Chapter 5: Democratization Scores
Figure 5.1: Illustrative Examples: Political Development, Power-Sharing and Foreign Aid Flows in the DRC and Liberia
# Libraries
library(tidyverse)
library(foreign)
library(countrycode)
library(cowplot)
library(tikzDevice)
# Read Data
load("./data/diss_df.rda")
aid <- read.dta("./data/aiddata_full.dta")
ucdp_cy <- read_csv("./data/133280_onset2012csv.csv")
p4 <- haven::read_spss("./data/p4v2012.sav")
names(p4)[2] <- "GWNo"
# Polity IV treats Serbia as Yugoslavia at some periods.
# I change it back to Serbia
p4[p4$GWNo == 347 & p4$year > 1991, "GWNo"] <- 345
# Prepare UCDP data
# load two small helper function to determine pre- and post-conflict years
ag_seq <- function(x) {
runs <- cumsum(c(0, diff(x) != 1 ))
return(runs)
}
source("./functions/identifyPostConflictYears.R")
# load and wrangle ucdp data
ucdp_cy <- ucdp_cy %>%
arrange(gwno, year) %>%
group_by(gwno) %>%
mutate(iso2c = countrycode(gwno, "cown", "iso2c"),
ucdp_dummy = ifelse(incidencev412 == 1, 1, 0),
pc = pcIdentifier(ucdp_dummy),
pc = ifelse(pc == 2, 1, 0),
conflict_ep_id = cumsum(c(ifelse(first(ucdp_dummy) == 1,
1, 0),
diff(ucdp_dummy) != 0)),
conflict_ep_id = as.numeric(ifelse(ucdp_dummy == 1,
conflict_ep_id,
NA)),
peace_ep_id = cumsum(c(ifelse(first(pc) == 2,
2, 1),
diff(pc) != 0)),
peace_ep_id = as.numeric(ifelse(pc == 1, peace_ep_id, NA)))%>%
group_by(gwno, conflict_ep_id) %>%
mutate(conflict_year = -1 * rev(cumsum(ucdp_dummy == 1))) %>%
group_by(gwno, peace_ep_id) %>%
mutate(peace_year = cumsum(pc == 1)) %>%
group_by(gwno) %>%
mutate(conf_and_peace_years = conflict_year + peace_year)
# Merge in Aid data
ucdp_cy <- left_join(ucdp_cy,
aid,
by = c("iso2c" = "iso2", "year")) %>%
filter(year >= 1989) %>%
dplyr::rename(wb_AidGNI = DT_ODA_ODAT_GN_ZS,
wb_AidPC = DT_ODA_ODAT_PC_ZS,
wb_AidGmentXP = DT_ODA_ODAT_XP_ZS,
oecd_Aid = aid_oecd_commitment2011USD,
oecd_Aid_mill = aid_oecd_commitment2011USD_mill,
aiddata_Aid = commitment_amount_usd_constant)
# Prepare data, i.e. subset UCDP data with only the countries in my sample etc.
ucdp_cy_diss_df <- ucdp_cy %>%
filter(gwno %in% unique(diss_df$GWNo)) %>%
group_by(recipient) %>%
mutate(mean_aid_gdp = mean(aiddata_Aid / GDP, na.rm = T)) %>%
ungroup() %>%
# mutate(ordered_recipient_by_aidmean = factor(recipient,
# levels = recipient[order(mean_aid_gdp,
# decreasing = T)])) %>%
left_join(., diss_df[, c("GWNo", "year", "cabinetINC", "cabinetCOUNT", "seniorCOUNT", "nonseniorCOUNT")],
by = c("gwno" = "GWNo", "year")) %>%
left_join(., p4[, c("GWNo", "year", "polity2")], by = c("gwno" = "GWNo", "year")) %>%
filter(year <= 2010)
# define function that generates illustrative country plots
plot_country_example <- function(country, year_min, year_max) {
# Aid Plot
cntry_illustration_plot <- ucdp_cy_diss_df %>%
filter(recipient == country & year >= year_min & year <= year_max) %>%
ggplot(., aes(x = year, y = aiddata_Aid / GDP * 100)) +
# Add conflict years to aid plot
geom_bar(data = ucdp_cy_diss_df %>% filter(incidencev412 == 1 &
recipient == country &
year >= year_min & year <= year_max),
aes(x = year, y = Inf),
fill = "#de2d26",
alpha = 0.2, stat = "identity", width = 1) +
geom_bar(stat = "identity",
position = "dodge",
color = "black",
fill = "#969696", width = 1) +
scale_x_continuous(breaks = year_min:year_max) +
labs(x = "", y = "Aid / GDP")
# Polity Plot
cntry_illustration_polity <- ucdp_cy_diss_df %>%
filter(recipient == country & year >= year_min & year <= year_max) %>%
ggplot(., aes(x = year, y = polity2)) +
geom_bar(stat = "identity", position = "dodge", color = "white", fill = "white", width = 1) +
# Add conflict years to polity plot
geom_bar(data = ucdp_cy_diss_df %>% filter(incidencev412 == 1 &
recipient == country &
year >= year_min & year <= year_max),
aes(x = year, y = Inf),
fill = "#de2d26", alpha = 0.2, stat = "identity", width = 1) +
geom_point() + geom_line() +
scale_x_continuous(breaks = year_min:year_max) +
labs(x = "", y = "Polity 2")
# Power-Sharing Plot
cntry_illustration_ps <- ucdp_cy_diss_df %>%
filter(recipient == country & year >= year_min & year <= year_max) %>%
dplyr::select(year, seniorCOUNT, nonseniorCOUNT) %>%
gather(key, value, -year) %>%
mutate(value = ifelse(is.na(value), 0, value)) %>%
ggplot(., aes(x = year, y = value, fill = factor(key))) +
geom_bar(stat = "identity",
position = "stack", color = "black", width = 1) +
scale_fill_manual("",
values = c("#bdc9e1", "#045a8d"),
labels = c("Nonsenior rebel seats", "Senior rebel seats")) +
# Add conflict years to ps plot
geom_bar(data = ucdp_cy_diss_df %>% filter(incidencev412 == 1 &
recipient == country &
year >= year_min & year <= year_max),
aes(x = year, y = Inf),
fill = "#de2d26", alpha = 0.2, stat = "identity", width = 1) +
scale_x_continuous(breaks = year_min:year_max) +
labs(x = "", y = "Number of Rebel Seats \n in Power-Sharing \nGovernment") +
theme(legend.position = "bottom", legend.direction = "vertical")
# combine plot output
cowplot::plot_grid(cntry_illustration_plot,
cntry_illustration_polity,
cntry_illustration_ps,
nrow = 3,
align = "v" )
}
#
# # Output for manuscript
# # Liberia
# options( tikzDocumentDeclaration = "\\documentclass[15pt]{article}" )
# tikz("../figures/Liberia_illu.tex", height = 7, width = 5, pointsize = 7)
# plot_country_example("Liberia", 2000, 2010)
# dev.off()
#
# # DRC
# options( tikzDocumentDeclaration = "\\documentclass[15pt]{article}" )
# tikz("../figures/DRC_illu.tex", height = 7, width = 5, pointsize = 7)
# plot_country_example("Congo, Dem. Rep.", 2000, 2010)
# dev.off()
# Output for Replication Archive
plot_grid(plot_country_example("Liberia", 2000, 2010),
plot_country_example("Congo, Dem. Rep.", 2000, 2010),
ncol = 2,
labels = c("Liberia", "DRC"),
vjust = -5,
hjust = -7,
scale = 0.9)
Figure 5.2: Foreign Aid and Polity Scores in Country-Years With and Without Power-Sharing Governments
# Libraries
library(tidyverse)
library(tikzDevice)
# Load Data
load("./data/diss_df.rda")
# Labels for easier plotting
diss_df$cabinetINClabel <- ifelse(diss_df$cabinetINC == 1, "Power-Sharing",
"No \nPower-Sharing")
# generate scatterplot, divided by power-sharing
aidps_democ_plot <- ggplot(diss_df, aes(x = log(aiddata_AidGDP),
y = polity2_t2)) +
geom_point(size = 1.7, alpha = 0.5) +
geom_smooth(method = "lm") +
# geom_rug() +
theme_bw() +
labs(x = "Aid / GDP (log)", y = "Polity score at t2") +
facet_wrap( ~ cabinetINClabel)
# output plot for manuscript
# options(tikzDocumentDeclaration = "\\documentclass[11pt]{article}" )
# tikz("../figures/aidps_democ_plot.tex", height = 3.5)
# print(aidps_democ_plot)
# dev.off()
# output for replication archive
print(aidps_democ_plot)
Figure 5.3: Temporal Dynamics of the Interactive Effect between Power-Sharing and Foreign Aid
# Libraries
library(tidyverse)
library(cowplot)
library(lfe)
library(tikzDevice)
# Data
load("./data/diss_df.rda")
# Prepare data frame for multiple plots
polity_vars <- list(
polity2_t1 = diss_df,
polity2_t2 = diss_df,
polity2_t3 = diss_df,
polity2_t4 = diss_df,
polity2_t5 = diss_df,
fh_t1 = diss_df,
fh_t2 = diss_df,
fh_t3 = diss_df,
fh_t4 = diss_df,
fh_t5 = diss_df
)
# create data frame with list column
polity_vars <- enframe(polity_vars)
# define function that will be applied to every data frame in the list column
main_model <- function(lead_type, data) {
data <- as.data.frame(data)
data$lead_var <- data[, grep(lead_type, names(data), value =T)]
if(grepl("fh", lead_type)) {
model <- lfe::felm(lead_var ~
cabinetCOUNT *
aiddata_AidGDP_ln +
log(GDP_per_capita) +
ln_pop +
conf_intens +
nonstate +
WBnatres +
fh | 0 | 0 | GWNo,
data=data)
return(model)
} else {
model <- lfe::felm(lead_var ~
cabinetCOUNT *
aiddata_AidGDP_ln +
log(GDP_per_capita) +
ln_pop +
conf_intens +
nonstate +
WBnatres +
polity2 | 0 | 0 | GWNo,
data=data)
return(model)
}
}
# fit models & post-process data for plotting
model_all <- polity_vars %>%
mutate(model = map2(name, value, ~ main_model(.x, .y)))
model_out <- model_all %>%
mutate(coef = map(model, broom::tidy)) %>%
unnest(coef) %>%
# keep only interaction term coefs
filter(term == "cabinetCOUNT:aiddata_AidGDP_ln") %>%
mutate(dem_score = ifelse(grepl("polity", name), "Polity", "Freedom House")) %>%
dplyr::select(dem_score, name, estimate, std.error) %>%
group_by(dem_score) %>%
mutate(name = 1:5)
temp_dyn_democ <- model_out
save(temp_dyn_democ, file = "./data/temp_dyn_democ.rda")
temp_dynamics_plot <- ggplot(model_out,
aes(x = name,
y = estimate,
group = dem_score, color = dem_score)) +
geom_point( size = 1.7,
position = position_dodge(width = .5)) +
geom_errorbar(aes(ymin = estimate - 1.67 * std.error,
ymax = estimate + 1.67 * std.error),
width = 0,
position = position_dodge(width = .5)) +
geom_hline(yintercept = 0, linetype = 2) +
facet_wrap(~dem_score, nrow = 2, scales = "free_y") +
scale_color_brewer("",palette = "Set1") +
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 = "none")
# Output for manuscript
# options(tikzDocumentDeclaration = "\\documentclass[11pt]{article}" )
# tikz("../figures/temp_dynamics_plot.tex", height = 3.5)
# print(temp_dynamics_plot)
# dev.off()
# Output for replication archive
print(temp_dynamics_plot)
Figure 5.4: Marginal Effects of Power-Sharing and Foreign Aid on Post-Conflict Democratization
# Libraries
library(gridExtra)
library(Cairo)
library(tikzDevice)
library(reshape)
# Polity DV + cabineINC
model_polity_cabinc <- ols(polity2_t2 ~
cabinetINC *
aiddata_AidGDP_ln +
log(GDP_per_capita) +
ln_pop +
conf_intens +
nonstate +
WBnatres +
polity2 ,
data=diss_df, x=T, y=T)
model_polity_cabinc <- robcov(model_polity_cabinc, diss_df$GWNo)
# Polity DV + cabCOUNT
model_polity_cabcount <- ols(polity2_t2 ~
cabinetCOUNT *
aiddata_AidGDP_ln +
log(GDP_per_capita) +
ln_pop +
conf_intens +
nonstate +
WBnatres +
polity2 ,
data=diss_df, x=T, y=T)
model_polity_cabcount <- robcov(model_polity_cabcount, diss_df$GWNo)
# source interaction plot function to plot marginal effects
source("./functions//interaction_plots.R")
# Output for manuscript
# options( tikzDocumentDeclaration = "\\documentclass[11pt]{article}" )
# tikz("../figures/interaction.tex", height = 3.5)
#
# # Output for replication archive
# par(mfrow=c(1,3),
# mar = c(5, 7, 4, 0.5),
# cex.lab = 1.3,
# cex.axis = 1.3,
# mgp = c(3.5, 1, 0))
#
# interaction_plot_continuous(model_polity_cabcount,
# "cabinetCOUNT", "aiddata_AidGDP_ln", "cabinetCOUNT * aiddata_AidGDP_ln",
# title = "",
# ylab = "Marginal effect of Power-Sharing (cabinet) \n on political development",
# xlab = "a) Aid / GDP (Log)\n",
# conf = .90)
# interaction_plot_continuous(model_polity_cabcount,
# "aiddata_AidGDP_ln", "cabinetCOUNT", "cabinetCOUNT * aiddata_AidGDP_ln",
# title = "",
# ylab = "Marginal effect of Aid\n on political development",
# xlab = "b) Power-Sharing \n(Number of rebel seats)",
# conf = .90)
# interaction_plot_binary(model_polity_cabinc,
# "aiddata_AidGDP_ln","cabinetINC", "cabinetINC * aiddata_AidGDP_ln",
# title = "",
# ylab = "Marginal effect of Aid\n on political development",
# xlab = "c) Power-Sharing \n(1 = Yes, 0 = No)",
# conf = .90)
# dev.off()
# Output for replication archive
par(mfrow=c(1,3),
mar = c(5, 7, 4, 0.5),
cex.lab = 1.3,
cex.axis = 1.3,
mgp = c(3.5, 1, 0))
interaction_plot_continuous(model_polity_cabcount,
"cabinetCOUNT", "aiddata_AidGDP_ln", "cabinetCOUNT * aiddata_AidGDP_ln",
title = "",
ylab = "Marginal effect of Power-Sharing (cabinet) \n on political development",
xlab = "a) Aid / GDP (Log)\n",
conf = .90)
interaction_plot_continuous(model_polity_cabcount,
"aiddata_AidGDP_ln", "cabinetCOUNT", "cabinetCOUNT * aiddata_AidGDP_ln",
title = "",
ylab = "Marginal effect of Aid\n on political development",
xlab = "b) Power-Sharing \n(Number of rebel seats)",
conf = .90)
interaction_plot_binary(model_polity_cabinc,
"aiddata_AidGDP_ln","cabinetINC", "cabinetINC * aiddata_AidGDP_ln",
title = "",
ylab = "Marginal effect of Aid\n on political development",
xlab = "c) Power-Sharing \n(1 = Yes, 0 = No)",
conf = .90)
Figure 5.5: Model Predictions for the Effect of Power-Sharing and Foreign Aid on Post-Conflict Democratization
# Libraries
library(tidyverse)
library(rms)
library(gridExtra)
library(tikzDevice)
# Load data
load("data/diss_df.rda")
# to predict substantive effects from this model, we need to define data
# distribution
diss_df$conflictID <- NULL
datadist_diss_df <- datadist(diss_df); options(datadist='datadist_diss_df')
# replicate Model from above Polity DV + cabCOUNT
model_polity_cabcount <- ols(polity2_t2 ~
cabinetCOUNT *
aiddata_AidGDP_ln +
ln_gdp_pc +
ln_pop +
conf_intens +
nonstate +
WBnatres +
polity2 ,
data=diss_df, x=T, y=T)
model_polity_cabcount <- robcov(model_polity_cabcount, diss_df$GWNo)
# Start predictions for aid
prediction_democ_aid <- Predict(model_polity_cabcount,
cabinetCOUNT = c(0, 10), # no / much power-sharing
aiddata_AidGDP_ln = seq(-5.7, 5.17, 0.1),# range of aid
polity2 = 2.6,
conf.int = 0.9)
subs_effects_pchng_aid <- ggplot(data.frame(prediction_democ_aid),
aes(x = exp(aiddata_AidGDP_ln),
y = yhat,
group = as.factor(cabinetCOUNT))) +
geom_line( color = "black", size = 1) +
geom_ribbon(aes(ymax = upper,
ymin = lower,
fill = as.factor(cabinetCOUNT)),
alpha = 0.7) +
scale_fill_manual(values = c("#b3cde3", "#e41a1c"),
name = "Number of Rebels \nin the Power-Sharing Coalition:") +
theme_bw() +
theme(text = element_text(size=8)) +
labs(x = "Aid / GDP",
y = "Predicted Polity Scores") +
theme(legend.position = "bottom")
# Predictions power-sharing
prediction_democ_ps <- Predict(model_polity_cabcount,
cabinetCOUNT = seq(0, 10, 1),
aiddata_AidGDP_ln = c(0, 3.4),
polity2 = 2.6,
conf.int = 0.9)
prediction_democ_ps$aiddata_AidGDP_ln <- round(exp(prediction_democ_ps$aiddata_AidGDP_ln))
subs_effects_pchng_ps <- ggplot(data.frame(prediction_democ_ps),
aes(x = cabinetCOUNT,
y = yhat,
group = as.factor(aiddata_AidGDP_ln))) +
geom_line( color = "black", size = 1) +
geom_ribbon(aes(ymax = upper,
ymin = lower,
fill = as.factor(aiddata_AidGDP_ln)),
alpha = 0.7) +
scale_fill_manual(values = c("#b3cde3", "#e41a1c"),
name = "Aid in per cent of GDP:") +
theme_bw() +
scale_x_continuous(breaks = seq(0, 10, 2)) +
theme(text = element_text(size=8)) +
labs(x = "Power-Sharing (No. of rebel seats in government)",
y = "Predicted Polity Scores") +
theme(legend.position = "bottom")
# output prediction plots
# output plot for predicted VDEM election quality variables
# options( tikzDocumentDeclaration = "\\documentclass[11pt]{article}" )
# tikz("../figures/aidps_pchng.tex", height = 4.5, width = 6.5)
# grid.arrange(subs_effects_pchng_ps,
# subs_effects_pchng_aid,
# nrow = 1)
# dev.off()
grid.arrange(subs_effects_pchng_ps,
