What happens when a skill you have becomes obsolete? No, this isn't a R vs. Stata thread---it's a thread about a working paper w/ @sonnytambe!
https://t.co/w6nLf1tnST

The skill we look at is Adobe Flash, which @apple decided to no longer support back in 2010, which in turn caused demand/interest to plummet, as measured on @StackOverflow and in online labor markets, one of which is our empirical context
Despite the big fall-off in Flash jobs posted, very little else appeared to change in the market for Flash skills: wages for Flash jobs didn't fall, jobs didn't become easier to fill & openings weren't inundated with out-of-work Flash programmers
What happened was that (a) new entrants stopped specializing in Flash and (b) at least some existing Flash specialists started moving to other skills. In short, the demand shock quickly became a supply shock
At the level of the individual Flash worker, using a matched sample, we find (a) no fall-off in their wages, (b) some decline on-platform hours-worked. The most-focused on Flash workers had substantial increases in application intensity and a movement towards new skills
In short, despite Flash skills being expensive to acquire, workers abandoning a skill with no perceived future create a de factor highly elastic supply curve, keeping wages "flat." We show how this is possible with a little toy model, of course.
We also conduct a survey of Flash workers affected by the decline. They confirm many of our stylized facts & give color to the adjustment process. For one, they report being highly-forward looking and market-oriented & deciding what skills to pick up
They also emphasize how critical on-the-job learning is to acquiring new skills. Sadly for us teachers, formal classroom learning gets almost no love
Anyway, lots more in the paper & thanks for reading this far- check it out! https://t.co/w6nLf1tnST Comments, feedback, suggested citations (even to/esp to your own papers) most welcome!

More from Tech

A brief analysis and comparison of the CSS for Twitter's PWA vs Twitter's legacy desktop website. The difference is dramatic and I'll touch on some reasons why.

Legacy site *downloads* ~630 KB CSS per theme and writing direction.

6,769 rules
9,252 selectors
16.7k declarations
3,370 unique declarations
44 media queries
36 unique colors
50 unique background colors
46 unique font sizes
39 unique z-indices

https://t.co/qyl4Bt1i5x


PWA *incrementally generates* ~30 KB CSS that handles all themes and writing directions.

735 rules
740 selectors
757 declarations
730 unique declarations
0 media queries
11 unique colors
32 unique background colors
15 unique font sizes
7 unique z-indices

https://t.co/w7oNG5KUkJ


The legacy site's CSS is what happens when hundreds of people directly write CSS over many years. Specificity wars, redundancy, a house of cards that can't be fixed. The result is extremely inefficient and error-prone styling that punishes users and developers.

The PWA's CSS is generated on-demand by a JS framework that manages styles and outputs "atomic CSS". The framework can enforce strict constraints and perform optimisations, which is why the CSS is so much smaller and safer. Style conflicts and unbounded CSS growth are avoided.

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I’ve always felt that the luckiest people I know had a talent for recognizing circumstances, not of their own making, that were conducive to a favorable outcome and their ability to quickly take advantage of them.

In other words, dumb luck was just that, it required no awareness on the person’s part, whereas “smart” luck involved awareness followed by action before the circumstances changed.

So, was I “lucky” to be born when I was—nothing I had any control over—and that I came of age just as huge databases and computers were advancing to the point where I could use those tools to write “What Works on Wall Street?” Absolutely.

Was I lucky to start my stock market investments near the peak of interest rates which allowed me to spend the majority of my adult life in a falling rate environment? Yup.