Class-Based Zero-Sum Beliefs

Feb 24: Analysis

Background

Broadly, this is our framework in this project: class/race ZSB’s -> cross-class/cross-race solidarity -> support for class/race policy.

Here, we look at the first link of that framework: ZSB’s -> solidarity. We look at solidarity through a perspective of linked fate. And we also added SDO and global ZSB’s as control/ecploratory variables.

Some models to look at from our email exchange:
class ZSBs predict class linked fate (LF) more strongly than race ZSBs, controlling for race LF [class_LF ~ class_zsb + race_zsb + race_LF]
race ZSBs predict race LF more strongly than class ZSBs, controlling for class LF [race_lf ~ class_zsb + race_zsb + class_LF]
These relationships hold when accounting for political orientation, SDO, global ZSBs
Exploring the interactive effect of ZSBs (classZSB X raceZSB) on linked fate perceptions (e.g., class_LF ~ classZSB X raceZSB + race_LF), so let’s for sure run these to see what we learn.

Demographics

Race

race	N	Perc
American Indian or Alaska Native	1	0.51
Asian	11	5.56
Black or African American	27	13.64
Hispanic, Latino, or Spanish origin	9	4.55
Middle Eastern or North African	1	0.51
Other (please specify)	1	0.51
White	133	67.17
multiracial	14	7.07
NA	1	0.51

Gender

gender	N	Perc
man	96	48.48
woman	98	49.49
NA	4	2.02

Age

age_mean	age_sd
38.63	12.62

Education

edu	N	Perc
GED	55	27.78
2yearColl	17	8.59
4yearColl	83	41.92
MA	36	18.18
PHD	7	3.54

Class

class	N	Perc
Lower Class	8	4.04
Working Class	32	16.16
Lower Middle Class	37	18.69
Middle Class	95	47.98
Upper Middle Class	24	12.12
Upper Class	2	1.01

Income

Politics

Ideology

Participants were asked about the extent to which they subscribe to the following ideologies on a scale of 1-7 (select NA if unfamiliar): Conservatism, Liberalism, Democratic Socialism, Libertarianism, Progressivism, Right-Wing Nationalism (removed from plot because it’s too heavily skewed).

Party ID

party_id	N	Perc
Democrat	99	50.00
Independent	51	25.76
Republican	48	24.24

Vote in 2020

vote_2020	N	Perc
Joe Biden	109	55.05
Donald Trump	47	23.74
I did not vote	34	17.17
Third-party candidate	7	3.54
	1	0.51

Vote in 2024

vote_2024	N	Perc
Joe Biden	98	49.49
Donald Trump	44	22.22
I will not vote	33	16.67
Other	12	6.06
Robert F. Kennedy Jr.	7	3.54
Cornel West	2	1.01
	1	0.51
Jill Stein	1	0.51

Measures

Class-based Zero-Sum Beliefs

Adapted from Chinoy et al., 2022: https://nathannunn.sites.olt.ubc.ca/files/2022/12/Zero_Sum_US_Political_Divides.pdf

1. If the upper class becomes richer, this comes at the expense of the working class
   
2. If the upper class makes more money, then the working class makes less money
   
3. If the upper class does better economically, this does NOT come at the expense of the working class [R]
   
   alpha = 0.87

Race-based Zero-Sum Beliefs

Adapted from Chinoy et al., 2022: https://nathannunn.sites.olt.ubc.ca/files/2022/12/Zero_Sum_US_Political_Divides.pdf

1. If racial minorities become richer, this comes at the expense of white people
   
2. If racial minorities make more money, then white people make less money
   
3. If racial minorities do better economically, this does NOT come at the expense of white people [R]
   
   alpha = 0.88

Linked Fate: Class

Adapted from Ho et al., 2017

1. What happens to working class white people in this country is closely related to what happens to upper class white people in this country
   
2. Upper class white people and working class white people share a common destiny
   
3. Issues that affect upper class white people also affect working class white people
   
4. What happens to upper class white people is not related at all to what happens to working class white people [R]
   
5. Progress for upper class white people also means progress for working class white people
   
6. When the way working class white people are treated in the U.S. changes, the way that upper class white people are treated in the U.S. will naturally follow
   
   alpha = 0.85

Linked Fate: Race

Adapted from Ho et al., 2017

1. What happens to working class black people in this country is closely related to what happens to working class white people in this country
   
