Poverty Among Elderly in India

Page 1

Poverty Among Elderly in India

Akanksha Srivastava • Sanjay K. Mohanty

Accepted: 24 July 2011� Springer Science+Business Media B.V. 2011

Abstract Using consumption expenditure data of the National Sample Survey

2004–2005, this paper estimates the size of elderly poor and tests the hypotheses that

elderly households are not economically better-off compared to non-elderly households in

India. Poverty estimates are derived under three scenarios—by applying the official cut-off

point of the poverty line to household consumption expenditure (unadjusted), consumption

expenditure adjusted to household size and consumption expenditure adjusted to household

composition. Results show that an estimated 18 million elderly in India are living below

the poverty line. On adjusting the consumption expenditure to household size and com-

position, there are no significant differences in the incidence of poverty among elderly and

non-elderly households in India. This is in contrast to the notion that elderly households are

better off than non-elderly households in India. Based on the findings, we suggest that the

age dimension should be integrated into social policies for evidence based planning.

Keywords Poverty � Household size � Household composition � MPCE � Elderly �Non-elderly � India

1 Introduction

Population ageing resulting from an increase in life expectancy and reduction in fertility is

gaining momentum in many developing countries including India. The life expectancy at

birth in India has increased from 50 years in 1972 to 64 years in 2004 and is projected to

reach 70 years by 2020 (Office of the Registrar General and Census Commissioner 1999,

2008). By 2005, nearly 11 of the 29 states in India had reached the replacement level of

fertility (IIPS and Macro International 2007). The proportion of population in the age

group 60 years and above has increased from 6.8% in 1991 (57 million) to 7.4% in 2001

A. Srivastava (&) � S. K. MohantyInternational Institute for Population Sciences, Govandi Station Road, Deonar, Mumbai 400088, Indiae-mail: [email protected]

S. K. Mohantye-mail: [email protected]

123

Soc Indic ResDOI 10.1007/s11205-011-9913-7

Page 2

(77 million) and is projected to be 12.4% by 2026 (174 million) (Office of the Registrar

General and Census Commissioner 2004). During 1991–2001, the annual growth rate of

the population aged 60 years and above in India was 3.04% compared to 1.8% for the total

population. The old age support ratio (ratio of number of persons aged 15–59 to 60?) is

expected to decline from 8.4 in 2001 to 5.2 by 2026.

Along with age-structural transition, India is experiencing an epidemiological transi-

tion, an economic transition, and social and institutional changes. On the epidemiological

front, the disease pattern has shifted from communicable to non-communicable diseases

and the older adults face an increasing health risk (RGI 2009; WHO 2010). Socio-

economic changes such as increased urbanisation and modernisation, increasing partic-

ipation of women in economic activities, mobility of the younger generation and the

growth of individualism are leading to the breakdown of the joint family structure, which

used to be the primary support for the elderly in India (Knodel et al. 1992; Pandey

2009). This has altered the conventional living arrangements of the elderly—an

increasing number of elderly are living alone or in small households and have to endure

economic vulnerability and rising poverty (Sherlock 2000; Dreze 1990). During the

period, 1995–1996 to 2004–2005, the percentage of elderly living alone or with spouse

in India has recorded a 20% increase (Table 1). Kumar et al. (2011) have similar

findings.

Old age poverty is a significant issue because the income of the elderly reduces, while

consumption expenditure increases (largely due to an increase in health expenditure) with

advancement of age (Mahal and Berman 2001). More specifically, the out of pocket

expenditure on health care significantly alters the household budget, reduces consumption

of non-health goods and services, reduces accessibility to health care utilization and pushes

many families into the medical poverty trap (Whithead et al. 2001). Thus poverty among

the elderly tends to be more permanent than that among the non elderly and elderly are

Table 1 Percent distribution ofelderly by living arrangement andthe state of economic indepen-dence in India, 1995–2005

Source: Computed from 52 (25.0)and 60 (25.0) rounds of NSS

Living arrangement/Economic independence

1995–1996 2004–2005

Living arrangement

Elderly living alone/with spouse only 14.6 17.4

Elderly living with spouse and children 46.7 45.6

Elderly living without spouse but withchildren

33.8 32.6

Elderly living with other relatives 4.5 3.9

Elderly living with non relatives 0.5 0.5

Total Percent 100 100

Total (Number) 33,656 34,104

Economic independence

Elderly not dependent on others 31.3 34.0

Elderly dependent on spouse 10.1 8.7

Elderly dependent on children 50.3 51.4

Elderly dependent on grandchildren 3.6 1.8

Elderly dependent on others 4.7 4.1

Total percent 100 100

Total (number) 32,496 34,229

A. Srivastava, S. K. Mohanty

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unlikely to come out of the poverty trap (Hurd 1990). Empirical evidence suggests a ‘‘U’’

shaped relationship of age and poverty; elderly population faces a higher incidence of

poverty compared to other groups (Barrientos et al. 2003; World Bank 2006; Mujahid et al.2008). Gasparini et al. (2007) found that in countries with weak social security systems,

there is no significant difference between old age poverty and the overall poverty rates,

while in countries with a well developed pension system, poverty rates are lower for the

elderly than for other age groups. Panda (1998), in his study among the elderly in rural

Orissa found that the households where elderly women live alone were the poorest and are

in the need of additional socio economic support by the government. Mohanty and Sinha

(2010) use a simple measure of deprivation and conclude that poverty among the elderly

living in nuclear households was higher compared to that among the elderly co-residing

with children or non-elderly households. Studies also documented that poverty among

elderly households is lower than that of non-elderly households and this variation was

attributed to the survival bias, that is, the positive correlation of household income and life

expectancy (Deaton and Paxson 1995; Pal and Palacios 2006, 2008). The incidence of

poverty in general and among the elderly in particular remains a serious issue in India

though the economy has been buoyant for the past two decades (Alam and Barrientos

2010).

