Maize Trade in Souhern Africa: Comparaive Advanage on Sorage Coss Anónio Sousa Cruz Discussion papers No. 32E Augus 2006 Naional Direcorae of Sudies and Policy Analysis Minisry of Planning and Developmen Republic of Mozambique
The inen of he discussion paper series is o simulae discussion and exchange ideas on issues perinen o he economic and social developmen of Mozambique. A mulipliciy of views exiss on how o bes fomen economic and social developmen. The discussion paper series aims o reflec his diversiy. As a resul, he ideas presened in he discussion papers are hose of he auhors. The conen of he papers do no necessarily reflec he views of he Minisry of Planning and Developmen or any oher insiuion wihin he Governmen of Mozambique. The Logo was kindly provided by he Mozambican aris Nlodzy. Anónio S. Cruz Conac: Anónio Sousa Cruz Direcção Nacional de Esudos e Análise de Políicas (DNEAP) Minisério de Planificação e Desenvolvimeno Av. Ahmed Sekou Touré nº 21, 4º andar Mapuo, Moçambique Tel: (+258) 2 1 499442 Fax: (+258) 2 1 492625 Web: www.mpd.gov.mz Email: anonioscruz@gmail.com ii
Resumo Diferenças em cusos de armazenagem, em paricular diferenças nas axas de juro reais são uma deerminane imporane de vanagens comparaivas e porano dos padrões de produção e comércio inernacional em seis dos maiores países da África Ausral (AA6). Usando um modelo espácio-emporal de equilíbrio de preços do comércio inraconinenal do milho, confirmamos a hipóese da vanagem comparaiva da África do Sul esar baseada em mercados financeiros e infraesruuras de armazenagem mais desenvolvidos ao invés de em cusos de produção do milho. Com uma redução nas axas de juro reais, os resulados das simulações indicam que Moçambique e Tanzania exporariam milho para ouros AA6. A comercialização do milho inra-aa6 inensificase, com uma queda simulânea no comércio com o reso do mundo. Considerando a variabilidade anual de produção enre os AA6, os resulados do modelo são semelhanes aos da versão deerminísica. Absrac Differences in sorage coss, in paricular differences in real ineres raes, are a significan deerminan of comparaive advanage and hence he paern of producion and rade wihin a se of six major Souhern African counries (SA6). Applying a spaialemporal price equilibrium model of regional maize rade, we confirm he hypohesis ha Souh African comparaive advanage is rooed in more developed financial marke and sorage infrasrucure raher han coss of maize producion. Wih a decline in real ineres raes, resuls indicae ha Mozambique and Tanzania would expor maize o he oher SA6. Inra-SA6 maize rade inensifies, wih a simulaneous decline in rade wih he res of he world. Accouning for year-o-year producion variabiliy among SA6, model resuls are similar o hose in he deerminisic version. Key words: inernaional maize rade, souhern Africa, sorage coss, real ineres raes, spaial-emporal price equilibrium. iii
Index Page Inroducion... 1 Maize Marke in SA6 Counries... 3 The Spaial-Temporal Price Equilibrium Model... 5 Daa and Model Specificaion... 7 Sochasic Oupu...9 Empirical Resuls... 10 Simulaion Cases...10 Simulaion Oucomes...10 Sochasic Version...14 Conclusion... 14 References... 16 Annex: Figures and Tables... 18 iv
Inroducion Maize is an imporan commodiy among SA6 counries represening an esimaed 46% in oal calorie inake in human consumpion, and 74% of he oal cereal oupu, in 2001. Maize marke prices in Malawi, Mozambique, Tanzania and Zambia increased from wo o five fold beween harves and he lean period, in he 2001-02 markeing year (Figure 1). Viewing maize price series as indicaive of sorage coss, he inra-seasonal rise of less han 50% in Souh Africa suggess ha his counry has a comparaive advanage in sorage over he oher SA6. Souh Africa expors maize o hese counries in he lean period of he markeing season when marke prices are relaively higher for he laer. Year-o-year producion variabiliy among differen SA6 is anoher imporan facor deermining rade paern. This sudy deermines and analyzes he effecs of a more efficien sorage, in paricular lower real ineres raes, on marke prices, rade paern, volume of producion and consumpion, and on welfare measures on he SA6 maize marke, using a spaialemporal price equilibrium model. The analysis emphasizes he role of sorage coss as a deerminan of he paern of rade. This paper also sudies he poenial for inra-sa6 supply of maize while compeing wih he supply from he inernaional marke, given he year-o-year producion variabiliy. This paern is capured by he oupu correlaion marix for years 1987-2002. Finally, a more efficien sorage scenario