Relationship between Life Expectancy at birth for male and female, - - PDF document

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Gender gap in mortality in India and role of age groups: A comparison between before and after male – female Life Expectancy at Birth crossover GIRIMALLIKA BORAH girimallikaborah@gmail.com Department of Geography, Bodoland University Kokrajhar, BTAD, Assam IUSSP Conference Paper, 2017, Cape Town

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ABSTRACT We assess the changing contribution of different age groups to temporal increase in life expectancy at birth (LEB) as well as increasing gender gap before and after female and male life expectancy crossover. We have used sample-survey-based age-specific mortality data available for the periods 1970–2006 to construct abridged life tables. During 1981–1985, female LEB caught up with male LEB for India and, by 2005, all major states completed the

• crossover. Using Arriaga’s method of decomposition, we provide added evidence that

temporal increase in life expectancy in India is largely a function of the decline in under-five mortality, especially from the 0–1 year age group, regardless of sex. The male – female crossover in LEB is remarkable on the face of continued female disadvantage from birth till adolescent, even for some richer states. Gender difference in LEB in favour of female is largely a function of adult age groups. Juxtaposing the results from contribution in absolute number of years and their relative contribution change over time, it is established that although the adult and old age groups contribute the highest in absolute number of years, the contribution of the reproductive age groups is the most relevant in relative terms.

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Gender gap in mortality in India and role of age groups: A comparison between before and after male – female Life Expectancy at Birth crossover Introduction Like many other Asian nations India began its health transition at low life expectancy of 24.8 years (Railey, 2005). At the turn of century, life expectancy increased to 62 years. The rate of growth has however slowed down after 1990s. The average life expectancy of global population in the year 2009 is 68 years (WHO) and life expectancy in India in the year 2008 is 66.1 years. Not only India lacked behind international standard, most of India’s neighbour countries are doing better than India. Using data from World Development Indicators (WDI) it is found that in the year 2011, India ranks 140 among 190 countries and except for Pakistan, rest of South Asia has higher life expectancy than India. Countries who started their transitions at the same time and at the same level as was India have reached higher life expectancy at birth. The reduction and where possible eradication of differentials in mortality has been a primary intention of World Health Organisation and it is implied in its stated goal

• f achieving ‘Health for all by the year 2000’ (Lopez et al., 1983). Life expectancy in India is

marked by differences among and between national populations, distinction by sex and place

• f residence. A better understanding of the age pattern of mortality is required to formulate

policy decisions and to follow target group approach to reduce mortality and improve life expectancy in general. [Figure 1 about here] Another striking distinction of India’s mortality transition is unlike most of the developed and many developing nations India continued to have higher male life expectancy when the transition was in process. During 1981-1985, female LEB has caught up with male LEB. “Crossover” is a term used in the literature to explain change in trend. The male female crossover was complete by 2005 in all the major states. Higher life expectancy among females is a general pattern found in majority of the life tables available across the world and almost universal to the developed countries (Lee 2003, Sandiford 2009). A clear north - east and south - west difference is observed among the states regarding timing of crossover. South and West Indian states passed the crossover earlier than the states located in the north and east India with only two exceptions, West Bengal from east and Tamil Nadu form south. We have identified three time period one before and one after the crossover. In this paper, we discuss gender gap in life expectancy at state level and what age group has contributed the most to the change in situation from 1970-’75 when life expectancy at zero was in favour of males to 1981-’85 when female crossed over male life expectancy at birth ? We will see how contribution from different age groups has changed after the crossover using time period 2006 – 2010. [Table 1 about here]

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Figure 2 shows the differences in male and female life expectancy at a particular level of life expectancy at birth at state level, 2006 - 2010. No state lies below the 45° line. It is very clear that by 2006-’10 female life expectancy for all states are higher at a given level of male life

• expectancy. As the gap between the trend line and the 45° line increases, the gender gap in

life expectancy in favour of female increases. Bihar, Orissa, Madhya Pradesh, Uttar Pradesh, Assam – from their position just above the 45° line it is apparent that sex differentials in mortality is very less. Kerala remained an outlier state during the whole period between 1970-’75 to 2006-’10, an occurrence that has been explained length and breadth in many earlier studies relating to this topic. [Figure 2 about here] Review of Literature There are some interesting findings related to pattern of sex differentials in life expectancy at birth. One, higher life expectancy among females is a general pattern found in majority of the life tables available across the world and almost universal to the developed countries.1,2 It is observed in literature from the developed nations that the gender gap in life expectancy at birth in favour of women has been a historical phenomenon for the developed nations, in Sweden life expectancy at Birth continues to be the highest until recently when Japan took over, higher female life expectancy is a historical phenomena right from early eighteenth century, and it increased thereafter until it started falling after the late nineteenth

• century. In England, Wales, The Netherlands, Iceland, Italy, Finland, Switzerland and

Norway female life expectancy at birth is higher than male since mid nineteenth century since when data is available3 . It is concluded in many literature that the gap increases with the increase in economic development. To quote one such assertion, “As society develops, mortality declines and, at the same time, the excess female mortality characteristic of pre- transition societies shifts to higher male mortality” (Fix and Fix: 1991, page- 212). It is also pointed out that economic development is more beneficial to female since absolute and relative increase of life expectancy at birth coincides with increase in income4. Developed nations started their mortality transition earlier gender gap has increased in favour of females and some of the developed nations are now at the stage from where gender gap in life expectancy is declining. Countries those are mentioned above are the high- income countries, they have started experiencing narrowing gap of gender differentials in mortality and it has pointed out new feature of epidemiological transition. Japan is an interesting case for this matter where in spite of having the highest life expectancy at birth still the gap is widening and showing no sign of narrowing down.5,6 In the 1980s and 90s sex differences in mortality

1 Sandiford, P. (2009). “Getting Back the Missing Men of Aotearoa: Declining Gender Inequality in NZ Life

Expectancy”, Journal of Primary Health Care, Vol. 1, No. 4.

2 Lee, R. (2003). “The Demographic Transition: Three Centuries of Fundamental Change”, Journal of Economic

Perspectives, Vol. 17, No. 4, pp. 167- 190.

3 Glei, A. D. and Shiro Horiuchi (2007). “The Narrowing sex Differentials in Life Expectancy in High- Income

Populations: Effects of Differences in the Age Pattern of Mortality”, Population Studies, Vol. 61, No. 2, p. 146.

