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The cyclically-adjusted primary balance:
A novel approach for the Euro area
Giovanni Carnazza (*)(*)
Paolo Liberati (§) (§)
Agnese Sacchi (**)
Abstract
This paper presents novel estimates for the cyclically-adjusted primary balance for 18 countries of the Euro area over years 1999-2017. We improve the methodology adopted by the European Commission by using quarterly rather than annual frequency data and providing accurate identification of the budgetary items whose response can be considered automatic to the economic cycle. This disaggregated outcome combined with high frequency data marks a significant improvement with respect to previous studies. The empirical analysis is implemented on two sub-periods to examine the impact of governments’ discretionary fiscal policy before and after the Great Recession. The most striking policy implication is that even though the budgetary policy of most European countries can be qualified in principle as anticyclical, this outcome has been weakened by the impact of discretionary policies of many governments especially after the crisis. The results are robust to the use of different de-trending methods.
Keywords: fiscal policy, cyclically-adjusted primary balance, automatic stabilizers, economic cycle, Great Recession, de-trending methods.
JEL Classification: H30; H61; H62; E62
(*)(*) University of Roma Tre, Department of Economics. E-mail: [email protected] (§) (§) University of Roma Tre, Department of Economics, E-mail: [email protected] ((**) Corresponding author - Sapienza University of Rome, Department of Economics and Law, Via del Castro Laurenziano 9, 00161, Rome (Italy). E-mail: [email protected]
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1. Introduction
The adoption of a balanced budget rule in almost all member states of the European
Union (EU) was aimed at affecting the budgetary policies with, at least, two objectives:
strengthening fiscal discipline (Hauptmeier et al., 2011); alleviating tensions in the
financial markets (Spilimbergo et al., 2009) in a context where national fiscal policies
coexist with a monetary policy moved at the supranational level.1
From a theoretical viewpoint, the new rule allows some flexibility for the policy-
makers as it defines the balanced budget in structural terms. This requires to split the
overall actual balance (OB) in two components: the cyclical balance (CB), which
isolates the action of the automatic stabilizers (Debrun and Kapoor, 2010) and whose
variations are due to factors that are mainly outside the direct control of governments;
and the cyclically-adjusted balance (CAB), which measures the impact of discretionary
fiscal policies implemented by governments. Since the CAB has to be equal to zero in
the medium term, the overall actual budget balance (OB) should only temporarily
deviate from the cyclical balance (CB).2
This new institutional setting has important consequences for the fiscal policy of the
countries of the Euro area. By limiting the use of discretionary policies to a zero-budget
balance, it implicitly confines the anticyclical function to the automatic response of the
public budget. This means that discretionary policies implemented by governments
might possibly fail to strengthen the anticyclical impact of automatic stabilizers or even
1 According to the ‘balanced budget rule’ introduced by the Fiscal Stability Treaty (FST), national budget has to be in balance (or surplus). More precisely, the structural budget balance should not exceed a country-specific Medium-Term budgetary Objective (MTO), which at most can be set at 0.5% of Gross Domestic Product (GDP) for member states with a debt-to-GDP ratio exceeding 60%, or at most 1.0% of GDP for states with debt levels below the 60% threshold. With regard to the moral hazard problem implicit in this framework, see Frankel (2015). It is worth noting that the recent fiscal tightening is part of a broader path started with the Maastricht Treaty, before the Monetary Union.2 By definition, the CAB is obtained by subtracting the CB from the OB.
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go in the opposite direction. Thus, in both cases, they might compromise the
anticyclical impulse, an information that is, in fact, crucial for policy-makers and that
has extremely important consequences on their decisions on how to spend and tax
(Blanchard, 1990; Larch and Turrini, 2010, Claeys et al., 2016).
It is worth noting, however, that the way in which policy-makers implement
discretionary fiscal policies is endogenous to the method in which the CAB is
calculated. To this regard, the empirical methodology adopted by the European
Commission (EC) has debatable characteristics, as it could lead a country to implement
a restrictive budgetary policy while experiencing a recessionary phase of the economic
cycle (Afonso and Clayes, 2008; D’Auria et al., 2010; Mourre et al., 2013; Mourre et
al., 2014;).
A crucial point of this vicious circle consists in the estimation of the non-observable
potential GDP by using a production function where the potential contribution of
labour depends on the unemployment rate consistent with the NAWRU (Non-
Accelerating Wage Rate of Unemployment). The latter, however, is estimated through
a methodology that can imply a strong variability over time and that makes the
estimated unemployment excessively dependent on the current level of unemployment
and on the wage rigidity in the short term (Gordon, 1997; Holden and Nymoen, 2002;
Schreiber and Wolters, 2007; FitzGerald, 2014). Faced with these problems, the
European Commission has recently revised the method for calculating the CAB (Havik
et al., 2014); however, this adjustment applies only to some countries of the Eurozone,
while it does not in others for which the detected critical issues remain.
Our paper tries to fill this gap by providing new estimates of the CAB that enrich the
information set and the data available for the policy-maker in order to properly assess
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the impact of discretionary fiscal policies as well as to identify the sign of the automatic
response of the public budget. In particular, this information is provided for 18
countries of the Euro area using quarterly budgetary data from 1999 to 2017. By
considering country-specific responses combined with high frequency, the paper marks
a significant improvement with respect to previous contributions aimed at analysing the
cyclical properties of the fiscal aggregates in the Euro area as a whole (see, for
example, Paredes et al., 2014). More importantly, it is in line with the good practice of
relying on infra-annual budgetary data with the aim of fiscal forecasting (Onorante et
al., 2010) and simulating fiscal policies targets in the monetary union (Brück and
Zwiener, 2006).
To this purpose, the focus of our paper is on the evolution of the cyclically-adjusted
primary balance (CAPB), i.e. excluding interest payments, over time.3 In detail, we
provide new series using an alternative empirical methodology based on Burnside and
Meshcheryakova (2005a; 2005b), which is not affected by the dependence among the
actual unemployment rate, the NAWRU and the potential GDP. The CAPB is
estimated for two non-overlapping sub-periods (1999-2007 and 2008-2017)4 in order to
capture the different fiscal attitudes of the countries of the Euro area before and after
the recent Great Recession started in 2008. Note that 2008 represents a structural break
in our analysis, as it is thought to give rise to a period of ‘secular stagnation’
(Summers, 2014) experienced by many Euro area countries (Craft, 2016). This might
involve relevant changes in the conduct of national fiscal policies, and, to this respect,
3 Interest expenditures are excluded from the analysis as they are not under the direct control of the policy-makers, and to avoid discrepancies in measuring interest payments. In doing that, we also follow the recent approach of the European Fiscal Board (2019) that uses the primary balance to evaluate the combined impact of discretionary fiscal policies and automatic stabilizers. 4 Due to lack of data, it has not been possible to estimate the CAPB for Estonia. The availability of data in some countries is reduced in relation to the first sub-period: Austria (2001-2007); Germany (2002–2007); Ireland (2002–2007); Luxembourg (2002–2007); Malta (2000–2007).
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the estimation of the CAPB will provide important information about the degree in
which discretionary policies may be affected by the institutional setting.
The most striking policy implication deriving from our estimates is that, even
though the automatic stabilizers have widely acted in an anticyclical way in almost all
countries of the Euro area, this impact has often been severely weakened by
discretionary policies by governments, especially after the Great Recession. This
outcome suggests that discretionary policies, rather than being the result of an
intentional pro-cyclical behaviour of policy-makers, can be severely constrained by the
institutional setting. Furthermore, the fact that this occurs in a great number of
countries of the Euro area also suggests that the pro-cyclical impact of discretionary
policies may be uncorrelated with the specific way in which national governments
decide to tax and spend.
It is worth noting that this outcome differs from the results obtained by previous
studies, according to which the impact of the automatic stabilizers in cushioning the
cycle is often stronger than that of the discretionary changes (Lane, 2003; Fatás and
Mihov, 2009; Egert, 2010). One possible reason of this difference is that those studies
analyze the fiscal policy stance using annual data and confine the analysis to years
immediately after the recent economic crisis, thus only partially capturing its impact.
At the same time, our results are more in line with the recent empirical evidence, based
on annual data, provided by the European Fiscal Board (2019) and Paredes et al.
(2014), concluding for a high frequency of pro-cyclical behavior of important fiscal
aggregates in EU countries.
The rest of the paper is organized as follows. Section 2 describes the empirical
methodology to estimate the CAPB for countries of our sample. Section 3 discusses the
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main results and the policy implications linked thereto, while some sensitivity checks
are included in Section 4. Section 5 concludes.
2. The empirical methodology
2.1 Selecting budgetary items
The first step of the methodology consists in seasonally adjusting the budgetary data of
the 18 EU member states through the TRAMO/SEATS approach (Bee Dagum and
Bianconcini, 2016). We use a model-based approach, which conceives the series as the
finite part of the realization of a stochastic process, whose probabilistic structure is
described by an Auto Regressive Integrated Moving Average (ARIMA) model.5 The
second step consists in converting budgetary data in real terms by using the GDP
deflator. Finally, fiscal series have been decomposed into the trend and the cycle
components by using the Hodrick-Prescott (HP) filter (Hodrick and Prescott, 1997).
