Identifying key sectors of sustainable development: A Bayesian framework estimating policy-impacts in a general equilibrium

Transformation of the previous centrally growth-oriented economic systems to a sustainable bio-economy is a global political trend, where public policy is a key factor in making this successful. Designing effective and efficient policies requires understanding the linkages between policy choices and outcomes. Most existing studies are missing a direct link to policy choices and ignore fundamental model uncertainty present in policy analysis. We empirically estimate a sector-specific, nested two-stage policy impact function to address these shortcomings. We apply a Bayesian estimation approach that combines existing statistical data with a priori information from political experts, thus reducing data and estimation problems. This is linked with a Computable General Equilibrium to model the entire link from policies to outcomes. We derive a theoretical framework that allows the definition of indicators for key sectors of an efficient Pro-Poor-Growth strategy. In our generalized framework, we show that indicators based only on growth-poverty linkages might be misleading. To deal with model uncertainty inherent in the application, we derive a set of metamodels via simulations conducted under different model parameter settings and apply Markov Chain Monte Carlo sampling. Applying Bayesian model selection allows drawing statistical inferences on competing models to generate relatively robust policy-relevant messages even under model uncertainty. The approach is empirically applied to Ghana, Senegal, and Uganda, analyzing the allocation of public spending on agriculture under the Comprehensive Africa Agriculture Development Programme. [EconLit Citations: C11—Bayesian Analysis: General; C63—Computational Techniques, Simulation Modeling; D58—Computable and Other Applied General Equilibrium Models; O55—Africa; Q01—Sustainable Development; Q18—Agricultural Policy].