This website is a replication package for the paper “Exposure to Asylum Seekers and Changing Support for the Radical Right: A Natural Experiment in the Netherlands” by Tolsma, Laméris, and Savelkoul (n.d.). It contains R code to replicate all Tables in the manuscript.
Use the top menu to navigate to the section of interest. The section replicates the Main Tables of the manuscript.
To copy the code click the button in the upper right corner of the code-chunks.
Data
All data and scripts can be found on Github
Additional dataprep
We start with a datafile in long format we obtained after performing our dataprep, see here.
- We delete observations of respondents where respondents were not allowed to vote or did not give an answer.
- We delete observations with missing values on zipcode information.
- We delete respondents with only one observation.
- We construct the dichotomous dependent variable.
- We construct the multinomial dependent variable.
- We decided to perform additional robustness checks and need to match more raw data on crisis ASC.
rm(list = ls())
library(haven)
dataset <- read_stata("data/evazc v22122017.dta")
# dependent variable. delete not allowed to vote
table(dataset$vote, useNA = "always")
sum(table(dataset$vote, useNA = "always"))
dataset <- dataset[dataset$vote != 16, ]
dataset <- dataset[dataset$vote != 17, ]
# add 'VNL' to other party
dataset$vote[dataset$vote == 12] <- 18
#'1'='vvd', '2'='pvda', '3'='pvv', '4'='cda', '5'='sp'
#'6'='d66', '7'='gl', '8'='cu', '9'='sgp', '10'='dier'
#'11'='50plus', '12'='vnl','13='blanco','14'='dontknow','15='no vote', '18'='other'
# define dichotomous dependent variable
dataset$y_pvv <- ifelse(dataset$vote == 3, 1, 0)
# define multinomial dependent variable
dataset$vote_rwsp <- NA
dataset$vote_rwsp[dataset$vote == 2 | dataset$vote == 6 | dataset$vote == 7 | dataset$vote == 8 | dataset$vote ==
10 | dataset$vote == 11 | dataset$vote == 12 | dataset$vote == 18] <- 0 #other
dataset$vote_rwsp[dataset$vote == 1 | dataset$vote == 4 | dataset$vote == 9] <- 1 # right-wing
dataset$vote_rwsp[dataset$vote == 5] <- 2 # anti-establishment
dataset$vote_rwsp[dataset$vote == 13 | dataset$vote == 14 | dataset$vote == 15] <- 3 #demobilized
dataset$vote_rwsp[dataset$vote == 3] <- 4
# 1: right-wing; 2: anti-establishment; 3: demobilized; 0: other; 4: PVV
# delete respondents with missing data on zipcode
sum(is.na(dataset$pc4))
dataset <- dataset[!is.na(dataset$pc4), ]
# delete respondents with only one observation.
dataset$one <- 1
test <- aggregate(dataset$one, by = list(dataset$PanelistIdQuestion), FUN = sum)
names(test) <- c("PanelistIdQuestion", "ncases")
dataset <- merge(dataset, test)
table(dataset$ncases)
dataset <- dataset[dataset$ncases != 1, ]
# recode tijd/time variable
table(dataset$tijd, useNA = "always")
dataset$tijd <- dataset$tijd - 1
# capacity of ASC s1 is regular, s2=temporary, s3=crisis, s123=total
dataset$s1pc4 <- ifelse(dataset$tijd == 0, dataset$s1cap_pc4t1rel, dataset$s1cap_pc4t2rel)
dataset$s2pc4 <- ifelse(dataset$tijd == 0, dataset$s2cap_pc4t1rel, dataset$s2cap_pc4t2rel)
dataset$s3pc4 <- ifelse(dataset$tijd == 0, dataset$s3cap_pc4t1rel, dataset$s3cap_pc4t2rel)
dataset$s123pc4 <- dataset$s1pc4 + dataset$s2pc4 + dataset$s3pc4
# compute variables for hybrid model: within individual means (between vars) and deviations from mean
# (within vars).
test <- aggregate(dataset[, c("contactnw", "threat", "s123pc4", "s1pc4", "s2pc4", "s3pc4", "y_pvv", "vote_rwsp")],
by = list(dataset$PanelistIdQuestion), FUN = mean)
names(test) <- c("PanelistIdQuestion", "contactnw_mean", "threat_mean", "s123pc4_mean", "s1pc4_mean",
"s2pc4_mean", "s3pc4_mean", "y_pvv_mean", "vote_rwsp_mean")
dataset <- merge(dataset, test)
dataset$contactnw_afw = dataset$contactnw - dataset$contactnw_mean
dataset$threat_afw = dataset$threat - dataset$threat_mean
dataset$s123pc4_afw = dataset$s123pc4 - dataset$s123pc4_mean
dataset$s1pc4_afw = dataset$s1pc4 - dataset$s1pc4_mean
dataset$s2pc4_afw = dataset$s2pc4 - dataset$s2pc4_mean
dataset$s3pc4_afw = dataset$s3pc4 - dataset$s3pc4_mean
# create filter vars for analyses: fe is fixed effects, fem is fixed effects multinomial
dataset$sel_fe = (dataset$y_pvv != dataset$y_pvv_mean)
dataset$sel_fem = (dataset$vote_rwsp != dataset$vote_rwsp_mean)
dataset$sel_s123pc4 = (dataset$s123pc4 != dataset$s123pc4_mean)
# match additional data on crisis ASC these data have been added in a later phase
duur <- read.csv2("data/Adressen alle AZCs_17022017.csv")
names(duur)
duur <- duur[, c("PC4", "Soort", "Cap_Gem", "Duur.tot.4.11")]
names(duur) <- c("pc4", "soort", "cap", "duur")
duur <- duur[duur$soort == 3, ]
sort(unique(duur$pc4[duur$cap > 0]))
# we are multiplying length of stay with number of AS
duur$s3_altop <- duur$duur * duur$cap
duur2 <- aggregate(duur[, c("cap", "duur", "s3_altop")], by = list(duur$pc4), FUN = sum)
duur2
names(duur2)[1] <- c("pc4")
dataset <- merge(dataset, duur2, all.x = T)
dataset$duur[is.na(dataset$duur)] <- 0
dataset$s3_altop[is.na(dataset$s3_altop)] <- 0
dataset$cap[is.na(dataset$cap)] <- 0
# asylum seekers in crisis centers per 1000 inhabitants weighted by length of stay
dataset$s3_altop2 <- 1000 * dataset$s3_altop/(dataset$inw2014_pc4 * dataset$pauto2014_pc4)
