This file replicates all figures and tables from Chapter 4: Data and Research Design.

Figure 4.1: Aid Flows Before and After Conflict Termination

# Load Libraries
library(tidyverse)
library(foreign)
library(countrycode)
library(cowplot)
library(tikzDevice)

# Read data files
load("./data/diss_df.rda")
aid <- read.dta("./data/aiddata_full.dta")
ucdp_cy <- read_csv("./data/133280_onset2012csv.csv")


# load two small helper function to determine pre- and post-conflict years
ag_seq <- function(x) {
  runs <- cumsum(c(0, diff(x) != 1 ))
  return(runs)
}

source("./functions/identifyPostConflictYears.R")


# load and wrangle ucdp data
ucdp_cy <- ucdp_cy %>% 
  arrange(gwno, year) %>% 
  group_by(gwno) %>% 
  mutate(iso2c = countrycode(gwno, "cown", "iso2c"),
         ucdp_dummy = ifelse(incidencev412 == 1, 1, 0),
         pc = pcIdentifier(ucdp_dummy),
         pc = ifelse(pc == 2, 1, 0),
         conflict_ep_id = cumsum(c(ifelse(first(ucdp_dummy) == 1, 
                                          1, 0), 
                                 diff(ucdp_dummy) != 0)), 
         conflict_ep_id = as.numeric(ifelse(ucdp_dummy == 1, 
                                            conflict_ep_id, 
                                            NA)),
         peace_ep_id = cumsum(c(ifelse(first(pc) == 2, 
                                       2, 1), 
                                diff(pc) != 0)),
         peace_ep_id = as.numeric(ifelse(pc == 1, peace_ep_id, NA)))%>% 
  group_by(gwno, conflict_ep_id) %>% 
  mutate(conflict_year = -1 * rev(cumsum(ucdp_dummy == 1))) %>% 
  group_by(gwno, peace_ep_id) %>% 
  mutate(peace_year = cumsum(pc == 1)) %>% 
  group_by(gwno) %>% 
  mutate(conf_and_peace_years = conflict_year + peace_year)

# Merge in Aid data
ucdp_cy <- left_join(ucdp_cy, 
                     aid, 
                     by = c("iso2c" = "iso2", "year")) %>% 
  filter(year >= 1989) %>% 
  dplyr::rename(wb_AidGNI = DT_ODA_ODAT_GN_ZS,
                wb_AidPC = DT_ODA_ODAT_PC_ZS,
                wb_AidGmentXP = DT_ODA_ODAT_XP_ZS,
                oecd_Aid = aid_oecd_commitment2011USD,
                oecd_Aid_mill = aid_oecd_commitment2011USD_mill,
                aiddata_Aid = commitment_amount_usd_constant)


ucdp_cy_df <- ucdp_cy %>% 
  filter(conf_and_peace_years >= -5 &
           conf_and_peace_years <= 5 &
           conf_and_peace_years != 0) %>% 
  group_by(conf_and_peace_years) 


conf_pc_aid_plot <- ggplot(ucdp_cy_df, 
                           aes(x = conf_and_peace_years, 
                               y = log(aiddata_Aid / population),
                               color = factor(pc))) +  
  geom_jitter(data = ucdp_cy_df %>%
                filter(log(aiddata_Aid / population) < 5 &
                         log(aiddata_Aid / population) > 0),
              width = 0.2,
              alpha = 0.55) +
  geom_smooth(data = ucdp_cy_df %>% 
                filter(pc == 1), 
              inherit.aes = T, 
              method = "loess") +  
  geom_smooth(data = ucdp_cy_df %>% 
                filter(pc == 0),
              method = "loess") +
  scale_x_continuous(breaks = -10:10) +
  scale_color_manual(values = c("#e41a1c", "#2ca25f" )) +
  geom_vline(aes(xintercept = 0), color = "black") +
  theme(legend.position = "none") +
  labs(x = "Years before / after end of internal armed conflict",
       y = "Aid per capita (log)")

# This piece of code generates the plot to included in the .tex doc
# options( tikzDocumentDeclaration = "\\documentclass[11pt]{article}" )
# tikz("../figures/pc_aid_flows.tex", height = 4.5)
# plot_grid(conf_pc_aid_plot,
#           align = "v", nrow = 1)
# dev.off()

# Output replication plot
print(conf_pc_aid_plot)

Figure 4.2: Distribution of Post-Conflict Democratic and Autocratic Change

# Libraries

library(dplyr)
library(ggplot2)
library(tikzDevice)
library(cowplot)
library(gridExtra)

# Democratization Scores

hist_polity <- ggplot(diss_df, aes(x = polity_chng)) + 
  geom_histogram(color = "black", fill = "white", bins = 10) + 
  theme_bw() +
  labs(x = "Polity", y = "Frequency") +
  geom_vline(aes(xintercept = mean(polity_chng, na.rm = T)), color = "red", 
           linetype = "dashed", size = 1)

hist_fh <-  ggplot(diss_df, aes(x = fh_chng)) + 
  geom_histogram(color = "black", fill = "white", bins = 7) +
  theme_bw() +
  labs(x = "Freedom House", y = "Frequency") +
  geom_vline(aes(xintercept = mean(fh_chng, na.rm = T)), color = "red", 
             linetype = "dashed", size = 1)


# Output manuscript
# options(tikzDocumentDeclaration = "\\documentclass[16pt]{article}")
# tikz("../figures/descriptive_stats_polityFH.tex", height = 2.5)
# plot_grid(hist_polity, hist_fh, ncol = 2)
# dev.off()

# Output replication archive
plot_grid(hist_polity, hist_fh, ncol = 2)

Figure 4.3: Elections in Post-Conflict Countries

# Libraries

library(tidyverse)
library(foreign)
library(countrycode)
library(cowplot)
library(tikzDevice)
library(lubridate)

# Load data

load("./data/nelda_ps.rda")

# prepare data
nelda_desc_obs_table <- nelda_ps %>% 
  mutate(mmdd = ifelse(nchar(mmdd) == 3, paste0("0", mmdd), mmdd),
         date = paste0(year, mmdd)) %>% 
  mutate(date = ymd(date)) %>% 
  arrange(country) %>% 
  dplyr::mutate(year = year(date),
                date = format(date, "%d.%m.%Y"),
         cabinetCOUNT = as.integer(cabinetCOUNT)) %>% 
  dplyr::mutate(election_string = paste(date, types, sep = " ")) %>% 
  group_by(country, year, types)  

# generate plot
election_plot <- ggplot(nelda_desc_obs_table, 
                        aes(x = year, fill = types)) + 
  geom_bar(aes(alpha = factor(-quality)),
           stat = "bin", bins = length(unique(nelda_desc_obs_table$year))) + 
  facet_wrap( ~ country, labeller = (country = label_wrap_gen(50)),
              ncol = 3) + 
  scale_y_continuous(breaks = 0:5) + 
  scale_fill_brewer(palette = "Dark2", guide = guide_legend(title = NULL)) + 
  theme_bw() +
  scale_alpha_discrete(guide = "none") + 
  theme(legend.position = "bottom") +
  labs(x = "", y = "Number of Elections in given year")

# Output for manuscript

# options( tikzDocumentDeclaration = "\\documentclass[12pt]{article}" )
# tikz("../figures/elections_instances.tex", height = 12)
# print(election_plot)
# dev.off()

# Output for replication archive
print(election_plot)

Figure 4.5: Foreign Aid after Civil Conflict, 1990-2010

library(forcats)
library(tidyverse)
library(countrycode)
library(cowplot)

load("./data/diss_df.rda")

aid_plot <- diss_df %>% 
  ungroup() %>% 
  mutate(Location = as.character(Location), 
         Location = ifelse(grepl("Cote", Location), "Ivory Coast", Location), 
         Location = ifelse(grepl("Zaire", Location), "DRC", Location)) %>% 
  group_by(Location) %>% 
  mutate(aidmean = mean(aiddata_AidGDP, na.rm = T),
         aidmedian = median(aiddata_AidGDP, na.rm = T), 
         meanps = mean(cabinetCOUNT), 
         hadPS = ifelse(any(1 %in% cabinetINC), "Yes", "No")) %>% 
  ungroup() %>% 
  mutate(Location = fct_reorder(Location, aidmedian)) %>% 
  