is combined wih lower ransporaion coss and inra-sa6 ariff free rade scenarios, o accoun for simulaneous effecs. Spaial equilibrium problems have claimed he aenion of economiss for a long ime (Courno 1838; Koopmans 1949; Enke 1951). However, i was wih he developmen of linear and non-linear programming echniques ha many auhors were able o consruc models of opimal allocaion of resources in space and ime for cases of one and more commodiies, and imperfec compeiion wih a single produc monopoly (Samuelson 1952; Takayama and Judge 1971). This ype of models were exended o deal wih cases of wo monopolies and a Courno-Nash oligopoly, and discriminaory ad valorem ariffs, using he variaional inequaliies approach (Harker 1986; Nagurney e al 1996). When dealing wih ad valorem ariffs, non-linear programming (NLP) was no a saisfacory approach. I could no solve cases when he coefficien marix of he demand and/or supply funcions was asymmeric - i.e., he inegrabiliy condiion was violaed. The NLP approach had o be solved hrough a sequence of ieraions, which was 1
inefficien and lacked ransparency. Takayama and Uri (1983) showed ha he linear complemenariy programming formulaion was more appropriae han quadraic programming when inegrabiliy was los. Ruherford (1995) applied he mixed complemenariy problem (MCP) approach o economic problems using he General Algebraic Modeling Sysem (GAMS) (Brooke e al 1992). He defined MCP as an economic equilibrium model formulaed as sysems of nonlinear equaions, complemenariy problems or variaional inequaliies. Mixed complemenariy problems may incorporae boh equaliy and inequaliy relaionships. Dirkse and Ferris (1995) developed he PATH solver ha allows he implemenaion of a sabilized Newon mehod for he soluion of mixed complemenariy problems. Ferris and Pang (1997) presened a few examples of non-linear complemenary problems in equilibrium modeling. Tradiionally, rade models focus on relaive producion efficiencies, rade barriers, such as ariffs, and ranspor coss as major drivers behind he paern of rade. However, Benirschka and Binkley (1995) show ha anoher facor, sorage coss he opporuniy cos of capial proxied by he real rae of ineres paid by soring agens plus direc sorage coss plus any risk premium have significan implicaions for he paern of commodiy rade. Arnd e al (2001) build upon his idea for he case of Mozambique. They show ha ineres rae differenials beween formal and informal secor marke paricipans due, for example, o he high ransacions coss of delivering credi o small borrowers in he rural secor subsanially influence maize markeing paerns and provide a plausible explanaion o he seasonal commodiy flow reversals observed in rural zones of many developing counries (Jones 1984; and Timmer 1974). This sudy applies he MCP version of a spaial-emporal price equilibrium framework wih differeniaed impor ariff raes and ineres raes by counry o a model of inernaional maize rade, given he relevan conribuion of his aciviy for he provision of food securiy in bad crop years among SA6 counries. In beer weaher condiions, price differences sill jusify maize rade among some SA6, which are currenly involved in a process of gradual rade liberalizaion wihin he Souhern African Developmen Communiy, in line wih he inernaional movemen of creaing blocks of free rade areas. The second secion idenifies he key feaures of he maize marke in SA6 counries. Third secion defines he spaial-emporal price equilibrium model, providing a lieraure review, presening he model, and is assumpions. Fourh secion presens daa, 2
and specificaion issues. Fifh secion defines and develops model simulaions and resuls, presening scenarios for he deerminisic and he sochasic versions of he model. Las secion concludes. Maize Marke in SA6 Counries In Malawi, Mozambique, Tanzania, Zambia and Zimbabwe (MMTZZ) maize is mosly grown by smallholder farmers. They grow from 65% in Zambia o 90% in Malawi, as a share of he oal naional maize oupu in each counry. In hese counries, smallholder farmers use predominanly low producive and labor inensive echnologies, local seed varieies, and a limied amoun of ferilizers (RATES 2003a-d). Hence, produciviy, measured in yields per hecare, is also low, ranging from 0.9 ons/ha in Mozambique o 1.5 ons/ha in Zambia (Pingali 2001). In Souh Africa, 89% of oal maize oupu is grown by commercial farmers, which is he excepion among SA6 counries. These farmers use capial inensive echnology, improved seed varieies, and have access o ferilizers and pesicides, conribuing for a higher maize produciviy of 2.3 ons/ha. Even hough