66 CHAPTER 3


We now look at the characteristics of each commodity. We would expect retail price changes to be lower than wholesale price changes, which is what we find on average, although the effect is not significant. Only marginally lower price changes occurred for processed and semi-processed goods. Each of these dummies predicts price changes that are 5–10 percentage points lower than the base (wholesale or unprocessed).


Turning next to regional effects, we find that relative to the base conti- nent, South America, price changes were low in East Asia but high in Central America, South Asia (except India) and Sub-Saharan Africa. The large price rise in Africa is somewhat surprising, as is the 33.5 percentage points extra rise in prices in landlocked countries relative to coastal countries. With the excep- tion of Afghanistan, all landlocked countries in our sample are in Africa, so we also ran a regression excluding the landlocked dummy variable. This regression shows that the average difference between price changes in Africa and South America is 33 percent. Thus there is something of an African price puzzle, which future research would do well to explore further. Finally, we look at individual commodities. Unlike that in Table 3.3, the regression model used in Table 3.4 nets out the effects of retail versus wholesale, processed versus unprocessed, and regional effects. Relative to a base consisting of such goods as barley, lentils, meats and seafoods, sorghum, teff, and other less common food types, price changes were highest in maize, potatoes, rice, and wheat; lower in beans and millet; and much lower in bananas and cassava. Price increases that are highest in those commodities characterizing the global food crisis again suggest high rates of transmission. However, future work could use other variables to explain cross-country pat- terns in price changes, such as monetary policies or trade and exchange rate policies.


Finally, we look at food-price impacts at the country level using GIEWS (2009). This is challenging, given the incomplete and unbalanced coverage of the data, but essentially we try to construct a food price index using several steps designed to make the data more comparable. First, a cross-country and cross-commodity regression (similar to the one used in Table 3.4) was used to determine price change differences between wholesale and retail commodi- ties and between unprocessed, semi-processed, and fully processed products. Wholesale, semi-processed, and fully processed items were then adjusted to give an unprocessed retail price equivalent. Next we again netted out sea- sonality effects by taking the average of differences between prices in each month of 2008 over corresponding months of 2007. Finally, we aggregated multiple commodities into a single price index using dietary energy shares as weights. Note, however, that in a few cases total energy shares of all com- modities for a country were quite low (in some cases a little less than 30 per-


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