Abstract
Purpose
This paper aims to explore the effect of non-resource tax revenue instability on non-resource tax revenue in developed and developing countries.
Design/methodology/approach
The analysis has used an unbalanced panel data set of 146 countries over the period 1981–2016, as well as the two-step system generalized methods of moment approach.
Findings
The empirical analysis has suggested that non-resource tax revenue instability influences negatively non-resource tax revenue share of gross domestic product. The magnitude of this negative effect is higher in less developed countries than in relatively advanced countries. This negative effect materializes through public expenditure instability: non-resource tax revenue instability exerts a higher effect on non-resource tax revenue share as the degree of public expenditure instability increases. Finally, non-resource tax revenue instability exerts a higher negative effect on non-resource tax revenue share as economic growth volatility rises, inflation volatility increases and terms of trade instability increases.
Research limitations/implications
The main policy implication of this analysis is that policies that help ensure the stability of non-resource tax revenue also contribute to improving countries’ non-resource tax revenue share. For example, governments’ measures that help cope with or prevent the severe adverse effects of shocks on economies (shocks that could translate into higher tax revenue instability) would ultimately help enhance countries’ tax revenue performance.
Practical implications
The severity of the current COVID-19 pandemic shock (which is a supply and demand shock) and the macroeconomic uncertainty that it has generated – inter alia, in terms of economic growth instability, terms of trade instability, inflation volatility and public expenditure instability – are likely to result in severe tax revenue losses. Governments in both developed and developing countries would surely learn from the management of this crisis so as to prepare for possible future economic, financial and health crises with a view to dampening their adverse macroeconomic effects, including here their negative tax revenue effects.
Originality/value
To the best of the author’s knowledge, this topic is being addressed in the empirical literature for the first time.
Keywords
Citation
Gnangnon, S.K. (2022), "Tax revenue instability and tax revenue in developed and developing countries", Applied Economic Analysis, Vol. 30 No. 88, pp. 18-37. https://doi.org/10.1108/AEA-09-2020-0133
Publisher
:Emerald Publishing Limited
Copyright © 2021, Sèna Kimm Gnangnon.
License
Published in Applied Economic Analysis. Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence maybe seen at http://creativecommons.org/licences/by/4.0/legalcode
1. Introduction
The instability of tax revenue has been a major concern for policymakers in both developed and developing countries as it can translate into a higher instability of public expenditure (Lim, 1983; Bleaney et al., 1995; Ebeke and Ehrhart, 2012), greater instability of public investment and lower levels of public investment (Ebeke and Ehrhart, 2012), which all can hamper economic growth[1]. In turn, factors of economic growth play an essential role in determining the breadth of a country’s tax base (Besley and Persson, 2014), and hence its public revenue performance, including tax revenue performance (also referred to as tax revenue share, measured by the share of tax revenue in gross domestic product [GDP]). At the same time, governments in both advanced and developing countries need to mobilize higher public revenue to finance their development needs. Much work [2] has been conducted on the determinants of countries’ public revenue, in particular their tax revenue performance. However, to the best of our knowledge, little attention has been paid to the relationship between tax revenue instability and tax revenue performance. The relevance of this topic lies in the fact that through its adverse effect on public expenditure instability, tax revenue instability can influence the elements of the tax base, as those elements (consumption, investment, etc.) ultimately determine a country’s tax revenue performance (“tax revenue performance” can also be referred to as “tax revenue share of GDP,” or “tax revenue share”). The present study aims to fill this gap in the literature by investigating the effect of tax revenue instability on tax revenue performance.
We postulate that tax revenue instability would negatively affect tax revenue performance through the public expenditure instability channel. As noted above, Lim (1983); Bleaney et al. (1995); and Ebeke and Ehrhart (2012) have reported that tax revenue instability results in higher public expenditure instability. As the rise in public expenditure instability can be associated with lower economic growth (Afonso and Furceri, 2010; Bernanke, 1983; Brunetti, 1998; Gong and Zou, 2002; Pindyck and Solimano, 1993), we expect tax revenue instability to be associated with lower tax revenue performance. This is because economic growth is an essential ingredient for widening the tax net and the expansion of the tax base and thus, for greater tax revenue performance (Besley and Persson, 2014; Tosun and Abizadeh, 2007).
The analysis has been conducted on a sample of 146 countries over the period 1981–2016, using the two-step system generalized methods of moments (GMM). It has suggested that tax revenue instability leads to lower tax revenue share, and this negative effect is higher in less advanced countries than in relatively advanced economies. The analysis has been deepened by investigating whether the effect of tax revenue instability on tax revenue share depends on the prevailing economic conditions, measured by the volatility of economic growth rate, the inflation rate volatility and terms of trade instability. As these three factors are important sources of tax revenue instability (Lim, 1983; Bleaney et al., 1995; Ebeke and Ehrhart, 2012; Brun and Gnangnon, 2019), we expect all of them to enhance the negative effect of tax revenue instability on tax revenue performance. The empirical analysis has supported this hypothesis, given that the negative effect of tax revenue instability on tax revenue performance is higher in countries with a higher economic growth volatility, greater inflation volatility and an increase in terms of trade instability.
The rest of the paper is organized as follows. Section 2 presents the model specification for the analysis, and Section 3 discusses the econometric method to estimate this model. Section 4 presents some data analysis, in particular for key variables under analysis. Section 5 interprets empirical outcomes, and Section 6 concludes.
