Data analysis and monetary policy during the pandemic - Philip R. Lane Member of the Executive Board
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Philip R. Lane
Member of the Executive Board Data analysis and monetary
policy during the pandemic
Central Bank of Ireland webinar “The importance of data:
statistics during the pandemic and beyond”
7 October 2021Introduction
Rubric
• Official statistics (monthly, quarterly, annual)
• Comprehensive national accounting framework (flows; balance sheets)
• Granular data (administrative, regulatory, surveys, commercial)
• Globalisation of firms; financial complexity; cross-border linkages
• High-frequency shocks and policy surveillance
• [European Statistical Forum, 26 April 2021]
www.ecb.europa.eu ©High-frequency
Rubric data: policy making
• Signal to noise ratio: often unfavorable, but pandemic an exception
• Financial market data: always available
• Macroeconomic variables: intra-quarter turning points; early warning
indicators
• Representativeness of indicators: scope; stability
• Supplementary role; not a replacement for official statistics
www.ecb.europa.eu ©Containment
Rubric measures and mobility
EA stringency of EA mobility indicators
containment measures (percentage change on baseline,
7-day moving average)
(index 0 to 100)
100 30
20
80
10
0
60
-10
40
-20
-30
20
-40
0 -50
Jan-20 Jul-20 Jan-21 Jul-21 Sep-21 Jan-20 May-20 Sep-20 Jan-21 May-21 Sep-21
Source: University of Oxford, Government Response Tracker, ECB calculations. Source: Google Mobility report, ECB calculations. Notes: The mobility indicator is the
Notes: The euro area stringency index is calculated as the weighted average of the aggregation of the mobility indicators for retail and recreation, workplaces and transit
stringency index for the 19 euro area countries. Latest observation: 24 September 2021. stations. The euro area indicator is calculated as the weighted average for the 19 euro
area countries. Latest observation: 28 September 2021.
4 www.ecb.europa.eu ©Weekly
Rubric economic activity tracker
Weekly economic activity tracker Weekly economic activity tracker
(index, 100=December 2019) (month-on-month percentages; contributions)
Weekly economic activity tracker Standard indicators
GDP Non-standard indicators
105
Weekly tracker
8
100
4
95 0
-4
90
-8
85
-12
80 -16
Dec-19 Mar-20 Jun-20 Sep-20 Dec-20 Mar-21 May-21 Oct-19 Jan-20 Apr-20 Jul-20 Oct-20 Jan-21 Apr-21
May-21
Source: Eurostat, ECB staff calculations.
Notes: The weekly economic activity tracker combines weekly and monthly indicators using principal components. The weekly frequency indicators are electricity consumption, German HGV toll mileage
index, Google searches (restaurants, jobs, travel, hotels) and financial indicators (CISS, EURO STOXX, VSTOXX). The monthly frequency indicators are airport cargo and employment for the four largest
euro area countries, euro area industrial production, industrial orders, car registrations, retail sales (volume), intra and extra euro area exports of goods (value), PMI composite output and economic
sentiment indicator. The contributions are not computed in real time, therefore the role of non-traditional indicators in 2020 could be underestimated. Latest Observation: 29 May 2021.
5 www.ecb.europa.eu ©Card spending in Ireland
Rubric
Daily card spending Breakdown of card spending
(7-day moving average; index, 100=1 Mar. 2020) (millions of euros; 7-day moving average)
Gross new card spending Groceries Other retail Transport
Accommodation Restaurants Other
200
300
180
250
160
200
140
150
120
100
100
80 50
60 0
Mar-20 Jun-20 Sep-20 Dec-20 Mar-21 Jun-21 Sep-21 Oct-20 Dec-20 Feb-21 Apr-21 Jun-21 Aug-21 Sep-21
Source: Central Bank of Ireland, ECB staff calculations. Source: Central Bank of Ireland, ECB staff calculations.
