Disability Statistics – Estimates Database Methods
Appendix 1: Disability Breakdown/Disaggregation and Groups
Appendix 2: Population Groups
Appendix 3: Geography
Appendix 4: Indicators
Appendix 5: Multidimensional Poverty
Appendix 6: Data Sources
Appendix 7: Datasets and Sampling
Appendix 1: Disability Breakdown/Disaggregation and Groups
Appendix 1 describes the methods used in the Disability Statistics – Estimates (DS-E) database to disaggregate estimates based the disability status of adults ages 15 and older (DDI 2025). It includes and expands the description of methods in Carpenter et al (2024).
DS-E makes it possible to compare indicators across groups by disability status within and across countries. Disaggregating an indicator (e.g. ever attended school) by disability status aims to establish the size of the gap that may be associated with disability, i.e. the disability gap, or inequalities associated with disability.
Disability is measured through questions on functional difficulties in household surveys and population and housing censuses. All the datasets have questions on functional difficulties that
- meet the United Nations (2017) Principles and Recommendations for Population and Housing Censuses with questions in at least four core domains (seeing, hearing, walking, cognition) and
- have graded answer scales that capture the severity of functional disability.
Some datasets have the internationally tested Washington Group Short Set (WG-SS) of disability questions covering six domains (seeing, hearing, walking, cognition, self-care, and communication) (Altman 2016). The WG-SS is included in Appendix 1 Table 1 and more information is available at 
. Other datasets have other functional difficulty questions. These are similar to the WG-SS but with differences in the wording of the answer scale or the questions, and some do not have questions for the self-care domain and/or the communication domain.
Appendix 1 Table 1. The Washington Group Short Set of Questions on Disability
| Introductory Statement: “The next questions ask about difficulties you may have doing certain activities.” | |
| (a) Vision | [Do/Does] [you/he/she] have difficulty seeing, even if wearing glasses? |
| (b) Hearing | [Do/Does] [you/he/she] have difficulty hearing, even if using a hearing aid(s)? |
| (c) Mobility | [Do/Does] [you/he/she] have difficulty walking or climbing steps? |
| (d) Cognition | [Do/does] [you/he/she] have difficulty remembering or concentrating? |
| (e) Self-Care | [Do/does] [you/he/she] have difficulty with self-care, such as washing all over or dressing? |
| (f) Communication | Using [your/his/her] usual language, [do/does] [you/he/she] have difficulty communicating, for example understanding or being understood? |
| For each question in (a) through (f), respondents are asked to answer with one of the following: 1. No Difficulty, 2. Some Difficulty, 3. A lot of difficulty, 4. Unable to do | |
Disability is diverse, both in terms of the type and severity of disability. Severity requires the establishment of a threshold using the answer scale of functional difficulties. Disability classification is challenging (Fujiura and Rutkowski-Kmitta 2001) and variation in the threshold of functional difficulty under consideration may lead to varying results regarding group sizes and inequalities (Hanass-Hancock et al 2023).
Disability disaggregation or breakdowns are described in Table 2 below.
First, it should be noted, that we set 50 observations as the minimum required to produce estimates for subgroups following common practice. Due to this constraint, for a given data set, disaggregation may be possible for some indicators but not others, especially when some indicators are constructed only for particular subgroups such as youth or workers.
The DS-E database offers four different ways to disaggregate adults based on answers to functional difficulty questions listed below and three of them are available by functional domain as well:
- Two groups (No disability — Any severity level): No difficulty — Any level of difficulty;
- Three groups: No difficulty — Some difficulty — A lot of difficulty or Cannot do
- Four groups: No difficulty — Some difficulty — A lot of difficulty — Cannot do
- Two groups (No disability or Not severe — Severe): No difficulty or Some difficulty — A lot of difficulty or Cannot do
Appendix 1 Table 2: Disability disaggregation methods and groups
| Disability breakdowns or disaggregations | Disability group(s) | Reference group | Disability disaggregation is also available by type/domain |
| Two groups (No disability — Any severity level): No difficulty — Any level of difficulty | “Any level of difficulty” refers to persons who were reported to have a difficulty in at least one domain of any degree (some difficulty, a lot of difficulty or Cannot do at all). | “No Difficulty” refers to persons who were reported to have no difficulty in all the domains. | Yes |
| Three groups: No difficulty — Some difficulty — A lot of difficulty or Cannot do | “Some difficulty” refers to persons who are reported to have some difficulty in one or more domain but no a lot of difficulty or cannot do at all responses in all domains. “A lot of difficulty or Cannot do” refers to persons who were reported as cannot do at all or to have a lot of difficulty in at least one domain. | “No Difficulty” refers to persons who were reported to have no difficulty in all the domains. | Yes |
| Four groups: No difficulty — Some difficulty — A lot of difficulty — Cannot do | “Some difficulty” refers to persons who are reported to have Some difficulty in one or more domain but no A lot of difficulty or Cannot do at all responses in all domains. “A lot of difficulty” refers to persons who were reported to have a lot of difficulty in at least one domain but no Cannot do at all responses in all domains. “Cannot do” refers to persons who answered cannot do at all in at least one domain. | “No Difficulty” refers to persons who were reported to have no difficulty in all the domains. | Yes |
| Two groups (No disability or Not severe — Severe): No difficulty or Some difficulty — A lot of difficulty or Cannot do | “A lot of difficulty or Cannot do” refers to persons who reported “Cannot do at all” or “A lot of difficulty” in at least one domain. | “No difficulty or Some difficulty” includes persons who are reported to have “some difficulty” in one or more domain but no “a lot of difficulty” or “Cannot do at all” responses in all domains and persons who were reported to have no difficulty in all the domains. | No: while the group with “A lot of difficulty or Cannot do” is broken down by type in DS-E, the reference group “No difficulty or Some difficulty” is not. |
Comparison to the disaggregation methods in DS-E (2024)
It should be noted that in its original version, the DS-E Database included the above disaggregation methods by severity except for the four groups. Note that in the original version of the DS-E, we labeled the disaggregation methods differently. We have now updated the labels to be more straightforward and consistent with the terminology used in the answer scale of the disability questions in surveys and censuses). The original DS-E Database also disaggregated by type but only for all levels of severity together (No disability — Any severity level).
