Background

The purpose of this study was to validate the correlational relationship between race+class zero-sum and linked fate.

We targeted white working class folks and wanted to see if their zs beliefs map onto to their perceived linked fate with either upper class white people (if they have more racial zero-sum beliefs or working class non white people (if they have more class zero-sum beliefs).

Eligibility

First, let’s see how many people entered the survey and did not pass the initial eligibility block.
We filtered on race (for white people) and on income (lower than 100k) and ses (anything other than upper class) and education (masters and above).

df_cbzs %>% 
  filter(elg == 0) %>% 
  group_by(race,
           income,
           ses,
           edu) %>% 
  summarise(N = n()) %>% 
  ungroup() %>% 
  kbl() %>% 
  kable_styling(bootstrap_options = "hover",
                full_width = F,
                position = "left")

race	income	ses	edu	N
Hispanic, Latino, or Spanish origin	$20,001-$40,000	Lower Middle Class	4yearColl	1
Hispanic, Latino, or Spanish origin	$20,001-$40,000	Middle Class	4yearColl	1
Hispanic, Latino, or Spanish origin	$40,001-$60,000	Middle Class	MA	1
Hispanic, Latino, or Spanish origin	$60,001-$80,000	Middle Class	2yearColl	1
Hispanic, Latino, or Spanish origin	$100,001-$120,000	Middle Class	4yearColl	1
Other (please specify)	$80,001-$100,000	Middle Class	4yearColl	1
White	$20,001-$40,000	Lower Middle Class	MA	1
White	$20,001-$40,000	Middle Class	MA	1
White	$20,001-$40,000	Upper Class	4yearColl	1
White	$60,001-$80,000	Middle Class	MA	1
White	$100,001-$120,000	Upper Middle Class	4yearColl	1
White	$120,001-$140,000	Middle Class	4yearColl	1
White	$160,001-$180,000	Upper Middle Class	4yearColl	1

ok cool. 13 people didn’t pass the eligibility criteria and were routed out of the survey. that leaves us with 200 eligible participants.

Demographics

Race

df_cbzs_elg %>% 
  group_by(race) %>% 
  summarise(N = n()) %>% 
  ungroup() %>% 
  mutate(Perc = round(100*(N/sum(N)),2)) %>% 
  ungroup() %>% 
  kbl() %>% 
  kable_styling(bootstrap_options = "hover",
                full_width = F,
                position = "left")

race	N	Perc
White	193	96.5
multiracial	7	3.5

Gender

df_cbzs_elg %>% 
  mutate(gender = ifelse(is.na(gender) | gender == "","other",gender)) %>% 
  group_by(gender) %>% 
  summarise(N = n()) %>% 
  ungroup() %>% 
  mutate(Perc = round(100*(N/sum(N)),2)) %>% 
  ungroup() %>% 
  kbl() %>% 
  kable_styling(bootstrap_options = "hover",
                full_width = F,
                position = "left")

gender	N	Perc
man	81	40.5
other	1	0.5
woman	118	59.0

Age

df_cbzs_elg %>% 
  summarise(age_mean = round(mean(age,na.rm = T),2),
            age_sd = round(sd(age,na.rm = T),2)) %>% 
  kbl() %>% 
  kable_styling(bootstrap_options = "hover",
                full_width = F,
                position = "left")

age_mean	age_sd
40.3	13.53

Education

df_cbzs_elg %>% 
  group_by(edu) %>% 
  summarise(N = n()) %>% 
  ungroup() %>% 
  mutate(Perc = round(100*(N/sum(N)),2)) %>% 
  ungroup() %>% 
  kbl() %>% 
  kable_styling(bootstrap_options = "hover",
                full_width = F,
                position = "left")

edu	N	Perc
noHS	1	0.5
GED	80	40.0
2yearColl	41	20.5
4yearColl	78	39.0

Subjective SES

df_cbzs_elg %>% 
  group_by(ses) %>% 
  summarise(N = n()) %>% 
  ungroup() %>% 
  mutate(Perc = round(100*(N/sum(N)),2)) %>% 
  ungroup() %>% 
  kbl() %>% 
  kable_styling(bootstrap_options = "hover",
                full_width = F,
                position = "left")

ses	N	Perc
Lower Class	42	21
Lower Middle Class	96	48
Middle Class	56	28
Upper Middle Class	6	3

