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Progress of Inequality in Age at Death in India: Role of Adult Mortality

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Abstract

India has seen a reduction in infant and child mortality rates for both the sexes since the early 1980s. However, a decline in mortality at adult ages is marked by significant differences in the subgroups of sex and regions. This study assesses the progress of inequality in age at death with the advances in mortality transition during 36 years period between 1981–1985 and 2012–2016 in India, using the Gini coefficients at the age of zero (G0). The Gini coefficients show that in the mid-2000s, women outpaced men in G0. The reduction in inequality in age at death is a manifestation of the process of homogeneity in mortality. The low G0 is concomitant of high life expectancy at birth (e0) in India. The results show the dominance of adult mortality over child mortality in the medium-mortality and low-mortality regimes. Varying adult mortality in the subgroups of sex and variance in the mortality levels of regions are the predominant factors for the variation in inequality in age at death. By lowering of the mortality rates in the age group of 15–29 years, India can achieve a high e0 that appears at high demographic development and the narrow sex differentials in e0 and G0 in a short time. Men in the age group of 15–29 years are the most vulnerable subgroup with respect to mortality. There is an immediate need for health policies in India to prioritise the aversion of premature deaths in men aged 15–29 years.

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Fig. 1

Source: Own calculations using Eqs. (1) and (2). Notes: 3MAV: Three-year Moving Average of ASDR; 5MAV: Five-year Moving Average of ASDR

Fig. 2

Source: Own calculations using Eq. (1). Notes: a and b show the graphs for demographically advanced (DA) states, and c and d show the graphs for demographically backward (DB) states, The names of Indian states are arranged in an alphabetical order by the category of demographically advanced and backward states

Fig. 3

Source: Own calculations using Eq. (1). Notes: a, b show the graph for demographically advanced (DA) states, and c, d show the graph for demographically backward (DB) states, The names of Indian states are arranged in an alphabetical order by the category of demographically advanced and backward states

Fig. 4

Source: Own calculations using Eq. (1). Notes: The Y values in per cent above zero represent equalising effect on G0. The Y values in per cent below zero represent disequalising effect on G0, The embedded age groups represent the respective areas in the graph, The areas in the graph show the age-specific contributions to G0 in per cent between the base year (1981–1985) and the reference period (say 2012–2016), The period 1982–1986 shows the age-specific contributions to G0 in per cent between the periods 1981–1985 and 1982–1986

Fig. 5

Source: Own calculations using Eq. (1). Notes: The graph shows the age-specific contributions to G0 in absolute values between the base year (1981–1985) and the reference period (say 2012–2016), The negative Y-axis values are the equalising effect on G0, and positive Y-axis values are the disequalising effect on G0

Fig. 6

Source: Own calculations using Eq. (1). Notes: a, b show the graph for demographically advanced (DA) states, and c, d show the graph for demographically backward (DB) states, The names of Indian states are arranged in an alphabetical order by the category of demographically advanced and backward states, Bigger marker size represents recent years during the period of 1981–2016, and the smallest marker size represents the base period 1981–1985, The negative Y-axis values are the equalising effect on G0, and positive Y-axis values are the disequalising effect on G0

Fig. 7

Source: Own calculations using Eq. (1). Notes: The positive Y-axis values in per cent are the equalising effect on G0, and negative Y-axis values in per cent are the disequalising effect on G0, The graph shows the age-specific contributions to G0 in per cent between the base period (1981–1985) and the selected periods, AP: Andhra Pradesh, ASS: Assam, BH: Bihar, GUJ: Gujarat, HAR: Haryana, IND: India, KAR: Karnataka, KER: Kerala, MHA: Maharashtra, MP: Madhya Pradesh, OD: Odisha, PUN: Punjab, RAJ: Rajasthan, TN: Tamil Nadu, UP: Uttar Pradesh, WB: West Bengal

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Notes

  1. The convention of age groups in this section is followed for the comparison with the available age-specific contributions to G0 in USA (Shkolnikov et al. 2003, p. 327).

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Acknowledgement

I am thankful to Prof. Arni S. R. Srinivasa Rao (PhD), Division of Health Economics and Modeling, Department of Population health Sciences and Director at the Laboratory for Theory and Mathematical Modeling, Medical College of Georgia, Augusta University, Georgia, USA and Dr. Manoj Alagarajan, Department of Development Studies, IIPS, Mumbai, India, for their critical suggestions and comments for this paper. I am thankful to Prof. Arokiasamy Perianayagam, Department of Development Studies, IIPS, Mumbai, India, for his help. I thank the IUSSP 2017, Cape Town, PAA 2018, Denver, Virtual PAA 2020, and APA 2018, Sanghai for giving an opportunity to present the paper in poster session. Author is grateful to the anonymous reviewers for their comments and suggestions.

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    Suryakant Yadav

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Yadav, S. Progress of Inequality in Age at Death in India: Role of Adult Mortality. Eur J Population 37, 523–550 (2021). https://doi.org/10.1007/s10680-021-09577-1

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