Latest observation: 27 September 2021. Latest observation: 27 September 2021.
6 www.ecb.europa.eu ©High-frequency
Rubric indicators key for short-term forecasting in COVID-19 times
Forecasts of euro area real GDP growth using non-standard models
(quarter-on-quarter growth rate in percentages)
2020Q2 2020Q3
Short-term forecast model
Adjusted short-term
forecast model
Pandemic cross-country
model
Adjusted PMI GDP tracker
GDP-at-risk model
Actual (latest estimate)
-14 -12 -10 -8 -6 -4 -2 0 0 2 4 6 8 10 12 14
Source: Eurostat, Hale et al. (2020), IHS Markit, ECB staff calculations.
Notes: The adjusted short-term forecast model includes information from weekly credit card payments and other standard indicators which were available 15
days before the release of the preliminary flash estimate of GDP. The adjusted PMI GDP tracker refers to a non-linear PMI composite output-based rule, which
takes into account both the quarterly change in this index and previous GDP growth. The GDP-at-risk model uses the 5% left tail of the conditional distribution
for the second quarter of 2020 and, given the expected sharp rebound, the 1% right tail of the conditional distribution for the third quarter of 2020. All of the
reported real GDP forecasts are real-time estimates.
7 www.ecb.europa.eu ©The ECB dialogue with non-financial companies
Rubric
Distribution of participating companies Summary of views on developments in
across broad economic sectors and the outlook for activity and prices
(share of total companies) (percentage of respondents; lhs: activity; rhs: prices)
Business Energy Previous quarter Current quarter
services
Agroindustry
60 60
Consumer
services 50 50
40 40
Intermediate
goods
30 30
Transport
20 20
10 10
0 0
Significant decrease
Significant increase
Significant decrease
Significant increase
Increase
Increase
No change
No change
Decrease
Decrease
Retail
Capital goods
Construction
and real
estate Consumer
goods
Source: ECB Corporate Telephone Survey (CTS) – January 2021. Source: ECB Corporate Telephone Survey (CTS) – July 2021.
Notes: The chart shows the distribution across sectors of companies contacted during Notes: The scores for the previous quarter reflect the ECB staff assessment of what
2020. The allocation refers to the organisation of the survey and not to the sectors for contacts said about developments in activity (sales, production and orders) and prices in
statistical classification purposes. Many companies operate in more than one sector (e.g. the second quarter of 2021. The scores for the current quarter reflect the assessment of
consumer goods manufacturers may also have retail outlets). what contacts said about the outlook for activity and prices in the third quarter of 2021.
8 www.ecb.europa.eu ©The ECB dialogue with non-financial companies (continued)
Rubric
These contacts normally used to gather anecdotal evidence helped gauge
the initial impact on activity at the height of the pandemic
(percentages; year-on-year growth rate)
Activity versus normal as gauged from contacts with companies
Industrial and services production in statistics (year-on-year)
5
0
-5
-10
-15
-20
-25
-30
Jan-20 Feb-20 Mar-20 Apr-20 May-20 Jun-20
Sources: ECB contacts with non-financial companies, Eurostat, ECB staff calculations.
The blue line reports a weighted average of what companies said about where activity in their firm/sector
stood compared with a “normal” level, weighted by the share of the corresponding sector in euro area gross
value added. The yellow line shows the year-on-year change in production of the corresponding sectors as
released by Eurostat. Latest observation: June 2020.
9 www.ecb.europa.eu ©Survey
Rubric responses on government support
Lower hours worked and Liquidity-constrained households
government support (x-axis: income percentiles; y-axis: percentage of respondents)
(x-axis: income percentiles; y-axis: percentage of respondents)
Share of respondents receiving government support in
response to COVID-19 Share of respondents having liquidity constraints
Share of respondents with decreased number of hours 60
worked due to COVID-19
40
50
35
40
30
30
25 20
20 10
15 0
0-20 20-40 40-60 60-80 80-100 0-20 20-40 40-60 60-80 80-100
Source: ECB Consumer Expectations Survey (CES) – June 2020 wave. Source: ECB Consumer Expectations Survey (CES) – June 2020 wave.
