PALGRAVE STUDIES IN DISABILITY AND
INTERNATIONAL DEVELOPMENT
Series Editors: Shaun Grech · Nora Groce
Sophie Mitra

DISABILITY,HEALTH AND
HUMAN
DEVELOPMENT
Sophie Mitra

Palgrave Studies in Disability and International
Development
Series editors
Shaun Grech
Director of the Critical Institute
Malta
Nora Groce
University College London
LondonUnited Kingdom
Sophie Mitra
Fordham University
New York, NY, USAWe are pleased to announce the new book series, the Palgrave Studies
in Disability and International Development. With this series, we open
space for innovative research, debate and critical writings aimed at pushing forward the frontiers of discourse, theory and practice. We are seeking strong new monographs reporting on empirical work, edited books,
as well as shorter theoretical writings, and are especially interested ininterdisciplinary offerings. We welcome unsolicited book proposals. We
accept completed manuscripts, but would also be happy to hear about
current research or aboutwriting projects still in-process. The series isintended to span a range of areas and we would welcome proposals on
any topic related to international development and disability, including,though not limited to: Inclusive education Employment and livelihoods
Social protection Disability and poverty Human rights and disability
rights Health and healthcare Discrimination and exclusion Religion andspirituality Disability definition and measurement (Data and Disability)
Rehabilitation and community based rehabilitation Enabling and disabling environments International development programs and theirimpacts on disabled people Disability cultures and identities Histories of
disability Postcolonial issues Indigenous concerns Inclusive research anddecolonizing approaches.
More information about this series at
http://www.springer.com/series/14633				

Sophie Mitra

Disability, Healthand
Human Development

Sophie Mitra
Department of Economics
Fordham University
New York, NY
USA

Palgrave Studies in Disability and International Development
ISBN 978-1-137-53637-2
ISBN 978-1-137-53638-9 (eBook)
DOI 10.1057/978-1-137-53638-9
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To my parents‘Sophie Mitra presents one of the most comprehensive analysis of disability to datein low resource settings. Lucidly written, this book should be essential reading for all
interested in evidence informed policy and in ensuring that people with disability are
not left behind in the development agenda.’—Somnath Chatterji, Team Leader, Surveys, Measurement and Analysis,
Department of Information, Evidence and Research,
World Health Organization.
‘Despite national and international guarantees to equal rights, research on persons
with disabilities and deprivation in low income countries remains sketchy at best. In
her book, Sophie Mitra addresses some of these issues and perhaps most importantly
presents a conceptual framework for a new model, the human development modelof disability, health and wellbeing, based on Amartya Sen’s capability approach. This
book is an excellent and insightful contribution to advancing the agenda for disability inclusion for policy makers and practitioners. Introducing new data, Mitra explores
some challenging concepts around disability measurement, functionality, wellbeing
and poverty with much needed research for low income countries and development
writ large.’—Charlotte V. McClain-Nhlapo,
Global Disability Advisor, The World Bank Group.
‘This pioneering book charts a way to think about the neglected causes and consequences of functional disabilities in low-income countries, and extends theconcept of human development to encompass not only the returns to early childdevelopment through nutrition and preventive healthcare, but in addition thesocial mechanisms for coping with the deprivations due to the widespread functional disabilities of adults in the world, especially among the elderly, women andthe poor.’—T. Paul Schultz, Malcolm K. Brachman Professor of Economics Emeritus,
Yale University, US.
‘This book is important, refreshing and timely. In contrast to much of the writing on
disability and poverty, Mitra takes a disciplined and careful empirical approach, basing
her work on her contribution to theory. She charts the difficult and contested waters
between a narrowly decontextualized quantitative approach and a rhetorical appeal toactivist politics. It is the role of academic researchers not simply to repeat or academicise the important slogans of disability activists. Mitra succeeds admirably in providing
a nuanced empirical analysis which will be of use to activists and to policy makers.’—Leslie Swartz, Distinguished Professor of Psychology, Stellenbosch University,
South Africa.

Foreword

Disability has not featured prominently on the development agenda.
Despite all the talk of twin tracks and inclusive development, the reality
on the ground is that disabled people have been forgotten. Investment
in new schools has created inaccessible buildings, which exclude the 5%of children who are disabled. Efforts toward economic development
have not taken into account the increased poverty faced by people with
impairments in the poorest countries.
One of the obstacles to addressing disability in a serious and sustained
way is the lack of data. Economists and policymakers reply to humanrights activists that there is no evidence that including disabled people
makes good financial sense. There is a world where people are talking
about the UN Convention on the Rights of Persons with Disabilities,
and there is a world where people are taking investment decisions, which
overlook these obligations about equality of opportunity.
Sophie Mitra has long been one of the most respected and committed
development economists working on disability. Her work replaces rhetoric with detailed evidence and critique. She is one of those who are fillingthe data gap and making it harder for policymakers to ignore the needsof people with disabilities in developing countries.
In this book, Mitra combines detailed data analysis with an interactional model of disability based on Amartya Sen’s capability approach.
Sen’s work fits very well into the disability human rights agenda. By
developing the human development model of disability, health, andix

x

Foreword

wellbeing, Mitra is able to illuminate for us the complex world of­disability and begins to supply the required solutions.
This short text is a much-needed contribution to the fields of development economics and disability studies. It builds on the data and analysis in the WHO/World Bank World Report on Disability, and helps us
understand the nuances of disability and development. We need more
research like this.
Tom Shakespeare
Professor of Disability Research
Norwich Medical School
University of East Anglia
Norwich, UKPreface

This book presents new research on disability, health, and wellbeing infour countries. The primary focus is empirical. It also makes a conceptual
contribution as it presents a new model of disability based on the humandevelopment and capability approach.
The audience for this book are academic researchers and policymakers interested in disability, poverty, global health, and wellbeing issues ingeneral and in low-income countries in particular. The book can also be
used as a teaching tool for students in development economics, development studies, disability studies, or global health courses.
There are other volumes dedicated to disability and international
development (e.g., Grech 2015, MacLachlan and Swartz 2009, Stone
1999). This book differs from these previous efforts in that it exploits
new internationally comparable data on disability in low-income countries and offers a quantitative analysis. To my knowledge, it is also thefirst book on disability set in the context of the human development andcapability approach, where human development refers to the expansionof freedoms (Sen 1999). My aim is to offer a new way of understanding global disability issues through the capability approach and panel­datasets.
The ideas and methods in this book grew out of my work on disability and wellbeing over the past 15 years.I am grateful to several valued
collaborators I have learned from and enjoyed working with. First, I owe
many thanks to the late Monroe Berkowitz for inspiring me to work in thefield of disability when I worked for the Program for Disability Research
xi

xii

Prefaceat Rutgers University in 2002–2005. I am thankful to Debra Brucker,
Patricia Findley, Nora Groce, Jill Hanass-Hancock, Todd Honeycutt,
Douglas Kruse, Ilionor Louis, Subha Mani, Suguru Mizunoya, DanielMont, Michael Palmer, Aleksandra Posarac, and Usha Sambamoorthi. I
am deeply grateful to Fordham University for financial support. I thank
Hoolda Kim for excellent research assistance with data. Shannon Kelsh,
Shannon Pullaro and Andrew Seger also very skillfully helped with initialstages.
I had the opportunity to present different parts of the book manuscript and related results at the United Nations Department of
Economic and Social Affairs, the 2015 and 2016 Annual Conferencesof the Human Development and Capability Association, at Fordham
University, Kolkata Institute of Development Studies, St Gallen
University and the World Bank. Comments and questions received byparticipants on each of these occasions helped refine the analysis presented here. I am also very grateful for comments on the entire draftfrom Somnath Chatterji, James English, Jill Hanass-Hancock, Nora
Groce, Daniel Mont, Julius Omona and Tom Shakespeare, and on draftsof selected chapters from Barbara Altman, Vandana Chaudhry, Kim
Hopper, Eileen McGinn, Gerald Oriol Jr and Jean-François Trani. Last,
but not least, I am grateful to Richard Mukaga who shared his life story
to provide examples for several points made in this book.
I personally thank Joydeep, Leela, Alain and Neel for their love,­support, joy, and patience.
New York, USA		

Sophie Mitra

References
Grech, S. (2015). Disability and poverty in the global South. Renegotiating
development in Guatemala. London: Palgrave Macmillan.
MacLachlan, M. and L. Swartz (eds) (2009). Disability and international development: Towards inclusive global health. New York: Springer.
Stone, E. (ed) (1999). Disability and Development: Learning from action andresearch on disability in the majority world. Leeds: The Disability Press.
Sen, A.K. (1999). Development as Freedom. New York: Alfred A. Knopf.

Contents

1

Introduction		1
1.1 Motivation		 3
1.2 Research Questions and Scope of the Book		
4
1.3 Book Overview		
4References		62

The Human Development Model of Disability, Healthand Wellbeing		9
2.1 The Capability Approach and Disability		 102.2 The Human Development Model		 122.3 Other Disability Models		 202.4 Comparison of the Human Development Model to Other
Models		23
2.5 Conclusion		 27References		29

3

Measurement, Data and Country Context		33
3.1 Implementing the Human Development Model		 343.2 Data		 453.3 Country Context		 47References		
54

xiii

xiv

Contents

4

Prevalence of Functional Difficulties		61
4.1 Literature on Disability Prevalence in LMICs		 624.2 Methodology		 644.3 Prevalence at the Individual Level		 644.4 Prevalence at the Household Level		 764.5 Conclusion: Summary and Implications		 80References		84

5Functional Difficulties and Inequalities
Through a Static Lens		89
5.1 Literature on Inequalities Associated with Disability		 905.2 Methodology		 945.3 Results and Discussion		 1045.4 Conclusion: Summary and Implications		 120References		123

6Dynamics of Functional Difficulties and Wellbeing		1296.1 Functional Trajectories and Their Correlates		 1306.2 Changes in Functional Difficulties and Economic
Wellbeing		
136
6.3 Functional Difficulties and Mortality Within 2 Years		 1436.4 Conclusion: Summary and Implications		 148References		150

7Main Results and Implications		1537.1 Summary and Some Implications		 1547.2 Implications for Policies and Programs		 1577.3 Implications for Data		 1587.4 Further Research		 160References		
161

Appendices		163Index		173Abbreviations

CRPD
GNI
HDI
HIC
ICD
ICF
LIC
LMIC
LSMS
OLS
PPP
PSNP
SAGE
SDG
SF12
UK
UNESCOWHO
WHS

Convention on the Rights of Persons with Disabilities
Gross National Income
Human Development Index
High Income Country
International Classification of Diseases
International Classification of Functioning, Disability and Health
Low-Income Country
Low- and Middle-Income Country
Living Standard Measurement Study
Ordinary Least Squares
Purchasing Power Parity
Productive Safety Net Program (Ethiopia)
Social Assistance Grant for Empowerment (Uganda)
Sustainable Development Goal
Short Form Survey with 12 questions
United Kingdom
United Nations Educational, Scientific and Cultural Organization
World Health Organization
World Health Survey

xvList of Figures

Fig. 2.1
Fig. 2.2
Fig. 3.1
Fig. 4.1
Fig. 4.2
Fig. 4.3
Fig. 4.4
Fig. 4.5
Fig. 4.6
Fig. 5.1
Fig. 5.2
Fig. 5.3
Fig. 5.4
Fig. 5.5
Fig. 6.1
Fig. 6.2

The human development model		
The ICF		
Application of the human development model		
Mean functional score by age		
Types of functional difficulties among persons
with severe difficulties		Age of onset among persons with severe difficulties		
Measure taken to improve performance at activities
among persons with severe difficulties		
Mean household functional score by asset index quintile		Prevalence of severe functional difficulties
for the poorest and richest quintiles (%)		
Predicted work hours by functional score		Rates of deprivation by dimension and functionaldifficulty status		
Multidimensional poverty and functional difficulty status		
Predicted multidimensional poverty adjusted
headcount (M0) by functional score		Odds ratio of being multi-dimensionally poorby type of functional difficulty		
Multidimensional poverty adjusted headcount(M0) by subgroup		Odds ratio of death within two years by functionaldifficulty type (any degree) in wave 1		

17
22
38
67
68
69
71
78
79
109
115
117
118
119
137
147

xvii

List of Tables

Table 3.1
Table 3.2
Table 4.1
Table 4.2
Table 4.3
Table 4.4
Table 4.5
Table 4.6
Table 4.7
Table 5.1
Table 5.2
Table 5.3
Table 5.4
Table 5.5
Table 5.6

Demographic and socioeconomic indicators		
Selected national policies and legislations directly
relevant to the wellbeing of adults with disabilities		Prevalence of functional difficulties by sex and age group (%)		
Prevalence of functional difficulties by mother’s
educational attainment		Prevalence of functional difficulties by severity
and trajectory (%)		
Descriptive Statistics of sample of individuals		
Prevalence of functional difficulties among households (%)		
Prevalence of functional difficulties among households
and economic inequalities (%)		
Household descriptive statistics		
Aspects of wellbeing and indicators		
Dimensions, indicators and weights in the multidimensional
poverty measure		
Individual wellbeing outcomes by functional status		
Regressions of individual outcomes on functionaldifficulty status and other covariates		
Regressions of individual outcomes on functionalscore and other covariates		
Household wellbeing outcomes by functional
status (means unless otherwise noted)		

48
52
65
70
72
74
76
77
81
96
100
105
107
108
110

xix

xx

List of Tables

Table 5.7
Table 5.8
Table 6.1
Table 6.2
Table 6.3
Table 6.4
Table 6.5

Regressions of household wellbeing outcomeson severe and moderate functional difficulties
and other covariates		 113Regressions of household wellbeing outcomeson functional score and other covariates		 115Descriptive statistics by functional difficulty trajectory		 134Odds ratio of deprivation by functional trajectory		 135Odds ratio of work exit or return to work and change
in functional difficulties		 141Rates of mortality within two years among adults(deaths/1000)		145Odds ratio of short term death by functional difficulty status		 146

List of Boxes

Box 1
Box 2
Box 3
Box 4
Box 5
Box 6

The Washington Group short set of questions on disability		
The Alkire and Foster (2011) multidimensional poverty measures		
Multivariate regressions of wellbeing
Multinomial logistic model of functional trajectories		
First-difference model of work status		
Lagged logistic model of mortality		

39
98
102
132
139
144

xxi

CHAPTER 1

Introduction

Abstract In low-income countries, there has been very little research on
disability and its link to deprivations. Much of the research is recent, andresearch using traditional poverty indicators (e.g., consumption expenditures) paints an unclear picture on the association between disability anddeprivations. This is important as the prevalence of health conditions andimpairments is expected to rise with an increasing life expectancy and as
more policies try to address deprivations in relation to disability. This
book asks the following: How should disability be defined to analyze andinform policies related to wellbeing? What is the prevalence of functionaldifficulties? What inequalities are associated with functional difficulties?
What are the economic consequences of functional difficulties? The
empirical work is focused on Ethiopia, Malawi, Tanzania, and Uganda.
Keywords Disability · Functional difficulties · Poverty · Low-income
countries · Africa
JEL I1 · I3 · O15
In December 2016, the United Nations Educational, Scientific andCultural Organization (UNESCO) published the profile of Richard
Mukaga, one of six children raised by his single mother in the rural
Namaingo District in Eastern Uganda where polio left him unable to© The Author(s) 2018
S. Mitra, Disability, Health and Human Development,
Palgrave Studies in Disability and International Development,
DOI 10.1057/978-1-137-53638-9_1

1

2

S. Mitrawalk from age six.1 In June 2016, The Guardian newspaper started aseries of online articles on disability rights. Many were about the challenges faced by persons with disabilities in low- and middle-income
countries (LMICs). It described them as being marginalized in theircommunities, excluded from work and among ‘the poorest of the poor.’2
This recent attention to disability is welcome from the perspective of the field of international development where disability has beena marginal issue. It is barely mentioned in landmark policy documents(World Bank 2006, 2017) and in textbooks (e.g. de Janvry and Sadoulet2016). Governments in LMICs and international donors in high-income
countries (HICs) rarely pay attention to it. The term ‘disability’ itself
is unclear and conceptually elusive. What does it mean exactly? Isn’t it
a subjective notion? If it is, how can it be studied and measured so as
to inform policy? Internationally comparable data has been missing on
disability, making it difficult to investigate the significance of the phenomenon. There is also the common perception that disability is an issue
that has more relevance in HICs where, due to aging and better survival chances in case of injuries or health conditions, people’s lives areextended and may thus experience disability. This perception is also
entertained by the presence in HICs of social safety net programs suchas disability insurance programs, often criticized for the potential disincentive to work and poverty traps they might create for persons with disabilities.
How does Richard live in a low-income country (LIC)? In a setting
where most people are poor and there is little in terms of a social safety
net, are deprivations more acute and more common for persons with disabilities or is disability not so relevant?
In this book, I present new research on disability and wellbeing infour LICs: Ethiopia, Malawi, Tanzania, and Uganda. This book analyzes four large longitudinal household survey datasets in Africa collected
as part of the Living Standard Measure Study. These datasets have theWashington Group short set of questions on disability (Altman 2016).
This set of questions identifies six functional or basic activity difficulties(functional difficulties for short): seeing, hearing, walking, concentrating/remembering, selfcare, and communicating. For instance, for seeing,
it asks if, due to a physical, mental, or emotional health condition, individuals experience any difficulty seeing even when wearing glasses.

1

INTRODUCTION

3

1.1   Motivation
This research is motivated by three main factors. First of all, there is very
little research on disability in the context of LMICs, and LICs in particular. Much of the research is from the last decade or so. The seminal World Report of Disability (WHO-World Bank 2011) contributed
some internationally comparable prevalence and situational analyses in
59 countries, including in some LICs. It showed that disability is notrare and is associated with lower educational attainment, lower employment rates, and limited access to health services. Some recent research in
LMICs has consistently found that disability is associated with a higher
likelihood of experiencing simultaneous multiple deprivations (multidimensional poverty) (Hanass-Hancock and McKensie 2017; Mitra et al.
2013; Trani and Cunning 2013; Trani et al. 2015, 2016). In contrast,some research using traditional poverty indicators (consumption expenditures and asset ownership) paints a mixed picture (Filmer 2008; Mitra
et al. 2013; Trani and Loeb 2010).
Second, the prevalence of health conditions and impairments is likely
going to increase in LMICs in the near future. Aging is on the risebecause of epidemiological transitions, including increased life expectancy due to a reduction in mortality from parasitic and other infections(WHO 2016). At the same time, chronic and degenerative diseases (e.g.
cardiovascular diseases) are becoming more common. People may survive conditions once fatal as the quality and accessibility of treatments
and healthcare improve (HIV/AIDS). Hence, there is a need to study
disability in LMICs.
Third, in the past decade, disability has received more attention inpolicies and programs worldwide and more knowledge is required to
inform them. As of January 2017, 172 countries have signaled theircommitment to protect the rights of persons with disabilities with theratification of the Convention on the Rights of Persons with Disabilities(CRPD) a decade after its adoption (United Nations 2006, 2016).
Disability also explicitly features in the sustainable development goals(SDGs) of the Agenda 2030 (UNDP 2016b). In LICs, there are numerous advocates who work toward the empowerment of persons with disabilities and they tend to work in NGOs or disabled people organizations.
For policies and programs in LICs, more knowledge is needed on topics
as basic as the prevalence of functional difficulties and their association
with wellbeing inequalities.

4

S. Mitra1.2  Research Questions and Scope of the Book
This book presents an empirical analysis of disability and wellbeing in
Ethiopia, Malawi, Tanzania, and Uganda. In resource-poor settings, thespecific research questions addressed in this book are as follows:
1. How should disability be defined to analyze and inform policies
related to wellbeing?
2. What is the prevalence of functional difficulties?
3. What inequalities are associated with functional difficulties?
4. What are the economic consequences of functional difficulties?
The analysis in this book is quantitative and limited to the analysisof large-scale household survey datasets. While other methodological approaches such as qualitative and/or participatory approaches arebeyond the scope of this book, I do believe that these other approaches
involving multiple stakeholders may assist in developing a deep understanding of issues around wellbeing and disability and complement the
research in this book. Stakeholders include, of course, persons with disabilities who can contribute their expertise from lived experience. They
could also include other stakeholders depending on the particular issue
under study, including family members, community leaders, employers,service providers (e.g., social workers), policymakers, and advocates. This
book does not attempt to cover the field comprehensively, nor does it
provide a full account on disability, health and wellbeing in Ethiopia,
Malawi, Tanzania, and Uganda. I do not cover important areas such as
education, transition from school to work, and noneconomic aspects
of wellbeing such as social relations. It does not cover the long-term
dynamics of disability and wellbeing, as individuals are followed over a
period of only two years.

1.3  Book Overview
The second chapter provides the conceptual framework of the book,the human development model of disability, health, and wellbeing.
It is based on the capability approach of Amartya Sen. The humandevelopment model highlights, in relation to wellbeing, the roles ofresources, conversion functions, and agency. It uses capabilities (practical ­opportunities) and/or functionings (achievements) as the metric for

1

INTRODUCTION

5

wellbeing. Impairments and health conditions are considered as determined by, and influencing, wellbeing. I believe the model generates
insights for this book and research and policy on wellbeing, disability,and health.
Chapter 3 introduces the empirical context of this study, from measurement to data and country contexts. This book uses nationally representative Living Standard Measurement Study datasets for Ethiopia,
Malawi, Tanzania, and Uganda, which include six questions on functional difficulties. The four countries under study have ratified the CRPD
with Disabilities and adopted national policies or legislations on disability.
Chapter 4 through 6 present the empirical analysis and results of thebook. These chapters have sections covering the literature review, methods, results/discussion, and a summary of results. The methods sections are quantitative, and not all readers will have the inclination to readthem. I have included statistical methods primarily in boxes that somereaders may want to consult.
Chapter 4 presents results regarding the prevalence of six functionaldifficulties (seeing, hearing, walking, concentrating, selfcare and communication) overall and by functional difficulty type, severity, age at onset,
age, sex, and socioeconomic status. It presents results on the use of assistive devices and healthcare services among persons with functional difficulties.
Chapter 5 focuses on inequalities that are associated with functionaldifficulties at a given point in time. Inequalities are considered for educational attainment, morbidity, work, household material wellbeing andeconomic security. Inequalities are also analyzed through multidimensional poverty measures.
Chapter 6 investigates three separate issues on the dynamics of functional difficulties and inequalities. It compares the wellbeing of persons
with different trajectories in terms of functional difficulties; for instance,
how do persons with persistent functional difficulties fare compared topersons with temporary difficulties? It also assesses if changes in functional difficulties are associated with changes in employment outcomes
and assets/living conditions. It analyzes if functional difficulties are correlated with mortality in the short run.
The last chapter presents concluding remarks that summarize themain results and derive implications for policy and future research.
It does not have all the nuances of the main text of each chapter andshould be read with this in mind. Overall, it shows that disability needs

6

S. Mitrato be considered from multiple angles including aging, gender, health,and poverty. This book concludes that disability policies are unlikely to
be conducive to human development for all if they exclusively use an
oppressed minority group approach and focus on barrier removal. It
makes a call for inclusion and prevention interventions as solutions to
the deprivations associated with impairments and health conditions.

Notes
1. http://www.unesco.org/new/en/education/resources/online-materials/
single-view/news/disability_education_and_work_a_life_spent_fighting_for/.
2. ‘Mexico City from a wheelchair: There is no second chance from thesestreets’ The Guardian Resilient Cities for the 21st century. Accessed on 23
June 2016 at: https://www.theguardian.com/cities/2016/jun/23/mexico-city-wheelchair-users-disability-street-workout-athlete-abraham-plaza.

References
Altman, B. M. (Ed.). (2016). International measurement of disability: Purpose,
method and application, the work of the Washington group. Social indicators
research series 61. Switzerland: Springer.
De Janvry, A., & Sadoulet, E. (2016). Development economics: Theory and practice. London: Routledge.
Filmer, D. (2008). Disability, poverty and schooling in developing countries:
Results from 14 household surveys. The World Bank Economic Review, 22(1),141–163.
Hanass-Hancock, J., & McKensie, T. (2017). People with disabilities and income
related social protection measures in South Africa: Where is the gap? African
Journal of Disability. In press.
Mitra, S., Posarac, A. and Vick, B. (2013). Disability and Poverty in Developing
Countries: A Multidimensional Study. World Development Vol. 41; pp.1–18.
Trani, J. F., & Canning, T. I. (2013). Child poverty in an emergency and conflict
context: A multidimensional profile and an identification of the poorest children in Western Darfur. World Development, 48, 48–70.
Trani, J., & Loeb, M. (2010). Poverty and disability: A vicious circle? Evidence
from Afghanistan and Zambia. Journal of International Development, 24(1),S19–S52.
Trani, J., Bakhshi, P., Myer Tlapek, S., Lopez, D., & Gall, F. (2015). Disability
and poverty in Morocco and Tunisia: A multidimensional approach. Journalof Human Development and Capabilities, 16(4), 518–548.

1

INTRODUCTION

7

Trani, J., Kuhlberg, J., Cannings, T., & Chakkal, D. (2016). Multidimensional
poverty in Afghanistan: Who are the poorest of the poor? Oxford Development
Studies, 44(2), 220–245.
WHO-World Bank. (2011). World report on disability. Geneva: World HealthOrganization.
World Bank. (2006). World development report 2006: Equity and development.
Washington, DC: World Bank.
World Bank. (2017). Monitoring global poverty: Report of the commission on global
poverty. Washington, DC: World Bank.
United Nations. (2006). Convention on the rights of persons with disabilities.
Accessed January 3, 2017, from https://www.un.org/development/desa/
disabilities/convention-on-the-rights-of-persons-with-disabilities.html.
United Nations. (2016). UN enable newsletter, December. Accessed January 3,
2017, from www.un.org/disabilities.

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CHAPTER 2

The Human Development Modelof Disability, Health and Wellbeing

Abstract This chapter sets the conceptual framework for the book.
It introduces a new model, the human development model of disability, health and wellbeing, based on Amartya Sen’s capability approach.
Disability is defined as a deprivation in terms of functioning and/or
capability among persons with health conditions and/or impairments.
The human development model highlights in relation to wellbeing theroles of resources, conversion functions, agency, and it uses capabilities
and/or functionings as metric for wellbeing. It does not consider impairments/health conditions as individual characteristics; instead, they arethemselves determined by resources, structural factors, and personal
characteristics, and thus the model is informed by the socioeconomic
determinants of health literature. This chapter also compares the humandevelopment model to the main disability models used in the literature.
Keywords Disability model · Capability approach · Humandevelopment model · ICF · Medical model · Social model
JEL I1 · I3 · O15
The notion of disability is enigmatic, even confusing. The term itself ‘disability’ has negative connotations, which is no surprise given the prefix‘dis’ meaning ‘absence’ or ‘negation’. Beyond the everyday semantic© The Author(s) 2018
S. Mitra, Disability, Health and Human Development,
Palgrave Studies in Disability and International Development,
DOI 10.1057/978-1-137-53638-9_2

910S. Mitramuddle around the term ‘disability’, how it is conceptually defined is also
challenging. Researchers have long wrestled with the definition, which
is important. This chapter develops a conceptual framework for disability based on Amartya Sen’s capability approach. I call this framework thehuman development model of disability, health, and wellbeing.
Any discussion of disability or analysis of data on disability is basedon one or more models of disability, whether explicitly or implicitly. A
model is a conceptual tool that helps make sense of a complex reality
and tries to offer a map of the relationships between concepts. It tries
to explain and describe a complex phenomenon as part of a coherent
framework. A model also clarifies terminology to promote a consistentuse. How disability is modeled influences our understanding of its determinants, consequences and how it is measured, and what data is thus
relevant. It also influences disability-related policies and programs, how
they are designed and operationalized. It also shapes how we respond topeople with disabilities, whether family or strangers, in everyday interactions. For the conceptual definition of disability, there is not a universally
agreed upon model. There are many models that are currently in use andthe differences among them feed lively debates. Several scholars haverecently argued that available models have all been developed in, and for,the context of HICs (e.g., Anand 2016).
The human development model proposed in this chapter attempts toaddress some of the limitations of existing models and is particularly relevant for resource-poor settings. Each model provides a particular lenson disability. In this chapter, I argue that it provides breadth and depth
relative to other models: breadth through the range of factors that canaffect health conditions, impairments and disabilities, and a broad rangeof consequences and depth through a consideration of agency, capabilities, resources and conversion factors.
This chapter starts by presenting the capability approach and its applications to disability. I then present the human development model. I
later compare it to the main disability models used in the literature.

2.1  The Capability Approach and Disability
Martha Nussbaum and Amartya Sen are the two original architects ofthe capability approach, extended and applied in the past two decades
by many scholars in a variety of disciplines to deal with a wide range ofissues, poverty, and justice in particular. Sen’s capability approach was2

THE HUMAN DEVELOPMENT MODEL OF DISABILITY, HEALTH …

11developed as a framework to analyze different concepts in welfare economics including the standard of living, wellbeing, and poverty. Taking
the case of the standard of living, it is traditionally measured throughthe ability to buy commodities. Sen argues that the standard of living
encompasses more than this. Under the capability approach, Sen focuseson the type of life that people are able to live, i.e., on their practical
opportunities, called capabilities, and on their achievements, called functionings. Sen has used the example of two women starving to contrast
the two terms: both women have the same functioning (not being wellnourished) but very different capabilities. One has the capability to bewell nourished but decided to starve for religious reasons, and the otherone does not, due to the inability to purchase enough food.
There has been a rapid growth of the literature on disability and thecapability approach in the past decade or so. The capability approachhas been used to deal with different disability-related issues by Martha
Nussbaum (2006) and Amartya Sen (2009). The capability approach has
been considered in how it may respond to the justice demands that may
be associated with disability (Nussbaum 2006). It has been used by other
scholars on a variety of issues including the philosophical grounding of
human rights in relation to disability (Venkatapuram 2014), the evaluationof disability-related policies (e.g. Díaz Ruiz et al. 2015), the challenges thatneed to be addressed for education to be disability-inclusive (Mutanga andWalker 2015) and comparative assessments of wellbeing across disability status (Mitra et al. 2013; Trani and Cunning 2013; Trani et al. 2015, 2016).
In fact, Sen’s capability approach of justice (2009) motivates comparative assessments of wellbeing that may lead to insights on the extent andnature of deprivations experienced by persons with disabilities that have
implications for policies and reforms designed to remediate them andthus could be justice enhancing. This ties in with the general message ofSen (2009): ‘Justice-enhancing changes or reforms demand comparative
assessments, not simply an immaculate identification of ‘the just society’(or ‘the just institutions’)’ (emphasis in original) (p. 401).
More related to this chapter, several scholars in philosophy and thesocial sciences have argued that Amartya Sen’s capability approach can beused to define disability as capability or functioning deprivation in general (Burchardt 2004; Mitra 2006; Terzi 2009; Wolff 2009), in the context of education (Terzi 2005a, b), public policy (Trani et al. 2011a), or
recovery from psychiatric disorder (Hopper 2007; Wallcraft and Hopper2015). There is not a single interpretation of the capability approach

12

S. Mitrawith respect to defining disability so this brief summary simplifies somepotential differences among scholars. A central idea of this literature isthat with a capability approach based conceptualization, whether theindividual with an impairment has a disability depends on whether his/her functionings or capabilities are restricted. An impairment is a featureof the individual that may or may not lead to a disability. Another idea isthat the deprivations in terms of capabilities or functionings come fromthe interaction of a variety of factors (personal factors, the environment,
and the impairment) and that the ability to convert resources into capabilities and functionings (conversion factors) is particularly relevant andshould not be ignored.

2.2  The Human Development Model
Out of Sen’s capability approach, I carve out concepts and normative
statements to form the human development model of disability, health,and wellbeing (the ‘human development model’ for short in what follows). The objective is to provide a conceptual framework to describeand explain health conditions, impairments, disability, their causes as wellas their consequences.
This model is also informed by growing evidence on the socioeconomic determinants of health from social epidemiology (Marmot2005). It also draws from the extensive literatures on the capability
approach, in general (Robeyns 2005, 2016) and in particular on disability (e.g. Burchardt 2004; Mitra 2006; Terzi 2005a, b) and health(e.g. Hopper 2007; Law and Widdows 2008; Venkatapuram 2011). Of
course, it also relies on the literature on disability models (e.g. Patston
2007; Shakespeare 2014; WHO 2001; Albrecht et al. 2001; Barnartt
and Altman 2001; Altman 2001). Compared to earlier works on disability, health, and the capability approach, it organizes and maps existing
concepts in a new way with the objective to describe and explain healthdeprivations, their causes and consequences on wellbeing. Unavoidably,
then, this means starting from definitions and maps of foundationalbuilding blocks.
2.2.1   Key Concepts and Statements of the Human Development Model
Functionings and capabilities are the main concepts of the capability
approach in general and of this model in particular. Functionings refer

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THE HUMAN DEVELOPMENT MODEL OF DISABILITY, HEALTH …

13

to achievements. ‘Capabilities’ do not have the everyday sense of ‘ability’and instead refer to ‘practical opportunities’.
Wellbeing in the capability approach includes functionings and capabilities related to one’s own life. It also includes functionings from sympathies (i.e., from helping another person and feeling thus better off).
Wellbeing is multidimensional, and the individual’s choices and values
are central.
The concept of wellbeing is closely linked to that of human development. Sen considers development to be the process that expands capabilities (Sen 1999; p. 3). This view of development is people-centered.
It is referred to as human development. It stands in contrast to a morecommon view focused on the growth of the gross national product. It
was championed by Mahbub ul Haq at the United Nations Development
Programme who led the Human Development Reports in the early
1990s.1
Health deprivations include impairments and health conditions,which are defined using WHO’s definitions. An impairment is a ‘problem in bodily function or structure as a significant deviation or loss’(WHO 2001). For instance, an impairment could be a significant deviation in terms of seeing. A health condition is defined broadly as per
WHO (2011; p. 12)2: it may refer to a disease, disorder, symptom,
or injury. Using the capability approach’s definition of functioning,
health conditions and impairments can be thought about as healthfunctioning deprivations, health deprivations for short. The capability to be health condition- or impairment-free is also a notion that is
important here.3
Disability is defined as a deprivation in terms of functioning(s) and/or capability(s) among persons with health deprivations. Disability resultsfrom the interaction between resources, personal and structural factors,
and health deprivations. Disability identifies a specific type of deprivation
or disadvantage that might be the target of policies.
Resources refer to goods, services, information owned by, or available
to, the individual.
Structural constraints in the environment are included here understructural factors. They include the physical environment (e.g., terrain,
climate, architecture), the economic environment (e.g., markets), social
attitudes, laws and institutions (e.g., home, school and work, services,
systems and policies (e.g., transportation, health, and social services)),
culture, products, and technology.