4Alachkar A. And William J. Serow, 1988, “The Socioeconomic Determinants of Mortality: An International

Comparison”, Genus, Vol. 44, No. ¾, pp. 131-151

5 Ibid.

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declined in many parts, the gender differences in life expectancy also declined and mostly it happened in the developed parts of the world, including The USA, Western Europe and Australia. Broadly Glei and Horiuchi (2007) grouped the causes for sex differentials in mortality into two- medical and behavioural. In analysis of the data from developed nations, Glei and Horiuchi (2007) found out steeper mortality curve for women than for men. The plausible reasons he explained are unhealthy behaviour among male of younger age groups like drinking, smoking and occupation hazard, increases the probability of dying at younger age

• group. While in the older age groups the mortality declines either because of the decline in

these fatal habits or because of selective survival among males in older age group. During reproductive periods women benefit greatly from sex hormone mainly estrogens.7 Two, female disadvantage over male life expectancy in Asia is observed by many

• scholars. Countries are at various stages of this transition. Interesting to note that during

1950s there were only 7 countries in the world where life expectancy at birth for female was lower than that of male and more interestingly 6 of them were from South Asia.8 Some of the underlying explanations for female disadvantages in south Asian countries are – environmental, high maternal mortality among women in Asia and traditional family and social system where son is given greater importance and provided with better educational, health facility than daughters. The attitude of the society towards gender equality has not changed but there are some improvement in the mortality situation particularly among the adult women resulted to higher life expectancy among females in South Asia. Most of these improvements came from effect

• f public health services in general and family planning services in particular. In her analysis

based on SRS data 1976- 1980, Karkal (1987) found that though some gains were experienced by Indian female but larger share of them is enjoyed by women in older age

• group. Younger women in India still remain the deprived group. Lopez (1983) adds

environmental factor another reason for sex differential in mortality. Due to environmental disadvantages for the part of women in developing countries the biological advantage they have over men cannot be seen. There is a relationship of fertility with maternal deaths. With decline in fertility, deaths during pregnancy should decline because fewer women will be exposed to the risk of pregnancy - related deaths. Fix and Fix (1991) in their study among Semai Senoi, a Malaysian Aboriginal group found out declining maternal rate at the same time when level of fertility was increasing. They explained, better health care leads to the significant decline in maternal mortality. Along with overall fertility also important to look who are exposed to the risk of fertility because it is not all women in the reproductive age group at the equal risk of

6 Trovato, F. (2005). “Narrowing Sex Differential in Life Expectancy in Canada and Austria: A Comparative

Analysis”, Vienne Yearbook of Population Research, Vol. 3, pp. 17-52

7 Glei, A. D. and Shiro Horiuchi (2007). “The Narrowing Sex Differentials in Life expectancy in High-Income

Populations: Effects of Differences in the Age Pattern of Mortality”, Population Studies, Vol. 61, No. 2, p.- 155

8 Karkal, M, (1987). “Differentials in Mortality by Sex”, Economic and Political Weekly, Vol. 22, No. 32, pp.

1343-1347.

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maternal death. Women at the younger and older age group are more prone to die during pregnancy.9 Excess female mortality is a South Asian phenomenon where female mortality is higher than male mortality not only in the reproductive age groups but also during childhood.10,11 Several explanations were given for this distinct pattern of higher female mortality over male mortality called “negative differentials” (Lopez and Ruzicka: 1983, page

• 13). Unequal child care with greater care for a male child is one explanation for the higher

mortality among female during childhood. With the introduction of modern health care prevailing unequal treatment is aggravated12. Basu and Basu’s (1991) findings are similar based in her study in Delhi among families originally from Uttar Pradesh and Tamil Nadu. In Uttar Pradesh female child mortality is higher than male child but malnutrition is more common among boy children. The reason for higher mortality is not for malnutrition among girl child but lack of medical care given to her. On the contrary in Tamil Nadu the opposite is

• bserved. More girls are likely to be malnourished but mortality is higher among boys. That

is, because of equal medical treatment given for both the gender. Biologically for many different species higher mortality among males than females has been observed (Lopez and Ruzicka, 1983, page - 142). There are different hypothesis regarding mortality differentials presented, one hypothesis “the double jeopardy” 13which pronounces being a woman and being old – both are not the very favourable situation. Hence it is predicted to be not good for health situations and mortality is supposed to increase among the older females. Alam and Karan (2011) in their paper among elderly in India prove this hypothesis right. Among many other interesting findings they also found that negative impact of health is associated with elderly women and increase with age. Saikia and Bhatt (2008) in their analysis among 15- 59 years old women is based on NFHS II data found higher female mortality than male mortality. Opposite to double jeopardy, the other theory “age - as - leveler”14 corresponded to the experience of the western countries and empirically proven one which believes the opposite.

9 Fix, G. Alan and Allan G. Fix (1991). “Changing Sex Ratio of Mortality in the Semai Senoi, 1969-1987”, Human

Biology, Vol. 63, No. 2, pp. 211-220

10 Ibid. 11 Pebley, R. A. and Sajeda Amin (1991). “The impact of a public- health intervention on sex differentials in

childhood mortality in rural Punjab, India”, Health Transition Review, Vol. 1, No. 2 (OCTOBAR 1991), pp. 143- 169

12 Fix, G. Alan and Allan G. Fix (1991). “Changing Sex Ratio of Mortality in the Semai Senoi, 1969-1987”, Human

Biology, Vol. 63, No. 2 (April 1991) pp. 211-220

13 Knodel J. and Mary Beth, 2003, “Gender and Ageing in the Developing World: Where Are the Men?,”

Population and Development Review, Vol. 29, No. 4, PP. 677-698

14 Ibid.

Data base and methodology The source of data used is Sample five years age groups has been estimated by Sample Registration System since 1970 2006 - 2010. The Sample Registration System is a large based on mechanism of a dual record system with the objective of providing reliable estimate

• f fertility and mortality indicators

is one problem of using SRS - based life tables. There are different ways of converting specific mortality rates to life table function life table is that life tables for the year 1970 method which life tables constructed afterwards are constructed us United Nations’ software package for mortal this particular problem we have constructed life tables using MORTPAK 4 specific death data from SRS. Decomposition techniques are used mortality in a particular age group has contributed to the change in life expectancy between two time periods or between sexes or between any subgroups of population Arriaga’s method based on discrete data is used. Arriaga (1984) explained age decomposition using three effects – direct, indirect and interaction effect. Direct effect on life expectancy is due to change in life years on an age group due to the change in mortality in that particular age group. Indirect effect capt change after the end of that particular age interval. The difference in survivors, after mortality change, will be added or subtracted from years lived assuming that mortality has not changed and remained at the same level. Both direct and indirect effects are generated because mortality has changed only within the age group under study (it is assumed that mortality has not changed in other ages). This gi exclusive effect (Arriaga : 1984). Here x is the initial age of age interval is the initial year of the observation period of To calculate gender differentials in life expectancy, male life expectancy is considered t and female life expectancy is considered t+n.

