1. Some Dilemmas Concerning the Collection of Ethnic Data in Europe. The Hungarian Case Andrea Krizsán Central European University krizsana@ceu.hu

2. The Contentious Issues How policy responds Classification used for collecting ethnic data  Recognition of ethnic groups  Defining the aim of the policy Defining boundaries of ethnic groups: identification of members of ethnic groups  For measuring discrimination  For positive action purposes

3. The Hungarian Context Minority protection policy  13 historical minorities  Focus on self-determination rights (Kymlicka 1995) Anti-discrimination policy  Follows EU norms  Inclusive of all differences on grounds of ethnicity  Wide scope: equal opportunity

4. The Hungarian Context (2) Roma policy  Focus on positive action programs  Transnational cooperation: Decade of Roma Inclusion Data protection policy  Historical sensibility: WWII abuse of ethnic data  Historical sensibility: intrusive nature of communist regimes

5. Classifications in Hungary Based on the minority protection system: 13 minorities Contentious issues:  Only historical minorities  The 13 recognized are the result of a political decision – under- and over-inclusive  Procedure to register new groups restrictive: move towards objectivation

6. Classifications: recognizing minority groups The purpose of the data collection policy undefined:  Equality-social inclusion vs. identity politics Procedure is not responsive to changes in social reality  Dilemma of social reality vs. politics of recognition  The democratic procedure narrowed down by historical and political procedure to freeze the classification

7. Identifying members of protected minorities Problem of under-inclusiveness: prevents the state from measuring disadvantage Problem of over-inclusiveness: makes difficult the targeting of positive action policies

8. Under-inclusiveness: measuring discrimination Systematic failure to produce accurate data on membership of ethnic minority groups in Hungary  Failure to capture the socially relevant diversity of groups  Prioritization of the principle of voluntary self- identification in combination with the unwillingness of minorities to identify with their groups Roma: low prestige of the group Historically determined anxiety to release such data

9. Overcoming the problem: non-state solution Generating survey data Purpose: Ethnic data is needed to measure discrimination and social exclusion. We need to know who is seen as minority by those in power positions A refined method of external identification. The agents are members of local communities whose identification makes a difference in distribution of resources

10. Over-inclusiveness: tailoring positive action Such data cannot be striped off its personal character. Opt in available for claimants Efficient positive action programs vs. preventing the state from holding sensitive personal data Initial Hungarian approach: emphasis on data protection at the expense of policy efficiency: minority self-governments  All Hungarian citizens had both passive and active electoral rights in minority elections

11. Solution Shift away from the voluntary self-identification principle to recognition of the need for sharper boundaries for groups claiming positive action Compromise between voluntary self- identification and more efficiently targeted positive action policy: electoral registries Pending issues:  Handling multiple identities  Introducing objective criteria for identifying holders of positive rights

12. Alternative non-state approaches Public interest groups gathering data for litigating discrimination:  Discrimination in the criminal justice system  Segregation in education Surveys:  Employment, education, health Problems  Extremely demanding of resources  Sporadic in their results

13. Way forward Collection of ethnic data is possible and feasible within even a very stringent data protection system. Electoral registries are the most extreme form of data collection Strategic policy thinking needed  Defines the purpose of the policy  Defines the scope of the policy: which groups  Designs systematic methods of data collection  Designs appropriate data protection guarantees against abuse  Does all of these in cooperation with the concerned groups