Though old age poverty is a significant issue in developing countries, policy and

research mainly focus on the pension program in the organised sector that has little rele-

vance for the poor elderly (Sherlock 2000). Also, the comprehension of old age poverty

suffers from methodological problems, international comparability and data limitations

(Treas and Logue 1976; Barrientos et al. 2003; Gasparini et al. 2007). Understanding the

extent of poverty among elderly in the wake of the breakdown of the joint family structure

and other socio-economic changes is essential for evidence based planning. Besides, the

existing social security system in the country is inadequate to meet the multifaceted need

of the growing elderly.

This paper attempts to provide a numerical estimate of the elderly poor and under-

stand the economic deprivation of elderly and non-elderly households in India. Con-

sumption expenditure is adjusted to household size and household composition because

of the differentials in the demographic structure of the households. We hypothesise that

there is no significant difference in the incidence of poverty between households with the

elderly and households without the elderly, and the elderly living alone or with other

elderly members are the poorest compared to any other type of households. The interest

for this paper is primarily due to the growing number of elderly living amidst abject

poverty in rural India. This may be partly attributed to the fragmentation of land and

disintegration of the family soon after the marriage of the son. This leaves the poor

elderly with little or no resources and often children do not undertake the responsibility

for the care of parents.

This study has bearing on the findings of two earlier studies by Dreze and Srinivasan

(1997) and Pal and Palacios (2006). Dreze and Srinivasan dealt extensively with poverty

among the widowed in India and estimated the incidence of poverty in 20 types of

households. They found significantly higher incidence of poverty among widowed

households when consumption expenditure was adjusted for household size and compo-

sition. However, their study did not include elderly households by their co-residence and

this study addresses the issue. Pal and Palacious had examined the incidence of poverty

among elderly and non-elderly households, but we believe that an analysis by co-residence

of the elderly would help to draw valid inferences.

Poverty Among Elderly in India

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2 Data and Methods

2.1 Data

In India, the National Sample Survey Organisation (NSSO) collects periodic data on

household consumption expenditure in various rounds. From the household consumption

expenditure, the monthly per capita consumption expenditure (MPCE) is computed and

used to explain the economic differentials and derive the incidence of poverty in the

population sub-groups. The poverty line is fixed according to the money values of cal-

ories intake (2,400 in rural and 2,100 in urban areas) and adjusted for the state level price

line. In 2004–2005, the national level poverty line was fixed at a monthly cut off of

rupees 356 and rupees 539 per person for rural and urban India, respectively (Planning

Commission, Government of India 2007). Among other things, these estimates are not

adjusted for age composition of household, size of the household and not segregated for

elderly and non-elderly households. It has been established that the consumption

expenditure and the poverty level vary by household size and the demographic compo-

sition of the households, and the choice of equivalence scale can sometimes systemati-

cally affect absolute and relative levels of poverty (Buhmann et al. 1988; Nelson 1988;

Meenakshi and Ray 2002; Pal and Palacios 2006). The need to adjust consumption

expenditure to household size is governed by the fact that larger households tend to have

a comparative advantage in their standard of living over smaller households due to

economies of scale. Several examples of scale economies include house rent, utilities

such as electricity and purchase of food items. Similarly, the consumption needs of the

children and those of adults are not identical and hence, consumption expenditure is

sensitive to the age composition of household members.

We have used the 61st round of consumption expenditure data (Schedule 1.0), collected

by the NSSO during 2004–2005. The 61st round (Schedule 1.0) of NSS was one of the

largest ever consumption expenditure surveys conducted in the country covering a total of

124,644 households. It was specifically designed to provide reliable estimates of poverty

for the districts of India (NSS 2006). The data on consumption expenditure were based on a

reference period of 30 days, that is, uniform recall period (URP) and with a reference

period of 30 days and 365 days that is, mixed recall period (MRP). We have used the

consumption expenditure based on URP and the MPCE is the dependent variable in the

analyses. We have used the state specific cut-off point of the poverty based on URP

separately for rural and urban areas to derive the unadjusted poverty estimates at the state

level. The population weight is used to derive the estimates of MPCE and poverty. Pop-

ulation projection by the expert group of the Registrar General of India (Office of the

Registrar General and Census Commissioner 2006) has been used to estimate the number

of elderly living below the poverty line. The statistical package STATA 10.0 has been used

for the analysis.

