Why in the News?
Recently, Nobel Prize in Economics has been awarded to three economists — Joel Mokyr, Philippe Aghion (College de France, INSEAD), and Peter Howitt (Brown University) — for contributions in studying role of technological change and creative destruction in economic growth. While works of Aghion and Howitt recognized readily by modern economists, Mokyr distinguished by adopting historical lens to examine relationship between knowledge, ideas, and economic growth.
Background: Laureates and Their Contributions
Current Nobel Prize in Economics conferred on Joel Mokyr, Philippe Aghion, and Peter Howitt for pioneering research on technological change and creative destruction as drivers of economic growth. Creative destruction, originally conceptualized by Schumpeter, refers to process where new innovations replace outdated ones, fostering long-term growth.
- Aghion and Howitt known for formal endogenous growth models integrating competition, R&D, and innovation incentives.
- Mokyr complements this with historical-institutional analysis, emphasizing evolution of knowledge systems across civilizations.
- Combined framework bridges economic theory with social history, offering insights into why some societies innovate faster.
This convergence vital for UPSC aspirants analyzing growth divergences, India’s innovation ecosystem, and policy for knowledge economy.
Mokyr’s Model: Propositional and Prescriptive Knowledge
Mokyr’s model centrally distinguishes between two types of knowledge essential for economic growth:
- Propositional knowledge: Defined as knowledge about scientific phenomena and principles (the “what” and “why” of nature).
- Prescriptive knowledge: Defined as knowledge about techniques (the “how” to apply principles in practice).
Economic growth triggered when both types expand, and society equipped to translate theory into technique. Mere possession of knowledge insufficient; access and diffusion critical:
- Majority of members in society must access knowledge.
- Social norms required to promote sharing, free exchange of ideas, and open communication.
- Technological progress not purely economic process;; instead, social and cultural outcome governing spread, sharing, and application of knowledge.
Implications of Mokyr’s Model
- Generation of new ideas alone inadequate; sharing and communication determine real impact.
- Free markets not automatically ensure growth; state intervention not necessarily cause negative growth.
- Any factor restricting free flow of information directly hampers innovation and long-term growth.
Role of Social Institutions: Caste System as Knowledge Barrier
Social institutions in India, particularly caste system, identified as major impediments to knowledge diffusion and growth:
- Caste historically ensured knowledge remained preserve of elite minority.
- Access restricted through social exclusion and violence to maintain hierarchy.
- Post-Independence, policy interventions like reservations introduced to correct historical imbalances.
- Despite progress, high-quality education still inaccessible to many segments.
- Slow retreat of public sector in education results in quality education again becoming preserve of elite.
Mokyr’s framework warns:
- Accumulated knowledge without meaningful access equivalent to no knowledge at all.
- Extreme fragmentation via caste not only limits education but enforces rigid conventions, preventing meaningful community interactions.
- Such isolation blocks cross-fertilization of ideas, experimentation, and innovation.
Relevance amplified by current trends:
- Rise in private universities with high fees.
- Lack of reservations in private institutions.
- State withdrawal from education sector, undermining inclusive access.
Automation, Job Polarisation, and Erosion of Prescriptive Knowledge
Labour markets profoundly disrupted by automation powered by AI, raising dual concerns:
- Job displacement: Immediate threat to employment.
- Knowledge transmission crisis: Deeper structural issue.
Job polarisation explained:
- Routine tasks increasingly performed by machines or AI.
- Human workforce bifurcated into:
- Highly skilled roles (requiring advanced propositional knowledge).
- Low-skill service occupations (e.g., restaurants, delivery).
Consequences for prescriptive knowledge:
- Share of workforce with hands-on experience of modern techniques declines.
- Transmission of practical knowledge depends on:
- Long-term familiarisation.
- Personal contact.
- Training programs.
- Hands-on operational experience.
- Automation may enhance productivity and growth over time, but reduces exposure to new techniques for majority.
Combined effect:
- Inaccessible education + restrictive institutions → Propositional knowledge confined.
