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Multidimentional Poverty in Bhutan: Estimates and Policy Implications Santos, Maria Emma; Karma Ura, 1928- between 2008-06 and 2008-08

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 Multidimensional Poverty in Bhutan: Estimates and Policy
Implications
Maria Emma Santos* and Karma Ura"
Abstract
This paper estimates multidimensional poverty in Bhutan
applying a recently developed methodology by Alkire and
Foster (2007) using the 2007 Bhutan Living Standard Survey
data. Five dimensions are considered for estimations in both
rural and urban areas (income, education, room availability,
access to electricity and access to drinking water) and two
additional dimensions are considered for estimates in rural
areas only (access to roads and land ownership). Also, two
alternative weighting systems are used: a baseline using equal
weights for every dimension and another one using weights
derived from the Gross National Happiness Survey. Estimates
are decomposed into rural and urban areas, by dimension and
between districts. It was found that multidimensional poverty
is mainly a rural phenomenon, although urban areas present
non-depreciable levels of deprivation in room availability and
education. Within rural areas, it was found that poverty in
education, electricity, room availability, income and access to
roads, contribute in similar shares to overall multidimensional
poverty, while poverty in land ownership and water have a
relatively smaller contributions. The districts of Samtse,
Mongar, Chukha, Trashigang and Samdrup Jongkhar are
identified as giving the highest contribution to overall
multidimensional poverty. The methodology is suggested as a
potential formula for national poverty measurement and for
budget allocation among the districts and sectors.
' Oxford Poverty and Human Development Initiative (OPHI), Oxford
University and  Consejo  Nacional  de  Investigaciones  Cientificas y
Tecnicas (CONICET)-Universidad Nacional del Sur, Argentina.
** President, The Centre for Bhutan Studies, Thimphu.
 Journal of Bhutan Studies
1. Introduction
Fostered by Sen's (1985, 1990, 1999) pioneering 'capabtiity
approach', there is now an increasing consensus that poverty
is an intrinsicaUy multidimensional phenomenon. This has
led scholars to propose different multidimensional poverty
measures. However, some of the proposed measures seem to
have incorporated a multi-dimensional perspective at the cost
of giving up the simplicity and intuition that characterise the
unidimensional measures. Departing from this, Alkire and
Foster (2007) propose a new family of multidimensional
poverty measures which is a variant of the extensively used
Foster, Greer and Thorbecke's (1984) class of one-dimension
poverty measures (FGT from now on). The dimension adjusted
FGT measures keep the simple structure of the one-
dimension case and satisfy a set of convenient properties,
among which decomposabitity across population subgroups
and the possibtiity to break it down by dimension are useful
for policy purposes.
In this paper, the mentioned new class of measures is applied
to estimate multidimensional poverty in Bhutan. Bhutan
constitutes an extremely interesting example of how a country
can define development goals, tailor its policies to these goals,
and see them materialized. Since 1961, the country
implemented coordinated efforts towards development
through consecutive five-years-plans. In particular, the
country has made significant progress in extending the
access to safe drinking water and sanitation, protecting and
managing the country's natural resources, providing basic
health care and increasing the access to primary education.
However, more can sttil be done in some of the mentioned
areas as weU as in others. Within this development agenda,
the MiUennium Development Goals play a key role since
Bhutan is seriously committed to contribute to the realisation
ofthe Millennium Declaration.
In this context, this paper intends not only to present
estimates of multidimensional poverty in Bhutan, which
would complement the income poverty estimates performed
 Multidimensional Poverty in Bhutan
by the National Statistics Bureau, but also to suggest the
applied methodology as a potential formula for budget
aUocation among the twenty districts, and within each
district, among the different gewogs, the lowest
administration units.
The data used in this paper correspond to the 2007 Bhutan
Living Standard Survey. It constitutes a unique data source of
this country, representative both at the national and district
levels. Estimations are performed for rural and urban areas
considering five dimensions and also for rural areas
exclusively, with two additional dimensions. Each measure is
also estimated at the district level, and in aU cases, using two
alternative weighting structures: a baseline of equal weights
and another one with weights derived from the ranking of
'sources of happiness' identified through the Gross National
Happiness Survey.
Results confirm that, indeed, income deprivation should not
be the only considered dimension. Deprivation in other
dimensions such as education, access to electricity and room
availabUity in the house, are significant both in rural and
urban areas, and not necessarily related to deprivation in
income. AdditionaUy, deprivation in access to roads is a
significant component of multidimensional poverty in the
rural areas. Land ownership in the rural areas and access to
drinking water in both rural and urban areas, seem to be
relatively less important. It was also found that
multidimensional poverty is mainly a rural problem, which is
particularly important given that 74% of the population in
Bhutan live in rural areas. When analysing at the district
level, it is found that Samtse, Mongar, Chukha, Trashigang
and Samdrup Jongkhar are the five districts with the highest
contributions to aggregate multidimensional poverty.
However, even in the other districts with lower contributions,
improvements in the mentioned dimensions are sttil
important.
 Journal of Bhutan Studies
The rest of the paper is organised as follows. Section 2 briefly
revises the literature on multidimensional poverty measures.
Section 3 presents the methodology used in the paper
(measures estimated, data-set used, selected dimensions,
deprivation cutoff values and weighting structures). Section 4
presents the estimation results. Finally, Section 5 contains
the concluding remarks.
2. Literature review
Since Sen (1976), the measurement of poverty has been
conceptualised as foUowing two main steps: identification and
aggregation. In the unidimensional space, the identification
step is relatively an easy one. Even when it is recognised that
the concept of a poverty line-as a threshold that dichotomises
the population into the poor and the non-poor- is somehow
artificial, it is agreed to be necessary. Greater consideration is
given to the properties that should be satisfied by the poverty
index that will aggregate individuals' data into an overall
indicator. However, in the multidimensional context, the
identification step is more complex. Given a set of
dimensions, each of which has an associated deprivation
cutoff or poverty line, it is possible to identify for each person
whether he/she is deprived or not in each dimension.
However, the difficult task is to decide who is to be considered
multidimensionaUy poor.
One proposed approach has been to aggregate achievements
in each dimension into a single cardinal index of weU-being
and set a deprivation cutoff value for the weU-being measure
rather than for each specific dimension to identify the
multidimensionaUy poor. This approach has some practical
drawbacks, in particular, in that it is based on a number of
restrictive assumptions, such as the existence of prices for aU
dimensions. Moreover, it does not agree with the conceptual
framework of the capability approach which considers each
dimension to be intrinsically important. Then, each
dimension with its corresponding deprivation cutoff value
needs to be considered at the identification step of the
multidimensionaUy poor.
 Multidimensional Poverty in Bhutan
In this perspective, two extreme approaches have been
traditionaUy used. On the one hand, there is the intersection
approach, which requires the person to be poor in every
dimension under consideration so as to be identified as
multidimensionaUy poor. Clearly, this is a demanding
identification criterion, by which the set of the poor is
reduced as the number of dimensions considered increases,
and may exclude people that are indeed deprived in several
important dimensions. On the other hand there is the union
approach, which requires the person to be poor in at least one
of the considered dimensions. Clearly, with this criterion, the
set of poor increases as the number of dimensions does, and
it may include people that many would not considered to be
multidimensionaUy poor (Alkire and Foster, 2007, pp.8). The
union approach has received important support both in the
theoretical and empirical literature. In particular, Tsui (2002)
and Bourguignon and Chakravarty (2003) adopt it for the
measures they propose.
Tsui (2002) develops an axiomatic framework for
multidimensional poverty measurement (which includes
subgroup consistency) and derives two relative
multidimensional poverty measures, one of which is a
generalization of Chakravarty's (1983) one-dimensional class
of poverty indices, and the other is a generalization of Watt's
(1968) poverty index. He also derives two absolute
multidimensional poverty measures, i
Bourguignon and Chakravarty (2003) distinguish two groups
of multidimensional poverty indices, depending on whether
they consider dimensions to be independent or to have some
substitutability   or   complementarity.   Those   that   consider
i The distinction between relative and absolute poverty indices is
due to Blackorby and Donaldson (1980). Relative poverty indices are
invariant to changes in scale, such as a doubling of the poverty line
and all incomes, while absolute indices are invariant to translations
or additions of the same absolute amount to each income and to the
poverty line (Foster and Shorrocks, 1991). In practice, relative
poverty indices are the ones that have been most frequently used.
 Journal of Bhutan Studies
attributes to be independent satisfy what they call the One
Dimensional Transfer Principle, by which poverty decreases
whenever there is a Pigou-Dalton progressive transfer of the
achievement in some dimension between two poor people. The
progressive nature of the transfer is judged by the
achievements of the two poor people in that specific
dimension, independently of the achievements in the other
dimensions. These indices are additively decomposable. The
second group of indices are non-additive -ie. non
decomposable- and by choosing appropriate values of the
parameters they can reflect either a substitutabitity or a
complementarity relationship between the dimensions. For
both groups of indices, extensions of the FGT class are
proposed.
On a more practice-based perspective, the Unsatisfied Basic
Needs Approach, widely used in Latin America, also uses a
union criterion, identifying as households with unsatisfied
basic needs those that are deprived in one or more of the
selected indicators.
