Despite its vast potential, the full capabilities of GST data remain largely unrealised. While aggregate data provides a broad overview of tax performance and health of the Indian economy, disaggregated sector-wise information is crucial for evidence-based policymaking.

A closer look at the performance of GST revenue collection raises several questions. First, though the growth of GST revenue collection improved in the last seven years (13 per cent) compared to the pre-GST period 2012-17 (11.8 per cent), there has been hardly an increase in the tax base of GST in the last seven years compared to the pre-GST period. The average of total taxes subsumed under GST from 2012 to 2017 amounted to 6.13 per cent of GDP. However, from 2017 to 2023, the GST-GDP ratio, excluding the GST compensation cess, decreased to 5.65 per cent.

Second, if we consider the GST revenue net of refunds, the GST has under-performed. A study published in the Economic and Political Weekly by Varun Agarwal, Theerdha Sara Reji, Josh Felman, and Arvind Subramanian shows that it converged to the pre-GST average in 2023-24 (barely 6 per cent of GDP). Third, while the GST/indirect collections tax buoyancy marginally improved to 1.18 post-GST compared to 1.11 pre-GST period, the net revenue was not buoyant. Fourth, the 17 major States, which account for over 90 per cent of total GST revenue collection in the country, have shown a lower average GST-GSDP ratio from 2017-18 to 2023-24 compared to the pre-GST regime from 2012-13 to 2016-17.

Disaggregated data

The emerging questions, such as below-par revenue performance, significant differences in revenue performance across States, poor compliance issues related to IGST settlement, delays in filing, and input tax credit, could be addressed using disaggregated data. The GST network publishes monthly data on registrations, return filing process, tax collection, IGST transactions and settlements, and the number of e-way bills generated at the State level every month.

While the available macro data is helpful in understanding how macroeconomic shocks affect GST collection at an aggregate level, it is not adequate to analyse and address the challenges and think of a way forward. The government must harness one of the unique features of GST, which is the introduction of e-way bills and e-invoice systems. They provide data at different levels of aggregation.

Since GST is high-frequency data and a leading indicator of the economy’s performance, understanding growth sources across different sectors and size classes is crucial. Aggregate data reveals overall GST growth but not key contributors. Further, factoring in GST collections net of refunds, primarily given to exporters, can alter the growth picture significantly. Hence, publishing the data that the system generates can immensely help in planning and implementing second-generation GST reforms, including rate restructuring.

The disaggregated sector-wise data can help us gauge economic performance across States. This will help detect prime revenue sources and consumption patterns across the States, along with differences in compliance and filing processes. It would help in understanding revenue buoyancy differences. Further, it is possible to detect any mismatches by comparing sectoral tax revenue growth with the actual sectoral economic growth, which will further help highlight areas needing targeted policy interventions. The differences in revenue buoyancy would help detect potential fraud activities and thus help combat tax evasion.

Studies by NIPFP and GIFT showed the present system of rates have unequal distribution of tax burden across consumer groups with the poorest segment facing more tax burden. The availability of revenue collection under different rates would be a useful tool to analyse the distribution of the tax burden across consumer groups.

The data generated by e-way bills is a goldmine that remains inaccessible for research. It offers valuable insights into goods movement within and across States. Using network analysis and big-data tools this data can uncover business clusters and patterns, helping States design targeted policies to enhance the business environment in specific sectors.

Dash teaches economics at IRMA. Kakarlapudi is an Assistant Professor of Economics at the Gulati Institute of Finance and Taxation, Thiruvanathapuram