produciviy in he SA6 counries is currenly below he world average of 4.3 ons/ha, maize has been an imporan crop whose oupu has been growing seadily in he pas five decades. In he period 1988-99 farmers produced a oal annual average oupu of abou 16.3 million ons compared wih approximaely 6.1 million ons in 1951-60 (FAO 2002-03; and Pingali 2001). This variaion in oupu of 165% is well above he oupu variaion of 43% for he enire world in he same period. Souh Africa has been consisenly he major maize producer among SA6, wih a share beween 50-60% of he aggregae oupu. The aggregae SA6 domesic maize balance for he 2001-02 markeing year is a posiive 1.2 million ons (SADC 2002). This resul is reached by subracing Gross Domesic Requiremens (GDR) from he Domesic Availabiliy (DA) being DA equal o Opening Socks plus Gross Harves. GDR includes maize used for human and animal consumpion, inpu for he processing indusry, seed and wase. The posiive balance is mainly due o he 125% raio of DA/GDR for Souh Africa. Mozambique (101%) and Tanzania (99%) are around he self-sufficiency saus (Jayne e al 1995). Malawi (89%), Zambia (70%) and Zimbabwe (95%) are defici maize producers, for he period under consideraion. 3
Alhough he mos prominen sources of inra-sa6 maize rade saisics do no show consisen figures among hemselves, he auhor esimaed a oal rade volume of 169 housand ons for he 2001-02 markeing year (FAO 2004; RATES 2003a-d; SADC 2002; and Whieside 2003). This rade volume is one fifh of he oal maize impored ino SA6, bu i is sill imporan given maize price differenials beween counries and is role in conribuing for food securiy. Maize rade is affeced by oupu correlaions among SA6 counries. I is assumed here ha variaions in oupu from one year o he oher are mainly due o changes in yields han in area planed. Keeping all oher facors consan, a srong posiive oupu correlaion indicaes lower possibiliy of rade beween he regions involved, as good crop years would be common among hem and vice-versa. A negaive oupu correlaion suggess higher chances of rade developmen beween a region wih bad crop season and anoher wih a bouny season. Low posiive correlaion coefficiens denoe some chances of rading beween wo regions. Souh Africa, Zambia and Zimbabwe reveal srong posiive oupu correlaion (Table 1). If year-o-year changes in maize yields are due mainly o he weaher paern, hese counries would have lower possibiliies o rade among hem. This resul coincides wih he one in Jayne e al (1995). The difference is ha he curren sudy includes addiional counries Malawi, Mozambique and Tanzania. Counry pairs Malawi-Zambia (wih correlaion coefficien of 0.51), Malawi-Mozambique (0.40) and Malawi-Zimbabwe (0.38) may have lower chances of rading maize among hem. However, since Souh Africa is a surplus maize producer, i is a regional source of maize supply o defici producer counries like Malawi, Zambia and Zimbabwe, or o regions like Mozambique- Souh. Tanzania, Mozambique-Cener and Mozambique-Norh are poenial surplus producers, hence inra-sa6 maize suppliers. I is no well undersood he economic effecs of differences in sorage coss among SA6 counries, as well as of exogenous shocks and policy measures aimed a improving ransacions coss in he regional maize marke. These facors are analyzed in he curren sudy applying an opimizaion framework. 4
The Spaial-Temporal Price Equilibrium Model A Spaial-Temporal Price Equilibrium (STPE) model is used o accoun for sorage, ransporaion and rade coss on he maize marke and inra-sa6 commodiy flows. The model simulaes he impac on producion, consumpion, rade paerns and on welfare measures of changes in economic condiions and alernaive policies affecing he maize marke in Malawi, Mozambique, Souh Africa, Tanzania, Zambia and Zimbabwe. The curren sudy exends he framework of Arnd e al (2001) in which a Mixed Complemenariy Problem approach is applied o a case of maize markeing wihin Mozambique in he presence of differeniaed ineres raes. The MCP is an efficien and more ransparen approach o solve an opimizaion problem in he presence of ad valorem ariff raes and differeniaed ineres raes, as is considered here (Takayama and Uri 1983; Ruherford 1995; Langyinuo e al 2005). In his STPE model, wih a parial equilibrium approach, i is assumed ha producers maximize profis, consumers maximize uiliy and rade is compeiive. I is assumed ha agens minimize coss when choosing quaniies of maize ranspored among SA6, and soring maize in each region. In inernaional rade, agens choose exporing and imporing quaniies ha maximize revenue and minimize coss, respecively. Maize