In view of the two prevailing extreme criteria to identify the
multidrmensionally poor, Alkire and Foster (2007) propose a
new identification methodology which, whtie containing the
two extremes, also aUows for intermediate options. Assume
that there are   k = l, , d considered dimensions,  and that
ci represents the number of dimensions in which individual
/' = 1, ,nis deprived, then an individual is considered to be
multidimensionaUy poor if c. > k . When k = 1, the approach
coincides with the union approach, whereas when k = d, it is
the intersection approach. For 1 < k < d , the identification
criterion lies somewhere in the middle between the two
extremes. Then, for the aggregation step, they use the weU-
known FGT class of poverty indices. The resulting farmly of
measures satisfies a set of convenient properties including
decomposabitity by population subgroups and the possibtiity
of being broken down by dimensions. These last properties
 Multidimensional Poverty in Bhutan
make it particularly suitable for policy targeting. Additionally,
the class includes measures that can be used with ordinal
data, which is very common in a multidimensional context. A
detailed description of this class of measures is presented in
Section 3.2.
A final note must acknowledge the probably most popular
multidimensional poverty measure, which is the Human
Poverty Index (HPI), developed by Anand and Sen (1997),
companion index of the Human Development Index (HDI).
Both indices are periodically estimated by the United Nations
Development Programme for all countries to monitor the level
of deprivation and development correspondingly with a
broader perspective than income. The components of the HPI
are survival deprivation (measured by the probability at birth
of not surviving to age 40), deprivation of education and
knowledge (measured by the adult literacy rate) and economic
deprivation (measured by the average of the percentage of
population without access to an improved water source and
chtidren under weight for age). In developed countries the
indicators for each of the components are specified according
to the higher living standards.2 An important advantage of the
HPI is that it only requires macro-data, which can be
especiaUy important for countries in which micro-data
coUection is sttil at its beginnings and its quality is not
assured. However, it has some disadvantages. Clearly, the
three selected dimensions can be argued to be arbitrary as
well as the weighting system used to calculate the measure.
When micro-data sets are available more informative
measures can be calculated, with a higher number of
dimensions and alternative weighting systems.
2 In particular, the survival deprivation is estimated as the
probability at birth of not surviving to age 60, the deprivation of
education and knowledge is defined as adults lacking functional
skills, the economic deprivation is defined as the percentage of
population below 50% of he median adjusted disposable income, and
a social exclusion component is also added, defined as the rate of
long-term unemployment (lasting 12 months or more).
7
 Journal of Bhutan Studies
3. Methodology
3.1 Data
The dataset used is the 2007 Bhutan Living Standard Survey
(BLSS) conducted by the National Statistics Bureau (NSB).
There are 9798 households in the sample and 49165 people.
This is the second BLSS performed; the previous one was
done in 2003. Both surveys have foUowed the Living Standard
Measurement Study methodology developed by the World
Bank. However, the 2007 survey has more than doubled the
2003 sample size and it has also extended the coverage, so
that the sample is representative both nationaUy and at each
of the 20 Bhutanese districts (Dzongkhags), in rural and
urban areas.
The unit of analysis to identify the poor is the household.
However, households are weighted by their size (as weU as by
their sample weights), so that results are presented in
population terms. Table A. 1 in the Appendix presents the
composition ofthe sample.
3.2 Multidimensional poverty measures
The poverty measure applied in this paper corresponds to
Alkire and Foster's (2007) family of multidimensional poverty
measures. Before introducing it, it is convenient to clarify
notation in the first place.
Let M"' denote the set of all nxd matrices, and interpret a
typical element y e M"' as the matrix of achievements of n
people in d different dimensions. For every i = l,2,...,n and
J' = 1,2,...,d, the typical entry y.. of y is individual i's
achievement in dimension j. The row vector
yt =(yi\,yi2,----,yidS) contains individual i's achievements in
the different dimensions; the column vector
y ■ = (y1 -,y2 ■,....,y„,-)' gives the distribution of achievements
8
 Multidimensional Poverty in Bhutan
in    dimension    /'    across    individuals.    Let     z, > 0 be    the
J j
deprivation cutoff value (or poverty line) in dimension j.
FoUowing Alkire and Foster (2007)'s notation, the sum of
entries in any given vector or matrix v is denoted by \v\,
whtie /u(v) is used to represent the mean of v (or | v\ divided
by the number of entries in v).
For any matrix y, it is possible to define a matrix of
deprivations g   = [g. ], whose typical element g..  is defined
by gtj = 1 when ytj < z ■, and gtj = 0 when ytj > z ■. That is,
the ij   entry of the matrix is 1 when person i is deprived in
dimension j, and 0 when he/she is not. From this matrix,
define a column vector of deprivation counts, whose ith entry
c. =| gt  |  represents the number of deprivations suffered by
person i. If the variables in y are cardinal, then a matrix of
normalised gaps g   = [g. ] can be defined, where the typical
element    g]}.= (z}.- yi}.) / z}.     when    yij<zj,    and    g\ = 0
otherwise. The entries of this matrix are non-negative
numbers between 0 and 1, and each non-zero entry gives the
extent of the deprivation experienced by person i in dimension
j. This matrix can be generalised to ga = [g"], with ot > 0,
whose typical element g" is the normalised poverty gap
raised to the a-power.
The methodology to identify the multidimensionaUy poor
proposed by Alkire and Foster (2007) compares the number of
deprivations with a cutoff level k When each selected
dimension has the same weight, the possible values of k go in
the  range  of  k— \ ,d.   However,   the  methodology  also
aUows other weighting systems, which will be explained at the
end of the section. In general, for any weighting system, let
pk be the identification method such that pk (yi, z) = 1 when
 Journal of Bhutan Studies
c. > k , and pk (yt, z) = 0 when ct < k . That means that an
individual is identified as multidimensionaUy poor if he/she is
deprived in at least k dimensions. This methodology is said to
be a dual cutoff method, because it uses the within dimension
cutoffs z ■  to determine whether an individual is deprived or
not in each dimension, and the across dimensions cutoff k to
determine who is to be considered multidimensionaUy poor. It
is also presented as a counting approach, since it identifies
the poor based on the number of dimensions in which they
are deprived. When equal weights are used, when k — \ , the
identification criterion corresponds to the union approach,
whereas when k — d, the identification criterion corresponds
to the intersection approach. This identification criterion
defines  the   set  of the  multidrmensionally  poor  people   as
Zk — {/ : pk {yi; z) = 1} . Once identification is applied, a
censored matrix g (k) can be obtained from g by replacing
the ith row with a vector of zeros whenever pk {yi, z) = 0.
Matrix ga (k) can be defined analogously for a > 0, with its
typical entry g" (k) = g" if i is such that c. > k, whtie
g^ (k) = 0 if z is such that c. < k .
A first natural measure to consider is the percentage of people
that are multidimensionaUy poor: the multidimensional
Headcount Ratio H = H(y,z) defined byH = ql n , where q is
the   number   of  people   in   set   Zk.   This   measure   is   the
analogous to the unidimensional Headcount Ratio, and it has
the advantages that it is easy to compute and understand,
and that it can be calculated with ordinal data. However, it
suffers from the disadvantages first pointed by Watts (1969)
and Sen (1976) for the one-dimensional case, namely, being
insensitive to the depth and distribution of poverty, violating
monotonicity and the transfer axiom. Moreover, in the
multidimensional context,  it also violates what Alkire  and
10
 Multidimensional Poverty in Bhutan
Foster (2007) call dimensional monotonicity: if a poor person
becomes deprived in an additional dimension (in which
he/she was not previously deprived), Hdoes not change.
Considering this, Alkire and Foster (2007) propose the
dimension        adjusted        FGT        measures,        given        by
Ma (y; z) = ju(ga (k)) for a > 0 . When a = 0, the measure is
the Adjusted Headcount Ratio, given by
M0 = ju(g (k)) = HA,    which    is    the    total    number    of
deprivations experienced by the poor (| c(^) |=| <? (£)|),
divided by the maximum number of deprivations that could
possibly be experienced by aU people (nd). It can also be
expressed as the product between the percentage of
multidimensionaUy poor individuals (H) and the average
deprivation share across the poor, which is given by
A =| c(k) | /(qd) . In words, A provides the fraction of possible
dimensions d in which the average multidimensionaUy poor
individual is deprived. In this way, Mo summarises
information on both the incidence of poverty and the average
extent of a multidimensional poor person's deprivation. As H,
this measure is easy to compute, and can be calculated with
ordinal data. However, it is superior to H in that it satisfies
dimension monotonicity: if a poor becomes deprived in an
additional dimension, A will increase and therefore Mo wiU
also increase.
When a = 1, the measure is the Adjusted Poverty Gap, given
byAffj = jtl(g (k)) = HAG , which is the sum ofthe normalised
gaps of the poor (| g (k) \)  divided by the highest possible
sum of normalised gaps (nd). It can also be expressed as the
product between the percentage of multidrmensionally poor
individuals (H), the average deprivation share across the poor
(A)    and   the    average   poverty   gap    (G),   which   is   given
by G =| g (k) | / | g (k) |. Mi summarises information on the
incidence of poverty, the average range of deprivations and
11
 Journal of Bhutan Studies
the average depth of deprivations of the poor. It satisfies not
only dimension monotonicity but also monotonicity: if an
individual becomes more deprived in a certain dimension, Mi
wiU increase.
