G O V E R N M E N T O F N E PA L

N AT I O N A L P L A N N I N G C O M M I S S I O N

N E PA L

Multidimensional
Poverty Index
A N A L Y S I S

T O W A R D S

2021

In partnership with

Empowered lives.
Resilient nations.

A C T I O NNEPAL'S MULTIDIMENSIONAL POVERTY INDEX 2021

G O V E R N M E N T O F N E PA L

N AT I O N A L P L A N N I N G C O M M I S S I O N

N E PA L

Multidimensional
Poverty Index
A N A L Y S I S

T O W A R D S

2021

In partnership with

Empowered lives.
Resilient nations.

A C T I O NNEPAL'S MULTIDIMENSIONAL POVERTY INDEX 2021

Nepal Multidimensional Poverty Index:
Analysis Towards Action

Copyright © 2021

Published byGovernment of Nepal
National Planning Commission
Singha Durbar, Kathmandu
Tel: (+977)-014211136
Fax: (+977)-014211700
Email: npcs@npc.gov.np
Web: www.npc.gov.np
Central Bureau of Statistics (CBS), Nepal with
Oxford Poverty and Human Development Initiative (OPHI)
University of Oxford
All rights reserved. No parts of this publication may be reproduced, stored in a retrieval
system or transmitted, in any form or by any means, electronic, mechanical, photocopying,recording or otherwise, without prior permission.
Cover Photo: pixabay.com
Printed in Nepal

Preface
It gives me an immense pleasure to present Nepal’s
second Multidimensional Poverty Index (MPI)
report – an update to the 2018 report applying revised global MPI methodology. The Multidimensional Poverty Index examines poverty from morethan an income perspective – it includes nutrition,
child mortality, tears of schooling, housing, andassets. It is an academically validated, transparent
and trusted measure of poverty and is globally comparable. The global MPI addresses a key subset of
SDG-poverty related indicators, which include themost pressing poverty issues. Nepal opted to produce a National MPI to enable it to compare its national MPI with the level and trends of other countries, which is important in helping to adjust andincentivize Nepal’s own progress.
This MPI Report 2021 is based on the Nepal Multiple Indicator Cluster Survey Report (NMICS)
2019. The MPI covers a subset of priorities articulated in the current 15th Plan, Sustainable
Development Goals: Status and Roadmap 20162030 Report and Constitution of Nepal 2015. I
am happy to note that Nepal has made substantive progress in reducing MPI from 30.1 percent
to 17.4 percent over the timeframe of five years.
This report reaffirms that Nepal is heading in theright direction in its commitment to Agenda 2030
and in attaining its aspiration of ‘Prosperous
Nepal - Happy Nepali’. Since the report uses a
2019 dataset, this rate might have been changed in
the COVID-19 pandemic context. The pandemic has already negatively impacted the socio-economic sectors of our economy and has slowed thecountry’s promising economic growth rate achievedsince 2015.

The MPI report also contains a detailed analysis of provincial MPIs. This empirical and analytical study on
multidimensional poverty will be useful for provincial
and local governments, enabling them to accelerate
poverty reduction by seeing the different forms it takesin each province. This report will be equally useful forother policy circles and academia.
I would like to thank honourable members of the
National Planning Commission (NPC), member-secretary and officials of the NPC and Central
Bureau of Statistics (CBS) for their rigorous andhard work during the preparation of this MPI report. I am particularly grateful to Sabina Alkire, theDirector of the Oxford Poverty and Human Development Initiative, and her team, for their professional support and guidance in this important endeavour, and to the UNDP and UNICEF country
offices for their generous technical support in preparing this MPI report.
This MPI report justifies our home-grown efforts inpoverty alleviation and I urge all national and international stakeholders to continue cooperation in
our endeavour for Nepal’s growth and development,
and an inclusive, resilient and sustainable economy.

Prof. Dr. Puspa Raj Kadel
Vice-Chair
National Planning Commission
June 2021

iii

NEPAL'S MULTIDIMENSIONAL POVERTY INDEX 2021

Forewords
It is a pleasurethat the National Planning
Commission is presenting second iteration of"Nepal Multidimensional Poverty Index (MPI
Report 2021)”. Multi-dimensional Poverty Index isboth a technically rigorous measure of poverty and ameasure of national and provincial policy priorities
towards poverty reduction. This report reveals thatthe multi-dimensional poverty has been reduced
significantly in Nepal as the country has achieved
significant progress in health, education and housing.
According to strictly harmonized data, Nepal has
been able to reduce multidimensional poverty from30.1 percent to 17.4 percent in between 2014 and2019. This change in MPI is driven by statisticallysignificant reduction in each of the 10 component
indicators. The findings of the report show thatChild Mortality rate has been reduced significantly
and Access to Electricity and Basic Drinking Water
has been increased sharply among the 10 indicators.
Across provinces, Province 2 has the highest MPI
followed by Karnali Province despite their fastest
poverty reduction over the period.
In an era of the SDGs, Nepal’s effort to use theglobal MPI structure, showing the level andcomposition both nationally and disaggregated bygroups such as provinces and age cohorts – may beof interest to other countries that are designing theirnational MPIs using similar datasets. Nepal’s second
national MPI does not contain all aspects relevant topoverty in Nepal due to some data constraints. Butiv

this powerful policy tool still provides meaningful
information to guide more effective policies andmonitor progress. This report presents not only
the level of and trends in poverty but also its
composition by dimensions. From the perspectiveof planning and policy design, this informationfrom the MPI can be used to target poor people andsocial groups, allocate resources to have the largest
poverty impact, coordinate multispectral policies,
and to manage interventions and make evidencebased policy adjustments.
In spite of short duration amidst the COVID-19
pandemic, the CBS team has performed an
appreciable task. As a member of the National
Planning Commission and focal for statistics, I
would like to extend my sincere thanks to theentire family of NPC, staff of CBS and Sabina
Alkine, Director of OPHI for their valuable efforts
to prepare this report. I equally thank UNDP and
UNICEF for their technical support and all persons
who contributed directly and indirectly in thisendeavor.

Dr. Ram Kumar Phuyal
MemberNational Planning Commission

NEPAL'S MULTIDIMENSIONAL POVERTY INDEX 2021

Message
This report presents not only the level of, and trends
in, poverty, but also its composition by dimension.
The report shows that Nepal has made significant
progress in reducing MPI. In terms of poverty
trends, the incidence of multidimensional poverty
nationally fell from 30.1 percent in 2014 to 17.4
percent in 2019. In 2019, the MPI was 0.074.
This means that 3.1 million people left poverty
in five years. The intensity of multidimensional
poverty also significantly decreased from 44.2
percent to 42.5 percent. Across indicators, thehighest number of people are deprived in housingmaterials, clean cooking fuel, years of schooling,assets and nutrition. Considering the indicatorweights, years of schooling and nutritional
deprivations contribute most to ongoing
multidimensional poverty in Nepal.
The report highlights that 28 percent of rural
dwellers are MPI poor as compared with 12.3
percent in urban areas. Over half of Nepal’s poor
population live in rural areas. Moreover, acrossprovinces, Karnali Province has the highest MPI
poor (39.5 percent of people) and followed by
25.3 percent in Sudurpashchim Province, and24.2 percent in Province 2. In Gandaki Province
9.6 percent of people are multidimensionally poor,
while in Bagmati Province it is only 7.0 percent.

From the perspective of planning and policy design,
the MPI information can be used for evidencebased policy adjustments to target poor people andgroups, allocate resources accordingly to have thebiggest poverty impact and accelerate impact onpoverty reduction.
As the data used in this report date from before the
COVID-19 pandemic, some new information may
be required to update the impact of COVID-19.
I am grateful to all the team members, particularly
Sabina Alkire, the Director of the Oxford Poverty
and Human Development Initiative, and her team,
who were engaged in producing this report. I would
also like to thank the UNDP and UNICEF country
offices for their technical support for this report.

Kewal Prasad Bhandari
Secretary
National Planning Commissionv

NEPAL'S MULTIDIMENSIONAL POVERTY INDEX 2021

Message
Nepal has made remarkable progress to lift millionsof men, women and children out of poverty overthe past decades. In more recent years, joint poverty
eradication efforts have been informed by a multidimensional approach to measuring poverty to better
inform prioritization and targeting and related resource allocations to achieve the goal to end poverty
in Nepal. UNDP and UNICEF are pleased to havebeen by Nepal’s side working closely with Government counterparts at all levels under the leadership/
coordination of the National Planning Commission
on our common overarching goal to leave no one
behind in Nepal.
There are at least three reasons to use a multidimensional approach to measuring poverty. First, poverty
and well-being are multidimensional. This reality
has been increasingly accepted both nationally andinternationally. A second important reason to use
a multidimensional approach is the commitment
to the Sustainable Development Goals (SDGs), in
particular SDG 1, which aims to “end poverty inall its forms everywhere”. Under the goal, target 1.2
is to reduce at least by half the proportion of men,
women and children of all ages living in poverty inall its dimensions according to national definitions.
Progress on this target depends on regularly measuring and tracking the national multidimensional
poverty index (MPI). A third reason for taking a
multidimensional approach is to measure non-income deprivations, such as child mortality, primaryschool completion rates or undernourishment, that
do not highly correlate with levels and trends of income poverty. The United Nations Development
Programme (UNDP) and the Oxford Poverty and
Human Development Initiative (OPHI) launchedvi

the multidimensional poverty index (MPI) in the2010 Human Development Report, allowing comparison across countries. Several countries have subsequently adapted the MPI to their national context
considering their national priorities.
It is good to note that the concept of MPI has already been integrated in Nepal’s national development discourse which is also manifested in its
development plans. For example, the current 15th
Development Plan aims to reduce MPI to 11.5%
from 28.6% . This report, second in series, also
shows strong commitment by the government of
Nepal. It is indeed promising that Nepal succeeded
to reduce the MPI from 30.1% in 2014 to 17.4%
in 2019, a 42% reduction in just five years. However, significant disparities between rural and urban,
between age groups as well as among provinces remain a matter of concern. The MPI in rural areas
is still more than two times higher than in urban
areas. Karnali Province’s MPI is more than fivetimes higher than that of Bagmati Province. The
MPI poor thus are concentrated in Karnali Province, Sudurpashim Province and Province 2. From
the age perspective, children represent the poorest
sub-group since more than one in five children are
multidimensionally poor (nearly 22%). This figure
implies that nearly 2.2 million children are poor in
Nepal. Thus, children who form 35% of the population constitute 44% of the multidimensionallypoor.
Moreover, this report also aims to analyse the MPI
in the context of the COVID-19 pandemic to help
identify priority populations for social protection
measures aimed at assisting the poorest/most de-NEPAL'S MULTIDIMENSIONAL POVERTY INDEX 2021

prived households to cope with the devastating secondary impact of the pandemic especially the loss
and/or further reduction of household income andassociated difficulties to pay for livelihood needs andto access health, nutrition, and education services.
This report will provide useful data and analysis not
only in identifying overlapping deprivations, anddeveloping evidence based multi-sectoral poverty
interventions, but also to strengthen the rationale

Elke Wisch
Representative
United Nations Children’s Fund Nepal

for resource allocation and for targeting. UNDP
and UNICEF remain committed to working withall three tiers of the Government of Nepal, particularly through the National Planning Commission
and province governments in applying this reportand its findings to identify the most vulnerable andprioritize them in planning and resource allocation,
lifting people out of existing multidimensional vulnerabilities as well as curtailing inequality to reachthe goal to leave no one behind.

Ayshanie Medagangoda-Labé
Resident Representative
United Nations Development Programvii

Photo: UNDP Nepal

NEPAL'S MULTIDIMENSIONAL POVERTY INDEX 2021

viii

NEPAL'S MULTIDIMENSIONAL POVERTY INDEX 2021

Acknowledgements
This report was prepared under the direction of Prof.
Dr. Puspa Raj Kadel, Honourable Vice-Chairmanof the National Planning Commission (NPC) of
the Government of Nepal in cooperation with the
Oxford Poverty and Human Development Initiative(OPHI) at the University of Oxford.
We are grateful for the guidance and support from
Dr. Ram Kumar Phuyal, Honourable Member,
NPC. We express our gratitude to all other Membersof NPC, Member-Secretary Kewal Prasad Bhandari
and Mr Khomraj Koirala, Joint Secretary, Economic
Management Division for their incisive andsubstantive contributions to this joint initiative. The
technical work was performed by the Central Bureauof Statistics (CBS), led by Director General Nebin
Lal Shrestha, with the support of Deputy Director
General Hem Raj Regmi, Suresh Basnyat, Krishna
Tuladhar, and Dilli Raj Joshi in collaboration with
Hector Moreno and Sophie Scharlin-Pettee, Jakob
Dirksen, Monica Pinilla and Sabina Alkire of OPHI.

We also thank to Dr. Narayan Raj Poudel and
Dr. Chakra Pani Acharya, Programme Directors
at NPC and Accelerating Implementation of
Sustainable Development Goals in Nepal (AISN)
project team members Pushpa Lal Shakya, NPM,
Padam Bhusal, Dr. Menuka Karki and Keshav
Dawadi, Policy Specialists, for their support inshaping this report. We are also grateful to others
whose inputs and insights improved this report,
including Dharma Swarnakar, Kalpana Sarkar,
and Deputy Resident Representative Bernardo
Cocco, and Resident Representative Ayshanie
Medagangoda-Labé of UNDP Nepal; and Thakur
Dhakal, Chief of Social Policy, Evidence andEvaluation Usha Mishra, and Representative Elke
Wisch of UNICEF Nepal. We would like to thank
UNDP and UNICEF Nepal office for providing thefinancial support to publish this report.

ix

NEPAL'S MULTIDIMENSIONAL POVERTY INDEX 2021

Executive Summary
This report provides the first update to Nepal’s official national Multidimensional Poverty Index (MPI).
While the first MPI used 2014 data from the Nepal Multiple Indicator Cluster Survey (NMICS), theupdated MPI in this report uses 2019 data from thesame NMICS report. In this report, minor adjustments were made to five indicators in 2021 in linewith Nepal’s national policy aspirations and to alignwith the global MPI and Sustainable Development
Goals (SDGs) to allow for ongoing international
comparability. The five indicators were nutrition,
child mortality, years of schooling, housing, and assets. The main findings using the previous definition
are also transparently presented in the appendix. The
results from both measures are similar in terms of thescope of poverty and patterns of its reduction.
In 2019, 17.4 percent of Nepalis are multidimensionally poor – just under five million persons, andthe MPI is 0.074. Across indicators, the highest number of people are deprived in housing materials, clean
cooking fuel, years of schooling, assets, and nutrition.
Considering the indicator weights, years of schoolingand nutritional deprivations contribute most to ongoing multidimensional poverty in Nepal.
Disparities exist: for example, 28 percent of rural
dwellers are MPI poor, compared with 12.3 percent
in urban areas (using the updated definitions of rural and urban zones), and over half of Nepal’s poor
population live in rural areas. Across provinces, 39.5
percent of people in Karnali Province are MPI poor
– by far the highest – followed by 25.3 percent in
Sudurpashchim Province and 24.2 percent in Province 2. In contrast, in Gandaki Province 9.6 percent
of people are multidimensionally poor, while thisfalls to 7.0 percent in Bagmati Province. However,xin terms of number of poor, the largest number livein Province 2, followed by Lumbini Province andProvince 1. Children are the poorest – 21.8 percent
of children under 18 years of age are MPI poor compared with 15.1 percent of adults. Furthermore, 44
percent of poor people are children under the age of
18, making this group pivotally important for policy. And if the household includes a member with adisability, overall poverty rises to 18.3 percent, versus 17.4 percent otherwise.
In terms of poverty trends, results show strong andequalizing progress in MPI since 2014. Using the
new MPI, the incidence of multidimensional poverty
nationally fell from 30.1 percent in 2014 to 17.4 percent, and MPI dropped from 0.133 to 0.074, nearing
being cut by nearly half in a mere five years. This is aremarkable result, given that the SDG aim is to cut
multidimensional poverty by half in fifteen years. In
human terms, it means that 3.1 million people left
poverty in a mere five years, with only 5 million left
to exit. The intensity of multidimensional poverty
also significantly decreased from 44.2 percent to 42.5
percent. Nepal’s results are also striking on the international stage in comparison to other countries; according to the 2020 global MPI trend data, no country with a similar starting level of poverty reduced
MPI or its incidence faster than Nepal.
How did this change happen? Progress was strong
and balanced, in that each of the 10 MPI indicators
significantly reduced in that period, with the largest absolute reductions in cooking fuel, sanitation
housing, years of schooling, and nutrition. While
reductions in cooking fuel and housing deprivations
were consistent across all provinces, patterns varied
across provinces. For example, Karnali ProvinceNEPAL'S MULTIDIMENSIONAL POVERTY INDEX 2021

showed strong reductions in water deprivations,whereas Provinces 1, 2, and Lumbini Province hadoutstanding progress in improving sanitation, andProvince 2 also made great strides in improving
child school attendance and nutrition.
Across provinces and age groups, the patterns ofpoverty reduction tended to be pro-poor and equalizing. In 2014, Province 2 was the poorest province,
whereas by 2019 it was the third poorest. Karnali Province, the second poorest province in 2014,
had the second fastest reduction of MPI, followedby Lumbini Province. Across age groups, children
reduced poverty faster than adults. Thus, it seems
that from 2014 to 2019, the poorest groups were
not being left behind but rather were catching up –a positive finding.

Looking forward, it is salutary that in 2019, 60 percent of all poor people would need to reduce just
1-2 deprivations in order to exit poverty, suggesting that further swift reductions would be possible.
However, for moving forward, the findings of theanalysis of COVID-19 related deprivations in thisreport are crucial: First of all, more than half of Nepal’s population (63.5 percent) experience at least
one of the COVID-19 related deprivations (Nutrition, Water, Cooking Fuel). Secondly, MPI poor
people are more affected by additional COVID-19
related deprivation (overcrowding, internet access,handwashing facilities). Thus, on top of their experiences of overlapping deprivations which already puts them at a disadvantage, the situation of
the poor may be more exposed and susceptible to
COVID-19 and its consequences.

xi

NEPAL'S MULTIDIMENSIONAL POVERTY INDEX 2021

Contents
Preface			
Forewords			
Message			
Message			
Acknowledgements
Executive Summary
Contents			Abbreviations and Acronyms

iii
iv
v
vi
ix
x
xii
xv

CHAPTER 1: INTRODUCTION

1CHAPTER 2: METHODOLOGICAL IMPROVEMENTS: BETTER INDICATORS FORGREATER CHALLENGES
2.1 Introduction
2.2 Nepal’s Revised National MPI
2.3 Data Source: NMICS 2019

5
5
5
7

CHAPTER 3: MULTIDIMENSIONAL POVERTY IN 2019 IN NEPAL
3.1 Nepal’s National MPI – Key Results
3.2 MPI across Rural and Urban Areas
3.3 The Composition of MPI by Indicator
3.4 MPI across Provinces
3.5 Disaggregation of the MPI by Age and Disability Status of Household Members
3.5.1 MPI by Age Group
3.5.2 Child Poverty with the MPI
3.5.3 MPI among Households with a Member with Disabilities
3.6 Indicator Deprivations at the National Level

9
9
11
12
14
18
19
20
20
21

CHAPTER 4: MULTIDIMENSIONAL POVERTY REDUCTION OVER TIME
4.1 Key Results: National and Provincial Levels 2014-2019
4.2 Changes in Multidimensional Poverty at Provincial Levels
4.3 Changes in Multidimensional Poverty by Age Group

25
25
30
34

CHAPTER 5: PROVINCIAL ANALYSIS OF MULTIDIMENSIONAL POVERTY
5.1 Province 1
5.1.1 Multidimensional Poverty in Province 1, 2019
5.1.2 Composition of MPI by Indicator in Province 1
5.1.3 Changes over time in Province 1, 2014-2019
5.1.4 Uncensored Headcount Ratios

37
37
37
38
38
40

5.2 Province 2
5.2.1 Multidimensional Poverty in Province 2
5.2.2 Composition of MPI by Indicator in Province 2
5.2.3 Changes Over time in Province 2, 2014-2019
5.2.4 Uncensored Headcount Ratios

41
41
41
42
43

5.3 Bagmati Province
5.3.1 Multidimensional Poverty in Bagmati Province
5.3.2 Composition of MPI by Indicator in Bagmati

44
44
44

xii

NEPAL'S MULTIDIMENSIONAL POVERTY INDEX 2021

5.3.3 Changes over time in Bagmati Province, 2014-2019
5.3.4 Uncensored Headcount Ratios 2019
5.4 Gandaki Province
5.4.1 Multidimensional Poverty in Gandaki Province
5.4.2 Composition of MPI by Indicator in Gandaki Province, 2019
5.4.3 Changes in MPI over time in Gandaki Province 2014-2019
5.4.4 Uncensored Headcount Ratios 2019

45
46
47
47
47
48
49

5.5 Lumbini Province
5.5.1 Multidimensional Poverty in Lumbini Province
5.5.2 Composition of MPI by Indicator in Lumbini Province
5.5.3 Changes over time in Lumbini Province, 2014-2019
5.5.4 Uncensored Headcount Ratios 2019

50
50
50
51
52

5.6 Karnali Province
5.6.1 Multidimensional Poverty in Karnali Province
5.6.2 Composition of MPI by Indicator in Karnali Province
5.6.3 Changes over time in Karnali Province 2014-2019
5.6.4 Uncensored Headcount Ratios 2019

53
53
53
54
55

5.7 Sudurpashchim Province
5.7.1 Multidimensional poverty in Sudurpashchim Province
5.7.2 Composition of MPI by Indicator in Sudurpashchim Province, 2019
5.7.3 Changes over time in Sudurpashchim Province, 2014-2019
5.7.4 Uncensored Headcount Ratios 2019

56
56
56
57
58

CHAPTER 6: MULTIDIMENSIONAL POVERTY AND COVID-19 IN NEPAL
6.1 Multidimensional Poverty and Vulnerability-to-Disease in Nepal in the Context of COVID-19
6.2 Multidimensional Poverty and Additional Deprivations in Nepal in the Context of COVID-19
6.3 Discussion of MPI and COVID-19

59
60
62
64

CHAPTER 7: CONCLUSION

65

REFERENCES

70

ANNEX 1: METHODOLOGY
1.1 A Step-by-Step Application of the Alkire-Foster Method
1.2 Alkire-Foster Method – Technical Details
1.2.1 The Multidimensional Poverty Index: An Adjusted Headcount Ratio
1.2.2 Properties of the Multidimensional Poverty Index
1.3 Measurement Design
1.3.1 Unit of Identification and Analysis
1.3.2 Dimensions, Indicators and Deprivation Cut-offs
1.3.3 Weights
1.3.4 Poverty Cut-off

73
73
75
75
76
77
77
77
77
77

STATISTICAL APPENDIX I. NEPAL MPI USING PREVIOUS METHODOLOGY78

STATISTICAL APPENDIX II: CHANGES OVER TIME 2014-2019: PREVIOUS METHODOLOGY

82
xiii

Photo: UNDP Nepal

NEPAL'S MULTIDIMENSIONAL POVERTY INDEX 2021

NEPAL'S MULTIDIMENSIONAL POVERTY INDEX 2021

Abbreviations and Acronyms
CBS

Central Bureau of Statistics

COVID-19		 Coronavirus Disease – 2019
MDGs		 Millennium Development Goals
MICS 		 Multiple Indicator Cluster Survey
MoF

Ministry of Finance

MPI 		 Multidimensional Poverty Index
NMICS 		 Nepal Multiple Indicator Cluster Survey
NPC		 National Planning Commission
OPHI 		 Oxford Poverty and Human Development Initiative
p.p. 		 Percentage Points
PSU		 Primary Sampling Unit
SDG 		 Sustainable Development Goal
UNDESA		 United Nations Department of Economic and Social Affairs
UNDP 		 United Nations Development Programme
UNICEF 		 United Nations Children’s Fund
WHO		 World Health Organization

xv

xvi

Photo: pixabay.com

NEPAL'S MULTIDIMENSIONAL POVERTY INDEX 2021

NEPAL'S MULTIDIMENSIONAL POVERTY INDEX 2021

CHAPTER 1:

Introduction
In 2018, the Government of Nepal published its firstnational Multidimensional Poverty Index (MPI) report. The MPI report shed a light on the provincial
poverty profiles, highlighting especially high levelsof poverty in Province 2 and in Karnali Province(formerly Province 6). It showed the composition ofpoverty according to each of the 10 included indicators, showcasing the need for different policy strategies across provinces based on the poverty contextsof each province. Given the interest sparked, the
MPI informed policy decisions including planning,budget allocation, and program formulation. The
MPI was then explicitly included in the National
Development Plan, with a clear goal set to reduce
MPI incidence to 11.5 percent by 2024. Anotherlandmark step was that Nepal reported its national
MPI in the global Sustainable Development Goals(SDGs) database as indicator 1.2.2, and its 2020
Voluntary National Review showcased its work on
multidimensional poverty.
As is evident, the 2019 NMICS report, and hence
the analysis in this report, provides a fine-grained
overview of multidimensional poverty levels andtrends in Nepal before the COVID-19 pandemic.
However, it is almost certain that the pandemic
will have affected the national profile of poverty inmultiple ways. Some persons may be newly impoverished; some may have exited poverty; some may
have migrated back to Nepal from abroad, or internally in search of livelihoods. Furthermore, somenecessary pandemic policies may have interrupted

child schooling, with a disproportionate effect on
the poorest children both because of limited phoneor online learning and home tutoring options, andbecause of a potential shortening of their overall
school life expectancy. Yet other poverty policies
during the pandemic will have had positive impacts,
countering or preventing potential deprivations
from occurring, or potentially further reducing
MPI. The NMICS 2019 dataset provides information required for a detailed analysis of poverty in2019 but cannot include changes after that period.
However, this report seeks to provide a discussionof COVID-19 related deprivations (of as well as additional to the deprivations included in the MPI)
to identify people at risk and evaluate heightened
vulnerabilities among the poor as well as the entire
population in Nepal.
This report marks the first update of Nepal’s national MPI since it was launched, and tracks trends
over time in multidimensional poverty 2014-2019,
finding strong reductions. In this chapter and thenext minor adjustments are made to some indicators used in 2018 to better align the new MPI with
the SDGs and with the global MPI, permitting ongoing international comparisons alongside national
insights. In the interest of transparency, all estimations using the un-adjusted structure of the MPI of2018 are provided in detail in the appendix, andthey show similar patterns.

