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replication_code.R
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replication_code.R
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################################################################################
## Project: Personal 2020 ##
## Title: Persistence and Change in Attitudes toward N.K. and Unification ##
## Updates: Sep. 12th 2020 ##
## Authors: Sanghoon Park (Univ. of South Carolina) ##
## Jaeyoung Hur (Yonsei Univ.) ##
################################################################################
## Import necessary packages
library(ggplot2)
library(ezpickr)
library(patchwork)
library(ggeffects)
library(MASS)
library(lmtest)
library(stargazer)
library(knitr)
library(kableExtra)
library(tidyverse)
load("Analysis_data/replication.RData")
## See the data for replication
glimpse(subset) # Survey
glimpse(approval) # Approval rates
glimpse(gdp) # GDPpc trends
## Figure 1
grid_gdp <- gdp %>%
tidyr::gather(variable, value, -Quarters) %>%
mutate(
variable = case_when(
variable == "Real_GDP" ~ "Nominal GDP",
variable == "Real_GDP_growth" ~ "Real GDP Growth",
T ~ NA_character_)
)
grid_gdp %>%
ggplot(aes(x=Quarters, y = value)) + geom_col() +
facet_wrap(variable~., scales = "free", ncol = 1) +
labs(x = "Year-Quarter", y = "",
caption = "Source: Bank of Korea <National Income>
Currency: Billion (Won), YoY %") +
scale_y_continuous(labels = scales::comma) + theme_bw() +
theme(strip.text = element_text(size = 6),
axis.text.x = element_text(size = 5),
axis.text.y = element_text(size = 7),
axis.title.x = element_text(size = 8),
plot.caption = element_text(size = 7))
## Figure 2
average <- approval %>% summarize()
p_approval <- approval %>% drop_na() %>% mutate(
media = case_when(
media == "A" ~ "Gallup Korea",
media == "B" ~ "Realmeter",
media == "Average" ~ "Average"
) %>% parse_factor(., levels = c("Gallup Korea", "Realmeter", "Average"),
include_na = F, ordered = T)
)
p_approval %>% ggplot(aes(x = period,
y = positive,
group = media,
color = media)) +
labs(x = "Year - Month", y = "President Approval (%)",
caption =
"Source: Gallup Korea. 2019. Gallup Korea Daily Opinion:\nMonthly Aggregate Statistics & Realmeter. Political Archive.\nThe monthly data of Realmeter is the averages of weekly data.") +
scale_color_manual(values =futurevisions::futurevisions("mars")) +
scale_x_discrete(guide = guide_axis(n.dodge = 2)) +
geom_point() + geom_vline(xintercept = 11.33,
color = "black", linetype = 2) +
geom_curve(
aes(x = 11.33, y = 82, xend = 10, yend = 84), color = "black",
arrow = arrow(length = unit(0.03, "npc"))
) +
geom_text(aes(x=8.5, y=84, label="4.27 Panmunjom\nDeclaration"),
color="black",
size=2) +
geom_line() + theme_bw() +
theme(axis.title.x = element_text(size = 8),
axis.text.x = element_text(size = 5),
axis.title.y = element_text(size = 8),
axis.text.y = element_text(size = 6),
plot.caption = element_text(size = 7),
legend.title = element_blank(),
legend.text = element_text(size = 8),
legend.position = c(0.5, 0.7),
legend.background = element_blank() )
subset <- subset %>% mutate(
Q1 = case_when(
Q1==1 ~ "Very Dissatisfied",
Q1==2 ~ "Dissatisfied",
Q1==3 ~ "Neutral",
Q1==4 ~ "Satisfied",
Q1==5 ~ "Very Satisfied",
T ~ NA_character_
) %>% parse_factor(., levels = c("Very Dissatisfied", "Dissatisfied",
"Neutral", "Satisfied", "Very Satisfied"),
include_na = F, ordered = T),
Q2 = case_when(
Q2==1 ~ "Hostile",
Q2==2 ~ "Vigilant",
Q2==3 ~ "Competitive",
Q2==4 ~ "Cooperative",
Q2==5 ~ "Supportive",
T ~ NA_character_
) %>% parse_factor(., levels = c("Hostile", "Vigilant", "Competitive",
"Cooperative", "Supportive"),
include_na = F, ordered = T)
)
## Figure 3
p1 <- subset %>%
ggplot(aes(x = Q1, fill = Q1)) +
geom_bar(aes(y = (..count..)/sum(..count..)),
position=position_dodge(), show.legend = F) +
geom_text(aes(x = Q1,
label = scales::percent((..count..)/sum(..count..)),
y= (..count..)/sum(..count..)), stat= "count", vjust = 0.5,
position = position_dodge(1), size = 2) +
scale_x_discrete(guide = guide_axis(n.dodge = 2)) +
scale_fill_grey(start = 0.8, end = 0.4) +
scale_y_continuous(labels = scales::percent) +
labs(subtitle = "Q. What do you think of Jong-Eun Kim’s
North Korean regime and leadership group?",
y = "Percent (%)", x = "") +
theme_bw() +
theme(plot.subtitle = element_text(size = 7.5),
axis.text.x = element_text(hjust = 0.5, size = 5),
axis.title.x = element_blank(),
axis.title.y = element_text(size = 7.5),
axis.text.y = element_text(size = 6),
legend.title = element_blank())
p2 <- subset %>%
ggplot(aes(x = Q2, fill = Q2)) +
geom_bar(aes(y = (..count..)/sum(..count..)),
