goods and service tax
Published on 5 April 2025
GST Collection Estimates and Trends from 2017 to 2024
Overview of GST Collection Estimates from 2017 Onwards
The Ministry of Finance has addressed Lok Sabha Unstarred Question No. 1012, providing insights into the Goods and Services Tax (GST) collected across various slabs (5%, 12%, 18%, and 28%) since 2017. While comprehensive annual figures for each slab are lacking, estimates for the financial year 2023-24 indicate the following distribution of GST collections:
- 5% Slab: Approximately 6-8%
- 12% Slab: Approximately 5-6%
- 18% Slab: Approximately 70-75%
- 28% Slab: Approximately 13-15%
Projected Yearly Estimates for 2017-18
According to initial projections from the 2016 GST Council Meeting, the expected GST revenue for 2017-18 from each slab was estimated as follows (amounts in Rs. Lakh Crores):
- 5% Slab: Rs. 0.22
- 12% Slab: Rs. 1.76
- 18% Slab: Rs. 2.9
- 28% Slab: Rs. 3.34
It should be noted that the projection for the 5% slab was estimated at a 6% collection rate.
Limitations in Data Availability
Despite these estimates, it is important to highlight some limitations in data availability:
- Year-wise Breakdown: Detailed year-on-year collections by product category are not available due to the absence of mandatory four-digit Harmonized System (HS) codes for reporting.
- Service Tax Revenue: Revenue data pertaining to services taxed at the standard 18% GST rate is also not readily accessible.
Responses from the Minister of State for Finance
Regarding the queries raised:
- (a) The Ministry clarified that specific annual GST collections segmented by slab rates cannot be accurately derived from collected data.
- (b) No disaggregated data on top product categories by GST collection is available due to the HS code limitations.
- (c) Revenue data for services taxed at 18% GST is currently not available.
In summary, while the GST collection framework has been outlined, the lack of detailed reporting mechanisms limits the ability to provide a comprehensive analysis of the data across different slabs and categories.