SCHRIFTEN DER GESELLSCHAFT FÜR WIRTSCHAFTS- UND SOZIALWISSENSCHAFTEN DES LANDBAUES E.V.

Witzke, H.P., Heckelei, T.: Modelling EU Sugar Market Reform Options with Capsim – An innovative profit function approach. In: Ahrens, H., Grings, M., Petersen, V.: Perspektiven der europäischen Agrar- und Ernährungswirtschaft nach der Osterweiterung der Europäischen Union. Schriften der Gesellschaft für Wirtschafts- und Sozialwissenschaften des Landbaues e.V., Band 38, Münster-Hiltrup: Landwirtschaftsverlag (2003), S. 265-274.

Schriften der Gesellschaft für Wirtschafts- und Sozialwissenschaften des Landbaues e.V., Bd. 38, 2002, S. 265-274

MODELLING EU SUGAR MARKET REFORM OPTIONS WITH CAPSIM: AN INNOVATIVE PROFIT FUNCTION APPROACH

von

H. P. WITZKE* und T. HECKELEI"

1 Introduction

The EU sugar market regime has withstood any agricultural policy reform in the last 4· decades. The "secret" of the production quota based policy was to not touch what seems to worry politicians more than diminished consumer. rents: the budget. But suddenly the invulnerability of the regime is threatened, mainly by committed tariff preferences and import guarantees to developing countries. Under the "everything but arms" (EBA) agreement ofthe EU with the least developed countries the EU has accepted duty free imports of "everything but arms", ineluding sugar. This agreement could trigger problems for existing WTO agreements and current negotiation strategies. Consequently, the EUROPEAN COMMISSION (2001) considers several reform options for the sugar market policy and has commissioned a major study on the sugar market which will encompass simulations with the Common Agricultural Policy Simulation System (CAPSIM).

The rest of this paper is organized as folIows: First, a profit maximisation framework is developed for an analysis ofthe sugar Common Market Organisation (CMO) which accounts for the existing heterogeneity of farm situations. Second we will explain the representation of the sugar CMO in the modelling tool CAPSIM. Finally we elose with brief conclusions.

2 Theoretical framework for an analysis ofthe EU Sugar CMO

The main political instruments of the EU's sugar CMO are support prices, export refunds, A and B quotas and the levy system which renders it largely neutral to the EU budget. They are evaluated and criticallyassessed in LINDE et al. (2000). BUREAU et al. (1997) and VIERLING (1997) investigate potential efficiency gains in agriculture through tradability of quotas. These studies point to the problem ofheterogeneity in the farm population, an issue difficult to come byempirically.

A theoretical framework to incorporate this heterogeneity in aggregate analysis may depart from the farm level cost function C of farm i:

(I) Ci(w yi yi )=min{w'xi 'Ti(Xi yi i )=o} J -S' s ., -s,Ys

X'

where

T farm level technology

* H. Peter Witzke, Institut für Agrarpolitik, Marktforschung und Wirtschaftssoziologie der Universität Bonn, Nußallee 21,53332 Bonn; email: peter.witzke@eurocare-bonn.de.

** Thomas Heckelei, IMPACT Center I Department of Agricultural Economics, Washington State University, Hulbert Hall, 123C, Pullman WA, 99164-6214; e-mail: heckelei@wsu.edu

265

W (Xi) column vector of input prices (quantities)

y~s = column vector of non sugar outputs

y! sugar beet production

This cost function will become part of the farm level profit maximisation problem for sugar beet farms, at least if we neglect consideration of uncertainty for the moment. A and B quotas (q'a, q'b) constrain the applicability of corresponding sugar beet prices (p", pb, Pe). First order conditions reveal - depending on quota endowment and the position of the marginal cost

curve C; = BCi / ry! - that the farm will operate according to one of the following five cases:

3) q~<y!<q~+q~~Pb=C;<Pa

4) y!=q~~Pb~C;~Pa

5) y! <q~ ~C; = Pa

These cases are ordered to represent increasing marginal cost and may be depicted graphically as in figure 1.

