Class-Based Zero-Sum Beliefs
April 2024a: Analysis
Background
The purpose of this study is to pilot our experimental manipulation
for class (class-frame vs. control) and zero-sum (zs-frame vs. control)
framing of progressive housing policy.
See materials here
We also measured support and race zero-sum beliefs, just to get a
sense.
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 |
|---|---|---|
| Asian | 13 | 6.5 |
| Black or African American | 26 | 13.0 |
| Hispanic, Latino, or Spanish origin | 13 | 6.5 |
| Other (please specify) | 1 | 0.5 |
| White | 134 | 67.0 |
| multiracial | 12 | 6.0 |
| NA | 1 | 0.5 |
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 | 100 | 50.0 |
| other | 3 | 1.5 |
| woman | 97 | 48.5 |
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 |
|---|---|
| 39.52 | 12.53 |
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 | 3 | 1.5 |
| GED | 52 | 26.0 |
| 2yearColl | 29 | 14.5 |
| 4yearColl | 84 | 42.0 |
| MA | 21 | 10.5 |
| PHD | 9 | 4.5 |
| NA | 2 | 1.0 |
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 | 20 | 10.0 |
| Lower Middle Class | 57 | 28.5 |
| Middle Class | 92 | 46.0 |
| Upper Middle Class | 29 | 14.5 |
| Upper Class | 1 | 0.5 |
| NA | 1 | 0.5 |
Working Class
Do you see yourself as part of the working class?
df_cbzs_elg %>%
group_by(wrkclass) %>%
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")| wrkclass | N | Perc |
|---|---|---|
| Yes | 154 | 77 |
| No | 30 | 15 |
| Not sure | 16 | 8 |
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 | 64 | 32 |
| Independent | 66 | 33 |
| Republican | 70 | 35 |
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 | 92 | 46.0 |
| Donald Trump | 63 | 31.5 |
| I did not vote | 37 | 18.5 |
| Third-party candidate | 8 | 4.0 |
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 |
|---|---|---|
| Joe Biden | 74 | 37.0 |
| Donald Trump | 70 | 35.0 |
| I will not vote | 24 | 12.0 |
| Robert F. Kennedy Jr. | 18 | 9.0 |
| Other | 10 | 5.0 |
| Jill Stein | 3 | 1.5 |
| Cornel West | 1 | 0.5 |
Measures
Manipulation check: Class
The policy proposal emphasizes the benefit for working class individuals seeking homeownership
df_cbzs_elg %>%
ggplot(aes(x = check_class)) +
geom_histogram(fill = "lightblue",
color = "black",
binwidth = 1) +
scale_x_continuous(limits = c(0,8),
breaks = seq(1,7,1)) +
geom_vline(xintercept = mean(df_cbzs_elg$check_class,na.rm = T),
color = "black",
linetype = "dashed",
size = 1.1) +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
axis.ticks = element_blank(),
axis.line = element_line(color = "grey66"),
axis.text.y = element_text(color = "black"),
axis.text.x = element_text(color = "black",
face = "bold"))
Manipulation check: Zero-Sum
This policy proposal emphasizes that one group has gained at the expense of another group
df_cbzs_elg %>%
ggplot(aes(x = check_zs)) +
geom_histogram(fill = "lightblue",
color = "black",
binwidth = 1) +
scale_x_continuous(limits = c(0,8),
breaks = seq(1,7,1)) +
geom_vline(xintercept = mean(df_cbzs_elg$check_zs,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"))
Support
To what extent do you oppose or support this policy?
