########################################################################################################### 
##### FIGURE 1: LIST OF COUNTRIES BY YEAR OF ADOPTING FIRST COIN/NOTES WITH NATIONAL SYMBOL

rm(list=ls())
library(knitr)
library(MASS)
library(mlogit)
library(cquad)
library(tidyverse)
library(plm)
library(stargazer)
library(dplyr)
library(lmtest)
library(pcse)
library(readstata13)
library(brglm)
library(sandwich) 
library(readxl)
library(dygraphs)
library(xts)
library(readxl)
library(stringr)

df <- read_excel("~/Box Sync/Ideasss (nn6nc@Virginia.EDU)/PolEcon ideas/Montenegro/Year_independence_Adoption_national_symbol.xlsx")
str(df)

df$independence_year <- str_sub(df$Independence_year, -4)
df <- dplyr::select(df, Country, independence_year, Year_adoption_national_currency, Explanation, Type)
df$independence_year <- as.numeric(df$independence_year)
df$Year_adoption_national_currency <- as.numeric(df$Year_adoption_national_currency)

df <- dplyr::mutate(df, duration = Year_adoption_national_currency-independence_year)

library(ggplot2)

df$duration <- as.character(df$duration)
str(df)

df <- mutate(df, duration = ifelse(is.na(duration) == T, "Censored", duration)) 
ggplot(df, aes(duration)) + geom_bar() + labs(caption = "Author's calculation",
                                              title="Fig.1: Years First Adoption since Indpendence 1945-2018",
                                              x ="Years", 
                                              y = "Count" ) +
  theme(plot.title = element_text(face = "bold"))


#ggsave("/Users/Nicola/Box Sync/Ideasss (nn6nc@Virginia.EDU)/PolEcon ideas/Montenegro/RIPE_Stats/Plots/FIG2.pdf")


########################################################################################################### 
##### FIGURE 2: LIST OF COUNTRIES BY YEAR OF ADOPTING FIRST COIN/NOTES WITH NATIONAL SYMBOL



### Symbolic Dimension of Balkan nationalism 2011 survey

df <- read_excel("/Users/Nicola/Box Sync/Ideasss (nn6nc@Virginia.EDU)/PolEcon ideas/Montenegro/Survey2011_complete.xlsx")
as.data.frame(df)


df$Country <- factor(df$Country,levels = c("Kosovo", "Albania", "Croatia", "Bosnia Herzegovina", "Macedonia", "Montenegro"))

df <- dplyr::filter(df, Country != "Serbia")

gg <- df %>% 
  dplyr::filter(Type=="Strong_identification_with_Europe" | Type=="European_integration_good")%>% 
  ggplot(df, mapping=aes(x=Country, y=Value, fill=factor(Type)))+
  geom_bar(position="dodge", stat="identity")+
  labs(title="Fig 2: Relationship with the EU: Western Balkans", y = "% Respondents", caption = "Source: Kolstø, 2016") +
  theme(axis.text.x=element_text(angle=45,hjust=1)) +
  scale_fill_discrete(name = "Legend", labels = c("EU Good Thing", "Strong European Feeling"))
gg + theme(plot.title = element_text(face = "bold"))

#ggsave("/Users/Nicola/Box Sync/Ideasss (nn6nc@Virginia.EDU)/PolEcon ideas/Montenegro/RIPE_Stats/Plots/FIG9.pdf")



########################################################################################################### 
##### FIGURE 3 Inflation Performances: Balkan countries
rm(list=ls())
library(knitr)
library(MASS)
library(mlogit)
library(cquad)
library(tidyverse)
library(plm)
library(stargazer)
library(dplyr)
library(lmtest)
library(pcse)
library(readstata13)
library(brglm)
library(sandwich) 
library(readxl)
library(dygraphs)
library(xts)

#rm(list=ls())

infl <- read_excel("/Users/Nicola/Box Sync/Ideasss (nn6nc@Virginia.EDU)/PolEcon ideas/Montenegro/NEW POLITICAL ECONOMY/NPE R&R/FINAL_replication_package/Inflationdata.xlsx", sheet = 5)
balkan <- filter(infl, Country == "Kosovo" |
                   Country == "Montenegro" |
                   Country == "Macedonia, FYR" |
                   Country == "Bosnia and Herzegovina" |
                   Country == "Albania" |
                   Country == "Slovenia" |
                   Country == "Croatia")
balkan2 <- dplyr::select(balkan, -"Indicator Type", -"Series Name", -"IMF Country Code")
balkan2 <- reshape(balkan2, 
                   direction = "long",
                   varying = list(names(balkan2)[3:51]),
                   v.names = "Value",
                   idvar = c("Country", "Country Code"),
                   timevar = "Year",
                   times = 1970:2018)

kosovo <- read.csv("/Users/Nicola/Box Sync/Ideasss (nn6nc@Virginia.EDU)/PolEcon ideas/Montenegro/NEW POLITICAL ECONOMY/NPE R&R/FINAL_replication_package/Kosovo_infl_WB.csv") # From World Bank
kosovo <- kosovo %>% rename('Country Code' = Country.Code)
balkan2 <- rbind(balkan2, kosovo)

rm(balkan)


df_balkan <- filter(balkan2, Year > 2001 & Year < 2018)
df_balkan <- dplyr::rename(df_balkan, Inflation =Value )
g3 <- ggplot(df_balkan, aes(x = Year, y = Inflation)) + 
  geom_line(aes(color = Country), size = 1) +
  theme_minimal() +
  labs(title = "Fig. 3: Inflation performance: Balkan Countries",
       caption = "Source: World Bank") +
  theme(plot.title = element_text(face = "bold"))

g3

#ggsave("/Users/Nicola/Box Sync/Ideasss (nn6nc@Virginia.EDU)/PolEcon ideas/Montenegro/RIPE_Stats/Plots/FIG3.pdf")







########################################################################################################### 

######################################################################################################### 
## ################################### FIGURE 4 and 5 Import and Export  #################################
rm(list=ls())

