library(rio)
library(dplyr)
library(stargazer)
library(texreg)
library(purrr)
library(htmlTable)
library(ggplot2)
# exclude the non EU member states which are in the dataset
exclusde.vc <- c("CY-TCC", "TR", "MK", "ME", "RS", "AL")
my.df.ex <- subset(my.df, !(isocntry %in% exclusde.vc))
View(my.df.ex)
table(my.df.ex$isocntry)
##
## AT BE BG CY CZ DE-E DE-W DK EE ES FI FR GB GR HR HU
## 1018 1012 1039 505 1013 505 1035 1022 1001 1008 1001 1014 1010 1008 1013 1011
## IE IT LT LU LV MT NL PL PT RO SE SI SK
## 1013 1023 1008 510 1000 501 1006 1008 1003 1058 1023 1007 1007
# Functions to make cleaning missings and rescaling easier.
item_test <- function(x, y){
# tests if the value x is in y and returns NA if evaluated to true or
# the original value if evaluated to false
return(ifelse(x %in% y, NA, x))
}
clean_na <- function(dataset, variables, values, suffix = "_m"){
# create a tibble with the same length as the dataset
new_tib <- tibble::tibble(x = 1:nrow(dataset))
for (i in variables){
# create new name by attaching the suffix to the old variable name
new_name <- paste0(i, suffix)
# updated version, grep had the issue to match all variables starting with the
# specified name. This meant that already existing variables with extensions
# would be matched to
# ends_with only matched the variables exactly as described because
# if they end with the whole specified name they cant be miss interpreted
# select gives us back a whole data frame, however a list is needed for map
# to get a list we use the pull function of the dplyr package and specify
# the first (and only) variable in the data frame
newcol <- dataset %>% select(ends_with(i)) %>%
pull(., var = 1) %>%
map(y = values, .f = item_test) %>%
as.numeric(unlist(.))
# add the new column to the before created tibble
# !! x := y syntax is so that new_name is evaluated as an object and not the
# directly specified name
new_tib <- new_tib %>% tibble::add_column(!! new_name := newcol)
}
# select all columns except the placeholder variable "x"
new_tib <- new_tib %>% select(-c(x))
# return the original data set and the tibble appended to it
return(cbind(dataset, new_tib))
}
remap <- function(old_value, values, new_values_){
if (old_value %in% values){
# match the first value, as there are only distinct values in our var
# match can be used instead to which. Which is slower
new_val <- match(old_value, values)[1] %>% new_values_[.]
return(new_val)
}
else{
# return the old_value if its not found in the values vector as it is not
# supposed to be decoder
return(old_value)
}
}
recode_variable <- function(dataset, variables, old_values, new_values, suffix = "_re"){
# takes two vectors and maps the old values to the new ones
# both vectors must be equal in length and in correct order
# index 1 of the first will be mapped to the index 1 of new_values
# check for duplicates
if (length(old_values) == length(new_values)){
# duplicates has one or more True values the sum function will evaluate
# to < 0 and the if statement will evaluate to FALSE and the remapping
# will not start
if (duplicated(values_old) %>% sum(., na.rm = TRUE) == 0) {
new_tib <- tibble::tibble(x = 1:nrow(dataset))
for (i in variables){
new_name <- paste0(i, suffix)
# here has to be a check if the new_name is already in the data set
# if so it has to be aborted
if (new_name %in% colnames(dataset)){
message(paste0(new_name, " was ommited because the variable name allready exists in dataframe."))
}
else {
newcol <- dataset %>% select(ends_with(i)) %>%
pull(., var = 1) %>%
map(., values = old_values, new_values_ = new_values, .f = remap) %>%
# list is returned by map and this converts it to a numeric vector
as.numeric(unlist(.))
new_tib <- new_tib %>% tibble::add_column(!! new_name := newcol)
}
}
# return the original data set and the tibble appended to it
new_tib <- new_tib %>% select(-c(x))
return(cbind(dataset, new_tib))
}
else{
print("Error: The old values to be mapped are not unique.")
return(none())
}
}
else{
print("Error: Vectors have different length. Must have equal length")
return(none())
}
}
Missing values
# Concept: Free Market and market integration in the EU/currency union
# Variables: QB2_1, QB2_2
missings_qb2 <- c(3, 4)
variables_qb2 <- c("qb2_1", "qb2_2")
my.df.ex <- my.df.ex %>% clean_na(variables_qb2, missings_qb2, "_m")
# Concept: Free Movement
# Variables: QB9_1, QB9_2, QB5_7
missings_qb9 <- c(3, 4)
missings_qb5 <- c(3, 4)
variables_qb9 <- c("qb9_1", "qb9_2")
variables_qb5 <- c("qb5_7")
my.df.ex <- my.df.ex %>% clean_na(variables_qb9, missings_qb9, "_m")
my.df.ex <- my.df.ex %>% clean_na(variables_qb5, missings_qb5, "_m")
# Concept: Further political integration
# Variables: QB5_1, QB5_2, QB5_3, QB5_4, QB5_5
missings_qb5_2 <- c(3, 4)
variables_qb5_2 <- c("qb5_1", "qb5_2", "qb5_3", "qb5_4", "qb5_5")
my.df.ex <- my.df.ex %>% clean_na(variables_qb5_2, missings_qb5_2, "_m")
# Concept: Identity
# Variables: QC1a_1, QC1a_2, QC1a_3
missings_qc1a <- c(5)
variables_qc1a <- c("qc1a_1", "qc1a_2", "qc1a_3")
my.df.ex <- my.df.ex %>% clean_na(variables_qc1a, missings_qc1a, "_m")
# Concept: Age
# Variable: D11
missings_d11<- c(99)
variables_d11<- c("d11")
my.df.ex <- my.df.ex %>% clean_na(variables_d11, missings_d11, "_m")
# Concept: Education
# Variable: D8
table(my.df.ex$d8, useNA = "ifany")
##
## 0 2 3 4 6 7 8 9 10 11 12 13 14 15 16 17
## 75 27 11 2 1 8 14 27 153 140 414 204 1157 1567 1835 1708
## 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33
## 5463 3002 1475 1171 1251 1170 1188 985 493 299 234 121 180 53 90 43
## 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49
## 42 55 21 22 28 24 61 19 32 21 21 30 15 5 18 14
## 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65
## 25 9 10 12 6 8 2 2 6 10 13 5 5 2 2 2
## 66 67 68 69 70 71 72 73 74 75 76 77 78 80 81 84
## 6 2 4 3 3 2 12 2 1 6 1 1 1 1 1 1
## 88 92 97 98 99
## 2 1 239 1630 355
# even though 97 is not a missing code finishing full time education at 97
# should resemble an exception however in this data apparently 239 individuals
# finished at 97. Which is unlikely and is more likely error by the respondents.
missings_d8<- c(00, 01, 97, 98, 99)
variables_d8<- c("d8")
my.df.ex <- my.df.ex %>% clean_na(variables_d8, missings_d8, "_m")
# Concept: Gender
# Variable D10
table(my.df.ex$d10)
##
## 1 2
## 12449 14933
### No missings. All either coded male or female. ###
# Concept: City vs. Countryside
# Variable: D25
missings_d25 <- c(4)
variables_d25 <- c("d25")
my.df.ex <- my.df.ex %>% clean_na(variables_d25, missings_d25, "_m")
# Concept: Immigration
# Variables: QB7_1. QB7_2
missings_qb7 <- c(5)
variables_qb7 <- c("qb7_1", "qb7_2")
my.df.ex <- my.df.ex %>% clean_na(variables_qb7, missings_qb7, "_m")
# Concept: Knowledge of the EU
# Variables: QA11_1, QA11_2, QA11_3, QA11_4, QA11_5, QA11_6, QA11_7, QA11_8, QA11_9, QA11_10
missings_qa11 <- c(3)
variables_qa11 <- c("qa11_1", "qa11_2", "qa11_3", "qa11_4", "qa11_5", "qa11_6", "qa11_7", "qa11_8", "qa11_9", "qa11_10")
my.df.ex <- my.df.ex %>% clean_na(variables_qa11, missings_qa11,"_m")
# Concept: Occupation
# Varaibles: D15a, D15b
table(my.df.ex$d15a)
##
## 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
## 1213 1630 1549 8959 249 8 364 798 535 750 275 1850 2534 962 2048 322
## 17 18
## 2526 810
table(my.df.ex$d15b)
