ECONOMICS

THE STATE OF INDIA’S INFORMAL ECONOMY, WHAT THE NUMBERS SAY ON JOBS

Source: Indian Express

Informal Sector in India

• The informal sector, also known as the unorganized sector, comprises economic activities that are not regulated directly by the government and are not included in the formal economy. It includes small-scale, self-employed, or casual workers and enterprises.

Characteristics of the informal sector

• Lack of Formal Contracts: Employment relationships in the informal sector are often based on verbal agreements without formal contracts.
• Absence of Social Security Benefits: Workers in the informal sector do not receive social security benefits such as health insurance, pensions, or paid leave.
• Small Scale of Operations: Informal sector enterprises are generally small in size, with low levels of investment and capital.
• Family Labor: Many informal sector businesses rely on family members for labour.
• Limited Access to Credit and Finance: Informal enterprises often have restricted access to formal financial institutions and rely on informal credit sources.

Unincorporated Enterprises

• Unincorporated enterprises are part of the informal sector, encompassing Micro, Small and Medium Enterprises (MSMEs), household units with hired workers, and own-account enterprises.
• They have the following characteristics:
  • Sole Proprietorships or Partnerships: Most unincorporated enterprises are owned and operated by individuals or families, often as sole proprietorships or partnerships.
  • No Separate Legal Identity: These enterprises do not have a separate legal identity from their owners, meaning the owners are personally liable for the business's debts and obligations.
  • Simple Organizational Structure: Unincorporated enterprises have a simple organizational structure with minimal administrative and managerial hierarchy.
  • Lack of Formal Accounting Systems: These enterprises often do not maintain formal accounting records or follow standardized accounting practices.
• These enterprises operate outside the formal regulatory framework and include a wide range of activities in manufacturing, trade, and other services.
  • Manufacturing: Includes units not covered under the Factories Act, 1948, and the organized manufacturing sector's Annual Survey of Industries (ASI). This also covers informal establishments engaged in activities like cotton-ginning, cleaning, bailing, and beedi and cigar manufacturing.
  • Trade and Other Services: Encompasses proprietary and partnership establishments (excluding Limited Liability Partnership establishments), societies, trusts, associations, clubs, cooperatives, self-help groups, and non-profit institutions.

Recent Surveys

The outcomes of the 2021-22 and 2022-23 surveys by the National Sample Survey Office (NSSO) are significant for understanding employment trends in the informal sector. The surveys help to:

• Assess the impact of events such as demonetization (2016), GST implementation (2017), and the national lockdown (2020-21) on the informal sector.
• Understand shifts in employment patterns, especially when the formal sector experiences a slowdown.

Key Findings from the Surveys
Employment Trends	The informal sector saw a reduction in employment, despite an increase in the number of enterprises between 2015-16 and 2022-23. Key trends include: ●Employment Decline: Employment in the informal sector decreased by 16.45 lakh (1.5%) to 10.96 crore in 2022-23 from 11.13 crore in 2015-16. ●Growth in Own-Account Enterprises: These enterprises increased by nearly 4%, while hired-worker enterprises contracted by 3.2%. This shift indicates a deterioration in employment quality, as more units moved towards self-owned rather than hired-worker setups. ●Sectoral Impact: Manufacturing units were the worst hit, contracting 9.3%, and the number of workers in manufacturing fell by 15% during this period. ○The trade sector saw a marginal decline in units but a slight increase in workers. The services sector, however, recorded increases in both the number of units and workers.
Sector-Specific Trends	●Manufacturing: The number of manufacturing enterprises decreased by 9.3% to 1.78 crore, and the number of workers fell by 15% to 3.06 crore. ●Trade: The trade sector contracted by 2% to 2.26 crore units, but the number of workers increased marginally by 0.8% to 3.90 crore. ●Services: The services sector saw a 19.1% increase in the number of establishments and a 9.5% increase in workers, reflecting growth in this area.
Gross Value Added (GVA)	●Current Prices: GVA per enterprise increased to Rs 2.38 lakh in 2022-23 from Rs 1.82 lakh in 2015-16, and GVA per worker rose to Rs 1.42 lakh from Rs 1.04 lakh. ●Real Terms: Real GVA growth was slower, with a compounded annual growth rate (CAGR) of 6.9% in 2022-23, lower than the pre-pandemic level. ○From 2015-16 to 2022-23, the real GVA CAGR contracted by 0.2%, compared to a 7.4% CAGR from 2010-11 to 2015-16.
Employment Patterns Across States	●Decline in Workers: Sixteen out of 34 states/Union Territories recorded a decline in informal sector workers in 2022-23 compared to 2015-16. ●Post-Pandemic Increase: The share of informal sector workers increased in most states immediately after the pandemic, indicating economic distress and a shift away from the formal sector.

