· Interview with Amitabh Chandra, one of the editors of ReStat in the ASHEcon newsletter. Good advice on both writing and reviewing papers – number 1 takeaways for writing are that simplicity and clarity are highly rewarded – “We don’t teach good writing in economics—and routinely confuse LaTeX equations with good writing—but as my little rant highlights, we actually value better-writing”; and for reviewing “write short reviews—don’t over referee or rewrite the paper—you are the reviewer, not the author. Be kind”.
· The Economist’s special report on migration includes “when a worker migrates, a family benefits”, which gives the estimate that 83% of the non-migrant citizens in OECD countries have benefited from migration. This comes from this paper, which concludes “Although labor market and fiscal effects are non-negligible in some countries, the greatest source of gain comes from the market size effect, i.e. the change in the variety of goods available to consumers”. This estimate comes from a calibrated model, but does suggest that perhaps the literature argues way too much about identifying labor market effects, and not nearly enough about product variety effects of migration. There is very little well-identified literature on the latter topic – I only know of this paper that looks at restaurants.
· You work really hard to get buy-in from policymakers on your research design of one of their signature programs, and then there is an election that leads to a change in government – what should you do to ensure your research doesn’t get kicked out along with the politicians? On the IPA blog, John Branch and Maria Gonzalez discuss how to navigate political transitions during a research project, based on their experiences in Mexico.
· Nathan Nunn has a new paper “Rethinking Economic Development” that is based on a lecture he gave at the Canadian Economic Association meetings. He gives nice examples of how things that get far less attention in research than aid-funded interventions may have far-reaching effects (examples include migration, consumer action on fair trade, anti-dumping policies, etc.); as well as a nice set of examples of how knowing local context was critical for understanding the impacts of some interventions.
· The MIT technology review summarizes ongoing work by Sean Higgins and co-authors in the Dominican Republic, which looks at whether training a machine-learning model to predict creditworthiness and allowing it to run separate models by gender reduces or increases gender bias. They find 93% of women would benefit from having gender explicitly taken into account (something that is illegal in current U.S. lending law).
· Reminder, our blog your job market series is still taking submissions, which close on Monday Nov 25 at NOON Eastern time.