1. Arbitrary and Biased: I feel like "arbitrary and biased" should have been the tagline for the faiV but it'll have to do as just the name this week's edition (I won't make the obvious joke). The reference here specifically is an update to my post at CGAP on impact evaluations and systematic reviews of financial inclusion interventions. Duvendack and Mader, authors of a systematic review of reviews that I've mentioned in the faiV and in that post, responded. And then I responded to them. The short version, if you don't want to click on all those links or do a lot of scrolling, is that we disagree substantially (though in good faith!) and particularly on the issues of arbitrariness and bias. My perspective on these issues have been substantially influenced by Deaton's and Pritchett's critiques of RCTs, which feels a bit ironic. Systematic reviews are useful, but they are no less arbitrary nor less biased than other attempts to synthesize the literature--they're just arbitrary and biased in different ways, albeit generally more transparent ways (though what we know about how disclosure affects people's trust leaves a question about the benefits of that disclosure). Reveling in the arbitrarily biased essential nature of the research enterprise, here are a couple of papers that raise different questions about how the literature on microcredit may be biased. Bedecarrats, Guerin, Morvant-Roux and Roubaudreplicate the Al-Amana microcredit impact study and find errors and issues with the data and code--though exactly how much it matters to the big picture conclusion isn't clear. Meanwhile Dahal and Fiala review the microcredit RCTs focusing on whether they have sufficient power to detect likely magnitude of effects (and find that they aren't) and find significant and meaningful effects on profits when the data is pooled. I need to read both these papers more closely, but they are interesting enough that I didn't want to wait before including them in the faiV.
2. Evidence-Based Policy/Methods: Speaking of arbitrarily biased research, the 5% statistical significance threshold is perhaps the most influential arbitrarily biased feature of modern academic research. Some people are trying to change that--well more than 800 who signed onto a letter in Nature protesting the cutoff. Before you come to a conclusion on whether that letter will make a difference, I must note, as many on Twitter did, that it's not a statistically significant portion of scientists who have signed on. Another arbitrary bias, according to Nick Lea, deputy chief economist at DfID, is the need to run regressions in economics papers. David Evans, now ensconced at CGD, responds with a defense of regressions and some ideas on how development economics can be better. Here's a reminder that "purely evidence-based policy doesn't exist" though I'm not sure how many people thought it did. And here's a reminder from Straight Talk on Evidence that short-term impact often fades out, something evidence-based policy really needs to take into account. And finally, here's an interesting piece from mathemetician Aubrey Clayton adjudicating a long-running dispute between Nate Silver and Nassim Taleb over probabilities, finding that Taleb "overplays his hand."
3. Household Finance: The mythology of Spanish colonialism in the Americas centered heavily on cities of gold (anybody remember this?). Here's a story about the reverse--Dominicans searching Spain (and Switzerland) for lost troves of gold. It's all a scam of course, of the sort immediately recognizable by anyone who has spent time in Latin America. It's a fascinating read because of how the story delves into the psychology that has led so many Dominicans to believe (and continue to believe) an ancestor secreted billions of dollars of gold in Spanish and Swiss banks that they stood to inherit--to the point that they quit jobs and made all sorts of other bad financial decisions. When there is little hope, believing that slow, steady abstemious frugality will matter may seem as much magical thinking as hidden inheritances. Here's a piece from Morgan Housel on how much our (macro)financial experiences affect our later decision making.