B.4. Poverty and social protection1 The Socio-Economic Impacts of COVID-19 in EAP Poverty in developing East Asian and Pacific could increase for the first time in 20 years: Up to 38 million more people are expected to be pushed into poverty as a result of the pandemic (Figure B.4.1). 2 The COVID-19 pandemic is expected to reverse the 20-year sustained trend of poverty reduction in the region. Prior to the onset of COVID-19, 14 million people were projected to escape poverty in 2020 based on the lower-middle income class poverty line (US$3.20/day, 2011 PPP) (Figure B.4.1, left panel). Instead, based on the latest GDP forecast, poverty is likely be 0.9 – 1.0 percentage points higher than it would have been in the absence of the pandemic, translating into between 18 to 21 million more poor people than in the pre-COVID-19 scenario. The differences between pre- and post- COVID-19 estimates are even starker at the higher, upper-middle income class poverty line (US$5.50/day, 2011 PPP) (Figure B.4.1, right panel). Based on that line, 33 million would have escaped poverty in the pre-COVID-19 scenario but due to the pandemic, poverty is expected to be 1.6 – 1.8 percentage points higher than previously projected. This represents between 33 and 38 million more poor people than expected before the crisis. While poverty in China is projected to continue to decline in 2020, poverty in the rest of the developing region is projected to increase (Figure B.4.2). China is projected to recover faster relative to the rest of the region, with positive GDP growth rates in 2020 in both baseline and downside scenarios. Translating these growth rates into per capita household consumption growth, even under the lower post- COVID-19 scenario 8 million people are expected to escape from poverty between 2019 and 2020 (US$5.50/day, 2011 PPP) (Figure B.4.2, left panel). 3 On the other hand, poverty the rest of developing EAP countries is expected to increase under either scenario, with up to 13 million more poor people in 2020 than in 2019 (Figure B.4.2, right panel). 4 Given their population size and contraction in GDP, more than three-quarters of those are expected to come from Indonesia and the Philippines. 1 This analysis was conducted to inform the October 2020 East Asia and Pacific Economic Update. 2 At the US$5.50 a day poverty line (2011PPP) which represents the poverty line for upper middle-income countries. 3 As household survey data is not available for China (only grouped data), poverty projections for China are done assuming neutral distribution growth from per capita GDP growth rates to per capita household consumption with a passthrough of 0.7. For most other countries in the region, projections are done based on prior per capita GDP growth elasticities of poverty. 4 In addition to China, Vietnam’s poverty is also expected to decline from 2019 to 2020, following the positive growth projections for 2020. 1 Figure B.4.1. Up to 38 million more people are being pushed into poverty as a result of the pandemic Change in number of poor from 2019 to 2020 in EAP A. US$3.20 a day poverty line B. US$5.50 a day poverty line 10 6.9 10 4.1 4.5 Change in number of poor (millions) Change in number of poor (millions) 0 0 0.0 -10 -10 -14.1 -20 -20 -30 -30 -40 -33.1 -40 Baseline Lower case Baseline Lower case pre-COVID-19 2020 forecast pre-COVID-19 2020 forecast scenario scenario Source: World Bank Staff estimations. Poverty estimates are based on growth forecasts, population projections, and historical growth elasticities of poverty. Note: The baseline and lower-case forecasts are as of September 17, 2020. US$3.20 per-person-per-day and US$5.50 per-person-per-day poverty lines (2011PPP) represent the typical value of poverty lines found in lower middle-income and upper middle-income countries, respectively (World Bank, 2018). Figure B.4.2. Poverty is expected to decrease in China but not in the rest of the developing EAP region Change in the number of poor from 2019 to 2020 at US$5.50 a day poverty line A. China B. Rest of developing EAP 20 20 Change in number of poor (millions) Change in number of poor (millions) 12.6 9.5 10 10 0 0 -10 -10 -9.6 -8.1 -8.1 -20 -20 -30 -25.0 Baseline Lower case -30 Baseline Lower case pre-COVID-19 2020 forecast scenario pre-COVID-19 2020 forecast scenario Source: World Bank Staff estimations. Poverty estimates are based on growth forecasts, population projections, and historical growth elasticities of poverty. Note: The baseline and lower-case forecasts are as of September 17, 2020. 2 The employment impacts of the pandemic have been generally large and widespread. High- frequency data collected by the World Bank in May and early July indicate that significant shares of workers have either lost their jobs or had to change jobs as a result of the pandemic - although the magnitude of the effects differ considerably across the countries surveyed (Figure B.4.3, panel A). Yet, significantly more households experienced declines in their incomes, since many who remained employed saw their earnings fall (Figure B.4.3, panel B). While both wage employees and those working in family businesses experience income reductions, in most countries surveyed a larger share of those relying on non-farm family businesses – many of whom operate in the informal sector – report a negative income shock. Figure B.4.3. Employment losses were large in some countries, whereas losses in earnings were widespread A. Share of workers who lost their job since the B. Share of households that experienced a reduction pandemic (%) in wages or family business income (%) 100 100 Percent of households with wage/business Percent of respondents working pre-crisis 85 79 79 80 80 66 57 59 60 60 53 income 40 40 33 24 22 19 20 13 14 20 3 0 0 Sources: EAP High frequency household phone surveys, first round. Notes: Dates of fieldwork are as follows: Indonesia (May 1-17, 2020), Cambodia (May 11-26, 2020), Myanmar (May 18 – June 3, 2020), Mongolia (May 22 – June 2, 2020), PNG (June 17 – July 2, 2020), Vietnam (June 5 – July 8, 2020) and Lao PDR (June 20 – July 17, 2020). Box 1. EAP High frequency phone surveys Since early May 2020, the World Bank conducted high-frequency household phone surveys in a total of nine countries throughout the East Asia and the Pacific region. The purpose was to monitor the socio-economic impacts of COVID-19 and inform policy responses to the crisis. At the time of this report, data were available for seven of nine countries, as listed in the table below. General aspects of the surveys are detailed as follows: 3 − Questionnaires. Each survey questionnaire covers a range of topics, including knowledge and behavior associated with COVID-19, employment, family business, incomes, access to food and basic services, methods of coping with the crisis, and safety nets. − Sampling method. Countries varied in sampling methods; some used a subsample of recent representative sample surveys, while others used random digit dialing when recent household surveys were not available. − Timing of surveys. Round 1 took place between early May to early July 2020, but the stringency of local containment measures and mobility restrictions at the time of each survey vary across countries. In general, countries with later survey periods had less stringent measures in place during the survey (see Annex). − Representativeness. Sampling weights were constructed for all surveys to ensure unbiased estimates from the sample. As the high-frequency surveys were phone-based, the sample is representative of households who have access to phones. This may limit representativeness in areas and subpopulations for which phone penetration is low. − Respondents. All surveys had one respondent per household, who is required to be an adult member of the household. In most surveys, the respondent is also the subject of the module on individual employment, although a few countries asked about the household head in addition to or instead of the respondent if the respondent was not the head. The analysis in this report uses respondents to analyze how the crisis impacted individual employment,5 although results are not qualitatively different when using a subsample of household heads. The following key variables are used in the present analysis: − Employment loss: Defined as the share of respondents working pre-crisis who lost their job or stopped working since a specified pre-crisis point in time. − Income loss: Defined as the share of households with labor income that experienced a reduction in income from wages, non-farm business, and/or farm business. − Welfare indicator: To look at the distributional effects of the crisis, a welfare indicator is either imputed using a basic set of welfare predictors in the high-frequency data or merged in from recent surveys for samples that are subsamples of previous surveys with consumption data. EAP high-frequency household phone surveys Fieldwork dates Subject(s) of Construction of Country Sampling method Sample size employment welfare (2020) module indicator Cambodia May 11- May 26 Subsample of 2019 700 Respondent Consumption Living Standard aggregate from Measurement previous survey Survey Indonesia May 1- May 17 Subsample of the 4,338 (R1) Household Proxy means 2018 Urban 4,119 (R2) head/ test May 25 - June 6 Perception, 2018 Breadwinner (R2*) Rural poverty, and employed the 2020 Digital pre-crisis** economy household surveys 5 In Indonesia, the respondent is not the subject of the employment module if she/he is not the head/breadwinner. For simplicity, we refer to these individuals as “respondents” in the analysis. 