Two Economist-Statistician Vacancies in Central Bank of Ireland

The Central Bank is looking to fill two economist-statistician roles, in the following two areas.

The first role will be responsible for Quarterly Financial accounts (QFA). The QFA provides comprehensive information not only on the economic activities of households, non-financial corporates, financial corporations and Government, but also on the interactions between these sectors as well as the rest of the world. The successful candidate will work on developing the methodological aspects of QFA, lead the research and analysis of the Distributional Financial Accounts and ensure the ECB and national requirements and timelines are met.

The second role will oversee analysis of the Pension Fund and Insurance Corporation statistical collections. The team is responsible for collection and analysis of data on the insurance corporation and pension fund sectors in Ireland. The successful candidate will contribute towards the analytical development of a new statistical series on the Irish pension fund sector and coordinate ECB statistical requirements across both areas.

The closing date for receipt of applications is 25th June. For further details, including how to apply, see the Central Bank careers webpage.

The Impact of COVID-19 Income Supports on Earnings

[Disclaimer: This blog represents my personal views and not those of the Central Bank of Ireland or the European System of Central Banks.]

Last week, the CSO released the latest in its Insights from Real Time Administrative Sources series on the “Impact of COVID-19 Income Supports on Employees”.  Using administrative data on employee earnings and COVID-19 support scheme payments (PUP and T/EWSS), it tracks the individual gross weekly earnings of matched cohorts to Q4 2020 (‘matched cohort’ means the CSO is comparing the (median) earnings of same group of individuals year-on-year).* 

For policy makers deliberating the future of COVID supports (PUP and WSS), the information in the release provides several useful insights.** I highlight three aspects in this post: (i) the strong earnings growth of non-recipients of supports throughout 2020; (ii) the diverse earnings experience of different demographic and income groups; and (iii) the different earnings growth experienced by PUP versus T/EWSS recipients, which, for the first time, is shown separately in this release.  Some more details on each below.

For non-recipients, earnings grew strongly throughout 2020
One of the most striking observations is the strong growth in earnings for non-recipients of either PUP or T/EWSS, as shown in Chart 1 below.

Note: A recipient is defined as an employee who received at least one payment of either PUP or WSS during the quarter. All the comparisons are of growth in the median. The median is the mid-point in the distribution of the level of earnings, i.e. half of earners in a given group earn more, half earn less.

The fourth quarter, with year-on-year earnings growth of 7.1%, is particularly strong for non-recipients (as we already knew from the previous release to Q3, the group of all recipients experienced large falls in earnings in 2020). Year-on-year growth for the bottom 20% per cent of (non-recipient) earners was over 21% in Q4 (see Figure 1.5 in the release). The release notes that this increase is mainly among younger age groups, and workers in the wholesale & retail and health sectors. Some of this likely reflects the steep slope of the age-earnings profile for younger workers, and also perhaps longer hours worked during the pandemic. However, the CSO also notes that it reflects individuals changing jobs during the pandemic, which could point to more permanent effects on the level of earnings.

But it is not just at the bottom where we see earnings growth for non-recipients. We see growth right across the distribution. For example, the top-20% of (non-recipient) earners registered growth of 4.7% in Q4 2020. In fact, the combination of strong growth with the size of the non-recipient group (75% of employees in Q4), puts growth for the median of all workers (i.e. non-recipients plus recipients) in positive territory in Q4 (+4.6%). This is a remarkable outturn for the year that was in it. As highlighted in this piece with Tara McIndoe-Calder in March, the fact that average household incomes actually grew during 2020 is one of the reasons for the large jump in savings we saw during the year.

The earnings growth of recipients differs significantly across demographic groups
Reflecting the concentration of shut-downs in face-to-face services and some retail sectors, we know that the pandemic has affected the employment of some groups more than others, notably females, lower paid and younger workers (see, for example, this recent speech by Deputy Governor Sharon Donnery).  However, when we look at earnings growth, and including supports, the same groups tend to be the least affected during 2020 (see Table 1 below, which is based on Table 1.2 in the release).  The fact that some groups fare less badly reflects, in part, the level of supports relative to pre-pandemic earnings. We know, for example, that younger and female workers generally work fewer hours during the week (i.e. part-time), and tend to work in lower-paying sectors.  So, these are almost like ‘reference effects’. Note that the CSO analysis is of gross earnings, i.e. before income tax, and there may be future tax liabilities for some recipients where supports were not taxed at source.

One thing we do not see in the Q4 data – at least for growth at the median – is much evidence of an impact from the change in payment rates from mid-October onwards, when payments were more closely aligned to pre-COVID gross earnings. In my paper with Brian Cahill (CSO) from February, when we only had data to Q3 2020, we thought this could change income for some groups. But we do not see it in this release. As the payment could have both risen for some (the top rate increased to €350) and fallen for others, it could be washed out in the median, and we would need to dig down to the individual data to see it.  The Christmas Bonus, when there was additional PUP payment in December, could also be a factor.