In the 2025 edition, we have made two enhancements. First, we added the four group method. This four group method provides the most granular approach to identifying inequalities that may vary with the degree of functional difficulty, i.e. a severity gradient. However, it should be noted that the four group method is not always feasible, even at the national level, especially with household surveys and in censuses for small countries where the group of adults in the Cannot group isoften below 50. For example, for the living alone indicator, among the datasets where this indicator is available, the four group breakdown is feasible in 67.9% of censuses, 37.8% of surveys, and 49.3% of datasets overall. In addition, the four group breakdown cannot be powered with sufficient observations for indicators that are available for subsets of the populations such as the youth idle rate available only for adults ages 15 to 24 or the BMI available only among women ages 15 to 49 in the DHS.
The second enhancement is that the DS-E database now disaggregates by both disability type and severity levels presenting estimates for each functional domain for the following severity levels: Some difficulty — A lot of difficulty — Cannot do, A lot of difficulty or Cannot do . For example, for the seeing domain, users are now able to compare an indicator, say multidimensional poverty, between persons with no difficulty and persons with various levels of difficulty seeing: persons who are cannot see at all (Cannot do), have a lot of difficulty seeing (A lot of difficulty), have some difficulty seeing (Some difficulty), persons with at least a lot of difficulty seeing (A lot of difficulty or Cannot do) and persons with a seeing difficulty of any degree (Any level of difficulty).
References
Altman, B. M. (Ed.). International measurement of disability: Purpose, method and application, the work of the Washington group. Social indicators research series 61. Switzerland: Springer 2016. Accessed on 24th July 2024 at: 
Carpenter, B., Kamalakannan, S., Patchaiappan, K., Theiss, K., Yap, J., Hanass-Hancock, J., Murthy, GVS, Pinilla-Roncancio, M., Rivas Velarde, M., Teodoro, D., and Mitra, S.. The Disability Statistics – Estimates Database (DS-E Database). An innovative database of internationally comparable statistics on disability inequalities. International Journal of Population Data Science. 2024.
DDI (2024). Disability Statistics – Estimates Database (DS-E Database). Disability Data Initiative collective. Fordham University: New York, USA. 2024.
Fujiura, G.T. and V. Rutkowski-Kmitta. Counting Disability. In Albrecht, G.L. and K.D. Seelman and M. Bury, Handbook of Disability Studies, Thousand Oaks, CA: Sage. 2001; pp.69-96. Accessed on 24 July 2024 at:
Hanass-Hancock, J., Kamalakannan, S., Murthy, G.V.S., Palmer, M., Pinilla-Roncancio, M., Rivas Velarde, M., Tetali, S., Mitra, S. What cut-off(s) to use with the Washington Group short set of questions?, Disability and Health Journal 2023; Volume 16, Issue 4, 101499.
Washington Group on Disability Statistics. An introduction to the Washington Group on Disability Statistics question set. 2020. Retrieved from Washington:
Appendix 2: Population Groups
There may be patterns of intersectional disadvantage that affect subgroups of persons with disabilities and their households, such as women or rural residents.
For each dataset under consideration, DS-E includes disaggregated estimates at the individual level based on disability as well as sex (female, male), rural/urban residence or age group (15 to 29, 30 to 44, 45 to 64, 65 and older).
At the household level, DS-E has disaggregated estimates based on disability as well as rural/urban residence.
Appendix 3: Geography
Estimates are representative and available at the national level and at the first subnational level as per ISO (International Organisation for Standardisation) 3166-2 for subnational divisions for all datasets except Mongolia 2020 Population and Housing Census, Marshall Islands HIES 2019, Tokelau Population and Housing Census 2022 and Wallis and Futuna Enquête Budget des Familles 2019.
For some datasets, results are available at an alternative subnational level that is different from ISO 3166-2 and is relevant to the country’s policy or historical context. This is the case for Guatemala Census 2018, Haiti DHS 2016-2017, Maldives DHS 2009, Mauritania DHS 2019-2020, Mexico Census 2020, Nigeria LSMS, Pakistan DHS 2017-2018, Palestine HIES 2009, Uganda2016, Vietnam2009.
For some population and housing censuses, results are also representative and available at a second subnational level below the ISO 3166-2 level: Bhutan 2017, Cambodia 2019, Ecuador 2022,Guatemala 2018, Ghana 2021, Kenya 2019, Kiribati 2015 and 2020, Mauritius 2011, Mexico 2020, Morocco 2014, Myanmar 2014, Philippines 2020, Senegal 2013, Tanzania 2012, Tanzania 2022, South-Africa 2011, Uganda 2014, Uruguay 2011, Vanuatu 2009, Vietnam 2009 and 2019.
Appendix 4: Indicators
The Disability Statistics – Estimates (DS-E) database covers indicators that are defined below. Each indicator has a short name that is noted below as it is used on the interactive platform.
Proportion with disabilities (Prevalence)
Adults with Disabilities
“Adults with disabilities”, “Individual prevalence” for short, is the proportion of adults ages 15 and older with disabilities. The proportion is available for adults with any disability (disability), by disability type, and by degree (moderate and severe disability). More details on the disability groups are in Appendix 1.
Households with Disabilities
“Households with disabilities”, “Household prevalence” for short, is the proportion of households who have at least one adult ages 15 and older with disability. The proportion is available for households with adults with any disability (disability), and by degree (moderate and severe disability). More details on the disability groups are in Appendix 1.
Demographics
Adults who live alone
“Adults who live alone”, “Lives Alone” for short, is the percentage of adults ages 15+ who live alone.
Education
Adults Who Have Ever Attended School
“Adults who have ever attended school”, “Ever attended school” for short, is the proportion of adults ages 15 and older who have ever been to school.
Adults Ages 25+ Who Have Completed Primary School or Higher
“Adults ages 25+ who have completed primary school or higher”, “At least primary” for short, is the proportion of adults ages 25 and older who at least completed primary school. It is calculated by dividing the number of population ages 25 and older who completed primary education by the total population of the same age group and multiplying by 100.
Adults Ages 25+ Who Have Completed Upper Secondary School or Higher
“Adults ages 25+ who have completed upper secondary school or higher”, “At least secondary” for short, is the proportion of adults ages 25 and older who completed upper secondary school, whether or not they also attended tertiary school. It is calculated by dividing the number of population ages 25 and older who completed upper secondary education by the total population of the same age group and multiplying by 100.
