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Drawing on ethnography of one family's life with diabetes in a poor settlement in Delhi's suburbs, this paper examines the relationship between emotional structures of care and kinship in the face of chronic illness. While anthropologists have argued for a relational understanding of care and discussed how, in India, modernity and social transformations have resulted in crises of familial care, less attention has been paid to the emotional terrains of care and its difficulties as they unfold in concrete relationships over time. This paper demonstrates how emotional intensities define the possibilities, limits, and ambivalence of kin care for the chronically ill. Described as care without heart, this mode of attention implies a continuation of care labour that maintains kinship ties and holds the possibility of kin futures, but is disinvested emotionally and feels unsatisfactory. The analytic of care without heart expresses a particular mode of care by which persons navigate dominant moral regimes around gendered family responsibilities and imperatives of love in relationships, but without fully subscribing to them. Care without heart at once signifies an inadequate form of care, invokes North Indian normative moral regimes around family care responsibilities and emotions, and acknowledges the shortcomings of these regimes and norms of relatedness. [ABSTRACT FROM AUTHOR]

Women in Manipur, India, practice marup to respond to poverty and support each other during a crisis and for planned social events. Based on in-depthinterviews of group members, this article presents a case study of marup. It examines thispractice from the feminist social work lens to discuss implications for feminist social work with groups. The practice of mutual aids, democratic functioning and solidarity in marup develops women's capacities and prioritises economic development. Marup is a robust group framework that strengthens women's social and economic positions of women by creating financial capital, extending their social network and building social support. From the feminist social work lens, marup comes into play to interrupt the power relations between men and women within and outside the household. Through the processes and dynamics of group activities, marup represents a robust framework of a group practice that responds to women's needs. [ABSTRACT FROM AUTHOR]

The analysis of household wealth dynamic remains an important methodology in the identification of poverty traps. To overcome measurement issues in survey data, livelihoods-based approaches of the dynamics of poverty are typically examined using panel regressions of a livelihoods regression on household assets and other socio-economic factors over time. In this paper, we characterise the livelihoods regression as a 'livelihoods technology', and use a latent class-technology approach to account for heterogeneity in how households generate a livelihood. We use a detailed dataset from rural India covering 213 households across 2001–2014, and control for selection issues through a Heckman Selection model. Our results are the first in the wealth dynamics literature to show that substantial heterogeneity exists in the technologies with which households generate their livelihoods. Importantly, we show that accounting for heterogeneity in household livelihoods 'technologies' more readily identifies different equilibria in wealth levels and provides previously foregone information on who is poor and why they remain poor. [ABSTRACT FROM AUTHOR]

Background: Tribal peoples are among the most marginalised groups worldwide. Evidence on birth outcomes in these groups is scant. We describe inequalities in Stillbirth Rate (SBR), Neonatal Mortality Rate (NMR), and uptake of maternal and newborn health services between tribal and less disadvantaged groups in eastern India, and examine the contribution of poverty and education to these inequalities. Methods: We used data from a demographic surveillance system covering a 1 million population in Jharkhand State (March 2017 – August 2019) to describe SBR, NMR, and service uptake. We used logistic regression analysis combined with Stata's adjrr-command to estimate absolute and relative inequalities by caste/tribe (comparing Particularly Vulnerable Tribal Groups (PVTG) and other Scheduled Tribes (ST) with the less marginalised Other Backward Class (OBC)/none, using the Indian government classification), and by maternal education and household wealth. Results: PVTGs had a higher NMR (59/1000) than OBC/none (31/1000) (rate ratio (RR): 1.92, 95%CI: 1.55–2.38). This was partly explained by wealth and education, but inequalities remained large after adjustment (adjusted RR: 1.59, 95%CI: 1.28–1.98). NMR was also higher among other STs (44/1000), but disparities were smaller (RR: 1.47, 95%CI: 1.23–1.75). There was a systematic gradient in NMR by maternal education and household wealth. SBRs were only higher in poorer groups (RRpoorest vs. least poor:1.56, 95%CI: 1.14–2.13). Uptake of facility-based services was low among PVTGs (e.g. institutional birth: 25% vs. 69% in OBC/none) and among poorer and less educated women. However, 65% of PVTG women with an institutional birth used a maternity vehicle vs. 34% among OBC/none. Visits from frontline workers (Accredited Social Health Activists [ASHAs]) were similar across groups, and ASHA accompaniment of institutional births was similar across caste/tribe groups, and higher among poorer and less educated women. Attendance in participatory women's groups was similar across caste/tribe groups, and somewhat higher among richer and better educated women. Conclusions: PVTGs are highly disadvantaged in terms of birth outcomes. Targeted interventions that reduce geographical barriers to facility-based care and address root causes of high poverty and low education in PVTGs are a priority. For population-level impact, they are to be combined with broader policies to reduce socio-economic mortality inequalities. Community-based interventions reach disadvantaged groups and have potential to reduce the mortality gap. [ABSTRACT FROM AUTHOR]