  # plot starts here
  ggplot(. , aes(Location)) +
  geom_boxplot(aes(y = aiddata_AidGDP, 
                   color = as.factor(hadPS), 
                   fill = as.factor(hadPS)), outlier.shape = NA, 
               alpha = 0.7) + 
  geom_jitter(aes(y = aiddata_AidGDP, 
                  color = as.factor(hadPS)), 
              size = 1.5) +
  scale_fill_manual("Country had Power-Sharing:", 
                     values = c("#b3cde3", "#e41a1c")) +
  scale_color_manual("Country had Power-Sharing:", 
                     values = c("#b3cde3", "#e41a1c")) +
  labs(y = "Aid / GDP", x = "") +
  theme_cowplot() +
  theme(legend.position = "bottom") +

  coord_flip()

# Output for Manuscript
# library(tikzDevice)
# options( tikzDocumentDeclaration = "\\documentclass[11pt]{article}" )
# tikz("../figures/aidoverview.tex", height = 12, width = 8.75)
# print(aid_plot)
# dev.off()

# Output for Replication Archive
print(aid_plot)

Figure 4.6: Establishing Face Validity of the Relationship between Levels of Aid Commitment and Theoretical Mechanisms

library(tidyverse)
library(countrycode)
library(cowplot)

# Load data

load("./data/nelda_ps.rda")

# data management prior to plotting
nelda_aid <- nelda_ps %>%
  mutate(dga = dga / (gdp_per_capita_un * population),
         commodity_aid = commodity_aid / (gdp_per_capita_un * population),
         program_aid = program_aid / (gdp_per_capita_un * population),
         aiddata_Aid = aiddata_Aid / (gdp_per_capita_un * population))

# Was aid threatened to be cut off?
aid_comparison_aidwithheld <- nelda_aid %>% 
  ungroup() %>% 
  rename(aid_withheld = nelda57) %>% 
  mutate(aid_withheld = ifelse(aid_withheld == "N/A", NA, aid_withheld),
         aid_withheld = ifelse(aid_withheld == "unclear", NA, aid_withheld),
         aid_withheld = ifelse(aid_withheld == 1, "yes", "no")) %>% 
  dplyr::select(dga, commodity_aid, program_aid, aiddata_Aid, aid_withheld, country, year) %>% 
  mutate_each(funs(log), -aid_withheld, -country, -year) %>% 
  gather(key, value, -aid_withheld, -country, -year) %>% 
  filter(!is.na(value)) %>% 
  filter(!is.na(aid_withheld)) %>% 
  mutate(key = forcats::fct_relevel(factor(key), 
                                    c("dga", "commodity_aid", "program_aid", "aiddata_Aid")))

# plot the data!
aid_comparison_aidwithheld_plot <- ggplot(aid_comparison_aidwithheld, 
                                          aes(x = aid_withheld, 
                                              y = value, 
                                              group = key)) + 
  geom_jitter(size = 1.7, alpha = 0.5) + 
  geom_boxplot(aes(fill = aid_withheld, 
                   group = aid_withheld), 
               alpha = 0.9) + 
  facet_wrap( ~ key, 
              nrow = 1, 
              labeller = labeller(key = c(dga = "DGA", 
                                          aiddata_Aid = "All Aid",
                                          commodity_aid = "Budget Aid",
                                          program_aid = "Program Aid"))) + 
  
  scale_fill_brewer(name = "NELDA57: \"Is aid threatened to be cut off?\"") + 
  theme_bw() +
  labs(x = "", y = "Aid / GDP (log)") +
  theme(legend.position = "bottom") 

# output for manuscript
# library(tikzDevice)
# options( tikzDocumentDeclaration = "\\documentclass[11pt]{article}" )
# tikz("../figures/aidcutoff_boxplots.tex", height = 4)
# print(aid_comparison_aidwithheld_plot)
# dev.off()

# output for replication archive
plot(aid_comparison_aidwithheld_plot)

Table 4.1: List of Countries

library(xtable)

# function to extract years of observation from the dataset
extract_years <- function(df) {
  year_start <- subset(df, firstFive == 1, c("year", "firstFive"))
  year_between <- subset(df, diff(firstFive) != 1, c("year", "firstFive"))
  year_end <- df[nrow(df), c("year", "firstFive")]
  years_full <- rbind(year_start, year_between, year_end)
  years_full <- years_full[order(years_full$year), ]
  
  # print(years_full)
  
  if(nrow(years_full) == 2) {
    years_full <- paste(years_full[, "year"], collapse = "-")
    return(years_full)
  }
  else {
    years_string <- vector()
    for(i in seq(2, nrow(years_full), 2)) {
      years_string <- c(years_string, paste(years_full[(i-1):i, "year"], collapse = "-"))

    }
    years_full <- paste(years_string, collapse = ", ")
    return(years_full)
  }
  
}

# helper function that extracts values from data set
generate_desc <- function(df) {
  z <- plyr::ddply(df,  plyr::.(GWNo), .fun = function(x) {
    c(Country = as.character(unique(x$Location)),
      Years = extract_years(x),
      MeanAidGDP = round(mean(x$aiddata_AidGDP, na.rm = TRUE), 2),
      HasPS = ifelse(1 %in% x$cabinetINC, "Yes", "No"),
      MeanPolity = round(mean(x$polity2, na.rm = TRUE), 1),
      MeanFH = round(mean(x$fh, na.rm = TRUE),1))
  })
  return(z)
}
  
    
countryList <- generate_desc(diss_df)

countryList <- countryList[order(countryList$Country), ]

# set NA in Elections to zero
# countryList$Elections.TRUE[is.na(countryList$Elections.TRUE)] <- 0 

countryList[countryList$GWNo == 437, "Country"] <- "Cote D'Ivoire"
countryList[countryList$GWNo == 490, "Country"] <- "Democratic Republic of Congo"

countryList <- plyr::rename(countryList, c("Years" = "Years obs.",
                                     "MeanAidGDP" = "$\\varnothing \\frac{\\textit{Aid}}{\\textit{GDP}}$",
                                     "HasPS" = "Has PS?", 
                                     "MeanPolity" = "$\\varnothing$  Polity IV",
                                     "MeanFH" = "$\\varnothing$  FH"))
                              #       "Elections.TRUE" = "No. of Elections"))

countryList[, 6] <- ifelse(countryList[, 6] == "NaN", 
                           NA, 
                           countryList[, 6])

countryList_p <- xtable(countryList[, 2:ncol(countryList)], digits = 1)

# Output for Document
print(countryList_p, 
      file = "../output/obs_descr.tex", 
      NA.string = ".", 
      include.rownames = FALSE,
      floating = FALSE, 
      booktabs = TRUE, 
      sanitize.colnames.function = function(x) return(x))