Notes: All reported numbers are aggregated using individual household weights. Notes: All reported numbers are aggregated using individual household size weights.
10 www.ecb.europa.eu ©Saving
Rubric and spending intentions
Household deposits, loans and currency How will accumulated net savings be
(change on December 2019; percentage point allocated in the next 12 months?
of disposable income) (per cent of net savings)
Currency in circulation Full sample
Loans to households (inverted sign)
COVID-19-related motivations
Household deposits
Saving rate Other motivations
18 80
16
14 60
12
10 40
8
6 20
4
2 0
Buy more Buy more long- Maintain a Borrow less
0 goods and lasting goods higher level of money or
services that and services savings or repay more
-2 don’t last for a financial debt
Jan-20 Jun-20 Nov-20 Apr-21 Aug-21 long time investments
Source: ECB. Source: ECB Pilot Consumer Expectations Survey – March 2021 wave.
Notes: Loans to households are reported with an inverted sign. As no breakdown by Notes: Net savings refer to the amounts of deposits and financial assets accumulated
holding sector is available, the contribution of currency flows should be considered as an since January 2020 by savers minus dissavers.
upper bound.
Latest observation: August 2021.
11 www.ecb.europa.eu ©The frontier: an example from the United States
Rubric
Employment Consumer spending
(index, 100=Jan. 2020) (index, 100=Jan. 2020)
Total employment Spending low income households
Employment low wage earners
Spending high income households
Employment high wage earners
140
120
Stimulus payment
Stimulus payment
Stimulus payment
130
110
120
100
110
90 100
90
80
80
70
70
60 60
Jan-20 May-20 Sep-20 Jan-21 May-21 Aug-21 Jan-20 May-20 Sep-20 Jan-21 May-21 Sep-21
Source: Opportunity Insights. Source: Opportunity Insights.
Notes: Change in employment, indexed to 4 - 31 January 2020, not seasonally adjusted. Low Notes: Change in average consumer credit and debit card spending, indexed to 4-31
and high wage earners are defined as the first and fourth earnings quartiles. The series is January 2020, and seasonally adjusted. Low and high income households are defined as
based on data from Earnin, Intuit, Kronos, and Paychex. first and fourth income quartiles. Vertical lines refer to the start of the three stimulus
Latest observation: 10 August 2021. payments to households (15 April 2020, 4 January 2021, 17 March 2021). The series is
based on data from Affinity Solutions. Latest observation: 13 September 2021.
12 www.ecb.europa.eu ©Weekly
Rubric tracker to monitor global trade developments
Global trade
(indices, 2019Q4 = 100)
Sources: CPB and ECB staff calculations.
Notes: The tracker is based on the first principal component of weekly and monthly variables
that have been chosen based on their correlation with global trade excl. the euro area.
Dynamic Factor Model (DFM) refers to a quarterly forecast from a dynamic factor model that
combines monthly trade indicators with a reduced version of the tracker above.
Latest observations: July 2021 (CPB), September 2021 (tracker), 2020Q2 (data), diamonds
refer to forecasts for Q3 and Q4 2021.
13 www.ecb.europa.eu ©High-frequency
Rubric labour market indicators: hiring rate and job postings
LinkedIn hiring rate (standardised) Indeed job postings
(%-deviations from sample mean) (year-on-year growth rates, percentages)
EA4 DE FR IT ES
EA4 DE FR IT ES
40 100
20
75
0
50
-20
25
-40
0
-60
-80 -25
-100 -50
Jan-20 May-20 Sep-20 Jan-21 Apr-21 2019 2020 2021 Sep 24
Sources: LinkedIn, Indeed and ECB calculations.
