A Microsimulation Analysis of the Distributional Impact over the Three Waves of the COVID-19 Crisis in Ireland

From a methodological point of view, the key challenge is the lack of up to date information. We propose to overcome this data gap by using a “nowcasting” methodology (O’Donoghue and Loughrey, 2014). Our methodology is summarized in Figure F1 (Appendix F), which highlights the main components:

• A household income generation model -- composed of a labour market module, an income/cost module and a tax-benefit simulator -- able to describe the overall household income distribution and to simulate counterfactual distributions of disposable incomes under alternative scenarios;

• A nowcasting component to calibrate the simulations from the income generation model to external statistics based on recent data on employment and prices with the objective to provide a "near real-time" picture of the distribution of income.

Our paper goes beyond existing methods that apply price inflation factors, change proportionally the employment rate in specific industries and apply tax-benefit transformations to explain the policy consequences (Navicke et al., 2013). In periods of significant volatility with large and rapid changes in the structure of the economy, such as during the COVID-19 crisis, it is more appropriate to utilise a dynamic-type income generation model approach to update the data and to capture the heterogeneity of changes in the population (see Li and O’Donoghue, 2014; Bourguignon et al., 2001). We follow a similar approach as studies looking at the first 2 waves of COVID-19 (e.g. Sologon et al. (2021), Sologon et al. (2021)).

At the core of our nowcasting approach lies an income generation model, which consists of a system of equations that describe the household market income distribution as a function of personal and household attributes (Sologon et al., 2021). The parameters of the income generation model, estimated using the latest available survey data (time t), are used to simulate counterfactual distributions of market incomes under alternative scenarios.⁶ In order to convert market incomes into disposable incomes, we utilize the NUI Galway tax-benefit simulator (O’Donoghue et al. (2013); O’Donoghue et al. (2018)).

This methodology was already used to understand the drivers of cross-national difference in inequality in disposable income (Sologon et al., 2021) and the drivers of changes in disposable income inequality (Černiauskas et al., 2021). In this paper, we use the infrastructure to update the latest available survey data by calibrating the simulations to external macro controls to reflect more timely Live Register, Price and Labour Force Survey data to undertake the COVID distributional impact assessment.

To accommodate the nature of the shock and the multi-faceted impact on household living standards, our core welfare variable of interest is an augmented definition disposable income which accounts for work-related and housing costs. Following the standard definition, disposable income, $Y_{D,t}$ , at time $t$ depends upon market income $Y_{M,t}$ , benefits $B\left(Y_{M,t},Z_t,{\theta }_t^B\right)$ and taxation $T(Y_{M,t},Z_t,{\theta }_t^T)$ , which are in turn dependent upon personal skills, family characteristics, Z, and tax-benefit parameters $\theta$ . Our analysis adjusts disposable income for:

• Work-related expenditures $C_t$ :

• Housing costs $H_t$ :

• Capital losses $Q_t$ :

In this paper we define:

• Market income – income from the market plus employer social insurance contributions;

• Gross income – market income plus benefits;

• Disposable income – gross income minus taxes and all social insurance contributions;

• Adjusted disposable income – disposable income minus child care, housing costs, commuting costs, and share losses.

To some extent, this turns the clock back to microsimulation analyses from the 1980’s where disposable income net of housing costs were used occasionally (Atkinson et al., 1993; Atkinson, 1995).

The core, a generic household income-generation model (IGM, similar to Sologon et al. (2021)), is composed of several modules as shown in Figure F1(Appendix F). The labour market module estimates the statistical distribution of labour market factors: the probability to be at work, to earn income from salaried employment or self-employment, the occupational, sector and industry, choices, the probability of being unemployed, retired (if not working), the prevalence of income sources (investment income, property income, private pension, other income), the probability of paying for housing (home owner, mortgage, rent), the probability of paying contributions (private pensions), the probability of having child care.

The market composition module involves two estimation techniques: (i) binary models for binary outcomes, and (ii) multinomial models for m outcomes, $m\S amp;gt;2$ . In order to use the estimated probabilities from logistic models within a Monte Carlo simulation, we draw a set of random numbers such that we predict the actual dependent variable in the base year (see Sologon et al., 2021 for the method). The disturbance terms are normally distributed, recovered directly from the data for those with observed incomes, or generated stochastically for those without a specific income source in the data.