1NEPAL'S MULTIDIMENSIONAL POVERTY INDEX 2021

The Government of Nepal took the path breaking
decision to use the global MPI as the basis for thedesign of its national MPI. This choice built on
far-reaching international knowledge on multidimensional poverty and inherits the best practices set
in place in the global MPI in the selection of dimensions, indicators, deprivation, and poverty cut-offs.
Although there were several reasons, both technicaland normative, for this choice, there were three major motivations. First, the 10 indicators that makeup the global MPI also were a good match for existing government priorities in terms of nutrition andhealth, education, access to services, and infrastructure. Second, having a measure that permitted both
intra-national and international comparisons “enabled Nepal to compare its national MPI with thelevel and trends of other countries” (National Planning Commission, 2018). Third, the Nepal MPI,
like the global MPI, uses the NMICS, which is implemented by the Central Bureau of Statistics withthe support of UNICEF, and the 2014 NMICS
was disaggregated by the newly defined provincial
structure, shedding immediate light on poverty byprovince. Since 2018, the MPI has been used to inform the national development plan, shape budget
allocation, and monitor multidimensional poverty
and other national goals taking worldwide standards
as the reference.
The global MPI developed by Oxford Poverty and
Human Development Initiative (OPHI) and releasedby the United Nations Development Programme(UNDP) in the Human Development Report since20101 , is a powerful measure for policy analysis, dialogue, and monitoring worldwide. In 2018, OPHI
and UNDP jointly undertook a comprehensive revision of the global MPI, drawing upon and subsum-

1

2

Alkire, S. & Santos, M.E. (2010). Multidimensional Poverty Index: OPHI Policy Briefing 01. OPHI: Oxford, UK. https://www.ophi.org.uk/wp-content/
uploads/OPHI-MPI-Brief.pdf.

ing the best of the previous MPIs by adjusting five of
its ten indicators (Alkire, Kanagaratnam, & Suppa,
2018). With this revision the global MPI now betteraligns with the SDGs. The global MPIs uses a measurement approach developed at OPHI by Alkire andFoster, which is described in detail in Annex 1.
In 2021, in consultation with the National Planning Commission, the Central Bureau of Statistics
decided to adjust the national MPI to match the revised global MPI. Ordinarily, a national poverty statistic is only changed after a minimum of 10 years.
However, the changes are minor, and furthermore,they were in the direction of improvement that had
already been considered during the MPI design process. In addition, alignment with the SDGs is a national priority. Furthermore, the inclusion of ‘roof ’in the housing indicator was already demanded byand introduced in Nepal’s national MPI; the global
MPI now followed this adjustment, and also added information on wall materials. Furthermore, theglobal MPI now includes child stunting, and theinclusion of stunting had also been considered by
NPC previously. So, the indicator changes in the
global MPI resonated with the national context as
well. Furthermore, by moving to the revised global
MPI structure, Nepal continues to be a pioneeringcountry in that its national MPI can be used forboth intra-national and international comparisons.
The next chapter sets out the indicator modifications,and subsequent chapters describe in detail the 2019
MPI, its disaggregation by provinces, rural/urban andother priority groups, including children, and how it
has changed since 2014. The final chapter will offer
the analysis of COVID-19 related deprivations.

Photo: UNDP Nepal

NEPAL'S MULTIDIMENSIONAL POVERTY INDEX 2021

3

4Photo: pixabay.com

NEPAL'S MULTIDIMENSIONAL POVERTY INDEX 2021

NEPAL'S MULTIDIMENSIONAL POVERTY INDEX 2021

CHAPTER 2:

Methodological
Improvements: Better
Indicators for Greater
Challenges
2.1 INTRODUCTION
Since its inception, Nepal built a national MPI toreflect national priorities and also permit international comparisons (see National Planning Commission, 2018). The internationally comparable
global MPI was modified in 2018 to better alignwith the SDGs. After due considerationNepal choseto adjust its national MPI accordingly. This chapter
introduces these minor improvements in the indicators of multidimensional poverty.
The main structure of the revised MPI – its dimensions, choice of indicators, poverty cut-off, etc. – isidentical to the version published in 2018 with revisions apparent in select indicator definitions. Bothconsider the same three equally weighted dimensions with 10 individual indicators in total: two indicators for health, two for education and six indicators in the dimension of living standards. Similarly,
each component indicator remains equally weighted
within its respective dimension.2 Finally, the poverty cut-off is still set at one-third of the weighted
sum of indicators; that is, a person who is deprived
in 33.33 percent or more of the weighted sum of
2

The individual indicators in the dimensions of Health and Education accrue
a weight of one-sixth while those in the Living Standards indicators receive aweight of one-eighteenth in both methodologies.

indicators is identified as multidimensionally poor.
Thus, the overall structure in terms of dimensions,indicators, and weights has not changed.
The adjusted indicators do not alter the outstanding
progress of Nepal in terms of MPI reduction – boththe original and revised MPIs show strong reductions (all computations using the previous MPI
structure are transparently included in Statistical
Appendix I and II). These results confirm the patternand distribution of poverty reduction over time.

2.2 NEPAL’S REVISED NATIONAL MPI
Figure 2.1 and Table 2.1 describe the dimensions andindicators included in the revised national MPI for
Nepal. The new structure uses the same three original dimensions, 10 indicators, the same weighting
structure and poverty cut-off. There are adjustments
in five indicators: Nutrition, Child Mortality, Yearsof Schooling, Housing, and Assets. These improvements run in parallel with Nepal’s aspirations. For
instance, in the case of Nutrition, previously, thisindicator only considered nutritional deprivations
based on children’s weight for age (underweight).
5

NEPAL'S MULTIDIMENSIONAL POVERTY INDEX 2021

The new indicator now considers both underweightand stunting for children.

and roof material. Finally, the list of assets now includes computer and animal cart.

The new Child Mortality indicator now focuses on
the unfortunate death of children if they were under 18 years of age. Furthermore, the indicator now
only considers deaths that occurred in the last 5 years
only. Previously, this indicator captured the historic
records of child mortality for children under 5 at any
time – even if the death occurred 20 years ago. In thecase of Years of Schooling, the new indicator is now
higher to reflect the idea of educational achievements
for the new generations and the deprivation cut-offchanged from five to six years of schooling.

Notes: a. Children under 5 years (60 months and younger) are considered undernourished if their z-score of either height-for-age (stunting)or weight-for-age (underweight) is below minus two standard deviations from the median of the reference population.

The changes in the Housing indicator of the global
MPI actually built upon Nepal’s own adaptation ofthat indicator in 2018 and considers a person to be
deprived if there are deprivations in any of the floor,roof, or walls of household dwelling – in contrast to
the previous indicator which only considered floor

b. The child mortality indicator of the global MPI is based on birth history data provided by mothers aged 15 to 49. The “under 18” condition refers to the age of the child who has unfortunately died – thereby using theinternational legal definition of a child, rather than capturing the deathsof any “child” of a mother (e.g., a son or daughter who was 30 years oldand died, reported by a mother who was 60 years old in the household).
c. A household is considered to have access to improved sanitation if
it has some type of flush toilet or latrine, or ventilated improved pit orcomposting toilet, if they are not shared.
d. A household has access to clean drinking water if the water source is
any of the following types: piped water, public tap, borehole, or pump,protected well, protected spring or rainwater, and it is within 30 minutes’ walk (roundtrip).
e. A household has inadequate housing materials if the household has a dirt,
sand, dung or other floor; if the walls are made of natural or rudimentarymaterials; or if the roof is made of natural or rudimentary materials.
Source: Adaptation of Alkire, Kanagaratnam, and Suppa (2020)

FIGURE 2.1: Nepal’s Revised MPI Matches the Global MPI Structure

Nutrition
HealthChild mortality

The
Dimensions ofPoverty

Years of Schooling
Education
School attendance

Cooking fuelSanitation
LivingStandards

Drinking waterElectricity
Housing
Assets6

NEPAL'S MULTIDIMENSIONAL POVERTY INDEX 2021

TABLE 2.1: Nepal’s Revised National MPI
Dimension

Indicator

Household is deprived if…

HealthNutrition

Any child for whom there is nutritional informationis undernourished in terms of weight for age(underweight) or height-for-age (stunting). (a)1/6Child Mortality

A child under 18 has died in the household in the fiveyear period preceding the survey. (b)1/6Years of SchoolingNo household member aged 11 years or older has
completed 6 years of schooling.

1/6

School Attendance

Any school-aged child is not attending school up to theage at which he /she would complete class 8.

1/6

Cooking Fuel

The household cooks with dung, wood, or charcoal

1/18

Improved Sanitation

The household’s sanitation facility is not improved, or
it is improved but shared with other households. (c)1/18

Improved drinking
water

The household does not have access to improved
drinking water or safe drinking water is at least a
30-minute walk from home, roundtrip. (d)1/18Electricity

The household has no electricity.

1/18

Housing

The household has inadequate housing materials in anyof the three components: floor, roof, or walls.

1/18

Assets ownership

The household does not own more than one of theseassets: radio, TV, telephone, computer, animal cart,
bicycle, motorbike, or refrigerator, and does not own a
car or truck.

1/18

Education

Living Standard

2.3 DATA SOURCE: NMICS 2019

Weightmany SDG indicators, and the surveys are implemented by Nepal’s Central Bureau of Statistics withsupport of UNICEF and provides one of the main
sources of information to track the poverty-related
SDGs in the country.

The NMICS 2019 was selected as the most appropriate dataset to analyze the levels of multidimensional poverty in Nepal. The is a recent nationally
representative dataset, containing all the relevant
indicators considered in the national MPI for Nepal
plus many SDG indicators, and was implemented
by the Central Bureau of Statistics with support
from UNICEF in 2019. The NMICS report provides one of the main sources of information to
track the poverty-related SDGs in the country, as it
includes questions on demographic characteristics,
education, health, employment, household assets,household amenities, water supply, and sanitation,
among others. It is representative by rural-urban areas and by province and can be compared to theprovince level – in line with the previous NMICS(2014) survey. Thus, the NMICS dataset was chosen as it is nationally representative, contains all theindicators in the national MPI and which reflect

The sample frame of NMICS 2019 is the National
Population and Housing Census 2011, as this is themost recent census conducted in Nepal.3 The survey contains six questionnaires: Household, Women(age 15-49), Men (age 15-49), Children under five,Children age 5-17, and Water Quality Testing. The
fieldwork occurred during May-November 2019and collected information on 12,800 householdswith a 99.7 percent response rate. Very importantly, the sample was designed to particularly provide
estimates for a large number of indicators on thesituation of children and women at the national level, for urban and rural areas, and of Nepal’s sevenprovinces (CBS, 2020).

For more information on the sampling design of the NMICS 2019
survey, please refer to the Appendix A: Sample Design (starting on p.532)
of the NMICS 2019 report, available online: https://mics.unicef.org/surveys.
According to the UN Population Division (Department of Economic andSocial Affairs) in the world population prospect, the estimates of the total
population for Nepal in 2019 was 28, 609 thousand.

7

3

8

Photo: Prabin Basnet, unsplash.com

NEPAL'S MULTIDIMENSIONAL POVERTY INDEX 2021

NEPAL'S MULTIDIMENSIONAL POVERTY INDEX 2021

CHAPTER 3:

Multidimensional Poverty in2019 in Nepal
This chapter presents the results of the national MPI
for Nepal using the NMICS 2019). It first describes
who is poor and provides the national MPI as wellas the incidence (H) and intensity (A) among thepoor. Disaggregated results are then discussed by rural and urban areas, provinces, and other individual
and household characteristics.

3.1 NEPAL’S NATIONAL MPI – KEY
RESULTS
Table 3.1 presents Nepal’s MPI for 2019, as well as
its partial indices: the incidence of poverty (or the
proportion of people identified as multidimensionally poor, H) and the intensity of poverty (or the
average proportion of weighted indicators in which
the poor are deprived in, A). As can be seen in thetable, the incidence of multidimensional poverty(H) is 17.4 percent. Since this estimate is based on asample, it has a margin of error. Thus, the 95 percent
confidence interval is also presented in the table,
which can be interpreted as with a 95 percent confidence level that the true multidimensional poverty
headcount ratio is between 15.8 percent and 19.1
percent of the population. The average intensity ofpoverty (A), which reflects the share of deprivations
each poor person experiences on average, is estimated at 42.5 percent. That is, each poor person is, on
average, deprived in less than half of the weighted
indicators. The MPI, which is the product of theincidence (H) and intensity (A), amounts to 0.074.
This indicates that multidimensionally poor people
in Nepal experience 7.4 percent of the total deprivations that would be experienced if all people in
Nepal were poor and deprived in all indicators. The
MPI is the official statistic of poverty used to declare whether poverty has fallen or risen over time,
because it considers improvements in both the incidence and intensity of poverty.

TABLE 3.1: Incidence (H), Intensity (A) and Multidimensional Poverty Index (MPI), Nepal 2019
Poverty Cut-off (k)

Index

Value

Confidence Interval (95%)

k value=33%

MPI

0.074

0.067

0.082

Headcount ratio (H, %)

17.4

15.8

19.1

Intensity (A, %)

42.5

41.8

43.2

Number of Poor

Total Population(Millions)
4.98

28.61

Source: Calculations based on data from NMICS 2019. Population figures from UNDESA (2021). Own calculations based on MPI results and population
projection from 2019. This was computed by multiplying the headcount by population of 2019 and rounding to the nearest thousand.

9

NEPAL'S MULTIDIMENSIONAL POVERTY INDEX 2021

Figures 3.1 and 3.2 depict the distribution of theintensity of poverty among the poor by different
deprivation score ranges. A deprivation score refers
to the individual’s sum of weighted deprivations –that is, how many of the weighted indicators they
are deprived in – and is used to identify whether
the individual is considered multidimensionallypoor, as in, if they are deprived in at least 1/3 of theweighted indicators.
The majority (60 percent) of all poor people in
Nepal experience deprivations in the lowest intensity band, which means their deprivation scoreis between 33.33 percent and 39.99 percent of allweighted indicators. About 16 percent of the poor
experience the next higher gradient of intensity (40-49.9 percent). Nearly one in four of the poor in Nepal, about 24 percent, face an intensity of poverty
beyond 50 percent of the weighted indicators.
In a global comparison, Nepal’s 2019 MPI value of
0.074 is below Bangladesh’s 2019 MPI of 0.104,
which is also computed from a NMICS survey, andis lower than the MPI values for all South Asian
countries except the Maldives. Nepal’s MPI matches the MPI of Botswana (2015-16), which is among
the least poor countries in Sub-Saharan Africa, issimilar to that of Nicaragua (2011-12) in Latin
America, and just a little less poor than Kiribati(2018-19 NMICS) in East Asia and Pacific region.

FIGURE 3.1: Intensity Gradient among the Poor, 2019

Source: Calculations based on data from NMICS (2019).

10

NEPAL'S MULTIDIMENSIONAL POVERTY INDEX 2021

FIGURE 3.2: Intensity Gradient among the Poor, 2019

Source: Calculations based on data from NMICS (2019).

3.2 MPI ACROSS RURAL AND URBAN
AREAS
IMPORTANT NOTE: The national definition ofrural and urban areas in Nepal changed between
2014 and 2019. The 2019 NMICS uses the new
definition of rural and urban areas. According tothis new definition, two-thirds of Nepalis live in urban areas and one-third in rural areas. Readers willobserve that this redefinition is significant, so findings cannot be compared with previous results forrural and urban areas. Applying the property of subgroup decomposability, we investigate the levels ofpoverty by rural and urban areas, and by provinces.
In Table 3.2, the MPI, the incidence (H), and theintensity (A) of poverty are presented by urban andrural areas. As can be seen in the table, the incidenceof rural poverty is higher than the one for urban
areas – 28.0 percent and 12.3 percent, respectively.
It is worth noticing that about two-thirds of Nepal’s
population of 28.6 million now live within urban
areas. Figure 3.3 compares the distribution of thepoor and general population by area. Although only
one of every three Nepalese (32.7 percent) resides
in rural areas, nearly half of the multidimensionallypoor live there (this is 52 percent). Therefore, rural
areas still have a disproportionately large share of
population that is multidimensionally poor when
the share of total population is taken as a reference.

TABLE 3.2: Multidimensional Poverty by Rural/Urban Areas, 2019
Index

Urban
Population
Share (%)

MPI
Headcount ratio (H, %)

Rural
Value

ConfidenceInterval (95%)

0.053

0.044

0.061

12.3

10.5

14.2

42.6

41.6

43.7

67.3

Intensity (A, %)Population
Share (%)
32.7

Value

Confidence Interval
(95%)

0.119

0.106

0.132

28.0

25.3

30.7

42.4

41.4

43.4

Source: Calculations based on data from NMICS (2019).

11

NEPAL'S MULTIDIMENSIONAL POVERTY INDEX 2021

FIGURE 3.3: Distribution of Poor and Population by Rural/Urban Areas, 2019

Source: Calculations based on data from NMICS (2019).

3.3 THE COMPOSITION OF MPI BY
INDICATOR
What deprivations create multidimensional poverty in Nepal? To answer these questions, the MPI isbroken down by indicators and its composition is
examined, by looking the censored headcount ratio
and the weighted contribution of each indicator to
the MPI. The censored headcount ratio of an indicator represents the proportion of the populationthat is multidimensionally poor and deprived inthat indicator. The MPI can also be computed as
the sum of the weighted censored headcount ratios.
So, reducing any of the censored headcount ratios
will reduce poverty.

12

Figure 3.4 shows that the largest censored headcount ratios are found in the indicators of Housing and Cooking Fuel. In 2019, 16.2 percent of the
population is multidimensionally poor are deprived
in housing and live in households that cook withdung, wood or charcoal. Over 11.6 percent of people live in households that have no member who
has completed six years of schooling and also are
multidimensionally poor, and nearly 10.3 percent
live in households that are deprived in assets and arepoor. Note that less than 1 percent of individuals in
Nepal are poor and live in a household deprived inChild Mortality (0.9 percent), that is, in a household where a child under the age of 18 years has
died in the five years preceding the survey.

NEPAL'S MULTIDIMENSIONAL POVERTY INDEX 2021

FIGURE 3.4: National ‘Censored Headcount Ratios’, 2019
Censored Headcount Ratios are the proportion of the population who are both MPI poor and are
deprived in each of the indicators.

Source: Calculations based on data from NMICS (2019).

For a more in-depth view on multidimensional poverty, it is useful to see the percentage contributionof each of the 10 indicators for the overall multidimensional poverty in both rural and urban areasof Nepal. Figure 3.5 illustrates the composition ofmultidimensional poverty by showing the weighted
percentage contribution of each indicator. Recall thatthe weights of the indicators in the Health and Education dimensions are higher than those includedin the dimension of Living Standard, therefore it isexpected that indicators in these two dimensions are
more likely to have a higher contribution to the MPI.
In Nepal, the deprivations in Years of Schooling andNutrition have the largest contribution to overall
multidimensional poverty (recall that contribution
considers deprivation levels and indicator weights).

A similar pattern is observed in both urban and rural
areas with some nuances. Figure 3.5 confirms thatthe highest contribution to urban or rural poverty
are the deprivations of years of schooling, followedby the deprivation of nutrition. Overall, the dimension of Living Standards has the largest contribution to poverty (at the national level as well as inrural and urban areas). The aggregate contributionof all Living Standard indicators to overall poverty isaround 43 percent. The dimension of Education is
the second largest contributor to multidimensional
poverty, with a contribution of 33 percent. Finally,
the dimension of Health has the lowest contribution
to overall poverty given the low incidence of Child
Mortality among the poor. Here, Nepal reflects thedecline in global rates of infant and child mortality
over the last half century.

13

NEPAL'S MULTIDIMENSIONAL POVERTY INDEX 2021

FIGURE 3.5: Percentage Contribution of Each Indicator to Rural and Urban MPI, 2019

Source: Calculations based on data from NMICS (2019).

3.4 MPI ACROSS PROVINCES
Table 3.3 presents the sub-national and national estimates for the MPI, the incidence (H), and the intensity of poverty (A). As shown in Map 1 and visualised in Figure 3.6, Karnali Province has the highest
levels of multidimensional poverty with an MPI of
0.169. In Karnali Province, four out of 10 individuals
are multidimensionally poor. Thus, the incidence ofpoverty is nearly 40 percent. Province 2 and Sudurpashchim Province ranks second and third in termsof multidimensional poverty although the differences
between these two provinces are not significant. There
are, however, some differences in terms of incidence
and intensity between these two regions. As shownin Map 2, the incidence is greater in Sudurpashchim
Province but the intensity of poverty, shown in Map
3, is significantly higher in Province 2.

14

The lowest level (MPI: 0.028) and incidence (H:7%) of poverty are found in Bagmati Province,with an incidence of nearly only one sixth of theincidence in Karnali Province. However, the lowest
number of poor people is to be found in Gandaki Province– the second least populated region. A
more detailed characterization of multidimensional
poverty is shown in individual sections for Provinces.

