Abstract
This paper attempts to extend the theoretical and empirical methodology employed in previous literature, by proposing a utility maximization process to estimate the optimal tax revenue from a sample of 30 countries. It is shown that an optimal tax system is defined solely by two crucial determining factors: The productive capacity of the country (GDP) and consumers’ preferences (consumption spending). All the other variables can be disregarded, as macroeconomic determinants (GDP, consumption) tend to capture the impact of all the remaining factors on tax revenue. It is also shown that our utility maximization method generates tax-effort indices which do not differ significantly from those of IMF and World Bank studies. The actual tax burden for most of the sample countries is shown to be below its optimal level. As expected, the tax-effort performance of each of the sample countries appears to be affected by the variety of approaches employed throughout the text.
Highlights
The main body of literature (Bahl 1971; Chelliah et al 1975; Crivelli and Gupta 2014; Cyan et al 2013; Garg et al 2017; etc.) on tax effort focuses on identifying the forces—in terms of administrative capacity and political, social or economic principles—which determine the capability of policy makers to impose taxes.A large number of determinants of the tax effort are available for empirical testing, across a broad sample of developing and developed countries and over an adequate number of time periods (Gupta 2007; Kim 2007; Le et al 2012; Stotsky and WoldeMariam 1997; Langford and Ohlenburg 2016; Leuthold 1991; Fenochietto and Pessino 2013; etc.)
We employ two discrete models, one with proportional taxes (Section 3.2) and the other with a progressive, income tax and a regressive consumption tax (Section 3.3). Both models lead to the same conclusion: an optimal tax revenue is one that exhausts the difference between income and private consumption, T = Y − C = G, whereas the tax effort is defined as the ratio of actual tax revenue to the optimal one, Y−C
The fundamental conclusion of this paper is that a conditionally distortionary tax system can lead to a Pareto optimal tax revenue and, to a Pareto optimal tax effort index that is argued to be more reliable than the relevant index that has been estimated by previous contributions to this issue
Summary
The main body of literature (Bahl 1971; Chelliah et al 1975; Crivelli and Gupta 2014; Cyan et al 2013; Garg et al 2017; etc.) on tax effort focuses on identifying the forces—in terms of administrative capacity and political, social or economic principles—which determine the capability of policy makers to impose taxes.A large number of determinants of the tax effort are available for empirical testing, across a broad sample of developing and developed countries and over an adequate number of time periods (Gupta 2007; Kim 2007; Le et al 2012; Stotsky and WoldeMariam 1997; Langford and Ohlenburg 2016; Leuthold 1991; Fenochietto and Pessino 2013; etc.). In estimating the tax effort regression equation, two methods are used to find an observable proxy for the unobserved dependent tax-effort variable, namely, the average tax ratio method and the potential tax revenue method. In the potential tax revenue method, a multiple regression analysis is used, where the tax share is defined as the ratio of the actual tax revenue to tax capacity, proxied by GDP. In both cases, the tax effort is defined as the ratio of the actual value of the dependent variable to the estimated (predicted) value (Kim 2007). The average tax ratio and the potential tax revenue methods provide biased estimators for tax effort, Kim (2007) applies the Kalman filter estimation technique to overcome this problem
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