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MENA Crisis Tracker – 10/6/2020
The MENA Crisis Tracker is a weekly newsletter that provides information on public health
indicators, expected economy-wide losses, and social consequences of the ongoing COVID-19
crisis in the Middle East and North Africa. COVID-19's spread, fatality, and economic costs are
particularly difficult to ascertain when testing is far from universal. Data transparency is key to
facilitate context-specific policy responses, which require tradeoffs between public health
outcomes and socio-economic conditions in the short run. But publicly available data must be
interpreted with caution because testing is far from universal. In addition to presenting COVID-19
related indicators with caveats, the Tracker provides links to publicly available research on the
economics of the pandemic and potential policy responses.
Highlights from this edition:
• Missing Data Alert: In the absence of universal testing, general mortality rates during 2020 can
be compared to pre-pandemic mortality as a proxy for the public health consequences of the
pandemic. Unfortunately, MENA countries do not offer publicly available data on deaths.
• High-income MENA economies lead in terms of the incidence of testing per capita – see Public
Health Tracker. Yemen no longer reports testing data. Syria’s testing remains unknown. Algeria
stopped reporting testing data in May when its test-positivity rate was 108%. Egypt’s testing data
has not changed since mid-May. Several MENA countries have test-positivity rates well above the
WHO’s recommendation of 5% or lower.
• Global evidence indicates that testing per person tends to rise with income per capita, after
controlling for population size and the quality of public health systems. This finding confirms that
developing economies are at a disadvantage relative to rich countries. See What Is Correlated with
Testing per Capita.
• Expected macroeconomic losses due to the crisis have surged during the past months, reaching
7.5% of MENA’s 2019 Gross Domestic Product (GDP) as of September 4 relative to the
counterfactual scenario of no crisis. The expected GDP losses are highest for Lebanon, with an
expected loss in 2020 equivalent to 18% of its 2019 GDP. See Macroeconomic Costs.
• The economic losses have increased poverty relative to the counterfactual scenario without the
crisis. Yet estimates of increases in the number of poor people might be underestimated. See
Poverty and Social Costs.
• This edition features a new piece on the use of phone surveys to analyze the impact of -19 on the
ability to earn income in MENA. See Insights from the MENA Welfare Observatory.
• In Insights from Academia, this edition features two new papers: one on the gendered nature of the
pandemic, and another on SME failures during COVID-19.
• A list of ongoing World Bank operations related to COVID-19 can be found here.
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Table of Contents
I. Public Health Tracker 3
II. What Is Correlated with Testing per Capita? 9
III. Macroeconomic Costs 10
IV. Poverty and Social Costs 12
V. Insights from the MENA Welfare Observatory (Poverty Team) 15
VI. Insights from Academia 16
VII. Useful Resources for Information on COVID-19 17
3
I. Public Health Tracker
Under the hypothetical of universal testing, the spread of the virus is measured by the number of
COVID-19 cases per capita, and its fatality rate is tracked by the number of deaths per capita.
Given that the incidence of testing around the world and in MENA is far from universal, indicators
of the spread are neither strictly reliable nor comparable across countries. In fact, it is likely that
countries with more widespread testing will present higher rates of spread and fatality. Hence the
degree of testing itself must be tracked to put the indicators of the spread and deaths in perspective.
Testing is tracked by two indicators: the number of tests per capita and the test positivity rate
(number of positive cases over total tests) which tends to decline with the incidence of testing.
Table 1 provides a summary of the indicators and their caveats.
Table 1: Summary of Public Health Indicators
Indicator Caveats
Testing Tests per capita Testing data is sparse for some economies
Test positivity rate (number of
positive cases over total tests)
Emerging rule-of-thumb: Test-positivity rate
should be below 5 percent
Spread Number of COVID-19 cases per
capita
Testing is not universal; many cases may be
missed
Fatality Deaths due to COVID-19 per
capita
COVID-19 deaths may be misattributed, or at-
home deaths may be missed; deaths may be
underestimated
Missing data alert: Given that testing is not universal, an arguably more trustworthy indicator of
the fatality rate is the difference between total deaths reported during the spread and pre-pandemic
mortality trends. Currently, most MENA countries do not provide readily accessible historical or
recent data on the number of deaths (due to any cause). This alone indicates that MENA faces a
transparency challenge.
