana demand marke plays a decsve role n he economc developmen and ganng foregn exchange of hese counres. Thus, he srucural and compeveness changes n he consumng demand of he U.S. fresh banana may have he possbly o cause severe economc shock n Lan Amercan counres - 1 -
whch largely depend on he banana rade. Analyzng demand condons of he mpor banana marke n he U.S. could provde nformaon for polcy makers n banana expor counres. In addon, bananas from hese counres are called dollar bananas because hey are expored o Norh Amerca and of he US-based ransnaonal corporaons (TNCs). The wo larges producer and markeers of bananas of US-based TNCs are Dole Food Co. (formerly Sandard Fru) and Chqua Brands Inernaonal (formerly known as he Uned Fru Company, hen Uned Brands). Each accouns for us over 25% of all bananas raded n he world. Then he hrd larges s Fresh Del Mone Produce, conrollng abou 16% of he banana rade. Fresh Del Mone Produce headquarers s n Mam, USA. In addon o hese US-based TNCs, he fourh larges banana expor company n he world s Exporadora Bananera Noboa, one of he larges exporers of Ecuador bananas and conrollng abou 12% of oal world rade. The U.S. banana marke s free of arffs or quanave mpor resrcons and bascally conrolled by hese four and some relavely small companes, ha s, has an olgopolsc naure. In addon, due o producng and markeng large quanes of bananas, hese TNCs can generae economes of scale a all levels of he markeng chan o make prof. Thus, s neresng o esmae he degree of mperfec compeon n he U.S. marke of banana mpors. Ohers have looked a he banana mpor marke. Deodhar and Sheldon (1995) esmaed he degree of marke mperfecon n he German marke for banana mpors usng a srucural economerc model and concluded ha he marke s mperfecly compeve. Harl, Jones, and Akas (2003) measured he marke power of he banana mpor marke n Turkey and concluded ha he marke s no perfecly compeve and he behavor of frms s much closer o prce-akng han o colluson. Burrell and Hennngsen (2001) nvesgaed he consumer demand for bananas and for oher frus n Germany and found ha demand for bananas s sgnfcanly responsve o own prce, suggesng ha polcy-nduced prce ncreases generae he usual dead-wegh losses. The banana marke n U.S. for he pas 20 years has become sauraed such ha he volume and prce (share, wholesale, and real prces) generally reman fxed even durng peak perods. Moreover, he U.S. s he larges counry of banana mpors n he world. Therefore, he prmary goal of hs analyss s o nvesgae he U.S. mpor demand for fresh bananas dfferenaed by counry of orgn o evaluae mplcaon for he sx man exporng counres. An ancllary goal s o apply a srucural economc model of marke power o evaluae he degree of mperfec compeon n he mpor marke of fresh bananas of he Uned Saes. In order o acheve hese goals, he new emprcal ndusral organzaon (NEIO) model by usng wo-sage and hree-sage leas squares (2SLS and 3SLS) and he nonlnear nverse almos deal demand sysem - 2 -
(IAIDS) by usng he maxmum lkelhood esmaon (MLE) for U.S. banana marke are calculaed and esmaed. Noe ha he U.S. banana demand nearly depends on mpor. In he U.S., Hawa s only a place where bananas are planed, bu s producon s very low and could no sasfy he consumpon demand of bananas. The paper s organzed as follows. The heorecal framework for he IAIDS, and NEIO s presened n he nex secon of he paper. Followng ha, he wo models used n he analyss are gven. Then, he daa used n he analyss are presened and dscussed along wh esmaon consderaons. Resuls and relevan dscusson s hen presened. A summary of he man fndngs s presened n he las secon of he paper. 