Multidimensional poverty measurement in Pakistan: time series trends and breakdown.
Salahuddin, Taseer ; Zaman, Asad
1. INTRODUCTION
In the recent literature, consensus has emerged that poverty is a
multidimensional phenomenon; see Alkire and Santos (2010) for a review
of the major arguments. Nonetheless, the most widely used measures of
poverty remain unidimensional, being based on income or caloric intake
cutoffs. The logic for the use of income based measures was that it was
only lack of income which led to deprivation--with sufficient income;
rational agents would automatically eliminate deprivations in all
dimensions in the right sequence of priorities. However, careful studies
like Thorbecke (2005) and Banerjee and Duflo (2006) show that this does
not happen. Even while malnourished and underfed, the poor spend
significant portions of their budgets on festivals, weddings, alcohol,
tobacco and other non-essential items. The move from abstract
theoretical speculation based on mathematical models of human behaviour
to experiments and observations of actual behaviour has led to dramatic
changes in the understanding of poverty and how to alleviate it. Some of
these insights are encapsulated in a new approach to poverty advocated
by Banerjee and Duflo (2011). (1)
Another motivation for more careful study of poverty is a silent
revolution in the understanding of development. Traditional economists
treat development as a process of accumulation of wealth, and current
textbooks endorse this idea for the most part. On this view, the poor
are regarded as labour inputs to the production function, and valued at
their marginal product of labour. Elementary as it might appear, the
idea that wealth is an input to improving human welfare, and that our
goal as economists should be to provide lives of comfort and dignity to
all human beings, is revolutionary. Experience with implementing
development schemes based on conventional growth theory led Mahbubul-Haq
to the following important insight: (2) "..., after many decades of
development, we are rediscovering the obvious--that people are both the
means and the end of economic development." Similarly, Sen (1975,
2006) has argued that development is about the process of development of
human capabilities, not the accumulation of wealth. A recent study of
the wealth of nations by the World Bank shows that most of the wealth on
this planet is generated by skills and capabilities of human beings,
rather than natural resources or accumulated capital. (3) Thus the poor
are the most valuable resources in the process of development, and
providing for them adequately is the key to rapid economic growth.
In this paper, we calculate the Alkire-Foster Measure (AFM) (2007)
of poverty on the basis of available Pakistani data. This is a true
multidimensional poverty index, which treats income as means to ends and
not an end in itself. We will show that it provides a substantially
clearer picture of poverty than large numbers of earlier studies based
on unidimensional measures. Because the measure is decomposable, we are
able to provide a breakdown across different dimensions, and also across
provinces. The sharper conclusions also provide much clearer guidance
for anti-poverty policy.
Before proceeding to provide details of this alternative
methodology, we provide a brief review of existing approaches to poverty
measurement in context of Pakistan. This will place our discussion in a
historical context, and provide a benchmark for comparisons.
2. HISTORY OF POVERTY MEASUREMENT IN PAKISTAN
Studies on poverty measurement in Pakistan used various
income-based definitions of poverty measurement. Increase in number of
measures led to increasing confusion about the true level of poverty.
Changes in cutoffs for calories, income, indexation methods, some of
which were politically motivated, led to conflicting and contradictory
pictures of poverty. A close study of Naseem (1973, 1977), Allaudin
(1975), Mujahid (1978), Irfan and Amjad (1984), Ahmed and Allison
(1990), and Malik (1988) showed that for same years and same data sets,
changes in models of poverty measurement, poverty lines and units of
analysis lead to these differing results and trends. Most confusing
aspect here was the fact that all models used same income and
expenditure poverty definitions and yet achieved different results.
In 2003-04 Economic Survey of Pakistan government admitted that:
"... many poverty estimates have ... neither helped in
understanding changes in the standard of living of a common man nor
facilitated in assessing how to reduce poverty through various policy
changes". (4) In this 2003-04 year for the very first time in the
history of Pakistan an official poverty line of 2350 calories/day/adult
was announced. This may have brought uniformity in poverty measurement
but it did not bring any improvement in policy area. Ultimately, it is
not of much help to know if the headcount of the poor is going up or
down, since it does not provide sufficient clues as to policies needed
to help them.
