Table C.1: Robustness checks: Individual Effect of Power-Sharing
# Libraries
library(rms)
library(texreg)
# Load data
load("./data/nelda_ps.rda")
# Models
model_ps_quality_cabinc <- ols(quality ~
cabinetINC.12 +
aid_ln +
WBnatres +
fh +
nonstate +
conf_intens +
log(gdp_per_capita_un) +
log(population)
,
data = nelda_ps, x = T, y = T)
model_ps_quality_cabinc <- robcov(model_ps_quality_cabinc, nelda_ps$country)
model_ps_quality_seniorinc <- ols(quality ~
seniorINC.12 +
aid_ln +
WBnatres +
fh +
nonstate +
conf_intens +
log(gdp_per_capita_un) +
log(population)
,
data = nelda_ps, x = T, y = T)
model_ps_quality_seniorinc <- robcov(model_ps_quality_seniorinc, nelda_ps$country)
model_ps_quality_nonseniorinc <- ols(quality ~
nonseniorINC.12 +
aid_ln +
WBnatres +
fh +
nonstate +
conf_intens +
log(gdp_per_capita_un) +
log(population)
,
data = nelda_ps, x = T, y = T)
model_ps_quality_nonseniorinc <- robcov(model_ps_quality_nonseniorinc, nelda_ps$country)
model_ps_v2xel_frefair_cabinc <- ols(v2xel_frefair ~
cabinetINC.12 +
aid_ln +
WBnatres +
fh +
nonstate +
conf_intens +
log(gdp_per_capita_un) +
log(population)
,
data = nelda_ps, x = T, y = T)
model_ps_v2xel_frefair_cabinc <- robcov(model_ps_v2xel_frefair_cabinc, nelda_ps$country)
model_ps_v2xel_frefair_seniorinc <- ols(v2xel_frefair ~
seniorINC.12 +
aid_ln +
WBnatres +
fh +
nonstate +
conf_intens +
log(gdp_per_capita_un) +
log(population)
,
data = nelda_ps, x = T, y = T)
model_ps_v2xel_frefair_seniorinc <- robcov(model_ps_v2xel_frefair_seniorinc, nelda_ps$country)
model_ps_v2xel_frefair_nonseniorinc <- ols(v2xel_frefair ~
nonseniorINC.12 +
aid_ln +
WBnatres +
fh +
nonstate +
conf_intens +
log(gdp_per_capita_un) +
log(population)
,
data = nelda_ps, x = T, y = T)
model_ps_v2xel_frefair_nonseniorinc <- robcov(model_ps_v2xel_frefair_nonseniorinc, nelda_ps$country)
# Output
model_list_ps_indrob <- list(model_ps_quality_cabinc,
model_ps_quality_seniorinc,
model_ps_quality_nonseniorinc,
model_ps_v2xel_frefair_cabinc,
model_ps_v2xel_frefair_seniorinc,
model_ps_v2xel_frefair_nonseniorinc)
coef_name_map <- list(
cabinetINC.12 = "Power-Sharing (binary)",
# seniorCOUNT.12 = "Power-Sharing (senior)",
seniorINC.12 = "Senior Power-Sharing (binary)",
# nonseniorCOUNT.12 = "Power-Sharing (nonsenior)",
nonseniorINC.12 = "Nonsenior Power-Sharing (binary)",
aid_ln = "Aid/GDP (log)",
# dga_gdp_null = "DGA/GDP (log)",
# program_aid_gdp_null= "Program Aid / GDP (log)",
# budget_aid_gdp_null = "Budget Aid / GDP (log",
WBnatres = "Nat. Res. Rents",
fh = "Regime Type (FH)",
nonstate = "Nonstate Conflict",
conf_intens = "Conflict Intensity",
population = "Population (log)",
gdp_per_capita_un = "GDP p/c (log)")
#
# # Output for Manuscript
# texreg(l = model_list_ps_indrob,
# stars = c(0.001, 0.01, 0.05, 0.1),
# custom.coef.map = coef_name_map,
# file = "../output/aid_ps_indeff_elections_rob.tex",
# symbol = "+",
# table = F,
# booktabs = T,
# use.packages = F,
# dcolumn = T,
# include.lr = F,
# include.rsquared = F,
# include.cluster = T,
# include.adjrs = T,
# caption = "")
# Output for Replication Archive
htmlreg(l = model_list_ps_indrob,
stars = c(0.001, 0.01, 0.05, 0.1),
custom.coef.map = coef_name_map,
symbol = "+",
table = F,
booktabs = T,
use.packages = F,
dcolumn = T,
include.lr = F,
include.rsquared = F,
include.cluster = T,
include.adjrs = T,
caption = "",
star.symbol = "\\*")
|
|
Model 1
|
Model 2
|
Model 3
|
Model 4
|
Model 5
|
Model 6
|
|
Power-Sharing (binary)
|
0.80+
|
|
|
0.20**
|
|
|
|
|
(0.44)
|
|
|
(0.07)
|
|
|
|
Senior Power-Sharing (binary)
|
|
1.53*
|
|
|
0.22**
|
|
|
|
|
(0.65)
|
|
|
(0.08)
|
|
|
Nonsenior Power-Sharing (binary)
|
|
|
0.96+
|
|
|
0.17*
|
|
|
|
|
(0.54)
|
|
|
(0.07)
|
|
Aid/GDP (log)
|
-0.10
|
-0.11
|
-0.11
|
-0.02
|
-0.02
|
-0.02
|
|
|
(0.09)
|
(0.08)
|
(0.09)
|
(0.01)
|
(0.01)
|
(0.01)
|
|
Nat. Res. Rents
|
-0.02
|
-0.02+
|
-0.02
|
-0.01**
|
-0.01***
|
-0.01**
|
|
|
(0.01)
|
(0.01)
|
(0.01)
|
(0.00)
|
(0.00)
|
(0.00)
|
|
Regime Type (FH)
|
0.36*
|
0.31*
|
0.34*
|
0.10***
|
0.09***
|
0.09***
|
|
|
(0.16)
|
(0.15)
|
(0.16)
|
(0.02)
|
(0.02)
|
(0.02)
|
|
Nonstate Conflict
|
-2.62**
|
-2.55**
|
-2.58**
|
-0.27***
|
-0.27***
|
-0.27***
|
|
|
(0.87)
|
(0.86)
|
(0.85)
|
(0.05)
|
(0.04)
|
(0.05)
|
|
Conflict Intensity
|
-0.62
|
-0.72
|
-0.59
|
0.00
|
-0.01
|
0.01
|
|
|
(0.46)
|
(0.47)
|
(0.47)
|
(0.06)
|
(0.06)
|
(0.06)
|
|
Population (log)
|
0.10
|
0.10
|
0.09
|
0.03*
|
0.03*
|
0.03+
|
|
|
(0.22)
|
(0.21)
|
(0.21)
|
(0.01)
|
(0.01)
|
(0.01)
|
|
GDP p/c (log)
|
-0.28
|
-0.32
|
-0.29
|
0.01
|
-0.00
|
0.00
|
|
|
(0.22)
|
(0.21)
|
(0.22)
|
(0.02)
|
(0.02)
|
(0.02)
|
|
Num. obs.