2. The Model 2.1 Inverse Almos Ideal Demand Sysem The radonal AIDS model of Deaon and Muellbauer (1980) s one of he mos commonly used demand models n he emprcal work. However, n case a many pershable goods such as fresh frus, vegeables, fsh and ec. he quany suppled s ofen predeermned and he prce mus adus n order o clear he marke. The predeermned quany s ofen renforced by he fac ha many pershable goods are no f for sorage and mus be consumed shorly afer harves. For hs reason, we make us of he Inverse Almos Ideal Demand Sysem (IAIDS) of Eales and Unnevehr (1994) o esmae he demand for bananas n he U.S. by counry of orgn usng quarerly daa., In he IAIDS model, he consumer preference s derved from he dsance funcon (ransformaon funcon), whch s dual o he cos funcon (expendure funcon) of he AIDS. As he properes of cos funcon, he dsance funcon s connuous n uly and quany, decreasng n uly, and non-decreasng, concave, and homogeneous of degree one n qualy (Moschn and Vssa, 1992). I measures he proporonal amoun by whch all quanes consumed need o be nflaed n order o reach a parcular ndfference curve. Le U q represen he drec uly funcon, where q denoes he vecor of quanes. Then, he dsance q F u, q funcon F u, q s mplcly defned by U u, where u denoes he reference uly level. The dsance funcon has a dervave propery smlar o he cos funcon (Deaon, 1979). Tha s, dfferenaon of he dsance funcon wh respec o he opmal quany of a parcular good yelds he compensaed demand for ha good n he same way ha dfferenaon of he cos funcon wh respec o a parcular prce yelds a compensaed demand funcon. Thus, followng Deaon and Muellbauer s dervaon of he AIDS model (1980), a logarhmc dsance funcon may be defned: - 3 -
ln F( u, q) (1 u)ln a( q) u ln b( q) (1) Because he dsance funcon possesses he same properes as he cos funcon, excep of subsung quanes for prces, ln a ( q) and ln b ( q) are bascally defned analogous o hose n he developmen of he AIDS model. ln a q 1 r ln q ln q (2) 0 k ln qk k 2 k q ln aq q k k * k k ln b (3) 0 Thus, he IAIDS dsance funcon s wren 1 * k ln Fu, q 0 ln p r ln q ln q u pk (4) k k k k 0 k 2 k The compensaed nverse demand funcon can be derved drecly from equaon (4). The quany dervaves of he dsance funcon are he normalzed prces demanded,.e., by F( u, q) p Shepherd s Lemma where x denoes oal expendure. Mulplyng boh sdes by q x q yelds F( u, q) k ln F( u, q) ln q pq x w (5) where w s he budge share of good. Hence, logarhmc dfferenaon of (4) gves he budge shares as a funcon of quanes and uly: w r ln q u0 q k k (6) 1 * * where r ( r r ). 2 Inverng he dsance funcon a he opmal quany yelds he drec uly funcon whch may be subsued no equaon (6). q /[ln b( q) ln a( )] U( q) ln a q (7) - 4 -
Ths yelds a sysem of nverse demand funcons ha Eales and Unnevehr call IAIDS. w r ln q ln Q (8) where ln Q s expressed as follows: 1 r ln q ln q (9) ln Q 0 k ln qk k 2 k k k. The IAIDS model, as specfed n equaon (8), s comprsed of sx share equaons: he sources of he U.S. banana mpors are dsngushed no Colomba, Cosa Rca, Ecuador, Guaemala, and Honduras. I s common n he leraure o lnearly approxmae he IAIDS model by usng Sone s nonparamerc sascal ndex nsead of ln Q. However, Pashardes (1993) showed ha errors resulng from ha approxmaon can be seen as an omed varable. In addon, Barne and Seck (2006) ndcaed ha he use of he lnear approxmaon by knds of prce ndces exacerbaes msclassfcaon of goods as complemens and leads o esmaed elasces dfferen from hose of he nonlnear AIDS model. Thus, n hs paper we esmae he nonlnear IAIDS model specfed by equaon (8). As wh he AIDS model, he heorecal resrcons of he fxed and unknown coeffcens are mposed as: 1, 0, and 0,, (addng up); 0 (homogeney); (symmery). Eales and Unnevehr (1993) also provde he relevan formulas for he uncompensaed (Marshallan) own-quany & cross-quany frequences ( ) and he expendure frequences ( ) when esmang he IAIDS model as follows, { w }/ w (10) 1 / w (11) where s he Kronecker dela, whch akes a value of 1 when = and zero oherwse. In our research we are usng quarerly quany daa. hus, before he me-seres daa are used o esmae he parameers of he IAIDS model, s necessary o check wheher he daa srucure - 5 -
