I’ve saved the most interesting result in Ken Lee’s thesis till today. The subject is how death rates vary with jobs. The big result: death rates depend on job details more than on race, gender, marriage status, rural vs. urban, education, and income
Interesting artcile, but I agree with Rinky: some more translation would be nice. When there is a correlation, it would be ince to know whether it is negative or positive and so on. Best,Olga
So in this table... does "higher ranked" = "more likely to die on the job" or "less likely..."?...and what is the significance of having 4 categories? What conclusions are laypeople supposed to draw from that?Forgive the stupid questions, but is it saying that some professions cause people to "reason themselves to death" or "people themselves to death" or "attention to detail themselves to death"?!(the physical category makes some kind of sense... I suppose the others are sort of talking about stress killing people or something?)
Help anyone? A translation? Imagine you have an audience of postgrads from every other department in your uni, and try and convert all this stats-talk into something they can relate to. :)
Looks statistically correct but not a reason to ask for higher wages. The market determines the wages for such jobs. If it is a dangerous job then people also need to consider where that job leads in the future such as will they get promoted to a desk in the future? should they get education for a less stressful position? Can't use these statistics to justify asking for higher wages especially in a bad economy where it is the employers market.
I'm guessing he just individually looked into correlation without controlling for secondary factors?
P.S. The article's language is confusing, for example Black men and Asian women die with equally frequently- once per person. (I know what you'll say, but see 2 posts above for why your first reaction to that line wasn't the right one either) It could be really helped by just providing clear, real life examples of what is being discussed.
Interesting - but the amount of data needed to fit a model scales as some base to the power of the number of variables in the model. How could he possibly have had enough data to compute risk ratios for 26 variables?
a poor badly-schooled unmarried urban black male dies 17.7 times as often as a rich well-educated married rural asian woman (of the same age), with a lifespan roughly thirty years shorter on average.
WTF? A lifespan 30 years shorter would seem to correlate to dying maybe two or at worst 3 times as often (per capita) as someone else. If rich well-educated asian women life to, say, 100 - do poor badly-schooled urban black males live, on average, to the ripe old age of 100/17.7 = 5.6 years old? As harsh as poor urban life can be, I find it hard to believe it's THAT bad!
Again, please assume we're not all statisticians and/or actuaries, and tell us what numbers like "17.7" actually mean!
Agreed - Robin, pretend for a moment that we aren't all statisticians who have nothing better to do than analyze statistical significance calculations and/or read/understand 273-page theses.
What are the "safe" jobs, and what are the "dangerous" jobs?
I certainly did not suggest otherwise. But for proper analysis you need to compensate for situations where several of your variables measure the same underlying dimension of variation. It's not clear to me that you've done that in your "100:1" figure.
Of course I could be entirely wrong. Can you find any two jobs in the American economy that have 100:1 differential mortality, or even close to it? If there is no such pair, that would strongly suggest that your number manipulation is empirically meaningless. If there are a bunch, then likely enough I'm wrong.
This might have been emphasized a bit more, because on first read, the table made no sense what so ever, and I had to look into the comments section to figure out this fact.
Granted, I have no economics background, but I did just get my PhD in theoretical computer science, so I thought it worth mentioning if you were indeed aiming for the general audience.
But if mulitplying all the risk ratios together is not valid—as I believe it would not be if the different risk factors are correlated—then your "overall importance" obtained by that multiplication is going to be exaggerated.
I'm not proposing looking for risk ratios for actual pairs of jobs because the jobs themselves are important, but as a way of checking whether the order of magnitude of effect you project even remotely makes sense.
Douglas and Steve, most discussion of jobs killing considers only on the job deaths, which is only a tiny fraction of the overall effect. Estimating fixed effects for 800 jobs could easily run into data limitations and let people just assume some hidden selection of people into jobs explains those fixed effects. WIth just five or ten factors, we have a much better chance of estimating and understanding the effects, and so also better understanding the degree to which they might be explained by selection.
The point is just to have a measure of the overall importance of a set of influences; its not importnat just how many people have all the extreme values together.
"At the very least we should try to tell people about the huge life and death consequences of their job choices."
There's a whole genre of reality TV programs that follow men doing dangerous jobs, such as "Most Dangerous Catch" about Alaska deep sea fishermen. I've read dozens of articles about death rates in different jobs and what the wage premiums are. USA Today runs that kind of article frequently.