Finally, when a = 2 , the measure is the Adjusted Squared
Poverty Gap, given by M2 = ju(g (k)) = HAS , which is the
sum of the squared normalised gaps of the poor (| g (k) \)
divided by the highest possible sum of normalised gaps (nd).
It can also be expressed as the product between the
percentage of multidrmensionally poor individuals (H), the
average deprivation share across the poor (A) and the average
severity       of       deprivations       (S),       which       is       given
hyS =| g (k) | / | g (k) |. M2 summarises information on the
incidence of poverty, the average range and severity of
deprivations of the poor. If a poor person becomes more
deprived in a certain dimension, M2 will increase more the
larger the initial level of deprivation was for this individual in
this dimension. This measure satisfies both types of
monotonicity and also transfer, being sensitive to the
inequality of deprivations among the poor.
All members of the Ma(y',z) farmly are decomposable by
population subgroups. Given two distributions x and y,
corresponding to two population subgroups of size n(x) and
n(y) correspondingly, the weighted average of    sum of the
subgroup poverty levels (weights being the population shares)
equals the overaU poverty level obtained when the two
subgroups are merged:
... n(x)   ...     .      n(y)   ...
M(x,y;z) = —^-M(x; z) + —±^-M(y; z)
n(x,y) n(x,y)
Clearly, this can be extended to any number of subgroups.
12
 Multidimensional Poverty in Bhutan
Additionally, once the identification step has been completed,
aU members of the Ma (y\ z) farmly can be broken down into
dimension subgroups. To see this, note that the measures
can        be        expressed        in        the        foUowing        way:
Ma(y;z) = Y^1=l/u(g*}(k))ld, where g"}  is the jth column of
the censored matrix ga (k). Strictly speaking, this is not
decomposabitity in terms of dimensions, since the
information on all dimensions is needed to identify the
multidimensionaUy poor. However, it is stiU a very convenient
break-down property. Once identification has been applied,
and the non-poor rows of ga have been censored to obtain
ga(k),    for    each   j,     (fi(g?}.(k))/d)/Ma(y;z)     can    be
interpreted as the post-identification contribution of
dimension j to overall multidimensional poverty.
TheMa(y,z)    farmly   adopts   the   neutral   assumption   of
considering dimensions as independent. In this way, it
satisfies a property, based on Atkinson and Bourguignon
(1982), caUed weak rearrangement. Imagine that one
individual that begins with weakly higher achievements in
every dimension than another individual, switches one or
more dimension achievement levels with this other individual,
so that this ranking no longer holds. This is called an
association decreasing rearrangement. Under such
rearrangement one would expect multidimensional poverty
not to increase. This is postulated by the weak rearrangement
axiom and it is precisely satisfied by the Ma (y, z) , which wiU
not change under such transformation. Because of its
completely additive form, it evaluates each individual's
achievements in each dimension independently of the
achievements   in   the    other    dimensions    and    of   others'
achievements.   In   this  way,   the   Ma(y',z)    family   can  be
13
 Journal of Bhutan Studies
associated with the first group of measures of Bourguignon
and Chakravarty (1983).3
Until now, the Ma (y\ z) family has been presented assuming
that aU dimensions receive the same weight. However, the
family can be extended into a more general form, admitting
different weighting structures. Let tt bead dimensional row
vector, whose typical element  w .   is the weight associated
with dimension j. Then, define the matrix ga  of size nxd,
where    the    typical    element    g" = w, ((z, — yi )/z .)" when
y   < z,, while g" = 0  otherwise. Then, as before, from this
•^ y J <J y
matrix, a column vector of deprivation counts can be defined,
whose ith entry  ci =| gi  |   represents the sum of weights for
the   dimensions   in   which   person   i  is   deprived.    ci varies
between 1 and d, and so the dimensional cutoff for the
identification step of the multidimensionaUy poor will be a
real number k, such that 0 < k < d. Note that when
k = min{w },    the    criterion    coincides    with    the    union
approach, whereas when k — d, it is the intersection
approach. Also note that when w . = 1, it is the previous case
where all dimensions receive the same weight and the
dimensional cutoff k is an integer. Then, the methodology
works   exactly   in   the   same   way   as   before,   defining   the
3 Alkire and Foster (2007) explain that their measures can be
converted into measures that consider either all dimensions as
substitutes or all dimensions as complements, and in this way, they
would be in line with the second type of measures considered by
Bourguignon and Chakravarty (2003). However, they remark that
imposing the same type of relationship between all dimensions, and
with the same assumed degree of either substitutability or
complementarity seems rather restrictive. Moreover, such
transformation would be at the cost of losing the possibility of
breaking down the measure into dimensions.
14
 Multidimensional Poverty in Bhutan
censored     matrices      c(k) and ga (k),     and     theMa(y,z)
measures.
3.3 Dimensions and deprivation cut-offs
The selection of the dimensions for the multidimensional
poverty measure is guided by the eight MiUennium
Development Goals (MDG) that Bhutan has defined to fulfil
the MUlennium Declaration, and it is subject to data
availabUity.4 Table 1 presents the dimensions with their
corresponding cutoff values.
Having an adequate income, and for rural households, having
access to roads and owing some land, can be framed into the
first MDG, which is to Eradicate Extreme Poverty and Hunger.
For the income cutoff, the official Bhutanese poverty line was
used, which is calculated in Nu 1,096.94 per capita per
month. During 2007, this was equivalent approximately to
US$25. This poverty line is composed of a food poverty line,
which is the cost of a food basket consisting of 53 items that
is considered to fulfil the requirement of 2,124 Kcal. per
person per day, plus a non-food aUowance.5 Given that the
4 The eight goals are: Goal 1: Eradicate extreme poverty and hunger,
Goal 2: Achieve universal primary education, Goal 3: Promote gender
equality and empower women, Goal 4: Reduce child mortality, Goal
5: Improve maternal health, Goal 6: Combat HIV/AIDS, malaria and
other diseases, Goal 7: Ensure environmental sustainability, Goal 8:
Develop a global partnership for development.
5 The 2,124 Kcal per person per day is the nutritional norm applied
in Nepal, and the NSB decided to follow it for the case of Bhutan.
The cost of the food poverty line is Nu 407.98, which in 2007 was
equivalent to US$ 9 approximately. The NSB does not account for
differences in nutritional requirements across age and sex, that is,
they do not use equivalised scales. They do not account for
economies of scale in the household either. Despite this, it is a
common practice to consider both issues in poverty estimates. It was
decided to stick to the NBS methodology to make the results of this
paper comparable to the official income poverty estimates. The nonfood allowance is estimated averaging the non-food per capita
expenditure of households in the reference population that spent for
15
 Journal of Bhutan Studies
percentage of people below the food poverty tine is only 6%
the target in Bhutan with respect to this MDG is more
demanding, and it consists of halving poverty, rather than
extreme poverty (2005 MDG Progress Report). This is why the
overaU poverty tine rather than the food poverty tine is used
for the multidimensional poverty estimation. If a household
does not make a monthly per capita income of at least Nu
1,096.94, it is considered income deprived, and so are all its
household members.
To achieve the mentioned target in terms of income poverty,
Bhutan faces some significant constraints, one of which is the
geographical isolation of some rural areas. Lack or limited
road access and links to markets impede the development of
the area and, more seriously, it can cause food shortage in
these remote regions. The further development of rural road
and communication infrastructure and access to markets has
become a priority in the country. Based on this, access to
services is included among the selected dimensions. A
household in a rural area that can not reach either a feeder or
a tarred road within 30 minutes by any means of transport, it
is considered to be access deprived, and so are all its
household members.
Another potential constraint to reduce poverty regards land
ownership. Households in rural areas with small land
holdings are at risk in terms of food access, since small land
holdings are usually compounded with low productivity,
inadequate storage facilities, poor irrigation and vulnerability
to natural disasters, crop depredation by wild animals, birds
and pests (2005 MDG Progress Report, pp. 26-28). The BLSS
has information on different type of land holdings: wet land,
dry land, orchard, sokshing (leaf titter wood lot), pasture and
tseri (swidden cultivation land). Given that sokshing and
pastures have been recently nationalised, it was decided to
only consider the other four types of land. Despite the
differences of land qualities between the different types of
food a value near the food poverty line.
16
 Multidimensional Poverty in Bhutan
land, a deprivation cutoff of 1 acre of total land holding (either
any of them or the sum of any combination) was defined. The
selected threshold is clearly debatable. However, 1 acre seems
a reasonable amount of land that would aUow cultivating for
subsistence, even considering that land quality may vary.6 A
household in a rural area with less than 1 acre of land
holdings is considered to be land deprived, and so are aU its
household members.
A third selected dimension is closely related to the second
MDG: Achieve Universal Primary Education. The target of the
country regarding this MDG is that by 2015 all children are
able to complete a fuU course of primary schooling. The
country has achieved significant progress towards this target,
raising the primary enrolment rate from 55% in 1990 to 84%
in 2004. Reaching chtidren in rural and remote communities,
reducing early dropouts, and improving the quality of
education are among the priorities of the education policy and
programs. A need to expand secondary school education has
also been identified, as the number of those completing
primary education continues to increase.
