Chapter 3: Theory
Figure 3.1: Distribution of Post-Conflict States’ Revenue Sources in Comparison to Other Income Groups, 1989-2010
library(foreign)
library(dplyr)
library(tikzDevice)
library(readr)
library(rio)
library(tidyr)
library(ggplot2)
library(gridExtra)
library(countrycode)
library(forcats)
library(cowplot)
# Load data
load("./data/income_types_WDI.rdta") # this gives the "taxes" data frame
load("./data/diss_df.rda")
# intrastate ucdp country-year data set
# 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 <- read_csv("./data/133280_onset2012csv.csv") %>%
filter(year > 1989) %>%
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,
pc_dummy = pc)
# UN GDP estimates
un_gdp <- read_csv("./data/UNdata_Export_20150703_055055592.csv") %>%
mutate(iso2c = countrycode(`Country or Area`, "country.name", "iso2c"),
year = Year,
gdp = Value)
# Aid
aid <- read.dta("./data/aiddata_full.dta") %>%
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,
aid = commitment_amount_usd_constant,
iso2c = iso2) %>%
select(-population)
# Merge & wrangle data for eventual plotting
taxes <- taxes %>%
rename(tax = GC.TAX.TOTL.GD.ZS,
natres = NY.GDP.TOTL.RT.ZS,
# gdp = NY.GDP.MKTP.CD,
population = SP.POP.TOTL) %>%
filter(region != "Aggregates") %>%
left_join(., ucdp_cy[, c("iso2c", "year", "ucdp_dummy", "pc_dummy")]) %>%
# left_join(., pc_dummy[, c("iso2c", "year", "pc_dummy")]) %>%
left_join(., un_gdp[, c("iso2c", "year", "gdp")]) %>%
left_join(., aid, by = c("iso2c", "year")) %>%
mutate(income = as.character(income),
aid = aid / (gdp * population),
aid = aid * 100) %>%
mutate(income = ifelse(!is.na(ucdp_dummy) & ucdp_dummy == 1, "Conflict-affected", income),
income = ifelse(!is.na(pc_dummy) & pc_dummy == 1, "Post-conflict", income),
income = ifelse(income == "High income: OECD" | income == "High income: nonOECD",
"High Income", income)) %>%
filter(country != "United States" & country != "United Kingdom" & country != "Spain") %>%
filter(population > 500000) %>%
mutate(income = ifelse(country == "Timor-Leste", "Conflict-affected", income),
income = ifelse(country == "Yemen, Rep.", "Conflict-affected", income),
income = ifelse(country == "West Bank and Gaza", "Conflict-affected", income)) %>%
ungroup()
# # take logs
# taxes <- taxes %>%
# mutate(tax = log(tax),
# natres = log(natres + 1),
# aid = log(aid + 1))
#
# this piece calculates means and medians for each distribution
plot_tax_data_median <- taxes %>%
group_by(income) %>%
summarise(median_tax = median(tax, na.rm = T),
median_natres = median(natres, na.rm = T),
median_aid = median(aid, na.rm = T),
mean_tax = mean(tax, na.rm = T),
mean_natres = mean(natres, na.rm = T),
mean_aid = mean(aid, na.rm = T)) %>%
filter(!is.na(median_tax)) %>%
gather(variable, value, 2:7) %>%
separate(variable, c("type", "variable")) %>%
mutate(variable = replace(variable, variable == "tax", values = "Tax Income / GDP"),
variable = replace(variable, variable == "aid", values = "Aid / GDP"),
variable = replace(variable, variable == "natres", values = "Resource Rents / GDP"))
taxes <- taxes %>% left_join(., plot_tax_data_median %>%
filter(type == "mean" & variable == "tax"), all.x = T) %>%
arrange(type, variable, value) %>%
filter(income != "Not classified")
# reorder factors for country group classification
taxes$income <- factor(taxes$income,
levels = c("Conflict-affected",
"Post-conflict",
"Low income",
"Lower middle income",
"Upper middle income",
"High Income"))
# get data in shape for plotting
taxes_gath <- taxes %>%
dplyr::select(iso2c, year, income, aid, natres, tax) %>%
rename(`Aid / GDP` = aid,
`Resource Rents / GDP` = natres,
`Tax Income / GDP` = tax) %>%
gather(variable, value, 4:6 ) %>%
arrange(iso2c, variable, year)
taxes_gath$income <- ordered(taxes_gath$income,
levels = c("Conflict-affected",