NEPAL'S MULTIDIMENSIONAL POVERTY INDEX 2021

TABLE 3.3: Multidimensional Poverty by Provinces, 2019
Province

Population
Share (%)

Value

MPI

Incidence (H, %)Intensity (A, %)Poor

ConfidenceInterval (95%)

Value

Value

ConfidenceInterval (95%)

Number(thousand)

ConfidenceInterval (95%)

Karnali

5.6

0.169

0.144

0.195

39.5

33.9

45.1

42.9

41.7

44.1

636

Province 2

18.7

0.109

0.085

0.132

24.2

19.1

29.3

45.0

43.3

46.6

1,296

Sudurpashchim

8.7

0.105

0.083

0.126

25.3

20.4

30.3

41.3

40.1

42.5

631

Lumbini

18.4

0.078

0.059

0.098

18.2

14.1

22.2

43.1

41.0

45.2

958

Province 1

17.0

0.066

0.050

0.082

15.9

12.3

19.5

41.4

39.6

43.3

773

Gandaki

8.2

0.035

0.027

0.044

9.6

7.3

11.9

36.4

35.7

37.2

227

Bagmati

23.3

0.028

0.018

0.038

7.0

4.8

9.3

40.3

38.6

42.0

470

National

100.0

0.074

0.067

0.082

17.4

15.8

19.1

42.5

41.8

43.2

4,980

Source: Calculations based on data from NMICS (2019). Provinces ordinarily ranked by the MPI.

Map 3.1 Multidimensional Poverty Index (MPI) Map by Province, 2019

Lumbini

Source: Calculations based on data from NMICS (2019).

15

NEPAL'S MULTIDIMENSIONAL POVERTY INDEX 2021

Map 3.2 Poverty Incidence (H) Map by Province, 2019

Lumbini

Source: Calculations based on data from NMICS (2019).

Map 3.3 Poverty Intensity (A) Map by Province, 2019

Lumbini

Source: Calculations based on data from NMICS (2019).

16

NEPAL'S MULTIDIMENSIONAL POVERTY INDEX 2021

FIGURE 3.6: MPI by Province, 2019

Sudurpaschim
Source: Calculations based on data from NMICS (2019), with 95% confidence intervals shown.

While the level of poverty is very important to consider, it provides an incomplete guide to policies,
such as budget allocation, because the population
shares of the provinces vary. The figure 3.7 below
shows where the poor live. It shows that Province 2
unambiguously houses the largest number of multidimensionally poor – more than two of the poorest
provinces, Karnali and Sudurpashchim Provinces,combined. Of the 5 million poor people in Nepal
2019, just under 1.3 million live in Province 2. Furthermore, almost one million live in Lumbini Province, and over 770,000 in Province 1. Thus, while
Province 1 has a much lower level of MPI than Karnali Province or Sudurpashchim Province, it has thethird highest number of poor due to its much larger
population. This must be considered in designinganti-poverty policies across the provinces.

FIGURE 3.7: Where the Poor Live (2019)(thousands)

Province 2, 1,296
Sudurpaschim, 631

Province 1, 773

Source: Calculations based on data from NMICS (2019).

17

NEPAL'S MULTIDIMENSIONAL POVERTY INDEX 2021

FIGURE 3.8: Percentage Contributions of Each Indicator to Provinces’ MPI, 2019

Sudurpaschim

Source: Calculations based on data from NMICS (2019).

Since the Alkire-Foster method allows for sub-groupdecomposability and dimensional breakdown, it ispossible to explore the dimensional composition of
the MPI not only at the national and urban/rurallevel but also at the provincial level. Figure 3.8 illustrates the percentage contribution of each indicator
to multidimensional poverty for each province. This
figure shows that multidimensional poverty varies
substantially across provinces with some general
patterns. For instance, Years of Schooling has thelargest contribution in all provinces. The contribution of this indicator to overall poverty, however,varies between 17 percent in Karnali Province tonearly 32.5 percent in Bagmati Province. The deprivation in Nutrition has the second largest contributor ranging 16 percent – 26 percent in Gandaki
Province and Karnali Province, respectively. The

18

contribution of Child Mortality is remarkably low
in all the Nepalese territory. In all cases, its percentage contribution is smaller than the analyticalweight attached to this indicator which confirms
the modest contribution of this indicator to overall
poverty in Nepal.

3.5 DISAGGREGATION OF THE MPIBY AGE AND DISABILITY STATUS OF
HOUSEHOLD MEMBERS
In this section, the variation of multidimensional poverty is examined according to household characteristics. This section shows the MPI by age group andhouseholds with at least one member with disability.

NEPAL'S MULTIDIMENSIONAL POVERTY INDEX 2021

3.5.1 MPI by Age Groupin it, households without children will automatically be non-deprived in these indicators. Still, given
this MPI structure it, is clear that children are disproportionally affected by multidimensional poverty. In Nepal, children represent nearly 35 percentof the population. The differences in multidimensional poverty between the two age groups – children aged 0-17 years; and adults, anyone aged 18 orover – are, in any case, significant, with 21.8 percent
of all children living in multidimensional poverty
compared to 15.1 percent of the adult population.
In international comparison, the situation is similar:
across the 5.9 billion people covered by the global
MPI, half of the poor people are children. Thus, theneed to focus on the plight of children is echoed
across the world.

Table 3.4 presents the MPI disaggregated by two
age groups: children (0-17), and adult population(18+). The table also includes the incidence and theintensity of poverty. As visualised in Figure 3.9, thechildren (0-17 years of age) are the poorest sincealmost one in five children are multidimensionallypoor (nearly 22 percent). This means that nearly 2.2
million children are poor.
As Nepal’s MPI is constructed at the household level, the information by age group is not as precise as
an individual child or adult MPI would be. There
are also some design issues: because the survey only
obtains information on undernutrition for children
under the age of five, and also has school attendance

TABLE 3.4: Multidimensional Poverty Index by Age, 2019
MPI

Incidence (H, %)Intensity (A, %)Poor

Age-group

Population
Share (%)

Value

ConfidenceInterval (95%)

Value

ConfidenceInterval (95%)

Value

ConfidenceInterval (95%)

Age 0-17

35.2

0.096

0.087

0.106

21.8

19.9

23.8

44.0

43.2

44.8

2,202

Age 18+

64.8

0.062

0.056

0.069

15.1

13.6

16.5

41.3

40.7

42.0

2,790

Number(thousand)Source: Calculations based on data from MICS (2019).

FIGURE 3.9: MPI by Age, 2019

Source: Calculations based on data from NMICS (2019).

19

NEPAL'S MULTIDIMENSIONAL POVERTY INDEX 2021

TABLE 3.5: MPI, Incidence and Intensity for Children, 2019
Age-group

Population
Share4(%)

MPI

Incidence (H, %)Intensity (A, %)Value

ConfidenceInterval (95%)

Value

ConfidenceInterval (95%)

Value

ConfidenceInterval (95%)

Poor
Number (thousand)Age 0-9

53.9

0.124

0.112

0.136

27.8

25.4

30.3

44.5

43.6

45.3

1,187

Age 10-17

46.1

0.064

0.056

0.072

14.9

13.2

16.5

43.0

42.0

44.0

1,015

Source: Calculations based on data from NMICS (2019).

FIGURE 3.10: MPI by Children, 201914.9 percent, and an intensity of 43.0 percent. The
differences in MPI and the incidence (H) are statistically significant between these two age groups,
although the difference in intensity (A) is insignificant. That younger children are themselves more
often deprived is an important finding for policy
strategies aiming to eradicate child poverty.
3.5.3 MPI among Households with a Member
with Disabilities

Source: Calculations based on data from NMICS (2019).

3.5.2 Child Poverty with the MPIThis section provides a more detailed analysis ofchildren, considered as those aged between 0 and
17 years. This age category can be broken down into
two further categories: early childhood (aged 0-9
years) and adolescence (aged 10-17 years). Table 3.5
shows that of the households living with a child – ahousehold member aged under 18 years – nearly 54
percent of this population live in a household where
the child is under 10 years. The MPI for these households is 0.124, with an incidence of poverty (H) of
27.8 percent, and an intensity (A) of 44.5 percent.
By contrast, the MPI for households living with anadolescent is 0.064, with an incidence of poverty of
4 These shares sum to 100 percent for the overall child population 0-17. The
population shares, in terms of the whole population, are 19.0 percent and16.2 percent for children aged 0-9 and 10-17 years, respectively.

20

The analysis now turns to households in which amember experiences some disability. Individuals
aged 15-49 are considered to have a disability if theyreport a very high level of difficulty in the following
domains: visual; hearing, walking or climbing steps;
remembering or concentrating; self-care, and incommunication. If at least one household member
has any of these difficulties, the household is categorised as having a member with disabilities.
Table 3.6 shows the MPI estimates for householdswith a member with disability and compares theseresults with those obtained for households without
members with disability. The table shows that nearly 3.2 percent of the population live in a household
where one of its members reports a disability. The
MPI for these households is 0.083, with an incidence (H) of poverty of 18.3 percent, and an intensity (A) of 45.5 percent. Although these figures
are larger than those observed in households withno members experiencing any disability, the results
are not statistically different given the sample size –see Figure 3.11. These results however reveal an important pattern which suggest that these households
may indeed experience larger levels of deprivations.

NEPAL'S MULTIDIMENSIONAL POVERTY INDEX 2021

TABLE 3.6: MPI, Incidence (H) and Intensity (A) by Disability Status of Household Members, 2019
Households in which
a person experiences
disability

Population
Share (%)

With a member withdisability
Without a member
with disability

MPI

Incidence (H,%)

Intensity (A, %)Poor
Number(thousand)

Value

ConfidenceInterval (95%)

Value

ConfidenceInterval
(95%)

Value

ConfidenceInterval
(95%)

3.2

0.083

0.060

0.107

18.3

13.4

23.2

45.5

42.2

48.7

166

96.8

0.074

0.066

0.081

17.4

15.8

19.0

42.4

41.7

43.1

4,826

Source: Calculations based on data from NMICS (2019). Refers to population living in households with a member (aged 15-49) is experiencing at least one the following difficulties: difficulty seeing, even if wearing glasses or contact lenses; difficulty hearing, even if using a hearing aid;
difficulty walking or climbing steps; difficulty remembering or concentrating; difficulty with self-care, such as washing all over or dressing; difficultycommunicating.

FIGURE 3.11: MPI among Households with a
Member with Disability, 2019

3.6 INDICATOR DEPRIVATIONS AT THE
NATIONAL LEVELThe uncensored headcount ratio of each indicator
is defined as the proportion of people who are deprived in that particular indicator across the total
population (including people who are poor andnon-poor). Figure 3.12 presents these rates basedon the NMICS (2019) dataset. The highest levels ofdeprivations are found in Cooking Fuel (56.8 percent of the population is deprived in this indicator),and Housing (56.0 percent), followed by deprivations in Sanitation (21.4 percent and 20.3 (18.4
percent). Some indicators, however, show far lower
rates of deprivation. In particular, the uncensoredheadcount ratios are the lowest for Child Mortality(1.6 percent) and Access to Water (6.6 percent).

Source: Calculations based on data from NMICS (2019).

21

NEPAL'S MULTIDIMENSIONAL POVERTY INDEX 2021

FIGURE 3.12: National Uncensored Headcount Ratios, Nepal 2019

Source: Calculations based on data from NMICS (2019).

22

Photo: unsplash.com

NEPAL'S MULTIDIMENSIONAL POVERTY INDEX 2021

23

Photo: unsplash.com

NEPAL'S MULTIDIMENSIONAL POVERTY INDEX 2021

24

NEPAL'S MULTIDIMENSIONAL POVERTY INDEX 2021

CHAPTER 4:

Multidimensional Poverty
Reduction over TimeTo monitor and track Nepal’s progress in achieving
the SDG targets by 2030 and its national development priorities, this chapter analyses trends in multidimensional poverty between 2014 and 2019. The
NMICS 2014 and 2019 datasets are used to createa strictly harmonized MPI to compare it and its
sub-indices across these two periods. As mentioned
in the methodology section, the NMICS for 2014
and 2019 share a common survey design and questionnaire. This allows for the creation of the same
indicators and MPI structure for each year to makerobust comparisons across time. All indicator definitions, weights, and poverty cut-offs used in the2014 to 2019 comparisons follow the same structure presented in Chapter 2. Such analysis allows
an inference of broad poverty alleviation trends overtime, to investigate the contributions and levels ofpoverty by each indicator, and to focus on poverty
reduction broken down by province and age group.
Statistical Appendix II presents these same results
according to the previous methodology, finding thatthe changes are similar.

5

4.1 KEY RESULTS: NATIONAL AND
PROVINCIAL LEVELS 2014-2019
All three key statistics of the MPI – the MPI, theheadcount ratio or incidence (H), and intensity (A)– decreased between 2014 and 2019, and the changes were statistically significant at the 99 percent level(Table 4.1). For example, the MPI dropped sharply
from 0.133 to 0.074. The incidence (H) reduced
from 30.1 percent5 to 17.4 percent, the intensity
also declined significantly, from 44.2 percent to42.5 percent. Thus, in a five-year period, 12.7 percent of the population of Nepal, a total of 3.1 million people, left poverty. In addition, the reduction
in intensity signifies that those who live in multidimensional poverty in 2019 face fewer deprivations,on average, than the multidimensionally poor population in 2014, which is an important achievement(Figures 1a-1c).

This percentage is based on the new methodology of calculation. It was 28.6
percent from the methodology used in MPI 2018.

25

NEPAL'S MULTIDIMENSIONAL POVERTY INDEX 2021

TABLE 4.1: Changes in MPI, H, and A at the National Level, 2014-2019
Index

Poverty across time

ChangesPopulation (million)

2014

2019

Absolute

Relative

significanceYear 1

Year 2

MPI

0.133

0.074

-0.059-44.2%

***

26.91

28.61

H

30.1%

17.4%

-12.6

-42.0%***

Poor people (million)

A

44.2%

42.5%

-1.7

-3.9%

***

8.10

5.00

Note: *** statistically significant at α=0.01, ** statistically significant at α=0.05, * statistically significant at α=0.10.

FIGURE 4.1A: MPI Nepal, 2014-2019

FIGURE 4.1B: Incidence (H) of
Multidimensional Poverty in Nepal, 2014-2019

Source: Calculations using the NMICS 2014 and NMICS 2019.

Source: Calculations using the NMICS 2014 and NMICS 2019.

FIGURE 4.1C: Intensity (A) of
Multidimensional Poverty Nepal, 2014-2019

Source: Calculations using the NMICS 2014 and NMICS 2019.

26

International comparisons can be useful to put Nepal’s trends into context. Considering the 32 othercountries whose MPI values ranged from zero to
0.350 according to NMICS surveys covered in the2020 global MPI (recall that Nepal’s MPI value in2006 was 0.350, so they are in this range), only one
country – eSwatini 2010-2014 – matched Nepal’sannualized rate of MPI reduction. In terms of theabsolute reduction of the headcount ratio, of all
countries considered in the 2020 global MPI trends
with a starting incidence of 33 percent or less, Nepal’s rate of reduction was only matched by eSwatini. The others were slower. As might be expected,
some countries with much higher levels of poverty
had higher rates of absolute reduction, but among
the natural comparison group for Nepal, its performance was commendable.

NEPAL'S MULTIDIMENSIONAL POVERTY INDEX 2021

To understand what drove poverty reduction in Nepal, we look to Figure 4.2, which depicts the censoredheadcount ratios of the 10 indicators – the percentageof people who are MPI poor and deprived in a given
indicator of the total population for both 2014 and2019. We can see that the indicators with the highest
deprivation levels among the multidimensionally poorin 2014 and 2019 are Cooking Fuel (29.4 percent and16.2 percent, respectively), Housing (28.6 percent and16.2 percent, respectively), and Years of Schooling(19.6 percent and 11.6 percent, respectively). Furthermore, the Child Mortality indicator observed the fewest deprivations among the multidimensionally poor,
with 1.7 percent in 2014 and only 0.9 percent in 2019of the multidimensional poor deprived in child mortality. To refine the story of substantial reduction in

multidimensional poverty over time, we turn to Figure
3, which depicts the absolute changes in these censoredheadcount ratios. Between 2014 and 2019, all indicators reduced significantly in Nepal at the 99 percent
confidence interval, with the greatest absolute reductions in Cooking Fuel (-13.2 p.p.), Sanitation (-13.0p.p.), and Housing (-12.4 p.p.).
In comparison to other countries included in the2020 MPI trend analysis, three-quarters of thecountries did not manage to achieve this balanced
result. Nepal is, therefore, among the 20 countries including Bangladesh and India in South Asiawith a significant reduction in all 10 indicators.
Three-quarters of the countries with trend data did
not manage to achieve this balanced result.

FIGURE 4.2: National Censored Headcount Ratios in Nepal, 2014-2019

Source: Calculations using the NMICS 2014 and NMICS 2019.

27

NEPAL'S MULTIDIMENSIONAL POVERTY INDEX 2021

FIGURE 4.3: Absolute Reduction in Censored Headcount Ratios, 2014-2019

Source: Calculations using the NMICS 2014 and NMICS 2019.

The population-wide trends in each indicator included in the MPI can be analysed alongside thetrends in deprivations of the poor. Figure 4.4 presents the uncensored headcount ratios which means
the proportion of the population deprived in eachof the 10 indicators used in the national MPI for
Nepal. The figure shows that nine of the 10 indicators have registered statistically significant improvements over time. The only reduction which was not

28

significant was in Assets. Figure 4.5 displays the absolute change in the uncensored headcount ratios
and their statistical significance between 2014 and2019. This figure shows that Sanitation, Cooking
Fuel, and Housing are the indicators showing thelargest absolute reductions (-18.2 and -17.7 and-13.3 percentage points, respectively). These population-wide trends therefore mirror the trends of themultidimensionally poor.

NEPAL'S MULTIDIMENSIONAL POVERTY INDEX 2021

FIGURE 4.4: National Uncensored Headcounts Ratios in Nepal, 2014-2019

Source: Calculations using the NMICS 2014 and NMICS 2019.

FIGURE 4.5: Absolute Change in Uncensored Headcount Ratios, 2014-2019

Source: Calculations using the NMICS 2014 and NMICS 2019.

29

NEPAL'S MULTIDIMENSIONAL POVERTY INDEX 2021

Another way to unpack the drivers of poverty reduction in Nepal is to focus on the contribution ofeach of the 10 indicators of the MPI. As the generalcomposition of the MPI stays relatively unchanged
between 2014 and 2019, one can conclude that themultidimensional poverty profile of the poor in Nepal remains largely the same across time. In both
years, an insufficient number of Years of schooling
contributes most strongly to poverty, followed by
Nutrition. The good news is that the contribution of
Sanitation decreased, although the Assets indicator’s
contribution saw a sizeable increase. Other indicators’ weighted contribution is smaller than these.
Although poverty at the urban and rural level in2019 is presented in Chapter 3, the changes inurban and rural poverty levels 2014 - 2019 is notpresented here, due to redrawing of administrative
boundaries between the surveys. Appendix A of the
NMICS 2019 report (p.532) states that, “[t]here
have been substantial changes in the administrative
structure of Nepal. Most notable is the extensive reclassification of geographical locations from rural tourban at the ward level. New municipalities havebeen declared and old municipalities have either
been upgraded to sub-metro city or been extended
in area by merging several old wards.”6 This reclassification at the urban and rural levels prevents strictly harmonized comparisons for multidimensional
poverty over time.

630

Central Bureau of Statistics (CBS) (2020). Nepal Multiple Indicator Cluster
Survey 2019, Survey Findings Report. Kathmandu, Nepal: Central Bureau of
Statistics and UNICEF Nepal.

4.2 CHANGES IN MULTIDIMENSIONAL
POVERTY AT PROVINCIAL LEVELS
Another key feature of the poverty reduction trends
in Nepal is the disaggregation of the MPI and its
associated statistics by province. Table 4.2 breaks
down the levels and changes of MPI, the incidence(H), and the intensity (A) between 2014 and 2019
for the seven provinces of Nepal. Six of the sevenprovinces see a statistically significant reduction at
the 99 percent level in MPI and incidence. Three
provinces, Gandaki, Karnali, and Sudurpashchim,significantly reduced the intensity of poverty – thus
driving the national average reduction in intensity.
This suggests that these three provinces had meaningful improvements in the lives of the multidimensionally poor population, reducing on average thenumber of deprivations per household, during thefive-year period.
Province 2, which was the second poorest province
in 2014, observes the largest reduction in both the
MPI and incidence of multidimensional poverty,
nearly halving its MPI and incidence between the
two time periods. Notably, as its total population
remains largely the same between the two years,
poverty eradication measures may have led to these
improvements. Furthermore, Province 2 also has
the largest population share of the provinces in
the initial year, so its poverty reduction – in which
800,000 people moved out of poverty – is a remarkable feat.

NEPAL'S MULTIDIMENSIONAL POVERTY INDEX 2021

TABLE 4.2: Changes in MPI, Incidence (H), and Intensity (A) at the Provincial Level, 2014-19
Province
Province 1

MPI
2014

2019

Absolute (p.p.)

Relative (%)

0.091

0.066

-0.025

-27.7

*

2014

2019

4,446

4,860

Province 2

0.222

0.109

-0.113

-50.9

***

5,554

5,355

Bagmati

0.061

0.028

-0.033

-53.4

***

5,243

6,673

Gandaki

0.078

0.035

-0.043

-54.9

***

2,565

2,353

Lumbini

0.138

0.078

-0.059

-43.1

***

4,854

5,266

Karnali

0.234

0.169

-0.065

-27.7

***

1,585

1,611

Sudurpaschim

0.144

0.105

-0.039

-27.3

***

2,659

2,491

National

0.133

0.074

-0.059-44.2***

26,91028,6102014

2019

Absolute (p.p.)

Relative (%)

Province 1

21.3

15.9

-5.4

-25.4

Province 2

49.1

24.2

-24.9

-50.7

Bagmati

14.4

7.0

-7.4

-51.1

***

Province

H (%)

ChangesPopulation Share
2014

2019

*

16.5%

17.0%

***

20.6%

18.7%

19.5%

23.3%

Gandaki

18.5

9.6

-8.8

-47.8

***

9.5%

8.2%

Lumbini

31.2

18.2

-13.0

-41.7

***

18.0%

18.4%

Karnali

50.9

39.5

-11.4

-22.4

***

5.9%

5.6%

Sudurpashchim

32.5

25.3

-7.1

-22.0

**

9.9%

8.7%

National

30.1

17.4

-12.6

-42.0

***

100.0%

100.0%

Province

A (%)

ChangesNumber of Poor (thousands)

2014

2019

Absolute (p.p.)

Relative (%)

Province 1

42.8

41.4

-1.3

-3.1

948

773

Province 2

45.1

45.0

-0.2

-0.3

2,729

1,296

2014

Bagmati

42.3

40.3

-2.0

-4.6

Gandaki

42.2

36.4

-5.7

-13.6

Lumbini

44.1

43.1

-1.0

-2.3

Karnali

46.1

42.9

-3.2

-6.9

***

Sudurpashchim

44.3

41.3

-3.0

-6.8

National

44.2

42.5

-1.7

-3.9

To dig deeper into the provincial level story, let
us look to the absolute changes in censored headcount ratios by province showcased in Figure 4.6.
Although no province saw significant reductions inall 10 indicators, Province 2 observed significant
reductions in all indicators except Child Mortality,
Water, and Assets. Of the seven indicators, it had
the fastest reductions among the provinces in Yearsof Schooling (-1.5 p.p.), School Attendance (-0.9p.p.), Nutrition (-1.2 p.p.), Sanitation (-3.1 p.p.),
7

Total Population (thousands)

Changes7

2019

755

470

474

227

1,515

958

807

636

***

864

631

***

8,100

5,000

***

Electricity (-1.1 p.p.), Cooking Fuel (-2.6 p.p.),and Housing (-2.3 p.p.). Bagmati Province led theprovinces in significant reductions in deprivations
among the poor and deprived in Child Mortality(-0.1 p.p.) and Assets (-0.3 p.p.). Karnali achieved
the fastest significant reductions in Water deprivations among the poor (-1.7 p.p.). Moreover, all provinces saw significant reductions among the poor anddeprived in Cooking Fuel and Housing. That said,
with attention paid to the administrative re-district-

Note: *** statistically significant at α=0.01, ** statistically significant at
α=0.05, * statistically significant at α=0.10.

