A Critical Assessment of Supply­Demand Models of Agricultural Trade

Everett B. Peterson, Thomas W. Hertel, and James V. Stout

We offer a critical assessment of static, deterministic, reduced form, supply-demand(SD) models of agricultural trade. A commonly used and well-documented model hassignificant limitations: no explicit treatment of factor mobility between farm andnonfarm sectors, nonzero profits in some sectors, no substitutability among feedstuffsin livestock sectors, violation of the law of one price, small marketing margins forsome commodities, and incomplete coverage of the agricultural sector. Suchlimitations have significant impacts on the magnitude and direction of projectedworld price and welfare changes. We believe agricultural policy analysis should firstfocus on the remediation of these limitations before proceeding to more complexissues like dynamics and uncertainty.

Key words: agricultural policy, static equilibrium models.

Numerical results from agricultural trade mod­els have been in great demand recently, largelybecause agriculture has had such a high profilein the Uruguay Round of the GATT negotia­tions. In fact, the same group of models hasbeen called upon time and again to analyze im­plications of liberalizing domestic and tradepolicies on farm and food products (Goldin andKnudsen). With the Uruguay Round approach­ing completion, a critical assessment of thetools used by agricultural economists to ana­lyze the consequences of multilateral trade lib­eralization seems appropriate, We will assessthe most commonly used modeling framework,the static, deterministic, reduced-form, supply­demand (SD) trade model (e.g., OEeD;Roningen and Dixit; Valdes and Zietz).

With some exceptions, SD trade models havetended to be multicommodity, numerical gener­alizations of the familiar one-good, supply-de­mand framework in introductory economicscourses. This approach has a number of impor-

The authors are assistant professor, Department of Agricultural andApplied Economics, Virginia Polytechnic Institute and State Uni­versity; professor, Department of Agricultural Economics, PurdueUniversity; and economist, USDA/ERS, respectively.

This research was conducted under a cooperative agreement be­tween Purdue, Virginia Polytechnic Institute & State University,and the Agriculture and Trade Analysis Division of USDA/ERS.The authors thank Jerry Sharples for his encouragement of thiswork. (Three anonymous reviewers provided helpful comments onearlier drafts.) Vern Roningen generously supplied much of thedata used in this paper.

Review coordinated by Steven Buccola.

tant virtues. First, it requires information ontraded commodity prices, quantities, supply anddemand elasticities of each commodity, andpolicy instruments in the various regions, al­lowing considerable model disaggregation. Sec­ond, the supply-demand framework corre­sponds to the familiar diagrammatic representa­tion of markets. Numerical results may then beexplained as shifts in, and movements along,supply and demand schedules, proving an ef­fective means of communicating model resultsto policy makers.

Despite their popularity, SD models have sig­nificant limitations. We identify these limita­tions, discuss their implications for projectingworld price changes and trade reform gains,and suggest priorities for future research. To beas specific as possible, we focus on the U.S.Department of Agriculture's SWOPSIM modelas a "representative" SD model. We choose thismodel because it is publicly available, is widelyused and exhaustively documented (Roningen,Sullivan, and Dixit), has been employed to rep­licate the results from a number of other widelycited SD trade models (Magiera and Herlihy),and has been adopted by numerous economistsaround the world.

Since the motivation for using the SD formu­lation is based largely on the availability ofdata and behavioral parameters, we begin byassessing the basic information and structurebehind empirical SD trade models. We thenconsider the most severe limitations of the typi-

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710 November 1994 Amer. 1. Agr. Econ.

cal SD formulation, some of which may bereadily addressed with available data sources,while others are more complex. In the contextof a complete, structural model of agriculturaltrade focusing on US/EEC trade liberalization,we assess quantitatively some of the majorproblems of existing SD trade models.

Aggregate Utilityof Consumption

Endogenous Food OtherFoodConsumption

tNonfood

Conceptual Issues in Modeling the Farm andFood Economy

CommodityMarkets I • F"""""'" Li...tock

~~

Figure 1. The structure of a generic, regionaleconomy

LivestockNonfeed Inputs

Nonagriculture

Feedstuftil

Aggregate ResourceEndowment

Agriculture

for the agricultural commodity, and l1q is a vec­tor of technology scale parameters. In many SDmodels, the intermediate demands are not de­pendent on relative prices, but are simply pro­portional to the total supply of the final good inwhich they are used (e.g., livestock, dairy prod­ucts). Final demands are a function of con­sumer prices of the endogenous foodcommodities and of income

where llh is a matrix of uncompensated de­mand elasticities, ll y is a vector of income elas­ticities, Ph is a vector of retail prices, and y isincome. Since most SD models do not exhaustfood consumption, competition between de­mand for modeled consumption and other foodproducts, as well as nonfood products, is miss­ing. Finally, changes in income are taken as ex­ogenous, so there is no link between sourcesand uses of income in the regional economy.

In equilibrium, supply must equal demand

(2)

Figure 1 provides an overview of a generic, re­gional economy in which the farm and foodsector plays a central role. Agricultural prod­ucts are supplied from the farm sector, whichcompetes with the nonfarm economy for scarceresources. Since livestock sectors are an impor­tant source of intermediate demands for manycrop products, the feeding decision is high­lighted in figure 1. The nonfeed component oflivestock production is shown to compete withother agricultural activities for scarce labor andcapital. All these products move through thecommodity markets (probably after some fur­ther processing) to their ultimate destination,household consumption.