Figure 1: Five different farm types in view ofthe EU sugar eMO

Pa

Pb

Pe

y~

In a certain region we observe several farm types at the same time because quota endowments and farm efficiency follow a distribution with significant variance. It is quite dear that the regional aggregate response to changing prices or quota quantities depends on this distribution and treating each region as homogenous in terms of one of the five farm types (see, for example, FRANDSEN et al., 2001) will cause some aggregation error. The aggregation error can be avoided, at least in theory, using an aggregate profit function for a heterogeneous population of farms which follows from integration of all farm level profit functions over the distribution of an appropriately defined farm efficiency index:

266

(2) TI(W'P-S.Pa'Pb'PC.Qa.Qb)="j 1C.(. ... i)f(i)di+"J 1C_.(. ..• e)g(e)de

where

1i,( ...• i)

~.( ...• e)

Q. (Q.)

iQ I

profit function of sugar beet farm with efficiency index i

profit function of non sugar beet farm with efficiency index (:

aggregate A (B) quotas

The arguments of farm level profit functions are omitted here because they will be different for each of the above 5 sugar beet farm types and other farms as well. The basic result is the possibility to obtain an ordinary aggregate profit function for a given allocation of quota rights. This profit function has usual properties, generates behavioural functions through Hotelling's Lemma, and will depend on all 3 prices and on the aggregate quotas. Its comparative static properties are investigated in more detail in WITZKE and HECKELEI, 2002.

The intuition may be presented graphically as well and for that purpose a change in the B sugar beet price P. is an interesting example:

Figure 2: Increase ofB sugar beet price with farm heterogeneity

Pa I-__ ~I-_.

P~F===~~~~-;~--~

y~

The depicted increase would cause some farmers (with efficiency index iu2>i>i./) who did not

make any use oftheir B-quota at the low price to use it partially and thus move from type 4 to type 3 (dashed lines in figure 2). Other farmers of type 3 both before and after the price change would simply move upwards on their marginal cost curve (solid lines in figure 2). Finally there will be farmers (i/ > i > i./) who did not fully exploit their B quota at low prices but would do so at increased prices and move from type 3 to type 2 (dotted lines in figure 2). Currently the largest groups will be made up of "old" type 2 producers and type I producers who will make full use of their B quota at both prices and hence do not contribute at all to any impact of the change in Pb. Furthermore, type 5 farmers and apart of type 4 farmers will not change their status for moderate but non-marginal changes. Hence the response of B-sugar to moderate changes in revenues may be expected to be .fairly smalI, but nonzero. A similar

267

nonzero response may be deduced for A beet, but this response will be very elose to zero if the shares of type 1 and 2 producers are negligible as in most EU countries. This would be different, however, in the case of drastic price drops, such as those which might be considered in certain reform options. The graphical representation above suggests that once a critical region is approached, an increasing percentage of farms would change their status from the B quota constrained type 2 to type 3 or even type 4 farms, accompanied with a market deeline of their production. On the aggregate level we would observe a strongly increasing supply elasticity being associated with strong price drops. This has been a problem for the specification of the supply side in CAPSIM the solution of which is explained in the next section.

Note that typical aggregate models would not correct1y capture the response to changes in the price of B-sugar beet. If the region were modeled as if it had a marginal cost curve index of i<i/, and hence remain in a type 1 or 2 situation, the aggregate response would incorrect1y predicted to be zero. A zero response would also result ifthe region were taken to operate as a pure type 4 or 5 farm with farm index i> i/ If the region were finally modeled as if it had a farm index of i/ <i< ig2 the aggregate response would be overestimated. An improved approach might be to distinguish farm types 1-5 and model their behavior separately. While being an improvement over a simple representative agent approach it would miss the endogenous regrouping of farms, which tends to limit the aggregate response. This is because the type-switching farmers (see the dotted lines in figure 2) are constrained in their response to a change in Pb.

