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date: 20 November 2019

Disability and Economic Development

Summary and Keywords

While definitional and measurement problems pose a challenge, there is no doubt that disability affects a noticeable share of the population, the vast majority of whom live in low- and middle-income countries (LMICs). The still comparatively scarce empirical data and evidence suggests that disability is closely associated with poverty and other indicators of economic deprivation at both the country and—if with slightly greater nuance—at the individual/household level. There is also a growing body of evidence documenting the sizeable additional costs incurred by persons with disabilities (PwDs) as a direct or indirect consequence of their disability, underlining the increased risk of PwDs (and the households they are part of) falling under the absolute poverty line in any given LMIC.

Looking ahead, there remains considerable scope for more evidence on the causal nature of the link between disability and poverty, as well as on the (cost-)effectiveness of interventions and policies attempting to improve the well-being of PwDs.

Keywords: disability, health economics, development, socioeconomic inequalities in health, poverty

Introduction

According to the 2011 WHO/World Bank report on disability, one billion people worldwide are living with at least one disability, which amounts to 15.6% of adults aged 18 and over. Out of those, a total of 80% are thought to reside in low- and middle-income countries (LMICs) (WHO/World Bank, 2011).

It is widely believed that there is a close relationship between disability and economic development, in the sense that the odds of being disabled are closely associated with those of living in poverty. And yet there appears to be preciously little empirical evidence on the precise extent and nature of the relationship. The thorough review of the disability and poverty nexus in LMICs by Groce et al. (2011) located a mere 27 studies of mixed quality. This article takes a critical look at the relevant evidence of the association between disability and measures of economic well-being, both across and within LMICs. It also reviews the evidence on the potential extra costs of disability (see also, Suhrcke, 2018).

Assessing the evidence base is important, as persons with disabilities (PwDs) have traditionally suffered from neglect within overall development policy and practice, arguably due to a lack of rigorous evidence. The Millennium Development Goals (MDGs) that were agreed upon among all 191 UN member states (and numerous international organizations) and set for 2015 failed to explicitly acknowledge disability as a development issue (Groce & Trani, 2011). This omission has at least partly been rectified in the Sustainable Development Goals (SDGs; i.e., the successor targets to the MDGs), which comprise a total of 17 goals and 169 targets—numbers high enough to accommodate several references to disability, mainly in the sections related to education, growth and employment, inequality, and accessibility of human settlements, as well as data collection and monitoring of the SDGs.

The second section of this article starts with a brief, general discussion of how the concept of disability can best be defined, and how it can be—or at least thus far has been—measured. How disability is defined and then measured is at the very heart of any assessment of the poverty consequences (and determinants) of disability. While there is variation across measures, there is no doubt about disability affecting a considerable and far from marginal share of the population in LMICs.

The third section examines how closely disability is linked to economic development (and hence poverty); that is, whether disability is—as one might expect—more likely to be found among poor countries and people than among the rich. Despite critical issues around data and measurement, this relationship appears to hold at the country level. At the individual and household level, the empirical evidence remains scarce in the LMIC context. However, a growing evidence base overwhelmingly supports the hypothesized relationship.

Showing correlation between disability and economic outcomes (including poverty) is important because it points to the potential of vicious circles of poverty and disability, yet it is precisely a causal understanding that is needed to credibly inform policymakers, for instance about the extra costs incurred by PwDs as a result of their disability. Therefore, in the fourth section, we critically review the existing literature on one direction of the causal link—the impact of disability on costs at the household and individual level. The final section briefly discusses the policy and research implications of the presented findings.

Defining and Measuring Disability

The UN Convention on the Rights of Persons with Disabilities (UNCRPD)—the international human rights treaty of the United Nations intended to protect the rights and dignity of PwDs—acknowledges that disability is an evolving concept. An early, traditional view took a predominantly medical perspective, viewing disability as a problem of the individual that is directly caused by a disease, an injury, or other health conditions, and that requires prevention interventions or medical care in the form of treatment and rehabilitation (Johnstone, 1998). This is the approach largely followed in the well-known Global Burden of Disease study (Murray & Lopez, 1996), which regularly undertakes to capture the extent of premature mortality and disability from different diseases, injuries, and risk factors, using the metric of Disability Adjusted Life Years (DALYs). The disability component of this metric seeks to capture the functional status of individuals in terms of their capacities, but it ignores the role of environmental factors taken into account in subsequent concepts of disability. This “medical model” of disability soon gave rise to the rather opposing view that attributed disability entirely to the social environment (“the social model”), which, as opposed to medical interventions, saw the solution primarily in social change (Shakespeare, 2006).