Literacy rate/ Able to Read and Write
“Literacy rate/Able to read and write”, “Literacy” for short, is the proportion of adults 15 and older who can read and write in any language.
Health
Adults in Households Using Safely Managed Drinking Water
“Adults in households using safely managed drinking water”, “Water” for short, is the proportion of adults ages 15 and older who live in households who have safely managed drinking water.
Water sources considered as safely managed include: piped water into dwelling, yard or plot; public taps or standpipes; boreholes or tubewells; protected dug wells; protected springs; packaged water; delivered water and rainwater. Water sources that are not considered as safely managed include: unprotected well, unprotected spring, tanker truck, surface water (river/lake, etc), cart with small tank” (UN Statistics 2017a).
Adults in Households Using Safely Managed Sanitation Services
“Adults in households using safely managed sanitation”, “Sanitation” for short, is the proportion of adults ages 15 and older who live in households who have safely managed sanitation services.
Adults are considered to have safely managed sanitation service if their household’s sanitation facility is improved and is not shared with other households. ‘Improved’ sanitation facilities include: flush or pour flush toilets to sewer systems, septic tanks or pit latrines, ventilated improved pit latrines, pit latrines with a slab, and composting toilets” (UN Statistics 2017b).
Women With Family Planning Needs Met
“Women with family planning needs met”, “Family planning” for short, is the proportion of women who report that they have their family planning needs met, i.e. who want and have access to modern contraceptive methods.
Women Subjected to Violence in The Previous 12 Months
“Women subjected to violence in the previous 12 months”, “Violence” for short, is the proportion of ever-married women who report being subject to domestic violence by their intimate partner in the past 12 months. Domestic violence may be physical, psychological or sexual.
Body Mass Index (BMI) (women age 15-49)
“Body Mass Index (BMI) (women age 15-49)”, “BMI” for short, is the weight over height squared.
Overweight or Obese (women age 15-49)
“Overweight or Obese (women age 15-49)”, “Overweight or Obese” for short, is the proportion of women ages 15 to 49 who are overweight or obese. Overweight is a body mass index (BMI) between 25 and 29.9. Obese is for a BMI at 30 or above.
Problems in accessing health care (women age 15-49)
“Problems in accessing health care (women age 15-49)”, “Problems accessing health care” for short is the percentage of women ages 15 to 49, who reported a big problem seeking medical advice or treatment when sick. This could be a problem getting permission, getting money, getting to the health facility, or safety issues traveling alone.
Under 5 child mortality (mothers age 15-49)
“Under 5 child mortality (mothers age 15-49)”, “Child mortality”, for short, is the percentage of mothers who have ever experienced the death of a child age 5 or younger.
Households with a recent death
“Households with a recent death”, “Recent death in the household” for short, is the percentage of households experienced a death in the past 12 months. Activities
Personal Activities
Adults who Have or Use a Computer
“Adults who have or use a computer”, “Computer” for short, is the proportion of adults ages 15 and older who have or use a computer.
Adults who Have or Use the Internet
“Adults who have or use the internet”, “Internet” for short, is the proportion of adults ages 15 and older who have or use the internet.
Adults who Own a mobile phone
“Adults who own a mobile phone”, “Own mobile” for short, is the proportion of adults ages 15 and older who have their own mobile phone.
Employment Population Ratio (Or Employment Rate)
The “employment population ratio”, also called the “employment rate”, “employment” for short, measures the proportion of the adult population ages 15 and older who work for pay, profit (self-employed) or for a family business/farm (whether paid or unpaid).
Youth Idle Rate
The youth idle rate, also called NEET (Not in Education, Employment or Training) is the proportion of youth ages 15 to 24 who are not enrolled in school and not employed. As information on training was not consistently available, estimates of the youth idle rate do not reflect whether youth might be in training.
Workers in Manufacturing
“Workers in manufacturing”, “manufacturing work” for short, is the proportion of workers ages 15 and older who work in the manufacturing sector.
Women in Managerial Positions
The “Women in managerial positions”, “managerial work” for short, is the proportion of women workers who have managerial positions.
Informal Workers
“Informal workers”, “informal work” for short, is the proportion of workers ages 15 and older who do informal work, i.e. who are self-employed, those who work for a microenterprise of five or fewer employees or in a firm that is unregistered, and those who have no written contract with their employers. Family workers without pay are included as informal workers.
Standard of Living
Adults in Households with Electricity
“Adults in households with electricity”, “Electricity” for short, is the proportion of adults ages 15 and older who live in households with access to electricity (United Nations 2017c).
Access is “only considered if the primary source of lighting is the local electricity provider, solar systems, mini-grids and stand-alone systems. Sources such as generators, candles, batteries, etc., are not considered due to their limited working capacities and since they are usually kept as backup sources for lighting (UN Statistics, 2017c).”
Adults in Households with Clean Cooking Fuel
“Adults in households with clean cooking fuel”, “Clean fuel” for short, is the proportion of adults ages 15 and older who live in households who use clean cooking fuel.
Clean fuel includes electricity, gaseous fuels (e.g. natural gas, biogas). Unclean fuels include kerosene and solid fuels (biomass (wood, crop waste, dung), charcoal, coal) (UN Statistics’ 2017d).
Adults in Households with Adequate Housing
“Adults in households with adequate housing”, “Adequate housing” for short, is the proportion of adults ages 15 and older who live in households with adequate housing.
Adequate housing refers to a household living in a place with quality floor, roof and wall materials. Quality floor conditions include laminates, cement, tiles, bricks, parquet. Poor floor conditions include earth, dung, stone, wood planks. Quality roof conditions include burnt bricks concrete, cement. Poor roof conditions refer to no roof or roofs made of natural or rudimentary materials (e.g. asbestos, thatch, palm leaf, mud, earth, sod, grass, plastic, polythene sheeting, rustic mat, cardboard, canvas, tent, wood planks, reused wood, unburnt bricks). Quality wall conditions include burnt bricks, concrete, cement. Poor wall conditions refer to no walls or walls made of natural or rudimentary materials (e.g. cane, palms, trunk, mud, dirt, grass, reeds, thatch, stone with mud, plywood, cardboard, carton/plastic, canvas, tent, unburnt bricks, reused wood.
Mean Percentage of Assets Owned by Household
The “Mean proportion of assets owned by household”, “Assets” for short, is the proportion of assets owned by an adult’s household among the following assets: a radio, TV, telephone, mobile phone, bike, motorbike, refrigerator, car (or truck) and computer.
