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Socio-economic Inequality in Longevity in India

Two new indices, the index of representation in longevity and the index of socio-economic inequality in longevity, are presented for examining socio-economic inequality in longevity in India. The India Human Development Survey data from the 2004–05 and 2011–12 rounds are used to investigate socio-economic inequality based on caste, occupation, economic classes, and geographic regions. The findings suggest that India suffers from substantial socio-economic inequality in longevity with the Scheduled Castes, Scheduled Tribes, and Muslims being worst off. Groups such as agricultural and non-agricultural labourers, petty traders, and lower economic classes were substantially under-represented in longevity. Regionally, the south and west have over-representation, whereas the central, east, and north-east have under-representation in longevity. 

Creating the possibility for people of all sections to live longer lives is a basic and foremost challenge for any society. Given the importance of longer lives and socio-economic parity therein, specific initiatives have been taken in many countries around the globe in measuring life span as well as socio-economic inequalities in the life spans of people. For example, “Healthy People 2000,” launched in 1990 by the United States (US) Department of Health and Human Services, seeks, as one of its broad health goals for the nation, to increase life expectancy among Americans of all ages (Singh and Siahpush 2006). Similarly, the “Longevity Science Advisory Panel” was established in the United Kingdom (UK) to bring actuarial science and epidemiology closer together to provide a better chance of understanding the past and the present as well as better forecasting of improvements in life expectancy (Wanless et al 2012).

That said, the concept that is generally used when it comes to measuring the length of individual lives in any society is longevity. Though it is often used as a synonym for life expectancy at a given age, it is also used to refer only to the long-lived members of a population. If increasing longevity is a des­irable objective of any society, then achieving socio-economic equality in it is another one. However, the first step towards striving for equality in longevity in any society is to measure the existing socio-economic inequalities associated with it. Further, when inequality in longevity is high, the decrease in life expectancy among those of a lower socio-economic status can outweigh the increase in life expectancy among those of a higher socio-economic status. This can lead to life expectancy below a level that is likely to be seen in a country with the same average level of social indicators (National Research Council and Committee on Population 2011: 118).

Given the above, it is important to mention that the scholarship on socio-economic inequalities in health, in general, across different countries is far and wide. But the scholarship on socio-economic inequalities in longevity is relatively recent and to some extent limited. As interest in the subject has been increasing across the globe, the scholarship too is growing, res­ulting in the publication of a few studies. For example, Qin et al (2017) investigated the spatio-temporal variation of longevity clusters in China. Neumayer and Plümper (2016) analysed the determinants of longevity inequality in 28 predominantly Western countries. The UK’s Office of National Statistics (2015) provided the trends in life expectancy by socio-economic position for England and Wales. Auger et al (2013) evaluated the life expectancy gaps between economically advantaged and disadvantaged Francophones and Anglophones in Canada. Wanless et al (2012) estimated the past and future variations in life expectancy by socio-economic groups in England and Wales. Brønnum-Hansen and Baadsgaard (2012) and Clarke and Leigh (2011) estimated the trends in social inequa­lity in life expectancy in Denmark and Australia, respectively. Magnolfi et al (2007) evaluated the characteristics of the ageing process in the Italian population using two indices, namely longevity and centenarity. Singh and Siahpush (2006) examined the changes in the extent of inequalities in life expect­ancy in the US between 1980 and 2000.

The studies on socio-economic inequalities in longevity are relatively few when it comes to developing countries in general, and India in particular. The association between the indicators of socio-economic status, such as income and education, and mortality implies that the distribution of socio-economic status within a country could affect mortality (and therefore, longevity). In particular, two countries with the same average ­income or education could have differences in health and mortality (and longevity) outcomes/indicators, if income or education were differentially distributed (National Research Council and Committee on Population 2011). Therefore, the trends in inequality in longevity in Western countries cannot be extrapolated to describe the possible scenario of socio-economic inequality in longevity in developing countries, such as India.

