Female Labour Force Participation Rate increased across states
Special Research Report
The Economic Department,
State Bank of India
FinTech BizNews Service
Mumbai, November 14, 2023: The recently released sixth annual PLFS report from the NSSO gauging unemployment rates across rural and urban areas for Pan-India as also states with different levels of disaggregation like gender, age, etc. reveal a significant decline in unemployment rate from 6.1% in FY18 to 3.2% in FY23, with an accompanying increase in Labour Force Participation Rate /LFPR from 36.9% to 42.4%, with the Female Labour Force Participation Rate far outstripping the overall gain in LFPR ? Unemployment is always a contentious and political issue, more so for developing economies, and it is no surprise that even as the unemployment rates in PLFS survey data for the 5-year period ended has revealed a significant decline, there is a plethora of misplaced & ignorant data interpretations in public domain, some political, some economic and laced with old fashioned rhetoric…regarding….for example, the jump in self employed populace and youth unemployment
• Firstly, interpreting the jump in self-employed populace within the employment estimates (57.3% in FY23 now against 52.2% in FY18) with main traction coming from rising share of household helpers evidentially has been wrongfully interpreted by labour economists and others as a signal of shrinking employment opportunities… The fact is however….(a) the central tendency of self employed in India’s labour force has always been trending much above 50%, even during the NSS EUS days through 1980s and 90s to 2000s…….(b) The Government emphasis on entrepreneurship through PMMY and even recent schemes post pandemic like PM SVANidhi for those at bottom of the pyramid is imparting a structural transformation in labour markets in India through formalization of credit for such family enterprises and it is heartening that the family enterprises are getting bigger and this is getting reflected through a rise in household helpers. Also, (c) with primary subsistence needs like food, shelter, medical needs being taken care by the Government through free ration for 80 crore people, PMAY and Ayushman Bharat, apart from additional state schemes, such people are making a clear trade-off between earnings & working in family enterprises. earnings have actually increased across all categories…
• Secondly, the youth unemployment rate in PLFS survey (between age group of 15-29 years) even though shows a decline from 12.9% to 10% for the 3-year period ended FY23 is still cited wrongfully as a proxy for shrinking unemployment opportunities. However, while there is a lot of noise about it being a barometer of serious youth unemployment, we believe it’s truly a reflection of changing employment-education pattern, with the men/women remaining in the education system at least until the age of 23-24 years which used to be only up to 17 years earlier. As this sub-group (41 million in 2020-21 per MHRD data, with 11 million from the northern states alone) is not counted in labor force, this could be pushing up the unemployment rate in the 15-29 age bracket as a pure statistical artefact….(low denominator in terms of labour force) if we re-estimate the unemployment rates for 30+ group separately…in 15-29 age group, the PLFS unemployment rate for urban male.
Back in the angst filled days of mid-70s, when the unemployment rate was pegged at just 2.5% in an era of Hindu growth rate chequered with abysmal level of Gross Capital formations and Savings, the decision to change the EUS with PLFS seems to be in sync with changing realities. Strikingly, EUS-NSSO survey pegging unemployment at a meagre 2.0% in the heydays of global financial crisis in 2009-10 raises eyebrows for sure
? Perhaps, the implausible variance in unemployment rates across the earlier NSSO and current PLFS survey stems from the starkly different methodologies employed by these two; PLFS considers education level of households where larger weights are assigned to households having higher number of 10th pass members (above 15 years) while previous survey of EUS-NSSO was based on expenditure (urban) or livelihood (rural) of households (it is thus natural to have an unemployment rate of 2.5% in 1977-78 when PFCE was more than 75% of GDP)
? The rapid increase in educational enrollments post secondary levels (as more students were lured to schools by state welfare initiatives in past decade), a greater portion qualified ‘naturally’ to seek post-secondary enrollments (dubbed higher education)..It however seems the time may have come again to tweak the absolute, omnipotent benchmarking given to higher educational qualifications in PLFS as education is the most critical factor in deciding the unemployment rate (In the last three years, maximum deceleration in unemployment rate is visible in the persons having education of secondary and above) and one needs to calibrate the education/employment matrix rationally.... As per PLFS, these people are not counted in labour force because they are still in colleges. This could thus push up the unemployment rate in the 15-29 age bracket as a pure statistical artefact (as unemployment rate is explained as a percentage of labour force).
An alternate employment survey by CMIE, CPHS too possibly suffers from innate flaws in its sample selection technique, thereby under-representing women and young children as also the poor while overrepresenting the other extreme (Somanchi 2021)… Interestingly, both PLFS and CMIE give urban unemployment rate and their mean are nearly same for quarterly data of June 2018 to June 2023 ? But CMIE urban unemployment is more skewed, i.e. concentration is more towards higher values, signifying that unemployment measured through CMIE urban employment is more on upside in comparison to PLFS…. As an example, kurtosis of CMIE urban unemployment is nearly double the kurtosis of PLFS signifying that there is too much concentration near high peak of central value, again signifying that CMIE urban employment is more on upside in comparison to PLFS…..Urban Kurtosis might be high, because the respondents might be changing…. People in the lower part moves a lot and thus household addresses changes. So CMIE unemployment survey might be concentrating more on the stable households, thereby on stable income resulting in average higher echelon ? PLFS might be more distributed as they do more granular stratified sampling.
