2. Tax revenue buoyancy in OECD countries

Since the OECD began publishing the Revenue Statistics report, tax-to-GDP ratios have gradually risen in most OECD countries. However, as discussed in Chapter 1, there have been numerous downturns in revenues across this period, some of which have been related to major external events including the oil shock, the Global Financial Crisis and, most recently, the COVID-19 pandemic. These downturns have affected different taxes in different ways.

This Special Feature uses Revenue Statistics data to examine the volatility of revenues over the past 40 years. By providing insights into the factors behind short- and long-run changes in revenues from different tax types, the chapter aims to inform strategies to optimise fiscal policy over the business cycle and to ensure fiscal sustainability over the longer term. It may also help governments to enhance the resilience of public finances in the event of future shocks.

Following a brief introduction to tax buoyancy, the chapter estimates the buoyancy of total tax revenues and of revenues from different tax types between 1980 and 2021 on average across the OECD and for individual countries. The chapter also investigates whether and how tax buoyancy in OECD countries has changed over this period. Finally, the Special Feature examines how short-run tax buoyancy varies over the business cycle in OECD countries and analyses the potential impact of high inflation or population ageing.

The buoyancy and elasticity of tax revenues are two of the most common indicators of tax revenue volatility (Box 2.1). In one of the earliest papers on the topic, (Musgrave and Miller, 1948[1]) measured tax elasticity as the ratio of the percentage change in tax yield to a given percentage change in income and defined the concept of “built-in flexibility”, the automatic compensatory movement of tax revenues in response to changes in national income. (Groves and Kahn, 1952[2]) developed a regression model to estimate tax elasticity using log tax revenues as the dependent variable and log income as the independent variable. This method is still widely used today to estimate both tax elasticity and tax buoyancy in the long run. (Prest, 1962[3]) introduced the Proportional Adjustment approach to remove the impact of discretionary tax policies on tax revenues and estimate tax elasticity. Later refined by (Mansfield, 1972[4]), this is one of the most common methods for estimating tax elasticity.1

Early papers rarely differentiated between short- and long-run tax buoyancy or elasticity, using the long-run estimate to study both the growth potential and cyclical variability of tax revenues until (Sobel and Holcombe, 1996[5]) proposed time series econometric models to produce unbiased estimates of short- and long-run buoyancy or elasticity. These econometric models have since been used in many studies of tax volatility, including (Belinga et al., 2014[6]), which estimates short- and long-run tax buoyancy in 34 OECD countries between 1965 and 2012; (Dudine and Jalles, 2017[7]), which produces tax buoyancy estimates for 107 countries between 1980 and 2014; and (Deli et al., 2018[8]), which estimates tax buoyancy in 25 OECD countries between 1965 and 2015. Other methods for estimating tax buoyancy have been used by the OECD Fiscal Network (Dougherty, de Biase and Lorenzoni, 2022[9]) and in a recent IMF working paper by (Cornevin, Corrales and Angel, 2023[10]).2

This Special Feature uses econometric models proposed by (Sobel and Holcombe, 1996[5]) and data from Revenue Statistics to estimate short- and long-run tax buoyancy in 38 OECD countries using two estimation approaches: the Mean Group (MG) estimator and the Pooled Mean Group (PMG) estimator.3 Like most research in this area, this Special Feature uses GDP as a proxy of the tax base.

This Special Feature considers revenues between 1980 and 20214 from six tax types: personal income tax (PIT), corporate income tax (CIT), social security contributions (SSCs), taxes on property, value added tax (VAT) and excises. Data exists for all years between 1980 and 2021 for 26 OECD countries, while the available data starts from a later period for 12 countries: from 1990 in Chile, Colombia and Costa Rica; 1991 in Hungary and Poland, 1993 in Czechia and 1995 for the remaining countries.5 Data exclude zero values and outliers (Box 2.2).

Figure 2.1 shows the evolution of total tax revenues as well as revenues from the six different tax types as a share of GDP on average across the OECD since 1980. Based on this graph, several observations can be made as context for the analysis of tax revenue buoyancy.

