Content uploaded by Shatakshee Dhongde
Author content
All content in this area was uploaded by Shatakshee Dhongde on Nov 06, 2017
Content may be subject to copyright.
Measuring
Multidimensional
Poverty and Deprivation
Incidence and Determinants
in Developed Countries
EDITED BY
Roger White
g
l
o
b
a
l
p
e
r
s
p
e
c
t
i
v
e
s
o
n
w
e
a
l
t
h
a
n
d
d
i
s
t
r
i
b
u
t
i
o
n
255
CHAPTER 10
Assessing Multidimensional Deprivation
Among the Elderly in the USA
Shatakshee Dhongde
© The Author(s) 2017
R. White (ed.), Measuring Multidimensional Poverty
and Deprivation, Global Perspectives on Wealth and Distribution,
DOI 10.1007/978-3-319-58368-6_10
S. Dhongde (*)
Georgia Institute of Technology, Atlanta, USA
10.1 introduction
Aging is not a single process but one that is inuenced by multiple
economic, social, and psychological factors. The number of Americans
aged 65 or older is projected to exceed 70 million, or 20% of the popu-
lation, by 2030 (CDC 2013). The economic costs of dependency and
underlying medical conditions at older ages are large and are projected
to grow rapidly as the number of older adults in the United States of
America (USA) continues to increase in the coming decades. Hence,
measuring deprivation among elderly adults provides valuable guidance
for the provision of health care and the estimation of health care costs.
This information will be critical to the formulation of smart public poli-
cies since it effectively allows for the simultaneous targeting of multiple
dimensions.
In the last few decades, the literature on multidimensional deprivation
has been at the frontier of poverty research. Several methodologies, such
as the latent variables analysis, factor analysis, fuzzy set, and information
theory, have been used to formulate multidimensional deprivation meas-
ures (Kakwani and Silber 2008). The most widely used index is the one
adopted by the United Nations to measure multidimensional poverty
across countries. The United Nations Multidimensional Poverty Index
rwhite1@whittier.edu
256 S. DHONGDE
(UN MPI) is based on an axiomatic framework proposed by Alkire and
Foster (2011). The method uses a counting approach to estimate multi-
dimensional deprivation; it tracks individuals across multiple dimensions
and counts the number of deprivations that are simultaneously experi-
enced by that individual. In a recent paper, Dhongde and Haveman
(2016) estimate that nearly 15% of the total population in the USA was
multidimensional deprived during the Great Recession. In this chapter,
I measure multidimensional deprivation exclusively among the elderly in
the USA.
The existing literature on the elderly typically provides summary sta-
tistics on health-related dimensions. Statistics on health outcomes, health
care, and health determinants are measured separately by different gov-
ernment agencies. For example, the Agency for Healthcare Research and
Quality (AHRQ) annually publishes the National Healthcare Quality
and Disparities Report which summarizes measures of health quality and
disparities by race and income. The latest report (AHRQ 2015) states
that people in poor households received worse care than people in high-
income households in 60% of quality measures; Blacks, Hispanics, and
American Indians and Alaska Natives received worse care than Whites
in 40% of quality measures. Similarly, the Center for Disease Control
and Prevention’s (CDC 2013) report on the state of aging and health
in America notes that two of every three older Americans have multi-
ple chronic conditions (e.g., arthritis, asthma, diabetes, heart disease, and
high blood pressure), and treatment for this population accounts for 66%
of the country’s health care budget.
These and similar reports provide information on the marginal
distribution of outcomes. However, the reports are often not based
on data coming from a single survey and, hence, they are not able to
track a single individual along multiple factors that affect her health.
For instance, these summary reports do not provide answers to ques-
tions such as how many of the elderly adults, who had hearing impair-
ment, were also uneducated and lived in poverty. Estimating the
number of individuals simultaneously experiencing these deprivations
will inform policy makers how many elderly individuals with hearing
impairments, for instance, had trouble nding an appropriate support
program.