A change in the B-quota is investigated in the following figure:

Figure 3: Decrease ofB-Sugar Beet Quota with Farm Heterogeneity

q~ y~

With a decrease in the B-quota, both indices for the borders between types 2 and 3 and between types I and 2 will increase. For the graphical presentation we assurne that some farms are of type 2 both before and after the change, i.e. i!/ <i/<i/ <i/ Some former type 3 farmers will become constrained type 2 farmers such that their cut in B-sugar beet production is less than their quota cut. Farms of type I will completely substitute an increase in C sugar beet for former B-sugar beet, whereas some type 2 producers will do so partially. Hence there is an expansionary impact on C sugar production of a quota cut and the decrease in total sugar

268

production will be clearly less than the cut in the B-quota. Again this is a characteristic of supply response which is to be built into the parameters ofCAPSIM.

3 Representation of sugar in CAPSIM 3.1 Objectives and Overview

CAPSIM is a straightforward partial equilibrium modelling tool with behavioural functions for activity levels, input demand, consumer demand and processing. Together with an assumption of exogenous yields they result in a complete set of market balances for 31 agricultural raw products covering the complete Economic Accounts for Agriculture (EAA). Currently either trade prices or quantities are considered exogenous. In terms of policy representation the system incorporates both border measures (tariffs, flexible levies/export subsidies, WTO constraints) as we11 as domestic support measures for agriculture (payments per ha or head, public intervention) and accociated quantity restrietions (milk quota, sugar market regime, ceilings on payments). A number of details relevant for sugar market modelling have been included recently.\

3.2 Supply Specification

The supply side in CAPSIM is innovative on a number of points and hence it will presented in some detail on these. It is driven on the one side by input producer prices PP;, on the other side by activity net revenues NREJj which fo11ow from gross revenues RE"Jj minus land rent:

(3) NREJj = REJj - Aj * PLND

where

= land requirement of activity j (Aj > 0 for crops, Ii:; = 0 for animals)

= (shadow) rental price of land

Due to regional variation of land quality and activity mix it is unrealistic to assurne the same absolute price of land used for, say, vegetables as for cereals and consequently the land requirements A; have been made crop specific. However, it is assumed that localland markets are sufficiently tied together to make for land prices of a11 qualities move together. This is an ad hoc solution to the inevitable regional aggregation problem in country level models.

In line with eq. (2), the behavioural functions will be derived from an aggregate profit function depending on prices, net revenues and the sugar beet quotas as fixed factors. For the simulation of sugar market reform options we require a functional form permitting to impose sma11 elasticities for local changes and large elasticities in case of drastic changes, because a drop in (A and B) sugar beet revenues will have negligible consequences for sma11 changes but will cause an increasing portion of farmers to leave their quotas unused when revenues are declining substantia11y. To introduce this flexibility in the empirical implementation we use a modification of the Generalised Leontief functional form to apply the theoretical framework of equation (2) above:

(4) I1..RN,QTS) = Ls Lq ~.q QTSq RNs + Ls LI as.1 RN/ RN/ + Ls .. LI" ßs.t RN/ RNl-r

where

The description of the modelling system has to be selective and concise here for space !imitations. For further detailed information there is the description in WIlZKE, VERHOOG, ZINTL, 2001 on the predecessor model MFSS99. Most recent on CAPSIM is WITZKE, 2002b, available upon request.

269

RN = vector (NREV, Pp)' formed by net revenues NRE~ and prices PP,

QTSq sugar quota (q = A, B) as fixed factor

Note that the functional form above may be considered a simplified Asymptotically Ideal Model (AlM, see e.g. HA VENNER and SAHA, 1999) where only one set of additional terms has been included. In the AlM applications it has been common practice to choose r= 2-n , for some positive integer n. Because this turned out too restrictive for our purposes, and because our form would always be a rudimentary form of the full AlM, we prefer to call the proposed functional form the "Power GL".