df_cbzs_elg %>%
ggplot(aes(x = support)) +
geom_histogram(fill = "lightblue",
color = "black",
binwidth = 1) +
scale_x_continuous(limits = c(0,8),
breaks = seq(1,7,1)) +
geom_vline(xintercept = mean(df_cbzs_elg$support,na.rm = T),
color = "black",
linetype = "dashed",
size = 1.1) +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
axis.ticks = element_blank(),
axis.line = element_line(color = "grey66"),
axis.text.y = element_text(color = "black"),
axis.text.x = element_text(color = "black",
face = "bold"))
Class-based Zero-Sum Beliefs
- If the upper class becomes richer, this comes at the expense of the
working class
- If the upper class makes more money, then the working class makes
less money
- If the upper class does better economically, this does NOT come at
the expense of the working class [R]
alpha = 0.91
df_cbzs_elg %>%
ggplot(aes(x = zs_class)) +
geom_density(fill = "lightblue",
color = "black") +
scale_x_continuous(breaks = seq(1,7,1),
limits = c(1,7)) +
ylab("density") +
geom_vline(xintercept = mean(df_cbzs_elg$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"))
Analysis
DV: Class manipulation check
Descriptives
df_cbzs_elg %>%
group_by(cond_class,cond_zs) %>%
summarise(mean = mean(check_class,na.rm = T),
sd = sd(check_class,na.rm = T)) %>%
ungroup() %>%
kbl() %>%
kable_styling(bootstrap_options = "hover",
full_width = F,
position = "left")## `summarise()` has grouped output by 'cond_class'. You can override using the
## `.groups` argument.| cond_class | cond_zs | mean | sd |
|---|---|---|---|
| 0 | 0 | 5.698113 | 1.1021815 |
| 0 | 1 | 5.446809 | 1.2821265 |
| 1 | 0 | 6.127660 | 0.8499878 |
| 1 | 1 | 5.679245 | 1.4381289 |
Stats
| Effect | DFn | DFd | F | p | p<.05 | ges |
|---|---|---|---|---|---|---|
| cond_class | 1 | 196 | 3.827 | 0.052 | 0.019 | |
| cond_zs | 1 | 196 | 4.275 | 0.040 |
|
0.021 |
| cond_class:cond_zs | 1 | 196 | 0.339 | 0.561 | 0.002 |
Plot
This is very weak.
DV: Zero-Sum manipulation check
Descriptives
df_cbzs_elg %>%
group_by(cond_class,cond_zs) %>%
summarise(mean = mean(check_zs,na.rm = T),
sd = sd(check_zs,na.rm = T)) %>%
ungroup() %>%
kbl() %>%
kable_styling(bootstrap_options = "hover",
full_width = F,
position = "left")## `summarise()` has grouped output by 'cond_class'. You can override using the
## `.groups` argument.| cond_class | cond_zs | mean | sd |
|---|---|---|---|
| 0 | 0 | 3.433962 | 1.562904 |
| 0 | 1 | 4.893617 | 1.644940 |
| 1 | 0 | 3.276596 | 1.814221 |
| 1 | 1 | 5.358491 | 1.630176 |
Stats
| Effect | DFn | DFd | F | p | p<.05 | ges |
|---|---|---|---|---|---|---|
| cond_class | 1 | 196 | 0.427 | 0.514 | 0.002 | |
| cond_zs | 1 | 196 | 56.588 | 0.000 |
|
0.224 |
| cond_class:cond_zs | 1 | 196 | 1.747 | 0.188 | 0.009 |
Plot
Much better.
DV: Support
Descriptives
df_cbzs_elg %>%
group_by(cond_class,cond_zs) %>%
summarise(mean = mean(support,na.rm = T),
sd = sd(support,na.rm = T)) %>%
ungroup() %>%
kbl() %>%
kable_styling(bootstrap_options = "hover",
full_width = F,
position = "left")## `summarise()` has grouped output by 'cond_class'. You can override using the
## `.groups` argument.| cond_class | cond_zs | mean | sd |
|---|---|---|---|
| 0 | 0 | 5.415094 | 1.167409 |
| 0 | 1 | 4.808511 | 1.676413 |
| 1 | 0 | 5.276596 | 1.715684 |
| 1 | 1 | 4.981132 | 1.715210 |
Stats
| Effect | DFn | DFd | F | p | p<.05 | ges |
|---|---|---|---|---|---|---|
| cond_class | 1 | 196 | 0.006 | 0.939 | 2.97e-05 | |
| cond_zs | 1 | 196 | 4.066 | 0.045 |
|
2.00e-02 |
| cond_class:cond_zs | 1 | 196 | 0.484 | 0.488 | 2.00e-03 |
Plot
Not great. Could be an artifact of the manipulation checks, though. Especially because it emphasizes that one group loses. But still, looks like the zero-sum conditions are under-performing, and that’s not what we’d want to see.
