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AFGHANISTAN
Multidimensional Poverty Index
2016–2017
Report and Analysis
OPHI
Oxford Poverty & Human
Development Initiative
AFGHANISTAN
Multidimensional Poverty Index 2016–2017
Report and Analysis
Islamic Republic of Afghanistan
National Statistics and Information Authority
OPHI
Oxford Poverty & Human
Development Initiative
Afghanistan Multidimensional Poverty Index 2016–2017: Report and Analysis The Afghanistan Multidimensional Poverty Index 2016–2017 was implemented by the National Statistics and Information Authority (NSIA) of the Government of the Islamic Republic of Afghanistan with technical assistance from the Oxford Poverty and Human Development Initiative (OPHI) at the University of Oxford. This publication has been produced with financial assistance from UNICEF. The contents of this publication are the sole responsibility of NSIA and OPHI, and can in no way be taken as to reflect the views of UNICEF. For further information, please contact: National Statistics and Information Authority Web: www.nsia.gov.af E-mail: mail@nsia.gov.af Oxford Poverty and Human Development Initiative (OPHI) Web: www.ophi.org.uk E-mail: ophi@qeh.ox.ac.uk UNICEF in Afghanistan Web: www.unicef.org/afghanistan E-mail: kabul@unicef.org © 2019 NSIA Recommended citation: National Statistics and Information Authority (2019). Afghanistan Multidimensional Poverty Index 2016–2017. NSIA, Kabul. ISBN: 978-9936-1-0309-2 Front cover and title page photo: Abbas Farzami / Rumi Consultancy / World Bank / Flickr CC BY-NC-ND Designed by: Maarit Kivilo | OPHI, Oxford Printed in Kabul, Afghanistan ii
AFGHANISTAN MPI 2016–2017 • Report and Analysis
Foreword
THE NATIONAL STATISTICS AND INFORMA
TION AUTHORITY (NSIA) has been authorized
by the High Council on Poverty Reduction to publish
a national Multidimensional Poverty Index of Afghan
istan (A-MPI). The A-MPI complements the mone
tary poverty measure and uncovers the deprivations
experienced by the Afghan people in various aspects
of their lives.
The report is produced in accordance with the Nation
al Priority Programs (NPP) and Afghanistan National
and Peace Development Framework (ANPDF). Both
give primacy to making integrated and evidence-based
policies in order to overcome the poverty, depriva
tions, and related consequences that the surviving
population of the country have suffered for decades.
This report vigorously helps the government of Af
ghanistan with budget allocation, policy coordination,
and integrated policies.
Following the High Council on Poverty Reduction’s
authorization, the current report is the result of the
efforts and collaboration of the NSIA and Oxford
Poverty and Human Development Initiative (OPHI)
towards publishing the first national MPI for Afghan
istan. OPHI’s training programs, technical support,
and in-person involvements were key to producing
this report.
NSIA is grateful and convinced that the results of the
A-MPI will be instrumental for developing effective
poverty reduction policies for the people of Afghanistan.
iii
AFGHANISTAN MPI 2016–2017 • Report and Analysis
MULTIDIMENSIONAL POVERTY in Afghanistan IT IS AN HONOUR to have been able to collaborate
is a situation in which people are affected by multiple with and learn from our colleagues at NSIA who de
and intersecting deprivations in health, education, liv signed the A-MPI. Based on data from ALCS 2016–17,
ing standards, employment, and security. the A-MPI is innovative in having a gendered education
indicator and cutting-edge indicators on employment
The Afghanistan Multidimensional Poverty Index
and security. It also includes an analysis of child poverty.
(A-MPI) is an official permanent poverty measure that
was developed by the National Statistics and Infor The aim is for the A-MPI to provide a prominent tool
mation Authority (NSIA) under the direction of the to coordinate the actors and programmes that address
Islamic Government of Afghanistan. The A-MPI aims distinct forms of poverty, bringing these into a cohe
to guide policies that will accelerate the reduction of rent whole and creating a common momentum.
interlinked deprivations. Using data from the Afghani
The A-MPI will also inform decentralized activities with
stan Living Conditions Survey (ALCS) 2016–17, the
detailed data on provincial challenges. When the A-MPI
A-MPI finds that more than half of the population are
is updated in future surveys, it will enable actors to cele
multidimensionally poor, but that one-third of MPI
brate visible progress and will provide updated evidence
poor people are not income poor.
for high-impact integrated policy interventions.
By providing one high-resolution picture of people’s lives,
the A-MPI will henceforth monitor poverty and hence
provide incentives for accelerating poverty reduction.
Sabina Alkire
Director
Hasibullah Mowahed Oxford Poverty and Human Development Initiative
(OPHI), University of Oxford
Deputy Director General
National Statistics and Information Authority
iv
AFGHANISTAN MPI 2016–2017 • Report and Analysis
Acknowledgements
We are deeply grateful to His Excellency the President
and to members of the High Council for Poverty for
the trust vested in the National Statistics and Infor
mation Authority to complete the important work of
establishing Afghanistan’s first multidimensional pov
erty index (MPI).
The design of the Afghanistan MPI (A-MPI) was
based on the Afghanistan National Peace and Devel
opment Framework and also on consultations with
many ministries. We are particularly grateful to the
Ministry of Economy, Ministry of Finance, Ministry
of Public Health, Ministry of Labor and Social Affairs,
Ministry of Rural Rehabilitation and Development,
and Ministry of Education, for their participation in
MPI-related consultations.
We are grateful to colleagues at the Oxford Poverty
and Human Development Initiative at the University
of Oxford, particularly Ricardo Nogales, and from the
Multidimensional Poverty Peer Network for sharing
technical details on MPI computations as well as case
studies of how other countries implemented national
MPIs as permanent official statistics and used national
MPIs to shape policies that accelerate poverty reduction.