dataset$cap2 <- 1000 * dataset$cap/(dataset$inw2014_pc4 * dataset$pauto2014_pc4)
dataset$s3_altop <- 1000 * dataset$s3_altop/dataset$inw2014_pc4
dataset$cap <- 1000 * dataset$cap/dataset$inw2014_pc4
Table 1
Fixed effects sample
library(psych)
require(DT)
dataset_sel <- dataset[dataset$sel_fe == 1, ]
# table(dataset_sel$y_pvv, dataset_sel$tijd)
prop.table(table(dataset_sel$y_pvv, dataset_sel$tijd), margin = 2)
# describeBy(dataset_sel[,c('s123pc4', 's1pc4', 's2pc4', 's3pc4', 'threat', 'contactnw',
# 's123pc4_afw', 's1pc4_afw', 's2pc4_afw', 's3pc4_afw', 'threat_afw', 'contactnw_afw')],
# group=dataset_sel$tijd)
test <- describeBy(dataset_sel[, c("s123pc4", "s1pc4", "s2pc4", "s3pc4", "threat", "contactnw", "s123pc4_afw",
"s1pc4_afw", "s2pc4_afw", "s3pc4_afw", "threat_afw", "contactnw_afw")], group = dataset_sel$tijd)
# kable(round(test[[1]],2), caption='Time 1')
#>
#> 0 1
#> 0 0.7588193 0.2411807
#> 1 0.2411807 0.7588193
- respondents in FE sample: 1389
- zipcodes in FE sample: 1002
- respondents experiencing change in FE sample: 116
- zipcodes experiencing change in FE sample: 81
hybrid model sample
table(dataset$y_pvv, dataset$tijd)
prop.table(table(dataset$y_pvv, dataset$tijd), margin = 2)
describeBy(dataset[, c("s123pc4", "s1pc4", "s2pc4", "s3pc4", "threat", "contactnw", "s123pc4_afw", "s1pc4_afw",
"s2pc4_afw", "s3pc4_afw", "threat_afw", "contactnw_afw")], group = dataset$tijd)
print("respondents in hybrid sample")
length(unique(dataset$PanelistIdQuestion))
print("zipcodes in FE sample")
length(unique(dataset$pc4))
print("respondents experiencing change in FE sample")
length(unique(dataset$PanelistIdQuestion[dataset$s123pc4 != dataset$s123pc4_mean]))
print("zipcodes experiencing change in FE sample")
length(unique(dataset$pc4[dataset$s123pc4 != dataset$s123pc4_mean]))
#>
#> 0 1
#> 0 15949 15230
#> 1 3200 3919
#>
#> 0 1
#> 0 0.8328894 0.7953418
#> 1 0.1671106 0.2046582
#>
#> Descriptive statistics by group
#> group: 0
#> vars n mean sd median trimmed mad min max range skew kurtosis se
#> s123pc4 1 19149 1.74 15.62 0 0.00 0.00 0.00 690.65 690.65 14.62 340.58 0.11
#> s1pc4 2 19149 1.65 15.44 0 0.00 0.00 0.00 690.65 690.65 15.07 357.57 0.11
#> s2pc4 3 19149 0.09 2.48 0 0.00 0.00 0.00 71.38 71.38 28.22 794.82 0.02
#> s3pc4 4 19149 0.00 0.00 0 0.00 0.00 0.00 0.00 0.00 NaN NaN 0.00
#> threat 5 19149 1.89 1.36 2 1.86 1.48 0.00 4.00 4.00 0.18 -1.15 0.01
#> contactnw 6 19149 2.48 2.11 2 2.37 2.97 0.00 6.00 6.00 0.18 -1.40 0.02
#> s123pc4_afw 7 19149 -1.04 8.23 0 0.00 0.00 -251.42 15.64 267.06 -17.56 409.53 0.06
#> s1pc4_afw 8 19149 -0.20 5.26 0 0.00 0.00 -251.42 15.64 267.06 -41.62 1938.93 0.04
#> s2pc4_afw 9 19149 -0.39 5.70 0 0.00 0.00 -125.14 0.00 125.14 -18.81 378.23 0.04
#> s3pc4_afw 10 19149 -0.46 2.90 0 0.00 0.00 -141.51 0.00 141.51 -17.55 607.67 0.02
#> threat_afw 11 19149 -0.05 0.52 0 -0.04 0.74 -2.00 2.00 4.00 -0.04 1.25 0.00
#> contactnw_afw 12 19149 -0.05 1.00 0 -0.04 0.74 -3.00 3.00 6.00 -0.05 1.26 0.01
#> ---------------------------------------------------------------------------
#> group: 1
#> vars n mean sd median trimmed mad min max range skew kurtosis se
#> s123pc4 1 19149 3.81 23.15 0 0.00 0.00 0.00 689.21 689.21 12.04 209.26 0.17
#> s1pc4 2 19149 2.04 19.31 0 0.00 0.00 0.00 689.21 689.21 16.77 392.10 0.14
#> s2pc4 3 19149 0.86 11.71 0 0.00 0.00 0.00 250.29 250.29 17.66 340.44 0.08
#> s3pc4 4 19149 0.92 5.80 0 0.00 0.00 0.00 283.02 283.02 17.55 607.67 0.04
#> threat 5 19149 1.98 1.33 2 1.98 1.48 0.00 4.00 4.00 0.08 -1.11 0.01
#> contactnw 6 19149 2.58 2.15 2 2.48 2.97 0.00 6.00 6.00 0.10 -1.45 0.02
#> s123pc4_afw 7 19149 1.04 8.23 0 0.00 0.00 -15.64 251.42 267.06 17.56 409.53 0.06
#> s1pc4_afw 8 19149 0.20 5.26 0 0.00 0.00 -15.64 251.42 267.06 41.62 1938.93 0.04
#> s2pc4_afw 9 19149 0.39 5.70 0 0.00 0.00 0.00 125.14 125.14 18.81 378.23 0.04
#> s3pc4_afw 10 19149 0.46 2.90 0 0.00 0.00 0.00 141.51 141.51 17.55 607.67 0.02
#> threat_afw 11 19149 0.05 0.52 0 0.04 0.74 -2.00 2.00 4.00 0.04 1.25 0.00
#> contactnw_afw 12 19149 0.05 1.00 0 0.04 0.74 -3.00 3.00 6.00 0.05 1.26 0.01
#> [1] "respondents in hybrid sample"
#> [1] 19149
#> [1] "zipcodes in FE sample"
#> [1] 3039
#> [1] "respondents experiencing change in FE sample"
#> [1] 1491
#> [1] "zipcodes experiencing change in FE sample"
#> [1] 175
Table 2
require(survival)
ev_sel <- dataset_sel
m1 <- clogit(y_pvv ~ s123pc4 + tijd + strata(PanelistIdQuestion), data = ev_sel)
summary(m1)
# via different estimation of fixed effects model (a first difference design) only select
# y_observation at timepoint 2
ev_sel2 <- ev_sel[ev_sel$tijd == 1, ]
# need to multiply _afw with 2 (thus becomes t2-t1, instead of t2 - mean(t2,t1)
ev_sel2$s123pc4_afw <- 2 * ev_sel2$s123pc4_afw
ev_sel2$s1pc4_afw <- 2 * ev_sel2$s1pc4_afw
ev_sel2$s2pc4_afw <- 2 * ev_sel2$s2pc4_afw
ev_sel2$s3pc4_afw <- 2 * ev_sel2$s3pc4_afw
ev_sel2$threat_afw <- 2 * ev_sel2$threat_afw
ev_sel2$contactnw_afw <- 2 * ev_sel2$contactnw_afw
m1_alt1 <- glm(y_pvv ~ s123pc4_afw, data = ev_sel2, family = binomial)
m1_alt2 <- glm(y_pvv ~ s1pc4_afw + s2pc4_afw + s3pc4_afw, data = ev_sel2, family = binomial)
m1_alt3 <- glm(y_pvv ~ s1pc4_afw + s2pc4_afw + s3pc4_afw + contactnw_afw + threat_afw, data = ev_sel2,
family = binomial)
model 1
#>
#> Call:
