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

Mukashov, Askar Working Paper Parameter uncertainty in policy planning models: Using portfolio management methods to choose optimal policies under world market volatility Working Papers of Agricultural Policy, No. WP2021-01 Provided in Cooperation with: Chair of Agricultural Policy, Department of Agricultural Economics, University of Kiel Suggested Citation: Mukashov, Askar (2021) : Parameter uncertainty in policy planning models: Using portfolio management methods to choose optimal policies under world market volatility, Working Papers of Agricultural Policy, No. WP2021-01, Kiel University, Department of Agricultural Economics, Chair of Agricultural Policy, Kiel This Version is available at: https://hdl.handle.net/10419/229438 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ WORKING PAPERS OF AGRICULTURAL POLICY ISSN: 2366-7109 AGRICULTURAL POLICY WORKING PAPER SERIES WP 2021-01 Parameter Uncertainty in Policy Planning Models: Using Portfolio Management Methods to Choose Optimal Policies under World Market Volatility Askar Mukashov Kiel Institute for the World Economy University of Kiel, Ph.D. Programme Quantitative Economics The Agricultural Working Paper Series is published by the Chair of Agricultural Policy at the University of Kiel. The authors take the full responsibility for the content. Askar Mukashov Parameter Uncertainty in Policy Planning Models: Using Portfolio Management Methods to Choose Optimal Policies under World Market Volatility Kiel Institute for the World Economy University of Kiel, Ph.D. Programme Quantitative Economics Kiel, January 2021 WP 2021-01 http://www.agrarpol.uni-kiel.de/de/publikationen/working-papers-of-agricultural-policy About the authors: Askar Mukashov is a Junior Researcher at the Kiel Institute for the World Economy and Doctoral Candidate at the University of Kiel. His current research focuses on development economics, in particular on such areas as economic growth, inequality, fiscal policies and macroeconomic stability. Corresponding author: askar.mukasho[email protected] 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 JEL classification: D58, C68, O13, Q11, I3, O21, G110 Funding This research is part of the project "Modeling and evaluation of political processes to implement sustainable economic systems in industrial and developing countries" funded by the German Federal Ministry of Education and Research (German: Bundesministerium für Bildung und Forschung). Acknowledgments I thank Johannes Ziesmser, Christian Henning, and Manfred Wiebelt for their valuable comments and suggestions that significantly improved this work. Any errors or omissions are my own. Declarations of interest None. 3 1. Introduction Modern evidence-based policy requires to quantitatively formulate the impact of policy options considered by policymakers. One of the standard tools utilized for such purpose is the Computable General Equilibrium (CGE) modeling (Dixon and Rimmer, 2016; Taylor, 2016; Henning et al., eds, 2018). This type of model allows estimating the economywide implications of potential policy shocks, and in various fields, CGE models are used to compare policy options and define optimal policy intervention1However, policy conclusions based on the classic deterministic CGE models can be corrupted by exogenous model parameter uncertainty. Reparameterization of a CGE model can affect the quantitative and even qualitative impact of policy simulations (e. g., Fugazza and Maur 2008; Olekseyuk and Schürenberg-Frosch 2016; Phimister and Roberts 2017), and comparative analysis of policy options in a standard deterministic fashion might not allow defining optimal policy. This paper suggests using portfolio management methods in CGE-based policy planning models as a practical tool for defining optimal policy under model parameter uncertainty. In addition to the standard policy impact estimates, we suggest analyzing policy options from the risk perspective, with indicators of policy impact variation caused by parameter uncertainty being used as volatility/risk metrics. Similar to portfolio theory, policy options are explicitly characterized by both policy return and risk perspective, and the selection of optimal policy depends on policymakers’ risk/return preferences (Markowitz, 1952, 1959; Sharpe, 1994). To our knowledge, this is the first paper that suggests the use of portfolio theory in CGE-based studies in order to explicitly account for model parameter uncertainty. We apply our approach to the CGE-based analysis of agricultural value chains in Senegal. Poverty reduction and pro-poor growth are declared as of the most important goals of the country (World Bank, 2020b; African Development Bank, 2010), and many studies demonstrate that agriculture remains the most significant sector in achieving these goals (e. g. Diao et al. 2010; Valdés and Foster 2010; Klasen and Reimers 2017). However, developing a country-specific pro-poor agricultural policy requires a comparative analysis of each agricultural value chain (e. g. Pauw and Thurlow 2015; Chhuor 2017; Benfica and Thurlow 2017; Otchia 2018; Ferrari 2018). In the case of the Senegalese CGE model with a focus on agricultural policy planning, the set of model parameters