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	11	5.47
Black or African American	15	7.46
Hispanic, Latino, or Spanish origin	9	4.48
White	146	72.64
multiracial	20	9.95

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	104	51.74
other	2	1.00
woman	95	47.26

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
38.63	12.93

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
GED	58	28.86
2yearColl	21	10.45
4yearColl	90	44.78
MA	24	11.94
PHD	7	3.48
NA	1	0.50

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	23	11.44
Lower Middle Class	53	26.37
Middle Class	95	47.26
Upper Middle Class	27	13.43
Upper Class	3	1.49

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	146	72.64
No	35	17.41
Not sure	20	9.95

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	73	36.32
Independent	58	28.86
Republican	70	34.83

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	88	43.78
Donald Trump	71	35.32
I did not vote	32	15.92
Third-party candidate	10	4.98

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	72	35.82
Joe Biden	72	35.82
I will not vote	26	12.94
Robert F. Kennedy Jr.	18	8.96
Other	7	3.48
Jill Stein	5	2.49
Cornel West	1	0.50

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 suggests that the benefits provided to one group come at the expense of another

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 = 0.94

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"))

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	5.056604	1.524355
0	1	5.340426	1.108791
1	0	5.938776	1.106951
1	1	6.250000	0.882843

Stats

Effect	DFn	DFd	F	p	ges
cond_class	1	197	28.726	2.00e-07	1.27e-01
cond_zs	1	197	3.168	7.70e-02	1.60e-02
cond_class:cond_zs	1	197	0.007	9.35e-01	3.41e-05

Cool.

Plot

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	4.377358	1.559418
0	1	4.276596	1.651115
1	0	3.551020	1.720722
1	1	3.326923	1.641629

uh oh.

Stats

Effect	DFn	DFd	F	p	ges
cond_class	1	197	14.656	0.000173	0.069000
cond_zs	1	197	0.490	0.485000	0.002000
cond_class:cond_zs	1	197	0.071	0.791000	0.000359

That’s no good. Looking at the item, I think we weren’t clear. More over email.

Plot

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	4.226415	1.782840
0	1	4.446809	1.665618
1	0	5.346939	1.283729
1	1	5.653846	1.480390

Stats

Effect	DFn	DFd	F	p	ges
cond_class	1	197	27.651	4.00e-07	0.123000
cond_zs	1	197	1.419	2.35e-01	0.007000
cond_class:cond_zs	1	197	0.038	8.45e-01	0.000194

oh bummer. but big caveat that the it came after the (not-so-great) manipulation checks.

Plot

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	4.207547	1.691136
0	1	4.503546	1.782730
1	0	4.517007	1.530646
1	1	4.448718	1.852849

Stats

Effect	DFn	DFd	F	p	ges
cond_class	1	197	0.275	0.601	0.001
cond_zs	1	197	0.220	0.640	0.001
cond_class:cond_zs	1	197	0.563	0.454	0.003

Plot

no difference. ok.

Class-Based Zero-Sum Beliefs

April 2024: Analysis

Background

Demographics

Race

Gender

Age

Education

Subjective SES

Working Class

Income

Politics

Ideology

Party ID

Vote in 2020

Vote in 2024

Measures

Manipulation check: Class

Manipulation check: Zero-Sum

Support

Class-based Zero-Sum Beliefs

Analysis

DV: Class manipulation check

Descriptives

Stats

Plot

DV: Zero-Sum manipulation check

Descriptives

Stats

Plot

DV: Support

Descriptives

Stats

Plot

DV: Class ZSB’s

Descriptives

Stats

Plot