After-Effects of the COVID-19 Pandemic: Prospects for ...

INTERNATIONAL MONETARY FUND 1

After-Effects of the COVID-19 Pandemic: Prospects for Medium-Term Economic Damage

APRIL 2021 WEO CHAPTER 2

Sonali Das, Weicheng Lian, Giacomo Magistretti, Evgenia Pugacheva, and Philippe Wingender

With support from Srijoni Banerjee and Savannah Newman, and contributions from Philip Barrett, Mariya Brussevich, Marina Conesa Martinez, Allan Dizioli, Jungjin Lee, and Futoshi Narita


Motivation

▪ The COVID-19 pandemic has led to a severe global recession

• Output declined three times as much as in GFC, in half the time

• A unique crisis with differential impacts

• An unprecedented policy response

• And great uncertainty about the recovery path

▪ This chapter tackles the following questions:

• What can we learn from historical experience about prospects for scarring, and the most relevant channels through which it occurs?

• How important are sectoral spillovers in propagating shocks to the broader economy?

• What are the implications of the COVID-19 crisis for the medium-term outlook?


Roadmap

1. Anatomy of the COVID-19 shock

2. Recent developments and near-term outlook (from WEO Chapter 1)

3. Three sections to the analysis:

1. Historical evidence on recessions

2. Historical evidence on sectoral spillovers

3. Implications of the COVID-19 crisis for the medium-term outlook


The COVID-19 shock: Demand and supply in a low-contact economy

▪ Sectors can be grouped into:

• High contact, affected: restaurants,

transportation, brick-and-mortal retail, etc.

• High contact, less affected: health, groceries,

construction, etc.

• Low-contact services: professional and

business services

• Other low contact: e.g. manufacturing

▪ High-contact, affected sectors most severely

impacted by lockdowns and other pandemic

containment measures

▪ But activity fell across the board, with demand

declining due to reduced mobility, heightened

uncertainty, and network spillovers

70

75

80

85

90

95

100

105

110

–2 –1 0 1 2 3 4 5 6

70

75

80

85

90

95

100

105

110

–2 –1 0 1 2 3 4 5 6

GFC COVID-19 Other

70

75

80

85

90

95

100

105

110

–2 –1 0 1 2 3 4 5 6

70

75

80

85

90

95

100

105

110

–2 –1 0 1 2 3 4 5 6

1. High Contact Affected 2. High Contact Less

Affected

4. Low Contact, Other3. Low-Contact Services

Sources: OECD; US Bureau of Economic Analysis; and IMF staff calculations.

Note: Data are for 1990:Q1–2020:Q4 from 38 countries (the number of countries used for

each recession line varies). Time since the shock (in quarters) on the x-axis. Lines are

averages weighted by country’s GDP. For the COVID-19 crisis, quarter 0 is 2019:Q4. For

the Global Financial Crisis (GFC), quarter 0 is the country-specific date of peak real GDP

during 2007–08. Other recessions are country-specific.

Value Added by Sector Group During Recessions

(Index, last prerecession quarter = 100)


The COVID-19 shock: Differential impacts within and across economies

Within:

▪ Employment declines disproportionately

among lower-skilled workers

▪ Exits for small businesses appear to be

increasing

Across:

▪ Countries with larger share of high-contact

sectors faced larger contractions

• Particularly those dependent on tourism,

and commodity exporters

Employment, by Sector Group

(Total hours worked, cumulative percent change from 2019:Q4)

Sources: International Labor Organization; and IMF staff calculations.

Note: Data are from 43 economies (27 AEs, 16 EMDEs) for 2019:Q4–

2020:Q3. AEs = advanced economies; EMDEs = emerging market and

developing economies.

–30

–25

–20

–15

–10

–5

0

5

Hig

h co

ntac

t, af

fect

ed

Hig

h co

ntac

t, le

ss a

ffect

ed

Low

-con

tact

ser

vice

s

Low

con

tact

, oth

er

Hig

h co

ntac

t, af

fect

ed

Hig

h co

ntac

t, le

ss a

ffect

ed

Low

-con

tact

ser

vice

s

Low

con

tact

, oth

er

2020:Q1 2020:Q2 2020:Q3

AEs EMDEs


Recent Developments and Near-Term Outlook

(from WEO Chapter 1)


Renewed waves, but activity not as sensitive to restrictions

7


Manufacturing-led recovery, merchandise trade rebound

–30

–25

–20

–15

–10

–5

0

5

10

70

75

80

85

90

95

100

105

110

2017 18 19 20 Feb.21

Global Activity Indicators(Three-month moving average, annualized percent change; deviations from 50 for manufacturing PMI, unless noted otherwise)

Sources: CPB Netherlands Bureau for Economic Policy Analysis; Haver Analytics; Markit Economics; and IMF staff estimates.