of each of he varables n he model s no-saonary usng an auoregressve model. In sascs, he augmened Dckey Fuller es (Dckey and Fuller, 1981) ha s vald n large samples s commonly used. In general, economc me seres daa s ofen volaed n he saonary assumpon,.e., he daa s he exsence of a long-erm rend. For hs suaon, s mporan for he daa o be dfferenced o render saonary. Accordng o Engle and Granger s (1987) explanaon, f a non-saonary me-seres daa s dfferenced d mes o reach saonary, expressed as I(d) (negraed of order d), here are d un roos n he seres. All varables o be employed n he IAIDS model are negraed of he order 1, I(1),.e. saonary n he frs dfference form. 2.2 New Emprcal Indusral Organzaon Wh complee cos nformaon, s approprae and sraghforward o oban he ndex of marke power or srucure,, by a measure of he devaon beween prce and margnal cos,.e. he ably of a frm/ndusry o rase prce above margnal cos. Here, Lerner s measure (Collns and Preson, 1969) s P MC L (12) P where s he marke elascy of demand and les n he closed se [0, 1]. However, whle prce nformaon s ofen readly observable, margnal cos s rarely so easly measured n realy. In general, mos of he researchers would use average cos nsead of margnal cos n calculang Lerner s measure. Moreover, excep for compeve frms n long-run equlbrum, average (varable) cos s no a good approxmaon o margnal cos and he dsadvanage of accoun cos daa for economc analyses are well-known. Hence, o avod usng cos daa, Bresnahan and Lau (1982) developed NEIO srucural economerc models, whch can be esmaed o deermne wheher marke power s beng exered a varous sages n supply chan. Marke demand plays a crcal role n deermnng marke power, snce denfes frms /ndusres perceved margnal revenue. The marke demand equaon n a gven ndusry s gven by he mplc funcon: Q Q P, Z (13) where Q s he oal quany demanded, P s he marke prce of oupu, exogenous varables whch could affec marke demand, and s a me subscrp. Snce Z s a vecor of Q and P are smulaneously deermned, he nverse marke demand funcon can be wren - 6 -
as P PQ, Z. Indusry revenue s defned as R P * Q and, hus, perceved margnal revenues { MR ( )} can be expressed as dp MR ( ) P Q (14) dq The value of represens he roaons of he perceved margnal revenue curve away from consumer demand (Bresnahan, 1982). If =0, he gven ndusry s perfecly compeve and he producers perceve ha hey face a horzonal demand cure. As >0, producers face a downward-slopng marke demand so here s a deparure of perceved margnal revenues from marke demand and some seller marke power exss. If =1, full monopoly marke power s beng exered and frms are behavng as f a sngle frm s acng as a monopoly. In equlbrum, MR and hs relaonshp can be expressed as MC P dp Q MC (15) dq Gven he above-menoned conceps, he followng equaons can be adoped o esmae he degree of mperfec compeon n he U.S. fresh banana mpor marke. For fresh banana mpors, he marke demand funcon s specfed n lnear form: IMPQ 0 1PCB 2Pr e1 (16) where IMPQ s he oal quany of bananas mpored no Uned Saes (housand pounds/year); PCB s per-capa consumpon of banana (pound/year); P r s he real prce of bananas (US$/housand pounds) and 2 e 1 s he error erm, where ~ N(, ). In addon, e1 suppose ha a margnal cos akes he followng funconal form: MC 0 1P w (17) where P w s he wholesale prce of bananas as an approxmaon of he cos of bananas o realers. Subsung he margnal cos funcon (17) no he prof-maxmzng condon (15) and rearrangng erms, we ge he opmaly equaon 2 Pr 0 1IMPQ 2 ln( Earnngs ) 3IMPI 4( Pw ) e2 (18) where ln() s he naural logarhm; Earnngs s average hourly earnngs n rade, ransporaon - 7 -