The International Classification of Functioning, Disability and Health (ICF) model, adopted by the WHO/World Bank 2011 flagship report on disability, among others, subsequently offered a compromise between the two extreme models. According to this “bio-psycho-social model,” functioning and disability is considered the outcome of a dynamic interaction among health conditions and contextual factors, both personal and environmental. “Disability” became the umbrella term for impairments, activity limitations, and participation restrictions. While the UNCRPD refrains from an explicit definition, this is also how it has conceptualized disability.

A further influential and not necessarily mutually exclusive conceptual view has been put forth by economics Nobel laureate Amartya Sen, who viewed disability as a double handicap that (1) reduces the ability to generate an income (“earning handicap”) and (2) reduces the ability to convert money into good living (“conversion handicap”; Sen, 2004). With such a multitude of definitions and concepts, it has been difficult to come up with a widely accepted harmonized measure of disability (even though in some cases differences in definition and measurement of disability may be explained by differences in the purposes for which they have been collected, e.g., the collection of prevalence data versus the provision of services).

Recent efforts have tried to harmonize measurement internationally. In 2001 the Washington Group on Disability Statistics was set up by the United Nations Statistical Commission as an international, consultative group of experts to facilitate the measurement of disability and the comparison of data on disability across countries. The questions selected by the group use the World Health Organization’s International Classification of Functioning, Disability, and Health as a conceptual framework, with the focus being on functioning in basic actions as opposed to approaches that are based on impairments or bodily functions. The severity scale is used in the response categories to capture the full spectrum of functioning from mild to severe.1

As definitions and measures of disability have evolved, so have global estimates of disability prevalence. In 1981, the WHO Expert Committee on Disability, Prevention, and Rehabilitation put the figure at 10% of the world population, based on “expert opinions” (WHO, 1981). The 2004 WHO Global Burden of Disease (GBD) adopted a predominantly medical perspective and suggested—based on extensive epidemiological modeling—that 19.4% of adults aged 15 and up suffer from severe or moderate disability and 3.8% have a severe disability. In the GBD, disability prevalence is inferred from data on health conditions and impairments alone using available data on distributions of limitations that may result from health conditions and impairments.

The WHO/World Bank (2011) flagship report, which put forth the one billion global disability prevalence estimate mentioned earlier, found that 15.6% of adults (aged 18+) could be considered as disabled, as derived from responses to 15 questions of the WHO’s World Health Survey in 59 countries. More recently, Mitra and Sambamoorthi (2014), using the same survey (but for 54 countries and using a subset of four questions to identify disability), estimated that 14% of adults (aged 18+) have a disability, as measured by having at least one severe or extreme difficulty.

Empirical Evidence on the Relationship Between Disability and (Economic) Development

In an article entitled “Disability and Economic Development,” the obvious question arises: what precisely is the link between disability and (economic) development? Is it indeed the case that—as one would expect—the poorest countries have the highest levels of disability and that as countries grow richer and out of poverty, disability prevalence does decline? Or, taking the relationship to the within-country level, are people with disabilities (or households with disabled members) more likely to be among the poor in a given country?

This section seeks to answer those questions in turn, relying on recent data and on the published evidence, first from a cross-country perspective and then from a within-country perspective. Both perspectives are relevant, and the observed patterns at the cross-country level may be different from those observed among individuals within countries, not least due to the well-known “ecological fallacy” (Robinson, 1950). Before turning to the empirical evidence, this section briefly discusses the potential mechanisms behind what is widely seen as a bidirectional relationship.

As Figure 1 illustrates in a somewhat stylized manner, there are multiple ways in which disability may lead to poverty. For instance, children with disabilities are less likely to attend school than their nondisabled counterparts, with long-term adverse effects on the human capital they accumulate and subsequent lower employment and earnings opportunities in the labor market (Filmer, 2008). Some of the adverse educational and employment outcomes may come about as the result of discrimination in (access to) schools and in the labor market, and some may be driven by reduced productivity (Mitra, 2017). In addition, there are a range of potential extra costs incurred by PwDs that affect the economic well-being of the individual or household concerned. At the same time, living in poverty exposes people to conditions that increase the chances for people to develop a disability, including the risk of malnutrition or infectious diseases, greater exposure to violence, and lack of access to safe water and sanitation infrastructure (Emerson, Hatton, Llewellyn, Blacher, & Graham, 2006; Peters et al., 2008).

