Table B.1: Individual Effects of Power-Sharing and Foreign Aid on Freedom House scores
# 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)",
"log(population)",
"conf_intens",
"nonstate",
"WBnatres",
"fh")
#### Power-Sharing only Models ####
# PS only + cabinetCOUNT
model_ps <- ols(formula(paste0("fh_t2 ~ cabinetCOUNT + ",
paste0(controlvars, collapse = " + "))),
data = diss_df, x = T, y = T)
model_ps <- robcov(model_ps, diss_df$GWNo)
# PS only + seniorCOUNT
model_seniorCOUNT <- ols(formula(paste0("fh_t2 ~ seniorCOUNT + ",
paste0(controlvars, collapse = " + "))),
data = diss_df, x = T, y = T)
model_seniorCOUNT <- robcov(model_seniorCOUNT, diss_df$GWNo)
# PS only + nonseniorCOUNT
model_nonseniorCOUNT <- ols(formula(paste0("fh_t2 ~ nonseniorCOUNT + ",
paste0(controlvars, collapse = " + "))),
data = diss_df, x = T, y = T)
model_nonseniorCOUNT <- robcov(model_nonseniorCOUNT, diss_df$GWNo)
#### Aid-only Models ####
# DGA
model_gov_aid <- ols(formula(paste0("fh_t2 ~ cabinetCOUNT + log(dga_gdp_zero + 1) + ",
paste0(controlvars, collapse = " + ")))
,
data=diss_df, x=T, y=T)
model_gov_aid <- robcov(model_gov_aid, diss_df$GWNo)
# Program Aid
model_program_aid <- ols(formula(paste0("fh_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$GWNo)
# Budget Aid
model_commodity_aid <- ols(formula(paste0("fh_t2 ~ cabinetCOUNT + log(commodity_aid_gdp_zero + 1) + ",
paste0(controlvars, collapse = " + ")))
,
data=diss_df, x=T, y=T)
model_commodity_aid <- robcov(model_commodity_aid, diss_df$GWNo)
# Output manuscript
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_fh.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 (Freedom House)",
# "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,
# # add.lines = list(c("Controls", rep("\\multicolumn{1}{c}{Yes}", 6))),
# use.packages = F,
# center = TRUE,
# include.lr = 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 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),
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 (Freedom House)",
"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,
booktabs = T,
use.packages = F,
center = TRUE,
include.lr = F,
star.symbol = "\\*")
|
|
Model 1
|
Model 2
|
Model 3
|
Model 4
|
Model 5
|
Model 6
|
|
Power-Sharing (cabinet)
|
0.01
|
|
|
0.01
|
0.01
|
0.01
|
|
|
(0.02)
|
|
|
(0.02)
|
(0.02)
|
(0.02)
|
|
Power-Sharing (senior)
|
|
0.04
|
|
|
|
|
|
|
|
(0.05)
|
|
|
|
|
|
Power-Sharing (nonsenior)
|
|
|
0.01
|
|
|
|
|
|
|
|
(0.02)
|
|
|
|
|
Democracy Aid/GDP (log)
|
|
|
|
0.13
|
|
|
|
|
|
|
|
(0.09)
|
|
|
|
Program Aid/GDP (log)
|
|
|
|
|
0.08
|
|
|
|
|
|
|
|
(0.11)
|
|
|
Budget Aid/GDP (log)
|
|
|
|
|
|
0.04
|
|
|
|
|
|
|
|
(0.07)
|
|
Aid / GDP (log)
|
-0.03
|
-0.03
|
-0.03
|
-0.05
|
-0.05
|
-0.04
|
|
|
(0.04)
|
(0.04)
|
(0.04)
|
(0.04)
|
(0.05)
|
(0.05)
|
|
GDP p/c (log)
|
-0.11+
|
-0.11+
|
-0.11+
|
-0.10+
|
-0.09
|
-0.11+
|
|
|
(0.06)
|
(0.06)
|
(0.06)
|
(0.06)
|
(0.07)
|
(0.06)
|
|
Population (log)
|
0.00
|
0.00
|
0.00
|
0.00
|
0.01
|
0.00
|
|
|
(0.03)
|
(0.03)
|
(0.03)
|
(0.03)
|
(0.03)
|
(0.03)
|
|
Conflict intensity
|
-0.05
|
-0.06
|
-0.05
|
-0.09
|
-0.07
|
-0.06
|
|
|
(0.08)
|
(0.09)
|
(0.08)
|
(0.09)
|
(0.09)
|
(0.09)
|
|
Non-State Violence
|
-0.36***
|
-0.36***
|
-0.36***
|
-0.33**
|
-0.35**
|
-0.35***
|
|
|
(0.11)
|
(0.11)
|
(0.11)
|
(0.11)
|
(0.11)
|
(0.11)
|
|
Nat. res. rents
|
-0.01+
|
-0.01*
|
-0.01+
|
-0.01*
|
-0.01*
|
-0.01+
|
|
|
(0.00)
|
(0.00)
|
(0.00)
|
(0.00)
|
(0.00)
|
(0.00)
|
|
Regime Type (Freedom House)
|
0.87***
|
0.87***
|
0.87***
|
0.87***
|
0.87***
|
0.87***
|
|
|
(0.04)
|
(0.04)
|
(0.04)
|
(0.04)
|
(0.04)
|
(0.04)
|
|
Num. obs.
|
272
|
272
|
272
|
272
|
272
|
272
|
|
R2
|
0.80
|
0.80
|
0.80
|
0.81
|
0.80
|
0.80
|
|
Adj. R2
|
0.80
|
0.80
|
0.80
|
0.80
|
0.80
|
0.80
|
|
***p < 0.001, **p < 0.01, *p < 0.05, +p < 0.1
|
Table B.2: Temporal Dynamics for the Interaction Effect between Power-Sharing and Foreign aid on Democracy Score
# 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$lead_var <- data[, grep(lead_type, names(data), value =T)]
if(grepl("fh", lead_type)) {
model <- lfe::felm(lead_var ~
cabinetCOUNT *
aiddata_AidGDP_ln +
log(GDP_per_capita) +
log(population) +
conf_intens +
nonstate +
WBnatres +
fh | 0 | 0 | GWNo,
data=data)
return(model)
} else {
model <- lfe::felm(lead_var ~
cabinetCOUNT *
aiddata_AidGDP_ln +
log(GDP_per_capita) +
log(population) +
conf_intens +
nonstate +
WBnatres +
polity2 | 0 | 0 | GWNo,
data=data)
return(model)
}
}
# fit models & post-process data for plotting
model_all <- polity_vars %>%
mutate(model = map2(name, value, ~ main_model(.x, .y)))
# output for manuscript
source("./functions/custom_texreg.R")
environment(custom_texreg) <- asNamespace('texreg')
#
# custom_texreg(model_all$model,
# file = "../output/temp_dynamics_democ_app.tex",
# stars = c(0.001, 0.01, 0.05, 0.1),
# include.adjrs = T,
# include.rsquared = F,
# center = TRUE,
# symbol = "+",
# reorder.coef = c(1, 2, 9, 3:8, 10),
# 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 (cabinet) * Aid",
# "Freedom House"),
# omit.coef = "Intercept",
# table = FALSE,
# custom.multicol = F,
# dcolumn = T,
# booktabs = T,
# use.packages = F)
# output replication archive
htmlreg(model_all$model,
stars = c(0.001, 0.01, 0.05, 0.1),
include.adjrs = T,
caption = "",
include.rsquared = F,
center = TRUE,
symbol = "+",
reorder.coef = c(1, 2, 9, 3:8, 10),
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 (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
|
Model 5
|
Model 6
|
Model 7
|
Model 8
|
Model 9
|
Model 10
|
|
Power-sharing (binary)
|
-0.10+
|
-0.17+
|
-0.22+
|
-0.26+
|
-0.31+
|
-0.01
|
-0.05+
|
-0.09**
|
-0.09*
|
-0.05
|
|
|
(0.06)
|
(0.09)
|
(0.12)
|
(0.14)
|
(0.17)
|
(0.02)
|
(0.03)
|
(0.03)
|
(0.04)
|
(0.05)
|
|
Aid / GDP (log)
|
-0.02
|
-0.02
|
-0.01
|
0.02
|
0.08
|
-0.01
|
-0.03
|
