"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.)
How much does financing matter for education? The Education Commission argued that to achieve access and quality education “will require total spending on education to rise steadily from $1.2 trillion per year today to $3 trillion by 2030 (in constant prices) across all low- and middle-income countries.” At the same time, the World Bank’s World Development Report 2004 showed little correlation between spending and access to school, and the World Development Report 2018 (for which I was on the team) shows a similarly weak correlation between spending and learning outcomes. (Vegas and Coffin, using a different econometric specification, do find a correlation between spending and learning outcomes up to US$8,000 per student annually.)
Sources: Left-hand figure is from WDR 2004. Right-hand figure is from WDR 2018.
And yet, correlation is not causation (or in this case, a lack of correlation is not necessarily a lack of causation)! Last month, Kirabo Jackson put out a review paper on this topic: Does School Spending Matter? The New Literature on an Old Question. This draws on a new wave of evidence from the United States’ experience, moving beyond correlations to efforts to measure the causal impact of spending changes. (Jackson and various co-authors have contributed significantly to this literature.) I’ll summarize his findings and then discuss what we might expect to be the same or different in low- or middle-income contexts.
This is the nineteenth in this year's series of posts by PhD students on the job market.
While sociable peers increase your social skills, higher-achieving peers do not improve your academic performance. That is the main conclusion of my job market paper.
As the world bends closer towards automation, social skills take a lead role on individuals' well-being and labor market success. According to Deming (2017), between 1980 and 2012, jobs demanding high levels of social interaction grew by nearly 12 percentage points as a share of the U.S. labor force. Similarly, a recent column by the Washington Post highlights the importance of social skills for team productivity and employment opportunities. It describes the results of Google’s Project Aristotle, which concludes that the best teams at Google exhibit high levels of soft skills, and particularly social skills. These include emotional safety, equality, generosity, curiosity towards the ideas of your teammates, empathy, and emotional intelligence
While there is extensive research on policies that improve academic learning, little is known about how social skills form. My job market paper addresses this challenge. I present the results of a large-scale field experiment at boarding schools in Peru. The intervention was designed to estimate social and cognitive peer effects. While other studies have exploited random assignment to dormitories and classrooms, I use a novel experimental design to generate large variation in peer skills. Specifically, I assign students to two cross-randomized treatments in the allocation to beds in a dormitory: (1) less or more sociable peers, and (2) lower- or higher-achieving peers. This design surmounts many of the challenges with traditional approaches to the study of peer effects (Manski, 1993; Angrist, 2014; Caeyers and Fafchamps, 2016).
This is the second in this year's series of posts by PhD students on the job market.
Each evening the sun sets more than 90 minutes later in west India than in the east of the country. This is because time on clocks across India are set to Indian Standard Time, regardless of location. In China all clocks are set to Beijing Time, which means in western part of the country the sun sets 3 hours later than the east of the country. The sun sets at least an hour later in Madrid than in Munich because Franco’s Spain switched clocks ahead one hour to be in sync with Nazi Germany in 1940, even though Spain is geographically in line with Britain, not Germany. Similarly, for a range of historical reasons, clocks in large parts of the planet – e.g., France, Algeria, Senegal, South Sudan, Russia, and Argentina – are set to be ahead of their (solar) time. Therefore, these places see the sun set later in the day. In my job market paper, I show that these arbitrary clock conventions -- by generating large discrepancies in when the sun sets across locations -- help determine the geographic distribution of educational attainment levels.
Last weekend, the North East Universities Development Consortium held its annual conference, with more than 160 papers on a wide range of development topics and from a broad array of low- and middle-income countries. We’ve provided bite-sized, accessible (we hope!) summaries of every one of those papers that we could find on-line. Check out this collection of exciting new development economics research!
The papers are sorted by topic, but obviously many papers fit with multiple topics. There are agriculture papers in the behavioral section and trade papers in the conflict section. You should probably just read the whole post.
If you want to jump to a topic of interest, here they are: agriculture, behavioral, climate change, conflict, early child development, education, energy, finance, firms and taxes, food security, gender, health and nutrition, households, institutions and political economy, labor and migration, macroeconomics, poverty and inequality, risk management, social networks, trade, urban, and water, sanitation, and hygiene (WASH).
Teachers are important. And many teachers in low- and middle-income countries would benefit from support to improve their pedagogical skills. But how to do it? Again and again, evidence suggests that short teacher trainings – usually held in a central location – don’t do much of anything to improve teacher practice. Likewise, much teacher training is overly theoretical and doesn’t translate into practical pedagogical improvements.
Providing teachers with one-on-one coaching is a popular alternative. A coach comes to the classroom, observes the teacher, and provides practical feedback. It makes sense. As Kotze and others put it (in turn paraphrasing earlier authors), “Teachers only learn to do the work by doing the work, and not by being told to do the work, or being told how to do the work, or being told that they will be rewarded or punished for outcomes associated with the work.” A recent review of U.S. evidence shows big impacts of coaching on both teacher practices and on student learning. But those big effects are concentrated in small-scale programs: Effects tend to be much smaller when implemented at scale. Outside a high-income environment, a teacher coaching pilot in South Africa compared coaching to a more traditional training at a central location. Students whose teachers received coaching learned twice as much as students who teachers received training.
But implementing coaching at scale presents a number of challenges. First, it’s costly. Second, where do you find the coaches? Technology has the potential to help. In a follow-up experiment, researchers in South Africa compared on-site coaching to “virtual coaching” to compare effectiveness. Initial results have just come out in Kotze, Fleish, and Taylor’s “Alternative forms of early grade instructional coaching: Emerging evidence from field experiments in South Africa.”
Many education investments focus on the first years of primary education or – even before that – early child education. The logic behind this is intuitive: Without a solid foundation, it’s hard for children and youth to gain later skills that use those foundations. If you can’t decipher letters, then it’s going to be tough to learn from a science textbook. Or even a math textbook. But it’s important to remember that for most “investors” (whether governments or parents or the children themselves), the most basic skills aren’t the ultimate goal. The objective is better life outcomes. Most of the justification for these early interventions are that they will translate into better lives once these children grow up.
In Gaile Parkin's novel Baking Cakes in Kigali, two women living in Kigali, Rwanda – Angel and Sophie – argue over the salary paid to a development worker: "Perhaps these big organisations needed to pay big salaries if they wanted to attract the right kind of people; but Sophie had said that they were the wrong kind of people if they would not do the work for less. Ultimately they had concluded that the desire to make the world a better place was not something that belonged in a person's pocket. No, it belonged in a person's heart."
It's not a leap to believe – like Angel and Sophie – that teachers should want to help students learn, health workers who want help people heal, and other workers in service delivery should want to deliver that service. But how do you attract and motivate those passionate public servants? Here is some recent research that sheds light on the topic.