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Socio-Economic Profile of Persons with Disabilities PDF Print E-mail
Tuesday, 06 September 2011 13:42

This chapter presents a socio-economic profile of persons with disabilities in India. It is based primarily on data from the 47th and 58th NSS rounds and the 2001 census, but is supplemented by analysis from a dedicated survey in rural UP and TN carried out in 2005.

Socio-economic profile of PWD

Disability Prevalence: The starting point is an estimation of the total number of PWD in India. On this point, the two major official sources of data on disability differ, with the census estimate around 18 percent higher than NSS estimates. The 2001 census found 21.91 million PWD (2.13 percent of the population), while the 2002 NSS round’s disability estimate is 1.8 percent of the population, which would come to around 18.5 million (Table 1.1). The difference in aggregate estimates is in part explainable on the basis of different definitions used in the NSS and census for disabilities (see Annex 1). Both sources find disability rates to be higher among men and higher in rural than urban areas. In fact, the 58 percent share of males in total PWD estimates is worthy of further exploration.

Just as importantly in terms both of the impact of disability in the population and in terms of political economy and voice among people with disabilities, is the share of households estimated to have a member with a disability. The NSS for 2002 estimates that 8.4 percent of rural households and 6.1 percent of urban households had a member with a disability. These higher figures are important for several reasons. Firstly, the direct impacts of a disabled household member will clearly go beyond the individual with the impairment (as, for example, the chapter in impacts on non-disabled family member work participation in Chapter * indicates). Secondly, 7.8 percent of households nationally represents a significant “vote bank” which suggests that there may be more political mileage in policies to promote inclusion of disabled people than is commonly thought.

Disability rates from census and survey sources, early 2000s.
PWD as share of… Census NSS 58th
All individuals 2.13 1.8
All urban individuals NA 1.50
All urban households NA 6.1
All rural individuals NA 1.85
All rural households NA 8.4
All males 2.37 2.12
All females 1.87 1.67

Sources: Census 2001 and NSS 2002.

Looking at the prevalence of specific disability types, the divergence between census and NSS estimates are very pronounced for locomotor and visual disabilities (see Figure 1.1). While they also differ significantly for both hearing and speech disabilities, the inclusion in NSS of multiple disabilities is a factor, as speech and hearing disabilities may be more likely to combine. The locomotor and visual disability differences are however much more sharp and cannot be explained by this. The major driver of the differences appears to be definitional, with the census defining visual disabilities more broadly and vice versa for movement disabilities.

Census and NSS sources give a divergent picture of the composition of disability
Figure 1.1: Disability shares by type, census and NSS, early 2000s (% of disabled people)
Source: NSS, 58th round and census, 2001.

The aggregate number of PWD in India is keenly disputed, with alternative estimates invariably higher than official ones. Higher estimates are based on several arguments:

exclusion of disability categories in both NSS and census. The reliance on PWD Act categories is a limiting factor. There are numerous examples of excluded disability categories, including autism, thalassemia, haemophilia, and many learning disabilities.

the method of questioning on disability in both census and NSS which relies on a traditional “diagnostic” identification of disability by untrained interviewers, which recent work coming out of a UN expert group suggests is the method which yields the lowest disability estimates. Box 1.1 notes the various methods and issues involved. Simply asking whether or not people have a disability (and what type) has been found worldwide to yield lower bound estimates of prevalence, with a strong bias towards more serious disabilities.

disability-specific surveys which have found often substantially higher rates of disability in cases where interviewers have been far better trained on detection and probing. Examples include: a meta-analysis of mental illness incidence gives an estimated prevalence of 5.8 percent of the population based on dedicated surveys. A GoI/WHO survey in the 1980s estimated visual disabilities at 1.5 percent of the population, a share that may have grown based on a national estimate of blind persons in 2000 of 18.7 million, of which 9.5 million were cataract-related and 3 million refractive error-related. Naturally, different studies are subject to differences in approach and definition, but careful work by well-trained interviewers using better instruments and with reasonable definitions of disability have produced substantially higher estimates of disability prevalence than official statistics. The higher estimates are also more consistent with comparable international estimates of disability from WHO and individual countries.

it is clear from primary field research from this report (and previous work) that significant categories of people who are functionally disabled will not typically be identified by households as being disabled. The primary example of this is elderly people with significant functional impairments who were not disabled before they became old. In field work, the standard answer on probing was that even seriously functionally impaired elderly people were “just old” or “like many other old people” rather than disabled. The main exception was elderly disabled people who were disabled before becoming old. Given the relatively higher rates of functional disability among elderly populations worldwide, such cultural factors are likely to be a significant source of under-estimation of disability prevalence.

the social stigma attached to disability is also likely to contribute to under-estimation. Chapter 2 provides insights on the strong stigma often attached to disability in India. Stigma – as in many countries – seems to be particularly pronounced for mental illness and mental retardation. Stigma is a factor in many countries, but Indian notions of karma seem likely to make the problem of lack of identification by households more pronounced in India.

