Principles of Policy Modeling in Food and Agriculture

The aspirations of quantitative policy modelers to aid the policy-making process have loomed large since the 1970s but have remained largely unfulfilled. We submit that the primary reason is that policy modelers have failed to realize that the principles of successful policy modeling are very different and considerably more complex than have served typical causal, exploratory, and forecasting purposes of quantitative economic modeling. In this chapter, we develop and explain with many practical examples 11 major principles with 18 subprinciples that have been lacking in policy modeling efforts and which we believe explain their failure to find sustained use in policy-making circles. Among our major points are that more successful policy modeling requires specific goal setting and interactions of policy makers and modelers from the outset; that policy modeling must be treated as a process rather than an end product with continuous model updating and use of accumulating data (in stable policy environments) so as to track structural change and facilitate learning; that more general equilibrium considerations must be incorporated to capture indirect effects while exploiting those relationships to reduce parameter spaces and thus increase the accuracy of conditional probability distributions; that general purpose data sets should be developed rather than general purpose models; and that operative political preference functions must be used to capture the role of special interest groups and reflect the possibilities for compensation that may be necessary in the realm of political feasibility.