UNECE Work Session on Gender Statistics 6-8 october 2008 Geneva 1 MEASUREMENT ISSUES AND MULTIDISCRIMINATION: GENDER AND ETHNICITY Ko Oudhof Statistics Netherlands

UNECE Work Session on Gender Statistics 6-8 october 2008 Geneva 2 FROM GENDER INEQUALITY TO ….(1) Gender: concerns issues in relation between women and men in specific social context Equality – equal treatment –equal outcomes – equal opportunities (equal chances to realize outcomes corresponding to own abilities or efforts)

UNECE Work Session on Gender Statistics 6-8 october 2008 Geneva 3 FROM GENDER INEQUALITY TO ….(2) Example 1: female outcomes 90% of male Example 2: minority outcomes 80% of majority outcomes Within variation: subgroups with different outcomes Example 3: female minority outcomes 72% of male majority outcomes (=MULTIPLE INEQUALITY) Further steps by disaggregation (until no more significant subgroup variation) = explanation Just common statistical production practice

UNECE Work Session on Gender Statistics 6-8 october 2008 Geneva 4 ….DISCRIMINATION (1) Q1: Which factors explain variation? A1: Social theory and statistical analyses Q2: Which variation is explained by justified factors? A2: Policy decision Q3: Which factors are justified? A3: (eh eh ……silence) Q4: Which factors are not acceptable as justification? A4: The grounds of discrimination

UNECE Work Session on Gender Statistics 6-8 october 2008 Geneva 5 …. DISCRIMINATION (2) Q5: How do we know when these grounds are actually involved? A5: When we know for sure that no other factors are left which might explain variation Q6: When will we have that certainty? A6: (eh eh ……silence) Q7: When will we have enough certainty? A7: That’s a policy decision.

UNECE Work Session on Gender Statistics 6-8 october 2008 Geneva 6 WHICH GROUPS DO YOU BELONG TO? Risk group = position along one or more discrimination grounds – female or male – ethnic minority or majority Classification method of social construct (paper) – e.g. register-based vs. self-identification Multiple risk groups – e.g. sex + ethnic minority

UNECE Work Session on Gender Statistics 6-8 october 2008 Geneva 7 Measuring discrimination No discrimination –Y y = f(x 1, x 2, …..x i, x r ) (1) Discrimination –Y n = f(x 1, x 2, …..x i ) (2) Multidiscrimination (comp. Makkonen) –Y y = f(x 1, x 2, …..x i, x r, x s, x rs ) (3) Multiple discr: no combined effects x rs = 0 Compound discr.: x r # 0, x s # 0, x rs # 0 Intersectional discr.: x r = 0, x s = 0, x rs # 0

UNECE Work Session on Gender Statistics 6-8 october 2008 Geneva 8 International comparability? Differences risk groups – groups (Surinams?) – concepts (self identification vs ‘objective’) – definitions (citizenship vs country of birth) – measurement (register or survey) – aggregates (size dependent) Difference in measure of inequality – concept (objective vs experienced/perceived) – domain (labour market vs income) – criterion (policy objective) – inequality vs discrimination (excluded factors)

UNECE Work Session on Gender Statistics 6-8 october 2008 Geneva 9

10 What should be comparable? Minimal version Starting from an unspecified national target value: has any inequality position of any nationally defined minority population on any domain (however measured) compared to reference population in country A become less in the period between t and t+1 while in some other country B the nationally specified equivalent of anu such inequality has not diminished?

UNECE Work Session on Gender Statistics 6-8 october 2008 Geneva 11 Can comparability be raised? Same risk group dimension –Same risk groups are often not meaningful Same domain – assumes harmonisation of specification level Same target variable –assumes harmonised data source –same target value is only sometimes meaningful Same model specification –Assumes relevance of same alternative explanatory factors

UNECE Work Session on Gender Statistics 6-8 october 2008 Geneva 12 Odds ratio (OR) as remedy? No simple interpretation of OR’s –Relative and reduced interpretation acceptable? –Metadata: really required for any interpretation! OR’s can be used in simple as well as in multivariate models OR’s do not require higher measurement level than dichotomy OR’s have large degree of independency of value on target variable OR’s can be produced on microdata as well as on aggregates How should OR’s be sold to politics and public?

UNECE Work Session on Gender Statistics 6-8 october 2008 Geneva 13 Illustration of minimum level indicator I MF W X Y Z

UNECE Work Session on Gender Statistics 6-8 october 2008 Geneva 14 THANK YOU VERY MUCH FOR YOUR ATTENTION DISCUSSION ISSUE What would you think of it as ……????? Statistical researcher Gender expert Politician Public