2.2 Methods

For national estimates, we have classified the households into three groups, namely,

households where the elderly lives alone or with other elderly members, households where

elderly live with non-elderly members, and non-elderly households. However, for state

level estimates, we have carried out the analysis for two groups, namely, elderly house-

holds and nonelderly households due to sample size constraint.

A. Srivastava, S. K. Mohanty

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We have derived three alternatives estimates of poverty using the same cut off point of

poverty to facilitate the comparison.

1. Official cut-off point of poverty applied to household consumption expenditure

(unadjusted poverty estimates).

2. Official cut-off point of poverty applied to consumption expenditure adjusted for

household size (poverty estimates adjusted to household size).

3. Official cut-off point of poverty applied to consumption expenditure adjusted for

household composition (poverty estimates adjusted to household composition).

The equation for deriving the poverty adjusted for economies of scale in its simplest

form may be given as:

X ¼ Y=Ah ð1Þ

where, X is the total household expenditure, A is the household size, and h is the degree of

household economies of scale, 0 B h B 1. The closer the value of h to 0, more weight is

assigned for household size and the closer the value of h to 1, less weight is assigned to the

value of household size and h may be assigned different intermediate values such as 0.2,

0.4, 0.6 0.7, 0.8 and 0.9. In the study, we have used the value of h as 0.9 for adjusting

MPCE.

Equation 1 can be extended to take into account the economies of scale and adult

equivalent scale and is given as:

Xih ¼ Yh= a1C1 þ a2C2 þ Að Þh ð2Þ

where, X is the MPCE of an individual i living in the household h, Y is the total expenditure

of the household. A is the number of adults, C1 is the number of children under 6 years of

age, and C2 is the number of children between 6 and 14 years in the household. We have

used the values of a as a1 = 0.5, a2 = 0.75 and A = 1 as suggested by Deaton and Zaidi

(2002) for adjusting MPCE for middle income countries. Coale and Hoover (1958)

developed an adult equivalent scale by assigning a weight of 1.0 for male age 10 years and

above, 0.9 for an adult female 10 years and above and 0.5 for a child of less than 10 years

of age.

3 Results

3.1 Living Arrangement and Economic Dependence

We begin the discussion by giving the context of living arrangement and economic

dependence of the elderly as these are important indicators to assess their well being in

lieu of the changing demography, economy and society (Table 1). To examine the

changes in the living arrangements and economic dependence, we have used the 52nd

(25.0) and 60th (25.0) rounds of NSS data, conducted in 1995–1996 and 2004–2005,

respectively. These surveys provide detailed information on socio-economic conditions,

living arrangements and health care utilisation of the elderly population. It is observed

that about one in five elderly lives alone or with the spouse and this trend is increasing.

On the other hand the economic dependence of elderly revealed that about one-third of

the elderly do not depend on others, while about half of the elderly depend on their

children for old age support.

Poverty Among Elderly in India

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3.2 Poverty by Age and Household Size

To understand the age dimension of consumption poverty, we have plotted the incidence of

poverty by age of the head of the household (Fig. 1). The unadjusted poverty is age

sensitive; higher in the younger age group and declines with increase in age (32% in the

age group of 31–40 compared to 20% in the age group of 71 years and above). The patterns

are similar for rural and urban India, and for major states.

We have further examined the variation in poverty estimates by household size. Based

on the literature, we have adjusted the age composition of household members to adult

equivalent scale by assigning a weight of 1 to adult (15 years and above), 0.75 for children

aged 6–14 years and 0.5 for children under 6 years to derive the adult equivalent scale.

Similarly, we put a small value of h = 0.9 to adjust for household size. We have used the

official poverty cut-off point to compare the incidence of poverty. On adjusting con-

sumption expenditure to household composition, we observed that the poverty differentials

across the age group have narrowed down substantially. The incidence of poverty varies in

a narrow range in all age groups except in the age group 71 and above. Similarly, by

adjusting the consumption expenditure for household size, the poverty differentials among

the age group narrowed down substantially. The relatively low poverty among the 71?

may be because of survival bias as attributed by Pal and Palacios (2008) (relatively better

off elderly survives bit longer). The differential in unadjusted poverty by household size

reveals that large households tend to have a higher incidence of poverty. For example,

among households with two members, 10% households were living below the poverty line

compared to 30% among households with five to seven members and 38% among

households with eight members and more (Fig. 2). The pattern holds true for rural and

urban India and for the states.

In the following section, we have estimated the number of elderly living below the

poverty line and the adjusted poverty estimates among elderly and nonelderly households

in India.

3.3 MPCE Among Elderly and Nonelderly Households

Table 2 summarises the proportion of elderly households, estimated number of elderly and

the mean MPCE of elderly and non-elderly households by place of residence and selected

Fig. 1 Percentage of population living below the poverty line by age of the head of the household in India,2004–2005. Source: Computed from 61(1) round data of NSSO data

A. Srivastava, S. K. Mohanty

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states in India. We define an elderly household as one with at least one member of 60 years

and above. In 2005, 28% rural households and 24% of urban households in India had at

least one elderly member and the estimated number of elderly households was about

62 million in the country (44 million in rural and 17 million in urban India). The size of

elderly population varies from 10 million in Uttar Pradesh to 1.2 million in Assam. With

respect to household consumption expenditure, there were no significant differentials in the

MPCE of elderly and non-elderly households in urban areas (rupees 1,052 vs.

rupees 1,053) while differentials were small (rupees 579 vs. rupees 550) in rural India.