|
142
|
142
|
142
|
141
|
141
|
141
|
|
Adj. R2
|
0.16
|
0.20
|
0.17
|
0.49
|
0.46
|
0.45
|
|
***p < 0.001, **p < 0.01, *p < 0.05, +p < 0.1
|
Table C.2: Individual Effects (V-Dem Election Quality)
# Libraries
library(rms)
library(texreg)
# Load data
load("./data/nelda_ps.rda")
# Models
model_ps_v2xel_frefair_cabcount <- ols(v2xel_frefair ~
cabinetCOUNT.12 +
aid_ln +
WBnatres +
fh +
nonstate +
conf_intens +
log(gdp_per_capita_un) +
log(population)
,
data = nelda_ps, x = T, y = T)
model_ps_v2xel_frefair_cabcount <- robcov(model_ps_v2xel_frefair_cabcount, nelda_ps$country)
model_ps_v2xel_frefair_seniorcount <- ols(v2xel_frefair ~
seniorCOUNT.12 +
aid_ln +
WBnatres +
fh +
nonstate +
conf_intens +
log(gdp_per_capita_un) +
log(population)
,
data = nelda_ps, x = T, y = T)
model_ps_v2xel_frefair_seniorcount <- robcov(model_ps_v2xel_frefair_seniorcount, nelda_ps$country)
model_ps_v2xel_frefair_nonseniorcount <- ols(v2xel_frefair ~
nonseniorCOUNT.12 +
aid_ln +
WBnatres +
fh +
nonstate +
conf_intens +
log(gdp_per_capita_un) +
log(population)
,
data = nelda_ps, x = T, y = T)
model_ps_v2xel_frefair_nonseniorcount <- robcov(model_ps_v2xel_frefair_nonseniorcount, nelda_ps$country)
# Models Aid
model_dga_v2xel_frefair <- ols(v2xel_frefair ~
cabinetCOUNT.12 +
log(dga_gdp_null + 1 )+
aid_ln +
WBnatres +
fh +
nonstate +
conf_intens +
log(gdp_per_capita_un) +
log(population)
,
data = nelda_ps, x = T, y = T)
model_dga_v2xel_frefair <- robcov(model_dga_v2xel_frefair, nelda_ps$country)
# program aid
model_prog_aid_v2xel_frefair <- ols(v2xel_frefair ~
cabinetCOUNT.12 +
log(program_aid_gdp_null + 1 )+
aid_ln +
WBnatres +
fh +
nonstate +
conf_intens +
log(gdp_per_capita_un) +
log(population)
,
data = nelda_ps, x = T, y = T)
model_prog_aid_v2xel_frefair <- robcov(model_prog_aid_v2xel_frefair, nelda_ps$country)
# budget aid
model_budgetaid_v2xel_frefair <- ols(v2xel_frefair ~
cabinetCOUNT.12 +
log(budget_aid_gdp_null + 1 )+
aid_ln +
WBnatres +
fh +
nonstate +
conf_intens +
log(gdp_per_capita_un) +
log(population)
,
data = nelda_ps, x = T, y = T)
model_budgetaid_v2xel_frefair <- robcov(model_budgetaid_v2xel_frefair, nelda_ps$country)
# Output
# individual effects model list
ind_effect_models_app <- list(model_ps_v2xel_frefair_cabcount,
model_ps_v2xel_frefair_seniorcount,
model_ps_v2xel_frefair_nonseniorcount,
model_dga_v2xel_frefair,
model_prog_aid_v2xel_frefair,
model_budgetaid_v2xel_frefair)
coef_name_map <- list(cabinetCOUNT.12 = "Power-Sharing (cabinet)",
seniorCOUNT.12 = "Power-Sharing (senior)",
nonseniorCOUNT.12 = "Power-Sharing (nonsenior)",
aid_ln = "Aid/GDP (log)",
dga_gdp_null = "DGA/GDP (log)",
program_aid_gdp_null= "Program Aid / GDP (log)",
budget_aid_gdp_null = "Budget Aid / GDP (log",
WBnatres = "Nat. Res. Rents",
fh = "Regime Type (FH)",
nonstate = "Nonstate Conflict",
conf_intens = "Conflict Intensity",
population = "Population (log)",
gdp_per_capita_un = "GDP p/c (log)")
source("./functions/extract_ols_custom.R")
# Output Manuscript
# texreg(l = ind_effect_models_app,
# stars = c(0.001, 0.01, 0.05, 0.1),
# custom.coef.map = coef_name_map,
# symbol = "+",
# file = "../output/aid_ps_indeff_elections_vdem.tex",
# table = F,
# booktabs = T,
# use.packages = F,
# dcolumn = T,
# include.lr = F,
# include.rsquared = F,
# include.adjrs = T,
# include.cluster = T,
# caption = "",
# star.symbol = "\\*")
# Output Replication Archive
htmlreg(l = ind_effect_models_app,
stars = c(0.001, 0.01, 0.05, 0.1),
custom.coef.map = coef_name_map,
symbol = "+",
table = F,
booktabs = T,
use.packages = F,
dcolumn = T,
include.lr = F,
include.rsquared = F,
include.adjrs = T,
include.cluster = T,
caption = "",
star.symbol = "\\*")
|
|
Model 1
|
Model 2
|
Model 3
|
Model 4
|
Model 5
|
Model 6
|
|
Power-Sharing (cabinet)
|
0.02**
|
|
|
0.02**
|
0.02***
|
0.02*
|
|
|
(0.01)
|
|
|
(0.01)
|
(0.01)
|
(0.01)
|
|
Power-Sharing (senior)
|
|
0.07***
|
|
|
|
|
|
|
|
(0.02)
|
|
|
|
|
|
Power-Sharing (nonsenior)
|
|
|
0.03*
|
|
|
|
|
|
|
|
(0.01)
|
|
|
|
|
Aid/GDP (log)
|
-0.01
|
-0.01
|
-0.01
|
-0.02**
|
-0.04***
|
-0.02+
|
|
|
(0.01)
|
(0.01)
|
(0.01)
|
(0.01)
|
(0.01)
|
(0.01)
|
|
DGA/GDP (log)
|
|
|
|
0.13***
|
|
|
|
|
|
|
|
(0.04)
|
|
|
|
Program Aid / GDP (log)
|
|
|
|
|
0.12*
|
|
|
|
|
|
|
|
(0.05)
|
|
|
Budget Aid / GDP (log
|
|
|
|
|
|
0.06
|
|
|
|
|
|
|
|
(0.04)
|
|
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 (FH)
|
0.09***
|
0.08***
|
0.09***
|
0.08***
|
0.09***
|
0.08***
|
|
|
(0.02)
|
(0.02)
|
(0.02)
|
(0.02)
|
(0.02)
|
(0.02)
|
|
Nonstate Conflict
|
-0.25***
|
-0.25***
|
-0.26***
|
-0.24***
|
-0.23***
|
-0.26***
|
|
|
(0.05)
|
(0.05)
|
(0.05)
|
(0.05)
|
(0.05)
|
(0.05)
|
|
Conflict Intensity
|
-0.01
|
0.00
|
-0.00
|
-0.02
|
-0.04
|
-0.02
|
|
|
(0.07)
|
(0.07)
|
(0.07)
|
(0.08)
|
(0.06)
|
(0.07)
|
|
Population (log)
|
0.02
|
0.02+
|
0.02
|
0.03*
|
0.03*
|
0.03+
|
|
|
(0.01)
|
(0.01)
|
(0.01)
|
(0.01)
|
(0.01)
|
(0.02)
|
|
GDP p/c (log)
|
0.01
|
0.02
|
0.01
|
0.03
|
0.05
|
0.02
|
|
|
(0.02)
|
(0.02)
|
(0.03)
|
(0.02)
|
(0.03)
|
(0.02)
|
|
Num. obs.