# Output for Replication Archive
print(countryList_p, 
      type = "html",
      NA.string = ".", 
      include.rownames = FALSE,
      floating = FALSE, 
      booktabs = TRUE, 
      sanitize.colnames.function = function(x) return(x))
Country Years obs. \(\varnothing \frac{\textit{Aid}}{\textit{GDP}}\) Has PS? \(\varnothing\) Polity IV \(\varnothing\) FH
Angola 2005-2006 1.03 Yes -2 2.5
Azerbaijan 1996-2000, 2006-2010 2.45 No -6.8 2.6
Bangladesh 1993-1997 3.02 Yes 6 4.9
Bosnia and Herzegovina 1996-2000 18.61 Yes . 3.1
Burundi 2009-2010 29.09 No 6 3.2
Cambodia 1999-2003 13.18 No 2 2.3
Central African Republic 2003-2005, 2007-2008 8.62 No -1 2.8
Comoros 1990-1994, 1998-2002 12.67 Yes 2.5 3.6
Congo 1994-1996, 2000-2001, 2003-2007 8.12 No -1.6 3.4
Cote D’Ivoire 2005-2009 3.73 Yes 0 2.1
Croatia 1996-2000 0.76 No -1.2 4.3
Democratic Republic of Congo 2002-2005, 2009-2010 24.38 Yes 3 2
Djibouti 1995-1998, 2000-2004 13.26 Yes -1.7 2.9
El Salvador 1992-1996 5.1 No 7 5
Georgia 1994-1998, 2005-2007, 2009-2010 6 No 5.6 4.2
Guatemala 1996-2000 1.97 No 8 4.5
Guinea-Bissau 2000-2004 23.9 No 2.6 3.5
Haiti 1992-1996, 2005-2009 10.4 No 3 2.7
Indonesia 1993-1996, 2006-2010 0.98 No 1.3 3.8
Lebanon 1991-1995 1.99 No . 2.8
Lesotho 1999-2003 9.62 No 5.6 4.6
Liberia 1991-1995, 2004-2008 42.1 Yes 2.6 2.9
Macedonia 2002-2006 4.9 Yes 9 5
Mali 1991-1993, 1995-1999 12.89 Yes 5.8 5.1
Mexico 1997-2001 0.22 No 6.8 4.9
Moldova 1993-1997 5.24 No 7 4
Mozambique 1993-1997 36.12 No 2.8 4
Nepal 2007-2010 5.38 Yes 6 3.9
Nicaragua 1991-1995 16.97 No 6.4 4.1
Niger 1998-2002, 2009-2010 12.01 Yes 2 3.4
Nigeria 2005-2008 2.57 No 4 3.9
Panama 1990-1994 2.92 No 8.2 5
Papua New Guinea 1997-2001 13.89 No 4 5.4
Paraguay 1990-1994 1.32 No 5 4.8
Peru 2000-2004 1.5 No 8.2 5.4
Rumania 1990-1994 0.98 No 5 3.6
Russia (Soviet Union) 1997-1998 2.04 No 3 4.2
Rwanda 2003-2007 23.78 No -3 2.5
Senegal 2004-2008 9 No 7.6 5.4
Serbia 1992-1996, 2000-2004 6.71 No 0 3.6
Sierra Leone 2001-2005 29.37 No 4.4 4.2
Somalia 1997-2000, 2003-2005 6.64 No 0 1.3
Tajikistan 1999-2003 16.53 No -1.4 2.2
Trinidad and Tobago 1991-1995 0.48 No 9 6.8
Uzbekistan 2001-2003, 2005-2009 1.67 No -9 1.2
Venezuela 1993-1997 0.29 No 8 5.2

Table 4.4: Countries with Power-Sharing and Elections

library(tidyverse)

load("./data/diss_df.rda")
load("./data/nelda_ps.rda")

countries_withPS <- diss_df %>%
  group_by(Location, GWNo) %>%
  summarise(country_hadPS = sum(1 %in% cabinetINC))

elections_withPS <- nelda_ps %>%
  group_by(country, ccode) %>%
    summarise(election_hadPS = sum(1 %in% cabinetINC.12)) 
  

countries_withPS <- left_join(countries_withPS,
                              elections_withPS,
                              by = c("GWNo" = "ccode")) %>% 
  group_by(Location) %>% 
  mutate()


countries_withPS %>% group_by(country_hadPS, election_hadPS) %>% 
  summarise(countries = paste(Location, collapse = ", ")) %>% 
  replace_na(list(election_hadPS = 0)) %>%
  mutate(category = c("withoutPS_election", "without_PS_noelection", "withPS_electionafterPS", 
                      "withPS_election", "withPS_noelection")) %>% 
  select(category, countries) %>% 
  mutate(no_countries = length(unlist(strsplit(countries, ",")))) %>% 
  spread(category, countries) %>% 
  select(withPS_election, withPS_electionafterPS, withPS_noelection, withoutPS_election, without_PS_noelection) %>% 
  knitr::kable()
withPS_election withPS_electionafterPS withPS_noelection withoutPS_election without_PS_noelection
Nepal, Democratic Republic of Congo (Zaire), Liberia, Comoros, Mali, Niger, Djibouti, Bosnia and Herzegovina, Macedonia Bangladesh Angola, Cote D’Ivoire Paraguay, Guatemala, Lebanon, Venezuela, Burundi, Peru, Cambodia, El Salvador, Indonesia, Mozambique, Panama, Papua New Guinea, Rumania, Rwanda, Senegal, Trinidad and Tobago, Georgia, Haiti, Sierra Leone, Serbia, Azerbaijan, Croatia, Moldova, Tajikistan, Mexico, Congo, Guinea-Bissau, Lesotho, Uzbekistan, Central African Republic, Nigeria Nicaragua, Somalia, Russia (Soviet Union)

How many countries had power-sharing? (Bottom row of table in manuscript)

load("./data/diss_df.rda")

diss_df %>% 
  group_by(Location) %>% 
  summarise(had_power_sharing = sum(any(1 %in% cabinetINC))) %>% 
  group_by(had_power_sharing) %>% 
  tally()
## # A tibble: 2 × 2
##   had_power_sharing     n
##               <int> <int>
## 1                 0    34
## 2                 1    12

Table 4.6: Summary Statistics I: Dependent and Key Independent Variables in the Cross-Sectional Sample

library(pastecs)
library(xtable)
load("./data/diss_df.rda")

# For all dependent variables in the cross-section time series sample
sum_stats <- stat.desc(diss_df[ , c("polity2_t2",
                                    "fh_t2",
                                    "v2x_jucon", 
                                    "LJI",
                                    "v2dlencmps", 
                                    "v2x_corr",
                                    "cabinetCOUNT", 
                                    "aiddata_AidGDP",
                                    "dga_gdp_zero", 
                                    "program_aid_gdp_zero", 
                                    "commodity_aid_gdp_zero",
                                    "nonstate", 
                                    "conf_intens", 
                                    "ln_gdp_pc", 
                                    "ln_pop", 
                                    "WBnatres"
                                    )])
# Better variable names
names(sum_stats) <- c("Polity", 
                      "Freedom House", 
                      "Judicial Independence (V-Dem)", 
                      "Judicial Independence (LJI)", 
                      "Particularistic Spending (V-Dem)",
                      "Political Corruption (V-Dem)",
                      "Power-Sharing (cabinet)", 
                      "Aid/GDP",
                      "Democracy and Governance Aid", 
                      "Program Aid", 
                      "Budget Aid",
                      "Nonstate Conflict", 
                      "Conflict Intensity",
                      "GDP p/c", 
                      "Population",
                      "Natural Resource Rents")

# transpose data.frame (rows to colums)
sum_stats <- t(sum_stats) 

# keep statistics of interest
sum_stats <- as.data.frame(sum_stats[, c("nbr.val", "min", "max", "mean", "std.dev",  "median")])

names(sum_stats) <- c("Obs", "Min", "Max", "Mean", "Std.Dev", "Median")

knitr::kable(sum_stats, digits = 1)
Obs Min Max Mean Std.Dev Median
Polity 263 -9.0 10.0 3.2 5.1 5.0
Freedom House 272 1.0 7.0 3.8 1.4 4.0
Judicial Independence (V-Dem) 273 0.0 0.9 0.4 0.2 0.5
Judicial Independence (LJI) 273 0.0 0.9 0.3 0.2 0.3
Particularistic Spending (V-Dem) 273 -3.0 1.7 0.0 1.0 0.1
Political Corruption (V-Dem) 273 0.2 0.9 0.7 0.2 0.8
Power-Sharing (cabinet) 273 0.0 17.0 0.6 2.2 0.0
Aid/GDP 273 0.0 176.7 10.2 15.5 6.6
Democracy and Governance Aid 273 0.0 13.6 1.0 1.8 0.3
Program Aid 273 0.0 37.8 5.2 5.8 3.6
Budget Aid 273 0.0 142.5 3.9 11.2 0.9
Nonstate Conflict 273 0.0 1.0 0.1 0.3 0.0
Conflict Intensity 273 0.0 1.0 0.3 0.5 0.0
GDP p/c 273 4.4 8.8 6.7 1.0 6.6
Population 273 12.9 19.3 15.9 1.4 15.8
Natural Resource Rents 273 0.0 82.6 10.9 16.2 3.4

The following code generates the sparklines of the variable distributions in the manuscript (which is only feasible for the TeX output):

