for online publication deconstructing bias: individual groupiness … · 2017-08-14 · for online...

19
1 FOR ONLINE PUBLICATION Deconstructing Bias: Individual Groupiness and Income Allocation Online Appendix Rachel Kranton, Matthew Pease, Seth Sanders, and Scott Huettel August 2017 This appendix contains the following: 1. Further details of the experiment, including screen shots, sample questions from the surveys that preceded the group treatments, and descriptions of the algorithms pairing subjects in the group treatments. 2. Further order effect study: predicted favoritism levels from separate estimations of nine-type latent class model for minimal-group-first subjects and for political-group-first subjects 3. Predicted by type difference in i’s and j’s payoffs in minimal group and political group. 4. Predicted by type payoffs for i and for j and predicted expected total payoffs per match. 5. Cross tabulation of subjects: groupiness designation by individual data versus type categorization 6. Non-group condition: parameter values for six-type latent class utility function estimation 7. Cross-tabulation of subjects non-group condition social preference sets and nine-types from group condition. 8. Regression of nine types categorizations on non-group social preference sets.

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Page 1: FOR ONLINE PUBLICATION Deconstructing Bias: Individual Groupiness … · 2017-08-14 · FOR ONLINE PUBLICATION Deconstructing Bias: Individual Groupiness and Income Allocation

1

FOR ONLINE PUBLICATION

Deconstructing Bias: Individual Groupiness and Income Allocation

Online Appendix

Rachel Kranton, Matthew Pease, Seth Sanders, and Scott Huettel

August 2017

This appendix contains the following:

1. Further details of the experiment, including screen shots, sample questions from the surveys that preceded the group treatments, and descriptions of the algorithms pairing subjects in the group treatments. 2. Further order effect study: predicted favoritism levels from separate estimations of nine-type latent class model for minimal-group-first subjects and for political-group-first subjects 3. Predicted by type difference in i’s and j’s payoffs in minimal group and political group. 4. Predicted by type payoffs for i and for j and predicted expected total payoffs per match. 5. Cross tabulation of subjects: groupiness designation by individual data versus type categorization 6. Non-group condition: parameter values for six-type latent class utility function estimation 7. Cross-tabulation of subjects non-group condition social preference sets and nine-types from group condition. 8. Regression of nine types categorizations on non-group social preference sets.

Page 2: FOR ONLINE PUBLICATION Deconstructing Bias: Individual Groupiness … · 2017-08-14 · FOR ONLINE PUBLICATION Deconstructing Bias: Individual Groupiness and Income Allocation

2

1. Further details of the experiment. Examples of questions used for the Minimal Group Treatment survey:

Question)4: Which%painting%do%you%prefer?

You$friendly$boatmen$and$mechanics!$You$roughs!

You$twain!$And$all$processions$moving$along$the$streets!

Question)8: Which%line%of%poetry%do%you%prefer?

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3

Sample questions on subjects’ political affiliation for the Political Treatment survey:

Pairings in the Minimal Group Treatment: Taken from a bank of other participant’s responses, the Own Group Member was selected as the one that answered similarly on the highest number

DEMOCRAT

1.#Do#you#consider#yourself#a(n):

REPUBLICAN NONE#OF THE#ABOVE

INDEPENDENT

1 2 3 4

STRONG

1(a).#Are#you#a#strong#or#moderate#DEMOCRAT?

MODERATE

1 2

1(a).#Do#you#consider#yourself#closer#to#the::

DEMOCRATIC*PARTY

REPUBLICAN+PARTY

1 2

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4

of questions as the subject while the Other Group Member answered most dissimilarly on survey questions. Specifically, an ordered list of subjects who have previously answered the survey is generated. Given a subject i, for the Own Group Member, the algorithm goes down this list, counting for each subject the number of similar answers to survey questions. If the second person on the list has a higher number of similar questions, then s/he replaces the first subject as the subject with the highest similarity rating. If a subject further down the list has a greater number of similar answers, s/he then replaces the current most similar subject. Ties are broken in favor of the ordered list. Then, from among the three categories of questions (poetry, painting, and image), whichever category with the highest number of similar responses is selected. Subject i is then given the following information: "Overall, the OWN GROUP MEMBER answered [the number of similarly answered questions, overall] survey questions with the same response as you" and "This participant preferred the same [chosen as most similar category, category name] as you on [number of similarly answered questions, in this category] out of 7 questions." A parallel procedure searched for the participant in the pool with the most dissimilar answers to select the Other Group Member and present the corresponding information. Pairings in the Political Group Treatment: Taken from a bank of other participant’s responses, the subjects were divided into two groups. Democrats and subjects answering they were "closer" to Democrats were assigned to the Democrat group. Similarly, Republicans and subjects "closer" to the Republican party were assigned to the Republican group. For Democrats and D-Independents, therefore, the Own Group was the Democrat group, and the Other Group was the Republican group. The subjects were given the following information: "Your OWN GROUP answered similarly on political survey questions," and "The OTHER GROUP answered differently on political survey questions." For the Own Group members selected to be the recipient (by an algorithm that identified a subject that answered similarly on at least one of the five political questions), subjects were presented with the statement "This participant identifies with the [Democrat/Republican] party and subjects were also told the question on which the subject and Own Group Member answered similarly. If the subject and Own Group Member answered several questions similarly, preference was given, in order, to - the abortion, gay marriage, Arizona immigration law, Bush tax cut, and government size questions. Parallel information was given for the Other Group member selected, except the algorithm searched for subjects in the Other Group who had answered dissimilarly on at least one political position question. Code available upon request.

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!5!

