Tag Archives: occupations

The most comprehensive analysis ever of the gender of New York Times writers

In this post I present the most comprehensive analysis ever reported of the gender of New York Times writers (I think), with a sample of almost 30,000 articles.

This subject has been in the news, with a good piece the other day by Liza Mundy — in the New York Times — who wrote on the media’s Woman Problem, prompted by the latest report from the Women’s Media Center. The WMC checked newspapers’ female byline representation from the last quarter of 2013, and found levels ranging from a low of 31% female at the NYT to a high of 46% at the Chicago Sun-Times. That’s a broad study that covers a lot of other media, and worth reading. But we can go deeper on the NYTimes. The WMC report, it appears (in full here), only focused on the A-section of each newspaper, with articles coded by topic according to unspecified criteria. Thanks to the awesome data collecting powers of my colleague Neal Caren, a sociology professor at UNC, we can do better.*

I started this project with a snap survey of the gender of writers on the front page of each section of NYTimes.com: result, 36% female from a sample of 164 writers. Then I followed the front page of the website for a month: result, 29% female from a sample of 421. For this, Neal gave me everything the NYTimes published online from October 23, 2013 to February 25, 2014 — a total of 29,880 items, including online-only and print items. After eliminating the 7,669 pieces that had no author listed (mostly wire stories), we tried to determine the gender of the the first author of each piece. To start, Neal gave me the gender for all first names that were more than 90% male or female in the Social Security name database in the years 1945-1970. That covered 97% of the total. For the remainder, I investigated the gender of all writers who had published 10 pieces or more during the period (attempting to find both images and gendered pronouns). That resolved all but 255 pieces, and left me with a sample of 21,440.** These are the results.

Women’s authorship

1. Women were the first author on 34% of the articles. This is a little higher than the WMC got with their A-section analysis, which is not surprising given the distribution of writers across sections.

2. Women wrote the majority of stories in five out of 21 major sections, from Fashion (52% women ), to Dining, Home, Travel, and Health (76% women). Those five sections account for 11% of the total.

3. Men wrote the majority of stories in the seven largest sections. Two sections were more than three-fourths male (Sports, 89%; and Opinion, 76%). U.S., World, and Business were between 66% and 73% male.

Here is the breakdown by section (click to enlarge):

nytpctfem

Gender words

Since we have all this text, we can go a little beyond the section headers served up by the NYTimes‘ API. What are men and women writing about? Using the words in the headlines, I compiled a list of those headline words with the biggest gender difference in rates of appearance. That is, I calculated the frequency of occurrence of each headline word, as a fraction of all headline words in female-authored versus male-authored stories.

For example, “Children” occurred 36 times in women’s headlines, and 24 times in men’s headlines. Since men used more than twice as many headline words as women, this produced a very big gender spread in favor of women for the word “Children.”  On the other hand, women’s headlines had 10 instances of “Iran,” versus 85 for men. Repeating this comparison zillions of times, I generated these lists:

NYTimes headline words used disproportionately in stories by

WOMEN MEN
Scene US
Israel Deal
London Business
Hotel Iran
Her Game
Beauty Knicks
Children Court
Home NFL
Women Billion
Holiday Nets
Food Music
Sales Case
Wedding Test
Museum His
Cover Games
Quiz Bitcoin
Work Jets
Christie Chief
German Firm
Menu Nuclear
Commercial Talks
Fall Egypt
Shoe Bowl
Israeli Broadway
Family Oil
Restaurant Shows
Variety Super
Cancer Football
Artists Hits
Shopping UN
Breakfast Face
Loans Russia
Google Ukraine
Living Yankees
Party Milan
Vows Mets
Clothes Kerry
Life Gas
Child Investors
Credit Plans
Health Calls
Chinese Fans
India Model
France Fed
Park Protesters
Doctors Team
Hunting Texas
Christmas Play

Here is the same table arranged as a word cloud, with pink for women and blue for men (sue me), and the more disproportionate words larger (click to enlarge):

nytmenwomenwords

What does it mean?

It’s just one newspaper but it matters a lot. According to Alexa, NYTimes.com is the 34th most popular website in the U.S., and the 119th most popular in the world — and the most popular website of a printed newspaper in the U.S. In the JSTOR database of academic scholarship, “New York Times” appeared almost four-times more frequently than the next most-commonly mentioned newspaper, the Washington Post.

