# 6.2. Early signs of an uneven jobs recovery

A key area of concern about the impacts of a shock is that economic recovery may occur more slowly among those who were most affected by it, thus exacerbating inequality over a longer term (as illustrated by the conceptual framework in Figure 4). This appears to be the case globally (see section 2) as recovery in 2021 appears to be concentrated in the upper part of the global income distribution, while the lower end of the distribution who suffered the greatest losses in 2020 fall further behind (Figure 1). In 2021, the top 60 percent are projected to return to pre-COVID welfare levels, while the bottom 40 percent will have incomes 2 percent smaller than that before the pandemic. In these distribution-neutral simulations, the widening gap during recovery is the consequence of GDP growth in low-income countries lagging growth rates in advanced economies in 2021.<sup>27</sup> What can we discern about the pattern of early recovery _within_ countries? Analysis of harmonized HFPS data from surveys conducted since July 2020, in those developing economies where policy restrictions became less stringent, allows an early look at the pattern of recovery across different groups in the population to provide some indication of what is likely to happen when economies rebound. To our knowledge, this is the only database that allows analysis of changes over time in the pandemic period in a consistent manner across many developing countries.

We study the recovery between May-June 2020 (Round 1) and August-September 2020 (Round 2) — periods chosen in a way that maximizes the number of countries in our HFPS sample with at least one wave in each round, while ensuring that the two surveys of each country are roughly equidistant. Around 40 percent of the countries (23 out of 59) with harmonized HFPS data have survey rounds conducted within the Round 1 and Round 2 windows.<sup>28</sup> To focus on the patterns associated with economic recovery, we restrict our attention to 20 countries where policy stringency declined between Round 1 and Round 2.<sup>29</sup> For the average country in our sample, policy restrictiveness — measured by the Oxford Stringency index — fell from 77 to 68 between rounds 1 and 2, where higher values indicate greater stringency.<sup>30</sup> Our findings are thus reflective of countries covered by HFPS where policies became more favorable toward economic activity between May and September 2020, and do not reflect what happened during subsequent months that included a second wave of the pandemic in some countries.

Since we are focusing on changes in three outcome variables that were the focus of the analysis of the initial impacts of COVID-19 – income loss, employment, and food security – over time, and disaggregating by several sociodemographic characteristics, the sample of countries varies because of differences in coverage of variables across surveys and quality of data. Our total sample of countries includes two countries in the World Bank’s Europe and Central Asia region, two in East Asia and Pacific, eight in Latin America and the Caribbean, and eight in Sub-Saharan Africa. Of these, seven are low-income countries, seven are lowermiddle-income, and six are either upper-middle-income or high-income countries. Eight countries fall under the World Bank’s IBRD lending classification and 12 under the IDA category (including those

> 27 Note that LICs account for most of the lower end of the global income distribution.

> 28 Survey waves were fielded at different times in each country. Therefore, these rounds do not necessarily correspond to waves 1 and 2 for all countries but can also correspond to waves 1 and 3.

> 29 Countries are Argentina Burkina Faso, Colombia, Costa Rica, Dominican Republic, Ethiopia, Guatemala, Honduras, Kenya, Cambodia, Mali, Mongolia, Mozambique, Malawi, Nigeria, Paraguay, El Salvador, Tajikistan, Uganda, and Uzbekistan.  Please see Annex A for further details. Chile and Mexico witnessed a rise in the Oxford Stringency index in this period and are hence omitted for our analysis. We exclude St. Lucia because of lack data on policy stringency. 30 The Oxford stringency index is also highly correlated with Google mobility data, which is another commonly used indicator of the impact of COVID related restrictions. For further details, see Bundervoet et al (2021).

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