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What changed under GST 2.0?
GST 2.0 took effect on 22 September 2025, according to Mishra and Mukherjee’s opinion article in Mint, published on 5 October 2026. The authors describe the earlier four main consumer-goods rates as consolidated into two, while retaining special rates and applying a higher rate to some goods. Their account is a summary, not a complete schedule for checking the tax treatment of a particular product or transaction.
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| Rate account | What the authors report |
|---|---|
| Earlier main consumer-goods slabs | 5%, 12%, 18% and 28% (Mishra and Mukherjee, Mint, 5 October 2026) |
| Main slabs after the change | 5% and 18% (Mishra and Mukherjee, Mint, 5 October 2026) |
| Special rates retained | 0.25% and 3% (Mishra and Mukherjee, Mint, 5 October 2026) |
| Higher rate for some goods | 40% (Mishra and Mukherjee, Mint, 5 October 2026) |
The authors estimate that the effective GST rate fell from 11.64% to 11.30%; this is their estimate, not an official audited result. They also say about 90% of the 506 goods covered by GST Council recommendations saw rate cuts. Separately, a Press Information Bureau announcement on 4 September 2025 described a simplified two-slab structure and selected sectoral changes. That announcement should not be treated as a current, exhaustive rate list.
Why does a wider tax base matter more to states than a simple rate comparison?
Mishra and Mukherjee frame GST as roughly half of states’ own tax revenue and argue that collection performance affects the room available for capital spending. The Press Information Bureau’s national figures provide context for the scale of the tax system, but do not independently verify that state-level share or establish the effect of any one reform.
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- The Press Information Bureau reported 66.5 lakh GST taxpayers in 2017 and 1.51 crore in 2025.
- It reported gross GST collections of ₹22.08 lakh crore in FY 2024–25.
These national historical figures show the expansion and scale of GST; they do not show that GST 2.0, or a particular state analytics project, caused that growth. The authors’ policy argument is instead that better administration could bring more existing economic activity into view without relying primarily on higher rates.
Why can a state’s GST figures change after IGST settlement?
State GST (SGST) is retained by the state where a transaction takes place. Integrated GST (IGST) applies to inter-state sales, with settlement accounting for the destination state’s share. For example, if a Maharashtra manufacturer sells furniture to a retailer in Karnataka, Karnataka receives its share because the goods are consumed there. This means collections recorded before settlement can tell a different story from state revenue after settlement.
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| State | Reported SGST and IGST collections before settlement | Reported after settlement | Context |
|---|---|---|---|
| Haryana | About 7.7% of state GDP (Mishra and Mukherjee, Mint, 5 October 2026) | Around 3.4% of state GDP (Mishra and Mukherjee, Mint, 5 October 2026) | The authors say industrial and services-base differences account for much of the pre-settlement gap. |
| Bihar | About 1.3% of state GDP (Mishra and Mukherjee, Mint, 5 October 2026) | Around 2.9% of state GDP (Mishra and Mukherjee, Mint, 5 October 2026) | The authors use the change to illustrate the destination-state share from IGST settlement. |
These are figures reported in the authors’ article, not a basis for ranking states’ tax administration. Production and services are geographically uneven, and the settlement mechanism changes how inter-state activity is reflected in a state’s revenue.
How can states use technology to improve GST administration?
The authors propose three administrative levers. They are recommendations and examples in an opinion article, not independently established proof that the same results will follow in every state.
1. Measure the tax base with administrative data
States can use digital GST records to compare registered activity with potential collections and identify sectors or locations where activity may be informal or under-registered. The point is to find gaps in coverage before assuming that a rate increase is the best route to more revenue.
2. Reduce compliance friction and focus scrutiny on risk
Simpler filing, reconciliation and dispute resolution, faster refunds, and clearer rules can make compliance less costly. Enforcement can then focus on higher-risk cases rather than applying broad scrutiny indiscriminately.
For an example of risk targeting, the authors describe Andhra Pradesh using artificial intelligence and machine learning with a 35-parameter risk matrix to select cases for scrutiny. They do not establish that this approach is a universally validated model or report a comparable outcome for every state.
3. Integrate records to improve taxpayer risk profiling
The authors describe Maharashtra as using a GST Network data warehouse for taxpayer risk profiling. They also describe Karnataka integrating registrations, returns and e-way bills in an analytics portal with IIT Hyderabad.
For earlier Karnataka analytics work, the authors report a 15-fold rise in detection of bogus entities, about ₹278 crore in fraudulent input tax credit claims blocked, and about ₹4,250 crore in fake turnover flagged. These operational measures are reported by the authors; the article does not independently verify them or isolate how much of each result was caused by the analytics system.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Can GST information help collect other state taxes?
The authors identify excise on alcohol, stamp duty and registration fees, vehicle taxes, electricity duties, and land revenue as sources that together account for roughly 25–35% of states’ own tax revenue. They argue that GST taxpayer and transaction records could help identify people or businesses missing from those other tax systems, particularly where identification is weak.
That is a proposed use of data, not a revenue forecast: the authors do not estimate how much additional tax cross-referencing would recover. A practical implementation would need to reconcile records across systems and direct follow-up toward verifiable mismatches, rather than treating a data match alone as proof of non-compliance.
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Mishra and Mukherjee argue that the end of GST compensation changes state incentives because additional revenue from improved administration would accrue to state finances. The article does not establish the precise legal timeline or transition mechanics, so this point is best understood as the authors’ fiscal argument rather than a detailed account of compensation law.
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