metadata: authors: 0: author: Decerf,Benoit Marie A
1: author: Friedman,Jed
2: author: Galego Mendes,Arthur
3: author: Pennings,Steven Michael
4: author: Yonzan,Nishant
count: World
docna: 0: docna: Lives, Livelihoods, and Learning : A Global
            Perspective on the Well-Being Impacts of the COVID-19 Pandemic
docty: Policy Research Working Paper
abstracts: cdata!: This study compares the magnitude of
            national level losses that the COVID-19 pandemic inflicted
            across three critical dimensions: loss of life, loss of
            income, and loss of learning. The well-being consequences of
            excess mortality are expressed in years of life lost, while
            those of income losses and school closures are expressed in
            additional years spent in poverty (measured by national
            poverty lines), either currently or in the future. While
            2020&#x2013;21 witnessed a global drop in life
            expectancy and the largest one-year increase in global
            poverty in many decades, widespread school closures may
            cause almost twice as large an increase in future poverty.
            The estimates of well-being loss for the average global
            citizen include a loss of 8 days of life, an additional two
            and half weeks spent in poverty in 2020 and 2021 (17 days),
            and the possibility of an additional month of life in
            poverty in the future due to school closures (31 days).
            Well-being losses are unequally distributed across
            countries. The typical high-income country suffered the
            least additional poverty years while low- and
            low-middle-income countries suffered far higher poverty
            losses with roughly the same degree of mortality shock as
            richer countries. Upper-middle income countries experienced
            the highest mortality shock of all and also high poverty
            costs. Aggregating total losses requires the valuation of a
            year of life lost vis-&#xe0;-vis an additional year
            spent in poverty. For the wide range of valuations
            considered, high-income countries experienced the lowest
            well-being loss. Aggregate losses were much higher among
            lower-income countries. This is especially true for
            countries in the Latin America region who suffered the
            largest mortality costs as well as large losses in learning
            and sharp increases in poverty.
display_title: Lives, Livelihoods, and Learning : A
            Global Perspective on the Well-Being Impacts of the COVID-19 Pandemic
listing_relative_url: /research/2024/03/34285119/lives-livelihoods-learning-global-perspective-well-being-impacts-covid-19-pandemic
topicv3: Poverty Measurement and Analysis m1326941 707,Education m1327048 470,Mortality m1504365 3661
publishtoextweb_dt: 2024-03-20T00:00:00Z
origu: Off of Sr VP Dev Econ/Chief Econ (DECVP)
no_of_pages: 27
author_keys: 0: author_key: 1947055
1: author_key: 669320
2: author_key: 1464295
3: author_key: 1324256
4: author_key: 1855473
model_extractions: Policy Research Working Paper 10728

# Lives, Livelihoods, and Learning

A Global Perspective on the Well-Being Impacts of the COVID-19 Pandemic _Benoit Decerf Jed Friedman Arthur Mendes Steven Pennings Nishant Yonzan_ Development Research Group Development Data Group March 2024 A verified reproducibility package for this paper is available at http://reproducibility.worldbank.org, click **here** for direct access.
Policy Research Working Paper 10728

# **Abstract**

This study compares the magnitude of national level losses that the COVID-19 pandemic inflicted across three critical dimensions: loss of life, loss of income, and loss of learning. The well-being consequences of excess mortality are expressed in years of life lost, while those of income losses and school closures are expressed in additional years spent in poverty (measured by national poverty lines), either currently or in the future. While 2020–21 witnessed a global drop in life expectancy and the largest one-year increase in global poverty in many decades, widespread school closures may cause almost twice as large an increase in future poverty. The estimates of well-being loss for the average global citizen include a loss of 8 days of life, an additional two and half weeks spent in poverty in 2020 and 2021 (17 days), and the possibility of an additional month of life in poverty in the future due to school closures (31 days). Well-being losses are unequally distributed across countries. The typical high-income country suffered the least additional poverty years while low- and low-middle-income countries suffered far higher poverty losses with roughly the same degree of mortality shock as richer countries. Upper-middle income countries experienced the highest mortality shock of all and also high poverty costs. Aggregating total losses requires the valuation of a year of life lost vis-à-vis an additional year spent in poverty. For the wide range of valuations considered, high-income countries experienced the lowest well-being loss. Aggregate losses were much higher among lower-income countries. This is especially true for countries in the Latin America region who suffered the largest mortality costs as well as large losses in learning and sharp increases in poverty.

This paper is a product of the Development Research Group and the Development Data Group, Development Economics. 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 bdecerf@worldbank.org. A verified reproducibility package for this paper is available at http://reproducibility.worldbank.org, click **here** for direct access.

