Tax buoyancy in OECD countries: New empirical evidence

Journal of Macroeconomics 63 (2020) 103189

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Tax buoyancy in OECD countries: New empirical evidence Raffaele Lagravinesea, Paolo Liberatib, , Agnese Sacchic ⁎

T

a

Department of Economics and Finance, University of Bari, Largo Abbazia Santa Scolastica, Bari 70124, Italy Department of Economics, University Roma Tre, Via S. d'Amico, 77, Rome 00145, Italy c Department of Economics and Law, Sapienza University of Rome, Via del Castro Laurenziano 9, Rome 00161, Italy b

ARTICLE INFO

ABSTRACT

JEL classifications: E32 E62 H24 H25

A renewed interest in the link between business cycle and tax revenues has recently emerged, especially during economic crises. In this paper, we provide an empirical analysis on 35 OECD countries over the period 1995–2016 to estimate both short-run and long-run tax buoyancies, taking into account the macroeconomic framework, changes in governments’ tax policies, budgetary and political variables possibly affecting how taxes react to GDP fluctuations. By adopting the dynamic common correlated effects estimator, we find that both short- and long-run tax responses are lower than those reported in previous cross-country studies. We suggest that this slightly lower than expected reaction of tax revenue can be interpreted as a reduced power of both automatic stabilization in the short-run and fiscal sustainability in the long-run. Results are robust to possible endogeneity issues between tax revenues and business cycles.

Keywords: Tax buoyancy Fiscal stabilization Business cycle Macroeconomic conditions Economic crises DCCE estimator

1. Introduction In recent years, international organizations, national governments, and academics have shown a renewed interest in the way in which tax revenues react to the business cycle, especially during economic crises (Arnold et al., 2011; Acosta-Ormaechea and Yoo, 2012; Gemmell et al., 2014; Baiardi et al., 2018; Aizenman et al., 2019). Measuring this reaction is crucial both for monitoring and forecasting governments’ public finances, as it helps to predict how to implement stabilization policies in response to expansionary or contractionary stages of the economic cycle (Blanchard and Perotti, 2002; Arachi et al., 2015; Lagravinese et al., 2018), and for the general sustainability of public finances. This issue has even greater importance in the presence of financial stress – as in the last decade – where the tax revenue management has been affected by a certain degree of uncertainty and instability. In this context, how taxpayers respond either to tax incentives or to discretionary tax changes might be unpredictable (Mourre and Princen, 2019), especially when the monetary policy has limited room for country-specific macroeconomic policies as for most European countries in the Euro area. Since the first contribution on the elasticity of tax revenues (Groves and Kahn, 1952), the literature has gradually enriched over the years. For example, many studies have estimated the elasticity of single taxes (e.g. Huton and Lambert, 1980; Fries et al., 1982; Clausing, 2007), while other contributions have provided either case-study analyses (Sobel and Holcombe, 1996; Dye 2004; Bruce et al., 2006; Wolswijk 2009; Poghosyan, 2011; Koester and Prismeier, 2012; Havranek et al., 2016; Lagravinese et al., 2018) or comparative analyses of tax buoyancy and tax elasticity using different samples of countries (Sancak et al., 2010; Brückner, 2012; Fricke and Sussmuth, 2014; Belinga et al., 2014; Dudine and Jalles, 2018; Boschi and d'Addona, 2019; Mourre and Princen, 2019). ⁎

Corresponding author. E-mail address: [email protected] (P. Liberati).

https://doi.org/10.1016/j.jmacro.2020.103189 Received 4 July 2019; Received in revised form 28 October 2019; Accepted 6 January 2020 Available online 09 January 2020 0164-0704/ © 2020 Elsevier Inc. All rights reserved.

Journal of Macroeconomics 63 (2020) 103189

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Over time, the econometric methodologies have also been refined, moving from the Ordinary Least Squares (OLS) and Dynamic OLS (DOLS) estimators to more sophisticated econometric techniques that take into account the presence of cross-sectional dependence among panel units (Pesaran, 2006; Chudik and Pesaran, 2015). This is a crucial point for fiscal policies, which are often influenced by supranational constraints (e.g., the European rules on public budget). For this reason, our analysis is carried out using a new panel data method that takes into account unobserved heterogeneity, temporal persistence, and cross-sectional dependence. In particular, in the presence of cross-sectional dependence in the data, the output obtained with the fixed effect estimator cannot be relied upon. Most importantly, when addressing the tax revenues reaction to the economic cycle, an important distinction between the concepts of tax buoyancy and tax elasticity should be made. Tax buoyancy is a measure of how taxes react to economic growth, without disentangling the impact of discretionary and automatic tax changes. Tax elasticity, instead, is a measure of the reaction of taxes due to the built-in flexibility of the tax system, which disregards the impact of discretionary tax changes. Compared to the previous studies, our paper concentrates exclusively on the tax buoyancy of 35 OECD countries during the period 1995–2016. By focusing on the tax buoyancy, instead of tax elasticity, we are able to capture the reaction of both structural characteristics and discretionary tax policies to GDP changes, not least because of the debatable methods by which discretionary changes are isolated in the common practice. Indeed, estimating tax elasticities through a regression of tax changes on tax bases may fail to adequately disentangle automatic and discretionary changes, thus being not able to assess their distinctive impacts (Sen, 2006). The time interval used in this paper is also particularly important as it crosses relevant institutional changes and economic cycles for the countries in our sample. In particular, we extend previous works that are normally based on a time span that ends at years 2013 or 2014 (Sancak et al., 2010; Belinga et al., 2014; Boschi and d'Addona, 2019; Dudine and Jalles, 2018), by adding further empirical evidence capturing the impact of the recent Great Recession on both short- and long-run responses of taxes to GDP changes. On the institutional side, all OECD countries belonging to the European Union (EU), since 1992, have experienced tighter budget constraints and fiscal discipline, which might have conditioned the response of taxes compared to those OECD countries that do not belong to the EU. As for the economic cycle, our period includes both a severe recession and a recovery. This element is particularly important for measuring both short- and long-run tax reactions. In doing so, we also exploit both the heterogeneity of tax responses across countries and the different intensity of recessions in different countries. From the econometric side, by limiting the analysis to OECD countries that share similar institutional systems and economic development, one can expect either weak or strong forms of cross-sectional correlation that, if ignored, may lead to biased estimates (Pesaran 2006; Pesaran and Tosetti, 2011; Everaert and De Groote, 2016). To address this issue, we adopt the Dynamic Common Correlated Effects estimator (DCCE) as developed by Ditzen (2018) to correct for small sample time-series bias (Chudik and Pesaran, 2015). The DCCE controls for dependence by adding cross-sectional means and lags (Everaert and De Groote, 2016; Pesaran and Tosetti, 2011) and by testing, at the same time, for cross-sectional dependence in the error terms. The main results of the paper show a significant mitigation of the levels of tax buoyancy over the period investigated both in the short- and the long-run, with respect to estimates provided by previous studies. Such divergence can be mostly attributed to the different time span included in the analysis and to the novel use of the dynamic estimator in calculating short- and long-run tax responses. In detail, the short-run coefficients for total revenue display a slightly lower reaction to the economic cycle than expected for a good automatic stabilizer. Nevertheless, this average response is the outcome of a significant variability of the short-run buoyancies of specific tax items. On the other hand, when considering long-run tax buoyancies, all coefficients are significantly lower than 1. Since long-run responses would give information on the sustainability of fiscal policies, they reveal that both the aggregate of total taxes and single tax items could not guarantee fiscal sustainability in the long-run, as they do not seem to converge to a long-run equilibrium in the period analysed. When considering the macroeconomic conditions, we note that inflation and unemployment contribute to foster tax buoyancy at least in the short-run, while they do not significantly affect estimates of long-run tax buoyancy. This would suggest that while the short-run tax reaction is impaired by automatic sources, the long-run response could be mostly affected by the combination of both automatic and discretionary changes in governments’ policy. Most interestingly, when taking into account the role of countryspecific economic downturns occurred during the observed period, short- and long-run tax buoyancies are lower than those obtained with the baseline results. Robustness checks are performed by considering changes in governments’ tax policy, and the presence of European institutions that could differently impact on the tax system of the selected countries and, eventually, on the tax reactions to the economic cycle. Finally, other robustness checks concern the use of instrumental variables to take into account for possible endogeneity issues and the use of fixed effects only to test the stability and the accuracy of our dynamic estimator, also compared to previous studies mostly based on the former. The rest of the paper is organized as follows. Section 2 describes the importance of tax buoyancy approach used in this paper. Section 3 presents the methodological framework. Section 4 shows the main results for the long and short-run tax buoyancies, also reporting country-specific coefficients, and some robustness checks. Section 5 provides additional evidence taking into account the role of budgetary parameters, shadow economy and political variables possibly affecting how taxes react to GDP changes. Section 6 briefly concludes.