Research (including this paper I wrote with Matt Huffman and Jessica Pearlman) shows that women in charge, on average, produce better outcomes for women below them in the organizational hierarchy. Jill Abramson, the NYTimes‘ executive editor, is the 19th most powerful woman in the world, behind only Sheryl Sandberg and Oprah Winfrey among media executives on that list. She is aware of this issue, and proudly told the Women’s Media Center that she had reached the “significant milestone” of having a half-female news masthead (which is significant). So why are women underrepresented in such prominent sections? That’s not a rhetorical question; I’m really wondering how this happens. The NYTimes doesn’t even do as well as the national average: 41% of the 55,000 “News Analysts, Reporters and Correspondents” working full-time, year-round in 2012 were women.

Organizational research finds that large companies are less likely to discriminate against women, and we suspect three main reasons: greater visibility to the public, which may complain about bias; greater visibility to the government, which may enforce anti-discrimination laws; and greater use of formal personnel procedures, which limits managerial discretion and is supposed to weaken old-boy networks. Among writers, however, an informal, back-channel norm still apparently prevails — at least according to a recent essay by Ann Friedman. Maybe NYTimes‘ big-company, formalized practices apply more to departments other than those that select and hire writers.

Finally, I am sorry I’m not doing this for race/ethnicity. It’s just a much different project to do that, because the names don’t tell you the identities as well. If someone wants to figure out the race/ethnicity of NYTimes authors (such as someone, say, inside their HR department) and send it to me, I would love to analyze it.

* Neal has a series of tutorials on analyzing text as data, and he has posted some slides on how to do this with the NYT’s application programming interface (API).

** A couple other notes. This is a count of stories by the gender of their authors, not a count of authors. If men or women write more stories per person then this will differ from the gender composition of authors. So it’s not a workplace study but a content study. It asks: When you see something in the NYTimes, what is the chance it was written by a woman versus a man? I combined Sunday Review (which was small) with Opinion, since they have the same editor and are the same on Sundays. I combined Style (which was small) into Fashion, since they’re “Fashion and Style” in the paper. I  combined T Mag (which was small) into T:Style, since they seem to be the same thing. Also, I coded Reed Abelson‘s articles as female because I know she’s a woman even though “Reed” is male more than 90% of the time.

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What do doctors, lawyers, police, and librarians Google?

Now with college teachers!

What do doctors, lawyers, police, and librarians Google? I’ll tell you. But first — if you are going to take this too seriously, please stop now.

Data and Method

Using IPUMS to extract data from the 2010-2012 American Community Survey, I count the number of people ages 25-64, currently employed, in a given occupation. I divide that by each state’s population in that age range (excluding Washington DC from all analyses). I enter those numbers into the Google Correlate tool to see which searches are most highly correlated with the distribution of each occupation across states (the tool reports the top 100 most correlated searches). In other words, these are searches that maximize the difference between, for example, high-lawyer and low-lawyer states — searches that are relatively popular where there are a lot of lawyers, and relatively unpopular where there are not a lot of lawyers.

Is this what lawyers actually Google? We can’t know. But I think so. Or maybe what people who work in law firms do, or people who live with lawyers. It’s a very sensitive tool. I made this case first in the post, Stuff White People Google. Check that out if you’re skeptical.

For each occupation, I first offer a few highly correlated searches that support the idea that the data are capturing what these people search for. Then I list some of the interesting other hits from each list.

Results

Police

Police per adult

Police per adult

The map of police per adult looks pretty random, but the list of correlated search terms doesn’t. On the list are “security training,” “tsa jobs,” “waist belt,” “weight vest,” and “air marshals.”

After all the security stuff, the only major category left in the 100 searches most correlated with police in the population is women. Specifically, their search taste includes tough actress Rachel Ticotin, body builder Denise Masino, Brazilian actress Alice Braga, actress Rosario Dawson, and, “israeli women.” (Remember, Google suppresses known porn terms, so this is just what got through the filter.) It’s a leap from this data to the statement, “police search for images of these women,” but this is who they would find if that were the case (is this a “type”?):

policewomensearches

Librarians

Librarians per adult

Librarians per adult

On the other hand, librarians. They are the smallest occupation I tried: the average state population aged 25-64 is only one tenth of one percent librarians. Yet, their distribution leaves an unmistakable trace in the Google search patterns. It especially seems to pick up terms associated with public libraries. Correlated terms include, “cataloguing,” and “quiet hours.” And then there are terms one might ask a librarian about, classic reference-desk questions such as, “which vs that,” “turn off track changes,” “think tanks,” “9/11 commission,” and “irs form 6251″; and term paper topics like Shakespeare titles or “human development report.”

