Parameter uncertainty in policy planning models: Using portfolio management methods to choose optimal policies under world market volatility

Askar Mukashov

No WP2021-01, Working Papers of Agricultural Policy from University of Kiel, Department of Agricultural Economics, Chair of Agricultural Policy

Abstract: This paper suggests using portfolio management methods in policy planning models as a practical tool for determining optimal policy under model parameter uncertainty. We suggest that in addition to calculating the standard policy return estimates, policy options should also be analyzed from the risk perspective by using metrics that inform the effect of parameter uncertainty on policy impact variation. We demonstrate the approach in a Computable General Equilibrium model that analyzes pro-poor agricultural value chains in Senegal under world market uncertainty. We show that prioritizing the rice sector is the most effective policy in terms of expected policy return, but this policy is also associated with the highest risk, leading to an increase in poverty under unfavorable yet realistic scenarios. Much like diversified portfolios in finance, mixed policies that assume the rice sector's promotion combined with other sectors such as milk, vegetables, oilseeds, or fishery, can offer risk reduction at the cost of reduced expected policy return.

Keywords: policy analysis; CGE modeling; portfolio management; pro-poor growth (search for similar items in EconPapers)
JEL-codes: C68 D58 G11 I3 O13 O21 Q11 (search for similar items in EconPapers)
Date: 2021
New Economics Papers: this item is included in nep-agr and nep-rmg
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https://www.econstor.eu/bitstream/10419/229438/1/1745661077.pdf (application/pdf)

Related works:
Journal Article: Parameter uncertainty in policy planning models: Using portfolio management methods to choose optimal policies under world market volatility (2023)
Working Paper: Parameter uncertainty in policy planning models: Using portfolio management methods to choose optimal policies under world market volatility (2023)
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