##
## 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
## 332 7 124 304 221 383 320 1433 1543 542 1672 315 2792 1680 1683
# has basically no missings, there are occupation which are non important to us
# but not missing. These will be dealt with further below.
Recoding/-scaling, accumulated scoring
# Concept: Free Market and market integration in the EU/currency union
# Variables: QB2_1, QB2_2
values_old <- c(1, 2)
values_new <- c(1, 0)
variables <- c("qb2_1_m", "qb2_2_m")
my.df.ex <- my.df.ex %>% recode_variable(variables, values_old, values_new)
my.df.ex <- my.df.ex %>% mutate(free_market_integration_score = (qb2_2_m_re +
qb2_1_m_re))
table(my.df.ex$free_market_integration_score, useNA = "ifany")
##
## 0 1 2 <NA>
## 2793 4707 13255 6627
# Concept: Free Movement
# Variables: QB9_1. QB9_2, QB5_7
values_old <- c(1, 2)
values_new <- c(1, 0)
variables <- c("qb9_1_m", "qb9_2_m", "qb5_7_m")
my.df.ex <- my.df.ex %>% recode_variable(variables, values_old, values_new)
my.df.ex <- my.df.ex %>% mutate(free_movement_score = (qb9_1_m_re +
qb9_2_m_re +
qb5_7_m_re))
table(my.df.ex$free_movement_score, useNA = "ifany")
##
## 0 1 2 3 <NA>
## 746 954 1153 18444 6085
# Concept: Further political integration
# Variables: QB5_1, QB5_2, QB5_3, QB5_4, QB5_5
values_old <- c(1, 2)
values_new <- c(1, 0)
variables <- c("qb5_1_m", "qb5_2_m", "qb5_3_m", "qb5_4_m", "qb5_5_m")
my.df.ex <- my.df.ex %>% recode_variable(variables, values_old, values_new)
my.df.ex <- my.df.ex %>% mutate(furhter_political_integration_score = (qb5_1_m_re +
qb5_2_m_re +
qb5_3_m_re +
qb5_4_m_re +
qb5_5_m_re))
table(my.df.ex$furhter_political_integration_score, useNA = "ifany")
##
## 0 1 2 3 4 5 <NA>
## 1075 1034 1517 2075 3434 12710 5537
# Concept: Identity
# Variables: QC1a_1, QC1a_2, QC1a_3
values_old <- c(1, 2, 3, 4)
values_new <- c(3, 2, 1, 0)
variables <- c("qc1a_1_m", "qc1a_2_m", "qc1a_3_m")
my.df.ex <- my.df.ex %>% recode_variable(variables, values_old, values_new)
### This is the independent variable and does not need a score. ###
### Further below is the splitting into the different ideal types of territorial identification. ###
# Concept: Age
# Variable: D11
# Age has to be subtracted by 15 so that 15 resembles the base age of respondents.
# Each increase in age means one ontop of the lowest possible to take part in
# the eurobaromenter
my.df.ex <- my.df.ex %>% mutate(age = d11_m - 15)
table(my.df.ex$age)
##
## 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19
## 81 161 174 263 298 270 291 290 281 267 308 261 312 321 349 388 331 379 377 350
## 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39
## 437 361 361 398 407 458 396 456 364 404 494 398 390 471 459 520 404 493 410 426
## 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59
## 562 465 428 530 496 599 477 503 489 543 601 524 525 550 543 577 473 530 408 367
## 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79
## 387 334 310 305 241 267 176 202 132 123 97 86 66 56 54 32 22 24 12 2
## 80 81 82 83
## 1 2 1 1
# Concept: Education
# Variable: D8
table(my.df.ex$d8_m)
##
## 2 3 4 6 7 8 9 10 11 12 13 14 15 16 17 18
## 27 11 2 1 8 14 27 153 140 414 204 1157 1567 1835 1708 5463
## 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34
## 3002 1475 1171 1251 1170 1188 985 493 299 234 121 180 53 90 43 42
## 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50
## 55 21 22 28 24 61 19 32 21 21 30 15 5 18 14 25
## 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66
## 9 10 12 6 8 2 2 6 10 13 5 5 2 2 2 6
## 67 68 69 70 71 72 73 74 75 76 77 78 80 81 84 88
## 2 4 3 3 2 12 2 1 6 1 1 1 1 1 1 2
## 92
## 1
my.df.ex <- my.df.ex %>% mutate(education = d8_m)
table(my.df.ex$education)
##
## 2 3 4 6 7 8 9 10 11 12 13 14 15 16 17 18
## 27 11 2 1 8 14 27 153 140 414 204 1157 1567 1835 1708 5463
## 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34
## 3002 1475 1171 1251 1170 1188 985 493 299 234 121 180 53 90 43 42
## 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50
## 55 21 22 28 24 61 19 32 21 21 30 15 5 18 14 25
## 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66
## 9 10 12 6 8 2 2 6 10 13 5 5 2 2 2 6
## 67 68 69 70 71 72 73 74 75 76 77 78 80 81 84 88
## 2 4 3 3 2 12 2 1 6 1 1 1 1 1 1 2
## 92
## 1
mean(my.df.ex$education, na.rm = TRUE)
## [1] 19.77339
### The code for No education is not in this sample. No further rescaling needed. ###
# Concept: Gender
# Variable D10
my.df.ex<- my.df.ex %>% mutate(female = case_when(is.na(d10) == T ~ NA_real_,
d10 == 1 ~ 0,
d10 == 2 ~ 1,
TRUE ~ NA_real_
))
table(my.df.ex$female)
##
## 0 1
## 12449 14933
### No score needed. ###
# Concept: Urban environment
# Variable: D25
my.df.ex <- my.df.ex %>% mutate(urban_env = case_when(is.na(d25_m) == T ~ NA_real_,
d25_m == 1 ~ 0,
d25_m == 2 ~ 0,
d25_m == 3 ~ 1,
TRUE ~ NA_real_
))
table(my.df.ex$urban_env, useNA = "ifany")
##
## 0 1 <NA>
## 19521 7858 3
# Concept: Immigration
# Variables: QB7_1. QB7_2
my.df.ex <- my.df.ex %>% mutate(EU_immigration = case_when(is.na(qb7_1_m) == T ~ NA_real_,
qb7_1_m == 1 ~ 3,
qb7_1_m == 2 ~ 2,
qb7_1_m == 3 ~ 1,
qb7_1_m == 4 ~ 0,
TRUE ~ NA_real_
))
my.df.ex<- my.df.ex %>% mutate(Non_EU_immigration = case_when(is.na(qb7_2_m) == T ~ NA_real_,
qb7_2_m == 1 ~ 3,
qb7_2_m == 2 ~ 2,
qb7_2_m == 3 ~ 1,
qb7_2_m == 4 ~ 0,
TRUE ~ NA_real_
))
table(my.df.ex$EU_immigration, useNA = "ifany")
##
## 0 1 2 3 <NA>
## 1602 5562 13328 5399 1491
table(my.df.ex$Non_EU_immigration, useNA = "ifany")
##
## 0 1 2 3 <NA>
## 4912 9646 8325 2774 1725
# Concept: Knowledge of the EU
# Variables: QA11_1, QA11_2, QA11_3, QA11_4, QA11_5, QA11_6, QA11_7, QA11_8, QA11_9, QA11_10
values_old <- c(1, 2)
values_new <- c(1, 0)
variables <- c("qa11_1_m", "qa11_2_m", "qa11_3_m", "qa11_4_m", "qa11_5_m", "qa11_6_m", "qa11_7_m", "qa11_8_m", "qa11_9_m", "qa11_10_m")
my.df.ex <- my.df.ex %>% recode_variable(variables, values_old, values_new)
my.df.ex <- my.df.ex %>% mutate(knowledge_EU = qa11_1_m_re +
qa11_2_m_re +
qa11_3_m_re +
qa11_4_m_re +
qa11_5_m_re +
qa11_6_m_re +
qa11_7_m_re +
qa11_8_m_re +
qa11_9_m_re +
qa11_10_m_re)
table(my.df.ex$knowledge_EU, useNA = "ifany")
##
## 0 1 2 3 4 5 6 7 8 9 10 <NA>
## 737 568 880 1475 1822 2524 3148 3158 2324 1437 7002 2307
# Concept: Occupation
# Varaibles: D15a, D15b
table(my.df.ex$d15a, useNA = "ifany")
##
## 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
## 1213 1630 1549 8959 249 8 364 798 535 750 275 1850 2534 962 2048 322
## 17 18
## 2526 810
table(my.df.ex$d15b, useNA = "ifany")
##
## 1 2 3 4 5 6 7 8 9 10 11 12 13
## 332 7 124 304 221 383 320 1433 1543 542 1672 315 2792
## 14 15 <NA>
## 1680 1683 14031
d15 <- my.df.ex %>% select(d15a, d15b)