Adults in Households with A Mobile Phone
“Adults in households with a mobile phone”, “Household mobile phone” for short, is the proportion of adults ages 15 and older who live in households with a mobile phone.
Insecurity
Adults Covered by Health Insurance
“Adults covered by health insurance”, “Health insurance” for short, is the proportion of adults ages 15 and older who live in households with health insurance.
Adults in Households Receiving Social Protection
“Adults in households receiving social protection”, “Social protection” for short, is the proportion of adults ages 15 and older who live in households who received social protection benefits in the past year or who currently receive them (e.g. cash benefits, in kind transfers). Benefits may be from Government of Non-Government institutions.
Adults In Food Insecure Households
Proportion of adults ages 15 and older “Adults in food insecure households”, “Food insecurity” for short, is the proportion of adults ages 15 and older who live in households that recently (in the past week, month or 12 months) did not have access to adequate food. More precisely, (i) the household respondent worried about the household not having enough food or (ii) the household respondent was faced with a situation when they did not have enough food to feed the household; or (iii) there was not enough money to buy food; or (iv) any adult or child in the household went hungry.
Adults In Households That Experienced a Shock Recently
“Adults in households that experienced a shock recently”, “Shock” for short, is the proportion of adults ages 15 and older who live in households that were recently exposed to at least one negative shock. The time frame is usually the past 12 months and shocks include:
- shocks related to the weather (drought, flood, heavy rains),
- negative events affecting household members (death of a household member, illness of a household member),
- economic hardships (loss of a job, crop damage) and disasters (e.g. fire, landslide).
Household Health Expenditures Out of Total Consumption Expenditures
“Household health expenditures out of total consumption expenditures”, “Health expenditures” for short, is the proportion of a household’s total consumption expenditures that are dedicated to health (inpatient care and outpatient care out of pocket expenditures, medicines).
Multidimensional Poverty
Adults who experience Multidimensional Poverty
“Adults who experience multidimensional poverty”, “Multidimensional poverty” for short, is the proportion of adults ages 15 and older who experience more than one deprivation such as not working. This indicator is also called the multidimensional poverty headcount or rate. More details on how it is calculated are in Appendix 5.
References
UN Statistics (2017a). Metadata 06-01-01. Accessed September 30th 2024 at:
UN Statistics (2017b). Metadata 06-02-01. Accessed September 30th 2024 at:
UN Statistics (2017c). Metadata 07-01-01. Accessed September 30th 2024 at:
UN Statistics (2017d). Metadata 07-01-02. Accessed September 30th 2024 at:
Appendix 5: Multidimensional Poverty
The Disability Statistics – Estimates (DS-E) database uses a multidimensional measure of poverty to investigate the experience of simultaneous deprivations following Alkire and Foster (2011). In brief, this method counts deprivations for a set of dimensions and indicators.
An individual is considered to experience multidimensional poverty if the number of deprivations of the individual exceeds a set threshold. Details on the calculation of this measure are included below. H is the multidimensional poverty headcount (or rate) and gives the percentage of the population who experiences multidimensional poverty or multiple deprivations. Dimensions are weighted and wj is the weight of dimension j. There are different possible methods for setting up weights, for instance, asking people’s opinions or using the observed distribution of successes or deprivations (Decancq and Lugo 2013).
In DS-E, as is often done in multi-dimensional poverty research, all dimensions were considered equally important and were given equal weights (each has a weight of 1) and when more than one indicator was used within a dimension, indicators were equally weighted within the dimension. For instance, for the health dimension with two indicators, each indicator weighs ½.
According to the method laid out in Alkire and Foster (2011), each individual i has a weighted count of dimensions where that person achieves deprivations (ci) across all measured dimensions: 0≤ ci ≤ d where d is the number of dimensions; with (ci) equal to one if individual i has a deprivation in dimension j, and zero otherwise. Let qi be a binary variable equal to one if the person is identified as deprived, and to zero otherwise. A person is identified as experiencing multidimensional poverty if the person’s count of deprivations is greater than some specified cutoff (k):
if ci > k, then qi = 1; if ci ≤ k, then qi = 0
In DS-E, k is set at 1. In other words, adults who experience more than one deprivation are considered as multidimensionally poor.
The multidimensional poverty headcount or the proportion of adults experiencing multidimensional poverty H is then the number of persons in multidimensional poverty ( qi) divided by the total population (n): H=q/n
Dimensions and indicators are laid out in Appendix 3 Table 1 below. Based on the information available in the datasets under study, four dimensions and eight indicators were selected for the calculation of the multidimensional poverty measure. The four dimensions are: education, personal activities, health, and standard of living. Each dimension has a weight of 1 and when more than one indicator was used within a dimension, indicators were equally weighted within the dimension.
All indicators in the multidimensional poverty measure are defined in Appendix 2, except for assets. In the multidimensional poverty measure, asset ownership status reflects whether a household owns more than one asset (among radio, TV, telephone, bike, or motorbike or fridge) and if the household does not own a car (or truck).
Education is measured through an indicator of educational attainment for adults ages 15 and older. Personal activities are captured through employment. Health is measured with two indicators and each has a weight of ½: access to safely managed drinking and sanitation services. Standard of living is measured through four indicators with each a weight of ¼: electricity, clean fuel, adequate housing and asset ownership status.
For each indicator, the cutoff for a deprivation is as follows: if a person (1) has less than primary schooling completion; (2) is not working; (3) lives in a household without safely managed drinking water; (4) lives in a household without safely managed sanitation services; (5) lives in a household without clean cooking fuel; (6) lives in a household without adequate housing, i.e. without adequate walls, floor and roof; (7) lives in a household that does not own more than one asset (among radio, TV, telephone, bike, or motorbike or fridge); and the household does not own a car (or truck).