15 United Nations Population Division (2003). Mortpak for Windows, Version 4, United Nations, New York

The source of data used is Sample Registration System (SRS). In India, life expectancy at five years age groups has been estimated by Sample Registration System since 1970 . The Sample Registration System is a large-scale demographic sample survey dual record system with the objective of providing reliable estimate

• f fertility and mortality indicators. There are several methods of constructing life tables; this

based life tables. There are different ways of converting specific mortality rates to life table function nqx. One problem in using SRS based abridge life table is that life tables for the year 1970 -’75 to 1981 -’85 are baesed on Grevelli’s method which life tables constructed afterwards are constructed using MORTPAK 4 United Nations’ software package for mortality measurement (Chaurasia, 2010). To nullify this particular problem we have constructed life tables using MORTPAK 4 Decomposition techniques are used in formal demography, to understand how change in mortality in a particular age group has contributed to the change in life expectancy between two time periods or between sexes or between any subgroups of population. In this analysis d on discrete data is used. Arriaga (1984) explained age decomposition direct, indirect and interaction effect. Direct effect on life expectancy is due to change in life years on an age group due to the rticular age group. Indirect effect captures the number of years after the end of that particular age interval. The difference in survivors, after mortality

• r subtracted from years lived as they pass through successive ages,

assuming that mortality has not changed and remained at the same level. Both direct and indirect effects are generated because mortality has changed only within the age group under study (it is assumed that mortality has not changed in other ages). This gives us the total and ). is the initial age of age interval i , a is the age at which life expectancy is calculated is the initial year of the observation period of n years. Both lx and Tx are life table functions. To calculate gender differentials in life expectancy, male life expectancy is considered t and female life expectancy is considered t+n.

United Nations Population Division (2003). Mortpak for Windows, Version 4, United Nations, New York

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Registration System (SRS). In India, life expectancy at five years age groups has been estimated by Sample Registration System since 1970-75 to scale demographic sample survey dual record system with the objective of providing reliable estimate . There are several methods of constructing life tables; this based life tables. There are different ways of converting age - . One problem in using SRS based abridge ’85 are baesed on Grevelli’s ing MORTPAK 415- a , 2010). To nullify this particular problem we have constructed life tables using MORTPAK 4 taking age in formal demography, to understand how change in mortality in a particular age group has contributed to the change in life expectancy between . In this analysis d on discrete data is used. Arriaga (1984) explained age decomposition Direct effect on life expectancy is due to change in life years on an age group due to the ures the number of years after the end of that particular age interval. The difference in survivors, after mortality as they pass through successive ages, assuming that mortality has not changed and remained at the same level. Both direct and indirect effects are generated because mortality has changed only within the age group under ves us the total and is the age at which life expectancy is calculated, t are life table functions. To calculate gender differentials in life expectancy, male life expectancy is considered t and

United Nations Population Division (2003). Mortpak for Windows, Version 4, United Nations, New York

lx= Number of people left alive at age Tx = Persons- years lived above age But since mortality changes in all age groups under study, the difference in the number of survivor will experience a change in the mortality at the end of age interval. The difference in mortality levels applied to the difference in survivors and produces an inter Interaction effect cannot be allocated to a particular age group alone. It is the difference between two components : (a) the one resulting from the years of life to be added because the additional survivors at the end of interval will conti because of indirect effect mentioned above (Arriaga: 1984 to OE minus indirect effect. Using the above three formulas direct, indirect and interaction effects are calculated separately and adding the three effects gives how much a particular age group is responsible for the change in life expectancy at birth over the time, or to gender gap i states at certain periods had lower female LEB than male LEB sometimes when the percentages are shown it goes very high and seems unreasonable, becomes difficult to

• interpret. If one age group contributed negative then the other groups

the actual difference and the actual age differences although for most of the age groups are very small, makes more sense. Total contribution from a particular age group in years is broken down to direct, indirect and interaction eff percentages. = Number of people left alive at age x years lived above age x mortality changes in all age groups under study, the difference in the number of survivor will experience a change in the mortality at the end of age interval. The difference in mortality levels applied to the difference in survivors and produces an inter Interaction effect cannot be allocated to a particular age group alone. It is the difference between two components : (a) the one resulting from the years of life to be added because the additional survivors at the end of interval will continue to live through new mortality; and (b) mentioned above (Arriaga: 1984). Hence interaction effect is equal Using the above three formulas direct, indirect and interaction effects are calculated separately and adding the three effects gives how much a particular age group is responsible for the change in life expectancy at birth over the time, or to gender gap in LEB. Since certain states at certain periods had lower female LEB than male LEB sometimes when the percentages are shown it goes very high and seems unreasonable, becomes difficult to

• interpret. If one age group contributed negative then the other groups contributes more than

the actual difference and the actual age differences although for most of the age groups are very small, makes more sense. Total contribution from a particular age group in years is broken down to direct, indirect and interaction effects and total effect is also shown in

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mortality changes in all age groups under study, the difference in the number of survivor will experience a change in the mortality at the end of age interval. The difference in mortality levels applied to the difference in survivors and produces an interaction effect. Interaction effect cannot be allocated to a particular age group alone. It is the difference between two components : (a) the one resulting from the years of life to be added because the nue to live through new mortality; and (b) Hence interaction effect is equal Using the above three formulas direct, indirect and interaction effects are calculated separately and adding the three effects gives how much a particular age group is responsible n LEB. Since certain states at certain periods had lower female LEB than male LEB sometimes when the percentages are shown it goes very high and seems unreasonable, becomes difficult to contributes more than the actual difference and the actual age differences although for most of the age groups are very small, makes more sense. Total contribution from a particular age group in years is ects and total effect is also shown in

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Though life expectancy at birth is higher for female it is does not necessarily mean that mortality among females will be lower in all age groups. Sex ratio of mortality (SRM) is calculated for all 17 states using the following formula; SRM = nMx/ nFx Where, nMx is the geometric mean of the 5-year age-specific death rate for the age group (X, X+n) from the male life table in a particular age group. nFx is the corresponding value for female age group. Sex ratio of mortality defined as male upon female mortality, if the value is unitary male and female mortality at that particular age group will be equal. SRM value above 1 means male mortality is higher and vice-versa. For a particular age group if mortality is higher among male population then the value of SRM will be higher than unitary and if male mortality is lower than female mortality then the SRM value will be lower than unitary. Results Life expectancy at birth: Contribution of age groups It was observed that in between the three time periods all the age groups for India and for most of the states (with the exception of few older age groups in some states) contributed positively to the temporal increase in life expectancy at birth. Not all age groups participated equally in the process; the concentration was confined largely at the first 5 years of life and especially from the 0 to1 age group, regardless of sex. It is also observed that the contribution from the first 5 years of life has not reduced but increased over the whole period. Definitely, mortality improvement among infants and children has contributed significantly to the change in life expectancy at birth. Contribution from adult population to change in life expectancy has been falling. It is falling for both the sexes. Relative contribution from 15 to 45 years of age was more during 1970- ’75 to 1981-’85 than from 1981-’85 to 2006-’10, while from 10 - 15 years age group contribution is increasing regardless of sex and more for females. Some age groups among adult population contributed negatively to life expectancy at birth, among men during 1981- ’85 to 2006- ’10, in Assam, Haryana, Himachal Pradesh and Karnataka contribution from 15