# Output for manuscript (adds sparklines)
source("./functions/gen_sparkline_hist.R")

digitmat <- matrix(c(5, 0, rep(1, 5), # Polity
                     5, 0, rep(1, 5), # FH
                     5, 0, rep(1, 5), # JudInd 1
                     5, 0, rep(1, 5), # JudInd 2
                     5, 0, rep(1, 5), # Part Spend
                     5, 0, rep(1, 5), # Corruption
                     5, 0, rep(1, 5), # Power-Shainrg
                     5, 0, rep(1, 5), # Aid
                     5, 0, rep(1, 5), # Aid
                     5, 0, rep(1, 5), # Aid
                     5, 0, rep(1, 5), # Aid
                     5, 0, rep(1, 5), # nonstate
                     5, 0, rep(1, 5), # conflict intensity
                     5, 0, rep(1, 5), # gdp 
                     5, 0, rep(1, 5), # population
                     5, 0, rep(1, 5)), # wbnatres

                   ncol = ncol(sum_stats) + 1,
                   nrow = nrow(sum_stats), byrow = TRUE)

# add column for sparkline
digitmat <- cbind(digitmat, rep(0, nrow(digitmat)))

sum_stats[1, "Distribution"] <- gen_sparkline_hist(diss_df$polity2_t2, 10)
sum_stats[2, "Distribution"] <- gen_sparkline_hist(diss_df$fh_t2, 10)
sum_stats[3, "Distribution"] <- gen_sparkline_hist(diss_df$v2x_jucon, 10)
sum_stats[4, "Distribution"] <- gen_sparkline_hist(diss_df$LJI, 10)
sum_stats[5, "Distribution"] <- gen_sparkline_hist(diss_df$v2dlencmps, 10)
sum_stats[6, "Distribution"] <- gen_sparkline_hist(diss_df$v2x_corr, 10)
sum_stats[7, "Distribution"] <- gen_sparkline_hist(diss_df$cabinetCOUNT, 10)
sum_stats[8, "Distribution"] <- gen_sparkline_hist(log(diss_df$aiddata_AidGDP), 10)
sum_stats[9, "Distribution"] <- gen_sparkline_hist(log(diss_df$dga_gdp_zero + 1), 10)
sum_stats[10, "Distribution"] <- gen_sparkline_hist(log(diss_df$program_aid_gdp_zero + 1), 10)
sum_stats[11, "Distribution"] <- gen_sparkline_hist(log(diss_df$commodity_aid_gdp_zero + 1), 10)
sum_stats[12, "Distribution"] <- gen_sparkline_hist(diss_df$nonstate, 2)
sum_stats[13, "Distribution"] <- gen_sparkline_hist(diss_df$conf_intens, 2)
sum_stats[14, "Distribution"] <- gen_sparkline_hist(diss_df$ln_gdp_pc, 10)
sum_stats[15, "Distribution"] <- gen_sparkline_hist(diss_df$ln_pop, 10)
sum_stats[16, "Distribution"] <- gen_sparkline_hist(diss_df$WBnatres, 10)


# print table
sum_stats <- xtable(sum_stats, digits=digitmat, align = "rrrrrrrc")
print.xtable(sum_stats,
             file="../output//summarystats.tex",
             type="latex", floating = FALSE, booktabs = TRUE,
             sanitize.text.function = function(x){x})

Table 4.6: Summary Statistics II: Dependent and Key Independent Variables in the Elections Sample

library(pastecs)
library(xtable)
load("./data/nelda_ps.rda")
nelda_ps$ln_gdp_pc <- log(nelda_ps$GDP_per_capita)
nelda_ps$ln_pop <- log(nelda_ps$population)

# For all dependent variables in the cross-section time series sample
sum_stats <- stat.desc(nelda_ps[ , c("quality",
                                    "v2xel_frefair",
                                    
                                    "cabinetCOUNT.12", 
                                    "aiddata_AidGDP",
                                    "dga_gdp_null", 
                                    "program_aid_gdp_null", 
                                    "budget_aid_gdp_null",
                                    "nonstate", 
                                    "conf_intens", 
                                    "ln_gdp_pc", 
                                    "ln_pop", 
                                    "WBnatres"
                                    
                                    )])
# Better variable names
names(sum_stats) <- c("NELDA Election Quality", 
                      "V-Dem Clean Election Index", 
                      "Power-Sharing (cabinet)", 
                      "Aid/GDP",
                      "Democracy and Governance Aid", 
                      "Program Aid", 
                      "Budget Aid",
                      
                      "Nonstate Conflict", 
                      "Conflict Intensity",
                      "GDP p/c", 
                      "Population",
                      "Natural Resource Rents")

# transpose data.frame (rows to colums)
sum_stats <- t(sum_stats) 

# keep statistics of interest
sum_stats <- as.data.frame(sum_stats[, c("nbr.val", "min", "max", "mean", "std.dev",  "median")])

names(sum_stats) <- c("Obs", "Min", "Max", "Mean", "Std.Dev", "Median")

knitr::kable(sum_stats, digits = 1)
Obs Min Max Mean Std.Dev Median
NELDA Election Quality 145 3.0 9.0 7.2 1.7 8.0
V-Dem Clean Election Index 144 0.1 0.9 0.5 0.2 0.5
Power-Sharing (cabinet) 145 0.0 18.0 0.8 2.9 0.0
Aid/GDP 142 0.0 54.1 10.4 13.0 7.3
Democracy and Governance Aid 145 0.0 13.6 1.2 2.2 0.5
Program Aid 145 0.0 36.7 5.3 6.6 3.4
Budget Aid 145 0.0 45.0 3.7 7.3 1.6
Nonstate Conflict 145 0.0 1.0 0.1 0.2 0.0
Conflict Intensity 145 0.0 1.0 0.2 0.4 0.0
GDP p/c 142 5.0 8.7 6.7 1.0 6.7
Population 145 12.9 19.3 15.8 1.3 15.8
Natural Resource Rents 145 0.0 51.2 7.9 12.1 2.3

Code for sparklines (histograms in the table)

# Output for manuscript (adds sparklines)
source("./functions/gen_sparkline_hist.R")

digitmat <- matrix(c(5, 0, rep(1, 5), # Polity
                     5, 0, rep(1, 5), # FH
                
                     5, 0, rep(1, 5), # Power-Shainrg
                     5, 0, rep(1, 5), # Aid
                     5, 0, rep(1, 5), # Aid
                     5, 0, rep(1, 5), # Aid
                     5, 0, rep(1, 5), # Aid
                     5, 0, rep(1, 5), # nonstate
                     5, 0, rep(1, 5), # conflict intensity
                     5, 0, rep(1, 5), # gdp 
                     5, 0, rep(1, 5), # population
                     5, 0, rep(1, 5)),

                   ncol = ncol(sum_stats) + 1,
                   nrow = nrow(sum_stats), byrow = TRUE)

# add column for sparkline
digitmat <- cbind(digitmat, rep(0, nrow(digitmat)))

sum_stats[1, "Distribution"] <- gen_sparkline_hist(nelda_ps$quality, 10)
sum_stats[2, "Distribution"] <- gen_sparkline_hist(nelda_ps$v2xel_frefair, 10)
sum_stats[3, "Distribution"] <- gen_sparkline_hist(nelda_ps$cabinetCOUNT.12, 10)
sum_stats[4, "Distribution"] <- gen_sparkline_hist(log(nelda_ps$aiddata_AidGDP), 10)
sum_stats[5, "Distribution"] <- gen_sparkline_hist(log(nelda_ps$dga_gdp_null + 1), 10)
sum_stats[6, "Distribution"] <- gen_sparkline_hist(log(nelda_ps$program_aid_gdp_null + 1), 10)
sum_stats[7, "Distribution"] <- gen_sparkline_hist(log(nelda_ps$budget_aid_gdp_null + 1), 10)
sum_stats[8, "Distribution"] <- gen_sparkline_hist(nelda_ps$nonstate, 2)
sum_stats[9, "Distribution"] <- gen_sparkline_hist(nelda_ps$conf_intens, 2)
sum_stats[10, "Distribution"] <- gen_sparkline_hist(nelda_ps$ln_gdp_pc, 10)
sum_stats[11, "Distribution"] <- gen_sparkline_hist(nelda_ps$ln_pop, 10)
sum_stats[12, "Distribution"] <- gen_sparkline_hist(nelda_ps$WBnatres, 10)

# print table
sum_stats <- xtable(sum_stats, digits=digitmat, align = "rrrrrrrc")
print.xtable(sum_stats,
             file="../output//summarystats_elections.tex",
             type="latex", floating = FALSE, booktabs = TRUE,
             sanitize.text.function = function(x){x})
---
title: "Chapter 4: Data and Research Design"
output: 
  html_document:
    toc: true
    toc_float: 
      collapsed: false
    code_download: true
    code_folding: "hide"
---

This file replicates all figures and tables from Chapter 4: Data and Research Design.

