Fiscal policy analysis in the euro area: Expanding the toolkit

Fiscal policy analysis in the euro area: Expanding the toolkit

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ScienceDirect Journal of Policy Modeling xxx (2014) xxx–xxx

Fiscal policy analysis in the euro area: Expanding the toolkit夽 Joan Paredes a,∗ , Diego J. Pedregal b , Javier J. Pérez c a

European Central Bank, Germany b U. Castilla-La Mancha, Spain c Banco de Espa˜ na, Spain

Received 2 December 2013; received in revised form 22 May 2014; accepted 29 June 2014

Abstract The absence of historical quarterly fiscal data has limited the analysis of the macroeconomic impact of fiscal policies in the euro area, including the interactions of fiscal and monetary policies. To overcome this gap, we construct a quite disaggregated euro area quarterly fiscal database for the period 1980Q1–2012Q4, based on a rich set of input fiscal data taken from national sources. We discuss how this dataset has allowed and can allow the profession to tackle new policy-relevant research topics. We also provide stylized facts on the cyclical properties of main euro area fiscal aggregates, focusing on the recent economic crisis period. © 2014 Society for Policy Modeling. Published by Elsevier Inc. All rights reserved. Keywords: Euro area; Fiscal database; Fiscal policies; Stylized facts; Mixed-frequencies’ models JEL classification: C53; E6; H6

夽 The views expressed in this paper are those of the authors and not necessarily those of the European Central Bank or the Bank of Spain. Initial versions of this paper circulated under the name: “A quarterly fiscal database for the euro area based on intra-annual fiscal information”. We thank seminar participants at the European Central Bank, Jacopo Cimadomo, Todd Clark, Günter Coenen, Giancarlo Corsetti, Francisco de Castro, Daniel Garrote, Domenico Giannone, Markus Kirchner, Michele Lenza, Albert Marcet, Henri Maurer, Agustín Maravall, Ad van Riet, Matthias Trabandt, and colleagues at the ECB’s Fiscal Policies Division and Government Finance Statistics Unit, for useful comments and suggestions. We also thank Lorenzo Forni, José Emilio Gumiel, Alexandru Isar, Sandro Momigliano and A. Jesús Sánchez for help with the data. Pedregal acknowledges financial support of the Spanish Education and Science Ministry under project SEJ2006-14732 (ECON). ∗ Corresponding author. Tel.: +49 69 1344 5676; fax: +49 69 1344 7809. E-mail addresses: [email protected] (J. Paredes), [email protected] (D.J. Pedregal), [email protected] (J.J. Pérez).

http://dx.doi.org/10.1016/j.jpolmod.2014.07.003 0161-8938/© 2014 Society for Policy Modeling. Published by Elsevier Inc. All rights reserved.

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1. Introduction The European Central Bank (ECB) sets the monetary policy for the European economies that have adopted the euro currency since January 1999. Therefore, macroeconomic analysis with euro area time series became a common place over the past decade.1 The construction of historical data (long series) for the euro area has been part of the academic and ECB agenda over the first part of the 2000s, see Beyer, Doornok, and Hendry (2001), Anderson, Dungey, Osborn, and Vahid (2007), Fagan, Henry, and Mestre (2001, 2005). Even though fiscal policy remains a national responsibility, interactions between monetary and fiscal policies are carefully monitored by the monetary authority, see for example, Duisenberg (2003), ECB (2008, 2009). In particular, the assessment of the impact of fiscal policies on euro area GDP and prices, and the constraints that fiscal policies might impose on monetary policy over the medium term is a very relevant endeavor, increasingly so, in the light of the recent policy responses to the EU sovereign debt crisis. Euro area governments introduced a number of discretionary fiscal policy packages: during 2008–2009 fiscal stimuli and since the end of 2009 fiscal consolidation measures. Indeed, these issues have recently attracted a great deal of attention.2 In addition, the analysis of spillover effects among economic areas, in particular between the US and the euro area, or the euro area and the UK/the rest of the EU are back to the forefront of the policy discussion.3 The appropriate assessment of the impact of fiscal policies at the euro area wide level and its interlinkages with other economic areas have been traditionally restricted by the shortcomings of existing quarterly data for the relevant euro area fiscal variables. The whole fiscal surveillance process at the European level is designed on the basis of annual data. The fact that budgetary plans are prepared following an annual budgetary cycle and the discretionary nature of the setup chosen by many government taking measures for the entire year, have traditionally limited the interest in high-frequency fiscal data.4 As recently claimed by Dilnot (2012) public policy analysis should not be undertaken lightly without thinking carefully and then finding out the numbers. Given the limitations and the scarcity of historical quarterly euro area fiscal data, we aim in this paper at reviewing existing, scattered national data sources, and on that basis we construct a quarterly fiscal database for the euro area aggregate5 for the period 1980Q1–2012Q4.6 The raw ingredients we use are closely linked to the ones used by national statistical agencies to provide their best estimates (intra-annual fiscal 1

Examples are Batini, Callegari, and Melina (2012), Coenen, Straub, and Trabandt (2012), Cimadomo (2011a, 2011b), de Castro and Garrote (2012), Kollmann, Ratto, Roeger, and in’t Veld (2013), Burriel et al. (2010), Forni, Monteforte, and Sessa (2009), Ratto, Roeger, and in’t Veld (2009), Dreger and Marcellino (2007), Fagan et al. (2005), Favero and Marcellino (2005), Smets and Wouters (2003), Bruneau and de Bandt (2003), Aarle, Garretsen, and Gobbin (2003) or Jacobs, Kuper, and Sterken (2003). 2 Just to quote a few examples, see Davig and Leeper (2011), Cogan, Cwik, Taylor, and Wieland (2009), Burriel et al. (2010), Cimadomo (2011a, 2011b), Cimadomo, Kirchner, and Hauptmeier (2010), or Coenen, Straub, and Trabandt (2013), Coenen et al. (2012). In the policy arena, ECB’s President introductory statement to the press conference typically incorporates an explicit reference to fiscal policies, see e.g. Draghi (2013). 3 See, for example, Auerbach and Gorodnichenko (2012). 4 Nevertheless, a recent strand of the literature has shown that intra-annual fiscal data, when modeled appropriately, contains valuable and useful information for forecasting annual aggregates (Pérez, 2007; Silvestrini, Salto, Moulin, & Veredas, 2008; Onorante, Pedregal, Pérez, & Signorini, 2010; Pedregal & Pérez, 2010; Asimakopoulos, Paredes, & Warmedinger, 2013; Leal, Pérez, Tujula, & Vidal, 2008). 5 The current euro area definition comprises the countries members of the euro area as of 31st December 2012. 6 The database is updated once a year with the latest available data and can be requested at euro area.fiscal [email protected]

