Three years into the COVID-19 pandemic, the world is still recovering from disruptions in lives and welfare that were unprecedented in recent times. In 2020, global poverty increased for the first time since 1998 and likely has not returned to pre-pandemic levels yet. Global supply-chain disruptions and containment measures including lockdowns, quarantines, and social distancing resulted in steep job losses, and recovery has been hindered by the energy and food price crises, climate shocks, and the war in Ukraine. But how has the welfare of households evolved after the initial COVID-19 shock?
A recent paper sheds light on the topic. The analysis was based on more than 300 high-frequency phone surveys (HFPS) in 80 countries during 2020 and 2021 representing a universe of over 2.5 billion people. It focused on the scarring effects of the initial losses of employment and income by documenting patterns of recovery within and across countries, as restrictions on mobility were gradually relaxed.
The main conclusion of the paper is that, unless proper remediation takes place in a timely way, the uneven impacts associated with COVID-19 on welfare may be amplified over the medium to long term, leading to future increases in global poverty and inequality. The statement is supported by four findings:
First, female, informal, and less-educated workers stopped working to a greater degree initially and they have recovered employment at a slower pace.
Women were less likely to gain or recover employment during 2020 and 2021, especially when there were young children in the household. This is in part because stay-at-home measures had a stronger effect on employment for women relative to men. We estimate that less educated women with children were between 14 and 17 percentage points more likely to have stopped working and 19 percentage points less likely to have returned to work compared to more educated men with children.
The employment gap along the dimensions of gender and education grew wider as the pandemic evolved, accentuated by the burden of childcare on women. This is especially worrisome because in countries with higher pre-pandemic income inequality, women and lower-educated workers were more likely to lose jobs than their counterparts (Figure 1), which points to a vicious circle from higher inequality to more unequal impacts to, potentially, more inequality in the future.
Second, new jobs started in the aftermath of the pandemic tend to be of lower quality.
Simply looking at employment levels may not represent the full picture, as it misses the pandemic-induced changes in the number of hours worked or wage rates. The surveys reveal substantial job market churning, including transitions across sectors and employment types. Across sectors, the most common transition was into agriculture, which likely helped absorb the labor market shock but where earnings are lower. There was also an increase in self-employment. Workers were twice as likely to transition from wage work before the pandemic into self-employment in 2021 compared to the opposite transition (figure 1). Self-employment is typically associated with lower earnings and higher income volatility. These trends toward lower-quality employment are most prominent among lower-income countries and lower-educated workers, which could exacerbate existing inequalities over time.
Third, income losses were more extensive than job losses and led to heightened food insecurity and negative coping strategies in places where safety nets were inadequate.
Early in the pandemic, as many as two-thirds of households reported income losses levels much higher than the share of households reporting job losses. Many countries provided additional social assistance during the pandemic, but support coverage was often incomplete or delayed, particularly in the poorest countries.
The extent of unmitigated losses is evident from the sizable share of households that faced food insecurity. One out of six households surveyed early in the pandemic reported that adults in the household had gone at least a day without eating in the previous 30 days. This ratio decreased only slightly, to one of seven households in 2021 surveys.
The situation likely deteriorated during the pandemic and was not just a continuation of pre-existing trends. The sale of assets during the pandemic was particularly common among households with lower access to education and in rural areas. Even the temporary adoption of such negative coping mechanisms can have longer term implications as they could represent damages to human and physical capital, reducing the productive capacity of the poor. Unmitigated losses could lead to heightened future poverty and inequality.
Finally, learning inequalities were accentuated by school closures, with possible long-term impacts on families welfare.
Over 20 percent of students in low-income and lower-middle-income countries had not returned to school in 2021. Children were less likely to access education if household adults had lower levels of education (Figure 3). As we show, these same households were more vulnerable to welfare losses during the COVID-19 economic crisis, consistent with negative feedback loops across generations that may worsen future inequalities.
Figure 3. Children in households with lower education were less likely to return to school
If not properly addressed, losses in welfare from the COVID-19 crisis could worsen inequality in the longer term, but there is still the opportunity to mitigate this risk before it is realized.