position=position_dodge(), show.legend = F) +
geom_text(aes(x = Q2,
label = scales::percent((..count..)/sum(..count..)),
y= (..count..)/sum(..count..)),
stat= "count", vjust = 0.5, size = 2,
position = position_dodge(1)) +
scale_x_discrete(guide = guide_axis(n.dodge = 2)) +
scale_fill_grey(start = 0.8, end = 0.4) +
scale_y_continuous(labels = scales::percent) +
labs(subtitle = "Q. What do you think of
North Korea is for South Korea?",
y = "Percent (%)", x = "") +
theme_bw() +
theme(plot.subtitle = element_text(size = 7.5),
axis.text.x = element_text(hjust = 0.5, size = 5),
axis.title.x = element_blank(),
axis.title.y = element_text(size = 7.5),
axis.text.y = element_text(size = 6),
legend.title = element_blank())
p1 + p2 + plot_layout(ncol = 1)
## Figure 4
subset <- subset %>%
mutate(
Q4_re = 5 - as.numeric(Q4),
Q4_re = case_when(
Q4_re==1 ~ "Not\npreferred",
Q4_re==2 ~ "Status quo",
Q4_re==3 ~ "Preferred\nwithout\nBurdens",
Q4_re==4 ~ "Preferred",
T ~ NA_character_
) %>% parse_factor(., levels = c("Not\npreferred","Status quo",
"Preferred\nwithout\nBurdens", "Preferred"),
include_na = F, ordered = T),
Q4_label = case_when(
Q4_re=="Not\npreferred" ~ "Not\npreferred\n(n = 89)",
Q4_re=="Status quo" ~ "Status quo\n(n = 252)",
Q4_re=="preferred\nwithout\nBurdens" ~ "preferred without\nBurdens\n(n = 455)",
Q4_re=="preferred" ~ "preferred\n(n = 204)",
T ~ NA_character_
) %>% parse_factor(., levels = c("Not\npreferred\n(n = 89)",
"Status quo\n(n = 252)",
"preferred without\nBurdens\n(n = 455)",
"preferred\n(n = 204)"),
include_na = F, ordered = T),
Q3 = case_when(
Q3==1 ~ "Never\nInterested",
Q3==2 ~ "Not\nInterested",
Q3==3 ~ "Interested",
Q3==4 ~ "Very\nInterested",
T ~ NA_character_
) %>% parse_factor(., levels = c("Never\nInterested", "Not\nInterested",
"Interested","Very\nInterested"),
include_na = F, ordered = T),
Q9 = case_when(
Q9==5 ~ "Never",
Q9==1 ~ "Less\nthan\n1%",
Q9==2 ~ "Less\nthan\n1~5%",
Q9==3 ~ "Less\nthan\n5~10%",
Q9==4 ~ "More\nthan\n10%",
T ~ NA_character_) %>%
parse_factor(., levels = c("Never", "Less\nthan\n1%", "Less\nthan\n1~5%",
"Less\nthan\n5~10%", "More\nthan\n10%"),
include_na = F, ordered = T),
Q6_fa = case_when(
Q6==1 ~ "Social\nDisorder",
Q6==2 ~ "Immigrants from\nthe North",
Q6==3 ~ "The Burden of\nUnification Costs",
Q6==4 ~ "Political/Economic\nDisorder",
Q6==5 ~ "Confusion of\nInternational Relations"
) %>% parse_factor(., levels = c("Social\nDisorder","Immigrants from\nthe North",
"The Burden of\nUnification Costs",
"Political/Economic\nDisorder",
"Confusion of\nInternational Relations"))
)
subset %>% ggplot(aes(x = Q4_re,
y = prop.table(stat(count)),
fill = Q4_re,
label = scales::percent(prop.table(stat(count))))) +
geom_bar(aes(y = (..count..)/sum(..count..)), show.legend = F) +
# facet_wrap(~ Q4_label, ncol = 2) +
geom_text(stat = 'count',
position = position_dodge(.1),
vjust = 0.5, size = 2)+
scale_fill_grey(start = 0.8, end = 0.4) +
scale_y_continuous(labels = scales::percent) +
labs(subtitle = "Q. What do you think about unification?",
y = "Percent (%)", x = "") +
theme_bw() +
theme(plot.subtitle = element_text(size = 7.5),
axis.text.x = element_text(hjust = 0.5, size = 5),
axis.title.x = element_blank(),
axis.title.y = element_text(size = 7.5),
axis.text.y = element_text(size = 6),
legend.title = element_blank())
## Figure 5
subset %>% ggplot(aes(x = Q6_fa,
y = prop.table(stat(count)),
fill = Q6_fa,
label = scales::percent(prop.table(stat(count))))) +
geom_bar(aes(y = (..count..)/sum(..count..)), show.legend = F) +
geom_text(stat = 'count',
position = position_dodge(.1),
vjust = 0.5, size = 2)+
scale_fill_grey(start = 0.8, end = 0.4) +
scale_x_discrete(guide = guide_axis(n.dodge = 2)) +
scale_y_continuous(labels = scales::percent) +
labs(subtitle = "Q. What do you think is most worrying about
the unification process?",
y = "Percent (%)", x = "") +
theme_bw() +
theme(plot.subtitle = element_text(size = 7.5),
axis.text.x = element_text(hjust = 0.5, size = 5),
axis.title.x = element_blank(),
axis.title.y = element_text(size = 7.5),
axis.text.y = element_text(size = 6),
legend.title = element_blank())
## Figure 6
subset %>% dplyr::filter(Q6_fa == "The Burden of\nUnification Costs") %>%
ggplot(aes(x = Q9,
y = prop.table(stat(count)),
fill = Q9,
label = scales::percent(prop.table(stat(count))))) +
geom_bar(aes(y = (..count..)/sum(..count..)), show.legend = F) +
geom_text(stat = 'count',
position = position_dodge(.9),
vjust = 1, size = 2)+
scale_fill_grey(start = 0.8, end = 0.4) +
scale_y_continuous(labels = scales::percent) +
labs(subtitle = "Preferred tax level to cover unification costs
when respondents think the burden of