and ules ndusres (US$); IMPI s he mpor prce ndex of fru and fru preparaons; P w s he wholesale prce of bananas (US$/housand pounds), and e 2 s he error erm. Conspcuously an absen explanaory varable n equaon (17) and (18) whch s used by Deodhar and Sheldon (1995) s me rend varable due o sascal nsgnfcance n he model. From (18) dp r 2 1 and e ~ (, ) dimpq 2 N. By dfferenang (16) wh respec o IMPQ, we derve ha dp r 1. Thus, Use he esmaes of equaon (16) and dimpq 2 (18), we can oban an esmae of he marke-power parameer 1 * 2. 3. Daa The dependen varables n our sx equaon IAIDS are he quarerly shares of he U.S. expendures on bananas from Colomba, Cosa Rca, Ecuador, Guaemala, Honduras and he res of he world.. The expendure shares were consruced usng U.S. expendures on mpored bananas from each of he sx counres dvded by he oal expendure on bananas from hese counres. The quarerly expendure and quarerly quany daa were obaned from he U.S. Inernaonal Trade Commsson (USITC) webse. To derve he marke-power parameer, usng he NEIO model, equaons (16) and (18) were esmaed wh 2SLS and 3SLS usng annual daa over he perod 1985~2008. These annual daa were obaned from dfferen sources. The per-capa consumpon and real prces of bananas were colleced from he Uned Saes Deparmen of Agrculure (USDA) webse, daa on average hourly earnngs n rade, ransporaon and uly ndusres along wh he mpor prce ndex of fru and fru preparaons were aken from he Uned Saes Deparmen of Labor webse, and he nformaon abou he wholesale prce of bananas s obaned from he Banana Sascs (2001, 2003, 2005, 2009) and he World Banana Economy 1985~2002 of Food and Agrculure Organzaon (FAO). All nomnal varables nvolvng prces and earnngs were deflaed by he consumer prce ndex. 4. Resuls Table 1 presens he economerc resuls from he MLE mehod of he nonlnear IAIDS model. Wh respec o he Cosa Rcan share equaon; seven ou of egh esmaed parameers - 8 -
were sascally sgnfcan a 5% level. In he Colomban, Ecuador, and Guaemala share equaons, sx parameers were sascally sgnfcan a 5% level. Nex, fve parameers were found o be sascally sgnfcan n he Honduran share equaon a 5% level of sgnfcance. Fnally, four parameers n he share equaon of he res of he exporng counres (Ohers) were sascally sgnfcan a 5% level. Table 1. Esmaed parameers from he nonlnear IAIDS model for fresh bananas Inercep q Columba qcosarca q Ecuador q Guaemala q Hoduras q ohers ln(q ) Columba -0.0504 0.1028* -.0323* -.02895* -.0162* -.0110* -.0141*.0101 (0.0338) (0.0058) (.0047) (.0043) (.0049) (.0034) (.0054) (.0067) Cosa Rca.1208* -.0323*.1604 * -.0686* -.0437* -.0215*.0059 -.0241* (.0403) (.0047) (.0088) (.0053) (.0059) (.0051) (.0042) (.0080) Ecuador -.0021 -.0289* -.0686*.1796* -.0472* -.0179* -.0167*.0005 (.0346) (.0043) (.0053) (.0062) (.0051) (.0039) (.0043) (.0069) Guaemala -.0373 -.0162* -.0437* -.0472*.1320* -.0122* -.0124*.0073 (.0352) (.0049) (.0059) (.0051) (.0080) (.0043) (.0048) (.0070) Honduras.0106 -.0110* -.0215* -.0179* -.0122*.0673* -.0044 -.0022 (.0338) (.0034) (.0051) (.0039) (.0043) (.0052) (.0029) (.0067) Ohers.9583* -.0141*.0059 -.0167* -.0124* -.0044.0418.0083 (.0500) (.0054) (.0042) (.0043) (.0048) (.0029) (.0086)* (.0099) Asympoc sandard errors are shown n parenheses. * ndcaes ha a coeffcen s sascally sgnfcan a he 5% sgnfcance level. Table 2 presens he own and cross flexbly esmaes as well as he scale (expendure) frequences along wh he approprae sandards errors. All frequences esmaes were calculaed a he sample means. Noe ha all own-quany frequences are negave as heorecally expeced. All own-quany frequences esmaes were less han one n absolue value, ndcang ha he fresh bananas of sx exporng counres are prce nflexble. In erms of he own-quany frequences a he prce-mpored level, he U.S. own prce for Honduran bananas wh respec o he quany of Honduran bananas appears o be he larges varaon n absolue value (0.4242). Tha s, a one percen ncrease (decrease) n he quany of Honduran bananas was found o decrease (ncrease) he mpor prce of Honduran bananas n he U.S. marke by approxmaely 0.4242%. A smlar change occurs n he prce of oher banana-exporng counres as her own quanes ncrease (decrease) by one percen. - 9 -