Tab

le O

A1.

M

inim

al G

roup

Fir

st S

ubje

cts:

Pr

edic

ted

Favo

ritis

m L

evel

s Nin

e T

ype

Mod

el

M

inim

al G

roup

Firs

t Sub

ject

s

Pred

icte

d Fa

vorit

ism

Lev

els

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A

Type

B

Type

C

Type

D

Type

E

Type

F

Type

G

Type

H

Type

I

M

G

1.48

2 -1

.999

-0

.118

3.

015

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18

33.0

0***

1.

536

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52

-6.6

42

(1

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) (1

.444

) (1

.931

) (3

.124

) (4

.206

) (3

.516

) (2

.280

) (5

.790

) (6

.717

) PO

L 5.

457*

**

1.71

7 14

.33*

**

2.88

5 15

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**

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8***

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805

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41

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1

(1

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) (1

.501

) (1

.964

) (2

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) (4

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) (3

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.031

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.438

) M

G m

inus

PO

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)

Frac

tion

of M

G F

irst S

ubje

cts

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81**

0.

0362

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0241

(0

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6)

(0.0

478)

(0

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6)

(0.0

288)

(0

.027

2)

(0.0

261)

(0

.023

5)

(0.0

205)

(0

.016

8)

Obs

erva

tions

8,

591

8,59

1 8,

591

8,59

1 8,

591

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Stan

dard

err

ors i

n pa

rent

hese

s **

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05, *

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!6!

Tab

le O

A2.

Po

litic

al G

roup

Fir

st S

ubje

cts:

Pr

edic

ted

Favo

ritis

m L

evel

s Nin

e T

ype

Mod

el

Polit

ical

Gro

up F

irst S

ubje

cts

Pr

edic

ted

Favo

ritis

m L

evel

s Ty

pe R

Ty

pe S

Ty

pe T

Ty

pe U

Ty

pe V

Ty

pe W

Ty

pe X

Ty

pe Y

Ty

pe Z

M

G

38.1

6***

-0

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7 10

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**

1.85

2 3.

581

0.98

6 9.

484

N/A

(2

.267

) (2

.050

) (2

.431

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.537

) (3

.185

) (1

.446

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.992

)

PO

L 42

.71*

**

1.51

6 8.

456*

**

9.64

9***

20

.85*

**

5.69

2***

-0

.394

N

/A

(2.0

79)

(2.0

80)

(1.9

49)

(2.2

72)

(3.5

84)

(1.8

50)

(6.0

81)

MG

min

us P

OL

4.55

6 1.

581

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34

7.79

8**

17.2

7***

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* -9

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N

/A

(3.0

75)

(2.9

28)

(3.1

06)

(3.3

92)

(4.7

95)

(2.3

48)

(8.5

25)

Frac

tion

of P

OL

Firs

t Sub

ject

s 0.

207*

**

0.19

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0.

172*

**

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121*

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* 0.

0172

0

(0

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2)

(0.0

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(0

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9)

(0.0

476)

(0

.042

9)

(0.0

373)

(0

.029

1)

(0.0

171)

0

Obs

erva

tions

5,

996

5,99

6 5,

996

5,99

6 5,

996

5,99

6 5,

996

5,99

6 5,

996

Stan

dard

err

ors i

n pa

rent

hese

s **

* p<

0.01

, **

p<0.

05, *

p<0

.1

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!7!

Tab

le O

A3.

Pr

edic

ted

Diff

eren

ces i

n D

ecis

ion

Mak

er’s

Pay

offs

by

Typ

e: Y

ou-O

wn

vs. Y

ou-O

ther

A

vera

ge

Mat

ch T

ype

Type

1

Type

2

Type

3

Type

4

Type

5

Type

6

Type

7

Type

8

Type

8

Subj

ect

M

G

-0.0

604

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45*

0.44

5 -4

.943

***

-0.0

899

-1.9

58

3.22

4 1.

941

1.54

3 -0

.330

(0

.706

) (0

.147

) (0

.490

) (1

.010

) (0

.499

) (1

.417

) (1

.968

) (1

.221

) (2

.656

) (0

.334

) PO

L -0

.767

0.

0926

-3

.883

***

-6.7

59**

* 0.

907*

**

-3.7

70**

* -2

.158

0.

918

0.11

8 -1

.933

***

(0

.731

) (0

.201

) (0

.504

) (0

.969

) (0

.336

) (1

.289

) (2

.120

) (1

.348

) (2

.477

) (0

.325

) M

G m

inus

PO

L 0.

706

-0.3

37

4.32

8***

1.

816

-0.9

97*

1.81

2 5.

383*

1.

022

1.42

5 1.

604*

**

(1

.016

) (0

.231

) (0

.701

) (1

.399

) (0

.605

) (1

.902

) (2

.893

) (1

.819

) (3

.632

) (0

.466

)

O

bser

vatio

ns

14,5

87

14,5

87

14,5

87

14,5

87

14,5

87

14,5

87

14,5

87

14,5

87

14,5

87

14,5

87

Stan

dard

err

ors i

n pa

rent

hese

s

***

p<0.

01, *

* p<

0.05

, * p

<0.1

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!8!

Tab

le O

A4.

Pr

edic

ted

Payo

ffs f

or R

ecip

ient

(j),

Dec

isio

n M

aker

(i),

and

Exp

ecte

d T

otal

Pay

offs

per

Mat

ch:

Min

imal

Gro

up

M

atch

and

Pay

offs

Ty

pe 1

Ty

pe 2

Ty

pe 3

Ty

pe 4

Ty

pe 5

Ty

pe 7

Ty

pe 8

Ty

pe 8

Ty

pe 9

j '

s pay

offs

MG

You

-Ow

n 10

8.2*

**

86.2

0***

95

.36*

**

97.3

9***

83

.24*

**

101.