31NEPAL'S MULTIDIMENSIONAL POVERTY INDEX 2021

FIGURE 4.6: Absolute Changes in Censored Headcount Ratios by Province
Sudurpaschim

Source: Calculations using the NMICS 2014 and NMICS 2019.

ing, harmonized comparisons at the provincial level
were possible by merging the appropriate districts.
Figure 4.7 shows the absolute changes in MPI byprovince. Karnali Province and Lumbini Province,
at -0.065 and -0.059, respectively, followed Province 2 in fastest reductions rates. Karnali Province
was the poorest province in 2014, and Lumbini
Province the third poorest. Furthermore, Province
2, Karnali Province, and Lumbini Province outperformed the national average. Province 1 only saw
a significant MPI reduction at the 90 percent significance level, but it did see a drop in the numberof poor by around 175,000 people despite overall
population growth in the province.

32

Figure 4.8 plots the starting level of MPI poverty
on the horizontal axis, with the poorest provinces,
Province 2 and Karnali Province, furthest to theright. The vertical axis is the pace of reduction of
MPI, with the lower bubbles showing fastest absolute poverty reduction. This dramatic figure showcases a pro-poor reduction among the provinces in
Nepal, with the poorest provinces, led by Province
2, having faster rates of MPI reduction. This meansthat there is an important story to tell at the provincial level in Nepal about its pro-poor poverty
eradication between 2014 and 2019. This provincial
story is the focus of the next chapter.

NEPAL'S MULTIDIMENSIONAL POVERTY INDEX 2021

FIGURE 4.7: Absolute Changes in MPI by Province, 2014-2019
Sudurpaschim

Source: Calculations using the NMICS 2014 and NMICS 2019.

FIGURE 4.8: Absolute Changes in MPI across Provinces, 2014-2019

Sudurpaschim

Source: Calculations using the NMICS 2014 and NMICS 2019.

33

NEPAL'S MULTIDIMENSIONAL POVERTY INDEX 2021

4.3 CHANGES IN MULTIDIMENSIONAL
POVERTY BY AGE GROUP
Table 4.3 shows the levels and changes of MPI, theincidence, and intensity of multidimensional poverty between 2014 and 2019 for the children andadult population. In 2014, nearly 36 percent ofchildren were multidimensionally. By 2019, only

22 percent of all the children remained in poverty
which implies a reduction of 14 percentage points.
These figures imply that nearly 1.7 million children
left multidimensional poverty in Nepal between
2014 and 2019 in comparison to nearly 1.4 millionof adults.

TABLE 4.3: Changes in MPI, Incidence (H), and Intensity (A) for Age Groups, 2014-2019
Age group

MPI

Change 2014 - 2019

Total Population (millions)

2014

2019

Absolute

Relative

significance

2014

2019

Age 0-17

0.164

0.096

-0.067-41.2%

***

10.8

10.1

Age 18+

0.112

0.062

-0.050-44.7%

***

16.1

18.5

Age group

H

Change 2014 - 2019

Population Share (%)

2014

2019

Absolute

Relative

significance

2014

2019

Age 0-17

35.9

21.8

-14.1

-39.2%

***

40.2

35.2

Age 18+

26.1

15.1

-11.1

-42.4%

***

59.8

64.8

Age groupA

Change 2014 - 2019

Number of Poor (million)

2014

2019

Absolute

Relative

significance

2014

2019

Age 0-17

45.5

44.0

-1.5

-3.3%

**

3.89

2.20

Age 18+

43.1

41.3

-1.7

-4.0%

***

4.20

2.79

34

Photo: UNDP Nepal

NEPAL'S MULTIDIMENSIONAL POVERTY INDEX 2021

35

Photo: unsplash.com

NEPAL'S MULTIDIMENSIONAL POVERTY INDEX 2021

36

NEPAL'S MULTIDIMENSIONAL POVERTY INDEX 2021

CHAPTER 5:

Provincial Analysis ofMultidimensional Poverty
IMPORTANT NOTE: This chapter delves into theprovincial analysis of the MPI in Nepal. The firstpart of the provincial analysis is devoted to the mostrecent information with NMICS (2019). The firstsection presents the most recent figures of multidimensional poverty in terms of the incidence, theintensity of poverty and the MPI overall. To provide
specific guidance of potential policy interventions,the chapter includes a detailed analysis of the censored headcount ratio (the percentage populationthat is multidimensionally poor and deprived ineach indicator) and the percentage contribution ofeach indicator. The last section examines the changes over time between 2014 and 2019 in provinces.
It is worth noting that the provinces did not exist
as their own territories in 2014. Sampling clusters
were used by CBS in the prior MPI – and in the
NMICS 2014 report – and so the data are takenfrom the PSUs that comprise provinces in the harmonized time-series analysis.
The composition of MPI in provinces is describedin the following terms:

the proportion of the population that is multidimensionally poor and also deprived in that specific indicator. The MPI can also be computed as thesum of the weighted censored headcount ratios. So
reducing any of the censored headcount ratios will
reduce poverty.
To analyse the contribution that each indicator
makes to the MPI and understand the compositionof poverty in each region, the percentage contribution per indicator is computed. It is important tohighlight that two indicators may have the same censored headcount ratios but different contributions
to overall poverty, because the contribution depends
both on the censored headcount ratio and on theweight assigned to each indicator. This means thatthe largest reductions in multidimensional poverty
can be obtained by tackling the deprivations withthe largest contributions. The percentage contribution of each indicator to overall multidimensional
poverty is therefore a valuable supplementary information to the censored headcount ratio.

What deprivations create multidimensional poverty in provinces and how can they be reduced? To
answer these questions, the MPI is broken down
by indicator and its composition is examined. The
censored headcount ratio of an indicator represents

37

NEPAL'S MULTIDIMENSIONAL POVERTY INDEX 2021

5.1 PROVINCE 1
5.1.1 Multidimensional Poverty in Province 1,
2019

FIGURE 5.1.1: Percentage Contribution to
MPI in Province 1

Province 1 has the third lowest MPI of all provinces,
at 0.066. Table 5.1.1 shows that the headcount ratioof multidimensional poverty in Province 1 is 15.9
percent. This is below the national multidimensional
poverty rate of 17.4 percent and substantially lower
than that of Karnali Province, the poorest province,
where nearly 40 percent of the population is identified as poor. The intensity of poverty in Province 1 is41.4 percent, which means that those who are identified as multidimensionally poor are deprived, on
average, in 41.4 percent of the weighted sum of indicators. Nationally, the poor in Nepal are deprived in
42.5 percent of indicators, so the intensity of deprivation is lower in this province than nationally. Province 1 is home to 17 percent of Nepal’s population.
This implies that in 2019 there were approximately
7,735,000 MPI poor people in Province 1.
TABLE 5.1.1: Incidence, Intensity andMultidimensional Poverty Index (MPI) in
Province 1, 2019
PovertyCut-off (k)

Index

Value

ConfidenceInterval (95%)

k value =
33%

MPI

0.066

0.050

0.082

Incidence (H, %)

15.9

12.3

19.5

Intensity (A, %)

41.4

39.6

43.3

Source: Calculations based on data from NMICS (2019).

5.1.2 Composition of MPI by Indicator in
Province 1

Table 5.1.3 shows that the largest censored headcount ratio is found in the indicator of cooking fuel.
In 2019, 15.2 percent of the population is multidimensionally poor and deprived in this indicator.
The second largest deprivation experienced by thepoor in Province 1 is housing (14.7 percent).
The bar graph in Figure 5.1.1 shows the percentage
contribution of each indicator in Province 1. The
height of each bar denotes the percentage contribution of each indicator to the overall MPI. All bars
38

Source: Calculations based on data from NMICS (2019).

add up to 100 percent. The figure also shows thecontribution at the national level which allows for
an immediate visual comparison. The indicator that
contributes the most to the MPI in Province 1 isYears of Schooling (27.8 percent), which is followedby Nutrition (18.2 percent). These two indicators
account for nearly half of the total contributions tooverall multidimensional poverty (46 percent) in
Province 1.
5.1.3 Changes over time in Province 1, 20142019

Between 2014 and 2019, this province reduced the
proportion of MPI poor people by nearly -5.4 percentage points, from 21.3 percent in 2014 to 15.9

NEPAL'S MULTIDIMENSIONAL POVERTY INDEX 2021

TABLE 5.1.2: Changes in MPI, H and A in Province 1
Index

Poverty across time

Change 2014 – 2019

Population (thousands)

2014

2019

Absolute

Relative

significanceYear 2014

Year 2019

MPI

0.091

0.066

-0.025-27.7%*

444,603.7

485,995.5

H

21.3%

15.9%

-5.4%

-25.4%*

Poor people (thousands)

A

42.8%

41.4%

-1.3%

-3.1%

948.2

773.5

Note: *** statistically significant at α=0.01, ** statistically significant at α=0.05, * statistically significant at α=0.10.
Source: Calculations based on data from NMICS (2019).

percent in 2019. This reduction is statistically significant with a 90 percent confidence level. This
represents a move out of poverty for nearly 174.7
thousand people in this province. Furthermore,
Table 5.1.2 shows that there was also a statisticallysignificant reduction in the MPI (with a 90 percent
confidence level), as well as a reduction in the intensity of poverty (not statistically significant).

change was observed in Sanitation with an absolute
reduction of 9.9 percent points in absolute terms.
In relative terms (this is, as a percentage of the value
observed in 2014) Sanitation was also the indicatorwith the largest reduction (-65 percent).
All these trends and figures are encouraging as they
confirm a consistent trend in poverty reduction overthis five-year period. A similar level of commitment
is needed to maintain this progress in the coming
years. This is particularly the case for the indicatorof Assets that showed the smallest variation, a slightincrease of 1.4 percentage points in absolute terms,
during this period.

To conclude the analysis of multidimensional
poverty in Province 1, the change in the censoredheadcount ratio – the proportion of the population that is multidimensionally poor and deprived
in each respective indicator – is presented. Province
1 managed to reduce the censored headcount ratio
in most of the indicators (Table 5.1.3). The largest

TABLE 5.1.3: Changes in Censored Headcount Ratio in Province 1
Value (percent)

Changes

Indicator

2014

2019

Absolute (p.p.)

Relative (%)

Child Mortality

1.3

0.9

-0.5

-34.1%

Nutrition

9.9

7.2

-2.7

-26.9%

significanceSchool Attendance

3.0

2.7

-0.3

-9.6%

Years of Schooling

15.3

11.0

-4.4

-28.4%

Electricity

6.6

5.1

-1.5

-22.7%

Water

2.8

2.6

-0.2

-6.1%

Sanitation

15.2

5.3

-9.9

-65.0%***

Housing

20.9

14.7

-6.3

-30.0%**

Cooking Fuel

21.0

15.2

-5.8

-27.5%**

Assets

9.0

10.4

1.4

15.1%

*

Note: *** statistically significant at α=0.01, ** statistically significant at α=0.05, * statistically significant at α=0.10.
Source: Calculations based on data from NMICS (2019).

39

NEPAL'S MULTIDIMENSIONAL POVERTY INDEX 2021

TABLE 5.1.4: Headcount Ratios in Province 1, 2019
Dimension
Health
Education

Living Standards

Indicator

Headcount ratios
Uncensored

CensoredChild Mortality

1.2%

0.9%

Nutrition

13.7%

7.2%

School Attendance

3.9%

2.7%

Years of Schooling

17.4%

11.0%

Electricity

11.6%

5.1%

Water

5.7%

2.6%

Sanitation

15.4%

5.3%

Housing

67.7%

14.7%

Cooking Fuel

67.1%

15.2%

Assets

21.7%

10.4%

Source: Calculations based on data from NMICS (2019).

5.1.4 Uncensored Headcount Ratios

Photo: unsplash.com

The analysis of poverty in Province 1 closes by presenting the uncensored (population wide) headcount ratio of deprivations for each of the 10 indicators in 2019. This represents the proportionof the population who are deprived in a particular
indicator, irrespective of their poverty status. This
information is useful to identify the main patternsof deprivation. Table 5.1.4 presents these rates using
NMICS (2019). In Province 1, the highest deprivation levels are found in Housing (67.7 percentof the population is deprived in this indicator) andCooking Fuel (67.1 percent) indicators show lower
rates of deprivation. The lowest deprivation is foundin Child Mortality indicator (1.2 percent).

40

NEPAL'S MULTIDIMENSIONAL POVERTY INDEX 2021

5.2 PROVINCE 2

5.2.2 Composition of MPI by Indicator in
Province 2

5.2.1 Multidimensional Poverty in Province 2

Table 5.2.3 shows that the largest censored headcount ratio is found in the indicator of Housing. In
2019, 23.1percent of the population is multidimensionally poor and deprived in this indicator. The
second most-widespread deprivation among thepoor in Province 2 is Cooking Fuel (11. percent).

Province 2 has the second highest MPI of any
province in Nepal, at 0.109. This is above the national MPI of 0.074. Table 5.2.1 shows that theincidence of multidimensional poverty in Province
2 is 24.2percent. Thus, the proportion of multidimensionally poor in Province 2 is higher than thenational incidence of multidimensional poverty of
17.4 percent but substantially lower than that of
Karnali Province, the poorest province, where nearly 40 percent the population is identified as poor.
Province 2 houses 18.7 percent of the populationin Nepal. So in 2019 there were approximately 1.2
million MPI poor people in Province 2. The intensity of poverty in Province 2 is 45 percent, whichmeans that those who are identified as multidimensionally poor are deprived, on average, in 45percentof the weighted indicators. Nationally, the poor in
Nepal are deprived in 42.5 percent of indicators, so
the intensity of deprivation is higher in this province than nationally.

Figure 5.2.1 shows the percentage contribution ofeach indicator in Province 2. The height of each bar
denotes the percentage contribution of each indicator
to the overall MPI. The figure shows the contribution
at the national level which allow for an immediate visual comparison. The indicator that contributes themost to the MPI in Province 2 is Years of Schooling(29 percent), which is followed by Nutrition (19 percent). These two indicators account for nearly half ofthe total contributions to overall multidimensional
poverty (48 percent) in Province 2.

TABLE 5.2.1: Incidence, Intensity and Multidimensional Poverty Index (MPI) in Province 2
Poverty Cut-off (k)k value = 33.33%

Index

Value

Confidence Interval (95%)

MPI

0.109

0.085

0.132

Incidence (H, %)

24.2

19.1

29.3

Intensity (A, %)

45.0

43.3

46.6

Source: Calculations based on data from NMICS (2019).

FIGURE 5.2.1: Percentage Contribution to MPI in Province 2

Source: Calculations based on data from NMICS (2019).

41

NEPAL'S MULTIDIMENSIONAL POVERTY INDEX 2021

TABLE 5.2.2: Changes in Multidimensional Poverty Index (MPI), Incidence and Intensity
in Province 2
Index

Poverty across time

Change 2014 – 2019

Population (thousands)

2014

2019

Absolute

Relative

significanceYear 2014

Year 2019

MPI

0.222

0.109

-0.113-50.9%***

555,434

535,457

H

49.1%

24.2%

-24.9%-50.7%***

Poor people (thousands)

A

45.1%

45.0%

-0.2%

-0.3%

2728.81296.2

Note: *** statistically significant at α=0.01, ** statistically significant at α=0.05, * statistically significant at α=0.10
Source: Calculations based on data from NMICS (2019).

5.2.3 Changes Over time in Province 2, 20142019

Between 2014 and 2019, this province reduced the
proportion of MPI poor people by nearly -24.9 percentage points, from 49.1 percent in 2014 to 24.2
percent in 2019. This reduction is statistically significant with a 99 percent confidence level. This represents a move out of poverty for nearly 1.4 million
people in this province. Furthermore, Table 5.2.2
shows that there was also a statistically significant reduction in the MPI (with a 99 percent confidence
level level), as well as a reduction in the intensity ofpoverty (not statistically significant).
To conclude the analysis of multidimensional poverty in Province 2 the analysis turns to the change inthe censored headcount ratio – the proportion of the
population that is multidimensionally poor and de-prived in each respective indicator. Province 2 managed to reduce the censored headcount ratio for mostof the indicators (Table 5.2.3). The greatest change
was observed in Sanitation with an absolute reduction of 31.1 percentage points in absolute terms. In
relative terms (this is, as a percentage of the value observed in 2014) the indicator with the largest reduction was Electricity (79.2 percent).
All these trends and figures are encouraging as they
confirm a consistent trend in poverty reduction overthis five-year period. A similar level of commitment
is needed to maintain this progress in the coming
years. This is particularly the case for the indicatorof Access to Clean Water that showed a slight increase of 0.3 percentage points in absolute terms
during this period.

TABLE 5.2.3: Changes in Censored Headcount Ratio in Province 2
Value (percent)Indicator

2014

2019

ChangesAbsolute(p.p.)

Relative (%)

significanceChild Mortality

1.4

0.7

-0.7

-49.0%

Nutrition

24.7

12.4

-12.3

-49.8%***

School Attendance

17.5

8.7

-8.8

-50.3%***

Years of Schooling

33.8

18.9

-14.9

-44.0%***

Electricity

14.0

2.9

-11.1

-79.2%

***

Water

1.4

1.7

0.3

24.8%

Sanitation

45.3

14.3

-31.1

-68.5%***

Housing

46.2

23.1

-23.2

-50.1%***

Cooking Fuel

48.5

22.3

-26.2

-54.0%***

Assets

11.0

9.3

-1.8

-16.0%

Note: *** statistically significant at α=0.01, ** statistically significant at α=0.05, * statistically significant at α=0.10.
Source: Calculations based on data from NMICS (2019).

42

NEPAL'S MULTIDIMENSIONAL POVERTY INDEX 2021

5.2.4 Uncensored Headcount Ratios

Finally, this section presents key findings based on
the uncensored (population-wide) headcount ratio
of deprivations for each of the 10 indicators in Province 2. This statistic represents the proportion ofpeople who are deprived in that particular indicator,
irrespective of their poverty status. This information
is useful to identify the main pockets of depriva-

tion. Table 5.2.4 presents these rates using NMICS(2019). In Province 2, the highest deprivation levels
are found in Cooking Fuel, with 65.4 percent of the
population is deprived in this indicator, and Housing (62.7 percent). However, some indicators show
lower rates of deprivation. The lowest deprivation isfound in Child Mortality indicator (1.7 percent).

TABLE 5.2.4: Headcount Ratios in Province 2, 2019
Dimension

Indicator

Headcount ratios
Uncensored

Censored

1.7%

0.7%

HealthChild Mortality
Nutrition

26.6%

12.4%

Education

School Attendance

11.6%

8.7%

Years of Schooling

27.3%

18.9%

Living Standards

Electricity

3.7%

2.9%

Water

3.3%

1.7%

Sanitation

27.4%

14.3%

Housing

62.7%

23.1%

Cooking Fuel

65.4%

22.3%

Assets

12.6%

9.3%

Photo: unsplash.com

Source: Calculation based on data from NMICS (2019).

43

NEPAL'S MULTIDIMENSIONAL POVERTY INDEX 2021

5.3 BAGMATI PROVINCE
5.3.1 Multidimensional Poverty in Bagmati
Province

Bagmati Province has the lowest MPI of any province in Nepal, at 0.028. This is below the national
MPI of 0.074. Table 5.3.1 shows that the headcountratio of multidimensional poverty in Bagmati Province is 7 percent. This figure is below the national
incidence of multidimensional poverty of 17.4 percent, and substantially lower than that of Karnali
Province, the poorest province, where nearly 40 percent the population is identified as poor. Bagmati
Province houses 23.3 % of the population in Nepal.
This implies that in 2019 there were approximately
470, 400 MPI poor people in Bagmati. The intensity of poverty in Bagmati Province is 40.3 percent,which means that those identified as multidimensionally poor are deprived, on average, in 40.3 percent of the weighted indicators. Nationally, the poorin Nepal are deprived in 42.5 percent of indicators,
so the intensity of deprivation is lower in this province than it is nationally.

Figure 5.3.1 shows the percentage contribution ofeach indicator in Bagmati. The height of each bar
denotes the percentage contribution of each indicator to the overall MPI. The figure also shows thecontribution at the national level which allow for
an immediate visual comparison. The indicatorthat contributes the most to the MPI in Bagmati
Province is Years of Schooling (32.4 percent), which
is followed by Nutrition (17.4 percent). These two
indicators account for nearly half of the total contributions to overall multidimensional poverty (49.7
percent) in Bagmati Province.
FIGURE 5.3.1: Percentage Contribution to
MPI in Bagmati Province

TABLE 5.3.1: Incidence, Intensity andMultidimensional Poverty Index (MPI) in
Bagmati Province
PovertyCut-off (k)k value =33.33%

Index

Value

ConfidenceInterval (95%)

MPI

0.028

0.018

0.038

Incidence (H, %)

7.0

4.8

9.3

Intensity (A, %)

40.3

38.6

42.0

Source: Calculations based on data from NMICS (2019).

5.3.2 Composition of MPI by Indicator in
BagmatiTable 5.3.1 shows that the largest censored headcount ratio is found in the indicator of Housing. In
2019, 6.1% of the population is multidimensionally poor and deprived in this indicator. The second
most-widespread deprivation among the poor in
Bagmati is Cooking Fuel (6 percent).

Source: Calculations based on data from NMICS (2019).

44

NEPAL'S MULTIDIMENSIONAL POVERTY INDEX 2021

TABLE 5.3.2: Changes in Multidimensional Poverty Index (MPI), Incidence and Intensity in
Bagmati Province
Index

Poverty across time

Change 2014 – 2019

Population (thousands)

2014

2019

Absolute

Relative

significanceYear 2014

Year 2019

MPI

0.061

0.028

-0.033-53.4%

H

14.4%

7.0%

-7.4%

-51.1%***

524,277.7

667,356.6***

Poor people (thousands)

A

42.3%

40.3%

-2.0%

-4.6%

755.5

470.4

Note: *** statistically significant at α=0.01, ** statistically significant at α=0.05, * statistically significant at α=0.10
Source: Calculations based on data from NMICS (2019).

5.3.3 Changes over time in Bagmati Province,
2014-2019

Between 2014 and 2019, this province reduced the
proportion of MPI poor people by -7.4 percentage
points, from 14.4 percent in 2014 to 7 percent in2019. This reduction is statistically significant witha 99 percent confidence level. This represents a moveout of poverty for nearly 285,100 people in this province. Furthermore, Table 5.3.2 shows that there wasalso a statistically significant reduction in the MPI(with a 99 percent confidence level), as well as in theintensity of poverty (not statistically significant).
To conclude the analysis of multidimensional poverty in Bagmati Province the analysis turns to thechange in the censored headcount ratio – the proportion of the population that is multidimensionally poor and deprived in each respective indicator. As

seen in Table 5.3.3, there was a statistically significant reduction (at 95% confidence interval) of thecensored headcount ratio in seven indicators The
largest change was observed in Cooking Fuel with
an absolute reduction of 6.9 percentage points inabsolute terms. In relative terms (this is, as a percentage of the value observed in 2014), the indicator with the largest reduction was Child Mortality(77.7 percent).
All these trends and figures are encouraging as they
confirm a consistent trend in poverty reduction overthis five-year period. A similar level of commitment
is needed to maintain this progress in the coming
years. This is particularly the case for the indicator of School Attendance that showed the smallest
change – a slight reduction of 0.6 percentage points
in absolute terms – during this period.

TABLE 5.3.3: Changes in Censored Headcount Ratio in Bagmati Province
Percentage

Changes

Indicator

2014

2019

Absolute(p.p.)

Relative (%)

significanceChild Mortality

0.9

0.2

-0.7

-77.7%**

Nutrition

6.5

3.0

-3.5

-54.3%***

School Attendance

1.1

0.5

-0.6

-52.3%

Years of Schooling

11.5

5.5

-6.0

-52.1%

Electricity

3.2

1.9

-1.3

-41.5%

***

Water

4.5

1.9

-2.6

-57.7%*

Sanitation

7.8

2.6

-5.2

-67.0%***

Housing

12.8

6.1

-6.8

-52.7%***

Cooking Fuel

12.9

6.0

-6.9

-53.3%***

Assets

8.6

5.2

-3.4

-39.7%**

Note: *** statistically significant at α=0.01, ** statistically significant at α=0.05, * statistically significant at α=0.10.
Source: Calculations based on data from NMICS (2019).