-0.03
|
-0.01
|
0.02
|
|
|
(0.09)
|
(0.15)
|
(0.18)
|
(0.20)
|
(0.21)
|
(0.03)
|
(0.04)
|
(0.04)
|
(0.05)
|
(0.06)
|
|
Power-sharing (cabinet) * Aid
|
0.06***
|
0.11***
|
0.14**
|
0.17**
|
0.19**
|
0.01
|
0.02*
|
0.04**
|
0.03**
|
0.02
|
|
|
(0.02)
|
(0.03)
|
(0.04)
|
(0.05)
|
(0.06)
|
(0.01)
|
(0.01)
|
(0.01)
|
(0.01)
|
(0.02)
|
|
GDP p/c (log)
|
-0.15
|
-0.08
|
0.19
|
0.56
|
0.83
|
-0.06+
|
-0.10+
|
-0.08
|
-0.03
|
0.04
|
|
|
(0.16)
|
(0.32)
|
(0.44)
|
(0.52)
|
(0.53)
|
(0.04)
|
(0.06)
|
(0.09)
|
(0.12)
|
(0.15)
|
|
Population (log)
|
0.03
|
-0.02
|
-0.03
|
-0.11
|
-0.04
|
0.00
|
0.00
|
0.01
|
0.00
|
0.02
|
|
|
(0.09)
|
(0.12)
|
(0.14)
|
(0.19)
|
(0.24)
|
(0.02)
|
(0.03)
|
(0.04)
|
(0.06)
|
(0.07)
|
|
Conflict intensity
|
0.08
|
0.09
|
0.16
|
0.01
|
-0.21
|
-0.03
|
-0.07
|
-0.08
|
-0.10
|
-0.12
|
|
|
(0.31)
|
(0.47)
|
(0.61)
|
(0.72)
|
(0.83)
|
(0.06)
|
(0.09)
|
(0.12)
|
(0.14)
|
(0.19)
|
|
Non-State Violence
|
-0.52
|
-0.45
|
-0.19
|
0.39
|
0.37
|
-0.22**
|
-0.38***
|
-0.36*
|
-0.33+
|
-0.34
|
|
|
(0.35)
|
(0.51)
|
(0.65)
|
(0.80)
|
(0.88)
|
(0.07)
|
(0.11)
|
(0.15)
|
(0.19)
|
(0.24)
|
|
Nat. res. rents
|
-0.02*
|
-0.04*
|
-0.05+
|
-0.07*
|
-0.07*
|
-0.01**
|
-0.01+
|
-0.01
|
-0.01
|
-0.01
|
|
|
(0.01)
|
(0.02)
|
(0.03)
|
(0.03)
|
(0.03)
|
(0.00)
|
(0.00)
|
(0.01)
|
(0.01)
|
(0.01)
|
|
Polity
|
0.90***
|
0.82***
|
0.75***
|
0.66***
|
0.58***
|
|
|
|
|
|
|
|
(0.04)
|
(0.07)
|
(0.09)
|
(0.10)
|
(0.11)
|
|
|
|
|
|
|
Freedom House
|
|
|
|
|
|
0.94***
|
0.87***
|
0.83***
|
0.80***
|
0.75***
|
|
|
|
|
|
|
|
(0.03)
|
(0.04)
|
(0.05)
|
(0.06)
|
(0.08)
|
|
Num. obs.
|
263
|
263
|
255
|
244
|
233
|
273
|
272
|
271
|
263
|
252
|
|
Adj. R2 (full model)
|
0.85
|
0.76
|
0.69
|
0.62
|
0.57
|
0.89
|
0.80
|
0.74
|
0.69
|
0.62
|
|
Adj. R2 (proj model)
|
0.85
|
0.76
|
0.69
|
0.62
|
0.57
|
0.89
|
0.80
|
0.74
|
0.69
|
0.62
|
|
***p < 0.001, **p < 0.01, *p < 0.05, +p < 0.1
|
Table B.3: Technical Robustness Checks I: Outlier Test and XPOLITY and Interregnum Bias Correction
# Library
library(rms)
library(dplyr)
# Load data
load("./data/diss_df.rda")
##### Outlier #####
# Load outlier function
source("./functions/outlier_analysis.R")
# Estimate baseline model
model_polity_cabcount <- ols(polity2_t2 ~
cabinetCOUNT *
aiddata_AidGDP_ln +
log(GDP_per_capita) +
log(population) +
conf_intens +
nonstate +
WBnatres +
polity2 ,
data=diss_df, x=T, y=T)
model_polity_cabcount <- robcov(model_polity_cabcount, diss_df$GWNo)
# selector variables
selectvars = c("Location", "year", "identifiers")
diss_df$identifiers <- paste(diss_df$GWNo, diss_df$year, sep = "-")
# Estimate outliers
democ_outliers <- check_outlier(model_polity_cabcount,
data = diss_df,
selectvars = selectvars,
clustervar = "GWNo")
#### XPOLITY & Interregnum Bias ####
# First load regular Plumper & Neumayer correction
load("./data/polity2_xpolity_plumper_corrected.rdata")
names(polity_rev)[2] <- "GWNo"
# Fix Serbia for correct merging
polity_rev$GWNo <- ifelse(polity_rev$country == "Yugoslavia", 345, polity_rev$GWNo)
polity_rev$GWNo <- ifelse(polity_rev$country == "Serbia and Montenegro", 345, polity_rev$GWNo)
polity_rev_orig <- polity_rev
# Second load xpolity combined with Plumper & Neumayer correction
load("./data/polity2_xpolity_plumper_corrected_combined.rdata")
names(polity_rev)[2] <- "GWNo"
# Fix Serbia
polity_rev$GWNo <- ifelse(polity_rev$country == "Yugoslavia", 345, polity_rev$GWNo)
polity_rev$GWNo <- ifelse(polity_rev$country == "Serbia and Montenegro", 345, polity_rev$GWNo)
polity_rev <- left_join(polity_rev_orig, polity_rev[, c("GWNo", "year", "xpolity_inter",
"xpolity_max",
"xpolity_min")],
by = c("GWNo", "year"))
# merge xpolity & interregnum data in main data set
testdf <- merge(diss_df, polity_rev[, c("GWNo", "year",
"polity2min", "polity2max", "polity2inter",
"xpolity_inter",
"xpolity_max",
"xpolity_min")], by = c("GWNo", "year"), all.x = TRUE)
# Data transformations -----------------------------------------------------
source("./functions/laggedVar.R")
#### For Plumper & Neumayer correction
# Polity 2 Min
testdf <- laggedVar(testdf, polity_rev, lVar="polity2min", tID ="year", gID="GWNo", lagBy = -2)
# Polity2 Max
testdf <- laggedVar(testdf, polity_rev, lVar="polity2max", tID ="year", gID="GWNo", lagBy = -2)
# Polity2 Inter
testdf <- laggedVar(testdf, polity_rev, lVar="polity2inter", tID ="year", gID="GWNo", lagBy = -2)
#### XPOLITY
# xpolity min
testdf <- laggedVar(testdf, polity_rev, lVar="xpolity_min", tID ="year", gID="GWNo", lagBy = -2)
# xpolity max
testdf <- laggedVar(testdf, polity_rev, lVar="xpolity_max", tID ="year", gID="GWNo", lagBy = -2)
# xpolity inter
testdf <- laggedVar(testdf, polity_rev, lVar="xpolity_inter", tID ="year", gID="GWNo", lagBy = -2)
## Run XPOLITY & Interregnum Bias models
# xpolity min
model_xpol_min <- ols(xpolity_min_t2 ~
cabinetCOUNT +
aiddata_AidGDP_ln +
cabinetCOUNT : aiddata_AidGDP_ln +
log(GDP_per_capita) +
log(population) +
conf_intens +
nonstate +
WBnatres +
xpolity_min,
data=testdf, x=T, y=T)
model_xpol_min <- robcov(model_xpol_min, testdf$GWNo)
# xpolity min
model_xpol_max <- ols(xpolity_max_t2 ~
cabinetCOUNT +
aiddata_AidGDP_ln +
cabinetCOUNT : aiddata_AidGDP_ln +
log(GDP_per_capita) +
log(population) +
conf_intens +
nonstate +
WBnatres +
xpolity_max,
data=testdf, x=T, y=T)
model_xpol_max <- robcov(model_xpol_max, testdf$GWNo)
# xpolity inter
model_xpol_inter <- ols(xpolity_inter_t2 ~
cabinetCOUNT +
aiddata_AidGDP_ln +
cabinetCOUNT : aiddata_AidGDP_ln +
log(GDP_per_capita) +
log(population) +
conf_intens +
nonstate +
WBnatres +
xpolity_inter,
data=testdf, x=T, y=T)
model_xpol_inter <- robcov(model_xpol_inter, testdf$GWNo)
# Models for Plumper & Neumayer (interregnum bias) ------------------------
# polity 2 min
model_pchng_p2min <- ols(polity2min_t2 ~
cabinetCOUNT +
aiddata_AidGDP_ln +
cabinetCOUNT : aiddata_AidGDP_ln +