|
141
|
141
|
141
|
141
|
141
|
141
|
|
Countries
|
41
|
41
|
41
|
41
|
41
|
41
|
|
Adj. R2
|
0.45
|
0.48
|
0.43
|
0.53
|
0.50
|
0.48
|
|
***p < 0.001, **p < 0.01, *p < 0.05, +p < 0.1
|
Table C.3: Outlier Analysis
# Libraries
library(rms)
library(texreg)
library(tidyverse)
# Load data
load("./data/nelda_ps.rda")
source("./functions/outlier_analysis.R")
# define selectvars which is necessary for the custom 'check_outliers()'
# function
selectvars = c("electionid", "country", "year", "identifiers")
outlierfit_nelda <- lm(quality ~
aid_ln *
cabinetCOUNT.12 +
WBnatres +
fh +
conf_intens +
# nonstate +
log(gdp_per_capita_un) +
log(population)
,
data = nelda_ps, x = T, y = T)
outlierfit_vdem <- lm(v2xel_frefair ~
aid_ln *
cabinetCOUNT.12 +
WBnatres +
fh +
conf_intens +
# nonstate +
log(gdp_per_capita_un) +
log(population)
,
data = nelda_ps, x = T, y = T)
outliers_election_nelda <- check_outlier(outlierfit_nelda,
data = nelda_ps,
selectvars = selectvars)
outliers_election_vdem <- check_outlier(outlierfit_vdem,
data = nelda_ps,
selectvars = selectvars)
## Output
coef_name_map <- list( cabinetINC.12 = "Power-Sharing (binary)",
"aid_ln * cabinetINC.12" = "Power-Sharing (binary) * Aid",
cabinetCOUNT.12 = "Power-Sharing (cabinet)",
"aid_ln * cabinetCOUNT.12" = "Power-Sharing (cabinet) * Aid",
aid_ln = "Aid/GDP (log)",
dga_gdp_null = "DGA/GDP (log)",
program_aid_gdp_null= "Program Aid / GDP (log)",
budget_aid_gdp_null = "Budget Aid / GDP (log",
WBnatres = "Nat. Res. Rents",
fh = "Regime Type (FH)",
nonstate = "Nonstate Conflict",
conf_intens = "Conflict Intensity",
population = "Population (log)",
gdp_per_capita_un = "GDP p/c (log)")
source("./functions/custom_texreg.R")
environment(custom_texreg) <- asNamespace('texreg')
# # Output Maucsript
# custom_texreg(l = list(outliers_election_nelda[[2]],
# outliers_election_nelda[[4]],
# outliers_election_nelda[[6]],
# outliers_election_vdem[[2]],
# outliers_election_vdem[[4]],
# outliers_election_vdem[[6]]),
# stars = c(0.001, 0.01, 0.05, 0.1),
# custom.coef.map = coef_name_map,
# symbol = "+",
# table = F,
# file = "../output/aidps_elections_outlierrobust.tex",
# custom.multicol = T,
# custom.model.names = c(" \\multicolumn{3}{c}{\\textbf{NELDA Election Quality}} &
# \\multicolumn{3}{c}{\\textbf{V-Dem Clean Elections Index}} \\\\
# \\cmidrule(r){2-4} \\cmidrule(r){5-7}
# & \\multicolumn{1}{c}{(1) Hat Values }",
# "\\multicolumn{1}{c}{(2) Cook's Distance}",
# "\\multicolumn{1}{c}{(3) DFBETA}",
# "\\multicolumn{1}{c}{(4) Hat Values}",
# "\\multicolumn{1}{c}{(5) Cook's Distance}",
# "\\multicolumn{1}{c}{(6) DFBETA}"),
# booktabs = T,
# use.packages = F,
# dcolumn = T,
# include.lr = F,
# include.rsquared = F,
# include.cluster = T,
# include.adjrs = T,
# caption = "")
# Output Replication Archive
htmlreg(l = list(outliers_election_nelda[[2]],
outliers_election_nelda[[4]],
outliers_election_nelda[[6]],
outliers_election_vdem[[2]],
outliers_election_vdem[[4]],
outliers_election_vdem[[6]]),
stars = c(0.001, 0.01, 0.05, 0.1),
custom.coef.map = coef_name_map,
symbol = "+",
table = F,
booktabs = T,
use.packages = F,
dcolumn = T,
include.lr = F,
include.rsquared = F,
include.cluster = T,
include.adjrs = T,
caption = "",
star.symbol = "\\*")
|
|
Model 1
|
Model 2
|
Model 3
|
Model 4
|
Model 5
|
Model 6
|
|
Power-Sharing (cabinet)
|
-2.85***
|
-0.23**
|
-0.19
|
-0.17+
|
-0.06***
|
-0.04**
|
|
|
(0.68)
|
(0.09)
|
(0.12)
|
(0.09)
|
(0.01)
|
(0.01)
|
|
Power-Sharing (cabinet) * Aid
|
1.34***
|
0.13***
|
0.11**
|
0.09+
|
0.03***
|
0.02***
|
|
|
(0.35)
|
(0.04)
|
(0.04)
|
(0.05)
|
(0.00)
|
(0.00)
|
|
Aid/GDP (log)
|
-0.16
|
-0.04
|
-0.15**
|
0.01
|
-0.02
|
0.01
|
|
|
(0.19)
|
(0.10)
|
(0.05)
|
(0.02)
|
(0.01)
|
(0.02)
|
|
Nat. Res. Rents
|
-0.05**
|
-0.03+
|
-0.05***
|
-0.01***
|
-0.01***
|
-0.01***
|
|
|
(0.02)
|
(0.02)
|
(0.01)
|
(0.00)
|
(0.00)
|
(0.00)
|
|
Regime Type (FH)
|
0.24
|
0.17
|
0.23
|
0.07***
|
0.06**
|
0.07***
|
|
|
(0.23)
|
(0.21)
|
(0.15)
|
(0.02)
|
(0.02)
|
(0.02)
|
|
Conflict Intensity
|
-0.50
|
-0.60
|
-1.15***
|
0.01
|
0.04
|
-0.03
|
|
|
(0.43)
|
(0.44)
|
(0.34)
|
(0.07)
|
(0.07)
|
(0.07)
|
|
Population (log)
|
-0.01
|
0.06
|
0.04
|
0.02
|
0.02
|
0.02
|
|
|
(0.23)
|
(0.20)
|
(0.18)
|
(0.02)
|
(0.02)
|
(0.01)
|
|
GDP p/c (log)
|
-0.22
|
-0.00
|
-0.51*
|
0.04+
|
0.04+
|
0.05*
|
|
|
(0.30)
|
(0.28)
|
(0.20)
|
(0.02)
|
(0.02)
|
(0.03)
|
|
Num. obs.
|
129
|
140
|
99
|
128
|
138
|
102
|
|
Adj. R2
|
0.13
|
0.07
|
0.25
|
0.42
|
0.46
|
0.61
|
|
***p < 0.001, **p < 0.01, *p < 0.05, +p < 0.1
|
Table C.4 and Figure C.1: Matching Diagnostics
library(MatchIt)
library(dplyr)
library(tidyr)
library(xtable)
library(rms)
library(texreg)
# Create matched data set for elections
# First: create values for matching at the beginning of post-conflict period. This
# serves the purpose of creating pre-treatment (or approximation to pre-treatment)
# values of the covariates
nelda_ps_matching <- nelda_ps %>%
ungroup() %>%
arrange(country, year) %>%
group_by(country, pc_period) %>%
mutate(matchyear = first(year)) %>%
mutate(match_natres = first(WBnatres),
match_aiddata_AidGDP = first(aiddata_AidGDP),
match_pop = first(population),
match_conf_intens = first(conf_intens),
match_nonstate = first(nonstate),
match_fh = first(fh),
match_nelda45 = first(nelda45),
match_gdp_per_capita_un = first(gdp_per_capita_un))
# add additional rebel parity variable as predictor
# parity_to_join_nelda <- psed_ag_y %>%
# dplyr::select(iso3c, pc_period, minyear, parity) %>%
# group_by(iso3c, pc_period) %>%
# dplyr::summarise(parity = unique(parity))
#
# nelda_ps_matching <- left_join(nelda_ps_matching, parity_to_join_nelda)
match_data_elections <- nelda_ps_matching %>%
dplyr::select(cabinetINC.12,
aiddata_AidGDP,
gdp_per_capita_un,
year,
country,
seniorINC.12,
nonseniorINC.12,
seniorCOUNT.12,
nonseniorCOUNT.12,
cabinetCOUNT.12,
population, conf_intens, nonstate, WBnatres, fh, nelda45,
quality, v2xel_frefair, electionid,
iso2c, aiddata_Aid, GDP,
match_natres, match_aiddata_AidGDP, match_pop, match_conf_intens,
match_nonstate, match_fh, match_nelda45, match_gdp_per_capita_un)
match_data_elections <- match_data_elections[complete.cases(match_data_elections), ]
match_data_elections <- as.data.frame(match_data_elections)
# Matching Mahalanobis
#I match only on post-conflict controls at the beginning of the pc period
match_data_elections <- match_data_elections %>%
arrange(country, pc_period, year)
set.seed(1234)
match_res <- matchit(cabinetINC.12 ~
log(match_aiddata_AidGDP) +
log(match_gdp_per_capita_un) +
log(match_pop) +
match_conf_intens +
log(match_natres + 1) +
match_nonstate +
match_fh ,
data = match_data_elections,
method = "nearest",
ratio = 2,
distance = "mahalanobis")
match_df_elec_reg <- match.data(match_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)
library(Matching)
# before matching
imb_before <- MatchBalance(cabinetINC.12 ~
log(match_aiddata_AidGDP) +
log(match_gdp_per_capita_un) +
log(match_pop) +
match_conf_intens +
log(match_natres + 1) +
match_nonstate +
match_fh,
data = match_data_elections,
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 <- baltest.collect(imb_before,
c("Aid / GDP (log)", "GDP / PC (log)",
"Population (log)", "Conflict Intensity",
"Natural Resource Rents (log)", "Nonstate Conflict",
"Regime Type (FH)"),
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")
# After Matching
imb_after <- MatchBalance(cabinetINC.12 ~
log(match_aiddata_AidGDP) +
log(match_gdp_per_capita_un) +
log(match_pop) +
match_conf_intens +
log(match_natres + 1) +
match_nonstate +
match_fh,
data = match_df_elec_reg,
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<- baltest.collect(imb_after,