The Indian Context

There is a dearth of scholarship on socio-economic inequalities in longevity in India. India has substantial diversity, in terms of caste, religion, region, rural/urban sector, and economic classes, with different groups being at different levels of economic and health conditions (Bhat and Zavier 1999; Singh 2012). Hence, there is always a possibility that the individuals of particular socio-economic groups, on an average, live longer compared to some others. Therefore, it is expected that the socio-economic inequalities in longevity in India will be substantial. To the best of our search, there is no study that has comprehensively examined such inequalities in longevity (and changes therein) over time in India.

Before proceeding further, it is important to take a look at Sauvaget et al (2011), who estimated the impact of the socio-economic levels on life expectancy in individuals living in ­Kerala. A cohort of 1,67,331 individuals, aged 34 years and above, was used in the analysis and it was found that at 40 years, men and women were expected to live another 34 and 37 years, res­pectively, with life expectancy varying across the participants’ different socio-economic categories. The above study has only presented life expectancies for various socio-economic groups rather than estimating socio-economic inequa­lities in longevity or life expectancies itself. Moreover, examining longevity using life expectancy at birth for different ­socio-economic groups is important. But it is quite different from say analysing the disparity in longevity in terms of disparity in the proportion of individuals in different groups actually living beyond a particularly old age, such as 65 years at a given time. The knowledge of the proportion of the population of different ­socio-economic groups living beyond a certain old age can help in developing targeted health programmes for their ­welfare.

Therefore, in this study, we not only investigate socio-­economic inequality in longevity (using old age population proportions, 65 years and above) in India at the national and regional levels but also examine the changes therein in India between 2004 and 2012. We have used data from the India Human Development Survey (IHDS) for the analysis. We have also taken a comprehensive list of socio-economic characteristics, including caste, religion, rural/urban, geographic regions, and economic and occupational classes, for analysing the ­socio-economic inequalities in longevity.

The data has been taken from the 2004–05 and 2011–12 rounds of the IHDS, a nationally representative survey conducted by the National Council of Applied Economic Research in collaboration with the University of Maryland (Desai et al 2010; Desai et al 2015). The IHDS-I was conducted in all the states and union territories of India except the Andaman and Nicobar, and the Lakshadweep islands during 2004–05, covering a total of 382 districts. It is a multi-topic survey of households conducted in 1,503 villages and 971 urban neighbourhoods across India. A two-stage stratified sampling design was followed to draw a sample consisting of 27,010 rural households (1,43,374 individuals) and 13,126 urban households (72,380 individuals). The IHDS is exclusive in its measurement of different dimensions of human development, including health, education, employment, economic status, marriage, fertility, gender relations, and social capital (Desai et al 2010).

The IHDS-II was conducted in 2011–12. This survey is also a nationally representative, multi-topic survey of 42,152 households across India. Out of the total households, 27,579 belong to rural areas whereas 14,573 belong to urban areas. These households were spread across 33 states and union territories, 384 districts, 1,420 villages and 1,042 urban blocks located in 276 towns and cities. Two one-hour interviews in each household sought to cover health, education, employment, economic status, marriage, fertility, gender relations, and social capital (Desai et al 2015).

Analysis and Measures

For estimating the socio-economic inequality in longevity in India, we have focused on the individuals above 65 years of age during the 2004–05 and 2011–12 surveys, respectively. The intention behind this is to consider those individuals who are above 65 years of age as individuals living a long life. The reference point of 65 years is informed by the fact that it is the retirement age in most central government jobs in India. After identifying the age of individuals, we categorised them into socio-economic groups based on different characteristics, such as caste, religion, occupation, economic classes, and geographic regions. Caste was categorised as Other Caste (OC), Other Backward Classes (OBC), Scheduled Caste (SC), and Schedules Tribe (ST).1 Religion was categorised into Hindu, Muslim, Christian, Sikh, and Other.2 Occupations (based on the main source of income of the household) were categorised into cultivation, agricultural labour, non-agricultural labour, petty trade, business, salaried, pension/rent, and others. Economic classes have been captured in terms of deciles based on the per capita consumption expenditure of the household to which the individual belongs. Finally, geographic regions were categorised into north, central, east, west, south, and north-east.3

