indicators (assets, living conditions) that might be affected due to the extra costs of living with disabilities. Some of these indicators were not found in the data sets under consideration and are thus not listed in Table 2.2 (for instance, Household expenditures on care as a share of total household expenditure and Information, Communication, and Technology (ICT) skills among adults).

For education, we use “Ever attended school’ as a first indicator for all adults and also for adults age 15 to 29 following UNESCO (2018)<sup>3</sup> . For school completion rates, we had to deviate from current international practice focused on specific age cohorts (e.g. secondary completion rate for adolescents age 15 to 17) due to small sample sizes for some surveys (UNESCO 2018). Instead, we group all adults age 15 and older in four mutually exclusive categories: less than primary school, primary school completion, secondary school completion and higher than secondary school. It should be noted that countries may have different thresholds for each type of schooling. For instance, for the completion of primary school, the years primary school requires may vary from five to eight years in the different countries. At the household level, we categorize the educational attainment of the household head according to the four categories above and present the mean educational attainment of household heads across functional status. At the household level, we calculate the percentage of children in the household between the age of six and 14 who are out of school and for data sets with a consumption expenditure module, the share of education expenditures out of total consumption expenditures.

For employment, this paper uses six indicators: employment population ratio, unemployment rate, idle rate among youths, shares of workers in the informal sector, in the manufacturing sector and in managerial positions. In four data sets, the unemployment rate could not be calculated as there is no information on the job search status of individuals (Ethiopia, Philippines, Tanzania (LSMS) and Uganda). The idle rate is commonly used for youths age 15 to 24 and reflects the share of youths who are not in school, not working, and not seeking employment. As job search information was not available in all countries, we did not include it in the idle rate. The share of workers in the informal sector was calculated based on information collected in some surveys on individuals’ employment status, whether they have a contract with their employer, and information provided on employers (e.g.business registration status).

# **Disaggregation**

There may be patterns of intersectional disadvantage that affect subgroups of people with disabilities and their households, such as women, rural residents, or indigenous peoples. Identifying sub-groups within this population is policy-relevant as many LMICs have social policies targeted at the groups that are particularly exposed to vulnerability.

For each data set under consideration, we considered further stratifying results at the individual level based on sex, age group, rural/urban residence and at the household level based on rural/urban residence. For data sets with the full population or random sampling, disaggregation is feasible based on sex, age groups, rural/urban. For data sets with complex survey design, disaggregation based on sex, age groups, rural/urban is feasible if sex, age, rural/urban residence

> 3 An adult who has not been to school at all or only attended nursery school is considered as not having ever attended school.

10