Analysis of Micro Datasets: Method

This report uses population census and Demographic and Health Survey (DHS) data for 15 countries in Sub-Saharan Africa, Asia & the Pacific, and Latin America & the Caribbean. Countries are predominantly lower-middle income countries. All countries have a Human Development Index rank at 90 or above and all except Tonga have ratified the CRPD (Appendix 3).

We use three population census datasets of the 2020 census round (2015 to 2024): Guatemala (2018), Kenya (2018) and Tonga (2016). We use data from the DHS program for 12 countries: Cambodia (2014), Haiti (2016-17), Maldives (2009), Mali (2018), Mauritania (2019-2021), Nigeria (2018), Pakistan (2017-18), Rwanda (2019), Senegal (2018), South Africa (2016), Timor-Leste (2016) and Uganda (2016). The 15 countries were selected given the availability of a dataset representative at both national and regional levels and has the WG-SS[4],[5]. We focus on adults 15 years and older as the WG-SS may not be adequate to capture disability among children (Loeb et al 2018).

What indicators does the report produce?

This report produces various indicators to capture the rights and human development situation of persons with disabilities. The indicators are in Table 4.1 and are further described in Method Brief 2. The list of indicators was developed by reviewing the questionnaires of datasets in light of the provisions of the CRPD and the SDGs that they could capture (IWGHS 2018; OHCHR 2021). Indicators reflect a variety of achievements (e.g., access to safely managed water) and deprivations (e.g., less than primary school completion). Taking the difference of indicators between persons with no difficulties and persons with difficulties may give a gap associated with disability, i.e. the disability gap or inequalities associated with disability[6]. How indicators are disaggregated by disability status is explained in Box 1 and Method Brief 1.

Box 1: How were indicators disaggregated by disability status?
All the datasets have the WG-SS. The WG-SS measures functional difficulties for individuals in six domains: (a) seeing, (b) hearing, (c) walking/climbing stairs, (d) concentrating or remembering things, (e) selfcare, and (f) communication. A household respondent reports the degree of difficulty in each domain on a four-point answer scale: 1-‘No difficulty’, 2-‘Some difficulty’, 3-‘A lot of difficulty’, and 4-‘Unable to do’.

To identify functional difficulty status groups, at least one cutoff has to be set on the answer scale of functional difficulties. Where the threshold is set can lead to varying results and may answer different data needs. This report’s results tables consistently present disaggregations using three ways to categorize individuals based on functional status and place them into mutually exclusive categories.

In disaggregation a, individuals are in two categories:
– No difficulty includes people who report ‘No difficulty’ in all domains.
– Any difficulty includes people who report ‘Some difficulty’, ‘A lot of difficulty’ or ‘Unable to do’ for at least one domain.
In disaggregation b, individuals are in three categories:
– No difficulty includes people who report ‘No difficulty’ in all domains.
-Some difficulty includes persons who report ‘Some difficulty’ in at least one domain but no ‘A lot of difficulty’ or ‘Unable to do’ in other domains.
– At least a lot of difficulty includes people who answer ‘A lot of difficulty’ or ‘Unable to do’ in at least one domain.
In disaggregation c, individuals are in two categories as follows:
– No difficulty or some difficulty includes persons who report no or some difficulty for all domains.
– At least a lot of difficulty includes people who answer ‘A lot of difficulty’ or ‘Unable to do’ in at least one domain.

In the results described below, we mostly use disaggregations a and b. Due to sample size constraints, disaggregation a is useful to compare persons with no difficulty to persons with any level of difficulty to enable disaggregations by functional domains and also for some subgroups (e.g. by sex, age). Disaggregation b is able to identify potential deprivations among persons with some difficulty and compare them to those experienced by persons with at least a lot of difficulty.

What results are available from the micro-data analysis?

Comprehensive Results Tables are available on the DDI website for each country. Results Tables have results for the three disaggregation methods above for each indicator. For prevalence rates, results tables report shares of adults with any difficulty, some difficulty and at least a lot of difficulty, and for any difficulty by domain.