Figure 1 may be related to basic equations ina typical SD model of agricultural trade. Com­modity supply equations are of the form

where <Is is a vector of variables representingthe percentage change in agricultural commod­ity supplies as a function of a vector of farm­level price changes (pa)' premultiplied by amatrix of supply elasticities (l1s)' Note that (1)only implicitly acknowledges the competitionbetween farm and nonfarm sectors for produc­tion factors.

Total demand for agricultural production

consists of a share-weighted sum of intermedi-" . "hate demands (q~) and final demands (qn). In-

termediate demands are a function of relativeprices and the quantity of final product sup­plied

(3)

(5) qn = qswhere v, is a matrix of output-constant feed de-mand elasticities, Pm is the vector of prices paid and this condition permits us to solve for a vee-Downloaded from https://academic.oup.com/ajae/article-abstract/76/4/709/101907

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Peterson, Hertel, and Stout Supply-Demand Trade Models 711

mined by the degree of resource mobility be­tween sectors within a region (O"T in figure 1). Ifthe expansion effect outweighs the substitutioneffect, agricultural products will be grosscomplements. Thus, by using an aggregate rev­enue function, we can distinguish betweenchanges in the production mix of agriculturalcommodities and the aggregate level of re­sources in the farm sector.

The responsiveness of primary factors in ag­riculture to changes in relative factor returns isdirectly related to the question of aggregate ag­ricultural supply response. Following Rendlemanand Hertel, and assuming constant returns toscale in agriculture, the aggregate supply elas­ticity (1]J in our framework can be expressed as

tor of price changes. However, producers, do­mestic and foreign consumers, and processorsgenerally face different commodity prices.Thus, the following price linkage equations arerequired:

(6) Ph Pm + t hPa Pm + isPm = Pw + i,

where Pw is a vector of world prices, and thevectors i h , is' and i; are changes in thepower of the ad valorem equivalent values ofinitial household, farm-level, and trade subsi­dies/taxes. In the experiments below, the initialsubsidies/taxes are removed, and the propor­tional price changes differ by region and agent.

(7) 11 = -c (Jv na T

Agricultural Technology

One of the main sources of uncertainty in SDtrade models is how liberalization affects inputuse in agriculture. If inputs are held fixed overthe course of simulation, the associated supplyelasticities may be viewed as compensated. Onthe other hand, if input levels are permitted toadjust in response to changing relative returnsbetween farm and nonfarm sectors, supply elas­ticities are uncompensated. Unfortunately, stan­dard SD trade models are silent on this criticalissue.

In order to explicitly incorporate factor mo­bility into such a framework, it is helpful to de­compose agricultural supply response. Agricul­tural technology can be represented by an ag­gregate revenue function, which is conditionedon an aggregate agricultural input. 1 If the agri­cultural resource base is fixed, as it may be inthe short run, this formulation yields compen­sated supply elasticities; agricultural supply re­sponse is a function of farmers' willingness todivert resources from one product to another inresponse to relative price changes (e.g.,by mov­ing along a fixed production possibilities fron­tier). Except for joint products such as wooland mutton, we expect a priori that productswill be net substitutes in the short run.

In the longer run, commodity supply re­sponse will include an expansion effect deter-

I We believe that treating agriculture as a multiproduct sector ismore natural than using a nonjoint representation because manyfarms produce several different products.

where Cna is the share of nonagricultural factorpayments to total factor payments, and O"T ~ 0 isthe CET parameter. The size of the aggregatesupply response has long been a subject of de­bate. Rao has found that estimates of the aggre­gate supply elasticity range from 0.1 to 0.3 inconventional time-series models to as high as1.66 in cross-sectional models. He concludesthat "cross-sectional estimates exaggerate sup­ply responsiveness to prices while time-seriesstudies underestimate that response" and thatthe true aggregate supply elasticity must fallbetween the two extremes.

Another limitation of many SD trade modelsis that they lack factor markets and cannot de­termine the impacts of policy liberalization onagricultural factor returns. Because informationon variable input usage is not readily availablefor many regions (feed use is an exception), weaggregate all inputs into a single endowment.Further refinements can easily be introduced,provided data on variable input use are avail­able, by replacing the revenue function with arestricted profit function. The latter then givesrise to a set of variable input demand equations.

Livestock Sector

Most SD models are relatively weak in the live­stock sector. (An exception is the OECD modelof agricultural trade.) To assess whether feeddemand and livestock supply elasticities areconsistent with an underlying livestock produc­tion structure requires a conceptual picture ofthe sectoral production process. In figure 1, thelivestock sector is assumed to combine feedDownloaded from https://academic.oup.com/ajae/article-abstract/76/4/709/101907

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712 November 1994

and nonfeed inputs into a finished livestockproduct. The nonfeed input is derived from theaggregate agricultural revenue function andcompetes with other farm activities for the ag­gregate agricultural factor. For example, the de­gree to which a short-to-medium run increasein pork production may bid farm labor andcapital away from other livestock and crop ac­tivities will determine the supply response ofpork, given exogenous feed prices.

