Retail prices of nutritious food rose more in countries with higher COVID-19 case counts

We use national CPIs and retail food prices from international agencies to describe changes in average consumer prices paid during the COVID-19 pandemic. We begin with changes from one calendar month to the next as categorical variable, and for correlation with the timing of each country’s epidemic, we use a cubic function of the country’s cumulative monthly case count. Analyzes control for fixed effects associated with each country in its CPI and each market location for the food price data, to adjust for differences over space. In addition, because different countries report prices for different numbers of foods, we use sample weights in the regression models to show means and CIs for the average country. The weight is defined as the reciprocal of the price observation number of country i in time t for food group fg divided by the total price observation number in time t for food group fg. This ensures that each country is equally represented in the regression results, as prices from countries with fewer observations are given greater weight and vice versa. All analyzes and data visualizations were conducted using Stata / SE version 17.1 and R version 4.1.0. Descriptions of the datasets are detailed below.


Our price index data were downloaded from the FAO, which disseminates food and agriculture data for all countries and territories of the world through FAOSTAT at We downloaded the CPI and FCPI in September 2021 for 203 and 200 countries, respectively. We then downloaded the COVID-19 data from Johns Hopkins University (, complemented with data before January 2020 from European Center for Disease Prevention and Control (, and merged it with the CPI database. After deleting countries without COVID-19 information and dropping Venezuela and Zimbabwe, which had multiple currencies in use due to hyperinflation, the resulting dataset spans 181 and 179 countries with CPI and FCPI data, respectively, from January 2019 to June 2021 (Supplementary Fig. 1).

Individual item prices

Our food item prices come from the international EWS data assembled by three different organizations: the World Food Program (WFP) ‘s Vulnerability Analysis and Mapping program (, the FAO’s Global Information and Early Warning System data for Food Price Monitoring and Analysis ( and the United States Agency for International Development (USAID) – funded Famine Early Warning System Network (FEWS NET) ( Each of these provides monthly food price reporting for specific market locations in LMICs. Unlike CPI data, the EWS prices are not intended to be nationally representative of all consumer expenditures. Instead, their aim is to cover the cost of basic foods needed by people at risk of undernutrition, primarily in more remote towns and open markets than the high-volume markets captured by CPI.

We compiled all available EWS data in September 2021, initially including 789 food items and 109 countries. We then categorized those food items into 8 food groups of breads and cereals; pulses, nuts and seeds; fruits and vegetables; dairy and eggs; sugar and confectionary; meats, fish and seafood; and oils and fats. We kept observations for which prices were reported for January 2019 to June 2021. To focus on percentage changes, we normalized each price to be 100 in January 2019. To remove extreme outliers that are almost certainly caused by data-entry errors, we trimmed the top and bottom 0.5% of normalized prices by food group and dropped observations with missing COVID-19 or normalized prices, leaving a total of 369,088 observations in the final dataset.

Supplementary Figs. 2, 3 and 4 provide a visual summary of the price dataset, which contains 1,344 country-items for 499 food items from 88 countries. As shown in Supplementary Fig. 2, a total of 52 countries (59%) have prices for 10 or more food items. A majority of country items (63%) and countries (an average of 70% for various food groups) have prices updated through September 2020, and food groups are well represented over time as shown in Supplementary Figs. 3 and 4.

The country and item coverage described in this annex reveals some risk of selection bias in global averages. To the extent that non-reporting is most common for the places and nutritious food items whose supply chains are most stressed, leading to scarcity and high prices, our global averages over the observed data are a lower bound that understates the actual rise in food prices associated with COVID-19. Future work will examine patterns of non-reporting and changes in observed prices, with respect to a variety of country characteristics including COVID-19 exposure and policy responses.

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