45

NEPAL'S MULTIDIMENSIONAL POVERTY INDEX 2021

5.3.4 Uncensored Headcount Ratios 2019

Finally, this section highlights the key findings
based on the uncensored (population-wide) headcount ratio of deprivations for each of the 10 indicators in Bagmati Province. This indicator represents
the proportion of people who are deprived in thatparticular indicator, irrespective of their poverty status. This information is useful to identify the main

pockets of deprivation. Table 5.3.4 presents these
rates using NMICS (2019). In Bagmati Province
the highest deprivation levels are found in Sanitation (29.5 percent of the population is deprived inthis indicator) and Housing (24.9 percent). However, some indicators show lower rates of deprivation.
The lowest deprivation is found in Child Mortality(0.5 percent).

TABLE 5.3.4: Headcount Ratios in Bagmati Province, 2019
Dimension

Indicator
Health

Education

Living Standards

Headcount ratios
Uncensored

CensoredChild Mortality

0.5%

0.2%

Nutrition

8.8%

3.0%

School Attendance

1.5%

0.5%

Years of Schooling

12.4%

5.5%

Electricity

2.7%

1.9%

Water

6.2%

1.9%

Sanitation

29.5%

2.6%

Housing

24.9%

6.1%

Cooking Fuel

24.7%

6.0%

Assets

13.0%

5.2%

Photo: unsplash.com

Source: Calculations based on data from NMICS (2019).

46

NEPAL'S MULTIDIMENSIONAL POVERTY INDEX 2021

5.4 GANDAKI PROVINCE
5.4.1 Multidimensional Poverty in Gandaki
Province

FIGURE 5.4.1: Percentage Contribution to
MPI in Gandaki Province

Gandaki Province has the second lowest MPI of
any province in Nepal, at 0.035. This is below thenational MPI of 0.074. Table 5.4.1 shows thatthe headcount ratio of multidimensional poverty
in Gandaki Province is 9.6 percent. This figure is
below the national incidence of multidimensional
poverty rate of 17.4 percent, and substantially lower
than that of Karnali Province, the poorest province,
where nearly 40 percent the population is identified
as poor. Gandaki houses 8.2 percent of the population in Nepal, so in 2019, there were approximately
226.7 thousand MPI poor people in Gandaki Province. The intensity of poverty in Gandaki Province
is 36.4 percent, which means that those who are
identified as multidimensionally poor are deprived,on average, in 36.4 percent of the weighted indicators. Nationally, the poor in Nepal are deprived in
42.5 percent of indicators, so the intensity of deprivation is lower in this province than nationally.
TABLE 5.4.1: Incidence, Intensity andMultidimensional Poverty Index (MPI) in
Gandaki Province
PovertyCut-off (k)k value =33.33%

Index

Value

ConfidenceInterval (95%)

MPI

0.035

0.027

0.044

Incidence (H,
%)

9.6

7.3

11.9

Intensity (A, %)

36.4

35.7

37.2

Source: Calculations based on data from NMICS (2019).

5.4.2 Composition of MPI by Indicator in
Gandaki Province, 2019

Table 5. 4.3 shows that the largest censored headcount ratios are found in the indicator of Housing
and Cooking Fuel. In 2019, 8.4 percent of the population is multidimensionally poor and deprived inthese indicators, respectively. The next most-widespread deprivation among the poor in Gandaki
Province is Assets (7 percent).

Source: Calculations based on data from NMICS (2019).

Figure 5.4.1 shows the percentage contribution ofeach indicator in Gandaki Province. The height ofeach bar denotes the percentage contribution of eachindicator to the overall MPI. The figure also shows
the contribution at the national level which allow
for an immediate visual comparison. The indicatorthat contributes the most to the MPI in Gandaki
Province is years of schooling (32 percent), which
is followed by Nutrition (16.5 percent). These two
indicators account for nearly half of the total contributions to overall multidimensional poverty (48.5
percent) in Gandaki Province.
47

NEPAL'S MULTIDIMENSIONAL POVERTY INDEX 2021

TABLE 5.4.2: Changes in MPI, H and A in Gandaki Province
Index

Poverty across time

Change 2014 - 2019

Population (thousands)

2014

2019

Absolute

Relative

significanceYear 2014

Year 2019

MPI

0.078

0.035

-0.043-54.9%***

256,479.7

235,259.3

H

18.5%

9.6%

-8.8%

-47.8%***

Poor people (thousands)

A

42.2%

36.4%

-5.7%

-13.6%

***

473.6

226.7

Source: Calculations based on data from NMICS (2019).

5.4.3 Changes in MPI over time in Gandaki
Province 2014-2019

Between 2014 and 2019, this province reduced the
proportion of poor people by nearly 8.8 percentage
points, from 18.5 percent in 2014 to 9.6 percent
in 2019. This reduction is statistically significant
with a 99 percent confidence level. This represents
a move out of poverty for nearly 246,900 people inthis province. Furthermore, Table 5.4.2 shows that
there was a statistically significant reduction in the
MPI as well as in the intensity of poverty – bothwith a 99 percent confidence level. This significant
reduction in intensity of poverty is a great achievement, considering that four of the seven provinces
did not see a significant decrease in the average shareof deprivations experienced by multidimensionallypoor households. As in Sudurpashchim and Karnali Provinces, poverty reduction efforts in Gandaki
Province between 2014 and 2019 offer lessons forother provinces and their poverty eradication strategies in the race to leave no one behind.

48

To continue the analysis of multidimensional poverty in Gandaki Province, the analysis turns to thechange in the censored headcount ratio – the proportion of the population that is multidimensionally poor and deprived in each respective indicator.
Gandaki Province managed to reduce censoredheadcount ratios in most of the indicators (Table
5.4.3). The largest change was observed in Cooking
Fuel with an absolute reduction of 9.5 percentage
points in absolute terms. In relative terms (this is,
as a percentage of the value observed in 2014) theindicator with the largest reduction was Electricity(92.4 percent).
All these trends and figures are encouraging as they
confirm a consistent trend in poverty reduction overthis five-year period. A similar level of commitment
is needed to maintain this progress in the coming
years. This is particularly the case for the indicatorof Child Mortality that showed the smallest change– a slight reduction of 0.1 percentage points in absolute terms – during this period. It is worth mentioning that the value of this indicator was alreadylow in 2014.

NEPAL'S MULTIDIMENSIONAL POVERTY INDEX 2021

TABLE 5.4.3: Changes in Censored Headcount Ratio in Gandaki Province
Indicator

Value (percent)2014

Changes2019

Absolute (p.p.)

Relative (%)

Child Mortality

0.4

0.3

-0.1

-19.4%

Nutrition

10.1

3.5

-6.6

-65.4%

significance***School Attendance

1.4

0.8

-0.6

-43.8%

Years of Schooling

13.4

6.7

-6.6

-49.6%**

Electricity

6.1

0.5

-5.7

-92.4%

**

Water

5.1

1.6

-3.5

-69.1%**

Sanitation

5.4

3.4

-2.0

-37.9%*

Housing

17.8

8.4

-9.4

-52.8%***

Cooking Fuel

17.9

8.4

-9.5

-53.1%***

Assets

12.1

7.0

-5.1

-42.0%*

Note: *** statistically significant at α=0.01, ** statistically significant at α=0.05, * statistically significant at α=0.10.
Source: Calculations based on data from NMICS (2019).

5.4.4 Uncensored Headcount Ratios 2019

Finally, this section highlights the key findings
based on the uncensored (population-wide) headcount ratio of deprivations in all the 10 indicators
in Gandaki Province. This indicator represents the
proportion of people who are deprived in that particular indicator, irrespective of their poverty status. This information is useful to identify the main

pockets of deprivation. In Gandaki Province, thehighest deprivation levels are found in Cooking
Fuel (49.8 percent of the population is deprived
in this indicator) and Housing (46 percent) (Table
5.4.4). However, some indicators show lower rates
of deprivation. The lowest deprivation is found inElectricity (1 percent).

TABLE 5.4.4: Headcount Ratios in Gandaki Province, 2019
Dimension
Health
Education

Living Standards

Indicator

Headcount ratios
Uncensored

CensoredChild Mortality

1.4%

0.3%

Nutrition

11.0%

3.5%

School Attendance

1.8%

0.8%

Years of Schooling

15.8%

6.7%

Electricity

1.0%

0.5%

Water

8.6%

1.6%

Sanitation

17.7%

3.4%

Housing

46.0%

8.4%

Cooking Fuel

49.8%

8.4%

Assets

19.0%

7.0%

Source: Calculations based on data from NMICS (2019).

49

NEPAL'S MULTIDIMENSIONAL POVERTY INDEX 2021

5.5 LUMBINI PROVINCE
5.5.1 Multidimensional Poverty in Lumbini
Province

FIGURE 5.5.1: Percentage Contribution to
MPI in Lumbini Province

Lumbini Province has the fourth highest MPIof any province in Nepal, at 0.078. This is above
the national MPI of 0.074. Table 5.5.1 shows thatthe headcount ratio of multidimensional poverty
in Lumbini Province is 18.2 percent. This meansthat in 2019 there were approximately 957,700
MPI poor people in Lumbini. In comparison to
the national incidence of multidimensional poverty of 17.4 percent, Lumbini’s incidence is higher,
but yet, it is substantially lower than that of Karnali
province, where nearly 40 percent of the population is identified as poor. The intensity of poverty
in Lumbini Province is 43.1 percent, which meansthat those who are identified as multidimensionallypoor are deprived, on average, in 43.1 percent of theweighted indicators. Nationally, the poor in Nepal
are deprived in 42.5 percent of indicators, so theintensity of deprivation is higher in this provincethan it is nationally.
TABLE 5.5.1: Incidence, Intensity andMultidimensional Poverty Index (MPI) in
Lumbini Province
PovertyCut-off (k)k value =33.33%

Index

Value

ConfidenceInterval (95%)

MPI

0.078

0.059

0.098

Incidence (H,
%)

18.2

14.1

22.2

Intensity (A, %)

43.1

41.0

45.2

Source: Calculations based on data from NMICS (2019).

5.5.2 Composition of MPI by Indicator in
Lumbini Province

Table 5.5.3 shows that the largest censored headcount ratio is found in the indicator of Cooking
Fuel. In 2019, 16.5% of the population is multidimensionally poor and deprived in this indicator.
The second most-widespread deprivation among
the poor in Lumbini is Housing (16.1percent).

50

Source: Calculations based on data from NMICS (2019).

Figure 5.5.1 shows the percentage contribution ofeach indicator in Lumbini. The height of each bar
denotes the percentage contribution of each indicator to the overall MPI. The figure also shows thecontribution at the national level which allow for
an immediate visual comparison. The indicatorthat contributes the most to the MPI in Lumbini isYears of Schooling (26.9 percent), followed by Nutrition (21.9 percent). These two indicators accountfor nearly half of the total contributions to overall
multidimensional poverty (48.8 percent) in Lumbini Province.

NEPAL'S MULTIDIMENSIONAL POVERTY INDEX 2021

TABLE 5.5.2: Changes in Multidimensional Poverty (MPI), Incidence and Intensity in Lumbini
Province
Index

Poverty across time

Change 2014 - 2019

Population (thousands)

2014

2019

Absolute

Relative

significanceYear 2014

Year 2019

MPI

0.138

0.078

-0.059-43.1%***

485,375.6

526,571.8

H

31.2%

18.2%

-13.0%-41.7%***

Poor people (thousands)

A

44.1%

43.1%

-1.0%

-2.3%

1515.1957.7

Note: *** statistically significant at α=0.01, ** statistically significant at α=0.05, * statistically significant at α=0.10
Source: Calculations based on data from NMICS (2019).

5.5.3 Changes over time in Lumbini Province,
2014-2019

Between 2014 and 2019, this province reduced the
proportion of MPI poor people by -13 percentage
points, from 31.2 percent in 2014 to 18.2 percent in2019. This reduction is statistically significant with a99 percent confidence level. This represents a moveout of poverty for nearly 557,400 people in this province. Furthermore, Table 5.5.2 shows that there wasa statistically significant reduction in the MPI (with a99 percent confidence level), as well as in the intensity of poverty (not statistically significant).
The analysis then turns to the change in the censoredheadcount ratio – the proportion of the populationthat is multidimensionally poor and deprived in each
respective indicator. Lumbini Province managed toreduce the censored headcount ratio in most of theindicators (Table 5.5.3). The largest change was observed in Cooking Fuel with an absolute reduction of
14.3 percentage points in absolute terms. In relative
terms (this is, as a percentage of the value observedin 2014) the indicator with the largest reduction was
Sanitation (69.6 percent).
These trends and figures are encouraging, as they
confirm a consistent trend in poverty reduction overthis five-year period. A similar level of commitment isneeded to maintain this progress in the coming years.
This is particularly the case for the indicator of Child
Mortality that showed the smallest variation, a slight
reduction of 1 percentage points in absolute terms,
during this period. It is worth mentioning that thevalue of this indicator was already low in 2014.

TABLE 5.5.3: Changes in Censored Headcount Ratio in Lumbini Province
Value (percent)Indicator

2014

2019

ChangesAbsolute (p.p.)

Relative (%)

Child Mortality

2.7

1.7

-1.0

-36.7%

Nutrition

17.2

10.3

-7.0

-40.4%

significance***School Attendance

7.4

4.0

-3.3

-45.3%*

Years of Schooling

19.6

12.6

-7.0

-35.6%***

Electricity

11.8

5.5

-6.2

-52.9%

***

Water

4.7

2.2

-2.5

-52.3%*

Sanitation

19.5

5.9

-13.6

-69.6%***

Housing

29.2

16.1

-13.1

-44.8%***

Cooking Fuel

30.7

16.5

-14.3

-46.4%***

Assets

11.2

8.8

-2.4

-21.2%

Note: *** statistically significant at α=0.01, ** statistically significant at α=0.05, * statistically significant at α=0.10.
Source: Calculations based on data from NMICS (2019).

51

NEPAL'S MULTIDIMENSIONAL POVERTY INDEX 2021

5.5.4 Uncensored Headcount Ratios 2019

Finally, this section highlights the key findings basedon the uncensored (population-wide) headcount ratio of deprivations in all the 10 indicators in Lumbini
Province. This indicator represents the proportion ofpeople who are deprived in that particular indicator,
irrespective of their poverty status. This information
is useful to identify the main pockets of deprivation.

Table 5.5.4 presents these rates using NMICS (2019).
In Lumbini the highest deprivations levels are foundin Cooking Fuel (61.6 percent of the population isdeprived in this indicator) and Housing (61.2 percent). Some indicators, meanwhile, show lower rates
of deprivation. The lowest deprivation is found inChild Mortality indicator (2.8 percent).

TABLE 5.5.4: Headcount Ratios in Lumbini Province, 2019
Dimension
Health
Education

Living Standards

Indicator

Headcount ratios
Uncensored

CensoredChild Mortality

2.8%

1.7%

Nutrition

22.7%

10.3%

School Attendance

4.9%

4.0%

Years of Schooling

17.4%

12.6%

Electricity

9.2%

5.5%

Water

4.5%

2.2%

Sanitation

16.8%

5.9%

Housing

61.2%

16.1%

Cooking Fuel

61.6%

16.5%

Assets

16.0%

8.8%

Photo: unsplash.com

Source: Calculations based on data from NMICS (2019).

52

NEPAL'S MULTIDIMENSIONAL POVERTY INDEX 2021

5.6 KARNALI PROVINCE
5.6.1 Multidimensional Poverty in Karnali
Province

FIGURE 5.6.1: Percentage Contribution to
MPI in Karnali Province

Karnali Province has the highest MPI of any province
in Nepal, at 0.169. This is above the national MPI of
0.074. Table 5.6.1 shows that the headcount ratio of
multidimensional poverty in Karnali province is 39.5
percent. This translates into approximately 636,200
MPI poor people in Karnali in 2019. The incidenceof Karnali is the highest provincial incidence in Nepal
and substantially higher than the national incidence
multidimensional poverty of 17.4 percent. The intensity of poverty in Karnali Province is 42.9 percent,which means that those who are identified as multidimensionally poor are deprived, on average, in 42.9
percent of the weighted indicators. Nationally, thepoor in Nepal are deprived in 42.5 percent of indicators, so the intensity of deprivation is higher in thisprovince than it is nationally.
TABLE 5.6.1: Incidence, Intensity andMultidimensional Poverty Index (MPI) in
Karnali Province
PovertyCut-off (k)

Index

Value

ConfidenceInterval (95%)

MPI

0.169

0.144

0.195

k value =33.33%

Incidence (H,
%)

39.5

33.9

45.1

Intensity (A, %)

42.9

41.7

44.1

Source: Calculations based on data from NMICS (2019).

5.6.2 Composition of MPI by Indicator in
Karnali Province

Table 5.6.3 shows that the largest censored headcount ratio is found in the indicator of Housing. In
2019, 39.2 percent of the population is multidimensionally poor and deprived in this indicator. The second most-widespread deprivation among the poor in
Karnali is Cooking Fuel (39.1 percent).
Figure 5.6.1 shows the percentage contribution ofeach indicator in Karnali. The height of each bar denotes the percentage contribution of each indicator

Source: Calculations based on data from NMICS (2019).

to the overall MPI. The figure also shows the contribution at the national level which allow for an immediate visual comparison. The indicator that contributes the most to the MPI in Karnali Province is
Nutrition (26.6 percent), which is followed by Yearsof Schooling (16.8 percent). These two indicators
account for nearly half of the total contributions tooverall multidimensional poverty (43.4 percent) in
Karnali Province.

53

NEPAL'S MULTIDIMENSIONAL POVERTY INDEX 2021

TABLE 5.6.2: Changes in MPI, H and A in Karnali Province
Index

Poverty across time

Change 2014 - 2019

Population (thousands)

2014

2019

Absolute

Relative

significanceYear 2014

Year 2019

MPI

0.234

0.169

-0.065-27.7%***

158,542.7

161,130.4

H

50.9%

39.5%

-11.4%-22.4%***

Poor people (thousands)

A

46.1%

42.9%

-3.2%

-6.9%

***

806.8

636.2

Note: *** statistically significant at α=0.01, ** statistically significant at α=0.05, * statistically significant at α=0.10.
Source: Calculations based on data from NMICS (2019).

5.6.3 Changes over time in Karnali Province
2014-2019

Between 2014 and 2019, this province reduced the
proportion of MPI poor people by 11.4 percentage
points, from 50.9 percent in 2014 to 39.5 percent in2019. This reduction is statistically significant with a99 percent confidence level. This represents a moveout of poverty for nearly 170,500 people in this province. Furthermore, Table 5.6.2 shows that there wasalso a statistically significant reduction in the MPI(with a 99 percent confidence level level), as well as in
the intensity of poverty (99 percent confidence level).
As in Gandaki and Sudurpashchim, Karnali’s reduction in intensity should be celebrated and singled out
as a pro-poor reduction among the provinces.
To continue the analysis of multidimensional poverty
in Karnali, the analysis turns to the change in censored headcount ratio – the proportion of the popula-tion that is multidimensionally poor and deprived ineach respective indicator. Karnali Province managed
to reduce censored deprivation in most of the indicators (Table 5.6.3). The largest change was observed inAccess to Clean Water with an absolute reduction of
17.5 percentage points in absolute terms. In relative
terms (this is, as a percentage of the value observedin 2014) the indicator with the largest reduction was
Access to Clean Water (65 percent) as well.
All these trends and figures are encouraging as they
confirm a consistent trend in poverty reduction overthis five-year period. A similar level of commitment isneeded to maintain this progress in the coming years.
This is particularly the case for the indicator of Child
Mortality that showed the smallest variation, a slight
reduction of 1.2 percentage points in absolute terms,
during this period. It is worth mentioning that thevalue of this indicator was already low in 2014.

TABLE 5.6.3: Changes in Censored Headcount Ratio in Karnali Province
Indicator

Value (percent)2014

Changes2019

Absolute (p.p.)

Relative (%)

significance*Child Mortality

3.5

2.3

-1.2

-34.3%

Nutrition

32.0

27.0

-5.0

-15.7%

School Attendance

7.7

3.1

-4.6

-59.9%******

Years of Schooling

26.6

17.1

-9.5

-35.7%

Electricity

32.7

27.6

-5.2

-15.7%

Water

26.8

9.4

-17.5

-65.0%***

Sanitation

13.7

7.5

-6.1

-44.9%*

Housing

50.8

39.2

-11.6

-22.9%***

Cooking Fuel

50.7

39.1

-11.6

-22.9%***

Assets

37.8

33.7

-4.1

-10.8%

Note: *** statistically significant at α=0.01, ** statistically significant at α=0.05, * statistically significant at α=0.10.
Source: Calculations based on data from NMICS (2019).

54

NEPAL'S MULTIDIMENSIONAL POVERTY INDEX 2021

This analysis of poverty in Karnali Province closes bypresenting the uncensored (population wide) headcount ratio of deprivations in all the 10 indicators
in 2019. This uncensored headcount ratio represents
the proportion of people who are deprived in an indicator, irrespective of their poverty status. This information is useful to identify the main pockets ofdeprivation. Table 5.6.4 presents these rates using
NMICS (2019). In Karnali Province, the highest levels of deprivations are found in Housing (90.2 percent of the population is deprived in this indicator)
and Cooking Fuel (89.4 percent). However, some indicators show lower rates of deprivation. The lowest
deprivation is found in Child Mortality (2.5 percent).

TABLE 5.6.4: Headcount Ratios in Karnali
Province, 2019
Dimension

Indicator

Health

Education

LivingStandards

Headcount ratios
Uncensored

CensoredChild
Mortality

2.5%

2.3%

Nutrition

30.1%

27.0%

SchoolAttendance

3.5%

3.1%

Years of
Schooling

18.3%

17.1%

Electricity

55.0%

27.6%

Water

20.1%

9.4%

Sanitation

14.8%

7.5%

Housing

90.2%

39.2%

Cooking
Fuel

89.4%

39.1%

Assets

67.4%

33.7%

Source: Calculations based on data from NMICS (2019).

Photo: UNDP Nepal

5.6.4 Uncensored Headcount Ratios 201955

NEPAL'S MULTIDIMENSIONAL POVERTY INDEX 2021

5.7 SUDURPASHCHIM PROVINCE

FIGURE 5.7.1: Percentage Contribution to
MPI in Sudurpashchim Province

5.7.1 Multidimensional poverty in
Sudurpashchim Province

Sudurpashchim Province has the third highest MPIof any province in Nepal, at 0.105. This is above
the national MPI of 0.074. Table 5.7.1 shows thatthe headcount ratio of multidimensional poverty
in Sudurpashchim Province is 25.3 percent whichtranslates to approximately 631,400 MPI poor
people in Sudurpashchim in 2019. The incidence
in this province is higher than the national multidimensional poverty rate of 17.4 percent and substantially lower than that of Karnali Province, thepoorest province, where nearly 40 percent the population is identified as poor. The intensity of poverty
in Sudurpashchim Province is 41.3 percent, whichmeans that those who are identified as multidimensionally poor are deprived, on average, in 41.3 percent of the weighted indicators. Nationally, the poorin Nepal are deprived in 42.5 percent of indicators,
so the intensity of deprivation is lower in this province than it is nationally.

Sudurpaschim

TABLE 5.7.1: Incidence, Intensity andMultidimensional Poverty Index (MPI),
Sudurpashchim Province
PovertyCut-off (k)k value =33.33%

Index

Value

ConfidenceInterval (95%)

MPI

0.105

0.083

0.126

Incidence(H, %)

25.3

20.4

30.3

Intensity(A, %)

41.3

40.1

42.5

Source: Calculations based on data from NMICS (2019).