<!-- Start of picture text --> TRANSPARENT<br>ANALYSIS<br>G<br>R E<br>N<br>S<br>I P<br>Y E<br>K A<br>C A<br>R P<br>LI R<br>O E<br>O C<br>W R<br>H<br>S<br>P<br><!-- End of picture text --> _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
# **Lives, Livelihoods, and Learning: A Global Perspective on the Well-Being Impacts of the COVID-19 Pandemic**<sup>**1**</sup>

Benoit Decerf, Jed Friedman, Arthur Mendes, Steven Pennings, Nishant Yonzan<sup>2</sup> Originally published in the Policy Research Working Paper Series on _March 2024._ This version is updated on _June 2024._ To obtain the originally published version, please email prwp@worldbank.org.

JEL codes: D63, I15, I31, I32, 015 Keywords: Covid, welfare, poverty, mortality, learning

> 1 We thank Aart Kraay and Deon Filmer for helpful comments, as well as Guido Damonte for excellent research assistance. This work was supported by the “Covid-19, lives, livelihoods and learning” RSB grant from the World Bank. We are grateful to the audience at the 2023 IPA/GRPL methods conference, the Institute of Social Science at the University of Tokyo and the Edhec Business School in Lille for providing insightful comments. Nishant Yonzan gratefully acknowledges financial support from the UK Government through the Data and Evidence for Tackling Extreme Poverty (DEEP) Research Program.  All errors remain our own. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors and should not be attributed in any manner to the World Bank, to its affiliated organizations, or to members of its Board of Executive Directors or the countries they represent. The World Bank does not guarantee the accuracy of the data included in this paper and accepts no responsibility for any consequence of their use.

2 All authors are with the World Bank’s Development Economics Research and Data group. Corresponding author: <u>bdecerf@worldbank.org.</u>
# **1. Introduction**

The COVID-19 pandemic caused dramatic increases in mortality in the two years following the disease’s emergence, but also severe declines in income and significant interruptions to student learning. Virtually all countries suffered losses in these dimensions, albeit to varying degrees determined in part by initial country conditions, post-outbreak policy choices, and the behavioral responses to such policies. A potential trade-off between lives and livelihoods was recognized early in the pandemic period as various papers investigated the economic consequences of COVID-19 control policies (Loayza and Pennings 2020; Decerf et al. 2021; Ma et al. 2022, Feindouno et al. 2023). This paper now measures the global net impacts of the pandemic on both lives and current and future livelihoods through a unified framework of assessment.

The analysis focuses on the magnitudes of well-being loss generated by the pandemic in the first two calendar years of its emergence, 2020 and 2021. A total of 122 countries, covering roughly 95% of global population in 2019, are included in this analysis.<sup>3</sup> Well-being loss is assessed across the three dimensions of excess mortality, monetary poverty, and school closures. Such a comparison requires a common denominator for the measurement of cost – in this analysis, this denominator is years of human life. The increase in the total number of years of life spent below the monetary poverty line in 2020 and 2021 directly captures the pandemic-induced global economic contraction. In contrast, the years of life lost from excess mortality or additional years spent in poverty due to school closures mostly take place in the future even though the cause of these losses arose in the initial outbreak period.<sup>4</sup> Three insights emerge from this analysis. First, the well-being losses in each dimension are substantive from a global perspective as well as, typically, the national perspective. Second, the impact of learning loss on future poverty is likely to exceed the contemporaneous loss from the poverty increase during the pandemic – three-quarters of countries are expected to have an increase in (discounted) future poverty years greater than the increase in contemporaneous poverty and the future poverty impact is almost twice as large in magnitude. Third, countries at different income levels had radically different profiles of well-being loss. The poverty impacts were greatest for lower income countries, while mortality impacts were greatest among upper-middle income countries. Combined well-being losses are smallest on average in high-income countries and largest on average for low- and middle-income countries.

# **2. Methodology**

_A unified analytic framework for cross-dimensional comparison_ 3 Included countries have population greater than 2.5 million and data on poverty, mortality, and school closures. 4 For example, the consequence of the premature death of a 60-year-old individual in 2020 in a given country is that she will lose the 18 years of her residual life expectancy over the period 2020 and beyond (assuming the conditional life expectancy of a 60-year-old individual in that country is 78). Similarly, even though the connection is less deterministic, the consequence of the learning loss a student experiences due to school closures in 2020–21 is that the student may spend additional years in poverty over the next decades because of lower aggregate growth.

2
We adopt a sparse version of lifecycle utility that simplifies levels of annual consumption to two states: monetarily poor or not (see Baland et al. (2021) and Decerf et al. (2021) for more discussion of this utilitarian analytic framework). Given this framework, the pandemic is assumed to reduce an individual’s lifecycle utility in three ways: • Years of Life Lost ( _YLL_ ). The excess mortality estimated over the period 2020–21 will have shortened the life of an individual who otherwise would have lived for a number of additional years. _YLL_ represents the discounted sum of future years of life lost.