The education indicator constructed for this paper is
composed of two requirements. In the first place, foUowing
Basu and Foster's (1998) idea of proximate literacy, it is
required that at least one household member is literate. The
logic behind this is that iUiterate people that live in a
household where at least someone is literate enjoy some of
the literate person's abilities; in other words, they enjoy an
intra-household externality. Despite that the literacy rate in
the country is stiU low (55%), the proximate literacy
requirement is a mtid one, since even if the adults in the
household are illiterate, as long as the children are literate -
which is very likely given the progress in primary school
enrolment,    the    household    will    be    considered    literate.
6 Although an absolute poverty line approach is followed for all
indicators in this paper, it is worth mentioning -for reference-, that 1
acre is half of the median rural land holdings and less than the
country's median land holdings (which is 1.32 acres).
17
 Journal of Bhutan Studies
However, the second requirement for the education indicator
is that all children between 6 and 16 years of age are
attending school. This is in tine with the mentioned MDG. On
the one hand, it is more demanding than the target, in that
chtidren are required to be in school even at an older age than
what primary education demands. On the other hand, it is
not excessively demanding since children are not required to
be in the school grade corresponding to their age (even if a 16
year old was in primary school, the household would satisfy
the requirement). A household with no-literate member and
with children between 6 and 16 years of age that are not
attending school is considered to be education deprived, and
so are aU its household members.
The following two dimensions are directly related to the
seventh MDG: Ensure Environmental Sustainability.
Increasing the access to electricity (especiaUy in rural areas)
is one of the key objectives within this goal, since it will not
only improve the living conditions of the rural population but
it will also reduce the proportion of population using solid
fuels improving the quality of the air. Bhutan would tike to
achieve "electricity for aU" by 2020 and it is working steadily
towards this goal. A household with no access to electricity is
considered to be electricity deprived, and so are all its
members. Access to safe drinking water is another key
objective within this goal and Bhutan has progressed
significantly in increasing this access. However, there are
areas in which more progress can stiU be made, so this
dimension was selected as one to be considered for
multidimensional poverty measurement. A household with no
access to either a pipe in dweUing, a neighbour's pipe, a
public outdoor tap or a protected weU, is considered to be
water deprived, and so are aU its members.
It is worth mentioning that within the goal to ensure
environmental sustarnabtiity, increasing the access to safe
sanitation is also considered. However, Bhutan has
progressed tremendously in extending the access to improved
sanitation, that only 3.6% of the population is deprived in
18
 Multidimensional Poverty in Bhutan
this dimension. Therefore, it was decided not to include it
among the dimensions of the multidimensional poverty
measure to be estimated.
FinaUy, the number of people per room in the household is
also considered. Although this is not included as a target in
any of the 8 Goals of Bhutan, it is a commonly used socioeconomic assessment indicator, since it provides a measure
of housing quality. It is mentioned as an indicator in the 2003
Indicators for Monitoring the MUlennium Development Goals.
It can be related to Goal 7, since dweUrng's overcrowding can
promote different type of diseases and it does not contribute
to a sustainable environment. A household with 3 or more
people per room is considered to be room deprived, and so are
aU its members. The number of rooms excludes kitchens,
bathrooms, toilets and balconies. The use of 3 or more people
is quite standard in different countries.
Table 1: Selected dimensions, deprivation cut-off values and
weights	
Dimension Deprivation Cutoff value	
Rural and Urban Areas
Related to MDG 1: Eradicate Extreme Poverty and Hunger
Income Have monthly per capita income of Nu 1096.94
pc p/month
(Bhutan Poverty Line)
Related to MDG 2: Achieve Universal Primary Education
Education At least one literate household member and
all children between 6 and 16 are going to
school.
Related to MDG 7: Ensure Environmental Sustainability
Access to Access to electricity
Electricity
Access to Access to drinking water (either pipe in
Drinking Water dwelling, neighbour's pipe, public outdoor
tap or protected well)
Room Availability      Less than 3 people per room
Rural Areas Only: Two additional MDG1 -related dimensions are
considered
Access to Roads        Access to either a feeder or a tarred road in 30
19
 Journal of Bhutan Studies
minutes or less (by any means of transport).
Land Ownership       Own at least 1 acre of land of any kind.
(Land is the sum of wet land, dry land, orchard
 and tsheri (swidden cultivation land)).	
Clearly, the list of dimensions is not intended to be
exhaustive. There are another four MDGs that Bhutan is
trying to accomplish, and within aU the eight goals there are
many other indicators which could be considered. However,
there are two difficulties. In the first place, not all goals and
targets are applicable to obtain a multidimensional poverty
estimate from micro-data that is relevant for the whole
population. For example, even when Improving Maternal
Health (Goal 5) is a goal of utmost importance, indicators that
account for these issues at the household level would only
have meaning for households with pregnant or recently
pregnant women. Secondly, even when indicators on some of
the other goals, such as Reducing Child Mortality (Goal 4), or
Combating HIV/AIDS, Malaria and Other Diseases (Goal 6)
could be included, the BLSS does not provide information on
these issues. Goal 3 of Promoting Gender Equality and
Empowering Women is also a fundamental one, but it is
centred on a specific part of the population. Finally, the
targets included in Goal 8 of Developing a Global Partnership
for Development (such as telephone density or computers in
use) might not be necessartiy associated with poverty,
especiaUy in a country that is in the first stages of
modernisation.
AU the selected dimensions refer to material-conditions.
However, there are basis to argue that other non-material
conditions should also be included in the measurement of
multidimensional poverty as it is suggested by the capabtiity
approach. The Oxford Poverty and Human Development
Initiative (OPHI), at the University of Oxford, has identified
five missing dimensions of poverty, namely: the quality of
employment, empowerment, physical safety, the abtiity to go
about without a shame and psychological and subjective
wellbeing (Alkire, 2007). Unfortunately data on any of these
20
 Multidimensional Poverty in Bhutan
dimensions is not available in the BLSS, so indicators related
to these dimensions can not be included in the estimations of
this paper. Most of these data are available in this GNH
Survey but the period of survey and respondents are not
compatible with the BLSS data set. On the other hand, GNH
Survey does not include questions on a few requisite data tike
water and sanitation. However, Bhutan is planning to
incorporate questions on these issues in poverty surveys in
the near future. This wiU eventually allow broadening and
enriching the present analysis.
In any case, given Bhutan's interest in non-traditional
dimensions and in a holistic approach to the measurement of
weU-being, the main purpose of this paper is an Ulustrative
one: to demonstrate the methodology and its potential both
for multidimensional poverty measurement as weU as for
budget allocation. A different list of dimensions could be used
eventuaUy.
Provided that four out of the seven selected indicators are
dichotomous variables, only the multidimensional Headcount
Ratio H and Mo axe estimated. These two measures are
estimated for both urban and rural areas considering the five
dimensions applicable to both areas: income, education, room
availabUity, electricity and water. The two measures H and Mo
are also estimated only for rural areas considering all the
seven dimensions.
3.4 Weighting
The selection of dimensions to be included is not the only
controversial task when measuring multidimensional poverty.
Defining the weights to give to each dimension is another
difficult issue since it implicitly entails value judgements
(Decanq and Lugo, 2008). In this paper, two groups of
estimations were performed for each measure. One of them
uses equal weights, assigning a value of one to each
dimension. This can be thought as a benchmark, since it
implicitly assumes that aU dimensions are equaUy important.
21
 Journal of Bhutan Studies
The second group of estimates uses a set of weights derived
from the 2007 Gross National Happiness Survey (GNHS). One
of the questions of this survey, which had a sample size of
950 people, required the respondent to rank his/her sources
of happiness. The question was an open one, so that the
respondent could mention any source of happiness that was
important for him/her. Answers were then grouped and
categorised. Interestingly, the seven dimensions selected in
this paper are among the dimensions ranked in the ten first
places.7 The percentage of people that placed each of the
selected dimensions at some point in the ranking was re-
scaled so as to add up to the total number of dimensions
used, obtaining the weights listed in Table 2.
Table 2: Weights derived from the Gross National
Happiness
Survey
% of responses
Derived
Derived
Dimension
by 950
Weight
Weight
respondents who
For the
For the
mentioned it as
urban &
urban &
a source of
rural
rural
happiness
estimates
estimates
Income
41%
2.0
2.0
Education
27%
1.3
1.3
Room
14%
0.7
0.7
Availability
Electricity
16%
0.8
0.8
Water
4%
0.2
0.2
Access to
27%
-
1.3
Roads
Land
15%
-
0.7
Ownership
7 The list of 'sources of happiness' derived from this question of the
GNHS, ranked in order of their preference reads: financial security,
transportation, education, good health, family relationships,
agricultural productivity, electricity, basic needs (food, clothing,
shelter, cleaning drinking water), land, housing, good governance,
health infrastructure and facilities, faith and spiritual practices,
community relationship, job, national security, communication
facilities, environment, sports and travelling.
22
 Multidimensional Poverty in Bhutan
7
Note: Room Availability was not listed itself as a source of
happiness, but 'Housing' was, so the percentage of people
mentioning this was used to derive this weight. Access to roads was
listed within 'Transportation'.
4. Estimation results
4.1 Aggregate deprivation by dimension
Figure 1 presents the estimated Headcount in each
dimension, ranked from highest to lowest. It also shows the
contribution to the overaU deprivation in each of them done
by rural and urban areas. Note that, by definition, all the
deprivation in access to roads and land ownership
corresponds to rural areas.