14

S. MitraPersonal factors (e.g. age and sex) may interact with health deprivations in the conversion of resources into wellbeing. For instance, in anenvironment where women are constrained in their movements outside
their homes, a wheelchair will not translate into mobility for women withspinal cord injury.
Conversion functions refer to people’s different abilities to convert
resources (goods and services) into capabilities and functionings. They
are particularly relevant for disability. For example, the same income maylead to very different capability sets for two persons—one without any
health deprivation, the other one with—who both live in an environment where medical and rehabilitative care expenses are born by individuals. The affected individual has to spend a significant amount of her
income on out-of-pocket health expenditures, while the former does not.
Conversion could also be very different for two individuals with the same
impairment in two very different environments. Converting a wheelchair
into mobility is not going to be efficient in a town with dirt roads andno public transportation, compared to a town where sidewalks are paved
and cut and buses are wheelchair accessible.
Human diversity: health deprivations may lead to differences in conversion factors and differences in capability sets and are thus sources ofdiversity. The capability approach also does not exclude persons withhealth deprivations from theories (Robeyns 2016) and, in fact, here they
are placed at the center of the human development model.
Agency cannot be ignored. Agency is the ability to pursue valued
objectives, to act and bring about change (Sen 1999; p. 19). A person
without agency is ‘forced, oppressed or passive’ (Deneulin and Alkire
2009; p. 37). In other words, one has to consider whether an individual
is able to act on behalf of what matters to him/her or what he/she ‘hasreason to value’ (Sen 1999). This is particularly important for disability
since in some contexts, there are differences in agency experienced bypersons with some health conditions or impairments (e.g., severe psychiatric condition (Hopper 2007)).
Means-ends distinction: the ultimate end of the capability approachand the human development model and its applications in particular are
to describe, explain, and compare people’s functionings and/or capabilities. For the human development model, the focus is on how healthdeprivations may relate to other dimensions of wellbeing. The end ofresearch or policy initiatives guided by this model is thus to enhance
human development, i.e., to expand the functionings/capabilities of

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THE HUMAN DEVELOPMENT MODEL OF DISABILITY, HEALTH …

15

individuals with health deprivations or to expand functionings/capabilities by preventing health deprivations. It affirms flourishing as the endof human development. Resources or structural factors (e.g., healthcare
services, assistive devices) and other means may be used to achieve thisend but are not ends per se.
This is a normative framework.4 It is normative in at least two ways:(i) functionings and/or capabilities are the evaluative space; and (ii) one
needs to specify which functionings or capabilities reflect the values of
the individuals under consideration or are relevant for a particular exercise and the criteria or reasoning used in making this specification. For
example, an analytical exercise to inform policies aimed at improving
school access for children with impairments may focus on capabilities orfunctionings related to school attendance, school progression, interactions with children in the classroom. Relevant structural factors include
physical accessibility of buildings, trainings of teachers, and school fees.
In contrast, an exercise focused on aging, health conditions, and retirement would obviously lead to a very different set of relevant functionings
or capabilities such as access to healthcare and social participation.
2.2.2  Examples
To illustrate how this model works, consider the case of Richard, who
had polio at the age of six. In a social policy environment where having limited mobility leads to forced institutionalization, he would haveto leave behind many valued functionings to go and live in an institution.
He would start a life of deprivation in terms of capabilities and functionings. In contrast, think of an environment where individuals are given
supports, as needed, to continue to go to the same school and live inthe same community and where there are no physical, cultural, politicalbarriers to participation in society. If he could continue to do what hewants to do and be who he wants to be, he would not have a disability, although he has an impairment. These are two extreme and opposite
cases above for the same person: a case with no deprivation and a casewith deprivations.
Alternatively, it could be a mixed assessment, and it was for Richard,
who remained in his community with his family but at the same time
faced considerable challenges with inaccessible schools, ridicule fromother children, and constrained by his family’s inability to raise tuition
to attend a school of choice. So in terms of family connectedness, there

16

S. Mitrawas no deprivation, but in terms of schooling, there was. Health deprivations may thus influence some functionings/capabilities but not others: a child could have a deprivation with respect to education but notin terms of where and with whom he can play. Disability thus encapsulates a multidimensional assessment of deprivations, and in this case, ityields a mixed assessment with deprivations in some dimensions but notin others.
Another example may help illustrate that the concept of capability isparticularly relevant to disability. A given health deprivation may affectcapabilities in different ways given personal and structural factors, whileleading to similar functionings. For instance, two older persons witharthritis and limited mobility are not working. One has the capability towork for pay but chooses to retire so as to care for young grandchildren
in a three generation household. Her children will work more and earnmore after she retires. The second person, on the other hand, does nothave the capability to work because based on her age and impairment,
no one in her village is willing to hire her. This example illustrates situations where people with similar health deprivations attain a similar functioning (in this case, not working) from vastly different capability sets.
Evaluating situations based on capability information may offer very useful insights compared to an assessment of functionings alone.
2.2.3  Terminology
While the concept of ‘disability’ under the human development model isimportant, the label ‘persons with disabilities’ or ‘disabled people’ may
be problematic. It refers to persons with impairments or health conditions who are deprived in wellbeing. The dichotomous term ‘disability’does not sit well with the continuous, multidimensional, and potentiallyheterogeneous notion of wellbeing and deprivation that this model uses
to define disability. Should Richard be considered to have a disability
with respect to education but not with respect to family life? The termis also potentially stigmatizing as persons with disability are by definition
deprived, and it becomes impossible to convey a neutral or potentiallyempowering discourse around them. Perhaps paradoxically, then, I’m
proposing a model that defines the concept of disability but notes theinadequacy of the term ‘disability’. Later in this book, when I apply thismodel to an analysis of wellbeing for persons with health deprivations,
I will use the precise term for the particular health deprivation under

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THE HUMAN DEVELOPMENT MODEL OF DISABILITY, HEALTH …

A. Personal factors

B. Resources

D. Health deprivations(Impairments and/or
Health Conditions)

17

C. Structural factors

E. Functionings and/or
Capabilities

Fig. 2.1 The human development model

consideration, here functional and basic activity difficulties (functionaldifficulties for short). I will also refer to persons at risk of disability torefer to persons with health deprivations.
2.2.4  Mapping
The human development model emphasizes many potential factorsthat may influence wellbeing: the personal factors, the resources,
and structural factors of the individual. These are represented inFig. 2.1. Arrows describe possible bidirectional links between different components of the model. Personal factors in Box A are individual characteristics. They may include simple demographics such assex, race/ethnicity, and age. They may also be more complex characteristics such as personality traits. Some are immutable (e.g., dateof birth!), others are not (e.g., personal attitudes). Resources in Box
B include goods, services, and information. They could be owned by
the individual herself, or denote resources that she can access throughfamily or community (public goods). Structural factors in Box C are
broad and cover physical, social, economic, epidemiological, political(and more) aspects of the individual’s context. Structural factors referto characteristics of the environment of the individual: the immediate environment (e.g., family, home, and workplace), the meso-environment (the community), and the macro-environment (regional,
national). At each of these levels, structural factors may influence capabilities and functionings.

18

S. MitraGoing back to the example of Richard, the human development
model focuses in part on describing and explaining his capabilities
and functionings and his agency. It also considers the conversion of
resources, structural, and personal factors into capabilities and functionings. One would need to select the relevant wellbeing dimensions in hiscase to be able to analyze his situation downstream from his impairment,
in other words how his impairment may affect his wellbeing.
The deprivation (or wellbeing) outcomes in Box E in Fig. 2.1 canhave one or more dimensions (e.g., social inclusion, political participation, and employment). It could be a health dimension such as mortality,
as long as it is different from the health deprivation(s) considered in D.
One could even investigate the links between a health condition in D
(say diabetes) and an impairment in E (e.g., missing limb).
In earlier analyses of the capability approach for the purpose of defining disability (Burchardt 2004; Mitra 2006; Terzi 2005a, b, 2009; Wolff
2009), the impairment was considered a given characteristic of the person that is part of the conversion factors and thus influences capabilities
and functionings. This is different in the human development model
which moves the analysis upstream and includes impairments as now
separate and unpacked, in that they are influenced by (and may influence) personal factors, resources, structural factors and capabilities/functionings. In addition to the impairment, the model also includes healthconditions, which are determined by (and may determine) resources,
personal, and structural factors, and wellbeing. This recognizes the broad
set of determinants of health conditions and impairments, now wellknown in social epidemiology (Marmot 2005).
Going back again to the example of Richard, the human development
model questions the determinants of his impairment and provides guidance in this upstream analysis. His impairment may have resulted from avariety of factors, including the extreme poverty setting he was growing
up in as he contracted polio. Resources and structural factors are partly
responsible for the impairment. For policy, this is useful to know as this
could inform prevention interventions in poor communities.
2.2.5   Characteristics of the Model
This is an interactional model where wellbeing results from the interaction between the health deprivations, personal factors, resources, and theenvironment (structural factors). The health deprivation is a necessary,2

THE HUMAN DEVELOPMENT MODEL OF DISABILITY, HEALTH …

19

but not a sufficient, ingredient for a disability. With this definition, notall persons with impairments/health conditions experience disability but
all are at risk of disability.
Of course, resources and structural factors may in some cases not besalient determinants of wellbeing outcomes. Disability may be inevitable
in a given environment: for instance, given a particular health condition
with no cure, the experience of pain and its effects on many dimensionsof wellbeing (leisure, work) may be inevitable. Sally French, as reported
by Shakespeare (2014), gives the example of a blind teacher who is notable to read nonverbal clues in interactions, hence potentially having difficulties interacting with her students. Some of the deprivations experienced by persons with health deprivations may not be able to be solved
by resources or changes in the environment.
The model can be used in a static or dynamic manner. The dynamic
lens is important for all components of the model, which may changeover time. For instance, a particular health condition such as cancer may
have subsided while leaving behind deprivations, perhaps due to the lingering consequences of treatment.
The model does not address what justice demands in terms of correction
and compensation for health deprivations and other wellbeing deprivations.
This model is restricted to describing and explaining links between healthdeprivations and wellbeing. However, results of analyses framed in thehuman development model can be used to demand justice. It may provide
supporting materials to mobilize advocacy and policy efforts and demandjustice. The model could also be used together with some of the justiceclaims of the capability approach in relation to disability (e.g. Terzi 2009),
health (e.g. Venkatapuram 2011) or wellbeing more broadly (Sen 2009).5
Like the broader capability approach, the human development modelis flexible and unspecified. The model is open-ended, in that not all
dimensions of wellbeing may be specified. Relevant personal factors,
resources, and structural factors will also vary depending on the issue
under focus. For instance, if the analyst is concerned about employment
as a wellbeing outcome for adults, educational attainment would be relevant as a personal factor in many settings. If on the other hand, the focus
is on educational attainment, then the latter is no longer a personal factor but becomes a wellbeing outcome—an end, not a means. Unlike inthe capability approach in general, this model imposes a structure by separating health deprivations, given that the goal of the model is to analyze
them in relation to other aspects of wellbeing.

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S. MitraHow does the disability phenomenon change or become any different if one moves to the human development model from another disability model? I try to answer this question below for three major disability
models that have been used in social science research. I first summarize
these models.

2.3  Other Disability Models
2.3.1   The Medical Model
The medical model (or individual model) considers disability as a problem of the individual that is directly caused by a disease, an injury orother health conditions, and requires prevention interventions or medical care in the form of treatment and rehabilitation. People are disabledon the basis of being unable to function as a ‘normal’ person does. So
this model is strongly normative. In the medical model, disability refers
to impairment, health condition or an ability to perform an activity in anormal way. It restricts disability to an individual phenomenon. Medical
rehabilitation then has an important role to play in bringing the personback or close to the norm. The major concern of the medical model at
the political level is to provide healthcare and rehabilitation services. The
medical model leads to ‘paternalism, pathologisation and benevolence’(Goodley 2016). For Richard, the concern under the medical model
would be about his access to physical rehabilitation and medical care andhis experience would justify a prevention strategy for polio.
2.3.2   The Social ModelIn contrast, the social model would be focused on Richard’s environment, for instance the physical environment (can he access his school?) or
the social/attitudinal environment (does he get discriminated against by
his teachers and classmates?). The social model sees disability as a social
creation. Within this framing, disability is not the attribute of the individual, but is instead created by the social environment and thus requires
social change. The terms ‘impairment’ and ‘disability’ have very different
meanings with impairment referring to an individual’s condition and disability referring to social disadvantage, discrimination, and exclusion.
There are several versions of the social model. UK disability activists
in the Union of the Physically Impaired Against Segregation (UPIAS)

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developed the UK social model. Societal oppression is at the heart ofthis model (Oliver 1990). The core definition of the British social model
comes in the UPIAS document, Fundamental Principles of Disability,
reported in Oliver (1996; p. 22): ‘In our view, it is society which disables
physically impaired people. Disability is something imposed on top of
our impairments by the way we are unnecessarily isolated and excluded
from full participation in society.’
The minority model is another version of the social model. It wasdeveloped in North America by activists and scholars. This version saysthat persons with disabilities face discrimination and segregation throughsensory, attitudinal, cognitive, physical and economic barriers, and theirexperiences are therefore perceived as similar to those of an oppressedminority group. Social inequalities by disability status are considered assimilar to those encountered by other minorities based on race/ethnicity
such as ‘extraordinary high rates of unemployment, poverty and welfare
dependency; school segregation; inadequate housing and transportation;and exclusion from many public facilities…’ (Hahn 2002; p. 171).
The social model has been very influential in policy. To some extent,
it has grounded human rights advances, such as the United Nations
CRPD, which has guided disability laws worldwide. The social model
born in HICs has recently gained prominence in LMICs. In recent years,
it has certainly dominated as a conceptual framework in research at theintersection of disability and development (Coleridge 1993; Stone 1999;
Turmusani 2003). For instance, using the social model, Turmusani(2003) advocates a move away from the medical model toward the social
model. Disadvantages are viewed as a result of social neglect, oppression
and discrimination, and thus unsurprisingly, it considers the environment
as the ‘focal point of action’ for a policy agenda on disability (p. 146).
Similarly, Amerena and Barron (2007) argue that change is needed tostop ‘the exclusion of disabled people from social, economic, politicaland community life’ (p. 19).
2.3.3   The ICF Model
There are many other models of disability, including several interactional models. One of the most influential interactional models is theInternational Classification of Functioning, Disability and Health (ICF)
developed by the World Health Organization (WHO) and presented
below.

22

S. MitraFig. 2.2 The ICF.
Source: WHO (2001)

The ICF model was developed as a synthesis of the medical andsocial models to model and classify the consequences of health conditions (WHO 2001). It is a revision of the International Classification of
Impairments, Disabilities and Handicaps (ICIDH) (WHO 1980). It wasdeveloped by WHO as part of its mandate to collect information about
the health of populations worldwide.
Briefly, under the ICF, disability is the result of the interaction of the environment and the person with a health condition. The different componentsof the ICF and their interactions are shown in Fig. 2.2. This model starts
with a health condition (disorder or disease) that within contextual factors
gives rise to impairments, activity limitations and/or participation restrictions.
An impairment, using WHO’s (2001) definition, is defined as a ‘problem
in bodily function or structure as a significant deviation or loss.’ An activity
is the execution of a task or action by an individual. Participation is understood in terms of an involvement in a life situation. Activity and participation
domains include among others, learning and applying knowledge, mobility,
Selfcare, education, remunerative employment, and economic self-sufficiency.

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THE HUMAN DEVELOPMENT MODEL OF DISABILITY, HEALTH …

23

Functioning and disability are umbrella terms, one the mirror imageof the other. Functioning6 covers body functions and structures, activities, and participation, while disability refers to impairments, activity
limitations, and participation restrictions. Contextual factors refer to theentire background of an individual’s life. It includes personal factors:
gender, age, coping styles, social background, education, profession, andbehavioral patterns character. Contextual factors also include structural
factors. They make up the ‘physical, social and attitudinal environment
in which people live and conduct their lives’ (WHO 2001). They include
the physical environment (terrain, climate, and architecture), social attitudes, laws and institutions (e.g., home, school and work, services, systems, and policies (e.g., transportation, health, social services)), products
and technology. Structural factors may be barriers or facilitators when it
comes to the individual’s functioning. Disability refers to impairments,
activity limitations and participation restrictions. Under the ICF, Richard
had a health condition (polio), has a functional limitation (walking) and
faced as a child restrictions to participation in school due to the interaction of his impairment and barriers in the environment.
The ICF has gained considerable influence globally. It is used for avariety of objectives, in descriptive as well as analytical studies and forpolicy (e.g., Cerniauskait et al. 2011; Resnik and Allen 2007; Okawa
and Ueda 2008). The World Report on Disability advocated an adoption of the ICF (WHO-World Bank 2011). It is sometimes adopted inpublic health curricula and endorsed by clinical associations as a conceptual framework (e.g., APTA 2008). In medicine, it is most often usedin rehabilitation settings (Nixon et al. 2011), but has also been used inother fields such as oncology (e.g. Bornbaum et al. 2013).

2.4  Comparison of the Human Development Modelto Other Models
The human development model enlarges an understanding of the deprivation process (called ‘disablement’ in some models) by highlighting therole of resources and conversion functions, by incorporating agency, by
including the determinants of health conditions/impairments and using
functionings and/or capabilities as metric of wellbeing.
Resources and conversion factors are particularly important in thecontext of LMICs. To my knowledge, other models do not explicitly
model resources.7 Resources are not ignored in the ICF where they are

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S. Mitraconsidered as part of environmental factors. However, they are not as
centrally placed as in the human development model where they are astand-alone set of factors, and the diversity that may result from theirconversion into wellbeing is acknowledged. In the case of Richard, growing up in poverty was a key factor shaping his life. This is explicitly considered under the human development model.
Unlike the ICF, this model incorporates determinants of health conditions and impairments: it includes them as being influenced by, and influencing, personal factors, resources, structural factors. This recognizes
that health conditions and impairments may be influenced by structural
factors and thus are socially created to some extent.
If this model is adopted, say to frame an intervention providing physical rehabilitation services to Richard and other persons who had polio,
then the outcomes of interest will be capabilities/functionings that
Richard values or ‘has reason to value’ (Sen 1999). Service provision is amean toward human development, i.e., to expand relevant capabilities/functionings. The human development model thus makes the selectionof relevant capabilities/functionings explicit and human flourishing as
the objective of rehabilitation services. Other models, including the ICF,
fall short of recognizing the importance and the challenge of selecting
relevant dimensions of wellbeing.
Among the three models reviewed earlier, the ICF is the closest to thehuman development model. Both are interactional models with disability
arising through the interaction of the individual and the environment.
Both offer normative metrics. The ICF offers a metric of body functions/structures, activity and participation; it has been used and can beused for prescription, and thus offers implicitly a normative metric.
Unlike the social and medical models but like other interactional models such as the ICF, the human development model provides a comprehensive account of the variety of factors that might lead to deprivations.
For instance, if a person’s impairment causes constant pain, due to which
the person is unable to have practical opportunities (e.g., go out of thehouse, work, and leisure), it is the intrinsic nature of the impairment thatdeprives the person of capabilities and makes her disabled. The humandevelopment model recognizes that the impairment/health condition
alone can lead to a deprivation, but unlike the medical model, it does not
focus on the impairment/health condition as the disabling factor. With thehuman development model, the environment alone can be disabling, but

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25

unlike the social model, it is not centered on the environment as the disabling factor.
2.4.1   The Human Development Model and the ICF
The ICF and the capability approach have been analyzed head-to-head in
the literature (e.g., Bickenback 2014; Mitra 2014). It is thus worth comparing the human development model and the ICF. The human development and the ICF models have a number of commonalities. Startingfrom the obvious, the description and explanation of the disability phenomenon is central to both the ICF and the human development model;
it is their common aim. There are both interactional models. Disability
arises at the interaction of the individual and the environment. Theyboth offer normative metrics.
The ICF offers a metric of body functions/structures, activity andparticipation, and it has been used and can be used for prescription, andthus offers implicitly a normative metric. The capability approach in general and the human development model in particular are explicitly normative in that human lives should be assessed in terms of functionings
and/or capabilities.
Compared to the human development model, the ICF falls short ofrecognizing the importance and challenge of selecting relevant dimensions of wellbeing and that health conditions may be determined bystructural factors. The ICF also falls short of incorporating several concepts such as resources and agency. The lack of an explicit and central
consideration of resources can be considered a shortcoming of the ICF,
especially if used for economically deprived countries, communities,groups, or individuals.
The ICF could benefit from becoming open-ended, with the recognition that not all dimensions of life may be specified and classified, andthus the classification does not, and cannot be expected to provide an
exhaustive account of the lived experience of health deprivations.
Having said that, the synergies between the ICF and the humandevelopment models need to be explored further. The human development model might be useful for potential revisions of the ICF model andclassification. Unlike the ICF, the human development model does notoffer a classification for operationalization.

26

S. Mitra2.4.2   Disability and Poverty Linkages
Because of the broad set of potential factors influencing wellbeing in thehuman development model, policy responses to improve wellbeing may
have several entry points: health deprivations (preventing health conditions and impairments, improving health in general), resources (enhancing access to goods and services), and structural factors (e.g., change of
attitude or physical environment). This comes in contrast to the individual and social models, which is illustrated with the example of policy
responses to the disability poverty association.
In the disability and poverty discourse, where disability typically refers
to impairment and poverty refers to low income or consumption, it isoften noted that disability and poverty go hand in hand and their relationship is very often portrayed as a vicious circle, especially in the LMIC
context. It has become part of the reasoned wisdom. ‘It is a two-way relationship—disability adds to the risk of poverty and conditions of poverty
increase the risk of disability’ (Elwan 1999, p. i). ‘The result of the cycleof poverty and disability is that people with disabilities are usually among
the poorest of the poor’ (DFID 2000, p. 2). This vicious circle has been
proposed and is widely accepted as the explanation for why persons with
impairments are more likely to be materially poorer than the rest of the
population. In the context of Fig. 2.1, this vicious circle focuses on thereinforcing links between impairments (Box D) and one functioning (lowincome or consumption) (Box E). The policy prescription is to break thecycle for poverty to be reduced among persons with impairments.
Which disability model is adopted to think about these disability–poverty linkages largely predetermines the course of action to break the circle.
The medical model predisposes the analyst to identify ways out of the circle
through preventive care and the provision of assistive technology, medicalcare, and rehabilitation services to persons with impairments. The social
model is set to point toward changes in the environment as ways out of
the circle though the removal of barriers to economic participation in theenvironment, for instance by changing attitudes toward disability in thecommunity, so that persons with impairments can find jobs. Interactionalmodels such as the ICF or the human development model may point
toward a mix of medical and social interventions and go beyond the false
dichotomy of having to invest in prevention or inclusion interventions.
The human development model can offer further insights. The conversion function explained above is of course very relevant here. It points
toward the insufficiency of using income or assets to assess poverty.

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THE HUMAN DEVELOPMENT MODEL OF DISABILITY, HEALTH …

27

The human development model also goes upstream by considering
health conditions and impairments as themselves potentially the resultsof resources, personal, and structural factors. For instance, it allows forpotential joint determinants of health conditions or impairments, on the
one hand, and wellbeing deprivations, on the other. Low quality andexpensive healthcare services may lead to impairments through a lack ofadequate care. It may also lead to poverty through high out-of-pocket
health expenditures pushing an individual to sell assets and leaving her/
him with little for nonhealth expenditures. In this case, there is not a‘vicious circle’ per se, yet some dynamic relations linking impairment andpoverty on the one hand, and health services, on the other. Education
may offer a way out of the poverty–disability association without again
breaking a vicious circle: education may lead to socioeconomic mobility by providing a way out of income poverty while simultaneously
enhancing behaviors that contribute to preventing health conditions
and impairments. The human development model thus seems useful inunderstanding links between impairments, health conditions, and wellbeing outcomes such as material poverty that go beyond the disability–
poverty vicious circle. It considers the role of other factors that may also
separately be linked to impairments and income/consumption poverty(personal and structural factors, resources) and may confound the relation between disability and poverty.

2.5  Conclusion
The human development model provides a conceptual framework for organizing the links between health conditions, impairments, and wellbeing. Failure to use an interactional model such as
the human development model may generate an unnecessary focus on
prevention/rehabilitation through the medical model or social oppression through the social model.
The human development model highlights in relation to wellbeing theroles of resources, conversion functions, agency, and it uses capabilities and/or
functionings as metric for wellbeing. It does not consider impairments/healthconditions as individual attributes; instead, they are themselves determinedby resources, structural factors, and personal factors and thus the model isinformed by the socioeconomic determinants of health literature.
The human development model is limited to defining, and explaining links between disability, health deprivations, and wellbeing. It canbe combined with justice claims from the capability approach such as

28

S. Mitrathe right to the capability to be healthy (Venkatapuram 2011). I use
the human development model because I think it can generate useful
insights for this book and research and policy on wellbeing, disability,and health deprivations. It is applied in the rest of this book using data
for Ethiopia, Malawi, Tanzania, and Uganda.

Notes
1. See Qizilbash (2009) for more details on the history of the human development concept and also on the very scant literature at the intersection ofhuman development and disability.
2. A disease is a set of dysfunction(s) in any of the body systems defined bysymptomology, etiology, course and outcome, treatment response, linkage
to genetic factors, and linkage to interacting environmental factors. A disorder/syndrome ‘refers to common patterns seen in clinical practice which
represent similar manifestations such as a typical constellation of symptoms’. A symptom/sign is the ‘manifestation of a dysfunction either identifiable by the affected person or the health worker.’ Injuries are ‘physicaldamages that results when a human body is suddenly or briefly subjected
to intolerable levels of energy.’ WHO (2011, p. 12).
3. Some research has used the capability approach to frame the capability to
be healthy in a social justice context, which is beyond the scope of thismodel. This literature is useful nonetheless in how it frames determinantsof the capability to be healthy (Venkatapuram 2011) or health capability(Ruger 2010).
4. See Sect. 2.2 above.
5. This is consistent with a cartwheel view of the capability approach as presented by Robeyns (2016).
6. As noted in Mitra (2006), the term ‘functioning’ has different meanings in
the ICF model and in the capability approach. In the ICF, it includes functionings that are directly related to health (body functions and structures)
as well as activities and participation in a wide range of life domains (e.g.,
education, Selfcare, and work). Sen’s concept of functionings is broader
in that it includes activities and participation as well as desirable states of
persons (e.g., being fit), and it can be general (e.g., being free of thirst), or
specific (e.g., drinking wine).
7. Bill et al. (2004) offer a version of the social model that does account forpoverty.

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29

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CHAPTER 3

Measurement, Data and Country Context

Abstract This chapter gives the empirical background to the analysis
in Chapters 4, 5 and 6. It first reviews measurement issues with respect
to implementing the human development model of Chapter 2. There
could be different ways to put the human development model into practice depending on the objective of the exercise and the context. This
chapter discusses some ways to operationalize the model and explains
the health and wellbeing deprivation measures that are adopted in therest of this book. It describes the data under use (Living Standards
Measurement Study) and the demographic, socioeconomic, and policy
contexts for the countries covered in the analysis: Ethiopia, Malawi,
Tanzania, and Uganda.
Keywords Disability · Capability approach · Human development
model · LSMS · Africa · Washington GroupJEL I1 · I3 · O15
This chapter gives the empirical background to the analysis in Chapters
4, 5, 6. It first reviews measurement issues with respect to implementing
the human development model of Chapter 2. Of course, there could be
different ways to operationalize the human development model depending on the objective of the exercise and the context. In Sect. 3.1 below,© The Author(s) 2018
S. Mitra, Disability, Health and Human Development,
Palgrave Studies in Disability and International Development,
DOI 10.1057/978-1-137-53638-9_3

33

34

S. MitraI discuss some ways to put the model into practice and explain the measures that are adopted in the rest of this book. Section 3.2 describes thedata (Living Standards Measurement Study), and Sect. 3.3 covers thecontexts for the countries covered in the analysis: Ethiopia, Malawi,
Tanzania, and Uganda.

3.1  Implementing the Human Development Model
To put the human development model into practice, one needs to identify
persons with health conditions or impairments who experience deprivations. This can be done using a variety of methods (qualitative, quantitative, mixed, and participatory) and by different stakeholders. Assessing
whether an individual with an impairment has a deprivation can be doneby the individual herself, by caregivers or professionals (e.g., medical doctor, rehabilitation expert, and teacher). Of course, this assessment of functionings and how they may relate to a health deprivation is something thatsome may already do without the human development model. Broadly,
the objective of such exercise may be to track social progress and wellbeing in general or for specific population groups: persons with impairments
or health conditions in general, persons with specific impairments or conditions (e.g., blindness, HIV). Such analyses may be done at the level ofa community, region, nation, or globally. Another possible objective is tounderstand determinants of wellbeing, whether personal, structural, or
resource factors, to find ways to improve wellbeing.
This section deals with implementing the human development
model described earlier in Chapter 2 through quantitative datasets suchas household surveys or censuses. It presents how this book puts thehuman development model into practice for the purpose of an assessment conducted toward social and political purposes at the national
level. It starts with a review of the methods that can be used to measure
impairments/health conditions and wellbeing.
3.1.1   Measuring Health Deprivations and Wellbeing
3.1.1.1 Direct and Indirect Approaches
There are at least two ways of measuring wellbeing associated with healthdeprivations through survey-based data using the human development
model: a direct and an indirect measurement. A direct approach asks

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MEASUREMENT, DATA AND COUNTRY CONTEXT

35

people to report, usually in only one question, if they are limited in theircapabilities (opportunities) or functionings (achievements) due to an
impairment/health condition. Such an approach has in fact been used inapplied disability research under questions on broad activity limitations.1
Many countries have in their general surveys broad activity limitationquestions that can be considered as direct measures of limitations in deprivations (in capabilities or functionings) due to impairments or healthconditions, as reported directly by respondents. For instance, in South
Africa, the General Household Survey had for several years a broad activity limitation question as follows: ‘Is the person limited in his/her daily
activities, at home, at work or at school, because of a long-term physical, sensory, hearing, intellectual, or psychological condition, lasting six
months or more?’ (Mitra 2008). This direct approach is convenient as it
takes little time and space in a survey. It, however, poses two main challenges.
A direct approach operationalizes in one variable a mix of concepts(health deprivations and wellbeing) and factors (e.g., the environment)
and thus does not allow the researcher to investigate the empirical relations between different concepts of the human development model.2
Moreover, this direct approach does not provide the necessary data tomonitor people’s lives over time. As an example, let us use a broad activity limitation question related to schooling among children: ‘is your childlimited in the amount or the type of schooling you can have due to aphysical, mental or emotional condition?’ Such question does identify
persons with perceived limited schooling opportunities due to a healthcondition, so it can be a way to identify children with deprivations dueto health conditions. This question does not identify children with healthconditions who have been able to access schooling, which is problematic. Using such a question, for example in an environment where education becomes more inclusive through the provision of accommodationsin schools, one would get a decline in the prevalence of disability overtime but the negative association between schooling attendance and disability would persist and perhaps worsen as people with disabilities would
likely include more and more people with the more severe health conditions. This could lead to the misleading result that inclusive education
is not working. It is therefore necessary to identify people with impairments or health conditions and the subset who are deprived, which is anindirect measurement.

36

S. MitraSecond, the direct measure is a subjective evaluation of the link
between health conditions/impairments, on the one hand, and wellbeing, on the other. Respondents may not be aware of the ways that theirhealth condition or impairment affects their capabilities or functionings.
Responses may also be subject to different types of biases. For instance,
the rationalization bias might encourage a person who does not work toreport a health condition as the primary reason for non-employment,even if it is not. People may have adapted to their impairment in such a
way that they no longer perceive how it affects their employment. The
environment of the person could also implicitly influence this subjective evaluation: for instance, in a study of work limitation, the numberof people receiving work disability insurance benefits in a person’s reference group influences self-reported work limitations and explains why
self-reported work limitations are higher in the Netherlands than say the
USA (Van Soest et al. 2012).
An alternative approach is to separate health conditions/impairment
measures from general wellbeing measures. I refer to this as an indirector stepwise measurement. This methodology consists in empirically making the distinction between a health deprivation and other aspects of wellbeing. Some of the literature linking wellbeing and health deprivations
perhaps can already be thought of as an operationalization of the humandevelopment model. For example, several studies have investigated thewellbeing of persons with mental illness (Simon et al. 2013; Mitra et al.
2013), and a growing literature assesses the multiple deprivations experienced by persons with and without functional difficulties (Mitra et al.
2013; Trani et al. 2013, 2015).3 Some qualitative work has identified
capabilities important to patients with chronic pain (Kinghorn 2010;
Kinghorn et al. 2015). The ICECAP instruments measure perceived
capabilities in several dimensions of wellbeing identified through participatory methods (Coast et al. 2008; Grewal et al. 2006; Al-Janabi et al.
2012). They have been shown to provide information that is complementary to a measure of health (Couzner et al. 2013; Davies et al. 2013).
The ICECAP instruments measure aspects of wellbeing that may thenbe related to health conditions and impairments. The stepwise approachdescribed above of assessing health conditions/impairments first, thenwellbeing or deprivations has been used in the literature on the wellbeingof health minorities using the capability approach. Mitchell et al. (2016)
review the findings of studies on the wellbeing of persons with psychiatriccondition using a variety of methods. Kinghorn (2010)’s qualitative work

3

MEASUREMENT, DATA AND COUNTRY CONTEXT37

was conducted so as to identify capabilities important to patients withchronic pain. These capabilities were developed into a long questionnaire, which was piloted on a separate sample and then refined (Kinghorn
et al. 2015). Tellez et al. (2016) assess the wellbeing of older people withand without Alzheimer’s disease from the point of view of their functionings and latent capabilities. They find that persons with Alzheimer’s disease have lower levels, and a smaller set, of capabilities, when comparedto persons without the disease, even when the latter have several impairments, thus demonstrating that Alzheimer’s disease considerably affects
wellbeing among older adults.
There are many challenges with respect to applying the human development model and more generally the capability approach, given challenges in measuring health deprivations and wellbeing. Some of thesechallenges are discussed below.
3.1.1.2 Measurement of Health Deprivations
To measure wellbeing for persons with health deprivations, one needs toidentify health deprivations. Health measures broadly are of two types‘self-perceived and observed’ (Murray and Chen 1992). Self-perceived
measures give an individual’s own perception of health deprivations,while observed measures rely on an external party’s assessment. Bothtypes of measures provide complementary and valuable information on
health (Murray and Chen 1992). There is often no observed health data
in LMICs, self-reported measures are used in this study and are therefore
the focus of this section.
Health Conditions/Impairments
Some surveys ask respondents if they have specific health conditions.
Health conditions may be temporary, episodic or chronic, physical ormental, life threatening or not, infectious or noncommunicable. They
may be coded according to the ICD specifications for health conditions(WHO 2011) (e.g., the National Health Interview Survey in the USA).
Not everyone has access to health services, which is necessary to havea diagnosis associated with a doctor/clinic visit or hospitalization. Suchquestions in fact identify those with a diagnosis among those who areable to access healthcare, who may be a small minority in LICs. In orderto capture health conditions among those who may not have received
a diagnosis, some questionnaires attempt to find if people experience38

S. MitraA. Personal factors(sex, age)

D. Health deprivations(Functional difficulties)

C. Structural factors(e.g. household size,
rural/urban,
healthcare)

B. Resources(e.g., mother’s
education)

E. Functionings (e.g.
education, morbidity, work)

Fig. 3.1 Application of the human development model

certain symptoms and figure out if the person has specific health conditions. This is the case for instance, of depression for which a questionnaire may ask about a person’s wish to die or about difficulty sleeping atnight (e.g. Radloff 1977). This method may require a lot of questions
and may not be feasible for all health conditions.
As for impairments, individuals may be directly queried about impairments that might include blindness, deafness, complete or partial paralysis. However, this nomenclature may be unknown or people may feelstigmatized and not self-report impairments, which will lead to underestimates (Mont 2007). They tend to capture visible and severe impairments.
Functional Difficulties
Given the challenges of measuring health conditions and impairments
through household survey data, this book uses instead questions on
basic activity or functional difficulties. Basic activities are basic actions
such as walking or activities of daily living such as bathing or dressing.
Functional difficulties refer to difficulties experienced with particularbodily functions such as seeing and hearing. In the context of the humandevelopment model, functional and basic activity difficulties (functionaldifficulties for short) are used here as measures of health deprivations.
Figure 3.1 illustrates the human development model as implemented in this study. It is similar to Fig. 2.1, with additional information

3

MEASUREMENT, DATA AND COUNTRY CONTEXT

39

on dimensions and indicators used for the empirical part of the study.
Functional difficulties are in Box D of Fig. 3.1.
In this study, the functional difficulties measured by the
Washington Group short set of questions are particular types of healthdeprivations that may result from health conditions or impairments ininteraction with personal, resource, structural factors and capabilities/functionings.
This measure of functional difficulties is the one developed by theUnited Nations’ Washington City Group on Disability Statistics4 (theWashington Group thereafter) (Madans et al. 2011; Altman 2016). The
Washington Group has recommended a short list of six questions to be
included in household surveys or censuses. They are presented in Box
1. The questions ask about difficulties in six domains: (a) seeing, (b)hearing, (c) walking/climbing stairs, (d) concentrating or remembering
things, (e) selfcare, and (f) communication. For each difficulty, individuals could respond on a scale of 1–4 as follows: 1-no difficulty, 2-somedifficulty, 3-a lot of difficulty, and 4-unable to do. The Washington
Group short set of questions has the advantage of brevity and international comparability. Albeit cognitively tested in 14 countries (Miller2016), these questions on functional difficulties are not without limitations. For instance, an understanding of functional difficulties may belimited in a context with limited access to healthcare (Schneider 2016).
This may lead to underreporting in the countries under study.
Box 1: Washington Group Short Set of Questions on Disability

The next questions ask about difficulties you may have doing certain activities because of a health problem.
(a) Do you have difficulty seeing even when wearing glasses?
(b) Do you have difficulty hearing even when wearing a hearing
aid?
(c) Do you have difficulty walking or climbing steps?
(d) Do you have difficulty remembering or concentrating?
(e) Do you have difficulty with selfcare such as washing all over ordressing?
(f) Do you have difficulty communicating, for example understanding others or others understanding you?