This choice is aware of the criticisms that have been raised on the use of the HP
filter (e.g., Harvey and Jaeger, 1993; Cogley and Nason, 1995; Guay and St-Amant,
2005; Hamilton, 2018), but also of the fact that the main critique rests on an inadequate
definition of the distortion it may create (Pedersen, 2001).6 More recently, Sakarya and
De Jong (2019) provide an additional formal justification of the use of the HP filter
5 In detail, this seasonal adjustment procedure is a model-based approach which consists of two parts: the first part (TRAMO, Time Series Regression with Arima Noise) preliminarily eliminates the deterministic effects from the time series; it interpolates any missing observations and identifies and estimates the ARIMA model that best fits the data. The second part (SEATS, Signal Extraction in ARIMA Time Series), based on the ARIMA model and the deterministic effects previously identified, carries out the real seasonal adjustment of the historical series. In this context, the identification of the so-called deterministic calendar effects carried out in TRAMO plays an important role, as the identification of the ARIMA model requires the historical series to be purely stochastic. Subsequently, these effects are attributed by SEATS to the seasonal component.6 While showing that the optimal value of the smoothing parameter of the HP filter, λ, lies in the range 1,000-1,050 for quarterly data, the difference in the distortionary effect when using λ =1,600, for which Hodrick and Prescott (1997) provide a theoretical background, is weak and the difference in computed business cycle stylized facts is small. For details about the procedure for adjusting the smoothing parameter for the data frequency, see Ravn and Uhlig (2002) and De Jong and Sakarya (2016).
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when the time series have not an exponential path, as in our case, and Drehmann and
Yetman (2018) underline that any filter lacks a clear theoretical foundation, thus
holding the HP filtered trend as preferred.7
With available quarterly data, the HP decomposition consists in choosing the values
y t¿ of a given variable at time t that minimize the following quadratic loss objective
function:
∑t=1
T
( y t− y t¿)2+λ∑
t=2
T −1
[ ( y t+1¿ − y t
¿ )−( y t¿− y t−1
¿ ) ]2 (1)
This minimization problem leaves a degree of freedom in relation to the choice of the
parameter λ. To this regard, the HP filter establishes a trade-off between the adherence
of the trend to the historical series and the regularity of the trend itself. In particular, by
setting λ=0, the trend that minimizes the previous function collapses to the original
series (y t= y t¿); if λ →+∞, the trend tends to a linear form.8 In our case, the HP filter
has been applied with a smoothing parameter λ equal to 1,600.
The methodology used in this paper to estimate the CAPB includes at least two
potential advantages for the policy-makers. The former concerns the use of quarterly
budgetary data. In detail, the availability of intra-annual fiscal information allows, for
instance, to acknowledge fiscal slippages early enough, and implement corrective
measures (Onorante et al., 2010). Moreover, providing high frequency budgetary data
to the policy-makers allow them to rely on information actually available at the time
7 In any case, we provide further discussion on alternative filters, also providing some robustness checks, in Section 4.8 Hodrick and Prescott (1997) show how their results do not significantly depend on the value of the smoothing parameter, λ, unless this value tends to infinity. Robustness checks with different values of the smoothing parameter are provided in Section 4.
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budgetary decisions were taken and not on the basis of the latest available (ex post) data
(Golinelli and Momigliano, 2006). Finally, employing those short-term fiscal indicators
could be relevant not only at a national level but also at the European level for fiscal
surveillance and forecasting issues.
The second advantage refers to the accurate identification of the budgetary items
whose response can be considered automatic with respect to the fluctuations of the
economic cycle is needed (Burnside and Meshcheryakova, 2005a; 2005b).9 This
identification could be affected by the shortcoming that even though it is known that
there are items in the public budget that are mostly affected by automatic responses, the
possibility that their movements are at least partially determined by discretionary
changes cannot be excluded. Thus, the choice to label a budgetary item as ‘automatic’
has been made when it can be safely assumed that the discretionary component may
represent only a small part of the overall trend. To this purpose, two different types of
information have been considered: the first is based on a qualitative examination of the
economic nature of such series; the second concerns the quantitative characteristics of
the historical budgetary series.
From a qualitative perspective, some budgetary items have not been classified as
automatic when it is known that they have been targeted by discretionary policies, even
though their cyclical component may have shown a significant correlation with the
economic cycle. This is the usual case, for instance, of capital taxes, capital transfers
and investments, whose trend is mainly due to discretionary policies of the policy-
makers.10
9 For example, on the expenditure side, some welfare programs, such as the unemployment benefits, automatically activate in response to negative fluctuations of the economic cycle showing a natural anticyclical response.10 It is worth noting that when one uses the CAB as an index of discretionary changes in fiscal policy, there is no adjustment for the one-off measures, as those measures are not related with the business cycle
8
From a quantitative point of view, instead, equation (1) has been applied to all items
of the public budget and to GDP in order to separate trend and cycle.11 This is done by
applying the HP filter to a generic item Y t, where the subscript t indicates quarterly
frequency, to obtain Y t=Y tc+Y t
¿, where Y tc is the cyclical component and Y t
¿ is the trend
component. Then, the cyclical component Y tc of each budgetary item has been
correlated with the cyclical component of GDP (i.e. the economic cycle). The aim is to
select only those budgetary items showing statistically significant correlations under
the assumption that, if the correlation is statistically significant, the cyclical movements
of each item are mostly due to the automatic response.12 Overall, this thorough
approach is not valuable per se but, as explained later, to the extent to which it provides
to the policy-makers specific information on which budgetary items are de facto, and
not only de jure, more reactive to GDP fluctuations and over different periods.
The outcome of this selection process is reported in Table 1, providing information
for each country in both sub-periods (1999-2007 and 2008-2017).
[Table 1 about here]
It is worth noting that for the cyclical component of direct and indirect taxes the
statistically significant correlation with the economic cycle occurs more frequently,
followed by social benefits on the expenditure side. Furthermore, as expected, current
budgetary variables are more correlated with the cycle than non-recurrent budgetary
but they depend on a discretionary intervention (Burnside and Meshcheryakova, 2005a; 2005b). The rationale of this choice relies on the seminal paper by Blanchard (1990), which describes the right uses and the potential abuses of the CAB.11 Results are not reported but they are available upon request.12 In order to be taken into consideration for the calculation of the CAPB, the elasticities of each selected budgetary item have also to be statistically significant.
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items, since both capital taxes and expenditures are usually not induced by the
economic cycle.
From a policy point of view, this preliminary analysis offers an interesting
perspective, highlighting the different reactions of the national public budget
components to the business cycle over time, distinguishing two different sub-periods.
Indeed, Table 1 reveals that the statistical significance of automatic responses is more
pronounced in the second sub-period, which includes the Great Recession. This aspect,
to some extent expected, is particularly evident on the revenue side, with regard to
indirect taxes and net social contributions, and, on the expenditure side, with regard to
intermediate consumption and social benefits. Considering the effect of the automatic
stabilizers, national fiscal policies seem to have worked in an anticyclical way. On the
contrary, capital expenditures, which are part of the discretionary item of the budget,
have showed a more frequent correlation with the business cycle in the first sub-period,
while discretionary capital expenditures policies have been characterized by an almost
total lack of connection with the economic cycle in the second sub-period.
2.2 Calculating the CAPB
Since Table 1 shows some heterogeneity of the correlation between single budgetary
items and the economic cycle in our sample, a country-specific approach is adopted to
calculate the CAPB in order to take into account those different reactions.
To this purpose, for each selected budgetary item and for each country – as resulting
from Table 1 – we have first calculated the elasticity of their cyclical components with
respect to the cyclical component of GDP by using the OLS technique (after
transforming the selected series in logarithms) as follows:
10
ykjtc =θkj gkt
c +ε kjt (2)
where ykjtc represents (the logarithm of) the cyclical component of the budgetary item j
of country k; gktc is (the logarithm of) the cyclical component of GDP for country k; θkj
is the country-specific elasticity of the budgetary item j, to which we are interested in,
and ε kjt is the error term.
Then, the estimated elasticities (θ̂kj) from equation (2) are used to calculate the
CAPB by clearing the actual public revenues and expenditures of the corresponding
cyclical components. In symbols:
CAPBkt= {Rkt−X kt }⏟Actual primary balance
−¿¿ (3)
where Rkt represents actual total revenues in country k , X kt stands for actual total
expenditures (excluding interest on public debt), Rkjt and X kjt are, respectively, single
revenue and expenditure items for which elasticities have been estimated according to
equation (2). It is worth recalling that these items are, by definition, those whose
reaction is mostly automatic, and for which the elasticities should capture the size of
these automatic responses that are removed from the actual figures to calculate the
CAPB.
The interpretation of equation (3) is straightforward: if the cyclical component of
GDP would be zero, then gktc =0, and ykjt
c =0, which implies exp (−θ̂kj gkjtc )=1, and no
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cyclical adjustment would be needed. When gktc >0 and θ̂kj>0 , the adjustment would be
negative. The intuition is that during a positive stage of the economic cycle, some items
of the public budget rise automatically (e.g., income taxes and VAT) because the
economy is growing. This means that, without the positive cycle, that specific item
would have not risen in the same way; thus, to neutralise the impact of the cycle, the
adjustment has to be negative. The opposite holds in the case of a negative phase of the
economic cycle. Furthermore, a specular reasoning can be made for those items for
which θ̂kj<0when gktc >0 such as, for example, the unemployment benefits and other
social transfers.
