Tax Forecasting Methodological Review 2019
Transcript of Tax Forecasting Methodological Review 2019
Department of Finance | Tax Forecasting Methodological Review 2019 Page | 1
Tax Forecasting Methodological Review 2019
December 2019
Department of Finance | Tax Forecasting Methodological Review 2019 Page | i
Executive Summary
This is the third methodological review of the Department of Finance’s approach to tax forecasting.
This Report assessed the accuracy of tax forecasts over the past decade. The overall forecasting
performance was found to be robust with the exception of the financial crisis years (2008 and 2009)
when errors were particularly large for all of the main tax heads. In more recent years, forecasts from
the Department have tended to err on the low side, pointing to a degree of prudence within the numbers.
Much of the over–performance in taxes has been driven by unexpectedly strong corporation tax receipts
since 2014. In general, forecasts were noticeably less accurate as the time horizon extends. That said,
the overall forecasting approach within the Department is in line with the approach taken by domestic
and international institutions such as the Central Bank of Ireland and the European Commission.
The performance of the four main tax heads – income tax, corporation tax (CT), value added tax (VAT)
and excise duties, which combined accounted for 92 per cent of the tax take in 2018, were assessed in
detail. The findings for both VAT and income tax pointed to relatively small forecasting errors. In
contrast, CT related errors were much larger. The difficulties inherent in forecasting CT have been well
documented but have been exacerbated in recent years as these receipts have grown in size.
A battery of more formal statistical tests and backcasting exercises were also carried out. On the whole,
these tests confirmed that the Department’s tax forecasts were both unbiased and also outperformed
alternative approaches. However, the weak performance of excise and CT forecasts were again
highlighted.
A number of recommendations are included in the report. These include the need to refine forecasts
for VAT, CT and excises. For VAT, the forecasts should be supplemented with a housing-specific
component. For CT, there appears to be merit in moving to a higher elasticity. Work is underway within
the Department in both of these areas. For excise, a more disaggregated approach is warranted bearing
in mind the diverse nature of the tax. The Group also noted and welcomed recent research in relation
to income tax elasticities, which have been incorporated into the forecasting methodology.
In light of the increasing prominence of general government aggregates, the Group also recommends
that the Department continues to develop its general government forecasting capacity alongside the
Exchequer tax forecasts considered in this report.
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Contents
Page
Executive Summary Tables, Figures, Boxes and Annexes
i
ii
1. Introduction 5
1.1 Background 5 1.2 Terms of reference 5
1.3 Group membership 5
1.4 Main findings of the 2008 Report 5
1.5 Follow up on the recommendations of the 2008 Report 6
1.6 Structure of the Report 6
2. Composition of Irish taxes in 2018 7
2.1 Trends in exchequer taxes 7 2.2 Macroeconomic context 11
3. Forecasting methodology and accuracy 14
3.1 Tax forecasting methodology 14 3.2 Approaches to forecasting in other agencies 16 3.2.1 The Central Bank of Ireland’s approach 16 3.2.2 The European Commission’s approach 17
3.2.3 Summary of the approaches 18 3.3 Forecasting accuracy 19 3.4 Current and year-ahead performance 20
3.4.1 Current year estimates 20
3.4.2 Year-ahead forecasts 20 3.5 Forecasts for the main tax heads 22 3.5.1 Income tax 22
3.5.2 VAT 25
3.5.3 Corporation tax 26 3.5.4 Excise duties 27 3.6 Smaller tax heads – CGT, CAT, stamps and customs duties 28
3.6.1 Capital taxes 28
3.6.2 Customs duties 30
4. Detailed forecast errors appraisal 32
4.1 Introduction 32
4.2 Forecast bias and benchmarking 32
4.2.1 Methodology 32
4.2.2 Results 33 4.3 A decomposition of one-year ahead forecast errors (2008-18) 34
4.4 Forecasts on a general government basis 39
4.4.1 Accuracy of forecasts 40
4.4.2 Benchmarking general government forecasts 41
5. Tax revenue elasticities 43
5.1 Introduction 43
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5.2 Elasticity concepts and recent research 43 5.3 Historical tax to output elasticities 44
5.4 Elasticity for the main tax heads 45
5.4.1 Income tax 46
5.4.2 VAT 47 5.4.3 Corporation tax 48
5.5 Lessons from quarterly data 48
6. Forecasting Recommendations 51
6.1 Introduction 51 6.2 Income Tax and USC 51
6.3 VAT 51
6.4 Corporation tax 52
6.5 Excise duties 53 6.6 Other taxes 53
6.7 Other recommendations 54
6.8 Summary of recommendations 54
7. Conclusion 56
Tables, Figures, Boxes and Annexes
Tables
Table 1 Recommendations from 2008 Report 6 Table 2 Exchequer tax take at end-year 7 Table 3 Current year (t) forecast errors 20 Table 4 Year ahead (t+1) forecast errors 21
Table 5 Capital and other taxes, selected years 28 Table 6 Forecast bias check 33
Table 7 Benchmark check – budget forecasts relative to a random walk 33
Table 8 Summary of forecast error decomposition 36
Table 9 General government forecast errors 41 Table 10 Income tax elasticities 47
Figures
Figure 1 Taxes in 2007 and 2018 8
Figure 2 Annualised exchequer taxes, 2007 to mid-2019 9 Figure 3 The big 4 taxes 9 Figure 4 Selected macroeconomic drivers 10 Figure 5 Macroeconomic developments 12 Figure 6 Flow-chart of forecasting approach 14 Figure 7 Income tax forecast errors 23 Figure 8 VAT forecast errors 25 Figure 9 CT forecast errors 26 Figure 10 Excise forecast errors 27 Figure 11 CGT forecast errors 29 Figure 12 Stamp Duty forecast errors 29 Figure 13 CAT forecast errors 30
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Figure 14 Customs duties forecast errors 31 Figure 15 VAT forecast error decomposition 37 Figure 16 Income tax (PAYE) forecast error decomposition 37 Figure 17 Corporation tax forecast error decomposition .38 Figure 18 Excise duties forecast error decomposition 39 Figure 19 Taxes on a general government and exchequer basis 40 Figure 20 General Government tax forecast errors 41 Figure 21 General Government tax forecast errors- current year 42 Figure 22 General Government tax forecast errors- year ahead (t+1) 42 Figure 23 Aggregate tax to output elasticities 44 Figure 24 Tax to GNI* elasticities with a 95 per cent confidence interval 45 Figure 25 Income taxes and employee compensation 49 Figure 26 Corporation taxes and net operating surplus 49 Figure 27 VAT and nominal consumption 50 Figure 28 Excises and consumption of goods 50
Boxes
Box 1 A heat-map approach to assessing tax outturns 11
Box 2 The timing of budget forecasts 22
Box 3 Forecasting pay related social insurance- performance and approach 24
Box 4 Research on tax revenue elasticities 46
Annexes
Annex A Group membership and meetings 57
Annex B Forecast summary decomposition 58
Annex C Alternative forecasting approaches 59
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Chapter 1 Introduction
1.1 Background
This is the third methodological review of tax forecasting undertaken by the Department of Finance
(herein the Department). This assessment was carried out by the Tax Forecasting Methodological
Review Group (TFMRG), a group of officials from within the Department and other organisations with a
particular expertise in taxes, economic forecasting and the public finances. The reviews are designed
to both assess forecasting accuracy and to make recommendation for improvements going forward.
The last report was published in 2008 (Department of Finance, 2008), and is detailed below.
1.2 Terms of Reference
For the 2019 review, the terms of reference tasked the Group with reviewing the existing tax forecasting
methodology and examining and quantifying tax forecast errors with a focus on the four largest tax
heads – income tax, corporation tax (CT), value added tax (VAT) and excise duties. The information
bases upon which forecasts are made was to be assessed looking at both relevant literature and
experiences from other jurisdictions. The Group was also tasked with making recommendations for
changes to the existing forecasting methodology, where appropriate. Finally, aside from the largest tax
heads, the Group was also required to consider some of the smaller taxes (capital taxes, stamp and
customs duties) as well as social contributions.1
1.3 Group Membership
The Group was comprised of officials from the Department of Finance and external bodies - the Central
Bank of Ireland, the Revenue Commissioners and the European Commission. The Department hosted
seven meetings between April and November. These meetings focused on specific tax heads -
specifically the composition of taxes, the approach and performance of forecasts and any
recommendations for change (for details see Annex A). Before proceeding it is worthwhile to briefly
review the findings of the 2008 report and the extent to which recommendations were followed.
1.4 Main Findings of the 2008 Report
The work underlying the 2008 tax forecasting review occurred at a time when the economy was
undergoing exceptionally strong growth, much of which was related to a property boom. In the run up
to the report, taxes had consistently outperformed budget day targets in large part due to housing
related transaction receipts. The latter showed up most clearly in stamp duties, capital taxes, VAT and
income tax receipts.2
The report focused on tax forecasts from 1999 to 2006. Over this period, taxes significantly diverged
from forecasts with an overall root mean square error (RMSE) of 6.1 per cent.3 The role of one-off
1 Receipts from social contributions are not forecast by the Department of Finance. 2 For a detailed discussion on the housing market and Irish taxation receipts see papers by Addison-Smyth and McQuinn (2010 and 2016). 3 The RMSE is a commonly used metric to assess forecast accuracy – for more details, see Chapter 3.
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factors and forecast errors in macroeconomic variables in influencing the size of the RMSE were
highlighted. When controls were put in place for these factors, the forecasting accuracy improved with
a decline in the RMSE to 4.0 per cent. The Report noted a prudent bias in forecasts, with outturns
typically in excess of Budget day forecasts. The approach to forecasting was similar to methods
followed internationally, although errors in Ireland were reported as being on the high side.
All of the main tax heads were assessed for forecast accuracy, resulting in a number of points being
raised. These included:
Income taxes – a pattern of undershooting in forecasts linked to a series of key changes in the
tax system.
VAT – complement the existing approach (based on consumption patterns) with a separate
forecast for VAT related to housing.
CT – consider the use of Gross Operating Surplus (GOS) in the forecasting equation.
Stamp Duty – use a disaggregated approach including forecasts for construction related
(housing and non-housing) activity.
Overall the report recommended that a cautious approach be adopted for property related revenues.
Similarly, the overarching guide for tax forecasting should be to maintain an overall tax to GDP elasticity
of unity as a ‘top-down’ check of tax forecasts.
1.5 Follow up on Recommendations of the 2008 Report
A summary of the main recommendations and their implementation is shown in table 1.
Table 1: recommendations from 2008 report
Recommendation Result
Maintain an aggregate tax-to-GDP elasticity of 1.0. Maintained
Project VAT receipts from housing separately from other VAT receipts Implemented but not maintained
Use Gross Operating Surplus (GOS) as a driver of CT forecasts Implemented
Maintain the disaggregated approach to forecasting stamp duty Maintained
Maintain cautious approach to forecasting property-related tax revenue Maintained
Undertake more regular analysis of tax forecasting performance Since establishment in 2011, the Irish
Fiscal Advisory Council (IFAC) has been assessing forecasts.
Source: Report of the Tax Forecasting Methodology Review Group 2008.
1.6 Structure of the Report
This report is structured as follows. Chapter 2 examines the composition of taxes in Ireland and changes
in both taxes and the macroeconomic backdrop over the past decade. The Department’s tax forecasting
methodology and overall accuracy is set out in Chapter 3, before a more detailed assessment is carried
out in Chapter 4. Tax elasticities are discussed in Chapter 5 before recommendations are put forward
in Chapter 6. Chapter 7 concludes.
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Chapter 2 Composition of Irish Taxes in 2018
2.1 Trends in Exchequer Taxes
The Exchequer tax take is summarised in table 2 below. In 2018, tax receipts amounted to €56 billion.
The 4 largest tax heads – income tax, VAT, corporation tax (CT) and excises accounted for 92 per cent
of the total. Since the last tax forecasting report, the composition and size of the tax base has changed
significantly (figure 1). Income tax and CT now account for a much larger share of overall tax revenue
with capital related taxes (capital gains tax (CGT), capital acquisitions tax (CAT) and stamp duties)
much smaller. Capital taxes had risen sharply prior to the last report due mainly to housing related
activity – peaking in 2006 at €7 billion (16 per cent of tax revenue), before declining abruptly to €1.5
billion in 2010. Since then, their share of the tax take has remained relatively stable at close to 5 per
cent.
Table 2: exchequer tax take at end-year, € billion
2005 2007 2010 2015
2016
2017 2018
Share of tax
in 2018
Custom duties 0.2 0.3 0.2 0.3 0.3 0.3 0.3 1
Excise duties 5.2 5.8 4.7 5.3 5.7 5.9 5.4 10
Capital gains Tax 2.0 3.1 0.3 0.7 0.8 0.8 1.0 2
Capital acquisitions Tax
0.2 0.4 0.2 0.4 0.4 0.5 0.5 1
Stamp duties 2.7 3.2 1.0 1.3 1.2 1.2 1.5 3
Income taxes 11.3 13.6 11.3 18.4 19.2 20.0 21.2 38
Corporation tax 5.5 6.4 3.9 6.9 7.4 8.2 10.4 19
VAT 12.1 14.5 10.1 11.9 12.4 13.3 14.2 26
Total 39.3 47.2 31.8 45.6 47.9 50.7 55.6 100
Source: Department of Finance.
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Figure 1: taxes in 2007 and 2018, percentage share
Since 2008, motor tax for private cars is assessed on the basis of CO2 emissions.
Source: Department of Finance.
In figure 2, annualised taxes are shown since 2007 with figure 3 indexing taxes to January 2008.4 These
charts highlight the marked fall and recovery in receipts as a result of the financial and economic crisis
in 2008/09. Taxes returned to pre-crisis peaks in 2016. The increasing prominence of the four largest
tax heads and in particular income tax and CT is notable. The latter two taxes have increased on an
average annual basis by 5.2 and 9.1 per cent, respectively over the past decade, outstripping the growth
rate of overall taxes (of 3.5 per cent).
Tax receipts have also been helped by the sharp recovery in underlying economic activity including
corporate profitability. Figure 4 shows the main macroeconomic drivers behind the largest tax receipts,
indexed to 2008. Aside from growth, a series of policy related changes that include the introduction of
the universal social charge (USC) have helped the tax take. A more detailed discussion of recent trends
in taxation is provided in the Department’s ‘Annual Taxation Report’ (Department of Finance, 2019a).
Taxation trends are also regularly monitored and reported on by the Department through the monthly
‘Fiscal Monitor’ publication and the use of heat maps (box 1).