Due the above and other factors, official disability estimates in India can therefore be considered a more reliable estimate of serious disabilities, particularly for mental retardation and mental illness. A more inclusive definition would appear to include around80-90 million people. It is stressed that the total figure of over 90 million (or around 8 percent of the population) is not intended to be “the” estimate of disability prevalence, but simply to indicate that reasonable alternative definitions and reliable sources find a possible prevalence rate of disability which is considerably higher than the official NSS and census estimates. It also points to be great importance of mental illness in overall prevalence, an area of particular difficulty in measurement. This points to the complex issues of both definitions for measurement and of conducting interviews with non-specialist interviewers.

International evidence from developing and developed countries provides useful insights for interpreting both official disability estimates for India and those from reliable alternative sources. Several pertinent findings on international experience in estimating disability prevalence are

how disability questions are asked matters, and India’s methods of asking in both NSS and census tend to generate the lowest disability estimates worldwide. This can be seen in Table 1.2, where prevalence rates are seen to vary sharply according to how the disability questions are asked. Interestingly, many countries find a prevalence range of 1-3 percent using India’s current method of asking disability questions, while activity-based questioning yields higher rates. More detail on the different approaches is provided in Box 1.1

Table 1.2: Disability Prevalence rates by country and disability question method, various years
Country and question type Disability prevalence rate (% of population)
Do you have a disability ? Yes/No
Jordan 1.2
Philippines 1.3
Turkey 1.4
Mauritania 1.5
Ethiopia 3.8
Jamaica 6.3
List of Conditions
Colombia 1.8
Mexico 1.8
Palestine 1.8
Chile 2.2
Uganda 3.5
Hungary 5.7
Activity-based questions
Poland 10.0
United Kingdom 12.2
Brazil 14.5
Canada 18.5
United States 19.4

Source: Mont (2007)

• it is not unusual for different official surveys from the same country to yield different estimates of disability, even where interviewer capacity is high. An extreme case is provided by Canada, which has estimates from various official sources ranging from 13.7 to 31.3 percent. In the region, there are also significant variations across source in disability estimates, e.g. in Sri Lanka, the census-based estimate is 1.6 percent, while a UNICEF survey estimated around 4 percent prevalence.

• official disability prevalence rates tend to rise with country income levels. This is a product of various factors, but would appear to be less about the “true” rates of disability (though older age structures are a factor in that respect) as other factors. These include more inclusive definitions of disability, better measurement and identification, social security systems which provide incentives to self-declare as disabled etc.

• despite the general pattern of rising disability rates with higher country income, developing countries which have implemented more advanced survey and census-based measurement have found notably higher prevalence rates of 10-15 percent of the population. Three notable examples are Brazil, Zambia and Nicaragua, which also demonstrate that new forms of measurement can be achieved even in very low income and capacity settings. Brazil for example moved from around a 1 percent prevalence rate to 14.5 percent with a change in the method of asking questions in its census. Using other household surveys with improved methodology, Nicaragua’s rate is just over 10 percent, while Zambia has a rate of around 13 percent. The example of the change in questions for Brazil is given in Annex 2.

Box 1.1: Different approaches to asking about disability in census and surveys

The UN Statistics Division has formed the Washington City Group on Disability Statistics, which is focused on measurement of disability in national censuses and surveys (website is nchs/citygroup.htm). There are broadly four methods of trying to identify disability in surveys, which are:

Diagnostic: An example of this approach would be “Is anyone in house deaf ?”. This method tends to generate the lowest prevalence estimates among those now available and is the one used in India for both NSS and census.

Activities of daily living (ADL): This method relies on a functional approach based on common activities of individuals. An example of this approach would be “Do you have trouble bathing or dressing yourself ?”. This yields higher prevalence estimates than the diagnostic approach, but can be very culturally sensitive for purposes of cross-country comparison (e.g. putting on a sari is a more demanding task than putting on a skirt).

