SYMMETRIC P OSITIVE EQUILIBRIUM PROBLEM: AF RAMEWORK FOR RATIONALIZING ECONOMIC BEHAVIOR WITH LIMITED INFORMATION:COMMENT WOLFGANG BRITZ,THOMAS HECKELEI, AND HENDRIK WOLFF In a recent contribution to this journal, Paris suggests a framework which extends positive mathematical programming (PMP)—a widely used calibration methodology for agricultural supply models—to a symmetric positive equi- librium problem (SPEP). He stresses three main contributions: (1) The PMP methodol- ogy is modified to incorporate more than one observation on production programs; (2) A solution to the “self-selection problem” with respect to the choice of crops produced by each farm is provided; (3) “Limiting inputs” are no longer considered fixed quantities as in PMP. We address several conceptual concerns with respect to the SPEP methodology and the presented application. We consider these to be substantial enough to question Paris’ claim to present “... a general framework of analysis that is capable of reproducing economic be- havior in a consistent way ...” (p. 1049). Our discussion is structured along Paris’ presenta- tion: The next three sections represent the core of the comment and deal with the method- ology itself. They refer to the three phases of SPEP: (i) recovery of unknown variable marginal costs and shadow prices of limited resources, (ii) use of these results to specify data constraints and parameter supports for generalized maximum entropy (GME) estima- tion of a cost function, and (iii) definition of a simulation model. Finally, concluding remarks are made regarding the application of SPEP to an analysis of the EU Common Agricul- tural Policy (CAP) based on Italian farm data. Throughout the comment we use the same Wolfgang Britz is a researcher at the Institute for Agricultural Policy, Market Research and Economic Sociology of Bonn Uni- versity; Thomas Heckelei is an assistant professor, IMPACT Center/Department of Agricultural and Resource Economics; and Hendrik Wolff is a graduate student, Department of Agricultural and Resource Economics, Washington State University. This comment is a genuinely joint effort. There is no senior authorship assigned. The research leading to this comment is sup- ported by the European Union in the context of project No QLK5- CT-2000-00394. mathematical notation as Paris and refer to his equation numbers to facilitate comparison. Phase 1: Estimation of Marginal Costs Two alternative ways to recover marginal cost and shadow prices of limiting inputs are sug- gested for Phase 1 depending on the number of limiting inputs: 1 The first alternative is equivalent to the typ- ical PMP procedure. Equations (1)–(4) (or equations (8)–(10) for the sample LP problem) maximize overall gross margin, (p n - c n ) ′ x n , subject to land availability and calibration constraints restricting the optimal production quantities to be less than or equal to ob- served quantities. The model’s solution im- plies a shadow value of land, y n , that is equal to the gross margin of the least profitable of the produced crops per unit of land, i.e., equal to min j [( p jRn - c jRn )/a jRn ]. For all ob- served cropping activities, the shadow values of the calibration constraints,  jn , are equal to p jn - c jn - y n · a jn . 2 Consequently, the values required in Phase 2 can be calculated analyti- cally as long as only one limiting input exists. More importantly, “the marginal cost of lim- iting inputs, (A ′ Rn y n ) and (A ′ NRn y n ), and the variable marginal cost associated with out- put levels, ( Rn + c Rn ) and ( NRn + c NRn ),” (p. 1051) are arbitrary outcomes with poten- tially significant influence on parameter esti- mates in Phase 2 through the data constraints (25)–(28). We consider them “arbitrary” in the sense that the combination of variable marginal cost and shadow prices is implied by this form of linear programming model, which is different from the ultimate simulation model used in Phase 3. Thus, other optimization 1 In the article, the “extension of SPEP to several limiting inputs” is actually given under Phase 2, but since the presented equilibrium problem is supposed to replace models (1)–(4) of Phase 1 we in- clude it in this section. 2 This implies a zero value of  jRn for the least profitable crop. Amer. J. Agr. Econ. 85(4) (November 2003): 1078–1081 Copyright 2003 American Agricultural Economics Association