Downloading the data is easy. At the microdata library, you'll see a screen that looks like this:
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.
Collecting data in education can be a tricky business. After spending considerable resources to design a representative study, enlist and train data collectors, and organize the logistics of data collection, we want to ensure that we capture as true a picture of the situation on the ground as possible. This can be particularly challenging when we attempt to measure complex concepts, such as child development, learning outcomes, or the quality of an educational environment.
Data can be biased by many factors. For example, the very act of observation by itself can influence behavior. How can we expect a teacher to behave “normally” when outsiders sit in her or his classroom taking detailed notes about everything they do? Social desirability bias, where subjects seek to represent themselves in the most positive light, is another common challenge. Asking a teacher, “Do you hit children in your classroom?” may elicit an intense denial, even if the teacher still has a cane in one hand and the ear of a misbehaving child in another.
I've been thinking about the role of data and digital technology in today's information landscape. New platforms and technologies have democratized access to much of the world’s knowledge, but they’ve also amplified disinformation that affects public discourse. In this context, the official statistics community plays a critical role in bringing credible, evidence-based information to the public.
A “post-truth” society is not an inevitable state of affairs that we must accept; it's an unacceptable state of affairs that we must address. To do so, we need reliable data that are trusted by the public. Institutions like national statistical offices must go beyond their traditional data production remit to become a trusted, visible force for reason in people’s lives by building trust, embracing relevance, and communicating better.
The EU’s new General Data Protection Regulation (GDPR) recently went into effect. You have probably received emails regarding your data resident on email servers and applications. And while the media focus has also remained on data concerns with Facebook and other personal data, the impact of the GDPR on developing countries has received little attention. Their exports of data-based services rely on the free flow of data across borders. Strengthened regulation can make international data transfers more difficult. And traditional trade rules and regulatory cooperation cannot resolve this conflict.
Human body measurements are used to evaluate health trends in various populations. We wanted a simple way to reliably measure someone’s height as part a field interview, using a photo of them holding a reference object. We’ve developed an approach and would highlight two things we learned during the process:
The ‘results agenda’ of donor agencies have inspired several heated debates. Value for money is one of the main tools that helps further this agenda. There is significant pressure on donor development agencies to ‘demonstrate’ what they have achieved (results), and further, examine whether these results have been achieved in a cost-effective manner (‘value for money’). This pressure to demonstrate ‘value for money’ often leads to plenty of frustration, as those designing and implementing aid programmes struggle to strike a balance between what is easy to prove versus the complex nature of an intervention designed to tackle a real-world problem.
There are several problems with the results agenda – development interventions take place in a wide range of contexts, that lend themselves to comparisons on some counts and not, on others. These contexts change every day, and certainly over the lifetime of a development project, and attempting a grand theory or mathematical formulae to capture the entire process is nearly impossible.
Besides technical problems, there are valid fears that focusing too closely on ‘value for money’ will lead development workers to focus on ‘bean-counting’ and preferring interventions that can be easily measured and whose costs and benefits are easy to estimate. Some researchers have gone further and argued that an obsession with such metrics essentially forces development workers into lying about how their projects actually work.
While some studies predict automation to eliminate jobs at a dizzying rate, disruptive technologies can also create new lines of work. Our working draft of the forthcoming 2019 World Development Report, The Changing Nature of Work, notes that in the past century robots have created more jobs than they have displaced. The capacity of technology to exponentially change how we live, work, and organize leaves us at the World Bank Group constantly asking: How can we adapt the skills and knowledge of today to match the jobs of tomorrow?
One answer is to harness the data revolution to support new pathways to development. Some 2.5 quintillion bytes of data are generated every day from cell phones, sensors, online platforms, and other sources. When data is used to help individuals adapt to the technology-led economy, it can make a huge contribution toward ending extreme poverty and inequality. Technology companies, however well intended, cannot do this alone.
This is true even in Africa, where the most studies have been published, due to shortcomings in both the quality and quantity of research on these questions.