Over the course of this year, I consumed three dozen books in an array of formats – audio, ebook, print, or a mix. As with my movies, my rating is as follows: 1 = bad; 2 = okay; 3 = good; 4 = great; 5 = wow!
Standout titles were Yaa Gyasi’s Homegoing in fiction and James Gleick’s Time Travel: A History in nonfiction. I managed to write short reviews for 27 of the books. In those cases, I include the link next to the title.
What were your favorite books of the year?
Over the course of the year, I saw many movies in many forms – in the theater, on planes, while exercising, during my commute, and at home with my family. I rate each movie on a scale of 1 to 5:
I generally enjoy movies, so of the 108 movies I’ve seen (so far!) this year, 81 received a good rating or above. Note that I rate movies within genre, so a silly comedy that’s great at being a silly comedy may get the same rating as a serious mediation on war violence that great at being that.
My top six movies for the year (with direct links so you can watch them right now) were Cinema Paradiso (1988 – Amazon/YouTube), Embrace of the Serpent (2016 – Amazon/YouTube), About Elly (2015 – YouTube), Hunt for the Wilderpeople (2016 – Amazon/YouTube), Sing Street (2016 – Amazon/YouTube), and The Wizard of Oz (1939 – Amazon/YouTube). The children’s movie that you probably didn’t see but should is Kubo and the Two Strings (2016 – Amazon/YouTube
Below is the full list, ordered by rating – and then alphabetically within ratings. Links are to Rotten Tomatoes, which gives a synthesis of critical reviews. Bolded titles are the 35 movies I saw in the theater. I occasionally include the movie’s release year to add clarity. Lastly, I didn’t re-calibrated the list at the end of the year, so some ordering may be affected by my mood or the quality of popcorn at the theater.
What did I miss? What did you love?
I always try to reduce things to three questions—even in simple interventions like passing out nutritional supplements to infants and toddlers. These are theory-driven questions that you should be using whenever you’re justifying any intervention whatsoever.
And the questions are
Karlan makes the point that experiments should absolutely be driven by theory. But theory doesn’t have to mean three pages of math. The theory can be simple, and the questions above sum up what your theory should predict.
from Tim Ogden’s interview with Dean Karlan in Experimental Conversations: Perspectives on Randomized Trials in Development Economics
George Bethell has put out a report via the World Bank, Mathematics Education in Sub-Saharan Africa: Status, Challenges, and Opportunities. It brings together an array of analysis on current mathematics performance and teaching around the continent.
I’d like to highlight two elements. First, a survey in 6 African countries shows teacher attitudes towards math. Now, it’s just 70 teachers per country (roughly 50 primary and 20 secondary), so this is suggestive only. Key findings: Almost all teachers agree that mathematical skills are useful for everyone (except in DRC). But between 24 percent and 62 percent of teachers don’t believe that “everyone has the potential to be good at mathematics,” depending on the country, and a majority of teachers in all countries except Cameroon believe that “you have to have the right sort of brain to be good at mathematics.” It’s easy to imagine that these kinds of beliefs then play into the way teachers teach. (You can read more about it in Appendix A of the report.) Of course, I also don’t know how different these are from other parts of the world. The point is not whether these 6 countries are worse or better than other countries, but that teachers in these country have these particular beliefs about math education which may well affect pedagogy.
The report also lays out what he sees as the big research questions that remain in this area.
Calling all researchers! To work!
Two years ago, Anna Popova and I put out a working paper examining whether beneficiaries of cash transfer programs are more likely than others to spend money on alcohol and cigarettes (“temptation goods”). That paper has just been published, in the journal Economic Development and Cultural Change.
The findings of the published version do not vary from the working paper: Across continents, whether the programs have conditions or don’t, the result is the same. The poor don’t spend more on temptation goods. But for the published version, we complemented our vote count (where you sum up how many programs find a positive effect and how many find a negative effect) with a formal meta-analysis. You can see the forest plot below. (The results are not substantively different from the “vote count” review that we did in the working paper and maintain in the published version as a complement to the meta-analysis.)
As you can see, while there are only two big negative effects, both from Nicaragua, most of the effects are slightly negative, and none of them are strongly positive. We do various checks to make sure that we’re not just picking up people telling surveyors what they want to hear, and we’re confident that cannot explain the consistent lack of impact across venues.
Why might there be a negative effect? After all, if people like alcohol, we might expect them to spend more on it when they have more money. We can’t say definitively, but even unconditional transfer programs almost always come with strong messaging: Recipients hear, again and again, that this money is for their family, that this money is to make their lives better, and so on and so on. We know from others areas of economics that labeling money has an effect (called the flypaper effect).
So you can be for cash transfers or against cash transfers, but don’t be against them because you think the poor will use the money on temptation goods. They won’t. To quote the last line of our paper, “We do have estimates from Peru that beneficiaries are more likely to purchase a roasted chicken at a restaurant or some chocolates soon after receiving their transfer (Dasso and Fernandez 2013), but hopefully even the most puritanical policy maker would not begrudge the poor a piece of chocolate.”
Yes and no. Better income and better social conditions, but also a black-white pay gap that changed little over time. Why?
Leah Platt Boustan, UCLA economist (and my friend), just wrote a book on it, Competition in the Promised Land: Black Migrants in Northern Cities and Labor Markets.
This is from James Ryerson’s New York Times review:
In her rich and technical account, the economist Leah Platt Boustan employs the tools of her trade — resourceful matching of data sets, rigorous modeling of labor phenomena, sweeping use of census figures — to analyze the demographics and economics of the Great Migration as a whole… Her investigation both deepens our understanding of what we think we know and adds new complexities and wrinkles.
I expect it’s excellent.
Esther Duflo and Abhijit Banerjee of MIT and JPAL weigh in during an interview in Tim Ogden’s forthcoming book, Experimental Conversations, which I am enjoying thoroughly:
Esther: “I think it’s been completely won in that I think it’s just happening. A lot of people are doing it without us. It’s being used. I think it is now understood to be one of the tools. The argument within the economics profession [over the value of RCTs] had two main consequences, both good. First, it raised the profile. If something was debated, people began to believe it must be significant. Second, it did force us to answer the challenges. There were a lot of valid points that were raised and it forced us to react. We’ve become more intelligent as a result.”
Abhijit: “I am less certain that it has been won. The acid test of whether an idea has come to stay is that it becomes something that no one needs to justify using. … RCTs aren’t there yet: it is true almost everyone is doing them, but many of them are taking the trouble to explain that what they do is better than a ‘mere RCT.’ We need to get to the point where people take RCTs to be the obvious tool to use when possible to answer a particular class of empirical questions.”
Earlier this month, Esther Duflo of MIT gave a talk at the IMF, and the slides are available here. I found the framing insightful.
She goes on to give three examples from impact evaluations in India that seek to improve “the rules of the game,” creating systems for better governance: (1) “fixing the pipes” — eplatform for workfare payments, (2) “changing the faucet” — biometric identification for welfare payments, and (3) “replacing the meter” — inspections on polluting compliance.
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