Appendix 5 Table 1: Dimensions, Indicators, and Weights in The Multidimensional Poverty Measure
| Dimension | Indicator(s) | Threshold: Deprived if… | Dimension Weight | Indicator Weight |
| Education | ||||
| At least primary | Individual has not completed primary school | 1 | 1 | |
| Personal activities | ||||
| Employment | Individual does not work | 1 | 1 | |
| Health | 1 | |||
| Water | Household without safely managed drinking water | 1/2 | ||
| Sanitation | Household without safely managed sanitation services | 1/2 | ||
| Standard of living | 1 | |||
| Electricity | Household without electricity | 1/4 | ||
| Clean fuel | Household without clean fuel | 1/4 | ||
| Adequate Housing | Households without quality floor, roof and wall materials | 1/4 | ||
| Asset ownership status | Household does not own more than one asset (among radio, TV, telephone, bike, or motorbike or fridge); and the household does not own a car (or truck). | 1/4 |
References
Alkire, S. and Foster, J. (2011). Counting and multidimensional poverty measurement. Journal of Public Economics 95(7–8):476–87.
Decancq, K. and Lugo, M. A. (2013). Weights in multidimensional indices of wellbeing: an overview. Econometric Reviews, 32, 7-34.
Appendix 6: Data Sources
Appendix 6 Table 1: Datasets under analysis and their disability questions
| Country | Dataset | Year | Disability questions |
| Afghanistan | Living Conditions Survey | 2016-2017 | WG-SS |
| Angola | Demographic and Health Survey (DHS) | 2023-2024 | WG-SS |
| Bangladesh | Household Income and Expenditure Survey | 2016-2017 | WG-SS |
| Benin | Enquête Harmonisée sur le Conditions de Vie des Ménages (EHCVM) (LSMS) | 2021-2022 | WG-SS |
| Bhutan | Population and Housing Census | 2016 | WG-SS |
| Bolivia | Encuesta de Hogares | 2021 | WG-SS |
| Burkina Faso | Enquête Harmonisée sur le Conditions de Vie des Ménages (EHCVM) (LSMS) | 2021-2022 | WG-SS |
| Cambodia | Demographic and Health Survey (DHS) | 2014 | WG-SS |
| Cambodia | Demographic and Health Survey (DHS) | 2021-2022 | WG-SS |
| Cambodia | General population census | 2019 | WG-SS |
| Colombia | Gran Encuesta Integrada de Hogares | 2023 | Other functional (A) |
| Congo Rep. | Recensement General de la Population et de l’habitation | 2023 | WG-SS |
| Cook Islands | Labour Force Survey (LFS) | 2019 | WG-SS |
| Côte d’Ivoire | Enquête Harmonisée sur le Conditions de Vie des Ménages (EHCVM) (LSMS) | 2021-2022 | WG-SS |
| Djibouti | Enquête Djiboutienne auprés des ménages pour les indicateurs sociaux | 2017 | Other functional (S) |
| Ecuador | National Population and Household Census | 2022 | WG-SS |
| Egypt | Labour Force Survey (LFS) | 2023 | WG-SS |
| Ethiopia | Socioeconomic Survey | 2018-2019 | WG-SS |
| Gambia | Labour Force Survey | 2018 | WG-SS |
| Ghana | Population and Housing Census | 2021 | WG-SS |
| Guatemala | National Census of Population and Housing | 2018 | WG-SS |
| Guinea Bisau | Enquête Harmonisée sur le Conditions de Vie des Ménages (EHCVM) (LSMS) | 2021-2022 | WG-SS |
| Haiti | Demographic and Health Survey (DHS) | 2016-2017 | WG-SS |
| Indonesia | Indonesia Survei Angkatan Kerja Nasional (Sakernas) (LFS) | 2025 | WG-SS |
| Jordan | Demographic and Health Survey (DHS) | 2023 | WG-SS |
| Kenya | Demographic and Health Survey (DHS) | 2022 | WG-SS |
| Kenya | Population and Housing Census | 2019 | WG-SS |
| Kiribati | Population and Housing Census | 2015 | Other functional (A) (W) |
| Kiribati | Population and Housing Census | 2020 | WG-SS |
| Liberia | Household Income and Expenditure Survey | 2016-2017 | WG-SS |
| Malawi | Integrated Household Survey | 2019-2020 | WG-SS |
| Malawi | Population and Housing Census | 2018 | Other functional (W) |
| Maldives | Demographic and Health Survey (DHS) | 2009 | WG-SS |
| Mali | Demographic and Health Survey (DHS) | 2018 | WG-SS |
| Mali | Enquête Harmonisée sur le Conditions de Vie des Ménages (EHCVM) (LSMS) | 2021-2022 | WG-SS |
| Malta | Population and Housing Census | 2021 | WG-SS |
| Marshall Islands | Poverty, Food Consumption, Labour, and Household Income and Expenditure (HIES) | 2019 | WG-SS |
| Mauritania | Demographic and Health Survey (DHS) | 2019-2020 | WG-SS |
| Mauritius | Housing and population census | 2011 | Other functional (W) |
| Mexico | Population and Housing Census | 2020 | WG-SS |
| Mongolia | Labour Force and Forced Labour Survey (LFS) | 2022 | WG-SS |
| Mongolia | Population and Housing Census | 2020 | WG-SS |
| Morocco | General Census of Population and Housing | 2014 | Other functional (A) (W) |
| Mozambique | Demographic and Health Survey (DHS) | 2022-2023 | WG-SS |
| Myanmar | Population and Housing Census | 2014 | Other functional (S) (C) |
| Namibia | Household Income and Expenditure Survey | 2015-2016 | WG-SS |
| Nauru | Mini Census | 2019 | WG-SS |
| Nepal | Demographic and Health Survey (DHS) | 2022 | WG-SS |
| Niger | Enquête Harmonisée sur le Conditions de Vie des Ménages (EHCVM) (LSMS) | 2021-2022 | WG-SS |
| Nigeria | Demographic and Health Survey (DHS) | 2018 | WG-SS |
| Nigeria | Living Standards Survey | 2018-2019 | WG-SS |
| Pakistan | Demographic and Health Survey (DHS) | 2017-2018 | WG-SS |
| Palestine | Household Income and Expenditure Survey | 2009 | Other functional (S) |