We have developed and estimated two indices for capturing socio-economic inequality in longevity in India. We have named them the Index of Representation in Longevity (IRL) and the Index of Socio-economic Inequality in Longevity (ISIL), respectively. The IRL is a representational measure similar to the “Distributional Fairness Index” proposed by Villemez and Rowe (1975) and “Group-specific Index of Relative Disadvantage” proposed by Jayaraj and Subramanian (2006). Whereas the ISIL, particularly well-suited for dichotomous outcomes, is a form of dissimilarity index based on the measure of the inequality of opportunities advanced by Barros et al (2009) (see also, for an application in the Indian context, Singh et al 2013, 2014). The indices have been estimated separately for rural and urban areas.


Index of Representation in Longevity: In simple terms, in the IRL, we are comparing the proportion of the 65 years plus (henceforth referred to as 65+) population of a socio-economic group in the overall 65+ population of the country to the overall proportion of population of the same socio-economic group in the overall population of the country. Intuitively, any group-specific measure of inequality in longevity must be some function of how large the group’s share in total longevity (65+) is in relation to the group’s share in total population.

This index could be defined as:

IRL (i) = SL (i)/a (i) ... (1)

where, IRL (i) is the index of representation in longevity for group i, where the groups (mutually exclusive and totally ­exhaustive) have been formed based on the basis of socio-­economic characteristics (caste, religion, occupation, economic class and geographic region) described earlier; SL (i) is the share of group i in longevity (number of 65+ individuals in group i/total number of 65+ individuals in the population) and; α (i) is the share of population of group i in the total population.

The above index has been estimated for each of the groups based on each of the characteristics. If IRL (i) for the group i is “x,” it means that group i’s share/representation in longevity (65+ population) is “x” times group i’s share in the total population. The IRL has a lower bound of “zero” (when share of a group in longevity is “zero”) but it does not have any upper limit. If IRL (i) for a group is more than “one,” it could be inf­erred that the group under consideration has excess or over-representation in longevity. If IRL (i) for a group is less than “one,” it could be inferred that the relevant group has under-representation in longevity. An IRL (i) of “one” indicates that the representation of group i in longevity is the same as its share in total population. The change in the values of IRL (i) over time indicates the extent of gains or losses experienced by each socio-economic group in longevity over time. One could also rank the groups by the values of the index, and examine what has happened to the initially most deprived sections (in terms of longevity) of the population between 2004–05 and 2011–12.


Index of Socio-economic Inequality in Longevity: As in the case of the previous index, if an individual’s age is more than 65 years then they will be considered as a long living individual. Once the information on the age of individuals and the socio-economic group to which they belong is obtained, the ISIL has been estimated for groups based on each characteristic (for example, groups based on caste, groups based on religion, etc). The ISIL is a form of dissimilarity index to estimate socio-economic inequalities in longevity and has been estimated at two time points: 2004–05 and 2011–12.

The dissimilarity-based ISIL is given by:

; j = 1, 2,…, m ... (2)

where, m is the total number of socio-economic groups in the population. For example, if we take caste, then the number of groups is four: SC, ST, OBC, and OC. p is the average longevity in the population (proportion of the individuals in the population aged more than 65 years). pj is the average longevity in the jth group (proportion of individuals within group j aged more than 65 years). αj is the proportion of the jth group in the population. The paper calculates ISIL separately for groups formed by caste, religion, occupation, economic classes, and geographic regions.

The ISIL is a simple summary and representational measure of group disparities, which is expressed as a normalised, weighted sum of the absolute deviations of group-specific ave­rage longevity from the overall (whole population) average longevity. The measure varies from “zero” (perfect intergroup equality in longevity) to unity (which is the upper bound on the index, and to which its value tends when the entire longevity is accounted for, in a polar extreme of concentration, by a single group of arbitrarily small size).