- Current Poverty Years ( _CPY_ ). The induced economic recession may have pushed a non-poor individual into monetary poverty for the years 2020 or 2021, or both.

• Future Poverty Years ( _FPY_ ). The school closures over 2020–21 lowered the stock of human capital, at least temporarily, and may depress future incomes thereby pushing more individuals into poverty. The total period considered for these future poverty spells spans 2020–2100, which covers the full working life of the affected student cohort and then subsequent cohorts that may enter a labor force already impacted by lower aggregate growth. _FPY_ represents the discounted sum of additional (future) poverty years over 2020–2100 due to school closures in 2020 and 2021.<sup>5</sup> Under these assumptions, the total well-being losses ( _WL_ ) over the whole population deriving from the mortality, poverty, and learning detriments are proportional to a weighted sum of years of life either prematurely lost to excess mortality or spent in poverty. In more formal terms, W +<sup>CYY+ FFCCYY</sup> where is a normative parameter that captures how many poverty years generate an equivalent well= YYW αα being loss as one lost year of life. is generally assumed to have a minimum value of 1 as this presumably αα represents a lower bound for almost all normative choices of α (i.e., a value of 1 gives equal importance αα to the well-being loss of a year in poverty and a year of life lost while a value greater than 1 gives more importance to the latter).<sup>6</sup> Our approach remains agnostic to the relative weight afforded to poverty years vis-à-vis years of life lost and will present results for a range of values. For poverty, both _CPY_ and _FPY_ , we adopt the societal poverty lines (SPL) of Jolliffe and Prydz (2021) held αα For poverty, both _CPY_ and _FPY_ , we adopt the societal poverty lines (SPL) of Jolliffe and Prydz (2021) held fixed at their 2019 values (i.e. an anchored-SPL).<sup>7</sup> The SPL is a relative poverty line whose country-specific value depends on the country’s median income. Keeping the SPL anchored in 2019 mimics a countryspecific absolute poverty line. The SPL is generally close in value to each country’s national poverty line and therefore a more appropriate benchmark to measure national poverty than a fixed global poverty line (for instance, the international poverty line of $2.15 per day line).

> 5 We apply a discount factor of 3% when computing both _YLL_ and _FPY_ . This is the value used in Azevedo et al. (2021) and Psacharopoulos et al. (2021) and a standard value in the analysis of health interventions (recommended, for example, by the Panels on Cost-Effectiveness in Health and Medicine (Sanders et al., 2016)). 6 We do require α > 0 as, conceptually, it captures the marginal rate of substitution between two undesirable goods, and not the exchange between one desirable good and one “bad”.

> 7 The societal poverty line for a given country is defined as max($2.15, $1.15 + 0.5 x median income per capita per day) in 2017 PPP values (Jolliffe et al. 2022). We follow Mahler et al. (2022) in using the anchored version of this line.

3
# _Excess mortality and years of life lost_

The impact of COVID-19 on mortality has been so severe that global life expectancy declined for the first time since at least 1950 (Heuveline 2022) and the World Health Organization (WHO) estimates that approximately 14.9 million excess deaths occurred between January 1, 2020, and December 31, 2021 (WHO 2022a). Excess mortality estimates are widely viewed as a more complete measure of pandemic mortality impacts than nationally certified COVID-19 deaths due to incomplete national death registers in many countries (Msemburi et al. 2023). Furthermore, and relevant to the inquiry here, excess mortality is due not only to the direct mortality effects of COVID-19 infection but also the net effect of disruptions in the availability of medical care and care-seeking behaviors as well as declines in real income.<sup>8</sup> To estimate the total years of life lost ( _YLL_ ) due to the excess mortality witnessed over the 2020-21 period we first take the WHO estimates of excess deaths in each country, disaggregated by age, over the period 2020–21 (WHO 2022b). The number of years of life lost from an excess death will depend on a country’s residual life expectancy at the age at which the excess death takes place. We use pre-pandemic reported life expectancies for 2019, conditional on surviving age, from the Population Division of the United Nations (United Nations 2022).<sup>9</sup>

# _The increase in poverty over 2020-2021_

We adopt poverty estimates for 2020 from Mahler et al. (2022), updated using fall 2023 data in the Poverty and Inequality Platform, who employ various data sources and methods to estimate poverty globally in 2020.<sup>10</sup> For 2021, we utilize the poverty nowcasting methodology described by the World Bank (2022). In short, this approach takes the prior year’s income or consumption distribution and adjusts this distribution forward using distribution-neutral growth from national account sources.<sup>11</sup> In our case, we use the welfare distribution for 2020 from Mahler et al (2022) and project them forward to 2021 using the per capita GDP growth rate from the World Bank’s World Development Indicators. We calculate poverty in each country for 2021 using these welfare distributions along with the anchored-SPL.