5.7.2 Composition of MPI by Indicator in
Sudurpashchim Province, 2019

Table 5.7.3 shows that the largest censored headcount ratio is found in the indicator of Housing.
In 2019, 24.6 percent of the population is multidimensionally poor and deprived in this indicator.
The second most-widespread deprivation among
the poor in Bagmati is Cooking Fuel (24.3 percent).

56

Source: Calculations based on data from NMICS (2019).

Figure 5.7.1 shows the percentage contribution ofeach indicator in Sudurpashchim Province. The
height of each bar denotes the percentage contribution of each indicator to the overall MPI. The figure also shows the contribution at the national level
which allow for an immediate visual comparison.
The indicator that contributes the most to the MPI in
Sudurpashchim Province is Nutrition (25 percent),
which is followed by Years of Schooling (20 percent).
These two indicators account for nearly half of the total contributions to overall multidimensional poverty(45 percent) in Sudurpashchim Province.

NEPAL'S MULTIDIMENSIONAL POVERTY INDEX 2021

TABLE 5.7.2: Changes in Multidimensional Poverty Index (MPI), Incidence, and Intensity in
Sudurpashchim Province
Index

Poverty across time2014

Change 2014 - 2019

Population (thousands)

2019

Absolute

Relative

significanceYear 2014

Year 2019

265,886.7

249,129.4

MPI

0.144

0.105

-0.039-27.3%

***

H

32.5%

25.3%

-7.1%

-22.0%**

Poor people (thousands)

A

44.3%

41.3%

-3.0%

-6.8%

***

863.9

631.4

Note: *** statistically significant at α=0.01, ** statistically significant at α=0.05, * statistically significant at α=0.10
Source: Calculations based on data from NMICS (2019).

5.7.3 Changes over time in Sudurpashchim
Province, 2014-2019

Between 2014 and 2019, this province reduced the
proportion of MPI poor people by nearly 7.1 percentage points, from 32.5 percent to 25.3 percent.
This reduction is statistically significant with a 99
percent confidence level. This represents a move outof poverty for nearly 232,500 people in this province.
Furthermore, Table 5.7.2 shows that there was alsoa statistically significant reduction in the MPI (witha 95 percent confidence level level), as well as in theintensity of poverty (99 percent confidence level).
As in Gandaki and Karnali Provinces, the significant
reduction in intensity touts a pro-poor reduction in
Sudurpashchim Province, as on average, the multidimensionally poor saw a reduction in the share ofdeprivations they experienced.
To continue the analysis of multidimensional poverty
in Sudurpashchim the analysis turns to the change

in the censored headcount ratio – the proportion of
the population that is multidimensionally poor anddeprived in each respective indicator. Sudurpashchim
managed to reduce the censored headcount ratio
in most of the indicators (Table 5.7.3). The largest
change was observed in Cooking Fuel with an absolute reduction of 8.1 percentage points in absoluteterms. In relative terms (this is, as a percentage of thevalue observed in 2014) the indicator with the largest
reduction was Access to Clean Water (58.5 percent.
All these trends and figures are encouraging as they
confirm a consistent trend in poverty reduction overthis five-year period. A similar level of commitment isneeded to maintain this progress in the coming years.
This is particularly the case for the indicator of Electricity that showed the smallest variation – a slight
reduction of 0.2 percentage points in absolute terms– during this period.

TABLE 5.7.3: Changes in Censored Headcount Ratio in Sudurpashchim Province
Indicator

Value (percent)2014

Changes2019

Absolute (p.p.)

Relative (%)

significanceChild Mortality

2.9

1.7

-1.2

-42.1%*

Nutrition

21.0

15.7

-5.3

-25.2%*****

School Attendance

7.5

3.3

-4.2

-55.8%

Years of Schooling

15.3

12.6

-2.7

-17.7%

Electricity

12.1

12.0

-0.2

-1.3%

Water

10.4

4.3

-6.1

-58.5%***

Sanitation

12.8

6.4

-6.4

-50.2%***

Housing

32.1

24.6

-7.5

-23.4%**

Cooking Fuel

32.4

24.3

-8.1

-25.1%**

Assets

19.6

17.2

-2.3

-11.9%

Note: *** statistically significant at α=0.01, ** statistically significant at α=0.05, * statistically significant at α=0.10
Source: Calculations based on data from NMICS (2019).

57

NEPAL'S MULTIDIMENSIONAL POVERTY INDEX 2021

5.7.4 Uncensored Headcount Ratios 2019

This analysis of poverty in Sudurpashchim Province
closes by presenting the uncensored (populationwide) headcount ratio of deprivations for each of the
10 indicators in 2019. This indicator represents the
proportion of people who are deprived in that particular indicator, irrespective of their poverty status.
This information is useful to identify the main pock-ets of deprivation. Table 5.7.4 presents these rates
using NMICS (2019) datasets. In Sudurpashchimrovince, the highest levels of deprivations are foundin Cooking Fuel (79.6 percent of the population isdeprived in this indicator) and Housing (78.8 percent). However, some indicators show lower rates ofdeprivation. The lowest deprivation is found in Child
Mortality (2.2 percent).

TABLE 5.7.4: Headcount Ratios in Sudurpashchim Province, 2019
Dimension
Health
Education

Living Standards

Indicator

Headcount ratios
Uncensored

CensoredChild Mortality

2.2%

1.7%

Nutrition

26.0%

15.7%

School Attendance

4.2%

3.3%

Years of Schooling

16.3%

12.6%

Electricity

22.8%

12.0%

Water

9.7%

4.3%

Sanitation

15.6%

6.4%

Housing

78.8%

24.6%

Cooking Fuel

79.6%

24.3%

Assets

33.2%

17.2%

Photo: UNDP Nepal

Source: Calculations based on data from NMICS (2019).

58

NEPAL'S MULTIDIMENSIONAL POVERTY INDEX 2021

CHAPTER 6:

Multidimensional Poverty
and COVID-19 in Nepal
The MPI for Nepal was constructed using 2019
NMICS data that pre-dated the pandemic, yet the
pandemic will have affected the shape of poverty because of changes in both deprivation patterns andin demography. This chapter uses the same 2019
NMICS dataset to probe additional pandemic-related deprivations. It needs to be complemented
by other analysis, which use more up-to-date data,
or include additional indicators, such as access tohealth facilities, health systems capacity, employment-related aspects, and diagnosed underlying
co-morbidities. As the global community reckonswith the impacts of the pandemic on the everyday
lives of people across the world, the focus must remain on the diverging experiences of the poor andtheir increased vulnerabilities due to pre-existing
inequalities.
The first case of SARS-CoV-2 in Nepal was confirmed in January 2020 (Shrestha, 2020). Over one
year later (as of June 17th, 2021), the pandemic has
caused well more than half a million COVID-19
cases (615,984), and at least 8,597 confirmed
COVID-19 related deaths in Nepal (John Hopkins
University & Medicine, 2021).8 The pandemic affects all provinces, districts, and population groupsof Nepal (Ministry of Health and Population,2021). However, its implications for health, so8

cio-economic realities, lives, and livelihoods, are unlikely evenly distributed across space and populationgroups within the country. Those already worse-off
and affected by multiple, overlapping deprivations
pre-pandemic are, in many ways, disproportionately
vulnerable to the various impacts and implicationsof the COVID-19 health and socio-economic emergency. In particular, already disadvantaged populations may be:•

more exposed to disease and less able to practicedisease prevention due to lack of handwashingfacilities or overcrowding.

•more susceptible and severely affected by thedisease due to weakened immune systems andco-morbidities, e.g. due to undernutrition, theeffects of air pollution, or lack of safe drinking
water.

•

more strongly affected by socio-economic implications and disease prevention and control
measures, e.g. due to lack of access to information and inability to participate in online education due to lack of internet access.

These additional deprivations, disadvantages, andstruggles come on top of the already heavy deprivation load they may be experiencing across dimensions of human development/poverty (see also The
Lancet, 2020 and Ahmad et al., 2020).

Both confirmed cases and confirmed deaths may be subject to underreporting.

59

NEPAL'S MULTIDIMENSIONAL POVERTY INDEX 2021

Using the same NMICS 2019 dataset as the MPI,
two sets of variables are further explored. First, theoverlaps between the following three deprivations
are probed, that if present as pre-existing conditions, might affect the course of the disease.9•••

Undernutrition, which is strongly associated with weakened immune systems, morbidity, and mortality – particularly among young
children, the elderly, and people with acuterespiratory infections (WHO, 2018; UNICEFWHO-The World Bank, 2020).10
Unsafe drinking water, which is associatedwith much of the global disease burden andweakened immune systems (WHO 2019;
UNICEF-WHO 2017). The drinking water indicator in the Nepal-MPI captures individuals
who do not have access to safe drinking wateraccording to SDG standards.
Unclean cooking fuel, which is associated withindoor air pollution and much of the global
disease burden, including acute respiratory infections, implying an increased vulnerability
to diseases such as COVID-19 (WHO 2018a;
Gordon et al. 2014). The cooking fuel indicator in Nepal’s MPI captures those who may beaffected by indoor air pollution in their homes
because of solid cooking fuels.

Then three, additionally relevant, indicators are explored that might affect the spread of the pandemic
and children’s experience:11•

Lack of handwashing facility with soap on
household premises. Households without
soap and handwashing facilities in their households face more difficulty practising some ofthe essential, hygiene-related disease prevention
measures. This may lead to more frequent viraltransmission and means that such households
may be more exposed to virus and disease on
their premises.

Based on Alkire, Dirksen, Nogales, and Oldiges (2020a,b) who performed
this analyses for all countries included in the global MPI 2020.
10
The undernutrition indicator is, due to data limitations, limited toanthropometric information for children below the age of five.
11
See Dirksen, Nogales, and Oldiges (2021) who further justify the explorationof these indicators.
9

60

•

Overcrowding. People living in households
that are ‘overcrowded’ – here defined as morethan three persons per sleeping room in thehousehold, in line with SDG 11.1.1. (UNHabitat, 2018) – may face additional difficulty
to stop viral transmission in their households,
as effective physical distancing to stop diseasetransmission may not be feasible.

•

Lack of access to the internet. During periods of lockdown, internet access is an essential
source of information for many households.
And it can also be crucial for households withschool-aged children to have internet access, toallow children to partake in online schooling.
People living in households without internet,
especially children or students, may thus be disadvantaged during lockdowns and periods ofschool closures.

These indicators are considered in combination
with the global MPI results for Nepal, and are disaggregated by rural-urban areas and by provinces.

6.1 MULTIDIMENSIONAL POVERTY AND
VULNERABILITY-TO-DISEASE IN NEPAL IN
THE CONTEXT OF COVID-19
Table 6.1 presents the percentage and the numberof people who were deprived in one or more of MPI
indicators identified as indicating vulnerability to
COVID-19: Nutrition, Water, and Cooking Fuel.
Some 18 million people in Nepal – 63.5 percent– are affected by at least one of the identified vulnerability conditions. As more than half of Nepal’s
population experiences increased vulnerability, by
facing at least one of the mentioned deprivation, theanalysis also shows the clear added value of looking
at deprivations one by one.
Furthermore, for targeted efforts that seek to prioritize the most vulnerable, considering overlaps ofvulnerability-to-disease indicators is critical. In Nepal, 17.0 percent or 4.9 million people are increased
vulnerable – affected by two out of the three vulner-

NEPAL'S MULTIDIMENSIONAL POVERTY INDEX 2021

TABLE 6.1: Overlap between Deprivation in Nutrition, Water and Cooking Fuel
Persons(thousand)Vulnerability Gradient

Percent

Confidence Interval (95%)

Highly vulnerable (all three deprivations)*

1.31%

1.0%

1.6%

374.8

Increased vulnerable (any two deprivations)

17.0%

15.5%

18.5%

4,863.5

Vulnerable (at least one deprivation)

63.5%

60.6%

66.3%

18,155.1

Source: Calculations based on data from NMICS (2019).

ability conditions. 375,000 (1.3 percent) are highly
vulnerable, affected by all three deprivations at thesame time. Arguably, these population subgroups
are one priority subgroups for disease prevention
measures. The analysis also shows the clear-value
added of looking at deprivations one by one – where
18 million people have at least one of the vulnerability-to-disease deprivations considered here. Whenconsidering their overlap between deprivations, twosubsets of vulnerable people emerge – i.e., the 4.9
million who are increasingly vulnerable and the 375
thousand who are highly vulnerable. For targeted
efforts that seek to prioritize the most vulnerable,considering such overlaps of vulnerability-to-disease
indicators is critical.

Table 6.2 presents the percentage of and numberof people in Nepal who are multidimensionallypoor and deprived in one, two, or three of the focal
deprivations. As the results show, 17.2 percent (or4.9 million of Nepal’s population are both multidimensionally poor and affected by at least one ofthese deprivations. 9.7 percent of the population or
2.8 million people in Nepal are both multidimensionally poor and deprived in two of them. 1.3 percent of Nepal’s population, which is 369,100 of the374,800 highly vulnerable people (or 98.5 percent),
are also MPI-poor. These results show that nearly
all of the highly vulnerable people are already MPIpoor and more than half (~57 percent) of those withtwo deprivations are also MPI poor, testifying thatdeprivations do indeed tend to cluster.

TABLE 6.2: Multidimensional Poverty and Deprivations in Nutrition, Water, and Solid Cooking Fuel
MPI and COVID-19 vulnerability in Nepal

Multidimensional Poor

Vulnerability Gradient

Percent

Confidence Interval (95%)

Persons (thousand)Highly vulnerable (all three deprivations)*

1.29%

1.0%

1.6%

369.1

Increased vulnerable (any two deprivations)

9.7%

8.6%

10.8%

2,780.8

Vulnerable (at least one deprivation)

17.2%

15.6%

18.7%

4,909.3*VI=Vulnerability Indicator
Source: Calculations based on data from NMICS (2019).

61

NEPAL'S MULTIDIMENSIONAL POVERTY INDEX 2021

TABLE 6.3: Additional Deprivations and Multidimensional Poverty by Deprivation Status
Deprivation Status
Overcrowding

Internet Access

Handwashing

Overall Population

MPI-Poor

Per cent

Persons (thousands)

Per cent

Persons (thousands)

Non-deprived

82.53

46,168.8

64.05

7,729.4

Deprived

17.47

9,770.2

35.95

4,337.6

Deprived

47.20

26,387.7

80.02

9,647.1

Non-Deprived

52.80

29,514.3

19.98

2,408.9

Non-deprived

61.81

34,577.3

34.84

4,203.7

Deprived

38.19

21,361.7

65.16

7,863.3

Source: Calculations based on data from NMICS (2019).

6.2 MULTIDIMENSIONAL POVERTY AND
ADDITIONAL DEPRIVATIONS IN NEPAL IN
THE CONTEXT OF COVID-19
Table 6.3 shows that the multidimensionally poorin Nepal are, on top of their already high burden ofoverlapping deprivations, more likely to be affectedby additional deprivations that make them exposedand susceptible to disease. Whilst 17.5 percent of
the overall population are affected by overcrowded
housing conditions that may impede effective disease prevention, 36.0 percent of multidimensionallypoor people in Nepal live in crowded households.
Similarly, whilst overall 38.2 percent of people in
Nepal do not have access to a handwashing facility
with soap on their household’s premises, among the
MPI poor this figure stands at 65.2 percent. Hence,
those already worse-off may not only be more vulnerable to disease if contracting a disease, but they
may also be less able to prevent disease transmission.
Furthermore, on top of their already more significant overall disadvantage, multidimensionally poor
people are also more likely not to have internet access. While 52.8 percent of the overall population of
Nepal in 2019 could access the internet, only 20.0
percent of multidimensionally poor people hadaccess. This means that they may face additional
struggles in accessing important information during
a health emergency and periods of lockdowns. Additionally, it may also mean that the students in62

these households become educationally left behindbecause they cannot participate in online educational activities. This may exacerbate pre-existing
disadvantages and inequalities.
Table 6.4 provides provincial and rural-urban disaggregations. Findings indicate that the three additionally considered deprivations are much more frequent
among the multidimensionally poor than non-poor
individuals in both urban and rural areas. For example, deprivation of internet access is more frequent
among the MPI-poor than MPI-non-poor across urban and rural areas. In addition, the patterns here also
confirm that the population in rural Nepal is suffering a greater deprivation load. Among both the poorand non-poor, rural Nepalis are more frequently affected by all three additional deprivations consideredhere than are their urban counterparts.
Table 6.4 also confirms spatial inequalities across
Nepal’s provinces for the additional set of relevant
indicators considered here. Karnali Province has
the highest proportion of people deprived of internet access and handwashing facilities, followed by
Sudurpashchim Province. These are also the two
provinces with the most prevalent overcrowding,topped only by Province 2. Across all provinces, theshare of multidimensionally poor people deprived
in these three indicators is higher than among theNEPAL'S MULTIDIMENSIONAL POVERTY INDEX 2021

TABLE 6.4: Additional Deprivations and Multidimensional Poverty in Nepal, Provinces and Areas
Disaggregation
Overcrowding

Internet

Handwashing

Population
Share (%)

Overall Population
Value

SE

MPI-Poor

Confidence Interval
(95%)

Value

SE

ConfidenceInterval (95%)

Province 1

17.0

14.7%

0.01

11.9%

17.5%

34.2%

0.03

28.6%

39.7%

Province 2

18.7

23.5%

0.02

19.9%

27.0%

43.6%

0.03

36.7%

50.4%

Bagmati
Province

23.3

14.2%

0.01

11.7%

16.7%

35.1%

0.06

23.6%

46.5%

Gandaki
Province

8.2

11.3%

0.01

9.0%

13.7%

27.9%

0.04

20.2%

35.6%

Lumbini
Province

18.4

18.8%

0.02

15.1%

22.4%

33.8%

0.04

25.7%

41.8%

Karnali
Province

5.6

20.4%

0.02

16.1%

24.8%

28.5%

0.03

22.1%

34.9%

Sudurpashchim
Province

8.7

19.7%

0.02

16.1%

23.3%

36.8%

0.03

30.4%

43.3%

Rural

32.7

22.6%

0.01

20.2%

24.9%

38.6%

0.02

34.4%

42.8%

Urban

67.3

15.0%

0.01

13.5%

16.5%

33.0%

0.02

29.0%

37.1%

Province 1

17.0

45.2%

0.03

40.0%

50.4%

82.5%

0.03

77.5%

87.6%

Province 2

18.7

53.3%

0.03

46.8%

59.8%

75.2%

0.02

71.2%

79.3%

Bagmati
Province

23.3

27.6%

0.02

24.3%

31.0%

80.1%

0.02

75.4%

84.7%

Gandaki
Province

8.2

37.8%

0.02

33.7%

42.0%

73.1%

0.04

65.0%

81.3%

Lumbini
Province

18.4

50.5%

0.02

46.0%

55.1%

76.9%

0.03

70.1%

83.6%

Karnali
Province

5.6

74.1%

0.03

69.0%

79.1%

87.6%

0.02

83.4%

91.9%

Sudurpashchim
Province

8.7

74.9%

0.02

71.0%

78.7%

86.2%

0.02

82.2%

90.3%

Rural

32.7

63.5%

0.01

60.9%

66.1%

83.3%

0.01

80.9%

85.6%

Urban

67.3

39.3%

0.01

36.7%

41.9%

76.4%

0.02

73.0%

79.9%

Province 1

17.0

23.5%

0.02

18.9%

28.2%

47.0%

0.04

38.8%

55.2%

Province 2

18.7

37.2%

0.03

31.1%

43.3%

54.3%

0.04

46.8%

61.8%

Bagmati
Province

23.3

23.1%

0.02

19.3%

26.9%

69.4%

0.05

59.3%

79.5%

Gandaki
Province

8.2

38.3%

0.04

30.4%

46.2%

63.0%

0.05

53.6%

72.3%

Lumbini
Province

18.4

51.0%

0.03

44.4%

57.6%

73.3%

0.04

65.2%

81.4%

Karnali
Province

5.6

68.3%

0.03

61.4%

75.1%

81.4%

0.02

76.6%

86.3%

Sudurpashchim
Province

8.7

62.7%

0.03

57.1%

68.3%

78.5%

0.03

71.9%

85.2%

Rural

32.7

54.1%

0.02

50.9%

57.3%

67.6%

0.02

63.7%

71.5%

Urban

67.3

30.5%

0.02

27.5%

33.4%

62.5%

0.03

57.1%

67.9%

Source: Calculations based on data from NMICS (2019).

63

NEPAL'S MULTIDIMENSIONAL POVERTY INDEX 2021

general population. This suggests that the clusteringof deprivations of multidimensional poverty and
COVID-19 vulnerabilities indeed affect those withan already high deprivation load more heavily in allseven provinces of Nepal.

6.3 DISCUSSION OF MPI AND COVID-19

Photo: unsplash.com

The results in this section show how combining the
MPI with information on deprivation patterns at theindividual and household level can be used to identify priority populations for social protection andpreventive public health interventions. They can also

guide vaccination prioritisation efforts to ensure anequitable roll-out that protects those most vulnerable
first. Beyond this, the analysis and results are relevant
for the context of the current global COVID-19 pandemic. Construction of the vulnerability-to-disease
gradient is based on epidemiologically ascertained
indicators that are not necessarily relevant only inthe context of this disease. Hence, the results of thisanalysis can also be used to inform preventative measures that improve overall public health and alleviate
vulnerabilities. Considering the overlapping deprivations or vulnerabilities that people face seems key toappropriately focusing on those most vulnerable andat risk of being left behind.