If the livestock sectors are perfectly competi­tive and operate under locally constant returnsto scale, the own-price supply elasticity (1100)and the cross-price supply elasticity with re­spect to the feed price (110F) can be related tounderlying technology parameters

where (J F is the elasticity of substitution be­tween feed and nonfeed inputs, and CF is thefeed cost share. The conditional feed demandelasticities are also related to (J F

where CL is the nonfeed cost share, VFF is theoutput constant own-price elasticity of demandfor feed in a given livestock sector, and vFL isthe output constant cross-price demand elastic­ity with respect to the livestock (nonfeed) in­put. However, the SWOPSIM data base is notconsistent with this livestock production struc­ture since it does not allow feed-livestock priceinteraction (i.e., vFL = 0, implying (JF = 0), and110F does not equal -1100 cF • Assuming that (JF =o may cause this type of SD trade model tooverestimate the impact of policy liberalizationon world feed grain and oilseed prices.

Food Processing and Marketing

Another important aspect of any region's farmand food economy is the processing and mar­keting of agricultural commodities. However,most SD trade models contain very little foodprocessing and marketing detail. (TheSWOPSIM model includes detail on oilseedand dairy processing. Fixed marketing marginsare applied to all other farm products to obtainretail prices.) This is a major weakness in theSD model, because the farm value share of totalfood expenditures in the United States for 1992was only about 22% (Dunham). In neglectingfood marketing activities, the SWOPSIM model

Amer. 1. Agr. Econ.

omits 78% of the food system! Indeed, otherfactors, such as labor costs, make up a largerpercentage of total food expenditures than doagricultural commodities. Thus, the impact oftrade liberalization on wage rates could influ­ence retail food prices as much as any subse­quent changes in agricultural commodityprices.

If the marketing margins used in the SDmodel are incorrect, the effect of farm gateprice changes on retail prices will be over- orunderestimated, and welfare changes will be in­correctly measured. For agricultural commodi­ties with easily identifiable retail products, likemeats, marketing margin estimates are likely tobe more reliable. However, for agriculturalcommodities used in a variety of retail prod­ucts, like sugar, cotton, grains, and oilseeds, es­timating the magnitude of the marketing marginbecomes problematic.

Finally, most SD trade models assume per­fectly competitive markets for farm and foodproducts, but there is a growing literature onnoncompetitive behavior in food processingand marketing (Azzam; Connor et al.; Connorand Peterson; Koontz, Garcia, and Hudson;Schroeter and Azzam; Stiegert, Azzam, andBrorsen; and Zellner). Ignoring the potentialexercise of oligopolistic and/or oilgopsonisticpricing behavior may cause SD trade models toinaccurately measure the impacts of policy lib­eralization on world prices and welfare. How­ever, this issue is beyond the scope of ourpaper.

Consumer Demand

Since consumer demand elasticities for SDmodels are typically drawn from diversesources, a major concern is whether these esti­mates are mutually consistent. In other words,can the demand elasticities be related to an un­derlying utility function? There are several rea­sons why we believe that a complete demandsystem should be used to represent consumerpreferences. First, there is no reason to believethat consumer preferences for SWOPSIM com­modities are separable from those for foodcommodities not covered by the model (e.g.,fruits and vegetables, and various processedproducts). Second, including a nonfood com­modity in the demand system allows the theo­retical properties of symmetry, homogeneity,and adding-up to be imposed. Finally, using acomplete demand system facilitates welfareanalysis.Downloaded from https://academic.oup.com/ajae/article-abstract/76/4/709/101907

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Peterson, Hertel. and SWill

Identifying an underlying utility function iscomplicated because most of the cross-priceelasticities in final demand are zero in theSWOPSIM data base." While zero is an accept­able choice, it is not plausible because the onlyunderlying utility function that generates zerouncompensated cross-price elasticities is theCobb-Douglas. However, the Cobb-Douglasutility function contradicts the nonhomotheticnature of the income elasticities in theSWOPSIM data base. Thus, the conceptualproblem is to identify an underlying utilitystructure replicating the own-price and incomedemand elasticities without requiring additionalinformation on the cross-price elasticities.

The matrix of final demand elasticities in theSWOPSIM data base exhibits a block diagonalpattern, suggesting that consumer preferencescan be modeled as a two-stage budgeting pro­cess. Seven separable food groups are identi­fied: meats and eggs, dairy products, grains,oilseeds and associated products, cotton, sugar,and tobacco. Within these groups, only a sprin­kling of cross-price elasticities are present. Be­cause of this limitation, we assume a single,constant elasticity of substitution (CES) be­tween all commodities at the lower stage of thebudgeting process.

Choosing a functional form to represent pref­erences at the top stage is more difficult due tothe predominance of zero cross-price elastici­ties between commodities in different groups ofthe demand matrix. In practical terms, we needa representation of preferences with n free pa­rameters to replicate the group-wise own-priceelasticities of demand, ruling out many of thewell-known functional forms. For example, aCES function does not have enough free param­eters to replicate all own-price elasticities.Thus, not all of the available information isused. Flexible functional forms, such as thetranslog, have too many free parameters[n(n - 1)/2], requiring that additional informa­tion be supplied to the model. The additionalinformation becomes problematic in trade mod­els with many regions. Fortunately, Hanoch in­troduced a class of implicitly additivepreference relationships with precisely n substi­tution parameters, allowing replication of acomplete vector of own-price elasticities andincome elasticities. We use one member of thisclass, the Constant Difference Elasticity of sub­stitution (CDE) expenditure function, to repre-

2 The zero cross-price elasticities undoubtedly reflect an ab­sence of information.

Supply-Demand Trade Models 713

sent preferences for the first stage.'In calibrating the CDE aggregate expenditure

function," we discovered the uncompensatedown-price and income elasticities in theSWOPSIM data base were not consistent, giventhe base expenditure shares and the maintainedhypothesis of utility maximization subject toimplicitly additive preferences.' This illustratesthat one cannot choose a set of income elastici­ties independently of price elasticities and bud­get shares.