4 Taxes are annualised (12 month rolling totals) to smooth the data and to counteract seasonal patterns.
Customs1%
Excises12%
Capital Gains
7%
Capital Acquisitions
1%
Stamps7%
Income29%
Corporation14%
VAT31%
Taxes in 2007
Total: €47bn
Customs1% Excises
10%
Capital Gains
2%
Capital Acquisitions
1%
Stamps2%
Income38%
Corporation19%
VAT26%
Motor Vehicle Duties
2%
Taxes in 2018
Total: €56bn
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Figure 2: annualised exchequer taxes, 2007 to mid-2019, € billions
Source: Department of Finance.
Figure 3: the big 4 taxes, January 2008 = 100
Source: Department of Finance.
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Figure 4: selected macroeconomic drivers, 2008 = 100
Note: wages are proxied by the non-agricultural wage bill. PCE refers to personal consumption expenditure. GOS and NOS measure the gross and net operating surplus, respectively. All series are in nominal terms. Source: Department of Finance.
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Box 1: a heat-map approach to assessing tax outturns Heat maps are a commonly used method of depicting a wide range of data in a convenient manner. The Department has
previously published heat maps for both macroeconomic and fiscal data.5 Following the same methodology, an exchequer
tax heat map is shown in the figure below based on an extended sample from 2005.6 For each of the tax heads, standardised
year-on-year growth rates are calculated. The shadings are based on the value of each tax in a particular quarter relative to
its mean. A growth rate of two standard deviations or more above the mean is assigned the darkest red; observations within
a standard deviation of the mean have a neutral shading. By highlighting deviations, the maps provide a fast high-level
overview of potential (or emerging) imbalances. It is important to stress that a variable ‘flashing red’ is not necessarily
problematic, although persistent strong colours may be symptomatic of emerging imbalances.
From the heat map, three distinctive phases stand out. First, the persistently strong growth rates across most of the tax
heads (and notably capital and transaction based taxes) in the lead-up to the financial crisis. Second, the comprehensive
and sustained collapse in taxes from 2008. It is also notable that receipts remained subdued for a prolonged period up until
mid-2011. Third, the broad-based recovery in taxes in recent years including robust CT receipts.
Figure: fiscal heat map for exchequer taxes, 2006Q1 to 2019Q3
Source: Department of Finance.
2.2 Macroeconomic Context
The economic backdrop is central to the changes in tax yields illustrated above, as the economy has
undergone a number of structural changes, encompassing a sharp contraction in activity and a
subsequent strong recovery. The economy is now two thirds larger than in 2007 (as measured on a
gross value added basis), with growth considerably more balanced across the sectors.
As evident in Figure 5 below, the labour market continues to perform strongly. Employment levels hit
new peaks in 2018, with more people in work than ever before. In 2007, approximately 2.2 million people
were employed, with 1 in 9 jobs in the construction sector and the services industry accounting for 70
per cent of total employment. In 2018, just 1 in 16 of the nearly 2.3 million people in work were employed
in construction while the services sector accounted for 76 per cent of total employment. Unemployment
continues to fall, with the unemployment rate now close to that seen in 2007. Furthermore, there is
5 For more details, see ‘Box 6 Fiscal Heat Maps’ 2019 Stability Programme Update (Department of Finance, 2019b) 6 The heat map above is based on year-on-year growth rates and therefore starts from Q1 2006.
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evidence of a pick-up in wage growth, in the first half of 2019 in particular, in line with a tightening labour
market.
While initially slow to recover following the crisis, since 2014 private consumption has grown at a robust
rate. Consumption growth still remains below the rates seen between 2005 and 2007, however.
Figure 5: macroeconomic developments
Source: Central Statistics Office, Department of Finance
At the time of the last report, the booming construction sector resulted in very strong growth in VAT and
capital related taxes. Activity levels within the construction sectors are currently much lower, with
approximately 18,000 new homes constructed in 2018, behind estimated demand of 30-35,000 units
required per annum, and significantly lower than the 78,000 homes built in 2007 thus creating
consequent knock-on effects for capital related taxes. Another key difference relates to the corporate
sector, with the increasingly globalised Irish economy experiencing exceptional rates of increase in
corporation taxes in recent years.
While a return to a fiscal surplus was observed in 2018, the level of public indebtedness (at 104 per
cent of GNI*) remains high by both historical and international standards.7 In total, the stock of debt
amounted to €206 billion last year or €42,500 on a per capita basis, one of the largest in the OECD.
Aside from the stock of debt, the annual interest bill continues to represent a significant operating cost
for the State, amounting to €5.2 billion in 2018.
Since 2007 and the subsequent economic and financial crisis, several measures have been taken to
broaden the tax base. The universal social charge (USC) was introduced in Budget 2011 and was
7 Department of Finance Annual Report on Public Debt in Ireland 2019. Available at: https://www.gov.ie/en/publication/d45694-annual-report-on-public-debt-in-ireland-2019/
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followed by a household charge in 2012 and the local property tax in 2013, while a carbon tax, was
introduced in 2010. At the same time as these tax increases, tax expenditures have been curtailed (e.g.
lapsed property reliefs).8
From a tax forecasting perspective, greater data availability and improvements in modelling capabilities
in the decade following the crisis, have increased the scope for wider and more detailed forecasts.
Despite this, forecasting the macroeconomic developments that underpin tax forecasts has become
increasingly challenging, given the growth in the economy in recent years. This is illustrated by
distortions to traditional measures of economic activity including GDP and the impact of globalisation
and capital mobility. As one of the most globally-integrated economies in the world, and with a large
foreign-owned component, interpreting conventional measures of economic activity is especially
challenging in an Irish context. Following the exceptional growth in GDP in 2015, the Central Statistics
Office (CSO) published an alternative measure of the size of the economy, so-called ‘modified gross
national income’ (denoted as GNI*). These changes and some of the implications are discussed in an
explanatory note by the Department (Department of Finance, 2018).
8 Further information on tax expenditures are available in the Department’s Annual Report on Tax Expenditures
available at: http://budget.gov.ie/Budgets/2020/Documents/Budget/Report%20on%20Tax%20Expenditures%20Incorporating%20the%20Outcomes%20of%20Certain%20Tax%20Expenditure%20and%20Tax%20Related%20Reviews%20completed%20since%20c.pdf. Further information on post-crisis base broadening measures can be found here: https://assets.gov.ie/5749/170119165651-e89ccd5500de476784176193cb6bef4c.pdf
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Chapter 3 Forecasting Methodology and Accuracy
This Chapter examines the methodology used in tax forecasting before an overall assessment is made
of accuracy, with a specific focus on the past decade.
3.1 Tax Forecasting Methodology
Forecasts for taxes are typically derived using a range of equations that link taxes to underlying
macroeconomic drivers. The latter can be a variable (or variables) that are known to be highly relevant
for the tax in question. The standard forecasting equation is set out below (also summarised in figure
6):
Taxt+1 = (Taxt – Tt)*(d(Macrot+1)*E) + Tt+1 + Mt+1 + Jt+1, (1)
Taxt+1 = tax take in year t+1
Tt = temporary or one-off factors affecting the tax take in the current year
Tt+1 = temporary/one-off factors affecting the tax take in the year t+1
d(Macrot+1) = growth rate in the macro driver (the proxy for growth in the tax base)
E = elasticity between the tax take and tax base
Mt+1 = effect of fiscal or budgetary measures impacting the tax take in year t+1
Jt+1 = judgement applied to alter the tax forecast for year t+1
Figure 6: flow-chart of forecasting approach
Source: Department of Finance
The elasticity is a key input as it shows how responsive each tax head is to the underlying driver. These
elasticities are revised periodically. While elasticities vary across time and by tax category, the overall
tax-to-GDP elasticity in Ireland has remained close to unity (see Chapter 5).
Budgetary measures consist of announced policy changes that will impact on the tax yield. These are
estimated by the Department and the Revenue Commissioners and are reported in the Budget on both
a current and full year basis. These include changes to tax rates, allowances, credits, exemptions and
tax bands. The Irish Fiscal Advisory Council published a working paper this year that includes a tax
policy measures dataset (Conroy 2019). This paper along with the IMF’s tax policy reform database,
Estimated
tax yield
(t)
+ (-) the effect of one-offs) affecting
the current year's base
Macro driver Elastcity
+ (-) the effect of one-offs) affecting the yield in year
t+1
+ (-) the effect of
Budgetary measures on yield in year t+1
+(-) Judgement in year t+1
Forecast tax yield
(t+1)
Department of Finance | Tax Forecasting Methodological Review 2019 Page | 15
which uses the OECD surveys of changes in taxation (IMF, 2018), are important additions to the public
knowledge base.
In summary, the Department’s forecasting approach for the main tax heads relies on a range of inputs
on macroeconomics aggregates, elasticities, budgetary measures, temporary factors and judgement.
The latter is also informed by information provided by the Revenue Commissioners over the course of
the year in relation to the performance of each tax head.
The most aggregative forecasts are for VAT and CT. For the former, the growth rate in nominal personal
consumption expenditure is used with an elasticity of one. Following the 2008 report, the Department
adopted a more disaggregated approach using a combination of consumption and housing related
macroeconomic drivers, but the collapse in the property market negated this need. For CT, the
Department uses the growth rate in gross operating surplus (GOS), also with an elasticity of one. This
followed the recommendations of the 2008 report.
The forecasts for income taxes are disaggregated based on a range of separate forecasts for the main
subcomponents, namely, PAYE, self-assessed (schedule D) receipts, USC income (PAYE and self-
assessed) as well as other smaller items.9 The main sub-components are driven by projected changes
in labour market conditions as proxied by wage and employment growth, and adjusted by an elasticity.
The latter were re-estimated in 2017 (see Chapter 5). This resulted in the employment elasticity
dropping out of the equation and being replaced by a more comprehensive elasticity measure, reflective
of the wage distribution. Separate elasticities are used depending on the income tax sub-component.
Excises are disaggregated into two main sub-components – namely, excises excluding vehicle
registration tax (VRT) and VRT. The latter is estimated based on forecasts for the price and volume of
new car sales, with the former linked to growth in the volume of personal consumption expenditure.
Capital taxes (CGT, CAT and stamp duties) and customs duties are forecast along similar lines. CGT
and CAT are both linked to nominal GDP with customs driven by the projected change in the volume of
merchandise imports. In contrast, stamp duties are highly disaggregated with residential and non-
residential elements as well as non-housing and equity components. Each of these in turn is linked to
a specific macroeconomic driver – housing volumes and prices, other building and construction
investment, GDP (for equities) and consumer price inflation (for residual categories).
9 Other smaller components of income tax, include receipts from Deposit Interest Retention Tax (DIRT), Life Assurance Exit Tax (LAET), Relevant Contracts Tax (RCT), Professional Services Withholding Tax (PSWT) and Dividend Withholding Tax (DWT), back duty receipts and miscellaneous items are estimated separately with input from Revenue.
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3.2 Approaches to Tax Forecasting in Other Agencies
3.2.1 The Central Bank of Ireland’s Approach
The Central Bank of Ireland (CBI) produces fiscal projections regularly throughout the year. The key
use for these is in the European Central Bank’s (ECB) Broad Macroeconomic Projection Exercise
(BMPE). These forecasts are published twice a year – in June and December – with interim projections
produced by ECB staff in March and September. The BMPE is a key input for the Governing Council in
its monetary policy decision making process. The BMPE includes short- and medium-term projections
(t+2 in June and t+3 in December) for the euro area and individual euro area countries for a wide range
of variables, including prices, real output, labour market and fiscal variables. It is a bottom up forecasting
exercise; national Central Banks (NCBs) produce individual country projections and these are
aggregated to generate a region wide outlook.10 The use of peer reviews and centralised assumptions
ensures a consistent approach across countries. The CBI’s fiscal projections are also used internally
as an input for Quarterly Bulletin forecasts and, more broadly, to support the CBI’s mission of
safeguarding monetary and financial stability.
With regard to tax forecasting, the methodology adopted by the CBI is broadly similar to that of both the
Department of Finance and the European Commission. Growth in individual tax heads are driven by
five main factors: (i) developments in an underlying macroeconomic base which drives the tax head, (ii)
the elasticity of the tax head to changes in this base, (iii) fiscal measures announced by the Government,
(iv) once off factors and (v) expert judgement. The latter is applied to take account of intra-year
information such as Exchequer returns, with projections over the medium-term more model based. The
macroeconomic variables which drive the fiscal projections are prepared by the CBI.
One key difference with the Department is that projections are made on a general government rather
than exchequer basis, with the focus on direct, indirect and capital taxes. Nevertheless, most individual
tax heads – such as income tax, corporation tax and VAT - are produced, both to support the
macroeconomic projections and to help build the broader tax components. Overall, broadly similar
macroeconomic bases and elasticities are used to those outlined by the Department of Finance and the
Commission. Focusing on the largest tax heads, changes to income tax are driven by a combination of
developments in non-agricultural employment and compensation per employee. CT is driven by gross
operating surplus, although with receipts decoupling from macroeconomic developments in recent
years a significant amount of judgement has been required. In the case of indirect taxes, the only sub
component projected is VAT, which is driven by developments in nominal private consumption.
A further difference is the role that peer reviews play in the forecasting process. To ensure that they are
realistic and prudent, each NCB projection is formally peer reviewed by both an ECB country expert
10 For a more detailed look at the Broad Macroeconomic Projection Exercise see ‘A guide to the Eurosystem / ECB staff macroeconomic projection exercises’, European Central Bank, July 2016.
Department of Finance | Tax Forecasting Methodological Review 2019 Page | 17
and another NCB. One tool used to aid this process is the Disaggregated Framework (DF).11 While the
DF is primarily used to identify the structural path of the general government balance and the main
expenditure and revenue categories, it can also be used to examine the impact of important factors on
both the structural and unadjusted balances. On the revenue side, changes in unadjusted ratios of taxes
and social contributions are broken down into the following factors: (i) the fiscal drag, (ii) the decoupling
of the tax base from GDP, (iii) discretionary fiscal policy measures of a permanent nature and (iv)
residual developments. The first two factors show the impact of macroeconomic developments, the third
identifies the impact of macroeconomic policy and the fourth captures the effects of other, mostly
country specific factors that need to be explained on a case by case basis.12 These residuals occur
because the underlying tax model can only be an approximation of actual developments. In the Irish
case, for example, high residuals would appear on CT in the period 2015 to 2018, because the
underlying base used to project them – gross operating surplus – was not a good driver of receipts over
the period in question. Having to explain residuals – particularly positive ones which imply the tax ratio
is higher than the underlying model would imply – plays an important role in checking the consistency
of forecasts.
