Technology and the Solow residual...

I recently wrote some Python code to compute paths of technology and the implied Solow residuals using data from the Penn World Tables.  Combining the results with some country metadata (i.e., income groupings) from the World Bank API yields this pretty interesting graphic...

If you don't already have the Penn World Tables data...no worries! The script will download the PWT data, compute the Solow residuals based on a method used by Hall and Jones (1999). Based on this decomposition, high income (i.e., red) countries had higher levels of technology in 1960 and higher subsequent growth rates of technology.  In fact, the low income (i.e., purple) countries have had effectively zero technological progress since 1960!

Enjoy!
  

Graph of the Day

A New Year and a new graph of the day! This graphic actually uses a new Python library, wbdata, for grabbing World Bank data via the World Bank's API.  Here is a plot of global inflation over the last 50 odd years for all available countries.  I have color-coded the countries according to income group: Low, Lower-Middle, Upper-Middle, or High.

I am not entirely thrilled with this graph.  It turned out to be hard to scale the y-axis to capture the full range of the data: Democratic Republic of Congo had an annual inflation rate over 23,000% in 1994! Zimbabwe would have had even higher annual inflation rates but they stopped reporting inflation statistics in 2006 (just prior to the onset of its recent bought of hyperinflation).

As always, code is available on GitHub.