Another caveat to keep in mind is that each country may be at a different stage of the pandemic.
A country may seem to be faring better than another, although at the peak of the outbreak it may
suffer more. Without universal testing, the true spread of the virus can only be understood by
random population testing.12 Notably, reported numbers are susceptible to selection bias, since it
is common for only those with symptoms to be tested. Random population testing has only been
undertaken in a few places. In New York State, random testing of 3000 individuals revealed that
14 percent were carriers of the COVID-19 antibody as of April 23.3 In Indiana, random population
testing in April suggested that the virus had a 2.8% prevalence rate in the state, implying that for
1 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7138654/
2 https://www.medrxiv.org/content/10.1101/2020.04.09.20059360v2
3 https://www.reuters.com/article/us-health-coronavirus-usa-new-york-idUSKCN2252WN
4
every officially reported case of COVID-19, 10 cases were unreported.4 In a state in southern
Brazil, a program was launched to randomly test 18,000 people. A significant upward trend was
observed over the course of three surveys, with an increase in seroprevalence from 0.135% in the
first round to 0.222% in the third during the early days since the arrival of the virus in southern
Brazil. 5 The spread of the virus is much higher than reported by official statistics.
News Highlights:
❖ Tunisia to ban gatherings, cut public-sector work hours due to pandemic
❖ COVID-19 surge in northwest Syria as cases multiply by 14 times in one month
❖ UAE records another 1,000 cases
❖ Pilgrims return to Mecca as Saudi Arabia eases COVID restrictions
❖ Iraq allows thousands of pilgrims entry despite COVID-19 epidemic
❖ 63 doctors die of COVID-19 in Yemen
❖ Jordan reimposes restrictions on schools, businesses after COVID-19 spike
The information below covers data for the date ending: October 4th, 2020
4 https://www.medrxiv.org/content/10.1101/2020.04.09.20059360v2
5 https://www.nature.com/articles/s41591-020-0992-3
5
1. Testing as of October 4th, 2020
Table 2 presents each country's tests per million of population and the test-positivity rate. A high
test-positivity rate implies that testing is selective and that it is insufficient relative to the spread
of the disease.
As reported last week, UAE (1,021,320) and Bahrain (861,271) continue to have the highest tests
per million and low test positivity rates, in both cases performing better than the median of 492
thousand per million among the top 5 countries with the highest per capita tests in the world (with
at least a population of 5 million). This indicates that these countries have both strong testing
capacity and widespread testing. Qatar, Saudi Arabia, and Kuwait have tests rates of more than
176 thousand per million. The World Health Organization has indicated that countries should aim
to have a test positivity rate of 5 percent or lower. 6 The median positivity rate of the top 5 highest
testing countries globally is 3 percent.
Finally, three countries lack testing data. Yemen recently joined that list. Yemen’s tests per million
of population was unchanged between early May and early September, and, given that its test
positivity rate exceeded 100 percent throughout that time period, it appears to have been selectively
testing and reporting untested or probable cases of COVID-19. Syria is the only country that has
not reported testing data since the beginning of the pandemic. Algeria stopped reporting testing
data in May when its test-positivity rate was 108 percent.
In Egypt, the total number of tests has remained constant for over two months, at 135,000 tests, in
the Worldometer database. The Johns Hopkins University lists Egypt as having no testing data.
However, World Bank staff has access to information from the UN-WHO team that collects data
from government sources and private laboratories on a daily basis.
