A guest post by Kenneth Devine (Central Bank of Ireland) on new occupational pension fund data highlighting household exposure, concentrated asset holdings and the impact of COVID-19. [Disclaimer: This blog represents the author’s views and not those of the Central Bank of Ireland]
Pensions are the primary source of income to households in retirement. The volatility and economic shock associated with COVID-19 have compounded pre-existing issues for pension systems. These include aging populations, the low interest rate environment and the prevailing low yields on safe assets (OECD, 2020).
In a recent Behind the Data publication, Ciarán Nevin, David Mulleady and I ask the question – What do we know about occupational pension funds in Ireland? Our note highlights the role of occupational pension funds as a household asset, outlines the breakdown of financial assets, and examines the impact of the pandemic on these holdings. An overview of the key findings can be seen in Figure 1 below.
work by the OECD
(2014) provided a comprehensive review of the Irish pension system, its analysis
of occupational pension funds was constrained by a lack of data. New
Central Bank of Ireland statistics covering occupational pension funds help
to fill this gap by providing a better understanding of the structure and asset
holdings of the sector.
We show that, in
June 2020, Irish occupational pension funds had assets of €118 billion,
accounting for 30 per cent of household financial assets. This is the second
largest household financial asset behind currency and deposits. Household
sector housing assets accounted for €542
billion in the same period.
According to the Pensions
Authority’s 2019 annual report, the Irish sector consists of over 75,000
active occupational pension funds, representing almost half a million active members.
This represents over 90 per cent of total euro area pension funds by number. The
size, and role, of occupational pensions varies across euro area countries (Curos
et al., 2020), with total assets of the pension fund sector amounting to €3
trillion at September 2020.
We have seen a transition away from Defined Benefit (DB) funds in recent years (fall of 50 per cent in number of active schemes since end-2009). For Defined Contribution (DC) pension funds, the member’s income in retirement is dependent on asset performance. Therefore, the switch from DB to DC pension funds has shifted investment risk from the corporate sector to households (Brown, 2016). Households, and their retirement income, are now increasingly exposed to financial market shocks.
The Behind the Data piece outlines that Irish pension funds primarily invest in investment funds shares and unit-linked insurance products. Combined, these two instruments account for three quarters of the sector’s balance sheet. However, structural differences in asset holdings exist across DB/DC pension funds. While the larger DB pension funds are seen to directly invest in hundreds of diverse assets, smaller DC pension funds tend to predominantly hold a limited number of investments.
As can be seen in Figure 2, at the onset of the COVID-19 pandemic the total value of pension fund assets fell by 6.5 percent (€7.9 billion). These asset values largely recovered across Q2 and Q3 2020 to sit at €118 billion. The movements were predominantly caused by financial market price gains and losses as the pandemic, and global policy responses, evolved. At Q3 2020, asset values were 1.8 per cent below pre-pandemic levels.
Going forward, the Central
Bank will publish Pension Fund Statistics information releases on a quarterly
basis. The next steps in developing this dataset will include an investigation
into asset breakdowns by their sector and geography, to further explore
Researchers interested in hearing
more about the data can contact Kenneth Devine.
Disclaimer: this post represents my own views and not those of the Central Bank of Ireland
Two recent CSO releases shed light on the evolution of earnings in the first three quarters of 2020. Alongside employment and hours, understanding the impact of COVID-19 on earnings tells us how household incomes are affected by the shock.
In previous work, using data from the Financial Crisis, we found that earnings in Ireland were sensitive to economic conditions, notably changes in the unemployment rate. The workers most exposed to lower pay when labour demand falls are those with weaker bargaining power. For example, in our paper we focused on the lower wages of new hires during the last crisis.
When looking at changes in average earnings, such as from the CSO’s Earnings and Labour Costs release, it is important to take account of changes in the composition of employment. For example, if changes in employment are concentrated amongst lower paid workers, average earnings could rise when there is a negative aggregate demand shock. In a SSISI paper in 2012, Kieran Walsh showed that these compositional effects can be large. The CSO also noted the potential for compositional effects in the context of COVID-19 average earnings changes.
Tracking the earnings of the same workers in the same jobs can remove some of these composition effects, giving a clearer picture of underlying wage developments. The CSO does something close to this in its Labour Market Insight Bulletin 4/2020, showing changes in average gross weekly earnings conditional on workers being in employment in Q1 and Q3 2020. Earnings includes wage subsidies, where applicable, but exclude PUP payments.
