1 Load the package

library(tidyverse)

2 Load the data

aff_eu <- rio::import("../Data_output/ESTaff_eu.dta")

#aff_highEU <- rio::import("../Data_output/ESTaff_highEU.dta")

#aff_oecd <- rio::import("../Data_output/ESTaff_oecd.dta")

#aff <- rio::import("../Data_output/ESTaff.dta")

3 Preparation

# Sector 
aff_eu$Sector <- "Agriculture & Mining"
aff_eu$Sector[aff_eu$FASectorClassNameAlph=="HeadQuarter"] <- "Services"
aff_eu$Sector[aff_eu$FASectorClassNameAlph=="Retail"] <- "Services"
aff_eu$Sector[aff_eu$FASectorClassNameAlph=="Service,Others"] <- "Services"
aff_eu$Sector[aff_eu$FASectorClassNameAlph=="Wholesale"] <- "Services"
aff_eu$Sector[aff_eu$FASectorClassNameAlph=="Manufacturing"] <- "Manufacturing"

# make country name
aff_eu$countryname <- countrycode::countrycode(
  aff_eu$country,
  origin = 'iso3c', 
  destination = "country.name"
)

# Rename
aff_eu$exitit <- ifelse(aff_eu$exit==1, "Exit", "Survive")


aff_eu2 <- aff_eu[, c("exitit", "GBR", "highEU", "Sector", "Aff_size", "N_Aff_EU", "countryname", "year", "Ratio")]

aff_eu2$GBR[aff_eu2$GBR==1] <- "UK"
aff_eu2$GBR[aff_eu2$GBR==0] <- "Other EU"

aff_eu2$highEU[aff_eu2$highEU==1] <- "High-income EU"
aff_eu2$highEU[aff_eu2$highEU==0] <- "Other EU"
# Rename

aff_eu2 <- aff_eu2 %>% 
  select(`High-income EU` = highEU,
         `Number of affiliates in EU` = N_Aff_EU,
         `Country name` = countryname,
         `Affiliate size` = Aff_size,
         `Exit dummy` = exitit,
         `Japanese ownership ratio` =  Ratio,
         `Sector` = Sector,
         `Year` = year,
         `UK` = GBR
         )

4 Country list

# N of obserbation by country and highEU 
agg <- aff_eu2 %>% 
  group_by(`Country name`, `High-income EU`) %>% 
  summarise(N = n())

# Make subsample by High-income EU
agg_highEU <- filter(agg, `High-income EU` == "High-income EU")
agg_otherEU <- filter(agg, `High-income EU` == "Other EU")

# Rename
agg_highEU$`High-income EU` <- NULL
agg_otherEU$`High-income EU` <- NULL

# Rename `Country name` with `High-income EU`

names(agg_highEU)[names(agg_highEU)=="Country name"] <- "High-income EU"
names(agg_otherEU)[names(agg_otherEU)=="Country name"] <- "Other EU"

names(agg_highEU)[names(agg_highEU)=="N"] <- "N1"
names(agg_otherEU)[names(agg_otherEU)=="N"] <- "N2"

# Add two observations to agg_otherEU
dat2 <- data.frame(matrix(nrow = 2, ncol = 2))
names(dat2) <- names(agg_otherEU)
agg_otherEU <- rbind(agg_otherEU, dat2)
# Replace NA with blank


# Combine the data 
agg2 <- cbind(agg_highEU, agg_otherEU)

5 tinytable

library(tinytable)

cap <- "Country list.\\label{tab:country-list}"
not <- "Note: This table shows the number of affiliates by the host country. The sample includes the Japanese affiliates in the EU."

c1 <- tt(agg2, 
   caption = cap, 
   notes = not, 
   width = 1) |> 
 group_tt(
   j = list("High-income EU" = 1:2, "Other EU" = 3:4))
colnames(c1) <- c("Country", "N of affiliates","Country", "N of affiliates")