- Automation + job polarisation → Prescriptive knowledge inaccessible.
- Result: Economy-wide innovation severely constrained.
Mokyr’s insight: Transformation of knowledge into innovations and growth governed by cultural and social norms determining costs of access to knowledge.
Democratisation of Education as Growth Enabler
Democratisation of education established as necessary condition for faster, inclusive growth:
- Far from being antithetical to efficiency, universal quality education accelerates innovation.
- Ensures tinkering, experimentation, and technique development across population.
- Reduces knowledge monopolies held by elites or machines.
Way Forward: Policy and Institutional Reforms
To operationalize knowledge-driven inclusive growth:
- Break down caste barriers through:
- Stronger social integration policies.
- Community interaction initiatives.
- Cultural norm shifts via education and media.
- Reverse state retreat in education:
- Increase public investment in quality school and higher education.
- Regulate private universities for affordability and inclusion.
- Mandate reservations and scholarships in private sector.
- Mitigate automation risks:
- Promote apprenticeships, on-job training, and skill bridging.
- Design AI policies ensuring human-machine collaboration.
- Support reskilling for polarised workforce.
- Foster open knowledge ecosystems:
- Reduce information restrictions.
- Incentivize idea sharing across sectors and communities.
- Build innovation clusters with diverse participation.
Conclusion
- Access to knowledge is a fundamental driver of innovation and economic growth.
- Restricting the free flow of information, whether through social institutions like caste or through technological changes like automation, hampers the ability of societies to innovate and progress.
- Ensuring universal access to quality education and promoting the sharing of both propositional and prescriptive knowledge are essential for sustained economic development.
- The insights from the Nobel-winning research provide a clear roadmap for policymakers to foster inclusive growth and innovation.
Automation and Job Polarisation
| Feature | Automation (Technological Displacement) | Job Polarisation (Structural Change) |
| Core Concept | Use of AI, robotics, and software to perform tasks traditionally done by humans, especially routine tasks (cognitive or manual). | Uneven impact of automation on labour demand, leading to growth in high- and low-skill jobs at the expense of middle-skill occupations. |
| Mechanism | Substitution Effect: Machines replace human labour in repetitive tasks (e.g., assembly line, data entry). | Routine-Biased Technological Change (RTC): Technology substitutes routine tasks but complements non-routine (complex problem-solving, creative, or basic service) tasks. |
| Significance/Impact | 📈 Productivity Boost: Reduces costs, enhances efficiency, and may lead to creation of new jobs due to lower prices (Reinstatement Effect). | 📉 Widening Inequality: Increases skill premium and wage gap between high-skilled and low-skilled workers. Shifts income share from labour to capital (owners of technology). |
| Challenges | Skill Mismatch: Workforce lacks advanced digital and analytical skills required by new industries. | Social Tensions: Concentrates job losses/wage stagnation in the middle class, impacting social mobility and consumer demand. |
| Knowledge Transmission Gap: Reduces the share of workers with hands-on experience and practical knowledge (prescriptive knowledge), hindering future innovation. | Informalisation of Work: Increased reliance on gig contracts and short-term work, eroding social security and job stability for low-skilled workers. | |
| Way Forward | Reskilling & Upskilling: Focus on lifelong learning and training in 21st-century skills (e.g., critical thinking, communication, creativity). | Universal Basic Income (UBI) / Social Safety Nets: Explore mechanisms for income support or assurance, decoupled from traditional employment models. |
| Innovation & Entrepreneurship: Incentivise transition from formal employee roles to self-employment and flexible work models (Form 16A over Form 16). | Labour Law Reform: Update laws to include gig and platform workers under social security and basic benefits coverage. | |
| Public Investment: Focus on labour-intensive infrastructure (roads, railways) and support to SMEs (Small and Medium Enterprises) for immediate job creation. | Human-Centric AI Policy: Ensure algorithmic management is transparent and ethical, prioritising human oversight and mitigating digital fatigue. |