6 https://coronavirus.jhu.edu/testing/testing-positivity
6
Table 2. COVID-19 Tests per Million of Population as of October 4th, 2020
Country Tests/1M pop Total Tests
Change in Tests during Past Week relative to
the Previous Week
Cases/Tests (%)
United Arab Emirates 1,021,320 10,132,865 7% 1%
Bahrain 861,271 1,478,163 5% 5%
Qatar 283,413 795,768 4% 16%
Saudi Arabia 191,055 6,678,019 5% 5%
Kuwait 176,777 757,815 3% 14%
Lebanon 130,538 889,920 9% 5%
Jordan 126,244 1,291,402 9% 1%
West Bank and Gaza 84,448 433,343 5% 10%
Djibouti 78,393 77,743 2% 7%
Oman 73,296 376,700 0% 27%
Morocco 73,229 2,711,203 6% 5%
Iraq 58,080 2,349,433 6% 16%
Iran, Islamic Rep. 48,928 4,123,173 5% 11%
Libya 31,716 218,698 8% 17%
Tunisia 21,136 250,479 10% 9%
Algeria - - - NA
Egypt, Arab Rep. - - - NA
Syrian Arab Republic - - - NA
Yemen, Rep. - - - NA
MENA REGION 71,259 32,564,724 6% 7%
Source: Authors’ calculations based on data from Worldometer (https://www.worldometers.info/coronavirus/). Color coordination done as follows:
0-5% Green, 6-10% Yellow, 11-20% Orange, 21% + Red. “NA” means data is not currently available. Countries should aim to be below the 5 percent test positivity rate threshold, according to a May 12th advisory statement by the World Health Organization.
“MENA Region” aggregate does not include Algeria, Egypt, Syria, and Yemen.
7
2. Spread of COVID-19 as of October 4th, 2020
Table 3 presents the number of reported COVID-19 cases per million of population. Qatar
(45,052), Bahrain (42,337), and Kuwait (24,966) have the highest rates, with all three countries
exceeding the median of 9,863 among the top 5 countries with the highest per capita tests in the
world (with at least a population of 5 million). In conflict economies such as Libya, Syria, and
Yemen, weak testing capacity leads to fewer reported positive cases and paints a potentially
misleading picture of low spread. Two countries faced an increase in COVID-19 cases in the past
week: Jordan and Tunisia, with at least 28 percent of total cases occurring in the past 7 days.
Table 3. Total Cases per Million Population as of October 4th, 2020
Country Cases/Million Total Cases This
week
Change in cases during the Past Week relative to the Previous
Week
Last week's cases as a % of total
Qatar 45,052 126,498 1,414 1%
Bahrain 42,337 72,662 3,301 5%
Kuwait 24,966 107,025 3,481 3%
Oman 19,704 101,270 3,820 4%
UAE 9,958 98,801 7,332 7%
Saudi Arabia 9,624 336,387 3,194 1%
Iraq 9,373 379,141 29,691 8%
West Bank & Gaza 8,087 41,498 2,795 7%
Lebanon 6,525 44,482 8,228 18%
Iran 5,598 471,772 25,324 5%
Djibouti 5,464 5,419 10 0%
Libya 5,338 36,809 3,725 10%
Morocco 3,600 133,272 15,587 12%
Tunisia 1,876 22,230 6,116 28%
Jordan 1,529 15,640 7,148 46%
Algeria 1,183 52,136 1,069 2%
Egypt 1,008 103,683 843 1%
Syria 248 4,366 294 7%
Yemen 68 2,041 11 1%
MENA REGION 4,714 2,154,412 123,383 6%
Source: Authors’ calculations based on data from Worldometer (https://www.worldometers.info/coronavirus/). Color coordination done as follows:
0-5% Green, 6-10% Yellow, 11-20% Orange, 21% + Red.
8
3. COVID-19 Fatality Rate as of October 4th, 2020
Table 4 shows the deaths per million of population. A limitation of this measure is that it may
underreport deaths by not counting deaths that occur at home or by misattributing COVID-19
deaths to other causes. In either case, the numbers reported may be underestimates. Iran (320) has
by far the highest rate in the region, followed by Iraq (232) and Oman (190). For reference, the
median fatality rate among the top-5 countries in the world in terms of testing is 43. Two
economies also experienced a spike in COVID-19 related deaths in the past 7 days: Tunisia and
Jordan, with at least 33 percent of total deaths occurring in the last 7 days.