is reaed as a single and homogenous good. The simplifying assumpion of marke compeiive behavior in he chosen model does no exclude he possibiliy of non-compeiive behavior in he real world (Varian 1992). However, i is expeced ha he STPE model generaes useful insigh in he SA6 maize marke. Excep for Mozambique (hree regions), Souh Africa (wo regions) and Zambia (wo regions), each one of he oher SA6 counries is aken as a region. Each region under sudy is considered a separae marke from all oher regions. The presence of differeniaed ransacion coss is manifesed hrough differences in sorage and ransporaion coss, and differeniaed impor ariff raes. In he model i is assumed ha producers and consumers are risk neural. These agens value heir fuure ransacions a he expeced value. In addiion, hey have perfec foresigh of maize prices wihin he enire markeing year. This simplifying assumpion allows he model o solve simulaneously all equaions for he 12 monhs. The non-linear formulaion of he opimizaion problem consiss of maximizing he presen value of he ne quasi-welfare funcion (1) by finding he opimal quaniies 5
for demand ( D, ), supply ( S, ), shipmen among SA6 regions ( g g X g gp,, ), sorage ( Z g, ), and impors from and expors o he res of he world ( M,, E, ), as follows (Arnd e al 2001; Harker 1986): g g Max. S g,, Dg,, X g, gp,, Z g,, M g,, Eg, = 1 s.. Z + 1 Z D g gp G, T, T + S 1 1 + r g G Dg, g G 0 Φ gp G M g, g G 0 gp G X X g, gp, PM gp, 0 g, + ( D) dd TC gp, ( M ) dm + g G X S g, g G 0 Ψ ( X ) dx gp, Eg, g G 0 + M PE ( S) ds Z g, g G 0 E SC ( E) de, ( Z) dz (1) (2) Dg,, S g,, X g, gp,, Z g,, M g,, E g, 0 g gp G, T, (3) S Z = 0, g G, and T NH (4) H = 0, g G, and T (5) NP M, E = 0, g G, and T. (6) Model funcions, variables and parameers are defined for ses of regions G ; regions wihou ocean pors periods NP G ; ime periods T ; and non-harves and harves ime NH T and T H, respecively. The objecive funcion is defined by he inverse demand funcion Φ ( ), he inverse supply funcion Ψ ( ), he parameer for g, D S ransporaion coss beween counries g and gp, TC ( ), he parameer of uni g, gp, X sorage coss SC g, ( Z), he parameer for impor price PM g, ( M ) and he parameer for expor price PE g, ( E). The presen value of he objecive funcion over 12 monhs (T ) is 1 discouned by he inverse of he real ineres rae r. 1+ The NLP formulaion is ransformed ino he MCP approach by deriving he firs order condiions from he Lagrangian form and adjusing hem o handle ad-valorem ariffs ( τ gp ), differences in sorage coss, and real ineres raes across space ( r g ). 6
Considering he firs order condiions wih respec o sric posiive values of inra-sa6 ranspored maize and sorage, i follows: L X * gp, = c gp + λ λ )(1 + r) = 0, (7) ( gp, L Z * = h g + ( λ λ 1)(1 + r) = 0, (8) where c g, gp corresponds o he inra-sa6 rade uni ransporaion cos, g h represens he uni sorage cos, and λ symbolizes he sorage consrain Lagrange muliplier. Equaion (7) provides he spaial dimension of he model. This equaion enails ha he uni ranspor cos ( c g, gp ) is equal o he difference beween prices in wo regions. Equaion (8) indicaes ha he uni sorage cos ( h g ) is equal o he difference in prices beween wo consecuive monhs. This equaion besows he ime elemen o he model, hrough he real ineres rae. When differeniaed by region, ineres raes operae like ad valorem ariff raes disinguished also by region. They violae he inegrabiliy condiion of he equilibrium equaions sysem, making he coefficien marix for he sysem of equaions asymmeric for each region (Takayama, and Uri 1983). I reinforces he need for choosing he MCP approach as a more ransparen alernaive o he NLP approach. Daa and Model Specificaion The SA6 counries included in his analysis are all locaed in souhern Africa mainland. They have been rading wih each oher in he recen pas wihou inerrupions caused by inernal wars, and are he mos relevan regarding oal populaion, and he oal volume of maize producion and consumpion. This group of counries are classified and divided ino regions as follows: Malawi, Mozambique-Cener, Mozambique-Norh, Mozambique- Souh, Souh Africa-Eas, Souh Africa-Wes, Tanzania, Zambia-Eas, Zambia-Wes, and Zimbabwe. Linear inverse demand funcions (IDF) for maize for each region are derived hrough a benchmarking procedure. The corresponding parameers are also derived for he linear inverse supply funcion (ISF). Table 2 provides daa used o derive boh IDF and ISF. The uni ransporaion cos for Mozambique is se o US$0.048 per meric on per kilomeer. This value is adjused for 3% inflaion during five years from he original 7