The chart below, from the data in the CSO Bulletin, shows that for all sectors earnings fell by almost 4 per cent for workers in employment in Q1 and Q3. In some sectors, like Administrative & Support Services and Financial, insurance & real estate the changes are double-digit. In others, like Construction and Accommodation & food, earnings are up.
For comparison, during the financial crisis, and controlling for composition effects, average weekly nominal pay also fell by around 4 per cent, most of it between 2008 and 2009 (Lydon & Lozej, Table 2). At that time, the declines were largest in sectors connected to property market, like construction and real estate. The emphasis on nominal pay is important. Between 2008 and 2010, prices (CPI) also fell sharply, by over 5 per cent. This helped offset the fall in nominal earnings, cushioning the impact on households’ purchasing power. Price levels have fallen in 2020, by around 1.5 per cent, which suggests a fall in real earnings in the first three quarters of the year of around 2.5 per cent.
… changes in earnings positively correlated with labour demand, but important to control for hours
The changes in earnings between Q1 and Q3 are generally positively correlated with changes in labour demand, such as changes in employment or job postings. There are some notable exceptions like Accommodation & food – where employment fell by over a fifth, but average earnings rose marginally, by 0.4 percent; or Industry, where employment grew by 2.4 per cent, but average earnings fell by 6.4%.
Despite conditioning on workers in employment in Q1 and Q3, there are likely still many factors affecting earnings dynamics that are not picked up in the conditional averages. One example is hours-worked. As weekly earnings are the product of hours worked and hourly pay, higher or lower earnings could be due to higher or lower hours. This could matter in sectors with seasonal hours, like Accommodation & Food services.
To get at the the change in hours worked, and for a sample broadly aligned to the administrative earnings data, the CSO provided me with average actual hours worked by sector for employed persons interviewed in Q1 and Q3, from the LFS. I use this data to back out change in average hourly pay as the change in weekly earnings minus change in weekly hours worked. Readers should note that earnings data is from administrative sources (including wage subsidies), whereas the hours data is from a survey. Furthermore, the two matched LFS samples are six months apart and may not be exactly representative.
The chart below shows the data. The line in the chart is the change in average gross weekly earnings, corresponding to the bars in Chart 1. Whilst there are offsetting increases in hours in several cases, they are usually small. Furthermore, the direction of the change in hourly pay and earnings is roughly the same for most sectors, with the notable exception of Accommodation and food services. In fact, the increase in hours worked (10.5%, an increase from 32.1 to 35.5 hours per week) offsets a large fall in hourly pay (minus 10.1%). This fall in hourly pay is more closely alinged with the fall in demand (employment and job postings) that we have seen during COVID-19. Looking at the historic LFS data, it is clear that this hours increase in Q3 is not unusual. In fact, it is entirely predictable: the historic Q1 to Q3 change in hours is almost exactly the same as the 2020 figure, at 10.3 per cent.
The third chart below shows the correlation between the estimated change in hourly pay (conditional on working in Q1 and Q3) and the change in job postings by sector from Indeed. Job postings are generally a good indicator of labour demand, and, whilst postings are down across the board, we find that sectors where postings have declined the most have generally see larger falls in hourly pay.
It should be said that three quarters of data is a relatively short time period. Added to this is the fact that the COVID-19 shock has generated a very high degree of uncertainty. For firms in some sectors – such as exporters, industry or multinationals – the demand shock may may turn out to be less bad than initially feared. This might help explain negative earnings growth for workers in Industry, but positive employment growth. It is quite possible that in Q4 or Q1 2021 we may see a strong earnings growth for some sectors as employers unwind pay freezes that were put in place early-on the crisis.
… looking ahead
By combining administrative and survey data in novel ways, the CSO provides timely and granular insights on the COVID-19 labour market. This is crucial information for understanding the impact of the shock, and how policy might help mitigate it.
The decline in earnings in 2020 for employees working in both Q1 and Q3 is similar to falls seen during the last recession, albeit with a different sectoral pattern. There are other differences this time around. The most significant difference is the large and decisive policy response to COVID-19 – both fiscal and monetary. In November, over a quarter of workers were supported by Pandemic Unemployment Payments or Wage Subsidies. Furthermore, the government has committed to these supports remaining in place while restrictions remain in in place. Another important difference is healthier state of household balance sheets going into 2020, a factor which dragged on domestic demand during the last recession.