40

S. MitraFor each question in (a) through (f), respondents are asked toanswer one of the following: 1-no difficulty, 2-some difficulty, 3-a
lot of difficulty, or 4-unable to do.
For a proxy respondent, each of the six questions starts with‘does <person> have difficulty…?’
Source http://www.washingtongroup-disability.com/
Functional difficulties can thus be thought about and measured on acontinuum or spectrum from ‘no difficulty’ to ‘unable to do’. This study
uses a score as in Stewart and Ware (1992, p. 80). The Functional Scoreis the normalized Sum of answers (each ranging from 1 to 4) to thesix questions with a minimum of six (MinScore) and a maximum of 24(MaxScore) as follows:

Functional Score =Sum − MinScore
MaxScore − MinScore

For example, if someone answers 1—no difficulty to the six difficultyquestions, the sum of answers is six and the functional score is as follows:

Functional Score =6−6=0
24 − 6

If someone answers 1—no difficulty to the six questions except 4—unable to do for seeing, then the sum of answers is nine and the functionalscore is:

Functional Score =19−6=
24 − 6
6

The functional score has a minimum of 0 and a maximum of 1, andmany possible values in between. Using such a score is consistent witha move toward a more plural understanding of health deprivations ingeneral, and functional status in particular, where every individual has ascore and may well change score from time to time and as part of the life
course. For the household-level analysis below, the household functionalscore is the highest individual score among the adults in a household.
With this score, every person or household is placed on a continuum.
In order to determine prevalence or identify a specific group, athreshold needs to be set on this continuum. This threshold represents
a social judgment to differentiate persons with and without functional

3

MEASUREMENT, DATA AND COUNTRY CONTEXT

41

difficulties. The Washington Group recommendation uses ‘a lot of difficulty’ as a threshold: persons who report ‘a lot of difficulty’ or ‘unable todo’ for at least one domain are considered to have a disability.
This study uses two thresholds and a trichotomy. It groups individuals
into three mutually exclusive categories of difficulties:
1. no moderate/severe functional difficulty in the six domains;
2. 
moderate functional difficulty (some difficulty in at least one
domain and no severe difficulty);
3. severe functional difficulty (a lot of difficulty or unable to do in atleast one domain).
The analysis conducted at the household level categorizes households
in the same way: households with no moderate/severe functional difficulty; households with at least one adult with a moderate functional difficulty (some difficulty in at least one domain and no severe difficultyin the household); and households with at least one adult with a severefunctional difficulty (a lot of difficulty or unable to do in at least one
domain). Moving away from a dichotomy (limited vs not limited) towarda functional score above or a trichotomy (severe, moderate, and no difficulty) is consistent with the human development model where healthdeprivations are considered aspects and factors of human diversity.
3.1.2   Measuring Functionings and Capabilities
Some of the challenges in putting the human development model into
practice are of course similar to those of putting the broader capability
approach into practice. These have been extensively covered in the literature, from the measurement of capabilities to the selection of relevant
dimensions, their weights, and thresholds for deprivations.
i. Capabilities measurement
In brief, the measurement of capabilities is very challenging since capabilities are not directly observable. So are capabilities measurable? Recently,
there have been efforts to collect data on a range of capabilities for thegeneral population (e.g., Al-Janabi et al. 2012; Anand et al. 2009), andfor some particular population groups such as older people (Coast et al.
2008). In general, this literature, although at an early stage, reports
encouraging results on the feasibility of measuring capabilities. This

42

S. Mitrastudy does not have information on capabilities in the datasets under use
and is therefore restricted to functionings.
ii. Selection of dimensions of wellbeing
One also needs a set of functionings (or capabilities), a method to measure them, and a threshold below which a person is considered to havea deprivation. The selection of dimensions for measures of wellbeing or
deprivations at an applied level is challenging (Alkire 2007). Sen did not
develop a definitive list of dimensions of what constitutes the good life.5
Relevant capabilities have been chosen based on people’s views (Coastet al. 2008) or from theory, based, for instance, on Nussbaum’s list
(Anand et al. 2009). Martha Nussbaum did develop a prescriptive list of‘central human capabilities’—ten ordered functions considered essential
to human life and universal across all cultures based on an Aristotelian‘objective’ view of ‘human flourishing’.6 This list is used to determine asocial minimum in each dimension.
While operationally attractive, this approach ignores the value of asking people themselves to construct the dimensions of the good life.
Nussbaum’s list has led some to worry about who decides which dimensions are part of the list and on what grounds, since item selection byresearchers gives the appearance of paternalism (Stewart 2001, 2005).
This question of who should decide is especially salient for groups
expected to have different lists of dimensions compared to the general
population. This is the case, for some persons with health conditions andimpairments who may require specific services or products (e.g., assistive
devices, care services).
However, members of disadvantaged groups may be so deprived for specific dimensions that they are not even aware of deprivations and not
likely to include them in their list. In this case, experts may then offer
insights into such omitted dimensions for the group. Most of the lists ofcapabilities that have been proposed (e.g., Nussbaum 2000) have beendeveloped by only one kind of expert, researchers. In the context of thecapability approach, it is easy to make an argument on democratic andethical grounds that people within relevant groups should decide, andin terms of human rights, a participatory approach engages the people
who are being studied directly in research (Viswanathan et al. 2004).
Furthermore, participation may be instrumentally valuable in improving the quality of the research output. Although I recognize the valueof including the voices of relevant groups or individuals with disabilities,

3

MEASUREMENT, DATA AND COUNTRY CONTEXT

43

using a participatory framework to select dimensions of wellbeing isbeyond the scope of this study.
This study uses for guidance the list of dimensions of wellbeing developed in the Stiglitz, Sen, and Fitoussi report (Stiglitz et al. 2009). This
list has been derived through an extended and international consultative
process toward developing and recommending indicators to measure
economic and social progress. Stiglitz et al. (2009) recommend the following eight dimensions as constitutive parts of wellbeing: material wellbeing (income, consumption, and wealth), health, education, personal
activities (including work), political voice and governance, social connections and relationships, environment (present and future) and security ofan economic and physical nature.
The datasets under use in this book were combed for indicators of the
wellbeing dimensions above. Due to data constraints, this book focuseson material wellbeing (consumption and assets/living conditions), health(morbidity), education, work, and economic insecurity. The datasets do
not have any information on political voice and governance, social connections and relationships and the environment, which are therefore notcovered in this study.
Chapter 2 defined disability as a deprivation in terms of functioning(and/or capability) among persons with health deprivations. Persons
with health deprivations are at risk of disability. What aspects of disability
is this study capturing then? Wellbeing deprivations are measured in different ways in Box E of Fig. 3.1. This study measures disability as a deprivation in terms of various functionings (e.g., education, work) among
persons with functional difficulties. People may experience disability in
one dimension of wellbeing, say education, but not in another, say work.
Should they still be considered as having a disability? For precision andclarity and due to the challenges of using the term disability raised in
Chapter 2, I will refer to specific deprivations (e.g., work, education,and material poverty) among persons with functional difficulties. I will
not use the term disability in the empirical analysis. The term disability
will, however, be used when relevant literatures and policies are analyzed,
with definitions and measures as used in the reviewed studies and policies. In literature reviews in Chapters 4, 5 and 6, ‘disability’ will be used
as an umbrella term, covering the different meanings in the literature,
typically including impairments, functional difficulties, or broad activity
limitations.

44

S. Mitra fter selecting relevant dimensions of wellbeing, one needs a method ofAmeasurement for each of them, and a threshold below which a person isconsidered to have a deprivation. The threshold needs to be establishedin relation to a standard that accounts for the context of the particular individual. Chapter 5 will explain indicators and thresholds for eachdimension. An advantage of the capability approach, as noted earlier, is
to expand the evaluative space of wellbeing beyond material wellbeing
and to multiple dimensions. For a broad assessment of wellbeing that
accounts for simultaneous achievements or deprivations in several areasof life, one can adopt a multidimensional measure of wellbeing, or of
the lack of wellbeing, i.e., a measure of multidimensional deprivations orpoverty. Chapter 5 will use the method developed by Alkire and Foster(2011) for multidimensional poverty based on the capability approach.
iii. Resources, personal and structural factors
Resources, personal, and structural factors are key components of thehuman development model in Chapter 2. They are potential determinants of wellbeing whether the person has functional difficulties
or not. Such factors could be related to functional difficulties or wellbeing. For instance, as noted in Chapter 2, personal and structural factors may interact to determine how resources may lead to capabilities orfunctionings. The set of relevant factors will of course vary dependingon the particular capability or functioning of interest. Household survey
information on resources, personal, and structural factors will be used in
Chapters 4, 5, 6 to investigate correlates of functional difficulties.
Resources refer to resources available to the individual, whether purchased in the market or shared within the family or community. Accessto material resources may be measured through asset ownership, living conditions and wealth, expenditures, or income. Income data israrely available in LMICs as it can be volatile. This study has information on some material resources (e.g. assets) but they are used here asfunctionings (wellbeing outcomes). Information can also be considered
as a resource, which is not directly captured in the datasets under use.
Instead, this study uses mother’s educational attainment as a proxy for
information.
Personal factors are individual characteristics. They may include simple
demographics such as sex, race/ethnicity, and age. They may also be
more complex characteristics such as personality traits, which are notavailable in the surveys under use. They are important so as to capture

3

MEASUREMENT, DATA AND COUNTRY CONTEXT

45

potential intersectional disadvantages, as noted in Chapter 2. This analysis will assess how age and sex interact with functional difficulties in theirassociation with deprivations. Information on ethnicity was not used inthis study as it was not available for all four countries.
Structural factors refer to characteristics of the environment: the immediate environment (e.g., family, home, and workplace), the meso-environment (the community), and the macro-environment (regional, national).
At each of these levels, the environment has cultural, economic, natural,
physical, social characteristics that may influence capabilities and functionings. Information about the environment may be collected in different ways (Altman and Meltzer 2016): structural reviews that describe theenvironment in a town or city; self-reports of difficulties experienced by
the person while interacting with the environment; and a person’s participation level and how the environment at home, school or work may playa barrier and/or facilitator role in activities. Household surveys generally
have few questions on the environment but potentially might sometimes
be merged with other datasets with structural reviews. In the household
surveys used in this study, the immediate environment of the person is
known (family) and some information is available on the community(distance to healthcare services, rural vs urban).

3.2  Data
This study uses data from the Living Standards Measurement Study(LSMS). It draws on the four LSMS panel datasets that have internationally comparable functional difficulty questions: the Ethiopia Rural
Socioeconomic Survey (2011/2012 and 2013/2014), the Malawi
Integrated Household Survey (2010/2011 and 2012/2013), theTanzania National Panel Survey (2010/2011 and 2012/2013), and the
Uganda National Panel Survey (2009/2010, 2010/2011, 2011/2012).
To my knowledge, the recent LSMS datasets collected in Ethiopia,
Malawi, Tanzania, and Uganda are the first longitudinal datasets that
include the recommended short questionnaire on functional difficultiesof the Washington Group.
These surveys were implemented by each country’s national statistics office, with technical support from the World Bank Development
Economics Research Group. These datasets are nationally representative,
except the Ethiopia Rural Socioeconomic Survey for 2011/2012, representative of rural areas and small towns.

46

S. MitraIn the four countries included in this study, the LSMS followed astratified sample design with weights. For each household, one household informant responded to a questionnaire including a roster withhousehold demographics (size number of children, age of each member of the household), questions on household economic wellbeing(e.g., expenditures, living conditions, assets). In addition, within eachhousehold, each individual or a household respondent was asked questions about each individual’s education, health, disability, and labor
force activities. The caregiver answered such questions on behalf ofchildren. This study focuses on individual respondents aged 15+ as the
Washington Group short set of questions was developed for this age
group.
These datasets are novel in different ways. To my knowledge, these
are the first longitudinal datasets that include the recommended shortquestionnaire on functional difficulties of the Washington Groupfor at least one wave.7 They thus provide internationally comparable data on disability using a tool that has been tested in different country contexts. The Washington Group short set of questions
was included in the following surveys (waves): Ethiopia Rural
Socioeconomic Survey (2011/2012 and 2013/2014), the Malawi
Integrated Household Survey (2010/2011), the Tanzania National
Panel Survey (2010/2011), and the Uganda National Panel Survey(2009/2010, 2010/2011). I use other waves as well that do not havethe Washington Group questions to investigate the association between
functional difficulties and short-term mortality (the Malawi Integrated
Household Survey (2012/2013), the Tanzania National Panel Survey(2012/2013), and the Uganda National Panel Survey (2011/2012)). In
addition, the LSMS surveys include a wide range of indicators of economic wellbeing. For instance, it has questions on employment in farmand nonfarm enterprises, while many other datasets have detailed activities for farm or nonfarm activities, but rarely both. Finally, the datasets
are internationally comparable with similar survey designs and questionnaires, and thus will be used in this study for cross-country comparisons.
Despite these similarities, what people may understand from the questionnaire and how they reply could differ given different languages, cultures, and contexts in ways that researchers cannot appreciate (Grech
2016).
The response rates for these surveys were very high. One limitation
though is that each survey only covers the household population in each

3

MEASUREMENT, DATA AND COUNTRY CONTEXT

47

country. They exclude the homeless and the institutionalized population(i.e., people in nursing homes, psychiatric hospitals). This is problematic as functional difficulties may affect the probability of living outside
the household. Institutionalization among adults is suspected to be
low in the four countries, but no data could be found to confirm this.
Homelessness may be a more significant problem in its potential to affectfunctional difficulties.
Although the functional difficulty questions are worded the same way,
there are a few differences in the questionnaires of the four countries.
Ethiopia, Malawi, and Tanzania have the Washington Group questions
as part of a longer health section in the questionnaire, while Uganda has
a separate section titled ‘disability’ after the health section. Tanzania has
a somewhat different answer scale including an additional category, nodifficulty with assistive device as follows: 1—no difficulty, 2—no difficulty with assistive devices, 3—some difficulty, 4—a lot of difficulty, and5—unable to do. Categories 1 and 2 were collapsed into one category(no difficulty) for comparability with other countries. Only in Ethiopia
does each individual in the household consistently answer about his/herfunctional difficulties. In the other countries, it is either the individual or
the household respondent. Finally, Ethiopia and Malawi surveys do nothave the introductory sentence recommended in the Washington Group
short set of questions prior to asking about functional difficulties, while
Tanzania and Uganda surveys do.8 Although these differences between
surveys may seem minor, such changes in question wording or in theplacement of the questions may significantly affect the resulting estimates(Mathiowetz 2001).

3.3  Country Context
The four countries under study are briefly described in this section interms of their overall human development, their labor markets and social
protection programs, and their disability laws and policies.
3.3.1   Overall Human Development
Ethiopia, Malawi, Tanzania, and Uganda are some of the poorest countries in the world. Key demographic and socioeconomic information for
the four countries are presented in Table 3.1. With varying population
sizes, all four countries have a young population overall, with almost half

48

S. MitraTable 3.1 Demographic and socioeconomic indicators
Ethiopia Malawi Tanzania Uganda
Total population, in millions (2015)
Share of population under 15 (2015)
Share of population 65 and older (2015)
Share of rural population (2015)
GDP growth rate (2015)
GDP from agriculture (2015)
Employed to total (15+) population ratio(2014)
GNI per capita (2014)
Life expectancy at birth (2014)
Mean years of schooling
Poverty headcount ratio $1.90 (PPP 2011)
Under 5 mortality (per 1000) (2015)
Health expenditures per capita (2014)
Prevalence of HIV among 15–49 (2014)
Human Development Index (HDI)HDI country rank
Multidimensional poverty headcount

99.4
41%
3%
81%
10%
42%
79%

17.2
45%
3%
84%
3%
31%
77%

53.5
45%
3%
68%
7%
32%
86%

39
48%
2%
84%
5%
25%
75%

$1428
64.1
2.4
33.5%
59
$27
1%
0.442
174th
87%

$747
62.8
4.3
70.9%
64
$29
10%
0.445
173th
56%

$2411
65
5.1
46.6%
49
$52
5%
0.521
151th
66%

$1613
58.5
5.4
34.6%
55
$52
7%
0.483
163th
70%

Sources United Nations Development Program country notes for the 2015 Human Development
Report for GNI, Life expectancy, mean years of schooling and HDI. OPHI (2016) Country Briefings
June 2016 for Multidimensional Poverty Headcount. World Bank Poverty and Equity data bank for$1.90 poverty headcount ratios. World Development Indicators database (2015) for all other indicators
Notes GNI stands for Gross National Income. $1.90 poverty headcounts are for 2010. Multidimensional
poverty headcounts are for 2011 for Ethiopia and Uganda, 2013/14 for Malawi and 2010 for Tanzaniaof the population under the age of 15. For Sub-Saharan Africa overall,with an expected decline in fertility and an increase in life expectancy, theshare of adults in the total population, including older people is expected
to increase to 72% by 2050 (UNPD 2015).
By international standards, these are economies largely reliant on agriculture. For instance, Malawi and Tanzania have about a third of theirgross domestic product (GDP) coming from agriculture. Ethiopia is,among the four economies, the one growing the fastest with an annual
growth rate of 10%. Ethiopia, Malawi, Tanzania, and Uganda are lowincome countries9 with gross national income (GNI) per capita ranging
from a low of $747 in Malawi to a high of $2411 in Tanzania. The mean
years of schooling fall between 2.4 in Ethiopia and 5.4 in Uganda, andlife expectancy at birth is around 60 years. The information on GNI per
capita, years of schooling and life expectancy can be considered together

3

MEASUREMENT, DATA AND COUNTRY CONTEXT

49

as part of the Human Development Index (HDI). These four countries
have low HDIs and are at the bottom of the global HDI ranking conducted for 187 countries annually by UNDP (2015). Their rankings are
between 151th (Tanzania) and 174th (Ethiopia). Using the international
poverty line of $1.90 a day (PPP 2011), the poverty headcount ratio
stands at 33.5% in Ethiopia, 70.9% in Malawi, 46.6% in Tanzania, and34.6% in Uganda. Using the Multidimensional Poverty Index (MPI),
poverty becomes even more common and affects a majority of the population in the four countries. The highest percentage of poor people using
the MPI is in Ethiopia at 87.3%. Finally, by world standards, under-five
mortality is high at about 60, health expenditures per capita are low
between $27 and $52 and the employed to total population ratio is highat 75% or higher. The prevalence of HIV is the highest in Malawi at 10%
followed by Uganda (7%), Tanzania (5%), and Ethiopia (1%). In fact, in
2012, HIV/AIDS was the leading cause of death in Malawi, Tanzania,
and Uganda while lower respiratory infections were the leading cause in
Ethiopia (WHO 2015). In addition to HIV, individuals face a high disease environment given widespread malnutrition, poor sanitation, a highprevalence of infectious diseases, and limited access to healthcare facilities(WHO 2015). Epidemics such as Ebola and Nodding diseases have also
been experienced in recent years (Deogratius et al. 2016). The diseaseenvironment combined with stringent resource constraints is expected tohave cumulative effects on survival, health deprivations, and wellbeing.
3.3.2   Labor Market and Social Protection
In all four countries, the labor market is largely informal with very limited access to formal insurance for on-the-job injuries, health, or old age.
For health insurance, coverage is very limited. Uganda’s National Health
Insurance scheme is still in draft form (Omona 2016). Both Ethiopia
and Tanzania have recently introduced community-level programs to
expand health insurance coverage: Community-Based Health Insurancein Ethiopia and Community Health Fund in Tanzania. These programs
are at early stages and cover only small shares of the population (United
Nations 2015).
Like many countries around the world and in Africa, Ethiopia,
Malawi, Tanzania, and Uganda have developed cash transfer programsin the past decade or so (World Bank 2012). Malawi, Tanzania, andUganda have pilot cash transfer programs (Oxford Policy Management

50

S. Mitra2015; World Bank 2012). Malawi’s Social Cash Transfer programis targeted at the ultra-poor and at labor constrained households(Government of Malawi 2016). Malawi also has a large Targeted Input
Program aimed at improving agricultural productivity and a large-scale
public works program under the Malawi Social Action Fund (UNDP
2012). A recent evaluation shows that the public works program was noteffective in achieving its aim of improving food security during the 2013lean season (Beegle et al. 2017). The Tanzania Social Action Fund has
been a leading and growing policy initiative in the area of social protection since the early 2000s. Public works have been a major part of the
Tanzania Social Action Fund, with further components more recently
added, including a pilot conditional cash transfer program since 2010
(United Nations 2015).
Uganda started a five-year pilot project in 2010/2011, the Social
Assistance Grants for Empowerment Program (SAGE), with cash transfers for older persons and vulnerable families. For the latter, vulnerability
indicators include age, sex, orphanhood, and disability. The Washington
Group short set of questions was used to assess disability (Schneider
et al. 2011). The 15% of families in 14 districts with the highest vulnerability indicators receive SAGE (Oxford Policy Management 2015). In
2015/2016, the program for older persons was rolled out in 20 more
districts, with a target of covering a total of 55 districts by 2019/2020.10
In Ethiopia, the Productive Safety Net Program (PSNP), started in
2005, is an integrated public works program for households with the socalled able-bodied adult laborers and an unconditional cash transfer for
those unable to work due to pregnancy, illness, or disability.11 PSNP is
also linked to interventions to boost agricultural productivity. The objective of the PSNP is ‘to provide transfers to the food insecure population in chronically food insecure woredas in a way that prevents asset
depletion at the household level and creates assets at the community
level’ (GFDRE 2004). A recent evaluation finds that participation in the
Public Works component of the PSNP has positive albeit modest effects
on food security (Berhane et al. 2014). An evaluation of PSNP’s targeting (Coll-Black et al. 2012) shows that in general it is targeted at worse
off households based on consumption and that the cash transfer component is targeted at households with older heads, older men, and fewer
younger men, and female-headed households are more likely to receive
these payments.

3

MEASUREMENT, DATA AND COUNTRY CONTEXT

51

Ethiopia, Malawi, Tanzania, and Uganda have grown their social
protection systems in recent years. However, it is unclear if households
that experience functional difficulties that lead to extra healthcare needs
receive the necessary services or if it comes with a financial burden given
limited access to health insurance. It is also unclear whether the social
protection systems, with large public works programs, may be able toassist households with adults who are unable to work permanently or
temporarily.
3.3.3   Disability Laws and Policies
This section describes disability laws and policies in Ethiopia, Malawi,
Tanzania, and Uganda. The term ‘disability’ is used within the definition of the relevant law or policy, which is often as impairment or as anumbrella term as in the ICF (impairment, activity limitation, and participation restriction).
Information on the disability policy background in each country ispresented in Table 3.2. All four countries aspire to improve the wellbeing of persons with disabilities, as signaled by several legislations
and policies on disability. Each country has disability included in its
Constitution, in one aspect or another, for instance with respect to antidiscrimination or resource allocation.12 Uganda is among the first countries worldwide to ratify the CRPD when it came into force in 2008.
Malawi and Tanzania followed suit soon after in 2009 and Ethiopia in
2010. The four countries also adopted national disability legislations.
Malawi, Tanzania, and Uganda had their policies in place prior to theratification of the CRPD, while Ethiopia adopted the policy two years
after. Several paradigms started in HICs seem to have been embraced
in these national legislations and policies. The social model of disability seems to have been very influential in the four countries with the
adoption of disability definitions consistent with the one in the CRPD:‘persons who have long-term physical, mental, intellectual or sensory
impairments which in interaction with various barriers may hinder theirfull and effective participation in society on an equal basis with others’ (Article 1). For instance, in Tanzania, the Persons with Disabilities
Act of 2010 uses the following definition of a person with disability: ‘aperson with a physical, intellectual, sensory or mental impairment andwhose functional capacity is limited by encountering attitudinal, environmental and institutional barriers.’ Uganda’s disability policy defines itNational Policy on
Disability (2004)

Persons with Disabilities
Act (2010)“<…> right to work, toself education and social
welfare at times of oldage, sickness or disability
and in other cases ofincapacity. Without
prejudice to those rights,
the state authority shallmake provisions toensure that every person
earns his livelihood”
2009

Tanzania

Sources MLSA (2012), Chilemba (2014), Government of Malawi (2006), Oyaro (2014), Shughuru (2013)
Note CRPD stands for the Convention on the Rights of Persons with Disabilities

National Policy
on Equalization ofOpportunities for
Persons with Disabilities(2006)Disability Act (2012)

Proclamation of theRights to Employment
for Persons with
Disabilities No.
568/2008
National Plan of
Action of Persons with
Disabilities (2012),
National Physical
Rehabilitation Strategy of
Ethiopia (2011)

Disability policy

2009

2010

Year of ratification of the
CRPD
Disability legislation“To support the disabled” and ban discrimination based on disability
and other categories(Section 20)

Malawi“The State shall, within
available means, allocate
resources to provide
rehabilitation and assistance to the physicallyand mentally disabled”(Article 41.5)

Ethiopia

National Policy on
Disability (2006)

Persons with Disabilities
Act (2006), National
Council for Disability
Act (2003)

2008

Article 21 bans discrimination based on disability and other categories

UgandaSelected national policies and legislations directly relevant to the wellbeing of adults with disabilities

Constitution

Table 3.2

52
S. Mitra3

MEASUREMENT, DATA AND COUNTRY CONTEXT53

as ‘permanent’ and substantial functional limitation of daily life activities
caused by physical, mental, or sensory impairment and environmental
barriers resulting in limited participation.’
In addition, certain strategies widely discussed and put forward in the
global discourse on disability and development have also been adopted
at the national level. For example, the twin-track approach (DFID 2000)
of both disability-targeted and mainstream policies and programs indisability and development is part of Malawi’s National Policy on the
Equalization of Opportunities for Persons with Disabilities (Governmentof Malawi 2006) and Ethiopia’s National Plan of Action for Persons with
Disabilities (MLSA 2012).
Overall, in the past decade or so, the four countries under study havemade great strides in developing a range of disability policies and legislations for disability inclusion well in line with the CRPD and the global
discourse around disability and human rights. Of course, there may well
be a gap between disability policies and legislations, on the one hand,and implementation and the reality experienced by persons with disabilities, on the other. This is a concern that some policy analysts havealready expressed (e.g., for Tanzania, Aldersey and Rutherford Turnbull
2011; GIZ 2016). The next three chapters attempt to investigate thispolicy–reality gap by researching empirically the socioeconomic inequalities that are associated with functional difficulties.

Notes
1. Such questions have also been included in general efforts to measure wellbeing under the capability approach (Anand et al. 2005).
2. A similar point is made by Altman (2001) with respect to measures ofActivities of Day Living or Instrumental Activities of Daily Living.
3. Mitchell et al. (2016) give a review of studies on multidimensional poverty in relation to health.
4. 
In June 2001, the United Nations International Seminar on the
Measurement of Disability recommended that principles and standard
forms for indicators of disability be developed (Altman 2016). There wasa broad consensus on the need for population-based measures of disability for country use and for international comparisons. The Washington
Group on Disability Statistics was formed to address this urgent need.
The main purpose of the Washington Group is to promote and coordinate international cooperation in the area of disability measures.
Specifically, the Washington Group has developed a short set of questions

54

S. Mitrafor use in censuses and national surveys in order to inform policy onequalization of opportunities. It also has developed an extended set ofquestions to measure disability to be used as part of population surveys or
as supplements to special surveys (Altman 2016).
5. Alkire (2002a, b) reviews several such lists including John Rawls’ list of
primary goods, Doyal and Gough’s list of needs and Martha Nussbaum’slist of capabilities.
6. Nussbaum’s (2000) list includes: 1. Life: not dying prematurely. 2.
Bodily health: to have good health, adequate nutrition, and shelter. 3.
Bodily integrity, including physical mobility. 4. Senses, imagination, andthought: including being able to use the senses, to imagine, think andreason. 5. Emotions: including being able to have attachments to thingsand people outside ourselves. 6. Practical reason: including being able toform a conception of the good. 7. Affiliation: including social interactions. 8. Other species: ‘Being able to live with concern for and in relation to animals, plants and the world of nature.’ 9. Play: ‘Being able tolaugh, to play, to enjoy recreational activities.’ 10. Control over one’s
environment. (A) Political: including political participation; (B) Material:‘Being able to hold property…; having the right to seek employment on
an equal basis as others…’.
7. The Nigeria General Household Survey also includes Washington Groupquestions. However, it was not included in this study due to inconsistencies in age/birth year self-reports in the two waves.
8. The introductory sentence reads as follows: ‘Because of a physical, mental
or emotional health condition…’.
9. Income country groups are as defined by the World Bank. Available at:https://datahelpdesk.worldbank.org/knowledgebase/articles/906519.
10. For this expansion, the vulnerable families component was dropped. Only
the senior citizens grant component (older persons) was rolled out.
11. I could not find from the PSNP literature how ‘disability’ is determined inthis context.
12. In Uganda, Article 21 of the Constitution bans discrimination based on
disability among other categories (gender, age, tribe). In Ethiopia, Article
415 of the Constitution is as follows: ‘The State shall, within available
means, allocate resources to provide rehabilitation and assistance to thephysically and mentally disabled.’

References
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S. MitraOpen Access This chapter is licensed under the terms of the Creative Commons
Attribution 4.0 International License (http://creativecommons.org/licenses/
by/4.0/), which permits use, sharing, adaptation, distribution and reproduction
in any medium or format, as long as you give appropriate credit to the original
author(s) and the source, provide a link to the Creative Commons license andindicate if changes were made.
The images or other third party material in this chapter are included in thechapter’s Creative Commons license, unless indicated otherwise in a credit line
to the material. If material is not included in the chapter’s Creative Commonslicense and your intended use is not permitted by statutory regulation or exceeds
the permitted use, you will need to obtain permission directly from the copyrightholder.

CHAPTER 4

Prevalence of Functional Difficulties

Abstract This chapter estimates the prevalence of disability measured
through functional difficulties. In Ethiopia, Malawi, Tanzania, andUganda, the prevalence of functional difficulties ranges from 10.8 to
15.1%. In the four countries, the prevalence of functional difficulties at
the household level ranges from one in five to one in three households.
Functional difficulties disproportionately affect older individuals andwomen. Seeing and walking limitations are the most prevalent limitationsin the four countries. A majority of individuals do not take any measure to reduce their functional difficulties, suggesting there may be scopefor prevention. There is a strong socioeconomic gradient in prevalence.
Prevalence is two to four times higher in households in the poorest
quintile compared to the richest quintile.
Keywords Disability · Prevalence · Poverty · Gender · Aging · Africa
JEL I1 · I3 · O15© The Author(s) 2018
S. Mitra, Disability, Health and Human Development,
Palgrave Studies in Disability and International Development,
DOI 10.1057/978-1-137-53638-9_4

61

62

S. Mitra

Policymakers in LMICs currently have very little guidance from statistics regarding the magnitude or nature of functional difficulties. Until
recently, data was often not collected, or of poor quality and not comparable across countries. The Washington Group questions allow us topresent nationally representative and comparable prevalence estimates.
The main goal of this chapter is to present nationally representative estimates of the prevalence of functional difficulties among adults
in Ethiopia, Malawi, Tanzania, and Uganda. Finding out about prevalence is important for several reasons. It helps policymakers, analysts, andresearchers understand functional status in their countries. It also helps
with the design of interventions in order to prevent functional difficulties
and to improve the wellbeing of persons who experience such difficulties,
including health, economic, and social wellbeing.
This chapter uses data for Ethiopia, Malawi, Tanzania, and Uganda
to answer several questions: how prevalent are functional difficulties?
What types of functional difficulties can be found? Do people take any
measure to curb their difficulties? Are functional difficulties consistently
experienced overtime? What are their correlates? In the context of thehuman development model, this chapter measures the prevalence of one
type of health deprivation (functional difficulties) and investigates its correlates with personal factors (age, sex), resources (mother’s education),and structural factors (rural/urban, distance to healthcare services).

