Content

1. India’s First National Anti-Terror Policy
2. Jnanpith Award & the Demise of Vinod Kumar Shukla
3. The Upskilling Gap: Why Women Risk Being Left Behind by AI
4. How India’s Exports Are Concentrated in a Few States
5. Rhino Dehorning and the Decline in Poaching
6. Made-in-Tihar Products Go Online

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India’s First National Anti-Terror Policy 

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Why is it in News?

• Union Government finalising India’s first comprehensive National Counter-Terrorism Policy and Strategy.
• Inputs consolidated by the Ministry of Home Affairs with operational feedback from National Investigation Agency.
• NIA anti-terror conference (26–27 December, New Delhi) to outline policy contours.

Relevance

• GS III – Internal Security
  • Terrorism and counter-terrorism strategies
  • Role of intelligence agencies (NIA, IB, NSG)
  • Border management (India–Nepal open border)
  • Terror financing, digital radicalisation, identity fraud

Strategic Context

• India lacked a unified anti-terrordoctrine; counter-terror responses have been:
  • Statute-based (UAPA, NIA Act)
  • Agency-driven (NIA, IB, NSG)
  • Incident-reactive rather than prevention-centric
• Contrast:
  • National Policy & Action Plan on LWE (2015) → integrated security-development model.
  • Terrorism domain lacked an equivalent pan-India template.

Key Threat Vectors Driving the Policy

Digital Radicalisation (High Priority)

• Shift from physical indoctrination to algorithm-driven online recruitment.
• NIA interrogation after Nov 10 car-borne suicide attack near Red Fort:
  • Perpetrators radicalised entirely online.
• Identified risks:
  • Encrypted messaging platforms
  • Social media micro-targeting
  • Foreign-hosted servers beyond Indian jurisdiction
• Institutional gap:
  • Very limited number of trained cyber-radicalisation spotters at police-station level.

Foreign-Funded Conversion & Radicalisation Networks

• Intelligence inputs point to:
  • Overseas religious centres acting as ideological nodes.
  • Suspected linkages with Pakistan’s ISI.
• Pattern:
  • Funding → conversion → ideological grooming → terror facilitation.
• Policy likely to integrate:
  • Financial intelligence
  • Social media monitoring
  • NGO & charity oversight (within constitutional limits).

Open Border Exploitation (Nepal Corridor)

• India–Nepal border:
  • ~1,750 km
  • Visa-free, largely unfenced
• Reported modus operandi:
  • Khalistani operatives enter Nepal on foreign passports.
  • Discard passports → enter India illegally → move via UP–Bihar corridor to Punjab.
• Policy focus:
  • Border intelligence fusion
  • Joint surveillance with Nepal
  • Technology-enabled profiling (without border closure).

Aadhaar Spoofing & Identity Fraud

• Emerging threat:
  • Synthetic identities used for SIM cards, bank accounts, logistics.
• Links to:
  • Arms trafficking
  • Drug-terror financing nexus
• Requires coordination between:
  • UIDAI
  • Financial Intelligence Units
  • State police cyber cells.

Institutional Architecture Being Integrated

Core Agencies

• National Investigation Agency – federal investigations, terror financing, international linkages.
• National Security Guard – tactical response, hostage rescue, urban counter-terror.
• Intelligence Bureau – threat anticipation, radicalisation tracking.
• State ATS & Special Branches – ground-level intelligence.

Technology Backbone

• National Intelligence Grid (NATGRID):
  • Secure access to 20+ databases (immigration, banking, telecom, vehicle, travel).
  • Shift from post-event investigation → pre-emptive detection.

Policy Orientation: From Reaction to Prevention

Old Approach	Proposed Policy Shift
Incident-led response	Intelligence-led prevention
Central agency dominance	State-centric capacity building
Post-attack prosecution	Early detection & disruption
Fragmented data	Integrated data grids
Elite-unit focus	Police station-level vigilance

Federal Dimension

• Policy designed as a template, not command-and-control.
• States consulted post-Pahalgam terror attack (April 22).
• Emphasis on:
  • Training local police
  • Standard operating procedures (SOPs)
  • Shared best practices across States.

