This paper examines the role of caste, tribe, and religion in determining energy inequality in India. We provide evidence by using the National Sample Survey Organisation data from the 68th round (2011-12) of 87,753 households. We estimate the inequalities in access to Liquid Petroleum Gas (LPG) and electricity usage by the households belonging to the three major disadvantaged groups in India, viz., the scheduled castes, the scheduled tribes, and the Muslims. The results of our empirical analysis suggest that, after controlling for the determinants which impinge on the households' microeconomic demand and regional supply characteristics, the households belonging to the scheduled tribe and scheduled caste communities do have significantly poorer access to LPG and electricity usage as compared to the upper caste households. The decomposition analysis of average differences in the predicted outcomes shows that it is the scheduled caste and scheduled tribe households who would appear to face most discrimination. The Muslim households too face significant inequality in accessing LPG. Policy implications of the findings are considered.

ObjectiveWe undertook a systematic review of strategies adopted to scale up COVID-19 testing in countries across income levels to identify successful approaches and facilitate learning.MethodsScholarly articles in English from PubMed, Google scholar and Google search engine describing strategies used to increase COVID-19 testing in countries were reviewed. Deductive analysis to allocate relevant text from the reviewed publications/reports to the a priori themes was done.Main resultsThe review covered 32 countries, including 11 high-income, 2 upper-middle-income, 13 lower-middle-income and 6 low-income countries. Most low- and middle-income countries (LMICs) increased the number of laboratories available for testing and deployed sample collection and shipment to the available laboratories. The high-income countries (HICs) that is, South Korea, Germany, Singapore and USA developed molecular diagnostics with accompanying regulatory and legislative framework adjustments to ensure the rapid development and use of the tests. HICs like South Korea leveraged existing manufacturing systems to develop tests, while the LMICs leveraged existing national disease control programmes (HIV, tuberculosis, malaria) to increase testing. Continent-wide, African Centres for Disease Control and Prevention-led collaborations increased testing across most African countries through building capacity by providing testing kits and training.ConclusionStrategies taken appear to reflect the existing systems or economies of scale that a particular country could leverage. LMICs, for example, drew on the infectious disease control programmes already in place to harness expertise and laboratory capacity for COVID-19 testing. There however might have been strategies adopted by other countries but were never published and thus did not appear anywhere in the searched databases.

A strand of research holds the view that restricting access to credit to regulate over-borrowing can worsen consumers' financial condition. Another strand of research holds the view that access to credit in the developing countries with subprime credit markets is determined by social groupings and ethnic affiliations. By merging these two strands of research, we investigate the impact of Andhra Pradesh microfinance act (2010) on the consumption expenditure of marginalised social groups and the upper caste Hindu groups in India. We construct an aggregated district level panel data for eight quarters and estimate the impact of unanticipated policy change. The results of our analysis show that the sudden restriction of access to credit has larger impact on the consumption levels of the marginalised social groups: lower castes, tribes, and Muslims. The findings also confirm the failure of contingency policy enacted for smoothing consumption.

Importance: Homelessness is a complex challenge with an estimated yearly economic burden of $6 billion in the United States. Mitigating homelessness requires an understanding of determinants of homelessness, their interaction with health factors, and quantification of impact. Objective: To investigate the health, social and policy factors influencing homelessness in a longitudinal integrative machine learning analysis. Data Sources and Study design: This retrospective longitudinal study integrated Global Burden of Disease (GBD), Health Inequality, and Housing and Urban Development (HUD) datasets for 3131 counties in the United States. We used the disease burden data of 2014, health inequalities data of 2001-2014, and homelessness count of 2015. Primary Outcome and Measurement Results: Homelessness, the burden of disease, health inequalities, economic policies, ethnic, social, and racial factors. Methods: Spearman rank correlation test was performed to check pairwise associations. A unified probabilistic model with temporal causality was fitted using a data-driven structure learning algorithm. The resulting associations adjusted for other variables in the network were quantified using network inference algorithms. Finally, counterfactual analysis was performed to quantify the potential impact of the learned interventions. Results: The total burden of homelessness was significantly (p<0.001) and positively associated with rates of HIV and hepatitis mortality. Inference from the unified probabilistic model indicated that a state with a high hepatitis mortality rate had a 9% higher homelessness. Further, the rate of rheumatic heart disease mortality had a 29% decrease with the provision of shelter in young adults experiencing homelessness (p<0.001). Finally, states with moderate tax progressivity had a mitigating effect on homelessness as compared to both high and low tax progressivity ( 2% and 5% respectively). We evaluated the counterfactual impact of policy interventions to provide more support to cancer patients to prevent homeless and provision of shelter to prevent rheumatic heart disease mortality in young adults experiencing homelessness. Conclusion and Relevance: Control of infectious diseases and the implementation of tax policies are critical interventions for the reduction of homelessness in the United States.

All BRICS countries suffer from gender inequality in different dimensions. Three of the BRICS countries—India, China, and Russia—have highly skewed sex ratios, while the other two—Brazil and South Africa—do not have the problem of skewed sex ratios. Highly skewed sex ratios have major economic and social consequences, as indeed have gender inequalities in other dimensions. A comparative perspective on these inequalities is provided in this chapter. Within BRICS, India probably has most to gain by reducing such inequalities. Equally, Russia stands to gain most by changing the gender order that estranges men from most family spheres.