measurement issues and multidiscrimination: gender and ethnicity

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

1

MEASUREMENT ISSUES AND MULTIDISCRIMINATION: GENDER AND

ETHNICITY

Ko Oudhof

Statistics Netherlands


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)


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


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


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.


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


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


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)


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?


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


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?


13

Illustration of minimum level

indicator I

M F

W 1.00 2.02

X 4.40 5.67

Y 1.23 1.56

Z 1.11 1.44


14

THANK YOU VERY MUCH FOR YOUR ATTENTION

DISCUSSION ISSUE

What would you think of it as ……?????• Statistical researcher• Gender expert• Politician• Public

measurement issues and multidiscrimination: gender and ethnicity

Documents