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B.5. Human Capital1
Public Disclosure Authorized
Even before COVID-19 caused schools to shut down, the region was in the midst of a learning crisis. About
21 percent of children in developing East Asia and Pacific (EAP) cannot read and understand an age-appropriate text at
the end of primary education by age 10, referred to as ‘learning poverty’ (World Bank, 2019). While this is lower than
the global average of 53 percent, it hides a high level of heterogeneity with learning poverty in developing EAP ranging
from 2 to 51 percent. A similar range is seen for lower secondary education. Using the latest PISA results, we observe that
the region has several of the top 10 performing countries, with China (represented by only four provinces); Singapore;
Macao SAR, China; and Hong Kong SAR, China in the first 4 positions, while the Republic of Korea was 9th. However,
31 percent of lower-secondary students are still performing below the grade-appropriate minimum proficiency level, and
it is where most of the largest declines in PISA scores are found. Between 2015 and 2018, there was a 26-point decline
(although in the context of impressive increases in enrollment) in Indonesia and a 16-point decline in Thailand.
The world is just beginning to get a better picture of how this crisis may be further compounded by school
closures. It will require school reopenings and months (if not years) of data on learning to gain a more precise empirical
picture of learning losses. In the meantime, researchers can simulate how students across the world may be affected,
using existing data. Existing simulations of learning losses stemming from COVID-19 school closures have largely focused
on the United States and other high-income countries, but some have also examined a selection of low- and middle-
income countries (Azevedo et al. 2020). Given the scope and seriousness of the learning crisis, a set of robust regional
Public Disclosure Authorized
estimates of learning loss is urgently needed to create a shared understanding on the magnitude of the cost of inaction,
generate at debate, and enable actions that can help save the future of millions of children.
As COVID-19 plays out, a dire situation is poised to become worse—particularly where safety nets are weakest.
Income shocks could lead families to put their children to work, and girls might marry early and start childbearing
(Bandeira et al. 2019). Many may never go back to school—particularly girls, persons with disabilities, and marginalized
groups. Governments too are showing signs of becoming cash strapped as they attempt to bolster funding to their
frontline response.
Any interruption in schooling, including scheduled vacations, can lead to a loss of learning for many children—
particularly for students from low-income backgrounds.2 Moreover, interruptions during critical schooling stages
of life can lead to much worse outcomes. For example, an interruption during third grade, when students are mastering
how to read, may lead to higher dropout rates and worse life prospects, including poverty.
School closures
To contain the pandemic, one of the first actions taken by governments around the World and in EAP was to
close schools resulting in an unprecedented number of out-of-school children. The EAP region was the first to be
hit by the crisis and to close their schools. Schools closed first in China; Hong Kong SAR, China; Mongolia; and Vietnam
(in January) and were followed by several others between the middle and the end of March. Schools were often closed
first in the regions most affected by the virus, then the closures were quickly scaled up to become nationwide. At the
height of the crisis, up to 20 of the school systems in EAP were affected by closures, leaving more than 400 million
1 This analysis was conducted to inform the October 2020 East Asia and Pacific Economic Update.
2 Kim and Quinn (2013); Andrabi et al. (2020); Cameron (2009); Shores and Steinberg (2017).
B.5. Human Capital 1
10187-EAP Economic Update_Human Capital_74701.indd 1 10/15/20 8:47 AMFigure B.5.1. Number of countries with schools closed by date (nationwide, in selected areas or open with limitations)
by date and number of students affected
100 20
80
15
Number of countries
Percent of students
60
10
40
5
20
0 0
Jan-20 Jan-20 Feb-20 Mar-20 Apr-20 May-20 Jun-20 Jul-20 Aug-20
Schools open with limitations
Schools closed (nationwide or selected areas)
Closed nationwide, selected areas, or open with limitations (number of countries, RHS)
Source: Author’s calculation using COVID monitoring database and inputs from World Bank task teams.
students out of school, out of 412 million students in EAP (Figure B.5.1), and starting in May several systems had already
moved to a situation where schools were open with limitations.
School closures have caught students at different moments of the school year. When scheduled school breaks
coincided with spikes in the virus transmission, governments had the option to either anticipate or extended the summer
breaks, and students simply did not return to schools (Japan, Mongolia, the Republic of Korea; Figure B.5.2). In those
cases, students had already been exposed to a substantial part of the school year, and governments were able to use the
summer break to plan for the reopenings. Students and systems in the southern hemisphere had a very different crisis.
In several of those cases school closures started after four weeks of the regular school year, while students were still
recovering from their “summer learning losses,” and educational systems were forced to roll out mitigation plans at the
same time they planned for the school reopening, adding a higher level of stress to the system.
Countries in EAP region were among the first to start reopening schools and they adopted various strategies,
including progressive reopening in selected areas, prioritizing selected grades, or reopening under blended
instruction modalities. In some countries, schools reopened progressively starting with regions where the virus was
more under control. For example, in Cambodia schools closed in Siem Reap town on March 7, followed by schools in
Phnom Penh on March 13, and then nationwide closure on March 16. In other cases, only students of certain grades could
go back to schools and received in-person instructions. This was the case, for example, in China where higher grades
(older students) returned first followed by lower grades with preprimary returning last; in Myanmar where only high
schools reopened, with delays, for the new academic year in July; and in Lao PDR where Grades 5, 9, and 12 reopened
first when the government lifted the lockdown in mid-May followed by remaining grades in early June. In other cases,
schools reopened under a blended model where students received in-person instructions within the school walls for a
certain portion of the week and participated in remote learning for the rest of the week. In Korea, Rep., some schools are
having students come in on alternate days, while others have adopted a hybrid in-class and online approach to lessons.
2 B.5. HUMAN CAPITAL
10187-EAP Economic Update_Human Capital_74701.indd 2 10/15/20 8:47 AMFigure B.5.2. Timing of school closures versus regular academic year calendar
Global peak of school closures
(March 1st to June 30th)
Korea, Rep.
Japan
Myanmar
Philippines
Indonesia
Micronesia, Fed. Sts.
Palau
Thailand
China
School closure
Hong Kong SAR, China
Lao PDR
Mongolia
Taiwan
Vietnam
Cambodia
Malaysia
Singapore
Fiji
Timor-Leste
Papua New Guinea
Australia
New Zealand
Tonga
Marshall Islands
No school closure
Brunei Darussalam
Solomon Islands
Tuvalu
Samoa
Vanuatu 9/1/2020
19
20
21
22
21
19
20
19
20
21
20
20
20
20
20
20
20
20
20
20
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
b
b
b
b
t
t
t
n
n
n
Oc
Oc
Oc
Fe
Fe
Fe
Fe
Ju
Ju
Ju
Legend
1st academic year
Summer break
2nd academic year
COVID-19 + scheduled break
Source: Authors calculations based on Education COVID monitoring database and inputs from World Bank task teams.
