Running a local government is not sexy. It’s making sure that roads are maintained, there is water to drink, health clinics are stocked and staffed, and schools are equipped to teach. Often, it means doing these things with limited resources, infrastructure, and manpower. With few exceptions, there is little fanfare and glamour. It’s a bit like being a soccer referee: you’re doing a good job when no one notices you’re there.
We are living in a learning crisis. According to the World Bank’s 2018 World Development Report, millions of students in developing countries are in schools that are failing to educate them to succeed in life. According to the UNESCO Institute of Statistics, there are 617 million children and youth of primary and secondary school age who are not learning the basics in reading, two-thirds of whom are attending school. The urgency to invest in learning is clear.
Along with the Center for Experimental Social Science at Nuffield College at Oxford, eMBeD co-organized a conference called “Measuring the Tricky Things.” The lineup included Susan Fiske presenting a magisterial overview of her decades-long work on the stereotype content model, Armin Falk on his groundbreaking study of time, risk, and social preferences among 80,000 individuals in 65 countries, Karla Hoff on using lab in field experiments to identify the honor ethic among higher caste villagers in North India, Ryan Enos on measuring racial attitudes, Rachel Glennerster on measuring women’s empowerment, Julian Jamison on how and why to use item count techniques to mitigate social desirability bias, Henry Travers on debiasing estimates of wildlife survival, Amandi Mani on assessing the effect of financial worry on cognitive performance with cell phones, and Sheheryar Banuri on using videos to probe the effect of pro-poor bonuses on doctor’s decisions on which patients to see. My eMBeD co-head Renos Vakis assessed the strengths and weaknesses of World Bank surveys on socio-emotional skills. I discussed the reliability and validity of measurements of social norms with respect to women’s labor force participation in Jordan.
The 2018 Poverty and Shared Prosperity Report shows how poverty is changing and introduces improved ways to monitor our progress toward ending it.
The landscape of extreme poverty is now split in two. While most of the world has seen extreme poverty fall to below 3 percent of the population, Sub-Saharan Africa is experiencing extreme poverty rates affecting more than 40 percent of people. The lamentable distinction of being home to the most people living in extreme poverty has shifted, or will soon shift, from India to Nigeria, symbolizing the increased concentration of poverty in Africa.
Last Thursday I attended a conference on AI and Development organized by CEGA, DIME, and the World Bank’s Big Data groups (website, where they will also add video). This followed a World Bank policy research talk last week by Olivier Dupriez on “Machine Learning and the Future of Poverty Prediction” (video, slides). These events highlighted a lot of fast-emerging work, which I thought, given this blog’s focus, I would try to summarize through the lens of thinking about how it might help us in designing development interventions and impact evaluations.
A typical impact evaluation works with a sample S to give them a treatment Treat, and is interested in estimating something like:
Y(i,t) = b(i,t)*Treat(i,t) +D’X(i,t) for units i in the sample S
We can think of machine learning and artificial intelligence as possibly affecting every term in this expression:
- If you could go back to the time you did not have any children and could choose exactly the number of children to have in your whole life, how many would that be?
- How many of these children would you like to be boys, how many would you like to be girls, and for how many would it not matter if it’s a boy or a girl?
About a year ago I reviewed Angela Duckworth’s book on grit. At the time I noted that there were compelling ideas, but that two big issues were that her self-assessed 10-item Grit scale could be very gameable, and that there was really limited rigorous evidence as to whether efforts to improve grit have lasting impacts.
A cool new paper by Sule Alan, Teodora Boneva, and Seda Ertac makes excellent progress on both fronts. They conduct a large-scale experiment in Turkey with almost 3000 fourth-graders (8-10 year olds) in over 100 classrooms in 52 schools (randomization was at the school level, with 23 schools assigned to treatment).
About a year ago, I wrote a blog post on issues surrounding data collection and measurement. In it, I talked about “list experiments” for sensitive questions, about which I was not sold at the time. However, now that I have a bunch of studies going to the field at different stages of data collection, many of which are about sensitive topics in adolescent female target populations, I am paying closer attention to them. In my reading and thinking about the topic and how to implement it in our surveys, I came up with a bunch of questions surrounding the optimal implementation of these methods. In addition, there is probably more to be learned on these methods to improve them further, opening up the possibility of experimenting with them when we can. Below are a bunch of things that I am thinking about and, as we still have some time before our data collection tools are finalized, you, our readers, have a chance to help shape them with your comments and feedback.