64

NEPAL'S MULTIDIMENSIONAL POVERTY INDEX 2021

CHAPTER 7:

Conclusion
This report represents the first update since thelaunch of Nepal’s MPI in 2018 (using NMICS
2014 report data) and provides a fine-grained overview of multidimensional poverty levels and trends
in Nepal until the COVID-19 pandemic, using themost recent NMICS 2019 survey data as well as the
NMICS 2014 survey.
The findings show that 17.4 percent or almost 5
million of the population of 28.6 million are poor
– and this has reduced strongly since the 2014 levelof 30.1 percent.12 This means that in the five-year
period 2014-2019, 12.7 percent of the populationof Nepal, a total of 3.1 million people, left poverty. Thus, in 2019, Nepal was on track to reach its
national target of 11.5 percent incidence of MPI
by 2024. Furthermore, the MPI value of 0.074 –meaning that poor people experience 7.4 percentof the deprivations that could be experienced ineveryone was deprived in all indicators – showed avery strong decrease from the 2014 level of 0.133.
And among the poor, the intensity of their poverty
declined from 44.2 percent to 42.5 percent. So ata national level, the trends were strong and significant. The majority of multidimensional poor (59.8
percent) experience between 33.33-39.99 percent.
Nearly one in four of the poor in Nepal, about 24
percent, experience an intensity of poverty above
50 percent of the weighted indicators. So while this
could be a chance that 60 percent of the current
poor could move out of poverty relatively easily, it
is especially important not to leave the poorest, but
12

Following the revised methodology, otherwise it is 28.6 percent

rather to continue the pro-poorest trends witnessed
between 2014 and 2019.
In this report, minor adjustments were made to fiveindicators to align the Nepal’s MPI with SDGs andwith the global MPI, facilitating international comparability. The overall structure of the revised MPI
remained the same, and the levels and trends of MPI
using the previous definition are transparently documented in Appendix 1, which finds similar strong
and pro-poor patterns of reduction between 2014
and 2019, and similar patterns of poverty in 2019.
The remainder of this section summarise main findings and actions.
Reductions of Deprivations experienced by the
Poor: Between 2014 and 2019, all indicators reduced significantly in Nepal at the 99 percent confidence interval, with the greatest absolute reductions
in Cooking Fuel (-13.2 p.p.), Sanitation (-13.0p.p.), and Housing (-12.4 p.p.).
Children in Poverty: In 2014, nearly 36 percent
of children were multidimensionally poor. By 2019,22 percent of all the children remained in poverty
which implies a strong and positive reduction of 14
percentage points. However, over one in five children is still poor, and children are still the poorest
age group. In numbers, this means that 2.2 million
children are poor – so roughly 44 percent of all poor
people in Nepal is a child, making this a vitally important group. While school attendance figures arestrong in most regions, 3.5 percent of people are stillpoor and live with an out-of-school child.
65

NEPAL'S MULTIDIMENSIONAL POVERTY INDEX 2021

Undernutrition in children aged 0 to 4 years alsostrongly contributes to national poverty, with nearly
one in 10 persons living with a little child who isundernourished. This shows that it is crucial to prioritize families with children, and child nutrition, isof pivotal importance.
Rural/Urban Divide: Following a new definition
for urban and rural areas, two-thirds of Nepalis
live in urban areas and only one third in rural areas according to the 2019 NMICS. More multidimensionally poor people (52.4 percent) live in rural
areas. This means that although one of every three
Nepalese (32.7%) reside in rural areas, nearly half ofthe multidimensionally poor live there, so action inrural areas must continue to be a national priority.
Sectoral Priorities: In 2019, 16 percent of the
population are multidimensionally poor and live inhouses with substandard roof, wall or flooring, andthe same number cook with dung, wood or charcoal. Over 11.6 percent people are MPI poor andlive in households that do not have any member
who has completed six years of schooling, while for
Assets it’s 10.3 percent, and for nutrition, 9.3 percent of the population. Note that less than 1 percent
of individuals in Nepal are poor and live in a household deprived in Child Mortality (0.9 percent), thatis, in a household where a child under the age of 18
years has died in the five years preceding the survey. In Nepal, the deprivations in Years of Schooling and Nutrition have the largest contribution to
overall multidimensional poverty, and this sectoralcomposition of poverty does not vary between ruraland urban areas. This leads to important policy recommendations to scale up efforts to address childundernutrition, lifelong learning, and housing.
One-third of the poor people lack an electricity connection but many will suffer from electrical powercuts, a situation that must be addressed in order tosustainably reduce reliance on solid cooking fuels.
While these deprivations are visible among the MPI
poor, they are also experienced by others in the population who are not multidimensionally poor, sec-66

toral efforts will therefore reduce both poverty andvulnerability across Nepal.
Disability Status: Nearly 3.2 percent of the population live in a household where one of its members
has a disability (someone who reported very highlevel of difficulty in at least one domain: visual;hearing, walking, or climbing steps; rememberingor concentrating; self-care, or in communication).
The MPI for these households is slightly higher than
the national average, at 0.083, with an incidence(H) of poverty of 18.3 percent, and an intensity (A)of 45.5 percent.
Provinces: In 2019, poverty levels were highest in
Karnali Province, where 40 percent of people werepoor, followed by Sudurpashchim Province. However, in terms of the number of poor persons, ofthe 5 million MPI poor persons, 1.3 million livein Province 2, followed by nearly one million in
Lumbini Province, and over 770,000 in Province 1.
Furthermore, Province 2 has the highest intensity,
so must be a focus of policy attention in the coming period. When funds are allocated across provinces they must consider, first, the number of poor
persons in each province, and, second, the level ofintensity in each province. Some countries adjust
subnational budgets according to the relative costsof reducing different indicators, but in Nepal, theoverall composition of poverty by indicator does
not vary greatly, so allocations based on the numberof poor and their intensity may suffice.
Equalizing Provincial trends: Six of the sevenprovinces (except Province 1) had a statistically significant reduction in MPI and incidence, and three
see a significant reduction in intensity, with all ofthese reductions at the 99 percent confidence level.
There is clearly an important provincial level story
to tell in Nepal about its pro-poor poverty eradication between 2014 and 2019. Province 2, which
was the second poorest province in 2014, had thelargest reduction in both MPI and the incidence ofmultidimensional poverty, nearly halving its MPI

NEPAL'S MULTIDIMENSIONAL POVERTY INDEX 2021

and incidence in the period. Notably, its total population remains largely the same between the twoyears, so the reductions appear to be the result ofpoverty eradication measures rather than migration.
Province 2 also has the largest population share of
the provinces in the initial year, so its poverty reduction – in which 800,000 people moved out ofpoverty – is a remarkable feat. The next fastest MPI
reduction was seen in Karnali Province (the poorest province in 2014) followed by Lumbini Province. With 1.3 million people remaining in poverty
in 2019, these positive trends must be continued
strongly in the next period.
Progress by Indicators across Provinces: No province saw significant reductions in all 10 indicators,
although this is partly due to large standard errors.
Province 2 had significant reductions in all indicators
except Child Mortality, Water, and Assets. Of the seven indicators, it had the fastest reductions among theprovinces in Years of Schooling (-1.5 p.p.), School Attendance (-0.9 p.p.), Nutrition (-1.2 p.p.), Sanitation(-3.1 p.p.), Electricity (-1.1 p.p.), Cooking Fuel (-2.6
p.p.), and Housing (-2.3 p.p.). Bagmati Province led
the way in significantly reducing deprivations among
the poor and deprived in Child Mortality (-0.1 p.p.)
and Assets (-0.3 p.p.), and while Karnali Province led
in significantly reducing Water deprivations among
the poor (-1.7 p.p.).
Other provinces can learn from these successes, so it
would be very beneficial if the success of Province 2
and Karnali Province at least is further studied, andlessons learned are published.
Sectoral Priorities by Province: This section offerssome policy priorities within each province. However, as Figure 7 showed, it is striking that, broadly speaking, the policy actions required at provincial levels do not vary as much as they do in othercountries. The patterns are relatively similar, inthat deprivations in Years of Schooling and Child
Nutrition contribute the most to MPI across allprovinces, followed by Housing and Cooking Fuel.

Province 2 is distinctive in having a higher contribution from school attendance, while Karnali has thelowest relative contribution from years of schooling
but the highest from child undernutrition and electricity deprivations. For convenience, particularitiesof each province are shared below.
In 2019, Karnali Province has the highest levels ofmultidimensional poverty where the MPI is 0.169.
Four out of 10 individuals are multidimensionalpoor, as the incidence of poverty is nearly 40 percent. The sectoral priorities in Karnali Province are
to improve child nutrition and supporting living
standards: clean energy, high quality housing, electricity, and assets. Improvements in these five indicators alone could reduce MPI by 75 percent.
Sudurpashchim Province and Province 2 rank
second and third in terms of multidimensional poverty: approximately 1 in 4 people are poor.
In Sudurpashchim Province, half of the MPI value is due to deprivations in child nutrition, housing, solid cooking fuel and assets. So investments inthese indicators, supported by investments in stableelectricity supply and years of schooling, will lead tothe fastest reduction of MPI.
In Province 2, the top priorities are improving yearsof schooling, through lifelong learning, and improving child school attendance and child nutrition. Thisis vital as deprivations in School attendance are highrelative to other provinces. An integrated programmeof nutrition and pre-primary education could beconsidered. Efforts to continue the very strong reductions in Sanitation should also continue.
In Lumbini Province, the next poorest province,
nearly half of the MPI value is due to deprivations
in years of schooling and child nutrition; these,then, are clear priorities, as is the provision of stableelectricity and clean cooking fuel, and good quality
housing materials.

67

NEPAL'S MULTIDIMENSIONAL POVERTY INDEX 2021

Province 1 has the third lowest MPI of all provinces,
at 0.066. This is below the national MPI of 0.074.
The incidence of MPI is 15.8 percent and this could
be reduced by investments in years of schooling, andnutrition – which together create nearly half of thevalue of MPI, followed by improvements in housing materials, and programmes to support the useof clean cooking fuel, or good ventilation systems.
Province 1 reduced MPI strongly, with particularly
strong reductions in sanitation deprivations.
The heart of Nepal, where poverty is lowest, are theprovinces of Gandaki and Bagmati, where 9.6 percent and 7 percent of their respective populations
live in poverty. The profiles of poverty, hence theactions required to reduce poverty, are similar inboth provinces, with investments in child nutrition,
years of schooling being the most critical, as they
constitute half of the value of MPI. These should becomplemented by investments to improve housingmaterials, advocate clean cooking fuel and stable energy sources, adequate sanitation, and assets.
These actions should not be difficult, consideringthat Bagmati Province, which has the lowest MPI
of all provinces in 2019, halved its MPI between
2014 and 2019 from 0.061 to 0.028 (reducing theincidence from 14.4 percent to 7 percent). Reduc-68

tions in that period were driven by reductions incooking fuel. Gandaki Province had a larger absolute decrease in MPI from 0.078 to 0.035 in thesame period, with the incidence of MPI falling from
18.5 percent in 2014 to 9.6 percent in 2019 predates the onset of the COVID-19 pandemic. As aresult, it is not able to reflect the possible implication and consequences of the pandemic. However,
the analysis of COVID-19 related deprivations (ofas well as additional to the deprivations included in
the MPI) show that a majority of Nepal’s population
is vulnerable to COVID-19 implications, as they
are deprived in at least one of the COVID-19 related MPI deprivations (Nutrition, Water, Cooking
Fuel). Almost five million (17 percent) experience
increased vulnerability, and around 375 are highly
considered to be highly vulnerable. Furthermore,considering additional COVID-19 related deprivation (overcrowding, internet access, handwashingfacilities), we find that MPI poor people are more
affected by these, making them particularly exposedand susceptible to COVID-19 and its consequences. Thus, on top of their experiences of overlapping
deprivations which already puts them at a disadvantage, the situation of the poor may be exacerbatedeven further which accentuates the necessity to effective and targeted poverty policy.

Photo: UNDP Nepal

NEPAL'S MULTIDIMENSIONAL POVERTY INDEX 2021

69

NEPAL'S MULTIDIMENSIONAL POVERTY INDEX 2021

References
Alkire, S., Dirksen, J., Nogales, R. and Oldiges, C. (2020a). ‘Multidimensional poverty and COVID-19 risk
factors: A rapid overview of interlinked deprivations across 5.8 billion people’, OPHI Briefing 53a,
Oxford Poverty and Human Development Initiative (OPHI), University of Oxford.
Alkire, S., Dirksen, J., Nogales, R. and Oldiges, C. (2020b). ‘Multidimensional poverty and vulnerability to
COVID-19: A rapid overview of disaggregated and interlinked vulnerabilities in Sub-Saharan Africa’,
OPHI Briefing 54a, Oxford Poverty and Human Development Initiative, University of Oxford.
Alkire, S., and Foster, J. (2019). “Counting and multidimensional poverty measurement”, Journal of Public
Economics, 95(7 8), 476-487.
Alkire, S., Foster, J., Seth, S., Santos, M. E. Roche, J. M. and Ballon, P. (2015). Multidimensional Poverty Measurement and Analysis, Oxford University Press.
Alkire, S., Kanagaratnam, U. and Suppa, N. (2018). ‘The Global Multidimensional Poverty Index (MPI): 2018
revision’, OPHI MPI Methodological Notes 46, Oxford Poverty and Human Development Initiative,
University of Oxford
Alkire, S. and Santos, M. E. (2014). “Measuring Acute Poverty in the Developing World: Robustness and Scopeof the Multidimensional Poverty Index”, World Development, 59, 251-274.
Alkire, S. and Santos, M. E. (2013). “Measuring Acute Poverty Using the Multidimensional Poverty Index:
Robust Comparisons and Future Prospects”, OPHI Working Paper 59. Oxford Poverty and Human
Development Initiative, University of Oxford.
Apablaza, M. and Yalonetzky, G. (2019). “Measuring the dynamics of multiple deprivations among children:the cases of Andhra Pradesh, Ethiopia, Peru and Vietnam”, Young Lives Research in Progress, Oxford:
University of Oxford.
Atkinson, A. B. (2003). “Multidimensional deprivation. Contrasting social welfare and counting approaches”,
Journal of Economic Inequality, 1, 51-65.
Bourguignon, F., and Chakravarty, S. (2003). “The measurement of multidimensional poverty”, Journal of
Economic Inequality, 1(1), 25-49.
Central Bureau of Statistics (CBS). (2014). Nepal Multiple Indicator Cluster Survey 2014. Survey Findings
Report. Kathmandu, Nepal: Central Bureau of Statistics and UNICEF Nepal.

70

NEPAL'S MULTIDIMENSIONAL POVERTY INDEX 2021

Central Bureau of Statistics (CBS). (2020). Nepal Multiple Indicator Cluster Survey 2019. Survey Findings
Report. Kathmandu, Nepal: Central Bureau of Statistics and UNICEF Nepal.
Dirksen, J., Nogales, R., and Oldiges, C. (2021). ‘Leave No One Behind. The COVID-19 Pandemic and Multidimensional Poverty in South Asia’. Mimeo.
Drèze, J. and Sen, A. (2013). An Uncertain Glory: India and its Contradictions. Princeton, New Jersey: Princeton
University Press.
Foster, J., Greer, J. and Thorbecke, E. (1984). “A class of decomposable poverty measures”, Econometrica: Journal of the Econometric Society, 761-766.
Gordon, S.B., Bruce, N.G., Grigg, J., Hibberd, P.L., Kurmi, O.P., Lam, K.B.H., Mortimer, K., Asante, K.P.,
Balakrishnan, K., Balmes, J. and Bar-Zeev, N. (2014). ‘Respiratory risks from household air pollution
in low- and middle-income countries’, The Lancet Respiratory Medicine, 2(10), pp. 823–860.
Johns Hopkins University & Medicine. ‘COVID-19 Dashboard’. Available online at: https://coronavirus.jhu.
edu/map.html.
Ministry of Finance, Nepal (2014). Annual Plan 2014-2015.
Ministry of Finance, Nepal (2015). Annual Plan 2015-2016.
National Planning Commission. (2018). “NEPAL Multidimensional Poverty Index”. National Planning Commission, Government of Nepal and Oxford Poverty and Human Development Initiative, University of
Oxford.
OCHA. (2020). Nepal – “Subnational Administrative Boundaries”. Accessed in February 2021. In: https://data.humdata.org/dataset/administrative-bounadries-of-nepal
Shrestha, R.; Shrestha, S.; Khanal, P.; Kc, Bhuvan. (2020). “Nepal’s first case of COVID-19 and public healthresponse”. Journal of Travel Medicine. 27 (3). February 2020.
UNDP (2010). Human Development Report 2010: The Real Wealth of Nations: Pathways to Human Development.
New York: Palgrave Macmillan.
UNICEF-WHO (2017). Safely Managed Drinking Water: Thematic Report on Drinking Water. World HealthOrganization, Geneva.
71

NEPAL'S MULTIDIMENSIONAL POVERTY INDEX 2021

UN HABITAT (2018). Metadata on SDGs Indicator 11.1.1. United Nations Human Settlements Program,
Nairobi, Kenya.
UN Population Division (DESA). 2021. “World Population Prospects 2019”. In https://population.un.org/
wpp/Download/Standard/Population/UN Population Division (DESA). 2021. “World Population
Prospects 2019”. Accessed in: https://population.un.org/wpp/Download/Standard/Population/
WHO (2019). Drinking Water. World Health Organization, June 14th 2019. In: https://www.who.int/en/
news-room/fact-sheets/detail/drinking-water
WHO (2018a). Household Air Pollution and Health. World Health Organization, May 8th 2018. In: https://www.who.int/news-room/fact-sheets/detail/household-air-pollution-and-health
WHO (2018b). Malnutrition. World Health Organization, February 16th 2018. In: https://www.who.int/en/
news-room/fact-sheets/detail/malnutrition72

NEPAL'S MULTIDIMENSIONAL POVERTY INDEX 2021

Annex 1: Methodology
The analysis is based on the Alkire-Foster methodology. This chapter presents this methodology, a description of the MPI and its properties, along withthe measurement design. It has three sections:
1. A Step-by-Step
Alkire-Foster Method

Application

of

the

Step 2: Choose Dimensions. The choice of dimensions is important but less haphazard than is often
assumed. In practice, most researchers implicitly
draw on five means of selection, either alone or in
combination:•

Ongoing deliberative participatory exercisesthat elicit the values and perspectives of stakeholders. A variation of this method is to use survey data on people’s perceived necessities.

•

A list that has achieved a degree of legitimacy
through public consensus, such as the Universal
Declaration of Human Rights, the MDGs, orsimilar lists at national and local levels.

•

Implicit or explicit assumptions about what
people do value or should value. At times theseassumptions are the informed guesses of theresearcher; in other situations, they are drawn
from convention, social or psychological theory, or philosophy.

•

Convenience or a convention that is taken to
be authoritative or used because these are theonly data available that have the required characteristics.

•

Empirical evidence regarding people’s values,
data on consumer preferences and behaviors,or studies of what values are most conducive to
people’s mental health or social benefit.

2. Alkire-Foster Method – Technical Details
3. Measurement Design

2.1 A STEP-BY-STEP APPLICATION OF THE
ALKIRE-FOSTER METHOD
Nepal’s multidimensional poverty measure uses
the Alkire-Foster method to count and identify themultidimensionally poor population of Nepal andits provinces. The Alkire-Foster method can be intuitively introduced in 12 steps. The first six steps arecommon to many multidimensional poverty measures; the remainder are specific to the Alkire-Foster
method. This section introduces the methodology
for non-technical audiences and is followed by amore comprehensive walk-through of the method
for technical readers.13
Step 1: Choose Unit of Analysis. The unit of analysis is most commonly an individual or household
but could also be a community, school, clinic, firm,
district, or other unit.

13This section has been adapted from the OPHI research, “How to Apply
the Alkire-Foster Method”, available here: https://ophi.org.uk/research/
multidimensional-poverty/how-to-apply-alkire-foster/.

Clearly these processes overlap and are often used intandem empirically; for example, nearly all exercises
need to consider data availability or data issues, andoften participation, or at least consensus, is required
to give the dimensions public legitimacy.

73

NEPAL'S MULTIDIMENSIONAL POVERTY INDEX 2021

Step 3: Choose Indicators. Indicators are chosen foreach dimension on the principles of accuracy (using
as many indicators as necessary so that analysis canproperly guide policy) and parsimony (using as few
indicators as possible to ensure ease of analysis forpolicy purposes and transparency). Statistical properties are often relevant—for example, when possible and reasonable, it is best to choose indicatorsthat are not highly correlated.
Step 4: Set Deprivation Cut-off. A deprivation cutoff is set for each indicator. This step establishes thefirst cut-off in the methodology. Every person canthen be identified as deprived or non-deprived withrespect to each indicator. For example, if the indicator is Schooling (‘How many years of schooling haveyou completed?’), ‘6 years or more’ might identify
non-deprivation, while ‘1-5 years’ might identify
deprivation in the indicator. Poverty thresholds canbe tested for robustness, or multiple sets of thresholds can be used to clarify explicitly different categories of the poor (such as deprived and extremely
deprived).
Step 5: Apply Deprivation Cut-offs. This step replaces the person’s achievement with his or her status with respect to each cut-off; for example, if in ameasure with the dimension of health, where the indicators are ‘access to health clinic’ and ‘self-reported morbidity body mass index,’ people are identified
as being deprived or non-deprived for each indicator. The process is repeated for all indicators for allother dimensions.
Step 6: Set Weights. Identify the weighting scheme
to be used by firstly choosing the relative weightsof the dimensions (for example, in the global MPI,
the three dimensions – health, education, and living standards – are all equally weighted as constituting 1/3 of the measure), and then dividing up
the weight of that dimension among its indicators.
These weighting decisions should be justified bynormative or empirical arguments.

74

Step 7: Count the Number of Deprivations for
Each Person. In this step, the number of deprivations (i.e., the number of indicators the person isdeprived in) is counted up to create a summaryscore for each person. It is important when calculating this sum that the weight of each indicator istaken into account, so each deprivation reflects its
relative importance to the overall deprivation score.
Step 8: Set the Poverty Cut-off. Assuming equal
weights for simplicity, set a second identification
cut-off, k, which gives the number of indicators inwhich a person must be deprived in order to be considered multidimensionally poor. In practice, it may
be useful to calculate the measure for several valuesof k. Robustness checks can be performed across allvalues of k
Step 9: Apply the Povety Cut-off k to Obtain the
Set of Poor Persons and Censor All Non-poor Data.
The focus is now on the profile of the poor and thedimensions in which they are deprived. All information on the non-poor is replaced with zeros (0) in
the deprivation matrix.
Step 10: Calculate the Incidence, H – also called thepoverty rate or headcount ratio. Divide the numberof poor people by the total number of people. The
multidimensional headcount is a useful measure,but it does not increase if poor people become moredeprived, nor can it be broken down by dimension
to analyse how poverty differs among groups. For
that reason, we need a different set of measures, described next.
Step 11: Calculate the Intensity, A. That is the average share of deprivations a poor person suffers. It
is calculated by adding up the proportion of total
deprivations each poor person suffers (deprivation
score) and dividing by the number of poor people.
Step 12: Calculate the MPI. If the data are binary or
ordinal, multidimensional poverty is measured by
the adjusted headcount, M0 or MPI which is calcu-

NEPAL'S MULTIDIMENSIONAL POVERTY INDEX 2021

lated as H times A. Headcount poverty is multipliedby the ‘average’ number of dimensions in which allpoor people are deprived to reflect the breadth ofdeprivations.

2.2 ALKIRE-FOSTER METHOD –TECHNICAL DETAILSThe global MPI, which was developed by Alkire
and Santos (2010, 2013) in collaboration with theUNDP, and first appeared in the 2010 Human Development Report, is one particular adaptation of theadjusted headcount ratio (M0 or MPI) proposed in
Alkire and Foster (2019) and elaborated in Alkire,
Foster, Seth, Santos, Roche, and Ballon (2015).
This section outlines the methodology and relevant
properties that are used in the subsequent sections
to understand the change in Nepal’s multidimensional poverty.
Sabina Alkire and James Foster created a new
method for measuring multidimensional poverty. It identifies who is poor by considering theintensity of deprivations they suffer, and includes
an aggregation method. Mathematically, theMPI combines two aspects of poverty:
MPI = H x A
1) Incidence ~ the percentage of people who are
multidimensionally poor, or the headcount: H
2) Intensity of people’s poverty ~ the average percentage of dimensions in which poor people are
deprived: A
14

15

The meaning of the terms ‘dimension’ and ‘indicator’ are slightly different
in Alkire and Foster (2019) and in Alkire and Santos (2010). In Alkire andFoster (2019), no distinction is made between these two terms. In Alkire andSantos (2010), however, the term ‘dimension’ refers to a pillar of wellbeing
and a dimension may consist of several indicators.
For k=100%, the identification approach is referred to as the intersection
approach; for 0<k≤min{w1,…,wd}, it is referred to as the union approach(Atkinson, 2003); and for minj{w1,…,wd}<k<1, it is referred to as the dual
cut-off approach by Alkire and Foster, or more generally as the intermediateapproach.

2.2.1 The Multidimensional Poverty Index: An
Adjusted Headcount Ratio

Suppose at a particular point in time, there are npeople in Nepal and their wellbeing is evaluated byd indicators.14 We denote the achievement of person i in indicator j by xijUR for all i=1,…,n and
j=1,…,d. The achievements of n persons in d indicators are summarized by an n×d dimensional matrix X, where rows denote persons and columns denote indicators. Each indicator is assigned a weightbased on the value of a deprivation relative to otherdeprivations. The relative weight attached to eachindicator j is the same across all persons and is de..
noted by wj, such that wj>0 and ∑For a single-dimensional analysis, people are identified as poor as long as they fail to meet a thresholdcalled the ‘poverty line’, and as non-poor otherwise.
In a multidimensional analysis based on a counting approach – as with the adjusted headcount ratio – a person is identified as poor or non-poor intwo steps. In the first step, a person is identified as
deprived or not in each indicator subject to a deprivation cut-off. We denote the deprivation cut-off
for indicator j by zj and the deprivation cut-offs are
summarized by vector z. Any person i is deprived
in any indicator j if xij<zj and non-deprived, otherwise. We assign a deprivation status score gij to each
person in each dimension based on the deprivation
status. If person i is deprived in indicator j, thengij=1; and gij=0 otherwise. The second step uses theweighted deprivation status scores of each person
in all d indicators to identify the person as poor ornot. An overall deprivation score ciU[0,1] is computed for each person by summing the deprivation
status scores of all d indicators, each multiplied by.
their corresponding weights, such that ∑A person is identified as poor if ci≥k, where kU(0,1];
and non-poor, otherwise.15 The deprivation scoresof all n persons are summarized by vector c.

