Tag Archives: occupations

Job turnover and divorce (preconference preprint)

As I was prepared to discuss Alison Pugh’s interesting and insightful 2015 book, The Tumbleweed Society: Working and Caring in an Age of Insecurity, on an author-meets-critics panel at the American Sociological Association meetings in Montreal next week (Monday at 4:30), I talked myself into doing a quick analysis inspired by the book. (And no, I won’t hijack the panel to talk about this; I will talk about her book.)

From the publisher’s description:

In The Tumbleweed Society, Allison Pugh offers a moving exploration of sacrifice, betrayal, defiance, and resignation, as people adapt to insecurity with their own negotiations of commitment on the job and in intimate life. When people no longer expect commitment from their employers, how do they think about their own obligations? How do we raise children, put down roots in our communities, and live up to our promises at a time when flexibility and job insecurity reign?

Since to a little kid with a hammer everything looks like a nail, I asked myself yesterday, what could I do with my divorce models that might shed light on this connection between job insecurity and family commitments? The result is a very short paper, which I have posted on SocArXiv here (with supporting data and code in the associated OSF project shared here). But here it is in blog form; someday maybe I’ll elaborate it into a full paper.


Job Turnover and Divorce

Introduction

In The Tumbleweed Society, Pugh (2015) explores the relationship between commitments at work – between employers and employees – and those at home, between partners. She finds no simple relationship such that, for example, people who feel their employers owe them nothing also have low commitment to their spouses. Rather, there is a complex web of commitments, and views of what constitutes an honorable level of commitment in different arenas. This paper is inspired by that discussion, and explores one possible connection between work and couple stability, using a new combination of data from the Current Population Survey (CPS) and the American Community Survey (ACS).

In a previous paper I analyzed predictors of divorce using data from the ACS, to see whether economic indicators associated with the Great Recession predicted the odds of divorce (Cohen 2014). Because of data limitations, I used state-level indicators of unemployment and foreclosure rates to test for economic associations. Because the ACS is cross-sectional, and divorce is often associated with job instability, I could not use individual-level unemployment to predict individual-divorce, as others have done (see review in Cohen 2014). Further, the ACS does not include any information about former spouses who are no longer living with divorced individuals, so spousal unemployment was not available either.

Rather than examine the association between individual job change and divorce, this paper tests the association between turnover at the job level and divorce at the individual level. It asks, do people who work in jobs that people are likely to leave themselves more likely to divorce? The answer – which is yes – suggests possible avenues for further study of the relationship between commitments and stressors in the arenas of paid work and family stability. Job here turnover is a contextual variable. Working in a job people are likely to leave may simply mean people are exposed to involuntary job changes, which is a source of stress. However, it may also mean people work in an environment with low levels of commitment between employers and employees. This analysis can’t differentiate potential stressors versus commitment effects, or identify the nature (and direction) of commitments expressed or deployed at work or within the family. But it may provide motivation for future research.

Do job turnover and divorce run together?

Because individual (or spousal) job turnover and employment history are not available in the ACS, I use the March CPS, obtained from IPUMS (Flood et al. 2015), to calculate job turnover rates for simulated jobs, identified as detailed occupation-by-industry cells (Cohen and Huffman 2003). Although these are not jobs in the sense of specific workplaces, they provide much greater detail in work context than either occupation or industry alone, allowing differentiation, for example, between janitors in manufacturing establishments versus those in government offices, which are often substantially different contexts.

Turnover is identified by individuals whose current occupation and industry combination (as of March) does not match their primary occupation and industry for the previous calendar year, which is identified by a separate question (but using the same occupation and industry coding schemes). To reduce short-term transience, this calculation is limited to people who worked at least 20 weeks in the previous year, and more than 20 hours per week. Using the combined samples from the 2014-2016 CPS files, and restricting the sample to previous-year job cells with at least 25 respondents, I end up with 927 job cells. Note that, because the cells are national rather than workplace-specific, the size cutoff does not restrict the analysis to people working in large workplaces, but rather to common occupation-industry combinations. The job cells in the analysis include 68 percent of the eligible workers in the three years of CPS data.

For descriptive purposes, Table 1 shows the occupation and industry cells with the lowest and highest rates of job turnover from among those with sample sizes of 100 or more. Jobs with low turnover are disproportionately in the public sector and construction, and male-dominated (except schoolteachers); they are middle class and working class jobs. The high-turnover jobs, on the other hand, are in service industries (except light truck drivers) and are more female-dominated (Cohen 2013). By this simple definition, high-turnover jobs appear similar to precarious jobs as described by Kalleberg (2013) and others.

t1

Although the analysis that follows is limited to the CPS years 2014-2016 and the 2015 ACS, for context Figure 1 shows the percentage of workers who changed jobs each year, as defined above, from 1990 through 2016. Note that job changing, which is only identified for employed people, fell during the previous two recessions – especially the Great Recession that began in 2008 – perhaps because people who lost jobs would in better times have cycled into a different job instead of being unemployed. In the last two years job changing has been at relatively high levels (although note that CPS instituted a new industry coding scheme in 2014, with unknown effects on this measure). In any event, this phenomenon has not shown dramatic changes in prevalence for the past several decades.

f1

Figure 1. Percentage of workers (20+ weeks, >20 hours per week) whose jobs (occupation-by-industry cells) in March differed from their primary job in the previous calendar year.