From this graph, it can be seen that while 23% of the
population do not earn enough income to afford the basic
needs basket, the incidence of deprivation in aU the other
dimensions except for water is higher. In particular, 35% of
the population in Bhutan live in a household with 3 or more
people per room, 32% live in a household where either no-one
is literate or there are children in school age not going to
school and 30% do not have access to electricity. Only 9% do
not have access to drinking water. Virtually all the deprived
population in electricity, income and water live in rural areas.
Most of the population deprived in room and education also
live in rural areas, although it is worth noting that 12% of aU
the deprived in room and 15% of all the deprived in education
live in urban areas, suggesting that improvement in these two
dimensions is also needed in urban areas. Among people
living in rural areas, 26.7% do not have access either to a
tarred or to a feeder room within 30 minutes, and the same
percentage owns less than one acre of land.
23
 Journal of Bhutan Studies
Figure 1: Head Count Ratio in each Dimension
Rural and Urban Contributions
I
Room Education      Electricity        Access Land Income Water
These figures provide a first basis for priorities within the
selected dimensions in terms of policy design. They also
suggest that deprivation is mainly a rural phenomenon,
where 74% of the population in Bhutan live. This provides a
strong reason to focus deprivation-reducing efforts in these
areas.
4.2 Aggregate multidimensional poverty estimates
4.2.2 Rural and urban estimates with five dimensions
Table 3 presents the estimates of the Multidimensional
Headcount Ratio (H) and the Adjusted Headcount Ratio for
both urban and rural areas using the five dimensions
applicable to both, for different values of k, using equal
weights and the weights derived from the GNHS.
It should be noted that the meaning of each /c-value in the
estimates using the GNHS weights differs from the meaning
when equal weights are used. With equal weights k=l
requires for someone to be considered multidimensionaUy
poor to be deprived in at least one of the five dimensions,
24
 Multidimensional Poverty in Bhutan
which can be any of them. With GNHS weights, k= 1 implies
requiring being deprived in at least a dimension or a
combination of dimensions which weights add to 1. For
example, someone deprived only in safe water is not
considered to be multidimensionaUy poor with k= 1, neither is
considered someone deprived only in room or in electricity.
However, someone deprived only in income or only in
education is considered multidimensionally poor with fc=l, as
well as someone deprived both in water and electricity,
electricity and room or electricity and water, for example. The
H and Mo measures using GNHS weights were estimated for
aU possible values of k, which range from 0.2 to 5, and not
only the entire values from 1 to 5. For simplicity and
comparison purposes Table 3 presents the estimates only for
the same five values of k for which the measures using equal
weights were estimated.
Clearly, both with equal weights and GNHS weights, the
multidimensional poverty estimates decrease as k increases.
With equal weights, estimates indicate that 64% of the
population is deprived in one or more of any the five
dimensions, and -on average- they are deprived in 2
dimensions, so that the Adjusted Headcount Ratio Mo is 0.26.
Analogously, 37% of the population in rural and urban areas
is deprived in two or more of the five dimensions, and on
average, they are deprived in 2.7 dimensions, so that the
Adjusted Headcount Ratio is 0.20. The percentage of people
deprived in 3 or more dimensions is 20%, with Mo being 0.14
and people being deprived on average in 3.5 dimensions. The
estimates are smaller for k=4 and finally only 1.4% of the
population is deprived in all the five dimensions. The
estimates using GNHS weights are smaller for k=l to k=3,
which is a consequence of the lower importance given to some
of the dimensions such as people per room, electricity and
water, so that combinations of these deprivations are
equivalent to being deprived only in income or only in
education. With k=4, H and Mo with GNHS weights are
slightly higher than with equal weights because people
deprived  in  a  combination  of three   dimensions   (such  as
25
 Journal of Bhutan Studies
income, education and room) are considered
multidimensionaUy poor (since their weights add up to 4) but
are not considered multidimensionaUy poor with k=4 in the
equal weighting system. Obviously, when it is required to be
deprived in aU 5 dimensions to be considered
multidimensionaUy poor, aU estimates coincide and are
indeed very low.
Table 3: Multidimensional Headcount Ratio (H) and Adjusted
Headcount Ratio (Mo) in rural and urban areas - Different k
values -Equal weights and GNHS weights
Five Dimensions considered
Equal Weights
GNHS Weights
K
H            Mo
Average
Deprivation
H            Mo
Average
Deprivation
1
0.64       0.26
2.0
0.48       0.23
2.4
2
0.37       0.20
2.7
0.34       0.19
2.8
3
0.20       0.14
3.5
0.17       0.12
3.5
4
0.08       0.06
3.75
0.11       0.08
3.6
5
0.014     0.014
5
0.014    0.014
5
The multidimensional poverty incidence (H) estimates can be
related to the one-dimensional (income) poverty incidence,
which is 23%. One should always present the estimates for
the different /c-values. However, if one had to choose a value
to define policy, k=2 might be a reasonable intermediate
cutoff which focused the attention on a set of people narrow
enough so as to ensure that they are indeed
multidimensionaUy deprived, and broader enough so as to
include people that, even if not deprived in a high number of
dimensions, they sttil experience deprivation in several
relevant ones.
A natural question is how does deprivation in each dimension
contributes to the overaU multidimensional poverty. This can
be analysed breaking down Mo by the dimensions, which is
precisely one of the advantages of this measure. Figure 2
presents this decomposition in the form of a bar graph for
each k value with each of the weighting systems.
26
 Multidimensional Poverty in Bhutan
Figure 2: Multidimensional Adjusted Headcount Ratio (Mo) in
rural and urban areas
Different k - Contributions by each of the five dimensions
I Income Poverty
I Room Poverty
] Water Poverty
I Education Poverty
I Electricity Poverty
(a) Equal Weights
I Income Poverty
I Room Poverty
] Water Poverty
I Education Poverty
I Electricity Poverty
(b) GNHS Weights
27
 Journal of Bhutan Studies
In the figure it can be seen that income, education, room and
electricity have roughly similar contributions to overaU Mo for
k=l to k=4 in both weighting systems, whereas water is
clearly the dimension with the smallest contribution. Within
the four dimensions with similar contributions, when equal
weights are used and k=l, room is the one with the highest
contribution (27%), foUowed by education (25%), electricity
(23%) and income (18%). Poverty in water contributes with
only 7%. When k=2, the ranking of contributions is simtiar,
except that electricity has a slightly higher contribution than
education (23% vs. 22%). When A^3 and k=4, the ranking
order is room, electricity, income, education and water. It is
interesting to note that when the GNHS weights are used, the
rankings of the contributions differ from the case of equal
weights. With k= 1, deprivation in education gives the highest
contribution (29%), foUowed by room (23%), electricity
(21.5%), income (20.5%) and water (6%). With k=2, the
ranking is income (25%), education (24.5%), room (23.5%),
electricity (21%) and water (6%). With /c=3 and /c=4, the
rankings are the same, except that with k=3, education
switches the place with room. The fact that education ranks
first with k=l, and income ranks first in the other cases, is
reflecting the higher weight given to these two dimensions
when GNHS weights are used. OveraU, and by definition, as k
approaches the maximum k value, the structure of
contributions by each dimension approaches to an equal-
contribution. When k=5, each dimension contributes with
20%.
Another interesting decomposition of the aggregate
multidimensional poverty measures is between rural and
urban areas. Figure 3 presents the estimates of H and Mo
contained in table 3 with the corresponding contributions of
rural and urban areas. These are consistent with what was
suggested in Graph 1. Only in the case of k= 1 does the urban
areas have some contribution to overaU H and Mo, which is
14% to overall H with equal weights and 9% with GNHS
weights, and it is 8% to overall Mo with equal weights and 5%
with GNHS weights. These results reinforce previous results
28
 Multidimensional Poverty in Bhutan
from the 2004 and 2007 Poverty Analysis Reports, which had
identified income poverty as a predominantly rural
phenomenon. The estimates in this paper suggest that
multidimensional poverty is also fundamentaUy a rural
problem.
Figure 3: Multidimensional Poverty Headcount Ratio and
Adjusted Headcount Ratio
Different k - Rural and Urban Contributions
Rural     ^Z^D Urban
ll.
Rural     ^Z^D Urban
(a) Equal Weights
29
 Journal of Bhutan Studies
(b) GNHS Weights
4.2.2 Rural estimates with seven dimensions
Given that multidimensional poverty is virtually aU
concentrated in rural areas, it is worth estimating H and Mo
only for these areas, expanding the set of dimensions to also
include deprivation in access to roads and land ownership.
These results are presented in table 4, both using equal
weights and GNHS weights, for different values of k The
same comment given when explaining table 3 on the meaning
ofthe k- cutoff with GNHS weights applies here.