Instrumental ADL (IADL): This asks about more complex functionings, e.g. “Do you have trouble maintaining the household ?”. This tends to yield the highest rates of disability, but can more often include those with chronic illness who may not otherwise be classified as disabled.

Participatory/social roles – This method is underpinned by a social model of disability. An example would be “Do you have a mental or physical impairment that limits the type/amount of work you can do ?”. This would tend to yield prevalence estimates between diagnostic and ADL/ IADL approaches.
Source: UN Washington City Group

• Such methods have recently been piloted in India by WHO/UNESCAP and lead to notably higher disability prevalence rates (of over 20 percent), pointing to the importance of not having a single prevalence rate for multiple purposes. While the surveys were not sampled to be nationally representative, they point to both the very different results that are obtained using different approach to investigation of disability.

The above estimates rely on national census and survey work. An alternative approach to estimating disability prevalence and trends by cause is to estimate the total years lost due to disability using the DALY methodology. This method is open to a number of criticisms 8, but is nonetheless of interest in comparative terms. An interesting insight the method provides is comparative estimates of the shift in the main drivers of years lost to disability between 1990s and 2020. This is presented below for India, China and other Asia/Pacific countries. All are in the midst of a disability transition, but the pace of that transition in India is predicted to be most rapid. Between 1990 and 2020, there is predicted to be a halving of disability due to communicable diseases, a doubling of disability years due to injuries/accidents, and a more than 40 percent increase in the share of disability years due to non-communicable diseases (e.g. cardiovascular and stroke). An additional point of note is that around half the disability from non-communicable disease for South Asia is estimated to be due to neuropsychiatric disorders (mainly mental illness and mental retardation), suggesting that 30 percent of total years lost to disability in India by 2020 could be due to these causes. This is of interest in that it appears to confirm that mental illness and retardation are significantly under-estimated in official statistics (as the national research referred to above also strongly suggests).

one notable difference between the characteristics presented is the urban share of the PWD and general household populations, with the urban share of the general population over 20 percent higher than for the PWD household population, pointing to issues with access to health care, the nature of work, and other factors. In terms of the relative poverty rates of PWD households, this is likely to have significant implications, as national estimates from 1999-00 find rural poverty rates more than double those in urban areas.

the share of illiterate or basic education HH heads in PWD households is only marginally higher than for the general population. However, the share of heads in PWD households with secondary or higher education is around 15 percent lower than the general population. These rates are of interest in terms of poverty correlates of PWD households, and would suggest slightly higher poverty rates among PWD households.

Table 1.3: PWD and general population social characteristics, 2002
Household characteristic Household characteristic HHs with PWD
ST 8.1% 6.9%
SC 20.2% 21.1%
OBC 39.9% 42.0%
Female headed 7.7% 7.2%
Urban 26.1% 21.6%
Land owned (hectares) 0.83 0.95
HH head illiterate or primary/less education 65.8% 66.7%
HH head with secondary/higher education 20.3% 17.2%
Household size 7.23 6.05
Age of HH head Age of HH head 50.04 years

Source: NSS, 58 th round, Bank staff estimates.

a finding that runs contrary to the above findings of higher of poverty in several indicators is that PWD households have higher than average land holdings. This is not simply an artifact of the higher rural population share among PWD households, as the rural-only sub-samples indicate around 10 percent larger average land holding than for general population. Another is that the differences by SC/ST status are not dramatic between PWD and non-PWD households, with PWD having only a slightly higher aggregate share of SC/ST/OBC. The share of SC/ST households among PWD is of significance in terms of likely poverty rates, both have higher than average poverty rates.

The share of disability impact due to communicable causes is falling sharply in India and much of Asia, with the share due to non-communicable diseases and injuries rising
Figure 1.2: Disability transition in various Asian countries, 1990-2020

Source: Murray and Lopez, 1997.

Numbers from a variety of reliable sources suggest that the real prevalence of disability in India could be easily around 40 million people, and perhaps as high as 80-90 million if more inclusive definitions of both mental illness and mental retardation in particular were used. The focus of this report is not on precise prevalence estimates of disability in India. However, the large range in estimates both of the number of disabled people and what is the composition of their impairments points to the need for improvements in public data collection efforts.

