Investment in family planning (FP) provides returns through a lifetime. Global evidence shows that FP is the second-best buy in terms of return on investment after liberalizing trade. In this study, we estimate the cumulative benefits of FP investments for India from 1991 to 2016 and project them up to 2061 with four scenarios of fertility levels. The findings suggest that India will have greater elasticity of FP investments to lifetime economic returns compared to the world average (cost–revenue ratio of 1:120). We have taken four scenarios for the goalpost, viz., 2.1, 1.8, 1.6, and 1.4. Although different scenarios of total fertility rate (TFR) levels at the goalpost (i.e., the year 2061) offer varied lifetime returns from FP, scenario TFR < 1.8 will be counterproductive and will reduce the potential benefits. With a comprehensive approach, if the country focuses more on improving the quality of FP services and on reducing the unmet need for FP to enhance reproductive health care and expand maximum opportunities for education and employment for both women and men, it can improve its potential to reap more benefits.

Implementing a large-scale survey involves a string of intricate procedures exposed to numerous types of survey errors. Uniform and systematic training protocols, comprehensive survey manuals, and multilayer supervision during survey implementation help reduce survey errors, providing a consistent fieldwork environment that should not result in any variation in the quality of data collected across interviewers and teams. With this background, the present study attempts to delineate the effect of field investigator (FI) teams and survey implementation design on the selected outcomes. Data on four of the bigger Empowered Action Group (EAG) states of India, namely Uttar Pradesh, Madhya Pradesh, Bihar, and Rajasthan, were obtained from the fourth round of the National Family Health Survey (NFHS-4) for analysis. A fixed-effect binary logistic regression model was used to assess the effect of FI teams and survey implementation design on the selected outcomes. To study the variation in the outcome variables at the interviewer level, a cross-classified multilevel model was used. Since one interviewer had worked in more than one primary sampling unit (PSU) & district and did not follow a perfect hierarchical structure, the cross-classified multilevel model was deemed suitable. In addition, since NFHS-4 used a two-stage stratified sampling design, two-level weights were adjusted for the models to compute unbiased estimates. This study demonstrated the presence of interviewer-level variation in the selected outcomes at both inter- and intra-field agencies across the selected states. The interviewer-level intra-class correlation coefficient (ICC) for women who had not availed antenatal care (ANC) was the highest for eastern Madhya Pradesh (0.23) and central Uttar Pradesh (0.20). For ‘immunisation card not seen’, Rajasthan (0.16) and western Uttar Pradesh (0.13) had higher interviewer-level ICC. Interviewer-level variations were insignificant for women who gave birth at home across all regions of Uttar Pradesh. Eastern Madhya Pradesh, Rajasthan, and Bihar showed higher interviewer-level variation across the selected outcomes, underlining the critical role of agencies and skilled interviewers in different survey implementation designs. The analysis highlights non-uniform adherence to survey protocols, which implies that not all interviewers and agencies performed in a similar manner in the field. This study recommends a refined mechanism for field implementation and supervision, including focused training on the challenges faced by FIs, random vigilance, and morale building. In addition, examining interviewer-level characteristics, field challenges, and field agency effects may also highlight the roots of interviewer-level variation in the data. However, based on the interviewer's performance in the field, the present study offers an intriguing insight into interviewer-level variations in the quality of data.

India has adopted a target-based approach to reduce the scourge of child malnourishment. Because the monitoring and evaluation required by this approach relies primarily on large-scale data, a data quality assessment is essential. As field teams are the primary mode of data collection in large-scale surveys, this study attempts to understand their contribution to variations in child anthropometric measures. This research can help disentangle the confounding effects of regions/districts and field teams on the quality of child anthropometric data. The anthropometric z-scores of 2,25,002 children below five years were obtained from the fourth round of India's National Family and Health Survey (NFHS-4), 2015–16. Unadjusted and adjusted standard deviations (SD) of the anthropometric measures were estimated to assess the variations in measurements. In addition, a cross-classified multilevel model (CCMM) approach was adopted to estimate the contribution of geographical regions/districts and teams to variations in anthropometric measures. The unadjusted SDs of the measures of stunting, wasting, and underweight were 1.7, 1.4, and 1.2, respectively. The SD of stunting was above the World Health Organisation threshold (0.8–1.2), as well as the Demographic and Health Survey mark. After adjusting for team-level characteristics, the SDs of all three measures reduced marginally, indicating that team-level workload had a marginal but significant role in explaining the variations in anthropometric z-scores. The CCMM showed that the maximum contribution to variations in anthropometric z-scores came from community-level (Primary Sampling Unit (PSU)) characteristics. Team-level characteristics had a higher contribution to variations in anthropometric z-scores than district-level attributes. Variations in measurement were higher for child height than weight. The present study decomposes the effects of district- and team-level factors and highlights the nuances of introducing teams as a level of analysis in multilevel modelling. Population size, density, and terrain variations between PSUs should be considered when allocating field teams in large-scale surveys.

The education sector in India was among the most affected sectors during the COVID-19 pandemic. While considerable attention has been paid to informal workers' return or reverse migration to their home communities, not much has been reported about the challenges faced by migrant students. Using a mixed-method approach, the current study presents an overview of internal student migration in India prior to the COVID-19 pandemic using data from the 2001 and 2011 Census of India and the 2007–2008 National Sample Survey Organization, and discusses challenges faced by selected migrant learners during the COVID-19 pandemic based on primary research. Based on the census data, nearly 3.3 million migrants in India move for study reasons with 2.9 million migrating within the state (with the duration of residence less than five years) from their last residence within India. The pattern of female student migration suggests an increasingly localized interdistrict migration. Findings from the qualitative data indicate that during the pandemic, students had compromised learning and placement experience, inadequate digital resources and pressure to repay loans. Student migrants experienced varying degrees of the impact of the COVID-19 pandemic based on their destination and migration stream.

Against the backdrop of the alarming rise in Caesarean section (C-section) births in India, this study aimed to examine the association between C-section births, fertility decline and female sterilization in the country. A cross-sectional design was used to investigate the association between C-section delivery and subsequent reproductive behaviour in women in India. Data were from the National Family Health Survey (NFHS-4). The study sample comprised 255,726 currently married women in the age group of 15–49 years. The results showed a strong positive relationship between C-section births and female sterilization. The predicted probabilities (PP) from the multivariate regression model indicated a higher chance of female sterilization in women with C-section births (PP = 0.39,p<0.01) compared with those with non-C-section births (PP = 0.20,p<0.01). Both state-level correlation plots and Poisson regression estimates showed a strong negative relationship between C-section births and mean children ever born (CEB). Based on the results, it may be concluded that the use of C-sections and sterilization were strongly correlated in India at the time of the NFHS-4, thus together contributing to fertility decline. A strong negative association was found between the occurrence of C-sections and CEB. The increased and undesired use of C-section births and consequent female sterilization is a regressive socio-demographic process that often violates women’s rights. Fertility decline should happen through informed choice of family planning and must protect the reproductive rights of women.