As the recent Poverty and Shared Prosperity Report argues, targeted social protection, inclusive labor market policies, and sustained efforts to recover lost learning have potential to help offset the uneven impacts of the pandemic and offer a path to income recovery. Based on what we all learned from the pandemic, governments should prepare finance and invest in support delivery systems to protect vulnerable households from future crises that threaten to lock in higher levels of inequality.
Note: A more recently updated version of the numbers and chart below is available here.
As the new year brings some hope for the fight against COVID-19, we are looking back and taking stock of the effect of the pandemic on poverty in 2020. In October 2020, using the June vintage of growth forecasts from the Global Economic Prospects, we estimated that between 88 and 115 million people around the globe would be pushed into extreme poverty in 2020. Using the January 2021 forecasts from GEP, we now expect the COVID-19-induced new poor in 2020 to rise to between 119 and 124 million. This range of estimates is in line with other estimates based on alternative recent growth forecasts.
As in our earlier estimates, the number of COVID-19-induced new poor is calculated as the difference between poverty projected with the pandemic and poverty projected without the pandemic. To predict poverty in the former we use the GEP growth forecasts from January 2021, and for the pandemic free world, we use GEP growth forecasts made in January 2020.[1] It is important to note that while we are now looking back at 2020, our estimates are still relying on extrapolations of household surveys that pre-date 2020.
The estimated increase in global poverty in 2020 is truly unprecedented. Figure 1 shows the annual change in the number of global extreme poor from 1992 to 2020. Each bar represents the net number of people who have either moved out of extreme poverty if they were poor in the last year or moved into extreme poverty if they were not poor in the previous year. Before COVID-19, the only other crisis-induced increase in the global number of poor in the past three decades was the Asian financial crisis, which increased extreme poverty by 18 million in 1997 and by another 47 million in 1998. In the two-decades since 1999, the number of people living in extreme poverty worldwide has fallen by more than 1 billion people. Part of this success in reducing poverty is set to be reversed due to the COVID-19 pandemic. For the first time in 20 years, poverty is likely to significantly increase. The COVID-19 pandemic is estimated to increase extreme poverty by between 88 million (baseline estimate) and 93 million (downside estimate) in 2020. Considering those who would have otherwise escaped extreme poverty but will not due to the pandemic (i.e. 31 million in 2020), the total COVID-19-induced new poor in 2020 is estimated to be between 119 and 124 million.
Utilizing various growth forecasts that have been available in 2020, Figure 2 shows both the change in the estimated number of COVID-19-induced new poor in 2020 and the contributions of various regions. The worsening impact of the pandemic has drastically changed our projections over the course of this year, especially when comparing with the April growth rates (while the increase from June to January GEP is smaller). This has been driven primarily by a worsening forecast for South Asia, which has also changed the regional profile of the COVID-19-induced new poor. It is important to note that the poverty estimate in South Asia in recent years, even before the pandemic, is subject to considerable uncertainty due to the absence of new household survey data for India since 2011/12.[2]
Using the growth forecast from April 2020 under the $1.90-a-day poverty line, we estimated that 62 million would fall into extreme poverty globally in 2020, with South Asia and Sub-Saharan Africa each contributing roughly two-fifths. We revised the global estimate to between 88 and 115 million using the June-2020 growth forecast, with about half of the new poor residing in South Asia. Using the January 2021forecast, we now estimate between 119 and 124 additional poor globally with around 60% living in South Asia.
The increase in the number of poor is also visible at the $3.20 poverty line. At the $3.20 line, the global new poor using the GEP-baseline scenarios has increased from 175 million to 228 million (from June 2020 to January 2021), again driven by South Asia. At the $5.50 line, we do not find a worsening of the global estimate, as our new estimates actually lie within the range we estimated using the June GEP. This is largely explained by a better than expected outlook for East Asia and Pacific, counteracting the upward revision in South Asia.