1***

80

.67*

**

58.3

7***

75

.22*

**

(0

.892

) (0

.726

) (1

.044

) (1

.602

) (1

.500

) (1

.818

) (2

.950

) (1

.384

) (4

.418

) j '

s pay

offs

MG

You

-Oth

er

109.

6***

84

.11*

**

94.8

0***

60

.74*

**

76.2

2***

99

.10*

**

76.1

5***

56

.68*

**

81.8

6***

(0

.786

) (0

.610

) (1

.118

) (1

.055

) (1

.550

) (1

.871

) (3

.020

) (1

.424

) (5

.059

) i '

s pay

offs

MG

You

-Ow

n 12

5.8*

**

138.

8***

13

5.8*

**

128.

0***

13

7.1*

**

125.

9***

13

0.6*

**

134.

1***

13

3.5*

**

(0

.494

) (0

.109

) (0

.296

) (0

.814

) (0

.336

) (0

.920

) (1

.271

) (0

.790

) (1

.673

) i '

s pay

offs

MG

You

-Oth

er

125.

9***

13

9.0*

**

135.

4***

13

2.3*

**

137.

1***

12

7.8*

**

127.

4***

13

2.2*

**

132.

1***

(0

.471

) (0

.085

5)

(0.3

43)

(0.5

53)

(0.3

39)

(1.0

29)

(1.4

38)

(0.9

12)

(1.9

42)

Expe

cted

Tot

al P

ayof

fs fo

r i M

G

117.

4***

11

2.0*

**

115.

4***

10

4.6*

**

108.

4***

11

3.5*

**

103.

7***

95

.33*

**

105.

7***

(0

.290

) (0

.215

) (0

.337

) (0

.536

) (0

.569

) (0

.653

) (1

.135

) (0

.753

) (1

.831

) Ex

pect

ed T

otal

Pay

offs

MG

12

5.9*

**

138.

9***

13

5.6*

**

130.

1***

13

7.1*

**

126.

8***

12

9.0*

**

133.

1***

13

2.8*

**

(0

.341

) (0

.073

2)

(0.2

32)

(0.4

92)

(0.2

51)

(0.6

90)

(0.9

60)

(0.6

03)

(1.2

82)

Obs

erva

tions

14

,587

14

,587

14

,587

14

,587

14

,587

14

,587

14

,587

14

,587

14

,587

Stan

dard

err

ors i

n pa

rent

hese

s

**

* p<

0.01

, **

p<0.

05, *

p<0

.1

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!9!

Tab

le O

A5.

Pr

edic

ted

Payo

ffs f

or R

ecip

ient

(j),

Dec

isio

n M

aker

(i),

and

Exp

ecte

d T

otal

Pay

offs

per

Mat

ch:

Polit

ical

Gro

up

M

atch

and

Pay

offs

Ty

pe 1

Ty

pe 2

Ty

pe 3

Ty

pe 4

Ty

pe 5

Ty

pe 6

Ty

pe 9

Ty

pe 8

Ty

pe 9

j '

s pay

offs

PO

L Y

ou-O

wn

108.

8***

86

.71*

**

98.6

2***

10

1.2*

**

77.9

9***

96

.95*

**

79.3

8***

65

.70*

**

113.

1***

(0

.832

) (0

.781

) (1

.063

) (1

.491

) (1

.172

) (1

.939

) (3

.028

) (2

.248

) (2

.433

) j '

s pay

offs

PO

L Y

ou-O

ther

10

7.1*

**

81.5

8***

86

.05*

**

59.5

1***

70

.90*

**

78.1

7***

79

.77*

**

58.1

5***

10

6.2*

**

(0

.923

) (0

.805

) (1

.078

) (0

.962

) (1

.231

) (2

.012

) (3

.056

) (1

.548

) (3

.712

) i '

s pay

offs

PO

L Y

ou-O

wn

125.

9***

13

8.7*

**

134.

0***

12

6.6*

**

138.

6***

12

8.7*

**

125.

0***

13

3.9*

**

121.

3***

(0

.485

) (0

.126

) (0

.417

) (0

.787

) (0

.181

) (0

.957

) (1

.482

) (0

.953

) (1

.510

) i '

s pay

offs

PO

L Y

ou-O

ther

12

6.6*

**

138.

7***

13

7.5*

**

132.

6***

13

7.7*

**

132.

2***

12

7.1*

**

132.

9***

12

1.0*

**

(0

.516

) (0

.117

) (0

.220

) (0

.525

) (0

.252

) (0

.813

) (1

.455

) (0

.904

) (1

.873

) Ex

pect

ed T

otal

Pay

offs

for i

in

POL

126.

3***

13

8.7*

**

135.

7***

12

9.6*

**

138.

2***

13

0.4*

**

126.

0***

13

3.4*

**

121.

2***

(0

.354

) (0

.084

3)

(0.2

39)

(0.4

73)

(0.1

55)

(0.6

30)

(1.0

40)

(0.6

57)

(1.2

03)

Expe

cted

Tot

al P

ayof

fs P

OL

117.

1***

11

1.4*

**

114.

0***

10

5.0*

**

106.

3***

10

9.0*

**

102.

8***

97

.67*

**

115.

4***

(0

.300

) (0

.256

) (0

.340

) (0

.505

) (0

.480

) (0

.725

) (1

.151

) (0

.896

) (1

.401

)

Obs

erva

tions

14

,587

14

,587

14

,587

14

,587

14

,587

14

,587

14

,587

14

,587

14

,587

Stan

dard

err

ors i

n pa

rent

hese

s

***

p<0.