In several countries, some or all schools had to be closed again after the first “reopening” to contain new
COVID-19 outbreaks and to carry out cleaning and contact tracing. In June, Beijing had to reclose schools due to
a new COVID-19 outbreak. In Hong Kong SAR, China, the government closed schools again in July, one week ahead of
the scheduled school year end due to an increase of local cases. In August, Australia started closing schools again in
the state’s capital, Melbourne, to all but children of essential workers and vulnerable children due to a second wave of
COVID; Korea, Rep. re-closed schools in Seoul after nearly 200 staff and student tested positive; and Myanmar re-closed
schools, which had just started reopening after a delay of the academic start, across the country following a new spike in
cases (especially in Rakhine state). Short closures to contain community/school infections are also taking place in many
countries.
B.5. Human Capital 3
10187-EAP Economic Update_Human Capital_74701.indd 3 10/15/20 8:47 AMWe estimate that between January 1 and August 31, schools in the region were closed (nationwide, selected
areas, or open with limitations) on average 2.7 months (or 46 percent of the time they would otherwise
have been open). As illustrated in Figure B.5.3 (and detailed in Table B.5.A1), the longest duration was in ASEAN-5
(4.1 months), the small East Asian economies (4 months), and China (4.9 months). The heterogenous nature of the
school closings and re-openings makes it challenging to precisely measure the duration of the closures and the exact
number of students affected. The closure durations above are higher bounds where we count as closed a system which
has some or all schools open “with limitations.” Under this scenario, Vietnam, Cambodia, Mongolia, Korea, Rep., and
China were closed the longest. Alternatively, we could limit the definition of closed to systems where schools are closed
nationwide or in selected areas. In that case, Mongolia, Cambodia, Malaysia, the Philippines, and Thailand were closed
the longest.
Figure B.5.3. Reported share of scheduled instruction time when schools were closed, by subgroup from January to August
100%
Share of scheduled instruction
80%
time (Jan–Aug 2020)
60%
40%
20%
0%
ASEAN-5 China High-income Pacific Island Small Total
EAP countries East Asia
economies
Percent of time closed (nationwide, selected)
Percent of time closed (nationwide, selected, limit)
Source: Authors calculations based on Education COVID monitoring database and inputs from task teams.
While few of the Pacific Islands Countries (PICs) have closed their schools, medium and longer impact of
the pandemic will likely leading to learning losses. Many of the PICs have managed to isolate themselves for
the pandemic, imposing stringent travel restrictions, and remained COVID free. Therefore, in most of these countries,
schools have remained open while, in a few others, schools were closed nationwide for a short period (1–2 weeks) before
reopening once the absence of cases was confirmed. The two important exceptions are Fiji and Palau, where schools were
closed for almost three months out of scheduled time. Also, in Tonga, the government closed schools at the end of June
to test how students, teachers, and parents cope with at-home learning.3 Despite schools remaining open, disruptions
in traveling abroad for educational programs (particularly for higher education) and for teacher training, as well delays
in planned procurement activities from abroad (textbook printing, education technology, pre-school supplies, etc.), may
affect the quality of education and learning in the medium term.
School reopening is not a return to normal. We must do things not only differently, but better. Just as the most
marginalized students were most at risk of being left behind by distance learning modalities, they must be the priority
3 McGarry and Cain (2020).
4 B.5. HUMAN CAPITAL
10187-EAP Economic Update_Human Capital_74701.indd 4 10/15/20 8:47 AMof any back-to-school strategy. Schools have to proactively bring them back and provide support. This can entail flexible
learning approaches, practices to expand access to previously out-of-school children, displaced and migrant children,
minorities, and other excluded groups.
For students across the region the COVID-19 pandemic has already changed the way they learn and where they
learn. Flexibility seems to be the foundation of any strategy on reopening schools: a cautious reopening, and readiness
to close again if outbreaks emerge. This is not easy in education systems that are historically steeped in tradition and
rigidity. But to balance safety and learning, a most effective approach has been to couple physical and remote education,
allowing a switch between the two with minimal disruptions.
As discussed in the World Bank report on the impact of the pandemic on education and the policy responses,4
we must capitalize on innovations and gather important lessons on the use of technology at this unprecedented
scale to move to a new normal. This can constitute a turning point to use new pedagogies to tackle the learning crisis
and provide more inclusive and creative learning models. Now is the time to build back better, to make education systems
more inclusive and better prepared to face and overcome possible crises in the future, including climate-related ones.
Mitigation
Students around the world are having very disparate experiences as education systems try to mitigate school
closures by providing remote learning. Mitigation strategies in the time of COVID-19 are often referred to as remote
learning because they represent alternative ways of providing instructions, which reduces or eliminates the need for
direct contact between students and teachers, among students, and among teachers. But in reality, many school systems
rolled out emergency response teaching delivered via a variety of modalities—such as paper-based homework sheets,
radio, TV, mobile phones, text messages, and the Internet—both instructor-directed and self-paced.5 Evidence is slowly
emerging of a great deal of inequality both within and across countries in the supply of, access to, and effectiveness of
mitigation strategies.6
Across the region, the adoption and use of the various remote learning modalities differ across countries
(Figure B.5.4). Some countries have moved their whole curriculum online (e.g., China7 and Korea, Rep.). Others are
offering televised programming, usually for selected grades in core subjects (Vietnam and Mongolia) or a range of
modalities of distance learning using both low tech (radio) and high tech (online).
Paper is the ubiquitous technology for remote learning in all countries in the region especially at primary
and secondary levels. However, there are also important differences (Figure B.5.4). While high-income countries
can focus their efforts in two complementary approaches such as paper and online, middle-income countries in the
region are pushed to embrace multimodality, not as complementary approaches to facilitate the learning experience,
but as substitute strategies in order to increase the outreach and ensure that students from different socioeconomic
backgrounds can receive some content. In China, according to teachers, 60 percent of online remote learning is provided
using synchronous approaches such as live streaming (Table B.5.A2).