At each step, we retrieve the parameters estimates and the individual specific errors for each estimated model, to be subsequently used in simulating counterfactuals. We use the IGM to simulate the impact of changing economic conditions over time. Bourguignon et al. (2007) and Černiauskas et al. (2021) used a similar methodology to disentangle the impact of macro-economic changes on inequality by generating counterfactual distribution - transformations of the income generation process by ’swapping coefficients’ between years for the various transformations.

In nowcasting, the simulations involve calibrating econometrically estimated equations in the income generation model to external control totals made available in more timely data than the estimation data, only available at a lag. The calibration mechanism or alignment is drawn from the dynamic microsimulation literature (Li and O’Donoghue, 2014) and aims to calibrate the microsimulation model in order to match the simulated output to exogenous totals, particularly in relation to the structure of the labour market (Bækgaard, 2002).

In our model we utilise three types of alignment:

1. for binary discrete data,

2. discrete data with more than two choices, and

3. continuous data, as discussed at length in Sologon et al. (2021).

In effect, this method incorporates a stochastic term in the parametric regression to select the weighted number from an exogenous calibration parameter according to the rank of the deterministic and stochastic component of the parametric regression.

The nowcasting processes involves a number of components:

• The simulation of the system of hierarchically structured, multiple equations in the income generation model that describe the presence $\left(I_{i,t}\right)$ and level $\left(Y_{i,t}\right)$ of market incomes and the alignment of these simulation to external statistics;

• A tax-benefit model, described in O’Donoghue et al. (2013), to simulate taxes and benefits T(), B();

• Income indexation – the change in the level of income resulting from changes to average wages $Y_{i,t}\left(\right)$ .

For work-related expenditures, we model and simulate commuting costs and childcare costs. For commuting costs, we first estimate the probability of commuting by car or by public transport as a function of occupation, industry; education, location, and age group (see Table B1 in Appendix B). Second, estimating models for both public transport and motor fuels as a function of household characteristics, disposable income, social group and number of workers, we predicted the proportional increase in these costs as a result of the number of workers in a household relative to not working. Without modelling the commuting distance as a function of income, which may have either a positive or a negative relationship, we assume a flat commuting cost across households, adjusted for the age.

The distribution of childcare costs per week by family type and disposable income decile is approximated using IPF. These averages are, in turn, used to calibrate the simulations based on the estimated models for having childcare and level of childcare expenditure (integrated in IGM, see Table C1 in Appendix C).

For those with capital income, we assign the probability of holding shares across the age-income distribution on the basis of Monte Carlo estimates using Iterative Proportional Fitting (IPF). On the basis that change in wealth (excluding savings) is an alternative measure of income and given the initial COVID related impact on stock market prices, we incorporate initial capital value losses in the model. We simulate an average change in the capital value or capital loss of financial assets at the median (see Tables D1-D2 in Appendix D).⁷

These allow us to “update” the microdata to the current period.

The simulations involve two steps.

• First, we nowcast the lastest available survey data to December 2019 which is our proxy distribution (D) for 2020 pre-COVID: $D\left({Y_D}_{t+1}\right)$ , t+1 = 2020 before the crisis;

• Second, we assess the impact of COVID on the base 2020 income distribution by comparing the counterfactual distribution $D^{\mathrm{*}}\left({Y_D}_{t+1}\left(L^{\mathrm{*}}\right)\right)$ under alternative shock scenarios (corresponding to different waves of the pandemic) to the pre-crisis nowcasted distribution in t+1:

As the main micro data source we use the 2017 version of the Survey on Income and Living Conditions (SILC). The SILC is a dataset that has been collected in Ireland since 2003 and which is used to form the Irish component of the European Union Statistics on Income and Living Conditions (EU-SILC). This representative survey contains information on socio-economic characteristics and incomes of households and living in them individuals, which is used for the construction of poverty and inequality indicators at the level of European Union. The Irish component is based on the information coming from two sources: a survey and register data. Overall, 80% of the respondents granted the permission to use their national social security numbers in order to access information on the benefit entitlement from administrative data (Callan et al., 2010).

In the context of this paper, the main advantage of the SILC data contains a rich set of variables needed for tax-benefit modelling. On the negative side, the dataset has a number of limitations, which might be challenging for microsimulation modelling. These include time mismatch in the measurement of income and personal characteristics, lack of information on some income components (e.g. wealth or property values) or tax-deductible expenditures (e.g. medical insurance), difficulties with attribution of some income variables to the appropriate unit of analysis (capital income, rental income, private transfers are recorded at the household level although they are often received by individuals), and aggregation of benefits. All these limitations are discussed in detail in O’Donoghue et al. (2013), whose strategy to address them we also follow in this paper.