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data, mostly on a public accounts basis), and our method preserves full coherence with official, annual data. In order to make the database a usable input for applied empirical studies, including the estimation of macroeconomic models7 we provide a quite disaggregated set of fiscal variables for the General Government sector.8 In fact, the potential for policy applications of our database has been proved in a number of recent papers that could not have been completed as they stand had our set of data not been developed (see, among others, Burriel et al. (2010), Batini et al. (2012), Coenen et al. (2012, 2013), Cimadomo (2011a,b), de Castro and Garrote (2012), Kollmann et al. (2013), European Commission (2012)).9 In addition, taking advantage of the dataset, and moving one step forward, we also offer in our study a description of fiscal policy developments in the euro area including the crisis years (2009Q1–2012Q4).10 In the historical period considered, that covers a number of economic downturns such as the 1980s, the mid-1990s, the 2000s and the most recent crisis, only the latest, also called the Great Recession, caused total euro area government revenues to enter into negative territory in nominal terms. The fiscal adjustment process that took place was mainly on the revenue side of the budget: indeed, the public deficit reduction between 2010Q4 and 2012Q4 was principally due to total revenues, which increased by 2.9 percentage points of 2012’s GDP over that period, and compensated the 0.6 percentage points of 2012’s GDP increase in total expenditure. Within total expenditure, though, a substantial reduction in public investment took place (−0.4 percentage points of 2012’s GDP), that partly compensated the boost in current expenditure, which can be attributed to the effect of automatic stabilizers. Thus, from the point of view of the composition of the fiscal adjustment, the features described in this paragraph would not be, inline with the available empirical and theoretical literature, the best in terms of impact on economic growth and conductive to successful consolidation (see e.g. Alesina and Ardagna (2010), Bi, Leeper, and Leith (2013)). Finally, we also provide detailed stylized facts on fiscal policies for the euro area on the basis of the dataset, for the whole sample (1980Q1–2012Q4) and for two relevant subsamples, namely, the pre-crisis period (i.e. the whole sample excluding 2008Q1–2012Q4) and the euro area period (since 1992Q1, i.e. including the run-up to the euro). First, as expected, we find a strong and procyclical behavior of total government revenue in the euro area, which follows the business cycle behavior in upturns and downturns, reflecting the operation of automatic stabilizers. When the 2008Q1–2012Q4 period is included the synchronicity of public revenues and real GDP increases compared to the pre-2008 sample. Second, and more interesting, are the results on the cyclical properties of government spending. When comparing the three analyzed samples, it seems that the crisis and subsequent fiscal consolidation period (2008–2012) reduced the pro-cyclical bias of public spending. On the one hand, some current spending items did not fall sharply during the Great Recession (2008–2009). On the other hand, during the most recent period (2010–2012), those items did not increase much, or even decreased due to the subsequent fiscal adjustment

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Like ECB’s AWM and NAWM, see Fagan et al. (2001, 2005), and Coenen, Christoffel, and Warne (2008), respectively. In National Accounts’ (NA) terms, i.e. according to ESA95 (European System of National Accounts) definitions. 9 In addition, since the September 2010 edition of ECB’s euro area AWM database, its initial fiscal block has been substituted by the early vintages of the historical dataset presented in the current paper. See “The AWM database”, September 2010, available at the official AWM site with the Euro Area Business Cycle Network (http://www.eabcn.org/data/awm/index.htm). 10 The studies mentioned in the previous paragraph used the beta version of the dataset that only covered the pre-crisis period. 8

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implemented by euro area economies. The latter result is particularly relevant in the case of public investment. The rest of the paper is organized as follows. In Section 2 we examine issues related to the availability of fiscal data for the euro area and the main features of the data used in our study (the details of which are further developed in Appendices A and B).11 In that section we also offer some descriptive evidence on the evolution of fiscal variables in the euro zone over the 1980–2012 period. In Section 3 we discuss the relevance of our dataset for the study of fiscal policy issues, first by showing how we deal with potential endogeneity problems, and second by reviewing the recent applied literature. In Section 4, in turn, we provide stylized facts on the cyclical properties of the main fiscal aggregates. Finally, Section 5 concludes. 2. Euro area fiscal data: fiscal variables over the economic crisis and the fiscal consolidation episode 2.1. General issues The euro area is an aggregation of member states’ country-specific time series. There is nothing like a “euro area fiscal policy”. Nevertheless, as claimed before, the monetary policy of the ECB is conducted taking into account euro area fiscal aggregates as if they were representing a single entity/country. Thus, the ECB has devoted a great deal of effort in building up consistent databases of country-specific data and in the development of aggregation tools to assemble euro area aggregates for the different macroeconomic variables. In the fiscal domain, Eurostat and the ECB provide annual series for euro area fiscal aggregates that dates back to the 1995s. In addition, Eurostat, on the basis of data provided by EU National Statistical Institutes, provides disaggregated quarterly non-seasonally adjusted12 government data for the euro area for the period starting in 1998Q1. The compilation practices follow the guidelines of the manual on quarterly non-financial accounts for general government (see European Commission (2006)). Using the latter accounting approach to extend back in the past existing euro area fiscal time series is not a feasible endeavor, though, given the limited information available. The main sources of intra-annual fiscal data in the euro area covering long periods of time are national sources. Most countries publish on a monthly and/or quarterly basis, for example, central government accounts. In Federal or quasi-federal countries, like Germany and Spain, regional and local government finances at the quarterly/monthly frequency are also covered, even though the details tend to be lower than the corresponding central government counterparts. With the exception of the United Kingdom (a country within the EU but outside the euro area), and to a lesser extent France, it is not possible to find official time series with a wide time and institutional coverage of the General Government sector in national accounts – see Onorante et al. (2010), or Pedregal and Pérez (2010) for some additional discussion on these issues. 2.2. Main features of the data A number of issues have to be dealt with carefully and explicitly for any set of non-official data (i.e. data not produced regularly by a National Statistical Institute or with an official status

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An additional document with further information is available from the authors upon request. Seasonal adjusted data started to be published recently by Eurostat only for total revenues and total expenditures.

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by an international organization) to be trustworthy, and thus usable by a wide audience. These are the main characteristics of the database used in the paper (see Appendices A and B for further, technical information): Input fiscal data. Our database is built up using only intra-annual fiscal information, i.e. we do not use general quarterly macroeconomic variables – like GDP, private consumption or total economy employment – in the interpolation process. This is a quite relevant issue because although government revenues and expenditures (e.g. unemployment benefits) may be endogenous to GDP or any other tax base proxy (e.g. private consumption for VAT collection) the relationship between these variables is at most indirect and extremely difficult to estimate. The decoupling of tax collection from the evolution of macroeconomic tax bases (revenue windfalls/shortfalls) is by now a proved stylized fact. In this respect, the direct use of intra-annual fiscal data, taken from public accounts’ sources, for interpolation purposes, might be instrumental to avoiding the potential problem of modeling an indirect relationship which, in addition, might be time-varying.13 Compilation approach. We choose in this paper an econometric approach rather than an accounting approach.14 Nevertheless, we tried to follow to the extent possible the principles outlined in the manual on quarterly non-financial accounts for general government: use of direct information from basic sources (public accounts’ data), computation of “best estimates”, and consistency of quarterly and annual data. In this respect, we chose intra-annual data from the public accounts of the individual countries, along the lines of the statement of the manual that quarterly data shall be based on direct information available from basic sources, such as for example public accounts or administrative sources. A description of the main fiscal indicators used in the study is described briefly in Table 1 (see also Appendix A). Aggregation of euro area data. The approach followed in our paper is an indicator-based one. This means that we do not aggregate data of the individual euro area member states as such. Instead, we use aggregated annual data as provided by the European Commission (Eurostat) and (when available) quarterly euro area data from the same source, as anchors for the interpolation procedure, while at the same time we set up statistical models that incorporate ingredients that closely resemble those used to compile available quarterly government finance statistics data by Eurostat, for the biggest euro area economies, namely Germany, France, Italy, Spain and the Netherlands. We do so for two main reasons. Firstly, to maximize data availability, and in particular, the length of the available series, an aggregation-based approach would have blocked many time series, and seriously limited the length of the feasible ones. In this respect it is worth mentioning that all the ingredients of the dataset are publicly available, i.e. we made no use of restricted or