To isolate the impact of the COVID-19 pandemic on poverty, we estimate counterfactual income or consumption distributions for each country in 2020 and 2021. These distributions are projected forward 8 While income declines were substantial (World Bank, 2022a), health service disruptions were also widespread and largely due to a combination of declines in spending, intentional service reductions, and fewer individuals seeking care (WHO 2022b). One review found a 37 percent reduction in the use of health care services across 20 economies over the initial pandemic period of January–May 2020 (Moynihan et al. 2021). Another, focused on maternal and child health services in eight Sub-Saharan African countries, reported disruptions in all assessed countries between March and July 2020, especially in critical services such as child vaccination and antenatal care (Shapira et al. 2021). Disruptions such as these may have increased the young child mortality rates in 2020 and 2021 by as much as 3.5% (Ahmed et al. 2022).

9 While the conditional life expectancies are reported for each year of life, the excess death information is only reported in ranges of age. We standardize the age ranges across the two sources and then assume that the age at which the excess death occurs takes place at the population-weighted average of the given range. The age ranges are the following: 0-24, 25-39, 40-49, 50-59, 60-69, 70-79, and 80+. For practical purposes, we assume the maximum lifespan to be 99, i.e., the conditional life expectancy for an individual 99 years and over is negligible.

10 These methods strive to account for the temporary social protection policies enacted that year as well as to allow for possible differential shocks by economic sector.

11 See the Mahler, Castaneda Aguilar, Newhouse (2022) for details on the nowcasting methodology used.

4
using the 2019 distribution from World Bank’s Poverty and Inequality Platform and the per capita GDP growth rates forecasted prior to the pandemic (World Bank, 2020). Poverty for both years is calculated using the same anchored-SPL threshold define above. The counterfactual poverty rates give us a baseline of poverty levels had there been no COVID-19 pandemic and are used to estimate the “excess” poverty due to the pandemic.

The estimate of _CPY_ – the additional number of people living in poverty each year – is the difference between the country specific number of poor estimated for 2020 and 2021 and the number of poor using the counterfactual distribution.

# _Future poverty from learning losses_

School closures, one of the primary social distancing policies enacted in many countries to limit COVID-19 transmission, can severely impact the future human capital of current school-age children. Various studies have estimated the aggregate income loss from reduced future earnings (if such learning loss is not remediated). Globally, estimates of the present value of future earning losses would range between US$10 trillion (Azevedo et al. 2021 and Psacharopoulos et al. 2021) and US$21 trillion (Schady et al. 2023), depending on the effectiveness of mitigation measures. More recently, Jedwab et al. (2023) project a welfare loss equivalent to a one-off loss of 63, 55, and 36 percent of the annual income in high-, middle-, and low-income countries, respectively.<sup>12</sup> Overall, this amounts to US$50 trillion.

Our alternative approach adopts a neoclassical growth model with a detailed treatment of human capital (the Long-Term Growth Model (LTGM) with the human capital extension (LTGM-HC)).<sup>13</sup> We apply the LTGM to evaluate the impact of school closures on future income levels _for each country in our sample_ due to lower human capital. The future income losses generated by the LTGM are then used to calculate the _FPY_ . We summarize the methodology below.

Cross-country data from UNESCO records the length and intensity of school closures in 2020-2021.<sup>14</sup> We then convert the school closure durations into Learning-Adjusted Years of Schooling (LAYS) lost - a measure that takes into account the national average quality of schooling (Kraay 2018 and Filmer et al. 2020). Next, we calculate the impact of school closures on the average human capital of the workforce from 2020 to 2100 in the LTGM-HC by tracking the human capital of successive population cohorts.<sup>15</sup> We then estimate future GDP per capita growth under the pre-pandemic baseline trend and the scenario with school closures from the pandemic. As a neoclassical growth model, GDP in the LTGM is calculated 12 To compare with Jedwab et al (2023), our estimates of future GDP losses (results below) would be equivalent to a one-off loss of 28, 45, 54 and 46 percent of the 2020 GDP in high, upper-middle, lower-middle, and low-income countries, respectively. The main reason for the different pattern is that Jedwab et al (2023) include losses in the return to experience, which is more important for high-income countries.

13 For more details about the LTGM, see Loayza and Pennings (2022) or visit https://www.worldbank.org/LTGM. 14 The UNESCO data records for each day of 2020 and 2021 whether schools are fully closed, partially closed, or open.