Income

df_cbzs_elg %>% 
  ggplot(aes(x = income)) +
  geom_bar() +
  theme(panel.grid.major = element_blank(),
        panel.grid.minor = element_blank(),
        panel.background = element_blank(),
        axis.ticks = element_blank(),
        axis.line = element_line(color = "grey66"),
        axis.text.y = element_text(color = "black"),
        axis.text.x = element_text(color = "black",
                                   face = "bold"),
        axis.title.x = element_blank(),
        axis.title.y = element_blank()) +
  coord_flip()

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.

means <- df_cbzs_elg %>%
  dplyr::select(PID,ideo_con:ideo_prog) %>% 
  pivot_longer(-PID,
               names_to = "ideo",
               values_to = "score") %>% 
  filter(!is.na(score)) %>% 
  group_by(ideo) %>% 
  summarise(score = mean(score)) %>% 
  ungroup()

df_cbzs_elg %>%
  dplyr::select(PID,ideo_con:ideo_prog) %>% 
  pivot_longer(-PID,
               names_to = "ideo",
               values_to = "score") %>% 
  filter(!is.na(score)) %>%  
  ggplot() +
  geom_density(aes(x = score), fill = "lightblue") +
  scale_x_continuous(limits = c(1,7),
                     breaks = seq(1,7,1)) +
  geom_vline(data = means,mapping = aes(xintercept = score),
             color = "black",
             linetype = "dashed",
             size = 1.1) +
  theme(panel.grid.major = element_blank(),
        panel.grid.minor = element_blank(),
        panel.background = element_blank(),
        axis.ticks = element_blank(),
        axis.line = element_line(color = "grey66"),
        axis.text.y = element_text(color = "black"),
        axis.text.x = element_text(color = "black",
                                   face = "bold")) +
  facet_wrap(~ideo,nrow = 2)

Party ID

df_cbzs_elg %>% 
  group_by(party_id) %>% 
  summarise(N = n()) %>% 
  ungroup() %>% 
  mutate(Perc = round(100*(N/sum(N)),2)) %>% 
  ungroup() %>% 
  kbl() %>% 
  kable_styling(bootstrap_options = "hover",
                full_width = F,
                position = "left")

party_id	N	Perc
Democrat	67	33.5
Independent	61	30.5
Republican	72	36.0

Vote in 2020

df_cbzs_elg %>% 
  group_by(vote_2020) %>% 
  summarise(N = n()) %>% 
  ungroup() %>% 
  mutate(Perc = round(100*(N/sum(N)),2)) %>% 
  ungroup() %>% 
  arrange(desc(N)) %>% 
  kbl() %>% 
  kable_styling(bootstrap_options = "hover",
                full_width = F,
                position = "left")

vote_2020	N	Perc
Joe Biden	80	40.0
Donald Trump	66	33.0
I did not vote	45	22.5
Third-party candidate	9	4.5

Vote in 2024

df_cbzs_elg %>% 
  group_by(vote_2024) %>% 
  summarise(N = n()) %>% 
  ungroup() %>% 
  mutate(Perc = round(100*(N/sum(N)),2)) %>% 
  ungroup() %>% 
  arrange(desc(N)) %>% 
  kbl() %>% 
  kable_styling(bootstrap_options = "hover",
                full_width = F,
                position = "left")

vote_2024	N	Perc
Donald Trump	80	40.0
Joe Biden	74	37.0
I will not vote	21	10.5
Robert F. Kennedy Jr.	15	7.5
Cornel West	5	2.5
Other	5	2.5