First, the total tax-to-GDP ratio has risen gradually for the OECD on average, from 30.1% of GDP in 1980 to 34.0% in 2022, without many major year-on-year changes. Second, revenues from four tax types (CIT, SSCs, taxes on property and VAT) as a share of GDP display an upward trend over the long term while revenues from PIT and excises have declined as a share of GDP. Third, individual tax types tend to be more volatile than total tax revenues as measured by the standard deviation. Fourth, revenues from CIT show greater volatility than most other tax types. The standard deviation6 of CIT revenues (indexed values), was 14.2 between 1980 and 2021, compared to only 2.9 for total tax revenues between 1980 and 2022.

This section presents the tax buoyancy results generated by the two estimation methods in turn, then analyses both sets of results together. It also identifies caveats that need to be taken into account when interpreting the estimates and examines these caveats in detail with respect to the buoyancy of CIT revenues and the possibility of a time lag in the impact of changes in GDP on revenues. Finally, it analyses whether the results differ when using real rather than nominal data.

Short- and long-run tax buoyancy estimated using the MG estimator are presented in Table 2.1. The results show that buoyancies for total tax revenues were close to unity8 on average across OECD countries between 1980 and 2021 (individual country estimates are shown in Annex 2.A.).

Long-run tax buoyancies were larger than 1.1 for seven OECD countries and were smaller than 0.9 in two countries; for the remainder, they were between these thresholds. For short-run tax buoyancy, estimates were above 1.1 for eleven countries and below 0.9 for nine countries. The standard deviation of buoyancy for total tax revenues across the OECD was smaller than for the six tax types, indicating that the buoyancy of total tax revenues was more narrowly distributed across countries.

Most of the six individual tax types displayed short- and long-run tax buoyancy close to unity. However, there were four notable exceptions:

  • For CIT, tax buoyancy was significantly larger than unity, especially in the short run.9 (Please see Box 2.3 for a deeper exploration of the CIT buoyancy estimates.)

  • Both short- and long-run VAT buoyancy were larger than unity, albeit to a lesser extent than CIT.

  • The short-run buoyancy of SSCs and long-run buoyancy of excises were slightly below unity.

Table 2.2 shows the panel regression results using the PMG estimator for total tax revenues and the six tax types. The buoyancy estimates produced by the panel regression are largely in line with those of the time series regression above. Both short- and long-run buoyancy of total tax revenues and PIT were close to unity between 1980 and 2021, while CIT had the highest buoyancy, especially in the short run. VAT had the second-highest short- and long-run buoyancy, while the short-run buoyancy of SSCs was below unity.

However, there were two notable differences between the results produced by the different methodologies. First, the short-run buoyancy of taxes on property was smaller than unity under the PMG estimator while it was close to unity under the MG estimator. Second, both the short- and long-run buoyancy of excises under the PMG were smaller than unity while, under the MG estimator, the short-run buoyancy of excises was close to unity and long-run buoyancy was slightly below unity.

The differences in results between the two methodologies may be caused by different estimation techniques, different ways of calculating averages, and the inherent differences between panel data and time series data. Panel data in general has advantages over time series data insofar as it contains more information and data variation, and it can deal with omitted variable bias (Torres-Reyna, 2007[19]). The advantages of panel data become more pronounced for tax types where data may be limited for some countries.

Several conclusions may be drawn from the short- and long-run tax buoyancy estimates generated by the two methodologies presented above:

  • Over the long term, tax revenues grew at the same pace as GDP for the OECD as a whole between 1980 and 2021.

  • On average, OECD tax revenues were as volatile as GDP over the business cycle.

  • With the highest short- and long-run buoyancy, revenues from CIT grew faster than GDP in the long term and was the most effective economic stabiliser in the short term.

  • VAT revenues also grew faster than GDP in the long term and VAT is a relatively good stabiliser.

  • SSCs were a relatively poor economic stabiliser over the business cycle and revenues from excises are least sensitive to changes in GDP.

These conclusions require several caveats. First, tax buoyancy includes the revenue impact of discretionary policy measures. As such, the response of tax revenues to changes in GDP over a given period of time is affected by tax policies implemented during the same period. Tax buoyancy estimates may change once the impact of policies is separated. Second, since GDP is used as a proxy for different tax bases, the response of tax revenues to changes in GDP is determined by how tax revenues react to changes in tax bases and how tax bases react to changes in GDP. The implications for CIT are discussed in Box 2.3. Lastly, there may be a temporal mismatch between tax revenues and GDP due to specific tax collection mechanisms or statutory lags in declaring the taxable base (Mourre and Princen, 2019[20]). Box 2.4 investigates this issue in greater detail.

