I just read "Linked" by Albert-Laszlo Barbasi and he talked about different kinds of networks. One example is the number of inbound/outbound links you find on a highway map versus a map showing links between airports. The highway map gives rise to a normal statistical distribution with only a few towns having so many or so few roads in/out that they fall far from the mean.
The airport map is a different story. There are many airports with just a few connecting flights and there are just a few airports with a large number of connections. When you make a plot that shows number of connections on the x-axis and the number of airports having that many connections on the y-axis it shows a power law. The graph is very high near the y-axis and drops off as you move to the right. Finally you get to the large airports like LAX, Chicago, and New York.
I bring this up here because I think the issue of programmer pay and productivity has similar properties. The salaries are distributed normally with a mean and the vast majority earning within 2 standard deviations. If you could measure productivity of each programmer and make a graph that showed the productivity level on the x-axis and the number of programmers working at that level on the y-axis then I don't think the graph would look like a normal distribution. Rather I think it would follow the power law just like the graph of airport hubs.
I know this is somewhat speculative on my part but I think it could also be that the top programmers are so productive because they are highly connected in terms of programming concepts and techniques. The article mentions their ability to come up with alternative solutions. I think they get like that by working on different projects over time. This is also how the power law networks (also called 'scale free' networks) are thought to form in other areas according to the book.
The airport map is a different story. There are many airports with just a few connecting flights and there are just a few airports with a large number of connections. When you make a plot that shows number of connections on the x-axis and the number of airports having that many connections on the y-axis it shows a power law. The graph is very high near the y-axis and drops off as you move to the right. Finally you get to the large airports like LAX, Chicago, and New York.
I bring this up here because I think the issue of programmer pay and productivity has similar properties. The salaries are distributed normally with a mean and the vast majority earning within 2 standard deviations. If you could measure productivity of each programmer and make a graph that showed the productivity level on the x-axis and the number of programmers working at that level on the y-axis then I don't think the graph would look like a normal distribution. Rather I think it would follow the power law just like the graph of airport hubs.
I know this is somewhat speculative on my part but I think it could also be that the top programmers are so productive because they are highly connected in terms of programming concepts and techniques. The article mentions their ability to come up with alternative solutions. I think they get like that by working on different projects over time. This is also how the power law networks (also called 'scale free' networks) are thought to form in other areas according to the book.