2. Model specification
There is a voluminous literature on the determinants of tax revenue (Agbeyegbe et al., 2006; Baunsgaard and Keen, 2010; Bird et al., 2008; Brun et al., 2015; Clist, 2016; Clist and Morrissey, 2011; Crivelli, 2016; Crivelli and Gupta, 2014; Ghura, 1998; Gnangnon and Brun, 2017, 2018, 2019; Khattry and Rao, 2002; Morrissey et al., 2016; Yohou et al., 2016). This literature has shown the existence of structural factors that influence countries’ tax revenue performance. These include the real per capita income (“GDPC”), which measures countries’ level of economic development; the inflation rate, whose transformation to reduce its skewness is denoted “INFL” (Appendix 1); countries’ structure of output measured by the share of the value added in the agricultural sector in the total output (denoted “SHAGRI”); the level of trade openness, denoted “OPEN”; the degree of democratization, which acts as a proxy for the institutional quality (denoted “POLITY2”); the terms of trade, denoted “TERMS”; and the demographic characteristics of the country, proxied by the population size, denoted “POP.”
In light of the foregoing, we postulate the following model:
i and t stand, respectively, for a country and the time period. The dependent variable “TAX” is the share of a country’s total non-resource tax revenue in percentage of GDP, and the regressor of key interest, namely, “TAXINST” is the measure of total non-resource tax revenue instability. Non-resource tax revenue (% GDP) is calculated as the difference between total tax revenue excluding grants and social contributions (in % GDP) and the resource tax revenue (in % GDP), the latter being the tax revenue collected on natural resources. In fact, the reliance on non-resource tax revenue (% GDP) – rather than on total tax revenue (which includes resource tax revenue) – arises from the fact that resource tax revenue is not significantly affected by economic policies. Additionally, using non-resource tax revenue over a large set of countries helps achieve a greater homogeneity in terms of the revenue components across countries in the sample[3] (Brun et al., 2015; Gnangnon and Brun, 2017, 2018).
Based on data available, model (1) has been estimated using an unbalanced panel data set of 146 countries (including developed and developing countries) over the period 1981–2016. In light of the practice in the literature, we have used non-overlapping sub-periods of five-year data so as to limit the impact of business cycles on variables in model (1). Thus, the variable measuring tax revenue instability has been computed as the standard deviation of annual growth rate of non-resource tax revenue (% GDP) over non-overlapping sub-periods of five-year data. The sub-periods are: 1981–1985; 1986–1990; 1991–1995; 1996–2000; 2001–2005; 2006–2010; and 2011–2016. α1–α9 are parameters to be estimated. µi are countries’ time invariant specific effects; γt are time dummies that act as global shocks that influence together all countries’ tax revenue share. ωit is a well-behaving error term. Note that the natural logarithm has been applied to all variables (except “POLITY2” – which contains negative values, but does not exhibit a high skewness) so as to reduce the skewness of variables such as “TAXINST,” “GDPC,” “OPEN” and “POP,” but also to obtain coefficients in terms of elasticity.
By reflecting an improvement in the level of economic and institutional sophistication, the rise in the real per capita income can be positively associated with tax revenue (Crivelli and Gupta, 2014). Nevertheless, it is possible that an increase in the real per capita income would be associated with lower tax revenue share of GDP. This could be explained by the fact that less developed countries make much more effort (in relation to their GDP) than relatively advanced economies in the mobilization of tax revenue. On another note, the majority of studies highlighted above have shown that an output structure featured by the rise in the share of value added in the agriculture sector in total output is negatively associated with the tax revenue-to-GDP ratio. This is because of the difficulties of countries to tax agricultural sector (Balh, 2003; Bird et al., 2008), in particular for political reasons. The effect of trade openness on tax revenue is ambiguous, as it can be positive, including through the productivity channel (Edwards, 1998; Föllmi et al., 2018; Melitz, 2003) or negative (Cagé and Gadenne, 2018; Khattry, 2003). Incidentally, an improvement in institutional quality is positively associated with tax revenue (Ghura, 1998; Bird et al., 2008). A rise in the population size can be negatively associated with tax revenue because in countries with faster growing populations, the tax system may lag behind in the ability to capture new taxpayers (Bahl, 2003). Terms of trade improvements can positively affect tax revenue, including through the rise in the related trading corporate tax revenue (owing to the increase in the profitability of exporting firms) (Agbeyegbe et al., 2006). However, governments may be less incentivized to collect tax revenue when countries experience terms of trade improvements, thereby leading to a negative relationship between terms of trade improvements and tax revenue. Finally, a higher inflation rate reduces tax revenue as the lags in tax payments reduce the real amount of tax collected by the inflation rate (Tanzi, 1977).
3. Data analysis
We present in Figure 1 the evolution of tax revenue instability and tax revenue over the full sample and the period 1981–2016 (using non-overlapping sub-periods data set). Figure 2 does the same for the sub-samples considered in the analysis. These sub-samples are derived from the World Bank classification of countries, and include low-income countries (LICs), lower-middle income countries (LMICs), upper-middle income countries (UMICs) and high-income countries (HICs). Figures 3 and 4 show the cross-plot between tax revenue instability and tax revenue, respectively, over the full sample and over the sub-samples (using non-overlapping data set).
Figure 1 indicates that tax revenue instability and tax revenue tended not to move in the same direction over the period under analysis. In particular, during the last sub-periods 2006–2010 and 2011–2016, there was a clear divergence between tax revenue instability and tax revenue. Figure 2 shows, as expected, that HICs experience the highest level of tax revenue, followed in the descending order by UMICs, LMICs and LICs. In the meantime, tax revenue has fluctuated strongly in the four sub-samples. Specially, from 1991 to 1994 onwards, HICs represent the group of countries that experienced the lowest degree of tax revenue instability. This group is followed by LMICs and UMICs. The level of tax revenue instability has been the highest in LICs, when compared to other sub-groups of countries. Figure 3 indicates a negative correlation between tax revenue instability and tax revenue over the full sample, and this pattern is observed in Figure 4 for each of the four sub-samples, although HICs experienced the steepest negative slope of the negative correlation pattern.