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2. Why tax buoyancy? The focus of the paper is on tax buoyancy, which is a measure of how taxes react to economic growth that does not disentangle the impact of discretionary and automatic tax changes. Controlling for discretionary tax changes, instead, would isolate tax elasticity, which is the reaction of taxes that is only due to the built-in flexibility of the tax system for a given tax structure (i.e. remaining constant when discretionary tax changes occur).1 Thus, tax buoyancy and tax elasticity give two different outcomes. In principle, tax elasticity is considered a better indicator to measure the reaction of the tax revenue to changes in the macroeconomic conditions, as it would assume the invariance of the tax laws. The most common way to disentangle discretionary and automatic changes is to make recourse to a proportional adjustment method, whose aim is to adjust a historical tax revenue series starting from an arbitrary base year and assuming that the tax structure in the base year is maintained throughout the period of analysis.2 In particular, the first step of the method consists of adjusting observed tax revenues in each year by removing from such data the estimated impact of discretionary changes until that year. Thus, the adjusted series would give an estimate of the changes that are exclusively due to the automatic response of the tax revenue. The second step of the method consists of converting the series to the chosen base year by adjusting the calculated changes with the ratio between the adjusted tax yield and the actual tax yield. Since the choice of the base year is arbitrary, this adjustment can be calculated by either going forward (i.e. a series of cumulated ratios) or going backward (i.e. a series of de-cumulated ratios).3 Even though widely supported when estimating tax elasticities, this methodology disregards that the impact of discretionary changes is usually measured by policy-makers by making an ex ante estimation of the tax base that may diverge from the ex post observed tax base, for example because policy-makers usually consider only the first-round effects of public policies. Thus, there is no guarantee that the ex ante estimation of the value of discretionary changes is equal to their realization ex post.4 Additionally, tax bases may themselves depend on changes in tax rates, which means that an observed tax base is not necessarily the tax base one could observe if tax changes were not applied.5 Finally, the proportional adjustment method may also be affected by a limited comparability across countries due to differences either in accounting rules or in the definition of the discretionary measure (Barrios and Fargnoli, 2010). Since these biases are not random, being directly related to the tax base, a regression of tax changes on tax bases to estimate tax elasticities may fail to properly disentangle automatic and discretionary changes, and thus to measure their impact (Sen, 2006). As an alternative to the proportional adjustment method, Singer (1968) proposed to rely on qualitative information provided by a narrative record of tax policy changes. This method has been recently applied by Boschi and d'Addona (2019) based on the period in which a tax policy change takes effect. The main drawback of the narrative method is, however, that it cannot take account of the size of the discretionary changes, which means that those changes can only be identified by dummy variables signaling policy intervention. To some extent, this is the main drawback that can apply to the contribution by Romer and Romer (2010), where historical tax reforms are used as a quasi-experiment to identify the effects of tax changes on GDP. In this case, the approach is based on constructing a series of exogenous tax reforms, where exogeneity is identified by a classification of tax reforms as either ideological or arising from long-term deficit concerns, excluding instead tax policy changes responding to the business cycle.6 In other contributions (e.g., Koester and Priesmeier, 2017; Barrios and Fargnoli, 2010), the issue of discretionary tax measures is disregarded because it does not seem to explain a significant part of the observable fluctuations of tax buoyancies. Because of these non-negligible and often disregarded shortcomings, we choose to estimate tax buoyancy instead of tax elasticity in this paper. While it is true that tax buoyancy conflates automatic and discretionary changes, it is also true that tax elasticities do not properly disentangle their impacts. Furthermore, since in a long-run perspective automatic and discretionary changes may be seen as complementary tools of fiscal policies, tax buoyancy may give, instead, a more comprehensive information about the long-run sustainability of the tax systems, i.e. about whether the tax revenue is growing consistently with national income. In other words, while tax elasticity may be interpreted as the relevant factor for forecasting purposes, as it excludes the impact of discretionary changes, tax buoyancy measures both the soundness of the tax bases and the effectiveness of tax changes in terms of revenue collection in a long-run perspective. Overall, this is a more comprehensive measure of the sustainability of the tax system related to the public spending needs. For example, if the tax elasticity were low, discretionary changes would be required to keep up with a given level of public spending, an information that is conveyed by tax buoyancy and not by tax elasticity. 1 See, for all, Hansen (1951). For a discussion of the difference between tax buoyancy and tax elasticity see also Leuthold and N'Guessan (1986). See also Jenkins et al. (2000), especially Chapter 3. 2 A recent contribution in this direction is Mourre and Princen (2019). 3 See, for example, Indraratna (2009) and, very recently, Mourre and Princen (2019). See also Wolswijk (2009) and Bettendorf and van Limbergen (2013). The introduction of the proportional adjustment method is due to Prest (1962); early use was made by Mansfield (1972). Assuming that the base year is t, that the aim is to back-cast the series for two years, and defining AT as the adjusted tax revenue, T as the observed T T tax revenue, and D as the amount of tax revenue due to discretionary changes, the formula at t 2 would be ATt 2 = Tt 2 [ T t D ][ T t D1 ]; the t t t 1 t 1 Tt formula at t 1 would be ATt 1 = Tt 1 [ T D ]; while at t (the base year) it is assumed that ATt = Tt , i.e. the absence of discretionary changes. 4

t

t

This is the case, for example, of the data on discretionary measures that are annually collected by the EU Member States in the context of the Output Gap Working Group (OGWG) managed by the European Commission. 5 It is worth noting that tax changes may affect tax bases not only in the year after they are implemented, but also in the following years, which would exacerbate the divergence. 6 For a detailed discussion, see the comment by Mertens to Barro and Furman (2018). An application of adjustments of tax revenues to the original tax series to take into account tax reforms is from Machado and Zuloeta (2012) for Latin American Countries. 3

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In particular, a tax is buoyant in the long-run when the tax revenue increases more than one percent for a one percent increase in the GDP. A tax is not buoyant in the opposite case. However, the value of tax buoyancy should converge to 1 in the long-run, if it is accepted that there exists a long-run relationship between GDP and tax revenue, as it should be if one considers that there is a limited possibility to avoid taxation when an increase in tax base occurs.7 Indeed, a value greater than 1 would imply that the ratio of taxes to GDP would increase indefinitely; while a value lower than 1 would mean that the same ratio would fall continuously. Both cases do not represent a long-run equilibrium for the sustainability of public finances. When observed in a given time interval, the coefficient of tax buoyancy may differ from 1, reflecting the possibility that the ratio between taxes and GDP has followed either a decreasing or an increasing path due to the specific combinations of automatic and discretionary changes in the observed period. Moving to the short-run, instead, tax buoyancy may give information on the stabilization power of taxes, more than measuring fiscal sustainability. This stabilization function is usually insufficient to bring about a stable rise in income and employment; rather it better serves the aim of either cushioning depressed cycles or braking excessive economic booms with inflationary pressures. With regard to specific tax items, long-run tax buoyancy is expected to be greater than 1 for personal progressive taxes and lower than 1 for levies that are mostly regressive, as it may be the case for social security contributions and most consumption taxes. However, this difference among taxes may vanish in the short-run, especially in the presence of wage rigidity, lack of indexation, and tight employment protection. Furthermore, consumption is also relatively stable in the short-run to the extent that it is smoothed over a longer period, which means that also consumption taxes may show a short-run buoyancy lower than 1. Thus, even though with some degree of variability, these taxes are not expected to provide a complete automatic stabilization in the short-run. Some exceptions may be expected for the corporation tax, as profits usually react more rapidly to business fluctuations. This would imply that both in the short-run and in the long-run the tax buoyancy of the corporation tax may be greater than one, especially when the share of profits in GDP is increasing over the period analysed (Stockhammer, 2013; Belinga et al., 2014). 3. The empirical methodology 3.1. Specification and estimation technique The empirical specification is based on a balanced panel of 35 OECD countries over the period 1995–2016.8 To estimate tax buoyancy, we adopt an Error Correction Model (ECM) (Engle and Grange, 1987; Wickens and Breusch, 1988), which considers both levels and changes of the relevant variables in order to identify both the long-run growth and short-run cyclical variability of tax revenues through a single equation (e.g., Wolswijk, 2009; Fricke and Sussmuth, 2014; Dudine and Jalles, 2018). In the most general form, one can consider the following ECM in a one-stage approach :9

ln Tit =

i

ln Tit

1

+

i

ln Yit

1

+

i0

lnYit +

i

ln Zit

1

+ µi0 lnZit +

it

(1)

Eq. (1) directly gives three parameters of interest: a) the short-run buoyancy, as measured by the coefficient γi0; b) the long-run i buoyancy, as measured by i = , i.e. by the ratio of the two estimated coefficients of the lagged GDP and the lagged tax revenue; c) i the speed of adjustment, as measured by λi, which measures how the long-run effect is distributed over the following years. The expected sign of the coefficients of both short-run and long-run buoyancy is positive (γi0 > 0 and θi > 0); the expected sign of the speed of adjustment is instead negative (λi < 0), which implies αi > 0. The novel approach of this paper – compared with the existing literature on tax buoyancy – is to estimate Eq. (1) by a heterogeneous coefficient model with common correlated effects in a dynamic panel, exploiting the Ditzen (2018) estimator. This method better applies with a large number of groups and observations over time. In order to prevent a small sample time series bias (Chudik and Pesaran, 2015), a correction is introduced by using a recursive method, according to which each observation at time t is calculated by the difference between the actual observation at time t and the average value of the observations until the period t 1. Before estimating Eq. (1), some diagnostic tests have been implemented, in order to consider the presence of cross-sectional dependence in the data. First, we use the CD-test (Pesaran, 2004) to measure the correlation coefficients between the time-series for each panel member. Second, stationarity has been controlled for in order to properly apply the ECM (Pesaran, 2006). The results of the tests are reported in Appendix (Tables A1 and A2). In what follows, the analysis is carried out by calculating short- and long-run responses of total revenue and of specific tax categories.

7

See, for example, Wolswijk (2009; 4). See also Sobel and Holcombe (1996) and Bruce et al. (2006). Countries are: Australia, Austria, Belgium, Canada, Chile, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Israel, Italy, Japan, Korea, Latvia, Luxembourg, Mexico, Netherlands, New Zealand, Norway, Poland, Portugal, Slovak Republic, Slovenia, Spain, Sweden, Switzerland, Turkey, United Kingdom, United States. 9 The ECM of Eq. (1) can be derived by the manipulation of a standard auto-regressive distributed lag model (ARDL). In principle, this procedure could not be implemented for the set of control variables. However, we choose to follow the same method in order to have both changes and lagged levels of those variables in the estimated regression. The model, however, allows for control variables only to affect short-run responses, as no longrun relation between them and the dependent variable is assumed. 8

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Fig. 1. Tax revenue composition (% of total taxation) by country (average 1995–2016). Note: PIT = Personal income tax; CIT = Corporation income tax; SSC = Social Security Contributions; GST = Goods and Services Tax. Source: Authors’ elaborations on OECD Revenue Statistics.

3.2. Data Data on tax revenues are from the OECD Revenue Statistics for 35 countries over the period 1995–2016. This period is particularly important for at least two reasons. First, it embraces a deep change of the fiscal policy in the countries of the European Union driven by the constraints introduced by the Maastricht Treaty in 1992 and by the Stability and Growth Pact in 1997. To some extent, this framework is likely to have changed the logic of the discretionary tax policy followed by many countries in Europe, especially those with high levels of deficit and debts. As a consequence, discretionary changes might also have affected the built-in flexibility of the various tax systems. Second, this period includes one of the most severe financial and economic crisis in many decades, extending between 2007 and 2013 with different lengths across countries. The impact of the recession has involved many countries, consequently stressing their tax systems. Indeed, during this period, national governments have still faced the need to collect an adequate amount of public resources to guarantee the provision of public services and of welfare programs, yet in the presence of a reduced level of economic activity and of an uncertain response of tax bases. Those elements may have affected both the short-run and the long-run reaction of taxes to GDP in such economic downturn. Finally, a third minor – but convenient – reason can be added, which is fully technical. Since not all countries considered in our sample were OECD members before 1995, starting with this year as the first year of the analysis allows for a balanced panel, by this way reducing the probability that the results could be driven only by those countries that are OECD members since a long time. Fig. 1 gives a first insight of the tax revenue composition calculated as the average of the period 1995–2016 for each country. As expected, personal income taxes (PIT) and goods and services taxes (GST), including VAT where applied, are the most relevant sources of total revenue in all countries, followed by social security contributions (SSC). Corporate income taxes (CIT), instead, contribute for a smaller fraction. Fig. 2, instead, shows the rates of growth of GDP and of PIT, CIT, SSC and GST (all in nominal terms) for our sample between 1995 and 2016. As can be observed, the series greatly fluctuate, with considerable volatility (especially for CIT). However, while between 1995 and 2000 the fluctuations of tax revenues were smaller, they have amplified after 2000 both in the case of expansions (2002–2007) and in the case of contractions (2007–2009), which suggests that short-run buoyancy may have been greater than 1 in both cases, with the largest deviations shown by the corporate income tax (CIT). Finally, some descriptive statistics and the source of the main variables are reported in Table A3 in Appendix.