4 Rogers and Sabarwal (2020).
5 Carvalho and Hares (2020).
6 UWEZO (2020); Andrew et al. (2020); Baker (2020).
7 Zhou et al. (2020); Zhang et al. (2020).
B.5. Human Capital 5
10187-EAP Economic Update_Human Capital_74701.indd 5 10/15/20 8:47 AMFigure B.5.4. Number of remote learning modalities offered by ministries of education by countries and grade levels
Preprimary Primary Lower secondary Upper secondary
Online Paper Radio TV Online Paper Radio TV Online Paper Radio TV Online Paper Radio TV
High- China, Hong Kong SAR, China
income
China, Macao SAR, China
EAP
Japan
Singapore
China China
ASEAN-5 Philippines
Malaysia
Viet Nam
Indonesia
Thailand
Small East Timor-Leste
Asian
Lao PDR
economies
Cambodia
Myanmar
Papua New Guinea
Pacific Tuvalu
Island
Fiji
countries
Tonga
Palau
Solomon Islands
Kiribati
Source: UNESCO-UNICEF-World Bank School Survey (round 1).
The evidence on the effectiveness of remote learning appears mixed at best in both developed and developing
countries.8 While we reference this literature, it bears noting that it does not assess interventions rolled out at scale
as emergency responses; nor does it measure their effectiveness at a time when the welfare and emotional well-being
of families and learners were deteriorating as rapidly as with COVID-19. For instance, we know that domestic abuse
charities have reported a spike in calls made to helplines since lockdown measures were announced, and food security
concerns are on the rise due to the interruption of school feeding programs.9
High-frequency household surveys carried out in a subset of countries indicates that most children remained
engaged in educational activities (Figure B.5.5). Except for Lao PDR where the survey was fielded during a holiday
period, most households with children enrolled prior to the crisis remained engaged in educational activities. Nearly
60 percent of households in Vietnam with children enrolled precrisis took part in these activities through an online
platform, and 45 percent through teacher assignments, both of which may require certain degree of active learning. At
the other extreme, in Mongolia, the majority of those engaged did so through television.
In China, a survey led by a research team from Central China Normal University reveals that the main challenges
relate to real-time interaction and group collaboration. The survey10 measured satisfaction rates of students, teachers,
and parents with the online learning experience during the crisis. For example, over 70 percent of teachers gave positive
comments on the platform tool’s resource management function, homework collection and correction functions, and multi-
terminal applicability and stability; about 40 percent of teachers disagreed with the platform’s teacher-student interaction
8 Murphy and Zhiri (1992); Bosch (1997).
9 Nicola et al. (2020); World Food Programme (2020); American Academy of Pediatrics (2020).
10 https://www.sohu.com/na/414527159_484992
6 B.5. HUMAN CAPITAL
10187-EAP Economic Update_Human Capital_74701.indd 6 10/15/20 8:47 AMFigure B.5.5. Share of households with children attending school precrisis by type of engagement in educational activities
(percent)
100
Percent of households with children
80
attending school precrisis
60
40
20
0
Cambodia Lao PDR Mongolia Vietnam
Engaged in educational activities Assignments provided by teacher Online/mobile platforms
Television Radio
Source: EAP high-frequency phone surveys.
function; and about 50 percent of teachers were not satisfied with its role in maintaining classroom order. Parents
satisfaction differs significantly between urban and rural areas, with parents of urban students being more satisfied.
Household access to technology, especially among the poorest, affects their access to remote learning. Access
to mobile phones includes from 83 percent in small East Asian economies to 94 percent in high-income countries
(Figure B.5.6). Mobile phone access is almost universal in China but reaches less than 80 percent of households in
Indonesia, Cambodia, and Myanmar. The divide for Internet access is even larger. In many countries, in particular
Cambodia, Indonesia, Lao PDR, and Myanmar, using only online modalities would leave out many of the students. And
within countries, the gap in access to these technologies between the richest 60 percent of households and the poorest
40 percent of households can be as large as 30 percentage points (as is the case for Vietnam, Lao PDR, and China for
Internet access). To mitigate this effect, some governments have partnered with the private sector during the pandemic,
including with network operators to offer free data services for education websites (Korea, Rep., Cambodia) and/or with
education technology firms to provide free online education resources (Indonesia).
High-frequency surveys also reveal welfare disparities in engagement in distance learning. Households with
enrolled children in the bottom 40 are less likely than those in the top 60 to have children who are engaged in
educational activities (Figure B.5.7). This may partly reflect access to different technologies in rural and urban areas. And
beyond unequal Internet access and access to a computer, issues that schools and governments confront when it comes
to home learning include gaps in basic resources needed by families to support home learning, in information and
communications technology resources and know-how, in individual students skills and know-how, and in preparation
and ability to manage and cope by parents.11 And this does not account for how suitable the model adopted by a school
or system is for home learning or how prepared teachers are to transition to online learning. In Australia, research
shows just under two in five teachers feel well prepared or very well prepared in how they use information technology
for teaching. In Indonesia, surveys revealed that the capacity to support distance education is lacking. For example,
67 percent of teachers reported difficulties in operating digital devices (including to use online learning platforms).
11 Paper commissioned by the federal government, the Centre for International Research on Education Systems (CIRES), and Mitchell Institute at Victoria University modeled the impact
of online learning (Impact of learning from home for disadvantaged children).
B.5. Human Capital 7
10187-EAP Economic Update_Human Capital_74701.indd 7 10/15/20 8:47 AMFigure B.5.6. Access to Internet and mobile phone, within and across EAP subregions
Between countries inequality of access
Mobile phone ownership—East Asia and Pacific Internet access—East Asia and Pacific
100 94% 94% 100 87%
Internet access
83% 70%
Mobile access
85%
62% 54%
50 50
0 0
Australia
Hong Kong SAR, China
Japan
Korea, Rep.
New Zealand
Singapore
China
Indonesia
Malaysia
Philippines
Thailand
Vietnam
Cambodia
Lao PDR
Mongolia
Myanmar
Australia
Hong Kong SAR, China
Japan
Korea, Rep.
New Zealand
Singapore
China
Indonesia
Malaysia
Philippines
Thailand
Vietnam
Cambodia
Lao PDR
Mongolia
Myanmar
HIC China ASEAN-5 Small East Asian HIC China ASEAN-5 Small East Asian
economies economies
Within countries inequality of access
Ratio of mobile ownership by the top 60 vs bottom 40 (of income) Ratio of internet access by the top 60 vs bottom 40 (of income)
1.67
1.50
1.5 1.5 1.46
Internet T60/B40 ratio
Mobile T60/B40 ratio
1.14 1.16
1.06 1.05 1.17
1.0 1.0
0.5 0.5
0.0 0.0
Australia
Hong Kong SAR, China
Japan
Korea, Rep.