For global convexity we require as,' ~ 0 Vt ;e s and Ps" ~ 0 Vt ;e s, if 0 < r< 1 and Ps" ~ 0 Vt ;es, if r< O. This implies global convexity based on the fact that the sum of convex functions is also convex. Homogeneity is met because the exponents sum to one, while symmetry follows for any functional form from Youngs theorem. The functional form above may be used as a regular profit function therefore. It implies the following behavioural functions for netputs:

(5) NETP'(RN,QTS) = oll/a?Ns = as.s +~q 4.q QTSq+~,,,,, as,t (RN,/ RNs}5

+ r ~''''' Ps" (RN, / RNs)(/·n +(1 -r) ~t"s ß,.s (RN, / RNs)Y

where netputs are activity levels LVLs or negative input demands [NPs :

(6) NETPs = LVLs for SE ACTIVITY

(7) NETPs = -lNPs for s E INPUT

The elasticities with respect to price variables may be derived as

(8) (iNETP,Ia?Ns) (RN/NETPs) =

[- .5 ~''''' as.' (RN, / RNs}5 - r(1 -r) ~''''' Ps,t (RN, / RNsf·n

- r(1 -r) ~''''' ß,.s (RN, / RNs)YJ!NETPs

(9) (iNETP/t'RN,) (RN/NETPs) =

[ .5 as,' (RN, / RNs}5 + r (1 -r) Ps" (RN, / RNsf·n +

r(1-n ß,.s (RN,/ RNs)1)1NETPs, t;es

Note that the parameter matrix pneed not be symmetrie, while a should be assumed symmetrie to permit identification of parameters. Consequently the above Power GL has n(n-l)+l additional parameters compared to the standard GL form. In an econometric estimation these parameters could be used to improve the fit. In our case the parameters will not be estimated but calibrated to reproduce as good as possible a set of starting values, measured in terms of cross entropy. However, as explained above, the benefit of the additional parameters for CAPSIM would be to permit an appropriate change of elasticities in case of drastic changes in price variables. Because our apriori expectations on these changes are currently fairly vague, with the exception of the sugar beet case, we plan to set all f:Js = 0 except those involving sugar beets. Based on the reasoning in seetion 2 and provisionally we

270

specified two starting values both for small revenue increases (typically around 0 .1 for B beet) and higher values for strong drops of B revenues (1.0-1.5 for a drop down to the level of C beet). The additional parameters of the Power GL form were thus used essentially to impose these a-priori expectations in an approximate way.

We may investigate numerically the effects of the additional parameters in a simple two product case, say sugar beets SB and other crops oe. In this case the sums above involve only a single element and it is easy to set up a numerical example. For the graphical presentation, we assume that initially NETPSB = 10, RNoe / RNsB = I, (t3NETP,It3RNs ) (RNslNETPs) = 0.1. The difference to the standard GL case can be stressed with a high negative 1, for example y = - 4.0. Assuming further that ß SB.oe = 0.025 = ß O<:'SB = 0.025 (even though ßneed not be symmetric), the parameters a sn.oe = O. and the constant a SB.SB = 9.975 may be determined residually. Sugar beet supply as a function of the relative sugar beet revenue RNs.! RNoe may be compared now with the standard GL supply function running through the same point with the same local elasticity:

Chal't 1: Standard GL supply function (Ien) and Powel' GL with y = -4,0 (right)

2.8

2.4

2

1.6 RNsol RNOC

1.2

0.8

0.4

o 2 4 6 8 10 12 14 16 18 20 o 2 4 6 8 10 12 14 16 18 20

NF.TPSH N f: TPSH

The standard GL would require a drop of sugar beet revenues to about 8.4 % of the initial value to predict NETPSB = 50 % of the initial value. The standard GL supply disappears only a revenues smaller than 2.8 % of the initial value. These predictions evidently do not reflect our apriori expectations. Contrasting with these results, the Power GL supply with y= -4 drops to about 50 % already at a revenue of 45.9 %. Similarly it vanishes completely already at a revenue of about 40 %.