What about the librarians themselves, or those close to them? Could it be they who are searching for Ann Taylor dresses, Garnet Hill free shipping, Lands End home, and textile museums? We can’t know for sure. Of course, if anyone knows how to cover their search tracks, it might be this crowd.

Doctors

Doctors per adult

Doctors per adult

You know they’re doctors, because the search terms most correlated the map include “md, mph,” “md, phd,” “nejm,” “journal medicine,” “tedmed,” and “groopman.” What else do they like? Chic Corea, Tina Fey, Larry David, Mad Men (season 1) and The West Wing, Laura Linney, John Oliver, Scrabble 2-letter words, and a bunch of Jewish stuff.

Lawyers

Lawyers per adult

Lawyers per adult

That’s the map of lawyers per adult across states. Is it really lawyers? The top 100 searches correlated with the distribution shown above include “general counsel,” and then a lot of financial terms like, “world economic forum,” “international finance corporation,” and “economist intelligence.” Then there are international travel terms, like, “rate euro dollar,” “royal air,” and “swiss embassy.”

Looks like lawyers in lawyer-land are richer and more finance-oriented than lawyers in general. On the cultural side, they search for clothing terms Massimo Dutti, Hugo Boss, and Benetton. They apparently like to eat at Zafferano in London, and drink Caipirinhas. Also, they like “vissi,” which is an aria from Tosca but also a Cypriot celebrity; I lean toward the latter, because Queen Rania is also on the list. Finally, they combine their interests in law, finance, and wealthy attractive women by searching for Debrahlee Lorenzana, the “too-hot-for-work” banker.

By popular demand: Post-secondary teachers

postsecondaryperadult

Finally, here without comment are the results for “post-secondary teachers,” which includes any college teacher who didn’t instead specify a specialty, such as “psychologist” or “economist.” (It’s hard to see on the map, but Rhode Island is the highest.) I broke the results into four rough categories:

Academic

attribution
balderdash
bmi index
body image
citation style
cpdl
critical theory
debt to equity
debt to equity ratio
democracy in america
dihedral
economic inequality
economic statistics
economists
educause
edward elgar
effect size
email forward
equals sign
exogenous
feminists
google scholar
growth rates
homomorphism
inflation rate
inflation rates
intelligibility
international study
isomorphic
journal of
journal of nutrition
marginal propensity
marginal propensity to consume
mediating
meters per second
milieu
overlaying
piano sonata
prefrontal
prefrontal cortex
profile of
psychology studies
quick ratio
rejection letter
returns to scale
routledge
scholar
subgroup
superscript
transglutaminase
ways to end a letter

Personal

1% milk
2006 olympics
best pump up songs
crib safety
easy halloween costume
graco snug
handel
ipod history
jackson superbowl
janet jackson superbowl
mastermind game
maxim online
minesweeper
most popular names
napping
national sleep foundation
olympic figure skating
olympics 2006
pairs figure skating
positioning
refereeing
sandra boynton
senior hockey
snl clips
stuff magazine
stumbled upon
toilet training
verum

Musical

1812 overture
acapella group
acapella groups
africa toto
ave verum
for the longest time
it breaks my heart
pdq bach
taylor swift

Birth control

apri
apri birth control
aviane

Conclusions

Poor social scientists, generations of them spending their lives raising a few thousand dollars to ask a few thousand people a few hundred stilted, arbitrary survey questions. Meanwhile, coursing through the cable wires below their feet, and through the air around them, billions of data bits carry so much more potential information about so many more people, in so many intimate aspects of their lives, then we could even dream of getting our hands on. Just think of the power!

RingfrodoNote: I’ve done many posts like this. Some use time series instead of geographic variation, some use terms from Google Books ngrams. Browse the series under the Google tag, or check out this selection:

 

 

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Why are only 29% of NYTimes.com front page authors women?