Measures

Class-based Zero-Sum Beliefs

If the upper class becomes richer, this comes at the expense of the working class
If the upper class makes more money, then the working class makes less money
If the upper class does better economically, this does NOT come at the expense of the working class [R]

alpha = 0.94

df_cbzs_elg %>% 
  ggplot(aes(x = zs_class)) +
  geom_density(fill = "lightblue",
                 color = "black") +
  scale_x_continuous(breaks = seq(1,7,1),
                     limits = c(1,7)) +
  ylab("density") +
  geom_vline(xintercept = mean(df_cbzs_elg$zs_class,na.rm = T),
             color = "black",
             linetype = "dashed",
             size = 1.1) +
  theme(panel.grid.major = element_blank(),
        panel.grid.minor = element_blank(),
        panel.background = element_blank(),
        axis.ticks = element_blank(),
        axis.line = element_line(color = "grey66"),
        axis.text.y = element_text(color = "black"),
        axis.text.x = element_text(color = "black",
                                   face = "bold"),
        axis.title.x = element_text(color = "black",
                                   face = "bold"))

Race-based Zero-Sum Beliefs

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

alpha = 0.91

df_cbzs_elg %>% 
  ggplot(aes(x = zs_race)) +
  geom_density(fill = "lightblue",
                 color = "black") +
  scale_x_continuous(breaks = seq(1,7,1),
                     limits = c(1,7)) +
  ylab("density") +
  geom_vline(xintercept = mean(df_cbzs_elg$zs_race,na.rm = T),
             color = "black",
             linetype = "dashed",
             size = 1.1) +
  theme(panel.grid.major = element_blank(),
        panel.grid.minor = element_blank(),
        panel.background = element_blank(),
        axis.ticks = element_blank(),
        axis.line = element_line(color = "grey66"),
        axis.text.y = element_text(color = "black"),
        axis.text.x = element_text(color = "black",
                                   face = "bold"),
        axis.title.x = element_text(color = "black",
                                   face = "bold"))

Linked Fate

White Working Class

Please indicate your agreement, as a white working class person, with the following statements.

1. Issues that affect me also affect white working class people
2. What happens to white working class people in this country will have something to do with what happens to me
3. White working class people and me share a common destiny
4. Progress for white working class people also means progress for me

alpha = 0.85

df_cbzs_elg %>% 
  ggplot(aes(x = lnktfate_wwc)) +
  geom_density(fill = "lightblue",
                 color = "black") +
  scale_x_continuous(breaks = seq(1,7,1),
                     limits = c(1,7)) +
  ylab("density") +
  geom_vline(xintercept = mean(df_cbzs_elg$lnktfate_wwc,na.rm = T),
             color = "black",
             linetype = "dashed",
             size = 1.1) +
  theme(panel.grid.major = element_blank(),
        panel.grid.minor = element_blank(),
        panel.background = element_blank(),
        axis.ticks = element_blank(),
        axis.line = element_line(color = "grey66"),
        axis.text.y = element_text(color = "black"),
        axis.text.x = element_text(color = "black",
                                   face = "bold"),
        axis.title.x = element_text(color = "black",
                                   face = "bold"))

White Upper Class

Please indicate your agreement, as a white working class person, with the following statements.

1. Issues that affect me also affect white upper class people
2. What happens to white upper class people in this country will have something to do with what happens to me
3. White upper class people and me share a common destiny
4. Progress for white upper class people also means progress for me

alpha = 0.89

df_cbzs_elg %>% 
  ggplot(aes(x = lnktfate_wuc)) +
  geom_density(fill = "lightblue",
                 color = "black") +
  scale_x_continuous(breaks = seq(1,7,1),
                     limits = c(1,7)) +
  ylab("density") +
  geom_vline(xintercept = mean(df_cbzs_elg$lnktfate_wuc,na.rm = T),
             color = "black",
             linetype = "dashed",
             size = 1.1) +
  theme(panel.grid.major = element_blank(),
        panel.grid.minor = element_blank(),
        panel.background = element_blank(),
        axis.ticks = element_blank(),
        axis.line = element_line(color = "grey66"),
        axis.text.y = element_text(color = "black"),
        axis.text.x = element_text(color = "black",
                                   face = "bold"),
        axis.title.x = element_text(color = "black",
                                   face = "bold"))

Non-White Working Class

Please indicate your agreement, as a white working class person, with the following statements.