Disability and Economic DevelopmentClick to view larger

Figure 1. The bidirectional relationship between disability and poverty.

Source: Modified based on WHO/World Bank (2011).

Note: The listed potential mechanisms do not necessarily present an exhaustive list, and neither does the framework account for potential interrelationships between the different channels.

Cross-Country Perspective

From a cross-country perspective, providing an accurate picture of the empirical relationship between disability and (economic) development hinges on the availability of data that is comparable across countries. There are notable challenges in this respect (which will hopefully be overcome as the ongoing harmonization efforts spurred on by the Washington Group take effect), as mentioned in the section on “Defining and Measuring Disability.” Figure 2 makes use of the latest (publicly) available data on disability prevalence, based on a dedicated effort by Mitra and Sambamoorthi (2014), and plotted against the standard proxy for the economic wealth of a country (which is closely correlated with absolute poverty levels across countries).

The first thing to note about Figure 2 is the considerable variation in disability prevalence among the lower-income countries alone, ranging from 3.1% in Malaysia to 30% in South Africa. While there is much variation, on average the expected inverse relationship is confirmed. (Using a slightly different disability measure with a similar data source, i.e., the one employed in the WHO/World Bank 2011 report but for more countries, a broadly similar pattern emerges.) Hence, across a large set of countries worldwide, disability indeed appears to be positively associated with poverty on average.

Disability and Economic DevelopmentClick to view larger

Figure 2. Disability prevalence (% of adults) and economic development (GNI per capita) in 54 countries worldwide.

Source: Disability data from Mitra and Sambamoorthi (2014), Gross national income (GNI) per capita data from World Bank World Development Indicators.

An important caveat when comparing self-reported disability data across countries, as shown in Figure 2 (or within countries, as in studies referred to below), is in the lack of clarity as to whether the measured differences in disability among countries reflect true differences in prevalence or whether they are due to systematic differences in reporting styles among countries (“reporting heterogeneity”; Bago d’Uva, Van Doorslaer, Lindeboom, & O’Donnell, 2008).

Within-Country Perspective

An inverse correlation between disability and economic status at the country level does not necessarily mean that the same correlation holds at the level of the individual or household within a given country. A review on disability and poverty in LMICs published in 2011 revealed that the evidence base (at least back then) was severely limited and that some studies did show strong links, but it also showed that existing evidence sometimes presented a somewhat nuanced complex relationship between disability and poverty (Groce & Trani, 2011). A similar picture emerged from a review on childhood disability and socioeconomic indicators in LMICs (Simkiss, Blackburn, Mukoro, Read, & Spencer, 2011).

A more recent review (Banks, Kuper, & Polack, 2017) has thoroughly reassessed and updated the evidence base by systematically and critically appraising the peer-reviewed literature published from 1990 to March 2016. This study represents the latest, most comprehensive stock-take of what is known on the subject. The study finds a total of 150 relevant studies, of which the majority (81%) found evidence supporting the hypothesis of a positive link between disability and poverty. Overall, the relationship appeared robust to the inclusion of controls for a set of measurable, potential confounders (e.g., age, gender, education), and across all regional contexts, impairment types, study designs, and age groups.2 There was also evidence of a dose-response relationship, with greater severity of disability being associated with greater odds of poverty, and vice versa. Table 1 categorizes the reviewed papers by geographic region, income grouping, disability type, and study design.

Table 1 shows that—reassuringly—each region is covered by at least some analysis, though the majority has been focused on East Asia and the Pacific, possibly due to better data availability. The geographic distribution is at least partly reflected in the distribution by income grouping: the richer the country covered, the more likely there is empirical evidence available on the link between disability and poverty. The majority of disability types addressed by the studies is based on mental disorders, a finding that may be driven by the fact that there is a distinct and growing research community that focuses on mental disorders in the context of global health.3 Classifying studies by study design reveals the dominance of cross-sectional designs, which severely limit the extent to which the nature of the relationship can be explored in any further depth, beyond the description of a correlation or lack thereof.