Purpose: This study on spatial analysis of child labour in India is a macro level analysis on child labour using the census data, 2011 of Government of India. The population census which is conducted once in 10 years only provides district level data on work-force distribution. The study has spatial analysis of child labour in the age group of 5–14 years in India. To assess the magnitude of the children in the labour force, district level data of Census 2011 has been used in the study. The study has made an attempt to identify the districts where there is high level of children in the labour force. This paper aims to estimate the magnitude and trends of children's workforce participation using the census data as it is the only data base, which is available at the district level since 1961 onwards. The study has made an attempt to identify the clustering of child labour across districts in India and how child labour is clustered by different background characteristics. Design/methodology/approach: The study has used ArcGIS software package, GeoDa software and local indicator of spatial association test. Findings: The findings of study reveal that the proportion of rural, total fertility rate (TFR) and poverty headcount ratio is positively associated, whereas female literacy and the pupil-teacher ratio are negatively associated with child labour. It suggests that in the hot-spot areas and areas where there is a high prevalence of child labour, there is need to increase the teacher's number at the school level to improve the teacher-pupil ratio and also suggested to promote the female education, promote family planning practices to reduce TFR in those areas for reducing the incidences of child labour. Research limitations/implications: The study also recommends that the incidences of child labour can be controlled by a comprehensive holistic action plan with the active participation of social workers. Practical implications: The promulgation of effective legislation, active involvement of judiciary and police, political will, effective poverty alleviation and income generation programmes, sensitisation of parents, corporates and media can play effective role in mitigating the incidences of child labour in India. To achieve the sustainable development goals (SDGs) adopted by world leaders in 2015 to eradicate child labour in all its forms by 2025. Social implications: The study aims to achieve the SDGs adopted by world leaders in 2015 to eradicate child labour in all its forms by 2025. Originality/value: The study is purely original and there are no such studies in Indian context by using the latest software. [ABSTRACT FROM AUTHOR]

Background: Conventional indicators used to access the nutritional status of children tend to underestimate the overall undernutrition in the presence of multiple anthropometric failures. Further, factors contributing to the rich-poor gap in the composite index of anthropometric failure (CIAF) have not been explored. This study aims to estimate the prevalence of CIAF and quantify the contribution of factors that explain the rich-poor gap in CIAF. Methods: The present study used data of 38,060 children under the age of five years and their biological mothers, drawn from the nationally representative Comprehensive National Nutrition Survey of children and adolescents aged 0–19 years in India. The CIAF outcome variable in this study provide an overall prevalence of undernutrition, with six mutually exclusive anthropometric measurements of height-for-age, height-for-weight, and weight-for-age, calculated using the World Health Organization (WHO) Multicenter Growth Reference Study. Multivariate regression and decomposition analysis were used to examine the association between covariates with CIAF and to estimate the contribution of different covariates in the existing rich-poor gap. Results: An overall CIAF prevalence of 48.2% among children aged aged under 5 years of age was found in this study. 6.0% children had all three forms of anthropometric failures. The odds of CIAF were more likely among children belonging to poorest households (AOR: 2.41, 95% CI: 2.12–2.75) and those residing in urban area (AOR: 1.06, 95% CI 1.00–1.11). Children of underweight mothers and those with high parity were at higher risk of CIAF (AOR: 1.51, 95% CI: 1.42–1.61) and (AOR: 1.15, 95% CI: 1.08–1.22), respectively. Children of mother exposed to mass media were at lower risk of CIAF (AOR: 0.87, 95% CI: 0.81–0.93). Conclusion: This study estimated a composite index to assess the overall anthropometric failure, which also provides a broader understanding of the extent and pattern of undernutrition among children. Findings show that maternal covariates contribute the most to the rich-poor gap. As well, the findings suggest that intervention programs with a targeted approach are crucial to reach the most vulnerable groups and to reduce the overall burden of undernutrition. [ABSTRACT FROM AUTHOR]

