Staff Paper No. 523 April The Effect of Ethanol Production on the U.S. National Corn Price. T. Randall Fortenbery and Hwanil Park

Transcription

1 Universiy of Wisconsin-Madison Deparmen of Agriculural & Applied Economics Saff Paper No. 523 April 2008 The Effec of Ehanol Producion on he U.S. Naional orn Price By T. Randall Forenbery and Hwanil Park AGRIULTURAL & APPLIED EONOMIS STAFF PAPER SERIES opyrigh 2008 T. Randall Forenbery & Hwanil Park. All righs reserved. Readers may make verbaim copies of his documen for non-commercial purposes by any means, provided ha his copyrigh noice appears on all such copies.

2 The Effec of Ehanol Producion on he U.S. Naional orn Price T. Randall Forenbery and Hwanil Park * * Renk Professor of Agribusiness (rforen@wisc.edu) and Graduae Suden (hipark@wisc.edu), respecively in he Deparmen of Agriculural and Applied Economics, Universiy of Wisconsin a Madison. Senior auhorship no assigned.

3 The Effec of Ehanol Producion on he U.S. Naional orn Price Absrac A sysem of equaions represening corn supply, feed demand, expor demand, food, alcohol and indusrial (FAI) demand, and corn price is esimaed by hree-sage leas squares. A price dependen reduced form equaion is hen formed o invesigae he effec of ehanol producion on he naional average corn price. The elasiciy of corn price wih respec o ehanol producion is hen obained. Resuls sugges ha ehanol producion has a posiive impac on he naional corn price and ha he demand from FAI has a greaer impac on he corn price han oher demand caegories. Thus, significan growh in ehanol producion is imporan in explaining corn price deerminaion. Keywords : corn price, ehanol, simulaneous equaions, hree-sae leas squares, elasiciy Inroducion Ehanol producion in he U.S. has grown remendously in he las decade. Producion was averaging 1 billion gallons per year in he early 1990s, grew o 4 billion gallons in 2005, and in 2006 exceeded 5 billion gallons (Renewable Fuels Associaion (RFA), Figure 1). If curren plans for new consrucion and expansion come o fruiion, producion capaciy will exceed 11 billion gallons by he end of Recen growh has been suppored by he combinaion of favorable public policy and high nominal gasoline prices. Mos U.S. ehanol is made from corn. The domesic indusry used a record 13% of domesic corn producion in 2005 (RFA) and is esimaed o have used over 20% in he 2006/2007 markeing year (USDA). 1 As he ehanol indusry has increased is share of corn use, concern has developed relaive o ehanol s impac on corn price, and as a resul oher corn users. The purpose of his paper is o examine he effec of ehanol producion on he U.S. naional corn price. While he popular press ofen refers o he impac, a comprehensive analysis of ehanol s impac has been lacking. This research invesigaes he effec of ehanol producion on corn price hrough esimaion of a sysem of simulaneous equaions ha represen he 2

4 supply/demand relaionships in he corn marke. The paper proceeds wih a review of relevan lieraure, followed by a descripion of he model, daa and mehods. Resuls and conclusions are presened a he end. Lieraure review There have been several sudies focused on relaing increased ehanol producion o changes in corn markes. Gusafson (2002) found ha farmers in he norhwes region of Norh Dakoa were readily able o expand corn acreage for ehanol producion, provided adequae marke incenives were available. He esimaed ha 154,000 addiional Norh Dakoa acres of corn could be obained wih marke premium of $0.11 per bushel. Ferris and Joshi (2004) considered several scenarios in analyzing he impac of increased ehanol demand on crop and feed prices, and on farm income and sae finances given he curren ax-subsidy srucure. This was done uilizing a muli-secor economeric model (AGMOD). Based on heir high demand scenario of 4.67 billion gallons of ehanol producion by 2010, hey esimaed an increase of eigheen percen in farm level corn prices for They furher concluded ha agriculural commodiy prices would increase sharply in he shor run followed by more moderae increases due o expanded corn acreage. McNew and Griffih (2005) examined local grain price impacs associaed wih ehanol plans. They based heir work on a sample of welve ehanol plans ha opened beween 2001 and They found ha he ehanol plans increased local grain prices (i.e., he basis), bu he impac was no uniform across plans nor around a specific plan. On average, corn prices increased by 12.5 cens per bushel a he plan sie, and some posiive price responses were fel up o 150 miles away. However, price responses a he plan ranged from less han 5 cens per 3