Significance for Internal Security (GS III)

• First attempt at doctrinal clarity in counter-terrorism.
• Acknowledges non-traditional threats: digital ecosystems, identity fraud, ideological financing.
• Balances:
  • National security
  • Federal autonomy
  • Civil liberties (critical for judicial sustainability).

Likely Challenges

• Online radicalisation vs freedom of speech.
• Inter-state coordination asymmetries.
• Capacity gaps at thana level.
• Managing foreign policy sensitivities (Canada, Nepal).

Conclusion

• The proposed policy marks India’s transition from event-driven counter-terrorism to ecosystem-based prevention.
• If implemented effectively, it can become the internal security equivalent of the LWE framework (2015)—but success hinges on State-level absorption, training depth, and tech-human integration.

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Jnanpith Award & Demise of Vinod Kumar Shukla

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• Vinod Kumar Shukla, celebrated Hindi poet and novelist, passed away at age 88 in Raipur.
• He was the 2024 Jnanpith Award recipient — India’s highest literary honour.
• First writer from Chhattisgarh to receive the Jnanpith Award.

Relevance

• GS I – Art & Culture
  • Indian literature and literary institutions
  • Regional language contributions (Hindi literature)
  • Cultural diversity and non-metropolitan voices
• Prelims
  • Jnanpith Award: year, nature, eligibility, administering body
  • First Jnanpith awardee from Chhattisgarh

Vinod Kumar Shukla

• Born: 1937, Rajnandgaon (present-day Chhattisgarh).
• Literary career began: 1971.
• Known for:
  • Minimalist language
  • Poetic treatment of everyday life
  • Quiet subversion of power, class, and alienation.

Major Works

• Poetry
  • Lagbhag Jaihind (debut, 1971)
  • Kavita Se Lambi Kavita
• Novels
  • Naukar Ki Kameez
    • Adapted into a critically acclaimed film.
  • Deewar Mein Ek Khidki Rehti Thi
• Literary style often compared with:
  • Post-Nayi Kavita humanism
  • Everyday realism rather than ideological grand narratives.

Awards & Recognitions

• Jnanpith Award (2024)
• Sahitya Akademi Award
• Numerous state and national recognitions for poetry and fiction.

Jnanpith Award:

• Instituted: 1961
• First awarded: 1965
• Administered by: Bharatiya Jnanpith (trust founded by Sahu Jain family).
• Eligibility:
  • Indian citizens
  • Works in any 8th Schedule language.
• Nature:
  • Awarded for overall literary contribution, not a single book.

Award Components

• Cash prize: ₹11 lakh
• Citation
• Bronze replica of Saraswati, Hindu goddess of knowledge.

Significance

• Considered the literary equivalent of the Bharat Ratna.
• Recognises lifetime achievement and cultural impact.

Conclusion

• Vinod Kumar Shukla’s death is not only a personal loss but a civilisational moment for Hindi literature.
• His 2024 Jnanpith Award symbolised the recognition of simplicity, empathy, and everyday realism as enduring literary values.

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The Upskilling Gap: Why Women Risk Being Left Behind by AI

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Why is it in News?

• Recent analysis based on India’s latest Time Use Survey (2024) highlights a structural time poverty faced by women, raising concerns that the AI-driven future of work may deepen gender inequality.
• As India pushes AI-led growth through initiatives like the India AI Mission, evidence shows women lack time, access, and flexibility required to upskill for AI-era jobs.
• Aligns with broader debates on:
  • Automation risks
  • Right to disconnect
  • Gender budgeting
  • India’s Viksit Bharat @2047 vision.