representing world markets is essential. In the context of 1For example, Ojha et al. (2013) use a CGE model of India and compare economic and distributional consequences of policies that promote growth of physical capital, human capital or technological progress. Liu et al. (2015) use China’s financial CGE model to investigate the effectiveness of various monetary policy options in response to oil price shocks. Ge and Lei (2017) use China’s bioethanol CGE model and argue that demand incentives are better than supply incentives for GDP growth, energy saving, and emission reduction. Benfica et al. (2019) use a CGE model of Mozambique and show that the government should have reallocated resources towards agricultural research and extension, as this is the most effective policy at raising growth and reducing poverty in all regions of the country. 4 increased volatility on the international markets (World Bank, 2020c), and recommendations for the policymakers in developing countries to explore counter-cyclical mechanisms for external shocks (FAO, 2011), the standard assumption about constant world market prices in policy planning models becomes particularly weak. When analyzing Senegal’s agricultural value chains, we use portfolio management methods to explicitly account for the world market uncertainty. As a domain of endogenous policy outcomes, we consider the Poverty-Growth Elasticities (PGE) of ten primary agricultural sectors, and as a domain of uncertain model parameters, we consider the Rest of the World (RoW) parameters - that is, the world market prices and the country’s current account. Similar to financial portfolio management, we treat indicators representing expected PGE and its variation due to RoW volatility as return and risk metrics of policy options. Furthermore, because PGE of agricultural sectors are not perfectly correlated (due to different production structures, trade characteristics, economic linkages, etc.), we consider portfolio diversification methods that can offer risk reduction (for a given return) or increase of return (for a given risk). In addition to standard sector-specific policy comparison scenarios, we sample mixed-policy scenarios and estimate the PGE of all policy scenarios under various RoW scenarios. We demonstrate that a policy that exclusively promotes the rice sector is the most effective in poverty reduction when only looking at expected policy return, but this option is also associated with the highest risk. Under the least favorable RoW scenarios, this policy can even lead to an increase in poverty. Mixed policies that assume the rice sector’s promotion combined with other sectors can offer risk reduction at expected return costs. We show that the promotion of milk, vegetables, oilseeds, or fishery sectors can mitigate the risks associated with the country’s reliance on the rice sector. Given the government’s current prioritization of the rice sector in response to the 2008 food crisis (Liesbeth et al., 2013), the suggested application of widely known portfolio management principles can be particularly beneficial for Senegal’s practical policymaking. The rest of this paper proceeds as follows. Section 2 provides a brief overview of the CGE modeling literature that addresses model parameter uncertainty and describes the suggested use of portfolio methods in the CGE-based policy studies. Section 3 describes the application to the analysis of the Senegalese pro-poor agricultural value chains. Finally, section 4 highlights the potential implications for policy analysis and concludes. 2. Portfolio management methods in CGE modeling Policy choices based on the estimates and point predictions produced by deterministic models can be very fragile, as policy conclusions often rest on critical assumptions, and communication of the uncertainty either of the researchers with the policymakers or of the policymakers with the public can be rarely found in practical policy analysis (Manski, 5 2011). In CGE modeling, the choice of exogenous model parameters is critical, as it can often affect the quantitative and even qualitative impact of policy simulations (see e. g. Fugazza and Maur 2008; Olekseyuk and Schürenberg-Frosch 2016). As a response to this problem, the concept of Systematic Sensitivity Analysis (SSA), popularized by Arndt and Pearson (1998), is increasingly used in CGE-based studies. The concept addresses the problem of parameter uncertainty by treating exogenous CGE parameters as random variables. The SSA implies estimating the variation of CGE endogenous variables of interest by sampling exogenous model parameters from assumed or estimated distributions. For example, Valenzuela et al. (2007) suggest using the SSA to validate the global agricultural CGE model by sampling output shocks and comparing simulated and historically observed price volatility in various world regions. Webster et al. (2008) use the SSA to address the uncertainty in projections of emissions and atmospheric stabilization costs for five climate scenarios. Phimister and Roberts (2017) use the SSA to investigate the implications of allowing uncertainty in exogenous shocks when modeling a new onshore wind sector in North East Scotland. Chatzivasileiadis et al. (2018)) conduct SSA to address parameter