unification costs are the biggest concern",
y = "Percent (%)", x = "") +
theme_bw() +
theme(plot.subtitle = element_text(size = 7.5),
axis.text.x = element_text(hjust = 0.5, size = 5),
axis.title.x = element_blank(),
axis.title.y = element_text(size = 7.5),
axis.text.y = element_text(size = 6),
legend.title = element_blank())
## Figure 7
Bivariate <- subset %>% dplyr::select(Q1, Q2, Q4_re, Q5, age) %>%
mutate(
age = case_when(
age == 1L ~ "20s",
age == 2L ~ "30s",
age == 3L ~ "40s",
age == 4L ~ "50s",
age == 5L ~ "over 60s",
T ~ NA_character_) %>%
parse_factor(., levels = c("20s", "30s", "40s", "50s", "over 60s"),
include_na = F, ordered = T),
Q5 = case_when(
Q5 == 1L ~ "Less than\n5 years",
Q5 == 2L ~ "6-10 years",
Q5 == 3L ~ "11-20 years",
Q5 == 4L ~ "21-30 years",
Q5 == 5L ~ "More than\n30 years",
Q5 == 6L ~ "Never"
) %>% parse_factor(., levels = c("Less than\n5 years", "6-10 years",
"11-20 years", "21-30 years",
"More than\n30 years", "Never"),
include_na = F, ordered = T)
)
Bivariate_sum <- Bivariate %>% group_by(age) %>%
summarise(
Q1.sum = mean(as.numeric(Q1), na.rm = T),
Q1.sd = sd(as.numeric(Q1), na.rm = T),
Q2.sum = mean(as.numeric(Q2), na.rm = T),
Q2.sd = sd(as.numeric(Q2), na.rm = T),
Q4.sum = mean(as.numeric(Q4_re), na.rm = T),
Q4.sd = sd(as.numeric(Q4_re), na.rm = T)
)
plot5a <- Bivariate_sum %>%
ggplot(aes(x = age, y = Q1.sum)) +
geom_col() +
geom_errorbar(aes(ymin = Q1.sum - Q1.sd, ymax = Q1.sum + Q1.sd), width=0.2) +
scale_y_continuous(limits = c(0, 5, by = 1)) +
geom_hline(yintercept = 2.73, color = "red", linetype = 2) +
scale_fill_grey(start = 0.8, end = 0.4) +
labs(subtitle = "NK ledership",
y = "Mean value", x = "") +
theme_bw() +
theme(plot.subtitle = element_text(size = 7.5),
axis.text.x = element_text(hjust = 0.5, size = 5),
axis.title.x = element_blank(),
axis.title.y = element_text(size = 7.5),
axis.text.y = element_text(size = 6),
legend.title = element_blank())
plot5b <- Bivariate_sum %>%
ggplot(aes(x = age, y = Q2.sum)) +
geom_col() +
geom_errorbar(aes(ymin = Q2.sum - Q2.sd, ymax = Q2.sum + Q2.sd), width=0.2) +
scale_y_continuous(limits = c(0, 5, by = 1)) +
geom_hline(yintercept = 3.03, color = "red", linetype = 2) +
scale_fill_grey(start = 0.8, end = 0.4) +
labs(subtitle = "NK general",
y = "Mean value", x = "Generations") +
theme_bw() +
theme(plot.subtitle = element_text(size = 7.5),
axis.text.x = element_text(hjust = 0.5, size = 5),
axis.title.x = element_text(size = 7.5),
axis.title.y = element_text(size = 7.5),
axis.text.y = element_text(size = 6),
legend.title = element_blank())
plot5a + plot5b + patchwork::plot_layout(ncol = 1)
## Figure 8
Bivaraiate_sum2a <- as.data.frame(table(Bivariate$Q4_re, Bivariate$age))
Bivaraiate_sum2b <- as.data.frame(table(Bivariate$Q5, Bivariate$age))
Bivaraiate_sum2a <- Bivaraiate_sum2a %>% rename(
Q4_re = Var1,
Generation = Var2,
Q4.freq = Freq
)
Bivaraiate_sum2b <- Bivaraiate_sum2b %>% rename(
Q5 = Var1,
Generation = Var2,
Q5.freq = Freq
)
Bivaraiate_sum2a <- Bivaraiate_sum2a %>% mutate(
Q4.Prop = case_when(
Q4_re == "Not\npreferred" & Generation == "20s" ~ 26/162,
Q4_re == "Status quo" & Generation == "20s" ~ 71/162,
Q4_re == "Preferred\nwithout\nBurdens" & Generation == "20s" ~ 45/162,
Q4_re == "Preferred" & Generation == "20s" ~ 20/162,
Q4_re == "Not\npreferred" & Generation == "30s" ~ 25/181,
Q4_re == "Status quo" & Generation == "30s" ~ 97/181,
Q4_re == "Preferred\nwithout\nBurdens" & Generation == "30s" ~ 42/181,
Q4_re == "Preferred" & Generation == "30s" ~ 17/181,
Q4_re == "Not\npreferred" & Generation == "40s" ~ 48/208,
Q4_re == "Status quo" & Generation == "40s" ~ 83/208,
Q4_re == "Preferred\nwithout\nBurdens" & Generation == "40s" ~ 63/208,
Q4_re == "Preferred" & Generation == "40s" ~ 14/208,
Q4_re == "Not\npreferred" & Generation == "50s" ~ 52/202,
Q4_re == "Status quo" & Generation == "50s" ~ 92/202,
Q4_re == "Preferred\nwithout\nBurdens" & Generation == "50s" ~ 42/202,
Q4_re == "Preferred" & Generation == "50s" ~ 16/202,
Q4_re == "Not\npreferred" & Generation == "over 60s" ~ 53/247,
Q4_re == "Status quo" & Generation == "over 60s" ~ 112/247,
Q4_re == "Preferred\nwithout\nBurdens" & Generation == "over 60s" ~ 60/247,
Q4_re == "Preferred" & Generation == "over 60s" ~ 22/247
),
Q4.Prop.round = round(Q4.Prop, 2),
`Attitudes toward unification` = case_when(
Q4_re == "Not\npreferred" ~ "Not preferred",
Q4_re == "Status quo" ~ "Status quo",
Q4_re == "Preferred\nwithout\nBurdens" ~ "Preferred without\nBurdens",
Q4_re == "Preferred" ~ "Preferred"
)
)
Bivaraiate_sum2b <- Bivaraiate_sum2b %>% mutate(
`Prospect of unification` = case_when(