Hence, this study is distinct from the previous literature; it estimates
indices of multidimensional deprivation among the elderly in the USA.
rwhite1@whittier.edu
10 ASSESSING MULTIDIMENSIONAL DEPRIVATION AMONG THE ELDERLY … 257
These indices are estimated using the largest household survey dataset
in the USA. The study tracks older adults’ deprivation in four differ-
ent dimensions of well-being: (i) Health (i.e., having two or more dis-
abilities), (ii) Standard of living (i.e., income below the poverty line),
(iii) Education (i.e., not completing grade 8), and (iv) Economic secu-
rity (i.e., severe housing costs). The analysis, thus, takes a comprehensive
review of factors that may be linked to each other and have a jointly sig-
nicant impact on older people’s overall welfare. To design cost-effective
health policies, it is imperative to provide policymakers with a broader
picture of the multiple deprivations jointly experienced by the elderly.
The chapter is organized as follows. Data are described in Sect. 10.2,
the methodology is discussed in Sect. 10.3, estimates for the elderly in
the general population are presented in Sect. 10.4, and Sect. 10.5 dis-
cusses estimates for the elderly by race and ethnicity. Conclusions are
summarized and policy implications are suggested in Sect. 10.6.
10.2 dAtA
An extensive literature has promulgated the importance of measuring
well-being in terms of multiple dimensions, such as education and health,
in addition to income. Hicks and Streeten (1979) propose a basic needs
approach, and Sen (1985) proposes a capabilities approach to measure
individuals’ quality of life. However, for practical purposes, the choice of
indicators to measure the quality of life largely depends on the availability
of data. The marginal approach in reports published by different agencies
(e.g., the AHRQ or the CDC study described in the previous section) is
less intensive in terms of data. It can be computed using aggregate statis-
tics on how many elderly individuals had low income, how many elderly
experienced hearing loss, and so on. The multidimensional approach is
much more data-intensive, since it requires survey data with person records
on multiple dimensions. All the data on each elderly adult’s income, edu-
cation, health records, and so on have to originate from the same survey.
This study uses the American Community Survey (ACS) which is
the largest nationally representative household survey in the USA. The
ACS is conducted each year and collects data on demographic, social,
economic, and housing characteristics in all counties across the nation.
The analysis uses observations on about 1 million elderly adults (aged 65
and above) from the 2013 wave of the ACS. All data used are publicly
rwhite1@whittier.edu
258 S. DHONGDE
available in the 1-year records at the Public Use Microdata Sample
(PUMS) les.1 The PUMS les provide data for areas with a population
of 65,000 or more. Data on individual records are matched with data
on individual’s household characteristics. Data on individuals less than
65 years of age and on those living in group quarters (GQ) are removed.
Elderly adults living in GQs such as residential treatment centers, skilled
nursing facilities, group homes, correctional facilities, and workers’ dor-
mitories are excluded on purpose.2 All individual records in the ACS les
are replicated using person weights. The sample represents almost 40
million elderly adults who are 65 years and older.
10.2.1 Disabilities
Elderly adults with one or more chronic diseases often experience dimin-
ished quality of life, largely due to a long period of decline and disabil-
ity associated with their disease. However, measuring a complex concept
such as disability is difcult. The operational disability denition used
in the ACS is based on the World Health Organization’s International
Classication of Functioning, Disability and Health. The concepts used
include impairment, activity limitation, participation restriction, and
disability. Data are collected on three types of impairments: 1. Sensory
impairment implies being deaf or having serious difculty in hearing
and/or being blind or having serious difculty with vision; 2. Physical
impairment means ambulatory difculty, that is, serious difculty walk-
ing or climbing stairs and 3. Mental impairment is referred to as cogni-
tive difculty.3 Additionally, data on activity limitation and participation
restriction are compiled by asking respondents questions about difcul-
ties with self-care such as difculty in bathing and dressing, and dif-
culty performing independent errands such as visiting a doctor’s ofce
or shopping. As such, people identied by the ACS as having a disabil-
ity experience difculty in at least one of the six areas: hearing, vision,
ambulation, cognition, self-care, or independent living. Note that the
series of six questions does not capture all types of disability or account
for the severity of an individual’s impairment. In this study, any elderly
person experiencing two or more disabilities is considered deprived. One
in ve or almost 20% of the elderly adults experience multiple (two or
more) disabilities.
