I don't quite understand why you are not happy / what you mean by these examples.

My command uses heatplot internally. Takes one line in Stata instead of many to make things look professional. I needed to fiddle with heatplot for a long time to get what I wanted. But I think now that this may be too marginal for a package release.

Thanks for the suggestion! That should be easy to implement.

My package is essentially a wrapper for heatplot. It uses heatplot but cuts away all the setup and figure layout so you can just type one line and it will look fine.

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.

Just like Economists are unaware that multilevel models exist!

I'll leave this here

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.