The Whitest Jobs in America | Hacker News Reader

The Whitest Jobs in America

theatlantic.com

77 pointskitcar92 comments

One thing to keep in mind, is different cultures (especially immigrant cultures) have a different idea of what a 'good' job is. Most non-white people would say being a veterinarian is a waste of time, for instance.

Having once upon a time done a construction stint, I found it's overwhelmingly white not because it's racist, but because most immigrants don't want to do the job (they'd rather have a service job at the entry level, or an office/business related job at the higher level).

Most immigrants and non-whites are more 'aspirational' you could say, they want better, more prestigious jobs than trades. I've noticed going through business school, there's a disproportionate amount of Asians, Indians and Africans (compared to the overall population). Indeed, in Universities in general, you'll see more immigrants than are proportional to the population.

I wouldn't be so quick to say it's racism, rather different values. And notice that the 'whitest' jobs are not what society would call 'prestigious'...

construction? In 2010, ~25% of construction workers were Hispanic [0]. You may have been in some kind of statistically anomalous area.

[0] - http://www.dol.gov/_sec/media/reports/hispaniclaborforce/

Canada, where we don't have many Hispanic immigrants... The vast majority of non-whites here are Asian or Indian.

That's probably an important detail when commenting on an article about demographics in America.

The point isn't so much demographics in America, but aspirations of white people vs. non-white people.

Your point was to make a foolishly broad generalization that "all non-white people are like this"? I'm glad I could prove you wrong in at least one small instance.

There are a fair number of Vietnamese doing residential construction work. Same for Koreans. There are a LOT of Polynesian people doing construction. They aren't numerous in absolute numbers, but something like half the men work in construction.

The main thing that pushes people toward construction work is a lack of education, poverty, and a desire for a middle class life. It's hard work, but generally pays well after you're at it a while. But I think there's limited upward mobility for some people, because I notice lots of Asians in residential, but not in commerical, and definitely not in public works projects.

The reason why public works construction's heavily white is due to construction unions, who have generally seen their numbers declining. The tendency is for fathers to give their jobs to their sons. That fills the apprentice pipeline, and keeps Black and Asian people out. (A lot of Latinos are in construction and get in unions just by force of numbers.) These are the best construction jobs, and can get you an upper middle class lifestyle.

I think the issue of declining jobs or declining union membership tends to cause racist outcomes, even if the people involved aren't racist. Basically, if the overall field is shrinking, the tendency is to avoid hiring. If there are provisions to pass jobs on to their kids, people will do that. The end result is entire fields where the racial composition doesn't change.

As for Asian vets versus physicians: the coursework to become a vet is similar, and vet school is very expensive, but the pay isn't that great compared to a physician's pay. Becoming a vet is really a field for people who come from upper middle class families.

There's another explanation for the difference between commercial and residential.

When you work residential, it's much easier to work for yourself (ie. start a business). Because the stakes are much lower the home-builders will hire smaller contractors.

On commercial work-sites only large contractors ever get any jobs because of the guarantees they bring.

Also take a look at statistics, immigrants are more likely to start a small business than white people. Their tendency to start businesses would automatically steer those who do go the construction route into the residential sector.

Anecdote - I had a friend who started his own construction business doing a specific subset of drywall (which also happened to be dirt simple), and made well over 6 figures within a year by simply doing it very efficiently.

Further, having at one point in my life (when I was much younger) done construction in both residential and commercial, while commercial does pay better than residential if you're an employee, the potential in residential is huge if you become a contractor.

Every time we take our dog to the vet, every single vet is white, but every single vet TECH is a minority.

Your thinking is the same kind of logic that says 'well women just aren't interested in STEM'.

And I can't count the amount of doctors I've gone to who are Asian, Indian or African (most of them).

Being married to a black woman (who's also an immigrant), I get to hear 'the other side' if you will, every single day. My inlaws and wife's family are quite numerous, and are all immigrants. Let's just put it this way - they don't think the same way white people do.

Most immigrants think going through so much schooling to become a vet is a waste of time. Taking a tech job temporarily en route to something better is at least understandable.