Relevance

• GS I – Society
  • Gender roles and unpaid care work
  • Time poverty and gender inequality
• GS II – Governance
  • Gender budgeting
  • Social infrastructure (childcare, transport, water, energy)

Women’s Workload & Time Poverty

• Labour force participation (women): ~40% (2024).
• Average daily work (paid + unpaid):
  • Women: ~9.6 hours/day
  • Peaks at 70–80 hours/week for ages 25–39.
• Key driver:
  • ~40% of women outside the labour force cite household & caregiving responsibilities.
• Nature of work increase:
  • Over 80% of recent rise in women’s workforce participation comes from:
    • Unpaid family work
    • Low-paid self-employment
    • Informal, low-productivity jobs.

Gender Gap in Paid vs Unpaid Work

Across the Life Cycle

• Men:
  • Total work: 54–60 hours/week
  • Unpaid work: minimal and stable across ages.
• Women:
  • Total work exceeds men at almost all ages.
  • 25–39 age group:
    • Women spend 2× more time on unpaid caregiving than men.
    • Childcare is the largest component.
• Even in later life:
  • Men’s unpaid work rises marginally (elderly care),
  • Structural inequality at home persists across income, occupation, and age.

AI-Specific Risks for Women

1. Higher Automation Exposure

• Women overrepresented in:
  • Routine, clerical, low-skill service jobs
  • Informal and home-based work
• These roles are more automation-prone under AI adoption.

2. Algorithmic Bias

• AI-driven productivity metrics:
  • Ignore caregiving interruptions
  • Penalise time constraints
  • Reward uninterrupted, long-hour availability
• Care work remains invisible to algorithms.

3. Upskilling Time Deficit

• Women spend ~10 hours less per week than men on:
  • Learning
  • Skill enhancement
  • Self-development
• Gap widens to 11–12 hours/week in prime working years.
• Result:
  • Limited transition from low-skill to high-value AI-linked jobs.

Health & Well-being Costs

• Women sleep 2–2.5 hours less per week than men during peak working years.
• Time adjustment happens at the cost of:
  • Rest
  • Mental health
  • Physical well-being
• Long-term impact:
  • Lower productivity
  • Higher burnout
  • Reduced career longevity.

Policy & Governance Solutions Highlighted

1. Time-Centric Policy Design

• Shift from job-counting to outcome-based employment metrics.
• Explicit use of time-use data in:
  • Labour policy
  • Skill missions
  • AI governance.

2. Gender Budgeting as an Enabler

• Integrate time-use indicators into gender budgeting.
• Prioritise sustained spending on:
  • Affordable childcare
  • Elderly care services
  • Piped water
  • Clean cooking energy
  • Safe public transport.

3. AI-Era Upskilling for Women

• Design lifelong, flexible, modular skilling:
  • Local delivery
  • Hybrid / online formats
  • Low time-intensity learning
• Scale targeted programmes:
  • India AI Mission
  • AI Careers for Women
• Focus on:
  • Digital literacy
  • Applied AI tools
  • Locally relevant vocational tech skills.

Conclusion

• AI will not automatically empower women; without time-sensitive policy design, it may entrench inequality.
• Until women’s time is valued, freed, and integrated into growth strategy, India’s AI ambitions and Viksit Bharat @2047 vision will remain constrained by:
  • Invisible labour
  • Time poverty
  • Underutilised human capital.

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How India’s Exports Are Concentrated in a Few States

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Why is it in News?

• Recent analysis using the RBI Handbook of Statistics on Indian States 2024–25 shows India’s export growth is increasingly concentrated in a handful of States, raising concerns about:
  • Regional inequality
  • Jobless export growth
  • Breakdown of the traditional export–industrialisation–employment link
• Despite a weakening rupee and record export values, export-led development is not translating into broad-based industrial employment.

