metadata: authors: 0: author: Brunckhorst,Ben James
1: author: Cojocaru,Alexandru
2: author: Hill,Ruth
3: author: Kim,Yeon Soo
4: author: Kugler,Maurice David
count: World
docna: 0: docna: Long COVID : The Evolution of Household
            Welfare in Developing Countries during the Pandemic
docty: Policy Research Working Paper
abstracts: cdata!: This paper examines the welfare impacts
            of the COVID-19 pandemic, using harmonized data from 343
            high-frequency phone surveys conducted in 80 economies
            during 2020 and 2021, representing more than 2.5 billion
            people. The analysis focuses on the scarring effects of the
            initial losses of employment and income by examining their
            evolution over time across and within countries, as
            restrictions on mobility and economic activity were
            introduced and then gradually relaxed. The employment and
            welfare outcomes of some groups that were impacted to a
            greater degree initially&#x2014;including women,
            informal workers, and those with less
            education&#x2014;have been improving at a slower pace.
            The social protection response in lower-income economies was
            largely insufficient to protect households from the pandemic
            shock. Unmitigated welfare losses, as seen for example from
            the large share of households indicating income losses well
            into 2021, are highly correlated with food insecurity, which
            likely led some households to sell physical assets and
            deplete their savings. Without proper remediation, the
            uneven welfare impacts associated with COVID-19 may be
            amplified over the medium to long term, leading to future
            increases in poverty and inequality.
display_title: Long COVID : The Evolution of
            Household Welfare in Developing Countries during the Pandemic
listing_relative_url: /research/2023/02/33997417/long-covid-evolution-household-welfare-developing-countries-during-pandemic
topicv3: Shocks and Vulnerability to Poverty
            m1326691 1160,Fiscal Policy and Crisis Responses
            m1327381 446,Inequality and Shared Prosperity m1327655 700,Jobs and Poverty m1327656 701,Coronavirus (COVID-19) m1939478 4828
publishtoextweb_dt: 2023-02-07T00:00:00Z
dois: 10.1596/1813-9450-10300
origu: Off of Sr VP Dev Econ/Chief Econ (DECVP)
author_keys: 0: author_key: 2109025
1: author_key: 648083
2: author_key: 663673
3: author_key: 1057128
4: author_key: 638972
model_extractions: Policy Research Working Paper 10300

# Long COVID

The Evolution of Household Welfare in Developing Countries during the Pandemic _Ben Brunckhorst Alexandru Cojocaru Ruth Hill Yeon Soo Kim Maurice Kugler_ Poverty and Equity Global Practice February 2023
Policy Research Working Paper 10300

# **Abstract**

This paper examines the welfare impacts of the COVID-19 pandemic, using harmonized data from 343 high-frequency phone surveys conducted in 80 economies during 2020 and 2021, representing more than 2.5 billion people. The analysis focuses on the scarring effects of the initial losses of employment and income by examining their evolution over time across and within countries, as restrictions on mobility and economic activity were introduced and then gradually relaxed. The employment and welfare outcomes of some groups that were impacted to a greater degree initially—including women, informal workers, and those with less education—have been improving at a slower pace. The social protection response in lower-income economies was largely insufficient to protect households from the pandemic shock. Unmitigated welfare losses, as seen for example from the large share of households indicating income losses well into 2021, are highly correlated with food insecurity, which likely led some households to sell physical assets and deplete their savings. Without proper remediation, the uneven welfare impacts associated with COVID-19 may be amplified over the medium to long term, leading to future increases in poverty and inequality.

This paper is a product of the Poverty and Equity Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at bbrunckhorst@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# Long COVID: The Evolution of Household Welfare in Developing Countries during the Pandemic<sup>∗</sup>

Ben Brunckhorst<sup>†</sup> Alexandru Cojocaru<sup>†</sup> Ruth Hill<sup>†</sup> Yeon Soo Kim<sup>†</sup> Maurice Kugler<sup>‡</sup>

**Keywords:** COVID-19, welfare, inequality, labor market, phone surveys **JEL Codes:** E24, J15, J16, J21

∗ Corresponding author: bbrunckhorst@worldbank.org. This paper is a product of the Poverty and Equity Global Practice and the Umbrella Facility for Poverty and Equity, administered by the World Bank. We thank Benu Bidani, Tom Bundervoet, Margarita Isaacs, Joana Silva and Michael Weber for helpful comments and advice.

> †Poverty and Equity Global Practice, The World Bank.

> ‡School of Policy and Government, George Mason University.
# **1 Introduction**

Almost three years into the COVID-19 pandemic, the world is still recovering from disruptions in lives and welfare that are unprecedented in recent times. In 2020, economic activity – contracted in nine out of ten economies worse than during the Great Depression of the 1930s, World War II or the global financial crisis of 2007-08 – while global poverty increased for the first time since 1998, increasing the number of extreme poor by 70 million in 2020 (World Bank 2022b). Labor market impacts have been particularly severe with workplace disruptions due to social distancing requirements and global supply chain disruptions affecting many sectors and occupations. Recovery has been slow and challenging, impeded by inflationary pressures and the war in Ukraine.

Analysis of household phone surveys early in the pandemic suggested that the impact of COVID-19 on employment and incomes was large and unequal across and within economies. Employment and incomes dropped sharply almost everywhere but particularly in middleincome countries (MICs) during the first few months of the pandemic when governments adopted sweeping containment measures, including lockdowns, quarantines and social distancing. Within countries, restrictions on mobility and economic activity were associated with larger losses in employment and income among more vulnerable population groups, including those with lower levels of education, women, and urban informal workers (Bundervoet et al. 2022; Khamis et al. 2021; Kugler, Viollaz, et al. 2021).

The objective of this paper is to update the analysis of the early employment and income impacts of the pandemic over a considerably larger sample of countries,<sup>1</sup> and to describe how the welfare impact of the economic crisis associated with the COVID-19 pandemic evolved throughout 2020 and 2021. Toward those ends, we utilize a comprehensive database of high-frequency phone surveys (HFPS) that covers 80 economies in five out of six World Bank regions, representing a combined population of over 2.5 billion.<sup>2</sup> The HFPS database spans April 2020 through December 2021, with at least four survey rounds available in most economies represented. While this database is unique in its coverage, especially during a period when data collection efforts were halted in many countries at least temporarily, it also comes with a set of caveats. Biases associated with phone surveys were discussed extensively in previous papers. This paper discusses additional caveats related to the way phone survey data measure employment, which are important for interpretation of the evidence.

> 1For comparison, the analysis of initial impacts covered around 30 economies in earlier papers such as Bundervoet et al. (2022).

> 2While phone survey data also exist for countries in South Asia, they are dropped from the analysis because, at the time of writing, data was available only from one survey round and differences in the survey instrument prevented the construction of comparable employment indicators that are representative of the population.