4. Empirical methodology
Many of the studies on the determinants of tax revenue share (Agbeyegbe et al., 2006; Baunsgaard and Keen, 2010; Crivelli and Gupta, 2014; Crivelli, 2016; Gnangnon and Brun, 2018, 2019) have used the GMM system approach, including the two-step system GMM estimator proposed by Arellano and Bover (1995) and Blundell and Bond (1998) to estimate the dynamic relationship between tax revenue and its determinants. The present analysis follows this practice, and estimates the dynamic model (1) using the two-step system GMM estimator. It is more efficient than the first-differenced GMM estimator developed by Arellano and Bond (1991), in particular when there is persistence in variables over time, and in the presence of weak instruments for first differences.
The two-step system GMM estimator helps to deal with the endogeneity issue related to the reverse causality from the dependent variable to some regressors, notably tax revenue instability, trade openness, the real per capita income and the institutional quality. Additionally, this estimator allows for addressing the likely endogeneity problem induced by the correlation between the one-period lag of the non-resource tax revenue variable (as a regressor) and countries’ time invariant specific effects. Such a correlation leads to biased estimates when the panel dataset features a short time dimension and a relatively larger cross-section (Nickell, 1981). The consistency of the two-step system GMM estimator rests on three tests, including the Arellano-Bond (AB) test of the presence of first-order serial correlation in the error term (denoted AR(1)), the Arellano Bond test of the absence of second-order autocorrelation in the residuals (denoted AR(2)), and the Sargan test of over-identifying restrictions (OID). The estimator would be consistent if the null hypotheses of the tests were not rejected. Finally, the diagnostic tests are powerful if the number of instruments used in the regressions are lower than the number of countries (Bowsher, 2002; Roodman, 2009). For this reason, in addition to reporting the outcomes of the diagnostic tests mentioned above, we also present the number of instruments used in the regressions. The latter have used a maximum of three lags of the dependent variables as instruments, and three lags of endogenous variables as instruments.
While the two-step system GMM approach is pour preferred estimator in this analysis, we also present the outcomes of the estimation of a variant of model (1) using two standard econometric estimators. These include the within fixed effects estimator (denoted “FE”) along with the Driscoll and Kraay (1998) technique to correct standard errors of the estimates of serial correlation, heteroscedasticity and cross-sectional dependence; and the feasible generalized least squares (denoted “FGLS”). The variant of model (1) estimated by means of these two estimators is model (1) without the one period lag of the dependent variable (i.e. a static version of model (1)). We have used the one-period lag of the variables capturing the real per capita income, trade openness and the proxy for the institutional quality so as to mitigate the endogeneity problem associated with simultaneity bias, i.e. the reverse causality from the dependent variable to each of these regressors. The outcomes of these estimations are provided in Table 1.
The regressions based on the two-step system GMM approach are as follows. Column (1) of Table 2 contains the results of the estimation of model (1). Columns (2)–(5) contain the outcomes of the estimation of different variants of model (1) that allow obtaining the net effect of non-resource tax revenue instability on non-resource tax revenue, respectively, on LICs, LMICs, UMICs and HICs. These variants are model (1) in which we introduce (separately) a dummy capturing each category of countries (or each sub-sample) as well as its interaction with the variable “TAXINST.” As the categorization of countries in each of these groups (sub-samples) by the World Bank may vary from one year to another year, we adopt a more general approach that entails estimating another specification of model (1). The latter includes the interaction between the variables “TAXINST” and “GDPC,” and thus help to assess how the effect of tax revenue instability on tax revenue varies across countries in the full sample. The result of the estimation of this model specification is provided in Column (6) of Table 2.
Estimates presented in Table 3 aim to help examine the channel through which tax revenue instability affects tax revenue. As noted in Section 2, the present study has considered that the effect of non-resource tax revenue instability on non-resource tax revenue can materialize through the instability of government expenditure (denoted “EXPINST”). Thus, we estimate a variant of model (1) that includes both the variable “EXPINST” and its interaction with the variable “TAXINST.” The instability of public expenditure has been calculated as the standard deviation of the growth rate of the general government total expenditure (% GDP), over each of the above-mentioned non-overlapping sub-periods of five years.
Finally, we investigate whether the effect of non-resource tax revenue instability on tax revenue depends on countries’ prevailing economic conditions. These conditions are measured by three factors, including the economic growth volatility, inflation volatility and terms of trade instability. Thus, we estimate three other variants of model (1) that include, respectively, each of these three variables, and the interaction between the relevant variable and the variable “TAXINST.” Economic growth volatility (denoted “GRVOL”) is measured by the standard deviation of annual economic growth rate (growth rate of real GDP) over non-overlapping sub-periods of five years. Similarly, inflation volatility (denoted “INFLVOL”) is computed as the standard deviation of inflation rate over five-year non-overlapping sub-periods. Terms of trade instability (denoted “TERMSINST”) is the standard deviation of annual terms of trade growth over five-year non-overlapping sub-periods. The terms of trade indicator is measured by the ratio of the export price index to import price index.
5. Empirical results
At the outset, we would like to note that for the sake of simplicity, we henceforth refer to “non-resource tax revenue share of GDP” as “tax revenue share,” and the “instability of non-resource tax revenue share of GDP” as “tax revenue instability.”
Results in Columns (1) and (2) of Table 1 show that tax revenue instability influences negatively and significantly the tax revenue share. In fact, the coefficients of the variable “TAXINST” in these two columns of Table 1 are negative and significant at the 1% level. However, the coefficient obtained based on the FGLS approach is almost four times the one obtained from the FE regression. As for control variables, results in Column (1) (i.e. the one based on the FE approach) show that trade openness and the population size exert a positive and significant effect on the tax revenue share at the 1% level, while inflation and terms of trade influences it negatively and significantly (at least at the 5% level). The other regressors do not appear to influence significantly the tax revenue share. The outcomes based on the FGLS approach reveal that tax revenue share is positively and significantly (at the 1% level) driven by higher real per capita income, greater trade openness, a lower share of value added in agriculture in total output, lower inflation rates, a better institutional quality and a lower population size. Terms of trade do not influence significantly the tax revenue share.
