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Applications open for third round of funding for collaborative data innovation projects

World Bank Data Team's picture
Photo Credit: The Crowd and The Cloud


The Global Partnership for Sustainable Development Data and the World Bank Development Data Group are pleased to announce that applications are now open for a third round of support for innovative collaborations for data production, dissemination, and use. This follows two previous rounds of funding awarded in 2017 and earlier in 2018.

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.

Scaling local data and synergies with official statistics

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.

If development data is so important, why is it chronically underfinanced?

Michael M. Lokshin's picture

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.

Data quality

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)

Beyond Proof of Concept: do we have the right structure to take disruptive technologies to production?

Michael M. Lokshin's picture
Figure 1: Azure Cognitive Services Algorithm compliments authors’
youthful appearances

“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:

  1. Disruptive technologies can make things look easy. The cost of experimentation, especially in the software domain, is often low. But quickly developed prototypes belie the complexity of creating robust systems that work at scale. There’s a lot more investment needed to get a prototype into production that you’d think.

  2. Organizations should monitor and invest in many proofs of concept because they can relatively inexpensively learn about their potential, quickly kill the ones that aren’t going anywhere, and identify the narrower pool of promising approaches to continue monitoring and investing resources in.

  3. But organizations should also recognize that the skills needed to make a proof of concept are very different to the skills needed to scale an idea to production. Without a structure or environment to support promising initiatives, even the best projects will die. And without an appetite for long-term investment, applications of disruptive technologies in international development will not reach any meaningful level of scale or usefulness.

The 2018 Atlas of Sustainable Development Goals: an all-new visual guide to data and development

World Bank Data Team's picture
Also available in: Español | العربية | Français
Download PDF (30Mb) / View Online

“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

We’re pleased to release the 2018 Atlas of Sustainable Development Goals. With over 180 maps and charts, the new publication shows the progress societies are making towards the 17 SDGs.

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.
 

Trends and analysis for the 17 SDGs

Survey specialists and data scientists meet: machine learning to measure a person’s height from a picture.

Michael M. Lokshin's picture
Also available in: Français
A test subject holding a reference image and a silhouette derived from the photo by Tensorflow/DeepLab semantic image segmentation model.

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:

  • With an iteratively refined method, it’s possible to get a measure of someone’s height accurate to 1% from a well-composed image of them holding a calibrated paper printout. We plan to integrate this functionality in to the free World Bank Survey Solutions CAPI tool.

  • We found working with an in-house team of survey specialists and data scientists the best way to tackle this problem. It’s only when we combined our domain knowledge and field experience with our data science skills and a healthy dose of creative problem solving, were we able to develop a working prototype.

Why time use data matters for gender equality—and why it’s hard to find

Eliana Rubiano-Matulevich's picture
Also available in: العربية | Français
Photo: © Stephan Gladieu / World Bank

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.

How do women and men spend their time?

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.

5 Reasons to Check out the World Bank’s new Data Catalog

Malarvizhi Veerappan's picture
Also available in: العربية

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.

Open data is at the heart of our strategy

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.

Chart: Why Are Women Restricted From Working?

Tariq Khokhar's picture
Also available in: العربية | Français | Español | 中文

Economies grow faster when more women work, but in every region of the world, restrictions exist on women’s employment. The 2018 edition of Women Business and the Law examines 189 economies and finds that in 104 of them, women face some kind of restriction. 30% of economies restrict women from working in jobs deemed hazardous, arduous or morally inappropriate; 40% restrict women from working in certain industries, and 15% restrict women from working at night.

 

How many companies are run by women, and why does it matter?

Masako Hiraga's picture
Also available in: Español | العربية

Happy International Women’s Day! This is an important year to celebrate – from global politics to the Oscars last weekend, gender equality and inclusion are firmly on the agenda.

But outside movies and matters of government, we see the effects on gender equality every day, in how we live and work. One area we have data on comes from companies: what share of firms have a female CEO or top manager?

Only 1 in 5 firms worldwide have a female CEO or top manager, and it is more common among the smaller firms. While this does vary by around the world – Thailand and Cambodia are the only two countries where the data show more women running companies than men.

Better representation of women in business is important. It ensures a variety of views and ideas are represented, and when the top manager of a firm is woman, that firm is likely to have a larger share of permanent female workers.

What data do decision makers really use, and why?

Sharon Felzer's picture
Also available in: العربية | Français

When it comes to revolutions, the data revolution has certainly been less bloody than, say, those in the 18th and 19th centuries. Equally transformative? A question for historians.

AidData, a research and innovation lab located at the College of William & Mary in the US, set out in 2017, to identify what data decision makers in low and middle-income countries use, whose data they use, why they use it, and which data are most helpful.

What can the World Bank learn from AidData’s study, and do data from our own Country Opinion Survey Program, align with AidData’s findings?

Decoding data use: 3500 leaders in 126 low- and middle-income countries.

In 2017 nearly 3500 leaders responded to AidData’s Listening To Leaders Survey (LTL) to help uncover how, when, and why this audience uses information from a range of sources.

This rich data is featured in the report “Decoding Data Use: How do Leaders Source data and Use It To Accelerate Development” and can help any institution target important audiences. For example, what are CSOs and NGOs using most frequently, and for what purpose? How about government respondents? Development partners? The private sector? Does it differ region to region?

Here are some of the key findings:

 

  • Policymakers consult information from the World Bank more than other foreign/international organizations.
  • If you want opinion leaders in client countries to be aware of the Bank’s data and knowledge, bring it to their attention. If you expect them to find it through an internet search, you might be disappointed.
  • Opinion leaders are most likely to regard the knowledge and information helpful if it helps them better understand challenging policy issues and will help them develop implementation strategies in response.
  • Make sure the knowledge and information reflects the local context (be inclusive).
  • Stay focused on policy recommendations to ensure value.

Now let’s see how AidData’s findings compare with the Bank’s Country Opinion Survey Data.

First thing’s first: Accessing data

The AidData survey findings demonstrate that in the world of information and knowledge, decision makers around the world are accessing the Bank’s data.

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