# Figure 4.1: Aid Flows Before and After Conflict Termination

```{r, fig.align = "center", message=F, warning=F, cache = T, comments = F}


# Load Libraries
library(tidyverse)
library(foreign)
library(countrycode)
library(cowplot)
library(tikzDevice)

# Read data files
load("./data/diss_df.rda")
aid <- read.dta("./data/aiddata_full.dta")
ucdp_cy <- read_csv("./data/133280_onset2012csv.csv")


# load two small helper function to determine pre- and post-conflict years
ag_seq <- function(x) {
  runs <- cumsum(c(0, diff(x) != 1 ))
  return(runs)
}

source("./functions/identifyPostConflictYears.R")


# load and wrangle ucdp data
ucdp_cy <- ucdp_cy %>% 
  arrange(gwno, year) %>% 
  group_by(gwno) %>% 
  mutate(iso2c = countrycode(gwno, "cown", "iso2c"),
         ucdp_dummy = ifelse(incidencev412 == 1, 1, 0),
         pc = pcIdentifier(ucdp_dummy),
         pc = ifelse(pc == 2, 1, 0),
         conflict_ep_id = cumsum(c(ifelse(first(ucdp_dummy) == 1, 
                                          1, 0), 
                                 diff(ucdp_dummy) != 0)), 
         conflict_ep_id = as.numeric(ifelse(ucdp_dummy == 1, 
                                            conflict_ep_id, 
                                            NA)),
         peace_ep_id = cumsum(c(ifelse(first(pc) == 2, 
                                       2, 1), 
                                diff(pc) != 0)),
         peace_ep_id = as.numeric(ifelse(pc == 1, peace_ep_id, NA)))%>% 
  group_by(gwno, conflict_ep_id) %>% 
  mutate(conflict_year = -1 * rev(cumsum(ucdp_dummy == 1))) %>% 
  group_by(gwno, peace_ep_id) %>% 
  mutate(peace_year = cumsum(pc == 1)) %>% 
  group_by(gwno) %>% 
  mutate(conf_and_peace_years = conflict_year + peace_year)

# Merge in Aid data
ucdp_cy <- left_join(ucdp_cy, 
                     aid, 
                     by = c("iso2c" = "iso2", "year")) %>% 
  filter(year >= 1989) %>% 
  dplyr::rename(wb_AidGNI = DT_ODA_ODAT_GN_ZS,
                wb_AidPC = DT_ODA_ODAT_PC_ZS,
                wb_AidGmentXP = DT_ODA_ODAT_XP_ZS,
                oecd_Aid = aid_oecd_commitment2011USD,
                oecd_Aid_mill = aid_oecd_commitment2011USD_mill,
                aiddata_Aid = commitment_amount_usd_constant)


ucdp_cy_df <- ucdp_cy %>% 
  filter(conf_and_peace_years >= -5 &
           conf_and_peace_years <= 5 &
           conf_and_peace_years != 0) %>% 
  group_by(conf_and_peace_years) 


conf_pc_aid_plot <- ggplot(ucdp_cy_df, 
                           aes(x = conf_and_peace_years, 
                               y = log(aiddata_Aid / population),
                               color = factor(pc))) +  
  geom_jitter(data = ucdp_cy_df %>%
                filter(log(aiddata_Aid / population) < 5 &
                         log(aiddata_Aid / population) > 0),
              width = 0.2,
              alpha = 0.55) +
  geom_smooth(data = ucdp_cy_df %>% 
                filter(pc == 1), 
              inherit.aes = T, 
              method = "loess") +  
  geom_smooth(data = ucdp_cy_df %>% 
                filter(pc == 0),
              method = "loess") +
  scale_x_continuous(breaks = -10:10) +
  scale_color_manual(values = c("#e41a1c", "#2ca25f" )) +
  geom_vline(aes(xintercept = 0), color = "black") +
  theme(legend.position = "none") +
  labs(x = "Years before / after end of internal armed conflict",
       y = "Aid per capita (log)")

# This piece of code generates the plot to included in the .tex doc
# options( tikzDocumentDeclaration = "\\documentclass[11pt]{article}" )
# tikz("../figures/pc_aid_flows.tex", height = 4.5)
# plot_grid(conf_pc_aid_plot,
#           align = "v", nrow = 1)
# dev.off()

# Output replication plot
print(conf_pc_aid_plot)



```

<!-- *Note:* The y-axis represents the logged value of aid per capita. Data points are individual country-years with extreme outliers removed for visual clarity (including outliers does not change the overall pattern). The x-axis codes the year before or after the end of a conflict as coded by the Uppsala Conflict Data Program (Themnér and Wallensteen 2013); negative values indicate the years before the end of a conflict, positive values are post-conflict years. The lines represent a regression of aid per GDP on year in the respective conflict / post-conflict group. -->


# Figure 4.2: Distribution of Post-Conflict Democratic and Autocratic Change

```{r, message=F, warning=F, cache = T, comments = F, fig.height = 3, fig.align = "center"}
# Libraries

library(dplyr)
library(ggplot2)
library(tikzDevice)
library(cowplot)
library(gridExtra)

# Democratization Scores

hist_polity <- ggplot(diss_df, aes(x = polity_chng)) + 
  geom_histogram(color = "black", fill = "white", bins = 10) + 
  theme_bw() +
  labs(x = "Polity", y = "Frequency") +
  geom_vline(aes(xintercept = mean(polity_chng, na.rm = T)), color = "red", 
           linetype = "dashed", size = 1)

hist_fh <-  ggplot(diss_df, aes(x = fh_chng)) + 
  geom_histogram(color = "black", fill = "white", bins = 7) +
  theme_bw() +
  labs(x = "Freedom House", y = "Frequency") +
  geom_vline(aes(xintercept = mean(fh_chng, na.rm = T)), color = "red", 
             linetype = "dashed", size = 1)


# Output manuscript
# options(tikzDocumentDeclaration = "\\documentclass[16pt]{article}")
# tikz("../figures/descriptive_stats_polityFH.tex", height = 2.5)
# plot_grid(hist_polity, hist_fh, ncol = 2)
# dev.off()

# Output replication archive
plot_grid(hist_polity, hist_fh, ncol = 2)


```


# Figure 4.3: Elections in Post-Conflict Countries


```{r, message=F, warning=F, cache = T, comments = F, fig.height=12, fig.align = "center"}

# Libraries

library(tidyverse)
library(foreign)
library(countrycode)
library(cowplot)
library(tikzDevice)
library(lubridate)

# Load data

load("./data/nelda_ps.rda")

# prepare data
nelda_desc_obs_table <- nelda_ps %>% 
  mutate(mmdd = ifelse(nchar(mmdd) == 3, paste0("0", mmdd), mmdd),
         date = paste0(year, mmdd)) %>% 
  mutate(date = ymd(date)) %>% 
  arrange(country) %>% 
  dplyr::mutate(year = year(date),
                date = format(date, "%d.%m.%Y"),
         cabinetCOUNT = as.integer(cabinetCOUNT)) %>% 
  dplyr::mutate(election_string = paste(date, types, sep = " ")) %>% 
  group_by(country, year, types)  

# generate plot
election_plot <- ggplot(nelda_desc_obs_table, 
                        aes(x = year, fill = types)) + 
  geom_bar(aes(alpha = factor(-quality)),
           stat = "bin", bins = length(unique(nelda_desc_obs_table$year))) + 
  facet_wrap( ~ country, labeller = (country = label_wrap_gen(50)),
              ncol = 3) + 
  scale_y_continuous(breaks = 0:5) + 
  scale_fill_brewer(palette = "Dark2", guide = guide_legend(title = NULL)) + 
  theme_bw() +
  scale_alpha_discrete(guide = "none") + 
  theme(legend.position = "bottom") +
  labs(x = "", y = "Number of Elections in given year")

# Output for manuscript

# options( tikzDocumentDeclaration = "\\documentclass[12pt]{article}" )
# tikz("../figures/elections_instances.tex", height = 12)
# print(election_plot)
# dev.off()

# Output for replication archive
print(election_plot)


