Work

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Background

Article 27 of the CRPD “recognizes the right of persons with disabilities to work, on an equal basis with others; this includes the opportunity to gain a living by work freely chosen or accepted in a labor market and work envi­ronment that is open, inclusive and accessible to persons with disabilities”. It also prohibits all forms of employment discrimination, calls for reasonable accommodation in the workplace, promotes access to vocational training and self-employment opportunities. This section describes and discusses the main results on work indicators. The entire set of results is available in the Work Results Tables.

The term “work” is broad and includes both paid and unpaid work. Unpaid work can be, for instance, working in a family enterprise. Meanwhile, paid work means being employed by another person or organization whether in the formal or informal economy. This report uses five work indicators for adults. The first one is the employment population ratio, also called the employment rate. It captures the share of the adult population who is employed, i.e. working for pay or those who are self-employed.

The youth idle rate measures the share of youth aged 15-24 years who are not enrolled in school and are not employed (SDG indicator 8.6.1). We measure the share of the employed in the manufacturing sector (SDG 9.2.2) and the share of women who hold managerial positions (SDG 5.5.2). Finally, the informal work indicator[12] captures the share of the adult population who do informal work, i.e. who are self-employed, those who work for a microenterprise of five or few employees or in a firm that is unregistered and those who have no written contract with their employers. It also includes persons who work unpaid, including for a family business.

Except for the youth idle rate which focuses on adults ages 15 to 24, the work indicators are generally reported for all adults ages 15 and older. For seven countries for which the Demographic and Health Survey (DHS) females and males modules were used (Cambodia, Colombia, Haiti, Maldives, Mali, Pakistan, Timor Leste), the work information in DHS covers ages 15 to 49 and ages 15 to 54 for women and men respectively.

Results

Adults with any functional difficulty have significantly lower employment population ratios in 30 out of 40 countries. For adults with at least a lot of difficulty, this is the case for 29 out of 35 countries. The disability gap in the employment population ratio ranges from a low of 2 percentage points in Malawi to a high of 48 percentage points in Mauritius. The median stands at eight percentage points.

For 15 of the 35 countries, there is a gradient in the employment population ratio, with persons with some difficulty having lower employment population ratios than persons with no difficulty but higher ones than persons with at least a lot of difficulty.

As shown in Tables 7.1 and 7.2, while women consistently have lower employment population ratios than men, the disability gap in the employment population ratio tends to be larger for men than for women with a median gap of 10 and five percentage points respectively. It is also often larger for older age groups.

Four countries have a reverse gap, i.e. a larger employment population ratio for persons with any difficulty compared to persons with no difficulty (disaggregation a: Colombia, Papua New Guinea, Timor Leste, Vanuatu). However, for all of them except Colombia, this result only holds for persons with some difficulty and not persons with at least a lot of difficulty with disaggregation b.

The youth idle rate shows a similar pattern as the employment population ratio across countries with a disability gap in 22 out of 38 countries. The disability gap in the youth idle rate is larger in rural areas compared to urban areas. In most countries, the share of persons in manufacturing and the share of women in managerial positions are similar across functional status but are significantly lower for persons with functional difficulty in 12 and eight countries respectively.

Adults with any functional difficulty are significantly more likely to do informal work in 26 out of 37 countries. Persons with some and at least a lot of difficulty have a higher rate of informal work in respectively 17 and 20 out of 29 countries.

Discussion

Overall, employment population ratios are lower for persons with functional difficulties compared to persons with no difficulty in most countries.

The significance and magnitude of the gap varies considerably across contexts. This may come from the variety of survey questions that capture work. It is noteworthy that most of the countries that do not have a disability gap are countries with DHS datasets, where work questions are only asked among a subsample of adults who are young or middle aged and do not capture older adults.

There are also contextual variations in the accessibility of the work environment, the availability of workplace accommodations, and the presence of discrimination. The cultural and social context also matters in so far as negative attitudes toward the employment potential of persons with disabilities in society at large or within the household might limit access to work. The policy context is also relevant: for instance, are there vocational rehabilitation programs available? Are there disability cash transfer programs? Such programs, depending on how they are designed and put into practice, might facilitate or limit access to work for persons with disabilities.