New Zealand
Singapore
China
Indonesia
Malaysia
Philippines
Thailand
Vietnam
Cambodia
Lao PDR
Mongolia
Myanmar
Australia
Hong Kong SAR, China
Japan
Korea, Rep.
New Zealand
Singapore
China
Indonesia
Malaysia
Philippines
Thailand
Vietnam
Cambodia
Lao PDR
Mongolia
Myanmar
HIC China ASEAN-5 Small East Asian HIC China ASEAN-5 Small East Asian
economies economies
Source: Gallup World Poll, 2019.
Figure B.5.7. Share of households engaged in educational activities in the past week, by bottom 40 status (percent)
Cambodia
Lao PDR
Mongolia
Vietnam
0 20 40 60 80 100
Percent of households with children attending school precrisis
Bottom 40 Top 60
Source: EAP high-frequency phone surveys.
8 B.5. HUMAN CAPITAL
10187-EAP Economic Update_Human Capital_74701.indd 8 10/15/20 8:47 AMSimulations: Effects of COVID-19 on learning and earnings12
In June 2020, the World Bank released a report with the first set of global estimates of the potential learning
losses due to COVID-19 school closures.13 This work provided simulations, based on three alternative scenarios
using the available information at that time, and a set of parameters which varied by countries’ income levels and GDP
projections as of June 2020. Our global estimate for the intermediate scenario suggested that today’s student cohort
stands to lose approximately US$10 trillion (2017 PPP) due to an average 0.6 reduction in the learning-adjusted years
of schooling (LAYS) and corresponding to its impact in future earning at present value. In the case of EAP, the same
scenario suggested a learning loss of 0.5 LAYS and a cumulative earnings loss of US$3.8 trillion (2017 PPP). As time
passes, and the consequences of the COVID-19 pandemic continues to build-up, our intermediate scenario is rapidly
becoming an optimistic perspective of what might actually happen.
This section builds on the methodology proposed by Azevedo et al. (2020) used in the World Bank (2020a)
report and improves it on four main fronts. First, it uses the revised version of the World Bank Human Capital Index
(HCI) and LAYS, released in September 2020, which has more recent data for all EAP countries and includes the Pacific
Islands. Second, it uses actual country data on the length of school closures and builds three scenarios in terms of no
additional days of closure (optimistic), 90 additional days of school closure (intermediate) and 180 additional days of
school closure (pessimistic). Third, the forward-looking scenarios now allow for the partial reopening of the educational
system. Fourth, the mitigation parameters are being informed by more specific and granular country-level information,
including revised economic projections, reflecting the latest expectations. For those reasons, the results presented in this
paper cannot be directly compared against the regional numbers previously reported.
Ì Simulation Scenarios for EAP
It is with the regional context in mind that we have designed a number of simulations of the potential learning
losses for countries in EAP. These results take into consideration the latest available data from countries in the region,
including actual school closure information to update the initial regional estimates provided in June, 2020 (World Bank,
2020a). These results can help ministries of education and their development partners advocate plan and for recovery
strategies. Such strategies, if well planned and executed, can prevent the learning losses documented in this paper from
becoming permanent. It is important to keep in mind that these results can only be compared to what was previously
reported for EAP to the extent that it might reflect improvement and worsening of expectations regarding the learning
losses in the region.
It is important to note that the effects simulated here are forward looking and do not consider any future
government response to negative effects of school closures (discussed in the following section) once lockdowns
lift and schools reopen. These simulations can be used to help motivate the importance and need for an education
sector response strategy and should not be used to guide decisions for reopening schools. As articulated in the UNESCO,
UNICEF, World Food Programme, and World Bank Framework for reopening schools, “[s]chool reopenings must be safe
and consistent with each country’s overall COVID-19 health response, with all reasonable measures taken to protect
students, staff, teachers, and their families.”14
12 For a detailed description of the methodology and assumptions please see Azevedo et al. (2020).
13 World Bank (2020a).
14 Framework for Reopening Schools. Available online at https://www.unicef.org/media/68366/file/Framework-for-reopening-schools-2020.pdf.ab
B.5. Human Capital 9
10187-EAP Economic Update_Human Capital_74701.indd 9 10/15/20 8:47 AMWe present two simulation exercises. The first uses the Learning-Adjusted Years of Schooling (LAYS) measure.15 This
is one of the components of the World Bank Human Capital Index, launched in 2018.16 The second simulation exercise
translates the impact of a PISA mean score shock into the share of children performing below the minimum proficiency
level, as defined by OECD and UNESCO Institute for Statistics (UIS) in the context of SDG 4.1.1c.17
One important element in these simulations is the possibility to present results in monetary terms. In order
to do that we use expected earnings information from International Labor Organization and World Bank (2020a), and
the expected long-run return to education. We also compute aggregate results by bringing all expected earning losses
to their present value, assuming a work life of 45 years and a 3 percent discount rate. In order to make these results
more realistic, we also adjust the aggregate loss by the expected adult survival rate (following the World Bank HCI), and
the fact that not all workers will always be in gainful employment (following the measure of Human Capital Utilization
described in Pennings, 2019).18
We propose three EAP specific regional scenarios, building on the observed school closure to date (Table B.5.1).
In the optimistic scenario, we assume that in most countries there will be no additional school closures beyond what has
already been planned and announced by governments. In the intermediate scenario, we expect the schools to be closed
for an additional three months. In the last, and most pessimistic scenario, we expect schools to be closed another six
months.
One additional parameter introduced in this work is that part of the educational system will be fully open. This
parameter reflects the reality that many educational system are operating with some regions in their territory fully open
or have moved to a blended approach in which students have both face-to-face and remote learning, depending on
the day of the week. As indicated earlier, in Cambodia, school closures started in Siem Reap, then Phnom Penh, before
becoming nationwide. In Australia, schools were never closed in the entire territory. For example, in Tasmania, schools
were not officially closed, although the government requested that parents keep children at home if they could, and
attendance hovered around 20 percent. In China, dates of reopening varied based on the date of the last reported case of
COVID in the province. Some schools in Korea, Rep., used a blended approach where students came in on alternate days,
while others adopted a hybrid in-class and online approach to lessons. In Cambodia, where schools are scheduled to
reopen in September, the plan is to limit the number of students by limiting the number of days for face-to-face classes
per week to two days for kindergarten and primary school students, and three days for secondary school students, while
the rest of the classes will be conducted remotely.