1
One less known aspect of the COVID-19 pandemic, and the focus of this paper, is not only how large the shock has been but also how persistent, potentially affecting poverty and inequality in the medium term. We document that extensive work stoppages in the second quarter of 2020 persisted, to some degree, through 2020 and in most economies all the way through 2021. We confirm earlier findings that female workers and those with lower levels of education experienced more severe employment losses on account of the pandemic disproportionately impacting services sectors such as retail and hospitality and low-tech manufacturing sectors in which they tend to work. The uneven distribution of care responsibilities left women assuming the bulk of the increased needs of care for children following massive school closures. We also find women and those with lower levels of education have experienced smaller gains in employment through the second half of 2020 and 2021 following the initial shock.

While there is evidence of work restarting after initial job losses, one prominent feature of the recovery is the increased prevalence of self-employment relative to prepandemic patterns. It appears that the mass job displacement that was caused by the COVID-19 labor market shock was buffered partially by informal employment. Given that informal self-employment in developing economies tends to be concentrated in low-return activities, this may indicate that while employment improves, the new jobs may be of lower quality than those held prior to the pandemic. Similarly, participation in the agriculture sector in low-income countries (LICs) and lower middle-income countries (LMICs) also increased and acted as a buffer to absorb shocks. However, income losses remained quite widespread throughout the pandemic.

Beyond employment losses, the welfare impacts of the COVID-19 crisis have been much broader, especially when impacts remained largely unmitigated. Most economies implemented COVID-19 fiscal packages to counter the pandemic impacts on the economy and people, however there was wide variation in the scale of the response, with higher-income economies spending significantly more than lower-income economies on support to households and firms. A synthesis of available microsimulation studies shows that increases in poverty were only partly mitigated in poorer countries World Bank (2022b). While the phone surveys are an imperfect source of data to assess receipt of assistance, they point to benefits expanding in coverage over the course of the crisis, but not reaching many affected households, especially in LICs. Many income losses went unmitigated, as reflected in the strong correlation between income losses and high levels of food insecurity. Households adopted various coping strategies, including the use of savings and sales of assets, which could hurt their future productive capacity and ability to recover from the crisis, leading to higher future poverty and inequality, an economic version of “long COVID”.

2
The remainder of this paper is structured as follows. Section 2 provides a brief review of the existing studies on the welfare impacts of the COVID-19 pandemic and describes the contributions of this paper to the existing literature. Section 3 describes the data used in the analysis, defines the main variables that the study relies upon to examine the different dimensions of welfare impacts, and discusses some of the data-related caveats. Section 4 outlines the main findings related to the patterns of employment losses and subsequent dynamics across different countries and population groups. Section 5 looks at how the broader welfare impacts and the response to the shock could lead to scarring and reinforce inequality in the longer-term if the losses are left unmitigated. Section 6 concludes.

# **2 Welfare impacts of COVID-19: Existing evidence and knowledge gaps**

A number of studies have documented the multifaceted welfare impacts of the COVID-19 pandemic both across and within countries, focusing primarily on the labor market impacts such as job and income losses. These resulted from the restrictions to mobility and economic activity that were imposed by governments in order to contain the spread of the coronavirus as well as the ensuing impact of global demand and supply shocks.<sup>3</sup> In developing countries, analyses mostly rely on HFPS data collected by the World Bank, as many countries had to put on hold the collection of household data through regular surveys such as Household Budget Surveys or Labor Force Surveys during 2020-2021. HFPS data revealed high levels of work stoppages early in the pandemic, particularly in the industry and services sectors, lack of payment for work performed, as well as income losses at the household level that correlate with the degree of stringency of the measures that were imposed to restrict mobility and economic activity (Bundervoet et al. 2022; Khamis et al. 2021; Kugler, Viollaz, et al. 2021). For instance, Bundervoet et al. (2022) find, based on a sample of 31 countries, that more than a third of respondents reported having stopped working since the onset of the pandemic, and almost two-thirds of households reported a decrease in total income. These job and income losses were associated with significantly higher levels of food insecurity at the household level.

The early analyses conclude that the welfare impacts precipitated by the economic crisis were not evenly distributed either across or within countries. Across countries, employment and incomes dropped sharply almost everywhere, and nearly all LICs and LMICs saw poverty increase in 2020. Globally, the pandemic led to a broad shock across the global income

> 3There is a substantive literature documenting the important benefits of public health policies to flatten the curve of COVID-19 contagion through masking, social distancing, contact tracing and vaccination in the context of developing countries that is beyond the scope and focus of this paper.

3
distribution, though households with per-capita incomes above $20 per day (in 2017 PPP) were much less affected than the rest (World Bank 2022).

Within countries, evidence suggests the brunt of the burden from the COVID-19 labor market shock was borne by women, less educated, and urban workers (Bundervoet et al. 2022; Kugler, Viollaz, et al. 2021). The disproportionate impact on these population groups has also been corroborated in a number of countries that continued standard survey data collection, including T ̈urkiye (Aldan et al. 2021), South Africa (K ̈ohler et al. 2023), Brazil (World Bank 2022a), Vietnam (Dang et al. 2023), and India (Deshpande 2020). Similar patterns are also observed in advanced economies. For instance, harmonized data from the EU Statistics on Income and Living Conditions (EU-SILC) show that in Q2 of 2020 the risk of temporary layoffs or reduced hours declines with household income. Chetty et al. (2020), based on data from the United States, find that employment losses between January and April 2020 decreased monotonically with income, from 37 percent in the bottom wage quartile to 14 percent in the top wage quartile.

These differential impacts across population groups are in part because the ability of workers to continue jobs remotely is higher among better-educated workers, as a result of the digital divide and the cognitive nature of work for high-skill occupations. In the case of developing countries, some studies have analyzed task characteristics across occupations to estimate the share of jobs that can be done at home. Dingel and Neiman (2020) find the share to be increasing with country income levels, with fewer than 25 percent of jobs in Mexico and T ̈urkiye being amenable to working from home. World Bank (2020) uses data from India to estimate that less than 10 percent of jobs are amenable to working from home at the 70<sup>th</sup> and lower percentiles of the earnings distribution and suggests that the actual estimate is likely much lower because of constraints to accessing digital technology. Gottlieb et al. (2021) find that just 13.3 percent of urban workers in Brazil and 10.6 percent in Costa Rica were actually working from home during the pandemic. In addition to feasibility to work from home, there is also considerable evidence of the gender gap in the care responsibilities precipitated by the pandemic and accompanying school closures which may have led to a reduction in women’s labor force participation (Albanesi and Kim 2021; Bau et al. 2022; Levine et al. 2021; Lee et al. 2021; Miguel and Mobarak 2022).<sup>4</sup> Beyond the initial impact, there is also evidence that those who suffered the larger initial

> 4Evidence is more abundant in high-income countries. Sostero et al. (2020) find that in Europe threequarters of employees in the top wage quintile were able to work remotely, compared to only 3 percent of those in the bottom quintile. Adams-Prassl et al. (2022) similarly find that in the UK workers with gross labor income above GBP 70,000 can accomplish some 60 percent of tasks remotely, compared to 20 percent among workers with gross labor income below GBP 10,000.

