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January 2018

Your Cow, Plant, Fridge and Elevator Can Talk to You (But Your Kids Still Won’t!)

Raka Banerjee's picture
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The Internet of Things (IoT) heralds a new world in which everything (well, almost everything) can now talk to you, through a combination of sensors and analytics. Cows can tell you when they’d like to be milked or when they’re sick, plants can tell you about their soil conditions and light frequency, your fridge can tell you when your food is going bad (and order you a new carton of milk), and your elevator can tell you how well it’s functioning.

At the World Bank, we’re looking at all these things (Things?) from a development angle. That’s the basis behind the new report, “Internet of Things: The New Government to Business Platform”, which focuses on how the Internet of Things can help governments deliver services better. The report looks at the ways that some cities have begun using IoT, and considers how governments can harness its benefits while minimizing potential risks and problems.

In short, it’s still the Wild West in terms of IoT and governments. The report found lots of IoT-related initiatives (lamppost sensors for measuring pollution, real-time transit updates through GPS devices, sensors for measuring volumes in garbage bins), but almost no scaled applications. Part of the story has to do with data – governments are still struggling how to collect and manage the vast quantities of data associated with IoT, and issues of data access and valuation also pose problems.

Chart: Global Wealth Grew 66% Between 1995 and 2014

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

Global wealth grew by 66% between 1995 and 2014 to a total of over 1,140 Trillion dollars. The share of the world’s wealth held by middle-income countries is growing — it increased from 19% to 28% between 1995 and 2014, while the share of high-income OECD countries fell from 75% to 65%. Read more in The Changing Wealth of Nations

 

Chart: Economic Development and the Composition of Wealth

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

The composition of wealth fundamentally changes with economic development. Natural capital—energy, minerals, land and forests—is the largest component of wealth in low-income countries. Its value goes up, but its share of total wealth decreases as economies develop. By contrast, the share of human capital, estimated as the present value of future incomes for the labor force, increases as economies develop. Overall, human capital accounts for two-thirds of the wealth of nations. Read more in The Changing Wealth of Nations

 

Announcing Funding for 12 Development Data Innovation Projects

World Bank Data Team's picture
Also available in: Français | 中文

We’re pleased to announce support for 12 projects which seek to improve the way development data are produced, managed, and used. They bring together diverse teams of collaborators from around the world, and are focused on solving challenges in low and lower middle-income countries in Sub-Saharan Africa, East Asia, Latin America, and South Asia.

Following the success of the first round of funding in 2016, in August 2017 we announced a $2.5M fund to support Collaborative Data Innovations for Sustainable Development. The World Bank’s Development Data group, together with the Global Partnership for Sustainable Development Data, called for ideas to improve the production, management, and use of data in the two thematic areas of “Leave No One Behind” and the environment. To ensure funding went to projects that solved real people’s problems, and built solutions that were context-specific and relevant to its audience, applicants were required to include the user, in most cases a government or public entity, in the project team. We were also looking for projects that have the potential to generate learning and knowledge that can be shared, adapted, and reused in other settings.

From predicting the movements of internally displaced populations in Somalia to speeding up post-disaster damage assessments in Nepal; and from detecting the armyworm invasive species in Malawi to supporting older people in Kenya and India to map and advocate for the better availability of public services; the 12 selected projects summarized below show how new partnerships, new methods, and new data sources can be integrated to really “put data to work” for development.

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.

2018 Innovation Fund Recipients

Over 1.25 Million People are Killed on the Road Each Year

David Mariano's picture
Also available in: Español | العربية | Français

Over 1.25 million people are killed each year on the road. And 20-50 million others are seriously impacted by road traffic injuries. While most regions have seen a decrease in road-traffic related death rates, Sub-Saharan Africa and Middle East and North Africa still see over 20 deaths per 100,000 people every year.

A new report produced by the World Bank and funded by Bloomberg Philantrophies estimates the social and economic benefits of reducing road traffic injuries in low- and middle-income countries​.

Data science competition: predicting poverty is hard - can you do it better?

Tariq Khokhar's picture
 

If you want to reduce poverty, you have to be able to identify the poor. But measuring poverty is difficult and expensive, as it requires the collection of detailed data on household consumption or income. We just launched a competition together with data science platform Driven Data, to help us see how well we can predict a household’s poverty status based on easy-to-collect information and using machine learning algorithms.

The competition supplies a set of training data with anonymized qualitative variables from household surveys in 3 countries, including the “poor” or “not poor” classification for each observation.

The challenge is to build models which can accurately classify households from a different set of test data (with the poor/not poor classification removed!) for the same 3 countries, and then submit them for scoring. Performance is measured by the mean log loss for the 3 countries which tells us how accurate the classification models developed are.

Prizes are $6,000; $4,000; and $2,500 for the top 3 performing entries, plus a $2,500 bonus prize for the top-performing entry from a low- or lower-middle income country. The deadline for entries is February 28th 2018.

You can read the full problem description and enter the competition here, and see the Driven Data team’s “benchmark solution” based on a random forest classifier.

Good luck - we look forward to seeing your solutions!

Going Deeper into TCdata360 Data Availability Leaders and Laggers

Reg Onglao's picture

Note: This is the second blog of a series of blog posts on data availability within the context of TCdata360, wherein each post will focus on a different aspect of data availability. The first blog post can be viewed here.

With open data comes missing data. In this blog series, we hope to explore data availability by looking at it from various perspectives within the context of the TCdata360 platform[1]: by country, dataset, topic, and indicator.

In our previous blog post, we took a look at the country-level data availability over time through an interactive motion bubble plot inspired by the famous Gapminder visualization. In this follow-up post, we’ll still look at data availability from a geographical lens – but now looking into country classifications and other details that aren’t evident in a bubble plot, as well as the data availability leaders and laggers over time.

Overall Data Availability Leaders and Laggers

First, let’s focus on comparing individual countries to get a better sense of country-level differences in data availability. We computed for each country’s overall data availability by taking the median data availability across all years (1955-2016). Looking at the top 20 and bottom 20 countries in terms of overall data availability generates a few interesting patterns.