Income poverty demonstrated following variations over years in
Pakistan.
[FIGURE 1 OMITTED]
The Headcount poverty estimates only show the percentage of
population of Pakistan below different poverty lines. They do not even
tell why this poverty is prevailing? Which province of Pakistan is
suffering more from poverty? Are the causes of poverty same for
different areas of Pakistan? Which policies will alleviate deprivation
in which dimension of poverty? They are all based on income and food
expenditure approaches of poverty measurement. Therefore, headcount
measures fail to depict a true and transparent picture of nature,
extent, causes and intensity of poverty in Pakistan.
Despite these deficiencies, the unidimensional poverty indices have
been widely used due to three aspects. Firstly, they are simple and easy
in application. Secondly, blind trust in 'trickledown' theory
suggests that growth is sufficient to remove poverty; this theory has
been repeatedly rejected across the globe. (5) Thirdly, nonexistence of
a sound and robust multidimensional poverty index also favoured the use
of one dimensional measure.
3. THE ALKIRE-FOSTER MEASURE
A large number of complex and difficult problems have hindered the
development of suitable multidimensional measure of poverty. How to
select the dimensions of poverty? How to decide upon cut-offs or poverty
lines within each dimension? How to aggregate all these different
dimensions? What weights to apply at each dimension? How to capture the
varying inter-relationships of these dimensions? The Alkire-Foster
measure provides satisfactory answers to all of these questions. For a
complete discussion of the debates and justifications for the choices,
the reader may consult training material for producing national human
development reports by Alkire and Seth (2011). (6) The Alkire Foster
methodology has been used to construct the MPI, a multi-dimensional
poverty, index which has specific dimensions and cutoffs. (7)
Internationally this index has been built using eleven different
indicators including health, education, shelter, occupation,
empowerment, child development, living standard, social exclusion,
assets, air quality, and security.
In attempting to adopt this methodology for Pakistan, we found data
was only accessible regarding seven dimensions. Another limitation was
unavailability of true panel data. Instead 'Household Income and
Expenditure Survey' (HIES) and 'Pakistan Social and Living
Standard Measurement Survey' (PSLM) were used for available years
since 1998-2006. It appears very hard to theoretically agree on the
dimensions which should be included as poverty constituents. There is a
vast debate going on to include many dimensions some of them are even
currently considered as immeasurable like self respect, social exclusion
etc. All the same, one does not have to be a genius to identify absolute
basic necessities for human survival e.g. health, education, shelter,
water and sanitation, nutrition etc. A composite index should include as
many of these real dimensions as possible. Data availability as
mentioned above hampers the true measurement of poverty. Though no
multidimensional poverty measurement has been done in Pakistan before,
but people like Zaidi and Devos (1994), Malik (1996), Kemal (2003),
Jamal (2005) and Haq (2005) have suggested that it is urgently needed.
They have also suggested health, education, living standard, assets,
occupation or livelihood to be some of the dimensions of poverty. We
have used all dimensions on which data was available to build a version
of the MPI in Pakistan.
Who is poor and who is not? A reasonable starting place is to
compare each individual's achievements against the respective
dimension-specific cutoffs. This is the first stage of dual cutoff
strategy to be applied. Within dimension cutoffs are based on the same
principle used by Alkire and Seth (2008) in India. Each question of a
survey has some answer options. Each option is then marked as deprived
or not deprived according to within dimension cut-off. For example
living standard is composed of two indicators type of housing and
electricity. Within each indicator and sub-indicator cutoffs are applied
as follows.
LIVING STANDARD: (Type of House + Electricity)
This dimension corresponds to Question 109,110,111 & 107 in the
PDHS questionnaire (similarly these questions are also present in HIES
and PSLM but with different numbers)
Poverty Cut-off [Z.sub.l]--in each question bold ones were
considered as poor (8) and allotted 1 value and non bold ones were
considered as non-poor and allotted 0 value. Poverty cut-off denotes the
situation under which a household is deprived in any two of the above
mentioned indicators.