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In a related fashion, the use of quarterly fiscal indicators in the interpolation, should be of use in capturing accurately the quarter(s) in which, for example, a change in a given tax rate took place, an issue that is crucial to assess the impact of fiscal policies on GDP and other macroeconomic aggregates, not least because of the existence of foresight effects. 14 The discussion in European Commission (2002a, 2002b, 2006) shows that there is some room for econometric estimation of intra annual fiscal variables. This is the case for two main reasons, highlighted in the previous references. Firstly, ESA95 does not consider the quarterly aspects of taxes and social payments with sufficient precision to ensure clarity of interpretation in all situations; this is because, when discussing non-financial accounts, the ESA95 guiding documents occasionally take a perspective which assumes an annual reference period is in mind, thus remaining silent on which quarter within a particular annual reference period is involved. Secondly, it is also the case that many accounting or legal events are annual events by definition (e.g. a tax levied in a complete year); this fact does not present a problem for the statistician compiling annual data (there is no need to establish the amount and time of recording to a particular annual reference period), but do pose problems for the compiler of quarterly data, that needs to attribute revenue and expenditure not merely to a reference year but also to quarters within that year.

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Spain

The Netherlands

Euro area aggregates

Banca d’Italia

IGAE (State Comptroller), National Statistical Institute (INE)

Ministry of Finance

Eurostat

Bank of International Settlements (BIS), Eurostat OECD Bank of International Settlements (BIS), Eurostat Bank of International Settlements (BIS), Eurostat Ministry of Finance, Eurostat

Eurostat

National Statistical Institute (INE), Eurostat

Eurostat

Eurostat

OECD –

OECD National Statistical Institute (INE), Eurostat, Social Security System Eurostat

OECD –

Eurostat Eurostat

Eurostat

Banca d’Italia

IGAE (State Comptroller), National Statistical Institute (INE)

Bank of International Settlements (BIS), Eurostat Ministry of Finance

Bank of International Settlements (BIS)

Giordano et al. (2007), Eurostat



Eurostat

Eurostat, Bank of International Settlements (BIS), Eurostat Eurostat, Bank of International Settlements (BIS), Eurostat

Eurostat

Eurostat

IGAE (State Comptroller), National Statistical Institute (INE) Eurostat

Eurostat

Eurostat

Eurostat

Eurostat, Bank of International Settlements (BIS), Eurostat

Eurostat

Eurostat

LGN

Eurostat

Eurostat

Eurostat, Bank of International Settlements (BIS), Eurostat Eurostat

Eurostat

Eurostat

COE

Federal Ministry of Finance

Giordano et al. (2007), Eurostat

Eurostat, Bank of International Settlements (BIS), Eurostat

IGAE (State Comptroller), National Statistical Institute (INE) IGAE (State Comptroller), National Statistical Institute (INE)



GIN

Eurostat, Bank of International Settlements (BIS), Eurostat Eurostat, Bank of International Settlements (BIS), Eurostat

OECD, Eurostat, Pérez and Sánchez (2011) Eurostat

Direct taxes

DTX

Corporate taxes Social security contributions

DTE SCT

OECD Bank of International Settlements (BIS)

Indirect taxes

ITX

Total expenditure

TOE

Interest payments

INP

Bank of International Settlements (BIS), Eurostat Federal Ministry of Finance, Eurostat, Bank of International Settlements (BIS) Federal Ministry of Finance

Government consumption

GCN

Real

GCR

Government employment Compensation of employees Government investment

Eurostat

Giordano et al. (2007), Eurostat

Eurostat, Bank of International Settlements (BIS), CBS

Eurostat

Eurostat

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Italy

Ministry of Finance, Eurostat

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France

Federal Ministry of Finance, Eurostat, Bank of International Settlements (BIS) Federal Ministry of Finance, Eurostat

TOR

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Germany Total revenue

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Table 1 Overview of main sources of national government’s monthly, quarterly and annual data, and of aggregate euro area annual and quarterly series.

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Table 2 Coverage and structure of the quarterly fiscal dataset. Deficit (DEF) = TOR − TOE Total revenues (TOR)

Total expenditure (TOE)

Direct taxes (DTX) -Paid by enterprises (DTE) -Paid by households (DTH = DTX − DTE) Social security contributions (SCT) -of which paid by employers (SCR) -of which paid by employees (SCE) Indirect taxes (TIN) Other revenues (OTOR = TOR − DTX − SCT − TIN)

Social payments (THN) -of which unemployment benefits (UNB) Government consumption (GCN) -Compensation of employees (COE) -Non-wage consumption expenditure (OGCN = GCN − COE) Subsidies (SIN) Government investment (GIN) Interest payments (INP) Other expenditure (OTOE = TOE − GCN − SIN − GIN − INP) Government employment (LGN) Real government consumption (GCR)

private information. The second reason is to avoid the controversial issues of weighting schemes, as discussed in Beyer et al. (2001), Bosker (2006), Brüggemann and Lütkepohl (2006) or Anderson et al. (2007). Coverage. The database covers the period 1980Q1–2012Q46 above, thus it includes the most recent crisis period. It encompasses the main components of the revenue and expenditure sides of the General Government sector (see Table 2) in NA terms. The net lending of the government, a key policy variable can be computed as the difference between total revenues and total expenditures. In addition, we also provide general government debt. Definitions. We provide seasonally adjusted series, which are consistently and jointly estimated within our models. The issue of seasonal adjustment of quarterly fiscal variables in Europe is an important one, as signaled in European Commission (2007). Currently, available disaggregated quarterly government finance official figures are presented mainly in non-seasonally adjusted terms, given the short time span available (the starting period is 1999Q1), two features that make difficult the economic analysis with those figures. Indeed, adjusting in a robust way for seasonality such short time series is a difficult endeavor. In this sense, given that we use a broad set of information and model explicitly seasonality for the whole set of series included in our models, for the period 1980Q1–2012Q4, we are in a position to provide, in particular, seasonally adjusted series computed in a robust way for the period for which the official statistics are available (1998Q1 onwards). 2.3. Some descriptive evidence Figs. 1–3 present year-on-year growth rates of the main general government variables for the euro area aggregate (seasonally adjusted). Fig. 1 shows total government revenue and its components, Fig. 2 total government expenditure and its components, and Fig. 3 zooms in the decomposition of government consumption into wage and non-wage expenditures. As regards the information displayed in Fig. 1, it is apparent that the most recent crisis had the largest negative impact on total government revenues in the analyzed sample. Indeed, in a Please cite this article in press as: Paredes, J., et al. Fiscal policy analysis in the euro area: Expanding the toolkit. Journal of Policy Modeling (2014), http://dx.doi.org/10.1016/j.jpolmod.2014.07.003

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Fig. 1. The evolution of total public revenue and its components in the euro area, 1980Q1–2012Q4. Year-on-year growth rates of seasonally adjusted figures in nominal terms.

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Fig. 2. The evolution of total public expenditure and its components in the euro area, 1980Q1–2012Q4. Year-on-year growth rates of seasonally adjusted figures in nominal terms.