We produce results at national and subnational levels. Administration Level 1 is the generic term for the largest subnational administrative unit of a country. It has different names across countries: for example, “island” and “departments” are the terms used in Tonga and Haiti respectively. Administration Level 2 is the generic term for the second largest subnational administrative unit of a country, for instance, ‘district’ in Tonga and ‘commune’ in Haiti. Similarly, administration Level 3 is the generic term for the third  subnational administrative unit, for instance, ‘village’ in Tonga and ‘section communale’ in Haiti.  To facilitate the presentation, this report uses the term ‘region’ for administrative level 1, ‘district’ for administrative level 2 and ‘village’ for administrative level 3. However, it is important to consider that this terminology changes between countries.

We develop results on human development and rights indicators disaggregated by functional difficulty status for a total of 119 regions across 12 countries with DHS data and 60 regions across three countries with census data. We also produce results at the district level for Guatemala, Kenya and Tonga using census data and also  include a map at the village level for Guatemala. The analysis was conducted in Stata 16: the codes to produce national and regional estimates are available in Method Brief 4.

Table 4.1: Indicators under study

Indicator  CRPD Article SDG indicator Indicator reference in results tables
Adults with functional difficulties P1
Adults with functional difficulties  by type of functional difficulty P2
Households with functional difficulties P3
Adults who have ever attended school 24 E1
Adults who have less than primary school completion 24 E2
Adults who have completed primary school 24 E3
Adults who have completed secondary  school or higher 24 E4
Adults who can read and write in any language 24 4.6.1 E5
Personal activities
Employment population ratio 27 W1
Youth idle rate (NEET) 27 8.6.1 W2
Working individuals in manufacturing 27 9.2.2 W3
Women in managerial positions 27 5.5.2 W4
Working individuals in informal work 27 8.3.1 W5
Adults who used a computer recently 9 PA2
Adults who used the internet recently 9 PA3
Adults who own a mobile phone 9 5.b.1 PA4
Adults in households using safely managed drinking water 25 6.1.1 H1
Adults in households using safely managed sanitation services 25 6.2.1 H2
Standard of living 
Adults in households with electricity 28 7.1.1 S1
Adults in households with clean cooking fuel 28 7.1.2 S2
Adults in households with adequate housing 28 S3
Assets owned by individual’s household (%) 28 S4
Adults in households with a mobile phone 28 5.b.1 S5
Multidimensional analysis
Adults who experience multidimensional poverty, i.e. deprivations in more than one dimension of wellbeing (education, health, work, standard of living) 24, 25, 27, 28 M1


Notes: Relevant SDG indicators are listed. The SDG indicators may be different from the indicators measured in this report. For instance, indicator 8.3.1 measures Proportion of informal employment in total employment while this report measures the proportion of workers doing informal work. All indicators are proportions except the one on assets. Indicator reference numbers follow those in the 2021 and the 2022 Disability Data Reports (PA1 was skipped due to a lack of data on exposure to mass media.

[4] The DHS follows a complex survey design: for each of the 12 countries under study, DHS data is representative at the national, regional, for rural and urban areas, for women and men. For Guatemala and Tonga, each population census includes the entire population. The Kenya population census dataset is a 10% random sample of the entire population.

[5] For some countries with DHS data (e.g. Haiti, Pakistan), the short set has been modified by adding two questions on whether the person wears glasses and hearing aids. In this setting, seeing difficulties are captured as follows: We consider a person to have seeing difficulties whether they wear glasses or not but report to have difficulty seeing. Similarly, we consider a person to have difficulty hearing whether they wear a hearing aid or not, but report having difficulty hearing. This allows us to create homogeneous cross-country indicators to capture functional difficulty seeing or hearing.

[6] The difference and its statistical significance are noted in the results tables. Positive/negative differences respectively reflect a disability gap in achievement/deprivation indicators respectively.