Notes: The methodology behind the high-frequency indicators on new hires and job postings is documented in the box entitled “High-frequency data developments in the euro area labour market”, Economic
Bulletin, Issue 5, ECB, 2020 and in the box entitled “Monitoring labour market developments during the pandemic”, which is published as Box 1 in the article entitled “Using machine learning and big data to
analyse the business cycle”, Economic Bulletin, Issue 5, ECB, 2021. Latest observation: April 2021 for the LinkedIn hiring rate and September 24, 2021 for the Indeed job postings.
14 www.ecb.europa.eu ©Job postings by sector and importance of “working from home”
Rubric
Job postings by sector Working from home
(30-day moving average, 2019H2=100) (% of employment)
Industry and construction Jun-Jul 2020 Before COVID-19
High-skilled market services
Low-to-medium-skilled market services 80
140
120 60
100 40
80 20
60 0
2018 2019 2020 2021 Sep 24 BE IE IT ES PT AT FI FR LT NL DE EE GR LV SI SK
Sources: Indeed and ECB calculations. Source: Eurofound (2020), Living, working and COVID-19 dataset, Dublin,
Notes: Aggregation of Indeed occupation data to high and low-to-medium skilled follows ISCO-88. http://eurofound.link/covid19data.
Data are available for 8 euro area countries: DE, FR, IT, ES, NL, BE, AT and IE.
Latest observation: 24 September, 2021.
15 www.ecb.europa.eu ©Role of job retention schemes and the “shadow” unemployment rate
Rubric
Job retention schemes “Shadow” unemployment rate – the U7
(percentage of the labour force) (percentage of the labour force)
DE ES FR IT Unemployment rate U7 rate
30 30
25
25
20
20
15
15
10
10
5
0 5
Feb-20 May-20 Aug-20 Nov-20 Feb-21 May-21 Aug-21 Jan-19 Aug-19 Mar-20 Oct-20 May-21 Aug-21
Sources: ECB calculations based on data from Eurostat, Institute for Employment Research (Institut für Sources: ECB calculations based on data from Eurostat, Institute for Employment Research (Institut
Arbeitsmarkt- und Berufsforschung – IAB), ifo Institute, Ministère du Travail, de l’Emploi et de l’Insertion, für Arbeitsmarkt- und Berufsforschung – IAB), ifo Institute, Ministère du Travail, de l’Emploi et de
Instituto Nazionale Previdenza Sociale (INPS), and Ministerio de Inclusión, Seguridad Social y Migraciones. l’Insertion, Instituto Nazionale Previdenza Sociale (INPS), and Ministerio de Inclusión, Seguridad
Latest observations: August 2021 for Germany, France and Spain, and June 2021 for Italy. Social y Migraciones. Latest observation: August 2021.
Notes: The job retention schemes data in Italy correspond to June 2021. The U7-series is calculated
by augmenting the standard unemployment rate with the number of workers in job retention schemes.
16 www.ecb.europa.eu ©Corporate
Rubric vulnerabilities and productivity developments
Estimated share of vulnerable firms Firms’ labour productivity
(percentages) (thousands of euros)
DE IT
Vulnerable
FR ES
Rest
35 80
30 70
25 60
50
20
40
15
30
10
20
5
10
0
liqudity risk liqudity risk - negative negative
policy working capital working capital 0
- policy France Germany Italy Spain
Sources: Orbis-iBACH, Eurostat and ECB calculations.
Notes: Predictions for 2021 based on latest available Orbis-iBACH data until 2018, combined with simulations for liquidity that are derived from sector-value added predictions. Policy includes job retention
schemes, government liquidity support, loan and interest moratoria. On the right-hand side, productivity is defined at the firm-level as real value added per employee. The figure reflects the ECB December
projections and refer to the peak of the crisis, which is Q4 2021 or Q1 2022 depending on the country. Vulnerable firms are defined as those with negative working capital and at the top 25% of the leverage
distribution within their country-sector.
