A Time-Series Analysis of the U.S. Durum Wheat and Pasta ...ageconsearch.umn.edu/bitstream/27818/1/32020001.pdf · A Time-Series Analysis of the U.S. Durum Wheat and Pasta Markets

A Time-Series Analysis of the U.S. Durum Wheat and PastaMarkets

Ronald A. Babula and Karl M. Rich

A quarterly, partial-equilibrium vector-autoregression model of the U.S. durum wheat and pasta markets was estimatedand simulated under three trade-barrier changes that are of potential relevance for the current round of WTO agriculturalnegotiations: a rise in the U.S. market-clearing durum wheat quantity from increased imports; a policy- or tariff-reduction-induced decline in U.S. durum wheat price; and a tariff-induced rise in U.S. pasta product prices. In responseto each shock, an array of quarterly dynamic response characteristics are examined: response reaction times, directionand pattern of quarterly responses, response durations, response multipliers, and strength of durum/pasta market inter-relationships.

The value-added side of the food industry hasoften been neglected as a topic for empirical re-search. One of the main reasons for this omissionis a lack of published data on these industries. Un-like commodities, such as corn, wheat, and soy-beans, the United States Department of Agricul-ture (USDA) and other agencies often do not pub-lish highly periodic (monthly or quarterly) data onquantities (demanded or supplied) or stocks ofvalue-added products. Moreover, food industriestypically keep information on their own prices, pro-duction, and distribution proprietary and thus outof the public purview. Any existing data on foodproducts typically concerns the retail side of theindustry. As a result there are few studies offeringempirical econometric parameter estimates that al-low policymakers, business strategists, and re-searchers to quantify and determine the monthlyor quarterly patterns of the impacts of market andpolicy changes on value-added products and theinteraction of value-added product markets withthose markets which produce commodities thatserve as inputs.

This paper addresses this gap in the literatureby examining the nature of the interactions that existwithin the durum wheat and pasta markets. Themarket-parameter and elasticity estimates from

Babula is an industry economist and Rich is an internationaltrade analyst with the Agriculture and Forest Products Division,U.S. International Trade Commission, 500 E. St., SW,Washington DC 20436. The opinions expressed herein arethose of the authors and not those of the U.S. InternationalTrade Commission or any of its Commissioners. This articleis a U.S. Government work, and as such, is in the public domainwithin the United States of America. The authors are gratfeulfor the comments and suggestions of three anonymousreviewers.

such analysis are then applied in the context of tradepolicy-for example, reduced tariffs or import re-strictions. These applications attribute a change inprice or quantity to a change in a trade barrier andthen map out the effects of the commodity marketfor durum wheat and the related downstream mar-kets (semolina and pasta). As with many compo-nents of the food industry, little or no publishedeconometric research exists to illuminate marketparameters of the U.S. durum wheat and pasta (dry,uncooked, non-egg)1 markets, or on the dynamicnature of the interactions of these two markets. Theprimary goals of this paper are to demonstrate howone can work around serious data deficiencies andapply vector-autoregression (VAR) econometricsto appropriately and effectively model U.S. durumand wheat and pasta markets, and to obtain marketparameters and market effects of proposed tradebarrier changes. In the following sections, this pa-per:

introduces the VAR methodology as an ap-propriate way of capturing dynamic inter-actions between a commodity market anddata-deficient commodity-using marketsdownstream generally and between theU.S. durum and pasta markets specifically.Focus is placed on how the VAR methods'reduced form properties are invoked to suc-cessfully work around severe data deficien-

'Herein, drypasta refers to the products included in HTS4-digit classification of 1902, in Chapter 19 of United StatesInternational Trade Commission (USITC 1999). Throughout,dry pasta refers to egg and non-egg dry, uncooked pasta, andexcludes fresh and frozen pasta as well as dry pasta incorporatedinto manufactured products.

Journal of Food Distribution Research

cies for downstream processed food mar-kets.

* specifies and estimates a reduced-formVAR model of the U.S. durum wheat andpasta markets. Emphasis is placed on howthe model meets rigorous diagnostic stan-dards of specification adequacy in order toshow that the reduced-form VAR model isappropriate and well-specified even in theface of severe data deficiencies and whatsome may deem abbreviated specificationsby structural model standards.

* uses the VAR model to simulate threeshocks likened to import-induced quantityincreases and price changes (perhaps frompolicy or tariff changes) in pasta and du-rum wheat prices. Focus is placed on thedynamic simulation results and market pa-rameter estimates which illuminate howsuch changes in policy or trade barriersaffect upstream and downstream marketsfor durum-dependent products, and howuseful such results would be to trade nego-tiators and policymakers. The results alsocapture the empirical nature of causal mar-ket relationships driving the U.S. durumwheat and pasta markets

*provides a summary, conclusions, and rec-ommendations for future research on thenature of U.S. durum wheat and pasta mar-ket reactions to proposed policy and tradebarrier changes implied by the imposedshocks that were simulated.

Motivation for the Study

A number of recent trade issues concerningdurum wheat and pasta demonstrate the need fortools that analyze the relationships between up-stream and downstream products in the context oftrade policy. One such issue concerns the imposi-tion of tariffs on imports of Italian and Turkishpasta. In 1995, the United States International TradeCommission (USITC) and the U.S. Department ofCommerce (USDOC) ruled affirmatively that cer-tain U.S. imports of Italian and Turkish dry pastawere materially injuring or threatening to injure theU.S. industry (USITC 1996). The USITC imposedcountervailing and antidumping duties (CVDs,

ADs) on such imports in 1995 and 1996 (USITC1996; Rich 1999, pp. 4.16-4.19).2 Remedial tariffpolicy is clearly a current issue for both the U.S.pasta market and the closely related U.S. durumwheat market. U.S. trade policy analysts wouldcurrently benefit-as they would during the CVD/AD investigations-from econometric estimatesand other empirical guidance regarding the marketparameters and dynamic relationships linking thesetwo markets. Such estimates and empirical guid-ance would permit analysts to better understand themarket effects of implementing and formulating theremedial pasta duties and the impact of proposedmeasures of future trade liberalization.

A second related issue revolves around the U.S./Canada wheat trade, which has been a longstanding,visible, and contentious issue for U.S. and Cana-dian Federal trade and agricultural authorities andpolicymakers (see Babula and Jabara 1999; Alston,Gray, and Sumner 1994 and 1999; Babula, Jabara,and Reeder 1996). There is currently increasingconcern by U.S. growers of wheat (particularlydurum wheat) over such issues. In September 2000the North Dakota Wheat Commission filed a peti-tion under Section 301 of the Trade Act of 1974against the Canadian Wheat Board (CWB) alleg-ing that the CWB is engaging in "unreasonable"trade practices which have resulted in economicharm to U.S. wheat growers; the U.S. Trade Rep-resentative initiated this investigation in October2000 (USTR 2000; Inside Washington Publishers2000a, b). The alleged practices were not specified

2 Imposed in 1996, these duties ranged as follows: ADrates from 0.67 percent to 46.67 percent and CVD rates from 0percent to 11.23 percent on subject imports of Italian pastaand AD rates from 56.87 percent to 63.29 percent and CVDrates from 3.87 percent to 15.82 percent on subject imports ofTurkish pasta (USITC 1996; Rich 1999, p. 4.16). Note thatthe U.S. International Trade Commission voted affirmativelyin the preliminary stages of the countervailing and antidumpingduty (CVD, AD) cases, imposing preliminary duties: CVDson October 17, 1995 and antidumping duties on January 19,1996. The Commission ultimately voted affirmatively in thefinal stages of the cases and imposed final duties in July andAugust of 1996. Duties are herein considered to have beenimposed from the onset of the preliminary duties. Hence boththe preliminary duties were imposed in the 1995/1996 June/May wheat market year: the second quarter for CVDs and thethird quarter for AD duties. Dates of imposition were providedby the cases' supervisory investigator in a USITC stafftelephone conversation with the authors on January 5, 2000.For an explanation of the workings of CVD and AD cases atthe U.S. International Trade Commission, see USITC (1998).