Our genuine and warm thanks are offered to the in
ternational donor community and the World Bank
and European Commission for prior investments in
data and social policies and to UNICEF for providing
solid cross-cutting support to a nationally owned and
nationally computed A-MPI that can be used to make
policies more integrated and more effective.
v
AFGHANISTAN MPI 2016–2017 • Report and Analysis
Executive Summary
This report presents the Islamic Republic of Afghan The consultations included individual meetings with
istan’s MPI (A-MPI), which is a new, official, and experts and roundtables convened by both the NSIA
permanent statistic of multidimensional poverty that and the Ministry of Economy (MOEC). A week-long
complements the monetary poverty indicator. The training was held at NSIA (previously known as the
A-MPI reflects the priorities present in the Afghanistan Central Statistics Organisation [CSO]), and, sub
National Peace and Development Framework (ANPDF) sequently, two lead statisticians joined an intensive
2017 to 2021. In the ANPDF, poverty is recognised training on multidimensional poverty measurement
to be multidimensional, and, furthermore, poverty re and analysis given by the Oxford Poverty and Human
duction is taken to be a central priority. A key priority Development Initiative (OPHI) at the University of
of the ANPDF is to overcome policy fragmentation Oxford. A further workshop on the use of the national
and link evidence and policy in an integrated man MPI for policy – covering topics such as budgeting,
ner. The MPI is an appropriate tool to support the policy coordination, and sectoral policies – was also
ANPDF because it has been used in many countries organised by NSIA. Candidate measures were devel
to improve policy design, coordination, and budget oped using ALCS data and were thoroughly analysed.
allocation, as well as the monitoring and evaluation of The High Council on Poverty Reduction selected one
ambitious targets to accelerate poverty reduction. of these and authorized NSIA to publish a national
MPI for Afghanistan within six months, using the
The A-MPI is based on the data from the Afghanistan
same five dimensions as the chosen measure. Extensive
Living Conditions Survey (ALCS) 2016–17, conduct
work was undertaken to improve certain indicators as
ed by the National Statistics and Information Author
well as to examine the final measure to ensure that it
ity (NSIA).
was robust to different plausible specifications and ap
The 2016–17 A-MPI value is 0.272, indicating that propriate as a policy tool.
poor people in Afghanistan experience more than
The A-MPI comprises five dimensions and 18 indica
27% of the deprivations that could be faced if all the
tors that were selected in a consultative process with
population were deprived in all indicators. The multi
high-level policymakers in the country and technical
dimensional poverty headcount ratio stands at 51.7%.
experts. It is a reflection of policy priorities in the
The A-MPI complements Afghanistan’s national mon
country and the data available in the ALCS 2016–17.
etary poverty measure. We find that the people who
are monetarily poor are not necessarily multidimen The A-MPI uses an equal nested-weight scheme,
sionally poor. In fact, while 51.7% of people are MPI assigning a weight of 1/5 to each of the five dimen
poor and 54.5% are monetary poor, only about 36.3% sions of education, health, living standards, work, and
of people in Afghanistan are poor by both measures. shocks. For the dimensions of education and shocks,
Both measures are needed to adequately illuminate two indicators have a weight equal to 1/20; however,
poverty in its many forms and dimensions. the indicators of school attendance and security have
a weight equal to 1/10. Child school attendance and
The design of the A-MPI draws directly on priorities as
adult years of schooling (male and female combined)
articulated in our pivotal national document, A
NPDF,
are roughly equal in importance, and gendered adult
and its associated National Priority Programmes, as
well as on extensive consultations across government
ministries and leaders.
vi
AFGHANISTAN MPI 2016–2017 • Report and Analysis
schooling indicators illuminate adult outcomes. Fi multidimensional poverty. Stark differences are found
nally, in the case of the shocks dimension, security in across provinces too. While 14.7% of the population
the context of Afghanistan covers the vital aspect of in Kabul are poor, the poverty rate reaches 80.2% and
personal security from violence, whereas production 85.5% in Nooristan and Badghis. However, consider
and income are related to security from sudden eco ing the size of the population in each province, Her
nomic hardship. A person is identified as poor if they at and Nangarhar are home to the highest number of
are deprived in at least 40% or more of the dimensions poor people.
or weighted indicators. The A-MPI was assessed and
Multidimensional poverty shows different patterns
found to be technically robust to a plausible range of
across an array of socioeconomic characteristics.
weights and poverty cut-offs.
While nearly 33.2% of people living in households of
In 2016–17, it is estimated that 51.7% of Afghans live in four members or less are poor, 60.2% of people liv
multidimensional poverty. On average, the intensity of ing in households with 15 or more members are poor.
multidimensional poverty is 52.5%, which means that, Multidimensional poverty is also higher in households
on average, Afghans are deprived in 52.5% of the 18 that lack education. More than 60% of people live in
weighted indicators that form the A-MPI. The A-MPI, households where the head has no education, while
estimated as the product of the percentage of poor peo only one in four persons are found to be poor when
ple and the average intensity of poverty, is 0.272. the head of house has a secondary education or higher.
An MPI of 0.272 means that in 2016–17, poor people In terms of age groups, multidimensional poverty is
experienced 27.2% of the deprivations that could be most prevalent among children. Fully, 56.4% of chil
experienced if all Afghans were poor and deprived in dren aged 0–17 are poor, while less than 49% of peo
each indicator. Deprivations in terms of school attend ple aged 18 and above are MPI poor.
ance (14.1%) and assisted delivery (12.5%) contribute The key motivation for designing an MPI in Afghani
the most to the value of the A-MPI. Furthermore, al stan is to guide evidence-based policies that accelerate
though the incidence of MPI and monetary poverty poverty reduction. Among the salient policy recom
are similar, the overlap between the two measures is mendations is a need to focus on children, as they are a
not perfect. In particular, nearly 16% of the popula particularly vulnerable group, whose high poverty rates
tion are not monetary poor but are multidimensional also pose additional challenges for the whole country in
ly poor. Thus, the lens of the A-MPI is a useful com the future. Actions to improve children’s health, educa
plement to the monetary approach to poverty because tion, employment opportunities, and survival chanc
it makes visible both people who are poor but not es also affect their potential in the future and hence
captured by a monetary metric and because it shows should be a priority in intersectional development pro
concretely how they are poor across 18 indicators. The grams. Another key observation is that deprivations are
shape and composition of multidimensional poverty interlinked, so an emphasis on integrated policies is
varies widely across the country. The urban poverty appropriate. Improvements in terms of school attend
rate is 18.1%, whereas the rural rate is 61.1%. It is ance, assisted delivery, and food security clearly should
estimated that 89% of the Kuchi population live in be priority components of these programs.
vii
AFGHANISTAN MPI 2016–2017 • Report and Analysis
The A-MPI can also guide evidence-based budgeting monitoring generates visibility, familiarity, and mo
across key social policies and infrastructure invest mentum to swiftly redress development gaps. Countries
ments. The budget allocations should reflect the level with fre
quent MPI moni tor
ing, such as Co lombia,
of poverty while also rewarding good performance in which updates measures annually, have been able to
poverty reduction in the most recent period, in order reduce their MPI swiftly because the same budget enve
to create strong incentives for policy reduction. How lope is spent more efficiently using the MPI evidence.