• 20 years was negative. During the same period in Himachal Pradesh, Punjab and Karnataka

20 - 25 years of age contributed negative. Contribution of 30 - 35 years was negative in Andhra Pradesh, Haryana and Karnataka. Contribution of 45 - 50 age groups was negative in Andhra Pradesh, Karnataka, Haryana, Madhya Pradesh and Punjab. Among all these states there was at least one age group where mortality has increased rather than decline among adult males. Increase in adult mortality among males in Karnataka is very prominent where all age groups in 10 - 50 and 55+ have contributed negatively to LEB. Its effect is seen male life expectancy. Rise in life expectancy during 1983 to 2008 was lowest in Punjab followed by Karnataka. Life expectancy in most of the states is around 60 years and significant increase has come up in the recent decades due to decline in infant and childhood mortality. Still there are few

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states where older ages add negatively to the increase in life expectancy at birth. Contribution from old aged has declined so much so that during 1981 - 85 to 2006 - 10 time periods older ages (70+) contributed negatively to the change in LEB, regardless of sexes. The shared contribution from 60+ age groups is negative to the change in LEB among males. It is a recent phenomenon in Kerala - which has been historically an “exceptional” achiever in life expectancy at zero - for both the sexes old ages contributed about one third in the increase in life expectancy at birth during 1970-’75 to 1981- ’85. The recent decline may not be due to deterioration of health but because of ageing. The shared contribution of the older ages was very high during 1970-‘75 to 1981-‘85; decline in contribution among old people in Kerala may be due to biological reasons. Deaths can be delayed but it is inevitable. In Gujarat, Karnataka and Rajasthan too among very old malesi and in Kerala and Gujarat among very

• ld females survivorship has declined. Old age mortality can be controlled with medical

intervention and life style changes16 and definitely in India there is room for lengthening of life after 60. Gender differentials in mortality Our preliminary finding is based on SRM values which establish maximum female advantage in mortality come from older age groups. The large sex differential in life expectancy in favour of female should reflect the fact that women have lower mortality than male in India for most age groups. Male – female life expectancy crossover in early 1980s is remarkable in India because higher female mortality in most age groups is not uncommon in India and in most of its states. Female disadvantage over male mortality is observed especially in the younger age groups. Sex ration of mortality values are lower than unitary for most states until age 15 which clearly indicate higher female mortality in the younger ages. Except for one state - Andhra Pradesh from south – all other states registered higher female mortality in 0 – 1 age group during 2006 – 2010. Similarly among children (1 – 5 age), female mortality is higher in all states except for two south Indian and one state from East. Three states - Karnataka, Tamil Nadu and West Bengal – where among children excess male mortality than female is observed. Two among the three states are from south India, one from the east. It is also interesting that all the states are at various levels of economic development. So there is no apparent similarity in terms of geography and economy of these states to explain the mortality situation of the states. The ubiquity of the phenomena of higher female mortality is

• bserved from wide range of LEBs, for example Kerala at 74.2 years to 61.9 years LEB in
• Assam. Kerala is the only state in India where life expectancy at birth is higher than life

expectancy at one and it has the highest LEB in India. During 1981-’85 SRM value for all the age groups in Kerala was higher than unity and for age groups from 40 to 60 SRM values were above 2, meaning males have twice high rate of mortality in those age groups than

• females. But SRM value for below one year age group in Kerala is lower than unity. SRM

among infants in Kerala is lowest in India; more number of female infants dies in comparison to the male counterpart.

16 Bongaarts, J. (2009). “Trends in senescent life expectancy”, Population Studies, Vol. 63, No. 3, pp. 203- 213

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Sex ratio of Mortality (SRM) curve shows a decline in the later years of life meaning a steeper decline in women’s mortality than that of men. As the life expectancy has been increasing over the years, women advantage is increasing. During 1970-75, life expectancy at birth at that time was 49 years, men enjoyed lower mortality than women till two- thirds of his life period. Life expectancy increased to 52 years in 1981-’85 but female disadvantages continued till 30 – 35 years. Female enjoyed advantage over male mortality only after the passing of reproductive age groups. In 2006 – 2010, the situation improved for women and they started having lower mortality after age 20. Based on their geographical location all the 18 major states are divided in to 4 categories- the Northern states, Southern states, Western states and eastern states. The northern and eastern states has more age groups in the younger ages having higher female mortality vis –a – vis

• men. Uttar Pradesh, Orissa and Assam are the classic examples where female disadvantage is

manifested in higher mortality among them than male counterpart until adolescence. [Figure 3 about here] Contribution of age groups Mortality decline among the younger section of population has proved to be beneficial more for males than among females. The recent change in gender differences in life expectancy at zero is largely a function of adulthood and population between 60 and 70 years of age, who are called as the ‘young olds’. Contribution to rise in gap between male and female life expectancy at zero in 2006 -’10 risen with age and felt a little towards the old-olds. Changing mortality among younger age groups however still contribute negative to the gender difference in life expectancy. Age decomposition analysis results for gender differences in life expectancy at birth shows the contribution of each age group. Contributions from early years of life have still remained negative in most states, for some states it is more prominent. During 2006 -’10, Assam, Himachal Pradesh, Madhya Pradesh, Orissa, Rajasthan, Gujarat were among those states where contribution is negative from birth till the adolescent years (see Appendix). Although contribution was very little but decline in the maternal mortality is one reason for increase in gender difference in recent time, for decline in maternal deaths per se and proximate variables including decline in fertility. Increase in the literacy as well as rise in age at marriage as well as improvement in anti-natal and peri - natal health facilities made that

• possible. Higher mortality among female at the adolescent age groups remains a concern for

India.

