phones.<sup>3</sup> The data presents changes in visits to these different places relative to some baseline value for a given geographic location, and it is available at the subnational level in some countries. These data are only obtained from mobile phone users with location services turned on but nonetheless can reflect useful signals in the data. We extracted Google mobility and search trends at the subnational level. The raw GPS data on mobility were first grouped by categories of places with similar characteristics, namely: grocery and pharmacy, parks, transit stations, retail and recreation, residential areas, and workplaces.<sup>4</sup> The resulting data shows how visits to categorized places change relative to the baseline period, which is the region-specific median value picked from the 5-week period between January 3 and February 6, 2020, just before the onset of the global pandemic. The Google mobility information is available only from February 2020, which naturally limits its usage—when tracking changes in economic activity—to the COVID-19 period.

Google search data—that is also available at the subnational level in some countries—provide information regarding the search intensity of a given combination of keywords for the target location and time period. Each data point can be interpreted as a share of a given request in the total searches in a given location and time period. However, as Abay et al. (2020) point out, the raw Google search series are not comparable across terms and geographic areas, which in our case are subnational regions. The primary reason is the following: when requesting Google search data for a given search term, it is possible to obtain the information only for a limited number of geographic locations per request. Thus, to obtain the information for the desired number of geographic locations, one has to file multiple requests in succession. The resulting data points are comparable across terms and geographic locations only within one request.

To achieve the desired comparability of Google search data points across different requests – and following Abay et al. (2020) – we constructed our data set as follows. First, we extracted the Google search data for every subnational region in our sample _and_ California jointly for a given search term and query period. In this case, the data on search terms from a given subnational region and California define one request. At the second stage, within each request, we normalized the time series from the target subnational region by the maximum value of the same search term index observed in California during the query period. Having repeated the above steps with every search term, we obtained a data set containing a list of search term indices comparable across different subnational regions. We extracted the information on the following search terms: food bank, assistance, online education, prayer, Bible, and Quran.

Finally, we extracted information on food prices from FAO’s Daily Food Price Monitor. The raw data set contains information on percentage changes in food prices in 180 countries relative to the baseline period (February 14, 2020). We have information on prices for the following food staples: apple, banana, bread, cheese, eggs, lettuce, meat, chicken, onions, oranges, potato, milk, and rice.

When aggregating data by regions or income levels, we weight each observation by the population in a given subnational region, normalized by the total country-level population in 2018. Thus, every country in our sample is given the same weight, whereas higher weights are given to more

> 3 https://www.google.com/covid19/mobility/ 4 This preprocessing takes place at Google.

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