ever, to create such incentives, the A-MPI must be Thus this document contains the MPI survey questions
updated frequently; thus, the questions used for MPI as an appendix in order that future surveys can easily
calculations – which are listed in the Appendix III – incorporate them and provide a sustained assessment of
should be included in the Income and Expenditure the pace of future MPI reduction in Afghanistan.
and Labor force survey (IE&LS) and the Afghanistan
Development Condition Surveys (ADCS). Frequent
FAO | Maryam Farzami | Flickr CC BY-NC
viiiAFGHANISTAN MPI 2016–2017 • Report and Analysis
Contents
Foreword iii
Acknowledgements v
Executive Summary vi
I. INTRODUCTION 1
II. METHODOLOGY 3
2.1 Methodological Basis of the A-MPI 3
2.2 Design of the A-MPI 3
2.3 Data for Analysis: ALCS 2016–17 6
III. RESULTS 7
3.1 Afghanistan’s National MPI: Key Results 8
3.2 Disaggregation by Urban, Rural, and Kuchi Areas, and Provinces 9
3.3 Robustness of the Results to Alternative Poverty Cut-offs 17
3.4 Multidimensional Poverty and Monetary Poverty 20
3.5 Performance across Household Size 21
3.6 Performance according to Education of Household Head 23
IV. MPI AMONG CHILDREN AND OTHER AGE GROUPS 26
V. NEXT STEPS 33
REFERENCES 35
APPENDICES 37
Appendix I Poverty Maps 37
Appendix II The Multidimensional Poverty Index: An Adjusted Headcount Ratio 39
Appendix III Questions Used for MPI Computations 41
ixAFGHANISTAN MPI 2016–2017 • Report and Analysis
I. Introduction
“Poverty in Afghanistan is multidimensional: it varies by region, by gender,
and by access to exit pathways.”
ANPDF 2017 to 2021
This report presents the Islamic Republic of Afghan The national MPI has been used as a policy tool in
istan’s Multidimensional Poverty Index (A-MPI), many countries to date, starting with Mexico, Bhu
which is a new, official, and permanent statistic of tan, and Colombia in 2009–11 and expanding from
multidimensional poverty that complements the con there to include over 18 countries. Learning from the
sumption poverty indicator. observations, data, and analyses of members of the
South-South Multidimensional Poverty Peer Network
The priority of poverty in all its forms: The move
(MPPN), it has become apparent that, where the po
to develop a national MPI was motivated by priori
litical will and organizational structures exist, a na
ties articulated in the Afghanistan National Peace and
tional MPI can be catalytic when it is used
Development Framework (ANPDF) 2017 to 2021. In
the ANPDF, poverty is recognized to be multidimen • To overcome fragmentation – by making visible
sional. Hence that pioneering five-year strategic plan interconnections across indicators and providing
articulated the vision of Afghanistan’s wellbeing and an integrated metric that different ministries can
self-reliance in terms that were far wider than mone directly affect and analyse;
tary measures alone. Furthermore, poverty reduction is
• To increase accountability – by monitoring the
taken to be a central priority: “The overarching goals of
trends in each component indicator over time, dis
our government are to reduce poverty and improve the
aggregated by subnational region, and also moni
welfare of our people” (p. 4). The plan’s components
toring the trends among the poor who are affect
encompass jobs, security, infrastructure, health, edu
ed by at least 40% of possible deprivations at the
cation, service delivery, and technology. It is therefore
same time;
natural that the traditional monetary poverty measure
should now be complemented by a multidimensional • To introduce appropriate policies – by providing
poverty index that establishes and tracks progress in a detailed and policy- salient infor
mation that can
cross-section of these non-monetary objectives. inform budget allocation by sector and region as
well as just-in-time policy adjustments, targeting,
Holistic policy: Furthermore, a key priority of the
and coordination across sectors and across levels of
ANPDF is to link evidence and policy in an integrated
government.
rather than fragmented way. The ANPDF vision state
ment closes with the words: “Achieving these goals re As these needs are evident in Afghanistan, they are de
quires a collective effort to overcome fragmentation, scribed more specifically below.
increase accountability, and introduce proper policies
for sustainable growth” (p. 3).
1AFGHANISTAN MPI 2016–2017 • Report and Analysis
BUDGET ALLOCATION: The MPI functions as PROCESS OF DESIGNING AFGHANISTAN’S
a headline indicator tracking outcomes of core pro NATIONAL MPI: Based on the alignment between
grammes, ministries, and priorities. It is used in many the ANPDF, with its multidimensional understanding
other countries to inform budget allocation across of poverty, and an overwhelming national motivation
sectors and subnational regions, and this consonance to reduce poverty insofar as is possible between 2017
between budget and evidence was called for in the and 2021, the NSIA chose to develop the baseline
ANPDF, which states: “Aligning the Cabinet, policy measure from the Afghanistan Living Conditions Sur
priorities, and the budget is at the heart of our na vey 2016–17 and then to update the survey(s) used to
tional development strategy. This will overcome the compute the A-MPI frequently in order that it might
fragmentation of the past by using a holistic approach be used for change management and evidence-based
to turning policies into effective expenditures” (p. 12). policy adjustments. In addition, the public, disaggre
gated, and intuitive nature of the A-MPI is intended
MAINSTREAMING AND INTEGRATING: The
to make transparent the successes and challenges that
ANPDF also called for attention to be paid to how the
people are experiencing across different regions and
language of and unwavering commitment to poverty
social groups in Afghanistan.
in all its forms is mainstreamed across many govern
ment priorities, “All National Priority Programs should This document presents Afghanistan’s national MPI.
articulate their approach to reducing poverty and sup Its indicators were selected in order to provide clear in
porting policies on gender” (p. 12). And it recognised sights as to how to design programs that deliberately tar
that “There has been an absence of poverty-focused in get the poor and follow the national priority to reduce
vestments over the long years of conflict” (p. 21). or eradicate multidimensional poverty. The A-MPI was
created to be used in monitoring and evaluating plans
CITIZEN ACTION: The National Citizens Char and programs at the national and subnational level, as
ter, which enables communities to shape their own well as in policy design, targeting, and coordination.
priorities and development responses, and whose
leadership is affirmed in the ANPDF, is a key coun
terpoint and important conversation partner for work
on multidimensional poverty because concerns about
water, health, education (both access and quality), and
electricity were already articulated and expressed in
the Charter and are reflected in the national MPI. In
addition, the actions of these communities have the
potential to catalyse and accelerate multidimensional
poverty reduction swiftly and definitively.
WOMEN AND CHILDREN: Naturally, a national
poverty measure must cover and address all groups.