Q5 == "Less than\n5 years" & Generation == "20s" ~ 13/162,
Q5 == "6-10 years" & Generation == "20s" ~ 40/162,
Q5 == "11-20 years" & Generation == "20s" ~ 47/162,
Q5 == "21-30 years" & Generation == "20s" ~ 25/162,
Q5 == "More than\n30 years" & Generation == "20s" ~ 19/162,
Q5 == "Never" & Generation == "20s" ~ 18/162,
Q5 == "Less than\n5 years" & Generation == "30s" ~ 7/181,
Q5 == "6-10 years" & Generation == "30s" ~ 45/181,
Q5 == "11-20 years" & Generation == "30s" ~ 63/181,
Q5 == "21-30 years" & Generation == "30s" ~ 27/181,
Q5 == "More than\n30 years" & Generation == "30s" ~ 16/181,
Q5 == "Never" & Generation == "30s" ~ 23/181,
Q5 == "Less than\n5 years" & Generation == "40s" ~ 9/208,
Q5 == "6-10 years" & Generation == "40s" ~ 57/208,
Q5 == "11-20 years" & Generation == "40s" ~ 85/208,
Q5 == "21-30 years" & Generation == "40s" ~ 22/208,
Q5 == "More than\n30 years" & Generation == "40s" ~ 18/162,
Q5 == "Never" & Generation == "40s" ~ 17/162,
Q5 == "Less than\n5 years" & Generation == "50s" ~ 21/202,
Q5 == "6-10 years" & Generation == "50s" ~ 62/202,
Q5 == "11-20 years" & Generation == "50s" ~ 61/202,
Q5 == "21-30 years" & Generation == "50s" ~ 21/202,
Q5 == "More than\n30 years" & Generation == "50s" ~ 11/162,
Q5 == "Never" & Generation == "50s" ~ 26/162,
Q5 == "Less than\n5 years" & Generation == "over 60s" ~ 23/247,
Q5 == "6-10 years" & Generation == "over 60s" ~ 63/247,
Q5 == "11-20 years" & Generation == "over 60s" ~ 61/247,
Q5 == "21-30 years" & Generation == "over 60s" ~ 19/247,
Q5 == "More than\n30 years" & Generation == "over 60s" ~ 12/162,
Q5 == "Never" & Generation == "over 60s" ~ 69/162
),
Q5.Prop.round = round(`Prospect of unification`, 2),
)
plot6a <- Bivaraiate_sum2a %>%
mutate(pos = cumsum(Q4.Prop.round) - (0.5 * Q4.Prop.round)) %>%
ggplot(aes(fill=`Attitudes toward unification`, x = Generation, y = Q4.Prop.round)) +
geom_bar(position="fill", stat="identity", width = .7, colour="black", lwd=0.1) +
geom_text(aes(label=ifelse(Q4.Prop.round != Q4.Prop.round,
paste0(sprintf("%.0f", Q4.Prop.round*100),"%", ""), y = pos)),
position=position_fill(vjust=0.5), colour="black", size = 3) +
scale_y_continuous(labels = scales::percent_format()) +
labs(subtitle = "Attitude toward unification",
fill = "Attitude toward\nunification",
x = "Generations", y = "") +
scale_fill_manual(values = futurevisions::futurevisions("cancri")) +
theme_bw() + theme(
legend.title = element_text(size = 7),
legend.position = "bottom",
plot.subtitle = element_text(size = 7.5),
axis.text.x = element_text(hjust = 0.5, size = 5),
axis.title.x = element_blank(),
axis.title.y = element_text(size = 7.5),
axis.text.y = element_text(size = 6),
legend.text = element_text(size = 5.5)) +
guides(fill=guide_legend(ncol=2))
plot6b <- Bivaraiate_sum2b %>% group_by(Q5,Generation) %>%
mutate(pos = cumsum(Q5.Prop.round) - (0.5 * Q5.Prop.round)) %>%
ggplot(aes(fill=Q5, x = Generation, y = Q5.Prop.round)) +
geom_bar(position="fill", stat="identity", width = .7, colour="black", lwd=0.1) +
geom_text(aes(label=ifelse(Q5.Prop.round != Q5.Prop.round,
paste0(sprintf("%.0f", Q5.Prop.round*100),"%", ""), y = pos)),
position=position_fill(vjust=0.5), colour="black", size = 3) +
scale_y_continuous(labels = scales::percent_format()) +
labs(subtitle = "Prospect of unification",
fill = "Prospect of\nunification",
x = "Generations", y = "Percent (%)") +
scale_fill_manual(values = futurevisions::futurevisions("cancri")) +
theme_bw() + theme(
legend.position = "bottom",
legend.title = element_text(size = 7),
plot.subtitle = element_text(size = 7.5),
axis.text.x = element_text(hjust = 0.5, size = 5),
axis.title.x = element_blank(),
axis.title.y = element_text(size = 7.5),
axis.text.y = element_text(size = 6),
legend.text = element_text(size = 5.5)) +
guides(fill=guide_legend(ncol=3))
plot6b + plot6a + patchwork::plot_layout(ncol = 1)
## Table 1: Ordered logistic regression:
## Attitudes toward North Korea after the Panmunjom Declaration
model1 <- polr(as.ordered(Q1) ~ as.factor(age) + Q19 + Honam + PK +
TK + gender + edu + income, data = subset,
method = "logistic", Hess = TRUE)
model1.null <- polr(as.ordered(Q1) ~ 1, data = subset,
method = "logistic", Hess = TRUE)
model1.cut1 <- summary(model1)$coefficients[12, 1] %>%
as.numeric %>% round(., 3)
model1.cut1.se <- summary(model1)$coefficients[12, 2] %>%
as.numeric %>% round(., 3)
model1.cut2 <- summary(model1)$coefficients[13, 1] %>%
as.numeric %>% round(., 3)
model1.cut2.se <- summary(model1)$coefficients[13, 2] %>%
as.numeric %>% round(., 3)
model1.cut3 <- summary(model1)$coefficients[14, 1] %>%
as.numeric %>% round(., 3)
model1.cut3.se <- summary(model1)$coefficients[14, 2] %>%