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Fig. 3. The evolution of government consumption and its components in the euro area, 1980Q1–2012Q4. Year-on-year growth rates of seasonally adjusted figures in nominal terms.

historical period, which covers a number of economic downturns such as the 1980s, the mid1990s, the 2000s and the most recent crisis, only the latest, also called Great Recession, caused total euro area government revenues to enter into negative territory in nominal terms. In fact, total revenues contracted for five consecutive quarters, namely since 2008Q4 till 2009Q4, presenting and average drop of 5% per quarter in year-on-year terms. The main components of public revenues presented a similar profile over the crisis, but with significantly different amplitudes. Direct and indirect taxes presented negative rates of growth for six consecutive quarters, while Social Security contributions displayed a more moderate drop, consistent with the more stable tax bases that are the source of such government incomes. In any case, the fall in social contributions is the biggest in the sample after the one observed at the end of the 1990s that was due to significant cuts in employees’ contributory rates in France. The significantly higher volatility of direct taxes as compared to other components is completely driven by the behavior of corporate tax receipts (see panel with total direct tax collection, corporate tax revenues and personal income tax revenues). Indeed, the relative standard deviation of direct taxes with respect to total revenues equals 1.5, while that for indirect taxes and social contributions is equal to 1.0. The most volatile component of revenue, in any case, is the residual aggregate “Other government revenue”, given that is comprises an aggregation of items much smaller in magnitude and more subject to discretionary Please cite this article in press as: Paredes, J., et al. Fiscal policy analysis in the euro area: Expanding the toolkit. Journal of Policy Modeling (2014), http://dx.doi.org/10.1016/j.jpolmod.2014.07.003

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impact, namely capital revenues, other current revenues (including interest receivable) and sales (non-market production). The crisis did not have, in any case, a significant impact on the percentage structure of government revenues in the euro area. Direct taxes, social contributions and indirect taxes represented in 2012 a share equal to 27%, 34% and 28% of total revenues, respectively, compared to 28%, 33% and 29% in 2007. Nevertheless, within direct tax collection the share of corporate taxes fell by 6 percentage points. As regards the aftermath of the 2008–2009 crisis, that coincided with the period of fiscal adjustment (generalized tax increases) that started in mid-2010, it is worth noticing that total revenues at the euro area level increased by 6.7% between 2010 and 2012 (compared to +0.7% of nominal GDP), in such a way that in cumulative terms in 2012Q4 an extra amount of D 276 bn was collected by euro area governments when compared with the year 2010, i.e. 2.9 percentage points of 2012’s GDP. In Fig. 2, in turn, we show total expenditure and its components. Government consumption and cash transfers to households represent the bulk of total expenditure, with shares (in 2012) of 43% and 35% respectively; the ratio of the standard deviation of consumption expenditure and transfers compared to the standard deviation of total spending stands at 1.2 and 1.0, respectively. The smaller components, in turn, present much higher relative volatility with respect to the aggregate, of 4.3 for government investment (5% weight), 3.3 for interest payments (6% weight), and, particularly, of 9 for “Other expenditures” (computed as a residual and amounting to some 8% of aggregate spending). On a related fashion, expenditure in subsidies is a small item amounting to some 3% of the total, and with a relative standard deviation that doubles that of the aggregate. We also show in the figure unemployment benefits, a subcomponent amounting to some 8% of social transfers, and some 4 times more volatile than this latter aggregate. Within government consumption, as shown in Fig. 3, non-wage consumption expenditure is more volatile than wage expenditure (compensation), 1.8 and 0.9 in terms of relative standard deviations to government consumption respectively, while both amount to a similar share of total consumption (some 50%). From an aggregate perspective, Figs. 2 and 3 provide an overview of the composition of the fiscal consolidation effort implemented by individual euro area countries. The expenditure adjustment process started with intensity approximately in 2010Q3, dominated by the deceleration in government consumption and significant cuts in government investment. As regards the latter, government investment expenditure presented the most prolonged and intense period of decline in the sample (including the 1980s), with negative average year-on-year nominal growth rates in each single quarter of −7% in 2010Q1–2012Q4. As regards the former, the fall in expenditure in compensation of government employees was the main driver in the nominal deceleration – and ultimately reduction – of consumption expenditure, in particular its real component, including public employment cutbacks. Overall, nevertheless, between 2010 and 2012, total euro area government expenditures increased by D 56 bn (+1.2% vs. the +0.7% of nominal GDP), due to the fact that the upsurge in social transfers (+D 64 bn) and interest payments (+D 35 bn) was only partly compensated by the nominal reductions in government investment (−D 34 bn), the wage bill (−D 5.9 bn) and other expenses (−D 3 bn). Overall, thus, 79% of the expenditure adjustment was due to cuts in government investment, a composition that has been typically advocated as neither being the less harmful for economic growth nor the most conductive to a successful fiscal consolidation process (within a huge literature, see e.g. Perotti (1996), Alesina and Ardagna (2010), Bi et al. (2013)). Please cite this article in press as: Paredes, J., et al. Fiscal policy analysis in the euro area: Expanding the toolkit. Journal of Policy Modeling (2014), http://dx.doi.org/10.1016/j.jpolmod.2014.07.003

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3. The relevance of the dataset for the study of fiscal policy issues in the euro area There are a number of areas of research and applied policy analysis in the field of fiscal policies in the euro area which can benefit from the detailed dataset presented in the current paper. In this section, first we outline some claimed advantages of the proposal we put forward in this paper and, second, we provide some examples of applications prepared with our dataset by other authors. 3.1. Fiscal revenues and theoretical macroeconomic tax bases In any standard macroeconomic model in which taxes are included, tax collection tends to be linearly related, via a tax rate, with a theoretical tax base. Let us take the example of consumption tax collection (Tc ): Tc = τ c C, where τ c is the implicit tax rate and C denotes private consumption. The latter is typically measured by the relevant National Accounts’ (NA) aggregate. For model consistency, this approach is a valid one. But from a practical point of view, it presents a number of problems. First, the definitions of taxes in OECD economies tend to encompass a number of taxable categories that are not always properly captured by NA main aggregates. In the previous example, following Eurostat (EU’s Statistical Agency), indirect taxes, defined as taxes on production and imports, comprise mainly tax categories such as VAT (the major item), property taxes, excise duties (such as taxes on gasoline and other fuels, and taxes on tobacco and alcohol) and taxes and duties on imports excluding VAT, and thus the relevant tax base should comprise in addition to NA private consumption, other variables like consumption of fuel, tobacco and alcohol, residential investment, house purchases, government intermediate consumption, certain imports, or exports of services like tourism. A second issue is related to the fact that tax systems are complex functions of the tax bases that determine the revenue responsiveness properties of different taxes to the state of the economy (see, for example, Creedy and Gemmell (2002, 2007)). These features, among others, make the relationship between tax collection and spending of endogenous items (like unemployment benefits) and their theoretical macroeconomic tax/spending bases a non-exact, indirect one. As an example, in Fig. 4(Panel A) we show the annual growth rates of indirect tax collection and private consumption, both deflated by the private consumption deflator. Even though average growth over the three decades displayed in the chart is broadly the same among the two series, the volatility of the growth rate of indirect taxes was almost 2 times higher than that of private consumption. Just focusing on the last decade, indirect tax collection was above this proxy macroeconomic base in the “good times” period 2002–2007, and quite below it in “bad times” (2000–2001, 2008–2009). Thus, in good times a typical indirect tax revenue equation would present positive residuals, while it would display negative residuals in recessions. This phenomenon is typically referred to as revenue windfalls/shortfalls. More specifically, the term revenue windfalls/shortfalls is usually used in the relevant literature15 to describe government revenues which fall short of (are in excess of) what would be expected in view of the impact of legislation changes and the actual or projected development of standard key macroeconomic aggregates. The database presented in our paper has been constructed by using only direct information on intra-annual fiscal developments. To give a flavor of the relevance of our approach compared to 15 See, among others, Barrios and Rizza (2010), Morris et al. (2009) and Morris and Schuknecht (2007). See also Penner (2008). A related literature is the one on “rainy day” funds. Budget stabilization or “rainy day” funds is a practice followed by many US states that consist of setting aside excess revenue for use in times of unexpected revenue shortfall. In fiscal year 2008, forty-seven states and the District of Columbia maintained rainy day funds.

