Understanding Labor Market Dynamics in Urban India Amidst the Pandemic: A Study Employing Supervised Learning Methods

• Published in: Journal of quantitative economics : journal of the Indian Econometric Society Vol. 22; no. 4; pp. 883 - 909
• Main Authors: Singha Roy, Namrata, Ghosh, Niladri
• Format: Journal Article
• Language: English
• Published: New Delhi Springer India 01.12.2024
• Subjects: Econometrics; Economics; Economics and Finance; Finance; Game Theory; Insurance; Management; Original Article; Social and Behav. Sciences; Statistics for Business; COVID-19; Multinomial logistic regression; Urban India; KNN algorithm; Employment; Labor market transition
• ISSN: 0971-1554; 2364-1045
• DOI: 10.1007/s40953-024-00413-x

This study provides insights into the dynamic job ladder and challenges in the Indian labor market, particularly when facing external shock. It examines the fluidity of job transitions among the ‘employed’, ‘unemployed’, and those ‘not in the laborforce’, focusing on the urban labor market of India during the COVID-19 pandemic. Using data from the 2020-21 Periodic Labour Force Survey, a longitudinal panel dataset was created to track individuals across four quarters, enabling the monitoring of their activity status. Employing K-Nearest Neighbour classification, the study identifies vulnerabilities in labor market engagement. It further explores factors driving transitions among the three states of labor market involvement, using a multinomial logistic model adjusted for selection bias. The research reveals significant movement within the labor force, with notable shifts between employment statuses. Even those currently employed are often vulnerable, at risk of detachment from the labor force at any time. Women were disproportionately affected, with evidence of discouraged worker effect, as many ceased jobs search duo to perceived job scarcity or unavailability of decent jobs. The study raised concerns about the sustainability of self-employment and the security of regular jobs. These findings expose enduring structural challenges exacerbated by the pandemic, calling for urgent action to address widespread unemployment, low female participation, and prevailing inequalities in the labor market.