rwhite1@whittier.edu
10 ASSESSING MULTIDIMENSIONAL DEPRIVATION AMONG THE ELDERLY … 259
10.2.2 Standard of Living
Economic resources are a well-documented social determinant of health
(Hajat et al. 2011). For example, the Preston curve shows that life
expectancy rates improve with rising incomes. There is evidence sug-
gesting that an increase in mortality rates occurs for older adults living
in neighborhoods with high poverty rates (Rehkopf et al. 2006). In this
study, standard of living is measured by comparing an individual’s total
family income in the last 12 months with the poverty threshold appro-
priate for that person’s family size and composition as dened by the
Census Bureau. A person is deprived in this dimension if her income is
less than the poverty threshold. About 9% of the elderly in the sample
lived in poverty.
10.2.3 Education
Income does not adequately reect an individual’s standard of living.
To a large extent, a person’s quality of life, even in old age, depends
on her level of education. Better-educated people typically have better
health status, longer life expectancies, lower unemployment, more social
connections, and greater engagement in civic and political life. Wallace
(2015) noted that the health status gap between those with the high-
est and lowest educational levels has been increasing for the past sev-
eral years. For instance, completion of a high-school education not only
improves chances of better paying jobs but also indicates an individual’s
ability to gain health literacy. Health literacy improves understanding of
the risks of smoking or high cholesterol and helps individuals make life
style changes, such as doing regular exercise and eating nutritious food.
Uneducated elderly adults often do not have enough information to
make the right decisions regarding their health. The CDC (2013) report
stated that among adult age groups, those aged 65 or older have the
smallest percentage of people with procient health literacy skills and the
largest percentage with “below basic” health literacy skills.
Hence, this study includes education levels as an important indica-
tor of well-being. Typically, in the USA, having obtained a high-school
diploma (i.e., completing grade 12) is used as a benchmark for educa-
tional attainment (Alkire and Foster 2011; Dhongde and Haveman
2016). However, since this study focuses on the elderly, I set the
rwhite1@whittier.edu
260 S. DHONGDE
benchmark lower than usual and identify an elderly person as deprived if
she has not completed education higher than grade 8. Slightly over 10%
of the elderly did not complete education beyond grade 8.
10.2.4 Housing Burden
Although the standard of living is measured by identifying the elderly
living in poverty, housing costs are also included in the study as an
additional dimension reecting economic insecurity. An important
shortcoming of using a poverty line to measure the standard of living
is that the poverty line is the same amount everywhere in the coun-
try; it does not vary across states. However, the cost of living—espe-
cially housing costs—varies widely across regions. Hence, the study
uses housing costs to better reect regional variation in standards of
living. The ACS provides information on monthly housing expenses
for residents. It reports selected monthly owner costs such as mort-
gage payments, taxes, insurance, utilities, fuel costs, and gross rent as a
percentage of household income. “Severe housing burden” is typically
dened as housing costs in excess of 50% of income. An individual is
deprived if the owner costs or gross rent in a year is greater than 50% of
the household income.4 Slightly over 13.4% of the elderly experienced
severe housing burden.
