Cumulative disadvantage; inter- and within-group inequalities


Step 1 Select a dimension of ex/inclusion Open

Selected: Intersecting risks and drivers

Some groups are at a higher risk of exclusion and inequality, but the status of excluded often transcends a single group affiliation and lies at the intersection of multiple identities.  Being a female – as a factor – may not automatically put someone at a high risk of exclusion from the labour market. But being a Roma woman from an under-served rural community in Central and Eastern Europe increases the risk dramatically.


The traditional group-based approach to ex/inclusion is primarily concerned with identification and support, through social insurance, of excluded groups vulnerable to uninsured risks. More recent approaches focus on individual risks, pointing out that the group-based lens may not provide strong evidentiary basis to weigh policy options in the case of multiple sources of exclusion.  Applied individually, both of these approaches may suffer from errors and blind spots. Yet a combination of the two – i.e., an approach of intersecting risks and drivers – is feasible and has a solid policy value.


Four inclusive policy markers are used to operationalize this dimension.

Step 2 Select an Inclusive Policy Marker Open

Selected: Exclusion risks and their intersections

Policy and practice need to be mindful of group-specific conditions but go deeper in their risk analysis to capture cumulative disadvantages, as well as prevalence and intensity of exclusion and inequality as experienced by in real-life by the affected individuals, categories and groups. Three key points elaborate on why and how this issue can be approached.  

Step 3 Select a Policy Design Consideration

Selected: Cumulative disadvantage; inter- and within-group inequalities

Inclusive policies exposeand tackle cumulative disadvantage and, in addition to the traditional inter-group, the within-group inequalities. Exclusion risks can be linked to gender, age, ethnicity, language, religion, health, and/or status and its markers such as income, employment, education, place of residence etc., which may or may not be correlated in a straightforward way. Taken one by one, these characteristics pose certain levels of risks. The “overlap” of multiple characteristics, however, proves to have a considerably more powerful impact. Take the example of exclusion and inequalities in education. From Guatemala and Peru to Cambodia and the Lao People’s Democratic Republic, indigenous young adults are far more likely than the non-indigenous to experience extreme education deprivation. The risk is higher if they are also poor and female. An indigenous person aged 17 to 22 in Peru has two years less education than the national average; poor indigenous girls lag even further behind by two additional years. This is one (of many) illustrative case of how the intersection of various risks – i.e., gender, age, income status and ethnicity – has a significantly more deleterious effects on education than the effects of ethnicity alone. “Averaging” such inequalities does not result in the appropriate design of policy instruments.


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