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[Figure 4 about here] Conclusion and Policy Implications One related question is how much of the increasing gender gap in life expectancy has come from change in sex differentials in mortality between the three time periods under study? While explaining the trend in life expectancy in India one of the findings was cross-over of female over male life expectancy during 1981 - ’85 which was watershed year for gender differences in life expectancy. Table 2 is the summary finding of differences in contribution from age groups before and following the crossover. [Table 2 about here] Juxtaposing the results from contribution in absolute number of years and their relative contribution change over time, it is interesting to note that contribution from the old age group was distinct during earlier period i.e. from 1971 - ’75 to 1981 - ’85. Shared contribution of 45 - 60 and 60+ was positive to the gender difference in life expectancy in favour of females. Although in absolute number of years the same age groups contribute the highest but the relative effect of mortality change among the reproductive age groups is mostly responsible for the increasing gender gap in the recent period. Relative contribution from adult age groups are distinct after 1981- ’85, while earlier it came mostly from the older age groups. Notwithstanding persistent female disadvantage in the initial years of life, as life expectancy is increasing, it is seen to be more beneficial for women in India. In India still a large number

• f deaths occur within first 1 year of life. So a great potential of saving persons years of life

lies in reduction of infant mortality. In Bihar, Madhya Pradesh, Uttar Pradesh, Orissa and Assam a large section of women in their reproductive age groups are too thin (NFHS III). Malnutrition among women is a trouble both for the women and for her newborn. High risk behaviour among adult population is another concern. Smoking in some parts of India has been relatively higher. We have seen in case of Karnataka which despite having low infant mortality in a state like Karnataka life expectancy at birth is still very low and part of it result in negative contribution from adult age groups to life expectancy. NFHS III gave HIV prevalence rate among women aged15- 49 and men in 15-54. Manipur and Nagaland along with Andhra Pradesh, Karnataka and Maharashtra have highest HIV prevalence rate among adult population. Cause specific analysis will help understanding adult mortality differential better. Farther researches on this topic should explore these areas. References 1) Alachkar A. And William J. Serow, 1988, “The Socioeconomic Determinants of Mortality: An International Comparison”, Genus, Vol. 44, No. ¾, pp. 131-151.

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2) Arriaga, E. E. (1984). “Measuring and explaining the changes in life expectancies”, Demography, Vol. 21, No. 1, pp. 83 – 96. 3) Bongaarts, J. (2009). “Trends in senescent life expectancy”, Population Studies, Vol. 63, No. 3, pp. 203- 213 4) Chaurasia, A. R. (2010). “Mortality transition in India 1970 – 2005”, Asian population Studies, pp. 47 – 68. 5) Glei, A. D. and Shiro Horiuchi (2007). “The Narrowing sex Differentials in Life Expectancy in High- Income Populations: Effects of Differences in the Age Pattern of Mortality”, Population Studies, Vol. 61, No. 2, p. 146. 6) Karkal, M, (1987). “Differentials in Mortality by Sex”, Economic and Political Weekly, Vol. 22, No. 32, pp. 1343-1347. 7) Lee, R. (2003). “The Demographic Transition: Three centuries of Fundamental Change”, Journal of Economic Perspectives, Vol. 17, No. 4, pp. 167- 190 8) Lopez, A. D. and Lado T. Ruzicka (Eds.) (1983). “Sex differentials in Mortality: Trends, Determinants and Consequences”, Canberra, Department of Demography, Australian National University 9) Ministry of Health and Family Welfare Government of India, (2005): National Family and Health Survey (NFHS – 3): Volume I. Mumbai: IIPS 10) Pebley, R. A. and Sajeda Amin (1991). “The impact of a public- health intervention

• n sex differentials in childhood mortality in rural Punjab, India”, Health Transition

Review, Vol. 1, No. 2 (OCTOBAR 1991), pp. 143-169 11) Railey, J. C. (2005). “The timing and pace of health transitions around the world”, Population and Development Review, Vol. 31, No. 4. 12) Registrar General of India (2008). SRS Based Abridge Life Tables 2006-‘10. New Delhi: Registrar General. 13) Sandiford P. (2009). “Getting back the Missing Men of Aotearoa: Declining Gender Inequality in NZ Life Expectancy”, Journal of Primary Health Care, Vol. 1, No. 4 14) Trovato, F. (2005). “Narrowing Sex Differential in Life Expectancy in Canada and Austria: A Comparative Analysis”, Vienne Yearbook of Population Research, Vol. 3,

• pp. 17-52

15) The World Development Report (WDR) is an annual report published since 1978 by the World Bank. Each WDR provides an in-depth analysis of a specific aspect of economic development. “The primary World Bank collection of development indicators, compiled from officially-recognized international sources. It presents the most current and accurate global development data available, and includes national, regional and global estimates” (http://data.worldbank.org/data-catalog/world- development-indicators)

14

Trend and Pattern of life expectancy at birth: INDIA Figure 1 Life expectancy at Birth, India, 1970-’75 to 2006-’10

45 50 55 60 65 70 75 1972 1983 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 Life expectancy at Birth (in years) Male Female Rural Urban

15

Table 1. Cross over time period for the states in India along with the life expectancy at birth at the time of crossover STATE Life expectancy at birth at the time of crossover MID- YEAR Male Female Andhra Pradesh Before 1970 – 1975 Gujarat Before 1970 – 1975 Kerala Before 1970 – 1975 Maharashtra Before 1970 – 1975 West Bengal Before 1976 – 1980 Karnataka 56.2 56.6 1978 Rajasthan 51 53 1978 INDIA 55.4 55.7 1983 Himachal Pradesh 58.5 62.9 1983 Jammu and Kashmir 60.2 60.7 1983 Punjab 62.5 53.3 1983

16

Tamil Nadu 56.5 57.4 1983 Haryana 62.2 62.2 1988 Assam 53.6 54.2 1988 Madhya Pradesh 56.1 57.1 1997 Orissa 57.4 57.5 1997 Uttar Pradesh 60 60.2 2001 Bihar 64.4 64.1 2005 Figure 2

y = 1.259x - 13.53 R² = 0.919 55 60 65 70 75 80 55 60 65 70 75 80 female Life expectancy at Birth (years) male life expectancy at birth (years)

Relationship between Life Expectancy at birth for male and female, 2008

45 degree line

K AP O B Ka G R A MPUP H M HP TN WB Key: A-Assam AP- Andhra Pradesh B- Bihar G- Gujarat

17

Figure 3 a) b)

0.00 0.50 1.00 1.50 2.00 2.50 3.00 3.50 5 15 25 35 45 55 65 75 85 Sex ratio of mortality (male/ female)

Sex Ratio of Mortality, South India, 2006-'10

Andhra P. Karnataka Kerala Tamil Nadu

18

c) d)

0.00 0.50 1.00 1.50 2.00 2.50 3.00 3.50 1 3 5 7 9 11 13 15 17 19 Sex Ratio of Mortality (male/ female)

Sex Ratio of Mortality, Northern India, 2006-'10

Haryana Himachal P. Madhya P. Punjab Uttar P. J & K 0.00 0.50 1.00 1.50 2.00 2.50 5 15 25 35 45 55 65 75 85 Sex ratio of mortality (male/female)

Sex ratio of Mortality, East India, 2006-'10

Assam Bihar

• W. bengal
• rissa

19

Figure 4: Decomposition Changes in Gender Life expectancy at Birth Over Time, India, 1970-’75, 1981-’85 and 2006-‘10 a)

0.00 0.50 1.00 1.50 2.00 2.50 3.00 5 15 25 35 45 55 65 75 85 Sex Ratio of Mortality (male/ female)