subs_effects_pchng_aid,
nrow = 1)
Figure 5.6: Mechanisms: Variation in Types of Power-Sharing and Aid
# Libraries
library(tidyverse)
library(rms)
library(plm)
library(lfe)
library(tidyverse)
library(tikzDevice)
# Load data
load("./data/diss_df.rda")
# Estimate Models
model_polity_cabinc <- felm(polity2_t2 ~
cabinetINC *
aiddata_AidGDP_ln +
log(GDP_per_capita) +
ln_pop +
conf_intens +
nonstate +
WBnatres +
polity2 | 0 | 0 | GWNo,
data=diss_df)
# Polity DV + seniorINC
model_polity_senior <- lfe::felm(polity2_t2 ~
seniorINC *
aiddata_AidGDP_ln +
log(GDP_per_capita) +
ln_pop +
conf_intens +
nonstate +
WBnatres +
polity2 | 0 | 0 | GWNo ,
data=diss_df)
# Polity DV + nonseniorINC
model_polity_nonsenior <- felm(polity2_t2 ~
nonseniorINC *
aiddata_AidGDP_ln +
log(GDP_per_capita) +
ln_pop +
conf_intens +
nonstate +
WBnatres +
polity2 | 0 | 0 | GWNo,
data=diss_df)
# DGA * PS
model_polity_cabinc_dga <- felm(polity2_t2 ~
cabinetINC * log(dga_gdp_zero + 1) +
aiddata_AidGDP_ln +
log(GDP_per_capita) +
ln_pop +
conf_intens +
nonstate +
WBnatres +
polity2 | 0 | 0 | GWNo,
data=diss_df)
model_polity_cabinc_programaid <- felm(polity2_t2 ~
cabinetINC * log(program_aid_gdp_zero + 1) +
aiddata_AidGDP_ln +
log(GDP_per_capita) +
ln_pop +
conf_intens +
nonstate +
WBnatres +
polity2 | 0 | 0 | GWNo,
data=diss_df)
model_polity_cabinc_budget <- felm(polity2_t2 ~
cabinetINC * log(commodity_aid_gdp_zero + 1) +
aiddata_AidGDP_ln +
log(GDP_per_capita) +
ln_pop +
conf_intens +
nonstate +
WBnatres +
polity2 | 0 | 0 | GWNo,
data=diss_df)
mechanism_models <- list("Cabinet PS (Baseline)" = model_polity_cabinc,
"Senior PS" = model_polity_senior,
"Nonsenior PS" = model_polity_nonsenior,
"DGA" = model_polity_cabinc_dga,
"Program Aid"= model_polity_cabinc_programaid,
"Budget Aid" = model_polity_cabinc_budget) %>%
enframe() %>%
mutate(tidymodel = map(value, broom::tidy)) %>%
unnest(tidymodel) %>%
filter(grepl(":", term)) %>%
mutate(name = forcats::fct_relevel(name, c("Cabinet PS (Baseline)",
"Senior PS",
"Nonsenior PS",
"DGA",
"Program Aid",
"Budget Aid")))
mechanism_models_democ <- mechanism_models
save(mechanism_models_democ, file= "./data/mechanism_models_democ.rda")
mechanims_democ_models_plot <- ggplot(mechanism_models, aes(x = name, y = estimate) ) +
geom_point() +
geom_errorbar(aes(ymin = estimate - 1.67 * std.error,
ymax = estimate + 1.67 * std.error),
width = 0) +
theme_bw() +
theme(axis.title.y = element_text(size = 10)) +
geom_hline(yintercept = 0, linetype = 2) +
labs(x = "", y = "Coefficient Estimate of Interaction between \n Different Types of Power-Sharing (binary) \n and Different Aid Types")
# Output for manuscript
# options(tikzDocumentDeclaration = "\\documentclass[11pt]{article}" )
# tikz("../figures/mechanims_democ_models_plot.tex", height = 2.75)
# print(mechanims_democ_models_plot)
# dev.off()
# Output for replication archive
print(mechanims_democ_models_plot)
Figure 5.7 & Table B.5: Covariate Balance Before and After Matching
# Libraries
library(dplyr)
library(MatchIt)
library(texreg)
library(rms)
library(tidyr)
library(ggrepel) # needed for variable labels
library(ggplot2)
library(tikzDevice)
library(xtable)
# Load data
load("./data/diss_df.rda")
# select relevant variables
match_democ_data <- diss_df %>%
ungroup() %>%
dplyr::select(cabinetINC, cabinetCOUNT, seniorINC,
seniorCOUNT, nonseniorINC, nonseniorCOUNT,
aiddata_AidGDP, population, nonstate,
WBnatres, fh, GDP_per_capita, conf_intens,
aiddata_AidGDP_ln,
GWNo, year,
polity2, polity2_t2, # check
fh, fh_t2,
pc_period, Location, ln_pop, ln_gdp_pc)
# keep only complete cases (necessary for matching)
match_democ_data <- match_democ_data[complete.cases(match_democ_data), ]
# generate pretreatment controls: control variables in first post-conflict year
match_democ_data <- match_democ_data %>%
arrange(GWNo, pc_period, year) %>%
group_by(GWNo, pc_period) %>%
mutate(match_aiddata_AidGDP_ln = first(aiddata_AidGDP_ln),
match_pop = first(population),
match_gdp = first(GDP_per_capita),
match_nonstate = first(nonstate),
match_WBnatres = first(WBnatres),
match_polity = first(polity2),
match_fh = first(fh))
# explicitly convert to data frame
match_democ_data <- as.data.frame(match_democ_data)
# 1.1 Perform matching algorithm ------------------------------------------
set.seed(123)
match_democ_res <- matchit(cabinetINC ~
match_aiddata_AidGDP_ln +
log(match_gdp) +
log(match_pop) +
conf_intens + # conf_intens is already pre-treatment
match_nonstate +
log(match_WBnatres + 1) +
match_polity,
method = "nearest",
distance = "mahalanobis",
ratio = 2,
data = match_democ_data)
# extract data
match_democ_df <- match.data(match_democ_res)
# Balance Improvement Plot ------------------------------------------------
balance_improvement <- summary(match_democ_res)$reduction
row.names(balance_improvement) <- c("Aid / GDP (log)",
"GDP / PC (log)",
"Population (log)",
"Conflict Intensity",
"Nonstate Conflict",
"Nat. Res. Rents (log)",
"Regime Type (Polity)")
balance_improvement <- data.frame(variable = row.names(balance_improvement),
percent_balance_improvement = round(balance_improvement[, 1], 2))
plot_balanceimpr <- data.frame(`All Data` = abs(summary(match_democ_res,
standardize = T)$sum.all$`Std. Mean Diff.`),
`Matched Data` = abs(summary(match_democ_res,
standardize = T)$sum.matched$`Std. Mean Diff.`),
cov = balance_improvement$variable) %>%
gather(key, value, -cov) %>%
filter(!is.nan(value) & !is.infinite(value)) %>%
mutate(key = gsub("\\.", " ", key))
# Plot balance improvement
plot_balanceimpr_out <- plot_balanceimpr %>%
ggplot(., aes(x = key, y = value)) +
geom_point(size = 2.5) +
geom_line(aes(group = cov),
linemitre = 2, size = 0.6) +
geom_hline(yintercept = 0.25, aes(group = key)) +
geom_label_repel(data = plot_balanceimpr %>% filter(key == "All Data"),
aes(label = cov),
nudge_x = -.3) +
theme_bw() +
labs( x = "", y = "Absolute Standardized Difference in Means")
# output balance improvement plot
# options( tikzDocumentDeclaration = "\\documentclass[11pt]{article}" )
# tikz("../figures/match_democ_balanceimpr.tex", height = 5)
# print(plot_balanceimpr_out)
# dev.off()
# Output for replication archive
print(plot_balanceimpr_out)
#### Estimate Models on Matched Sample #####
model_democ_matched <- ols(polity2_t2 ~
cabinetINC *
aiddata_AidGDP_ln +
log(GDP_per_capita) +
log(population) +
conf_intens +
nonstate +
WBnatres +
polity2
,
data=match_democ_df , x=T, y=T)
model_democ_matched <- rms::robcov(model_democ_matched, match_democ_df$GWNo)
model_democ_matched_fh <- ols(fh_t2 ~
cabinetINC *
aiddata_AidGDP_ln +
log(GDP_per_capita) +
log(population) +
conf_intens +
nonstate +
WBnatres +
polity2 ,
data=match_democ_df , x=T, y=T)
model_democ_matched_fh <- rms::robcov(model_democ_matched_fh, match_democ_df$GWNo)
## Order of coefficients in output table
name_map <- list(cabinetINC = "Power-Sharing (binary)",
"cabinetINC * aiddata_AidGDP_ln" = "Power-Sharing (binary) * Aid",
aiddata_AidGDP_ln = "Aid / GDP (log)",
GDP_per_capita = "GDP p/c",
population = "Population",
conf_intens = "Conflict Intensity",
nonstate = "Non-State Violence",
WBnatres = "Nat. Res. Rents",
polity2 = "Regime Type")
source("./functions/will_lowe_texreg_reorder.R")
# Model list
model_list <- list(model_democ_matched, model_democ_matched_fh)
oldnames <- all.varnames.dammit(model_list)
ror <- build.ror(oldnames, name_map)
# Output for replication archive
# texreg(l = model_list,
# stars = c(0.001, 0.01, 0.05, 0.1),
# symbol = "+",
# table = F,
# booktabs = T,
# use.packages = F,
# dcolumn = T,
# file = "../output/democ_matching_results.tex",
# custom.model.names = c("(1) Polity",
# "(2) Freedom House"),
# custom.coef.names = ror$ccn,
# omit.coef = ror$oc,
# reorder.coef = unique(ror$rc),
# include.lr = F)
# Output for replication archive
htmlreg(l = model_list,
stars = c(0.001, 0.01, 0.05, 0.1),
symbol = "+",
caption = "",
table = F,
booktabs = T,
use.packages = F,
dcolumn = T,
custom.model.names = c("(1) Polity",
"(2) Freedom House"),
custom.coef.names = ror$ccn,
omit.coef = ror$oc,
reorder.coef = unique(ror$rc),
star.symbol = "\\*",
include.lr = F)| (1) Polity | (2) Freedom House | ||
|---|---|---|---|
| Power-Sharing (binary) | -2.01* | -1.08** | |
| (0.86) | (0.35) | ||
| Power-Sharing (binary) * Aid | 1.15*** | 0.50*** | |
| (0.33) | (0.14) | ||
| Aid / GDP (log) | -0.36 | -0.12 | |
| (0.24) | (0.10) | ||
| GDP p/c | -0.04 | 0.20 | |
| (0.27) | (0.14) | ||
| Population | -0.04 | -0.01 | |
| (0.12) | (0.09) | ||
| Conflict Intensity | 0.15 | -0.44 | |
| (0.42) | (0.37) | ||
| Non-State Violence | -0.91 | -0.83* | |
| (0.56) | (0.33) | ||
| Nat. Res. Rents | -0.02 | -0.01 | |
| (0.01) | (0.01) | ||
| Regime Type | 0.80*** | 0.18*** | |
| (0.08) | (0.03) | ||
| Num. obs. | 108 | 108 | |
| R2 | 0.79 | 0.62 | |
| Adj. R2 | 0.77 | 0.59 | |
| ***p < 0.001, **p < 0.01, *p < 0.05, +p < 0.1 | |||
Figure 5.8: The The Relationship between Foreign Aid and Aid (Instrumented)
# load libraries
library(AER)
library(ivpack)
library(lmtest)
library(tikzDevice)
# load data with instrument; this gives "instrument_df" data frame
load(file = "./data/instrumentedAid2.RData")
load("./data/diss_df.rda")
diss_df_iv <- merge(diss_df,
instrument_df,
by = c("iso2c", "year"), all.x = TRUE)
# subset only complete.cases / necessary for cluster.robust.se()
iv_na <- na.omit(diss_df_iv[, c("polity_chng",
"cabinetCOUNT",
"cabinetINC",
"aiddata_Aid",
"aiddata_AidGDP_ln",
"aiddata_AidPC_ln",
"fh",
"fh_chng",
"GDP_per_capita",
"population",
"conf_intens",
"WBnatres",
"polity2",
"polity2_t2",
"total_sum_except",
"year",
"GWNo",
"GDP",
"nonstate")])
# 3.1 Plot Z_it against aid -----------------------------------------------
plot_democ_aid_iv <- ggplot(iv_na, aes(x = log(total_sum_except),
y = log(aiddata_Aid))) +
geom_point(alpha = 0.7, size = 3) +
geom_smooth(method = "lm") +
theme_bw() +
labs(x = "Aid (instrumented) Log",
y = "Aid (AidData) Log ")
# Output Manuscript
# options( tikzDocumentDeclaration = "\\documentclass[10pt]{article}" )
# tikz("../figures/aid_instr_democ.tex", height = 4)
# print(plot_democ_aid_iv)
# dev.off()
# Output Replication Archive
print(plot_democ_aid_iv)
Table 5.1: Individual Effects of Power-Sharing and Foreign Aid on Post-Conflict Political Development
# Libraries
library(texreg)
library(rms)
# Load data
load("./data/diss_df.rda")
# specify vector with control vars
controlvars <- c("log(aiddata_AidGDP)",
"log(GDP_per_capita)",
"ln_pop",
"conf_intens",
"nonstate",
"WBnatres",
"polity2")
#### Power-Sharing only Models ####
# PS only + cabinetCOUNT
model_ps <- ols(formula(paste0("polity2_t2 ~ cabinetCOUNT + ",
paste0(controlvars, collapse = " + "))),
data = diss_df, x = T, y = T)
model_ps <- robcov(model_ps, diss_df$GWNo)
# PS only + seniorCOUNT
model_seniorCOUNT <- ols(formula(paste0("polity2_t2 ~ seniorCOUNT + ",
paste0(controlvars, collapse = " + "))),
data = diss_df, x = T, y = T)
model_seniorCOUNT <- robcov(model_seniorCOUNT, diss_df$GWNo)
# PS only + nonseniorCOUNT
model_nonseniorCOUNT <- ols(formula(paste0("polity2_t2 ~ nonseniorCOUNT + ",
paste0(controlvars, collapse = " + "))),
data = diss_df, x = T, y = T)
model_nonseniorCOUNT <- robcov(model_nonseniorCOUNT, diss_df$GWNo)
#### Aid-only Models ####
# DGA
model_gov_aid <- ols(formula(paste0("polity2_t2 ~ cabinetCOUNT + log(dga_gdp_zero + 1) + ",
paste0(controlvars, collapse = " + ")))
,
data=diss_df, x=T, y=T)
model_gov_aid <- robcov(model_gov_aid, diss_df$GWNo)
# Program Aid
model_program_aid <- ols(formula(paste0("polity2_t2 ~ cabinetCOUNT + log(program_aid_gdp_zero + 1) + ",
paste0(controlvars, collapse = " + ")))
,
data=diss_df, x=T, y=T)
model_program_aid <- robcov(model_program_aid, diss_df$GWNo)
# Budget Aid
model_commodity_aid <- ols(formula(paste0("polity2_t2 ~ cabinetCOUNT + log(commodity_aid_gdp_zero + 1) + ",
paste0(controlvars, collapse = " + ")))
,
data=diss_df, x=T, y=T)
model_commodity_aid <- robcov(model_commodity_aid, diss_df$GWNo)
# Output table for manuscript
source("functions/extract_ols_custom.R")
source("./functions/custom_texreg.R")
environment(custom_texreg) <- asNamespace('texreg')
#
custom_texreg(l = list(model_ps,
model_seniorCOUNT,
model_nonseniorCOUNT,
model_gov_aid,
model_program_aid,
model_commodity_aid),
file = "../output/ind_effects_democ.tex",
reorder.coef = c(1, 9, 10, 11:13, 2:8),
stars = c(0.001, 0.01, 0.05, 0.1),
symbol = "+",
# custom.multicol = T,
custom.coef.names = c("Intercept",
"Power-Sharing (cabinet)",
"Aid / GDP (log)",
"GDP p/c (log)",
"Population (log)",
"Conflict intensity",
"Non-State Violence",
"Nat. res. rents",
"Regime Type (Polity)",
"Power-Sharing (senior)",
"Power-Sharing (nonsenior)",
"Democracy Aid/GDP (log)",
"Program Aid/GDP (log)",
"Budget Aid/GDP (log)"),
omit.coef = "Intercept",
table = FALSE,
dcolumn = T,
groups = list("Power-Sharing" = 1:3, "Aid" = 4:7, "Controls" = 8:13),
booktabs = T,
use.packages = F,
center = TRUE,
include.cluster = T,
include.lr = F,
include.rsquared = F)
# custom.model.names = c(" \\multicolumn{3}{c}{ \\textbf{Power-Sharing}} & \\multicolumn{3}{c}{ \\textbf{Foreign Aid}} \\\\ \\cmidrule(r){2-4} \\cmidrule(l){5-7} & \\multicolumn{1}{c}{(1) }",
# "\\multicolumn{1}{c}{(2) }",
# "\\multicolumn{1}{c}{(3) }",
# "\\multicolumn{1}{c}{(4) }",
# "\\multicolumn{1}{c}{(5) }",
# "\\multicolumn{1}{c}{(6) }"))
#
# Output table for replication archive
htmlreg(l = list(model_ps,
model_seniorCOUNT,
model_nonseniorCOUNT,
model_gov_aid,
model_program_aid,
model_commodity_aid),
reorder.coef = c(1, 9, 10, 11:13, 2:8),
# file = "./output/psresults.tex",
stars = c(0.001, 0.01, 0.05, 0.1),
symbol = "+",
caption = "",
custom.multicol = T,
custom.coef.names = c("Intercept",
"Power-Sharing (cabinet)",
"Aid / GDP (log)",
"GDP p/c (log)",
"Population (log)",
"Conflict intensity",
"Non-State Violence",
"Nat. res. rents",
"Regime Type (Polity)",
"Power-Sharing (senior)",
"Power-Sharing (nonsenior)",
"Democracy Aid/GDP (log)",
"Program Aid/GDP (log)",
"Budget Aid/GDP (log)"),
omit.coef = "Intercept",
groups = list("Power-Sharing" = 1:3, "Aid" = 4:7, "Controls" = 8:13),
table = FALSE,
dcolumn = T,
booktabs = T,
use.packages = F,
center = TRUE,
star.symbol = "\\*",
include.lr = F)| Model 1 | Model 2 | Model 3 | Model 4 | Model 5 | Model 6 | ||
|---|---|---|---|---|---|---|---|
| Power-Sharing | |||||||
| Power-Sharing (cabinet) | 0.10* | 0.10* | 0.10* | 0.10* | |||
| (0.05) | (0.05) | (0.05) | (0.05) | ||||
| Power-Sharing (senior) | 0.31* | ||||||