The tax buoyancy coefficients shown above are estimated using nominal tax and GDP data, which include a price component and a real component. To understand how price movements affect tax buoyancy, Annex Table 2.A.8. shows the time series regression results when short- and long-run tax buoyancy are estimated using real data. These results suggest that both short- and long-run tax buoyancy estimated with real data are larger than those estimated with nominal data, albeit to different extents for total tax revenues and across most tax types. The panel regression results are mixed: while most buoyancy estimates using real data are close to the estimates based on nominal data, they decreased for PIT and excises in the long run, and SSCs in the short run. The real buoyancy of CIT revenues has increased in the short run (see Annex Table 2.A.9).

Other studies on real tax buoyancy show differing results: (Haughton, 1998[11]) argues that nominal buoyancy could understate the responsiveness of tax revenues to changes in GDP and tax buoyancy in nominal terms may bias towards one. (Belinga et al., 2014[6]) and (Dudine and Jalles, 2017[7]) find that total tax buoyancy in real terms is smaller than in nominal terms, particularly in the long run; (Deli et al., 2018[8]) does not find any significant differences between nominal and real total tax buoyancy; (Cornevin, Corrales and Angel, 2023[10]) investigates the real short-run buoyancies of PIT, CIT and VAT, finding no significant differences for PIT and VAT but higher real short-run CIT buoyancy. The different conclusions may be caused by differences in methodology, country sample or time coverage.

This section investigates how tax buoyancy evolved in OECD countries between 1980 and 2021. The dataset is divided into three sub-periods: 1980-1999, 2000-2010 and 2011-2021. The first period is longer than the second and third periods because revenue data are not available for some OECD countries in the 1980s. Table 2.4 presents short- and long-run tax buoyancy estimates using the PMG estimator for these different periods using nominal data. The same results are also plotted in Figure 2.2.

For total tax revenues, long-run buoyancy gradually increased over the three periods. Improved tax capacity and changes in tax structures in the OECD are possible explanations for this increase. Short-run buoyancy, which measures tax revenue fluctuation over the business cycle, jumped substantially in 2000-2010 before falling back to unity. The greater volatility in this second period may be due to the Global Financial Crisis, which had a greater negative impact on OECD tax revenues than the recent COVID-19 crisis, in particular revenues from PIT and CIT (OECD, 2021[25]).

Considering the evolution of tax buoyancy for specific tax types, the main findings are as follows:

  • For PIT, long-run buoyancy increased significantly after 1980 but fell in the third period. One possible explanation for the higher buoyancy in the first two periods is an increase in the progressivity of PIT systems in the OECD (Belinga et al., 2014[6]). The short-run buoyancy of PIT displayed a similar pattern to total tax revenues, rising above unity before falling back, likely due to the impact of the Global Financial Crisis. (OECD, 2021[25]) found that revenues from PIT as a share of GDP dropped on average in 2009, implying a larger decrease of tax revenues than GDP, whereas the opposite occurred in 2020.

  • For CIT, long-run buoyancy exhibits the opposite trajectory to PIT: it decreased significantly over the three periods despite rising slightly in the third period. (Deli et al., 2018[8]) also find a substantial decline in long-run CIT buoyancy in the post-2000 period compared to the pre-2000 period. One possible explanation for this tendency is that many OECD countries have reduced CIT rates in recent decades, which may have suppressed CIT buoyancy (Box 2.3). The short-run buoyancy of CIT increased substantially in 2000-2010 but has declined in recent years, similar to the short-run buoyancy of total tax revenues and PIT.

  • The increase in long-run CIT buoyancy in the third period (as well as the decrease in PIT buoyancy) may be related to tax arbitrage. In many OECD countries, a tendency has recently been documented of taxpayers shifting part of their income taxable under PIT to CIT in order to reduce their tax liability, benefitting from lower CIT rates and other tax advantages (OECD, Forthcoming[26]).

  • Both the short- and long-run buoyancy of SSCs fell over the periods under analysis, a finding consistent with other studies. Changes in the age structure of the population in OECD countries may be a factor behind this change.

  • For taxes on property, both short- and long-run buoyancy declined after 2000. Results are not statistically significant for 1980-1999. The decline in short-run buoyancy in the third period is likely to be related to subdued property markets in OECD countries after the Global Financial Crisis.