Table 4. COVID-19 Fatality Rate – Deaths per Million of Population as of October 4th, 2020
Country Deaths/Million Total Deaths Change in deaths during the
Past Week relative to the Previous Week
Last week's
deaths as a % of total
Iran, Islamic Rep. 320 26,957 1,368 5%
Iraq 232 9,399 409 4%
Oman 190 977 68 7%
Bahrain 151 260 18 7%
Kuwait 146 624 23 4%
Saudi Arabia 139 4,875 192 4%
Libya 86 592 72 12%
Qatar 77 216 2 1%
West Bank and Gaza 64 330 39 12%
Morocco 63 2,330 261 11%
Djibouti 62 61 - 0%
Lebanon 60 406 59 15%
Egypt, Arab Rep. 58 5,981 98 2%
United Arab Emirates 43 426 14 3%
Algeria 40 1,760 46 3%
Tunisia 27 321 107 33%
Yemen, Rep. 20 591 4 1%
Syrian Arab Republic 12 205 13 6%
Jordan 10 101 56 55%
MENA REGION 123 56,412 2,849 5%
Source: Authors’ calculations based on data from Worldometer (https://www.worldometers.info/coronavirus/). Color coordination done as follows:
0-5% Green, 6-10% Yellow, 11-20% Orange, 21% + Red.
9
II. What Is Correlated with Testing per Capita?
Using data on testing as of October 4th, 2020, we can draw some insights about the correlates of
testing across countries. The regression results reported in Table 5 below show that countries that
are more developed, have better health security and capabilities, smaller, or have had a longer
incidence of the virus tend to test more per capita. The regional fixed effects (not presented in
Table 5) show that North America has the largest coefficient followed by the GCC, South Asia,
and Europe and Central Asia. These are the only regions with statistically significant coefficients
relative to East Asia and the Pacific. The conclusion is that richer countries test more, but there
are no scale effects from being a larger economy. The GCC countries on average appear to have
more testing per capita than countries from other regions after controlling for population size and
GDP per capita.
Table 5. Correlates of Testing per Capita – Scale vs. Per Capita Income
Model OLS
Outcome Variable Log of Tests per Million of Population (as of October
4th, 2020)
(1) (2) (3)
Log of GDP per capita (constant 2010
US$), 2018 0.809 *** 0.726 *** 0.664 ***
(0.085) (0.095) (0.094)
Log of Population, 2018 -0.098 -0.209 *** -0.185 *** (0.060) (0.069) (0.068)
Global Health Security Index, 2019 0.008 -0.003 -0.004 (0.009) (0.009) (0.009)
Days since the 100th case (October 4th,
2020) 0.011 *** 0.008 *
(0.004) (0.004)
Constant 4.919 *** 6.489 *** 6.791 *** (1.216) (1.347) (1.272)
Region Fixed Effects NO NO YES
Number of observations 153 140 140
R2 0.670 0.719 0.757
Adjusted R2 0.663 0.710 0.736
Note: *** p<0.01, ** p<0.05, * p<0.1, Robust Standard Errors. East Asia & Pacific omitted
10
III. Macroeconomic Costs Updated consensus growth forecasts by the private sector were released on September 8, 2020
containing information available through September 4, 2020. We compute the effect of the crisis on
the level of economic activity (GDP) as the growth downgrade for 2020 plus the impact of the changes
in growth forecasts for 2021. MENA's 2020 GDP level has been downgraded by 7.5 percentage points
on average (see Panel A of Figure 1). The largest GDP-level downgrade is seen in forecasts for
Developing Oil Exporters (9.1 percentage points lower than what was implied by the forecasts of
December 2019), followed by the GCC (7.3 percentage points) and Developing Oil Importers (5.0
percentage points). These GDP-level downgrades can be interpreted as the expected macroeconomic
costs of the COVID-19 pandemic and oil price collapse as a percent of MENA’s 2019 GDP.