as.numeric %>% round(., 3)
model1.cut4 <- summary(model1)$coefficients[15, 1] %>%
as.numeric %>% round(., 3)
model1.cut4.se <- summary(model1)$coefficients[15, 2] %>%
as.numeric %>% round(., 3)
lr1 <- round(model1.null$deviance - model1$deviance, 3)
model2 <- polr(as.ordered(Q2) ~ as.factor(age) + Q19 + Honam + PK +
TK + gender + edu + income, data = subset,
method = "logistic", Hess = TRUE)
model2.null <- polr(as.ordered(Q2) ~ 1, data = subset,
method = "logistic", Hess = TRUE)
model2.cut1 <- summary(model2)$coefficients[12, 1] %>%
as.numeric %>% round(., 3)
model2.cut1.se <- summary(model2)$coefficients[12, 2] %>%
as.numeric %>% round(., 3)
model2.cut2 <- summary(model2)$coefficients[13, 1] %>%
as.numeric %>% round(., 3)
model2.cut2.se <- summary(model2)$coefficients[13, 2] %>%
as.numeric %>% round(., 3)
model2.cut3 <- summary(model2)$coefficients[14, 1] %>%
as.numeric %>% round(., 3)
model2.cut3.se <- summary(model2)$coefficients[14, 2] %>%
as.numeric %>% round(., 3)
model2.cut4 <- summary(model2)$coefficients[15, 1] %>%
as.numeric %>% round(., 3)
model2.cut4.se <- summary(model2)$coefficients[15, 2] %>%
as.numeric %>% round(., 3)
lr2 <- round(model2.null$deviance - model2$deviance, 3)
stargazer(model1, model2,
title = "Ordered Logistic Regressions: Attitudes toward North Korea after the Panmunjom Declaration",
label = "tab1",
dep.var.caption = "Attitudes toward North Korea",
dep.var.labels.include = F,
single.row = F,
model.names = T,
model.numbers = T,
column.labels = c("Leadership",
"General"),
covariate.labels = c(
"Generation: 30s", "Generation: 40s", "Generation: 50s",
"Generation: 60+", "Evaluation of the Govt.'s NK policy",
"Region: Honam", "Region: PK", "Region: TK",
"Socio-demografic: Gender","Socio-demografic: Education",
"Socio-demografic: Income"),
header = F,
keep.stat = c("n"),
add.lines = list(c("cut1", model1.cut1, model2.cut1),
c("", paste0("(", model1.cut1.se,")"),
paste0("(",model2.cut1.se,")")),
c("cut2", model1.cut2, model2.cut2),
c("", paste0("(", model1.cut2.se,")"),
paste0("(", model2.cut2.se,")")),
c("cut3", model1.cut3, model2.cut3),
c("", paste0("(", model1.cut3.se,")"),
paste0("(", model2.cut3.se),")"),
c("cut4", model1.cut4, model2.cut4),
c("", paste0("(", model1.cut4.se,")"),
paste0("(", model2.cut4.se),")"),
c("Log-likelihood ratio", lr1, lr2)),
star.cutoffs = c(0.05, 0.01, 0.001),
notes = c("Standard errors in parentheses.",
"PK/TK are abbreviations of the regions:",
"PK: Pusan/Ulsan/Kyeongnam",
"TK:Taegu/Kyeongbuk"),
table.placement = "H",
type = "text",
table.layout = "=lc-tas-n")
## Table1 -> Coefplot
model1.tidy <- broom::tidy(model1)
model1.tidy_conf <-broom::confint_tidy(model1)
model1.tidy <- bind_cols(model1.tidy[1:11,], model1.tidy_conf)
coefplot1 <- model1.tidy %>% mutate(
term = case_when(
term == "as.factor(age)2" ~ "30대",
term == "as.factor(age)3" ~ "40대",
term == "as.factor(age)4" ~ "50대",
term == "as.factor(age)5" ~ "60대\n이상",
term == "Q19" ~ "정부의\n대북정책\n평가",
term == "Honam" ~ "호남",
term == "PK" ~ "부울경",
term == "TK" ~ "대구\n경북",
term == "gender" ~ "성별",
term == "edu" ~ "교육\n수준",
term == "income" ~ "소득\n수준") %>%
parse_factor(., levels = c("30대","40대","50대","60대\n이상",
"정부의\n대북정책\n평가","호남",
"부울경","대구\n경북","성별",
"교육\n수준","소득\n수준")),
sig = ifelse(conf.high >= 0 & conf.low <=0, "Insig", "sig") %>%
parse_factor(., levels = c("Insig", "sig"), ordered = T,
include_na = F)
)
coefplot1 %>%
ggplot(aes(x = term, y = estimate,
color = as.factor(sig))) +
geom_point(size = 3, show.legend = F, shape = 10) +
geom_pointrange(size = 1, show.legend = F,
aes(ymin = conf.low,
ymax = conf.high)) +
labs(y = "계수값값", x = "") +
scale_color_manual(values = wesanderson::wes_palette("Royal1")) +
geom_hline(yintercept = 0, color = "red") +
theme_bw() + theme(
plot.background = element_rect(fill = "transparent", color = NA)
)
ggsave("figure/figure_ko_3.png", bg = "transparent",
width = 8, height = 3.5, dpi = 600)
## Figure 9
## Simulate coefficiets and cutpoints (King, Tomz, and Wittenberg 2000)
set.seed(1234)
simb <- mvrnorm(n = 1000, mu = c(coef(model1), model1$zeta),
Sigma = vcov(model1))
cut <- cbind(-Inf, simb[, 12:15], Inf)
# Create the baseline profile + Calculate the Predicted Probs
# Here, let's assume that the "middle income" means income at the median
x0 <- c(0, 0, 0, 0,
mean(subset$Q19, na.rm = T),
mean(subset$Honam, na.rm = T),
mean(subset$PK, na.rm = T),
mean(subset$TK, na.rm = T),
mean(subset$gender, na.rm = T),
mean(subset$edu, na.rm = T),
mean(subset$income, na.rm = T))
x0b <- simb[, 1:11] %*% x0
pr0 <- matrix(NA, nrow = 1000, ncol = 5)
for (i in 1:5){ # for each value of the dependent variable...