3.2.2 The European Commission’s Approach
The European Commission undertakes regular tax forecasts as part of the cross country projection
exercises.13 The forecasts are prepared by “country desk” experts within the Directorate General for
Economic and Financial Affairs (DG ECFIN). The exercises are conducted on a no-policy change (NPC)
basis.14 A three-step process is followed:
(i) Building a trend by extrapolating past revenue and expenditure trends and relationships.
When the revenue/expenditure items have a well-established link with some
(macroeconomic) aggregates, the forecast uses these relationships, i.e. elasticities with
respect to macroeconomic variables. For items that are not clearly correlated to other
aggregates, the method suggests extrapolation of past behaviour. The latter can take the
form of using rules of thumb, such as past ratios and/or applying the average growth rate
over an appropriate reference period.
(ii) Specifying the working assumptions that complement the trend projections and determining
the NPC baseline. This is useful, when time series contain structural breaks, specific multi-
year patterns observed in the past that are deemed likely to recur or when available monthly
indicators point to a deviation from an established long-term trend (for the in-year forecast).
11 For more details on the Disaggregated Framework see ‘A disaggregated framework for the analysis of structural developments in public finances’, ECB Working Paper Series, No.579 January 2006. A revised Disaggregated Framework was introduced this year by the ECB, although this primarily relates to structural rather than unadjusted developments. For more on the new methodology see ‘The new ESCB methodology for the calculation of cyclically adjusted budget balances: an application to the Portuguese case’, Braz et al, April 2019. 12 The fiscal drag captures the increase in average tax rates in a progressive income tax regime where changes to
tax bands do not keep pace with nominal income increases. The decoupling of the tax base from GDP can occur when growth in the tax base deviates from the growth rate of nominal GDP. 13 The forecast exercises take place four times a year, however the fully fledged forecasts, including fiscal, are bi-annual in spring and the autumn. 14 European Commission (2016), Report on Public Finances in EMU 2016, Institutional Paper 045, December 2016, Part II, Section 1, available at https://ec.europa.eu/info/sites/info/files/ip045_en_0.pdf
Department of Finance | Tax Forecasting Methodological Review 2019 Page | 18
(iii) Incorporating the fiscal policy measures which are discretionary items that have been
credibly announced and are known in sufficient detail. This includes one-off measures.15
Fiscal forecasts by the Commission will typically extrapolate taxes forward in a way that is consistent
with past behaviour whilst accounting for measures that imply a change to any of these orientations on
the condition that they are sufficiently well detailed, or are either credibly announced or already adopted.
Forecasts include a limited number of working assumptions, especially to deal with possible structural
breaks.
The Commission’s fiscal forecast for Ireland closely mirrors this overall approach. Tax forecasts are
compiled using a bottom-up approach based on general government and national accounts data, as
this is the basis for multilateral fiscal surveillance undertaken by the Commission, i.e. the preventive
and corrective arms of the Stability and Growth Pact.16 As regards revenue items that are correlated
with macroeconomic variables, their trend is linked to the projections of these variables, which are
prepared by the Irish country desk in DG ECFIN within the same forecast exercise. Forecasts of the
main tax revenue aggregates (taxes on production and income on a general government basis) are
also compiled on a bottom-up basis and are then compared with various indicators, notably ex-ante
elasticities with respect to key macroeconomic aggregates - namely private consumption, nominal GDP
and compensation of employees (wages). The elasticities and discretionary budgetary announcements
are periodically revised based on the most recent information available at the time. The methodology
for the four main tax heads is briefly described below.
The Commission splits personal income tax into four sub-categories – PAYE, USC, Schedule D and
Other. For PAYE, USC and Other, the Commission use forecasts for wages and salaries as their main
macro driver whereas Schedule D is modelled based on potential GDP and inflation (HICP). For VAT,
a weighted macroeconomic driver is used based on household consumption expenditure, general
government intermediate consumption, and both household and general government gross fixed capital
formation. CT is modelled on gross operating surplus with an elasticity based on OECD estimates. A
highly disaggregated approach is used for excises with several of the sub-components estimated
separately, linked to imports and trends in consumption.
3.2.3 Summary of the Approaches
Overall, both the Central Bank and the Commission’s approaches to forecasting are similar to that of
the Department. The Department’s forecasts provide a more detailed breakdown of taxes, particularly
on an exchequer basis. All of the organisations rely heavily on exchequer based tax data as these are
timelier and more frequent than accruals based series. The difficulty in linking the exchequer data
15 One-off and temporary measures are defined by the Commission as government transactions that have a transitory budgetary effect that does not lead to a sustained change in the budgetary position. European Commission (2015), Report on Public Finances in EMU 2015, Institutional Paper 014, December 2015, Part II, Section 3, available at https://ec.europa.eu/info/sites/info/files/file_import/ip014_en_2.pdf 16 The approach used for Ireland is also similar to that used for other small open economies. During the meetings
the Commission presented on both approaches to tax forecasting in Ireland and in Denmark.
Department of Finance | Tax Forecasting Methodological Review 2019 Page | 19
(particularly at a more granular level) to broader general government tax aggregates was noted as a
particular issue for tax forecasting in Ireland.
A report on macroeconomic and revenue forecasting by the Australian Treasury noted that most
countries performed tax forecasts on this “bottom-up” basis, with the approach followed in the US,
Canada and New Zealand (Australian Treasury 2012). Specifically, the Treasury noted the wide use of
such “mapping models” whereby revenue forecasts are prepared alongside macroeconomic forecasts.
The latter in turn is used to grow the economic base thereby ensuring a consistency between the
revenue and macroeconomic numbers.
3.3 Forecasting Accuracy
For this report, forecast errors were calculated on a current year (t) and year-ahead (t+1) basis. The
former measures in-year estimates, whereas the latter reflects forecasts for the following year.17 The
forecasts were taken from successive Budget day publications and compared with end-year exchequer
outturns.18 The main period for analysis consists of the decade since the last report, although longer
time series were also examined. The past decade is a rich period for analysis as it incorporates the
economic collapse in 2008/09, the subsequent recovery and very strong growth, including a surge in
CT receipts, since 2014.
For each tax head, the forecast error was calculated based on the following equation:
𝐸𝑡,𝑖 = 𝑇𝑎𝑥𝑜𝑢𝑡𝑡𝑢𝑟𝑛,𝑡,𝑖 − 𝑇𝑎𝑥𝑓𝑜𝑟𝑒𝑐𝑎𝑠𝑡𝑡,𝑖
(2)
where:
Et,i: difference between the outturn and forecast for a given time period (i).
The mean error (ME) is the average forecast error for a given period (equation 3). The sign of the ME
gives an indication of the direction of errors. A positive number indicates that forecasts erred on the low
side and vice versa.
𝑀𝐸 =1
𝑇∗ ∑ 𝐸𝑡,𝑖 (3)
One potential problem with the mean error relates to cases where positive and negative errors largely
offset, thereby resulting in a misleadingly low number. To counter this, two alternative measures of
forecast accuracy are used - the mean absolute error (MAE) and the root mean squared error (RMSE).
The former measures the average (absolute) error without considering the direction. The latter (shown
in equation 4) is a commonly used metric in evaluation exercises, with larger errors more heavily
penalised, which is important from a tax forecasting perspective.
𝑅𝑀𝑆𝐸 = √[1
𝑇∗ ∑ 𝐸𝑡,𝑖
2 ] (4)
17 For example, Budget 2017 is published in October 2016 - the current year error refers to the outturn for taxes in 2016 whereas the year-ahead forecast relates to 2017. 18 The supplementary budget which was published in April 2009 was not included in the analysis.
Department of Finance | Tax Forecasting Methodological Review 2019 Page | 20
3.4 Current and Year-ahead Forecasting Performance
3.4.1 Current-year Estimates
Looking at the period since 2008, the mean error was positive (i.e. tax forecasts erred on the low side)
at 0.2 per cent with a RMSE of 1.5 per cent (table 3). In nominal amounts, (absolute) errors have
averaged close to €0.3 billion per annum over the past decade with a particularly large error in 2015
(€1 billion) related primarily to unexpectedly strong CT receipts (see below). Errors for income tax and
VAT were relatively low, particularly given their size in the tax take. The overall errors were also heavily
affected by the unexpected collapse in tax revenues during the crisis. If the years 2008 and 2009 are
excluded, the overall RMSE improves to 0.8.
Table 3: current year (t) forecast errors, per cent
ME 2008-18
RMSE 2008-18
ME 2000-18
RMSE 2000-18
RMSE 2000-18 (excl crisis)
Custom duties -1.5 3.4 -0.9 3.2 3.3
Excise duties 0.6 2.4 0.3 1.9 1.8
CGT 8.5 18.1 1.8 16.7 15.5
CAT -2.1 14.8 -1.1 11.3 11.9
Stamp duties -1.6 4.0 -0.5 3.3 2.8
Income taxes 0.1 0.7 0.0 1.0 1.0
CT 0.5 7.5 0.3 5.8 4.2
VAT -0.3 0.8 -0.2 0.7 0.8
Total 0.2 1.5 0.1 1.2 0.8
Source: Department of Finance.
3.4.2 Year-ahead Forecasts
Errors are larger for year-ahead forecasts with a much higher RMSE (table 4). Since 2008, there was
a negative mean error (i.e. forecasts were too high) but this was heavily affected by the crisis years and
2008 in particular. If 2008 and 2009 are excluded, the mean error turns positive, averaging €0.7 billion
since 2010. Looking at the extended period from 2000, the magnitude of the errors were similar, with a
RMSE of 6.4 per cent. The stand-out number is the double digit RMSE for CT, an error that has also
risen in size over the past decade. In nominal terms, the (absolute) error for overall taxes has averaged
€1.0 billion over the past decade, although this has been dominated recently by errors relating to CT.
Similar to the case above for current year errors, there is also a notable improvement in the RMSE (by
close to a third) if the years coinciding with the financial crisis (2008 and 2009) are excluded.
Department of Finance | Tax Forecasting Methodological Review 2019 Page | 21
Table 4: year ahead (t+1) forecast errors, per cent
ME 2008-18
RMSE 2008-18
ME 2000-18
RMSE 2000-18
RMSE 2000-18 (excl crisis)
Custom duties -2.9 11.6 -2.3 16.1 16.0
Excise duties -1.0 4.2 -1.6 5.5 5.2
CGT -1.1 42.2 2.1 38.0 26.2
CAT -5.8 14.2 1.1 16.7 16.2
Stamp duties -4.4 25.5 0.7 22.8 16.3
Income taxes -1.4 2.7 -0.3 3.8 3.5
CT 5.2 17.3 2.2 13.7 12.2
VAT -2.4 5.7 -1.4 5.3 3.8
Total -0.9 6.7 0.0 6.4 4.6
Source: Department of Finance.
One key development over the past decade that has influenced forecasting has been the change in the
date of the Budget from December to October (since 2013). This has complicated the assessment of
in-year tax estimates which in turn feed into forecasts for the following year. This is discussed in more
detail in box 2.
Department of Finance | Tax Forecasting Methodological Review 2019 Page | 22
Box 2: the timing of budget forecasts A key development since the last TFMRG report has been the movement of the Budget from December to October since
2013, as part of reforms under the European semester. This change has had a number of implications for forecasting as the
dataset or base for taxes is more compressed. To get a sense of how timing matters in the chart below the share of taxes
received in post-Budget periods is plotted. Prior to 2014, the Budget was published in December which meant that tax
forecasts were compiled with 11 months of data for the current year – meaning that 94 per cent of the tax take was received.
In contrast, with the movement to October, forecasts are finalised with 9 months of data, meaning that just under a third of
tax revenues have yet to be received. Within this, income taxes account for a substantial share of receipts with close to a
third arising in the final quarter of the year. November is a particularly large month for (self-employed) income tax. Similarly,
receipts from CT and VAT are also sizable in the final quarter of the year.
Figure: share of taxes post-Budget, per cent of tax revenue
Source: Department of Finance calculations.
The movement of the Budget affects forecasts in a number of ways. First, estimates for the current year’s tax take are likely
to be less accurate with nine months of data. Second, forecasts for the following year (t+1) are directly affected by errors in
estimates for taxes in the current year. This is referred to as the ‘starting-point’ error (discussed in more detail in Chapter 4).
To investigate this issue, end-year tax returns were regressed on the tax take at end-September and end-November using
annual data from 1984, using an equation of the form:
𝐿𝑜𝑔(𝑡𝑎𝑥𝑡) = 𝛼 + 𝛽𝐿𝑜𝑔(𝑡𝑎𝑥𝑚=𝑗) + 𝜀
Where t refers to a given tax year, and m=j reflects the month chosen (receipts to September or November).
The resulting regressions were then used to perform in-sample forecasts from 2013 (see Table). The mean absolute error
for forecasts based on 11 months of data was lower, at 0.9 per cent, relative to 1.2 per cent for forecasts based on 9 months
of data. There was also a notable difference for 2018 with the end-November equation much closer to the annual outturn.
Table: tax outturns and estimates, € billions
2013 2014 2015 2016 2017 2018
Tax outturn 37.8 41.3 45.6 47.9 50.7 55.6
Estimate (data to end-September) 38.8 41.7 45.7 48.3 50.9 54.3
Error -1.0 -0.4 -0.1 -0.4 -0.2 1.3
Estimate (data to end-November) 37.5 40.7 44.8 47.8 50.5 55.0
Error 0.3 0.6 0.8 0.1 0.2 0.6
Source: Department of Finance calculations.
3.5 Forecasts for the Main Tax Heads
3.5.1 Income Tax
Income tax (IT) is the largest tax head and since the 2008 report, its share of revenue has increased
significantly, from 29 to 38 per cent. Receipts totalled €21.2 billion in 2018, up 6 per cent in the year.
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Department of Finance | Tax Forecasting Methodological Review 2019 Page | 23
As an Exchequer heading, IT includes income tax, USC and DIRT. Within IT, PAYE income tax receipts
account for 70 per cent with USC accounting for close to a fifth. The balance is made up of self-assessed
income tax and other smaller items. The contribution from USC has flat lined in recent years as
economic growth balanced out policy changes.
Forecast errors for IT are plotted in figure 7 (and reported in the tables above). On average, errors are
relatively small with no clear evidence of any bias. In recent years, there has been a tendency for taxes
to slightly underperform relative to forecasts. The summary RMSE statistics for IT were 0.7 and 2.7 per
cent, on a current and year-ahead basis since 2008 with the accuracy of forecasts improving over the
period. In nominal amounts, one year-ahead (absolute) forecast errors have averaged €255 million over
the past decade.
