One of the things we do at the Open Data Institute (ODI) is incubate start ups. Start ups, I have learnt in my 12 months working here, usually begin as being just one or two individuals with a good idea. They have some sort of plan to make that idea a reality. They have some manifestation of the entrepreneurial leadership qualities to at least try to make that idea work. They never have enough time, money or people, and they ordinarily start out surrounded by people telling them all the reasons why it won’t succeed.
The term "big data" is much in the news lately – alternatingly touted as the next silver bullet potentially containing answers to myriad questions on natural and human dynamics, and dismissed by others as hype. We are only beginning to discover what value exists in the vast quantities of information we have today, and how we are now capable of generating, storing, and analyzing this information. But how can we begin to extract that value? More importantly, how can we begin to apply it to improving the human condition by promoting development and reducing poverty?
That is precisely the question that motivated the World Bank Group and Second Muse to collaborate on the recently released report Big Data in Action for Development. Interviews with big data practitioners around the world and an extensive review of literature on the topic led us to some surprising answers.
(Source: FRED Economic Data)
A recent World Bank Group feature story broke down country by country the potential regional consequences. And according to the Bank Group’s Global Economic Prospects report, the decline in oil prices will dampen growth prospects for oil-exporting countries.
There are various factors that can be used to assess the impact of falling oil prices on countries. One such factor is trade. Countries exporting mostly fuel products will lose export revenue as oil prices drop. The chart below shows the top 15 countries that exported fuel in 2012. You can visualize the data for other years and products using the World Integrated Trade Solution’s (WITS) product analysis visualization tool.
Every minute, dozens of people in East Asia move from the countryside to the city.
The massive population shift is creating some of the world’s biggest mega-cities including Tokyo, Shanghai, Jakarta, Seoul and Manila, as well as hundreds of medium and smaller urban areas.
This transformation touches on every aspect of life and livelihoods, from access to clean water to high-speed trains that transport millions of people in and out of cities during rush hour each weekday.
A revolution starts with an idea, but to become real, it has to move quickly to a practical proposition about getting stuff done. And getting things done needs money. If the ideas generated last year, in the report of the UN Secretary General’s Independent Expert Advisory Group and elsewhere, about how to improve data production and use are to become real, then they will need investments. It’s time to start thinking about where the money to fund the data revolution might come from, and how it might be spent.
Getting funding for investment in data won’t be easy. As hard-pressed statistical offices around the world know to their cost, it’s tough to persuade governments to put money into counting things instead of, say, teaching children or paying pensions. But unless the current excitement about data turn into concrete commitments, it will all fade away once the next big thing comes along, leaving little in the way of lasting change.
Last August, the UN Secretary-General Ban Ki-moon asked an Independent Expert Advisory Group (IEAG) to make concrete recommendations on bringing about a Data Revolution in sustainable development. In response, the IEAG delivered its report, and among other items, recommends, “a new funding stream to support the Data Revolution for sustainable development should be endorsed at the Third International Conference on Financing for Development,” in Addis Ababa in July 2015.Three Issues Papers for Consultation
To support this request and to stimulate conversation, the World Bank Group has drafted issues papers that focus on three priority areas:
The papers aim to flesh out the specific development needs, as well as financing characteristics needed to support each area. A fuller understanding of these characteristics will determine what kind of financing mechanism(s) or instrument(s) could be developed to support the Data Revolution.
Most countries in the world measure their poverty using an absolute threshold, or in other words, a fixed standard of what households should be able to count on in order to meet their basic needs. A few countries, however, have chosen to measure their poverty using a relative threshold, that is, a cutoff point in relation to the overall distribution of income or consumption in a country.
The chart above shows the differences between relative and absolute poverty headcount ratios for countries that have measured both. You can select other countries from the drop down list, but for example, you can see that Romania switched from measuring poverty in absolute terms to measuring poverty in relative terms in 2006. Absolute poverty headcount ratios steadily declined from 35.9% in 2000 to 13.8% in 2006. However, by relative measures, the national poverty headcount ratio in 2006 was 24.8%. This does not mean that poverty bumped up in 2006. These two numbers are simply not comparable, but what exactly do they both mean?
A month ago, the Kenya National Bureau of Statistics (KNBS) Kenya released a set of re-based and revised National Accounts Statistics (NAS), the culmination of an exercise that started in 2010. Press coverage, reactions from investors and the public have been generally favorable, but some confusion still looms regarding some of the facts and consequences. We wrote this blog post to debunk some of the myths.
NAS, including Gross Domestic Product (GDP), are typically measured by reference to the economic structure in a “base” year. Statisticians sample businesses in different industries to collect data that measures how fast they are growing. The weight they give to each sector depends on its importance to the economy in the base year. As time passes and the structure of the economy changes, these figures become less and less accurate.
Re-basing is a process of using more recently collected data to replace an old base year with a new one to reflect the structural changes in the economy. Re-basing also provides an opportunity to add new or more comprehensive data, incorporate new or better statistical methods, and apply advancements in classification and compilation standards. The current gold standard is the 2008 System of National Accounts (SNA).