log(GDP_per_capita) +
log(population) +
conf_intens +
nonstate +
WBnatres +
polity2min,
data=testdf, x=T, y=T)
model_pchng_p2min <- robcov(model_pchng_p2min, testdf$GWNo)
# polity 2 max
model_pchng_p2max <- ols(polity2max_t2 ~
cabinetCOUNT +
aiddata_AidGDP_ln +
cabinetCOUNT : aiddata_AidGDP_ln +
log(GDP_per_capita) +
log(population) +
conf_intens +
nonstate +
WBnatres +
polity2max ,
data=testdf, x=T, y=T)
model_pchng_p2max <- robcov(model_pchng_p2max, testdf$GWNo)
# polity 2 interpolated
model_pchng_p2inter <- ols(polity2inter_t2 ~
cabinetCOUNT *
aiddata_AidGDP_ln +
log(GDP_per_capita) +
log(population) +
conf_intens +
nonstate +
WBnatres +
polity2inter,
data=testdf, x=T, y=T)
model_pchng_p2inter <- robcov(model_pchng_p2inter, testdf$GWNo)
# Output Replication Archive
## Determine order of coefficients in output table
name_map_robustness <- list(cabinetCOUNT = "PS (cabinet)",
aiddata_AidGDP_ln = "Aid / GDP (log)",
"cabinetCOUNT * aiddata_AidGDP_ln" = "PS (cabinet) * Aid",
GDP_per_capita = "GDP p/c",
population = "Population",
conf_intens = "Conflict Intensity",
nonstate = "Non-State Violence",
WBnatres = "Nat. Res. Rents",
polity2 = "Polity",
polity2inter = "Polity (Interpolated)",
polity2max = "Polity (Max)",
polity2min = "Polity (Min)",
xpolity_inter = "XPOLITY (Interpolated)",
xpolity_max = "XPOLITY (Max)",
xpolity_min = "XPOLITY (Min)")
source("./functions/will_lowe_texreg_reorder.R")
# Model list
app_rob_models <- list(democ_outliers[[2]],
democ_outliers[[4]],
democ_outliers[[6]],
model_pchng_p2inter,
model_pchng_p2max,
model_pchng_p2min,
model_xpol_inter,
model_xpol_max,
model_xpol_min)
oldnames <- all.varnames.dammit(app_rob_models)
ror <- build.ror(oldnames, name_map_robustness)
# texreg
# texreg(l = app_rob_models,
# stars = c(0.001, 0.01, 0.05, 0.1),
# symbol = "+",
# table = F,
# booktabs = T,
# use.packages = F,
# dcolumn = T,
# file = "../output/democ_app_tech_robustness.tex",
# # custom.model.names = c("(1) ELF",
# # "(2) PKO",
# # "(3) Cabinet Size",
# # "(4) Random Effects",
# # "(5) Region FE",
# # "(6) Country FE"),
# custom.coef.names = ror$ccn,
# omit.coef = ror$oc,
# reorder.coef = unique(ror$rc),
# include.lr = F)
# Replication Archive output
htmlreg(l = app_rob_models,
stars = c(0.001, 0.01, 0.05, 0.1),
symbol = "+",
table = F,
booktabs = T,
caption = "",
use.packages = F,
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
|
Model 5
|
Model 6
|
Model 7
|
Model 8
|
Model 9
|
|
PS (cabinet)
|
-0.60
|
-0.17***
|
-0.14***
|
-0.09
|
-0.09
|
-0.13
|
-0.07
|
-0.07
|
-0.11+
|
|
|
(0.55)
|
(0.04)
|
(0.04)
|
(0.07)
|
(0.08)
|
(0.08)
|
(0.05)
|
(0.05)
|
(0.06)
|
|
Aid / GDP (log)
|
-0.01
|
-0.02
|
-0.02
|
-0.02
|
-0.00
|
-0.01
|
0.01
|
0.02
|
0.02
|
|
|
(0.16)
|
(0.07)
|
(0.05)
|
(0.15)
|
(0.14)
|
(0.15)
|
(0.11)
|
(0.11)
|
(0.11)
|
|
PS (cabinet) * Aid
|
0.28
|
0.10***
|
0.08***
|
0.08**
|
0.06+
|
0.13***
|
0.06**
|
0.05*
|
0.09***
|
|
|
(0.26)
|
(0.02)
|
(0.02)
|
(0.03)
|
(0.03)
|
(0.03)
|
(0.02)
|
(0.02)
|
(0.02)
|
|
GDP p/c
|
0.00
|
0.08
|
-0.02
|
-0.11
|
-0.07
|
-0.11
|
-0.03
|
-0.01
|
-0.03
|
|
|
(0.35)
|
(0.16)
|
(0.10)
|
(0.32)
|
(0.31)
|
(0.32)
|
(0.23)
|
(0.23)
|
(0.23)
|
|
Population
|
0.01
|
-0.06
|
-0.05
|
-0.03
|
-0.03
|
0.01
|
0.01
|
0.02
|
0.04
|
|
|
(0.12)
|
(0.07)
|
(0.06)
|
(0.11)
|
(0.11)
|
(0.11)
|
(0.09)
|
(0.09)
|
(0.09)
|
|
Conflict Intensity
|
0.25
|
0.02
|
0.04
|
0.05
|
0.05
|
0.11
|
-0.02
|
-0.02
|
0.03
|
|
|
(0.54)
|
(0.25)
|
(0.16)
|
(0.53)
|
(0.53)
|
(0.55)
|
(0.39)
|
(0.39)
|
(0.41)
|
|
Non-State Violence
|
-0.55
|
-0.10
|
-0.20
|
-0.04
|
-0.23
|
0.04
|
-0.24
|
-0.34
|
-0.09
|
|
|
(0.58)
|
(0.23)
|
(0.14)
|
(0.52)
|
(0.50)
|
(0.59)
|
(0.45)
|
(0.44)
|
(0.49)
|
|
Nat. Res. Rents
|
-0.05**
|
-0.01
|
-0.01+
|
-0.04+
|
-0.04*
|
-0.04+
|
-0.03+
|
-0.03*
|
-0.03+
|
|
|
(0.02)
|
(0.01)
|
(0.00)
|
(0.02)
|
(0.02)
|
(0.02)
|
(0.01)
|
(0.01)
|
(0.01)
|
|
Polity
|
0.80***
|
0.94***
|
0.97***
|
|
|
|
|
|
|
|
|
(0.07)
|
(0.03)
|
(0.02)
|
|
|
|
|
|
|
|
Polity (Interpolated)
|
|
|
|
0.82***
|
|
|
|
|
|
|
|
|
|
|
(0.07)
|
|
|
|
|
|
|
Polity (Max)
|
|
|
|
|
0.82***
|
|
|
|
|
|
|
|
|
|
|
(0.07)
|
|
|
|
|
|
Polity (Min)
|
|
|
|
|
|
0.80***
|
|
|
|
|
|
|
|
|
|
|
(0.07)
|
|
|
|
|
XPOLITY (Interpolated)
|
|
|
|
|
|
|
0.82***
|
|
|
|
|
|
|
|
|
|
|
(0.07)
|
|
|
|
XPOLITY (Max)
|
|
|
|
|
|
|
|
0.82***
|
|
|
|
|
|
|
|
|
|
|
(0.06)
|
|
|
XPOLITY (Min)
|
|
|
|
|
|
|
|
|
0.80***
|
|
|
|
|
|
|
|
|
|
|
(0.07)
|
|
Num. obs.
|
242
|
252
|
223
|
254
|
254
|
254
|
254
|
254
|
254
|
|
R2
|
0.76
|
0.90
|
0.97
|
0.78
|
0.78
|
0.78
|
0.77
|
0.77
|
0.76
|
|
Adj. R2
|
0.75
|
0.89
|
0.97
|
0.77
|
0.77
|
0.77
|
0.76
|
0.76
|
0.76
|
|
***p < 0.001, **p < 0.01, *p < 0.05, +p < 0.1
|
Table B.4: Technical Robustness Checks II: Binary DV, Time Trends, and Power-Sharing Aggregation
# Library
library(rms)
library(dplyr)
library(texreg)
# Load data
load("./data/diss_df.rda")
# Binary DV
model_polinc <- lrm(pol_inc ~
cabinetCOUNT *
aiddata_AidGDP_ln +
log(GDP_per_capita) +
log(population) +
conf_intens +
nonstate +
WBnatres +
polity2 + pcy + pcy2 + pcy3 ,
data=diss_df, x=T, y=T)
model_polinc <- robcov(model_polinc, diss_df$GWNo)
# Time Trend
model_polity_time <- ols(polity2_t2 ~
cabinetCOUNT *
aiddata_AidGDP_ln +
log(GDP_per_capita) +
log(population) +
conf_intens +
nonstate +
WBnatres +
polity2 +
pcy + pcy2 + pcy3,
data=diss_df, x=T, y=T)
model_polity_time <- robcov(model_polity_time, diss_df$GWNo)
# year FE
diss_df$yearFE <- as.factor(diss_df$year)
model_polity_yearfe <- ols(polity2_t2 ~
cabinetCOUNT *
aiddata_AidGDP_ln +
log(GDP_per_capita) +
log(population) +
conf_intens +
nonstate +
WBnatres +
polity2 +
yearFE,
data=diss_df, x=T, y=T)
model_polity_yearfe <- robcov(model_polity_yearfe, diss_df$GWNo)
# different cabinet aggregation types
model_polity_cabmax <- ols(polity2_t2 ~
cabinetCOUNT_max *