15 For example, consider a one-year loss of formal schooling for 15–19-year-olds in 2020-2021. Adjusting for the (median) quality of education, the cohort losses are 2/3 of one LAYS, leading to an 8% fall in the cohort’s future productivity – based on international empirical evidence of an 8% return to raw years of schooling, the LTGM-HC applies a 12% return to one LAYS. The oldest cohort members enter the workforce immediately, leading to an instant reduction in human capital of the workforce. Human capital growth continues to be depressed until 2035, when the youngest cohort at school age during the pandemic enters the labor force.

5
using a standard Cobb-Douglas production function of human and physical capital, labor, and total factor productivity. In the short term, the effect of a fall in human capital growth on GDP growth is given by the labor share ( , see online Appendix 1). In the long term, induced changes to physical capital accumulation lead to a larger effect of human capital on GDP growth, an effect which is subsequently ββ< 1 proportional.<sup>16</sup> Appendix 1 discusses the assumptions taken to calibrate the LTGM to each country, including the length of school closures and the quality of education, as well as further details of the model.

The LTGM-simulated paths for GDP per capita growth under both the pre-pandemic baseline (without school closures) and school closure scenario are then applied in a distributionally neutral fashion to the pre-pandemic (2019) national income distributions and with the fixed 2019 poverty lines. The additional number of poor individuals in each year in 2020-2100 is derived as the difference between the number of poor individuals obtained for the school closure scenario and the pre-pandemic baseline. These estimates of future poverty due to learning loss may be conservative as they assume that the future income of all students is affected proportionately even though in reality disadvantaged individuals may have suffered heavier learning losses (Agostinelli et al., 2022; Bundervoet, Davalos, and Garcia, 2022; Moscoviz and Evans, 2022). Of course, this projected future increase in poverty may be averted, at least in part, through remedial public and private actions.

# **3. Results**

# _Well-being loss by dimension_

Figure 1 presents the relationship between well-being loss in each of the three dimensions at the national level and 2019 per-capita national income (i.e., at the start of the pandemic). Table 1 then summarizes these estimates of well-being loss by country-income groups. Several patterns emerge, beginning with the observation that the well-being losses accrued over 2020-2021 are substantial in all three dimensions. Overall, the estimates imply that an average person in the world (population weighted national estimates) lost almost 8 days of life, spent an additional two and half weeks in poverty in the years 2020 and 2021 (17 days), and faces the possibility of an additional month of future life in poverty due to school closures (31 days).

> 16 For natural-resource rich countries, we use the Natural Resource Extension of the LTGM, which decomposes the economy into resource and non-resource sectors. This allows us to account for heterogeneous , with typically higher values for the non-resource sector. ββ

6
<!-- Start of picture text --> 15<br>—— Years of Life Lost (YLL)<br>~ TAL . : E === Future Poverty Years (FPY)<br>“SA . : : |="=: Current Poverty Years (CPY)<br>: ee : z x<br>> ve os! : ~~ «J : Sse us cos<br>Low Income : Low-Middle” : Nt na digh Income<br>: : : ~ .<br>250 500 1000 2000 4000 8000 16000 32000 64000<br>GNI PC, Atlas Method (2020 US$, log, scale)<br><!-- End of picture text -->
Regarding _FPY_ , low-income countries, whose school systems are typically of lower quality, experienced severe learning losses due to extended school closures. Their relatively young populations further imply that a large fraction of the total population was affected by these learning losses. The estimate of _FPY_ declines monotonically with national income despite the corresponding increase in the quality of education.

**Table 1: Estimates of Years of Lives Lost (** **_YLL_ ), discounted Future Poverty Years (** **_FPY_ ), and Current Poverty Years (** **_CPY_** **<u>) per 100 people by country income groups</u>**

||**Not we**|**ighted by popu**<br>|**lation**|**Weig**|**hted by popula**<br>|**tion**|
|---|---|---|---|---|---|---|
||**Excess**<br>**mortality**<br>**_YLL_ per 100**|**School**<br>**closures**<br>**_FPY_ per 100**|**Current**<br>**poverty**<br>**_CPY_ per 100**|**Excess**<br>**mortality**<br>**_YLL_ per 100**|**School**<br>**closures**<br>**_FPY_ per 100**|**Current**<br>**poverty**<br>**_CPY_ per 100**|
|LICs|0.9|13.5|4.4|1.0|15.5|5.3|
|LMICs|1.6|10.4|6.3|2.6|8.7|9.4|
|UMICs|3.9|9.4|3.9|1.8|6.8|1.6|
|HICs|1.6|4.8|1.0|2.1|7.4|-1.2|
|World|2.0|9.0|3.7|2.1|8.4|4.7|