In December I picked a moment to audit the gender composition of authors at the New York Times and Washington Post websites. Not many were women. Here’s a follow-up with more data.

For some context, according to the American Community Survey (IPUMS data extraction tool), there were about 55,000 “News Analysts, Reporters and Correspondents” working full-time, year-round in 2012. Of those, 41% were women. This pool of news writers is small compared with the number FTYR workers who report their college major was in journalism: about 315,000, of whom 53% are women. Lots of journalism majors work in other careers; lots of news writers weren’t journalism majors.

So, how will the premier newspaper in the country compare?

Methods

I stuck with NYTimes.com, and checked the gender composition of the bylines that appeared on the front page of the website just about every day between January 8 (the first day of their website redesign) and February 9, for 26 observations over 32 days. I checked whenever I thought of it, aiming for once a day and never more than once per calendar day. I excluded those in the “most-emailed” or “recommended for you” lists. I included Op-Eds and Opinion columnists if they were named (e.g., “Friedman: Israel’s Big Question”) but not if they weren’t (e.g., “Op-Ed Contributor: Czar Vladimir’s Illusions”). On average there were 16 bylines on the front page.

Someone — looking at you, Neal Caren — could scrape the site for all bylines, but in the absence of that I figured a simple rule was best. To check the gender of authors, I used my personal knowledge of common names, and when I wasn’t sure Googled the author’s photo and eyeballed it (all the authors I checked had a photo easily accessible). Overall, I counted 421 named authors (including duplicates, as when the same story was on the front page twice or the same author wrote again on a different day).

Results

Twenty-nine percent of the named authors were women (124 / 421). Women outnumbered men once (8-to-6), on February 8 at 2:35 AM. At the most extreme, men outnumbered women 18-to-1, at 8:12 AM on January 14.

Here are the details:

nytimes percent female authors.xlsx

Discussion

The New York Times is just one newspaper, and one employer, but it matters a lot, and the gender composition of the writers featured there is important. According to Alexa, NYTimes.com is the 34th most popular website in the U.S., and the 119th most popular in the world — and the most popular website of a printed newspaper in the U.S. In the JSTOR database of academic scholarship, “New York Times” appeared in 117,683 items in January 2014, 3.7-times more frequently than the next most-common newspaper, the Washington Post.

I don’t know the overall composition of New York Times writers, or their pool of applicants, or the process by which articles are selected for the website front page, so I can’t comment on how they end up with a lower female composition on the website than the national average for this occupation.

However, it is interesting to hold this up to the organizational research on how organization size and visibility affect gender inequality. Analyzing data from almost 300,000 workplaces over three decades, Matt Huffman, Jessica Pearlman and I found strong evidence that larger establishments are less gender segregated. To explain that, we wrote (with references removed for brevity):

Institutional research on organizational legitimacy implies that size promotes gender integration within establishments, because size increases both visibility to the public and government regulatory agencies and pressure to conform to societal expectations. Size is positively correlated with the formalization of personnel policies and other practices, and formalization is thought to reduce gender-based ascription by limiting managers’ discretion and subjectivity and holding decision makers accountable for their decisions.

The New York Times certainly is a high-visibility corporation, and the effects of its staffing practices are splashed all over its products through bylines and the masthead. In fact, maybe that visibility is to thank for the integration it has accomplished already. Of course it’s complicated; we also found that the gender of managers, firm growth, and other factors affect gender integration. Maybe to help figure this out someone should repeat this count over a longer time period to see how it’s changed, and how those changes correspond with other characteristics of the company and its social context.

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Percent female among bylined New York Times website authors, circa 3 p.m. on December 24, 2013

Now with the Washington Post from the next morning added…

New York Timesnyt-female-writers

The total is 36% female. The segregation score is .42, meaning 42% of men or women would have to switch sections to get an even gender distribution across sections. If that’s what you want.

Washington Post

wapo-female-writers

The total is 32% female and the segregation is .41. The grey couple appears pretty homophilous.

Note: Authors counted as many times as they appeared (e.g., if a piece appeared in more than one section, or they had two pieces in the same section).