```

<!-- *Figure Note from Manuscript:* 145 elections in 41 countries. Opacity is adjusted according to election quality: translucent bars indicate less instances of election manipulation; more solid bars indicate more instances of election manipulation. For the construction of the election quality indicator from the NELDA data see subsection 4.2.2. Constituent assembly elections took place in Nepal and Paraguay. -->


# Figure 4.4: Trends in Power-Sharing Governments

```{r, message=F, warning=F, cache = T, comments = F, fig.align = "center"}

# Libraries
library(tidyverse)
library(cowplot)
library(tikzDevice)

# Data
load("./data/diss_df.rda")

# Plot Power-Sharing

plotpscount <- diss_df %>% 
  group_by(year) %>% 
  summarise(cabinetCOUNT = sum(cabinetCOUNT),
            seniorCOUNT = sum(seniorCOUNT),
            nonseniorCOUNT = sum(nonseniorCOUNT)) %>% 
  gather(pstype, count, seniorCOUNT, nonseniorCOUNT) %>% 
  ggplot(., aes(x = year, y = count, group = pstype)) + 
 geom_area( aes(fill = pstype)) +
  scale_fill_brewer("", 
                    breaks=c("seniorCOUNT", "nonseniorCOUNT"), 
                    labels = c("Power-Sharing (senior positions)", 
                               "Power-Sharing (nonsenior positions)")) +
  theme(legend.position = "none") + 
  labs(y = "Number of Cabinet Seats", 
       x = "Year",
       title = "Counts of Rebel Seats \n in all country-years") 


plotpsinc <- diss_df %>% 
  group_by(year) %>% 
  summarise(cabinetINC = sum(cabinetINC),
            seniorINC = sum(ifelse(seniorCOUNT > 0, 1, 0)),
            nonseniorINC = sum(ifelse(nonseniorCOUNT > 0, 1, 0)),
            cabinetCOUNT = sum(cabinetCOUNT),
            seniorCOUNT = sum(seniorCOUNT),
            nonseniorCOUNT = sum(nonseniorCOUNT)) %>% 
  gather(pstype, count, seniorINC, nonseniorINC) %>% 
  ggplot(., aes(x = year, y = count, group = pstype)) + 
  scale_y_continuous(breaks = c(0, 2, 4, 6, 8)) +
  geom_area( aes(fill = pstype)) +
  scale_fill_brewer("", 
                    breaks=c("seniorINC", "nonseniorINC"), 
                    labels = c("Power-Sharing (senior positions)", 
                               "Power-Sharing (nonsenior positions)")) +
  theme(legend.position = "none") + 
  labs(y = "Number of Country-Years", 
       x = "Year", 
       title = "Instances of Power-Sharing \n in all country-years") 

psplots_legend <- get_legend(plotpscount + theme(legend.position = "bottom",
                                                 legend.direction = "vertical"))


# Plot using tikz

# this is required to get point sizes right
options( tikzDocumentDeclaration = "\\documentclass[15pt]{article}" )

# tikz("../figures/trendps.tex", height = 4)
# plot_grid(plot_grid(plotpsinc,
#                     plotpscount,
#                     nrow = 1,
#                     align = 'vh'),
#           psplots_legend,
#           ncol = 1,
#           rel_heights = c(1, .2))
# dev.off()

# Output for replication archive
plot_grid(plot_grid(plotpsinc, 
                    plotpscount,
                    hjust = -1,
                    nrow = 1,
                    align = 'vh'), 
          psplots_legend, 
          ncol = 1,   
          rel_heights = c(1, .2))
```




# Figure 4.5: Foreign Aid after Civil Conflict, 1990-2010

```{r, message=F, warning=F, cache=T, comments = F,fig.height = 12, fig.align = "center", dev="CairoPNG"}

library(forcats)
library(tidyverse)
library(countrycode)
library(cowplot)

load("./data/diss_df.rda")

aid_plot <- diss_df %>% 
  ungroup() %>% 
  mutate(Location = as.character(Location), 
         Location = ifelse(grepl("Cote", Location), "Ivory Coast", Location), 
         Location = ifelse(grepl("Zaire", Location), "DRC", Location)) %>% 
  group_by(Location) %>% 
  mutate(aidmean = mean(aiddata_AidGDP, na.rm = T),
         aidmedian = median(aiddata_AidGDP, na.rm = T), 
         meanps = mean(cabinetCOUNT), 
         hadPS = ifelse(any(1 %in% cabinetINC), "Yes", "No")) %>% 
  ungroup() %>% 
  mutate(Location = fct_reorder(Location, aidmedian)) %>% 
  
  # plot starts here
  ggplot(. , aes(Location)) +
  geom_boxplot(aes(y = aiddata_AidGDP, 
                   color = as.factor(hadPS), 
                   fill = as.factor(hadPS)), outlier.shape = NA, 
               alpha = 0.7) + 
  geom_jitter(aes(y = aiddata_AidGDP, 
                  color = as.factor(hadPS)), 
              size = 1.5) +
  scale_fill_manual("Country had Power-Sharing:", 
                     values = c("#b3cde3", "#e41a1c")) +
  scale_color_manual("Country had Power-Sharing:", 
                     values = c("#b3cde3", "#e41a1c")) +
  labs(y = "Aid / GDP", x = "") +
  theme_cowplot() +
  theme(legend.position = "bottom") +

  coord_flip()

# Output for Manuscript
# library(tikzDevice)
# options( tikzDocumentDeclaration = "\\documentclass[11pt]{article}" )
# tikz("../figures/aidoverview.tex", height = 12, width = 8.75)
# print(aid_plot)
# dev.off()

# Output for Replication Archive
print(aid_plot)

```


# Figure 4.6: Establishing Face Validity of the Relationship between Levels of Aid Commitment and Theoretical Mechanisms

```{r, message=F, warning=F, cache=T, comments = F, fig.align = "center"}

library(tidyverse)
library(countrycode)
library(cowplot)

# Load data

load("./data/nelda_ps.rda")

# data management prior to plotting
nelda_aid <- nelda_ps %>%
  mutate(dga = dga / (gdp_per_capita_un * population),
         commodity_aid = commodity_aid / (gdp_per_capita_un * population),
         program_aid = program_aid / (gdp_per_capita_un * population),
         aiddata_Aid = aiddata_Aid / (gdp_per_capita_un * population))

# Was aid threatened to be cut off?
aid_comparison_aidwithheld <- nelda_aid %>% 
  ungroup() %>% 
  rename(aid_withheld = nelda57) %>% 
  mutate(aid_withheld = ifelse(aid_withheld == "N/A", NA, aid_withheld),
         aid_withheld = ifelse(aid_withheld == "unclear", NA, aid_withheld),
         aid_withheld = ifelse(aid_withheld == 1, "yes", "no")) %>% 
  dplyr::select(dga, commodity_aid, program_aid, aiddata_Aid, aid_withheld, country, year) %>% 
  mutate_each(funs(log), -aid_withheld, -country, -year) %>% 
  gather(key, value, -aid_withheld, -country, -year) %>% 
  filter(!is.na(value)) %>% 
  filter(!is.na(aid_withheld)) %>% 
  mutate(key = forcats::fct_relevel(factor(key), 
                                    c("dga", "commodity_aid", "program_aid", "aiddata_Aid")))

# plot the data!
aid_comparison_aidwithheld_plot <- ggplot(aid_comparison_aidwithheld, 
                                          aes(x = aid_withheld, 
                                              y = value, 
                                              group = key)) + 
  geom_jitter(size = 1.7, alpha = 0.5) + 
  geom_boxplot(aes(fill = aid_withheld, 
                   group = aid_withheld), 
               alpha = 0.9) + 
  facet_wrap( ~ key, 
              nrow = 1, 
              labeller = labeller(key = c(dga = "DGA", 
                                          aiddata_Aid = "All Aid",
                                          commodity_aid = "Budget Aid",
                                          program_aid = "Program Aid"))) + 
  
  scale_fill_brewer(name = "NELDA57: \"Is aid threatened to be cut off?\"") + 
  theme_bw() +
  labs(x = "", y = "Aid / GDP (log)") +
  theme(legend.position = "bottom") 

# output for manuscript
# library(tikzDevice)
# options( tikzDocumentDeclaration = "\\documentclass[11pt]{article}" )
# tikz("../figures/aidcutoff_boxplots.tex", height = 4)
# print(aid_comparison_aidwithheld_plot)
# dev.off()

# output for replication archive
plot(aid_comparison_aidwithheld_plot)
```