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Fig. 4. Real private consumption and real indirect tax collection. Panel A: Annual data (year-on-year growth rates), 1981–2012. Panel B: Cross-correlation function of real private consumption and real indirect tax collection (quarter-onquarter growth rates), by means of two interpolation alternatives. Sample 1980Q2–2012Q4.

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the alternative of interpolating annual fiscal accounts with quarterly macro indicators (as in the cases of the editions of ECB’s AWM database published prior to 2009 or the companion dataset in Forni et al. (2009)), we present the following exercise. First, we compute quarterly series of (seasonally adjusted, real) indirect tax collection using the same econometric methodology as in our database but using only quarterly private consumption as the indicator to distribute the annual figures among quarters. Second, we compute the cross-correlation functions (CCFs) of the quarter-on-quarter growth rates of the indirect tax series obtained with the two alternatives (fiscal indicators versus private consumption) and real private consumption for 4 leads and lags. The two CCFs are plotted in Fig. 4(Panel B). It is clear that the second alternative provides a higher contemporaneous correlation of consumption tax collection with NA private consumption. As a conclusion, one may claim that approaches that use macroeconomic indicators to interpolate fiscal series may inflate the underlying “true” relationship between the so-constructed quarterly fiscal series and the headline macro figures. Maybe this cost is worth assuming in certain studies for the sake of model consistency, but it would certainly harm estimated relationships in empirical studies. 3.2. The impact of fiscal policies in the euro area economy Given the potential importance of the spillover effects of fiscal policies in a highly integrated region such as the euro area, the results available for some specific countries16 do not necessarily provide a good guidance for analyzing the macroeconomic impact of fiscal shocks in the euro area as a whole.17 While several studies have focused on the United States, results for the euro area have been scarcer, primarily because of lack of data availability. Some recent empirical studies stress this data availability problem, and use the historical dataset described and presented in the current paper to extend the set of available facts on the effects of government shocks on euro area GDP and inflation: Cimadomo et al. (2010) and Burriel et al. (2010), Batini et al. (2012) or European Commission (2012). From a more structural point of view, some additional recent studies, namely Coenen et al. (2012, 2013), make use of the database presented in this paper to estimate DSGE models for the euro area. In particular, in the second paper the authors conduct a quantitative evaluation of discretionary fiscal policy on euro area economic activity during the Great Recession, and to this end, they use a DSGE model characterized by a rich specification of the fiscal sector and estimate it utilizing a large set of euro area fiscal data.18

16

For euro area country studies see, among others, Heppke-Falk, Tenhofen, and Wolff (2006) and Baum and Koester (2011) for Germany, de Castro (2006) and de Castro and Hernández de Cos (2008) for Spain, Giordano, Momigliano, and Neri (2007) for Italy, Marcellino (2006) for the four largest countries of the euro area or Afonso and Sousa (2009a, 2009b) for Germany, Italy and Portugal, and Bénassy-Quéré and Cimadomo (2012) and Beetsma and Giuliodori (2011) for a group of EU countries. On different grounds, Jacobs, Kuper, and Verlinden (2007) incorporate a fiscal closure rule in a VAR for the euro area. 17 For empirical studies that include EU countries and focus on cross-country spillovers see Canzoneri, Cumby, and Diba (2006), Beetsma, Giuliodori, and Klaasen (2006), Cwik and Wieland (2010), and Corsetti, Meier, and Müller (2010) for theoretical considerations. See also Pappa (2009) that compares the transmission of government spending shocks in Canada, Japan, the UK, the US and the euro area. See also Canova and Pappa (2011). 18 In order to address the potential problem of mismeasurement associated with the use of interpolated data, the authors allow for errors in the measurement of the fiscal variables. In particular, for all fiscal data iid measurement errors with a variance of 0.5% are assumed.

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4. The cyclical properties of the main fiscal aggregates in the euro area We provide in this section the cyclical properties of the main fiscal aggregates in the euro area. We have chosen this topic because of its policy relevance and because there are relatively few empirical studies on the pro- versus counter-cyclicality of fiscal policies for the euro area. Previous studies have typically focused on a limited number of euro area or euro area countries’ fiscal aggregates and used annual data (Fatas & Mihov, 2009; Lamo, Pérez, & Schuknecht, 2013; Marcellino, 2006). The studies find mostly pro-cyclical fiscal behavior. The most comprehensive study of public expenditure cyclicality in the OECD is Lane (2003), who estimates the elasticity of government expenditure and its components with respect to output for a number of countries for the period 1960–1998. Lane finds that government consumption in most countries behaves pro-cyclically, mainly due to the behavior of wages. In Table 3, we report dynamic cross-correlation functions. We look at the unconditional correlations between detrended series at the standard business cycle frequencies. Following standard practice we measure the co-movement between two series using the cross-correlation function (CCF thereafter). Each row of this table displays the CCF between a given detrended fiscal variable at time t + k, and detrended GDP at time t. For the sake of robustness, we show results for a set of standard filters19 as applied to seasonally adjusted time series in real terms. Each row of this table displays the CCF between a measure of detrended real GDP at time t, and a detrended fiscal variable at time t + k. Following the standard discussion in the literature, it is said that two variables co-move in the same direction over the cycle if the maximum value in absolute terms of the estimated correlation coefficient of the detrended series (call it dominant correlation) is positive, that they co-move in opposite directions if it is negative, and that they do not co-move if it is close to zero. A cut-off point of 0.20 roughly corresponds in our sample to the value required to reject at the 5% level of significance the null hypothesis that the population correlation coefficient is zero. Finally, the fiscal variable is said to be lagging (leading) the private sector variable if the maximum correlation coefficient is reached for negative (positive) values of k. The results in the table show the strong and pro-cyclical behavior of total government revenue in the euro area, which follows the business cycle behavior in upturns and downturns, reflecting the operation of automatic stabilizers. In addition, public revenues are much more volatile than GDP, more than 1.5 times, on average. This reflects the fact that a number of taxes, most notably corporate taxes, property taxes and other indirect taxes, tend to follow boom–bust dynamics and do react to the cycle more than proportionally (Morris & Schuknecht, 2007). Finally, it is worth mentioning that the dominant correlation is the contemporaneous one (zero lag), reflecting that tax receipts are particularly endogenous with respect to the business cycle. For the whole sample the correlation is 0.75, while if the most recent crisis years are removed from the sample (“pre-crisis sample”) the dominant correlation is lower of 0.61, reflecting that the 2008–2012 has increased the synchronicity of public revenues and real GDP. Given the not-quite-surprising feature that government revenues are strongly pro-cyclical, most studies look at the cyclical properties of government spending (see Frankel, Vegh, and Vuletin 19 The selected filters are: (i) first difference filter; (ii) linear trend; (iii) Hodrick–Prescott filter for two alternative values of the band-pass parameter (the standard 1600, that is a fair approximation of the cycles of France and Italy, while a higher value would be more appropriate for countries with more volatile cycles like Spain, as shown by Marcet and Ravn (2004)); and (iv) Band-Pass filter (with two different band-pass parameters, capturing fluctuations between 1.5 and 8 years and between 1.5 and 12 years, an observation closer to average euro area business cycle duration).