If the spread of the virus can be brought under control in 2021, this points to a potentially shorter duration shock than before. However, the longer restrictions continue, the greater the potential for behaviour to change – like less business travel or less bricks-and-mortar retail, for example – and the harder it becomes for some businesses to reopen. This would lead to permanent job losses, even after restrictions are lifted. Furthermore, if employment and (real) earnings shocks persist, there is greater potential for precautionary savings, with negative feedback loops for domestic demand. The fact most people who have experienced reduced employment or been laid off due to COVID-19 said they expected to return to the same job suggests a widespread perception of this as a short-term or temporary shock – albeit this was in Q3, before the most recent Level 5 restrictions.
Related to this, in services – the sector most affected by the shock – turnover picked up sharply during the summer easing of restrictions. Although some sub-sectors, like travel and accommodation remained far below pre-COVID levels. Job postings in services track tend to track turnover very closely, rising in the summer, before declining again in the move to Level 5. This suggests that permanent relaxation of restrictions, leading to increased demand, could undo some, but not all, of the labour market damage we have seen in 2020.
A guest post by Niall McGeever (Central Bank of Ireland) on new company registrations and corporate insolvency in Ireland during the pandemic. [Disclaimer: This blog represents the author’s views and not those of the Central Bank of Ireland]
The severity of the COVID-19 shock
and the modest liquid asset holdings of many Irish firms (Financial Stability Review 2020 I; McGeever et al., 2020) raises the
question of how the pandemic is affecting business dynamism and failure rates. A
marked reduction in new firm formation or a spike in insolvencies could lower
the productive capacity of the economy and negatively affect output and
There’s lots of economic research
showing the importance of new firms for productivity and employment growth. Lawless (2013),
for example, shows that young firms contribute disproportionately to employment
growth in Ireland.
While a certain level of insolvency over time is inevitable
and even desirable to ensure resource re-allocation to productive firms, the
failure of otherwise viable firms due to the pandemic could reduce output and
productivity growth. See Lambert
et al. (2020) for more discussion on this point.
The chart below, Figure 2 from the Letter, shows the new company registration rate between January 2001 and September 2020. The rate averages around 9.5 per cent per annum and is broadly pro-cyclical.
The initial Covid-19 shock coincided with a sharp decline in new
company registrations, with the rate falling to 5.3 per cent in April and 6.1
per cent in May. The Companies Registration Office tell us that over 90
per cent of applications to register a new company are made online, so the
decline in April and May cannot be explained by procedural delay due to the
pandemic. Instead, it likely reflects a temporary decline in both new
enterprise formation and stalled investment decision-making by pre-existing
The largest declines during this period were, perhaps
unsurprisingly, in Accommodation and Food and in Arts, Entertainment and
Recreation. New registrations in these sectors were down 50 per cent on the
same period in 2019.
Whilst the number of registrations in the first nine months of
2020 were down around 12 per cent on the same period of 2019, new company
registrations rebounded quite strongly over the summer and had returned to
roughly pre-pandemic levels by September. An emerging trend in the
Wholesale and Retail trade category is the consistent increase in new
registrations in “retail sales via mail order houses or via internet” and in
“other retail sales not in stores, stalls or markets” between June and
September relative to the same period in 2019. This trend is also reflected
Census Bureau data, for example, shows higher new business applications by
non-store (e.g., internet sales) retailers during 2020.
We next look at insolvent liquidations. The next chart (Figure 4 in the Letter) shows the insolvent liquidation rate from January 2001 to September 2020. The rate generally tracks macroeconomic conditions very closely and it is worth noting that it rose notably rose with the unemployment rate in early 2008.
immediate impact of Covid-19 shock was to sharply reduce insolvent liquidations. The annualised rate was
exceptionally low at 0.07 per cent in April 2020 and only a touch higher at
0.10 per cent in May. This is due principally to the inability of company
directors to safely convene creditors’ meetings. Prior to the pandemic, it was
a requirement to hold a physical meeting with creditors to initiate a
creditors’ voluntary liquidation. This became impractical during the acute
phase of public health restrictions and so the main channel for insolvent
liquidations was blocked. This procedural issue was quickly resolved and the
Oireachtas passed a company law amendment to facilitate creditors’ meetings by
liquidation rate reverted to pre-pandemic levels in June and showed no signs of
a marked increase up to September. At a sectoral level, Accommodation and Food
and Wholesale and Retail Trade show signs of higher liquidations both during
the pandemic and relative to 2019. These patterns are aligned with the negative
labour market shocks in both sectors.. To a lesser extent, we also see the Arts
and health sectors recording higher numbers.
