I got an email that my post from yesterday was having trouble accepting comments.

Regardless, Catie J had a good question as to whether the 35+ hour people evaluated were salaried, hourly, or both.

From what I can tell of individual studies, everyone is lumped together….including those who work for commission, bonuses, or those who bill hourly/by procedure as well (therapists, lawyers, MDs….).

One other interesting factor I’ve read is that many women are likely to take pay cuts in order to carry the health benefits for their household.  I have only anecdotal evidence, but when I’ve mentioned the “construction worker husband admin assistant wife” combo, most people I talk to have examples at their work places of the same thing.  In these cases, the husband out earns the wife monetarily, but not in total compensation.

I have no idea how big of an effect this has on earnings, but with the ACA still standing and the cost of health care continuing to rise, subsidized health insurance should not be left out of income calculations for much longer, if we want to truly see what’s going on.

Gender pay gap – Categories and equivalencies

Amy Alkon (aka the Advice Goddess) had an interesting piece over on her blog about the gender pay gap stat that keeps getting floated around.  By the way, if you’re at all libertarian leaning and hate the TSA, she’s a good read.

Anyway, I’ve posted before about the gender pay gap stat, and how it’s fairly deceptive, but her post triggered a point I hadn’t thought about previously.  Apparently the stat (that women make 75-81 cents per dollar that men make) is based on full time year round workers.  Alkon quotes another article that mentions that “full time” means anything over 35 hours.  
Now obviously this accounts for some of the disparity in pay gap.  We all know high achievers who work 70 hours a week, and to lump them in with those working 35 is silly.  
It’s an interesting study in categories though.  When I took my assessment class in grad school, the professor showed us a study that had been done on the number of sex partners a person had.  The options were:
  1. 0
  2. 1
  3. 2
  4. 3
  5. 4+
He pointed out two issues with this setup.  First, is there truly a meaningful difference between those who had 2 partners and 3?  How about those who had 3 partners and “4+”?  Is everyone with 4 or more partners really equal?
This mirrors the paycheck issue pretty well.  We’d all expect someone work 40 hours to make more than someone working 20 hours, but none of the calculations take in to account that someone working 60 hours will also make more than someone working 40 hours.  Alkon also links to this piece by Kay Hymowitz that gives this quote:

In 2007, according to the Bureau of Labor Statistics, 27 percent of male full-time workers had workweeks of 41 or more hours, compared with 15 percent of female full-time workers; meanwhile, just 4 percent of full-time men worked 35 to 39 hours a week, while 12 percent of women did. Since FTYR men work more than FTYR women do, it shouldn’t be surprising that the men, on average, earn more.

She also mentions the term “proofiness”….the use of misleading statistics to confirm what you already believe. Love that.

Anyway, I’m all for equal pay for equal work, but only if we’re really talking about equal pay AND equal work.

Bad categories, bad.

Too hot to hire?

….or why psych undergrads would make lousy hiring managers.

I saw this study pop up on Instapundit, and while the number of “that happens to me all the time” jokes are infinite, I’m pretty sad this study got mentioned at all.  Here’s the Router’s recap:

Attractive women faced discrimination when they applied for jobs where appearance was not seen as important. These positions included job titles like manager of research and development, director of finance, mechanical engineer and construction supervisor.

Oh the sad sad existence of beautiful women.  To work so hard on your career and then get denied a job because you’re too attractive.  Now, out of curiosity, exactly how many women got rejected from these jobs for this study?


This study didn’t study women or men actually applying for jobs.  They studied what happens when you give a bunch of psych undergrads a huge stack of pictures, a list of job titles and say “sort these pictures in to groups of who you think would be most qualified for a job based solely on the pictures“.  Seriously, that’s what they did.  Read the full study here.

It turns out that when you ask 65 undergrads (mostly women) to rank a whole bunch (204) of photos of people using no criteria other than what they look like, people might judge other people based on what they look like.  There was some lovely statistical analysis in here, but at no point did they attempt to prove that asking a 20 year old (who presumably had no first hand knowledge about any of the fields other than psych) to sort a picture reflected at all what goes on in hiring offices.