Industrial production

Manufacturing PMI: New orders

Services PMI: New Business

Monthly world trade volumes (seasonally adjusted, 2018=100, right scale)

Sources: Haver Analytics; and IMF staff calculations.

Global Imports: Contributions, by Types of Goods and Regions(Contribution to year-over-year percent change, percentage points; based on value in US dollars)

–21

–18

–15

–12

–9

–6

–3

0

3

6

9

12

Jan.2020

Mar.20

May20

Jul.20

Sep.20

Nov. 20

Jan.21

Cars, AEs Cars, EMsConsumer goods, AEs Consumer goods, EMsCapital goods, AEs Capital goods, EMsIndustrial supplies, AEs Industrial supplies, EMsOther goods Total excluding petroleum


Access to vaccines a key driver of the recovery

A Race between Virus and Vaccines(Per thousand, seven-day moving average; latest observation: March 8, 2021)

Sources: Johns Hopkins University COVID-19 statistics; and national government reports via Our World in Data.

0.00

0.50

1.00

1.50

2.00

2.50

3.00

0.00 0.50 1.00 1.50 2.00 2.50 3.00

New

peo

ple

fully

vac

cina

ted

New cases

March 8February 2

Confirmed Vaccine Procurement(Percent of population)

Sources: Duke Global Health Innovation Center; Johns Hopkins University COVID-19 statistics; and national government reports via Our World in Data.

0

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40

60

80

100

120

140

160

180

200

0.0

2.0

4.0

6.0

8.0

10.0

12.0

G20 AEs G20 EMs Non-G20 EMs LIDC

By the end of June 2021By the end of December 2021UnknownPeople vaccinated (at least one shot), latest observation: March 8, 2021 (right scale)

World

Advanced

Economies U.S. Euro Area Japan U.K. Canada Korea

Other

Advanced

Asia

2020 -3.3 -4.7 -3.5 -6.6 -4.8 -9.9 -5.4 -1.0 -1.3

Revision from

January 2021 0.2 0.2 -0.1 0.6 0.3 0.1 0.1 0.0 0.2

2021 6.0 5.1 6.4 4.4 3.3 5.3 5.0 3.6 4.3

Revision from

January 2021 0.5 0.8 1.3 0.2 0.2 0.8 1.4 0.5 0.7

Growth projections: Advanced economies (percent change from a year earlier)

16Source: IMF, World Economic Outlook, April 2021.

World

Emerging

Market and

Developing

Economies

Emerging

Market and

Developing

Economies

ex. China China India Brazil Russia

Commodity

Exporting

Economies

Low Income

Developing

Countries

2020 -3.3 -2.2 -4.3 2.3 -8.0 -4.1 -3.1 -3.9 0.0

Revision from

January 2021 0.2 0.2 0.4 0.0 0.0 0.4 0.5 0.5 0.8

2021 6.0 6.7 5.8 8.4 12.5 3.7 3.8 4.1 4.3

Revision from

January 2021 0.5 0.4 0.3 0.3 1.0 0.1 0.8 0.3 -0.8

Growth projections: Emerging markets and LIDCs (percent change from a year earlier)

17Source: IMF, World Economic Outlook, April 2021.

Risks related to vaccine rollout

12

AEs Real GDP(Percent; deviation from baseline)

Sources: IMF, G20 Model simulations.

Upside scenario Downside scenario

–4

–2

0

2

4

2020 21 22 23 24 25

EMDEs Real GDP(Percent; deviation from baseline)

Sources: IMF, G20 Model simulations.

Upside scenario Downside scenario

–4

–2

0

2

4

2020 21 22 23 24 25


Historical evidence on economic recessions


Channels of scarring

▪ Scarring: persistent damage to supply potential from job losses and firm

bankruptcies, and resulting loss of economic ties in production and

distribution networks

• loss of firm-specific know-how due to bankruptcies

• reduction in technology adoption and innovation

• obstacles to resource reallocationTFP

• delayed or reduced investment during the crisis

• debt overhang, uncertainty, and tighter financial conditions after the recession

Capital

• discouraged unemployed exit labor force

• education interruptions affect long-term human capital accumulation

Labor


Medium-term output losses are larger for certain types of recessions

▪ We estimate the impact of recessions for a

sample of about 600 recession episodes in

115 countries, from 1957-2019

▪ Local projection method with standard

macroeconomic control variables, interaction

terms for recession types, and country and

year fixed effects

• There are permanent output losses, on

average, after all types of recessions

• The greatest scarring has occurred following

financial crises

• Previous epidemics and pandemics featured

lower scarring than recessions with financial

crises

Impact on Real GDP per Capita

(Percentage points)

Sources: Penn World Table 10.0; and IMF staff calculations.