Given that the 2017 SILC data contains income information, which refers to 2016, it cannot be used directly for the evaluation of the distributional impacts of the COVID-19 crisis. In order to account for the changes that elapsed between 2016 and 2020, we adjust the SILC data using a set of calibration control totals capturing the change in macroeconomic situation in Ireland over this period. This information is drawn from the Live-Register data and official statistics provided by the Irish Central Statistics Office. In what follows, we describe in more detail all the adjustments made to SILC data in order to make it timely appropriate for the analysis of the COVID-19 impacts as well as policy measures introduced to cushion individual incomes.

Individuals who lose their job as a result of the COVID-19 crisis are eligible for a COVID-19 Pandemic Unemployment Payment. This instrument is available to workers who have lost their job on (or after) March 13. The payment is a flat rate non-means tested benefit (without additional payments for dependents) provided to individuals aged 18-66.

The payment structure has changed a number of times over the crisis. In the first incarnation, at the start of the crisis on March 13 2020, it was at €203 per week. This was increased in response to social partnership negotiations in relation to matters such as the levying of childcare fees to €350 per week on March 24 2020.

From June 29^th, payments were partially linked to previous earnings, with the introduction of several rates of payment: €300 per week for those with previous earnings of €300 or more, and €300, €250 and €203 for those with earning falling in the ranges €300-€400, €200-€300 and less than €200 respectively.

From September 17, there were two rates of payment: €203 for those earning less than €200 and €350 for all others. From October 16 2020, there were three rates of payment: €350 per week for those with previous earnings of €400 or more, and €250 and €203 respectively for those earning in the ranges €200-€300 and less than €200. From February 2021, there will be a reversion to two rates: €250 if earnings are over €300 and €203 otherwise.

The numbers and type of individuals eligible for payment and directly affected by the crisis are simulated using the income generation model. The overall employment rate is first used to calibrate the income generation model. This is characterised by the number of people in work relative to the population of a particular age group. The Labour Force Survey (LFS) collects data on this topic. However, as a quarterly survey, even with a relatively quick turn-around time from collection to publication, there is typically a 2-3 month lag between data collection and publication. In near real time modelling within a period of economic volatility such as the COVID-19 crisis data that is closer to the period of the crisis is required.

The impact of the crisis is not a general demand shock, but a highly asymmetric change in employment, with “essential” industries remaining at work and some sectors such as the public sector remaining on full pay, while other industries are experiencing almost a full shut down over the period of the virus. There is relatively limited data in close to real time as to the sectoral impact of the crisis. The most suitable data to perform such calculations is the Administrative Data that is available on a monthly basis, typically 2-3 days after the end of the month, together with weekly updates in relation to aggregates that have been made. As is well documented, Live-Register data does not capture the level of unemployment equivalent to ILO measures. People can be working part-time whilst in receipt of benefits and conversely, someone can be out of work and seeking work, but not eligible for unemployment benefits. However, as an indicator in the short term, of a change in economic circumstances, the changes observed in the live register are an approximate indicator of changes in the numbers out of work (or non-employment rate). In this paper, the LFS is used to nowcast to December 2019, with the Administrative Data used to nowcast to May, June, August and November 2020 and January 2021. For much of the crisis, the Pandemic Unemployment Payment was paid to those who lost their jobs due to the COVID crisis, while the COVID Wage Subsidy supports those who remained at work. The latter was paid on the basis of bridging the pre-crisis income rather than covering specific hours. As a result labour supply as opposed to labour participation is not that relevant.

Taking the change in the receipt of the pandemic unemployment benefit at the end of March 2020, we firstly model using the Nowcasting procedure the change in the employment rate at these 4 different points, reflecting different stages of the crisis in terms of employment. We then simulate the changes in the design of the policy instruments. Table 1 outlines the assumed change in employment by sector, consistent with the overall change in live-register numbers as a result of COVID-19. We apply age specific changes identified in the Live Register and expressed as a proportion of the population in the SILC. In other words we take the change in those in receipt of the Pandemic Unemployment Payment for different age groups and sex and subtract that from the total in employment calibration totals.