#> glm(formula = y_pvv ~ s123pc4_afw, family = binomial, data = ev_sel2)
#>
#> Deviance Residuals:
#> Min 1Q Median 3Q Max
#> -2.3876 0.5200 0.7521 0.7521 0.9127
#>
#> Coefficients:
#> Estimate Std. Error z value Pr(>|z|)
#> (Intercept) 1.11803 0.06373 17.543 <2e-16 ***
#> s123pc4_afw 0.02166 0.01165 1.859 0.063 .
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> (Dispersion parameter for binomial family taken to be 1)
#>
#> Null deviance: 1534.7 on 1388 degrees of freedom
#> Residual deviance: 1528.8 on 1387 degrees of freedom
#> AIC: 1532.8
#>
#> Number of Fisher Scoring iterations: 5
model 2
#>
#> Call:
#> glm(formula = y_pvv ~ s1pc4_afw + s2pc4_afw + s3pc4_afw, family = binomial,
#> data = ev_sel2)
#>
#> Deviance Residuals:
#> Min 1Q Median 3Q Max
#> -1.9985 0.4944 0.7539 0.7539 0.8090
#>
#> Coefficients:
#> Estimate Std. Error z value Pr(>|z|)
#> (Intercept) 1.112850 0.063957 17.400 <2e-16 ***
#> s1pc4_afw 0.007758 0.013869 0.559 0.5759
#> s2pc4_afw 0.056311 0.062301 0.904 0.3661
#> s3pc4_afw 0.027981 0.016846 1.661 0.0967 .
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> (Dispersion parameter for binomial family taken to be 1)
#>
#> Null deviance: 1534.7 on 1388 degrees of freedom
#> Residual deviance: 1527.5 on 1385 degrees of freedom
#> AIC: 1535.5
#>
#> Number of Fisher Scoring iterations: 7
model 3
#>
#> Call:
#> glm(formula = y_pvv ~ s1pc4_afw + s2pc4_afw + s3pc4_afw + contactnw_afw +
#> threat_afw, family = binomial, data = ev_sel2)
#>
#> Deviance Residuals:
#> Min 1Q Median 3Q Max
#> -2.1981 0.4287 0.6897 0.7635 1.1680
#>
#> Coefficients:
#> Estimate Std. Error z value Pr(>|z|)
#> (Intercept) 1.083612 0.064778 16.728 < 2e-16 ***
#> s1pc4_afw 0.008048 0.014410 0.558 0.577
#> s2pc4_afw 0.061377 0.066976 0.916 0.359
#> s3pc4_afw 0.029132 0.017235 1.690 0.091 .
#> contactnw_afw 0.034130 0.029358 1.163 0.245
#> threat_afw 0.265371 0.058514 4.535 5.76e-06 ***
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> (Dispersion parameter for binomial family taken to be 1)
#>
#> Null deviance: 1534.7 on 1388 degrees of freedom
#> Residual deviance: 1504.4 on 1383 degrees of freedom
#> AIC: 1516.4
#>
#> Number of Fisher Scoring iterations: 7
Table 3
require(sandwich)
require(lmtest)
ev <- dataset
## hybrid model because we include ethnic density of NB and SES of NB need to remove missing values.
# sum(is.na(ev$pnwal2014_pc4))
ev_sel4 <- ev[!is.na(ev$pnwal2014_pc4), ]
# lost 90 respondents
ev_sel4 <- ev_sel4[!(ev_sel4$woz2012_pc4 == 0), ]
# lost 22 respondents
ev_sel4 <- ev_sel4[!is.na(ev_sel4$woz2012_pc4), ]
# lost 3 respondents
# center variables!
ev_sel4$age_c <- ev_sel4$age - mean(ev_sel4$age)
ev_sel4$educ_c <- ev_sel4$educ - mean(ev_sel4$educ)
ev_sel4$pnwal2014_pc4_c <- ev_sel4$pnwal2014_pc4 - mean(ev_sel4$pnwal2014_pc4)
ev_sel4$woz2012_pc4_c <- ev_sel4$woz2012_pc4 - mean(ev_sel4$woz2012_pc4)
# run models
m1h <- glm(y_pvv ~ tijd + s123pc4_afw + s123pc4_mean + gender + age_c + educ_c + pnwal2014_pc4_c + woz2012_pc4_c,
data = ev_sel4, family = binomial)
m2h <- glm(y_pvv ~ tijd + s1pc4_afw + s2pc4_afw + s3pc4_afw + s1pc4_mean + s2pc4_mean + s3pc4_mean +
gender + age_c + educ_c + pnwal2014_pc4_c + woz2012_pc4_c, data = ev_sel4, family = binomial)
m3h <- glm(y_pvv ~ tijd + s1pc4_afw + s2pc4_afw + s3pc4_afw + s1pc4_mean + s2pc4_mean + s3pc4_mean +
threat_mean + contactnw_mean + gender + age_c + educ_c + pnwal2014_pc4_c + woz2012_pc4_c, data = ev_sel4,
family = binomial)
m4h <- glm(y_pvv ~ tijd + s1pc4_afw * pnwal2014_pc4_c + s2pc4_afw * pnwal2014_pc4_c + s3pc4_afw * pnwal2014_pc4_c +
s1pc4_mean + s2pc4_mean + s3pc4_mean + threat_mean + contactnw_mean + gender + age_c + educ_c + woz2012_pc4_c,
data = ev_sel4, family = binomial)
# summary(m1h) summary(m2h) summary(m3h) coeftest(m1h, vcov = vcovCL(m1h, cluster = ~
# PanelistIdQuestion)) coeftest(m2h, vcov = vcovCL(m2h, cluster = ~ PanelistIdQuestion))
# coeftest(m3h, vcov = vcovCL(m3h, cluster = ~ PanelistIdQuestion))
model 1
#>
#> z test of coefficients:
#>
#> Estimate Std. Error z value Pr(>|z|)
#> (Intercept) -2.126 0.040 -52.973 <2e-16 ***
#> tijd 0.258 0.014 19.077 <2e-16 ***
#> s123pc4_afw 0.001 0.001 1.665 0.096 .
#> s123pc4_mean 0.000 0.001 0.019 0.985
#> gender 0.581 0.043 13.396 <2e-16 ***
#> age_c -0.014 0.002 -8.946 <2e-16 ***
#> educ_c -0.154 0.006 -27.170 <2e-16 ***
#> pnwal2014_pc4_c 0.772 0.239 3.233 0.001 ***
#> woz2012_pc4_c 0.000 0.000 -0.515 0.607
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
model 2
#>
#> z test of coefficients:
#>
#> Estimate Std. Error z value Pr(>|z|)
#> (Intercept) -2.128 0.040 -52.866 <2e-16 ***
#> tijd 0.256 0.014 18.852 <2e-16 ***
#> s1pc4_afw 0.001 0.001 0.949 0.343
#> s2pc4_afw 0.001 0.001 0.732 0.464
#> s3pc4_afw 0.003 0.002 1.535 0.125
#> s1pc4_mean 0.000 0.001 -0.040 0.968
#> s2pc4_mean -0.001 0.003 -0.156 0.876
#> s3pc4_mean 0.005 0.006 0.838 0.402
#> gender 0.582 0.043 13.402 <2e-16 ***
#> age_c -0.014 0.002 -8.955 <2e-16 ***
#> educ_c -0.154 0.006 -27.165 <2e-16 ***
#> pnwal2014_pc4_c 0.775 0.239 3.246 0.001 ***
#> woz2012_pc4_c 0.000 0.000 -0.527 0.598
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
model 3
#>
#> z test of coefficients:
#>
#> Estimate Std. Error z value Pr(>|z|)
#> (Intercept) -5.324 0.084 -63.717 <2e-16 ***
