A new publication released today by UNECE, Approaches to Measuring Social Exclusion, examines the many and varied ways in which statistics can assess progress towards the policy goal of leaving no-one behind.
Social exclusion, or being left out of societal processes and structures, often goes hand-in-hand with being left out of the associated statistics.
One reason is that it isn’t always exactly clear what ‘social exclusion’, or its converse, social inclusion, actually mean. They are broad concepts, with specific understandings that vary according to cultural norms and values and national contexts, as well as to the policy or other goals in question. Some aspects of social exclusion are measured alongside measurements for other policy goals, such as improving well-being, reducing poverty or building social cohesion. Other aspects, though, are poorly-defined and therefore remain unmeasured. While many countries collect data on some elements of social exclusion, there are very few dedicated surveys or statistical approaches, and no overarching conceptual framework, designed to measure the phenomenon as a whole.
The new publication examines this lack of conceptual clarity, explaining that while it is often used interchangeably with related terms such as poverty and inequality, social exclusion is more often intended to cover a broader idea. Based on collective rather than individual resources, and community relationships rather than only on what people have or lack, this idea helps to focus attention on the way that social exclusion arises and persists within societies. In fact, as the publication explains, some understandings of social exclusion define it not only as an outcome – a state of disadvantage – but also as a process by which individuals or groups become or remain systematically disadvantaged.
The report reviews the range of current practices across 31 countries participating in the Conference of European Statisticians, UNECE’s highest statistical decision-making body. In the Netherlands, for example, a social exclusion index is produced by means of a special module in the Dutch Survey of Income and Living Conditions (EU-SILC). An index is constructed using 42 items across four dimensions of social exclusion: limited social participation; inadequate access to basic social rights and institutions; material deprivation; and lack of normative integration. In Bosnia and Herzegovina, the government worked with the United Nations Development Programme to develop a measure of social inclusion based on data from a household survey. The work aimed to measure accomplishment of the European Commission’s definition of the concept—that is, that people should be able to participate fully in economic, social and cultural life, to enjoy a standard of living and well-being that is considered normal in the society in which they live, and to participate in making the decisions that affect their lives.
Assessing the potential of these practices for delivering policy-relevant and comparable results, the publication distils emerging good practices and offers recommendations for moving this challenging area forward:
- Social exclusion is not just economic. Statistical concepts are more well-developed and data collection more commonplace for economic variables such as income, poverty and material deprivation, but the development of indicators on the social dimensions is less advanced. A full picture of social exclusion requires information on a wide range of social topics such as employment, education and skills, health and disability, access to healthcare, public services, essential infrastructure, and social, political and civic engagement.
- Those most at risk of social exclusion are also those most likely to be hidden within conventional data sources and statistical approaches. Inclusive data collection methods and special attention to hard-to-reach populations are essential to produce truly granular data that can shed light on marginalized groups. Analyses conducted through an intersectional lens—permitting, for example, studies of how gender, ethnicity and location interact— depend on such detailed data.
- The potential for linking data across sources and combining survey and register data with statistical modelling is a potential avenue for producing richer insights that can counter the many limitations of current data sources.
Countries across the world are attempting to craft inclusive approaches to strengthening societies drastically impacted by the COVID-19 pandemic. Understanding how far their policy responses for social and economic recovery are truly inclusive will depend on precisely the kinds of statistics and frameworks described in the new UNECE publication.
At the European Union level, for example, a European Statistical Recovery Dashboard has been created to track economic and social developments during the recovery from the pandemic in the Member States and the EU as a whole. The dashboard focuses on economic variables and includes indicators of economic activity such as unemployment and the share of young people who are not in employment, education or training.
The publication was endorsed by the 69th plenary session of the Conference of European Statisticians in 2021. It is available in both English and Russian.