This paper estimates and decomposes multidimensional poverty in 82 natural regions in India using unit data from the Indian Human Development Survey (IHDS), 2011–12. Multidimensional poverty is measured in the dimensions of health, education, living standard and household environment using eight indicators and Alkire-Foster methodology. The unique contributions of the paper are inclusion of a direct economic variable (consumption expenditure, work and employment) to quantify the living standard dimension, decomposition of MPI across the dimensions and the indicators, and estimates of multidimensional poverty at the sub-national level. Results indicate that 43% of India’s population are multidimensional poor with large regional variations. The average intensity of poverty was 45.5% with a MPI value of 19.3. Six states in India—Bihar, Chhattisgarh, Jharkhand, Madhya Pradesh, Odisha and West Bengal who have a share of 45% of the total population—account for 58% of the multidimensional poor. Across regions, more than 70% of the population are multidimensional poor in the southern region of Chhattisgarh and the Ranchi plateau, while they comprise less than 10% in the regions of Manipur, Mizoram and Chandigarh. The economic poor have a weak association with health and household environment dimensions. The decomposition of MPI indicates that the economic dimension accounts for 22%, the health dimension accounts for 36%, the education dimension accounts for 11% and the household environment accounts for 31% of the deprivation. Based on these analyses, the authors suggest target based interventions in the poor regions to reduce poverty and inequality in India.

Using the unit data from the second round of the Indian Human Development Survey (IHDS-II), 2011–2012, which covered 42,152 households, this paper examines the association between multidimensional poverty, household environmental deprivation and short-term morbidities (fever, cough and diarrhoea) in India. Poverty is measured in a multidimensional framework that includes the dimensions of education, health and income, while household environmental deprivation is defined as lack of access to improved sanitation, drinking water and cooking fuel. A composite index combining multidimensional poverty and household environmental deprivation has been computed, and households are classified as follows: multidimensional poor and living in a poor household environment, multidimensional non-poor and living in a poor household environment, multidimensional poor and living in a good household environment and multidimensional non-poor and living in a good household environment. Results suggest that about 23% of the population belonging to multidimensional poor households and living in a poor household environment had experienced short-term morbidities in a reference period of 30 days compared to 20% of the population belonging to multidimensional non-poor households and living in a poor household environment, 19% of the population belonging to multidimensional poor households and living in a good household environment and 15% of the population belonging to multidimensional non-poor households and living in a good household environment. Controlling for socioeconomic covariates, the odds of short-term morbidity was 1.47 [CI 1.40–1.53] among the multidimensional poor and living in a poor household environment, 1.28 [CI 1.21–1.37] among the multidimensional non-poor and living in a poor household environment and 1.21 [CI 1.64–1.28] among the multidimensional poor and living in a good household environment compared to the multidimensional non-poor and living in a good household environment. Results are robust across states and hold good for each of the three morbidities: fever, cough and diarrhoea. This establishes that along with poverty, household environmental conditions have a significant bearing on short-term morbidities in India. Public investment in sanitation, drinking water and cooking fuel can reduce the morbidity and improve the health of the population.