Hi Robin - I sent you an email about this today, but it might have gone to your spam folder. Let me know if you can't see it.
Interesting artcile, but I agree with Rinky: some more translation would be nice. When there is a correlation, it would be ince to know whether it is negative or positive and so on. Best,Olga
So in this table... does "higher ranked" = "more likely to die on the job" or "less likely..."?...and what is the significance of having 4 categories? What conclusions are laypeople supposed to draw from that?Forgive the stupid questions, but is it saying that some professions cause people to "reason themselves to death" or "people themselves to death" or "attention to detail themselves to death"?!(the physical category makes some kind of sense... I suppose the others are sort of talking about stress killing people or something?)
Help anyone? A translation? Imagine you have an audience of postgrads from every other department in your uni, and try and convert all this stats-talk into something they can relate to. :)
Looks statistically correct but not a reason to ask for higher wages. The market determines the wages for such jobs. If it is a dangerous job then people also need to consider where that job leads in the future such as will they get promoted to a desk in the future? should they get education for a less stressful position? Can't use these statistics to justify asking for higher wages especially in a bad economy where it is the employers market.
I'm guessing he just individually looked into correlation without controlling for secondary factors?
P.S. The article's language is confusing, for example Black men and Asian women die with equally frequently- once per person. (I know what you'll say, but see 2 posts above for why your first reaction to that line wasn't the right one either) It could be really helped by just providing clear, real life examples of what is being discussed.
Interesting - but the amount of data needed to fit a model scales as some base to the power of the number of variables in the model. How could he possibly have had enough data to compute risk ratios for 26 variables?
a poor badly-schooled unmarried urban black male dies 17.7 times as often as a rich well-educated married rural asian woman (of the same age), with a lifespan roughly thirty years shorter on average.
WTF? A lifespan 30 years shorter would seem to correlate to dying maybe two or at worst 3 times as often (per capita) as someone else. If rich well-educated asian women life to, say, 100 - do poor badly-schooled urban black males live, on average, to the ripe old age of 100/17.7 = 5.6 years old? As harsh as poor urban life can be, I find it hard to believe it's THAT bad!
Again, please assume we're not all statisticians and/or actuaries, and tell us what numbers like "17.7" actually mean!
Agreed - Robin, pretend for a moment that we aren't all statisticians who have nothing better to do than analyze statistical significance calculations and/or read/understand 273-page theses.
What are the "safe" jobs, and what are the "dangerous" jobs?
I certainly did not suggest otherwise. But for proper analysis you need to compensate for situations where several of your variables measure the same underlying dimension of variation. It's not clear to me that you've done that in your "100:1" figure.
Of course I could be entirely wrong. Can you find any two jobs in the American economy that have 100:1 differential mortality, or even close to it? If there is no such pair, that would strongly suggest that your number manipulation is empirically meaningless. If there are a bunch, then likely enough I'm wrong.
This might have been emphasized a bit more, because on first read, the table made no sense what so ever, and I had to look into the comments section to figure out this fact.
Granted, I have no economics background, but I did just get my PhD in theoretical computer science, so I thought it worth mentioning if you were indeed aiming for the general audience.
It is just not true that regression analysis is invalid if variables are correlated with each other.
But if mulitplying all the risk ratios together is not valid—as I believe it would not be if the different risk factors are correlated—then your "overall importance" obtained by that multiplication is going to be exaggerated.
I'm not proposing looking for risk ratios for actual pairs of jobs because the jobs themselves are important, but as a way of checking whether the order of magnitude of effect you project even remotely makes sense.
Douglas and Steve, most discussion of jobs killing considers only on the job deaths, which is only a tiny fraction of the overall effect. Estimating fixed effects for 800 jobs could easily run into data limitations and let people just assume some hidden selection of people into jobs explains those fixed effects. WIth just five or ten factors, we have a much better chance of estimating and understanding the effects, and so also better understanding the degree to which they might be explained by selection.
I've given what concrete examples I have.
The point is just to have a measure of the overall importance of a set of influences; its not importnat just how many people have all the extreme values together.
Robin writes:
"At the very least we should try to tell people about the huge life and death consequences of their job choices."
There's a whole genre of reality TV programs that follow men doing dangerous jobs, such as "Most Dangerous Catch" about Alaska deep sea fishermen. I've read dozens of articles about death rates in different jobs and what the wage premiums are. USA Today runs that kind of article frequently.