The ISIL can be interpreted as the amount of longevity opp­ortunities that need to be rearranged (as a proportion of the number of individuals who already have it) from the better-off groups (in which average longevity is higher than the population average) to the worse-off groups (in which average longevity is lower than the population average) to have equal ­average longevity in all groups.4



Socio-economic characteristics in sample (65+ years): ­Table 1 (p 60) presents the distribution of socio-economic characteristics among the elderly (65+) in rural and urban areas of India for 2004 and 2012. The table shows that in 2004, in rural areas, 19%, 7%, 43%, and 31% of elderly population belonged to the SC, ST, OBC, and OC, respectively. The corresponding figures for urban areas were 14%, 2%, 38%, and 46%, respectively. In 2012, there was some increase in the percentage of SCs and some decrease in the percentage of OCs in rural areas. In terms of religion, the percentages of Hindu and Muslim elders have almost remained unchanged in rural as well as urban areas during the study period. There has been some mild decrease in the proportion of elderly Christians in rural areas during the same period.

If we talk in terms of occupation in rural areas, there was a decrease in the proportion of elderly among cultivators, agricultural labourers, businesspersons, and salaried indivi­duals during 2004 to 2012. There was an increase in the proportion of elderly among the non-agricultural labourers, petty traders, and pensioners during the same period. In urban areas, a dec­rease was observed in the proportion of elders involved in agri­cultural labour, self-employed in business, and salaried jobs during the study period, whereas an increase was obser­ved in the proportion of elders involved in non-agricultural ­labour, petty trade, and pension (and rent) during the same period. The findings indicate that there seems to be some shift from involvement in agriculture-related activities to non-­agriculture-related activities, self-employed in business to petty trade, and salaried to non-salaried during the study period.

In rural as well as urban areas, the percentage of elderly population was highest in the southern region and lowest in the north-eastern region. The highest proportion of elders in the southern region is in line with the fact that the southern region comprises of states that are among the most developed as far as economic and demographic indicators are concerned (Bhat and Zavier 1999; Bose 1991; Singh 2012).


Group-specific proportion of elderly (65+ years): Table 2 reveals the proportion of the elderly within their socio-economic groups. The figures indicate that among the caste groups, the OCs have the highest proportion of elderly amongst themselves throughout, be it India, rural or urban areas or the years 2004 and 2012. It is not surprising that longevity was relatively more in the OCs because they comprise of the socially and economically most advanced castes of India (Deshpande 2011). On the other hand, the STs lay at the other end of the spectrum, which is also not surprising given that they are among the most disadvantaged as far as social and economic development is concerned (Deshpande 2011). Talking about religion, it was the Muslims (the majority in minority), which were the most disadvantaged, which is again not surprising as their social, demographic and economic development is at the level of the SCs and the STs in India (Government of India 2006).

Among the occupation categories, the proportion of elderly was highest among the pensioners, which is understandable. The proportion of the elderly increased among all the occupation-based groups during the study period. Coming to economic classes, the proportion of elderly was relatively higher among the richer classes. Further, talking about regions, it was the southern and western regions where the proportion of ­elderly or longevity was more. Once again, longevity increased during 2004 to 2012 in all occupation categories, in all economic classes and all geographic regions.


Index of Representation in Longevity: The results, based on IRL (Table 3), if observed in the light of caste groups, show that the OCs are over-represented in both 2004 and 2012 in rural as well as urban areas. The OBCs are marginally over-represented and slightly under-represented in rural and urban areas, respectively, in 2004 as well as 2012. The SCs and the STs are severely under-represented (with the STs the worst off) in almost all cases (be it rural or urban) with the representation of the STs decreasing from 0.75 in 2004 to 0.69 in 2012 (at the all-India level), which is the least among all the caste categories.