2 July 2001

U.S. Durum Wheat and Pasta Markets 3

but reportedly concern how the CWB marketswheat, particularly durum and hard red springwheat, in the United States and third country mar-kets (USTR 2000; Inside Washington Publishers2000a, b).3 These issues have also resulted in otherevents and investigations since the early 1990s: aUSITC competitive conditions study of the U.S.and Canadian durum wheat industries (USITC1990); a USITC Section-22 investigation of whetherU.S. imports of largely Canadian wheat materiallyinjured the U.S. wheat program (USITC (1994)); astudy by a U.S./Canadian Joint Commission onGrains of the U.S./Canadian wheat and grain trade(Canada/U.S. Joint Commission on Grains 1995;Reeder 1995); and separate temporary U.S. importquotas for durum and non-durum (mainly Cana-dian) wheat (Glickman and Kantor 1995; Reeder1995).

Seidband (1999, pp. 1-2) recently noted agrowing concern over increasing U.S. imports ofCanadian durum wheat, as market year (MY) 1998/99 (i.e., June 1998-May 1999) imports surged byabout one- third from previous MY levels at a timewhen U.S. production was at a five-year high(Seidband 1999, pp. 1-2). Seidband also notes thatwhile U.S. pasta exports to Canada are rising, Ca-nadian durum wheat sales to the United States farexceed U.S. pasta sales in Canada in value; in fact,the value of Canada's pasta sales in the U.S. mar-ket exceeds the value of the U.S. pasta exports toCanada, with Canadian durum wheat sales to theU.S. notwithstanding (Seidband 1999, pp. 1-2). 4

Osorio (1999) noted an increased interest inmodeling the trade-induced impacts not only on themarkets for raw commodities (e.g. grains) but ondownstream markets that use these raw commodi-ties. He contended that this is because downstream

3 According to the U.S. Trade Representative (USTR

2000), the Section 301 investigation provides a means for U.S.business, farmers, and workers to solicit Federal Governmentaid in obtaining relief from burdensome and U.S. commerce-restricting trade practices. After an investigation that can lastas long as one year, USTR has discretion on whether or not totake retaliatory action (Inside Washington Publishers 2000b;USITC 1998, p. 29).

4 It should be noted that the majority of pasta importsfrom Canada in 1998 were produced by an Americanmultinational that had moved a portion of its capacity fromthe United States to Canada. Before 1998 most pasta importsfrom Canada were produced by Canadian firms. See Rich(1999, p. 4-18).

value-added products are often subject to highertariff and nontariff barriers than raw commodities;indeed, the current round of WTO agricultural ne-gotiations may well focus on such commodity-based value-added markets (Osorio 1999). Tradein wheat-derived products such as pasta may thusbe as important an issue as trade in durum wheatduring the current WTO agricultural round. Tradereforms in processed products such as pasta and itscomponent inputs (in this case durum wheat andsemolina) will have some level of impact along theproduction chain. Yet for trade negotiators to ne-gotiate optimally they must also be able to identifythe empirical and dynamic nature of the direct orprimary market effects of a proposed barrier re-duction and should know the magnitude and na-ture of any upstream or downstream effects in theeconomy.

Tools and modeling methods that providepolicy makers and negotiators with empirical esti-mates of and empirical guidance concerning theeffects of proposed changes in trade barriers on U.S.durum wheat and pasta markets are urgentlyneeded, but data limitations have precluded theconstruction of such models and have thus pre-cluded the provision of such empirical estimatesand guidance. The next sections will introduce theVAR methodology as an appropriate means to workaround these data deficiencies and apply the avail-able data to the study of the dynamics in the durumwheat and pasta markets in the context of tradepolicy.

VAR Econometrics

Under general conditions an m-component vec-tor, indexed by time period t, admits anautoregressive representation expressed as

(1) x(t)= Z [b(s)*x(t-s) + e(t)s=1

where the S with subscripts and superscripts de-notes the summation operator from lag period 1through infinity (oo). Underlined characters repre-sent matrices or vectors. Theb(s) term is an m bym matrix of autoregressive (AR) regression coeffi-cients, and e(t) is an m-element vector of whitenoise residuals or innovations (Bessler 1984, Sims1980). The white noise nature of e(t) satisfies

Babula, Ronald A. and Karl M. Rich


(2) E(e(t))= 0 for all t, and

(3) E(e(t)*e(t)')= 0 for t• s;= S, a positive-definite, m*m co-

variance matrix, if t=s.

"E" denotes the expected value multiplier. Forapplied work, the infinite lag sequence of equation1 must be small enough to be operational but largeenough for the residuals to approximate white noise(Bessler 1984, p. 112; Hamilton 1994, ch. 11). Alag structure was chosen with methods presentedbelow.

Compared to more conventional "structural"econometric models, VAR econometrics is an ap-proach that reveals empirical regularities from time-ordered data. The approach imposes as few aprioritheoretical restrictions as possible on the data.Rather, VAR models loosely utilize theory to sug-gest which variables constitute a dynamic systemin equation 1 and permits the regularities embed-ded in the time-ordered data to reveal themselves(Bessler 1984). All variables in the system are ini-tially considered endogenous and each variable in-fluences itself and all other variables in the systemwith lags.

Strength of relationships among a model's en-dogenous variables may be revealed through a sec-ond tool or operation of VAR econometrics-theanalysis of forecast error variance (FEV) decom-positions. By strength of relationships we meanwhether or not one variable's movements causechanges in another variable's movements, the de-gree of such influence, and the timing of such ef-fects (Bessler 1984, p. 111). FEV decompositionsare, at alternative horizons, attributed to shocks ineach of the dynamic system's series such that thedesired measurement of strength of relationshipsemerges (Bessler 1984, p. 117). Bessler (1984, p.1I1) established that FEV decomposition analysisis closely related to Granger causality testing. Yetunlike Granger causality testing, which simply dis-cerns whether a causal link between variables ex-ists, analysisofFEV decompositions provides fur-ther information on the timing and strength of suchcausal relationships (Bessler 1984, pp. 111-117).

VAR Model Specification, EconometricEstimation, and Goals of Estimation andSimulation

This paper's purpose is to reveal the dynamicand empirical nature of the market parameters andrelationships that drive and interrelate the U.S. du-rum wheat and pasta markets. This is done by esti-mating a quarterly VAR model of relevant U.S.durum wheat and pasta market relationships, simu-lating the model's impulse-response function withshocks attributable to notable current trends or pro-posed trade barrier changes, and analyzing themodel's FEV decompositions.

VAR Model Specification

To capture U.S. durum wheat and pasta mar-ket relationships, a VAR model of the followingvariables (hereafter denoted interchangeably by theparenthetical labels) was specified and estimated:1. U.S. durum wheat price (PDURUM): This is

reflected by the per-bushel dollar price of Min-neapolis no. 1 hard amber durum wheat pub-lished by the USDA's Economic Research Ser-vice (USDA, ERS 1999b, p. 73). Quarterly MYvalues were calculated from published monthlyvalues.

2. Durum wheat quantity supplied to and con-sumed in the U.S. market (QDURUM): This isreflected by the sum of the unpublished quar-terly values of beginning stocks, imports, andproduction, obtained from the Economic Re-search Service (USDA, ERS, 1999a).

3. U.S. semolina price (PSEMOL): This price wascalculated into quarterly MY prices using themonthly prices in dollars per hundredweightof semolina in Minneapolis obtained from Mill-ing and Baking News (M&BN 1999).

4. U.S. price of pasta products (PPASTA): Thisprice is reflected by the producer price indexof pasta products, Series Number WPU021402,from the U.S. Department of Labor, Bureau ofLabor Statistics (Labor, BLS 1999). QuarterlyMY values were calculated from publishedmonthly values.Detailed derivations and summaries of VAR

econometric methods are provided by Sims (1980),Bessler (1984), and Hamilton (1994, ch. 11). AVAR model posits each of the above four endog-

4 July 2001


enous variables as a function of a specified num-ber of lags of all four variables (Sims 1980; Bessler1984; and Hamilton 1994, ch. 11). Schwarz's(1978) and Tiao and Box's (1978) lag selection cri-teria applied to the above data both suggested a one-order lag structure. The four-equation VAR is speci-fied as:

(4) X(t) = a0 + a 1*PDURUM(1) +a,*QDURUM(1) + a *PSEMOL(l) +ax4*PPASTA(l) + aT*TREND + RX(t)

The parenthetical numbers refer to a value'stime period: t for the current period and 1 for theone-order quarterly lagged value. The X(t) =PDURUM(t), QDURUM(t), PSEMOL(t), andPPASTA(t). The 0-subscript refers to the intercept.The a-coefficients reflect regression parameter es-timates. The x-subscript denotes the x-th equation,the T-subscript refers to the time trend (TREND),and the second numeric subscript refers to each ofthe four lagged regressors. RX(t) refers to the cur-rent period t-estimates of the x-th equation's whitenoise residuals.