value in Arnd e al (2001). Transpor coss are differeniaed as follows: Mozambique- Cener (US$0.048), Mozambique-Norh (US$0.050), and Mozambique-Souh (US$0.046). The uni ransporaion cos for boh regions of Souh Africa (US$0.038) corresponds o he disance beween Gaueng and Cape Town (Poonyh e al 2002). The ransporaion cos for Zimbabwe as repored by Masers and Nuppenau (1993) is adjused o reflec a lower cos han among MMTZZ counries (US$0.042). Uni ransporaion cos for Malawi, Zambia and Tanzania are se wih he same value beween he Mozambican and he Zimbabwean levels (US$0.045). Uni ransporaion coss include freigh coss, insurance, and discharging coss, wherever i applies (Poonyh e al 2002). Monhly real ineres rae is se a 2.5% for Mozambique which is he average for urban areas and rural areas. Corresponding rae for Souh Africa is se a 1.5%, for Malawi and Tanzania are se a 2.75%, and for Zambia and Zimbabwe a 3%. Monhly uni sorage cos is assumed o be US$3 per meric on in Mozambique, which is $0.5 above he value menioned in MICTUR e al (1999). Sorage cos in Souh Africa is 2/3 of he cos in Mozambique. Corresponding values for Zimbabwe, Zambia, Malawi and Tanzania are US$2.5, US$2.7, US$2.8, and US$2.9, respecively. Excep for Souh Africa wih an assumed sorage loss rae of 0.5%, all oher counries have a sorage loss rae of 0.85%. This value is skewed owards he 1% sorage loss in rural areas in Mozambique as compared wih 0.5% in urban areas. The ransporaion loss rae is se o 1.1%, and 0.6% for MMTZZ and for Souh Africa, respecively. In 2001-2002, Malawi, and Souh Africa applied a zero ariff rae on maize impors from oher SA6 counries (RATES 2003a; SADC 2000-01). Tanzania applied an uniform ariff rae of 30%. Mozambique and Zambia applied ariff raes of 2.5% and 5% o impors from Souh Africa. Zimbabwe imposed a ariff rae of 30% o impors from Souh Africa and 17.5% o impors from all oher SA6. Even if effecive ariff raes are below legal ariff raes, i imposes a burden on imporers ha is no always measured accuraely. Demand for maize is a monhly even. For simpliciy, i is assumed ha each region harvess maize once a year, in he firs monh of he period beween April 2001 and March 2002. This period is referred o as he markeing season. I is assumed no beginning socks. In he firs period, provision of maize is made by farmers. Thereafer, each region will source heir maize from domesic sorage, from oher SA6 regions, or from he res of he world. Each region wih ocean pors is allowed 8
o expor o he res of he world in he firs period, and o impor afer he firs period. The world price for expors and impors are se a $79/on and $141/on, respecively (World Bank 2003; MARD, and MSU 2001-02a, 2001-02b; MIC e al 2001; SAGIS 2001; Couler 1996; Miller 1996). These price hresholds deermine which regions wih ocean pors are exporing o or imporing from he res of he world. Each region is allowed o sore maize, wihou capaciy consrains. Transporaion of maize occurs beween SA6 regions, using he poin-represenaion approach (Mwanaumo e al 1997). Sochasic Oupu In realiy, maize oupu varies from year-o-year mainly due o changes in weaher, alhough oher non-economic facors may have an impac. Incorporaing he sochasic naure of oupu changes hroughou he years, he comparison of various scenarios would have o consider he crierion of he degree of risk. The STPE model is adjused so ha oupu becomes exogenous, reproducing hisorical oupu variaions for he period 1987-2002. The same simulaions run for he deerminisic version of he model are also used in he sochasic version. The analysis of ransacion coss improvemen is relevan for maize, which is a food and cash crop whose producion is subjec o weaher vagaries. Using he acual oupu ime series for maize S, ), i is esimaed he expeced oupu ( S ˆ g, y ), hrough an Ordinary Leas Squares ( g y regression on ime, y (FAO 2002-03). Running he model in GAMS, simulaed oupu values ( S g, y ) are obained hrough: S S y = * E( S g ) S base, (9) y ˆ g, y where E ( S g ) base is he esimaed oupu in he Base scenario, for each SA6 counry. Resuls from model simulaions provide a ime series on ne social welfare (NSW), corresponding o 1987-2002 for each scenario. These daa are he basis o esimae cumulaive densiy funcions (CDF). 9