d15
# if they have a value on the first variable then the second will be missing
# it does not matter for me if the second variable has an NA or not.
# As long as it equals 9 or 11 the respondent is from the management level.
my.df.ex <- my.df.ex %>% mutate(management = ifelse(
d15a %in% c(1, 2, 3, 4, 5, 6, 7, 8, 10, 12, 13, 14, 15, 16, 17, 18) &
d15b %in% c(1, 2, 3, 4, 5, 6, 7, 8, 10, 13, 14, 15, NA),
0,
1
))
table(my.df.ex$management, useNA = "ifany")
##
## 0 1
## 23042 4340
Country Factor Variable
table(my.df.ex$isocntry, useNA = "ifany")
##
## AT BE BG CY CZ DE-E DE-W DK EE ES FI FR GB GR HR HU
## 1018 1012 1039 505 1013 505 1035 1022 1001 1008 1001 1014 1010 1008 1013 1011
## IE IT LT LU LV MT NL PL PT RO SE SI SK
## 1013 1023 1008 510 1000 501 1006 1008 1003 1058 1023 1007 1007
my.df.ex <- my.df.ex %>% mutate(country = ifelse(isocntry == "DE-E" |
isocntry =="DE-W", "DE", isocntry))
table(my.df.ex$country, useNA = "ifany")
##
## AT BE BG CY CZ DE DK EE ES FI FR GB GR HR HU IE
## 1018 1012 1039 505 1013 1540 1022 1001 1008 1001 1014 1010 1008 1013 1011 1013
## IT LT LU LV MT NL PL PT RO SE SI SK
## 1023 1008 510 1000 501 1006 1008 1003 1058 1023 1007 1007
Constructing the six different ideal types of identification
# Concept: Identity
# Variables: QC1a_1, QC1a_2, QC1a_3, QC1a_4
# Is that orientation the best?
my.df.ex$id_type <- 100 * my.df.ex$qc1a_1_m_re +
10 * my.df.ex$qc1a_2_m_re +
1 * my.df.ex$qc1a_3_m_re
table(my.df.ex$id_type, useNA = "ifany")
##
## 0 1 2 3 10 11 12 13 20 21 22 23 30 31 32 33
## 106 10 9 2 18 15 17 1 26 31 44 9 30 17 34 13
## 100 101 102 103 110 111 112 113 120 121 122 123 130 131 132 133
## 32 11 7 5 96 383 125 19 128 312 397 58 86 151 208 82
## 200 201 202 203 210 211 212 213 220 221 222 223 230 231 232 233
## 25 20 14 3 82 232 153 57 475 2220 3218 188 228 753 1261 406
## 300 301 302 303 310 311 312 313 320 321 322 323 330 331 332 333
## 25 11 16 8 50 120 56 11 207 651 714 105 1340 3179 5065 3449
## <NA>
## 558
class(my.df.ex$id_type)
## [1] "numeric"
# classifing the id_types as the 6 different territorial identities
supranationalist.vc <- as.numeric(c(2:3,
12:13,
22:23,
32,
102:103,
112:113,
122:123,
132:133,
223))
localist.vc <- as.numeric(c(200:201,
210:211,
301,
310:311,
320:321))
nationalist.vc <- as.numeric(c(20:21,
30:31,
120:121,
130:131,
220:221,
230:231,
330:331))
cosmopolit.vc <- as.numeric(c(33,
222,
232:233,
322:323,
332:333))
apolide.vc <- as.numeric(c(0:1,
10:11,
100:101,
110:111))
glocalist.vc <- as.numeric(c(202:203,
212:213,
302:303,
312:313))
my.df.ex <- my.df.ex %>% mutate(supranationalist =
if_else(id_type %in% supranationalist.vc, 1, 0))
my.df.ex <- my.df.ex %>% mutate(localist =
if_else(id_type %in% localist.vc, 1, 0))
my.df.ex <- my.df.ex %>% mutate(nationalist =
if_else(id_type %in% nationalist.vc, 1, 0))
my.df.ex <- my.df.ex %>% mutate(cosmopolit =
if_else(id_type %in% cosmopolit.vc, 1, 0))
my.df.ex <- my.df.ex %>% mutate(apolide =
if_else(id_type %in% apolide.vc, 1, 0)) # will be excluded later
my.df.ex <- my.df.ex %>% mutate(glocalist =
if_else(id_type %in% glocalist.vc, 1, 0))
table(my.df.ex$supranationalist, useNA = "ifany")
##
## 0 1
## 26177 1205
table(my.df.ex$localist, useNA = "ifany")
##
## 0 1
## 25984 1398
table(my.df.ex$nationalist, useNA = "ifany")
##
## 0 1
## 18406 8976
table(my.df.ex$cosmopolit, useNA = "ifany")
##
## 0 1
## 13151 14231
table(my.df.ex$apolide, useNA = "ifany")
##
## 0 1
## 26711 671
table(my.df.ex$glocalist, useNA = "ifany")
##
## 0 1
## 27064 318
# appolides dropped, resulting in 671 cases "lost"
# dropped them because they do not hold any identity and hence can not be
# interpreted along the exclusive/inclusive axis as they do not fit any of these
# categories
# edge cases in the topology above will not result in unclear results
# because they are most likely ending up in the same category anyway
# in this step
my.df.ex <- my.df.ex %>% mutate(inclusive = ifelse(supranationalist == 1 |
cosmopolit == 1 |
glocalist == 1, 1, 0))
my.df.ex <- my.df.ex %>% mutate(exclusive = ifelse(localist == 1 |
nationalist == 1, 1, 0))
table(my.df.ex$inclusive)
##
## 0 1
## 11628 15754
table(my.df.ex$exclusive)
##
## 0 1
## 17008 10374
linear regression analysis
vars_in_model.vc <- c("inclusive",
"exclusive",
"country",
"age",
"knowledge_EU",
"EU_immigration",
"Non_EU_immigration",
"urban_env",
"female",
"education",
"free_market_integration_score",
"furhter_political_integration_score",
"free_movement_score",
"management")
my.df.filtered <- my.df.ex[vars_in_model.vc]
#View(my.df.de.filtered)
#nrow(my.df.de.filtered)
#names(my.df.de.filtered)
#Listwise deletion
my.df.listw <- na.omit(my.df.filtered)