Another important set of assumptions are related to the effectiveness of mitigation strategies. We assume that
remote learning is never as effective as classroom instruction. It is hard to keep children engaged cognitively with all
the distractions in the household, and devices having to be shared between siblings, and it can be hard for families
to decipher television programming. Moreover, access to a television or the Internet (the main channels of delivering
remote learning) is highly unequal. We also assume that the economic shock that families are experiencing will also have
detrimental effects on the ability of children to make effective use of any available mitigating strategies, especially as
family incomes drop, family and child food security worsen, and household stress increases.
15 Filmer et al. (forthcoming).
16 Kraay (2018).
17 PISA defines minimum reading proficiency as a score below level 2 which is 407.47 points.
18 Pennings (2019).
10 B.5. HUMAN CAPITAL
10187-EAP Economic Update_Human Capital_74701.indd 10 10/15/20 8:47 AMTable B.5.1. Parameters for global LAYS estimates and scenarios
Small East Asian
Developing EAP
Pacific Island
High-income
economies
Total EAP
countries
ASEAN-5
China
EAP
A. Learning gains or school productivity 40 36 40 36 30 38 50
(in Harmonized Learning Outcomes (HLO) points/year)
B. Actual school closure to date (calendar days) 81 71 147 122 119 19 110
C. Share of countries affected by additional school closures 70 76 30 50 50 100 50
(percent)
Optimistic scenario
D1. Additional school closures (months) 0 0 0 0 0 0 0
E1. Mitigation effectiveness (0 to 100 percent) 42 37 40 35 28 39 60
F1. HLO decrease (points) = A*(B+C*D1)*(1–E1) 4.9 4.4 9.9 7.8 7.2 1.2 6.2
Intermediate scenario
D2. Additional school closures (months) 3 3 3 3 3 3 3
E2. Mitigation effectiveness (0 to 100 percent) 21 18 20 18 14 20 30
F2. HLO decrease (points) = A*(B+C*D2)*(1–E2) 12.1 11.0 15.6 13.7 11.9 9.4 15.3
Pessimistic scenario
D3. Additional school closures (months) 6 6 6 6 6 6 6
E3. Mitigation effectiveness (0 to 100 percent) 11 9 10 9 7 10 15
F3. HLO decrease (points) = A*(B+C*D3)*(1–E3) 19.8 18.4 20.3 19.3 16.4 19.3 23.9
Macro poverty outlook (GDP per capita growth percent) [g] –1.0 –0.6 1.3 –3.9 –1.8 –10.4 –5.3
Note: Learning gains (A) were attributed by country income level and do not vary across scenarios. Actual school closure to date (B) and projected economic shock (g) are country-specific parameters, with the same value
across scenarios. Additional school closures (D) were the same for all countries, varying by scenario only. Mitigation-effectiveness (E) was attributed by country income level for each scenario. To calculate effect in HLO
points (F), the length of school closures (B and D) are converted to share of a year. Growth [g] is in per capita terms, weighted by student enrollment within country groups.
Ì Results
If schools are closed for an additional three months, COVID-19 could result in a loss of 0.7 learning-adjusted
years of schooling (LAYS). While a child born today in EAP should expect to receive an average of 11.9 years of
schooling throughout their lifetimes, this amounts to only 8.3 years of schooling when adjusted for the quality of
learning they experience during this time. In the intermediate scenario, school closures due to COVID-19 could bring
the average learning that students achieve during their lifetime down by 0.5 learning-adjusted years (Figure B.5.8). The
developing countries in EAP, are expected to experience a 0.2 to 0.7 drop in the LAYS, while in the high-income EAP
countries, the LAYS is expected to drop by up to 1.0 LAYS.
B.5. Human Capital 11
10187-EAP Economic Update_Human Capital_74701.indd 11 10/15/20 8:47 AMFigure B.5.8. Loss in Learning-Adjusted Years of Schooling (LAYS)
1.2
1.0 1.0
0.8 0.8 0.8 0.8
LAYS loss
0.7 0.6 0.7
0.6
0.4 0.4 0.4
0.2 0.2 0.3 0.3
0.2
0.1
0.0
Total Developing China ASEAN-5 Small EA Pacific Island High-income
EAP EAP economies countries EAP
Baseline 8.3 7.3 9.3 8.7 6.9 6.6 11.4
Intermediate Optimistic Pessimistic
Source: Authors’ calculation. Results expressed in Learning-Adjusted Years of Schooling (LAYS), Simulation 1 results based on latest available LAYS data for EAP countries (unweighted average).
Note: Developing EAP are: China; ASEAN-5; Small East Asian economics; Pacific Islands.
The region stands to lose from US$2.3 trillion to US$5.0 trillion19 in private earnings at present value. The
average student from the cohort in school today could, in the intermediate scenario, face a reduction of US$865 (in 2017
PPP dollars) in yearly earnings, or an average reduction of 4 percent in expected earnings every year (Figure B.5.9). The
range from the optimistic to the pessimistic scenario is US$414 to US$1,308, or from 2 percent to 7 percent of annual
expected earnings loss, respectively. For EAP high-income countries, estimates range from US$979 in the optimistic
scenario to US$3,113 in the pessimistic scenario. In China the losses per student per year would range from US$567
to US$1,079. If added up and brought to the present value, using the same assumptions of Azevedo et al. (2020),
the total earnings loss in the EAP region, for the intermediate scenario, add up to US$3.8 trillion (2017 PPPs), and
for the developing countries in EAP, this same number adds up to US$2.8 trillion (2017 PPPs), with most of this loss
concentrated in China (see Table B.5.A5 in the Annex).
Figure B.5.9. Decrease on average annual earning per student (2017 PPP S)
3,500
per student (2017 PPP US$)
3,000 3,113
Loss annual earnings
2,500
2,000
1,500
1,308
1,000 1,079 979
680 761 746
500 567 425
414 342
218 204 138
0
Total Developing China ASEAN-5 Small EA Pacific Island High-income
EAP EAP economies countries EAP
Baseline $19,651 $12,928 $16,468 $11,502 $7,914 $15,334 $39,073
Intermediate Optimistic Pessimistic
Source: Authors’ calculation. Decrease on average lifetime earnings per student at present value (2017 PPP $). Simulation 1 results based on latest available LAYS (unweighted average), with the change in LAYS expressed
in foregone annual earnings per student.