| (0.13) | |||||||
| Power-Sharing (nonsenior) | 0.14+ | ||||||
| (0.08) | |||||||
| Aid | |||||||
| Democracy Aid/GDP (log) | 0.49 | ||||||
| (0.40) | |||||||
| Program Aid/GDP (log) | 0.33 | ||||||
| (0.44) | |||||||
| Budget Aid/GDP (log) | 0.06 | ||||||
| (0.24) | |||||||
| Aid / GDP (log) | -0.03 | -0.02 | -0.02 | -0.10 | -0.12 | -0.05 | |
| (0.14) | (0.14) | (0.14) | (0.18) | (0.20) | (0.14) | ||
| Controls | |||||||
| GDP p/c (log) | -0.12 | -0.12 | -0.12 | -0.10 | -0.02 | -0.12 | |
| (0.31) | (0.31) | (0.31) | (0.31) | (0.35) | (0.32) | ||
| Population (log) | -0.02 | -0.02 | -0.02 | -0.02 | 0.01 | -0.02 | |
| (0.11) | (0.11) | (0.11) | (0.11) | (0.12) | (0.11) | ||
| Conflict intensity | 0.19 | 0.17 | 0.20 | 0.04 | 0.16 | 0.17 | |
| (0.45) | (0.45) | (0.45) | (0.44) | (0.44) | (0.46) | ||
| Non-State Violence | -0.38 | -0.35 | -0.38 | -0.25 | -0.33 | -0.36 | |
| (0.52) | (0.52) | (0.52) | (0.52) | (0.53) | (0.51) | ||
| Nat. res. rents | -0.04* | -0.04* | -0.03* | -0.04* | -0.03* | -0.04* | |
| (0.02) | (0.02) | (0.02) | (0.02) | (0.02) | (0.02) | ||
| Regime Type (Polity) | 0.82*** | 0.82*** | 0.82*** | 0.81*** | 0.82*** | 0.82*** | |
| (0.07) | (0.07) | (0.07) | (0.07) | (0.07) | (0.07) | ||
| Num. obs. | 263 | 263 | 263 | 263 | 263 | 263 | |
| Adj. R2 | 0.76 | 0.76 | 0.76 | 0.76 | 0.76 | 0.76 | |
| ***p < 0.001, **p < 0.01, *p < 0.05, +p < 0.1 | |||||||
Table 5.2: Interaction Effects of Power-Sharing and Foreign Aid on Post-Conflict Political Development
# Libraries
library(tidyverse)
library(rms)
# library(texreg)
# load
load("./data/diss_df.rda")
# to predict substantive effects from this model, we need to define data
# distribution
diss_df$conflictID <- NULL
datadist_diss_df <- datadist(diss_df); options(datadist='datadist_diss_df')
# Polity DV + cabineINC
model_polity_cabinc <- ols(polity2_t2 ~
cabinetINC *
aiddata_AidGDP_ln +
log(GDP_per_capita) +
ln_pop +
conf_intens +
nonstate +
WBnatres +
polity2 ,
data=diss_df, x=T, y=T)
model_polity_cabinc <- robcov(model_polity_cabinc, diss_df$GWNo)
# Polity DV + cabCOUNT
model_polity_cabcount <- ols(polity2_t2 ~
cabinetCOUNT *
aiddata_AidGDP_ln +
log(GDP_per_capita) +
ln_pop +
conf_intens +
nonstate +
WBnatres +
polity2 ,
data=diss_df, x=T, y=T)
model_polity_cabcount <- robcov(model_polity_cabcount, diss_df$GWNo)
# FH + cabINC
model_fh_cabinc <- ols(fh_t2 ~
cabinetINC * aiddata_AidGDP_ln +
log(GDP_per_capita) +
ln_pop +
conf_intens +
nonstate +
WBnatres +
fh, x= T, y = T,
data=diss_df)
model_fh_cabinc <- robcov(model_fh_cabinc, diss_df$GWNo)
# FH + cabcount
model_fh_cabcount <- ols(fh_t2 ~
cabinetCOUNT * aiddata_AidGDP_ln +
log(GDP_per_capita) +
ln_pop +
conf_intens +
nonstate +
WBnatres +
fh ,
data=diss_df, x=T, y=T)
model_fh_cabcount <- robcov(model_fh_cabcount, diss_df$GWNo)
# tex output
source("functions/extract_ols_custom.R")
source("./functions/custom_texreg.R")
environment(custom_texreg) <- asNamespace('texreg')
#
custom_texreg(l=list(model_polity_cabinc,
model_polity_cabcount,
model_fh_cabinc,
model_fh_cabcount),
file = "../output/mainresults2.tex",
stars = c(0.001, 0.01, 0.05, 0.1),
custom.note = "%stars",
center = TRUE,
symbol = "+",
reorder.coef = c(1, 9:11, 2:8, 12),
custom.coef.names = c("Intercept",
"Power-sharing (binary)",
"Aid / GDP (log)",
"GDP p/c (log)",
"Population (log)",
"Conflict intensity",
"Non-State Violence",
"Nat. res. rents",
"Polity",
"Power-sharing (binary) * Aid",
"Power-sharing (cabinet)",
"Power-sharing (cabinet) * Aid",
"Freedom House"),
omit.coef = "Intercept",
table = FALSE,
custom.multicol = T,
dcolumn = T,
booktabs = T,
include.rsquared = F,
include.cluster = T,
use.packages = F,
custom.model.names = c("\\multicolumn{2}{c}{\\textbf{Polity}} & \\multicolumn{2}{c}{\\textbf{Freedom House}} \\\\ \\cmidrule(r){2-3} \\cmidrule(l){4-5} & \\multicolumn{1}{c}{(1)}",
"\\multicolumn{1}{c}{(2) }",
"\\multicolumn{1}{c}{(3) }",
"\\multicolumn{1}{c}{(4) }"))
# output replication archive
htmlreg(l=list(model_polity_cabinc,
model_polity_cabcount,
model_fh_cabinc,
model_fh_cabcount),
# file = "../output/mainresults.tex",
stars = c(0.001, 0.01, 0.05, 0.1),
# custom.note = "%stars",
center = TRUE,
symbol = "+",
reorder.coef = c(1, 9:11, 2:8, 12),
custom.coef.names = c("Intercept",
"Power-sharing (binary)",
"Aid / GDP (log)",
"GDP p/c (log)",
"Population (log)",
"Conflict intensity",
"Non-State Violence",
"Nat. res. rents",
"Polity",
"Power-sharing (binary) * Aid",
"Power-sharing (cabinet)",
"Power-sharing (cabinet) * Aid",
"Freedom House"),
omit.coef = "Intercept",
table = FALSE,
custom.multicol = T,
dcolumn = T,
booktabs = T,
use.packages = F,
star.symbol = "\\*",
include.lr = F)| Model 1 | Model 2 | Model 3 | Model 4 | ||
|---|---|---|---|---|---|
| Power-sharing (binary) | -1.08+ | -0.26+ | |||
| (0.63) | (0.16) | ||||
| Power-sharing (binary) * Aid | 0.81*** | 0.12+ | |||
| (0.24) | (0.07) | ||||
| Power-sharing (cabinet) | -0.17+ | -0.05+ | |||
| (0.09) | (0.03) | ||||
| Power-sharing (cabinet) * Aid | 0.11*** | 0.02* | |||
| (0.03) | (0.01) | ||||
| Aid / GDP (log) | -0.05 | -0.02 | -0.03 | -0.03 | |
| (0.14) | (0.14) | (0.04) | (0.04) | ||
| GDP p/c (log) | -0.09 | -0.08 | -0.10+ | -0.10+ | |
| (0.31) | (0.31) | (0.06) | (0.06) | ||
| Population (log) | 0.01 | -0.02 | 0.01 | 0.00 | |
| (0.11) | (0.11) | (0.03) | (0.03) | ||
| Conflict intensity | 0.16 | 0.09 | -0.07 | -0.07 | |
| (0.45) | (0.46) | (0.09) | (0.09) | ||
| Non-State Violence | -0.37 | -0.45 | -0.36*** | -0.38*** | |
| (0.50) | (0.50) | (0.11) | (0.11) | ||
| Nat. res. rents | -0.03* | -0.04* | -0.01+ | -0.01* | |
| (0.02) | (0.02) | (0.00) | (0.00) | ||
| Polity | 0.82*** | 0.82*** | |||
| (0.07) | (0.07) | ||||
| Freedom House | 0.87*** | 0.87*** | |||
| (0.04) | (0.04) | ||||
| Num. obs. | 263 | 263 | 272 | 272 | |
| Adj. R2 | 0.76 | 0.76 | 0.80 | 0.80 | |
| ***p < 0.001, **p < 0.01, *p < 0.05, +p < 0.1 | |||||
Table 5.3: Robustness Checks
# Libraries
library(tidyverse)
library(rms)
library(plm)
library(texreg)
library(countrycode)
library(texreg)
# Load Data
load("./data/diss_df.rda")
# Models
# Fractionalization
democ_model_elf <- ols(polity2_t2 ~
cabinetCOUNT * aiddata_AidGDP_ln +
ln_gdp_pc +
ln_pop +
conf_intens+
nonstate +
WBnatres +
polity2 +
Ethnic,
data=diss_df,
x = T, y = T)
democ_model_elf <- robcov(democ_model_elf, diss_df$GWNo)
# PKO
democ_model_pko <- ols(polity2_t2 ~
cabinetCOUNT * aiddata_AidGDP_ln +
ln_gdp_pc +
ln_pop +
conf_intens+
nonstate +
WBnatres +
polity2 +
DS_ordinal,
data=diss_df,
x = T, y = T)
democ_model_pko <- robcov(democ_model_pko, diss_df$GWNo)
# Cabinet Size
library(readxl)
cnts <- read_excel("./data/CNTSDATA.xls")DEFINEDNAME: 21 00 00 01 0b 00 00 00 01 00 00 00 00 00 00 0d 3b 00 00 00 00 e5 3b 00 00 c1 00 DEFINEDNAME: 21 00 00 01 0b 00 00 00 01 00 00 00 00 00 00 0d 3b 00 00 00 00 e5 3b 00 00 c1 00 DEFINEDNAME: 21 00 00 01 0b 00 00 00 01 00 00 00 00 00 00 0d 3b 00 00 00 00 e5 3b 00 00 c1 00 DEFINEDNAME: 21 00 00 01 0b 00 00 00 01 00 00 00 00 00 00 0d 3b 00 00 00 00 e5 3b 00 00 c1 00
cnts <- cnts %>% filter(year >= 1989)
cnts$iso3c <- countrycode(cnts$country, "country.name", "iso3c")
cnts <- cnts %>% filter(country != "SOMALILAND")
testcabsize <- left_join(diss_df, cnts[, c("iso3c", "year", "polit10")])
testcabsize$ps_share <- testcabsize$cabinetCOUNT / testcabsize$polit10 * 100
model_cabsize <- ols(polity2_t2 ~
ps_share *
aiddata_AidGDP_ln +
ln_gdp_pc +
ln_pop +
conf_intens +
nonstate +
WBnatres +
polity2,
data = testcabsize, x = T, y = T)
model_cabsize <- robcov(model_cabsize, testcabsize$GWNo)
# Random Effects
country_re_democ <- plm(polity2_t2 ~
cabinetCOUNT +
aiddata_AidGDP_ln +
cabinetCOUNT * aiddata_AidGDP_ln +
ln_gdp_pc +
ln_pop +
conf_intens+
nonstate +
WBnatres +
polity2 ,
data=diss_df, model = "random", index =c("GWNo", "year"))series conflictID is NA and has been removed series conflictdummy, newconflictinyearv412, onset1v412, onset2v412, onset5v412, onset8v412, onset20v412, maxintyearv412, govonlyv412, terronlyv412, bothgovterrv412, sumconfv412, pcyears, is.pc, codingend are constants and have been removed
country_re_democ$vcov <- vcovHC(country_re_democ, cluster="group")
# Region Fixed Effects
diss_df$region <- countrycode(diss_df$Location, "country.name", "region")
diss_df$country_year <- paste0(diss_df$Location, diss_df$year, sep = "-")
region_fe_democ <- plm(polity2_t2 ~
cabinetCOUNT * aiddata_AidGDP_ln +
ln_gdp_pc +
ln_pop +
conf_intens+
nonstate +
WBnatres +
polity2 ,
data=diss_df,
model = "within",
index =c("region", "country_year"))series conflictID is NA and has been removed series conflictdummy, newconflictinyearv412, onset1v412, onset2v412, onset5v412, onset8v412, onset20v412, maxintyearv412, govonlyv412, terronlyv412, bothgovterrv412, sumconfv412, pcyears, is.pc, codingend are constants and have been removed
region_fe_democ$vcov <- vcovHC(region_fe_democ, cluster="group")
# Country FE
country_fe_democ <- plm(polity2_t2 ~
cabinetCOUNT * aiddata_AidGDP_ln +
ln_gdp_pc +
ln_pop +
conf_intens+
nonstate +
WBnatres +
polity2 ,
data=diss_df,
model = "within",
index =c("GWNo", "year"))series conflictID is NA and has been removed series conflictdummy, newconflictinyearv412, onset1v412, onset2v412, onset5v412, onset8v412, onset20v412, maxintyearv412, govonlyv412, terronlyv412, bothgovterrv412, sumconfv412, pcyears, is.pc, codingend are constants and have been removed
country_fe_democ$vcov <- vcovHC(country_fe_democ, cluster="group")
## Order of coefficients in output table
name_map_robustness <- list(cabinetCOUNT = "PS (cabinet)",
"cabinetCOUNT * aiddata_AidGDP_ln" = "PS (cabinet) * Aid",
"cabinetCOUNT:aiddata_AidGDP_ln" = "PS (cabinet) * Aid",
ps_share = "PS (cabinet share)",
"ps_share * aiddata_AidGDP_ln" = "PS (cabinet share) * Aid",
aiddata_AidGDP_ln = "Aid / GDP (log)",
ln_gdp_pc = "GDP p/c",
ln_pop = "Population",
conf_intens = "Conflict Intensity",
nonstate = "Non-State Violence",
WBnatres = "Nat. Res. Rents",
polity2 = "Polity",
Ethnic = "Ethnic Frac.",
DS_ordinal = "UN PKO")
source("./functions/will_lowe_texreg_reorder.R")
# Model list
rob_models <- list(democ_model_elf,
democ_model_pko,
model_cabsize,
country_re_democ,
region_fe_democ,
country_fe_democ)
oldnames <- all.varnames.dammit(rob_models)
ror <- build.ror(oldnames, name_map_robustness)
# Output manuscript
#
# source("./functions/custom_texreg.R")
# environment(custom_texreg) <- asNamespace('texreg')
#
custom_texreg(l = rob_models,
stars = c(0.001, 0.01, 0.05, 0.1),
symbol = "+",
table = F,
booktabs = T,
use.packages = F,
dcolumn = T,
file = "../output/robustness.tex",
custom.model.names = c("(1) ELF",
"(2) PKO",
"(3) Cab. Size",
"(4) RE",
"(5) Region FE",
"(6) Country FE"),
custom.coef.names = ror$ccn,
omit.coef = ror$oc,
include.rsquared = F,
include.cluster = T,
reorder.coef = unique(ror$rc),
star.symbol = "\\*",
include.lr = F, include.variance = F)
# Output replication archive
htmlreg(l = rob_models,
stars = c(0.001, 0.01, 0.05, 0.1),
symbol = "+",
table = F,
booktabs = T,
use.packages = F,
dcolumn = T,
# file = "../output/robustness.tex",
custom.model.names = c("(1) ELF",
"(2) PKO",
"(3) Cabinet Size",
"(4) Random Effects",
"(5) Region FE",
"(6) Country FE"),
custom.coef.names = ror$ccn,
omit.coef = ror$oc,
include.rsquared = F,
include.cluster = T,
caption = "",
reorder.coef = unique(ror$rc),
star.symbol = "\\*",
include.lr = F, include.variance = F)| (1) ELF | (2) PKO | (3) Cabinet Size | (4) Random Effects | (5) Region FE | (6) Country FE | ||
|---|---|---|---|---|---|---|---|
| PS (cabinet) | -0.17+ | -0.19+ | -0.17+ | -0.15 | -0.14 | ||
| (0.10) | (0.10) | (0.10) | (0.18) | (0.09) | |||
| PS (cabinet) * Aid | 0.11*** | 0.11*** | 0.09** | 0.11* | 0.06* | ||
| (0.03) | (0.03) | (0.03) | (0.04) | (0.03) | |||
| PS (cabinet share) | -0.03 | ||||||
| (0.04) | |||||||
| PS (cabinet share) * Aid | 0.03* | ||||||
| (0.01) | |||||||
| Aid / GDP (log) | -0.02 | -0.03 | -0.19 | -0.03 | 0.11 | 0.05 | |
| (0.14) | (0.15) | (0.17) | (0.17) | (0.22) | (0.21) | ||
| GDP p/c | -0.08 | -0.06 | -0.01 | 0.11 | -0.14 | -0.36 | |
| (0.31) | (0.32) | (0.37) | (0.30) | (0.31) | (0.50) | ||
| Population | -0.02 | -0.02 | -0.08 | -0.04 | 0.11 | 0.66 | |
| (0.11) | (0.11) | (0.14) | (0.14) | (0.14) | (2.88) | ||
| Conflict Intensity | 0.09 | 0.05 | 0.27 | 0.62 | 0.18 | 3.31* | |
| (0.47) | (0.44) | (0.62) | (0.52) | (0.50) | (1.44) | ||
| Non-State Violence | -0.45 | -0.43 | -0.12 | -0.51 | -0.59 | -0.87 | |
| (0.52) | (0.50) | (0.66) | (0.48) | (0.61) | (0.59) | ||
| Nat. Res. Rents | -0.04* | -0.04* | -0.05* | -0.03* | -0.03* | 0.02 | |
| (0.02) | (0.02) | (0.02) | (0.02) | (0.01) | (0.02) | ||
| Polity | 0.82*** | 0.82*** | 0.76*** | 0.71*** | 0.67*** | 0.46** | |
| (0.07) | (0.07) | (0.08) | (0.09) | (0.09) | (0.15) | ||
| Ethnic Frac. | 0.00 | ||||||
| (0.79) | |||||||
| UN PKO | 0.08 | ||||||
| (0.12) | |||||||
| Num. obs. | 263 | 263 | 204 | 263 | 263 | 263 | |
| Countries | 44 | 44 | 40 | ||||
| Adj. R2 | 0.76 | 0.76 | 0.74 | 0.64 | 0.58 | 0.37 | |
| ***p < 0.001, **p < 0.01, *p < 0.05, +p < 0.1 | |||||||
Table 5.4: Additional Aid Lags
# Libraries
library(tidyverse)
library(rms)
library(texreg)
# load
load("./data/diss_df.rda")
# Estimate models with time lags -------------------------------------
# 1 year lag
lag1_pchng <- ols(polity2_t2 ~
cabinetCOUNT * aiddata_AidGDP_ln_tmin1 +
log(GDP_per_capita) +
log(population) +
conf_intens +
nonstate +
WBnatres +
polity2,
data=diss_df, x=T, y=T)
lag1_pchng <- robcov(lag1_pchng, diss_df$GWNo)
lag1_fhchng <- ols(fh_t2 ~
cabinetCOUNT * aiddata_AidGDP_ln_tmin1 +
log(GDP_per_capita) +
log(population) +
conf_intens +
nonstate +
WBnatres +
fh ,
data=diss_df, x=T, y=T)
lag1_fhchng <- robcov(lag1_fhchng, diss_df$GWNo)
# 2 years aid lag
lag2_pchng <- ols(polity2_t2 ~
cabinetCOUNT * aiddata_AidGDP_ln_tmin2 +
log(GDP_per_capita) +
log(population) +
conf_intens +
nonstate +
WBnatres +
polity2,
data=diss_df, x=T, y=T)
lag2_pchng <- robcov(lag2_pchng, diss_df$GWNo)
lag2_fhchng <- ols(fh_t2 ~
cabinetCOUNT * aiddata_AidGDP_ln_tmin2 +
log(GDP_per_capita) +
log(population) +
conf_intens +
nonstate +
WBnatres +
fh ,
data=diss_df, x=T, y=T)
lag2_fhchng <- robcov(lag2_fhchng, diss_df$GWNo)
#### Model Output ####
## Order of coefficients in output table
name_map <- list(cabinetCOUNT = "Power-Sharing (cabinet)",
aiddata_AidGDP_ln_tmin1 = "Aid / GDP (log)",
aiddata_AidGDP_ln_tmin2 = "Aid / GDP (log)",
"cabinetCOUNT * aiddata_AidGDP_ln_tmin1" = "Power-Sharing (cabinet) * Aid",
"cabinetCOUNT * aiddata_AidGDP_ln_tmin2" = "Power-Sharing (cabinet) * Aid",
