Visualization data for world development

by on October 30, 2012 at 5:47 am in Data Source, Economics, Education | Permalink

From Damian Clarke:

I am a PhD student in economics at the University of Oxford, and a fan of your blog.  Much of my work focuses on the microeconomics of development (principally fertility and education), however I am also working on the use of open data in economic development – quite an exciting area.  I write you with regards to this open data work.  Recently I have written a module for Stata which allows anyone to automatically import any of the over 5000 indicators maintained by the World Bank, and produces both a geographic and time series representation of the data (I provide a png attachment of this graph here if you are interested in seeing it)…

Whilst this program may be useful for researchers, I think its prinicipal benefit is in pedagogy – perhaps even users of MRUniversity would be interested in visualising for example fertility, GDP, current account balances, etc in a simple command.  The syntax really is very easy: “worldstat Africa, stat(GDP)”.

I provide at the end of this email a brief description, and more details are available on my site: https://sites.google.com/site/damiancclarke/computation#TOC-worldstat

…worldstat is a module which allows for the current state of world development to be visualised in a computationally simple way. worldstat presents both the geographic and temporal variation in a wide range of statistics which represent the state of national development. While worldstat includes a number of “in-built” statistics such as GDP, maternal mortality and years of schooling, it is extremely flexible, and can (thanks to the World Bank’s module wbopendata) easily incorporate over 5,000 other indicators housed in World Bank Open Databases.

…it is automatically available from Stata’s command line by typing: “ssc install worldstat”

Ely Spears October 30, 2012 at 7:23 am

Is the contributor interested in collaborating on a port of this to the Pandas library in Python? It has programmatic access to the FRED data sets, many of Ken French’s data library files, and Yahoo finance. This would be a nice addition and could help target an audience different from the Stata/R world.

Anon. October 30, 2012 at 8:19 am

What is it that economists like so much about Stata? I was forced to use it for a couple of courses back at uni and it’s an abomination compared to every other math/stats computing environment.

ymous October 30, 2012 at 8:50 am

Agreed. I always prefer to have “The Power To Know” :)

Turpentine October 30, 2012 at 9:26 am

Dear Damian,

I want to thank you warmly for your contribution. As I believe will anyone who uses development data in teaching, this is a fantastic initiative. The fact that you are not retributed for this also provides a great pedagogical example of potential market failures due to public good provisions (which you have, admirably, decided to overcome).

Best

Sean October 30, 2012 at 10:01 am

Hear, hear!

Vincent Arel-Bundock October 30, 2012 at 10:01 am

Ely Spears: I just put together a quick port of my WDI package for R which allows users to search and download data series from the World Bank. It is definitely alpha software (error catching is horrible), but it seems to do fine in downloading data and shaping them into a useable Pandas DataFrame. This should at least give you a sense of how the call structure of the World Bank API works.

I’m always interested in improving the packages, so write me an email if you would like to collaborate on expanding this or the R version: varel@umich.edu

Python version: https://github.com/vincentarelbundock/pyWDI
R version with plots: https://github.com/vincentarelbundock/WDI

Dave Backus @ NYU October 30, 2012 at 11:07 am

Thank you!

Vincent Arel-Bundock October 30, 2012 at 10:03 am

Also see this other project for Stata:

http://data.worldbank.org/developers/apps/wbopendata

Steve Sailer October 30, 2012 at 4:44 pm

Don’t forget to include PISA, TIMMS, and IQ scores, the later available from Rindermann’s papers: some very nice correlations to lots of important variables.

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