Relevance

• GS III – Indian Economy
  • Export-led growth model
  • Industrialisation and jobless growth
  • Capital deepening and labour absorption
• GS I – Regional Development
  • Inter-State disparities
  • Core–periphery model

Core Export Concentration:

• Top 5 exporting States:
  • Maharashtra
  • Gujarat
  • Tamil Nadu
  • Karnataka
  • Uttar Pradesh
• Share of national exports:
  • ~65% (5 years ago)
  • ~70% now
• Implication:
  • National export aggregates mask severe regional divergence.

Rising Geographic Concentration (HHI Evidence)

• Export geography measured using Herfindahl–Hirschman Index (HHI):
  • Rising HHI → increasing concentration.
• Pattern emerging:
  • Core–periphery structure
    • Coastal western & southern States → tightly integrated into global supply chains.
    • Northern & eastern hinterland → decoupling from export growth.
• Agglomeration logic:
  • Firms prefer existing industrial clusters due to:
    • Logistics efficiency
    • Supplier ecosystems
    • Skilled labour pools

Global Context: Why Convergence Is Failing ?

Shrinking Global Trade Window

• World Trade Organization data:
  • Merchandise trade volume growth slowed to 0.5–3% band.
• UN Trade and Development (2023):
  • Top 10 exporters control ~55% of global merchandise trade.
• Consequence:
  • Latecomers face entry barriers.
  • Global capital now seeks complexity, not just cheap labour.

Shift from Volume to Value

Economic Complexity Trap

• Modern exports cluster around dense product spaces:
  • Automobiles
  • Electronics
  • Precision machinery
• These sectors:
  • Require advanced logistics
  • Depend on accumulated industrial capabilities
• Regions exporting low-complexity goods face:
  • High barriers to upgrading
  • Weak backward–forward linkages.

Export Growth ≠ Employment Growth

Capital Deepening Evidence

• Annual Survey of Industries (ASI) 2022–23:
  • Fixed capital growth: +10.6%
  • Employment growth: +7.4%
  • Fixed capital per worker: ₹23.6 lakh
• Indicates:
  • Rising capital–labour ratio
  • Factories becoming less labour-absorptive.

Manufacturing Employment Stagnation

• Periodic Labour Force Survey (PLFS):
  • Manufacturing employment share:
    • Stuck at ~11.6–12%
  • Despite record export values.
• Interpretation:
  • Employment elasticity of exports has collapsed.
  • Exports are generating value, not mass jobs.

Capital Bias & Wage Compression

• ASI data shows:
  • Wage share in Net Value Added (NVA) declining.
  • Productivity gains in:
    • Petrochemicals
    • Electronics
  • Gains accrue disproportionately to capital owners.
• Outcome:
  • High industrial GDP growth
  • Limited mass prosperity

Spatial Stickiness of New-Age Exports

• Electronics exports (PLI-driven):
  • ~47% YoY growth
  • Locked into:
    • Kancheepuram (TN)
    • Noida (UP)
• Reason:
  • High supply-chain complexity
  • Precision logistics unavailable in hinterland districts.

Financial Divide: Credit-Deposit Ratios

Coastal vs Hinterland

• RBI Credit–Deposit (CD) ratios:
  • Tamil Nadu, Andhra Pradesh: >90%
    • Local savings reinvested locally.
  • Bihar, eastern Uttar Pradesh: <50%
    • Savings mobilised but lent elsewhere.
• Effect:
  • Capital flight from hinterland to coast
  • Reinforces regional divergence.

Structural Diagnosis

• Exports no longer act as:
  • A bridge from agriculture → industry
  • A mass employment generator
• Instead, exports are now:
  • An outcome of prior structural capacity
  • A mirror of accumulated industrial wealth.

Policy Implications

Why Old Assumptions Fail ?

• Export-led growth ≠ labour-intensive industrialisation.
• India bypassing East Asian trajectory of:
  • Low-skill manufacturing
  • Broad middle-class creation.

Need for New Metrics

• Export growth ≠ inclusive development.
• Policy must track:
  • Employment elasticity
  • Wage share
  • Regional diffusion
• Otherwise:
  • Risk mistaking outcomes for instruments.