# On WITS: Country, year range, trade flow (import or export)
library(readxl)
library(dplyr)
library(tidyr)

# Albania
albania_imp <- read_excel("/Users/Nicola/Box Sync/Ideasss (nn6nc@Virginia.EDU)/PolEcon ideas/Montenegro/NEW POLITICAL ECONOMY/NPE R&R/FINAL_replication_package/AlbaniaWITS-Partner-Timeseries.xlsx", sheet="Partner-Timeseries")
albania_exp <- read_excel("/Users/Nicola/Box Sync/Ideasss (nn6nc@Virginia.EDU)/PolEcon ideas/Montenegro/NEW POLITICAL ECONOMY/NPE R&R/FINAL_replication_package/AlbaniaWITS-Partner-Timeseries-2.xlsx", sheet="Partner-Timeseries")

albania <- rbind(albania_imp,albania_exp)
rm(albania_imp,albania_exp)
albania <- albania[,c(2,3,7:22)]
albania <- albania %>% rename( country = "Partner Name", type = "Trade Flow")
albania <- gather(albania, year, value, "2001":"2016", factor_key=TRUE)
albania <- mutate(albania, export = ifelse(type == "Export", value, NA))
albania <- mutate(albania, import = ifelse(type == "Import", value, NA))
albania_total <- albania %>%
  group_by(year) %>%
  summarise(export_tot = sum(export, na.rm = TRUE), import_tot = sum(import, na.rm = TRUE))
albania$year <- as.numeric(albania$year)
albania_euro <- filter(albania, country == "Germany" |
                         country == "Netherlands" |
                         country == "Ireland" |
                         country == "France" |
                         country == "Austria" |
                         country == "Belgium" |
                         country == "Finland" |
                         country == "Greece" |
                         country == "Italy" |
                         country == "Luxembourg" |
                         country == "Portugal" |
                         country == "Spain" |
                         (country == "Slovenia" & year > 2006) |
                         (country == "Cyprus" & year > 2007) |
                         (country == "Malta" & year > 2007))
albania_euro <- albania_euro %>%
  group_by(year) %>%
  summarise(export_euro = sum(export, na.rm = TRUE), import_euro = sum(import, na.rm = TRUE)) %>%  select(-year)
albania_EU <- filter(albania, country == "Germany" |
                       country == "Netherlands" |
                       country == "Ireland" |
                       country == "France" |
                       country == "Austria" |
                       country == "Belgium" |
                       country == "Finland" |
                       country == "Greece" |
                       country == "Italy" |
                       country == "Luxembourg" |
                       country == "Portugal" |
                       country == "Spain" |
                       country == "Denmark" |
                       country == "United Kingdom" |
                       country == "Sweden" |
                       (country == "Slovenia" & year > 2003) |
                       (country == "Cyprus" & year > 2003) |
                       (country == "Estonia" & year > 2003) |
                       (country == "Hungary" & year > 2003) |
                       (country == "Latvia" & year > 2003) |
                       (country == "Lithuania" & year > 2003) |
                       (country == "Poland" & year > 2003) |
                       (country == "Slovakia" & year > 2003) |
                       (country == "Slovenia" & year > 2003) |
                       (country == "Malta" & year > 2003) |
                       (country == "Bulgaria" & year > 2006) |
                       (country == "Romania" & year > 2006))
albania_EU <- albania_EU %>%
  group_by(year) %>%
  summarise(export_EU = sum(export, na.rm = TRUE), import_EU = sum(import, na.rm = TRUE)) %>%  select(-year)
albania <- cbind(albania_total, albania_euro, albania_EU)
rm(albania_total, albania_euro, albania_EU)
albania <- albania %>% 
  mutate(imp_EU_share = (import_EU) / import_tot,
         exp_EU_share = (export_EU) / export_tot,
         imp_euro_share = (import_euro) / import_tot,
         exp_euro_share = (export_euro) / export_tot) %>% 
  dplyr::select(year, imp_EU_share, exp_EU_share, imp_euro_share, exp_euro_share)

albania <- mutate(albania, country = "Albania")


# Bosnia
imports <- read_excel("/Users/Nicola/Box Sync/Ideasss (nn6nc@Virginia.EDU)/PolEcon ideas/Montenegro/NEW POLITICAL ECONOMY/NPE R&R/FINAL_replication_package/BosniaWITS-Partner-Timeseries-2.xlsx", sheet="Partner-Timeseries")
exports <- read_excel("/Users/Nicola/Box Sync/Ideasss (nn6nc@Virginia.EDU)/PolEcon ideas/Montenegro/NEW POLITICAL ECONOMY/NPE R&R/FINAL_replication_package/BosniaWITS-Partner-Timeseries.xlsx", sheet="Partner-Timeseries")

country <- rbind(imports,exports)
rm(imports,exports)
country <- country[,c(2,3,6:19)]
country <- country %>% rename( country = "Partner Name", type = "Trade Flow")
country <- gather(country, year, value, "2003":"2016", factor_key=TRUE)
country <- mutate(country, export = ifelse(type == "Export", value, NA))
country <- mutate(country, import = ifelse(type == "Import", value, NA))
country_total <- country %>%
  group_by(year) %>%
  summarise(export_tot = sum(export, na.rm = TRUE), import_tot = sum(import, na.rm = TRUE))

country$year <- as.numeric(country$year)
country_euro <- filter(country, country == "Germany" |
                         country == "Netherlands" |
                         country == "Ireland" |
                         country == "France" |
                         country == "Austria" |
                         country == "Belgium" |
                         country == "Finland" |
                         country == "Greece" |
                         country == "Italy" |
                         country == "Luxembourg" |
                         country == "Portugal" |
                         country == "Spain" |
                         (country == "Slovenia" & year > 2006) |
                         (country == "Cyprus" & year > 2007) |
                         (country == "Malta" & year > 2007))