01, *

* p<

0.05

, * p

<0.1

Page 10: FOR ONLINE PUBLICATION Deconstructing Bias: Individual Groupiness … · 2017-08-14 · FOR ONLINE PUBLICATION Deconstructing Bias: Individual Groupiness and Income Allocation

!10!

T

able

s OA

6.

Cro

ss-T

ype

Diff

eren

ces i

n Pr

edic

ted

Favo

ritis

m L

evel

s and

Pre

dict

ed P

ayof

fs:

Typ

e 1

vs. T

ypes

2-9

and

vs.

Ave

rage

Sub

ject

C

ross

Typ

e D

iffer

ence

Ty

pe 1

v.

2 Ty

pe 1

v.

3 Ty

pe 1

v.

4 Ty

pe 1

v.

5 Ty

pe 1

v.

6 Ty

pe 1

v.

7 Ty

pe 1

v.

8 Ty

pe 1

v.

9 Ty

pe 1

v.

Ave

rage

D

iffer

ence

in F

avor

itism

leve

ls M

G

-3.5

11**

-1

.970

-3

8.07

***

-8.4

35**

* -3

.431

-5

.928

-3

.098

5.

227

-6.5

85**

*

(1

.527

) (1

.932

) (2

.258

) (2

.389

) (2

.900

) (4

.386

) (2

.315

) (6

.822

) (1

.428

) D

iffer

ence

in F

avor

itism

leve

ls P

OL

-3.4

37**

-1

0.88

***

-40.

02**

* -5

.389

***

-17.

09**

* 2.

091

-5.8

54*

-5.1

99

-9.5

96**

*

(1

.688

) (1

.950

) (2

.166

) (2

.040

) (3

.066

) (4

.478

) (2

.999

) (4

.609

) (1

.463

) D

iffer

ence

in E

xpec

ted

Tota

l Pay

offs

MG

5.

353*

**

2.04

4***

12

.80*

**

8.96

5***

3.

918*

**

13.6

9***

22

.07*

**

11.7

3***

5.

718*

**

(0

.361

) (0

.444

) (0

.609

) (0

.639

) (0

.713

) (1

.171

) (0

.807

) (1

.854

) (0

.349

) D

iffer

ence

in E

xpec

ted

Tota

l Pay

offs

PO

L 5.

687*

**

3.08

0***

12

.13*

**

10.8

1***

8.

125*

**

14.3

1***

19

.45*

**

1.71

7 6.

199*

**

(0

.395

) (0

.454

) (0

.587

) (0

.566

) (0

.777

) (1

.189

) (0

.945

) (1

.433

) (0

.357

) D

iffer

ence

in i'

s Exp

ecte

d Pa

yoff

s MG

-1

3.05

***

-9.7

42**

* -4

.239

***

-11.

25**

* -0

.962

-3

.117

***

-7.2

52**

* -6

.909

***

-6.6

60**

*

(0

.349

) (0

.412

) (0

.599

) (0

.423

) (0

.770

) (1

.019

) (0

.693

) (1

.326

) (0

.377

) D

iffer

ence

in i'

s Exp

ecte

d Pa

yoff

s PO

L -1

2.43

***

-9.4

60**

* -3

.336

***

-11.

89**

* -4

.149

***

0.24

5 -7

.123

***

5.11

0***

-6

.418

***

(0

.364

) (0

.427

) (0

.590

) (0

.386

) (0

.722

) (1

.098

) (0

.746

) (1

.254

) (0

.387

) D

iffer

ence

in D

iffer

ence

of M

G, P

OL

Favo

ritis

m L

evel

s -0

.073

5 8.

909*

**

1.94

8 -3

.045

13

.65*

**

-8.0

18

2.75

6 10

.43

3.01

0

(2

.238

) (2

.720

) (3

.139

) (3

.160

) (4

.257

) (6

.265

) (3

.797

) (8

.237

) (2

.061

)

Obs

erva

tions

14

,587

14

,587

14

,587

14

,587

14

,587

14

,587

14

,587

14

,587

14

,587

Stan

dard

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ors i

n pa

rent

hese

s

***

p<0.

01, *

* p<

0.05

, * p

<0.1

Page 11: FOR ONLINE PUBLICATION Deconstructing Bias: Individual Groupiness … · 2017-08-14 · FOR ONLINE PUBLICATION Deconstructing Bias: Individual Groupiness and Income Allocation

!11!

Tab

les O

A7.

C

ross

-Typ

e D

iffer

ence

s in

Pred

icte

d Fa

vori

tism

Lev

els a

nd P

redi

cted

Pay

offs

: T

ype

2 vs

. Typ

es 3

-9 a

nd v

s. A

vera

ge S

ubje

ct

Cro

ss T

ype

Diff

eren

ce

Type

2 v

. 3

Type

2 v

. 4

Type

2 v

. 5

Type

2 v

. 6

Type

2 v

. 7

Type

2 v

. 8

Type

2 v

. 9

Type

2 v

. A

vera

ge

D

iffer

ence

in F

avor

itism

leve

ls M

G

1.54

1 -3

4.56

***

-4.9

24**

0.

0794

-2

.417

0.

413

8.73

8 -3

.074

**

(1

.849

) (2

.143

) (2

.309

) (2

.787

) (4

.327

) (2

.203

) (6

.784

) (1

.238

) D

iffer

ence

in F

avor

itism

leve

ls P

OL

-7.4

42**

* -3

6.58

***

-1.9

52

-13.

