Estimating Food Price Inflation from Partial Surveys

The traditional consumer price index is often produced at an aggregate level, using data fromfew, highly urbanized, areas. As such, it poorly describes price trends in rural or poverty-stricken areas, where largepopulations may reside in fragile situations. Traditional price data collection also follows a deliberate sampling andmeasurement process that is not well suited for monitoring during crisis situations, when price stability maydeteriorate rapidly. To gain real-time insights beyond what can be formally measured by traditional methods, this paperdevelops a machine-learning approach for imputation of ongoing subnational price surveys. The aim is to monitorinflation at the market level, relying only on incomplete and intermittent survey data. The capabilities arehighlighted using World Food Programme surveys in 25 fragile and conflict-affected countries where real-time monthly foodprice data are not publicly available from official sources. The results are made available as a data set that coversmore than 1200 markets and 43 food types. The local statistics provide a new granular view on importantinflation events, including the World Food Price Crisis of 2007–08 and the surge in global inflation following the 2020pandemic. The paper finds that imputations often achieve accuracy similar to direct measurement of prices. Theestimates may provide new opportunities to investigate local price dynamics in markets where prices are sensitive tolocalized shocks and traditional data are not available.