In India, the issue of unemployment persists as a multifaceted challenge, with a significant portion being attributed to structural factors. Structural unemployment, characterized by a mismatch between the skills possessed by the labor force and those demanded by the job market, has emerged as a prominent concern. While assessing the magnitude of this issue, it’s imperative to scrutinize the methodologies employed to compute unemployment statistics in the country. Currently, India primarily relies on household surveys, such as the Periodic Labour Force Survey (PLFS) conducted by the National Sample Survey Office (NSSO), and administrative data from government agencies like the Labor Bureau. These methods often capture only a portion of the workforce, particularly those in the formal sector, thereby potentially underrepresenting the true extent of unemployment, especially among the informal and rural sectors. Moreover, the methodologies employed may not adequately account for discouraged workers who have exited the labor force due to prolonged job search without success. To enhance the accuracy and comprehensiveness of unemployment measurements, several improvements can be suggested. Firstly, incorporating a broader spectrum of data sources including administrative records, private sector surveys, and real-time labor market indicators can provide a more holistic understanding of the employment landscape. Additionally, implementing advanced statistical techniques like longitudinal studies and econometric modeling can offer deeper insights into the dynamics of unemployment, enabling policymakers to formulate targeted interventions. Furthermore, fostering collaboration between government agencies, academia, and industry stakeholders can facilitate the development of a robust and adaptable framework for measuring unemployment, tailored to the diverse socio-economic contexts prevalent across different regions of the country. By refining the methodologies for computing unemployment and embracing a more inclusive approach to data collection and analysis, India can better address the structural challenges underlying its unemployment crisis, paving the way for more effective policy interventions and sustainable economic growth.
Answer:
Introduction:
As per NSSO report, the unemployment rate is around 7.33% (2022-23) and the structural unemployment is due to drastic change in the economic structure that is leading to mismatch between the skills possessed by the workforce and the skills demanded by the employers.One of the reasons why NITI Ayog focuses on skill development is to address the problem of structural unemployment in the country.
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Reasons behind the structural difference
- Skill Mismatch- as per CMIE 4/5th of the people do not have the skills as per the demand of the market.The education system in India is often criticised for being too theoretical, leading to graduates with skill deficiencies. Initiatives like Skill India have not fully bridge.
- Technology Shifts: Rapid technological changes, including automation and AI, have made some jobs obsolete, creating unemployment for workers lacking the required technical skills.
- Industrial Changes: Traditional sectors like agriculture are declining in GDP contribution and job opportunities, leading to unemployment among workers transitioning into new roles.
- Regional Disparities: Metropolitan cities like Bangalore and Mumbai offer tech and finance jobs, but rural areas such as UP and Bihar face limited employment opportunities. Special Economic Zones (SEZs) exacerbate this regional disparity.
- Outdated Economic Policies: Economic policies that don’t align with current conditions can lead to structural unemployment, as many educated youth lack market-relevant skills.
- Informal Sector Dominance: The informal sector absorbs a significant portion of the workforce but often lacks job security(comprises 80% of the jobs), benefits, and career progression.
- Wage Expectations: Mismatched wage expectations, particularly among recent graduates, can hinder job placements. E.g.MBA may expect a high starting salary but must accept lower offers due to market conditions.
Methodology for Computing Unemployment in India:
- Census of India: The Census of India, conducted every ten years, offers a baseline population count used as a reference for estimating the labour force. For example, the 2011 Census revealed India’s population was approximately 1.2 billion.
- NSSO (National Sample Survey Office) Surveys: NSSO conducts extensive sample surveys on employment and unemployment, providing crucial insights into the country’s employment situation. The Periodic Labor Force Survey (PLFS), conducted annually since 2017-18, is an example. For instance, the 2017-18 PLFS revealed an urban unemployment rate of 7.8%.
- PLFS (Periodic Labor Force Survey): PLFS is a significant source for estimating employment and unemployment in India, replacing previous NSSO surveys. It provides detailed data on labour force participation and employment status and is conducted annually. For instance, the 2018 PLFS reported a 49.8% labour force participation rate.
- Labor Bureau Surveys: The Labor Bureau of India conducts surveys to gather data on employment and unemployment.Surveys like Employment and Unemployment Surveys (EUS)and Annual Employment- Unemployment Surveys (EUS-AEUS) collect employment data, such as a 10% decline in manufacturing jobs in 2020 compared to 2018.
- Employment classification:
- Usual Principal Status (UPS) Approach: It categorises individuals as employed, unemployed, or not in the labour force based on their primary activity in the year before the survey.
- Current Weekly Status (CWS) Method: This classifies people based on their activities in the week preceding the survey, providing a real-time employment snapshot.
- Unemployment Rate Calculation: The unemployment rate is determined by dividing the number of unemployed individuals by the total labour force, including both employed and unemployed.
- Segmented Data Analysis: Unemployment data is divided by age, gender, education, and urban/rural locations, helping analyse jobless trends among different population groups.
Limitations with the unemployment calculation in India:
- Delayed Release of Employment Data: The delayed release of employment data, such as the NSSO’s 2017-18 employment survey, led to the resignation of the last two independent members of the National Statistical Commission.
- Constraints from Social Norms: Social norms strongly influence work-seeking decisions. Many women in domestic roles are willing to work but may not be actively seeking jobs, leading to the undercounting of the unemployed.
- Informal Sector Complexity: The complexity of informal jobs in India poses categorization challenges. People engage in various activities throughout the year, making it difficult to clearly define their employment status.
- Rural vs. Urban Disparities: In agrarian economies, low employment thresholds result in lower rural unemployment rates compared to urban areas. Access to family farms or casual agrarian work increases the chances of finding some form of employment.
Measures needed:
- Real-time Data: Utilise Big Data analytics and IoT for gathering real-time unemployment statistics, allowing timely interventions and policy adjustments, e.g., through job portal analytics.
- Skill Mapping: Conduct regular sector-specific skill mapping surveys to identify education-industry mismatches and effectively address structural unemployment.
- Transparency: Make all collected data and reports easily accessible to the public, with real-time employment statistics dashboards for public monitoring.
- Policy Feedback Loop: Establish a system for immediate policy adjustments based on enhanced data, addressing high unemployment in specific sectors through interventions like skill development programs.
- Incorporate Underemployment: Include underemployment metrics in official statistics to offer a more nuanced understanding of the labour market, as done in countries like Australia
Conclusion:
Hence,Effective policy measures require a deep understanding of structural unemployment. While current Indian unemployment calculation methods provide a foundation, there’s room for improvement. Utilising innovative data collection and analysis can offer more precise insights, enabling targeted interventions to reduce structural unemployment.
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