Uttar Pradesh (UP) is the most populous state in India with historically high levels of fertility rates than the national average. Though fertility levels in UP declined considerably in recent decades, the current level is well above the government’s target of 2.1. Fertility and family planning data obtained from the different rounds of Sample Registration System (SRS) and the National Family Health Survey (NFHS). We analyzed fertility and family planning trends in India and UP, including differences in methods mix, using SRS (1971–2016) and NFHS (1992–2016). Bivariate and multivariate regression analyses were used. From 2000, while the total fertility rate (TFR) declined in UP, it is still well above the national level in 2015–16 (2.7 vs 2.18, respectively). The demand for family planning satisfied increased from 52 to 72% during 1998–99 to 2015–16 in UP, compared to an increase from 75 to 81% in India. Traditional methods play a much greater role in UP than in India (22 and 9% of the demand satisfied respectively), while use of sterilization was relatively low in UP when compared to the national averages (18.0 and 36.3% of current married women 15–49 years in UP and India, respectively in 2015–16). Within UP, district fertility ranged from 1.6 to 4.4, with higher fertility concentrated in districts with low female schooling, predominantly located in north-central UP. Fertility declines were largest in districts with high fertility in the late nineties (B = 7.33, p < .001). Among currently married women, use of traditional methods increased and accounted for almost one-third of users in 2015–16. Use of sterilization declined but remained the primary method (ranging from 33 to 41% of users in high and low fertility districts respectively) while condom use increased from 17 and 16% in 1998–99 to 23 and 25% in 2015–16 in low and high fertility districts respectively. Greater reliance on traditional methods and condoms coupled with relatively low demand for modern contraception suggest inadequate access to modern contraceptives, especially in district with high fertility rates. Family planning activities need to be appropriately scaled according to need and geography to ensure the achievement of state-level improvements in family planning programs and fertility outcomes.

In 2001, India prioritized eight most socioeconomically disadvantaged states known as Empowered Action Group (EAG) states and in 2013, it prioritized 190 of the 718 as high priority districts (HPDs) to accelerate the decline in maternal and newborn mortality. This paper assesses whether the HPDs achieved a greater coverage of maternal and newborn health interventions than the non-HPDs and HPDs in EAG states achieved greater coverage than those in non-EAG states. We used data from the Sample Registration System to assess rural neonatal mortality trends in EAG states and all India. We computed a co-coverage index based on seven maternal and newborn health interventions from the 2015/16 National Family Health Survey. Difference in differences (DID) analyses were used to examine the contribution of district prioritization, considering the HPDs and the illiterate as treatment groups and 2013 as the time cut-off for the pre- and post-treatment. Neonatal mortality declined in rural India from 36 to 27 per 1000 live births during 2010-2016 at 4.5% per year. Four EAG states experienced faster rates of decline than the national rate. From 2013, the co-coverage index increased significantly more in the HPDs compared to non-HPDs (DID = 0.11, P ≤ 0.005). The district prioritization effect on co-coverage was statistically significant in only EAG states (DID = 0.13, P ≤ 0.05). The coverage gains for illiterate mothers were greater than for literate mothers, especially in the HPDs. The district prioritization in India is associated with greater improvements in the coverage of maternal and newborn health services in EAG states and the HPDs, including reductions in inequalities within those states and districts. There are however still large gaps between states and districts and within districts by the mother's literacy status that need further prioritization to make progress towards the SDG targets by 2030.

Universal health coverage is central to the development agenda to achieve maternal and neonatal health goals. Although there is evidence of a growing preference for institutional births in India, it is important to understand the pattern of switching location of childbirth and the factors associated with it. This study used data from the fourth round of the National Family and Health Survey (NFHS-4) conducted in India in 2015–16. The study sample comprised 59,629 women who had had at least two births in the five years preceding the survey. Bivariate and multivariate logistic regression analyses were applied to the data. About 16.4% of the women switched their location of childbirth between successive births; 9.1% switched to a health facility contributing to a net increment of 1.9% in institutional delivery, varying greatly across states and regions. There was at least a 4 percentage point net increment in institutional births in Chhattisgarh, Bihar, Punjab and Haryana, but the shift was more in favour of home births in Madhya Pradesh, Odisha and West Bengal. Women with high parity and a large birth interval had higher odds of switching their place of childbirth, and this was in favour of a health facility, while women with higher education, from lower social groups, living in urban areas, who had not received four antenatal care visits, and who belonged to a higher wealth quintile had higher odds of switching their place of childbirth to a health facility, despite having lower odds of switching their childbirth location. The study provides evidence of women in India switching their location of childbirth for successive births, and this was more prevalent in areas where the rate of institutional delivery was low. Only a few states showed a higher net increment in favour of a health facility. This suggests that there is a need for action in specific states and regions of India to achieve universal health coverage.