Chapter 9: Conclusion
Figure 9.1: The Significance of Senior Level Power-Sharing
library(tidyverse)
library(tikzDevice)
load("./data/mechanism_models_democ.rda")
load("./data/mechanism_models_elec.rda")
load("./data/mechanism_models_rol.rda")
load("./data/mechanism_models_pubgoods.rda")
mechanism_models_democ <- mechanism_models_democ %>%
mutate(element = "Democracy Scores")
mechanism_models_elec <- mechanism_models_elec %>%
mutate(element = "Election Quality")
model_out_rol <- model_out_rol %>%
mutate(element = "Rule of Law")
model_out_pubgoods <- model_out_pubgoods %>%
mutate(element = "Public Goods")
all_effects <- bind_rows(mechanism_models_democ, mechanism_models_elec) %>%
bind_rows(., model_out_rol) %>%
bind_rows(., model_out_pubgoods)
all_effects <- all_effects %>%
filter(grepl("(S|s)enior", name)) %>%
mutate(name = stringr::str_replace_all(name, "nonseniorCOUNT", "Nonsenior PS"),
name = stringr::str_replace_all(name, "seniorCOUNT", "Senior PS")) %>%
mutate(element = forcats::fct_relevel(element, c("Democracy Scores",
"Election Quality",
"Rule of Law",
"Public Goods")))
all_effects_plot <- ggplot(all_effects,
aes(x = name,
y = estimate)) +
geom_point() +
geom_errorbar(aes(ymin = estimate - 1.67 * std.error,
ymax = estimate + 1.67 * std.error),
width = 0) +
facet_wrap( ~ element, scales = "free_y", nrow = 1) +
scale_x_discrete(labels = c("Nonsenior \n PS", "Senior \n PS")) +
theme_bw() +
labs(y = "Coefficient Estimate \n of the Interaction Term", x = "")
# Output for Manuscript
# options( tikzDocumentDeclaration = "\\documentclass[11pt]{article}" )
# tikz("../figures/seniorPScomparison.tex", height = 2.5)
# print(all_effects_plot)
# dev.off()
# Output for Rep. Archive
print(all_effects_plot)
Figure 9.2: Comparing different aid types
library(tidyverse)
library(tikzDevice)
load("./data/mechanism_models_democ.rda")
load("./data/mechanism_models_elec.rda")
load("./data/mechanism_models_rol.rda")
load("./data/mechanism_models_pubgoods.rda")
mechanism_models_democ <- mechanism_models_democ %>%
mutate(element = "Democracy Scores")
mechanism_models_elec <- mechanism_models_elec %>%
mutate(element = "Election Quality")
model_out_rol <- model_out_rol %>%
mutate(element = "Rule of Law")
model_out_pubgoods <- model_out_pubgoods %>%
mutate(element = "Public Goods")
all_effects <- bind_rows(mechanism_models_democ, mechanism_models_elec) %>%
bind_rows(., model_out_rol) %>%
bind_rows(., model_out_pubgoods)
all_effects <- all_effects %>%
filter(grepl("(A|a)id|DGA|dga|bga|pga", name)) %>%
mutate(name = stringr::str_replace_all(name, "dga_gdp_ln", "DGA"),
name = stringr::str_replace_all(name, "bga_gdp_ln", "Budget Aid"),
name = stringr::str_replace_all(name, "pga_gdp_ln", "Program Aid")) %>%
mutate(element = forcats::fct_relevel(element, c("Democracy Scores",
"Election Quality",
"Rule of Law",
"Public Goods"))) %>%
mutate(name = forcats::fct_relevel(name, c("DGA",
"Program Aid",
"Budget Aid")))
#
all_effects_plot <- ggplot(all_effects,
aes(x = name,
y = estimate)) +
geom_point() +
geom_errorbar(aes(ymin = estimate - 1.67 * std.error,
ymax = estimate + 1.67 * std.error),
width = 0) +
scale_x_discrete(labels = c("DGA",
"Program \n Aid",
"Budget \n Aid")) +
geom_hline(yintercept = 0, linetype = 2) +
facet_wrap( ~ element, scales = "free_y", nrow = 1) +