# standardization of the beta coefficients to compare who has the greatest impact
# but I cant use the country variable as it cant be standardized.
`%!in%` = Negate(`%in%`)
x <- my.df.listw$country
country_codes <- c()
# getting every country code
for (i in x){
if (i %!in% country_codes){
country_codes <- append(country_codes, i)
}
}
# creating dummy variables for every country
for (i in country_codes){
my.df.listw <- my.df.listw %>% mutate(., !! i := ifelse(country == i, 1, 0))
}
my.df.std <- as.data.frame(scale(select(my.df.listw, -one_of("country"))))
Summary statistics
[1] 12687 12687 12687 12687 12687 12687 12687 12687 12687 12687 12687 12687 [1] 1.5609679 4.1415622 2.7298810 0.6609127 37.2858044 20.0284543 [7] 7.2747694 0.5177741 0.1559864 0.2934500 1.9820288 1.4435249 [1] 0.6847700 1.3612667 0.7212514 0.4734182 16.2743207 5.7738581 [7] 2.6368601 0.4997037 0.3628568 0.4553608 0.7801890 0.9105200 [1] 0 0 0 0 0 2 0 0 0 0 0 0 [1] 2 5 3 1 81 81 10 1 1 1 3 3
|
Variable
|
Variable label
|
Obs
|
Mean
|
SD
|
Min
|
Max
|
|
free_market_integration_score
|
Free Market Integration Support
|
12687
|
1.561
|
0.685
|
0
|
2
|
|
furhter_political_integration_score
|
further Political Integration Support
|
12687
|
4.142
|
1.361
|
0
|
5
|
|
free_movement_score
|
Free Movement Supportt
|
12687
|
2.730
|
0.721
|
0
|
3
|
|
inclusive
|
Inclusive Identity
|
12687
|
0.661
|
0.473
|
0
|
1
|
|
age
|
Age
|
12687
|
37.286
|
16.274
|
0
|
81
|
|
education
|
Education
|
12687
|
20.028
|
5.774
|
2
|
81
|
|
knowledge_EU
|
Knowledge about the EU
|
12687
|
7.275
|
2.637
|
0
|
10
|
|
female
|
Female
|
12687
|
0.518
|
0.500
|
0
|
1
|
|
management
|
Management Position
|
12687
|
0.156
|
0.363
|
0
|
1
|
|
urban_env
|
Urban Environment
|
12687
|
0.293
|
0.455
|
0
|
1
|
|
EU_immigration
|
EU Immigration Support
|
12687
|
1.982
|
0.780
|
0
|
3
|
|
Non_EU_immigration
|
Non-EU Immigration Support
|
12687
|
1.444
|
0.911
|
0
|
3
|
|
Variable
|
Variable label
|
Obs
|
Mean
|
SD
|
Min
|
Max
|
|
free_market_integration_score
|
Free Market Integration Support
|
12687
|
1.561
|
0.685
|
0
|
2
|
|
furhter_political_integration_score
|
further Political Integration Support
|
12687
|
4.142
|
1.361
|
0
|
5
|
|
free_movement_score
|
Free Movement Supportt
|
12687
|
2.730
|
0.721
|
0
|
3
|
|
inclusive
|
Inclusive Identity
|
12687
|
0.661
|
0.473
|
0
|
1
|
|
age
|
Age
|
12687
|
37.286
|
16.274
|
0
|
81
|
|
education
|
Education
|
12687
|
20.028
|
5.774
|
2
|
81
|
|
knowledge_EU
|
Knowledge about the EU
|
12687
|
7.275
|
2.637
|
0
|
10
|
|
female
|
Female
|
12687
|
0.518
|
0.500
|
0
|
1
|
|
management
|
Management Position
|
12687
|
0.156
|
0.363
|
0
|
1
|
|
urban_env
|
Urban Environment
|
12687
|
0.293
|
0.455
|
0
|
1
|
|
EU_immigration
|
EU Immigration Support
|
12687
|
1.982
|
0.780
|
0
|
3
|
|
Non_EU_immigration
|
Non-EU Immigration Support
|
12687
|
1.444
|
0.911
|
0
|
3
|
Full Model
# first do inclusvie b1a.lw
# then do inclusive plus country dummy b1b.lw
b1a.lw <- lm(free_market_integration_score ~ inclusive, data = my.df.listw)
b1b.lw <- lm(free_market_integration_score ~ inclusive + country, data = my.df.listw)
b2a.lw <- lm(furhter_political_integration_score ~ inclusive, data = my.df.listw)
b2b.lw <- lm(furhter_political_integration_score ~ inclusive + country, data = my.df.listw)
b3a.lw <- lm(free_movement_score ~ inclusive, data = my.df.listw)
b3b.lw <- lm(free_movement_score ~ inclusive + country, data = my.df.listw)
m1.lw <- lm(free_market_integration_score ~ inclusive +
age +
education +
knowledge_EU +
female +
management +
urban_env +
EU_immigration +
Non_EU_immigration +
country,
data = my.df.listw)
m2.lw <- lm(furhter_political_integration_score ~ inclusive +
age +
education +
knowledge_EU +
female +
management +
urban_env +
EU_immigration +
Non_EU_immigration +
country,
data = my.df.listw)
m3.lw <- lm(free_movement_score ~ inclusive +
age +
education +
knowledge_EU +
female +
management +
urban_env +
EU_immigration +
Non_EU_immigration +
country,
data = my.df.listw)