  • Among taxes on goods and services, the short- and long-run buoyancy of VAT increased between 1980 and 2021. The long-run buoyancy of excises is the lowest among the six tax types in almost every time period, increasing only slightly since 1980, although short-run buoyancy has increased substantially, albeit from a low base.

For comparison, Annex Table 2.A.10 and Annex Figure 2.A.6 show tax buoyancy estimates for the three periods using real tax and GDP data. Most of the conclusions drawn from nominal data hold for real tax buoyancy, with a few exceptions concerning long-run results.

To broaden the analysis of the factors behind revenue trends in OECD countries, this section investigates the interaction between tax buoyancy and three other socio-economic factors of interest: the business cycle, inflation and the age structure of the population.

Numerous studies have found that short-run tax buoyancy varies at different stages of the business cycle, indicating asymmetric responses of tax revenues to changes in GDP during periods of economic expansion and contraction. To better understand the power of OECD tax systems to stabilise the economy across the business cycle, short-run tax buoyancy is estimated using equation (3), following the method used in (Belinga et al., 2014[6]) and (Deli et al., 2018[8]).

lnTaxi,t=λilnTaxi,t-1-βilnGDPi,t-1+θi*Dummyi,t*lnGDPi,t+ϕi*1-Dummyi,t*lnGDPi,t+μi+ϵi,t (3)

Equation (3) is a panel regression ECM similar to Equation (2) but includes the country dimension denoted by i and country fixed effect μi as well as a dummy variable. The dummy variable in the equation takes a value of one for years of growth and a value of zero for years of contraction.

This Special Feature defines years of growth and contraction in two scenarios: a moderate boom-bust cycle where annual real GDP changes of larger than 0.5% (-0.5%) constitute economic growth (economic contraction); and a strong boom-bust cycle, where real GDP changes in excess of 1.0% (-1.0%) constitute economic growth (economic contraction).

Table 2.5 shows that, in a moderate business cycle, the short-run buoyancy of total tax revenues is largely the same for years of economic growth as for years of economic contraction. However, it is larger during economic contractions for most tax types except taxes on property and VAT. In a strong business cycle, short-run buoyancy is significantly larger during years of economic contraction than in years of growth for total tax revenues, CIT, SSCs, taxes on property and excises. This indicates that OECD taxes are on average more buoyant during recessions than during growth periods, consistent with findings in other studies (Belinga et al., 2014[6]; Dudine and Jalles, 2017[7]; Deli et al., 2018[8]). The results also indicate that, as economic recession deepens, tax revenues become more volatile relative to GDP.

High inflation in OECD countries in 2022 revived interest in the impact of inflation on tax revenues. This sub-section investigates the impact of inflation on short-run tax buoyancy using the same approach as above but creates a dummy variable that takes a value of one for years of higher inflation and a value of zero for years of lower inflation. Analysis is conducted for two scenarios: in a baseline scenario, lower/higher inflation is defined as a rate of inflation smaller/larger than 2%, which is the inflation target of the European Central Bank; in an extreme scenario, lower inflation is defined as a rate of inflation below 1% and higher inflation as a rate of inflation above 3%.

Table 2.6 shows that, in the baseline scenario, OECD average short-run buoyancy remained stable regardless of the inflation level for total tax revenues, PIT, taxes on property and VAT. It was larger during years of higher inflation for SSCs and larger during years of lower inflation for CIT and excises. In the extreme scenario, short-run tax buoyancy was larger during years of higher inflation for total tax revenues and VAT and larger during years of lower inflation for CIT and excises.

(OECD/WHO, 2020[28]) qualifies a country as having an “ageing society” if the share of people aged 65 years or more is between 7% and 14% of the total population, as an “aged society” if this share is between 15% and 20%, and as a “super-aged society” if this share is 21% or higher. By this measure, many OECD countries have ‘aged’ societies: in recent decades, the share of the population aged 65 years and over has nearly doubled on average across OECD countries, increasing from less than 9% in 1960 to more than 17% in 2019 (OECD, 2021[29]).

To analyse the impact of population ageing on tax buoyancy, this Special Feature considers a country as having a “younger society” if the share of its population aged 65 years or older is smaller than 15%, while a country has an “older society” if the share is above this threshold. Results shown in Table 2.7 indicate that short-run buoyancy was notably larger in a “younger society” for total tax revenues, CIT, SSCs and VAT, while it was slightly larger for PIT and taxes on property. The short-run buoyancy of excises was larger in an “older society”, although the difference was trivial. The results suggest that tax revenues were more stable over the business cycle in countries with an ageing population.

