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Subject: Economics

  • DAE’s nuclear pivot: Light water push to tap global markets, retain heavy water edge

    Why in the News

    India’s nuclear establishment is prioritising Light Water Reactors (LWRs) for global market integration while retaining Pressurised Heavy Water Reactors (PHWRs) as a domestic and strategic strength. This marks a significant departure from India’s earlier inward-looking PHWR-centric approach. The shift is urgent as Light Water Reactors account for over 85% of global nuclear capacity. India seeks export competitiveness as emerging economies like the UAE, Bangladesh, Saudi Arabia, and Turkey expand nuclear power.

    What is a Light Water Reactor?

    1. A Light Water Reactor (LWR) is a nuclear reactor that uses ordinary water (H₂O) as both the coolant and neutron moderator.
    2. Fuel type: Operates on low-enriched uranium fuel.

    What is a Pressurised Heavy Water Reactor?

    1. Definition: A Pressurised Heavy Water Reactor (PHWR) is a nuclear reactor that uses heavy water (D₂O) as both the coolant and neutron moderator.
    2. Fuel type: Operates on natural uranium, avoiding the need for enrichment.

    Why do Light Water Reactors dominate the global nuclear market?

    1. Global share: LWRs constitute over 85% of installed civil nuclear reactor capacity worldwide.
    2. Design simplicity: Uses normal water as coolant and moderator, reducing engineering complexity.
    3. Cost structure: Lower construction costs due to economies of scale and standardised designs.
    4. Operational efficiency: Higher thermal efficiency compared to heavy water reactors.
    5. Fuel ecosystem: Reliance on enriched uranium, readily accessible in Western markets.

    Why has India relied on PHWRs despite global LWR dominance?

    1. Fuel flexibility: PHWRs operate on natural uranium, reducing enrichment dependence.
    2. Resource alignment: Matches India’s limited uranium and abundant thorium reserves.
    3. Indigenous capability: Strong domestic expertise in design, manufacturing, and operation.
    4. Strategic autonomy: Minimises external fuel supply vulnerabilities.
    5. Limitation: Reduced export competitiveness in markets structured around LWR ecosystems.

    Why is the current LWR push a strategic departure for India?

    1. Export integration: Enables Indian firms to enter the global nuclear supply chain.
    2. Private participation: Supported by legal reforms opening nuclear power to non-state actors.
    3. Project acceleration: DAE fast-tracking the 900 MWe indigenous LWR design, initiated in 2015.
    4. Negotiating leverage: Enhances bargaining capacity with foreign vendors on imports and technology.
    5. Market realism: Aligns reactor strategy with international demand patterns.

    What challenges have exposed the limits of LWR-led imports?

    1. Cost escalation: Higher capital costs translate into elevated electricity tariffs.
    2. Domestic displacement: Risk of sidelining indigenous PHWR manufacturing capacity.
    3. Case study: Jaitapur: Project delays due to tariff concerns, liability issues, and Areva’s financial instability.
    4. Market absorption: Indian electricity markets struggle to absorb high-cost nuclear power.

    How are PHWRs repositioned in India’s nuclear future?

    1. Fuel innovation: PHWRs using thorium and low-enriched uranium lower scale-up constraints.
    2. Export differentiation: Positions PHWRs as a niche solution for resource-constrained economies.
    3. Manufacturing depth: Builds on India’s experience from 220 MWe to 700 MWe PHWR units.
    4. Growth alignment: Supports nuclear expansion without overreliance on imported enrichment services.

    What role do SMRs play in India’s nuclear ambitions?

    1. Capacity range: 30-300 MWe Small Modular Reactors (SMRs).
    2. Cost efficiency: Enables modular, scalable deployment.
    3. Export potential: Enhances attractiveness for emerging economies.
    4. Strategic competition: China pursuing SMR leadership as a Global South diplomatic tool, similar to EV sector disruption.

    Conclusion

    India’s nuclear pivot reflects strategic pragmatism rather than abandonment of legacy strengths. The dual-track approach, global LWR integration combined with PHWR-based differentiation, balances export ambition, energy security, and industrial capability. Success depends on managing costs, protecting indigenous capacity, and converting legislative reform into manufacturing scale.

    PYQ Relevance

    [UPSC 2018] With growing energy needs should India keep on expanding its nuclear energy programme? Discuss the facts and fears associated with nuclear energy?