Sex Ratio of Mortality, Western India, 2006-'10

Gujarat Rajasthan Maharastra

• 2.000
• 1.500
• 1.000
• 0.500

0.000 0.500 1 5 10 15 20 25 30 35 40 45 50 55 60 65 70

Change in LEAB in years

India, 1970-'75, Male vs Female Direct Effect Indirect Effect Interaction Effect Total Effect

20

b) c) Table 2 : Age decomposition of female - male difference in life expectancy (the contribution

• f mortality differences within a given age group to the gender difference in life expectancy

at birth and its change between two periods): India Age groups Contribution of a age group in increasing LEAB (in absolute years) Change Percentage Change 1970-’75 (T1) 1981-’85 (T2) T2 – T1 00 - 01

• 0.2814

0.002697 0.284099 16.14399 01 - 05

• 1.43151
• 1.119867

0.311647 17.70939 05 - 15

• 0.2127
• 0.232625
• 0.01992
• 1.13199

15 - 45

• 0.75273
• 0.328406

0.424329 24.11257

• 1.5
• 1
• 0.5

0.5 1 5 10 15 20 25 30 35 40 45 50 55 60 65 70

Change in LEAB (in years)

India, 1981-'85, Male vs Female Direct Effect Indirect Effect Interaction Effect Total Effect

• 0.6
• 0.4
• 0.2

0.2 0.4 0.6 1 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85

Change in LEAB in years

India, 2006-'10, Male vs Female Direct Effect Indirect Effect Interaction Effect Total Effect

21

45 - 60 0.664183 0.861075 0.196892 11.18842 60+ 0.319415 0.882152 0.562737 31.97762 Total

• 1.69476

0.065025 1.759783 100 Age groups Contribution of a age group in increasing LEAB (in absolute years) Change Percentage Change 1981-’85 (T2) 2006-’10 (T3) T3 – T2 00 - 01 0.002697

• 0.10736
• 0.11006
• 3.60978

01 - 05

• 1.11987
• 0.39051

0.729356 23.92243 05 - 15

• 0.23263
• 0.02648

0.206141 6.761287 15 - 45

• 0.32841

0.858258 1.186664 38.92185 45 - 60 0.861075 1.322908 0.461833 15.14785 60+ 0.882152 1.457052 0.5749 18.85636 Total 0.065025 3.113863 3.048838 100

i Very olds meaning population above 70 years of age.

APPENDIX

Decomposition changes in Life expectancy at Birth over time (male and females separately)

0.5 1 1.5 2 5 15 25 35 45 55 65

Changes in LEB (in years)

India, 1970-'75 vs 1981-'85, Male

0.5 1 1.5 2 5 15 25 35 45 55 65

Changes in LEB (in years)

India, 1970-'75 vs 1981-'85, Male

22

1 2 3 4 5 15 25 35 45 55 65

Changes in LEB (in years)

INDIA, 1981-'85 vs 2006-'10, Female

1 2 3 4 5 15 25 35 45 55 65

Changes in LEB (in years)

INDIA, 1981-'85 vs 2006-'10, Female

1 2 3 5 15 25 35 45 55 65

Change in LEB (in years)

Andhra Pradesh, 1970-'75 vs 1981-'85, Male

• 1

1 2 3 5 15 25 35 45 55 65

Change in LEB (in years)

Andhra Pradesh, 1981-'85 vs 2006-'10, Male

1 2 3 4 5 15 25 35 45 55 65

Change in LEB (in years)

Andhra Pradesh, 1970-'75 vs 1981- '85, Female

1 2 3 5 15 25 35 45 55 65

Change in LEB (in years)

Andhra Pradesh, 1981-'85 vs 2006- '10, Female

• 0.5

0.5 1 1.5 2 2.5 3 5 15 25 35 45 55 65

Change in LEB (in years)

Assam, 1970-'75 vs 1981-'85, Male

• 1

1 2 3 4 5 15 25 35 45 55 65

Change in LEB (in years)

Assam, 1981-'85 vs 2006-'10, Male

23

0.5 1 1.5 2 5 15 25 35 45 55 65

Change in LEB (in years)

Assam, 1970-'75 vs 1981-'85, Female

1 2 3 5 15 25 35 45 55 65

Change in LEB (in years)

Assam, 1981-'85 vs 2006-'10, Female

• 1

1 2 3 4 5 15 25 35 45 55 65

Change in LEB (in years)

Gujarat, 1970-'75 vs 1981-'85, Male

• 2

2 4 5 15 25 35 45 55 65

Change in LEB (in years)

Gujarat, 1981-'85 vs 2006-'10, Male

• 2

2 4 6 5 15 25 35 45 55 65

Change in LEB (in years)

Gujarat, 1970-'75 vs 1981-'85, Female

• 2
• 1

1 2 3 4 5 5 15 25 35 45 55 65

Change in LEB (in years)

Gujarat, 1981-'85 vs 2006-'10, Female

• 0.5

0.5 1 5 15 25 35 45 55 65

Change in LEB (in years)

Himachal Pradesh, 1970-'75 vs 1981- '85, Male

• 1

1 2 3 4 5 15 25 35 45 55 65

Change in LEB (in years)

Himachal Pradesh, 1981-'85 vs 2006-'10, Male

24

• 2
• 1

1 2 3 5 15 25 35 45 55 65

Change in LEB (in years)

Himachal Pradesh, 1970-'75 vs 1981-'85, female

1 2 3 4 5 15 25 35 45 55 65

Change in LEB (in years)

Himachal Pradesh, 1981-'85 vs 2006- '10, Female

• 2
• 1

1 2 5 15 25 35 45 55 65

Change in LEB (in years)

Haryana, 1970-'75 vs 1981-'85, Male

• 0.5

0.5 1 1.5 2 2.5 5 15 25 35 45 55 65

Change in LEB (in years)

Haryana, 1981-'85 vs 2006-'10 Male

• 1

1 2 3 5 15 25 35 45 55 65

Change in LEB (in years)

Haryana, 1970-'75 vs 1981-'85, Female

1 2 3 4 5 15 25 35 45 55 65

Change in LEB (in years)

Haryana, 1981-'85 vs 2006-'10 Female

• 0.5

0.5 1 1.5 2 5 15 25 35 45 55 65

Change in LEB (in years)

Karnataka, 1970-'75 vs 1981-'85, Male

• 0.5

0.5 1 1.5 2 2.5 5 15 25 35 45 55 65

Change in LEB (in years)

Karnataka, 1981-'85 vs 2006-'10, Male

25

0.5 1 1.5 2 5 15 25 35 45 55 65

Change in LEB (in years)

Karnataka, 1970-'75 vs 1981-'85, Female

1 2 3 4 5 15 25 35 45 55 65

Change in LEB (in years)

Karnataka, 1981-'85 vs 2006-'10, Female

• 0.5

0.5 1 1.5 2 5 15 25 35 45 55 65

Change in LEB (in years)