4 Lao PDR June 20 - July 16 Random digit 2,500 Respondent Multiple dialing & household imputation head (if not using Proxy the same) Means Test variables. Mongolia May 22- June 2 Subsample of 2018 1,334 Respondent Consumption Household Socio- aggregate from Economic Survey previous survey Myanmar May 18 - June 3 Random digit 1,500 Respondent Multiple dialing from an & household imputation existing list owned head (if not using Survey of by contracted firm the same) Well-being via for the survey Instant and Frequent Tracking Papua New June 18 - July 3 Random digit 3,114 Respondent Wealth index Guinea dialing & household head (if not the same) Vietnam June 5 - July 8 Subsample from 6,217 Respondent Consumption representative 2018 aggregate from survey previous survey Note: *The analysis shown in this report uses only round 1 (R1) data of all high-frequency surveys except Indonesia, for which several indicators were only available in R2. **In Indonesia, the respondent is not the subject of the employment module if she/he is not the head/breadwinner. For simplicity, we refer to these individuals as “respondents” in the analysis. Income losses from wage employment and non-farm family businesses have been experienced across the welfare distribution in most countries (Figure B.4.4, panel A). These patterns in part reflect the sectoral and geographic distribution of crisis impacts, including that poor, rural agricultural households have often been less affected than those working in services or manufacturing. Nonetheless, there is suggestive evidence from Indonesia and Vietnam, that the wealthier, more educated workers may be less vulnerable to the shock as they are more likely to be engaged in more formal sector occupations and with higher ability to work from home. 6 6 Regression analyses confirm that, conditional on other characteristics, tertiary educated workers are less likely to lose their job or seen their wages decline than their less educated counterpart. This result that is driven by the sectors in which they are engaged in, is consistent with the evidence that richer more educated workers are more able to perform their work from home. This evidence suggests potentially growing inequalities that may have lasting effects. 5 Workers in key service sectors and industries, predominantly living in urban areas were hit hardest. Due to lockdowns and shrinking demand, job losses were more prevalent amongst those working in accommodation and food services, transportation, construction and manufacturing (Figure 5, panel A). Informality rates tend to be higher and the ability to work from home tend to be lower in these activities than in other non-agricultural sectors, such as in public administration or finance, which show a lower share of job losses. In Indonesia, Mongolia and Vietnam, since the highly affected sectors are concentrated in urban areas, job losses tend to be more prevalent among those living in urban areas (Figure B.4.4, panel B). On the other hand, in predominantly rural Papua New Guinea (PNG), Cambodia and Myanmar, the employment shock was also felt by those in the agricultural sector and rural industries (for example, mining in PNG), likely reflecting declines in external demand. Still, to the extent that workers have changed sectors of employment, agriculture (largely, own farms) appears to be playing a role as employer of last resort across most countries (Figure B.4.5, panel B) – a phenomenon that has been seen in past economic crises in the region. Figure B.4.4. COVID-19-related income losses from wage employment and non-farm family businesses have been widespread in most countries A. Share of households that experienced a reduction B. Share of respondents working pre-crisis who lost in wage or family business income (%) their job (%) Bottom 40 Middle 40 Top 20 Urban Rural 100 70 Share of households with wag/business Percent of hrespondents working pre-crisis 90 60 80 70 50 60 40 income 50 40 30 30 20 20 10 10 0 0 Sources: EAP High frequency household phone surveys, first round. Notes: “Bottom 40”, “middle 40” and “top 20” refer to the position of the respondent prior to the crisis. See Box 1 for further details. 6 Figure B.4.5. The contraction has led to a loss of jobs in services and manufacturing Changes in employment status from before to after the Covid-19 crisis, by pre-crisis sub-sector Lost job since crisis Switched job since crisis Construction 29 15 Transportation and storage 33 11 Accommodation and food service 30 12 Education 29 8 Human health and social work… 25 11 Manufacturing 29 7 Other service activities 25 8 Mining and quarrying 22 10 Wholesale and retail trade 22 7 Electricity, gas, water supply,… 16 3 Financial, insurance, real estate 17 2 Agriculture, forestry and fishing 14 4 Professional, scientific,… 12 6 Public administration and defence 7 3 0 20 40 60 P ercent of respondents working pre-crisis Sources: EAP High frequency household phone surveys, first round. Pooled sample includes Cambodia, Indonesia, Lao PDR, Mongolia, Myanmar, Papua New Guinea, and Vietnam. Population numbers are in millions of individuals. Note: Mongolia does not report on jobs switches. It is noteworthy that the crisis seems to have negatively impacted women more than their male counterparts (Figure B.4.6). Across all sampled countries, women are more likely than men to have lost their job with the onset of the crisis, even after considering differences in age and education. This is partially due to higher employment among women in service sectors such as retail and accommodation and food services that have been hard hit by COVID-19 containment measures. The differences are particularly large in Myanmar and PNG. 7 Figure B.4.6. Female workers are more likely to have lost their job and their male counterparts Female Male Cambodia Indonesia Lao PDR Mongolia Myanmar PNG Vietnam 0 10 20 30 40 50 60 70 Percent of respondents working pre-crisis Sources: EAP High frequency phone surveys (first round). Data also indicate that employment (and income) effects have evolved over time – although the adverse impacts of the crisis on employment appear to be quite persistent. A recent study found significant and widespread employment and income effects of COVID-19 in rural China (Rozelle and Wang 2020). 7 In February 2020, during the strict lockdown period, virtually no one in rural areas surveyed was working; in March 2020, roughly 34 percent of workers had returned to work; in April, that figure had risen to 46 percent. By late July 2020, 4 ½ months after the government relaxed the initial lockdown between 25-35 percent of rural workers that were working last year were still without jobs. Moreover, workers who do have jobs report that both their hours and wages are lower than at the same time last year. On the other hand, recent data from Indonesi a’s high frequency survey present an encouraging picture. Three-quarters of breadwinners that had stopped working in May had resumed work by August. 8 These improvements were largest amongst those working in services, as economic activities resumed. There is also a recovery in labor incomes though still 50 percent of respondent who are still working report income losses. Households relying on domestic or international family transfers also were likely to see these decline since the outbreak of the pandemic. Across the region, many households rely on transfers from migrant family members to complement their own earnings. In the sample of countries for which recent survey data is available, between 12 and 20 percent of households receive transfers from a family member or relative from other cities or from abroad (Figure B.4.7, panel A). As men are more likely to migrate, female-headed households in these countries are at least twice as likely to rely on domestic or international remittances than male-headed households. Most households surveyed reported that such transfers declined in the month prior to the survey, except in PNG. In Cambodia, Indonesia and Myanmar, roughly three-quarters of households receiving remittances have seen those transfers decline or stop completely (Figure B.4.7, panel B). 7 Based on a survey of 726 randomly selected village informants across seven rural Chinese provinces outside Hubei, as part of the Rural Education Action Program at Stanford University. 8 Purnamasari, Ririn. 