Gross domestic product, perhaps the most commonly used statistic in the world for evaluating economic progress, has some issues.
Increasingly, one of the biggest problems is that GDP generally underestimates the value of free goods and services—checking facts on Wikipedia or sharing photos on Instagram, for instance. GDP is best at measuring the impact of TV and car sales—not of things available for free or that require you to view ads, like broadcast TV or Facebook, explains the Financial Times’s Gillian Tett.
What makes GDP buggy
If a company buys proprietary data science software—like SPSS, SAS or STATA—that purchase makes it into GDP. Yet if the company chooses to use a free, open-source language for statistics—like Python or R—that choice never makes into the GDP, even though they are very similar products.
Closing the open-source loop
They find that based on the typical pay of computer programmers, the cost to develop the Github code for these languages would be over $3 billion—much of which was unpaid for. This is a very low-end estimate: A great deal of the code is not on Github and these are only four of the many open-source languages. The actual value could be magnitudes greater.
With numbers like these missing from GDP, it’s hard to know exactly how much economic growth is happening. Dour reports of slow growth in worker productivity may be oversold.
It’s good that researchers are beginning to assess the value of “free” goods. It probably won’t completely change our views, but it is likely that the economy is doing a bit better than conventional statistics suggest.