The Economy-Wide Multi-Market (EMM) model is based on neoclassical microeconomic theory. In the model, an aggregate producer represents a specific zone’s production of a specific sector. In this application for Ethiopia, there are a total of 2,352 (42 sub-sectors x 56 zones) such representative producers. Consistent with the setup of many other multi-market models, the supply function, rather than the production function, is used to capture each representative producer’s response to market conditions. Specifically, the supply functions are derived under producer profit-maximization and based on the producer prices of all commodities (including the prices for the 10 non-agricultural commodities). Risk and market imperfections are not taken into account and therefore do not affect producers’ profit-maximization decision in the model.
In the crop sub-sectors, the supply functions have two components: (i) yield functions that are used to capture supply response to the own prices given farmland allocated to each crop and (ii) land allocation functions that are functions of all prices and hence are responsive to changing profitability across crops given the total available land. The own-price elasticities employed in the yield functions are the combination of authors’ estimates, assumptions, and results drawn from other studies, while the cross-price elasticities in the area functions are calibrated according to the share of each commodity in regional total production.
The production of major staple crops and livestock products involves a variety of technologies. For staple crops, modern inputs and their effects on crop productivity are captured through the identification of 15 different technologies; maize production, for example, incorporates four primary modern inputs —fertilizer, improved seeds, pesticide, and irrigation (individually or jointly)—and also includes production without modern inputs. While the model captures the average difference in crop yields across technologies, the marginal effect of increased use of an input for a given technology is not captured because input uses are not explicitly included in the supply function.
The yield gaps for a specific crop among the 15 technologies are defined at the zonal level and are consistent, by zone, with data from the national agricultural sample surveys for 1997 and 2000. Data on irrigation was also available for cash crop production and hence was employed in supply functions for those crops.
For livestock, the model captures the productivity difference between traditional and modern technologies. For example, three types of cattle are raised to produce beef: draught animals, from which beef is a by-product; beef animals, using traditional technology; and beef stock, using improved technology. The productivity (yield) gaps resulting from the use of different types of technologies in animal production are reflected in the supply function. Moreover, the supply function also captures the difference in feed use between traditional and modern technologies. Livestock production under modern technology requires feed grain, while under traditional production, it assumes feeding via grazing only. The feed-grain demand function is therefore defined only for improved technology and is a function of grain crop prices. Different technologies are similarly defined for dairy, poultry, and sheep and goats.
Demand functions are also disaggregated to the zonal level on per capita (rural or urban) basis. A representative consumer’s demand for each consumption good is derived from maximizing a Stone-Geary utility function and the subsistence level of consumption is calibrated to the first quintile households' consumption (rural and urban separately). Data used to calibrate the demand functions are from the 1999/2000 Household Income, Consumption, and Expenditure Survey (HICES [CSA 2000]). Both income and price elasticities for any specific commodity vary across zones due to different consumption patterns and income levels (see Wamisho and Yu (2006) for the estimation of income and price elasticities). Such differences not only imply that the aggregate effect of consumers’ market responses is often non-linear and much more complicated than when demand is defined at the national level, but also indicate the possible differential effect on poverty reduction with similar income increases. These are a focus of model simulations which will be discussed later.
Distinguished from most multi-market models that are usually partial equilibrium in nature, the per capita income at the zonal-level is an endogenous variable in the EMM model. It is determined by the zonal-level value added divided by population, rural and urban, respectively. Because of such a setup, the model has a general equilibrium nature, which allows production and consumption decisions to be linked at the zonal level. Similar to a CGE model, intermediate inputs are explicitly included in the model through a fixed input-output relationship with the sector’s production. The IO coefficients are drawn from the SAM developed in Taffesse, Belay, and Wamisho (2006) for the purpose of growth linkages analysis. The aggregate of agricultural production value added equals agricultural GDP (henceforth, AgGDP), and the sum-total of agricultural and non-agricultural value added equals national GDP. 22 Both AgGDP and GDP are endogenous in the model.
The model does not capture bilateral trade flows across sub-national regions, although it does identify sub-national regions as being food surplus or deficit by comparing regional-level demand and supply for total food commodities. While producers and consumers in different regions operate in the same national markets for specific commodities, prices can vary across regions due to differences in transportation and market costs. For example, domestic marketing margins are defined at the regional level according to the distance to Addis Ababa, which represents the central market for the country. For a food surplus region, food crop prices faced by local producers are equal to the prices in the central market subtracting marketing margins, while for a food deficit region, local prices are higher than those in the central market due to marketing margins.
Information on this model is provided for AGRODEP members but the model in itself is not provided or maintained by the AGRODEP team. The AGRODEP team will be happy to help the AGRODEP members to be in touch with the model developers but will not provide guarantee or provide direct support in the use of the model.