clear evidence of financial distress facing many firms, there is no evidence yet
of a marked increase in corporate insolvencies. The striking contrast between
the insolvent liquidation rate and current labour market conditions is unusual
and points to the significant role of government supports, loan payment breaks,
and forbearance from other creditors in helping firms to stay cash-flow
Today the Bank published its fourth and final Quarterly Bulletin for 2020. The report contains a detailed overview of developments in the economy since the publication of last Bulletin in July as well as our latest macroeconomic forecasts out to 2022.
The forecast for GDP growth has been revised upwards to -0.4 per cent in 2020 reflecting more positive developments in consumption, strong export performance and an enhanced level of fiscal support arising from the July stimulus package. Growth prospects for next year and 2022 are more subdued compared to the previous Bulletin due to the implications of a WTO Brexit. As outlined Box A, a disruptive transition to a WTO trading relationship would frontload associated output and employment losses. In this baseline scenario, the growth rate of the Irish economy is 2 percentage points lower in 2021 relative to a Free Trade Agreement due to the introduction of tariff and non-tariff barriers. The ILO unemployment rate is projected to average 5.3 per cent for this year, rising to 8 per cent in 2021 following the closure of income-support schemes at the end of the first quarter (Box D in the Bulletin discusses the challenges that arise for measuring unemployment in the time of COVID-19).
Since re-opening from a period of lockdown, the recovery of the
Irish economy has been uneven as levels of domestically focussed economic
activity remain well below pre-pandemic levels. In particular, consumer-facing services
sectors, such as tourism, hospitality and retail services, which are also more
labour-intensive, have been slower to recover contributing to a projected
decline in underlying domestic demand of 7.1 per cent this year. The strong
performance of exports, which are expected to decline by just 0.3 percent in
2020, is the main factor driving an upward revision in the baseline projection for
GDP. Box C details the
relative resilience of high-value exports such as computer services and
pharmaceuticals during a period of declining trade-weighted world demand.
The Central Bank’s Business Cycle Indicator (BCI), a monthly
summary indicator of overall economic conditions estimated from a larger dataset
of high-frequency releases, fell sharply during the months of March and April
reaching a historical low (Figure 1). The latest estimates show that economic
conditions continued to improve into July and August, but the rate of recovery has
slowed down. Despite the improvement over the four months to August, the
overall level of the BCI remains substantially below that observed prior to the
emergence of the COVID-19 crisis.
Figure 1: Business Cycle Indicator (BCI) for Ireland’s Economy
The outlook remains highly uncertain, depending not only on the economic
consequences of COVID-19 and its containment, but also on the nature of the
trading relationship between the EU and the UK. Recognising this uncertainty, Box E analyses the
impact of a ‘severe’ COVID-19 scenario as an alternative to the baseline
forecasts in which there is a strong resurgence of the pandemic, leading to the
restoration of widespread and stringent containment measures for a more
prolonged period. Underlying domestic demand is projected to fall by 8.5 per
cent in 2020 in this case with a continued contraction of -1.3 per cent into
2021. While the economy does not begin to recover until 2022, underlying
domestic demand remains 6 percentage points below 2019 levels. In the ‘severe’
scenario, the unemployment rate rises to 12.5 per cent in 2021 before
moderating to 10.1 per cent the following year.
A guest post by Fergal McCann (Central Bank of Ireland) on SME finances and firm supports during the pandemic.
The Central Bank has published a Financial Stability Note, written by Derek Lambert, Fergal McCann, John McQuinn, Samantha Myers and Fang Yao, entitled “SME finances, the pandemic, and the design of enterprise support policies”. The Note estimates the likely losses that are being experienced in the SME sector over from March 2020 to year end, introduces a model of SME financial distress which can be used to evaluate the effect of announced SME support policies, and discusses policy implementation issues in the current climate.