There is little doubt that 2020 has been an exceptionally difficult year in recent history. While there has been progress in the development of vaccines, it doesn't appear that the increase in poverty of this past year will be reversed in 2021. Figure 3 presents the nowcast of poverty up to 2021 using the pre-COVID-19 and the COVID-19 baseline and downside scenarios using the GEP January 2021 forecast. As reported above, the pandemic-induced global new poor is estimated to be between 119 and 124 million in 2020. In 2021, the estimated COVID-19-induced poor is set to rise to between 143 and 163 million. While the estimates for 2021 are very preliminary, it goes to show that for millions of people around the globe this crisis will not be short-lived. Following the Asian financial crisis (see Figure 1), 42 million people moved out of extreme poverty in 1999 and, on average, 54 million people have moved out of extreme poverty annually in the two decades before the pandemic. We hope that when we finally take stock of 2021 in a years time, the coming year will have fared much better for poverty reduction than what we expect at the start of the year. However, the continued worsening of growth outlooks that we observed in the past year could suggest otherwise. Increasing inequality is another downside risk that we have not explored here (we have assumed that inequality remains unchanged), but discussed in more detail elsewhere. Perhaps the only certainty in this crisis is that it is truly unprecedented in modern history.
We gratefully acknowledge financial support from the UK government through the Data and Evidence for Tackling Extreme Poverty (DEEP) Research Programme.
[1] As before, we use a 0.85 global pass-through to adjust the growth rates for the projected years, i.e. 2019-2021 (for details on the pass-through calculation see Lakner et al. 2020). We use the most recent January-2021 GEP forecast for all scenarios in 2019.
Governments across the globe will spend about $5 trillion on K-12 education this year. But unless they get all children and young people back to school, keep them in class, and recover the central elements of learning, this generation could lose twice or three times that amount in earning losses.
The first impact was the millions of lives lost due to the disease caused by the COVID 19 virus. The second was the human suffering caused by job instability and poverty. The third is on children and youth who should have been in school but were told to stay at home.
It is two years since the pandemic started. Nearly all countries decided that one of the main ways to fight the pandemic was to keep students out of school and universities. Public health experts had decided that keeping education institutions open would lead to further spread of the virus. To flatten the curve and prevent overcrowding of hospitals, kids would have to stay home.
Many European and some East Asian countries reopened school relatively quickly, conscious of both the obvious costs for kids and the scant evidence of the benefits of the complete closure. But in many countries in South Asia, Latin America, Middle East, and even in East Asia, school closures were maintained for exceptionally long periods. Our own countries, India and Peru, are tragically representative.
By the end of 2021, school days lost were well above two hundredthats about a school year and a half. This prolonged interruption in learning could have grave long lasting effects, particularly in middle income and poor countries.
Most of the impact will be on children and youth who happened to be between 4 and 25 years old in 2020 and 2021, generating a huge intergenerational inequality. Being out of school for that long means that children not just stop learning, they also tend to forget a lot of what they have learned. In late 2020, the World Bank estimated that a 7-month absence from schools would increase the share of students in learning poverty from 53 to 63 percent. An additional 7 million students would drop out of school. The effects on marginalized minorities and girls will be even worse. Our loss estimates have been revised upwards, and now we expect that, unless swift and bold action is taken, learning poverty can reach 70%.
Biggest losses for those with the least
In all countriesrich, middle-income and poorkids from the poorest families are bearing the largest losses as their opportunities to maintain any educational engagement through remote learning are limited. Internet access for them is poor: only half of all students in middle income countries and just a tenth in the poorest countries have web access. The use of TV and radio and facilitating learning materials has helped, but it cannot replace interactive education. Learning cannot just mean watching television or listening to the radio for a few hours a day.
The result is a widening of the already large inequality of opportunity. In the developing world, COVID-19 might lead to lower growth, higher poverty and more inequality for a generation, a terrible triple threat to global prosperity for decades to come.