3. Main results and policy implications
3.1 Correlations between the cyclical components of the primary balance and the
economic cycle
Figures 1 and 2 provide a visual impact of the correlation between the cyclical
component of the primary balance (the continuous line), i.e. the automatic stabilizers,
and the cyclical component of GDP (the dotted line), i.e. the economic cycle, before
and after the crisis respectively, both expressed as a percentage of the HP trend of the
real GDP. In each figure, the comparison between the two lines identifies the impact of
the automatic stabilizers. If the automatic stabilizers work, it is expected that when the
cyclical component of GDP lies in the negative quadrant, the cyclical component of the
primary balance lies in the positive one.13
13 In order to visualize the countercyclical action of the automatic stabilizers, the continuous line, which represents the cyclical component of the primary balance, is expressed by the difference between the CAPB and the actual primary balance. In this case, a positive value of the cyclical component implies a net expenditure, while a negative value a net revenue. Put differently, the continuous line can also be interpreted as the correction that has to be applied to the actual primary balance to get the CAPB. For example, in Figure 2 for Austria 2008_Q1 the value is about –2 percent, which means that the actual primary balance is higher than the CAPB due to the impact of automatic stabilizers amounting to about 2
12
With regard to the period after the crisis, i.e. 2008-2017 (Figure 2), this effect is
more evident in 11 of 18 countries of our sample: Austria, Belgium, Finland, France,
Germany, Italy, Latvia, Lithuania, the Netherlands, Slovakia and Slovenia. In the
remaining countries, the impact of the automatic stabilizers is either weaker or absent
(as in Cyprus, Ireland, Luxembourg, Portugal, and Spain). The comparison with the
pre-crisis period in Figure 1 confirms these characteristics with some interesting
exceptions: Austria and Italia exhibit a weaker impact of the automatic stabilizers,
while Cyprus, Portugal, and Spain reveal a more anticyclical path.
Thus, even though at a different degree, there is the general impression that
automatic stabilizers work in almost all countries in the period considered. From a
policy point of view, the interesting question now arises about whether and in which
country this anticyclical impact is either reinforced or mitigated by discretionary fiscal
policies.
[Figures 1 & 2 about here]
3.2 The fiscal policy stance
In order to investigate the relationship between the impact of the automatic stabilizers
and that of discretionary fiscal policies, Table 2 reports the correlations of both the
actual primary balance and the CAPB with the economic cycle by each country and for
each sub-period. In general, a positive correlation means that the primary balance
percentage points of the HP trend of the real GDP. The opposite holds when the continuous line lies in the positive quadrant. In this case, the automatic stabilizers have worsened the actual primary balance, which is thus lower than the CAPB.
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improves during the expansionary phase of the cycle and worsens during recessions.
This features the traditional function of an anticyclical budgetary policy, with an
important caveat: the actual primary balance contains both automatic and discretionary
fiscal policies, whereas the CAPB isolates the impact of discretionary policies only.
[Table 2 about here]
Table 2 reports two important policy outcomes of the analysis. The first is that the
actual primary balance shows some anticyclical power in most countries. In particular,
there are countries in which the anticyclical impact holds in both sub-periods (Austria,
Finland, France, Germany, Luxembourg, the Netherlands); and countries in which this
impact is less stable (Belgium, Cyprus, Italy, Lithuania, Slovakia, and Spain), together
with countries for which the impact is neutral (Ireland, Malta, Portugal, and Slovenia).14
However, the second even more important result from a policy perspective is that
most of the countries, where an anticyclical impact of the actual primary balance has
been found, fail to implement anticyclical discretionary policies. This is evident not
only in the first sub-period, where discretionary fiscal policies have an anticyclical
impact only in Austria, Finland, and Luxembourg, but also – and more surprisingly –
during and after the Great Recession, where a robust statistically significant anticyclical
impact is found only in Greece. If one combines this outcome with the fact that in the
second sub-period the anticyclical impact of the actual primary balance is more
widespread across countries, it can be concluded that discretionary budgetary policies
14 It is worth noting that Greece and Latvia, in the first sub-period, shows a pro-cyclical impact of the actual primary budget balance.
14
in many countries have worked in the opposite direction to the automatic stabilizers,
thus weakening their impact.
In a nutshell, the sign of discretionary fiscal policies in those countries suggests that
the bulk of the economic stabilization is now provided by the automatic stabilizers,
leaving the discretionary side of the policies to obey the institutional rule of balancing
the structural budget, regardless of the stage of the economic cycle. The great number
of countries for which this occurs also marks a fundamental outcome according to
which the impact of discretionary fiscal policies seems rather independent of the
specific tax and spending provisions implemented in different countries. This policy
conclusion is reinforced by the observations that our result also holds for countries with
very different fiscal positions in terms of debt-to-GDP ratios.15 Thus, if the aim would
be to reinforce the action of the automatic stabilizers, the policy conclusion one can
draw from the previous outcome is that tax and spending policies should not be used as
they have been used during and after the crisis.
Our findings – even though with different methodologies – are in line with and
extend the conclusions proposed by Debrun and Kapoor (2010). Since their analysis
includes a period before the recent economic crisis, our results not only confirm their
conclusion that before 2008 the power of the discretionary policy is weaker than that of
automatic stabilizers, but also provide evidence that after the crisis the power of the
discretionary policy to counteract adverse economic cycles has even weakened.
In our study, pro-cyclicality or a-cyclicality is, indeed, a frequent outcome of the
estimates. From the point of view of national policy-makers, this would suggest that the
15 In detail, it happens for: ‘very high-debt’ countries (i.e. with debt ratios above 90% of GDP) such as Italy and Belgium; ‘high-debt’ countries (i.e. between 60% and 90% of GDP) such as Austria, Germany, France and the Netherlands; ‘low-debt’ countries (i.e. below 60% of GDP) such as Finland, Luxembourg, Slovakia, Latvia and Lithuania. We adopt the thresholds proposed by the European Fiscal Board (2019) for defining very high-, high-, and low-debt countries.
15
European reforms that have moved the attention from nominal to structural variables,
with the aim of reducing pro-cyclicality, do not appear to have encouraged anticyclical
discretionary policies. As a policy suggestion, an improvement of the overall
anticyclical impact of the public budgets should be possible by changing at least two
elements. The first should consists in removing those rules based on unobservable
variables whose estimates are often a source of uncertainty, and the second in enlarging
the time horizon within which targets have to be achieved.16
4. Sensitivity analysis
In order to check the robustness of the results obtained with the standard application of
the HP filter, we replicate the previous estimates by using different methods of de-
trending.17 First, we used the HP filter with five new values of the parameter based on
the contribution provided by Pedersen (2001).18 Second, we apply new band-pass filters
have been developed over time, such as the Baxter-King filter (Baxter and King, 1999)
and the Christiano-Fitzgerald filter (Christiano and Fitzgerald, 2003).19 However, it is
worth noting that the Baxter-King filter truncates observations from both the beginning
and the end of the original sample, which implies the loss of important information
16 For a discussion about the uncertainty about countries’ compliance with the Stability and Growth Pact, see Ferré (2012). The perspective of the new member states is dealt with in Orban and Szapáry (2004).17 We decide to not take into consideration the Beveridge-Nelson approach (Beveridge and Nelson, 1981) as an alternative method of de-trending. Indeed, as highlighted in Bouthevillain et al. (2001), the main problem of that approach is the theoretical assumption according to which the cyclical component is highly correlated with the first differences of the original series especially in the typical case of a positively auto-correlated original series. 18 In detail, Pedersen (2001) identifies five optimal values for the smoothing parameter when using the HP filter: =1,007; =1,038; =1,041; =1,103; =1,269.19 The band-pass filter by Baxter and King (1999) and that by Christiano and Fitzgerald (2003) are very similar in their design; they only differ in the approximation of the ideal band-pass filter to a filter that can be applied in reality. An approximation of the ideal filter is necessary as the ideal filter requires an infinite-order moving average which implies a data series of infinite length. The most important difference is the amount of output data, resulting from the different assumptions with respect to the symmetry of the weights: Baxter and King assume symmetric weights while Christiano and Fitzgerald omit this assumption.
16
about the response of discretionary fiscal policy in the period before and after 2008.
The same would happen in the case of the Hamilton filter (Hamilton, 2018), with the
loss of relevant data whose extension depends on the value of the back-shifting
parameter at the beginning of the sample. Additionally, such assumption is not neutral
and it significantly influences the properties of the estimated cyclical component,
implying a filter that eliminates two-year cycles and overweight frequencies that are
longer than the typical business cycle frequencies. This tends also to create
inconsistencies with the NBER business cycle definition (Schüler, 2018).20
For all these reasons, to the purpose of providing robustness checks, we decide to
use only the Christiano-Fitzgerald filter, which does not cause any loss of data. Finally,
we apply a polynomial filter of three different orders (linear, quadratic and cubic time
trend).21 Results are reported in the Appendix (Table A1 and Table A2 for the two sub-
periods, respectively).
