# <u>Calibrated model behavior</u>

Recall that the model is calibrated around a set of prices and quantities, together with two distributions of supply shocks that the model randomly draws on in simulation. Because of this structure, the mean values of the simulated series will be near their calibrated values. However, the variations in the simulated variables are affected by the sets of randomly drawn supply shocks. Because these distributions are not specified directly, we use the ex post variability in the simulated distributions of price, demand, supply and trade as to provide a validation check on the aggregate consequences of our ex ante parameter selections. With this as background, the simulated and historical coefficients of variation are reported in Table 3. As the table makes clear, orders of magnitude are similar between the historical and simulated distributions.

As emphasized in our earlier discussion of the link between storage and price expectations, all other things equal, low inventories should be associated with both higher expected price and more volatile price outcomes. This relationship is evident in Figure 3, which maps next-period simulated prices and expected prices against carry-in stock levels. Expected prices rise as carry-in inventories fall and the model-generated prices become more dispersed.

Before moving to a discussion of alternative policies, a couple of points should be made about trade and storage outcomes in the baseline model. First, given the large gap between production and demand in MENA, the simulated model never generates an outcome in which wheat is exported from the region. This is consistent with expectations since MENA has not been a net exporter of wheat in the last fifty years.

The second point has to do with speculative storage—that is, storage that is held in order to profit from expected temporal price changes. Data series, including the data reported in Figure 2, contain all forms of storage: speculative storage, public storage, and pipeline storage that is needed to keep shipping and processing operations running smoothly. It is impossible to isolate speculative storage in the data; however, it is speculative storage that is key to the model’s pricing mechanics. Conceptually, when one country is a perpetual importer, speculative storage will always take place in the exporting country, absent physical impediments to trade, large differences in physical storage costs or changing transfer costs (Gouel and Jean, 2012). This is because an importing speculator pays interest on the costs of importing in addition to interest on the price of the commodity, a cost that a speculator in an exporting country does not have to pay. Unless there is an offsetting benefit to storing locally, competition will result in adjustments in trade rather than storage in response to changes in availability in the importing country.<sup>17</sup> This general finding applies to our model outcomes as well, and speculative private storage always takes place in RoW.

> 17 The result also depends on the assumption that trade is instantaneous in our model. If trade takes time, it is rational to store while importing in order to be able to wait for the next shipment (Coleman, 2009). In a related way, variations in production generate greater price variability in landlocked and isolated countries (Byerlee, Jayne and Myers, 2006).

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