Significance of the Informal Sector

• Employment Generation: The informal sector provides employment to more than 90% of India's workforce, absorbing a significant portion of semi-skilled and unskilled labour.
• Economic Contribution: It accounts for nearly half of India's economic output, including various small and medium enterprises (SMEs) and household proprietary establishments.
• Flexibility and Adaptability: Informal enterprises are often more flexible and adaptable to economic changes compared to formal sector firms.
• Poverty Alleviation: By providing jobs and income opportunities, the informal sector plays a crucial role in poverty alleviation and socio-economic development.
• Entrepreneurship and Innovation: The sector fosters entrepreneurship and innovation, as many businesses start informally before transitioning to formal enterprises.

Challenges Facing the Informal Sector

• Regulatory Burden: Compliance with regulatory requirements and inclusion in the tax net have imposed additional burdens on informal enterprises.
• Access to Finance: Limited access to formal financial services and credit restricts the growth and sustainability of informal businesses.
• Lack of Social Security: Workers in the informal sector often lack social security benefits, including health insurance, pensions, and unemployment benefits.
• Low Productivity and Quality of Employment: The shift towards own-account enterprises indicates a decline in employment quality, with fewer opportunities for hired labour.
• Impact of Economic Disruptions: Events such as demonetization, the introduction of GST, and the COVID-19 pandemic have disproportionately affected the informal sector.

Steps Taken to Support the Informal Sector

• Pradhan Mantri Mudra Yojana (PMMY): Launched to provide financial support to micro and small enterprises through easily accessible loans.
• Skill Development Programs: Initiatives like the Pradhan Mantri Kaushal Vikas Yojana (PMKVY) aim to enhance the skills of workers in the informal sector.
• Social Security Schemes: Programs such as the Pradhan Mantri Shram Yogi Maandhan (PMSYM) provide pension benefits to workers in the unorganized sector.
• Digitalization and Formalization: Efforts to promote digital payments and formalize businesses to increase their access to formal financial systems and markets.
• Infrastructure Development: Schemes like the National Urban Livelihoods Mission (NULM) focus on providing infrastructure and support services to informal enterprises.

Way Forward for the Informal Sector

• Improving Access to Finance: Expanding financial inclusion initiatives and ensuring easier access to credit for informal enterprises.
• Enhancing Social Security: Strengthening social security nets and ensuring broader coverage of health, pension, and insurance schemes for informal workers.
• Skill Development and Training: Investing in skill development programs as per the needs of the informal sector to improve productivity and job quality.
• Streamlining Regulatory Frameworks: Simplifying regulatory processes and reducing compliance burdens to encourage the formalization of informal enterprises.
• Promoting Digital Inclusion: Enhancing digital literacy and promoting the use of digital tools to improve business operations and access to markets.
• Supporting Women Entrepreneurs: Providing targeted support to women entrepreneurs in the informal sector to foster inclusive growth.
• Data Collection and Research: Strengthening data collection efforts to better understand the informal sector and design more effective policies and interventions.
• Public-Private Partnerships: Encouraging collaboration between the government, private sector, and civil society to address the challenges faced by the informal sector.

Conclusion

• The informal sector in the Indian economy accounts for almost half of the economic output and more than 90% of employment. However, the sector faces ongoing challenges, particularly in manufacturing, where job losses and enterprise closures have been significant. The data highlights the need for targeted policies to support and stabilize the informal sector, especially in the face of economic disruptions and structural changes.