    Linkage: The question links directly to India’s energy security and clean baseload power needs, where nuclear energy complements renewables. The current debate on LWR expansion, PHWR indigenisation, high costs, and safety concerns reflects the balance between the facts and fears of nuclear power.

  • Chips to Start up (C2S) Programme

    Why in the News?

    The Chips to Start up (C2S) Programme has reported strong outcomes, including 56 student designed chips fabricated, 75 plus patents filed, and large scale national training in chip design, reflecting India’s growing indigenous semiconductor design capability.

    About Chips to Start up (C2S) Programme

    • The Chips to Start up (C2S) Programme is a national capacity building and innovation initiative to develop industry ready chip design talent and strengthen India’s indigenous semiconductor ecosystem through hands on training, research and fabrication exposure.
    • Launched in: 2022
    • Implemented by: Ministry of Electronics and Information Technology

    Aim

    • Create a strong pipeline of skilled chip designers
    • Enable hands on chip fabrication for students
    • Promote start ups, patents and IP creation
    • Support technological self reliance in semiconductors

    Key Features

    • Financial outlay: ₹250 crore for 5 years
    • Infrastructure and tools: Access to shared EDA tools, High Performance Computing (HPC), FPGA boards, and SMART laboratories across institutions
    • Hands on fabrication: Shared wafer runs provided through Semi Conductor Laboratory.
    • Chip design enablement: National ChipIN Centre and Operated by Centre for Development of Advanced Computing, Bengaluru
    • Innovation outcomes: Student designed ASICs and SoCs, Start up incubation, Patents, IP cores and chip prototypes
    • Industry collaboration: Training partnerships with global EDA and semiconductor firms

    Significance

    • Addresses the global semiconductor skill gap
    • Democratises chip design by providing nationwide access to advanced tools and fabrication
    • Reduces dependence on foreign design ecosystems
    • Strengthens Atmanirbhar Bharat in a strategic and security sensitive sector
    • Complements India’s broader semiconductor manufacturing and design policy

    Prelims Takeaways

    • C2S Programme launched in 2022
    • Implemented by MeitY
    • Focus on chip design plus fabrication exposure
    • Uses SCL Mohali for wafer runs
    • ChipIN Centre operated by C DAC Bengaluru
    • Key pillar of India’s indigenous semiconductor capability building
    [2025] Consider the following statements: 

    1. It is expected that Majorana 1 chip will enable quantum computing

    2. Majorana 1 chip has been introduced by Amazon Web Services (AWS)

    3. Deep learning is a subset of machine learning

    Which of the statements given above are correct? 

    (a) I and only I (b) II and III only (c) I and III only (d) I, II and III

  • Nearly 44,000 startups registered in 2025, highest since the launch of Startup India

    Why in the News

    India registered nearly 44,000 startups in 2025, the highest annual addition since the launch of Startup India in 2016, marking a decisive acceleration in entrepreneurial activity. The Prime Minister announced that India now hosts over 2 lakh startups and nearly 125 unicorns, reflecting a structural shift from a risk-averse economy to one driven by innovation, capital formation, and job creation. This scale-up positions India as the third-largest startup ecosystem globally, indicating a transformation in growth drivers over the past decade.

    How has Startup India altered the scale of entrepreneurship in India?

    1. Startup Proliferation: Expanded from fewer than 500 startups a decade ago to over 200,000 registered startups, indicating ecosystem maturity.
    2. Annual Acceleration: Addition of 44,000 startups in 2025 alone, the largest single-year increase since inception.
    3. Global Standing: Establishes India as the third-largest startup ecosystem, enhancing economic visibility and investor confidence.

    What does the rise in unicorns indicate about ecosystem depth?

    1. Unicorn Expansion: Growth from four unicorns in 2014 to nearly 125 active unicorns, reflecting scale viability.
    2. Capital Maturity: Transition of unicorns towards initial public offerings (IPOs) signals capital market integration.
    3. Employment Generation: Scaling startups contribute to job creation beyond traditional sectors, supporting inclusive growth.

    How has societal perception of risk-taking changed?

    1. Cultural Shift: Risk-taking normalised and respected, replacing preference for fixed-salary employment.
    2. Entrepreneurial Aspiration: Acceptance of ideas previously considered fringe, strengthening innovation culture.
    3. Labour Market Impact: Encourages self-employment and venture creation as mainstream career choices.

    What role has state-backed risk capital played?