Figure 7: income tax forecast errors, per cent
Source: Department of Finance.
The Group’s discussions on IT focused on a range of factors, specifically the highly disaggregated
nature of the forecast and the split between PAYE and non-PAYE receipts and contributions from the
USC.19 Much of the debate centred on elasticities. The latter were updated in 2017 following research
conducted under the Department’s Joint Research Programme (JRP) with the ESRI, details of which
are included in chapter 5.20 The relatively strong forecasting performance was noted as was the
increasing reliance on income tax as a source of revenue. As highlighted in the Department’s Fiscal
Monitors, aside from income tax and USC, a significant amount of money received is in the form of pay
related social insurance (PRSI) each month. Although these are not forecast by the Department, they
are detailed in box 3, given their size and importance.
19 The USC was introduced (replacing the health and income levy) in 2011. In 2018, total PAYE income tax receipts amounted to €17.7 billion, with €3.2 billion accounted for by the USC. In addition, a further €2.3 billion was raised through self-assessed income tax which included €0.5 billion in USC. 20 https://www.esri.ie/current-research/joint-research-programme-on-the-macroeconomy-taxation-and-banking.
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Department of Finance | Tax Forecasting Methodological Review 2019 Page | 24
Box 3: forecasting pay related social insurance – performance and approach Overall Exchequer revenues amounted to €68.2 billion in 2018, of which €55.6 billion can be attributed to taxes. The
remainder is accounted for by a range of items, the biggest of which are PRSI receipts (€9.4 billion) which are used to finance
both the Social Insurance (SIF) and National Training Funds (NTF), although both funds include income from other sources.21
The latter includes income from investments, insurance awards, EU related monies and other miscellaneous items.
PRSI forecasts are compiled by the Department of Employment Affairs and Social Protection (DEASP). Given their size, this
box documents how these forecasts are prepared. Up until 2013, PRSI receipts were forecast by individual fund stakeholder
departments. This included the Departments of health, social protection, education, etc. Since 2013, the DEASP has taken
ownership of these forecasts. The approach to forecasting PRSI closely mirrors that for wider taxes. At budget time, an
estimate of the current year’s expected outturn is compiled based on data available at end-September. This estimate is
largely based on trends in the current year relative to the previous year but also reflects any changes in the macroeconomic
outlook (relative to the last set of estimates). For year-ahead forecasts, the estimated outturn in the current year is driven by
wage and employment macros provided by the Department of Finance. This number is then adjusted first, for the impact of
any previously announced budget measures (as is done for taxes) and second, to reflect any new measures announced in
the current budget.
PRSI forecasts are sub-divided into separate Schedule E (PAYE) and Schedule D receipts. Schedule E receipts (employer,
employee and self-employed directors) are also apportioned between the Social Insurance and National Training Funds
based on an analysis of receipts. For Schedule D (self-assessment from self-employed sole traders), forecasts are more
problematic due to the timing of receipts with close to 70 per cent received in November, with a further 13 per cent between
October and December. PRSI estimates and forecasts are finalised at the start of October once September receipts are
confirmed. Since 2013, PRSI outturns have exceeded forecasts on average by 2.8 per cent (€257 million), relative to initial
estimates. In general, these numbers tend not to be revised for the annual revised estimates for the public services volume
(REV).22
In recent years, PRSI receipts have tended to rise faster than overall employment and wages. This can be attributed to a
number of factors including a greater number of public service employees paying PRSI at a higher rate; proportionally more
employers paying contributions at the full class A rate; thresholds for PRSI not moving in sync with wages and changes in
the distribution of payers across different wage and salary deciles. Prior to 2013, forecast errors were considerably higher
mainly reflecting the effects of the economic and financial crisis. In 2009 and 2010 for example, PRSI receipts significantly
fell short of forecasts by close to 35 per cent (€2.5 billion) reflecting the unexpectedly large and severe shock to the labour
market. At the same time, in-year estimates proved too high and were significantly overstated in 2010 thus contributing to
sizable forecast errors. A further complicating issue arose from some employers incorrectly filing PRSI returns as USC during
2011. This resulted in social insurance fund income being overstated in 2011 before being offset in 2012.
Figure: PRSI forecast errors, per cent
Source: Department of Employment Affairs and Social Protection calculations.
21 PRSI receipts of €11.15 billion were received in 2018. The data published in the monthly fiscal monitor relates to government accounting reporting arrangements for PRSI. 22 Revised Estimates for Public Services 2019, available at: https://www.gov.ie/pdf/?file=https://assets.gov.ie/5035/201218100533-227c552da63d401ca85493a67c93e5de.pdf#page=1
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Department of Finance | Tax Forecasting Methodological Review 2019 Page | 25
3.5.2 VAT
VAT has consistently been Ireland’s second largest source of tax revenue. In 2018, receipts amounted
to €14.2 billion – 26 per cent of Exchequer revenue. There are two main rates, the standard (at 23 per
cent) and a reduced rate (13.5 per cent). The standard rate applies to most goods and services although
certain categories are either exempt or liable at the reduced rate.23
The forecasting performance of VAT has been robust, particularly over the past decade, across a range
of metrics (figure 8). The RMSE statistics for current- and year-ahead forecasts were 0.8 and 5.7 per
cent, respectively, although the latter improves to 3.8 per cent if the crisis years are excluded. These
compare well with the other tax heads, repeating a pattern also evident in the 2008 TFMRG report. The
absolute size of one-year ahead forecast errors is similar in magnitude to IT at €289 million per annum.
Figure 8: VAT forecast errors, per cent
Source: Department of Finance.
The Group’s main deliberations on VAT focused on data sources, the macroeconomic driver (nominal
personal consumption expenditure) and the role of the housing market. The Group discussed the extent
to which the existing macro driver fully captured online trades (and e-commerce) and VAT
developments. The Group noted that a number of items are included in the national accounts measure
of consumption that are zero-rated or exempt from VAT (e.g. food and rent). Ultimately, the Group
adjudged that the existing methodology was working satisfactorily although there appeared to be merit
in moving towards a more disaggregated approach, that better captures developments in the housing
market. This was also advocated in the 2008 report, though subsequently discontinued and is discussed
in more detail in chapter 6.
23 There are special reduced rates below the 13.5 per cent for a limited number of goods and services such as newspapers and electronically supplied publications (9 per cent) and agriculture (4.8 per cent). Most tourism related activities moved from the special reduced rate of 9 per cent to the 13.5 per cent rate following changes announced in Budget 2019.
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Department of Finance | Tax Forecasting Methodological Review 2019 Page | 26
3.5.3 Corporation Tax
In 2018, Corporation tax (CT) amounted to €10.4 billion – 19 per cent of Exchequer revenue – increasing
by 27 per cent in the year. In recent years, CT receipts have surged – up 125 per cent since 2014.
Traditionally, CT has been a difficult tax to forecast – a fact compounded in recent years. In the 2008
report, CT had the largest error of the main tax heads. This was attributed to the size of the multinational
sector as well as a number of changes to the tax regime.24
Similar to the findings of the 2008 Report, forecast errors were largest for CT (bearing in mind the size
of the tax). In recent years, taxes have significantly overshot relative to forecasts with sizable errors
since 2014 (figure 9). In absolute terms, the one-year ahead errors are very high, averaging €739 million
per annum over the past decade, rising to €1.1 billion over the past 5 years. Overall this has meant that
close to three quarters of the total tax forecasting errors can be attributed to CT.
Figure 9: CT forecast errors, per cent
Source: Department of Finance.
A number of points were raised during the Group’s discussions in relation to CT including the inherent
difficulty in forecasting such a tax. This is exacerbated in Ireland by the concentrated nature of receipts
and the marked rise in revenues since 2014 (when receipts increased by nearly 50 per cent). In respect
of the former, the large reliance on a relatively small number of companies and specifically the
contribution made by the top-10 companies is a particular issue.25
24 At the time of the 2008 report, there were a number of important changes to the corporation tax regime over this period, most notably the phased reduction to a standard 12½ per cent tax rate for trading income generally but also the decision to bring forward the payment date for preliminary corporation tax by 7 months, effectively to a current year payment basis. The 5 year transition period for the gradual move to a current year payment basis for corporation tax ended in 2006. The transition arrangements – whereby 1/5th of the amount due was brought forward in each year – generated cash-flow gains in each of the transitional years but from 2007 this cash-flow gain was lost. 25 McGuinness, G., and Smyth, D (2019), ‘Modelling recent developments in corporation tax’. Available at: https://www.gov.ie/en/publication/29cb12-modelling-recent-developments-in-corporation-tax/ Also various Revenue annual research papers on CT: https://www.revenue.ie/en/corporate/information-about-revenue/research/research-reports/corporation-tax-and-international.aspx.
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Department of Finance | Tax Forecasting Methodological Review 2019 Page | 27
Much of the work of the Group focused on the relationship between receipts and corporate profitability.
These links are not always clear-cut, particularly in real time. It was noted that in 2017 for example,
profitability remained relatively unchanged yet receipts increased by 12 per cent. One of the factors
behind this was a reduction in the repayment of the R&D tax credit among a small number of large
corporate tax payers. Recent developments in the international tax sphere including BEPS were also
noted.
In light of the size of forecast errors, the Group proposed testing to see if alternative elasticities or
macroeconomic drivers would improve accuracy (Chapter 4). The merits of changing the timing of CT
payments was also discussed, however it was agreed that a large administrative change like this would
need to lead to substantial benefits in terms of forecasting performance given the burden it would place
on companies. It was also noted that the impact of the change in the date of the Budget from December
to October has most likely contributed to forecast errors given that a high proportion of receipts are
received in the final quarter of the year (box 2 and chapter 4).
3.5.4 Excise Duties
Excise duties are arguably the most diverse tax with several rates applying to a multitude of products,
principally vehicles, alcohol, fuels and tobacco. In 2018, receipts amounted to €5.4 billion, down 9 per
cent in the year. The share of overall tax revenue has also fallen since the last report.
The forecasting performance of excises has been mixed. Over the past decade, the one-year ahead
absolute error has averaged €112 million. However, in 2018 the error was much larger (€402 million).
The 2018 error largely reflected a timing issue with tobacco taxes and the movement to plain packaging.
Figure 10: excise forecast errors, per cent
Source: Department of Finance.
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Department of Finance | Tax Forecasting Methodological Review 2019 Page | 28
The Group discussions focused on the multitude of sub-components under excises. Overall, the steady
fall in the share of taxes deriving from excises was noted (falling from 20 per cent in 1988 to 10 per cent
in 2018). Within excises, close to 80 per cent is accounted for by receipts from alcohol, tobacco and
fuels (excluding carbon). Changes in VRT over the economic cycle were also discussed. It was noted
that the recovery in VRT receipts (which accounts for 16 per cent of excises) and new car sales have
not reached pre-crisis levels. The significant shift towards diesel cars since 2008 was also discussed
and the likely impact of future switching of consumers towards electric and hybrid cars. These structural
changes could affect the base upon which forecasts are derived.
The Group discussed whether a more disaggregated forecasting approach was warranted going
forward given the range of subcomponents within excises and their susceptibility to policy changes. For
VRT, the merit in the existing approach was questioned, specifically the need for an exchange rate
channel given recent trends in car sales between Ireland and the UK.
3.6 Smaller Tax Heads – CGT, CAT, Stamps and Customs Duties
Aside from the four main tax heads, remaining taxes include capital gains tax (CGT), capital acquisitions
tax (CAT), stamp duty, customs duties and motor taxes. These taxes are diverse and relatively small.
That said, they are important sources of revenue and featured heavily in the recommendations arising
from the 2008 Report. At that time, capital taxes (CGT, CAT and stamp duties) accounted for over 14
per cent of the tax take, in large part due to the housing boom. In 2018 however, this share has fallen
to 5 per cent (see table 5). These taxes are briefly discussed below.
Table 5: capital and other taxes, selected years
2000 €bn
%
2007 €bn
%
2010 €bn %
2018 €bn
%
A. Custom duties 0.2 0.8 0.3 0.6 0.2 0.7 0.3 0.6
B. CGT 0.8 2.9 3.1 6.6 0.3 1.1 1.0 1.8
C. CAT 0.2 0.8 0.4 0.8 0.2 0.7 0.5 0.9
D. Stamp duties 1.1 4.1 3.2 6.7 1.0 3.0 1.5 2.6
E. Capital taxes (=B+C+D)
2.1 7.8 6.7 14.1 1.5 4.9 3.0 5.3
Other taxes 24.8 91.5 40.3 85.3 30.0 94.4 52.3 94.1
Total 27.1 100.0 47.2 100.0 31.8 100.0 55.6 100.0
Source: Department of Finance.
3.6.1 Capital Taxes
Capital Gains Tax (CGT) is paid on the gains from the sale/gift or exchange of assets. Typically this
includes land, buildings and shares with different rates applying, the main one being 33 per cent. In
2018, receipts amounted to €1.0 billion (2 per cent of the tax take). Forecast errors for CGT are much
higher than for other tax heads. There has also been a clear tendency in recent years for forecasts to
err on the low side (figure 11). Despite their small weighting in overall taxes, absolute errors for CGT
were high averaging €112 million (the same as excises, despite being a fifth of the size) over the past
decade.
Department of Finance | Tax Forecasting Methodological Review 2019 Page | 29
Figure 11: CGT forecast errors, per cent
Note: t+1 forecast error in 2008 peaked at 73 per cent (not shown).
Source: Department of Finance.
Stamp duty is incurred on the transfer of property, equity and financial cards with several rates
applying.26 The share of stamps in overall receipts has declined sharply from a peak of €3.7 billion in
2006 (8 per cent of the tax take) to €1.5 billion (3 per cent of the tax take) last year. Forecast errors for
stamps were heavily affected by the period in the run-up to and following the financial crisis. Over the
past decade the absolute error has averaged close to €151 million – broadly similar in scale to CGT
and excises.
Figure 12: stamp duty forecast errors, per cent
Source: Department of Finance.
26 In relation to stamp duty, it is important to note an annual pension levy (of 0.6 per cent) applied on the market value of assets in the years from 2011 to 2014.
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Department of Finance | Tax Forecasting Methodological Review 2019 Page | 30
Capital acquisitions tax (CAT) is payable on a gift or inheritance with at a rate of 33 per cent. In 2018,
receipts amounted to €0.5 billion or 1 per cent of the tax take – a share that has remained unchanged
over the past 2 decades. Absolute forecast errors on a one-year basis are small – averaging €29 million
over the past decade.
