Search results

With the launch of the Swachh Bharat Mission (SBM), India accelerated access to improved sanitation in a 'mass movement' emphasising people's participation and political leadership. However, SBM continues to be implemented at the administrative unit of districts, disassociated from the political and electoral units of Parliamentary Constituencies (PC). We provide estimates of India's 543 PCs by their performance on three important Water Sanitation and Hygiene (WASH) indicators: unsafe disposal of child stool, unimproved drinking water supply, and unimproved sanitary facilities. We used multilevel modelling to generate precision-weighted estimates of each indicator at PC-level, based on recently developed methodologies linking cluster GPS data from the National Family Health Survey (NFHS), 2016 to potential PCs. We found very high heterogeneity across PCs ranging from 0.95 per cent–95.85 per cent for unsafe stool disposal, 0.35 per cent–64.17 per cent for unimproved drinking water source, and 0.19 per cent–90.69 per cent for unimproved sanitation facility. Unsafe child stool disposal and unimproved sanitary facility were strongly correlated ( r = 0.85, Pearson and r = 0.83, Spearman). Monitoring of SBM data at the PC level will allow parliamentarians to effectively improve WASH conditions in their constituencies, while accounting for critical between-PC variability that may be obfuscated in an approach focussed on state or district means.

In India, data on key developmental indicators used to formulate policies and interventions are routinely available for the administrative unit of districts but not for the political unit of parliamentary constituencies (PC). Recently, Swaminathan et al. proposed two methodologies to generate PC estimates using randomly displaced GPS locations of the sampling clusters ('direct') and by building a crosswalk between districts and PCs using boundary shapefiles ('indirect'). We advance these methodologies by using precision-weighted estimations based on hierarchical logistic regression modeling to account for the complex survey design and sampling variability. We exemplify this application using the latest National Family Health Survey (NFHS, 2016) to generate PC-level estimates for two important indicators of child malnutrition – stunting and low birth weight – that are being monitored by the Government of India for the National Nutrition Mission targets. Overall, we found a substantial variation in child malnutrition across 543 PCs. The different methodologies yielded highly consistent estimates with correlation ranging r = 0.92-0.99 for stunting and r = 0.81-0.98 for low birth weight. For analyses involving data with comparable nature to the NFHS (i.e., complex data structure and possibility to identify a potential PC membership), modeling for precision-weighted estimates and direct methodology are preferable. Further field work and data collection at the PC level are necessary to accurately validate our estimates. An ideal solution to overcome this gap in data for PCs would be to make PC identifiers available in routinely collected surveys and the Census.