Quarterly data on the four endogenous variableswere available from the first quarter of market year1985 (hereafter 1985:1) through the fourth quarterof market year 1998 (or 1998:4).5 The model wasestimated over MY 1986:1-1998:4, because the fourquarterly MY 1985 observations were "saved" foruse in the Tiao-Box and Schwarz lag searches. Forwell-known reasons detailed by Bessler (1984) andSims (1980) the four-equation VAR model wasappropriately estimated with ordinary least squares.Doan's (1996) package, RATS, was used.

A number of considerations governed the VARmodel's specification and estimation. First, themodel was estimated in natural logarithms soshocks to and impulse responses in the logged vari-ables reflect approximate proportional changes in

5 The U.S. wheat market year runs from 1 June of a yearthrough 31 May of the following year. Throughout, thenumerals right of a quarterly date's colon refers to the MYquarter. For example, 1998:1 refers to the quarter spanningJune, July and August; 1998:2 refers to the quarter spanningSeptember, October, and November; 1998:3 to the quarterspanning December 1998 and January and February 1999; and1998:4 to the quarter spanning March, April, and May of 1999.For quarterly market-year dates only the first year is listed;1998/99 market year is taken as MY1998 and its quarters as1998:1 through 1998:4.

the nonlogged variables. Second, dynamic resultswere desired, requiring quarterly (preferablymonthly) data. Quarterly data were used becausemonthly QDURUM data are not available (USDA,ERS 1999b, pp. 42-47).

Third, unavailability of quarterly (or monthly)data on U.S. supply, consumption, shipments, orstock quantities of pasta precluded the inclusion ofquantity variables for the pasta market downstreamfrom the durum wheat market. While inclusion ofpasta price and quantity variables would have beenpreferable, such pasta quantity data does not exist.In fact, such production data are no longer avail-able even on an annual basis. Previous research andliterature demonstrates how VAR econometricmodels, beset with data unavailability, may rely ona VAR model's reduced-form qualities and on thetheory of the stochastic process to capture amarket's forces of demand and supply through in-clusion of a single price equation (Hamilton 1994,pp. 324-329; Babula 1996, p. 71). The diagnosticspresented below show that the PPASTA equationappears to be an adequately specified reduced-formrelation that likely reflects as much of the down-stream pasta demand and supply conditions as lim-ited data sources will allow. But further, and per-haps most importantly, this method of invokingVAR model reduced-form attributes is a viableeconometric way to obtain empirical estimates ofhighly periodic market relationships given the un-availability of quarterly stock and quantity data formany downstream wheat-based products (Babula2000).

Fourth, reliable quarterly data on U.S. supply,consumption, or stocks of semolina were not avail-able.6 The VAR model's reduced-form attributes

6 A quarterly market-year variable for U.S. semolinaproduction was assembled from 1985-1996 monthly data and1997-1999 quarterly data provided by the U.S. Department ofLabor, Bureau of the Census (Labor, Census 1985-1999).However, the variable did not exhibit "rational" relationshipswith semolina price. In all simulations the quantity of U.S.semolina rose and fell (moved positively) with semolina price.According to reliable industry sources in a telephoneconversation with the authors (18 January 2001), the reliabilityof this quantity variable is suspect because of allegedincompleteness of the surveys used to gather data. Thisconversation's contentions were confirmed by staff of Millingand Baking News (2000) in a front-page article concerninginaccuracies of the data. Sources for this data included Labor,Census (1985-1999). Calendar-year quarterly data wereobtained from the quarterly reports for 1997-1999 (Commerce,



were again invoked to have a single reduced-formPSEMOL price equation capture the demand andsupply elements of the U.S. semolina market. Aswith PPASTA, diagnostic evidence presented be-low suggests that PSEMOL is adequately specified,and that the reduced-form relationship likely cap-tures as many of the market's demand and supplyelements as limited data resources permit.

A number of binary (dummy) variables wereconsidered. Following previous quarterly econo-metric research on U.S. wheat markets, centeredseasonal binary variables were included to captureexogenous seasonal influences (Babula 2000;Babula, Jabara, and Reeder 1996; USITC 1994, pp.11.80-11.96 and appendix N). A binary variable, de-fined as 1.Q for 1995:2 and subsequent quarters andzero otherwise, was initially included in each VARequation to account for the influences of the pre-liminary and final antidumping and/or counter-vailing duties imposed in 1995-1996 on certain U.S.imports of Turkish and Italian dry pasta (see foot-note 2 and USITC 1996). We ultimately excludedthis binary from all equations because evidence atthe five-percent-significance level suggested thatthe coefficients were statistically zero in all fourcases.

VAR Model Estimation and Diagnostics

VAR model specified above was chosen over mod-eling the system as a vector error correction (VEC)model using Johansen and Juselius' (1990, 1992)maximum likelihood methods. When a vector sys-tem of individually nonstationary variables movesin tandem and in a stationary manner, the variablesare said to be cointegrated (Johansen and Juselius1990, 1992). With more than two cointegrated vari-ables one should model the vector system as a VECmodel with Johansen and Juselius' (1990, 1992)maximum-likelihood methods. Cointegration wasnot an issue here because evidence from severalunit root tests suggested that the four endogenousvariables are likely stationary in logged levels.

Two well-known unit root tests were appliedto the logged levels of the VAR model's four en-dogenous variables: The Sargan-Bhargava (SB) testand the Dickey-Fuller (DF) test (or the augmentedDickey-Fuller [ADF] test where appropriate). TheDF or ADF T[1 tests were applied to all variablesexcept PDURUM, and the DF or ADF TT test wasapplied to PDURUM. 7 Harris (1995, pp. 27-29) andKwiatowski, Phillips, Schmidt, and Shin (1992)discuss the well-known DF-type test problems ofgenerating false conclusions of nonstationarity,particularly when samples are-as in this study-finite and when variables are stationary but haveroots that are near-unity (i.e., variables which are

A number of important points concerning theadequacy of the estimated model's specification aremade. Among these are the rationalization of thechoice of a VAR model in logged levels over a vec-tor error-correction model; adequacy of VAR modelspecification supported by an established batteryof diagnostic test results; and evidence of time-in-variance of parameters.

Issue of cointegration. Enough evidenceemerged from the logged levels data to suggest thatcointegration among the four logged endogenousvariables may not be an issue. Consequently, the

Census 1997-1999). Monthly data for 1990-1996 wereobtained from the annual summary issues (Commerce, Census1990-1996). These monthly data for 1990 -1996 were thenconverted to calendar-year quarterly values. Monthly 1985-1989 data were obtained from monthly issues of Commerce,Census (1985-1990). These 1985-1989 monthly data werealso converted to calendar year quarterly values. The 1985-1999 quarterly data were then converted to a market yearstarting in July of one year and extending through June of thenext year.

7 Throughout, levels data and first differences are levelsand first differences of the data in natural logarithms. TheSargan-Bhargava (SB) test is outlined in Sargan and Bhargava(1983); the Dickey-Fuller (DF) I: and IJ tests are detailed inFuller (1976) and Dickey and Fuller (1979), while the"augmented" forms of the DF tests are outlined in Hamilton(1994, pp. 516, 528). The SB test entails running a regressionof the variable against a constant-and a trend whenappropriate-and concluding that evidence at the 5-percent-significance level is sufficient to reject the null hypothesis (null)of nonstationarity when the Durbin-Watson value equals orexceeds 0.26. The DF IJ or I: tests entail running a regressionof a variable's first differences against a constant and a lag ofthe differenced dependent variable, with and without a trend,respectively. The augmented forms of these two tests, the ADFI: and IJ tests, are often used where at least one lagged dependentvariable is included in the DF I: or DF IJ regression. In thispaper the number of lagged dependent variables was determinedusing Akaike's (1973) information criterion program in Doan(1996, p. 5.18). Evidence is sufficient in the DF or ADF testswhen the pseudo-t (I: or IJ) value on the non-differenced laggedregressor is both negative and of an absolute value exceedingthe following critical values: -2.89 (5-percent level) and -2.58(10-percent level) for the I: test, and -3.45 (5-percent level)and -3.15 (10-percent level) for the IJ tests (Hamilton (1994).

