India is probably known to most N-IUSSP readers for its macro characteristics, which are often compared to those of other large developing countries, as in Table 1. The picture that emerges is mixed: a synthetic measure like the human development index (HDI) indicates that India occupies an intermediate position, behind Brazil, China, and Indonesia, but ahead of Bangladesh, Pakistan, and Nigeria.

This article, however, does not set out to compare India internationally; rather, it focuses on its internal heterogeneity, which is extremely high – higher than what most N-IUSSP readers probably imagine. Indeed, the famous north-south dichotomy is not limited to demography but concerns several key domains of human development, education, health and income. Regional differences and disparities with respect to each of these domains are analysed in depth in a recent book (Mohanty, Mishra and Chauhan 2019); here we highlight a few of the most interesting features that emerge.

640 Indian realities

Our unit of analysis are the 640 Indian districts. These are reasonably large (some 2 million inhabitants each, on average) and at different stages of their demographic transition, with wide differences in terms of growth rate, age and sex structure, fertility, mortality, and migration. Not surprisingly, they also exhibit huge disparities in socio-economic development, which is here measured with the district development index (DDI), a composite indicator ranging between 0 and 1, which is analogous to the more famous HDI, and combines social, health and economic variables (Mohanty et al 2019).

Figure 1, illustrating the mean level of the DDI in 2011 across a range of population growth rates between 2001 and 2011, displays a very distinct U shape. Moderate population growth is associated with higher development; however, robust population growth rates characterize not only relatively backward areas (with high fertility), but also highly dynamic and urbanized districts, on the right of the scale, because they attract migrants.

Child sex ratio and level of development

Child sex ratio (CSR), defined as the number of girls per 1,000 boys aged 0-6, has been declining in India over the last two decades, and this is a major concern to both national and state governments. Figure 2 plots the CSR by DDI for the Indian districts in 2011. This indicator is very frequently below, sometimes largely below, its “expected” value of 952 (horizontal violet line). It is generally higher in less developed districts (DDI below 0.4), and tends to decrease in the more developed one (DDI above 0.6), although the trend is slightly masked by an increase in the variability of the phenomenon (right part of Figure 2). Son preference (which often translates into sex-selective abortion) is surely the main cause of this pattern: low fertility levels in a good number of the more developed districts are associated with (and lead to) low CSRs.

Changing age structure and level of development

Along with fertility transition and rising longevity, the size and share of the older population (e.g., aged 60 and over) is increasing in India. On average, districts with higher level of development have higher proportion of older adults (Figure 3). Districts where this share exceeds 10% (the horizontal line) typically have medium or high levels of development. Deviations from this pattern are probably caused by migration to the more developed districts, because migrants are typically of young working ages. Even here, however, declining fertility and improved survival will eventually lead to ageing: provision of healthcare, pensions and social support to the elderly will be a formidable challenge in the coming years.

Gender differentials in educational progression

The conventional educational indicators fail to capture the geographical variations and gender gap in educational progress in India. For instance, while gender gaps in literacy and primary enrolment are narrowing, they remain disparate at higher educational levels. This is clearly shown by a gendered analysis of the Educational Progression Ratio (EPR), the probability of attaining higher secondary level by those who completed their junior secondary education (Chatterjee and Mishra, 2019).

As Figure 4 shows, inter-district variations in EPR are extremely large and responsive to the differential levels of development across districts. However, at all levels of DDI, EPRs are consistently lower for females than for males.

Conclusions

Districts of India remain highly heterogeneous in both demographic and development terms, with no apparent signs of convergence, yet. As the divergence is not limited to the few dimensions illustrated here, the need for policy intervention arises. In our opinion, the key dimensions on which to intervene, among the many that are needed, are:

1) arrest population increase, especially in rapidly growing districts,

2) put special emphasis on gender equity, and

3) identify districts with an urgent need for development intervention, within and across states.

Without these, or similar, policy actions, there are reasons to fear that the already existing, profound inequalities of the country will soon grow even larger.

References

Chatterjee S., & Mishra U.S.(2019). Educational Development and Disparities in India: District-Level Analyses. In The Demographic and Development Divide in India (pp. 259-328). Springer, Singapore.

Mohanty S. K., Mishra U.S. & Chauhan R.K. (2019). The Demographic and Development Divide in India. Springer, Singapore.

Mohanty S.K., Dehury B., Mishra U.S., Dash A. & Singh R.R. (2019). Spatial Pattern of Development in Districts of India. In The Demographic and Development Divide in India (pp. 509-551). Springer, Singapore.