I love how everyone is so quick to jump on the discrimination train. Maybe travel to some non-white countries and hang out with non-white people who aren't in the same industry and you'll realize there ARE cultural differences.

In Canada over half of all small businesses are owned by women, yet most 'tech' start-ups are owned by men. If you break down the numbers further, approx 1/4 of CS graduates are women, yet less than that number are in tech start-ups. Maybe there's a valid reason, beyond discrimination...

On the flip-side, I wonder what the "blackest" jobs in America are? (Aside from Olympic level sprinters that is)

[EDIT] - Ok, I amend my last bit to "Outside of Professional sports"

NBA

Please don't downmod the parent comment for being racist. Before rurounijones' edit, this answer was probably the most accurate. According to Wikipedia, "In 2011, the NBA was composed of 83 percent non-white players, including 78 percent black, four percent Latino, and one percent Asian; 17 percent of the players were white." [1] Furthermore, a 1999 analysis of the top 150 NBA point scorers found that 135 (90%) were Black and 15 (10%) were White. Given that 78% of the US is white and 13% of the US is black, Blacks are substantially over-represented in the NBA. The author also estimated that in order to explain this effect, the mean "athletic quotient" of Blacks must be about 0.87 standard deviations higher than the mean "athletic quotient" of Whites. Furthermore, this athletic superiority is also found in other sports. [2] Very interesting read, if you're reading this far I suggest you look through it.

[1] https://en.wikipedia.org/wiki/Race_and_ethnicity_in_the_NBA

[2] http://www.lagriffedulion.f2s.com/blackathlete.htm

I am downmoding the parent because "NBA" isn't a meaningful job category.

I mean, given the rarity of professional sports success, one could just as well list "lottery win" as an occupation category.

Professional basketball player is an occupation. It's concentration of a specific group of people shows that it's exactly not a lottery.

You're downvoting it because it doesn't appear to agree with your worldview.

The point isn't it's randomness but that it's very, very minute portion of the population.

99% of would-be professional athletes fail to make a living at it. Professional athletics isn't a factor affecting where the vast majority of people go to work after they finish school, except for the way it might take away from the time they spend preparing for what they wind-up doing.

Why would anyone downvote him for being "racist"?

Scanning the BLS report, I think it's barbers. 38.2% of barbers are black.

Rappers.

The title is misleading. These graphs are of unionized workers. I am an electrician I Chicago. Most non-union work here (construction, carpentry, plumbing, electrical) is done by Mexicans who vastly outnumber us. 9 in 10 of all workers. Hah.

Before we jump to theories, has anyone eliminated the null hypothesis?

That is: assuming a totally random distribution of individuals into professions, purely by chance, some professions will have curious ratios. The first thing a statistician aims to do is to test the probability that the observed effect is due to chance.

A second thing to test is whether the ratios are stable across time.

This comment represents 20% of your RDA for nitpicky HN comments.

Yeah I was thinking something like that, because as the article said 81% of all workers are white - simplified it means 8/10 are white - and these 33 jobs (out of how many?) have 9/10 ratio - does that really mean discrimination or is this story just a filler hit piece against perceived injustices that don't actually exist?

#1) This article was not a "hit piece", more a "isn't this curious?" piece

#2) There's already enough statistics out there that show injustice on a much larger scale - pretty much pick any statistic - life expectancy, wealth, employment, incarceration, etc and you'll see rank racial disparities -- in many cases racial disparities that haven't closed over the past 50 years.

Without knowing how many jobs there are total it doesn't sound fully like a "isn't this curious" piece. At best I'd put it with the "50 weird things about the 80's" type of piece. At worst it could detrimentally affect those professions in the name of progress.

I think I'll try testing this statistically, even just as a cute example for Statistics Done Wrong. Thanks for the idea.

(If I get interesting results I'll post back here in a few days)

edit: I can't replicate the results in their charts using this BLS table:

http://www.bls.gov/cps/cpsaat11.htm

For example, for veterinarians I get 90.7% white, not 96.5%. Am I missing something?