uncertainty when analyzing the effects of sea-level rise on the global economy. Mukashov et al. (2019); Ziesmer et al. (2020) extend SSA’s principles to policy parameters and demonstrate how the model uncertainty can affect policy impact estimations and optimal policy choice. Methodologically this paper follows recent literature strands and uses the SSA methods to represent the impact of parameter uncertainty on endogenous variables of interest. However, our approach’s peculiarity is the suggestion to integrate SSA methods into the portfolio management framework. Our approach is mainly targeted at those cases when a comparison of policy options under standard SSA methods fails to define optimal policy. Specific policy options might be robustly superior regardless of the model’s reparameterization. In this case, SSA sampled parameters/scenarios affect policy impact estimates, but not policy rankings themselves. However, if policy scenarios are not robust under certain SSA reparametrizations (e. g., Fugazza and Maur 2008; Olekseyuk and Schürenberg-Frosch 2016), it becomes difficult to define univocally optimal policy. As a solution to this problem, we suggest explicitly representing risk/return trade-offs of policy options and use portfolio management principles to select an optimal policy. The important prerequisite to refer to portfolio management tools is the standard tools’ inability to define optimal policy. Therefore, as a first step, it is necessary to investigate important shock transmission mechanisms of a specific model and define the set of uncertain model parameters that can affect policy impact estimates. The SSA methods should then be used to compare policy options under the uncertain exogenous model parameters; if none of the policies is robustly superior under the SSA sampled scenarios, portfolio management tools can be used. 6 Modern portfolio and risk management methods are very diverse, and the specifics of mathematical finance concepts are beyond this paper’s scope. In our application to CGE-based analysis of agricultural value chains in Senegal, we use simple portfolio theory concepts defined in Markowitz (1952, 1959); Sharpe (1994); Jorion (2007). To represent risk/return trade-offs, we use simple metrics such as the average value of policy impact (represents expected policy return), standard deviation of policy impact (represents the volatility/risk of a policy), and minimum policy impact (represents the worst-case policy return). Furthermore, because considered agricultural policy options are not perfectly correlated, much like diversified portfolios in finance, we consider diversified (mixed) policies. We rank all policy options based on expected return or risk and represent a (sub)set of efficient policies where higher expected return requires taking more risk, or lower risk requires lowering return expectations. Thus, investors (policymakers) faced with a tradeoff between expected return and risk have to select an optimal policy based on their risk/return preferences. Although our method does not offer a universal tool to define optimal policy, the suggested application of portfolio management methods contributes to practical policymaking by offering widely-known financial instruments for direct communication of model uncertainty with policymakers. In this context, our framework can encourage policymakers to express their risk and return preferences explicitly and, therefore, increase the transparency of their policy choices (see Manski 2011, 2018, for more details). 3. Agricultural policy in Senegal under the world market uncertainty 3.1. The standard approach to define the pro-poor agricultural value chain We use the recursive-dynamic CGE model of the International Food Policy Research Institute (IFPRI) developed by Löfgren et al. (2002) and Diao and Thurlow (2012) and the 2015 Social Accounting Matrix (SAM) constructed by Randriamamonjy and Thurlow (2019). We select five years as our simulation horizon (2020-2024)2and tailor the IFPRI CGE model and the SAM to reflect specific adjustment possibilities of the Senegalese economy in the medium-term (see appendix A.1 and A.2 for more details). Used SAM and selected functional forms and closures define the set of CGE parameters, and we use different sources, estimates, and approximations to assign fixed parameter values (see appendix A.3 for more details). 2Years 2015-2019 are run in the background to approximate already known developments. 7 As a set of policy parameters, we consider the Total Factor Productivity (TFP) growth of ten primary agriculture sectors (table 1, col. 1-2). In general, productivity increase means that a country can increase output with the same available production factors (capital, labor, and land). Consequently, households (who own most of the country’s production factors) should receive a higher income and increase consumption. In turn, a productivity increase of agricultural sectors should particularly benefit poorer rural residents3, who own most of the agricultural capital, labor, and land. However, depending on the sectors’ structural characteristics, the effectiveness of specific sectors within agriculture might vary significantly. Table 1: Considered agricultural sectors Sector Short GDP, % Export