Let us now turn to outcomes contained in the other tables. The estimations’ outcomes presented in Tables 2–4 show that all the conditions required to ensure the consistency of the two-step system GMM estimator are met, thereby confirming the appropriateness of this estimator for the empirical analysis. In fact, as shown by results presented at the bottom of the three tables, the null hypothesis of the presence of the first-order serial correlation in the error term (denoted AR(1)) and the absence of the second-order autocorrelation in the error term (denoted AR(2)) are not rejected. Additionally, the p-values of the OID test are higher than 0.10, and thus confirm the validity of the instruments used in the regressions. Incidentally, the number of instruments is always lower than the number of countries. Finally, as observed in many other studies that have used the two-step system GMM estimator, the one-period lag of the dependent variable shows positive and significant coefficients at the 1% level across all columns of the three tables, thereby showing the state dependence nature of non-resource tax revenue share of GDP.
Results in Column (1) of Table 2 show a negative and significant coefficient (at the 1% level) of the variable “TAXINST.” This suggests that tax revenue instability is associated with lower tax revenue share. A 1 percentage increase in the degree of tax revenue instability is associated with a 0.107 percentage decrease in tax revenue share[4]. Results in Columns (2)–(5) suggest that at the 5% level, the net effect of tax revenue instability on tax revenue share in LICs, LMICs, UMICs and HICs amounts, respectively, to −0.100; −0.011 (= −0.128 + 0.117); −0.0896; and −0.137 (= −0.0749−0.0625). It, therefore, appears that a 1 percentage rise in tax revenue instability is associated with a 0.1 percentage fall in tax revenue share in LICs, a 0.011 percentage decline in tax revenue share in LMICs, a 0.09 percentage decrease in tax revenue share in UMICs and a 0.137 percentage fall in HICs. Thus, HICs appear to be the group of countries that experiences the highest negative effect of tax revenue instability on tax revenue. This group is followed (in the descending order) by LICs, UMICs and finally LMICs. Results in Column (6) of Table 2 show a negative and significant (at the 1% level) coefficient of the interaction variable “[Log(TAXINST)]*[Log(GDPC)],” while the coefficient of the variable “[Log(TAXINST)]” is not significant at the conventional levels. We conclude that the magnitude of the negative effect of tax revenue instability on tax revenue share increases as countries experience a rise in their real per capita income. In other words, less developed countries experience a higher magnitude of the negative effect of tax revenue instability on tax revenue share than do relatively advanced economies. This means that relatively advanced economies managed relatively well the adverse effect of tax revenue instability on tax revenue share, compared to less developed countries.
Turning to control variables, we obtain (in particular from Column (1) of Table 2, although similar outcomes are more or less observed in other columns of the table) that at the 5% level, an improvement in tax revenue share is positively driven by a fall in the real per capita income, lower share of value added in agriculture in total output, lower inflation rate, an improvement in the institutional quality and a deterioration in terms of trade. The negative effect of the real per capita income on non-resource tax revenue runs in contrast with the theoretical expectation of a positive effect of real per capita income on tax revenue found in many studies in the literature. This peculiar outcome may be because of the inclusion of tax revenue instability in the model specification (which has not been the case in previous empirical studies). This may, therefore, signify that the effect of the real per capita income on non-resource tax revenue is dependent on the level of non-resource tax revenue instability. This was, indeed, what we found above.
The population size does not influence the tax revenue share. Finally, at the 5% level, there is no significant effect of trade openness on tax revenue share. Similar results on the control variables are obtained in Tables 2 and 3.
Turning to the results reported in Table 3, we find that the interaction term associated with the variable “[Log(TAXINST)]*[Log(EXPINST)]” and the coefficient of the variable “[Log(TAXINST)]” are both negative and significant at the 1% level. Based on these results, we draw the following conclusions: tax revenue instability is consistently associated with a fall in tax revenue share as the level of instability of public expenditure rises, and the higher this level, the greater is the magnitude of the negative effect of tax revenue instability on the tax revenue share.
Results in Column (1) of Table 4 suggest a negative and significant coefficient (at the 1% level) of the interaction variable “[Log(TAXINST)]*[Log(GRVOL)],” while the coefficient of the variable “[Log(TAXINST)]” is positive and significant at the 1% level. These two results indicate that there is a turning point of the economic growth volatility below which tax revenue instability is positively associated with the tax revenue share; above this threshold, the relationship between tax revenue instability and tax revenue share becomes negative. This turning point amounts to 1.468 [= exponential (0.0346/0.0902)]. Given that in the data set considered in the analysis, values of economic growth volatility range between 0.14 and 63, we conclude that it is only for very small degrees of economic growth (comprising between 0.14 and 1.47) that tax revenue instability results in higher tax revenue share (the lower the degree of economic growth volatility, the greater is the positive effect of tax revenue instability on tax revenue share). For levels of economic growth volatility higher than the threshold 1.47, tax revenue instability leads to lower tax revenue share and the higher the level of economic volatility, the greater is the magnitude of the negative effect of tax revenue instability on the tax revenue share.
We also obtain from Columns (2) and (3) of Table 4 that not only are the coefficients of the variable “[Log(TAXINST)]” negative and significant at the 1% level, but the interaction terms related to the interactions variables “[Log(TAXINST)]*[Log(INFLVOL)]” and “[Log(TAXINST)]*[Log(TERMSINST)]” are also negative and significant at the 1% level. Therefore, we deduce that tax revenue instability consistently leads to lower tax revenue share in the context of rising inflation volatility or terms of trade instability. The higher the inflation volatility, the greater is the magnitude of the negative effect of tax revenue instability on the tax revenue share. Additionally, the magnitude of the reducing effect of tax revenue instability on tax revenue share rises as countries experience an increase in the degree of terms of trade instability.