4
– – shocks women, urban workers, and the low-educated recovered more slowly during the initial relaxation of policy stringency measures during the summer of 2020 (Narayan et al. 2022). ILO (2021) using labor force survey data finds that the labor market recovery stalled during 2021, with little progress since the last quarter of 2020. Kim et al. (2021) using phone survey data from East Asian countries, find that employment impacts were widespread across the distribution when mobility restrictions were stringent, but it was more difficult for poorer workers to regain employment once stringencies had been relaxed. This pattern is also found in the US (Chetty et al. 2020; Lee et al. 2021), Colombia (Alvarez and Pizzinelli 2021), and India (Kugler and Sinha 2020).

– This paper’s first contribution is the use of a unique data source arguably the most – comprehensive data collection effort during the pandemic to provide an analysis of the initial welfare impact, notably employment and income, and its evolution in the first two years of the pandemic, across and within countries.

Focusing solely on the levels of employment could obscure some of the welfare effects of the pandemic. In particular, transitions into agriculture and self-employment would likely be associated with lower quality informal jobs, yielding lower remuneration vis-`a-vis prepandemic wage employment. For instance, Kugler, Viollaz, et al. (2021) document a fall in wage employment and an equivalent increase in the share of self-employment among younger workers during the early stages of the pandemic. Such coping strategies may be more prevalent among lower-income households, who lack access to safety nets and cannot sustain prolonged unemployment spells for fear of not being able to cover basic necessities. Therefore, another contribution of this paper is the analysis of employment transitions for different population groups during the pandemic to examine not only the scope of employment changes, but also changes in the quality of employment across different population groups.

Despite the uneven impact of the pandemic across population groups, the estimated shortrun impacts of COVID-19 on aggregate within-country income inequality so far appear to be mixed (World Bank 2022b).<sup>5</sup> However, research on the inequality impacts of previous pandemics suggests that they could peak 4-5 years after the onset of the pandemic (Furceri et al. 2022). The impacts of the COVID-19 pandemic on inequality could worsen over time

> 5While traditionally vulnerable groups appear to have experienced greater losses as seen in group-wise comparisons, the final impact on within-country inequality depends on where these groups are located in the overall income distribution. In an analysis combining prepandemic household survey data with information from high-frequency phone surveys and macroeconomic growth projections, Mahler et al. (2022) estimate the change in the Gini index after COVID-19. These projections complement Ginis published by National Statistical Offices and estimates from the literature and household surveys conducted in 2020. The result shows a mix of positive and negative changes in within-country inequality, with the magnitude being small in most cases.

5
if the scarring effects of sustained employment and income losses as well as distress asset sales and learning disruptions for children in poorer households limit the recovery of poorer households relative to better off households over the medium-term (Kim et al. 2021; World Bank 2022b; Neidh ̈ofer et al. 2021).

In this regard, the dynamics of income losses examined in this paper would reflect employment changes in the intensive as well as extensive margin and indicate the extent of unrecovered welfare losses throughout the pandemic. This paper goes on to argue that the wider welfare losses beyond job losses, if left unrecovered, could amplify inequality in the longer term. The pandemic has led to significant losses of productive assets and human capital, particularly in poorer countries. Households disposed of productive assets as a way of coping with the income shock since the social protection response was largely inadequate, often relied on inefficient means, and did not last long enough in many lower-income countries (World Bank 2022b). The loss of assets could hurt the productive capacity of those households that need it the most during the recovery.

# **3 Data and methods**

The main data source for this paper is the November 2022 vintage of the World Bank’s harmonized HFPS data which was collected to monitor the impact of the pandemic on households. The first surveys were conducted in April 2020, with data collection continuing into 2022 in some economies. Since questionnaires differed by economy and across survey waves, responses were harmonized globally to construct an internationally comparable dataset with up to half a million observations for a given indicator. Data used for analysis in this paper were collected from 80 countries in five of the six World Bank regions over 343 survey waves between April 2020 and December 2021. The breakdown of countries with harmonized survey data by income level, region and timing is shown in Table 1. More than half are LICs and LMICs, including 19 Fragile and Conflict-Affected Situations (FCS). Of the 80 countries with surveys, 53 are located in Latin America and the Caribbean (LAC) and Sub-Saharan Africa (SSA), so the country averages reported are strongly influenced by these regions (Figure 1).<sup>6</sup> Previous papers using the HFPS describe the harmonization process and dataset in more detail (Bundervoet et al. 2022; Khamis et al. 2021; Kugler, Viollaz, et al. 2021).

> 6Annex 1 provides more detail on the coverage and timing of surveys. Figure A1.1 shows countries with HFPS waves by the time periods used in the analysis, and Figure A1.2, by income level. Figure A1.3 lists all countries with HFPS data included in the analysis and the timing of HFPS waves between April 2020 and December 2021 at the country level.

6
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| er §<br>7PS Waves Cer * . a an, % 4<br>1 bs r) yy ve. uf |<br>ns ' y |<br><!-- End of picture text -->
quite low compared to in-person interviews and attempts to correct for nonresponse bias may not be perfect. Third, the scope and type of questions that can be asked is often limited compared to in-person surveys in order to keep the survey short and to minimize survey refusal.

Two main sampling strategies were used for the World Bank COVID-19 phone surveys, with important limitations intrinsic to the mode of data collection and leading to implications for the populations they were intended to represent. Surveys in 36 countries, especially those in LAC, used pure Random Digit Dialing (RDD) or assisted RDD. This method samples from all active landline and mobile phone numbers, such that RDD surveys would be representative of the population (over 18 years) with an active phone number if survey completion and response were perfect. On the other hand, surveys in 31 countries used a subsample drawn from a prepandemic nationally representative survey. The latter often collected the contact details of the household head, meaning that phone surveys using this sampling frame tend to overrepresent household heads and underrepresent members who are neither heads nor spouses. This means that individual level indicators such as employment outcomes may be biased due to respondent selection within households. The remainder of surveys use other pre-existing lists to identify the sampling frame.

To help correct for the non-representativeness of surveyed households, household sampling weights were calculated. These adjust for differential response rates among subgroups of the population, with the objective of obtaining estimates as close to nationally representative as possible.<sup>7</sup> Addressing the non-representative selection of individuals poses a greater challenge to generating statistics that are representative of individual level outcomes such as engagement in the labor market. Kugler, Viollaz, et al. (2021) examined this for five countries where surveys collected employment information for all household members and find that phone surveys overstate employment rates for the full population even once sampling weights are used. Encouragingly, their analysis suggests that phone surveys do reasonably well at tracking disparities and changes across gender, education, and urban/rural groups. However, for age group comparisons the bias was greater.<sup>8</sup> We use household sampling weights for all our analysis. Household sampling weights are used even for respondent level employment indicators, so these should be interpreted as the share of households with a respondent having that employment outcome, rather than the share of individuals.<sup>9</sup> Countries are weighted equally such that summary statistics are

> 7Surveys in Democratic Republic of Congo, Central African Republic and Mozambique were conducted only in urban areas or the capital city and are not representative of the rural population.