Other Modeling Issues

Most SD models assume perfectly competitivemarkets, but the zero profit conditions arerarely enforced in all markets. This is a severeproblem when conducting welfare analyses. Ifthe initial data base contains a sector for whichexpenditures exceed revenues (as in severalcases in the SWOPSIM model), any stimulus tothis sector reduces the welfare of the region inquestion. Further, given the medium term equi­librium assumptions embedded in most SDmodels, it is unclear how such a disequilibriumcan be explained.

The law of one price, which must hold in anyconsistent model of trade in homogeneousproducts, reveals another dimension of theequilibrium/disequilibrium problem. Lackinginformation on bilateral trade flows, many SDmodels use net trade flows in their world mar­ket clearing conditions. Therefore they assume,either explicitly or implicitly, that all commodi­ties are homogeneous and the law of one priceholds. However, in the absence of transporta­tion costs, the underlying data bases contain re­gional price differentials, contrary to theassumption of homogenous commodities. Thus,shipments of homogeneous goods from a low­price region to a high-price region will (spuri­ously) raise world welfare.

A final modeling issue is where to draw theline on sectoral coverage in partial equilibriumSD trade models. Our main concern is whetherit is necessary to have complete coverage of allagricultural and nonagricultural commodities

J See Hertel et al. (1991) for an example of how to incorporatethe CDE functional form into equilibrium models.

4 Hertel. Peterson, and Stout have developed a systematicframework to augment the SWOPSIM data base to calibrate the ex­penditure, revenue, and cost functions.

5 The base elasticities values violated the regularity conditionsof the CDE expenditure function. To satisfy these conditions, ei­ther the compensated own-price or income elasticities must be ad­justed. We choose to adjust the income elasticities.Downloaded from https://academic.oup.com/ajae/article-abstract/76/4/709/101907

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714 November 1994 Amer. J. Agr. Econ.

Table 1. Price Wedges in the United States and EEC-12, 1989

United States EEC-12

Commodity Trade3 DPSWb CSW b Trade DPSW CSW

Ad Valorem Equivalents

Wheat 0.0 21.5 0.0 23.7 6.5 0.04Corn 0.0 24.4 0.0 -61.3 7.4 0.5Other coarse grains 0.0 23.9 0.0 49.5 2.4 0.0Rice 0.0 35.5 0.0 55.9 12.4 1.9Soybeans 0.0 5.6 0.0 0.0 80.9 0.0Other oil seeds -9.4 5.6 0.0 0.0 44.4 0.0Cotton 0.0 20.3 0.0 0.0 6.0 0.0Sugar -39.7 6.9 0.0 64.2 -5.3 0.7Tobacco 0.0 7.1 0.0 0.0 6.0 0.0Other agriculture 0.0 5.9 0.0 0.0 6.0 0.0Beef and veal -1.7 6.4 0.0 71.5 11.1 0.0Pork 0.0 3.6 0.0 9.3 4.2 0.0Mutton and lamb 0.0 19.8 0.0 -115.3 72.3 0.0Poultry meat 0.0 6.5 0.0 37.3 9.2 0.0Poultry eggs 0.0 3.4 0.0 14.8 4.1 0.0Soymeal 0.0 0.0 0.0 0.0 0.0 0.0Soyoil 0.0 0.0 0.0 0.0 0.0 0.0Other oilmeal 0.0 0.0 0.0 0.0 0.0 0.0Other oil 0.0 0.0 0.0 0.0 0.0 0.0Milk -37.5 5.6 0.0 69.1 7.3 0.2Butter -39.1 0.0 0.0 -79.4 0.0 2.7Cheese -35.2 0.0 0.0 66.5 0.0 0.0Powdered milk -11.9 0.0 0.0 -13.8 0.0 21.0

a A positive value indicates an export subsidy, negative value is an import tax.b DPSW and CSW represent producer and consumer subsidy wedges respectively.

when analyzing trade and policy liberalization.Because this is a difficult problem, we high­light the implications of model closure in ourempirical analysis.

Numerical Illustration of Selected Issues

We focus on a subset of issues identified in theprevious section, selected on the basis of ame­nability to numerical illustration. Included arethe importance of expansion effects in agricul­tural supply, livestock-feed price relationships,marketing margins, violation of the law of oneprice, implications of nonzero profits, and thebreadth of sectoral coverage. By quantifyingthe impacts of these issues in the context of asimple agricultural trade liberalization exercise,we hope to aid researchers and managers seek­ing to prioritize future work, as well as the us­ers of SD model results.

A three-region (US-EEC-ROW) static, neo­classical trade model based on figure 1 (Hertel,Peterson, and Stout) is employed in the numeri­cal illustrations. The base year of this model is

1989. Each region is represented by a singlehousehold. There are twenty-three agricultur­ally related commodities and one nonagricul­tural commodity in the model, with all com­modities assumed to be homogeneous. All mar­kets are assumed to be perfectly competitive;revenues are exhausted on factor paymentswhich, when combined with net tax revenues,determine consumers' income. We assign the roleof numeraire to the nonagricultural commodity.The magnitude of producer, consumer, andtrade interventions for the U.S. and the EEC isgiven in table 1. These have been calculatedbased on GECD estimates of PSEs and CSES.6To simplify the experiments, all distortions areremoved in each experiment." The residualROW region is assumed passive in this exercise.