It should be noted that as the calibration totals used in the model are drawn from benefit administrative data, that the total modelled recipients are approximately the same as the number of actual recipients. The only difference relates to rounding that occurs due to the weighting in the survey. As individuals in the survey represent multiple people, the weighted total will never be exactly the same, but nevertheless as near as makes no qualitative difference. Of course an issue in doing this is that the survey may not necessarily have the same population base as a benefit instrument. This may be an issue for some social protection measures targeting vulnerable groups such as homeless and asylum seekers that may be under represented or not reported in the survey. However in the case of the Pandemic Payments that were mainly replacing the incomes of those that had been in work, we are confident that the base populations are close. We feel therefore that calibrating to benefit totals will not unduly bias the analysis.

As the administrative data is more timely that the labour force survey data, we utilise the original labour force survey, less the number of benefit recipients as the calibration totals as the size of the labour force by gender and sector. In addition as those in receipt of temporary out of work payments are technically not unemployed from an ILO definitional perspective and many are in fact employed, we prefer not to use the LFS totals. In any case they do not align inter-temporally to peaks and troughs that we have considered which are based upon weekly totals rather than the quarterly estimate contained in the LFS, which tends to smooth the extent of the peak.

The Tax-Benefit model used in this study relies on the EU-SILC which has an annual period of analysis. However given the volatility over the crisis, we transform the unit of analysis into current monthly income at the employment level of a particular week. This is in effect the assumption that were made within EUROMOD in earlier versions when current incomes were used prior to the use of SILC (O’Donoghue, 1998), where the parameters are assumed to be those that hold in the month of June. This is reasonable as current income is taxed on a PAYE basis and current status is relevant for benefit entitlement, with a correction in the end of year tax return. This approach can thus be considered as an approximation of current cash flow, rather than an annualised value of disposable implications. This is equivalent to the assumption made in incorporating deferred housing costs that are not eliminated, but rather reduced temporarily to improve cash flow during the crisis.

Individuals who have to stop working due to the COVID-19 infection or due to having been in a close contact with someone who contracted the disease are eligible for the COVID enhanced Illness Benefit (CEIB). The benefit is paid at the same rate as the PUP.

Both workers and non-workers get sick as a result of COVID-19. Table 2 outlines our random allocation of cases across in-work and out-of-work, within the national age distribution of the COVID-19 cases.⁸ Dividing by the proportion of workers in each age group, we derive the recipient rate of the COVID-19 related illness benefit.

Individuals who have to endure mortgage repayments received a possibility to freeze up to 3 months of such repayments during the COVID-19 crisis. This resulted in 28000 applications for mortgage deferrals in March 2020 with the numbers going steadily up in subsequent months. As of July, 160000 deferrals had been made (see Table 3).

Due to intensified work from home and business closures, the size of work-related expenses, such as commuting costs and childcare costs, decreased substantially for most workers. In order to account for the reductions in these expenses we use the Household Budget Survey data from 2016 to simulate the amount of commuting costs typically incurred by an average worker (Table 4). It should be noted that those who do not work also have transport costs for other purposes. While the actual cost of commuting for work may be higher, it is assumed that there would be some substitution if an individual was not working.

Following the outbreak of the pandemics, the State took a decision to support childcare providers in order to maintain the sustainability of the childcare sector and relieve parents from childcare payments while keeping childcare places.⁹ Utilising IPF to data collected within the Household Budget Survey, we derive the distribution of childcare costs per week by family type and disposable income decile (Table 5). These averages are simulated across households in the sample on the basis of the regressions outlined in Table C1 in Appendix C.

It is assumed in the simulations that those who receive Pandemic Payments or those who are non-essential workers working from home do not incur commuting costs or childcare expenses. We assume non-working families can utilise child care, but correct for the fact that working families and in particular those where all adults work, have a much higher probability of purchasing child care.

On March 24 2020 the State introduced a COVID-19 Temporary Wage Subsidy Scheme to ensure that employers would keep their employees even if the revenues go down. In order to be eligible for the scheme businesses would need to have a minimum of 25% decline in turnover. For the initial period between March 13 and March 24, the scheme was a flat rate payment of €203. From March 26 to April 20, it was redesigned to cover up to 70% of an employee’s net earnings. The payment was limited, however, depending on the employee’s average take home pay:

• Average pay from €0 to €586 limits it to €410;

• Average pay from €586 to €960 limits it to €350;

• Average pay above €960 is not entitled to the subsidy.

On April 20^th, the rates of temporary wage subsidy were changed as follows:

• 70% to 85% for employees with a previous average take home pay below €412 per week.

• €350 per week for employees with a previous average take home pay between €412 and €500 per week.

• The subsidy remained the same for employees with a previous average take home pay of between €500 and €586 per week.