> 8This paper does not make comparisons across age groups.

> 9Individual level sampling weights are only available in some countries with RDD surveys, and it would be

8
interpreted as averages of country averages, intended to represent a global picture of country experiences rather than the story of global population aggregates. The same approach to weighting is used for all regression analysis.

To keep the analysis tractable, we present descriptive statistics for three time periods broadly based on different stages of the pandemic, as shown in Figure 2, and the timing of surveys (Figure A1.3). The first period from April 2020 to June 2020 represents the initial impact of the pandemic, marked by stringent policy measures in many countries including lockdowns and school closures, as captured by the Oxford Covid-19 Government Response Tracker (OxCGRT) stringency index.<sup>10</sup> This corresponds with a substantial decrease in visits to workplaces and transit stations, while people spent more time in residential areas, as reflected in Google Community Mobility data. The second period, July 2020 to December 2020, is characterized by a gradual withdrawal of some containment measures and increased mobility following the initial shock, though there are regional differences. The third period covering 2021 is distinguished by stalled progress in many countries, with little change in policy stringency on aggregate, but some return to normal levels of mobility. More significant differences emerge between regions and countries in this latter period (Figure A1.4).

When countries fielded multiple surveys within one of these time periods, we select a single survey to include in the calculation of summary statistics for each period (Figure A1.3). For the initial period, we choose the survey month corresponding with the highest stringency index to measure peak welfare impacts at the onset of the pandemic. To look at subsequent recovery in welfare and employment outcomes, we use the survey month corresponding to the lowest stringency index in the second half of 2020, and the most recent survey collected in 2021 for the third period.<sup>11</sup> For employment outcomes, the sample is restricted to households in which the respondent was between 18 and 64 years old, since employment questions were only asked of the respondent. Survey waves were only included in the calculation of summary statistics if the indicator had a response rate of at least 50 percent. Table 2 describes welfare outcomes of interest related to employment, income, food security, assistance, and coping strategies. Annex 2 provides more detail on the construction of these indicators.

> challenging to use these for cross-country comparisons with countries without individual level weights.

> 10The OxCGRT stringency index records the strictness of ‘lockdown style’ policies that primarily restrict people’s behavior (Hale et al. 2021). It is calculated using 9 indicators including school closures; workplace closures; cancellation of public events; restrictions on public gatherings; closures of public transport; stayat-home requirements; public information campaigns; restrictions on internal movements; and international travel controls.

> 11The main findings from the descriptive analysis are consistent if we instead pool surveys within each period. The regression analysis does not compare across time periods and uses all HFPS rounds (countries are weighted equally).

9
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Table 2. Harmonized welfare indicators

|**Topic**|**Indicator**|**Countries**|**N**|
|---|---|---|---|
||Respondent was currently working at time of survey|77|471,967|
||Respondent stopped/started working at time of survey rel-<br>ative to prepandemic|75|443,902|
||Respondent changed job, sector or employment type relative<br>to prepandemic, if working before the pandemic and at the|Job: 69<br>Sector: 54|184,324<br>115,828|
|**Employment**|time of survey|Type: 43|96,152|
||Respondent gained employment during the pandemic, if not<br>working in a previous HFPS wave (panel respondents)|52|136,478|
||Respondent recovered employment during the pandemic,<br>if stopped working relative to prepandemic in a previous<br>HFPS wave (panel respondents)|52|78,019|
||Household income decreased relative to the prepandemic<br>reference period|54|186,073|
||Household received no or partial payment for wage work in<br>the past week, if the respondent was a wage worker|47|36,421|
|**Income**|Household gained income during the pandemic since the<br>date of the previous HFPS wave|29|102,091|
||Household recovered income during the pandemic – income<br>increased since the date of the previous HFPS wave – if the<br>household reported income loss relative to prepandemic in<br>a previous HFPS wave (panel households)|23|56,360|
|**Food security**|Household adults went a full day without eating in the last<br>30 days|61|309,457|
||Household received any form of public assistance since the<br>start of the pandemic|70|359,736|
|**Assistance**<br>**di**|Household<br>reduced<br>consumption<br>of<br>goods<br>during<br>the<br>pandemic|49|229,090|
|**an copng**<br>**strategies**|Household used money saved for emergencies to cover basic<br>living expenses|52|285,059|
||Household sold assets such as property to pay for basic living<br>expenses|51|283,790|

_Notes_ : The number of observations includes all survey rounds; questionnaires varied across survey rounds and countries.

and individuals across panel survey rounds where possible. This both minimizes the recall period and reduces potential variation in recall bias across survey rounds. We observe that prepandemic employment estimates are stable across surveys, suggesting the constant recall bias assumption is valid in our data (Figure A3.2).

A further cross-check on employment statistics from HFPS can be provided by comparisons with labor force survey (LFS) data, primarily in UMICs and high-income countries (HICs) that continued to collect LFS after initial interruptions due to the pandemic. It 11
should be noted that employment estimates from the two sources are not strictly comparable, and any differences may be the result of several factors, including the aforementioned recall bias, differences in the number, sequence and wording of questions, differences in the timing of surveys, and important distinctions between the populations represented.<sup>13</sup> More details on this comparison exercise can be found in Annex 4. The analysis suggests that despite some differences in magnitude, the HFPS and LFS capture similar employment dynamics in most countries with overlapping data (Figure A4.1). The share of respondents currently working in the HFPS is strongly correlated with the employment population rate reported in the ILO database (Pearson correlation coefficient=0.604, _p<_ 0.001; R<sup>2</sup> =0.869 controlling for country fixed effects; see Figure A4.2). Furthermore, discrepancies in terms of how closely employment trends mirror each other at the country level tend to be less consequential in country or population group aggregates, which is the level of analysis in this paper (Figure A4.3). The number of LICs with LFS data remains very small, and as such HFPS data continue to be one of the few available sources of information for many developing countries throughout the pandemic.

# **4 Employment dynamics during the pandemic**

This section uses the most comprehensive and up-to-date cross-country data available to (i) update the phone survey estimates of the initial impacts of the pandemic for a considerably larger set of countries vis-`a-vis analysis in previous papers; (ii) trace the evolution of employment, work stoppages and acquisition of new jobs; and (iii) examine transitions across sectors and types of employment throughout 2020 and 2021, focusing on the question of the quality of the employment gains following initial losses.

# **4.1 What are the main patterns of initial employment losses and subsequent employment gains?**

There are several ways through which labor market outcomes could be affected during the crisis. These include job losses, changes in jobs, and earnings losses which can be observed in

> 13For example, the HPFS question asking whether a respondent worked before the COVID-19 pandemic could be capturing broader recall of past employment which may or may not be coded as employment in a considerably more detailed LFS instrument. Respondents reporting not working in the HFPS after the onset of COVID-19 could still be classified as employed in the LFS as they are temporarily absent from work but believe they still have a job to go back to in the future. Differences in the magnitude of employment impacts are in line with expectations considering these factors. Also, the LFS available for comparison are conducted quarterly which does not line up with the timing of HFPS and therefore may not capture the same short-term disruptions to the labor market.