aiddata_AidGDP_ln +
log(GDP_per_capita) +
log(population) +
conf_intens +
nonstate +
WBnatres +
polity2 ,
data=diss_df, x=T, y=T)
model_polity_cabmax <- robcov(model_polity_cabmax, diss_df$GWNo)
model_polity_cabmin <- ols(polity2_t2 ~
cabinetCOUNT_min *
aiddata_AidGDP_ln +
log(GDP_per_capita) +
log(population) +
conf_intens +
nonstate +
WBnatres +
polity2 ,
data=diss_df, x=T, y=T)
model_polity_cabmin <- robcov(model_polity_cabmin, diss_df$GWNo)
model_polity_cabsix <- ols(polity2_t2 ~
cabinetCOUNT_six *
aiddata_AidGDP_ln +
log(GDP_per_capita) +
log(population) +
conf_intens +
nonstate +
WBnatres +
polity2 ,
data=diss_df, x=T, y=T)
model_polity_cabsix <- robcov(model_polity_cabsix, diss_df$GWNo)
# list models
tech_rob_models <- list(model_polinc, model_polity_time,
model_polity_yearfe, model_polity_cabmax,
model_polity_cabmin, model_polity_cabsix)
coef_map <- list(cabinetCOUNT = "Power-Sharing (cabinet)",
cabinetCOUNT_max = "Power-Sharing (cabinet)",
cabinetCOUNT_min = "Power-Sharing (cabinet)",
cabinetCOUNT_six = "Power-Sharing (cabinet)",
"cabinetCOUNT * aiddata_AidGDP_ln" = "Power-Sharing (cabinet) * Aid",
"cabinetCOUNT_max * aiddata_AidGDP_ln" = "Power-Sharing (cabinet) * Aid",
"cabinetCOUNT_min * aiddata_AidGDP_ln" = "Power-Sharing (cabinet) * Aid",
"cabinetCOUNT_six * aiddata_AidGDP_ln" = "Power-Sharing (cabinet) * Aid",
aiddata_AidGDP_ln = "Aid / GDP (log)",
GDP_per_capita = "GDP p/c",
population = "Population",
conf_intens = "Conflict Intensity",
nonstate = "Non-State Violence",
WBnatres = "Nat. Res. Rents",
polity2 = "Polity",
pcy = "Time",
pcy2 = "Time$^2$",
pcy3 = "Time$^3$")
# Output Manuscript
#
# texreg(tech_rob_models,
# custom.coef.map = coef_map,
# file = "../output/democ_app_tech_robustness2.tex",
# custom.model.names = c("(1) Logit: Binary DV",
# "(2) OLS: Cubic Time Trend",
# "(3) OLS: Year FE",
# "(4) PS: Max",
# "(5) PS: Min",
# "(6) PS: Six Months"),
# stars = c(0.001, 0.01, 0.05, 0.1),
# symbol = "+",
# table = F,
# booktabs = T,
# use.packages = F,
# dcolumn = T,
# include.lr = F,
# include.rsquared = F,
# caption = "")
# Output Replication Archive
htmlreg(tech_rob_models,
custom.coef.map = coef_map,
custom.model.names = c("(1) Logit: Binary DV",
"(2) OLS: Cubic Time Trend",
"(3) OLS: Year FE",
"(4) PS: Max",
"(5) PS: Min",
"(6) PS: Six Months"),
stars = c(0.001, 0.01, 0.05, 0.1),
symbol = "+",
table = F,
booktabs = T,
use.packages = F,
dcolumn = T,
include.lr = F,
include.rsquared = F,
caption = "")
|
|
(1) Logit: Binary DV
|
(2) OLS: Cubic Time Trend
|
(3) OLS: Year FE
|
(4) PS: Max
|
(5) PS: Min
|
(6) PS: Six Months
|
|
Power-Sharing (cabinet)
|
-2.75***
|
-0.14
|
-0.05
|
-0.14+
|
-0.18
|
-0.16+
|
|
|
(0.76)
|
(0.09)
|
(0.09)
|
(0.07)
|
(0.11)
|
(0.09)
|
|
Power-Sharing (cabinet) * Aid
|
0.83***
|
0.09**
|
0.06*
|
0.09***
|
0.11**
|
0.11***
|
|
|
(0.21)
|
(0.04)
|
(0.03)
|
(0.03)
|
(0.04)
|
(0.03)
|
|
Aid / GDP (log)
|
0.27
|
-0.03
|
0.10
|
-0.03
|
-0.01
|
-0.03
|
|
|
(0.22)
|
(0.14)
|
(0.16)
|
(0.14)
|
(0.14)
|
(0.14)
|
|
GDP p/c
|
0.09
|
-0.08
|
0.00
|
-0.08
|
-0.08
|
-0.08
|
|
|
(0.34)
|
(0.31)
|
(0.30)
|
(0.31)
|
(0.32)
|
(0.31)
|
|
Population
|
-0.27
|
-0.02
|
0.10
|
-0.02
|
-0.01
|
-0.02
|
|
|
(0.23)
|
(0.11)
|
(0.13)
|
(0.11)
|
(0.11)
|
(0.11)
|
|
Conflict Intensity
|
-0.27
|
0.07
|
0.02
|
0.09
|
0.14
|
0.10
|
|
|
(0.76)
|
(0.45)
|
(0.43)
|
(0.46)
|
(0.46)
|
(0.46)
|
|
Non-State Violence
|
-1.23
|
-0.46
|
-0.77
|
-0.44
|
-0.37
|
-0.46
|
|
|
(1.36)
|
(0.51)
|
(0.52)
|
(0.50)
|
(0.52)
|
(0.50)
|
|
Nat. Res. Rents
|
-0.07*
|
-0.04*
|
-0.03+
|
-0.04*
|
-0.04*
|
-0.04*
|
|
|
(0.03)
|
(0.02)
|
(0.02)
|
(0.02)
|
(0.02)
|
(0.02)
|
|
Polity
|
-0.20***
|
0.82***
|
0.82***
|
0.82***
|
0.82***
|
0.82***
|
|
|
(0.05)
|
(0.07)
|
(0.07)
|
(0.07)
|
(0.07)
|
(0.07)
|
|
Time
|
-1.83
|
-2.82+
|
|
|
|
|
|
|
(2.42)
|
(1.54)
|
|
|
|
|
|
Time\(^2\)
|
0.46
|
0.90+
|
|
|
|
|
|
|
(1.00)
|
(0.54)
|
|
|
|
|
|
Time\(^3\)
|
-0.04
|
-0.09
|
|
|
|
|
|
|
(0.12)
|
(0.06)
|
|
|
|
|
|
Num. obs.
|
263
|
263
|
263
|
263
|
263
|
263
|
|
Pseudo R2
|
0.31
|
|
|
|
|
|
|
Adj. R2
|
|
0.76
|
0.76
|
0.76
|
0.76
|
0.76
|
|
p < 0.001, p < 0.01, p < 0.05, +p < 0.1
|
Table B.5: Matching Tables and Balance Diagnostics
# Balance diagnostics (table) ---------------------------------
# Libraries
library(dplyr)
library(MatchIt)
library(texreg)
library(rms)
library(tidyr)
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,
polity_chng, polity2, polity2_t2, # check
fh, fh_chng, 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)
# 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)
# Imbalance Reduction Tables ------------------------------------------
# This section creates the matching diagnostics: First the imbalance reduction as
# tables including the t-test and K-S test for significant difference between treated
# and control. And also a balance improvment plot.
# First: the imbalance reduction tables before and after matching.
library(ebal)
# before matching
imb_before <- Matching::MatchBalance(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,
data = match_democ_data,
digits = 2,
ks = T, print.level = 0) # data before matching
# note that order of covariates in MatchBalance is important to get row names right
imbalance_tab_before <- ebal::baltest.collect(imb_before,
c("Aid / GDP (log)",
"GDP / PC (log)",
"Population (log)",
"Conflict Intensity",
"Nonstate Conflict",
"Natural Resource Rents (log)",
"Regime Type (Polity)"),
after = F)
# do not report all statistics
imbalance_tab_before <- imbalance_tab_before[, c("mean.Tr",
"mean.Co",
"T pval",
"KS pval")]
colnames(imbalance_tab_before) <- c("Mean Treated",
"Mean Control",
"P-Value T-Test",
"P-Value K-S Test")
# Imbalance After Matching
imb_after <- Matching::MatchBalance(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,
data = match_democ_df,