    1. Fund of Funds (FoF): Over ₹25,000 crore invested through government-backed FoF mechanisms.
    2. Capital Crowding-In: Public capital reduces early-stage risk, enabling private investment participation.
    3. Policy Signalling: Demonstrates long-term state commitment to entrepreneurship.

    Why is deep tech now a strategic priority?

    1. FoF 2.0 Corpus: ₹10,000 crore approved in April 2025, with targeted deployment.
    2. Sectoral Focus: Artificial Intelligence, Machine Learning, Quantum Technologies, Defence, Aerospace.
    3. Gestation Support: Addresses long proof-of-concept cycles and capital intensity in frontier technologies.
    4. Strategic Autonomy: Aligns startup policy with national security and technological self-reliance goals.

    Conclusion:

    A decade of Startup India demonstrates a decisive shift in India’s growth strategy from capital-scarce, risk-averse entrepreneurship to a scale-oriented, innovation-driven ecosystem. The record surge in startups, expansion of unicorns, and targeted deep-tech financing indicate that startups are increasingly complementing MSMEs and manufacturing, strengthening employment creation, capital formation, and India’s long-term economic resilience.

    Value Addition

    Startup India Mission

    1. Launch Year: 2016
    2. Nodal Ministry: Ministry of Commerce and Industry (DPIIT)
    3. Core Objective: Enables innovation-led entrepreneurship through regulatory easing, funding access, and ecosystem support.
    4. Policy Significance: Shifts India’s growth model from job-seeking to job-creating; strengthens formalisation and innovation capacity.

    PYQ Relevance

    [UPSC 2023] Faster economic growth requires increased share of the manufacturing sector in GDP, particularly of MSMEs. Comment on the present policies of the Government in this regard. 

    Linkage: This question directly links to GS III (Economic Growth, Industrial Policy, MSMEs) by examining manufacturing-led growth as a driver of jobs and productivity. Government initiatives like Startup India, PLI schemes, and Fund of Funds strengthen MSME manufacturing, capital access, and scale-up, addressing this requirement.

  • NITI Aayog Report on MSME Scheme Convergence 

    Why in the News?

    In January 2026, NITI Aayog released a report proposing convergence of MSME schemes to reduce duplication, improve efficiency and strengthen last mile delivery.

    About the Report

    • Title: Achieving Efficiencies in MSME Sector through Convergence of Schemes
    • Prepared by Administrative Staff College of India
    • Analyses 18 centrally administered MSME schemes
    • Recommends information convergence and process convergence
    • Focus on better coordination, outcomes and resource utilisation

    Key Facts about MSME Sector

    • GDP contribution about 29 to 30 percent
    • Employment over 28.7 crore, second only to agriculture
    • Share in exports about 45 to 46 percent
    • Total MSMEs more than 6.3 crore
    • Around 51 percent located in rural areas
    • Government MSME budget increased sharply from 2019–20 to 2023–24, raising efficiency concerns

    Why Convergence is Needed

    • Multiple schemes with overlapping objectives
    • Fragmented implementation across ministries
    • High compliance burden for MSMEs
    • Duplication of resources and limited outreach
    • Weak translation of spending into outcomes

    Framework for Convergence

    1. Information Convergence
    • Integration of central and state government data
    • Enables evidence based policymaking
    • Improves coordination and governance
    1. Process Convergence
    • Alignment and rationalisation of schemes
    • Merging similar components
    • Collaboration across ministries and states
    • Creation of a unified MSME support ecosystem
    [2023] With reference to India, consider the following statements: 

    1. According to the ‘Micro, Small and Medium Enterprises Development (MSMED) Act, 2006’, the ‘medium enterprises’ are those with investments in plant and machinery between Rs. 15 crore and Rs. 25 crore

    2. All bank loans to the Micro, Small and Medium Enterprises qualify under the priority sector. 

    Which of the statements given above is/are correct? 

    (a) 1 only (b) 2 only (c) Both 1 and 2 (d) Neither 1 nor 2

  • Export Preparedness Index (EPI) 2024

    Why in the News?

    NITI Aayog released the Export Preparedness Index (EPI) 2024, assessing export readiness of Indian States and Union Territories. This is the 4th edition of the Index, first launched in August 2020.