Socio-economic characteristics of households with disabled members:
Despite the above concerns, it is useful to examine the socio-economic profile of PWD in India that emerges from NSS and census. With respect to education, health and employment, more detail is provided in Chapters 3-5. The socio-economic characteristics of households with PWD and others can be done using the 58th and 47th rounds of NSS. The PWD schedule and main schedule are not the same sample, but are representative of the PWD and general populations respectively. Key points are in Table 1.3

The rural UP and TN survey provides insights into a wider range of comparable welfare measures between PWD and non-PWD households. The results are presented in Figures 1.3 and 1.4 below. Figure 1.3 presents the incidence of a disabled or severely disabled household member by quintile, using an asset index to rank household welfare. The incidence of disability was estimated based on an ADL approach. Figure 1.4 presents findings again by quintile for two measures – first a per capita consumption ranking and secondly an asset ranking. The difference is that presence of disability is in this figure based on community identification of households with a disabled member. A few points emerge: {mospagebreak}

• there is a clear decline in the proportion of people with disabilities of all severity (using an ADL measure) as the wealth of households rises. There are more PWD in poorer households in rural UP and TN.

• a similar pattern can be seen where the measure of disability is community identification of whether or not a household has a disabled member.

a further finding of interest is that consumption measure captured only a very weak disability and poverty correlation when the ADL measure was used to identify PWDs, while the link between poverty and disability is quite pronounced using the consumption measure when community identification of PWDs is used. This is an interesting finding for research on disability survey methodology.

It is also important to recall Sen’s “conversion handicap” for disabled people, i.e. that equivalent levels of income are less easily converted into individual welfare by PWD. Taking account of this factor would further widen the gap between PWD and non-PWD households.

In rural UP and TN, disability is clearly associated with lower economic status

Figure 1.3: Relative share of PWD and severe PWD by asset quintile, UP and TN, 2005
relative share
Figure 1.3: Relative share of PWD and severe PWD by asset quintile, UP and TN, 2005

There are several other non-income indicators at the household level from the village survey which are of interest, and most of which also point in the direction of households with PWD being worse off than average. Those which were statistically significant are presented in Table 1.4. While the differences are not in most cases dramatic, several points are worth noting:

the key welfare indicator of three meals a day year-round shows a clear difference, with PWD households almost one quarter less likely to report a positive answer.

there are also significantly lower rates of ownership of key assets for PWD households.

interestingly, the share of SC households with disabled members was substantially lower than the households without disabilities.

Table 1.4: Non-income indicators for households with and without PWD, UP and TN, 2005
Indicator HH without PWD HH with PWD HH with severe PWD
Three meals per day year round
47.9% 36.7%** 37.4%**
Pucca floor

39.9% 34.1%** 34.9%
Good light source

48.3% 42.2%** 42.8%
Good toilet

6.9% 4.1%** 4.0%*
Making some savings

35.9% 32.7% 30.1%**

16.9% 12.5%** 12.1%**
SC household

28.2% 20.4%** 20.9

Source: UP and TN village survey, 2005, Bank staff estimates. ** = significant at 5%; * = significant at 10%.

A final point which the NSS allows to explore is the relative welfare among households with a disabled member by disability type. This is not subject to the same problems of a different consumption measure as with comparisons to households without a PWD. Results for both per capita household consumption and land holdings are presented in Figure 1.5. Overall, households with a hearing disabled member are the relatively best off among households with disabled members whether measured by consumption or land. Equally, households with a visually disabled member seem to be the worst off across both measures, around 12 percent lower PC expenditure and 12 percent smaller average land holding. What is harder to interpret are the differences in relative situation of other disability types across the expenditure and land measures. Equally, the relatively good position of households with a person with mental disability is unexpected.

Figure 1.5: Relative per capital HH consumption and land holdings by disability type, 2002 (hearing disabled as reference)
Figure 1.2: Disability transition in various Asian countries, 1990-2020
relative per capital

Source: NSS, 58th round, Bank staff estimates.

Socio-economic profile of people with disabilities:
The above characteristics relate to households within which PWD live. Of even greater importance is the socio-economic profile of PWD themselves. This is dealt with in greater detail in chapters on health, education and employment. However, some details are presented in the following section, above and beyond the incidence data already discussed.


