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 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.
3. American Exceptionalism: Chetty et al have a piece in Science on declining income mobility in the US since 1940. Here's Katz and Krueger's take on the policy options available. Matt Yglesias contends that the idea that the economy is becoming more concentrated is a myth. Opportunity however is becoming more concentrated in cities which have a declining share of the population because housing costs (due to limiting the housing supply) are too high. The Hamilton Project has an overview on the increasing gap in what you might call health opportunity as a consequence of decreasing access to economic opportunity.
4. Financial Inclusion: One of the long-standing discussions in microfinance is whether competition via the expansion of microcredit drives down interest rates. Hoffman et. al. see that microcredit via a subsidized self-help group does drive down informal lending rates in rural India. Meanwhile, the penetration of mobile money agents may be much smaller than estimated--the dominant way of counting agents may be doubling the number of actual agents.
5. Spring Cleaning: There was a reason for "spring cleaning" being in the title. Here are some things that I think are definitely worth your time that I've been either holding onto for awhile or don't really have a connection to the usual themes here. Here's the story of the first African American woman to earn an economics PhD (or a PhD at all apparently). Her doctoral thesis was a financial diaries project (there's nothing new under the sun!) of African American migrants to Philadelphia. And here's an essay on what might have been if she had been able to pursue a career in economics (she eventually became the first African American woman admitted to the Pennsylvania bar). Here's the story of 9 different attempts to create Wikipedia that failed before Wikipedia existed. Given all the attention to Wikipedia and the crowdsourcing-mania it inspired, this XKCD comic that's been making the rounds comes to mind. A few weeks ago there were a bunch of tributes to the first woman to run the Boston Marathon, who ran it again (and finished) at age 70. But she wasn't the first woman to run the Boston Marathon.
And this one I confess is pretty personal. At the 20 week ultrasound for our first-born, we were told our son had a birth defect that was "incompatible with life." It was about 2 weeks before further testing revealed he had a different rare disease (he's 11 now). Here's the heartbreaking and uplifting story of a family who heard the same thing--but the initial diagnosis was correct.
You don't have to learn anything in school for it to be good for you. Maybe what the world needs is more bad schools. Source: CGD
