ISSN (Print) - 0012-9976 | ISSN (Online) - 2349-8846

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Robust Parliamentary Constituency Estimates

Geographic Data Science Approaches

This article is a response to Srinivas Goli’s article “Unreliable Estimates of Child Malnutrition” (EPW, 9 February 2019) that had questioned the reliability of methodologies of Akshay Swaminathan et al’s article “Burden of Child Malnutrition in India: A View from Parliamentary Constituencies” (EPW, 12 January 2019). The reliability and usability of the methodologies proposed by Swaminathan et al have been reiterated, emphasising that these can provide broad assessments at the parliamentary constituency level.

(Figures 1–8 accompanying this article are available on the EPW website).


Parliamentary constituencies (PCs) represent an important geographic unit of governance and agenda-setting for health, nutrition, and development domains in India. Each of the 543 PCs has a representative member of Parliament (MP) who is responsible for representing the interests of the people living within the PC. Yet, data on key developmental indicators are not collected at the PC level. Instead, most existing data is available at the administrative unit of districts. Given the lack of direct corres­pondence between the PC and district boundaries, and the absence of data at the PC level, we recently proposed two methodologies in Swaminathan et al (2019) to estimate PC level burden of child malnutrition, using the available geographic information system (GIS) shapefiles and nationally representative data.

The first methodology involved building an indirect crosswalk between districts and PCs using boundary shapefiles; and the second involved aggregating individual-level data to a potential PC directly linked via the randomly displaced global positioning system (GPS) locations of the National Family Health Survey (NFHS-4) sampling clusters (Swaminathan et al 2019). In a subsequent article, we further refined these methodologies by applying precision-weighted estimations based on multilevel modelling to account for the multilevel data structure of the NFHS-4 and sampling variability, and presented PC estimates for child stunting, underweight, wasting, low birth weight, and anaemia (Kim et al 2019).

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Updated On : 13th May, 2019
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