An important issue for policy and planning purposes is age at onset of disability. In all countries, this will shift over time, generally towards later onset as maternal-child health systems improve, infectious diseases are superseded by age and lifestyle related conditions, and the share of accidents in total disability causes shifts. For India, 2002 data on age at onset of disability are in Figure 1.6, and show an expected double peak, with the highest rate of disability occurring at or shortly after birth, though with a second noticeable hump in onset from the 50s to early 60s. Just as important is that the disability-specific patterns of onset vary considerably. This is discussed in detail in Chapter 3, but show the critical importance of early childhood identification of disability and intervention.

Figure 1.6: Kernel Density of Age at Onset of All Disability - 2002
kernal density

A second issue with obvious implications for policy is how severe are the disabilities of people with disabilities in India? Table 1.5 presents the NSS indicator, which is extent of reliance on aids and appliances and other people for self-care. The key point is that the bulk of PWD are only mildly/moderately impaired by the NSS measure. In addition, a further sixth of PWD are capable of self-care with the necessary aids and appliances. This is an important point to stress, and has significant implications for education policy and implications for the employment capacities of PWD.

Table 1.5: Reported extent of disability among PWD, 2002
Extent of disability Share of all PWD
Can not take care of self even with aid-appliance 13.6%
Can take care of self only with aid-appliance 17.2%
Can take care of self without aid-appliance 60.2%
Aid-appliance not tried/available 9.0%

Source: NSS, 58th round. Bank staff estimates.

A second key social indicator that the NSS reports is education enrollment and attainment. Educational indicators were also captured for PWD in the 2001 census. The summary results are reported below, with more detailed analysis and econometric findings presented in the education chapter. Figure 1.7 presents educational attainment levels for PWD and the general population averaged across all age groups, using 2001 census data for both groups. It shows substantially higher rates of illiteracy among the PWD population relative to general, and conversely lower shares of PWD with higher levels of educational attainment. Across all PWD, illiteracy is 52 percent, versus only 35 percent in the general population. For specific disability categories, the illiteracy rates are higher again: with almost two thirds of both speech and mentally disabled people being illiterate. Conversely, those with locomotor disabilities have 44 percent illiteracy rate, significantly lower than the PWD average but still one quarter higher than the general population rate.13

As with the general population, there are strong gender differences in educational attainment among PWD, with PWD female illiteracy rates on average 64 percent (against a male PWD average of 43 percent), and as high as 73 percent for the visually disabled. There are also strong locational differences as one would expect, with the total PWD illiteracy rate for rural areas as high as 57 percent, against a rate of 37 percent in urban areas.

While Figure 1.7 is important, of most immediate relevance is school attendance of the current batch of children in general education, as this is a group for whom improvements could occur in time to affect their lifetime attainment. Nationally representative figures from survey conducted in 2005 are presented in Figure 1.8 which show the proportion of children out of school along various social indicators, including disability.14 The share of disabled children who are out of school is dramatically higher than other major social categories, with the average out-of-school rate for CWD five and a half times the rate for all children, and around four times even that of the ST population (generally considered to have poor educational outcomes).

PWD have high rates of illiteracy relative to the general population, for some disabilities close to double national averages
Figure 1.2: Disability transition in various Asian countries, 1990-2020
educational attainment

Source: Census, 2001

In addition to the very high average rates of out-of-school children among disabled children, the rates among some disability categories are extremely high, with more than 60 percent of multiple disability and almost half of mentally disabled 6-13 year olds out of school. Even the lowest disability group (surprisingly, visual) have almost 30 percent of children in the general education group out of school. It is very clear from these numbers that India’s hopes of reaching the educational MDGs are highly unlikely to be realized unless there is major improvement in getting CWD into school.

The proportions of children with disabilities out of basic education is dramatically higher than national averages for non-disabled children
Figure 1.8: Share of 6-13 year olds out of school by social category, 2005

Source: SRI (2005)

In terms of individual and household welfare, employment is a key variable for analysis among disabled adults. This is discussed in detail in Chapter 5, but summary statistics are presented in Figure 1.9, which indicates that PWD employment rates were substantially below those of the general population in both urban and rural areas and for both genders. Chapter 5 also shows that the relative employment situation of people with disabilities has deteriorated during the 1990s, with those with the lowest educational levels doing the worst.

Figure 1.9: Employment rates of PWD and non-PWD by gender and location, early 2000s
employments rates

Source: Mitra and Sambamoorthi, based on NSS 58th and 47th rounds in 2002 and 1991
(PWD) and 55th and 50th rounds in 1993/94 and 1999/00 (non-PWD).