b1a.lw
##
## Call:
## lm(formula = free_market_integration_score ~ inclusive, data = my.df.listw)
##
## Coefficients:
## (Intercept) inclusive
## 1.2799 0.4253
b1b.lw
##
## Call:
## lm(formula = free_market_integration_score ~ inclusive + country,
## data = my.df.listw)
##
## Coefficients:
## (Intercept) inclusive countryBE countryBG countryCY countryCZ
## 1.1741885 0.3924055 0.1900415 -0.0591105 0.3614935 -0.3478740
## countryDE countryDK countryEE countryES countryFI countryFR
## 0.2756664 -0.3061030 0.4735795 0.2695252 0.2124429 0.1549873
## countryGB countryGR countryHR countryHU countryIE countryIT
## -0.4008313 0.2757694 0.0008418 0.0813340 0.3303251 -0.0307192
## countryLT countryLU countryLV countryMT countryNL countryPL
## 0.3523734 0.2684351 0.3497893 0.2721016 0.2276105 -0.1846925
## countryPT countryRO countrySE countrySI countrySK
## 0.3041094 0.0458101 -0.4414521 0.4156892 0.2414589
b2a.lw
##
## Call:
## lm(formula = furhter_political_integration_score ~ inclusive,
## data = my.df.listw)
##
## Coefficients:
## (Intercept) inclusive
## 3.5823 0.8462
b2b.lw
##
## Call:
## lm(formula = furhter_political_integration_score ~ inclusive +
## country, data = my.df.listw)
##
## Coefficients:
## (Intercept) inclusive countryBE countryBG countryCY countryCZ
## 2.8661 0.8197 0.9238 0.8973 1.4104 0.2120
## countryDE countryDK countryEE countryES countryFI countryFR
## 1.0898 0.3706 0.8130 1.1808 0.3999 0.8977
## countryGB countryGR countryHR countryHU countryIE countryIT
## 0.1142 1.0674 0.8170 0.3697 0.8886 0.4194
## countryLT countryLU countryLV countryMT countryNL countryPL
## 1.2361 0.9160 0.7473 0.8183 0.9952 0.5551
## countryPT countryRO countrySE countrySI countrySK
## 0.8501 0.5613 0.3008 1.0589 0.4230
b3a.lw
##
## Call:
## lm(formula = free_movement_score ~ inclusive, data = my.df.listw)
##
## Coefficients:
## (Intercept) inclusive
## 2.4986 0.3499
b3b.lw
##
## Call:
## lm(formula = free_movement_score ~ inclusive + country, data = my.df.listw)
##
## Coefficients:
## (Intercept) inclusive countryBE countryBG countryCY countryCZ
## 2.05910 0.34578 0.33829 0.58006 0.62040 0.49773
## countryDE countryDK countryEE countryES countryFI countryFR
## 0.52594 0.38275 0.66954 0.56741 0.57467 0.44237
## countryGB countryGR countryHR countryHU countryIE countryIT
## 0.20410 0.50429 0.53594 0.31509 0.47631 0.05204
## countryLT countryLU countryLV countryMT countryNL countryPL
## 0.64859 0.54761 0.54135 0.53522 0.42370 0.35078
## countryPT countryRO countrySE countrySI countrySK
## 0.57562 0.21704 0.48091 0.49137 0.44475
m1.lw
##
## Call:
## lm(formula = free_market_integration_score ~ inclusive + age +
## education + knowledge_EU + female + management + urban_env +
## EU_immigration + Non_EU_immigration + country, data = my.df.listw)
##
## Coefficients:
## (Intercept) inclusive age education
## 0.7860720 0.3204436 -0.0003070 0.0001788
## knowledge_EU female management urban_env
## 0.0269554 -0.0239004 -0.0173994 0.0104355
## EU_immigration Non_EU_immigration countryBE countryBG
## 0.1102858 0.0216987 0.2293725 -0.0696785
## countryCY countryCZ countryDE countryDK
## 0.3845033 -0.2741017 0.2820351 -0.3125707
## countryEE countryES countryFI countryFR
## 0.4820770 0.2186708 0.1827724 0.2036708
## countryGB countryGR countryHR countryHU
## -0.3970487 0.2839797 -0.0076985 0.1127347
## countryIE countryIT countryLT countryLU
## 0.2852110 0.0220782 0.3501848 0.2501540
## countryLV countryMT countryNL countryPL
## 0.4291180 0.2737118 0.2514142 -0.1738668
## countryPT countryRO countrySE countrySI
## 0.2660433 0.0504636 -0.4233962 0.4020582
## countrySK
## 0.2906875
m2.lw
##
## Call:
## lm(formula = furhter_political_integration_score ~ inclusive +
## age + education + knowledge_EU + female + management + urban_env +
## EU_immigration + Non_EU_immigration + country, data = my.df.listw)
##
## Coefficients:
## (Intercept) inclusive age education
## 1.9289235 0.6625305 0.0008618 -0.0007864
## knowledge_EU female management urban_env
## 0.0556230 0.0529638 0.0482060 -0.0179294
## EU_immigration Non_EU_immigration countryBE countryBG
## 0.2561005 0.0482663 1.0054989 0.8848587
## countryCY countryCZ countryDE countryDK
## 1.4567105 0.3820092 1.0896743 0.3509701
## countryEE countryES countryFI countryFR
## 0.8195324 1.0658571 0.3148121 0.9907898
## countryGB countryGR countryHR countryHU
## 0.1138505 1.0908714 0.7873753 0.4307867
## countryIE countryIT countryLT countryLU
## 0.7807888 0.5230995 1.2134238 0.8755878
## countryLV countryMT countryNL countryPL
## 0.9011392 0.8221144 1.0152291 0.5732232
## countryPT countryRO countrySE countrySI
## 0.7593104 0.5754552 0.3283908 1.0301337
## countrySK
## 0.5171326
m3.lw
##
## Call:
## lm(formula = free_movement_score ~ inclusive + age + education +
## knowledge_EU + female + management + urban_env + EU_immigration +
## Non_EU_immigration + country, data = my.df.listw)
##
## Coefficients:
## (Intercept) inclusive age education
## 1.4249458 0.2292343 -0.0010522 0.0002844
## knowledge_EU female management urban_env
## 0.0337767 0.0098835 0.0374268 -0.0104015
## EU_immigration Non_EU_immigration countryBE countryBG
## 0.2503935 -0.0115543 0.4098042 0.5438453
## countryCY countryCZ countryDE countryDK
## 0.6524004 0.6072042 0.5279240 0.3560368
## countryEE countryES countryFI countryFR
## 0.6644791 0.4968654 0.5143730 0.5167497
## countryGB countryGR countryHR countryHU
## 0.2190506 0.5165861 0.5099501 0.3512261
## countryIE countryIT countryLT countryLU
## 0.4054057 0.1397117 0.6364943 0.5201978
## countryLV countryMT countryNL countryPL
## 0.6503121 0.5378338 0.4559306 0.3572875
## countryPT countryRO countrySE countrySI
## 0.5358963 0.2371276 0.5015406 0.4706039
## countrySK
## 0.5140533
Standardized Beta
# only needed for the full models as the point of standardized beta is to
# compare the coefficients of the variables
# country variable can not be included as it can not be standardized.
# Either do a dummy variable for every country or so.
m1.lw.std <- lm(free_market_integration_score ~ inclusive +
age +
education +
knowledge_EU +
female +
management +
urban_env +
EU_immigration +
Non_EU_immigration +
BE +
BG +
CY +
CZ +
DE +
DK +
EE +
ES +
FI +
FR +
GB +
GR +
HR +
HU +
IE +
IT +
LT +
LU +
LV +
MT +
NL +
PL +
PT +
RO +
SE +
SI +
SK,
data = my.df.std)
m2.lw.std <- lm(furhter_political_integration_score ~ inclusive +
age +
education +
knowledge_EU +
female +
management +
urban_env +
EU_immigration +
Non_EU_immigration +
BE +
BG +
CY +
CZ +
DE +
DK +
EE +
ES +
FI +
FR +
GB +
GR +
HR +
HU +
IE +
IT +
LT +
LU +
LV +
MT +
NL +
PL +
PT +
RO +
SE +
SI +
SK,
data = my.df.std)
m3.lw.std <- lm(free_movement_score ~ inclusive +
age +
education +
knowledge_EU +
female +
management +
urban_env +
EU_immigration +
Non_EU_immigration +
BE +
BG +
CY +
CZ +
DE +
DK +
EE +
ES +
FI +
FR +
GB +
GR +
HR +
HU +
IE +
IT +
LT +
LU +
LV +
MT +
NL +
PL +
PT +
RO +
SE +
SI +
SK,
data = my.df.std)
summary(m1.lw.std)
##
## Call:
## lm(formula = free_market_integration_score ~ inclusive + age +
## education + knowledge_EU + female + management + urban_env +
## EU_immigration + Non_EU_immigration + BE + BG + CY + CZ +
## DE + DK + EE + ES + FI + FR + GB + GR + HR + HU + IE + IT +
## LT + LU + LV + MT + NL + PL + PT + RO + SE + SI + SK, data = my.df.std)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.1798 -0.4389 0.2005 0.5755 2.1509
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) -0.000000000000005623 0.007742548162061573 0.000
## inclusive 0.221539810355539823 0.008310637906067207 26.657
## age -0.007297263971145803 0.008141285825490728 -0.896
## education 0.001507296400899489 0.008251245278214433 0.183
## knowledge_EU 0.103797832660394995 0.008144740694760568 12.744
## female -0.017441089995939407 0.007852750468428966 -2.221
## management -0.009219853066675129 0.007938913859223216 -1.161
## urban_env 0.006939437657800075 0.007986743109826500 0.869
## EU_immigration 0.125653548700062734 0.009048560519445656 13.887
## Non_EU_immigration 0.028852153772762925 0.009366570846780642 3.080
## BE 0.072218815382963333 0.011498792016549705 6.281
## BG -0.016172767238060159 0.009968274524347636 -1.622
## CY 0.078653550208589287 0.009499119051985073 8.280
## CZ -0.074749661349720747 0.010715797377532817 -6.976
## DE 0.098044195937440751 0.012164553274237532 8.060
## DK -0.070588196292991984 0.009950170035815612 -7.094
## EE 0.123467396188947406 0.010415067604880023 11.855
## ES 0.064637565095944871 0.011123173031499614 5.811
## FI 0.047202198769654972 0.010541387415216966 4.478
## FR 0.052412949792162947 0.010402931476225785 5.038
## GB -0.098585843722477376 0.010242378834097191 -9.625
## GR 0.081767583315534548 0.011010435233198581 7.426
## HR -0.002369377310409559 0.011305549293477208 -0.210
## HU 0.035711387915485823 0.011525129234201856 3.099
## IE 0.083335650449251508 0.011048508419972637 7.543
## IT 0.005880107730322175 0.010565133965627695 0.557
## LT 0.099790627288889086 0.010897140374852905 9.158
## LU 0.047044973324925597 0.009288911700876385 5.065
## LV 0.116408472548668041 0.010733159908306358 10.846
## MT 0.042343713907177880 0.008813772109451569 4.804
## NL 0.069842589825330051 0.010851220556214951 6.436
## PL -0.047958026147258540 0.010723142535793550 -4.472
## PT 0.073458283259783833 0.010782511393557645 6.813
## RO 0.013747388127490006 0.010648352681947360 1.291
## SE -0.109730777223954898 0.010630180364956813 -10.323
## SI 0.119885583241121224 0.011182581609993081 10.721
## SK 0.080180909299152517 0.010782447835699996 7.436
## Pr(>|t|)
## (Intercept) 1.00000
## inclusive < 0.0000000000000002 ***
## age 0.37009
## education 0.85506
## knowledge_EU < 0.0000000000000002 ***
## female 0.02637 *
## management 0.24552
## urban_env 0.38493
## EU_immigration < 0.0000000000000002 ***
## Non_EU_immigration 0.00207 **
## BE 0.000000000348411813 ***
## BG 0.10474
## CY < 0.0000000000000002 ***
## CZ 0.000000000003196126 ***
## DE 0.000000000000000832 ***
## DK 0.000000000001370612 ***
## EE < 0.0000000000000002 ***
## ES 0.000000006356976235 ***
## FI 0.000007607717970172 ***
## FR 0.000000476199558535 ***
## GB < 0.0000000000000002 ***
## GR 0.000000000000118768 ***
## HR 0.83400
## HU 0.00195 **
## IE 0.000000000000049173 ***
## IT 0.57784
## LT < 0.0000000000000002 ***
## LU 0.000000414939392999 ***
## LV < 0.0000000000000002 ***
## MT 0.000001571022193955 ***
## NL 0.000000000126772414 ***
## PL 0.000007802576524706 ***
## PT 0.000000000010010340 ***
## RO 0.19672
## SE < 0.0000000000000002 ***
## SI < 0.0000000000000002 ***
## SK 0.000000000000110261 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8721 on 12650 degrees of freedom
## Multiple R-squared: 0.2416, Adjusted R-squared: 0.2395
## F-statistic: 111.9 on 36 and 12650 DF, p-value: < 0.00000000000000022
summary(m2.lw.std)