country_euro <- country_euro %>%
  group_by(year) %>%
  summarise(export_euro = sum(export, na.rm = TRUE), import_euro = sum(import, na.rm = TRUE)) %>%  select(-year)
country_EU <- filter(country, country == "Germany" |
                       country == "Netherlands" |
                       country == "Ireland" |
                       country == "France" |
                       country == "Austria" |
                       country == "Belgium" |
                       country == "Finland" |
                       country == "Greece" |
                       country == "Italy" |
                       country == "Luxembourg" |
                       country == "Portugal" |
                       country == "Spain" |
                       country == "Denmark" |
                       country == "United Kingdom" |
                       country == "Sweden" |
                       (country == "Slovenia" & year > 2003) |
                       (country == "Cyprus" & year > 2003) |
                       (country == "Estonia" & year > 2003) |
                       (country == "Hungary" & year > 2003) |
                       (country == "Latvia" & year > 2003) |
                       (country == "Lithuania" & year > 2003) |
                       (country == "Poland" & year > 2003) |
                       (country == "Slovakia" & year > 2003) |
                       (country == "Slovenia" & year > 2003) |
                       (country == "Malta" & year > 2003) |
                       (country == "Bulgaria" & year > 2006) |
                       (country == "Romania" & year > 2006))
country_EU <- country_EU %>%
  group_by(year) %>%
  summarise(export_EU = sum(export, na.rm = TRUE), import_EU = sum(import, na.rm = TRUE)) %>%  select(-year)
country <- cbind(country_total, country_euro, country_EU)
rm(country_total, country_euro, country_EU)
country <- country %>% 
  mutate(imp_EU_share = (import_EU) / import_tot,
         exp_EU_share = (export_EU) / export_tot,
         imp_euro_share = (import_euro) / import_tot,
         exp_euro_share = (export_euro) / export_tot) %>% 
  dplyr::select(year, imp_EU_share, exp_EU_share, imp_euro_share, exp_euro_share)
bosnia <- mutate(country, country = "Bosnia")
rm(country)


# Croatia
imports <- read_excel("/Users/Nicola/Box Sync/Ideasss (nn6nc@Virginia.EDU)/PolEcon ideas/Montenegro/NEW POLITICAL ECONOMY/NPE R&R/FINAL_replication_package/CroatiaWITS-Partner-Timeseries.xlsx", sheet="Partner-Timeseries")
exports <- read_excel("/Users/Nicola/Box Sync/Ideasss (nn6nc@Virginia.EDU)/PolEcon ideas/Montenegro/NEW POLITICAL ECONOMY/NPE R&R/FINAL_replication_package/CroatiaWITS-Partner-Timeseries-2.xlsx", sheet="Partner-Timeseries")
country <- rbind(imports,exports)
rm(imports,exports)
country <- country[,c(2,3,6:21)]
country <- country %>% rename( country = "Partner Name", type = "Trade Flow")
country <- gather(country, year, value, "2001":"2016", factor_key=TRUE)
country <- mutate(country, export = ifelse(type == "Export", value, NA))
country <- mutate(country, import = ifelse(type == "Import", value, NA))
country_total <- country %>%
  group_by(year) %>%
  summarise(export_tot = sum(export, na.rm = TRUE), import_tot = sum(import, na.rm = TRUE))
country$year <- as.numeric(country$year)
country_euro <- filter(country, country == "Germany" |
                         country == "Netherlands" |
                         country == "Ireland" |
                         country == "France" |
                         country == "Austria" |
                         country == "Belgium" |
                         country == "Finland" |
                         country == "Greece" |
                         country == "Italy" |
                         country == "Luxembourg" |
                         country == "Portugal" |
                         country == "Spain" |
                         (country == "Slovenia" & year > 2006) |
                         (country == "Cyprus" & year > 2007) |
                         (country == "Malta" & year > 2007))
country_euro <- country_euro %>%
  group_by(year) %>%
  summarise(export_euro = sum(export, na.rm = TRUE), import_euro = sum(import, na.rm = TRUE)) %>%  select(-year)
country_EU <- filter(country, country == "Germany" |
                       country == "Netherlands" |
                       country == "Ireland" |
                       country == "France" |
                       country == "Austria" |
                       country == "Belgium" |
                       country == "Finland" |
                       country == "Greece" |
                       country == "Italy" |
                       country == "Luxembourg" |
                       country == "Portugal" |
                       country == "Spain" |
                       country == "Denmark" |
                       country == "United Kingdom" |
                       country == "Sweden" |
                       (country == "Slovenia" & year > 2003) |
                       (country == "Cyprus" & year > 2003) |
                       (country == "Estonia" & year > 2003) |
                       (country == "Hungary" & year > 2003) |
                       (country == "Latvia" & year > 2003) |
                       (country == "Lithuania" & year > 2003) |
                       (country == "Poland" & year > 2003) |
                       (country == "Slovakia" & year > 2003) |
                       (country == "Slovenia" & year > 2003) |
                       (country == "Malta" & year > 2003) |
                       (country == "Bulgaria" & year > 2006) |
                       (country == "Romania" & year > 2006))
country_EU <- country_EU %>%
  group_by(year) %>%
  summarise(export_EU = sum(export, na.rm = TRUE), import_EU = sum(import, na.rm = TRUE)) %>%  select(-year)
country <- cbind(country_total, country_euro, country_EU)
rm(country_total, country_euro, country_EU)
country <- country %>% 
  mutate(imp_EU_share = (import_EU) / import_tot,
         exp_EU_share = (export_EU) / export_tot,
         imp_euro_share = (import_euro) / import_tot,
         exp_euro_share = (export_euro) / export_tot) %>% 
  dplyr::select(year, imp_EU_share, exp_EU_share, imp_euro_share, exp_euro_share)
croatia <- mutate(country, country = "Croatia")
rm(country)


##
# Montenegro
library(readxl)
library(dplyr)
library(tidyr)

imports <- read_excel("/Users/Nicola/Box Sync/Ideasss (nn6nc@Virginia.EDU)/PolEcon ideas/Montenegro/NEW POLITICAL ECONOMY/NPE R&R/FINAL_replication_package/MontenegroWITS-Partner-Timeseries-2.xlsx", sheet="Partner-Timeseries")
exports <- read_excel("/Users/Nicola/Box Sync/Ideasss (nn6nc@Virginia.EDU)/PolEcon ideas/Montenegro/NEW POLITICAL ECONOMY/NPE R&R/FINAL_replication_package/MontenegroWITS-Partner-Timeseries.xlsx", sheet="Partner-Timeseries")

country <- rbind(imports,exports)
rm(imports,exports)
country <- country[,c(2,3,6:16)]
country <- country %>% rename( country = "Partner Name", type = "Trade Flow")
country <- gather(country, year, value, "2006":"2016", factor_key=TRUE)