2. Working class black people and working class white people share a common destiny
   
3. Issues that affect working class white people also affect working class black people
   
4. What happens to working class black people is not related at all to what happens to working class white people [R]
   
5. Progress for working class white people also means progress for working class black people
   
6. When the way working class black people are treated in the U.S. changes, the way that working class white people are treated in the U.S. will naturally follow
   
   alpha = 0.85

SDO

Ho et al., 2015

1. An ideal society requires some groups to be on top and others to be on the bottom
   
2. Some groups of people are simply inferior to other groups
   
3. No one group should dominate in society [R]
   
4. Groups at the bottom are just as deserving as groups at the top [R]
   
5. Group equality should not be our primary goal
   
6. It is unjust to try to make groups equal
   
7. We should do what we can to equalize conditions for different groups [R]
   
8. We should work to give all groups an equal chance to succeed [R]
   
   alpha = 0.91

Zero-Sum Mindset

Andrews-Fearson, 2019

1. The success of one person is usually the failure of another person
   
2. Life is such that when one person gains, someone else has to lose
   
3. When someone does much for others, they lose
   
4. In most situations, different people’s interests are incompatible
   
5. When one person is winning, it does not mean that someone else is losing [R]
   
6. Life is like a tennis game - A person wins only when another person loses
   
7. One person’s success is not another person’s failure [R]
   
   alpha = 0.88

Analysis

Correlation Matrix

first glance - i like what i’m seeing. Alright - let’s run some models.

Outcome: Cross-Class Linked Fate

Main effects of zero-sum

(#tab:unnamed-chunk-25)

Predictor	\(b\)	95% CI	\(t\)	\(\mathit{df}\)	\(p\)
Intercept	5.38	[4.84, 5.93]	19.49	196	< .001
Zs class	-0.40	[-0.50, -0.29]	-7.51	196	< .001

(#tab:unnamed-chunk-26)

Predictor	\(b\)	95% CI	\(t\)	\(\mathit{df}\)	\(p\)
Intercept	2.80	[2.45, 3.16]	15.71	196	< .001
Zs race	0.24	[0.12, 0.36]	3.89	196	< .001

(#tab:unnamed-chunk-27)

Predictor	\(b\)	95% CI	\(t\)	\(\mathit{df}\)	\(p\)
Intercept	4.78	[4.20, 5.37]	16.14	195	< .001
Zs class	-0.40	[-0.50, -0.30]	-7.86	195	< .001
Zs race	0.24	[0.13, 0.34]	4.44	195	< .001

(#tab:unnamed-chunk-28)

Predictor	\(b\)	95% CI	\(t\)	\(\mathit{df}\)	\(p\)
Intercept	4.16	[3.27, 5.05]	9.19	194	< .001
Zs class	-0.39	[-0.49, -0.29]	-7.76	194	< .001
Zs race	0.26	[0.15, 0.37]	4.75	194	< .001
Lnktfate race	0.12	[-0.01, 0.25]	1.81	194	.071

(#tab:unnamed-chunk-29)

Predictor	\(b\)	95% CI	\(t\)	\(\mathit{df}\)	\(p\)
Intercept	3.90	[2.91, 4.89]	7.77	192	< .001
Zs class	-0.35	[-0.46, -0.23]	-5.90	192	< .001
Zs race	0.22	[0.08, 0.36]	3.10	192	.002
Lnktfate race	0.11	[-0.02, 0.24]	1.63	192	.106
SDO	0.11	[-0.03, 0.26]	1.57	192	.118
Zsm	-0.03	[-0.20, 0.14]	-0.36	192	.719

(#tab:unnamed-chunk-30)