When using the HP filter with different value of the smoothing parameter , we get
the same results as those previously observed, regardless of the five selected values of
.22 In relation to the other methods of de-trending, the soundness of our findings is
confirmed. In relation to the period 1999-2007, most of the countries show stable
results as reported in Table A1 in the Appendix. In particular, no significant differences
emerge in the relationships between the primary budget balances and the economic
20 In detail, Hamilton (2018) proposes to use simple forecasts of a series to remove their cyclical nature by fitting values from a regression of a variable y on four lagged values of the same variables back-shifted by a number of observations that depends on both the data frequency and the nature of the series. Following this approach, we would lose the first eleven observations for each country, using quarterly data, with a significant loss of meaning especially in relation to the response of discretionary fiscal policy to GDP changes in the second sub-period.21 Historically, the removal of linear (or log-linear) trends has been a standard method for separating trends and cycles. In any case, it should be kept in mind that recent evidence suggests that many macroeconomic series contain unit root (stochastic trend) components that would not be removed by this procedure (Baxter and King, 1999). For this reason, the results deriving from the use of this kind of filter have to be interpreted in a careful way.22 Results are not reported but they are available upon request.
17
cycle. In any case, a small group of countries show some differences in the estimated
correlations of the primary budget balances with the economic cycle.
This represents an interesting aspect, especially when we compare the results from
the sensitivity analysis between the two sub-periods. Estimates for the period 2008-
2017 are reported in Table A2 in the Appendix. With the exception of the Netherlands,
the sensitivity analysis confirms the direction of the discretionary government’s
intervention aimed at counteracting the anticyclical effects of the automatic stabilizers
in the post-crisis period.
5. Conclusions
This paper has estimated the CAPB for 18 countries of the Euro area using quarterly
budgetary data for the period 1999-2017. The methodology here used differs from that
based on the EC approach and gives the opportunity to better characterize the
discretionary fiscal policy in two different sub-periods marked off by the recent
economic crisis.
We find that, especially in relation to the period after the crisis (i.e. 2008-2017), the
fiscal policy could be defined as anticyclical in most countries of our sample. However,
once the automatic impact of the economic cycle is removed from the budget balance,
the CAPB loses its positive correlation with the business cycle. This means that
discretionary fiscal policies have weakened the automatic anticyclical nature of the
budgetary policy. Concerning the effectiveness of the fiscal policy, this result
highlights to what extent the discretionary action of national policy-makers has failed
to provide an expansionary contribution in the most severe phases of the economic
crisis.
18
These findings challenge the core of recent reforms of the European fiscal rules
aimed at reducing the pro-cyclicality of the public budgets since, in most cases, they
have neither reduced pro-cyclicality nor encouraged anti-cyclicality. From a policy
viewpoint, we can argue that this unsatisfactory outcome may be partially due to two
main elements. The first is the excessive difficulty in managing the European fiscal
rules, which are mainly based on variables that are not observable and that need to be
estimated under debatable assumptions. The second is that the time horizon in the
application of the rules is too short (i.e. the annual basis), a characteristic that may
particularly affect the incentives to activate those public investments that are
anticyclical in the medium term. In this perspective, the possibility of reforming at least
these two settings of the European fiscal rules should be seriously considered.
Acknowledgements
We thank the participants at the: XXXI Conference of the Italian Society of Public Economics; XXX Conference of the Italian Economic Society; INFER Workshop on New Challenges for Fiscal Policy for their comments. Special thanks are due to Massimo Bordignon, Daniela Monacelli, Alberto Petrucci and Luìs Pinheiro de Matos for insightful suggestions on a previous version of the paper. We are also indebted to anonymous referees and to the Editor for valuable comments on the paper.
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Figures
Figure 1 – The economic cycle and the cyclical component of the primary balance,
by country (1999-2007)
2001
_Q1
2001
_Q2
2001
_Q3
2001
_Q4
2002
_Q1
2002
_Q2
2002
_Q3
2002
_Q4
2003
_Q1
2003
_Q2
2003
_Q3
2003
_Q4
2004
_Q1
2004
_Q2
2004
_Q3
2004
_Q4
2005
_Q1
2005
_Q2
2005
_Q3
2005
_Q4
2006
_Q1
2006
_Q2
2006
_Q3
2006
_Q4
2007
_Q1
2007
_Q2
2007
_Q3
2007
_Q4
-2%
-1%
0%
1%
2%
Austria
1999
_Q1
1999
_Q2
1999
_Q3
1999
_Q4
2000
_Q1
2000
_Q2
2000
_Q3
2000
_Q4
2001
_Q1
2001
_Q2
2001
_Q3
2001
_Q4
2002
_Q1
2002
_Q2
2002
_Q3
2002
_Q4
2003
_Q1
2003
_Q2
2003
_Q3
2003
_Q4
2004
_Q1
2004
_Q2
2004
_Q3
2004
_Q4
2005
_Q1
2005
_Q2
2005
_Q3
2005
_Q4
2006
_Q1
2006
_Q2
2006
_Q3
2006
_Q4
2007
_Q1
2007
_Q2
2007
_Q3
2007
_Q4
-2%
-1%
0%
1%
2%
Belgium
22
1999
_Q1
1999
_Q2
1999
_Q3
1999
_Q4
2000
_Q1
2000
_Q2
2000
_Q3
2000
_Q4
2001
_Q1
2001
_Q2
2001
_Q3
2001
_Q4
2002
_Q1
2002
_Q2
2002
_Q3
2002
_Q4
2003
_Q1
2003
_Q2
2003
_Q3
2003
_Q4
2004
_Q1
2004
_Q2
2004
_Q3
2004
_Q4
2005
_Q1
2005
_Q2
2005
_Q3
2005
_Q4