Figure 13: CAT forecast errors, per cent
Source: Department of Finance.
The Group’s main discussions on capital taxes focused on the size of forecast errors, particularly for
CGT. It was noted that capital taxes in general are harder to forecast given the lack of suitable
macroeconomic drivers, specifically in relation to transactions. It was also highlighted that a relatively
small number of transactions could significantly influence receipts and thereby the size of forecast
errors. Timing was also an issue with the vast majority of payments (in the case of CGT) typically due
in December. The underperformance in stamp duty receipts was noted as was their relationship to
movements in equity and housing markets. The need for the current disaggregated approach to
forecasting stamp duty was queried, particularly given its size and whether a more streamlined
approach could be used.
3.6.2 Customs Duties
Customs duties are normally charged on the value of the item being brought into Ireland with a large
proportion collected and paid to the EU as part of Ireland’s annual budget contribution. Receipts from
customs duties have been broadly stable in recent years, totalling €333 million in 2018. The forecasting
performance has been mixed although in absolute terms, errors remain small.
While custom duties are currently only a small proportion of the tax take, the importance of this tax head
is likely to increase as a result of Brexit. Indeed at Budget time, the Department forecast custom duties
to more than triple between 2019 and 2020. This significant growth reflects the no-deal Brexit
assumption contained in the Budget 2020 forecasts. As pointed out in the Budget, this increase is purely
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Department of Finance | Tax Forecasting Methodological Review 2019 Page | 31
technical in that the majority (80 per cent) of this figure will go towards an increased EU Budget
contribution.27
Figure 14: customs duties forecast errors, per cent
Source: Department of Finance.
27 Budget 2020. Economic and Fiscal Outlook. Available at : http://budget.gov.ie/Budgets/2020/Documents/Budget/Budget%202020_Economic%20and%20Fiscal%20Outlook_B.pdf
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Department of Finance | Tax Forecasting Methodological Review 2019 Page | 32
Chapter 4 Detailed Forecast Errors Appraisal
4.1 Introduction
This chapter takes a more detailed look at sources of forecast error with a particular focus on bias,
benchmarking and backcasting. Much of the analysis follows the methodology of previous reports and
more recent work in Ireland and the UK (Hannon, Leahy and O’Sullivan, (2015) and Bank of England
(2015)).
4.2 Forecast Bias and Benchmarking
4.2.1 Methodology
The unbiasedness of tax forecasts was assessed using a standard ordinary least squares (OLS)
regression involving the forecast error and a constant term with a null hypothesis that the latter is zero
(equation 5).
𝐸𝑡+𝑖 = 𝛼0 + 𝜇𝑡+𝑖 (5)
where 𝐸𝑡+𝑖 measures the forecast error in a given period (t and t+1).
The null hypothesis for unbiasedness is that 𝛼0 = 0. Values of 𝛼0 > 0 point to forecasts being too low
and similarly 𝛼0 < 0 point to forecasts being systematically too high.
An alternative check of the forecasts involved running benchmarking checks (common in forecasting
evaluation exercises). This involved comparing forecasts from successive budgets with those of a
random walk, as proxied by the year-end tax outturn.
More formally, the process can be described by the following equation:
𝑇𝑎𝑥𝑡 = 𝑇𝑎𝑥𝑡−1 + 𝜇𝑡 (6)
with E(𝜇𝑡) = 0.
The forecast errors from the random walk were then compared with budget day forecasts using a
Diebold-Marino test, with an equation of the form:
𝐷𝑖𝑓𝑓𝑡,𝑖 = 𝐹𝑜𝑟𝑒𝑐𝑎𝑠𝑡 𝐸𝑟𝑟𝑜𝑟𝑡,𝑖2 − 𝑅𝑎𝑛𝑑𝑜𝑚 𝑊𝑎𝑙𝑘 𝐸𝑟𝑟𝑜𝑟𝑡,𝑖
2 (7)
where 𝐷𝑖𝑓𝑓𝑡,𝑖 refers to the difference between the squared forecast errors from the two approaches,
with ‘i’ distinguishing between current and one-year ahead periods. The next step then involved testing
to see the size, sign and significance of the difference, by regressing it on a constant term:
𝐷𝑖𝑓𝑓𝑡,𝑖 = 𝛼0 + 𝜇𝑡 (8)
Department of Finance | Tax Forecasting Methodological Review 2019 Page | 33
The null hypothesis is that there is no difference between the forecasts (i.e. 𝛼0 = 0). In contrast, if 𝛼0 is
positive, then it can be asserted that the random walk process outperforms the budget forecast, and
vice versa.
4.2.2 Results
The bias and benchmark checks were done using regressions covering the period 1998 to 2018, with
a focus on both current and year- ahead forecast errors for the main tax heads. While the sample period
is small, the results are informative.28 Tests showed no evidence of any systematic bias over the
forecast horizon with the results summarised in table 6.
Table 6: forecast bias check
Current year bias (yes/no)
Constant term (t-statistic)
Year ahead bias (yes/no)
Constant term (t-statistic)
Income tax No 0.0
(0.8) No
-41.1 (-0.4)
VAT No -21.9 (-1.3)
No
-155.6 (-1.0)
Corporation tax No 47.9 (0.7)
No
218.5 (1.0)
Excise duties No 18.3 (1.2)
No
-77.0 (-1.4)
Total taxes No 54.5 (0.6)
No 79.8 (0.1)
Source: Department of Finance calculations.
For the benchmark tests, the forecasts significantly outperformed random walk series in most cases
(table 7).29 The only exception was for one-year ahead forecasts for excise duties. This finding coupled
with other metrics suggests that changes may be needed in relation to this tax head. Proposals are
outlined in Chapter 6.
Table 7: benchmark check – budget forecasts relative to a random walk
Current year outperforms benchmark
(yes/no)
Constant term (t-statistic)
Year-ahead outperforms benchmark
(yes/no)
Constant term (t-statistic)
Income tax Yes 68.1 (-4.0) Yes
-17.9 (-3.1)
VAT Yes -98.6 (-3.1) Yes
-70.6 (-2.9)
Corporation tax Yes -201.4 (-3.9) Yes
-47.1 (-2.0)
Excise duties Yes -37.8 (-3.3) No
-11.6 (-0.6)
Total taxes Yes -97.2 (-3.3)
Yes -58.3 (-3.0)
Source: Department of Finance calculations.
28 The estimations were carried out by ordinary least squares and also using HAC (Newey West) standard errors. 29 Forecast errors were compared with random walk errors, with the latter based on the outturn for the preceding year.
Department of Finance | Tax Forecasting Methodological Review 2019 Page | 34
4.3 A Decomposition of One-year Ahead Forecast Errors (2008-2018)
There are many steps involved in generating a tax forecast. Before discussing recommendations, it is
important to get a fuller sense of errors through a more detailed forecast decomposition exercise. To
do this, each of the four tax heads were examined over the past decade. From Section 3.1, there are
several key inputs into a forecast. These include the estimate for the current year’s outturn, the forecast
for the macroeconomic driver (and the tax revenue elasticity), the assumed effect of budget measures
and judgement. Much of the analysis builds on the previous TFMRG report and work by Hannon et al.,
(op.cit,).30
Given this, four main sources of forecast error were assessed, namely:
starting point
macroeconomic
judgement
residual/other (which includes the tax revenue elasticity).
The components largely follow each of the main elements of the standard tax forecasting equation
outlined above and are discussed below. The tax revenue elasticity was not separately assessed as its
value in the forecasting equation does not change from year to year. If its value is an under/over-
estimate of the true parameter value, this will be picked up in the residual in this decomposition exercise.
A step-by-step backcasting exercise was followed whereby tax forecasts at the time of the budget were
taken and then retrospectively adjusted one at a time for a change in inputs.
The starting point error was assessed first. This is the proportion of the error caused by an incorrect
estimate for the current year’s tax take being used in the equation. In simple terms, the forecast for a
tax head in period t+1 is based on an assumed outturn for the tax in period t. Errors in the latter will
automatically feed through into the year-ahead forecast. To quantify this error, the correct tax outturns
were used in each budget day forecast (as opposed to estimates), leaving all other components of the
equation unchanged. This approach then highlights the proportion of the overall error accounted for by
an incorrect starting point.
The macroeconomic error was calculated in a similar fashion. Once again, the budget day forecast was
taken and adjusted solely for the latest observation of the macroeconomic driver, rather than the
estimate assumed at budget time.31 All other elements of the equation were kept unchanged. This then
helps to isolate the proportion of the error accounted for by the macroeconomic driver. This is typically
a large source of error.
30 The latter focused on tax forecasts over the period 2004 to 2014, finding that macroeconomic and other (residual) factors accounted for a substantial proportion of errors. 31 A further complication arises from the fact that macroeconomic aggregates themselves get revised (see Casey and Smyth, 2016). For the decomposition work here, the latest observed values of macroeconomic drivers were used.
Department of Finance | Tax Forecasting Methodological Review 2019 Page | 35
To evaluate the extent to which judgement affected forecasts, the forecasts were amended to remove
adjustments imposed by the Department. Typically, these judgements involved either positive or
negative additions to the tax forecast at budget time to reflect additional information, not fully captured
in the macroeconomic driver. Following the approach above, all other elements of the budget day
equation were unchanged bar judgement to again isolate its impact.
Finally, other sources of error were captured by subtracting the three aforementioned errors from the
overall forecast error. This residual error is important as it acts as a “catch-all” variable, thereby
reflecting events or factors not accounted for by standard forecasting equations. Such factors could
allude to the elasticity being incorrectly estimated and/or the limits of a specific macroeconomic driver
in reflecting movements in the tax base.
A summary of each of these sources of error is provided in Table 8. Interpretation of the results is not
always intuitive as some of the factors can offset one another. For these reasons, the large forecast
error for CT in 2015 (an excess of €2.3 billion, 33.4 per cent) provides a useful example. Stripping this
down, the starting point component was small. The Department had assumed in the Budget that the CT
outturn in 2014 (the starting point for the forecast) would be €4.5 billion, whereas the actual outturn was
close to this at €4.6 billion. Similarly, there were very modest adjustments applied by the Department
to the forecast – these amounted to €35 million. These two factors alone therefore clearly indicate that
macroeconomic and other forecasting errors must have been at the heart of the €2.3 billion excess. At
the time the budget was prepared it was assumed that the macroeconomic driver (GOS) would increase
by 5.3 per cent in 2015. The actual outturn proved very different for well documented reasons with the
growth in GOS revised up to 60.0 per cent. This effectively captures most of the forecasting error. There
is also an important timing dimension to this type of retrospective analysis. If this decomposition
exercise had been replicated back in 2016, all of the error would have shown up in the residual, as the
CSO’s first estimate for the main macroeconomic aggregate in 2015 were much closer to the
Department’s forecast.32 However, in time, with data revisions, the macroeconomic driver was
substantially revised with the result that the error moved from the residual into the macroeconomic
component.
Overall, the findings are mixed as many of the errors tend to offset with at times no clear discernible
pattern. Looking at the past 5-years, macroeconomic factors have been a frequent and large source of
error, particularly for CT but also rather surprisingly for excises. Starting point errors have been a factor
for CT, VAT and excises although less important for income tax. Looking at judgements applied in
successive budgets, these tend to be relatively small. Perhaps the most notable finding from the
decomposition, is the sizable contribution from other/residual factors particularly for CT and excises
suggesting the need for modifications going forward. Each of the four main taxes are discussed in turn
below.
32 In 2016 for example, the CSO initially estimated nominal GDP and GNP growth rates for 2015 of 13.5 and 11.2 per cent, respectively. Most recently, these growth rates were estimated by the CSO at 34.9 and 22.9 per cent.
Department of Finance | Tax Forecasting Methodological Review 2019 Page | 36
Table 8: summary of forecast error decomposition, per cent
Note: table measures one-year ahead forecast errors. Source: Department of Finance.
For VAT, the overall forecasting performance is strong with the main source of error arising from the
macroeconomic and other/residual components. In 2008 and 2009, the forecasts were too high primarily
as a result of macroeconomic forecasting error, with the residual operating in the same direction. This
reflects both the unprecedented fall in domestic demand in the economy at the time and the sharp
unwinding in the construction sector (which was a large source of VAT). In more recent years, the
residual component has been a factor but overall errors remain low. Looking at 2018, the
macroeconomic and other component captured much of the error, with consumption stronger than was
foreseen at budget time.
2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018
Income Tax (PAYE)
starting point 0.5 -2.0 -0.4 1.5 0.0 0.4 0.1 -0.1 0.0 -0.1 0.0
macroeconomic -1.9 -15.5 -1.9 -3.3 -0.3 -1.1 -2.1 1.9 0.9 -0.7 -2.0
judgement 1.3 -3.9 -3.9 -5.1 -3.8 -0.6 0.0 1.0 0.6 1.2 0.3
other -1.4 2.3 5.9 6.0 6.9 0.6 5.8 -3.2 0.0 -0.3 3.2
Total -1.4 -19.1 -0.4 -0.9 2.9 -0.7 3.8 -0.4 1.4 0.0 1.6
VAT
starting point -1.1 -0.9 0.3 -0.7 0.1 -0.2 -0.3 0.7 -0.7 -1.7 -0.9
macroeconomic -5.0 -17.3 2.8 -1.5 0.2 -0.2 0.2 -0.1 0.3 -0.7 1.1
judgement 0.4 2.3 0.1 0.0 1.3 -0.5 -0.1 -1.4 -1.5 -0.5 -0.7
other -10.2 -14.9 -3.1 -3.1 0.2 -1.3 3.9 2.2 -1.7 2.4 1.5
Total -15.8 -30.8 0.1 -5.3 1.7 -2.2 3.7 1.4 -3.5 -0.5 1.0
CT
starting point 0.8 -23.9 2.9 4.4 -5.2 5.0 -1.9 1.4 10.8 -2.1 2.4
macroeconomic -4.8 -14.8 0.9 0.3 -0.7 -0.9 8.6 29.4 -3.0 4.2 3.7
judgement 1.3 13.8 4.3 0.9 9.7 -3.0 -0.2 0.3 -2.2 2.4 -1.0
other -29.6 -27.6 11.4 -19.8 6.7 2.0 -1.5 2.4 4.5 1.4 13.0
Total -32.3 -52.5 19.5 -14.2 10.6 3.2 5.1 33.4 10.0 5.9 18.1
Excises
starting point 0.5 -2.0 2.6 -2.3 -3.9 -1.5 0.1 -5.0 -2.2 -1.8 0.4
macroeconomic -5.4 -14.3 8.7 3.3 -0.4 -4.5 1.5 1.4 0.5 -1.9 1.6
judgement -4.7 -6.8 -7.2 -4.7 -2.8 -3.9 -2.8 -1.5 -1.1 -0.8 -4.9
other -0.4 1.5 -0.6 3.7 4.9 9.3 4.8 6.0 4.0 3.5 -4.4
Total -10.0 -21.6 3.5 0.1 -2.3 -0.6 3.6 0.9 1.2 -1.0 -7.4
Department of Finance | Tax Forecasting Methodological Review 2019 Page | 37
Figure 15: VAT forecast error decomposition, per cent
Source: Department of Finance.