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Gender devaluation, in one comparison

You can divide the reasons women earn less money than men do, on average, into three categories, in declining order or importance:

  1. Working fewer years, weeks, and hours
  2. Working in different occupations
  3. Being paid less in the same occupations

The first has to do with families and children. That has a large voluntary, or at least kind of voluntary, component (or it reflects hiring discrimination, which is hard to prove, prevent or punish under our legal regime). The third is illegal and sometimes actionable, as in the Lilly Ledbetter situation.

The second — occupational segregation — is a difficult hybrid. Segregation reflects both discrimination in hiring and promotions, and socialization-related choices, including in education. And it is wrapped up with divisions that may even be relatively harmless in a separate-but-equal kind of way — that is, not directly harmful, but contributing to the categorical divisions that make gender inequality more intractable. But the different pay in female- versus male-dominated occupations is a problem, well documented (see here and here) but virtually impossible to address under current law.

nurse-truck

Today’s example: nursing assistants versus light truck drivers

The government’s O*Net job classification system provides detailed descriptions of the qualifications, skills, and conditions of hundreds of occupations. The comparison between nursing assistants (1.5 million workers) and light truck or delivery services drivers (.9 million) is instructive for the question of gender composition. Using the 2009-2011 American Community Survey, I figure nursing assistants are 88% female, compared with 6% female for the light truck drivers. Here are some other facts:

  • The nursing assistants are better educated on average, with only 50% having no education beyond high school, compared with 67% of the light truck drivers.
  • But in terms of job skills, they are both in the O*Net “Job Zone Two,” with 3 months to 1 year of training “required by a typical worker to learn the techniques, acquire the information, and develop the facility needed for average performance in a specific job-worker situation.”
  • The O*Net reported median wage for 2012 was $11.74 for nursing assistants, compared with $14.13 for light truck drivers, so nursing assistants earn 83% of light truck drivers’ hourly earnings.

To make a stricter apples-to-apples comparison, I took those workers from the two occupations who fit these narrow criteria in 2009-2011:

  • Age 20-29
  • High school graduate with no further education
  • Employed 50-52 weeks in the previous year, with usual hours of exactly 40 per week
  • Never married, no children

This gave me 748 light truck drivers and 693 nursing assistants, with median annual earnings of $22,564 and $20,000, respectively — the light truck drivers earn 13% more. Why?

The typical argument for heavy truck drivers’ higher pay is that they spend a lot of time on the road away from home. But that’s not the case with the light truck drivers. They are more likely to work longer hours, but I restricted this comparison to 40-hour workers only. Here are comparisons of the O*Net database scores for abilities and conditions of the two jobs. For each I calculated score differences, so the qualities with bars above zero have higher scores for nursing assistants and those with bars below zero have higher scores for light truck drivers. See what you think (click to enlarge the figures). My comments are below.

abilities

context

You can stare at these lists and see which skills should be rewarded more, or which conditions compensated more. Or you could derive some formula based on the pay of the hundreds of occupations, to see which skills or conditions “the market” values more. But you will not be able to divine a fair market value for these differences that doesn’t have gender composition already baked into it. And “the market” doesn’t make this comparison directly, because nursing assistants and light truck drivers generally don’t work for the same employers or hire from the same labor pools. You might see reasons in these lists for why women choose one occupation and men choose the other, but I don’t see how that fairly leads to a pay difference.

The only solution I know of to the problem of unequal pay according to gender composition is government wage scales according to a “comparable worth” scheme (the subject of old books by Joan Acker and Paula England, but not high on the current political agenda). Under our current legal regime no one woman, or class of women, can successfully bring a suit to challenge this disparity.* That means occupational integration might be the best way to break this down.

*One exception to this is the public sector in Minnesota, in which local jurisdictions have their pay structures reviewed at regular intervals for evidence of gender bias, based on the required conditions and abilities of their jobs (as reported by me by Patricia Tanji of the Pay Equity Coalition of Minnesota).

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Choose that job?

This is a quick note following up on some posts about the gender gap in pay (like this one on long-hours workers, and this one on the use and abuse of the gender gap statistic).

One of the worst headlines I saw on these subjects was this one from a Time.com post: “The Pay Gap Is Not as Bad as You (and Sheryl Sandberg) Think. [Subhead:] Women don’t make 77 cents to a man’s dollar. They make more like 93 cents, as long as they don’t major in art history.”

