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In very large countries, demographic behaviours are often geographically diverse, so analyses must be carried out at subnational level to capture this heterogeneity. The finer the level of observation, the greater the degree of accuracy in identifying the geographical patterns of similar or divergent behaviours. India, a country of considerable economic, cultural and demographic diversity, is very interesting in this respect. Abhishek Singh, Kaushalendra Kumar, Praveen Kumar Pathak, Rajesh Kumar Chauhan and Adrita Banerjee study the spatial variations in fertility across the 640 districts of India to map its levels and determinants. The maps obtained provide demographers with a useful tool for analysing the spatial concentration of demographic behaviours.
This article examines the spatial patterns of fertility in India and its determinants using data from the 2011 Indian Census and round 3 of the District Level Household Survey conducted in India in 2007-08. We estimated all the independent variables and the dependent variable for each of the 640 districts of India as defined for the 2011 Indian Census. Moran's I, univariate and bivariate LISA, ordinary least squares (OLS) and two-stage least squares were used to analyse the data, and spatial error and two-stage spatial regression models were applied to account for the effects of spatial clustering. The four statistical models reveal different relationships between childlessness and fertility across districts and regions. A statistical association between son preference and fertility is also observed. Our findings demonstrate the importance of using spatial econometric models to analyse the determinants of fertility at district or lower levels.
Cette recherche a pour objectif d'examiner les structures spatiales de la fécondité en Inde et ses déterminants. Nous avons utilisé les données du recensement indien de 2011 et celles de la troisième phase de l'Enquête auprès des ménages au niveau des districts (District Level Household Survey) menée en Inde en 2007-2008. Les variables dépendante et indépendantes sont définies au niveau de chacun des 640 districts composant le territoire indien, conformément au découpage du recensement de 2011. Pour analyser ces données, nous avons recours à l'indice I de Moran, aux indices LISA univarié et bivarié, à la méthode des moindres carrés ordinaires (MCO) et aux doubles moindres carrés, ainsi qu'à des modèles à terme d'erreur spatiale pour tenir compte des effets de...