In fact, this is what the “practical implications” section of the paper said:

Although the findings reported here demonstrate the “what is beautiful is good” and “beauty is beastly” effects, it is important to address the likelihood of such stereotypes influencing actual employment decisions. For example, in situations where there is a high cost of making a mistake, as would be the case for a hiring decision, one would expect the decision maker to rely more on individuating information, rather than on stereotypes about physical appearance. However, it is important to note that the bias for the physically attractive, unlike other stereotypes, seems to impact impression formation in a broader range of circumstances. Recent meta-analyses suggest that the what is beautiful is good effect is pervasive, even when the perceiver has additional information about the target   (Hosoda et al., 2003; Langlois et al., 2000). Attractiveness may influence decision  making at a subconscious level, where exposure to an attractive individual elicits positive feelings in the decision maker, causing him or her to judge the target more favorably (Eagly et al., 1991). Moreover, in situations where a decision maker is under a high cognitive load or under time pressure, he or she may be more likely to rely on stereotypes (Fiske & Taylor, 1991; Pendry & Macrae, 1994).

So there is some proof that people favor attractive people no matter what, but no similar proof that they might discriminate against an attractive person if they had real world information.  Which leads me to get a little weirded out by quotes like this from the researcher (in interviews, not the article):

“In every other kind of job, attractive women were preferred,” said Johnson, who chided those who let stereotypes affect hiring decisions. 

Putting aside the fact that equality in this case appears to mean that everyone should prefer attractive people….what hiring managers was she chiding?  The ones she never studied?  Since the largest bias against attractive women was found when the mostly female undergrads were asked about who was qualified for male dominated fields….does that say more about what men think about women in non traditional fields, or what women think about women in non traditional fields?

While I’m sure that physical appearance does make a difference in hiring practices, I would have loved to see a little more time dedicated mimicking the real world before announcing that women were facing discrimination in certain professions.  To allow these results to be propagated as proof of what goes on at legitimate companies is a bit of a stretch, and points the finger at people who never even got asked what they would do.  

Opinions, everybody’s got one

I was listening to a management podcast recently where a man named John Blackwell was being interviewed.  He was talking about how he was constantly reading things about how the whole workplace was changing, but he was getting curious as to why he felt like the companies he worked with weren’t reflecting this.  When he tried to investigate, he found out that the ongoing surveys commonly used in British management journals (can’t find a link) were being done on the “up and coming business leaders”.  When he looked in to what that meant, he realized it was people who were second year MBA students.

The problem with this, of course, was that this was asking people not in the workforce what the workforce was going to look like 10 years from now.  They found, not surprisingly, that young people in grad school tend to be very optimistic about things like “working from home” or “flex time” when they’re in school, but when they got in to business, they towed toed the line.  Thus, every survey done was essentially useless.  
This all reminded me of a conversation I got in to several years ago when I was working the overnight shift.  Someone had brought in a magazine (People or Vogue or something like that) and they had a ranking of the 100 most beautiful women in Hollywood.  Drew Barrymore was number one that year, and one of my (young, male) coworkers was actively scoffing at that.  “She’s unattractive,” he stated definitively.  “All the guys I know think so too.”
Now, I was feeling a little feisty feminist that night, so I thought about how to challenge him on that.  Leaving aside that “Hollywood unattractive” would still turn heads in any average crowd (and be more attractive than any girl he’d dated), something about his comment irked my data side.  “So maybe the voting was done by women,” I replied.  
He was floored.
I noted that it was not a men’s magazine that ran the story, so really women’s opinions of other women’s attractiveness would actually be more relevant to this list.  Furthermore, as most of the leading women in Hollywood make their money on romantic comedies, professionally women’s opinions of their attractiveness (which presumably included a certain likeability factor) would actually matter more than men’s.
I was fascinated that this clearly disturbed him.  It had clearly never occurred to him that straight men may not be the target audience for female attractiveness, or even that the relevance of his opinion might get questions.  He wasn’t trying to be a jerk, he was legitimately confused at the whole idea.
A long intro, but the bigger point is important.  In any opinion survey or research, it’s important to figure out whose opinion is most relevant to what you’re trying to get at and why.  When it comes to law and public policy questions, I think every voter is relevant.  When it comes to workplace trends?  You may need to narrow your sample.
Sampling bias is a huge problem in many contexts, but my primary one for today’s post is when the survey was not conducted with the end in mind.  For any sample, you have to figure out how much your subject’s opinions actually matter given what you’re trying to find out.  In social conversation it may be interesting to find out what a particular person thinks of a topic, but for good data, show me why I care.