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<!-- Start of picture text --> S3=< Stopped working, started working, net change in employment<br>6<br>i3<br>Q 0-- aan a a ~<br>oD® —_—<br>—<br>2 -10 =<br>zOo$ -20 —<br>fe)<br>g2 -30-<br>n HIC UMIC LMIC LIC<br>GB Apr-Jun 2020 Jul-Dec 2020 [© 2021<br>— Netchange<br>HICs: 8; UMICs: 24; LMICs: 25; LICs: 18.<br><!-- End of picture text --> <!-- Start of picture text --> <&Stopped working, started working, net change in employment<br>cc 20-<br>g<br>3<br>romoO<br>Do = - a —<br>&D F F L<br>z -20- _<br>rel<br>fe)<br>o5 «40<br>ra East AsiaPacific and CentralAsiaEurope and andLatinCaribbean America Middle EastNorth Africa and Sub-SaharanAfrica<br>GB Apr-Jun 2020 Jul-Dec 2020 [© 2021<br>— Netchange<br>EAP: 11; ECA: 8; LCA: 24; MNA: 6; SSA: 26.<br><!-- End of picture text -->
labor market losses. Overall, the share of the working age population who reported being employed in April-June 2020 was, on average, 31 percent lower than prepandemic levels. However, as we note in the previous section, the absolute magnitudes of these initial job losses, inclusively in the earlier studies, should be interpreted with caution, as the magnitude of any recall bias associated with the framing of the prepandemic employment question in the HFPS is unknown.<sup>15</sup> Given that many countries did not have a survey in all three periods, it is possible that dynamics of certain indicators across the three time periods may be a function of the composition of the sample within each time window. To address this concern, we also consider employment estimates for a sample restricted to countries with data points in all three periods, such that the sample composition remains constant across the three time periods. We attempt this whenever possible and include results for a “country panel” in Annex 6, however, data coverage varies significantly depending on the harmonized indicator. Overall, employment trends for a panel of 31 countries are similar to the full sample (Table A6.1).

Updated estimates reaffirm that the initial impacts, as well as the pace of the subsequent gains, varied considerably across country groups. MICs experienced the largest employment losses in the initial stage of the pandemic, followed by an increase in employment levels in the second half of 2020 and through 2021. Among a small sample of HICs, the initial magnitude of job losses was much smaller, but has been followed by a much slower pace of recovery. In LICs, losses of employment were subdued, compared to those reported in MICs. There could be several contributing factors behind lower employment losses in LICs. First, the extent of restrictions to mobility and economic activity, as measured by the OxCGRT stringency index, was somewhat lower on average in LICs. In addition, LICs have a much higher share of agricultural employment and a higher share of population residing in rural areas, which have not been affected as much by mobility restrictions as those living in urban areas. Finally, the HFPS data do not directly capture changes at the intensive margin, such as changes in hours worked which have been shown to have been affected significantly (ILO

> 15One possible estimate of recall bias within the HFPS is to compare a respondent’s recalled employment status for the previous wave and their employment status reported in that wave (in countries asking both questions). Another estimate of recall bias is possible by comparing the HFPS estimate based on recall with prepandemic LFS data. This is likely to be an upper bound estimate because several other sources of error are expected to add to any recall bias when making this comparison. Using these methods, we estimate prepandemic employment levels in HFPS include recall bias less than zero, on average, in the first case (24 countries), and up to 6 percentage points using the upper bound LFS method (26 countries). Upper bound recall bias estimates remain smaller than employment losses in 2021 estimated from HFPS for 16 of the 21 countries with both data sources, suggesting evidence of unrecovered employment is quite robust (Figure A4.4).

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2021). This may explain why a much larger share of households in LICs report to have lost income since the pandemic started. Employment recovery among LICs appears to stall in 2021, with gains driven only by new entrants and, unlike in MICs, the share of adults that had stopped working since the pandemic did not continue to fall between the second half of 2020 and 2021 (Table A6.2 and Table A6.3).

Across all regions, employment fell most in the early phase of the pandemic and was substantially lower in LAC compared to other regions. Employment levels recovered during the second half of 2020, most significantly in Europe and Central Asia (ECA), LAC and SSA, relative to prepandemic levels. The EAP region diverges from these patterns when considering results in a panel of five countries, where there is limited recovery from initial labor market losses throughout 2020 and into 2021. Results for a smaller but stable sample of countries also suggest little recovery in employment levels during 2021 in ECA and SSA (Table A6.1).

# **4.2 We’re all in this together? Employment gains across different population groups**

One of the main findings in studies of initial COVID-19 impacts described in Section 2 related to the uneven impacts of the pandemic on different population groups—those with lower levels of education, informal employment, and women have been impacted to a greater degree initially by job losses.

Table 3 reports the percentage change in employment across different population groups in 2020 and 2021, relative to each group’s prepandemic employment level (based on recall), both for the full set of countries and for the country panel. Our main interest is in comparing the magnitudes of relative employment changes across different population groups over time. To make comparisons across countries in terms of education level, we define low education as primary or less in LICs and LMICs, and secondary or less in UMICs and HICs.

The estimates from the expanded HFPS sample confirm that the initial impacts of the pandemic on employment were more pronounced for women, urban workers, and those with lower levels of education. For instance, there was proportionally greater employment loss among women relative to prepandemic levels, and this gap persists into 2021 (Table 3). Work – – stoppages the main component of net employment changes decreased over time, but the rate of decrease tended to be lower among women, who report a higher incidence of work stoppages in the later part of the pandemic (Table A6.2). For a number of countries, particularly in the LAC region, women experienced larger proportionate declines in employment 15
Table 3. Evolution of employment across different population groups

||**Net change **<br>**to prep**|**in employment relative**<br>**andemic level (%)**|**Sample**|
|---|---|---|---|
||**Apr-Jun20**|**Jul-Dec20**<br>**2021**|**Countries**<br>**N**|
|**All countries**|_−_30_._6|_−_16_._8<br>_−_8_._2|75<br>246,618|
|_Country panel_|_−_31_._0|_−_19_._2<br>_−_12_._0|31<br>140,798|
|**Female**|_−_33_._6|_−_20_._1<br>_−_10_._0|73<br>103,900|
||_−_35_._8|_−_22_._6<br>_−_13_._0|31<br>60,950|
|**Male**|_−_27_._2|_−_14_._6<br>_−_7_._4|73<br>139,142|
||_−_27_._7|_−_16_._1<br>_−_10_._4|31<br>79,353|
|**Non-urban/Rural**|_−_28_._7|_−_15_._1<br>_−_7_._5|67<br>92,426|
||_−_28_._4|_−_16_._6<br>_−_11_._6|27<br>53,174|
|**Urban**|_−_31_._5|_−_17_._3<br>_−_9_._3|69<br>135,127|
||_−_31_._3|_−_20_._0<br>_−_14_._2|27<br>73,999|
|**Low education**|_−_33_._6|_−_19_._4<br>_−_8_._1|59<br>92,320|
||_−_35_._2|_−_22_._5<br>_−_12_._6|23<br>55,169|
|**High education**|_−_29_._1|_−_17_._8<br>_−_7_._9|59<br>89,509|
||_−_28_._2|_−_20_._7<br>_−_12_._7|23<br>47,497|

_Source_ : World Bank estimates based on COVID-19 High-Frequency Phone Surveys.

in the initial phase of the pandemic, and subsequent gains in employment either proceed at roughly the same pace among women and men, or gender disparities actually widened over time (Figure A6.1).

