Reading the geography of India’s district-level fertility differentials: a spatial econometric approach

Abstract

India has gradually progressed into fertility transition over the last few decades. However, the timing and pace of this transition has varied notably in terms of both its geography and the demographic groups most affected by it. While much literature exists on the relationships between fertility level and its influence on demographic, economic, socio-cultural and policy-related factors, the potential spatial variations in the effects of these factors on the fertility level remain unaddressed. Using the most recent district-level census data (of 2011) for India, this nationwide study has identified plausible spatial dependencies and heterogeneities in the relationships between the district-wise Total Fertility Rates (TFRs) and their respective demographic, socioeconomic and cultural factors. After developing a geocoded database for 621 districts of India, spatial regression and Geographically Weighted Regression (GWR) models were used to decipher location-based relationships between the district-level TFR and its driving forces. The results revealed that the relationships between the district-level TFR and the considered selected predictors (percentage of Muslims, urbanization, caste group, female mean age at marriage, female education, females in the labour force, net migration, sex ratio at birth and exposure to mass media) were not spatially invariant in terms of their respective strength, magnitude and direction, and furthermore, these relationships were conspicuously place- and context-specific. This study suggests that such locality-based variations and their complexities cannot be explained simply by a single narrative of either socioeconomic advancement or government policy interventions. It therefore contributes to the ongoing debate on fertility research in India by highlighting the spatial dependence and heterogeneity of the impacts made by demographic, socioeconomic and cultural factors on local fertility levels. From a methodological perspective, the study also discerns that the GWR local model performs better, in terms of both model performance and prediction accuracy, compared with the conventional global model estimates.

A Decomposition Analysis of Fertility: Evidence from DKI Jakarta and East Nusa Tenggara

“…Similar to the findings from previous studies, socioeconomic indicators can reflect fertility (Haque, Das & Patel 2019;Morgan, Zhigang & Hayford 2009). Several other indicators between the two censuses showed that DKI Jakarta has better socioeconomic conditions than East Nusa Tenggara.…”

“…significant in a society with low economic status (Haque, Das & Patel 2019). In India, women with higher educational level and lower fertility are those with higher economic status.…”

Cash‐based maternal health interventions can improve childhood vaccination—Evidence from India

“…12 Nevertheless, we try to test this conjecture in our data. We cannot create a test of differential fertility with conventional measures like total fertility rate; such data are not readily available and often difficult to calculate in India (Guilmoto & Rajan, 2013;Haque, Das, & Patel, 2019). Therefore, we develop our test following the intuition of Hoynes, Miller, and Simon (2015) by adopting the same DID strategy to see if the treatment districts had significantly higher birth than the control ones.…”

Rural–Urban Fertility Convergence, Differential Stopping Behavior, and Contraceptive Method Mix in West Bengal, India: A Spatiotemporal Analysis

“…[37][38][39][40] However, the relationship between urbanization and fertility decline in India has been found to be weak. 41 One can argue that starting from similar fertility levels, rural areas experienced a sharp increase in excess fertility over urban areas in the first phase because of earlier and faster decline in cities, but reached fertility levels comparable to urban areas in the later phase of the demographic transition. 42 Reliable estimates of the birth rates in West Bengal were exceptionally deficient before about 1979.…”

“…However, a recent study by Haque et al (2019) depicts that there is no significant spatial dependence in the districtlevel TFR of West Bengal with the proximate districts of the adjacent states; rather it is surrounded by the high-TFR districts of east Bihar, Sikkim, and some districts of Jharkhand. 26 The demographic behavior of West Bengal has not received adequate attention among demographers, where fertility decline began well before independence. 27 Currently, West Bengal is one of the lowest fertility states in India.…”