The expected GDP losses for 2020 have grown larger as more information became available. In
addition, the recovery in GDP level in 2021 will not be a V-shaped recovery (Panel B of Figure 1).
The 2020 GDP level downgrade for MENA, using the baseline December 2019 forecasts, was 0.50
percentage points in March 2020, 1.8 percentage points in April 2020, 5.1 percentage points in May
2020, 6.4 percentage points in June 2020, 6.9 percentage points in July 2020, 7.3 percentage points in
August 2020, and 7.5 percentage points in September 2020. This trend reflects private sector
forecasters' increasingly pessimistic view of the cost of the crisis.
Figure 1. Not a V-Shape Recovery Relative to the Counterfactual of No Crisis: The
Expected GDP Losses of the Crisis
Sources: World Bank Staff calculations based on data from Focus Economics.
Notes: “GCC” includes Bahrain, Kuwait, Oman, Qatar, Saudi Arabia and UAE. “Developing Oil Exporters” includes Algeria,
Iran, Iraq and Yemen. “Developing Oil Importers” includes Egypt, Jordan, Lebanon, Morocco and Tunisia. “MENA” includes
countries in all three groups. Data for Egypt correspond to its fiscal year, running from July 1 to June 30 in Egypt.
-10.0
-9.0
-8.0
-7.0
-6.0
-5.0
-4.0
-3.0
-2.0
-1.0
0.0
2019 2020e 2021fMENA
GCC
Developing Oil Exporters
Panel A: By MENA Country Groups(September 2020 minus December 2019 forecasts)
-9.00
-8.00
-7.00
-6.00
-5.00
-4.00
-3.00
-2.00
-1.00
0.00
1.00
2019 2020e 2021fMarch 2020 - December 2019
April 2020 - December 2019
May 2020 - December 2019
June 2020 - December 2019
July 2020 - December 2019
August 2020 - December 2019
September 2020 - December 2020
Panel B: MENA Region by Time of
11
Figure 2 presents expected GDP-level downgrades by various private sector forecasters for each
country. The 2020 GDP-level forecasts for most countries were downgraded sharply (compared
with the 2020 GDP-level forecasts in December 2019), with Lebanon witnessing the most
significant downgrade. Moreover, Figure 2 also reveals that the expected GDP losses during
2020 are not expected to be recovered during 2021. All countries’ GDP are expected to remain
well below their 2019 levels in 2021.
Figure 2. Not a V-Shape Recovery Relative to the Counterfactual of No Crisis:
The Expected GDP-Level Downgrades of the Crisis by Country in 2020 and 2021
Source: World Bank Staff calculations based on data from Focus Economics.
12
IV. Poverty and Social Costs
The crisis shock will increase poverty in 2020. The uncertainty of the magnitude of the economic
shock caused by the pandemic, as well as the uncertainty of the distribution of its effects on
household per capita consumption, imply that any estimate of the expected percent changes in
poverty due to the pandemic relies on restrictive assumptions. Tables 6 and 7 present alternative
estimates of expected percent changes in poverty headcounts for 8 developing MENA economies.
Both tables show estimated impacts of the pandemic by applying poverty-rate-to-growth
elasticities to changes in GDP forecasts by Focus Economics. In both sets of estimates, the
elasticities are based on the assumption that the economic shock is “inequality-neutral,” which
means that they rely on the assumption that all households are impacted by a constant proportion
of the GDP shock equal to 0.85, which is known as the “pass-through rate.”
Table 6 uses a common elasticity for the eight MENA countries at each poverty threshold, which
is the median elasticity for the sample of MENA countries listed in the table at each poverty line.
These elasticities were estimated with pre-crisis data by Mahler, Lakner, Aguilar and Wu (2020).7
In contrast, the estimates reported in Table 7 allow for the poverty-to-GDP elasticities to vary
across countries as well as across poverty thresholds. These estimates were provided to the Tracker
by the World Bank’s MENA Poverty team.