Yet the need to empower women, whose leadership
can effectively redress so many other deprivations, is
presented in the ANPDF as fundamental to poverty
reduction. Thus any measure of multidimensional
poverty must be able to make visible the success of
gendered policies. Further, it is recognized that child
poverty is “particularly pernicious” (p. 7); hence a
multidimensional poverty measure must be disaggre
gated to reflect these needs.
2AFGHANISTAN MPI 2016–2017 • Report and Analysis
II. Methodology
Afghanistan’s national MPI is estimated using the In order to identify people who suffer multidimen
Alkire-Foster (AF) method. This chapter presents this sional poverty in Afghanistan, the deprivation score c
method in general terms along with the measure’s de is compared to a poverty cut-off or the k-value. All
sign and the dataset used for its computation. In this people suffering deprivations in a number of weighted
chapter, we cover the following subjects: deprivations equal to or greater than this cut-off are
identified as multidimensionally poor.
2.1 Methodological basis of the A-MPI;
2.2 Design of the A-MPI; Once the poor people in Afghanistan are identified,
the MPI is computed as the product of two compo
2.3 Data for analysis: ALCS 2016–17.
nent indices: the multidimensional headcount ratio
2.1. METHODOLOGICAL BASIS OF THE A-MPI and the intensity of multidimensional poverty.
The A-MPI is calculated using the AF method, which
consists of counting the simultaneous deprivations
The headcount ratio (H) is the proportion of the
that negatively affect a person’s life. The AF method
population who are multidimensionally poor.
allows the construction of individual deprivation pro
The intensity of poverty (A) reflects the pro-
files that can then be used to identify multidimen portion of the weighted indicators in which, on
sionally poor people. The number of people living in average, multidimensionally poor people are de-
multidimensional poverty and the intensity of their prived.
poverty are combined in the value of the MPI. The MPI combines these two aspects of poverty
in the following way:
By applying this method, the A-MPI reflects simul
MPI = H x A
taneous deprivations in the 18 indicators that were
chosen based upon a detailed analysis of relevance as
well as data availability. In order to identify whether It is important to note that the MPI can be equivalent
or not a person in Afghanistan is deprived in an indi ly computed as the weighted sum of censored head
cator, a deprivation cut-off was set for each indicator. count ratios – which show the percentage of people
This yields a set of 18 binary variables for every person, who are identified as poor and are also deprived in a
each one taking the value of 1 if the individual is de particular indicator. Because of this structure, the MPI
prived in that indicator and 0, otherwise. can be broken down by indicator to show the com
Once the set of binary variables is calculated, each per position of multidimensional poverty. This feature of
son is assigned a deprivation score denoted as c, indi dimensional detail brings added policy relevance to
cating the proportion of deprivations weighted by the the analysis.
relative importance of each indicator in the structure 2.2 DESIGN OF THE A-MPI
of the MPI. The deprivation score c is defined to take Afghanistan’s national MPI uses a set of dimensions,
values ranging between 0 (indicating that the person indicators, and cut-offs that reflect its priorities as
does not experience any weighted deprivations) and 1 expressed in the ANPDF and the National Citizen’s
(indicating that they experience weighted deprivations Charter (NCC), and via the consultations described
in all the 18 indicators). in Chapter I.
3AFGHANISTAN MPI 2016–2017 • Report and Analysis
TABLE 2.1 Dimensions, indicators, and weights of the A-MPI
Dimensions
Indicator Household is deprived if… Weight
of Poverty
Food security There is no borderline or acceptable food consumption. 1/10
Health Any woman who was pregnant in the last 5 years preceding the interview
Assisted delivery received fewer than 4 antenatal care visits OR the delivery did not take 1/10
place at a health facility OR was not attended by a doctor or a nurse.
School attendance At least one child aged 7–16 is not attending school or never did. 1/10
No woman aged 10+ has completed primary schooling or knows how to
Female schooling 1/20
Education read and write.
No man aged 10+ has completed primary schooling or knows how to read
Male schooling 1/20
and write.
Access to water They lack access to improved water sources.[1] 1/30
Sanitation They lack access to improved sanitation facilities. [2]
1/30
There is no adequate lighting source (i.e. there is no lighting, or it comes
Electricity 1/30
from candles or solid fuel)
Living There are no adequate fuel cooking sources (i.e. they use animal dung, crop
Standards Cooking fuel residue or cooking is done in the dwelling using bushes, twigs, firewood or 1/30
charcoal).[3]
Housing Dwelling is made of inadequate roof, floor or wall materials.[4] 1/30
They own less than 3 assets (refrigerator, washing machine, vacuum clean-
Asset ownership
er, gas cylinder, iron, television, mobile, satellite dish, bicycle and motor- 1/30
and agriculture
bike) OR agricultural items (land and livestock).[5]
Dependency There is less than one household member who works for every 6 people. 1/20
Unemployment No one in the household is employed in the labour force. 1/20
Work Underemployment One or more people in the household are underemployed. 1/20
There are one or more people aged 17–24 who are not employed, and do
Youth NEET 1/20
not attend school or any training program.
They have experienced one or more of the following shocks, with a strong
negative effect on household members: i) reduced drinking or agriculture
Production water, ii) unusually high crop pests or disease, iii) severe loss of opium 1/20
production, iv) unusually high livestock disease, v) reduced availability of
grazing area or reduced availability of Kuchi migration route.
They have experienced one or more of the following shocks, with a strong
Shocks
Income negative effect on household members: i) increased food prices, ii) a reduc- 1/20
tion in household income or iii) a decrease in farm food prices.
One or more of the following situations apply: i) they have suffered violence
or theft, ii) they live in a district rated as very insecure, iii) they are dis-
Security 1/10
placed or iv) they respond that the government’s first priority should be to
disarm local militia or to increase local security.
[1] Improved sources are those that have the potential to deliver safe water by nature of their design and construction. These include
piped supplies and non-piped supplies (such as boreholes, protected wells and springs, rainwater and packaged or delivered
water, e.g. by tanker trucks). Unimproved drinking water sources that do not protect against contamination are unprotected
springs and wells. The category ‘no service’ identifies surface water, such as rivers, streams, irrigation channels and lakes.
[2] An improved sanitation facility is defined as one that hygienically separates human excreta from human contact. These facilities
include wet sanitation technologies (flush and pour flush toilets connecting to sewers, septic tanks or pit latrines) and dry sani
tation technologies (ventilated improved pit latrines, pit latrines with slabs and composting toilets).
[3] The use of inadequate (solid) cooking fuels is a direct cause of household air pollution, and thus directly associated to respiratory
diseases, disabilities and death.