# Table 4.1: List of Countries

```{r, message=F, warning=F, cache = T, results = "asis", comments = F }


library(xtable)

# function to extract years of observation from the dataset
extract_years <- function(df) {
  year_start <- subset(df, firstFive == 1, c("year", "firstFive"))
  year_between <- subset(df, diff(firstFive) != 1, c("year", "firstFive"))
  year_end <- df[nrow(df), c("year", "firstFive")]
  years_full <- rbind(year_start, year_between, year_end)
  years_full <- years_full[order(years_full$year), ]
  
  # print(years_full)
  
  if(nrow(years_full) == 2) {
    years_full <- paste(years_full[, "year"], collapse = "-")
    return(years_full)
  }
  else {
    years_string <- vector()
    for(i in seq(2, nrow(years_full), 2)) {
      years_string <- c(years_string, paste(years_full[(i-1):i, "year"], collapse = "-"))

    }
    years_full <- paste(years_string, collapse = ", ")
    return(years_full)
  }
  
}

# helper function that extracts values from data set
generate_desc <- function(df) {
  z <- plyr::ddply(df,  plyr::.(GWNo), .fun = function(x) {
    c(Country = as.character(unique(x$Location)),
      Years = extract_years(x),
      MeanAidGDP = round(mean(x$aiddata_AidGDP, na.rm = TRUE), 2),
      HasPS = ifelse(1 %in% x$cabinetINC, "Yes", "No"),
      MeanPolity = round(mean(x$polity2, na.rm = TRUE), 1),
      MeanFH = round(mean(x$fh, na.rm = TRUE),1))
  })
  return(z)
}
  
    
countryList <- generate_desc(diss_df)

countryList <- countryList[order(countryList$Country), ]

# set NA in Elections to zero
# countryList$Elections.TRUE[is.na(countryList$Elections.TRUE)] <- 0 

countryList[countryList$GWNo == 437, "Country"] <- "Cote D'Ivoire"
countryList[countryList$GWNo == 490, "Country"] <- "Democratic Republic of Congo"

countryList <- plyr::rename(countryList, c("Years" = "Years obs.",
                                     "MeanAidGDP" = "$\\varnothing \\frac{\\textit{Aid}}{\\textit{GDP}}$",
                                     "HasPS" = "Has PS?", 
                                     "MeanPolity" = "$\\varnothing$  Polity IV",
                                     "MeanFH" = "$\\varnothing$  FH"))
                              #       "Elections.TRUE" = "No. of Elections"))

countryList[, 6] <- ifelse(countryList[, 6] == "NaN", 
                           NA, 
                           countryList[, 6])

countryList_p <- xtable(countryList[, 2:ncol(countryList)], digits = 1)

# Output for Document
print(countryList_p, 
      file = "../output/obs_descr.tex", 
      NA.string = ".", 
      include.rownames = FALSE,
      floating = FALSE, 
      booktabs = TRUE, 
      sanitize.colnames.function = function(x) return(x))

# Output for Replication Archive
print(countryList_p, 
      type = "html",
      NA.string = ".", 
      include.rownames = FALSE,
      floating = FALSE, 
      booktabs = TRUE, 
      sanitize.colnames.function = function(x) return(x))




```

<!-- <p>&nbsp;</p> -->
<!-- *Table caption from manuscript:*:  A total of 46 countries. Aid data is taken from the AidData project (Tierney et al. 2011). GDP data is taken from the World Bank (see http://data.worldbank.org ). Polity IV data from the Polity IV project (Marshall, Jaggers, and Gurr 2015), Freedom House data from Freedom House (2013). For a discussion of data sources, see below. Freedom House scores have been inverted to increase comparability between Polity and FH (i.e. higher values indicate a higher level of democracy). An “.” indicates missing data. -->

# Table 4.4: Countries with Power-Sharing and Elections

```{r, message=F, warning=F, cache = T, comments = F,  fig.align = "center"}

library(tidyverse)

load("./data/diss_df.rda")
load("./data/nelda_ps.rda")

countries_withPS <- diss_df %>%
  group_by(Location, GWNo) %>%
  summarise(country_hadPS = sum(1 %in% cabinetINC))

elections_withPS <- nelda_ps %>%
  group_by(country, ccode) %>%
    summarise(election_hadPS = sum(1 %in% cabinetINC.12)) 
  

countries_withPS <- left_join(countries_withPS,
                              elections_withPS,
                              by = c("GWNo" = "ccode")) %>% 
  group_by(Location) %>% 
  mutate()


countries_withPS %>% group_by(country_hadPS, election_hadPS) %>% 
  summarise(countries = paste(Location, collapse = ", ")) %>% 
  replace_na(list(election_hadPS = 0)) %>%
  mutate(category = c("withoutPS_election", "without_PS_noelection", "withPS_electionafterPS", 
                      "withPS_election", "withPS_noelection")) %>% 
  select(category, countries) %>% 
  mutate(no_countries = length(unlist(strsplit(countries, ",")))) %>% 
  spread(category, countries) %>% 
  select(withPS_election, withPS_electionafterPS, withPS_noelection, withoutPS_election, without_PS_noelection) %>% 
  knitr::kable()
  


```

How many countries had power-sharing? (Bottom row of table in manuscript)

```{r, message=F, warning=F, cache = T, comments = F,  fig.align = "center"}

load("./data/diss_df.rda")

diss_df %>% 
  group_by(Location) %>% 
  summarise(had_power_sharing = sum(any(1 %in% cabinetINC))) %>% 
  group_by(had_power_sharing) %>% 
  tally()

```


# Table 4.6: Summary Statistics I: Dependent and Key Independent Variables in the Cross-Sectional Sample

```{r, message=F, warning=F, cache = T, results = "asis", comments = F }

library(pastecs)
library(xtable)
load("./data/diss_df.rda")

# For all dependent variables in the cross-section time series sample
sum_stats <- stat.desc(diss_df[ , c("polity2_t2",
                                    "fh_t2",
                                    "v2x_jucon", 
                                    "LJI",
                                    "v2dlencmps", 
                                    "v2x_corr",
                                    "cabinetCOUNT", 
                                    "aiddata_AidGDP",
                                    "dga_gdp_zero", 
                                    "program_aid_gdp_zero", 
                                    "commodity_aid_gdp_zero",
                                    "nonstate", 
                                    "conf_intens", 
                                    "ln_gdp_pc", 
                                    "ln_pop", 
                                    "WBnatres"
                                    )])
# Better variable names
names(sum_stats) <- c("Polity", 
                      "Freedom House", 
                      "Judicial Independence (V-Dem)", 
                      "Judicial Independence (LJI)", 
                      "Particularistic Spending (V-Dem)",
                      "Political Corruption (V-Dem)",
                      "Power-Sharing (cabinet)", 
                      "Aid/GDP",
                      "Democracy and Governance Aid", 
                      "Program Aid", 
                      "Budget Aid",
                      "Nonstate Conflict", 
                      "Conflict Intensity",
                      "GDP p/c", 
                      "Population",
                      "Natural Resource Rents")

# transpose data.frame (rows to colums)
sum_stats <- t(sum_stats) 

# keep statistics of interest
sum_stats <- as.data.frame(sum_stats[, c("nbr.val", "min", "max", "mean", "std.dev",  "median")])

names(sum_stats) <- c("Obs", "Min", "Max", "Mean", "Std.Dev", "Median")

knitr::kable(sum_stats, digits = 1)

