Table of Contents
THE CONTEXT: The KLEMS database, a crucial tool for analyzing productivity and employment trends in India, faces scrutiny over its methodological approach, raising concerns about the accuracy of reported employment figures. Addressing these issues is vital to ensure reliable data that can inform effective policy-making and economic planning.
METHODOLOGICAL CONCERNS:
- Data Sources and Interpolation: The KLEMS database relies on various sources like the Employment-Unemployment Surveys (EUS), Periodic Labour Force Surveys (PLFS), National Account Statistics, and the Annual Survey of Industries. In the absence of yearly data from the National Statistical Office, available data are used as benchmarks and interpolated for other years, which may introduce inaccuracies. The PLFS data, for instance, shows a rise in the worker population ratio, but these are largely self-employed and own-family work, not regular wage employment
- Population Projections: In recent years, the database has used population projections from the Ministry of Health and Family Welfare (MoHFW), which are reportedly higher due to assumptions about fertility rates and uniform growth across rural and urban areas. This could lead to overestimated employment figures, particularly in rural areas with higher Worker Population Ratios (WPR).
- Sectoral Employment Distribution: The methodology involves distributing worker numbers among industry groups based on their employment shares in PLFS, which may not accurately reflect the actual employment situation, especially when using projected population figures. The PLFS data shows discrepancies in employment figures compared to For example, while KLEMS reported 1.9 crore new jobs for 2022-23, the PLFS cited 4.1 crore, showing a discrepancy of 2.2 crore jobs.
- Temporal Comparability Issues: The WPR significantly dropped from 2011-12 to 2017-18 as data sources shifted from EUS to PLFS. The KLEMS database assumes no issues with temporal comparability, which could be misleading. The shift from EUS to PLFS data sources has led to a significant drop in the WPR from 2011-12 to 2017-18, raising concerns about temporal comparability. The PLFS data indicates an increase in WPR from 46.8% in 2017-18 to 56% in 2022-23, but these figures may not be directly comparable due to changes in survey methodology.
- Inflated Employment Numbers: Higher population estimates and increased WPRs for certain demographics, such as rural women, result in inflated employment numbers. This is particularly evident in sectors like agriculture and services, where employment figures have risen sharply in recent years. The KLEMS data shows a surprising jump in employment, with 46.7 million jobs reportedly created in FY24, despite poor consumption growth of just 4%.
- Quality of Employment: The database includes individuals with subsidiary employment, often unpaid family workers, which may not accurately reflect the quality or stability of employment. Therefore, using these figures to claim robust job creation without considering job quality can be misleading.
THE WAY FORWARD:
- Enhance Data Collection and Methodology: The International Labour Organization (ILO) emphasizes the importance of developing international standards for labor statistics to enhance comparability and measurement accuracy. The National Sample Survey Office (NSSO) should be responsible for primary data collection, ensuring consistency and accuracy in employment data. The Periodic Labour Force Survey (PLFS) launched by the NSSO in 2017 aims to provide more frequent estimates of employment and unemployment indicators in urban and rural areas.
- Improve Population Projections: The RBI’s KLEMS database has shown discrepancies in employment figures due to population projection assumptions. To refine population projections, more accurate demographic data should be used by collaborating with institutions like the Ministry of Health and Family Welfare (MoHFW) and the Census Bureau. Projections should be adjusted based on recent trends in fertility rates and urban-rural migration patterns to avoid overestimating employment figures.
- Sectoral Employment Distribution: The RBI data showed a 3.31% increase in employment across 27 sectors in 2022-23, indicating the need for sector-specific analysis. Combine enterprise and household surveys to capture employment distribution across sectors better. Integrate data from the Annual Survey of Industries and the Periodic Labour Force Survey (PLFS) to create a more comprehensive employment picture. The India Employment Report 2024 emphasizes the need for active labor market policies to improve employment data accuracy.
- Address Temporal Comparability Issues: The Ministry of Labour’s Group on Labour Statistics recommends developing a standardized methodology for comparing data across different survey periods. The Task Force on Improving Employment Data suggested improving the comparability of data across different periods.
- Adjust for Inflated Employment Numbers: The PLFS data showed an increase in the female Worker Population Ratio in urban areas from 20.6% to 23.4% during January-March 2023 to January-March 2024, indicating changes in employment dynamics. Re-evaluate the Worker Population Ratio (WPR) calculations by considering the quality and stability of employment. Focus on creating high-quality jobs and improving job security.
- Improve Employment Quality: The RBI data indicated that employment growth mainly comes from self-employment and unpaid work, which are not equivalent to formal jobs. Develop policies promoting decent work and addressing the informal sector’s challenges. Encourage entrepreneurship and skill development through government schemes like PMMY and the National Skill Development Mission.
THE CONCLUSION:
By adopting international standards, refining demographic projections, and focusing on job quality, India can enhance the reliability of its employment data. These improvements will support sustainable job creation and clarify the nation’s labor market dynamics.
UPSC PAST YEAR QUESTIONS:
Q.1 Most of the unemployment in India is structural. Examine the methodology adopted to compute unemployment in the country and suggest improvements. 2023
Q.2 Explain the difference between the computing methodology of India’s gross domestic product (GDP) before the year 2015 and after 2015. 2021
Q.3 The nature of economic growth in India in described as jobless growth. Do you agree with this view? Give arguments in favor of your answer. 2015
Q.4 While we found India’s demographic dividend, we ignore the dropping employability rates. What are we missing while doing so? Where will the jobs that India desperately needs come from? Explain. 2014
MAINS PRACTICE QUESTION:
Q.1 Critically analyze the methodological challenges associated with the KLEMS database in measuring employment trends in India.
SOURCE:
Spread the Word