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The Goods and Services Tax (GST), implemented in India from July 2017, was intended to unify a fragmented indirect tax structure and, through mechanisms such as input tax credit, e-way bills, mandatory digital invoicing, and the Udyam registration framework, to draw a large share of Micro, Small and Medium Enterprises (MSMEs) out of the informal economy. This paper combines a systematic review of literature published between 2017 and 2026 with an original secondary-data analysis to examine whether the formalisation and revenue benefits associated with GST have been shared evenly across Indian states or have instead reinforced pre-existing regional economic imbalances. The review synthesises twenty-three studies across three themes: GST-led MSME formalisation, state-wise GST revenue performance, and interstate disparity. This is supplemented with an original cross-sectional dataset of twenty-two major states, compiled from Press Information Bureau GST revenue releases, Ministry of Statistics and Programme Implementation (MoSPI) Gross State Domestic Product (GSDP) statistics, and Udyam registration compilations, on which descriptive statistics, Gini coefficients, Pearson and Spearman correlations, and ordinary least squares (OLS) regression are computed. GST collection is very strongly and significantly correlated with GSDP (Pearson r = 0.90, p < 0.001) and strongly correlated with MSME registration counts (r = 0.84, p < 0.001); GSDP alone explains approximately 80 percent of the cross-state variance in GST collection. The Gini coefficient for GST collection across the sampled states (0.47) exceeds that for GSDP (0.39) and MSME density (0.38), indicating that GST revenue is somewhat more concentrated than the underlying economic base itself. These findings support the view that GST, rather than acting as a levelling reform, is reproducing the regional economic geography that predates it. The paper concludes with policy recommendations for GST administrative capacity building, targeted MSME formalisation support, and revised fiscal-transfer criteria for lagging states, and identifies the panel-data and spatial-econometric extensions that a fuller data infrastructure would allow.
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Manikandan et al. (2026) studied this question.
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