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Fig. 2. Growth rates of nominal GDP and main tax revenues in the OECD countries (%, 1995–2016). Note: The growth rates are calculated considering the yearly difference of the aggregate value of each variable across countries in each year. Source: Authors’ elaborations on OECD data.

Table 1 The baseline model. Source: Authors’ elaborations on OECD data

Lag (dep) Delta GDP Lag GDP Constant Short-run Long-run Speed of adjustment Observations R2 CD Comparison with recent studies Boschi and d'Addona (2019) 15 EU countries, 1980–2013 Short run Long run Dudine and Jalles (2018) 31 advanced economies, 1980–2014 Short run Long run Belinga et al. (2014) OECD countries Short run Long run Sobel and Holcombe (1996) USA Short run Long run

Total revenue

PIT

CIT

GST

Total taxes

−0.32⁎⁎⁎ 0.76⁎⁎⁎ 0.29⁎⁎⁎ 0.004 0.76⁎⁎⁎ 0.91⁎⁎⁎ −0.32⁎⁎⁎ 770 0.71 19.65⁎⁎⁎

−0.39⁎⁎⁎ 1.16⁎⁎⁎ 0.32⁎⁎⁎ 0.02⁎⁎⁎ 1.16⁎⁎⁎ 0.81⁎⁎⁎ −0.39⁎⁎⁎ 770 0.68 13.98⁎⁎⁎

−0.46⁎⁎⁎ 2.16⁎⁎⁎ 0.31⁎⁎⁎ 0.04⁎⁎⁎ 2.16⁎⁎⁎ 0.67⁎⁎⁎ −0.46⁎⁎⁎ 770 0.55 8.24⁎⁎⁎

−0.35⁎⁎⁎ 0.56⁎⁎⁎ 0.28⁎⁎⁎ 0.03⁎⁎⁎ 0.56⁎⁎⁎ 0.79⁎⁎⁎ −0.35⁎⁎⁎ 770 0.59 12.95⁎⁎⁎

−0.37⁎⁎⁎ 0.90⁎⁎⁎ 0.33⁎⁎⁎ 0.01 0.90⁎⁎⁎ 0.88⁎⁎⁎ −0.37⁎⁎⁎ 770 0.68 19.21⁎⁎⁎

0.74 1.11

2.62 2.75

0.48 0.71

0.98 1.00

0.80 0.91

2.94 1.52

0.87 0.95

1.04 1.06

1.10 0.97

1.96 1.26

0.92 0.98

1.16 1.22

3.37 0.67

1.04 0.66

PIT = Personal income tax; CIT = Corporate income tax; GST = Goods and Services Tax. The aggregate “Total revenue” includes social security contributions (SSC). The aggregate “Total taxes” does not include SSC. ⁎⁎⁎ indicates 1% level significance level.

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Table 2 The baseline model controlling for unemployment and inflation. Source: Authors’ elaborations on OECD data.

Lag (dep) Delta GDP Lag GDP Constant Short-run Long-run Speed of adjustment Observations R2 CD With instruments Short-run Long-run

Total revenue

PIT

−0.37 0.50⁎⁎⁎ 0.33⁎⁎⁎ 0.03⁎⁎⁎ 0.50⁎⁎⁎ 0.89⁎⁎⁎ −0.37⁎⁎⁎ 750 0.85 7.33⁎⁎⁎

−0.52 0.69⁎⁎⁎ 0.42⁎⁎⁎ 0.04 0.69⁎⁎⁎ 0.81⁎⁎⁎ −0.52⁎⁎⁎ 750 0.84 4.75⁎⁎⁎

−0.62 1.21⁎⁎⁎ 0.43⁎⁎⁎ 0.16⁎⁎⁎ 1.21⁎⁎⁎ 0.69⁎⁎⁎ −0.62⁎⁎⁎ 743 0.72 3.41⁎⁎⁎

−0.40 0.41⁎⁎⁎ 0.31⁎⁎⁎ 0.05⁎⁎⁎ 0.41⁎⁎⁎ 0.77⁎⁎⁎ −0.40⁎⁎⁎ 750 0.80 10.41⁎⁎⁎

−0.41⁎⁎⁎ 0.59⁎⁎⁎ 0.35⁎⁎⁎ 0.03⁎⁎⁎ 0.59⁎⁎⁎ 0.87⁎⁎⁎ −0.41⁎⁎⁎ 750 0.83 6.76⁎⁎⁎

0.43⁎⁎⁎ 0.64⁎⁎⁎

0.72⁎⁎⁎ 0.83⁎⁎⁎

1.15⁎⁎⁎ 0.68⁎⁎⁎

0.50⁎⁎⁎ 0.80⁎⁎⁎

0.71⁎⁎⁎ 0.90⁎⁎⁎

⁎⁎⁎

CIT ⁎⁎⁎

GST ⁎⁎⁎

Total taxes ⁎⁎⁎

PIT = Personal income tax; CIT = Corporate income tax; GST = Goods and Services Tax. The aggregate “Total revenue” includes social security contributions (SSC). The aggregate “Total taxes” does not include SSC. ⁎⁎⁎ indicates 1% level significance level ⁎⁎indicates 5% significance level. Table 3 The baseline model controlling for unemployment, inflation, and economic crisis. Source: Authors’ elaborations on OECD data.

Lag (dep) Delta GDP Lag GDP Constant Short-run Long-run Speed of adjustment Observations R2 CD Dummy for negative output gap Short-run Long-run Speed of adjustment Output gap Short-run Long-run Speed of adjustment

Total revenue

PIT

⁎⁎⁎

−0.37 0.47⁎⁎⁎ 0.32⁎⁎⁎ 0.03⁎⁎⁎ 0.47⁎⁎⁎ 0.89⁎⁎⁎ −0.37⁎⁎⁎ 750 0.87 5.56⁎⁎⁎

⁎⁎⁎

CIT

−0.50 0.66⁎⁎⁎ 0.41⁎⁎⁎ 0.04 0.66⁎⁎⁎ 0.81⁎⁎⁎ −0.50⁎⁎⁎ 750 0.85 3.99⁎⁎⁎

⁎⁎⁎

GST

−0.63 0.96⁎⁎⁎ 0.44⁎⁎⁎ 0.16⁎⁎⁎ 0.96⁎⁎⁎ 0.69⁎⁎⁎ −0.63⁎⁎⁎ 743 0.75 2.54⁎⁎⁎

⁎⁎⁎

Total taxes

−0.39 0.38⁎⁎⁎ 0.30⁎⁎⁎ 0.06⁎⁎⁎ 0.38⁎⁎⁎ 0.77⁎⁎⁎ −0.39⁎⁎⁎ 750 0.82 7.65⁎⁎⁎

−0.42⁎⁎⁎ 0.56⁎⁎⁎ 0.36⁎⁎⁎ 0.36⁎⁎⁎ 0.56⁎⁎⁎ 0.86⁎⁎⁎ −0.42⁎⁎⁎ 750 0.86 5.28⁎⁎⁎

0.47⁎⁎⁎ 0.89⁎⁎⁎ −0.38⁎⁎⁎

0.62⁎⁎⁎ 0.81⁎⁎⁎ −0.52⁎⁎⁎

1.03⁎⁎⁎ 0.67⁎⁎⁎ −0.64⁎⁎⁎

0.41⁎⁎⁎ 0.77⁎⁎⁎ −0.43⁎⁎⁎

0.56⁎⁎⁎ 0.86⁎⁎⁎ −0.41⁎⁎⁎

0.27⁎⁎⁎ 0.92⁎⁎⁎ −0.40⁎⁎⁎

0.53⁎⁎⁎ 0.81⁎⁎⁎ −0.62⁎⁎⁎

0.29 0.67⁎⁎⁎ −0.72⁎⁎⁎

0.13 0.79⁎⁎⁎ −0.45⁎⁎⁎

0.35⁎⁎⁎ 0.89⁎⁎⁎ −0.44⁎⁎⁎

PIT = Personal income tax; CIT = Corporate income tax; GST = Goods and Services Tax. The aggregate “Total revenue” includes social security contributions (SSC). The aggregate “Total taxes” does not include SSC. ⁎⁎⁎ indicates 1% level significance level ⁎⁎ indicates 5% significance level.

Table 4 The baseline model controlling for unemployment and inflation, Euro countries. Source: Authors’ elaborations on OECD data.

Lag (dep) Delta GDP Lag GDP Constant Short-run Long-run Speed of adjustment Observations R2 CD

Total revenue

PIT

−0.59 0.53⁎⁎⁎ 0.54⁎⁎⁎ −0.01 0.53⁎⁎⁎ 0.91⁎⁎⁎ −0.59⁎⁎⁎ 224 0.82 4.27⁎⁎⁎

−0.70 0.63⁎⁎⁎ 0.57⁎⁎⁎ 0.003 0.63⁎⁎⁎ 0.82⁎⁎⁎ −0.70⁎⁎⁎ 224 0.78 2.57⁎⁎⁎

⁎⁎⁎

CIT

GST

−0.75 1.11⁎⁎⁎ 0.55⁎⁎⁎ −0.09 1.11⁎⁎⁎ 0.72⁎⁎⁎ −0.75⁎⁎⁎ 224 0.77 −0.47

⁎⁎⁎

⁎⁎⁎

Total taxes

−0.55 0.53⁎⁎⁎ 0.45⁎⁎⁎ 0.02 0.53⁎⁎⁎ 0.81⁎⁎⁎ −0.55⁎⁎⁎ 224 0.75 5.15⁎⁎⁎ ⁎⁎⁎

−0.63⁎⁎⁎ 0.62⁎⁎⁎ 0.55⁎⁎⁎ −0.01 0.62⁎⁎⁎ 0.88⁎⁎⁎ −0.63⁎⁎⁎ 224 0.78 3.35⁎⁎⁎

PIT = Personal income tax; CIT = Corporate income tax; GST = Goods and Services Tax. The aggregate “Total revenue” includes social security contributions (SSC). The aggregate “Total taxes” does not include SSC. ⁎⁎⁎ indicates 1% level significance level ⁎⁎ indicates 5% significance level. 7

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Table 5 The baseline model controlling for unemployment and inflation, non-Euro countries. Source: Authors’ elaborations on OECD data.