1. Issues that affect me also affect non-white working class people
2. What happens to non-white working class people in this country will have something to do with what happens to me
3. Non-white working class people and me share a common destiny
4. Progress for non-white working class people also means progress for me

alpha = 0.88

df_cbzs_elg %>% 
  ggplot(aes(x = lnktfate_nwwc)) +
  geom_density(fill = "lightblue",
                 color = "black") +
  scale_x_continuous(breaks = seq(1,7,1),
                     limits = c(1,7)) +
  ylab("density") +
  geom_vline(xintercept = mean(df_cbzs_elg$lnktfate_nwwc,na.rm = T),
             color = "black",
             linetype = "dashed",
             size = 1.1) +
  theme(panel.grid.major = element_blank(),
        panel.grid.minor = element_blank(),
        panel.background = element_blank(),
        axis.ticks = element_blank(),
        axis.line = element_line(color = "grey66"),
        axis.text.y = element_text(color = "black"),
        axis.text.x = element_text(color = "black",
                                   face = "bold"),
        axis.title.x = element_text(color = "black",
                                   face = "bold"))

Non-White Upper Class

Please indicate your agreement, as a white working class person, with the following statements.

1. Issues that affect me also affect non-white upper class people
2. What happens to non-white upper class people in this country will have something to do with what happens to me
3. Non-white upper class people and me share a common destiny
4. Progress for non-white upper class people also means progress for me

alpha = 0.88

df_cbzs_elg %>% 
  ggplot(aes(x = lnktfate_nwuc)) +
  geom_density(fill = "lightblue",
                 color = "black") +
  scale_x_continuous(breaks = seq(1,7,1),
                     limits = c(1,7)) +
  ylab("density") +
  geom_vline(xintercept = mean(df_cbzs_elg$lnktfate_nwuc,na.rm = T),
             color = "black",
             linetype = "dashed",
             size = 1.1) +
  theme(panel.grid.major = element_blank(),
        panel.grid.minor = element_blank(),
        panel.background = element_blank(),
        axis.ticks = element_blank(),
        axis.line = element_line(color = "grey66"),
        axis.text.y = element_text(color = "black"),
        axis.text.x = element_text(color = "black",
                                   face = "bold"),
        axis.title.x = element_text(color = "black",
                                   face = "bold"))

Analysis

Correlation matrix

df_cbzs_elg <- df_cbzs_elg %>% 
  mutate(income_num = as.numeric(income),
         edu_num = as.numeric(edu),
         ses_num = as.numeric(ses))

df_cbzs_elg %>%
  dplyr::select(zs_class:lnktfate_nwuc,ideo_con:ideo_prog,income_num:ses_num) %>%
  corPlot(upper = TRUE,stars = TRUE,xsrt = 270)

I like what I’m seeing! A few notes.
1. Class ZS is doing exactly what we want it to do. positive correlation with working class linked fate (both white and non-white) and negative correlations with upper class linked fate. That’s great.
2. Race ZS is also doing what we want it to do: Positively correlated with white upper class linked fate and negatively correlated with non-white working class linked fate.
3. Despite being filtered for, we still see meaningful correlations with SES, and in the direction you’d expect. So, that’s cool. I bet our effects are stronger for lower SES.

Regressions

Outcome: White Upper Class Linked Fate

m1 <- lm(lnktfate_wuc ~ zs_race,data = df_cbzs_elg)

apa_lm <- apa_print(m1)
apa_table(
  apa_lm$table, 
  placement = "H"
)

(#tab:unnamed-chunk-26)
Predictor	$b$	95% CI	$t$	$\mathit{df}$	$p$
Intercept	2.62	[2.25, 3.00]	13.86	198	< .001
Zs race	0.31	[0.19, 0.43]	5.09	198	< .001

m1 <- lm(lnktfate_wuc ~ zs_race + ideo_con,data = df_cbzs_elg)

apa_lm <- apa_print(m1)
apa_table(
  apa_lm$table, 
  placement = "H"
)

