Using Intersectionality to Understand Post Secondary Pathways of ‐

Marginalized GroupsKaren Robson (York University), Paul Anisef (York University), Rob Brown (TDSB).

What is intersectionality?

K. Crenshaw: When we don’t pay attention to the margins, when we don’t acknowledge the intersection, where the places of power overlap, we not only fail to see the women who fall between our movements, sometimes we pit our movements against each other. Women, Power & Peace conference, September , 2007.

P. Collins: By embracing a paradigm of race, class, and gender as interlocking systems of oppression, Black feminist thought reconceptualizes the social relations of domination and resistance. Black Feminist Thought: Knowledge, Consciousness, and the Politics of Empowerment, 1990.

Situating the analysis

• Toronto is one of the most ethnically diverse cities in the world

Home language

Place of birth

Data from Toronto, Canada

• Constructed from various sources:– Survey of all Toronto secondary school students

(2006) – 2006 Toronto student Census– Administrative data from the school board– College and university application centers– Neighbourhood census information (2001)– N=approximately 17,000

Questions of interest

• How do race, immigration status, and gender interact to impact PSE choices?

And once we know these things…then what?

Interactions (technical stuff)

• Many interesting findings in the social sciences involve ‘‘interaction” effects, also known as ‘‘moderator” or ‘‘synergistic” effects

• Two variables have an interaction effect on a dependent variable if the relationship of either independent variable with the dependent variable changes across values of the other independent variable.

Race/region of origin in our sample

Black11%

E Asian23%

Latin2%

MiddleE5%

S Asian21%

SE Asian3%

White35%

Low neighbourhood income by race/region 0

.1.2

.3.4

.5P

ropo

rtion

livi

ng in

low

est q

uarti

le o

f nei

ghbo

urho

od in

ome

Black E Asian Latin MiddleE S Asian SE Asian White

% in poor lowest quartile of income

% of overall sample

Black 19% 10%E Asian 20% 23%Latin 3% 2%MiddleE 7% 5%S Asian 29% 21%SE Asian 4% 3%White 18% 37%

Within-race proportions

As percentage of total sample

Generation status and race/region0

.2.4

.6.8

Black E Asian Latin MiddleE S Asian SE Asian White

First GenerationSecond Generation

Third Generation

Race and generation cannot be separated from each other

Generation status and low neighbourhood income

First generation

33% Second Generation

22%Third Generation

10%

Generation and income cannot be separated from each other.

Generation status and race/region by lowest neighbourhood quartile

0.2

.4.6

.8

Black E Asian Latin MiddleE S Asian SE Asian White

First Generation Second GenerationThird Generation

PSE Confirmations by Race

0.2

.4.6

.8P

ropo

rtion

con

firm

ed P

SE

Black E Asian Latin MiddleE S Asian SE Asian White

Confirmations by Race

0.2

.4.6

.8P

ropo

rtion

Con

firm

ed

Black E Asian Latin MiddleE S Asian SE Asian White

College University

Race and PSE pathways differ according to type of institution

Sex by PSE confirmations

48

16

35

males

5915

25

femalesConfirmed university

Confirmed college acc

Did not confirm Ontar

Confirmations by Race and Gender0

.2.4

.6.8

Black

E Asia

nLa

tin

MiddleE

S Asia

n

SE Asia

nW

hite

Black

E Asia

nLa

tin

MiddleE

S Asia

n

SE Asia

nW

hite

Males Females

College University

Gender is also a major determinant of type of PSE transition

Multinomial logistic regression (South Asian Students)

SA male, no sn, not in lowest

SA male, no sn, lowest

SA male with sn, lowest

SA female, no sn, not in lowest

SA female in lowest

SA female in lowest with sn

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

university college

Multinomial logistic regression (Black students)

black male, no sn, not in lowest

black male, no sn, lowest

black male with sn, lowest

black female, no sn, not in lowest

black female in lowest

black female in lowest with sn

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

universitycollege

Multinomial logistic regression (white students)

white male, no sn, not in lowest

white male, no sn, lowest

white male with sn, lowest

white female, no sn, not in lowest

white female in lowest

white female in lowest with sn

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

university college

Average Probabilities of Confirming College - Interaction of Black*SEN*Applied

With

out S

EN and n

ot in

appli

ed

With

SEN, not

in ap

plied

With

out S

EN and

in ap

plied

With

SEN and i

n app

lied

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

Not BlackBlack

Conclusions (?)

• PSE trajectories affected by range of characteristics

• Mustn’t forget the “gappers”• How can policy be targeted?

Thank you!

• klrobson@yorku.ca

• Ontario Ministry of Training, Colleges and Universities financially supports this research

References• Choo, Hae Yeon, and Myra Marx Ferree. "Practicing Intersectionality

in Sociological Research: A Critical Analysis of Inclusions, Interactions, and Institutions in the Study of Inequalities*." Sociological Theory 28.2 (2010): 129-149.

• Dubrow, Joshua Kjerulf. "How can we account for intersectionality in quantitative analysis of survey data? Empirical illustration for Central and Eastern Europe."ASK. Research&Methods 17 (2008): 85-100.

• McCall, Leslie. "The complexity of intersectionality." Signs 30.3 (2005): 1771-1800.

• Patricia Hill Collins, 1990. Black Feminist Thought: Knowledge, Consciousness, and the Politics of Empowerment (Boston: Unwin Hyman).