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D10.2 – Report on analysis of European datasets on employment, inactivity and unemployment rates

Anderson, Bridget; Walker, Sarah; Shutes, Isabel; Lepianka, Dorota; Baricevic, Vedrana; Hoffman, Drazen; Finlay, Graham ; Murphy, Karen; Gal, John; Havely, Dana

With the increasing value placed on data collection, and the growth of migration control as a policy topic, this report as part of WP10 of bEUcitizen, Barriers to EU citizenship: insiders and outsiders, seeks to explore how migrants are captured in datasets and what this can tell us about the in/exclusion of different groups as explored in our previous report, D10.1 Report on the rights and obligations of citizens and non-citizens in selected countries1. For this report, WP10 partner countries (Ireland, Netherlands, UK, Spain, Croatia and Israel) explored their national datasets, in the form of national labour force surveys (LFS), administrative and register datasets. We also looked at Eurostat harmonised data sources: the European Union Labour Force Survey (EU-LFS) and European Union Statistics on Income and Living Conditions (EU-SILC) as well as the publicly available Eurostat database on migration statistics.

Comparing different states’ datasets it becomes clear that it is necessary to engage with their political history. For example, while there were understandable concerns about migration data and representativeness, data on asylum seekers was far easier to come by. The perceived importance of collecting data on asylum (required under EU harmonisation of asylum procedures) has resulted in it being hypervisibilised. While for the purposes of our next deliverable, D10.3 which will develop more in-depth case studies on groups that have been invisibilised in data, this does not mean that making groups visible in data is necessarily a good thing for them. Identifying a population as a population can stigmatise and risk reducing complex social processes to matters of identity. The politics of visibility are complex and also nationally particular. As this report shows, it is thus important to remember that statistical processes are not necessarily the neutral and benign form of enumeration they can be taken to be (Sussman, 2004), but can contribute to processes of ‘othering’ and normalised ideas of in/exclusion. Data about populations can “render rigid new conceptualizations of the human being” through their categorization (Hacking, 1982 in Sussman, 2004: 102). Processes of labelling can lead to the construct of bureaucratic identities (Zetter, 1991) and Werbner (2000) similarly argues that some ethnic minority categorisation is ‘imagined’ by the state for the control of populations. Population data systems in European colonies, for example, were used to control colonial subjects (Anderson, 1991 in Selzer & Anderson, 2001). Thus, one needs to look behind the numbers at the framing of concepts embedded in statistical systems and what the data may be masking.

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