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

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Income earner who stopped working

Income earner who stopped working

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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