4.1   Literature on Disability Prevalence in LMICs
As of June 2016, there are a number of estimates of disability prevalencein LMICs from both country and global-level data collection efforts. Let
us take the example of Ethiopia. What do we know so far about disability
prevalence in this populous country of the horn of Africa? In Ethiopia,
in 2007, the Census came up with a national disability prevalence of 1%(CSA 2007). This is not unusual in LMICs (WHO-World Bank 2011,
Appendix 1). Like many low-income or African countries, prevalence
was found to be low compared to prevalence estimates in HICs often
between 15 and 20%. Is there underreporting of disability in LICs? Is
there excessive mortality associated with disability? Does it reflect a very
different population pyramid? Are disability measures radically different
from those used in HICs? There is of course a combination of factors,
but clearly measurement plays an important role in explaining the vast
range of estimates across country groups. Ethiopia’s 2007 Census used

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PREVALENCE OF FUNCTIONAL DIFFICULTIES

63

a single question asking if the person has ‘a problem of seeing, hearing, speaking and/or standing/walking/seating, body parts movement,functioning of hands/legs or mental retardation or mental problem or
mental/physical damages?’A single question asking directly about ‘disability’ or about impairments (e.g., mental retardation), or about a mixof impairments and functional difficulties as in the case of Ethiopia, tends
to capture very extreme and permanent disabilities only and lead to very
low prevalence rates (Mont 2007). Estimates using such questions arethus not comparable to those usually much higher found in HICs using
several questions on functional difficulties (e.g., difficulty seeing) andactivity limitations (e.g., selfcare difficulty).
Some global data initiatives have also provided estimates of prevalence for countries in LMICs. As part of the Global Burden of Disease(GBD) study (Murray and Lopez 1996), disability prevalence is inferredfrom data on health conditions and impairments alone using assumptions
on distributions of limitations that may result from health conditions
and impairments. According to the GBD study, disability prevalence in
Ethiopia stands at 11.3% (WHO 2008).
Another global effort to estimate disability prevalence in LMICs(and globally) is in the World Report on Disability (WHO–World Bank
2011). It uses a score that aggregates answers to 15 questions in theWorld Health Survey (WHS) on difficulties experienced in eight domains(vision, mobility, cognition, selfcare, pain, interpersonal relationships,
sleep and energy, affect) (WHO–World Bank 2011).1 According to theWorld Report on Disability, disability prevalence in Ethiopia stands at
17.6% among adults using a standardized population structure. Using
also the WHS dataset, Mitra and Sambamoorthi (2014) measure disability as having at least one severe or extreme difficulty with bodilyfunctions (seeing) and basic activities (concentrating, moving around,selfcare). For Ethiopia, Mitra and Sambamoorthi (2014) find a disability
prevalence among adults of 14.2% using a standardized population structure, and 12.7% for its actual population structure.2,3This range of estimates for Ethiopia from 1 to 17.6% is potentiallyconfusing and not helpful for policy and may curb policy and research
initiatives with respect to disability. They illustrate that considerable
uncertainty remains on disability prevalence, especially in LICs and in
Africa in particular, where very few surveys have been conducted. To
our knowledge, very few country estimates are available in Africa using
the internationally comparable and tested Washington Group questions

64

S. Mitraexcept for a few countries where estimates have become available inrecent years (South Africa (Statistics South Africa 2014 and NDSD
2015); Zambia (Eide and Loeb 2006); Tanzania (NBS 2008); andUganda (UBOS 2016). This chapter attempts to fill part of this gap for
Ethiopia, Malawi, Tanzania, and Uganda using recent datasets with the
Washington Group short set of questions.

4.2   Methodology
This chapter uses cross-sectional samples that are nationally representative for Malawi, Tanzania, and Uganda and representative of rural
areas and small towns for Ethiopia. For Malawi and Tanzania, in eachcase, the only wave with the Washington Group questions is used: for
Malawi, the 2010/2011 Third Integrated Household Survey and for
Tanzania, the 2010/2011 National Panel Survey, respectively. For
Ethiopia and Uganda, I use the initial wave of the panel dataset in which
the Washington Group questions are used: the 2011/2012 wave of theEthiopia Rural Socioeconomic Survey and the 2009/2010 wave of the
Uganda National Panel Survey.
The questions on functional difficulties are as explained earlier in
Chapter 3. Basic proportions are used to calculate prevalence in eachcountry, and adjustments are made for complex sampling (clustering,strata, and weights). Although one of the objectives of this book is tomake cross-country comparisons of prevalence rates, the estimates are
not age and sex standardized. As seen in Chapter 3, the population
structures of the four countries are somewhat similar. The objective is topresent prevalence estimates for the current population structure in eachcountry and their implications for policy, and thus the age/sex standardization is not necessary.

4.3   Prevalence at the Individual Level
Table 4.1 presents results on prevalence overall among adults and by sex
and age group. Prevalence is presented for the entire adult population
defined as ages 15 and over and for four age groups, overall and by sex.4
The prevalence of moderate and severe functional difficulties (at least
some difficulty in one domain) stands at 12.85% in Ethiopia, 10.78% in
Malawi, 15.05% in Tanzania, and 15.36% in Uganda. Prevalence rates
for severe difficulties (at least a lot of difficulty in one domain) are as

1.36
1.25
1.47

3.02
3.77
2.3

6.82
9.11
4.41

19.44
20.81
18.53

All aged 15-39
Women aged 15–39
Men aged 15-39

All aged 40-49
Women aged 40-49
Men aged 40-49

All aged 50-64
Women aged 50-64
Men aged 50-64

All aged 65+
Women aged 65+
Men aged 65+

48.68
53.26
45.61

28.89
33.71
23.85

16
15.58
16.4

5.84
6.2
5.45

12.85
13.24
12.44

8.75
8.94
7.40

2.59
3.29
1.85

0.92
1.21
0.66

0.60
0.54
0.67

1.39
1.55
1.22

46.93
50.21
42.88

22.58
26.54
18.37

11.60
15.31
8.15

5.26
5.86
4.63

10.78
12.50
8.98

Moderate& Severe

20.69
24.14
16.36

5.61
5.81
5.39

3.20
3.85
2.46

1.01
0.99
1.02

3.88
4.32
3.36

SevereTanzania

55.09
55.09
48.75

26.76
27.67
25.82

16.55
17.57
15.36

6.10
6.42
5.68

15.05
15.65
14.33

Moderate& Severe

23.24
27.24
18.36

8.73
8.03
9.60

2.60
2.57
2.64

1.49
1.31
1.68

3.76
3.96
3.54

SevereUganda

62.70
67.59
56.71

36.20
39.89
31.60

18.55
21.90
15.16

7.39
7.63
7.13

15.36
17.16
13.45

Moderate& Severe

Moderate and Severe difficulty includes reporting at least "some difficulty" in at least one domain. Estimates are weighted.

Sources Author's calculations using Ethiopia Rural Socioeconomic Survey (2011/12), Malawi Integrated Household Survey (2010/11), Tanzania National
Panel Survey (2010/11), Uganda National Panel Survey (2009/10)
Notes Each number is the share of the population in a given age group who reports experiencing a certain level of difficulty for one of the six domains of
the Washington Group short set of questions. A severe difficulty includes reporting "a lot of difficulty" or "being unable to do" for at least one domain.

3.46
3.58
3.34

SevereSevereModerate& Severe

Malawi

Ethiopia

Prevalence of functional difficulties by sex and age group (%)

All aged 15+
Women aged 15+
Men aged 15+

Table 4.1

4
PREVALENCE OF FUNCTIONAL DIFFICULTIES

65

66

S. Mitrafollows: 3.46% in Ethiopia, 1.39% in Malawi, 3.88% in Tanzania, and3.76% in Uganda. While Malawi seems to be somewhat of an outlier
with lower prevalence rates, the other countries have rates that are relatively close to each other.
Overall, these prevalence estimates are consistent with the resultsof recent studies using the Washington Group questions in LMICs:
for severe difficulties, 9.6% in Maldives (age 5+) (Loeb 2016), 8.5%
in Zambia (all ages) (Eide and Loeb 2006), and 3.3% in South Africa(5 years and older) (Statistics South Africa 2015). For moderate andsevere difficulties, 13.6% in Uganda (all ages) (UBOS 2016) and 9.1%
in Bangladesh (as reported in Loeb 2016). In the 2008 Tanzania
Disability Survey with a threshold of at least one severe difficulty or
two moderate difficulties, prevalence stands at 7.8% (NBS 2008) for
persons age seven and older, which is in between the prevalence rates
found in this study for severe disability (3.88%) and severe/moderate difficulties (15.05%). The prevalence of severe difficulties found in
this study for four LICs are lower than those found in two HICs thathave used the Washington Group questions: Israel (14.8% for persons
20 years or older) and in the USA (9.5% for persons 18 years or older)(Loeb 2016).
4.3.1  AgeAs expected, the prevalence of difficulties, whatever the severity, ishigher for older age groups. For instance, in Ethiopia, 1.36% of adults
age 15–39 have severe difficulties compared to 19.44% among people
age 65 and older. This is further illustrated in Fig. 4.1 where the meanfunctional difficulty score is plotted by age for each country.5 In all
four countries, functional difficulties tend to increase with age among
adults, especially from mid to late 40s. This result is consistent withmuch evidence worldwide that functional difficulties become morecommon with age (WHO–World Bank 2011; Mitra and Sambamoorthi2014). There is also country evidence showing that prevalence
increases with age in the four countries under study for functional difficulties (Wandera et al. (2014) and for other disability measures (CSA
2007; Payne et al. 2013). This finding contributes to fill the considerable gap on the functional status of older adults in LICs (Chatterji
et al. 2015).

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PREVALENCE OF FUNCTIONAL DIFFICULTIES

67

Fig. 4.1 Mean functional score by age. Note The upper limit of age is at 75 due
to small sample sizes beyond that age

4.3.2  SexIn Table 4.1, prevalence for all adults is higher among women thanmen in the four countries. The gender gap in prevalence is the largest
in Malawi where the prevalence of moderate/severe difficulties is 3.5
percentage points higher among women (12.5 for women vs. 8.98 formen). The gender gap is not consistently found in all age groups for all
countries. In fact, it is among adults age 50 and older that there is a gender gap in all countries. It is as large as 10 percentage points for moderate/severe difficulties in Ethiopia (aged 50–64) and Uganda (aged 65+).
Based on the results for all four countries, women overall, but especiallyin older age groups are found to have higher prevalence than men. This
result is consistent with findings on gender differences in disability fromrecent international studies among adults (e.g. Mitra and Sambamoorthi
2014; OECD 2003; WHO–World Bank 2011) and among older adults
in high-income countries (Crimmings et al. 2011), while results of country level surveys and censuses are more mixed. For instance, for Uganda,
the 2014 Census has a higher prevalence for women compared to men(14.5% vs 10%, respectively),6 while for Tanzania, the 2008 Disability
Survey found a rate of 7.8% for both men and women (NBS 2008).

68

S. MitraMore research is needed on the extent of a gender gap in prevalence,on gender differences in the determinants as well as the consequencesof functional difficulties. Several gender-related factors may be at play inthe higher prevalence among women including maternal care, access tohealthcare, domestic violence, HIV/AIDS, and intra-household distribution of resources.
4.3.3   Type of Functional Difficulty
Figure 4.2 provides the distribution of difficulties by type of functionaldifficulty among persons with severe difficulties. Seeing and walking difficulties are the most common types of difficulties among persons withsevere difficulties in all four countries. Hearing and cognitive difficulties are the third or fourth most common types of difficulties in the four
countries. Communication difficulties are the least prevalent difficulties.
A similar breakdown can be found within persons with moderate difficulties in Appendix A1 and persons with moderate and severe difficulties inAppendix A2. Comparing Fig. 4.2 and Appendix A1, seeing difficulties
are more common among persons with moderate difficulties than severe
difficulties.

UgandaSeeingHearing

Tanzania

Walking
Concentrating
MalawiSelfcareCommunication

Ethiopia
0%

50%

100%

Fig. 4.2 Types of functional difficulties among persons with severe difficulties

4

PREVALENCE OF FUNCTIONAL DIFFICULTIES

69

These results above on difficulty types are consistent with results fromseveral other studies in the four countries, although such studies do not
all use the Washington Group short set of questions (e.g., Groce et al.
2014; Loeb and Eide 2004; NBS 2008; Wandera et al. 2014).
4.3.4   Age at Onset
In Tanzania and Uganda, respondents were asked about their age at theonset of the difficulty. Age at onset is important as it could be a determinant of wellbeing. An onset during childhood may impact education due
to barriers to schools, which would affect school outcomes and in turn
economic wellbeing later in life. An age of onset in the 50s would not
impact individual educational outcomes but could still affect economic
wellbeing, for instance, if the person does not retain her job. Figure 4.3
shows the distribution of age at onset in three age groups: birth toage 14, age 15–49, and age 50 and over. In both countries, about halfof onsets took place at age 50 or over. Only 16% and 25% of persons
with severe difficulties had an onset during childhood in Tanzania andUganda, respectively. Information on age at onset is rarely available insurveys so far, so there is little to compare these results to. For Tanzania,
this is overall consistent with results from the 2008 Disability Survey(NBS 2008) showing that functional difficulties arise at various ages.

Tanzania

UgandaBirth to 14
16%

50 and over54%

15 to 4930%

50 and over48%

Fig. 4.3 Age of onset among persons with severe difficulties

Birth to 14
25%

15 to 49
27%

70

S. MitraTable 4.2 Prevalence of functional difficulties by mother’s educational attainment

Prevalence of severe difficulty
Mother had no schooling
Mother had some schooling
Prevalence of moderate and severe difficulty
Mother had no schooling
Mother had some schooling
Functional limitation score
Mother had no schooling
Mother had some schooling

Ethiopia

MalawiTanzania

3.38%***
0.89%

1.61%***
0.74%

5.56%**
1.39%

12.03%***
3.90%

12.44%***
9.60%

20.77%**
8.73%

0.01***(0.00)
0.00(0.00)

0.01***(0.00)
0.01(0.00)

0.03**(0.00)
0.03(0.00)

Sources Author’s calculations based on data described in the text and in Table 4.1 except for Ethiopia
based on Ethiopia Rural Socioeconomic Survey 2013/2014. Notes For Uganda (both waves) andEthiopia (wave 1), data on mother’s education was largely missing. No result can be presented for
Uganda. Estimates are weighted. ***indicates significance at 1% level of the difference compared topersons whose mother had some schooling. Statistical significance is tested with Pearson’s Chi square
test for prevalence and t-test for the functional score. For Tanzania, the category with ‘no school’ in factrefers to individuals with mothers with less than primary education. Standard errors are in parentheses.

4.3.5   Mother’s Educational Attainment
Table 4.2 shows that the prevalence of functional difficulties and thefunctional score are significantly higher for persons whose mother had noschooling. For instance, in Ethiopia, 3.38% of persons whose mother had
no schooling have a severe functional difficulty compared to only 0.89%
for other individuals. This result has been found in at least one otherstudy (Mont et al. 2014).
4.3.6   Healthcare or Rehabilitation Measures Taken
In Malawi, Tanzania, and Uganda, persons who reported at least one
functional difficulty were asked if they took any measure to improve
performance such as using assistive devices (e.g., glasses, braces, hearing aid), medication, surgical operation, spiritual/traditional means.
Figure 4.4 shows the answers of respondents with at least one severe
difficulty. More than 50% of people with severe difficulties do not takeany measure to curb their difficulties. While more than a quarter of

4

PREVALENCE OF FUNCTIONAL DIFFICULTIES

100%

Other measures
Spiritual/traditional

90%

Assistive devices(e.g. glasses, braces,
hearing aid)
Medication
Surgical operation

80%
70%

71

No measure taken

60%
50%
40%
30%
20%
10%
0%

MalawiTanzania

UgandaFig. 4.4 Measure taken to improve performance at activities among persons
with severe difficulties

individuals have used medication, a very small share has used assistive
devices (e.g., glasses, wheelchairs). This could be due to a variety of reasons including the lack of availability of assistive devices or services, ortheir lack of affordability.
More broadly, results in Fig. 4.4 suggest that rehabilitation needs arelarge in Africa and are rarely fulfilled in a healthcare setting (Mulumba
et al. 2014). No significant gender difference is found in the extent towhich individuals took any measure to curb functional difficulties,7
which is different from results in May-Teerink (1999) for Uganda.
This result is consistent with earlier research in Africa and in lowincome settings in general.8 The potential to prevent functional difficulties

72

S. Mitrasuch as seeing and hearing has been noted globally, in LMICs and in
Africa in particular.9 This result points toward the need for secondary prevention in the form of assistive technology, rehabilitation services in lowincome settings that can help curb functional difficulties. The preventionof functional difficulties through assistive technology, rehabilitation or
healthcare needs to receive more attention and resources in human development whether from individual countries or international stakeholders.
The results above are also consistent with a small literature on disparities in access to care across disability status in LMICs. WHO–WorldBank (2011) shows that persons with disabilities face barriers in accessing
care. World Bank (2009) and Trani et al. (2011) show that individuals
with disabilities have a reduced access to healthcare in India and urban
Sierra Leone, respectively.
4.3.7   Transitions Over Time
Disability is often characterized or assumed to be a static phenomenon
but do functional difficulties change over time? This could have implications for the identification of the group of persons with disabilities
and for policies aimed at improving wellbeing for this group. Table 4.3
gives additional prevalence estimates for Ethiopia and Uganda, where
Table 4.3 Prevalence of functional difficulties by severity and trajectory (%)
Ethiopia

UgandaSevereModerate &severeSevereModerate &severe

5.54
3.39
3.17
1.17
2.07

21.16
13.34
11.50
5.58
6.82

6.07
4.15
3.74
1.82
1.92

19.53
12.84
11.2
4.51
6.69

Wave 1 only
2.30
(reduction in difficulty)

8.76

2.34

8.32

Any wave
Wave 1
Wave 2
Both waves
Wave 2 only
(increase in difficulty)

Source Author’s calculations using a balanced panel from Ethiopia Rural Socioeconomic Survey(2011/2012, 2013/2014) and Uganda NPS (2009/2010, 2010/2011). Notes The sample sizes are
7913 for Ethiopia and 5990 for Uganda respectively. These are longitudinal stamples. Other notes from
Table 4.1 apply. Estimates are weighted

4

PREVALENCE OF FUNCTIONAL DIFFICULTIES

73

functional difficulty questions were asked in two waves. Prevalence estimates are close in both waves: for instance, for severe functional difficulties in Uganda, they stand at 4.15 for wave 1 (2009/2010) and 3.74
in wave 2 (2010). However, these prevalence rates for both waves capture in part different people. Indeed, only 1.82% of individuals reporta severe difficulty in both waves in Uganda. There is thus some transitioning in and out of severe difficulties. These transitions may be due
to actual changes in the severity of functional difficulties over time or tochanges in reporting behavior. Perhaps some individuals may get used
to experiencing functional difficulties, especially in the context of aging,and may stop reporting them. Changes between waves could also reflect
some measurement error, as noted by Altman (2001).
This churning is consistent with transitions in disability status foundin the literature in the context of HICs (Burchardt 2000; Burchardt
2003; Burkauser and Daly 1996; Drum 2014; Gannon and Nolan 2007;
Jenkins and Rigg 2003) and in relation to aging (Grundy and Glaser
2000; Maddox et al. 1994). This literature has shown that transitions
into or out of disability status are not rare. A small but growing literature
on disability transitions can also be found in middle-income countries
such as China (e.g., Liang et al. 2001) and Mexico (Diaz-Venegas et al.
2016a, b). In Malawi, Payne et al. (2013) find a relatively high numberof transitions between disability states (none, moderate, severe) using an
SF12 measure of functional status.10
4.3.8   Descriptive Statistics
Table 4.4 shows descriptive characteristics for individuals across functional status. First, it shows the share of respondents who answered forthemselves instead of via a proxy. In Ethiopia, all individuals responded
for themselves while in other countries, the share varies between about
half to 90%. In Malawi, Tanzania, and Uganda, persons with functionaldifficulties are more likely to have responded to questions themselves
perhaps suggesting different reporting behavior for functional difficulties
between self reports and proxy reports.
Table 4.4 indicates that moderate and severe functional difficulties areassociated with a somewhat different profile. In terms of personal factors,
persons with functional difficulties are significantly older and more often
female. With respect to resources, persons with functional difficulties are
more likely to have a mother with no schooling in Ethiopia and Malawi

Self
respondent
Personalfactors:
Age 15-39
Age 40-49
Age 50-64
Age 65+
Male
Resources:
Mother noschooling 1
Structural
factors:
Household:
MarriedHeadhh size

Table 4.4

0.32***
0.20***
0.27***
0.21***
0.48*

0.99***

0.27***
0.12***
0.22***
0.39***
0.48

0.98**

0.93

0.73
0.14
0.09
0.04
0.50

1.00

0.58*** 0.71*** 0.63
0.60*** 0.53*** 0.35
4.25*** 5.42*** 5.88
(0.16)(0.11)(0.34)

1.00

1.00

0.94***

0.35***
0.14***
0.22***
0.29***
0.40***

0.69

0.94

0.75
0.12
0.09
0.04
0.50

0.47

0.50*** 0.61
0.62
0.56*** 0.57*** 0.40
3.43*** 3.37*** 3.87
(0.13)(0.05)(0.02)

0.97***

0.31***
0.08***
0.19***
0.42***
0.43*

0.60

Moderate None

Moderate None

0.81***

0.27***
0.20***
0.26***
0.28***
0.45*

0.91***

0.63

0.67
0.16
0.12
0.05
0.46

0.84

0.36*** 0.53**
0.47
0.53*** 0.59*** 0.40
3.45
3.39*** 3.53
(0.12)(0.06)(0.03)

0.86***

0.16***
0.13***
0.20***
0.51***
0.39**

0.78***

SevereModerate NoneNA

0.36***
0.18***
0.26***
0.21***
0.42***

0.73***

NA

0.76
0.12
0.08
0.03
0.50

0.46

(continued)

0.38*** 0.54*** 0.49
0.51*** 0.57*** 0.31
7.81*** 7.78*** 8.46
(0.45)(0.22)(0.08)

NA

0.28***
0.09***
0.25***
0.38***
0.46

0.58***

SevereSevereSevereModerate None

Malawi

Ethiopia

UgandaS. MitraDescriptive Statistics of sample of individuals
Tanzania

74

NA

NA

345

N

8,323

NA

15.34
(0.23)

406

0.93***

29.21(13.58)2,698

0.87***

23.73(4.63)

26,0560.83

22.33(13.58)Moderate None349

0.78***

6.11(0.79)

SevereTanzania

1,028

0.71

5.10
(0.33)

8,275

0.69

5.79
(0.79)

Moderate None330

0.87***

936

0.83***

5,318

0.76

24.85
(0.58)

Moderate None

30.14*** 26.12(2.97)(1.30)

SevereUgandaNotes Notes of Table 4.1 apply. hh stands for household. Table includes sample means and standard errors (between brackets). ***, **, * indicate significance at 1%, 5% and 10% levels respectively of the difference compared to persons with no difficulty. Statistical significance is tested with t-test for continuous variable, Pearson’s Chi square test for binary variables and the Wilcoxon-Mann-Whitney test for ordinal variables (age group). Distance to healthcare
services refers to the distance to the nearest facility in kilometers (health clinic or post or hospital)
1. For Tanzania, this shows the share of individuals with mothers with less than primary education

897

14.74
(0.60)

15.85(1.12)

SevereSevereModerate None

Malawi

Ethiopia(continued)

Distance tohealthcare
services
Rural

Community:

Table 4.4

4
PREVALENCE OF FUNCTIONAL DIFFICULTIES

75

76

S. Mitraand with less than primary schooling in Tanzania.11 Regarding structural factors, persons with functional difficulties tend to live in smaller
households are more often household heads and less often married. No
consistent difference is found with respect to healthcare services. Persons
with severe functional difficulties on average live further away from ahealth clinic but the difference is statistically significant only in Uganda.

4.4   Prevalence at the Household Level
Prevalence estimates at the household level are shown in Table 4.5.
When the focus is on severe difficulties, prevalence estimates standat 8.06% in rural Ethiopia, 3.35% in Malawi, 8.85% in Tanzania, and10.01% in Uganda. Like at the individual level, Malawi is an outlier with
Table 4.5 Prevalence of functional difficulties among households (%)

Severe difficulty
Overall– current wave
– current or later wave
Rural
– current wave
– current or later wave
Urban
– current wave
– current or later wave
Moderate and severe difficulty
Overall– current wave
– current or later wave
Rural
– current wave
– current or later wave
Urban
– current wave
– current or later wave

Ethiopia

MalawiTanzania

Uganda

NA
NA

3.35
NA

8.85
NA

10.01
14.41

8.06
12.60

3.68
NA

5.64
NA

11.05
15.28

NA
NA

1.5
NA

10.74
NA

5.72
10.85

NA
NA

21.96
NA

29.80
NA

37.18
44.76

26.42
38.51

22.64
NA

25.60
NA

36.87
47.42

NA
NA

18.07
NA

32.35
NA

24.38
33.83

Notes NA indicates not available. For each country the current wave refers to the one listed in Table 4.1.
For Ethiopia, the later wave is Ethiopia Rural Socioeconomic Survey (2013/2014). For Uganda, thelater wave is Uganda National Panel Survey (2010/2011). For Ethiopia, the current wave covers rural
areas only, while the later wave also covers small towns. Hence, estimates for urban areas are not available for Ethiopia. Estimates are weighted

4

PREVALENCE OF FUNCTIONAL DIFFICULTIES

77

a lower household prevalence estimate compared to the other three
countries where one in 10–12 households has at least one severe functional difficulty. Functional difficulties of any degree affect between one
in five households in Malawi (21.80%) to more than one in three households in Uganda (34.4%). Functional difficulties of any degree thus seem
relatively common among households. There is no consistent pattern
across rural and urban areas. Prevalence is higher in rural areas in Malawi
and Uganda but the opposite is true in Tanzania.
For Ethiopia and Uganda, where longitudinal data on functional difficulties is available, Table 4.5 also presents prevalence estimates for functional difficulties in any wave, leading as expected to higher rates: for
instance, 12.60% and 14.41% of households have an adult with a severe
difficulty in at least one wave in rural Ethiopia and in Uganda, respectively, with thus an increase in the prevalence rates of 4 percentage points.
Table 4.6 provides prevalence rates by household economic status. The Malawi, Tanzania, and Uganda datasets have information on
Table 4.6 Prevalence of functional difficulties among households and economic inequalities (%)

Severe difficulty
Below $1.90 poverty line
– current wave
– current wave or later wave
At or above $1.90 poverty line
– current wave
– current wave or later wave
Moderate and severe difficulty
Below $1.90 poverty line
– current wave
– current wave or later wave
At or above $1.90 poverty line
– current wave
– current wave or later wave

MalawiTanzania

Uganda4.06***
NA

15.80***
NA

12.03**
15.98

2.58
NA

8.64
NA

8.1
12.94

22.26
NA

36.59***
NA

36.3
46.67

21.54
NA

30.9
NA

32.65
42.96

Notes No result is available for Ethiopia due to a lack of data on consumption expenditures. For eachcountry the current wave refers to the one listed in Table 1 (Notes). For Uganda, the later wave is Uganda
National Panel Survey (2010/2011). Estimates are weighted. Per capita expenditures is total household
expenditures divided by adult equivalent. ***, **indicate significance at 1% and 5% levels respectively of thedifference in prevalence between households below the $1.90 poverty line compared to households at or
above the $1.90 poverty line. Statistical significance is tested with Pearson’s Chi square test

78

S. Mitra

0.07
0.06
0.05
Quintile 1 (poorest)
0.04

Quintile 2
Quintile 3

0.03

Quintile 4

0.02

Quintile 5 (richest)

0.01
0.00
Ethiopia

MalawiTanzania

UgandaFig. 4.5 Mean household functional score by asset index quintile

household consumption expenditures, which makes it possible to calculate the poverty headcount using the international poverty line of $1.90.
Severe functional difficulties are more common for households below
the poverty line. For instance, in Uganda, 12.03% of households below
the $1.90 poverty line have an adult with a severe functional difficulty,
compared to 8.1% for households beyond the poverty line. The share ofhouseholds in poverty with an adult with a severe difficulty goes up toalmost 15.98% in Uganda if one includes reports of functional difficulties
in the current or following wave.
By quintile, whether by asset index or per capita consumption expenditure, there is not always a linear gradient of prevalence rates, but prevalence is consistently higher in the bottom quintile compared to the top
one. This is shown in Fig. 4.5 where the mean household functionalscore of each quintile of asset index is plotted for each quintile in eachcountry.
Comparing the poorest and richest quintiles, there is a consistent contrast between the poorest and the richest quintiles in Fig. 4.5.
This is consistent with results in Fig. 4.6, which shows the prevalenceof severe functional difficulties in the poorest and richest quintiles. The
difference is striking in the four countries with a prevalence two to four

4

PREVALENCE OF FUNCTIONAL DIFFICULTIES

79

By asset index quintile
16
14
12
10
8

Poorest quintile

6

Richest quintile

420
Ethiopia

MalawiTanzania

UgandaBy per capita expenditure quintile
16
14
12
10
8

Poorest quintile

6

Richest quintile

420
Ethiopia

MalawiTanzania

UgandaFig. 4.6 Prevalence of severe functional difficulties for the poorest and richest
quintiles (%)

times higher in the bottom quintile compared to the top quintile. For
instance, in Tanzania, 14% of households in the bottom asset index quintile have a severe functional limitation, compared to 5% in the top quintile. As noted by Grech (2015), there is a common guess-estimate that
one in five of the poorest people have a disability. Defining the poorest

80

S. Mitraas those in the bottom quintile, prevalence estimates in Fig. 4.6 are
below this guess-estimate for severe functional difficulties but above inAppendix A3 for moderate or severe difficulties. For Tanzania, for example, one in seven of the poorest have a severe functional difficulty andone in three have a severe or moderate difficulty. Other countries’ estimates are close to the Tanzania estimates (Fig. 4.6 and Appendix A3).12
This result is consistent with results from Hosseinpoor et al. (2013)using an asset quintile, a disability measure similar to that in WHO–
World Bank (2011) and 2002–2004 World Health Survey data for 49
countries, including Ethiopia and Malawi.
Table 4.7 gives descriptive statistics of households across functional
status. It shows that households with functional difficulties have different
characteristics in terms of structural factors. Households with functionaldifficulties have heads who tend to be older and less often female or married. They are significantly smaller households and tend to have more
older or female members. They are also more likely to be in rural areas.
For these characteristics, significant differences are found between households with severe or moderate functional difficulties, on the one hand,and households with no difficulty, on the other. However, the differences
are larger for households with severe vs. moderate functional difficulties.

4.5  Conclusion: Summary and Implications
This chapter has several noteworthy results on disability prevalence for
Ethiopia, Malawi, Tanzania, and Uganda.
1. The prevalence of moderate and severe functional difficulties
among adults ranges from 10.8 to 15.1%, while the prevalence ofsevere difficulties alone spans 1.4–3.9%.
2. In the four countries, prevalence of functional difficulties at thehousehold level ranges from one in five to one in three households.
Overall, this chapter shows that functional difficulties affect sizeable
shares of individuals and households in Ethiopia, Malawi, Tanzania, andUganda and thus require policy and research attention.
3. Persons with functional difficulties are a diverse group in terms of
demographics (age, sex) but also with respect to age at onset, typeof functional difficulty, and severity.

Severe57.39***(1.48)
Head is mar0.68***
ried
Head is male 0.71***
Head has
0.24***
no formal
education
hh size
4.34***(0.18)
Share of hh
0.30***
members(0.02)
under age 15
Share of hh
0.24***
members(0.02)
over age 60

Structural
factors:
Household :Age of head
43.37
(0.36)
0.83
0.82
0.44

5.24
(0.05)
0.44(0.01)
0.07(0.00)

51.48***(0.76)
0.77***

0.77***
0.34***

5.38(0.12)
0.40***(0.01)

0.13***(0.01)

0.28***(0.02)

4.31(0.16)
0.27***(0.02)

0.63***
0.44***

58.06***(1.31)
0.58***

Moderate None Severe

MalawiHousehold descriptive statistics

Ethiopia

Table 4.7

0.22***(0.10)

4.25**(0.06)
0.32***(0.01)

0.68***
0.27***

52.89***(0.48)
0.64***

0.06(0.00)

4.47(0.03)
0.40(0.00)

0.78
0.22

0.29***(0.03)

3.37***(0.15)
0.23*(0.02)

0.51**
0.41***

39.39 60.11***(0.19) (1.27)
0.75
0.45

Moderate None Severe

Tanzania

0.18***(0.02)

3.22***(0.08)
0.25***(0.01)

0.72***
0.28**

53.32***(0.79)
0.54***

0.06(0.01)

2.74(0.05)
0.30(0.01)

0.76
0.20

42.32
(0.44)
0.49

Moderate None

0.25***(0.02)

7.00***(0.51)
0.35***(0.02)

0.62
0.75***

60.67***(1.13)
0.54***

SevereUganda0.06(0.00)

5.78(0.09)
0.43(0.01)

0.77
0.73

40.13
(0.78)
0.78(continued)

0.15***(0.01)

6.42***(0.19)
0.37***(0.01)

0.63
0.62***

51.01***(0.77)
0.63***

Moderate None

4
PREVALENCE OF FUNCTIONAL DIFFICULTIES

81

0.99

0.99

657

15.40
(0.73)

15.64(1.16)

295

0.49***(0.01)

0.47***(0.02)

SevereMalawi

2,710

1.00

15.86
(0.44)

0.50(0.01)

359

0.94

20.71
(10.64)0.43***(0.02)

Moderate None Severe(continued)

Ethiopia

2,011

0.87***

26.95(5.97)

0.45***(0.01)

8,591

0.84

21.30
(2.53)

0.50(0.00)

226

0.77***

6.08(0.66)

0.45***(0.02)

Moderate None Severe

Tanzania549

0.68***

4.54(0.35)

0.48***(0.01)

1,701

0.65

5.54
(0.32)

0.51(0.01)

Moderate None275

0.882**

29.90(3.52)

0.50***(0.02)

SevereUganda639

0.85***

27.95(1.73)

0.48*(0.01)

1,557

0.77

25.48
(1.17)

0.50(0.01)

Moderate None

Notes Table includes sample means and standard errors (between brackets). hh stands for household. ***, **, * indicate significance at 1%, 5% and 10%
levels respectively of the difference compared to persons with no difficulty. Statistical significance is tested with t-test for continuous variable and Pearson’s
Chi square test for binary variables. Distance to healthcare services refers to the distance to the nearest facility in kilometers (health clinic or post or hospital)N

Share ofmale hhmembers
Community:
Distance tohealthcare
services
Rural area

Table 4.7

82
S. Mitra4

PREVALENCE OF FUNCTIONAL DIFFICULTIES

83

4. There are also some patterns. They tend to affect older individuals more, as well as women more often than men. Seeing andwalking limitations are the most prevalent limitations in the four
countries. A majority of individuals do not take any measure to
reduce their functional difficulties, suggesting there may be scopefor prevention.
5. 
There is a strong socioeconomic gradient in prevalence. In fact,comparing the poorest and richest quintiles based on an asset index,
prevalence is two to four times higher in the poorest quintile.
More research on gender is needed given the higher prevalence foundamong women in this study and in other studies. Functional difficulties are significantly associated with aging in the four countries. Moreresearch is also needed on older adults in less-resourced settings, forwhom, little is known on health and wellbeing. These results overall suggest that functional status needs to be considered and included as part ofaging, gender, public health, and broadly as part of human development
policy and research.
The estimates in this book are of course not the final word on disability prevalence in Ethiopia, Malawi, Tanzania, and Uganda. They
likely offer a lower bound estimate of prevalence given that only six
functional difficulties are measured. More data collection efforts areneeded to inform policy further. For instance, data using the extended
set of questions of the Washington Group would offer information on
mental health related functional difficulties (e.g., Loeb 2016). Surveys
that can collect detailed information on the environment would provide
information to help understand the determinants of functional difficulties. Because functional difficulties affect sizeable shares of individuals
and households in the four countries under study, a study of the association and causal links between such difficulties and wellbeing inequalities
is thus warranted and is conducted in the rest of this book.