Lag (dep) Delta GDP Lag GDP Constant Short-run Long-run Speed of adjustment Observations R2 CD

Total revenue

PIT

−0.31 0.43⁎⁎⁎ 0.27⁎⁎⁎ 0.05⁎⁎⁎ 0.43⁎⁎⁎ 0.86⁎⁎⁎ −0.31⁎⁎⁎ 416 0.86 0.44

−0.54 0.57⁎⁎⁎ 0.44⁎⁎⁎ 0.05 0.57⁎⁎⁎ 0.80⁎⁎⁎ −0.54⁎⁎⁎ 416 0.87 0.52

⁎⁎⁎

CIT

GST

−0.63 0.82⁎⁎ 0.44⁎⁎⁎ 0.22⁎⁎ 0.82⁎⁎⁎ 0.70⁎⁎⁎ −0.63⁎⁎⁎ 416 0.73 1.59

⁎⁎⁎

⁎⁎⁎

Total taxes

−0.33 0.34⁎⁎ 0.24⁎⁎⁎ 0.08⁎⁎⁎ 0.34⁎⁎⁎ 0.72⁎⁎⁎ −0.33⁎⁎⁎ 416 0.82 4.87⁎⁎⁎ ⁎⁎⁎

−0.34⁎⁎⁎ 0.46⁎⁎⁎ 0.29⁎⁎⁎ 0.05⁎⁎⁎ 0.46⁎⁎⁎ 0.84⁎⁎⁎ −0.34⁎⁎⁎ 416 0.85 0.87

PIT = Personal income tax; CIT = Corporate income tax; GST = Goods and Services Tax. The aggregate “Total revenue” includes social security contributions (SSC). The aggregate “Total taxes” does not include SSC. ⁎⁎⁎ indicates 1% level significance level ⁎⁎ indicates 5% significance level.

Table 6 The baseline model controlling for unemployment and inflation, European countries. Source: Authors’ elaborations on OECD data.

Lag (dep) Delta GDP Lag GDP Constant Short-run Long-run Speed of adjustment Observations R2 CD

Total revenue

PIT

−0.51 0.57⁎⁎⁎ 0.46⁎⁎⁎ 0.02 0.57⁎⁎⁎ 0.90⁎⁎⁎ −0.51⁎⁎⁎ 378 0.84 6.15⁎⁎⁎

−0.59 0.66⁎⁎⁎ 0.49⁎⁎⁎ 0.03 0.66⁎⁎⁎ 0.82⁎⁎⁎ −0.59⁎⁎⁎ 378 0.79 3.42⁎⁎⁎

⁎⁎⁎

CIT

GST

−0.66 1.15⁎⁎ 0.47⁎⁎⁎ −0.02 1.15⁎⁎⁎ 0.71⁎⁎⁎ −0.66⁎⁎⁎ 378 0.73 2.87⁎⁎⁎

⁎⁎⁎

⁎⁎⁎

Total taxes

−0.58 0.54⁎⁎ 0.46⁎⁎⁎ 0.02⁎⁎⁎ 0.54⁎⁎⁎ 0.80⁎⁎⁎ −0.58⁎⁎⁎ 378 0.78 5.86⁎⁎⁎ ⁎⁎⁎

−0.62⁎⁎⁎ 0.64⁎⁎⁎ 0.53⁎⁎⁎ 0.03⁎⁎⁎ 0.64⁎⁎⁎ 0.86⁎⁎⁎ −0.62⁎⁎⁎ 378 0.82 5.17⁎⁎⁎

PIT = Personal income tax; CIT = Corporate income tax; GST = Goods and Services Tax. The aggregate “Total revenue” includes social security contributions (SSC). The aggregate “Total taxes” does not include SSC. ⁎⁎⁎ indicates 1% level significance level ⁎⁎ indicates 5% significance level.

4. Main results Tables 1–8 show the results from the DCCE estimator for total revenue and disaggregated taxes as dependent variables, without and with some control variables.10 The tables also report the Pesaran (2004) CD statistic that confirm a sizeable reduction in the degree of cross-sectional dependence compared to the fixed effect estimator (reported in Table A4 in the Appendix), as measured by the average pairwise correlation coefficient. It follows that the inclusion of cross sectional averages in the ADF regressions seems to better capture the cross-sectional dependence in the data compared to other estimators, as confirmed by the significance of the CD test compared with fixed effects. This means that the output obtained with the fixed effect estimation cannot be relied upon, regardless of the outcome, unless a DCCE estimation is performed once cross-sectional dependence is detected. 4.1. The baseline model Table 1 reports the outcome of the baseline model described in Eq. (1) without control variables. All coefficients are statistically significant across specifications. Short-run and long-run responses are calculated as explained in Section 3.1 and reported in the middle part of the table. For total revenue, the short-run coefficient suggests an average tax buoyancy of 0.76, which means that the reaction to the economic cycle is slightly lower than expected even for a good automatic stabilizer. It can imply, on average, either that discretionary changes are not enough to compensate a low tax elasticity or that they excessively counteract a high tax elasticity. Comparing our 10 The analysis of disaggregated taxes does not include SSC, because in most cases these levies are targeted to the financing of specific items of public spending (e.g., pensions). Even though they are technically similar to other forms of taxation, their behavior – and thus their reaction to GDP changes – cannot be fully assimilated to that of general taxes.

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Table 7 The baseline model controlling for unemployment and inflation, non-European countries. Source: Authors’ elaborations on OECD data.

Lag (dep) Delta GDP Lag GDP Constant Short-run Long-run Speed of adjustment Observations R2 CD

Total revenue

PIT

−0.29 0.41⁎⁎⁎ 0.25⁎⁎⁎ 0.05⁎⁎ 0.41⁎⁎⁎ 0.87⁎⁎⁎ −0.29⁎⁎⁎ 260 0.87 −1.31

−0.45 0.75⁎⁎⁎ 0.37⁎⁎⁎ 0.07 0.75⁎⁎⁎ 0.81⁎⁎⁎ −0.45⁎⁎⁎ 260 0.88 −0.41

⁎⁎⁎

GST −0.64 1.30⁎⁎ 0.42⁎⁎⁎ 0.34⁎⁎ 1.30⁎⁎⁎ 0.65⁎⁎⁎ −0.64⁎⁎⁎ 260 0.76 −1.34

⁎⁎⁎

Total taxes

−0.28 0.18 0.21⁎⁎⁎ 0.08⁎⁎⁎ 0.18⁎⁎⁎ 0.72⁎⁎⁎ −0.28⁎⁎⁎ 260 0.84 −1.03

⁎⁎⁎

−0.33⁎⁎⁎ 0.49⁎⁎⁎ 0.28⁎⁎⁎ 0.05* 0.49⁎⁎⁎ 0.86⁎⁎⁎ −0.33⁎⁎⁎ 260 0.86 −0.75

⁎⁎⁎

PIT = Personal income tax; CIT = Corporate income tax; GST = Goods and Services Tax. The aggregate “Total revenue” includes social security contributions (SSC). The aggregate “Total taxes” does not include SSC. ⁎⁎⁎ indicates 1% level significance level ⁎⁎ indicates 5% significance level.

Table 8 Controlling for changes in tax policy. Source: Authors’ elaborations on OECD data.

Short-run Long-run Differences Short-run Long-run With instruments Short-run Long-run Differences Short-run Long-run

Total taxes (a)

PIT (b)

PIT (c)

CIT (d)

0.61⁎⁎⁎ 0.86⁎⁎⁎

0.68⁎⁎⁎ 0.79⁎⁎⁎

0.72⁎⁎⁎ 0.80⁎⁎⁎

1.12⁎⁎⁎ 0.61⁎⁎⁎

0.03⁎⁎⁎ 0.00⁎⁎⁎

−0.01⁎⁎⁎ −0.02⁎⁎⁎

0.03⁎⁎⁎ −0.01⁎⁎⁎

−0.09⁎⁎⁎ −0.08⁎⁎⁎

0.70⁎⁎⁎ 0.90⁎⁎⁎ 0.11 0.03⁎⁎⁎

0.71⁎⁎⁎ 0.78⁎⁎⁎ 0.01⁎⁎⁎ −0.04⁎⁎⁎

0.73⁎⁎⁎ 0.77⁎⁎⁎ 0.03⁎⁎⁎ −0.04⁎⁎⁎

0.94⁎⁎⁎ 0.35⁎⁎⁎ −0.27⁎⁎⁎ −0.34⁎⁎⁎

(a) Controlling for income average tax rates and corporate tax rates; (b) Controlling for income average tax rates; (c) Controlling for the highest marginal tax rate; (d) Controlling for corporate tax rates. Controls for unemployment and inflation are included in all regressions. PIT = Personal income tax; CIT = Corporate income tax. The aggregate “Total taxes” does not include SSC.

results with previous studies estimating tax buoyancy, it emerges that short-run coefficients for total revenue are significantly lower. As reported in the bottom part of Table 1, in Dudine and Jalles (2018) the short-run coefficient for total revenue, estimated for the period 1980 to 2014, is very close to 1, as well as in Belinga et al. (2014), where the coefficient estimated for the period 1989–2012 slightly exceeds 1. Thus, while these two studies measure a short-run buoyancy in line with the function that should be performed by a pure automatic stabilizer, our results suggest an insufficient degree of tax buoyancy for the same purpose. It is worth recalling that, however, this insufficiency may be due to the inclusion of social security contributions in the definition of total tax revenue. As stated above, this component is usually much less elastic than other tax items and it could lower, ultimately, the estimated tax buoyancy. In order to explore this issue and understand whether the outcome for total revenue is driven by specific tax items, we replicate the analysis for each of them. Table 1 shows that for PIT and CIT tax buoyancy significantly exceeds one (1.16 and 2.16, respectively). It is, instead, well below 1 for taxes on goods and services (0.56). In this case, our results for PIT are in line with those estimated by Belinga et al. (2014) and by Sobel and Holcombe (1996), which are however limited to the US and very far from the crisis period. Both Dudine and Jalles (2018) and Boschi and d'Addona (2019), for PIT, report short-run coefficients below 0.8. The tax buoyancy for CIT, instead, are well above 1 in all cases, the closest outcome being again Belinga et al. (2014). This effect can be imputed to the speed with which profits adjust in either recessions or booms, suggesting that CIT has worked as a satisfactory stabilizer. More homogeneity in terms of results is found when estimating the short-run tax buoyancy of GST, which is below 1 in all cases (with the exception of Sobel and Holcombe, 1996, for the US). Actually, our coefficient is lower than those estimated by Dudine and Jalles (2018) and Belinga et al. (2014) and more in line with that by Boschi and d'Addona (2019). The main reason of this low level of tax buoyancy is that the excise taxes included in this aggregate are usually inelastic tax items, as they are per unit taxes rather than set on an ad valorem basis. Furthermore, consumption is usually smoothed in both expansions and recessions, which may yield a lower average reaction of these taxes to the business cycle. Finally, Table 1 reports the result for total taxes, i.e. excluding social security contributions; in this case, the short-run coefficient 9

