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.
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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?
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.
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.
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.
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