[4] Adequacy is related to durability. Housing of which the outer walls, roof and floor are made of durable materials that protect its
inhabitants from the extremes of climatic conditions, such as rain, heat, cold and humidity. Fired brick, concrete, mud bricks and
stone are considered durable materials. For roofs, wood is regarded as durable.
[5] A person is identified as deprived in assets if their household owns less than three of the considered agricultural items.
Source: Author’s calculations based on data from ALCS 2016–17.
4AFGHANISTAN MPI 2016–2017 • Report and Analysis
2.2.1 Dimensions, indicators, and deprivation The unit of analysis, which is the unit for which re
cut-offs sults are reported and analysed, is the individual. This
The A-MPI comprises five dimensions and 18 indica means that the headcount ratio is the percentage of
tors that were selected in a consultative process with individuals who are identified as poor.
high-level policymakers in the country and technical
experts. These choices reflect both policy priorities and 2.2.2 Weights
data availability. The weights for each indicator, which The A-MPI uses an equal nested-weight scheme, as
mirror their relative importance in the MPI, were set signing a weight of 1/5 to each of the five dimensions
based upon the same priorities (see Table 2.1). of education, health, living standards, work, and
shocks. The five dimensions were approved by the
The unit of identification for the A-MPI is the house High Council on Poverty, which also approved equal
hold. This approach assumes intra-household caring and weights between each of them. The adopted scheme of
sharing, and thus considers a household as a unit formed equal weights for every dimension implies an identical
by individuals whose lives are deeply intertwined. relative importance for each one. Within the dimen
For instance, if one household member is unemployed, sions of health, living standards, and work each indica
other household members are affected. Notice that tor is equally weighted. Each of the two health indica
this approach allows the measure to include indicators tors has a weight of 1/10; each of the living standards
that are specific to certain age groups (such as school indicators has a weight of 1/30; and each of the work
attendance or youth ‘not in employment, education or indicators has a weight of 1/20. This is because the
training’ [NEET]). indicators were considered to be roughly similar in
importance across the index. Within the dimensions
©UNICEF-Afghanistan / 2017 / Aziz Froutan
5AFGHANISTAN MPI 2016–2017 • Report and Analysis
of education and shocks, two indicators have a weight 2.3 DATA FOR ANALYSIS: ALCS 2016–17
equal to 1/20; however, the indicators for school at The data used for the national poverty measure is from
tendance and security have a weight equal to 1/10. the ALCS 2016–17, which is the longest-running and
Child school attendance and adult years of schooling most comprehensive source of information about the
(male and female combined) are roughly equal in im living conditions of people in Afghanistan. It is the
portance, but gendered adult schooling indicators were flagship of the Central Statistics Organization (now
created in order to illuminate gender disparities. NSIA), and it covers 45 indicators of which 15 are
And in the case of shock, security in the context of Af SDG indicators.
ghanistan covers the vital aspect of personal security The sampling design of the ALCS 2016–17 is repre
from violence, whereas production and income cover sentative at the national level, as well as at the provincial
security from sudden economic hardship. Robustness level. In total, 35 strata were specified, corresponding
tests to weights are found in section 3.3. to the number of provinces (34) in the country plus
2.2.3 Poverty and deprivation cut-offs the nomadic Kuchi population. For analytical purpos
Two kinds of thresholds are used to decide whether a es, the Kuchi population is not designated as rural or
person is deprived and whether they are poor: (1) an urban but is treated as an area of its own.
indicator-specific poverty cut-off (deprivation cut-off, The data provides information for around 20,000 house
shown in Table 2.1), according to which a person is holds and 155,000 people. Out of the 2102 originally
considered deprived in each indicator if their achieve sampled clusters, 304 (14%) were not covered mainly
ment falls below the cut-off, and (2) a cross-indicator because of security reasons. A total of 176 clusters were
cut-off (or poverty cut-off), which sets the minimum replaced, meaning that 1926 out of the 2102 (92%)
share of deprivations (or deprivation score) needed for originally sampled clusters were effectively covered.
a person to be considered poor. In Afghanistan, the
poverty cut-off or the k-value was set at 40%, based on
the reasoning that this threshold is equivalent to being
deprived in two or more dimensions, or the equiva
lent of weighted indicators. It is thus aligned with the
notion of poverty in multiple dimensions. In the MPI
estimation process, all poverty cut-offs were applied
and Figure 3.8 demonstrates results for all poverty cut-
offs by province.
6AFGHANISTAN MPI 2016–2017 • Report and Analysis
III. Results
This chapter presents details of Afghanistan’s national 3.1 Afghanistan’s national MPI: Key results;
MPI estimation results based on the ALCS 2016–17 3.2 Disaggregation by urban, rural, and Kuchi areas,
data. We first present the A-MPI as well as the poverty and provinces;
rate and intensity among the poor. We then list disag
3.3 Robustness of the results to alternative poverty
gregated results by household and individual charac
cut-offs;
teristics. The third section presents robustness tests for
3.4 Multidimensional poverty and monetary
the choice of weights and of the k-value. This chapter
poverty;
has the following sections:
3.5 Performance across household size;
3.6 Performance according to education of
household head.
FIGURE 3.1 National uncensored headcount ratios, 2016–17
%
80 74.81
70 63.01
60 52.73
49.57
45.47 46.73 45.78
50
Percentages
41.2
37.73 38.80
40 33.64
35.01
31.30
27.78
30 25.59
16.55 17.40
20
10 5.17
0
FS AD SA FS MS AW SN EL CF HO AA DE UN UD YO PR IN SE
FS Food security SN Sanitation UN Unemployment
AD Assisted delivery EL Electricity UD Underemployment
SA School attendance CF Cooking fuel YO Youth NEET
FS Female schooling HO Housing PR Production
MS Male schooling AA Assets and agriculture IN Income
AW Access to water DE Dependency SE Security
Source: Author’s calculations based on data from ALCS 2016–17.