Kerala, 1970-'75 vs 1981-'85, Male

• 1
• 0.5

0.5 1 1.5 2 5 15 25 35 45 55 65

Change in LEB (in years)

Kerala, 1981-'85 vs 2006-'10, male

0.5 1 1.5 2 2.5 3 5 15 25 35 45 55 65 Change in LEB (in years)

Kerala, 1970-'75 vs 1981-'85, Female

• 0.5

0.5 1 1.5 5 15 25 35 45 55 65

Change in LEB (in years)

Kerala, 1981-'85 vs 2006-'10, Female

26

• 2
• 1

1 2 5 15 25 35 45 55 65

Change in LEB (in years)

Madhya Pradesh, 1970-'75 vs 1981-'85, Male

• 2

2 4 6 5 15 25 35 45 55 65

change in LEB (in years)

Madhya Pradesh, 1981-'85 vs 2006- '10, Male

0.5 1 1.5 2 5 15 25 35 45 55 65

Change in LEB (in years)

Madhya Pradesh, 1970-'75 vs 1981- '85, Female

1 2 3 4 5 5 15 25 35 45 55 65

Change in LEB (in years)

Madhya Pradesh, 1981-'85 vs 2006- '10, Female

• 0.5

0.5 1 1.5 2 2.5 3 5 15 25 35 45 55 65

Change in LEB (in years)

Orissa, 1970-'75 vs 1981-'85, Male

1 2 3 4 5 5 15 25 35 45 55 65

Change in LEB (in years)

Orissa, 1981-'85 vs 2006-'10, Male

0.5 1 1.5 2 2.5 5 15 25 35 45 55 65

Change in LEB (in years)

Orissa, 1970-'75 vs 1981-'85, Female

1 2 3 4 5 5 15 25 35 45 55 65

Change in LEB (in years)

Orissa, 1981-'85 vs 2006-'10, Male

27

• 0.5

0.5 1 1.5 2 5 15 25 35 45 55 65

Change in LEB (in years)

Rajasthan, 1970-'75 vs 1981-'85, Male

• 2

2 4 5 15 25 35 45 55 65

Change in LEB (in years)

Rajasthan, 1981-'85 vs 2006-'10, Male

• 0.5

0.5 1 1.5 2 2.5 5 15 25 35 45 55 65

Change in LEB (in years)

Rajasthan, 1970-'75 vs 1981-'85, Female

1 2 3 4 5 6 5 15 25 35 45 55 65

Change in LEB (in years)

Rajasthan, 1981-'85 vs 2006-'10, Female

• 0.5

0.5 1 1.5 2 2.5 5 15 25 35 45 55 65

Change in LEB (in years)

Tamil Nadu, 1970-'75 vs 1981-'85, Male

1 2 3 4 5 15 25 35 45 55 65

Change in LEB (in years)

Tamil Nadu, 1981-'85 vs 2006-'10, Male

0.5 1 1.5 2 2.5 5 15 25 35 45 55 65

Change in LEB (in years)

Tamil Nadu, 1970-'75 vs 1981-'85, Female

1 2 3 4 5 5 15 25 35 45 55 65

Change in LEB (in years)

Tamil Nadu, 1981-'85 vs 2006-'10, Female

28

• 1

1 2 3 5 15 25 35 45 55 65

Change in LEB (in years)

Uttar Pradesh, 1970-'75 vs 1981-'85, Male

• 1

1 2 3 4 5 6 5 15 25 35 45 55 65

change in LEB (in Years)

Uttar Pradesh, 1981-'85 vs 2006-'10, Male

• 1

1 2 3 4 5 5 15 25 35 45 55 65

Change in LEB (in years)

Uttar Pradesh, 1970-'75 vs 1981-'85, Female

2 4 6 5 15 25 35 45 55 65

Change in LEB (in Years)

Uttar Pradesh, 1981-'85 vs 2006- '10, Female

0.5 1 1.5 5 15 25 35 45 55 65

Change in LEB (in years)

Maharastra, 1976-'80 vs 1981-'85, Male

1 2 3 4 5 15 25 35 45 55 65

Change in LEB (in years)

Maharastra, 1981-'85 vs 2006-'10, Male

• 0.5

0.5 1 1.5 2 5 15 25 35 45 55 65

Change in LEB (in years)

Maharastra, 1976-'80 vs 1981-'85, Female

1 2 3 4 5 15 25 35 45 55 65

Chage in LEB (in years)

Maharastra, 1981-'85 vs 2006-'10, Female

29

• 1
• 0.5

0.5 1 1.5 2 2.5 5 15 25 35 45 55 65

Change in LEB (in years)

Punjab, 1976-'80 vs 1981-'85, Female

• 3
• 2
• 1

1 2 3 5 15 25 35 45 55 65

Change in LEB (in years)

Punjab, 1981-'85 vs 2006-'10, Female

• 2
• 1

1 2 5 15 25 35 45 55 65

Change in LEB (in years)

Punjab, 1976-'80 vs 1981-'85, Male

• 1

1 2 3 5 15 25 35 45 55 65

Change in LEB (in years)

Punjab, 1981-'85 vs 2006-'10, Male

1 2 3 4 5 5 15 25 35 45 55 65

Change in LEB (in years)

Bihar, 1981-'85 vs 2006-'10, Male

1 2 3 4 5 5 15 25 35 45 55 65

Change in LEB in years

Bihar, 1981-'85 vs 2006-'10, Female

30

• 0.5

0.5 1 5 15 25 35 45 55 65

Change in LEB (in years)

Jammu and Kashmir, Male, 1976 - '80 vs 1981 - '85

• 1

1 2 3 5 15 25 35 45 55 65

change in LEB (in years)

Jammu and Kashmir, 1981 -'85 Vs. 2006- 10, Male

• 1

1 2 3 5 15 25 35 45 55 65

Change in LEB (in years)

Jammu and Kashmir, 1976-'80 Vs. 1981 - '85, Female

1 2 3 5 15 25 35 45 55 65

Change in LEB (in years)

Jammu and Kashmir, 1981 - '85 Vs. 2006 - '10, Female

31 Decomposition changes in Gender Differences in Life Expectancy at Birth

• 2.000
• 1.000

0.000 1.000 5 15 25 35 45 55 65

Change in LEB (in years)

India, 1970-'75, Male vs Female

• 1

1 5 15 25 35 45 55 65

Change in LEB (in years)

Andhra Pradesh, 1970-'75, Male vs Female

• 2
• 1

1 5 15 25 35 45 55 65

Change in LEB (in years)

India, 1981-'85, Male vs Female

• 1

1 5 15 25 35 45 55 65

Change in LEB (in years)

Andhra Pradesh, 1981-85, Male vs Female

• 0.5

0.5 5 15 25 35 45 55 65 75 85

Change in LEB (in years)