I can appreciate a joke, but this just underscores how this debate over job “choice” is going on among the 28% of U.S. adults that have a bachelor’s degree or more. That bias shows up in the telltale use of “profession,” as in Hanna Rosin’s phrase, “Women congregate in different professions than men do, and the largely male professions tend to be higher-paying.” People who are scraping by in dead-end jobs aren’t “congregating in professions.”

In the language of economics, this may be expressed as, “differences in educational attainment, work experience and occupational choice contribute to the gender wage gap.”

Really?

Really?

Many of the critics of my NYTimes op-ed on gender inequality shared the view of “wmdawesrode”:

What about free choice? Nothing holds anyone of whatever gender from pursuing a career in whatever field they prefer.

Occupations

Technically, this comes down to how you handle the issue of occupations in employment data, and transitions between them. In a paper analyzing the job changes of nurses’ aides, Vanesa Ribas, Janet Dill and I found that for 30% of those who left the job, their next job was in an even worse-paid service job.

We looked at nurses’ aides because it is a poorly-paid job, disproportionately female, which employers fret over because of its high turnover rate. But there are hundreds of occupations in the federal statistical system. Some of them reflect career choices made by people with professional options (e.g., “economist” versus “sociologist”). But what about the 3.1 million workers who are “cashiers” versus the 3.1 million workers who are “first-line supervisors/managers of retail sales workers.”? Treating this difference as an occupational choice, rather than as an unequal outcome, is iffy at best.

If you go to the IPUMS archive of Current Population Survey data, you can experiment with this using the “occ” (what is your occupation now?) versus “occly” (what was your occupation last year?) questions. For example, to see what those retail sales supervisors and managers were doing last year, fill out the online analysis window like this:

occoccly

And you will find the major feeder occupations were (in descending order):

For women:

  1. First-line supervisors/managers of non-retail sales workers
  2. Cashiers
  3. Sales representatives, wholesale and manufacturing
  4. Customer service representatives
  5. Food service managers

For men:

  1. First-line supervisors/managers of non-retail sales workers
  2. Sales representatives, wholesale and manufacturing
  3. Marketing and sales managers
  4. Retail salespersons
  5. Driver/sales workers and truck drivers

It looks to me like some of those people were making lateral moves in pursuit of career dreams (e.g., from non-retail to retail sale manager), but for most of them the job is a promotion. (I pooled 10 years of data because the numbers for these are pretty small, since there are so many occupations, and the vast majority of people don’t change jobs each year).

If you look through the list of occupations, many of them reflect hierarchies in vertical career paths. This is empirically observable, but analyzing it systematically requires a creative approach I haven’t figured out (but maybe someone else has).

Of course, a gender disparity in rates of transitioning from cashier to supervisor isn’t necessarily employer discrimination. Some people, for example, have family obligations (“choices”) that make them less dedicated workers and legitimately less desirable for promotion. The gender system is complicated. If fathers are more likely to move out when their children have disabilities, as suggested by data on living arrangements, then single mothers whose children have disabilities might have a tough time giving 110% to their cashier jobs — to get that promotion at Wal-Mart. And then Hanna Rosin would catch them congregating in the less lucrative professions.

 

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Gender Gap Statistic Gets it from All Sides

I was very happy to write this post for the Gender & Society blog, where it first appeared.

The “gender gap” has gotten a lot of attention this fall. This hard-working statistic is as often abused and attacked by antifeminists as it is misused and misunderstood by those sympathetic to feminism. But it is good for one thing: information.

The statistic is released each year with the U.S. Census Bureau’s income and poverty report. This year they reported 2012 annual earnings as recorded in the March 2013 Current Population Survey (CPS): the median earnings of full-time, year-round working women ($37,791) was 76.5% of men’s ($49,398). That is the source of (accurate) statements such as, “Women still earned only 77 cents for every dollar that men earned in 2012.”

In the category of reading too much into a single number, I put this data brieffrom the Institute for Women’s Policy Research, which helpfully informed us that, “Most Women Working Today Will Not See Equal Pay during their Working Lives.” Here is the chart:

Cohen_image1

Of course, real life projections are not usually made by simply extending a trend with a straight line. The future is not that easy to foresee. If you did want to fit a line to that trend, however, the bad news is that it’s not a straight line that fits, but a third-order polynomial (which improves the measure of fit from .90 to .98). And projected this way, the trend will never reach equality:

Cohen_image2

Fortunately, curvy lines are often no better at predicting the future than straight ones.

