1. Mobile Money: The GSMA published it's annual mobile money "state of the industry" report, except that this time it's a review of the 2006-2016. Here's a summary (I know which one I would click on). As you'd expect, the GSMA heavily touts some impressive statistics on growth and usage. And I suppose you can't be surprised at the sometimes more than implied leaps from outputs to outcomes. But the more I look at things like this, the more I'm reminded of Lant Pritchett's book The Rebirth of Education: Schooling Ain't Learning and the history of using school enrollment as a very bad proxy for the outcome that everyone actually cares about, learning. Or to use a closer to the finance industry analogy, was there anyone tracking the spread of ATMs and debit cards and getting excited about how much it was going to help the poor?
2. The New Redlining: Fisman, Paravisini, and Vig have a new paper (and a summary) in AER on the effect of loan officer "cultural proximity" with borrowers in India. Loan officers who share religion, ethnicity and other traits with a borrower provide larger loans on better terms, and borrowers have higher repayment rates, meaning the loans are more profitable for the bank. The proposed mechanism is reduced "information frictions" in the lending process. It's a more subtle form of redlining--a systematic way that banks denied credit to minority communities in the United States. Fisman et. al. suggest hiring and promoting minority loan officers as the obvious way to combat the discrimination they document (that's a version of "immigrant" banks that you can still find in places like New York and San Francisco). It's also part of the reason that algorithmic approaches to credit, like this effort to use exam scores as a proxy for student lending in Kenya--remain appealing: you can simply skip past the bias inherent in human-to-human interactions! If only. The long battle against algorithmic redlining is only just beginning and will be much harder to win as long as we succumb to the fiction that algorithms fix bias. I wonder which socioeconomic class the people doing better on exams come from?