17 www.ecb.europa.eu ©Assessing
Rubric corporate vulnerabilities and insolvency gaps
Firm deaths – past and predicted Estimated insolvency gaps in 2020
(number of firms in thousands) (percentages)
Firm deaths predicted using observed insolvencies
Firm deaths predicted using predicted insolvencies from BVAR model 90
Firm deaths
325 80
300 70
60
275
50
250
40
225
30
200
20
175 10
150 0
2015 2016 2017 2018 2019 2020 2020Q4 EA-4 DE FR IT ES
Sources: Haver, Cerved, Eurostat, ECB staff calculations.
Notes: (LHS) Quarterly data. The paths of firm deaths beyond 2018 is based on the number of observed and predicted insolvencies using the 2018 weighted average share across the four largest EA
countries. (RHS) Annual data, seasonally adjusted numbers. Insolvency forecast for 2020 conditional on the observed paths of GDP and unemployment. The gaps are computed as the percent distance
between the predicted annual number of insolvencies and the annual observed insolvencies. Predictions based on country-level BVARs for unemployment rate, insolvencies, and the logarithm of real GDP.
18 www.ecb.europa.eu ©Using
Rubricmicro-data to assess impact of lockdowns on product availability and sales
Number of distinct products available Annual percentage change in the share of
online products offered at a discount
(index, January 2020 = 100) (annual percentage change)
Germany Spain France Germany Spain Italy Netherlands
Italy Netherlands
110 75
100 50
90
25
80
0
70
-25
60
50 -50
Jan-20 Feb-20 Mar-20 Apr-20 Jan-20 Feb-20 Mar-20 Apr-20
Sources: PriceStats, web scraped price data. Sources: PriceStats, web scraped price data.
Notes: Microdata on online prices provided by PriceStats for one online supermarket per country. The Notes: The chart shows the 5-week moving average of the year-on-year percentage change in the
chart shows a weekly index of the number of products available online by country, computed as the ratio weekly median of the share of products offered at a discount. France is excluded from the analysis of
of the weekly median of the number of distinct products to the median number of products in January temporary discounts, as no information on temporary discounts was available from the French online
2020. See for further information: O’Brien, D,, Dumoncel, C. and Goncalves, E (2021). “The role of supermarket. The vertical lines indicate the start of the country-specific lockdowns. See for further
demand and supply factors in HICP inflation during the COVID-19 pandemic – a disaggregated information: O’Brien, D,, Dumoncel, C. and Goncalves, E (2021). “The role of demand and supply
perspective”, ECB Economic Bulletin, Issue 1/2021, ECB, Frankfurt am Main. factors in HICP inflation during the COVID-19 pandemic – a disaggregated perspective”, ECB Economic
19 www.ecb.europa.eu ©
Latest observations: 30 April 2020. Bulletin, Issue 1/2021, ECB, Frankfurt am Main. Latest observations: 30 April 2020.Assessing
Rubric price rigidities in the euro area
Euro area implied duration of price spells based on Share and size of price
Inflation Persistence Network (IPN) and PRISMA increases/decreases in the euro area
(months) (percentages)
IPN PRISMA Price decreases Price increases
20
PRISMA
18
Size
16
IPN
14
12
PRISMA
Share
10
8 IPN
6
Food NEIG Services -50 -25 0 25 50 75
Source: Consolo, A., G. Koester, C. Nickel, M. Porqueddu and F. Smets (2021) "The need for an inflation buffer in the ECB’s price stability objective – the role of nominal rigidities and inflation
differentials”, ECB Occasional Paper No 279.
Notes:The Eurosystem Inflation Persistence Network (IPN) conducted an in-depth study of inflation persistence and price stickiness in the euro area. See: Altissimo, F., Ehrmann, M. and Smets, F.