6. Conclusion
This analysis has investigated the effect of tax revenue instability on non-resource tax revenue share in a set of 146 countries over the period 1981–2016. It has suggested that tax revenue instability affects negatively non-resource tax revenue share, with the magnitude of this negative effect declining as countries enjoy a rise in the real per capita income. This negative effect of tax revenue instability on non-resource tax revenue share translates through the channel of public expenditure instability. Specially, countries experience a higher negative effect of non-resource tax revenue instability on non-resource tax revenue share when their public expenditure instability increases. Finally, the magnitude of the negative effect of non-resource tax revenue instability on non-resource tax revenue share increases as countries experience higher economic volatility, higher inflation volatility and a rise in terms of trade instability.
The mobilization of tax revenue is essential to, inter alia, supply public services, and prevent or cope with economic, financial and health shocks that developed and developing countries may face. The main policy implication of this analysis is that policies that help ensure the stability of non-resource tax revenue also contribute to improving countries’ non-resource tax revenue share. For example, governments’ measures that help cope with or prevent the severe adverse effects of shocks on economies (shocks that could translate into higher tax revenue instability) would ultimately help enhance countries’ tax revenue performance. The current COVID-19 pandemic, which has the specific feature to be simultaneously both a demand shock and a supply shock is a case in point. The severity of these negative shocks and the subsequent macroeconomic uncertainty that it has generated – inter alia, in terms of economic growth instability, terms of trade instability, inflation volatility and public expenditure instability – are likely to result in severe tax revenue losses. Governments in both developed and developing countries would surely learn from the management of this crisis so as to prepare for possible future economic, financial and health crises with a view to dampening their adverse macroeconomic effects, including here their negative tax revenue effects.
Figures
Figure 1.
Evolution of TAX and TAXINST_Over full sample
Figure 2.
Evolution of TAX and TAXINST_Over sub-samples
Figure 3.
Correlation pattern between TAX and TAXINST_Over full sample
Figure 4.
Correlation pattern between TAX and TAXINST_Over sub-samples
Impact of tax revenue instability on tax revenue
| FE | FGLS with panel-specific AR(1) | |
|---|---|---|
| Variables | Log(TAX) | Log(TAX) |
| (1) | (2) | |
| Log(TAXINST)t−1 | −0.0281*** (0.00628) | −0.0848*** (0.00737) |
| Log(GDPC)t−1 | 0.0677 (0.0562) | 0.0919*** (0.0131) |
| Log(OPEN)t−1 | 0.0660*** (0.0143) | 0.0996*** (0.00999) |
| Log(SHVAAGRI) | 0.0645 (0.0461) | 0.151*** (0.0120) |
| INFL | −0.0601*** (0.00928) | −0.0483*** (0.00773) |
| POLITY2t−1 | 0.000434 (0.00120) | 0.0171*** (0.00145) |
| Log(TERMS) | −0.0497** (0.0218) | −0.0163 (0.0174) |
| Log(POP) | 0.365*** (0.0298) | −0.0707*** (0.00697) |
| Constant | −7.527*** (0.282) | MMMMM (0.270) |
| Observations – Countries | 654 – 144 | 650 – 140 |
| Within R-squared | 0.2494 | |
| Pseudo R-squared | 0.6238 |
Notes: *p-value < 0.1; **p-value < 0.05; ***p-value < 0.01. Robust standard errors are in parenthesis. The pseudo R2 has been computed for the regression based on the FGLS estimator as the correlation coefficient between the dependent variable and its predicted values.
Estimators: FE and FGLS
Effect of tax revenue instability on tax revenue across sub-samples
| Variables | Log(TAX) | Log(TAX) | Log(TAX) | Log(TAX) | Log(TAX) | Log(TAX) |
|---|---|---|---|---|---|---|
| (1) | (2) | (3) | (4) | (5) | (6) | |
| Log(TAX)t−1 | 0.735*** (0.0160) | 0.748*** (0.0172) | 0.765*** (0.0169) | 0.768*** (0.0178) | 0.796*** (0.0146) | 0.728*** (0.0152) |
| Log(TAXINST) | −0.107*** (0.0106) | −0.100*** (0.0124) | −0.128*** (0.0138) | −0.0896*** (0.0112) | −0.0749*** (0.0145) | 0.0213 (0.0401) |
| [Log(TAXINST)]*LIC | −0.0329* (0.0173) | |||||
| [Log(TAXINST)]*LMIC | MM (0.0220) | |||||
| [Log(TAXINST)]*UMIC | 0.00140 (0.0209) | |||||
| [Log(TAXINST)]*HIC | −0.0625*** (0.0229) | |||||
| [Log(TAXINST)]*[Log(GDPC)] | −0.0181*** (0.00460) | |||||
| LIC | −0.0779 (0.0564) | |||||
| LMIC | 0.280*** (0.0689) | |||||
| UMIC | 0.0632 (0.0571) | |||||
| HIC | −0.287*** (0.0890) | |||||
| Log(GDPC) | −0.0637*** (0.0179) | −0.0884*** (0.0196) | −0.0941*** (0.0170) | −0.0894*** (0.0193) | −0.0832*** (0.0214) | −0.170*** (0.0256) |
| MM | −0.0156* (0.00938) | 0.00140 (0.0140) | 0.0187 (0.0118) | −0.00697 (0.0139) | 0.0100 (0.0120) | 0.0251** (0.0125) |
| Log(SHAGRI) | −0.0765*** (0.0202) | −0.0754*** (0.0179) | −0.0489** (0.0242) | −0.0983*** (0.0186) | −0.0637*** (0.0232) | −0.0684*** (0.0199) |
| INFL | −0.0165*** (0.00622) | −0.0132* (0.00675) | −0.0123 (0.00761) | −0.0214*** (0.00629) | −0.0226*** (0.00766) | −0.0137* (0.00741) |
| POLITY2 | 0.0107*** (0.00179) | 0.0108*** (0.00179) | 0.00937*** (0.00172) | 0.0121*** (0.00212) | 0.0114*** (0.00250) | 0.00903*** (0.00187) |
| Log(TERMS) | −0.0536*** (0.0194) | −0.0804*** (0.0142) | −0.0864*** (0.0217) | −0.0591*** (0.0170) | −0.0768*** (0.0194) | −0.0699*** (0.0176) |
| Log(POP) | −0.00577 (0.0111) | −0.00256 (0.0144) | −0.0246* (0.0138) | −0.00960 (0.0145) | −0.00627 (0.0128) | −0.0274** (0.0121) |
| Observations – Countries | 688 – 146 | 688 – 146 | 688 – 146 | 688 – 146 | 688 – 146 | 688 – 146 |
| Number of Instruments | 97 | 97 | 97 | 97 | 97 | 97 |
| AR1 (p-value) | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |
| AR2 (p-value) | 0.2189 | 0.1933 | 0.2149 | 0.2205 | 0.1887 | 0.2331 |
| OID (p-value) | 0.2252 | 0.2986 | 0.1451 | 0.1787 | 0.2523 | 0.2612 |
Notes: *p-value < 0.1; **p-value < 0.05; ***p-value < 0.01. Robust standard errors are in parenthesis. In the two-step system GMM estimations, the variables “TAXINST,” “OPEN,” “GDPC,” “POLITY2” and the interaction variables have been considered as endogenous. The other variables have been considered as exogenous. Time dummies have been included in the regressions. The latter have used a maximum of three lags of the dependent variables as instruments and three lags of endogenous variables as instruments.
