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1Clear out junk files and repair common Windows errors2Fix the driver behind crashes, sound loss and screen glitches3Repair Windows errors before they cause bigger problemsStates may be able to strengthen their finances by bringing more taxable activity into view and improving compliance, rather than relying mainly on higher GST rates. That is the policy case made by Prachi Mishra and Shohan Mukherjee in a Mint opinion article published on 5 October 2026. Their proposals focus on better measurement, simpler compliance and more targeted enforcement, and using GST data to identify taxpayers who may be missing from other state tax systems.
What GST 2.0 changed—and what the rate figures mean
Mishra and Mukherjee describe GST 2.0 as taking effect on 22 September 2025. In their account, the four main consumer-goods slabs—5%, 12%, 18% and 28%—were consolidated into 5% and 18%, while special rates of 0.25% and 3% remained and a 40% rate applied to some goods. These are the authors’ summary of the changes, not a comprehensive rate schedule for deciding how a particular product or transaction is taxed.
The Press Information Bureau’s 4 September 2025 announcement described a simplified two-slab structure and selected sectoral changes. It is a dated announcement, not an exhaustive current list of GST rates.
The authors estimate that the effective GST rate fell from 11.64% to 11.30%, a reduction of 0.34 percentage points. They also say that about 90% of the 506 goods covered by GST Council recommendations received rate cuts. These are estimates and claims in an opinion article, not an official audited assessment of the reforms’ results.
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Why state GST collections can look different after settlement
GST receipts are not a simple measure of how much economic activity a state hosts. State GST (SGST) is retained by the state where a transaction takes place. Integrated GST (IGST) applies to inter-state sales and is settled so that the destination state receives its share. For example, when a Maharashtra manufacturer sells furniture to a Karnataka retailer, Karnataka receives a share because the goods are consumed there.
Mishra and Mukherjee use state GDP comparisons to illustrate how settlement changes the picture. Their reported figures are specific to the article’s comparison and should not be treated as a general ranking of state tax performance.
| State | Before settlement | After settlement |
|---|---|---|
| Haryana | About 7.7% | Around 3.4% |
| Bihar | About 1.3% | Around 2.9% |
The authors attribute much of the gap in pre-settlement collections to differences in industrial and services bases. The contrasting post-settlement figures show why production location alone does not determine which state ultimately receives the tax share: destination matters too.
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Why the authors focus on administration, not just rates
The authors frame GST as contributing roughly half of states’ own tax revenue and argue that collection performance affects the room available for capital spending. Those are their fiscal framing and interpretation. For national context, the Press Information Bureau reported 66.5 lakh GST taxpayers in 2017, 1.51 crore in 2025 and gross GST collections of ₹22.08 lakh crore in FY 2024–25. These national historical figures do not establish that GST 2.0 or any single state initiative caused the increase.
Their central distinction is between increasing rates and widening the reach of the tax system. If taxable activity is unregistered, under-reported or not identified for the right tax, higher rates alone do not address the coverage problem. The proposed state agenda has three connected parts.
1. Measure the potential tax base
States can use existing digital GST records to compare registered activity with indicators of economic activity and potential collections. Better measurement can help identify where registration or reporting may be incomplete, including activity that appears informal or under-registered. The authors present this as an administrative opportunity, not as a quantified estimate of recoverable revenue.
2. Make compliance easier and scrutiny more focused
The authors recommend reducing friction in filing, reconciliation and dispute resolution, speeding refunds and clarifying rules. They pair simpler compliance with risk-based checks: rather than scrutinising taxpayers uniformly, direct attention towards cases whose data indicate higher risk.
Andhra Pradesh is described in the article as using artificial intelligence and machine learning with a 35-parameter risk matrix to select cases for scrutiny. This is an example of an approach reported by the authors, not independent evidence that the method improves collections or that the same model would work equally well elsewhere.
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3. Use GST information to find gaps in other state taxes
The article identifies excise on alcohol, stamp duty and registration fees, vehicle taxes, electricity duties and land revenue as revenue streams that together account for roughly 25–35% of states’ own tax revenue. The authors argue that GST taxpayer and transaction information could help identify people or businesses who may also need to be identified in these systems, where taxpayer records are weak.
They do not estimate how much additional revenue such cross-referencing could raise. GST data may help point to a potential compliance gap; it does not, by itself, establish that tax is due under another tax or determine the amount owed.
What state analytics examples show—and do not show
Mishra and Mukherjee describe different administrative uses of data rather than a state-by-state performance league table. Maharashtra is described as using a GST Network data warehouse for taxpayer risk profiling. Karnataka is described as 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. Those operational results are reported in the opinion article; the figures alone do not establish the methods used to calculate them or prove that analytics caused each outcome.
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The examples suggest distinct functions: combining records to see activity more completely, profiling taxpayers for risk, and selecting cases for scrutiny. Their usefulness depends on the quality of the data and on whether checks distinguish genuine errors or risk indicators from a proven tax liability.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Why state finances matter to residents
State tax receipts help determine the resources available for public spending, including capital investment. The authors also argue that the end of GST compensation changed states’ incentives because additional revenue from better administration would accrue to state finances. That is their account of the incentive effect; the article’s discussion does not establish the precise legal timeline or transition mechanics, so it should not be read as a detailed explanation of compensation rules.
For residents, the relevant question is not only whether collections rise, but how they rise. A broader base and easier compliance could, in principle, improve receipts without depending primarily on rate increases. Whether that happens—and whether the additional fiscal room translates into better services or investment—depends on implementation and subsequent budget choices, not on data integration alone.
Quick Recap
How to read the reform’s prospects
- Rate changes are only one part of the story. The authors’ effective-rate estimate and account of goods receiving cuts describe rate effects; they do not measure the success of state administration reforms.
- Settlement changes comparisons. A state’s pre-settlement collections reflect where transactions are recorded, while IGST settlement also directs a share to the destination state.
- Analytics identifies leads, not automatic assessments. Risk flags and mismatches can guide scrutiny, but they are not, on their own, proof of evasion or a final tax liability.
- Cross-tax data use remains a proposal. The authors argue it could improve taxpayer identification in other state tax systems, but do not quantify recoverable revenue.
- National growth figures are context, not a causal test. The PIB’s taxpayer and collection totals document GST’s scale over time, not the effect of any one reform or state initiative.
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