There is a large variation in MPCE in elderly and non-elderly households among the states

of India. In urban areas where fertility level is close to the replacement level, 14 of the 17

states (except Andhra Pradesh, Haryana, Jharkhand, Tamil Nadu and Uttar Pradesh) had

higher MPCE in non-elderly households compared to elderly households. In the states of

Kerala, Maharashtra and Karnataka (rural), the mean MPCE in non-elderly households is

higher compared to elderly households. In general, the state level patterns of MPCE among

elderly and non-elderly households are mixed.

3.4 MPCE and Poverty Adjusted to Household Size and Composition

This section depicts the relative economic conditions of elderly households and provides

methodological insight on sensitivity of consumption expenditure to economies of scale

and adult equivalents. We have carried out this exercise for the country; separately for rural

and urban areas and by household type.

We begin the discussion by plotting the unadjusted MPCE among non-elderly and

elderly households in India (Fig. 3a, b). We have truncated 2,469 of 124,642 households

that had MPCE of more than rupees 3,000. The distribution of MPCE among elderly and

non-elderly households is similar indicating that the economic conditions are probably

similar in elderly and non-elderly households.

Following this, we had assigned different values of theta (h) and alpha (a) to understand

the impact of household size and composition on MPCE for elderly households and non

elderly households. To understand the economic well being of elderly by their co resi-

dence, we have categorised elderly households into households where elderly live alone or

with other elderly members, and households where elderly live with non elderly members.

Table 3 provides the average household size and the adjusted MPCE for different values of

h and a for elderly and non-elderly households by place of residence. The average

Fig. 2 Percentage of households living below the poverty line (unadjusted) by household size in India,2004–2005. Source: Computed from 61(1) round data of NSSO

Poverty Among Elderly in India

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A. Srivastava, S. K. Mohanty

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household size varies largely; from 1.6 in households where the elderly lives alone or only

with other elderly members, 6 in households where the elderly live with non-elderly

members and 4.5 among non-elderly households. As the value of theta tends to 1, there are

no economies of scale. On the other hand, when the value of theta tends to 0, the adjusted

MPCE increases, indicating that there are full economies of scale. It has been recom-

mended that for transitional economies, a moderate value of theta captures size economies

well.

In rural areas, the unadjusted mean MPCE was rupees 703 among the households where

elderly live alone or with other elderly members, rupees 611 among the households where

elderly live with nonelderly members and rupees 597 among the nonelderly households.

On assigning a value of h = 0.8, the mean MPCE for households with the elderly living

alone or with other elderly members (rural) was rupees 758 compared to rupees 850 for

elderly living with other members and rupees 783 for nonelderly households. The differ-

ences are smaller for rural and urban areas.

Similarly, the adult equivalent scale under the two scenarios has been derived for both

elderly and non-elderly households. In the first scenario, a weight of 1 is assigned to an

adult (15 years and above), 0.6 for children of 6–14 years of age and of 0.4 for children of

0–5 years of age. In the second scenario, a weight of 1 is assigned to adults (15 years and

above), 0.75 to children in the age group 6–14 years, and 0.5 for children in the age group

0–5. By adjusting for household composition, we found that the differences in mean MPCE

by household type are smaller. The mean MPCE among the household where elderly live

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Fig. 3 Unadjusted monthly per capita consumption expenditure among elderly and nonelderly householdsin India, 2004–2005. Source: Computed from 61st round data of NSSO