Bottom Line

• India’s export success is real but narrow.
• Without correcting:
  • Capital bias
  • Financial asymmetry
  • Human capital gaps
• Export growth will deepen regional inequality rather than resolve it, making inclusive industrialisation increasingly elusive.

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Rhino Dehorning & Poaching Decline 

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Why is it in News?

• A peer-reviewed study published in Science reports that rhino dehorning led to a near-elimination of poaching in African wildlife reserves.
• The study analysed 7 years of data (2017–2023) from 11 reserves in South Africa’s Greater Kruger ecosystem, home to the world’s largest rhino population.
• Findings challenge the dominance of technology-heavy anti-poaching strategies and reframe conservation economics.

Relevance

• GS III – Environment & Conservation
  • Wildlife protection strategies
  • Anti-poaching models
  • Biodiversity conservation
• GS II – Governance
  • Evidence-based policymaking
  • Institutional capacity vs incentives

Global Rhino Status:

• Global rhino population (2024): < 28,000 (all five species combined).
• Major threat: Poaching for horns, driven by illicit international demand.
• Greater Kruger losses:
  • 1,985 black & white rhinos killed (2017–2023).
  • ~6.5% population loss per year, despite heavy surveillance.
• Anti-poaching expenditure:
  • ~$74 million spent on:
    • Armed patrols
    • Tracking dogs
    • AI cameras
    • Aerial surveillance.

Core Findings of the Study

Impact of Dehorning

• 2,284 rhinos dehorned across 8 reserves.
• Poaching outcomes:
  • 75% reduction compared to pre-dehorning levels.
  • 78% drop where dehorning was implemented rapidly (1–2 months).
  • 95% lower poaching risk for dehorned rhinos vs horned rhinos.
• Cost efficiency:
  • Achieved using only 1.2% of total anti-poaching budgets.

Methodology

• Data type: Quarterly poaching records (2017–2023).
• Analytical method:
  • Hierarchical Bayesian regression modelling.
• Comparison:
  • Dehorned vs non-dehorned reserves.
  • Before–after intervention analysis.
• Outcome:
  • Strong causal inference rather than correlation.

Why Dehorning Works ?

Economics of Poaching

• Rhino horn:
  • Composed of keratin (same as hair & nails).
  • No proven medicinal value.
• Illicit market value:
  • $874 million – $1.13 billion (2012–2022), per Wildlife Justice Commission.
• Removing horns:
  • Eliminates primary incentive, not the animal.

Behavioural Reality of Poachers

• Killing the rhino allows:
  • Faster removal
  • No resistance
• Dehorned rhinos:
  • Offer minimal reward
  • Increase risk–reward imbalance for poachers.

Limits of Enforcement-Only Models

• Arrests and patrols showed limited deterrence due to:
  • Corruption
  • Weak prosecution
  • Cross-border trafficking loopholes
• Surveillance ≠ prevention when incentives remain intact.

How Dehorning Is Done (Animal Welfare) ?

• Conducted by veterinarians:
  • Sedation, blindfolding, earplugs.
  • 90–93% of horn removed, above the germinal layer.
  • Horn regrows naturally.
• Stump sealed to prevent infection.
• Considered non-lethal and reversible.

India–Africa Contrast

African Context

• Large landscapes.
• High-value illicit trade routes.
• Enforcement stretched thin.

Indian & Nepali Model

• India & Nepal do not dehorn.
• Losses:
  • 1–2 rhinos in last 3 years.
• Kaziranga National Park success drivers:
  • Smart patrolling
  • Community participation
  • Local intelligence

Role of Local Communities & Rangers

• Research involved:
  • 1,000+ hours of workshops with rangers.
• Rangers:
  • Often local residents.
  • Hold critical ecological knowledge.
• Study highlights:
  • Ranger welfare (pay, safety, training) is as vital as technology.

Conservation Economics:  

• Dehorning shifts strategy from:
  • Policing supply → collapsing incentive.
• Represents preventive conservation, not reactive enforcement.
• More cost-effective than high-tech surveillance alone.