Flog that stat

Some defenders of equal pay for women misstate the statistic, as President Bill Clinton did when he said:

“How would you like to show up for work every day, but only get to take home three out of every four paychecks? … if you get paid 75 percent for the same kind of work, it’s as if you were only picking up three paychecks, instead of four, in four pay periods. The average woman has to work, therefore, an extra 17 weeks a year to earn what a similarly-qualified man in the same kind of job makes.”

The mistake here is that he said “same kind of work” and “similarly-qualified man.” That led to the screaming headline on the American Enterprise Institute website, “Still Hyping the Phony Pay Gap.”

But he also went on to say:

“Yes, some of this can be explained — by differences in education, experience and occupation. But even after you make all those adjustments, there is still a very significant gap.”

So he belatedly acknowledged the complexities, and that second statement is true.

Oh, and that exchange occurred in 2000. How far we’ve come.

When Clinton, ever a repository for handy statistics, essentially repeated his statement on September 29 of this year, he played right into the screaming headlines of today’s anti-feminists, including Hanna Rosin, who declared, “I feel the need to set the record straight” in a piece she titled, “The Gender Gap Lie.” Kay Hymowitz also has written extensively to debunk the gender gap, arguing that it mostly results from women’s choices – the educations and occupations they choose, the hours they choose, the “mommy track” they prefer. (Naturally, sociologists are very interested in that construction of “choice.”)

There is no single number that can tell us the true state of gender inequality. But if you had to pick one, this one is pretty good. That’s because it combines factors that affect employment levels, work experience, occupational distributions, and pay discrimination – to give a sense of the place of the typical worker. As long as that number is not zero, there is a gender inequality problem to discuss, whether it results from socialization, family demands, educational sorting and tracking, hiring and promotion discrimination, or pay discrimination – and the details depend on further scrutiny.

Take your pick

We could use a different gender gap. The next figure shows some gender gaps for earnings among full-time, full-year workers in the 2011 American Community Survey (ACS). I’ve cut the sample to compare men and women by education, long-hours status (50+ hours), parenthood (no co-residential children) and marital status (never married). As you can see, the gaps range from a low of 65% for women with an MA degree and no children all the way up to 93% for never-married professional degree or PhD holders with no kids. Generally, the 50-hour limit doesn’t help, but marriage and children make a big difference.Cohen_image3

Another way of restricting the data to consider real-world gaps is shown in the next figure. Here, from the same data, I’ve taken full-time, full-year workers who have a bachelor’s degree and no further education, and sorted them by college major. So these gaps account for educational specialization, and reflect – in addition to any hiring and pay discrimination – occupational sorting within those categories, as well as other educational processes such as university prestige and school performance. The gaps range from 69% for transportation science majors all the way up to 94% for architecture majors.

Cohen_image4

Finally, we might look more closely at occupations. In this figure, again from the 2011 ACS, I have sorted 484 detailed occupational categories according to the median earnings wage gap within them, for full-time, year-round workers. The y-axis shows the cumulative percentage of women who work at or below each level as you move from less equal occupations on the left to more equal ones on the right. I’ve labeled the 25th, 50th and 75th percentile, showing, for example, that half of women work in occupations with a wage gap of 83% or worse.Cohen_image5

Although this figure shows inequality within occupations, it is occupational segregation itself, which extends the gender division of labor into the labor market, that lies behind much of the gender gap – representing the culmination of historical and contemporary processes of allocating people to tasks.

In summary, the wage gap clearly is smaller in some situations than others – smaller for workers without children, especially if they’re never married, smaller for some college majors and in some occupations. Each of these comparisons tells us something different. (More complete statistical analysesthat control for several factors at once create counterfactuals that don’t actually exist, but that do help us isolate important dynamics behind the gender gap.)

We mustn’t read into these numbers more than they can tell us. None of the numbers I’ve shown can discern occupational choice from employer discrimination, for example; or the cumulative effects of time out of the labor force versus discrimination in previous jobs. But the gender gap numbers are measures of inequality. And as long as we are accurate and responsible in our use of these numbers, they are useful sources of information.

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