Question 109 main material of floor (MFM): natural floor,
earth/sand/mud floor, finished floor: chips/terrazzo, ceramic tiles,
marble, cement, carpet, bricks, mats, other.
Question 110 main material of roof (MRM): natural roofing: thatch/
bamboo/wood/mud, rudimentary roofing, cardboard/plastic, finished
roofing: iron sheets/asbestos, t-iron/wood/brick, reinforced brick
cement/ RCC, other.
Question 111 main material of walls (MWM): natural walls:
mud/stones, bamboo/sticks/mud, rudimentary walls: unbaked bricks/mud,
plywood sheets, carton/plastic, finished walls: stone, blocks, baked
bricks, cement blocks/cement, tent, others.
Question 107 House has electricity: yes, no.
Similarly these within dimension cutoffs are applied on other
dimensions of the study. But dimension specific cutoffs alone do not
suffice to identify who is poor; we must consider additional criteria
that look across dimensions to arrive at a complete specification of
identification method. This is the second stage of dual cutoff method.
The most commonly used identification criterion is called the union
method of identification. In this approach, a person i is said to be
multidimensional poor if there is at least one dimension in which the
person is deprived (k = 1). The other extreme identification method is
the intersection approach, which identifies person / as being poor only
if the person is deprived in all dimensions (k=d) (where d is the number
of dimensions under study). This criterion would accurately identify the
poorest of the poor but excludes those who are above the poverty
threshold in even one dimension, even if they are poor in all others.
Secondly, as the dimensions grow the proportion of the population
appearing as poor declines to nearly zero. A natural alternative is to
use an intermediate cutoff level for ci that lies somewhere between the
two extremes of 1 and d. In other words, k identifies person i as poor
when the number of dimensions in which i is deprived is at least k;
otherwise, if the number of deprived dimensions falls below the cutoff
k, then i is not poor according to k. Since k is dependent on both the
within dimension cutoffs and the across dimension cutoff k, Alkire and
Foster have referred to k as the dual cutoff method of identification.
Here k includes the union and intersection methods as special cases
where k = 1 and k = d.
Result and Discussion
[FIGURE 2 OMITTED]
Alkire-Foster Measure when applied on PDHS data set 2006-07 showed
that only 0.7 percent of Pakistani population is not deprived in any of
the six dimensions. (9) We can see that if union definition (deprived in
at least one dimension) is considered 99.3 percent of Pakistan is poor
according to this multidimensional poverty measure. Whereas, if
intersection definition (deprived in all dimensions) is taken into
consideration even then as high as 28.5 percent population suffers
poverty. It was observed that increase in the number of dimensions
augmented poverty. This leaves us with anticipation that if further
dimensions were included like health, empowerment and child status etc.
probably the analysis would have shown a bleaker picture. Nearly 47
percent of the population is poor in four dimensions.
In Table 3, the number of poor in multiple dimensions; the cut-off
based headcount ratios and the adjusted headcount ratios are shown. The
union approach would identify 92.5 percent of rural population as poor.
On the other hand, the intersection approach leads to 28.5 percent
poverty. If the poverty cut-off is two that means people are deprived in
two or more than two out of six dimensions. 65.6 percent of population
belongs to poor households and it denotes the multidimensional headcount
ratio for this k=4 cut-off. To avoid criticisms of the multidimensional
headcount ratio (it does not take into account the breadth of
multidimensional poverty, does not satisfy dimensional monotonicity, and
is not decomposable) the adjusted headcount ratio ([M.sub.0]) as a
measure of poverty has been used instead of a multidimensional
headcount. For theoretical properties of [M.sub.0], see Alkire-Seth
(2008).
We use the cut-off of two out of six subsequently, because leaving
aside union definition k= 2 is the cut-off showing the broadest picture
of deprivation. The third column of Table 3 reports the adjusted
headcount poverty rates for different cut-offs. If the poverty cut-off
is four out of six dimensions, then [M.sub.0] is 0.568. As [M.sub.0] =
HA. For the poverty cut-off of four out of six dimensions, H is equal to
0.656 and A is equal to 0.568/.656 = 0.866. A can be interpreted as the
poor being deprived in 86.6 percent of all dimensions on average. Thus,
the fourth column reports the average depth of poverty among the
population from the poor households. This shows that if k=6 is
considered then 28.5 percent of population is poor with 100 percent
average deprivation in all dimensions.