(#tab:unnamed-chunk-27)
Predictor	$b$	95% CI	$t$	$\mathit{df}$	$p$
Intercept	2.25	[1.81, 2.69]	10.06	190	< .001
Zs race	0.26	[0.13, 0.38]	4.00	190	< .001
Ideo con	0.14	[0.05, 0.23]	3.10	190	.002

m1 <- lm(lnktfate_wuc ~ zs_race + ideo_con + lnktfate_wwc,data = df_cbzs_elg)

apa_lm <- apa_print(m1)
apa_table(
  apa_lm$table, 
  placement = "H"
)

(#tab:unnamed-chunk-28)
Predictor	$b$	95% CI	$t$	$\mathit{df}$	$p$
Intercept	1.77	[0.52, 3.02]	2.79	189	.006
Zs race	0.26	[0.13, 0.39]	4.02	189	< .001
Ideo con	0.14	[0.05, 0.23]	3.09	189	.002
Lnktfate wwc	0.08	[-0.12, 0.29]	0.81	189	.418

m1 <- lm(lnktfate_wuc ~ zs_race + ideo_con + lnktfate_wwc + lnktfate_nwwc,data = df_cbzs_elg)

apa_lm <- apa_print(m1)
apa_table(
  apa_lm$table, 
  placement = "H"
)

(#tab:unnamed-chunk-29)
Predictor	$b$	95% CI	$t$	$\mathit{df}$	$p$
Intercept	1.40	[0.00, 2.81]	1.97	188	.050
Zs race	0.28	[0.15, 0.42]	4.16	188	< .001
Ideo con	0.15	[0.06, 0.24]	3.22	188	.002
Lnktfate wwc	0.04	[-0.18, 0.26]	0.38	188	.702
Lnktfate nwwc	0.10	[-0.08, 0.28]	1.10	188	.272

m1 <- lm(lnktfate_wuc ~ zs_race*zs_class,data = df_cbzs_elg)

apa_lm <- apa_print(m1)
apa_table(
  apa_lm$table, 
  placement = "H"
)

(#tab:unnamed-chunk-30)
Predictor	$b$	95% CI	$t$	$\mathit{df}$	$p$
Intercept	4.60	[3.53, 5.66]	8.50	196	< .001
Zs race	0.34	[-0.03, 0.71]	1.80	196	.074
Zs class	-0.38	[-0.58, -0.18]	-3.83	196	< .001
Zs race $\times$ Zs class	-0.01	[-0.08, 0.06]	-0.33	196	.744

interact_plot(m1,
              pred = "zs_class",
              modx = "zs_race",
              interval = T)

class zs just takes over. that’s cool. If you believe the rich are gaining at your expense, you have nothing in common with upper class white people, even if you have racial zero-sum beliefs.

m1 <- lm(lnktfate_wuc ~ zs_race*zs_class + lnktfate_wwc,data = df_cbzs_elg)

apa_lm <- apa_print(m1)
apa_table(
  apa_lm$table, 
  placement = "H"
)

(#tab:unnamed-chunk-32)
Predictor	$b$	95% CI	$t$	$\mathit{df}$	$p$
Intercept	3.61	[2.24, 4.98]	5.20	195	< .001
Zs race	0.32	[-0.05, 0.69]	1.73	195	.086
Zs class	-0.41	[-0.60, -0.21]	-4.10	195	< .001
Lnktfate wwc	0.20	[0.02, 0.37]	2.22	195	.028
Zs race $\times$ Zs class	-0.01	[-0.08, 0.06]	-0.22	195	.827

interact_plot(m1,
              pred = "zs_class",
              modx = "zs_race",
              interval = T)

Outcome: Non-White Working Class Linked Fate

m1 <- lm(lnktfate_nwwc ~ zs_class,data = df_cbzs_elg)

apa_lm <- apa_print(m1)
apa_table(
  apa_lm$table, 
  placement = "H"
)