Notes
1. Each answer is on a scale of 1–5: (1) no difficulty; (2) mild difficulty; (3)moderate difficulty; (4) severe difficulty; (5) extreme difficulty/unable to
do. The disability score aggregates all answers, including mild and moderate and ranges from zero to 100. An Item Response Theory approach
using a Rasch model was applied to construct the disability score. It is

84

S. Mitracompared to a threshold so as to identify who experiences a significant
disability. This threshold was set at 40, which is the average of the disability scores of people who report at least one extreme limitation on anyof the items and/or a chronic health condition (e.g., asthma, arthritis,
diabetes, depression) explaining that ‘such chronic diseases are associatedwith disability, it is justifiable to use them as indicator conditions for estimating the average levels of functioning across all the chronic conditions
that were assessed in the WHS, in order to set a meaningful threshold.’(WHO–World Bank 2011).
2. Disability prevalence estimates that are not standardized are available in
the working paper version of Mitra and Sambamoorthi (2014) available
at: http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2329676.
3. Ethiopia is not unique in the vast range of disability estimates it receives.
Another example among the countries under study is Malawi: 3.8% using
the 2008 Census (NSO 2010); 4.2% as per Loeb and Eide (2004); 14%
in WHO–World Bank (2011).
4. It should be noted that prevalence estimates had very low standard errors
producing very narrow confidence intervals, which are not presented.
5. The median is zero and is therefore not used.
6. 
A number of other studies in LMICs also find a higher prevalence
among women compared to men (Mexico: Diaz-Venegas et al. 2016a;
Bangladesh: Moniruzzaman et al. 2016).
7. Sex-disaggregated results are not reported here.
8. 
See for instance, Borg et al. (2011), Eide and Øderud (2009), MayTeerink (1999), Magnusson et al. (2013), Harniss et al. (2015),
McPherson (2014), WHO (2011).
9. Global Burden of Disease collaborators Study 2016; Kulua et al. (2011),
Muller et al. (2011), Lewallen and Courtright (2001).
10. SF12 is a twelve-item functional health measure. More information can befound in Burdine et al. (2000).
11. This information is not available in Uganda.
12. This result also holds using per capita expenditures, with for instance inMalawi 5.17% with severe functional limitations in the lowest quintile and2.78% in the highest quintile (this result is not shown in Tables/graphs).

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CHAPTER 5

Functional Difficulties and Inequalities
Through a Static Lens

Abstract This chapter is about the association between disability andinequalities. Results from both descriptive statistics and regressions indicate that functional difficulties have significant and large associations
with both individual and household deprivations in Ethiopia, Malawi,
Tanzania, and Uganda. This is found through an indicator-by-indicatoranalysis as well as through an assessment of multidimensional poverty.
There are four wellbeing dimensions for which functional difficulties
were systematically associated with deprivations in the four countries:
education, morbidity, employment, and economic security. Some persons
with functional difficulties do achieve levels of wellbeing comparable topersons with no difficulty. The association between functional difficulties
and deprivations was found for both severe and moderate functional difficulties, although it was typically larger and more often significant for
the former.
Keywords Disability · Poverty · Multidimensional poverty · Gender ·Aging · Africa
JEL I1 · I3 · O15© The Author(s) 2018
S. Mitra, Disability, Health and Human Development,
Palgrave Studies in Disability and International Development,
DOI 10.1057/978-1-137-53638-9_5

89

90

S. MitraThis chapter investigates the association between functional difficulties
on the one hand, and different deprivations, on the other. For Ethiopia,
Malawi, Uganda, and Tanzania, it uses cross-sectional LSMS data and thus
draws a static snapshot of this association. Framing this question within
the human development model, the objective is to assess the association
between functional difficulties and deprivations measured in terms of educational attainment, morbidity, employment, material wellbeing, economic
security and through the experience of multidimensional poverty.

5.1   Literature on Inequalities Associatedwith Disability
Globally, the evidence on the wellbeing inequalities associated with disability is limited, although the situation greatly differs between HICs andLMICs. Most of the evidence pertains to HICs. Overall, in HICs, theevidence suggests that persons with disabilities have lower educational
attainment and experience lower employment rates, lower wages whenemployed, and are more likely to be income poor (Brucker et al. 2015;
Grammenos 2013). They are also more likely to be chronically poor
(She and Livermore 2009). In LMICs, there is very limited empiricalresearch on disability and poverty or deprivations in general (Groce et al.
2011; Grech 2015; Banks and Polack 2014). The peer-reviewed literature, while still small, has recently grown. The literature review below islimited to deprivations in dimensions of wellbeing later analyzed in thischapter and to peer reviewed papers published since 2000.1 The qualitative evidence that gives space to the voices and perceptions of persons
with disabilities is beyond the scope of this review.2
5.1.1   Material Wellbeing
This section starts with material wellbeing, typically measured throughconsumption expenditures, assets, and living conditions. There has notbeen consistent evidence of material deprivations for households withdisabilities relative to other households. Hoogeveen (2005) (Uganda)
and Mont and Cuong (2011) (Vietnam) find that households withdisabilities have lower expenditures than households without, but
Rischewski et al. (2008) (Rwanda) does not. A cross-country study of
LMICs (Filmer 2008) finds that in eight out of 12 countries, disability
in adulthood is associated with a higher probability of being in poverty,

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91

where poverty refers to belonging to the lowest two quintiles in terms ofhousehold expenditures or asset ownership. Another cross-country study(Mitra et al. 2013) finds a significant difference in household per capita
expenditures across disability status in only three out of 15 LMICs.
There are, however, challenges in using household expenditures toassess the wellbeing of households with disabilities, as they may reflect
additional expenditures associated with a disability (NDSD 2015). These
expenditures may relate to general items that any household may need(e.g., healthcare, food) as well as to disability-specific items (e.g., assistive devices, rehabilitation), although this is perhaps less of a concern in
the LICs under consideration in this study where disability-specific goods
and services may not be available. Having similar or higher expendituresat the household level across disability status does not necessarily imply
that the standard of living is similar. This empirical concern regarding theuse of household expenditures is related to the conversion function andits particular relevance to disability, as discussed earlier in Chapter 2.
With respect to asset ownership, several studies show that householdswith disabilities have fewer assets and worse living conditions comparedto other households.3 However, Eide et al. (2003a) and Trani and Loeb(2010) find no significant difference in Zimbabwe and Afghanistan/
Zambia, respectively. Mitra et al. (2013) find a significant difference inthe rate of asset deprivation in only four of 15 LMICs.
5.1.2   Educational Attainment
There is extensive and consistent evidence that adults with disabilities
have lower educational attainment in a number of LMICs.4 This association consistently found among adults may result from lower school
attendance among children with disabilities (Filmer 2008; Mizunoya
et al. 2016), but may also be due to more frequent onsets among adults
with limited educational attainment because they are more exposed tomalnutrition, lack of access to healthcare, and risky working conditions.
5.1.3  Employment
How disability may impact employment is an empirical question, andrealities in LMICs may differ from HICs. In an agrarian economy, as isoften the case in LICs, many jobs are in the primary sector (agriculture,
forestry, mining) and may involve heavy manual labor, which people with

92

S. Mitraphysical difficulties may not be able to do. People with hearing or cognition difficulties, on the other hand, may not experience barriers to physical labor. The effect of disability on employment will also depend on theworkplace, its accessibility, available accommodations and transport, andwhether there is discrimination that might prevent access to employment
and/or might lead to lower wages (Baldwin and Johnson 2005; Mitra andSambamoorthi 2008). The policy context is also relevant; for instance,
vocational rehabilitation, disability insurance, or social assistance programs could facilitate, limit or not affect access to employment for persons
with disabilities depending on how they are designed and implemented.
In some LMICs (e.g., South Africa), social protection benefits have been
introduced to provide financial support to persons with disabilities.
Several studies in LMICs find that persons with disabilities are lesslikely to be employed.5 In a study of 15 countries, Mizunoya and Mitra(2013) have results that are somewhat mixed with a significant disability gap in employment rates in nine countries out of 15. In these ninecountries with a disability gap, the size of the gap varies greatly acrosscountries.
Finally, it should be noted that not working may not be an option.
So people may be begging or selling small items on the roadside earning very little but working. Hence, the type of employment needs to beconsidered. In most LMICs, a large majority are in the informal sector.
Some studies have shown that persons with disabilities are disproportionately more likely to be working in the informal sector than persons without disabilities (e.g., Adioetomo et al. 2014; Mizunoya and Mitra 2013).
5.1.4   Morbidity and Healthcare Expenditures
Disability is associated with a wide range of health conditions (WHOWorld Bank 2011); some of which may result in morbidity and highhealthcare needs. These may lead to higher health expenditures. Trani
and Loeb (2010) also show that on average, ‘persons with severe orvery severe disabilities spent 1.3 times more on healthcare than nondisabled respondents’ (p. 36). Mitra et al. (2013) show that householdswith disabilities have a higher ratio of medical to total expenditures innine out of 15 countries while WHO-World Bank (2011) finds that persons with disabilities are more likely to experience catastrophic healthexpenditures.

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93Overall, in LMICs, there is not a consistent overall pattern of evidence
on disability and deprivations. The evidence thus far points toward individuals with disabilities being worse off in terms of educational attainment, morbidity, and health expenditures, while in terms of employment
and household material wellbeing, the evidence is more mixed.
5.1.5   Multidimensional Poverty
The literature review so far considered inequalities in one dimension of
wellbeing at a time. Recently, several studies have found that disability is
associated with a higher likelihood of experiencing multidimensional poverty (Mitra et al. 2013; Trani and Cunning 2013; Trani et al 2015, 2016).
These deprivations can be in terms of employment, health, education,material wellbeing, social participation or psychological wellbeing. This
growing literature has provided consistent evidence that in LMICs, disability is correlated with the experience of multidimensional poverty whilethe very nature of deprivations may vary across countries. For instance, it
could be in terms of employment and healthcare access in one country,
but in terms of educational attainment and living conditions in another.
This consistent association between disability and multidimensional
poverty comes in contrast to the more mixed evidence on disability andmaterial wellbeing. This literature, however, remains small and so far
separate from the growing general research on multidimensional poverty.
The MPI offers a measure of the experience of simultaneous deprivations
at the household level and is increasingly used in policy and research(Alkire and Santos 2014). It is yet to present separate results for households with disabilities.
5.1.6  Overview
Deriving any definitive conclusion on inequalities across disability status isproblematic in this literature with varying measures for disability, wellbeing indicators, data sources, and methodologies. First, studies use different methods: some studies only present means and frequency counts ofeconomic indicators across disability status (e.g., Hoogeveen 2005), whileother studies resort to multivariate analysis using a variety of empiricalstrategies which can be difficult to compare.6 Some studies measure disability through functional difficulties (e.g., Mont and Cuong 2011), whileothers use broad activity limitations (e.g., Mitra 2008). Several of these94

S. Mitrastudies (Mitra et al. 2013; Mizunoya and Mitra 2013; WHO-World Bank
2011) rely on the World Health Survey (WHS) that was designed to collect a detailed health and disability profile of individuals but provides only
summary measures of economic wellbeing, for instance, on employment
and household expenditures. Besides, not every individual in a householdin the WHS was interviewed, only one individual per household. Hence,differences across disability status may be underestimated for household
wellbeing indicators. Finally and more importantly, results vary acrosswellbeing dimensions, making the evidence mixed. It could be read in different ways. Someone relying on traditional poverty measures based on
consumption expenditures or asset ownership data will not find any consistent significant association between disability and poverty. Someone relying on multidimensional poverty measures will. This is surprising given theconsistent evidence found in HICs, whatever the measure of poverty.
As a result, despite a growing body of research on disability-related
inequalities in LMICs, more work is needed with internationally comparable and tested disability measures and detailed economic indicators
suitable to the LMIC context to understand disability and inequalities.
Research is particularly needed in the context of LICs. Mizunoya and
Mitra (2013) note that the six countries in this study that do not have adisability gap in employment are LICs, while only two of the nine countries with a disability gap (Bangladesh and Burkina Faso) are in the lowincome category. This is consistent with the results on multidimensional
poverty in Mitra et al. (2013). In both studies, the authors hypothesizethat economic inequalities associated with disability may be more common in middle-income countries compared to LICs because as countries
develop, there may be growing barriers to employment and economic
activities for persons with disabilities. It could also be that disability is
associated with premature mortality in LICs, more so than in middleincome countries, which would drive down the association between
disability and economic deprivations. This chapter aims to fill some ofthese gaps in the literature by offering evidence for Ethiopia, Malawi,
Tanzania, and Uganda.

5.2   Methodology
This Chapter presents for several indicators of wellbeing at the individual and household levels bivariate and multivariate analyses to investigate the association between functional difficulties and wellbeing in a

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95

number of domains. For this analysis, as described in Chapter 3, thefollowing datasets are used: the 2010/11 Malawi Third Integrated
Household Survey, the 2010/11 Tanzania National Panel Survey, the2011/12 Ethiopia Rural Socioeconomic Survey and the 2009/10
Uganda National Panel Survey. The measures of moderate or severefunctional difficulties and the functional score are as explained earlier in
Chapter 3.
5.2.1   Wellbeing Indicators
The household and individual wellbeing indicators analyzed in this chapter are presented in Table 5.1. As explained in Chapter 3, they were chosen based on a review of the datasets and guidance from Stiglitz et al.
(2009) for a list of dimensions of wellbeing. In the four countries, anindex of assets and living conditions is used (Filmer and Pritchett 2001).
Assets include ownership of a bike, a car, a refrigerator, a fixed-line telephone, a cell phone, a television set, and a computer. Living condition
variables include building quality (high-quality floor and wall materials),water source (from pipes, from protected wells, and from unprotectedsources), type of toilet (flush, latrine, other/none), and use of a gas orelectric cooking stove.7 The index is normalized to range from zero to100 (Table 5.1).
For Malawi, Tanzania, and Uganda, a comprehensive range ofannual expenditure variables are analyzed: total, total nonhealth,health,8 and education.9 In Ethiopia, expenditures were collected only
on food items, so these expenditures-based indicators cannot be used.
The monetary poverty status of the household is determined using theinternational $1.90 poverty line. Detailed income data from earned
and unearned sources is not available in the four countries, but data on
income received from social protection transfers is. Social Protection
transfers include assistance received by the household from government or nongovernment institutions (such as church). Two measures of economic insecurity are also used. One covers food insecurity;
it measures whether the household faced a situation where it did nothave enough food. The other one measures if the household has experienced a shock recently.
Several issues should be noted with regard to using household (nonhealth) expenditures as a dimension of economic wellbeing in thecontext of this study. First, as pointed earlier, if poverty is measured

96

S. MitraTable 5.1 Aspects of wellbeing and indicators
Wellbeing

Indicators

Household level:
Assets/Living conditions Asset index1
Household Consumption Household’s daily per capita expenditures (PCE) is under$1.90 a day
Total expenditures: Annual expenditures for the household
and in local currency
Total expenditures (nonhealth): Annual expenditures for thehousehold and in local currency
Health expenditures: Annual expenditures for the household
and in local currency
Education expenditures: Annual expenditures for the household and in local currency
IncomeSocial protection transfers for the household in local currency
per year2
Food insecurity
In the past 12 months, household respondent faced with asituation when did not have enough food to feed the household.
ShocksHousehold experienced a shock recently3
Individual level:
Educational attainment
Ever attended school
Morbidity
Sick or injured recently4
Work status
Worked in the past week5
Hours worked6
Type of work (family farm, family business, wage work, otherwork)
Notes 1See text for details on the asset index
2Social protection transfers refer to assistance received by the household from government or nongovernment (such as church) institutions
3Each household was asked about shocks experienced in the past 12 months in Ethiopia, Malawi and
Uganda and in the past 5 years in Tanzania. For each country, the question was asked for a list of under
20 types of shocks (e.g. drought or floods, livestock died or stoken, loss of land). A variable was constructed to indicate that a household experienced at least one shock
4For Malawi and Uganda, the question refers to an ‘illness or injury’ in the past 2 weeks and 30 days
respectively. For Ethiopia, the question refers to ‘a health problem’ in the past 2 months. For Tanzania
only, the question asks if the person visited a healthcare provider in the last 4 weeks: there is no question
on recent health problem or illness/injury
5Work indicates if an individual worked in past 7 days or did not work in past 7 days but has a job toreturn to. Work can be of any type for pay, profit, barter or home use and also includes apprenticeships
6Hours worked refer to hours worked in the past week among individuals who worked in the past week.
In all countries but Uganda, individuals were queried about hours worked by type of work (e.g. farm,business). In Uganda, work hours were asked for each day of the past week for the individual’s main job
and secondary job

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through per capita expenditures (PCE) against a poverty line, the comparison of households with a functional difficulty to other households
may be biased due to the conversion factors: households with disabilities may have additional (nonhealth) needs and hence expenditures(e.g., transportation, personal assistance) due to the functional difficulty. Evidence on the additional costs of living with a disability is available only in very few LMICs.10 Second, there is the possibility that the
intra-household distribution of expenditures is unequal across functional difficulty status. For these two reasons, PCE may not be an accurate indicator of economic disparities across functional difficulty status.
In contrast, assets or living conditions, at least the ones included inthis study as described earlier, can be, to a larger extent, considered ashousehold common goods, so the issue of intra-household distributionis less likely to arise.
Several individual wellbeing indicators are also assessed. Educational
attainment is used with an indicator of whether an individual everattended school. Morbidity is captured by a question asking persons
whether they recently experienced a health problem (illness or injury).
However, for Tanzania, the question asks if the person visited a healthcare provider in the last 4 weeks; there is no question on recent healthproblem or illness/injury. Three labor market outcomes are analyzed:
work status, hours worked during the last week, and work type. Workstatus refers to working in the past week or having a job to return to.
Work types include working at a family-owned farm or business, a wagejob (working for wage, salary or commission) or some other type ofwork (unpaid family worker or apprenticeship). For Ethiopia, Malawi,
and Tanzania, individuals report hours of work during the past week intheir first and second job or by type of job; these hours across jobs areadded up to get weekly hours of work. For Uganda, daily hours of work
are available for each day of the past week, which are added up.
Overall, and in the context of the human development model of
Chapter 2, one kind of health deprivations (functional difficulties) is considered in its association with other deprivations related to education,
morbidity, work, material wellbeing, and economic security.
5.2.2   Multidimensional Poverty
In addition to an indicator-by-indicator analysis, this study estimates a setof measures of multidimensional poverty developed by Alkire and Foster

98

S. Mitra(2011) to investigate the experience of simultaneous deprivations. This
is in line with a multidimensional understanding of wellbeing and poverty in the capability approach in general, and in the human development model, in particular. In brief, this method counts deprivations for aset of dimensions that affect an individual at the same time. An individual is considered multidimensionally poor if the number of deprivationsof the individual is equal or above a set threshold. Three multidimensional poverty measures are calculated. The poverty headcount H gives
the percentage of the population who are multidimensionally poor. The
average deprivation share A gives the share of deprivations experienced
by the poor out of all of their dimensions. The adjusted headcount ratio
M0 is the product of H and A; in other words, it is the headcount ratio
adjusted for the intensity of the deprivations experienced by the poor. It
is on a continuum from 0 to 1. Details on the calculation of this set ofmeasures are included in Box 2.
Box 2: The Alkire and Foster (2011) Multidimensional Poverty
Measures

Dimensions are weighted: wj is the weight of dimension j. Eachindividual i has a weighted count of dimensions where that person
is deprived (ci) across all measured dimensions: 0 ≤ ci ≤ d where dd
is the number of dimensions; ci =wj cij with cij a binary variaj=1

ble equal to one if individual i is deprived in dimension j, and zerootherwise. Let qi be a binary variable equal to one if the person
is identified as poor, and to zero otherwise. A person is identified
as multidimensionally poor if the person’s count of deprivations isgreater than some specified cutoff (k):if ci ≥ k, then qi = 1
if ci < k, then qi = 0
The headcount ratio for a given population is then the numberof poor persons (q = Σqi) divided by the total population (n):
q
(5.1)
H=
n
To capture the breadth of deprivations experienced by the multidimensionally poor, in other words, the experience of deprivation inseveral dimensions, the average number of deprivations that a multidimensionally poor person faces is computed. The total number

5

FUNCTIONAL DIFFICULTIES AND INEQUALITIES …

99

of deprivations experienced by multidimensionally poor people c(k)is calculated as follows: c(k) = Σ(qici) for i = 1…n. The average
deprivation share is the total number of deprivations of the disadvantaged (c(k)) divided by the maximum number of deprivations
that the deprived could face (qd):
c(k)
A=
(5.2)
qd
The adjusted headcount ratio, M0, combines information on
the prevalence of multidimensional poverty and its breadth, as the
product of the headcount ratio and average deprivation share:c(k)M0 = HA =nd(5.3)
It is important to note that this method has a number of limitations.
First, the three measures above are a function of the weights allocatedarbitrarily to dimensions. Thus, any poverty calculation using this framework is sensitive to the assumptions used in setting weights. Second,
this method is sensitive to the selection of dimensions, and there is no
guidance on how to select them. Furthermore, this method also requiresthat a cutoff is set for each dimension/indicator. Deciding on a specificcutoff point is an arbitrary choice, although it can be an informed one.
The cutoff across dimensions—the share of dimensions whereby one
needs to experience deprivation—also needs to be specified. As noted in
Alkire and Foster (2011), setting the cutoff points ‘establishes the minimum eligibility criteria for poverty in terms of breadth of deprivation
and reflects a judgment regarding the maximally acceptable multiplicity
of deprivations’ (p. 483). This judgment is based on expert opinion andseems particularly difficult to make in a cross-country study such as thisone. Since multidimensional poverty measures require assumptions forthe selection of dimensions, weights, and thresholds, these assumptions
are described in detail below.
Based on the information available in the datasets above and theguidance of Stiglitz et al. (2009), five dimensions were selected for the
calculation of the multidimensional poverty measure as presented inTable 5.2. The five dimensions include three dimensions of individual
wellbeing—education, health and personal activities (work)—and two
dimensions at the household level material wellbeing and economic

Individual is not working

Individual suffered from an illness or injury recently

Individual has less than primary schooling

Food insecurity
Shock

In the past 12 months, household respondent was faced with a situation where did not have enough food to feed the household.
Household experienced at least one shock in the past 12 months.

The household’s sanitation facility is not improved, or it is improved
but shared with other households.
Water
The household does not have access to safe drinking water or safe
drinking water is more than a 30-min walk from home, round trip.
Cooking fuel The household cooks with dung, wood, or charcoal.
Electricity
The household has no electricity.
Flooring
The household has a dirt, sand, or dung floor.
Assets
The household does not own more than one asset (among radio, TV,
telephone, bike, motorbike, or refrigerator) and does not own a car ortruck.

Sanitation

Work status

Morbidity

Educational
attainment

Notes 3, 4 and 5 of Table 5.1 apply

Insecurity

Material wellbeing

Personal activities

Health

Education

1/5

1/5

1/5

1/5

1/5

1/10

1/10

1/30
1/30
1/30
1/30

1/30

1/30

1/5

1/5

1/5

Indicator
WeightDimension

Dimension
WeightTable 5.2 Dimensions, indicators and weights in the multidimensional poverty measureThreshold: Deprived if…

S. MitraIndicator(s)

100

5

FUNCTIONAL DIFFICULTIES AND INEQUALITIES …

101

insecurity. In the context of the human development model, the multidimensional poverty measures capture deprivations in terms of functionings, not capabilities. These functionings are related to material
wellbeing, economic security, education, morbidity, and work (Fig. 3.1).
The five dimensions are equally weighted and when more than one
indicator is used within a dimension, indicators are equally weighted
within the dimension. An individual is identified as multidimensionallypoor if he or she is deprived in some combination of indicators whose
weighted sum exceeds 40%.
The within dimension indicator cutoffs are given in Table 5.2.
The selection of indicators and cutoffs was based on a review of theliterature measuring the wellbeing dimensions above. As shown in
Table 5.2, for household material wellbeing, six indicators are used
for assets and living conditions similar to Alkire and Santos (2010),
and for household insecurity, two indicators are used: food insecurity
and exposure to a shock.11 Each of the other dimensions uses only
one indicator. The cutoffs for the indicators are as follows: if a person (1) has less than primary schooling; (2) has been sick or injured
recently; (3) does not work; (4) The household’s sanitation facility
is not improved, or it is improved but shared with other households;(5) The household does not have access to safe drinking water orsafe drinking water is more than a 30-min walk from home, roundtrip; (6) The household cooks with dung, wood, or charcoal; (7) The
household has no electricity; (8) The household has a dirt, sand, or
dung floor; (9) The household does not own more than one asset(among radio, TV, telephone, bike, motorbike, or refrigerator) anddoes not own a car or truck; (10) In the past 12 months, household
respondent faced with a situation when did not have enough food tofeed the household; (11) Household experienced at least one shockin the past 12 months. One could argue that some of the thresholds
may not capture deprivations. For instance, not working is considered
as a deprivation while it may not be, if no or limited decent work isavailable.
For each of the wellbeing indicators described above, descriptive statistics are presented and include cross-tabulations for each indicator
across functional difficulty status. Multivariate regression analysis is also
used.

102

S. Mitra5.2.2.1 Multivariate Analysis
A linear relationship is specified in which a wellbeing indicator is afunction of functional difficulties, individual, household, and community characteristics. For each of the individual wellbeing indicators in
Table 5.1 and the multidimensional poverty status described above, amodel is used as described in Box 3.
Box 3: Multivariate regression of wellbeing

An OLS or a logistic regression is run in turn for individual wellbeing outcomes as follows:

IndivWellbeingi = α + β1 Severei + β2 Moderatei +γk xi,k + εi

k

(5.4)
where
- IndivWellbeingi is an individual wellbeing outcome for person
i which is in turn: ever attended school, sick or injured in the pastmonth, work and work hours (definitions are in Table 5.1).
- α is the intercept;- Severei is a variable equal to 1 if individual i has a severe functional difficulty, 0 otherwise;- Moderatei is a dummy variable equal to 1 if individual i has amoderate functional difficulty, 0 otherwise;- β1 and β2 are the coefficients of the functional difficulty variables, to be estimated;
They are the coefficients of interest and their values are reported
for each country in Table 5.4.
– xi,k is a set of k control variables for personal factors (age categories, male), resources and structural factors (being married, being
the household head, having a mother with no prior schooling,household size, distance to healthcare).12– γk are the estimated coefficients for the set of k control variables
but are not reported in Table 5.4.
– εi is the error term for person i.

5

FUNCTIONAL DIFFICULTIES AND INEQUALITIES …

103

In a variant of (5.4), the functional difficulty variables are replacedby the functional score defined earlier in Chapter 3.
For each of the household wellbeing indicators in Table 5.1, asimilar regression model as (5.4) above is estimated:
HHWellbeingi = α + β1 Severei + β2 Moderatei +kγk xi,k + εi

(5.5)

where

HHWellbeingi is a household wellbeing outcome for household
i which is in turn is: asset score, below the $1.90 per day poverty
line, total expenditures, total expenditures (nonhealth), education
expenditures, health expenditures, social protection transfers, foodinsecurity, shocks (definitions are in Table 5.1).
Other symbols are as above for (5.4) except for the set of control variables at the household level (household head’s age, marital
status, educational attainment, household size, shares of members
under age 15 and over age 60, share of male members13) and thecommunity level (rural, distance to healthcare services).
It is essential to note that the models above suffer from several
important limitations. The first limitation deals with multicollinearity in each of the models. In other words, the control variables arethemselves interrelated. As shown in Chapter 4, functional statusis related to age, gender, and rural residence.14 This, on the other
hand, may lead to biased estimates of the coefficient of the functional status variables in the regressions. More importantly, theabove models suffer from omitted variable bias. For instance, they
do not control for potential confounders, which can affect both
wellbeing indicators and functional status. Possible confounders
include, for example, violence in the community or household,community services (e.g., health and education facilities, roads),
which could affect both functional status and wellbeing indicators. The community control variables (rural residence, distance tohealthcare services) in (5.4) and (5.5) above are a very crude way
to adjust for these potential structural factors at the community
level that may impact household or individual wellbeing as well asfunctional difficulties.

104

S. Mitra5.3  Results and Discussion
Results are presented in a set of tables and graphs using the data
described in Chapter 3 starting with individual, then household wellbeing and finally multidimensional poverty.
5.3.1   Individual Wellbeing
Table 5.3 compares individual outcomes for persons with severe, moderate, or no difficulty for all adults, and then separately for women andmen. In all four countries, individuals with moderate or severe functionaldifficulty have less often ever been to school and are more likely to havebeen sick or injured recently. The gap in educational attainment acrossfunctional difficulty status is large in the four countries. For instance, in
Ethiopia, only 15% and 24% of persons with severe and moderate difficulties, respectively, have ever attended school compared to 48% of persons with no difficulty.
The gap in morbidity is large in Ethiopia, Malawi, and Uganda but
not in Tanzania. This likely results from the different measures used in
Tanzania which is healthcare use and not morbidity.
Individuals with severe difficulties in all four countries are less likely to
be working and have fewer work hours than persons with no difficulty.
There is a large gap in employment rates in all four countries between
persons with severe and no difficulty. The largest is in Tanzania where
53% of persons with severe difficulties work, compared to 85.4% among
persons without any difficulty. This result of a consistent gap in employment rates for severe functional difficulty stands in contrast to the resultsin Mizunoya and Mitra (2013) which found a significant gap in only twoout of eight LICs using a measure of severe functional difficulty in seeing, moving, concentrating, or selfcare. For moderate difficulty, a significantly lower employment rate is found in Uganda only and significantly
lower work hours are found in Ethiopia, Tanzania, and Uganda.
Regarding work type, persons with severe difficulties are less likely to
be in wage work and more likely to do household business work in threeout of four countries. There is no consistent pattern for farm work withpersons with functional difficulties less often in farm work in Ethiopia
and Malawi, more often in Tanzania. No significant difference is foundin Uganda.

Ever attended
schoolSick or injured inpast month
Work
Work hours perweek

0.42***

0.56***

0.50*** 0.68
25.55*** 29.75

0.15***

0.01
0.06

0.00**
0.06

0.14***

0.23*

0.26**

0.68
31.75

0.19

0.37

0.01
0.05

0.21

0.73

0.70**

0.67***

0.17
0.75
33.91

0.39***

0.57***

0.48

0.55*** 0.75
28.23*** 32.06**

0.24***

0.15***
0.31***

0.68***

Moderate

0.33***

0.60***

0.09**
0.19

0.09

0.63

0.60*** 0.78
12.43*** 17.83**

0.41***

0.35***

0.08**
0.28***

0.06**

0.58***

0.62*** 0.81
15.88*** 20.49

0.38***

0.48***

SevereNone

SevereModerate

Malawi

Ethiopia

0.78
17.64

0.16

0.75

0.11
0.18

0.10

0.61

0.81
21.05

0.14

0.81

None

Individual wellbeing outcomes by functional status

All adults
Ever attended
schoolSick or injured inpast month
Work
Work hours per
weekType of work:
Household farmwork
Household business work
Wage work
Other work
Women

Table 5.3

0.20***

0.65***

Moderate

0.23***

0.57***

0.09***
0.07

0.14

0.70**

0.54*** 0.81
17.65*** 21.71*

0.23

0.30***

0.07***
0.07

0.13

0.73**

0.53*** 0.84
20.92*** 25.88**

0.19**

0.42***

SevereTanzania

0.83
23.81

0.18

0.74

0.14
0.07

0.14

0.65

0.85
27.82

0.15

0.80

None

0.65***

0.73***

Moderate

0.69***

0.63***

0.14***
00.22***

0.64

0.80
26.86

0.42

0.84

0.19
0

0.20

0.61

0.89
29.47

0.37

0.89

None

(continued)

0.51*** 0.88***
19.32*** 22.02***

0.76***

0.48***

0.13***
00.27***

0.61

0.56*** 0.89***
18.58*** 23.35***

0.68***

0.60***

SevereUganda5
FUNCTIONAL DIFFICULTIES AND INEQUALITIES …

105

0.01
0.06

0.00
0.10

0.15
0.26

0.10

0.49

0.63***
20.14**

0.34***

0.65***

0.02
0.30***

0.03*

0.65

0.14**
0.18

0.09

0.59***

0.84*
24.10

0.27***

0.81***

0.05
0.19

0.09

0.67

Moderate

0.17
0.19

0.10

0.54

0.84
24.22

0.12

0.87

0.04
0.19

0.09

0.68

None

0.12*
0.02

0.11

0.75***

0.51***
26.30*

0.14***

0.61***

0.05
0.09

0.14

0.70***

SevereTanzania

0.14**
0.04

0.10 ***

0.72***

0.88
30.66

0.17***

0.74***

0.06*
0.09

0.17***

0.68*

Moderate

0.20
0.04

0.18

0.59

0.88
32.56

0.11

0.86

0.08
0.10

0.11

0.70

None

0.61***

0.87***

0.10
0.00

0.18**

0.71

Moderate

0.20*
0

0.30

0.50

0.19***
00.27***

0.54

0.60*** 0.89
17.76*** 25.10***0.60***

0.74***

0.50**
0.00

0.23**

0.72

SevereUganda

0.26
0.01

0.21

0.52

0.86
32.05

0.32

0.94

0.12
0.00

0.18

0.69

None

Notes ***significant at the 1% level, **significant at the 5% level, *significant at the 10% level for the difference with individuals with no difficulty. Moreinformation on the wellbeing indicators is in Table 5.1. Statistical significance is tested with ttest for continuous variable and the Pearson’s Chi square test
for binary variables. Estimates are weighted

0.02
0.05

0.13

0.13**

0.09**

0.15

0.81

0.36***

0.59***

0.59

0.80*

0.33***

0.16***

0.01
0.05

0.81***

0.01
0.06

0.00
0.02

0.31

0.82
35.71

0.35

0.46

0.63

0.61*** 0.82
30.57*** 34.08*

0.59

0.53

SevereNone

SevereModerate

Malawi

Ethiopia(continued)

Type of work:
Household farmwork
Household business work
Wage work
Other work
Men
Ever attended
schoolSick or injured
recently
Work
Work hours per
weekType of work:
Household farmwork
Household business work
Wage work
Other work

Table 5.3

106
S. Mitra1.00−3.15**
0.80
2.42***
0.95
−2.41
0.87
2.52***
1.11−3.74*

0.42***−6.21***

0.60
4.03***

0.45***−7.14***

0.33***
5.66***

0.39***−5.36**

0.19***−6.42***

0.89
3.19***

0.37***−4.22***

0.57***
2.31***

0.28***−5.18***

0.71**
3.00***

0.62***
0.43

1.32**
2.30***

0.85**
0.64

1.18***
2.04***

0.79***
0.57

1.20***
2.16***

Moderate

0.09***
−3.12

0.66*
1.47

0.36***−6.70***

0.66
1.36

0.19***−4.80*

0.59***
1.37*

SevereTanzania

0.49***−1.77

0.83
1.51***

0.93**−1.48

1.06
1.34**

0.67***−0.40

1.07
1.38***

Moderate

0.21***−8.96***

0.26***
2.37***

0.30***
3.30

0.74
3.50***

0.25***−6.00***

0.46***
2.80***

SevereUganda0.86−6.12***

0.73
2.20***

1.35*−3.28**

0.99
2.09***

1.17−4.50***

0.85*
2.20***

Moderate

Notes For each wellbeing indicator in a given row, a multivariate regression is run and the coefficients of moderate difficulty and severe difficulty dummies
are reported on the same row. No functional difficulty is the reference category. All regressions are run as logistic regressions except for work hours run
as OLS. Coefficients are then odds ratios except for work hours. ***significant at the 1% level, **significant at the 5% level, *significant at the 10% level.
More information on the dependent variables is in Table 5.1. Descriptive statistics are in Table 5.3 for the dependent variables and in Table 4.4 for control
variables. The regression controls are as follows: age categories, sex (for the entire sample of all adults), being married, being the household head, having amother with no prior schooling, household size, rural and distance to healthcare services. For Tanzania, data was missing for distance to healthcare services
for a sizeable share of the sample, community fixed effects were used instead

0.84*
2.46***

0.39***
4.76***

SevereSevereModerate

Malawi

Ethiopia

Regressions of individual outcomes on functional difficulty status and other covariates

All adults
Ever attended school
Sick or injured inpast month
Work
Work hours
Women
Ever attended school
Sick or injured inpast month
Work
Work hours
Men
Ever attended school
Sick or injured inpast month
Work
Work hours

Table 5.4

5
FUNCTIONAL DIFFICULTIES AND INEQUALITIES …

107

108

S. Mitra

These results above largely hold in subsamples of women and men in
the bottom two panels of Table 5.3. Comparing now women to men,
women are less likely to have ever been to school, more likely to havebeen sick or injured recently and less likely to work, whatever the functional difficulty status.
Perhaps these results so far reflect to some extent cohort effects, withpersons with functional difficulties being on average older and having
less education. The association between functional difficulty and deprivations at the individual level is further considered through the regression
model (5.4) of Box 3 controlling for various characteristics, including
age. As shown in Table 5.4, results are quite consistent across countries.
Moderate and severe difficulties are significantly associated with lower
odds of ever attending school and higher odds of being sick or injured.
For work, lower odds of working and lower hours of work are associated with severe difficulty in all countries. For instance, in Uganda, theodds of working for a person with a severe difficulty are 0.25 the odds ofworking of a person with no functional difficulty, everything else equal.
Results are more mixed for moderate difficulty with a significant association with lower odds of working in Malawi and Tanzania, and significantly lower work hours in Ethiopia, Tanzania, and Uganda.
Similar regressions are run with the individual functional difficultyscore in Table 5.5 instead of the severe and moderate difficulty binary
variables. The functional difficulty score is consistently and significantly
associated with worse individual wellbeing outcomes for all country–
indicator pair except schooling in Malawi. For example, in Ethiopia, a
10% higher functional score is associated with a 32.7% lower probabilityof working.
Table 5.5 Regressions of individual outcomes on functional score and othercovariates
EthiopiaEver attended school
Sick or injured in past month
Work
Work hours−4.13***
7.06***−3.27***−36.08***Malawi0.37
7.37***−5.47***−11.65**

Tanzania

Uganda–1.59***
2.15***–5.82***−18.08**−3.35***
5.69***−5.84***−37.48***Note The notes of Table 5.4 apply. The right hand side variable of interest for which the estimated coefficient is reported is the functional score.