    The Index aligns with India’s targets of USD 1 trillion merchandise exports by 2030

    About Export Preparedness Index

    • Evidence based framework to assess strength, resilience and inclusiveness of subnational export ecosystems
    • Recognises the critical role of States and districts in India’s global trade performance
    • Identifies
      • Structural challenges
      • Growth levers
      • Policy opportunities
    • Focus on districts as core units of export competitiveness

    Top Performing States and Union Territories

    A. Large States

    1. Maharashtra
    2. Tamil Nadu
    3. Gujarat
    4. Uttar Pradesh
    5. Andhra Pradesh

    B. Small States, North Eastern States & Union Territories

    1. Uttarakhand
    2. Jammu and Kashmir
    3. Nagaland
    4. Dadra and Nagar Haveli & Daman and Diu
    5. Goa
    [2020] With reference to the international trade of India at present, which of the following statements is/are correct? 

    1. India’s merchandise exports are less than its merchandise imports

    2. India’s imports of iron and steel, chemicals, fertilisers and machinery have decreased in recent years

    3. India’s exports of services are more than its imports of services

    4. India suffers from an overall trade/current account deficit

    Select the correct answer using the code given below: 

    (a) 1 and 2 only (b) 2 and 4 only (c) 3 only (d) 1, 3 and 4 only

  • PFRDA forms high-level committee for assured payouts under NPS

    Why in the news?

    The Pension Fund Regulatory and Development Authority (PFRDA) has constituted a high-level committee to frame guidelines and regulations for assured pension payouts under the National Pension System (NPS), aimed at strengthening retirement income security.

    About the committee

    • Chairperson: Dr. M. S. Sahoo, Former Chairperson, Insolvency and Bankruptcy Board of India (IBBI)
    • Composition: 15 members from legal, actuarial, finance, insurance, capital markets and academia
    • Flexibility: Power to invite external experts and intermediaries as special invitees
    • Nature: Standing advisory committee on structured pension payouts

    Key objectives and terms of reference

    • Assured payout framework: Draft regulations for assured and structured pension payouts, based on PFRDA consultation paper dated 30 September 2025
    • Seamless transition: Smooth shift from accumulation phase to decumulation payout phase
    • Market based assurance: Explore novation and settlement mechanisms for legally enforceable guarantees
    • Operational design: Define lock in period, withdrawal limits, pricing mechanisms and fee structures
    • Risk and legal oversight: Specify capital and solvency norms and examine tax implications for in-system payouts
    • Consumer protection: Standardised disclosure framework to prevent mis selling and manage subscriber expectations

    Significance

    • Enhances predictability and security of retirement income
    • Strengthens trust and attractiveness of NPS
    • Supports the vision of Viksit Bharat 2047 with financial dignity in old age
    [2017] Who among the following can join the National Pension System (NPS)? 

    (a) Resident Indian citizens only 

    (b) Persons of age from 21 to 55 only 

    (c) All State Government employees joining the services after the date of notification by the respective State Governments 

    (d) All Central Governments Employees including those of Armed Forces joining the services on or after 1st April, 2004

  • India must focus on AI and its environmental impact

    Why in the News?

    Artificial Intelligence is expanding rapidly across sectors. However, its environmental costs remain largely ignored in policy discussions. The global ICT sector contributes 1.8-2.8% of global greenhouse gas emissions, with estimates rising to 2.1-3.9%. For the first time, clear data is available on the energy, water, and carbon footprint of AI systems, including Large Language Models (LLMs).

    A clear gap exists between perceived digital efficiency and actual environmental impact. A single ChatGPT query consumes 10 times more energy than a Google search. Training one LLM can emit up to 3,00,000 kg of carbon dioxide. Despite these costs, India has no formal system to measure or disclose AI’s environmental impact. This contrasts with the EU and the US, highlighting a major governance gap.

    What is the scale of AI’s environmental footprint?

    1. Global ICT emissions: Accounts for 1.8-2.8% of global GHG emissions, with upper estimates reaching 3.9%.
    2. Carbon-intensive training: Training a single LLM can emit ~3,00,000 kg of carbon dioxide.
    3. Comparative impact: Emissions from one deep learning model equal emissions from five cars over their lifetime.
    4. Data gap: Carbon footprint data of AI models and users remains fragmented and inconsistent.

    How does AI affect energy consumption patterns?

    1. High energy intensity: Each ChatGPT query consumes 10× more energy than a Google search.
    2. Hidden electricity demand: AI workloads rely on energy-intensive data centres and specialised hardware.
    3. Misleading averages: Claims such as 0.24 watt-hours per AI query underestimate system-wide consumption.