in output, % Import in consumption, % TFP growth per year Standard PGE Rank Sorghum, millet sorg 1.46 - 0.0 1.49 -1.04 10 Rice rice 1.15 19.4 53.6 2.94 0.84 1 Groundnuts gnut 1.01 6.0 0.0 2.70 -0.53 9 Other oilseeds oils 1.48 4.3 1.2 1.91 -0.40 7 Vegetables vege 1.78 10.2 4.6 1.49 -0.38 6 Fruits frui 1.55 7.4 3.9 1.30 -0.40 7 Cattle catt 1.21 0.0 0.2 2.53 -0.30 4 Poultry poul 0.85 0.1 0.3 2.99 -0.30 4 Raw milk milk 1.20 - - 2.04 0.02 2 Fishery fish 1.43 8.2 0.0 2.22 -0.29 3 Source: SAM (2015) and CGE simulations by the authors. Following Wiebelt et al. (2020), who conducted the standard CGE-based policy analysis of the Senegalese agricultural value chains, we define standard TFP sectoral scenarios such that 1 percent growth of total agriculture by 2024 is achieved uniquely by respective sectors (table 1, col. 6). Then, obtained policy impact estimates represent sectors’ effectiveness in poverty reduction per 1 percent of agricultural growth. Therefore, we define policy impact estimates under standard TFP sectoral scenarios as (semi) Poverty-Growth elasticities (PGE4, table 1, col. 7) and rank sectors (table 1, col. 8). We obtain a similar conclusion to Wiebelt et al. (2020) that rice and milk are the only sectors that can reduce the national poverty headcount, with the rice sector being outstandingly more effective than milk. 3.2. Uncertain model parameters The rice sector’s estimated high effectiveness can be primarily attributed to its trade intensiveness (table 1 1, col. 4 and 5). The increased output should be absorbed by 3Per-capita consumption of rural households is 3.7 times lower than of urban residents; poverty incidence in rural areas is 57 percent compared to 26 percent in Dakar and 41 percent in other cities (Randriamamonjy and Thurlow, 2019; ANSD, 2013). 4In order to operate with the term ‘return’ in the meaning similar to finance, we calculate PGE as percentage difference of poverty headcount under the no-policy vs. policy scenario. In other words, positive PGE indicates poverty reduction and vise-versa. 8 representing the expected policy impacts can be treated as expected policy returns, and indicators representing the dispersion of policy impacts due to model reparameterization can be used as risk metrics. Furthermore, much like diversified portfolios in finance, it might be beneficial to consider mixed policies that can offer risk reduction for a given return or increase return for a given risk. As a case study, we investigate pro-poor agricultural policy in Senegal and demonstrate that a simple comparison of policy options under the SSA sampled RoW uncertainty scenarios does not define optimal policy. Therefore, we apply portfolio management methods and analyze the spectrum of agricultural policy options from a risk and return perspective. We find that the policy that exclusively promotes the rice sector is the most effective in poverty reduction in expected policy return, but this option is also associated with the highest risk. Mixed policies that assume rice promotion combined with promotion of other sectors can offer less risk at the cost of reduced expected return. While the optimal policy’s exact determination depends on policymakers’ risk/return preferences, it is possible to conclude that the promotion of milk, vegetables, oilseeds, or fishery sectors can help to mitigate the risks associated with the current country’s prioritization of the rice sector (Liesbeth et al., 2013). The suggested concept can be used in other CGE-based studies aimed at comparing policy options. A set of varying/uncertain model parameters and target outcomes can be adjusted for specific research interests, and portfolio management methods can be used when the SSA methods do not establish a robustly superior policy option. For example, many environmental and ecological CGE models are naturally characterized by high uncertainty because they are used for long-term simulations and projections (e. g. Webster et al. 2008; Chatzivasileiadis et al. 2018). Consequently, our method can be used in such models if it is necessary to compare various policy options and define optimal policy intervention. Furthermore, applying commonly known portfolio management principles can find its use in practical policymaking as it allows to communicate model uncertainty with the policymakers explicitly and, therefore, increase the transparency of their policy choices (see Manski 2011, 2018 for more details). 15 References African Development Bank, “Republic of Senegal Country Strategy Paper 2010 - 2015,” 2010. Country Operations Department West Region. Aguiar, Angel, Badri Narayanan, and Robert McDougall, “An Overview of the GTAP 9 Data Base,” Journal of Global Economic Analysis, may 2016, 1(1), 181–208. ANSD, “Deuxiéme enquéte de suivi de la pauvreté au Sénégal,” ESPS-II 2011, Agencenationale de la statistique et de la démographie, Dakar, Sénégal 2013. Arndt, C. and K. R. 