30
 Multidimensional Poverty in Bhutan
Table 4: Multidimensional Headcount Ratio (H) and Adjusted
Headcount Ratio (Mo) in rural areas only - Different k values -
Equal weights and GNHS weights
Seven Dimensions considered
Equal Weights
GNHS Weights
K
H
Mo
Average
Deprivation
H            Mo
Average
Deprivation
1
0.84
0.31
2.6
0.68       0.28
2.9
2
0.60
0.27
3.1
0.54       0.25
3.2
3
0.38
0.21
3.9
0.32       0.18
3.9
4
0.21
0.14
4.6
0.24       0.14
4.1
5
0.09
0.07
5.4
0.09       0.07
5.4
6
0.024
0.021
6.1
0.05       0.04
5.6
7
0.002
0.002
7
0.002     0.002
7
Table 4 shows higher estimates than before both because
these refer only to rural areas, where multidimensional
poverty is higher, and because a higher number of
dimensions are being considered. Using equal weights,
estimates suggest that 84% of the population in rural areas is
deprived in at least one of the seven considered dimensions,
being deprived on average in 2.6 dimensions giving a Mo value
of 0.31. 60% are deprived in two or more, 38% in three or
more and 21% in four or more, increasing the average
deprivation among these groups and reducing Mo
correspondingly. Using GNHS weights, the multidimensional
poverty estimates in the rural areas are lower for k=l to k=3
than the ones obtained with equal weights for simtiar reasons
to the ones explained in table 3. Note that starting with k=5,
estimates both using equal weights and GNHS weights
decrease significantly and are only 0.2% for k=7. This
suggests that a k cutoff value of 5 or higher is extremely
demanding for estimating multidimensional poverty in the
rural areas of Bhutan.
The Income Poverty Headcount Ratio is 30.9% in the rural
areas of Bhutan, which can be compared with the
Multidimensional Poverty Headcount Ratios for the different k
values  and  the  two  weighting  systems.   Analogous  to  the
31
 Journal of Bhutan Studies
analysis for both urban and rural areas, even when estimates
for the different k values must be considered, a cutoff value of
k=3 might be a good option for monitoring multidimensional
poverty in the rural areas.
As in the case of the overaU estimates, it is worth analysing
the decomposition of overaU Mo in rural areas among the
seven dimensions. The results of this decomposition are
presented in figure 4.
Figure 4: Multidimensional Adjusted Headcount Ratio (Mo) in
rural areas only
Different k - Contributions by each ofthe seven dimensions
;"n
3
Income Poverty
Room Poverty
Water Poverty
Land Poverty
I Education Poverty
| Electricity Poverty
I Access Poverty
3
| Income Poverty
I Room Poverty
] Water Poverty
| Land Poverty
| Education Poverty
I Electricity Poverty
I Access Poverty
(a) Equal Weights
(b) GNHS Weights
In the figure it can be seen that for all k and for the two
weighting systems, poverty in electricity, education, room and
income are among the highest contributors to overall poverty
in rural areas, coinciding with the contributions analysed for
both rural and urban areas. These are followed in all cases by
deprivation in access to roads, deprivation in land ownership
and, finally, water. This means that the two additional
dimensions considered in rural areas do not affect
significantly the ranking of the other deprivations; rather,
they are placed after the mentioned four and before water.
32
 Multidimensional Poverty in Bhutan
Within the four dimensions with the highest contributions,
when equal weights are used and k=l, electricity ranks first
(contributing with 18.7% to overall poverty), then room (with
18.5%), education (17.9%) and income (14.3%). Poverty in
access and land contributes in both cases with 12.4% and
water with 5.7% When k=2 and k=3, room poverty ranks
first, and it is followed by electricity, education, income,
access, land and water. And when k=4, income switches
positions with education. For higher values of k, the four
main contributors have more and more equal contributions.
As it happened with estimates for both rural and urban areas,
when the GNHS weights are used, education and income tend
to have higher contributions to overaU poverty relative to
electricity and room because of the higher importance
attributed to these two dimensions.
4.3 Overlapping and correlation between dimensions
The typical argument to focus poverty analysis exclusively on
income is that income is highly correlated with achievements
in other dimensions, such as education. If this was the case,
by targeting the income-poor, one would be targeting the
deprived in other dimensions. However, this does not seem to
be the case of Bhutan.
A first simple exercise is to analyse the Spearman correlation
between any pair of variables. Table 5 (a) presents this
coefficient between deprivations in the different pairs of
dimensions used to estimate multidimensional poverty in
both rural and urban areas, using the total sample. Table 5
(b) presents the same, but for all pairs of dimensions used for
the estimations only in rural areas.
33
 Journal of Bhutan Studies
Table 5: Spearman correlation coefficients between deprivations
(a) Rural and Urban Areas-Five Dimensions
Income         Education    Room
Electricity     Water
Deprived      Deprived       Deprived      Deprived       Deprived
Income Deprived        1
Education
0.24
1
Deprived
Room Deprived
0.36
0.17
1
Electricity
0.30
0.22
0.25
1
Deprived
Water Deprived
0.14
0.14
0.11
0.22
1
(b) Rural Areas Only- Seven Dimensions
Income
Education
Room
Electricity
Water
Access
Land
Deprived
Deprived
Deprived
Deprived
Deprived
Deprived
Deprived
Income Deprived
1
Education
0.21
1
Deprived
Room Deprived
0.36
0.16
1
Electricity
0.22
0.16
0.23
1
Deprived
Water Deprived
0.09
0.11
0.09
0.17
1
Access Deprived
0.22
0.14
0.20
0.36
0.22
1
Land Deprived
-0.09
-0.03
0.01
-0.08
-0.015
-0.08
1
34
 Multidimensional Poverty in Bhutan
In both tables it can be seen that any pair of deprivations has
a high correlation coefficient, and even though deprivation in
income is the one with higher correlations with the others, it
never exceeds 0.36. This suggests that a multidimensional
analysis is indeed important: a policy targeted to the income
poor might not reach other segments of the population
deprived in other dimensions.
A second exercise consists of analysing whether there is
overlap between the group of poor identified with the
multidimensional approach and the group of poor identified
with the traditional income approach. Ruggeri-Laderchi, Saith
and Stewart (2003) present empirical evidence of significant
lack of overlap in the identification by the monetary and the
capability approach for the case of India and Peru. Similar
evidence is found in the case of Bhutan.
Table 6 (Panels a and b) present the percentage of population
that is income non-poor but multidimensionaUy poor, and the
percentage of the population that is income poor but
multidimensionaUy non-poor, for the different k values in the
estimates of rural and urban areas using equal weights and
GNHS weights. Simtiar tables can be constructed for the
estimates of rural areas only. By definition, the percentage of
the income non-poor that are multidimensionaUy poor
decreases as k increases, being zero when k=d, since all the
multidimensionaUy poor in that case are deprived in every
considered dimension, including income. For the same
reason, the percentage of income poor that are not
multidimensionaUy poor increases as k increases. It goes from
0 when k=l, since in that case all the income deprived are
considered multidimensionaUy poor, to a percentage close to
the aggregate income Headcount Ratio when k=d, since in
that case only the few income deprived that are also deprived
in all the other dimensions are considered multidimensionaUy
poor.
This suggests that, if one would want to reach the
multidimensionaUy poor by using the income poor as a 'proxy'
35
 Journal of Bhutan Studies
variable there would always be some non-depreciable error:
either a group that is only income poor but not
multidimensionaUy poor would be included, which would be a
Type-1 error, or a part of the multidimensionaUy poor would
be excluded for not being income poor, which would be a
Type-II error. If one considers the minimum possible k value
to be the relevant to identify the multidimensionaUy poor,
using an income approach in that case minimises the Type-II
error but maximises Type-I error. On the other hand, if one
considers that k=d, is the relevant deprivation cutoff to
identify the multidimensionaUy poor, using an income
approach minimises Type-I error but maximises Type-II error.
For /c-values in the middle of the extremes, there is some
combination of each error type when an income approach is
used.
Table 6: Lack of overlap between Income and Multidimensional
Poverty
(a) Rural and urban areas, five dimensions, equal weights	
% of Population k=l k=2 k=3      k=4        k=5
Income Non-Poor but     40.7%       15.8%      4.6%    0.5%       0%
MultidimensionaUy
Poor
Income Poor but 0% 2.1%        8.1%     15.9%    21.8%
MultidimensionaUy
Non-Poor
(b) Rural and urban areas, five dimensions, GNHS weights
% of Population k=l k=2 k=3 k=4 k=5
Income Non-Poor but      24.4%       10.4%      0.54%     0% 0%
MultidimensionaUy
Poor
Income Poor but 0% 0% 6.7%        12.5%     21.8%
MultidimensionaUy
Non-Poor
36
 Multidimensional Poverty in Bhutan
4.4 Analysis at the district level
Given that the 2007 BLSS is representative at the district
level, the multidimensional poverty measures H and Mo were
estimated for each district. Table 7 presents these estimates
for both rural and urban areas of each district, using five
dimensions, with k=2, using the GNHS weights. It also
presents the income Headcount Ratio in each district. Two
type of analysis can be done at the district level. On the one
hand, it is interesting to analyse the estimates of each
measure in each district, which are presented in columns (2),
(6) and (10) for Income H, Multidimensional H and Mo
correspondingly. Districts can be ranked according to the
estimate in each measure, which is done in descending order
in columns (3), (7) and (11), and then rankings can be
compared. Column (14) presents the difference in the rank
order obtained by each district when ranked by Income H and
when ranked by Mo.
On the other hand, provided that the three measures can be
decomposed by population subgroups, it is worth analysing
the contribution of each district to the aggregate estimate of
each measure. This is obtained weighting the measure
estimate in each district by the district's population share,
such     that:      Cf = [(100ns I n)Ps IP],     where     Cf is     the
contribution of district s (with 5 = 1, ,20) to the aggregate
poverty measure P,  Ps  is the poverty estimate in district s,
and   (\00ns In)   is the population share  of district  s.  The
population share of each district is presented in column (1) of
the table and the contribution of each district to the aggregate
Income H, Multidimensional H and Mo estimates are
presented in columns (4), (8) and (12) correspondingly.