2020. Indonesia High-frequency Monitoring of Covid-19 Impacts. Round 3: 20 July – 2 August 2020. World Bank. 8 Figure B.4.7. Up to three-quarters of households reliant on domestic or international transfers have seen these transfers stopped or declined A. Share of households receiving remittances B. Change in remittances in the last month, among (%) receipient households (%) Decreased/Stopped Stayed the same Increased Share of households receiving remittances 100 Share of households receiving remittances 20 80 60 (%) (%) 10 40 20 0 0 PNG Lao PDR Indonesia Cambodia Myanmar Cambodia Indonesia Myanmar Lao PDR PNG Sources: EAP High frequency household phone surveys, first round. Households commonly reduced their food consumption due mainly to income losses (rather than due to disruptions in the supply chain). Overall, 65.4 percent of households surveyed in Cambodia and 49.2 percent of those surveyed in Myanmar indicated that they reduced family food consumption as a way to cope with the income shock associated with COVID-19 – although these rates are significantly higher among households that experienced income losses (Figure 8). In Indonesia, a follow-up survey in June 2020 suggested that those facing food shortages had declined, although still over one-quarter of households reported facing at least some food shortages. In rural China, 55 percent of households reported reducing spending on food as a means to cope with decreased income (Rozelle and Wang 2020). According to official data, national households’ food expenditure dropped almost 13 percent in the first quarter relative to the same period in 2019. In the second quarter, the economy started to recover but food expenditure remained still 5.6 percent lower than in the previous year (World Bank 2020a). Food insecurity has increased as a result. Indeed, estimates from the World Food Programme (WFP) suggest that the number of people in the region (excluding China) who are food insecure rose from roughly 93 million in February 2020 to about 112 million in June (Figure 9). 9 While this figure has since declined slightly – to about 108 million as of mid-September 2020 – the rise in food insecurity raises the concern that the negative income shock could translate into higher incidence of malnutrition and stunting, 9The Food Security Score is a proxy indicator for food security that measures diversity of diets and how frequent it is consumed. WFP collects daily household information in over 30 countries, largely in Sub -Saharan Africa, through mobile phone (mVAM, mobile vulnerability analysis and mapping). This informatio n is used to predict food security around the world, using key metrics —food security information, weather, population size, conflict, hazards, nutrition information, and macro -economic data—and a machine learning– based predictive model. Source: https://hungermap.wfp.org/ and https://mvam.org/info/ 9 particularly among poorer households in the region. If so, there is also a risk that even a relatively short- term income could translate into longer-term losses of human capital, affecting both people’s well -being and economic productivity. Figure B.4.8. Lowering food expenditure Figure B.4.9. Over 110 million people faced food was a common coping strategy, especially insecurity in selected EAP countries among those that lost income Share of households that reduced food Estimated number of people that are food insecure (in consumption to cope with the shock (%) millions) 120 112 Business/wage income loss No business/wage income loss 106 108 104 103 106 95 12.7 80 100 93 15.4 12.7 12.7 15.4 12.7 70 15.2 15.4 22.5 P ercent of households with labor income 80 24.5 27.0 29.0 28.5 27.6 60 16.8 28.7 29.3 60 14.9 14.9 50 15.7 15.8 15.2 40 40 14.0 14.9 52.8 46.7 39.0 39.3 46.6 30 20 38.1 28.5 28.6 20 0 10 Feb 11 March 11 April 11 May 11 June 16 July 11 Aug 11 Sept 11 Cambodia Fiji Indonesia 0 Lao, PDR Mongolia Myanmar PNG Myanmar Mongolia Lao PDR Indonesia Cambodia Philippines Solomon Islands Timor-Leste Vanuatu Vietnam Sources: EAP High frequency household phone Source: World Food Programme surveys, second round for Indonesia, first round for (https://hungermap.wfp.org/) remaining countries. The crisis may also have long lasting effects in the accumulation of human capital, as government containment measures have resulted in school closures in most countries in the region. As detailed in the next chapter, schools closed in China, Hong Kong SAR, China, Vietnam, and Mongolia in January; several other countries followed in mid-to-late March. At the peak of the crisis, as many as 20 school systems in the region had been affected by closures, leaving more than 400 million students (out of a total of 412 million) out of school. Starting in May, school several systems began to re-open, but with limitations. Since then, some countries have re-closed schools, at least temporarily, due to renewed outbreaks. All-in-all, between January 1 and August 31, 2020, schools in the region were either closed nationwide, closed in selected areas, or open with limitations for 2.7 months, on average. This is equivalent to 46 percent of the time that schools would have otherwise been in session (Figure B.4.10). The extent to which classroom learning has been interrupted varies considerably across groups of countries in the region. School closures or COVID-related limitations have affected China most, followed by the ASEAN-5 counties and the small economies in developing East Asia. A number of countries in the Pacific Islands have managed to remain COVID-free by imposing stringent travel restrictions; as a result, relatively few school systems in the Pacific have had to close so far during the pandemic. 10 Figure B.4.10. Students in the region have lost significant school time due to COVID-19 Number of countries with schools closed or open with limitations due to COVID-19 and the percentage of the region’s students affected Source: Cloutier et al (2020), based on Education COVID monitoring database and inputs from country task teams. Notes: “Schools closed nationwide” means that all schools in the country are closed; “Schools closed in selected areas” means that schools are open in some geographical areas and closed in others; “Open with limitations” means that schools in the country are open but serving only certain grades or serving students on a rotating schedule. Children affected by school closures during the crisis have had varying degrees of access to remote learning. During school closures, many countries have attempted to provide academic continuity to students through online learning platforms, applications on mobile phones, educational programs on television and radio, as well as printed materials. Except for Lao PDR where the survey was field during a holiday period, most households with children enrolled prior to the crisis, remained engaged in educational activities (Figure B.4.11, panel A). But countries differ widely in the method of instruction for home-based learning, with potentially different levels of effectiveness. Nearly six out of ten households in Vietnam with children enrolled pre-crisis 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 though television. Nevertheless, welfare disparities in distance learning are also apparent within several countries (Figure B.4.11, panel B). 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, which may partly reflect access to different technologies in rural and urban areas. In Cambodia, the survey reveals that more than a third of children were not engaged in education or learning activities in the week prior to the survey, with higher shared among those in the bottom 40. 11 Figure B.4.11. Most children remained engaged in educational activities though poorer students were less likely to do so A. Share of households with children B. Share of households engaged in attending school pre-crisis (%) educational activities in the past week (%) Engaged in educational activities Bottom 40 Top 60 Assignments provided by teacher Online/Mobile platforms 100 Cambodia Percent of households with children attending school pre-crisis 80 Indonesia 60 Lao PDR 40 Mongolia Vietnam 20 0 50 100 0 Percent of households with children attending Cambodia Indonesia Lao PDR Mongolia Vietnam school pre-crisis Sources: EAP High frequency household phone surveys, second round for Indonesia, first round for remaining countries. Another potential negative impact to human capital may be related to access to preventative and treatment services. Countries are reporting significant disruptions in immunization and fewer patients than normal at hospitals. Over half of community based services reported a suspension of immunization services in Indonesia, 10 measles coverage in Lao PDR dropped from 74 to 46 percent from end of 2019 to April of 202011 , and the number of vaccinations in PNG dropped by 34 percent in January-March 2020 compared to the same period in 2019. 12 In Vietnam, the number of under-5 children getting immunizations in commune health centres decreased by 75 percent. 13 These countries also experienced substantial decline in antenatal care coverage and children routine growth and development monitoring visits. While there is no evidence on major disruptions in access to health services when needed, some groups appear to be left out. According to the EAP high frequency phone surveys, the share of households that report needing medical services but not able to access them is less than 4 percent among the households in the sample (Figure B.4.12, left panel). However, in PNG there are significant urban- rural disparities; while only 7 percent of urban households report limited access to medical services, nearly one quarter of rural households report it. In the region, female-headed households in urban areas are significantly more likely to be unable to access necessary medical services than their counterparts in rural areas or male-headed households in either rural or urban areas (Figure B.4.12, right panel). 