Note: The solid lines represent the estimated cumulative impulse response

functions and shaded areas represent 90 percent confidence intervals. Past

modern pandemics and epidemics include Hong Kong flu, SARS, H1N1,

MERS, Ebola, and Zika.

Years since the shock

–14

–12

–10

–8

–6

–4

–2

0

2

–1 0 1 2 3 4 5 6 7


Channels of medium-term output losses

▪ For typical recessions, medium-term losses in GDP per capita can be attributed

mainly to persistently weaker productivity

▪ Financial crises are associated with permanent deteriorations in TFP, capital-to-

worker ratio, and employment

Sources: Penn World Table 10.0; and IMF staff calculations.

Note: The solid lines represent the estimated cumulative impulse response functions and shaded areas represent 90 percent confidence intervals. Past modern pandemics

and epidemics include Hong Kong flu, SARS, H1N1, MERS, Ebola, and Zika.

Impact on Total Factor Productivity

(Percentage points)Impact on Capital-Employment Ratio

(Percentage points)

Impact on Employment per Capita

(Percentage points)

Years since the shock

–10

–8

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2

–1 0 1 2 3 4 5 6 7–10

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–1 0 1 2 3 4 5 6 7–10

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–6

–4

–2

0

2

–1 0 1 2 3 4 5 6 7


Education losses worse in EMDEs

0

10

20

30

40

50

60

70

All economies Advanced economies

Emerging market economies

Low-income developing countries

Global Education Losses Due to the COVID-19 Pandemic(Average missed days of instruction in 2020)

Sources: UNESCO-UNICEF-World Bank Survey on National Education Responses to COVID-19 School Closures; and IMF staff calculations.

Where Are Education Losses Larger?

(Average number of missed days of instruction)

Sources: UNESCO-UNICEF-World Bank Survey on National Education

Responses to COVID-19 School Closures; World Bank, WDI; and IMF staff

calculations.

Note: Each bar corresponds to the average number of missed days of

instruction across countries with a given infrastructure characteristic. The

differences are statistically significant at the 5 percent level.

No Yes

0 20 40 60 80

Teachers were provided with ICT

tools during school closure

Internet access above world

average

Electricity access above world

average

68

40

65

33

58

25


Historical evidence on sectoral spillovers


Economic shocks: the sectoral dimension

▪ We quantify size and persistence of effects from historical sectoral shocks

• Focus on outcomes at sector-level

• Effect from shocks in own sector vs. shocks in other sectors/countries

• Inter-country input-output tables to quantify linkages for 4 transmission channels

➢ From suppliers: Downstream effects, Domestic and Foreign

➢ From customers: Upstream effects, Domestic and Foreign

• Sample: 31 AEs and 12 EMEs over 1995–2014

▪ Building on Acemoglu and others (2016), local projections

Δ log𝑌s,𝑐,𝑡 = 𝜷𝒐𝒘𝒏𝑆ℎ𝑜𝑐𝑘s,𝑐,𝑡 + 𝜷𝑫𝒏,𝑫𝒐𝒎𝐷𝑛s,𝑐,𝑡𝐷𝑜𝑚 + 𝜷𝑫𝒏,𝑭𝒐𝒓𝐷𝑛s,𝑐,𝑡

𝐹𝑜𝑟

+𝜷𝑼𝒑,𝑫𝒐𝒎𝑈𝑝s,𝑐,𝑡𝐷𝑜𝑚 + 𝜷𝑼𝒑,𝑭𝒐𝒓𝑈𝑝s,𝑐,𝑡

𝐹𝑜𝑟 + Γ(𝑠,𝑐,𝑡) + 𝜀𝑠,𝑐,𝑡

▪ Analyze different shocks

• SUPPLY: sectoral TFP changes

• DEMAND: sectoral government spending changes

Own and Spillover Effects

Source: IMF staff.

Note: Solid, black arrows correspond to (net) trade flows. Dashed,

colored arrows correspond to shocks and their resulting effects on

the focal sector (automotive).

Automotive

Downstream

effects

Automobile dealers

Steel producers

Electricity producers

Car rental companies

Upstream

effects

Own

effect


Supply shocks: large and persistent downstream effects

▪ Shock: year-on-year change in sectoral TFP

▪ Outcome: cumulative sectoral real value-added growth

• Total network effects 2 times larger than own effect

• Downstream effects dominate

• Effects persist up to 5 years

Own and Network Effects of TFP Shock

(Cumulative change in GVA, percentage points)

Sources: World Input-Output Database; and IMF staff

calculations.