SCIENCE AND TECHNOLOGY

IMPLEMENTING AI IN HEALTHCARE: PROMISES AND CHALLENGES

Source: Indian Express

Implementing AI in healthcare

Artificial Intelligence (AI) in healthcare is revolutionizing the way medical services are delivered and managed, with the potential to greatly enhance the quality, accessibility, and efficiency of care.

Promises of AI in Healthcare

Enhanced Diagnostics and Treatment

• Medical Imaging: AI algorithms can analyze medical images (such as X-rays, MRIs, and CT scans) with high precision, identifying abnormalities and aiding in early diagnosis. For instance, AI models can detect cancers, fractures, and other conditions more accurately than traditional methods.
• Predictive Analytics: AI can predict disease outbreaks, patient deterioration, and the likelihood of developing specific conditions based on historical data and patterns, enabling proactive healthcare measures.

Personalized Medicine

• Tailored Treatments: AI can analyze genetic, lifestyle, and environmental data to recommend personalized treatment plans, improving the effectiveness of interventions and reducing adverse effects.
• Drug Discovery: AI accelerates drug discovery by analyzing vast datasets to identify potential drug candidates, reducing the time and cost of bringing new drugs to market.

Operational Efficiency

• Automated Administrative Tasks: AI can automate routine administrative tasks such as appointment scheduling, billing, and documentation, freeing up healthcare professionals to focus on patient care.
• Resource Optimization: AI can optimize resource allocation in healthcare facilities, ensuring that staff, equipment, and supplies are used efficiently.

Remote and Accessible Care

• Telemedicine: AI-driven telemedicine platforms facilitate remote consultations, enabling healthcare access for patients in remote or underserved areas. Virtual health assistants can provide real-time health monitoring and guidance.
• Wearable Devices: AI-powered wearable devices can continuously monitor vital signs, track health metrics, and alert users and healthcare providers to potential issues.

Predictive and Preventive Care

• Risk Assessment: AI can assess individual risk factors and predict the likelihood of developing certain diseases, allowing for early intervention and preventive care.
• Health Management: AI can assist in managing chronic conditions by providing personalized recommendations for lifestyle changes and medication adherence.

Challenges in Implementing AI in Healthcare

Data Quality and Availability

• Fragmented Data: Patient data is often scattered across multiple systems and formats, making it difficult to aggregate and analyze. High-quality, labelled data is essential for training effective AI models.
• Data Privacy: Ensuring the privacy and security of patient data is crucial, and compliance with data protection regulations (such as GDPR) is necessary.

Infrastructure Limitations

• Digital Divide: Inadequate digital infrastructure, particularly in rural areas, affects the deployment and effectiveness of AI solutions. Poor internet connectivity and outdated technology can hinder AI adoption.
• Integration Challenges: Integrating AI systems with existing healthcare infrastructure can be complex and require significant investment.

Skill Gap and Training

• Lack of Expertise: There is a shortage of professionals with expertise in both AI and healthcare. Training healthcare providers to use AI tools effectively is essential.
• Continuous Education: Ongoing education and training programs are needed to keep healthcare professionals updated with the latest AI advancements.

Regulatory and Ethical Issues

• Regulatory Framework: A comprehensive regulatory framework specific to AI in healthcare is needed to ensure safe and effective use. This includes guidelines for validation, accountability, and ethical considerations.
• Bias and Fairness: AI systems can inherit biases from training data, leading to unfair or inaccurate outcomes. Addressing bias and ensuring fairness in AI models is critical.

Financial Constraints

• High Costs: The initial investment required for AI technologies and infrastructure can be substantial. Financial constraints can limit the adoption of AI, particularly in resource-limited settings.
• Cost-Benefit Analysis: Demonstrating the cost-effectiveness of AI solutions through rigorous analyses can help justify the investment.

Awareness and Adoption

• Resistance to Change: Healthcare providers and patients may resist adopting new AI technologies due to skepticism or lack of understanding. Building trust and demonstrating benefits are key to overcoming resistance.
• Awareness Programs: Educating stakeholders about the benefits and potential of AI in healthcare is essential for encouraging adoption.