10 UNICEF rapid assessment survey (July 2020) https://www.unicef.org/indonesia/reports/report-rapid-health- assessment 11 Data from DHIS2, MoH and analyzed by WHO EPI team. 12 Data from eNHIS analyzed by the World Bank team in collaboration with the NDOH. 13 UNICEF rapid assessment survey (August 2020) https://www.unicef.org/eap/media/6506/file/Impact%20of%20COVID- 19%20on%20children%20in%20Viet%20Nam.pdf 12 Figure B.4.12. While access to health services was mostly unaffected, some groups were more likely to be left out Share of households needing medical services but were unable to access them Female head Male head Cambodia Urban Rural Cambodia Indonesia Indonesia Lao PDR Lao PDR Mongolia Mongolia PNG PNG Vietnam Vietnam 0 5 10 15 20 25 0 5 10 15 Percent of households that needed medical Percent of households that needed medical services services in the past week in the past week Sources: EAP High frequency household phone surveys, second round for Indonesia, first round for remaining countries. 13 Government Social Protection Responses While East Asian and Pacific countries have tended to spend relatively little on social protection prior to COVID-19, governments’ response to the crisis has been substantial in most countries. Data from prior to the shock indicate, for example, that East Asian and Pacific countries spent less on social assistance on average than any other region (Figure B.4.13). Public spending on social assistance has risen substantially in response to the crisis, however. In Malaysia, incremental spending on social protection is expected to be around 2.0 percent of GDP compared with around 0.5 percent of GDP pre- COVID-19. In the Philippines, the government has authorized incremental spending of around 1.5 percent of GDP compared to a total of 0.5 percent of GDP pre-COVID. Indonesia, Mongolia, and Vietnam are also planning to more than double their pre-crisis social protection spending levels as a share of GDP in response to the crisis, while in Timor-Leste, spending may triple. Current spending on the region’s overall social protection response is estimated at around US$167 billion to date or roughly US $142 per capita, multiples higher than other developing regions. 14 Figure B.4.13. While most East Asian and Pacific countries have spent relatively little on social protection pre-COVID, the response to the crisis has been substantial in most countries Public spending on social assistance (percent of GDP) A. Average public spending on social assistance, by B. Public spending on social assistance in selected East region and worldwide Asian countries, pre-COVID-19 and including countries’ COVID-19 responses 2.5 9.0 8.0 2.0 7.0 Percent of GDP Percent of GDP 6.0 1.5 5.0 4.0 1.0 3.0 2.0 0.5 1.0 0.0 0.0 ECA AFR LAC MNA SAR EAP World Average Annual Spending, Pre-COVID-19 Spending, Pre-Covid-19 Spending, including COVID-19 Response Source: Panel A, World Bank staff calculations based on ASPIRE database data; Panel B, World Bank staff estimates. Note: Figures capture central government spending on social assistance. In contexts where social assistance spending by local governments is important, as in China, figures may underestimate pre-COVID spending levels as well as the magnitude of the response. 14 World Bank staff estimates. 14 The region’s response to the pandemic is reflected in the significant increase in the number of social protection initiatives across a wide range of instruments. Since March 2020, countries in developing East Asia and the Pacific have planned or put in place at least 124 different social protection measures in response to the crisis. This represents more than a tripling in the number of initiatives in place prior to the pandemic (Figure B.4.14, panel A). Building on existing programs where possible, countries have employed the gamut of social protection instruments in their response – social assistance targeted to the poor or to specific groups affected by the shock, social insurance for those employed in the formal sector pre-COVID, as well as labor market programs (Figure B.4.14, panel B). These initiatives have sought to protect populations through several means – by increasing the benefit amounts associated with existing programs, by increasing the number of people covered by existing programs, and by initiating new programs aimed at supporting specific crisis-affected groups. Set up as part of countries’ emergency responses, most social protection programs initiated in response to COVID-19 were set up in a time- limited manner. Although timeframes differ across countries, the average duration of COVID-19 related initiatives was originally just over 3 months. As the crisis has continued, several countries have made adjustments in program durations, however, to reflect evolving realities on the ground. Figure B.4.14. Governments of East Asian and Pacific countries scaled up their COVID-19 response quickly, using a range of social protection instruments A. Increases in social protection measures planned or B. Composition of instruments used in the region’s implemented as a result of the crisis social protection response Source: Adapted from Gentilini et al 2020 . Data in this figure capture developments through September 18, 2020. The majority of governments’ social protection responses in the region have been in the form of social assistance (Figure B.4.14, panel B). These have largely taken the form of cash transfer programs, although in-kind transfers, including food or food vouchers, school feeding programs, along with other assistance measures, such utility subsidies. Where possible, to enable rapid mobilization, countries have sought to build on existing social assistance platforms, such the Program Keluarga Harapan (PKH) conditional cash transfer (CCT) program in Indonesia, or the Pantawid Pamilyang Pilipino Program (4P) 15 CCT program in the Philippines. 15 Key to the scale-up has been efforts to identify and reach affected workers in urban areas, along with other “new poor,” who have not traditionally been beneficiaries of these programs. In the case of China, for example, the Government allowed temporary inclusion of migrant workers, without local hukou16 , to participate in the Dibao program for the first time, as part of its crisis response. In some cases, governments also initiated one-off cash transfers to specific groups affected by the shock. In Malaysia, for example, the government targeted one-off transfers to a range of affected groups, including e-hailing drivers, taxi drivers, tour guides, and students enrolled in institutes of higher learning. Social insurance policies, including unemployment insurance and paid sick leave, have also played an important role in countries’ social protection responses. For example, in Cambodia, China, Indonesia, Mongolia, Vietnam, and several Pacific Island countries the governments have enacted some form of exemption, reduction or deferral of social security contributions. Several countries, including Cambodia China, Fiji, Korea, the Philippines, and Thailand have also eased conditions under which workers can receive unemployment insurance benefits and/or increased benefit levels. In some countries, including China, and in several Pacific Island countries where the capacity to deliver social assistance is relatively underdeveloped, social insurance has played a central role in the response. In most East Asian and Pacific countries, the impact of social insurance programs has been limited, however, given that the share of the labor force in working in the formal sector is relatively low. To help address this limitation, several countries in the region, including China, Thailand, and Vietnam, have relaxed the usual eligibility conditions to broaden people’s access to benefits. In Thailand, for example, the government has supplemented social insurance with emergency benefits for informal sector workers who otherwise would not be eligible for social insurance. The region’s governments have also made use of labor market instruments in their COVID -19 response, including provision of direct support to workers and firms to promote job retention. The Wages subsidies have played a particularly important role in several countries’ responses, including in China, Malaysia, Mongolia, and the Philippines. In Malaysia, for example, the government is providing a 3-month wage subsidy to support worker retention, targeted to employees earning below a specified threshold. The amount of the subsidy differs by firm size. Firms are not allowed to lay off workers receiving wage subsidies nor to reduce those workers’ current wages. In the Philippines, wage subsidies are being provided to affected workers in private enterprises that have adopted flexible work arrangements or have had to close temporarily. Several governments in the region have also enacted regulatory adjustments to provide greater flexibility to employers with respect to the terms of employment for workers whose contracts are ending. Skills training and other labor “activation” measures including job search assistance, coaching, and job referrals, have also played a part in governments’ social protection responses in several countries. Most countries in the region are aiming to significantly increasing social protection coverage during the crisis. It is estimated that nearly 400 million individuals in developing East Asia and the Pacific have benefitted – or will benefit – from countries’ social protection responses. Coverage of cash transfer 15 In light of COVID-19 related school closures, many countries have temporarily suspended enforcement of the conditions associated with receipt of CCT payments. 