2006
_Q1
2006
_Q2
2006
_Q3
2006
_Q4
2007
_Q1
2007
_Q2
2007
_Q3
2007
_Q4
-3%
-2%
-1%
0%
1%
2%
3%
Cyprus
1999
_Q1
1999
_Q2
1999
_Q3
1999
_Q4
2000
_Q1
2000
_Q2
2000
_Q3
2000
_Q4
2001
_Q1
2001
_Q2
2001
_Q3
2001
_Q4
2002
_Q1
2002
_Q2
2002
_Q3
2002
_Q4
2003
_Q1
2003
_Q2
2003
_Q3
2003
_Q4
2004
_Q1
2004
_Q2
2004
_Q3
2004
_Q4
2005
_Q1
2005
_Q2
2005
_Q3
2005
_Q4
2006
_Q1
2006
_Q2
2006
_Q3
2006
_Q4
2007
_Q1
2007
_Q2
2007
_Q3
2007
_Q4
-4%
-2%
0%
2%
4%
Finland19
99_Q
119
99_Q
219
99_Q
319
99_Q
420
00_Q
120
00_Q
220
00_Q
320
00_Q
420
01_Q
120
01_Q
220
01_Q
320
01_Q
420
02_Q
120
02_Q
220
02_Q
320
02_Q
420
03_Q
120
03_Q
220
03_Q
320
03_Q
420
04_Q
120
04_Q
220
04_Q
320
04_Q
420
05_Q
120
05_Q
220
05_Q
320
05_Q
420
06_Q
120
06_Q
220
06_Q
320
06_Q
420
07_Q
120
07_Q
220
07_Q
320
07_Q
4
-2%
-1%
0%
1%
2%
France
2002
_Q1
2002
_Q2
2002
_Q3
2002
_Q4
2003
_Q1
2003
_Q2
2003
_Q3
2003
_Q4
2004
_Q1
2004
_Q2
2004
_Q3
2004
_Q4
2005
_Q1
2005
_Q2
2005
_Q3
2005
_Q4
2006
_Q1
2006
_Q2
2006
_Q3
2006
_Q4
2007
_Q1
2007
_Q2
2007
_Q3
2007
_Q4
-3%
-2%
-1%
0%
1%
2%
Germany
1999
_Q1
1999
_Q2
1999
_Q3
1999
_Q4
2000
_Q1
2000
_Q2
2000
_Q3
2000
_Q4
2001
_Q1
2001
_Q2
2001
_Q3
2001
_Q4
2002
_Q1
2002
_Q2
2002
_Q3
2002
_Q4
2003
_Q1
2003
_Q2
2003
_Q3
2003
_Q4
2004
_Q1
2004
_Q2
2004
_Q3
2004
_Q4
2005
_Q1
2005
_Q2
2005
_Q3
2005
_Q4
2006
_Q1
2006
_Q2
2006
_Q3
2006
_Q4
2007
_Q1
2007
_Q2
2007
_Q3
2007
_Q4
-3%
-2%
-1%
0%
1%
2%
3%
Greece
2002
_Q1
2002
_Q2
2002
_Q3
2002
_Q4
2003
_Q1
2003
_Q2
2003
_Q3
2003
_Q4
2004
_Q1
2004
_Q2
2004
_Q3
2004
_Q4
2005
_Q1
2005
_Q2
2005
_Q3
2005
_Q4
2006
_Q1
2006
_Q2
2006
_Q3
2006
_Q4
2007
_Q1
2007
_Q2
2007
_Q3
2007
_Q4
-4%
-2%
0%
2%
4%
Ireland
1999
_Q1
1999
_Q2
1999
_Q3
1999
_Q4
2000
_Q1
2000
_Q2
2000
_Q3
2000
_Q4
2001
_Q1
2001
_Q2
2001
_Q3
2001
_Q4
2002
_Q1
2002
_Q2
2002
_Q3
2002
_Q4
2003
_Q1
2003
_Q2
2003
_Q3
2003
_Q4
2004
_Q1
2004
_Q2
2004
_Q3
2004
_Q4
2005
_Q1
2005
_Q2
2005
_Q3
2005
_Q4
2006
_Q1
2006
_Q2
2006
_Q3
2006
_Q4
2007
_Q1
2007
_Q2
2007
_Q3
2007
_Q4
-3%
-2%
-1%
0%
1%
2%
3%
Italy
1999
_Q1
1999
_Q2
1999
_Q3
1999
_Q4
2000
_Q1
2000
_Q2
2000
_Q3
2000
_Q4
2001
_Q1
2001
_Q2
2001
_Q3
2001
_Q4
2002
_Q1
2002
_Q2
2002
_Q3
2002
_Q4
2003
_Q1
2003
_Q2
2003
_Q3
2003
_Q4
2004
_Q1
2004
_Q2
2004
_Q3
2004
_Q4
2005
_Q1
2005
_Q2
2005
_Q3
2005
_Q4
2006
_Q1
2006
_Q2
2006
_Q3
2006
_Q4
2007
_Q1
2007
_Q2
2007
_Q3
2007
_Q4
-4%
-3%
-2%
-1%
0%
1%
2%
3%
4%
5%
Latvia
23
1999
_Q1
1999
_Q2
1999
_Q3
1999
_Q4
2000
_Q1
2000
_Q2
2000
_Q3
2000
_Q4
2001
_Q1
2001
_Q2
2001
_Q3
2001
_Q4
2002
_Q1
2002
_Q2
2002
_Q3
2002
_Q4
2003
_Q1
2003
_Q2
2003
_Q3
2003
_Q4
2004
_Q1
2004
_Q2
2004
_Q3
2004
_Q4
2005
_Q1
2005
_Q2
2005
_Q3
2005
_Q4
2006
_Q1
2006
_Q2
2006
_Q3
2006
_Q4
2007
_Q1
2007
_Q2
2007
_Q3
2007
_Q4
-3%
-2%
-1%
0%
1%
2%
3%
4%
5%
Lithuania
2002
_Q1
2002
_Q2
2002
_Q3
2002
_Q4
2003
_Q1
2003
_Q2
2003
_Q3
2003
_Q4
2004
_Q1
2004
_Q2
2004
_Q3
2004
_Q4
2005
_Q1
2005
_Q2
2005
_Q3
2005
_Q4
2006
_Q1
2006
_Q2
2006
_Q3
2006
_Q4
2007
_Q1
2007
_Q2
2007
_Q3
2007
_Q4
-3%
-2%
-1%
0%
1%
2%
3%
4%
Luxembourg
2000
_Q1
2000
_Q2
2000
_Q3
2000
_Q4
2001
_Q1
2001
_Q2
2001
_Q3
2001
_Q4
2002
_Q1
2002
_Q2
2002
_Q3
2002
_Q4
2003
_Q1
2003
_Q2
2003
_Q3
2003
_Q4
2004
_Q1
2004
_Q2
2004
_Q3
2004
_Q4
2005
_Q1
2005
_Q2
2005
_Q3
2005
_Q4
2006
_Q1
2006
_Q2
2006
_Q3
2006
_Q4
2007
_Q1
2007
_Q2
2007
_Q3
2007
_Q4
-4%
-3%
-2%
-1%
0%
1%
2%
3%
Malta
1999
_Q1
1999
_Q2
1999
_Q3
1999
_Q4
2000
_Q1
2000
_Q2
2000
_Q3
2000
_Q4
2001
_Q1
2001
_Q2
2001
_Q3
2001
_Q4
2002
_Q1
2002
_Q2
2002
_Q3
2002
_Q4
2003
_Q1
2003
_Q2
2003
_Q3
2003
_Q4
2004
_Q1
2004
_Q2
2004
_Q3
2004
_Q4
2005
_Q1
2005
_Q2
2005
_Q3
2005
_Q4
2006
_Q1
2006
_Q2
2006
_Q3
2006
_Q4
2007
_Q1
2007
_Q2
2007
_Q3
2007
_Q4
-3%
-2%
-1%
0%
1%
2%
3%
Netherlands
1999
_Q1
1999
_Q2
1999
_Q3
1999
_Q4
2000
_Q1
2000
_Q2
2000
_Q3
2000
_Q4
2001
_Q1
2001
_Q2
2001
_Q3
2001
_Q4
2002
_Q1
2002
_Q2
2002
_Q3
2002
_Q4
2003
_Q1
2003
_Q2
2003
_Q3
2003
_Q4
2004
_Q1
2004
_Q2
2004
_Q3
2004
_Q4
2005
_Q1
2005
_Q2
2005
_Q3
2005
_Q4
2006
_Q1
2006
_Q2
2006
_Q3
2006
_Q4
2007
_Q1
2007
_Q2
2007
_Q3
2007
_Q4
-2%
-1%
0%
1%
2%
3%
Portugal
1999
_Q1
1999
_Q2
1999
_Q3
1999
_Q4
2000
_Q1
2000
_Q2
2000
_Q3
2000
_Q4
2001
_Q1
2001
_Q2
2001
_Q3
2001
_Q4
2002
_Q1
2002
_Q2
2002
_Q3
2002
_Q4
2003
_Q1
2003
_Q2
2003
_Q3
2003
_Q4
2004
_Q1
2004
_Q2
2004
_Q3
2004
_Q4
2005
_Q1
2005
_Q2
2005
_Q3
2005
_Q4
2006
_Q1
2006
_Q2
2006
_Q3
2006
_Q4
2007
_Q1
2007
_Q2
2007
_Q3
2007
_Q4
-3%
-2%
-1%
0%
1%
2%
3%
4%
5%
6%
Slovakia
1999
_Q1
1999
_Q2
1999
_Q3
1999
_Q4
2000
_Q1
2000
_Q2
2000
_Q3
2000
_Q4
2001
_Q1
2001
_Q2
2001
_Q3
2001
_Q4
2002
_Q1
2002
_Q2
2002
_Q3
2002
_Q4
2003
_Q1
2003
_Q2
2003
_Q3
2003
_Q4
2004
_Q1
2004
_Q2
2004
_Q3
2004
_Q4
2005
_Q1
2005
_Q2
2005
_Q3
2005
_Q4
2006
_Q1
2006
_Q2
2006
_Q3
2006
_Q4
2007
_Q1
2007
_Q2
2007
_Q3
2007
_Q4
-2%
-1%
0%
1%
2%
3%
Slovenia
1999
_Q1
1999
_Q2
1999
_Q3
1999
_Q4
2000
_Q1
2000
_Q2
2000
_Q3
2000
_Q4
2001
_Q1
2001
_Q2
2001
_Q3
2001
_Q4
2002
_Q1
2002
_Q2
2002
_Q3
2002
_Q4
2003
_Q1
2003
_Q2
2003
_Q3
2003
_Q4
2004
_Q1
2004
_Q2
2004
_Q3
2004
_Q4
2005
_Q1
2005
_Q2
2005
_Q3
2005
_Q4
2006
_Q1
2006
_Q2
2006
_Q3
2006
_Q4
2007
_Q1
2007
_Q2
2007
_Q3
2007
_Q4
-2%
-1%
0%
1%
2%
Spain
Notes: in order to visualize the countercyclical action of the automatic stabilizers, the continuous line, which represents the cyclical component of the primary balance, is expressed by the difference between the CAPB and the actual primary balance. The dotted line represents the cyclical component of GDP
24
(i.e. the economic cycle). Each series is presented as a percentage of the HP trend of the real GDP. The availability of data in some countries is reduced in relation to the first sub-period: Austria (2001-2007); Germany (2002–2007); Ireland (2002–2007); Luxembourg (2002–2007); Malta (2000–2007).Source: authors’ elaborations on Eurostat data
