The paper explores the available housing options for migrant students based on a field survey in the National Capital Region. The study finds that most migrant students depend on private rental housing, particularly in the form of paying guests and independent flats at relatively higher charges. Students from affluent families prefer independent private accommodation in a better location, whereas those from lower-income groups prefer to stay in distant places from educational institutions. Further, the paper also finds that female students often incur higher accommodation costs as compared to their male counterparts. Besides being expensive, rental housing poses serious challenges to living experiences ranging from sustaining accommodation to managing higher education.
This article examines the trends and patterns of unpaid work performed by women in India’s North Eastern States and account for the factors that underlie these trends. It uses the two unit-level datasets from the National Sample Survey Office Employment and Unemployment Survey 2011–2012 and Periodic Labour Force Survey 2018–2019. The multinomial regression results found that illiterate and lower social stratum have more chances to engage in unpaid activities. It then explores the impact of COVID-19 on unpaid work activities among women in the northeast states. The telephonic conversation and informal interviews with different regional stakeholders have been substantiated along with the utilisation of the Centre for Monitoring Indian Economy report on employment and unemployment for the second quarter of 2020 for nuanced analysis. The study found that women are losing their livelihood very fast during the pandemic and the effects are likely to linger for a more extended period. JEL Codes: J16, J21, J22, R23
Objectives: This study explores the patterns and determinants of seasonal migration of human resources in India. Moreover, the paper also attempts to review the emerging literature in the contemporary migration situation on the wake-up COVID-19 pandemic. Method: This study used the 64th National Sample Survey Organization data on employment and unemployment survey 2007-08. The statistical tools have used, like, percentage and cross-tabulation. The Binary Logistic Regression has used to understand the factors associated with the probability of seasonal migration in India. Findings: The study found that the majority of seasonal migration has undoubtedly from socially backward communities, low income, and residing in rural areas. However, if we look at the relative change in the odds of seasonal migration for different layers of the socio-economic strata, it is found that those with lower socio-income more likely to migrate seasonally for livelihoods. Novelty: This study looks at the empirical rigour with a particular focus on the systematic review of seasonal labour migration across the country, focusing on Covid-19. This study gives rise to engage with better policy implication for seasonal migration, which constitutes a larger segment of total migrants in the country.
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