16 A local residency permit in China that traditionally has allowed residents access to local services. 16 programs alone could increase two-and-a-half times as a result of COVID-related initiatives (Figure B.4.15, panel A) – although there remains significant variation in the extent to which countries are working to scale up (Figure B.4.15, panel B). This heterogeneity in responses across countries is driven by a number of factors, including the number and quality of social protection programs operating pre- COVID, the development of countries’ social protection delivery systems, including information systems, access to digital-mobile services, the extent of financial inclusion in the country, and the capacity of the relevant government ministries and agencies to implement a significant scale-up of programs in the midst of the crisis. Figure B.4.15. East Asian and Pacific countries’ responses to the pandemic are intended to increase coverage of cash transfer programs significantly, at least temporarily, during the crisis A. Percent of population covered by cash transfers, B. Percent of population covered by cash transfers, pre-Covid-19 and to be covered by the COVID-19 pre-Covid-19 and to be covered by the COVID-19 crisis response, by region crisis response, selected EAP countries Source: Adapted from Gentilini et al (2020). A critical challenge across East Asian and Pacific countries has been to reach the “new poor” – those affected by COVID-19 who were outside existing social protection programs, particularly those working in the informal sector. By virtue of working in the informal sector, many of those affected by the crisis are not covered by traditional social insurance programs associated with formal sector employment; nor have they been covered by countries’ safety net programs targeted toward the countries’ poorest citizens. Indeed, informal employment remains significant across much of the region. As noted above, many of those who have experienced income losses run family-owned businesses, which operate in the informal sector. In Thailand, Timor-Leste, and Vietnam over 50 percent of the non-agricultural labor is informal, for example, while in Indonesia, Lao PDR, and Myanmar, more than three-quarters of the non-agricultural labor force in informal (World Bank 2020b). In short, there have been holes in East Asian and Pacific countries safety nets that have required filling as a result of the pandemic. To address this challenge, countries in the region sought practical innovations to reach those outside existing programs – including through innovative use of digital and mobile technologies. In Thailand, for example, the government launched an online registration platform, “Leave No One Behind.” This platform helped screen and enroll as many as 16 million informal workers and 10 million farmers 17 into support programs. Transfers to new beneficiaries were made via an e-payment application, Promptpay. In other settings, with less developed digital and mobile infrastructure, countries called on more traditional community resources to support program scale-up. In Cambodia, for example, authorities have augmented their centralized registry, “IDPoor”, with applications for support at the village level. In Myanmar, where information was lacking due to the absence of a social registry, local authorities and communities have worked to identify new beneficiaries. While most countries’ responses have focused on crisis relief to date, a few countries have also begun use social protection measures to help crisis-affected workers reintegrate into the labor market as economies have begun to recover. This shift in emphasis could be seen in China, where the government moved quickly at the start of the outbreak to mitigate the economic impact of the lockdown on households, workers and firms, using a combination of existing programs and new initiatives. lt then shifted from emergency relief to economic recovery as the economy began to reopen. This included a public works program introduced in the rural areas to increase the labor income of poor workers; efforts to facilitate labor mobility, such as the “point -to-point” chartered transportation to assist stranded migrants in returning to work, and initiatives to upgrade workers skills to meet the demands associated with new employment opportunities (World Bank 2020a). A similar shift could be seen in in Malaysia where, in March, the Government announced and implemented a comprehensive series of social protection measures focused pr otecting people’s livelihoods. This was followed by the announcement in June of the National Economic Recovery Plan which has had a greater focus on active labor market programs and reintegration of workers into the economy. How have scale-up efforts performed to date? Many governments’ efforts have been ambitious – in terms of scale and generosity of programs – to offset the worst effects of the crisis on people’s wellbeing. Recent simulation analysis done for Indonesia, for example, indicated that if cash transfers were perfectly targeted and there was no program “leakage”, Indonesia’s response would go far to offsetting the poverty impacts associated with COVID -19 (Box 2). Similar analysis for Mongolia suggests that adjustments in the Child Money Program, the country’s universal child allowance, should go far in shielding poorer households from the effects of the shock. Nonetheless, much of the ultimate impact of countries’ social protection responses will depend on timely and effective implementation of the scale-up. Box 2 : The Scope and Potential of Indonesia’s COVID-19 Social Protection Response Without GOI's emergency economic support measures for households, Covid-19 could push as many as 8 million Indonesians into poverty. The same modeling exercise also shows that without the emergency social assistance and economic support measures for households, Covid-19 could push between 5.5 and 8 million In- donesians into poverty in 2020. This would imply a potential wiping out accumulated gains in poverty reduction achieved over the last seven years. Government of Indonesia’s response to the crisis has been strong with budgetary allocations on new relief measures approximately doubling the spending on core social assistance. Measures to mitigate adverse impacts among the poor and vulnerable include vertical and horizontal expansion and repurposing of several existing social assistance programs, as well as addition of new ones. Most of the measures target households in the bottom 40 18 percent including those that were not covered by existing programs. New programs have been introduced to address the needs of those who reside in areas with high rates of infection and mobility restrictions ( Sembako for Jabodetabek); the needs of the majority of poor and vulnerable who live in rural areas (reallocation of the Dana Desa funds for an unconditional cash transfer) and those that have lost work and wish to undertake skills training (Kartu Pra Kerja). Most of the programs are likely to be implemented for a duration of between 3 and 12 months. This emergency response has the potential to mitigate the pandemic’s impact on the poor and vulnerable population in Indonesia. World Bank (WB) modelling of ex-ante poverty impacts of the pandemic shows that if the planned economic relief measures are fully delivered to the intended target population and people are able to return to work by the third quarter of 2020, then the poverty rate could decline from 9.4 percent in 2019 to between 8.2 and 9.0 percent in 2020 (Box Figure 2.1). This suggests that irrespective of whether the overall macroeconomic impact of the pandemic is mild (0 percent growth) or more severe (-2 percent growth), the government’s social protection response may be sufficient to shield the poor from the worst effects of the crisis. How well this potential will be realized depends crucially on the performance of the targeting and delivery systems, however. Such a large-scale expansion of social assistance programs implies additional burden on delivery systems, and this can affect the speed of rollout and program realization. Some of the newer cash transfer programs, such as BLT Dana Desa , are designed to provide safety nets to affected households who are not already covered by existing programs, such as the PKH, Indonesia’ s flagship conditional cash transfer program, and Sembako, the country’s food assistance program. Ensuring that the targeting mechanism correctly identifies and delivers assistance to the deserving households will be equally important in determining how su ccessfully this package of programs shields the poor and this is something that will need to be monitored closely in the coming months. With mitigation of the pandemic’s impacts hinging on the efficacy of program targeting and implementation, the crisis has exposed the gaps in the existing social protection system. In that context, the crisis also presents an opportunity to for policymakers to rethink Indonesia’s