_ESTIMATING FOOD PRICE INFLATION_ _21_ surge in inflation that followed the 2020 pandemic and subsequent expansion in the global monetary base.

The paper used out-of-sample validation techniques to estimate the reliability of the augmented data. The share of missing data (20.4% to 79.18%), the number of markets (3 to 77), and the number of food items (3 to 16), varied widely across the country-specific applications, but the imputation methods were shown to remain robust across these aspects, even when data coverage was relatively low. A linear regression that correlated the cross-validated prediction performance with key properties of the country applications showed that data coverage itself was not a correlate of prediction accuracy. Instead, the strength of inflation and the natural volatility in prices are strong predictors of the imputation accuracy.

The accuracy of the imputations was judged by estimating the total price variation explained by the imputation models. On average, across the countries, the models predicted 85% of the observed price variation. In individual countries, the errors were in the 5% to 30% range of observed prices. This puts the accuracy of imputations in a similar range of that of the direct measure of prices at major urban markets in countries with well-established CPI methods. In particular, direct measure of prices is prone to error with respect to true price levels simply because of the natural volatility in prices. For example Lebow and Rudd (2003) estimated that measurement error in CPI change rates in the United States, a country with strong statistical capacities, could have been as high as 0 _._ 3% to 1 _._ 4% points over a period where the change rates were typically in a 3% _−_ 6% range, which, similarly, places the measurement error of official CPI change in a 5% to 45% range of true values.

An important result is thus that as long as incomplete and intermittent survey data has a at least a 20% to 40% rate of completeness, it can be augmented reliably with predictions to monitor subnational food price trends continuously. This contributes to a previously non-existing capacity to provide insights beyond the major (urban) markets where prices are formally measured with traditional methods. In situations with great spatial heterogeneity, and localized vulnerabilities — such as in fragile environments or during crisis — this can provide important new insight. Additionally, in those markets where prices are very sensitive to localized shocks, the statistical estimates may provide new opportunities to investigate local price dynamics with a similar confidence as one would have obtained otherwise with measured prices.

A cross-country analysis of inflation trends, and comparison with inflation trends at global markets such as captured by the FAO or World Bank Food Price Index, would be interesting for future analyses, but is beyond the scope of the paper as it would require dealing with the differences in exchange rates. In a few countries, such as the Republic of Yemen, local unofficial exchange rates are also surveyed by WFP. In a separate analysis, the methods of the paper have been applied to track prices of 23 items covering foods and non-foods in the Repub-