Ethnically homogeneous?

The other day I was at a conference, and Poland was described as ethnically homogeneous. This is not a controversial observation, I guess. The speaker was then using this homogeneity as an ‘explanation’ for current government rhetoric against Muslims in the country — compared to government rhetoric in a more heterogeneous country. This struck me as an odd explanation, after all we all know that ethnic groups and their boundaries are socially constructed. This way, the observation that in a country where the common view is one of internal homogeneity also features exclusivity to ‘others’ seemed trivial if not circular. I’m far from claiming that social construction renders ethnic differences meaningless — the consequences are very real indeed — but as an ‘explanation’ this way I’m struggling a bit.

Ruedin, Didier. 2009. ‘Ethnic Group Representation in a Cross-National Comparison’. The Journal of Legislative Studies 15 (4):335–54. https://doi.org/10.1080/13572330903302448.
Ruedin, Didier. 2013. Why Aren’t They There? The Political Representation of Women, Ethnic Groups and Issue Positions in Legislatures. Colchester: ECPR Press.

Level of Descriptive Representation Unrelated to Population Size

Here’s a null result I’ve long wanted to share: The level of descriptive representation seems unrelated to population size. More specifically, the share of women in national legislatures and the share of ethnic minority groups in national legislatures (relative to their size in the population) does not correlate substantially with the overall population size. I’ve looked into this a few years back after receiving comments that the nature of political representation could be expected to be different in small countries (microstates). I’m still unsure why this would be the case — except that in really small chambers including an additional woman adds much to the share of women.

Do Ethnically Diverse Associations Lead to Positive Attitudes?

When it comes to inter-ethnic relations, contact between groups has long been recognized as a factor potentially reducing tensions. In this sense, ethnically diverse voluntary associations have been lauded as a means to foster positive attitudes towards other groups. In a recent paper Tom van der Meer has a close look at the role of ethnically diverse associations, and concludes that in this particular case, we’re looking at self-selection effects.

The paper concludes that voluntary associations do not live up to their socializing potential to reduce tensions between different ethnic groups. Self-selection in this case means that people who are more open towards other groups in society are more likely to be in ethnically mixed association.

While the paper is a step forward in many aspects, it really would have needed panel data to support the strong conclusions it makes. In the meantime, we’re left with a caution and encouraged to dig deeper.

van der Meer, Tom. 2016. ‘Neither Bridging nor Bonding: A Test of Socialization Effects by Ethnically Diverse Voluntary Associations on Participants’ Inter-Ethnic Tolerance, Inter-Ethnic Trust and Intra-Ethnic Belonging’. Social Science Research 55 (January): 63–74. doi:10.1016/j.ssresearch.2015.09.005.

Mapping Ethnic Representation Scores in R

Here’s a demonstration of how easy it is to map with the R package rworldmap by Andy South. I map the ethnic representation scores in my 2009 JLS article, available from my Dataverse. I used the tab-delimited file, which contains the country name, Q-scores, R-scores, and a binary indicator of minority presence. I searched&replaced the tabs with commas, and added a new column for the ISO3 country codes.

After that, it’s just a few lines in R:

library(rworldmap)
dta <- read.csv("Representation.csv") # these are the data described above.

> head(dta) gives:
Country ISO3 QScore RScore Present
1 Afghanistan AFG 0.803 0.697 1
2 Albania ALB 0.980 0.510 1
3 Algeria DZA NA NA 1
4 Andorra AND 0.980 0.000 0
5 Angola AGO NA NA 1
6 Antigua & Barbuda ATG 0.910 0.000 0

Next we have to identify the countries. joinCode specifies that I used ISO3, nameJoinColumn specifies the variable with the country abbreviations:

jcd <- joinCountryData2Map(dta, joinCode="ISO3", nameJoinColumn="ISO3")

Next a line from the package vignette that makes the plot use the space available.
par(mai=c(0,0,0.2,0),xaxs="i",yaxs="i")

Now, while mapCountryData(jcd, nameColumnToPlot="QScore") would suffice to draw a map, I used some of the options available (e.g. a blue ocean, light grey for missing data), and drew the legend separately for a little extra control:

mapParams <- mapCountryData(jcd, nameColumnToPlot="QScore", addLegend=FALSE, mapTitle="Ethnic Representation Scores", oceanCol="light blue", missingCountryCol="light grey")
do.call(addMapLegend, c(mapParams, legendWidth=0.5, legendMar = 4))

The title is a bit off, but other than that, I’m pretty happy for a first cut with so little coding.

ers-map
Ruedin, Didier. 2009. ‘Ethnic Group Representation in a Cross-National Comparison.’ The Journal of Legislative Studies 15 (4): 335–54. doi:10.1080/13572330903302448.

Explaining MIPEX Scores with Patterns of Democracy

I’m always happy to see research published that I hoped to get done ‘one of theses days’. A recent paper in West European Politics uses a sophisticated model to statistically explain immigration policies using patterns of democracy. Different aspects of democracy are associated in different ways, but I’m a bit puzzled by the decision of the authors to downplay the influence of GDP. Perhaps there’s still a difference between political science and sociology after all, and institutional differences count more, so to speak, than for example a modernization thesis.

Wasn’t it already published, I’d include this paper as an example in my recent paper on recombining MIPEX. It’s just one of these instances where aggregated MIPEX scores (and in the supplementary material MIPEX dimensions) are used. Well, if you’re not into recombining MIPEX, a look at a pure reliability assessment of MIPEX might have helped making a slightly stronger case. With just 30 countries, more sensitivity analysis would also help. For instance, is there something about “settler legacies” or is it just Anglosaxon countries with a longer tradition of regulating race and ethnicity — something that MIPEX honours?

Future efforts should make use of the fact that MIPEX data have been collected over time, which makes for stronger conclusions (institutions or otherwise). They may also use theory other than the empirically refuted assumption that proportional systems are good for all kinds of minorities under all circumstance. Irrespective of these quibbles, with the paper by Anita Manatschal and Julian Bernauer we have a good basis to build on.