India, 2006-'10, Male vs Female

• 1

1 5 15 25 35 45 55 65 75

Change in LEB (in yeras)

Andhra Pradesh, 2006-'10, Male vs Female

• 2

2 5 15 25 35 45 55 65

Chaneg in LEB (in years)

Assam, 1970-'75, Male vs Female

• 1
• 0.5

0.5 5 15 25 35 45 55 65

change in LEB (in years)

Assam, 1981-85, Male Vs Female

• 2
• 1

1 5 15 25 35 45 55 65

Change in LEB (in years)

Bihar, 1981-85, Male Vs. Female

• 0.5

0.5 1 5 15 25 35 45 55 65 75 85

Change in LEB (in years)

Assam, 2006-'10, Male vs Female

• 1
• 0.5

0.5 5 15 25 35 45 55 65 75 85

Change in LEB (in years)

Bihar, 2006-'10, Male vs Female

32

• 5

5 5 15 25 35 45 55 65

Change in LEB (in years)

Gujarat, 1970-'75, Male vs Female

• 4
• 2

2 0 1 5 10152025303540455055606570

Change in LEB (in years)

Haryana, 1970-'75, Male vs Female

• 1

1 2 5 15 25 35 45 55 65

Change in LEB (in years)

Gujarat, 1981-85, Male vs. Female

• 2

2 5 15 25 35 45 55 65

Change in LEB (in years)

Haryana, 1981-85, Male vs. Female

• 0.5

0.5 1 5 15 25 35 45 55 65 75 85

Change in LEB (in years)

Gujarat, 2006-'10, Male vs Female

• 0.5

0.5 5 15 25 35 45 55 65 75 85

Change in LEB (in years)

Haryana, 2006-'10, Male vs Female

• 2

2 5 15 25 35 45 55 65

change in LEB (in years)

Karnataka, 1970-'75 Male vs Female

• 1

1 5 15 25 35 45 55 65

Change in LEB (in years)

Kerala, 1970-'75, Male vs Female

33

• 1
• 0.5

0.5 1 5 15 25 35 45 55 65

Change in LEb (in years)

Karnataka, 1981-85, Male Vs. Female

0.5 1 5 15 25 35 45 55 65

Change in LEB (in years)

Kerala, 1981-85, Male Vs. Female

• 0.5

0.5 1 5 15 25 35 45 55 65 75 85

Change in LEB (in years)

Karnataka, 2006-'10, Male vs Female

• 0.5

0.5 1 5 15 25 35 45 55 65 75 85

Change in LEB( in years)

Kerala, 2006-'10, Male vs Female

• 2

2 5 15 25 35 45 55 65

Change in LEB (in years)

Madhya Pradesh, 1970-'75, Male vs Female

• 0.5

0.5 5 15 25 35 45 55 65

Change in LEB (in years)

Orissa, 1970-'75, Male vs Female

• 2

2 5 15 25 35 45 55 65

change in LEB (in years)

Madhya Pradesh, 1981-85, Male vs. Female

• 1

1 5 15 25 35 45 55 65

change in LEB (in years)

Orissa, 1981-85, Male vs. Female

• 0.5

0.5 1 5 15 25 35 45 55 65 75 85

Change in LEB (in years)

Madhya Pradesh, 2006-'10, Male vs Female

• 0.5

0.5 5 15 25 35 45 55 65 75 85

Change in LEB (in years)

Orissa, 2006-'10, Male vs Female

• 4
• 2

2 5 15 25 35 45 55 65

Change in LEB (in years)

Rajasthan, 1970-'75, Male vs Female

• 1

1 5 15 25 35 45 55 65

Change in LEB (in years)

Tamil Nadu, 1970-'75, Male vs Female

34

• 2

2 5 15 25 35 45 55 65

Chaneg in LEB ( in years)

Rajasthan, 1981-85, Male Vs. Female

• 1

1 5 15 25 35 45 55 65

Change in LEB (in years)

Tamil Nadu, 1981-85, Male Vs. Female

• 1

1 5 15 25 35 45 55 65 75 85

Change in LEB (in years)

Rajasthan, 2006-'10, Male vs Female

• 0.5

0.5 1 5 15 25 35 45 55 65 75 85

Change in LEB (in years)

Tamil Nadu, 2006-'10, Male vs Female

• 4
• 2

2 5 15 25 35 45 55 65

Change in LEB (in years)

Uttar Pradesh, 1970-'75, Male vs Female

• 10

10 5 15 25 35 45 55 65

Change in LEB (in years)

West Bengal, 1976- '80, Male vs. Female

• 4
• 2

2 5 15 25 35 45 55 65

Change in LEB (in years)

Uttar Pradesh, 1981-85, Male vs. Female

• 10

10 5 15 25 35 45 55 65 Change in LEB ( in years)

West Bengal, 1981-85, Male Vs. Female

• 1
• 0.5

0.5 1 5 15 25 35 45 55 65 75 85

Change in LEB (in years)

Uttar Pradesh, 2006-'10, Male vs Female

• 0.5

0.5 5 15 25 35 45 55 65 75 85

Change in LEB (in years)

West Bengal, 2006-'10, Male vs Female

• 2
• 1

1 5 15 25 35 45 55 65

Change in LEB (in years)

Himachal Pradesh, 1976-'80, Male vs Female

• 1
• 0.5

0.5 1 5 15 25 35 45 55 65

Change in LEB (in years) Maharashtra, 1976-'80, Male vs Female

35

• 2

2 5 15 25 35 45 55 65

Change in LEB (in years)

Himachal Pradesh, 1981-85, Male Vs. Female

• 1

1 5 15 25 35 45 55 65 Change in LEB (in years)

Maharastra, 1981-85, Male vs. Female

• 2

2 5 15 25 35 45 55 65 75 85

Change in LEB (in years)

Himachal Pradesh, 2006-'10, Male vs Female

• 0.5

0.5 1 5 15 25 35 45 55 65 75 85

Change in LEB (in years)

Maharastra, 2006-'10, Male vs Female

• 4
• 2

2 4 5 15 25 35 45 55 65

Change in LEB (in years)

Punjab, 1976-'80, Male vs Female

• 2

2 5 15 25 35 45 55 65

Change in LEB (in years)

Jammu and Kashmir, 1976 - '80, Male

• Vs. Female
• 2

2 4 5 15 25 35 45 55 65

Change in LEB (in Years)

Punjab, 1981-85, Male Vs. Female

• 1

1 5 15 25 35 45 55 65

chaneg in LEB (in years)

Jammu and Kashmir, 1981- '85, Male Vs. Female

• 0.5

0.5 1 5 15 25 35 45 55 65 75 85

Change in LEB (in years)

Punjab, 2006-'10, Male vs Female

• 0.5

0.5 5 15 25 35 45 55 65 75 85

change in LEB (in years)

Jammu and Kashmir, 2006- '10, Male Vs. Female