Oct 292012
 

Like social networks, multilevel data structures are everywhere once you start thinking about it. People live in neighbourhoods, neighbourhoods are nested in municipalities, which make up provinces – well, you get the picture. Even if we have no substantive interest in their effects, it often makes sense to control for structures in our data to get more realistic standard errors.

Now the good folks over at the European Social Survey have reacted and spent the Descartes Prize money on compiling multilevel information and merging them with their own data. So far, the selection is a little bit disappointing in some respects. Homicide rates, for instance, are reported on the national level only. But there are some pleasant surprises (I guess due to Eurostat, who collect such things): We get unemployment, GDP growth and even student numbers at the NUTS-3 level. Since you asked, NUTS is the Nomenclature of (subnational) Territory, and level 3 is the lowest level for which comparative data are normally published.

Regrettably, the size and number of level 3 units is not necessarily comparable across countries: For Germany, level 3 corresponds to about 400 local government districts, while France is divided into 96 European Departments. But if you need to combine top-notch survey data with small(ish) regional data, it’s a start, and not a bad one.

Sep 292008
 

In a recent article in the European Journal of Political Research, Kestilä and Söderlund claim (amongst other things) that in the French regional elections of 2004, turnout and district magnitude have significant negative effects on the extreme right vote whereas the effects of the number of party lists and unemployment are positive and significant. Most interestingly, immigration (which is usually a very good predictor for the radical right vote) had no effect on the success of the Front National. More generally, they argue that a subnational approach can control for a wider range of factors and provide more reliable results than cross-national analyses (now the most common approach to this phenomenon). My colleague Liz Carter and I disagreed and engaged in a massive replication/re-analysis endeavour. The outcome is a critique of the KS model of subnational political opportunity structures in regional elections. In this paper, we dispute Kestilä’s and Söderlund’s claims on theoretical, conceptual and methodological grounds and demonstrate that their findings are spurious. Today, the European Journal has accepted the article for publication (probably in 2009) 🙂

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Mar 192008
 

If you are interested in subnational politics, France is an interesting case for many reasons. On the one hand, the country is highly centralised and divided into 96 (European) Departements (administrative units) with equal legal rights (though Corsica is a bit of an exception to this). In fact, Departements were created after the revolution in an attempt to replace the provinces of the Ancien Regime with something rational and neat. On the other hand, the Departements are vastly different in terms of their size, population, economic, political and social structure, which gives you a lot of variance that can be modelled. Electoral data is often made available at the level of the Departement (see e.g. the useful book by Caramani for historical results and the CDSP and government websites for recent elections) or can be aggregated to that level since electoral districts are nested in Departements. The French National Insitute for Statistics and Economic Studies (INSEE) has a wealth of data from the 1999 census and other sources, and even more is available from Eurostat. One thing that is incredibly annoying, however, is that many sources like Caramani, INSEE and the Wikipedia use the traditional French system. This system (which is part of the ISO standard ISO 3166-1) assigns numbers from 1 to 95 that once reflected the alphabetical order of the Departments’ names, though this initial order was a bit scrambled by territorial changes. The most obvious result of these are the odd 2A/2B codes for Corsica (after 1975, see this article on the French Official Geographic Code for the details). Rather unsurprisingly, Eurostat (and a few others) prefer the European NUTS-3 codes, which have a hierarchical structure that consists of a country (FR), region, and subregion (=Departement) code. If you want to merge Departmental data from various sources you obviously have to map one system to the other, which is cumbersome and prone to error. That’s why I wrote a little script in Perl that reads a table of Departmental Codes and creates a do-File for Stata, which does the actual mapping. From within Stata, you can simply type net from http://www.kai-arzheimer.com/stata to get the whole package. It should be fairly easy to adopt this to your own needs – enjoy!

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