5 bushel o jus under 20 cens per bushel. Similarly, he range of price impacs up o 150 miles away was also quie large. Taylor, Mason, Andino and Koo (2006) developed a simulaion model o esimae he impac of changes in ehanol producion on corn producion, consumpion, expors and price. They found ha changes in ehanol producion impac corn producion, feed use, and expors, as well as corn price under a variey of scenarios. They esimaed ha he corn price for 2014 will average $2.46 per bushel if ehanol producion reaches he 7 billion gallon mark as oulined in he 2005 Energy Bill. If 14 billion gallons of ehanol are produced, hey esimaed he price of corn would average $3.00 per bushel in Since ehanol producion has no ye reached 14 billion gallons ye average corn prices in spring 2007 far exceeded Taylor e al. s esimae for 2014, he naional average impac deserves furher consideraion. Srucural Model and Specificaion This work differs from previous research in ha i focuses on esimaing he shor-run corn price elasiciy associaed wih ehanol producion. I does his by way of a sysem of supply/demand equaions ha reflec he naional corn marke. All equaions in he sysem are specified as log-log models (some call his specificaion a log-linear model). The parameers of he log-log model can be direcly inerpreed as elasiciies (Gujarai). The log-log model assumes a consan elasiciy over all values of he daa se. The iniial model specificaion is of he form : β γ δ D ε (1) y = α z x e e 4

6 where α, β, γ, and δ are parameers o be esimaed, and z and x are endogenous and exogenous variables, respecively. D is a ime reflecing dummy variable and e is he exponenial funcion. Taking logs of variables in equaion (1) yields Y = α + βz + γx + δd + ε (2) 2 wih he radiional assumpions for he error erm, namely ~ N ( 0, ε ) ε σ. All daa are ransformed by logs excep he dummies and a rend variable. As usual, i is assumed ha he disurbance erm is uncorrelaed wih he exogenous variables used as insrumens, bu is correlaed wih he endogenous variables. Tha is, ( ε ) ( ) E z 0 and E xε = 0. Prices of many agriculural producs are relaed, and his ofen resuls in specifying mulimarke parial equilibrium models, e.g., models of boh he feed grain and livesock secors ha accoun for ineracion across markes (Tomek and Myers, 1993). As an example, Arzac and Wilkinson (1979) presen a quarerly economeric model of he U.S. livesock and feed grain markes wih 42 equaions. For he purposes here, however, model specificaion is limied o he U.S. corn marke even hough ineracion wih oher markes is o be expeced. Exension of he curren work o a muli-marke srucure is he opic of fuure research. Based on Unied Saes Deparmen of Agriculure (USDA) defined caegories, corn is uilized for feed, expors, and food, alcohol and indusrial use (FAI). 2 hambers and Jus (1981) aggregaed food disappearance and feed disappearance for domesic corn use o invesigae he effec of exchange rae flucuaions on he corn marke, while ohers usually disaggregae he demand ino several componens. 3 Since he focus of his work is on he effec of each caegory of corn demand on U.S. corn price, corn demand is separaed ino feed, expor and FAI. Demand from feed and expors have been relaively fla over ime (hough hey show seasonaliy), bu 5

7 FAI consumpion has been increasing rapidly (Figure 2). urrenly, abou half of FAI demand goes o he producion of ehanol (Figure 3). I is assumed he price of corn is deermined by supply and he hree secors of demand simulaneously. In his sysem here are separae equaions for corn supply, corn price, feed demand, expor demand and FAI demand. Each equaion is explained below. The approach adoped here is differen from many previous applicaions of supply/demand models. For insance, hambers and Jus (1981) and Devadoss e al. (1989) firs model corn supply and use funcions and hen derive a price dependen reduced form equaion from equilibrium condiions (supply is equal o disappearances and socks). Tha is, he price equaion is expressed as a funcion of all exogenous variables. However, his makes i difficul o esimae effecs of oher endogenous variables on he corn price. orn supply in his research is predeermined in he sense ha i is he value of ending socks from he previous period. The decision of how much corn o carry forward is dependen on physical sorage coss and he opporuniy cos of capial ied up in invenory. If he sorage coss (including he opporuniy coss) are sufficienly high minimal socks will be carried forward. On he oher hand, if he carrying coss are relaively low compared o expeced price appreciaion, ending socks will end o be large. The carrying coss can be approximaed by he differences in curren and laer prices (reurns o physical sorage) and discouned by curren ineres raes (as a proxy for foregone income resuling from holding invenory). Thus, corn supply in he curren period is deermined by he previous period s corn price and ineres rae. However, because of serial correlaion in he corn price, i seems reasonable o include corn supply in he lis of endogenous variables even hough i is predeermined. Tomek and Myers (1993) discussed his issue for apples and argued ha curren producion and beginning 6