Note: Bars and dots represent the estimated coefficients

of the cumulative impulse response function for sectoral

GVA from a one standard deviation increase in each

shock type.–0.5

0.0

0.5

1.0

1.5

2.0

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3.0

0 1 2 3 4 5

Own Upstream, domesticUpstream, foreign Downstream, domesticDownstream, foreign


Demand shocks: large and persistent network effects

▪ Shock: year-on-year change in government spending in each sector

▪ Outcome: cumulative sectoral real value-added growth

• Total network effects 7 times larger than effect of own shock

• Network effects persist up to 5 years

Own and Network Effects of Govt. Spend Shock

(Cumulative change in GVA, percentage points)

–0.5

0.0

0.5

1.0

1.5

2.0

2.5

3.0

0 1 2 3 4 5

Sources: World Input-Output Database; and IMF staff

calculations.

Note: Bars and dots represent the estimated coefficients

of the cumulative impulse response function for sectoral

GVA from a one standard deviation increase in each

shock type.


Network effects amplified the COVID-19 shock

▪ Back-of-the envelope quantification of COVID-19 sectoral spillovers

▪ Relative size of spillovers from ‘peripheral sectors’ is smaller than in previous recessions, still sizable

▪ Downstream domestic spillovers from supply shocks dominate, limited role of foreign shocks

Relative Own and Spillover Effects from the

COVID-19 Shock

(Percent contribution to the 2020 GVA decline)

Sources: World Input-Output Database; OECD Quarterly National

Accounts; International Labor Organization; and IMF staff

calculations.–100

–90

–80

–70

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–40

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–20

–10

0

10

Average sector High-contact sectors Low-contact sectors

Own Upstream, domesticUpstream, foreign Downstream, domesticDownstream, foreign


Implications of COVID-19 for the Medium Term


A wide range of possible medium-term outcomes

▪ Historical evidence suggests that most recessions leave persistent scars

• Mainly through lower productivity growth and slower capital accumulation

• Sectoral shocks can also propagate, contributing to a broader downturn

▪ Divergent recoveries could result from differences in:

• Path of the pandemic

• Sectoral composition across countries and capability of businesses and workers to

adapt

• Policy responses and policy space

• Amplification through the financial system

• Global spillover channels (e.g. portfolio flows and remittances)


Expected medium-term output losses

Medium-term Output Losses

(Percent difference from precrisis forecast)

Source: IMF staff estimates.

Note: Bars show the percent difference in real GDP four years after the

crisis and anticipated GDP for the same period prior to the crisis for the

indicated group.

–11

–10

–9

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–7

–6

–5

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–3

–2

–1

0

World AEs EMDEs EMEs LICs

Global financial crisis COVID-19

Medium-Term GDP Losses Relative to Pre-COVID-19, by Region(Revisions to projected 2024 GDP levels between the January 2020 and April 2021 WEO forecasts, percent)

Source: IMF staff estimates.

–9

–8

–7

–6

–5

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–3

–2

–1

0

1

EM Asiaex.

CHN

LAC SSA MECA EMDE World China EMEur.

AE United States


Cross-country variation in expected medium-term output losses

▪ The average income level, sectoral

structure of economy, and size of fiscal

policy response in 2020 help explain the

variation across economies:

• Countries that implemented larger

fiscal responses are projected to

experience smaller losses

• Largest impacts of the crisis are on

the most tourism-dependent

economies

• Economies with larger services

sectors are also projected to

experience persistent output losses

▪ But large uncertainty remains

Expected Medium-Term Output Losses: Explanatory

Factors

(Percentage points)

Sources: World Bank, World Development Indicators; World Travel and Tourism

Council; and IMF staff calculations.

Notes: Chart shows point estimate and two standard error ranges for

coefficients of a cross-sectional, cross-country regression of forecast revisions

on explanatory variables.

2022 23 24 2022 23 24–4

–3

–2

–1

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2022 23 24

1. Policy Response and Economic Structure

Above-the-line fiscal measures

Tourism Service sector


A sequenced approach for policies to limit persistent damage

▪ Important to avoid financial distress

▪ Targeted support to the most-affected sectors and workers may be most effective while

supply constraints remain in place, whereas public investment can help boost both supply

and demand as constraints ease

▪ Where fiscal space permits, policymakers should deploy a combined package of better-

targeted support for affected households and firms and public investments aimed at the

following:

• Reversing setbacks to human capital accumulation and encouraging employment

• Supporting productivity, including policies to facilitate resource reallocation

• Boosting investment


Conclusion

▪ Historically, deep recessions have led to persistent output losses, driven

largely by TFP losses

▪ Spillovers have been large amplifiers of sectoral shocks in the past, and

have also amplified the COVID-19 shock

▪ World output anticipated to be 3 percent lower in 2024, with variation across

countries

• Emerging market and developing economies are expected to suffer more

scarring

▪ Addressing the setback to human capital accumulation, measures to support

reallocation, and lift investment will be key to limit long-run GDP losses

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World Economic OutlookApril 2021

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