Interoperability

• Standardization: Lack of standardized protocols and formats for data exchange between different healthcare systems can hinder the integration of AI solutions. Promoting interoperability is crucial for effective implementation.

Cultural and Socioeconomic Factors

• Diverse Needs: India's diverse population presents varied healthcare needs, making it challenging to develop standardized AI solutions. Solutions must be adaptable to different cultural and socioeconomic contexts.
• Equitable Access: Socioeconomic disparities can lead to unequal access to AI technologies. Addressing these disparities is important to ensure equitable healthcare delivery.

Steps Taken to Support AI in Healthcare

National Strategies and Reports

• NITI Aayog’s National Strategy for AI: Emphasizes the need for AI to address the shortage of healthcare professionals and improve accessibility.

Government Initiatives

• Ayushman Bharat Health Accounts (ABHA): Nationwide promotion of digital health IDs linked to personalized AI solutions.
• Ayushman Bharat Digital Mission (ABDM): Aims to create a unified digital health infrastructure.

Collaborations

• Partnerships with Tech Giants: Collaborations with companies like Microsoft and startups like Forus Health for AI-based healthcare solutions.
• International Centre for Transformational Artificial Intelligence (ICTAI): Focus on rural healthcare, in collaboration with NITI Aayog and the Maharashtra government.

Research and Development

• Encouraging Investments: Government incentives for industry players to invest in R&D for innovative healthcare technologies.

Overcoming Challenges

Improving Data Quality and Availability

• Data Integration: Develop systems for integrating fragmented data from different sources. Implement data management practices to ensure high-quality, labelled data.
• Privacy Measures: Strengthen data protection regulations and practices to safeguard patient information and build trust in AI systems.

Strengthening Infrastructure

• Invest in Digital Infrastructure: Improve internet connectivity and technological infrastructure, especially in rural and underserved areas.
• Upgrade Facilities: Equip healthcare facilities with the necessary technology to support AI implementations.

Bridging the Skill Gap

• Educational Programs: Develop and promote educational programs focused on AI and healthcare to build a skilled workforce.
• Training Initiatives: Implement continuous training and professional development programs for healthcare providers.

Developing Regulatory Frameworks

• Create Guidelines: Establish clear regulatory guidelines for the use of AI in healthcare, including validation, accountability, and ethical considerations.
• Address Bias: Implement strategies to identify and mitigate biases in AI systems to ensure fairness and accuracy.

Ensuring Financial Sustainability

• Funding Opportunities: Explore funding opportunities and partnerships to support the initial costs of AI technologies and infrastructure.
• Cost-Benefit Analysis: Conduct cost-benefit analyses to demonstrate the potential savings and benefits of AI adoption in healthcare.

Enhancing Awareness and Encouraging Adoption

• Awareness Campaigns: Launch campaigns to educate stakeholders about the benefits of AI in healthcare and address concerns.
• Pilot Projects: Implement pilot projects to demonstrate the effectiveness of AI solutions and build confidence among healthcare providers and patients.

Promoting Interoperability

• Standardization: Develop and adopt standardized protocols and formats for data exchange between healthcare systems to facilitate interoperability.
• Collaborative Efforts: Encourage collaboration between stakeholders to develop and implement interoperable solutions.

Addressing Cultural and Socioeconomic Barriers

• Customized Solutions: Develop AI solutions that are adaptable to diverse cultural and socioeconomic contexts.
• Equitable Access: Implement policies and programs to ensure equitable access to AI technologies across different regions and communities.

Conclusion

• The integration of AI in healthcare in India holds immense promise, from improving access and efficiency to reducing costs and enhancing the quality of care. However, realizing this potential requires addressing significant challenges related to data quality, infrastructure, skills, regulations, financial constraints, and socioeconomic factors. By overcoming these challenges, AI-driven healthcare solutions can play an important role in transforming India’s healthcare system, making it more equitable, efficient, and responsive to the diverse needs of its population.