75

NEPAL'S MULTIDIMENSIONAL POVERTY INDEX 2021

After identifying the set of poor and their deprivation scores, we obtain the adjusted headcount ratio(M0). Many countries refer to this as the MPI or
Multidimensional Poverty Index. The focus axiom
requires that while measuring poverty the focus
should remain only on those identified as poor.16
This entitles us to obtain the censored deprivation
score vector c(k) from c, such that ci(k)=ci if ci≥kand ci (k)=0, otherwise. The M0 is equal to the average of the censored deprivation scores:( )∑2.2.2 Properties of the Multidimensional
Poverty Index

We now outline some of the features of M0 that are
useful for policy analysis. The first is that M0 can be
expressed as a product of two components: the shareof the population who are multidimensionally poor
or Multidimensional Headcount Ratio (H) and theaverage of the deprivation scores among the poor
only, or Intensity (A). Technically:∑( )where q is the number of poor.17 This feature has
an interesting policy implication for inter-temporal
analysis. A certain reduction in M0 may occur either by reducing H or by reducing A. This difference
cannot be understood by merely looking at M0. If
a reduction in M0 occurs by merely reducing thenumber of people who are marginally poor, then H
decreases but A may not. On the other hand, if a
reduction in M0 occurs by reducing the deprivationof the poorest of the poor, then A decreases, but H
may not.18
16

17

18

19

76

In the multidimensional context, there are two types of focus axioms. One isdeprivation focus, which requires that any increase in already non-deprived
achievements should not affect a poverty measure. The other is poverty focus,
which requires that any increase in the achievements of non-poor persons
should not affect a poverty measure. See Bourguignon and Chakravarty(2003), and Alkire and Foster (2019).
This feature is analogous to that of the Poverty Gap Ratio, which is similarly
expressed as a product of the Headcount Ratio and the Average Income Gap
Ratio among the poor.
Apablaza and Yalonetzky (2019) have shown that the change in M0 can be
expressed as ΔM_0=ΔH+ΔA+ΔH×ΔA, where Δx is referred to as change in x.
See Foster, Greer and Thorbecke (1984) for a discussion of this property.

The second feature of M0 is that if the entire population is divided into m mutually exclusive and collectively exhaustive groups, then the overall M0 can be
expressed as a weighted average of the M0 values ofm subgroups, where weights are the respective population shares. We denote the achievement matrix,
the population, and the adjusted headcount ratio ofsubgroup l by Xl, nl, and M0 (Xl), respectively. Thenthe overall M0 can be expressed as:∑

This feature is also known as subgroup decomposability and is useful for understanding the contributionof different subgroups to overall poverty levels.19
Note that the contribution of a subgroup to theoverall poverty depends both on the poverty level ofthat subgroup and that subgroup’s population share.
The third feature of M0 is that it can be expressed as
an average of the censored headcount ratios of indicators weighted by their relative weight. The CensoredHeadcount Ratio of an indicator is the proportion of
the population that is multidimensionally poor andis simultaneously deprived in that indicator. Let us
denote the Censored Headcount Ratio of indicator j
by hj. Then M0 can be expressed as:
∑

∑

[ ∑

]

Where gij (k)=gij if ci≥k and gij (k)=0, otherwise. Similar relationships can be established between A andthe deprivations among the poor. Let us denote theproportion of poor people deprived in indicator j by
. Then, dividing both sides of the above relationship by H, we find:∑∑

Breaking down poverty in this way allows an analysis of multidimensional poverty to depict clearly
how different indicators contribute to poverty andhow their contributions change over time. Let us

NEPAL'S MULTIDIMENSIONAL POVERTY INDEX 2021

denote the contribution of indicator j to M0 by ϕ j.
Then, the contribution of indicator j to M0 is:

2.3 MEASUREMENT DESIGN
Nepal’s national MPI utilizes a set of dimensions,indicators, and cut-offs that reflect its priorities as
expressed in the national plans, and that can be implemented using the MICS (2019) dataset. This section describes these parameter choices.
2.3.1 Unit of Identification and Analysis

The unit of identification refers to the entity that is
identified as poor or non-poor – usually the individual or the household. In the case of Nepal’s MPI, theunit of identification is the household: the household members’ information is considered together,and all household members receive the same deprivation score. This acknowledges intra-household
caring and sharing – for example, educated household members reading for each other, and multiple
household members being affected by someone’s
severe health conditions. In addition, it allows themeasure to include indicators that are specific tocertain age groups (as, for instance, school attendance). The unit of analysis, meaning how the results are reported and analysed, is the individual.
This means that, for instance, the headcount ratio isthe percentage of people who are identified as poor,
rather than the percentage of households that are
identified as poor.

2.3.2 Dimensions, Indicators and Deprivation
Cut-offs

As described in Chapter 1, Nepal’s MPI builds upon
the global MPI, and retains the same three dimensions. In total, 10 indicators are used in this national
index; all of them are the same as the international MPI. The selection of the dimensions, as well as
the particular indicators, deprivation cut-offs, andweights were based in a thorough discussion withinternational organizations and experts in the field.
2.3.3 Weights

The weights used in this report assign one third of
the total weight to each of the three dimensions ofeducation, health and living standards.
2.3.4 Poverty Cut-off

Thresholds are used to decide whether a person ismultidimensionally poor, using the Alkire and Foster method. It involves the following steps: (a) a indicator-specific cut-off (deprivation cut-off) – wherea person is considered deprived in each indicator if
their achievement falls below the cut-off; and (b) across-indicator cut-off (or poverty cut-off) - wherea person is considered to be poor if the weighted
sum of their deprivations meets or exceeds the poverty cut-off. In line with the global MPI, in Nepal’s
MPI the poverty cut-off is chosen to be at one-thirdof all weighted indicators; that is, a person who isdeprived in k ≥ 33.33% of the weighted indicators
is considered multidimensionally poor.

77

NEPAL'S MULTIDIMENSIONAL POVERTY INDEX 2021

Statistical Appendix I.
Nepal MPI using Previous
Methodology.
TABLE 1.1: Nepal’s Previous MPI – Indicators, Deprivations Cut-offs, and Weights
Dimensions ofPoverty

Indicator

Household is deprived if…

Nutrition

Any child for whom there is nutritional information isundernourished (underweight) in terms of weight for age (a)1/6Child Mortality

Any child has died in the family in the 5-year period preceding thesurvey1/6Years of SchoolingNo household member aged 10 years or older has completed 5 yearsof schooling

1/6

School Attendance

Any school-aged child is not attending school up to the age at
which he /she would complete class 8

1/6

Cooking Fuel

The household cooks with dung, wood, or charcoal

1/18

Improved Sanitation

The household’s sanitation facility is not improved (according to
MDG guidelines) or it is improved but shared with other households
(b)

1/18

Improved drinking
water

The household does not have access to improved drinking water(according to MDG guidelines) or safe drinking water is at least a
30-minute walk from home, roundtrip (c)1/18Electricity

The household has no electricity

1/18

Housing

The household has inadequate housing materials in any of the threecomponents: floor and roof.

1/18

Assets ownership

The household does not own more than one of these assets: radio,
TV, telephone, bicycle, motorbike, or refrigerator, and does not
own a car or truck

1/18

Health

Education

LivingStandard

Source: TABLE 2.1 Nepal’s National MPI in National Planning Commission (2018).
Notes:
a. Children are considered malnourished if their z-score of weight-for-age is below minus two standard deviations from the median of thereference population. Nepal’s MICS data only include child nutrition; the DHS data include both child and adult nutritional data.
b. A household is considered to have access to improved sanitation if it has some type of flush toilet or latrine, or ventilated improved pit orcomposting toilet, provided that they are not shared.
c. A household has access to clean drinking water if the water source is any of the following types: piped water, public tap, borehole or pump,protected well, protected spring or rainwater, and it is within 30 minutes’ walk (roundtrip).

78

WeightNEPAL'S MULTIDIMENSIONAL POVERTY INDEX 2021

TABLE A1.1: Uncensored Headcount Ratios 2019, Previous Methodology
Dimension

Indicator

Health
Education

Living Standards

Percentage of Population Deprived

Child Mortality

9.0

Nutrition

12.2

School Attendance

4.9

Years of Schooling

10.0Electricity

10.2

Water

7.6

Sanitation

21.3

Housing

51.6

Cooking Fuel

56.8

Assets20.8

Source: Calculations based on data from NMICS (2019).

TABLE A1.2: Incidence, Intensity and Multidimensional Poverty Index (MPI) 2019, Previous
Methodology
Poverty Cut-off (k)k value=33.33%

Index

Value

Confidence Interval (95%)

MPI

0.062

0.055

0.068

Incidence (H, %)

14.7

13.3

16.2

Intensity (A, %)

41.9

41.1

42.7

Source: Calculations based on data from NMICS (2019).

TABLE A1.3: Censored Headcount Ratios 2019, Previous Methodology
Dimension
Health
Education

Living Standards

Indicator

Percentage of Population MPI Poor and Deprived

Child Mortality

4.3

Nutrition

6.4

School Attendance

3.4

Years of Schooling

6.7

Electricity

4.9

Water

2.1

Sanitation

5.6

Housing

13.4

Cooking Fuel

13.7

Assets

8.9

Source: Calculations based on data from NMICS (2019).

79

NEPAL'S MULTIDIMENSIONAL POVERTY INDEX 2021

TABLE A1.4: Contribution of each Indicator to National, Urban and Rural MPI 2019, Previous
Methodology
Percentage Contribution of each Indicator to MPI

Indicator

Urban

Rural

National

Child Mortality

12.4%

10.9%

11.6%

Nutrition

18.4%

16.2%

17.2%

School Attendance

9.9%

8.6%

9.2%

Years of Schooling

17.1%

19.1%

18.2%

Electricity

3.7%

5.1%

4.4%

Water

1.9%

1.9%

1.9%

Sanitation

5.9%

4.3%

5.0%

Housing

11.7%

12.4%

12.1%

Cooking Fuel

11.9%

12.7%

12.3%

Assets

7.0%

8.8%

8.0%

Source: Calculations based on data from NMICS (2019).

TABLE A1.5: Multidimensional Poverty by Rural/Urban Areas 2019, Previous Methodology
Urban
Index

Population Share (%)

MPI
Incidence (H, %)

Rural

Value

ConfidenceInterval (95%)

0.044

0.037

0.051

10.6

8.9

12.3

41.6

40.4

42.8

67.3

Intensity (A, %)Population
Share (%)
32.7

Value

ConfidenceInterval (95%)

0.098

0.086

0.110

23.3

20.8

25.8

42.2

41.1

43.2

Source: Calculations based on data from NMICS (2019).

TABLE A1.6: Multidimensional Poverty by Sub-National Region 2019, Previous Methodology
Sub-National
RegionPopulation
Share (%)

Province 1

MPI

Incidence (H, %)Value

ConfidenceInterval (95%)

16.9

0.053

0.038

Province 2

18.8

0.095

Bagmati
Province

23.2

Gandaki
Province

Intensity (A, %)

Poor

Value

ConfidenceInterval (95%)

Value

ConfidenceInterval (95%)

0.068

13.0

9.4

16.5

40.8

39.2

42.4

629

0.074

0.116

21.6

17.1

26.2

43.9

42.0

45.7

1,167

0.020

0.013

0.028

5.0

3.3

6.8

40.4

38.9

41.9

334

8.2

0.025

0.019

0.031

6.7

5.1

8.3

37.1

36.0

38.2

157

Lumbini
Province

18.4

0.063

0.045

0.080

14.9

11.3

18.4

42.1

39.7

44.5

781

Karnali Province

5.6

0.151

0.123

0.179

35.6

29.5

41.6

42.4

41.0

43.9

575

Sudurpashchim
Province

8.7

0.092

0.072

0.112

22.7

17.9

27.4

40.6

39.3

41.8

567

Source: Calculations based on data from NMICS (2019).

80

Number(thousand)NEPAL'S MULTIDIMENSIONAL POVERTY INDEX 2021

TABLE A1.7: Multidimensional Poverty by Age Groups 2019, Previous Methodology
MPI

Incidence (H, %)Intensity (A, %)Poor

Age-group

Population
Share (%)

Value

ConfidenceInterval (95%)

Value

ConfidenceInterval (95%)

Value

ConfidenceInterval (95%)

Age 0-17

35.2

0.078

0.070

0.087

18.2

16.4

20.0

43.1

42.1

44.0

1,833

Age 18+

64.7

0.053

0.047

0.058

12.8

11.6

14.1

41.0

40.3

41.8

2,377

ConfidenceInterval (95%)

Number(thousand)Number(thousand)Source: Calculations based on data from NMICS (2019).

TABLE A1.8: Multidimensional Poverty by Sex 2019, Previous Methodology
GenderPopulation
Share (%)

MPI

Incidence (H, %)ConfidenceInterval (95%)

Value

Value

Intensity (A, %)ConfidenceInterval (95%)

Value

Poor

Male

47.3

0.060

0.053

0.067

14.3

12.9

15.8

42.0

41.2

42.8

1,941

Female

52.7

0.063

0.056

0.070

15.1

13.6

16.5

41.9

41.0

42.7

2,269

Source: Calculations based on data from NMICS (2019).

TABLE A1.9: Multidimensional Poverty by Disability status of household members 2019, Previous
Methodology
MPI

Incidence (H, %)Intensity (A, %)Poor

ConfidenceInterval (95%)

ConfidenceInterval
Value
(95%)

Number(thousand)

19.6

14.4

24.9

43.8

41.3

46.3

178

14.6

13.1

16.0

41.8

41.0

42.7

4,032

MPI among households
in which a person
experiences disability

Population
Share (%)

Value

ConfidenceInterval (95%)

Value

With a member withdisability

3.2

0.086

0.063

0.109

Without a member
with disability

96.8

0.061

0.054

0.067

Source: Calculations based on data from NMICS (2019). Refers to population living in households with a member (aged 15-49) is experiencing at least
one the following difficulties: difficulty seeing, even if wearing glasses or contact lenses; difficulty hearing, even if using a hearing aid; difficulty walking or
climbing steps; difficulty remembering or concentrating; difficulty with self-care, such as washing all over or dressing; difficulty communicating.

81

NEPAL'S MULTIDIMENSIONAL POVERTY INDEX 2021

Statistical Appendix II:
Changes over time 20142019: Previous Methodology
TABLE A-1: Changes in MPI, H, and A at the National Level 2014-19, Previous Methodology
Index

Poverty across time

ChangesPopulation (millions)

2014

2019

Absolute p.p.)

Relative (%)

significanceYear 1

Year 2

MPI

0.127

0.062

-0.065-51.3%

***

26.91

28.61

H

28.6%

14.7%

-13.9

-48.6%***

Poor people (millions)

A

44.2%

41.9%

-2.3

-5.2%

***

7.70

4.21

Note: *** statistically significant at α=0.01, ** statistically significant at α=0.05, * statistically significant at α=0.10.

TABLE A-2: Changes in Uncensored Headcount Ratios, National Level 2014-19, Previous
Methodology
Dimension
Education
HealthLivingStandards

Indicator

Uncensored Headcount Ratios (%)

Changes

2014

2019

Absolute (p.p.)

Relative (%)

significanceYears of Schooling

15.7

10.0

-5.7

-36.4%***

School Attendance

7.9

4.9

-3.0

-38.0%***

Nutrition

15.9

12.2

-3.6

-22.9%***

Child Mortality

13.9

9.0

-4.9

-35.4%***

Sanitation

39.6

21.3

-18.3

-46.2%

***

Water

11.2

7.6

-3.6

-32.3%***

Electricity

15.7

10.2

-5.5

-35.2%***

Cooking Fuel

74.5

56.8

-17.7

-23.8%***

Housing

67.3

51.6

-15.6

-23.3%***

Assets

21.0

20.8

-0.2

-0.7%

Note: *** statistically significant at α=0.01, ** statistically significant at α=0.05, * statistically significant at α=0.10.

82

NEPAL'S MULTIDIMENSIONAL POVERTY INDEX 2021

TABLE A-3: Changes in Censored Headcount Ratios, National Level 2014-19, Previous
Methodology
Dimension
Education
HealthLivingStandards

Indicator

Censored Headcount Ratios (%)
2014

Changes2019

Absolute (p.p.)

Relative (%)

significanceYears of Schooling

13.5

6.7

-6.7

-50.1%***

School Attendance

7.3

3.4

-3.9

-53.3%***

Nutrition

12.1

6.4

-5.7

-47.1%***

Child Mortality

9.5

4.3

-5.2

-54.5%***

Sanitation

18.7

5.6

-13.1

-70.0%

***

Water

5.2

2.1

-3.1

-58.8%***

Electricity

9.9

4.9

-5.0

-50.3%***

Cooking Fuel

28.2

13.7

-14.5

-51.5%***

Housing

27.2

13.4

-13.8

-50.6%***

Assets

11.9

8.9

-3.1

-25.7%***

Note: *** statistically significant at α=0.01, ** statistically significant at α=0.05, * statistically significant at α=0.10.

TABLE A-4: Contribution of Each Indicator to National MPI 2014 & 2019, Previous Methodology
Dimension
Education
HealthLiving Standards

Indicator

Percentage Contribution to MPI (%)
2014

2019

Years of Schooling

17.7

18.2

School Attendance

9.6

9.2

Nutrition

15.9

17.2

Child Mortality

12.5

11.6

Sanitation

8.2

5.0

Water

2.3

1.9

Electricity

4.3

4.4

Cooking Fuel

12.4

12.3

Housing

11.9

12.1

Assets

5.2

8.0

83

NEPAL'S MULTIDIMENSIONAL POVERTY INDEX 2021

TABLE A-5: Changes in MPI, H, and A at the National Level 2014-19, Previous Methodology
MPI

Province
Province 1

Total Population(thousands)

Changes20

2014

2019

Absolute (p.p.)

Relative (%)

0.085

0.053

-0.032-37.7%

**

2014

2019

4,448

4,848

Province 2

0.217

0.095

-0.122-56.3%

***

5,553

5,391

Bagmati Province

0.051

0.020

-0.031-60.3%

***

5,243

6,651

Gandaki Province

0.061

0.025

-0.036-59.2%

***

2,553

2,347

Lumbini Province

0.133

0.063

-0.070-52.8%

***

4,835

5,258

Karnali Province

0.230

0.151

-0.079-34.3%

***

1,586

1,616

Sudurpashchim Province

0.146

0.092

-0.054-37.0%

***

2,688

2,498

Province

H (%)

ChangesPopulation Share

2014

2019

Absolute (p.p.)

Relative (%)

Province 1

19.7

13.0

-6.7

-34.1%

2014

2019

**

16.5%

16.9%

Province 2

47.9

21.6

-26.2

Bagmati Province

12.2

5.0

-7.2

-54.8%

***

20.6%

18.8%

-58.9%

***

19.5%

23.2%

Gandaki Province

14.2

6.7

-7.5

-52.9%

***

9.5%

8.2%

Lumbini Province

29.9

14.9

-15.1

-50.3%

***

18.0%

18.4%

Karnali Province

51.2

35.6

-15.6

-30.6%

***

5.9%

5.6%

Sudurpashchim Province

33.6

22.7

-10.9

-32.4%

***

10.0%

8.7%

A (%)

Province

Number of Poor
(thousands)

Changes

2014

2019

Absolute (p.p.)

Relative (%)

Province 1

43.2

40.8

-2.4

-5.6%

Province 2

45.3

43.9

-1.5

-3.2%

Bagmati Province

41.9

40.4

-1.4

-3.4%

Gandaki Province

42.9

37.2

-5.7

-13.3%

Lumbini Province

44.3

42.1

-2.2

-5.1%

2014
*

***

2019

875

629

2,659

1,167

642

334

362

157

1,447

781

Karnali Province

44.9

42.4

-2.4

-5.4%

**

812

575

Sudurpashchim Province

43.5

40.6

-2.9

-6.8%

***

902

567

20

84

Note: *** statistically significant at α=0.01, ** statistically significant at
α=0.05, * statistically significant at α=0.10.

NEPAL'S MULTIDIMENSIONAL POVERTY INDEX 2021

TABLE A-6: Changes in Censored Headcount Ratios by Province 2014-19, Previous Methodology
Absolute Changes (p.p.)
Province

Years of
Schooling

SchoolAttendance

Nutrition

Child
Mortality

Sanitation

Water

Electricity

Cooking
Fuel

Housing

AssetsProvince 1-4.1**-1.3-2.6**-3.2***-9.6***

-0.6

-0.8

-7.1**-7.3**

0.8

Province 2-13.3***-9.8***-13.3***-5.1***-30.3***-0.1-11.0***-27.6***-24.7***-1.4

Bagmati-4.6***-0.6-2.3***-2.0**-4.3***-2.4**-2.0-7.2***-7.4***-3.8**

Gandaki-3.4**-0.7-4.7***-2.3*-2.0**-2.5**-4.9**-8.0***-8.0***-5.7**

Lumbini-6.2***-3.2*-5.4***-8.0***-15.0***-2.7**-6.1***-15.8***-14.7***-3.3*

Karnali-7.7***-4.3***-2.3-9.6***-6.9**-17.3***-6.8-15.9***-16.6***-6.7

Sudurpashchim-3.1***-4.5***-2.2-8.3***-8.6***-6.8***-0.3-12.0***-11.2***-3.8

Relative Changes (%)
Province

Years of
Schooling

SchoolAttendance

Nutrition

Child
Mortality

Sanitation

Water

Electricity

Cooking
Fuel

Housing

AssetsProvince 1-37.6**-31.4-38.6**-48.0***-68.3***

-24.3

-13.7

-36.9**-38.1**

9.7

Province 2-52.0***-53.6***-58.6***-49.8***-70.2***-9.9-82.1***-58.1***-55.3***-14.7

Bagmati-60.9***-62.1-62.3***-56.5**-68.4***-61.7**-60.3-61.2***-63.9***-47.1**

Gandaki-46.4**-51.1-71.8***-57.1*-45.0**-71.2**-92.9**-58.4***-58.0***-55.5**

Lumbini-49.2***-46.1*-42.1***-61.1***-76.6***-63.6**-56.3***-54.0***-53.2***-31.5*

Karnali-45.3***-56.1***-11.0-44.1***-48.6**-65.7***-21.6-31.2***-32.6***-17.8

Sudurpashchim-33.8***-56.5***-16.0-53.1***-59.4***-63.0***-2.8-35.8***-33.9***

-20.0

85

NEPAL'S MULTIDIMENSIONAL POVERTY INDEX 2021

TABLE A-7: Changes in MPI, H, and A for Age Groups 2014-19, Previous Methodology
Age group

MPI

Change 2014 - 2019

Total Population (thousands)

2014

2019

Absolute

Relative

significance

2014

2019

Age 0-17

0.154

0.078

-0.076-49.3%

***

10.8

10.1

Age 18+

0.108

0.053

-0.055-51.2%

***

16.1

18.5

Age group

H

Change 2014 - 2019

Population Share

2014

2019

Absolute

Relative

significance

2014

2019

Age 0-17

34.0

18.2

-15.8

-46.5%

***

40.2

35.2

Age 18+

25.0

12.8

-12.2

-48.7%

***

59.8

64.7

Age groupA

Change 2014 - 2019

Number of Poor (thousands)

2014

2019

Absolute

Relative

significanceAge 0-17

45.5

43.1

-2.4

-5.3%

***

3,676.6

1,833.1

Age 18+

43.1

41.0

-2.1

-4.8%

***

4,022.8

2,376.9

86

2014

2019

NEPAL'S MULTIDIMENSIONAL POVERTY INDEX 2021

87

NEPAL'S MULTIDIMENSIONAL POVERTY INDEX 2021

G O V E R N M E N T O F N E PA L

N AT I O N A L P L A N N I N G C O M M I S S I O N
K AT H M A N D U