c("Aid / GDP (log)",
"GDP / PC (log)",
"Population (log)",
"Conflict Intensity",
"Natural Resource Rents (log)",
"Nonstate Conflict",
"Regime Type (FH)"),
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")
# output imbalance tables
print(xtable(imbalance_tab_before), booktabs = T, type = "latex", floating = F,
file = "../output/matching_imbalance_reduction_elections_before.tex")
print(xtable(imbalance_tab_after), booktabs = T, type = "latex", floating = F,
file = "../output/matching_imbalance_reduction_elections_after.tex")
print(xtable(imbalance_tab_before), booktabs = T, type = "html", floating = F)
|
|
Mean Treated
|
Mean Control
|
P-Value T-Test
|
P-Value K-S Test
|
|
Aid / GDP (log)
|
2.51
|
1.23
|
0.00
|
0.00
|
|
GDP / PC (log)
|
6.07
|
6.78
|
0.00
|
0.00
|
|
Population (log)
|
15.36
|
15.92
|
0.07
|
0.00
|
|
Conflict Intensity
|
0.38
|
0.18
|
0.08
|
|
|
Natural Resource Rents (log)
|
1.48
|
1.54
|
0.85
|
0.06
|
|
Nonstate Conflict
|
0.00
|
0.07
|
0.00
|
|
|
Regime Type (FH)
|
2.77
|
3.37
|
0.00
|
0.06
|
print(xtable(imbalance_tab_after), booktabs = T, type = "html", floating = F)
|
|
Mean Treated
|
Mean Control
|
P-Value T-Test
|
P-Value K-S Test
|
|
Aid / GDP (log)
|
2.51
|
2.14
|
0.05
|
0.02
|
|
GDP / PC (log)
|
6.07
|
6.30
|
0.27
|
0.30
|
|
Population (log)
|
15.36
|
15.38
|
0.95
|
0.06
|
|
Conflict Intensity
|
0.38
|
0.38
|
1.00
|
|
|
Natural Resource Rents (log)
|
1.48
|
1.34
|
0.67
|
0.17
|
|
Nonstate Conflict
|
0.00
|
0.00
|
1.00
|
|
|
Regime Type (FH)
|
2.77
|
3.15
|
0.07
|
0.22
|
# imbalance reduction plot, see http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2943670/
library(tidyr)
library(ggplot2)
library(ggrepel)
balance_improvement <- summary(match_res)$reduction
row.names(balance_improvement) <- c("Aid / GDP (log)", "GDP / PC (log)",
"Population (log)", "Conflict Intensity",
"Natural Resource Rents (log)", "Nonstate Conflict",
"Regime Type (FH)")
balance_improvement <- data.frame(variable =
row.names(balance_improvement),
percent_balance_improvement = round(balance_improvement[, 1], 2))
plot_balanceimpr <- data.frame(`All Data` = abs(summary(match_res,
standardize = T)$sum.all$`Std. Mean Diff.`),
`Matched Data` = abs(summary(match_res,
standardize = T)$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_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 = -.4,
nudge_y = -.1) +
theme_bw() +
labs(x = "", y = "Absolute Standardized Difference in Means")
print(plot_balanceimpr_out)
Table C.5: First Stage and Reduced Form Regressions for Instrumental Variable Analysis
# Libraries
library(tidyverse)
library(texreg)
# Load elections data
load("./data/nelda_ps.rda")
# load instrument data for aid
load(file = "./data/instrumentedAid2.RData")
nelda_ivreg <- left_join(nelda_ps, instrument_df,
by = c("year", "iso2c"))
nelda_ivreg$total_sum_except <- as.numeric(nelda_ivreg$total_sum_except)
nelda_ivreg$instr_aid_gdp <- log(nelda_ivreg$total_sum_except / nelda_ivreg$GDP)
nelda_ivreg$date <- NULL
nelda_ivreg$StartDate2 <- NULL
nelda_ivreg$yearmon.x <- NULL
nelda_ivreg$yearmon.y <- NULL
nelda_ivreg[nelda_ivreg == ""] <- NA
# first stage and reduced form models
firststage <- ols(log(aiddata_Aid / GDP) ~
cabinetCOUNT.12 +
instr_aid_gdp +
WBnatres +
fh +
nonstate +
conf_intens +
log(gdp_per_capita_un) +
log(population),
data = nelda_ivreg, x = T, y = T)
firststage <- robcov(firststage, nelda_ivreg$country)
firststage_inter <- ols(log(aiddata_Aid / GDP) * cabinetCOUNT.12 ~
cabinetCOUNT.12 * instr_aid_gdp +
WBnatres +
fh +
nonstate +
conf_intens +
log(gdp_per_capita_un) +
log(population),
data = nelda_ivreg, x = T, y = T)
firststage_inter <- robcov(firststage_inter, nelda_ivreg$country)
reduced_form <- ols(v2xel_frefair ~
instr_aid_gdp *
cabinetCOUNT.12 +
WBnatres +
fh +
nonstate +
conf_intens +
log(gdp_per_capita_un) +
log(population),
data = nelda_ivreg, x = T, y = T)
reduced_form <- robcov(reduced_form, nelda_ivreg$country)
reduced_form_nelda <- ols(quality ~
instr_aid_gdp *
cabinetCOUNT.12 +
WBnatres +
fh +
nonstate +
conf_intens +
log(gdp_per_capita_un) +
log(population),
data = nelda_ivreg, x = T, y = T)
reduced_form_nelda <- robcov(reduced_form_nelda, nelda_ivreg$country)
source("./functions/custom_texreg.R")
environment(custom_texreg) <- asNamespace('texreg')
# custom_texreg(l = list(firststage,
# firststage_inter,
# reduced_form, reduced_form_nelda),
# omit.coef = "Intercept",
# file = "../output/iv_elections_firststage_reduced.tex",
# custom.model.names = c("\\multicolumn{2}{c}{\\textbf{First Stage}} &
# \\multicolumn{2}{c}{\\textbf{Reduced Form}} \\\\ \\cmidrule(r){2-3} \\cmidrule(r){4-5}
# & \\multicolumn{1}{c}{ (1) DV: Aid / GDP (log)}",
# "\\multicolumn{1}{c}{ (2) DV: Aid /GDP * P-S}",
# "\\multicolumn{1}{c}{ (3) V-Dem CEI) }",
# "\\multicolumn{1}{c}{ (4) NELDA)}"),
# custom.coef.names = c("Intercept",
# "Power-Sharing (cabinet)",
# "Aid / GDP (log) - Instrumented",
# "Natural Resource Rents",
# "Regime Type (FH)",
# "Nonstate Conflict",
# "Conflict Intensity",
# "GDP / PC (log)",
# "Population (log)",
# "Aid / GDP (log) - Instrumented * Power-Sharing (cabinet)",
# "Aid / GDP (log) - Instrumented * Power-Sharing (cabinet)"),
# reorder.coef = c(1, 2, 9, 3:8),
# stars = c(0.001, 0.01, 0.05, 0.1),
# booktabs = TRUE,
# center = TRUE,
# symbol = "+",
# table = FALSE,
# custom.multicol = TRUE,
# column = T,
# dcolumn = TRUE,
# include.rsquared = F,
# include.cluster = T,
# use.packages = F)
htmlreg(l = list(firststage, firststage_inter,
reduced_form, reduced_form_nelda),
omit.coef = "Intercept",
custom.coef.names = c("Intercept",
"Power-Sharing (cabinet)",
"Aid / GDP (log) - Instrumented",
"Natural Resource Rents",
"Regime Type (FH)",
"Nonstate Conflict",
"Conflict Intensity",
"GDP / PC (log)",
"Population (log)",
"Aid / GDP (log) - Instrumented * Power-Sharing (cabinet)",
"Aid / GDP (log) - Instrumented * Power-Sharing (cabinet)"),
reorder.coef = c(1, 2, 9, 3:8),
stars = c(0.001, 0.01, 0.05, 0.1),
booktabs = TRUE,
center = TRUE,
symbol = "+",
caption = "",
table = FALSE,
custom.multicol = TRUE,
column = T,
dcolumn = TRUE,
use.packages = F)
|
|
Model 1
|
Model 2
|
Model 3
|
Model 4
|
|
Power-Sharing (cabinet)
|
0.02
|
-17.16***
|
-0.38***
|
-1.83**
|
|
|
(0.03)
|
(1.13)
|
(0.07)
|
(0.70)
|
|
Aid / GDP (log) - Instrumented
|
0.40***
|
-0.02
|
-0.01
|
-0.01
|
|
|
(0.06)
|
(0.02)
|
(0.01)
|
(0.08)
|
|
Aid / GDP (log) - Instrumented * Power-Sharing (cabinet)
|
|
0.72***
|
0.02***
|
0.09**
|
|
|
|
(0.05)
|
(0.00)
|
(0.03)
|
|
Natural Resource Rents
|
0.01
|
0.01*
|
-0.01***
|
-0.03*
|
|
|
(0.01)
|
(0.01)
|
(0.00)
|
(0.02)
|
|
Regime Type (FH)
|
0.08
|
0.02
|
0.08***
|
0.27+
|
|
|
(0.11)
|
(0.04)
|
(0.02)
|
(0.16)
|
|
Nonstate Conflict
|
0.70
|
-0.16
|
-0.26***
|
-2.52**
|
|
|
(0.60)
|
(0.15)
|
(0.06)
|
(0.83)
|
|
Conflict Intensity
|
0.71***
|
0.02
|
0.01
|
-0.64
|
|
|
(0.18)
|
(0.17)
|
(0.07)
|
(0.50)
|
|
GDP / PC (log)
|
-0.68***
|
-0.15
|
0.02
|
-0.12
|
|
|
(0.13)
|
(0.10)
|
(0.02)
|
(0.24)
|
|
Population (log)
|
-0.28***
|
0.11
|
0.03*
|
0.11
|
|
|
(0.07)
|
(0.07)
|
(0.01)
|
(0.21)
|
|
Num. obs.
|
142
|
142
|
141
|
142
|
|
Adj. R2
|
0.62
|
0.98
|
0.50
|
0.18
|
|
L.R.
|
144.69
|
597.46
|
106.55
|
37.05
|
|
p < 0.001, p < 0.01, p < 0.05, +p < 0.1
|
---
title: "Appendix: Chapter 6"
output: 
  html_document:
    toc: true
    toc_float: 
      collapsed: false
    code_download: true
    code_folding: "hide"
---



# Table C.1: Robustness checks: Individual Effect of Power-Sharing
```{r, results="asis", message=F, warning=F, cache = T, comments = F}

# Libraries
library(rms)
library(texreg)

# Load data
load("./data/nelda_ps.rda") 

# Models
model_ps_quality_cabinc <- ols(quality ~