To summarize, deprivation is measured by the following four
dimensions5: (i) Presence of two or more disabilities, (ii) Income below
the poverty threshold, (iii) No education beyond grade 8, and (iv)
Severe housing burden. Figure. 10.1 shows the percent of elderly adults
who were deprived in each of the four dimensions. Among the four
dimensions, the highest percent (20.4%) of elderly had two or more disa-
bilities; about 1 in 4 elderly adults had multiple disabilities. On the other
hand, fewer older individuals lived in poverty; only 9.1% of the elderly
had incomes below the poverty line.
10.3 metHodology
Figure 10.1 presents the marginal distribution of deprivation; it shows
the percent of individuals deprived in each dimension. The focus of this
paper, however, is on studying the joint distribution of deprivations. The
extent of multiple deprivations experienced simultaneously by a person
is estimated using three indices described below. These indices are based
rwhite1@whittier.edu
10 ASSESSING MULTIDIMENSIONAL DEPRIVATION AMONG THE ELDERLY … 261
on an axiomatic framework (Alkire and Foster 2011), well established in
the literature on poverty and inequality measures (Kakwani and Silber
2008). The framework insures that the deprivation indices satisfy certain
properties.
10.3.1 Multidimensional Deprivation Indices
Let
i=1, 2, ...n
be the number of individuals and
j
=
1, 2, ...d,d
≥
2
be the multiple dimensions of well-being. This analysis considers four
dimensions, so
d=4
. Let
wj
be the weights attached to each dimension
so that
d
j=
1wj=
d
; for simplicity, assume that each dimension carries
equal weight
wj
=
1, j
=
1, 2, ...d
. Let
yij
denote individual i’s perfor-
mance in j, and let
zj
be a threshold for dimension j. For instance, in
the case of standard of living,
zj
is equal to the poverty threshold. If
individual i is deprived in j, i.e.,
y
ij
<z
j
, then her deprivation score is
g0
ij
=
1
; else the score is equal to zero. The sum of dimensions in which
an individual is deprived is given by
c
0
i=
d
j
=1
g0
ij
.
Each individual is iden-
tied as being multidimensionally deprived if her weighted deprivation
score is at least equal to
k
;
c0
i
(k)=c
0
i
if
c0
i
≥
k
, and
c0
i
(k)=
0
otherwise.
Suppose
k=2
, then, the multidimensional deprived are those individuals
Fig. 10.1 Percent of elderly adults deprived in each dimension
rwhite1@whittier.edu
262 S. DHONGDE
whose deprivation score is two or more; that is, they are deprived in two
or more dimensions.
Following Alkire and Foster (2011), I estimate three specic depri-
vation indices: (i) Index 1 gives the percentage of multidimensional
deprived,
Index 1
=
(q/n)
∗
100
, where
q
is the number of individu-
als who are deprived in at least
k
dimensions, (ii) Index 2 is the aver-
age intensity of deprivation experienced by the multidimensional
deprived,
Index 2
=1/qd
n
i=1
c
0
i
(k
)
, and (iii) Index 3 gives the actual
number of deprivations among the deprived as a share of the maxi-
mum deprivations
(nd)
the society could potentially experience,
Index 3
=
1/nd
n
i=1
c
0
i
(k)
∗
100
. Index 1 is the headcount ratio, which
measures the spread of deprivation in the population, and Index 2 meas-
ures the extent or the depth of deprivation. Index 3 is the adjusted head-
count ratio, which is equal to the product of Index 1 and Index 2. Index
1 is the easiest to understand as it shows the prevalence of deprivation.
However, Index 1 does not satisfy the axiom of dimensional monotonic-
ity. For instance, if a deprived individual becomes deprived in another
dimension, Index 1 will not increase. However, Index 2 and Index 3 sat-
isfy the axiom and will show an increase in their values.