Lastly, please note that if a country has negligible pre-crisis poverty rates at low poverty-line
thresholds, the absolute change in poverty rates (the number of poor people as a share of the
population) can also be negligible. This is the case of Lebanon in Tables 6 and 7.
7 The median MENA regional inequality-neutral elasticity for the international poverty rate ($1.9 in 2011 PPP) is -4.8, for the lower
middle-income poverty rate ($3.2 in 2011 PPP) is -3.3, and for upper middle-income poverty rate ($5.5 in 2011 PPP) is -2.3. All
these MENA-specific elasticities are larger in absolute values than median elasticities for the world as provided by World Bank
Economist, Daniel Mahler of the Development Economics Data Group (DECDG) on May 1, 2020. The median global elasticities
are lower: -1.4 for the $1.9 threshold (1.4% decline in $1.90 headcount ratio per 1% increase in GDP), the median elasticity for
$3.2 is -1.2, and the median elasticity for $5.5 is -0.9.
13
Table 6. Estimates of Increases in Poverty Headcounts due to the Crisis based on Private-
Sector Growth Forecasts as of September 2020 and Median MENA Poverty Elasticities
(percentage of pre-crisis poverty rates)
Country
Change in
Forecasts (%)
% Changes in Poverty Rates Due to the Crisis GDP Losses
International poverty
rate ($1.9 in 2011
PPP)
Lower middle-
income poverty rate
($3.2 in 2011 PPP)
Upper middle-
income poverty rate
($5.5 in 2011 PPP)
2020 2020 2020 2020
Algeria -9.5 16.5 27.2 16.1
Egypt -1.8 9.2 8.2 2.8
Iran -8.1 31.1 17.4 13.1
Iraq -11 50.4 23.7 10.0
Jordan -7.1 25.2 17.5 13.0
Lebanon -18.0 - 21.8* 51.0*
Morocco -7.5 18.5 16.2 11.2
Tunisia -7.4 27.8 16.3 13.2
Source: MNACE Staff calculations based on data from Focus Economics and poverty-GDP elasticities by Daniel Mahler (World
Bank, DECDG). * indicates that pre-crisis poverty rates at the indicated thresholds were estimated at zero. Forecasts for Egypt
are based on data from its fiscal year of 2021, which runs from July 1st 2020 to June 30, 2021.
14
Table 7. Estimates of Increases in Poverty Headcounts due to the Crisis based on Private-
Sector Growth Forecasts as of September 2020 using Varying Elasticities (percentage of
pre-crisis poverty rates)
Country
Change in
forecasts (%)
% Changes in Poverty Headcount Due to Expected GDP Losses from
the Crisis
2020
International poverty
rate ($1.9 in 2011
PPP)
International poverty
rate ($3.2 in 2011
PPP)
International poverty
rate ($5.5 in 2011
PPP)
Algeria -9.5 29.6 49.8 28.1
Egypt -1.8 9.2 4.6 1.4
Iran -8.1 31.7 20.4 14.4
Iraq -11 65.2 32.2 15.2
Jordan -7.1 25.2 30.5 18.1
Lebanon -18 - 21.8 73.5
Morocco -7.5 26.6 24.3 16.4
Tunisia -7.4 40.9 26.8 20.7 Source: World Bank Staff calculations based on data from Focus Economics and varying poverty-GDP elasticities. “—" indicates
that pre-crisis poverty rates at the indicated thresholds were estimated at zero. 8
As mentioned, the estimates of the impact of the crisis on the number of poor people presented in
Tables 6 and 7 rely on the weak assumption that the impact is “inequality neutral.” Yet, it is likely
that some individuals or households will be more severely affected than others. Across the region,
those at risk of falling into poverty are probably self-employed, informal sector workers who lack
social protection, and individuals working in sectors directly hit by the COVID-19 crisis. Migrant
workers—for example in GCC countries—are excluded from safety nets available to citizens. In
addition, the crisis is affecting some industries more than others, which implies that the economic
risk of individuals depends on their sector of employment. For example, hard-hit sectors include
tourism, retail, textile, and garment industries, which are particularly salient for the economies of
Lebanon, Tunisia, Morocco, and Egypt. Individuals whose livelihoods are tied to these sectors are
probably at a higher risk of falling into poverty. Thus, the estimates of the expected increases in
the number of poor people need to be interpreted with a grain of salt. But it suffices to say that
poverty is expected to rise, possibly by large numbers.