8 invenories are predeermined, bu prices and allocaions of quaniies o alernae end-uses may be simulaneously deermined. Following he Tomek and Myers argumen, five endogenous variables are idenified in he corn sysem. These include price, supply, feed, expor and FAI. For purposes of comparison, an alernaive sysem of equaions ha reas corn supply as exogenous is also esimaed. The iniial sysem of five equaions is esimaed by hree-sage leas squares (3SLS). The alernaive sysem (reaing supply as exogenous) firs esimaes he supply equaion via OLS, and hen he oher four equaions via 3SLS. Prior o esimaion, each equaion is esed for auocorrelaion by applicaion of he Breusch-Godfrey Lagrange Muliplier es. This is done by esimaing each equaion via OLS individually. Resuls confirm ha he supply, expor and price equaions exhibi firs order auocorrelaion. This is correced by adding a one period lagged dependen variable o he righ hand side of he hree equaions. Supply equaion The supply of corn for each quarer is composed of beginning socks (same as ending socks from he previous quarer) and producion (we ignore impors). 4 Harves occurs only in he 1s quarer of he year. Thus, he supply of corn for he 1s quarer is he sum of he beginning socks and producion. For he remainder of quarers supply is represened only by he beginning socks. The supply of corn is a funcion of one period lagged corn price and one period lagged ineres rae. Since he supply in each period is a funcion of he ending socks he previous period, i is deermined by he previous period's price and ineres rae. If he corn price was high in he previous period relaive o fuures prices for subsequen periods, farmers will end o reduce carryover because of low expeced reurns o sorage. Also, if he ineres rae was high in 7

9 he previous period farmers will reduce carry over since he opporuniy cos of holding invenory is high. 5 This resuls in a supply equaion of he form : S = α + α P + α R + α S + α D + α D + α D +ε 1 (3) where is supply of corn for quarer, S 1 P is he lagged corn price and R 1 is he lagged ineres rae. D, D and D are quarerly dummies for he 1 s quarer, he 2 nd quarer and he 3 rd quarer, respecively. The signs of α1 and α2 are expeced o be negaive and α 3 posiive. The supply equaion has one endogenous variable ( (, R, S, D, and D ). P 1 1 Feed equaion 1 1 D2 3 S ) and six exogenous variables The firs demand equaion is he feed equaion. The corn feed equaion is specified as a funcion of corn price, soybean meal price (a subsiue for corn) and number of animals on feed (specifically cale, hogs and broilers). I akes he form : F = β + β P + β P + β B + β OF + β H + β D + β D + β D +ε SM (4) SM where is feed consumpion, is corn price, P is soybean meal price, B is he number of F P broilers, OF is he number of cale on feed and H is he number of hogs. The expeced sign of β 1 is negaive and he expeced signs of 2, 3, 4 and 5 β β β β are all posiive. There are wo endogenous variables (, ) and seven exogenous variables F P SM ( P, B, OF,,, and D ) in he feed equaion. H D1 D2 3 Expor equaion The nex demand equaion accouns for corn expors. orn expors are modeled as a funcion of corn price, whea producion in oher counries, per capia GDP of major U.S. corn 8

10 imporers, and exchange raes. More han 60% of U.S. corn expors go o five counries; Japan, Mexico, Taiwan, Egyp and Korea. 6 The per capia GDP for he five major imporers is calculaed as he weighed average of he five counries' per capia GDP. The weigh is deermined by he proporion of corn expored o each counry from 1997 o The impac of exchange raes is measured by he dollar index. The dollar index is a weighed average of he exchange raes beween he U.S. Dollar and six major world currencies. 7 In hambers and Jus (1981), corn expors are represened as a linear funcion of own-deflaed price, he exchange rae, socks of corn in he oher major exporing naions, he price of soybeans, he lagged dependen variable, and he quarerly indicaor variables. The exchange rae hey used was he Special Drawing Righs (SDR) per dollar. 8 Today, use of SDR is limied and is main funcion is o serve as he uni of accoun of he Inernaional Moneary Fund (IMF) and some oher inernaional organizaions (IMF). As a resul, use of he dollar index as a proxy for he U.S. Dollar exchange rae is now more accurae. The expor equaion akes he form : EX = γ + γ P + γ EX + γ W + γ DX + γ GDP + γ D + γ D + γ D +ε row (5) row where EX is expors, W is whea producion in he res of world, DX is he dollar index, and GDP is he per capia GDP of he main corn imporing counries. orn expors will decrease as he U.S. Dollar srenghens (higher dollar index) and world whea producion increases, and will increase wih increases in impor counries GDPs. Therefore, γ 5 is expeced o be posiive and γ1, γ3 and γ 4 all negaive. The expor equaion has wo endogenous variables ( EX, ) and seven exogenous variables ( W row, EX 1, DX, GDP, D1, D2 and D3 ). FAI equaion P 9