5

FUNCTIONAL DIFFICULTIES AND INEQUALITIES …

Tanzania

UgandaEthiopia

109

Malawi40
35

work hours

30
25
20
15
10
5
0
0.0

0.1

0.3
0.4
Functional score

0.6

0.7

Fig. 5.1 Predicted work hours by functional score
Notes This is the predicted mean work hours by functional score among working
adults using an OLS regression with control variables as follows: age categories,
sex, being married, being the household head, having a mother with no priorschooling, household size and distance to healthcare services

Figure 5.1 gives the predicted value of work hours vs. the individualfunctional score, based on the regression model in Table 5.5. For all
four countries, there is a negative relationship between work hours andfunctional score, which extends from low to high values of the functionalscore. It also applies to values of the functional score in the moderate difficulty range, for example from 0.05 to 0.10. There is a gradient in work
hours across severe, moderate, and no difficulty.
5.3.2   Household Wellbeing
Table 5.6 compares wellbeing outcomes of households with at least an
adult with a severe or moderate difficulty to households with no functional difficulty. In all four countries, households with an adult with amoderate or severe functional difficulty tend to have worse living conditions or own fewer assets as reflected by a lower asset score. They are also
more prone to economic insecurity with higher shares of food insecurehouseholds and households subject to a recent shock.

NA
NA

Total exp. (nonhealth)

Health exp.

Share of food insecurehouseholds
Share of households
subject to a shock
recentlySocial transfers

Education expenditures

NA

NA

Share of households
below $1.90 per day
Total expenditures

182.49(18.23)47.69 *
(13.26)
0.21***
0.59***

91.39**(14.94)62.68(15.28)0.19***

0.55***

NANA

14.72**(0.61)
NA

13.76***(0.82)NA

Asset index

0.48

168.40(14.15)
29.59(10.65)
0.16

NA

NA

NA

15.81
(0.33)
NA

0.76***

191,601.30(9,201.33)
187,078.50 *(19,046.16)
4,572.80***(720.16)
3,019.83(458.19)477.13(349.82)
0.43***

11.22***(0.59)
0.64***

SevereNone

SevereModerate

MalawiE thiopia

0.69***

250,328.10(9,824.46)
246,508.10(18,733.02)
3,820.06***(285.56)
7,419.06**(1,126.22)
328.46(129.57)
0.34***

14.9(0.40)
0.52

Moderate

Table 5.6 Household wellbeing outcomes by functional status (means unless otherwise noted)

15.14(0.20)
0.53(continued)

0.65

248,146.80(4,370.98)
245,031.60(14,314.64)
3,115.10(235.62)5,588.35(411.64)575.13(267.50)
0.30

None

110
S. Mitra(continued)

23.02***(0.91)
0.12
2,876,496.00*(1133,625.50)
2,759,560.00(1128,816.40)
116,935.70**(113,066.96)
207,056.70*(28159.92)
372.59(113.86)
0.43***
0.88***

16.82***(1.28)
0.20***

2,036,795.00***(1142,123.40)
1,930,099.00***(1137,693.70)
106,696.50**(116,281.18)
84,097.38(22586.38)
1,525.19***(623.87)
0.42***

0.88*

0.80

2,690,722.00(168,999.53)
2,599,069.00(1066,860.65)
91,653.09(16,191.04)
133,104.90(13075.30)
309.62(59.63)0.32

25.33(0.62)
0.12

0.56***

0.34***

257,213.20(19523.56)
256,764.70(19518.04)
448.45**(18.82)375,679.30(79736.80)
NA28.24***(1.02)
0.57*

SevereNone

SevereModerate

UgandaTanzania

0.48***

0.30**

267,776.90(14086.71)
267,365.50(14084.40)
411.38**(14.04)
370,427.90**(47387.87)
NA

29.67***(0.88)
0.46

Moderate

0.37

0.18

263,203.80(8961.32)
262,806.50(8959.48)
397.36(9.61)
295,928.70(26629.60)
NA

33.25
(0.59)
0.45

None

Notes Table includes sample means and standard errors (between brackets) for household indicators of Table 5.1. NA stands for not available. Estimates areweighted. Expenditures and tranfers are in domestic currencies (Birrs for Ethiopia, Kwachas for Malawi, Tanzania Shillings for Tanzania, Uganda Shillingsfor Uganda). 1) exp. stands for expenditures 2) Social transfers stands for Social protection transfers

Share of foodinse­cure households
Share of households
subject to a shock
recentlySocial transfers

Education exp.

Health exp.

Total exp. (nonhealth)

Share of households
below $1.90 per day
Total expenditures

Asset index

Table 5.6

5
FUNCTIONAL DIFFICULTIES AND INEQUALITIES …

111

112

S. MitraThe poverty headcount using the $1.90 a day poverty line is about
10 percentage point higher among households with severe functionaldifficulties in Malawi, Tanzania, and Uganda. No significant difference isfound for households with moderate difficulties. Mean total expenditures
and total nonhealth expenditures are not significantly different acrossgroups except in Tanzania where they are significantly lower amonghouseholds with severe difficulties. Households with functional difficulties incur significantly higher health expenditures than other households
in Malawi and Tanzania, but not in Uganda. In Ethiopia, significantly
lower educational expenditures are found, but not in other countries.
Households with functional difficulties receive significantly higher social
protection transfers in Tanzania, but not in Ethiopia and Malawi.
This association between household economic indicators and functional difficulties may be due to differences in household characteristics. Perhaps the lower education expenditures found in Ethiopia forhouseholds with a severe functional difficulty result from the older agesof household members when a household has a severe functional difficulty. The associations are analyzed further in Table 5.7 with multivariate regressions. It gives the estimated coefficient of the two variables thatindicate if at least one adult in the household experiences a severe or moderate functional difficulty. The model includes as controls the household
head and household characteristics described in Box 3 and Table 4.7.
In the four countries, households with functional difficulties, whether
moderate or severe, are consistently more likely to be food insecure and to
experience a shock. For instance, in Uganda, households with moderate orsevere functional difficulties, respectively, have 1.3 or 1.8 higher odds ofbeing subject to shocks than households without any functional difficulty.
For other household outcomes, results vary across countries. Having
an adult with a functional difficulty in the household is significantly associated with lower asset ownership in Ethiopia and Tanzania, lower total
expenditures in Tanzania, lower education expenditures in Ethiopia andMalawi and higher health expenditures in two out of three countries(Malawi and Tanzania). It is significantly correlated with higher social
protection transfers in Ethiopia, Malawi, and Tanzania for moderate orsevere functional difficulties.
Total expenditures do not differ across functional status in all countries, except for moderate functional difficulty in Malawi. This is consistent with Mitra et al. (2013), who, based on a bivariate analysis, findno significant difference in expenditures in 15 LMICs using WHS data,

−0.85
NA
NA
NA
NA
0.04
0.11
1.30**
1.45***

0.83
1.18
−0.02
−0.03
0.82***−0.56***
0.04
1.59***
1.51***

0.67
0.88*
0.04**
0.03*
0.87***
0.10
0.13***
1.20***
1.28***

Moderate−3.39***
1.14
−0.04
−0.07
1.09***
0.58
0.29**
1.57**
1.39

SevereTanzania−2.11**
1.23
0.01
0.00
0.67**
0.12
0.07
1.80***
2.10***

Moderate
−0.76
0.89
0.01
0.01
0.20
−0.18
NA
1.83**
1.42**

SevereUganda

−0.60
1.18
0.02
0.02
0.00
−0.27
NA
1.29*
1.29**

Moderate

Notes Each row for each country gives the estimated coefficients of the household moderate and severe functional difficulty binary variables in a regression of a household wellbeing outcome. No functional difficulty is the reference category. The dependent variable is the row header (e.g. asset index). All
expenditures variables are logged. For continuous dependent variables (asset index, expenditure variables, social protection transfers) the coefficients arefrom an OLS regression. For binary dependent variables (below $1.90 a day, food insecurity and household subject to shocks in the past year), the coefficients are odds ratios from a logistic regression. ***significant at the 1% level, **significant at the 5% level, *significant at the 10% level. NA stands for notavailable. Descriptive statistics for all variables are in Table 4.7 and 5.6. The regression controls are as follows: household head’s age, marital status, educational attainment, household size, share of members under age 15 and over age 60, share of male members, rural and distance to healthcare services. For
Tanzania, data was missing for distance to healthcare services for a sizable share of the sample, community fixed effects were used instead

13.76***
NA
NA
NA
NA
−0.49***
0.33***
1.64***
1.50***

SevereSevereModerate

Malawi

Ethiopia

Regressions of household wellbeing outcomes on severe and moderate functional difficulties and other

Asset index
Below $1.90 per day
Total expenditures
Total expenditures (nonhealth)
Health expenditures
Education expenditures
Social protection transfers
Food insecurity
ShocksTable 5.7
covariates

5
FUNCTIONAL DIFFICULTIES AND INEQUALITIES …

113

114

S. Mitrawhere the expenditures survey questions were few and not detailed.
Using a very detailed expenditure questionnaire from the LSMS, no significant difference is found here either.
Some differences are found though for certain types of expenditures.
Health expenditures information is available in three countries. Having amoderate or severe functional difficulty is associated with higher healthexpenditures in Malawi and Tanzania but not for Uganda. This result
supports the hypothesis of conversion factors associated with functionaldifficulty as explained in Chapter 2. Households with functional difficulties have on average higher health expenditures in Malawi and Tanzania,
which may make the conversion of income into wellbeing more challenging. In particular, higher health expenditures may crowd out otherexpenditures and indirectly make households more prone to worse living
conditions, asset accumulation and food insecurity as shown earlier.
Significantly lower education expenditures are associated with a severefunctional difficulty in Ethiopia and Malawi, but not in Tanzania andUganda. This might suggest an allocation of expenditures away fromeducation and toward health or other expenditures affected by the functional difficulty as found in Mitra et al. (2016) for Vietnam.
In the four countries, a consistent result is that having a moderatedifficulty is less strongly associated with a household wellbeing deprivation than having a severe difficulty across all household wellbeing indicators. Nonetheless, households with a moderate difficulty are more often
deprived, everything else held constant, than households with no functional difficulty especially with respect to food insecurity and shocks.
Similar regressions are run with the household functional difficultyscore15 in Table 5.8 instead of the severe and moderate difficulty binary
variables in Table 5.7. The functional difficulty score is associated withworse household wellbeing outcomes for three to five wellbeing indicators by country. For instance, for a household in Ethiopia, a 10% higher
functional difficulty score is associated with 13.4% higher odds of having
experienced a recent shock and 11.2% higher odds of being food insecure. It is also associated with education expenditures lower by 16.3 ETB(Ethiopian Birr) and social protection transfers higher by 10.2 ETB.
5.3.2.1 Multidimensional Poverty
Results of the multidimensional poverty analysis using Alkire and Foster(2011) and the dimensions, indicators and weights in Table 5.2 are
first shown in the spider charts in Fig. 5.2, which gives the deprivation
rates across functional difficulty status for each of the five dimensions.

5

FUNCTIONAL DIFFICULTIES AND INEQUALITIES …

115

Table 5.8 Regressions of household wellbeing outcomes on functional score
and other covariates

Asset index
Below $1.90 per day
Total expenditures
Total expenditures (nonhealth)
Health expenditures
Education expenditures
Social protection transfers
Food insecurity
ShocksEthiopia

MalawiTanzania

Uganda

−3.61
NA
NA
NA
NA
−1.63***
1.02***
1.12*
1.34***

−0.03
1.08
−0.03
−0.10
6.42***−1.52**
1.05***
2.87***
2.11***−13.1***
1.28
−0.05
−1.16
4.81***−1.63***
0.66
2.04***
1.73**−6.70**
2.53
0.00
−0.01
0.15
−0.58
0.66
0.52***
0.36*

Notes Each row for each country gives the estimated coefficient of the household functional score in aregression of a household wellbeing outcome. Other notes on dependent and independent variables ofTable 5.7 apply. NA stands for not available

Fig. 5.2 Rates of deprivation by dimension and functional difficulty status.
Note Deprivations using dimensions, thresholds and indicators described inTable 5.2

116

S. Mitra

The three lines, from dark to light green, connect the deprivation
rates for persons with severe, moderate, or no difficulty, respectively.
Unsurprisingly, the darker lines are on the outskirts of the lighter line
for each country, showing higher deprivation rates for persons with functional difficulties. The gaps between the lines are larger for individual
wellbeing dimensions (less than primary school, sick or injured and notworking) than for household wellbeing dimensions (materially deprived
and economically insecure). The gap between persons with moderate
and no difficulty is smaller than the gap between persons with severe andno difficulty.
Results for multidimensional poverty measures are given in Fig. 5.3
and Appendix A4 for all adults. A higher headcount (H) is found among
persons with moderate or severe functional difficulties, and the difference
across functional difficulty status is found to be statistically significant inall countries. More than eight in 10 adults with functional difficulties
experience multidimensional poverty. Roughly, the difference in the multidimensional headcount ratio is around 20 and 10 percentage points inthe four countries comparing, respectively, persons with severe and moderate difficulties to persons without any difficulty.
The average deprivation share (A), i.e. the share of dimensions inwhich the poor have deprivations, is significantly higher among persons
with severe or moderate difficulty in all countries (Appendix A4). In
other words, the poor with functional difficulties face more deprivations
than the poor without any functional difficulty. Appendix A4 also presents the adjusted headcount ratio (M0). The adjusted headcount ratio
is found to be higher among persons with functional difficulties thanpersons without in all countries. The difference in the adjusted headcount ratio across functional difficulty status is the largest in Uganda; it ismore than twice higher among persons with severe functional difficulties
compared to persons without any difficulty. The gaps across functionaldifficulty status found in almost all dimensions of wellbeing earlier in
Fig. 5.2 suggest that the gaps also found with multidimensional poverty
measures are not sensitive to the dimension weights used in the analysis(Table 5.2).
Appendix A5 gives multidimensional poverty measures for sex and age
subgroups. While women almost always have higher multidimensional
poverty than men whatever the functional difficulty status, women withfunctional difficulties also have higher H, A and M0 than women without
difficulties. It shows that women with functional difficulties experience5

FUNCTIONAL DIFFICULTIES AND INEQUALITIES …

Multidimensional Poverty
Headcount

UgandaEthiopia
1.0
0.8
0.6
0.4
0.2
0.0

Severe difficulty
MalawiModerate difficulty
No difficulty

Tanzania

Average Deprivation Share
Ethiopia
0.8
0.6
0.4
Severe difficulty

0.2
Uganda0.0

MalawiModerate difficulty
No difficulty

Tanzania

Adjusted Multidimensional
Headcount

UgandaEthiopia
0.8
0.6
0.4
0.2
0.0

Severe difficulty
MalawiModerate difficulty
No difficulty

Tanzania

Fig. 5.3 Multidimensional poverty and functional difficulty status

117

118

S. Mitra

1
0.9
0.8
0.7
0.6
M0 0.5
0.4
0.3
0.2
0.1
0
0.0

0.1
Ethiopia

0.3
0.4
0.6
Functional score

0.7

Malawi

UgandaTanzania

Fig. 5.4 Predicted multidimensional poverty adjusted headcount (M0) by
functional score. Note This is the mean adjusted multidimensional headcount byfunctional score using an OLS regression as in Box 3

a double disadvantage associated with gender and functional difficulty.
This double disadvantage is, for instance, stark in Uganda where 96% of
women with severe functional difficulties are multidimensionally poor
compared to 52% of men with no functional difficulty. By age group,
the assessment is somewhat different. Among persons with no functionaldifficulty, being older than 50 is not always associated with being more
often multidimensionally poor than those younger than 50; for H, itis the case in Ethiopia and Malawi, but not in Tanzania and Uganda.
However, having a functional difficulty is consistently associated withbeing more often multidimensionally poor within each age group.
Figure 5.4 gives the predicted M0 by functional score after adjusting
for a number of covariates listed in Box 3. In the four countries, there
is a positive and steep gradient in the functional score. Appendix A6
gives the full results of the regression. The functional difficulty score isthe covariate with the highest coefficient, above those of sex, age groups,
marital status, mother’s schooling, rural residence, and the distance tohealthcare services.

5

FUNCTIONAL DIFFICULTIES AND INEQUALITIES …

119

Fig. 5.5 Odds ratio of being multi-dimensionally poor by type of functionaldifficulty

5.3.2.2 Type of Functional Limitation and Age of Onset
Finally, Fig. 5.5 presents results of a logistic regression of the probabilityof being multidimensionally poor. It gives the odds ratio of being multidimensionally poor for each functional difficulty type among all adults.
For instance, for seeing in Malawi, an odds ratio close to one indicatesthat having this difficulty is associated with odds of being multidimensionally poor that are similar to those of a person without such difficulty,
everything else held constant. A lower bound of the confidence interval above one for the odds ratio indicates a higher likelihood of being
multidimensionally poor associated with a functional difficulty type.
Figure 5.5 shows that having a walking difficulty is associated withhigher odds of being multidimensionally poor in all four countries, followed by having a concentrating difficulty in three countries and a seeing, hearing, or selfcare difficulty in two countries, and communicationdifficulty in no country.
While Fig. 5.5 is focused on multidimensional poverty, Appendix A7
considers in turn a deprivation in a domain (e.g., education, morbidity)
and its association with the different functional disability types among all

120

S. Mitraadults. Results are consistent with those in Fig. 5.5, with walking as thefunctional difficulty the most often associated with deprivations.
Finally, for Tanzania and Uganda, information is available on age
at onset among persons with functional difficulties as shown earlier in
Fig. 4.3. The model of Box 3 was used this time replacing moderate andsevere functional difficulties with age at onset during working age years(15–49 years old) and age at onset during old age (50 and above). The
sample was restricted to persons with functional difficulties. Results are
not shown. Having an onset from birth to age 14 was the reference category. Among persons with functional limitations, no significant association was found between having an onset during working age or older
years and having different odds of deprivation in turn for each wellbeing
domain (e.g., education, morbidity) and for multidimensional poverty.
The exception was for Tanzania, where having an onset during working
age years was significantly associated with higher odds of being materiallydeprived.

5.4  Conclusion: Summary and Implications
The results in this chapter add to a small but growing quantitative literature on the association between disability and inequalities by using aninternationally comparable disability measure and very detailed economic
wellbeing information. Compared to earlier studies, it has indeed moredetailed information on employment, household expenditures, and economic insecurity. I summarize below the main results of interest of thischapter and derive policy and research implications.
1. Results from both descriptive statistics and regressions indicate thatfunctional difficulties, whether moderate or severe, are significant
and large correlates of both individual and household wellbeing
deprivations in Ethiopia, Malawi, Tanzania, and Uganda. This isfound through an indicator-by-indicator analysis as well as throughan assessment of multidimensional poverty, in descriptive and multivariate analyses. More than eight in ten persons with severe functional difficulties experience multidimensional poverty in Ethiopia,
Malawi, Tanzania, and Uganda.
2. The association is found to be strong and consistent for several
dimensions of wellbeing at both the individual (education, morbidity, work status) and the household levels (economic insecurity).

5

FUNCTIONAL DIFFICULTIES AND INEQUALITIES …

121

The association seems stronger and more consistent than in earlier
studies (e.g., Filmer 2008; Mitra et al. 2013; Trani et al. 2015).
These results support the inclusion of moderate and severe functional difficulties as potential correlates of deprivations in poverty
monitoring, evaluation, and programmatic efforts in LMICs. It
also supports a disaggregation by functional difficulty status of theindicators used to monitor the SDGs, in particular SDG #1, whichstates as a goal the eradication of hunger and poverty ‘in all its
forms’ (UNDP 2016).
The result on household-level economic insecurity is consistent
with results from qualitative research that deprivations are not contained to the individual and are also a ‘family affair’ (Grech 2016).
This result also implies that policies aimed to improve household
wellbeing need to pay attention to functional difficulties. Indeed,
despite the development of social protection programs in recent
years in the four countries, deprivations in terms of material wellbeing and food insecurity are widespread, and disproportionately soamong households with functional difficulties.
3. 
For some individual and household outcomes (job type, assets,
total expenditures), results were not consistent across countries. In
three countries, total expenditures do not differ across functional
status, which is consistent with earlier results in the literature. Total
expenditures may reflect additional expenditures associated with afunctional difficulty (e.g., health, transportation and care expenditures).
4. This analysis also shows that not all persons with functional difficulties are poor; persons with disabilities are not always among
the poorest of the poor. Some persons with functional difficulties
do achieve levels of wellbeing comparable to persons with no difficulty.
5. 
The association between functional difficulties and deprivations
was found for both severe and moderate functional difficulties,
although it was typically larger and more often significant for theformer. Moderate functional difficulties are also correlates of deprivations, and there is a severity gradient in the association between
deprivations and functional difficulties, consistent with other findings in the literature (Banks et al. 2014).
6. Analyses should try to incorporate the degree of functional difficulties through several categories (e.g., moderate, severe) or a

122

S. Mitrascore. Yet recommendations of the use of the Washington Groupquestions focus on the group with severe functional difficulties.
Analyses that focus on severe functional difficulties leave out persons with moderate functional difficulties who are also at risk ofpoverty.
7. In addition to functional difficulties, older ages and being female
are also correlated with deprivations. This makes older persons
with functional difficulties and women with functional difficulties
more likely to be multidimensionally poor. These results underscore the importance of considering and addressing age and sex
differences when formulating prevention and inclusion strategies
with respect to functional status.

Notes
1. There was very limited research conducted prior to 2000 as shown in
Elwan (1999). The gray literature has been growing but is included primarily if it pertains to the four countries under study or other countries inAfrica.
2. 
Examples of such qualitative studies include Grech (2015); Eide andIngstad (2011). For reviews of the literature, see Grech and Soldatic(2016).
3. Eide and Kamaleri (2010) (Lesotho), Eide et al. (2003b) (Namibia), Loeband Eide (2004) (Malawi), Eide and Kamaleri (2009) (Mozambique),Palmer et al. (2010) (Vietnam), World Bank (2009) (India).
4. 
Evidence is offered in the following studies, among others: Eide andMmatli (2016) (Botswana), Loeb and Eide (2004) (Malawi), GayleGeddes (2015) (Jamaica), Hoogeveen (2005) (Uganda), Loeb et al.
(2008) (South Africa), Mete (2008) (Eastern Europe), Mitra et al.
(2013) (15 LMICs), Mont and Cuong (2011) (Vietnam), Rischewski
et al. (2008) (Rwanda), Trani and Loeb (2010) (Afghanistan andZambia), World Bank (2009) (India), WHO-World Bank 2011 (59countries).
5. Eide and Jele (2011) (Swaziland), Eide and Kamaleri (2010) (Lesotho),
Eide and Mmatli (2016) (Botswana), Gayle-Geddes (2015) (Jamaica),
Hoogeveen (2005) (Uganda), Mete (2008) and Mussida and Sciulli(2016) (Eastern Europe), Mitra (2008) and NDSD (2015) (South
Africa), Mitra and Sambamoorthi (2008) (India), Payne et al. (2013)(Malawi), World Bank (2009) (India), Trani and Loeb (2010)(Afghanistan and Zambia), UNESCAP (2016) (Asia), WHO-World Bank
(2011) (59 countries).

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123

6. One can get a sense of the variety of methods under use by reading Filmer(2008), Mitra and Sambamoorthi (2008) and Trani and Loeb (2010).
7. Each variable is weighted using the corresponding eigenvector for the firstprincipal component, found by a principal component analysis.
8. Health expenditures include those associated with inpatient and outpatient contacts, nonprescription medication and medical equipment.
9. Education expenditures are collected at the individual level for all household
members who attend school and then aggregated at the household level.
10. Mitra, Palmer et al. (2017) offer a recent review of this literature.
11. A shock is an unexpected negative event. The list of shocks covered in thesurvey is in Table 5.1.
12. Descriptive statistics are in Table 4.4.
13. Descriptive statistics are in Table 4.7.
14. Results in Sect. 4 on prevalence illustrate this for gender and rural residence.
15. The household functional difficulty score is the highest functional difficulty score among adults aged 15 and above in the household.

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from Afghanistan and Zambia.Journal of International Development, 24(1),pp. S19–S52.
Trani, J.F., & Canning, T.I.. (2013). Child poverty in an emergency and conflict
context: A multidimensional profile andan identification of the poorest children in Western Darfur. World Development, 48, 48–70.
Trani, J., Bakhshi, P., Myer Tlapek, S., Lopez, D., & Gall, F. (2015). Disability
and poverty in Morocco and Tunisia: A multidimensional approach. Journalof Human Development and Capabilities, 16(4), 518–548.
Trani, J., Kuhlberg, J., Cannings, T., & Chakkal, D. (2016). Multidimensional
poverty in Afghanistan: Who are the poorest of the poor? Oxford Development
Studies. doi:10.1080/13600818.2016.1160042.
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Programme. Available: http://www.undp.org/content/undp/en/home/sustainable-development-goals.html. Accessed August 20, 2016.
UNESCAP. (2016). Disability at a glance 2015. Accessed March 3, 2017
http://www.unescap.org/sites/default/files/SDD%20Disability%20
Glance%202015_Final.pdf.
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World Bank. (2009). People with disabilities in India: From commitments to outcomes. Washington, DC: World Bank.

5

FUNCTIONAL DIFFICULTIES AND INEQUALITIES …

127Open Access This chapter is licensed under the terms of the Creative Commons
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by/4.0/), which permits use, sharing, adaptation, distribution and reproduction
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CHAPTER 6

Dynamics of Functional Difficulties
and Wellbeing

Abstract This chapter uses the longitudinal data for Ethiopia, Malawi,
Tanzania and Uganda to investigate some dynamic links between disability and wellbeing. The functional difficulty trajectories of individuals
are significantly associated with different levels of wellbeing. Persons withpersistent functional difficulties are worse off than persons with functional difficulties in one or no period. Women with persistent difficulties and older persons with persistent difficulties are the most deprived
groups. New functional difficulties lower the odds to continue working
and no longer reporting any difficulty increases the odds of returning
to work. Functional difficulties are also associated with mortality in the
short-term. More research is needed on the links between disability, on
the one hand, and poverty dynamics and mortality, on the other.
Keywords Disability · Panel data · Mortality · Gender · Aging · Africa
JEL I1 · I3 · O15 · J1
In this chapter, I exploit the longitudinal data available for Ethiopia,
Malawi, Tanzania, and Uganda to study three questions related to the© The Author(s) 2018
S. Mitra, Disability, Health and Human Development,
Palgrave Studies in Disability and International Development,
DOI 10.1057/978-1-137-53638-9_6

129

130

S. Mitradynamic links between functional difficulties and wellbeing. The first section
asks whether individuals with different trajectories in terms of functionaldifficulties (e.g., new functional difficulty in wave 2, functional difficulty inboth waves) tend to have different characteristics and wellbeing outcomes.
The second section investigates if short-term changes in functional difficulties are associated with changes in asset ownership and work status. The lastsection considers if functional difficulties predict mortality in the short term.
Each of the three sections starts with a literature review, then moves
onto methodology and ends with results and discussion. I conclude witha summary of the main results of this chapter. Because the first and second sections require information on functional difficulties in two waves,which was only available in Ethiopia and Uganda, results are limited tothese two countries.

6.1   Functional Trajectories and Their Correlates
6.1.1  Literature
Results in Chapter 4 earlier show that transitions in and out of functionaldifficulties are common. They affect about half of individuals with functional difficulties at a given point in time in Ethiopia and Uganda. This
result is consistent with results from studies in HICs. The question thenarises as to what such transitions may be correlated with.
There is a small body of literature on these correlates that tries to separate demographic from socioeconomic correlates. It also asks to what
extent these transitions are due to factors amenable to policy change(e.g., poverty, employment, and education). The literature is mostly
focused on transitions into disability, i.e., on the predictors of onsets. It
has consistently shown that older persons are more likely to experiencetransitions into disability (e.g., Gannon and Nolan 2007). As age may
be correlated with educational attainment, studies have to consider theextent to which, within age groups, education predicts onsets. Results
are mixed with some studies finding that education predicts onsets(Jenkins and Rigg 2003; Jagger et al. 2007; Burchardt 2003) and otherstudies finding that it does not (Gannon and Nolan 2007). There is also
evidence that poverty is a significant predictor of later disability onset
while gender and family composition are not (Burchardt 2003; Gannon
and Nolan 2007; Jenkins and Rigg 2003).

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DYNAMICS OF FUNCTIONAL DIFFICULTIES AND WELLBEING131

The literature on the determinants of persistent difficulties and of transitions out of disability is even smaller. Gannon and Nolan (2007) findthat persons who are older, male and unemployed, and have no education
are more likely to have a persistent illness or disability. They also find thatpeople who work are more likely to no longer report an illness or disability in a subsequent wave compared to people who do not work. Jagger
et al. (2007) show that persons with limited educational attainment areless likely to experience a transition out of a mobility or ADL limitation.
The literature above uses several disability measures: a broad activity limitation or work limitation measure (Burchardt 2003; Jenkins andRigg 2003), a mobility or ADL limitation (e.g., Jagger et al. 2007)or broadly, a health problem, illness, or disability (Gannon and Nolan2007). The question then arises as to whether similar correlates can also
be found when one uses the Washington Group short set of questions on
disability, which are now increasingly used in surveys and censuses internationally, but largely in cross-sectional datasets. The literature above is
set in the context of a few HICs (Ireland, UK, and USA). Do similar
results hold in very different contexts, in LICs in particular where healthcare and rehabilitation services, and the socioeconomic environment arevery different? This is what the rest of this section attempts to answer for
Ethiopia and Uganda.
6.1.2  Methodology
The objective of this section is to determine the correlates of different
trajectories in functional difficulty status in the short term. If persons
with persistent functional difficulties are found to have a different profile
compared to persons with transitory or no difficulty, it will be important
to monitor this group for policy and to understand the determinants andconsequences of persistent difficulties.
This section is focused on the cases of Ethiopia and Uganda, wherecomparable data on functional difficulties using the Washington Groupquestions was collected in two waves: the Ethiopia Rural Socioeconomic
Survey (2011/2012, 2013/2014) and the Uganda National Panel
Survey (2009/2010, 2010/2011). The sample includes individuals
whose functional status is known in both waves.1
Individuals are categorized into one of four functional difficulty categories depending on their trajectory: (1) A functional difficulty in wave 1
only; (2) A functional difficulty in wave 2 only; (3) A functional difficulty

132

S. Mitrain both waves; and (4) No functional difficulty in any wave. The analysis not only considers any degree of functional difficulty (moderate orsevere), but also later separates transitions by degree (severe, then moderate). Given that the longitudinal data is available for the short term
only, the permanent or temporary nature of a functional difficulty cannot
be determined. (1) and (2) may get at exits or entries into a functionaldifficulty status, but may also capture episodic functional difficulties.
Group (1) captures persons with medium, long-term, or permanentfunctional difficulties. As noted in Chapter 4, there could be a variety ofreasons for reporting a functional difficulty in one wave and not in theother, including changes in reporting behavior, measurement error, andactual changes in functional limitations.
After presenting descriptive statistics, this section will give results of amultinomial logit model of the probability of experiencing a particularfunctional trajectory as presented in Box 4.
Box 4: Multinomial logit model of functional trajectories

Functional Trajectoryi,t+1 = α + β Deprivationi,t +where:

kγk xi,k,t + εi,t(6.1)

• Functional Trajectoryi,t+1 of individual i at time t + 1 refers to thefour categories of functional trajectory above (1) through (4)with (4) No functional difficulty in any wave as the reference category.
• α is the intercept;• Deprivationi,t is a dummy variable equal to 1 if the individual
experiences a deprivation in the previous wave (in turn withrespect to education, morbidity, work, material wellbeing, economic insecurity, and multidimensional poverty), 0 otherwise;• β is the coefficient of the deprivation status to be estimated.
Results are reported in Table 6.2.
• xi,k,t is a set of k control variables at the individual, household orcommunity level in time t (age categories, male, married, household head, household size, rural residence, distance to healthcare
services);• γk is the set of estimated coefficients of the k control variables;• εi,t is the error term for person i at time t.