"Post-conflict",
"Low income",
"Lower middle income",
"Upper middle income",
"High Income"))
plot_tax_data_median$income <- factor(plot_tax_data_median$income,
levels = c("Conflict-affected",
"Post-conflict",
"Low income",
"Lower middle income",
"Upper middle income",
"High Income"))
# generate final plot
plot_tax <- ggplot(taxes_gath,
aes(x = value, fill = income)) +
geom_histogram(aes(y = ..density..), bins = 20, color = "black") +
facet_grid( income ~ variable, scales = "free") +
geom_vline(data = plot_tax_data_median %>% filter(type == "median"),
aes(xintercept = value),
size = 1, color = "black", linetype = "dashed") +
geom_vline(data = plot_tax_data_median %>% filter(type == "mean"),
aes(xintercept = value),
size = 1, color = "black") +
theme_bw() +
theme(legend.position = "none") +
scale_fill_manual(values = RColorBrewer::brewer.pal(6, "RdYlGn")) +
labs(x = "", y = "") +
theme(text = element_text(size = 13))
# output for manuscript
# library(tikzDevice)
# options( tikzDocumentDeclaration = "\\documentclass[16pt]{article}" )
#
# tikz("../figures/taxes.tex", height = 7)
# print(plot_tax)
# dev.off()
# output for replication archive
print(plot_tax)
Figure 3.2: Foreign Aid, Conditionality, and Armed Conflict
# Libraries ---------------------------------------------------------------
library(readxl)
library(dplyr)
library(countrycode)
library(tidyr)
library(ggplot2)
library(foreign)
library(readr)
# Load data
aid <- read.dta("./data/aiddata_full.dta")
# WB and IMF structural adjustment programs -------------------------------
wb_adj <- read_excel("./data/dreherWB_IMF.xls", sheet = "WB adjustment agreed")
imf_adj <- read_excel("./data/dreherWB_IMF.xls", sheet = "IMF PRGF")
# data cleaning
wb_adj <- wb_adj %>% gather(year, WB_adj, `1970.000000`:`2004.000000`) %>%
mutate(year = as.numeric(as.character(year))) %>%
arrange(`Country Name`, year)
imf_adj <- imf_adj %>% gather(year, IMF_adj,`1970.000000`:`2011.000000`) %>%
mutate(year = as.numeric(as.character(year)),
IMF_adj = as.numeric(as.character(IMF_adj))) %>%
arrange(`Country Name`, year)
all_countries <- left_join(wb_adj, imf_adj)
# create data frame with WB Structural Adjustment Dummies
test <- left_join(diss_df, imf_adj, by = c("iso3c" = "Country Code", "year"))
plot_IMF <- test %>%
group_by(Location) %>%
summarise(IMF = ifelse(sum(IMF_adj, na.rm = T) > 0, 1, 0))
# create data frame with IMF SAP dummies
test2 <- left_join(diss_df, wb_adj, by = c("iso3c" = "Country Code", "year"))
plot_WB <- test2 %>%
group_by(Location) %>%
summarise(WB = ifelse(sum(WB_adj, na.rm = T) > 0, 1, 0))
# combine information of IMF and WB adjustment programs
plot_adj_df <- left_join(test, test2 %>%
ungroup() %>%
dplyr::select(year, iso3c, WB_adj)) %>%
mutate(pcsampledummy = 1)
all_countries <- left_join(all_countries,
plot_adj_df[, c("iso3c", "year", "pcsampledummy")],
by = c("Country Code" = "iso3c", "year")) %>%
replace_na(list(pcsampledummy = 0))
# prepare data for plots
all_countries <- all_countries %>%
group_by(year, pcsampledummy) %>%
dplyr::summarise(WB_adj_year = sum(WB_adj, na.rm = T) / n(),
IMF_adj_year = sum(IMF_adj, na.rm = T) / n()) %>%
gather(key = adj_donor, value, -year, -pcsampledummy) %>%
filter(year <= 2004 & year >= 1990)
# generate plot
plot_adj <- ggplot(all_countries %>% filter(adj_donor == "WB_adj_year"),
aes(x = year,
y = value,
color = factor(pcsampledummy))) +
geom_point(size = 2) +
geom_line(size = 0.8) +
scale_color_manual("Sample:",
values = c("#b3cde3", "#e41a1c"),
labels = c("All countries", "Post-Conflict Countries")) +
labs(x = "", y = "Share of \n Structural Adjustment \nPrograms \n per year") +
theme(legend.position = "bottom")
# Democracy Aid -----------------------------------------------------------
dga_plot_df <- diss_df %>%