Districts can be ranked according to their contribution to
each of the aggregate measures. These rankings are done in
columns (5), (9) and (13), and again, changes in the rankings
can be analysed. In particular, column (15) presents the
difference in the rank order obtained by each district when
37
 Journal of Bhutan Studies
ranked by their contribution to Income H and when ranked
by their contribution to Mo.
Regarding the first type of analysis, one interesting point to
note is that the districts having the lowest estimates of
Income H are not necessarily the ones having the lowest
estimates of multidimensional H and Mo. Looking at column
(14), it can be seen that although the change in the rank
order of the districts when moving from Income H to Mo is not
striking, there are some interesting cases, such as the
districts of Gasa and Tsirang. Note that when ranked in
descending order by Income H, the district of Gasa ranks in
the 18th place, since its income His one ofthe lowest (only 4%
of the population is income poor), and the district of Tsirang
ranks in the 15th place (with only 14% of the population being
income poor). However, when ranked by Mo, Gasa is ranked
in the 8th place, that is, it climbs 10 places in the ranking
because its Mo estimate is 0.25, whereas Tsirang ranks in the
10th place, with an Mo estimate of 0.22, climbing 5 places in
the ranking.
38
 Multidimensional Poverty in Bhutan
Table 7: Income and Multidimensional Headcount Ratio and Multidimensional Adjusted Headcount Ratio (Mo) decomposed by districts
Urban and Rural Areas- Five dimensions considered, k=2, GNHS weights
Desc.
Contri.
Desc.
Multi.
Desc.
Contri
Desc.
M0
Des
Contrib.
Desc.
Diff.
Diff.
Pop.
Income
Rank
Overall
Rank
H
Rank
b.
Rank
(k=2
c.
Overall
Rank
b/w.
b/w
District
Share
H
Order
Income
Order
(k=2)
Order
Overall
Order
)
Ran
Mo
Orde
Rank
Rank
(%)
Inc.
H
Contr
Inc. H
Multi.
Contr
k
(k=2)
r
Order
Order
H
(%)
ib
H (k=2)
ib
Ord
(%)
Contr
in Inc.
in
(1)
(2)
(4)
to
(7)
(%)
to
er
ib.
H
Contrib.
(3)
Inc.
H
(5)
(6)
(8)
Inc.
H
P)
(10)
Mo
(11)
(12)
to Mo
(13)
and Mo
(3)-(H)
=(14)
Inc. H
and Mo
(5)-(13)
= (15)
Zhemgang
3.11
0.53
1
7.09
6
0.58
1
5.42
1
0.33
1
5.59
7
0
-1
Samtse
8.85
0.47
2
17.84
1
0.55
2
14.44
2
0.32
3
15.08
1
-1
0
Mongar
6.06
0.44
3
11.61
2
0.54
3
9.82
3
0.32
2
10.40
2
1
0
Lhuntse
2.49
0.43
4
4.62
8
0.53
4
3.96
4
0.27
6
3.67
11
-2
-3
S/Jongkhar
5.55
0.38
5
9.08
5
0.49
5
8.02
5
0.29
4
8.63
5
1
0
Dagana
3.00
0.31
6
4.02
10
0.50
6
4.45
6
0.29
5
4.63
10
1
0
Trashingang
7.58
0.29
7
9.56
3
0.43
7
9.74
7
0.23
9
9.30
4
-2
-1
Pemagatshel
3.76
0.26
8
4.24
9
0.44
8
4.97
8
0.25
7
5.09
8
1
Trongsa
2.32
0.22
9
2.21
13
0.37
9
2.56
9
0.20
11
2.54
14
-2
-1
Chukha
10.74
0.20
10
9.38
4
0.29
10
9.20
10
0.17
13
9.55
3
-3
Sarpang
6.38
0.19
11
5.35
7
0.31
11
5.84
11
0.18
12
6.21
6
-1
Punakha
4.03
0.16
12
2.71
11
0.26
12
3.16
12
0.12
14
2.56
9
-2
2
Wangdue
5.70
0.16
13
3.89
12
0.31
13
5.29
13
0.15
16
4.66
13
-3
-1
T/Yangtse
2.89
0.14
14
1.79
15
0.29
14
2.49
14
0.15
15
2.32
16
-1
-1
Tsirang
3.01
0.14
15
1.80
14
0.38
15
3.45
15
0.22
10
3.61
12
5
2
Haa
1.99
0.13
16
1.13
18
0.20
16
1.17
16
0.11
17
1.20
17
-1
Bumthang
2.55
0.11
17
1.20
17
0.18
17
1.35
17
0.08
18
1.07
18
-1
-1
Gasa
0.60
0.04
18
0.11
20
0.38
18
0.68
18
0.25
8
0.79
19
10
39
 Journal of Bhutan Studies
Paro
5.63
0.04
19
0.96
19
0.05
19
0.84
19
0.02
20
0.71
20
-1
-1
Thimp
ihu
13.77
0.02
20
1.42
16
0.08
20
3.15
20
0.03
19
2.38
15
1
1
Bhutan 100%       0.23 100% 0.34 100% 0.19 100%
40
 Multidimensional Poverty in Bhutan
The explanation for this sort of change in the relative
positions of these two districts can be found in figure 5,
where the 20 districts have been ranked from highest to
lowest by the Mo estimates. The bar for each district also
presents the composition of multidimensional poverty by each
of the dimensions. There, it can be seen that in the case of
Gasa only a very small fraction of the multidimensional
poverty in this district is explained by income. However, even
if not highly deprived in income, significant parts of the
population in this district are deprived in the other
considered dimensions. Deprivation in education accounts for
31% of the overaU multidimensional poverty estimate,
deprivation in electricity accounts for 27% deprivation in
drinking water accounts for 21.4% and deprivation in room
accounts for 17% of Mo. The high levels of deprivation in the
other dimensions relative to the income deprivation explain
the big change between the ranking by income H and by Mo.
Moreover, it is a paradox that given Bhutan's achievement in
terms of access to drinking water (such that only 9% of the
population in the country is deprived in this dimension),
being Gasa one of the richest districts in income terms, it is
the one that has the highest deprivation in access to water.
Something simtiar happens with Tsirang, in which the
deprivation in education, room and electricity accounts for
most part of the Mo estimate. On the contrary, in most of the
other districts, deprivation in income accounts for a very
significant part of overaU multidimensional poverty, which
explains why they do not have such striking changes in the
rank order when moving from Income H to Mo. However, this
does not mean that deprivation in income would suffice for a
comprehensive poverty analysis since these districts are
highly deprived in the other considered dimensions,
suggesting that they have coupled disadvantages, which
makes them particularly vulnerable. Simtiar conclusions are
obtained when the analysis is performed on the estimate
results of the rural areas only.
Figure 5: Composition ofthe Adjusted Headcount Ratio (Mo) in
each Bhutanese district
41
 Journal of Bhutan Studies
Rural and urban areas - Five Dimensions - k=2 - GNHS
weights
r n
llliiiiiin ._
Income Poverty
Room Poverty
Water Poverty
Education Poverty
Electricity Poverty
In terms of policy design, the second type of analysis seems
particularly important. When governments are faced to the
difficult task of assigning public budget among the different
districts in order to reduce poverty, it is necessary to consider
the contribution of each district to the aggregate poverty
estimate, that is, it is necessary to weight district-level
estimates by their population share. Figure 6 summarises two
type of relevant information for policy purposes. In the first
place, it presents the contribution of each district to overall
Mo, given by the height of each bar. In the second place, the
figure also presents, within each district, the contribution of
deprivation in each dimension to overaU Mo in the district.
Panel (a) is referred to estimates in both rural and urban
areas, using five dimensions, k=2, and GNHS weights; panel
(b) is referred to estimates in rural areas only, using seven
dimensions, k=3, and GNHS weights. In Panel (a) it can be
seen that Samtse, Mongar, Chukha, Trashigang and
Samdrup Jongkhar are the districts with the highest
contribution to aggregate Mo.  Note that Gasa is one of the
42
 Multidimensional Poverty in Bhutan
districts with the lowest contribution to aggregate Mo despite
it is one with the highest estimates of Mo. This is because its
population share is below 1% Within the districts with the
highest contribution to aggregate Mo, improving income
conditions, extending the access to electricity, guaranteeing
that chtidren in school age attend to school and that at least
one household member becomes literate, and improving
housing conditions to reduce overcrowding seem to be the
most urgent needs. Extending even further the access to
drinking water comes at a second place. It is worth noting
that improvements in the mentioned dimensions should also
be priorities even among the districts with lower contributions
to aggregate Mo. Similar conclusions can be drawn from the
rural estimates of Panel (b), with the addition that access to
roads is a also key dimension that should be added to
priorities in the case of rural areas, whereas land ownership
comes -together with access to drinking water- seems to be
less relevant.