Estimator: Two-step system GMM
Impact of tax revenue instability on tax revenue for varying levels of public expenditure variability and economic growth rate
| Variables | Log(TAX) |
|---|---|
| (1) | |
| Log(TAX)t−1 | 0.737*** (0.0125) |
| Log(TAXINST) | −0.205*** (0.0139) |
| [Log(TAXINST)]*[Log(EXPINST)] | −0.0721*** (0.00467) |
| Log(EXPINST) | −0.265*** (0.0164) |
| Log(GDPC) | −0.0492*** (0.0145) |
| Log(OPEN) | −0.0148 (0.0136) |
| Log(SHAGRI) | −0.0829*** (0.0133) |
| INFL | −0.0124** (0.00495) |
| POLITY2 | 0.0143*** (0.00162) |
| Log(TERMS) | −0.112*** (0.0180) |
| Log(POP) | 0.00990 (0.0125) |
| Observations – Countries | 661 – 144 |
| Number of instruments | 111 |
| AR1 (p-value) | 0.0000 |
| AR2 (p-value) | 0.5442 |
| OID (p-value) | 0.4653 |
Notes: *p-value < 0.1; **p-value < 0.05; ***p-value < 0.01. Robust standard errors are in parenthesis. In the two-step system GMM estimations, the variables “TAXINST,” “OPEN,” “GDPC,” “POLITY2” and the interaction variables have been considered as endogenous. The other variables have been considered as exogenous. Time dummies have been included in the regressions. The latter have used a maximum of three lags of the dependent variables as instruments and three lags of endogenous variables as instruments.
Estimator: Two-step system GMM
Impact of tax revenue instability on tax revenue taking into account the prevailing economic conditions (economic volatility, inflation volatility, terms of trade instability)
| Variables | Log(TAX) | Log(TAX) | Log(TAX) |
|---|---|---|---|
| (1) | (2) | (3) | |
| Log(TAX)t−1 | 0.760*** (0.0131) | 0.734*** (0.0130) | 0.808*** (0.0104) |
| Log(TAXINST) | 0.0346*** (0.0122) | −0.0714*** (0.0122) | −0.183*** (0.00999) |
| [Log(TAXINST)]*[Log(GRVOL)] | −0.0902*** (0.00570) | ||
| Log(GRVOL) | −0.302*** (0.0151) | ||
| [Log(TAXINST)]*[Log(INFLVOL)] | −0.0139*** (0.00439) | ||
| Log(INFLVOL) | −0.0979*** (0.0112) | ||
| [Log(TAXINST)]*[Log(TERMSINST)] | −0.0637*** (0.00441) | ||
| Log(TERMSINST) | −0.234*** (0.0145) | ||
| Log(GDPC) | −0.0892*** (0.0174) | −0.0865*** (0.0157) | −0.103*** (0.0146) |
| Log(OPEN) | −0.0165* (0.00951) | 0.0106 (0.0107) | −0.00414 (0.00922) |
| Log(SHAGRI) | −0.130*** (0.0144) | −0.0566*** (0.0142) | −0.105*** (0.0176) |
| INFL | −0.00215 (0.00599) | 0.0519*** (0.00728) | −0.0112** (0.00455) |
| POLITY2 | 0.00952*** (0.00113) | 0.00308** (0.00138) | 0.00255** (0.00118) |
| Log(TERMS) | −0.0676*** (0.0129) | −0.0461*** (0.0161) | −0.0812*** (0.0143) |
| Log(POP) | 0.00308 (0.00905) | −0.0131 (0.0135) | −0.0122 (0.00986) |
| Observations – Countries | 687 – 146 | 688 – 146 | 662 – 146 |
| Number of instruments | 111 | 111 | 111 |
| AR1 (p-value) | 0.0009 | 0.0003 | 0.0001 |
| AR2 (p-value) | 0.3141 | 0.1388 | 0.1017 |
| OID (p-value) | 0.2795 | 0.2515 | 0.2869 |
Notes: *p-value < 0.1; **p-value < 0.05; ***p-value < 0.01. Robust standard errors are in parenthesis. In the two-step system GMM estimations, the variables “TAXINST,” “OPEN,” “GDPC,” “POLITY2,” “GRVOL” and the interaction variables have been considered as endogenous. The other variables have been considered as exogenous. Time dummies have been included in the regressions. The latter have used a maximum of three lags of the dependent variables as instruments and three lags of endogenous variables as instruments.