Turning to religion, it was found that the maximum representation in longevity is of the Christians at the all-India level.
Although over-represented, their representation observed a decrease from 1.40 in 2004 to 1.18 in 2012. The other over-­represented groups were the Sikhs and Hindus. It is the Muslims that are seriously under-represented (0.71) in longevity in both the years. Looking at the rural–urban pattern, the representation of the Christians was found to be the highest in rural areas (1.53 in 2004 and 1.24 in 2012). The representation of the Hindus was found to be the same in rural areas as it was at the all-India level in both the years. Not surprisingly, the Muslims were under-represented in rural areas as well. In urban areas, in both the years, the highest over-representation was of the Sikhs. The (under) representation of Muslims remained the same in both the years (0.77).

In terms of occupation, pensioners, cultivators and “other occupation” categories had an over-representation in longevity in both years and areas, whereas, it was the agricultural and non-agricultural labourers and petty traders who were under-represented throughout. The lowest under-representation is seen in the case of non-agricultural labourers. Also, looking by economic classes, the poorer classes (barring a few exceptions) were generally under-represented, whereas the richer classes were generally over-represented.

Figure 1 (p 62) shows the representation of longevity among the elderly across different geographic regions of India during 2004. It can be seen that the over-representation of longevity was highest in the south followed by the west and the north; whereas, there was an under-representation in the case of the central, east, and north-east, with the north-east being most severely under-represented. It may be noted that the central, eastern, and north-eastern regions comprise of states, which are among the poorest as far as economic, social, and demographic indicators are concerned (Bhat and Zavier 1999; Bose 1991; Singh 2012).

Similar results are found in Figure 2, which is for 2012. The representation in longevity across the six geographic regions by rural and urban areas have been presented in Figure 3 (2004) and Figure 4 (2012) (p 63), respectively. Figure 3 indicates that the representation of longevity in rural parts is quite similar to that of Figure 1 (all-India level, 2004), but in the case of urban ­areas, the picture is slightly changed where the eastern region also had an over-representation in longevity. In 2012 (Figure 4), a similar pattern has been found in the representation in longevity as is the case in 2004. In rural areas, the northern, western and southern regions have an over-representation, whereas, in the urban areas, the eastern region (in place of the northern region) along with the western and southern regions have an over-representation in longevity.


ISIL: Table 4 reports the socio-economic inequality (as per the ISIL) based on different socio-economic characteristics for the all-India as well as rural and urban areas for 2004–05 and 2011–12. The maximum socio-economic inequality in longevity is observed when the groups are constructed on the basis of ­occupation, be it 2004 or 2012. In 2012, about 15% longevity opportunities need to be shifted from the better-off groups (where average longevity is higher than the average longevity for the whole population) to the worse-off groups such as non-agricultural labour, etc (where average longevity is lower than the average longevity for the whole population) to bring equa­lity in longevity in society at the all-India level. The corresponding figure for 2004 is 14%. Followed by occupation, the second and third highest socio-economic inequalities in longevity in both 2004 and 2012 were observed in the case of geographic regions and caste, respectively. The results are similar in terms of patterns in the case of rural and urban areas.

Further, the lowest socio-economic inequality in longevity was observed in the case of economic classes based on per capita consumption. This was surprising because it is expected that if people are divided into groups based on income (or consumption) then longevity should be much higher in the richer groups compared to the poorer groups. That is, variation in longevity across groups should be relatively higher when the groups are formed based on income (or consumption expenditure) compared to variation when the groups are formed based on other factors like caste, religion, etc. In other words, it can be said that the caste-based or religion-based or occupation-based variation in longevity is relatively higher than economic class-based variation. Further, barring the case of economic class and geographic regions, the socio-economic inequality in longevity, based on all other characteristics, was higher in ­urban areas compared to that of rural areas.

It is also important to note that the socio-economic inequa­lity in longevity based on occupation increased during the study period, but based on caste and geographic region decre­ased at the all-India level during the same period. However, it increased during 2004 to 2012 in the urban areas.