19 At 2017 U.S. dollars PPP.
12 B.5. HUMAN CAPITAL
10187-EAP Economic Update_Human Capital_74701.indd 12 10/15/20 8:47 AMThese simulations reflect the ability of governments to mitigate this shock on their student populations. As
presented in Table B.5.1, our mitigation effectiveness, for a country such as China, ranges from 40 percent to 10 percent.
However, if we take the parents’, teachers’, and students’ subjective satisfaction with the government, responses showed
a 70 percent satisfaction rate as a proxy for the mitigation effectiveness.20 China’s private earning losses at present
value would drop from a range of 1.5 trillion to 2.9 trillion (U.S. dollars 2017 PPP) to 900 million to 1.2 trillion (U.S.
dollars 2017 PPP). Going forward it will be critical to continue to monitor both the subjective assessments of different
stakeholders in this process, but also be prepared to measure actual learning losses.
School closures will have an effect on both the mean and the share of students below minimum proficiency
in lower secondary school. Average learning levels will fall. To assess what effect school closures might have on test
scores, we use the average learning gains between Grades 9 and 10 in the PISA and PISA for Development datasets. In
the intermediate scenario, the average student will lose 15 PISA points, or the equivalent of just under half a year of
learning.
The share of children in early secondary education below the minimum proficiency level will increase by
7 percentage points, or 21 percent in the intermediate scenario assuming that the distribution skews.21 This
means a rise in the share of students not able to identify the main idea in a text of moderate length, finding information
based on explicit though sometimes complex criteria, and reflecting on the purpose and form of texts when explicitly
directed to do so (PISA’s definition of a minimum level of proficiency) (Figure B.5.10).
Countries in the developing part of EAP will also experience a 6 percentage points increase; however, since the
baseline is much higher, the relative increase is close to 12 percent. In the case of the high-income countries of
EAP, the increase is also close to 8 percent; however, as they start from a much lower level, the relative increase is close
to 42 percent.
Figure B.5.10. Increase in share of lower-secondary students below minimum proficiency (BMP)
12
11
11
10
9
BMP increase (p.p.)
8 8
7
6 6
5 5
4
2
1
0
Total Developing China ASEAN-5 High-income
EAP EAP EAP
Baseline 36% 54% 14% 54% 20%
Intermediate Optimistic Pessimistic
Source: Authors’ calculation. Share Students Below Minimum Proficiency (BMP). Simulation 3 results based on the latest available PISA and PISA-D of 15 countries. Unweighted average. Student coverage as share of
lower secondary enrollment: 94 percent EAP. No Pacific Island countries and a single small East Asian economy (Cambodia) participated in PISA. Cambodia’s results are included in Developing EAP and Total EAP, but not
reported individually.
20 Over 70 percent of parents support “online learning,” and their overall satisfaction is high. 74.1 percent of parents believe that “the decision to organize children’s home online
learning during the epidemic is wise.” However, only 31.5 percent of parents are willing to support their children to use online education platforms to study after the epidemic (https://
www.sohu.com/na/414527159_484992).
21 In this scenario we assume that all students will lose learning, but some will lose more than others; hence the learning distribution will skew to the left. For a longer discussion on the
details of this scenario please see Azevedo et al. (2020).
B.5. Human Capital 13
10187-EAP Economic Update_Human Capital_74701.indd 13 10/15/20 8:47 AMHowever, this crisis is likely to have significant effects in the learning distribution, as the learning loss of the
most vulnerable students will be relatively greater. The achievement gap between the poorest and richest students
is highly heterogenous. As Figure B.5.11 suggests, in Cambodia this gap is relatively low as the system seems to be
underperforming for both groups, with 96 percent and 80 percent of the poorest and richest students in lower secondary
below the minimum proficiency level (BMP). In other countries such as the Philippines, we find far greater heterogeneity,
with 94 percent of the poorest students performing below minimum proficiency, while the richest students have a
BMP of 36 percent. Hence, when doing our simulations, it is critical to understand to what extent this shock is likely to
increase this gap. Table B.5.2, shows the change in the share of lower-secondary students below minimum proficiency
for the poorest and richest quintiles, and how COVID-related school closures might affect each one of these groups. In
all country groups the absolute increase in students below minimum proficiency is more than two times higher for the
poorest than the richest quintile. This result suggests the widening of the achievement gap between the poor and rich.
Figure B.5.11. Illustration of learning distribution between the poorest and richest students using PISA data
PISA-D 2017 Cambodia reading PISA 2018 Philippines reading
Shaded areas Shaded areas
denote students below denote students below
minimum proficiency minimum proficiency
0.0 0.0
Minimum Minimum
proficiency proficiency
Distribution
Distribution
0 0
0 200 400 600 800 1,000 0 200 400 600 800 1,000
Score Score
dist. ( μ , σ, BMP ) dist. ( μ , σ, BMP )
φall (321, 62, 92%) φall (340, 80, 81%)
φpoor (313, 55, 96%) φpoor (309, 59, 94%)
φrich (359, 64, 80%) φrich (440, 88, 36%)
Dotted lines denote students below Dotted lines denote students below
minimum proficiency minimum proficiency
1.0 1.0
Cumulative distribution
Cumulative distribution
0.8 0.8
0.6 0.6
0.4 0.4
0.2 0.2
0.0 0.0
0 200 400 600 800 1,000 0 200 400 600 800 1,000
Score Score
Source: Authors’ calculation based on PISA 2018.and PISA-D 2017.
One limitation of this measure is the fact that it is not sensitive to movement below the minimum proficiency
threshold. This problem can be solved using a Learning Gap measure, analogous to the FGT1 in the poverty measurement
literature. Panel II of Table B.5.2 shows the results in terms of the learning gaps. In this case, the increase in the learning
gap is twice as large for the poor students, when compared against the rich students. This suggests that not only the
gap between poor and rich students will widen, but the relative depth of the learning gaps will also increase, which the
learning poor students from the poorest household falling further behind. This ratio is significantly greater in ASEAN-5
14 B.5. HUMAN CAPITAL
10187-EAP Economic Update_Human Capital_74701.indd 14 10/15/20 8:47 AMTable B.5.2. Effect on inequality of learning on lower-secondary by socioeconomic status quintile
Post-COVID-19
Baseline Intermediate Ratio delta
(A–C)
Poorest A (%) Richest B (%) Poorest C (%) Richest D (%) (B–D)
Panel I. Share students below minimum proficiency (BMP)
Total EAP 52 18 57 20 2.0
Developing EAP 60 25 64 28 1.3
China 12 2 15 2 4.7
ASEAN-5 67 22 72 25 1.5
High-income EAP 43 11 49 13 3.3
Panel II. Learning gap (LG)
Total EAP 17 13 18 13 2.2
Developing EAP 18 12 19 12 1.9
China 11 5 12 5 0.4
ASEAN-5 18 11 19 12 2.7
High-income EAP 16 14 17 14 3.0
Source: Authors’ calculation. Share Students Below Minimum Proficiency (BMP) and Learning Gap (LP) for the richest and poorest quintiles. Simulation 3 results based on the latest available PISA and PISA-D of 15 countries.