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Dominant correlation

k −6

−3

−2

−1

0

1

2

3

4

5

6

0.13 0.51 0.27 0.37 0.19 0.41

0.14 0.61 0.43 0.52 0.40 0.57

0.24 0.71 0.59 0.65 0.59 0.71

0.30 0.78 0.71 0.76 0.72 0.80

0.47 0.82 0.78 0.82 0.78 0.84

0.40 0.81 0.75 0.79 0.75 0.81

0.23 0.76 0.64 0.70 0.63 0.72

0.23 0.69 0.50 0.58 0.46 0.59

0.09 0.59 0.33 0.43 0.27 0.43

0.01 0.10 0.48 0.38 0.16 0.03 0.28 0.15 0.07 −0.11 0.27 0.11

→ → → → → →

pro-cycl., contemp. pro-cycl., contemp. pro-cycl., contemp. pro-cycl., contemp. pro-cycl., contemp. pro-cycl., contemp.

0.02

0.17

0.31

0.45

0.58

0.68

0.75

0.72

0.61

0.51

0.36

0.21

0.11



pro-cycl., contemp.

Pre-crisis sample (1980Q1–2007Q4) 1.9 Average

0.15

0.30

0.36

0.42

0.49

0.53

0.61

0.60

0.54

0.53

0.45

0.38

0.34



pro-cycl., contemp.

Euro area sample (1992Q1–2012Q4) 1.7 Average

0.79

0.72

0.58

0.40

0.21

0.04 −0.10



pro-cycl., contemp.

−0.01 0.02 −0.05 0.03 0.06 0.12 0.18 0.25 −0.17 −0.11 −0.03 0.10 −0.02 0.06 0.16 0.28 −0.31 −0.28 −0.20 −0.09 −0.09 0.01 0.12 0.26

0.16 0.32 0.25 0.41 0.07 0.39

0.28 0.38 0.39 0.53 0.24 0.53

0.22 0.43 0.50 0.63 0.39 0.65

→ → → → → →

pro-cycl., lagged pro-cycl., lagged pro-cycl., lagged pro-cycl., lagged pro-cycl., lagged pro-cycl., lagged

Average

1.6

0.01

0.15

0.31

0.49

0.63

0.73

F = Total expenditure Whole sample (1980Q1–2012Q4) 0.6 First diff. filter 1.0 Linear trend HP 1600 0.8 0.9 HP 3200 0.6 BP (1.5, 8 years) 0.9 BP (1.5, 12 years)

0.01 −0.39 −0.25 −0.34 −0.10 −0.45

0.05 −0.32 −0.24 −0.30 −0.12 −0.41

−0.04 −0.25 −0.24 −0.26 −0.17 −0.36

−0.05 −0.18 −0.24 −0.21 −0.22 −0.31

0.04 −0.10 −0.22 −0.16 −0.27 −0.25

−0.04 −0.02 −0.21 −0.10 −0.30 −0.18

Average

−0.25 −0.22 −0.22 −0.20

0.8

Pre-crisis sample (1980Q1–2007Q4) 1.1 Average

−0.34 −0.25 −0.21

Euro area sample (1992Q1–2012Q4) 0.5 Average

−0.15 −0.15 −0.18 −0.20

−0.16 −0.14 −0.09 −0.03

0.03

0.14

0.27

0.39

0.47



pro-cycl., lagged

0.44

0.55

0.62

0.67

0.64



pro-cycl., lagged

−0.24 −0.28 −0.29 −0.28 −0.21 −0.10

0.08

0.26

0.40



pro-cycl., lagged

−0.14 −0.02

0.08

0.23

0.36

Note: Nominal fiscal variables are deflated using AWM’s GDP deflator. Quarterly real GDP is also taken from the latter database.

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F = Total revenue Whole sample (1980Q1–2012Q4) 1.8 First diff. filter 1.1 Linear trend 1.7 HP 1600 1.5 HP 3200 1.9 BP (1.5, 8 years) 1.6 BP (1.5, 12 years)

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Table 3 Stylized facts on the cyclical properties of euro area aggregate total government revenues and total government expenditures: whole sample (1980Q1–2021Q4), pre-crisis sample (1980Q1–2007Q4) and euro area sample (1992Q1–2012Q4, including the Maastricht, run-up to EMU period).

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(2011), and the references quoted therein). Indeed, an important reason for the usual finding of procyclical spending is precisely that government receipts get increased in booms, typically beyond expectations (see discussion on revenue windfalls above), and thus governments use the surplus to increase spending proportionately as a consequence of political pressure or just following certain social-welfare-improving objectives. As expected, in Table 3 total expenditure appears pro-cyclical as well, but lagged, inline with available evidence for the euro area obtained with annual data (see Lamo et al. (2013)); this behavior can be rationalized on the basis of the political economy arguments mentioned in the previous paragraph. The lag detected with quarterly data implies that total expenditure follows GDP with a delay of 1 to 1½ years. Budgetary patterns on the spending side tend to be quite persistent, in particular as regards sizeable items like public wages or public employment. For example, only in the period following an economic downturn are fiscal consolidation measures implemented, while in expansions, fresh government revenues tend to expand the public sector wage bill with some delay. When comparing the three analyzed samples, it seems that the crisis and subsequent fiscal consolidation period (2008–2012) reduced the pro-cyclical correlation of public spending. While for the whole sample the dominant correlation stands at about 0.5, for the pre-crisis period it was significantly higher, of the order of 0.7. This may reflect the fact that, firstly, the 2008–2009 crisis did not affect a number of current spending items as much as in other similar periods in the past, while, secondly, and on different grounds, the most recent (and ongoing) fiscal consolidation period may have somewhat weakened the traditional political economy channels outlined above, given sizeable consolidation needs. The three main components of public spending (Table 4), namely government consumption, social payments and government investment, reflect the same pro-cyclical pattern than total spending, overall. Interestingly, the estimated cyclical pattern and correlation of consumption expenditure was not significantly affected by the past five years (dominant correlation of 0.58 for the whole sample, and 0.64 when 2008–2012 is excluded). Thus, the weakening of the correlation is a reflection of the impact of the most recent data on social payments and government investment. As regards social payments other than unemployment benefits, the weak pro-cyclical, lagged pattern of 0.34 estimated with the pre-crisis sample, gets reduced when the whole sample is used; what is more, when the 1980s and the first part of the 1990s are excluded, the overall pattern is a weak, counter-cyclical one. Again, in this respect, the containment of social transfers like pensions over the crisis and the subsequent period of fiscal prudence/consolidation may explain the change in the (in any case weak) cyclical pattern. Regarding the most volatile component of social payments, namely unemployment expenditures, they became more responsive to the cycle, as the negative correlation (counter-cyclical pattern) was of −0.49 for the whole sample but turned out to increase (in absolute value) to −0.79 when the 1992Q1–2012Q4 period is considered. Unemployment-related benefits increase, as expected, in downturns and decrease in upturns; at the same time, unemployment spending seems to lead real GDP by 1–2 quarters. The latter evidence is consistent with an interpretation whereby employment losses at the beginning of a cyclical downturn tend to be associated with new unemployed receiving full-entitlement benefits (given that downturns do occur after a good times period), coupled with the fact that the average duration of the entitlement tends to be lower than the number of quarters the economy is below trend. Finally, government investment presents a pro-cyclical and lagged behavior, with a dominant correlation of 0.48 for the whole sample, a number lower than the correlation estimated for the pre-crisis sample of 0.59. The lessening of the pro-cyclical bias in investment expenditure reflects the fact that the fiscal consolidation process that started by 2010 hinged heavily, and in a quite Please cite this article in press as: Paredes, J., et al. Fiscal policy analysis in the euro area: Expanding the toolkit. Journal of Policy Modeling (2014), http://dx.doi.org/10.1016/j.jpolmod.2014.07.003