digits = 2,
ks = T, print.level = 0) # data before matching
# note that order of covariates in MatchBalance is important to get row names right
imbalance_tab_after<- ebal::baltest.collect(imb_after,
c("Aid / GDP (log)",
"GDP / PC (log)",
"Population (log)",
"Conflict Intensity",
"Nonstate Conflict",
"Natural Resource Rents (log)",
"Regime Type (Polity)"),
after = F)
imbalance_tab_after <- imbalance_tab_after[, c("mean.Tr",
"mean.Co",
"T pval",
"KS pval")]
colnames(imbalance_tab_after) <- c("Mean Treated",
"Mean Control",
"P-Value T-Test",
"P-Value K-S Test")
# Balance tables before / after output
# output imbalance tables
# print(xtable(imbalance_tab_before), booktabs = T, type = "latex", floating = F,
# file = "../output/matching_imbalance_reduction_democ_before.tex")
# print(xtable(imbalance_tab_after), booktabs = T, type = "latex", floating = F,
# file = "../output/matching_imbalance_reduction_democ_after.tex")
# output imbalance tables fro replication archive
print(xtable(imbalance_tab_before), booktabs = T, type = "html")
|
|
Mean Treated
|
Mean Control
|
P-Value T-Test
|
P-Value K-S Test
|
|
Aid / GDP (log)
|
2.16
|
1.41
|
0.00
|
0.00
|
|
GDP / PC (log)
|
6.32
|
6.55
|
0.13
|
0.07
|
|
Population (log)
|
15.54
|
15.91
|
0.17
|
0.02
|
|
Conflict Intensity
|
0.29
|
0.26
|
0.70
|
|
|
Nonstate Conflict
|
0.45
|
0.12
|
0.00
|
|
|
Natural Resource Rents (log)
|
1.71
|
1.78
|
0.78
|
0.17
|
|
Regime Type (Polity)
|
2.05
|
1.70
|
0.68
|
0.34
|
print(xtable(imbalance_tab_after), booktabs = T, type = "html")
|
|
Mean Treated
|
Mean Control
|
P-Value T-Test
|
P-Value K-S Test
|
|
Aid / GDP (log)
|
2.16
|
2.09
|
0.74
|
0.40
|
|
GDP / PC (log)
|
6.32
|
6.34
|
0.89
|
0.17
|
|
Population (log)
|
15.54
|
15.77
|
0.44
|
0.01
|
|
Conflict Intensity
|
0.29
|
0.26
|
0.77
|
|
|
Nonstate Conflict
|
0.45
|
0.33
|
0.23
|
|
|
Natural Resource Rents (log)
|
1.71
|
1.75
|
0.91
|
0.44
|
|
Regime Type (Polity)
|
2.05
|
1.82
|
0.80
|
0.56
|
Table B.6: First Stage and Reduced Form Results
# load libraries
library(AER)
library(ivpack)
library(lmtest)
library(lfe)
library(rms)
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$population)
# First Stage Results
iv_fs_aid <- ols(aiddata_AidGDP_ln ~
log(total_sum_except / GDP) +
cabinetCOUNT +
log(GDP_per_capita) +
log(population) +
conf_intens +
nonstate +
WBnatres +
polity2,
data = iv_na, x = T, y = T)
iv_fs_aid <- robcov(iv_fs_aid, iv_na$GWNo)
# IV * cabinetCOUNT => aid * cabinetCOUNT
iv_fs_aidinteraction <- ols(aiddata_AidGDP_ln * cabinetCOUNT ~
log(total_sum_except / GDP) *
cabinetCOUNT +
log(GDP_per_capita) +
log(population) +
conf_intens +
nonstate +
WBnatres +
polity2,
data = iv_na, x = T, y = T)
iv_fs_aidinteraction <- robcov(iv_fs_aidinteraction, iv_na$GWNo)
# reduced form: Polity
reduced_form_polity <- ols(polity2_t2 ~
cabinetCOUNT *
log(total_sum_except / GDP) +
log(GDP_per_capita) +
log(population) +
conf_intens +
nonstate +
WBnatres +
polity2,
data=iv_na, x=T, y=T)
reduced_form_polity <- robcov(reduced_form_polity, iv_na$GWNo)
# reduced form: Freedom House
reduced_form_fh <- ols(fh_t2 ~
cabinetCOUNT *
log(total_sum_except / GDP) +
log(GDP_per_capita) +
log(population) +
conf_intens +
nonstate +
WBnatres +
fh,
data=iv_na, x=T, y=T)
reduced_form_fh <- robcov(reduced_form_fh, iv_na$GWNo)
# Output: create variable list order
name_map <- list(total_sum_except = "Aid/GDP (Instrumented)",
"total_sum_except * cabinetCOUNT" = "Aid/GDP (Instrumented) * Power-Sharing (cabinet)",
"cabinetCOUNT * total_sum_except" = "Aid/GDP (Instrumented) * Power-Sharing (cabinet)",
cabinetCOUNT = "Power-Sharing (cabinet)",
"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 (Polity)",
fh = "Regime Type (Freedom House)")
# Output for Manuscript
# texreg(l = list(iv_fs_aid,
# iv_fs_aidinteraction,
# reduced_form_polity,
# reduced_form_fh),
# custom.coef.map = name_map,
# file = "../output/iv_democ_firststage_reduced.tex",
# stars = c(0.001, 0.01, 0.05, 0.1),
# symbol = "+",
# table = F,
# custom.model.names = c("(1) DV: Aid", "(2) Aid * PS Interaction",
# "(3) Reduced Form - Polity", "(4) Reduced Form - FH"),
# booktabs = T,
# use.packages = F,
# dcolumn = T,
# include.lr = F)
# Output for Replication Archivie
htmlreg(l = list(iv_fs_aid,
iv_fs_aidinteraction,
reduced_form_polity,
reduced_form_fh),
custom.coef.map = name_map,
caption = "",
stars = c(0.001, 0.01, 0.05, 0.1),
symbol = "+",
table = F,
custom.model.names = c("(1) DV: Aid", "(2) Aid * PS Interaction",
"(3) Reduced Form - Polity", "(4) Reduced Form - FH"),
booktabs = T,
use.packages = F,
dcolumn = T,
star.symbol = "\\*",
include.lr = F)
|
|
(1) DV: Aid
|
(2) Aid * PS Interaction
|
(3) Reduced Form - Polity
|
(4) Reduced Form - FH
|
|
Aid/GDP (Instrumented)
|
0.70***
|
-0.09
|
0.26
|
0.10***
|
|
|
(0.08)
|
(0.07)
|
(0.17)
|
(0.03)
|
|
Aid/GDP (Instrumented) * Power-Sharing (cabinet)
|
|
1.03***
|
0.10*
|
0.03**
|
|
|
|
(0.22)
|
(0.05)
|
(0.01)
|
|
Power-Sharing (cabinet)
|
0.04*
|
-19.18***
|
-2.08*
|
-0.72**
|
|
|
(0.02)
|
(4.67)
|
(0.99)
|
(0.25)
|
|
Conflict Intensity
|
0.51**
|
0.37*
|
0.00
|
-0.12
|
|
|
(0.19)
|
(0.18)
|
(0.43)
|
(0.10)
|
|
Non-State Violence
|
0.02
|
0.74
|
-0.29
|
-0.31*
|
|
|
(0.31)
|
(0.57)
|
(0.53)
|
(0.13)
|
|
Nat. Res. Rents
|
-0.00
|
0.00
|
-0.04*
|
-0.01*
|
|
|
(0.01)
|
(0.01)
|
(0.02)
|
(0.00)
|
|
Regime Type (Polity)
|
-0.00
|
-0.01
|
0.80***
|
|
|
|
(0.02)
|
(0.02)
|
(0.07)
|
|
|
Regime Type (Freedom House)
|
|
|
|
0.84***
|
|
|
|
|
|
(0.04)
|
|
Num. obs.
|
260
|
260
|
260
|
260
|
|
R2
|
0.68
|
0.94
|
0.77
|
0.81
|
|
Adj. R2
|
0.67
|
0.94
|
0.76
|
0.80
|
|
***p < 0.001, **p < 0.01, *p < 0.05, +p < 0.1
|
---
title: "Appendix for Chapter 5: Democratization Scores"
output: 
  html_document:
    toc: true
    toc_float: 
      collapsed: false
    code_download: true
    code_folding: "hide"
---

