With a measure of household predicted welfare in hand, it is straightforward to report averages of outcomes from the first survey wave by predicted welfare quintile, to get a sense of how the initial impacts of the crisis varied over the predicted per capita consumption distribution. Analyzing how these differences evolved over time, however, is more complicated because countries enter and exit the sample, forming an unbalanced sample. To analyze the evolution of the impact of Covid-19, we therefore pooled data from 73 phone surveys from the 14 countries with imputed welfare. The goal of the analysis is to investigate how trends in income loss, employment, food insecurity, and social protection evolved for households in the bottom two quintiles as opposed to the top three. To examine trends, we estimate a logit model using the following model specified at the household level: hc 1 2 hc hc is the outcome of interest observed in household h, region (i.e. state/province) cc **_r_** _,_ country **_c_** , and yy = ββ ff(tt) + ββ ff(tt) ∗BB40 + θθ + εε month **_t_** . In this case it is in this case is one of eight indicators: “Household received decreased total hrr(cc)cc yy income”, “Respondent could work as usual”, ”Household with children engaged in any learning/education activities since school closures”, “Households with adult member skipped a meal in the last month due to lack of money or other resources”, “Household engaged in farming activities since the beginning of 2020”, “Households owning a non-farm enterprise”, “Households receiving government assistance” , and “Households that sold assets to cope with the pandemic”. hc is a dummy variable taking on one if household h is in the bottom 40 percent. is a country fixed effect, and U hc is a dummy variable indicating that household h resides in an urban area. The term BB40 1 is the cubic cc polynomial in the month of the survey, measured since March 2020. Thus, the θθ 1 term reflects the UU average trend across months for the top three quintiles and the 1 2 ββ ff(tt) term ββ ff(tt) represents the average trend for households in the bottom 40 percent. Finally, hc is the error term, hrrcc ββ ff(tt) + ββ ff(tt) ∗BB40 clustered at the level of the survey wave. After estimating the model, we plot the average predictive εε margins for the bottom 40 and top 60 percent by month, evaluated at the means of the other covariates and treating all dummy variables as balanced.

# Results

This section contains the main findings of this analysis based on a S2S imputation of per capita household computation in HFPS based on a model of consumption in GMD. We examine the impact of Covid-19 on changes in household income and labor, food security, children’s education, and coping strategies implemented by households across the consumption distribution.

## **<u>Household Income and labor</u>**

The initial impact of Covid-19 on the prevalence of reported total income declines was widespread, with minor differences across the distribution. Figure 2a plots the share of households that experienced a decrease in total income immediately after Covid-19 struck. Across the 14 countries analyzed in this sample, the average country reported that more than 76% of households experienced a decrease in total income. This share is about 5 percentage points higher for the poorest quintile (Q1). Appendix Figure 3 show a similar pattern for farm income. Declines in wage income, in the initial wave of the HFPS, were most prevalent in the third and fourth quintile. Figure 2c describes how the prevalence of total income declines has evolved since the beginning of the pandemic, after controlling for country and urban location.

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