Dominant correlation

k −6

−3

−2

−1

−0.14

−0.15

−0.13

−0.08

−0.04

0.03

0.08

0.17

0.29

0.40

0.49

0.58



pro-cycl., lagged

−0.15

−0.11

−0.02

0.08

0.17

0.29

0.35

0.44

0.54

0.60

0.63

0.64



pro-cycl., lagged

−0.12

−0.15

−0.18

−0.19

−0.18

−0.12

−0.10

0.01

0.14

0.27

0.40

0.52



pro-cycl., lagged

−0.14

−0.13

−0.12

−0.08

−0.03

0.02

0.09

0.15

0.23

0.27



pro-cycl., lagged

−0.01

0.05

0.10

0.16

0.23

0.28

0.32

0.32

0.34

0.32



pro-cycl., lagged

−0.34

−0.35

−0.35

−0.31

−0.27

−0.21

−0.12

−0.03

0.11

0.21



counter-cycl., lead.

F = Social payments other than unemployment benefits Whole sample (1980Q1-2012Q4) −0.09 −0.10 −0.13 Average 0.7 Pre-crisis sample (1980Q1–2007Q4) 1.2 −0.07 −0.04 −0.03 Average Euro area sample (1992Q1–2012Q4) 0.4 −0.25 −0.29 −0.32 Average F = Unemployment benefits Whole sample (1980Q1–2012Q4) Average 37.0 −0.03 Pre-crisis sample (1980Q1–2007Q4) −0.31 Average 60.1 Euro area sample (1992Q1–2012Q4) 0.38 Average 23.2 F = Government investment Whole sample (1980Q1–2012Q4) −0.04 Average 12.7 Pre-crisis sample (1980Q1–2007Q4) 0.08 Average 18.7 Euro area sample (1992Q1–2012Q4 −0.10 Average 13.3

0

1

2

3

4

5

6

−0.10

−0.17

−0.27

−0.37

−0.44

−0.49

−0.48

−0.40

−0.26

−0.10

0.05

0.18



counter-cycl.

−0.33

−0.37

−0.40

−0.42

−0.44

−0.39

−0.32

−0.22

−0.08

0.05

0.17

0.28



counter-cycl., lead.

0.25

0.10

−0.12

−0.36

−0.54

−0.72

−0.79

−0.71

−0.54

−0.30

−0.09

0.08



counter-cycl.

−0.01

0.07

0.18

0.25

0.26

0.33

0.37

0.38

0.41

0.48

0.47

0.45



pro-cycl., lagged

0.14

0.22

0.38

0.48

0.50

0.58

0.59

0.53

0.51

0.52

0.45

0.38



pro-cycl., lagged

−0.05

0.05

0.11

0.15

0.17

0.22

0.22

0.26

0.34

0.40

0.41

0.42



pro-cycl., lagged

Note: Nominal fiscal variables are deflated using AWM’s GDP deflator. Quarterly real GDP is also taken from the latter database.

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F = Government consumption Whole sample (1980Q1–2012Q4) 0.9 −0.13 Average Pre-crisis sample (1980Q1–2007Q4) 1.3 −0.21 Average Euro area sample (1992Q1–2012Q4) 0.8 −0.09 Average

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Table 4 Stylized facts on the cyclical properties of the main components of euro area fiscal expenditures: whole sample (1980Q1–2021Q4), pre-crisis sample (1980Q1–2007Q4) and euro area sample (1992Q1–2012Q4, including the Maastricht, run-up to EMU period).

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persistent fashion, on this budgetary item, as discussed above, and as a consequence traditional boom–bust dynamics were broken over the past few years.

5. Conclusions The fiscal database developed in this paper presents the potential of constituting (and has already constituted) a useful input for broader macroeconomic and policy analyses using euro area data and involving fiscal variables. Before the production of the current dataset those exercises were mainly conducted either with annual data or with limited availability of quarterly fiscal information. This type of studies have recently received renewed attention and include simulation exercises to assess the impact fiscal stimulus and fiscal adjustment packages, analyses of the interaction between monetary and fiscal policies, or the estimation of fiscal policy rules. As an illustration along these lines, we provide two types of empirical evidence. First, we characterize the evolution of the main euro area fiscal aggregates over the past four decades. In the case of the fiscal consolidation episode of 2010–2012, we show how it was basically hinging on tax increases and sizeable government investment cuts, a composition that cannot be considered to be inline with the best policy strategies, as advocated by the literature. Secondly, we provide stylized facts on the cyclical behavior of fiscal policies in the euro area. Here the main highlight is that the headline public spending euro area aggregates behave in a pro-cyclical fashion, and follow GDP with a delay of 1 to 1½ years. At the same time we provide evidence that this pro-cyclical bias has weakened in recent years.

Appendix A. Input database The bulk of annual euro area data in ESA95 terms for the period 1995–2012 is taken from AMECO, the database of the Directorate-General for Economic and Financial Affairs of the European Commission. There are two exceptions to this source: the series for annual euro area direct taxes on corporations for the period 1980–2008 was obtained from the OECD Economic Outlook database, while the series for employers’ social contributions (for the period 1991–2012) was taken from Eurostat’s ESA95 database. For the prior period 1980–1994, we had to deal with the presence of a break in accounting standards (ESA79–ESA95) and the German unification. In order to obtain homogeneous levels for the whole period 1980–2012, we removed level discontinuities by applying backwards the growth rates of the series in ESA79 terms (that exclude East Germany) to the levels of the ESA95 series. Quarterly figures for the euro area aggregate for the period 1999Q1–2012Q4 are taken from Eurostat. The impact of one-off proceeds from the allocation of mobile licenses (UMTS), that sizeably distort some years, was removed from the relevant series. Quarterly and monthly fiscal variables (indicators) for the biggest euro area economies, namely Germany, France, Italy, Spain and the Netherlands, are taken from Eurostat (available ESA95 series), several national sources, the Bank of International Settlements (BIS), and other sources, as described in Table 1. When necessary, country variables are set into euros using the official fixed euro conversion rates. Also, when necessary, German series were corrected for the impact of the Unification. For additional details on some data sources of monthly/quarterly “indicator” series, the interested reader can also consult Onorante et al. (2010) and ECB (2004). Finally, annual information in ESA79/ESA95 definitions for the individual countries is taken from the AMECO database when needed, and Please cite this article in press as: Paredes, J., et al. Fiscal policy analysis in the euro area: Expanding the toolkit. Journal of Policy Modeling (2014), http://dx.doi.org/10.1016/j.jpolmod.2014.07.003