GDP_per_capita = "GDP p/c",
population = "Population",
conf_intens = "Conflict Intensity",
nonstate = "Non-State Violence",
WBnatres = "Nat. Res. Rents",
polity2 = "Regime Type (Polity)",
fh = "Regime Type (FH)")
source("./functions/will_lowe_texreg_reorder.R")
# Model list
model_list <- list(lag1_pchng,lag2_pchng, lag1_fhchng, lag2_fhchng)
oldnames <- all.varnames.dammit(model_list)
ror <- build.ror(oldnames, name_map)
#
# # Output for Manuscript
#
source("./functions/custom_texreg.R")
environment(custom_texreg) <- asNamespace('texreg')
#
custom_texreg(l = model_list,
file = "../output/laggedresults2.tex",
stars = c(0.001, 0.01, 0.05, 0.1),
symbol = "+",
table = F,
booktabs = T,
use.packages = F,
dcolumn = T,
custom.model.names = c("\\multicolumn{2}{c}{\\textbf{Polity}} & \\multicolumn{2}{c}{\\textbf{Freedom House}} \\\\ \\cmidrule(r){2-3} \\cmidrule(l){4-5} & \\multicolumn{1}{c}{(1) 1 year}",
"\\multicolumn{1}{c}{(2) 2 years}",
"\\multicolumn{1}{c}{(3) 1 year}",
"\\multicolumn{1}{c}{(4) 2 years}"),
custom.coef.names = ror$ccn,
omit.coef = ror$oc,
reorder.coef = unique(ror$rc),
custom.multicol = T,
include.rsquared = F,
include.cluster = T,
include.lr = F)
# Output for Replication Archive
htmlreg(l = model_list,
stars = c(0.001, 0.01, 0.05, 0.1),
symbol = "+",
table = F,
booktabs = T,
use.packages = F,
caption = "",
dcolumn = T,
custom.coef.names = ror$ccn,
omit.coef = ror$oc,
reorder.coef = unique(ror$rc),
star.symbol = "\\*",
include.lr = F)| Model 1 | Model 2 | Model 3 | Model 4 | ||
|---|---|---|---|---|---|
| Power-Sharing (cabinet) | -0.09 | -0.05 | -0.06** | -0.08* | |
| (0.09) | (0.11) | (0.02) | (0.03) | ||
| Aid / GDP (log) | -0.08 | -0.01 | -0.06 | 0.04 | |
| (0.15) | (0.14) | (0.04) | (0.04) | ||
| Power-Sharing (cabinet) * Aid | 0.08* | 0.06+ | 0.03*** | 0.03** | |
| (0.03) | (0.03) | (0.01) | (0.01) | ||
| GDP p/c | 0.15 | 0.29 | -0.08 | 0.05 | |
| (0.34) | (0.34) | (0.07) | (0.08) | ||
| Population | -0.15 | -0.29* | -0.00 | -0.00 | |
| (0.10) | (0.12) | (0.04) | (0.03) | ||
| Conflict Intensity | 0.12 | -0.06 | 0.01 | 0.00 | |
| (0.41) | (0.42) | (0.08) | (0.10) | ||
| Non-State Violence | 0.29 | 0.33 | -0.32** | -0.22* | |
| (0.52) | (0.44) | (0.11) | (0.11) | ||
| Nat. Res. Rents | -0.04+ | -0.03 | -0.01 | -0.00 | |
| (0.02) | (0.02) | (0.00) | (0.01) | ||
| Regime Type (Polity) | 0.84*** | 0.86*** | |||
| (0.07) | (0.07) | ||||
| Regime Type (FH) | 0.90*** | 0.92*** | |||
| (0.04) | (0.03) | ||||
| Num. obs. | 202 | 141 | 209 | 146 | |
| Adj. R2 | 0.79 | 0.81 | 0.83 | 0.85 | |
| ***p < 0.001, **p < 0.01, *p < 0.05, +p < 0.1 | |||||
Table 5.6: 2SLS Second Stage Results
# load libraries
library(AER)
library(ivpack)
library(lmtest)
library(lfe)
library(tikzDevice)
# load data with instrument; this gives "instrument_df" data frame
load(file = "./data/instrumentedAid2.RData")
load("./data/diss_df.rda")
diss_df_iv <- merge(diss_df,
instrument_df,
by = c("iso2c", "year"), all.x = TRUE)
# subset only complete.cases / necessary for cluster.robust.se()
iv_na <- na.omit(diss_df_iv[, c("polity_chng",
"cabinetCOUNT",
"cabinetINC",
"aiddata_Aid",
"aiddata_AidGDP_ln",
"aiddata_AidPC_ln",
"fh",
"fh_t2",
"GDP_per_capita",
"population",
"conf_intens",
"WBnatres",
"polity2",
"polity2_t2",
"total_sum_except",
"year",
"GWNo",
"GDP",
"nonstate")])
# to proceed with IV estimation I first hard-code the instrument
iv_na$instr_aid_gdp_ln <- log(iv_na$total_sum_except / iv_na$GDP)
# data transformation for Stata
iv_na$ln_gdp_pc <- log(iv_na$GDP_per_capita)
iv_na$ln_pop <- log(iv_na$population)
# and save data to Stata format
foreign::write.dta(iv_na, "./data/iv_na_democ.dta")
iv_na$cabinetCOUNTxaid_ln <- iv_na$cabinetCOUNT * iv_na$aiddata_AidGDP_ln
iv_na$cabinetCOUNTxaid_ln_instr <- iv_na$cabinetCOUNT * iv_na$instr_aid_gdp_ln
# IV for Polity
democ_pchng_iv <- lfe::felm(polity2_t2 ~
cabinetCOUNT +
log(GDP_per_capita) +
log(population) +
nonstate +
conf_intens +
WBnatres +
polity2
| 0 | (cabinetCOUNTxaid_ln|aiddata_AidGDP_ln ~
cabinetCOUNTxaid_ln_instr + instr_aid_gdp_ln) | GWNo,
data = iv_na)
# IV for FH
democ_fhchng_iv <- lfe::felm(fh_t2 ~
cabinetCOUNT +
log(GDP_per_capita) +
log(population) +
nonstate +
conf_intens +
WBnatres +
fh
| 0 | (cabinetCOUNTxaid_ln|aiddata_AidGDP_ln ~
cabinetCOUNTxaid_ln_instr + instr_aid_gdp_ln) | GWNo,
data = iv_na)
# Output Second Stage Results
## Order of coefficients in output table
name_map <- list(cabinetCOUNT = "Power-Sharing (cabinet)",
"`aiddata_AidGDP_ln(fit)`" = "Aid / GDP (log)",
"`cabinetCOUNTxaid_ln(fit)`" = "Power-Sharing (cabinet) * Aid",
"log(GDP_per_capita)" = "GDP p/c",
"log(population)" = "Population",
conf_intens = "Conflict Intensity",
nonstate = "Non-State Violence",
WBnatres = "Nat. Res. Rents",
polity2 = "Regime Type",
fh = "Regime Type")
source("./functions/will_lowe_texreg_reorder.R")
# Model list
model_list <- list(democ_pchng_iv,
democ_fhchng_iv)
oldnames <- all.varnames.dammit(model_list)
ror <- build.ror(oldnames, name_map)
# Output for Manuscript
# library(texreg)
# source("./functions/extract_felm_custom.R")
# # # options(expressions = 1500)
# setMethod("extract", signature = className("felm", "lfe"),
# definition = extract.felm.custom)
source("./functions/custom_texreg.R")
environment(custom_texreg) <- asNamespace('texreg')
#
# # library(texreg)
# custom_texreg(l = model_list,
# file = "../output/ivresults_democ.tex",
# stars = c(0.001, 0.01, 0.05, 0.1),
#
# add.lines = list(c("Kleibergen-Paap rk Wald F statistic",
# "40.651",
# "40.651"),
# c("Adj. R$^2$",
# round(summary(democ_pchng_iv)$adj.r.squared, 2),
# round(summary(democ_fhchng_iv)$adj.r.squared, 2))),
# symbol = "+",
# table = F,
# booktabs = T,
# use.packages = F,
# dcolumn = T,
# custom.model.names = c("(1) Polity",
# "(2) Freedom House"),
#
# custom.coef.map = name_map,
#
# include.lr = F,
# include.rsquared = F,
# include.adjrs = F)
# Replication Archive Output
texreg::htmlreg(l = model_list,
stars = c(0.001, 0.01, 0.05, 0.1),
symbol = "+",
table = F,
booktabs = T,
use.packages = F,
caption = "",
dcolumn = T,
custom.model.names = c("(1) Polity",
"(2) Freedom House"),
# custom.coef.map = name_map,
custom.coef.names = ror$ccn,
omit.coef = ror$oc,
reorder.coef = unique(ror$rc),
star.symbol = "\\*",
include.lr = F)| (1) Polity | (2) Freedom House | ||
|---|---|---|---|
| Power-Sharing (cabinet) | -0.21+ | -0.11** | |
| (0.11) | (0.04) | ||
| Aid / GDP (log) | 0.38 | 0.16** | |
| (0.26) | (0.05) | ||
| Power-Sharing (cabinet) * Aid | 0.12** | 0.04** | |
| (0.04) | (0.01) | ||
| GDP p/c | 0.40 | 0.13 | |
| (0.46) | (0.09) | ||
| Population | 0.08 | 0.04 | |
| (0.15) | (0.04) | ||
| Conflict Intensity | -0.25 | -0.22+ | |
| (0.47) | (0.13) | ||
| Non-State Violence | -0.38 | -0.35** | |
| (0.47) | (0.11) | ||
| Nat. Res. Rents | -0.04+ | -0.01 | |
| (0.02) | (0.00) | ||
| Regime Type | 0.80*** | 0.83*** | |
| (0.07) | (0.04) | ||
| Num. obs. | 260 | 260 | |
| R2 (full model) | 0.76 | 0.78 | |
| R2 (proj model) | 0.76 | 0.78 | |
| Adj. R2 (full model) | 0.75 | 0.78 | |
| Adj. R2 (proj model) | 0.75 | 0.78 | |
| ***p < 0.001, **p < 0.01, *p < 0.05, +p < 0.1 | |||
---
title: "Chapter 5: Democratization Scores"
output: 
  html_document:
    toc: true
    toc_float: 
      collapsed: false
    code_download: true
    code_folding: "hide"
---
# Figure 5.1:  Illustrative Examples: Political Development, Power-Sharing and Foreign Aid Flows in the DRC and Liberia

```{r, fig.align = "center", message=F, warning=F, cache = T, comments = F, fig.height= 10, fig.width = 15, dev = "CairoPNG"}

# Libraries
library(tidyverse)
library(foreign)
library(countrycode)
library(cowplot)
library(tikzDevice)

# Read Data
load("./data/diss_df.rda")
aid <- read.dta("./data/aiddata_full.dta")
ucdp_cy <- read_csv("./data/133280_onset2012csv.csv")

p4 <- haven::read_spss("./data/p4v2012.sav")
names(p4)[2] <- "GWNo"
# Polity IV treats Serbia as Yugoslavia at some periods. 
# I change it back to Serbia
p4[p4$GWNo == 347 & p4$year > 1991, "GWNo"] <- 345


# Prepare UCDP data

# load two small helper function to determine pre- and post-conflict years
ag_seq <- function(x) {
  runs <- cumsum(c(0, diff(x) != 1 ))
  return(runs)
}

source("./functions/identifyPostConflictYears.R")

# load and wrangle ucdp data
ucdp_cy <- ucdp_cy %>% 
  arrange(gwno, year) %>% 
  group_by(gwno) %>% 
  mutate(iso2c = countrycode(gwno, "cown", "iso2c"),
         ucdp_dummy = ifelse(incidencev412 == 1, 1, 0),
         pc = pcIdentifier(ucdp_dummy),
         pc = ifelse(pc == 2, 1, 0),
         conflict_ep_id = cumsum(c(ifelse(first(ucdp_dummy) == 1, 
                                          1, 0), 
                                 diff(ucdp_dummy) != 0)), 
         conflict_ep_id = as.numeric(ifelse(ucdp_dummy == 1, 
                                            conflict_ep_id, 
                                            NA)),
         peace_ep_id = cumsum(c(ifelse(first(pc) == 2, 
                                       2, 1), 
                                diff(pc) != 0)),
         peace_ep_id = as.numeric(ifelse(pc == 1, peace_ep_id, NA)))%>% 
  group_by(gwno, conflict_ep_id) %>% 
  mutate(conflict_year = -1 * rev(cumsum(ucdp_dummy == 1))) %>% 
  group_by(gwno, peace_ep_id) %>% 
  mutate(peace_year = cumsum(pc == 1)) %>% 
  group_by(gwno) %>% 
  mutate(conf_and_peace_years = conflict_year + peace_year)

# Merge in Aid data
ucdp_cy <- left_join(ucdp_cy, 
                     aid, 
                     by = c("iso2c" = "iso2", "year")) %>% 
  filter(year >= 1989) %>% 
  dplyr::rename(wb_AidGNI = DT_ODA_ODAT_GN_ZS,
                wb_AidPC = DT_ODA_ODAT_PC_ZS,
                wb_AidGmentXP = DT_ODA_ODAT_XP_ZS,
                oecd_Aid = aid_oecd_commitment2011USD,
                oecd_Aid_mill = aid_oecd_commitment2011USD_mill,
                aiddata_Aid = commitment_amount_usd_constant)


# Prepare data, i.e. subset UCDP data with only the countries in my sample etc.
ucdp_cy_diss_df <- ucdp_cy %>% 
  filter(gwno %in% unique(diss_df$GWNo)) %>% 
  group_by(recipient) %>% 
  mutate(mean_aid_gdp = mean(aiddata_Aid / GDP, na.rm = T)) %>% 
  ungroup() %>% 
  # mutate(ordered_recipient_by_aidmean = factor(recipient, 
  #                                              levels = recipient[order(mean_aid_gdp, 
  #                                                                                  decreasing = T)])) %>% 
  left_join(., diss_df[, c("GWNo", "year", "cabinetINC", "cabinetCOUNT", "seniorCOUNT", "nonseniorCOUNT")],
            by = c("gwno" = "GWNo", "year")) %>% 
  left_join(., p4[, c("GWNo", "year", "polity2")], by = c("gwno" = "GWNo", "year")) %>% 
  filter(year <= 2010)



# define function that generates illustrative country plots 
plot_country_example <- function(country, year_min, year_max) {

  
# Aid Plot
cntry_illustration_plot <- ucdp_cy_diss_df %>% 
  filter(recipient == country & year >= year_min & year <= year_max) %>% 
  ggplot(., aes(x = year, y = aiddata_Aid / GDP * 100)) +

  # Add conflict years to aid plot
  geom_bar(data = ucdp_cy_diss_df %>% filter(incidencev412 == 1 &
                                                 recipient == country &
                                                 year >= year_min & year <= year_max),
           aes(x = year, y = Inf),
           fill = "#de2d26", 
           alpha = 0.2, stat = "identity", width = 1) +
  geom_bar(stat = "identity",
           position = "dodge", 
           color = "black", 
           fill = "#969696", width = 1) +
  scale_x_continuous(breaks = year_min:year_max) +
  labs(x = "", y = "Aid / GDP")

# Polity Plot
cntry_illustration_polity <- ucdp_cy_diss_df %>% 
  filter(recipient == country & year >= year_min & year <= year_max) %>% 
  ggplot(., aes(x = year, y = polity2)) +
  geom_bar(stat = "identity", position = "dodge", color = "white", fill = "white", width = 1) +
  
  # Add conflict years to polity plot
  geom_bar(data = ucdp_cy_diss_df %>% filter(incidencev412 == 1 &
                                                 recipient == country &
                                                 year >= year_min & year <= year_max),
           aes(x = year, y = Inf),
           fill = "#de2d26", alpha = 0.2, stat = "identity", width = 1) +

  geom_point() + geom_line() + 
  scale_x_continuous(breaks = year_min:year_max) +
  labs(x = "", y = "Polity 2")

# Power-Sharing Plot
cntry_illustration_ps <- ucdp_cy_diss_df %>% 
  filter(recipient == country & year >= year_min & year <= year_max) %>% 
  dplyr::select(year, seniorCOUNT, nonseniorCOUNT) %>% 
  gather(key, value, -year) %>% 
  mutate(value = ifelse(is.na(value), 0, value)) %>% 
  ggplot(., aes(x = year, y =  value, fill = factor(key))) +
  geom_bar(stat = "identity", 
           position = "stack", color = "black", width = 1) +
  scale_fill_manual("", 
                    values = c("#bdc9e1", "#045a8d"),
                    labels = c("Nonsenior rebel seats", "Senior rebel seats")) + 
  
  # Add conflict years to ps plot
  geom_bar(data = ucdp_cy_diss_df %>% filter(incidencev412 == 1 &
                                                 recipient == country &
                                                 year >= year_min & year <= year_max),
           aes(x = year, y = Inf),
           fill = "#de2d26", alpha = 0.2, stat = "identity", width = 1) +
  scale_x_continuous(breaks = year_min:year_max) +
  labs(x = "", y = "Number of Rebel Seats \n in Power-Sharing \nGovernment") +
  theme(legend.position = "bottom", legend.direction = "vertical")

# combine plot output
cowplot::plot_grid(cntry_illustration_plot, 
                   cntry_illustration_polity, 
                   cntry_illustration_ps,
                   nrow = 3, 
                   align = "v" )

}

# 
# # Output for manuscript
# # Liberia
# options( tikzDocumentDeclaration = "\\documentclass[15pt]{article}" )
# tikz("../figures/Liberia_illu.tex", height = 7, width = 5, pointsize = 7)
# plot_country_example("Liberia", 2000, 2010)
# dev.off()
# 
# # DRC
# options( tikzDocumentDeclaration = "\\documentclass[15pt]{article}" )
# tikz("../figures/DRC_illu.tex", height = 7, width = 5, pointsize = 7)
# plot_country_example("Congo, Dem. Rep.", 2000, 2010)
# dev.off()

# Output for Replication Archive
plot_grid(plot_country_example("Liberia", 2000, 2010), 
          plot_country_example("Congo, Dem. Rep.", 2000, 2010), 
          ncol = 2, 
          labels = c("Liberia", "DRC"), 
          vjust = -5,
          hjust = -7,
          scale = 0.9)