##
## Call:
## lm(formula = furhter_political_integration_score ~ inclusive +
## age + education + knowledge_EU + female + management + urban_env +
## EU_immigration + Non_EU_immigration + BE + BG + CY + CZ +
## DE + DK + EE + ES + FI + FR + GB + GR + HR + HU + IE + IT +
## LT + LU + LV + MT + NL + PL + PT + RO + SE + SI + SK, data = my.df.std)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.6256 -0.3179 0.2248 0.5836 1.9674
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) -0.00000000000001135 0.00800391946698140 0.000
## inclusive 0.23041330308572011 0.00859118666453262 26.820
## age 0.01030251700636753 0.00841611763220270 1.224
## education -0.00333548626525790 0.00852978907289795 -0.391
## knowledge_EU 0.10774534014494654 0.00841968913021937 12.797
## female 0.01944232025167897 0.00811784195952233 2.395
## management 0.01284971707269694 0.00820691403585742 1.566
## urban_env -0.00599759692140784 0.00825635789619665 -0.726
## EU_immigration 0.14678002282821234 0.00935401991357668 15.692
## Non_EU_immigration 0.03228423208954058 0.00968276556634894 3.334
## BE 0.15925462679725497 0.01188696580784674 13.397
## BG 0.10331441274550060 0.01030478142952834 10.026
## CY 0.14989703564644496 0.00981978830585835 15.265
## CZ 0.05240501414309681 0.01107753900124608 4.731
## DE 0.19055350908176913 0.01257520169340186 15.153
## DK 0.03987083849961567 0.01028606577349777 3.876
## EE 0.10558531426549543 0.01076665725646973 9.807
## ES 0.15848740135038145 0.01149866675646445 13.783
## FI 0.04089822829698343 0.01089724134427372 3.753
## FR 0.12826048894659545 0.01075411143894865 11.927
## GB 0.01422025262381267 0.01058813889465049 1.343
## GR 0.15800429713541894 0.01138212317938891 13.882
## HR 0.12190214626435360 0.01168719963775932 10.430
## HU 0.06864559975286423 0.01191419211172756 5.762
## IE 0.11476242109589065 0.01142148163275755 10.048
## IT 0.07008224919081006 0.01092178952571577 6.417
## LT 0.17394277798496602 0.01126500373715345 15.441
## LU 0.08283369181018874 0.00960248481940597 8.626
## LV 0.12297058774418947 0.01109548765266491 11.083
## MT 0.06397778036569884 0.00911130556604682 7.022
## NL 0.14187179231647273 0.01121753376698039 12.647
## PL 0.07953713230826420 0.01108513211580741 7.175
## PT 0.10546522899708580 0.01114650513492776 9.462
## RO 0.07885961998334370 0.01100781752187754 7.164
## SE 0.04281284379143144 0.01098903174764968 3.896
## SI 0.15451588012233286 0.01156008083719817 13.366
## SK 0.07175432453144803 0.01114643943149738 6.437
## Pr(>|t|)
## (Intercept) 1.000000
## inclusive < 0.0000000000000002 ***
## age 0.220922
## education 0.695774
## knowledge_EU < 0.0000000000000002 ***
## female 0.016634 *
## management 0.117440
## urban_env 0.467594
## EU_immigration < 0.0000000000000002 ***
## Non_EU_immigration 0.000858 ***
## BE < 0.0000000000000002 ***
## BG < 0.0000000000000002 ***
## CY < 0.0000000000000002 ***
## CZ 0.000002261162833 ***
## DE < 0.0000000000000002 ***
## DK 0.000107 ***
## EE < 0.0000000000000002 ***
## ES < 0.0000000000000002 ***
## FI 0.000175 ***
## FR < 0.0000000000000002 ***
## GB 0.179284
## GR < 0.0000000000000002 ***
## HR < 0.0000000000000002 ***
## HU 0.000000008522837 ***
## IE < 0.0000000000000002 ***
## IT 0.000000000144191 ***
## LT < 0.0000000000000002 ***
## LU < 0.0000000000000002 ***
## LV < 0.0000000000000002 ***
## MT 0.000000000002302 ***
## NL < 0.0000000000000002 ***
## PL 0.000000000000763 ***
## PT < 0.0000000000000002 ***
## RO 0.000000000000827 ***
## SE 0.000098313628559 ***
## SI < 0.0000000000000002 ***
## SK 0.000000000125910 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9015 on 12650 degrees of freedom
## Multiple R-squared: 0.1895, Adjusted R-squared: 0.1872
## F-statistic: 82.18 on 36 and 12650 DF, p-value: < 0.00000000000000022
summary(m3.lw.std)
##
## Call:
## lm(formula = free_movement_score ~ inclusive + age + education +
## knowledge_EU + female + management + urban_env + EU_immigration +
## Non_EU_immigration + BE + BG + CY + CZ + DE + DK + EE + ES +
## FI + FR + GB + GR + HR + HU + IE + IT + LT + LU + LV + MT +
## NL + PL + PT + RO + SE + SI + SK, data = my.df.std)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.2126 -0.1205 0.1918 0.4920 1.8795
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) -0.00000000000001249 0.00802657502889756 0.000
## inclusive 0.15046581647216392 0.00861550452057984 17.465
## age -0.02374124635897282 0.00843993994512035 -2.813
## education 0.00227670654656636 0.00855393313947309 0.266
## knowledge_EU 0.12348587273744874 0.00844352155247092 14.625
## female 0.00684760328465412 0.00814081998571291 0.841
## management 0.01882916151223077 0.00823014418576672 2.288
## urban_env -0.00656693987275517 0.00827972799984286 -0.793
## EU_immigration 0.27085454715906321 0.00938049701372633 28.874
## Non_EU_immigration -0.01458631972317059 0.00971017320028542 -1.502
## BE 0.12250205617001800 0.01192061255941214 10.276
## BG 0.11984473101893257 0.01033394971572515 11.597
## CY 0.12670407954380256 0.00984758379066860 12.867
## CZ 0.15721367954870658 0.01110889462292077 14.152
## DE 0.17424013070459676 0.01261079653687178 13.817
## DK 0.07633730152782314 0.01031518108393618 7.400
## EE 0.16157542289312379 0.01079713290919341 14.965
## ES 0.13944117750778745 0.01153121440486709 12.092
## FI 0.12612110954101155 0.01092808662289115 11.541
## FR 0.12625486676873607 0.01078455158000247 11.707
## GB 0.05163844572096456 0.01061810924071584 4.863
## GR 0.14121948414008170 0.01141434094438415 12.372
## HR 0.14900947162356906 0.01172028093950305 12.714
## HU 0.10563157888282453 0.01194791592893759 8.841
## IE 0.11246374846773992 0.01145381080415572 9.819
## IT 0.03532750493829222 0.01095270428939572 3.225
## LT 0.17220468507602738 0.01129688998872129 15.244
## LU 0.09288216793241234 0.00962966521399546 9.645
## LV 0.16748958327088767 0.01112689407904663 15.053
## MT 0.07899535604891469 0.00913709564904805 8.646
## NL 0.12025061029596081 0.01124928565202281 10.690
## PL 0.09356653251676626 0.01111650923023672 8.417
## PT 0.14048412379182179 0.01117805596927522 12.568
## RO 0.06133126438647994 0.01103897579282935 5.556
## SE 0.12340868558778087 0.01102013684436943 11.198
## SI 0.13322680645739091 0.01159280232174625 11.492
## SK 0.13462041178337789 0.01117799007986744 12.043
## Pr(>|t|)
## (Intercept) 1.00000
## inclusive < 0.0000000000000002 ***
## age 0.00492 **
## education 0.79012
## knowledge_EU < 0.0000000000000002 ***
## female 0.40028
## management 0.02216 *
## urban_env 0.42771
## EU_immigration < 0.0000000000000002 ***
## Non_EU_immigration 0.13308
## BE < 0.0000000000000002 ***
## BG < 0.0000000000000002 ***
## CY < 0.0000000000000002 ***
## CZ < 0.0000000000000002 ***
## DE < 0.0000000000000002 ***
## DK 0.000000000000144 ***
## EE < 0.0000000000000002 ***
## ES < 0.0000000000000002 ***
## FI < 0.0000000000000002 ***
## FR < 0.0000000000000002 ***
## GB 0.000001168673676 ***
## GR < 0.0000000000000002 ***
## HR < 0.0000000000000002 ***
## HU < 0.0000000000000002 ***
## IE < 0.0000000000000002 ***
## IT 0.00126 **
## LT < 0.0000000000000002 ***
## LU < 0.0000000000000002 ***
## LV < 0.0000000000000002 ***
## MT < 0.0000000000000002 ***
## NL < 0.0000000000000002 ***
## PL < 0.0000000000000002 ***
## PT < 0.0000000000000002 ***
## RO 0.000000028179156 ***
## SE < 0.0000000000000002 ***
## SI < 0.0000000000000002 ***
## SK < 0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9041 on 12650 degrees of freedom
## Multiple R-squared: 0.1849, Adjusted R-squared: 0.1826
## F-statistic: 79.74 on 36 and 12650 DF, p-value: < 0.00000000000000022
stargazer(m1.lw.std,
m2.lw.std,
m3.lw.std,
title = "Table 2: Standardized models",
covariate.labels = c("Inclusive Identity",
"Age",
"Education",
"Knowledge of the EU",
"Female",
"Management Position",
"Urban Environment",
"EU Immigration",
"Non EU Immigration",
"Intercept"),
omit = c("BE",
"BG",
"CY",
"CZ",
"DE",
"DK",
"EE",
"ES",
"FI",
"FR",
"GB",
"GR",
"HR",
"HU",
"IE",
"IT",
"LT",
"LU",
"LV",
"MT",
"NL",
"PL",
"PT",
"RO",
"SE",
"SI",
"SK"), # excludes the country variable to be displayed in the table.
