Saturday, February 18, 2012

High-wage skills on oDesk (or why you might want to learn Clojure if you're not a lawyer)

Update: Hello HackerNews readers. One thing that I discussed but probably didn't emphasize enough is that this data show the correlation between listed skills and offered wages---you absolutely cannot infer a causal relationship (my cheeky title notwithstanding). Unless I get to create and run a massive skills training program experiment, it's going to be hard to get at causality. But I can do something about the offered/earned distinction. If you don't want to miss my follow-on blog post where I explore the relationship between skills and actual earned wages from actual projects, follow me on twitter.     

oDesk recently introduced a controlled, centralized vocabulary of about 1,400 skills for buyers and contractors to use when posting jobs and creating profiles. The primary motivation for the change was to make it easier for buyers and sellers to find each other: without a standardized vocabulary, would-be traders can fail to match simply because they use different terms for the same skill.

A side effect of this transition is that high quality data on the relationships between skills and wages are now available. I recently built a dataset of contractors' hourly wages by skill: for each skill, I identified all contractors listing that skill on their profiles and averaged their offered hourly wages. Although contractors are free to offer any hourly wage they like, in my experience, wages offered closely map to actual earnings. However, to reduce the influence of outliers, I restricted the sample to contractors offering between 50 cents and 100 dollars per hour. I also only included skills for which there were 30 or more observations.

In the bar chart below (made using the very cool googleVis package for R), I plotted the top 50 skills, ordered by average hourly wage (here is a "live" version with mouse-over). The top of the list is dominated by high-end consulting areas (e.g., patents and venture capital consulting) or hot newer technologies (e.g., redis and Amazon RDS). The programming language that commands the highest wage is Clojure, which is a rather esoteric skill: it's a lisp dialect that compiles to the Java Virtual Machine (JVM). Perhaps this is the market reflecting Paul Graham's "Python Paradox":

"if a company chooses to write its software in a comparatively esoteric language, they'll be able to hire better programmers, because they'll attract only those who cared enough to learn it. And for programmers the paradox is even more pronounced: the language to learn, if you want to get a good job, is a language that people don't learn merely to get a job."

At the time Graham wrote this, Python was a far less mainstream language, probably analogous to how Clojure is regarded today. It's an interesting pattern, and although they'd cut up my economist membership card if I made a causal claim between knowing Clojure and being able to command hire wages, I'm intrigued by the idea of using online labor markets as a bellwether to help guide human capital choices.


  1. All these rates are very low. Try searching for Murex and see what you get.

  2. Yes but Murex is a god awful system in an awful industry, only a sadomasochist would work on Murex or someone in it purely for the money hence the ridiculous contracting rates it commands

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  4. I work in NLP in the bio domain. The role of ontology as controlled vocabulary is a big one as gene names are full of synonyms and different ways to spell them (do you abbreviate, spell out or use the greek letter alpha?). One thing I'm curious about here is how to make them more specific. Some of the skills could be either admin or programming (mongodb and hadoop), and I wonder how the different modes would pay differently.
    It's probably as hard as identifying the species a paper's gene is about as they often have identical names too.

    1. These are exactly the kinds of issues we wrestled with when we made the vocabulary. In our case, we had over 250K "skills" of which only 16% were used more than once; I think I've seen every possible permutation of "microsoft powerpoint." My emerging view is that there is no silver bullet---each decision to make a skill more or less general, split it, remove it etc. takes some judgement. That being said, I think there's hope for at least creating automated ways of finding badly performing tags. Managing the skill ontology can/should be a blog post in itself.

  5. "if a company chooses to write its software in a comparatively esoteric language, they'll be able to hire better programmers, because they'll attract only those who cared enough to learn it."

    I call BS (par for the course with Paul Grapham).

    You can find top talent for any language. Choosing an esoteric language is not a guarantee that you'll find top talent any easier.

    I.e there could be 20,000 C++ gurus, but only 10 Haskell gurus.

    What you *do* get with an esoteric language is lots of tinkerers though, guys that played with the language for several years, in no actual projects (it's esoteric, after all, not many actual projects use it), and consider themselves 'above the average' because they "know" it.

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  7. @Unknown

    My guess (and that's all we really have, guesses) is that with an esoteric language you'd have less-than-expected middle-level programmers; You'd have mostly those that'd played with it out of curiosity and those who dedicated themselves despite the low chance of external reward.

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