6 July 2001


almost nonstationary). 8 In order to avoid errone-ous treatment of such "almost nonstationary" vari-ables as nonstationary, Kwiatowski et. al. recom-mend their KPSS test, which has versions with andwithout a time trend, as complementary evidencewhen variables generate marginal or ambiguous DFtest evidence of nonstationarity. We placed in-creased reliance on KPSS test results in such mar-ginal or ambiguous cases. Evidence from the SB,DF or ADF, and KPSS tests applied to the loggedlevels of the four endogenous variables suggests,on balance, that all series are stationary. 9

8 Harris (1995, pp. 27-29) notes that DF-type unit roottests often fail to reject the null hypothesis of nonstationaritywhen samples are small and/or when time series are stationarybut have near-unity roots. Kwiatowski et. al. (1992) furthercontend that classical hypothesis testing usually requires strongevidence to reject a null hypothesis, which is nonstationaryfor the DF-type tests, and note that using their KPSS test withthe opposite null, a null of stationarity, is useful as acomplementary test for consideration with marginal orambiguous DF test results. We followed this recommendationand complemented the DF and/or ADF tests with the KPSStest and placed increased reliance on the latter when the DF orADF test results were marginally or ambiguously indicativeof nonstationarity. The KPSS test was used to "break the tie"in such cases.

9 SB and DF evidence at the 5-percent-significance level (5-percent level) is sufficient to reject the null that QDURUM isnonstationary: the SB test value of 1.39 exceeds the criticalvalue of 0.26 and the DF I: test value of-5.7 exceeds in absolutevalue the critical value of-2.89.

PSEMOL's SB and ADF test evidence is marginallysufficient to reject the null of nonstationarity: while the SBtest value of 0.30 exceeds the 0.26 critical value (5-percentlevel) the ADF I: value of-2.5 falls below in absolute valuebut nearly equals the critical value of-2.58 (1 0-percent level).Complementary KPSS test evidence at the 5-percent level isinsufficient to reject the null that PSEMOL is stationary, asthe test value of 0.254 falls below the critical value of 0.463.Coupled with the marginal SB- and DF-type evidence, thisKPSS test evidence led to our conclusion that PSEMOL islikely stationary.

SB and ADF evidence concerning PPASTA's stationarityis ambiguous. While the ADF I: test value of-2.6 exceeds inabsolute value the critical value of -2.58, suggesting thatevidence at the 10-percent level is sufficient to reject the nullof nonstationarity, the SB value of 0.09 falls below the critical0.26 critical value, suggesting that evidence at the 5-percentlevel is insufficient to reject the null of nonstationarity. DFevidence suggests stationarity, but only at thel O-percent level,while SB evidence is insufficient to conclude stationarity.However, complementary KPSS test evidence at the 1-percentlevel fails to reiect the null of PPASTA's stationarity sincethe test value of 0.64 falls below the critical value of 0.739.The ambiguous evidence from the DF and SB tests combined

Estimation andAdequacy of Specification. Fol-lowing Babula et al. (1998) the model was judgedadequately specified based on evidence generatedby Ljung-Box portmanteau and DF unit root testson the residual estimates of the four VAR equa-tions. The Ljung-Box "Q" statistic is used to testthe null hypothesis that the equation has been ad-equately specified with the null rejected for highQ-values (see Granger and Newbold 1986, pp. 99-101). Granger and Newbold (1986, pp. 99-101)caution against the exclusive reliance on the port-manteau tests for model adequacy. Consequently,DF Tii unit root tests were conducted on each VARequation's residual estimates since stationary re-sidual estimates provide evidence of adequatemodel specification (Babula et al. 1998, pp. 44-45). Evidence at the 1-percent-significance levelfrom both tests suggests that all four equations havebeen adequately specified.' 0

Time Invariance of Regression Parameter Es-timates. Two market developments may have in-duced structural change or time variance of param-eters during the 1986:1-1998:4 quarterly sample:developments in U.S. consumer demand for pastaproducts during the mid-1990s and the previously

with the KPSS evidence of stationarity led to our conclusionthat PPASTA is likely stationary.

PDURUM's SB and ADF test evidence is ambiguousconcerning the acceptance or rejection of the null ofnonstationarity: while SB's test value of 0.29 exceeds thecritical value of 0.26 which rejects the null at the 5 percentlevel, the ADF IJ value of-1.8 fails to exceed the critical valueof -3.15 and to reject the null of nonstationary at even the 10-percent level. However, complementary KPSS test evidenceat the 5-percent level fails to reject the null of stationarity,since the test value of 0.075 is less than the critical value of0.146 (note the KPSS test version inclusive of a trend wasused for PDURUM). Taken with the ambiguous SB and DF-type evidence of stationarity the KPSS test evidence ofPDURUM's stationarity led to the conclusion that the series islikely stationary.

'0 The VAR model's four equations generated Ljung -Box (Q) values ranging from 11.2 to 19.7, which are all lessthan the critical chi-square value of 27.7 (13 degrees offreedom; 1-percent significance), suggesting that evidence inall four cases is insufficient to reject the null hypothesis ofadequate specification (Granger and Newbold 1986, pp. 99-101). The four equations' residuals generated DF I: values thatranged from -6.0 to -8.4, which are all negative and haveabsolute values in excess of those of the -3.51 critical value,suggesting that evidence at the 1-percent-significance level issufficient to reject the null hypothesis of nonstationary residualsand to conclude that all four equations are likely adequatelyspecified.



mentioned 1995 and 1996 imposition of antidump-ing and countervailing duties on certain U.S. im-ports of Italian and Turkish dry pasta. Existence ofstructural change generally signifies that marketrelationships embedded in the regression coeffi-cients have changed such that the regression esti-mates themselves vary over time and that the coef-ficients estimated over the entire sample period areinvalid. Existence of structural change usually re-quires division of the sample into subsamples atjunctures of the change's occurrence andreestimation of the model separately for thesubperiods (Larue and Babula 1995, pp. 163-164).If patterns of change-perhaps from the consumerdevelopments and/or imposition of antidumpingand countervailing duties-were not adequate toinduce structural change and time variance of pa-rameters, it is appropriate to estimate over the en-tire sample period and proceed as if parameter es-timates are time invariant (Babula 1997).

Developments in consumer demand for pastain the mid-to-late 1990s in the United States couldhave induced structural change or time-variance ofparameters over the 1986:1-1998:4 quarterlysample. The growth of consumption in the pastamarket, which accelerated during the early 1990s,slowed considerably in the mid-1990s. Accordingto Leath (1999), per-capita consumption of semo-lina-based products (mainly pasta) peaked in 1994at 13.9 pounds per person, and has since fallen to12.5 pounds per person in 1997 and 11.6 poundsper person in 1998. These trends are reflected inthe sluggish sales figures for pasta, with dry pastasales down 4 percent over 1997-98 (Food InstituteReport 1998: 5). Moreover, consumer tastes maybe changing, as evidenced by the pasta industry'sdevelopment of new niche markets such as flavoredpasta, organic pasta, and dessert pastas (Sjerven,1996, pp. 30-31), and by new applications of pastaproducts, including complete pasta dinners and non-Italian food applications of pasta (Bloom 1997, pp.30 and 34).

A second potential source of structural changein the pasta industry concerns the CVD and ADduties petitioned by the U.S. industry and imposedon selected U.S. imports of Italian and Turkishpasta. In 1996, the USDOC assessed CVD ratesranging from 0 to 11.23 percent on certain Italianpasta manufacturers and from 3.87 to 15.82 per-cent on certain Turkish manufacturers (Rich, 1999:

4-16). Antidumping margins imposed on certainItalian pasta manufacturers (weighted-average less-than-fair-value margins) were much larger andranged from 0.67 to 46.67 percent, while these ADmargins for Turkish manufacturers ranged between56.87 and 63.29 percent.