(#tab:unnamed-chunk-34)
Predictor	$b$	95% CI	$t$	$\mathit{df}$	$p$
Intercept	4.08	[3.53, 4.63]	14.62	198	< .001
Zs class	0.17	[0.06, 0.27]	3.14	198	.002

m1 <- lm(lnktfate_nwwc ~ zs_class + ideo_con,data = df_cbzs_elg)

apa_lm <- apa_print(m1)
apa_table(
  apa_lm$table, 
  placement = "H"
)

(#tab:unnamed-chunk-35)
Predictor	$b$	95% CI	$t$	$\mathit{df}$	$p$
Intercept	4.66	[3.87, 5.45]	11.62	190	< .001
Zs class	0.12	[0.01, 0.24]	2.09	190	.038
Ideo con	-0.08	[-0.17, 0.00]	-1.98	190	.049

m1 <- lm(lnktfate_nwwc ~ zs_class + lnktfate_wwc,data = df_cbzs_elg)

apa_lm <- apa_print(m1)
apa_table(
  apa_lm$table, 
  placement = "H"
)

(#tab:unnamed-chunk-36)
Predictor	$b$	95% CI	$t$	$\mathit{df}$	$p$
Intercept	1.83	[0.82, 2.84]	3.57	197	< .001
Zs class	0.13	[0.03, 0.23]	2.56	197	.011
Lnktfate wwc	0.44	[0.27, 0.61]	5.11	197	< .001

m1 <- lm(lnktfate_nwwc ~ zs_class + lnktfate_wwc + lnktfate_wuc,data = df_cbzs_elg)

apa_lm <- apa_print(m1)
apa_table(
  apa_lm$table, 
  placement = "H"
)

(#tab:unnamed-chunk-37)
Predictor	$b$	95% CI	$t$	$\mathit{df}$	$p$
Intercept	1.86	[0.69, 3.03]	3.13	196	.002
Zs class	0.13	[0.01, 0.24]	2.17	196	.031
Lnktfate wwc	0.44	[0.27, 0.61]	5.06	196	< .001
Lnktfate wuc	-0.01	[-0.13, 0.12]	-0.10	196	.919

m1 <- lm(lnktfate_nwwc ~ zs_class*zs_race,data = df_cbzs_elg)

apa_lm <- apa_print(m1)
apa_table(
  apa_lm$table, 
  placement = "H"
)

(#tab:unnamed-chunk-38)
Predictor	$b$	95% CI	$t$	$\mathit{df}$	$p$
Intercept	3.94	[2.95, 4.94]	7.83	196	< .001
Zs class	0.33	[0.15, 0.52]	3.62	196	< .001
Zs race	0.13	[-0.21, 0.47]	0.75	196	.457
Zs class $\times$ Zs race	-0.08	[-0.14, -0.02]	-2.43	196	.016

interact_plot(m1,
              pred = "zs_class",
              modx = "zs_race",
              interval = T)

oh this is cool. These are the racial divides obstacles to class solidarity. If you’re high on race zs, it doesn’t matter how much you believe that the rich are gaining at your expense. If you’re low on race zs, it matters a lot.

m1 <- lm(lnktfate_nwwc ~ zs_class*zs_race + lnktfate_wwc,data = df_cbzs_elg)

apa_lm <- apa_print(m1)
apa_table(
  apa_lm$table, 
  placement = "H"
)

(#tab:unnamed-chunk-40)
Predictor	$b$	95% CI	$t$	$\mathit{df}$	$p$
Intercept	1.88	[0.67, 3.09]	3.07	195	.002
Zs class	0.28	[0.11, 0.45]	3.21	195	.002
Zs race	0.10	[-0.23, 0.42]	0.58	195	.560
Lnktfate wwc	0.42	[0.26, 0.57]	5.27	195	< .001
Zs class $\times$ Zs race	-0.07	[-0.13, -0.01]	-2.32	195	.021

interact_plot(m1,
              pred = "zs_class",
              modx = "zs_race",
              interval = T)

Class-Based Zero-Sum Beliefs

July 2024: Analysis