la crise du coronavirus: mesurer l’impact économique en ......furniture manufacture of textiles,...
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19 mai 2020
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2
3) What can be the long-term damage?
1) How bad is the initial shock?
2) A stylised scenario (NBB-BFP)
“We plegen economische harakiri.”
“Even if this containment regime only lasts for close to
a month and the economic situation does not get worse,
the direct shock to output in annual terms is probably
already larger than the Great Recession.”
(e-mail to Governor Wunsch, 18 March 2020)
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COVID-19-related requests for
temporary unemployment(thousands of persons, daily data)
The Great Recession of 2008/09 is now the small recession …
4Source: NEO, classified data.1 Including staff suspensions.
Max = 226k March 2009
Actual use of temporary unemployment(thousands of persons, monthly data, not verified from October 2019 on)
March:
1 030
April:
1 179
-33 -33-36
-33 -34-32
-29-31
-50
-40
-30
-20
-10
0
Week 1
(23 March)
Week 2
(30 March)
Week 3
(6 April)
Week 4
(14 April)
Week 5
(20 April)
Week 6
(27 April)
Week 7
(5 May)
Week 8
(12 May)
5
Lockdown = about 1/3 of private sector was gone …
Source: weekly ERMG surveys (BECI, Boerenbond, NSZ-SNI, UNIZO. UWE, VOKA coordinated by NBB, VBO-FEB).
¹ Weighted average based on industry value added and sales. This approach excludes the firms that were
identified as belonging to a miscellaneous ‘other’ industry. The value added weight of the real estate activities
is corrected (so as not to reflect imputed rents).
Covid-19 impact¹ on turnover of Belgian companies
and self-employed (in %)
6
The Corona damage strongly differs by industry
Source: weekly ERMG survey. 1 Weights based on sales.
Weighted1 average impact of Covid-19 on turnover(in %)
-100
-80
-60
-40
-20
0
Accommodation
and food
services
Events Retail sales
(non-food)
Manufacture of
furniture
Manufacture of
textiles, wearing
apparel and
shoes
Road transport
(persons)
Agriculture and
fishing
Manufacture of
pharmaceutical
and chemical
products
Financial and
insurance
activities
Retail sales
(food)
Average Week 1 - 6
Detailed industry information can be found on the NBB web-site (detailed tables)
7Source: ERMG surveys (weeks 2-6).
Reported decline in turnover due to the coronavirus, by staff size(in % of responding firms)
Smallest firms / self-employed suffer most
0
10
20
30
40
50
60
70
80
90
100
Self-employed 1 - 10 10 - 20 20 - 50 50 - 250 >250
100% 75 - 99% 50 - 75% 20 - 50% 0 - 20% No impact Positive impact
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3) What can be the long-term damage?
1) How bad is the initial shock?
2) A stylised scenario (NBB-BFP)
◆ Unprecedented shock: acute, instant and complex (supply and demand).
◆ Quick policy response to mitigate liquidity stress and income loss.
◆ Urgent: contain permanent damage to the economy additional measures?
If so, what type (supply, demand, both)?
◆ Need to assess the extent of the damage on impact and the benefits of avoiding
permanent job and capacity destruction.
◆ Elaborate most-plausible scenario, not a forecast results as good as
underlying assumptions.
◆ Joint work by NBB and FPB teams published April 8.
9
Context and approach
◆ How bad is this? Extremely bad.
◆ What are the challenges? Risks tilted to the downside, bolder and more
comprehensive policy action required despite fragile public finances.
◆ How does the recovery look like ? V, U, W or ?
10
Key questions and messages
◆ Hard lockdown for 7 weeks (until May 4).
◆ Loss during lockdown: 1/3 of private sector value added (see survey).
◆ Steep recovery post-lockdown, then gradual convergence to pre-crisis baseline
(minus 2 percent) by mid-2021.
◇ pent-up demand unwinds post-lockdown, but
◇ gradual lifting of lockdown,
◇ gradual repair of value chains and production lines,
◇ negative spillbacks from international trade,
◇ some capacity and employment losses.
◆ No sharp price response (offsetting shocks).
◆ Automatic stabilizers (income support,…) taken into account, but no other measure.
11
Assumptions
12
Real GDP growth(annual ratel)
Real GDP(Indices 2018 Q1 = 100)
Recession in 2020 (-8 %) followed by steep recovery in 2021
80
85
90
95
100
105
1102018
T1
2018
T2
2018
T3
2018
T4
2019
T1
2019
T2
2019
T3
2019
T4
2020 T
1
2020 T
2
2020 T
3
2020 T
4
2021
T1
2021
T2
2021
T3
2021
T4
Trajectoire initiale Scénario
-10
-8
-6
-4
-2
0
2
4
6
8
10
2019 2020 2021
13
The recession curve …
Cumulative GDP loss: 30 billion by June; 45 by end-2020 hit on
state budget
-70
-60
-50
-40
-30
-20
-10
0
2020 T1 2020 T2 2020 T3 2020 T4 2021 T1 2021 T2 2021 T3 2021 T4
-6
-4
-2
0
2
4
6
8
2020 2021
Households' real disposable income
Private consumption
◆ Households’ real disposable income falls
modestly (-1,5 %) compared to GDP
Shock is smoothed.
◆ Constrained consumption and higher
aggregate savings pent-up demand
likely (excess savings of about 11 billion
in 2020).
14
Households disposable income and
private consumption (real)(y-o-y percentage change)
Impact on households
-40
-30
-20
-10
0
10
20
30
40
50
60
2020 2021
◆ Extreme but transitory shock.