_Note: Income groups are classified according to World Bank FY23 income classification. Acronyms: LIC – low income country; LMIC – lower middle income country; UMIC – upper middle income country; HIC – high income country. FPY represents the sum of additional (future) poverty years over 2020–2100 due to school closures in 2020 and 2021, discounted at 3%. YLL estimates are also discounted at 3%._ From a global perspective, the wellbeing loss from school closures dominates the loss from _CPY_ with the global _FPY_ estimate more than twice as large as _CPY_ . This dominance also holds within all country income categories, except for lower-middle income countries when adjusted for the population size. The wellbeing loss from _YLL_ equals or exceeds that from _CPY_ in both high- and upper-middle income countries for any calibration that exceeds one in the value of a year of life lost relative to a year in poverty (i.e. for all . Among lower- and lower-middle-income countries, the _CPY_ estimates are far higher than _YLL,_ indicating that higher valuations of are necessary for the wellbeing loss from _YLL_ to surpass that from _CPY_ αα≥1) . αα Finally, while the global averages do not appreciably change if we weight the cross-national average estimates by population, the relative size of each dimensional loss can shift within income categories when impacts are population weighted. In high-income countries, the US and other larger countries actually experienced declines in contemporaneous poverty, and hence a negative _CPY_ , due to effective social protection policies. This lowers the high-income _CPY_ estimate after weighting by population. China experienced very low excess mortality over 2020-2021 in part because of its restrictive zero-COVID-19 policy (only experiencing an increase in excess deaths in the second half of 2022, outside our timeframe, when some of these restrictions were relaxed), thereby appreciably lowering the _YLL_ among uppermiddle-income countries once weighted for population. India experienced one of the largest increases in 8
current poverty and thus we see a population-weighted lower-middle-income country estimate of _CPY_ that is almost 50% higher than the unweighted estimate.

# _Relative loss within countries_

We next contrast the relative sizes of the three types of well-being losses at the country level. The results are again presented by country-income groups in Table 2, which examines the proportion of countries where (a) the _FPY_ estimate exceeds _CPY_ , (b) the _YLL_ estimate exceeds the _CPY_ estimate, and (c) the _YLL_ exceeds the sum of _CPY_ and _FPY_ . Regardless of national income level, the long-term effect of school closures may have larger poverty consequences than the immediate poverty impacts due to social distancing (first column). For 77% of all countries and no less than 68% in any income category, the _FPY_ estimate is greater than _CPY_ . This underscores the importance of mitigating learning losses associated with school closures in nearly every country.

Second, the relative importance of excess mortality tends to increase with national income (second and third columns). Only 13 percent of low-income countries have a larger _YLL_ than _CPY_ , while 61 percent of high-income countries have a larger _YLL_ than _CPY_ . A similar gradient exists when comparing mortality with the combined poverty shock of contemporaneous changes and learning losses. Only 4 percent of lowincome countries have a larger _YLL_ than the sum of _CPY_ and _FPY_ . This rises to 29 percent of upper-middleincome countries and 24 percent of high-income countries. High-income countries were more successful at mitigating the possible poverty impact of the pandemic rather than its mortality impact. This is not the case for lower-income countries with fewer available resources.

**Table 2: Within-country comparison of** **_YLL_ ,** **_CPY_ and** **_FPY_ : proportion of countries, by country income group, where one dimension exceeds others**

||**Future vs Current poverty**|**Life Years lost vs ad**|**ditional poverty years**|
|---|---|---|---|
||**_FPY_>****_CPY_**|**_YLL_>****_CPY_**|**_YLL_>****_CPY_+****_FPY_**|
|LICs|0.83|0.13|0.04|
|LMICs|0.73|0.18|0.03|
|UMICs|0.68|0.46|0.29|
|HICs|0.84|0.61|0.24|
|World|0.77|0.37|0.16|

_Note: The table reports unweighted proportions of countries where the inequality holds._ While Table 2 explores “how frequently” _YLL_ estimates are larger than _CPY_ (or _CPY+FPY_ ), Figure 2 quantifies the degree to which poverty impacts exceed or fall short of mortality costs. The figure plots for each country the ratio _CPY_ / _YLL_ (blue circles) as well as the ratio ( _CPY_ + _FPY_ )/ _YLL_ (red crosses). These two ratios thus provide the number of additional poverty years estimated in the country for each year of life lost. These two ratios can be interpreted as “break-even” values, which is the value of the normative parameter for which one would conclude that the well-being loss coming from the country’s years of αα life lost is exactly equal to the well-being loss coming from its additional poverty years. These break-even αα values can help the reader assess which channel leads to the larger wellbeing loss. If the reader’s preferred 9
value is larger than the break-even value, then the well-being loss from _YLL_ is larger than the wellbeing loss from _CPY_ (or from _CPY+FPY_ ).<sup>17</sup> αα When considering the ratio _CPY_ / _YLL_ , the blue regression line suggests that the average break-even value is fairly constant across the range of national income at an approximate value of five.<sup>18</sup> The interpretation for this value is that _CPY_ and _YLL_ have on average generated equal well-being losses when assuming that αα five years lived in poverty generate the same ill-being as one year of life lost. If the preferred value is larger than five, then the reader may conclude the well-being loss from YLL is on average larger than the αα wellbeing loss from CPY.