```




# Figure 5.2: Foreign Aid and Polity Scores in Country-Years With and Without Power-Sharing Governments


```{r, fig.align = "center", message=F, warning=F, cache = T, fig.height = 3.5, comments = F, dev = "CairoPNG"}

# Libraries
library(tidyverse)
library(tikzDevice)

# Load Data
load("./data/diss_df.rda")

# Labels for easier plotting
diss_df$cabinetINClabel <- ifelse(diss_df$cabinetINC == 1, "Power-Sharing", 
                                    "No \nPower-Sharing")

# generate scatterplot, divided by power-sharing
aidps_democ_plot <- ggplot(diss_df, aes(x = log(aiddata_AidGDP), 
                                            y = polity2_t2))  +
  geom_point(size = 1.7, alpha = 0.5) +
  geom_smooth(method = "lm") +
  # geom_rug() +
  theme_bw() +
  labs(x = "Aid / GDP (log)", y = "Polity score at t2") +
  facet_wrap( ~ cabinetINClabel) 

# output plot for manuscript
# options(tikzDocumentDeclaration = "\\documentclass[11pt]{article}" )
# tikz("../figures/aidps_democ_plot.tex", height = 3.5)
# print(aidps_democ_plot)
# dev.off()

# output for replication archive
print(aidps_democ_plot)


```

<!-- *Note:* Individual data points represent country-years. Low values on the y-axis indicate more autocratic Polity 2 scores; positive values indicate more democratic Polity 2 scores. -->


# Figure 5.3: Temporal Dynamics of the Interactive Effect between Power-Sharing and Foreign Aid
```{r, fig.align = "center", message=F, warning=F, cache = T, comments = F, width = 9, height = 7, dev = "CairoPNG"}

# Libraries
library(tidyverse)
library(cowplot)
library(lfe)
library(tikzDevice)

# Data
load("./data/diss_df.rda")

# Prepare data frame for multiple plots
polity_vars <- list(
  polity2_t1 = diss_df,
  polity2_t2 = diss_df, 
  polity2_t3 = diss_df, 
  polity2_t4 = diss_df, 
  polity2_t5 = diss_df, 
  fh_t1 = diss_df,
  fh_t2 = diss_df, 
  fh_t3 = diss_df, 
  fh_t4 = diss_df, 
  fh_t5 = diss_df
)

# create data frame with list column
polity_vars <- enframe(polity_vars)

# define function that will be applied to every data frame in the list column
main_model <- function(lead_type, data) {
  data <- as.data.frame(data)
  data$lead_var <- data[, grep(lead_type, names(data), value =T)]
  
  if(grepl("fh", lead_type)) {
      model <- lfe::felm(lead_var ~
                       cabinetCOUNT *
                       aiddata_AidGDP_ln +
                       log(GDP_per_capita) +
                       ln_pop +
                       conf_intens +
                       nonstate +
                       WBnatres +
                       fh | 0 | 0 | GWNo,
                       data=data)
    return(model)
  } else {
    model <- lfe::felm(lead_var ~
                       cabinetCOUNT *
                       aiddata_AidGDP_ln +
                       log(GDP_per_capita) +
                       ln_pop +
                       conf_intens +
                       nonstate +
                       WBnatres +
                       polity2 | 0 | 0 | GWNo,
                       data=data)
    return(model)
  }
}


# fit models & post-process data for plotting
model_all <- polity_vars %>% 
  mutate(model = map2(name, value, ~ main_model(.x, .y))) 

model_out <- model_all %>% 
  mutate(coef = map(model, broom::tidy)) %>% 
  unnest(coef) %>% 
  # keep only interaction term coefs
  filter(term == "cabinetCOUNT:aiddata_AidGDP_ln") %>% 
  mutate(dem_score = ifelse(grepl("polity", name), "Polity", "Freedom House")) %>% 
  dplyr::select(dem_score, name, estimate, std.error) %>% 
  group_by(dem_score) %>% 
  mutate(name = 1:5)

temp_dyn_democ <- model_out
save(temp_dyn_democ, file = "./data/temp_dyn_democ.rda")

temp_dynamics_plot <- ggplot(model_out, 
       aes(x = name, 
           y = estimate, 
           group = dem_score, color = dem_score)) +
  geom_point( size = 1.7, 
       position = position_dodge(width = .5)) + 
  geom_errorbar(aes(ymin = estimate - 1.67 * std.error, 
                    ymax = estimate + 1.67 * std.error), 
                width = 0, 
       position = position_dodge(width = .5)) +
  geom_hline(yintercept = 0, linetype = 2) +
  facet_wrap(~dem_score, nrow = 2, scales = "free_y") + 
  scale_color_brewer("",palette = "Set1") +
  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 = "none")

# Output for manuscript
# options(tikzDocumentDeclaration = "\\documentclass[11pt]{article}" )
# tikz("../figures/temp_dynamics_plot.tex", height = 3.5)
# print(temp_dynamics_plot)
# dev.off()

# Output for replication archive
print(temp_dynamics_plot)

```

<!-- *Note:* The plot shows the coefficient for the interaction term between Power-Sharing (binary) and Aid/GDP for different annual leads of the dependent variable. The x-axis shows the year after t 0 (as a reference, the coefficient for the year 2 is the one reported in Table 1.2); the y-axis depicts coefficient size with 90% confidence intervals. The full model results for this plot are reported in the Appendix -->

