4 ms·

> Early in the article I adjust this for various controls to better get at causality. This includes observable factors like age, years of experience, hours work

by whack 7y ago

> Early in the article I adjust this for various controls to better get at causality. This includes observable factors like age, years of experience, hours worked, size of employer, programming languages used, etc. There are likely many other factors which aren't "observable" by your methodology, which are causing the 9.4% pay-premium. Factors like competency, domain knowledge, and how essential they are to the organization. I find it much more believable that these unobservable factors are causing both the pay-premium, and working-remote flexibility. This is a common problem that comes up in such studies. Adjusting for various observable factors is great, but it still leaves behind the other unobservable factors. At which point you have to use your judgement to figure out whether those unobservable factors are more compelling than the hypothesis being tested. That said, this is still a very cool analysis that demonstrates an interesting correlation. Thanks for sharing!

whoisnnamdi 7y ago
Totally. The "etc" I used in my quote is doing a lot of work here - I control for a fairly extensive list of observed factors from the dataset. I just listed out a few because otherwise the list gets absurdly long. I control for domain knowledge via what programming languages, technologies, and tools the developers know. I use a proxy for importance to the organization by controlling for the control the developer has over buying decisions within their organization. I control for education levels and college major. I control for employment status (full-time, part-time, independent). Hours worked per week. The list goes on but those are a few. But your point remains that unobservables could be confounding the results, so one can never be sure. Thanks for reading!
braythwayt 7y ago
My go-to quip for making decisions based on observable variables when the actual causal relationship is between unobservable variables and outcomes is, "Looking for your watch under the street lamp." --- For those who aren't familiar with the aphorism, it's from a joke that goes: One night, I came upon a man staring at the ground under a street lamp. "Looking for something?" I asked, and the man nodded, "My watch." "You lost it here?" I asked, but the man shook his head. "I lost it further down the street." "Why look here, if the watch is over there?" The light's better under the street lamp."

by whack 7y ago

whoisnnamdi 7y ago

Totally. The "etc" I used in my quote is doing a lot of work here - I control for a fairly extensive list of observed factors from the dataset. I just listed out a few because otherwise the list gets absurdly long. I control for domain knowledge via what programming languages, technologies, and tools the developers know. I use a proxy for importance to the organization by controlling for the control the developer has over buying decisions within their organization. I control for education levels and college major. I control for employment status (full-time, part-time, independent). Hours worked per week. The list goes on but those are a few. But your point remains that unobservables could be confounding the results, so one can never be sure. Thanks for reading!

braythwayt 7y ago

My go-to quip for making decisions based on observable variables when the actual causal relationship is between unobservable variables and outcomes is, "Looking for your watch under the street lamp." --- For those who aren't familiar with the aphorism, it's from a joke that goes: One night, I came upon a man staring at the ground under a street lamp. "Looking for something?" I asked, and the man nodded, "My watch." "You lost it here?" I asked, but the man shook his head. "I lost it further down the street." "Why look here, if the watch is over there?" The light's better under the street lamp."