theme_bw() +
labs(y = "Coefficient Estimate \n of the Interaction Term", x = "")
# Output for Manuscript
# options( tikzDocumentDeclaration = "\\documentclass[11pt]{article}" )
# tikz("../figures/aid_type_comparison.tex", height = 2.5)
# print(all_effects_plot)
# dev.off()
# Output for Rep. Archive
print(all_effects_plot)
9.3: Temporal Dynamics
library(tidyverse)
library(tikzDevice)
load("./data/temp_dyn_democ.rda")
load("./data/temp_dyn_rol.rda")
load("./data/temp_dyn_pubgoods.rda")
all_temp_dyn <- temp_dyn_democ %>%
bind_rows(., temp_dyn_rol) %>%
bind_rows(., temp_dyn_pubgoods)
all_temp_dyn <- all_temp_dyn %>%
ungroup() %>%
# mutate(estimate = ifelse(dem_score == "Freedom House", estimate * 10, estimate),
# std.error = ifelse(dem_score == "Freedom House", estimate * 10, std.error)) %>%
mutate(dv = c(rep("Democracy Scores", 10),
rep("Rule of Law", 10),
rep("Public Goods", 10))) %>%
group_by(dv) %>%
mutate(legend = c(rep(1,5), c(rep(2, 5)))) %>%
ungroup() %>%
mutate(dv = forcats::fct_relevel(dv, c("Democracy Scores", "Rule of Law", "Public Goods")))
all_temp_dyn_plot <- ggplot(all_temp_dyn,
aes(x = name,
y = estimate,
group = dem_score,
color = factor(legend))) +
geom_point( aes(group = dem_score), size = 1.7,
position = position_dodge(width = .5)) +
geom_errorbar(aes(ymin = estimate - 1.67 * std.error,
ymax = estimate + 1.67 * std.error,
linetype = factor(legend)),
width = 0,
position = position_dodge(width = .5)) +
geom_hline(yintercept = 0, linetype = 2) +
scale_color_manual("", values = c("#4575b4", "#e41a1c"),
labels = c("Democracy Scores: Polity\nRule of Law: LJI\nPublic Goods: Pub/Part. Spending", "Democracy Scores: FH\nRule of Law: V-Dem\nPublic Goods: Political Corruption")) +
scale_linetype_manual("", values = c(1, 5),
labels = c("Democracy Scores: Polity\nRule of Law: LJI\nPublic Goods: Pub/Part. Spending", "Democracy Scores: FH\nRule of Law: V-Dem\nPublic Goods: Political Corruption")) +
theme_bw()+
labs(x = "Year after t0", y = "Estimate of Interaction Coefficient \n between Power-Sharing (cabinet)\n and Aid/GDP (log)") +
theme(legend.position = "bottom") +
theme(legend.key.size=unit(3,"lines")) +
facet_wrap(~dv, nrow = 1, scales = "free_y")
# Output for Manuscript
# options( tikzDocumentDeclaration = "\\documentclass[11pt]{article}" )
tikz("../figures/all_temp_dyn_plot.tex", height = 3.5)
print(all_temp_dyn_plot)
dev.off()## pdf
## 2# Output for Rep. Archive
print(all_temp_dyn_plot)
---
title: "Chapter 9: Conclusion"
output: 
  html_document:
    toc: true
    toc_float: 
      collapsed: false
    code_download: true
    code_folding: "hide"

---

# Figure 9.1: The Significance of Senior Level Power-Sharing

```{r, fig.align = "center", message=F, warning=F, cache = T, comments = F, fig.width = 8, fig.height = 3, dev = "CairoPNG" }
library(tidyverse)
library(tikzDevice)

load("./data/mechanism_models_democ.rda")
load("./data/mechanism_models_elec.rda")
load("./data/mechanism_models_rol.rda")
load("./data/mechanism_models_pubgoods.rda")


mechanism_models_democ <- mechanism_models_democ %>% 
  mutate(element = "Democracy Scores")

mechanism_models_elec <- mechanism_models_elec %>% 
  mutate(element = "Election Quality")

model_out_rol <- model_out_rol %>% 
  mutate(element = "Rule of Law")

model_out_pubgoods <- model_out_pubgoods %>% 