| Philippines | Census of Population and Housing | 2020 | WG-SS |
| Rwanda | Demographic and Health Survey (DHS) | 2019-2020 | WG-SS |
| Rwanda | Labour Force Survey (LFS) | 2018 | WG-SS |
| Senegal | Demographic and Health Survey (DHS) | 2018 | WG-SS |
| Senegal | General Census of Population and Housing, Agriculture and Livestock | 2013 | WG-SS |
| Senegal | Recensement général de la Population et de l’Habitat | 2023 | WG-SS |
| Senegal | Enquête Harmonisée sur le Conditions de Vie des Ménages (EHCVM) (LSMS) | 2021-2022 | WG-SS |
| Sierra Leone | Integrated Household Survey (SLIHS) | 2018 | WG-SS |
| Somalia | Labour Force Survey (LFS) | 2019 | WG-SS |
| South Africa | Census | 2011 | WG-SS |
| South Africa | Community Survey | 2016 | WG-SS |
| South Africa | Demographic and Health Survey (DHS) | 2016 | WG-SS |
| Suriname | Census | 2012 | Other functional (A) |
| Tajikistan | Poverty Diagnostic of Water Supply, Sanitation, and Hygiene Conditions | 2016 | WG-SS |
| Tanzania | Demographic and Health Survey (DHS) | 2022 | WG-SS |
| Tanzania | Population and Housing Census | 2012 | WG-SS |
| Tanzania | Population and Housing Census | 2022 | WG-SS |
| Timor-Leste | Demographic and Health Survey (DHS) | 2016 | WG-SS |
| Togo | Enquête Harmonisée sur le Conditions de Vie des Ménages (EHCVM) (LSMS) | 2021-2022 | WG-SS |
| Tokelau | Population and Housing Census | 2022 | WG-SS |
| Tonga | Population and Housing Census | 2016 | WG-SS |
| Tuvalu | Mini Census | 2017 | WG-SS |
| Uganda | Demographic and Health Survey (DHS) | 2016 | WG-SS |
| Uganda | National Population and Housing Census | 2014 | Other functional (S) (C) |
| Uruguay | General Population Census | 2011 | Other functional (S) (C) |
| Vanuatu | National Population and Housing Census | 2009 | Other functional (A) (S) (C) |
| Vietnam | Population and Housing census | 2009 | Other functional (A) (S) (C) |
| Vietnam | Population and Housing census | 2019 | WG-SS |
| Wallis and Futuna | Enquête Budget des Familles | 2019 | WG-SS |
| Zimbabwe | Poverty Income Consumption and Expenditure Survey | 2017 | Other functional (W) |
| Note: For datasets with other functional difficulty questions, the legend is as follows: (A) Answer scale is different from that in the WGSS (W) Wording of one question or more is different from the WGSS (S) Does not have the selfcare domain (C) Does not have the communication domain | |||
Appendix 6 Table 2. Data sources of default estimates for countries with more than one dataset
| Country | Cambodia | Kenya | Kiribati | Mongolia | Mali | Malawi | Nigeria | Rwanda | Senegal | South Africa | Tanzania | Uganda | Vietnam | ||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Indicator/Dataset | IPUMS 2019 | DHS 2014 | DHS 2021-2022 | Census 2019 | DHS 2022 | Census 2015 | Census 2020 | Census 2020 | LFS 2022 | DHS 2018 | LSMS 2021 | LSMS 2019-2020 | Census 2018 | DHS 2018 | LSMS 2018 | DHS 2019- 2020 | LFS 2018 | IPUMS 2013 | DHS 2018 | LSMS 2021 | Census 2023 | IPUMS 2011 | IPUMS 2016 | DHS 2016 | IPUMS 2012 | DHS 2022 | Census 2022 | IPUMS 2014 | DHS 2016 | IPUMS 2009 | IPUMS 2019 |
| Living Arrangment | |||||||||||||||||||||||||||||||
| Living alone | 1 | x | x | 1 | x | 1 | x | 1 | x | 1 | x | 1 | x | 1 | x | 1 | x | x | x | 1 | 1 | x | x | x | x | 1 | 1 | x | x | 1 | |
| Prevalence | |||||||||||||||||||||||||||||||
| Adults with disability | 1 | x | x | 1 | x | x | 1 | x | 1 | x | 1 | x | 1 | x | 1 | x | 1 | x | x | x | 1 | x | 1 | x | x | x | 1 | 1 | x | x | 1 |
| Adults with disability by type | 1 | x | x | 1 | x | x | 1 | x | 1 | x | 1 | x | 1 | x | 1 | x | 1 | x | x | x | 1 | x | 1 | x | x | x | 1 | 1 | x | x | 1 |
| Households with disability by type | 1 | x | x | 1 | x | x | 1 | x | 1 | x | 1 | x | 1 | x | 1 | x | 1 | x | x | x | 1 | x | 1 | x | x | x | 1 | 1 | x | x | 1 |
| Education | |||||||||||||||||||||||||||||||
| Ever attended school | 1 | x | x | 1 | x | x | 1 | x | 1 | x | 1 | x | 1 | x | 1 | x | 1 | x | x | x | 1 | x | 1 | x | x | x | 1 | 1 | x | x | 1 |
| Primary school or high | 1 | x | x | 1 | x | x | 1 | x | 1 | x | 1 | x | 1 | x | 1 | x | 1 | x | x | x | 1 | x | 1 | x | x | x | 1 | 1 | x | x | 1 |
| Secondary school or higher | 1 | x | x | 1 | x | x | 1 | x | 1 | x | 1 | x | 1 | x | 1 | x | 1 | x | x | x | 1 | x | 1 | x | x | x | 1 | 1 | x | x | 1 |
| Literacy | 1 | x | x | 1 | x | 1 | x | 1 | x | 1 | x | 1 | x | 1 | x | x | x | 1 | 1 | x | x | 1 | 1 | x | 1 | ||||||
| Personal activities | |||||||||||||||||||||||||||||||
| Computer | 1 | ||||||||||||||||||||||||||||||
| Internet | 1 | x | 1 | x | x | 1 | 1 | 1 | 1 | 1 | 1 | x | 1 | 1 | 1 | 1 | |||||||||||||||
| Own mobile | 1 | x | 1 | x | 1 | 1 | 1 | 1 | 1 | 1 | 1 | x | 1 | 1 | 1 | 1 | |||||||||||||||
| Employment | 1 | x | x | 1 | x | x | 1 | x | 1 | x | 1 | x | 1 | x | 1 | x | 1 | x | x | x | 1 | 1 | x | x | x | 1 | 1 | x | x | 1 | |
| Youth idle | 1 | 1 | x | 1 | x | 1 | 1 | x | 1 | 1 | 1 | x | x | 1 | 1 | x | 1 | 1 | x | 1 | |||||||||||
| Manufacturing | 1 | 1 | x | 1 | x | 1 | 1 | x | 1 | 1 | 1 | x | x | 1 | x | 1 | x | 1 | |||||||||||||
| Managerial work | 1 | x | 1 | x | 1 | 1 | x | 1 | 1 | 1 | x | x | 1 | x | 1 | x | 1 | ||||||||||||||
| Informal work | 1 | 1 | 1 | 1 | 1 | x | 1 | 1 | 1 | x | x | 1 | x | 1 | 1 | x | 1 | ||||||||||||||
