_ESTIMATING FOOD PRICE INFLATION_ _5_ local currency quotes. As a result, the full data set is highly heterogeneous and challenging to work with. For example, while it contains common staples like rice, sorghum, and maize, that are monitored in many countries, there are also items specific only to one country.

The focus of the paper is purely on monitoring food price inflation, in a reasonably cross-comparable manner.<sup>8</sup> The strategy has been to extract from the very large set of price data set a stable set of commodity prices that are defined as homogeneously as possible across countries, and that are as widely available as possible across markets, while having the best coverage over time. The period of analysis starts in January 2007, or the next first date at which data was available. After carefully examining the prevalence of price data across commodities, markets, and time for each country, 43 foods were identified for which price data are reasonably abundant across multiple markets in at least one country. Tables A2 and A3 list the selected food items by country. The foods are either staples, agricultural produce, or dairy products.<sup>9</sup> Aside from non-foods, the selection excludes only fish and meat products due to the very high dietary heterogeneity in these foods. The resulting country-specific baskets thus consists mostly of staples, often similar in nature as the type of foods traded at global markets. For example, maize, sorghum, millet, wheat, vegetable oil, to name a few common food items, are also tracked in the World Bank Commodities Price Data (The Pink Sheet) that is used to construct the World Bank Food Price Index used to track international food price developments. In most cases, the different food items can substitute one another to certain degrees, or may act as complements, so that the prices of most foods will be strongly associated with the price developments in others. For example, in Afghanistan there are only four food items: bread, rice, wheat and wheat flour. These prices are likely strongly associated. First, rice and wheat are both cereals and likely to move together in price, as either will replace the other as sources of carbohydrates to a certain degree if their price ratios diverge too strongly. The price of wheat is likely a good predictor of the price of wheat flour, which in turn is used to make bread. Thus, a regression chain, that cycles over the individual food items, can make good imputations for all products as soon as the price of one of these goods is available. When more food items are observed, the chances increase that for each item with a missing price quote, at

> 8In several countries, the data covers non-food items that include common household items, prevailing day labor wages and local exchange rates, but generally the availability of such data is highly specific to the selected area. The proposed statistical methods, however, will not necessarily discriminate between types of commodities and could be applied to generate country-specific results on non-foods.

> 9The items covered are _bread, cooking oil, pulses, rice, sugar, wheat, wheat flour, beans, groundnuts, maize, millet, sorghum, bananas, cassava flour, maize flour, onions, potatoes, tomatoes, cassava, cocoyam, cowpeas, eggs, milk, peas, plantains, sesame, cabbage, carrots, cucumbers, dates, fonio, garlic, oranges, tomatoes paste, watermelons, yogurt, bulgur, cheese, chickpeas, lentils, maize meal, gari_ , and _parsley_ . The selection of these items has been fully codified in a set of flexible rules around minimum data-availability thresholds. The selected items in this way depend on the period of analysis. Generally, if the focus is on recent data, without requiring a long history, a higher number of food items could be selected. The code to scrape and run the price monitor has been made available as open source and can be customized for improved country-specific results.