country <- mutate(country, export = ifelse(type == "Export", value, NA))
country <- mutate(country, import = ifelse(type == "Import", value, NA))

country_total <- country %>%
  group_by(year) %>%
  summarise(export_tot = sum(export, na.rm = TRUE), import_tot = sum(import, na.rm = TRUE))

country$year <- as.numeric(country$year)

country_euro <- filter(country, country == "Germany" |
                         country == "Netherlands" |
                         country == "Ireland" |
                         country == "France" |
                         country == "Austria" |
                         country == "Belgium" |
                         country == "Finland" |
                         country == "Greece" |
                         country == "Italy" |
                         country == "Luxembourg" |
                         country == "Portugal" |
                         country == "Spain" |
                         (country == "Slovenia" & year > 2006) |
                         (country == "Cyprus" & year > 2007) |
                         (country == "Malta" & year > 2007))

country_euro <- country_euro %>%
  group_by(year) %>%
  summarise(export_euro = sum(export, na.rm = TRUE), import_euro = sum(import, na.rm = TRUE)) %>%  select(-year)

country_EU <- filter(country, country == "Germany" |
                       country == "Netherlands" |
                       country == "Ireland" |
                       country == "France" |
                       country == "Austria" |
                       country == "Belgium" |
                       country == "Finland" |
                       country == "Greece" |
                       country == "Italy" |
                       country == "Luxembourg" |
                       country == "Portugal" |
                       country == "Spain" |
                       country == "Denmark" |
                       country == "United Kingdom" |
                       country == "Sweden" |
                       (country == "Slovenia" & year > 2003) |
                       (country == "Cyprus" & year > 2003) |
                       (country == "Estonia" & year > 2003) |
                       (country == "Hungary" & year > 2003) |
                       (country == "Latvia" & year > 2003) |
                       (country == "Lithuania" & year > 2003) |
                       (country == "Poland" & year > 2003) |
                       (country == "Slovakia" & year > 2003) |
                       (country == "Slovenia" & year > 2003) |
                       (country == "Malta" & year > 2003) |
                       (country == "Bulgaria" & year > 2006) |
                       (country == "Romania" & year > 2006))

country_EU <- country_EU %>%
  group_by(year) %>%
  summarise(export_EU = sum(export, na.rm = TRUE), import_EU = sum(import, na.rm = TRUE)) %>%  select(-year)


country <- cbind(country_total, country_euro, country_EU)
rm(country_total, country_euro, country_EU)

country <- country %>% 
  mutate(imp_EU_share = (import_EU) / import_tot,
         exp_EU_share = (export_EU) / export_tot,
         imp_euro_share = (import_euro) / import_tot,
         exp_euro_share = (export_euro) / export_tot) %>% 
  dplyr::select(year, imp_EU_share, exp_EU_share, imp_euro_share, exp_euro_share)

montenegro <- mutate(country, country = "Montenegro")
rm(country)




# Slovenia
imports <- read_excel("/Users/Nicola/Box Sync/Ideasss (nn6nc@Virginia.EDU)/PolEcon ideas/Montenegro/NEW POLITICAL ECONOMY/NPE R&R/FINAL_replication_package/SloveniaWITS-Partner-Timeseries-2.xlsx", sheet="Partner-Timeseries")
exports <- read_excel("/Users/Nicola/Box Sync/Ideasss (nn6nc@Virginia.EDU)/PolEcon ideas/Montenegro/NEW POLITICAL ECONOMY/NPE R&R/FINAL_replication_package/SloveniaWITS-Partner-Timeseries.xlsx", sheet="Partner-Timeseries")

country <- rbind(imports,exports)
rm(imports,exports)
country <- country[,c(2,3,6:21)]
country <- country %>% rename( country = "Partner Name", type = "Trade Flow")
country <- gather(country, year, value, "2001":"2016", factor_key=TRUE)
country <- mutate(country, export = ifelse(type == "Export", value, NA))
country <- mutate(country, import = ifelse(type == "Import", value, NA))

country_total <- country %>%
  group_by(year) %>%
  summarise(export_tot = sum(export, na.rm = TRUE), import_tot = sum(import, na.rm = TRUE))

country$year <- as.numeric(country$year)

country_euro <- filter(country, country == "Germany" |
                         country == "Netherlands" |
                         country == "Ireland" |
                         country == "France" |
                         country == "Austria" |
                         country == "Belgium" |
                         country == "Finland" |
                         country == "Greece" |
                         country == "Italy" |
                         country == "Luxembourg" |
                         country == "Portugal" |
                         country == "Spain" |
                         (country == "Slovenia" & year > 2006) |
                         (country == "Cyprus" & year > 2007) |
                         (country == "Malta" & year > 2007))

country_euro <- country_euro %>%
  group_by(year) %>%
  summarise(export_euro = sum(export, na.rm = TRUE), import_euro = sum(import, na.rm = TRUE)) %>%  select(-year)

country_EU <- filter(country, country == "Germany" |
                       country == "Netherlands" |
                       country == "Ireland" |
                       country == "France" |
                       country == "Austria" |
                       country == "Belgium" |
                       country == "Finland" |
                       country == "Greece" |
                       country == "Italy" |
                       country == "Luxembourg" |
                       country == "Portugal" |
                       country == "Spain" |
                       country == "Denmark" |
                       country == "United Kingdom" |
                       country == "Sweden" |
                       (country == "Slovenia" & year > 2003) |
                       (country == "Cyprus" & year > 2003) |
                       (country == "Estonia" & year > 2003) |
                       (country == "Hungary" & year > 2003) |
                       (country == "Latvia" & year > 2003) |
                       (country == "Lithuania" & year > 2003) |
                       (country == "Poland" & year > 2003) |
                       (country == "Slovakia" & year > 2003) |
                       (country == "Slovenia" & year > 2003) |
                       (country == "Malta" & year > 2003) |
                       (country == "Bulgaria" & year > 2006) |
                       (country == "Romania" & year > 2006))

country_EU <- country_EU %>%
  group_by(year) %>%
  summarise(export_EU = sum(export, na.rm = TRUE), import_EU = sum(import, na.rm = TRUE)) %>%  select(-year)


country <- cbind(country_total, country_euro, country_EU)
rm(country_total, country_euro, country_EU)

country <- country %>% 
  mutate(imp_EU_share = (import_EU) / import_tot,
         exp_EU_share = (export_EU) / export_tot,
         imp_euro_share = (import_euro) / import_tot,
         exp_euro_share = (export_euro) / export_tot) %>% 
  dplyr::select(year, imp_EU_share, exp_EU_share, imp_euro_share, exp_euro_share)

slovenia <- mutate(country, country = "Slovenia")
rm(country)