For income tax, the table and figure below show decompositions for PAYE related income tax. This is
the main sub-component of income tax, although USC is discussed below. For PAYE, the forecasting
performance is reasonably robust. With the exception of the financial crisis years, errors have been
relatively small. One potential source of concern relates to the extent to which the residual component
largely offsets errors on the macroeconomic side. The Group felt that this could be reflective of the
wage side of the forecasting equation, where there are a number of sources of information on
compensation levels within the economy.
Figure 16: income tax (PAYE) forecast error decomposition, per cent
Source: Department of Finance.
USC (PAYE) forecasts were also assessed looking at the period from 2012. The methodology
underlying USC forecasts mirrors that for overall income tax with separate PAYE and Schedule D
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Department of Finance | Tax Forecasting Methodological Review 2019 Page | 38
components using both wages and employment as macroeconomic drivers. The main difference relates
to the (wage) elasticity measures which are lower for USC forecasts (1.2 for PAYE and 1.4 for Schedule
D). In contrast to most of the other tax heads, USC PAYE forecasts tended to err on the high side,
averaging -3.9 per cent. Much of this reflected a large undershoot in 2014 of close to 13 per cent. A
decomposition of the forecasts pointed to the residual as one of the main factors behind errors.
For CT, forecast errors are appreciably higher than for income tax or VAT. The main source of this
relates to the macroeconomic driver, confirming the difficulty in forecasting CT and limitations with the
existing equation. The large residuals in more recent years are difficult to account for but one potential
explanation could be linked to the tendency for macroeconomic aggregates to get revised (see Casey
and Smyth, op.cit,). This could mean that the published profitability figures (as encapsulated in gross
operating surplus) are not fully capturing activity levels in real time.
Figure 17: corporation tax forecast error decomposition, per cent
Source: Department of Finance.
From the decomposition, there are three distinct phases with forecasts too high in 2008 and 2009,
reflecting macroeconomic forecasting errors linked to the turning point in the economy at the onset of
the global crisis and the housing market collapse. The latter two factors are also more likely to appear
in the residual series. From 2010 to 2014, CT forecasts performed reasonably well. The third phase
encompassing the period post 2014, however, is marked by much larger errors with outturns
significantly outperforming forecasts, notably in 2015 and 2018. This primarily reflects contributions
from macroeconomic aggregates, the residual and some larger starting point errors. The latter reflects
the fact that a large proportion of CT receipts are received post-Budget. The residuals in recent years,
as outlined above, could signal issues in respect of the macroeconomic outturns. It is clear that CT
remains a difficult tax to forecast, a point that was also made at the time of the 2008 report.
In this respect, recent research published by the Department in relation to CT modelling (McGuinness
and Smyth, 2019) is particularly timely. This work made use of error-correction models to generate one-
year ahead forecasts for in-sample comparison. Even with a range of macroeconomic drivers including
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variants of both gross and net operating surplus, the models could not fully account for some of the
recent rises in CT. The research also noted the role of firm-level and international developments in
determining receipts. The paper highlighted that for current year estimates, the standard approach
adopted by the Department at budget time (which relies more on in-year trends and analysis from
Revenue survey data) worked well and outperformed a series of econometric models. This pointed to
the importance of specialist in-house knowledge within the Department and Revenue and the role of
judgement.
For excises, the effects of the financial crisis from 2008 are evident with the unexpectedly large and
persistent drop in consumer spending culminating in a large forecast error. More recently, outturns have
tended to exceed forecasts although the persistent nature of the residual – it has been positive in 6 of
the last 7 years - points to potential issues with the forecasting equation. In 2018, the large negative
error is accounted for judgment and the residual. The Group suggested that this was most likely
reflective of a carryover from the distorting impact of the tobacco plain packaging initiative. This resulted
in excise duties being persistently above profile through 2017 (as payments were frontloaded) and
below profile during 2018.
Figure 18: excise duties forecast error decomposition, per cent
Source: Department of Finance.
A summary of the retrospective analysis is shown in Annex B. This analysis suggests that in the majority
of cases, judgement improved forecasting performance (errors were smaller when judgement was
included). The same finding applied for the use of the correct starting point for all taxes bar excise. For
macroeconomic aggregates, this was a source of error for CT, excise and VAT, although less so for
income tax. Overall, the Group was of the view that periodic forecast decompositions were useful while
recognising some of the limitations with such retrospective analysis.
4.4 Forecasts on a General Government Basis
While this report is primarily concerned with taxes on an Exchequer basis, the Group felt it useful to
also consider general government tax forecasts. These are broader than their exchequer counterparts
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Department of Finance | Tax Forecasting Methodological Review 2019 Page | 40
and feed directly into general government deficit and debt aggregates. General government data are
prepared in line with the European System of Accounts (ESA) 2010 methodology, with flows recorded
on an accruals basis. In simple terms, this means that flows are recorded when the related economic
activity takes place. A series of adjustments are required to convert the cash-based (exchequer) data
to accruals – particularly for taxes such as VAT, excises and income tax (PAYE).33
The main sub-components of general government revenue are taxes (74 per cent of the total) and social
contributions (16 per cent). Other smaller elements include interest, sales and transfer related income
accruing to government. In figure 19, taxes on an exchequer and general government basis are plotted.
In 2018, general government taxes reached a new peak of €61 billion.
Figure 19: taxes on a general government and exchequer basis, € millions
Source: Department of Finance.
4.4.1 Accuracy of Forecasts
In figure 20, current and year- ahead forecast errors for general government taxes are shown for the
period 2002 to 2018. With the exception of the financial crisis, forecasts have consistently erred on the
low side – on just four occasions have receipts underperformed relative to forecasts (and in 2016/17
the differences were marginal). The mean errors for current and year ahead tax forecasts were 3.9 and
3.5 per cent respectively (table 9). On a RMSE basis, the respective errors were 4.9 and 8.8 per cent.
Over the past decade, the mean absolute error for taxes on a current year basis averaged €1.5 billion
and €2.5 billion in year-ahead terms. The accuracy of forecasts improves once the financial crisis years
are omitted (2008/09) with the RMSE falling to 7.0 and 7.4 per cent for overall revenues and taxes,
respectively.
An important development since the last tax forecasting report involved the setting up of the Irish Fiscal
Advisory Council (IFAC) in 2011. IFAC is an independent statutory body and as part of its mandate it is
required to formally assess the Department’s official forecasts, which includes budgetary and general
government forecasts. This involves IFAC publishing a bi-annual Fiscal Assessment reports. These
33 The ‘walk’ from the exchequer to the general government balance is documented in Annex 3 of Budget 2020.
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Department of Finance | Tax Forecasting Methodological Review 2019 Page | 41
contain detailed analysis on the Department’s macroeconomic and budgetary forecasts. In addition,
IFAC has also published several analytical pieces on tax forecasting. This includes work on elasticities,
corporation tax, deposit interest retention tax as well as detailed analysis on tax forecasting errors.34
Table 9: general government forecast errors, per cent
Mean error (€mn)
Mean error RMSE RMSE
(excluding crisis years)
Total revenue - current-year (t) 1,897 3.0 4.0 4.1
Total revenue - year-ahead (t+1) 2,123 3.3 8.0 7.0
Total taxes - current-year (t) 1,622 3.9 4.9 4.7
Total taxes - year-ahead (t+1) 1,538 3.5 8.8 7.4
Note: sample period from 2002 to 2018.
Source: Department of Finance calculations.
Figure 20: general government tax forecast errors, per cent
Source: Department of Finance.
4.4.2 Benchmarking General Government Forecasts
There are a relatively limited number of agencies that publish detailed general government forecasts to
compare with the Department. The European Commission, as set out above, produces regular
economic forecasts for Ireland as part of cross-country fiscal surveillance exercises. For benchmarking
purposes, the Commission’s autumn forecasts were used as these were closest in time to the
Department’s budget day numbers.
To proceed, forecasts for total revenue and tax receipts on a general government basis were compared
on a current and year-ahead basis. The results are summarised in figures 21 and 22. On average, the
forecasting performance was similar, with outturns typically exceeding forecasts and with errors notably
34 For working papers click on this link with analytical notes detailed here and fiscal assessment reports here.
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Department of Finance | Tax Forecasting Methodological Review 2019 Page | 42
higher on a one-year ahead basis. There also appears to have been a marked improvement in
forecasting accuracy over the past 5-years.
A final check involved testing to see if forecasts were statistically different from one another. This
involved regressing the differences in forecast errors between the two agencies on a constant term for
both overall revenue and taxes. The results, bearing in mind a relatively small sample, indicate that tax
forecasts were statistically different on both a current and year-ahead basis, although this did not hold
for overall government revenue.
Figure 21: general government tax forecast errors - current year, per cent
Source: Department of Finance.
Figure 22: general government tax forecast errors - year ahead (t+1), per cent
Source: Department of Finance.
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Department of Finance | Tax Forecasting Methodological Review 2019 Page | 43
Chapter 5 Tax Revenue Elasticities
5.1 Introduction
From the standard tax forecasting equation in Chapter 3, the choice of the elasticity parameter is clearly
of significant importance. Historically, these parameters have averaged close to unity in Ireland,
although depending on the tax head in question the number can vary. Tax elasticities are periodically
reviewed and revised. This Chapter takes a more detailed look at elasticities and research in the area.
5.2 Elasticity Concepts and Recent Research
Before proceeding it is useful to distinguish between a tax revenue elasticity and buoyancy. The latter
is the observed change in revenues in response to changes in economic activity, while the elasticity is
the automatic change in revenues in response to changes in economic activity. Automatic changes
refer to revenue growth in the absence of discretionary policy changes. All other things being equal, if
there are no policy changes, then the two concepts coincide. Although not formally assessed in this
Report, a recent paper by the Parliamentary Budget Office (2019) estimated an average buoyancy for
Ireland of close to unity suggesting that taxes move in line with economic growth.
A tax revenue elasticity can be characterised as a structural elasticity, in the sense that it relates directly
to the design features of a given tax system. Another important form of elasticity is a behavioural
elasticity (which measures the extent to which economic decisions change in response to changes in
the tax rate). Generally speaking however, the Department and Revenue prepare forecasts on a static
basis, i.e. ignoring any behavioural change in response to the tax rate, such as changes in compliance
or economic activity. This is common practice across other countries and international agencies, given
the uncertainty inherent in predicting behavioural responses to tax.
The current exception to this approach relates to excise duties. Revenue apply behavioural elasticities
to petrol, diesel, alcohol, cigarettes and cars. These elasticities are based on empirical estimation of
consumer responses to changes in tax rates. They indicate, for example, strong consumer (taxpayer)
sensitivity to changes in the rate of duty applied to alcohol. If the forecast did not account for such a
behavioural response, future revenues would be mis-estimated.
Under the Joint Research Programme (JRP), the Department, Revenue and the ESRI recently
completed a study on behavioural elasticities for income tax (Department of Finance 2018b). The
parameter of interest, the elasticity of taxable income (ETI), measures how individual tax-payers adjust
their taxable income in response to changes in the amount of income which they retain after taxation
(known as the net-of-tax rate). The adjustment captures both labour supply and tax planning responses.
The research estimates a central ETI of 0.168, meaning that for each 1 per cent decrease in the
marginal net-of-tax rate, taxable income falls by 0.168 per cent on average. This lies in the bottom half
of the international range, which may be due to Ireland-specific factors such as a relatively smaller level
Department of Finance | Tax Forecasting Methodological Review 2019 Page | 44
of tax deductions and reliefs here compared to elsewhere, a high degree of income tax compliance and
labour market frictions for PAYE earners in particular. The relatively low responsiveness compared to
other countries, coupled with the well-known progressivity of the Irish income tax system, suggest that
the trade-off involved in pursuing both equity and efficiency objectives in the Irish income tax system is
reasonably limited.
5.3 Historical Tax to Output Elasticities
In the two previous tax forecasting reports (in 1998 and 2008) long-run tax to GDP elasticities of 1.1
and 0.9 per cent were estimated. Neither of the equations controlled for policy changes over the period,
so strictly speaking they are indications of tax buoyancy. To update this analysis, using an extended
time series to 1970, tax elasticities were re-estimated using both GDP and adjusted Gross National
Income (GNI*).The results are summarised in figures 23 and 24, showing elasticities (including 95 per
cent confidence intervals) of close to unity, particularly over longer time horizons.
Recent work by the Department (Department of Finance, 2019c) also highlighted a number of issues in
relation to the aggregate tax to output elasticity and principally the increasing prominence of CT receipts
in complicating standard measures. This work noted that taxes excluding CT appear to be more closely
aligned with movements in nominal GNI*, perhaps reflective of the volatility in both GDP and CT receipts
over the past decade.
Figure 23: aggregate tax to output elasticities, 1970-2018
Source: Department of Finance
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Department of Finance | Tax Forecasting Methodological Review 2019 Page | 45
Figure 24: tax to GNI* elasticities with a 95 per cent confidence interval, 1970-2018
Source: Department of Finance
In terms of tax forecasting methodology, the Department’s approach builds the forecast for total tax
revenues from the bottom upwards on a tax-by-tax basis. These elasticities nevertheless provide a
useful cross-check as the overall tax revenue number should be close to the projected growth rate in
nominal output, save in the cases of significant policy changes, one-off factors, or periods of unusual
growth.
5.4 Elasticities for the Main Tax Heads
Under the JRP, the Department has reviewed elasticities for different tax heads, specifically in terms of
estimation and interpretation. Some of these are detailed below in box 4, followed by the main findings
to date for income tax, USC, VAT and CT.
1.00
1.05
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1.15
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1.25
1970-85 1970-90 1970-95 1970-00 1970-05 1970-10 1970-15 1970-18
Sample period
Mid low high
Department of Finance | Tax Forecasting Methodological Review 2019 Page | 46
Box 4: research on tax revenue elasticities The Department has published several related pieces of work on elasticities under the JRP. These include papers on tax
elasticities particularly in relation to income tax, USC and VAT, while a further paper on CT is ongoing. A revenue elasticity
estimates the automatic growth potential of a tax. It represents a useful baseline to judge the impact of discretionary tax
policy against, as the elasticity estimates how revenues respond to tax base growth under a ‘no policy change’ scenario,
hence the term automatic growth.
