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Labour Market Flows and Gender Differentials in Urban Unemployment over the Pandemic

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Abstract

Utilising data from the Periodic Labour Force Survey (PLFS), we estimate quarterly changes in urban labour market flows over the period 2018–2022 and the impact on unemployment rates for men and women. Our analysis provides non-intuitive explanations for established findings as well as pointing out important questions for further study. Both men and women’s unemployment rates have reduced in 2022 compared to 2018, showing rapid reductions following the high levels reached during the lockdown. Women’s unemployment rates have consistently been higher than men throughout this period. The gap between men and women’s unemployment rates reduced during the lockdown, but have shown signs of increasing since 2021, even as unemployment rates have fallen. For women, flows from the labour force to non-participation play a larger role in explaining changes in unemployment rates as compared to men. Flows from the labour force to non-participation, however, have reduced since the pandemic, providing an explanation as to why labour force participation rates (LFPRs) have increased, namely women staying for longer in the labour force rather than more women entering it. Despite rising LFPRs, the gender gap in unemployment rates have risen, in contrast to developed economies.

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Fig. 1

Source PLFS Quarterly Bulletins, various reports

Fig. 2

Source PLFS Quarterly Bulletins, various reports

Fig. 3

Source PLFS Quarterly Bulletins, various reports

Fig. 4
Fig. 5

Source Author’s calculation from the PLFS Quarterly Bulletins, various reports

Fig. 6

Source Author’s calculation from the PLFS Quarterly Bulletins, various reports

Fig. 7

Source Author’s calculation from the PLFS Quarterly Bulletins, various reports

Fig. 8

Source Author’s calculation from the PLFS Quarterly Bulletins, various reports

Fig. 9

Source Author’s calculation from the PLFS Quarterly Bulletins, various reports

Fig. 10

Source Author’s calculation from the PLFS Quarterly Bulletins, various reports

Fig. 11

Source Author’s calculation from the PLFS Quarterly Bulletins, various reports

Fig. 12

Source Author’s calculation from the PLFS Quarterly Bulletins, various reports

Fig. 13

Source Author’s calculation from the PLFS Quarterly Bulletins, various reports

Fig. 14

Source Author’s calculation from the PLFS Quarterly Bulletins, various reports

Fig. 15

Source Author’s calculation from the PLFS Quarterly Bulletins, various reports

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Notes

  1. Based on a survey of 8530 individuals between May–July 2020, Bhalotia et al. (2020) report that while 15.5% of the sample report being unemployed in the week before the interview, a figure which rises to 21.7% if those receiving 0 income were included. Mishra and Das (2022) classify those workers who received zero income while being employed as unemployed. Abraham (2020) highlights how during a significant exogenous shock like a pandemic, individuals – particularly women—who would have lost their jobs may not classify themselves as being “unemployed”, but may report themselves as being out of the labour force. These studies stress the important point that during a pandemic, the normal classifications of employment and unemployment may not capture the full reality of work loss and its impact on income, earnings and well-being. While these criticisms of standard definitions of work status is important, we refrain from widening the ambit of the category “unemployment”: our aim is to analyse the factors driving changes in the measured unemployment rate—as understood by the standard notation—over a longer time period that does not just include the pandemic.

  2. https://www.hindustantimes.com/india-news/india-s-gdp-growth-rises-falls-by-23-9-per-cent-in-april-june-quarter/story-Yj1GGTR7fuHAQ6QNL0jpBL.html

  3. https://www.thehindu.com/business/Economy/indias-gdp-grows-16-in-january-march-shrinks-73-in-2020-1/article34690310.ece

  4. See Jha and Kumar (2020) for an extensive discussion of findings from field surveys.

  5. https://www.hindustantimes.com/business/the-poorest-have-been-worst-hit-by-pandemic-101611781491571.html

  6. The study notes that in the second wave, the brunt of job losses was experienced by urban men.

  7. A note of caution with regard to the use of the steady-state unemployment rate: we do not attempt to analyse the behaviour of the economy outside of the steady state and its possible adjustments to equilibrium, and make no comment on whether an economy can actually reach a steady-state equilibrium within a period as short as a quarter. We use simply use the concept as a framing device so we may focus our attention on different labour market flows and its impact on changes in unemployment rates.

  8. This gender differential in flows out of the labour market remain significant even after demographic characteristics like age and education are controlled for, as shown in Menon and Nath (2022).

  9. This is not to imply that these unemployed women permanently leave the labour force, just that in every quarter, a fifth of unemployed women leave the labour force, possibly returning in later periods. As shown by Deshpande and Singh (2021), women make multiple entries and exits to the labour force. Since this study deals with aggregate transitions and is not an individual-level analysis, we do not deal with the question of whether these moves are permanent or only temporary.

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Authors and Affiliations

  1. B3-4 Cb01, Azim Premji University, Survey No 66, Burugunte Village, Bikkanahalli Main Road, Sarjapura, Bengaluru, 562125, India

    Paaritosh Nath

  2. O.P. **dal Global University, Sonipat, India

    Rahul Menon

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  1. Paaritosh Nath
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Correspondence to Paaritosh Nath.

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Nath, P., Menon, R. Labour Market Flows and Gender Differentials in Urban Unemployment over the Pandemic. Ind. J. Labour Econ. 67, 73–96 (2024). https://doi.org/10.1007/s41027-024-00481-w

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