6

DYNAMICS OF FUNCTIONAL DIFFICULTIES AND WELLBEING

133

Model (6.1) of Box 4 does not identify the causal link from recent
characteristics or deprivations to recent changes in functional difficulty.
Instead, it estimates an association. For instance, finding a positive andsignificant coefficient for material wellbeing deprivation in wave 1 for a
new functional status in wave 2 does not indicate that the material deprivation in wave 1 caused the functional difficulty in wave 2. It mightreflect reverse causality from functional difficulty to a deprivation: thefunctional difficulty may have been a long term but transitory functionaldifficulty that was not measured in wave 1 and yet had already affected
the employment, earnings, and material wellbeing of the individual prior
to wave 1. The material wellbeing deprivation and the functional difficulty may also both be caused by factors not measured in the model,such as violence, natural disasters or an absence of public goods in thecommunity (e.g., infrastructure, health services).
6.1.2.1 Results and Discussion
Table 6.1 gives descriptive statistics for four groups of individuals basedon their functional difficulty trajectory. Consistent with the descriptive statistics with cross-sectional data earlier in Chapter 4 (Table 4.4),
persons with no functional difficulty tend to be younger and belong
to larger households. In Ethiopia, males and females are evenly spread
across the four groups, while in Uganda, 61% of persons with persistent functional limitations are women. There is no significant differencein the distance to healthcare services across groups, except in Uganda
where persons with persistent difficulties are further away on average.
Table 6.1 also shows that the functional trajectory is associated withpatterns with respect to deprivations in five dimensions (education, morbidity, work, material wellbeing, and insecurity) and in the multidimensional poverty indicators of Chapter 5 (H, A, M0). Persons with persistentfunctional difficulties are worse off than persons with functional difficulties in wave 1 or 2 and persons with no functional limitation in any wave.
This is shown by significantly higher rates of deprivation in each dimension and of multidimensional poverty. For instance, in Ethiopia, 87% of
those with persistent difficulties are multidimensionally poor compared to
69% of those who do not experience any difficulty in waves 1 and 2, and,
respectively, 85 and 74% of those with a difficulty in wave 1 or 2 only.
Results from the model in Box 4 are shown in Table 6.2 for transitions
in severe functional difficulty in the top panel, moderate difficulties in
the middle panel, and then for all difficulties (any degree) in the bottom

0.93***
0.24***
0.23
0.99
0.3
0.74
0.57***(0.01)
0.42**(0.02)
485

16.5(0.96)
NA

0.69
0.6***
5.35***(0.14)

0.35***
0.19***
0.28***
0.18***
0.49

0.97***
0.48***
0.33***
0.99**
0.37***
0.87***
0.63***(0.01)
0.55**(0.02)
457

14.41(0.83)
NA

0.63***
0.65
4.5***(0.15)

0.18***
0.19***
0.24***
0.39***
0.5*

0.81
0.18
0.24
0.98
0.34
0.69
0.56
(0.00)
0.38(0.00)
6360

15.4(0.26)
NA

0.69
0.38
5.8
(0.04)

0.72
0.15
0.09
0.03
0.5

0.64***
0.46***
0.21*
0.71***
0.59***
0.71***
0.54***(0.01)
0.38***(0.01)
504

28.38(1.91)
0.85***

0.55**
0.57***
6.95***(0.23)

0.4***
0.19***
0.27***
0.14***
0.47**

0.55***
0.55***
0.24
0.7***
0.6***
0.72***
0.55***(0.01)
0.4***(0.03)
401

26.45(2.23)
0.89**

0.54***
0.58
7.31***(0.52)

0.35***
0.23***
0.27***
0.15***
0.48

Difficulty inwave 2 only

Notes The characteristics and deprivations are for wave 1. *** significant at the 1% level, ** significant at the 5% level, *significant at the 10% level.

N

M0

Education
Morbidity
Employment
Material
Insecurity
HA

Deprivations

Rural

0.93***
0.4***
0.25
0.99**
0.39***
0.85***
0.6***(0.01)
0.52***(0.01)
611

15.41(0.77)
NA

Distance to healthcare
services

Community

0.74**
0.57***
5.44***(0.12)

0.39***
0.18***
0.29***
0.15***
0.47

MarriedHousehold head
Household size

Household

Structural factors

Age 15–39
Age 40–49
Age 50–64
Age 65+
Male

Personal factors

No difficulty

Difficulty inwave 1 only

Persistent diff.
in both waves

Difficulty inwave 1 only

Difficulty inwave 2 only

UgandaEthiopia

Table 6.1 Descriptive statistics by functional difficulty trajectory

0.73***
0.62***
0.36***
0.71***
0.62***
0.79***
0.61***(0.01)
0.48***(0.02)***
558

28.06**(1.94)
0.88***

0.49
0.62
6.47***(0.27)

0.22***
0.12***
0.27***
0.39***
0.39***

Persistent diff.
in both waves

0.4
0.29
0.25
0.66
0.48
0.49
0.5
(0.00)
0.24(0.00)
4527

26.37(0.72)
0.84

0.52
0.33
7.5
(0.07)

0.76
0.13
0.08
0.02
0.5

No difficulty

134
S. Mitra6

DYNAMICS OF FUNCTIONAL DIFFICULTIES AND WELLBEING

135

Table 6.2 Odds ratio of deprivation by functional trajectory
Ethiopia
Difficultyin wave 1
only

UgandaDifficultyin wave 2
only

Persistentdifficultyin bothwaves

Difficultyin wave 1
only

Difficultyin wave 2
only

Persistentdifficultyin bothwaves

3.94**
1.2
1.65*
1.7
0.45**
0.99

3.98***
5.47***
3.76***
4.32**
2.47***
5.1***

2.57***
1.23
1.03
1.19
2.15***
2.31***

1.79
7.1***
2.54**
0.81
3.1***
4.98***

3.56***
3.76***
5.61***
1.45
1.38
4.75***

Severe difficulty transitions
Education
Morbidity
Employment
Material wellbeing
Insecurity
Multidimensional
poverty

1.21
4.02***
2.18***
3.25*
1.87**
2.27***

Moderate difficulty transitions
Education
Morbidity
Employment
Material wellbeing
Insecurity
Multidimensional
poverty

1.44**
2.5***
1
1.72*
1.55
1.95***

1.01
1.17
1.03
0.8
0.85
0.91

1.93**
2.24***
1.02
0.87
0.99
1.82***

1.34**
1.53***
1.07
1.29*
1.39***
1.63***

1.17
2.41***
1.06
1.08
1.68***
2.01***

1.19
2.51***
1.07
0.92
1.86***
1.96***

1.41**
2.7***
1.14
1.97***
1.79***
1.99***

1.16
1.17
1.08
0.61*
0.72**
0.92 NS

2.62***
2.94***
1.54***
1.24
1.51***
2.49***

1.48***
1.45***
1.05
1.3**
1.42***
1.7***

1.24
2.56***
1.15
1.06
1.73***
2.16***

1.75***
3.01***
2.02***
1.11
1.97***
2.92***All transitions
Education
Morbidity
Employment
Material wellbeing
Insecurity
Multidimensional
povertyNotes For each deprivation in a given row, a multinomial logit regression as in Box 4 is run and the odds
ratio of difficulty in wave 1 only, difficulty in wave 2 only and difficulty in both waves are reported on
the same row. No functional difficulty in any wave is the reference category. The upper (middle) panel
covers severe (moderate) difficulty transitions and excludes persons with a moderate (severe) difficulty in
any wave. ***significant at the 1% level, **significant at the 5% level, *significant at the 10% level. Moreinformation on the dependent variables is in Table 5.2. Descriptive statistics are in Table 6.1

136

S. Mitrapanel. Persons with persistent functional difficulties are significantly more
likely to have experienced multidimensional poverty and a deprivation inany dimension in Ethiopia and most dimensions in Uganda compared topersons with no functional difficulty in any wave. A similar association isfound for persons with a severe limitation in wave 1 or wave 2, albeit lessstrong. Comparing the results of severe and moderate functional difficulty transitions, overall similar results are found for moderate difficulties
but with smaller odds ratios.
Next, the mean adjusted multidimensional poverty headcount (M0)2
is calculated for different groups of persons with functional difficulties
based on age, sex, and the severity and trajectory of the functional difficulty.3 The trajectory covers a functional difficulty in one wave vs. both
waves. Results are presented in Fig. 6.1. Groups are ranked from theleast (bottom) to the most (top) multidimensionally poor as measured by
M0. There is a considerable variation in M0 across the subgroups ranging
0.45–0.7 in Ethiopia and 0.3–0.6 in Uganda.
There are some patterns in both countries. Older persons, older
women, and persons experiencing severe difficulties in both waves tend
to be the groups the most multidimensionally poor. These are groups forwhom personal factors, structural factors, resources, and functional difficulties may interact to create situations of extreme deprivations. Further
research is needed that explores the heterogeneity in wellbeing of persons with functional difficulties and how they are shaped by personal,
structural, and resource factors.

6.2  Changes in Functional Difficulties and Economic
Wellbeing
6.2.1   Literature Review
As noted in Chapter 2, it is often stated that ‘disability and poverty area cause and a consequence of each other’ (DFID 2000; Yeo and Moore
2003). Yet, the poverty dynamics literature has been largely silent on disability. From the poverty dynamics literature, we know that households
in LMICs have a limited set of coping mechanisms to deal with theeconomic consequences of illness, hospitalization or broad activity limitations (Santos et al. 2011; Mitra et al. 2016). In fact, they sometimes
adopt coping mechanisms that are detrimental in the medium and long

6

DYNAMICS OF FUNCTIONAL DIFFICULTIES AND WELLBEING

137

Ethiopia
50 & older/ Severe func. diff. both waves
Under 50/ Severe func. diff. both waves
50 & older/ Women/ func. diff. both waves
50 & older/ Severe func. diff. one wave
50 & older/ Women/ func. diff. one wave
50 & older/ Men/ func. diff. both waves
Under 50/ Severe func. diff. one wave
50 & older/ Moderate func. diff. one wave
Under 50/ Women/ func. diff. both waves
50 & older/ Moderate func. diff. both waves
Under 50/ Men/ func. diff. both waves
50 & older/ Men/ func. diff. one wave
Under 50/ Women/ func. diff.one wave
Under 50/Moderate func. diff. both waves
Under 50/Moderate func. diff. one wave
Under 50/ Men/ func. diff. one wave
0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.2

0.3

0.4

0.5

0.6

0.7

Uganda50 & older/ Severe/ func. diff. both waves
50 & older/ Severe/ func. diff. one wave
50 & older/ Women/ func. diff. both waves
Under 50/ Severe/ func. diff. both waves
50 & older/ Men/ func. diff. both waves
50 & older/ Moderate/ func. diff. one wave
Under 50/ Women/ func. diff. both waves
50 & older/ Women/ func. diff. one wave
50 & older/ Moderate func. diff. both waves
Under 50/ Women/ func. diff. one wave
Under 50/ Severe func. diff. one wave
50 & older/ Men/ func. diff. one wave
Under 50/ Moderate func. diff. one wave
Under 50/ Moderate func. diff. both waves
Under 50/ Men/ func. diff. both waves
Under 50/ Men/ func. diff. one wave
0.0

0.1

Fig. 6.1 Multidimensional poverty adjusted headcount (M0) by subgrouprun, such as selling productive assets. WHO–World Bank (2011) notesthat the onset of disability is linked with a decline in social and economic
wellbeing and an increase in poverty through a number of channels
including stigma, education, employment, inaccessible basic services, andincreased disability-related expenditures. However, the evidence for these138

S. Mitracomplex relationships between disability and deprivations is very limited.
The evidence is scarce (Grech 2015) and is mainly correlational and does
not separate the many causal pathways between disability and wellbeing(Groce et al. 2011; Mitra et al. 2013; Minuzoya and Mitra 2013).
The causal literature is mainly on the pathways from poverty to disability through channels such as malnutrition and working conditions. Thissuggests that policies aiming at poverty reduction in general will reduce
disability prevalence. Poverty, and more broadly, inequalities, may increase
the risk of disability through several pathways, many of which are related
to poor health and its determinants. Poverty may lead to the onset of ahealth condition that results in disability. In LMICs, there is evidence thatmalnutrition leads to disability (Maulik and Damstadt 2007). Other possible pathways include diseases whose incidence and prevalence are strongly
associated with poverty, lack of inadequate public health interventions(e.g., immunization), poor living conditions (e.g., lack of safe water),environmental exposures (e.g., unsafe work environments), and injuries.
Poverty, as a contextual factor, may also increase the likelihood that ahealth condition/impairment/functional difficulty may result in a disability, for instance, if there is a lack of healthcare and rehabilitation services
or if there are barriers to access the services that are available. In addition, stigma associated with a health condition or impairment may leadto deprivations. It might also be worsened by the stigma associated withpoverty. Limited resources in the community, for instance to build accessible roads or buildings, may also make it difficult for an individual withmobility impairment to participate in the community life.
In reverse, the onset of a disability may lead to lower living standards
and poverty through adverse impacts on education, employment, earnings, and increased expenditures related to disability. Disability may prevent school attendance for children and youth with disabilities (Filmer
2008; Mizunoya et al. 2016) and restrict their human capital accumulation, thus leading to limited employment opportunities and reduced productivity (earnings) in adulthood. For onsets during adulthood, disability
may prevent work, or constrain the kind and amount of work a person
can do (Gertler and Gruber 2002), lowering income for the individual
and the household and potentially resulting in poverty. In addition, disability may lead to additional expenditures for the individual and thehousehold, in particular in relation to specific services such as healthcare,transportation, assistive devices, personal care (Mont and Cuong 2011;
Mitra et al. 2017).

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DYNAMICS OF FUNCTIONAL DIFFICULTIES AND WELLBEING

139

6.2.2  Methodology
6.2.2.1 Household Material Wellbeing
I exploit the longitudinal data available for Ethiopia and Uganda to investigate if changes in functional difficulties are associated with changes in
economic wellbeing between two waves two years apart for Ethiopia andone year apart for Uganda. For the asset index, a first-difference modelis estimated where changes in asset score between two waves are associated with changes in control variables at the household level (Table 4.7).
Based on the human development model (Chapter 2) and the associations between economic deprivations and functional difficulties found in
Chapter 5, a functional difficulty decrease/increase is expected to be associated with an increase/decrease in economic wellbeing, respectively.
For work status, the model is described in Box 5. I split the analysis
between individuals who are working at baseline and those who are not anddifferentiate between increasing and decreasing functional difficulties. The
objective is to investigate separately determinants of work exits, on the one
hand, and return to work, on the other (Mitra and Jones 2017). Among
persons working at wave 1, I also restrict the sample to persons without afunctional difficulty at baseline. I then investigate if an increase in functionaldifficulty is associated with an increase in the probability of transitioning into
not working. Likewise, among persons not working at wave 1, I investigate thedecrease in functional difficulty and its association with returning to work forthe sample of the initially not working with a functional difficulty in wave 1.
Box 5: First-difference model of work status

�yi = β �Fi +5γk �xk,i + �εi

(6.2)

k=1

where:– The symbol  refers to the difference of a given variable between
wave t+1 and t;– yi denotes changes in work status for individual i: work exit andreturn to work transitions are considered in turn as dependent
variable.
– Fi is the change in functional difficulties: for work exit, thesample includes persons with no difficulty and working in wave
1 and a value of ‘1’ refers to a new difficulty in wave 2, a value of

140

S. Mitra‘0’ refers to no change; for return to work, the sample includes
persons with a difficulty and not working in wave 1, a value of‘1’ refers to no longer having a difficulty in wave 2, and ‘0’ refers
to still having a difficulty in wave 2.
– β is the estimated coefficient for Fi and is reported in Table 6.3.
– The set of control variables includes k potentially time-varying
characteristics xk,i (marital status, household head, household
size, distance to healthcare services, rural) while time-invariant
characteristics (e.g., sex, birth year) are differenced out.
– γk is the set of estimated coefficients of the k control variables.
– �εi captures changes in time-varying unobserved factors.

6.2.3   Results and Discussion
6.2.3.1 Household Material Wellbeing
Are changes in functional status associated with changes in householdmaterial wellbeing in the short-term? It might be that following an onsetof a functional difficulty, households sell assets to pay for healthcare orcompensate for lower earnings.
In the interest of space, results of the asset index are discussed here
but are not shown in a table. Changes in functional difficulties are notsignificantly associated with changes in asset ownership for Ethiopia andUganda.
The differences in asset ownership shown earlier in Chapter 5(Table 5.7) for Ethiopia may reflect cumulative effects that take placeover the medium and long term and could not be identified here withdata following households over the short term. They may also reflect
other links between functional difficulties and assets/living conditions,
including of course reverse causality from poor assets/living conditions
to the onset of functional difficulties or other factors that affect both
assets/living conditions and functional difficulties.
6.2.3.2 Work Status
Results are presented in Table 6.3 for changes in work status and functional difficulties. Column (i) considers if new functional difficulties in wave 2 are associated with work exits among workers in wave 1.
The model gives a significant association between increased functional

6

DYNAMICS OF FUNCTIONAL DIFFICULTIES AND WELLBEING

141

Table 6.3 Odds ratio of work exit or return to work and change in functionaldifficulties
Dependent variable
Functional difficulty transition

Work exit
Difficulty in wave 2 only vs.
No difficulty in any wave(i)Return to work
Difficulty in wave 1 only vs.
Difficulty in both waves(ii)

1.72**
1.37
2.19***
1.11
2.14***

4.61***
3.60*
7.92**
4.90*
4.69**

2.04**
2.11**
1.63
0.86
3.08***

3.47***
2.32
6.10**
3.31
4.08**

Severe difficulty
Ethiopia
All
FemalesMales
Age 15–49
Age 50 and older
UgandaAll
FemalesMales
Age 15–49
Age 50 and older

Any difficulty (moderate and severe)
Ethiopia
All
FemalesMales
Age 15–49
Age 50 and older

1.13
1.14
1.11
0.86
1.44**

2.99***
2.39***
5.40***
1.78
3.66***

0.77
0.63
0.94
0.56*
1.25

2.67***
2.38**
3.32**
2.16*
2.37*

UgandaAll
FemalesMales
Age 15–49
Age 50 and older

Notes Diff. stands for difficulty. Each estimated coefficient is from a separate logistic regression as
explained in Box 5. In column (i), the sample includes all individuals working in wave 1 and not reporting a functional difficulty in wave 1. In column (i), a difficulty in wave 2 only refers to: in the upper
panel, among persons with no severe difficulty in wave 1, a new severe difficulty in wave 2; and in thelower panel, among persons with no difficulty in wave 1, a new moderate or severe difficulty in wave
2. In column (ii), the sample includes all individuals not working in wave 1 and reporting a functionaldifficulty in wave 1: no longer experiencing a severe difficulty in the upper panel and no longer experiencing any difficulty at all in the lower panel. ***significant at the 1% level, **significant at the 5% level,*significant at the 10% level

142

S. Mitradifficulty and rising odds of a work exit when individuals experience new
severe difficulties in wave 2 (top panel). This result holds for the entire
sample of workers and for older workers in both countries. For instance,
for Ethiopia, having a new severe functional difficulty is associated withhaving 1.7 times higher odds of leaving work.
In contrast, for both countries, when all new functional difficulties,
whether moderate or severe, are considered (bottom panel), no significant association is found for the entire sample and all subsamples except
persons 50 and older in Ethiopia and persons 15–49 in Uganda.
Column (ii) assesses if changes in functional difficulties are associatedwith return to work among persons who did not work and had functional difficulties in wave 1. In both countries, no longer experiencing
a functional difficulty in wave 2, whether of any degree or severe only,is significantly associated with higher odds of working in wave 2. For
Uganda, persons who no longer experience a functional difficulty haveodds of working in wave 2 that are 2.7 times higher than persons who
still have a functional difficulty.
Does this model identify the causal impact of functional difficulties on work? Compared to the regression models used in Chapter 5(Table 5.4), the model of Box 5 removes the potential bias of omitted
variables associated with time-invariant characteristics (e.g., personality traits such as low self-esteem) that may be correlated with both economic outcomes on the one hand, and reports of functional difficulties,
on the other. However, there is still the possibility that these estimatesare biased by other factors that change over time, affect both functionaldifficulties and work status and are not measured here (e.g., exposure
to violence). In addition, in each wave, the data on functional difficulties and work were collected at the same time. In other words, in column (i), the new functional difficulty and work exits are observed at thesame time in wave 2, and there is no indication of which one preceded
the other. Hence, although it is plausible that this temporal associationreflects a mechanism whereby functional difficulties impact work status,
these results cannot for certain establish a causal link from functionaldifficulties to work status. More econometric research and qualitative
research are needed to isolate and demonstrate the causal links between
functional difficulties and work in the context of LICs. Longitudinal
datasets that follow individuals over longer periods of time and for morethan two waves would help further research in this field.

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DYNAMICS OF FUNCTIONAL DIFFICULTIES AND WELLBEING

143

Nonetheless, these results offer suggestive evidence that functionaldifficulties may have a negative economic impact through work. New
functional difficulties are associated with lower odds of work and nolonger reporting difficulties comes with higher odds of return to workin the short term. These findings have implications for public policy.
There may be a need for rehabilitation services in an LMIC context toassist people continue working or return to work following the onsetof a health deprivation. The availability of vocational rehabilitation services in an LMIC context is limited (WHO–World Bank 2011). In some
LMICs, there are programs focused on those injured in the workplace(e.g., Malaysia). In more and more LMICs, there are community-based
rehabilitation programs, the efficacy of which is often not evaluated(WHO–World Bank 2011; Mitra et al. 2014). Exceptions are some studies reviewed by Sharma (2007) and recently Mauro et al. (2014, 2015).

6.3   Functional Difficulties and Mortality Within
2 Years
LICs have the highest adult mortality rates in the world (Rajaratnam
et al. 2010), and reducing premature adult mortality rates is fundamental
to improve wellbeing and to promote sustainable development. Yet dataon adult mortality is severely lacking in LICs, as they often do not havevital registry systems. Population-based surveys can offer a way of assessing the overall health of a population (e.g., Rathod et al. 2016).
The objective of this section is to use longitudinal population-level dataon mortality collected as part of the LSMS in Ethiopia, Malawi, Tanzania,
and Uganda to investigate the association between functional difficulty, on
the one hand, and short term mortality, on the other. If important associations are found, then functional difficulty indicators may be considered
as potential indicators of vulnerability to mortality for policy purposes.
6.3.1  Methodology
Mortality data was collected as part of the four longitudinal LSMS surveys described in Chapter 3. During a household revisit, the household respondent was asked about each member of the household who
was listed as member of the household during the prior wave. In case amember is no longer a part of the household, the household respondent

144

S. Mitrawas asked why the person is no longer a member and death is one ofthe possible reasons listed in the questionnaire. While this survey-based
data may provide useful insights, it is limited in that the death cannot be
verified and the cause and timing of death are also not known. Certain
stigmatized causes of death such as HIV/AIDS may lead to death underreporting. In the four countries under study, and especially in Malawi, it
is likely that HIV/AIDS is a significant cause of death. This section uses
a lagged model that exploits the longitudinal data as shown in Box 6.
Box 6: Lagged logistic model of mortality

A logistic regression is run as follows:Mortalityi,t+1 = α + β1 Severei,t + β2 Moderatei,t +


k

where

γk xi,t,k + δzi,j,t + εi,t(6.3)

– Mortalityi,t+1 is a variable indicating if individual i interviewed ininitial wave t died by wave t+1 two years later (1 if dead, 0 otherwise).
– α is the intercept;– Severei,t is a variable equal to 1 if the individual i had a severefunctional difficulty in the initial wave t, 0 otherwise;– Moderatei,t is a dummy variable equal to 1 if the individual i hada moderate functional difficulty in initial wave t, 0 otherwise;– β1 and β2 are the coefficient of the functional difficulty variables,
to be estimated;
They are the coefficients of interest, and their values are reported
for each country in Table 6.5.
– xi,t,k is a set of k control variables for personal factors (age categories, male) and structural factors (being married, being
the household head, having a mother with no prior schooling,household size, distance to healthcare services.4)– γk are the estimated coefficients for the set of k control variables.
– zi,j,t is a binary variable indicating if the person’s household
is in the bottom quintile based on the asset index described in
Chapter 5.
– δ is the estimated coefficient for zi,j,t.
– εi,t is the error term for person i.

6

145

DYNAMICS OF FUNCTIONAL DIFFICULTIES AND WELLBEING

In a variant of (6.3), the functional difficulty variables are replacedby the functional score defined earlier in Chapter 3. Results are also
reported in Table 6.5.

6.3.2   Results and Discussion
Adult mortality rates are presented in Table 6.4. They range from alow of 12.4/1000 persons in Uganda to a high of 29/1000 persons
in Malawi. In all four countries, men have higher mortality rates than
women. Mortality rates are consistent with rates found from other population-based surveys in LICs in Africa.5 Malawi’s mortality rates stand at
more than twice those of Ethiopia, Tanzania, and Uganda, which may be
due in part of the higher prevalence of HIV/AIDS in Malawi.
Descriptive statistics for the entire sample and the subsamples of persons who died are in Appendix A8. Entire samples have a mean age ofabout 34 years and mostly include rural residents. As expected, comparedwith the entire sample, persons who have died were older, more likely toreport a functional difficulty and to have fewer assets in the prior wave.
Table 6.5 reports results of the model of Box 6. It gives the odds of
dying in the short run, given a functional difficulty (or a functional scorevalue) in the prior wave. Results are given separately by sex and age group.
Severe functional difficulties and the functional score are significantly andpositively associated with short-term mortality in all four countries for men
and women, for people younger or older than 50. For example, in Tanzania,
Table 6.4 Rates of mortality within two years among adults (deaths/1000)

All adults
Men
Women
Adults younger than 50
Adults 50 and older

Ethiopia

MalawiTanzania

Uganda

13.4
16.2
10.6
7.4
40.3

29
31.3
26.7
17.1
85.4

12.7
15.5
10.2
8
32.7

12.4
14.7
10.2
6
43.1

Sources Author’s calculations using Ethiopia Rural Socioeconomic Survey (2011/2012, 2013/2014),
Malawi Integrated Household Survey (2010/2011, 2012/2013), Tanzania National Panel Survey(2010/2011, 2012/2013) and Uganda National Panel Survey (2009/2010, 2011). Note Estimates areweighted

146

S. MitraTable 6.5 Odds ratio of short term death by functional difficulty status
Ethiopia

MalawiTanzania

UgandaModerate functional difficulty
Severe functional difficulty

2.53***
13.86***

2.13***
8.98***

1.82*
6.18***

1.71
9.23***

Functional difficulty score

8.07***

10.37***

6.81***

7.69***

Moderate functional difficulty
Severe functional difficulty

2.84**
10.79***

2.55***
2.99**

2.52**
9.99***

7.62***
26.44***

Functional difficulty score

7.97***

5.37***

7.89***

8.70***

Moderate functional difficulty
Severe functional difficulty

2.49**
9.90***

1.21
6.79***

3.13***
7.26***

2.55**
8.75***

Functional difficulty score

9.59***

7.67***

10.12***

6.65***

Moderate functional difficulty
Severe functional difficulty

1.39
7.05**

1.45
2.28**

1.01
4.28***

1.39
5.64***

Functional difficulty score

5.99**

3.46***

5.36***

5.75***

Men

Women

Younger than 50

50 and older

Sources Author’s calculations using Ethiopia Rural Socioeconomic Survey (2011/2012, 2013/2014),
Malawi Integrated Household Survey (2010/2011, 2012/2013), Tanzania National Panel Survey(2010/2011, 2012/2013) and Uganda National Panel Survey (2009/2010, 2011). Notes For eachcountry, two logistic regressions are run. For the first one, the coefficients of moderate difficulty andsevere difficulty dummies are reported on separate rows (no functional difficulty is the reference category). For the second one, the coefficient of the functional difficulty score (marginal effect) is reported.
Coefficients are odds ratios. ***significant at the 1% level, **significant at the 5% level, *significant at
the 10% level. The regression controls are as follows: age, sex (for the sub sample by age), being married,
having a mother with no prior schooling, household asset bottom quintile, being the household head,household size, distance to healthcare services and rural. For Tanzania, data was missing for distance tohealthcare services for a sizeable share of the sample, community fixed effects were thus used insteadthe odds ratios of dying within two years for a woman with a severe functional difficulty are 9.99 times those of a woman with no functional difficulty, everything else held constant. For moderate functional difficulties, anassociation is found for women in the four countries and for people younger
than 50 in three out of four countries. Across all countries and subgroups, a
1% increase in the functional score increases the odds of dying by 5 to 10%.

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DYNAMICS OF FUNCTIONAL DIFFICULTIES AND WELLBEING

147

Fig. 6.2 Odds ratio of death within two years by functional difficulty type (anydegree) in wave 1.
Note: The estimated odds ratio for selfcare in Tanzania stands at 7.74 and is notshown in the graphThis model is also used by replacing the severe and moderate functional difficulty variables with the type of functional difficulty the person
experienced in the initial wave. Results are presented in Fig. 6.2. It plots
the odds ratios of death within two years given functional difficulty types(e.g., seeing) in the initial wave and their confidence intervals. A lower
bound of the confidence interval above one indicates significantly higher
odds of experiencing death within two years compared to a person without this functional difficulty type, everything else held constant. Only one
type of functional difficulty, walking, is consistently and significantly associated with higher odds of death in all countries. Having a seeing difficulty is associated with higher odds of death in three countries (Ethiopia,
Malawi, and Uganda). There is an association for Selfcare in two countries(Tanzania and Uganda), for communication in Ethiopia and concentrating in Uganda, and none for hearing. Similar results were reached when
the analysis was restricted to severe functional difficulties (Appendix A9).6
Of course, this model is unable to determine a causal relationshipfrom functional difficulties to mortality. It only points at an association.

148

S. MitraDespite this caveat, the results have noteworthy implications. The
association between functional difficulties and mortality in the shortterm has implications for research on disability and poverty. The association between disability and economic inequalities measured at a given
point in time as done earlier in Chapter 5 may be affected by a disproportionate risk of mortality associated with disability in the context of
LICs. It is therefore possible that due to mortality, the association
between disability and poverty using survey data at one point in time
may underestimate the true extent of the association between disability
and poverty given the disproportionate risk of mortality among the poor
with disabilities. Further research is needed on the links between disability, poverty, and mortality.
More broadly, persons with functional difficulties experience higher
odds of mortality in the short term. This should be taken into account inpolicies and programs aimed at reducing mortality among adults, including premature mortality. Functional difficulties may be part of, or linked
to, determinants of premature mortality, and yet they are not part of initiatives such as the 25 × 25 initiative signed by WHO members states in2011 to cut mortality due to noncommunicable diseases by 25% by 2025
(WHO 2013).

6.4  Conclusion: Summary and Implications
This chapter uses longitudinal data for Ethiopia, Malawi, Tanzania, andUganda to assess some potential dynamic links between functional difficulties and wellbeing.
1. The functional difficulty trajectories of individuals are significantly
associated with different levels of wellbeing. Persons with persistent functional difficulties are worse off than persons with functional difficulties in only one wave, who are in turn worse off thanpersons with no functional limitation in any wave.
2. Among persons with functional difficulties, older persons, older
women and persons experiencing persistent severe difficulties tend
to be the groups the most multidimensionally poor.
Longitudinal data collection and monitoring of functional difficulties and wellbeing outcomes are needed to identify the trajectory

6

DYNAMICS OF FUNCTIONAL DIFFICULTIES AND WELLBEING

149

of functional difficulties and vulnerable groups. This is required forpolicies and programs that aim to reduce extreme poverty in general and to target vulnerable groups in particular.
3. No significant association was found between changes in functional
status and changes in assets/living conditions in the short term.
4. New functional difficulties lower the odds to continue working,and no longer reporting functional difficulties increases the odds ofreturning to work.
These findings suggest that there may be a causal link from functional difficulties to work status.
These findings, together with the consistent and strong association between functional difficulties and work outcomes foundin Chapter 5, suggest that there may be a need for rehabilitation services in an LMIC context to assist people to continue
working or return to work following the onset of a healthdeprivation.
5. Functional difficulties are consistently found to be associated withmortality in the short term.
More attention is needed to functional difficulties as potential
determinants of mortality, including premature mortality. Moreresearch is also needed on the links between disability, poverty, andmortality as excess mortality may reduce the association between
disability and poverty in LICs.

Notes
1. Attrition was very limited for both datasets.
2. M0 is explained earlier in Chapter 5.
3. Groups could not be further disaggregated due to small sample sizes: for
instance, the sample could not be disaggregated by age, sex, trajectory andseverity.
4. Descriptive statistics are in Table 4.4.
5. For instance, for Zambia, Rathod et al. (2016) find a mortality rate of
16.2/1000 for men and 12.3/1000 for women.
6. Given the association between functional difficulties and resources, especially asset ownership, the model is also run without economic quintile asa control. Results were overall similar for the functional difficulty/scorevariables.

150

S. Mitra

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S. MitraOpen Access This chapter is licensed under the terms of the Creative Commons
Attribution 4.0 International License (http://creativecommons.org/licenses/
by/4.0/), which permits use, sharing, adaptation, distribution and reproduction
in any medium or format, as long as you give appropriate credit to the original
author(s) and the source, provide a link to the Creative Commons license andindicate if changes were made.
The images or other third party material in this chapter are included in thechapter’s Creative Commons license, unless indicated otherwise in a credit line
to the material. If material is not included in the chapter’s Creative Commonslicense and your intended use is not permitted by statutory regulation or exceeds
the permitted use, you will need to obtain permission directly from the copyrightholder.

CHAPTER 7

Main Results and Implications

Abstract This chapter summarizes the main results of the book. It
derives implications for policies and programs, data and research.
Overall, this book offers a new understanding and analysis of the links
between disability and wellbeing through the human development
model and panel data. The book shows that disability needs to be considered from multiple angles including aging, gender, health, and poverty. It also suggests that disability policies are unlikely to be conducive
to human development for all if they focus exclusively on changing theenvironment and are based on an oppressed minority group approach.
This book concludes with a call for inclusion and prevention interventions as the sustainable solutions to the deprivations associated with
impairments and health conditions.
Keywords Disability · Inclusion · Prevention · Washington Group ·AfricaJEL I1 · I3 · O15 · O19
This chapter summarizes the main results of this book for each of thefour research questions. I then derive implications for policy, data, andfurther research.
© The Author(s) 2018
S. Mitra, Disability, Health and Human Development,
Palgrave Studies in Disability and International Development,
DOI 10.1057/978-1-137-53638-9_7

153

154

S. Mitra7.1  Summary and Some Implications
7.1.1   How Should Disability Be Defined to Analyze and Inform
Policies Related to Wellbeing?
1. This book introduces the human development model of disability,
health, and wellbeing. It is a conceptual framework developed to define
disability, describe and explain health deprivations, their causes, andtheir consequences on wellbeing. The model is based on the capability
approach of Amartya Sen and informed by the socioeconomic determinants of health. It defines disability as a deprivation in terms of functionings and/or capabilities among persons with health deprivations(impairment and/or health condition). Health deprivations and disability result from the interaction of personal factors (e.g., sex, age), structural factors (e.g., policies, social attitudes, and physical environment),
and resources (e.g., assets, information). It highlights the role of conversion factors and agency in shaping health deprivations and wellbeing. It
is universal in that any individual is vulnerable to health deprivations andthus is at risk of disability. It points toward the need for inclusion andprevention interventions in health and disability for human development.
The human development model is applied using panel household survey data for Ethiopia, Malawi, Tanzania, and Uganda with the
Washington Group short set of questions on six functional difficulties(e.g., seeing, walking) as a measure of health deprivation. The primary
focus of the empirical part of the book is descriptive given the scarcity
of studies on functional difficulties and wellbeing. Some of the results
did vary across countries, while at the same time some patterns emerged,
and these patterns for the four countries under study are summarized
below. These results dispel some myths around disability when the latteris measured through functional difficulties.
7.1.2   What Is the Prevalence of Functional Difficulties?
2. Functional difficulties are not rare among adults in the four countries.
The prevalence of functional difficulties (moderate or severe) ranges
from 11% in Malawi to 15% in Tanzania and Uganda. The prevalenceof severe difficulties is under 2% in Malawi and close to 4% in Ethiopia,
Malawi, and Uganda.