# Replace NA with blank
c1 <-  format_tt(c1, replace = "")

c1
High-income EU Other EU
Country list.\label{tab:country-list}
Country N of affiliates Country N of affiliates
Note: This table shows the number of affiliates by the host country. The sample includes the Japanese affiliates in the EU.
Austria 162 Bulgaria 27
Belgium 855 Czechia 450
Denmark 144 Greece 54
Finland 99 Hungary 288
France 1782 Poland 315
Germany 3960 Portugal 126
Ireland 189 Romania 81
Italy 846 Slovakia 90
Luxembourg 90 Slovenia 27
Netherlands 2394 Spain 648
Sweden 261
United Kingdom 4464 
# Save as LaTex file
c1 |> 
  save_tt("../Tables/Table_countrylist.tex", overwrite = TRUE)

For LaTex, add the following code to the preamble:

\usepackage{tabularray}
\usepackage{float}
\usepackage{graphicx}
\usepackage{rotating}
\usepackage[normalem]{ulem}
\UseTblrLibrary{booktabs}
\UseTblrLibrary{siunitx}
\newcommand{\tinytableTabularrayUnderline}[1]{\underline{#1}}
\newcommand{\tinytableTabularrayStrikeout}[1]{\sout{#1}}
\NewTableCommand{\tinytableDefineColor}[3]{\definecolor{#1}{#2}{#3}}

6 Descriptive statistics of the data

# Remove "countryname"
aff_eu2$`Country name` <- NULL


caption <- 'Descriptive statistics. \\label{tab:desc}'
longnote <- 'This table shows the descriptive statistics of the estimation sample. The sample includes the Japanese affiliates in the EU. The variable `Exit` is a dummy variable that takes 1 if the affiliate exits the host country. The variable `High-income EU` is a dummy variable that takes 1 if the affiliate is located in the high-income EU countries. The variable `Sector` is the sector of the affiliate. The variable `Affiliate size` is the size of the affiliate. The variable `Number of affiliates in EU` is the number of affiliates in the EU. The variable `Country name` is the name of the country where the affiliate is located.'

library(modelsummary)

t2 <- datasummary_balance(~UK,
                    data = aff_eu2,
                    title = caption,
                    #output = "latex",
                    )
t2
Descriptive statistics.
Other EU (N=12888)
UK (N=4464)
Mean Std. Dev. Mean Std. Dev.
Number of affiliates in EU 5.7 7.6 5.8 8.3
Affiliate size 167.5 624.9 179.5 695.7
Japanese ownership ratio 0.7 0.4 0.7 0.4
Year 2016.3 3.5 2016.3 3.5
N Pct. N Pct.
High-income EU High-income EU 10782 83.7 4464 100.0
Other EU 2106 16.3 0 0.0
Exit dummy Exit 3602 27.9 1272 28.5
Survive 9286 72.1 3192 71.5
Sector Agriculture & Mining 108 0.8 99 2.2
Manufacturing 3465 26.9 981 22.0
Services 9315 72.3 3384 75.8 
t2 <- datasummary_balance(~UK,
                    data = aff_eu2,
                    title = caption,
                    output = "latex",
                    )

t2
# Save as Latex file
library(kableExtra)

t2 |> 
kableExtra::footnote(general = longnote, threeparttable = TRUE) %>%
kableExtra::save_kable("../Tables/Table_descriptive.tex")

7 Country list: simpler method

library(modelsummary)

t1 <- datasummary_crosstab(`Country name` ~ `High-income EU`, 
                     statistic =  ~ N ,
                     title = 'Country list \\label{tab:countrylist}',
                     data = aff_eu2)



t1

t1 <- datasummary_crosstab(`Country name` ~ `High-income EU`, 
                     statistic =  ~ N ,
                     title = 'Country list \\label{tab:countrylist}',
                     output = "latex",
                     data = aff_eu2)

t1

# Save as Latex file
library(kableExtra)

t1 |> 
#kableExtra::footnote(general = longnote, threeparttable = TRUE) %>%
kableExtra::save_kable("../Tables/Table_countrylist.tex")