The negative slope for the regression line that compares _YLL_ with _CPY_ and _FPY_ , focusing on the red scatter plot, underscores that the relative burden of excess mortality increases with national income per capita. The higher GDP per capita the lower the break-even value for α. The fitted line suggests that one needs to consider approximately fourteen years lived in poverty ( _CPY_ + _FPY_ ) as the same well-being loss as one _YLL_ to equalize well-being losses from the two sources in the average low-income country. In contrast, only six or seven ( _CPY_ + _FPY_ ) equate with one _YLL_ for the same conclusion in the average high-income country.

Note that many upper-middle and high-income countries are below the solid line that corresponds to a ratio of one. Any country whose ratio is below one experienced greater aggregate years of life lost than years in poverty and, as a result, experienced larger well-being losses from excess mortality than from poverty (as long as ). αα≥1

> 17 Note that, for figure clarity, we top-code ratios to 20 for countries in which _YLL_ from excess mortality is dwarfed by years of poverty (either _CPY_ or _CPY+FPY_ ). This data truncation does not qualitatively affect the regression lines shown in the figure – i.e. top coding this ratio at a value higher than 20 does not substantively affect the slope estimates. We also bottom code this ratio to 0 for countries where years of poverty ( _CPY_ ) are negative, as the meaning of a negative value is not clear.

> 18 Computing the break-even values at country income groups from Table 1 gives a different picture. Indeed, the ratio _CPY/YLL_ for low-income countries is 5 while this ratio is 0.6 for high-income countries. Hence, this would yield αα decreasing break-even values as national income increases. The difference with the fairly flat regression line in αα Figure 2 is that this line is regressed over break-even values computed at country level that have been truncated to fall between 0 and 20, while Table 1 averages the numerator and the denominator of this ratio across countries, αα and then calculates the ratio. In particular, the presence of negative αα _CPY_ values for particular countries that inform estimates in Table 1, and the absence of negative ratios in Figure 2, largely account for this difference.

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However, there is significant variation for , ranging from 1 to 10, with generally larger values associated with higher-income countries.<sup>19</sup> αα Turning now to the comparison of total well-being losses from all three dimensions, Table 3 presents total loss for a range of values, including the value of 4 derived from the exercise above. The first three columns adopt a value for that is common to all countries (1, 4, and 10). The last column considers the αα calibrated country-specific presented in Appendix 2. Analysis of this sort enables a determination of αα whether the larger excess mortality in high-income than in low-income countries created a greater toll on well-being than the larger poverty impacts ( αα _CPY_ and _FPY_ ) in low-income countries.

**Table 3: Average total well-being loss by income group in life-year equivalent terms (not weighed by** **<u>population)</u>**

||**α = 1**<br>**(****_YLL_ per 100)**|**α = 4**<br>**(****_YLL_ per 100)**|**α = 10**<br>**(****_YLL_ per 100)**|**country-specific α**<br>**(****_YLL_ per 100)**|
|---|---|---|---|---|
|LICs|18.7|5.3|2.7|13.6|
|LMICs|18.3|5.8|3.2|9.2|
|UMICs|17.2|7.2|5.2|8.1|
|HICs|7.4|3.0|2.1|2.6|
|World|14.7|5.1|3.2|7.7|

_Note: Total well-being loss is measured as YLL+(CPY+FPY)/α. Country-specific_ α values are explained in Appendix 2.

Regardless of the value assumed for α in Table 3, the typical high-income country experiences the smallest total well-being loss – not unexpected as the average high-income country suffered among the lowest well-being losses from all three sources (Table 1). The distribution of pandemic costs was highly unequal globally, with high-income countries best able to weather the disruptions (relatively speaking). Total wellbeing loss for the typical high-income country would only surpass that of the typical low-income country if α were given a value larger than 17. Aggregate well-being losses among low- and middle-income countries are more similar to each other. For lower values of alpha, or for the country specific alphas (which have the lowest alpha values among low-income countries), low-income countries are the group that experienced the worst welfare losses. As alpha increases in value, the upper-middle income countries emerge as the group with the greatest welfare loss due to their relatively high mortality costs.