Aggregate revenue shortfalls We begin by updating an aggregate model of SME revenue shortfalls. McGeever, McQuinn and Myers (‘MMM’, 2020) used a first variant of this model to estimate three-month initial liquidity needs for SMEs of €2.4bn to €5.7bn. We update this model to account for reported reductions in wage and non-wage costs, using both PUP/TWSS take-up rates and CSO surveys on the business impact of COVID-19. Firstly, we show that, using observed data, our estimates for Q2 are at the very high end of the MMM estimates, suggesting the effect of the COVID-19 shock is about as severe as we were willing to project back in March/April when the initial MMM work was being carried out. Revenue shortfalls for 2020 are estimated between €10.3bn and €11.7bn across the SME sector, which are of course subject to significant uncertainty, both due to the uncertain outlook and the use of firms’ survey responses during the pandemic.
We highlight in the paper that such aggregate revenue shortfall estimates are not necessarily estimates of the size of required government support. These shortfalls already account for wage cost reductions through the TWSS. In aggregate, these shortfalls can be met by a combination of utilisation of pre-existing cash reserves, draw-down of existing credit commitments, new borrowing, additional cost reductions or loss-sharing, or if necessary governmental non-wage grants, reliefs and guaranteed loans. In this vein, we also model that across the board non-personnel cost reductions of 30 per cent could have large effects in reducing the overall shortfalls, suggestive of the importance of burden-sharing and cost efficiencies, along with fiscal support, in addressing the crisis.
Protection or liquidation We outline considerations for designing policy responses to
SME financial distress. Some enterprises entered the COVID-19 shock with
unsustainable business models and during a typical downturn the closure of such
companies can be seen as part of the overall process of economic restructuring
and dynamism. However, identification of such firms is difficult given the
nature of the COVID-19 shock.
There is a risk that, if traditional financial signals were being used, widespread liquidation could arise in the short run. We also point out that there are employee, supplier and customer relationships tied up in all firms, with relationship-specific capital on the line as firms are liquidated. Blanchard, Philippon and Pisani-Ferry (2020) summarises much of our thinking in this area:
“In normal times, policies should help the reallocation process, letting some firms fail and others expand, and helping the reallocation of workers across sectors. These are not normal times, however: many firms may fail because they are insolvent even if they are viable. Given the very high uncertainty, banks may be reluctant to advance credit. Unemployment is extremely high, making it difficult for laid off workers to find other jobs. For these reasons we think that protection (of workers) and preservation (of firms) should be given a higher priority than in normal times.”
Despite the above, we of course acknowledge that those designing SME supports must do whatever feasible to use taxpayer funds as efficiently as possible, given the nature of deficit dynamics during the pandemic. Targeting is difficult, especially in the current climate, but it is not fiscally or economically sustainable for supports to be provided without regard to viability, particularly as the likely duration of the pandemic period elongates.
Firm supports: loans, grants or equity? Outside of the over €5bn that will be used to support wages
through the TWSS and EWSS, there is a 60/40 split between debt and grants in
the announced SME support packages. If tax warehousing is accounted for as a
debt, this rises to 70/30. We point out that the amounts announced are committed funds – in the case of
debt-based supports in particular, final take-up rates are unknown.
We highlight the risk that debt-based supports may have weak
demand from firms wary of borrowing, may lead to debt overhang issues over the
medium term and face implementation issues when channelled through lenders. However,
debt-based supports benefit from lenders’ access to information and incentives
to screen credit risk, in cases where banks retain appropriate levels of risk
(such as the 80-20 split embedded in the Irish Credit Guarantee Scheme).
Direct fiscal supports, such as grants or tax or rate waivers, provide liquidity and support the economy but raise issues regarding costs, targeting and moral hazard. Relative to a guaranteed loan, where costs only arise as defaults occur, grant funding is far more expensive up-front for the taxpayer. One potential option to address this drawback is for the State is to provide equity-like or “conditional grant” injections to SMEs which involve an element of clawback or potential return, lowering the cost of intervention relative to a direct grant. In this light, the proposal of Boot et al. (2020) is worthy of further consideration, where higher future tax rates are agreed in exchange for up-front aid.
A model of financial distress Finally we present key results from a forthcoming model of SME financial distress (McCann and Yao, 2020). The financial distress (FD) indicator is based on SMEs’ capacity to meet losses through cash holdings, or to service interest expenses during the shock. We calibrate 2018-2019 data on SME balance sheets to the revenue and cost reductions reported by SMEs in 2020, and use the model to assess the role of various policy support options.