# Macedonia
imports <- read_excel("/Users/Nicola/Box Sync/Ideasss (nn6nc@Virginia.EDU)/PolEcon ideas/Montenegro/NEW POLITICAL ECONOMY/NPE R&R/FINAL_replication_package/MacedoniaWITS-Partner-Timeseries-2.xlsx", sheet="Partner-Timeseries")
exports <- read_excel("/Users/Nicola/Box Sync/Ideasss (nn6nc@Virginia.EDU)/PolEcon ideas/Montenegro/NEW POLITICAL ECONOMY/NPE R&R/FINAL_replication_package/MacedoniaWITS-Partner-Timeseries.xlsx", sheet="Partner-Timeseries")
country <- rbind(imports,exports)
rm(imports,exports)
names(country)
country <- country[,c(2,3,13:28)]
country <- country %>% rename( country = "Partner Name", type = "Trade Flow")
country <- gather(country, year, value, "2001":"2016", factor_key=TRUE)
country <- mutate(country, export = ifelse(type == "Export", value, NA))
country <- mutate(country, import = ifelse(type == "Import", value, NA))
country_total <- country %>%
  group_by(year) %>%
  summarise(export_tot = sum(export, na.rm = TRUE), import_tot = sum(import, na.rm = TRUE))
country$year <- as.numeric(country$year)
country_euro <- filter(country, country == "Germany" |
                         country == "Netherlands" |
                         country == "Ireland" |
                         country == "France" |
                         country == "Austria" |
                         country == "Belgium" |
                         country == "Finland" |
                         country == "Greece" |
                         country == "Italy" |
                         country == "Luxembourg" |
                         country == "Portugal" |
                         country == "Spain" |
                         (country == "Slovenia" & year > 2006) |
                         (country == "Cyprus" & year > 2007) |
                         (country == "Malta" & year > 2007))

country_euro <- country_euro %>%
  group_by(year) %>%
  summarise(export_euro = sum(export, na.rm = TRUE), import_euro = sum(import, na.rm = TRUE)) %>%  select(-year)

country_EU <- filter(country, country == "Germany" |
                       country == "Netherlands" |
                       country == "Ireland" |
                       country == "France" |
                       country == "Austria" |
                       country == "Belgium" |
                       country == "Finland" |
                       country == "Greece" |
                       country == "Italy" |
                       country == "Luxembourg" |
                       country == "Portugal" |
                       country == "Spain" |
                       country == "Denmark" |
                       country == "United Kingdom" |
                       country == "Sweden" |
                       (country == "Slovenia" & year > 2003) |
                       (country == "Cyprus" & year > 2003) |
                       (country == "Estonia" & year > 2003) |
                       (country == "Hungary" & year > 2003) |
                       (country == "Latvia" & year > 2003) |
                       (country == "Lithuania" & year > 2003) |
                       (country == "Poland" & year > 2003) |
                       (country == "Slovakia" & year > 2003) |
                       (country == "Slovenia" & year > 2003) |
                       (country == "Malta" & year > 2003) |
                       (country == "Bulgaria" & year > 2006) |
                       (country == "Romania" & year > 2006))

country_EU <- country_EU %>%
  group_by(year) %>%
  summarise(export_EU = sum(export, na.rm = TRUE), import_EU = sum(import, na.rm = TRUE)) %>%  select(-year)


country <- cbind(country_total, country_euro, country_EU)
rm(country_total, country_euro, country_EU)

country <- country %>% 
  mutate(imp_EU_share = (import_EU) / import_tot,
         exp_EU_share = (export_EU) / export_tot,
         imp_euro_share = (import_euro) / import_tot,
         exp_euro_share = (export_euro) / export_tot) %>% 
  dplyr::select(year, imp_EU_share, exp_EU_share, imp_euro_share, exp_euro_share)

macedonia <- mutate(country, country = "Macedonia")
rm(country)




############### From Kosovo Central Bank
kos_exp <- read_excel("/Users/Nicola/Box Sync/Ideasss (nn6nc@Virginia.EDU)/PolEcon ideas/Montenegro/NEW POLITICAL ECONOMY/NPE R&R/FINAL_replication_package/Kosovo_exports_central_bank.xlsx")
kos_exp <- kos_exp[-c(1,2,3),]

kos_imp <- read_excel("/Users/Nicola/Box Sync/Ideasss (nn6nc@Virginia.EDU)/PolEcon ideas/Montenegro/NEW POLITICAL ECONOMY/NPE R&R/FINAL_replication_package/Kosovo_Imports_central_bank.xlsx")
kos_imp <- kos_imp[-c(1,2,3),]


imp_total <- dplyr::select(kos_imp, Year, Total)
imp_total <- dplyr::rename(imp_total, tot_import = Total)

exp_total <- dplyr::select(kos_exp, Year, Total)
exp_total <- dplyr::rename(exp_total, tot_export = Total)

kos_exp <- dplyr::select(kos_exp, -Total)
kos_imp <- dplyr::select(kos_imp, -Total)


kos_imp <- gather(kos_imp, country, value, "Austria":"Others", factor_key=TRUE)
kos_exp <- gather(kos_exp, country, value, "Austria":"Others", factor_key=TRUE)


kos_imp$Year <- as.numeric(kos_imp$Year)
kos_exp$Year <- as.numeric(kos_exp$Year)

exp_euro <- filter(kos_exp, country == "Germany" |
                     country == "Netherlands" |
                     country == "Ireland" |
                     country == "France" |
                     country == "Austria" |
                     country == "Belgium" |
                     country == "Finland" |
                     country == "Greece" |
                     country == "Italy" |
                     country == "Luxembourg" |
                     country == "Portugal" |
                     country == "Spain" |
                     (country == "Slovenia" & Year > 2006) |
                     (country == "Cyprus" & Year > 2007) |
                     (country == "Malta" & Year > 2007))