Poverty Among Elderly in India

123

Page 10

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liv

ing

wit

hn

on

-el

der

lym

emb

ers

No

n-

eld

erly

ho

use

ho

lds

Eld

erly

liv

ing

alo

ne/

wit

ho

ther

eld

erly

mem

ber

s

Eld

erly

liv

ing

wit

hn

on

-el

der

lym

emb

ers

No

n-

eld

erly

ho

use

ho

lds

Un

adju

sted

MP

CE

70

36

11

59

71

,88

91

,150

1,2

19

95

67

47

77

4

Av

erag

eh

ou

seh

old

size

1.6

6.1

4.6

1.6

5.7

4.1

1.6

6.0

4.5

Dif

fere

nt

val

ues

of

h

h=

0.2

97

72

,414

1,8

74

2,7

85

4,1

29

3,2

59

1,3

62

2,8

48

2,2

68

h=

0.3

93

32

,012

1,6

11

2,6

40

3,4

94

2,8

51

1,2

97

2,3

91

1,9

63

h=

0.4

89

31

,689

1,3

88

2,5

05

2,9

62

2,5

02

1,2

37

2,0

12

1,7

05

h=

0.5

85

51

,418

1,1

98

2,3

82

2,5

17

2,2

01

1,1

81

1,6

96

1,4

84

h=

0.6

82

11

,192

1,0

37

2,2

67

2,1

43

1,9

43

1,1

29

1,4

33

1,2

95

h=

0.7

78

81

,005

90

02

,16

11

,828

1,7

20

1,0

81

1,2

13

1,1

33

h=

0.8

75

88

50

78

32

,06

31

,563

1,5

28

1,0

36

1,0

31

99

4

h=

0.9

73

07

20

68

31

,97

31

,339

1,3

62

99

58

77

87

6

Dif

fere

nt

val

ues

of

a

a 1=

0.4

,a 2

=0

.6,

A=

1.0

70

36

64

66

91

,88

91

,253

1,3

62

95

68

32

89

2

a 1=

0.5

,a 2

=0

.75,

A=

1.0

70

36

88

70

51

,88

91

,222

1,3

14

95

68

06

85

2

a 1=

0.5

,a 2

=0

.75,

A=

1.0

and

h=

0.9

75

79

11

86

02

,06

31

,645

1,6

28

1,0

36

1,0

97

1,0

78

Per

cen

tage

of

po

pu

lati

on

liv

ing

bel

ow

po

ver

tyli

ne

wit

ho

ffici

alcu

t-o

ffp

oin

ta

14

.72

6.4

29

.31

4.5

26

.72

5.3

14

.72

6.5

28

.3

Per

cen

tage

of

po

pu

lati

on

liv

ing

bel

ow

po

ver

tyli

ne

by

adju

stin

gto

ho

use

ho

ldsi

zeo

f0

.9b

10

.05

.17

.49

.67

.78

.59

.95

.77

.7

A. Srivastava, S. K. Mohanty

123

Page 11

Ta

ble

3co

nti

nu

ed

Un

adju

sted

and

adju

sted

MP

CE

for

ho

use

ho

ldsi

zean

dco

mp

osi

tio

n

MP

CE

(rura

l)M

PC

E(u

rban

)M

PC

E(c

om

bin

ed)

Eld

erly

liv

ing

alo

ne/

wit

ho

ther

eld

erly

mem

ber

s

Eld

erly

liv

ing

wit

hn

on

-el

der

lym

emb

ers

No

n-

eld

erly

ho

use

ho

lds

Eld

erly

liv

ing

alo

ne/

wit

ho

ther

eld

erly

mem

ber

s

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erly

liv

ing

wit

hn

on

-el

der

lym

emb

ers

No

n-

eld

erly

ho

use

ho

lds

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erly

liv

ing

alo

ne/

wit

ho

ther

eld

erly

mem

ber

s

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erly

liv

ing

wit

hn

on

-el

der

lym

emb

ers

No

n-

eld

erly

ho

use

ho

lds

Per

cen

tage

of

ho

use

ho

lds

liv

ing

bel

ow

po

ver

tyli

ne

by

adju

stin

gfo

rco

mp

osi

tio

nc

14

.71

6.9

16

.01

4.5

20

.91

7.4

14

.71

7.9

16

.3

Per

cen

tage

of

ho

use

ho

lds

liv

ing

bel

ow

po

ver

tyli

ne

by

adju

stin

gfo

rsi

zean

dco

mp

osi

tio

nd

10

.02

.63

.39

.65

.15

.09

.93

.23

.7

aC

on

sum

pti

on

exp

end

iture

isn

ot

adju

sted

for

size

and

com

po

siti

on

bH

ou

seho

ldsi

zeis

adju

sted

to0

.9c

Ass

ign

eda

wei

ght

of

0.5

for

chil

dre

nu

nd

erag

e6

,0

.75

for

chil

dre

n6

–1

4an

d1

for

adu

ltd

Con

sum

pti

on

exp

endit

ure

adju

sted

tosi

zean

dco

mp

osi

tio

n

Poverty Among Elderly in India

123

Page 12

alone or with other elderly members remained the same (rupees 703), while that of elderly

living with non elderly members was rupees 688. On the other hand, the mean MPCE

among non elderly households was rupees 705. We have adjusted the consumption

expenditure to both size (h = 0.9) and composition (0.5, 0.75 and 1 for age groups 0–5,

6–14 and 15?, respectively) to understand the combined effect. We found that elderly

households living in rural areas are the poorest compared to other groups (MPCE of

rupees 757 among elderly living alone or with non-elderly members, rupees 911 among

elderly living with nonelderly members and rupees 860 among nonelderly households).

We have estimated the incidence of poverty in three types of households by using the

official cut off point of poverty. We have used the uniform cut-off point not to estimate the

number of poor in each type of household but to understand and compare the sensitivity of

poverty estimates to household size and composition. If we apply the official cut-off point

to the unadjusted estimates of MPCE, 15% households where elderly live alone or with

other elderly members are below the poverty line, compared to 27% households where

elderly co-reside with non elderly members and 28% among non-elderly households. But

the difference in poverty estimates among elderly and non-elderly households reduces

substantially when adjusted for age composition of the household. On adjusting for both

household size and household composition, it was found that the differentials in poverty

estimates reduced significantly. From the analyses, it is clear that on adjusting for either

household size or household composition or both household size and household compo-

sition, the economic condition of elderly and non-elderly households are similar. This is in

contrast to the notion that the elderly households are better off compared to non-elderly

households in India.