Estimator: Two-step system GMM
Definition and source of variables
| Variables | Definition | Sources |
|---|---|---|
| TAX | This is the measure of the non-resource tax revenue as a share of GDP, which represents the difference between the variable the Total Tax Revenue excluding grants and social contributions as a share of GDP and the resource tax revenue as a share of GDP. The resource tax revenue represents the tax revenue collected on natural resources. Note that all ratios are not expressed in percentage. | Public Revenue Dataset developed by the United Nations University World Institute for Development Economics Research (UNU-WIDER). Available at: www.wider.unu.edu/project/government-revenue-dataset |
| TAXINST | This is the measure of the instability of non-resource tax revenue. It has been calculated as the standard deviation of annual growth rate of non-resource tax revenue (% GDP) over non-overlapping sub-periods of five-year data. | Authors’ calculation based on data from the UNU-WIDER database |
| GDPC | GDP per capita (constant 2010 US$) | WDI |
| OPEN | This is the measure of trade openness suggested by Squalli and Wilson (2011). It is calculated as the ratio of exports and imports to GDP, adjusted by the proportion of a country’s trade level relative to the average world trade (Wilson, 2011, p. 1758). | Authors’ calculation based on data extracted from the WDI |
| SHAGRI | Share of value added in agriculture, in percentage of total output | WDI |
| POLITY2 | This variable is an index extracted from Polity IV Database (Marshall et al., 2018). It represents the degree of democracy based on competitiveness of political participation, the openness and competitiveness of executive recruitment and constraints on the chief executive. Its values range between −10 and +10, with lower values reflecting autocratic regimes, and greater values indicating democratic regimes. Specifically, the value +10 for this index represents a strong democratic regime, while the value −10 stands for strong autocratic regime. | Polity IV Database (Marshall et al., 2018) |
| INFL | The variable “INFL” has been calculated using the following formula (Yeyati et al., 2007): INFL = sign (INFLATION)*log (1+|INFLATION|) (2), where |INFLATION| refers to the absolute value of the annual inflation rate (%), denoted “INFLATION.” | Authors’ calculation based on data from the WDI |
| The annual inflation rate (%) is based on consumer price index (CPI) (annual %) where missing values have been replaced with values of the GDP Deflator (annual %). | ||
| TERMS | This is the measure of terms of trade. Terms of trade represent the ratio of the export price index to import price index. | Authors’ calculation based on data from the World Development Indicators (WDI) of the World Bank |
| POP | Total population | WDI |
| EXPINST | This is the measure of the instability of total public expenditure. It has been calculated as the standard deviation of the growth rate of the general government total expenditure (percent of GDP), over non-overlapping sub-periods of five years. | Authors’ calculation based on data on the general government total expenditure (percent of GDP) has been extracted from the International Monetary Fund Database |
| GRVOL | This is the measure of the volatility of economic growth rate. It has been calculated as the standard deviation of annual economic growth rate (growth rate of real GDP) over non-overlapping sub-periods of five years. | Authors’ calculation based on economic growth rate data extracted from the WDI |
| INFLVOL | This is the measure of inflation volatility, calculated as the standard deviation of inflation rate over five-year non-overlapping sub-periods. | Authors’ calculation based on inflation data extracted from the WDI |
| TERMSINST | This is the measure of terms of trade instability. Terms of trade represent the ratio of the export price index to import price index. Terms of trade volatility has been calculated as the standard deviation of annual terms of trade growth over five-year non-overlapping sub-periods. | Authors’ calculation based on terms of trade data previously described |
Standard descriptive statistics on the variables used in the analysis
| Variable | Observations | Mean | Standard deviation | Minimum | Maximum |
|---|---|---|---|---|---|
| TAX | 688 | 0.158 | 0.075 | 0.006 | 0.409 |
| TAXINST | 688 | 0.109 | 0.271 | 0.002 | 5.420 |
| GRVOL | 687 | 3.045 | 2.744 | 0.143 | 30.798 |
| INFLATION | 688 | 25.310 | 258.705 | −5.111 | 6,517.110 |
| INFLVOL | 688 | 27.576 | 382.074 | 0.144 | 9,730.510 |
| TERMS | 688 | 103.112 | 49.040 | 16.781 | 750.973 |
| TERMSINST | 662 | 0.170 | 0.235 | 0.010 | 3.122 |
| SHAGRI | 688 | 15.081 | 13.485 | 0.033 | 71.779 |
| POLITY2 | 688 | 3.536 | 6.304 | −10.000 | 10.000 |
| OPEN | 688 | 0.0046 | 0.0105 | 0.0000000098 | 0.0875 |
| GDPC | 688 | 11,081.43 | 17,108.13 | 161.66 | 106,478.8 |
| POP | 688 | 44,100,000 | 148,000,000 | 295,163.4 | 1,360,000,000 |
List of countries of the full sample
| Full sample | |||||
|---|---|---|---|---|---|
| Albania | Republic of the Congo | Indonesia | Mongolia | Solomon Islands | |
| Algeria | Costa Rica | Iran, Islamic Rep. | Morocco | South Africa | |
| Angola | Croatia | Iraq | Mozambique | Spain | |
| Argentina | Cuba | Ireland | Myanmar | Sri Lanka | |
| Armenia | Cyprus | Israel | Namibia | Sudan | |
| Australia | Czech Republic | Italy | Nepal | Eswatini | |
| Austria | Cote d’Ivoire | Jamaica | Netherlands | Sweden | |
| Azerbaijan | Dominican Republic | Japan | New Zealand | Switzerland | |
| Bahrain | Ecuador | Jordan | Nicaragua | Tajikistan | |