exp_euro <- exp_euro %>%
  group_by(Year) %>%
  summarise(export_euro = sum(value, na.rm = TRUE))

imp_euro <- filter(kos_imp, country == "Germany" |
                     country == "Netherlands" |
                     country == "Ireland" |
                     country == "France" |
                     country == "Austria" |
                     country == "Belgium" |
                     country == "Finland" |
                     country == "Greece" |
                     country == "Italy" |
                     country == "Luxembourg" |
                     country == "Portugal" |
                     country == "Spain" |
                     (country == "Slovenia" & Year > 2006) |
                     (country == "Cyprus" & Year > 2007) |
                     (country == "Malta" & Year > 2007))

imp_euro <- imp_euro %>%
  group_by(Year) %>%
  summarise(import_euro = sum(value, na.rm = TRUE))

imp_EU <- filter(kos_imp, country == "Germany" |
                   country == "Netherlands" |
                   country == "Ireland" |
                   country == "France" |
                   country == "Austria" |
                   country == "Belgium" |
                   country == "Finland" |
                   country == "Greece" |
                   country == "Italy" |
                   country == "Luxembourg" |
                   country == "Portugal" |
                   country == "Spain" |
                   country == "Denmark" |
                   country == "United Kingdom" |
                   country == "Sweden" |
                   (country == "Slovenia" & Year > 2003) |
                   (country == "Cyprus" & Year > 2003) |
                   (country == "Estonia" & Year > 2003) |
                   (country == "Hungary" & Year > 2003) |
                   (country == "Latvia" & Year > 2003) |
                   (country == "Lithuania" & Year > 2003) |
                   (country == "Poland" & Year > 2003) |
                   (country == "Slovakia" & Year > 2003) |
                   (country == "Slovenia" & Year > 2003) |
                   (country == "Malta" & Year > 2003) |
                   (country == "Bulgaria" & Year > 2006) |
                   (country == "Romania" & Year > 2006))

imp_EU <- imp_EU %>%
  group_by(Year) %>%
  summarise(import_EU = sum(value, na.rm = TRUE))




exp_EU <- filter(kos_exp, country == "Germany" |
                   country == "Netherlands" |
                   country == "Ireland" |
                   country == "France" |
                   country == "Austria" |
                   country == "Belgium" |
                   country == "Finland" |
                   country == "Greece" |
                   country == "Italy" |
                   country == "Luxembourg" |
                   country == "Portugal" |
                   country == "Spain" |
                   country == "Denmark" |
                   country == "United Kingdom" |
                   country == "Sweden" |
                   (country == "Slovenia" & Year > 2003) |
                   (country == "Cyprus" & Year > 2003) |
                   (country == "Estonia" & Year > 2003) |
                   (country == "Hungary" & Year > 2003) |
                   (country == "Latvia" & Year > 2003) |
                   (country == "Lithuania" & Year > 2003) |
                   (country == "Poland" & Year > 2003) |
                   (country == "Slovakia" & Year > 2003) |
                   (country == "Slovenia" & Year > 2003) |
                   (country == "Malta" & Year > 2003) |
                   (country == "Bulgaria" & Year > 2006) |
                   (country == "Romania" & Year > 2006))

exp_EU <- exp_EU %>%
  group_by(Year) %>%
  summarise(export_EU = sum(value, na.rm = TRUE))

df <- full_join(exp_EU, imp_EU)
df <- full_join(exp_euro, df)
df <- full_join(imp_euro, df)
df <- full_join(exp_total, df)
kosovo <- full_join(imp_total, df)

rm(exp_EU); rm(imp_EU); rm(exp_euro); rm(imp_euro); rm(exp_total); rm(imp_total); rm(df)
rm(kos_exp); rm(kos_imp)

kosovo <- kosovo %>% 
  mutate(imp_EU_share = (import_EU) / tot_import,
         exp_EU_share = (export_EU) / tot_export,
         imp_euro_share = (import_euro) / tot_import,
         exp_euro_share = (export_euro) / tot_export) %>% 
  dplyr::select(Year, imp_EU_share, exp_EU_share, imp_euro_share, exp_euro_share)

kosovo <- mutate(kosovo, country = "Kosovo")
kosovo <- rename(kosovo, year = Year)
kosovo$year <-as.factor(kosovo$year)

trade <- full_join(albania, bosnia)
trade <- full_join(croatia, trade)
trade <- full_join(kosovo, trade)
trade <- full_join(macedonia, trade)
trade <- full_join(montenegro, trade)
trade <- full_join(slovenia, trade)
trade <- full_join(bosnia, trade)


### Linearly interpolating one obsrvation (for Macedonia)
library(zoo); library(ggplot2)
trade$exp_euro_share <- na.approx(trade$exp_euro_share)
trade$exp_EU_share <- na.approx(trade$exp_EU_share)

trade$year <- as.numeric(as.character(trade$year))
trade <- dplyr::filter(trade, year < 2017)

# To make the Graph more discernible, let's increase size for Montenegro and Kosovo
trade <- mutate(trade, Highlight= 1)
trade <- trade %>%  mutate(Highlight = ifelse(country == "Kosovo" | country == "Montenegro", 2, Highlight ))


cbPalette <- c("#999999", "#E69F00", "#56B4E9", "#009E73", "#F0E442", "#CC79A7", "#FF0000")

imports_euro <- ggplot(trade, aes(x = year, y = imp_euro_share)) + 
  geom_line(aes(color = country), size = 1) +
  scale_color_manual(values = cbPalette) +
  xlab("Year") + ylab("Imports (% of total imports)") +
  labs(title = "Fig. 6: Imports from Eurozone countries (%)", 
       caption = "Source: World Bank")

imports_euro + theme(plot.title = element_text(face = "bold"))


#ggsave("/Users/Nicola/Box Sync/Ideasss (nn6nc@Virginia.EDU)/PolEcon ideas/Montenegro/RIPE_Stats/Plots/FIG6.pdf")


exports_euro <- ggplot(trade, aes(x = year, y = exp_euro_share)) + 
  geom_line(aes(color = country), size = 1) +
  scale_color_manual(values = cbPalette) +
  xlab("Year") + ylab("Exports (% of total imports)") +
  theme(legend.position = "none") +
  labs(title = "Fig. 7: Exports from Eurozone countries (%)", 
       caption = "Source: World Bank")

exports_euro  + theme(plot.title = element_text(face = "bold"))