Employment also fell proportionately more among those with lower levels of education in April-June 2020, following the onset of the pandemic. Relative to prepandemic employment levels, this gap appears to close in subsequent HFPS waves, in the sample of all countries and in the country panel. However this is driven by previously unemployed workers in the low education group starting jobs at a faster rate, rather than those that stopped working since the pandemic returning to jobs more quickly (Table A6.2 and Table A6.3).

To get a cleaner picture of job losses and subsequent recovery, we employ the panel structure of the data to analyze the characteristics associated with employment loss and, separately, with employment recovery. In particular, we draw on a subsample of almost 40 countries with panel surveys to predict conditional probabilities that those who stopped working subsequently returned to work, as reported in a later HFPS wave. Figure 4a shows that those working before the pandemic were significantly more likely to stop working within each country if they were women, lower educated, and during a period of higher policy stringency. Workers in urban places were also marginally more likely to stop work. Figure 4b shows women were also less likely to gain or recover employment during the pandemic period, especially if they had children in their household. Those with lower education were 16
<!-- Start of picture text --> Urban it<br>Female —_<br>Children in hh<br>Female w/ children —_—<br>Low education —<br>Stringency index (std.) +<br>-.05 0 .05 ra 15<br>. a .<br>Marginal effect on probability of stopping work<br>if working prepandemic<br>ry If N=198,495; reported C=52 PanelN=148,939;householdsC=37<br><!-- End of picture text --> <!-- Start of picture text --> Urban +<br>Female ° |<br>Children in hh<br>Female w/ children .<br>Low education. —e—<br>Striringency i ndexndex (std.)(std. a<br>-15 : -1 -05 0 a: .05 41. 15<br>Marginal effect on probability of starting work<br>during pandemic<br>@ duringN=72,723;If not workingpandemicC=36 vs.N=41,752;If stoppedprepandemic workingC=36<br><!-- End of picture text -->
<!-- Start of picture text --> 2<br>romBo 36 H:<br>BE<br>525& H<br>2s<br>Be 3 i<br>Qo<br>Ux :<br>Bs<br>3<br>& 25<br>-1 -5 0 Re} 1 1.5<br>Stringency index<br>(standard deviations from Apr20-Dec21 mean)<br>— Non-urban/rural Urban<br>N=148,769; C=37<br><!-- End of picture text --> <!-- Start of picture text --> Do<br>-€é5S 6 ::<br>ana<br>SsPe=: 6 —_—S :<br>5 55<br>oe :<br>os<br>Be :<br>ge 4<br>Sx<br>a g A :<br>-1 -5 0 Re} 1 1.5<br>Stringency index<br>(standard deviations from Apr20-Dec21 mean)<br>— Male Female<br>N=36,673; C=30<br><!-- End of picture text -->
<!-- Start of picture text --> Stopped working (female - male)<br>2 25<br>68& 20 ; ee e Ce o%° e $o e °@<br>fy=< 15 e e ge °<br>e e<br>5 10 e ry<br>© ; e<br>a 5 @@ ee e e@<br>Qao ee e e e e<br>Oo -+5<br>oO<br>Zz -10<br>6 -15<br>30 35 40 45 50 55<br>Gini index (prepandemic)<br>HICs: 6; UMICs: 20; LMICs: 12; LICs: 9.<br><!-- End of picture text --> <!-- Start of picture text --> _~ Stopped working (low education - high education)<br>z 25<br>2 20 °<br>o bd e<br><g 15- e © e e ° e rCe<br>® 10 @ bd<br>i) . ° ee 2 ° e<br>3 5 e<br>od©D e eo %% e e<br>c -9 e e@<br>2 ee<br>% -10 e<br>3S -15<br>Ww<br>30 35 40 45 50 55<br>Gini index (prepandemic)<br>HICs: 6; UMICs: 20; LMICs: 9; LICs: 6.<br><!-- End of picture text -->
port measures from the government and may need to maintain some means of livelihood to cover basic necessities (Franklin 2018).

We do not observe job quality directly in the HFPS data, so we rely on several proxies to analyze the quality of net employment changes. In particular, we examine transitions between sectors and types of employment (wage employment vs. self-employment) across different population groups before and after the onset of the pandemic. In the case of agriculture, in poorer countries the sector oftentimes acts as a shock absorber and workers unable to find work elsewhere may resort to jobs in the agriculture sector. As the economic fallout caused by the pandemic lingers over a longer period, households may rely on agricultural employment as a coping strategy. Since we observe the prepandemic sector of employment for each respondent, the analysis here relies on the plausible assumption that the movement from non-agricultural employment immediately preceding the COVID-19 pandemic to agricultural employment after the onset of the pandemic is a push factor related to the need to mitigate income losses, rather than a pull factor associated with better remunerated employment in agriculture. Similarly, self-employment which is oftentimes in the informal sector can serve as a buffer against the labor market shock, particularly among households who do not have access to formal or informal safety nets that would allow them to wait it out for a better job. As such, it could be argued that transitions from prepandemic wage employment to pandemic self-employment are primarily push factor transitions into immediate, but more precarious and lower-quality employment vis-`a-vis the jobs held previously.

HFPS data record a significant share of workers changing jobs during the COVID-19 pandemic. In the early phase of the pandemic, a period also characterized by substantial work stoppages, an additional 9 percent of workers reported working in jobs different from their prepandemic employment. Over time, this figure rose to more than 20 percent of workers in the second half of 2020, before slowing in 2021, at which point around one in four workers reported having a different job than before the pandemic (Table A6.6). The incidence of job switches is particularly high in LICs, primarily on account of SSA countries, with 40 percent of workers reporting having changed jobs by 2021 since before the pandemic.