While these properties are desired, we also see that the constant does not act as an upper asymptotic value anymore, because the supply becomes s-shaped. This would mean that the netput quantity go es to infinity as the relative revenue ofthe activity increases. The Power GL shares this feature with other functional forms such as the normalised quadratic and it could only be avoided setting 0 < y< I (but this would imply smaller curvature). The point where supply is strongly bending rightwards or leftwards depends on the parameters ß SIJ. oc and ß oe.SB . The rightward bending part can be eliminated completely with ß OC.SB = 0.0, thus reestablishing the upper bound property ofthe constant. On the other hand due to the common

271

profit function framework, this may be shown to imply a strongly rightward bending supply with a lower bound for the substitute crop oe in the two product case. The last implication may be undesirable. However, the land market dampens any excessive expansion of other crops in CAPSIM. First simulations of strong sugar price drops with the Power GL specification (and r = -1.4 ) were quite promising. We expect the new Power GL functional form therefore to become a theoretical development which has practical usefulness, possibly in other quantitative research efforts as weil.

3.3 Demand Specification and Market Clearing The modeling of food demand is based on utility maximization subject to a budget constraint. Consumer prices, total private expenditure, and population determine derived demands. Again, standard microeconomic conditions are imposed including full curvature. The specification is derived from a standard GL expenditure function (see WITZKE, 2002a).

For most products, a fixed margin is assumed between producer and consumer prices, important exceptions being oilseeds, milk, and sugar beet. In this case sugar beet prices PPSBx

(x = A,B) are a certain percentage (I - OJIND) ofthe industry's revenues from sugar (SUCA) and molasses (MOLA) net ofthe relevant levies and variable processing costs per ton ofbeet.2

(10) PPSBx = (I - OJIND)*( 'f/SUGA.SB (EPsUGA -LEVA * Olx -LEVB * (1- Olx » + 'f/MOLA,SB EPMOLA - 'f/PRC,SB)

where

'f/hSB

'f/PRC,SB

WIND

01,

LEV,

EU market price of item i E {SUCA, MOLA}

processing coefficient: output h E {SUCA, MOLA} per ton of sugar beet

variable processing cost per ton of processed sugar beet

share of net revenue to cover sugar industry fixed cost and profit

weight of A levies in determination of beet price PPSB, (x = A, B)

= levy on sugar of type x (x = A, B)

In Member states implementing a classical A, B, C system, the weight of A levies in the price determination of A beet (OJA) equals I. However in countries implementing a pool price system t~e weight will be equal to the share of the A quota in the combined A+B quota. Unfortunately our information on the implementation of pool price systems is still sketchy, mainly based on LINDE et al., 2000 and COMBETTE, GIRAUD-HERAUD and REQUILLARD, 1997. At the same time we are still lacking reliable information on sugar prices in EU member states, such that the simulations below are based on a preliminary specification.

This hold as weil for the issue of reclassification, While it is true that actual production in a certain percentage of farms has fallen short of quota endowments in almost every year and country in the past, aggregate underuse of the national quotas is rare. Consequently we assurne that unused farm level quotas are effectively filled withC beets from neighbour farmers. This may be due to the carry forward possibility of the sugar CMO, due to some limited tradability or to informal transfers between farmers in case of quota underuse. On the member state level we assurne therefore that quotas are fully used until the C beet production is exhausted.

In the case of e sugar, world market prices take over the role of EU prices in eq. (10),

272

3.4 Other poliey Representation

Overall the most important support instrument of the current CAP is the system of premiums for crop and live stock activities. These premiums have been constrained by ceilings, again reflecting the overriding importance ofbudgetary considerations in the EU. An obligatory set aside rate accompanies the system ofpremiums. To account for the counteracting response of voluntary set aside to a change in the obligatory set aside rate, the latter translates less than proportionally to the effective set aside area in the model. The milk quota regime is implemented in a standard way. This leads to a divergence of market revenues and shadow revenues, the latter ofwhich have been specified in view ofresults in BARKAOUI, BUTAULT and GUYOMARD (1997).