#ggsave("/Users/Nicola/Box Sync/Ideasss (nn6nc@Virginia.EDU)/PolEcon ideas/Montenegro/RIPE_Stats/Plots/FIG7.pdf")





###### PLEASE NOTICE THAT THE ANALYSIS BELOW WILL NOT REPLICATE TH ESTIAMTES OF TABLE 1 ACCURATELY
###### I AM AFRAID I CANNOT RECOVER WHAT I DID IN THE ORIGINAL ANALYSIS AND I CANNOT REPLICATE THE EXACT RESULTS
###### NOTICE, THOUGH, THAT THE ANLAYSIS IS CONSISTENT WITH EVERYTHING WRITTEN IN THE PAPER, (ACTUALLY THE RESULTS ARE EVEN MORE
##### STRONGLY IN FAVOR OF MY HYPOTHESIS). IN PARTICULAR IN P. 12 I WRITE: "Montenegro’s price co-movement is the least synchronised to the eurozone after that of Serbia. 
# On the output side, Montenegro is less synchronised with the eurozone than any other country in the sample.". THIS REMAINES TRUE.

########################################################################################################### 
##### Table 1: Eurozone - Balkans co-movements

library(readr)
library(tidyr)
rm(list=ls())

#Loading packages
Packages <- c("tidytext", "data.table", "stringr", "rvest", "zoo", "quanteda", "tm.plugin.lexisnexis", 
              "tm.plugin.factiva", "xml2", "ggplot2", "tm", "reshape2", "knitr", "DataCombine", 
              "dplyr", "MASS", "qgraph", "stringr", "plyr", "NLP", "readr", "readxl",
              "EGAnet", "Hmisc")

lapply(Packages, library, character.only = TRUE)



# Notice that it is constant ($2017)
gdp_ppp <- read_csv("/Users/Nicola/Box Sync/Ideasss (nn6nc@Virginia.EDU)/PolEcon ideas/Montenegro/NEW POLITICAL ECONOMY/NPE R&R/FINAL_replication_package/API_NY.GDP.MKTP.PP.KD_DS2_en_csv_v2_5343796.csv", 
                    skip = 3)

class(gdp_ppp$`Country Name`)


gdp_ppp <- dplyr::filter(gdp_ppp, `Country Name` == "Albania" |
                           `Country Name` == "Bosnia and Herzegovina" |
                           `Country Name` == "Bulgaria" |
                           `Country Name` == "Euro area" |
                           
                           `Country Name` == "Croatia" |
                           #`Country Name` == "Kosovo" |
                           `Country Name` == "North Macedonia" |
                           `Country Name` == "Montenegro" |
                           `Country Name` == "Serbia" )


gdp_ppp <- dplyr::select(gdp_ppp, "Country Name", "2008":"2021")


gdp_ppp <- gather(gdp_ppp, year, gdp_ppp, "2008":"2021", factor_key=TRUE)
gdp_ppp


er_wb <- read_csv("/Users/Nicola/Box Sync/Ideasss (nn6nc@Virginia.EDU)/PolEcon ideas/Montenegro/NEW POLITICAL ECONOMY/NPE R&R/FINAL_replication_package/API_PA.NUS.FCRF_DS2_en_csv_v2_5337029.csv", 
                  skip = 3)


er_wb <- dplyr::filter(er_wb, `Country Name` == "Albania" |
                         `Country Name` == "Bosnia and Herzegovina" |
                         `Country Name` == "Bulgaria" |
                         `Country Name` == "Croatia" |
                         `Country Name` == "Euro area" |
                         #`Country Name` == "Kosovo" |
                         `Country Name` == "North Macedonia" |
                         `Country Name` == "Montenegro" |
                         `Country Name` == "Serbia" )


er_wb <- dplyr::select(er_wb, "Country Name", "2008":"2021")


er_wb <- gather(er_wb, year, er, "2008":"2021", factor_key=TRUE)
er_wb



# Merge

df <- full_join(gdp_ppp,er_wb )

# Notice that euro area has no ER with US prior to 1999 in the dataset from the World Bank.
# Nevertheless, theoretical historical ER can be found here https://fxtop.com/en/historical-exchange-rates.php?A=1&C1=USD&C2=EUR&YA=1&DD1=01&MM1=01&YYYY1=1990&B=1&P=&I=1&DD2=24&MM2=03&YYYY2=2023&btnOK=Go%21
# I directly insert the values for 1998 and 1997

df <- mutate(df, er = ifelse(`Country Name` == "Euro area" & year == 1998, 0.898283, er))
df <- mutate(df, er = ifelse(`Country Name` == "Euro area" & year == 1997, 0.885013, er))



euroarea <- dplyr::filter(df, `Country Name` == "Euro area" )

euroarea_tmp <- dplyr::select(euroarea, year, er)
euroarea_tmp <- dplyr::rename(euroarea_tmp, er_euro = er)

df <- full_join(df,euroarea_tmp )



df <- mutate(df, er = ifelse(is.na(er), er_euro, er))

df <- dplyr::select(df, -er_euro)


# Step 1: P_i = (PPP of GDP) / (ex. rate) measures how many units of U.S. output can with one unit of country i's output, 
# that is, it measures the relative price of output with respect to that of the

df <- dplyr::mutate(df, P_i = gdp_ppp/er)

# Step 2: P_j , i.e. same as above but for the anchor (eurozone)
euroarea <- dplyr::filter(df, `Country Name` == "Euro area" )
df <- dplyr::filter(df, `Country Name` != "Euro area" )

euroarea <- dplyr::select(euroarea, year, P_i)
euroarea <- dplyr::rename(euroarea, P_j = P_i)


df <- full_join(df,euroarea )

# Step 3: relative prices between country and anchor, P_i / P_j, in log form

df <- dplyr::mutate(df, P_ratios_log = log(P_i/P_j))

df <- dplyr::rename(df, country = "Country Name")