# Table B.1: Individual Effects of Power-Sharing and Foreign Aid on Freedom House scores

```{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)",
                 "log(population)",
                 "conf_intens", 
                 "nonstate",
                 "WBnatres",
                 "fh")

#### Power-Sharing only Models ####

# PS only + cabinetCOUNT
model_ps <- ols(formula(paste0("fh_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("fh_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("fh_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("fh_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("fh_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("fh_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 manuscript
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_fh.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 (Freedom House)",
#                                     "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,
#               # add.lines = list(c("Controls", rep("\\multicolumn{1}{c}{Yes}", 6))), 
#               use.packages = F,
#               center = TRUE,
#               include.lr = 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 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),
        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 (Freedom House)",
                              "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,
        booktabs = T,
        use.packages = F,
        center = TRUE,
        include.lr = F,
        star.symbol = "\\*")


```

# Table B.2: Temporal Dynamics for the Interaction Effect between Power-Sharing and Foreign aid on Democracy Score
```{r, results="asis", message=F, warning=F, cache = T, comments = F}

# 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) +
                       log(population) +
                       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) +
                       log(population) +
                       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))) 

# output for manuscript
source("./functions/custom_texreg.R")
environment(custom_texreg) <- asNamespace('texreg')
# 
# custom_texreg(model_all$model,
#               file = "../output/temp_dynamics_democ_app.tex",
#               stars = c(0.001, 0.01, 0.05, 0.1),
#         include.adjrs = T, 
#         include.rsquared = F,
#               center = TRUE,
#               symbol = "+",
#               reorder.coef = c(1, 2, 9, 3:8, 10),
#         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 (cabinet) * Aid",
#                               "Freedom House"),
#         omit.coef = "Intercept",
#         table = FALSE, 
#         custom.multicol = F, 
#         dcolumn = T,
#         booktabs = T,
#         use.packages = F)

# output replication archive
htmlreg(model_all$model,
        stars = c(0.001, 0.01, 0.05, 0.1),
        include.adjrs = T, 
        caption = "", 
        include.rsquared = F,
        center = TRUE,
        symbol = "+",
        reorder.coef = c(1, 2, 9, 3:8, 10),
        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 (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 B.3: Technical Robustness Checks I: Outlier Test and XPOLITY and Interregnum Bias Correction

```{r, results="asis", message=F, warning=F, cache = T, comments = F}

# Library
library(rms)
library(dplyr)

# Load data
load("./data/diss_df.rda")

##### Outlier #####

# Load outlier function
source("./functions/outlier_analysis.R")

# Estimate baseline model
model_polity_cabcount <- ols(polity2_t2 ~  
                        cabinetCOUNT * 
                        aiddata_AidGDP_ln +
                        log(GDP_per_capita) +
                        log(population) +
                        conf_intens +
                        nonstate + 
                        WBnatres +
                        polity2 ,
                      data=diss_df, x=T, y=T)
model_polity_cabcount <- robcov(model_polity_cabcount, diss_df$GWNo)

# selector variables
selectvars = c("Location", "year", "identifiers")
diss_df$identifiers <- paste(diss_df$GWNo, diss_df$year, sep = "-")

# Estimate outliers
democ_outliers <- check_outlier(model_polity_cabcount, 
                                      data = diss_df,
                                      selectvars = selectvars, 
                                clustervar = "GWNo")

#### XPOLITY & Interregnum Bias ####

# First load regular Plumper & Neumayer correction
load("./data/polity2_xpolity_plumper_corrected.rdata")
names(polity_rev)[2] <- "GWNo"

# Fix Serbia for correct merging
polity_rev$GWNo <- ifelse(polity_rev$country == "Yugoslavia", 345, polity_rev$GWNo)
polity_rev$GWNo <- ifelse(polity_rev$country == "Serbia and Montenegro", 345, polity_rev$GWNo)

polity_rev_orig <- polity_rev


# Second load xpolity combined with Plumper & Neumayer correction
load("./data/polity2_xpolity_plumper_corrected_combined.rdata")
names(polity_rev)[2] <- "GWNo"

# Fix Serbia
polity_rev$GWNo <- ifelse(polity_rev$country == "Yugoslavia", 345, polity_rev$GWNo)
polity_rev$GWNo <- ifelse(polity_rev$country == "Serbia and Montenegro", 345, polity_rev$GWNo)


polity_rev <- left_join(polity_rev_orig, polity_rev[, c("GWNo", "year", "xpolity_inter", 
                                                        "xpolity_max", 
                                                        "xpolity_min")], 
                        by = c("GWNo", "year"))

# merge xpolity & interregnum data in main data set
testdf <- merge(diss_df, polity_rev[, c("GWNo", "year", 
                                       "polity2min", "polity2max", "polity2inter",
                                       "xpolity_inter", 
                                       "xpolity_max", 
                                       "xpolity_min")], by = c("GWNo", "year"), all.x = TRUE)



# Data transformations -----------------------------------------------------

source("./functions/laggedVar.R")

#### For Plumper & Neumayer correction

# Polity 2 Min
testdf <- laggedVar(testdf, polity_rev, lVar="polity2min", tID ="year", gID="GWNo", lagBy = -2)

# Polity2 Max
testdf <- laggedVar(testdf, polity_rev, lVar="polity2max", tID ="year", gID="GWNo", lagBy = -2)

# Polity2 Inter
testdf <- laggedVar(testdf, polity_rev, lVar="polity2inter", tID ="year", gID="GWNo", lagBy = -2)

#### XPOLITY

# xpolity min
testdf <- laggedVar(testdf, polity_rev, lVar="xpolity_min", tID ="year", gID="GWNo", lagBy = -2)

# xpolity max
testdf <- laggedVar(testdf, polity_rev, lVar="xpolity_max", tID ="year", gID="GWNo", lagBy = -2)

# xpolity inter
testdf <- laggedVar(testdf, polity_rev, lVar="xpolity_inter", tID ="year", gID="GWNo", lagBy = -2)


## Run XPOLITY & Interregnum Bias models

# xpolity min
model_xpol_min <- ols(xpolity_min_t2 ~  
                          cabinetCOUNT + 
                          aiddata_AidGDP_ln +
                          cabinetCOUNT : aiddata_AidGDP_ln +
                          log(GDP_per_capita) +
                          log(population) +
                          conf_intens +
                          nonstate + 
                          WBnatres +
                          xpolity_min,
                        data=testdf, x=T, y=T)
model_xpol_min <- robcov(model_xpol_min, testdf$GWNo)


# xpolity min
model_xpol_max <- ols(xpolity_max_t2 ~  
                        cabinetCOUNT + 
                        aiddata_AidGDP_ln +
                        cabinetCOUNT : aiddata_AidGDP_ln +
                        log(GDP_per_capita) +
                        log(population) +
                        conf_intens +
                        nonstate + 
                        WBnatres +
                        xpolity_max,
                      data=testdf, x=T, y=T)
model_xpol_max <- robcov(model_xpol_max, testdf$GWNo)