# Figure 5.4: Marginal Effects of Power-Sharing and Foreign Aid on Post-Conflict Democratization

```{r, fig.align = "center", message=F, warning=F, cache = T, comments = F, fig.width = 8, fig.height=3.5,  dev = "CairoPNG"}

# Libraries
library(gridExtra)
library(Cairo)
library(tikzDevice)
library(reshape)


# Polity DV + cabineINC
model_polity_cabinc <- ols(polity2_t2 ~  
                        cabinetINC * 
                        aiddata_AidGDP_ln +
                        log(GDP_per_capita) +
                        ln_pop +
                        conf_intens +
                        nonstate + 
                        WBnatres +
                        polity2 ,
                      data=diss_df, x=T, y=T)
model_polity_cabinc <- robcov(model_polity_cabinc, diss_df$GWNo)

# Polity DV + cabCOUNT
model_polity_cabcount <- ols(polity2_t2 ~  
                        cabinetCOUNT * 
                        aiddata_AidGDP_ln +
                        log(GDP_per_capita) +
                        ln_pop +
                        conf_intens +
                        nonstate + 
                        WBnatres +
                        polity2 ,
                      data=diss_df, x=T, y=T)
model_polity_cabcount <- robcov(model_polity_cabcount, diss_df$GWNo)


# source interaction plot function to plot marginal effects
source("./functions//interaction_plots.R")

# Output for manuscript
# options( tikzDocumentDeclaration = "\\documentclass[11pt]{article}" )
# tikz("../figures/interaction.tex", height = 3.5)
# 
# # Output for replication archive
# par(mfrow=c(1,3),
#     mar = c(5, 7, 4, 0.5),
#     cex.lab = 1.3,
#     cex.axis = 1.3,
#     mgp = c(3.5, 1, 0))
# 
# interaction_plot_continuous(model_polity_cabcount, 
#                             "cabinetCOUNT", "aiddata_AidGDP_ln", "cabinetCOUNT * aiddata_AidGDP_ln", 
#                             title = "", 
#                             ylab = "Marginal effect of Power-Sharing (cabinet) \n on political development", 
#                             xlab = "a) Aid / GDP (Log)\n", 
#                             conf = .90)
# interaction_plot_continuous(model_polity_cabcount, 
#                             "aiddata_AidGDP_ln", "cabinetCOUNT", "cabinetCOUNT * aiddata_AidGDP_ln", 
#                             title = "", 
#                             ylab = "Marginal effect of Aid\n on political development",
#                             xlab = "b) Power-Sharing \n(Number of rebel seats)",
#                             conf = .90)
# interaction_plot_binary(model_polity_cabinc, 
#                              "aiddata_AidGDP_ln","cabinetINC", "cabinetINC * aiddata_AidGDP_ln", 
#                             title = "", 
#                             ylab = "Marginal effect of Aid\n on political development",
#                             xlab = "c) Power-Sharing \n(1 = Yes, 0 = No)",
#                             conf = .90)
# dev.off()

# Output for replication archive
par(mfrow=c(1,3),
    mar = c(5, 7, 4, 0.5),
    cex.lab = 1.3,
    cex.axis = 1.3,
    mgp = c(3.5, 1, 0))

interaction_plot_continuous(model_polity_cabcount, 
                            "cabinetCOUNT", "aiddata_AidGDP_ln", "cabinetCOUNT * aiddata_AidGDP_ln", 
                            title = "", 
                            ylab = "Marginal effect of Power-Sharing (cabinet) \n on political development", 
                            xlab = "a) Aid / GDP (Log)\n", 
                            conf = .90)
interaction_plot_continuous(model_polity_cabcount, 
                            "aiddata_AidGDP_ln", "cabinetCOUNT", "cabinetCOUNT * aiddata_AidGDP_ln", 
                            title = "", 
                            ylab = "Marginal effect of Aid\n on political development",
                            xlab = "b) Power-Sharing \n(Number of rebel seats)",
                            conf = .90)
interaction_plot_binary(model_polity_cabinc, 
                             "aiddata_AidGDP_ln","cabinetINC", "cabinetINC * aiddata_AidGDP_ln", 
                            title = "", 
                            ylab = "Marginal effect of Aid\n on political development",
                            xlab = "c) Power-Sharing \n(1 = Yes, 0 = No)",
                            conf = .90)

```


<!-- *Note:*  Marginal effect calculations based on Model 2 in Table 5.2. Dashed lines indicate 90% confidence intervals. The underlying histogram depicts the distribution of observations in the sample on the variable on the x-axis.  -->


# Figure 5.5: Model Predictions for the Effect of Power-Sharing and Foreign Aid on Post-Conflict Democratization

```{r, fig.align = "center", message=F, warning=F, cache = T, comments = F, fig.width = 6.5, fig.height = 4.5, dev = "CairoPNG"}

# Libraries
library(tidyverse)
library(rms)
library(gridExtra)
library(tikzDevice)

# Load data
load("data/diss_df.rda")

# to predict substantive effects from this model, we need to define data 
# distribution
diss_df$conflictID <- NULL
datadist_diss_df <- datadist(diss_df); options(datadist='datadist_diss_df')

# replicate Model from above Polity DV + cabCOUNT
model_polity_cabcount <- ols(polity2_t2 ~  
                        cabinetCOUNT * 
                        aiddata_AidGDP_ln +
                        ln_gdp_pc +
                        ln_pop +
                        conf_intens +
                        nonstate + 
                        WBnatres +
                        polity2 ,
                      data=diss_df, x=T, y=T)
model_polity_cabcount <- robcov(model_polity_cabcount, diss_df$GWNo)

# Start predictions for aid
prediction_democ_aid <- Predict(model_polity_cabcount, 
                           cabinetCOUNT = c(0, 10), # no / much power-sharing
                           aiddata_AidGDP_ln = seq(-5.7, 5.17, 0.1),# range of aid
                           polity2 = 2.6,
                           conf.int = 0.9) 

subs_effects_pchng_aid <- ggplot(data.frame(prediction_democ_aid), 
                                    aes(x = exp(aiddata_AidGDP_ln), 
                                        y = yhat, 
                                        group = as.factor(cabinetCOUNT))) + 
  geom_line( color = "black", size = 1) + 
  geom_ribbon(aes(ymax = upper, 
                  ymin = lower, 
                  fill = as.factor(cabinetCOUNT)), 
              alpha = 0.7) +
  scale_fill_manual(values = c("#b3cde3", "#e41a1c"), 
                    name = "Number of Rebels \nin the Power-Sharing Coalition:") +
  theme_bw() +
  theme(text = element_text(size=8)) +
  labs(x = "Aid / GDP", 
       y = "Predicted Polity Scores") +
  theme(legend.position = "bottom") 

# Predictions power-sharing
prediction_democ_ps <- Predict(model_polity_cabcount, 
                              cabinetCOUNT = seq(0, 10, 1), 
                              aiddata_AidGDP_ln = c(0, 3.4),
                              polity2 = 2.6,
                              conf.int = 0.9)

prediction_democ_ps$aiddata_AidGDP_ln <- round(exp(prediction_democ_ps$aiddata_AidGDP_ln))


subs_effects_pchng_ps <- ggplot(data.frame(prediction_democ_ps), 
                                           aes(x = cabinetCOUNT, 
                                               y = yhat, 
                                               group = as.factor(aiddata_AidGDP_ln))) + 
  geom_line( color = "black", size = 1) + 
  geom_ribbon(aes(ymax = upper, 
                  ymin = lower, 
                  fill = as.factor(aiddata_AidGDP_ln)), 
              alpha = 0.7) +
  scale_fill_manual(values = c("#b3cde3", "#e41a1c"), 
                    name = "Aid in per cent of GDP:") +
  theme_bw() +
  scale_x_continuous(breaks = seq(0, 10, 2)) +
  theme(text = element_text(size=8)) +
  labs(x = "Power-Sharing (No. of rebel seats in government)", 
       y = "Predicted Polity Scores") +
  theme(legend.position = "bottom") 

# output prediction plots


# output plot for predicted VDEM election quality variables 

 
# options( tikzDocumentDeclaration = "\\documentclass[11pt]{article}" )
# tikz("../figures/aidps_pchng.tex", height = 4.5, width = 6.5)
# grid.arrange(subs_effects_pchng_ps,
#              subs_effects_pchng_aid,
#              nrow = 1)
# dev.off()

grid.arrange(subs_effects_pchng_ps, 
             subs_effects_pchng_aid, 
             nrow = 1)

```


<!-- *Note:* Marginal effects simulated with all other variables held at their mean/median, based on Model 2 in Table 1.2. The dependent variable Polity 2 at t 2 . The shaded areas represent 90% confidence intervals around predicted effects. All simulations were conducted with Frank Harrell’s rms package for R (Harrell 2014). -->

<!-- Left Panel: The blue (light grey) line represents the simulated effect for an aid level of 1% of the recipient’s GDP; the red line (dark grey) for an aid level of 30% of recipient GDP. -->

<!-- Right Panel: The blue (light grey) line represents the simulated effect of no rebel seats in the post-conflict government and red (dark grey) simulates the effect of 10 rebel seats. The x-axis represents the actual, not the logged value of foreign aid / GDP. -->

# Figure 5.6: Mechanisms: Variation in Types of Power-Sharing and Aid

```{r, fig.align = "center", message=F, warning=F, cache = T, comments = F, fig.width = 9, fig.height = 4, dev = "CairoPNG"}

# Libraries
library(tidyverse)
library(rms)
library(plm)
library(lfe)
library(tidyverse)
library(tikzDevice)

# Load data
load("./data/diss_df.rda")

# Estimate Models
model_polity_cabinc <- felm(polity2_t2 ~  
                        cabinetINC * 
                        aiddata_AidGDP_ln +
                        log(GDP_per_capita) +
                        ln_pop +
                        conf_intens +
                        nonstate + 
                        WBnatres +
                        polity2 | 0 | 0 | GWNo,
                      data=diss_df)

# Polity DV + seniorINC
model_polity_senior <- lfe::felm(polity2_t2 ~  
                        seniorINC * 
                        aiddata_AidGDP_ln +
                        log(GDP_per_capita) +
                        ln_pop +
                        conf_intens +
                        nonstate + 
                        WBnatres +
                        polity2 | 0 | 0 | GWNo ,
                      data=diss_df)

# Polity DV + nonseniorINC
model_polity_nonsenior <- felm(polity2_t2 ~  
                        nonseniorINC * 
                        aiddata_AidGDP_ln +
                        log(GDP_per_capita) +
                        ln_pop +
                        conf_intens +
                        nonstate + 
                        WBnatres +
                        polity2 | 0 | 0 | GWNo,
                      data=diss_df)

# DGA * PS
model_polity_cabinc_dga <- felm(polity2_t2 ~  
                        cabinetINC * log(dga_gdp_zero + 1) +
                        aiddata_AidGDP_ln +
                        log(GDP_per_capita) +
                        ln_pop +
                        conf_intens +
                        nonstate + 
                        WBnatres +
                        polity2 | 0 | 0 | GWNo,
                      data=diss_df)

model_polity_cabinc_programaid <- felm(polity2_t2 ~  
                        cabinetINC * log(program_aid_gdp_zero + 1) +
                        aiddata_AidGDP_ln +
                        log(GDP_per_capita) +
                        ln_pop +
                        conf_intens +
                        nonstate + 
                        WBnatres +
                        polity2 | 0 | 0 | GWNo,
                      data=diss_df)

model_polity_cabinc_budget <- felm(polity2_t2 ~  
                        cabinetINC * log(commodity_aid_gdp_zero + 1) +
                        aiddata_AidGDP_ln +
                        log(GDP_per_capita) +
                        ln_pop +
                        conf_intens +
                        nonstate + 
                        WBnatres +
                        polity2 | 0 | 0 | GWNo,
                      data=diss_df)

mechanism_models <- list("Cabinet PS (Baseline)" = model_polity_cabinc, 
                         "Senior PS" = model_polity_senior, 
                         "Nonsenior PS" = model_polity_nonsenior, 
                         "DGA" = model_polity_cabinc_dga, 
                         "Program Aid"= model_polity_cabinc_programaid, 
                         "Budget Aid" = model_polity_cabinc_budget) %>% 
  enframe() %>% 
  mutate(tidymodel = map(value, broom::tidy)) %>% 
  unnest(tidymodel) %>% 
  filter(grepl(":", term)) %>% 
  mutate(name = forcats::fct_relevel(name, c("Cabinet PS (Baseline)", 
                                      "Senior PS", 
                                      "Nonsenior PS", 
                                      "DGA", 
                                      "Program Aid", 
                                      "Budget Aid")))
mechanism_models_democ <- mechanism_models
save(mechanism_models_democ, file= "./data/mechanism_models_democ.rda")

mechanims_democ_models_plot <- ggplot(mechanism_models, aes(x = name, y = estimate) ) +
  geom_point() +
  geom_errorbar(aes(ymin = estimate - 1.67 * std.error, 
                    ymax = estimate + 1.67 * std.error), 
                width = 0) +
  theme_bw() +
  theme(axis.title.y = element_text(size = 10)) +
  geom_hline(yintercept = 0, linetype = 2) +
  labs(x = "", y = "Coefficient Estimate of Interaction between \n Different Types of Power-Sharing (binary) \n and Different Aid Types")

# Output for manuscript
# options(tikzDocumentDeclaration = "\\documentclass[11pt]{article}" )
# tikz("../figures/mechanims_democ_models_plot.tex", height = 2.75)
# print(mechanims_democ_models_plot)
# dev.off()

# Output for replication archive
print(mechanims_democ_models_plot)

``` 

<!-- *Note:* Coefficients for the interaction between different types of power-sharing and aid. Reference model is Model 1 in Table 5.2. 90% confidence intervals. -->

# Figure 5.7 & Table B.5: Covariate Balance Before and After Matching

```{r, results="asis", message=F, warning=F, cache = T, comments = F, fig.align="center", dev = "CairoPNG"}
# Libraries
library(dplyr)
library(MatchIt)
library(texreg)
library(rms)
library(tidyr)
library(ggrepel) # needed for variable labels
library(ggplot2)
library(tikzDevice)
library(xtable)

# Load data 
load("./data/diss_df.rda")

# select relevant variables
match_democ_data <- diss_df %>% 
  ungroup() %>% 
  dplyr::select(cabinetINC, cabinetCOUNT, seniorINC,
                seniorCOUNT, nonseniorINC, nonseniorCOUNT,
                aiddata_AidGDP, population, nonstate,
                WBnatres, fh, GDP_per_capita, conf_intens,
                aiddata_AidGDP_ln, 
                GWNo, year, 
                 polity2, polity2_t2, # check 
                fh, fh_t2, 
                pc_period, Location, ln_pop, ln_gdp_pc)

# keep only complete cases (necessary for matching)
match_democ_data <- match_democ_data[complete.cases(match_democ_data), ]

# generate pretreatment controls: control variables in first post-conflict year
match_democ_data <- match_democ_data %>% 
  arrange(GWNo, pc_period, year) %>% 
  group_by(GWNo, pc_period) %>% 
  mutate(match_aiddata_AidGDP_ln = first(aiddata_AidGDP_ln),
         match_pop = first(population),
         match_gdp = first(GDP_per_capita),
         match_nonstate = first(nonstate),
         match_WBnatres = first(WBnatres),
         match_polity = first(polity2), 
         match_fh = first(fh))

# explicitly convert to data frame
match_democ_data <- as.data.frame(match_democ_data)

# 1.1 Perform matching algorithm ------------------------------------------

set.seed(123)
match_democ_res <- matchit(cabinetINC ~
                          match_aiddata_AidGDP_ln +
                          log(match_gdp) +
                          log(match_pop) +
                          conf_intens + # conf_intens is already pre-treatment
                          match_nonstate +
                          log(match_WBnatres + 1)  +
                          match_polity,
                        method = "nearest",
                        distance = "mahalanobis",
                        ratio = 2,
                        data = match_democ_data)

# extract data
match_democ_df <- match.data(match_democ_res)

# Balance Improvement Plot ------------------------------------------------

balance_improvement <- summary(match_democ_res)$reduction
row.names(balance_improvement) <- c("Aid / GDP (log)", 
                                    "GDP / PC (log)",
                                    "Population (log)", 
                                    "Conflict Intensity",
                                    "Nonstate Conflict",
                                    "Nat. Res. Rents (log)", 
                                    "Regime Type (Polity)")

balance_improvement <- data.frame(variable = row.names(balance_improvement), 
                                  percent_balance_improvement = round(balance_improvement[, 1], 2))


plot_balanceimpr <- data.frame(`All Data` = abs(summary(match_democ_res, 
                                                        standardize = T)$sum.all$`Std. Mean Diff.`), 
                               `Matched Data` = abs(summary(match_democ_res, 
                                                            standardize = T)$sum.matched$`Std. Mean Diff.`),
                               cov = balance_improvement$variable) %>% 
  gather(key, value, -cov) %>% 
  filter(!is.nan(value) & !is.infinite(value)) %>% 
  mutate(key = gsub("\\.", " ", key))

# Plot balance improvement
plot_balanceimpr_out <- plot_balanceimpr %>% 
  ggplot(., aes(x = key, y = value)) + 
  geom_point(size = 2.5) + 
  geom_line(aes(group = cov),
            linemitre = 2, size = 0.6) + 
  geom_hline(yintercept = 0.25, aes(group = key)) +
  geom_label_repel(data = plot_balanceimpr %>% filter(key == "All Data"),
                   aes(label = cov),
                   nudge_x = -.3) +
  theme_bw() +
  labs( x = "", y = "Absolute Standardized Difference in Means")

# output balance improvement plot
# options( tikzDocumentDeclaration = "\\documentclass[11pt]{article}" )
# tikz("../figures/match_democ_balanceimpr.tex", height = 5)
# print(plot_balanceimpr_out)
# dev.off()

# Output for replication archive
print(plot_balanceimpr_out)


#### Estimate Models on Matched Sample #####

model_democ_matched <- ols(polity2_t2 ~  
                             cabinetINC *
                             aiddata_AidGDP_ln +
                             log(GDP_per_capita) +
                             log(population) +
                             conf_intens +
                             nonstate + 
                             WBnatres + 
                             polity2
                           ,
                           data=match_democ_df , x=T, y=T)
model_democ_matched <- rms::robcov(model_democ_matched, match_democ_df$GWNo)

model_democ_matched_fh <- ols(fh_t2 ~  
                             cabinetINC *
                             aiddata_AidGDP_ln +
                             log(GDP_per_capita) +
                             log(population) +
                             conf_intens +
                               nonstate + 
                               WBnatres + 
                               polity2                           ,
                             data=match_democ_df , x=T, y=T)
model_democ_matched_fh <- rms::robcov(model_democ_matched_fh, match_democ_df$GWNo)

## Order of coefficients in output table
name_map <- list(cabinetINC = "Power-Sharing (binary)",
                 "cabinetINC * aiddata_AidGDP_ln" = "Power-Sharing (binary) * Aid", 
                 aiddata_AidGDP_ln = "Aid / GDP (log)",
                 GDP_per_capita = "GDP p/c",
                 population = "Population",
                 conf_intens = "Conflict Intensity",
                 nonstate = "Non-State Violence",
                 WBnatres = "Nat. Res. Rents",
                 polity2 = "Regime Type")

source("./functions/will_lowe_texreg_reorder.R")

# Model list
model_list <- list(model_democ_matched, model_democ_matched_fh)

oldnames <- all.varnames.dammit(model_list)
ror <- build.ror(oldnames, name_map)

# Output for replication archive
# texreg(l = model_list,
#         stars = c(0.001, 0.01, 0.05, 0.1),
#         symbol = "+",
#         table = F,
#         booktabs = T,
#         use.packages = F,
#         dcolumn = T,
#         file = "../output/democ_matching_results.tex",
#         custom.model.names = c("(1) Polity",
#                                "(2) Freedom House"),
#         custom.coef.names = ror$ccn,
#         omit.coef = ror$oc,
#         reorder.coef = unique(ror$rc),
#         include.lr = F)

# Output for replication archive
htmlreg(l = model_list,
        stars = c(0.001, 0.01, 0.05, 0.1),
        symbol = "+",
        caption = "", 
        table = F,
        booktabs = T,
        use.packages = F,
        dcolumn = T,
        custom.model.names = c("(1) Polity", 
                               "(2) Freedom House"),
        custom.coef.names = ror$ccn, 
        omit.coef = ror$oc, 
        reorder.coef = unique(ror$rc),
        star.symbol = "\\*", 
        include.lr = F)