25
Figure 2 – The economic clycle and the cyclical component of the primary balance,
by country (2008-2017)20
08_Q
120
08_Q
220
08_Q
320
08_Q
420
09_Q
120
09_Q
220
09_Q
320
09_Q
420
10_Q
120
10_Q
220
10_Q
320
10_Q
420
11_Q
120
11_Q
220
11_Q
320
11_Q
420
12_Q
120
12_Q
220
12_Q
320
12_Q
420
13_Q
120
13_Q
220
13_Q
320
13_Q
420
14_Q
120
14_Q
220
14_Q
320
14_Q
420
15_Q
120
15_Q
220
15_Q
320
15_Q
420
16_Q
120
16_Q
220
16_Q
320
16_Q
420
17_Q
120
17_Q
220
17_Q
320
17_Q
4
-4%
-2%
0%
2%
4%
6%
Austria
2008
_Q1
2008
_Q2
2008
_Q3
2008
_Q4
2009
_Q1
2009
_Q2
2009
_Q3
2009
_Q4
2010
_Q1
2010
_Q2
2010
_Q3
2010
_Q4
2011
_Q1
2011
_Q2
2011
_Q3
2011
_Q4
2012
_Q1
2012
_Q2
2012
_Q3
2012
_Q4
2013
_Q1
2013
_Q2
2013
_Q3
2013
_Q4
2014
_Q1
2014
_Q2
2014
_Q3
2014
_Q4
2015
_Q1
2015
_Q2
2015
_Q3
2015
_Q4
2016
_Q1
2016
_Q2
2016
_Q3
2016
_Q4
2017
_Q1
2017
_Q2
2017
_Q3
2017
_Q4
-4%
-2%
0%
2%
4%
Belgium
2008
_Q1
2008
_Q2
2008
_Q3
2008
_Q4
2009
_Q1
2009
_Q2
2009
_Q3
2009
_Q4
2010
_Q1
2010
_Q2
2010
_Q3
2010
_Q4
2011
_Q1
2011
_Q2
2011
_Q3
2011
_Q4
2012
_Q1
2012
_Q2
2012
_Q3
2012
_Q4
2013
_Q1
2013
_Q2
2013
_Q3
2013
_Q4
2014
_Q1
2014
_Q2
2014
_Q3
2014
_Q4
2015
_Q1
2015
_Q2
2015
_Q3
2015
_Q4
2016
_Q1
2016
_Q2
2016
_Q3
2016
_Q4
2017
_Q1
2017
_Q2
2017
_Q3
2017
_Q4
-4%
-3%
-2%
-1%
0%
1%
2%
3%
4%
5%
Cyprus
2008
_Q1
2008
_Q2
2008
_Q3
2008
_Q4
2009
_Q1
2009
_Q2
2009
_Q3
2009
_Q4
2010
_Q1
2010
_Q2
2010
_Q3
2010
_Q4
2011
_Q1
2011
_Q2
2011
_Q3
2011
_Q4
2012
_Q1
2012
_Q2
2012
_Q3
2012
_Q4
2013
_Q1
2013
_Q2
2013
_Q3
2013
_Q4
2014
_Q1
2014
_Q2
2014
_Q3
2014
_Q4
2015
_Q1
2015
_Q2
2015
_Q3
2015
_Q4
2016
_Q1
2016
_Q2
2016
_Q3
2016
_Q4
2017
_Q1
2017
_Q2
2017
_Q3
2017
_Q4
-6%
-4%
-2%
0%
2%
4%
6%
Finland
2008
_Q1
2008
_Q2
2008
_Q3
2008
_Q4
2009
_Q1
2009
_Q2
2009
_Q3
2009
_Q4
2010
_Q1
2010
_Q2
2010
_Q3
2010
_Q4
2011
_Q1
2011
_Q2
2011
_Q3
2011
_Q4
2012
_Q1
2012
_Q2
2012
_Q3
2012
_Q4
2013
_Q1
2013
_Q2
2013
_Q3
2013
_Q4
2014
_Q1
2014
_Q2
2014
_Q3
2014
_Q4
2015
_Q1
2015
_Q2
2015
_Q3
2015
_Q4
2016
_Q1
2016
_Q2
2016
_Q3
2016
_Q4
2017
_Q1
2017
_Q2
2017
_Q3
2017
_Q4
-4%
-2%
0%
2%
4%
France
2008
_Q1
2008
_Q2
2008
_Q3
2008
_Q4
2009
_Q1
2009
_Q2
2009
_Q3
2009
_Q4
2010
_Q1
2010
_Q2
2010
_Q3
2010
_Q4
2011
_Q1
2011
_Q2
2011
_Q3
2011
_Q4
2012
_Q1
2012
_Q2
2012
_Q3
2012
_Q4
2013
_Q1
2013
_Q2
2013
_Q3
2013
_Q4
2014
_Q1
2014
_Q2
2014
_Q3
2014
_Q4
2015
_Q1
2015
_Q2
2015
_Q3
2015
_Q4
2016
_Q1
2016
_Q2
2016
_Q3
2016
_Q4
2017
_Q1
2017
_Q2
2017
_Q3
2017
_Q4
-6%
-4%
-2%
0%
2%
4%
6%
Germany
2008
_Q1
2008
_Q2
2008
_Q3
2008
_Q4
2009
_Q1
2009
_Q2
2009
_Q3
2009
_Q4
2010
_Q1
2010
_Q2
2010
_Q3
2010
_Q4
2011
_Q1
2011
_Q2
2011
_Q3
2011
_Q4
2012
_Q1
2012
_Q2
2012
_Q3
2012
_Q4
2013
_Q1
2013
_Q2
2013
_Q3
2013
_Q4
2014
_Q1
2014
_Q2
2014
_Q3
2014
_Q4
2015
_Q1
2015
_Q2
2015
_Q3
2015
_Q4
2016
_Q1
2016
_Q2
2016
_Q3
2016
_Q4
2017
_Q1
2017
_Q2
2017
_Q3
2017
_Q4
-6%
-4%
-2%
0%
2%
4%
6%
Greece
2008
_Q1
2008
_Q2
2008
_Q3
2008
_Q4
2009
_Q1
2009
_Q2
2009
_Q3
2009
_Q4
2010
_Q1
2010
_Q2
2010
_Q3
2010
_Q4
2011
_Q1
2011
_Q2
2011
_Q3
2011
_Q4
2012
_Q1
2012
_Q2
2012
_Q3
2012
_Q4
2013
_Q1
2013
_Q2
2013
_Q3
2013
_Q4
2014
_Q1
2014
_Q2
2014
_Q3
2014
_Q4
2015
_Q1
2015
_Q2
2015
_Q3
2015
_Q4
2016
_Q1
2016
_Q2
2016
_Q3
2016
_Q4
2017
_Q1
2017
_Q2
2017
_Q3
2017
_Q4
-10%
-8%
-6%
-4%
-2%
0%
2%
4%
6%
8%
10%
Ireland
26
2008
_Q1
2008
_Q2
2008
_Q3
2008
_Q4
2009
_Q1
2009
_Q2
2009
_Q3
2009
_Q4
2010
_Q1
2010
_Q2
2010
_Q3
2010
_Q4
2011
_Q1
2011
_Q2
2011
_Q3
2011
_Q4
2012
_Q1
2012
_Q2
2012
_Q3
2012
_Q4
2013
_Q1
2013
_Q2
2013
_Q3
2013
_Q4
2014
_Q1
2014
_Q2
2014
_Q3
2014
_Q4
2015
_Q1
2015
_Q2
2015
_Q3
2015
_Q4
2016
_Q1
2016
_Q2
2016
_Q3
2016
_Q4
2017
_Q1
2017
_Q2
2017
_Q3
2017
_Q4
-4%
-2%
0%
2%
4%
6%
Italy
2008
_Q1
2008
_Q2
2008
_Q3
2008
_Q4
2009
_Q1
2009
_Q2
2009
_Q3
2009
_Q4
2010
_Q1
2010
_Q2
2010
_Q3
2010
_Q4
2011
_Q1
2011
_Q2
2011
_Q3
2011
_Q4
2012
_Q1
2012
_Q2
2012
_Q3
2012
_Q4
2013
_Q1
2013
_Q2
2013
_Q3
2013
_Q4
2014
_Q1
2014
_Q2
2014
_Q3
2014
_Q4
2015
_Q1
2015
_Q2
2015
_Q3
2015
_Q4
2016
_Q1
2016
_Q2
2016
_Q3
2016
_Q4
2017
_Q1
2017
_Q2
2017
_Q3
2017
_Q4
-10%
-5%
0%
5%
10%
15%
Latvia
2008
_Q1
2008
_Q2
2008
_Q3
2008
_Q4
2009
_Q1
2009
_Q2
2009
_Q3
2009
_Q4
2010
_Q1
2010
_Q2
2010
_Q3
2010
_Q4
2011
_Q1
2011
_Q2
2011
_Q3
2011
_Q4
2012
_Q1
2012
_Q2
2012
_Q3
2012
_Q4
2013
_Q1
2013
_Q2
2013
_Q3
2013
_Q4
2014
_Q1
2014
_Q2
2014
_Q3
2014
_Q4
2015
_Q1
2015
_Q2
2015
_Q3
2015
_Q4
2016
_Q1
2016
_Q2
2016
_Q3
2016
_Q4
2017
_Q1
2017
_Q2
2017
_Q3
2017
_Q4
-15%
-10%
-5%
0%
5%
10%
15%
Lithuania
2008
_Q1
2008
_Q2
2008
_Q3
2008
_Q4
2009
_Q1
2009
_Q2
2009
_Q3
2009
_Q4
2010
_Q1
2010
_Q2
2010
_Q3
2010
_Q4
2011
_Q1
2011
_Q2
2011
_Q3
2011
_Q4
2012
_Q1
2012
_Q2
2012
_Q3
2012
_Q4
2013
_Q1
2013
_Q2
2013
_Q3
2013
_Q4
2014
_Q1
2014
_Q2
2014
_Q3
2014
_Q4
2015
_Q1
2015
_Q2
2015
_Q3
2015
_Q4
2016
_Q1
2016
_Q2
2016
_Q3
2016
_Q4
2017
_Q1
2017
_Q2
2017
_Q3
2017
_Q4
-6%
-4%
-2%
0%
2%
4%
6%
8%
Luxembourg
2008
_Q1
2008
_Q2
2008
_Q3
2008
_Q4
2009
_Q1
2009
_Q2
2009
_Q3
2009
_Q4
2010
_Q1
2010
_Q2
2010
_Q3
2010
_Q4
2011
_Q1
2011
_Q2
2011
_Q3
2011
_Q4
2012
_Q1
2012
_Q2
2012
_Q3
2012
_Q4
2013
_Q1
2013
_Q2
2013
_Q3
2013
_Q4
2014
_Q1
2014
_Q2
2014
_Q3
2014
_Q4
2015
_Q1
2015
_Q2
2015
_Q3
2015
_Q4
2016
_Q1
2016
_Q2
2016
_Q3
2016
_Q4
2017
_Q1
2017
_Q2
2017
_Q3
2017
_Q4
-5%
-4%
-3%
-2%
-1%
0%
1%
2%
3%
4%
5%
Malta
2008
_Q1
2008
_Q2
2008
_Q3
2008
_Q4
2009
_Q1
2009
_Q2
2009
_Q3
2009
_Q4
2010
_Q1
2010
_Q2
2010
_Q3
2010
_Q4
2011
_Q1
2011
_Q2
2011
_Q3
2011
_Q4
2012
_Q1
2012
_Q2
2012
_Q3
2012
_Q4
2013
_Q1
2013
_Q2
2013
_Q3
2013
_Q4
2014
_Q1
2014
_Q2
2014
_Q3
2014
_Q4
2015
_Q1
2015
_Q2
2015
_Q3
2015
_Q4
2016
_Q1
2016
_Q2
2016
_Q3
2016
_Q4
2017
_Q1
2017
_Q2
2017
_Q3
2017
_Q4
-3%
-2%
-1%
0%
1%
2%
3%
Netherlands
2008
_Q1
2008
_Q2
2008
_Q3
2008
_Q4
2009
_Q1
2009
_Q2
2009
_Q3
2009
_Q4
2010
_Q1
2010
_Q2
2010
_Q3
2010
_Q4
2011