As has been explained above, the sugar quotas are considered fixed factors. The self-financing character of the sugar market organization has been incorporated in CAPSIM. This accounts for an increase in the producer price of B sugar beet after a decrease in the sugar quota. As long as they are below international prices, EU market prices proportionally follow any change in the EU intervention price. Consequently, it is assumed that EU authorities adjust export subsidies exacdy by the amount necessary to bring about this parallel movement wh ich has been observed in the past even though market prices are usually somewhat higher than intervention prices.

4 Conclusions

This paper has made a number of contributions. In theoretical terms we offer a consistent framework to reflect farm heterogeneity in aggregate supply modeling of the EU quota regime. Furthermore we have developed a new functional form for supply analysis which is weil suited for an analysis of reform options on the sugar CMO. lt appears thus that applied, problem oriented research can be innovative at the same time.

In the next months the methodoIogy presented above will be put to work. Ongoing work in the wider research project will improve the empirical foundation ofparameters and the precise specification chosen. This is desirable most of all for the implementation of the sugar CMO, on the farm level distribution of quota use in EU member states, and, of course, on the relevant elasticities. -

Referenees

BARKAOUI, A., BUTAULT, J.-P., GUYOMARD, H. (1997): Mobilite des droits a produire dans I'Union europeenne - Consequences d'un marche des quotas lai tiers a I'echelle regionale, nationale ou communautaire, Cahiers d' economie et socioIogie rurales 44: 6-28.

BUREAU, J.-CH., GUYOMARD, H. and MORIN, L., REQUILLART, V. (1997): Quota Mobility in the European Sugar Regime. European Review of Agricultural Economics 24: 1-30.

COMBETTE, P., GIRAUD-HERAUD, E. and REQUILLARD, V. (1997): La Politique sucriere europeenne apres les accords du Gatt - Une analyse de quelques scenarios d'evolution, Economie & Prevision (127): 1-13.

EUROPEAN COMMISSION (2001): Open Call for Proposals - Study to Assess the Impact of Options for the Future Reform of the Sugar Common Market Organisation. Agricuitural Directorate General, Brussels.

FRANDSEN, S. E., JENSEN, H. J., Yu, W. and WALTER-JORGENSEN, A. (2001): Modeling EU Sugar Policy - A study of policy reform scenarios. Danish Institute of Agricultural and Fisheries Economics, SJFI Working Paper no. 13/2001.

273

HA VENNER, A. and SAHA, A. (1999): Globally Flexible Asymptotically Ideal Models, American Journal of Agricultural Economics (81), pp. 703-710.

LINDE, M. VAN DER, MINNE, V., WOONING, A. andZEE,F. VAN DER (2000): Evaluation ofthe Common Organisation ofthe Markets in the Sugar Sector, Report to the EU Commission, Netherlands Economic Institute (NEI), Agricultural Economics and Rural Development Division, Rotterdam: http://europa.eu.intlcommlagriculture/eval/reports/sugar/index _ en.htm.

VIERLING, G. (1997): Die regionale Wettbewerbsfahigkeit der ZuckeITÜbenproduktion in der Europäischen Union - Mögliche Effekte eines flexibilisierten Quotenmarkts auf das Rübenangebot, Agrarwirtschaft, Sonderheft (155).

WITZKE, H. P. (2002a): Conceptual framework for the respecification of the demand side in CAPSIM. (Revision 2). Working Paper, Eurocare, Bonn.

WITZKE, H. P. (2002b): CAPSIM intennediate version: Methodology (Revision I). Working Paper, Eurocare, Bonn.

WITZKE, H. P. and HECKELEI, T. (2002): EU Sugar Policy Refonn: Quota Reductionand Devaluation, Paper at the AAEA meeting on Juli 28-31, Long Beach, USo

WITZKE, H. P., VERHOOG, D. and ZINTL, A. (2001): Agricultural Sector Modelling. A new medium-tenn forecasting and simulation system (MFSS99). Research in Official Statistics, European Commission.

274

------