data from the World Bank’s high-frequency phone surveys (HFPS), complemented by simulations using official household surveys conducted before the pandemic.

To identify the long-term threats to inequality and social mobility, one is required to speculate, based on observable patterns in how the immediate impacts of the pandemic were distributed among households, on how the economic recovery is shaping up among households, and how the short-term impacts on children’s education can affect human capital development. Simulations can be used to extrapolate how learning losses due to the pandemic pose a risk to social mobility of the current generation of children. The objective of the forward-looking simulations and extrapolations in this paper is not to predict the future. Rather, it is to identify the sources of risk of long-term inequality and quantify the potential impacts, so that the necessary policy packages can be designed and employed to prevent the risks from materializing.

As noted earlier in the introduction, the conclusions one may draw about the distributional impacts of the pandemic will depend on the types of welfare impacts and inequalities that are being considered. In this paper, we make inference on the welfare impacts by looking at distributional patterns for several indicators, including (i) job losses; (ii) income losses; (iii) food insecurity; and (iv) learning losses. This is primarily driven by the types of questions that are available in the HFPS data. Likewise, since HFPS data do not have detailed income or consumption modules that allow us to compute income or consumption deciles, we rely on data on educational attainment, area of residence, household size and types of jobs to identify households with characteristics that would commonly be more prevalent at the bottom of the income distribution, in order to assess the distributional implications of the pandemic and early recovery.

The remainder of the paper is structured as follows. Section 2 provides an overview of the existing evidence and simulations on the distributional impacts of the COVID-19 pandemic and a conceptual framework that draws out the implications for inequality of the initial impacts and of the recovery patterns. Section 3 provides details of the HFPS data that the bulk of the empirical analysis relies upon, as well as other sources of data, such as the Oxford Policy Stringency index, and data on the size and targeting of government policy response packages. Section 4 presents the main findings of the analysis of the distributional patterns of the initial impacts of the pandemic for the countries in the HFPS sample. Section 5 builds on the patterns observed in the HFPS data to present a first account of the global inequality impacts of the pandemic, based on a simulation exercise. Section 6 examines the distributional patterns associated with the early recovery by linking them to the evolution of the policy stringency measures. Section 7 concludes with some reflections on the longer-term impacts and the importance of having an equity lens for the building back better policy agenda.

# **2. How COVID-19 can amplify existing inequalities, with potential longer-term impacts**

The impact of COVID-19 on the global income distribution is driven by its effects on inequality both _between_ and _within_ countries. On between-country inequality, simulations based on economic growth projections as well as the evidence from HFPS and other phone surveys suggests that household level socioeconomic impacts have been larger for poorer countries, widening the divide between households in rich and poor countries.

# 2.1. Impact on between-country inequality

The incidence (or distribution) of income growth across the global income distribution can be simulated using projected GDP growth rates for countries, under the assumption of no change in inequality within countries since 2019. This assumption is necessary since income distribution within countries has not been observed in the data since the pandemic. Figure 1 shows the results based on simulations using distribution3