65**

* 5.

528

-2.4

17

-1.7

62

-6.1

58**

*

(1

.962

) (2

.110

) (2

.019

) (3

.004

) (4

.452

) (2

.959

) (4

.583

) (1

.379

) D

iffer

ence

in E

xpec

ted

Tota

l Pay

offs

MG

-3

.309

***

7.45

2***

3.

612*

**

-1.4

35**

8.

339*

**

16.7

1***

6.

378*

**

0.36

5

(0

.397

) (0

.577

) (0

.612

) (0

.688

) (1

.155

) (0

.783

) (1

.844

) (0

.290

) D

iffer

ence

in E

xpec

ted

Tota

l Pay

offs

PO

L -2

.607

***

6.43

8***

5.

125*

**

2.43

7***

8.

625*

**

13.7

7***

-3

.970

***

0.51

1

(0

.424

) (0

.566

) (0

.531

) (0

.769

) (1

.179

) (0

.932

) (1

.424

) (0

.322

) D

iffer

ence

in i'

s Exp

ecte

d Pa

yoff

s MG

3.

307*

**

8.81

0***

1.

798*

**

12.0

9***

9.

932*

**

5.79

6***

6.

140*

**

6.38

9***

(0

.235

) (0

.498

) (0

.259

) (0

.694

) (0

.963

) (0

.608

) (1

.284

) (0

.177

) D

iffer

ence

in i'

s Exp

ecte

d Pa

yoff

s PO

L 2.

974*

**

9.09

8***

0.

548*

**

8.28

5***

12

.68*

**

5.31

0***

17

.54*

**

6.01

6***

(0

.247

) (0

.480

) (0

.182

) (0

.635

) (1

.043

) (0

.662

) (1

.206

) (0

.178

) D

iffer

ence

in D

iffer

ence

of M

G, P

OL

Favo

ritis

m

Leve

ls

8.98

3***

2.

021

-2.9

72

13.7

3***

-7

.945

2.

830

10.5

0 3.

084*

(2

.523

) (2

.967

) (3

.056

) (4

.070

) (6

.181

) (3

.656

) (8

.173

) (1

.788

)

O

bser

vatio

ns

14,5

87

14,5

87

14,5

87

14,5

87

14,5

87

14,5

87

14,5

87

14,5

87

Stan

dard

err

ors i

n pa

rent

hese

s

**

* p<

0.01

, **

p<0.

05, *

p<0

.1

Page 12: FOR ONLINE PUBLICATION Deconstructing Bias: Individual Groupiness … · 2017-08-14 · FOR ONLINE PUBLICATION Deconstructing Bias: Individual Groupiness and Income Allocation

!12!

Tab

les O

A8.

C

ross

-Typ

e D

iffer

ence

s in

Pred

icte

d Fa

vori

tism

Lev

els a

nd P

redi

cted

Pay

offs

: T

ype

3 vs

. Typ

es 4

-9 a

nd v

s. A

vera

ge S

ubje

ct

Cro

ss T

ype

Diff

eren

ce

Type

3 v

. 4

Type

3 v

. 5

Type

3 v

. 6

Type

3 v

. 7

Type

3 v

. 8

Type

3 v

. 9

Type

3 v

. A

vera

ge

Diff

eren

ce in

Fav

oriti

sm le

vels

MG

-3

6.10

***

-6.4

65**

-1

.462

-3

.958

-1

.129

7.

197

-4.6

16**

*

(2

.448

) (2

.586

) (3

.034

) (4

.486

) (2

.501

) (6

.887

) (1

.713

) D

iffer

ence

in F

avor

itism

leve

ls P

OL

-29.

14**

* 5.

489*

* -6

.207

**

12.9

7***

5.

024

5.67

9 1.

283

(2

.325

) (2

.214

) (3

.163

) (4

.557

) (3

.115

) (4

.685

) (1

.690

) D

iffer

ence

in E

xpec

ted

Tota

l Pay

offs

MG

10

.76*

**

6.92

1***

1.

874*

* 11

.65*

**

20.0

2***

9.

687*

**

3.67

4***

(0

.633

) (0

.655

) (0

.735

) (1

.184

) (0

.824

) (1

.862

) (0

.389

) D

iffer

ence

in E

xpec

ted

Tota

l Pay

offs

PO

L 9.

046*

**

7.73

2***

5.

045*

**

11.2

3***

16

.37*

**

-1.3

63

3.11

9***

(0

.609

) (0

.580

) (0

.800

) (1

.200

) (0

.958

) (1

.442

) (0

.392

) D

iffer

ence

in i'

s Exp

ecte

d Pa

yoff

s MG

5.

503*

**

-1.5

08**

* 8.

780*

**

6.62

5***

2.

490*

**

2.83

3**

3.08

2***

(0

.544

) (0

.348

) (0

.727

) (0

.987

) (0

.646

) (1

.302

) (0

.282

) D

iffer

ence

in i'

s Exp

ecte

d Pa

yoff

s PO

L 6.

124*

**

-2.4

26**

* 5.

311*

**

9.70

5***

2.

337*

**

14.5

7***

3.

042*

**

(0

.530

) (0

.285

) (0

.673

) (1

.067

) (0

.699

) (1

.226

) (0

.286

) D

iffer

ence

in D

iffer

ence

of M

G, P

OL

Favo

ritis

m

Leve

ls

-6.9

61**

-1

1.95

***

4.74

6 -1

6.93

***

-6.1

53

1.51

7 -5

.899

**

(3

.346

) (3

.382

) (4

.356

) (6

.372

) (3

.970

) (8

.318

) (2

.364

)

Obs

erva

tions

14

,587

14

,587

14

,587

14

,587

14

,587

14

,587

14

,587

Stan

dard

err

ors i

n pa

rent

hese

s

***

p<0.

