Patents & Innovation
Over dinner, a friend mentioned that she thought a particular country produced the most patents, and although I remember reading the same article about 10 years ago, - I believe it was in the NY Times - it is no longer true if it ever was. Looking at patents per capita, I found a variety of articles based on quality sources, and although the country does not rank in the top 10, it does rank well in Bloomberg's Innovation Index.
The latter is not solely based on patent numbers since one needs to consider other measures of innovation. Bloomberg's scoring includes indicators such as R&D spending, manufacturing, the number of high-tech companies, secondary education attainment, and the number of research personnel.
On a separate note, countries with large engineering and semiconductor industries and those that score well in international comparisons on science and math will dominate patents and innovation, as well as those countries with freer cultures, although this is synergistic, in that both the industries and social capital measures feed each other.
Some of my own informal research into Hofstede's cultural dimensions and patent production found that the two (2) dimensions with the highest correlations and P-values under .01 were Uncertainty Avoidance and Individuality. Essentially, cultures that tolerate ambiguity and are the least rule-based, along with having high individuality, produce a larger number of patents.
Because of the high tech industries they support, their high levels of education, and their generally free culture, Scandinavia performs well. It is similarly so for South Korea and Japan, although they generally do not have what we would think of as free cultures, being much more rigid and rule-based, they do have very high levels of technical education and industries that rely on those skills.
The Low Probability of Hiring Software Engineers
A fairly complicated description of hiring, and although somewhat obvious, more easily described by a simple probability equation. So, excluding the likelihood of getting past the recruiter:
P(hire) = P(phone screen) * P(sample project) * P(2 interview teams) * P(accepting)
Even including some kind of Bayesian inference, increasing odds for passing subsequent steps, or tilting candidate characteristics, it still leaves the probability of a hire fairly low, and an increased likelihood of a rejecting a good candidate, a false negative, but one can understand the aversion to a false positive, as it can be very expensive.
Source: Bayesian Inference for Hiring Engineers