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MEASUREMENT ISSUES AND MULTIDISCRIMINATION: GENDER AND ETHNICITY

MEASUREMENT ISSUES AND MULTIDISCRIMINATION: GENDER AND ETHNICITY. Ko Oudhof Statistics Netherlands. FROM GENDER INEQUALITY TO ….(1). Gender: concerns issues in relation between women and men in specific social context Equality equal treatment equal outcomes

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MEASUREMENT ISSUES AND MULTIDISCRIMINATION: GENDER AND ETHNICITY

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  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 • Yy = f(x1, x2, …..xi, xr ) (1) • Discrimination • Yn = f(x1, x2, …..xi) (2) • Multidiscrimination (comp. Makkonen) • Yy = f(x1, x2, …..xi, xr, xs, xrs ) (3) • Multiple discr: no combined effects xrs= 0 • Compound discr.: xr # 0 , xs # 0, xrs# 0 • Intersectional discr.: xr = 0 , xs = 0, xrs# 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. UNECE Work Session on Gender Statistics 6-8 october 2008 Geneva

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

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