group_by(year) %>%
summarise(dga_year = sum(dga, na.rm = T),
all_aid = sum(aiddata_Aid, na.rm = T))
dga_plot <- ggplot(dga_plot_df,
aes(x = year,
y = dga_year / 1000000)) +
geom_line(size =0.8) +
geom_point(size = 2) +
labs(x = "", y = "Democracy and Governance Aid \n(in 2011 USD Millions)")
# Aid after conflict ------------------------------------------------------
ag_seq <- function(x) {
runs <- cumsum(c(0, diff(x) != 1 ))
return(runs)
}
source("./functions/identifyPostConflictYears.R")
ucdp_cy <- read_csv("./data/133280_onset2012csv.csv") %>%
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)
# add aiddata
ucdp_cy <- left_join(ucdp_cy,
aid,
by = c("iso2c" = "iso2", "year")) %>%
filter(year >= 1989)
ucdp_cy_df <- ucdp_cy %>%
filter(conf_and_peace_years >= -10 &
conf_and_peace_years <= 10 &
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( commitment_amount_usd_constant / population),
color = factor(pc))) +
# geom_jitter(data = ucdp_cy_df %>%
# filter(log(aiddata_Aid / population) > 0 ),
# width = 0.2,
# alpha = 0.5) +
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( "#fc8d59", "#4575b4")) +
geom_vline(aes(xintercept = 0), color = "#e41a1c") +
theme(legend.position = "none") +
labs(x = "Years before / after end of internal armed conflict",
y = "Aid per capita (log)")
# # aid / GDP
# conf_pc_aidgdp_plot <- ggplot(ucdp_cy_df,
# aes(x = conf_and_peace_years,
# y = log(aiddata_Aid / GDP),
# color = factor(pc))) +
# 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( "#fc8d59", "#4575b4")) +
# geom_vline(aes(xintercept = 0), color = "#e41a1c") +
# theme(legend.position = "none") +
# labs(x = "Years before / after end of internal armed conflict",
# y = "Aid / GDP (log)")
# Plot --------------------------------------------------------------------
# Output plot for manuscript
# library(tikzDevice)
# library(cowplot)
# options( tikzDocumentDeclaration = "\\documentclass[11pt]{article}" )
#
# tikz("../figures/WB_IMF.tex", height = 6)
#
#
# plot_grid(plot_adj,
# dga_plot,
# # conf_pc_aid_plot,
# align = "v", nrow = 2)
#
#
# dev.off()
# Output plot for replication archive
library(cowplot)
plot_grid(plot_adj,
dga_plot,
# conf_pc_aid_plot,
align = "v", nrow = 2)
---
title: "Chapter 3: Theory"
output: 
  html_document:
    toc: true
    toc_float: 
      collapsed: false
    code_download: true
    code_folding: "hide"
---


# Figure 3.1: Distribution of Post-Conflict States’ Revenue Sources in Comparison to Other Income Groups, 1989-2010


```{r, fig.align = "center", message=F, warning=F, cache = T, comments = F, fig.height= 10}

library(foreign)
library(dplyr)
library(tikzDevice)
library(readr)
library(rio)
library(tidyr)
library(ggplot2)
library(gridExtra)
library(countrycode)
library(forcats)
library(cowplot)

# Load data
load("./data/income_types_WDI.rdta") # this gives the "taxes" data frame
load("./data/diss_df.rda")

# intrastate ucdp country-year data set

# 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 <- read_csv("./data/133280_onset2012csv.csv") %>% 
  filter(year > 1989) %>% 
  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, 
         pc_dummy = pc)



# UN GDP estimates
un_gdp <- read_csv("./data/UNdata_Export_20150703_055055592.csv") %>% 
  mutate(iso2c = countrycode(`Country or Area`, "country.name", "iso2c"),
         year = Year,
         gdp = Value)

# Aid
aid <- read.dta("./data/aiddata_full.dta") %>% 
  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,
                aid = commitment_amount_usd_constant, 
                iso2c = iso2) %>% 
  select(-population)

# Merge & wrangle data for eventual plotting
taxes <- taxes %>% 
  rename(tax = GC.TAX.TOTL.GD.ZS,
         natres = NY.GDP.TOTL.RT.ZS,
#         gdp = NY.GDP.MKTP.CD,
         population = SP.POP.TOTL) %>% 