Given the scarce nature of scholarship on socio-economic inqualities in longevity in India, we have used data from the ­IHDS–I and II, and have critically and comprehensively examined the socio-economic inequality in longevity in India and its various geographic regions. We have included caste, religion, occupation, economic classes, and geographic regions to investigate the socio-economic inequalities in longevity. We have also developed and estimated two indices for the examination, namely the IRL and ISIL.

Our findings support the general conclusions of earlier studies (for example, Drèze and Sen 2013). First, though India has shown impressive economic growth in the last two decades, it has not converted into desirable improvement in the health conditions of the population in the country; and second, India suffers from serious socio-economic inequalities in economic, demographic, and health outcomes with different socio-­economic groups being at different levels of economic, demographic and health conditions. That said, we find that India suffers from substantial socio-economic inequalities in longevity. The result that among the caste groups, the OCs have an over-representation in longevity, whereas the SCs and STs have a severe under-representation in longevity, is in line with the existing narrative on caste-based disparity (with the OCs in an advantageous and the SCs as well as STs in a disadvantageous position) in various economic, demographic and social indicators of welfare in ­India (Deshpande 2011). Among the religious groups, the Muslims’ severe under-representation in longevity is again in line with the existing discourse on the economic, demographic and social conditions of Muslims in India on the one hand and the rampant religion-based inequalities in economic, demographic and social indicators in India on the other (Drèze and Sen 2013; Government of India 2006).

We also find that groups such as agricultural and non-­agricultural labourers, petty traders, lower economic classes, etc, were substantially under-represented in longevity. This again fits with the existing literature (for example, see Motiram and Singh 2012) on the demographic, social, and economic conditions of these groups in India. One of the most glaring ­socio-economic inequalities in longevity is observed in the case of geographic regions, where the demographically, economically, and socially advanced regions of the south and the west have an over-representation in longevity, whereas the demographically, economically and socially disadvantaged ­regions of the central, east and northeast have an under-representation in longevity. The above finding is also in line with the existing scholarship on the increasing nature of region-based inequality in health, social, and economic outcomes in India (Singh 2011).

Our results, based on the summary indicator ISIL, further support the finding that India suffers from substantial socio-economic inequality in longevity, which was maximum when the groups were formed based on occupational categories. About 15% longevity opportunities (in 2012) need to be shifted from the better-off groups (where average longevity is higher than the average longevity for the whole population) to the worse-off groups (where average longevity is lower than the average longevity for the whole population) to bring equality in longevity in the Indian society. Further, the socio-economic inequality based on the above index has increased over time. Followed by occupation, the second and third highest socio-economic inequalities in longevity were observed in the case of geographic regions and caste, respectively; these findings once again are in line with existing studies (Pathak and Singh 2009; Sauvaget et al 2011) and complement as well as build on their evidence base.

Our study has some strengths and a few limitations. It is perhaps the first attempt to comprehensively examine the socio-economic inequality in longevity in India and its various geographic regions. We have also analysed the trends over time. Further, we have used a comprehensive list of socio-­economic characteristics to examine the inequalities in longevity. Moreover, we have developed and estimated two new indices suitable for examining the socio-economic inequalities in longevity. Some of the limitations of our study include the use of the cut-off age of 65 years to identify longevity. The ­results might vary if a different threshold is chosen but the variation is not expected to be significant. Because whatever the threshold or cutoff chosen, it will have to be applied uniformly across all socio-economic groups. Further, as the duration of time between the two surveys (2004–05 and 2011–12) is not very large, it limits the predictive power in the trends presented in the paper. An important aspect that can be taken up for future research is to examine why there are such enormous differences in longevity by factors, like caste, etc, which are factors beyond the control of an individual and get assigned automatically at birth.