Table 1. Selected Descriptive Characteristics of Disability and Poverty Studies

Number

Percent

Region

East Asia/Pacific

40

27

Latin America/Caribbean

31

21

South Asia

26

17

Sub-Saharan Africa

22

15

Middle East/North Africa

11

7

Europe/Central Asia

4

3

Multi-region

16

11

Income group

Low

38

25

Lower-middle

42

28

Upper-middle

48

32

Mix

16

11

Disability type

Visual impairment

12

8

Hearing impairment

2

1

Physical impairment

12

8

Intellectual/cognitive impairment

23

15

Mental disorders

73

49

Mixed impairments/functional limitations

37

25

Study design

Cross-sectional

124

83

Case-control

11

7

Cohort

13

9

Other

2

1

Source: Banks, Kuper, and Polack (2017).

Despite the mostly consistent picture emerging on the close link between disability and poverty, this evidence does not allow an assessment of the direction of causality between the two, or indeed whether the relationship may be driven by other unmeasured or unobservable factors.

The Costs of Disability

This section reviews what is known about some of the dimensions of the (broadly interpreted) “costs” of disability. This is important for several reasons. First, in the case of labor market consequences (e.g., employment chances, earnings), it provides an indication of the benefits of more effective inclusion in the labor market, and it may point to potential discrimination for PwDs in the labor market.4 Second, understanding the additional costs PwDs experience as a consequence of their disability can inform the extent to which social security systems should compensate those costs through the provision of benefits. Third, estimates of the extra costs of disability also help improve the assessment of the adequate poverty level in a given country, and especially in LMICs where the majority of the world’s population with disabilities reside. Fourth, knowledge of the true costs of disability is important from a policy perspective, considering the commitments countries have made under the UNCRPD. The CRPD requires signatories to protect the right of persons with disabilities to have an adequate standard of living for themselves and their families, including adequate food, clothing, and housing, as well as to safeguard access by families living in situations of poverty to social protection assistance with disability-related expenses (Palmer, Williams, & McPake, 2016).

In the literature, multiple approaches have been used to estimate the extra costs of disability.5 A recent review by Mitra, Palmer, Kim, Mont, and Groce (2017) synthesized the evidence base on the extra costs of disability worldwide—measured in different ways—as published in the peer-reviewed literature from 1995 to 2014. The review identified 20 relevant studies, the majority of which were in high-income countries, hinting at the lack of reliable evidence on the subject, particularly for LMICs, where the clear majority of PwDs live. That said, the evidence does indicate that individuals with disabilities face sizeable extra costs. These direct costs appear to vary according to the severity of disability, life-cycle, and household composition. As the methods used across the studies are highly heterogeneous, we focus on the findings based on one method: the increasingly popular, so-called Standard-of-Living (SoL) approach.

The SoL approach is related to Sen’s concept of the “double handicap,” which implies that the additional expenditures incurred by PwDs for goods and services as a result of their disability have the effect of creating disadvantage because higher income is required so that households with disabled members can achieve the same level of well-being as otherwise similar households.6 The extra expenditures may relate directly to disability (e.g., assistive devices or medication) or indirectly (e.g., transport). Because of these additional costs, PwDs experience a lower standard of living than their nondisabled counterparts. The absolute costs of disability can then be identified as the additional income required by a disabled person to reach the same standard of living as a nondisabled person, holding constant other characteristics. A key advantage of the “indirect” estimation followed by the SoL approach is that it does not rely on the often-challenging collection of individual disability-related expenditures.

Table 2 draws on the results of the studies in Mitra, Palmer, Kim, Mont, and Groce (2017), complemented by further primary studies, and reveals that the extra cost estimates, expressed as a share of average income, vary significantly, from 3% to 158%. Where studies have assessed costs by severity of the disability, they find a clear, expected gradient in the costs. Comparing results for high-income countries (HICs) versus LMICs, it is apparent that the percentage of extra costs is lower in the latter. (The intuitively surprising pattern of households with disabled members in richer countries facing on average higher disability costs than poorer ones also emerges from Antón, Braña, and de Bustillo’s 2016 study that compares disability costs in 31 EU countries.) This may be explained by a relatively low level of household resources to devote to disability-related costs or lower levels of availability of, and accessibility to, disability goods and service markets (e.g., rehabilitation services) in less-wealthy countries. In LMICs, there may also exist stronger family and community networks to care for people with disabilities. Moreover, overall living standards will be low, which may further mitigate the extent of disability costs estimated under an SoL approach.