Empirical Resuls Simulaion Cases The role of sorage coss, ransporaion coss and impor ariffs on he paern of producion, consumpion and rade in SA6 regions is sudied hrough a se of four simulaions (Table 3). Table 4 and Table 5 show parameer values used in each simulaion. The Base scenario is run wih benchmarked parameers (Table 6). This scenario is se as he sandard from which all oher simulaions are defined, by changing specified parameers. Simulaion 1 verifies how Souh Africa s comparaive advanage in sorage shapes rade paerns wihin SA6 regions. Only parameers for Souh Africa are changed, revealing a developmen scenario wih lower sorage coss. In order o achieve his sae, he Governmen could offer incenives o build new silos a lower coss, o adop equipmen wih modern echnology, and/or he macro policy environmen could be conducive o lower real ineres raes. Alernaively, simulaion 2 assesses rade and welfare effecs of a more efficien sorage in MMTZZ. These counries are assumed o cach-up wih Souh Africa s sorage efficiency. Simulaion 3 represens a more efficien sorage scenario in all SA6 regions, keeping relaive differences in coss among hem. Alhough here is an improvemen in sorage efficiency among MMTZZ counries, Souh Africa sill mainains an advanage in his aciviy wih lower real ineres raes. Simulaion 4 combines he more efficien sorage scenario wih a more efficien ransporaion and rade free from impor ariffs among SA6 counries. This las simulaion provides a view on simulaneous effecs from combining he reducion in he hree ypes of ransacion coss in he maize marke. Simulaion Oucomes The Base scenario illusraes wo disinc cases of paern of rade. Souh Africa-Eas, a region wih comparaive advanage in sorage coss relaive o oher SA6 regions, sores maize hroughou he enire markeing season. I expors 27% of is raded maize o Mozambique-Souh and sells he remaining o Souh Africa-Wes hroughou he markeing year. This model resul is consisen wih realiy regarding Mozambique-Souh impors from oher counries. Moreover, i arrived o a similar oucome as in Arnd e al (2001), whose model resuls show ha Mapuo sars imporing maize in Sepember. In 10
he curren sudy, he Base scenario indicaes ha Mozambique-Souh impors from Souh Africa beween Augus and Ocober. Mozambique-Cener, Mozambique-Norh and Tanzania expor maize o he res of he world, immediaely afer harves imporing back in February-March of he following year. These are relaively low sorage efficien regions. Again, model resuls wih respec o Mozambique-Cener are consisen wih realiy as repored by Uaiene (2004) based on a field survey carried ou in Manica. Farmers end o sell maize immediaely afer harves. Par of his maize is expored by large scale raders. In normal years, maize food lass abou 10 monhs for rural families, afer which smallholders have o buy back maize from he marke. The curren sudy shows ha Mozambique-Cener sars imporing maize in February, which is he elevenh monh afer harves. The inernaional marke provides he larges proporion (82%) of he oal impored maize, being he remaining sourced wihin SA6. Simulaion 1 resuls in larger ne inra-sa6 rade (+94%), reducing boh Res of he World (ROW) impors and expors by 15.2% and 20.5%, respecively. Maize farmers in Mozambique-Souh and in Zambia-Wes mus sell a lower prices due o Souh Africa s comparaive advanage in sorage coss, cuing in supply. Consumers in he former wo regions and in Souh Africa are hose who benefi from lower prices, leading o an improvemen of 1.6% in he ne social welfare indicaor (Table 7). The oucome in simulaion 1 is driven by a more efficien sorage in Souh Africa, which reduces marke prices laer in he markeing season in boh SA-Eas and SA-Wes. A reducion in boh he real ineres rae (0.75%) and he sorage loss rae (0.25%) reduces he rae of increase in marke prices hroughou he markeing season. Prices a harves in he SA-Eas are anchored a expor pariy. This is largely a surplus producer region, which expors o he res of he world, sores for domesic consumpion, and for selling o oher SA6 regions laer in he markeing year. Toal reducion in sorage coss are no enough o affec prices a harves, hence, i does no change maize oupu. However, he volume of maize socks in May increases (6.2%). When facing a reducion in direc sorage coss, sorage losses and he opporuniy cos of capial producers will have higher reurns in he overall markeing season, by soring more maize a harves and selling i during he markeing season a relaively higher prices compared o he Base scenario. For he case of Souh Africa-Eas, expors o he res of he world are reduced in order o sell more maize o oher SA6 regions hroughou he markeing season. 11