| Bangladesh | Egypt, Arab Rep. | Kazakhstan | Niger | Tanzania | |
| Belgium | El Salvador | Kenya | Nigeria | Thailand | |
| Benin | Equatorial Guinea | Korea, Rep. | Norway | Togo | |
| Bhutan | Eritrea | Kuwait | Pakistan | Tunisia | |
| Bolivia | Estonia | Kyrgyz Republic | Panama | Turkey | |
| Botswana | Ethiopia | Lao PDR | Papua New Guinea | Turkmenistan | |
| Brazil | Finland | Latvia | Paraguay | Uganda | |
| Bulgaria | France | Lebanon | Peru | Ukraine | |
| Burkina Faso | Gabon | Lesotho | Philippines | United Arab Emirates | |
| Burundi | Gambia, The | Liberia | Poland | United Kingdom | |
| Cabo Verde | Georgia | Libya | Portugal | United States | |
| Cambodia | Germany | Lithuania | Romania | Uruguay | |
| Cameroon | Greece | Luxembourg | Russian Federation | Uzbekistan | |
| Canada | Guatemala | North Macedonia | Rwanda | Venezuela, RB | |
| Central African Republic | Guinea | Madagascar | Saudi Arabia | Vietnam | |
| Chad | Guinea-Bissau | Malawi | Senegal | Zambia | |
| Chile | Guyana | Malaysia | Serbia | Zimbabwe | |
| China | Haiti | Mauritania | Sierra Leone | ||
| Colombia | Honduras | Mauritius | Singapore | ||
| Comoros | Hungary | Mexico | Slovak Republic | ||
| Congo, Dem. Rep. | India | Moldova | Slovenia | ||
List of countries in the sub-samples
| LICs | LMICs | UMICs | HICs |
|---|---|---|---|
| Benin | Armenia | Albania | Australia |
| Burkina Faso | Bangladesh | Algeria | Austria |
| Burundi | Bhutan | Angola | Bahrain |
| Central African Republic | Bolivia | Argentina | Belgium |
| Chad | Cabo Verde | Azerbaijan | Canada |
| Comoros | Cambodia | Botswana | Chile |
| Congo, Dem. Rep. | Cameroon | Brazil | Croatia |
| Eritrea | Congo, Rep. | Bulgaria | Cyprus |
| Ethiopia | Cote d'Ivoire | China | Czech Republic |
| Gambia, The | Egypt, Arab Rep. | Colombia | Estonia |
| Guinea | El Salvador | Costa Rica | Finland |
| Guinea-Bissau | Guatemala | Cuba | France |
| Haiti | Guyana | Dominican Republic | Germany |
| Liberia | Honduras | Ecuador | Greece |
| Madagascar | India | Equatorial Guinea | Hungary |
| Malawi | Indonesia | Gabon | Ireland |
| Mozambique | Kenya | Georgia | Israel |
| Nepal | Kyrgyz Republic | Iran, Islamic Rep. | Italy |
| Niger | Lao PDR | Iraq | Japan |
| Rwanda | Lebanon | Jamaica | Korea, Rep. |
| Senegal | Lesotho | Jordan | Kuwait |
| Sierra Leone | Mauritania | Kazakhstan | Latvia |
| Tanzania | Moldova | Libya | Lithuania |
| Togo | Mongolia | North Macedonia | Luxembourg |
| Uganda | Morocco | Malaysia | Netherlands |
| Zimbabwe | Myanmar | Mauritius | New Zealand |
| Nicaragua | Mexico | Norway | |
| Nigeria | Namibia | Poland | |
| Pakistan | Panama | Portugal | |
| Papua New Guinea | Paraguay | Saudi Arabia | |
| Philippines | Peru | Singapore | |
| Solomon Islands | Romania | Slovak Republic | |
| Sri Lanka | Russian Federation | Slovenia | |
| Sudan | Serbia | Spain | |
| Eswatini | South Africa | Sweden | |
| Tajikistan | Thailand | Switzerland | |
| Tunisia | Turkey | United Arab Emirates | |
| Ukraine | Turkmenistan | United Kingdom | |
| Uzbekistan | Venezuela, RB | United States | |
| Vietnam | Uruguay | ||
| Zambia |
Notes
Studies in this regard include for example, Afonso and Furceri (2010); Aschauer (1989); Barro (1990); Bertola and Drazen (1993); Devarajan et al. (1996); Easterly and Rebelo (1993); Fischer (1993); Furceri (2007); Gali (1994); Gong and Zou (2002); and Kormendi and Meguire (1985).
These include, for example, Agbeyegbe et al. (2006); Baunsgaard and Keen (2010); Bird et al. (2008); Brun et al. (2015); Clist (2016); Clist and Morrissey (2011); Crivelli (2016); Crivelli and Gupta (2014); Ghura (1998); Gnangnon and Brun (2017, 2018, 2019); Khattry and Rao (2002); Morrissey et al. (2016); and Yohou et al. (2016).
It is important to underline that the UNU-WIDER data set (Appendix 1) can be considered as being the best source of information on government revenue at this moment, and one can argue that the distinction between resource and non-resource revenue is a tricky one and involves some choices, particularly regarding the poorer countries. To some extent, this choice can affect the estimations’ outcomes. As noted in footnote 4 below, the inclusion of the variable capturing the resource revenue share of GDP does not alter qualitatively or quantitatively estimations’ results, particularly the magnitude of the effect of tax revenue instability on tax revenue share.
It is worth noting at this stage of the analysis that we have also estimated a variant of model (1) (with the two-step system GMM approach) that included the variable capturing the “resource tax revenue share of GDP.” We have observed that the results (notably the ones concerning the tax revenue instability variable) are qualitatively and quantitatively similar to those presented in Column (1) of Table 2. Additionally, the coefficient of the “resource tax revenue share of GDP” variable is not statistically significant at the 10% level. On another note, the introduction of the variable “resource tax revenue share of GDP” in all other variants of model (1) described in the previous section does not alter qualitatively or quantitatively the estimations’ outcomes.
Appendix 1
Appendix 2
Appendix 3
Appendix 4
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Acknowledgements
This paper represents the personal opinions of individual staff members and is not meant to represent the position or opinions of the WTO or its members, nor the official position of any staff members. Any errors or omissions are the fault of the author. The author thanks the two reviewers for their very insightful comments on the paper. These comments helped improve the quality of the paper.