Some Dilemmas Concerning the Collection of Ethnic Data in Europe.

The Hungarian Case

Andrea KrizsánCentral European University

krizsana@ceu.hu

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

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

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

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

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

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

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

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

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

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

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

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