If two or more than two dimensions are considered (union definition
with respect to k=2) then 92.5 percent of Pakistan's population is
poor. However, considering k=6 only people those are deprived in all six
dimensions available, 28.5 percent of Pakistani population is extremely
poor with poor living standard (either with a kaccha house or no
electricity, with equal weightage), poor water and sanitation (no access
to safe drinking water and no proper toilet facilities), poor air
quality (unsuitable cooking fuels), with limited or no asset holdings
(fridge, TV, car, AC, washing machine), very little or no education
(less than primary) and with no proper means of livelihood. This is not
a very bright picture compared to results of same measure calculated by
Alkire and Seth (2008) for India. Even though more dimensions were
considered for India, she is almost free of extreme poverty using same
definition.
A household identified as poor on the basis of k=2 (deprived in two
out of six dimensions) showed that Punjab is the least poor province of
Pakistan. Sindh has the second lowest poverty rate according to the
[M.sub.0] measure. Khyber Pakhtunkhwa is the third followed by
Balochistan, which is the poorest. These are not just poverty
distribution results of provinces, rather they point out the areas of
deprivations in every province. These results may help us understand the
consequences of these deprivations to Pakistan. Today the deep political
frustrations and unrest in Balochistan may have a simple solution:
removal of deprivations from the lives of people of Balochistan.
In Table 5, we present the decomposition of poverty across
different dimensions within these provinces. This will help us to
identify causes and intensity of poverty for each province. Analysis
depicts that, education and livelihood in all provinces entail close
attention of policy-makers. Punjab and Sindh are not close in their
respective [M.sub.0] values but causes of poverty are same. For example
both have done well in terms of living standard, water and sanitation
and air quality. However, assets, education and livelihood show high
deprivation levels. On the other hand KPK and Balochistan have close
[M.sub.0] value, but the causes of poverty in both these provinces are
different. This type of decomposition enables the policy-makers to make
proper policy recommendations by focusing on exact issues to be
resolved. As a result precise causes of poverty can be combated with
more targeted planning.
After implementation of this index on 2006-07 PDHS data set, we had
to shift to HIES and PSLM for building time-series trends. This analysis
showed that over years Pakistani population 'not deprived in any of
the dimensions' had declined. In 1998-99, 1.3 percent of this class
existed which reduced to 0.7 percent in 2001-02 and then from 2004
onwards this class was totally eliminated.
We can see that if union definition (deprived in at least one
dimension) is considered, 98.7 percent of Pakistan in 1998-99 which
increased to 100 percent in 2005-06. Whereas, if intersection definition
(deprived in all dimensions) is taken into consideration then 1.7-1.8
percent population suffered from poverty in 1998-99 which increased to
8.8 percent in 2005-06. Also, majority of people were deprived in 4/6
dimensions which increases to 5/6 dimensions later on.
Multidimensional poverty measure represents a more in-depth and
detailed picture of poverty. As a result it was observed results that
poverty in Pakistan has both increased in its depth and breath, during
last decade. Its incline became sharper in the last quarter. For any
cut-off, from union to intermediate and intersection definitions, these
conclusions hold.
The most depressing discovery done by this data analysis is that
over years educational poverty in Pakistan has increased from 2.4
percent to 20.64 percent. This deterioration in education also nullified
slight improvements in health, empowerment, living standard and water
and sanitation.
Education plays pivotal role in the development of any country.
Seminal research by Barro (1997) shows that long run growth is primarily
determine by investment in education. Its deprivation leads to
tribulations in long-run growth and progress. Pakistan not only needs to
find out the causes of this education poverty but also should try and
make policies for a quick recovery.
If we compare the respective Government poverty % ages for the
years we have studied, it becomes evident that multidimensional poverty
index presents a far clearer picture of poverty as compared to
unidimensional poverty indices in use. Income based poverty is a number
which does not tell anything about poverty beyond head count of poor.