                                 cabinetINC.12 +
                                 aid_ln +
                                 WBnatres +
                                 fh +
                                 nonstate +
                                 conf_intens +
                                 log(gdp_per_capita_un) +
                                 log(population)
                               ,
                               data = nelda_ps, x = T, y = T)
model_ps_quality_cabinc <- robcov(model_ps_quality_cabinc, nelda_ps$country)


model_ps_quality_seniorinc <- ols(quality ~
                                 seniorINC.12 +
                                 aid_ln +
                                 WBnatres +
                                 fh +
                                 nonstate +
                                 conf_intens +
                                 log(gdp_per_capita_un) +
                                 log(population)
                               ,
                               data = nelda_ps, x = T, y = T)
model_ps_quality_seniorinc <- robcov(model_ps_quality_seniorinc, nelda_ps$country)


model_ps_quality_nonseniorinc <- ols(quality ~
                                 nonseniorINC.12 +
                                 aid_ln +
                                 WBnatres +
                                 fh +
                                 nonstate +
                                 conf_intens +
                                 log(gdp_per_capita_un) +
                                 log(population)
                               ,
                               data = nelda_ps, x = T, y = T)
model_ps_quality_nonseniorinc <- robcov(model_ps_quality_nonseniorinc, nelda_ps$country)

model_ps_v2xel_frefair_cabinc <- ols(v2xel_frefair ~
                                 cabinetINC.12 +
                                 aid_ln +
                                 WBnatres +
                                 fh +
                                 nonstate +
                                 conf_intens +
                                 log(gdp_per_capita_un) +
                                 log(population)
                               ,
                               data = nelda_ps, x = T, y = T)
model_ps_v2xel_frefair_cabinc <- robcov(model_ps_v2xel_frefair_cabinc, nelda_ps$country)


model_ps_v2xel_frefair_seniorinc <- ols(v2xel_frefair ~
                                    seniorINC.12 +
                                    aid_ln +
                                    WBnatres +
                                    fh +
                                    nonstate +
                                    conf_intens +
                                    log(gdp_per_capita_un) +
                                    log(population)
                                  ,
                                  data = nelda_ps, x = T, y = T)
model_ps_v2xel_frefair_seniorinc <- robcov(model_ps_v2xel_frefair_seniorinc, nelda_ps$country)


model_ps_v2xel_frefair_nonseniorinc <- ols(v2xel_frefair ~
                                       nonseniorINC.12 +
                                       aid_ln +
                                       WBnatres +
                                       fh +
                                       nonstate +
                                       conf_intens +
                                       log(gdp_per_capita_un) +
                                       log(population)
                                     ,
                                     data = nelda_ps, x = T, y = T)
model_ps_v2xel_frefair_nonseniorinc <- robcov(model_ps_v2xel_frefair_nonseniorinc, nelda_ps$country)


# Output
model_list_ps_indrob <- list(model_ps_quality_cabinc, 
                             model_ps_quality_seniorinc,
                             model_ps_quality_nonseniorinc,
                             model_ps_v2xel_frefair_cabinc, 
                             model_ps_v2xel_frefair_seniorinc, 
                             model_ps_v2xel_frefair_nonseniorinc)

coef_name_map <- list(
                      cabinetINC.12 = "Power-Sharing (binary)",
                 # seniorCOUNT.12 = "Power-Sharing (senior)", 
                 seniorINC.12 = "Senior Power-Sharing (binary)",
                 # nonseniorCOUNT.12 = "Power-Sharing (nonsenior)", 
                 nonseniorINC.12 = "Nonsenior Power-Sharing (binary)",
                 aid_ln = "Aid/GDP (log)", 
                 # dga_gdp_null = "DGA/GDP (log)", 
                 # program_aid_gdp_null= "Program Aid / GDP (log)", 
                 # budget_aid_gdp_null = "Budget Aid / GDP (log", 
                 WBnatres = "Nat. Res. Rents", 
                 fh = "Regime Type (FH)", 
                 nonstate = "Nonstate Conflict", 
                 conf_intens =  "Conflict Intensity", 
                 population = "Population (log)", 
                 gdp_per_capita_un = "GDP p/c (log)")
# 
# # Output for Manuscript
# texreg(l = model_list_ps_indrob, 
#           stars = c(0.001, 0.01, 0.05, 0.1),
#           custom.coef.map = coef_name_map,
#        file = "../output/aid_ps_indeff_elections_rob.tex", 
#         symbol = "+",
#           table = F,
#           booktabs = T,
#           use.packages = F,
#           dcolumn = T,
#           include.lr = F,
#           include.rsquared = F,
#         include.cluster = T,
#           include.adjrs = T,
#         caption = "")

# Output for Replication Archive
htmlreg(l = model_list_ps_indrob, 
          stars = c(0.001, 0.01, 0.05, 0.1),
          custom.coef.map = coef_name_map,
        symbol = "+",
          table = F,
          booktabs = T,
          use.packages = F,
          dcolumn = T,
          include.lr = F,
          include.rsquared = F,
        include.cluster = T,
          include.adjrs = T,
        caption = "", 
        star.symbol = "\\*")


```


# Table C.2:  Individual Effects (V-Dem Election Quality)

```{r, results="asis", message=F, warning=F, cache = T, comments = F}

# Libraries
library(rms)
library(texreg)

# Load data
load("./data/nelda_ps.rda") 

# Models
model_ps_v2xel_frefair_cabcount <- ols(v2xel_frefair ~ 
                                   cabinetCOUNT.12 +
                                   aid_ln +  
                                   WBnatres + 
                                   fh + 
                                   nonstate + 
                                   conf_intens + 
                                   log(gdp_per_capita_un) + 
                                   log(population)
                                 ,
                                 data = nelda_ps, x = T, y = T)
model_ps_v2xel_frefair_cabcount <- robcov(model_ps_v2xel_frefair_cabcount, nelda_ps$country)

model_ps_v2xel_frefair_seniorcount <- ols(v2xel_frefair ~ 
                                      seniorCOUNT.12 +
                                      aid_ln +  
                                      WBnatres + 
                                      fh + 
                                      nonstate + 
                                      conf_intens + 
                                      log(gdp_per_capita_un) + 
                                      log(population)
                                    ,
                                    data = nelda_ps, x = T, y = T)
model_ps_v2xel_frefair_seniorcount <- robcov(model_ps_v2xel_frefair_seniorcount, nelda_ps$country)


model_ps_v2xel_frefair_nonseniorcount <- ols(v2xel_frefair ~ 
                                         nonseniorCOUNT.12 +
                                         aid_ln +  