#> tijd 0.361 0.019 18.927 <2e-16 ***
#> s1pc4_afw 0.002 0.002 0.894 0.371
#> s2pc4_afw 0.001 0.001 0.657 0.511
#> s3pc4_afw 0.005 0.003 1.601 0.109
#> s1pc4_mean 0.000 0.001 0.366 0.715
#> s2pc4_mean 0.002 0.003 0.436 0.663
#> s3pc4_mean 0.007 0.007 1.114 0.265
#> threat_mean 1.416 0.023 61.905 <2e-16 ***
#> contactnw_mean -0.056 0.011 -4.933 <2e-16 ***
#> gender 0.234 0.050 4.695 <2e-16 ***
#> age_c -0.013 0.002 -6.953 <2e-16 ***
#> educ_c -0.074 0.007 -11.085 <2e-16 ***
#> pnwal2014_pc4_c -1.670 0.295 -5.661 <2e-16 ***
#> woz2012_pc4_c 0.000 0.000 -1.127 0.260
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Model 4: no interaction with ethnic density
#>
#> z test of coefficients:
#>
#> Estimate Std. Error z value Pr(>|z|)
#> (Intercept) -5.323 0.084 -63.712 <2e-16 ***
#> tijd 0.360 0.019 18.885 <2e-16 ***
#> s1pc4_afw 0.002 0.002 1.034 0.301
#> pnwal2014_pc4_c -1.672 0.295 -5.668 <2e-16 ***
#> s2pc4_afw 0.001 0.002 0.748 0.454
#> s3pc4_afw 0.005 0.003 1.600 0.110
#> s1pc4_mean 0.000 0.001 0.335 0.738
#> s2pc4_mean 0.002 0.003 0.435 0.663
#> s3pc4_mean 0.007 0.007 1.135 0.256
#> threat_mean 1.416 0.023 61.904 <2e-16 ***
#> contactnw_mean -0.056 0.011 -4.934 <2e-16 ***
#> gender 0.234 0.050 4.694 <2e-16 ***
#> age_c -0.013 0.002 -6.951 <2e-16 ***
#> educ_c -0.074 0.007 -11.087 <2e-16 ***
#> woz2012_pc4_c 0.000 0.000 -1.128 0.260
#> s1pc4_afw:pnwal2014_pc4_c 0.019 0.033 0.587 0.557
#> pnwal2014_pc4_c:s2pc4_afw 0.013 0.031 0.422 0.673
#> pnwal2014_pc4_c:s3pc4_afw 0.024 0.052 0.466 0.641
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Inflow asylum seekers not related to PVV support T1.
In our manuscript we state the following:
Voters of neighbourhoods that would experience an inflow of asylum seekers did not differ with respect to radical right support from voters that would not experience an inflow of refugees: pre-crises support for the PVV for both the ‘treated’ and ‘untreated’ groups was approximately 17%.
dichtomeous treatment variable (total exposure)
# define treatment variable: whether there has been an increase in total asylum seekers.
dataset$treatment <- ifelse(dataset$tijd == 1, as.numeric(dataset$s123pc4_afw > 0), as.numeric(dataset$s123pc4_afw <
0))
# check if treatment is not correlated with pretreatment outcome
test <- table(dataset$treatment[dataset$tijd == 0], dataset$y_pvv[dataset$tijd == 0])
dimnames(test)[[1]] <- c("treatment NO", "treatment YES")
dimnames(test)[[2]] <- c("PVV NO", "PVV YES")
test
prop.table(test, margin = 1)
test1 <- glm(y_pvv ~ treatment, family = binomial, data = dataset[dataset$tijd == 0, ])
summary(test1)
#>
#> PVV NO PVV YES
#> treatment NO 14780 2963
#> treatment YES 1169 237
#>
#> PVV NO PVV YES
#> treatment NO 0.8330046 0.1669954
#> treatment YES 0.8314367 0.1685633
#>
#> Call:
#> glm(formula = y_pvv ~ treatment, family = binomial, data = dataset[dataset$tijd ==
#> 0, ])
#>
#> Deviance Residuals:
#> Min 1Q Median 3Q Max
#> -0.6076 -0.6045 -0.6045 -0.6045 1.8920
#>
#> Coefficients:
#> Estimate Std. Error z value Pr(>|z|)
#> (Intercept) -1.60707 0.02013 -79.842 <2e-16 ***
#> treatment 0.01123 0.07402 0.152 0.879
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> (Dispersion parameter for binomial family taken to be 1)
#>
#> Null deviance: 17283 on 19148 degrees of freedom
#> Residual deviance: 17283 on 19147 degrees of freedom
#> AIC: 17287
#>
#> Number of Fisher Scoring iterations: 3
This is what we report in the manuscript.
- No selective treatment!
- Among both groups, PVV support just below 17%
continuous treatment variable (total exposure)
# define treatment variable: whether there has been an increase in total asylum seekers.
dataset$treatment_c <- ifelse(dataset$tijd == 1, dataset$s123pc4_afw, -dataset$s123pc4_afw)
# check if treatment is not correlated with pretreatment outcome
summary(glm(y_pvv ~ treatment_c, family = binomial, data = dataset[dataset$tijd == 0, ]))
#>
#> Call:
#> glm(formula = y_pvv ~ treatment_c, family = binomial, data = dataset[dataset$tijd ==
#> 0, ])
#>
#> Deviance Residuals:
#> Min 1Q Median 3Q Max
#> -0.6131 -0.6052 -0.6052 -0.6052 1.9898
#>
#> Coefficients:
#> Estimate Std. Error z value Pr(>|z|)
#> (Intercept) -1.604437 0.019527 -82.165 <2e-16 ***
#> treatment_c -0.001810 0.002572 -0.704 0.482
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> (Dispersion parameter for binomial family taken to be 1)
#>
#> Null deviance: 17283 on 19148 degrees of freedom
#> Residual deviance: 17282 on 19147 degrees of freedom
#> AIC: 17286
#>
#> Number of Fisher Scoring iterations: 3
Conclusion: No selectivity in treatment with respect to pre-crisis PVV support
dichtomeous treatment variable (crisis exposure)
# define treatment variable based on crisis ASC only: whether there has been an increase in crisis
# asylum seekers.
dataset$treatment2 <- ifelse(dataset$tijd == 1, as.numeric(dataset$s3pc4_afw > 0), as.numeric(dataset$s3pc4_afw <
0))
# check if treatment2 is not correlated with pretreatment outcome
test <- table(dataset$treatment2[dataset$tijd == 0], dataset$y_pvv[dataset$tijd == 0])
dimnames(test)[[1]] <- c("treatment NO", "treatment YES")
dimnames(test)[[2]] <- c("PVV NO", "PVV YES")
test
prop.table(test, margin = 1)