pr0[, i] <- plogis(cut[, i + 1] - x0b) - plogis(cut[, i] - x0b)
}
# Matrix to store the results
out.mat <- matrix(NA, nrow = 5 * 4, ncol = 3)
rownames(out.mat) <- paste0(rep(c("30s", "40s", "50s", "60+"), each = 5),
"-", rep(1:5, times = 3))
colnames(out.mat) <- c("Mean", "Lower", "Upper")
# Then compute the first differences in each covariates
# 20s -> 30s
x1 <- x0
x1[1] <- 1
x1b <- simb[, 1:11] %*% x1
for (i in 1:5){ # for each value of the dependent variable...
pr1 <- plogis(cut[, i + 1] - x1b) - plogis(cut[, i] - x1b)
fdiff <- pr1 - pr0[, i]
out.mat[i, 1] <- mean(fdiff)
out.mat[i, 2:3] <- quantile(fdiff, probs = c(0.025, 0.975))
}#
# 20s -> 40s
x1 <- x0
x1[2] <- 1
x1b <- simb[, 1:11] %*% x1
for (i in 1:5){ # for each value of the dependent variable...
pr1 <- plogis(cut[, i + 1] - x1b) - plogis(cut[, i] - x1b)
fdiff <- pr1 - pr0[, i]
out.mat[i + 5, 1] <- mean(fdiff)
out.mat[i + 5, 2:3] <- quantile(fdiff, probs = c(0.025, 0.975))
}#
# 20s -> 50s
x1 <- x0
x1[3] <- 1
x1b <- simb[, 1:11] %*% x1
for (i in 1:5){ # for each value of the dependent variable...
pr1 <- plogis(cut[, i + 1] - x1b) - plogis(cut[, i] - x1b)
fdiff <- pr1 - pr0[, i]
out.mat[i + 10, 1] <- mean(fdiff)
out.mat[i + 10, 2:3] <- quantile(fdiff, probs = c(0.025, 0.975))
}#
# 20s -> 60s
x1 <- x0
x1[4] <- 1
x1b <- simb[, 1:11] %*% x1
for (i in 1:5){ # for each value of the dependent variable...
pr1 <- plogis(cut[, i + 1] - x1b) - plogis(cut[, i] - x1b)
fdiff <- pr1 - pr0[, i]
out.mat[i + 15, 1] <- mean(fdiff)
out.mat[i + 15, 2:3] <- quantile(fdiff, probs = c(0.025, 0.975))
}#
# Visualize the results
out.mat <- as.data.frame(out.mat)
out.mat$leadership <- rep(1:5, times = 4)
pr.table <- bind_rows(pr.table40 <-
out.mat[1:5,] %>% mutate(
generation = "30s"
),
pr.table40 <-
out.mat[6:10,] %>% mutate(
generation = "40s"
),
pr.table50 <-
out.mat[11:15,] %>% mutate(
generation = "50s"),
pr.table60 <-
out.mat[16:20,] %>% mutate(
generation = "60+"
)) %>%
mutate(
generation = generation %>%
parse_factor(., levels = c("30s", "40s", "50s",
"60+"),
include_na = F, ordered = T),
leadership = case_when(
leadership == 1L ~ "Very\nDissatisfied",
leadership == 2L ~ "Dissatisfied",
leadership == 3L ~ "Neutral",
leadership == 4L ~ "Satisfied",
leadership == 5L ~ "Very\nSatisfied"
) %>% parse_factor(., levels = c("Very\nDissatisfied",
"Dissatisfied",
"Neutral",
"Satisfied",
"Very\nSatisfied"), ordered = T,
include_na = F)
)
pr.table %>% #dplyr::filter(generation %in% c("40s", "50s")) %>%
ggplot(aes(x = leadership, y = Mean,
color = generation,
fill = generation)) +
geom_point(
#position = position_dodge2(0.6),
show.legend = T,
size = 1) +
geom_pointrange(aes(ymin = Lower, ymax = Upper),
# position = position_dodge2(0.6),
show.legend = F, size = 0.3) +
labs(x = "", y = "First Differences in Predicted Probabilities") + #ylim(-0.14, 0.09) +
scale_y_continuous(labels = scales::percent_format()) +
facet_wrap(~generation, ncol = 2) +
geom_hline(aes(yintercept = 0), linetype = "dashed") +
scale_color_manual(values = futurevisions::futurevisions("mars")) +
scale_fill_manual(values = futurevisions::futurevisions("mars")) +
theme_bw() +
theme(
legend.title = element_blank(),
legend.position = "bottom",
plot.subtitle = element_text(size = 7.5),
axis.text.x = element_text(hjust = 0.5, size = 5),
axis.title.x = element_blank(),
axis.title.y = element_text(size = 7.5),
axis.text.y = element_text(size = 6),
legend.text = element_text(size = 6.5),
strip.text = element_text(size = 6))
## Table 2: Ordered logistic regressions:
## Attitudes toward N.K. after the Panmunjom Declaration
table(subset$Q4_re)
table(subset$Q5_re)
subset$Q3 <- as.numeric(subset$Q3)
model3 <- polr(as.ordered(Q4_re) ~ Q3 + Q19 + Honam + PK +
TK + gender + edu + income, data = subset,
method = "logistic", Hess = TRUE)