Predictor	\(b\)	95% CI	\(t\)	\(\mathit{df}\)	\(p\)
Intercept	3.78	[1.21, 6.35]	2.91	163	.004
Zs class	-0.33	[-0.47, -0.20]	-4.75	163	< .001
Zs race	0.19	[0.04, 0.34]	2.44	163	.016
Lnktfate race	0.18	[0.03, 0.33]	2.37	163	.019
SDO	0.05	[-0.10, 0.21]	0.69	163	.491
Zsm	0.01	[-0.18, 0.19]	0.06	163	.953
Ideo con	0.03	[-0.09, 0.15]	0.45	163	.650
As numericincome	-0.03	[-0.11, 0.05]	-0.74	163	.460
As numericedu	-0.03	[-0.18, 0.13]	-0.32	163	.747
As numericclass	0.19	[-0.01, 0.38]	1.91	163	.058
Age	-0.01	[-0.02, 0.00]	-1.36	163	.175
Genderwoman	-0.09	[-0.41, 0.24]	-0.53	163	.595
RaceAsian	-0.58	[-2.80, 1.64]	-0.51	163	.607
RaceBlack or African American	0.05	[-2.12, 2.23]	0.05	163	.960
RaceHispanic, Latino, or Spanish origin	-0.67	[-2.90, 1.57]	-0.59	163	.557
RaceMiddle Eastern or North African	-1.39	[-4.43, 1.64]	-0.91	163	.365
Racemultiracial	-0.24	[-2.47, 1.99]	-0.21	163	.832
RaceWhite	-0.22	[-2.36, 1.92]	-0.20	163	.840

Interaction

(#tab:unnamed-chunk-32)

Predictor	\(b\)	95% CI	\(t\)	\(\mathit{df}\)	\(p\)
Intercept	5.38	[4.81, 5.95]	18.60	392	< .001
Zs	-0.40	[-0.50, -0.29]	-7.17	392	< .001
Typerace	-2.58	[-3.24, -1.92]	-7.67	392	< .001
Zs \(\times\) Typerace	0.63	[0.48, 0.79]	7.87	392	< .001

With controls:

Table: (#tab:unnamed-chunk-34)

Predictor	\(b\)	95% CI	\(t\)	\(\mathit{df}\)	\(p\)
Intercept	3.34	[1.44, 5.24]	3.45	343	< .001
Zs	-0.23	[-0.35, -0.11]	-3.84	343	< .001
Typerace	-1.47	[-2.18, -0.76]	-4.07	343	< .001
Lnktfate race	0.15	[0.04, 0.26]	2.76	343	.006
SDO	0.14	[0.03, 0.25]	2.54	343	.011
Zsm	-0.02	[-0.14, 0.10]	-0.36	343	.722
Ideo con	0.12	[0.03, 0.20]	2.81	343	.005
Income num	-0.04	[-0.09, 0.02]	-1.21	343	.228
Edu num	-0.02	[-0.13, 0.10]	-0.29	343	.774
Class num	0.25	[0.11, 0.39]	3.52	343	< .001
Age	-0.01	[-0.02, 0.00]	-1.63	343	.103
Genderwoman	-0.14	[-0.37, 0.10]	-1.15	343	.249
RaceAsian	-0.67	[-2.27, 0.93]	-0.82	343	.412
RaceBlack or African American	-0.03	[-1.60, 1.54]	-0.04	343	.970
RaceHispanic, Latino, or Spanish origin	-0.81	[-2.43, 0.80]	-0.99	343	.323
RaceMiddle Eastern or North African	-1.63	[-3.80, 0.55]	-1.47	343	.143
Racemultiracial	-0.29	[-1.89, 1.31]	-0.36	343	.721
RaceWhite	-0.37	[-1.91, 1.17]	-0.47	343	.637
Zs \(\times\) Typerace	0.36	[0.18, 0.54]	4.03	343	< .001

Outcome: Cross-Race Linked Fate

Main effects of zero-sum

(#tab:unnamed-chunk-35)

Predictor	\(b\)	95% CI	\(t\)	\(\mathit{df}\)	\(p\)
Intercept	4.91	[4.58, 5.24]	29.38	196	< .001
Zs race	-0.17	[-0.29, -0.06]	-3.02	196	.003

(#tab:unnamed-chunk-36)