6 Herlihy, Haley, and Johnson have explored the implications ofhow agricultural policies are modeled in the context of theSWOPSIM model.

7 We choose a complete liberalization scenario for its ease inproviding numerical illustrations. In particular, this allows us tocircumvent the question of how particular policies operate at themargin. While this is an important issue, it is beyond the scope ofour paper.Downloaded from https://academic.oup.com/ajae/article-abstract/76/4/709/101907

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Peterson, Hertel. and Stout Supply-Demand Trade Models 715

Table 2. Substitution and Expansion Effects in U.S. Agriculture Under Alternative Assump­tions About Factor Mobility

aT = a aT = -1

Commodity

Non-feed inputs suppliedto livestock assembly

Beef and vealPorkMutton and lambPoultry meatPoultry eggsDairy

WheatCornOther coarse grainsRiceSoybeansOther oilseedsCottonSugarTobaccoOther agriculture

Compensated

0.510.360.710.280.350.410.600.480.990.400.600.550.740.500.250.50

Expansion"

0.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.00

Compensated

0.390.310.700.260.330.310.550.360.970.390.530.530.720.490.240.23

Expansion

0.120.050.010.020.020.100.050.120.020.010.070.020.020.010.010.27

a The uncompensated supply elasticities are the sum of the compensated or substitution effects and the expansion effect.

Importance of Expansion Effect

To illustrate the importance of the expansioneffect, we consider two alternative scenarios.First, we assume no expansion effect with (IT =oin all regions, implying the own-price supplyelasticities in the SWOPSIM data base are com­pensated (e.g., the agricultural resource base isfixed in this scenario). Second, we assume that(IT =-1 in all regions, implying the SWOPSIMsupply elasticities to which we calibrate are un­compensated. The associated aggregate supplyresponse in this scenario is derived from (7)and equals 0.94 for the U.S., 0.92 for the EEC,and 0.91 for the ROW. Table 2 lists the com­pensated and uncompensated supply elasticitiesused in the two experiments.

No Expansion Effect. The first columns intables 3 and 4 present results based on theelimination of all distortions in the U.S. andEEC, but factors are not permitted to move be­tween the agricultural and nonagricultural sec­tors. This is our base case. As shown in table 3,world price changes reflect agricultural produc­tion shifts away from relatively heavily sup­ported commodities to less supported comrnodi-

ties. Thus, the prices of feed grains," cotton,sugar, beef, mutton and lamb, poultry meat, but­ter, and cheese all increase as supplies fall.

Interestingly, unlike the results from multi­lateral liberalization studies, the world price ofwheat does not increase." Because wheat is oneof the relatively least subsidized commoditiesin the EEC, disallowing resources to flow outof EEC agriculture leads to an increase in EECwheat production. This negates any decrease inU.S. production, highlighting the importance ofthe expansion effect in SD trade models.

Region-specific results for the base case arereported in the first column of table 4. As ex­pected, when support is removed, agriculturalfactor returns decrease relative to the price ofthe nonagricultural good in the U.S. and EEC.In the absence of mobility, any decreases infarm-level producer prices will be borne by theagricultural factor. Of course, the opposite is

8 The change in grain production depends on what happens toidled acreage, especially in the U.S. We assume that none of theidled acreage returns to production.

9 The world price decrease for pork contradicts the results ofmultilateral liberalization studies. The world price decline occursin our model because the reduction in feed cost in the EEC resultsin a strong expansion of EEC pork production.

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716 November 1994

Table 3. Changes in World Prices from U.S.lEEC Agricultural Liberalization

Amer. J. Agr. Econ.

Commodity Base Factor Feedstuff Margins Prices Zero- C1 C2case mobility sub. profit

Percentage ChangeWheat -0.1 8.6 -0.2 0.7 -0.8 0.0 0.3 1.7Corn 4.9 12.9 7.9 5.6 4.7 4.9 5.3 6.9Other coarse grains 4.8 12.8 4.5 5.6 4.4 4.8 5.2 6.7Rice -0.4 -2.0 -0.3 0.2 -2.7 -0.2 -0.2 0.5Soybeans -2.9 3.7 -1.6 -1.8 -3.8 -2.8 -2.5 -0.6Other oilseeds 0.1 5.6 0.8 1.0 -1.0 0.4 0.4 1.8Cotton 1.2 4.0 1.3 1.5 -0.5 1.3 1.5 2.9Sugar 3.8 9.7 3.9 5.6 1.8 3.9 4.1 5.0Tobacco -4.0 0.7 -3.9 -2.0 -6.1 -3.8 -3.5 -2.5Other agriculture -1.2 -2.7 -1.1 -0.5 -2.3 -1.1 -0.9 N/ABeef and veal 6.1 13.6 6.3 7.1 4.5 6.1 6.5 7.8Pork -2.5 7.4 -1.8 -1.4 -3.3 -2.8 -2.1 -0.9Mutton and lamb 13.6 19.7 14.2 14.5 14.6 13.7 14.1 15.3Poultry meat 3.5 12.6 4.3 4.6 2.1 3.5 4.0 5.3Poultry eggs -0.9 6.5 -0.3 0.0 -1.4 -0.9 -0.5 0.5Soymeal -4.4 0.9 2.4 -3.5 -5.4 -4.7 -4.0 -2.9Soyoil -0.5 2.2 -1.1 0.3 -0.9 -0.5 -0.3 0.4Other oilmeal -4.0 1.7 4.8 -3.1 -5.0 -4.2 -3.7 -2.5Other oil -0.5 1.7 -0.5 0.1 -1.3 -0.5 -0.3 0.2Butter 12.1 19.9 12.3 14.0 6.9 12.2 12.4 13.6Cheese 21.3 37.7 21.2 22.5 18.8 21.3 21.7 23.5Powdered milk 9.5 32.2 9.2 9.0 11.6 9.4 9.8 11.8

true in the ROW, where most producer pricesincrease, leading to higher agricultural factorreturns. Overall welfare gains are small, due tothe absence of farm-nonfarm factor mobility.