3.5 Estimates of Elderly Poor in India

Table 4 presents the estimated number of elderly living below the poverty line in states of

India using the state specific poverty line for rural and urban areas (Planning Commission,

Government of India 2004–2005). The percentage of elderly households living below the

poverty line in rural areas varies from 5% in Punjab to 45% in Orissa. It is higher in those

states where the overall poverty level is high (Jharkhand, Madhya Pradesh and Chhattis-

garh) and lower in those states where the overall poverty level is low. The average number

of elderly in rural households varies from 1.2 in Assam to 1.4 in Punjab. Based on the

number of elderly households, the proportion of elderly households living below the

poverty line and the average number of elderly in elderly households, the estimated

number of elderly living below the poverty line was 17.7 million in the country of which

12.8 million resides in rural areas and 4.9 million resides in urban areas. The estimated

number of elderly (both rural and urban) varies from 3.2 million in Uttar Pradesh to

1.6 million in Bihar and is relatively lower in the state of Punjab.

3.6 Regional Pattern of Elderly Poverty Adjusting for Household Size

and Composition

We have further examined the sensitivity of poverty estimates to household size and

composition for selected states in India. We found that the mean MPCE of the household

increases when consumption expenditure is adjusted for either household size or household

composition. Thus, applying the official poverty cut-off point to adjusted MPCE yields

lower estimates of poverty. The poverty line can be inflated, but that will not affect the

A. Srivastava, S. K. Mohanty

123

Page 13

Ta

ble

4E

stim

ated

nu

mb

ero

fel

der

lyli

vin

gb

elow

the

po

ver

tyli

ne

inm

ajo

rst

ates

of

Ind

ia,

20

04–

20

05

Sta

tes

Ru

ral

Urb

an

Un

adju

sted

po

ver

tycu

to

ffp

oin

tin

rup

ees

Est

imat

edn

um

ber

of

eld

erly

ho

use

ho

lds

in(0

000 )

Av

erag

en

um

ber

of

eld

erly

inel

der

lyh

ou

seh

old

s

Per

cen

tag

eo

fel

der

lyh

ou

seh

old

sli

vin

gb

elo

wp

ov

erty

lin

e

Est

imat

edno

of

eld

erly

liv

ing

bel

ow

po

ver

tyli

ne

(00

00 )

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adju

sted

po

ver

tycu

to

ffp

oin

tin

rup

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imat

edn

um

ber

of

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erly

ho

use

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in(0

000 )

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erag

en

um

ber

of

eld

erly

inel

der

lyh

ou

seh

old

s

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cen

tag

eo

fel

der

lyh

ou

seh

old

sli

vin

gb

elo

wp

ov

erty

lin

e

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imat

edno

of

eld

erly

liv

ing

bel

ow

po

ver

tyli

ne

(00

00 )

An

dh

raP

rad

esh

29

2.9

53

,848

1.2

7.7

33

69

54

2.8

91

,059

1.2

27

.33

49

Ass

am3

87

.64

1,0

30

1.2

20

.56

25

03

78

.84

14

41

.14

.06

Bih

ar3

54

.36

3,5

34

1.3

30

.76

1,4

35

43

54

57

1.2

28

.61

60

Chh

atti

sgar

h3

22

.41

96

61

.33

1.3

93

82

56

02

26

1.2

39

.01

09

Gu

jara

t3

53

.93

1,7

64

1.3

13

.25

31

15

41

.16

1,1

82

1.3

11

.71

86

Har

yan

a4

14

.76

92

91

.41

0.5

71

36

50

4.4

93

70

1.4

9.5

50

Him

ach

alP

rad

esh

39

4.2

84

04

1.3

8.5

46

50

4.4

93

21

.25

.62

Jam

mu

&K

ash

mir

39

1.2

64

10

1.3

4.0

42

25

53

.77

16

11

.43

.37

Jhar

kh

and

36

6.5

69

41

1.3

41

.03

48

64

51

.24

32

91

.41

3.5

61

Kar

nat

aka

32

4.1

72

,226

1.2

20

.82

55

15

99

.66

1,1

19

1.2

29

.13

89

Ker

ala

43

0.1

22

,145

1.3

12

.25

33

15

59

.39

80

21

.31

9.7

20

3

Mad

hy

aP

rad

esh

32

7.7

82

,309

1.4

33

.81

,05

35

70

.15

89

21

.33

8.9

45

1

Mah

aras

htr

a3

62

.25

4,1

34

1.3

23

.57

1,2

96

66

5.9

2,6

27

1.3

28

.29

49

Ori

ssa

32

5.7

92

,117

1.3

45

.31

1,2

09

66

5.9

27

91

.24

5.3

14

9

Pu

nja

b4

10

.38

1,0

30

1.4

5.2

37

84

66

.16

53

11

.35

.23

7

Raj

asth

an3

74

.57

2,3

56

1.3

13

.45

42

55

59

.63

64

41

.32

7.3

22

8

Tam

ilN

adu

35

1.8

62

,466

1.2

19

.35

58

75

47

.42

2,1

63

1.3

21

.05

74

Poverty Among Elderly in India

123

Page 14

Ta

ble

4co

nti

nu

ed

Sta

tes

Ru

ral

Urb

an

Un

adju

sted

po

ver

tycu

to

ffp

oin

tin

rup

ees

Est

imat

edn

um

ber

of

eld

erly

ho

use

ho

lds

in(0

000 )