By late last year, we had hard data about learning losses in middle-income countries like Brazil and India. In the state of So Paulo, for example, educators decided to measure the status of learning continuouslyas opposed to many countries who have postponed any type of learning measurement, maybe to avoid getting bad news. They find that after a year without in-person classes, students had learned 27 percent less what they would have learned in normal times. Indias Pratham, a well-regarded education NGO, has found that minimum proficiency levels have been cut by half in the state of Karnataka.
Three new "Rs" for a novel coronavirus
Encouragingly, by the end of 2021, schools had reopened in many countries. But nearly one in four education systems were still closed and many systems had reopened only partially. 1.5 billion children were back in class, though 300 million kids still need to be brought back to school safely. But that was before the Omicron variant of the virus. These numbers have changed since the start of this year.
We think a blend of reopened schools, remote learning and remedial programs can limit the damage caused by the disruptions and serve as a model response for future shocksand maybe even make public education better than it was a couple of years ago.
Reopen schools safely. If youre unsettled by the image of millions of kids sitting and staring at the TV, consider this: more than half the households in 30 African countries dont even have electricity. For just too many children in the world, conditions at home are not conducive for learning, too many do not have Internet access, a decent device, or money to pay for data or books, and a space to study at home. And education is inherently a social endeavor: it requires incessant interaction. This means brick-and-mortar schools, which must open and be made safe for students and teachers. Investments are needed. Quite often there is money available for this and there is no dearth of guidelines from international agencies on how to reopen schools safely. What is generally missing in many countries is a national sense of urgency.
Invest in remote learning. World Bank and OECD teams completed an assessment of remote learning during the two years of the pandemic. The results are not always encouraging. But the pandemic showed that hybrid learning innovations -combining in-person and remote instruction through smart use of digital technologies are here to stay. But investments in technology have to be cleverly coupled with investments in learning skills. The pandemic has quickened a change in mindset about the use of technology, and we have a small window of opportunity to get teachers and administrators to see technology as part of the learning process. Besides, this is not the last pandemic or natural disaster that might force schools to close. By facilitating the continuation of the learning process at home, better learning technologies in the classroom can also make the system more effective both when schools are open and when they have to be closed.
Remediate to recoup missed and lost learning. In the United States, students returned last Fall with about a third less learning in reading during the 2019-20 academic year than they normally would have. In many countries with long school closures, students are attending a grade without having grasped even a small portion of what was taught in the previous grade. If kids dont catch up, particularly those in the early grades where losses are larger, they might eventually even drop out. Across the world, schools must adapt to the needs of studentsboth the fundamental skills of literacy and numeracy, and their mental health and well-being. Encouragingly, though, students who learned less last year tend to rebound faster than othersif they are provided access to remedial instruction. But this cannot be done without additional support to teachers and principals.
Averting a permanent loss
To help in these efforts, the World Bank Group is working on nearly a hundred COVID-related education projects in more than 60countries. These projects sum upto $ 11 billion. These are record numbers for the World Bank, buta fraction of the $72 billion the US federal government is making available for public schools to reopen safely. We are supporting countries as diverse as Chile, Jordan and Pakistan. More efforts are needed to finance the return to classroom-based teaching, and help public schools adopt teaching techniques that blend on-line and in-classroom learning and teach students at the level they need today after the months and years they have been denied of an education, focusing on foundation skills and in their emotional wellbeing
The future of a billion kids around the world is at risk. Unless we get them back in school again and find ways to remedy the effects of the interruption, COVID-19 will result in a huge setback for this generation. When the fallout of the coronavirus is finally tallied, it will become clear that its biggest damage is the lost learning of school-goers.
A decade from now, we may look back and find that the biggest permanent loss of this pandemic was avoidable. We can act now and avoid the regret.
Note: A version of this blog was first published on the Future Development Blog of the Brookings Institution on January 28th, 2022.
Images of Earth taken at night are revolutionizing our ability to measure and understand nearly every dimension of human activity on Earth and allow us to get a glimpse into human-Earth interactions in close to real time. The COVID-19 outbreak exemplifies how nighttime lights can help understand the impacts of shocks on populations, economies, and markets.
