Dynamic modelling of agricultural policies: The role of expectation schemes

Abstract

The highly disputed effects of agricultural trade liberalisation are mostly simulated with static models. Our main objective in this paper is to evaluate the robustness of the static simulation results to the consistent modelling of dynamic behaviours and to the linked specification of price/return expectations. Focusing on a complete trade liberalisation scenario of arable crop markets by developed countries, we find that available static results are quite robust to dynamic specifications and to most expectation schemes. Endogenous market fluctuations due to expectation errors may appear following trade liberalisation. These fluctuations are nevertheless limited by the many feedback effects revealed by our general equilibrium framework.

Impacts of interannual climate and biophysical variability on global agriculture markets

“…The expected rental profit, , earned from land use using irrigation option (irrigation or rainfed) and fertilizer technology (high or low fertilizer) for producers in water basin , region , and period can be derived from the zero pure profit condition, as shown in Equation (1). We employ the Nerlove Adaptive expectation (Equation 2), which has been extensively studied in the literature [33][34][35] and also explored in recent studies 12,14,36 . It depicts that the expectation of a variable ( 9 ) is adaptively revised in proportion to the difference between the previous observation ( 9JK ) and the previous expectation ( .…”

“…Previous studies focused on assessing economic consequences of climate impacts in a future period (e.g., 2050) as most economic models were designed for mid-or long-term projections. More importantly, perfect foresight has been a standard assumption used in economic modeling even though its lack of realism has been criticized 12,13 . With perfect foresight, agricultural producers can perfectly predict future climate and market information and make adaptations accordingly and immediately (e.g., adjusting land use and management practices to compensate for changes in productivity).…”

“…That is, agricultural producers make production and land allocation decisions at planting time based on their expectations of prices and yield at harvesting time and adaptively adjust their future expectations with new information. In contrast to perfect foresight, adaptive expectation offers an intuitive and effective approach to characterize the "endogenous" market fluctuations in agricultural market equilibrium modeling 12 . Furthermore, we rely on biophysical yield projections estimated from combinations of two climate models, HadGEM2-ES and GFDL-ESM2M and two crop models, EPIC and LPJ-GUESS under representative concentration pathway (RCP) 8.5.…”

Tariff Impact on Industrialization in Tanzania: Evidence from Edible Oil Sub-Sector

“…The PET-EDTASIM model framework used for this study is a recursive dynamic computable general equilibrium model developed by Decaluwe et al (2013). The model does not take into consideration intertemporal optimization in the way that intertemporal dynamic models do (Féménia and Gohin 2011;Pratt et al 2013;Taylor 2016;Van Ha and Kompas 2016;Minor et al 2017;Raihan et al 2017). Therefore, dynamism is handled in two ways in PEP-EDTASIM; first, each period is considered as static equilibrium, with starting variables inherited from the previous period and, second, there is an accumulation of capital over time that is assumed to accelerate economic growth.…”

Computable General Equilibrium Models for Policy Evaluation and Economic Consequence Analysis

“…18and fixing the complementary variable p M i,r ). 4 More analytically-oriented papers have investigated the manner in which the macroeconomic effects of shocks are modulated by imperfect competition (Konan and Assche, 2007), agents expectations (Boussard et al, 2006;Femenia and Gohin, 2011), and international mechanisms of price transmission (Siddig and Grethe, 2014). Still others studies advance the state of modeling, extending the canonical model beyond just trade into the realm of international macroeconomics by introducing foreign direct investment and its potential to generate domestic productivity spillovers (Lejour et al, 2008;Latorre et al, 2009;Deng et al, 2012), and financial assets and interregional financial flows (Maldonado et al, 2007;Lemelin et al, 2013;Yang et al, 2013).…”