The statistics done wrong bit is where they don't control for socioeconomic status.

Even ignoring those two chumps on the radio, the socioeconomic status of auto body repair (90.7% white) is not very high.

I don't think that's really true. That's generally something that requires at least tech school/community college. It's not manual labor.

For the individual occupations, presumably they all have enough participants to avoid a high chance of any one of them being that far off (for example, there are 100,000 veterinarians [1]) [Edit: and see sanskritabelt's comment below that seems to match my intuition.] Then the question is how many different occupations are being considered here, and it doesn't seem like it would be high enough to make numbers like 96% very likely. So if we were measuring the whole population, it seems--just eyeballing-- pretty unlikely.

But of course we're not measuring the whole population. The data from which this chart was actually derived might well be caused by random chance. The article's failure to cite a more specific source than the BLS generally is a bit annoying here. Edit: I guess the source is [2], but I'm still unclear on what was measured.

[1] https://www.avma.org/KB/Resources/Statistics/Pages/Market-re...

[2] http://www.bls.gov/cps/cpsaat11.htm

pg. 25 in this pdf (http://www.bls.gov/cps/cpsrs.pdf).

Found the source here: http://www.bls.gov/cps/cps_over.htm#methodology

A profession with 10K workers should randomly expect to be about 80-82% white.

From here: http://en.wikipedia.org/wiki/Binomial_proportion_confidence_...

p=0.81 n=10000 and z = 2 gives a 99% confidence interval

And 90% white is firmly in the "basically impossible by random chance" category.

I guess that depends on a uniform distribution of the location of the jobs to the racial characteristics in said location.

For instance I would imagine there would be more vets in an area with more animals, perhaps a country or town border region. At the same time those areas might have a higher concentration of white people than the average (with other races preferring a more urban environment).

They need EMTs just about everywhere (92% white).

but they need EMTs who live nearby, just about everywhere - and it does not take much for neighbourhoods and states to clear into distinct patterns by house-purchase preferences (game theoretic economist whose name cannot remember won a prize for this) basically all that is needed is a prefernce for a particular neighbourhood attribute (walk to shops, less black people, more black people, close to hiking routes) and for that preference to be available at that persons clearing price.

so, a dribbling knuckle dragging KKK member would pay a lot to avoid living in Harlem (or rather would sacrifice opportunity by staying in Fuckwatsr Alabama.)

Take a nice black professional couple - happy to live in well integrated area of Manhattan - till along comes baby. at this point a slightly larger house nearer good schools is desirable - and preferably closer to grandma. Alternatively there is a nice area of Queens (?) that is predominately white but has good schools. Now all it takes is a preference or a feeling of being more comfortable near grandma and near other young mothers "just like me".

Since grandma will likely be living in an area where there was traditionally a larger percentage of black families the comfortable preference is near grandma. and who would argue with an expectant mother wanting to be comfortable.

At a certain point the cost of being near grandma will exceed the amount they can afford without realising they are making an explicit racist choice. which is I think my preferred definition - not emotional preference but when a conscious decision is made with race as a factor.

All this shakes out to mean that levels of preference few if any of us would consider racist, lead to segregated neighbourhoods. luckily other preferences intrude masking the effect.

So to help with the discussion, this implies that if everyone was perfectly color blind we would expect to see a 81% ratio. if not what is the level, the error bar, that we accept as emotional preference that does not cross the line ?

And how do we tell that difference if the starting population is not 81% in that area ? and if the school district in the area is really bad / good? (nb nationally funded schools systems I would expect see nationally less segregated societies than the USA for this reason)

anyway my 2 cents

Doesn't your segregation hypothesis actually argue in favor of an EMT distribution closer to 81%?

Also I think the fact that you had to put a question mark after "Queens (?)" suggests that you might be speaking from a position of shelter.

I simply ran out of parts of New York I can name and associate with good / bad neighbourhoods. hence the ? as a sop to anyone who knows queens enough to laugh out loud at the idea there are any nice parts.

