1. Sweatshops:  You've probably seen the New York Times piece by Chris Blattman and Stefan Dercon about their experiment with Ethiopian factory employment--finding that while many people wanted the jobs initially, they quickly learned that they didn't want them after all. The jobs are dangerous and unpleasant, and people who didn't get the jobs did just as well if not better via self-employment. Meanwhile, Lee et. al. look at urban-to-rural remittances from Bangladesh factory workers and find large positive effects for the folks back home, while the factory workers were less likely to be poor, but also less healthy. Morduch (one of the et als) also notes the workers felt pressure to work more despite poor conditions in order to send money home. It's an interesting compare/contrast.

I'm of several minds about this. First, the Blattman/Dercon piece notes that much of the problem in the Ethiopian factories is that they were poorly run, not that the owners were deliberately trying to exploit workers. If you're a reader of the faiV you know I'm somewhat obsessed with the "technology of management" and how to spread it (and that there's a good bit of evidence that its a big problem, Google Nick Bloom for more). Second, there's the perennial issue of external validity: what do these experiments tell us about sweatshops more generally in other places and times. Here's an overview by Heath and Mobarak (HT Asif Dowla) on the impact of factory labor on Bangladeshi women; and here are some emerging financial diaries of garment workers in several different countries. Third, factory jobs have almost always been terrible, despite the romanticization of those jobs in developed countries of late--and they still are even in places like the United States. So what to make of the fact that they do seem instrumental in the process of countries and households becoming wealthier? And what of my strong prior that most people in developing countries are "frustrated employees and not frustrated entrepreneurs"?

2. Our Algorithmic Overlords: Continuing last week's theme on Seeing Like A State and algorithms, the Royal Society has a new report suggesting easier access to public data sets so that machine learning can help improve policy. You'll be shocked, shocked, to learn that Google DeepMind, Amazon and Uber leaders were all part of drafting the report. The New Inquiry has used data to create a predictive algorithm and heat map for people and places likely to commit white-collar crime. Here's the methodology behind it, which you should definitely at least glance through through to see Figure 4 on page 4. On a related note, here's a story about racial and gender biases being "learned" by machine learning programs.

The white collar crime piece came via Matt Levine, and it's worth scrolling down to his item on Facebook for this gem: "What if human history isn't a video game at all?" Hopefully that will soon be a standard response to FinTech triumphalism: "What if people's financial lives aren't a video game at all?" It all brings to mind this piece from several years ago: The Reductive Seduction of Other People's Problems. You should definitely read it. It's about social entrepreneurs from developed countries traveling to developing countries but it does easily apply to algorithms, fintech and seeing like a state. Hat tip to Lee Crawfurd and Justin Sandefur for reminding me about it.

1. The World's Largest Financial Inclusion Experiment(s): That's a descriptor that applies to basically any Indian or Chinese national policy, but India is definitely where the interesting financial inclusion experiments via sweeping policy changes action is right now. Whether we'll learn from these experiments remains to be seen. Here's an attempt to learn something from the Indian government's JDY scheme to make bank accounts widely available--finding that usage is growing, and the primary actions are person-to-person transfers (sounds familiar doesn't it?). And here's a story from the FT on how insurance companies are attempting to use the shock of demonetisation and the increasing use of bank accounts (via JDY) to increase penetration of microinsurance.

2. Economists' Social Skills: A big concern in developed economies is the "skill mismatch": the skills that people have today (and by implication that our education system is focused on imparting) are not the skills that will be required for jobs in the near future (if not the present). The two items I see most frequently in such discussions is "coding/programming" and "social skills." I've never understood the former--computers are obviously better at coding than people are already. Social skills though, those do seem important. The surprising thing in this piece on skills and future jobs is the chart about mid-way through which says that economists require strong social skills, even more so than physicians, nurses' aides, and police(!). Perhaps that mismatch between practicing economists and social skills explains why the chart also notes that employment share for economists is stagnant.