Gross GST collections surge 16.4 per cent to over Rs 2.01 lakh crore in Might 2025

In Might, income from home transactions elevated by 13.7 per cent, totalling roughly Rs 1.50 lakh crore. In the meantime, GST from imports jumped 25.2 per cent to Rs 51,266 crore.
India’s Items and Companies Tax (GST) collections continued their upward development within the month of Might, rising 16.4 per cent year-on-year to surpass Rs 2.01 lakh crore, in accordance with authorities knowledge launched on Sunday (June 1). This follows the record-breaking GST income of Rs 2.37 lakh crore in April, marking back-to-back months of robust tax collections.
In Might, income from home transactions elevated by 13.7 per cent, totalling roughly Rs 1.50 lakh crore. In the meantime, GST from imports jumped 25.2 per cent to Rs 51,266 crore.
The breakdown of Might’s gross GST income contains Rs 35,434 crore from Central GST (CGST), Rs 43,902 crore from State GST (SGST), and Rs 1.09 lakh crore from Built-in GST (IGST). Moreover, the federal government collected Rs 12,879 crore by means of the GST compensation cess.
Refunds issued in Might declined 4 per cent to Rs 27,210 cr
Internet GST revenue- after accounting for refunds- stood at round Rs 1.74 lakh crore, reflecting a sturdy 20.4% progress in comparison with Might 2024, when collections have been at Rs 1.72 lakh crore. Notably, complete refunds issued in Might declined 4% to Rs 27,210 crore.
Nonetheless, the expansion in collections was not uniform throughout all states. Deloitte India Accomplice MS Mani highlighted that whereas main states like Maharashtra, West Bengal, Karnataka, and Tamil Nadu reported robust will increase of 17% to 25%, others, corresponding to Gujarat, Andhra Pradesh, and Telangana, confirmed extra modest progress, as much as 6%. States like Madhya Pradesh, Haryana, Punjab, and Rajasthan recorded common will increase of round 10%.
Mani prompt that these discrepancies could possibly be attributed to sector-specific or seasonal components and emphasised the necessity for an in depth data-driven evaluation to grasp the underlying traits.