_ANDR EE_<sup>_ ́_</sup> _DECEMBER - 2021_ _6_ least one other item can be found that carries very strong predictive power over its price.<sup>10</sup> Table A1 summarizes the raw data availability in each country. The table shows that the data of the analyzed countries on average include 7 food items measured across 27 markets, with around half of the survey data missing. The number of markets is generally a strong improvement in geographic detail when compared to traditional price indicators. For example, World Bank (2021) analyzes official CPI at the subnational component level in South Sudan. The analysis highlights the limits of relying purely on primary data collection, as the indexes are only compiled for the three major urban areas and not for any rural areas. This is despite an estimated 80% of the country’s population that lives in rural areas, and the fact that only a fraction of the remainder urban population buys goods at these three markets. Many urban households rely on markets that are less connected to international markets than primary markets in the capital city.

In total, the application uses data from 676 different markets. There are an additional 547 markets without observations, which are markets where WFP tracks commodities that are not covered by the application. These locations are not modeled but are spatial interpolated separately. Table A1 provides a country breakdown of data availability, for the country-product combinations analyzed by the paper. Data coverage of around 70%, as a percent of all _time × location with data_ , is relatively high, while 30% can be considered relatively low. Naturally, these figures depend on the selection of markets, food items, and the period of analysis. Higher data coverage could be achieved by focusing on a shorter time period, working with fewer markets, or tracking a very narrow basket. The statistics are thus not representative of the WFP data base, but represent the data selection of the paper which seeks to balance reasonable data coverage and data availability.

The raw price data of these selected food items remains a challenging source to deal with. Available data is regularly contaminated by outliers, which could be due to incorrect survey entries (e.g. a misplaced digit). Moreover, many local price series are incomplete and price quotes of different products may become available at different times and locations. The data availability constraints become more problematic when the interest is in tracking inflation, which requires prices quotes to be matched with historical quotes using a fixed time interval. For any given country, if the data selection was made such that the data coverage was 100%, the selection would collapse to zero cases unless the focus would be on an extremely narrow selection of data points that introduces strong sampling

> 10There may also be nonlinear patterns that carry important information about unobserved prices. First, a change in price ratio between two prices may be an indicator that a certain food of interest will likely trade against an elevated price ratio to some other food. Second, governments may use price controls and so certain prices may remain fixed for long periods. This can for example be observed in the bread prices in Afghanistan here (Andr ́ee, 2021). These fixed prices regularly break once input prices have risen too sharply, and such level shifts in the data can signal that all price levels must seek new price equilibria. To that regard, there may also be time-varying relations between various prices that, for example, change depending on key level shifts in the prices of certain foods.