(2006), “Inflation persistence and price-setting behaviour in the euro area – a summary of the IPN evidence”, Occasional Paper Series, No 46, ECB, Frankfurt am Main, June. European Central Bank
(2005) “Price-setting behaviour in the euro area”, Monthly Bulletin, Frankfurt am Main, November. Within the ESCB PRIce-Setting Microdata Analysis Network (PRISMA) a study analysed price
flexibility based on micro data underlying euro area CPIs. See Gautier E., Conflitti, C., Fabo, B., Faber, R., Fadejeva, L., Jouvanceau, V., Menz, J.-O., Messner, T., Petroulas, P., Roldan-Blanco, P.,
Rumler, F., Santoro, S., Wieland, E. and Zimmer, H. (2021), “New Facts on Consumer Price Rigidity in the Euro Area”, ECB Working Paper (forthcoming).
20 www.ecb.europa.eu ©VAT change in Germany: assessing pass-through based on daily web-scraped data
Rubric
Size of price changes Frequency of price changes
(monthly percentage change) (percentages)
Frequency of price decreases
Month-on-month price change (%) Frequency of price increases
3 Frequency of price changes
100%
2
80%
1
60%
0
40%
-1
-2 20%
-3 0%
Mar-20
May-20
Jul-20
Mar-21
May-21
Jul-21
Nov-20
Sep-20
Jan-21
Mar-20
May-20
Jul-20
Mar-21
May-21
Jul-21
Nov-20
Sep-20
Jan-21
Source: ECB staff calculations.
Notes: Web-scraped data from German online supermarkets, containing information mainly on food, beverages and personal care items. Data is collected daily. The right chart shows the daily
unweighted average of 4-week price changes. 4-week price changes are calculated as the percentage change of the price of a product on a given day compared to the price of the same product
on the same weekday four weeks before. The left chart shows the daily share of products that experienced a price change compared to four weeks before.
Latest observation: 26 July 2021.
21 www.ecb.europa.eu ©Using
Rubricfinancial market data to assess distance to insolvency/short-term inflation outlook
Distribution of the “distance to insolvency” of euro Inflation rates implied by market-based
area firms measures of inflation compensation
(x-axis: “distance of insolvency” value – degree of vulnerability, y-axis: density) (percentages per annum)
Year-on-year HICPxT inflation
Jan-20 Mar-20 Sep-21
Fixings latest (4 Oct. 21)
1.4 1.4
One-year forward ILS rates (4 Oct. 21)
Vulnerable Premia-adj. one-year forward LS rates (4 Oct. 21)
1.2 1.2 4.5 4.5
4.0 4.0
1.0 1.0 3.5 3.5
3.0 3.0
0.8 0.8 2.5 2.5
2.0 2.0
0.6 0.6
1.5 1.5
1.0 1.0
0.4 0.4
0.5 0.5
0.0 0.0
0.2 0.2
-0.5 -0.5
0.0 0.0 -1.0 -1.0
0 2 4 6 8 2019 2020 2021 2022 2023 2024 2025 2026
Sources: Refinitiv and ECB calculations. Sources: Bloomberg, Refinitiv, and ECB calculations.
Notes: The “distance to insolvency” is based on the inverse of the total volatility of the return of a large Notes: HICPxT refers to HICP excluding tobacco. Premia-adjusted forward inflation linked
sample of euro area listed firms. The densities are obtained through a kernel estimator applied to the swap (ILS) rates are average estimates based on two affine term structure models
cross-sectional values of the “distance to insolvency” at the selected dates. The vertical line marks the following Joslin, Singleton and Zhu (2011), applied to ILS rates (non-adjusted for the
threshold below which firms are characterised as being vulnerable. indexation lag).
Latest observation: September 2021 (monthly data).
Latest observation: August 2021 for HICPxT.