    Why is water consumption emerging as a major concern?

    1. UNEP projection: AI data centres may consume 4.2-6.6 billion cubic metres of water by 2027.
    2. Cooling requirements: Water is extensively used to cool AI servers.
    3. Water security risks: High freshwater withdrawal threatens water-stressed regions.

    What global governance responses are emerging?

    1. UNESCO framework (2021): Recognises negative environmental impacts of AI; adopted by ~190 countries.
    2. European Union leadership:
      1. AI Act, 2024: Introduces environmental accountability in AI governance.
      2. Harmonised AI rules: Address sustainability alongside ethics and safety.
    3. United States approach: Sector-specific regulations addressing AI’s environmental externalities.

    Why does India need a regulatory shift?

    1. Unaccounted externalities: Environmental costs of AI development remain outside policy evaluation.
    2. Regulatory vacuum: No mandatory assessment of AI’s environmental impact.
    3. Climate obligations: AI expansion risks undermining India’s climate mitigation commitments.
    4. Policy imbalance: Focus on innovation without parallel sustainability safeguards.

    How can Environmental Impact Assessment be extended to AI?

    1. EIA framework: India’s EIA Notification, 2006 mandates environmental assessment for infrastructure projects.
    2. Proposed extension: Inclusion of AI development and deployment within EIA scope.
    3. Lifecycle evaluation: Assessment of energy use, water consumption, and emissions across AI lifespans.

    What role can disclosure standards play?

    1. ESG integration: Environmental impact of AI included under ESG disclosure norms.
    2. SEBI alignment: Disclosure of emissions from data centres and computing activities.
    3. EU precedent: Corporate Sustainability Reporting Directive (CSRD) mandates emission disclosure, including AI training.
    4. Transparency outcome: Enables informed policymaking and accountability.

    Which sustainable practices can mitigate AI’s impact?

    1. Pre-trained models: Reduces repeated energy-intensive training.
    2. Renewable energy: Powering data centres through clean energy sources.
    3. Efficiency reporting: Disclosure of AI-specific environmental metrics.
    4. Resource optimisation: Minimising water and energy intensity of AI infrastructure.

    Conclusion

    India’s AI ambitions must align with environmental sustainability. Institutionalising environmental assessment, disclosure norms, and sustainable practices is essential to prevent AI-driven ecological externalities. A regulatory framework that integrates innovation with environmental accountability will ensure AI remains a tool for inclusive and sustainable development.

    PYQ Relevance

    [UPSC 2023] How can Artificial Intelligence help clinical diagnosis? Do you perceive any threat to privacy of the individual in the use of AI in healthcare?

    Linkage: Earlier, UPSC focused on how AI helps healthcare and affects patient privacy. Now, as AI use expands, questions are likely to include its environmental impact, especially energy- and data-intensive AI systems.

  • If data is the new oil, what does that make data centres?

    Why in the News?

    India is increasingly seen as a likely destination for global “data dumping” as large data centres expand due to AI growth, government incentives, and geopolitical changes. This is a serious issue because data centres place heavy pressure on electricity, water, land, and environmental regulation, especially in water-stressed cities. Unlike earlier views that treated digital infrastructure as low-impact, data centres are now emerging as resource-intensive industrial units, raising concerns about sustainability, weak regulation, and long-term environmental costs.

    What are Data centers?

    1. Physical Digital Infrastructure: Large facilities that store, process, and manage digital data using servers, storage systems, and networking equipment.
    2. Backbone of the Digital Economy: Support cloud computing, e-governance, AI, fintech, e-commerce, and social media services.

    Why is India vulnerable to becoming a “data dumping” destination?

    1. Geopolitical Stability: Provides predictability compared to other global regions, increasing investor preference.
    2. Fiscal Incentives: Offers subsidised land, power, and expedited clearances for data infrastructure.
    3. Domestic Market Scale: Ensures long-term demand for data storage and processing.
    4. AI-Driven Demand: Accelerates need for hyperscale facilities with high energy density.

    Why are data centres no longer “clean” digital infrastructure?

    1. Electricity Intensity: Requires massive grid capacity, substations, and uninterrupted power supply.
    2. Water Dependence: Uses large volumes for cooling, especially where air cooling is not feasible.
    3. Thermal Pollution: Releases waste heat, intensifying urban heat stress.
    4. Industrial Footprint: Mirrors heavy industry in land use, emissions, and infrastructure strain.