01, *

* p<

0.05

, * p

<0.1

Page 13: FOR ONLINE PUBLICATION Deconstructing Bias: Individual Groupiness … · 2017-08-14 · FOR ONLINE PUBLICATION Deconstructing Bias: Individual Groupiness and Income Allocation

!13!

Tab

les O

A9.

C

ross

-Typ

e D

iffer

ence

s in

Pred

icte

d Fa

vori

tism

Lev

els a

nd P

redi

cted

Pay

offs

: T

ype

4 vs

. Typ

es 5

-9 a

nd v

s. A

vera

ge S

ubje

ct

C

ross

Typ

e D

iffer

ence

Ty

pe 4

v.

5 Ty

pe 4

v.

6 Ty

pe 4

v.

7 Ty

pe 4

v.

8 Ty

pe 4

v.

9 Ty

pe 4

v.

Ave

rage

Diff

eren

ce in

Fav

oriti

sm le

vels

MG

29

.63*

**

34.6

4***

32

.14*

**

34.9

7***

43

.30*

**

31.4

8***

(2

.823

) (3

.245

) (4

.636

) (2

.760

) (6

.985

) (2

.073

)

Diff

eren

ce in

Fav

oriti

sm le

vels

PO

L 34

.63*

**

22.9

3***

42

.11*

**

34.1

6***

34

.82*

**

30.4

2***

(2

.401

) (3

.296

) (4

.653

) (3

.255

) (4

.779

) (1

.935

)

Diff

eren

ce in

Exp

ecte

d To

tal P

ayof

fs M

G

-3.8

39**

* -8

.886

***

0.88

7 9.

262*

**

-1.0

74

-7.0

87**

*

(0

.781

) (0

.844

) (1

.255

) (0

.924

) (1

.908

) (0

.570

)

Diff

eren

ce in

Exp

ecte

d To

tal P

ayof

fs P

OL

-1.3

13*

-4.0

01**

* 2.

187*

7.

327*

**

-10.

41**

* -5

.927

***

(0.6

96)

(0.8

83)

(1.2

57)

(1.0

28)

(1.4

89)

(0.5

41)

D

iffer

ence

in i'

s Exp

ecte

d Pa

yoff

s MG

-7

.012

***

3.27

7***

1.

122

-3.0

14**

* -2

.670

* -2

.421

***

(0.5

52)

(0.8

48)

(1.0

79)

(0.7

79)

(1.3

73)

(0.5

18)

D

iffer

ence

in i'

s Exp

ecte

d Pa

yoff

s PO

L -8

.550

***

-0.8

13

3.58

1***

-3

.788

***

8.44

6***

-3

.082

***

(0.4

98)

(0.7

88)

(1.1

42)

(0.8

09)

(1.2

92)

(0.4

98)

D

iffer

ence

in D

iffer

ence

of M

G, P

OL

Favo

ritis

m

Leve

ls

-4.9

93

11.7

1**

-9.9

66

0.80

8 8.

479

1.06

3

(3

.713

) (4

.628

) (6

.562

) (4

.267

) (8

.464

) (2

.836

)

Obs

erva

tions

14

,587

14

,587

14

,587

14

,587

14

,587

14

,587

Stan

dard

err

ors i

n pa

rent

hese

s

***

p<0.

01, *

* p<

0.05

, * p

<0.1

Page 14: FOR ONLINE PUBLICATION Deconstructing Bias: Individual Groupiness … · 2017-08-14 · FOR ONLINE PUBLICATION Deconstructing Bias: Individual Groupiness and Income Allocation

!14!

Tab

les O

A10

. C

ross

-Typ

e D

iffer

ence

s in

Pred

icte

d Fa

vori

tism

Lev

els a

nd P

redi

cted

Pay

offs

: T

ype

5 vs

. Typ

es 6

-9 a

nd v

s. A

vera

ge S

ubje

ct

Cro

ss T

ype

Diff

eren

ce

Type

5 v

. 6

Type

5 v

. 7

Type

5 v

. 8

Type

5 v

. 9

Type

5 v

. A

vera

ge

Diff

eren

ce in

Fav

oriti

sm le

vels

MG

5.

003

2.50

7 5.

336*

13

.66*

1.

849

(3

.339

) (4

.702

) (2

.869

) (7

.029

) (2

.216

) D

iffer

ence

in F

avor

itism

leve

ls P

OL

-11.

70**

* 7.

480

-0.4

65

0.19

0 -4

.206

**

(3

.215

) (4

.597

) (3

.173

) (4

.724

) (1

.793

) D

iffer

ence

in E

xpec

ted

Tota

l Pay

offs

MG

-5

.047

***

4.72

6***

13

.10*

**

2.76

6 -3

.247

***

(0

.866

) (1

.270

) (0

.944

) (1

.918

) (0

.601

) D

iffer

ence

in E

xpec

ted

Tota

l Pay

offs

PO

L -2

.688

***

3.50

1***

8.

640*

**

-9.0

95**

* -4

.614

***

(0

.869

) (1

.247

) (1

.016

) (1

.481

) (0

.518

) D

iffer

ence

in i'

s Exp

ecte

d Pa

yoff

s MG

10

.29*

**

8.13

3***

3.

998*

**

4.34

1***

4.

591*

**

(0

.735

) (0

.992

) (0

.653

) (1

.306

) (0

.298

) D

iffer

ence

in i'

s Exp

ecte

d Pa

yoff

s PO

L 7.

