No going back: COVID-19 disease threat perception and male migrants' willingness to return to work in India

Arora, Varun; Chakravarty, Sujoy; Kapoor, Hansika; Mukherjee, Shagata; Roy, Shubhabrata; Tagat, Anirudh

doi:10.1016/j.jebo.2023.03.017

Cited by 4 publications

(2 citation statements)

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“…Since both risk perception and farmers' willingness to withdraw from homesteads are subjective variables, which may be endogenously determined due to the presence of random measurement errors. Therefore, referring to Luo et al [ 55 ] and Arora et al [ 56 ], the adequacy of contact with villages was selected as the instrumental variable of the models in our study. The instrumental variable was defined as whether the respondent interacted with half of the people in the village on a regular basis.…”

Section: Methodsmentioning

confidence: 99%

How risk perception and loss aversion affect farmers' willingness to withdraw from rural homesteads: Mediating role of policy identity

He,

Dai,

Sun

2023

Heliyon

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Section: Methodsmentioning

confidence: 99%

How risk perception and loss aversion affect farmers' willingness to withdraw from rural homesteads: Mediating role of policy identity

He,

Dai,

Sun

2023

Heliyon

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“…The study also found that most migrant laborers failed to benefit from government assistance. Arora et al also found that there was a reduced willingness and prevalence of fear among male migrants to resume work [33].…”

Section: Introductionmentioning

confidence: 95%

Spatio-temporal Analysis of COVID-19 Hotspots in India Using Geographic Information Systems

2024

IJG

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The aim of this study is to identify hotspot regions of COVID-19 in India from March 2020 to August 2023. Identifying hotspots is essential for effective pandemic management, as it helps policymakers understand the dynamics of virus spread and allows for more precise public health campaigns. The present study is a district level analysis of India at five different points in time, where we calculate the cumulative incidence rate (CIR), cumulative fatality rate (CFR) and recovery rate (RR) for COVID-19. Further, we apply Global Moran's I, Getis-Ord Gi* and Anselin local Moran's I index to identify COVID-19 hotspots by using Geographic Information Systems (GIS) technology. The results show that the spatial and temporal variation of the CIR is very high across India. The CIR was recorded lower in May 2020 as the affected people were immobilized due to the lockdown. However, the CFR was high and RR was low due to inadequate medical facilities and treatment. The findings revealed that mainly two hotspot regions existed in India until May 2021, the National Capital Region, Haryana, Punjab, Rajasthan, Uttar Pradesh and Maharashtra in the south. However, this scenario has entirely changed since January 2022, when northern India has changed into a cold-spot and the southern coastal states have become the pandemic hot-spot region. Combining hot-spot analysis with Global & Anselin local Moran's I offers a precise method for locating statistically significant COVID-19 case cluster areas and identifying high-risk areas.

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Understanding counter-urbanization and re-urbanization in pandemic: Insights from People's migration behavior in China

Zhang,

Yiwen,

2024

Habitat International

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No going back: COVID-19 disease threat perception and male migrants' willingness to return to work in India

Cited by 4 publications

References 65 publications

How risk perception and loss aversion affect farmers' willingness to withdraw from rural homesteads: Mediating role of policy identity

How risk perception and loss aversion affect farmers' willingness to withdraw from rural homesteads: Mediating role of policy identity

Spatio-temporal Analysis of COVID-19 Hotspots in India Using Geographic Information Systems

Understanding counter-urbanization and re-urbanization in pandemic: Insights from People's migration behavior in China

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