11 FAI represens a broad demand caegory ha includes all indusrial uses, wih ehanol use as a sub-caegory of indusrial uses. orn FAI consumpion is specified as a funcion of corn price, ehanol producion and U.S. populaion. A linear rend variable is added o capure he increase in ehanol producion over ime. The model is specified as : FAI = δ + δ P + δ Eh + δ Pop + δ T + δ D + δ D + δ D + ε (6) where FAI is FAI consumpion, Eh is ehanol producion in he U.S., Pop is U.S. populaion and is a linear rend. The expeced sign of δ is negaive and δ 2 posiive. The FAI equaion T 1 has wo endogenous variables ( FAI, P ) and six exogenous variables ( Eh, Pop, T, D1 D2 3, and D ). Price equaion The final equaion in he corn supply/demand sysem is he corn price equaion. The price of corn is deermined by supply and demand simulaneously, bu price also affecs he supply and demand of corn. The corn price is modeled as : P = ζ + ζ S + ζ F + ζ EX + ζ FAI + ζ P + ζ D + ζ D + ζ D + ε (7) The sign of ζ 1 is expeced o be negaive and he signs of ζ 2, ζ 3 and ζ 4 o be posiive. The price equaion has five endogenous variables ( P, S, F, EX and FAI ) and four exogenous variables ( P, D, and D ). 1 1 D2 3 Daa and Mehodology Daa The daa used o esimae he sysem of equaions above is comprised of quarerly daa for 11 years. I spans 2nd quarer 1995 (Dec, 1995) o 1s quarer 2006 (Nov, 2006). The daa are srucured o coincide wih he markeing year for U.S. corn. Tha is, he 1s quarer is from Sep 10

12 o Nov, he 2nd quarer from Dec o Feb, he 3rd quarer from Mar o May and he 4h quarer runs from Jun o Aug. Quarerly daa are used o mach he quarerly release of USDA daa on socks. Similar o Lowry e al (1987), i is assumed ha he preceding year s crop is harvesed in quarer 1 and he curren year s crop is planed in quarer 3. Mos price, sock, producion, corn usage and livesock daa are obained from various USDA repors. The corn price is measured as he quarerly average of he USDA monhly repored average farm level price. The soybean meal price (49-50 percen proein) is he quarerly average of monhly wholesale prices in Illinois. orn socks are measured via he USDA quarerly socks repors, and represen he size of he beginning socks as of Mar 1, Jun 1, Sep 1 and Dec 1. The variables used o explain feed, expors and FAI consumpion are also measured quarerly. The number of cale on feed is he quarerly average of monhly daa, and he number of broilers is he quarerly average of weekly daa. Hog numbers are measured as he average of he beginning invenory and ending invenory in each quarer. The dollar index is obained from he web sie of he Board of Governors of he Federal Reserve Sysem. I is a nominal broad dollar index and is a quarerly average of monhly daa. The GDP per capia for imporing counries and he U.S. populaion are from he IMF, and are consan during a year. GDP per capia is he annual number in curren price. Ehanol producion is calculaed from Energy Informaion Adminisraion, and is he quarerly sum of monhly producion. Mehodology The impac of ehanol producion on naional average corn price is measured by a sysem of equaions ha model he supply/demand fundamenals of he U.S. corn marke. The sysem is 11