social protection system, as maintaining some of the recent expansions and introducing several additional reforms would lead to more a stronger, more durable social protection system in the longer-run. As such, there is an argument for some the recent improvements to social assistance to be locked in even after the pandemic abates. Box Figure 2.1: Projected poverty rates in Indonesia under compensated and uncompensated scenarios (Poverty rate, % of population) 14 11.6 12 10.7 10 8.7 9.0 8.2 8 6 4 2 0 Uncompensated Compensated Uncompensated Compensated (with Full SA package) (with Full SA package) Benchmark Mild case Severe case 19 Note: ‘ Benchmark’ shows the estimated poverty rate in 2020 pre -COVID. The results for severe case assumes -2 percent GDP growth. ‘Uncompensated’ case shows the impact on per capita consumption by decile without the government’s relief measures. ‘Compensated (with Full SA package)’ case shows the impact on per capita consumption by decile after accounting for the full central government’s social assistance (SA) package Source: Tiwari et al (2020). While the full effects of government response efforts are yet to be observed, available evidence suggests that program implementation has been varied considerably across countries. Several countries in the region, including China, Malaysia, and Thailand, appear to have been able to mobilize their responses quickly. In Malaysia, for example, the Government was able to implement a large-scale, one-off cash transfer, Bantuan Prihatin Nasional, achieving close to a 99 percent implementation rate and reaching roughly 10.4 million beneficiaries (World Bank 2020c). Other countries (e.g., the Myanmar, Vietnam) announced ambitious plans early but have been slower in implementing them. 17 And still others – with fewer existing programs and less developed systems – have faced challenges in mounting an effective scale-up. In Lao PDR, for example, the government has not implemented a specific COVID- related response. High-frequency data collected by the World Bank highlights this cross-country variation in reaching those who have experienced negative income shocks as a result of the crisis (Figure B.4.16). The data also show that, to date, programs have reached a higher share those in the bottom 40 percent of the population (Figure B.4.16, panel B) – the traditional targets of social assistance – than those whose incomes fell due to the pandemic (Figure B.4.16, panel A). Figure B.4.16. Coverage of programs providing support to households during the pandemic has varied across countries Share of households receiving government assistance at the time of the first-round survey* A. Share of households experiencing a negative income shock B. Share of households in the bottom 40% of the that received government assistance (%) distribution that received government assistance (%) 100 100 Percent of households with wage/business 80 80 Percent of households 60 60 income 40 40 20 20 0 0 Mongolia Indonesia Vietnam Lao PDR Mongolia Indonesia Vietnam Lao PDR Source: EAP High frequency household phone survey, first round. 17 High frequency data from Vietnam suggest that the incremental increase in coverage due to the country’s crisis response has been small. Some observers argue that this reflects the fact that the virus has been relatively well- contained in Vietnam and that the economic impacts of the pandemic have been less pronounced there than in many countries. 20 Note: Share of households receiving government assistance at the time of the first-round survey. Assistance programs include existing and new programs that were in place at the time of the survey flagged as part of the emergency response to the pandemic. Dates for the first-round of data collection for each of the four countries in the figures are as follows: Indonesia (May 1-17, 2020), Lao PDR (June 20 – July 17, 2020), Mongolia (May 22 – June 2, 2020), and Vietnam (June 5 – July 8, 2020). An important lesson from the crisis to date is that countries that already had well-functioning social protection programs and good social protection infrastructure – e.g., national ID systems, social registries, broad digital/mobile and access – were better placed to scale up quickly in response to the COVID-19 crisis. Indeed, part of Malaysia’s ability to mount a quick social protection response to the crisis lay precisely in the fact that it was able to leverage existing social protection programs along with strong fundamental implementation conditions, including the existence of a universal national ID system, high mobile phone coverage, and high financial inclusion. Similarly, Thailand’s ability to identify, screen, and provide support to millions of previously unregistered workers and farmers was the result of having in place a robust and ubiquitous national ID, a broad and accessible digital-mobile infrastructure and sound information systems. Cross-country data reinforce notion of an important correlation in the region between countries’ “delivery capacity” and their ability to undertake rapid scale - up of social protection (Figure B.4.17). Figure B.4.17. There is a positive relationship between a country’s “delivery capacity” and the scale of their COVID-19 social protection response (actual or planned Covid-19 cash transfer coverage, percent of population) 21 Source: World Bank staff calculations Notes: The JAM Index, which combines a measure of the prevalence of citizen identification, mobile phone coverage, and bank accounts.18 While JAM Indexes also exist for China, Lao PDR, and Mongolia, no national level data are available on cash transfers in China. In Lao PDR, the government did not launch a specific COVID-19 response. In Mongolia, coverage by the Child Money Program was near-universal prior to the pandemic. So, rather than expanding coverage, the Government of Mongolia raised the amung of the associated cash transfer. Longer-term risks and challenges As noted above, short-term impacts of the crisis may also have long lasting effects in the accumulation of human capital which, in turn, can affect people’s productivity and well -being along with economic growth. Food insecurity can result in greater child malnutrition and stunting, particularly among families that were already poor pre-COVID-19. School closings, even where alternative learning arrangements are implemented, can lead to weaker learning outcomes which, again, may be most serious among children living in households that were poor or living in lagging areas pre-COVID-19. Similarly, weaker access to non-COVID-19 related health care, including vaccines, during the pandemic can contribute to poorer health outcomes with potentially longer-term impacts for children’s human capital accumulation. Together, these effects on human capital accumulation can impede young people’s ability to work and to earn as adults reinforcing intergenerational cycles of poverty. They can affect people’s productivity and earnings in the workplace and, ultimately, economic growth. Several simulation analyses have been carried out to assess the potential costs – to learning and to peoples’ earnings – of pandemic-related school closures. One simulation examines how school closures could affect learning outcomes, taking into consideration four main channels, namely the learning that will not take place, the forgetting of past learning, the impact on future learning, and an increase in the drop-out rate, and is expressed in terms of Learning-Adjusted Years of Schooling (LAYS), a component of the World Bank’s Human Capital Index (HCI). 19 A related simulation examines the effect of school closures on the share of children scoring below the minimum proficiency level on lower- secondary (using PISA, the OECD’s international educatio nal assessment tests). 20 A related element of the simulations examines the monetary costs of lost learning due to the pandemic. The different scenarios were simulated: an optimistic, mid-case, and pessimistic scenario. 21 The simulations take into consideration mitigation scenarios based on current information on government responses in the region, households’ abilities to benefit from remote learning initiatives, and assumptions regarding the effectiveness of these strategies; they do not, however, take into account potential future government policy responses to address the negative impacts of school closures. 22 18 https://www.cgdev.org/sites/default/files/citizens-and-states-how-can-digital-id-and-payments-improve-state- capacity.pdf 19 Kraay (2018). 20 PISA defines minimum reading proficiency as a score below level 2 which is 407.47 points. 21 Details on the methodology used for these simulations can be found in Azevedo et al (2020). 22 For additional details, see Cloutier et al (2020) on the learning and human capital impacts of the COVID-19 crisis. 