A final key individual welfare indicator for women is marriage and widowhood rates. It has been clearly shown that widows in India have much lower average living standards, and much sociological literature attests also to their low social standing and high levels of vulnerability. NSS data indicate that women with disabilities have much higher rates of widowhood than women without disabilities in both urban and rural areas – in both cases around four times the non-WWD rate. Conversely, the proportion of women with disabilities who are currently married is much lower than non-disabled women. This can be seen for the first half of the 1990s in Figure 1.10. The explanation for such differential rates can most likely be found in the common practice of marrying of women with disabilities to men much older than themselves – men who are unable to find more “marketable” brides.

Rates of widowhood for women with disabilities are around four times those of women without disability, and the share of women with disabilities who are married is around half
Figure 1.10: Marital Status of Women with and without Disability (1991 and 1993-4 for ages >15
martial status

Conclusions and recommendations

A range of non-income indicators, together with the asset and consumption findings from the UP and TN survey, cumulatively suggest that PWD households and individuals are notably worse off than average. These include:

A range of non-income indicators, together with the asset and consumption findings from the UP and TN survey, cumulatively suggest that PWD households and individuals are notably worse off than average. These include:

much lower educational attainment rates, associated in India with lower living standards.

substantially lower employment rates. This is not definitively an indicator of lower living standards in India, due to cultural preferences with respect to womens’ work outside the home and features such as higher unemployment rates among the educated. However, taken together with the much lower education rates of PWD, it is probably a good indication of lower living standards.

clear evidence from the UP and TN survey of declining rates of disability in households as they become richer.

non-income indicators from UP and TN like frequency of three meals a day.

the higher than average rural share in the PWD household population, with considerably higher than average poverty rates in rural areas nationally.

the very high rates of widowhood for women, implying higher probabilities of being poor.

consistent findings from qualitative work (both in the UP/TN study and in other studies in India on disability) of community perceptions that households with disabled members tend to be poorer and more vulnerable.

Clearly much remains to be done in getting a clearer picture of the scale and composition of PWD in India. This is a large agenda, but some initial recommendations are:

there is a need to harmonize definitions of disability categories across NSS and census. In this process, there is also a need to improve and harmonize the approaches across disability types within each survey. This has been recognized by GoI in the work of its Technical Advisory Group on Disability Statistics. Specifically : (i) in the census, there are inconsistencies across disability types as to whether the functional limitation applies to a situation where an assistive device is used, an overly wide definition of visual disability, and an overly subjective definition of some elements of mental illness; and (ii) in the NSS, the current definitions of disability and disability types are a mix of activity limitation (general definition, visual and mental disability), functional limitations (e.g., speech) and impairments (e.g. locomotor) which result in inconsistent approaches across disability type.

in both census and NSS, piloting new methods of asking disability questions which are more in line with good international practice as exemplified by the UN’s Washington Group and by WHO. Box 1.2 below gives examples from the Washington Group census questions, which have already been field tested in 7 developing countries including India. While these questions also have their challenges (e.g. dressing may be more difficult in some countries than others, or for women than men), and are still not well-tuned to capturing a number of mental disabilities, the general approach to look at activities of daily life is instructive.

either adding additional specific categories of disability to such investigations or at least allowing for a broad “other category”.

The Washington Group has developed some model census questions on disability, as follows:
Because of a physical, mental or emotional health condition…
Do you have difficulty seeing even if wearing glasses ?

Do you have difficulty hearing even if using hearing aid/s or are you deaf ?

Do you have difficulty walking or climbing stairs

Do you have difficulty remembering or concentrating ?

Do you have difficulty (with self-care such as ) washing all over or dressing?

Do you have difficulties communicating (for example, understanding or being understood by others) ?

Question response options are No, Some, a Lot, and Unable.
Source: UN Washington group, cited in Mont, 2007.

• revising the NSS disability module for the next dedicated round, in particular improving how questions about disability are asked, PWD household consumption information and other welfare indicators. GoI, state governments and the non-government sector should also facilitate more detailed analysis of existing data than has been typical to date, and consider financing future analysis more systematically to address the large knowledge gaps on the socio-economic situation of people with disabilities.

• incorporating disability questions into mainstream health, education and other surveys to an extent not done to date. This includes the main schedules of NSS, so that more reliable comparisons between the disabled and non-disabled populations on critical indicators like educational attainment can be made.

Last Updated on Thursday, 19 January 2012 16:43

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