• Multidimensional child poverty is notably more prevalent among STs and SCs. • Standard of living, ECD, and WASH are the dimensions that need committed thrust. • STs and SCs contribute more to the national MCP than their share of the population. • Head of household characterised by female gender, older age, and higher level of education is associated with a lower likelihood of MCP. • MCP tends to be more pronounced in nuclear families, households with 7+ members, rural areas, and central and eastern regions. Children are among the most vulnerable groups worldwide, bearing a disproportionate share of the burden of poverty. One-third of children, compared to one-sixth of adults, are multidimensionally poor. Experiencing poverty in childhood can have long-term negative consequences across generations. As a result, multidimensional poverty measures have recently been investigated in the child poverty literature. This study analysed multidimensional poverty among children (0–59 months) in India using the Alkire-Foster's counting approach and looked into how it differs across the country's well-known social stratification, the Caste system. The determinants of multidimensional child poverty (MCP) have been determined using multiple logistic regression. By using National Family Health Survey, 2019–21 data, the study revealed that the incidence of MCP in India is 32 %; highest among Scheduled Tribes (STs) (50 %), followed by Scheduled Castes (SCs) (37 %), Other Backward Classes (OBCs) (30 %) and 'Others' (22 %). Across all the Castes highest rates of deprivation were recorded in the indicators of standard of living and early childhood development (ECD) dimensions; together, they contribute more than 50 % to the MCP index (MCPI). The contribution of the water, sanitation, and hygiene (WASH) dimension in MCPI of STs is also noticeably higher (20.2 %). Of the 15 indicators considered, availability of TV/Radio, followed by immunisation, sanitation, cooking fuel, housing condition, and stunting, are the leading contributors with a combined contribution of more than 55 %. A child being a male, of higher birth order and born to a mother with higher years of schooling was more likely to experience multidimensional poverty. The household head's sex, age, and years of schooling were significantly associated with MCP. Across all castes, children from nuclear families, 7 + household members, higher number of children per woman, rural areas, central, eastern and north-eastern (except STs) regions were more likely to experience multidimensional poverty. [ABSTRACT FROM AUTHOR]

• Poverty and child labour have a positive relationship in India. • Agriculture employs more than half of all child labourers in India. • The average age at which children begin working is 16 years, with and average of 6 to 8 years of education. • Children from economically disadvantaged families and the Muslim community are more likely to engage in child labour. Child labour continues to be a major concern in India despite the implementation of various policies and programmes aimed at its eradication. This study investigates factors that influence child labour in India with a focus on regional differences. The data is drawn from the 2019–20 Periodic Labour Force Survey. Binary logistic regression is used to identify factors that influence child labour. The findings show that children with pre-primary and primary education are less likely to be in the workforce, whereas children with secondary and higher secondary education are more likely to be in the workforce. This reflects the higher rates of secondary school dropouts attempting to enter the labour force. Likewise, poor families are more likely to send their children to work. Similarly, children from economically and socially disadvantaged communities, and Muslim households are more likely to be sent to work. The findings suggest that increasing children's educational attainment and providing financial assistance to poor families could effectively eradicate child labour in India. [ABSTRACT FROM AUTHOR]