Gender and Multicultural Leadership: The Future of Governance

American Political Science AssociationAnnual Meeting

Chicago

September 2, 2004

© 2004 Gender and Multicultural Leadership Project

Principal Investigators:Christine Marie Sierra, University of New Mexico

Carol Hardy-Fanta, University of Massachusetts BostonPei-te Lien, University of Utah

Dianne M. Pinderhughes, University of Illinois Urbana

Research Associate: Wartyna Davis, William Paterson University

The GMCL Project is supported by a major grant from the Ford Foundation.

Introductions

Today’s Roundtable

• Overview of project goals, research questions & methodology

• A “first look” at some preliminary results

• Theoretical and methodological issues/ challenges

• Future project activities

Project Goals

• Provide baseline data on leadership in the 21st century

• Provide within/across group comparisons • Expand scholarship & examine empirically

the category of “women of color”

• Identify prospects for coalition and/or competition

Research Questions

1. To what extent have women and men of color achieved descriptive representation in Congress, statewide offices, state legislatures, local governments, and school boards?

2. Compared to men of color and white women, do women of color hold distinctive political and social identities and conceptions of representation?

Research Questions (cont’d)

3. What are the prospects for coalition, competition, and conflict within and across the three groups, especially with regard to their positions on selected policy areas?

4. Do women of color provide a bridging function that facilitates cooperation and coalition across diverse groups of decision makers (and constituents)?

Project Components

1. National database of elected officials of color

2. Annotated bibliography and analytical framework on the intersection of gender, race/ethnicity, class

3. Telephone survey of elected officials of color

4. Project website (under construction) www.gmcl.org

Report on Component #1: National Database

• Combined data from existing directories on African-American, Latino/a, and Asian elected officials into uniform dataset

• Included Native American elected officials at the congressional and legislative levels

• Added variables on gender, party, and aggregate demographic data from the US Census linked to state/municipal jurisdictions

• Conducted preliminary bivariate analysis by gender, race/ethnicity: Who are our current elected officials of color?

Elected Officials of Color(N=12,095)

Black: 7583, 63%

Native American

Legislators: 43, 0.4%

Asian: 344, 3%

Latino: 4113, 34%

Elected Officials of Color by Sex(N=12,095)

Male67%

Female33%

Elected Officials of Color by Race & Sex(N=12,095)

25.6% 26.2%34.7% 29.9%

74.4% 73.8%65.3% 70.1%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Native AmericanLegislators (N=43)

Asian (N=344) Black (N=7,583) Latino (N=4,113)

Per

cent

of R

ace

Female Male

Partisanship by Race & Sex (N=4,547)

0102030405060708090

100

DEM IND REP DEM REP DEM IND REP DEM IND REP

Native American(Legislators)

Asian Black Latino

Per

cent

Female

Male

Note: Excludes elected officials in non-partisan races and those for whom no party affiliation was given.

Elected Officials of Color by Level & Race

33.7%24.7%

40.9%

33.1% 54.1%

37.9%

8.4%

12.5% 10.6%21.8%8.1% 5.6%

0.1%0.6% 0.2%0.5%1.5% 0.6%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Asian (N=344) Black(N=7,583)

Latino(N=4,113)

Pe

rce

nt

Congress

Statewide

Legislative

County

Municipal

SchoolBoard

Parity Ratios by Race/Ethnicity & Gender, Congress & State Legislatures, 2002* Race*Gender U.S. Congress State Legislature

White men 2.33 2.04

Black men .66 .84

Asian men .43 .43

Latino men .38 .30

Black women .35 .37

White women .28 .53

Latina women .18 .13

Asian women .09 .18* Parity ratio is calculated as the percent in the office divided by the percent in the population.

© 2004 Gender and Multicultural Leadership Project

Ratio of State Legislators of Color to White Legislators by Sex

(Number of States, N=50)

27

41

8

7

9

1

15 1

WOC:White Women MOC:White Men

Nu

mb

er o

f S

tate

s# EOCs equal oroutnumber whiteEOs #EOCs GT 75%but LT 100% of #white EOs# EOCs between50% & 74% of #white EOs# EOCs between25% & 49% of#white EOs# EOCs LT 25% of# white EOs

Presidential “Battleground States”: Legislators of Color (%) & Population of Color

EOC’s PopOC (%)

(%)

AR 11.9 21.4

AZ 24.5 36.2

CO 15.0 25.5

FL 26.3 34.6

IA 2.7 7.4

LA 22.9 37.5

MO 8.6 16.2

NC 15.9 29.8

NH 1.2 4.9

NM 49.1 55.3

NV 14.3 34.8

OH 13.6 16.0

OR 4.4 16.5

PA 8.3 15.9

WI 7.6 12.7

Elected Officials of Color by Sex(N=12,095)

Male67%

Female33%

Ratios of State Legislators of Color to White Legislators by Sex

(Number of States, N=50)

27

41

8

7

9

1

15 1

WOC:White Women MOC:White Men

Nu

mb

er

of

Sta

tes

# EOCs equal oroutnumber whiteEOs #EOCs GT 75%but LT 100% of #white EOs# EOCs between50% & 74% of #white EOs# EOCs between25% & 49% of#white EOs# EOCs LT 25%of # white EOs

Presidential “Battleground States”: Legislators of Color (%) & Population of Color

EOC’s PopOC (%)

(%)

AR 11.9 21.4

AZ 24.5 36.2

CO 15.0 25.5

FL 26.3 34.6

IA 2.7 7.4

LA 22.9 37.5

MO 8.6 16.2

NC 15.9 29.8

NH 1.2 4.9

NM 49.1 55.3

NV 14.3 34.8

OH 13.6 16.0

OR 4.4 16.5

PA 8.3 15.9

WI 7.6 12.7

Elected Officials of Color by Level & Race

33.7%24.7%

40.9%

33.1% 54.1%

37.9%

8.4%

12.5% 10.6%21.8%8.1% 5.6%

0.1%0.6% 0.2%0.5%1.5% 0.6%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Asian (N=344) Black(N=7,583)

Latino(N=4,113)

Pe

rce

nt

Congress

Statewide

Legislative

County

Municipal

School Board

Methodological Challenges

• Language is political: Elected officials of color? Non-White? “Minority”

• Difficulty collecting data on Native Americans• Who counts as elected officials of color? • Surprising: A multicultural identity?• Complexity #1: multiple races/ethnicities and levels• Complexity #2: the challenge of municipal &

county government structure across state.• Lack of standard collection method • Correspondence between district & census data