Av

erag

en

um

ber

of

eld

erly

inel

der

lyh

ou

seh

old

s

Per

cen

tag

eo

fel

der

lyh

ou

seh

old

sli

vin

gb

elo

wp

ov

erty

lin

e

Est

imat

edno

of

eld

erly

liv

ing

bel

ow

po

ver

tyli

ne

(00

00 )

Un

adju

sted

po

ver

tycu

to

ffp

oin

tin

rup

ees

Est

imat

edn

um

ber

of

eld

erly

ho

use

ho

lds

in(0

000 )

Av

erag

en

um

ber

of

eld

erly

inel

der

lyh

ou

seh

old

s

Per

cen

tag

eo

fel

der

lyh

ou

seh

old

sli

vin

gb

elo

wp

ov

erty

lin

e

Est

imat

edno

of

eld

erly

liv

ing

bel

ow

po

ver

tyli

ne

(00

00 )

Utt

arP

rad

esh

36

5.8

47

,309

1.4

26

.38

2,6

41

48

3.2

61

,647

1.3

26

.75

86

Wes

tB

eng

al3

82

.82

3,0

71

1.2

20

.21

75

14

49

.32

1,7

30

1.2

11

.52

45

Ind

ia3

56

.34

4,2

43

1.3

22

.17

12

,75

15

38

.61

7,3

87

1.3

22

.44

,97

8

A. Srivastava, S. K. Mohanty

123

Page 15

general inferences on the relative economic deprivation of elderly and non-elderly

households.

On adjusting the consumption expenditure for household composition, the differentials

in poverty estimates among elderly and non-elderly households have narrowed down in

many of the states of India. In 9 of the 17 states of India (rural), namely, Assam, Haryana,

Jharkhand, Kerala, Madhya Pradesh, Maharashtra, Rajasthan, Tamil Nadu and Uttar Pra-

desh, poverty among elderly households is higher than that of non-elderly households

where as the differences are small in the states of Gujarat, Punjab and West Bengal

(Table 5). The pattern is similar in urban India. This confirms that a small adjustment in

household composition affects the poverty estimates in India. We have adjusted the

household size by assigning a value of 0.9 and found similar results. The estimated poverty

in elderly households is high in 12 of the 17 states of India (rural), namely, Bihar,

Chhattisgarh, Gujarat, Karnataka, Kerala, Madhya Pradesh, Orissa, Rajasthan, Tamil

Nadu, Uttar Pradesh, Haryana and Jharkhand. This brings out the fact that the relative

differences in the economic condition of elderly and non elderly households are small on

adjusting for household size and composition. These findings are similar to those of Dreze

and Srinivasan (1997).

3.7 Determinants of Elderly Poverty

To understand the significant predictors of elderly poverty, we have carried out two set of

logistic regression analysis separately for rural and urban areas. The dependent variable is

the economic deprivation of elderly (elderly being poor or non-poor) and the person file is

used in the analysis. Model 1 examines the significant predictor of elderly poverty without

adjusting consumption expenditure to household size and composition while Model 2

examines the significant predictor of poverty when consumption expenditure is adjusted to

household size and composition. The dependent variable in Model 1 is the elderly being

poor or non poor, based on unadjusted poverty estimates. In Model 2 the dependent

variable is elderly being poor or non poor by adjusting for adult equivalent and economies

of scale. In Model 1, the significant predictors of poverty are age, educational attainment,

social group, religion, number of elderly in the household and type of household. For

example, elderly in the age group 80 years and above are 32% less likely to be poor

compared to elderly in the age group 60–69 years (Table 6). Similarly, elderly having

education up to graduation and above are less likely to be poor. The pattern is similar in

urban areas.

On adjusting the consumption expenditure to household size and composition and

applying the official-cut-off point in defining the poor (Model 2), we found that age is not a

significant predictor of poverty both in rural and urban areas. Education, religion and caste

are significant predictors of poverty among the elderly. In rural areas, households with

three or more elderly are more likely to be poor, whereas, in urban areas households with

three or more elderly are 46% less likely to be poor.

4 Discussion

As a consequence of the ongoing demographic transition, India is experiencing a rapid

shift in the age structure leading to an increase in the size of elderly population. Ageing

diminishes the capacity to participate in the work force, delimits the sources of income

generation and increases the likelihood of falling into poverty (Sherlock 2000).