# The variable is only used to control for effects and does not have to be in the table.
keep.stat = c("n", "rsq"),
notes = "Country dummies not displayed in table, but included in regression",
out = "models_standardized_beta.html")
% Table created by stargazer v.5.2.2 by Marek Hlavac, Harvard University. E-mail: hlavac at fas.harvard.edu % Date and time: Sat, Sep 03, 2022 - 12:41:02
library(stargazer)
stargazer(b1a.lw,
b1b.lw,
b2a.lw,
b2b.lw,
b3a.lw,
b3b.lw,
m1.lw,
m2.lw,
m3.lw,
title = "Table 1: All models",
covariate.labels = c("Inclusive Identity",
"Age",
"Education",
"Knowledge of the EU",
"Female",
"Management Position",
"Urban Environment",
"EU Immigration",
"Non EU Immigration",
"Intercept"),
omit = c("country"), # excludes the country variable to be displayed in the table.
# The variable is only used to control for effects and does not have to be in the table.
keep.stat = c("n", "rsq"),
notes = "Country dummies not displayed in table, but included in regression: 2,4,6,7,8,9",
out = "models.html")
% Table created by stargazer v.5.2.2 by Marek Hlavac, Harvard University. E-mail: hlavac at fas.harvard.edu % Date and time: Sat, Sep 03, 2022 - 12:41:03
Graphs
b1a.lw # 1.2799 0.4253
##
## Call:
## lm(formula = free_market_integration_score ~ inclusive, data = my.df.listw)
##
## Coefficients:
## (Intercept) inclusive
## 1.2799 0.4253
b1b.lw # 1.1741885 0.3924055
##
## Call:
## lm(formula = free_market_integration_score ~ inclusive + country,
## data = my.df.listw)
##
## Coefficients:
## (Intercept) inclusive countryBE countryBG countryCY countryCZ
## 1.1741885 0.3924055 0.1900415 -0.0591105 0.3614935 -0.3478740
## countryDE countryDK countryEE countryES countryFI countryFR
## 0.2756664 -0.3061030 0.4735795 0.2695252 0.2124429 0.1549873
## countryGB countryGR countryHR countryHU countryIE countryIT
## -0.4008313 0.2757694 0.0008418 0.0813340 0.3303251 -0.0307192
## countryLT countryLU countryLV countryMT countryNL countryPL
## 0.3523734 0.2684351 0.3497893 0.2721016 0.2276105 -0.1846925
## countryPT countryRO countrySE countrySI countrySK
## 0.3041094 0.0458101 -0.4414521 0.4156892 0.2414589
b2a.lw # 3.5823 0.8462
##
## Call:
## lm(formula = furhter_political_integration_score ~ inclusive,
## data = my.df.listw)
##
## Coefficients:
## (Intercept) inclusive
## 3.5823 0.8462
b2b.lw # 2.8661 0.8197
##
## Call:
## lm(formula = furhter_political_integration_score ~ inclusive +
## country, data = my.df.listw)
##
## Coefficients:
## (Intercept) inclusive countryBE countryBG countryCY countryCZ
## 2.8661 0.8197 0.9238 0.8973 1.4104 0.2120
## countryDE countryDK countryEE countryES countryFI countryFR
## 1.0898 0.3706 0.8130 1.1808 0.3999 0.8977
## countryGB countryGR countryHR countryHU countryIE countryIT
## 0.1142 1.0674 0.8170 0.3697 0.8886 0.4194
## countryLT countryLU countryLV countryMT countryNL countryPL
## 1.2361 0.9160 0.7473 0.8183 0.9952 0.5551
## countryPT countryRO countrySE countrySI countrySK
## 0.8501 0.5613 0.3008 1.0589 0.4230
b3a.lw # 2.4986 0.3499
##
## Call:
## lm(formula = free_movement_score ~ inclusive, data = my.df.listw)
##
## Coefficients:
## (Intercept) inclusive
## 2.4986 0.3499
b3b.lw # 2.05910 0.34578
##
## Call:
## lm(formula = free_movement_score ~ inclusive + country, data = my.df.listw)
##
## Coefficients:
## (Intercept) inclusive countryBE countryBG countryCY countryCZ
## 2.05910 0.34578 0.33829 0.58006 0.62040 0.49773
## countryDE countryDK countryEE countryES countryFI countryFR
## 0.52594 0.38275 0.66954 0.56741 0.57467 0.44237
## countryGB countryGR countryHR countryHU countryIE countryIT
## 0.20410 0.50429 0.53594 0.31509 0.47631 0.05204
## countryLT countryLU countryLV countryMT countryNL countryPL
## 0.64859 0.54761 0.54135 0.53522 0.42370 0.35078
## countryPT countryRO countrySE countrySI countrySK
## 0.57562 0.21704 0.48091 0.49137 0.44475
m1.lw # 0.760720 0.3204436
##
## Call:
## lm(formula = free_market_integration_score ~ inclusive + age +
## education + knowledge_EU + female + management + urban_env +
## EU_immigration + Non_EU_immigration + country, data = my.df.listw)
##
## Coefficients:
## (Intercept) inclusive age education
## 0.7860720 0.3204436 -0.0003070 0.0001788
## knowledge_EU female management urban_env
## 0.0269554 -0.0239004 -0.0173994 0.0104355
## EU_immigration Non_EU_immigration countryBE countryBG
## 0.1102858 0.0216987 0.2293725 -0.0696785
## countryCY countryCZ countryDE countryDK
## 0.3845033 -0.2741017 0.2820351 -0.3125707
## countryEE countryES countryFI countryFR
## 0.4820770 0.2186708 0.1827724 0.2036708
## countryGB countryGR countryHR countryHU
## -0.3970487 0.2839797 -0.0076985 0.1127347
## countryIE countryIT countryLT countryLU
## 0.2852110 0.0220782 0.3501848 0.2501540
## countryLV countryMT countryNL countryPL
## 0.4291180 0.2737118 0.2514142 -0.1738668
## countryPT countryRO countrySE countrySI
## 0.2660433 0.0504636 -0.4233962 0.4020582
## countrySK
## 0.2906875
m2.lw # 1.9289235 0.6625305
##
## Call:
## lm(formula = furhter_political_integration_score ~ inclusive +
## age + education + knowledge_EU + female + management + urban_env +
## EU_immigration + Non_EU_immigration + country, data = my.df.listw)
##
## Coefficients:
## (Intercept) inclusive age education
## 1.9289235 0.6625305 0.0008618 -0.0007864
## knowledge_EU female management urban_env
## 0.0556230 0.0529638 0.0482060 -0.0179294
## EU_immigration Non_EU_immigration countryBE countryBG
## 0.2561005 0.0482663 1.0054989 0.8848587
## countryCY countryCZ countryDE countryDK
## 1.4567105 0.3820092 1.0896743 0.3509701
## countryEE countryES countryFI countryFR
## 0.8195324 1.0658571 0.3148121 0.9907898
## countryGB countryGR countryHR countryHU
## 0.1138505 1.0908714 0.7873753 0.4307867
## countryIE countryIT countryLT countryLU
## 0.7807888 0.5230995 1.2134238 0.8755878
## countryLV countryMT countryNL countryPL
## 0.9011392 0.8221144 1.0152291 0.5732232
## countryPT countryRO countrySE countrySI
## 0.7593104 0.5754552 0.3283908 1.0301337
## countrySK
## 0.5171326
m3.lw # 1.4249458 0.2292343
##
## Call:
## lm(formula = free_movement_score ~ inclusive + age + education +
## knowledge_EU + female + management + urban_env + EU_immigration +
## Non_EU_immigration + country, data = my.df.listw)
##
## Coefficients:
## (Intercept) inclusive age education
## 1.4249458 0.2292343 -0.0010522 0.0002844
## knowledge_EU female management urban_env
## 0.0337767 0.0098835 0.0374268 -0.0104015
## EU_immigration Non_EU_immigration countryBE countryBG
## 0.2503935 -0.0115543 0.4098042 0.5438453
## countryCY countryCZ countryDE countryDK
## 0.6524004 0.6072042 0.5279240 0.3560368
## countryEE countryES countryFI countryFR
## 0.6644791 0.4968654 0.5143730 0.5167497
## countryGB countryGR countryHR countryHU
## 0.2190506 0.5165861 0.5099501 0.3512261
## countryIE countryIT countryLT countryLU
## 0.4054057 0.1397117 0.6364943 0.5201978
## countryLV countryMT countryNL countryPL
## 0.6503121 0.5378338 0.4559306 0.3572875
## countryPT countryRO countrySE countrySI
## 0.5358963 0.2371276 0.5015406 0.4706039
## countrySK
## 0.5140533
average_scores_on_integration_process <- c(1.2799,