We construct transition matrices that trace changes in individuals’ sector of activity or employment type. Because of limitations in the HFPS data, job changes within the same sector or within the same employment type are not captured. For example, the transition matrices would not capture workers who had a job in the services sector in February 2020 and switched jobs to a different services sector in 2021, but they would record a movement from services to agriculture, or from wage employment to self-employment. As such, job change estimates in the transition matrices are lower bound estimates of the total incidence of job 20
<!-- Start of picture text --> Self-employed<br>Wage-earner<br>Not<br>working<br>I |<br>February 2020 2021<br>HICs: 4; UMICs: 23; LMICs: 7; LICs: 4.<br><!-- End of picture text -->

<!-- Start of picture text --> Agriculture —— ——_ ~—s<br>Mining/Manuf. a<br>Commerce 7<br>X ra<br>Other services WN<br>—ZA— =<br>Not working<br>| i<br>February 2020 2021<br>HICs: 4; UMICs: 21; LMICs: 10; LICs: 6.<br><!-- End of picture text -->
not working in 2021 was notably smaller in comparison with those who were employed in the commerce or other services sectors in February 2020 (Table A6.9), congruent with earlier evidence that rural areas and agricultural livelihoods were relatively less affected by pandemic-related restrictions and the impact of the crisis on the economy in comparison to the urban services sector. Among those who were employed before the pandemic, transitions from mining/manufacturing and commerce into other services sectors were relatively more prevalent than transitions from these two sectors into agriculture, which might reflect both the location and transferable skills of workers most affected.

Finally, the pace of employment gains appears to be particularly slow among those with low levels of education. Disaggregating the transition matrices by level of education shows that the largest difference along the socio-economic gradient is found in the transitions out – of the labor market among those with low education, 29 percent of prepandemic wage workers and self-employed had left the labor force by 2021, compared to 19-21 percent of higher educated workers with the same type of employment (Table A6.11). While the rates of transition from self-employment to wage work were similar by education level, low-educated wage workers were 3 percentage points more likely to transition to self-employment compared to their higher educated peers. In all sectors, those with lower education were more likely to transition to agriculture between February 2020 and 2021 (Table A6.12). In addition, low-educated workers in services sectors were especially more likely to exit the labor market, and those without work before the pandemic were less likely to start jobs (Figure A6.3).

# **5 Beyond the labor market: Extensive income losses and weak safety nets led to heightened food insecurity and negative coping strategies**

Outright job losses are but one of the ways in which household welfare was affected. Many – – workers ended up working fewer hours and receiving smaller pay even if they managed to hold on to their jobs throughout the COVID-19 pandemic. In poorer countries where formal wage employment is less common, informal workers may have continued to work while earning less. As such, many households could have experienced income losses on account of COVID-19 even without any household members losing their jobs. Relatedly, it has been documented that poor households are more exposed to underemployment risks. Therefore, vulnerable workers may have worse labor market outcomes not only in the extensive margin of employment, documented as job loss above, but also in the intensive margin by working fewer hours and thus experiencing a loss of income.

Income losses were considerable in the first few months of the pandemic but fell over 22
<!-- Start of picture text --> Total income decreased (vs. prepandemic)<br>80—<br>=<br>2=£fe) 60-<br>o<br>3<br>3 40-<br>=fe)<br>&»i<br>wo 20-<br>o-<br>HICs UMICs LMICs LICs<br>GB Apr-Jun20.) Jul-Dec20. =) 2021<br>HICs: 8; UMICs: 22; LMICs: 16; LICs: 5.<br><!-- End of picture text --> <!-- Start of picture text --> Total income decreased (vs. prepandemic)<br>80—<br>&<br>S 60-<br>£°o<br>a<br>3 40-<br>£<br>fo)<br>@<br>© 20-<br>norat<br>o-<br>EastAsiaand Europeand Latin America Middle East and Sub-Saharan<br>Pacific CentralAsia and Caribbean North Africa Africa<br>GB Apr-Jun20.) Jul-Dec20. =) 2021<br>EAP: 8; ECA: 8; LCA: 24; MNA: 2; SSA: 9.<br><!-- End of picture text -->
working hours were lost in 2020 compared with the fourth quarter of 2019, a loss equivalent to 255 million full-time jobs. About half of the losses are estimated to have come from working hour reductions within employment. In fact, many more households reported losing incomes than households with a respondent stopping work, especially in lower income countries. This is consistent with the nature of the labor market in these countries where many workers are self-employed or hold otherwise informal jobs. Within countries, income losses were strongly correlated with work stoppages, job changes to self-employment, and sectoral transitions to agriculture (Figure A6.4).

During 2021, the share of households reporting that total incomes are below prepandemic levels remains very high – almost half of the households globally, and 70 percent of households in LICs are in this category. This may also be because the mitigating measures put forth by governments were largely limited in scope and duration (World Bank 2022b). While we do not have sufficient data to disaggregate trends by region and time window, it appears that apart from the ECA region, a relatively high share of households has still not recovered their lost incomes. The setback in 2021 could have been due to the emergence of new variants and re-imposition of partial lockdowns in several countries.

Evidence of “long COVID” effects on particular groups can be further seen by comparing characteristics associated with income loss and recovery using multivariate regressions. Similar to analysis of employment recovery, we draw on panel surveys in about 20 countries that collected information on income changes between survey waves during the pandemic, as well as recording initial income losses relative to before the pandemic. We can then identify which households reporting income loss relative to prepandemic were more likely to recover at least some income, as observed in a subsequent HFPS wave. Figure 9a shows that households were significantly more likely to lose income in each country if they were larger in size, less educated, the respondent stopped working, and during a period of higher policy stringency.<sup>18</sup> Figure 9b shows that some of the same households were less likely to gain income during the pandemic. Those who returned to work (or did not stop working) were more likely to recover income, implying that the same groups less likely to recover work (Figure 4b) were also less likely to recover income. Moreover, results are consistent with changes in income occurring at the intensive margin of employment, especially for households where respondents have lower education levels. Households with less access to education were significantly less likely to gain or recover income during the pandemic, whereas urban households were better off than rural households in terms of income recovery. Controlling for household characteristics, policy stringency remains a significant determinant of initial income losses and was

> 18Regression results are reported in Table A6.19. Results are robust to alternate definitions of the outcomes or specifications of the model (logit, probit).

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<!-- Start of picture text --> Urban a<br>Larger household +<br>Low education<br>Stopped working<br>F F a ad<br>Stringency index (std.)<br>-.05 0 05 1 15<br>Marginal effect on probability of income loss<br>vs. prepandemic<br>ry N=71,958;If reported C=41 PanelN=21,174;householdsC=19<br><!-- End of picture text --> <!-- Start of picture text --> Urban °<br>LargerLow householdeducation ° |<br>Back working °<br>Did not stop working ,<br>Stringency index (std.) .<br>-06 -.04 -02 0 02 .04 .06<br>Marginal effect on probability of income gain<br>during pandemic<br>o IfN=44,459; reported C=22 IfN=19,119; lost incomeC=17<br><!-- End of picture text -->
rose from 6 percent in LICs and 28 percent in UMICs during April-June 2020 to 19 percent and 52 percent respectively in 2021 (Table A6.21). While the number of beneficiaries increased over time, coverage likely fell short of the many households in need during the pandemic.

