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Disproportionately Common Names by Profession

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Re: Disproportionately Common Names by Profession

#91

Post by pawelk »

stygiansonic wrote: post #26
Camillo wrote: post #9

This is worthless unless they correct for age. Which they most likely don't, since age is not even mentioned in the post.

Indeed, the time period in which someone was born seems to have a significant effect on the likelihood of certain names. Here's one study that documents that effect in the US: http://fivethirtyeight.com/features/how-to-tell-someones-age...

Wolfram Alpha parses queries like "how old is [name]" and renders the age distribution, e.g. http://www.wolframalpha.com/input/?i=how+old+is+jane

Re: Disproportionately Common Names by Profession

#92

Post by jlees »

Lambdanaut wrote: post #73
teamhappy wrote: post #3

> People with these names are more likely than others to have these professions. Shouldn't it say: "People with these names happen to be in those professions more often than others"? Anyway, there are a couple of fun ones in there, but I'll let you figure those out yourself. Unfortunately neither my name nor my profession are covered — I'm not quite sure what to make of that. :/ --- They use the same language in thei…

I've heard mentioned in books on psychology before about this effect, and it seems at least from a clinical perspective, that people do tend to choose their professions based on their names. I'm sorry I can't provide sources.

It's mentioned in "Yes! 50 Scientifically Proven Ways to be Persuasive"[1], which despite the title, is a really interesting book. The Freakonomics blog quotes it here:

http://freakonomics.com/2009/04/24/yes-part-ii/

referencing this paper "Why Susie Sells Seashells by the Seashore: Implicit Egotism and Life Decisions" http://www.stat.columbia.edu/~gelman/stuff_for_blog/susie.pd...

[1]http://www.amazon.com/Yes-Scientifically-Proven-Ways-Persuas...

Re: Disproportionately Common Names by Profession

#93

Post by lmm »

learnstats2 wrote: post #75
V-2 wrote: post #52

Earlier quoted context omitted.

> Name inequality represents clear evidence against the existence of a meritocracy. Meritocracy in what sense? Coming from wealthier background, you're more likely to be well educated. While we may dislike that it is so, it doesn't mean there's no meritocracy in the sense that employers don't hire people based on their qualifications alone. Only that they don't care where these qualifications come from.

> Coming from wealthier background, you're more likely to be well educated. So, by your admission, education is not meritocratic. I claim that employment is non-meritocratic first by your measure: if access to a better education is not merited, then employers concentrating on qualifications alone are not hiring according to merit. I claim also that employment is non-meritocratic independently. The most obvious exampl…

> I claim that employment is non-meritocratic first by your measure: if access to a better education is not merited, then employers concentrating on qualifications alone are not hiring according to merit.

That doesn't follow. Maybe education changes your merit, and people with a better education actually are better at their jobs.

Re: Disproportionately Common Names by Profession

#95

Post by crpatino »

stygiansonic wrote: post #26
Camillo wrote: post #9

This is worthless unless they correct for age. Which they most likely don't, since age is not even mentioned in the post.

Indeed, the time period in which someone was born seems to have a significant effect on the likelihood of certain names. Here's one study that documents that effect in the US: http://fivethirtyeight.com/features/how-to-tell-someones-age...

No study, but first hand experience.

My father is a Pediatrician, and he has always commented on the strong correlations between relatively common names in his current crop of patients and the names of 5-year-ago-popular TV shows' protagonists.

The first time I was able to make the connection, it was Brandon/Brenda/Dylan.

Re: Disproportionately Common Names by Profession

#98

Post by coherentpony »

emodendroket wrote: post #88
coherentpony wrote: post #34

Earlier quoted context omitted.

What you're implying here is that correlation implies causation. If 99% of farmers are Elwoods, you can't claim that one's name being Elwood means one is more likely to become a farmer.

Do we have to write comments here as though we're writing the final draft of a math textbook? What's meant is clear enough.

What's clear to you may not be clear to someone else.

Re: Disproportionately Common Names by Profession

#100

Post by stolio »

colanderman wrote: post #19
teamhappy wrote: post #3

> People with these names are more likely than others to have these professions. Shouldn't it say: "People with these names happen to be in those professions more often than others"? Anyway, there are a couple of fun ones in there, but I'll let you figure those out yourself. Unfortunately neither my name nor my profession are covered — I'm not quite sure what to make of that. :/ --- They use the same language in thei…

EDIT2: OK folks we're smart, let's use MATH. Take above quote, which compares "1.9% of Arnolds are accountants" to the "0.55% of Shanes [are accountants]". They're implying that the probability of being an accountant (J), given that ones name (N) is Arnold, is above the expected probability of being an accountant in general. So they're looking for a high P[J|N]/P[J]. Now compare with what we were expecting to see. We…

After some pencil work I was convinced of EDIT2, but now I'm confused..

Before we get to my confusion, one interesting thing I found along the way is the following situation, call J1 "job 1" and N1 "name 1", and use the P(N|J)/P(N) (or its equivalent) metric:

  J1  J2
  ------
  N1  N1
  N1  N1
  N1  N3
  N2
If we limit each job to it's top name, N1 doesn't get attached to either despite being the most common name in each. J1 gets N2 while J2 gets N3.

If this is the method then don't use this chart to guess the names of people in a profession, use it to guess the professions of people whose names you know. "Guy" may be listed for investment bankers, but an investment banker is still more likely to be named Dave, but if you meet a Dave he's likely to be a mechanic.

For the same situation say we use the top value for P(N|J), then J1 gets N1 and so does J2. P(J|N) goes back to J1 getting N2 and J2 getting N3 and N1 being left out in the cold.

But here's where it's unclear, I think this:

> In our sample of two and a half million people, a whopping 1.9% of Arnolds are accountants. Contrast that with just 0.55% of Shanes. Arnolds therefore appear to have a much higher tendency to be accountants than Shanes

implies they're listing the top P(J|N) values for each J. *(edit: they're comparing Arnolds to Shanes, not Arnolds to all accountants?) I think your approach is the most consistent but is it what they're using?

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