# Step 4: estimate the second order autoregressive model
library("plm")
panel_df <- pdata.frame(df, index=c("country","year"))
panel_df <- dplyr::select(panel_df, country, year, P_ratios_log)


panel_df <- panel_df %>% dplyr::group_by(country) %>% dplyr::mutate(P_ratios_log1 = dplyr::lag(P_ratios_log, 1))
panel_df <- panel_df %>% group_by(country) %>% dplyr::mutate(P_ratios_log2 = dplyr::lag(P_ratios_log, 2))

panel_df <- dplyr::filter(panel_df, !is.na(P_ratios_log))


class(panel_df$year)


emp.gmm <- pgmm(P_ratios_log~lag(P_ratios_log, 1) + lag(P_ratios_log, 2)| lag(P_ratios_log, 2:99),
                data = panel_df, effect = "individual", model = "onestep")
summary(emp.gmm)


# Step 6: calculate the RMSE of the residuals as in Alesina et al., 2002, p. 313 
res <- as.data.frame(residuals(emp.gmm))
#res <- as.data.frame(t(res))
# Step 6a: squared residuals
res2 <- res^2
# Step 6b: summation over time
sum <- as.data.frame(colSums(res2))
# Step 6c: multiply by 1/(t-3) 
# Notice T = 13 for all countries 
sum <- dplyr::mutate(sum, t = 1/(13-3))
#sum[6,2] = 1/(14-3)
sum <- dplyr::rename(sum, residuals_sq_sum = `colSums(res2)`)
sum <- dplyr::mutate(sum, residuals_sq_sum_t = residuals_sq_sum * t)
# Step 6d: square root
comov <- dplyr::mutate(sum, comov_price = sqrt(residuals_sq_sum_t))

#Interpretation: the lower the value the greater the comovement with euro area





######## Now comovement in output
rm(list = ls())

output <- read_csv("/Users/Nicola/Box Sync/Ideasss (nn6nc@Virginia.EDU)/PolEcon ideas/Montenegro/NEW POLITICAL ECONOMY/NPE R&R/FINAL_replication_package/API_NY.GDP.PCAP.KD_DS2_en_csv_v2_5338219.csv", 
                   skip = 3)


output <- dplyr::filter(output, `Country Name` == "Albania" |
                          `Country Name` == "Bosnia and Herzegovina" |
                          `Country Name` == "Bulgaria" |
                          `Country Name` == "Euro area" |
                          `Country Name` == "Croatia" |
                          #`Country Name` == "Kosovo" |
                          `Country Name` == "North Macedonia" |
                          `Country Name` == "Montenegro" |
                          `Country Name` == "Serbia" )


output <- dplyr::select(output, "Country Name", "2006":"2021")


output <- gather(output, year, output, "2006":"2021", factor_key=TRUE)
output


euroarea <- dplyr::filter(output, `Country Name` == "Euro area" )
output <- dplyr::filter(output, `Country Name` != "Euro area" )

euroarea <- dplyr::rename(euroarea, output_euro = output)
euroarea <- dplyr::select(euroarea, -`Country Name`)


df <- left_join(output,euroarea )


# Step 3: relative prices between country and anchor, P_i / P_j, in log form

df <- dplyr::mutate(df, Y_ratios_log = log(output/output_euro))

df <- dplyr::rename(df, country = "Country Name")


# Step 4: estimate the second order autoregressive model
library("plm")
panel_df <- pdata.frame(df, index=c("country","year"))
panel_df <- dplyr::select(panel_df, country, year, Y_ratios_log)


panel_df <- panel_df %>% group_by(country) %>% dplyr::mutate(Y_ratios_log1 = dplyr::lag(Y_ratios_log, 1))
panel_df <- panel_df %>% group_by(country) %>% dplyr::mutate(Y_ratios_log2 = dplyr::lag(Y_ratios_log, 2))




emp.gmm <- pgmm(Y_ratios_log~lag(Y_ratios_log, 1) + lag(Y_ratios_log, 2)| lag(Y_ratios_log, 2:99),
                data = panel_df, effect = "individual", model = "onestep")
summary(emp.gmm)

# Step 6: calculate the RMSE of the residuals as in Alesina et al., 2002, p. 313 
res <- as.data.frame(residuals(emp.gmm))
#res <- as.data.frame(t(res))
# Step 6a: squared residuals
res2 <- res^2
# Step 6b: summation over time
sum <- as.data.frame(colSums(res2))
# Step 6c: multiply by 1/(t-3) 
# Notice T = 25 for all countries but Kosovo (T = 14)
sum <- dplyr::mutate(sum, t = 1/(16-3))
#sum[6,2] = 1/(14-3)
sum <- dplyr::rename(sum, residuals_sq_sum = `colSums(res2)`)
sum <- dplyr::mutate(sum, residuals_sq_sum_t = residuals_sq_sum * t)
# Step 6d: square root
comov <- dplyr::mutate(sum, comov_output = sqrt(residuals_sq_sum_t))

#Interpretation: the lower the value the greater the comovement with euro area



### TABLE 2: STATE CAPACITY

rm(list=ls())
library(haven)
library(dplyr)
df <- read_dta("/Users/Nicola/Box Sync/Ideasss (nn6nc@Virginia.EDU)/PolEcon ideas/Montenegro/NEW POLITICAL ECONOMY/NPE R&R/FINAL_replication_package/StateCapacityDataset_v1.dta")

df <- dplyr::select(df, cntrynum, country, year, Capacity)


df <- dplyr::filter(df, country ==  "Montenegro" | country == 
                      "Cameroon" | country ==  "Senegal" | country ==  "Togo" | country ==  "Benin" |
                      country == "Burkina Faso" |country ==  "Niger"|country ==  "Ivory Coast"|country ==  "Gabon" |
                      country == "Mauritania" |country ==  "Liberia")



df2 <- df %>%
  group_by(country) %>%
  arrange(country, year) %>%
  filter(row_number()==1:8)

df4 <- df %>%
  group_by(country) %>%
  arrange(country, year) %>%
  filter(row_number()==2)

df3 <- df2 %>%
  group_by(country) %>%
  summarise_at(vars(Capacity), list(name = mean))

# Manually insert into Table 2 the following numbers
View(df4)
View(df3)