  mutate(element = "Public Goods")

all_effects <- bind_rows(mechanism_models_democ, mechanism_models_elec) %>% 
  bind_rows(., model_out_rol) %>% 
  bind_rows(., model_out_pubgoods)

all_effects <- all_effects %>% 
  filter(grepl("(S|s)enior", name)) %>% 
  mutate(name = stringr::str_replace_all(name, "nonseniorCOUNT", "Nonsenior PS"), 
         name = stringr::str_replace_all(name, "seniorCOUNT", "Senior PS")) %>% 
  mutate(element = forcats::fct_relevel(element, c("Democracy Scores", 
                                                     "Election Quality", 
                                                     "Rule of Law", 
                                                     "Public Goods")))
         

all_effects_plot <- ggplot(all_effects, 
                           aes(x = name, 
                               y = estimate)) +
  geom_point() + 
  geom_errorbar(aes(ymin = estimate - 1.67 * std.error, 
                    ymax = estimate + 1.67 * std.error),
                width = 0) +
  facet_wrap( ~ element, scales = "free_y", nrow = 1) +
  scale_x_discrete(labels = c("Nonsenior \n PS", "Senior \n PS")) +
  theme_bw() +
  labs(y = "Coefficient Estimate \n of the Interaction Term", x = "")


# Output for Manuscript
# options( tikzDocumentDeclaration = "\\documentclass[11pt]{article}" )
# tikz("../figures/seniorPScomparison.tex", height = 2.5)
# print(all_effects_plot)
# dev.off()

# Output for Rep. Archive
print(all_effects_plot)

  
```



# Figure 9.2: Comparing different aid types

```{r, fig.align = "center", message=F, warning=F, cache = T, comments = F,, fig.width = 8, fig.height = 3,dev = "CairoPNG" }
library(tidyverse)
library(tikzDevice)

load("./data/mechanism_models_democ.rda")
load("./data/mechanism_models_elec.rda")
load("./data/mechanism_models_rol.rda")
load("./data/mechanism_models_pubgoods.rda")


mechanism_models_democ <- mechanism_models_democ %>% 
  mutate(element = "Democracy Scores")

mechanism_models_elec <- mechanism_models_elec %>% 
  mutate(element = "Election Quality")

model_out_rol <- model_out_rol %>% 
  mutate(element = "Rule of Law")

model_out_pubgoods <- model_out_pubgoods %>% 
  mutate(element = "Public Goods")

all_effects <- bind_rows(mechanism_models_democ, mechanism_models_elec) %>% 
  bind_rows(., model_out_rol) %>% 
  bind_rows(., model_out_pubgoods)

all_effects <- all_effects %>% 
  filter(grepl("(A|a)id|DGA|dga|bga|pga", name)) %>% 
  mutate(name = stringr::str_replace_all(name, "dga_gdp_ln", "DGA"), 
         name = stringr::str_replace_all(name, "bga_gdp_ln", "Budget Aid"),
         name = stringr::str_replace_all(name, "pga_gdp_ln", "Program Aid")) %>% 
  mutate(element = forcats::fct_relevel(element, c("Democracy Scores",
                                                   "Election Quality",
                                                   "Rule of Law",
                                                   "Public Goods")))  %>%
  mutate(name = forcats::fct_relevel(name, c("DGA",
                                             "Program Aid",
                                             "Budget Aid")))
  #        

all_effects_plot <- ggplot(all_effects, 
                           aes(x = name, 
                               y = estimate)) +
  geom_point() + 