The antidumping and countervailing duties(ADs and CVDs) imposed on certain imports ofItalian and Turkish pasta may have induced struc-tural change by eliciting changes in the U.S. mar-ket shares captured by foreign suppliers and bychanging the volumes of imported product. This isbecause, as is well known, the ADs and CVDs im-posed on Italian and Turkish pasta were not time-enduring and equivalent duties levied on all sup-plies of each affected country (USITC 1993, pp. 8-8; 1996). Instead, the orders reflected an array offirm-specific duties that varied across each affectednation's suppliers and were not necessarily imposedon all of each country's dry pasta exports to theUnited States. Further, these duties vary annuallyas the U.S. Department of Commerce reviews andadjusts the firm-specific duties in accordance withthe remaining dumping or subsidy margin.'1

Following established econometric researchprocedures, each estimated equation was subjectedto a two-tiered structural-change test method thatcombines the CUSUM/CUSUM-squared and Chowtest procedures (Larue and Babula 1994; USITC1997, pp. 5.54-5.66 and appendix G; and Babulaet. al. 1998 pp. 45-46). In the first tier the recur-sive residuals for the VAR equations were gener-ated using Doan's (1996) RATS software and thedata-analytic CUSUM/CUSUM-squared plot-testmethods detailed in Harvey (1990, pp. 153-155)were applied to each equation's recursive residu-als to discern potential points or junctures of struc-tural change. Three junctures of potential structuralchange were prescribed by the CUSUM/CUSUM-squared tests: two breaks at 1991:1 and 1993:2 forthe PSEMOL equation and one break at 1997:2 forthe PPASTA equation. In the second tier a Chowtest for structural change was conducted for therelevant equation at each potential juncture ofchange indicated by the CUSUM/CUSUM-squaredtests (see USITC 1997, pp. 5.54-5.66 and appen-

" For details on how an AD and/or CVD order isimplemented, see USITC (1998, pp. 8-9; 1995; 1996). Thesedetails relevant here are concisely summarized in Babula (1997,pp. 82-83).

8 July 2001


dix G; Babula et al. 1998, pp. 45-46; Larue andBabula 1994, pp. 163-164). One concludes that nostructural change occurred if the Chow F-test gen-erates evidence at the one-percent level that is in-sufficient to reject the null of no structural changeat each potential juncture (Larue and Babula 1994,pp. 163-164). Evidence at the one-percent-signifi-cance level was insufficient to reject the null hy-pothesis of no structural change for the semolinaprice equation at 1991:1 and 1993:2 or for the pastaprice equation at 1997:2.12 The failure of the latterperiod is especially noteworthy given that the pe-riod 1997:2 is conceivably a potential juncture atwhich market effects from the CVDs and ADs im-posed on certain U.S. imports of Italian and Turk-ish pasta in 1995 and 1996 could have becomemanifest. 13

Competing circumstances could have contrib-uted to the lack of evidence supporting structuralchange, particularly with respect to pasta. On theconsumption side the lack of evidence supportingstructural change implies that the decline in theconsumption of pasta may simply have resultedfrom lower demand for dry pasta rather than froma wholesale change in the structure of the market.From the standpoint of the CVD and AD duties,the failure to induce structural change is more in-teresting and likely due to a number of simulta-neously occurring events that undermined any pos-sibility for the market to be significantly altered.Results from the USITC (1996, pp. II. 1 and II.15)suggest that there are moderate to high degrees ofsubstitutability between both U.S.-produced andimported dry pasta and between dry pasta supplies

12 The Chow F-tests at the 3 potential junctures ofstructural change were conducted at the 1-percent-significancelevel. For PSEMOL the test value of 1.71 fell below the criticalF-value of 2.97 (9 and 34 degrees of freedom) for the 1991:1break and the test value of 1.87 fell below the critical F-valueof 2.97 (9 and 24 degrees of freedom) at the 1993:2 break. ForPPASTA the test value of 0.67 fell below the critical F-valueof 3.18 (7 and 36 degrees of freedom). In all 3 cases the testvalues fell below the critical F-values, suggesting insufficientevidence to reject the null hypothesis of no structural change.

13 As noted earlier, binary variables were included tocapture effects of the imposed ADs and CVDs imposed in 1995and 1996 on certain U.S. imports of Turkish and Italian drypasta. The statistical insignificance of this binary variableinitially included in each VAR equation supports this evidencethat suggests an absence of structural change and of timevariance of parameters.

from competing assessed and non-assessed foreigndry pasta suppliers. It is also noteworthy that notall Italian or Turkish manufacturers were assessedAD or CVD duties. Given this degree of substitut-ability it is possible that the share of the U.S. pastamarket lost by assessed Italian and Turkish suppli-ers may simply have been offset by increased U.S.sales of close substitutes by non-assessed foreignsuppliers from Italy, Turkey, and other countriessuch as Canada. Indeed, Rich (1999) noted thatwhile U.S. imports of assessed Turkish pasta sup-pliers plummeted during the 1996-1998 period fol-lowing the duties' imposition, imports of non-as-sessed Italian suppliers rose along with U.S. salesby foreign suppliers from other countries-mostnotably Canada-to the point that U.S. aggregatevolumes of pasta imports were not reduced for sus-tained periods after imposition of the duties (Rich1999, pp. 4.17-4.19).

Three Model Simulations with the ImpulseResponse Function

One aspect of VAR econometrics useful in ap-plied work is the impulse-response function. Theimpulse-response function simulates over time theeffect of a one-time shock in one of the system'sseries on that series and on the other series in thesystem. This is done by converting the VAR modelinto its moving average (MA) representation(Bessler 1984; Hamilton 1994, ch. 11). The param-eters of the MA representation are complex non-linear combinations of the VAR regression coeffi-cients. By imposing a one-time exogenous shockon one of the VAR variables on the system, onemay examine the quarterly impulse responses ofthe other respondent endogenous variables and dis-cern what the sample's long-run and historicaltrends would generate as the five dynamic andempirical attributes of U.S. durum wheat and pastamarket relationships and market interactions(Bessler 1984; Babula et al. 1998; and Hamilton1994, ch. 11). These include (a) reaction times ofquarterly responses of affected or "respondent"variables, (b) directions and patterns of quarterlyresponses, (c) response durations, (d) magnitudesof overall responses, and (e) strength of relation-ships among U.S. durum wheat and pasta marketvariables (Bessler 1984).

Babula, Ronald A. and Karl M Rich


The model's four equations may have contem-poraneously correlated current errors or innova-tions. Failure to correct for contemporaneously cor-related current errors may produce impulse re-sponses that do not reflect historical patterns (Sims1980; Bessler 1984). Procedures from previous re-search using quarterly reduced-form U.S. wheatmodels were followed and a different Choleski de-composition was imposed on each of the model'sthree simulations described below (Babula 2000;USITC 1994, pp. II.80-II.96 and appendix N). AsSims (1980) and Bessler (1984) note, the Choleskidecomposition resolves the issue of contempora-neously correlated current innovations. Each de-composition requires an arbitrary imposition of atheoretically-based Wold causal ordering amongthe current values of the model's four variables ineach simulation. These three orderings are pre-sented below.

Using literature-established methods, multipli-ers are calculated from each simulation's statisti-cally nonzero impulse responses (see Babula,Coiling and Gajewski 1994, p. 380). The multipli-ers are similar to elasticities and indicate history'slong-run average percentage change in a responsevariable per percentage change in the shock vari-able. Sign is important: a positive multiplier sug-gests that each percentage change in the shock vari-able directionally coincided with the shock vari-able changes (hereafter, "similarly directed" re-sponses), while a negative multiplier suggests thata variable's response was in the opposite directionof the shock (hereafter, "oppositely directed" re-sponses).

Following previous VAR econometric re-search, Kloek and Van Dijk's (1978) Monte Carlosimulation methods were used to generate t-statis-tics for the impulse responses (Babula 2000; Babulaet al. 1994, 1996, 1998; and USITC 1994, pp. II.80-II.96 and appendix N). We focused our analysis onthe impulse responses which were statistically non-zero at the 1 O-percent-significance level.

The VAR model's impulse-response functionwas simulated with three shocks discussed below.Following accepted procedures in the literature, im-posed shocks are of the magnitude of a single stan-dard error of the variable's innovation (Bessler1984; Babula et. al. 1994). Nonetheless, the modelis linear, so the chosen size of the shock is arbi-

trary. 14 Three VAR model simulations were cho-sen:' 5

Simulation 1: a one-standard-error (11.5 percent)rise in QDURUM to examine the dynamic as-pects of the elicited quarterly responses inPDURUM, PSEMOL, and PPASTA. Thisshock could be the result of increased domes-tic production or imports from relaxed quan-tity quota restrictions.

Simulation 2: a one-standard-error (10.9 percent)decline in PDURUM to examine the dynamicaspects of elicited quarterly responses inQDURUM, PSEMOL, and PPASTA. ThisPDURUM shock could arise from a reductionin import tariffs.