test2 <- glm(y_pvv ~ treatment2, family = binomial, data = dataset[dataset$tijd == 0, ])
summary(test2)
#>
#> PVV NO PVV YES
#> treatment NO 15188 3051
#> treatment YES 761 149
#>
#> PVV NO PVV YES
#> treatment NO 0.8327211 0.1672789
#> treatment YES 0.8362637 0.1637363
#>
#> Call:
#> glm(formula = y_pvv ~ treatment2, family = binomial, data = dataset[dataset$tijd ==
#> 0, ])
#>
#> Deviance Residuals:
#> Min 1Q Median 3Q Max
#> -0.6051 -0.6051 -0.6051 -0.6051 1.9024
#>
#> Coefficients:
#> Estimate Std. Error z value Pr(>|z|)
#> (Intercept) -1.60504 0.01984 -80.90 <2e-16 ***
#> treatment2 -0.02565 0.09176 -0.28 0.78
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> (Dispersion parameter for binomial family taken to be 1)
#>
#> Null deviance: 17283 on 19148 degrees of freedom
#> Residual deviance: 17283 on 19147 degrees of freedom
#> AIC: 17287
#>
#> Number of Fisher Scoring iterations: 3
Conclusion:
continuous treatment variable (crisis exposure)
# define treatment variable based on crisis ASC only: whether there has been an increase in crisis
# asylum seekers.
dataset$treatment2_c <- ifelse(dataset$tijd == 1, dataset$s3pc4_afw, -dataset$s3pc4_afw)
summary(glm(y_pvv ~ treatment2_c, family = binomial, data = dataset[dataset$tijd == 0, ]))
# table(dataset$treatment2,dataset$treatment2_c) summary(dataset$s3pc4_afw[dataset$tijd==0])
# summary(dataset$treatment2_c[dataset$tijd==0])
#>
#> Call:
#> glm(formula = y_pvv ~ treatment2_c, family = binomial, data = dataset[dataset$tijd ==
#> 0, ])
#>
#> Deviance Residuals:
#> Min 1Q Median 3Q Max
#> -0.6048 -0.6048 -0.6048 -0.6048 1.8940
#>
#> Coefficients:
#> Estimate Std. Error z value Pr(>|z|)
#> (Intercept) -1.606e+00 1.961e-02 -81.905 <2e-16 ***
#> treatment2_c -7.577e-05 6.689e-03 -0.011 0.991
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> (Dispersion parameter for binomial family taken to be 1)
#>
#> Null deviance: 17283 on 19148 degrees of freedom
#> Residual deviance: 17283 on 19147 degrees of freedom
#> AIC: 17287
#>
#> Number of Fisher Scoring iterations: 3
Conclusion:
Note 3 in manuscript
- In the manuscript we state that the treatment is not related to pretreatment PVV support.
- In note 3 we state that treatment is not related to an increase in contact or threat.
We first define a treatment variable: 1 (YES, increase in asylum seekers); 0 (NO, no increase in asylum seekers)
# show that treatment is not related to changes in threat and contact in fixed effect sample. note 3
# in revised manuscript. only select y_observation at timepoint 2
ev_sel2 <- dataset_sel[dataset_sel$tijd == 1, ]
ev_sel2$treatment <- ev_sel2$s123pc4_afw > 0
mean(ev_sel2$contactnw_afw)
#> [1] 0.1285097
by(ev_sel2$contactnw_afw, ev_sel2$treatment, function(x) sd(x))
#> ev_sel2$treatment: FALSE
#> [1] 1.08223
#> ---------------------------------------------------------------------------
#> ev_sel2$treatment: TRUE
#> [1] 1.095071
with(ev_sel2, t.test(contactnw_afw ~ treatment))
#>
#> Welch Two Sample t-test
#>
#> data: contactnw_afw by treatment
#> t = 2.2851, df = 129.37, p-value = 0.02394
#> alternative hypothesis: true difference in means is not equal to 0
#> 95 percent confidence interval:
#> 0.03319162 0.46156370
#> sample estimates:
#> mean in group FALSE mean in group TRUE
#> 0.1482786 -0.0990991
# treated group decrease in contact. untreated group increase in contact. difference in difference is
# significant.
mean(ev_sel2$threat_afw)
#> [1] 0.08243341
by(ev_sel2$threat_afw, ev_sel2$treatment, function(x) sd(x))
#> ev_sel2$treatment: FALSE
#> [1] 0.5565433
#> ---------------------------------------------------------------------------
#> ev_sel2$treatment: TRUE
#> [1] 0.5852402
with(ev_sel2, t.test(threat_afw ~ treatment))
#>
#> Welch Two Sample t-test
#>
#> data: threat_afw by treatment
#> t = 0.53467, df = 127.89, p-value = 0.5938
#> alternative hypothesis: true difference in means is not equal to 0
#> 95 percent confidence interval:
#> -0.08330397 0.14499242
#> sample estimates:
#> mean in group FALSE mean in group TRUE
#> 0.08489828 0.05405405
# treated group increase in threat. untreated (less) increase in threat. difference in difference is
# not significant.
Tolsma, Jochem, Joran Laméris, and Michael Savelkoul. n.d. “Exposure to Asylum Seekers and Changing Support for the Radical Right: A Natural Experiment in the Netherlands.” PLOS ONE - (-): –. https://journals.plos.org/plosone/.
---
title: "Exposure to Asylum Seekers"
author: '[Jochem Tolsma](https://www.jochemtolsma.nl) - Radboud University, the Netherlands'
bibliography: references.bib
date: "Last compiled on `r format(Sys.time(), '%B, %Y')`"
output: 
  html_document:
    toc:  true
    toc_float: true
    number_sections: false
    code_folding: show
    code_download: yes
---

```{r, globalsettings, echo=FALSE, warning=FALSE}
library(knitr)
opts_chunk$set(tidy.opts=list(width.cutoff=100),tidy=TRUE, warning = FALSE, message = FALSE,comment = "#>", cache=TRUE, class.source=c("test"), class.output=c("test2"))
options(width = 100)
rgl::setupKnitr()


colorize <- function(x, color) {
  if (knitr::is_latex_output()) {
    sprintf("\\textcolor{%s}{%s}", color, x)
  } else if (knitr::is_html_output()) {
    sprintf("<span style='color: %s;'>%s</span>", color, 
      x)
  } else x
}