My guess would be that a large fraction of EMTs are in fire departments. Fire departments are notorious for being full of racists. They were pretty segregated until the mid 20th century, too.

As the system was taken over by the government, the pay got good, and it's one of the best jobs someone without college can get. So you can bet that the incumbents would use any weapon, including racism, to discourage competition for these jobs.

For some jobs listed, one could make the argument. For some others, like say - mining machine operator- you'd need a beefier formula that accounts for the fact of where the jobs are located. In many(but not all) areas, the most diverse are the bigger cities. Not going to find many mines in Chicago or New York.

That said, the veterinarian one surprised me a bit. I'd be fascinated to read a deeper dive into it. My suspected guess is that in rural areas, many veterinarians make 'house calls', and every resident has a -lot- of livestock, vs inner cities where one veterinarian can service a big chunk of the population. But, again, just a guess.

No. What you meant to say was that a random selection from a population of 10K white and black balls in the ratio 80% white balls & 20% black balls should be expected to be about 80% white.

People aren't black or white balls. Other factors (e.g., education, IQ, health, social class, and racism, etc.) are what make the difference. To imply that the difference is attributable purely to racism is naive and wrong-headed.

Isn't the difference between random chance and the complex realities of race and socioeconomic the entire point?

I don't think a definition of racism as historically and culturally naive as to be blind selection bias that exists only in the current moment is worthy of any serious consideration - only useful as a straw man for arguments such as yours here.

Any serious consideration of racism involves precisely the factors you mention. Education, nutrition, IQ, are all proxies for social class and I think you have to be quite biased to ignore how race relations throughout history have had direct impact on the current social status of race groups.

I think you must have misread the context here. The grandparent's hypothesis was that maybe the results seen here are merely the result of random chance (e.g. white and black balls being drawn from a bag). The parent was testing that hypothesis by checking the probability of getting those results purely by random chance. The answer was no, but no comment was being made about the source of that non-randomness. So yes, attributing all the differences to racism would be naive and wrongheaded (for most definitions of racism), but who's doing that?

> The grandparent's hypothesis was that maybe the results seen here are merely the result of random chance

Not necessarily my hypothesis per se. Rather, it's the default hypothesis that you're supposed to disprove before you go on to propose other hypotheses.

My problem is that I remember the existence and requirement to test the null hypothesis, but not the mechanics of how to do that. Luckily we have some people who are across basic statistics and so the conversation is able to move past "it's probably just chance" to speculate about whys.

Right, I didn't mean to imply that you believed the hypothesis, just that you were proposing it. The distinction doesn't matter much for my explanation anyway.

"I think you must have misread the context here. "

Yes, you are correct. My apology to all.

I am not a gifted statistician, so I just made a monte carlo simulation to test. This is not too scientific because in reality different jobs have different numbers of people, and a thousand other variables, but anyway, here is the code and results:

    from random import random, randint

    p_white = .81

    n_occupations = 100
    n_workforce = 150000000

    occupations = [[] for i in xrange(n_occupations)]

    for i in xrange(n_workforce):
        occupation = randint(0,n_occupations-1)
        race = "white" if random() < p_white else "other"
        occupations[occupation].append(race)

    percentages = map(lambda x:x.count("white")/float(len(x)), occupations)
    print sorted(percentages)