  filter(region != "Aggregates") %>% 
  left_join(., ucdp_cy[, c("iso2c", "year", "ucdp_dummy", "pc_dummy")]) %>% 
  # left_join(., pc_dummy[, c("iso2c", "year", "pc_dummy")]) %>% 
  left_join(., un_gdp[, c("iso2c", "year", "gdp")]) %>% 
  left_join(., aid, by = c("iso2c", "year")) %>% 
  mutate(income = as.character(income),
         aid = aid / (gdp * population), 
         aid = aid * 100) %>% 
  mutate(income = ifelse(!is.na(ucdp_dummy) & ucdp_dummy == 1, "Conflict-affected", income),
         income = ifelse(!is.na(pc_dummy) & pc_dummy == 1, "Post-conflict", income),
         income = ifelse(income == "High income: OECD" | income == "High income: nonOECD",
                         "High Income", income)) %>% 
  filter(country != "United States" & country != "United Kingdom" & country != "Spain") %>% 
  filter(population > 500000) %>% 
  mutate(income = ifelse(country == "Timor-Leste", "Conflict-affected", income),
         income = ifelse(country == "Yemen, Rep.", "Conflict-affected", income),
         income = ifelse(country == "West Bank and Gaza", "Conflict-affected", income)) %>% 
  ungroup() 

# # take logs
# taxes <- taxes %>% 
#   mutate(tax = log(tax), 
#          natres = log(natres + 1), 
#          aid = log(aid + 1))


# 

# this piece calculates means and medians for each distribution
plot_tax_data_median <- taxes %>% 
  group_by(income) %>% 
  summarise(median_tax = median(tax, na.rm = T),
            median_natres = median(natres, na.rm = T),
            median_aid = median(aid, na.rm = T),
            mean_tax = mean(tax, na.rm = T),
            mean_natres = mean(natres, na.rm = T),
            mean_aid = mean(aid, na.rm = T)) %>% 
  filter(!is.na(median_tax)) %>% 
  gather(variable, value, 2:7) %>% 
  separate(variable, c("type", "variable")) %>% 
  mutate(variable = replace(variable, variable == "tax", values = "Tax Income / GDP"),
         variable = replace(variable, variable == "aid", values = "Aid / GDP"),
         variable = replace(variable, variable == "natres", values = "Resource Rents / GDP"))

taxes <- taxes %>% left_join(., plot_tax_data_median %>% 
                               filter(type == "mean" & variable == "tax"), all.x = T) %>% 
  arrange(type, variable, value) %>% 
  filter(income != "Not classified")

# reorder factors for country group classification
taxes$income <- factor(taxes$income, 
                       levels = c("Conflict-affected", 
                                  "Post-conflict", 
                                  "Low income", 
                                  "Lower middle income", 
                                  "Upper middle income",
                                  "High Income"))

# get data in shape for plotting
taxes_gath <- taxes %>% 
  dplyr::select(iso2c, year, income, aid, natres, tax) %>% 
  rename(`Aid / GDP` = aid,
         `Resource Rents / GDP` = natres,
        `Tax Income / GDP` = tax) %>% 
  gather(variable, value, 4:6 ) %>% 
  arrange(iso2c, variable, year) 


taxes_gath$income <- ordered(taxes_gath$income, 
                            levels = c("Conflict-affected", 
                                       "Post-conflict", 
                                       "Low income",
                                       "Lower middle income", 
                                       "Upper middle income",
                                       "High Income"))


plot_tax_data_median$income <- factor(plot_tax_data_median$income, 
                             levels = c("Conflict-affected", 
                                        "Post-conflict", 
                                        "Low income",
                                        "Lower middle income", 
                                        "Upper middle income",
