Goods and Services Tax efficiency across Indian States: panel stochastic frontier analysis
Sacchidananda Mukherjee ()
Indian Economic Review, 2020, vol. 55, issue 2, No 3, 225-251
Abstract In public finance, estimation of tax potential of a government—either federal or provincial—has immense importance to understand future streams of tax revenue. Tax potential depends on tax capacity and tax effort (TE) and, therefore, joint estimation of both the functions is desirable. There are several frameworks to estimate tax capacity and tax efficiency (tax effort); in the present paper, time-variant-truncated panel Stochastic Frontier Approach (SFA) is adopted to estimate the functions jointly for the period 2012–13 to 2019–20. The findings of the study could be useful for policy and especially for the sitting Fifteen Finance Commission. The results of the study show that GST capacity of states depends on size and structural composition of the economy. Introduction of GST has reduced states’ GST capacity and the impact is restricted to scale only. The study has used data from GST Network (GSTN) database for the post-GST period and given all other factors at their levels, GSTN data show lower GST capacity for high-income states and higher capacity for low-income states. The relationship between per capita income (PCI) of states and tax efficiency is non-linear and as PCI rises, TE falls and thereafter it rises. Minor states (special category states and UTs with legislative assembly) have lower tax efficiency. Delhi and Goa have the highest GST gap and on average major states could increase their GST collection by 0.52 percent of GSVA and minor states by 1.15% if they increase their tax efforts.
Keywords: Tax capacity; Tax efficiency; Goods and services tax (GST); Value added tax (VAT); Stochastic frontier approach; Panel data analysis; States of India (search for similar items in EconPapers)
JEL-codes: H21 H71 H77 (search for similar items in EconPapers)
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Working Paper: Goods and Services Tax Efficiency across Indian States: Panel Stochastic Frontier Analysis (2020)
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