There are two main methods of estimation: a micro-data based analytical method and a macro-data based time-series
approach. The analytical approach, considered under the JRP work, involves defining how the design parameters of a tax
lead to revenues and linking this to the underlying tax base distribution. In this way, the determinants of the elasticity can be
easily understood and changes in these factors can be linked to changes in the elasticity over time.
Under the analytical approach, the revenue elasticity can be expressed as the ratio of the marginal tax rate to the average
tax rate. As such, it has a further interpretative function which goes beyond the tax forecasting context: it equates to a
measure of tax progressivity. Whenever the marginal tax rate exceeds the average tax rate, the average tax rate will increase
as the tax base increases. This is the most commonly used definition of progressivity in tax policy.
The trade-off between revenue volatility and progressivity can thus be summarised in a single number, the revenue elasticity.
For example, the income tax elasticity (across both PAYE and non-PAYE) is estimated to be 2.0, but the USC elasticity
(across both PAYE and non-PAYE) is estimated to be 1.2. Income tax is therefore more progressive than USC, but it is also
more sensitive to the economic cycle (in an upturn it increases by more than USC but, in a downturn, it falls by more). The
analytically constructed elasticities mean that design features of both taxes can be shown to play a role in this difference:
tax credits, which occur in income tax but not USC, contribute to progressivity by reducing average tax rates at the lower
end of the income distribution while also contributing to revenue volatility by increasing marginal tax rates at the income
point at which they are exhausted.
Knowing that a revenue elasticity can be expressed as the ratio of the marginal tax rate to the average tax rate is also helpful
for judging the revenue growth potential of any new tax or an existing tax for which a revenue elasticity has not been
empirically estimated. If the marginal tax rate and the average tax rate are the same, for example, then the revenue elasticity
will always be one.
Revenue elasticities are also useful to understand spill-overs between taxes, as the tax base can be expressed with respect
to secondary influences, which can include other tax policies. For example, the VAT revenue elasticity for the household
sector declined between the 1990s and the 2000s. One explanation for this is the introduction of a tax credit-based income
tax system in 2001 which increased the progressivity of the income tax system, but appeared to have a knock-on effect on
VAT elasticities (on the margin, a progressive income tax system reduces the gross income available for expenditure,
particularly for higher income households).
In summary, employing an analytical approach to revenue elasticities can result in greater clarity on the determinants of
revenue responsiveness, as well as allowing for a richer interpretation of the relationship between tax revenues and the tax
base.
5.4.1 Income Tax
An extensive study of income tax elasticities was conducted and published in 2017 (Acheson, et al
(2017)). The paper estimated elasticities over the period 2003 to 2013, using Revenue’s micro data on
the distributions of taxable income. There were a number of steps taken to estimate a range of income
tax elasticities both accounting for and not accounting for changes in income growth and the effects of
tax deductions. The paper highlighted the importance of thresholds and credits in effecting tax elasticity
estimates. The main results pointed to a highly progressive income tax system given that the overall
income tax elasticity was estimated at 2.0.
A range of USC revenue elasticities were also estimated. This pointed to an overall elasticity of 1.2,
much lower than the income tax elasticity. One interpretation from these results is that USC is more
Department of Finance | Tax Forecasting Methodological Review 2019 Page | 47
stable but less progressive than income tax.35 A further interesting finding was that the USC estimates
for PAYE and non-PAYE earners were identical, unlike that for income tax. This reflects the fact that
the USC treats taxpayers more similarly than income tax (under income tax, an extra PAYE credit
explains the difference between the PAYE and non-PAYE elasticities). In recent budgets many of the
findings from this paper have been incorporated in the forecasting process, with the income tax and
USC elasticity estimates updated as summarised in table 10.
Table 10: income tax elasticities, coefficients in successive budgets
IT PAYE Wage
IT PAYE Employm
ent
IT Schedule D Wage
IT Schedule
D Employm
ent
USC PAYE Wage
USC PAYE
Employment
USC Schedule
D Wage
USC Schedule
D Employm
ent
Budget 2015
2.15 0.90 1.67 0.90 2.15 0.90 1.67 0.90
Budget 2016
2.15 0.90 1.67 0.90 2.15 0.90 1.67 0.90
Budget 2017
2.15 0.90 1.67 0.90 2.15 0.90 1.67 0.90
Budget 2018
2.10 n/a 1.40 n/a 1.20 n/a 1.20 n/a
Source: JRP research work.
5.4.2 VAT
In 2018, a comprehensive study on VAT revenue elasticities was published under the JRP (Acheson et
al (2018)). This work was motivated by the large weighting of VAT in tax revenue and the need to better
understand the relationship between VAT revenues and underlying activity as captured by the elasticity.
The approach in the paper modelled VAT revenues from the household sector as a function of the tax
system, incomes, savings and expenditures using five waves of the Household Budget Survey (from
1994/95 to 2015/16). Overall, two aggregate VAT elasticities were derived based on both income and
expenditure growth. The responsiveness of VAT to gross income is helpful for thinking about the impact
of income growth on VAT and income tax revenues combined, whereas the expenditure based measure
is more useful for VAT forecasting purposes.
Multiple factors impact on the VAT elasticity, including changes in earnings, savings, expenditure
patterns and both VAT and income tax policies. Intuitively, the VAT elasticity increases if marginal
income tax rates decline, as households have more to spend on the margin (holding
savings/expenditure pattern behaviour constant). The elasticity falls as savings rise, as households
spend less on the margin.
The work provided rich insights into the VAT elasticity, particularly the fact that the elasticity was likely
to vary over time, unlike income tax and USC counterparts. The spillovers from the income tax system
to VAT and the importance of changing saving patterns were identified as important explanations for
the varying elasticity over time (more so than changing expenditure patterns). The results from this work
suggested a lower elasticity of 0.7 (relative to the Department’s value of one), however this was not
35 USC was still progressive as the elasticity exceeded unity.
Department of Finance | Tax Forecasting Methodological Review 2019 Page | 48
deemed suitable for the Department’s current forecasting approach as it was based solely on household
final expenditure (whereas the public sector also pays VAT and not all business will pass VAT on to the
final consumer). Overall, this pointed to the need for further work on the elasticity before any firm
recommendations were made.
5.4.3 Corporation Tax
The Department is engaged in ongoing research examining CT elasticities under the JRP. This research
aims to broaden the existing suite of micro-founded revenue elasticity papers produced by the
Department. Previous research in this area has been macro-founded and derived dynamic CT
buoyancy estimates using OECD panel data controlling for both discretionary policy changes and
business cycle fluctuations. The current project uses data on profit distributions to estimate elasticities.
Currently, the CT revenue elasticity is assumed to be one for forecasting purposes. Given that a revenue
elasticity can be expressed as the proportional change in revenue divided by the proportional change
in the tax base, the currently used value for the elasticity implies that the marginal tax rate equals the
average tax rate for CT. However, there are many reasons why the average tax rate would be lower
than the marginal tax rate for CT taxpayers (as in other tax heads) – for example the use of tax credits
such as the foreign tax credit or the R&D tax credit which will lower the net tax liability. Similarly, reliefs
such as double taxation relief would have the same effect. On the basis of this logic, the Group
considered that it is advisable to revise the CT revenue elasticity upward. Current research under the
JRP aims to provide a more definitive value for the elasticity making use of tax returns data. The Group
recommends waiting until the project results are available and tested before formally changing the
elasticity, but agreed that it is reasonable to assume an upward revision in the near future.
5.5 Lessons from Quarterly Data
An alternative to the analytical method above, centres on the analysis contained in the Department’s
annual taxation reports (Department of Finance, 2019a) and specifically co-movements between the
main tax heads and underlying macroeconomic series. In the figures below, taxes on a quarterly basis
(annualised) are plotted relative to a number of key macroeconomic variables over an extended sample
(2001 to 2019). This alternative approach is informative for a range of reasons, both in terms of
identifying tax trends, movements in effective rates and also in inputting into annual budget forecasts.
In figure 25, income tax receipts are plotted against compensation of employees. The figure highlights
the relationship between the two series. The ‘step change’ in the graph coincides with the crisis and
consolidation years, when taxes rose disproportionately in relation to wages as a result of policy-
induced changes. The strong recovery in the labour market since 2012 has had a favourable impact on
the income tax yield, with employment close to a fifth higher than the low point during the crisis. More
recently, wage inflation has also contributed to income taxes with the cumulative increase in income
taxes closely aligned with compensation of employees.
Department of Finance | Tax Forecasting Methodological Review 2019 Page | 49
Figure 25: income taxes and employee compensation, annualised data in logs
Source: Department of Finance.
In figure 26, CT receipts are plotted, again on an annualised basis, relative to a profitability series, as
captured here by net operating surplus. The upward trajectory in CT has been well documented in
recent years with economy-wide profitability measures also rising.
Figure 26: corporation taxes and net operating surplus, annualised data in logs
Source: Department of Finance.
In figure 27, VAT receipts are plotted against nominal personal consumption expenditure. Two distinct
patterns are evident. In the lead up to the financial crisis (red-dashed line), VAT receipts increased
rapidly partly in response to VAT received from new housing. If looked at in terms of level (rather than
logs), VAT receipts as a share of personal consumption peaked at 16 per cent during this period. The
second-post crisis era (blue dashed line) shows spending that is less VAT rich, with the share falling
y = 1.2106x - 3.9861R² = 0.7374
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Department of Finance | Tax Forecasting Methodological Review 2019 Page | 50
towards 14 per cent, reflecting weaker non-discretionary spending (which typically faces the standard
rate of VAT), an increase in the level of VAT-exempt spending, and more subdued construction levels.
Figure 27: VAT and nominal consumption, annualised data in logs
Source: Department of Finance.
In figure 28, excise receipts are plotted against goods related consumption spending (in real terms).
The relationship between the series has been affected in recent years by the introduction of ‘plain
packaging’ for tobacco related products. The latter resulted in a front loading of excise payments in
2017 and a consequent decline in 2018.
Figure 28: excises and consumption of goods, annualised data in logs
Source: Department of Finance.
The quarterly analysis outlined above provides an additional information base to the Department when
compiling annual tax forecasts. This analysis can form an important part of the judgement variable in
the Department’s standard forecasting equation, as set out in Chapter 3.
y = 0.9058x - 0.8925R² = 0.7364
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T
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Department of Finance | Tax Forecasting Methodological Review 2019 Page | 51
Chapter 6 Forecasting Recommendations
6.1 Introduction
This Chapter summarises the main deliberations of the TFMRG in relation to the four largest tax heads.
The main goal was to propose improvements to the forecasting methodologies where appropriate. In
some cases, there appears to be a need to change existing approaches, however this was not always
clear-cut particularly where a balance needs to be struck between complexity and parsimony.
6.2 Income tax and USC
Income tax forecasts were found to be reasonably accurate and amongst the best performing tax heads.
The forecasting approach within the Department is disaggregated, particularly in comparison to the
other large tax heads. Aside from the disaggregated approach, more aggregative data, such as the
relationship between income tax receipts and economy-wide compensation figures (depicted in Chapter
5) helps in terms of parsimony and are a useful cross-check of the income tax forecasts.
The Group welcomed the research undertaken and published in relation to income tax (including USC)
elasticities (Acheson et al, 2017). This work has facilitated forecasts and the new elasticities were
deemed to better capture compositional changes within the labour market given the use of distributional
income data. This work also negated the need for a separate employment elasticity. The Group noted
that the annual estimates of the elasticities were relatively stable, despite covering a period of labour
market expansion and contraction over 2003-2013.
While not a major topic of discussion in the Group, the quality and reliability of data in relation to the
labour market are also high. These data are less likely to be revised and are deemed to capture
underlying dynamics well, thereby, facilitating income tax forecasts. The Group also saw merit in
progressing the work undertaken in relation to the elasticity of taxable income (ETI). Further work in this
area could explore the potential to incorporate behavioural responses to tax policy changes, alongside
the standard static tax forecasts.
Overall, the Group welcomed the new research on income tax including USC elasticities and the
continued use of these elasticities going forward.
6.3 VAT
The Group noted the consistently strong performance of VAT forecasts over the past decade. Most of
the discussions focused on the macroeconomic driver (personal consumption), the composition of VAT
revenue, the role of online purchases and the housing market. The main concerns in relation to the
macroeconomic driver centred on its coverage as it includes food, rents and many items that are zero-
rated or exempt from VAT. Notwithstanding this, however, the Group underlined that the existing
macroeconomic driver was performing well and should be maintained. The Group also discussed the
Department of Finance | Tax Forecasting Methodological Review 2019 Page | 52
potential for spillovers in income tax policy, and changes in savings patterns and consumption
preferences to affect the VAT elasticity over time.
In the 2008 report, the role of the housing market on VAT forecasts featured heavily. Following this, the
Department’s VAT forecasting methodology was modified to include a proxy for new housing market
activity and related VAT. However, this approach was discontinued after Budget 2011 following the
marked downturn in the sector.36
Despite the strong VAT forecasting performance, the Group was of the opinion that the main VAT
forecast should be supplemented to include a housing market element given recent growth in the sector
and prospects going forward. One proposal (detailed in Annex C) involved splitting VAT into housing
and non-housing components (similar to the approach undertaken for the 2008 report). The former was
compiled based on data available from the Property Price Register (which is based on Revenue stamp
duty tax returns). This sub-component in turn is driven by forecasts for the housing market as proxied
by budget day projections for investment in dwellings. This dis-aggregated approach was tested from
Budget 2011 to Budget 2018 and yielded some improvements in terms of forecast accuracy. That said,
given the relatively small weight of housing related transactions, the impacts were small, albeit more
sizable in recent years as the housing sector has grown. For these reasons, the Group recommends
that future VAT forecasts be supplemented with a housing component.
6.4 Corporation Tax
Discussions on CT focused on the sizable forecasting errors, particularly in recent years. The pattern
of over-performance was discussed and whether this was indicative of either prudent bias in the forecast
or more structural factors. There was a strong consensus that CT forecasts needed to be improved
while a broad recognition that this was extremely challenging for well documented reasons. Notably,
the concentrated and lumpy nature of receipts and the difficulty in predicting corporate profits. Aside
from these factors, the Group discussed the role of allowances, credits, BEPS, losses and their potential
impact on forecasts.