                                        "High Income"))
# generate final plot

plot_tax <- ggplot(taxes_gath, 
                   aes(x = value, fill = income)) + 
  geom_histogram(aes(y = ..density..), bins = 20, color = "black") + 
  facet_grid( income ~ variable, scales = "free") +
  geom_vline(data = plot_tax_data_median %>% filter(type == "median"), 
             aes(xintercept = value),
             size = 1, color = "black", linetype = "dashed") +
  geom_vline(data = plot_tax_data_median %>% filter(type == "mean"), 
             aes(xintercept = value),
             size = 1, color = "black") +
  
  theme_bw() +
  theme(legend.position = "none") +
  scale_fill_manual(values = RColorBrewer::brewer.pal(6, "RdYlGn")) +
  labs(x = "", y = "") +
  theme(text = element_text(size = 13))

# output for manuscript
# library(tikzDevice)
# options( tikzDocumentDeclaration = "\\documentclass[16pt]{article}" )
# 
# tikz("../figures/taxes.tex", height = 7)
# print(plot_tax)
# dev.off()

# output for replication archive
print(plot_tax)

```



<!-- *Note from the manuscript:* The histograms reflect the distribution of the x values (i.e. Taxes, Natural Resource Rents per GDP, and Aid / GDP). The y-axis represents the percentage of observations in the category. Since the maximum percentage score varies depending on the respective distribution, the y-axes have different scales in the respective country categories. The solid vertical line indicates the mean of each distribution; the dashed line indicates the median. -->

<!-- *Interpretation:* Many values to the left indicate little income from the respective resource type in a given country category (i.e. the strongly right-skewed distribution of resource rents in high-income countries (middle column, bottom row) indicates that only very few high-income countries receive income from natural resources), and vice versa.  -->


# Figure 3.2: Foreign Aid, Conditionality, and Armed Conflict

```{r, fig.align = "center", message=F, warning=F, cache = T, comments = F, fig.height= 6}

# Libraries ---------------------------------------------------------------

library(readxl)
library(dplyr)
library(countrycode)
library(tidyr)
library(ggplot2)
library(foreign)
library(readr)

# Load data

aid <- read.dta("./data/aiddata_full.dta")

# WB and IMF structural adjustment programs -------------------------------

wb_adj <- read_excel("./data/dreherWB_IMF.xls", sheet = "WB adjustment agreed")

imf_adj <- read_excel("./data/dreherWB_IMF.xls", sheet = "IMF PRGF")

# data cleaning
wb_adj <- wb_adj %>% gather(year, WB_adj,  `1970.000000`:`2004.000000`) %>% 
  mutate(year = as.numeric(as.character(year))) %>% 
  arrange(`Country Name`, year)
  
imf_adj <- imf_adj %>% gather(year, IMF_adj,`1970.000000`:`2011.000000`) %>% 
  mutate(year = as.numeric(as.character(year)),
         IMF_adj = as.numeric(as.character(IMF_adj))) %>% 
  arrange(`Country Name`, year)

all_countries <- left_join(wb_adj, imf_adj)

# create data frame with WB Structural Adjustment Dummies
test <- left_join(diss_df, imf_adj, by = c("iso3c" = "Country Code", "year"))
plot_IMF <- test %>% 
  group_by(Location) %>% 
  summarise(IMF = ifelse(sum(IMF_adj, na.rm = T) > 0, 1, 0))

# create data frame with IMF SAP dummies
test2 <- left_join(diss_df, wb_adj, by = c("iso3c" = "Country Code", "year"))
plot_WB <- test2 %>% 
  group_by(Location) %>% 
  summarise(WB = ifelse(sum(WB_adj, na.rm = T) > 0, 1, 0))


# combine information of IMF and WB adjustment programs
plot_adj_df <- left_join(test, test2 %>% 
                        ungroup() %>%  
                        dplyr::select(year, iso3c, WB_adj)) %>% 
  mutate(pcsampledummy = 1)

all_countries <- left_join(all_countries, 
                           plot_adj_df[, c("iso3c", "year", "pcsampledummy")], 