                                         WBnatres + 
                                         fh + 
                                         nonstate + 
                                         conf_intens + 
                                         log(gdp_per_capita_un) + 
                                         log(population)
                                       ,
                                       data = nelda_ps, x = T, y = T)
model_ps_v2xel_frefair_nonseniorcount <- robcov(model_ps_v2xel_frefair_nonseniorcount, nelda_ps$country)


# Models Aid

model_dga_v2xel_frefair <- ols(v2xel_frefair ~ 
                           cabinetCOUNT.12 +
                           log(dga_gdp_null + 1 )+ 
                           aid_ln +  
                           WBnatres + 
                           fh + 
                           nonstate + 
                           conf_intens + 
                           log(gdp_per_capita_un) + 
                           log(population)
                         ,
                         data = nelda_ps, x = T, y = T)
model_dga_v2xel_frefair <- robcov(model_dga_v2xel_frefair, nelda_ps$country)

# program aid
model_prog_aid_v2xel_frefair <- ols(v2xel_frefair ~ 
                                cabinetCOUNT.12 +
                                log(program_aid_gdp_null + 1 )+ 
                                aid_ln +  
                                WBnatres + 
                                fh + 
                                nonstate + 
                                conf_intens + 
                                log(gdp_per_capita_un) + 
                                log(population)
                              ,
                              data = nelda_ps, x = T, y = T)
model_prog_aid_v2xel_frefair <- robcov(model_prog_aid_v2xel_frefair, nelda_ps$country)

# budget aid
model_budgetaid_v2xel_frefair <- ols(v2xel_frefair ~ 
                                 cabinetCOUNT.12 +
                                 log(budget_aid_gdp_null + 1 )+ 
                                 aid_ln +  
                                 WBnatres + 
                                 fh + 
                                 nonstate + 
                                 conf_intens + 
                                 log(gdp_per_capita_un) + 
                                 log(population)
                               ,
                               data = nelda_ps, x = T, y = T)
model_budgetaid_v2xel_frefair <- robcov(model_budgetaid_v2xel_frefair, nelda_ps$country)


# Output 
# individual effects model list
ind_effect_models_app <- list(model_ps_v2xel_frefair_cabcount, 
                         model_ps_v2xel_frefair_seniorcount, 
                         model_ps_v2xel_frefair_nonseniorcount, 
                         model_dga_v2xel_frefair, 
                         model_prog_aid_v2xel_frefair, 
                         model_budgetaid_v2xel_frefair)


coef_name_map <- list(cabinetCOUNT.12 = "Power-Sharing (cabinet)",
                 seniorCOUNT.12 = "Power-Sharing (senior)", 
                 nonseniorCOUNT.12 = "Power-Sharing (nonsenior)", 
                 aid_ln = "Aid/GDP (log)", 
                 dga_gdp_null = "DGA/GDP (log)", 
                 program_aid_gdp_null= "Program Aid / GDP (log)", 
                 budget_aid_gdp_null = "Budget Aid / GDP (log", 
                 WBnatres = "Nat. Res. Rents", 
                 fh = "Regime Type (FH)", 
                 nonstate = "Nonstate Conflict", 
                 conf_intens =  "Conflict Intensity", 
                 population = "Population (log)", 
                 gdp_per_capita_un = "GDP p/c (log)")

source("./functions/extract_ols_custom.R")

# Output Manuscript
# texreg(l = ind_effect_models_app, 
#           stars = c(0.001, 0.01, 0.05, 0.1),
#           custom.coef.map = coef_name_map,
#         symbol = "+",
#        file = "../output/aid_ps_indeff_elections_vdem.tex",
#           table = F,
#           booktabs = T,
#           use.packages = F,
#           dcolumn = T,
#           include.lr = F,
#           include.rsquared = F,
#           include.adjrs = T,
#         include.cluster = T,
#         caption = "", 
#         star.symbol = "\\*")


# Output Replication Archive
htmlreg(l = ind_effect_models_app, 
          stars = c(0.001, 0.01, 0.05, 0.1),
          custom.coef.map = coef_name_map,
        symbol = "+",
          table = F,
          booktabs = T,
          use.packages = F,
          dcolumn = T,
          include.lr = F,
          include.rsquared = F,
          include.adjrs = T,
        include.cluster = T,
        caption = "", 
        star.symbol = "\\*")

```

# Table C.3: Outlier Analysis

```{r, results="asis", message=F, warning=F, cache = T, comments = F}

# Libraries
library(rms)
library(texreg)
library(tidyverse)

# Load data
load("./data/nelda_ps.rda") 


source("./functions/outlier_analysis.R")


# define selectvars which is necessary for the custom 'check_outliers()' 
# function 

selectvars = c("electionid", "country", "year", "identifiers")

outlierfit_nelda <- lm(quality ~ 
                         aid_ln *  
                         cabinetCOUNT.12 +
                         WBnatres + 
                         fh + 
                         conf_intens + 
                         # nonstate +
                         
                         log(gdp_per_capita_un) + 
                         log(population)
                       ,
                       data = nelda_ps, x = T, y = T)


outlierfit_vdem <- lm(v2xel_frefair ~ 
                        aid_ln *  
                        cabinetCOUNT.12 +
                        WBnatres + 
                        fh + 
                        conf_intens + 
                        # nonstate +
                        log(gdp_per_capita_un) + 
                        log(population)
                      ,
                      data = nelda_ps, x = T, y = T)


outliers_election_nelda <- check_outlier(outlierfit_nelda,
                                         data = nelda_ps, 
                                         selectvars = selectvars)
outliers_election_vdem <- check_outlier(outlierfit_vdem,
                                    data = nelda_ps, 
                                    selectvars = selectvars)

## Output
coef_name_map <- list( cabinetINC.12 = "Power-Sharing (binary)",
                       "aid_ln * cabinetINC.12" = "Power-Sharing (binary) * Aid", 
                       cabinetCOUNT.12 = "Power-Sharing (cabinet)",
                       "aid_ln * cabinetCOUNT.12" = "Power-Sharing (cabinet) * Aid", 
                       aid_ln = "Aid/GDP (log)", 
                       dga_gdp_null = "DGA/GDP (log)", 
                       program_aid_gdp_null= "Program Aid / GDP (log)", 
                       budget_aid_gdp_null = "Budget Aid / GDP (log", 
                       WBnatres = "Nat. Res. Rents", 
                       fh = "Regime Type (FH)", 
                       nonstate = "Nonstate Conflict", 
                       conf_intens =  "Conflict Intensity", 
                       population = "Population (log)", 
                       gdp_per_capita_un = "GDP p/c (log)")


source("./functions/custom_texreg.R")
environment(custom_texreg) <- asNamespace('texreg')