The Group also recognised that CT remains a difficult tax to forecast internationally. Work by the Office
for Budget Responsibility in the UK (OBR, 2017) highlighted particular issues with CT with nearly two-
thirds of the gap between tax forecasts and outturns in 2015/16 accounted for by this tax head
(specifically, onshore related receipts including accounting treatments). The OBR also noted issues
around company specific factors that influences yields and a tendency to under predict outturns. Other
work more garnered to small open economies, such as research for Sweden (Shahnazarian and
Solberger, 2017) also noted high corporate tax forecast errors.
The Group discussed ongoing Department work in relation to CT elasticities (Chapter 5). An overarching
aim of this research project is to derive more data-driven estimates of the elasticity, which exploit
36 For example, data on housing completions (as proxied by ESB connections) show a decline from a peak 93,419 units in 2006 to a low of 8,301 units in 2013.
Department of Finance | Tax Forecasting Methodological Review 2019 Page | 53
information from the distribution of taxable income, and to see how it performs against the currently
used elasticity of one.
In the meantime, the Group carried out a series of tests for CT using higher retrospective elasticities. A
summary of these results is shown in Annex C. Looking at the past five years, there appears to be merit
in moving to a higher elasticity, in the region of 1.2 to 1.3, although a cautious and prudent approach is
warranted given the uncertainty in forecasting CT. Ultimately, the elasticity will be informed by ongoing
JRP work and more aggregative analysis on long-run trends between CT and profitability.
The Group also noted recent work published in the area of CT modelling by the Department
(McGuinness and Smyth, op.cit,). This paper highlighted a number of challenges in modelling CT in
part due to the concentrated nature of receipts in Ireland. In relation to forecasting however, the authors
highlighted that in-year estimates for CT tended to outperform a range of econometric models. This re-
affirms the importance of in-house knowledge, judgement and expertise within the Department and
Revenue in relation to CT. The research also pointed to benefits arising from the use of error correction
and vector error correction models as checks on year-ahead forecasts.
While the Group also explored alternative macroeconomic drivers (including net operating surplus),
there was a strong view that CT remained a particularly difficult tax to forecast with much of the errors
linked to the underlying macro driver. In light of the recent pattern of forecast errors, the Group
recommended the use of a higher elasticity and, in the absence of a notable better-performing macro,
the continued use of gross operating surplus.
6.5 Excise Duties
The mixed performance of excises and the heterogeneous nature of this tax was noted. The Group felt
that a more disaggregated forecast is warranted focusing on the main sub-components – alcohol, fuels
and tobacco. These items are also highly susceptible to policy changes and behavioural effects. Going
forward it is recommended that the current approach within the Department is supplemented with a
more disaggregated bottom-up approach building on Revenue’ s estimates from detailed excise data.
6.6 Other Taxes
Forecast errors are typically larger for the smaller tax heads, specifically capital taxes. The difficulty in
forecasting these taxes was noted to be principally due to the lack of appropriate macroeconomic
drivers given their transactions based nature. At the same time, forecasts are also heavily affected by
a relatively small number of transactions, many of which arise at end-year. These taxes are also
influenced by equity and asset market developments, which are inherently unpredictable. Finally, given
the impact of Brexit on trading arrangements, the Group advised on the need to closely monitor customs
duties and consider developing new approaches in the near-term. Also, in relation to Brexit the Group
noted the links between the accuracy of tax forecasts and economic turning points, suggesting the need
for an even greater degree of caution in tax forecasting in the near-term.
Department of Finance | Tax Forecasting Methodological Review 2019 Page | 54
6.7 Other Recommendations
The long-run tax to output elasticity should be close to unity. While the overall elasticity of close to one
still holds on the basis of both nominal GDP and GNI*, there has been a fall in the elasticity over time.
While tax forecasts are made on a bottom-up basis, it is recommended that a more formalised
aggregative tax forecast be prepared in conjunction with the disaggregated approach. Any significant
divergences between the two methods should be explained and documented.
Given the importance of general government metrics and the extent to which international agencies
forecast on this basis, the Group advised that the Department continue to develop its general
government tax forecasting capability. It is hoped that this could be a stand-alone exercise although
clearly linked to the exchequer based tax forecasts. The Group also felt that this should be easier to
facilitate given much richer data sources and modelling capabilities than were the case at the time of
the last Report.
6.8 Summary of Recommendations
Income Tax:
The Group endorsed the new income tax and USC elasticities. The Group also advised
that the elasticities be reviewed and re-estimated on a more periodic basis, to reflect the
latest labour market data.
VAT:
The Group recommends that existing forecasts for VAT be supplemented with a housing
specific component drawing on expected trends in the housing market.
Corporation Tax
Consideration should be given to increasing the elasticity used in the corporation tax
forecasts from its current value of one. In time, the elasticity should be informed by the
ongoing JRP research work in this area.
For in-year estimates, the ongoing and detailed exchange of information between
Revenue and the Department of Finance remains an integral part of the forecasting
process. For year-ahead estimates, future forecasts should be informed by newly
developed error correction models within the Department.
Excise Duties
The Group recommends moving to a new more disaggregated, bottom-up approach for
forecasting excise duties with a focus on the main sub-components - alcohol, fuels and
tobacco.
Department of Finance | Tax Forecasting Methodological Review 2019 Page | 55
Other Taxes
Given the potential changes to the future trading relationship between the United Kingdom
and the European Union, the Group recommended placing a greater focus on the
forecasting of customs duties.
Given the uncertainties surrounding the impact of Brexit on the Irish economy and the
strong links between economic turning points and the accuracy of tax forecasts, the Group
suggested the need for caution in the near-term.
Other Recommendations
The Group recommends that a formalised aggregative (top-down) tax forecast be
prepared in conjunction with the disaggregated approach. Any significant divergences
between the two forecasts should be explained and documented.
Given the importance of general government aggregates, the Department should continue
to develop its general government forecasts.
Department of Finance | Tax Forecasting Methodological Review 2019 Page | 56
Chapter 7 Conclusion
This is the third methodological review of the Department of Finance’s approach to tax forecasting with
a focus on tax developments over the past decade. Looking at recent literature in relation to tax
modelling both within Ireland and abroad, as well as approaches at institutions such as the Central
Bank of Ireland and the European Commission, the Group was of the view that the Department’s
approach was in line with and similar to international practice.
Over the past decade (the period since the last Report), the accuracy of forecasts was heavily skewed
by the scale of the economic and financial crisis that ensued from 2008. Excluding this period, the
Department’s tax forecasts performed reasonably well, although there has been a tendency for
forecasts to err on the low side.
Income taxes and VAT forecasts have performed well over the past decade with no major changes
advised. That said, in relation to income tax (including USC), recent research in relation to elasticities
has facilitated a greater understanding of tax developments and has assisted forecasts. VAT forecasts
should, however, be supplemented with a more disaggregated approach that explicitly recognises
housing market developments.
More recent years have been marked by large forecasting errors in relation to CT. Going forward, the
Group recommends that a higher elasticity be used for CT. For excises, there appears to be strong
merit in the Department adopting more of a disaggregated approach in generating an overall forecast.
Finally, in light of the increasing prominence of general government aggregates, the Group
recommends that the Department continues to develop more stand-alone general government tax
forecasts. These would provide a useful cross-check of the budget day exchequer based tax forecasts.
Department of Finance | Tax Forecasting Methodological Review 2019 Page | 57
Annex A: Group Membership and Meetings
The Group was comprised of officials from the Department of Finance and external bodies - the Central
Bank of Ireland, the Revenue Commissioners and the European Commission.
The Group consisted of: Matt McGann (Chair), David Hughes, Gerard McGuinness, Eimear Nolan, Leo
Redmond and Diarmaid Smyth (all Department of Finance); Rónán Hickey and Linda Kane (Central
Bank of Ireland); Keith Walsh, Fionnuala Ryan and Jean Acheson (Revenue Commissioners); Simona
Pojar and Allen Monks (European Commission).37
The Department hosted seven meetings between April and November, with meetings focusing on
specific tax heads, with the structure as follows:
Meeting 1 (26 April): introduction, terms of reference, tax forecasting performance.
Meeting 2 (24 May): approaches to forecasting in the European Commission, the Central Bank
of Ireland and the Revenue Commissioners. VAT – forecasting approach and performance.
Meeting 3 (11 June): VAT – elasticities and recommendations. Corporation tax - forecasting
approach and performance.
Meeting 4 (21 June): Corporation Tax – elasticities, micro data and recommendations. Income
Tax – forecasting approach and performance.
Meeting 5 (05 July): Income Tax – elasticities and recommendations. Excise Tax – forecasting
approach, performance and recommendations.
Meeting 6 (25 September): Capital taxes and Pay Related Social Insurance – forecasting
approach, performance and recommendations.
Meeting 7 (19 November): Drafting of report, main findings, recommendations and sign-off for
publication.
37 The group would also like to acknowledge the contribution of Thomas Conefrey (Central Bank of Ireland), Philip O’Rourke and Donnchadh O’Donovan (Revenue Commissioners).
Department of Finance | Tax Forecasting Methodological Review 2019 Page | 58
Annex B: Forecast Summary Decomposition
The Table below summarises the decomposition exercise from Chapter 4. Overall, forecast errors were
noted and then adjusted for the correct starting point, the removal of any judgements/adjustments and
the most recent macroeconomic outturn. This was done on sequential basis. Taking the case of
judgement as an example, if the removal of judgement from the equation led to a larger error, we can
assert that judgement improved the forecast (and vice-versa) – thereby “y” for yes is inserted in the
Table. Similarly, for the starting point, if the use of the correct starting point (the outturn) resulted in a
lower error, than we insert “y” to note that it improved the forecast. The final column reports the
percentage of times that either the correct starting point, the most recent macroeconomic driver and the
use of judgement improved the forecast.
Table B1: forecast decomposition summary
2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 %
Did the starting point improve forecasts? Yes (y) or No (n)
Income tax n y y n y n y y n n y 55
VAT y y n y y y n y y n n 64
Corporation tax y n y y n y n y y n y 64
Excise n y y n y n y n n y n 45
Did the macro driver outturn improve forecasts? Yes (y) or No (n)
Income tax y y n n n y n n y n n 36
VAT y y n y y y y n n y y 73
Corporation tax y y y n n n y y n y y 64
Excise y y n n y n y y y y n 64
Did judgement improve forecasts? Yes (y) or No (n)
Income tax n y y y n y y y y y y 82
VAT y y n y n n y y n n y 55
Corporation tax y y n y n y y n y n y 64
Excise y y y y y y n n n y y 73
Source: Department of Finance.
Department of Finance | Tax Forecasting Methodological Review 2019 Page | 59
Annex C: Alternative Forecasting Approaches
In the approaches documented below, budget day year-ahead projections were adjusted to reflect
alternative approaches involving different macroeconomic drivers or elasticities. In order to try and
maintain comparability with budget forecasts, changes were made on a sequential basis with all other
inputs left unchanged looking back at recent budgets.
VAT Forecasts
To investigate the role of housing in shaping VAT forecasts, the existing methodology was
supplemented with a housing, non-housing split. For housing, the Property Price Register details VAT
receipts on new housing transactions. This is available from 2010. These receipts on a year-ahead
basis are then driven by Budget day forecasts for activity in the housing market as proxied by the growth
rate and deflator for investment in new dwellings. The non-housing related element is driven by the
existing macroeconomic driver (consumer spending). The results are reported in the table C1 below.38
In 3 of the 5 years, the forecasts with housing were closer to the outturns although improvements were
relatively minor.
Table C1: retrospective VAT forecasts, year-ahead basis
€ billions 2014 2015 2016 2017 2018
Outturn 11.15 11.94 12.42 13.30 14.23
Budget forecast 10.74 11.77 12.86 13.37 14.09
Error 0.41 0.17 -0.44 -0.07 0.14
Forecast with housing included 10.75 11.79 12.89 13.41 14.15
Error 0.40 0.15 -0.47 -0.11 0.08
Improvement (Yes (y)/No (n)) y y n n y
Note: numbers may not sum due to rounding.
Source: Department of Finance.
38 The exercise was tested back to Budget 2011. The mean absolute error for the VAT forecast adjusted for housing was marginally lower than for the unadjusted version.
Department of Finance | Tax Forecasting Methodological Review 2019 Page | 60
CT Forecasts
For CT, a number of alternative approaches were investigated, a subset of which are shown below. The
first table (C2) show forecasts using an alternative higher elasticity with gross operating surplus.39 This
led to an improvement in forecast accuracy although improvements at times were marginal. The second
table (C3), looks at CT forecasts based on outturns for gross and net operating surplus, keeping the
elasticity at unity. While forecast errors were smaller relative to budget day projections, errors remain
large. This re-affirms the difficulties with the macroeconomic driver. While CT remains hard to predict,
there appears to be merit in moving away from an elasticity of unity towards a higher number in future
budgets.
Table C2: retrospective CT forecasts – alternative elasticity estimates, year-ahead basis
€ billions 2014 2015 2016 2017 2018
Outturn 4.61 6.87 7.35 8.20 10.39
Budget forecast 4.38 4.58 6.61 7.72 8.50
Error 0.23 2.30 0.74 0.48 1.88
Forecast with elasticity of 1.3 4.41 4.64 6.73 7.81 8.59
Error 0.21 2.23 0.62 0.39 1.79
Improvement (y/n) y y y y y
Note: numbers may not sum due to rounding. Source: Department of Finance.
Table C3: retrospective CT forecasts – alternative macroeconomic drivers, year-ahead basis
€ billions 2014 2015 2016 2017 2018
Outturn 4.61 6.87 7.35 8.20 10.39
Forecasts with outturns for gross and net operating surplus (elasticity of 1.0)
GOS outturn 4.77 7.00 6.29 8.24 9.09
Error -0.15 -0.12 1.06 -0.04 1.30
NOS outturn 4.83 5.98 6.13 7.84 8.90
Error -0.21 0.89 1.23 0.36 1.48
Note: numbers may not sum due to rounding. Source: Department of Finance.
39 The elasticity of 1.3 is based on long-run movements between the tax head and macro driver. However, the
latest available information for CT (the 2017 CT returns) indicates an average tax rate, in the aggregate, of 10 per cent. Given the revenue elasticity is the ratio of the marginal tax rate to the average tax rate, this implies an aggregate elasticity of 1.25 (or 1.3 after rounding). Aggregate data available at: https://revenue.ie/en/corporate/information-about-revenue/statistics/income-distributions/ct-calculation.aspx
Department of Finance | Tax Forecasting Methodological Review 2019 Page | 61
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