10.4 estimAtes of multidimensionAl dePrivAtion
Table 10.1 shows estimates of the three deprivation indices among
the elderly. The indices are estimated for different deprivation cut-off
values
(k)
. Estimates of Index 1 in Table 10.1 show that around 38% of
the elderly were deprived in at least one dimension, 12% in at least two
dimensions, about 3% in more than two dimensions, and only about
0.4% were deprived in all four dimensions. Index 2 shows that for a
Table 10.1 Multidimensional deprivation indices among the elderly
Source Author’s calculations based on 2013 ACS wave
Index 1 Index 2 Index 3
Minimum dimensions of
deprivation (k)
(%) (%)
1 37.86 0.35 13.33
2 12.27 0.57 6.94
3 2.85 0.78 2.23
4 0.38 1 0.38
rwhite1@whittier.edu
10 ASSESSING MULTIDIMENSIONAL DEPRIVATION AMONG THE ELDERLY … 263
cut-off of two or more deprivations
(k
=
2)
, the extent of deprivation was
equal to 0.6, which means that, on average, the elderly were deprived in
2.4 out of 4 dimensions. Note that for
k=d=4
, Index 2 will always
be equal to 1, thus implying that Index 1 and Index 3 are exactly equal.
Finally, Index 3 shows the percent of deprived adjusted by the average
deprivation intensity. Thus, for a cut-off of
k=2
, the average deprivation
experienced was 7% of the maximum deprivations.
In Table 10.1, Index 1 estimates the proportion of deprived in, say,
at least
k
dimensions. For instance, if
k=2
, then the persons included
in Index 1, may be deprived in education and housing, or education and
standard of living, and so on. However, to implement policies to reduce
the deprivation experienced by the elderly, it is important to probe what
combinations of dimensions individuals were most deprived in.
10.4.1 Graphical Representation
Figure. 10.2 illustrates the combinations of deprivations by a Venn dia-
gram. The four circles denote the four well-being dimensions. The
percent of the elderly who are deprived in any one dimension or in a
combination of dimensions are shown in Fig. 10.2. We have already
seen, previously in Fig. 10.1, the proportion of deprived in any single
dimension, which we refer to as the marginal distribution of deprivation.
For instance, 20.4% among the elderly had multiple disabilities, and 9.1%
of the elderly adults were living in poverty.
Figure 10.2 shows the overlap between dimensions. It shows that
2.9% of the disabled were living in poverty and 3.6% were under severe
housing burden. Previous evidence in the literature supports the nding
that disability rates tend to be higher among elders with low incomes
(Schoeni et al. 2005). Among the elderly deprived of a high-school edu-
cation, 2.3% lived in poverty and 1.9% had severe housing burden. Note
that with four dimensions, there are six possible dimensional pairs. Only
four such pairs are shown in Fig. 10.2. The remaining two pairs not
shown in the gure are: Disabilities and Education (4.0%) and Standard
of Living and Housing Burden (4.4%). The gure also shows the percent
of the population that experience deprivation in more than two dimen-
sions. For example, 1.3% of the elderly were deprived in three dimen-
sions: disabilities, standard of living, and housing burden. Among elderly
adults, only 0.4% were deprived in all four dimensions.
rwhite1@whittier.edu
264 S. DHONGDE
10.5 multidimensionAl dePrivAtion by rAce
Despite improvements in overall health-related outcomes, race is known
to be an important social determinant of health status. Racial and eth-
nic minorities in the USA experience a lower quality of health services;
they are less likely to receive routine medical procedures and they have
higher rates of morbidity and mortality. Much of the health disadvantage
is due to variations in non-medical determinants of health, such as differ-
ences in health care, individual behavior, socioeconomic inequalities, and
the physical environment (Avendano and Kawachi 2014). While there
has been substantial progress in improving health and reducing health
disparities since the 1980s, signicant disparities still exist among diverse
groups.