Given the interdisciplinary nature of remote sensingbased socioeconomic research, a Special Issue of Remote Sensing will bring together original and novel studies demonstrating innovative applications of analyses based on nighttime lights, to help broaden our understanding of human activity, how its changing, and a wide range of implications.
Seeing the Impacts of COVID-19 at Night
In mid-December 2019, COVID-19 began emerging in Wuhan, China, and rapidly spread, causing significant impacts not only on peoples health, but on the entire economy, the job market and the daily life across the country. Within several weeks the disease was spreading globally, with millions of confirmed cases recorded around the world and significant implications for the global economy.
The need to track and predict outbreaks, as well as understand the impacts of COVID-19 on economies, has led to the utilization of unique sources of data that could help track the spread of the pandemic in close to real time. Satellite observations - including those taken at night - are becoming a primary source of data for tracking the progress of the pandemic and its impacts on energy consumption, transportation, social interactions, the functionality of critical infrastructure, tourism, trade emissions, etc. These images provide a compelling and striking picture of the large-scale impacts of COVID-19 on Earth, from the impacts of the pandemic on businesses and transportation networks to monitoring the gradual recovery of cities around the world.
The idea of using nighttime lights to understand pandemics is not new, and previous studies have already shown, for example, how nighttime lights can be used to estimate seasonal measles epidemics, which are directly linked to spatio-temporal changes in population density as measured by anthropogenic light emissions.
Changes in the intensity of nighttime lights can be used to illustrate pace of recovery. These images show changes in nighttime lights between March 2020 and February 2020. Cyan = lighting brightened, Red = lighting dimmed.Source: Elvidge et al., 2020. The Payne Institute for Public Policy.
Shedding Light on Earth
The use of nighttime lights observations to monitor pandemics is only one example of how satellite observations can be used to help us better understand processes on Earth. Since the early 1990s, with the launch of DMSP-OLS, remotely sensed observations of nighttime lights have been a key instrument for understanding almost every aspect of human activity on Earth, particularly in the data-scarce region, without needing to be filtered through national data agencies.
Today, newer sensors, such as VIIRS/DNB, provide nighttime light data at an even higher spatial resolution and granularity. With advances in the availability and the quality of nighttime light data, together with improvements in data storage capabilities and the development of new analytical methods and workflows for analyzing the data, there is an ongoing increase in the number of scientific applications that exploit remotely sensed nighttime lights to measure our world.
Nighttime lights observations or measurements of the intensity of light emitted from Earth at night - provide a unique glimpse into human behavior and socioeconomic patterns as well as into the nature of human-Earth interactions. These observations are especially vital in countries where timely, accurate, and reliable statistical or administrative data is poor. Here, nighttime light measurements can provide important insights into where people are and how they move; they can also help us understand patterns of economic development or evaluate the economic impacts of investments in infrastructure.
While in some cases, nighttime light observations may carry inherent measurement errors especially when compared across space and time, there is a general consensus that nighttime lights are able to represent many dimensions of human presence and activity on Earth.
Today, nighttime lights are being used to measure the extent and characteristics of urbanization processes; estimate economic growth at both national and subnational levels; map global poverty as well as population density, migration and mobility patterns; track local household wealth, education and health; understand armed conflicts; measure access to electricity and electrification as well as community resilience, fishing activity, coral reef health, and more. Recently, researchers have also shown that nighttime lights can even help explain brain development and human behavior.
Moreover, by looking at the relation between the distribution of the population on Earth and the occurrence of different types of hazards, nighttime lights measurements can be used to evaluate how humans adapt and respond to these hazards. This makes them useful as an instrument to guide resilience planning. For example, the City Resilience Program utilizes nighttime light data in its City Scan product to highlight where hotspots of economic activity may be developing in flood-prone areas.
The City Resilience Program incorporates nighttime light data in its City Scan product to highlight where hotspots of economic activity may be developing in flood-prone areas. The map on the left illustrates the cumulative area flooded since 2015. The map on the right illustrates the relative change in the intensity of nighttime lights as a proxy for identifying accelerating economic activity since 2013.
