Most of the unemployment in India is structural in nature. Examine the methodology adopted to compute unemployment in the country and suggest improvements.

Q11. Most of the unemployment in India is structural in nature. Examine the methodology adopted to compute unemployment in the country and suggest improvements. (15m) Economy

Introduction

Structural unemployment is a category of involuntary unemployment caused by differences between the skills possessed by the unemployed population and the jobs available in the market. Structural unemployment is a long-lasting condition that is caused by fundamental changes in the economy. Most of the unemployment in India is structural because of lack of requisite skills and poor education level.

Why is most of the unemployment structural in India?

- Education and Skill Gap: Workers often lack the skills needed for modern jobs due to an outdated education system.
  - E.g.: Most of the graduates lack the practical skills needed for jobs; an engineering graduate may lack programming skills for IT roles.
- Rural-Urban Divide: Rural areas have fewer job opportunities compared to cities.

E.g.: Rural areas have limited non-agricultural job opportunities; a farmer’s child may struggle to find non-farming work.

Labor Market Regulations: Stringent labor laws can discourage hiring and make layoffs difficult.
- E.g.: Stringent labor laws can discourage hiring; small business owners may hesitate to expand due to complex labor regulations.
Population Growth: Rapid population growth strains the job market’s capacity.
- E.g.: Rapid population growth strains job creation; the economy struggles to keep up with new entrants.
Technology Disruption: Automation and technology shifts are replacing traditional jobs.
- E.g.: Automation replaces jobs; bank tellers face reduced demand due to ATMs.
Geographic Disparities: Economic development varies between regions, leading to uneven job availability.
- E.g.: Economic development varies; IT jobs are abundant in cities but scarce in less-developed regions.
Wage Expectation Gap: Mismatch in wage expectations can hinder job matches.
- E.g.: Graduates have unrealistic wage expectations; an MBA may expect a high starting salary but must accept lower offers due to market conditions.

Key Methodology adopted to compute unemployment in India:

Census of India: The Census of India is conducted once every ten years. While it does not provide detailed information on employment and unemployment, it does provide a baseline population count that is used as a reference for estimating the labor force.

E.g.: Provides baseline population count for the country. (Example: 2011 Census revealed India’s population was approximately 1.2 billion.)

NSSO (National Sample Survey Office) Surveys: The NSSO conducts large-scale sample surveys on employment and unemployment in India. These surveys are crucial in understanding the employment situation in the country. The Periodic Labor Force Survey (PLFS) was one such survey conducted annually from 2017-18 onwards.
E.g.: Conducted annually, offer insights into employment and unemployment. (Example: 2017-18 PLFS showed an urban unemployment rate of 7.8%.)
PLFS (Periodic Labor Force Survey): The PLFS is a significant source for estimating employment and unemployment in India. It replaced the earlier employment-unemployment surveys conducted by the NSSO. PLFS provides detailed information on labor force participation, employment status, and other relevant factors. It’s conducted annually.

E.g.: Detailed data on labor force participation and employment status. (Example: 2018 PLFS reported a 49.8% labor force participation rate.)

Labor Bureau Surveys: The Labor Bureau of India conducts surveys to collect data on employment and unemployment. The Employment and Unemployment Surveys (EUS) and Annual Employment-Unemployment Surveys (EUS-AEUS) are examples of such surveys.

E.g.: Collect employment data, e.g., a 10% decline in manufacturing jobs in 2020 compared to 2018.

State Employment Exchanges: State Employment Exchanges collect data on job seekers and job placements. While this data does not provide a comprehensive picture of unemployment, it can give insights into the number of people actively seeking employment.

Suggestive Measures:

Enhanced Frequency and Timeliness of Surveys: Increase the frequency of labour force surveys to ensure more up-to-date information on employment and unemployment trends.
Regional and Industry-specific Breakdowns: Improve the granularity of data by offering more detailed information on unemployment rates at regional and sectoral levels. Given India’s diverse labor market, this granularity can aid in crafting more precisely targeted policies.
Leverage Technology for Data Collection: Embrace technology for data gathering and analysis, utilizing digital surveys, mobile applications, and data analytics tools to streamline the data collection process. This can reduce the time lag between data collection and dissemination.
Incorporate the Informal Sector: Develop methodologies to capture informal employment and underemployment more effectively, considering the substantial presence of the informal sector in India. Innovative survey techniques and alternative data sources may be explored.

Improving the methodology for measuring unemployment in India is essential for policymakers to make informed decisions and for society to address the structural issues contributing to unemployment effectively. This should be done in a transparent and collaborative manner to ensure the credibility and relevance of the data.

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