work could investigate the ability of real-time indicators observed in the past to produce _exante_ predictions of the economic losses implied by the actual values of predictors obtained during the COVID-19 pandemic. The accuracy of such predictions can be validated by comparing them against the actual GDP figures that usually come with a one-year lag. Also, the results point out potential caveats associated with the use of the “digital” real-time indicators, as a non-trivial proportion of people in low-income countries face limited internet access. With the availability of data at a higher geographic resolution, it would be possible to assess the models’ performance in settings with comparable rates of internet penetration—e.g., at the subnational level in a given country—and examine how the prediction accuracy depends upon the internet coverage in a more detailed way.

Despite these potential extensions, the analysis above can help shed light on how best to monitor the economic impact of a crisis like COVID-19 when survey data is scarce. Real-time indicators have several advantages in monitoring the impact of crises. They are often readily available, low cost, and do not involve face-to-face interactions, which is particularly important in a pandemic. Some indicators, particularly those relating to mobility, may be particularly valuable during the COVID-19 crisis when mobility was restricted. Nonetheless, the analysis suggests that Google search terms and food prices are promising indicators for tracking the trajectory of economic impacts during crises, particularly when combined in a parsimonious model that predicts postcrisis outcomes.

# References

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- <mark>Beyer, R. C., Chhabra, E., Galdo, V., and Rama, M. (2018).</mark> _<mark>M</mark> easuring districts' monthly economic activity from outer spac_ _<mark>e</mark>_ <mark>. The World Bank.</mark>

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- <mark>Bundervoet, T., Davalos, M. E., and Garcia, N. (2021). The short-term impacts of COVID-19 on households in developing countries: An overview based on a harmonized data set of highfrequency surveys. Policy Research Working Paper No. 9582. World Bank, Washington, DC.</mark>

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- <mark>Dietrich, S., Giuffrida, V., Martorano, B., & Schmerzeck, G. (2021). COVID‐19 policy responses, mobility, and food prices.</mark> _American Journal of Agricultural Economics_ .

- <mark>Donaldson, D., and Storeygard, A. (2016). The view from above: Applications of satellite data in economics.</mark> _<mark>J</mark> ournal of Economic Perspectives_ , _30_ <mark>(4), 171-98.</mark>

- <mark>Dong, E., Du, H., & Gardner, L. (2020). An interactive web-based dashboard to track COVID-19 in real time.</mark> _<mark>The Lancet Infectious Diseases</mark>_ <mark>,</mark> _<mark>20</mark>_ <mark>(5), 533-534.</mark>

- <mark>Elvidge, C. D., Baugh, K., Zhizhin, M., Hsu, F. C., & Ghosh, T. (2017). VIIRS night-time lights.</mark> _<mark>I</mark> nternational Journal of Remote Sensing_ , _38_ <mark>(21), 5860-5879.</mark>

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