Causal Identification and Policy Relevance Go Hand in Hand

On what to avoid when submitting an article for publication in development economics (and applied micro in general, I would add):

Q: Any pet peeves with submissions or with referees that would be good for people to avoid?

A: Unfortunately yes. Our main two criteria in selecting papers for publication are rigorous identification and policy relevance. The two go together as we cannot have credible policy recommendations without strong causal inference. Too many of the submitted papers offer simple “determinants” that are partial correlates with no causal value, and yet are the basis for bold policy recommendations, sometimes of first order of importance for development practice. This includes a large number of cross-country panel regressions with only mechanical, and hence not credible, identification, and yet eventually huge claims of policy implications. Regarding policy relevance, papers too often address issues of nth order of importance for development, clearly not something that will change outcomes and interest readers.

Some wise words from World Bank Economic Review editors Alain de Janvry and Élisabeth Sadoulet in an interview with David McKenzie and Berk Özler of the Development Impact blog which, if you do academic research in development policy, is a must. The emphasis is mine, as I feel the knowledge that rigorous identification is a necessary condition for policy relevance is not widespread enough.