```




# Figure 5.8: The The Relationship between Foreign Aid and Aid (Instrumented) 
```{r, fig.align = "center", message=F, warning=F, cache = T, comments = F, width = 9, height = 7, dev = "CairoPNG"}


# load libraries
library(AER)
library(ivpack)
library(lmtest)
library(tikzDevice)


# load data with instrument; this gives "instrument_df" data frame
load(file = "./data/instrumentedAid2.RData")
load("./data/diss_df.rda")

diss_df_iv <- merge(diss_df, 
                      instrument_df, 
                      by = c("iso2c", "year"), all.x = TRUE)


# subset only complete.cases / necessary for cluster.robust.se()
iv_na <- na.omit(diss_df_iv[, c("polity_chng", 
                               "cabinetCOUNT", 
                               "cabinetINC", 
                               "aiddata_Aid",
                               "aiddata_AidGDP_ln",
                               "aiddata_AidPC_ln",
                               "fh",
                               "fh_chng",
                               "GDP_per_capita", 
                               "population", 
                               "conf_intens", 
                               "WBnatres", 
                               "polity2", 
                               "polity2_t2",
                               "total_sum_except", 
                               "year", 
                               "GWNo", 
                               "GDP",
                               "nonstate")])


# 3.1 Plot Z_it against aid -----------------------------------------------

plot_democ_aid_iv <- ggplot(iv_na, aes(x = log(total_sum_except), 
                                       y = log(aiddata_Aid))) +
  geom_point(alpha = 0.7, size = 3) + 
  geom_smooth(method = "lm") +
  theme_bw() + 
  labs(x = "Aid (instrumented) Log",
       y = "Aid (AidData) Log ")


# Output Manuscript
# options( tikzDocumentDeclaration = "\\documentclass[10pt]{article}" )
# tikz("../figures/aid_instr_democ.tex", height = 4)
# print(plot_democ_aid_iv)
# dev.off()

# Output Replication Archive
print(plot_democ_aid_iv)


```

<!-- *Note:* Each data point represents one country-year. The correlation between both variables is ρ = 0.73, ( p < 0.001 ), represented by the blue regression line. The grey area represents a 95% confidence band around the regression line  -->

# Table 5.1: Individual Effects of Power-Sharing and Foreign Aid on Post-Conflict Political Development

```{r, results="asis", message=F, warning=F, cache = T, comments = F}

# Libraries
library(texreg)
library(rms)

# Load data
load("./data/diss_df.rda")

# specify vector with control vars
controlvars <- c("log(aiddata_AidGDP)",
                 "log(GDP_per_capita)",
                 "ln_pop",
                 "conf_intens", 
                 "nonstate",
                 "WBnatres",
                 "polity2")
#### Power-Sharing only Models ####

# PS only + cabinetCOUNT
model_ps <- ols(formula(paste0("polity2_t2 ~ cabinetCOUNT + ", 
                               paste0(controlvars, collapse = " + "))),
                data = diss_df, x = T, y = T)
model_ps <- robcov(model_ps, diss_df$GWNo)

# PS only + seniorCOUNT
model_seniorCOUNT <- ols(formula(paste0("polity2_t2 ~ seniorCOUNT + ", 
                                        paste0(controlvars, collapse = " + "))),
                         data = diss_df, x = T, y = T)
model_seniorCOUNT <- robcov(model_seniorCOUNT, diss_df$GWNo)

# PS only + nonseniorCOUNT
model_nonseniorCOUNT <- ols(formula(paste0("polity2_t2 ~ nonseniorCOUNT + ", 
                                           paste0(controlvars, collapse = " + "))),
                            data = diss_df, x = T, y = T)
model_nonseniorCOUNT <- robcov(model_nonseniorCOUNT, diss_df$GWNo)

#### Aid-only Models ####

# DGA
model_gov_aid <- ols(formula(paste0("polity2_t2 ~ cabinetCOUNT + log(dga_gdp_zero + 1) + ", 
                                    paste0(controlvars, collapse = " + ")))
                     ,
                     data=diss_df, x=T, y=T)
model_gov_aid <- robcov(model_gov_aid, diss_df$GWNo)


# Program Aid
model_program_aid <- ols(formula(paste0("polity2_t2 ~ cabinetCOUNT + log(program_aid_gdp_zero + 1) + ", 
                                        paste0(controlvars, collapse = " + ")))
                         ,
                         data=diss_df, x=T, y=T)
model_program_aid <- robcov(model_program_aid, diss_df$GWNo)


# Budget Aid
model_commodity_aid <- ols(formula(paste0("polity2_t2 ~ cabinetCOUNT + log(commodity_aid_gdp_zero + 1) + ", 
                                          paste0(controlvars, collapse = " + ")))
                           ,
                           data=diss_df, x=T, y=T)
model_commodity_aid <- robcov(model_commodity_aid, diss_df$GWNo)


# Output table for manuscript
source("functions/extract_ols_custom.R")
source("./functions/custom_texreg.R")
environment(custom_texreg) <- asNamespace('texreg')
# 
custom_texreg(l = list(model_ps,
                       model_seniorCOUNT,
                       model_nonseniorCOUNT,
                       model_gov_aid,
                       model_program_aid,
                       model_commodity_aid),
              file = "../output/ind_effects_democ.tex",
              reorder.coef = c(1, 9, 10, 11:13, 2:8),
              stars = c(0.001, 0.01, 0.05, 0.1),
              symbol = "+",
              # custom.multicol = T,
              custom.coef.names = c("Intercept",
                                    "Power-Sharing (cabinet)",
                                    "Aid / GDP (log)",
                                    "GDP p/c (log)",
                                    "Population (log)",
                                    "Conflict intensity",
                                    "Non-State Violence",
                                    "Nat. res. rents",
                                    "Regime Type (Polity)",
                                    "Power-Sharing (senior)",
                                    "Power-Sharing (nonsenior)",
                                    "Democracy Aid/GDP (log)",
                                    "Program Aid/GDP (log)",
                                    "Budget Aid/GDP (log)"),
              omit.coef = "Intercept",
              table = FALSE,
              dcolumn = T,
              groups = list("Power-Sharing" = 1:3, "Aid" = 4:7, "Controls"  = 8:13),
              booktabs = T,
              use.packages = F,
              center = TRUE,
              include.cluster = T, 
              include.lr = F, 
              include.rsquared = F)
# custom.model.names = c(" \\multicolumn{3}{c}{ \\textbf{Power-Sharing}} & \\multicolumn{3}{c}{ \\textbf{Foreign Aid}} \\\\ \\cmidrule(r){2-4} \\cmidrule(l){5-7} & \\multicolumn{1}{c}{(1)  }",
#                        "\\multicolumn{1}{c}{(2)  }",
#                        "\\multicolumn{1}{c}{(3)  }",
#                        "\\multicolumn{1}{c}{(4)  }",
#                        "\\multicolumn{1}{c}{(5)   }",
#                        "\\multicolumn{1}{c}{(6)   }"))
# 

# Output table for replication archive

htmlreg(l = list(model_ps, 
                 model_seniorCOUNT, 
                 model_nonseniorCOUNT, 
                 model_gov_aid, 
                 model_program_aid, 
                 model_commodity_aid), 
        reorder.coef = c(1, 9, 10, 11:13, 2:8),
        # file = "./output/psresults.tex",
        stars = c(0.001, 0.01, 0.05, 0.1),
        symbol = "+",
        caption = "", 
        custom.multicol = T,
        custom.coef.names = c("Intercept",
                              "Power-Sharing (cabinet)",
                              "Aid / GDP (log)",
                              "GDP p/c (log)",
                              "Population (log)",
                              "Conflict intensity",
                              "Non-State Violence",
                              "Nat. res. rents",
                              "Regime Type (Polity)",
                              "Power-Sharing (senior)",
                              "Power-Sharing (nonsenior)", 
                              "Democracy Aid/GDP (log)", 
                              "Program Aid/GDP (log)", 
                              "Budget Aid/GDP (log)"),
        omit.coef = "Intercept",
        groups = list("Power-Sharing" = 1:3, "Aid" = 4:7, "Controls"  = 8:13),
        table = FALSE, 
        dcolumn = T,
        booktabs = T,
        use.packages = F,
        center = TRUE,
        star.symbol = "\\*", 
        include.lr = F)

```
<!-- Notes: Dependent variable is $\textit{Polity}_{t2}$. Standard errors clustered by country. Intercept estimated, but not reported. -->

<!-- <p> &nbsp; </p> -->



# Table 5.2: Interaction Effects of Power-Sharing and Foreign Aid on Post-Conflict Political Development

```{r, results="asis", message=F, warning=F, cache = T, comments = F}
# Libraries
library(tidyverse)
library(rms)
# library(texreg)

# load 
load("./data/diss_df.rda")

# to predict substantive effects from this model, we need to define data 
# distribution
diss_df$conflictID <- NULL
datadist_diss_df <- datadist(diss_df); options(datadist='datadist_diss_df')

# Polity DV + cabineINC
model_polity_cabinc <- ols(polity2_t2 ~  
                        cabinetINC * 
                        aiddata_AidGDP_ln +
                        log(GDP_per_capita) +
                        ln_pop +
                        conf_intens +
                        nonstate + 
                        WBnatres +
                        polity2 ,
                      data=diss_df, x=T, y=T)
model_polity_cabinc <- robcov(model_polity_cabinc, diss_df$GWNo)

# Polity DV + cabCOUNT
model_polity_cabcount <- ols(polity2_t2 ~  
                        cabinetCOUNT * 
                        aiddata_AidGDP_ln +
                        log(GDP_per_capita) +
                        ln_pop +
                        conf_intens +
                        nonstate + 
                        WBnatres +
                        polity2 ,
                      data=diss_df, x=T, y=T)
model_polity_cabcount <- robcov(model_polity_cabcount, diss_df$GWNo)

# FH + cabINC
model_fh_cabinc <- ols(fh_t2 ~  
                      cabinetINC * aiddata_AidGDP_ln +
                      log(GDP_per_capita) +
                      ln_pop +
                      conf_intens +
                      nonstate + 
                      WBnatres +
                      fh, x= T, y = T,
                    data=diss_df)
model_fh_cabinc <- robcov(model_fh_cabinc, diss_df$GWNo)

# FH + cabcount
model_fh_cabcount <- ols(fh_t2 ~  
                      cabinetCOUNT * aiddata_AidGDP_ln +
                      log(GDP_per_capita) +
                      ln_pop +
                      conf_intens +
                      nonstate + 
                      WBnatres +
                      fh ,
                    data=diss_df, x=T, y=T)
model_fh_cabcount <- robcov(model_fh_cabcount, diss_df$GWNo)

# tex output
source("functions/extract_ols_custom.R")
source("./functions/custom_texreg.R")
environment(custom_texreg) <- asNamespace('texreg')
# 
custom_texreg(l=list(model_polity_cabinc,
                     model_polity_cabcount,
                     model_fh_cabinc,
                     model_fh_cabcount),
              file = "../output/mainresults2.tex",
              stars = c(0.001, 0.01, 0.05, 0.1),
              custom.note = "%stars",
              center = TRUE,
              symbol = "+",
              reorder.coef = c(1, 9:11, 2:8, 12),
        custom.coef.names = c("Intercept",
                              "Power-sharing (binary)",
                              "Aid / GDP (log)",
                              "GDP p/c (log)",
                              "Population (log)",
                              "Conflict intensity",
                              "Non-State Violence",
                              "Nat. res. rents",
                              "Polity",
                              "Power-sharing (binary) * Aid",
                              "Power-sharing (cabinet)",
                              "Power-sharing (cabinet) * Aid",
                              "Freedom House"),
        omit.coef = "Intercept",
        table = FALSE,
        custom.multicol = T,
        dcolumn = T,
        booktabs = T,
        include.rsquared = F, 
        include.cluster = T,
        use.packages = F,
        custom.model.names = c("\\multicolumn{2}{c}{\\textbf{Polity}} & \\multicolumn{2}{c}{\\textbf{Freedom House}} \\\\ \\cmidrule(r){2-3} \\cmidrule(l){4-5} & \\multicolumn{1}{c}{(1)}",
                               "\\multicolumn{1}{c}{(2) }",
                               "\\multicolumn{1}{c}{(3) }",
                               "\\multicolumn{1}{c}{(4) }"))


# output replication archive
htmlreg(l=list(model_polity_cabinc, 
                     model_polity_cabcount, 
                     model_fh_cabinc,
                     model_fh_cabcount),
              # file = "../output/mainresults.tex",
              stars = c(0.001, 0.01, 0.05, 0.1),
              # custom.note = "%stars",
              center = TRUE,
              symbol = "+",
              reorder.coef = c(1, 9:11, 2:8, 12),
        custom.coef.names = c("Intercept",
                              "Power-sharing (binary)",
                              "Aid / GDP (log)",
                              "GDP p/c (log)",
                              "Population (log)",
                              "Conflict intensity",
                              "Non-State Violence",
                              "Nat. res. rents",
                              "Polity",
                              "Power-sharing (binary) * Aid",
                              "Power-sharing (cabinet)",
                              "Power-sharing (cabinet) * Aid",
                              "Freedom House"),
        omit.coef = "Intercept",
        table = FALSE, 
        custom.multicol = T, 
        dcolumn = T,
        booktabs = T,
        use.packages = F,
        star.symbol = "\\*", 
        include.lr = F)
```


