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Life expectancy at birth and life disparity: an assessment of sex differentials in mortality in India

reas the male life expectancy at birth was higher in the 1970s and 1980s (Dyson, 1984). However, in
recent decades, in spite of higher child and maternal mortality, life expectancy at birth of females has
surpassed that of males. The increase in life expectancy at birth was more rapid in rural than in urban
areas. The increase in life expectancy at birth also varied across Indian states. There was a strong
North-South gradient across the states, with great variations in the level and the pace of mortality
reduction over time (Bhat, 1987; Saikia, Jasilionis, Ram et al., 2011). The pace of mortality decline
was faster than international standard for males in Kerala and Tamil Nadu, and for females in Kerala,
Tamil Nadu, Himachal Pradesh, and Uttar Pradesh (Chaurasia, 2010).
The sex differentials in mortality indicators also vary by age, in India. Research on infant
and child mortality in India showed significant sex differentials (Subramanian, Nandy, Irving et al.,
2006; Claeson, Bos, Mawji et al., 2000). Although the sex differential in adult mortality was not
very high, it has been increasing continuously since the declining female mortality outpaced male
mortality (Saikia and Ram, 2010). At older ages, the probability of survival is much higher among
females than males in India in the recent decades (Chaurasia, 2010).
The distribution of deaths in 2013 by age showed that child (aged 0–4) deaths constituted 3% of
the total deaths in Kerala compared with 25% in Uttar Pradesh, whereas adult (aged 15–59)
deaths constituted almost 30% to 35% of the total deaths in all the major states of India (RGI, 2014).
Saikia et al. (2013) further demonstrated that there was a substantial rural–urban difference in infant
mortality at both national and state levels. Such a rural-urban difference in infant mortality was
found in both socio-economically advanced states (e.g., Goa, Kerala) and disadvantaged states (e.g.,
Madhya Pradesh, Assam, and Orissa).
2. Data and Methods

2.1 Data

The Sample Registration System (SRS), under the auspices of the Office of the Registrar General of
India (RGI), is the major source of mortality data and life tables in India (RGI, 2014). SRS is a dual
record system with the continuous registration of birth and deaths in a nationally representative sam-
ple of villages and urban blocks in addition to a half-yearly survey for an independent count of
events to update the demographics of the sample population. Events recorded in both the operations
are matched to identify the unmatched and partially matched events, which can be referred to the
field for verification. SRS provides information on age-specific death rates in different age groups
and the abridged life tables starting 1970–1975. The data on death rates along with the abridged life
tables from 1970–1975 till the recent time period (ORG 1984, 1985, 1989; RGI 1994, 1996–2010,
1998) were used in this study.
The quality of death statistics, in particular, uneven completeness of death registration by age, and
systematic age misreporting can have an adverse impact on the accuracy of a life table and estimated
life expectancy and life disparity. Nevertheless, SRS data are considered the most reliable of all
death statistics in India (Roy and Lahiri, 1988; National Commission on Population, 2001; Mathers,
Fat, Inoue et al., 2005). Definition of terms, administrative guidelines, and data collection methods
of SRS are consistent over time, allowing for comparisons across time periods. An SRS representa-
tive character allows for estimation of vital statistics for India and the major states. The death regis-
tration completeness was about 95% for both males and females during 1971–1980. Evaluation for
the recent time period 1990–1997 suggested no substantial changes in the completeness of reporting
of either deaths or births in SRS (Bhat, 2002). With exceptions at the older ages, the registration of
deaths at both childhood and adult ages was considered to be reliable because they were consistent
with the SRS data (Saikia, Jasilionis, Ram et al., 2011).

2.2 Complete Life Table Construction

nq x (the probability of dying from age x to x+n) was segregated into 1q x (the probability of dying

40 International Journal of Population Studies | 2016, Volume 2, Issue 1