Last month, while World Bank President Jim Yong Kim launched the gender data portal, U.S. Secretary of State Hillary Clinton remarked that “data not only measures progress, it inspires it”. Indeed when data is both relevant and effectively communicated, it can help to inform policies, identify challenges, and catalyze changes and innovations that deliver development results.
With that goal in mind, we started an Open Data Lab. One of our objectives is to help the development community become more effective data communicators by experimenting with different data visualization techniques and tools. The human brain finds it easier to process data and information if it is presented as an image rather than raw numbers or words. And visualizations that let and encourage users to interact with data can deepen their understanding of the information presented.
We decided to begin with data that defines our challenging work and mission: poverty reduction. Over 20 years ago Martin Ravallion and Shaohua Chen with various collaborators along the way developed the now widely used methods, tools and indicators to monitor the population “living on less than $ a day”. Earlier this year they released the latest update of the World Bank’s global poverty monitoring data along with a re-vamped version of PovcalNet: an interactive on-line computational poverty analysis tool. We downloaded the table with regional poverty estimates and used PovcalNet to calculate the results for additional poverty lines. We then combined all the data in the three-panel visualization embedded below.
Click here for a guide on using this data visualization.
You probably already know that the global incidence of extreme poverty (defined as living on less than $1.25 a day) has fallen from 52 percent in 1981 to 22 percent in 2008. And three regions in particular – East Asia and the Pacific, South Asia and Sub-Saharan Africa – have experienced very different poverty trends over this period. But did you know that:
• Between 1981 and 2008, three regions (East Asia and Pacific, South Asia and Sub-Saharan Africa) have together consistently accounted for over 95 percent of the developing world’s population living on less than $1.25 a day?
• Even if we doubled the extreme poverty line to $2.5 a day, these three regions would still account for 93 percent the poor in the developing world?
• Even if we doubled the poverty line further to $5 a day, 88 percent of the developing world’s poor population would be still found in these regions.
You can explore more interactive country and regional data dashboards with the latest World Bank poverty statistics here: http://povertydata.worldbank.org/poverty/home
What do you think? Did you learn or discover something new about global poverty data? Did it help improve your understanding of these data?
Comments
It can be a very helpful
Data
Wow!
Blank Formatted Diskette
Playing with Tableau
Check out this recent working paper and try this in PovcalNet
Thanks Amparo. Yes we'll add inequality on our to-do-list. Using the Mean Log Deviation (MLD) measure, as outlined in Shaohua and Martin’s recent working paper “more relatively-poor people in a less absolutely-poor world”, is a good approach for global and regional aggregation/decomposition of inequality. And note that PovcalNet allows you to quickly get one measure of vulnerability by varying the poverty line by x%. A $1.25 per day is equivalent to $38 per month ($1.25*365/12) and 20% increase would mean setting the poverty line equal to 45.6. Enter this poverty line in PovcalNet here and let us know what you find.
Poverty Data
Keep doing what you're doing
Re: Keep doing what you're doing
Excellent idea!
Interesting work
Thanks and small suggestions
Thank you for the suggestion
Great Job! It's the Tableau Viz of the Day.
Great charts - digging deeper into your visuals?
Great map
Thanks Nathan. Good to hear you like our spruced up DataBank interface and data sharing functionalities. We'll follow up with graphic design folks. And glad to see you are keeping busy in Namibia. Your recent map of Namibian childcare facilities mashed up with OVC data is very informative.
Seeing between the lines
Average useful floor area per dwelling and per person
Check out our household survey microdata catalog
We do not curate indicator data on such housing characteristics for different countries. But do maintain a library with household survey microdata and DDI compliant metadata which you can search at the variable level: http://microdata.worldbank.org/index.php/home. This will likely turn up some data that you will find useful.
Great tool, great insights
It was excellent method to
No doubt the poverty rate in South Asia has declined
Do the figures take into account inflation?
Good point, and yes, the
Thanks for letting us use your visualization!
Re: Thanks for letting us use your visualization!
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