◆ Key assumption: no mass bankruptcies/job
losses.
◆ Aggregate result. Reality is one of diverse
firms in terms of:
◇ Incidence of the shock (HORECA vs.
grocery stores),
◇ Structural liquidity buffers.
◆ Need additional support measures:
temporary, targeted, and commensurate
to the damage.
15
Gross operating surplus(y-o-y percentage change)
Impact on businesses
◆ Recall: scenario no “confidence interval.”
◆ Extreme uncertainty ongoing adjustments to the most plausible scenario (more gradual exit,
greater job losses, weaker domestic and external demand).
◆ More severe scenarios assuming realization of bad risks:
◇ Epidemic not under control: slower reopening, return to hard lockdown;
◇ Larger and more persistent production losses (lasting disruptions in value chains, tight sanitary
rules constraining operations).
◇ Bankruptcies and job losses as liquidity runs out and diminished prospects for some sectors
force restructuring (structural lack of demand post-lockdown).
◇ More likely: deflation if fear leads to precautionary saving. Less likely: temporary inflation burst if
demand recovers “too fast” compared to supply.
◇ Long-lasting damage to international trade and global value chains if pandemic keep hitting
countries differently and at different time.
16
Risks as assessed on April 8: all on the downside
17Source: Consensus Economics, NBB-FPB, IMF, EC1 All forecasts above/below the line imply full/less than full recovery to 2019 GDP levels by end 2021
Belgium: Real GDP growth forecasts for 2020 vs 2021 1
(%)
V or no V?
KBC
NBB-FPB
IMF WEO
Morgan Stanley
EC Spring
Bank America
Merrill
Capital Economics
Citigroup
IHS Markit
Oxford Economics
INGKBC
-2
0
2
4
6
8
10
12
14
-14 -12 -10 -8 -6 -4 -2 0 2
Real G
DP
gro
wth
fo
reca
st 2
021
Real GDP growth forecast 2020
March 16 - April 3 April 6-24 April 27 - May 15
V
No V
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18
3) What can be the long-term damage?
1) How bad is the initial shock?
2) A stylised scenario (NBB-BFP)
19Source: ERMG survey, week 8.
Impediments for a recovery of sales to pre-crisis levels after the lockdown(in % of responding firms, multiple answers possible)
The economy reopens gradually but demand is yet to recover …
0
20
40
60
80
Lack of demand Social distancing Supply chain
problems
Liquidity problems Staff shortage Other None
20Source: ERMG survey, week 6.
… and investment is massively postponed
30
33
14
24
Yes, to later in 2020 or 2021
Yes, new date unknown
No
Did not have any investment plan
Do you envisage to postpone your investments
as a result of Covid-19? YES for 63%(in %, weighted by sectoral share in Belgian value added)
Plans to postpone investment per firm size(distribution, in % of responding firms of that size, size defined
as number of employees)
0
20
40
60
80
100
No
Did not have any investment plan
Yes, new date unknown
Yes, to later in 2020 or 2021
Bankruptcy risks are elevated in the worst-hit industries
21
Share of respondents that considered bankruptcy to be likely or highly likely(in %)
0
5
10
15
20
25
30
35
40
45
Arts,
entertainment
and recreation
Accommodation
and food services
Real estate
activities
Transportation
and logistics
Information and
communication
Support services Manufacturing Wholesale and
retail trade
Construction Agriculture Financial and
insurance
activities
Belgium¹
Source: ERMG survey, week 7 (5 May).
¹ Average, weighted by sectoral share in Belgian value added.
Bankruptcy risks are elevated in the worst-hit industries
22
Share of respondents that considered bankruptcy to be likely or highly likely(in %)
0
5
10
15
20
25
30
35
40
45
Arts,
entertainment
and recreation
Accommodation
and food services
Real estate
activities
Transportation
and logistics
Information and
communication
Support services Manufacturing Wholesale and
retail trade
Construction Agriculture Financial and
insurance
activities
Belgium¹
Source: ERMG survey, week 7 (5 May).
¹ Average, weighted by sectoral share in Belgian value added.
0
5
10
15
20
25
30
35
40
45
50
Arts,
entertainment
and recreation
Transportation
and logistics
Support services Accommodation
and food services
Construction Real estate
activities
Information and
communication
Wholesale and
retail trade
Manufacturing Financial and
insurance
activities
Agriculture
23
Source: ERMG survey, week 7 (5 May). 1 This corresponds to one fifth of 900 000 temporary unemployed in March (the most recent estimation from ONEM/RVA).2 Average, weighted by the estimated number of temporary unemployed in the industries.3 No estimate available as there are too few firms with temporary unemployed workers in the sample.
Temporary unemployed that would be dismissed, by industry(in % of the temporarily unemployed staff of the responding firms)
One out of five temporary unemployed may lose his/her job
(180 000 people1)
NA3
Belgium2
24Source: ERMG survey, week 7 (5 May).
Firms with a high bankruptcy risk have more than half of their staff in
temporary unemployment, of which about half will be fired
Temporary unemployed, by bankruptcy risk(in % of the staff of the responding firms, excluding the self-employed)
0
10
20
30
40
50
60
Highly likely Likely Possibly Unlikely Highly
unlikely
Temporary unemployed that will be dismissed,
by bankruptcy risk(in % of the temporarily unemployed staff of the responding firms)
0
10
20
30
40
50
60
Highly likely Likely Possibly Unlikely Highly
unlikely
25
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