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is 0.90. This outcome, and the difference with total revenue, may be due to the fact that SSC are relatively rigid also in the short-run and not expected to promptly react to the economic cycle. A different framework emerges in Table 1 when considering long-run tax buoyancies. For total revenue, the long-run tax reaction is about 0.91, very similar to the coefficient of total taxes (0.88), which suggests that in the long-run the inelastic short-run automatic behavior of social security contributions might be compensated by discretionary changes in line with what happens to other taxes. Unlike what happens in the short-run, however, all tax categories now show a tax buoyancy significantly lower than 1, where the only consistent outcome with the short-run effect is the limited long-run tax buoyancy of GST. In particular, the coefficients of PIT and CIT suggest that, notwithstanding their short-run buoyancy, these taxes could not guarantee fiscal sustainability in the long-run, as they do not seem to converge to a long-run equilibrium in the period analysed. To some extent, this may be due to a general decline both in the progressivity of income taxes and of taxes on profits that have characterized many countries of our sample. More generally, these lower tax buoyancies may be interpreted as an indicator of ineffective discretionary policy measures to compensate for either low or high elasticities (Jenkins et al., 2000). Put differently, over the period of analysis, low tax buoyancies signal the possibility that the mix of tax policies and automatic changes were not effective in keeping revenue collection at par with changes in national incomes. It is worth noting that this partial convergence occurs even in the presence – as expected – of a negative sign of the coefficient of the speed of adjustment, which in principle would allow convergence in the long-run. This specific set of results significantly differ from those reported by Boschi and d'Addona (2019), Dudine and Jalles (2018), and Belinga et al. (2014), where the long-run coefficients are almost always close to 1 or even exceeding 1, especially in the case of GST. It is worth saying, however, that not all results are directly comparable. For instance, Boschi and d'Addona (2019) estimate tax elasticities after having isolated discretionary tax changes by a set of dummy variables. In turn, Belinga et al. (2014) use a narrower time span that may not fully capture the impact of the recent Great Recession, which has certainly contributed to reduce the level of tax buoyancy in the long-run if one considers the fact that the in the last four years included in our analysis there is evidence of a slower growth in almost all countries. Finally, a more general difference concerns the use, in our case, of the DCCE estimator that takes into account the cross-sectional dependence, an issue that is not directly addressed by the other studies. In order to better explain the advantage of using a DCCE estimator, we have first re-estimated the baseline model by using fixed effects for the whole period as reported in Table A4 in Appendix. In this case, while slightly diverging in size, the path of long-run buoyancy below 1 is hardly challenged. When limiting the time span before 2008, the outcome with the DCCE estimator is rather stable (tax buoyancies below 1), while by using fixed effects results are more in line with the previous studies (e.g., for PIT and CIT the coefficient largely exceeds 1, while for total taxes it is very close to unity). Thus, our methodology, combining a longer period and a more proper dynamic estimator, is able to generate important differences in estimating tax buoyancies, an issue that the previous literature has overlooked, as it has been demonstrated by the fact that by using fixed effects in a sub-sample we can replicate the existing results. In any case, the presence of cross-sectional dependence makes harder to rely on fixed effect estimation, even when results do not seem to change compared to the use of the DCCE. The average outcomes described above are the product of deeply differentiated responses at country level, reported in tables A5 (short-run) and A6 (long-run) in Appendix. Short-run buoyancy of total revenue is statistically significant in 23 out of 35 countries, but only in nine cases it exceeds 1 (Chile, Czech Republic, Iceland, Japan, Latvia, Poland, Portugal, Spain, United States). In all other cases, the reaction is below what would be required to perform as a satisfactory stabilizer. This result, in terms of frequency of cases, does not significantly diverge from Belinga et al. (2014). Even though they report short-run buoyancies exceeding 1 for a set of countries greater than ours, our set is included in theirs, with the exception of Iceland (a tax buoyancy lower than 1) and Latvia, whose tax buoyancy is not estimated. A greater divergence, instead, can be found with respect to Dudine and Jalles (2018), where a short-run tax buoyancy greater than 1 is found for 21 out of 31 countries; for Chile, Czech Republic, Japan, Portugal, Spain, and US, tax buoyancies are in both cases greater than 1. Also in this case Iceland has a diverging result, while Latvia is not included. Things are slightly different if one considers disaggregated taxes. In the case of PIT and CIT more than 20 countries show a coefficient greater than 1, suggesting that they may be good stabilizers. In the case of GST, as expected, the number of statistically significant tax buoyancies is lower; thus, the conclusion about the effectiveness of stabilization function is less general than in the other two cases, which is mainly due to the presence of relatively rigid excise taxes. A remarkable result of our analysis, instead, concerns the estimation of long-run tax buoyancies at the country level. As reported in Appendix in Table A6, the country-specific coefficients are below 1 for all taxes, suggesting that in terms of fiscal sustainability the tax system may have lost some of its power in the period analysed. This loss concerns not only aggregate taxes but also single tax items. These results diverge from other studies extending the analysis to previous periods. For example, in Dudine and Jalles (2018) long-run buoyancy of total taxes is estimated above 1 for 16 countries out of the 31 included in their study. In Belinga et al. (2014), the same outcome occurs for 23 out of 34 countries. It is not straightforward to understand the reasons of these differences in country estimation. However, as already observed, the inclusion in our analysis of the most recent years of slow growth may play a role, as well as the fact that in both cases alternative estimation techniques to the DCCE are used (FMOLS, MG, PMG, fixed effects), thus without taking into account the presence of cross-sectional dependence.11 Finally, the speed of adjustment is also reported in Table A7 in the Appendix. As expected, the sign of the adjustment is negative in all countries, with few exceptions that might give rise to a possible divergence in the long-run. According to this line of reasoning, the 11 It is also worth noting that in some cases the coefficients of “Total revenue” and “Total taxes” are identical. This mainly happens in those countries where social security contributions are either not used or used at a low level to finance social spending.

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main implication of our result is that in the long-run neither aggregate taxes nor single tax items are in line with the requirement of the fiscal sustainability, whose counterpart is the sustainability of public spending in the near future. It means that – at least from what observed in the period of analysis – the mix of discretionary and automatic tax changes could be insufficient for the achievement of a long-run equilibrium in most of the countries analysed, beyond being insufficient on average. 4.2. The role of macroeconomic conditions As already explained in Section 2, the results in terms of tax buoyancy do not give any possibility to disentangle built-in reactions and responsiveness to discretionary changes due to governments’ fiscal policies and macroeconomic conditions. Yet, an indirect way to check whether automatic stabilization may have played a significant role is to control for both unemployment and inflation. Indeed, inflation tends to increase the tax burden of PIT, CIT, and GST (at least for ad valorem taxes), whereas unemployment tends to decrease it to the extent that it drives a reduction of wages and profits, giving rise to two important automatic sources of stabilization. The results reported in Table 2 show that short-run buoyancies are now much lower than those measured in Table 1 for both total tax revenue and disaggregated taxes, even though inflation and unemployment are not always statistically significant. When they are significant, as expected, the sign of unemployment is negative, which means that tax buoyancy is reduced by recessions when unemployment increases, and the sign of inflation is positive, as inflation may increase the reaction of important tax items like the personal income tax and the good and service taxes. Furthermore, by controlling for inflation, these results could be to some extent interpreted as a proxy of the real tax buoyancy, which is significantly lower than nominal tax buoyancy in the short-run, even for CIT that was the most effective short-run stabilizer in Table 1. In other words, inflation and unemployment contribute to foster the reaction of taxes to GDP changes in the short-run; once their impact is isolated, the real tax buoyancy reduces significantly, which implies that at least part of the tax buoyancy measured in Table 1 is driven by automatic responses to macroeconomic conditions. On the other hand, estimates of long-run tax buoyancy are not significantly affected when controlling for unemployment and inflation, as the corresponding coefficients are very similar to those obtained without controlling for them. Following the same line of reasoning, this outcome seems to imply that while the short-run tax response is significantly impaired by automatic sources, the longrun reaction could be mostly affected by the combination of both automatic and discretionary changes in governments’ policy. Finally, in order to take into account the possibility of endogeneity between tax revenues and business cycles, the same regressions have been run by using the instrumental variable (IV) approach with the DCCE estimator. In detail, given the difficulty to find suitable external instruments, we instrument the lag of GDP with further lags of the same variable (i.e. lag of order two). This method, whose results are added in Table 2, does not alter the general path of our main findings. This would suggest that, in our case, the endogeneity issue is not critical. To corroborate this statement, we provide further robustness checks in the next paragraphs to address the fact that changes in taxes might also drive changes in GDP (see Section 4.4). In order to deeply consider the macroeconomic framework, attention has also been paid to the role of the various economic crises occurred during the observed period. From a theoretical viewpoint, a negative shock, such as the crisis, could generate temporary fluctuations in tax buoyancy, eventually leading to a real structural break. For instance, the recent Global Recession has been characterized by different persistence and intensity across countries in our sample, possibly leading to different tax reactions to such GDP downturn. In particular, countries of the Euro area have been struck by a ‘double dip’ (the euro area sovereign crisis in 2012–2013, with a loss of access to financial markets for a couple of countries and a risk of contagion for other countries of the same area), while other OECD countries started recovering earlier in 2011–12, after a severe recession in 2008–2010. To take into account this kind of heterogeneity across the countries of our sample, a country-specific dummy variable to capture the occurrence of an economic crisis is introduced, which allows taking into account that the duration of the crisis could be not equal across countries. In particular, the dummy is equal to 1 when the country's yearly growth rate of GDP is negative and 0 otherwise. This allows us to exploit the variability of recessionary phases across countries in different years and to verify the possibility of different tax responses. Results including the dummy for crisis are reported in Table 3. Also in this case, short- and long-run tax buoyancies show lower values with respect to the results obtained from the baseline model in Table 1. Actually, they do not significantly differ from the results of Table 2, where only controls for unemployment and inflation were introduced, the reason being that the dummy crisis is statistically significant (with a negative sign) only for CIT and GST. Once controlling for the economic crisis, even the short-run tax buoyancy of CIT falls slightly below 1, which could be interpreted as the fact that most of the CIT tax buoyancy in the short-run is due to its automatic response to macroeconomic conditions. The impact on GST, instead, has a lower intensity (from 0.41 to 0.38). For the other tax items, by accepting the hypothesis that unemployment and inflation capture two important sources of stabilization, the results of Table 3 may imply that the presence of the crisis – even at different stages in different countries – do not add very much in affecting tax buoyancy. In other words, unemployment and inflation seem to absorb most of the impact due to the economic crisis, not least because the three variables may capture similar responses. As a matter of fact, by replicating the regressions in Table 3 using only the dummy crisis as a control, the coefficient is always negative and statistically significant, which means that the economic crisis has reduced the tax buoyancy of all tax items. When added to unemployment and inflation, however, the dummy is not always statistically significant (as described above), possibly because some of the impact of the crisis is already captured by the unemployment variable. In order to give further support to this interpretation, a measure of the output gap has been used as an additional proxy instead of the dummy for economic crises. This approach aims at verifying whether the dummy for crisis could be too a simple method to consider bad macroeconomic conditions, as the short-run fluctuations of output cannot be exclusively attributed to changes in the business cycle, but also to a slowdown in potential economic growth (Boschi and d'Addona, 2019). To this purpose, 11