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MAIN RESULTS AND IMPLICATIONS

155

These results are consistent with recent findings on prevalence (e.g.,
WHO–World Bank 2011; Mitra and Sambamoorthi 2014) that dispel
the myths that disability is rare and affects a small minority and that disability is an issue pertinent only in the context of HICs.
3. Seeing and walking difficulties are the most common functionaldifficulties, followed by hearing and concentrating difficulties.
4. A strong age and socioeconomic gradient in the prevalence of functional difficulties is found. About half of individuals 65 or older reportfunctional difficulties. Women are disproportionately more likely to experience difficulties, whatever their age group. Households in the bottom
quintile of the asset or expenditure distribution are 1.5–2 times more
likely to have a functional difficulty compared to households in the top
quintile.
5. Very few persons with functional difficulties use assistive devices(e.g., glasses) or healthcare services that could reduce such difficulties,
so some of these difficulties may be preventable. This provides suggestive
evidence that poverty may cause functional difficulties, at least in part.
6. Functional difficulties are not static. For Ethiopia and Uganda,
where individuals are interviewed twice about their functional difficulties,
a lot of persons with functional difficulties at baseline do not report suchdifficulties a year or two years later, and vice versa. Functional difficulties may change overtime and are fluid. This result dispels the myth thatfunctional difficulties are static or permanent.
7. Individuals experience various degrees of functional difficulties anddeprivations. The diversity in the degree of functional difficulties is correlated with the intensity of deprivations. There is not a dichotomous
state of disability vs. no disability.
The previous two results imply that we are not referring here to awell-defined minority group, contrary to common perceptions andsome of the arguments under the social model and the identity politics
approach of the disability rights movement. Persons with functional difficulties are a large and fluid group of people, some with intermittent or
temporary difficulties. Some would likely not self-identify as having adisability and may never be connected to disabled people organizations.
This point, made earlier (Shakespeare 2014), is supported by the empirical evidence in this book. It is also consistent with the point made by
Fujiura (2001) that disability is an ambiguous demographic.

156

S. Mitra7.1.3   What Inequalities Are Associated with Functional Difficulties?
8. There is a significant, consistent, and large association between
functional difficulties and deprivations. Among adults, functional difficulties are significantly associated with deprivations in employment,
morbidity, and living conditions, economic insecurity and short-term
mortality. Functional difficulties are also correlated with multidimensional poverty.
9. While persons with functional difficulties are a disproportionately
large share of the poor, not all persons with functional difficulties arepoor. Some persons with functional difficulties do achieve levels of wellbeing comparable to persons with no difficulty. This result dispels themyth that persons with disabilities are always among the poorest of thepoor. Having a functional difficulty is not synonymous with being poor
but considerably increases the odds of being poor, even in the poorest
countries.
These last two results indicate that in Ethiopia, Malawi, Tanzania, andUganda, persons with functional difficulties often experience deprivations. In a context where most people are poor and where there is little
in terms of a social safety net, persons with functional difficulties experience a greater breadth and depth of deprivations than persons without
any difficulty. Structural and resource factors contribute to this situation,although this book could not precisely isolate the extent to which structural barriers and resource constraints contribute to deprivations and tofunctional difficulties.
10. The association between functional difficulties and deprivations
varies depending on the trajectory of functional difficulties overtime in
Ethiopia and Uganda: Persons with persistent functional difficulties areworse off than persons with a one-time self-report of functional difficulty. Several subgroups are worse off among persons with functional difficulties: older persons, older women, and persons with persistent severefunctional difficulties.
11. In all four countries, individuals with functional difficulties havehigher odds of mortality within the next two years, everything else held
constant. There is a large and consistent association between severe functional difficulties and mortality. There is a smaller but significant association between moderate difficulties and mortality for women and adults
younger than 50.

7MAIN RESULTS AND IMPLICATIONS

157

7.1.4   What Are the Economic Consequences of Functional
Difficulties?
12. Having increasing functional difficulties is associated with higher
odds of leaving work in Ethiopia and Uganda, especially among older
adults. It provides suggestive evidence that functional difficulties are acausal factor of poverty through the work channel.

7.2  Implications for Policies and Programs
This book’s conceptual framework and empirical findings have several
policy implications.
Results on the prevalence of functional difficulties and their association with deprivations show that functional difficulties are relevant todevelopment policy. Disability measured through functional difficulties isindeed highly correlated with deprivations and poverty, whether material
or multidimensional. Although Ethiopia, Malawi, Tanzania, and Uganda
have national disability policies and legislations and have ratified the
CRPD, more policy work is needed to curb the stark inequalities acrossfunctional status shown in this book. Current economic systems andsocieties in the LICs under consideration fail to provide ways to include
persons with functional difficulties.
These findings provide ammunitions to demand interventions andpolicies in the form of the prevention of functional difficulties and theinclusion of persons with functional difficulties. Broadly, education,social protection programs, healthcare coverage, and labor market interventions are policy areas that need to address disability for inequalities tobe reduced. In the context of recent calls to ‘leave no one behind’ in theSDGs, this book shows some of the gaps that need to be closed: ‘taking
on inequality’1 requires taking on disability.
The results also show that disability is a crosscutting, not a specialist,
issue. The human development model and its application to four countries in Africa show that in policy and research, disability needs to beconsidered in policies related to aging, health, gender, and poverty.
The findings imply that disability should not be seen as a policy issue
that is the luxury of high-income and aging economies.
Functional difficulties seem to be preventable, at least in part, as evidenced by very limited access to assistive devices (e.g., glasses) andhealthcare services, pointing toward the need for prevention policies with

158

S. Mitrarespect to health conditions/injuries (global health, public health) andfunctional difficulties (assistive devices, rehabilitation).
Functional difficulties seem to cause poverty, at least via work exits.
While accessing work has received attention in the disability and development field, more attention is needed with respect to retaining work following the onset of a functional difficulty.
Overall, these empirical results as well as the human development
model suggest that multiple track approaches are needed including atleast inclusion and prevention interventions. Disability models and policies that leave out prevention are unlikely to be conducive to humandevelopment for all. They do not cover the many people with temporarydifficulties or late life onsets who may not self-identify as having a disability and are not connected to disabled people organizations. They also
ignore the potential wellbeing enhancements that prevention may bringabout.
Despite the recent development of social protection programs in thefour countries under study, including cash transfer and public works programs, inequalities across functional difficulty status are stark. The exact
impact of social protection programs related to disability needs to beassessed.
More specific policy implications need further analysis at the country
level. For instance, on employment policy in the context of Ethiopia,
Malawi, Tanzania, and Uganda, with a relatively low employment rate
for persons with functional difficulties, one needs to find out why thisis the case. It could be due to how the underlying health deprivations
reduce the productivity of persons with functional difficulties for thetypes of jobs that are available. It could be due to a lack of access to
assistive devices. It could be due to structural factors, for instance, aphysically inaccessible work environment or negative attitudes in thecommunity. Once the main causes for low employment rates are better
understood, it becomes feasible to develop evidence-based programs andpolicies to facilitate employment. The results and data presented in thisbook show the need for such analysis.

7.3  Implications for Data
Functional difficulty indicators need to become standard in household
surveys in LMICs, as well as in the monitoring systems of NGOs andgovernments, to inform the development of disability-inclusive policies

7

MAIN RESULTS AND IMPLICATIONS

159

and programs. The use of the Washington Group recommended questions in surveys and monitoring systems would provide some of the necessary data for this monitoring to become feasible across countries.
A measure of functional difficulties should be included as a standard correlate in studies of poverty and economic wellbeing. It would beinconceivable not to include age or gender or marital status variables ascorrelates. Likewise, applied researchers should at least include a measure such as the Washington Group short set as a potential correlate ofpoverty. There is also a need to disaggregate poverty statistics such as the$1.90 a day or the MPI and more broadly relevant SDG indicators of the2030 Agenda for persons with functional difficulties.
More generally, there is a need for internationally produced disability statistics with an academic or an international organization as thescorekeeper. It may have an immense effect on development practice anddebates related to disability, health and human development.
More work is needed in terms of disability measurement. For instance,
recommendations on the use of the Washington Group questions focus
on the group with severe functional difficulties. Analyses should try toincorporate the degree of functional difficulties through different categories or a functional score. Analyses that focus on persons experiencing
severe functional difficulties leave out persons with moderate functionaldifficulties who are at risk of poverty.
In addition, information is lacking considerably and more data isneeded on structural factors (e.g., social norms, attitudes, and physicalenvironment) and on health deprivations (e.g., health conditions) that
may lead to functional difficulties and/or deprivations. Data collection
efforts that collect information on health deprivations such as the Studyof Global Aging and Adult Health of WHO and on environmental factors such as the Model Disability Survey (WHO–World Bank 2015) are
steps in this direction.
The LSMS data used in this book is rich and yet ripe with limitations for the purpose of this study. It focused on a small set of wellbeing
dimensions, often economic in nature. For instance, it had no information on individual subjective wellbeing or on social connections.
Finally, the LSMS data used here could only follow individuals for up
to two years. Because of the particularly dynamic nature of functionaldifficulties during adulthood and the common transitions experienced byadults, it is important to avoid a single point-in-time survey contact andincorporate functional difficulties in longitudinal datasets.

160

S. Mitra7.4   Further ResearchThis book has highlighted a number of areas where more research isneeded.
The human development model needs to be developed further, andits synergies with the ICF need to be considered. It also needs to be
applied with data that captures more aspects of the model, including
agency and structural factors (e.g., stigma).
There are puzzles with respect to the links between mortality andfunctional difficulties: What interventions would prevent onsets, improve
recoveries or at least delay their progression to mortality? To what extent
are there ‘missing persons with disabilities’2? In other words, to what
extent is the excessive mortality the result of the negative treatment or
neglect of persons with disabilities and how can that be stopped?
More research is necessary on dimensions of wellbeing that this study
did not have data on, such as subjective wellbeing, political voice andgovernance, social connections, and relationships.
In addition, qualitative, mixed methods and participatory studies are
required to complement the quantitative analysis in this book by trying
to understand the results in their complex contexts and by listening tovoices and perceptions.
More research is also needed on program or policy evaluations. Social
protection programs seem to disproportionately reach persons withfunctional difficulties in some countries and yet do not appear to manage to do away with inequalities. Some policies and programs that some
LMICs have adopted after ratifying the CRPD need to be assessed. Not
all such assessments need to be quantitative and large scale in nature. For
instance, Díaz Ruiz et al. (2015) do a content analysis of the intentionsof a home-based care program in Chile targeted at persons with severe
disabilities and find that the program is unlikely to enhance the wellbeingof this group as per the capability approach. This methodology could be
used for other policies and programs and is not so resource intensive; itrelies primarily on a desk review of policy documents.
Finally, this book’s empirical findings were focused on deprivations.
Research is needed on successful case studies. The case of Richard who
accompanied us throughout Chapters 1 and 2 illustrates this point. Since
contracting polio at age six, Richard has had a severe walking difficulty.
Richard grew up facing countless challenges associated with poverty anddisability. Yet, today, as an adult, Richard does not experience any of the7

MAIN RESULTS AND IMPLICATIONS

161

deprivations measured in this study. If not all persons with functional difficulties are poor, and some do achieve levels of wellbeing comparable topersons with no difficulty, it seems key to understand why. Are there personal, structural, or resource factors that helped them maintain or boost
their wellbeing and to what extent can these factors work for other people?

Notes
1. ‘Taking on Inequality’ is in reference to World Bank (2016).
2. This is in reference to ‘missing women’ in Sen (1990).

References
Díaz Ruiz A., N. Sánchez Durán & A. Palá (2015). An analysis of the intentionsof a Chilean disability policy through the lens of the capability approach. Journalof Human Development and Capabilities, 6(4), 483–500.
Fujiura, G. T. (2001). Emerging trends in disability. Population Today, 29(6),9–10.
Mitra, S., & Sambamoorthi, U. (2014). Disability prevalence among adults:
Estimates for 54 countries and progress toward a global estimate. Disability
and Rehabilitation, 36, 940–947.
Sen, A. K. (1990). More than 100 million women are missing. New York Review
of Books, Dec. 20th, 61–66.
Shakespeare, T. (2014). Disability rights and wrongs revisited (2nd ed.). London:
Routledge Taylor & Francis.
WHO–World Bank. (2011). World report on disability. Geneva: World HealthOrganization.
WHO–World Bank. (2015). Model disability survey. Geneva: World HealthOrganization. http://www.who.int/disabilities/data/mds/en/. Accessed 14
April 2017.
World Bank. (2016). Poverty and shared prosperity 2016: Taking on inequality.
Washington, DC: World Bank. doi:10.1596/978-1-4648-0958-3.
Díaz Ruiz A., N. Sánchez Durán & A. Palá (2015). An analysis of the intentions of a Chilean disability policy through the lens of the capability approach.
Journal of Human Development and Capabilities, 6(4), 483-500.

162

S. MitraOpen Access This chapter is licensed under the terms of the Creative Commons
Attribution 4.0 International License (http://creativecommons.org/licenses/
by/4.0/), which permits use, sharing, adaptation, distribution and reproduction
in any medium or format, as long as you give appropriate credit to the original
author(s) and the source, provide a link to the Creative Commons license andindicate if changes were made.
The images or other third party material in this chapter are included in thechapter’s Creative Commons license, unless indicated otherwise in a credit line
to the material. If material is not included in the chapter’s Creative Commonslicense and your intended use is not permitted by statutory regulation or exceeds
the permitted use, you will need to obtain permission directly from the copyrightholder.

Appendices

A.1 Types of functional difficulties among persons
with moderate difficulties

UgandaTanzania

Malawi

Ethiopia
0%
20%
40%
60%
80%
100%
Hearing Walking
SeeingConcentrating Selfcare Communication© The Editor(s) (if applicable) and The Author(s) 2018
S. Mitra, Disability, Health and Human Development,
Palgrave Studies in Disability and International Development,
DOI 10.1057/978-1-137-53638-9

163

164

AppendicesA.2 Types of functional difficulties among persons
with moderate and severe difficulties

UgandaTanzania

Malawi

Ethiopia
0%

20%

40%

60%

80%

100%

SeeingHearing

Walking

Concentrating

SelfcareCommunication

Appendices165

A.3 Prevalence of severe and moderate functionaldifficulties for the poorest and richest quintiles (%)

By asset index quintile
45
40
35
30
25
20
15
10
5
0
Ethiopia

Malawi

Poorest quintile

Tanzania

UgandaRichest quintile

By per capita expenditurequintile
45
40
35
30
25
20
15
10
5
0
Ethiopia

Malawi

Poorest quintile

Tanzania

UgandaRichest quintile

Note Per capita expenditures are not available for Ethiopia

166

AppendicesA.4 Multidimensional poverty measures
by functional difficulty status

H
Ethiopia
MalawiTanzania
UgandaA
Ethiopia
MalawiTanzania
UgandaM0
Ethiopia
MalawiTanzania
UgandaSevere difficulty

Moderate difficulty

No difficulty

0.89***
0.81***
0.94***
0.90***

0.84***
0.68***
0.87***
0.71***

0.68
0.56
0.79
0.57

0.68***
0.61***
0.62***
0.61***

0.60***
0.55***
0.55***
0.57***

0.56
0.51
0.53
0.53

0.61***
0.50***
0.58***
0.53***

0.51***
0.38***
0.48***
0.37***

0.38
0.28
0.42
0.21

Notes H, A and M0 are defined in Box 2. ***indicates significance at 1% level of the difference comparedto persons with no difficulty. Statistical significance is tested with t-test for A and M0, Pearson’s Chi
square test for H

Appendices167

A.5 Multidimensional poverty by functional difficulty
status, sex and age group
SevereH
Ethiopia
MalawiTanzania
UgandaA
Ethiopia
MalawiTanzania
UgandaM0
Ethiopia
MalawiTanzania
UgandaH
Ethiopia
MalawiTanzania
UgandaA
Ethiopia
MalawiTanzania
UgandaM0
Ethiopia
MalawiTanzania
UgandaModerate

None

SevereModerate

None

Men

Women
0.90***
0.89***
0.95***
0.96***

0.89***
0.74***
0.88***
0.75***

0.73
0.62
0.82
0.62

0.89***
0.71***
0.94***
0.82***

0.79***
0.61***
0.87***
0.65***

0.63
0.50
0.75
0.52

0.68***
0.62***
0.63***
0.62***

0.61***
0.56***
0.56***
0.58***

0.58
0.52
0.54
0.54

0.68***
0.60***
0.60***
0.60***

0.59***
0.54***
0.53***
0.55***

0.53
0.50
0.52
0.52

0.61***
0.55***
0.59***
0.59***

0.54***
0.41***
0.49***
0.41***

0.42
0.32
0.44
0.25

0.61***
0.42***
0.56***
0.46***

0.46***
0.33***
0.46***
0.32***

0.33
0.25
0.39
0.18

Persons 50 and older

Persons under age 50
0.85***
0.72***
0.93***
0.79***

0.80***
0.60***
0.86**
0.66***

0.66
0.54
0.79
0.57

0.92***
0.87***
0.95***
0.94***

0.88***
0.77***
0.89***
0.80***

0.79
0.66
0.80
0.68

0.66***
0.58***
0.58***
0.56***

0.58***
0.53***
0.54***
0.55*

0.55
0.51
0.53
0.50

0.69***
0.63***
0.63***
0.63***

0.62***
0.57***
0.55***
0.58***

0.58
0.52
0.52
0.53

0.56***
0.42***
0.54***
0.43***

0.46***
0.32***
0.47***
0.36***

0.37
0.28
0.42
0.24

0.63***
0.55***
0.60***
0.59***

0.55***
0.44***
0.49***
0.44***

0.46
0.34
0.42
0.33

Notes H, A and M0 are defined in Box 2. Estimates are weighted. ***, ** and * indicates significance at
1%, 5% and 10% levels respectively of the difference compared to persons with no difficulty. Statisticalsignificance is tested with t-test for A and M0, Pearson’s Chi square test for H

168

AppendicesA.6 Regression of the adjusted

multidimensional poverty headcount (M0)Ethiopia
Functional score
Personal factors
Age 15–39 (reference)
Age 40–49
Age 50–64
Age 65+
Male
Resources
Mother had no schooling
Structural factors
Household
MarriedHousehold head
Household size
Community
Distance to healthcare services
Rural
Constant

MalawiTanzania

Uganda0.37***

0.55***

0.34***

0.24***

0.04***
0.09***
0.14***−0.09***

0.02***
0.05***
0.12***−0.08***

−0.02
0.00
0.08***−0.04***

0.03**
0.06***
0.19***−0.09***

0.09***

0.10***

0.03***

NA***

0.02***
0.00
0.00−0.02***−0.01*
0.00**−0.05***−0.04***
0.00

0.03***
0.02*−0.01***

0.00*
0.16***
0.15***

0.00*
0.14***
0.13***

0.00***
0.02**
0.43***

0.00***
0.08**
0.22***

Notes Table includes coefficients of all independent variables in a multivariate regression of the adjusted
multidimensional headcount M0. ***, **, * indicate significance at 1, 5, and 10% levels. Mother’s
schooling is not available for Uganda

Appendices169

A.7 Odds ratio of being deprived in each dimension
by functional difficulty type
Ethiopia
Having less than primary school completion
Seeing
0.84
Hearing
1.64*
Walking
1.48*
Concentrating
1.72*
Communication
1.18
Selfcare0.85
Being sick or injured recently
Seeing2.01***Hearing
1.32**Walking
2.42***
Concentrating
2.03***
Communication
1.28
Selfcare
0.57**
Not working
Seeing1.22**Hearing
1.15
Walking
1.24*
Concentrating
1.56***
Communication
0.99
Selfcare
1.41*
Being materially deprived
Seeing
1.1
Hearing
1.59
Walking
2.31*
Concentrating
0.95
Communication
1
Selfcare0.76
Being economically insecure
SeeingHearing
Walking
Concentrating
Communication
Selfcare

1.2**
1.12
1.05
1.26
0.98
0.97

MalawiTanzania

Uganda0.74***
1.51***
1.36***
1.07
1.38
2.09**

1.04
1.05
2.95***
1.45
2.7
0.59

0.82*
1.63***
1.59***
1.53**
1.82*
4.45***

1.56***
1.22*
2.29***
1.46***
0.86
1.45*

1.06
1.31
1.59***
0.61*
0.51
1.70

1.53***
1.15
1.66***
1.38*
0.93
1.08

1.17**
1.3**
1.78***
1.49***
0.94
3.78***

1.45***
1.07
1.93***
3.01***
2.31*
13.21***

1.33**
1
2.21***
1.87***
2.27***
9.07***

0.85**
1.85***
1.2*
0.84
1.43
1.02

0.83
1.18
1.3
1.19
4.38
0.89

0.97
1.57**
1.33**
1.01
0.72
0.94

1.04
1.44***
1.34***
1.44**
0.79
1.02

3.74***
0.74
1.19
0.82
1.13
1.45

1.34***
1.28*
1.39***
1.52***
0.54**
1.14

Notes For each country, results show the estimated odds ratio of being deprived in one dimension (e.g.
not working) for each functional difficulty (e.g. seeing) compared to a person without such functionaldifficulty in the entire sample of adults. All regressions are run as logistic regressions. Coefficients are
odds ratios. ***significant at the 1% level, **significant at the 5% level, *significant at the 10% level.
More information on the dependent variables is in Table 5.2. The regression controls are as follows:
age categories, sex, being married, being the household head, having a mother with no prior schooling,household size and distance to healthcare

0.17**
0.27***
0.10***(0.02)
53.64***(2.79)
0.60***
NA
0.74***
0.55***
5.21**(0.29)
17.49(1.90)
0.99

0.09
0.03
0.01
(0.00)
34.48(0.24)
0.50
NA
0.63
0.38
5.72
(0.03)
15.30(0.21)
0.99

Died

22.23
(0.39)
0.73

0.62
0.41
5.54
(0.03)

0.92

34.15(0.22)
0.43

0.10
0.01
0.01
(0.00)

MalawiAll

22.79(0.75)
0.70

0.64
0.61***
4.98***(0.19)

0.93*

52.93***(1.83)
0.54

0.18***
0.05***
0.03***(0.01)

Died

5.76(0.13)
0.71

0.40
0.35
3.85
(0.02)

0.59

34.72(0.19)
0.48

0.09
0.04
0.18
(0.00)

TanzaniaAll

5.30(0.77)
0.73

0.44
0.52***
4.17(0.23)

0.80***

53.73***(2.08)
0.53

0.15***
0.21***
0.10***(0.02)

Died

25.51(0.54)
0.78

0.49
0.34
8.46
(0.08)

NA

33.31(0.23)
0.49

0.12
0.04
0.02
(0.00)

UgandaAll

21.93(24.8)
0.84

0.49
0.51***
7.86(0.50)

NA

57.98***(2.50)
0.58

0.23***
0.28***
0.09***(0.13)

Died

Sources Author’s calculations using Ethiopia Rural Socioeconomic Survey (2011/12, 2013/2014), Malawi Integrated Household Survey (2010/2011,2012/2013), Tanzania National Panel Survey (2010/2011, 2012/2013) and Uganda National Panel Survey (2009/2010, 2011). Notes Estimates areweighted
1Mother's schooling, data is not available for a large share of the sample in Uganda and in Ethiopia for the initial wave

Rural

Community
Distance to healthcare

Male
Resources
Mother had no schooling1
Structural factors
Household
MarriedHousehold head
Household size

Personal factors
Age

Moderate functional difficulty
Severe functional difficulty
Functional limitation score

EthiopiaAll

A.8 Descriptive statistics for all adults by mortality status within two years

170
Appendices

n

io

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ar
ic

un

m

m

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at

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n

g

tin
ar
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un

m

m

lfc

Se

tra

ng

ki

g

ng
rin

ea

en

nc

Co

Co

Uganda

Co

lfc

Se

g

tin

ng

ki

tra

en

nc

Co

Tanzania
W
al

H

ei

Se

10
9
8
7
6
5
4
3
2
1
0

W
al

g

rin

ea

H

ng

10
9
8
7
6
5
4
3
2
1
0

ei

io

at

e

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g

tin

ar

ic

un

m

m

Co

lfc

Se

ng

ki

tra

en

nc

Co

Ethiopia

Se

n

io

at

e

ar

ic

un

m

m

Co

lfc

Se

g

tin

ng

ki

tra

en

nc

Co

g

ng

rin

ea

W
al

H

ei

Se

10
9
8
7
6
5
4
3
2
1
0

W
al

g

rin

ea

H

ng

ei

Se

Appendices171

A.9 Odds ratio of short-term death and severefunctional difficulty types

Malawi

10
9
8
7
6
5
4
3
2
1
0

IndexA
Africa, 2, 48, 49, 62, 63, 71, 72, 145,
157
Age, 2, 5, 16, 17, 23, 45, 46, 48, 50,
62, 64, 66, 67, 69, 102, 108,
120, 122, 130, 136, 145, 154,
159, 161
Age at onset, 69, 80, 120
Agency, 4, 10, 14, 18, 23, 25, 27,
154, 160
Agriculture, 48, 91
AIDS, 3, 49, 68, 144, 145
Arthritis, 16, 84
Asset, 3, 5, 26, 43, 44, 46, 50, 78, 83,
91, 94, 97, 101, 103, 109, 112,
130, 139, 140, 144, 149, 154,
155
Assistive devices, 5, 15, 42, 47, 70, 91,
138, 155, 157, 158
B
Barriers, 15, 21, 23, 26, 51, 69, 72,
92, 94, 138, 156
Begging, 94

CCapabilities. See Capability
Capability, 4, 10–14, 16, 18, 19, 25,
27, 28, 36, 37, 41, 42, 44, 53,
98, 154, 160
Capability approach, 10–12, 19
Care, 5, 14, 16, 20, 26, 27, 37, 39,
63, 68, 71, 91, 97
Care providers, 20, 97
Cash transfers, 50
Causal links, 83, 142
CBR, 143
Communication, 39, 68, 119, 147
Community Based Rehabilitation. See
CBRConcentrating, 2, 5, 39, 63, 104, 119,
147, 155
Consumption expenditures, 3, 90, 94
Conversion factors, 10, 12, 14, 18, 23,
97, 114, 154
Conversion function, 4, 14, 23, 26,
27, 91
Convention on the Rights of Persons
with Disabilities (CRPD), 3, 51,
53, 157, 160
Costs of disability, 97© The Editor(s) (if applicable) and The Author(s) 2018
S. Mitra, Disability, Health and Human Development,
Palgrave Studies in Disability and International Development,
DOI 10.1057/978-1-137-53638-9

173

174

Index

D
Data, 2, 4, 5, 10, 28, 33–35, 37, 38,
41, 44–47, 62–64, 77, 80, 83,
90, 93–95, 104, 112, 129, 131,
133, 139, 140, 142–144, 148,
153, 154, 158–160
Deprivation, 2, 3, 6, 11–16, 18,
19, 23, 24, 27, 28, 34–37, 40,
43, 44, 49, 62, 90, 93, 94, 98,
99, 108, 114, 116, 119–122,
132, 133, 136, 138, 143, 149,
154–157, 159, 160
Development studies, 36, 38, 47
Diabetes, 18, 84
Disability
and poverty, 6, 26, 27, 90, 94, 136,
138, 148, 149, 160
Defining disability, 12, 18
Global, 23, 34, 49, 53, 62, 63, 84,
90, 158
policy, 2, 5, 21, 26, 28, 51, 53, 157,
158
prevalence, 3, 35, 62, 63, 80, 83,
138, 157
rights, 2, 155statistics, 39, 159
Disability studies, 3, 43, 63, 91, 93,
94, 130, 161
Disabled people’s organizations(DPOs), 3, 155, 158
Disadvantage, 13, 20, 21, 45, 118
Donors, 2
E
Education, 1, 4, 11, 16, 22, 23, 27,
28, 35, 43, 46, 62, 69, 93, 95,
97, 99, 103, 108, 112, 114, 119,
120, 130, 132, 133, 137, 138,
157
Educational attainment, 3, 5, 19, 44,
90, 91, 93, 97, 103, 104, 131

Education expenditures, 114, 123
Empirical research, 90
Empowerment, 3, 50
Environment. See structural factors
Ethiopia
National Plan of Action, 53
Productive Safety Net Program(PSNP), 50
Rural Socioeconomic Survey, 45,
46, 64, 95, 131
Ethnicity, 17, 21, 44, 45
Extreme poverty, 18, 149
F
Family, 4, 10, 15–17, 44, 45, 97, 121,
130
Food insecurity, 95, 101, 103, 114,
121
Functional difficulty, 5, 41, 45, 47, 66,
68, 70, 73, 77, 78, 80, 97, 101,
103, 104, 108, 109, 112, 114,
116, 118–121, 123, 130, 131,
133, 136, 138, 139, 142, 143,
145, 147, 149, 156, 158
Functional trajectory, 132, 133
Functioning(s), 13G
Gender, 6, 23, 54, 67, 71, 83, 118,
123, 130, 157
H
Healthcare, 3, 45, 49, 51, 62, 103,
132, 133, 140, 157
Health conditions, 2, 3, 5, 6, 10,
12–16, 18–20, 22–27, 34–39, 42,
63, 92, 158, 159
Health deprivations, 13–16, 19,
25–27, 34, 35, 37, 39, 41, 97,
154, 158, 159

IndexHealth expenditures, 14, 27, 49, 92,
93, 103, 112, 114, 123
Hearing, 39, 68, 70, 72, 92, 155
HIC, 2, 10, 21, 62, 63, 66, 73, 90,
91, 130, 155
HIV/AIDS, 144
Household characteristics, 112
Human Development Model, 4, 6, 10,
13, 14, 17, 19, 25
Human Development Index. See HDI
Human rights, 11, 21, 42, 53
Hunger, 121
I
ICF, 21–26, 51, 160
Illness, 36, 50, 97, 131, 136
Impairments, 3, 5, 6, 10, 12–16,
18–20, 22–24, 26, 27, 34–36, 38,
42, 43, 51, 63, 138, 154
Inclusion, 6, 26, 53, 121, 122, 154,
157, 158
Inclusive, 11, 35, 158
Individual characteristics, 17, 44
Inequalities, 3–5, 21, 53, 83, 90, 93,
94, 120, 138, 148, 157, 158, 160
Inequality, 157
Informal sector, 92
Injuries, 2, 49, 138, 158
International Classification of
Functioning, Disability and
Health. See ICF
JJustice, 10, 11, 19, 27
L
Law, 12, 13, 21, 23, 47, 51
Legislation, 5, 51, 53, 157

175

Living conditions, 5, 43, 44, 46, 90,
91, 93, 95, 97, 101, 109, 114,
138, 140, 149, 156
Living Standards Measurement Study.
See LSMS
Low and Middle Income Country(LMIC), 2, 3, 21, 23, 26, 37, 44,
62, 63, 66, 72, 90–94, 97, 112,
121, 136, 143, 149, 158, 160
Low Income Country (LIC), 2, 3,
37, 62, 63, 66, 91, 94, 104, 131,
142, 143, 145, 148, 149, 157
LSMS, 45, 46, 90, 114, 143, 159
M
MalawiIntegrated Household Survey, 45,
46, 64, 95
National Policy on the Equalizationof Opportunities for Persons
with Disabilities, 53
Material wellbeing, 5, 43, 44, 62, 90,
93, 97, 99, 101, 121, 132, 133,
139, 140
Medical care, 20, 26
Medical model, 20, 21, 24, 26, 27
Medication, 71, 123
Millennium Development Goals. See
MDGs
Minority model of disability, 21
Morbidity, 5, 43, 90, 92, 97, 101,
104, 119, 120, 132, 133, 156
Mortality, 3, 5, 18, 46, 49, 62, 94,
130, 143–145, 147–149, 156,
160
Multidimensional poverty, 3, 5, 44,
90, 93, 94, 97–99, 101, 104,
114, 116, 119, 120, 132, 133,
156
Multidimensional poverty index(MPI), 49, 93

176

Index

N
Non-government organizations(NGOs), 3, 158
Nussbaum, Martha, 10, 11, 42, 54
O
Old age, 49, 120
Older people, 37, 41, 48
Opportunities. See Capabilities
P
Personal factors, 12, 14, 17–19, 23,
24, 27, 44, 62, 73, 102, 136,
144, 154
Poverty, 2, 3, 10, 11, 21, 24, 26,
27, 43, 44, 49, 78, 90, 93–95,
97–99, 102, 103, 112, 116,
120–122, 130, 133, 136, 138,
148, 155, 157–159
Poverty dynamics, 136
Prevalence, 3–5, 35, 40, 49, 62–64,
66–68, 72, 76, 78, 80, 83, 99,
145, 154, 155
Prevention, 6, 18, 20, 26, 27, 72, 83,
122, 154, 157, 158
Productivity, 50, 138, 158
Q
Qualitative research, 121, 142R
Race, 17, 21, 44
Rehabilitation, 20, 23, 24, 26, 27, 34,
71, 91, 92, 131, 138, 143, 149,
158
Resources, 4, 10, 12, 13, 15, 17–19,
23, 25–27, 34, 44, 49, 51, 62, 68,
72, 73, 136, 138, 154, 156, 160

S
Schooling, 16, 35, 48, 70, 73, 101,
102, 108, 118, 144
Seeing, 2, 5, 13, 38–40, 63, 68, 72,
83, 119, 147, 154
Seeing difficulty, 147
Selfcare, 2, 5, 22, 39, 63, 119, 147
Sen, Amartya, 4, 10–12, 14, 24, 42,
43, 154
Socioeconomic determinants of health,12, 27, 154
Social model, 20–22, 24, 26, 27, 51,
155
Social protection, 47, 49, 51, 95, 103,
112, 114, 121, 157, 158, 160
Structural factors, 13, 15–19, 23–25,
27, 39, 44, 45, 62, 76, 80, 102,
103, 136, 144, 154, 158–160
Sustainable Development Goals(SDGs), 3, 121, 157, 159
T
Tanzania
National Panel Survey, 45, 46, 64,
95
Persons with Disabilities Act, 5, 51
Social Action Fund, 50
Twin track approach, 53
UUgandaNational Council for Disability Act,
52
National Panel Survey, 45, 46, 64,
95, 131
Persons with Disabilities Act, 51
Social Assistance Grant for
Empowerment Program(SAGE), 50
UNDP, 3, 49, 50, 121IndexUnited Nations, 1, 3, 13, 21, 39, 49
WWalking, 2, 5, 38, 39, 63, 68, 83, 119,
120, 147, 154, 155, 160
Washington Group on Disability
Statistics, 53
Washington Group questions, 46, 47,
62–64, 66, 131, 159
Wealth, 43, 44
Wellbeing, 2–5, 9, 11–14, 16–19, 23,
24, 26, 27, 34–36, 42–44, 46,

177

47, 49, 51, 53, 62, 69, 72, 83,
90, 91, 93–95, 98, 101–103,
108, 114, 116, 120, 121, 130,
133, 136, 139, 140, 143, 148,
154, 156, 158–161
Work
work exit, 139, 142, 158
work hours, 102, 104, 108
work status, 97, 120, 130, 139,
140, 142, 149
World Report on Disability, 23, 63© The Editor(s) (if applicable) and The Author(s) 2018. This book is an open
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