Finally, we conduct the same analysis by regions of the world rather than by country-income groups in Appendix Table 1. The results suggest wide variation across regions. Countries in Latin America and the Caribbean (LAC) suffered particularly high well-being losses, greater than those found in all other regions ( ). The average country in the LAC region experienced large well-being losses from all three sources, _YLL, FPY, and CPY_ . These countries typically have a substantial share of older population ∀αα, 1 ≤αα≤10 cohorts combined with relatively limited resources to treat or prevent COVID-19 infection. Additionally,

> 19 One reason for these larger values is that poor individuals in higher-income countries have higher incomes than poor individuals in lower-income countries and are therefore a greater distance away from the stipulated level of a subsistence income (see Appendix 2 for more details).

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these countries also implemented longer school closures and could not uniformly enact sufficient social protection policies to shield their population from immediate income losses. The average country in the Eastern Europe and Central Asia (ECA) likewise experienced very high excess mortality losses. This is again the result of relatively elderly populations that could not be fully protected from COVID-19 risk. However, different from LAC, the ECA region had among the lowest increases in contemporary and future poverty and therefore their aggregate welfare losses are among the lowest for a wide range of alpha.

# **4. Conclusion**

All countries suffered losses of life, income, and human capital due to COVID-19, although the impacts in each dimension varied substantially across countries. While some countries bore high mortality and education losses, they were able to limit the increases in monetary poverty through ambitious social protection policies. Other countries witnessed comparatively modest increases in mortality but recorded significant monetary poverty increases or education losses, while others suffered to a relatively high degree in all three dimensions.

For most countries, and across all levels of income, the immiserating effects of school closures over the pandemic period will exceed the rise in poverty experienced at the start of the pandemic (unless soon mitigated). For the poorest countries, the combined poverty costs of learning deficits and short-run income loss exceed the mortality costs except under a high valuation of the trade-off parameter . For higher income countries, the mortality costs often exceed the income costs even at lower values of . αα When combining all dimensions, the unequal distribution of loss across the world becomes very clear. αα High-income countries suffered the lowest degree of aggregate loss, regardless of the choice of considered, as they experienced the lowest poverty increases and a relatively modest level of mortality αα increase. Upper-middle-income countries suffered the greatest mortality loss and experienced significant income and learning losses. For a range of , this country income group experienced the worst combined losses. For lower values that put relatively more weight on poverty increases, it is the lowest income αα countries that experienced the greatest welfare loss. Looking across regions, Latin American countries αα bore the greatest loss due to some of the highest mortality impacts combined with steep income and learning losses.

Of course, an exercise of this nature must come with caveats. Many important aspects of well-being are not considered, such as income losses for households well above the poverty line or the quality-of-life reductions for individuals suffering from “long COVID”. Likewise, the analysis considers only the incidence of poverty and not the depth of poverty. Importantly, there is unavoidable uncertainty around the implications of learning loss for future poverty, including whether the documented losses may instead be alleviated over time through a combination of private actions and public policies and the degree to which there are intergenerational implications of learning loss (Neidhofer et al., 2021; Jang and Yum, forthcoming). The analysis also assumes that a COVID-related fatality would otherwise have lived until the average conditional life expectancy is reached. If instead fatalities (direct or indirect) from COVID were more likely among those already in ill-health, then the true _YLL_ values would be less in magnitude than those estimated here.

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We used “years of life” as common denominator to express well-being losses coming from mortality and reduced income (current and future). An alternative common denominator would be “currency units”, using the Value of Statistical Life approach (VSL) to express excess mortality in currency units. Neither years of life nor currency units provide an unambiguously better lens than the other. The currency units approach faces limitations including that many people find offensive the mere idea of placing a “price” on a human life. Further, the VSL typically attributes smaller monetary values to human life in lower income countries than in higher income countries (Kniesner and Viscusi, 2019), while our approach can attribute the same value to a year of life lost in all countries.

Any adopted national pandemic policy implicitly involved trade-offs between lives and current and future livelihoods. Regardless of the exact magnitude of loss in each dimension, the losses estimated here suggest that optimal polices would have been best attuned to national conditions – global policy recommendations would clearly not have reflected differences in mortality risk or the importance of schooling in aggregate growth as both are partially a function of population age structure and other country specific factors. If national policy makers at the start of 2020 were somehow made aware of an analysis of this nature, it is interesting to speculate whether the same courses of action would be chosen. For some countries, especially low-income ones, perhaps greater efforts would have been made to limit school closures and otherwise ensure continuity of learning. For other countries, especially those that suffered the greatest mortality shocks, perhaps even more attention would have been paid to the design of efficient protective disease control policies as well as greater advocacy for more equitable early global access to vaccines.

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