```

```{r klippy, echo=FALSE, include=TRUE}
klippy::klippy(position = c('top', 'right'))
#klippy::klippy(color = 'darkred')
#klippy::klippy(tooltip_message = 'Click to copy', tooltip_success = 'Done')
```

```{css, echo=FALSE}
pre.test {
  max-height: 300px;
  overflow-y: auto;
  overflow-x: auto;
  margin: 0px;
}

pre.test2 {
  max-height: 300px;
  overflow-y: auto;
  overflow-x: auto;
  margin: 0px;
  background-color: white
}


h1, .h1, h2, .h2, h3, .h3 {
    margin-top: 24px;
}


```

----

This website is a replication package for the paper "Exposure to Asylum Seekers and Changing Support for the Radical Right: A Natural Experiment in the Netherlands" by @Tolsma2020. It contains R code to replicate all Tables in the manuscript. 

Use the top menu to navigate to the section of interest. The section replicates the **Main Tables** of the manuscript. 

To copy the code click the button in the upper right corner of the code-chunks. 


----

## Data
All data and scripts can be found on [Github](https://github.com/JochemTolsma/ExposureASC2020)

----

## Contact
Questions can be addressed to the first author of the corresponding article, [Jochem Tolsma](mailto:j.tolsma@ru.nl). 

----  

## Additional dataprep  

We start with a datafile in long format we obtained after performing our dataprep, see [here](data/evazc v22122017.dta).  

- We delete observations of respondents where respondents were not allowed to vote or did not give an answer.  
- We delete observations with missing values on zipcode information.  
- We delete respondents with only one observation.  
- We construct the dichotomous dependent variable.  
- We construct the multinomial dependent variable. 
- We decided to perform additional robustness checks and need to match more raw data on crisis ASC. 


```{r, results='hide', class.source=c("test")}
rm(list=ls())

library(haven)

dataset <- read_stata("data/evazc v22122017.dta")

#dependent variable.
#delete not allowed to vote
table(dataset$vote, useNA = "always")
sum(table(dataset$vote, useNA = "always"))
dataset <- dataset[dataset$vote!=16,]
dataset <- dataset[dataset$vote!=17,]
#add 'VNL' to other party
dataset$vote[dataset$vote==12] <- 18
#"1"="vvd", "2"="pvda", "3"="pvv", "4"="cda", "5"="sp"
#"6"="d66", "7"="gl", "8"="cu", "9"="sgp", "10"="dier"
#"11"="50plus", "12"="vnl","13="blanco","14"="dontknow","15="no vote", "18"="other"
#define dichotomous dependent variable
dataset$y_pvv <- ifelse(dataset$vote==3, 1, 0)
#define multinomial dependent variable
dataset$vote_rwsp <- NA
dataset$vote_rwsp[dataset$vote==2 | dataset$vote==6 | dataset$vote==7 | dataset$vote==8 | dataset$vote==10 | dataset$vote==11 | dataset$vote==12 | dataset$vote==18] <- 0 #other
dataset$vote_rwsp[dataset$vote==1 | dataset$vote==4 | dataset$vote==9] <- 1 # right-wing
dataset$vote_rwsp[dataset$vote==5] <- 2 # anti-establishment 
dataset$vote_rwsp[dataset$vote==13 | dataset$vote==14 | dataset$vote==15] <- 3 #demobilized
dataset$vote_rwsp[dataset$vote==3] <- 4
# 1: right-wing; 2: anti-establishment; 3: demobilized; 0: other; 4: PVV

#delete respondents with missing data on zipcode
sum(is.na(dataset$pc4))
dataset <- dataset[!is.na(dataset$pc4),]

#delete respondents with only one observation.
dataset$one <- 1 
test <- aggregate(dataset$one, by=list(dataset$PanelistIdQuestion), FUN=sum)
names(test) <- c("PanelistIdQuestion", "ncases")
dataset <- merge(dataset, test)
table(dataset$ncases)
dataset <- dataset[dataset$ncases!=1,]

#recode tijd/time variable
table(dataset$tijd, useNA = "always")
dataset$tijd <- dataset$tijd - 1

#capacity of ASC
#s1 is regular, s2=temporary, s3=crisis, s123=total
dataset$s1pc4 <- ifelse(dataset$tijd==0, dataset$s1cap_pc4t1rel,dataset$s1cap_pc4t2rel)
dataset$s2pc4 <- ifelse(dataset$tijd==0, dataset$s2cap_pc4t1rel,dataset$s2cap_pc4t2rel)
dataset$s3pc4 <- ifelse(dataset$tijd==0, dataset$s3cap_pc4t1rel,dataset$s3cap_pc4t2rel)
dataset$s123pc4 <- dataset$s1pc4 + dataset$s2pc4 + dataset$s3pc4

#compute variables for hybrid model: within individual means (between vars) and deviations from mean (within vars). 
test <- aggregate(dataset[,c("contactnw", "threat", "s123pc4", "s1pc4", "s2pc4", "s3pc4", "y_pvv", "vote_rwsp" )], by=list(dataset$PanelistIdQuestion), FUN=mean)
names(test) <- c("PanelistIdQuestion", "contactnw_mean", "threat_mean",    "s123pc4_mean" ,  "s1pc4_mean" ,    "s2pc4_mean" ,    "s3pc4_mean" ,    "y_pvv_mean", "vote_rwsp_mean")
dataset <- merge(dataset, test)
dataset$contactnw_afw = dataset$contactnw - dataset$contactnw_mean
dataset$threat_afw = dataset$threat - dataset$threat_mean
dataset$s123pc4_afw = dataset$s123pc4 - dataset$s123pc4_mean
dataset$s1pc4_afw = dataset$s1pc4 - dataset$s1pc4_mean
dataset$s2pc4_afw = dataset$s2pc4 - dataset$s2pc4_mean
dataset$s3pc4_afw = dataset$s3pc4 - dataset$s3pc4_mean

#create filter vars for analyses: fe is fixed effects, fem is fixed effects multinomial
dataset$sel_fe = (dataset$y_pvv != dataset$y_pvv_mean)
dataset$sel_fem = (dataset$vote_rwsp != dataset$vote_rwsp_mean)
dataset$sel_s123pc4 = (dataset$s123pc4 != dataset$s123pc4_mean)

#match additional data on crisis ASC
#these data have been added in a later phase 
duur <- read.csv2("data/Adressen alle AZCs_17022017.csv")
names(duur)
duur <- duur[,c("PC4", "Soort", "Cap_Gem", "Duur.tot.4.11")]
names(duur) <- c("pc4", "soort", "cap", "duur")
duur <- duur[duur$soort==3,]
sort(unique(duur$pc4[duur$cap>0]))

#we are multiplying length of stay with number of AS
duur$s3_altop <- duur$duur * duur$cap
duur2 <- aggregate(duur[,c("cap", "duur", "s3_altop")], by=list(duur$pc4), FUN=sum)
duur2
names(duur2)[1] <- c("pc4")

dataset <- merge(dataset, duur2, all.x=T)
dataset$duur[is.na(dataset$duur)] <- 0
dataset$s3_altop[is.na(dataset$s3_altop)] <- 0
dataset$cap[is.na(dataset$cap)] <- 0

#asylum seekers in crisis centers per 1000 inhabitants weighted by length of stay
dataset$s3_altop2 <- 1000 * dataset$s3_altop / (dataset$inw2014_pc4 * dataset$pauto2014_pc4)
dataset$cap2 <- 1000 * dataset$cap / (dataset$inw2014_pc4 * dataset$pauto2014_pc4)
dataset$s3_altop <- 1000 * dataset$s3_altop / dataset$inw2014_pc4
dataset$cap <- 1000 * dataset$cap / dataset$inw2014_pc4


