Following up on the last entry (see below), here’s make a quick estimate of the proportion of people who choose a career based on their first name:
p1 * first_letter_effect + p2 * first_2_letters_effect + p3 * first_3_letters_effect
Here, p1 is the proportion of careers that begin with the first letter of your name, and the “first letter effect” is the extra proportion of people in a specific career beginning with the same first letter of their name. Similarly, p2 is the proportion of careers that share the first 2 letters of your name, and the “first 2 letters effect” is the extra proportion with that career, and similarly for p3. One could go on to p4 etc., but the idea is that, after p3, the probability of actually sharing the first 4 letters is so low as to contribute essentially nothing to the total.
Now, for some quick estimates: The simplest estimates for p1, p2, p3 are 1/26, 1/26^2, 1/26^3, but that’s not quite right because all letters are not equally likely. Just to make a guess, I’ll say 1/10 for p1, 1/50 for p2, and 1/150 for p3.
What about the “letter effects”? For “Dennis” the effect was estimated to be about 221/(482-221) = .85–that is, about 85% more dentists named Dennis than would be expected by chance alone. But “Dennis” and “dentist” sound so much alike, so let’s take a conservative value of 50% for the “first-3-letters-effect.” The first-2-letters-effect and first-letter effects must be much smaller–I’ll guess them at 5% and 15%, respectively.
In that case, the total effect is
(1/10)*.05 + (1/50)*.15 + (1/150)*.50 = 0.011, or basically a 1% effect.
So, my quick estimate, based on the work of Pelham, Mirenberg, and Jones, is that approximately 1% of people choose their career based on their first name. As I said, I’m taking their results at face value; you can read their article for detailed discussions of potential objections to their findings.