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Table 9: Functional forms and closures of the CGE Block Category Form / closure (endogenous variables) Production Value-added Constant Elasticity of Substitution (CES) Intermediate Leontief Top of technology Leontief Trade Import CES Export Constant Elasticity of Transformation (CET) Consumption - Linear Expenditure System (LES) Closures Numeraire The exchange rate is the model numeraire5; Consumer Price Index and domestic producers’ price level are flexible; Rest of the World the current account balance and world market prices are given exogenously (exogenous shocks) Government Fixed government tax rates; (dis)savings adjust to available net revenues; Savings/Investment Balanced closure6 Factors Fully employed and mobile7 5The exchange rate of the CFA franc to the French franc (and later euro) is fixed since 1994. See Boogaerde and Tsangarides (2005) for more details. 6‘S-I’ closure 4, with enterprises adjusting marginal propensity to save. See Löfgren et al. (2002) for more details. 7Capital: ‘putty-clay’ assumption, see Diao and Thurlow (2012) for more details. 20 A.2. SAM adjustments The original SAM by Randriamamonjy and Thurlow (2019) has 462 accounts, including 262 (regionalized) production activities or sectors, 75 commodities, 45 (regionalized) factors of production, 65 (regionalized) household types, and other institutional, tax, and savings or investment accounts. However, some accounts in the SAM were incompatible with our theoretical, empirical, or computational limitations. In particular: •Certain commodities are reexported (export >domestic production); •Certain sectors, factors, or households are tiny and can cause computational problems for the GAMS solver; •Public goods and services are produced and consumed by both private and public entities. Due to these incompatibilities, we perform the following adjustments: •we net out imports for those commodities that have the reexport problem; •Sectors or commodities that are less than 0.5 percent of GDP or absorption are aggregated with the closest matching sectors or commodities; •Household and factor accounts are aggregated within regions; •Public goods and services are consumed and produced only by the public sector. This amendment allows us to emphasize that public goods and services should be outside of the consumers’ demand function and that the prices of these specific goods are determined by production costs only. The resulting SAM (available upon request) has 263 accounts, including 183 accounts representing (regionalized) activities, 48 accounts representing commodities, 12 accounts representing primary production factors, 9 accounts representing households. A.3. Sources used to define model parameters •We use approximations and assumptions when the necessary estimates are not available. For example, we use Aguiar et al. (2016) to define values of elasticity parameters; •based on the observed productivity decline over 2006-2015 (see IMF 2017 for more details), we assume zero productivity growth for all non-policy sectors; •we use Euro as a trade currency and convert US dollars growth rates of world market prices to Euro or French Franc growth rates; •full parameter specification of all model simulations is available upon request. 21 Table 10: Sources used to define model parameters Parameter Used sources Non-policy TFP Own assumption Factor supply ILO (2020) for labor; FAO (2020) for land; Feenstra et al. (2015) for economywide capital Population UN (2020) World market prices World Bank (2020a) and FRED (2020) for services Current account deficit IMF (2020) Production and trade elasticities Based on Aguiar et al. (2016) Income elasticities Own estimates based on ANSD (2013); King and Byerlee (1978)) Frisch parameters Own estimates based on ANSD (2013); World Bank (2020d); Ramprakash et al. (1979) A.4. Sampling RoW scenarios •we use the same sources as in Table 10 and construct the historical sample of yearly growth rates throughout 1980-2019 and estimate moments of the multivariate Gaussian distribution (Table 11); •we use Latin Hypercube Sampling implemented in the R-package ‘EnvStats’ by Millard (2013) and sample RoW scenarios from dimensions of specified multivariate Gaussian distribution; •in order to avoid computational problems with the GAMS solver in the final years of model simulations, we truncate 0.1 percentiles from left and right. Table 11: Mean values, standard deviations and correlations pw_agri pw_MUVi pw_ener pw_fert pw_mtmn pw_serv fsav mean 0.00 0.00 0.00 0.00 0.00 1.26 0.00 σ11.41 8.23 23.95 24.32 20.55 2.84 30.96 pw_agri 1.00 0.69 0.41 0.65 0.50 0.11 0.21 pw_MUVi 0.69 1.00 0.43 0.44 0.41 0.03 0.12 pw_ener 0.41 0.43 1.00 0.43 0.43 -0.09 0.40 pw_fert 0.65 0.44 0.43 1.00 0.38 0.05 0.61 pw_mtmn 0.50 0.41 0.43 0.38 1.00 -0.05 0.18 pw_serv 0.11 0.03 -0.09 0.05 -0.05 1.00 0.00 fsav 0.21 0.12 0.40 0.61 0.18 0.00 1.00 22 A.5. Sampling of mixed-policy scenarios •we use R-package ‘xsample’ by den Meersche et al. (2009) to sample 1000 mixed policy scenarios; •the Markov Chain Monte Carlo method produces sampled solutions for the underdetermined problem with linear equality constraints: 10 X i=1 shareitfpi=tfptot subject to tfpi>0(1) where shareiis share of sector iin total agricultural GDP; tfpiis sampled TFP shock of sector i; tfptot = 1 is the targeted TFP growth of the whole agriculture (1 percent by 2024); •produced sample is distributed (jointly) uniformly over the feasible space of TFP parameters, and we assume that the sample size of 1000 scenarios is sufficient to cover the parameter space of potential policy options. 23