model3.null <- polr(as.ordered(Q4_re) ~ 1, data = subset,
method = "logistic", Hess = TRUE)
lr3 <- round(model3.null$deviance - model3$deviance, 3)
model4 <- polr(as.ordered(Q4_re) ~ I(as.factor(age)) + Q3 + Q19 + Honam + PK +
TK + gender + edu + income, data = subset,
method = "logistic", Hess = TRUE)
model4.null <- polr(as.ordered(Q4_re) ~ 1, data = subset,
method = "logistic", Hess = TRUE)
lr4 <- round(model4.null$deviance - model4$deviance, 3)
model5 <- polr(as.ordered(Q4_re) ~ I(Q5_re) + Q3 + Q19 + Honam + PK +
TK + gender + edu + income, data = subset,
method = "logistic", Hess = TRUE)
model5.null <- polr(as.ordered(Q4_re) ~ 1, data = subset,
method = "logistic", Hess = TRUE)
lr5 <- round(model5.null$deviance - model5$deviance, 3)
model6 <- polr(as.ordered(Q4_re) ~ I(as.factor(age)) + I(Q5_re) +
I(as.factor(age))*I(Q5_re) +
Q3 + Q19 + Honam + PK +
TK + gender + edu + income, data = subset,
method = "logistic", Hess = TRUE)
model6.null <- polr(as.ordered(Q4_re) ~ 1, data = subset,
method = "logistic", Hess = TRUE)
lr6 <- round(model6.null$deviance - model6$deviance, 3)
model3.cut1 <- summary(model3)$coefficients[9, 1] %>%
as.numeric %>% round(., 3)
model3.cut1.se <- summary(model3)$coefficients[9, 2] %>%
as.numeric %>% round(., 3)
model3.cut2 <- summary(model3)$coefficients[10, 1] %>%
as.numeric %>% round(., 3)
model3.cut2.se <- summary(model3)$coefficients[10, 2] %>%
as.numeric %>% round(., 3)
model3.cut3 <- summary(model3)$coefficients[11, 1] %>%
as.numeric %>% round(., 3)
model3.cut3.se <- summary(model3)$coefficients[11, 2] %>%
as.numeric %>% round(., 3)
model4.cut1 <- summary(model4)$coefficients[13, 1] %>%
as.numeric %>% round(., 3)
model4.cut1.se <- summary(model4)$coefficients[13, 2] %>%
as.numeric %>% round(., 3)
model4.cut2 <- summary(model4)$coefficients[14, 1] %>%
as.numeric %>% round(., 3)
model4.cut2.se <- summary(model4)$coefficients[14, 2] %>%
as.numeric %>% round(., 3)
model4.cut3 <- summary(model4)$coefficients[15, 1] %>%
as.numeric %>% round(., 3)
model4.cut3.se <- summary(model4)$coefficients[15, 2] %>%
as.numeric %>% round(., 3)
model5.cut1 <- summary(model5)$coefficients[10, 1] %>%
as.numeric %>% round(., 3)
model5.cut1.se <- summary(model5)$coefficients[10, 2] %>%
as.numeric %>% round(., 3)
model5.cut2 <- summary(model5)$coefficients[11, 1] %>%
as.numeric %>% round(., 3)
model5.cut2.se <- summary(model5)$coefficients[11, 2] %>%
as.numeric %>% round(., 3)
model5.cut3 <- summary(model5)$coefficients[12, 1] %>%
as.numeric %>% round(., 3)
model5.cut3.se <- summary(model5)$coefficients[12, 2] %>%
as.numeric %>% round(., 3)
model6.cut1 <- summary(model6)$coefficients[18, 1] %>%
as.numeric %>% round(., 3)
model6.cut1.se <- summary(model6)$coefficients[18, 2] %>%
as.numeric %>% round(., 3)
model6.cut2 <- summary(model6)$coefficients[19, 1] %>%
as.numeric %>% round(., 3)
model6.cut2.se <- summary(model6)$coefficients[19, 2] %>%
as.numeric %>% round(., 3)
model6.cut3 <- summary(model6)$coefficients[20, 1] %>%
as.numeric %>% round(., 3)
model6.cut3.se <- summary(model6)$coefficients[20, 2] %>%
as.numeric %>% round(., 3)
stargazer(model3, model4, model5, model6, type = "text")
stargazer(model3, model4, model5, model6,
title = "Ordered Logistic Regressions: Attitudes toward North Korea after the Panmunjom Declaration",
label = "tab2",
dep.var.caption = "Attitudes toward Unification",
dep.var.labels.include = F,
single.row = F,
model.names = T,
model.numbers = T,
column.labels = c("Base", "Generation", "Prospects",
"Full"),
covariate.labels = c(
"30s", "40s", "50s", "60+",
"Prospects",
"Interests",
"NK policy Eval.",
"Honam", "PK", "TK",
"Gender","Education",
"Income",
"30s$\\times$Prospects",
"40s$\\times$Prospects",
"50s$\\times$Prospects",
"60s$\\times$Prospects"),
header = F,
keep.stat = c("n"),
add.lines = list(c("cut1",