Simulation 3: a one-standard-error (2.37 percent)rise in PPASTA to examine the dynamic as-pects of the elicited quarterly responses inPSEMOL, PDURUM, and QDURUM. Thisshock could arise from raising the zero importduty on dry pasta, especially given that domes-tic and imported pasta products are highly sub-stitutable.Table 1 provides the dynamic and empirical

aspects of the responses generated from these threereduced-form model simulations which could pos-sibly arise from, among other things, a rise in U.S.durum wheat market access (simulation 1), a de-cline in the U.S. tariff on durum wheat (simulation2), and a tariff-induced rise in U.S. pasta price(simulation 3). It is important to point out that thereis some subjective leeway in identifying the sourceof the shocks imposed on this (or on another) re-duced-form model. While the assumed sources ofthe shocks in the simulations above are valid, theshocks could have arisen from other sources, sincethe VAR model's estimated reduced-form relationsare neither prices nor quantities supplied or de-

14 Babula et al. (1994, p. 377) pointed out that because ofa VAR model's linearity one can characterize the impulseresponse simulations of a 20 percent shock simply bymultiplying the impulse responses from a 10 percent shock bythe scalar 2.0. Likewise, one can obtain the simulation resultsfor a negative shock by multiplying the results from thesimulation of a positive shock by the scalar -1.0.

15 As required for each simulation's Choleskidecomposition, a theoretically-based Wold causal ordering ischosen for each simulation. The orderings are those reflectedin the descriptions of each simulation with the shock variableplaced atop each ordering. So for example, simulation 1'sordering is QDURUM, PDURUM, PSEMOL, and PPASTA.

10 July 2001


Table 1. Dynamic Aspects of Responses to Shocks in QDURUM, PDURUM, and PPASTA

Reaction Patterns ofRespondent times Response quarterly Responsevariable (quarters) directions responses Durations Multipliers

Simulation 1: Rise in durum wheat quantity (QDURUM)

PDURUM 0 quarters decline U-shaped 4 -0.90

PSEMOL 0 quarters decline sharp, then 4 -0.88decaying

PPASTA NSR

Simulation 2: Decline in durum wheat price (PDURUM)

QDURUM 0 quarters increase U-shaped 7 -0.59

PSEMOL 0 quarters decline sharp, then 8 0.96decaying

PPASTA 1 quarter decline increasing 5 0.08magnitudes

Simulation 3: Rise in pasta price (PPASTA)

PSEMOL NSR

PDURUM NSR

QDURUM 0 quarters decrease U-shaped 5 -2.91

Notes: Impulse responses were considered statistically nonzero at the 10%-significance level. "NSR" denotes that there were noimpulse responses that were statistically nonzero at the 10%-significance level.

manded but market clearing prices which emergeafter a full interplay of all-and often counterbal-ancing-demand and supply adjustments (Babula2000). Other sources could have given rise to thesame shocks. For example, simulation 1 's shock ofa presumed rise in QDURUM from increased im-ports could be explained by a rise in productionwhile presumed tariff-induced shocks in wheat andpasta prices for simulations 2 and 3, respectively,could also have arisen from changes in productioncosts. A shock in a reduced-form model's price orquantity can therefore have a number of valid hy-pothesized sources. We chose to look at theseshocks in the context of change in trade policy (e.g.changes in import, tariff levels, or negotiated

changes in a quantity- or price-influencing farmpolicy).

Simulation 1: An Imposed Rise in Durum WheatQuantity (QDURUM).

An increase in durum wheat quantity, perhapsresulting from a surge in U.S. durum wheat pro-duction or highly substitutable imports, was im-posed on the model. The QDURUM increase in-duces a series of oppositely directed durum priceresponses (declines), which have what is hereinconsidered an "immediate" or zero-quarter reac-tion time-that is the responses begin during thesame quarter as (within 89 days of) the imposed



shock. These declines take a "U-shaped pattern"-that is, a pattern of declines that initially acceler-ate, level off, and then decelerate in (absolute)magnitude. The PDURUM declines last an aver-age of four quarters. The multiplier of -0.9 sug-gests that each one-percent rise in QDURUM elic-its an average decline of 0.9 percent in durum wheatprice. The reduced-form model cannot explain thereasons for the U-shaped response pattern forPDURUM, and we appeal to theory and marketknowledge for such explanations. In the context oftrade it is plausible that an injection of durum wheatimports, for example, will cause a decline in theprice of durum wheat since the market will be sup-plied with both domestic and imported durumwheat. Domestic farmers, upon seeing the lowerprices for durum wheat, may reduce production inan attempt to raise prices, but the effect of this be-havior will occur with a lag, given the planting de-cisions involved. Prices will therefore fall untildomestic planting decisions (namely, to lower pro-duction) have been accounted for in the market-place, providing the bell-shaped shock in this mar-ket. So a hypothetical surge in imports would likelyhave a sustained negative impact on prices as theeffects of the imports are transmitted through theeconomy and stocks of durum wheat.

Semolina price responses to the positiveQDURUM shock are similar to durum wheat priceresponses, with a decline of about 0.9 percent foreach percent rise in QDURUM. Cheaper durumwheat will clearly induce cheaper semolina. As withPDURUM's response pattern, we rely on theoryand market knowledge for insights to help explainPSEMOL's pattern of responses-a sharp-then-decaying pattern which differs from the PDURUMresponse pattern just examined. PSEMOL histori-cally begins responding sharply during the imposedshock's same quarter. Subsequent responses, whichlast over a four-quarter period, are much less dra-matic than the first quarter response. In response tothe price drop in semolina, users of semolina mayincrease market coverage (through 30 to 120 daycontracts) of semolina at the lower price. Prices thusadjust quickly in the same quarter as the initial in-jection of durum wheat into the system. The surgein initial demand could moderate price pressuresin future quarters, however. Pasta prices, on theother hand, do not seem to be affected directly byshocks in QDURUM. Instead, results suggest that

durum wheat quantity changes influence the pastamarket through impacts on durum wheat and semo-lina prices.

The data-embedded long-run market forcessuggest that QDURUM increases elicit rather im-mediate declines in durum wheat and semolinaprices. These PDURUM and PSEMOL declines lastfour quarters and are nearly proportional to the per-centage rise in QDURUM. Increases inQDURUM-for instance, through an increase inimports-are thus likely to swiftly and noticeablyaffect durum wheat and semolina prices.

Simulation 2: An ImposedDecline in Durum WheatPrice (PDURUM).

Simulation 2 imposed a decline in U.S. durumwheat price that could arise from a reduction in thetariffs on durum wheat or a negotiated change inU.S. wheat policy. In response to the imposed re-duction in PDURUM, the reduced-form model sug-gests that durum quantity supplied to and consumedin the market begins rising during the shock's samequarter, possibly due to augmented durum wheatdemand. Each one-percent decline in durum wheatprice ultimately elicits an average 0.6-percent in-crease in QDURUM. The QDURUM increases takea U-shaped pattern and last for an average of sevenquarters. Theory and market knowledge provide asimilar rationale for the shape of QDURUM's re-sponse pattern as in the case where there is an in-jection of durum wheat in the system. The fall inprices may allow for greater demand for durumwheat, supplied initially from imports that are partof QDURUM that was actually modeled. Theseprice movements would likely be abated, however,as domestic farmers adjust their planting decisionsto take into account the lower price received fordurum wheat.

As expected, the imposed decline in durumwheat price elicits declines in both semolina andpasta prices. Semolina price decreases occur dur-ing the PDURUM shock's same quarter and takeon a pattern of quarterly responses which initiallyare more pronounced in magnitude and then decaygradually over a period of up to eight quarters. Thereasons for the shape of the shock are consistentwith the results in the previous simulation. WithPSEMOL's response multiplier of 0.96, semolinaprice declines are about proportional with declines

12 July 2001


in PDURUM.PDURUM-induced pasta price declines take a

full quarter to respond (that is, they have a one-quarter reaction time) and assume a pattern of ac-celerating magnitudes for about one year. ThePPASTA response is muted, however, as reflectedby the 0.08 multiplier that suggests each percentdecline in PDURUM has elicited only an average0.08-percent decline in PPASTA. Compared to themore marked PSEMOL response, the muted levelof PPASTA in response to a change in durum wheatprice is not surprising. Pasta is more of a manufac-tured good than semolina and embodies substan-tial value added through processing and manufac-turing, so durum wheat price has less of a propor-tional impact on pasta prices.

