The Swiss Ban on Minarets Confirms the Contact Hypothesis

In November 2009, Switzerland held a referendum that sought approval for a constitutional ban on minarets. 57.5% of the voters supported the ban. The ban was supported by the Swiss right wing party SVP. Poster ads that supported the ban used images as the ones below, tapping on anti-muslim sentiments and on diffuse fear towards muslims. Thus, the referendum has been widely interpreted as a vote against foreigners by the media.

A student of mine, Michael Jäckli, had the idea to use archival polling data from the referendum for a test of Alport's Contact Hypothesis. It suggests that "interpersonal contact is one of the most effective ways to reduce prejudice between majority and minority group members". Thus, Swiss municipalities (the smallest unit for which polling data is available from the Swiss bureau of statistics) with a low percentage of foreign nationals should show a high rate of approval of the ban, while municipalities with a high percentage of foreigners should show a lower rate of approval.

In order to test this assumption, Michael obtained the agreement to the ban on the municipal level from the Swiss bureau of statistics, along with indicators for educational level and social-economic status for the municipalities. He was then able to regress the agreement for the ban on the percentage of foreigners:

As visible in the plot, municipalities with a below-average percentage of foreigners tended to accept the ban at above-average levels. Without controlling for other variables, an increase of the percentage of foreigners in a municipality by 1% would decrease its agreement to the ban by 0.37%.

As lower education and social status is usually associated with more right-wing political views, Michael tested whether the negative relationship between the percentage of foreigners and the approval of the ban still holds under statistical control of these two variables. The according stepwise hierarchical regression is presented below.

It reveals that low economic status and low educational levels are indeed associated with an acceptance of the ban. But even if these factors are controlled for, there is still a significant yet small negative effect of the percentage of foreigners on the approval of the ban: Municipalities with a low percentage of foreigners tended to support the ban.

Why does an increase of the percentage of foreigners decrease the support for the ban on minarets? Possibly because municipalities can profit from their foreign population: An inspection of the bivariate correlations of the measurement variables (see below) reveals that the percentage of foreigners is associated with higher levels of education and social-economic status.

The R code that produced these results can be found below. Michael's data set can be downloaded in .CSV-Format here.

# import the data

minarettdata <- read.csv(file = "minarettdata.csv")

# inspect the data

head(minarettdata)

length(minarettdata$municipality_nr)

# make the data available

attach(minarettdata)

# generate the plot

plot(foreigners, minarett_approval, main = "Approval of the minaret ban in all Swiss municipalities\nas a function of municipal share of foreign nationals", xlab = "Municipal share of foreign nationals (in %)", ylab = "Municipal share of voters approving the ban (in %)")

abline(lm(minarett_approval ~ foreigners), col = "blue")

abline(h = 57.5, lty = 2)

abline(v = 22, lty = 2)

text(x = 60, y= 48, round(coef(lm(minarett_approval ~ foreigners))[2], digits = 2), col = "blue")

text(x = 22, y = 26, "Swiss-wide share of foreign nationals (22%)", cex = .70, pos = 4)

text(x = 49, y = 60, "Swiss-wide approval of the\nban on minarets (57.5%)", cex = .70, pos = 4)

mean(foreigners)

mean(minarett_approval)

# test the stepwise regression

library(QuantPsyc)

lmodel.1 <- lm(minarett_approval ~ SES + edu_level)

summary(lmodel.1)

lm.beta(lmodel.1)

lmodel.2 <- lm(minarett_approval ~ SES + edu_level + foreigners)

summary(lmodel.2)

lm.beta(lmodel.2)

# determine the significance of the increase in R-squared

anova(lmodel.1, lmodel.2)

# print the correlation table

corstarsl2(minarettdata[,2:5])

library(xtable)

xtable(print(corstarsl2(minarettdata[,2:5])))

# clean up

rm(minarettdata, lmodel.1, lmodel.2)