# # Output Maucsript
# custom_texreg(l = list(outliers_election_nelda[[2]],
#                    outliers_election_nelda[[4]], 
#                    outliers_election_nelda[[6]], 
#                    outliers_election_vdem[[2]], 
#                    outliers_election_vdem[[4]], 
#                    outliers_election_vdem[[6]]),
#           stars = c(0.001, 0.01, 0.05, 0.1),
#           custom.coef.map = coef_name_map,
#         symbol = "+",
#           table = F,
#         file = "../output/aidps_elections_outlierrobust.tex",
#           custom.multicol = T, 
#            custom.model.names = c(" \\multicolumn{3}{c}{\\textbf{NELDA Election Quality}} & 
#                               \\multicolumn{3}{c}{\\textbf{V-Dem Clean Elections Index}} \\\\ 
#                               \\cmidrule(r){2-4} \\cmidrule(r){5-7} 
#                               & \\multicolumn{1}{c}{(1) Hat Values }",
#                               "\\multicolumn{1}{c}{(2)  Cook's Distance}",
#                               "\\multicolumn{1}{c}{(3)  DFBETA}",
#                               "\\multicolumn{1}{c}{(4)  Hat Values}",
#                               "\\multicolumn{1}{c}{(5)  Cook's Distance}",
#                               "\\multicolumn{1}{c}{(6)  DFBETA}"),
#           booktabs = T,
#           use.packages = F,
#           dcolumn = T,
#           include.lr = F,
#           include.rsquared = F,
#         include.cluster = T,
#           include.adjrs = T,
#         caption = "")

# Output Replication Archive
htmlreg(l = list(outliers_election_nelda[[2]],
                   outliers_election_nelda[[4]], 
                   outliers_election_nelda[[6]], 
                   outliers_election_vdem[[2]], 
                   outliers_election_vdem[[4]], 
                   outliers_election_vdem[[6]]),
          stars = c(0.001, 0.01, 0.05, 0.1),
          custom.coef.map = coef_name_map,
        symbol = "+",
          table = F,
          booktabs = T,
          use.packages = F,
          dcolumn = T,
          include.lr = F,
          include.rsquared = F,
        include.cluster = T,
          include.adjrs = T,
        caption = "", 
        star.symbol = "\\*")

```


# Table C.4 and Figure C.1: Matching Diagnostics
```{r, results="asis", message=F, warning=F, cache = T, comments = F}
library(MatchIt)
library(dplyr)
library(tidyr)
library(xtable)
library(rms)
library(texreg)


# Create matched data set for elections

# First: create values for matching at the beginning of post-conflict period. This
# serves the purpose of creating pre-treatment (or approximation to pre-treatment) 
# values of the covariates

nelda_ps_matching <- nelda_ps %>% 
  ungroup() %>% 
  arrange(country, year) %>% 
  group_by(country, pc_period) %>% 
  mutate(matchyear = first(year)) %>%
  mutate(match_natres = first(WBnatres),
         match_aiddata_AidGDP = first(aiddata_AidGDP),
         match_pop = first(population),
         match_conf_intens = first(conf_intens),
         match_nonstate = first(nonstate),
         match_fh = first(fh),
         match_nelda45 = first(nelda45),
         match_gdp_per_capita_un = first(gdp_per_capita_un)) 

# add additional rebel parity variable as predictor
# parity_to_join_nelda <- psed_ag_y %>% 
#   dplyr::select(iso3c, pc_period, minyear, parity) %>% 
#   group_by(iso3c, pc_period) %>%  
#   dplyr::summarise(parity = unique(parity))
# 
# nelda_ps_matching <- left_join(nelda_ps_matching, parity_to_join_nelda)
  
match_data_elections <- nelda_ps_matching %>% 
  dplyr::select(cabinetINC.12, 
                aiddata_AidGDP, 
                gdp_per_capita_un, 
                year, 
                country,
                seniorINC.12, 
                nonseniorINC.12, 
                seniorCOUNT.12, 
                nonseniorCOUNT.12,
                cabinetCOUNT.12, 
                population, conf_intens, nonstate, WBnatres, fh, nelda45,
                quality, v2xel_frefair, electionid, 
                iso2c, aiddata_Aid, GDP,
                match_natres, match_aiddata_AidGDP, match_pop, match_conf_intens,
                match_nonstate, match_fh, match_nelda45, match_gdp_per_capita_un)



match_data_elections <- match_data_elections[complete.cases(match_data_elections), ]
match_data_elections <- as.data.frame(match_data_elections)


# Matching Mahalanobis

#I match only on post-conflict controls at the beginning of the pc period

match_data_elections <- match_data_elections %>% 
  arrange(country, pc_period, year)

set.seed(1234)

match_res <- matchit(cabinetINC.12 ~ 
                       log(match_aiddata_AidGDP) + 
                       log(match_gdp_per_capita_un) + 
                       log(match_pop) + 
                       match_conf_intens + 
                       log(match_natres + 1)  + 
                       match_nonstate +
                       match_fh , 
                     data = match_data_elections, 
                     method = "nearest",
                     ratio = 2,
                     distance = "mahalanobis")

match_df_elec_reg <- match.data(match_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)
library(Matching)

# before matching

imb_before <- MatchBalance(cabinetINC.12 ~ 
                             log(match_aiddata_AidGDP) + 
                             log(match_gdp_per_capita_un) + 
                             log(match_pop) + 
                             match_conf_intens + 
                             log(match_natres + 1)  + 
                             match_nonstate +
                             match_fh, 
                           data = match_data_elections, 
                           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 <- baltest.collect(imb_before, 
                                        c("Aid / GDP (log)", "GDP / PC (log)",
                                          "Population (log)", "Conflict Intensity",
                                          "Natural Resource Rents (log)", "Nonstate Conflict",
                                          "Regime Type (FH)"), 
                                        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")

# After Matching

imb_after <- MatchBalance(cabinetINC.12 ~ 
                             log(match_aiddata_AidGDP) + 
                             log(match_gdp_per_capita_un) + 
                             log(match_pop) + 
                             match_conf_intens + 
                             log(match_natres + 1)  + 
                             match_nonstate +
                             match_fh, 
                           data = match_df_elec_reg, 
                           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<- baltest.collect(imb_after, 
                                      c("Aid / GDP (log)", 
                                        "GDP / PC (log)",
                                        "Population (log)", 
                                        "Conflict Intensity",
                                        "Natural Resource Rents (log)", 
                                        "Nonstate Conflict",
                                        "Regime Type (FH)"), 
                                      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")

# output imbalance tables
print(xtable(imbalance_tab_before), booktabs = T, type = "latex", floating = F,
      file = "../output/matching_imbalance_reduction_elections_before.tex")
print(xtable(imbalance_tab_after), booktabs = T, type = "latex", floating = F,
      file = "../output/matching_imbalance_reduction_elections_after.tex")


print(xtable(imbalance_tab_before), booktabs = T, type = "html", floating = F)
print(xtable(imbalance_tab_after), booktabs = T, type = "html", floating = F)


# imbalance reduction plot, see http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2943670/
library(tidyr)
library(ggplot2)
library(ggrepel)

balance_improvement <- summary(match_res)$reduction