    [0.8093594044124566, 0.8093716670190719, 0.8093837105400035, 0.8093941116532061, 0.8094080377696309, 0.809459598174709, 0.8094606757892058, 0.8094949877814243, 0.8095051091912103, 0.8095619154480623, 0.8095855138023271, 0.8096009184415862, 0.8096130265928078, 0.8096471477224493, 0.8096569834673563, 0.8096622916149537, 0.8096635533608949, 0.8096712436008832, 0.8096812597976153, 0.8096856369286581, 0.8096963532163073, 0.8097067434989362, 0.809721568598737, 0.8097215751436514, 0.8097328871910747, 0.8097572581628716, 0.809766392402407, 0.8097706403995997, 0.8097853662894858, 0.8097907743218179, 0.8098200848588418, 0.8098433273181519, 0.8098759161986114, 0.8098761662131717, 0.8098826202953597, 0.8098902227694705, 0.8098932712388633, 0.8099027774814941, 0.8099132209630405, 0.8099239110036907, 0.8099288512271164, 0.809935064025657, 0.8099377895998511, 0.8099442967886306, 0.8099576910748759, 0.8099684818490649, 0.8099723061883299, 0.8099772955347885, 0.8099790775887737, 0.8099951683577414, 0.8099964813792994, 0.8100114768250255, 0.8100264485053597, 0.8100326219727435, 0.8100426685467571, 0.8100538851385188, 0.8100624027564277, 0.8100772456940655, 0.8100799987206602, 0.8100878460787697, 0.8101051239951382, 0.8101238836646114, 0.8101259241335592, 0.8101299639808958, 0.8101333990942324, 0.8101444590699302, 0.810176363997584, 0.8101767343321185, 0.8101835910494692, 0.810185024612142, 0.8101881998783053, 0.8102189975744003, 0.8102348016041733, 0.810247294219311, 0.8102501899417512, 0.8102612850637286, 0.8102728907675896, 0.8102876025226937, 0.8102915035431886, 0.8103243498099062, 0.8103428626649917, 0.8103475670573029, 0.8103618541773706, 0.8103623158464366, 0.8103870909678774, 0.8104014543756358, 0.8104238667513951, 0.8104306535452401, 0.8104537503309478, 0.81046437557304, 0.8104696732444567, 0.8104846773483934, 0.8104918990629446, 0.810527481205214, 0.8105324346430826, 0.8105520454920258, 0.8106940187094273, 0.8107141451872244, 0.8107156884282282, 0.8108683817976586]

As we can see by the results, the min and max are around a tenth of a percent off from the expected value. Even if the job distribution is skewed (certain occupations have a larger workforce), if N is sufficiently large, numbers such as +15% should be completely unrealistic.

Please correct me if I have made a grave error that I did not mention (I know that this is very very hand wavy, unscientific).

No, that fits with the remarks made by others.

I wasn't sure whether the null hypothesis is approached by looking at the likelihood of sample variation from the general population of individuals, or whether by calculating how many job titles in the population of job titles would have such outlying numbers.

The consensus seems to be that most of these are not by chance. Roll on the debate.

I think you are making a grave error - near as I can tell, each of your simulated occupations has about 1.5 million people in it. Of the few professions listed that I checked, the biggest (farmer) has only 758k people in it:

Veterinarian 61k, farmer 758k, mining machine operators 21k, Speech language pathologist 123k.

The outcome doesn't change much (you go from 81.1% to 81.4% max), I'm just being a stats geek.

http://www.bls.gov/ooh/Healthcare/Veterinarians.htm http://www.bls.gov/ooh/farming-fishing-and-forestry/agricult... http://www.bls.gov/ooh/About/Data-for-Occupations-Not-Covere... http://www.bls.gov/ooh/Healthcare/Speech-language-pathologis...

Geography probably plays a big role in some of these. I expect that most veterinarians and farmers are located in farming and ranching states for example, and Iowa/Idaho are not exactly known for racial diversity.

Yes, I was aware of this, but in 5 minutes of googling I couldn't find a good answer to the total number of possible professions, so I went with an even 100. As I said, very hand-wavy and not too scientific. But yes, even for a workforce of 150,000 the results don't get close to breaching into the 90's.

Some of these could be due to training costs. On average in the US, white kids from wealthier families than black kids, and on average white adults have more money than black adults. If the trainee has to pay the training costs for a given job, then it is going to favor whites.

For instance, take "aircraft pilots", which the article says are 90% white. A small plane can cost in the neighborhood of $100/hour to rent, and one of the requirements for a commercial license is 250 hours flying as pilot. Add on to that instructor costs, and you need to have a fair bit of money available to even get to the point where piloting can be your job. There are some aviation scholarships, I believe, but they are highly competitive. I believe most students have to come up with the money themselves at least through getting their commercial license.