• A tiered system has been introduced for employees with a previous average take home pay of over €586 per week.

• Employees who were taking home more than €960 per week would be able to avail of the scheme, with tapers depending upon the proportion paid by the employer.

From the 1^st of September 2020, the Temporary Wage Subsidy Scheme was replaced by the Employment Wage Subsidy Scheme, where employers and new firms in sectors impacted by COVID-19 whose turnover has fallen 30% get a flat-rate subsidy per week.

Between 1 July 2020 and 19 October 2020, the following subsidy rates applied:

The subsidy rates from 20 October 2020 to 31 January 2021 are

The Wage Subsidy itself has a limited distributional impact on incomes of those who are employed but it shifts the burden of payments from the private sector to the public sector. This subsidy also does not take into account the impact of wage reductions where employers did not have the cash flow to make these payments as in the case of individuals whose take-home pay exceeds the wage subsidy limit. Prior to the introduction of the subsidy scheme there had been pay reductions for staff in certain sectors most affected by the crisis, where staff were not made redundant, such as the airline sector. For example, the two main airlines (Ryanair and Aer Lingus) halved the pay of staff when flights were grounded.¹⁰

The outbreak of the COVID-19 pandemics lead to a fall in stock markets. Only between January 1 and April 1 2020 the Irish index ISEQ fell by 32% which had a further impact on the financial situation of households holding shares. Using data from the Household Finance and Consumption Survey in 2018, Table 6 reports how the holding of shares is spread along the distributions of household income and age. It shows that individuals located in the top quantile of the household income distribution are 8 times more likely to have financial assets than those located in the bottom quantile of the distribution, with the values of assets being 9 times higher. The distribution of financial assets across the age distribution is not as extreme, with those aged 40-79 more likely than other age groups to hold shares.

The data equivalent to Table 7, with change in share values by age and income group, was not available during the analysis of this paper. In order to utilise this information in a microsimulation model, Iterative Proportional Fitting (IPF) was used to create an approximation of the share value holdings across the age- income distribution (Wong, 1992). The average share holding and the median value of holdings were generated separately and then multiplied to get the average value per person in the cell (see Appendix D). Applying the ISEQ index to January 1 2020 and then to April 1, 2020, Table 7 models the net change in the value of shares across the age-income distribution. The Table shows that the biggest losses were experienced by those with the highest incomes and the oldest.

Table 8 reports the trend in average equivalised incomes (measured in Euros per month) using three definitions:

• Market Income

• Disposable Income

• Adjusted Disposable Income.¹¹

Market income, unsurprisingly, fell over the course of the crisis. The decline was the most drastic in the first wave (around 32 percent) with a slight recovery taking place in June and August. However, the onset of the second wave in November saw a reduction in market incomes again, albeit to a much lower degree than in May, reflecting the fact that the restrictions were not as great. The end of the second wave at Christmas, saw a slight improvement, albeit not to the same degree as in the summer, before worsening again after Christmas in the 3^rd wave. However, sectors affected in the second wave were more likely to be concentrated at the bottom of the distribution than at the top in the second and third wave, rather than the first wave.

The drop in market incomes was partially compensated by the increase in benefit payments, which can be seen from the trends in disposable incomes. The average size of disposable income decreased by almost 10 percent at the start of the crisis, before making a slight recovery later on. Nevertheless, it remained 6-7% below the pre-crisis level during the second and third waves of the pandemic.

As mentioned above, the onset of the COVID-19 crisis has pushed a substantial share of employees to work from home or to take up temporary unemployment. This led to a decrease in commuting costs and childcare expenses. In addition, some individuals applied for mortgage deferrals, which further reduced their current expenditures. All these expenses are captured with the adjusted disposable income, which remained largely unchanged over the crisis.

Hidden below these averages are quite a differential impact on different parts of the income distribution. Figure 3 summarizes the distribution of market (Panel A), disposable (Panel B) and adjusted disposable (Panel C) incomes before the crisis and at various points during the crisis calculated per adult equivalent (based on Table E1) . Deciles are derived from adjusted disposable incomes.

Figure 3

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In terms of market income, the decline was larger at the top than at the bottom of the distribution, however proportionally the loss at the top was slightly less than at the bottom. Disposable incomes at the bottom of the distribution were largely maintained over the three waves and even higher in some cases where benefits were higher than pre-crisis market income. The gradual targeting of payments in later waves reduced the incidence of those at the bottom of the income distribution with disposable incomes that were higher than at the pre-crisis level. Disposable incomes at the top of the distribution fell by 10-20% at the start of the crisis, and recovered to about 10% in later waves.