22 www.ecb.europa.eu ©Market
Rubric expectations of high inflation and EURO STOXX 50 correction
Option-implied probabilities of high Option-implied risk-neutral probability of
inflation on average over the next five years large EURO STOXX 50 correction
(percentages) (percentages)
EA: Headline above 3% EA: Headline above 4% Risk-neutral probability of price drop in the next three
months larger than 20%
US: Headline above 3% US: Headline above 4%
25 25
40 40
20 20
30 30
15 15
20 20
10 10
10 10
5 5
0 0 0 0
2014 2015 2016 2017 2018 2019 2020 2021 4/Oct 2006 2008 2010 2012 2014 2016 2018 2020 4/Oct
Sources: Bloomberg and ECB calculations. Sources: Refinitiv and ECB calculations.
Notes: Probabilities implied by five-year zero-coupon inflation options, smoothed over five Notes: The probability is derived from the implied risk-neutral distribution of the EURO
business days. Risk-neutral probabilities may differ significantly from physical, or true, STOXX 50 priced in option contracts. The methodology for obtaining the distribution
probabilities. follows Shimko (1993). The 3-month horizon is obtained by interpolating between the
Latest observation: 4 October 2021. option contracts available with closest horizons above and below 3 months.
Latest observation: 4 October 2021.
23 www.ecb.europa.eu ©SAFE
Rubricdata on investment decisions
Change in investment decisions of euro Change in investment decisions of euro
area NFCs: all firms area NFCs – firms with up to 250 employees
(over the preceding six months; net percentage of respondents) (over the preceding six months; net percentage of respondents)
Micro Small Medium Large DE ES FR IT
30 20
20
10
10
0
0
-10 -10
-20
Pre-Covid Oct19-Mar20 Apr20-Sep20 Oct20-Mar21 -20
Pre-Covid Oct19-Mar20 Apr20-Sep20 Oct20-Mar21
Source: ECB and European Commission survey on the access to finance of enterprises Source: ECB and European Commission survey on the access to finance of enterprises
(SAFE). (SAFE).
Note: For the pre-Covid-19 period figures refer to rounds 11 (April-September 2014) to 21 Note: All firms with up to 250 employees. . For the pre-Covid-19 period figures refer to
(April-September 2019) of the survey. rounds 11 (April-September 2014) to 21 (April-September 2019) of the survey.
24 www.ecb.europa.eu ©Importance
Rubric of government support based on SAFE
Euro area companies that received government Importance of government support for
support in response to the pandemic Euro area SMEs long-term obligations
(percentage of respondents) (percentage of respondents)
Very important Not applicable
Yes
Moderately important Don't know
No
Not important
Don't know
100 100
90
90
80
70 80
60 70
50
60
40
30 50
20
40
10
0 30
Large SMEs Large SMEs Large SMEs
20
A) Government B) Tax cuts and C) Other
support to tax moratoria government 10
alleviate the wage support schemes 0
bill DE ES FR IT EA
Source: ECB and European Commission survey on the access to finance of enterprises Source: ECB and European Commission survey on the access to finance of enterprises
(SAFE). (SAFE)
Note: Results for small and medium-sized enterprises (SMEs). Note: Results for small and medium-sized enterprises (SMEs).
25 www.ecb.europa.eu ©Use of granular data to assess risks to monetary policy transmission
Rubric
Potential impact on CET1 capital ratio Cumulated loan volume to NFCs by
under a scenario of sustained mark-to- duration of interest rate fixation in August
market losses (percentages) 2020 (percentages of total loan volume)
Range across countries Cumulated share of total volume
15 100%
75%
14
50%
13
25%
12
0%
20+ years
3 months
6 months
1 week
10 years
15 years
1 month
1 year
5 years
11
CET1 ratio in Sovereign Other Equity CET1 ratio
2019Q4 bonds securities after impact
Mark-to-market losses Duration of interest rate fixation
Sources: ECB Supervisory Banking Statistics, SHS-G, CSDB, Bloomberg and ECB calculations. Notes: The Sources: AnaCredit and ECB calculations.