To analyse the volatility of tax revenues in the OECD, this Special Feature estimates the buoyancy of total tax revenues and revenues from six main tax types for all 38 OECD countries between 1980 and 2021. It finds that tax revenues in the OECD typically increased at the same pace as GDP growth over the long term and had similar volatility to the business cycle in the short run. Revenues from CIT and VAT were more buoyant than revenues from other tax types. SSCs and excises were more stable revenue sources during short-term economic fluctuations. The long-run buoyancy of total tax revenues and most tax types has increased since 1980, with the exception of CIT and SSCs.

The short-run buoyancy for all taxes except excises rose sharply in 2000-2010, likely due to the asymmetric impact of the Global Financial Crisis on revenues and GDP in 2008-09, before falling in 2011-2021. Short-run tax buoyancy tended to be larger during periods of economic contraction than during economic growth. It was also larger during periods of high inflation for most taxes except CIT and excises. Tax revenues appear to be more stable over the business cycle in countries with an aged population.

The results of the tax buoyancy estimates must be taken with caution as the estimates are affected by tax policies implemented over the sample period; estimates may change once the impact of tax policies is removed. This caveat warns against using historical estimates of tax buoyancy to make predictions for the evolution of tax revenues in the future. In addition, since GDP is used as a proxy tax base, the response of tax revenues to changes in GDP may be partly affected by how real tax bases react to changes in GDP.

Further analysis of tax volatility in OECD countries could include estimates of tax elasticity to disentangle the automatic response of tax revenues to changes in GDP and discretionary tax policy impacts. Tax buoyancy in non-OECD countries could also be estimated using the Global Revenue Statistics database (Box 2.5). In addition, measuring changes in tax revenues relative to changes in tax bases and changes in tax bases relative to changes in GDP can provide more information about the sources of tax buoyancy variation and why it differs across taxes, between countries and over time.


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← 1. Other estimation methods for tax elasticity include introducing dummy variables to represent tax policies, keeping tax policy parameters (e.g. tax rate and tax base) unchanged and using the Divisia index. When reliable and sufficient information is available, the Proportional Adjustment method produces the best results as it can take into account large and small discretionary policy changes without bias.

← 2. (Dougherty, de Biase and Lorenzoni, 2022[9]) use an auto-regressive distributed lag model estimated with Ordinary Least Squares in a one-step approach to obtain long-run tax buoyancy in OECD countries across revenue type and level of government. (Cornevin, Corrales and Angel, 2023[10]) use a one-step ECM estimated with three “generation” estimators. They define the Mean Group estimator and Pooled Mean Group estimator as “first generation” estimators; the common correlated effects estimators, which control for cross-sectional dependence, are defined as “second generation” estimators, and the dynamic common correlated effects estimators are the “third generation” estimators.

← 3. These two estimators are also used in (Belinga et al., 2014[6]), (Dudine and Jalles, 2017[7]), (Deli et al., 2018[8]) and (Cornevin, Corrales and Angel, 2023[10]).

← 4. While tax data of some OECD countries starts from 1965, the starting year of the analysis is set to 1980 to reduce missing data while maintaining a sufficiently large sample.

← 5. These countries are Estonia, Israel, Lithuania, Latvia, Slovak Republic and Slovenia.

← 6. Standard deviation is used to measure dispersion of data around its mean.

← 7. In 2016, Iceland received revenues from one-off stability contributions from entities that previously operated as commercial or savings banks and were concluding operations. These one-off stability contributions amounted to about 15.7% of Iceland’s GDP in 2016, leading to unusually high tax revenues for that year.

← 8. In this Special Feature, “close to unity” is defined as tax buoyancy falling roughly between 0.9 and 1.1, "above unity" is defined as tax buoyancy larger than 1.1, and “below unity” is defined as tax buoyancy smaller than 0.9 for the sake of simplicity.

← 9. This is consistent with findings elsewhere in the literature, for example (Belinga et al., 2014[6]); (Dudine and Jalles, 2017[7]); (Deli et al., 2018[8]); (Dougherty, de Biase and Lorenzoni, 2022[9]); (OECD, 2022[34]); and (Cornevin, Corrales and Angel, 2023[10]).

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