# xpolity inter
model_xpol_inter <- ols(xpolity_inter_t2 ~  
                    cabinetCOUNT + 
                    aiddata_AidGDP_ln +
                    cabinetCOUNT : aiddata_AidGDP_ln +
                    log(GDP_per_capita) +
                    log(population) +
                    conf_intens +
                    nonstate + 
                    WBnatres +
                    xpolity_inter,
                  data=testdf, x=T, y=T)
model_xpol_inter <- robcov(model_xpol_inter, testdf$GWNo)

# Models for Plumper & Neumayer (interregnum bias) ------------------------

# polity 2 min
model_pchng_p2min <- ols(polity2min_t2 ~  
                     cabinetCOUNT + 
                     aiddata_AidGDP_ln +
                     cabinetCOUNT : aiddata_AidGDP_ln +
                     log(GDP_per_capita) +
                     log(population) +
                     conf_intens +
                     nonstate + 
                     WBnatres +
                     polity2min,
                   data=testdf, x=T, y=T)
model_pchng_p2min <- robcov(model_pchng_p2min, testdf$GWNo)

# polity 2 max
model_pchng_p2max <- ols(polity2max_t2 ~  
                           cabinetCOUNT + 
                           aiddata_AidGDP_ln +
                           cabinetCOUNT : aiddata_AidGDP_ln +
                           log(GDP_per_capita) +
                           log(population) +
                           conf_intens +
                           nonstate + 
                           WBnatres +
                           polity2max ,
                         data=testdf, x=T, y=T)
model_pchng_p2max <- robcov(model_pchng_p2max, testdf$GWNo)

# polity 2 interpolated
model_pchng_p2inter <- ols(polity2inter_t2 ~  
                           cabinetCOUNT * 
                           aiddata_AidGDP_ln +
                           log(GDP_per_capita) +
                           log(population) +
                           conf_intens +
                           nonstate + 
                           WBnatres +
                           polity2inter,
                         data=testdf, x=T, y=T)
model_pchng_p2inter <- robcov(model_pchng_p2inter, testdf$GWNo)


# Output Replication Archive

## Determine order of coefficients in output table
name_map_robustness <- list(cabinetCOUNT = "PS (cabinet)",
                            aiddata_AidGDP_ln = "Aid / GDP (log)",
                            "cabinetCOUNT * aiddata_AidGDP_ln" = "PS (cabinet) * Aid", 
                            GDP_per_capita = "GDP p/c",
                            population = "Population",
                            conf_intens = "Conflict Intensity",
                            nonstate = "Non-State Violence",
                            WBnatres = "Nat. Res. Rents",
                            polity2 = "Polity", 
                            polity2inter = "Polity (Interpolated)", 
                            polity2max = "Polity (Max)", 
                            polity2min = "Polity (Min)", 
                            xpolity_inter = "XPOLITY (Interpolated)", 
                            xpolity_max = "XPOLITY (Max)", 
                            xpolity_min = "XPOLITY (Min)")

source("./functions/will_lowe_texreg_reorder.R")

# Model list
app_rob_models <- list(democ_outliers[[2]], 
                       democ_outliers[[4]], 
                       democ_outliers[[6]], 
                       model_pchng_p2inter, 
                       model_pchng_p2max, 
                       model_pchng_p2min, 
                       model_xpol_inter, 
                       model_xpol_max, 
                       model_xpol_min)

oldnames <- all.varnames.dammit(app_rob_models)
ror <- build.ror(oldnames, name_map_robustness)

# texreg
# texreg(l = app_rob_models,
#         stars = c(0.001, 0.01, 0.05, 0.1),
#         symbol = "+",
#         table = F,
#         booktabs = T,
#         use.packages = F,
#         dcolumn = T,
#         file = "../output/democ_app_tech_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, 
#         reorder.coef = unique(ror$rc),
#         include.lr = F)

# Replication Archive output
htmlreg(l = app_rob_models,
        stars = c(0.001, 0.01, 0.05, 0.1),
        symbol = "+",
        table = F,
        booktabs = T,
        caption = "", 
        use.packages = F,
        dcolumn = T,
        custom.coef.names = ror$ccn, 
        omit.coef = ror$oc, 
        reorder.coef = unique(ror$rc),
        star.symbol = "\\*", 
        include.lr = F)


```


# Table B.4: Technical Robustness Checks II: Binary DV, Time Trends, and Power-Sharing Aggregation

```{r, results="asis", message=F, warning=F, cache = T, comments = F}

# Library
library(rms)
library(dplyr)
library(texreg)

# Load data
load("./data/diss_df.rda")

# Binary DV 
model_polinc <- lrm(pol_inc ~  
                        cabinetCOUNT * 
                        aiddata_AidGDP_ln +
                        log(GDP_per_capita) +
                        log(population) +
                        conf_intens +
                        nonstate + 
                        WBnatres +
                        polity2 +  pcy + pcy2 + pcy3 ,
                      data=diss_df, x=T, y=T)
model_polinc <- robcov(model_polinc, diss_df$GWNo)

# Time Trend
model_polity_time <- ols(polity2_t2 ~  
                        cabinetCOUNT * 
                        aiddata_AidGDP_ln +
                        log(GDP_per_capita) +
                        log(population) +
                        conf_intens +
                        nonstate + 
                        WBnatres +
                        polity2 +
                          pcy + pcy2 + pcy3,
                      data=diss_df, x=T, y=T)
model_polity_time <- robcov(model_polity_time, diss_df$GWNo)

# year FE
diss_df$yearFE <- as.factor(diss_df$year)
model_polity_yearfe <- ols(polity2_t2 ~  
                        cabinetCOUNT * 
                        aiddata_AidGDP_ln +
                        log(GDP_per_capita) +
                        log(population) +
                        conf_intens +
                        nonstate + 
                        WBnatres +
                        polity2 +
                        yearFE,
                      data=diss_df, x=T, y=T)
model_polity_yearfe <- robcov(model_polity_yearfe, diss_df$GWNo)

# different cabinet aggregation types
model_polity_cabmax <- ols(polity2_t2 ~  
                        cabinetCOUNT_max * 
                        aiddata_AidGDP_ln +
                        log(GDP_per_capita) +
                        log(population) +
                        conf_intens +
                        nonstate + 
                        WBnatres +
                        polity2 ,
                      data=diss_df, x=T, y=T)
model_polity_cabmax <- robcov(model_polity_cabmax, diss_df$GWNo)

model_polity_cabmin <- ols(polity2_t2 ~  
                        cabinetCOUNT_min * 
                        aiddata_AidGDP_ln +
                        log(GDP_per_capita) +
                        log(population) +
                        conf_intens +
                        nonstate + 
                        WBnatres +
                        polity2 ,
                      data=diss_df, x=T, y=T)
model_polity_cabmin <- robcov(model_polity_cabmin, diss_df$GWNo)


model_polity_cabsix <- ols(polity2_t2 ~  
                        cabinetCOUNT_six * 
                        aiddata_AidGDP_ln +
                        log(GDP_per_capita) +
                        log(population) +
                        conf_intens +
                        nonstate + 
                        WBnatres +
                        polity2 ,
                      data=diss_df, x=T, y=T)
model_polity_cabsix <- robcov(model_polity_cabsix, diss_df$GWNo)


# list models

tech_rob_models <- list(model_polinc, model_polity_time, 
                        model_polity_yearfe, model_polity_cabmax, 
                        model_polity_cabmin, model_polity_cabsix)

coef_map <- list(cabinetCOUNT = "Power-Sharing (cabinet)",
                   cabinetCOUNT_max = "Power-Sharing (cabinet)",
                   cabinetCOUNT_min = "Power-Sharing (cabinet)",
                   cabinetCOUNT_six = "Power-Sharing (cabinet)",
                   "cabinetCOUNT * aiddata_AidGDP_ln" = "Power-Sharing (cabinet) * Aid", 
                   "cabinetCOUNT_max * aiddata_AidGDP_ln" = "Power-Sharing (cabinet) * Aid", 
                   "cabinetCOUNT_min * aiddata_AidGDP_ln" = "Power-Sharing (cabinet) * Aid", 
                   "cabinetCOUNT_six * aiddata_AidGDP_ln" = "Power-Sharing (cabinet) * 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 = "Polity", 
                   pcy = "Time", 
                   pcy2 = "Time$^2$", 
                   pcy3 = "Time$^3$")

