Syndicate content

#opendata

Boosting demand for open aid data: lessons from Kenya’s e-ProMIS

Daniel Nogueira-Budny's picture

One journalist used it as a data source for a story on solar energy in Makueni County. Another accessed the data for inclusion in a piece on sanitary napkin distribution in East Pokot. Development partners reported relying on the data to coordinate specific activities in the Central Highlands of Kenya. And this is to say nothing of the government users of the data managed by the Electronic Project Monitoring Information System for the Government of Kenya (e-ProMIS), Kenya’s automated information management system on development projects funded by both domestic and foreign resources.
 

 

Global Data Lab: a resource for subnational development indicators from household surveys

Jeroen Smits's picture

This is a guest blog written by Jeroen Smits of the Global Data Lab, an initiative hosted by the Nijmegen Center for Economics (NiCE) at Radboud University in the Netherlands.  

Disaggregation of indicators at the subnational level is one of the key elements to effectively monitor the Sustainable Development Goals (SDGs). At the same time, this is a great challenge, as in the case for many countries, only indicators at the national level are available.
 
This is particularly the case for poor countries, where administrative systems are less equipped and capable to generate reliable and representative information. Strengthening those systems is the preferred solution, but that takes time and does not produce the indicators for earlier years required for tracing developments over time.

The all-new Open Data website is here

Tim Herzog's picture
The time has come to bid a fond farewell to the open data website that has served us well for almost six years. Next week we will launch the most significant upgrade to the World Bank’s Open Data website since its initial debut in 2010. We first announced this upgrade when we launched the site as a public beta a few months ago.

Children nearly twice more likely to be poor than adults in Latin America

Oscar Calvo-González's picture
Also available in: Español | Portuguese

Childhood poverty in Latin America has declined steadily but remains much higher than poverty among adults. In 2014 poverty among children stood at 36 percent, almost twice the rate for adults (19 percent - see briefing note). The chart below shows that poverty has decreased for both adults and children, but a closer look at the data reveals that childhood poverty has been declining at a slower pace than among adults.
 

European countries making clear progress with Open Data

Tariq Khokhar's picture
Editor’s note: This is a guest blog from Margriet Nieuwenhuis, Eva van Steenbergen and Wendy Carrara on behalf of the European Data Portal. The indicator “Open Data readiness” mentioned in the analysis below is unrelated to the Open Data Readiness Assessment tool developed by the World Bank.
 
The public sector is providing increasing amounts of Open (Government) Data free of charge. Open Data refers to the information collected, produced or paid for by public bodies and can be freely used, modified and shared by anyone for any purpose. In Europe, the maturity of Open Data varies between the countries, as recent research shows. In 2015, the European Data Portal team conducted an assessment of where European countries stood with regard to Open Data. The countries included are the EU Member States (28 countries in total) plus Iceland, Liechtenstein, Norway and Switzerland – further on referred to as the EU28+ countries.
 
Two key indicators have been selected to measure Open Data maturity; Open Data readiness and the maturity of the national Open Data portal. Open Data Readiness looks at the presence of Open Data policies, at the use made of the available Open Data, and at the political, social and economic impact of Open Data. Portal Maturity measures the usability of a web-based Open Data portal with regard to the availability of functionalities, the overall re-usability of data, as well as the spread of data. The two key indicators as well as the sub indicators are depicted in the table below.
Open Data Maturity indicators.

Classifying countries by income: A new working paper

Neil Fantom's picture
Also available in: 中文 | العربية | Français | Español


"The World By Income"

We’ve
just released a working paper reviewing the Bank’s classification of countries by income. As Tariq Khokhar and Umar Serajuddin pointed out in their recent blog about whether we should call countries developing or not, there’s a strong appetite for classifying and ranking countries. Where is the best country to live, according to the OECD? (it depends, but it might be Australia, Norway or Sweden.) Which are making the most social progress, according to the Social Progress Imperative? (Norway and Sweden again.) Where is it easiest to do business, according to the World Bank? (Singapore.) Which countries have highest or lowest human development, according to the United Nations Development Program? (that’s Norway once more, and Niger is lowest.).

Using GNI per capita

The World Bank has used a specific measure of economic development - gross national income (GNI) per capita - for the purpose of ranking and classifying countries for over 50 years. The first compendium of these statistics was called the World Bank Atlas, published in 1966 - it had just two estimates for each country: its population, and its per capita gross national product in US dollars, both for 1964. Then, the highest reported average income per capita was Kuwait, with $3,290. In second place was the United States, with $3,020, third was Sweden, a fair way behind, with $2,040. The bottom three were Ethiopia, Upper Volta (now Burkina Faso), and Malawi, with GNP per capita estimates of $50, $45 and $40 respectively (GNI used to be called GNP). It probably comes as no surprise that today Norway is top. Malawi is still bottom.

What makes a data visualization memorable?

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

Image via Borkin et al

It may surprise some readers that’s there’s a thriving academic discipline concerned with quantifying the effectiveness of information visualization techniques. As John Wihbey notes, “by applying some cognitive and behavioral science”, we can get beyond some of the “rules”, and often subjective criticisms of visualization to see what works in an experimental setting.

In a fascinating paper, Michelle Borkin asked subjects to look at a selection of visualizations from the Massviz corpus while tracking their eye movements, and then later asked them to describe in detail what they recalled of the visualizations.


So what did the study find?

Pages