22 The simulations suggest that the costs to human capital accumulation of the pandemic, if not remediated, could be substantial. If schools are closed for an additional 4 months, beyond what was experienced from January to August 2020, the crisis could result in a loss of 0.7 learning-adjusted years of schooling. While a child born in East Asia and the Pacific today should expect to receive an average of 11.9 years of schooling over their lifetimes, this amounts only to 8.3 years of schooling when adjustments are made for the quality of their education and resultant learning. The simulation analysis suggests that school closures in the region due to COVID-19 could reduce the average learning that students achieve during their lifetime from 8.3 years to the equivalent of 7.8 learning-adjusted years of schooling. The estimated impacts are expected to differ across country groupings in the region. In China, for example, learning-adjusted years of schooling are expected to drop by 0.7 points, whereas learning-adjusted years of schooling are expected to decline by 0.6 and 0.5 points in the ASEAN5 and Small East Asian Economies, respectively (Figure B.4.18). Because school closures have been less common in the Pacific Island countries, the expected decline in learning-adjusted years of schooling is somewhat less, estimated at 0.3 points. Figure B.4.18. School closures are expected to have lasting impacts on students’ learning in the region Simulated effects of school closures on learning-adjusted years of schools by sub-region in East Asia and the Pacific Source: Cloutier et al (2020). Results expressed in Learning-Adjusted Years of Schooling (LAYS), Simulation results based on latest available LAYS data for EAP countries (unweighted averages). Fewer learning-adjusted years of schooling translate into substantial losses in individuals’ earnings over their working lives. Indeed, the average student in the region, from the cohort in school today, could face a reduction of $865 (in 2017 PPP dollars) in yearly earnings in the mid-case scenario compared to the in which there was no pandemic. This is equivalent to a reduction, on average, of 4 percent in expected earnings every year. Estimated losses differ across the region, ranging from just over $850 per year in China to about $320 per year in the Small East Asian Economies, under the mid-case scenario (Figure B.4.19). The largest earning losses are expected to be experienced by those in the High- 23 income Economies, estimated at just over $2,000 per year. If estimated losses over time are added up and brought to present value, the total earnings loss in the developing countries in the region could reach as much as 2.8 trillion dollars (2017 PPPs) under the mid-case scenario, with most of this loss concentrated in China. Figure B.4.19. Lost learning-adjusted years of schooling have real costs in terms of peoples’ earnings Effects of lower learning-adjusted years of schools on individuals’ yearly earnings, by sub -region Source: Cloutier et al (2020). The adverse effects of the COVID-19 crisis on human capital are expected to greater among the poor than the non-poor. There are a number of reasons for this, including less access of poor households to mobile technologies that could enable distance learning during periods of school closures. Simulation analysis suggests that learning outcomes among the poor will be more adversely affects by school closures than those of the non-poor. Figure B.4.20 shows the change in the share of lower- secondary students in the poorest and richest quintiles performing below the minimum proficiency level on PISA tests pre- and post-COVID. As can be seen in the figure, a higher share of students in the poorest quintile performed below the minimum proficiency level prior to the pandemic. Moreover, for all country groups in the region for which PISA scores are available, the increase in the learning gap among those performing below the minimum proficiency level is higher among students in the poorest than in the richest quintile. The results suggest that in the absence of decisive policy measures, there will indeed be a widening of learning disparities between the poor and rich as a result of the crisis. 24 Figure B.4.20. The effects of the COVID-19 shock on learning are likely to be larger among the poor than among the wealthy The share of students achieving below minimum proficiency on PISA/PISA-D tests (measured by scores PISA below 2) 80% minimum proficiency levels 70% Share of students below 60% 50% 40% 30% 20% 10% 0% Poorest Richest Poorest Richest Poorest Richest China ASEAN5 High-Income East Asia Pre-COVID Post-COVID (Mid-Case Scenario) Source: Cloutier et al (2020). Share of students below minimum proficiency levels, as measured by a PISA score of less than 2. Simulation results based on the latest available PISA and PISA-D scores for 15 countries (unweighted average). Implications for Policy Social protection policies have a central role to play in East Asian and Pacific governments’ responses to the pandemic – in mitigating the immediate impacts of the crisis, in supporting workers to reintegrate productively in the economy as countries recover, and in ensuring that short-term impacts of the crisis do not result in long-term harm to people’s human capital, productivity, and economic opportunity. Crisis relief With respect to crisis relief, there remains a need in many countries to continue strengthening program roll out and implementation – by continuing to strengthen social protection delivery systems and building the capacity to identify and reach those in need. Country experience with the pandemic response highlights that having in place well-functioning social assistance programs and solid information systems, enables more rapid scale-up in event of a crisis. Where the relevant programs and systems are in place – including national ID systems, social registries, and broad digital/mobile and access – countries have been able to implement scale-up plans relatively quickly and effectvely. Developing systems and building staff capacity in the midst of a crisis is very challenging, but may be essential to the successful implementation of countries responses. Continuing to build staff and administrative capacity to implement expanded programs is also critical and represents an investment in future, as well as current, crisis response. 25 Given the considerable uncertainty about the duration of the crisis, building flexibility into countries’ crisis response is also important. As noted, most measures countries have put in place to address the COVID-19 crisis are of limited duration – between 1-6 months. However, it will be important for governments in the region to maintain flexibility to adjust the time horizon of countries’ crisis responses, for example, in the event of renewed flare-ups of the coronavirus or a prolonged global or regional economic slump that dampens recovery. In addition, hard data is only now becoming available on the household-level impacts of the crisis. As countries obtain a more evidence-based understanding of the effects of the crisis on household well-being, governments in the region may also wish to have flexibility in adjusting the mix of specific interventions used to address those effects. Measures to mitigate the short-term impacts of the crisis will be particularly important to ensuring that the crisis does not result in long-term losses in human capital. COVID-19 shocks, including child malnutrition, stunting and reduced student learning, can have persistent impacts on people’s well - being, productivity, and economic opportunity. The poor remain particularly vulnerable to long-term impacts, as they possess fewer assets and have less access to services. During the Asian Financial Crisis, as well as the Global Financial Crisis, countries in the region used financial incentives – for example, fee waivers and other subsidies – to help ensure that children in crisis affected households could afford to stay in or return to school. Cash transfers, along with waivers on fees for basic services that incentivize access to basic services, including education, can thus play a role as part of a broader, multi-sector strategy to ensure that the short-term impacts of the pandemic on human capital does not cause long-term harm, especially to poor and vulnerable populations. Early investments in strategies to prevent and recoup COVID-19 induced learning losses will also be important to averting long-term losses in human capital. Experiences with distance learning in the region (and beyond) have been quite mixed. As a result, in-person education continues dominates remote education in terms of learning outcomes. As of early September 2020, only 12 countries still had schools closed or open with limitations (down from as many as in April). But the situation remains fluid and countries are occasionally reclosing in response to new outbreaks. Sustained opening requires measures to protect students, staff, teachers and their families. Such measures include sanitary protocols, social distance practices, and initiatives to support student re-enrollment. Learning losses can also be mitigated through measures to adjust school curricula, as needed, and to develop rapid catch-up periods when schools reopen. In the longer-term, countries should seek to develop more resilient and inclusive education systems that can deliver learning in the event of future crises, including through remote learning. In this context, there is an increasing recognition of the need for improved digital infrastructure and affordable digital/mobile access to enable poor and others affected by crises to access social protection benefits, remote learning opportunities, and other forms of support. The importance of strengthening digital