The cross-quany frequences measure he percenage change n he prce of a good when he quany demanded of anoher good ncrease by one percen. From Table 2 all cross-quany frequences were found o be negave whch classfes all mpor bananas as gross quany-subsues excep he Cosa Rca-Ohers and Ohers-Cosa Rca frequences (gross quany-complemens). The Cosa Rcan bananas were found o exhb a relavely srong cross-quany subsuon effec wh he Colomban bananas (-0.2589), he Ecuador bananas (-0.2840), and Honduran bananas (-0.1786). For he Ecuador bananas exhbs a relavely srong cross-quany subsuon effec wh he Cosa Rcan bananas (-0.2520), he Guaemalan bananas (-0.3107), and he oher bananas (-0.3528). The cross-quany frequences for he Honduran bananas reveal ha a change n quany of Honduran bananas would have very low mpac on he real prce of bananas expored n oher counres. Scale frequences measure he percenage change n he normalzed prce of a commody due o one percen change n he oal expendure. The scale frequences are ranged from -0.8442 o -1.0967. Based on hese esmaes, he prce of he oher bananas s leas affeced by he quany of oal mpor bananas. Table 2. IAIDS banana frequences Colomba Cosa Rca Ecuador Guaemala Honduras Oher Scale Colomba -0.2446* -0.2589* -0.2351* -0.1358* -0.0921* -0.1101* -0.9247* (0.0157) (0.0051) (0.0054) (0.0033) (0.0021) (0.0025) (0.0015) Cosa -0.1180* -0.3334* -0.2520* -0.1580* -0.0742* 0.0305* -1.0967* Rca (0.0012) (0.0137) (0.0049) (0.0034) (0.0017) (0.0007) (0.0021) Ecuador -0.1198* -0.2840* -0.2589* -0.1959* -0.0744* -0.0692* -0.9978* (0.0022) (0.0053) (0.0134) (0.0036) (0.0013) (0.0012) (0.00004) Guaemala -0.1090 * -0.2903* -0.3107* -0.1747-0.0804* -0.0806* -0.9534* (0.0126) (0.0327) (0.0351) (0.0949) (0.0094) (0.0101) (0.0053) Honduras -0.0916* -0.1786* -0.1486* -0.1018* -0.4242* -0.0369* -1.0191* (0.0022) (0.0042) (0.0035) (0.0021) (0.0134) (0.0010) (0.0004) Oher -0.2875* 0.0786* -0.3528* -0.2610* -0.1034* -0.2285* -0.8442* (0.0321) (0.0089) (0.0387) (0.0362) (0.0106) (0.0881) (0.0176) Sandard errors are approxmaed usng he boosrap echnque over 3000 drawngs wh replacemen. * ndcaes ha a coeffcen s sascally sgnfcan a he 5% sgnfcance level. Tha s, he one percen proporonae ncrease n all mpor bananas would reduce he prce of he oher bananas by abou 0.8442% whle he prce of Cosa Rcan bananas declnes 1.0967%. 2SLS and 3SLS esmaon procedures were employed o esmae he sysem of equaon (16) - 10 -
and (18); however, no mprovemen over he 2SLS resuls was observed. The resuls of esmang hese equaons are shown n Table 3. The whole model s plausble n erms of adused r-squared, he sandard error of esmaes, and sascal sgnfcance of ndvdual parameers. The adused R square values of demand and opmaly equaons are 0.92 and 0.88, respecvely. Alhough he Durbn-Wason raos le n he nconclusve range for reecng he hypohess of he exsence of auocorrelaon, s also clear ha hey are very close o he upper bound where he hypohess of he exsence of auoroaon can be reeced. In he below-menoned regressons, he relevan parameers for calculang marke power are 2 7053.964 and 1 0. 000044, boh beng sacally sgnfcan a he 5 percen level. Therefore, he marke power parameer for hs ndusry s =-(-7053.964)*(0.000044)=0.31. The resuls sugges ha he fresh banana mpor marke n he U.S. s closer o compeon han monopoly. Ths mples ha alhough he banana marke presens an olgopolsc srucure, hs does no acually mean ha TNCs have a grea marke power o se sellng prces for bananas because her producs bananas are ndfferen,.e., homogenous n he pon of vew of consumers. If any of hese TNCs waned o rase prces unlaerally, could be expeced for hs company o lose s marke share and decrease he ably o compee wh he oher companes sellng fresh bananas excep, consumers perceve he dfference among hese bananas, for example organc and usual bananas. All he parameers have he expeced sgns, and hey are sascally sgnfcan. The prce elascy for he real prce of bananas s -0.66, mplyng ha a gven change n prce wll resul n a less han proporonae change n quany mpored. - 11 -