| Health | |||||||||||||||||||||||||||||||
| Water | 1 | x | x | 1 | x | x | 1 | 1 | x | 1 | x | 1 | x | 1 | x | 1 | x | x | x | 1 | x | 1 | x | x | x | 1 | 1 | x | x | 1 | |
| Sanitation | 1 | x | x | 1 | x | x | 1 | 1 | x | 1 | x | 1 | x | 1 | x | 1 | x | x | x | 1 | x | 1 | x | x | x | 1 | 1 | x | x | 1 | |
| Family planning | 1 | x | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | |||||||||||||||||||||
| Violence | 1 | x | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | |||||||||||||||||||||
| BMI | 1 | x | 1 | 1 | 1 | 1 | 1 | 1 | 1 | ||||||||||||||||||||||
| Overweight or Obese | 1 | x | 1 | 1 | 1 | 1 | 1 | 1 | 1 | ||||||||||||||||||||||
| Problems in Healthcare Access | 1 | x | 1 | 1 | 1 | 1 | 1 | 1 | 1 | ||||||||||||||||||||||
| Under5 18 Children mortality | 1 | x | 1 | 1 | 1 | 1 | 1 | 1 | 1 | ||||||||||||||||||||||
| Mortality | 1 | 1 | x | 1 | |||||||||||||||||||||||||||
| Standard of living | |||||||||||||||||||||||||||||||
| Electricity | 1 | x | x | 1 | x | x | 1 | 1 | x | 1 | x | 1 | x | 1 | x | 1 | x | x | x | 1 | x | 1 | x | x | x | 1 | 1 | x | x | 1 | |
| Clean cooking | 1 | x | x | 1 | x | x | 1 | 1 | x | 1 | x | 1 | x | 1 | x | 1 | x | x | x | 1 | x | 1 | x | x | x | 1 | 1 | x | x | 1 | |
| Adequate housing | 1 | x | 1 | x | 1 | x | 1 | x | 1 | x | 1 | x | 1 | x | x | x | 1 | 1 | x | x | x | 1 | 1 | x | x | 1 | |||||
| Assets owned | 1 | x | x | 1 | x | x | 1 | x | 1 | x | 1 | x | 1 | x | 1 | x | x | x | 1 | x | 1 | x | x | x | 1 | 1 | x | x | 1 | ||
| Households mobile | 1 | x | 1 | x | x | 1 | 1 | x | 1 | x | 1 | x | 1 | x | 1 | x | x | x | 1 | x | 1 | x | x | x | 1 | 1 | |||||
| 1 | |||||||||||||||||||||||||||||||
| Insecurity | |||||||||||||||||||||||||||||||
| Health insurance | 1 | x | 1 | x | 1 | x | 1 | 1 | x | 1 | 1 | 1 | 1 | ||||||||||||||||||
| Social protection | x | 1 | 1 | 1 | 1 | ||||||||||||||||||||||||||
| Food insecure | 1 | 1 | 1 | 1 | |||||||||||||||||||||||||||
| Experienced shock | 1 | 1 | 1 | 1 | |||||||||||||||||||||||||||
| Health expenditures | 1 | 1 | 1 | 1 | |||||||||||||||||||||||||||
| Poverty | |||||||||||||||||||||||||||||||
| Multidimensional poverty | 1 | x | x | 1 | x | x | 1 | 1 | x | 1 | x | 1 | x | 1 | x | 1 | x | x | x | 1 | x | 1 | x | x | x | 1 | 1 | x | x | 1 | |
| Notes: An empty cell indicates the indicator cannot be estimated due to missing relevant questions; a number 1 or x indicates that the indicator was estimated; a number 1 indicates the default estimate for a given indicator. | |||||||||||||||||||||||||||||||
Appendix 7: Datasets and Sampling
Appendix 7 Table 1: Datasets under analysis and sampling design
| Country | Dataset | Year | Sampling |
|---|---|---|---|
| Afghanistan | Living Conditions Survey | 2016-2017 | Stratified, two-stage sample design |
| Angola | Demographic and Health Survey (DHS) | 2023-2024 | Stratified, two-stage sample design |
| Bangladesh | Household Income and Expenditure Survey | 2016-2017 | Stratified, two-stage sample design |
| Bhutan | Population and Housing Census | 2016 | Entire population |
| Bolivia | Encuesta de Hogares | 2021 | Stratified, two-stage sample design |
| Burkina Faso | Enquête Harmonisée sur le Conditions de Vie des Ménages (EHCVM) (LSMS) | 2021-2022 | Stratified, two-stage sample design |
| Cambodia | Demographic and Health Survey (DHS) | 2014 | Stratified, two-stage sample design |
| Cambodia | Demographic and Health Survey (DHS) | 2021-2022 | Stratified, two-stage sample design |
| Cambodia | General population census | 2019 | 10% systematic sample |
| Colombia | Gran Encuesta Integrada de Hogares | 2023 | Stratified, two-stage sample design |
| Congo Rep. | Recensement General de la Population et de l’habitation | 2023 | 5% sample from the census data. |
| Cook Islands | Labour Force Survey (LFS) | 2019 | Stratified, two-stage sample design |
| Côte d’Ivoire | Enquête Harmonisée sur le Conditions de Vie des Ménages (EHCVM) (LSMS) | 2021-2022 | Stratified, two-stage sample design |
| Djibouti | Enquête Djiboutienne auprés des ménages pour les indicateurs sociaux | 2017 | Stratified, two-stage sample design |
| Ecuador | National Population and Household Census | 2022 | Entire population |
| Egypt | Labour Force Survey (LFS) | 2023 | Stratified, two-stage sample design |
| Ethiopia | Socioeconomic Survey | 2018-2019 | Stratified, two-stage sample design |
| Gambia | Labour Force Survey | 2018 | Stratified, two-stage sample design |
| Ghana | Population and Housing Census | 2021 | 10% systematic sample |
| Guatemala | National Census of Population and Housing | 2018 | Entire population |
| Guinea Bisau | Enquête Harmonisée sur le Conditions de Vie des Ménages (EHCVM) (LSMS) | 2021-2022 | Stratified, two-stage sample design |
| Haiti | Demographic and Health Survey (DHS) | 2016-2017 | Stratified, two-stage sample design |
| Indonesia | Indonesia Survei Angkatan Kerja Nasional (Sakernas) (LFS) | 2025 | Stratified, two-stage sample design |
| Jordan | Demographic and Health Survey (DHS) | 2023 | Stratified, two-stage sample design |
| Kenya | Demographic and Health Survey (DHS) | 2022 | Stratified, two-stage sample design |
| Kenya | Population and Housing Census | 2019 | 10% systematic sample |