In addition, the results in this report point out that work differences across functional difficulty status are more pronounced among males than females.  For women, barriers to work may well be primarily gender-related.

This paper finds a significantly higher proportion of adults with functional difficulties who do informal work in a majority of countries. Other studies do find a higher share of workers with disabilities in self-employment compared to workers without disabilities in several countries (e.g., Eide and Kamaleri, 2009; Mitra and Mizunoya 2013). The data used in this study does not help us determine if they are constrained into informal work due to barriers to the formal sector, which should be the subject of further research.

Table 7.1: Females’ Employment Population Ratio

Country No Difficulty Any difficulty Difference Statistical Significance
Afghanistan 15 12 3 ***
Bangladesh 11 8 3 ***
Cambodia 76 71 4 *
Colombia 55 61 -6 ***
Djibouti 10 11 -1 NS
Dominican Rep. 35 26 9 ***
Ethiopia 43 38 5 **
Gambia 42 40 1 NS
Haiti 43 53 -11 ***
Indonesia 46 29 17 ***
Kiribati 33 31 3 ***
Liberia 70 65 5 **
Malawi 74 73 1 NS
Maldives 40 41 -1 NS
Mali 53 49 3 NS
Mauritius 39 10 29 ***
Mexico 37 18 19 ***
Morocco 15 9 7 ***
Myanmar 46 24 22 ***
Namibia 58 56 2 NS
Nigeria 53 49 5 NS
Pakistan 17 23 -6 ***
Panama 39 15 25 ***
Papua New Guinea 67 70 -3 *
Peru 62 35 26 ***
Puerto Rico 40 12 27 ***
Rwanda 50 34 16 ***
Senegal 20 18 1 ***
South Africa 37 29 8 ***
Suriname 41 36 5 ***
Tajikistan 17 14 3 **
Tanzania 83 76 6 ***
Timor Leste 26 37 -12 ***
Tonga 39 27 12 ***
Uganda 68 64 4 NS
Uruguay 55 28 28 ***
Vanuatu 58 60 -2 ***
Vietnam 73 34 39 ***
West Bank and Gaza 17 9 8 ***
Zimbabwe 64 65 -1 NS
Source: Own calculations based on datasets in Table 4.1
Notes: *, **, *** indicate that the difference is statistically significant at the 10%, 5% and 1% levels respectively. NS stands for not significant

Table 7.2: Males’Employment Population Ratio

Country No Difficulty Any difficulty Difference Statistical significance
Afghanistan 69 55 14 ***
Bangladesh 79 53 26 ***
Cambodia 90 88 3 NS
Colombia 89 92 -3 ***
Djibouti 34 30 4 *
Dominican Rep. 60 44 16 ***
Ethiopia 62 58 5 *
Gambia 61 60 1 NS
Haiti 71 79 -8 ***
Indonesia 81 61 20 ***
Kiribati 49 45 4 ***
Liberia 79 77 2 NS
Malawi 82 80 2 NS
Maldives 96 90 6 ***
Mali 87 93 -6 ***
Mauritius 73 22 51 ***
Mexico 76 41 35 ***
Morocco 70 41 29 ***
Myanmar 81 52 29 ***
Namibia 67 58 9 ***
Nigeria 66 53 13 ***
Pakistan 97 93 4 **
Panama 73 35 38 ***
Papua New Guinea 66 71 -5 ***
Peru 78 46 32 ***
Puerto Rico 51 16 35 ***
Rwanda 65 44 21 ***
Senegal 58 45 13 ***
South Africa 53 42 11 ***
Suriname 71 61 10 ***
Tajikistan 57 40 17 ***
Tanzania 90 80 9 ***
Timor Leste 62 79 -17 ***
Tonga 61 49 12 ***
Uganda 74 67 7 **
Uruguay 76 45 30 ***
Vanuatu 77 79 -2 ***
Vietnam 82 48 34 ***
West Bank and Gaza 57 32 25 ***
Zimbabwe 74 69 5 ***
Source: Own calculations based on datasets in Table 4.1
Notes: *, **, *** indicate that the difference is statistically significant at the 10%, 5% and 1% levels respectively. NS stands for not significant

[12] It is related to, but is not exactly the same as, SDG indicator 8.3.1.