Multidimensional index for same year not only gives a detailed picture
from slightly poor to absolute poor people but also provides a
deprivation degree spectrum. It makes multidimensional poverty
measurement a better guide for policy designing.
CONCLUSIONS
Poverty being a multidimensional phenomenon should have an equally
multidimensional measure for its true representation. A dimension level
breakdown of poverty analysis will help policy-makers to design proper
targeted policy of poverty alleviation on the basis of area, demographic
distributions, ethnicity and gender. These results will help people to
relate to the other issues in the society as a consequence of
deprivations in different dimensions of poverty. Whereas headcount
measures do not provide clues to suitable policy, our multidimensional
measure shows that the critical fronts are Health and Education. On both
of these fronts, we have had a dramatic rise in poverty. Both research
and common sense agree on the idea that the future of the nation lies
with our youth. Failings on the educational front do not bode well for
the future, and it is an urgent need to take suitable measures to
rectify this problem.
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(1) http://pooreconomics.com/
(2) In another place, he writes that "we were told to take
care of our GNP as that would take care of poverty--let us reverse this
and take care of poverty as this will take care of our GNP." See
Bari (2012) for an account of the intellectual journey of Mahbub-ul-Haq
from conventional wealth oriented views of development to human oriented
views based on his experiences with development.
(3) "Where is the Wealth of Nations?" study by World Bank
(2006) http://siteresources.worldbank.org/
INTEEI/214578-1110886258964/20748034/All.pdf
(4) Economic Survey of Pakistan 2003-04. p. 41.
(5) "For the 1 percent, of the 1 percent, by the 1
percent," Stiglitz has recorded how the top 1 percent of the USA
population has massively increased their share of the income and wealth
of the entire country over the past two decades,
http://www.vanityfair.com/society/features/2011/05/top-one-percent-201105
(6) http://www.ophi.org.uk/wp-content/uploads/MPI-Primerl.pdf?cda6c1
(7) See Alkire and Foster (2007) Counting and Multidimensional
Poverty Measures. Oxford University. Oxford Poverty and Human
Development Initiative. (OPHI Working Paper 7).
(8) See Alkire and Seth (2008) Measuring Multidimensional Poverty
in India: A New Proposal. (OPHI Discussion Paper 15).
(9) For the detail of the selection criteria of each dimension, see
Annex 1.
Taseer Salahuddin <salahuddin@gmail.com> is Assistant
Professor, Faculty of Arts and Social Sciences, University of Central
Punjab, Lahore. Asad Zaman <asadzaman@alum.mit.ed> is Director
General, International Institute of Islamic Economics, International
Islamic University, Islamabad.
Table 1
Dimensions and Indicators Used for Current Study
Dimensions Indicators
Living Standard: [Housing (main floor, roof and wall material)
+ electricity]
Health: [vaccination]
Water and Sanitation: [drinking water + type of toilet facility]
Air Quality: [type of cooking fuel]
Assets: [refrigerator, TV, car, AC/ room cooler, washing machine]
Education:[max education attained by any member]
Livelihood: [occupation of respondent and partner]