The difference in social assistance receipt tends to be relatively small across population groups (Table A6.21). Rural households were slightly more likely to benefit from assistance measures, though the difference with urban households remained small even later in the pandemic. Households where respondents had higher education were also less likely to receive assistance. Households with children were more likely to receive government assistance than households without any children, initially and also later in the pandemic as discussed in more detail in World Bank and UNICEF (2022).<sup>22</sup> The inadequacy of safety nets to protect households and the extent of unmitigated losses from the pandemic shock are also seen from the sizable share of households that faced food insecurity during the pandemic.<sup>23</sup> In the HFPS, food security is commonly measured using a binary variable that indicates whether adults in the household went a day without food in the last 30 days.<sup>24</sup> Initially, LICs had a significantly higher share of households that reported food insecurity, compared with countries in higher income groups (Table A6.22). The situation improved somewhat over the next year, but food insecurity remained elevated in LICs and LMICs in 2021. Nationwide school closures that led to the suspension of school feeding programs may have further exacerbated households’ lack of food access in some countries (Abay, Amare, et al. 2021). Bivariate regressions of food insecurity on a range of respondent characteristics show that within countries, respondents that were female and less educated

> transfers, but was less likely to include support received in the form of subsidies or tax/payment exemptions. The timing of the survey is also not ideal for capturing the timing of support. Nevertheless, these phone surveys are a rare source of cross-country information on the extent to which some form of fiscal support reached households at different times over the pandemic.

> 22The challenges with targeting are in part due to existing constraints of the social protection system in poorer countries, in particular high levels of informality and the lack of a social registry which could be used to quickly reach those in need. In fact, cash transfers, the main form of income support provided during the pandemic, were directed at informal workers in 79 percent of programs. In contrast, wage subsidies which mainly benefit formal workers were the most common labor market policy directed at individuals in HICs (Kamran et al. 2023). Many governments relied on inefficient subsidies instead of targeted transfers to respond to the crisis (World Bank 2022b).

> 23In some countries, there is evidence that well-established cash transfer programs helped mitigate the impact. In Ethiopia, Abay, Berhane, et al. (2023) show that worsening food insecurity was offset for households participating in the country’s flagship social safety net program. Aggarwal et al. (2022) presents similar findings in Liberia and Malawi in that receipt of cash transfers improved food security of rural households.

> 24This indicator is part of a set of self-reported questions that make up the Food Insecurity Experience Scale (FIES) and is associated with increased difficulties in accessing food due to resource constraints. The indicator was available for a total of 60 countries, including 4 HICs, 16 UMICs, 23 LMICs and 17 LICs.

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<!-- Start of picture text --> Respondent charateristics<br>Female —o<br>Rural oe<br>Low education —o<br>Employment transitions<br>Stopped working —o<br>Any job change —o—<br>Became self-employed —_o——-<br>Became a wage worker ——_o———_<br>Moved into agriculture —— ><br>Moved out of agriculture<br>Income loss<br>Total income decreased —oe<br>Coping strategies<br>Reduced consumption —o—_<br>Sold assets ——_o——_-<br>-.05 0 05 1<br>Marginal effect on probability of adults<br>going a day without food in the past 30 days<br><!-- End of picture text -->
To counter the welfare shock from the COVID-19 crisis, some households resorted to adverse coping strategies. These are captured in the HFPS using questions about whether the household used emergency savings to cover necessities, reduced the consumption of goods, or sold assets such as property. A large share of households adopted some type of negative coping strategy, including in HICs (Table A6.23, Table A6.24 and Table A6.25). In particular, the sale of assets was more pronounced among households with lower access to education and those in rural areas. While the share of households resorting to adverse measures fell over time in most instances, even the temporary adoption of such negative coping mechanisms can have longer term implications as they could represent damages to human capital (for example, if reduced consumption leads to suboptimal nutrition levels) and reductions in productive capital (in the case of selling assets) that can impact future poverty and inequality if left unmitigated.

# **6 Concluding remarks**

This paper analyzed the welfare impacts of the COVID-19 pandemic using the latest available harmonized global database of the World Bank’s high-frequency phone surveys from 80 countries—the largest known source of data on households during the pandemic. Focusing on the scarring effects of the pandemic, we show that, while there have been some improvements in employment, the labor market impacts continue to be felt over two years into the pandemic. The dynamics of labor market outcomes suggest that the initial disparities that were observed early in the COVID-19 pandemic narrowed rather slowly over time and sometimes not at all, while women and the less educated continued to report a higher incidence of work stoppages than men and better educated groups.

Another contribution of this paper is on the findings related to broader labor market impacts, beyond adjustments at the extensive margin. Analysis of job transitions throughout the pandemic show significant churning in the labor market, including significant shifts from wage employment to self-employment and from non-agriculture sectors to agriculture, likely reflecting a rise in informal and more precarious employment accompanied by lower earnings. Lower socio-economic groups, as proxied by the respondent’s level of education, were more likely to transition into these lower-quality jobs, while also being less likely to recover lost employment. This is consistent with income losses being more prevalent among households with less educated respondents, higher dependency ratios and greater incidence of labor market disruptions. Beyond labor market impacts, and likely reflecting that safety nets were insufficient to mitigate the full extent of the pandemic-induced shock, we observe heightened food insecurity and a high share of households resorting to negative coping strate28
gies, especially in the form of reduced consumption and drawdown of savings, with only slow improvements through the second year of the pandemic.

In addition to the short-term impacts analyzed in this paper, one of the key pathways of longer-term welfare impacts will be through the learning inequalities that have been accentuated by school closures. It is estimated that globally, about 1.6 billion students in over 190 countries were affected over the pandemic (Munoz-Najar et al. 2022). While a detailed investigation of learning losses throughout the pandemic is beyond the scope of this paper, the HFPS data also show that in LICs, children stopped learning in more than half of households where children had been enrolled prior to the pandemic, in contrast to much lower figures for UMICs where governments and households had more resources available to support distance learning or facilitate in-person learning with containment measures (Table A6.26). Over 20 percent of students in LICs and LMICs had not returned to school in 2021. Moreover, children were less likely to access education if household adults had lower levels of education (Figure A6.6). Evidence from COVID-19 indicates these same households are more vulnerable to economic crises, consistent with negative feedback loops that may worsen future inequalities. The loss in human capital could be detrimental for future poverty among the poorest. Azevedo et al. (2022) estimate that the share of children with more years of – – education than their parents a measure of absolute inter-generational mobility could decline by 8 percentage points or more in UMICs, with the largest declines in the Latin America region.

In conclusion, unrecovered and unmitigated welfare losses from the COVID-19 crisis could worsen inequality in the longer term, an economic version of “long COVID”. Jobs and income losses went largely unmitigated, as reflected in the strong correlation between income losses and food insecurity. The adoption of negative coping strategies during the pandemic, including the use of savings and sale of assets, could hurt households’ productive capacity and ability to recover from the crisis. Combined with the impact of the unequal learning losses, which by some estimate could result in a total of $17 trillion of lost lifetime earnings, equivalent to 14 percent of today’s global annual GDP, the full impact of the crisis may only be seen over the long term.

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