Estimates of multidimensional deprivation by race and ethnicity are
summarized in Table 10.2. These indices are estimated by identifying the
deprived as those with two or more deprivations
(k=2)
. Index 1 shows
that, compared to the population average (12.27%), Whites had less
Fig. 10.2 Identifying the joint distribution of deprivations in the multidimen-
sional framework
rwhite1@whittier.edu
10 ASSESSING MULTIDIMENSIONAL DEPRIVATION AMONG THE ELDERLY … 265
than average deprivation prevalence (10.42%), whereas all other groups
had above-average deprivation prevalence. Deprivation was high among
Asians (19.20%) and Blacks (22.10%) and it was signicantly higher
(30%) among Hispanics. A similar trend is observed in the estimates of
Index 2 and Index 3. Index 2 shows that the average intensity of dep-
rivation was 0.56 among Whites and 0.6 among Hispanics. Recall that
the adjusted headcount ratio (Index 3) is the product of Index 1, which
is the headcount ratio, and Index 2, which is the average intensity index.
The adjusted headcount ratio was 6.94% in the total population. It was
much higher among Asians (10.99%), Blacks (12.86%) and Hispanics
(17.99%).
10.5.1 Dimension-Specic Deprivation by Race and Ethnicity
In Table 10.3, I estimate Index 1, which is the percent of deprived in
each racial/ethnic group for each different combination of dimensions
(groups of 2, 3 and all 4 dimensions). The last column gives the per-
cent deprived in total population as a benchmark to make comparisons
across groups (some of these values have been illustrated in Fig. 10.2 in
the previous section).
Of the 4 dimensions, deprivation in the general population was
highest in terms of disability, followed by housing burden, educa-
tion and standard of living (also seen in Fig. 10.1). Disability rate was
highest among Blacks (26.25%); older African-Americans consistently
have higher rates of major health problems, including highest rates of
functional limitations (Wallace 2015). Among Asians and Hispanics,
deprivation is highest in terms of education and is then followed by
Table 10.2 Multidimensional deprivation by race and ethnicity
Source Author’s calculations based on 2013 ACS wave; + Includes Hispanic, Spanish and Latinos
Race/ethnicity Percent population Index 1 Index 2 Index 3
(%) (%)
White alone 81.41 10.42 0.56 5.83
Black alone 8.17 22.1 0.58 12.86
Asian alone 3.52 19.2 0.57 10.99
Hispanic+6.91 30.21 0.6 17.99
Total population 100 12.27 0.57 6.94
rwhite1@whittier.edu
266 S. DHONGDE
Table 10.3 Dimension-specic deprivation by race and ethnicity
Source Author’s calculations based on 2013 ACS wave; + Includes Hispanic, Spanish and Latinos
Dimensions White Black Asian Hispanic+Tot. pop.
Disability 19.59 26.25 19.83 25.52 20.39
Std. Living 7.63 18.72 13.36 19.13 9.13
Education 8.46 15.9 22.94 42.16 10.44
Housing 12.3 20.87 17.2 19.58 13.4
Disability Std Living 2.37 6.68 3.94 6.38 2.93
Disability Education 3.24 6.87 7.89 13.96 3.99
Disability Housing 3.26 6.05 3.67 5.57 3.58
Std Living Education 1.65 4.92 5.02 11.22 2.3
Std Living Housing 3.71 9.46 5.93 8.98 4.43
Education Housing 1.37 3.51 4.53 8.88 1.87
Disability Std Living Education 0.69 2.23 1.89 4.2 0.97
Disability Std Living Housing 1.09 3.24 1.43 2.81 1.34
Disability Education Housing 0.56 1.49 1.49 3.05 0.74
Std Living Education Housing 0.64 2.17 2.1 4.91 0.94
Disability Std Living Education Housing 0.26 0.95 0.68 1.72 0.38
rwhite1@whittier.edu
10 ASSESSING MULTIDIMENSIONAL DEPRIVATION AMONG THE ELDERLY … 267
disability, housing and standard of living. Compared to 10.4% of the
total elderly, 42% of Hispanics and 23% of Asians had not completed
education beyond grade 8. This is probably because most of the Asians
and Hispanics in this age group migrated to the USA at least 50 years
ago, without much prociency in the English language and most likely
did not undertake any formal education.
