Purpose

This paper aims to illustrate the usefulness of the proposed procedure to evaluate the Goods and Services Tax (GST) in India by applying it to provide evidence on optimal commodity tax rates.

Design/methodology/approach

In the optimal commodity tax literature, the commonly used Ramsey–Samuelson–Diamond–Mirrlees framework assumes invariance of budget allocation between pre- and posttax regimes and uses only the first-order conditions in the two-stage optimization procedure. This paper proposes an iterative method that overcomes the above limitations in obtaining the optimal tax rates.

Findings

It is found that the optimal commodity taxes are highly sensitive to the procedure used to estimate them. The resulting tax rates turn out to be close to the GST tax slabs. The results also show that on both absolute and relative measures, for all the selected states considered here and for All-India, the optimal tax systems are progressive for two chosen values of the inequality aversion parameter.

Originality/value

There is a very limited literature on the computational methodology to calculate optimal commodity taxes, and consequently, the evidence is quite scarce as well. In combining contributions to the computation of optimal taxes with empirical evidence, this paper fills a significant gap in the literature. The context of India gives it added value since the GST has recently been introduced in India, and to the best of the authors’ knowledge, this is one of the first attempts at evaluating the Indian GST through the spectacles of optimal taxes.

中文翻译：

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最优商品税收——一种适用于印度的新计算程序

目的

本文旨在说明拟议程序在评估印度商品和服务税 (GST) 方面的有用性，并将其应用于提供最佳商品税率的证据。

设计/方法/方法

在最优商品税文献中，常用的 Ramsey-Samuelson-Diamond-Mirrlees 框架假设税前和税后制度之间的预算分配不变，并且在两阶段优化过程中仅使用一阶条件。本文提出了一种迭代方法，克服了上述在获得最优税率方面的限制。

发现

研究发现，最优商品税对用于估算它们的程序高度敏感。由此产生的税率结果接近 GST 税率。结果还表明，在绝对和相对测量上，对于此处考虑的所有选定州和全印度，最优税收制度对于不平等厌恶参数的两个选定值是累进的。

原创性/价值

关于计算最优商品税的计算方法的文献非常有限，因此，证据也非常稀缺。本文将最优税收计算的贡献与经验证据相结合，填补了文献中的一个重大空白。自从 GST 最近在印度引入以来，印度的背景赋予了它附加值，据作者所知，这是通过最优税收视角评估印度 GST 的首次尝试之一。