Unweighted average. Student coverage as share of lower secondary enrollment: 94 percent EAP.
and high-income EAP, where this distance will increase 2.7 and 3.0, respectively. This finding has direct implications on
potential policy responses and shows that as the system reopens, teachers will be faced with a larger share of students
at a lower level of proficiency.
This crisis is not over, and our understanding of the ramifications to the economy and household welfare are
being updated daily. Since March 2020 regional growth projections have been frequently revised, and the recently
released EAP Regional Update (World Bank, 2020b) indicates that growth projections are likely to continue to go down.
In each of these revisions, our expected number of student’s dropping out due to the household income shock is revised
upward. Our initial estimate, based on the March Macro Poverty Outlook (MPO) suggested that approximately 300,000
students would drop out of the education system; by May, this number had already been revised to 1.1 million; and in
the most recent EAP regional update, it has been revised upward to 2.6 million (Figure B.5.12).
Taken together these estimates are sobering. Yet they do not fully capture important aspects such as COVID-19’s
immense impact on equity that would stem from household and individual characteristics.22 For example, the impact
of COVID-19 is likely to be worse for vulnerable and marginalized populations, such as persons with disabilities. We do
not yet know the full picture of the impact of the pandemic on the youngest learners,23 the marginalized, and persons
with disabilities.24 For instance, initial reports suggest that returning to school for children with disabilities is likely to
be more complex than for their peers. Parents of children with disabilities are concerned about their children’s ability to
social distance (both en route to school and while in school) and about the availability of accessible Water, Sanitation
and Hygiene (WASH) facilities. They are also worried about underlying health conditions that may make their children
more susceptible to contracting the virus. This could result in parents opting to keep children with disabilities at home.
In turn this may ultimately result in them dropping out.
22 Bassett and Arnhold (2020).
23 Devercelli (2020).
24 Alasuutari (2020).
B.5. Human Capital 15
10187-EAP Economic Update_Human Capital_74701.indd 15 10/15/20 8:47 AMFigure B.5.12. Estimates of student dropout by 2020 growth projections release
GDP per capita growth projections 2020 (p.p.)*
3 2
1
(in millions of students)
Number of dropouts
2
0
–1
1
–2
0 –3
MPO-Mar WEO-Apr MPO-June MPO-Sep (base) MPO-Sep (down)
Student dropout estimates by growth projection
GDP per capita growth projections for 2020 (p.p.)
Note: GDP per capita growth projections in for 2020 from WB-MPO (March, 2020); IMF-WEO (April, 2020); and WB-MPO (May, 2020); MPO-Sep-Base and MPO-Sep-Down (September, 2020) (*) Growth projections are
weighted by the student cohort of each country, will different from other global averages reported in the original publications, and are weighted by GDP.
Those from more disadvantaged backgrounds—indigenous peoples, refugees, displaced children, Afro-
descendants, and children who identify as LGBTI—often face structural and historical marginalization, both
in access and in the effectiveness of services they receive. For many of these groups, there is a significant pre-
existing deficit that is likely to be compounded by school closures, and they may thus face an even greater risk of being
left behind. Factors as diverse as language of instruction, number of other children in the home, access to technology,
parental capacity to assist in homework or home-learning—either due to their own literacy and schooling levels or due
to their availability—are all likely to play important roles in how effective government mitigation strategies end up
being for different groups in the population.
Indigenous children lag considerably in access to education and have much lower primary enrollment rates as
compared to national averages in their countries. Additionally, the education they receive in many countries does
not respect their culture and language, with deleterious impacts on learning outcomes. There is also evidence of greater
vulnerability to shocks. For example, in Vietnam in the 1970’s war, school enrollment for indigenous groups dropped
much more than the rest of the population, widening inequities (Macdonald, 2012). This heightened vulnerability of
indigenous groups to shocks has also been observed in Latin American countries, and during economic downturns,
indigenous consumption levels took longer to regain to precrisis levels (Hall and Patrinos, 2006).
Historical global evidence indicates that school closures will put some girls at risk of falling behind. The
combination of being out of school and the loss of family livelihoods caused by the pandemic may leave girls especially
vulnerable. There is also the potential increase in caregiving responsibilities due to an increased likelihood of needing
to look after younger siblings or sick family members. And the burden of care work often tends to fall disproportionately
on women and girls. This may increase the likelihood of adolescent pregnancies due to an escalation of sexual abuse
and risky behavior, including transactional sex. During the Ebola outbreak, teenage pregnancies increased in some
communities by as much as 65 percent,25 and some girls never returned to the classroom after schools reopened, due
to increased rates of sexual abuse and exploitation, as well as teenage pregnancies.26 In some countries, pregnant girls
25 Rissa-Gill and Finnegan 2015; Peterman et al. 2020.
26 Bandiera et al. (2019).
16 B.5. HUMAN CAPITAL
10187-EAP Economic Update_Human Capital_74701.indd 16 10/15/20 8:47 AMwere not allowed to enroll in school. There is also the potential increase in early marriage associated with the negative
income shock once schools start reopening, supported by evidence that shocks such as droughts can push families to
“marry off” their daughters earlier than otherwise (“famine brides”).
Even in the scenario of having systems in place for remote learning, gender norms will play a role in investment
decisions, as is the case of gender differences in the amount of time that can be allocated to learning (at home).
Intra-household allocation of ICT resources for homeschooling and/or at the community leve l might be redirected to boys
(as a future investment) over girls. Even if we know from past epidemics that girls are likely to be the hardest hit, it is
important to mention that pressure to contribute to the family income may impact boys’ likelihood to re-engage in school.
Policy response—building back better
Where schools have reopened, students are returning to schools in an environment that is quite different from
the one they left earlier in 2020 or 2019. For most countries, conditions to reopen schools included (i) reduced
number of new cases and (ii) the readiness to roll out strategies and tools to ensure safe operation of schools to reduce
virus transmission. And when reopening, in some cases, strategies also include measures to catch up on the learning loss,
making sure everyone returns to school, and building back better.