Lastly, our findings have some important policy implications. We have found that there is substantial inequality in longevity in India and it has generally increased over the study period. For any ideal society, how long a person lives, should not depend on factors such as caste and religion. The government should pay attention to the idea of longevity (and inequality in longevity) itself and bring it to the forefront of the public health debate in India. Policymakers need to take a cue from projects like the US’s national health initiative, “Healthy People 2000,” which seeks to increase longevity among Americans of all ages (Singh and Siahpush 2006), and the UK’s “Longevity Science Advisory Panel” to provide a better chance of understanding the past and the present, for better forecasting in improvements in longevity. Finally, policymakers should also address the rampant socio-economic inequalities in the healthcare services in India which (among others) lead to diff­erent mortality rates across different groups in India, which, in turn, might lead to substantial socio-economic inequalities in longevity in India.


1 Individuals belonging to the SC/ST community have suffered severe social exclusion and discrimination since historic times and lag behind the non-scheduled groups in different indicators of welfare (Deshpande 2011).

2 Muslims (the largest minority religious group) in India lag behind their Hindu (the majority) counterparts, to a large extent, in access to various governmental and non-governmental services, including education, income and employment (Government of India 2006).

3 The northern region comprises Jammu and Kashmir, Himachal Pradesh, Delhi, Punjab, Haryana (and the union territory of Chandigarh), Rajasthan, and Uttarakhand. Uttar Pradesh, Madhya Pradesh, and Chhattisgarh come under the central region. The eastern region comprises Bihar, Jharkhand, West Bengal, Odisha, and Sikkim. The north-eastern region includes Assam, Arunachal Pradesh, Meghalaya, Manipur, Mizoram, Tripura, and Nagaland. The western region includes Maharashtra, Goa, Gujarat, and the two union territories of Daman and Diu and Dadra and Nagar Haveli. Finally, the southern region comprises Andhra Pradesh, Karnataka, Kerala, Tamil Nadu, and the union territories of Pondicherry and Lakshadweep. The categorisation of states into regions is similar to Singh (2011, 2012).

4 See Barros et al (2009) for a formal proof and other properties, especially the range (0–1) of ISIL and the insensitivity of ISIL to a “balanced increase” in the outcome analysed. A balanced increase is a situation in which new longevity opportunities are assigned to the socio-economic groups in the same way as the pre-existing ones were in the past.


Auger, Nathalie, Sam Harper and Amadou D Barry (2013): “Diverging Socio-economic Inequality in Life Expectancy of Francophones and Anglophones in Montréal, Québec: Tobacco to Blame?” Journal of Public Health, Vol 21, No 4, pp 317–24.

Barros, R P, F H G Ferreira, J R M Vega and J S Chanduvi (2009): Measuring Inequality of Opportunities in Latin America and the Caribbean Countries, Palgrave Macmillan and the World Bank.

Bhat, P N M and F Zavier (1999): “Findings of National Family Health Survey: Regional Analysis,” Economic & Political Weekly, Vol 34, pp 3008–32.

Bose, A (1991): Demographic Diversity of India: 1991 Census, State and District Level Data, Delhi: B R Publishing Corporation.

Brønnum-Hansen, Henrik and Mikkel Baadsgaard (2012): “Widening Social Inequality in Life
Expectancy in Denmark: A Register-based Study on Social Composition and Mortality Trends for the Danish Population,” BMC Public Health, Vol 12, Article No 994,

Clarke, Philip and Andrew Leigh (2011): “Death, Dollars and Degrees: Socio-economic Status and Longevity in Australia,” Economic Papers: A Journal of Applied Economics and Policy, Vol 30, No 3, pp 348–55.

Desai, S, A Dubey, B L Joshi, M Sen, A Shariff and R Vanneman (2010): Human Development in India: Challenges for a Society in Transition, New Delhi, ­India: Oxford University Press.

Desai, S, A Dubey and R Vanneman (2015): India Human Development Survey-II 2011–12, Ann Arbor, MI: Inter-university Consortium for Political and Social Research.

Deshpande, Ashwini (2011): The Grammar of Caste: Economic Discrimination in Contemporary India, New Delhi: Oxford University Press.

Drèze, Jean and Amartya Sen (2013): An Uncertain Glory: India and Its Contradictions, Princeton, NJ: Princeton University Press.

Government of India (2006): “Social, Economic and Educational Status of the Muslim Community of India,” New Delhi.