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Sancak et al. (2010) have proposed two alternatives, defining bad times either as periods where GDP growth is below potential or as periods where GDP growth is at least one percentage point below potential GDP growth. In the same vein, we use two alternative methods of identifying the crisis, the first consisting in building a dummy variable that is equal to 1 when the country's output gap is negative in a given year and 0 otherwise, and the second consisting in using the actual values of the output gap for each country over the observed period. The outcomes of both approaches are reported in Table 3. When using the dummy of the output gap results are significantly in line with those using the dummy crisis, as somehow expected. When using the values of the output gap, the coefficients of long-run are extremely stable, while the coefficients of short-run are even lower. It would imply that the output gap mainly affects short-run responses, and this may occur when the output gaps have mostly a cyclical rather than a permanent nature, an element that can be observed, indeed, in our sample. Thus, as it stands, the baseline model of Table 1 appears best integrated by using inflation and unemployment as control variables, a specification that will be used in what follows, unless differently specified. 4.3. Controlling for the European institutions The sample used in the analysis includes European and non-European countries; furthermore, among the European countries, some of them belong to the Euro area, while other countries maintain their national currency. To this respect, it is worth noting that since the introduction of the Euro, fiscal policies in many European countries have undergone a process of restructuring to meet the fiscal constraints introduced in terms of both deficit and debt threshold over GDP. Similar differences between Euro and non-Euro countries may also occur in terms of output movements as shown by Kolasa (2013). Overall, this could imply that the impact of discretionary changes and their impact on the built-in flexibility of taxes might affect tax buoyancy differently from what can be observed in non-Euro countries. To this regard, Tables 4 and 5 show the results obtained by running two separate regressions, distinguishing Euro and non-Euro countries, and controlling for possible different paths of both unemployment and inflation between groups of countries. In both cases, results do not change significantly, as long-run buoyancy is still below 1 for all categories. This seems to suggest that the observed reduced power of tax systems in ensuring fiscal sustainability does not strictly depend on the adoption of the euro. Yet, the long-run buoyancy measured in non-Euro countries is slightly lower than that measured in Euro countries, an outcome possibly due to the greater heterogeneity of tax systems between the two groups than within each of them. As a possible consequence, also short-run buoyancies are lower in non-Euro countries. In both cases, however, differences are not dramatic. To control for whether these differences are due to splitting the sample – and thus to reducing the number of observations in each regression – the regression of the base model has been run by introducing a dummy for countries with the common currency regime. This dummy is not statistically significant, suggesting that the model of tax buoyancy does not diverge significantly between these two groups of countries, as indeed observed by comparing Tables 4 and 5.12 As a further line of investigation, one can consider that the European Union does not only include countries that adopted the Euro as a common currency. Thus, differences in tax responses may emerge with non-European countries rather than with non-Euro countries. Tables 6 and 7 report the results of splitting the sample according to this geographical classification. While the long-run tax buoyancies only slightly differ between the two groups, more marked differences occur in the short-run, especially for GST, possibly because nonEuropean countries make larger recourse to the relatively more rigid excise taxes. Furthermore, once again, CIT is the only tax showing a short-run buoyancy in both cases. Overall, when considering total taxes, the short-run function of stabilization seems to be more effective in the European countries compared with the non-European ones, mainly because of the fundamental role played by GST. 4.4. Changes in tax policy It is worth noting that changes in taxes might also determine changes in GDP. Indeed, we could have an endogenous response of GDP growth to changes in the tax burden of a country due, for example, to a fiscal reform, or to a new tax regime involving changes in tax rates (e.g., on labor and capital), tax exemptions or incentive schemes. If it is true, both short- and long-run estimations might be biased. Additionally, in the aftermath of the recent economic crisis, many member States have implemented tax cuts either by relaxing fiscal discipline in a context of much improved financial conditions or by consolidating the budget through reduction in public expenditures (Attinasi and Klemm, 2016; Alesina et al., 2019). Hence, the lower tax buoyancy can in fact capture changes in governments’ tax policy rather than the reaction of tax revenue per se. For instance, the low buoyancy of PIT could refer to the large tax cuts during the period 2009–2010, combined with the decade-wide trend of shifting PIT toward consumption taxes. Moreover, during the years 2013–2016 the economic recovery in many EU countries was driven by net exports, which are tax-poor (VAT-exempt), compared with domestic demand, subject to VAT. Overall, the composition effects affecting tax bases – not captured by GDP developments – can also contribute to explain the results of the paper. To check for this possibility, we run regressions for total taxes, PIT and CIT, by including income tax rates and corporate tax rates as additional controls, beyond unemployment and inflation, in each estimation. In the presence of multiple statutory tax rates, as in the case of progressive taxes, average tax rates are used, which help overcome the difficulty of identifying and measuring changes in 12 Results are not reported in the table. The analysis in Tables 4 and 5 has also been replicated by using the IV approach. Also in this case, the coefficients of tax buoyancies are extremely stable, with the short-run coefficient of CIT falling below 1 in this case. All these outcomes are available upon request.

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Table 9 The baseline model controlling for other relevant variables. Source: Authors’ elaborations on OECD data. Tax revenue (a)

Shadow economy (a)

Below median

0.260⁎⁎

0.232⁎⁎⁎

Above median

0.594⁎⁎⁎

0.545⁎⁎⁎

0.889⁎⁎⁎

0.891⁎⁎⁎

0.882⁎⁎⁎

Tax revenue (b)

Shadow economy (b)

Public sector corruption index (b) 0.565⁎⁎⁎

Political corruption index (b) 0.584⁎⁎⁎

Quality of government (b) 0.606⁎⁎⁎

Below median

0.292⁎⁎

0.267⁎⁎⁎

Above median

0.701⁎⁎⁎

0.595⁎⁎⁎

0.860⁎⁎⁎

0.855⁎⁎⁎

0.866⁎⁎⁎

0.860⁎⁎⁎

0.855⁎⁎⁎

Short-run

Long-run

Short-run

Long-run

Base (a)

Debt (a)

Deficit (a)

0.503

0.503

0.502

⁎⁎⁎

0.892⁎⁎⁎

⁎⁎⁎

0.888⁎⁎⁎

Base (b)

Debt (b)

Deficit (b)

0.585

0.572

0.585

⁎⁎⁎

0.865⁎⁎⁎

⁎⁎⁎

0.858⁎⁎⁎

⁎⁎⁎

0.862⁎⁎⁎

⁎⁎⁎

(a) The dependent variable is total revenue including SSC; (b) the dependent variable is total revenue excluding SSC (i.e. total taxes). ⁎⁎⁎ indicates 1% level significance level ⁎⁎ indicates 5% significance level. The model is estimated including controls for unemployment and inflation.

the effective tax, especially in a cross-country perspective. In particular, four regressions have been run. The first considers total taxes with the mean income tax rates and the corporate tax rate used as control variables. The second and the third regression estimate the PIT tax buoyancy by including, in turn, mean income tax rate and the highest marginal tax rate. The last regression considers CIT by controlling for the corporate tax rate. Results are shown in Table 8, where the rows under the heading “Differences” measure the distance between short- and long-run buoyancies after controlling for the tax rates and the estimated buoyancies in the baseline model of Table 2. The general outcome is that when controlling for the tax rates, both short- and long-run buoyancies are very similar, with the more significant distance in the case of CIT. This finding diverges from the results in Belinga et al. (2014), suggesting that in our period, changes in PIT and CIT tax rates may not be strictly correlated with GDP changes. Additionally, all these regressions have been replicated by using the IV approach as a further check for the possible endogeneity between tax revenues and business cycles. As reported in Table 8, the greatest difference again concerns the CIT estimation of both short- and long-run tax buoyancies, which are significantly lower when the IV method is used. On the other hand, the differences with the other tax items are negligible. Thus, endogeneity does not seem a critical issue in determining the general outcome of both short- and long-run coefficients below unity. 5. Additional empirical evidence In this section, we provide some additional results on tax buoyancies taking into account the role of budgetary parameters, shadow economy and political variables possibly affecting how taxes react to changes in GDP. The related results for total revenue and total taxes are reported in Table 9, respectively in the upper panel and in the bottom panel. Details on each column are described in the next sub-sections. 5.1. Budgetary policies The heterogeneity of the fiscal situation across the countries of our sample might represent a relevant issue for the story of the paper. Beyond controlling for the Euro area, one substantial distinction concerns the uneven implementation of the EU fiscal rules, especially in countries still having high debt and high deficits. To check for whether these parameters may affect tax buoyancy, we provide new estimates by introducing a dummy variable assuming value 1 when the ratio of public debt to GDP is higher than the median ratio calculated on the whole sample over time and across countries (which is equal to 61.2%). When the dummy is included (in the second column of Table 9) on the right-hand side of the equation together with inflation and unemployment, its coefficient is not statistically significant. This means that both short- and long-run buoyancies are not significantly affected by the level of public debt over GDP. This could be explained by the fact that what happens to GDP might be more directly relevant for the measurement of tax buoyancy than what happens to the level of public debt. In other words, it might be the case that the tax buoyancy reacts to public debt only indirectly, that is only to the extent that the public debt affects the level of economic activity. If true, it would imply that including public debt does not capture any additional effect for tax reactions. The same outcome is obtained when controlling for public deficit as reported in the third column of Table 9. In detail, a dummy assuming value 1 when the country deficit-to-GDP ratio is above the median of the sample (i.e. −2.16%) is introduced. Running the regressions for total revenues and total taxes does not provide statistically significant coefficients for the dummy and does not 13