Marke prices in Zambia-Wes and in Mozambique-Souh decline laer in he markeing season (pre-harves period), following he rend in Souh Africa. These prices also decline a harves reducing maize oupu. Given he Base scenario, his is he expeced resul when reducing real ineres raes in Souh Africa, which is also consisen wih he curren rade paern among SA6 regions. Souh Africa s rade advanage reduces par of maize producion in defici producer regions, which will increase impors from Souh Africa, driving down marke prices. Consumers in imporing regions are he winners. Simulaion 2 brings MMTZZ sorage coss down o Souh Africa s levels. Overall, maize farmers in MMTZZ largely benefi in a scenario wih improved sorage echnology, reduced sorage coss and lower opporuniy cos of capial. Consumers benefi from marginal welfare gains, resuling in an improvemen of 2.9% in ne social welfare for he overall MMTZZ regions (Table 7). Inra-SA6 rade increases by 49%. Impors from and expors o he ROW decline by 87% and 27%, respecively. A harves, he reducion of sorage coss, keep consan marke prices a he expor pariy price level, $79 per on, in Mozambique-Cener and in Mozambique-Norh. Boh regions are surplus maize producers among MMTZZ, in he 2001-02 crop season, keeping supply consan. Conversely, harves prices rise in Tanzania, Malawi, Zambia and Zimbabwe, increasing maize oupu. The reducion in sorage coss reduces he growh rae of prices hroughou he markeing season, bu increases prices a harves in order o compensae sorekeepers from selling maize a lower prices a he end of he season. Simulaion 3 improves general SA6 sorage efficiency, keeping consan cos differenials beween MMTZZ and Souh Africa. The opporuniy cos of capial is sill relaively lower in he laer counry. Under his scenario, farmers and consumers benefi wih an increase in boh he producer surplus (7.6%) and consumer surplus (2.4%). Ne social welfare reaches he bes resul (+3.9%) compared wih previous simulaions (Table 7). Trade paern changes wih an increase in inra-sa6 maize flows by 94%, and a decline in rade wih he ROW. The degree of SA6 self-sufficiency in maize increases, as boh oupu (2.4%) and demand (1.7%) rise (Table 8 only shows levels). The growh rae of prices is reduced hroughou he season in Mozambique- Cener, Mozambique-Norh and Souh Africa-Eas, bu prices a harves are kep a he expor pariy price level (no shown). These regions are maize surplus producers, whose oupu levels do no change, compared o he Base scenario. Souh Africa-Eas increases 12
inra-sa6 expors, namely o Mozambique-Souh (178%) and sales o Souh Africa-Wes (11.5%), by reducing expors o he res of he world (22.4%). The lower annual average price increases domesic demand by 3.3%. Mozambique-Cener and Mozambique-Norh also increase domesic consumpion of maize by 3.2% and 2.7%, respecively, bu hey reduce expors o he ROW (65.2% and 55.5%, respecively), responding o price movemens. Mozambique-Souh, Zambia and Zimbabwe are defici maize producers, whose producers, and consumers benefi from welfare gains under he curren scenario. Zambia- Wes and Zimbabwe obain similar resuls as in simulaion 2. In Mozambique-Souh, consumers increase demand due o lower annual average prices. Bu in Zambia-Eas demand declines as annual average prices rise. Malawi, Souh Africa-Wes and Tanzania have he expeced change in prices, rising a harves, bu declining owards he end of he markeing season. Farmers increase maize oupu by 4.3%, 1.0% and 6.2%, respecively. The volume of maize in sorage increases. In Tanzania, he decline in he growh rae of prices does no compensae for he subsanial increase in prices a harves, reducing domesic demand for maize by 0.2%. Conversely, Malawi (+0.3%) and Souh Africa- Wes (+3.1%) increase heir domesic demand for maize. Excep for Souh Africa-Wes, rade declines wih he res of he world. Malawi reduces drasically (-77%) her impors from Mozambique-Cener. Tanzania reduces re-expors o Zambia-Eas, eliminaes expors o he ROW, and reduces impors from he laer origin. However, inra-sa6 rade increases by 205. ons. Simulaion 4 combines scenarios wih improved ransacion coss: lower opporuniy cos of capial, more efficien ransporaion wihin MMTZZ counries and inra-sa6 rade free from ariffs. Considering he aggregae SA6 regions, simulaion 4 obains beer resuls han all oher previous simulaions in erms of ne social welfare (+4.0%), wih gains in producer surplus (+6.5%) and consumer surplus (+2.9%). Consisen wih he welfare measures for join SA6 counries, oupu increases by 312 housand ons and demand rises by 281 housand ons of maize (Table 8). Trade wihin SA6 regions, ne of re-expors, improve by 163% (no shown), bu impors from and expors o he ROW decline by 94% and 47%, respecively. 13
