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Big Data needs better questions

Elizabeth Sabet's picture

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.

Which countries could be affected by plunging oil prices: a data perspective

Siddhesh Kaushik's picture
Tumbling oil prices continue to dominate the headlines. Although oil prices have started to rise earlier in the week, this issue is still of concern to many oil-exporting countries.
 


(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.

Tracking Urbanization: How big data can drive policies to make cities work for the poor

Axel van Trotsenburg's picture

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.

Tracking Urbanization: How big data can drive policies to make cities work for the poor

Axel van Trotsenburg's picture
 Measuring a Decade of Spatial Growth

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.

Funding The Data Revolution

Claire Melamed's picture

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.

Buffet of Champions: What Kind Do We Need for Impact Evaluations and Policy?

Heather Lanthorn's picture
I realize that the thesis of “we may need a new kind of champion” sounds like a rather anemic pitch for Guardians of the Galaxy. Moreover, it may lead to inflated hopes that I am going to propose that dance-offs be used more often to decide policy questions. While I don’t necessarily deny that this is a fantastic idea (and would certainly boost c-span viewership), I want to quickly dash hopes that this is the main premise of this post. Rather, I am curious why “we” believe that policy champions will be keen on promoting and using impact evaluation (and subsequent evidence syntheses of these) and to suggest that another range of actors, which I call “evidence” and “issue” champions may be more natural allies. There has been a recurring storyline in recent literature and musings on (impact) evaluation and policy- or decision-making:
  • First, the aspiration: the general desire of researchers (and others) to see more evidence used in decision-making (let’s say both judgment and learning) related to aid and development so that scarce resources are allocated more wisely and/or so that more resources are brought to bear on the problem.
  • Second, the dashed hopes: the realization that data and evidence currently play a limited role in decision-making (see, for example, the report, “What is the evidence on evidence-informed policy-making”, as well as here).
  • Third, the new hope: the recognition that “policy champions” (also “policy entrepreneurs” and “policy opportunists”) may be a bridge between the two.
  • Fourth, the new plan of attack: bring “policy champions” and other stakeholders in to the research process much earlier in order to get up-take of evaluation results into the debates and decisions. This even includes bringing policy champions (say, bureaucrats) on as research PIs.

There seems to be a sleight of hand at work in the above formulation, and it is somewhat worrying in terms of equipoise and the possible use of the range of results that can emerge from an impact evaluation study. Said another way, it seems potentially at odds with the idea that the answer to an evaluation is unknown at the start of the evaluation.

Next step for the Data Revolution: financing emerging priorities

Grant Cameron's picture

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:

  1. Data innovation
  2. Public-private partnerships for data
  3. Data literacy and promotion of data use

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.

#1 from 2014: Anecdotes and Simple Observations are Dangerous; Words and Narratives are Not

Heather Lanthorn's picture
Our Top Ten blog posts by readership in 2014.
This post was originally posted on January 23, 2014

 

In a recent blog post on stories, and following some themes from an earlier talk by Tyler Cowen, David Evans ends by suggesting: “Vivid and touching tales move us more than statistics. So let’s listen to some stories… then let’s look at some hard data and rigorous analysis before we make any big decisions.” Stories, in this sense, are potentially idiosyncratic and over-simplified and, therefore, may be misleading as well as moving. I acknowledge that this is a dangerous situation.

However, there are a couple things that are frustrating about the above quote, intentional or not.

  • First, it equates ‘hard data’ with ‘statistics,’ as though qualitative (text/word) data cannot be hard (or, by implication, rigorously analysed). Qualitative twork – even when producing ‘stories’ – should move beyond mere anecdote (or even journalistic inquiry).
  • Second, it suggests that the main role of stories (words) is to dress up and humanize statistics – or, at best, to generate hypotheses for future research. This seems both unfair and out-of-step with increasing calls for mixed-methods to take our understanding beyond ‘what works’ (average treatment effects) to ‘why’ (causal mechanisms) – with ‘why’ probably being fairly crucial to ‘decision-making’ (Paluck’s piece worth checking out in this regard).

In this post, I try to make the case that there are important potential distinctions between anecdotes and stories/narratives that are too often overlooked when discussing qualitative data, focusing on representativeness and the counterfactual. Second, I suggest that just because many researchers do not collect or analyse qualitative work rigorously does not mean it cannot (or should not) be done this way. Third, I make a few remarks about numbers.

Government by numbers: How can we solve the gaming problem?

Willy McCourt's picture

Riccardo Ghinelli under creative commons

In my last blog post, I showed that while governments are increasingly using the technology to demonstrate that services are improving, their efforts risk being undermined by “gaming” – in other words, fiddling the performance statistics to make things look better than they really are.
 
We focused on the problem in the UK.  In this blog, I look at what the UK has and hasn’t done to address the problem, and what we can learn from that. 

Weekly Wire: The Global Forum

Roxanne Bauer's picture

These are some of the views and reports relevant to our readers that caught our attention this week.

Illicit financial flows growing faster than global economy, reveals new report
The Guardian
$991.2bn was funneled out of developing and emerging economies through crime, corruption and tax evasion in 2012 alone, according to the latest report by the Washington-based group, Global Financial Integrity (GFI), published on Monday.  The report finds that, despite growing awareness, developing countries lose more money through illicit financial flows (IFF) than they gain through aid and foreign direct investment. And IFFs are continuing to grow at an alarming rate – 9.4% a year. That’s twice as fast as global GDP growth over the same period. Though China tops the list of affected countries in terms of the total sum of money lost, as a percentage of the economy, sub-Saharan Africa was the worst affected region as illicit outflows there average 5.5% of GDP.
 
Development’s New Best Friend: the Global Security Complex
International Relations and Security Network
The United Nations’ blueprints for the upcoming Sustainable Development Goals (SDGs) reveal an interesting trend. Whereas the Millennium Development Goals (MDGs) focused exclusively on development initiatives, the SDGs look set to interweave security into what was once solely a development sphere with the inclusion of objectives that seek to secure supply chains, end poaching and protect infrastructure. This shift reflects lessons learned from 15 years of implementing the MDGs and, even more so, broader global trends to integrate security and development initiatives.


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