Abstract
This paper analyzes the effect of changes in the structural progressivity of national income tax systems on observed and actual income inequality. Using several unique measures of progressivity over the 1981–2005 period for a large panel of countries, we find that progressivity reduces inequality in observed income, but has a significantly smaller impact on actual inequality, approximated by consumption-based Ginis. An empirical comparative analysis shows that the differential effect on observed versus actual inequality is much larger in countries with weaker legal institutions. We also find that structural progressivity has a greater equalizing effect in environments that support pro-poor redistribution. Substantial differences in inequality response to changes in top versus bottom rates are also uncovered.
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Notes
A recent paper by Lakner and Milanovic (2015) finds that global inequality declined during the period 1988 to 2008 even as within-country income inequality increased.
The term structural progressivity denotes changes in the average (or marginal) tax rate along the income distribution.
Duncan (2014) and Doerrenberg and Duncan (2013) address a similar question to ours using different datasets and empirical techniques. Duncan (2014) uses data from the Russian Longitudinal Household Survey to simulate the redistributive effects of the Russian 2001 personal income tax reform, while Doerrenberg and Duncan (2013) use data from a laboratory experiment to estimate the impact of tax rate changes on income inequality.
It is important to emphasize that we focus on the personal income tax only. As such, any equity offsets that may come from other taxes such as the corporate income tax or sales taxes are not taken into account. In principle, policymakers could achieve the same level of income inequality by substituting reduced progressivity of the personal income tax with increased progressivity of the corporate tax.
We use the term avoidance to refer to all legal non-real responses to taxation such as income shifting, timing, and accounting responses. We acknowledge that evasion and avoidance are different, and our primary interest is in the tax evasion response. Because it is often difficult to distinguish evasion responses from avoidance responses empirically, we use the terms interchangeably from here on.
We acknowledge that the level of evasion tends to be more prominent at both tails of the income distribution. However, this should not have any qualitative effect on our results as long as the responsiveness of evasion is relatively higher in the right tail of the income distribution.
The RED data are taken from papers published in the special issue on Cross Sectional Facts for Macroeconomists; Volume 13, Issue 1 (January 2010). It includes data from Canada, Germany, Italy, Mexico, Russia, Spain, Sweden, UK, and the USA.
This represents less than 15 % of the sample. Alternatively, we impute missing net Gini using predicted values from the linear regression of net Gini on gross Gini, time period fixed effects, region fixed effect, standard macroeconomic variables (log of per capita GDP, log of population, government size, and inflation), and characteristics of the country’s tax system such as the type of tax allowance (none, standard, complex), an indicator for having local PIT taxes (no local tax, local tax is less than 5 %, and local tax is higher than 5 %), an indicator for having PIT surtaxes, and the number of tax brackets in quadratic form. The explanatory power of the model is fairly high (R-squared is 83 %). The results stay the same when the predicted net Gini is used instead of gross Gini, and the results are available upon request.
We also remove a small number of outliers (less than 1 % of the sample) if the reported Gini coefficient deviates from its country-specific linear trend by more than 3 standardized residuals.
We do not account for deductions, allowances, and credits that vary by individual characteristics; for example, child credits are not included in our calculations.
This corresponds to 4–200, 100–300, and 200–400 % of per capita GDP, respectively.
While the between-countries standard deviation of the net income Gini is 11.6, the within-country standard deviation is only three points. Comparable numbers for the consumption Gini are 9.3 and 3, for between and within standard deviations, respectively.
This allows us to control for time constant variables that are likely correlated with our measures of progressivity. We acknowledge that this approach does not fully solve the problem and is a limiting feature of the empirical analysis. It is worth noting that a similar approach is used by Dobson and Ramlogan-Dobson (2012) who use the same inequality measure as we do. Table 7 in Online Appendix A reports summary statistics for the covariates.
We use financial sector deposit as a share of GDP and interest rate spread as our primary measure of financial development (Beck et al. 2000), and the ratio of deposit money bank assets to all bank assets (central bank plus money bank) as alternative measures in robustness checks. We also replace inflation rate with currency depreciation rate in robustness checks. These changes do not affect the results, which are available upon request.
Table 9 in Online Appendix A presents results for the complete OLS and IV specification of the baseline model with MRP and ARP as measures of progressivity. The OLS estimates of progressivity are negative and statistically significant in ARP specification, but they are much smaller than the IV estimates, as implied in Sect. 4.1. The other coefficients are mostly consistent with expectations.
We note that if consumption expenditures used in Gini estimates are disproportionately underreported among the rich, then the difference between consumption inequality and income inequality based on survey data will be smaller than the actual difference in inequality. This implies that the differential effect posited in hypothesis 3 based on true expenditures will be even bigger in absolute value than the effect we estimate. Our estimate in this case can be interpreted as a lower-bound estimate.
Neighbors’ ARP-middle appear to be a weaker predictor for tax progressivity in the first stage compared to our other models (\(F\hbox {-stat}<4\)). We had to use additional instruments to make sure that IVs are valid before interpreting the results. The result in Table 5 is not affected by the use of two IVs; results are available upon request.
The results reported in Table 6 are estimated without sample weights. The use of sample weights does not affect the results for corruption. However, the law and order results are somewhat sensitive to the inclusion of weights; sign and magnitude are similar, but estimates are imprecise in some specifications.
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Acknowledgments
We would like to thank participants at the 71st Annual Congress of the International Institute of Public Finance, and two anonymous referees for their feedback and suggestions.
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Duncan, D., Sabirianova Peter, K. Unequal inequalities: Do progressive taxes reduce income inequality?. Int Tax Public Finance 23, 762–783 (2016). https://doi.org/10.1007/s10797-016-9412-5
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DOI: https://doi.org/10.1007/s10797-016-9412-5