More significantly, the myriad pricing, dis-count, and promotion practices that govern retailpasta sales (see USITC (1996), pp. 4.8-4.10 andappendix I) may cause wholesale prices actuallypaid to differ from published wholesale prices andhence from PPASTA constructed by Labor, BLS(1999) from these published prices. Three practicesexist. First, some suppliers (mainly foreign suppli-ers) practice "line pricing" by charging one unit-price averaged across a full line of pasta products,ranging from low levels of specialization and lowunit-production costs (e.g. spaghetti) to more spe-cialized products with higher levels of specializa-tion and higher unit-production costs (e.g. largeshells) (USITC 1996, pp. 4.8-4.10). As a result,line prices may not vary with production costs forspecific pasta products. Second, slotting fees-feeswhich pasta wholesalers or suppliers are requiredto pay retail chains in return for optimal amountsand locations of shelf space-represent lump-sumreductions in the prices charged by pasta produc-ers/wholesalers to the grocery outlets that retail thepasta products and are thus discounts that are notreported in the wholesale prices (USITC 1996, pp.v.9-v. 10). Third, pasta producers/wholesalers havea set of discount and promotion procedures thatsubsidize certain retailer or grocery promotion andmerchandising activities on behalf ofthe pasta prod-ucts. These activities include retailer discountsbased on sales volumes, cooperative advertisingallowances that subsidize grocery store advertis-ing in local newspapers and other media, in-kindgoods payments to the grocery retail outlets in placeof slotting fees, and retailer or manufacturer dis-

count coupons given directly to the retail consumer(USITC 1996, pp. v.8-v.10 and appendix I). Whole-sale pasta prices actually paid are net of such slot-ting fees and promotional/discount arrangements,but published wholesale prices and PPASTA maynot be.' 6 Thus it is likely that movements inPPASTA actually modeled in the VAR modelwould be more muted or sluggish than the pricesactually paid and may partly explain the sluggishresponse implied by the PPASTA multiplier.

Therefore, a drop in durum wheat price-per-haps from the reduction of an import tariff on highlysubstitutable durum wheat imports-will elicit arise in the quantity of durum wheat supplied to andconsumed in the U.S. market. This QDURUM in-crease will begin reacting immediately, will be en-during and may last up to two years, and will accu-mulate average increases of about 0.6 percent foreach one-percent drop in durum wheat price. Theimposed PDURUM decline influences the pastaindustry through swift, pronounced, and sustaineddeclines in semolina prices and in more delayedand muted declines in pasta prices.

Simulation 3: An Imposed Increase in Pasta Price(PPASTA).

The third and final simulation involved impos-ing an increase in pasta prices. Such an increase inpasta price could arise from increasing import du-ties on pasta product imports that are close substi-tutes with U.S.-produced products, allowing U.S.producers to raise pasta prices by the entire or par-tial margin of the tariff. The USITC (1996, p. 1.23)uncovered evidence that domestically produced andimported dry pasta are probably moderately tohighly substitutable for one another.

16 One author contacted an analyst of the U.S. Departmentof Labor involved with collecting information for andcalculating the PPI for pasta products (Series Number WPU021402) used as PPASTA in this study. The exact degree towhich the series' values account for all of the slotting fees,discounts, and promotion.practices is unknown. While theanalyst noted that a sampling of the responding agent'squestionnaires indicated that some of the discounts weresubtracted from the prices underlying the PPI values, thesesubtracted items varied among responding agents.Furthermore, the analyst did not find any specific "slottingfees" in the sample of questionnaires he reviewed. So, whilethere is an effort to account for such discounts and promotionalitems, the exact degree to which Series WPU021402 accountsfor such items appears unknown.

Bah ula, Ronald A. and Karl M: Rich


The summarized dynamics suggest that thesluggishly responsive pasta price has probably notfluctuated enough to directly influence semolinaand durum wheat prices. This result is not surpris-ing given that the U.S. pasta prices as published(and PPASTA formulated from these publishedprices) may not be prices actually paid and maynot include variations inherent in line pricing, slot-ting fees, and the promotional/discount arrange-ments previously discussed (USITC 1996, pp. v.8-v.10 and appendix I). These results are supportedby the results of the first two simulations showingthat shocks to durum wheat quantity and durumwheat price have little or no effect on PPASTA.

More interestingly, while pasta price move-ments do not appear to directly influence durumwheat and semolina prices, pasta price increasesdo seem to influence the quantity of durum wheat,which is likely to ultimately influence the otherdurum-related variables. Each one-percent rise inthe pasta price results in an immediate and greater-than-proportional (2.9 percent) decline in the quan-tity of durum wheat supplied to and consumed inthe U.S. market that endures for up to five quar-ters. While the magnitude of the multiplier forQDURUM is relatively large it is not surprising inthe context of the durum wheat market. For mostnon-durum varieties of wheat, there are many end-products (breads, flours, pastries, starches) that canbe derived from these relatively substitutable typesof wheat, so a price change for one particluar endproduct would exert a relatively small impact onthe wheat market. In the case of durum wheat, how-ever, there are few alternative uses for quality du-rum wheat other than the production of pasta (seeUSITC 1994, pp. 11.5). Thus the impact from ashock in the pasta market would be expected toproduce noticeable repercussions in the durumwheat market that negotiators and policy makersmay well find interesting. Model results suggestthat durum quantity's decline is U-shaped, with thelargest decline occurring in the third and fourthquarters. Since the reduced-form model cannot pro-vide reasons for this response pattern's shape, weappeal to theory and market knowledge for insights.We posit that while suppliers of durum wheat wouldreact favorably to an increase in pasta prices, thiseffect would be initially overwhelmed by a nega-tive effect on demand. Over subsequent quartersproduction may respond favorably to the price in-

creases for pasta-although possibly not by enoughto overwhelm the disincentives to consumption-creating favorable production effects that offsetsome of the demand-driven declines in QDURUM.

As noted earlier, the PPASTA increase imposedas a model shock could arise from, among otherthings, an increase in a tariff when imported anddomestic pasta are highly substitutable. Given thehigh degree of substitutability between importedand domestically-produced pasta (USITC, 1996)and the duty-free status accorded to dry pasta notsubject to the mentioned CVDs and ADs'7 (USITC,HTS 2000, chapter 19), the results of scenario 3suggest that raising the general duty rate on dryuncooked pasta-as occurred with the AD/CVDduties placed on certain Italian and Turkish manu-facturers of pasta-would likely have adverse up-stream effects on the U.S. durum wheat market.An increase in the duty on imported dry pasta mayallow domestic producers to raise prices on theirimport-substitutable products. The reduced-formmodel results suggest that the rise in pasta pricemay generate declines in derived demand forQDURUM, given that durum wheat has virtuallyno end-uses other than semolina and pasta. Thesenet declines in QDURUM could result from de-mand declines for both domestic and importedpasta, both of which are included in the modeledQDURUM variable.' 8Each percent rise in PPASTAwould immediately induce a series of five quar-ters' worth of QDURUM declines, which wouldultimately register a 2.9 percent drop for each one-percent rise in pasta price.

Strength of Relationships: Analyses of ForecastError Variance Decompositions

Analysis of decompositions of forecast errorvariance (FEV) is another tool of VAR economet-rics for discerning relationships among the mod-

17 Dry, uncooked, and otherwise unprepared pasta andpackaged without sauces or other preparations is classified inSections 1902.11.20 (if it contains eggs) and 1902.19.20 (if itdoes not contain eggs) of the Harmonized Tariff Schedule andenters the United States duty-free.

18 Such QDURUM declines would be net and presumablywould occur because demand-induced declines in QDURUMwould outweigh any incentives to increase durum wheatproduction, since pasta, virtually the sole end use of durumwheat, is now higher-priced.

14 July 2001


eled system's time series. As noted by Bessler(1984, p. 111), analysis of FEV decompositions isclosely related to Granger causality analysis, andboth tools provide evidence concerning the simpleexistence of a causal relationship among two mod-eled variables. But analysis of FEV decompositionsgoes further than Granger causality tests. A mod-eled endogenous variable's FEV is attributed at al-ternative time horizons to shocks in each modeledendogenous variable (including itself), and not onlyprovides evidence of the existence of a relation-ship among two endogenous variables but illumi-nates the strength and dynamic timing of such arelationship (Bessler 1984, p. 111). Error decom-positions attribute within-sample variance to alter-native series and thus provide measures that are

useful in applied work (Bessler 1984). Table 2 pro-vides the FEV decompositions for the estimatedVAR.