While COVID-19 is creating a new demand for what these observations can tell us, the insights they enable will also be important inputs into many aspects of countries recovery when the health emergency subsides.
Since the beginning of the COVID-19 pandemic, we have been exploring its effects in the Democratic Republic of Congo (DRC) through household high-frequency phone surveys implemented in Kinshasa, Kasai and Central Kasai, and East DRC (Beni, Bunia, Goma, and Lubero). Overall, we have found that COVID-19 pandemic-related shocks have adverse social and economic impacts, such as declined labor and non-labor income and the resulting harmful coping strategies, as well as disruptions in goods and services markets and health and education services.
Most obviously, job losses and reductions in non-labor income have resulted in a decline in income. In fact, in the aftermath of the outbreak of the COVID-19 pandemic, a significant proportion of household heads or members did not work. In Kinshasa, nearly half of household heads reported not having worked, mainly for reasons related to the COVID-19 pandemic, while more than 10% of households experienced job losses for some household members. This is also the case in East DRC, where about a quarter of respondents reported COVID-19-related job losses in June 2020.
At the same time, the proportion of households receiving remittances in Kinshasa declined from 24.3% before the COVID-19 outbreak to 16.3% in April 2020, with a downward trend in amounts and frequencies. In East DRC, we observed drops in remittances for half of households regarding transfers from abroad, and for 80% of households, regarding family transfers from the country.
Making matters worse for the poor, disruption of goods and services markets have led to inflation in the country, with a 2020 cumulative inflation rate of nearly 16% against 4.6% for the previous year. We found similar patterns for cities like Kinshasa (16.6%) and Goma (16.4%), denoting a reduction in relative income and consequently a deterioration of household well-being.
As a result, there has been a loss of income for households in the aftermath of the outbreak of the pandemic. The decline in absolute and relative incomes led to an increase in poverty. In fact, according to simulations, the 16.6% inflation rate recorded in Kinshasa in 2020 may have resulted in an increase in poverty of 7 percentage points. Also, drops in remittances may increase the incidence of poverty up to 1.4 percentage points in the extreme case where all remittances are removed.
Lower income and limited access to markets have led to an increase of food insecurity and hunger. In fact, 85% of Kinshasa households seem to have reduced food consumption, while severe food insecurity remained high in East DRC with a prevalence of 63% at the end of 2020, and down from around 70% in the preceding months. During the same period, the proportion of households affected by food insecurity in Kasai and Central Kasai was around 87%.
Disruptions in basic services, especially in education and health sectors, are likely to have exacerbated the adverse effects of the pandemic, leading to increased inequality and vulnerability. For example, the very limited and unequal access to digital tools such as internet and account ownership with a mobile-money service provider prevented most Kinshasa schoolchildren from engaging in remote learning activities during school closures and from benefiting from money transfer opportunities. In Eastern DRC, only 4 out of 10 households reported having children engaged in some form of distance learning activity in August 2020.
While the poorest households seem to have been the most affected, the situation remained more worrying for female-headed households. Therefore, to reverse the adverse effects of the crisis and limit long-lasting effects on poor and vulnerable people, it is appropriate to target the latter group.
COVID-19 main facts in Kinshasa and cumulative inflation in the DRC
How can DRC return to normalcy? Only with mass vaccination of the population. We found a certain reluctance of the Congolese population to be vaccinated, with only nearly 39% of Kinshasa households willing to get vaccinated. The main reasons for not accepting vaccines are distrust of the international community (44%), general distrust of vaccines (36.3%), and concerns about vaccine safety, including health risks for adults and children (20%). Overall, the poorest households seemed to be more willing than other households to be vaccinated. This may be because they have less access to information, including controversial messages conveyed by social media. The willingness to be vaccinated is also higher for households headed by women compared to those headed by men.
Households in Eastern DRC show less reluctance in vaccine acceptance. As in the case of Kinshasa, this higher vaccine acceptance rate may be explained by the fact that respondents in the East are among the poorest as they are all eligible for social transfers and have previous experience with pandemics, Ebola most notably. Among this group, almost 70% of respondents would be willing to get themselves and other households members vaccinated once tested vaccines become available for free. It is worth noting that among those refusing the vaccine, the most common reasons cited were either concerns about the safety of the vaccine (77.5%) and distrust in the international community (32.5%).