Predictor	\(b\)	95% CI	\(t\)	\(\mathit{df}\)	\(p\)
Intercept	4.74	[4.17, 5.31]	16.42	196	< .001
Zs class	-0.05	[-0.16, 0.06]	-0.96	196	.337

(#tab:unnamed-chunk-37)

Predictor	\(b\)	95% CI	\(t\)	\(\mathit{df}\)	\(p\)
Intercept	5.18	[4.55, 5.80]	16.29	195	< .001
Zs race	-0.17	[-0.29, -0.06]	-3.02	195	.003
Zs class	-0.05	[-0.16, 0.05]	-0.99	195	.325

(#tab:unnamed-chunk-38)

Predictor	\(b\)	95% CI	\(t\)	\(\mathit{df}\)	\(p\)
Intercept	4.51	[3.56, 5.47]	9.35	194	< .001
Zs race	-0.21	[-0.33, -0.09]	-3.45	194	< .001
Zs class	0.00	[-0.12, 0.12]	0.03	194	.980
Lnktfate class	0.14	[-0.01, 0.29]	1.81	194	.071

(#tab:unnamed-chunk-39)

Predictor	\(b\)	95% CI	\(t\)	\(\mathit{df}\)	\(p\)
Intercept	4.51	[3.49, 5.53]	8.74	192	< .001
Zs race	-0.14	[-0.29, 0.01]	-1.78	192	.076
Zs class	0.06	[-0.07, 0.20]	0.93	192	.351
Lnktfate class	0.12	[-0.03, 0.27]	1.63	192	.106
SDO	0.09	[-0.06, 0.24]	1.18	192	.241
Zsm	-0.21	[-0.39, -0.04]	-2.41	192	.017

(#tab:unnamed-chunk-40)

Predictor	\(b\)	95% CI	\(t\)	\(\mathit{df}\)	\(p\)
Intercept	4.08	[1.52, 6.64]	3.14	163	.002
Zs race	-0.14	[-0.29, 0.02]	-1.76	163	.080
Zs class	0.12	[-0.03, 0.27]	1.63	163	.106
Lnktfate class	0.18	[0.03, 0.34]	2.37	163	.019
SDO	-0.03	[-0.18, 0.13]	-0.32	163	.749
Zsm	-0.15	[-0.33, 0.03]	-1.68	163	.096
Ideo con	0.16	[0.04, 0.27]	2.61	163	.010
As numericincome	-0.05	[-0.13, 0.03]	-1.26	163	.208
As numericedu	0.02	[-0.13, 0.18]	0.28	163	.777
As numericclass	0.01	[-0.19, 0.20]	0.07	163	.943
Age	0.01	[-0.01, 0.02]	1.04	163	.299
Genderwoman	-0.17	[-0.50, 0.16]	-1.03	163	.304
RaceAsian	-0.38	[-2.61, 1.85]	-0.34	163	.736
RaceBlack or African American	-0.93	[-3.10, 1.25]	-0.84	163	.401
RaceHispanic, Latino, or Spanish origin	-0.83	[-3.07, 1.41]	-0.73	163	.467
RaceMiddle Eastern or North African	-1.71	[-4.75, 1.32]	-1.11	163	.267
Racemultiracial	-0.35	[-2.58, 1.89]	-0.31	163	.759
RaceWhite	-0.37	[-2.52, 1.78]	-0.34	163	.733

Interaction

(#tab:unnamed-chunk-42)

Predictor	\(b\)	95% CI	\(t\)	\(\mathit{df}\)	\(p\)
Intercept	4.74	[4.18, 5.30]	16.58	392	< .001
Zs	-0.05	[-0.16, 0.05]	-0.97	392	.332
Typerace	0.17	[-0.48, 0.82]	0.51	392	.608
Zs \(\times\) Typerace	-0.12	[-0.28, 0.04]	-1.52	392	.131

With controls

Table: (#tab:unnamed-chunk-44)