Sochasic Version Cumulaive densiy funcions of ne social welfare annual values are esimaed o compare all simulaions, informing on risk differences among hem. Simulaions 2, 3 and 4 firs degree sochasic dominae simulaions 0 and 1. Therefore, any of he simulaions in he firs group (2, 3 and 4) is preferred o any oher in he second group (0 and 1). In Figure 2, simulaions sorsa6 and combinaion represen he firs group and he Base scenario represens he second group. Simulaions 3 and 4 are he mos relevan in erms of represening he lowes degree of risk among hose in he second group (Figure 2). None of hese wo simulaions firs degree sochasic dominaes over he oher. The sochasic dominance analysis emphasizes he imporance of improving sorage efficiency, i.e., reducing he opporuniy cos of capial for he economic performance in he maize marke (Table 9). In boh simulaions 3 (sorsa6) and 4 (combined) consumers are beer off by facing lower marke prices and increasing heir demand for maize. In simulaion 3 more maize is available o consumers due o an increase in domesic oupu wihin each region, while in simulaion 4, consumers benefi from an increase in inra-sa6 rade and a reducion in rade wih he res of he world. Conclusion Two disinc rade paerns are highlighed from he sudy. Timmer s heory of he seasonal commodiy flow reversals is well suied o he case of Mozambique-Cener, Mozambique-Norh and Tanzania. These regions expor immediaely afer harves, imporing back laer in he hungry season. This paern of rade is ypically associaed wih inefficien sorage, paricularly in rural areas, which is he case of he hree menioned regions. In he Benirschka and Binkley paern of rade, grain is sored in he producer region and is released ino he marke laer in he markeing season when prices are high enough o benefi producers, i.e., when he grain prices grow a he rae of ineres. This heory successfully explains he case of Souh Africa-Eas a sorage efficien region, which is selling maize o Mozambique-Souh and o any oher SA6 region, in he preharves period. Sorage coss, in paricular he opporuniy cos of capial, play an imporan role in maize marke price rises beween harves and he lean season wihin he markeing 14
season. In considering he hypohesis ha differences in sorage coss beween Souh Africa and oher SA6 are a source of inernaional comparaive advanage, resuls from simulaion 1 confirms i. Due o more efficien sorage in Souh Africa-Eas, is inraseasonal maize price growh is furher reduced. As a surplus maize producer, his region increases inra-sa6 rade by 94% hrough addiional expors o Mozambique-Souh and Zambia-Wes, which are boh defici maize producers. Considering he main hypohesis ha sorage coss are a major deerminan of he volume and paern of rade, scenarios 2 and 3 simulae a reducion is sorage coss including he opporuniy cos of capial in he MMTZZ counries and in all SA6, respecively. Inra-SA6 rade rises by 49% and 94% in each scenario, respecively. Simulaion 2 reveals ha improving sorage efficiency in MMTZZ counries has a significan effec on producer welfare, increasing i by 9.4%. Viewing he economic developmen process as based on he improvemen of agriculural producive performance, simulaion 2 confirms he imporance of reducing sorage coss among MMTZZ. Simulaion 3 represens a more regional inegraed scenario, where producers increase heir welfare by 7.6%, bu consumers also benefi from a 2.5% raise in heir welfare measure. A srenghening of specializaion beween surplus and defici producer regions, leads o a more efficien allocaion of resources and provides a greaer conribuion o food securiy in SA6. Simulaion 3 also reveals ha oher surplus maize producer regions, Tanzania and Mozambique-Cener, conribue o supply defici SA6 regions. This is a clear deviaion from an exclusive focus on Souh Africa as he SA6 sole surplus maize producer and provider. Mozambique-Norh is also a poenial surplus maize producer. Combined scenario, simulaing a SA6 generalized reducion in sorage coss including he opporuniy cos of capial, improvemen in ransporaion efficiency for MMTZZ counries and rade liberalizaion obains he bes performing resuls among all scenarios, in he deerminisic model version. Ne social welfare increases by 4.0%, wih consumers benefiing by he larges absolue change in welfare (2.9%), and producers welfare improving by 6.5%. An economic scenario where hese hree ypes of ransacion coss are improved shows beer resuls, such ha he drawback of one ype e.g. producer welfare losses under rade liberalizaion is compensaed by he virue of he oher ype e.g. producer welfare gains under a scenario wih sorage efficiency improvemen, in paricular lower real ineres raes. 15
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