Expansion Effect. The impact of including anexpansion effect is seen by comparing the sec­ond and first columns in tables 3 and 4. Be­cause of decreases in agricultural factor returnsin the U.S. and EEC, resources employed in ag­riculture decline by 4.6% and 17.2%, leading toa decrease in the supply of agricultural com­modities from these regions and a rise in worldprices. The increase in world agriculturalprices, except for rice and "other agriculture,"leads to a 2.4% expansion in ROW agriculturalproduction. The added production of rice andother agriculture in the ROW, compared to thebase case, leads to reductions in world pricesfor these commodities.

When resources are permitted to flow tohigher-return uses, liberalization yields largergains in aggregate regional welfare, comparedto the no-mobility case. Indeed, the equivalentvariation of liberalization with factor mobilityis over four times higher for the U.S., comparedwith no-mobility, and nearly twice as large forthe EEC. In addition, factor mobility changesthe distribution of any welfare gains from liber-

alization. When resources move out of agricul­ture in the U.S. and the EEC, factor returns fallby only half as much as in the no-mobility case,while agricultural factor returns are higher inthe ROW. Consumers in the U.S. enjoy an over­all decline in retail food and fiber prices withno factor mobility, but face price increases inthe case of factor mobility.

Implications of Feedstuff Substitution

In the base case, we allow for substitution be­tween feed and nonfeed inputs, and amongfeedstuffs in livestock sectors. The elasticity ofsubstitution between feed and nonfeed inputsfor each sector is derived using (8) and infor­mation in the SWOPSIM data base. Since thereare multiple values of TJOF for any given live­stock product, there are also multiple estimatesof (JF' implying that different feed types substi­tute differentially for the nonfeed input. Givena lack of evidence, we assume the optimal feedmix is invariant to the price of nonfeed inputsand use a weighted average for (JF in the model.We also permit substitution among grains andproteins based on Hertel and Tsigas.

To examine the importance of feedstuff sub­stitution, we rerun the base case with no substi-Downloaded from https://academic.oup.com/ajae/article-abstract/76/4/709/101907

by gueston 01 April 2018

Peterson, Hertel, and St ou : Supply-Demand Trade Models 717

Table 4. Regional Impacts of U.S.lEEC Agricultural Liberalization

Commodity Basecase

Factor Feedstuff Margins Pricesmobility sub.

Zero­profit

Cl C2

Percentage change in agricultural factor returns

USEECROW

-10.1-26.9

0.5

-5.1-19.3

2.9

-10.0-27.3

0.6

-9.3-26.0

1.4

~10.5

-24.9-1.0

-10.1-26.9

0.6

-9.8-26.6

0.8

-7.0-24.6

1.9

USEECROW

-0.4-6.5

0.2

Percentage change in retail food and fiber prices1.3 -0.4 -0.1 -0.9 -0.4 -0.3

~4.0 -6.4 -5.3 -6.0 -6.5 -6.31.0 0.2 0.4 -0.4 0.2 0.3

0.1-6.0

0.6

USEECROW

0.504.41

-0.41

2.298.24

-0.96

Equivalent variation - $ billions0.76 0.613.49 4.54 N/Aa NIA

-0.35 -0.31N/A N/A

a Equivalent variation is not an accurate welfare measure for these experiments.

tution among/between proteins and grains inthe feed ration of each livestock industry, andno substitution between feed and the nonfeedinput. The simulation results are reported in thethird columns of tables 3 and 4. In general, de­mand for feedstuffs is higher in this experi­ment, compared to the base case, because of theassumption that producers cannot substitute be­tween feed and nonfeed inputs. In the basecase, a relative reduction in the price ofnonfeed inputs, due to more agricultural re­sources flowing into the livestock industriesfollowing liberalization, reduces total feed de­mand. Without this substitution possibility,world prices of soymeal, other meal, and cornincrease. However, note that wheat and othercoarse grains have lower world prices becausethese feedstuffs are not allowed to substitutefor corn, thereby lowering total demand forthese commodities.

In addition to the impact on feed grain andoilseed prices, imposing no feedstuff substitu­tion also has substantial welfare effects. Be­cause the U.S. is a major producer of corn andsoybeans, regional income increases comparedto the base case. Combined with no change inretail prices and a term of trade improvement,the equivalent variation from liberalization is52% higher for the U.S. than in the base case.However, in the EEC, the equivalent variationis 20.9% lower than in the base case because ofhigher prices for livestock products. Thus, thedegree of feedstuff substitution is critical inmeasuring the welfare effects of liberalization.