| Kiribati | Population and Housing Census | 2015 | Entire population |
| Kiribati | Population and Housing Census | 2020 | Entire population |
| Liberia | Household Income and Expenditure Survey | 2016-2017 | Stratified, two-stage sample design |
| Malawi | Integrated Household Survey | 2019-2020 | Stratified, two-stage sample design |
| Malawi | Population and Housing Census | 2018 | 10% sample from the census data. Systematic sample of every 10th household. |
| Maldives | Demographic and Health Survey (DHS) | 2009 | Stratified, two-stage sample design |
| Mali | Demographic and Health Survey (DHS) | 2018 | Stratified, two-stage sample design |
| Mali | Enquête Harmonisée sur le Conditions de Vie des Ménages (EHCVM) (LSMS) | 2021-2022 | Stratified, two-stage sample design |
| Malta | Population and Housing Census | 2021 | Entire population |
| Marshall Islands | Poverty, Food Consumption, Labour, and Household Income and Expenditure (HIES) | 2019 | Stratified, two-stage sample design |
| Mauritania | Demographic and Health Survey (DHS) | 2019-2020 | Stratified, two-stage sample design |
| Mauritius | Housing and population census | 2011 | 10% systematic sample |
| Mexico | Population and Housing Census | 2020 | One stage stratified cluster 10% sample |
| Mongolia | Labour Force and Forced Labour Survey (LFS) | 2022 | Stratified, two-stage sample design |
| Mongolia | Population and Housing Census | 2020 | 1% systematic sample |
| Morocco | General Census of Population and Housing | 2014 | 10% systematic stratified sample |
| Mozambique | Demographic and Health Survey (DHS) | 2022-2023 | Stratified, two-stage sample design |
| Myanmar | Population and Housing Census | 2014 | 10% systematic stratified sample |
| Namibia | Household Income and Expenditure Survey | 2015-2016 | Stratified, two-stage sample design |
| Nauru | Mini Census | 2019 | Entire population |
| Nepal | Demographic and Health Survey (DHS) | 2022 | Stratified, two-stage sample design |
| Niger | Enquête Harmonisée sur le Conditions de Vie des Ménages (EHCVM) (LSMS) | 2021-2022 | Stratified, two-stage sample design |
| Nigeria | Demographic and Health Survey (DHS) | 2018 | Stratified, two-stage sample design |
| Nigeria | General Household Survey | 2018-2019 | Stratified, two-stage sample design |
| Pakistan | Demographic and Health Survey (DHS) | 2017-2018 | Stratified, two-stage sample design |
| Palestine | Household Income and Expenditure Survey | 2009 | Stratified, two-stage sample design |
| Philippines | Census of Population and Housing | 2020 | Systematic cluster sampling |
| Rwanda | Demographic and Health Survey (DHS) | 2019-2020 | Stratified, two-stage sample design |
| Rwanda | Labour Force Survey (LFS) | 2018 | Stratified, two-stage sample design |
| Senegal | Demographic and Health Survey (DHS) | 2018 | Stratified, two-stage sample design |
| Senegal | General Census of Population and Housing, Agriculture and Livestock | 2013 | 10% sample |
| Senegal | Recensement général de la Population et de l’Habitat | 2023 | 10% data file with random sampling. |
| Senegal | Enquête Harmonisée sur le Conditions de Vie des Ménages (EHCVM) (LSMS) | 2021-2022 | Stratified, two-stage sample design |
| Sierra Leone | Integrated Household Survey (SLIHS) | 2018 | Stratified, two-stage sample design |
| Somalia | Labour Force Survey (LFS) | 2019 | Stratified, two-stage sample design |
| South Africa | Census | 2011 | 8.6% systematic stratified sample |
| South Africa | Community Survey | 2016 | 5.8% systematic stratified sample |
| South Africa | Demographic and Health Survey (DHS) | 2016 | Stratified, two-stage sample design |
| Suriname | Census | 2012 | 10% systematic sample |
| Tajikistan | Poverty Diagnostic of Water Supply, Sanitation, and Hygiene Conditions | 2016 | Stratified, two-stage sample design |
| Tanzania | Demographic and Health Survey (DHS) | 2022 | Stratified, two-stage sample design |
| Tanzania | Population and Housing Census | 2012 | 10% systematic sample |
| Tanzania | Population and Housing Census | 2022 | 10% data file, random sampling |
| Timor-Leste | Demographic and Health Survey (DHS) | 2016 | Stratified, two-stage sample design |
| Togo | Enquête Harmonisée sur le Conditions de Vie des Ménages (EHCVM) (LSMS) | 2021-2022 | Stratified, two-stage sample design |
| Tokelau | Population and Housing Census | 2022 | Entire population |
| Tonga | Population and Housing Census | 2016 | Entire population |
| Tuvalu | Mini Census | 2017 | Entire population |
| Uganda | Demographic and Health Survey (DHS) | 2016 | Stratified, two-stage sample design |
| Uganda | National Population and Housing Census | 2014 | 10% systematic sample |
| Uruguay | General Population Census | 2011 | 10% systematic sample |
| Vanuatu | National Population and Housing Census | 2009 | Entire population |
| Vietnam | Population and Housing census | 2009 | 15% stratified systematic sample |
| Vietnam | Population and Housing census | 2019 | 8.5% stratified two-stage sample design |
| Wallis and Futuna | Enquête Budget des Familles | 2019 | Stratified by single stage random sampling. |
| Zimbabwe | Poverty Income Consumption and Expenditure Survey | 2017 | Stratified, two-stage sample design |