A Note on Industrial Relations Code, 2020.

In this note, we assess the newly introduced Industrial Relations Code (IRC) 2020. First, we compare the provisions in IRC 2020 with its predecessor, Industrial Disputes Act (IDA) 1947, and the amendments to IDA 1947 in 1976 and 1982 regarding the employment protection provisions against layoffs, retrenchments, and closures. We find that IRC 2020 merely restored the factory-size threshold to the initial IDA amendment 1976 level of 300 or more workers to which IDA 1947 originally applied from 100 or more workers as notified in the 1982 IDA amendment. We calculate the effect of the threshold-size change on the workers and factories on the firms registered under the Factories Act 1948 2(m). We find from the ASI 2017-2018 data that an estimated 7.07% of the factories and 15.97% of the workers will be affected due to the change in the threshold size introduced through IRC, 2020. Finally, we conclude by discussing the current relevance of the employment protection legislation during the pandemic time and the need for discretionary usage and temporary expansion of coverage of employment protection provisions under the IRC if warranted.

Hierarchical Modelling of COVID-19 Death Risk in India in the Early Phase of the Pandemic.

We improve upon the modelling of India's pandemic vulnerability. Our model is multidisciplinary and recognises the nested levels of the epidemic. We create a model of the risk of severe COVID-19 and death, instead of a model of transmission. Our model allows for socio-demographic-group differentials in risk, obesity and underweight people, morbidity status and other conditioning regional and lifestyle factors. We build a hierarchical multilevel model of severe COVID-19 cases, using three different data sources: the National Family Health Survey for 2015/16, Census data for 2011 and data for COVID-19 deaths obtained cumulatively until June 2020. We provide results for 11 states of India, enabling best-yet targeting of policy actions. COVID-19 deaths in north and central India were higher in areas with older and overweight populations, and were more common among people with pre-existing health conditions, or who smoke, or who live in urban areas. Policy experts may both want to 'follow World Health Organisation advice' and yet also use disaggregated and spatially specific data to improve wellbeing outcomes during the pandemic. The future uses of our innovative data-combining model are numerous.

Dans le contexte du développement international, on peut améliorer la modélisation de la vulnérabilité à une pandémie en combinant différentes disciplines, en combinant des données et en reconnaissant les nombreux niveaux imbriqués de l'épidémie. Des modèles de transmission ont été élaborés à l'échelle nationale ou pour plusieurs pays. A l'inverse, nous construisons un modèle permettant de prendre en compte les différents niveaux de risque selon les groupes sociaux, ainsi que le conditionnement des facteurs régionaux et des facteurs liés au mode de vie. La forme grave de la COVID-19 est notre résultat clé innovant. Nous utilisons trois sources de données simultanément: l'enquête nationale sur la santé des familles en Inde, le recensement de la population indienne de 2011 et les décès liés à l'épidémie de COVID-19. Nous fournissons des résultats pour 11 États en Inde, ce qui permet un meilleur ciblage des actions politiques. Les utilisations futures de ces modèles sont nombreuses. Dans le nord et le centre de l'Inde, les décès liés à la COVID-19 étaient plus nombreux dans les régions avec populations âgées et populations en surpoids. Ces décès étaient plus fréquents chez les personnes ayant déjà des problèmes de santé, ou chez celles qui fument ou qui vivent dans les zones urbaines. Les experts en politiques publiques pourront souhaiter à la fois « suivre les conseils de l'Organisation mondiale de la santé ¼ tout en utilisant des données désagrégées et spatiales pour améliorer les résultats en matière de bien-être pendant la pandémie.

Escaping and Falling into Poverty in India Today.

The study examines the dynamic nature of movements into and out of poverty over a period when poverty has fallen substantially in India. The analysis identifies people who escaped poverty and those who fell into it over the period 2005 to 2012. The analysis identifies people who escaped poverty and those who fell into it over the period 2005 to 2012. Using panel data from the India Human Development Survey for 2005 and 2012, we find that the risks of marginalized communities such as Dalits and Adivasis of falling into or remaining in poverty were higher than those for more privileged groups. Some, but not all of these higher risks are explained by educational, financial, and social disadvantages of these groups in 2005. Results from a logistic regression show that some factors that help people escape poverty differ from those that push people into it and that the strength of their effects varies.

Caste in 21st Century India: Competing Narratives.

Recent debates regarding inclusion of caste in 2011 Census have raised questions about whether caste still matters in modern India. Ethnographic studies of the mid-20th century identified a variety of dimensions along which caste differentiation occurs. At the same time, whether this differentiation translates into hierarchy remains a contentious issue as does the persistence of caste, given the economic changes of the past two decades. Using data from a nationally representative survey of 41,554 households conducted in 2005, this paper examines the relationship between social background and different dimensions of well-being. The results suggest continued persistence of caste disparities in education, income and social networks.