13 comprised of a single supply equaion, a se of hree demand equaions, each focused on a specific caegory of demand (as defined by he World Agriculural Supply and Demand Esimaes, and ERS), and a price equaion. Before esimaing he sysem of equaions, idenificaion is verified by calculaing order and rank condiions. All five equaions are found o saisfy he requiremen for he order and rank condiion. 9 The order condiion is a necessary condiion and he rank condiion is a necessary and sufficien condiion for idenificaion. Since he model mees hese condiions, i can be solved for a unique soluion. There are several mehods for esimaing sysems of simulaneous equaions. The wosage leas squares esimaor (2SLS), one of he mos popular, is efficien and consisen bu i ignores informaion associaed wih endogenous variables ha appear in he sysem bu no in individual equaions (Judge e al.). Informaion concerning he error covariances is also los (Judge e al.). Anoher popular mehod, seemingly unrelaed regression (SUR), accouns for he correlaion in he error erms across equaions bu does no consider he endogeneiy issues associaed wih each equaion. Three-sage leas squares is considered a combinaion of 2SLS and SUR. I accouns for he conemporaneous correlaion in he error erms across equaions and he correlaion of he righ hand side variables wih he error erm. Furhermore, i is asympoically more efficien han 2SLS (Judge e al.). Because of his, 3SLS is used o esimae he sysem of simulaneous equaions for he corn marke idenified here. Two approaches are considered. The firs, as menioned earlier, assumes ha he endogenous variables are correlaed wih he error erm in each equaion and he error erms across he equaions are also correlaed. This assumpion leads o all five equaions being esimaed by 3SLS and is refered o as he base model. As an alernaive, i is assumed ha 12

14 supply is compleely exogenous, and he supply equaion is esimaed by OLS, wih he remaining four equaions esimaed by 3SLS. This esimaion is referred o as he alernaive model. The wo ses of resuls are compared o deermine wheher here are advanages of one model srucure (and associaed assumpions) over he oher. Resuls Regression resuls are presened in ables 1 and 2. The ables specify he firs model (5 endogenous variables) as he base and he second model (4 endogenous variables) as he alernaive. In boh cases, all esimaed coefficiens have he expeced signs. The coefficiens direcly imply elasiciy since he values are ransformed o logarihms. All bu he expor equaions exhibi high R 2 values. Over 90 percen of he variaion in he supply, feed and FAI variables are explained by he models. Variaion s in he price variable is also explained o a large degree. In he case of he expor equaion, however, only abou 50 percen of he variaion is explained. This is maybe due o he influence of inernaional facors no included in he model, or he inadequacy of he dollar index o appropriaely proxy he dollar exchange in he impor communiy. The Roo Mean Squared Errors associaed wih boh model sysems are quie similar and sable. onsequenly, i does no appear ha one model dominaes he oher. As such, he following discussion is based on he base model. In he hree demand equaions (feed, expor and FAI), he corn price coefficien is negaive as expeced. However, he price equaion reveals an ineresing resul. The effec of each demand facor on he corn price varies significanly. The impac of FAI consumpion on corn price is he greaes in erms of he magniude of coefficiens. Expor consumpion has he second greaes impac, and feed consumpion follows. 10 However, he impac of feed 13

15 consumpion on he corn price is no saisically significan even hough feed consumpion is he larges single use of corn. Resuls sugges ha increasing demand from FAI is more imporan in explaining corn price han oher use caegories. Thus, growh in ehanol producion is imporan in explaining corn price deerminaion. The effec of increasing producion of ehanol on he corn price, ha is he elasiciy of corn price wih respec o ehanol producion, c P Eh, can be calculaed from he price dependen reduced form equaion. Afer subsiuing equaions (3), (4), (5) and (6) ino he price equaion (7), we have he following reduced form equaion. ( ) sm αζ 1 1+ ζ5 P 1+ αζ 2 1R 1+ αζ 3 1S 1+ βζ 2 2P + βζ 3 2B + βζ 4 2OF + βζ 5 2H B 1 row P = + + γζ 2 3EX 1+ γζ 3 3W + γζ 4 3DX + γζ 5 3GDP + δζ 2 4Eh + δζ 3 4Pop + δζ 4 4T + v A A + D 1+ DD 2 + ED 3 where Then A = 1 β ζ γ ζ δζ B = ζ0 + α0ζ1+ β0ζ2 + γ0ζ3+ δ0ζ4 = αζ + βζ + γζ + δζ + ζ D = αζ + βζ + γζ + δζ + ζ E = αζ + βζ + γζ + δζ + ζ v = A [ ζ ε ζ ε ζ ε ζ ε ε ] P δζ 2 4 δζ 2 4 = = Eh A 1 βζ γ ζ δ ζ The resuls sugges ha ζ 2 (he feed consumpion coefficien in he price equaion) is no saisically significan a he 5% level. However, he variable is kep in he reduced form 14