7AFGHANISTAN MPI 2016–2017 • Report and Analysis
3.1 AFGHANISTAN’S NATIONAL MPI:
KEY RESULTS
The basic building block of multidimensional pover al poverty is nearly 52%. Since this estimate is based
ty analysis is the set of uncensored headcount ratios. on a sample, it has a margin of error. Thus, the 95%
These headcount ratios are estimated for each indica confidence interval is also presented in the table. This
tor, and they represent the proportion of the popula means that we can say with 95% confidence that the
tion who are deprived in the corresponding indicator, true multidimensional poverty headcount ratio of the
irrespective of their poverty status. As Figure 3.1 shows, population is between 50.3% and 53.1%.
the highest deprivations at the national level are found
The average intensity of poverty, which reflects the
for female schooling (with 75% of the population de
share of deprivations each poor person experiences
prived in this indicator), cooking fuel (63%), school
on average, is 52.5%. That is, each poor person is, on
attendance (53%), and dependency (50%). On the
average, deprived in more than half of the weighted
other hand, some indicators show much lower rates
indicators. With 95% confidence, the true value of the
of deprivation. In particular, the rate of deprivation in
intensity of poverty lies between 52.2% and 52.9%.
electricity (5%) is the lowest among all indicators, and
relatively fewer people are deprived in youth NEET The MPI, which is the product of H and A, has a val
(17%)6 and asset ownership and agriculture (17%). ue of 0.272. This means that multidimensionally poor
people in Afghanistan experience 27.2% of the total
Complementing the uncensored headcount ratios, Ta
deprivations that would be experienced if all people
ble 3.1 shows the main figures related to the A-MPI
were deprived in all indicators. The MPI is the official
for 2016–17, including its partial indices: the head
statistic of poverty used to declare whether poverty has
count ratio or poverty rate, H, which is also called
fallen or risen over time, because it considers progress
the incidence of poverty (or the proportion of people
on two levels – H and A. From analytical and policy
identified as multidimensionally poor), and the inten
making viewpoints, it is important to notice that there
sity of poverty (or the average proportion of weighted
are situations in which only one statistic goes down
indicators in which the poor are deprived, A). As can
over time and not the other, but it is important to al
be seen in the table, the incidence of multidimension
ways keep in mind that both are important. If we use
TABLE 3.1 Incidence, intensity, and MPI, 2016–17
Poverty cut-off (k) Index Value Confidence interval (95%)
MPI 0.272 0.264 0.28
k-value = 40% Headcount ratio (H, %) 51.7 50.3 53.1
Intensity (A, %) 52.5 52.2 52.9
Source: Author’s calculations based on data from ALCS 2016–17.
only the headcount ratio, for example, we might see a
rise in poverty in some years, where, if we use MPI, the
[6] In this particular indicator, it is worth noting that 37.4%
of the population live in households where there is no one
fuller picture would reveal a fall in multidimensional
aged 17–24. Also, 16.1% of people aged 17–24 belong to poverty – if there had been a sufficiently large decrease
the NEET group. Finally, gender differences are noticeable: in intensity.
21% of female youth can be classified as NEET, as opposed
Figure 3.2 depicts the distribution of the intensity of
to 11.2% of male youth. This disaggregation shows the extent
to which female youth are disadvantaged compared to their poverty among the poor. It gives an idea of the c-vector
male counterparts. schedule for values equal to or greater than 40%, thus
8AFGHANISTAN MPI 2016–2017 • Report and Analysis
FIGURE 3.2 Incidence, intensity, and MPI, 2016–17
5% 1%
40% – 49.99%
18%
50% – 59.99%
42%
60% – 69.99%
70% – 79.99%
80% – 89.99%
90% – 100%
34%
Source: Author’s calculations based on data from ALCS 2016–17.
corresponding to the population that has been identified In Table 3.2, the MPI, incidence, and intensity of pov
as multidimensionally poor. Around 42% of all poor erty are shown by urban, rural, and Kuchi areas. As can
people in Afghanistan are in the lowest intensity band, be seen in the table, the vast majority of the popula
which is between 40% and 49.99%, and one-quarter tion lives in rural areas (70%), which have particularly
of the poor have deprivation scores of more than 60%. high levels of poverty compared to urban areas. More
This suggests that further progress in MPI is a legitimate than 60% of the rural population are multidimension
policy objective even in the short and medium term, as ally poor, which greatly contrasts with the 18.1% mul
most of the poor are very near to the multidimensional tidimensional poverty headcount ratio in urban areas.
poverty line. However, 6% of the poor experience the
On average, poor people in rural areas experience dep
highest intensities of poverty, as they are deprived in
rivations in nearly 53% of the weighted indicators, a
more than 70% of the weighted indicators.
figure that is slightly under 50% in urban areas. As
3.2 DISAGGREGATION BY URBAN, RURAL, a result, the MPI in rural areas is 0.312, whereas in
AND KUCHI AREAS, AND PROVINCES urban areas it amounts to 0.088. The Kuchi represent
The nomadic Kuchi population is treated as an area of 5% of the Afghan population, and the levels of pov
their own, as they are not considered as members of erty they experience deserve particular attention. The
the rural and urban areas in the ALCS. Thus, apply vast majority of this population (89%) lives in mul
ing the property of subgroup decomposability of the tidimensional poverty, and, on average, they are de
MPI, it is possible to disaggregate the levels of poverty prived in more than 56% of the weighted indicators.
for different areas of Afghanistan – urban, rural and The MPI for the Kuchi population (0.500) is higher
Kuchi areas as well as provinces. than that in rural areas, and thus they can be consid
ered as nomadic pockets of poverty in the country. To
some extent, this may reflect the selected living stand
ards indicators. However, it is useful for policy purpos
9AFGHANISTAN MPI 2016–2017 • Report and Analysis
TABLE 3.2 Multidimensional poverty by rural/urban areas, 2016–17
Urban Rural
Index
Population Confidence Population Confidence
Value Value
share (%) interval (95%) share (%) interval (95%)
MPI 0.088 0.075 0.1 0.312 0.313 0.33
Headcount ratio
25% 18.1 15.7 20.5 70% 61.1 59.7 62.6
(H, %)
Intensity (A, %) 48.5 47.6 49.4 52.6 52.2 53
Source: Author’s calculations based on data from ALCS 2016–17.
es to have an objective gauge of the Kuchi population of the population. Overall, poverty is heavily concen
that is identical to the measure applied to the rest of trated in rural areas, as they are home to more than
the country’s population. 83% of the poor population, while 70% of the total
population live in rural areas.
Figure 3.3 compares the distribution of the poor and
general population by area of residence and for the Turning now to an analysis at the province level, Table
Kuchi population. Although only 5% of the popu 3.3 shows the province-level estimates for the MPI,
lation belong to the Kuchi population, nearly 9% of incidence of poverty, and intensity of poverty. The in
multidimensionally poor people belong to this no cidence of poverty is above 70% in eight out of the 34
madic part of the population. This figure also covers provinces, namely Badghis (85%), Nooristan (80%),
those living in urban areas, which are home to 25% Kunduz (77%), Zabul (77%), Helmand (74%), Sa
mangan (73%), Urozgan, (71%), and Ghor (70%).