```

The following code generates the sparklines of the variable distributions in the manuscript (which is only feasible for the TeX output):

```{r,eval = F, message=F, warning=F, cache=T, comments=F }

# Output for manuscript (adds sparklines)
source("./functions/gen_sparkline_hist.R")

digitmat <- matrix(c(5, 0, rep(1, 5), # Polity
                     5, 0, rep(1, 5), # FH
                     5, 0, rep(1, 5), # JudInd 1
                     5, 0, rep(1, 5), # JudInd 2
                     5, 0, rep(1, 5), # Part Spend
                     5, 0, rep(1, 5), # Corruption
                     5, 0, rep(1, 5), # Power-Shainrg
                     5, 0, rep(1, 5), # Aid
                     5, 0, rep(1, 5), # Aid
                     5, 0, rep(1, 5), # Aid
                     5, 0, rep(1, 5), # Aid
                     5, 0, rep(1, 5), # nonstate
                     5, 0, rep(1, 5), # conflict intensity
                     5, 0, rep(1, 5), # gdp 
                     5, 0, rep(1, 5), # population
                     5, 0, rep(1, 5)), # wbnatres

                   ncol = ncol(sum_stats) + 1,
                   nrow = nrow(sum_stats), byrow = TRUE)

# add column for sparkline
digitmat <- cbind(digitmat, rep(0, nrow(digitmat)))

sum_stats[1, "Distribution"] <- gen_sparkline_hist(diss_df$polity2_t2, 10)
sum_stats[2, "Distribution"] <- gen_sparkline_hist(diss_df$fh_t2, 10)
sum_stats[3, "Distribution"] <- gen_sparkline_hist(diss_df$v2x_jucon, 10)
sum_stats[4, "Distribution"] <- gen_sparkline_hist(diss_df$LJI, 10)
sum_stats[5, "Distribution"] <- gen_sparkline_hist(diss_df$v2dlencmps, 10)
sum_stats[6, "Distribution"] <- gen_sparkline_hist(diss_df$v2x_corr, 10)
sum_stats[7, "Distribution"] <- gen_sparkline_hist(diss_df$cabinetCOUNT, 10)
sum_stats[8, "Distribution"] <- gen_sparkline_hist(log(diss_df$aiddata_AidGDP), 10)
sum_stats[9, "Distribution"] <- gen_sparkline_hist(log(diss_df$dga_gdp_zero + 1), 10)
sum_stats[10, "Distribution"] <- gen_sparkline_hist(log(diss_df$program_aid_gdp_zero + 1), 10)
sum_stats[11, "Distribution"] <- gen_sparkline_hist(log(diss_df$commodity_aid_gdp_zero + 1), 10)
sum_stats[12, "Distribution"] <- gen_sparkline_hist(diss_df$nonstate, 2)
sum_stats[13, "Distribution"] <- gen_sparkline_hist(diss_df$conf_intens, 2)
sum_stats[14, "Distribution"] <- gen_sparkline_hist(diss_df$ln_gdp_pc, 10)
sum_stats[15, "Distribution"] <- gen_sparkline_hist(diss_df$ln_pop, 10)
sum_stats[16, "Distribution"] <- gen_sparkline_hist(diss_df$WBnatres, 10)


# print table
sum_stats <- xtable(sum_stats, digits=digitmat, align = "rrrrrrrc")
print.xtable(sum_stats,
             file="../output//summarystats.tex",
             type="latex", floating = FALSE, booktabs = TRUE,
             sanitize.text.function = function(x){x})


```



# Table 4.6: Summary Statistics II: Dependent and Key Independent Variables in the Elections Sample

```{r, message=F, warning=F, cache = T, results = "asis", comments = F }

library(pastecs)
library(xtable)
load("./data/nelda_ps.rda")
nelda_ps$ln_gdp_pc <- log(nelda_ps$GDP_per_capita)
nelda_ps$ln_pop <- log(nelda_ps$population)

# For all dependent variables in the cross-section time series sample
sum_stats <- stat.desc(nelda_ps[ , c("quality",
                                    "v2xel_frefair",
                                    
                                    "cabinetCOUNT.12", 
                                    "aiddata_AidGDP",
                                    "dga_gdp_null", 
                                    "program_aid_gdp_null", 
                                    "budget_aid_gdp_null",
                                    "nonstate", 
                                    "conf_intens", 
                                    "ln_gdp_pc", 
                                    "ln_pop", 
                                    "WBnatres"
                                    
                                    )])
# Better variable names
names(sum_stats) <- c("NELDA Election Quality", 
                      "V-Dem Clean Election Index", 
                      "Power-Sharing (cabinet)", 
                      "Aid/GDP",
                      "Democracy and Governance Aid", 
                      "Program Aid", 
                      "Budget Aid",
                      
                      "Nonstate Conflict", 
                      "Conflict Intensity",
                      "GDP p/c", 
                      "Population",
                      "Natural Resource Rents")

# transpose data.frame (rows to colums)
sum_stats <- t(sum_stats) 

# keep statistics of interest
sum_stats <- as.data.frame(sum_stats[, c("nbr.val", "min", "max", "mean", "std.dev",  "median")])

names(sum_stats) <- c("Obs", "Min", "Max", "Mean", "Std.Dev", "Median")

knitr::kable(sum_stats, digits = 1)

```

Code for sparklines (histograms in the table)

```{r, eval = F, message=F, warning=F, cache=T, comments=F }

# Output for manuscript (adds sparklines)
source("./functions/gen_sparkline_hist.R")

digitmat <- matrix(c(5, 0, rep(1, 5), # Polity
                     5, 0, rep(1, 5), # FH
                
                     5, 0, rep(1, 5), # Power-Shainrg
                     5, 0, rep(1, 5), # Aid
                     5, 0, rep(1, 5), # Aid
                     5, 0, rep(1, 5), # Aid
                     5, 0, rep(1, 5), # Aid
                     5, 0, rep(1, 5), # nonstate
                     5, 0, rep(1, 5), # conflict intensity
                     5, 0, rep(1, 5), # gdp 
                     5, 0, rep(1, 5), # population
                     5, 0, rep(1, 5)),

                   ncol = ncol(sum_stats) + 1,
                   nrow = nrow(sum_stats), byrow = TRUE)

# add column for sparkline
digitmat <- cbind(digitmat, rep(0, nrow(digitmat)))

sum_stats[1, "Distribution"] <- gen_sparkline_hist(nelda_ps$quality, 10)
sum_stats[2, "Distribution"] <- gen_sparkline_hist(nelda_ps$v2xel_frefair, 10)
sum_stats[3, "Distribution"] <- gen_sparkline_hist(nelda_ps$cabinetCOUNT.12, 10)
sum_stats[4, "Distribution"] <- gen_sparkline_hist(log(nelda_ps$aiddata_AidGDP), 10)
sum_stats[5, "Distribution"] <- gen_sparkline_hist(log(nelda_ps$dga_gdp_null + 1), 10)
sum_stats[6, "Distribution"] <- gen_sparkline_hist(log(nelda_ps$program_aid_gdp_null + 1), 10)
sum_stats[7, "Distribution"] <- gen_sparkline_hist(log(nelda_ps$budget_aid_gdp_null + 1), 10)
sum_stats[8, "Distribution"] <- gen_sparkline_hist(nelda_ps$nonstate, 2)
sum_stats[9, "Distribution"] <- gen_sparkline_hist(nelda_ps$conf_intens, 2)
sum_stats[10, "Distribution"] <- gen_sparkline_hist(nelda_ps$ln_gdp_pc, 10)
sum_stats[11, "Distribution"] <- gen_sparkline_hist(nelda_ps$ln_pop, 10)
sum_stats[12, "Distribution"] <- gen_sparkline_hist(nelda_ps$WBnatres, 10)

# print table
sum_stats <- xtable(sum_stats, digits=digitmat, align = "rrrrrrrc")
print.xtable(sum_stats,
             file="../output//summarystats_elections.tex",
             type="latex", floating = FALSE, booktabs = TRUE,
             sanitize.text.function = function(x){x})

```