A highly exogenous variable has large propor-tions of its FEV attributed to its own variation andlower proportions to variation in other endogenousvariables. Similarly, a highly endogenous variablehas small proportions of its FEV attributed to ownvariation, and large FEV proportions attributed tothe innovations of other variables (Bessler 1984, p.111).

Perhaps one of the most evident results in Table2 is durum wheat price's high degree ofexogeneity,and its role as the modeled system's central anddriving force. Durum wheat price is largely deter-mined by production and planted acreage in Canada

Table 2. Decompositions of Forecast Error Variance

Percent explanation of forecast error variance fromVariableexplained: STEP PDURUM QDURUM PSEMOL PPASTA

PDURUM 1 99.04 0.67 0.23 0.06

2 97.99 0.84 0.74 0.434 95.81 0.85 2.09 1.256 94.09 0.78 3.42 1.718 92.88 0.73 4.52 1.87

QDURUM 1 25.03 61.17 0.95 12.84

2 31.47 50.03 1.73 16.764 40.37 39.19 3.03 17.426 45.26 34.13 4.13 16.488 47.79 31.52 5.03 15.66

PSEMOL 1 93.31 0.48 6.03 0.18

2 91.92 0.44 7.22 0.424 89.67 0.37 9.21 0.766 88.10 0.32 10.70 0.878 87.05 0.30 11.76 0.89

PPASTA 1 4.43 2.98 1.14 91.46

2 9.18 2.67 1.40 86.754 18.48 2.38 2.04 77.116 25.10 2.18 2.75 69.978 29.10 2.05 3.44 65.42

Babula, Ronald A. and Karl M: Rich


as well as in the United States, and, in turn, on whatfarmers received at the end of the previous marketyear. As a result, data-embedded regularities sug-gest that PDURUM drives QDURUM to a far-greater degree than QDURUM drives PDURUM.U.S. durum wheat price's high degree ofexogeneitymay arise from the dependence of price on marketactions-particularly planting decisions, past pricemovements, and stock movements-on both sidesof the U.S./Canadian border, a political divisionruns through the center of North American durumwheat production. It is therefore not surprising thatat all of Table 2's horizons more than 90 percent ofthe uncertainty of durum wheat price is attributedto its own variation.

Durum wheat quantity is relatively endogenouscompared to PDURUM, with no more than 61 per-cent and no less than about 32 percent of its varia-tion attributed to its own movements. The degreeof QDURUM's exogeneity rapidly declines at ho-rizons beyond two quarters. As expected, other thanits own variation, the key explicator of durum wheatquantity behavior is durum wheat price, especiallyat longer horizons. Durum wheat price has moder-ate influence on QDURUM at shorter horizons (25to 31 percent), but takes a more proactive role inexplaining QDURUM movements at longer hori-zons, when nearly half of QDURUM's behavior isattributed to durum wheat price variation. One re-sult that is perhaps surprising but that coincides withthe third simulation's results is the noticeable pro-portion (up to about 17 percent) of QDURUM'svariation attributed to pasta price movements.

Semolina price is highly endogenous, with nomore than about 12 percent of its uncertainty at-tributed to own variation. As expected, the price ofdurum wheat-semolina's primary productive in-put-accounts for the majority (no less than 87percent) of the variation in semolina price. Thisresult coincides closely with the result of scenario2, in which a price shock to durum wheat elicits animmediate, lengthy, similarly directed, and nearlyproportional change in PSEMOL.

Pasta price appears largely exogenous, with noless than about 65 percent of its uncertainty attrib-uted to own variation. Supporting impulse-responseresults of the second simulation, where PDURUMchanges elicit similarly directed changes inPSEMOL, FEV decompositions suggest that du-rum wheat price contributes moderately to explain-

ing pasta price variation. Such PDURUM contri-butions are lower at shorter horizons, and increase(up to 29 percent) at longer horizons.

Summary and Conclusions

The specification, estimation, and simulationsof the VAR model reveal a rich set of data-embed-ded, long-run, and dynamic forces that not onlygovern the U.S. durum wheat and pasta markets,but characterize the nature of the two markets' in-teraction as well. These dynamics are highly rel-evant to farm- and trade-policy makers,agribusiness agents, and researchers involved withissues relevant to U.S. durum wheat and pasta mar-kets. This is because changes in prices or quanti-ties produced or consumed-arising perhaps fromnegotiated changes in tariff and nontariff trade bar-riers or from legislated or negotiated farm policychanges-affect prices and quantities in the imme-diate market as well as the related markets upstreamor downstream.

The paper's first result emerges from the struc-tural change test evidence: changing pasta con-sumption patterns and the 1995-1996 impositionof antidumping and countervailing duties on cer-tain U.S. imports of Italian and Turkish dry pastafailed to induce structural change. This may arisefrom what the USITC (1996, pp. II.11 and 11.15)reported as moderate to high degrees of substitut-ability, and hence fungibility, between U.S. andimported dry pasta, and between U.S. imports fromcompeting foreign suppliers. The imposed dutiesfailed to reduce imports because the U.S. marketturned from assessed or subject dry-pasta importsto competing and substitutable non-assessed sup-plies in order to offset any duty-induced shortages(see Rich 1999).

The impulse-response simulations and analy-ses of FEV decompositions generated several re-sults. First, a rise in the U.S. quantity of durumwheat-resulting perhaps from increased domes-tic production or a surge in imports-affects themarket immediately with about a year's worth ofprice declines for both durum wheat and semolina.The pasta market, however, does not appear directlyaffected by the imposed durum wheat quantity in-crease. Second, policy changes that reduce durumwheat prices, such as changes in tariffs or farmpolicies that lead to lower farm prices for durum

16 July 2001


wheat, affect all modeled markets, including thepasta market. The impact of such a policy immedi-ately affects the durum wheat and semolina mar-kets with durum quantity increases and price de-clines lasting up to 7 or 8 quarters. The reductionin durum wheat prices causes durum wheat quan-tity to increase, as the reduced-form model cap-tures demand increases that likely outweigh thesupply declines, and causes a nearly proportionaldecline in semolina price as lower input costs arepassed on to semolina millers. Pasta prices alsodecline for over a year, but such declines are farless proportional than the decline in PDURUM,presumably because pasta prices are far less du-rum-dependent than are semolina prices. The slug-gishness or reluctance of PPASTA response mayalso arise from the exclusion by PPASTA of pricevariation from slotting fees, line pricing, and thevarious discount and promotional procedures thatinfluence the pasta prices actually paid. Finally,while changes in durum wheat quantities and priceshave little or no effect on pasta price, the final simu-lation-an increase in pasta price that could resultfrom a tariff increase-shows adverse upstreameffects on the durum wheat market: oppositely di-rected movements (declines) in durum wheat quan-tity. Decreased volumes of domestically demandedand imported durum wheat, both components in themodeled QDURUM variable, may account for thiseffect. This is not surprising given that there arefew other demands for high-quality durum wheatand semolina other than for pasta production.

Future research along these lines could be three-fold. First, in terms of the pasta-durum market,policy simulations incorporating greater sources ofdata, such as reliable quantity data for semolina andpasta, would provide a much richer analysis andallow for a wider range of policy simulations thatwould be of importance to practitioners. Given thatboth quantity and stock data are unavailable forpasta products on a highly periodic (monthly orquarterly) basis, the methods used herein to invokepasta market demand and supply elements throughestimation of a single pasta price reduced-formequation were the only located method of obtain-ing highly periodic (quarterly) pasta-related econo-metric estimates of market parameters. Second,expanding this analysis to other countries of inter-est, particularly Canada and Italy, would be instruc-tive in comparing the market effects in these coun-

tries relative to those in the United States. Such ananalysis would elucidate market conditions andinteractions that could cause bottlenecks in tradenegotiations. More significantly, this type of analy-sis could be useful if extended to the analysis ofother food markets, where empirical estimates ofthe parameters behind all segments of the value-added chain remain few and far between. Thiswould be especially insightful in the context of thenext round of trade negotiations. The UruguayRound did little to reduce tariffs among many pro-cessed food products, as tariffs were often reducedby only the minimum amount (15 percent) or, wheretariffs replaced non-tariff barriers such as quotas,the tariffs devised were prohibitively high. A greaterempirical understanding of the structure of the pro-cessed food market from the finished product downto the raw inputs (which are often protected in for-eign markets) would be extremely beneficial fortrade practitioners to formulate modalities thatcould bring forth meaningful tariff reductions inthis sector.

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