Conclusion

• Rhino dehorning is not a silver bullet, but it is:
  • Highly effective
  • Cost-efficient
  • Data-validated
• The study redefines conservation success:
  • Remove incentives, not just criminals.
• Policy lesson:
  • Conservation outcomes improve when economics, ecology, and local capacity align.

Rhinoceros  

• Species & Distribution
  • Five species globally: White, Black (Africa); Greater one-horned, Javan, Sumatran (Asia).
  • India hosts the Greater one-horned rhinoceros, mainly in Assam (Kaziranga, Pobitora).
• Conservation Status
  • IUCN:
    • Javan & Sumatran – Critically Endangered
    • Black – Critically Endangered
    • Greater one-horned – Vulnerable
    • White – Near Threatened
  • Global population (2024): < 28,000.
• Major Threats
  • Poaching for horn (illegal trade worth ~$0.9–1.1 billion, 2012–22).
  • Habitat loss, fragmentation, and human–wildlife conflict.
• Biology & Horn
  • Rhino horn is made of keratin (same as hair and nails); no proven medicinal value.
  • Used for digging, defence, and mating displays.

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Made-in-Tihar Products Go Online 

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Why is it in News?

• Delhi Prison Administration plans to sell Made-in-Tihar products on major e-commerce platforms such as Flipkart and Amazon.
• Marks a shift from offline-only sales (jail canteens, courts, exhibitions) to nationwide digital marketplaces.
• Aimed at improving inmate rehabilitation, skill utilisation, and post-release employability.

Relevance

• GS II – Governance & Social Justice
  • Prison reforms
  • Rehabilitation of convicts and undertrials
  • Reformative vs retributive justice

What Are “Made-in-Tihar” Products?

• Produced by inmates inside Tihar Jail, South Asia’s largest prison complex.
• Product range includes:
  • Food items: cookies, mustard oil
  • Handicrafts: bags, footwear
  • Household items: furniture, paper products
• Around 13 categories currently marketed.

Scale of the Programme 

• Inmate workforce:
  • ~5,000 inmates engaged daily in manufacturing and vocational work.
• Production units:
  • Multiple industrial workshops across Tihar Jail complex.
• Revenue generation:
  • ₹2.42 crore turnover in FY 2023–24.
  • Average inmate earnings:
    • ₹412 per day (credited to prison accounts).
• Skill coverage:
  • About 70 different products across food processing, carpentry, tailoring, and handicrafts.

Economic Dignity of Prison Labour

• Shifts narrative from:
  • “Prison labour” → “Correctional industry”.
• Supports constitutional values:
  • Article 21 (right to live with dignity).
• Reinforces Supreme Court guidance on:
  • Fair remuneration
  • Voluntary skill-based work.

Governance & Implementation Aspects

• Sales via:
  • Government-approved accounts on platforms.
• Branding:
  • “Made-in-Tihar” already has recall value due to:
    • Quality perception
    • Ethical consumption appeal.
• Oversight:
  • Delhi Prison Department ensures:
    • No forced labour
    • Wage crediting
    • Product quality control.

Comparative Context

• Similar initiatives:
  • Open prisons in Rajasthan
  • Prison handicraft programmes in Kerala & Maharashtra.
• Distinction:
  • Tihar is among the largest prison-based industrial ecosystems in India.

Challenges & Caveats

• Pricing competitiveness with private brands.
• Logistics and supply consistency.
• Ensuring:
  • Non-exploitation
  • Transparency in revenue sharing.
• Need for:
  • Skill certification linkage with NSQF
  • Post-release job placement pipelines.

Conclusion

• Taking Made-in-Tihar products online transforms prison labour into a scalable rehabilitation model.
• If implemented with safeguards, it can:
  • Humanise incarceration
  • Generate ethical livelihoods
  • Recast prisons as institutions of correction, not exclusion.