16 equaion o capure he join ineracion of corn price and feed demand. The ehanol producion elasiciy of corn price and asympoic variance are equal o P 0.40*0.45 = = 0.16 Eh 1 (.30)*.09 (.26)*.27 ( 0.08)*0.45 P AsyVar. = Eh This suggess ha a 1% increase in ehanol producion causes a 0.16% increase in he corn price in he shor run, ceeris paribus. 11 Ehanol producion capaciy expanded from abou 5.5 billion gallons on lae 2006 o almos 11 billion gallons by lae 2007 (Renewable Fuels Associaion). USDA has repored he average U.S. cash corn price for firs quarer 2006/2007 o be $2.54 per bushel. By firs quarer 2007/2008 he average price had risen o $3.34, and by December 2007 he average price was $3.88 per bushel. Thus, corn prices increased 32 percen beween he firs quarer of he 2006/2007 markeing year and he firs quarer of he 2007/2008 markeing year, and anoher 16 percen during December Since ehanol producion capaciy essenially doubled beween he firs wo quarers of he las and curren markeing years, he model resuls above sugges ha ehanol s conribuion o he price rise was abou 41 cens per bushel, ceeris paribus. This would have resuled in an average 2007/2008 firs quarer price of $2.95 per bushel had nohing else changed. While his is a significan year over year increase, i is subsanially less han he acual price appreciaion beween he sar of 2006/2007 and he sar of he 2007/2008 markeing year. As a resul, while ehanol producion has had a significan and posiive impac on corn price, i does no fully explain price level changes in he 2006/2007 markeing year. Wha else could have happened? One facor conribuing o increased prices is likely growh in expor demand. While he resuls here do no show a significan impac from he 15

17 dollar index on oal expors (suggesing ha he dollar index no a good proxy for he expor opporuniy), he expor variable is found o be saisically significan in he price equaion, and is of he expeced sign. USDA has forecas corn expors for 2007/2008 o oal 2.45 billion bushels (World Agriculural Supply and Demand Esimaes). If realized his will be an increase of jus over 15 percen relaive o he previous year. Based on he expor price elasiciy esimaed in able 2, his explains anoher 10 cen per bushel average price increase from 2006/2007 o 2007/2008. However, oal corn supply firs quarer 2007/2008 was 15 percen greaer han firs quarer 2006/2007, and based on resuls in able 2 his has a negaive impac on price and offse some of he posiive impacs from he demand side. Based on he resuls in able 2, firs quarer 2007/2008 were well above wha would be projeced, and canno be explained based simply on ehanol producion and associaed corn use (as has been he pracice in he popular press). This suggess ha here may be an ouside facor influencing prices beyond hose capured in he supply/demand framework esimaed here. One unique aspec of he marke he las year has been he size of he non-commercial posiions in he fuures marke for corn. Speculaive raders have significanly increased heir ne long posiion over he las year, while non-commercial raders have ended o be ne shor. Figure 4 shows he ne fuures posiions of reporing non-commercial raders and commercial raders relaive o he price of corn on a weekly basis. The ne posiions are long posiions minus shor posiions. Noe ha corn prices have been highly correlaed wih he ne posiions of noncommercial raders since he firs quarer of 2006/2007, and he speculaors have had large ne long posiions mos of he las year. I is imporan o noe ha his does no imply causaliy, only correlaion. However, here does appear o be reason o sudy more carefully he impac of speculaive aciviy on boh price levels and volailiy. Forenbery and Zapaa (2004) have 16

18 shown ha speculaors can influence price levels and volailiy in cash markes based on fuures posiions in hese markes. If speculaors end o be echnical raders (as is he case wih large funds) hen hey may ake long posiion, a leas for shor periods of ime, ha appear inconsisen wih he hisorical fundamenal balance beween price and marke condiions. Researching more carefully he impac on price discovery resuling from a large increase in he amoun of risk capial coming from he speculaive side of a marke seems jusified, and his is he focus of curren work. In shor, here is no empirical evidence o dae o jusify a suggesion ha prices have exceeded heir fundamenal levels as a resul of marke srucure (i.e., growh in he speculaive componen), bu is also clear ha aemping o explain curren price levels simply as a funcion of ehanol producion is a bi naïve and inaccurae. onclusions A sysem of five equaions represening he U.S. corn marke is esimaed by 3SLS. Resuls show ha increasing ehanol producion has a significan impac on he naional average U.S. corn price. The posiive price change is consisen wih previous research. However, in conras o wha is wrien in much of he popular press, resuls do no sugges he exremely high corn prices in spring of 2007 can be compleely aribued o ehanol. Despie his, corn growers in he U.S. have benefied in he form of higher prices as a resul of growh in he ehanol indusry. To more fully undersand he overall impac of ehanol on corn prices, fuure research includes measuring ineracion across oher commodiy markes, and combining he srucural simulaneous equaion models presened here wih ime series models (as a proxy for echnical ype rading rules). In addiion, long run effecs of ehanol producion on he grain, livesock and gasoline markes should be invesigaed by inroducing dynamics. 17