infrastructure and making digital/mobile services broadly affordable and accessible, especially to the poor and those living in remote areas of countries, goes well beyond providing social protection in the time of COVID-19. Nonetheless, COVID-19 has reinforced its importance in identifying, screening, and ultimately assisting those who were outside the system of social protection pre-COVID. Investments to make digital/mobile access universal represents a longer-term agenda, but 26 one that will be critical to building the capacity for rapid and effective crisis response in the event of future crises and shocks. Economic Recovery As countries’ economies begin to recover, social protection can play an important role in supporting that recovery – by enabling the reintegration of workers into the post-COVID economy. This role for social protection will require a different mix of policies and instruments than has been used during the crisis phase. While some households will continue to need social assistance as countries’ economies emerge from the crisis, the focus, there should also be an increased emphasis on building new skills and on supporting workers in finding new jobs and rising firms in findings new talent. There will be a particularly important role for labor market initiatives that • Provide workers opportunities for training and skills upgrading to help them meet the needs of the post-COVID economy skills. Training should be designed and delivered with close links between government and the private sector to ensure that skills upgrading is demand driven. One important focus will almost certainly be to strengthen of workers’ digital literacy and skills. • Increase the availability of employment support services/job placement. In most country contexts, this will need to be accompanied by with strengthened labor market information services (LMIS) that help create a “market” in which employers’ needs can be matched with workers’ skills. While traditionally employment support services have been provided by governments, there is an increasingly important role for the private sector and for the use of digital platforms in connecting people and jobs. • Provide special employment support measures for selected groups, including for youth, workers from poor households, and older workers. Such measures may include targeted wage subsidies and/or public works programs to help those in vulnerable economic circumstances to earn. One critical consideration is the role and utilization of wage subsidies in the recovery support phase (in contrast to the crisis relief phase). As noted above, several countries have used wage subsidies during as part of their crisis response – to assist firms and incentivize worker retention. One risk associated with the use of wage subsidies as the crisis progresses is that they increasingly support the creation of “zombie firms” as the nature of the region’s economies evolve. There still may be a valuable role for wage subsidies as East Asian and Pacific economies emerge from the crisis. But that role should be different. Rather than focusing on retention of workers, conditional wage subsidies can be used to incentivize new hiring of workers among expanding firms and/or to incentive firms to provide training for workers to help upgrade their skills in the face of new, post-COVID-19 labor market demands. In short, social protection has an integral role to play as part of East Asian and Pacific countries’ social and economic policy response to the COVID-19 crisis – helping to protect people’s basic well- being during the crisis phase and enabling better job matches and greater worker productivity during the recovery phase. In contributing to stronger worker skills and productivity, social protection can also help 27 build the foundation for stronger growth performance as East Asian and Pacific countries emerge into the post-COVID world. Opportunities and challenges in sustaining momentum post-crisis … The COVID-19 crisis is creating tremendous challenges for countries’ social protection systems in East Asia and the Pacific; it is also creating a tremendous opportunity to pursue durable, long-term improvements in those systems. Country experience shows that having in place well-functioning social protection programs and solid information systems, enables more rapid and effective scale-up in event of a crisis. In doing so, the crisis provides a political opportunity to catalyze delivery system reforms for the post-COVID period: expanding social registries beyond beneficiary databases; expanding digitized payments; diversifying payment providers. This can help countries in the region better support their poor and vulnerable populations during stable economic times as well as help them create the foundations for faster, more robust responses in the event of future crises. There are, however, countervailing risks that will accompany the post-crisis period and that will need to be managed if durable improvements in countries’ social protection systems are to be made. The significant scale-up of countries’ social protection systems during the crisis has required significant fiscal resources. But increased spending is stretching many countries’ fiscal capacity. At a minimum, this will cause intensive competition for resources across sectors post-COVID. In many cases, the crisis will create an urgent need for fiscal reforms, both on the revenue and expenditure sides. A key challenge post- crisis, therefore, will be to maintain the impetus for continued strengthening of countries’ social protection systems in the face of competing interests and priorities for public spending and investments in other areas. In doing so, it will be critical to keep both the social and economic policy roles of social protection in mind. 28 References Azevedo, J. P., A. Hasan, D. Goldemberg, S. A. Iqbal, and K. M. Geven. 2020. “Simulating the Potential Impacts of COVID-19 School Closures on Schooling and Learning Outcomes: A Set of Global Estimates, ” Policy Research Working Paper 9284. Washington, D.C.: World Bank Group. http://hdl.handle.net/10986/33945. Cloutier, M.H., J. P. Azevedo, and D. Goldemberg. 2020. “Back to the Future: Why as schools reopen we cannot go back to the past,” Washington, DC: World Bank. Gentilini U., M. Almenfi, P. Dale, R. Palacios, H. Natarajan, G. A. Galicia Rabadan, Y. Okamura, J. Blomquist, M. Abels, G. Demarco, and I. Santos. 2020. “Social Protection and Jobs Responses to COVID-19: A Real-Time Review of Country Measures, ” Living paper” version 13, September 18. Kraay, Aart. 2018. “Methodology for a World Bank Human Capital Index,” Policy Research Working Paper 8593. Washington, D.C.: World Bank Group. Rozelle, Scott and Huan Wang. 2020. “How Rural China is Coping with COVID-19: Examining the Impacts on China’s 600 Million Rural Residents … 1, 2, 3, and 6 months after”, presented at IFPRI Policy seminar on July 28th , 2020. https://www.ifpri.org/event/virtual-event-evolving-effects- covid-19-poverty-and-food-security-what-are-we-learning-china Tiwari, S., V. A. Sari, I. Setiawan, and J. Pinxten. 2020. “Pandemic, Poverty and Policy: Distributional Impact of the COVID-19 Shock in Indonesia, ” Jakarta, Indonesia: World Bank Group. Wang, H., M. Zhang, R. Li, O. Zhong, H. Johnstone, H. Zhou, H. Xue, S. Sylvia, M. Boswell, P. Loyalka, and S. Rozelle. 2020. “Tracking the impact of COVID-19 in Rural China over time,” Rural Education Action Program Stanford University, Working paper, Stanford, California. World Bank. 2018. Poverty and Shared Prosperity 2018: Piecing Together the Poverty Puzzle. Washington, DC: World Bank World Bank. 2020a. “Leaning Forward: COVID-19 and China’s Reform Agenda, ” China Economic Update – July 2020, Washington, D.C. World Bank. 2020b. “World Bank East Asia and Pacific Economic Update, April 2020: East Asia and Pacific in the Time of COVID-19, ” Washington, D.C. World Bank. 2020c. “Malaysia’s Social Protection and J obs Response to COVID -19 - Lessons Learned and Challenges Ahead , ” Malaysia COVID-19 Social Protection and Jobs Note No. 4, Kuala Lumpur, Malaysia. 29 Annex B.5 Timing of high-frequency surveys relative to local containment measures The timing and stringency of government containment measures in response to COVID-19 differ in each country in the EAP region. The figures below show each country’s survey implementation period against measures of stringency of local containment measures and mobility fluctuations over time. The stringency index comes from Oxford University (OxCGRT) and measures the stringency of government regulations in response to COVID-19. Mobility data come from the Google Mobility Report, and the index has been created using data from travel to retail, parks, transit stations, and workplaces. Higher values of the mobility index indicate more restricted movement compared to the median value of the pre-crisis baseline period (January 3 – February 6, 2020). Both indices have been standardized, with zero indicating the non- missing EAP region average. The two-day running average of the mobility index is shown in the figures below. Survey rounds are indicated in gray shading. The pre-crisis reference period specified in the employment module of round 1 of each questionnaire is also indicated in each figure by a vertical line and/or text. 30 31