Table 3. 2SLS esmaon of he model Inercep PCB P r Coeffcen P-value Elascy 4,542,441 ** (980,911.3) 273,126.6 ** (29084.96) -7,053.96 ** (661.32) Adused R square 0.92 Durbn-wason Inercep IMPQ ln( Earnngs IMPI 2 ( P w ) Adused R square 0.88 Durbn-wason ) 0.000 0.000 0.959<DW=1.218<1.298-537.4846 * (245.2847) 0.000044 * (0.0000186) 252.3885 ** (51.6981) 1.855343 ** (0.5055642) 0.0004011 * (0.0001865) 0.000-0.66 0.041 0.029 0.000 0.002 0.045 0.805<DW=1.433<1.527 * and ** ndcae ha a coeffcen s sascally sgnfcan a he 5% and 1% sgnfcance level, respecvely. 5. Summary and Concluson Banana consumpon n he U.S. s hghly dependen on mpors and hese mpors come from a concenraed marke ha s conrolled by a few TNCs. Frs, usng a srucural economerc model, based on a mehod orgnally developed by Bresnahan (1982), he resuls show ha he U.S. fresh banana mpor marke s mperfecly compeve and mples ha he TNCs are exercsng some marke power. Furhermore, he fndngs of hs sudy show wo varables, real prce of bananas and per-capal consumpon of bananas, o have a sgnfcan mpac on mpor quany. Nex, we employed he nonlnear IAIDS model developed by Eales and Unnevehr (1993) o serve he followng purpose. We beleve ha esmang he relaonshp - 12 -
beween he U.S. fresh banana mpors usng prces as he dependen s a beer specfcaon gven ha bananas are hghly pershable and s he prce (no quanes) ha clears he marke. From he perspecve of he U.S. marke hs s an mporan sep oward undersandng demand condons and he dfference of compeveness. Own-quany effecs are relavely nflexble (flexble) n he Guaemalan (Honduran) equaon so ha a one percen ncrease n he quany of bananas nduces a less han (more han) one percen fall n he own-prce. In addon, n erm of he cross quany effecs, he resuls sugges ha Cosa Rcan and Ecuador bananas have sronger quany-subsuon effec on he oher rval bananas whle Honduran bananas: weaker quany-subsuon effec on he oher rval bananas. References Barne, W.A., and O. Seck. 2008. Roerdam Model versus Almos Ideal Demand Sysem: Wll he Bes Specfcaon Please Sand Up? Journal of Appled Economercs 23:795-824. Burrell, A., and A. Hennngsen. 2001. An Emprcal Invesgaon of he Demand for Bananas n Germany. Agrarwrschaf 50(4), pp. 242-249. Chern, W.S., Ishbash, K., Tanguch, K., Yokoyama, Y., 2003. Analyss of Food Consumpon Behavor by Japanese Households. FAO Economc and Socal Developmen Paper, 152. Deaon, A., and J. Muellbauer. 1990. An Almos Ideal Demand Sysem. Amercan Economc Revew 70:312-326. Deodhar, S.Y., and I.M. Sheldon. 1995. Is Foregn Trade (IM) Perfecly Compeve?: An Analyss of he German Marke for Banana Impors. Journal of Agrculural Economcs 46(3):336-348 Eales, J.S., and L.J. Unnevehr. 1994. The Inverse Almos Ideal Demand Sysem. European Economc Revew 38:101-115 Gran, J.H., D.M. Lamber and K.A. Foser. 2010. ASeasonal Inverse Almos Ideal Demand Sysem for Norh Amercan Fresh Tomaoes. Canadan Journal of Agrculural Economcs 58:215-234. Haden, K. 1990. The Demand for Cgarees n Japan. Amercan Journal of Agrculural Economcs 72:446-50. - 13 -
Harl, S.A., Jones, E., and A.R. Akas. 2003. Measurng he Marke Power of he Banana Impor Marke n Turkey. Turksh Journal of Agrculure and Foresry 27: 367-373. Huang, K.S. 1988. An Inverse Demand Sysem for U.S. Compose Foods. Amercan Journal of Agrculural Economcs 70:902-909. Moschn, G., and A. Vssa. 1992. A Lnear Inverse Demand Sysem. Journal of Agrculural and Resource Economcs 17:292-302. - 14 -