19 Anoher area of work is focusing on he role of speculaors in he U.S. corn fuures marke, and aemping o measure wheher hey have conribued o he recen price appreciaion as a resul of moving larger han usual (based on hisorical norms) amouns of risk capial ino long fuures posiions. 18

20 Fig.1 Ehanol Producion in U.S. Million Gallon q3 1998q1 2000q3 2003q1 2005q3 Time Source : EIA and auhor's calculaion 19

21 Million Bushel Fig.2 Uilizaion of U.S. corn 1995q3 1998q1 2000q3 2003q1 2005q3 Time feed FAI expor Source : USDA and auhor's calculaion 20

22 Fig.3 orn used in Ehanol producion Million Bushel q3 1998q1 2000q3 2003q1 2005q3 Time FAI corn_used_ehanol Source : USDA and auhor's calculaion 21

23 Fig. 4 orn Price vs Trader Aciviy $ ,000 $ ,000 $ ,000 orn Price $4.50 $4.00 $3.50 $ , , ,000 Marke Open Ineres $ ,000 $ ,000 $ ,000 Source: ommimen of Traders Repors orn Price Non-ommercials ommercials 22

24 Table 1 : Resuls of esimaion I. Base (5 endogenous) II. Alernaive (4 endogenous) RMSE * 2 R RMSE 2 R Supply Feed FAI Expor Price *: Roo Mean Squared Error 23

25 Table 2 : Resuls of esimaion I. Base (5 endogenous) II. Alernaive (4 endogenous) coef(s.e) _value coef(s.e) _value ons (.6809) (.6094) 3.07 Price S (.1912) (.1863) F.0868(.2573) (.2946) 1.25 EX.2678(.0851) (.0904) 3.80 FAI.4470(.0843) (.0760) 4.45 P.3424(.0902) (.0799) D.5941(.0902) (.0774) D.4689(.0629) (.0529) D.2810(.0358) (.0301) Supply ons (.7999) (.9994) 2.36 P (.1272) (.1534) R (.0483) (.0597) S.3524(.1413) (.1772) D.6178(.0387) (.0471) D.2688(.0441) (.0541) D.1610(.0288) (.0341)

26 - coninued Feed I. Base (5 endogenous) II. Alernaive (4 endogenous) coef(s.e) _value coef(s.e) _value ons (1.6279) (1.6971) -.79 P (.0824) (.0832) sm P.1813(.0536) (.0559) 4.14 B.1999(.1040) (.1065) 2.36 OF.3885(.1906) (.1961) 1.24 H.4423(.3298) (.3514) 1.43 D.3851(.0107) (.0107) D.2410(.0144) (.0147) D.1212(.0122) (.0124) FAI ons ( ) ( ) 1.50 P (.0307) (.0300) Eh.4016(.0555) (.0559) 7.15 Pop (4.2911) (4.2910) T.0081(.0054) (.0054) 1.25 D (.0063) (.0063) D (.0119) (.0120) D.0048(.0072) (.0072).45 3 Expor ons (2.4389) (2.5246) 1.51 P (.1318) (.1327) EX.6059(.1071) (.1145) row W (.3420) (.3584) DX (.3240) (.3332) -.92 GDP.3632(.2820) (.3005) D (.0185) (.0185) -.18 D (.0184) (.0184) D (.0184) (.0184)

27 End Noes 1 The markeing year for U.S. corn runs from Sepember o Augus. 2 The Economic Research Service, USDA issues corn uilizaion values for feed and residual, expor, and FAI quarerly. They have a separae value for seed use bu we ignore he seed use since i is small. 3 In Devadoss e al. (1989), demand for corn is disaggregaed ino food use, feed use, seed use, socks, and expors. 4 orn producion is defined as a funcion of acreage planed, yield and governmen program in several previous papers (Arzac and Wilkinson, 1979, Tomek and Myers, 1993, and Garcia and Leuhold, 1997). Price expecaion, risk and echnology also play a role in deerminaion of producion. In he presen research, producion is reaed as exogenous. However, corn supply (including producion) is reaed as endogenous and he 1 s quarer dummy variable capures he producion effec in he supply equaion. 5 A similar argumen is provided in hambers and Jus (1981), bu he invenory equaion in heir paper is deermined by lagged invenory, own price, producion and quarerly indicaor variables. 6 This was calculaed by he auhors based on daa from USDA. 7 The dollar index consiss of he Euro, he Japanese Yen, he Briish Pound, he anadian Dollar, he Swedish Krona,and he Swiss Franc (from New York Board of Trade). 8 This is defined as a baske of currencies consising of he U.S. Dollar, he Deusche Mark, he Japanese Yen, he Briish Pound, and he French Franc, and is calculaed by he IMF. 9 The verificaion is available upon reques. 26