8 The estimates of the increase in the number of poor people relative to the counterfactual scenario of no crisis are based on
simulations. The results are sensitive to the pre-Covid distribution of household consumption per capita. In the case of Lebanon,
the original data come from the 2011/2012 household survey. The poverty rates since then were estimated by applying a pass
through of GDP per capita growth to household per capita consumption, assuming that all households were affected by the same
proportion -- the inequality-neutral shock assumption. Earlier this month, the revised 2011 purchasing power parities data (released
in May 2020 from the International Comparison Program (ICP)) was updated in the poverty calculation. The result was that
measured poverty in 2011 and all subsequent years were estimated to be lower than previously thought. More importantly, the
distribution of per capita consumption at the bottom tail (low levels of per capita consumption) is flat, and thus the poverty elasticity
with respect to GDP shocks also fell. This explains why the current estimates in Table XX for Lebanon and other countries are
lower than those previously reported in this Tracker.
15
V. Insights from the MENA Welfare Observatory
(Poverty Team)
1. Impact of COVID-19 on Income in MENA
Restrictions on mobility resulting from lockdown measures strained the economic wellbeing of
many. Though the phone surveys did not collect information on the impact of Covid-19 on the
ability to earn an income in a harmonized way, Figure 3 still illustrates how the ability to earn
income was severely constrained across MENA.
Figure 3: Impact of Covid-19 on the Ability to Earn an Income
Source: MENA phone survey reports
In Yemen, where the phone survey was fielded in the early days of the pandemic, only 19 percent,
reported difficulties in their ability to go to work. In Libya, by contrast, the percent of workers
who could not reach their job was 63 percent. In Djibouti, 19 percent of the breadwinners who
were working no longer did so after the outbreak of the pandemic; in Tunisia, 41 percent were
unable to continue working. In Egypt, the containment measures sharply affected breadwinners’
likelihood to work as well. Altogether, 41 percent of interviewed workers stopped working.
19
63
19
41
41
72
0 10 20 30 40 50 60 70 80
Can't reach job
Can't reach job
Income earner who stopped working
Income earner who stopped working
Income earner who stopped working
Income earner who stopped working
Yem
enLi
bya
Djib
ou
tiEg
ypt
Tun
isia
Pal
esti
ne
16
VI. Insights from Academia
1. Gender dimensions of the COVID-19 pandemic
Given that the COVID-19 pandemic is not gender-blind, the response to it should not be either.
This note, based on existing evidence and emerging trends, summarizes key gender
considerations for policymaking in health, education, agency, and economic activity.
2. COVID-19 and SME Failures
The COVID-19 pandemic has hit small and medium sized enterprises (SMEs) hard. This article
studies the impact of the crisis on business failures among SMEs across seventeen countries.
The authors estimate a large increase in the failure rate of SMEs under COVID-19 of nearly 9
percentage points, absent government support. The most affected sectors are accommodation
and food services, arts, entertainment & recreation, and education.
3. COVID-19 Shifted Patent Applications toward Technologies that Support Working from
Home
This note presents evidence that COVID-19 has shifted the direction of innovation toward new
technologies that support working remotely, such as video conferencing telecommuting, and
remote interactivity. The share of WFH (work from home) patent applications rises from 0.53
percent in January 2020 to 0.77 percent in February, even before the World Health
Organization declared the novel coronavirus outbreak a global pandemic. By May, the value
for WFH patent applications is nearly twice as large as that of January.
4. Winners and Losers from COVID-19: Global Evidence from Google Search
Using historical and near real-time Google search data, this paper estimates the immediate
impacts of COVID-19 on demand for selected services across 182 countries. In the span of
three months, the pandemic has resulted in a 63 percent reduction in demand for hotels, while
increasing demand for ICT by a comparable rate. This impact appears to be driven by supply
contractions due to social distancing and lockdown measures.