_Q1
2011
_Q2
2011
_Q3
2011
_Q4
2012
_Q1
2012
_Q2
2012
_Q3
2012
_Q4
2013
_Q1
2013
_Q2
2013
_Q3
2013
_Q4
2014
_Q1
2014
_Q2
2014
_Q3
2014
_Q4
2015
_Q1
2015
_Q2
2015
_Q3
2015
_Q4
2016
_Q1
2016
_Q2
2016
_Q3
2016
_Q4
2017
_Q1
2017
_Q2
2017
_Q3
2017
_Q4
-3%
-2%
-1%
0%
1%
2%
3%
Portugal
2008
_Q1
2008
_Q2
2008
_Q3
2008
_Q4
2009
_Q1
2009
_Q2
2009
_Q3
2009
_Q4
2010
_Q1
2010
_Q2
2010
_Q3
2010
_Q4
2011
_Q1
2011
_Q2
2011
_Q3
2011
_Q4
2012
_Q1
2012
_Q2
2012
_Q3
2012
_Q4
2013
_Q1
2013
_Q2
2013
_Q3
2013
_Q4
2014
_Q1
2014
_Q2
2014
_Q3
2014
_Q4
2015
_Q1
2015
_Q2
2015
_Q3
2015
_Q4
2016
_Q1
2016
_Q2
2016
_Q3
2016
_Q4
2017
_Q1
2017
_Q2
2017
_Q3
2017
_Q4
-6%
-4%
-2%
0%
2%
4%
6%Slovakia
27
2008
_Q1
2008
_Q2
2008
_Q3
2008
_Q4
2009
_Q1
2009
_Q2
2009
_Q3
2009
_Q4
2010
_Q1
2010
_Q2
2010
_Q3
2010
_Q4
2011
_Q1
2011
_Q2
2011
_Q3
2011
_Q4
2012
_Q1
2012
_Q2
2012
_Q3
2012
_Q4
2013
_Q1
2013
_Q2
2013
_Q3
2013
_Q4
2014
_Q1
2014
_Q2
2014
_Q3
2014
_Q4
2015
_Q1
2015
_Q2
2015
_Q3
2015
_Q4
2016
_Q1
2016
_Q2
2016
_Q3
2016
_Q4
2017
_Q1
2017
_Q2
2017
_Q3
2017
_Q4
-5%
-4%
-3%
-2%
-1%
0%
1%
2%
3%
4%
5%
Slovenia
2008
_Q1
2008
_Q2
2008
_Q3
2008
_Q4
2009
_Q1
2009
_Q2
2009
_Q3
2009
_Q4
2010
_Q1
2010
_Q2
2010
_Q3
2010
_Q4
2011
_Q1
2011
_Q2
2011
_Q3
2011
_Q4
2012
_Q1
2012
_Q2
2012
_Q3
2012
_Q4
2013
_Q1
2013
_Q2
2013
_Q3
2013
_Q4
2014
_Q1
2014
_Q2
2014
_Q3
2014
_Q4
2015
_Q1
2015
_Q2
2015
_Q3
2015
_Q4
2016
_Q1
2016
_Q2
2016
_Q3
2016
_Q4
2017
_Q1
2017
_Q2
2017
_Q3
2017
_Q4
-3%
-2%
-1%
0%
1%
2%
Spain
Notes: in order to visualize the countercyclical action of the automatic stabilizers, the continuous line, which represents the cyclical component of the primary balance, is expressed by the difference between the CAPB and the actual primary balance. The dotted line represents the cyclical component of GDP (i.e. the economic cycle). Each series is presented as a percentage of the HP trend of the real GDP.Source: authors’ elaborations on Eurostat data
28
TablesTable 1 – Budgetary items and the economic cycle
1999 - 2007
Direct
taxes
Indirect taxes
Net social contributio
ns
Other curren
t taxes
Capital taxes
Compensation of
employees
Intermediate
consumption
Social benefit
s
Other current
expenditures
Capital expenditur
es
Austria x
Belgium x x x ° °
Cyprus * x ° °
Finland x x x * x ° x ° x
France x x x °
Germany x x x x ° x
Greece x x x
Ireland ° °
Italy x °
Latvia x x x ° ° x
Lithuania x x x ° xLuxembourg x ° °
Malta x * x xNetherlands x ° ° ° X x
Portugal x * x x *
Slovakia ° x °
Slovenia * * ° x x
Spain * x x °
2008 - 2017
Direct
taxes
Indirect taxes
Net social contributio
ns
Other curren
t taxes
Capital taxes
Compensation of
employees
Intermediate
consumption
Social benefit
s
Other current
expenditures
Capital expenditur
es
Austria x x x x x x
Belgium x x x x x
Cyprus ° ° x x °
Finland x x x x x
France x x x x x x x x
Germany x x x * * x x
Greece x x ° °
Ireland * x °
Italy ° x x x * °
Latvia x x x * x * x
Lithuania x x x x *Luxembourg x x x
Malta ° x °Netherlands ° x x x
Portugal x x x x °
Slovakia x x * ° °
Slovenia x x x x x
Spain ° x x x ° x
29
Note: the symbol ‘x’ means that the correlation between the cyclical component of each budgetary item and the economic cycle is statistically significant at 1%, ‘°’ at 5% and ‘*’ at 10%. In any case, the elasticities of each budgetary item have also to be significant to be taken into consideration for the calculation of the CAPB.Source: authors’ elaborations on Eurostat data
30
Table 2 – Correlations between the primary balances and the economic cycle
1999 - 2007 2008 – 2017
Actual primary
balance CAPB Actual primary balance CAPB
Austria 0.51*** 0.41** 0.57*** 0.13
Belgium 0.24 0.06 0.63*** 0.14
Cyprus 0.59*** 0.25 0.1 0.16
Finland 0.82*** 0.36** 0.66*** 0.14
France 0.50*** 0.1 0.73*** 0.12
Germany 0.43** 0.11 0.51*** 0.05
Greece -0.35** -0.25 0.26 0.36**
Ireland -0.01 -0.2 -0.06 -0.08
Italy 0.12 0.03 0.38** -0.04
Latvia -0.35** -0.42** 0.38** 0.01
Lithuania -0.2 -0.07 0.45*** 0.05
Luxembourg 0.65*** 0.50** 0.41*** 0.29*
Malta 0.29 0.01 0.15 -0.07
Netherlands 0.40** 0.15 0.48*** 0.30*
Portugal 0.12 -0.1 -0.15 -0.17
Slovakia -0.05 -0.09 0.41*** 0.13
Slovenia 0.09 0.01 0.25 0.12
Spain 0.33** -0.06 0.14 0.11
Notes: the actual primary balance and the CAPB are expressed as a percentage of the HP trend of the real GDP; the economic cycle is expressed in the same way. The correlation coefficients are statistically significant at three different levels: 0.01 (***); 0.05 (**); 0.1 (*). The availability of data in some countries is reduced in relation to the first sub-period: Austria (2001-2007); Germany (2002–2007); Ireland (2002–2007); Luxembourg (2002–2007); Malta (2000–2007). Source: authors’ elaborations on Eurostat data
Appendix A
31
Table A.1 – Sensitivity analysis (1999–2007)
CF filter Order 1 polynomial trend
Order 2 polynomial trend
Order 3 polynomial trend
AustriaActual primary balance 0.49 0.57 -0.01 -0.03
(0.0077***) (0.0016***) (0.9454) (0.8873)
CAPB 0.40 0.30 0.00 -0.01(0.0355) (0.1236) (0.9849) (0.9515)
BelgiumActual primary balance
0.24 0.30 0.20 0.06
(0.1615) (0.0735*) (0.2329) (0.7232)
CAPB0.08 0.04 0.01 -0.10
(0.6518) (0.8241) (0.9429) (0.573)
CyprusActual primary balance 0.54 0.69 0.45 0.36
(0.0007***) (0.0***) (0.0057***) (0.0314**)
CAPB 0.38 0.19 -0.01 0.31(0.0208) (0.2623) (0.9491) (0.0619*)
FinlandActual primary balance 0.81 0.78 0.81 0.45
(0.0***) (0.0***) (0.0***) (0.0057***)
CAPB 0.44 0.09 0.13 0.11(0.0075***) (0.5899) -0.4571 -0.5127
FranceActual primary balance 0.68 0.56 0.48 0.43
(0.0***) (0.0004***) (0.0031***) (0.0087***)
CAPB 0.34 0.09 0.11 -0.03(0.0446**) (0.5995) (0.5199) (0.8547)
GermanyActual primary balance 0.50 0.44 0.08 0.08
(0.0136**) (0.0316**) (0.7019) (0.706)
CAPB 0.20 -0.08 -0.05 -0.06(0.3374) (0.7034) (0.8255) (0.7984)
