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A revisit on the tax burden distribution and GDP growth: fresh evidence using a consistent nonparametric test for causality for the USA

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Abstract

We revisited the evidence of Karagianni et al. (Int Rev Econ Fin 21:186–194, 2012) and Tiwari (Econ Bull 32:147–159, 2012) by employing a recently developed and more powerful nonlinear Granger-causality test proposed by Nishiyama et al. (J Econ 165:112–127, 2011) to investigate the existence of Granger-causality from a set of alternative tax burden (ratios) to GDP (per capita GDP), for the period 1947:q1–2009:q3 for the United States of America (USA). The nonlinear Granger-causality test provides strong evidence that personal current taxes and taxes on production and imports Granger-cause GDP and weak evidence that CR Granger-cause GDP. As a consequence, in order to influence (rebalance) the USA’s GDP through taxation, it is recommended to the USA government to adjust the tax structure, focusing on PCT and taxes on production and imports’ shocks. In this case, the tax policy is oriented especially on labour supply and investments.

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Notes

  1. One may argue that the transformation in constant prices could act as a filter producing distortions, especially when the underlying mechanism generating data is nonlinear. To test whether our transformed series in the level and in first difference form for both cases is nonlinear, we applied Brock et al. (1996)-proposed test—using \(\varepsilon \) equal to 0.5, 1, 1.5, and two times the standard deviations of the data. We found for all cases evidence for nonlinearity in the data.

  2. It is important to mention that from the OECD online database, we could get data starting from 1950 for population and before that period, it was obtained from https://www.tsl.state.tx.us/ref/abouttx/census.html. Since population data were of annual observations, it was interpolated to get quarterly observation with simple linear interpolation. However, results do not change even if we use other methods like cubic interpolation.

  3. Please refer to Nishiyama et al. (2011) for detailed illustration and computation of this test statistic. Because of space problem, we have avoided the illustration.

  4. A descriptive statistics and graphical plot of the variables (without any transformation but in logarithms form) analysed are presented in Appendix to match the results with Tiwari (2012). For testing of stationarity property of data series, we used ADF and PP test. Results of unit root are not presented, but can be accessed from the authors upon request.

  5. The results are obtained following the original bandwidth choice of Nishiyama et al. (2011). They used \(\text{ C } \times \text{ T }^{-0.3}\) as bandwidth, where T is the length of time series and C is about 7, except for T \(=\) 100 case.

  6. The causality in variance is a widely studied topic in the finance or international finance studies, and mostly applied method is GARCH models and various variants such as GARCH-M, IGARCH, EGARCH, TGARCH, CCC-GARCH, and DCC-GARCH among others. More details about causality in variance, their economic interpretation, and theoretical reasoning can be found in such studies which proposed these models and applied them.

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Acknowledgments

We are thankful to the anonymous referees whose comments has benefited paper considerably. We also thank to Y. Nishiyama, K. Hitomi, Y. Kawasaki, and K. Jeong for making codes available to us without which this study might not have happened.

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Authors and Affiliations

  1. Faculty of Management, ICFAI University Tripura, Kamalghat, Sadar, Pin-799210, West Tripura, India

    Aviral Kumar Tiwari

  2. Faculty of Economics and Business Administration, West University of Timisoara, 16, J.H. Pestalozzi Street, 300115, Timisoara, Romania

    Mihai Mutascu

  3. Laboratoire d’Economie d’Orléans (LEO) UMR7322, Faculté de Droit d’Economie et de Gestion, University of Orléans, Rue de Blois, B.P. 6739, 45067 , Orléans, France

    Mihai Mutascu

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  1. Aviral Kumar Tiwari
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  2. Mihai Mutascu
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Correspondence to Mihai Mutascu.

Appendix

Appendix

See Tables 1, 2, 3, 4 and Figure 2.

Table 1 Descriptive statistics
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Table 2 Results of Granger-causality
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Table 3 Results of bootstrap causality
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Table 4 Results of nonlinear causality
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Tiwari, A.K., Mutascu, M. A revisit on the tax burden distribution and GDP growth: fresh evidence using a consistent nonparametric test for causality for the USA. Empir Econ 46, 961–972 (2014). https://doi.org/10.1007/s00181-013-0706-5

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