The MIPEX (Migrant Integration Policy Index) is a relatively widely used index. I have demonstrated empirically that it can be used as a scale, but have voiced some concerns about the weak theoretical foundation.
The number of countries covered by the MIPEX is increasing, and there are 148 indicators available. In an attempt to make most of these data, I have picked the parts of the MIPEX that most closely fit the typology developed in Koopmans et al. (2005).
To get a better handle of developments over time, I use the SOM extension of MIPEX, and here is how the situation in Austria and the Netherlands has changed over time.
We can discuss the labels, but there are clear differences between countries, and citizenship regimes are clearly dynamic. This means that, yes, citizenship regimes are worth investigating, but country dummies will fail to provide an accurate picture.
Koopmans, Ruud, Paul Statham, Marco Giugni, and Florence Passy. 2005. Contested Citizenship: Immigration and Cultural Diversity in Europe. Minneapolis: Minnesota University Press.
Ruedin, Didier. 2011. “The reliability of MIPEX indicators as scales.” SOM Working Paper 3: 1–19.
Immigration to Western European countries is nothing new. Arguably, the diversity of immigrants has increased in recent years. Inevitably, this leads to a more divers population in most European countries, and this diversity is viewed by some with scepticism. The fear is that increased (ethnic) diversity due to immigration threatens social cohesion. However, despite similar demographic developments across Western European countries, reactions to increased diversity have been quite different across countries. The reason for this can be found in historical legacies and the development of the welfare state — an institution that is inclusive by design.
The concept of social cohesion is broad, to say the least. A simple definition can be derived from shared values and feelings of togetherness in society. Depending on the political colour, either aspect tends to be highlighted. A minimalistic definition thus insists on individuals feeling part of society, and trusting each other — other groups and individuals accepted as full members of society.
In the context of immigration, five indicators can be considered: generalized trust, naturalization rates, confidence in key institutions, early leavers, and voter turnout.
In the last two weeks I had several conversations on how to best measure descriptive representation (i.e. the numerical representation of groups). I treated this in my recent monograph, but also in a conference paper in 2011. In my view, there are three important points: (1) What’s best depends on your research question. (2) It is important to include the population and the representatives. (3) I recommend two measures as follows: Ri / Pi for measuring the representation of a single group (e.g. a specific minority group, or all minorities combined as opposed to the majority population); for the situation at the national level, I prefer the Rose index (1 – 0.5 * |Ri – Pi|) over the Gallagher index (but following recent simulations I have undertaken, less strongly than previously). Ri stands for the proportion of a group among the representatives, Pi for the proportion among the population.
In this working paper (The Paradox of Manifestos) Ian Budge replies to some of the methodological critiques of the MARPOR/CMP/MRG manifesto projects. The paradox lies in the contrast between the positive research experience of most users of the manifesto data, and the at times rather harsh methodological critiques of manifesto data.
Unfortunately the paper is quite selective in which critiques it engages with (table 2 is rather short). The biggest issues I have come across with the data in question are a rigid coding scheme (this of course has advantages, but the data can struggle to reflect the situation on the ground adequately), and party rankings that defy common sense and expert judgements. In my view the many happy users Budge identifies are a sign of good enough data (not necessarily good data), and also of the extensive coverage of the data.
Having tried and compared different methods to measure party positions, I have serious doubts whether we’re even close to measuring party positions with precision — or do precise party positions exist at all? No, I don’t want to give up on measuring party positions, after all the different methods correlate enough to agree on the ranking of parties. We should, however, always express the precision and error in measuring party positions, not just the point estimates.
Brian Gaines and Rein Taagepera have recently clarified how to measure two-partiness (or should that be two-partyness? It isn’t in my dictionary, so I went with the analogy with happiness, although both forms seem to be in use). They highlight that the effective number of parties (Neff) is inadequate as a measure of two-partiness: very different constellations can lead to similar values of Neff.
Interestingly, the suggest two distinct measures for measuring two-partiness (T, D2), and I have implemented them in my R package polrep, available on R-Forge. It’s probably worth reiterating at this point that Gaines and Taagepera do not suggest we stop using the effective number of parties in general, but that we use more appropriate measures if we’re interested in two-partiness.
Gaines, Brian J., and Rein Taagepera. 2013. “How to Operationalize Two-Partyness.” Journal of Elections, Public Opinion & Parties 0 (0): 1–18. doi:10.1080/17457289.2013.770398.