I have paid to see Bob Dylan twice and I have left Bob Dylan concerts early twice. If Bob Dylan is in a city near me again I will buy another ticket and I will again leave early. He really should stop touring, but he hasn’t, and he won’t, and I’m glad. Two options for why he will not stop touring (1) He has nothing better to do (2) He’s getting paid $225,000 on average for each performance. There are a lot of things I would do on stage in every metropolitan area if you paid me $225,000 each time.
I got that number, and many more, from a leaked list from Degy Entertainment that shows the price for booking an artist. Degy Entertainment acts as a middle man between artist managers and booking agents for venues. They are the definition of a middle man. Anyway, from their information I made a dataset that contains all artists that command at least $100,000 to perform. From there I divide by genre (learning towards how the artist is marketed) to examine relative popularity of popular musical genre as live acts.
Grouping my data by genre is interesting because genre’s are super subjective and are blended really closely in popular music. If I were to only take song structure then almost every genre that is popular enough to command $100,000+ per performance would be the exact same genre. Life’s a rant.
The problem is that any one of these columns does not convey the whole story. The large variance of sample size in the count of artists complicates both the total commanded and the average. The count itself does a decent job, but how can you know that a large number of artists is not just a bunch of artists hovering around the 100,000s, while a smaller sample could have a couple artists in the exclusive 1,000,000+ sphere?
Unfortunately I don’t have a test for this. But by using a test for correlation (.58) and doing a student’s t-test (p value < .05) on count and average I know that more artists increases how much each artist is paid, which is possibly the most interesting outcome. Having a greater supply should drive price down, but apparently we have a half-decent argument for Say’s law, which in intro Micro says “supply creates its own demand”. I’m not getting too excited though, look what blog this is on and please lower your expectations accordingly, by cutting my sample off at $100,000 there’s plenty of stuff going on I don’t account for.
For clarity’s sake
5. Alternative Rock
7. Hip Hop
12. Soft Rock
This list could of course be expanded if I went below $100,000, but I’m happy with a 150ish row dataset when I’m the one making it from scratch. I really need to see about setting up a Shitty Data intern for work-study.
Want my data? God help your soul http://sharesend.com/o17wc46m