```




---  



## Table 1 {.tabset .tabset-fade}

### Fixed effects sample  

```{r, results="hold"}
library(psych)
require(DT) 

dataset_sel <- dataset[dataset$sel_fe==1,]
#table(dataset_sel$y_pvv, dataset_sel$tijd)
prop.table(table(dataset_sel$y_pvv, dataset_sel$tijd), margin=2)

#describeBy(dataset_sel[,c("s123pc4", "s1pc4", "s2pc4", "s3pc4", "threat", "contactnw", "s123pc4_afw", "s1pc4_afw", "s2pc4_afw", "s3pc4_afw", "threat_afw", "contactnw_afw")], group=dataset_sel$tijd)


test <- describeBy(dataset_sel[,c("s123pc4", "s1pc4", "s2pc4", "s3pc4", "threat", "contactnw", "s123pc4_afw", "s1pc4_afw", "s2pc4_afw", "s3pc4_afw", "threat_afw", "contactnw_afw")], group=dataset_sel$tijd)
#kable(round(test[[1]],2), caption="Time 1")
```

```{r, echo=FALSE, results="asis"}
datatable(round(test[[1]],2), caption="Time 1")
```

```{r, echo=FALSE, results="asis"}
datatable(round(test[[2]],2), caption="Time 2")
```


* respondents in FE sample: `r length(unique(dataset_sel$PanelistIdQuestion))`  
* zipcodes in FE sample: `r length(unique(dataset_sel$pc4))`  
* respondents experiencing change in FE sample: `r length(unique(dataset_sel$PanelistIdQuestion[dataset_sel$s123pc4!=dataset_sel$s123pc4_mean]))`  
* zipcodes experiencing change in FE sample: `r length(unique(dataset_sel$pc4[dataset_sel$s123pc4!=dataset_sel$s123pc4_mean]))`

---  


### hybrid model sample

```{r, results="hold"}

table(dataset$y_pvv, dataset$tijd)
prop.table(table(dataset$y_pvv, dataset$tijd), margin=2)

describeBy(dataset[,c("s123pc4", "s1pc4", "s2pc4", "s3pc4", "threat", "contactnw", "s123pc4_afw", "s1pc4_afw", "s2pc4_afw", "s3pc4_afw", "threat_afw", "contactnw_afw")], group=dataset$tijd)


print("respondents in hybrid sample")
length(unique(dataset$PanelistIdQuestion))

print("zipcodes in FE sample")
length(unique(dataset$pc4))

print("respondents experiencing change in FE sample")
length(unique(dataset$PanelistIdQuestion[dataset$s123pc4!=dataset$s123pc4_mean]))

print("zipcodes experiencing change in FE sample")
length(unique(dataset$pc4[dataset$s123pc4!=dataset$s123pc4_mean]))

```
--- 

## Table 2 {.tabset .tabset-fade}

```{r, results="hide"}
require(survival)
ev_sel <- dataset_sel 
m1 <- clogit(y_pvv ~ s123pc4 + tijd + strata(PanelistIdQuestion), data=ev_sel)
summary(m1)

#via different estimation of fixed effects model (a first difference design)
#only select y_observation at timepoint 2
ev_sel2 <- ev_sel[ev_sel$tijd==1,]
#need to multiply _afw with 2 (thus becomes t2-t1, instead of t2 - mean(t2,t1)
ev_sel2$s123pc4_afw <- 2*ev_sel2$s123pc4_afw
ev_sel2$s1pc4_afw <- 2*ev_sel2$s1pc4_afw
ev_sel2$s2pc4_afw <- 2*ev_sel2$s2pc4_afw
ev_sel2$s3pc4_afw <- 2*ev_sel2$s3pc4_afw
ev_sel2$threat_afw <- 2*ev_sel2$threat_afw
ev_sel2$contactnw_afw <- 2*ev_sel2$contactnw_afw

m1_alt1 <- glm(y_pvv ~ s123pc4_afw , data=ev_sel2, family=binomial)
m1_alt2 <- glm(y_pvv ~ s1pc4_afw + s2pc4_afw + s3pc4_afw , data=ev_sel2, family=binomial)
m1_alt3 <- glm(y_pvv ~ s1pc4_afw + s2pc4_afw + s3pc4_afw + contactnw_afw + threat_afw , data=ev_sel2, family=binomial)