Importance of Food Marketing Margins

To highlight the importance of food marketingmargins in the analysis of agricultural policyreform, we construct an experiment with lowmarketing margins for grains, oilseeds, sugar,cotton, and tobacco. We choose these commodi­ties because the farm value share of retail ex­penditures in the SWOPSIM model is very high(or conversely the marketing margins are low)compared to other independent USDA esti­mates (Hertel, Peterson, and Stout). For ex­ample, the farm value share for wheat impliedby the SWOPSIM marketing margin is 82.7%.However, the farm value share of wheat flour,one of the least processed of all wheat-basedfood products, was 32% in 1989 (Dunham). Anextreme example is that the farm value of cornand other coarse grains exceeds the retail valuein the SWOPSIM model! These examples illus­trate the difficultly in determining marketingmargins for agricultural commodities that re­ceive a lot of further processing. The implica­tion of using lower marketing margins is that alarger portion of agricultural price changes istransmitted to consumers.

To examine the impact of lower marketingmargins, we rerun the base case using the origi­nal SWOPSIM marketing margins (see table 5).Results are reported in the fourth columns oftables 3 and 4. In the base case, world prices ofgrain, cotton, and sugar increase following lib­eralization, while rice and oilseed prices de­cline. Transmitting a larger portion of these

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718 November /994

Table 5. Initial and Revised Food Value Shares

Farm value share

Commodity Initial Revised"

PercentageBeef and veal 55.2 58.2Pork 50.3 41.1Mutton and lamb 50.0 46.0Poultry meat 55.3 47.8Poultry eggs 60.1 56.4Milk 50.3 49.2Butter 81.7 70.0Cheese 71.1 70.0Powdered milk 82.4 70.0Wheat 82.7 10.0Corn 109.6 10.0Other coarse grains 108.7 10.0Rice 72.2 19.0Soybeans 95.9 21.0Soymeal 79.9 70.0Soyoil 50.0 70.0Other oilseeds 90.0 21.0Other meals 80.0 70.0Other oils 50.0 70.0Cotton 50.0 10.0Sugar 50.3 39.5Tobacco 50.0 5.9

a Producer price (p,) divided by the consumer price (po). Forprocessed products, this represents processor value share.

b Value share estimates from Connor and Weimer; Dunham; andHertel, Peterson, and Stout.

price changes to consumers leads to substitu­tion away from grains and cotton to other foodproducts, such as meats and dairy products. In­creases in V.S. and EEC sugar demand, due toreductions in retail prices following liberaliza­tion, lead to a global increase in sugar demand.Finally, income is higher in all three house­holds, leading to a demand increase for allcommodities. The combined effect of thesechanges is an overall increase in world prices.

V sing lower marketing margins for grains,oilseeds, sugar, cotton, and tobacco leads to anoverestimate of liberalization welfare impacts.The equivalent variation of agricultural liberal­ization is $0.11 billion higher in the U.S., $0.13billion higher in the EEC, and $0.10 billionhigher in the ROW than in the base case. Thedistributional effects of smaller margins under­estimate producer surplus loss in the V. S. andthe EEC, while overestimating producer surplusgains by ROW producers. Consumer surplusgains in the U.S. and EEC are underestimated,while the consumer surplus loss is overesti­mated in the ROW, compared to the base case.

Amer. 1. Agr. Econ.

Violation of the Law of One Price

The SWOPSIM model assumes all goods arehomogeneous and provides information onwhether a region is a net importer or exporterfor each commodity. The internal price level isdetermined by first applying a fixed percentageadjustment to the world price, then adjustingfor any policy wedges. This adjustment is madeto "match up" with observed FOB export pricesof various commodities in each region. How­ever, in several instances, a region with a rela­tively high commodity price is a net exporter(EEC pork) and a relatively low-price region isa net importer (EEC oilseeds). Thus, spuriouswelfare gains are possible for any exogenousshock to the model that reduces shipments froma high-cost region to a low-cost region.

To evaluate the significance of this problem,we perform an experiment that maintains theregional price differentials in the SWOPSIMdatabase. Results are given in the sixth columnsof tables 3 and 4. It is difficult to make com­parisons between this experiment and the basecase because of differences (e.g., cost shares) inthe underlying data sets. However, such pricedifferentials lead to an overstatement of con­sumer surplus gains in the U.S. and ROW butunderstate the gains in the EEC. On the pro­ducer side, agricultural factor returns are lowerin the U.S. and the ROW, but higher in theEEC. Indeed, a reversal occurs in the ROW,with agricultural factor returns declining, com­pared to an increase in the base case. Becausethe law of one price is violated, Walras' Law isviolated, rendering welfare measures such ascompensating or equivalent variation incorrect.The leakages equal $324 million, over half theV.S. welfare gains in the base case.

Implications ofNonzero Profits

Cost and revenue information of oilseed pro­cessing in the SWOPSIM data base is incom­patible with zero profits. In particular, totalexpenditures (on raw products alone) at marketprices exceed revenue at producer prices, andthis difference is very large in the case of otheroilseeds. To rectify the problem, we adjustcosts and revenues in these sectors to ensurezero profits (Hertel, Peterson, and Stout).