Figure 6: Contribution to overall Mo by each district and
contribution of each dimension to the Mo value in each district
fl_i_
trongsa      trashiyangtse    Bumthang
Income Poverty
Room Poverty
Water Poverty
Education Poverty
Electricity Poverty
(a) Rural and urban areas - Five Dimensions - k=2 - GNHS
weights
43
 Journal of Bhutan Studies
Income Poverty
Room Poverty
Water Poverty
Land Poverty
Education Poverty
Electricity Poverty
Access Poverty
(b) Rural areas only - Seven Dimensions - k=3 - GNHS weights
Clearly, the ranking of the districts by their estimates of Mo
(as the one presented in Figure 5) as well as by their
contribution to aggregate Mo (as the ones presented in Figure
6) are subject to the selected value of k, the weighting system,
the chosen dimensions and the deprivation cutoffs.
Figure 7 plots the contribution of two groups of districts for
the different k values in the rural and urban estimates (with
five dimensions) when GNHS weights are used.8 One group is
composed by the districts of Samtse, Mongar, Chukha,
Trashigang and Samdrup Jongkhar. These districts have the
highest contributions across all the different k values. Among
them, Samtse always has a higher contribution, whtie the
ranking of the other four changes with k, as it can be seen by
the lines crossing with each other. The other group is
composed by the districts of Paro,  Gasa,  Bhumtang,  Haa,
8 When GNHS weights are used, the minimum k value is 0.2. Then it
is increased in 0.1 until its maximum level, which is 5 in the case of
the estimates for both rural and urban areas (since 5 dimensions are
considered). That gives 49 different possible k cutoff values.
44
 Multidimensional Poverty in Bhutan
Trashiyaste, Trongsa and Punakha. These are at the other
extreme, always having the lowest contributions to the
aggregate Mo estimate. Within this group, the ranking
changes with the k value. In the middle of these two groups
tie the contributions of the other districts: Dagana, Lhuntse,
Pemagatshel, Sarpang, Thimphu, Tsirang, Wangdue and
Zhemgang. This type of analysis can facilitate assigning
priorities of public budget distribution among districts.
Figure 7: Contribution to overall Mo by each district for different
k values
Rural and urban areas - Five Dimensions - GNHS weights
Samtse
Chhukha
Samdrupjongkhar
Gasa
Paro
Trongsa
Monggar
Trashingang
Bhumtang
Haa
Trashiyangste
Punakha
5. Concluding remarks
This paper has estimated multidimensional poverty in Bhutan
using a recently developed methodology by Alkire and Foster
(2007). The selection of dimensions was based on the
MUlennium Development Goals that are applicable for
estimations of poverty at the household level and for which
the BLSS provides data. For the case of both urban and rural
areas five dimensions were selected: income (access to the
45
 Journal of Bhutan Studies
basic basket), education (at least one literate person in the
household and aU chtidren attending school), number of
people per room (less than three), access to electricity and
access to drinking water. Estimations for rural areas included
two additional dimensions: access to roads (in 30 minutes or
less) and land ownership (at least one acre). In each case, two
alternative weighting structures were applied: one using equal
weights and one using weights derived from the ranking of
'sources of happiness' identified through the Gross National
Happiness Survey.
Estimates suggest that 37% of the population in both rural
and urban areas is deprived in two or more of the five
considered dimensions, and 20% are deprived in three or
more. When these Headcount Ratios are adjusted by the
average deprivation, the Mo estimates are 0.20 and 0.14
correspondingly. If the dimensions are weighted using the
ranking of sources of happiness obtained from the Gross
National Happiness Survey, the estimates of the Headcount
Ratio and the Adjusted Headcount Ratio are slightly lower for
these values of k The results also indicate that
multidimensional poverty is mainly a rural phenomenon,
although urban areas present non-depreciable levels of
deprivation in room avatiabtiity and education. In the rural
areas of Bhutan, poverty in education, electricity, room
avatiabtiity, income and access to roads, contribute in similar
shares to overaU multidimensional poverty, whtie poverty in
land ownership has a relatively smaller contribution, being
poverty in water the smaUest one. When the aggregate
multidimensional poverty estimate is decomposed by
districts, it is found that Samtse, Mongar, Chukha,
Trashingang and Samdrup Jongkhar are the ones with the
highest contribution to overall multidimensional poverty.
The paper is innovative not only in that it changes the focus
from the traditional unidimensional perspective of poverty,
centred on income, to a broader multidimensional one, but it
also provides with a methodology that is potentiaUy useful for
aUocating the budget among the districts and within them,
46
 Multidimensional Poverty in Bhutan
among the different dimensions. The property of Alkire and
Foster's (2007) Mo measure of being decomposable in
population subgroups and suitable for breaking it down into
dimensions is what makes it suitable for such purpose.
Clearly, other dimensions could be incorporated and
alternative deprivation cutoff values could be considered in
the analysis. In any case, Bhutan constitutes a striking
example of how significant and fast progress can be made
towards development when goals are clearly set and policies
specificaUy designed to fulfil them. The proposed methodology
could prove to be a useful instrument to monitor such
progress.
References
Alkire, S. (2007). "The Missing Dimensions of Poverty Data:
Introduction to the  Special Issue."   Oxford Development
Studies 35: 347-359.
Alkire,    S.    and    J.     E.     Foster    (2007).    "Counting    and
Multidimensional Poverty Measurement."  OPHI Working
Paper Series No. 07, OPHI.
Anand,    S.    and    S.    Sen    (1997).    "Concepts    of   Human
Development       and       Poverty:       A       Multidimensional
Perspective."   Human   Development   Papers.   New  York:
UNDP.
Atkinson, A. and F. Bourguignon (1982). 'The Comparison of
Multidimensional Distribution of Economic Status."  The
Review of Economic Studies, 49:183-201.
Basu, K. and J. E. Foster (1998). "On Measuring Literacy."
Economic Journal, 108: 1733-749.
Blackorby, C. and D. Donaldson (1980). "Ethical Indices for
the  Measurement  of Poverty."   Econometrica 48:   1053-
1060.
Bourguignon,     F.     and     S.     Chakravarty     (2003).     "The
Measurement   of  Multidimensional   Poverty."   Journal  of
Economic Inequality, 1, 25-19.
Chakravarty,     S.     (1983).     "A    New    Index    of    Poverty."
Mathematical Social Sciences, 6: 307-13.
47
 Journal of Bhutan Studies
Decanq,   K.   and   M.   A.   Lugo   (2008).   "Setting  Weights  in
Multidimensional Indices of WeU-Berng."
http://www.ophi.org.uk/pubs/Wl.Decancq-Lugo.pdf.pdf
Foster, J. E. and A. Shorrocks (1991). "Subgroup Consistent
Poverty Indices." Econometrica, 59: 687-709.
Foster, J.E., J. Greer and E. Thorbecke (1984). "A Class of
Decomposable Poverty Indices", Econometrica, 52: 761-6.
National  Statistics Bureau  (2004).   Poverty Analysis Report
2004.
 (2007). Poverty Analysis Report 2007.
 (2000-2006). National Accounts Statistics.
Office of the Census Commissioner (2005).  Population and
Housing Census of Bhutan 2005.
Royal Government of Bhutan (2005). Millennium Development
Goals Progress Report 2005.
Rugeri Laderchi, C, R. Saith and F. Stewart (2003). "Does it
Matter that We Do Not Agree on the Definition of Poverty?
A Comparison of Four Approaches." Oxford Development
Studies 31,: 244-274.
Sen,     A.     (1976).     "Poverty:     An     Ordinal    Approach    to
Measurement." Econometrica, 44: 219-231.
    (1985).   Commodities  and  Capabilities.  Amsterdam:
North-HoUand.
    (1990).   "Development  as  Capabtiity  Expansion"   in
K.G.   a.   J.   KNIGHT  (ed.)   Human Development  and  the
International Development Strategy for the 1990s. London:
Mc. MiUan.
  (1999).  Development as Freedom.  Oxford University
Press.
Tsui,   K.   (2002).   "Multidimensional  Poverty Indices."   Social
Choice and Welfare, 19: 69-93.
United Nations Development Program (2003).  Indicators for
Monitoring Millennium Development Goals. New York.
Watts, H. W. (1969). "An Economic Definition of Poverty" in
Moynihan,   D.   P.   (ed.)   On  Understanding Poverty.   New
York: Basic Books.
48
 Multidimensional Poverty in Bhutan
Annex
Table A. 1: Sample size by district and by rural and urban
areas
District
Rural
Urban
Total
Weighted
sample
Bumthang
1051
286
1337
16,033
Chukha
2930
2088
5018
67,606
Dagana
1362
104
1466
18,867
Gasa
1076
48
1124
3749
Haa
1079
138
1217
12,511
Lhuntse
1206
81
1287
15,705
Mongar
2529
436
2965
38,187
Paro
2615
175
2790
35,475
Pemagatshel
1649
184
1833
23,646
Punakha
1879
134
2013
25,346
Samdrup Jongkhar
2027
679
2706
34,940
Samtse
3490
717
4207
55,727
Sar pang
2119
802
2921
40,182
Thimphu
662
5482
6144
86,717
Trashingang
3301
388
3689
47,704
Trashi Yangtse
1274
175
1449
18,216
Trongsa
1097
176
1273
14,585
Tsirang
1385
121
1506
18,970
Wangdue
2223
564
2787
35,890
Zhemgang
1257
176
1433
19,606
Bhutan
36,211
12,954
49,165
629,662
49
 50

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