exercise is based on granular information on SIs’ securities portfolios, quantifying the devaluation between Notes: Based on a sample of 2,495 banks and 3,688,312 NFCs. Shares of loan volume up
end 2019 and March 2020 of securities in accounting portfolios where changes in valuation are reflected in to a given duration of interest rate fixation in years, last category (20+) refers to durations
regulatory capital. The assessment can be seen as an upper bound for the actual impact, since it considers above 20 years. Diamonds refer to the euro area, the light grey area reports the min-max
only direct hedging through short sales but not the potential use of derivatives. The distribution of price range across euro area countries.
changes across securities has been trimmed at 1%-99% percentiles in order to account for outliers. Latest
26 www.ecb.europa.eu ©
observation: 2019 Q4 for securities holdings and CET1 ratio; 31 March 2020 for bond prices.Use of the BLS as a timely indicator to anticipate loan developments
Rubric
Net demand for loans to NFCs MFI loans to firms
by purpose (monthly flows in EUR bn)
(net percentages)
Financing need for working capital
Financing need for fixed investment
Loan demand
75 140
120
50 100
80
25
60
40
0
20
0
-25
-20
-50 -40
2019 2019 2020 2020 2020 2020 2021 2021 2015 2016 2017 2018 2019 2020 2021 Aug
Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2
Source: Bank Lending Survey (BLS). Source: ECB(BSI).
Notes: The net percentage refers to the difference between the sum of the percentages Notes: MFI loans adjusted for seasonality, sales, securitisation and cash pooling activities.
for “increased considerably” and “increased somewhat” and the sum of the percentages Latest observation: August 2021.
for “decreased somewhat” and “decreased considerably".
Latest observation: 2021Q2 (July 2021 BLS).
27 www.ecb.europa.eu ©Proxies
Rubric for real-time monitoring of bank lending by maturity
Bank lending flows to firms by initial maturity
(percentages)
Up to five years Over five years
100%
80%
60%
40%
20%
0%
Average 2019 1 Jan. - 20 Feb. 2020 21 Feb. - 19 Apr. 2020 March - April 2020
BSI real-time data real-time data BSI (publ. end May
2020)
Sources: ECB(BSI), Bloomberg Syndicated loan issuance data and ECB calculations.
Notes: The BSI data for reference date April 2020 was made available on 29 May 2020.
Latest observation: April 2020 for BSI; 19 April 2020 for Bloomberg (real-time data).
28 www.ecb.europa.eu ©Assessing
Rubric the role of public guarantees for loan developments
Take-up of and credit standards on loans covered by COVID-19-related public guarantees
(left panel: EUR bn; middle and right panel: net percentages)
With guarantees
Germany Spain Without guarantees
France Italy
Credit Demand
120 30 100
100 20 80
10 60
80
0 40
60
-10 20
40
-20 0
20 -30 -20
0 -40 -40
20Q2 20Q3 20Q4 21Q1 21Q2 H1 H2 H1 H1 H2 H1
2020 2021 2020 2021
Sources: National authorities and authors’ calculations and Bank Lending Survey (BLS). Notes: left panel: The take-up data refer to approved amounts
of guaranteed loans. As guaranteed loans can also be granted in the form of revolving credit facilities, the approved amount is higher than the amount
actually disbursed. In the absence of a breakdown by firm size for Italy, it is assumed that guaranteed loans to SMEs are those granted via the Fondo di
Garanzia, while guaranteed loans to large firms are those granted via SACE (the Italian export credit agency). Middle and right panel: The net
percentages of banks for credit standards (demand) are constructed as the difference between the sum of the percentages for “tightened (increased)
considerably” and “tightened (increased) somewhat” and the sum of the percentages for “eased (decreased) somewhat” and “eased (decreased)
considerably". Latest observation: 2021Q2 for data on guarantee take-up; 2021Q2 for BLS (July 2021 BLS). www.ecb.europa.eu ©You can also read