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significantly impair short- and long-run tax responses. Thus, as before, it seems that neither public debt nor deficit are able to capture additional and statistically significant effects on tax buoyancies compared with the base case (reported in the first column of Table 9). From a policy viewpoint, we could suggest that budgetary parameters related to countries’ fiscal discipline and sustainability issues do not significantly affect tax reactions to GDP, as their impact on tax buoyancies is likely to be mediated by output fluctuations. 5.2. Tax burden and shadow economy Similar exercises can be implemented by taking into account other relevant variables that might possibly interact with short-run coefficients, in this way affecting the cyclical response of taxes to GDP changes. Two natural candidates are the level of the tax burden (the ratio of tax revenue over GDP) and the level of the shadow economy. Since for both of them it does not exist a precise cut-point to locate countries either below or above, one possibility is to interact short-run responses with a dummy variable taking value 1 when the level of either tax revenue or shadow economy is above the median. Consider first the level of tax burden, and define a dummy variable taking value 1 when the level of tax revenue is above the median. Results in Table 9 (the fifth column) show that short-run responses are lower if the starting point is a lower than the median ratio of tax revenue to GDP, while the short-run response seems more effective when the tax burden is higher. This is probably due to the great ability of the tax system to provide built-in flexibility when the structure and the burden of taxes is more developed and greater in size. Consider now the shadow economy.13 To control for it, the same procedure as before is followed. Results are shown in Table 9 in the sixth column. In this case, even though countries with a higher level of the shadow economy show a higher short-run response, it is worth noting that in countries where a lower level of the shadow economy prevails, the short-run response reduces significantly (0.232 and 0.267 with and without SSC, respectively). A partial justification of this outcome may be that, at high levels, the shadow economy works in the direction of a greater built-in flexibility of the tax system. Put differently, a possible interpretation is that the payment of taxes would adjust more rapidly to both expansionary cycles (through a greater emersion of the tax base) and contractionary periods (through a more rapid reduction of the regular tax bases). 5.3. Political variables Table 9 completes the set of controls by introducing some control variables linked with political and institutional characteristics of countries for the case in which the dependent variable is total taxes (in the bottom panel).14 In principle, the way in which government institutions work may well affect not only the shape of national tax systems but also the way in which they respond to both automatic and discretionary changes.15 In particular, regressions have been run by introducing, one at a time, an index of corruption in the public sector, an index of political corruption, and an index of the quality of government (Coppedge et al., 2017). Results, reported in the last columns of Table 9, do not significantly differ with the base model, neither in the case of short- nor in the case of long-run tax buoyancies. Thus, the quality of the political and institutional system is not particularly relevant for tax reactions to GDP changes in the case of advanced economies. 6. Conclusions The most striking result of this paper is that the long-run tax buoyancy in OECD countries is consistently below 1 for various specifications of the base model. By interpreting the long-run responses in terms of fiscal sustainability, a first conclusion emerges that both the institutional changes and the deep recession occurred in the period analysed have reduced both the power of taxes in performing automatic stabilization and their ability to grow in line with GDP growth. This would imply that, if not automatic, discretionary changes should be implemented to favor the long-run convergence. This result differs from those reported in other studies, where especially the long-run coefficients have often been found either above 1 or not significantly different from 1. Our results, instead, seems to suggest a general decline of the power of taxes to properly follow the evolution and the changes of tax bases, which may also be caused by a reduced progressivity of the tax system as a whole. Overall, the robustness checks and the additional empirical evidence show that some volatility may occur in the short-run, especially with regard to the corporate income tax, while the size of the long-run coefficient is significantly stable and always below 1, even with different econometric techniques. This suggests that – on average – the sustainability of the tax systems is not ensured in the period analysed, and that changes are needed to guarantee the convergence, an outcome that significantly differ from the findings of other contributions on the topic.

13

We use the estimates for the shadow economy as provided by Medina and Schneider (2017). Estimations were run also in the case of total revenue including SSC. Results are the same as for total revenue excluding SSC. They are not reported in the table and available upon requests. 15 This factor is more often taken into account when investigating tax responses in developing countries because of a less developed institutional framework. See, for example, Ashraf and Sarwar (2018), where it is shown that bureaucratic efficiency, rules of law, and corruption may affect tax collection. 14

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Appendix

Table A1 Cross-sectional dependence in the first differences of all variables (in log). Source: Authors’ elaborations on OECD data. Variable

CD-test

p-value

Corr

abs(corr)

Δ Δ Δ Δ Δ Δ

49.28 39.8 27.88 32.01 47.44 49.51

0.000 0.000 0.000 0.000 0.000 0.000

0.444 0.36 0.253 0.288 0.428 0.449

0.45 0.368 0.292 0.315 0.432 0.453

Total revenue PIT CIT GST Total taxes GDP

The Pesaran CD-test employs the correlation-coefficients between the time-series for each panel member. The table reports the CD tests for the first differences of the logarithm of all the variables, regressed on a country-specific intercept. Results indicate the presence of cross-sectional correlation between pairs of countries for all variables.

Table A2 Panel unit root test (CIPS, Pesaran, 2006). Source: Authors’ elaborations on OECD data.

Levels Total revenue PIT CIT GST Total taxes GDP First difference Δ Total revenue Δ PIT Δ CIT Δ GST Δ Total taxes Δ GDP

Number of lags 0

1

2

−1.860 −2.042 −2.566⁎⁎ −1.829 −2.023 −1.580

−1.883 −2.125 −2.200* −1.996 −1.972 −2.075

−1.865 −2.004 −1.943 −1.710 −1.831 −2.031

−3.462⁎⁎⁎ −3.924⁎⁎⁎ −4.464⁎⁎⁎ −3.774⁎⁎⁎ −3.646⁎⁎⁎ −3.639⁎⁎⁎

−2.611⁎⁎⁎ −2.852⁎⁎⁎ −2.910⁎⁎⁎ −2.953⁎⁎⁎ −2.748⁎⁎⁎ −2.706⁎⁎⁎

−2.172* −2.252⁎⁎⁎ −2.241⁎⁎⁎ −2.274⁎⁎⁎ −2.238⁎⁎⁎ −2.017*

All variables are in logarithms. The table shows the CIPS statistics for the logarithm of our variables. We report these results for lag orders p = 0; 1; 2. As we can see from the table, most of the variables are non-stationary when adding an intercept only. On the other hand, they are stationary when the unit root tests are applied to the first differences of these variables. Given the sizeable amount of cross-country dependence detected by tests reported in the table, we believe that the CIPS unit root tests give more reliable inference than those that do not account for cross-sectional dependence, and we conclude that the variables under study are nonstationary.

15

16

Total tax revenue expressed in millions of USD dollar (current value) Personal Income tax revenue expressed in current millions of USD dollar (current value) Corporate Income tax revenue expressed in millions of USD dollar (current value) Goods and Services Tax revenue expressed in millions of USD (current value) Total taxes revenues minus Social Security contributions revenue expressed in millions of USD dollar. (current value) Gross Domestic Product expressed in millions of USD (current value)

Total revenue PIT CIT GST Total taxes

GDP

Description

Variable

Table A3 Descriptive statistics of the main variables (1995–2016). Source: Authors’ elaborations on OECD data.

770

770 770 763 770 770

Obs.

1077.92

317.88 118.55 28.12 78.17 228.37

Mean

2323.17

625.29 275.94 55.85 119.78 459.18

Std. Dev.

6302.00

1192.44 315.46 45.75 417.42 787.64

Min

18,700.00

4821.59 2313.11 425.22 815.38 3662.38

Max

OECD (Revenue Statistics 1995–2016) OECD (Revenue Statistics 1995–2016) OECD (Revenue Statistics 1995–2016) OECD (Revenue Statistics 1995–2016) Authors’ calculation on OECD RS 1995–2016 OECD National accounts 1995–2016

Source

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Table A4 The baseline model with fixed effects. Source: Authors’ elaborations on OECD data.

Lag (dep) Delta GDP Lag GDP Constant Short-run Long-run Speed of adjustment Observations R2 Sub-sample until 2007 Short-run Long-run

Total revenue

PIT

−0.07 0.45⁎⁎⁎ 0.05⁎⁎⁎ 0.16⁎⁎ 0.45⁎⁎⁎ 0.74⁎⁎⁎ −0.07⁎⁎⁎ 750 0.67

−0.12 0.65⁎⁎⁎ 0.10⁎⁎⁎ −0.03 0.65⁎⁎⁎ 0.84⁎⁎⁎ −0.12⁎⁎⁎ 750 0.37

−0.21 1.27⁎⁎⁎ 0.18⁎⁎⁎ −0.47 1.27⁎⁎⁎ 0.89⁎⁎⁎ −0.21⁎⁎⁎ 743 0.23

−0.09 0.27⁎⁎⁎ 0.06⁎⁎⁎ 0.19⁎⁎ 0.27⁎⁎⁎ 0.67⁎⁎⁎ −0.09⁎⁎⁎ 750 0.56

−0.08⁎⁎⁎ 0.52⁎⁎⁎ 0.06⁎⁎⁎ 0.13* 0.52⁎⁎⁎ 0.76⁎⁎⁎ −0.08⁎⁎⁎ 750 0.56

0.40⁎⁎⁎ 0.95⁎⁎⁎

0.47⁎⁎⁎ 1.35⁎⁎⁎

0.94⁎⁎⁎ 1.56⁎⁎⁎

0.29⁎⁎⁎ 0.76⁎⁎⁎

0.45⁎⁎⁎ 1.00⁎⁎⁎

⁎⁎⁎

CIT ⁎⁎⁎

GST ⁎⁎⁎

Total taxes ⁎⁎⁎

PIT = Personal income tax; CIT = Corporate income tax; GST = Goods and Services Tax. The aggregate “Total revenue” includes social security contributions (SSC). The aggregate “Total taxes” does not include SSC. ⁎⁎⁎ indicates 1% level significance level ⁎⁎ indicates 5% significance level.

Table A5 Short-run tax buoyancy by country. Source: Authors’ elaborations on OECD data.

Australia Austria Belgium Canada Chile Czech Republic Denmark Estonia Finland France Germany Greece Hungary Iceland Ireland Israel Italy Japan Korea Latvia Luxembourg Mexico Netherlands New Zealand Norway Poland Portugal Slovak Republic Slovenia Spain Sweden Switzerland Turkey United Kingdom United States

Total revenue

PIT

GST

CIT

Total taxes

0.538 0.482 0.776 0.624 1.364⁎⁎⁎ 1.129⁎⁎⁎ 0.559 0.779⁎⁎⁎ 0.922⁎⁎⁎ 0.834* 0.718* 0.494⁎⁎ 0.595* 1.295⁎⁎⁎ 0.624⁎⁎⁎⁎ 0.884⁎⁎⁎ 0.672 1.171⁎⁎⁎ 0.799⁎⁎⁎ 1.793⁎⁎⁎ 0.575⁎⁎⁎ 0.451 0.990⁎⁎⁎ 0.536 0.794⁎⁎⁎ 1.195⁎⁎⁎ 1.256⁎⁎⁎ 0.720⁎⁎⁎ 0.884⁎⁎⁎ 1.667⁎⁎⁎ 0.622⁎⁎ 0.638⁎⁎ 0.184 0.875⁎⁎ 2.817⁎⁎⁎

0.595 0.381 1.037 0.878 3.065⁎⁎⁎ 1.968⁎⁎⁎ 0.715 0.475 1.609⁎⁎⁎ 2.272⁎⁎⁎ 1.662⁎⁎ 0.437 1.036* 1.171⁎⁎⁎ 0.838⁎⁎⁎ 1.456⁎⁎⁎ 0.978 3.004⁎⁎⁎ −0.245 3.045⁎⁎⁎ 0.242 1.180⁎⁎ 0.972* 0.776 1.563⁎⁎⁎ 2.370⁎⁎⁎ 1.493⁎⁎ 1.116⁎⁎⁎ 1.749⁎⁎⁎ 2.179⁎⁎⁎ 1.174⁎⁎ 1.017⁎⁎⁎ −0.251 0.780 5.032⁎⁎⁎