FIGURE 3.3 Distribution of the poor and population by Although these regions are relatively small in that each
urban, rural, and Kuchi Areas, 2016–17 of them is home to less than 4% of the population,
90 they deserve particular attention as a very large pro
portion of their populations live in multidimensional
80
poverty.
70
Conversely, the incidence of poverty is below 20%
60 only in the capital, Kabul (15%), which is home to
Percentages
50 16% of the population and thus represents the most
densely populated province in the country. As a gener
40
al pattern, people who live in multidimensional pov
30 erty suffer relatively similar levels of poverty intensity.
20 In all regions, the intensity of poverty is around 50%,
but it ranges from 46.1% in Logar to 59.3% in Noor
10
istan. Maps for these and other figures are presented
0 in Appendix I.
Urban Rural Kuchi
The MPI for each province and its corresponding 95%
Poor people confidence intervals are depicted in Figure 3.4. If these
Distribution of population (%)
confidence intervals do not overlap, then a significant
Source: Author’s calculations based on data from ALCS 2016–17.
10AFGHANISTAN MPI 2016–2017 • Report and Analysis
TABLE 3.3 Multidimensional poverty by province, 2016–17
MPI Headcount Ratio (H, %) Intensity (A, %)
Subnational Population
Region Share (%)
Confidence Confidence Confidence
Value Value Value
Interval (95%) Interval (95%) Interval (95%)
Kabul 16.00 0.071 0.056 0.085 14.7 11.8 17.7 48.0 46.6 49.4
Kapisa 1.60 0.119 0.074 0.164 24.7 16.6 32.9 48.0 45.0 51.0
Parwan 2.40 0.217 0.171 0.263 42.4 34.6 50.3 51.1 48.6 53.7
Wardak 2.20 0.337 0.303 0.370 67.1 61.5 72.7 50.2 49.2 51.3
Logar 1.70 0.140 0.084 0.197 30.4 19.4 41.5 46.1 44.1 48.2
Nangarhar 5.80 0.349 0.305 0.393 66.3 58.3 74.2 52.7 51.2 54.1
Laghman 1.70 0.341 0.286 0.396 62.7 54.3 71.2 54.4 52.3 57.0
Panjsher 0.50 0.117 0.090 0.143 25.0 19.1 30.8 46.8 45.2 48.4
Baghlan 3.20 0.291 0.253 0.329 58.0 51.1 64.9 50.2 49.0 51.0
Bamyan 1.60 0.309 0.271 0.346 59.3 52.7 66.0 52.1 50.1 53.1
Ghazni 4.40 0.305 0.255 0.354 58.7 49.7 67.7 51.9 50.6 53.2
Paktika 1.50 0.140 0.102 0.179 29.7 21.7 37.7 47.1 46.0 48.2
Paktya 1.90 0.235 0.203 0.266 48.3 41.9 54.8 48.5 47.5 49.6
Khost 2.20 0.252 0.210 0.294 51.6 43.6 59.7 48.8 47.8 49.8
Kunarha 1.60 0.302 0.263 0.342 57.0 50.5 63.4 53.1 51.6 54.5
Nooristan 0.50 0.476 0.387 0.565 80.2 67.4 93.0 59.3 55.7 62.9
Badakhshan 3.40 0.348 0.307 0.389 64.9 58.4 71.5 53.7 52.1 55.2
Takhar 3.40 0.259 0.221 0.296 51.9 45.0 58.9 49.8 48.7 51.0
Kunduz 3.80 0.430 0.392 0.469 77.3 71.6 83.0 55.6 54.1 57.2
Samangan 1.30 0.409 0.364 0.455 72.7 65.8 79.6 56.3 54.6 58.0
Balkh 4.80 0.237 0.192 0.282 45.0 37.3 52.8 52.6 50.4 54.7
Sar-e-Pul 2.00 0.324 0.283 0.364 61.3 54.3 68.4 52.8 51.3 54.3
Ghor 2.60 0.365 0.319 0.412 70.1 62.8 77.4 52.1 50.3 54.0
Daykundi 1.60 0.348 0.309 0.388 67.4 60.4 74.5 51.7 50.4 52.9
Urozgan 1.30 0.378 0.322 0.434 71.2 60.6 81.8 53.1 51.4 54.7
Zabul 1.20 0.416 0.375 0.457 76.9 70.4 83.3 54.1 53.0 55.2
Kandahar 4.40 0.342 0.303 0.380 66.7 59.8 73.5 51.3 50.2 52.3
Jawzjan 1.90 0.207 0.166 0.247 43.0 34.9 51.0 48.1 47.2 49.0
Faryab 3.90 0.388 0.337 0.438 68.3 60.4 76.2 56.2 54.3 58.1
Helmand 3.30 0.376 0.340 0.411 73.9 66.6 81.2 50.8 49.6 52.1
Badghis 2.40 0.504 0.470 0.537 85.5 81.2 89.8 58.9 57.4 60.4
Herat 7.10 0.316 0.274 0.358 57.6 50.6 64.5 54.8 53.3 56.4
Farah 2.10 0.367 0.318 0.416 66.7 58.2 75.3 55.0 53.9 56.1
Nimroz 0.70 0.237 0.186 0.288 47.5 37.5 57.4 49.9 48.8 51.0
Source: Author’s calculations based on data from ALCS 2016–17.
11AFGHANISTAN MPI 2016–2017 • Report and Analysis
Eric Sutphin | Flickr CC BY
12AFGHANISTAN MPI 2016–2017 • Report and Analysis
difference in multidimensional poverty is clearly ob graph is particularly important because it combines
tained. With this important technical detail in mind, the size of the province in terms of population with
it is not possible to pinpoint the poorest province by the intensity of multidimensional poverty.
the MPI.
It is important to note that more than a quarter of poor
On average, however, MPI values in Badghis (0.504) people live in just four provinces. Herat is home to
and Nooristan (0.476) are the highest. The capital, nearly 8% of poor people in the country, followed by
Kabul, has an MPI of 0.071. This value is significant Nangarhar (7%), Kandahar, and Kunduz (6% each).
ly lower than nearly every other province in country,
At this point, it is natural to ask what deprivations
with Kapisa and Logar being the only exceptions.
create this poverty and how can they be reduced? To
Figure 3.5 depicts where the MPI poor people live answer these questions, we break the MPI down by
across the different provinces in Afghanistan. This indicator and examine its composition. The censored
FIGURE 3.4 MPI by province 2016–17
0.6
0.5
0.4
MPI
0.3
0.2
0.1
0.0
Panjsher
Logar
Samangan
Kabul
Kapisa
Paktika
Parwan
Jawzjan
Paktya
Balkh
Nimroz
Khost
Takhar
Baghlan
Kunarha
Bamyan
Herat
Sar-e-Pul
Wardak
Laghman
Kandahar
Badakhshan
Daykundi
Nangarhar
Ghor
Farah
Helmand
Urozgan
Faryab
Zabul
Kunduz
Nooristan
Badghis
Ghazni
Source: Author’s calculations based on data from ALCS 2016/17.
