How Well Did Real-Time Indicators Track Household Welfare Changes in Developing Countries during the COVID-19 Crisis? - Newhouse, David; Swindle, Rachel; Wang, Shun; Merfeld, Joshua; Pape, Utz; Tafere , Kibrom; Weber, Michael (World Bank, 2024)
This paper investigates the extent to which real-time indicators derived from internet search, cell phones, and satellites predict changes in household socioeconomic indicators across approximately 300 administrative level-1 regions in 20 countries during the COVID-19 crisis. Measures of changes in socioeconomic status in each region are taken from high-frequency phone surveys. When using the first wave of data, fielded between April and August 2020, models selected using the least absolute shrinkage and selection operator explain 37 percent of the cross-regional variation in the share of households reporting declines in total income and 34 percent of the share of respondents reporting work stoppages since the onset of the crisis. Real-time indicators explain a lower amount of the within-region variation in income losses and current employment over time, with an R2 of 15 percent for current employment and 22 to 26 percent for the prevalence of income declines. When limiting the sample to urban regions, real-time indicators are far more effective at explaining within-region variation in income losses and current employment, with R2 values of approximately 0.54 and 0.38, respectively. Income gains, self-reported food insecurity, social distancing behavior, and child school engagement are more difficult to predict, with R2 values ranging from 0.06 to 0.17. Google search terms related to food, money, jobs, and religion were the most powerful predictors of work stoppage and income declines in the first survey wave, while those related to food, exercise, and religion better tracked changes in income declines and employment over time. Google mobility measures are also strong predictors of changes in employment and the prevalence of specific types of income declines. In general, satellite data on vegetation, pollution, and nighttime lights are far less predictive. Google mobility and search data, and to a lesser extent vegetation and pollution data, can provide a meaningful signal of regional economic distress and recovery, particularly during the early phases of a major crisis such as COVID-19.