Panel C in Figure 3 suggests that adjusted household equivalized disposable incomes were somewhat cushioned during the crisis, which held them from the same decline as we observe in market incomes. Furthermore, there was even a rise in adjusted equivalised disposable incomes amongst lower deciles in the first and second waves. This reflects both the generosity of the instruments and the fact that the squeezed middle, who are at the bottom of the distribution have normally both low incomes and high fixed costs of working and housing. This progressivity was visible for all periods. However, we see that the differential proportional change between the top and the bottom of the distribution has shrunk in the second and third wave as a result of the increased targeting of benefits that occurred later on.

Table 9 summarizes changes in inequality of different types of incomes (measured with the Gini coefficient) during six time points over the crisis as compared to the pre-crisis period. It shows that inequality in market income increased by 0.12 points at the start of the crisis, declining gradually as the economy reopened and then increased again as the country went into the second and third waves. In contrast, inequality in gross income, disposable income, and especially adjusted disposable income decreased throughout the crisis. The largest declines were observed at the pick of the first and second waves following the strengthening of the lockdown measures, activation of COVID-19 related benefit payments, and decreases in work-related expenses.

Table 10 reports the redistributive impact of public policy. The contribution of benefits to redistribution is derived as the difference in the Gini coefficients calculated for gross and market incomes. The contribution of taxes to redistribution is derived as the difference in the Gini coefficients calculated for disposable and gross incomes. The contribution of work-related and housing costs to redistribution is derived as the difference in the Gini coefficients for disposable income adjusted for work-related and housing expenditures and disposable income without these adjustments. Out of three policy instruments, the redistributive role of benefits increased the most over the course of the crisis. It was the highest at the pick of the first wave and slightly declined afterwards remaining, nevertheless, much higher than in the pre-crisis period. In contrast, the redistributive role of taxes and work-related expenses remained relatively stable during the first three waves of the COVID-19 crisis. In general, taxes help to decrease inequality in incomes whereas the opposite applies to work-related expenses.

We decided to evaluate the impact of the COVID-19 crisis at different points of the income distribution rather than focusing on its point-specific summary measures (e.g. poverty or richness) in order to obtain a general picture of the distributional consequences of the crisis.

While in theory it may be possible to run analyses in real time when aggregate data is available, in practice policies are changing and the characteristics of the data are changing and, hence, there is a need for adjusting the model. In the absence of a major change, we can do an analysis about a week after the release of the data and a little longer if definitions or policy changes.

Another literature has produced nowcasted aggregate poverty rates (Álvarez et al., 2014).

Figure A1 in Appendix A also outlines the phases of economic restrictions over the course of the two waves, with the trend in expenditure per Revolut user over the period. It shows the decline and then recovery of expenditure in the first wave and subsequent fall again in the second wave as restrictions, particularly in retail and hospitality sectors, were applied.

Bruckmeier et al. (2020), for example, use business survey data and a structural labour market model for integrating the shock into a microsimulation model of household income, Clark et al. (2020) use actual survey data collected during the COVID crisis in a number of European countries, whereas the works of Brewer and Gardiner (2020); Brewer and Tasseva (2020), Figari and Fiorio (2020), and Almeida et al. (2021) rely on the use of EUROMOD microsimulation system based on European Survey of Income and Living Conditions data, which they adjust for changes in the macroeconomic conditions using external data sources.

Please refer to Sologon et al. (2021) for an in-depth discussion of simulating counterfactuals.

It should be noted that this is not a comprehensive approach to changes in capital values. Housing wealth initially fell as a result of concerns about the impact on the economy. However, later as a result of the closure of the sector and a reduction in housing supply, house prices rose. We have not incorporated these in our estimations. A more comprehensive approach would merit a detailed analysis on its own.

This is amended for later periods using actual control data.

https://www.gov.ie/en/press-release/e37415-minister-katherine-zappone-announces-measures-to-support-childcare-p/

https://wwwirishtimescom/business/work/coronavirus-employers-should-seek-consent-for-pay-cuts-lawyer-14221405.

Note that all results presented below refer to the total population (both working and non-working).

The authors are grateful to the Irish Health Research Board and Irish Research Council for funding of this research.

Not applicable.

1. Version of Record published: August 31, 2021 (version 1)

© 2021, O’Donoghue et al.

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