row.names(balance_improvement) <- c("Aid / GDP (log)", "GDP / PC (log)",
                                    "Population (log)", "Conflict Intensity",
                                    "Natural Resource Rents (log)", "Nonstate Conflict",
                                    "Regime Type (FH)")

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_res, 
                                                        standardize = T)$sum.all$`Std. Mean Diff.`), 
                               `Matched Data` = abs(summary(match_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_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 = -.4,
                   nudge_y = -.1) +
  theme_bw() +
  labs(x = "", y = "Absolute Standardized Difference in Means")

print(plot_balanceimpr_out)

```

# Table C.5:  First Stage and Reduced Form Regressions for Instrumental Variable Analysis
```{r, results="asis", message=F, warning=F, cache = T, comments = F, dev = "CairoPNG"}
# Libraries
library(tidyverse)
library(texreg)


# Load elections data
load("./data/nelda_ps.rda")

# load instrument data for aid
load(file = "./data/instrumentedAid2.RData")

nelda_ivreg <- left_join(nelda_ps, instrument_df,
                         by = c("year", "iso2c"))

nelda_ivreg$total_sum_except <- as.numeric(nelda_ivreg$total_sum_except)

nelda_ivreg$instr_aid_gdp <- log(nelda_ivreg$total_sum_except / nelda_ivreg$GDP)

nelda_ivreg$date <- NULL
nelda_ivreg$StartDate2 <- NULL
nelda_ivreg$yearmon.x <- NULL
nelda_ivreg$yearmon.y <- NULL
nelda_ivreg[nelda_ivreg == ""] <- NA



# first stage and reduced form models

firststage <- ols(log(aiddata_Aid / GDP)  ~ 
                    cabinetCOUNT.12 +
                    instr_aid_gdp  + 
                    WBnatres + 
                    fh + 
                    nonstate + 
                    conf_intens + 
                    log(gdp_per_capita_un) + 
                    log(population),
                  data = nelda_ivreg, x = T, y = T)

firststage <- robcov(firststage, nelda_ivreg$country)

firststage_inter <- ols(log(aiddata_Aid / GDP) * cabinetCOUNT.12  ~ 
                    cabinetCOUNT.12 * instr_aid_gdp  + 
                    WBnatres + 
                    fh + 
                    nonstate + 
                    conf_intens + 
                    log(gdp_per_capita_un) + 
                    log(population),
                  data = nelda_ivreg, x = T, y = T)

firststage_inter <- robcov(firststage_inter, nelda_ivreg$country)

reduced_form <- ols(v2xel_frefair ~
                    instr_aid_gdp *
                    cabinetCOUNT.12 +
                    WBnatres + 
                    fh + 
                    nonstate + 
                    conf_intens + 
                    log(gdp_per_capita_un) + 
                    log(population),
                  data = nelda_ivreg, x = T, y = T)
reduced_form <- robcov(reduced_form, nelda_ivreg$country)


reduced_form_nelda <- ols(quality ~
                      instr_aid_gdp *
                      cabinetCOUNT.12 +
                      WBnatres + 
                      fh + 
                      nonstate + 
                      conf_intens + 
                      log(gdp_per_capita_un) + 
                      log(population),
                    data = nelda_ivreg, x = T, y = T)
reduced_form_nelda <- robcov(reduced_form_nelda, nelda_ivreg$country)

source("./functions/custom_texreg.R")
environment(custom_texreg) <- asNamespace('texreg')

# custom_texreg(l = list(firststage, 
#                        firststage_inter,
#                        reduced_form, reduced_form_nelda),
#               omit.coef = "Intercept",
#               file = "../output/iv_elections_firststage_reduced.tex",
#                custom.model.names = c("\\multicolumn{2}{c}{\\textbf{First Stage}} &
# \\multicolumn{2}{c}{\\textbf{Reduced Form}}   \\\\                              \\cmidrule(r){2-3} \\cmidrule(r){4-5}
#                               & \\multicolumn{1}{c}{ (1) DV: Aid / GDP (log)}",
# "\\multicolumn{1}{c}{ (2) DV: Aid /GDP * P-S}",
# "\\multicolumn{1}{c}{ (3) V-Dem CEI) }",
# "\\multicolumn{1}{c}{ (4) NELDA)}"),
#               custom.coef.names = c("Intercept",
#                                     "Power-Sharing (cabinet)",
#                                     "Aid / GDP (log) - Instrumented",
#                                     "Natural Resource Rents",
#                                     "Regime Type (FH)",
#                                     "Nonstate Conflict",
#                                     "Conflict Intensity",
#                                     "GDP / PC (log)",
#                                     "Population (log)",
#                                     "Aid  / GDP (log) - Instrumented * Power-Sharing (cabinet)",
#                                     "Aid  / GDP (log) - Instrumented * Power-Sharing (cabinet)"),
#               reorder.coef = c(1, 2, 9, 3:8),
#               stars = c(0.001, 0.01, 0.05, 0.1),
#               booktabs = TRUE,
#               center = TRUE,
#               symbol = "+",
#               table = FALSE,
#               custom.multicol = TRUE,
#               column = T,
#               dcolumn = TRUE,
#               include.rsquared = F,
#               include.cluster = T,
#               use.packages = F)

htmlreg(l = list(firststage, firststage_inter, 
                reduced_form, reduced_form_nelda),
       omit.coef = "Intercept",
  
       custom.coef.names = c("Intercept", 
                             "Power-Sharing (cabinet)", 
                             "Aid / GDP (log) - Instrumented",
                             "Natural Resource Rents", 
                             "Regime Type (FH)",
                             "Nonstate Conflict",
                             "Conflict Intensity",
                             "GDP / PC (log)",
                             "Population (log)",
                             "Aid  / GDP (log) - Instrumented * Power-Sharing (cabinet)",
                             "Aid  / GDP (log) - Instrumented * Power-Sharing (cabinet)"),
       reorder.coef = c(1, 2, 9, 3:8),
       stars = c(0.001, 0.01, 0.05, 0.1),
       booktabs = TRUE,
       center = TRUE,
       symbol = "+",
       caption = "",
       table = FALSE, 
       custom.multicol = TRUE,
       column = T,
       dcolumn = TRUE,
       use.packages = F)


```

# Figure C.2: The Relationship between Foreign Aid and Aid (Instrumented) in the Election Sample

```{r, results="asis", message=F, warning=F, cache = T, comments = F, dev = "CairoPNG"}
# Libraries
library(tidyverse)

# Load elections data
load("./data/nelda_ps.rda")

# load instrument data for aid
load(file = "./data/instrumentedAid2.RData")

nelda_ivreg <- left_join(nelda_ps, instrument_df,
                         by = c("year", "iso2c"))

nelda_ivreg$total_sum_except <- as.numeric(nelda_ivreg$total_sum_except)

nelda_ivreg$instr_aid_gdp <- log(nelda_ivreg$total_sum_except / nelda_ivreg$GDP)

nelda_ivreg$date <- NULL
nelda_ivreg$StartDate2 <- NULL
nelda_ivreg$yearmon.x <- NULL
nelda_ivreg$yearmon.y <- NULL
nelda_ivreg[nelda_ivreg == ""] <- NA

# write to Stata file, so we can compute F statistics later
foreign::write.dta(nelda_ivreg, file = "./data/ivelections.dta", 
          version = 10)

# plot relationship between instrument and aid
plot_instrument <- ggplot(nelda_ivreg, 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
library(tikzDevice)
# options( tikzDocumentDeclaration = "\\documentclass[10pt]{article}" )
# tikz("../figures/aid_instr_elections.tex", height = 4)
# print(plot_instrument)
# dev.off()

print(plot_instrument)





```