28 10 hambers and Jus (1981) repor he own price elasiciy of domesic disappearances and expor as and -.465, respecively. The presen research finds a less inelasic resul for domesic disappearances and a more inelasic resul for expors. 11 The elasiciy measure derived from he alernaive model is 0.25, somewha larger han ha from he base model. Regardless of he elasiciy used, however, i is clear ha corn price levels have been impaced o a large degree by influences oher han ehanol producion. 27

29 References Arzac, E. and M. Wilkinson "A Quarerly Economeric Model of Unied Saes Livesock and Feed Grain Markes and Some of is Policy Implicaions" American Journal of Agriculural Economics 61(2). Board of Governors of he Federal Reserve Sysem. Available a hp:// hambers, R. and R. Jus Effecs of Exchange Rae hanges on U.S. Agriculure : A Dynamic Analysis American Journal of Agriculural Economics 63(1). Devadoss, S., P. Weshoff, M. Helmar, E. Grundmeier, K. Skold, W. Meyers, and S. Johnson The FAPRI Modeling Sysem a ARD : A Documenaion Summary ener for Agriculural and Rural Developmen, Iowa Sae Universiy. Economic Research Service, USDA, various repors and issues. Available a hp:// Energy Informaion Adminisraion. Available a hp:// Ferris, J. and S. Joshi "Evaluaing he Impacs of an Increase in Fuel-ehanol Demand on Agriculure and he Economy" Michigan Sae Universiy. Forenbery, T.R. and H. Zapaa Developed Speculaion and Underdeveloped Markes The Role of Fuures Trading on Expor Prices in Less Developed ounries, European Review of Agriculural Economics, 31(4). Garcia, P. and R. Leuhold ommodiy Marke Modeling Agro-food Markeing, Oxford, UK : AB Inernaional. Greene Economeric Analysis. 5 h ediion, Pearson Educaion. Gujarai Basic Economerics. 3 rd ediion, McGraw-Hill, Inc. 28

30 Gusafson, "Poenial orn Acreage Expansion for Ehanol Producion : Wesern Norh Dakoa-Mino" Norh Dakoa Sae Universiy. Hayashi Economerics. 1 s ediion, Princeon Universiy Press. Inernaional Moneary Fund. Available a hp:// Judge, Hill, Griffihs, Lukepohl and Lee Inroducion o The Theory and Pracice of Economerics. 2 nd ediion, John Wiley & Sons, Inc. Lowry, M., J. Glauber, M. Miranda and P. Helmberger "Pricing and Sorage of Field rops : A Quarerly Model Applied o Soybeans" American Journal of Agriculural Economics 69(4). McNew, K. and D. Griffih "Measuring he Impac of Ehanol Plans on Local Grain Prices" Review of Agriculural Economics 27(2). Naional Agriculural Saisics Service, USDA, various repors and issues. Available a hp:// New York Board of Trade. Available a hp:// Pohidee, A., A. Allen and D. Hudson "Impacs of orn and Soybean Meal Price hanges on he Demand and Supply of U.S.Broilers" Mississippi Sae Universiy. Renewable Fuels Associaion. Available a hp:// Taylor, R., J. Mason, J. Andino and W. Koo "Ehanol's impac on he U.S. orn Indusry" Norh Dakoa Sae Universiy. Tomek, W ommodiy Fuures Prices as Forecass Review of Agriculural Economics 19(1). Tomek, W. and R. Myers Empirical Analysis of Agriculural ommodiy Prices : A Viewpoin Review of Agriculural Economics 15(1). 29

31 Ugare, D "An Energy Sraegy Based on Energy Dedicaed rops or orn : Differenial Economics and Regional Impacs" Universiy of Tennessee. Wesco, P. and L. Hoffman "Price Deerminaion for orn and Whea" ERS, USDA. World Agriculural Supply and Demand Esimaes, USDA, various issues. Available from hardcopies and a hp:// 30