For vaccination to be successful, it is imperative to better inform Congolese people, especially those in Kinshasa, about its benefits and the need to fight the pandemic at the global level.
This joint blog post has been produced by speakers following a conversation organized by WISE on the occasion of the International Womens Day (March 8th), on the consequences of the pandemic on girls and womens education. The following summarizes their contributions and highlights recommendations on potential solutions to remedy to this issue. It originally appeared here.
The returns to schooling are especially high for women. That is, the earnings increment associated with more education is in fact higher for women than for men. One additional year of schooling and womens wages go up 12% (compared with 10% for men).
However, the schooling gains of girls and women are under threat. Prior to the COVID-19-induced school closures, girls were staying in school longer and learning more than at any other time. The longstanding disadvantage for girls in terms of enrollment had been declining. In some cases, this led to a reverse gap where girls outperform boys in both enrollment rates and learning outcomes a female learning premium.
COVID-19 may put a temporary halt to this progress. COVID-19-induced school closures may slow or reverse these gains and may further prevent girls and women from realizing the potential returns representing a hidden future cost. Urgent action is needed to ensure that girls and women can realize the returns to their schooling.
The World Bank is forecasting lower levels of schooling, learning, and future earnings because of school closures due to COVID-19. Learning loss is expected and predicted. Recent evidence from several countries shows that the COVID-19 slide is real. For women and girls, who are already being significantly negatively affected by the pandemic, there is a particular risk in the realm of education. The pandemic puts girls at an increased risk of:
UNESCO has projected that 11 million girls may never return to school following the pandemic.
What can be done, what might work?
Remote learning was a useful action in the Spring of 2020; but its not the answer now. Rather, we need to open schools safely in order to mitigate and reverse learning losses and get girls back in school. For many girls, especially the youngest, the learning lost during the pandemic can be limited and even reversed by improving distance education during school closures and by implementing learning recovery programs such as Teach to the Right Level and High Dosage Tutoring, which the evidence has shown to be effective. However, for older girls, the risk of dropping out is real, and they may leave school before their learning losses can be recovered, unless innovative programs are put in place in a timely manner.
The risk of dropping out needs to be addressed right away by providing extra support to students and their families to ensure that they stay in school, making sure to target girls as being at a high risk of dropout and learning losses. Targeted support may also be needed to overcome constraints specific to girls, especially adolescent girls. For example, the Keeping Girls in School Program in Zambia provides cash transfers to families of adolescent girls so they can afford to keep their daughters in school and has set up an early warning system to identify girls at risk of dropping out and of other vulnerabilities.
Organizations and communities can work to ensure students continue their education while at home but at the same time ensure that they are receiving other crucial services as well, so they do not lose momentum.
Malaika Foundation in the Democratic Republic of Congo, consisting of a school for 370 girls, a community center serving 5,000 youth and adults annually, a clean water initiative with 20 wells servicing more than 32,000 people and a sustainable agriculture program, for example, developed a comprehensive plan with staff to distribute food, hygiene items and other necessities to their students during lockdowns, keeping them safe and healthy while they remain academically up to date. This resulted in 98 percent of students returning to school after closure, which is a hugely higher rate than the average.
Going forward, we know that the classroom is forever changed. The post-pandemic classroom will be equipped with modern equipment and homes will be connected. The education system will be a combination of traditional and distance (online) education. For this and other reasons, we need to invest in girls skills, including ICT tools.
The role of government is key. Progress will only come when are able to implement at scale, and once policymakers, business leaders, nonprofits, and individual communities work hand in hand.
The ongoing COVID-19 pandemic has exacerbated global income inequality, partly reversing the decline of the previous two decades. Weak recoveries in emerging market and developing economies (EMDEs) are expected to raise between-country inequality. Within-country income inequality is also estimated to have increased somewhat in EMDEs because of severe job and income losses among lower-income population groups. Rising inflation, as well as pandemic-related disruptions in education could further increase within-country inequality in the longer run. Reversing the increase in global income inequality requires measures to reduce between-country and within-country inequality, underpinned by support from the global community.
