Leo Ahrens

Today at 2 and tomorrow at 12, these two panels take place at @apsa.bsky.social. The papers are so intriguing that I finished reading them pre-arrival! 😱 @leoahrens.bsky.social & me also present. Including work on the class biases in pledge breaking

#APSA2025 #polisky @apsainequality.bsky.social ‬

I feel the same! At the same time, it has lead me to believe that policy preferences are not as important as suggested by the literature.

What do you think about Achen/Bartel's Democracy for Realists? I really liked the book but its implications are dire for my research field (& my past research), which commonly relies on the "folk theory" of democracy

1/3) 📣Out in Party Politics:
We trace the equal rights and economic equality positions of 69 center-right and far-right parties since 1970 in 12 countries. We find that center right parties did not react to/address equal rights concerns and economic inequality

journals.sagepub.com/doi/epub/10....

📬🚩
Our article on parties' programmatic responses to inequality - and more often the lack of responses - will come out in APSR!

Here is the @excinequality.bsky.social working paper:

kops.uni-konstanz.de/bitstreams/4...

with @klueserthan.bsky.social & Martin Haselmayer 🧗🙏

Just like Economists are unaware that multilevel models exist!

I'll leave this here

Did German left parties abandon economic equality in favor of equal rights, as the diagnosis of the "Lifestyle-Left" suggests? Analyzing party programs since 1970, we caution against sweeping claims in Unequal German Democracy (open access). 3 key qualifications👇
www.tandfonline.com/doi/full/10....

That would be much appreciated! Thank you

data from a wide variety of countries.

more credible identification to lend credibility to the theory. Even in this optimal world, the cross-sectional analysis still has value—it offers evidence supporting the claim that the credible causal effect estimates from, say, an experiment are “replicated” in observational

I am fully aware of the weaknesses relating to causal identification of the present paper, but it can show that real-world data is consistent with the presented theory. Optimally, further people will do connected research and use a different approach with

In my opinion, important topics should be analyzed with a mix of different methods due to their unique strengths and weaknesses—and cross-sections have their place in this methods mix, despite their weaknesses.

But it always remains unclear to what extent the results from the survey/lab actually translate into real-life politics. Experiments offer an artificial environment with often unclear generalizability. For me, that is the unique strength of observational data.

I have a similar reservation regarding experiments. Of course, it would be possible to set up a survey or lab experiment that analyzes the research question at hand, for example by triggering people to see their government as incompetent, etc.

Multi-country cross-sections, as this paper, analyze a much broader set of contexts, which weakens this concern.

And what about social media use other than early-stage Facebook, which was quite different to today? We don’t know from this paper.

Economists recently used the geographically staggered rollout of Face-book in a natural experiment for causal estimates. This is great, but what about social media use in other countries?

Natural experiments tend to analyze a very specific case, and it remains unclear to what extent the specific results hold across contexts. A good example is the relationship between social media use and depressions.

The superior methods (from a causal id perspective) also have problems, or to put it more positively, cross-sections offer some advantages relative to them. The advantages relate to generalizability.

I consider the research topic to be important enough to warrant research with a suboptimal methodological approach.

The alternative would be to drop the research topic, but this creates a world where researchers only look for evidence where there is an identification strategy, which leaves many blind spots akin to the drunk who only looks for his/her keys under the street light.

In an optimal world, there would be a natural experiment available for analysis. But I found no case where there is also access to suitable survey data. In my opinion, the best approach is to take the next best solution, that is, use a cross-section.

Next, assuming that the research topic is worthwhile and that it addresses an important topic (which I would argue the present paper does), cross-sectional analyses may be the only approach to study the topic in a broad range of settings.

However, I fully agree that causal language should be avoided in the presentation of the empirical results, and I edited the manuscript accordingly (precisely because of the weak identification).

Substituting “drives” for “associates with”, etc., strikes me as obfuscation. This is why I decidedly use causal language – it is not because I fully believe in the validity of causal id assumptions underpinning the design.

Many authors simply avoid using causal language and depict their research as „descriptive“ to avoid this line of critique. But, if you carefully read their theories, they remain causal (as most theories on the relationship between concepts are).

First, the decision to use causal language despite the weak id strategy is conscious. I think it is the most honest approach to make explicit causal claims in the theory because the theory is causal.

Unfortunately, this point cannot be addressed easily. While it would be easy to dismiss all cross-sectional research like this paper, I will hereafter offer my perspective of why this research approach, and thus the present paper, is worthwhile nonetheless.

and it should be expected that the presented estimates deviate from the true causal effect (despite my best attempts to avoid this).