To ensure schools are safe, measures put in place in different countries that have reopened and in the plans
for countries that haven’t, include mandatory face masks for students and teachers and handwashing regularly
during the day. In Papua New Guinea, schools began gradually reopening in late April, with mandatory face masks and
handwashing, and the option for remote learning if parents wished. In China, reopening varied across provinces, with
social distance measures, such as use of plastic dividers and stacking of activities. In Vietnam, the Ministry of Education
and Training developed a list of priority responses for the basic education sector, including availability of hygiene
materials and part-time in-person classes for crowded classrooms. Korea, Rep., returned to learning in May, after over
two months of closures, using a policy of delaying reopening in regions till infections were diminished, and re-closing/
resorting to remote learning as they began to rise. Schools now have temperature checks at school entrances and require
students to wear masks, socially distance (including requirement to sit alone in classes and at lunch), and frequently
wash their hands.
Enhanced school safety is also important to reduce potential dropouts (not captured through the simulations)
related to parents refusing to send their children back to school. For example, in the Philippines, only approximately
85 percent of students are currently enrolled in schools compared to 2019. While 60 percent of survey responses
indicated that their children have already enrolled in basic education, 20 percent indicated that their children would not
enroll in school if they reopened in August. Of those who would not enroll their children, 88 percent cited concerns over
school safety.27 A similar phenomenon was observed in Tuvalu, where families were reluctant to send kids back to the
one boarding high school after the longer (due to COVID) break in April.
Beyond safety and health, reopening also involves (or is expected to involve) significant modifications to the
school calendar, content, and pedagogical approaches, but this varies across countries and groups. In countries
surveyed in the World Bank-UINCEF-UNESCO survey, adjustments to the school calendar are reported in more than
50 percent of the cases (Figure B.5.13). Changes in the start and end dates and accelerated learning programs are
27 IPA (Innovations for Poverty Action) Philippines.
B.5. Human Capital 17
10187-EAP Economic Update_Human Capital_74701.indd 17 10/15/20 8:47 AM18
High-income Yes No High-income Yes No
EAP 33% 67% EAP 50% 50%
Yes Yes
China 100% China 100%
Yes No Yes
Panel II. If "Yes", specify
ASEAN-5 20% 80% ASEAN-5 100% to adjust it)?
Small East Asian No Small East Asian Yes No
10187-EAP Economic Update_Human Capital_74701.indd 18
when schools re-open?
. . . increase class time
economies 100% economies 60% 40%
adjusted (or are there plans in place
Has the current school calendar been
Pacific Island Yes No Pacific Island Yes No
countries 67% 33% countries 83% 17%
High-income Yes No High-income No
EAP 33% 67% EAP 100%
Yes Yes
China 100% China 100%
Source: UNESCO-UNICEF-World Bank Survey on National Education Responses to COVID-19 School Closures.
Yes No Yes No
ASEAN-5 40% 60% ASEAN-5 75% 25%
Small East Asian Yes Small East Asian Yes No
Is there a new end date?
economies 100% economies 33% 67%
. . . introduce remedial programs?
Pacific Island Yes No Pacific Island Yes No
countries 67% 33% countries 25% 75%
Panel I. Has the current school calendar been adjusted (or are there plans in place to adjust it)? (q3)
High-income No High-income No
EAP 100% EAP 100%
Yes No
China 100% China 100%
Figure B.5.13. How EAP countries are planning to adjust their school year, by subregional groupings
No Yes No
ASEAN-5 100% ASEAN-5 50% 50%
learning programs?
Small East Asian Yes No Small East Asian No
50% 50% 100%
for the next school year?
. . . introduce accerlerated
economies economies
Is there a new starting date
Pacific Island Yes No Pacific Island Yes No
countries 17% 83% countries 20% 80%
B.5. HUMAN CAPITAL
10/15/20 8:47 AMcommon except for higher-income schools. For example, Cambodia started the 2020–21 academic year several weeks
earlier than scheduled (in September), and Vietnam extended the 2019–20 year, having it end in mid-July instead of the
regular end date in late May. To the contrary, all higher-income countries (and China) are planning to adjust the scope
of the content (Figure B.5.14). In terms of pedagogical approaches, learning is expected to be blended (comprised of
face-to-face as well as remote instruction rotating system, with a certain number of days/weeks on-site and the balance at
home) and some plans involved staggered returning (from mostly online and moving progressively to all in-person). In
China, school calendars are being readjusted to make up for lost face-to-face instruction time, simple health screening is
routinely implemented, schools must enforce an “all-closure” management approach (no outsiders can enter) to reduce
possible infection, and implement a clear contingency plan if/when an active case of COVID is found in classroom. In
Vietnam, measures to bridge the learning gap included distance learning materials in ethnic minority language, among
others.
Figure B.5.14. How the education sector in EAP countries are planning to adjust the next school year, by countries income groups
Panel I. Is there a plan to adjust the scope of contents to be Panel II. Are there expectations that the next school year
covered? (q4) 33% calendar will be affected? (q5)
40%
40%
No
No
No
50%
No
60%
No
80%
No
100%
100%
100%
100%
No
No
Yes
Yes
67%
60%
Yes
60%
Yes
Yes
50%
Yes
40%
Yes
20%
Yes
High-income
EAP
China
ASEAN-5
Small East Asian
economies
Pacific Island
countries
High-income
EAP
China
ASEAN-5
Small East Asian
economies
Pacific Island
countries
Source: UNESCO-UNICEF-World Bank Survey on National Education Responses to COVID-19 School Closures.
Despite the seemingly overwhelming nature of the pandemic, options remain open to policy makers as they
plan for reopening schools. Governments and schools can use the period of school closures to plan for sanitary
protocols, social distance practices, differentiated teaching, and possible re-enrollment drives. Countries should also use
this opportunity to build a more resilient and inclusive education system that can continue to deliver learning in future
crises.28
The simulations presented here indicate the region is poised to face a substantial setback to the goal of halving
the number of learning poor and will be unable to make even a modest progress toward the Education SDGs by
2030 unless drastic remedial action is taken. An ongoing learning crisis could well be amplified if appropriate policy
responses are not prepared. The projections in this paper should inform mitigation, recovery, and resilience strategies to
ensure that these numbers prove overblown.
28 Rogers and Sabarwal (2020).
B.5. Human Capital 19
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