[Edited to add: Please carefully read the comments to this post. There are remarks from people with expertise in data analysis. I would also urge everyone to read this post at Dear Author. Note as well that my expertise is in building databases. On a daily basis, I see how bad data architecture renders data untrustable. This is related, but not the same as, expertise in conducting a study and analyzing the results.
Basically, we have three flawed "studies" and my argument here is that publishers and authors alike may be missing the point.
Here's another post to take a look at: from Courtney Milan - who also has the data analysis expertise.]
So, Digital Book World did this study of authors and income from writing.
Then Beverly Kendall did a study …
Then Hugh Howey sponsored a study.
I would like to observe that Beverly Kendall’s study was closer in type to the DBW study but a girl did it so nobody cares about the results — except the mostly women who understand the point very well, thank you.
The DBW study polled authors. Anywhere from 30-60% of whom were unpublished.
Beverly Kendall’s study polled self-published authors (some of whom still traditionally publish) 100% of whom had at least one book on sale.
The Howey study grabbed 24 hours of Amazon sales ranking data, so it’s not really the same as either of the other two studies. With the Howey data, there are several weaknesses: 24 hours of data is not a basis for extrapolating future performance. You’d have to gather the data over a period of time before you could say much about trends, for example. From what I could see, the data analysis did not account for the fact that a price could, theoretically, change during the 24 hours polled. (A book could go on sale at 10 AM PST such that from 00:00 to 10:00 PST the book sold at price x and from 10:01 PST to 23:59 PST the book sold at price y.)
What’s clear from the Howey data is that Indie books are a significant presence in the top 7000 books.
And now the DBW and Howey camps are all arguing and missing the point, which I will make for everyone in just a bit. For once, a DBW data analysis post was reasoned — because it was written by the data guy. His points about the flaws in his own data and the flaws in the Howey data are well taken. NOTE: I am NOT a statistician.
DBW insists: authors as a whole just don’t make very much money. (DON’T LOOK AT BELLA ANDRE!!!)
This is true.
DBW suggests that the authors who are making money are the elite. The authors of the Howey 7000 (titles) are the elite, trad pubbed or self-pubbed. (Nice. Let’s just define authors who make money out of the analysis. Because that leaves you with the ones who are aren’t.)
The DBW/Trad pubbed camp continually harps on the fact that most authors (where you define “author” to include “anyone who wants to write even if they have no books on sale”) don’t make very much money.
The not-so-subtle subtext behind an observation framed in this way is this: why self-publish when you can trad-publish and have all the hard work of covers, editing, and marketing done for you! LOOK AT NORA!!! — And STILL make not very much money, but whatever.
Allow me to make the point
The point is NOT that as an aggregate, authors don’t make much money.
The point is that if you define author as “someone who has at least one book on sale” AND it is true that the author writes well enough that a traditional publisher would pay them to write for their house, the data from the Howey 7000 AND the Kendall 100% points to a very different conclusion.
The conclusion is that such an author has compelling reasons to choose self-publishing over traditional publishing.
Beverly Kendall’s data shows quite clearly the set of conditions that lead to making money as a writer, but that’s the girl talking and as usual, the boys can’t hear her.