Table 2
Indicators and Cut-offs of Dimensional Poverty Rates
Dimensions %age Poverty Rate
0 0.7
1 6.8
2 12.9
3 13.9
4 15.2
5 21.9
6 28.5
Total 100
Table 3
Pakistan: Multidimensional Poverty Measures
Adjusted Head Count Average Deprivation
Poverty Headcount Ratio Share
Cut-offs (k) Ratio (H) [M.sub.0] = HA A = [M.sub.0]/H
2 0.925 0.682 0.737
3 0.795 0.638 0.802
4 0.656 0.568 0.866
5 0.509 0.471 0.925
6 0.285 0.285 1
Table 4
Province-wise Decomposition of Poverty for Unequal Weighting and 2-6
Cut-off
Regions Population H = q/n H Rank Mo Mo Rank
(Provinces) share (% age)
Punjab 41.8% .901 1 .632 1
Sindh 27.1% .92 2 .685 2
KPK 19.5% .95 3 .722 3
Balochistan 11.6% .97 4 .776 4
Table 5
Poverty Decomposition by Dimensions at Province Level
Mo Living Water Air
Rank Provinces Standard and San. Quality Assets
1 Punjab 0.023 0.035 0.041 0.051
- Breakdown% 8.6 13.4 14.7 16.9
2 Sindh 0.021 0.028 0.023 0.033
- Breakdown% 11.7 14.8 12.0 17.3
3 KPK 0.019 0.017 0.022 0.023
- Breakdown% 13.5 11.7 15.6 16.3
4 Balochistan 0.017 0.011 0.013 0.014
- Breakdown% 18.3 12.0 14.2 14.9
Mo Lively-
Rank Provinces Education hood Mo
1 Punjab 0.057 0.111 0.63
- Breakdown% 21.3 25.1 100
2 Sindh 0.04 0.044 0.69
- Breakdown% 21.1 23.3 100
3 KPK 0.030 0.032 0.72
- Breakdown% 20.5 22.3 100
4 Balochistan 0.021 0.019 0.77
- Breakdown% 19.5 21.1 100
Table 6
Indicators and Cut-offs of Dimensional Poverty Rates
%age Poverty %age Poverty %age Poverty %age Poverty
Rate Rate Rate Rate
Dimensions (1998-1999) (2001-2002) (2004-05) (2005-06)
0 1.3 0.7 0.0 0.0
1 4.0 2.9 0.6 0.3
2 6.8 5.9 2.8 2.0
3 7.9 7.4 5.7 4.4
4 49.7 60.8 32.1 8.5
5 28.5 20.9 50.1 76.1
6 1.7 1.8 8.6 8.8
7 -- -- 0.0 --
Table 7
Deprivation Index [M.sub.0] for Different Cut-offs K (Various HIES
Data Sets)
[M.sub.0]
K 1998-99 2001-02 2004-05 2005-06
1 0.655 0.656 0.647 0.807
2 0.648 0.652 0.647 0.806
3 0.625 0.632 0.639 0.800
4 0.586 0.595 0.614 0.778
5 0.255 0.196 0.431 0.722
6 0.017 0.018 0.074 0.088
7 -- -- 0.0002 --
Table 8
Time Series Trends in Dimension-wise Poverty Breakdown (%age)
HIES Data
Dimensions/ Years 1998-99 2001-02
[M.sub.0] %age [M.sub.0] %age
Occupation 0.043 6.64 0.038 6.0
Education 0.016 2.4 0.016 2.0
Health 0.145 22.2 0.144 22.0
Women Empowerment 0.147 22.5 0.149 22.8
Living Standard 0.156 23.9 0.158 24.2
Water Sanitation 0.146 22.4 0.147 22.4
Assets -- -- -- --
Air Quality -- -- -- --
Dimensions/ Years 2004-05 2005-06
[M.sub.0] %age [M.sub.0] %age
Occupation 0.024 3.75 0.019 2.43
Education 0.091 13.9 0.166 20.64
Health 0.131 20.1 0.154 19.16
Women Empowerment -- -- 0.154 19.09
Living Standard 0.137 21.0 0.159 19.86
Water Sanitation 0.127 19.5 0.015 18.80
Assets 0.121 18.5 -- --
Air Quality 0.017 2.59 -- --
Table 9
Comparative Poverty Percentage of Both Uni-dimensional and
Multidimensional Poverty Indices for Given Years
FBS Multidimensional Poverty % Ages Spectrum
Pakistan
% age Slightly (2/6) (3/6) (4/6) (5/6) Absolutely
Poverty poor Poor
Years Rates (1/6) (6/6)
1998-99 28.2 4 6.8 7.9 49.7 28.5 1.7
2001-02 32.1 2.9 5.9 7.4 60.8 20.9 1.8
2004-05 23.1 0.6 2.8 5.7 32.1 50.1 8.6
2005-06 23 0.3 2.0 4.4 8.5 76.1 8.8
Fig. 3. Province-wise Population and Deprivation Shares
population share Mo deprivation
Punjab 41.80% 0.632
Sindh 27.10% 0.685
NWFP 19.50% 0.722
Baluchistan 11.60% 0.776
Note: Table made from bar graph.