13AFGHANISTAN MPI 2016–2017 • Report and Analysis
headcount ratio of an indicator represents the propor
tion of the population that is multidimensionally poor
and also deprived in that indicator. Recall that the
MPI can also be computed as the sum of the weight
ed censored headcount ratios. Therefore, reducing any
of the censored headcount ratios – by reducing any
deprivation of any poor person – naturally results in a
reduction in the MPI.
Figure 3.6 shows that the largest censored headcount
ratio corresponds to female schooling (with 48% of
the population being poor and deprived in this in
dicator), cooking fuel (41%), and school attendance
FIGURE 3.5 Proportion of Afghanistan’s poor people in each province (numbers sum to 100%)
Herat 7.72%
Nangarhar 7.35%
Kandahar 5.53%
Kunduz 5.50%
Faryab 5.08%
Ghazni 4.84%
Helmand 4.69%
Kabul 4.49%
Badakhshan 4.20%
Balkh 4.07%
Badghis 3.93%
Baghlan 3.56%
Ghor 3.47%
Takhar 3.37%
Wardak 2.75%
Farah 2.62%
Sar-e-Pul 2.36%
Khost 2.11%
Daykundi 2.06%
Laghman 1.96%
Parwan 1.95%
Samangan 1.85%
Paktya 1.77%
Urozgan 1.76%
Bamyan 1.76%
Zabul 1.75%
Kunarha 1.72%
Jawzjan 1.54%
Logar 0.98%
Paktika 0.85%
Nooristan 0.78%
Kapisa 0.75%
Nimroz 0.66%
Panjsher 0.25%
0% 1% 2% 3% 4% 5% 6% 7% 8%
Source: Author’s calculations based on data from ALCS 2016–17.
14AFGHANISTAN MPI 2016–2017 • Report and Analysis
FIGURE 3.6 National censored headcount ratios, 2016–17
60
47.9
50
41.3
39.1
40 34.6 34.2
32.2
Percentages
31.6
30.6
27.3
30 24.8
22.4 23.2
19.8 18.8 19.4
20
13.5
10 6.7
4.2
0
FS AD SA FS MS AW SN EL CF HO AA DE UN UD YO PR IN SE
Health Education Living Standards Work Shocks
Source: Author’s calculations based on data from ALCS 2016–17.
(39%). On the other hand, some indicators show FS Food security
much lower rates of deprivation while being poor. In
AD Assisted delivery
particular, deprivations are the lowest for electricity
SA School attendance
(4%) and youth NEET (7%).
FS Female schooling
Deprivation in assisted delivery is above 15% in Log
ar, Paktika, Takhar, Ghor, and Nimroz. Deprivation in MS Male schooling
school attendance is above 15% for a larger number of AW Access to water
provinces: Kabul, Kapisa, Logar, Paktika, Paktya, Khost, SN Sanitation
Kunarha, Urozgan, Kandahar, Jawzjan, Helmand, and EL Electricity
Nimroz. In fact, deprivation in school attendance is
CF Cooking fuel
below 10% only in Panjsher and Ghor. Combined,
the contribution of these three indicators is above 40% HO Housing
in Logar, Paktika, Paktya, Badakhshan, Takhar, Balkh, AA Assets and agriculture
Kandahar, Jawzjan, and Nimroz. These results clearly DE Dependency
show that deprivations in health and education often
UN Unemployment
overlap among the poor in several provinces around the
UD Underemployment
country. Thus a set of coordinated intersectoral policies
regarding health and education is needed to boost poor YO Youth NEET
people’s chances of exiting poverty. PR Production
Security is another particularly important indicator. IN Income
The contribution of deprivation in security to the MPI SE Security
is above 15% in three provinces, namely Kunduz,
15AFGHANISTAN MPI 2016–2017 • Report and Analysis
FIGURE 3.7 Percentage contribution of each indicator to urban, rural, and Kuchi MPI, 2016–17
SE Security
%
IN Income
100
PR Production
90 YO Youth NEET
UD Underemployment
80
UN Unemployment
70 DE Dependency
AA Assets and agriculture
60
HO Housing
50
CF Cooking fuel
40 EL Electricity
SN Sanitation
30
AW Access to water
20 MS Male schooling
10 FS Female schooling
SA School attendance
0
AD Assisted delivery
Urban Rural Kuchi National
FS Food security
Source: Author’s calculations based on data from ALCS 2016–17.
Eric Sutphin | Flickr CC BY
16AFGHANISTAN MPI 2016–2017 • Report and Analysis
3.3 ROBUSTNESS OF THE RESULTS TO
ALTERNATIVE POVERTY CUT-OFFS
Urozgan, and Helmand. The only province where the Figure 3.9 plots the value of H for each province
contribution is below 2% is Logar. The contribution and various levels of the poverty cut-off k. The cross
of all the other indicators is regularly below 10% in ing lines in this figure show that there is not a clear
every single region. The only exception is the contri ranking in terms of poverty between provinces for all
bution of deprivation in female schooling in Paktika, possible poverty cut-offs. However, on average, the
Paktya, and Jawzjan. poverty rate in Kabul, the capital, is the lowest among
all provinces for every cut-off between 10% and 50%.
Thus, Kabul’s average incidence of multidimensional
FIGURE 3.8 Percentage contributions of each indicator to provinces’ MPI, 2016–17
%
100
90
80
70
60
50
40
30
20
10
0
Logar
Nangarhar
Panjsher
Takhar
Ghor
Kandahar
Kabul
Kapisa
Parwan
Wardak
Laghman
Baghlan
Bamyan
Ghazni
Paktika
Paktya
Khost
Kunarha
Nooristan
Badakhshan
Kunduz
Samangan
Balkh
Sar-e-Pul
Daykundi
Urozgan
Zabul
Jawzjan
Faryab
Helmand
Badghis
Herat
Farah
Nimroz
FS Food security SN Sanitation UN Unemployment
AD Assisted delivery EL Electricity UD Underemployment
SA School attendance CF Cooking fuel YO Youth NEET
FS Female schooling HO Housing PR Production
MS Male schooling AA Assets and agriculture IN Income
AW Access to water DE Dependency SE Security
Source: Author’s calculations based on data from ALCS 2016–17.
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