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India’s total fertility rate (TFR) has declined steadily, falling from 3.6 in 1991 to 2.4 by 2011. Over the same period, per capita income increased from 1221 US$ to 3755 US$. The increase in national income has not only yielded substantial reductions in poverty, but has also been accompanied by major improvements in female literacy and child survival. The female literacy rate increased from 39 percent in 1991 to 65 per cent in 2011; under-five mortality declined to 55 per 1,000 live births in 2011. In the composite index of human development that combines education, health and income, the country increased its score from 0.428 in 1990 to 0.609 by 2014 (UNDP 2015).

The very positive recent national trends conceal large differences across states, regions and districts of India. As of the 2011 census, India comprises 29 states and six union territories spread across 640 districts. Conventional demographic analyses examine the pattern and determinants of fertility change using four proximate determinants, namely, contraception, marriage, induced abortion and postpartum insusceptibility at state level or at individual level. The average population size of an Indian district is about two million, with large variations in fertility and developmental parameters. Disaggregated analyses at district level are useful for multiple stakeholders as districts are the central focus of planning and program implementation in India. Such analyses could also shed light on the puzzling decrease in fertility levels despite a recent decline in use of modern contraception in eight of the 15 states of India (IIPS 2016).

No evidence of fertility convergence at the district level

Fertility convergence implies that fertility declines faster in high fertility areas than in low fertility areas. Maps 1 and 2 present the fertility levels recorded in districts of India in 1991 and 2011. In 2011, the TFR varied widely across districts of India; from 5 births per woman in Khagaria district in the state of Bihar to 1.1 in Kolkata district in the state of West Bengal. While one-fifth of the districts had reached a below-replacement level of fertility by 2011, about half of the districts had a TFR of more than 3.
Figure 1 shows average annual declines in TFR relative to the initial 1991 TFR levels at the district level. While most districts experienced substantial declines over the 1991-2011 period, there was no clear relationship between initial levels and subsequent changes; on average, the largest relative reductions were observed for districts with an initial TFR in the middle (3-4) range.

Pace of socio-economic change: female literacy, under-five mortality and poverty

Socio-economic change has been consistently highlighted as a key predictor of fertility change. Among other factors, the extant literature has shown that female literacy, under-five mortality and poverty are of critical importance. Female educational attainment is associated with increasing use of contraception, increasing age at marriage, and birth spacing. Maps 2(a) and 2(b) present female literacy rates at the district level for 1991 and 2011, respectively. Overall, tremendous progress in female literacy has been achieved in the last two decades, which probably triggered fertility reduction. Very low female literacy (less than 40% of women able to read and write), which was widespread in 1991 (60% of all districts) had become rare by 2011 (less than 3% of all districts). Most districts have female literacy in the range of 60-80 percent.
Fig 2 plots the change in female literacy by level of initial female literacy in districts of India. The negative sign of the slope and the convex nature of the curve indicate remarkable convergence in female literacy, with greater progress observed in the districts that most lagged behind in 1991.
Fig 3 plots the convergence in under-five mortality across districts of India. Districts with higher under-five mortality have generally shown a greater reduction in the last two decades; the convergence in under-five mortality is undoubtedly higher than that of fertility. However, the same does not hold for poverty: in this case the trajectories were heterogeneous and bore little relation to the starting point.When jointly analyzing poverty, literacy and mortality in multivariable models (not shown here), we found literacy to be the factor most predictive of changes in fertility; strong associations were also found between child mortality and changes in fertility, while the associations found with poverty were relatively minor and often not statistically significant.

Conclusion

Fertility differentials across districts of India are large. While fertility has declined in virtually all areas of India, evidence on convergence in fertility is limited, with a surprisingly large number of districts still having TFR of 4 and higher. High TFR rates in these districts, together with the generally young population, will continue to drive rapid population growth. Regional differences in population growth are likely to widen regional imbalances, increasing regional inequality and inter-state migration. Focusing on areas with high fertility will be critical; increasing access to modern methods of contraception, improving child survival and promoting female education in high-fertility districts are all potentially effective strategies for reaching this goal.

References

IIPS (2016). National Family and Health Survey. 2015-16. State Fact Sheet

Mohanty SK, Fink G, Chauhan RK and Canning D (2016). “Distal Determinants of fertility decline: Evidences from 640 Indian districts” Demographic Research, 34(13):373-406

UNDP (2015). Human Development Report 2015, New York: USA

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