0.444 0.370 1.068* 0.355 0.378* 0.597 0.579 0.873⁎⁎⁎ 0.420 0.422 0.505 0.246 0.137 1.084⁎⁎⁎ 0.562⁎⁎⁎ 0.460 0.844 −0.070 1.278⁎⁎⁎ 1.376⁎⁎⁎ 0.637⁎⁎ 0.054 0.679 0.084 0.162 1.990⁎⁎⁎ 1.244⁎⁎⁎ 0.866 0.611* 3.443⁎⁎⁎ 0.357 0.388 0.315⁎⁎ 0.879* 1.639⁎⁎⁎

1.619 1.535 2.405 1.342 3.002⁎⁎⁎ 2.726⁎⁎ 3.799⁎⁎⁎ 0.268 5.086⁎⁎⁎ 5.819⁎⁎⁎ 3.366⁎⁎ 1.504⁎⁎ 0.892 3.190⁎⁎⁎ 1.692⁎⁎⁎ 2.492⁎⁎⁎ 1.972 5.086⁎⁎⁎ 0.285 5.166⁎⁎⁎ 0.299 1.844 3.433⁎⁎⁎ 1.141 3.582⁎⁎⁎ 3.710⁎⁎ 2.108 1.635 3.769⁎⁎⁎ 4.136⁎⁎⁎ 2.673⁎⁎ 1.835 0.069 1.587 9.376⁎⁎⁎

0.538 0.489 1.021* 0.671 1.445⁎⁎⁎ 1.203⁎⁎⁎ 0.561 0.898⁎⁎⁎ 1.115⁎⁎⁎ 1.176⁎⁎ 1.07⁎⁎ 0.354 0.363 1.523⁎⁎⁎ 0.708⁎⁎⁎ 0.97⁎⁎⁎⁎ 0.744* 1.566⁎⁎⁎ 0.913⁎⁎⁎ 2.023⁎⁎⁎ 0.713⁎⁎ 0.566 0.852⁎⁎ 0.536 1.049⁎⁎⁎ 2.024⁎⁎⁎ 1.509⁎⁎⁎ 0.976⁎⁎⁎ 1.124⁎⁎⁎ 2.628⁎⁎⁎ 0.794⁎⁎ 0.744⁎⁎ 0.183 0.953⁎⁎ 3.432⁎⁎⁎

PIT: Personal Income Tax; CIT: Corporate Income Tax; GST: Good and Services Tax; Total taxes = Total revenue excluding Social Security Contributions.

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Table A6 Long-run tax buoyancy by country. Source: Authors’ elaborations on OECD data.

Australia Austria Belgium Canada Chile Czech Republic Denmark Estonia Finland France Germany Greece Hungary Iceland Ireland Israel Italy Japan Korea Latvia Luxembourg Mexico Netherlands New Zealand Norway Poland Portugal Slovak Republic Slovenia Spain Sweden Switzerland Turkey United Kingdom United States

Total revenue

PIT

GST

CIT

0.897* 0.962 0.957 0.933 0.67⁎⁎ 0.884 1.012 0.779 0.931 0.958 0.953 0.857⁎⁎⁎ 0.863⁎⁎ 0.662⁎⁎ 1.085 0.895⁎⁎ 0.939 0.95 0.792 0.829 0.925 0.907 0.933 0.904 0.976 0.861⁎⁎⁎ 0.874⁎⁎ 0.855 1.049 0.898⁎⁎⁎ 0.989 0.99 1.041⁎⁎⁎ 0.931 0.839

0.852* 0.854⁎⁎⁎ 0.889 0.874 0.392⁎⁎⁎ 0.795⁎⁎⁎ 0.941 0.676⁎⁎⁎ 0.839 0.851⁎⁎⁎ 0.859 0.801⁎⁎⁎ 0.776⁎⁎⁎ 0.57⁎⁎⁎ 0.917 0.858⁎⁎⁎ 0.859⁎⁎ 0.841 0.673⁎⁎⁎ 0.664⁎⁎ 0.819⁎⁎ 0.217 0.808 0.878 0.878* 0.702⁎⁎⁎ 0.739⁎⁎⁎ 0.72 0.64 0.770⁎⁎⁎ 0.911* 0.867 0.741⁎⁎⁎ 0.887 0.703

0.793 0.856 0.828 0.865 0.653 0.730⁎⁎ 0.931 0.554 0.9 0.856 0.861 0.800⁎⁎ 0.718* 0.674⁎⁎⁎ 1.082 0.781 0.847 0.879⁎⁎ 0.745 0.354 0.766⁎⁎ 0.737⁎⁎⁎ 0.839 0.774 0.955 0.747⁎⁎ 0.802* 0.743* 0.705⁎⁎ 0.798⁎⁎⁎ 0.851 0.213 0.977⁎⁎⁎ 0.833 0.789 ⁎⁎

Total taxes

0.710 0.678⁎⁎⁎ 0.706 0.756⁎⁎ 0.191 0.735⁎⁎ 0.719⁎⁎⁎ 0.527⁎⁎⁎ 0.726⁎⁎⁎ 0.747⁎⁎⁎ 0.673⁎⁎⁎ 0.845⁎⁎⁎ 0.680⁎⁎ 0.075 0.771 0.659⁎⁎⁎ 0.782⁎⁎ 0.763 0.580⁎⁎⁎ 1.328 0.861 0.537 0.741 0.74 0.778 0.614 0.694⁎⁎ 0.52 0.106 0.722⁎⁎⁎ 0.706⁎⁎⁎ 0.716 0.720⁎⁎⁎ 0.796 0.630⁎⁎⁎ ⁎⁎

0.897 0.928* 0.923 0.922 0.66⁎⁎⁎ 0.822* 1.009 0.717 0.91 0.927* 0.911 0.812⁎⁎⁎ 0.848* 0.653⁎⁎ 1.048* 0.882⁎⁎ 0.93 0.925 0.788 0.836 0.887⁎⁎ 0.901 0.892 0.904 0.937 0.805⁎⁎⁎ 0.841⁎⁎ 0.808* 0.861 0.851⁎⁎⁎ 0.952 0.957 1.050⁎⁎⁎ 0.918* 0.799

Notes: PIT: Personal Income Tax; CIT: Corporate Income Tax; GST: Good and Services Tax; Total taxes = Total revenue excluding Social Security Contributions.

Table A7 Speed of adjustment by country. Source: Authors’ elaborations on OECD data.

Australia Austria Belgium Canada Chile Czech Republic Denmark Estonia Finland France Germany Greece Hungary Iceland Ireland Israel Italy Japan

Total revenue

PIT

GST

−0.490* −0.568* −0.5 −0.272 −0.254⁎⁎⁎ −0.324* −0.186 −0.165 −0.174 −0.419 −0.268 −0.927⁎⁎⁎ −0.302⁎⁎ −0.287⁎⁎ −0.108 −0.623⁎⁎ −0.434 −0.246

−0.568 −0.889⁎⁎⁎ −0.444 −0.372 −0.324⁎⁎⁎ −0.386⁎⁎⁎ −0.427 −0.486⁎⁎⁎ −0.32 −0.536⁎⁎⁎ −0.196 −0.547⁎⁎⁎ −0.210⁎⁎⁎ −0.516⁎⁎⁎ −0.192 −0.381⁎⁎⁎ 0.847⁎⁎ −0.197

CIT

−0.585 −0.928⁎⁎⁎ −0.514* −0.728⁎⁎ −0.268⁎⁎⁎ −0.448* −0.530⁎⁎⁎ −0.422⁎⁎⁎ −0.428⁎⁎⁎ −0.791⁎⁎⁎ −0.648⁎⁎⁎ −0.308⁎⁎⁎ −0.263⁎⁎ −0.541⁎⁎⁎ −0.133 −0.556⁎⁎ −0.745⁎⁎ −0.255

⁎⁎

⁎⁎⁎

Total taxes

−0.527 −0.396 −0.717 −0.109 −0.137 −0.758⁎⁎ −0.133 −0.240* −0.141 −0.222 −0.571 −0.548⁎⁎ −0.701* −0.585⁎⁎⁎ −0.093 −0.222 −0.517 −0.246⁎⁎ ⁎⁎

−0.49 −0.645⁎⁎ −0.512 −0.332 −0.266⁎⁎⁎ −0.395* −0.179 −0.115 −0.274 −0.517* −0.286 −0.898⁎⁎⁎ −0.292* −0.345⁎⁎ −0.123 −0.588⁎⁎ −0.323 −0.266

(continued on next page)

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Table A7 (continued)

Korea Latvia Luxembourg Mexico Netherlands New Zealand Norway Poland Portugal Slovak Republic Slovenia Spain Sweden Switzerland Turkey United Kingdom United States

Total revenue

PIT

GST

CIT

Total taxes

−0.279 0.1013 −0.677⁎⁎ −0.116 −0.641 −0.215 −0.251 −0.505⁎⁎⁎ −0.515⁎⁎⁎ −0.172 −0.069 −0.418⁎⁎⁎ −0.231 −0.048 −0.163⁎⁎⁎ −0.539* −0.153

−0.779⁎⁎⁎ 0.228⁎⁎ −0.706⁎⁎ 0.072 −0.437 −0.19 −0.287⁎⁎ −0.343⁎⁎⁎ −0.583⁎⁎ −0.315⁎⁎ −0.069 −0.421⁎⁎⁎ −0.399* 0.37 −0.267⁎⁎⁎ 0.357 0.167

−1.068⁎⁎⁎ 0.0968 −0.442 −0.347 −0.477⁎⁎⁎ −0.33 −0.178 −0.479⁎⁎⁎ −0.645⁎⁎⁎ −0.236 −0.141 −0.410⁎⁎⁎ −0.710⁎⁎⁎ −0.386 −0.211 −0.542⁎⁎ −0.500⁎⁎⁎

−0.371 −0.035 −0.596⁎⁎ −0.324⁎⁎⁎ −0.309 −0.23 −0.047 −0.724⁎⁎⁎ −0.368⁎⁎ −0.351* −0.844⁎⁎ −1.169⁎⁎⁎ −0.378 0.02 −0.157⁎⁎⁎ −0.224 −0.428

−0.386 0.0938 −1.032⁎⁎ −0.119 −0.295 −0.215 −0.477 −0.525⁎⁎⁎ −0.593⁎⁎ −0.344* −0.11 −0.727⁎⁎⁎ −0.431 −0.086 −0.166⁎⁎⁎ −0.566 −0.166

PIT: Personal Income Tax; CIT: Corporate Income Tax; GST: Good and Services Tax; Total taxes = Total Revenue excluding Social Security Contributions.

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