"In 2016, 61 million children of primary school age...were not in school, along with 202 million children of secondary school age." That's a tragic number, and it's also a concrete image. While we may have trouble envisioning 61 million children, we have a clear picture in our heads as to what a child not in school looks like, and we have a picture of what it looks like to have a child formerly not in school now in school.
But what about learning? What does improved learning look like? There are lots of studies that examine how to improve learning in low- and middle-income countries. Some report striking learning gains: A technology-aided instruction program in India finds that participation for 90 days would increase math scores by ... 0.6 standard deviations. For the vast majority of people in the world, the first response to that would be, "What's a standard deviation?" Even for educationists and economists, it's hard to envision the difference between the child with and without 0.6 standard deviations additional mathematical learning. (FYI, 0.6 standard deviations is a big learning gain.)
- A nice summary of the research on different strategies for reducing self-control failures by an all-star psychology/econ team of Duckworth, Milkman and Laibson in the open-access Psychological Science in the Public Interest journal. See in particular, Figure 2, which categorizes strategies by whether they need to be self-imposed vs can be imposed by others, and between approaches that “modify one’s situation and approaches that modify one’s cognitions, depending on whether they target the objective situation or, in contrast, one’s mental representation of the environment”. What is notable from reading this overview is how short-term many of the studies are, and how easy it is for best-intentions to get derailed – e.g. a study that “tested the benefits of temptation bundling...this study showed substantial initial increases in self-controlled decisions from allowing people to enjoy tempting audio novels only when exercising ... In Week 1 of the intervention, participants in the treatment group exercised 55% more than those in the control group. These benefits lasted for several weeks but ended when the gym closed over Thanksgiving.”
- Related to the above, Alice Evans interviews Gautam Rao about behavioral development economics, with discussions of where he sees the big puzzles that behavioral economics helps us answer – e.g. why people don’t invest in high-return projects, and why demand for preventative health is not higher – and a nice discussion of the complementarity between insiders and outsiders in knowing what questions to ask.
First, how much of a home bias is there?
I thought I would put numbers to Paolo’s casual observation. The job market seems a reasonable place to start, since people have a designated job market paper, many people list citizenship on their C.V., or other features that can be used to determine citizenship, and it captures the country choices of students before they have found jobs. The latter point is important if the choice of country to work on affects the position these students end up receiving.
Looking at job market students at 20 top universities in the U.S., U.K. and France, I count 29 Ph.D. students on the economics job market who list development economics as one of their fields, and who are from a developing country.
- Home bias is prevalent: 24 of the 29 students (83%) had a job market paper on their country of origin. Only Colombians were more likely to work on another country than their home country (4 out of 7 were working on other countries, such as Mexico and Peru).
Pros and Cons of this for Individual Researchers
- This is the best thing I’ve read all week, particularly because it contrasts so much what my usual workflow looks like with what I would like more of it to look like – Cal Newport (of Deep Work fame) asks in the Chronicle Review “is email is making professors stupid?”. He notes that in the modern environment professors/researchers act more like middle managers than monks and suggests reforms to significantly restructure work culture to provide professors more uninterrupted time for thinking and teaching, and require less time on email and administrative duties. He gives the example of Donald Knuth, who does not have email and has an executive assistant who “intercepts all incoming communication, makes sense of it, brings to Knuth only what he needs to see, and does so only at ideal times for him to see it. His assistant also directly handles the administrative chores — things like scheduling meetings and filing expenses — that might otherwise add up to a major time sink for Knuth. It’s hard to overstate the benefits of this setup. Knuth is free to think hard about the most important and specialized aspects of his work, for hours at a time, disconnected from the background pull of inboxes”. It does make me think back to this old post I wrote on O-ring and Knowledge Hierarchy production functions for impact evaluations though, and the continued ability of O-ring issues to stymie my projects.
Now that I’ve noted that, here’s plenty of things to distract you from working deeper:
A new collection of papers – Towards Gender Equity in Development – sets out to “explore key sources of female empowerment and discuss the current challenges and opportunities for the future” in three categories: marriage, outside options, and laws and cultural norms. The final published book is available for free, and the individual chapters are available as working papers.
In the introduction, Anderson, Beaman, and Platteau discuss the current landscape of gender discrimination in low- and middle-income countries. In a set of tables that I’ve transformed into a single, completely unwieldy figure. We see discrimination in social norms, legal rights, and marriage indicators. (In all of these indicators, 100% is the worst; 0% is the best.) What stands out is that while no single region dominates the discrimination landscape, every region has significant room to improve. West Africa has high rates of female genital mutilation, South Asia has high rates of son bias, Central Africa has high rates of polygyny, West Asia has high mobility restrictions on women, and the Caribbean has few to no laws against harassment.
“Who can it be now?”
I turn my head around from my seat at the seminar table to see who it is this time that has interrupted the seminar speaker for the Nth time before she even got through her introductory slides: it was a man, of course.
A lot of people at econ seminars get annoyed at questions that would have been answered naturally had the audience just been patient enough to wait for, sometimes literally, another slide; the back and forth that sometimes ensues between a questioner and the speaker; and, of course, the inevitable consequence of the speaker rushing through their results because too much time has been sent on answering questions.
- Rachel Glennerster on lessons from a year as DFID’s Chief Economist, including the importance of knowledge work “As countries get richer, helping them spend their own money more effectively will become a more important route to reducing poverty than the UK directly paying for services”
- Seema Jayachandran and Ben Olken offer their thoughts on new exciting areas in development research and advice for young development researchers: “taking the time to actually immerse yourself in the environments that you are studying. That means going to the countries that you’re studying and making sure that you understand the environment firsthand” and “not over-strategize about what topics or methods have career returns at the expense of not working on what you are personally most excited about.”
- A reminder that not all research has to make policy recommendations: There is a new World Bank report on the mobility of displaced Syrians, which looks at the voluntary return decisions of over 100,000 refugees to understand key factors influencing these decisions, combined with simulations of how different security scenarios might influence voluntary returns. But I particularly liked this in the Q&A about the report “What policy recommendations do emerge from this report? This report does not aim to design policies. It focuses on informing such policies by providing the necessary data, analysis, and framework that demonstrate the tradeoffs between various policy choices.”
- Fabrizio Zilibotti on how inequality shapes parenting styles – next time your kids complain you are being too strict, you can blame the economic environment.
- research uptake
- Working with big datasets in Stata? Then the package gtools might be for you – I love that they have to give the caveat “Due to a Stata bug, gtools cannot support more than 2^31-1 (2.1 billion) observations”. Meanwhile, the Stata blog has the second post on doing power calculations via simulations in Stata.
- More on industrial policy: A nice summary at VoxDev by Ernest Liu of his work on industrial policies in networks, and a reason to prioritize upstream sectors.
- New SIEF note on using phone monitoring to help more money reach target beneficiaries: an example where small effects are meaningful when cheap and scaled to many people – the treatment group were only 1.3% more likely to get their money, but this meant about $1 million more funding reached farmers when officials knew they would be phone monitored, and the monitoring only cost $36,000.