                           by = c("Country Code" = "iso3c", "year")) %>% 
  replace_na(list(pcsampledummy = 0))

# prepare data for plots
all_countries <- all_countries %>% 
  group_by(year, pcsampledummy) %>% 
  dplyr::summarise(WB_adj_year = sum(WB_adj, na.rm = T) / n(),
            IMF_adj_year = sum(IMF_adj, na.rm = T) / n()) %>% 
  gather(key = adj_donor, value, -year, -pcsampledummy) %>% 
  filter(year <= 2004 & year >= 1990)


# generate plot
plot_adj <- ggplot(all_countries %>% filter(adj_donor == "WB_adj_year"), 
                   aes(x = year, 
                       y = value, 
                       color = factor(pcsampledummy))) + 
  geom_point(size = 2) +
  geom_line(size = 0.8) +
  scale_color_manual("Sample:", 
                     values = c("#b3cde3", "#e41a1c"),
                     labels = c("All countries", "Post-Conflict Countries")) +
  labs(x = "", y = "Share of \n Structural Adjustment \nPrograms \n per year") +
  theme(legend.position = "bottom") 


# Democracy Aid -----------------------------------------------------------

dga_plot_df <- diss_df %>% 
  group_by(year) %>% 
  summarise(dga_year = sum(dga, na.rm = T),
            all_aid = sum(aiddata_Aid, na.rm = T))

dga_plot <- ggplot(dga_plot_df, 
                   aes(x = year, 
                       y = dga_year / 1000000)) +
  geom_line(size =0.8) + 
  geom_point(size = 2) +
  labs(x = "", y = "Democracy and Governance Aid \n(in 2011 USD Millions)")




# Aid after conflict ------------------------------------------------------

ag_seq <- function(x) {
  runs <- cumsum(c(0, diff(x) != 1 ))
  return(runs)
}

source("./functions/identifyPostConflictYears.R")

ucdp_cy <- read_csv("./data/133280_onset2012csv.csv") %>% 
  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)

# add aiddata


ucdp_cy <- left_join(ucdp_cy, 
                     aid, 
                     by = c("iso2c" = "iso2", "year")) %>% 
  filter(year >= 1989)

ucdp_cy_df <- ucdp_cy %>% 
  filter(conf_and_peace_years >= -10 &
           conf_and_peace_years <= 10 &
           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( commitment_amount_usd_constant / population),
                               color = factor(pc))) +  
  # geom_jitter(data = ucdp_cy_df %>% 
  #               filter(log(aiddata_Aid / population) > 0 ),
  #             width = 0.2,
  #             alpha = 0.5) +  
  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( "#fc8d59", "#4575b4")) +
  geom_vline(aes(xintercept = 0), color = "#e41a1c") +
  theme(legend.position = "none") +
  labs(x = "Years before / after end of internal armed conflict",
       y = "Aid per capita (log)")

# # aid / GDP
# conf_pc_aidgdp_plot <- ggplot(ucdp_cy_df, 
#                            aes(x = conf_and_peace_years, 
#                                y = log(aiddata_Aid / GDP),
#                                color = factor(pc))) +  
#   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( "#fc8d59", "#4575b4")) +
#   geom_vline(aes(xintercept = 0), color = "#e41a1c") +
#   theme(legend.position = "none") +
#   labs(x = "Years before / after end of internal armed conflict",
#        y = "Aid / GDP (log)")


# Plot --------------------------------------------------------------------

# Output plot for manuscript
# library(tikzDevice)
# library(cowplot)
# options( tikzDocumentDeclaration = "\\documentclass[11pt]{article}" )
# 
# tikz("../figures/WB_IMF.tex", height = 6)
# 
# 
# plot_grid(plot_adj, 
#           dga_plot, 
#           # conf_pc_aid_plot,
#           align = "v", nrow = 2)
# 
# 
# dev.off()

# Output plot for replication archive
library(cowplot)

plot_grid(plot_adj, 
          dga_plot, 
          # conf_pc_aid_plot,
          align = "v", nrow = 2)


```