These wealth differences could also indirectly affect some of the jobs on the list. For instance, black people are more likely to live in poor and high crime neighborhoods where gangs are prevalent, and there is a lot of pressure on young people to get involved with the local gang. As a consequence, young black people are probably more likely than young white people to have an arrest record. That could make it harder or impossible to get a private detective license.

I'm gently surprised they didn't say what the Blackest jobs in America are. Because that seems like an important part of the comparison.

Well, it seems perhaps telling that you don't mention the most-Hispanic, most-Asian, most-Vietnamese etc. occupations given that these would also hypothetically be points of comparison. The reflex of viewing American race relations as black versus white seems rather deeply engrained.

The longer article on race and occupations linked by the main article does go into detail on multiple dimensions of race and employment. I recall it mentions domestic attendant and bus driver as black-dominated occupations.

FYI, the BLS report referenced in the article[1] says that 64% of software developers are white, 74% of computer programmers are white, and 85% of web developers are white.

[1]http://www.bls.gov/cps/cpsrace2012.pdf

"Misc. Agricultural Workers" is 92% white? So much for the theory that agricultural work is done by illegal migrant workers.

It looks like that data comes from the Bureau of Labor Statistics. I'm going to go ahead and guess that they don't include undocumented workers.

A lot of those construction jobs are probably only looking at union work. The author says as much below. I don't know what it's like for electricians or carpenters, but the concrete business has a lot of undocumented workers, especially on non-government jobs. And even when they are somewhat legit they don't typically join the unions or get tagged as 'cement masons' (finishers) even though a lot of the time they do finishing work as well as the general labor.

Who holds that theory? As a farmer myself, I'm not really surprised at all and it was my immediate thought upon reading the headline. Most farm work is highly skilled, which: a) pushes for higher pay, allowing locals to compete. b) requires skills that will often not be easily obtained by those coming from poorer countries. The jobs that are easily handed to migrant workers are a relatively small segment of the industry, and I would expect a dwindling segment at that.

I'll add that it is striking how non-diverse rural areas tend to be. I can probably count on one hand the number of non-white people living in my rural community. That doesn't help with the numbers when hiring from the local worker pool.

I guess it's more of a stereotype. With the higher value / higher skill farming work, as compared to migrant fruit pickers, I am sure the demographics are quite different.

if they're illegal, they pertinent aren't counted in the BLS statistics.

They may count hispanics as white.

Yes, this accounts for the disparity. The BLS report has miscellaneous agricultural workers at 92% white, with 48.9% of all workers having Hispanic or Latino ethnicity.

Most Hispanics identify themselves as white, but 2-3% of Hispanic Americans identify as black.

There are probably a lot of intertwined reasons for these, different for each job category. Culture, racism, geography are the big categories. A lot of job choices are about knowing people who know people who do that job or having family history in that profession. All these interact. So maybe steel unions were racist a generation ago and admitted fewer blacks. Then we have fewer sons and nephews of steel workers and so fewer people likely to go into that profession.

There are all sorts of professions that are "Jewish professions." The history of these is both speculative and complicated. You had a cultural tendency towards literacy when this was uncommon. Racism & transience preventing land ownership & farming (most Europeans' job) which lead to urban populations and non land assets. Certain guilds barring Jews girded them into unguilded professions or guilds without these rules. There were competitive advantages from transience (contacts in other places) that encouraged certain professions.

Then you get interacting second and third and forth order effects from having one set of grandparents that were actors another set who were merchants, the industries that exist in the place you live (not everyplace has many Jews), etc.

Some of these professions might be dominated by some subgroup of white, like descendants of Polish immigrants or late 18th century migrants. There is no reason to expect that the ratio of whites in a certain profession reflect their overall ratio in a country. Professions aren't randomly allocated.

IMO, more interesting would be the Blackest, Jewiest professions or some other minority and trying to figure out how that came to be.

Relevant: http://www.pewresearch.org/fact-tank/2013/08/21/through-good...

I am unhappy with the whole premise of labeling people as white. What do a Polish immigrant laborer, a Jewish professor and a WASP homemaker have in common - oh, they're not black. Let's lump them all together!