DV: Class ZSB’s
Descriptives
df_cbzs_elg %>%
group_by(cond_class,cond_zs) %>%
summarise(mean = mean(zs_class,na.rm = T),
sd = sd(zs_class,na.rm = T)) %>%
ungroup() %>%
kbl() %>%
kable_styling(bootstrap_options = "hover",
full_width = F,
position = "left")## `summarise()` has grouped output by 'cond_class'. You can override using the
## `.groups` argument.| cond_class | cond_zs | mean | sd |
|---|---|---|---|
| 0 | 0 | 4.138365 | 1.528132 |
| 0 | 1 | 4.730497 | 1.509452 |
| 1 | 0 | 4.446809 | 1.743555 |
| 1 | 1 | 4.389937 | 1.602263 |
Stats
| Effect | DFn | DFd | F | p | p<.05 | ges |
|---|---|---|---|---|---|---|
| cond_class | 1 | 196 | 0.005 | 0.943 | 2.57e-05 | |
| cond_zs | 1 | 196 | 1.400 | 0.238 | 7.00e-03 | |
| cond_class:cond_zs | 1 | 196 | 2.058 | 0.153 | 1.00e-02 |
Plot
Extra analyses
Ideology moderator
Is ZSB framing lowering support for all participants, across poli ideological spectrum?
Descriptives
df_cbzs_elg %>%
mutate(is_conservative = ifelse(ideo_con > 4,1,0),
is_liberal = ifelse(ideo_lib > 4,1,0)) %>%
group_by(is_conservative,cond_class,cond_zs) %>%
summarise(mean = mean(support,na.rm = T),
sd = sd(support,na.rm = T)) %>%
ungroup() %>%
filter(!is.na(is_conservative)) %>%
kbl() %>%
kable_styling(bootstrap_options = "hover",
full_width = F,
position = "left")## `summarise()` has grouped output by 'is_conservative', 'cond_class'. You can
## override using the `.groups` argument.| is_conservative | cond_class | cond_zs | mean | sd |
|---|---|---|---|---|
| 0 | 0 | 0 | 5.535714 | 1.137969 |
| 0 | 0 | 1 | 5.037037 | 1.531251 |
| 0 | 1 | 0 | 5.758621 | 1.057462 |
| 0 | 1 | 1 | 5.281250 | 1.611138 |
| 1 | 0 | 0 | 5.428571 | 1.247855 |
| 1 | 0 | 1 | 4.133333 | 1.922300 |
| 1 | 1 | 0 | 4.285714 | 2.431478 |
| 1 | 1 | 1 | 4.400000 | 1.759186 |
Plot
Class ZSB’s as control
| Predictor | \(b\) | 95% CI | \(t\) | \(\mathit{df}\) | \(p\) |
|---|---|---|---|---|---|
| Intercept | 4.38 | [3.69, 5.08] | 12.41 | 195 | < .001 |
| Cond class | -0.22 | [-0.82, 0.39] | -0.70 | 195 | .484 |
| Cond zs | -0.75 | [-1.36, -0.14] | -2.44 | 195 | .016 |
| Zs class | 0.25 | [0.11, 0.38] | 3.65 | 195 | < .001 |
| Cond class \(\times\) Cond zs | 0.47 | [-0.39, 1.33] | 1.08 | 195 | .279 |
SES as control
| Predictor | \(b\) | 95% CI | \(t\) | \(\mathit{df}\) | \(p\) |
|---|---|---|---|---|---|
| Intercept | 6.28 | [5.48, 7.08] | 15.47 | 194 | < .001 |
| Cond class | -0.10 | [-0.71, 0.51] | -0.33 | 194 | .739 |
| Cond zs | -0.62 | [-1.23, -0.02] | -2.03 | 194 | .044 |
| As numericses | -0.32 | [-0.57, -0.07] | -2.49 | 194 | .014 |
| Cond class \(\times\) Cond zs | 0.29 | [-0.57, 1.16] | 0.67 | 194 | .501 |
Income as control
| Predictor | \(b\) | 95% CI | \(t\) | \(\mathit{df}\) | \(p\) |
|---|---|---|---|---|---|
| Intercept | 5.80 | [5.20, 6.40] | 19.15 | 191 | < .001 |
| Cond class | -0.17 | [-0.80, 0.45] | -0.54 | 191 | .588 |
| Cond zs | -0.59 | [-1.22, 0.03] | -1.86 | 191 | .064 |
| As numericincome | -0.08 | [-0.16, 0.01] | -1.78 | 191 | .077 |
| Cond class \(\times\) Cond zs | 0.28 | [-0.60, 1.16] | 0.62 | 191 | .537 |
By Party ID
hmm independents don’t like the zs conditions
By voting intentions