Relative to a no-policy scenario, we implement firms’ lowering of wage costs, both through TWSS wage supports and the transition of employees to the Pandemic Unemployment Payment (PUP), as well as non-wage policies worth €7.5bn, capturing the role of the credit guarantee, other lending, tax warehousing and enterprise grant policies. When the full package of policies announced in 2020 are included in the model, distress rates fall from 18.7 to 15.8 per cent (or 25.9 to 14.9 per cent when weighting firms by their debt balances outstanding).
Why does a cohort of SMEs remain in financial distress (FD) after policy supports are modelled? There are a number of factors at play. Firstly, the schemes’ total availability of €7.5bn means there is an aggregate maximum on the number of SMEs that can access funds. Secondly, specific schemes have specific maximum amounts, which in the cases of those experiencing the most severe financial effects of the pandemic, may not suffice to alleviate FD. Thirdly, the schemes have reasonably wide eligibility criteria, often related to firms experiencing a fall in revenues of a certain amount. This means that many firms that were never at risk of entering FD by our definition are just as entitled to draw down funds as firms experiencing the greatest losses. This issue of widespread access to funding was a necessary feature of scheme designs across the globe in response to the pandemic.
The latter finding on debt-weighted distress suggests that support schemes will have more beneficial financial stability effects than are visible when looking at a simple share of enterprises falling into financial distress. The greater efficacy of policy in lowering debt-weighted distress relates to the tendency of larger SMEs to have larger debts, implying that these firms draw down larger amounts of total scheme funds available, as well as to the concentration of SME debt among affected sectors such as the accommodation, food, wholesale and retail sectors. The table below reports the impact of the sequential addition of specific policy supports, highlighting that the effects of lowering wage bills through PUP and TWSS are larger than the effect of other supports.
Financial Distress (%), by firm count
Financial Distress (%), by debt balance
+ Income and Wage Supports
+ Tax Warehouse
Finally we show that, relative to currently calibrated support policy, a hypothetical “viability-based” grant system that targets firms based directly on the size of their operating losses, supporting firms closest to viability first, would reduce distress rates to about half the levels modelled under currently-designed policy (comparing the middle and right hand side sections of Figure 1). Such a hypothetical system would prioritise solely the minimization of the financial distress rate, for a given fiscal outlay, and is therefore not intended as a specific recommendation but rather to illustrate the effect of current supports relative to a benchmark model. In practice of course, policy must take on board sector-specific, regional and longer-run considerations that go beyond solely the minimization of financial distress rates.
Source: Model-based estimates from McCann and Yao (2020). Notes: “Targeted Grants” replicate payroll supports modelling from the “Current Supports” scenario, but replace the grant, credit and tax components with a €7.5bn grant that provides support to firms in order of their viability (with firms closest to exiting financial distress receiving support first). By construction, the debt-weighted exercise relates only to firms with debt balances above zero in the 2018-19 data
Conclusions and wider policy issues The model suggests that existing policy supports are likely to have mitigated SME financial distress in some cases, but challenges will remain for a relatively large group of SMEs. From a policy perspective many of the firms modelled as being in financial distress may be viable over the medium term – the identification of FD does not imply enterprise liquidation. This points to the importance of a dual approach to policy for SMEs, where targeted and effective financial support is required in the first instance, but a focus is also placed on the system-wide capacity to restructure the liabilities of potentially-viable firms. This latter step will ensure that the set of firms with the greatest prospects of survival over the medium term are given a chance to trade through the current challenges posed by the pandemic. The Central Bank is a key stakeholder in this process, with oversight of lenders’ approach to loan restructuring as SMEs’ payment breaks begin to expire. The Central Bank’s approach to this process was outlined by Deputy Governor Ed Sibley on Monday September 28th and can be accessed here.
References McCann, Fergal and F. Yao (2020, forthcoming), Modelling financial distress in SME sectors during the Covid-19 pandemic – from liquidity to solvency, Central Bank of Ireland, Mimeo. McGeever, Niall, John McQuinn, and Samantha Myers (2020). SME liquidity needs during the COVID-19 shock. Central Bank of Ireland Financial Stability Note, 2020 No. 2