```

### model 1

```{r, echo=FALSE}
summary(m1_alt1)
```

---  

### model 2

```{r, echo=FALSE}
summary(m1_alt2)
```

---  


### model 3

```{r, echo=FALSE}
summary(m1_alt3)
```


---  


## Table 3 {.tabset .tabset-fade}

```{r, results="hide"}
require(sandwich)
require(lmtest)
ev <- dataset

##hybrid model 
#because we include ethnic density of NB and SES of NB need to remove missing values.

#sum(is.na(ev$pnwal2014_pc4))
ev_sel4 <- ev[!is.na(ev$pnwal2014_pc4),]
#lost 90 respondents

ev_sel4 <- ev_sel4[!(ev_sel4$woz2012_pc4==0),]
#lost 22 respondents
ev_sel4 <- ev_sel4[!is.na(ev_sel4$woz2012_pc4),]
#lost 3 respondents

#center variables! 
ev_sel4$age_c <- ev_sel4$age - mean(ev_sel4$age)
ev_sel4$educ_c <- ev_sel4$educ - mean(ev_sel4$educ)
ev_sel4$pnwal2014_pc4_c <- ev_sel4$pnwal2014_pc4 - mean(ev_sel4$pnwal2014_pc4)
ev_sel4$woz2012_pc4_c <- ev_sel4$woz2012_pc4 - mean(ev_sel4$woz2012_pc4)

#run models
m1h <- glm(y_pvv ~ tijd + s123pc4_afw + s123pc4_mean + gender + age_c + educ_c + pnwal2014_pc4_c + woz2012_pc4_c, data=ev_sel4, family=binomial)
m2h <- glm(y_pvv ~ tijd + s1pc4_afw + s2pc4_afw + s3pc4_afw + s1pc4_mean + s2pc4_mean + s3pc4_mean + gender + age_c + educ_c + pnwal2014_pc4_c + woz2012_pc4_c, data=ev_sel4, family=binomial)
m3h <- glm(y_pvv ~ tijd + s1pc4_afw + s2pc4_afw + s3pc4_afw + s1pc4_mean + s2pc4_mean + s3pc4_mean + threat_mean + contactnw_mean + gender + age_c + educ_c + pnwal2014_pc4_c + woz2012_pc4_c, data=ev_sel4, family=binomial)
m4h <- glm(y_pvv ~ tijd + s1pc4_afw*pnwal2014_pc4_c + s2pc4_afw*pnwal2014_pc4_c + s3pc4_afw*pnwal2014_pc4_c + s1pc4_mean + s2pc4_mean + s3pc4_mean + threat_mean + contactnw_mean + gender + age_c + educ_c + woz2012_pc4_c, data=ev_sel4, family=binomial)


# summary(m1h)
# summary(m2h)
# summary(m3h)
# 
# coeftest(m1h, vcov = vcovCL(m1h, cluster = ~ PanelistIdQuestion))
# coeftest(m2h, vcov = vcovCL(m2h, cluster = ~ PanelistIdQuestion))
# coeftest(m3h, vcov = vcovCL(m3h, cluster = ~ PanelistIdQuestion))
```

### model 1  
```{r, echo=FALSE}
round(coeftest(m1h, vcov = vcovCL(m1h, cluster = ~ PanelistIdQuestion)), 3)
```

---  

### model 2  
```{r, echo=FALSE}
round(coeftest(m2h, vcov = vcovCL(m2h, cluster = ~ PanelistIdQuestion)), 3) 
```

---  

### model 3  
```{r, echo=FALSE}
round(coeftest(m3h, vcov = vcovCL(m3h, cluster = ~ PanelistIdQuestion)), 3)
```


--- 

### Model 4: no interaction with ethnic density

```{r, echo=FALSE}
round(coeftest(m4h, vcov = vcovCL(m4h, cluster = ~ PanelistIdQuestion)), 3)
```

--- 


## Inflow asylum seekers not related to PVV support T1. {.tabset .tabset-fade}

In our manuscript we state the following: 

> Voters of neighbourhoods that would experience an inflow of asylum seekers did not differ with respect to radical right support from voters that would not experience an inflow of refugees: pre-crises support for the PVV for both the ‘treated’ and ‘untreated’ groups was approximately 17%.

### dichtomeous treatment variable (total exposure)
```{r, results='hold'}
#define treatment variable: whether there has been an increase in total asylum seekers.  
dataset$treatment <- ifelse(dataset$tijd==1, as.numeric(dataset$s123pc4_afw>0), as.numeric(dataset$s123pc4_afw<0))

#check if treatment is not correlated with pretreatment outcome
test <- table(dataset$treatment[dataset$tijd==0],dataset$y_pvv[dataset$tijd==0])
dimnames(test)[[1]] <- c("treatment NO", "treatment YES" )
dimnames(test)[[2]] <- c("PVV NO", "PVV YES" )
test
prop.table(test, margin=1)
test1 <- glm(y_pvv ~ treatment, family=binomial, data=dataset[dataset$tijd==0,])
summary(test1)

```
This is what we report in the manuscript.  

- No selective treatment!  
- Among both groups, PVV support just below 17%

---  


### continuous treatment variable (total exposure)

```{r, results='hold'}
#define treatment variable: whether there has been an increase in total asylum seekers.  
dataset$treatment_c <- ifelse(dataset$tijd==1, dataset$s123pc4_afw, - dataset$s123pc4_afw)

#check if treatment is not correlated with pretreatment outcome
summary(glm(y_pvv ~ treatment_c, family=binomial, data=dataset[dataset$tijd==0,]))

```
Conclusion: No selectivity in treatment with respect to pre-crisis PVV support

---  

### dichtomeous treatment variable (crisis exposure)
```{r, results='hold'}
#define treatment variable based on crisis ASC only: whether there has been an increase in crisis asylum seekers. 
dataset$treatment2 <- ifelse(dataset$tijd==1, as.numeric(dataset$s3pc4_afw>0), as.numeric(dataset$s3pc4_afw<0))
#check if treatment2 is not correlated with pretreatment outcome
test <- table(dataset$treatment2[dataset$tijd==0],dataset$y_pvv[dataset$tijd==0])
dimnames(test)[[1]] <- c("treatment NO", "treatment YES" )
dimnames(test)[[2]] <- c("PVV NO", "PVV YES" )
test
prop.table(test, margin=1)
test2 <- glm(y_pvv ~ treatment2, family=binomial, data=dataset[dataset$tijd==0,])
summary(test2)
```
Conclusion:  

- no selectivity

---  

### continuous treatment variable (crisis exposure)

```{r, results='hold'}
#define treatment variable based on crisis ASC only: whether there has been an increase in crisis asylum seekers. 
dataset$treatment2_c <- ifelse(dataset$tijd==1, dataset$s3pc4_afw, - dataset$s3pc4_afw)
summary(glm(y_pvv ~ treatment2_c, family=binomial, data=dataset[dataset$tijd==0,]))
#table(dataset$treatment2,dataset$treatment2_c)
#summary(dataset$s3pc4_afw[dataset$tijd==0])
#summary(dataset$treatment2_c[dataset$tijd==0])
```
Conclusion:  

- no selectivity

---  

## Note 3 in manuscript  

- In the manuscript we state that the treatment is not related to pretreatment PVV support.  
- In note 3 we state that treatment is not related to an increase in contact or threat. 

We first define a treatment variable: 1 (YES, increase in asylum seekers); 0 (NO, no increase in asylum seekers)

```{r}

#show that treatment is not related to changes in threat and contact in fixed effect sample. note 3 in revised manuscript. 
#only select y_observation at timepoint 2
ev_sel2 <- dataset_sel[dataset_sel$tijd==1,]
ev_sel2$treatment <- ev_sel2$s123pc4_afw>0
mean(ev_sel2$contactnw_afw)
by(ev_sel2$contactnw_afw, ev_sel2$treatment, function(x) sd(x))
with(ev_sel2, t.test(contactnw_afw~treatment))
#treated group decrease in contact. untreated group increase in contact. difference in difference is significant. 

mean(ev_sel2$threat_afw)
by(ev_sel2$threat_afw, ev_sel2$treatment, function(x) sd(x))
with(ev_sel2, t.test(threat_afw~treatment))
#treated group increase in threat. untreated (less) increase in threat. difference in difference is not significant. 

```

---  


Copyright © 2020 Jochem Tolsma