Low-income countries face the highest risk of financial catastrophe due to surgery and have made the slowest progress
We’re pleased to launch new dashboards in the Health, Nutrition and Population Portal, following the portal’s revamp last year. The renewed HNP portal has two main dashboards covering Population and Health. Both dashboards are designed to be interactive data visualization tools where users can see various population and health indicators. Users can access various charts and maps by selecting specific time, country or region and indicators. We have added new indicators, charts and new health topics such as Universal Health Coverage and Surgery and Anesthesia. Below are some examples of stories gleaned from our dashboards.
China, with 1.4 billion people, is the most populous country in the world in 2017. However, India, the second most populous country with 1.3 billion people, is projected to surpass China’s population by 2022. China’s total fertility rate (the number of children per woman) has also declined sharply since the 1970s.
A colleague stopped me by the elevators while I was leaving the office.
“Do you know of any paper on (some complicated adjustment) of standard errors?”
I tried to remember, but nothing came to mind – “No, why do you need it?”
“A reviewer is asking for a correction.”
I mechanically took off my glasses and started to rub my eyes – “But it will make no difference. And even if it does, wouldn’t it be trivial compared to the other errors in your data?”
“Yes, I know. But I can’t control those other errors, so I’m doing my best I can, where I can.”
This happens again and again — how many times have I been in his shoes? In my previous life as an applied micro-economist, I was happily delegating control of data quality to “survey professionals” — national statistical offices or international organizations involved in data collection, without much interest in looking at the nitty-gritty details of how those data were collected. It was only after I got directly involved in survey work that I realized the extent to which data quality is affected by myriad extrinsic factors, from the technical (survey standards, protocols, methodology) to the practical (a surprise rainstorm, buggy software, broken equipment) to the contextual (the credentials and incentives of the interviewers, proper training and piloting), and a universe of other factors which are obvious to data producers but usually obscure and typically hidden from data users.
This initiative is supported by the World Bank’s Trust Fund for Statistical Capacity Building (TFSCB) with financing from the United Kingdom’s Department for International Development (DFID), the Government of Korea and the Department of Foreign Affairs and Trade of Ireland.
The themes for this year’s call for proposals are scaling local data for impact, which aims to target innovations that have an established proof of concept which benefits local decision-making, and fostering synergies between the communities of non-official data and official statistics, which looks for collaborations that take advantage of the relative strengths and responsibilities of official (i.e. governmental) and non-official (e.g.,private sector, civil society, social enterprises and academia) actors in the data ecosystem.
Few will argue against the idea that data is essential for the design of effective policies. Every international development organization emphasizes the importance of data for development. Nevertheless, raising funds for data-related activities remains a major challenge for development practitioners, particularly for research on techniques for data collection and the development of methodologies to produce quality data.
If we focus on the many challenges of raising funds for microdata collected through surveys, three reasons stand out in particular: the spectrum of difficulties associated with data quality; the problem of quantifying the value of data; and the (un-fun) reality that data is an intermediate input.
First things first – survey data quality is hard to define and even harder to measure. Every survey collects new information; it’s often prohibitively expensive to validate this information and so it’s rarely done. The quality of survey data is most often evaluated based on how closely the survey protocol was followed.
The concept of Total Survey Error sets out a universe of factors which condition the likelihood of survey errors (Weisbeg 2005). These conditioning factors include, among many other things: how well the interviewers are trained; whether the questionnaire was tested and piloted and to what degree; whether the interviewers’ individual profiles could affect the respondent answers, etc. Measuring some of these indicators precisely is effectively impossible—most of the indicators are subjective by nature. It may be even harder to separate the individual effects of these components in the total survey error.
Imagine you are approached with a proposal to conduct a cognitive analysis of your questionnaire. - How often were you bothered by the pain in the stomach over the last year? A cognitive psychologist will tell you that this is a badly formulated question: the definition of stomach varies drastically among the respondents; last year could be interpreted as last calendar year, 12 months back from now, or from January 1st until now; one respondent said: it hurt like hell, but it did not bother me, I am a Marine... (from a seminar by Gordon Willis)
“Every company is a technology company”. This idea, popularized by Gartner, can be seen unfolding in every sector of the economy as firms and governments adopt increasingly sophisticated technologies to achieve their goals. The development sector is no exception, and like others, we’re learning a lot about what it takes to apply new technologies to our work at scale.
Last week we published a blog about our experience in using Machine Learning (ML) to reduce the cost of survey data collection. This exercise highlighted some challenges that teams working on innovative projects might face in bringing their innovative ideas to useful implementations. In this post, we argue that:
“The World Bank is one of the world’s largest producers of development data and research. But our responsibility does not stop with making these global public goods available; we need to make them understandable to a general audience.
When both the public and policy makers share an evidence-based view of the world, real advances in social and economic development, such as achieving the Sustainable Development Goals (SDGs), become possible.” - Shanta Devarajan
It’s filled with annotated data visualizations, which can be reproducibly built from source code and data. You can view the SDG Atlas online, download the PDF publication (30Mb), and access the data and source code behind the figures.
This Atlas would not be possible without the efforts of statisticians and data scientists working in national and international agencies around the world. It is produced in collaboration with the professionals across the World Bank’s data and research groups, and our sectoral global practices.
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:
Time use data is increasingly relevant to development policy. This data shows how many minutes or hours individuals devote to activities such as paid work, unpaid work including household chores and childcare, leisure, and self-care activities. It is now recognized that individual wellbeing depends not just on income or consumption, but also on how time is spent. This data can therefore improve our understanding of how people make decisions about time, and expand our knowledge of wellbeing.
Time use data reveals how, partly due to gender norms and roles, men and women spend their time differently. There is an unequal distribution of paid and unpaid work time, with women generally bearing a disproportionately higher responsibility for unpaid work and spending proportionately less time in paid work than men.
In a forthcoming paper with Mariana Viollaz (Universidad Nacional de La Plata, Argentina), we analyze gender differences in time use patterns in 19 countries (across 7 regions and at all levels of income). The analysis confirms the 2012 World Development Report findings of daily disparities in paid and unpaid work between women and men.
Please help us out by completing this short user survey on the new data catalog.
Data is the key ingredient for evidence based policy making. A growing family of artificial intelligence techniques are transforming how we use data for development. But for these and more traditional techniques to be successful, they need a foundation in good data. We need high quality data that is well managed, and that is appropriately stored, accessed, shared and reused.
The World Bank’s new data catalog transforms the way we manage data. It provides access to over 3,000 datasets and 14,000 indicators and includes microdata, time series statistics, and geospatial data.
Since its launch in 2010, the World Bank’s Open Data Initiative has provided free, open access to the Bank’s development data. We’ve continuously updated our data dissemination and visualization tools, and we’ve supported countries to launch their own open data initiatives.
We’re strong advocates for open data, but we also recognize that some data, often by virtue of how it has been acquired or the subjects it covers, may have limitations on how it can be used. In the new data catalog, rather than having such data remain unpublished, we’re making many of these previously unpublished datasets available, and we document any restrictions on how they can be used. This new catalog is an extension of the open data catalog and relies heavily on the work previously done by the microdata library.