1.2799 + 0.4253,
1.1741885,
1.1741885 + 0.3924055,
0.760720,
0.760720 + 0.320443,
3.5823,
3.5823 + 0.8462,
2.8661,
2.8661 + 0.8197,
1.9289235,
1.9289235 + 0.6625305,
2.4986,
2.4986 + 0.3499,
2.05910,
2.05910 + 0.34578,
1.4249458,
1.4249458 + 0.2292343)
Models <- c("b1a",
"b1a",
"b1b",
"b1b",
"m1",
"m1",
"b2a",
"b2a",
"b2b",
"b2b",
"m2",
"m2",
"b3a",
"b3a",
"b3b",
"b3b",
"m3",
"m3")
inclusive_exclusive <- c("exclusive", "inclusive",
"exclusive", "inclusive",
"exclusive", "inclusive",
"exclusive", "inclusive",
"exclusive", "inclusive",
"exclusive", "inclusive",
"exclusive", "inclusive",
"exclusive", "inclusive",
"exclusive", "inclusive")
graph.df <- tibble::tibble(inclusive_exclusive, Models, average_scores_on_integration_process)
plot <- ggplot(data = graph.df, aes(x = Models, y = average_scores_on_integration_process, fill = inclusive_exclusive)) +
geom_bar(stat="identity", position = "dodge") +
geom_text(aes(label=average_scores_on_integration_process), color="black", size = 3.5, nudge_x = c(-0.22, 0.22)) +
labs(x = "Bi- and Multivariate Models",
y = "Average attitude score",
title = "Graph 1: All models",
caption = "Plotted using Eurobarometer 92.3 data") +
guides(fill=guide_legend(title="Identity")) +
theme_minimal() +
theme(plot.title = element_text(hjust = 0.5))
plot
ggsave(filename = "all_plots.pdf", device = "pdf", dpi = "retina")
market integration plot
average_score_market_integration <- c(1.2799,
1.2799 + 0.4253,
1.1741885,
1.1741885 + 0.3924055,
0.760720,
0.760720 + 0.320443)
Models_market_integration <- c("b1a", "b1a",
"b1b", "b1b",
"m1", "m1")
inclusive_exclusive2 <- c("exclusive", "inclusive",
"exclusive", "inclusive",
"exclusive", "inclusive")
graph_market_integration.df <- tibble::tibble(inclusive_exclusive2, Models_market_integration, average_score_market_integration)
plot_market_integration <- ggplot(data = graph_market_integration.df, aes(x = Models_market_integration, y = average_score_market_integration, fill = inclusive_exclusive2)) +
geom_bar(stat="identity", position = "dodge") +
geom_text(aes(label=average_score_market_integration),color="black", size = 3.5, nudge_x = c(-0.22, 0.22)) +
labs(x = "Graph 2: Market Integration Attitude Models",
y = "Average Attitude Score",
title = "Attitudes towards Market Integration",
caption = "Plotted using Eurobarometer 92.3 data") +
guides(fill=guide_legend(title="Identity")) +
theme_minimal() +
theme(plot.title = element_text(hjust = 0.5))
plot_market_integration
ggsave(filename = "market_integration_plot.pdf", device = "pdf", dpi = "retina")
further political integration
average_score_further_political_integration <- c(3.5823,
3.5823 + 0.8462,
2.8661,
2.8661 + 0.8197,
1.9289235,
1.9289235 + 0.6625305)
Models_further_political_integration <- c("b2a", "b2a",
"b2b", "b2b",
"m2", "m2")
inclusive_exclusive2 <- c("exclusive", "inclusive",
"exclusive", "inclusive",
"exclusive", "inclusive")
graph_further_political_integration.df <- tibble::tibble(inclusive_exclusive2, Models_further_political_integration, average_score_further_political_integration)
plot_further_political_integration <- ggplot(data = graph_further_political_integration.df, aes(x = Models_further_political_integration, y = average_score_further_political_integration, fill = inclusive_exclusive2)) +
geom_bar(stat="identity", position = "dodge") +
geom_text(aes(label=average_score_further_political_integration),color="black", size = 3.5, nudge_x = c(-0.22, 0.22)) +
labs(x = "Graph 3: Further Politcal Integration Attitude Models",
y = "Average Attitude Score",
title = "Attitudes towards Further Political Integration",
caption = "Plotted using Eurobarometer 92.3 data") +
guides(fill=guide_legend(title="Identity")) +
theme_minimal() +
theme(plot.title = element_text(hjust = 0.5))
plot_further_political_integration
ggsave(filename = "furhter_integration_plot.pdf", device = "pdf", dpi = "retina")
freedom of movement plot
average_score_free_movement <- c( 2.4986,
2.4986 + 0.3499,
2.05910,
2.05910 + 0.34578,
1.4249458,
1.4249458 + 0.2292343)
Models_free_movement <- c("b3a", "b3a",
"b3b", "b3b",
"m3", "m3")
inclusive_exclusive2 <- c("exclusive", "inclusive",
"exclusive", "inclusive",
"exclusive", "inclusive")
graph_free_movement.df <- tibble::tibble(inclusive_exclusive2, Models_free_movement, average_score_free_movement)
plot_freedom_of_movement <- ggplot(data = graph_free_movement.df, aes(x = Models_free_movement, y = average_score_free_movement, fill = inclusive_exclusive2)) +
geom_bar(stat="identity", position = "dodge") +
geom_text(aes(label=average_score_free_movement),color="black", size = 3.5, nudge_x = c(-0.22, 0.22)) +
labs(x = "Graph 4: Freedom of Movement Attitude Models",
y = "Average Attitude Score",
title = "Attitudes towards Freedom of Movement within the EU",
caption = "Plotted using Eurobarometer 92.3 data") +
guides(fill=guide_legend(title="Identity")) +
theme_minimal() +
theme(plot.title = element_text(hjust = 0.5))
plot_freedom_of_movement
ggsave(filename = "freedom_of_movement_plot.pdf", device = "pdf", dpi = "retina")
max_length <- max(length(supranationalist.vc),
length(cosmopolit.vc),
length(glocalist.vc),
length(nationalist.vc),
length(localist.vc),
length(apolide.vc))
table.df <- data.frame(supranationalist = c(supranationalist.vc, rep(NA, max_length - length(supranationalist.vc))),
cosmopolit = c(cosmopolit.vc, rep(NA, max_length - length(cosmopolit.vc))),
glocalist = c(glocalist.vc, rep(NA, max_length - length(glocalist.vc))),
nationalist = c(nationalist.vc, rep(NA, max_length - length(nationalist.vc))),
localist = c(localist.vc, rep(NA, max_length - length(localist.vc))),
apolide = c(apolide.vc, rep(NA, max_length - length(apolide.vc))))
typologytable.html <- table.df %>% addHtmlTableStyle(col.rgroup = c("none", "#F7F7F7")) %>%
htmlTable(
caption = "Table 4",
tfoot = "Typology after Kuhn and Nicoli 2020",
ctable = "double")
typologytable.html
|
Table 4
|
|
|
supranationalist
|
cosmopolit
|
glocalist
|
nationalist
|
localist
|
apolide
|
|
1
|
2
|
33
|
202
|
20
|
200
|
0
|
|
2
|
3
|
222
|
203
|
21
|
201
|
1
|
|
3
|
12
|
232
|
212
|
30
|
210
|
10
|
|
4
|
13
|
233
|
213
|
31
|
211
|
11
|
|
5
|
22
|
322
|
302
|
120
|
301
|
100
|
|
6
|
23
|
323
|
303
|
121
|
310
|
101
|
|
7
|
32
|
332
|
312
|
130
|
311
|
110
|
|
8
|
102
|
333
|
313
|
131
|
320
|
111
|
|
9
|
103
|
|
|
220
|
321
|
|
|
10
|
112
|
|
|
221
|
|
|
|
11
|
113
|
|
|
230
|
|
|
|
12
|
122
|
|
|
231
|
|
|
|
13
|
123
|
|
|
330
|
|
|
|
14
|
132
|
|
|
331
|
|
|
|
15
|
133
|
|
|
|
|
|
|
16
|
223
|
|
|
|
|
|
|
Typology after Kuhn and Nicoli 2020
|
htmltools::save_html(typologytable.html, file = "typology.html")
# some percentage calculations
0.32 / 0.786 * 100
## [1] 40.71247
0.32 / 2 * 100
## [1] 16
0.663 / 1.929 * 100
## [1] 34.37014
0.663 / 5 * 100
## [1] 13.26
0.229 / 1.425 * 100
## [1] 16.07018
0.229 / 3 * 100
## [1] 7.633333