model3.cut1, model4.cut1,
model5.cut1, model6.cut1),
c("",
paste0("(", model3.cut1.se,")"),
paste0("(", model4.cut1.se,")"),
paste0("(", model5.cut1.se,")"),
paste0("(", model6.cut1.se,")")),
c("cut2",
model3.cut2, model4.cut2,
model5.cut2, model6.cut2),
c("",
paste0("(", model3.cut2.se,")"),
paste0("(", model4.cut2.se,")"),
paste0("(", model5.cut2.se,")"),
paste0("(", model6.cut2.se,")")),
c("cut3",
model3.cut3, model4.cut3,
model5.cut3, model6.cut3),
c("",
paste0("(", model3.cut3.se,")"),
paste0("(", model4.cut3.se,")"),
paste0("(", model5.cut3.se,")"),
paste0("(", model6.cut3.se,")")),
c("Log-likelihood ratio",
lr3, lr4, lr5, lr6)),
star.cutoffs = c(0.05, 0.01, 0.001),
notes = c("Standard errors in parentheses."),
# table.placement = "H",
table.layout = "=lc-tas-n")
## Table2 -> Coefplot2
model2.tidy <- broom::tidy(model6)
model2.tidy_conf <-broom::confint_tidy(model6)
model2.tidy <- bind_cols(model2.tidy[1:19,], model2.tidy_conf)
coefplot2 <- model2.tidy %>% mutate(
term = case_when(
term == "I(as.factor(age))2" ~ "Gen:\n30s",
term == "I(as.factor(age))3" ~ "Gen:\n40s",
term == "I(as.factor(age))4" ~ "Gen:\n50s",
term == "I(as.factor(age))5" ~ "Gen:\n60+",
term == "I(as.factor(age))2:I(Q5_re)" ~ "30s\nX\nProspect",
term == "I(as.factor(age))3:I(Q5_re)" ~ "40s\nX\nProspect",
term == "I(as.factor(age))4:I(Q5_re)" ~ "50s\nX\nProspect",
term == "I(as.factor(age))5:I(Q5_re)" ~ "60+\nX\nProspect",
term == "I(Q5_re)" ~ "Prospect\nof\nunification",
term == "Q19" ~ "Govt.NK\nPolicy\neval.",
term == "Honam" ~ "Region:\nHonam",
term == "PK" ~ "Region:\nPK",
term == "TK" ~ "Region:\nTK",
term == "gender" ~ "Gender",
term == "edu" ~ "Education",
term == "income" ~ "Income",
T ~ NA_character_) %>%
parse_factor(., levels = c(
"Gen:\n30s","Gen:\n40s","Gen:\n50s","Gen:\n60+",
"30s\nX\nProspect", "40s\nX\nProspect",
"50s\nX\nProspect", "60+\nX\nProspect",
"Prospect\nof\nunification",
"Govt.NK\nPolicy\neval.","Region:\nHonam",
"Region:\nPK", "Region:\nTK", "Gender", "Education",
"Income")),
sig = ifelse(conf.high >= 0 & conf.low <=0, "Insig", "sig") %>%
parse_factor(., levels = c("Insig", "sig"), ordered = T,
include_na = F)
)
coefplot2 %>% dplyr::filter(!term %in% c(NA_character_)) %>%
ggplot(aes(x = term, y = estimate,
color = as.factor(sig))) +
geom_point(size = 3, show.legend = F, shape = 10) +
geom_pointrange(size = 1, show.legend = F,
aes(ymin = conf.low,
ymax = conf.high)) +
labs(y = "Estimates", x = "") +
scale_color_manual(values = wesanderson::wes_palette("Royal1")) +
geom_hline(yintercept = 0, color = "red") +
theme_bw() + theme(
legend.title = element_blank(),
legend.position = "bottom",
axis.text.x = element_text(size = 7),
plot.background = element_rect(fill = "transparent", color = NA),
legend.background = element_rect(fill = "transparent", color = NA),
legend.box.background = element_rect(fill = "transparent", color = NA)
)
ggsave("figure/figure15.png", bg = "transparent",
width = 8, height = 3.5, dpi = 600)
## Figure 10
subset$Q4_int <- as.factor(as.numeric(subset$Q4_re))
subset$age_fa <- as.factor(subset$age)
model6a <- polr(Q4_int ~ age_fa + Q5_re + age_fa*Q5_re +
Q3 + Q19 + Honam + PK +
TK + gender + edu + income, data = subset,
method = "logistic", Hess = TRUE)
df <- ggeffects::ggpredict(model6a, terms = c("age_fa", "Q5_re"))
df <- df %>% mutate(
response.level = as.numeric(response.level)
)
df <- df %>% mutate(
Generation = case_when(
x == 1L ~ "20s",
x == 2L ~ "30s",
x == 3L ~ "40s",
x == 4L ~ "50s",
x == 5L ~ "60+"),
DV = case_when(
response.level == 1L ~ "Not\npreferred",
response.level == 2L ~ "Preferred without\nBurdens",
response.level == 3L ~ "Status quo",
response.level == 4L ~ "Preferred",
) %>% parse_factor(., levels = c("Not\npreferred","Status quo","Preferred without\nBurdens","Preferred"),