A lack of education combined with deprivation in other dimen-
sions make the elderly in these two racial groups more vulnerable. For
instance, whereas only 2.3% of all elderly individuals were deprived of
education and standard of living, 11.22% of the Hispanic elderly were
deprived in these two dimensions. Similarly, 3.99% of the total popula-
tion was deprived in education and had more than two disabilities, but
only 7.89% of the Asian population was deprived in these two dimen-
sions. Among the elderly who lived in poverty and faced a severe housing
burden, the highest percent (9.46%) were Blacks. The percent of elderly
who had three overlapping deprivations was high among Black and
Hispanics but quite low among the White and Asian groups. In the total
population, only 0.38% of the elderly were deprived in all four dimen-
sions, whereas 0.68% of Asians, 0.95% of Blacks and 1.72% of Hispanics
were deprived in all four dimensions, namely disability, standard of living,
education, and housing.
These ndings are important in light of the fact that the elderly pop-
ulation in the USA will become more diverse in the coming decades.
Whereas in 2010, non-Hispanic White adults comprised 80% of the
older population, their share is projected to decrease to 58% by 2050
(CDC 2013). In fact, between 2010 and 2050, the proportion of older
Hispanics will almost triple from 7% to nearly 20%, the proportion of
older Asian-Americans will more than double from 3.3 to 8.5%, and the
proportion of older African-Americans will increase from 8.3 to 11.2%
(CDC 2013). Hence, estimates of multidimensional deprivation by race
and ethnicity will be important to the design of future health-related
public policies.
10.6 conclusions
This study analyzes deprivation among the elderly population in a mul-
tidimensional framework. The analysis was carried out using the larg-
est household survey in the USA. Deprivation among the elderly was
measured in four different dimensions of well-being. Individuals were
rwhite1@whittier.edu
268 S. DHONGDE
deprived of healthy living if they experienced two or more disabilities:
hearing, vision, ambulatory, cognitive, independent living, and self-care.
The elderly were deprived of income if they had incomes less than the
poverty line and were deprived of education if they had not completed
grade 8. Finally, individuals with housings costs in excess of 50% of their
income were considered deprived of economic security. Three different
indices of multidimensional deprivation were estimated. It was found
that 37.86% of the elderly were deprived in at least one dimension, and
12.27% were deprived in two or more dimensions. In particular, the
elderly experienced overlapping deprivations in terms of disabilities and
education as well as poverty and housing burden.
Measuring disparity in overlapping dimensions is particularly useful
to design effective policies. For instance, increasing literacy among the
uneducated elderly will help them make better choices regarding their
health. Similarly, older adults will have more resources to help them
cope with their disabilities if there are policies that help to reduce hous-
ing costs and if affordable insurance is available to guarantee economic
security. Being deprived in multiple dimensions, such as lacking housing
facilities, being less educated and poor, amplies the disadvantages expe-
rienced by the elderly.
The study also analyzes variance in multidimensional deprivation
when the elderly are classied by race and ethnicity. Health inequities are
the result of avoidable differences between population sub-groups. The
literature that analyzes health inequities among older adults has largely
focused on the medical system, which has its greatest impact on health
outcomes after a person becomes ill. However, preventing illness has the
greatest potential for reducing health inequities and for reducing the
need for expensive medical care (Wallace 2015). Income, education, and
standard of living often play an important role in preventing long-term
illnesses. This study measures the percent of elderly deprived in socio-
economic factors such as living in poverty, education, and housing costs.
Results show that deprivation prevalence was high among Asians and
Blacks and particularly higher among the Hispanic community. Older
Hispanic adults were predominantly deprived of education and standard
of living, whereas older Blacks were deprived of standard of living and
faced severe housing burden.
