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