Political Parties, the Eurozone Crisis, and ICEWS Data

Large-scale event data based on worldwide media reports already help us to explain and forecast crises events such as civil wars or insurgencies. But the millions of data points provided by ICEWS or GDELT are a treasure trove for social scientists interested in all kinds of topics, whether they involve violence or not.

For example, they can be used to look at the way politicians interact with each other. A lot of research on political competition in the past two or three decades has focused on party positions and politicians’ ideological leanings, fueled by the convenient availability of suitable data (i.e. NOMINATE and the Comparative Manifestos Project). But political competition is about more than just ideology and policy positions. Recent contributions on the Monkey Cage (here and here) have pointed out that the discussion about polarization in the US is to a significant degree about the way politicians interact with each other: that they are more interested in attacking each other verbally, rather than “getting things done” for the good of the country. Arguably, this kind of behavior is responsible for at least part of the gridlock and lack of legislative productivity in Washington even in areas where there is significant bipartisan consensus about policy. However, serious empirical investigations into the way politicians interact with each other have been largely absent, the main reason being a lack of suitable data. But the availability of large-scale media event data can help to change that.

The machine-coded media stories that make up the ICEWS (or GDELT) data provide fine-grained information about how politicians publicly interact with each other, and with other societal actors. They record when one politician criticizes or denounces someone, and they also document when two actors praise each other or express a desire to work together. This allows us to analyze conflict and cooperation between political actors in a systematic manner. In a new working paper, I use the ICEWS event data to analyze the way parties interacted in the 11 Eurozone countries between 2001 and 2011.

I divide the events into two categories, cooperative (e.g. one actor praises another) and conflictual (e.g. one actor criticized another), based on the CAMEO codebook. For each country, the data provide between 2000 and 30,000 events, involving between 125 and almost 450 actors (parties, NGOs, military, etc.). The actors have a complex network of interactions with each other. To summarize them in a simple and intuitive manner, I estimate latent network models for each country-year. Without getting into the technical details, these models estimate the position of each actor in a hypothetical latent space. Actors that are positioned close together in the latent space have a higher probability of interacting with each other frequently in a cooperative way, while actors positioned far away from each other are likely to interact in a conflictual manner.

Posterior latent space estimates for Greece in 2002, 2006, and 2010. Parties: PASOK (green), ND (blue), KKE (red). All other actors in gray.

Latent space estimates for Greece in 2002, 2006, and 2010. Parties: PASOK (green), ND (blue), KKE (red). All other actors in gray.

The graph above shows the posterior distributions of the latent positions for Greece from 2002, 2006, and 2010. The left-of-center Panhellenic Socialist Movement (PASOK) is in green, the right-of-center New Democracy (ND) in blue, and the third party in Greece, the Communist Party (KKE), in red. All other actors are plotted in gray. In 2002, PASOK formed the government, with ND and KKE sitting in opposition. The latent space estimates reflect this by positioning the two parties not in government relatively close together, indicating a high probability of a cooperative relation. PASOK, however, is located far away from either one, signaling a contentious public relationship between the government party and its opposition. In 2006, ND was the government party. Again, this shows in the latent space estimates. Now PASOK  (green) and KKE (red) are located relatively close together, while ND (blue) is located far away in the latent space.

In 2010, this government-opposition dichotomy has broken down. All parties are located closely together in the latent space, indicating mutually cooperative relationships. The picture looks very similar in 2009. Of course, this was at the height of the economic crisis, when Greece recorded a dramatic drop in its GDP per capita growth from 3.1% in 2007 to -5.2% in 2010. This suggests that there was something akin to a “rallying around the flag” effect going on. It has been shown that this effect is a frequent byproduct of wars or terrorist attacks, where politicians “put politics aside” and work together in a cooperative manner to try to address the fundamental challenges faced by their country. In many ways, the economic problems that hit the Eurozone at the end of the last decade posed an existential threat to the livelihood of the countries most affected by it, endangering standards of living that were built up over many decades. In Greece, this seems to have been accompanied by parties putting aside their differences and interacting in a cooperative manner.

But we can see this pattern not just in Greece. In the paper, I estimate a series of models of the average distance between parties in the latent space for all 11 Eurozone countries, using different specifications and control variables. The results are clear: The worse the economic situation, the more cooperative the relationship between political parties. In “normal” times, parties in the Eurozone countries interacted mostly in a conflictual manner. But as economic growth collapsed towards the end of the decade, the relationships between parties in countries most affected by the economic crisis became overwhelmingly cooperative, that is they moved closer together in the latent network space. This finding suggests that just like war or terrorist attacks, a severe economic crisis can lead to a suspension of the regular pattern of political interaction.

Of course, it remains to be seen if this kind of “rallying around the flag” is a general phenomenon that can be observed in other economic crises as well, or whether it was restricted to the rather extraordinary Eurozone crisis. Future work will look at conflict and cooperation between parties in other parts of the world (and/or in the last century). What is clear from this research already, however, is the usefulness of machine-coded event data like ICEWS and GDELT. Although they were primarily developed to provide insights into protests, civil wars, or international conflicts, they can help us answering questions in other areas of political science as well. And party competition is just one of them.

More information can be found here and at www.simonweschle.com

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