To illustrate the importance of ensuring thezero profit conditions are met, we construct anexperiment where the initial level of negativeprofits is maintained in the new equilibrium, af-

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Peterson. Hertel, and S/Ol/!

ter agricultural liberalization occurs. Becauseof the small size of oilseed processing indus­tries (value-added in all regions contributes0.07% of global income) there is very little ef­fect on global and regional prices compared tothe base case. However, the presence of non­zero profits violates Walras' Law, renderingwelfare measures incorrect. The leakage causedby the violation of zero profits in these two in­dustries is $30 million, or 0.2% of their value­added. While this amount is not large, it maymean the difference between a welfare gain orloss in some cases. Further, if the same propor­tional leakage occurred in a much larger sector,like agriculture, $3.5 billion would be "lost"from the model, leading to a more substantialmisstatement of welfare changes.

Implications ofModel Closure

We explore the importance of sectoral coveragein SD models using two alternative model clo­sures. In the first case (C 1), we restrict themodel by fixing the price of the nonagriculturalproduct, the level of regional income, and drop­ping the associated market clearing and zeroprofit equations. However, many SD models donot cover all farm and food commodities. Thus,we consider a second scenario (C2), where themodel is further restricted by fixing the price of"other" agricultural products. In this case, "fullliberalization" only applies to the SWOPSIMcommodities.

Simulation results for these scenarios are re­ported in the last two columns in tables 3 and 4.The results for scenario (C 1) are quite similarto the base case (except for other oilseeds,where there is a slight increase in world price,rather than a slight decrease). In other words, ifone is only interested in the farm sector effectsof farm and food policies, it is sufficient totreat the nonfood sector as exogenous. How­ever, differences between (C2) and the basecase are rather large in many cases. When theprice of the "other" agricultural commodity isfixed, more resources are employed in its pro­duction following liberalization than in the basecase. This reduces supplies of all other agricul­tural commodities, leading to higher worldprices compared to the base case. Thus, to as­sess U.S./EEC liberalization, complete cover­age of the agricultural sector is more importantthan endogenizing the nonfood sector.

Supply-Demand Trade Models 719

Summary and Assessment

When reviewing static, deterministic models ofagricultural trade, it is easy to suggest that thefirst priority is to introduce dynamics and un­certainty. Indeed, many such efforts have beenundertaken in the last decade. However, even inmodels with dynamics/uncertainty options, re­sults most frequently cited are almost alwaysthose based on comparative static solutions.Thus, we conclude that, in the context of thepublic debate over farm subsidies, the simplic­ity of a comparative static analysis has consid­erable value.

While many policy researchers and managersbelieve that comparative statics have been fullymastered and that there is little room for im­provement, we disagree. Taking one of the mostcommonly used SD models of agriculturaltrade, we identify a series of limitations thatneed to be addressed if the potential of this toolis to be realized. We illustrate these limitationsin a series of simulations of U.S.-EEC agricul­tural liberalization.

At the top of our list of issues deserving fur­ther research is the question of expansion ef­fects in agricultural supply. The typical SDmodel is silent on the question of resourcemovements between farm and nonfarm sectors.Yet factor mobility, or the lack of it, is at thecrux of the "farm problem" faced in most in­dustrialized economies. To understand the im­plications of policy changes for factor returnsin agriculture, we must incorporate more detailon the movement of resources between farmand nonfarm sectors. In our simulations, the as­sumption of factor immobility generates twicethe decline in farm factor returns than in a sce­nario allowing factor mobility. In addition, dif­fering mobility assumptions can lead to largedifferences in the magnitude and even the signof world price changes when the U.S. and theEEC reform their farm policies.

Next on our agenda are two issues which mayappear mundane, but are critical for obtainingcredible welfare analyses from agriculturaltrade models. The first involves developing afull accounting of processing sector costs. Inthe model we examine, a number of sectorswere operating at significant losses. While thisis credible in the short run, it is not a validstarting point for medium-run, comparativestatic, equilibrium analysis. Any production ex­pansion in a sector incurring losses leads to a

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720 November 1994

reduction in welfare. Second, regional pricedifferentials must be consistent with underlyingmodel assumptions. The SD model we examineassumes homogeneous commodities but in­cludes regional price differentials that are notsupported by transportation costs or policywedges. This leads not only to a violation ofWalras' Law, but to incorrect welfare measure­ment.

Another area deserving further attention isthe proper specification of vertical marketingchannels of agricultural commodities. We focuson two particular issues: feed use in livestocksectors and marketing margins. The SD modelwe examine has an incomplete treatment offeed usage by livestock producers, with no sub­stitution allowed among feedstuffs. We illus­trate that the assumed degree of substitutabilityis a critical issue if one is interested in the sup­ply/demand/price of corn and soybeans, re­gional terms of trade, and welfare. Future workin this area should be directed at estimation ofsuch elasticities of substitution by sector andregion.

In our analysis of the SWOPSIM data base,we find that marketing margins for severalcommodities are much lower than other USDAestimates. Using smaller marketing margins,one risks overstating the welfare impacts of ag­ricultural policy liberalization by transmittingto consumers a larger portion of producer pricechanges. In addition, the predicted direction ofworld price changes from liberalization can beaffected by the marketing margin. Thus, furtherwork is needed in developing more accuratemargins for all commodities and regions. Suchwork would fit nicely with a research programdesigned to incorporate more off-farm detail inagricultural trade models.

Finally, we illustrate that SD model closure iscrucial to obtaining an accurate assessment ofthe effects of agricultural policy liberalization.While a general equilibrium closure is not cru­cial if one is interested in the farm-level effectsof liberalization, it is important for SD modelsto provide complete coverage of the agricul­tural sector.

[Received March 1993;final revision received April 1994.]

Amer. 1. Af;'r. Econ.

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