MIPEX Over Time

As part of the SOM project, we have expanded the MIPEX indicators backward over time for seven countries. In line with the MIPEX approach, we collected data every few years. This is useful to get an overview of how policies develop over time, but is restrictive for analyses with yearly data. Here are three possibilities to overcome this restriction.
First, we could focus on a smaller set of indicators, and collect data for every year. My earlier analysis suggests that this could be done without serious loss of information, but it would still entail a serious amount of work, especially for countries where relevant legislation covers many legal texts. This would be the correct course of action.
Second, we could impute the missing data. Without additional variables that cover the years in between, multiple imputation is impossible; but we can use manual imputation at the aggregate level. I have used geometric means, but gradual policy changes are in most cases unrealistic, even if they may offer good approximations.
Third, we can use the documentation available to estimate when policy changes occurred. Again, working at the aggregate level, many of the policy changes can be assigned more precisely. There are a few drawbacks to this approach, of course. First, if there were two policy changes within a period, or it is unclear which policy strands are affected by a change in law. It becomes difficult to choose. Fortunately this did not occur often, and I picked the year that – based on the text available – seemed more significant. Second, the documentation available does not cover all changes with sufficient detail. Here I resorted to a heroic assumption: If I observed many changes in a particular year in some of the strands, I assumed that this particular year was also likely to be the year the policies in other strands changed.
Short of collecting new data, does it matter whether I impute my data or look at the documentation? On the one hand, I get a Pearson correlation coefficient of 0.96 (overall, with most individual strands per country correlating at r>0.85). [The SOM asylum indicators are included here, although they turn out the most problematic ones. The biggest discrepancies occur when policies change radically, obviously. This is rare, but they occur particularly in the asylum indicators.]
Here is an example comparing three indicators over time for Austria. GM refers to imputation using geometric means; DOC to the use of documentation. For the years in yellow, data are collected. Obviously, both approaches pick up the same trends, but if we are interested in the level of anti-discrimination policy in Austria in 2001, we’d get rather different estimates.

Inequality = Politicization of Immigration?

An article by Frederick Solt (2011) shows an association between nationalism and economic inequality: more inequality prompts more nationalism (as diversion). It is conceivable that this idea can be expanded and applied to the politicization of immigration. Yes, it’s a wide shot.

I use the data from the SOM project to capture the politicization of immigration. Political claims in newspapers are used as the basis, and I consider two aspects: (more claims about immigration) and polarization (positions of claims differ more), along with a compound measure. To capture economic inequality, I use the Gini coefficient provided by the World Bank (I simply assumed that standardization would not be an issue with Western European countries).

The figures (simple scatter plots is all I did) suggests there is no direct association between economic inequality and the politicization of immigration in the Western European countries examined, except perhaps in the United Kingdom.

Solt, F. 2011. Diversionary Nationalism: Economic Inequality and the Formation of National Pride. The Journal of Politics 73 (3): 821–830. doi:10.1017/S002238161100048X.

Do POS Converge Over Time?

Last week I looked at the (lack of) convergence of immigration policies over time. Today, I examine whether indicators of political opportunity structure (POS) converge over time. We collected data to examine this as part of the SOM project (description of indicators).

In the following table I consider just a few indicators of POS. I include both issue-specific and generic aspects of POS for the seven countries covered in the SOM project (AT, BE, CH, ES, IE, NL, UK).

Indicator Convergence
Effective number of parties in legislature No convergence
Seat-share of anti-immigrant parties No convergence
Political parties have special arrangements for immigrant candidates No convergence
Public money for immigrant organizations No convergence
Specific department for migration Clear convergence
Right to vote at national level, TCN All countries the same
Embedded consultation No convergence

There is no convergence for most of the indicators considered here. There is only one real exception: we observe a clear trend to have a dedicated department for migration. In 1995 only Switzerland had such an organizational arrangement, in 2010 only Austria did not have one.

Do Immigration Policies Converge Over Time?

Do immigration policies in Europe converge over time? As part of the SOM project, we collected data to examine this. Using the MIPEX criteria, we cover seven countries 1995 to 2010. We complemented this with an indicator on asylum policies.

Strand Higher Lower SD
Labour Market 7 -7
Family Reunion 3 4 +3
Long-Term Residence 4 3
Political Participation 4 3 -3
Access to Nationality 4 3 +6
Anti-Discrimination 6 1 -7
Asylum Seekers 2 4

This table gives the number of countries where MIPEX scores in 2010 are higher than in 1995 (respectively lower), as well as the difference in standard deviations (AT, BE, CH, ES, IE, NL, UK). So we can see convergence in two strands only: labour market and anti-discrimination.

Do Civil Society Organizations See Immigration Positively?

Civil society organizations (CSO) are important political actors in the debate on immigration. As part of the SOM project we examined the politicization of immigration in seven Western European countries, 1995 to 2009. Civil society organizations are responsible for between 11 and 28 per cent of claims in the news.

With the exception of the UK, most of the claims by civil society organizations are positive: Between around 70 and 80 per cent of claims by civil society organizations on immigration are positive.

Edited on 1 Feb 2013: Removed some incorrect numbers; the patterns is generally observed.