Jayaraj, D and Sreenivasan Subramanian (2006): “Horizontal and Vertical Inequality: Some Interconnections and Indicators,” Social Indicators Research, Vol 75, No 1, pp 123–39.

Magnolfi, S U, E Petruzzi, P Pinzani, F Malentacchi, M Pazzagli and F M Antonini (2007): “Longevity Index (LI%) and Centenarity Index (CI%): New Indicators to Evaluate the Characteristics of Aging Process in the Italian Population,” Archives of Gerontology and Geriatrics, Vol 44, No 3, pp 271–76.

Motiram, Sripad and Ashish Singh (2012): “How Close Does the Apple Fall to the Tree? Some Evidence from India on Intergenerational Occ­upational Mobility,” Economic & Political Weekly, Vol 47, No 40, pp 56–65.

National Research Council and Committee on Population (2011): Explaining Divergent Levels of Longevity in High-income Countries, Washington, DC: National Academies Press.

Neumayer, Eric and Thomas Plümper (2016): “Inequalities of Income and Inequalities of Longevity: A Cross-country Study,” American Journal of Public Health, Vol 106, No 1, pp 160–65.

Office of National Statistics (2015): “Trend in Life Expectancy at Birth and at Age 65 by Socio-economic Position based on the National Statistics Socio-economic Classification, England and Wales: 1982–1986 to 2007–2011,” Statistical Bulletin, London, UK: Office of National ­Statistics.

Pathak, Praveen Kumar and Abhishek Singh (2009): “Geographical Variation in Poverty and Child Malnutrition in India,” Population, Poverty and Health: Analytical Approaches, K K Singh, R C Yadava and A Pandey (eds), New Delhi: Hindustan Publishing Corporation, pp 183–206.

Qin, J, G Yu, T Xia, Y Li, X Liang, P Wei, B Long, M Lei, X Wei, X Tang and Z Zhang (2017): “Spatio-temporal Variation of Longevity Clusters and the Influence of Social Development Level on Lifespan in a Chinese Longevous Area (1982–2010),” International Journal of Environmental Research and Public Health, Vol 14, No 7, p 812.

Sauvaget, C, K Ramadas, J M Fayette, G Thomas, S Thara and R Sankaranarayanan (2011): “Socio-economic Factors & Longevity in a Cohort of Kerala State, India,” Indian Journal of Medical Research, Vol 133, No 5, p 479.

Singh, Ashish (2011): “Inequality of Opportunity in Indian Children: The Case of Immunization and Nutrition,” Population Research and Policy Review, Vol 30, No 6, pp 861–83.

— (2012): “Inequality of Opportunity in Earnings and Consumption Expenditure: The Case of Indian Men,” Review of Income and Wealth, Vol 58, No 1, pp 79–106.

Singh, Ashish, Upasak Das and Tushar Agrawal (2013): “How Inclusive Has Regular Employment Been in India? A Dynamic View,” European Journal of Development Research, Vol 25, No 3, pp 486–94.

Singh, Ashish, Abhishek Singh, Saseendran Pallikadavath and Faujdar Ram (2014): “Gender Differentials in Inequality of Educational Opportunities: New Evidence from an Indian Youth Study,” European Journal of Development Research, Vol 26, No 5, pp 707–24.

Singh, G K and M Siahpush (2006): “Widening Socioeconomic Inequalities in US Life Expectancy 1980–2000,” International Journal of Epidemiology, Vol 35, No 4, pp 969–79.

Villemez, Wayne J and Alan R Rowe (1975): “Black Economic Gains in the Sixties: A Methodological Critique and Reassessment,” Social Forces, Vol 54, No 1, pp 181–93.

Wanless, D, J Pattison, K McPherson, S Haberman, C Blakemore, W Wong and J Lu (2012): “Life Expectancy: Past and Future Variations by Socio-economic Group in England & Wales,” Longevity Science Advisory Panel Paper 2,


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Updated On : 8th Aug, 2022
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