737*

**

12.1

3***

4.

763*

**

17.0

0***

5.

468*

**

(0

.649

) (1

.051

) (0

.675

) (1

.213

) (0

.220

) D

iffer

ence

in D

iffer

ence

of M

G, P

OL

Favo

ritis

m L

evel

s 16

.70*

**

-4.9

73

5.80

1 13

.47

6.05

6**

(4

.642

) (6

.571

) (4

.283

) (8

.472

) (2

.859

)

Obs

erva

tions

14

,587

14

,587

14

,587

14

,587

14

,587

Stan

dard

err

ors i

n pa

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hese

s

***

p<0.

01, *

* p<

0.05

, * p

<0.1

Page 15: FOR ONLINE PUBLICATION Deconstructing Bias: Individual Groupiness … · 2017-08-14 · FOR ONLINE PUBLICATION Deconstructing Bias: Individual Groupiness and Income Allocation

!15!

Tab

les O

A11

. C

ross

-Typ

e D

iffer

ence

s in

Pred

icte

d Fa

vori

tism

Lev

els a

nd P

redi

cted

Pay

offs

: T

ype

6, 7

, 8, 9

vs.

Typ

es 7

-9 a

nd v

s. A

vera

ge S

ubje

ct

Cro

ss T

ype

Diff

eren

ce

Type

6 v

. 7

Type

6 v

. 8

Type

6 v

. 9

Type

6 v

. A

vera

ge

Type

7 v

. 8

Type

7 v

. 9

Type

7 v

. A

vera

ge

Type

8 v

. 9

Type

8 v

. A

vera

ge

Type

9 v

. A

vera

ge

D

iffer

ence

in F

avor

itism

leve

ls M

G

-2.4

96

0.33

3 8.

658

-3.1

54

2.82

9 11

.15

-0.6

58

8.32

5 -3

.487

-1

1.81

*

(4

.969

) (3

.286

) (7

.209

) (2

.734

) (4

.664

) (7

.933

) (4

.294

) (7

.004

) (2

.136

) (6

.763

) D

iffer

ence

in F

avor

itism

leve

ls P

OL

19.1

8***

11

.23*

**

11.8

9**

7.49

0***

-7

.945

-7

.290

-1

1.69

***

0.65

5 -3

.741

-4

.396

(5

.121

) (3

.894

) (5

.236

) (2

.884

) (5

.095

) (6

.181

) (4

.371

) (5

.210

) (2

.836

) (4

.505

) D

iffer

ence

in E

xpec

ted

Tota

l Pay

offs

MG

9.

773*

**

18.1

5***

7.

812*

**

1.80

0***

8.

375*

**

-1.9

61

-7.9

74**

* -1

0.34

***

-16.

35**

* -6

.013

***

(1

.308

) (0

.996

) (1

.944

) (0

.681

) (1

.361

) (2

.154

) (1

.151

) (1

.980

) (0

.777

) (1

.842

) D

iffer

ence

in E

xpec

ted

Tota

l Pay

offs

PO

L 6.

188*

**

11.3

3***

-6

.407

***

-1.9

26**

5.

140*

**

-12.

60**

* -8

.114

***

-17.

74**

* -1

3.25

***

4.48

2***

(1

.360

) (1

.152

) (1

.577

) (0

.750

) (1

.458

) (1

.813

) (1

.167

) (1

.663

) (0

.917

) (1

.414

) D

iffer

ence

in i'

s Exp

ecte

d Pa

yoff

s MG

-2

.155

* -6

.290

***

-5.9

47**

* -5

.697

***

-4.1

35**

* -3

.792

**

-3.5

42**

* 0.

343

0.59

3 0.

249

(1

.183

) (0

.917

) (1

.456

) (0

.709

) (1

.134

) (1

.601

) (0

.973

) (1

.417

) (0

.624

) (1

.292

) D

iffer

ence

in i'

s Exp

ecte

d Pa

yoff

s PO

L 4.

394*

**

-2.9

74**

* 9.

259*

**

-2.2

69**

* -7

.369

***

4.86

5***

-6

.663

***

12.2

3***

0.

705

-11.

53**

*

(1

.217

) (0

.910

) (1

.358

) (0

.649

) (1

.229

) (1

.590

) (1

.051

) (1

.370

) (0

.675

) (1

.213

) D

iffer

ence

in D

iffer

ence

of M

G, P

OL

Favo

ritis

m L

evel

s -2

1.67

***

-10.

90**

-3

.228

-1

0.64

***

10.7

7 18

.44*

11

.03*

7.

670

0.25

4 -7

.416

(7

.129

) (5

.097

) (8

.911

) (3

.976

) (6

.900

) (1

0.05

) (6

.119

) (8

.729

) (3

.550

) (8

.126

)

O

bser

vatio

ns

14,5

87

14,5

87

14,5

87

14,5

87

14,5

87

14,5

87

14,5

87

14,5

87

14,5

87

14,5

87

Stan

dard

err

ors i

n pa

rent

hese

s

**

* p<

0.01

, **

p<0.

05, *

p<0

.1

Page 16: FOR ONLINE PUBLICATION Deconstructing Bias: Individual Groupiness … · 2017-08-14 · FOR ONLINE PUBLICATION Deconstructing Bias: Individual Groupiness and Income Allocation

!16!