    What environmental risks?

    1. Water Stress: Many Indian cities already face chronic water shortages.
    2. Grid Overload: Clustered data centres require grid upgrades and load balancing.
    3. Externalised Costs: Environmental and infrastructure costs often borne by the public sector.
    4. Weak Enforcement: Post-clearance monitoring and compliance remain inadequate.

    What are the governance and regulatory gaps?

    1. Institutional Lacunae: Noted by the Comptroller and Auditor General, Supreme Court, and National Green Tribunal.
    2. Zoning Weaknesses: Data centres not uniformly classified as heavy infrastructure.
    3. Opacity: Non-disclosure agreements restrict public scrutiny.
    4. Fragmented Oversight: Multiple agencies without integrated regulation.

    What lessons emerge from international and domestic resistance?

    1. United States Experience: Community resistance in Virginia, North Carolina, and Minnesota due to water and energy stress.
    2. Transparency Failures: Projects stalled due to non-disclosure and lack of public consultation.
    3. Course Correction: Developers increasingly engaging communities early to reduce backlash.
    4. Indian Parallel: Similar conditions exist but with weaker civic engagement and regulatory checks.

    Risks of unchecked expansion

    1. Capital Intensity: Limits government bargaining power once investments are sunk.
    2. Subsidy Distortions: Shifts public resources toward private digital infrastructure.
    3. Environmental Injustice: Local communities bear costs without proportional benefits.
    4. Governance Risk: Early-stage policy failures become irreversible later.

    Conclusion

    Data centres must be treated as heavy infrastructure, not neutral digital assets. Without enforceable zoning, water-use ceilings, transparent disclosures, and robust environmental oversight, India risks replicating extractive development models under the guise of digital growth. Sustainable digitalisation requires aligning data infrastructure with ecological limits and democratic accountability.

    PYQ Relevance

    [UPSC 2015] Discuss the advantages and security implications of cloud hosting of servers vis-a-vis in-house machine-based hosting for government businesses.

    Linkage: This question examines the trade-offs between efficiency-driven digital governance and strategic data control. It also connects with current debates on data centres, cloud infrastructure, and data sovereignty, where reliance on cloud hosting raises concerns of security, resilience, and regulatory oversight for government systems.

  • District Led Textiles Transformation (DLTT) Plan

    Why in the News?

    The Ministry of Textiles has launched the District Led Textiles Transformation (DLTT) Plan to convert 100 high potential districts into Global Export Champions and upgrade 100 Aspirational Districts into self reliant textile hubs.

    What is the DLTT Plan

    • A sector specific, district level transformation strategy for textiles
    • Uses data driven categorisation to tailor interventions
    • Covers districts at different stages, from advanced export clusters to foundation stage districts

    Objectives

    • Drive inclusive, sustainable, and export oriented growth in textiles
    • Decentralise policy execution to districts
    • Strengthen MSMEs and formalise the workforce
    • Build globally competitive textile clusters

    Significance

    • Moves India up the textile value chain
    • Diversifies export baskets
    • Strengthens MSMEs and formalises labour
    • Boosts women led and SHG led enterprises
    • Accelerates development in aspirational, eastern, and north eastern districts

    Prelims Pointers

    • DLTT follows a district first approach
    • Uses data driven classification
    • Integrates skilling, infrastructure, and exports
    • Strong focus on inclusive and regional development
    [2022] Which of the following activities constitute the real sector in the economy? 

    1. Farmers harvesting their crops 2. Textile mills converting raw cotton into fabrics 

    2. A commercial bank lending money to a trading company 

    3. A corporate body issuing Rupee Denominated Bonds overseas 

    Select the correct answer using the code given below: 

    (a) 1 and 2 only (b) 2, 3 and 4 only (c) 1, 3 and 4 only (d) 1, 2, 3 and 4

  • [9th January 2026] The Hindu OpED: GSDP share as criterion for central-State transfers

    PYQ Relevance

    [UPSC 2020] Explain the rationale behind the Goods and Services Tax (Compensation to States) Act, 2017. How has COVID-19 impacted the GST compensation fund and created new federal tensions?

    Linkage: COVID-19 exposed structural weaknesses in the GST compensation mechanism.

    This intensified Centre-State fiscal tensions and revived debates on fair and transparent transfer mechanisms in India’s federal framework.

