A disconcerting picture behind the headline numbers

The Hindu     3rd August 2021     Save    

Context: The third annual round of the Periodic Labour Force Survey (PLFS) data conducted during July 2019-June 2020 was released recently. There is evidence to suggest that the PLFS data may underestimate the loss of earnings and fall in consumption.

Key findings of the third annual round of PLFS 2019-20: From a paradox of jobless growth in the past, India seems to have come full circle with ‘growth less employment.’

  • Positive trends in employment scenario: 
    • Fall in unemployment rate: Fell from 6.1% in 2017-18 to 4.8% in 2019-20.
    • Rise in Labour Force Participation Rate, LFPR: Increased from 36.9% to 40.1% (same period).
    • Rise in Worker Population Ratio, WPR: Increased from 34.7% to 38.2% (same period).
  • Decline in GDP growth: The quarterly GDP growth declined for successive quarters, sliding from 8.2% in January-March 2018 to 3.1% in January-March 2020.
  • Impact of lockdown: 
    • GDP contraction by 23.9% during April-June 2020.
    • The current weekly status unemployment rate in April-June 2020 quarter was 14%, and the urban unemployment rate was around 20%.
    • Impact on income: The average monthly income for the salaried increased by 2%, whereas the monthly earnings of self-employed declined by 16% and daily wage for casual workers declined by 5.6% during the April-June 2020 quarter. 
    • The real monthly per capita consumer expenditure declined by 7.6%.

What are the headline numbers of PLFS data misses? Understanding the puzzle of fall in unemployment despite decline in GDP.

  • No quality gains from the rise in employment status: 
    • Rise in unpaid family workers: While the workforce increased by 2.9%, the proportion of all other employment categories in the workforce declined, except unpaid family helpers.
    • High proportion of households that did not earn any income from economic activities, classified as ‘others’ - 9.1% in rural India and 14.7% in urban areas.
  • Regressive structural shifts: 
    • Agriculture continues to perform the function of a sink: The proportion of the rural workforce in agriculture has gone up from 59.4% to 61.5%.
    • Workers employed in the manufacturing sector have reduced from 12.1% to 11.2%.
  • Gender dimension of the changing composition of workforce: 
    • Feminisation of agriculture and unpaid labour: Declining unemployment rate can largely be explained by a movement of women from domestic work to agriculture and other petty activities.
    • Unemployment rate among urban females at 8.6% is almost double the national rate.
  • Loose definition of work that underestimates open unemployment: 
    • The registered fall in unemployment is when measured on usual status, which reflects the activity of an individual over a relatively long period of the last 365 days.
    • Using the current weekly status approach (Last 7 days), the unemployment rate was estimated to be 8.8%, unchanged during the last three years.
    • Rise in income for salaried does not correlate with falling consumption: 
    • According to PLFS, the average monthly income for the salaried increased by 2% in April-June 2020 over April-June 2019.
    • However, private final consumption expenditure declined by 26.7% in April-June 2020 over the same quarter in 2019. 
  • Limited scope of PLFS and gaps in India’s data regime: 
    • No official data on poverty after 2011, on-farm income after 2013, and no recent data on migrants.
    • The consumer expenditure data for 2017-18 was buried, and the data on situation assessment of agricultural households are not yet released.

Way forward: Steps needed to ensure credibility of data

  • Methodological fine-tuning: Minor tweaks in future PLFS surveys can fill the data gaps
  • Adding questions on costs and returns from cultivation and related activities can also capture more accurate data on agricultural incomes.
  • Lengthening the questionnaire: It has its costs, but the costs of absence of reliable and timely data on important policy-relevant indicators are far higher.