Read more on the topic in theJanuary 2022 Global Economic Prospects.
The COVID-19 pandemic jeopardizes the progress made in reducing global income inequality achieved in the previous two decades. In contrast to the global financial crisis of 2008-10, the deep recession triggered by the pandemic and the lagging economic recovery in EMDEs compared with advanced economies have raised between-country income inequality. By some measuresbetween-country Gini and Theil indices based on real per capita GDPbetween-country inequality reversed back to levels of the early 2010s.
Change in global between-country income inequality
Sources: World Bank.Note: The figure shows annualized changes in the between-country Gini and Theil indices in 2008-10 and 2019-21. The calculations are based on a strongly balanced panel of 176 countries. The Gini index is on 0-100 scale. The Theil generalized entropy GE (1) index and the Gini index are computed using GDP per capita, purchasing power parity-adjusted (constant 2017 international dollars), based on the World Banks World Development Indicators and growth estimates.
The pandemic is estimated to have increased within-country income inequality in EMDEs, although the magnitude of the increase has been small, on average. Simulations for a sample of 34 EMDEs with sufficient data, suggest that income inequality, as measured by the Gini index, has increased in 2020 by 0.3 points, reversing the continued decline in income inequality observed in EMDEs since the 2000s. The magnitude of the increase, however, is relatively smallcomparable to an annual average decline in within-country inequality in the preceding two decades in this sample. The increase in within-country inequality reflected severe job and income losses among low-skilled workers, low-income households, informal workers, and women.
Change in within-country income inequality in EMDEs, 2019-20
Sources: Narayan et al. (2022); World Bank.Note: EMDEs = emerging market and developing economies; LICs = low-income countries. The figure shows the difference between the estimated average change in the Gini index in the COVID-19 scenario and the no-pandemic counterfactual scenario. The simulations estimate the changes in the income distribution of households in 2020 against a counterfactual 2020 income distribution that assumes the last pre-pandemic sectoral output growth forecast for 2020. The sample includes 34 countries. The simulations are based on country-specific sectoral growth projections and the Harmonized High-Frequency Phone Surveys data as of July 2021.
The increase in within-country inequality caused by the pandemic may become entrenched as pandemic-induced disruptions to education and the disproportionate adverse effects on low-income households may worsen intergenerational mobility. High inflation and surging public debt levels may hamper countries ability to support vulnerable groups and facilitate recovery and sustainable growth.
Children engaged in education during school closures in EMDEs, 2020
Sources: World Bank.Note: EMDEs = emerging market and developing economies; LICs = low-income countries. Calculations based on the Harmonized High-Frequency Phone Surveys data from the COVID-19 Household Monitoring Dashboard for wave 1 in 2020. Response to survey question about children engaged in any education activities since school closures (percent of household with school age children who attended school before the pandemic), by income group (simple averages). Sample consists of 49 EMDEs, including 14 LICs.
Comprehensive policy effort to lower income inequality.
A comprehensive strategy is needed to steer the global economy onto a more inclusive development path. Such a strategy needs to include measures to reduce both between-country and within-country inequality through national reforms and with support from the global community. This involves a rapid global rollout of vaccination and productivity-enhancing reforms in EMDEs to lower between-country inequality; fiscal support targeted at vulnerable population groups and measures to broaden access to education, health care, digital services and infrastructure; and assistance from the global community to resolve debt overhangs and to ensure an open and rules-based global trade and investment climate that nurtures faster productivity growth in EMDEs.
References
Narayan, A., A. Cojocaru, S. Agrawal, T. Bundervoet, M. Davalos, N. Garcia, C. Lakner, et al. 2022. COVID-19 and Economic Inequality: Short-Term Impacts with Long-Term Consequences. Policy Research Working Paper 9902, World Bank, Washington, DC.
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