The main purpose of this study is to underscore the importance of
measuring multidimensional deprivation among elderly adults in the USA.
The analysis can be extended in many ways. The proposed framework is
rwhite1@whittier.edu
10 ASSESSING MULTIDIMENSIONAL DEPRIVATION AMONG THE ELDERLY … 269
highly exible and lends researchers the ability to estimate different indi-
ces. These indices can be based on (i) different dimensions of well-being
than the four dimensions considered here, (ii) different source/type of
data, (iii) different deprivation thresholds, and (iv) different weights
applied to each dimension. Further investigation in this direction will
shed more light on ways to mitigate multiple deprivations experienced by
the elderly population in the USA.
notes
1. Data Source: https://www.census.gov/programs-surveys/acs/.
2. Dimensions such as housing costs and standard of living are not very
relevant for people living in GQ. Hence we exclude all records on indi-
viduals from GQ.
3. Cognitive difculty was assessed by asking respondents if they had serious
difculty concentrating, remembering, or making decisions.
4. The housing burden categories are: No housing burden (under 30%
of income spent on housing costs), moderate burden (between 30 and
49.9%), and severe burden (over 50%).
5. Lack of health insurance is often used as an important indicator of depriva-
tion (see Dhongde and Haveman 2016). However, since this study exclu-
sively measures deprivation among elderly adults, less than 1 percent of the
elderly adults did not have any kind of health insurance; hence, the indica-
tor is not included here.
references
Alkire, S., and J. Foster. 2011. Counting and Multidimensional Poverty
Measurement. Journal of Public Economics 95 (7–8): 476–487.
AHRQ’s National Healthcare Quality and Disparities Report. 2015. Retrieved
Nov 23, 2016. http://www.ahrq.gov/research/ndings/nhqrdr/nhqdr15/
index.html.
Avendano, M., and I. Kawachi. 2014. Why Do Americans Have Shorter Life
Expectancy and Worse Health Than Do People In Other High-Income
Countries? Annual Review of Public Health 35: 307–325.
CDC’s State of Aging and Health in America Report. 2013. Retrieved Nov
20, 2016. http://www.cdc.gov/aging/agingdata/data-portal/state-aging-
health.html.
Dhongde, S., and R. Haveman. 2016. Multidimensional Deprivation in the
U.S. Social Indicators Research, http://link.springer.com/article/10.1007%
2Fs11205-016-1379-1.
rwhite1@whittier.edu
270 S. DHONGDE
Hajat, A., J. Kaufman, K. Rose, A. Siddiqui, and J. Thomas. 2011. Long-term
Effects of Wealth on Mortality and Self-rated Health Status. American
Journal of Epidemiology 173 (2): 192–200.
Hicks, N., and P. Streeten. 1979. Indicators of Development: The Search for a
Basic Needs Yardstick. World Development 7: 567–580.
Kakwani, N., and J Silber, eds. 2008. Quantitative Approaches to Multidimensional
Poverty Measurement. Palgrave Macmillan.
Rehkopf, D., L. Haughton, J. Chen, P. Waterman, S. Subramaniam, and
N.Krieger. 2006. Monitoring Socioeconomic Disparities in Death: Comparing
Individual-Level Education and Area-based Socioeconomic Measures.
American Journal of Public Health 96 (12): 2135–2138.
Schoeni, R., L. Martin, P. Andreski, and V. Freedman. 2005. Persistent And
Growing Socioeconomic Disparities in Disability Among the Elderly:
1982–2002. American Journal of Public Health 95 (11): 2065–2070.
Sen, A. 1985. Commodities and capabilities. Amsterdam: North-Holland.
UNDP MPI. Retrieved Nov 26, 2016 from http://hdr.undp.org/en/content/
multidimensional-poverty-index-mpi.
Wallace, S. 2015. Equity and Social Determinants of Health among Older
Adults. Generations (Winter issue).
rwhite1@whittier.edu