5. Digital Cash Transfers in the Time of COVID-19: Opportunities and Considerations for
Women’s Inclusion and Empowerment
The digitization of cash transfers has emerged as an attractive policy solution for countries
seeing to expand social assistance to alleviate the economic hardships. The authors of this note
identify five common barriers that need to be addressed in order to design and implement the
most inclusive and effective cash-based responses to COVID-19.
17
VII. Useful Resources for Information on COVID-19
COVID-19 & Government
Response Trackers Description Link
World Bank World Bank COVID-19 Operations Projects https://www.worldbank.org/en/about/what-we-do/brief/world-bank-
group-operational-response-COVID-19-coronavirus-projects-list
Worldometer Daily updates of data on COVID-19 spread, fatalities, and
testing per capita https://www.worldometers.info/coronavirus/
Coronavirus News Tracker Daily updates on COVID-19 media coverage including
the levels of panic and misinformation https://coronavirus.ravenpack.com/
WHO Tracker Daily updates of new COVID-19 cases, total confirmed
cases, and death totals https://covid19.who.int/
Our World in Data Visualization and downloadable data on daily COVID-19
statistics https://ourworldindata.org/coronavirus
Bloomberg Live COVID-19 visuals including global map of travel
restrictions
https://www.bloomberg.com/graphics/2020-coronavirus-cases-world-
map/
Johns Hopkins Coronavirus
Research Center COVID-19 totals of cases, deaths, and testing with visuals https://coronavirus.jhu.edu/map.html
Financial Times Coronavirus
Tracker
Visualization of COVID-19 daily deaths per country
including government response stringency index https://www.ft.com/coronavirus-latest
Oxford University Government response Tracker https://www.bsg.ox.ac.uk/research/research-projects/coronavirus-
government-response-tracker
Ugo Gentilini (World Bank
Social Protection Expert) Social Protection Response to COVID-19 https://www.ugogentilini.net/
Worldwide Lockdown Dataset Dataset of lockdowns by country https://www.kaggle.com/jcyzag/covid19-lockdown-dates-by-
country#countryLockdowndates.csv
IMF Global Fiscal Support Monitor with a breakdown of
country-specific fiscal responses to COVID-19
https://blogs.imf.org/2020/05/20/tracking-the-9-trillion-global-fiscal-
support-to-fight-COVID-19/
The Guardian COVID vaccine tracker: when will a coronavirus be
ready?
https://www.theguardian.com/world/ng-interactive/2020/aug/31/covid-
vaccine-tracker-when-will-a-coronavirus-vaccine-be-ready
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Human Mobility Data Description Link
Cuebiq
Analysis of mobility and shelter in place analysis by
tracking movement of its users through their devices
(mostly US so far). Cuebiq maintains direct relationships
with 80+ apps that reach a diverse base of anonymous,
opted-in users, giving the ability to collect accurate and
precise SDK location data at scale on a daily basis.
https://www.cuebiq.com/visitation-insights-covid19/
Facebook Disease Prevention
Maps Mobility patterns tracked using Facebook data https://dataforgood.fb.com/tools/disease-prevention-maps/
Satellite Data (to capture
COVID-19 effects) Description Link
ESA: Sentinel 5P Air Pollution Maps https://earth.esa.int/web/guest/missions/esa-eo-missions/sentinel-5p
NASA Goddard: Black Marble Night Lights maps https://blackmarble.gsfc.nasa.gov/#home
Social media and Crowd-
sourced data Description Link
Premise
Custom questions as part of on-going micro-surveys, for
example perceptions of social distancing measures,
government support, livelihood impacts
https://www.premise.com/
Google Trends High frequency data COVID-19 related searches https://trends.google.com/trends/story/US_cu_4Rjdh3ABAABMHM_en
Waze
Crowd-sourced data on quarantine-related road closures,
medical testing centers, and emergency food distribution
centers
https://www.waze.com/covid19