Very different earnings growth by type of support and demographic group
The release provides, for the first time, a breakout of earnings growth (plus supports) for the two different types of supports, PUP and T/EWSS***. Whilst I focus on earnings here, the update on numbers receiving various supports released last week is also relevant.  

In Q2 and Q3, earnings for PUP (only) recipients fell by around 5% (for the median), less than half of the fall for T/EWSS recipients (Chart 2, below). So, even with employer top-ups, the gross earnings of (all) wage subsidy recipients fall a lot during this period (at the median). The one positive is that by Q4 both groups’ earnings were back at around Q4 2019 levels.   

Focusing on all recipients misses some of the variation within demographic or regional groups (Charts 3-5). For some workers – like males, older workers and workers in Dublin – wage subsidy recipients fare ‘better’ in terms of earnings growth, than PUP recipients in these groups. I say ‘better’, but in all quarters, earnings plus wage subsidies are still significantly lower than pre-COVID earnings for these sub-groups.

In contrast – for females, younger workers and workers outside of Dublin – PUP recipients fare better than wage subsidy recipients, and, in almost all quarters, earnings plus PUP are higher than pre-COVID earnings (although this is less so the case for the non-Dublin group).  For wage subsidy recipients in these sub-groups, earnings in Q2 and Q3 are below pre-COVID levels (except for under-25s), before recovering somewhat in Q4.

The timing of different health restrictions, and specifically which sectors were closed and when, likely explains some of the differences we observe between certain groups. For example, the closure of construction, with higher earnings on average and more male workers, could explain some of the gender differences we observe (this is also related to the ‘reference effect’ I mentioned above).  Related to this, we also need to be careful about comparing medians across individual quarters, as the composition of matched cohorts varies from quarter-to-quarter, as shown in Table 1.9 in the release.

With the above caveats in mind – plus the fact that this is aggregate data, albeit medians for specific groups – these patterns point to at least two important considerations when considering the future for these supports as the economy re-opens.

First, and most obviously, not all workers will be affected equally by changes to PUP or wage subsidies.  Taking PUP as an example, changes to the scheme as different sectors reopen could impact younger, female and, to a lesser extent, workers outside Dublin more.

Second, and related to the first point, for some recipient groups median earnings increase during 2020, notably PUP recipients. Therefore, whatever changes are made, and at what speed, could have a bearing on work incentives and labour supply.  I am not saying that workers on PUP do not want to return to work, but rather we should pay close attention to the literature and evidence on how unemployment benefits affect unemployment (see, for example, this recent paper on youth unemployment duration and unemployment benefits by Maynooth economists Aedin Doris, Donal O’Neill and Olive Sweetman). Furthermore, labour supply is a multi-dimensional issue, of which pay is just one. In the current context, other factors like the spread of the virus, migration (and how the pandemic affects cross-border flow of workers), caring responsibilities and health concerns also play a role.

Notes: (*) Average weekly earnings are estimated by summing all earnings during the quarter and dividing by weeks worked in the quarter. (**) Revenue pays the wage subsidy (T/EWSS) directly to qualifying employers who then pay employee wages. So, formally, it is a support for employers. For convenience, I refer to it broadly as an ‘income support’ in this post. (***) The release also provides data on the earnings growth of a third group: employees who receive both PUP and T/EWSS during a quarter (but not necessarily at the same time). This group accounts for around one-in-five recipients in Q4. For a clear comparison between the experience of recipients on the two types of supports, I omit this third group here.

I thank, without implicating, Brian Cahill (CSO) for answering my many queries about the data in the release.

Occupational Pension Funds in Ireland: What do we know?

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.

Figure 1: Overview of key findings

While previous 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.

Figure 2: Impact of COVID-19 on pension fund asset prices

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 these household investment exposures.

Researchers interested in hearing more about the data can contact Kenneth Devine.

Changes in earnings during COVID-19

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.

Source: CSO (2020, Figure 5).
Notes: Gross weekly earnings, percentage change (nominal)

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.

Source: CSO (2020, Figure 5) and LFS actual hours worked by sector

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.

Source: Own calculations using CSO data. Job postings data from Indeed.

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.

Company births and insolvencies

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 employment.

Cecilia Sarchi, Maria Woods, and I look at recent trends in a new Economic Letter on Irish company births and insolvent liquidations during the COVID-19 shock.

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 corporate groups.

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 internationally. US 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.

The 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 electronic means.

The insolvent 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.

Despite the 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 solvent.