Table 2. Estimates of Extra Costs of Disability, Using the Standard-of-Living Approach

Study

Country

Age

Extra costs as % of average income

Brana and Anton (2011)

Spain

17+

40% (moderate disability)

70% (severe disability)

Braithwaite and Mont (2009)

Bosnia

All ages

14%

Braithwaite and Mont (2009)

Vietnam

All ages

9%

Cullinan et al. (2011)

Ireland

All ages

23%

30% (moderate)

33% (severe)

Cullinan et al. (2013)

Ireland

65+

40%

Loyalka et al. (2014)

China

NA

For households with disabled adults: 8%–43%

For households with disabled children: 18%–31%

Moderate 3%–116%

Severe 14%–158%.

Mont and Cuong (2011)

Vietnam

NA

12%

Saunders (2007)

Australia

NA

29%

30% (moderate)

40% (severe)

Zaidi and Burchardt (2005)

UK

11% (mild)

34% (moderate)

64% (severe)

Morciano et al. (2015)

UK

65+

62%*

Palmer et al. (2016)

Cambodia

All ages

17%

Anton et al. (2016)

EU-countries (31)

All ages

ca. 18%–98%**

Sources: Mitra, Palmer, Kim, Mont, and Groce (2017); Morciano, Hancock, and Pudney (2015); Palmer, Williams, and McPake (2016); Antón, Braña, and de Bustillo, (2016).

Notes: 1. In Morciano et al. (2015), the cost is expressed as a share in the net weekly predisability household income. 2. Precise numbers are estimated from visual inspection of Figure 1 in Antón, Braña, and de Bustillo (2016), as no precise numeric information was given in the paper.

Some of the studies summarized in Table 2 assessed disability costs against the receipt of income support from government. In the United Kingdom, Cambodia, and China, public transfers have been found to fall significantly short of disability cost estimations (Loyalka, Liu, Chen, & Zheng, 2014; Palmer, Williams, & McPake (2016); Zaidi & Burchardt, 2005). This suggests that public support programs are not sufficiently taking account of the extra costs associated with disability.

The study on Cambodia (Palmer et al., 2016) also estimated what the extra costs meant for the absolute poverty levels of households with disabled members. If the additional costs of disability are accounted for, the poverty rate among households with disabled members almost doubles, increasing from 18% to 34%. This underlines the point made earlier about the importance of taking proper account of the extra costs of disability when measuring poverty levels in the context, for instance, of SDG poverty reduction progress assessments.

Concluding Remarks

This article briefly reviews selected (but by no means all) aspects of the relationship between disability and (economic) development, with a primary interest in LMICs. We have seen that, despite the still-significant measurement challenges and the overall scarcity of evidence in this field, several key conclusions emerge.

  1. 1. Disability affects a noticeable share of the population, the vast majority of whom live in LMICs.

  2. 2. While it is not (yet) known exactly how many PwDs live in absolute poverty, the existing data and evidence indicates that disability is closely associated with poverty and other indicators of economic deprivation at the country level, as well as—for the most part—at the individual/household level.

  3. 3. There is a growing body of evidence documenting the sizeable additional costs incurred by PwDs as a direct or indirect consequence of their disability, underlining the increased risk of PwDs (and the households they are part of) falling under the absolute poverty line in any given LMIC.

Taken together, this adds significant weight to the case for making disability a significant consideration of any serious, comprehensive poverty-reduction effort.

Looking ahead, there remains massive scope for more evidence on the link between disability and poverty, particularly as far as the causal nature of the relationship is concerned, of which we currently know little. Collecting and using longitudinal survey data would go some way toward this end, particularly if analyzed with advanced econometric methods able to provide causal inference. More and better causal evidence will provide more reliable information about relevant entry points for policies to break the cycle between disability and poverty. More evidence would also be required to improve our understanding of how disability leads to extra costs of living in LMICs, as this would provide useful information about the design of social protection programs (e.g., via social insurance programs, pension or cash transfer programs).

A related, significant evidence gap that could not be discussed within the space of the present article is in the rigorous evaluation of the impact of programs and policies intended to improve the well-being of PwDs, for instance in the domains of social protection, labor market, education, or health (see White, Saran, & Kuper, 2018, for a comprehensive review of the evidence on interventions targeting PwDs in LMICs). Ideally, such evaluation should not only consider effectiveness but should also take into account suitable concepts of “value for money.” While the latter is well developed in the field of health technology assessment (Drummond, Sculpher, Claxton, Stoddart, & Torrance, 2015), where it can usefully inform priority-setting under given budget constraints, significant conceptual and measurement challenges remain when trying to apply similar assessments in the context of disability.