Poverty Among Elderly in India

123

Page 16

Ta

ble

5P

erce

nta

ge

of

eld

erly

and

no

nel

der

lyh

ou

seh

old

sli

vin

gb

elow

po

ver

tyli

ne

by

adju

stin

gco

nsu

mp

tio

nex

pen

dit

ure

toh

ou

seh

old

size

and

com

po

siti

on

inm

ajo

rst

ates

of

Ind

ia,

20

04–

20

05

Sta

tes

Un

adju

sted

po

ver

tycu

to

ffC

on

sum

pti

on

exp

endit

ure

adju

stin

gfo

rh

ou

seh

old

com

po

siti

on

Con

sum

pti

on

exp

endit

ure

adju

stin

gfo

rh

ou

seh

old

size

Ru

ral

Urb

anR

ura

lU

rban

Rura

lU

rban

Po

ver

tycu

to

ffp

oin

tin

rup

ees

Po

ver

tycu

to

ffp

oin

tin

rup

ees

Per

cen

tag

eo

fel

der

lyh

ou

seh

old

sli

vin

gb

elo

wp

ov

erty

lin

e

Per

centa

ge

of

no

nel

der

lyh

ou

seh

old

sli

vin

gb

elow

po

ver

tyli

ne

Per

cen

tag

eo

fel

der

lyli

vin

gb

elow

po

ver

tyli

ne

Per

centa

ge

of

no

nel

der

lyli

vin

gb

elow

po

ver

tyli

ne

Per

cen

tag

eo

fel

der

lyh

ou

seh

old

sli

vin

gb

elow

po

ver

tyli

ne

Per

cen

tag

eo

fn

on

eld

erly

ho

use

ho

lds

liv

ing

bel

ow

adju

sted

po

ver

tyli

ne

Per

cen

tag

eo

fel

der

lyh

ou

seh

old

sli

vin

gb

elow

po

ver

tyli

ne

Per

cen

tage

of

no

nel

der

lyh

ou

seh

old

sli

vin

gb

elow

po

ver

tyli

ne

An

dh

raP

rad

esh

29

3.0

54

2.9

5.4

6.3

22

.31

4.7

3.9

6.4

16

.31

4.6

Ass

am3

87

.63

78

.81

1.4

9.4

2.8

1.0

8.7

9.1

2.3

0.6

Bih

ar3

54

.44

35

.01

8.9

19

.91

9.0

15

.81

4.5

23

.21

4.3

14

.8

Chh

atti

sgar

h3

22

.45

60

.02

1.2

23

.73

6.0

29

.21

7.4

22

.23

0.4

25

.6

Gu

jara

t3

53

.95

41

.27

.37

.86

.86

.64

.17

.95

.66

.1

Har

yan

a4

14

.85

04

.56

.45

.66

.96

.95

.75

.25

.67

.4

Jhar

kh

and

36

6.6

45

1.2

32

.62

4.1

12

.39

.52

4.0

23

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Population ageing in India is taking place in the context of decreased familial support and

a weak social security system. There has been research on health and health care utili-

zation, morbidity and living arrangements, but little is known about the extent of poverty

and deprivation among the elderly in India. Though India has a long history of providing

estimates of consumption poverty by state and residence, we do not have estimates of the

elderly poor. We are interested in the relative economic deprivation of the elderly in the

context of a changing familial structure that was the primary support of the elderly in

India.

We found that the poverty estimates vary by age of head of household and by

household size. The estimates are robust. However, these inferences does not hold true

when we analyse the poverty by type of household (elderly and non-elderly households)

and adjust the consumption expenditure to household size and composition. When

consumption expenditure is adjusted for household size (h = 0.9), households in rural

India where elderly lives alone or with other elderly members are the poorest compared

to households where the elderly co-resides with non-elderly members and non-elderly

households. Similarly, the differentials narrowed down when consumption expenditure is

adjusted for household composition. This validates our hypotheses and suggests that the

economic deprivations are similar among elderly and non-elderly households. These

findings holds true for many states in India and similar to previous studies on poverty

among the widowed in India. Results from the multivariate analyses confirm that

poverty among the elderly living alone or with other elderly members is higher, com-

pared to that among the elderly living with nonelderly members. Education is a sig-

nificant predictor of poverty among the elderly. Poverty increases with an increase in the

number of elderly in a household in rural areas, but it is relatively less in urban areas.

This perhaps can be attributed to the greater economic independence of the elderly in

urban areas.

We estimated that approximately 18 million elderly in the country were living below

the poverty line in 2004–2005, based on only one dimension of poverty, that is,

consumption poverty. We have not given any estimates of multidimensional poverty,

which will be much higher than the consumption poverty. For instance, age related

morbidity is higher at older ages and inclusion of the health dimension will increase

the incidence of poverty substantially. Existing literature suggests that the differentia-

tion in health across gradients of wealth and poverty is one of the key determinants of

health in later life (Zimmer 2008; Pandey 2009). We also believe that increased health

care expenditure is pushing many individuals and households into the poverty trap. We

suggest that future research be undertaken to understand multidimensional poverty in

later life.

Based on these findings, the study recommends that incentives be given for co-residence

of the elderly to encourage non-nuclear households. The universal pension program for the

elderly living in nuclear households with little or no education should be prioritized.

Within the existing program, all the elderly living in nuclear households without or with

little education should be included in the Old Age Pension Scheme (OASP). Surveys on the

elderly should incorporate details about the type of family or household, and analyse the

incidence of poverty by type of household. There is an urgent need to undertake a lon-

gitudinal study to understand the well being of the elderly.

Acknowledgments The authors thank Dr. Rajesh K. Chauhan, Joint Director, Population Research Centre,University of Lucknow, for his help in data decoding.

A. Srivastava, S. K. Mohanty

123

Page 21

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