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quarterly information following ESA95 standards from Eurostat, as mentioned above for the euro area aggregate.20 Appendix B. Econometric approach The basic model is of the Unobserved Components class known as the Basic Structural Model (Harvey, 1989), that decomposes a set of time series in unobserved though meaningful components from an economic point of view (mainly trend, seasonal and irregular). The exposition in this subsection follows closely Harvey (1989), Pedregal and Young (2002) and Young and Pedregal (1999). The model is multivariate, and may be written as Eq. (1), where t is a time sub-index measured in months (for models set up at the monthly frequency),   zt = Tt + St + et (1) ut where [zt , ut ]T , Tt , St and et denote the m-dimensional output time series (broken down into a scalar output, zt , and indicators, ut ), trend, seasonal and irregular components, respectively. Eq. (1) is in fact a set of observation equations in a State Space system, which has to be completed by the standard transition or state equations. The state equations qualify the dynamic behavior of the components. In this particular case, the transition equations for models of the trend and seasonal components are a Local Linear Trend and the Trigonometric Seasonal (see either Harvey (1989), or Pedregal and Young (2002), for details). The mixture of frequencies, and the estimation of models at the quarterly frequency, implies combining variables that at the quarterly frequency can be considered as stocks with those being pure flows. An annual ESA95 series cast into the quarterly frequency is a set of missing observations for the first three-quarters of the year and the observed value assigned to the last month of each year. Theoretically the annual ESA95 series would be obtained from a quarterly ESA95 series by summation of the 4 quarters of a year (Q1–Q4) had them been available. Model (1) then, has to be adapted to the fact that in the same model one variable is on an annual sampling interval, while others are sampled at a quarterly rate. This is the so-called temporal aggregation problem, which is relatively easy to handle in the State Space framework. The way the models are defined specifically may be seen in Pedregal and Pérez (2010) and Leal, Pedregal, and Pérez (2011). For each specific variable considered in this study, models of type (1) are estimated. In each model, the variable {zt } corresponds to the target time series to be interpolated, composed of annual observations for the period 1980–1998, and quarterly observations for the period 1999–2012. The vector of indicator variables {ut }, in turn, comprises a set of variables with quarterly (for quarterly models) observations, typically (but not always) available for the full period 1980–2012. Estimation of model (1) provides estimates for the missing values in {zt } (missing quarterly data points) and estimates of the vector comprising the unobserved components that include the estimated seasonal components. Thus, it is possible to compute model-consistent seasonally adjusted interpolated series for the target variables {zt } just by subtraction of the correspondingly estimated seasonal components from {zt }. For all euro area models the vector {zt } encompasses annual ESA95 euro area data for the period 1980–1998, and quarterly, non-seasonally adjusted, ESA95 data for the period 1999Q1–2012Q4. On the other hand, as it is clear from the description

20

The vintages of data used are the ones that were available in April 2013.

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of data sources in Table 1, in some instances it was necessary to use more than one source of intra-annual information in order to compute the indicator variable finally included in the euro area model within the vector {ut }.21 A final remark on the dimensionality of the models is worth mentioning. In order to reduce the dimensionality of our models and somewhat avoid the “curse of dimensionality” we opted for variable-by-variable models. By this we mean that, in all cases, {zt } encompasses just one time series (annual/quarterly), and {ut } the set of indicators corresponding to the latter variable, with a maximum of five indicators (one per country for each variable). The alternative would have been to run models in which {zt } would have included several variables, and thus {ut } would have been a matrix with indicators by blocks for each component of {zt }. Examples of other suitable models include a joint model for TOR and TOE, as in Pedregal and Pérez (2010), i.e. {zt } = {TOR, TOE}, a joint model for the revenue side of the governments accounts, i.e. {zt } = {TOR, DTX, SCT, TIN, OTOR}, or a joint model for the expenditure side, i.e. {zt } = {TOE, THN, GCN, GIN, INP, SIN, OTOE}. We preferred to use for interpolation purposes more parsimonious models, and thus disregarded the alternative approach, quite valid in different frameworks (like forecasting). References Aarle, B. van., Garretsen, H., & Gobbin, N. (2003). Monetary and fiscal transmission in the euro-area: Evidence from a structural VAR analysis. Journal of Economics & Business, 55, 609–638. Afonso, A., & Sousa, R. M. (2009a). The macroeconomic effects of fiscal policy. ECB Working Paper Series No. 991 Afonso, A., & Sousa, R. M. (2009b). The macroeconomic effects of fiscal policy in Portugal: A Bayesian SVAR analysis. School of Economics and Management Working Papers No. 09/2009/DE/UECE. Alesina, A., & Ardagna, S. (2010). Large changes in fiscal policy: Taxes versus spending. In J. R. Brown (Ed.), Tax policy and the economy (Vol. 24). University of Chicago Press. Anderson, H., Dungey, M., Osborn, D. R., & Vahid, F. (2007). Constructing historical euro area data. Australian National University, Centre for Applied Macroeconomic Analysis. CAMA Working Papers No. 2007-18. Asimakopoulos, S., Paredes, J., & Warmedinger, T. (2013). Forecasting fiscal time series using mixed frequency data. ECB Working Paper Series No. 1550. Auerbach, A. J., & Gorodnichenko, Y. (2012). Output spillovers from fiscal policy. Vox.EU, 10 December. Barrios, S., & Rizza, P. (2010). Unexpected changes in tax revenues and the stabilisation function of fiscal policy: Evidence for the European Union, 1999–2008. European Commission Economic Papers No. 404. Batini, N., Callegari, G., & Melina, G. (2012). Successful Austerity in the United States, Europe and Japan. IMF Working Papers WP/12/190. Baum, A., & Koester, G. B. (2011). The impact of fiscal policy on economic activity over the business cycle – evidence from a threshold VAR analysis. In Discussion Paper Series 1: Economic Studies 2011,03. Deutsche Bundesbank, Research Centre. Beetsma, R., & Giuliodori, M. (2011). The effects of government purchases shocks: Review and estimates for the EU. Economic Journal, 121, F4–F32. Beetsma, R., Giuliodori, M., & Klaasen, F. (2006). Trade spill-overs of fiscal policy in the European Union: A panel analysis. Economic Policy, 21, 640–687. Bénassy-Quéré, A., & Cimadomo, J. (2012). Changing patterns of domestic and cross-border fiscal policy multipliers in Europe and the US. Journal of Macroeconomics, 34, 845–873. Beyer, A., Doornok, J. A., & Hendry, D. F. (2001). Constructing historical euro-zone data. Economic Journal, 111, 102–121. Bi, H., Leeper, E. M., & Leith, C. (2013). Uncertain fiscal consolidations. Economic Journal, 123, F31–F63.

21 In a document available upon request we provide a quite detailed description of the implementation of the general methodology and the data inputs described in the case of each one of the variables included in our study, and also the description of the components of {ut } in each case.

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