Step 1 Select a dimension of ex/inclusion Open
Multidimensional
Relational
Intersecting risks and drivers
Dynamic
Multi-layered and contextual
ParticipatorySome 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
In sectionalized policy environments, inclusive interventions take a systems view of the positive and negative corollaries of policies and their distribution across societal groups and categories. One key consideration explores this in further detail.
Step 3 Select a Policy Design Consideration
Any policy intervention has the potential of having a differentiated impact – both positive and negative – on various categories and groups in a given society; inclusive policies are aware and control for these. Take for example the distributional concerns and possible regressive effects of policies and measures associated with environmental transition. Although it is difficult to summarise in any detail proposals for such a transition, higher carbon pricing is likely to be a part of it. Analysis of climate change and public policy in the UK, for example, shows that, however achieved, carbon pricing will have serious social policy implications by severely impacting disadvantaged households and those in lower income brackets, putting on them a considerably higher burden of the cost as a percentage of their income, and driving even further the country’s already high fuel poverty. Yet, due to the heterogeneity of these households, which may include people in poorly insulated housing, rural households dependent on car transport, and single-person households, compensation through social security system is problematic. This is a telling but by no means unique example. In such complex contexts, inclusive interventions control for (a) inter-sectoral spillovers of risks, and (b) when it comes to the exclusion populations, account for possible group and category-specific negative corollaries.
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