Hi, welcome to living in America.

I was expecting high end "professional" jobs. Vetinarian fits the bill, I suppose, but I was expecting surgeons and programmers. Instead, there's a lot of heavy industry and manual labor in the list.

The note at the bottom says that this could be because some unions are terribly racist, but carpenter? Painter? You don't have to be in a union to work with wood or paint, do you?

Or maybe you do. American labor laws are utterly nuts as far as I can tell, and this is probably yet another sign of just how insane they really are.

I'm surprised that so many blue collar jobs are less white than Chief Executives. Interesting example of counter-intuitive data. I suspect that the sample sizes are too big for large differences to be random. That said, there could be other issues like "on the books" versus "off the books" and similar.

"Farmers" was surprising.

Is "Land Owners" less surprising?

True.

It wouldn't surprise me if they were using the term "Farmer" loosely.

EDIT: Now that I look closer, they did say "Farmer/Rancher". So I guess that could be taken as a hint that this is not the low end manual guy they are talking about, so much as the landowner.

Farm labourers are generally not considered farmers. I would not really expect them to deviate from that classification. However, you do not need to own land to be considered a farmer either (i.e. you could rent land to meet the criteria).

Though misc. agriculture workers doesn't fall much further behind on the list.

"Groundskeeping workers" was a surprise.

Well that they explained in the article.

"...Grounds cleaning/maintenance workers are 44 percent black, but groundskeepers are 90 percent white..."

So [I assume] they draw a distinction between the worker and the boss.

Also, the Groundskeepers MAY have specialized knowledge. Degrees in botany or business for instance. (Maybe even both???) Not saying they do... just pointing out that we don't know a lot about that profession and so there may be more going on than can be seen from the numbers.

It just seems a very odd way to describe bosses - as 'workers'. Usually 'workers' refers to the people doing the grunt work. It's a bit like calling middle management "management workers"; techinically correct but not the way people actually reference them. I wonder why they didn't just say 'groundskeepers'?

When the revolutionaries say "workers of the world unite", they usually don't mean to include the bosses :)

Digging into the tables, the 96% figure is for management only.

48.9% of "Miscellaneous agricultural workers" are Hispanic or Latino, however.

Aren't these numbers a moot point when you consider that 70 percent of the US is white?

There's literally not enough minorities to change those numbers (not significantly at least)... Or am I too tired to thing straight?

There's some discussion about this above. If the population is 70% white, then the job categories should be 70% white. 90% white isn't random.

We should really understand what whiteness actually _is_ if we want to parse these kind of things correctly: http://www.ohio.edu/people/hartleyg/docs/abolishthepoint.pdf

Great point. My father identifies as "white" despite being clearly non-white, however because of an adoptive ancestry does not have a clear picture, thus he took the race of his adoptive parents.

I also select "white" on the form because I was brought up thinking that was correct. Only recently have I been selecting "Other" where available.

On what forms do you have to state your "race" or skin color? Ich have never been asked for it anywhere. I live in Germany.

Klu Klux Klan entry forms, perhaps?

I suppose anywhere there's an affirmative action policy in place.

Lots of official UK government or healthcare forms. Still surprises me every time I see it.

http://en.wikipedia.org/wiki/Classification_of_ethnicity_in_...

So far for me government and medical forms have been the majority of the forms where it is asked.

Funny enough, when I was in Munich, I was denied entry to a bar because I would "cause trouble." My German friend informed me it was basically because I looked Turkish. So in that case, while not on paper, my race was questioned.

This. Never been comfortable with the whole white/black thing, especially in the way it's used in the US.

Americans would consider me to be a white European. My father was a black South-American, my grand-father Chinese. To refer to me as "white" and draw any kind of conclusion from that is ludicrous and insulting.

I was surprised by this.

Before I read it, my guess would have been the less a job involved creating or doing anything real (ie. banker, stock trader, financier, etc.) the more "white" it would be.

ps. what is a "cost estimator" - do they mean appraiser?

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