Predictor	\(b\)	95% CI	\(t\)	\(\mathit{df}\)	\(p\)
Intercept	4.48	[2.68, 6.27]	4.90	343	< .001
Zs	0.08	[-0.04, 0.19]	1.29	343	.199
Typerace	0.60	[-0.09, 1.30]	1.70	343	.090
Lnktfate class	0.14	[0.04, 0.24]	2.76	343	.006
SDO	-0.06	[-0.16, 0.05]	-1.11	343	.266
Zsm	-0.17	[-0.28, -0.06]	-3.09	343	.002
Ideo con	0.13	[0.05, 0.20]	3.16	343	.002
Income num	-0.05	[-0.10, 0.01]	-1.71	343	.088
Edu num	0.01	[-0.09, 0.12]	0.27	343	.787
Class num	-0.01	[-0.14, 0.13]	-0.11	343	.915
Age	0.01	[0.00, 0.02]	1.41	343	.159
Genderwoman	-0.16	[-0.39, 0.06]	-1.41	343	.161
RaceAsian	-0.45	[-1.99, 1.09]	-0.58	343	.564
RaceBlack or African American	-0.99	[-2.49, 0.51]	-1.30	343	.196
RaceHispanic, Latino, or Spanish origin	-0.86	[-2.41, 0.70]	-1.08	343	.279
RaceMiddle Eastern or North African	-1.86	[-3.94, 0.23]	-1.75	343	.081
Racemultiracial	-0.45	[-1.99, 1.08]	-0.58	343	.563
RaceWhite	-0.42	[-1.90, 1.06]	-0.55	343	.579
Zs \(\times\) Typerace	-0.17	[-0.34, 0.01]	-1.89	343	.059

Exploratory Analysis

Interactions with race?

Let’s see if we see any difference for the cross-class items for white people vs. people of color.

(#tab:unnamed-chunk-45)

Predictor	\(b\)	95% CI	\(t\)	\(\mathit{df}\)	\(p\)
Intercept	5.31	[4.30, 6.33]	10.32	193	< .001
Zs class	-0.40	[-0.60, -0.20]	-4.04	193	< .001
Is whitewhite	0.12	[-1.09, 1.32]	0.19	193	.847
Zs class \(\times\) Is whitewhite	0.01	[-0.23, 0.24]	0.05	193	.963

hmm about for race?

(#tab:unnamed-chunk-46)

Predictor	\(b\)	95% CI	\(t\)	\(\mathit{df}\)	\(p\)
Intercept	4.10	[3.55, 4.65]	14.70	193	< .001
Zs race	-0.03	[-0.21, 0.15]	-0.31	193	.756
Is whitewhite	1.19	[0.51, 1.86]	3.47	193	< .001
Zs race \(\times\) Is whitewhite	-0.21	[-0.44, 0.01]	-1.85	193	.066

ok, not much to see here.

Zero-sum mindsets

Moderation?

Class ZSB * ZS Mindset -> Linkedfate Class

(#tab:unnamed-chunk-47)

Predictor	\(b\)	95% CI	\(t\)	\(\mathit{df}\)	\(p\)
Intercept	4.79	[3.52, 6.06]	7.42	194	< .001
Zs class	-0.38	[-0.61, -0.14]	-3.17	194	.002
Zsm	0.25	[-0.20, 0.70]	1.09	194	.276
Zs class \(\times\) Zsm	-0.02	[-0.10, 0.06]	-0.42	194	.677

Race ZSB * ZS Mindset -> Linkedfate Race

(#tab:unnamed-chunk-49)

Predictor	\(b\)	95% CI	\(t\)	\(\mathit{df}\)	\(p\)
Intercept	5.21	[4.38, 6.03]	12.47	194	< .001
Zs race	-0.04	[-0.38, 0.31]	-0.21	194	.831
Zsm	-0.18	[-0.44, 0.07]	-1.40	194	.164
Zs race \(\times\) Zsm	-0.01	[-0.09, 0.07]	-0.22	194	.826

Mediation?

ZS Mindset -> Class ZSB -> Linkedfate Class

a = 0.27 (p = 0.002)
b = -0.42 (p = 0)
direct = 0.04 (p = 0.569)
indirect = 0.16 (p = 0.019)

ZS Mindset -> Race ZSB -> Linkedfate Race

a = 0.7 (p = 0)
b = -0.07 (p = 0.305)
direct = -0.25 (p = 0)
indirect = -0.2 (p = 0.016)