GreeceActual primary balance -0.37 -0.37 -0.38 -0.35
(0.0282**) (0.0258**) (0.0238**) (0.0351**)
CAPB -0.30 -0.27 -0.26 -0.29(0.0728*) (0.1048) (0.1216) (0.0808*)
IrelandActual primary balance 0.12 -0.10 0.29 0.16
(0.5841) (0.6356) (0.1637) (0.4466)
CAPB -0.24 -0.26 0.06 0.16(0.249) (0.2288) (0.7752) (0.4466)
ItalyActual primary balance 0.30 0.11 0.17 0.05
(0.0759*) (0.5376) (0.3189) (0.78)
CAPB 0.16 0.02 -0.11 -0.03(0.3497) (0.8894) (0.541) (0.8542)
LatviaActual primary balance -0.23 -0.37 -0.14 -0.15
(0.1747) (0.0244**) (0.412) (0.3935)
CAPB -0.21 -0.46 -0.16 -0.20(0.2115) (0.0044***) (0.3444) (0.2415)
Lithuania Actual primary balance -0.10 -0.08 -0.03 -0.02
32
(0.5774) (0.6505) (0.8565) (0.9005)
CAPB -0.22 -0.22 -0.16 -0.14(0.197) (0.2026) (0.3564) (0.4147)
LuxembourgActual primary balance 0.63 0.70 -0.10 -0.02
(0.001***) (0.0001***) (0.6339) (0.9209)
CAPB 0.53 0.11 -0.19 -0.12(0.0082***) (0.6182) (0.3737) (0.5667)
MaltaActual primary balance 0.02 0.34 0.21 0.24
(0.9139) (0.0548**) (0.2447) (0.1842)
CAPB -0.05 0.05 0.01 -0.03(0.7728) (0.7844) (0.9447) (0.8669)
NetherlandsActual primary balance 0.53 0.56 0.26 0.47
(0.001***) (0.0004***) -0.12 (0.0042***)
CAPB 0.34 0.06 -0.03 0.05(0.0422**) (0.7229) (0.8666) (0.7804)
PortugalActual primary balance 0.07 0.13 0.16 -0.04
(0.6687) (0.4627) (0.3454) (0.8154)
CAPB 0.00 -0.09 -0.05 -0.08(0.9991) (0.6008) (0.7732) (0.6244)
SlovakiaActual primary balance 0.34 -0.22 0.20 0.38
(0.0436**) -0.2026 -0.2487 (0.0242**)
CAPB 0.31 -0.37 0.15 0.35(0.0665*) (0.0250**) (0.3699) (0.0372**)
SloveniaActual primary balance 0.07 0.16 -0.11 0.05
(0.6753) (0.3501) (0.5277) (0.7558)
CAPB -0.02 -0.23 -0.15 0.02(0.9104) (0.1857) (0.3787) (0.9203)
SpainActual primary balance 0.36 0.41 0.23 0.58
(0.029**) (0.0127**) -0.1704 (0.0002***)
CAPB 0.01 0.05 -0.15 0.07(0.972) (0.7555) (0.384) (0.6908)
Notes: The correlation with the economic cycle is calculated taking into account the actual primary balance and the CAPB as a percentage of the four different trends of the real GDP; the economic cycle is expressed in the same way. The correlation coefficients are statistically significant at three different levels: 0.01 (***); 0.05 (**); 0.1 (*). The availability of data in some countries is reduced in relation to the first sub-period: Austria (2001-2007); Germany (2002–2007); Ireland (2002–2007); Luxembourg (2002–2007); Malta (2000–2007). Source: authors’ elaborations on Eurostat data
Table A.2 – Sensitivity analysis (2008–2017)
CF filter Order 1 polynomial
Order 2 polynomial
Order 3 polynomial
33
trend trend trend
AustriaActual primary balance 0.49 0.58 0.54 0.51
(0.0013***) (0.0001***) (0.0004***) (0.0009***)
CAPB 0.08 0.16 0.06 0.08(0.6164) (0.331) (0.7179) (0.6154)
BelgiumActual primary balance
0.34 0.74 0.53 0.52
(0.0336**) (0.0***) (0.0005***) (0.0006***)
CAPB-0.26 0.15 0.02 0.13
(0.109) (0.3439) (0.9192) (0.4083)
CyprusActual primary balance 0.09 0.24 -0.02 0.15
(0.5798) (0.1427) (0.9033) (0.3501)
CAPB 0.15 0.16 0.15 0.13(0.3706) (0.3244) (0.3718) (0.4219)
FinlandActual primary balance 0.49 0.80 0.54 0.54
(0.0012***) (0.0***) (0.0003***) (0.0004***)
CAPB -0.13 0.06 -0.01 -0.02(0.4333) (0.7331) (0.9369) (0.9193)
FranceActual primary balance 0.56 0.77 0.69 0.60
(0.0002***) (0.0***) (0.0***) (0.0001***)
CAPB -0.11 0.05 0.01 -0.01(0.5021) (0.7483) (0.9715) (0.9334)
GermanyActual primary balance 0.44 0.52 0.52 0.41
(0.0042***) (0.0006***) (0.0006***) (0.008***)
CAPB -0.01 0.02 0.01 -0.03(0.9704) (0.914) (0.9312) (0.8529)
GreeceActual primary balance 0.18 0.30 0.26 0.26
(0.2727) (0.0644*) (0.1039) (0.1039)
CAPB 0.27 0.28 0.39 0.34(0.0942*) (0.0818*) (0.0135**) (0.0333**)
IrelandActual primary balance -0.08 0.00 -0.17 -0.21
(0.6265) (0.9958) (0.299) (0.2011)
CAPB -0.10 -0.09 -0.19 -0.22(0.5338) (0.5632) (0.2515) (0.1803)
ItalyActual primary balance 0.39 0.25 0.34 0.39
(0.0119**) (0.1203) (0.0313**) (0.0122**)
CAPB -0.04 -0.16 0.05 -0.04(0.8007) (0.3167) (0.7468) (0.7922)
LatviaActual primary balance 0.48 0.14 0.45 0.43
(0.0019***) (0.3909) (0.0038**) (0.006***)
CAPB 0.10 -0.03 0.02 0.00(0.5482) (0.8614) (0.9096) (0.9967)
LithuaniaActual primary balance 0.35 0.42 0.45 0.23
(0.0264**) (0.0068***) (0.0038***) (0.145)
CAPB 0.00 -0.17 -0.05 -0.04(0.986) (0.2904) (0.7494) (0.8275)
Luxembourg Actual primary balance 0.15 0.56 0.35 0.11
34
(0.3486) (0.0002***) (0.0286**) (0.484)
CAPB 0.06 0.13 0.20 0.08(0.7346) (0.4077) (0.2151) (0.6424)
MaltaActual primary balance 0.27 0.21 0.0 0.16
(0.0901**) (0.1929) (0.9889) (0.3285)
CAPB 0.18 -0.03 -0.04 0.09(0.275) (0.8579) (0.829) (0.5954)
NetherlandsActual primary balance 0.37 0.60 0.25 0.29
(0.0188**) (0.0***) (0.1181) (0.0664*)
CAPB 0.09 0.06 0.10 0.09(0.5878) (0.707) (0.5208) (0.5721)
PortugalActual primary balance -0.08 -0.01 -0.28 -0.14
(0.6064) (0.9462) (0.0763*) (0.3779)
CAPB -0.11 -0.15 -0.24 -0.17(0.5042) (0.3522) (0.1303) (0.3025)
SlovakiaActual primary balance 0.43 0.38 0.39 0.32
(0.0058***) (0.0171**) (0.0128**) (0.0472**)
CAPB 0.09 -0.07 0.13 0.17(0.5652) (0.6876) (0.4152) (0.2901)
SloveniaActual primary balance 0.11 0.40 0.09 0.09
(0.4959) (0.0115**) (0.5837) (0.5688)
CAPB 0.00 0.16 0.02 0.02(0.9974) (0.3161) (0.9022) (0.9217)
SpainActual primary balance 0.13 0.25 -0.20 0.04
(0.4209) (0.1262) (0.2218) (0.8061)
CAPB 0.01 -0.07 -0.05 -0.05(0.9561) (0.6841) (0.7576) (0.7596)
Notes: The correlation with the economic cycle is calculated taking into account the actual primary balance and the CAPB as a percentage of the four different trends of the real GDP; the economic cycle is expressed in the same way. The correlation coefficients are statistically significant at three different levels: 0.01 (***); 0.05 (**); 0.1 (*).Source: authors’ elaborations on Eurostat data
35
