Partial equilibrium (PE) trade models

PE trade models examine interactions within only one or a few industry sectors (e.g., agriculture) of the economy and assume that the impacts on the rest of the economy are exogenous, non-existent or small (Piermartini & Teh, 2005). There are a wide range of PE trade models ranging from specified single-sector single-country models through to multi-market models and multi-regional multi-commodities models. Multi-commodities models capture the demand and supply interrelationships among different commodities. These are specified as functions of prices and income in either linear or log linear behavioural equations. The PE framework can further incorporate exogenous variables such as technical change, population growth and household income but also feedbacks into other sectors (e.g., energy) impacted can be included exogenously (Piermartini & Teh, 2005; Tongeren et al., 2001).

A purpose of agricultural PE models is to provide detailed insights into the implications for national and international agricultural markets of existing and alternative agricultural policies. In particular, the models provide information on the effects of such polices on domestic supply, demand, trade volumes and global and domestic market prices (see Section 3.4). This information can be used to determine the welfare effects for consumers and producers and to analyse the impact on aggregate net imports or exports (see Section 3.5.1) (Tongeren et al., 2001; Blandford, 1990). However, more complex models can provide for both exports and imports of similar products and allocate these trade flows between regions to allow for differentiated markets and bilateral trade flows (Roningen, 1997). For this, there are two approaches (Cagatay & Saunders, 2003). In the pooled approach (non-spatial approach), the global market represents a pool to which each country supplies and others demand from, without further specification of bilateral relationships. In this approach, supply and demand for a good is aggregated into one figure and then equilibrated on a market-wide basis. In the bilateral approach, interactions between each buyer and seller for each commodity on the global market is explicitly represented, and trade flows between specific regions can be identified (Cagatay & Saunders, 2003).

The information that agricultural PE models can provide differ according to country coverage, commodity coverage and temporal properties. This is largely determined by the model structure and the way agricultural policies are incorporated in the model (Blandford, 1990).

PE models can treat commodities as either homogenous or heterogeneous. Commodities are called homogenous when the goods of one producer perfectly substitute for those of another. Each actor in the market is either a buyer or a seller of the goods, but never both. In contrast,

heterogeneous commodities are goods that are imperfect substitutes, thus there is product differentiation on the market. Each actor in the market may be both a buyer and a seller at the same time. The Armington (1969) method is one way to introduce product differentiation by assuming that products are differentiated by country of origin (Armington, 1969; Tongeren et al., 2001; Cagatay & Saunders, 2003).

Another important characteristic concerns the temporal property of PE models. In general, a model can be either dynamic or (comparative) static. Static models compare the new equilibrium state to the base equilibrium state after all changes have occurred and markets have cleared (Roningen, 1997). In contrast, dynamic models can be used to follow the accumulation of stock variables through time. Hence, they are more complex and resource intensive to run than static models (Roningen, 1997). A widely used approach to incorporate dynamic features into equilibrium models is to specify a recursive sequence of temporary equilibria for each time period. In each time period, the model is solved for an equilibrium based on the exogenous conditions predominating in that particular period. In between periods, stock variables are updated as a result of the equilibrium outcomes of the previous period (Roningen, 1997). Examples of recursive dynamic PE models are AGLINK of OECD, FAO World Model, FAPRI, GAPsi and LTEM (Tongeren et al., 2001; Cagatay & Saunders, 2003).

In PE models, key parameters include own- and cross-price elasticities of demand and supply systems, income elasticities of demand, substitution elasticities in supply systems, Armington (substitution) elasticities in import demand, among others. (Tongeren et al., 2001). There are two main approaches to estimating parameters in behavioural equations. Parameters can be econometrically estimated, typically by single-equation specifications, using either time series or cross-sectional data (Huang, Jun, Xu, Rozelle & Li, 2007). This can be a reasonably complex method to apply and is often not feasible due to lack of data. Parameters can also be incorporated into a model using a synthetic approach where initial estimates of parameters (e.g., elasticities) are obtained from secondary sources and other parameters in the given functional forms are calibrated to the initial equilibrium dataset (Tongeren et al., 2001).

Advantages of the PE approach include the level of commodity disaggregation, ease of traceability of interactions, transparency of results, relatively small model size and the relatively small number of behavioural variables. In addition, by concentrating on a limited set of factors such as a few prices and policy variables, PE modelling allows for a relatively fast and transparent analysis of a wide range of policy issues (Francois & Hall, 1997). These are the main features that draw many researchers to use PE frameworks for assessing the effects of agricultural and trade policy changes (Francois & Hall, 1997; Roningen, 1997). However, it is

often argued that PE models do not give a complete representation of the economy as they show only part of the economy and assume that the impact of that sector on the rest of the economy and vice versa are either non-existent or very small (Piermartini & Teh, 2005). However, as long as limitations are kept in mind, useful insights can be provided under time and data constraints that hinder more complex forms of analysis (Francois & Reinert, 1997). Also, PE modelling can be a useful analytical tool for environmental issues as they are often associated with specific production processes or products (Tongeren et al., 2001).

Since the 1990s, there has been an increasing interest in agricultural focused PE trade models by international institutions and organisation due to shifts in applied policies towards a more liberal agricultural industry (Cagatay & Saunders, 2003; Tongeren et al., 2001). The most widely used multi-country, multi-commodity PE trade models are FAO commodity model of FAO (FAO, 2003), AGLINK and MTM of OECD, ESIM and SWOPSIM of USDA/ERS (Tangerman & Josling, 1994; Roningen & Dixit, 1990), GLS model of Tyers and Anderson (1986), IMPACT of IFPRI (Rosegrant, 2012), FAPRI model of the Food and Agricultural Policy Research Institute (FAPRI, 2004) and GAPsi (Frenz & Manegold, 1988).