  geom_errorbar(aes(ymin = estimate - 1.67 * std.error, 
                    ymax = estimate + 1.67 * std.error),
                width = 0) +
  scale_x_discrete(labels = c("DGA",
                            "Program \n Aid",
                            "Budget \n Aid")) +
  geom_hline(yintercept = 0, linetype = 2) + 
  facet_wrap( ~ element, scales = "free_y", nrow = 1) +
  theme_bw() +
  labs(y = "Coefficient Estimate \n of the Interaction Term", x = "")
  

# Output for Manuscript
# options( tikzDocumentDeclaration = "\\documentclass[11pt]{article}" )
# tikz("../figures/aid_type_comparison.tex", height = 2.5)
# print(all_effects_plot)
# dev.off()

# Output for Rep. Archive
print(all_effects_plot)

```

# 9.3: Temporal Dynamics

```{r, fig.align = "center", message=F, warning=F, cache = T, comments = F, , fig.width = 8, fig.height = 4,dev = "CairoPNG" }
library(tidyverse)
library(tikzDevice)

load("./data/temp_dyn_democ.rda")
load("./data/temp_dyn_rol.rda")
load("./data/temp_dyn_pubgoods.rda")


all_temp_dyn <- temp_dyn_democ %>% 
  bind_rows(., temp_dyn_rol) %>% 
  bind_rows(., temp_dyn_pubgoods)

all_temp_dyn <- all_temp_dyn %>% 
  ungroup() %>% 
  # mutate(estimate = ifelse(dem_score == "Freedom House", estimate * 10, estimate), 
  #        std.error = ifelse(dem_score == "Freedom House", estimate * 10, std.error)) %>% 
  mutate(dv = c(rep("Democracy Scores", 10), 
                rep("Rule of Law", 10), 
                rep("Public Goods", 10))) %>% 
  group_by(dv) %>% 
  mutate(legend = c(rep(1,5), c(rep(2, 5)))) %>% 
  ungroup() %>% 
  mutate(dv = forcats::fct_relevel(dv, c("Democracy Scores", "Rule of Law", "Public Goods")))

all_temp_dyn_plot <- ggplot(all_temp_dyn, 
                             aes(x = name, 
                                 y = estimate,
                                 group = dem_score, 
                                 color = factor(legend))) +
  geom_point( aes(group = dem_score), size = 1.7, 
              position = position_dodge(width = .5)) + 
  
  geom_errorbar(aes(ymin = estimate - 1.67 * std.error, 
                    ymax = estimate + 1.67 * std.error, 
                linetype = factor(legend)), 
                width = 0,
                position = position_dodge(width = .5)) +
  
  geom_hline(yintercept = 0, linetype = 2) +
  scale_color_manual("", values = c("#4575b4", "#e41a1c"),
                     labels = c("Democracy Scores: Polity\nRule of Law: LJI\nPublic Goods: Pub/Part. Spending", "Democracy Scores: FH\nRule of Law: V-Dem\nPublic Goods: Political Corruption")) +
  scale_linetype_manual("", values = c(1, 5), 
                        labels = c("Democracy Scores: Polity\nRule of Law: LJI\nPublic Goods: Pub/Part. Spending", "Democracy Scores: FH\nRule of Law: V-Dem\nPublic Goods: Political Corruption")) +
  theme_bw()+ 
  labs(x = "Year after t0", y = "Estimate of Interaction Coefficient \n between Power-Sharing (cabinet)\n and Aid/GDP (log)") +
   theme(legend.position = "bottom") +
  theme(legend.key.size=unit(3,"lines")) +
  facet_wrap(~dv, nrow = 1, scales = "free_y") 

# Output for Manuscript
# options( tikzDocumentDeclaration = "\\documentclass[11pt]{article}" )
tikz("../figures/all_temp_dyn_plot.tex", height = 3.5)
print(all_temp_dyn_plot)
dev.off()

# Output for Rep. Archive
print(all_temp_dyn_plot)

```