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From the ideal to the real: 20 lessons from scaling up innovations at the World Bank

Soren Gigler's picture

On a brisk February morning in 2010, a small group of my World Bank colleagues, a few AidData partners, and I were in brainstorming mode.  Our topic of discussion: how we could make a meaningful, measurable difference in making our development projects more open, transparent, and effective.

One idea lit us all up: putting development on a map. We envisioned an open platform that citizens around the world could use to look up local development projects and provide direct feedback. We were inspired by “open evangelists” like Beth Novek, Hans Rosling and Viveck Kundra.

 

 Testing of the citizen feedback platform with local community members in rural Cochabamba, Bolivia
 

However, there was one challenge: how could we help make the World Bank’s data and numerous data sets fully open, free, shareable, and easily accessible to anyone? At the time, the large majority of these data sets were proprietary, and those who had access to key data sets were a relatively limited number of technical specialists.

 

In addressing this issue, we were fortunate. We worked closely as a small, creative, and highly committed team of innovators from different parts of the Bank to gradually open up the Bank’s data. To be honest, no one on our small team of incubators could have predicted that we would be able to scale up our early innovations so rapidly and that they would result in such important changes in the Bank’s approach to data and openness.

 

Are we ready to embrace big private-sector data?

Andrew Whitby's picture



The use of big data to help understand the global economy continues to build momentum. Last week our sister institution, the International Monetary Fund, launched their own program in big data, with a slate of interesting speakers including Hal Varian (Google Chief Economist), Susan Athey (Professor at Stanford GSB and a former Microsoft Chief Economist) and DJ Patil (Chief Data Scientist of the United States).
 
The day's speakers grappled with the implications of big data for the Fund's bread-and-butter macroeconomic analysis--a topic of great interest to the World Bank Group too. Examples were presented in which big data is used to generate macroeconomic series that have traditionally been the preserve of national statistical offices (NSOs): for example, MIT's Billion Prices Project, which measures price inflation in a radically different way from traditional CPI statistics.
 

Should we continue to use the term “developing world”?

Tariq Khokhar's picture
Also available in: 中文 | العربية | Español | Français
class-map-3.gif

Comparing the classification of countries.

Humans, by their nature, categorize. Economists are no different. For many years, the World Bank has produced and used income classifications to group countries.  

The low, lower-middle, upper-middle and high income groups are each associated with an annually updated threshold level of Gross National Income (GNI) per-capita, and the low and middle income groups taken together are referred to in the World Bank (and elsewhere) as the “developing world.

This term is used in our publications (such as the World Development Indicators and the Global Monitoring Report) and we also publish aggregate estimates for important indicators like poverty rates for both developing countries as a group and for the whole world.

But the terms “developing world” and “developing country” are tricky: even we use them cautiously, trying to make it clear that we're not judging the development status of any country.

Income growth in Latin America has stopped being pro-poor during the slowdown

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

The team behind the World Bank’s LAC Equity Lab is starting this new blog series to showcase our favorite charts and visuals that help tell the story of recent developments in poverty and equity in Latin America and the Caribbean. We welcome your comments and ideas, and invite you to explore our LAC Equity Lab and World Bank Poverty websites to learn more.
 
In this first installment, we are tackling a pressing issue for the region – income growth and its implications on inequality.
 
Income growth in Latin America has stopped being pro-poor during the slowdown
 

Source: SEDLAC (World Bank and CEDLAS). Note: growth incidence curves (GIC) show the annualized growth rate of income for every percentile of the income distribution and are calculated using pooled harmonized data from 17 countries. In order to analyze the same set of countries every year, interpolation was applied when country data were not available for a given year.

Climate change's biggest effect on poverty? Agriculture.

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

The biggest impact climate change will have on the poor will be through agriculture. Under a pessimistic "poverty" scenario with high climate change impacts, there could be more than 100 million additional people in poverty by 2030, largely due to changing crop yields and prices. Under an optimistic "prosperity" scenario, these effects are greatly reduced. Read more in the new "Shock Waves" report.

 

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?

2014 Global Findex microdata provides a closer look at people’s use of financial services

Leora Klapper's picture
Also available in: 中文 | Español | Français | العربية
We’ve just rolled out the 2014 Global Findex microdata, which features about 1,000 individual-level surveys on financial inclusion for 143 economies worldwide. Check it out at the Findex homepage or in the World Bank's Data Catalogue.
 

 

How we made #OpenIndia

Ankur N's picture

Cross posted from the End Poverty in South Asia blog

open india

It has been a season ripe with new ideas and shifts in the open data conversation. At the Cartagena Data Festival in April, the call for a country-led data revolution was loud and clear. Later in June at the 3rd International Open Data Conference in Ottawa there was an emphasis on the use of open data-beyond mere publishing.

Mulling on these takeaways, a logical question to ask may be: what would a country-focused data project that aims to put data to use look like?

How long does it take to start a business in your country?

Tariq Khokhar's picture
Also available in: العربية | Français | Español | 中文
One of the most interesting trends in Doing Business is the reduction in the number of days it takes to start a business across the world. Navigate through the graphic below using the arrows next to the captions and then select any countries or groupings you'd like to see with the dropdown menu at the bottom.
 
 

Starting a business gets easier around the world

Tariq Khokhar's picture
Also available in: Español

On average, it took 20 days to start a business in 2015 vs 51 in 2003. The 2016 edition of Doing Business finds that low and middle income countries are making big strides in improving business climates. Notably, a total of 45 economies, 33 of which were developing economies, undertook reforms to make it easier for entrepreneurs to start a business. The report presents quantitative data on 189 economies, including many city-level analyses. You can download the report and the data behind it from the Doing Business website.

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