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Much of this may be the effect of averaging a power law. Whenever you remove barriers to entry (whether geographic, skill-based, or social), you get regression
by nostrademons 7y ago
Much of this may be the effect of averaging a power law.
Whenever you remove barriers to entry (whether geographic, skill-based, or social), you get regression to the mean: if workers are actually identical, they should be paid the same. The mean of a power law distribution is always higher than the media. Hence, if you remove geographic barriers to software development work, the majority of software developers will see their salaries increase. This comes at the expense of a few developers in high-paying areas (Silicon Valley, SF, NYC), who will see their salaries dramatically decreased. We already see this - as someone living in Silicon Valley, I can't take the vast majority of remote jobs because they don't pay enough to live on here.
Someone's inevitably going to ask "If salaries are so unsustainably high in Silicon Valley, why do companies keep hiring there and paying those unsustainable salaries?"
And the answer to that is that opportunities are not normally distributed. In today's consumer markets, consumers don't split their dollars evenly between all firms providing a good, they tend to overwhelmingly go for the best product available in that market, particularly if that product gets on the market early. And that means going for a location where you have deep talent pools in the many subspecialties needed to build that best product, and putting them all in one cube farm so that communication costs between them are at an absolute minimum. Silicon Valley's wage premium is a consequence of growth and of consumer herd behavior: if everybody either thought entirely for themselves or bought solely based on price without regards to features, distributed teams would have an advantage.
It's a very similar phenomena as globalization. In the 80s all of America enjoyed a large wage premium over all of the rest of the world. As globalization broke down labor & trade barriers and innovation slowed, the trend has been toward "equal pay for equal work", which means that the factory worker in America will make something close to what the factory maker in China makes. Global median wages rise (which they have done, substantially), but the gains are at the cost of the majority of American wages, with the exception of a few professions (tech, finance, some marketing) that have substantial skill-based barriers to entry. Those professions have seen their wages increase dramatically as markets get bigger.
- sbacic 7y ago> We already see this - as someone living in Silicon Valley, I can't take the vast majority of remote jobs because they don't pay enough to live on here. You also can't afford _not_ to take them since the high cost of living limits how much you can save each month. This is one of those things that isn't mentioned often - somebody with cheaper costs of living can play hardball for months in search of a good gig. Somebody in NYC or San Fran, not so much.
- nostrademons 7y agoNot so much of an issue for me personally because I built up a bunch of savings before COL became insane. Every time I've been on the job market, it hasn't been an issue to wait several months for a better offer to come around. You can rebuild savings awfully quickly when living costs $120K/year but your job pays $500K/year. I feel for newer developers that just moved out here with nothing to take $120K/year jobs when living costs $120K/year, though.
- whoisnnamdi 7y agoAuthor here I agree with everything in your comment except the first line. I don't think software developer pay follows a power law, or at least not in this dataset. Ignoring equity (which can cause power law type behavior if enough folks strike it rich), the distribution of pay is probably closer to a lognormal distribution. It's just not skewed enough to constitute a power law — that would imply serious skew. Additionally, this dataset excludes outliers making multiple millions of dollars, so that also reduces the likelihood of a power law-style distribution. Lastly, this dataset is only U.S. developers, so there isn't skew across meaningfully different geographies. With a lognormal distribution, your point remains valid, but the effect is not nearly so dramatic.