Tab

le O

A12

. C

ross

Tab

ulat

ion

of In

divi

dual

Dat

a G

roup

ines

s Des

igna

tion

and

Typ

e A

ssig

nmen

t

Gro

upin

ess f

rom

N

ot G

roup

y

C

ond

Gro

upy

Gro

upy

Sum

by

Indi

vidu

al

In

divi

dual

Dat

a Ty

pe 1

Ty

pe 7

Ty

pe 9

Ty

pe 2

Ty

pe 3

Ty

pe 6

T

ype

4 Ty

pe 5

Ty

pe 8

G

roup

ines

s

Not

Gro

upy

29

6 2

20

18

8 1

10

2 96

Con

d G

roup

y 0

0 0

2 6

1 12

5

1 27

Gro

upy

1 1

0 2

4 6

2 1

1 18

Tota

l in

Each

Typ

e 30

7

2 24

28

15

15

16

4

141

Page 17: FOR ONLINE PUBLICATION Deconstructing Bias: Individual Groupiness … · 2017-08-14 · FOR ONLINE PUBLICATION Deconstructing Bias: Individual Groupiness and Income Allocation

!17!

Tab

le O

A13

. S

ix T

ype

Lat

ent C

lass

Est

imat

ion

Non

-Gro

up C

ontr

ol

U

tility

Pa

ram

eter

s ty

pe1

type

2 ty

pe3

type

4 ty

pe5

type

6

Bet

a 0.

150*

**

0.06

55**

* 0.

0865

***

0.01

79**

* 0.

0043

5 0.

0441

***

(0

.015

8)

(0.0

0773

) (0

.014

7)

(0.0

0520

) (0

.003

19)

(0.0

0728

)

Rho

0.

0050

0**

0.02

31**

* -

0.05

08**

* 0.

0258

***

0.00

931*

**

0.00

281

(0

.001

98)

(0.0

0410

) (0

.008

48)

(0.0

0422

) (0

.001

97)

(0.0

0246

)

M

ixin

g Pr

opor

tions

0.

246*

**

0.20

6***

0.

0600

* 0.

152*

**

0.18

9***

0.

147*

(0

.043

3)

(0.0

545)

(0

.031

3)

(0.0

582)

(0

.057

9)

(0.0

791)

O

bser

vatio

ns

3,63

6 3,

636

3,63

6 3,

636

3,63

6 3,

636

Stan

dard

err

ors i

n pa

rent

hese

s

***

p<0.

01, *

* p<

0.05

, * p

<0.1

Page 18: FOR ONLINE PUBLICATION Deconstructing Bias: Individual Groupiness … · 2017-08-14 · FOR ONLINE PUBLICATION Deconstructing Bias: Individual Groupiness and Income Allocation

!18!

T

able

OA

14.

Cro

ss T

abul

atio

n N

on-G

roup

Con

ditio

n So

cial

Pre

fere

nce

Cla

ss v

s. G

roup

ines

s

NG

Par

amet

ers

N

on G

roup

y

Con

d G

roup

y

Gro

upy

To

tal N

G C

lass

Ty

pe 1

Ty

pe 7

Ty

pe 9

Ty

pe 2

Ty

pe 3

Ty

pe 6

T

ype

4 Ty

pe 5

Ty

pe 8

0

< B

eta/

Rho

≤ 1

25

5

2 0

1 11

2

1 0

47

Bet

a/R

ho >

1

5 2

0 24

27

4

11

14

0 87

R

ho <

0

0 0

0 0

0 0

2 1

4 7

To

tal E

ach

Type

30

7

2 24

28

15

15

16

4

141

Page 19: FOR ONLINE PUBLICATION Deconstructing Bias: Individual Groupiness … · 2017-08-14 · FOR ONLINE PUBLICATION Deconstructing Bias: Individual Groupiness and Income Allocation

!19!

Tab

le O

A15

. R

egre

ssio

ns: N

ine

Typ

e Po

ster

ior

Prob

abili

ties o

n N

on-G

roup

Pos

teri

or P

roba

bilit

ies o

f Soc

ial P

refe

renc

es

NG

Soc

ial

Pref

eren

ces

Type

1

Type

2

Type

3

Type

4

Type

5

Type

6

Type

7

Type

8

Ty

pe 9

B

eta/

Rho

> 1

0.

0481

0.

287*

**

0.30

6***

0.

123*

**

0.17

8***

0.

0333

0.

0261

-5

.35e

-05

-0.0

0160

(0

.037

2)

(0.0

374)

(0

.039

8)

(0.0

323)

(0

.033

9)

(0.0

306)

(0

.023

7)

(0.0

121)

(0

.012

8)

0<B

eta/

Rho

≤1

0.55

4***

-0

.019

7 0.

0253

0.

0386

0.

0077

0 0.

238*

**

0.11

2***

3.

73e-

06

0.04

44**

(0

.050

4)

(0.0

507)

(0

.053

9)

(0.0

438)

(0

.046

0)

(0.0

415)

(0

.032

1)

(0.0

165)

(0

.017

4)

Rho

< 0

-5

.90e

-05

-0.0

0035

1 -0

.000

374

0.28

6***

0.

143

-4.0

7e-0

5 -3

.19e

-05

0.57

2***

1.

94e-

06

(0

.128

) (0

.128

) (0

.137

) (0

.111

) (0

.116

) (0

.105

) (0

.081

2)

(0.0

417)

(0

.044

0)

Obs

erva

tions

14

1 14

1 14

1 14

1 14

1 14

1 14

1 14

1 14

1 R

-squ

ared

0.

468

0.29

5 0.

298

0.13

7 0.

172

0.19

8 0.

089

0.57

1

Stan

dard

err

ors i

n pa

rent

hese

s

***

p<0.

01, *

* p<

0.05

, * p

<0.1