    Mentor’s Comment

    Debates on fiscal federalism in India often oscillate between equity and efficiency. The article examines whether Gross State Domestic Product (GSDP) can be a fair and reliable basis for sharing Central tax revenues among States, especially in the post-GST era where tax attribution has become complex.

    Why in the News

    The article gains significance amid ongoing debates on Central-State fiscal relations, especially after the implementation of GST, which has weakened the direct link between tax collection and the place of economic activity. The issue is critical because ₹75.12 lakh crore was transferred to States between 2020-21 and 2024-25, and the method used to distribute this amount affects State fiscal autonomy and perceived fairness. A key finding is the very high correlation (0.99) between actual transfers and GSDP, compared to a much weaker link with Finance Commission devolution, making GSDP a stronger alternative measure.

    Introduction

    India’s system of fiscal transfers relies heavily on the recommendations of successive Finance Commissions, which distribute Central tax revenues through tax devolution, grants-in-aid, and Centrally Sponsored Schemes (CSS). However, the post-GST tax regime has disrupted the traditional linkage between tax collection location and economic value creation, raising questions about whether existing criteria adequately capture States’ real contribution to national revenues.

    Why is tax collection an unreliable indicator of State-level contribution?

    1. GST structure: Breaks the link between the location of production and the location of tax collection due to destination-based taxation.
    2. Corporate taxation: Attributes tax payments to the registered office location rather than where economic activity occurs.
    3. Multi-State operations: Dilutes State-wise attribution due to labour migration, inter-State supply chains, and inter-corporate transactions.
    4. Example distortion: Automobile manufacturers pay taxes where offices are registered, not necessarily where factories operate; plantation companies record profits centrally despite dispersed production.
    5. Outcome: Direct tax figures reflect collection points, not value creation.

    Why does GSDP emerge as a credible proxy for tax accrual?

    1. Economic base representation: Captures the size and intensity of economic activity within a State.
    2. Uniform tax base assumption: Assumes broadly similar tax administration efficiency across States.
    3. Empirical validation: Correlation between GSDP and GST collections stands at 0.75 for 2023-24.
    4. High correlation with transfers: Correlation of 0.91 between GSDP and total Central tax transfers.
    5. Policy neutrality: Avoids contentious attribution disputes inherent in GST accounting.

    How do actual transfers align with GSDP shares?

    1. Overall transfers: ₹75.12 lakh crore transferred during 2020-25, including FC devolution, grants, and CSS.
    2. High-alignment States:
      1. Uttar Pradesh: 15.81% transfer share vs 16.85% population share.
      2. Maharashtra: High tax contribution (40.3%) but only 6.64% of transfers, reflecting redistribution.
    3. Mismatch States:
      1. Bihar: Receives 8.65% transfers despite only 4.66% GSDP share.
      2. West Bengal: 6.96% GSDP share vs 6.69% transfers.
    4. Interpretation: Transfers broadly track economic output, not tax collections.

    How does the equity-efficiency trade-off emerge in fiscal transfers?

    1. Redistributive bias: FC criteria prioritize equity over efficiency by favoring population and income distance.
    2. Regional disparities: Persist due to differential expenditure needs and fiscal capacity.
    3. Efficiency trade-off: GSDP-based transfers better reflect contribution but reduce redistributive scope.
    4. Evidence: Correlation between GSDP and FC devolution shares is only 0.58, indicating weak alignment.
    5. Outcome: GSDP balances fairness and efficiency more transparently than current metrics.

    Which States gain or lose under a pure GSDP-based system?

    1. Major gainers: Tamil Nadu and Karnataka: High production but lower tax attribution due to GST mechanics.
    2. Major losers: Uttar Pradesh, Bihar, Madhya Pradesh: Benefit currently from redistributive weights.
    3. Exception States: Haryana, Karnataka, Maharashtra: GSDP share lower than tax collection due to tax concentration effects.
    4. Inference: GSDP corrects distortions arising from centralized tax accounting.

    Conclusion

    The debate on using GSDP as a basis for Central-State transfers highlights the need to realign India’s fiscal federal framework with the realities of the post-GST economy. While redistribution remains essential for equity, greater reliance on GSDP can improve transparency, efficiency, and trust by linking transfers more closely with economic activity. A calibrated approach, combining GSDP-based devolution with targeted grants, offers a balanced pathway to strengthen cooperative federalism.