# Output Manuscript
# 
# texreg(tech_rob_models, 
#         custom.coef.map = coef_map, 
#        file = "../output/democ_app_tech_robustness2.tex", 
#         custom.model.names = c("(1) Logit: Binary DV", 
#                                "(2) OLS: Cubic Time Trend", 
#                                "(3) OLS: Year FE",
#                                "(4) PS: Max", 
#                                "(5) PS: Min", 
#                                "(6) PS: Six Months"),
#         stars = c(0.001, 0.01, 0.05, 0.1),
#         symbol = "+",
#         table = F,
#         booktabs = T,
#         use.packages = F,
#         dcolumn = T,
#         include.lr = F,
#         include.rsquared = F,
#         caption = "")



# Output Replication Archive
htmlreg(tech_rob_models, 
        custom.coef.map = coef_map, 
        custom.model.names = c("(1) Logit: Binary DV", 
                               "(2) OLS: Cubic Time Trend", 
                               "(3) OLS: Year FE",
                               "(4) PS: Max", 
                               "(5) PS: Min", 
                               "(6) PS: Six Months"),
        stars = c(0.001, 0.01, 0.05, 0.1),
        symbol = "+",
        table = F,
        booktabs = T,
        use.packages = F,
        dcolumn = T,
        include.lr = F,
        include.rsquared = F,
        caption = "")


```


# Table B.5: Matching Tables and Balance Diagnostics

```{r, results="asis", message=F, warning=F, cache = T, comments = F}

# Balance diagnostics (table) ---------------------------------

# Libraries
library(dplyr)
library(MatchIt)
library(texreg)
library(rms)
library(tidyr)
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, 
                polity_chng, polity2, polity2_t2, # check 
                fh, fh_chng, 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)


# 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)

# Imbalance Reduction Tables ------------------------------------------

# This section creates the matching diagnostics: First the imbalance reduction as
# tables including the t-test and K-S test for significant difference between treated
# and control. And also a balance improvment plot. 

# First: the imbalance reduction tables before and after matching.
library(ebal)

# before matching
imb_before <- Matching::MatchBalance(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, 
                           data = match_democ_data, 
                           digits = 2,
                           ks = T, print.level = 0) # data before matching

# note that order of covariates in MatchBalance is important to get row names right
imbalance_tab_before <- ebal::baltest.collect(imb_before, 
                                        c("Aid / GDP (log)", 
                                          "GDP / PC (log)",
                                          "Population (log)", 
                                          "Conflict Intensity",
                                          "Nonstate Conflict",
                                          "Natural Resource Rents (log)", 
                                          "Regime Type (Polity)"), 
                                        after = F)
# do not report all statistics
imbalance_tab_before <- imbalance_tab_before[, c("mean.Tr", 
                                                 "mean.Co", 
                                                 "T pval", 
                                                 "KS pval")]

colnames(imbalance_tab_before) <-  c("Mean Treated", 
                                     "Mean Control", 
                                     "P-Value T-Test", 
                                     "P-Value K-S Test")

# Imbalance After Matching
imb_after <- Matching::MatchBalance(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, 
                          data = match_democ_df, 
                          digits = 2,
                          ks = T, print.level = 0) # data before matching

# note that order of covariates in MatchBalance is important to get row names right
imbalance_tab_after<- ebal::baltest.collect(imb_after, 
                                      c("Aid / GDP (log)", 
                                        "GDP / PC (log)",
                                        "Population (log)", 
                                        "Conflict Intensity",
                                        "Nonstate Conflict",
                                        "Natural Resource Rents (log)", 
                                        "Regime Type (Polity)"), 
                                      after = F)
imbalance_tab_after <- imbalance_tab_after[, c("mean.Tr", 
                                               "mean.Co", 
                                               "T pval", 
                                               "KS pval")]

colnames(imbalance_tab_after) <- c("Mean Treated", 
                                   "Mean Control", 
                                   "P-Value T-Test", 
                                   "P-Value K-S Test")


# Balance tables before / after output

# output imbalance tables
# print(xtable(imbalance_tab_before), booktabs = T, type = "latex", floating = F,
#       file = "../output/matching_imbalance_reduction_democ_before.tex")
# print(xtable(imbalance_tab_after), booktabs = T, type = "latex", floating = F,
#       file = "../output/matching_imbalance_reduction_democ_after.tex")

# output imbalance tables fro replication archive
print(xtable(imbalance_tab_before), booktabs = T, type = "html")
print(xtable(imbalance_tab_after), booktabs = T, type = "html")


```




# Table B.6: First Stage and Reduced Form Results

```{r, results="asis", message=F, warning=F, cache = T, comments = F}

# load libraries
library(AER)
library(ivpack)
library(lmtest)
library(lfe)
library(rms)
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)


# First Stage Results
iv_fs_aid <- ols(aiddata_AidGDP_ln ~
                   log(total_sum_except / GDP) + 
                   cabinetCOUNT  +
                   log(GDP_per_capita) +
                   log(population) +
                   conf_intens +
                   nonstate + 
                   WBnatres +
                   polity2, 
                 data = iv_na, x = T, y = T)

iv_fs_aid <- robcov(iv_fs_aid, iv_na$GWNo)

# IV * cabinetCOUNT => aid * cabinetCOUNT
iv_fs_aidinteraction <- ols(aiddata_AidGDP_ln * cabinetCOUNT ~
                   log(total_sum_except / GDP) * 
                   cabinetCOUNT  +
                   log(GDP_per_capita) +
                   log(population) +
                   conf_intens +
                   nonstate + 
                   WBnatres +
                   polity2, 
                 data = iv_na, x = T, y = T)

iv_fs_aidinteraction <- robcov(iv_fs_aidinteraction, iv_na$GWNo)


# reduced form: Polity
reduced_form_polity <- ols(polity2_t2 ~  
                             cabinetCOUNT * 
                             log(total_sum_except / GDP) +
                             log(GDP_per_capita) +
                             log(population) +
                             conf_intens +
                             nonstate + 
                             WBnatres +
                             polity2,
                           data=iv_na, x=T, y=T)
reduced_form_polity <- robcov(reduced_form_polity, iv_na$GWNo)

# reduced form: Freedom House
reduced_form_fh <- ols(fh_t2 ~  
                             cabinetCOUNT * 
                             log(total_sum_except / GDP) +
                             log(GDP_per_capita) +
                             log(population) +
                             conf_intens +
                             nonstate + 
                             WBnatres +
                             fh,
                           data=iv_na, x=T, y=T)
reduced_form_fh <- robcov(reduced_form_fh, iv_na$GWNo)


# Output: create variable list order

name_map <- list(total_sum_except = "Aid/GDP (Instrumented)",
                 "total_sum_except * cabinetCOUNT"  = "Aid/GDP (Instrumented) * Power-Sharing (cabinet)",
                 "cabinetCOUNT * total_sum_except"  = "Aid/GDP (Instrumented) * Power-Sharing (cabinet)",
                 cabinetCOUNT = "Power-Sharing (cabinet)",
                 "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 (Polity)",
                 fh = "Regime Type (Freedom House)")

# Output for Manuscript
# texreg(l = list(iv_fs_aid, 
#                  iv_fs_aidinteraction, 
#                  reduced_form_polity, 
#                  reduced_form_fh),
#         custom.coef.map = name_map,
#        file = "../output/iv_democ_firststage_reduced.tex",
#         stars = c(0.001, 0.01, 0.05, 0.1),
#         symbol = "+",
#         table = F,
#         custom.model.names = c("(1) DV: Aid", "(2) Aid * PS Interaction", 
#                                "(3) Reduced Form - Polity", "(4) Reduced Form - FH"),
#         booktabs = T,
#         use.packages = F,
#         dcolumn = T,
#         include.lr = F)

# Output for Replication Archivie
htmlreg(l = list(iv_fs_aid, 
                 iv_fs_aidinteraction, 
                 reduced_form_polity, 
                 reduced_form_fh),
        custom.coef.map = name_map,
        caption = "", 
        stars = c(0.001, 0.01, 0.05, 0.1),
        symbol = "+",
        table = F,
        custom.model.names = c("(1) DV: Aid", "(2) Aid * PS Interaction", 
                               "(3) Reduced Form - Polity", "(4) Reduced Form - FH"),
        booktabs = T,
        use.packages = F,
        dcolumn = T,
        star.symbol = "\\*", 
        include.lr = F)

```