Further Reading

Banks, L. M., Kuper, H., & Polack, S. (2017). Poverty and disability in low- and middle-income countries: A systematic review. PLoS One, 12(12), e0189996.Find this resource:

Cullinan, J., Lyons, S., & Brian, N. (Eds.). (2015). The economics of disability: Insights from Irish research. Oxford, U.K.: Oxford University Press.Find this resource:

Haveman, R., & Wolfe, B. (2000). The economics of disability and disability policy. Handbook of Health Economics, 1(January), 995–1051.Find this resource:

Mitra, S. (2017). Disability, health and human development. Palgrave Studies in Disability and International Development. London, U.K.: Palgrave.Find this resource:

Morciano, M., Hancock, R., & Pudney, S. (2015). Disability costs and equivalence scales in the older population in Great Britain. Review of Income and Wealth, 61(3), 494–514.Find this resource:

UNICEF. (2013). The state of the world’s children: Children with disabilities. New York, NY: United Nations.Find this resource:

United Nations. (2018). Realization of the sustainable development goals by, for and with persons with disabilities. UN flagship report on disability and development 2018. New York: United Nations.Find this resource:

White, H., Saran, A., & Kuper, H. (2018). Evidence and Gap Map of Studies Assessing the Effectiveness of Interventions for People with Disabilities. (Inception Paper 12). London, U.K.: Centre of Excellence for Development Impact and Learning.Find this resource:

World Health Organization and the World Bank. (2011). World report on disability. Geneva, Switzerland: World Health Organization.Find this resource:

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Braithwaite, J., & Mont, D. (2009). Disability and poverty: A survey of World Bank poverty assessments and implications. Alter, 3, 219e232.Find this resource:

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Notes:

(1.) The exact wording of the short version of the set of questions is as follows: “The next questions ask about difficulties you may have doing certain activities because of a HEALTH PROBLEM. (1) Do you have difficulty seeing, even if wearing glasses? (2) Do you have difficulty hearing, even if using a hearing aid? (3) Do you have difficulty walking or climbing steps? (4) Do you have difficulty remembering or concentrating? (5) Do you have difficulty (with self-care such as) washing all over or dressing? (6) Using your usual (customary) language, do you have difficulty communicating (for example understanding or being understood by others)?” For each question, four response categories are used: (1) No, no difficulty, (2) Yes, some difficulty, (3) Yes, a lot of difficulty, and (4) Cannot do it at all.

(2.) At the same time, it is important to recognize that the evidence is not without nuance. For instance, studies in low-income countries or in certain regions (notably sub-Saharan Africa and Europe/Central Asia) were less likely to observe a relationship between disability and poverty, perhaps due to challenges in accurately and appropriately measuring poverty in complex and varying economies. Or it may be the case that PwDs are left behind as regions develop economically, so that the gap in poverty between those with and without disabilities will be larger in areas that are less poor. There was also some slight variation in the results by age group. Analyses focused on older adults were slightly less likely to reveal a positive association, compared to working-age adults and children. This may be attributable to the possibility that poorer individuals who survive into older age may be healthier than their wealthier counterparts.

(3.) See for example the Lancet Special Series on Global Mental Health in 2007 and 2011, as well as major reviews focusing particularly on the link between mental health and poverty (Lund et al., 2010, 2011).

(4.) The studies reviewed below do not focus specifically on the labor market consequences of disability. For a thorough assessment in some LMICs, see Mitra (2017).

(5.) See Morciano, Hancock, and Pudney (2015) for a description of five different measurement approaches, including (1) assuming that the political process has resulted in an acceptable evaluation of disability costs by using an income measure for distributional analysis which excludes any receipt of disability benefit, on the assumption that income from disability benefit is exactly offset by the extra costs of disability, (2) asking a panel of experts or disabled people themselves, (3) an “objective” equivalence approach constructing an equivalence scale by using the consumption pattern as an indicator of living standards in a comparison of a sample of disabled people with matched individuals who are unaffected by disability, (4) a “subjective” equivalence approach, based on individuals’ reported satisfaction with their well-being, and (5) the SoL approach described in the text.

(6.) The premise underlying the method is that households with disabilities are considered as having a different conversion from income into SoL due to the extra costs of disability.