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Consumer Pyramidsdx


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URL:
https://link.springer.com/epdf/10.1007/s41027-022-00420-7?sharing_token=WF9WbAZwQHJqPk0L3DgnHve4RwlQNchNByi7wbcMAY6ouzQ47YcvLHKN72xxwFyLoNVpgcg0MFdChyO3WBDTr1peecnR343v8W0U_rg6SSUM5Rm7MZ7eNkSZ9xH9dO00bCo0Lo0rhqv4JGUVcoNaTDu91W7V-AY2i5JlQBikLJg=
Authors: Partha Chatterjee
Shiv Nadar University
Aakash Dev
Shiv Nadar University
Published: Indian Journal of Labour Economics, Dec 2022 JEL Codes: JEL code(s)
not specified. Versions: ● Labour Market Dynamics and Worker Flows in India:
Impact of Covid19

Labour Market Dynamics and Worker Flows in India: Impact of Covid19

Tracking and analyzing the labour market dynamics at regular, frequent intervals
is critical. However, this was not possible for India, a large emerging economy
with a significant population undergoing demographic transition, due to a
paucity of data. We use the new dataset Centre for Monitoring Indian Economy
(CMIE)Consumer Pyramids Household Survey (CPHS) and use a panel to create Labour
Flow Charts and Transition Matrices for India from January 2019 to December
2021. To the best of our knowledge, this is the first time these were created
for India. We then use that to look at the impact of Covid-19 on the Indian
labour market. We not only look at transitions between employment, unemployment
and out of labour force, but also across types of employmentfull-time and
part-time. The rich data also allows us to consider heterogeneity in the labour
market and look at the differential impact of the pandemic across different
education groups and gender. From the labour flow charts and transition
probabilities, we find that while all groups have been impacted, the magnitude
of the impact is different across groups. The recovery is also uneven, and the
extent depends on education levels. Further, we do an event study analysis to
examine the likelihood of getting a full-time job across different educational
and gender groups. Men, on average, enjoy a higher likelihood of getting a
full-time job than women. The likelihood coefficients also go up with increasing
educational qualifications. Looking at skill heterogeneity, while the likelihood
of getting a full-time job either goes down for most groups during the pandemic
or the change is minuscule, strikingly it goes up for those with no education,
for both men and women. The likelihood coefficients remain elevated for men even
after the restrictions are removed, and that for women reverts to the level seen
before the pandemic. Finally, this paper provides a way to continuously monitor
the dynamics of the labour market as data is released in the regular intervals
in the future, which would be of great value for researchers and policymakers
alike.

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