# Table 5.3: Robustness Checks
```{r, results="asis", message=F, warning=F, cache = T, comments = F}

# Libraries
library(tidyverse)
library(rms)
library(plm)
library(texreg)
library(countrycode)
library(texreg)

# Load Data
load("./data/diss_df.rda")

# Models

# Fractionalization
democ_model_elf <- ols(polity2_t2 ~
                         cabinetCOUNT * aiddata_AidGDP_ln +
                         ln_gdp_pc +
                         ln_pop +
                         conf_intens+
                         nonstate +
                         WBnatres +
                         polity2 +
                         Ethnic,
                       data=diss_df,
                       x = T, y = T)
democ_model_elf <- robcov(democ_model_elf, diss_df$GWNo)

# PKO
democ_model_pko <- ols(polity2_t2 ~
                         cabinetCOUNT * aiddata_AidGDP_ln +
                         ln_gdp_pc +
                         ln_pop +
                         conf_intens+
                         nonstate +
                         WBnatres +
                         polity2 +
                         DS_ordinal,
                       data=diss_df,
                       x = T, y = T)
democ_model_pko <- robcov(democ_model_pko, diss_df$GWNo)

# Cabinet Size

library(readxl)
cnts <- read_excel("./data/CNTSDATA.xls")

cnts <- cnts %>% filter(year >= 1989)

cnts$iso3c <- countrycode(cnts$country, "country.name", "iso3c")
cnts <- cnts %>% filter(country != "SOMALILAND")

testcabsize <- left_join(diss_df, cnts[, c("iso3c", "year", "polit10")])

testcabsize$ps_share <- testcabsize$cabinetCOUNT / testcabsize$polit10 * 100

model_cabsize <- ols(polity2_t2 ~
                       ps_share *
                       aiddata_AidGDP_ln +
                       ln_gdp_pc +
                       ln_pop +
                       conf_intens +
                       nonstate +
                       WBnatres +
                       polity2,
                     data = testcabsize, x = T, y = T)
model_cabsize <- robcov(model_cabsize, testcabsize$GWNo)

# Random Effects
country_re_democ <- plm(polity2_t2 ~
                  cabinetCOUNT +
                  aiddata_AidGDP_ln +
                  cabinetCOUNT * aiddata_AidGDP_ln +
                  ln_gdp_pc +
                  ln_pop +
                  conf_intens+
                  nonstate +
                  WBnatres +
                  polity2 ,
                data=diss_df, model = "random", index =c("GWNo", "year"))
country_re_democ$vcov  <- vcovHC(country_re_democ, cluster="group")


# Region Fixed Effects
diss_df$region <- countrycode(diss_df$Location, "country.name", "region")
diss_df$country_year <- paste0(diss_df$Location, diss_df$year, sep = "-")

region_fe_democ <- plm(polity2_t2 ~
                     cabinetCOUNT * aiddata_AidGDP_ln +
                     ln_gdp_pc +
                     ln_pop +
                     conf_intens+
                     nonstate +
                     WBnatres +
                     polity2 ,
                   data=diss_df, 
                   model = "within", 
                   index =c("region", "country_year"))
region_fe_democ$vcov  <- vcovHC(region_fe_democ, cluster="group")

# Country FE 
country_fe_democ <- plm(polity2_t2 ~
                     cabinetCOUNT * aiddata_AidGDP_ln +
                     ln_gdp_pc +
                     ln_pop +
                     conf_intens+
                     nonstate +
                     WBnatres +
                     polity2 ,
                   data=diss_df, 
                   model = "within", 
                   index =c("GWNo", "year"))
country_fe_democ$vcov  <- vcovHC(country_fe_democ, cluster="group")


## Order of coefficients in output table
name_map_robustness <- list(cabinetCOUNT = "PS (cabinet)",
                            "cabinetCOUNT * aiddata_AidGDP_ln" = "PS (cabinet) * Aid", 
                            "cabinetCOUNT:aiddata_AidGDP_ln" = "PS (cabinet) * Aid",
                            ps_share = "PS (cabinet share)",
                            "ps_share * aiddata_AidGDP_ln" = "PS (cabinet share) * Aid",
                            aiddata_AidGDP_ln = "Aid / GDP (log)",
                            ln_gdp_pc = "GDP p/c",
                            ln_pop = "Population",
                            conf_intens = "Conflict Intensity",
                            nonstate = "Non-State Violence",
                            WBnatres = "Nat. Res. Rents",
                            polity2 = "Polity",
                            Ethnic = "Ethnic Frac.",
                            DS_ordinal = "UN PKO")

source("./functions/will_lowe_texreg_reorder.R")

# Model list
rob_models <- list(democ_model_elf,
     democ_model_pko,
     model_cabsize,
     country_re_democ,
     region_fe_democ,
     country_fe_democ)

oldnames <- all.varnames.dammit(rob_models)
ror <- build.ror(oldnames, name_map_robustness)



# Output manuscript
# 
# source("./functions/custom_texreg.R")
# environment(custom_texreg) <- asNamespace('texreg')
# 
custom_texreg(l = rob_models,
        stars = c(0.001, 0.01, 0.05, 0.1),
        symbol = "+",
        table = F,
        booktabs = T,
        use.packages = F,
        dcolumn = T,
        file = "../output/robustness.tex",
        custom.model.names = c("(1) ELF",
                               "(2) PKO",
                               "(3) Cab. Size",
                               "(4) RE",
                               "(5) Region FE",
                               "(6) Country FE"),
        custom.coef.names = ror$ccn,
        omit.coef = ror$oc,
        include.rsquared = F,
        include.cluster = T,
        reorder.coef = unique(ror$rc),
        star.symbol = "\\*",
        include.lr = F, include.variance = F)

# Output replication archive
htmlreg(l = rob_models,
        stars = c(0.001, 0.01, 0.05, 0.1),
        symbol = "+",
        table = F,
        booktabs = T,
        use.packages = F,
        dcolumn = T,
        # file = "../output/robustness.tex",
        custom.model.names = c("(1) ELF", 
                               "(2) PKO", 
                               "(3) Cabinet Size", 
                               "(4) Random Effects", 
                               "(5) Region FE", 
                               "(6) Country FE"),
        custom.coef.names = ror$ccn, 
        omit.coef = ror$oc, 
          include.rsquared = F,
        include.cluster = T,
        caption = "", 
        reorder.coef = unique(ror$rc),
        star.symbol = "\\*", 
        include.lr = F, include.variance = F)



```


# Table 5.4: Additional Aid Lags


```{r, results="asis", message=F, warning=F, cache = T, comments = F}



# Libraries
library(tidyverse)
library(rms)
library(texreg)

# load 
load("./data/diss_df.rda")


# Estimate models with time lags -------------------------------------

# 1 year lag
lag1_pchng <- ols(polity2_t2 ~  
                    cabinetCOUNT * aiddata_AidGDP_ln_tmin1 +
                    log(GDP_per_capita) +
                    log(population) +
                    conf_intens +
                    nonstate + 
                    WBnatres +
                    polity2,
                  data=diss_df, x=T, y=T)
lag1_pchng <- robcov(lag1_pchng, diss_df$GWNo)


lag1_fhchng <- ols(fh_t2 ~  
                     cabinetCOUNT * aiddata_AidGDP_ln_tmin1 +
                     log(GDP_per_capita) +
                     log(population) +
                     conf_intens +
                     nonstate + 
                     WBnatres +
                     fh ,
                   data=diss_df, x=T, y=T)
lag1_fhchng <- robcov(lag1_fhchng, diss_df$GWNo)


# 2 years aid lag
lag2_pchng <- ols(polity2_t2 ~  
                    cabinetCOUNT * aiddata_AidGDP_ln_tmin2 +
                    log(GDP_per_capita) +
                    log(population) +
                    conf_intens +
                    nonstate + 
                    WBnatres +
                    polity2,
                  data=diss_df, x=T, y=T)
lag2_pchng <- robcov(lag2_pchng, diss_df$GWNo)

lag2_fhchng <- ols(fh_t2 ~  
                     cabinetCOUNT * aiddata_AidGDP_ln_tmin2 +
                     log(GDP_per_capita) +
                     log(population) +
                     conf_intens +
                     nonstate + 
                     WBnatres +
                     fh ,
                   data=diss_df, x=T, y=T)
lag2_fhchng <- robcov(lag2_fhchng, diss_df$GWNo)


#### Model Output ####


## Order of coefficients in output table
name_map <- list(cabinetCOUNT = "Power-Sharing (cabinet)",
                 aiddata_AidGDP_ln_tmin1   = "Aid / GDP (log)",
                 aiddata_AidGDP_ln_tmin2   = "Aid / GDP (log)",
                 "cabinetCOUNT * aiddata_AidGDP_ln_tmin1" = "Power-Sharing (cabinet) * Aid", 
                 "cabinetCOUNT * aiddata_AidGDP_ln_tmin2" = "Power-Sharing (cabinet) * Aid", 
                 GDP_per_capita = "GDP p/c",
                 population = "Population",
                 conf_intens = "Conflict Intensity",
                 nonstate = "Non-State Violence",
                 WBnatres = "Nat. Res. Rents",
                 
                 polity2 = "Regime Type (Polity)",
                 fh = "Regime Type (FH)")

source("./functions/will_lowe_texreg_reorder.R")

# Model list
model_list <- list(lag1_pchng,lag2_pchng, lag1_fhchng, lag2_fhchng)

oldnames <- all.varnames.dammit(model_list)
ror <- build.ror(oldnames, name_map)


# 
# # Output for Manuscript
# 
source("./functions/custom_texreg.R")
environment(custom_texreg) <- asNamespace('texreg')

# 
custom_texreg(l = model_list,
       file = "../output/laggedresults2.tex",
        stars = c(0.001, 0.01, 0.05, 0.1),
        symbol = "+",
        table = F,
        booktabs = T,
        use.packages = F,
        dcolumn = T,
        custom.model.names = c("\\multicolumn{2}{c}{\\textbf{Polity}} & \\multicolumn{2}{c}{\\textbf{Freedom House}} \\\\ \\cmidrule(r){2-3} \\cmidrule(l){4-5} & \\multicolumn{1}{c}{(1) 1 year}",
                              "\\multicolumn{1}{c}{(2)  2 years}",
                              "\\multicolumn{1}{c}{(3)  1 year}",
                              "\\multicolumn{1}{c}{(4)  2 years}"),
        custom.coef.names = ror$ccn,
        omit.coef = ror$oc,
        reorder.coef = unique(ror$rc),
               custom.multicol = T,
  include.rsquared = F,
        include.cluster = T,
        include.lr = F)

# Output for Replication Archive
htmlreg(l = model_list,
        stars = c(0.001, 0.01, 0.05, 0.1),
        symbol = "+",
        table = F,
        booktabs = T,
        use.packages = F,
        caption = "", 
        dcolumn = T,
        custom.coef.names = ror$ccn, 
        omit.coef = ror$oc, 
        reorder.coef = unique(ror$rc),
        star.symbol = "\\*", 
        include.lr = F)


```


# Table 5.6: 2SLS Second Stage Results

```{r, results="asis", message=F, warning=F, cache = T, comments = F}


# load libraries
library(AER)
library(ivpack)
library(lmtest)
library(lfe)
library(tikzDevice)

# load data with instrument; this gives "instrument_df" data frame
load(file = "./data/instrumentedAid2.RData")
load("./data/diss_df.rda")

diss_df_iv <- merge(diss_df, 
                      instrument_df, 
                      by = c("iso2c", "year"), all.x = TRUE)


# subset only complete.cases / necessary for cluster.robust.se()
iv_na <- na.omit(diss_df_iv[, c("polity_chng", 
                               "cabinetCOUNT", 
                               "cabinetINC", 
                               "aiddata_Aid",
                               "aiddata_AidGDP_ln",
                               "aiddata_AidPC_ln",
                               "fh",
                               "fh_t2",
                               "GDP_per_capita", 
                               "population", 
                               "conf_intens", 
                               "WBnatres", 
                               "polity2", 
                               "polity2_t2",
                               "total_sum_except", 
                               "year", 
                               "GWNo", 
                               "GDP",
                               "nonstate")])

# to proceed with IV estimation I first hard-code the instrument
iv_na$instr_aid_gdp_ln <- log(iv_na$total_sum_except / iv_na$GDP)

# data transformation for Stata
iv_na$ln_gdp_pc <- log(iv_na$GDP_per_capita)
iv_na$ln_pop <- log(iv_na$population)

# and save data to Stata format
foreign::write.dta(iv_na, "./data/iv_na_democ.dta")

iv_na$cabinetCOUNTxaid_ln <- iv_na$cabinetCOUNT * iv_na$aiddata_AidGDP_ln
iv_na$cabinetCOUNTxaid_ln_instr <- iv_na$cabinetCOUNT * iv_na$instr_aid_gdp_ln

# IV for Polity
democ_pchng_iv <- lfe::felm(polity2_t2 ~ 
                          cabinetCOUNT + 
                          log(GDP_per_capita) +
                          log(population) +
                          nonstate +
                          conf_intens +
                          WBnatres +
                          polity2 
                          | 0 | (cabinetCOUNTxaid_ln|aiddata_AidGDP_ln ~ 
                                   cabinetCOUNTxaid_ln_instr + instr_aid_gdp_ln) | GWNo,
                          data = iv_na)


# IV for FH
democ_fhchng_iv <- lfe::felm(fh_t2 ~ 
                          cabinetCOUNT + 
                          log(GDP_per_capita) +
                          log(population) +
                          nonstate +
                          conf_intens +
                          WBnatres +
                          fh 
                          | 0 | (cabinetCOUNTxaid_ln|aiddata_AidGDP_ln ~ 
                                   cabinetCOUNTxaid_ln_instr + instr_aid_gdp_ln) | GWNo,
                          data = iv_na)

# Output Second Stage Results

## Order of coefficients in output table
name_map <- list(cabinetCOUNT = "Power-Sharing (cabinet)",
                 "`aiddata_AidGDP_ln(fit)`" = "Aid / GDP (log)",
                 "`cabinetCOUNTxaid_ln(fit)`" = "Power-Sharing (cabinet) * Aid", 
                 "log(GDP_per_capita)" = "GDP p/c",
                 "log(population)" = "Population",
                 conf_intens = "Conflict Intensity",
                 nonstate = "Non-State Violence",
                 WBnatres = "Nat. Res. Rents",
                 polity2 = "Regime Type",
                 fh = "Regime Type")

source("./functions/will_lowe_texreg_reorder.R")

# Model list
model_list <- list(democ_pchng_iv, 
                   democ_fhchng_iv)

oldnames <- all.varnames.dammit(model_list)
ror <- build.ror(oldnames, name_map)

# Output for Manuscript

# library(texreg)
# source("./functions/extract_felm_custom.R")
# # # options(expressions = 1500)
# setMethod("extract", signature = className("felm", "lfe"),
#     definition = extract.felm.custom)

source("./functions/custom_texreg.R")
environment(custom_texreg) <- asNamespace('texreg')
# 
# # library(texreg)
# custom_texreg(l = model_list,
#               file = "../output/ivresults_democ.tex",
#         stars = c(0.001, 0.01, 0.05, 0.1),
# 
#         add.lines = list(c("Kleibergen-Paap rk Wald F statistic",
#                            "40.651",
#                            "40.651"),
#                          c("Adj. R$^2$",
#                            round(summary(democ_pchng_iv)$adj.r.squared, 2),
#                            round(summary(democ_fhchng_iv)$adj.r.squared, 2))),
#         symbol = "+",
#         table = F,
#         booktabs = T,
#         use.packages = F,
#         dcolumn = T,
#         custom.model.names = c("(1) Polity",
#                                "(2) Freedom House"),
# 
#         custom.coef.map = name_map,
# 
#         include.lr = F,
#         include.rsquared = F,
#         include.adjrs = F)


# Replication Archive Output
texreg::htmlreg(l = model_list,
        stars = c(0.001, 0.01, 0.05, 0.1),
        symbol = "+",
        table = F,
        booktabs = T,
        use.packages = F,
        caption = "", 
        dcolumn = T,
        custom.model.names = c("(1) Polity", 
                               "(2) Freedom House"),
        # custom.coef.map = name_map,
        custom.coef.names = ror$ccn, 
        omit.coef = ror$oc,
        reorder.coef = unique(ror$rc),
        star.symbol = "\\*", 
        include.lr = F)
```