Using the occupation industry codes from the CPS and ACS, which match for the years under study, I attach the job turnover rates from the 2014-2016 CPS data to individuals in the 2015 ACS (Ruggles et al. 2015). The analysis then uses the same modeling strategy as that used in Cohen (2014). Using the marital events variables in the ACS (Cohen 2015), I combine people, age 18-64, who are currently married (excluding those who got married in the previous year) and those who have been divorced in the previous year, and model the odds that individuals are in the divorced group. In this paper I essentially add the job turnover measure to the basic analysis in Cohen (2014, Table 3) (the covariates used here are the same except that I added one category to the education variable).

One advantage of the ACS data structure is that the occupation and industry questions refer to the “current or most recent job,” so that people who are not employed at the time of the survey still have job characteristics recorded. Although that has the downside of introducing information from jobs in the distant past for some respondents, it has the benefit of including relevant job information for people who may have just quit (or lost) jobs as part of the constellation of events involved in their divorce (for example, someone who divorces, moves to a new area, and commences a job search). If job characteristics have an effect on the odds of divorce, this information clearly is important. The ACS sample size is 581,891, 1.7 percent of whom reported having divorced in the previous year.

Results from two multivariate regression analyses are presented in Table 2. The first model predicts the turnover rate in the ACS respondents’ job, using OLS regression. It shows that, ceteris paribus, turnover rates are higher in the jobs held by women, younger people (the inflection point is at age 42), people married more recently, those married few times, those with less than a BA degree, Blacks, Asians, Hispanics, and immigrants. Thus, job turnover shows patterns largely similar to labor market advantage generally.

Most importantly for this paper, divorce is more likely for those who most recent job had a higher turnover rate, as defined here. In a reduced model (not shown), with just age and sex, the logistic coefficient on job turnover was 1.39; the addition of the covariates in Table 2 reduced that effect by 39 percent, to .84, as shown in the second model. Beyond that, job turnover is predicted by some of the same characteristics as those associated with increased odds of divorce. Divorce odds are lower after age 25, with additional years of marriage, with a BA degree, and for Whites. However, divorce is less common for Hispanics and immigrants. (The higher divorce rates for women in the ACS are not well understood; this is a self-reported measure, not a count of administrative events.)

t2

To illustrate the relationship between job turnover and the probability of divorce, Figure 2 shows the average predicted probability of divorce (from the second model in Table 2) for each of the jobs represented, with markers scaled according to sample size and a regression line similarly weighted. Below 20 percent job turnover, people are generally predicted to have divorce rates less than 2 percent per year, with predicted rates rising to 2.5 percent at high turnover rates (40 percent).

job changing effect 2015 ACS-CPS

Figure 2. Average predicted probability of divorce within jobs (from logistic model in Table 2), by turnover rate. Markers are scaled according to sample size, and the linear regression line shown is weighted by sample size.

Conclusion

People who work in jobs with high turnover rates – that is, jobs which many people are no longer working in one year later – are also more likely to divorce. A reading of this inspired by Pugh’s (2015) analysis might be that people exposed to lower levels of commitment from employers, and employees, exhibit lower levels of commitment to their own marriages. Another, noncompeting explanation would be that the stress or hardship associated with high rates of job turnover contributes to difficulties within marriage. Alternatively, the turnover variable may simply be statistically capturing other aspects of job quality that affect the risk of divorce, or there are individual qualities by which people select into both jobs with high turnover and marriages likely to end in divorce. This is a preliminary analysis, intended to raise questions and offer some avenues for analyzing these questions in the future.

References

Cohen, Philip N. 2013. “The Persistence of Workplace Gender Segregation in the US.” Sociology Compass 7 (11): 889–99. http://doi.org/10.1111/soc4.12083.

Cohen, Philip N. 2014. “Recession and Divorce in the United States, 2008–2011.” Population Research and Policy Review 33 (5): 615–28. http://doi.org/10.1007/s11113-014-9323-z.

Cohen, Philip N. 2015. “How We Really Can Study Divorce Using Just Five Questions and a Giant Sample.” Family Inequality. July 22. https://familyinequality.wordpress.com/2015/07/22/how-we-really-can-study-divorce/.

Cohen, P. N., and M. R. L. Huffman. 2003. “Individuals, Jobs, and Labor Markets: The Devaluation of Women’s Work.” American Sociological Review 68 (3): 443–63. http://doi.org/10.2307/1519732.

Kalleberg, Arne L. 2013. Good Jobs, Bad Jobs: The Rise of Polarized and Precarious Employment Systems in the United States 1970s to 2000s. New York, NY: Russell Sage Foundation.

Pugh, Allison J. 2015. The Tumbleweed Society: Working and Caring in an Age of Insecurity. New York, NY: Oxford University Press.

Steven Ruggles, Katie Genadek, Ronald Goeken, Josiah Grover, and Matthew Sobek. Integrated Public Use Microdata Series: Version 6.0 [dataset]. Minneapolis: University of Minnesota, 2015. http://doi.org/10.18128/D010.V6.0.

Sarah Flood, Miriam King, Steven Ruggles, and J. Robert Warren. Integrated Public Use Microdata Series, Current Population Survey: Version 4.0. [dataset]. Minneapolis: University of Minnesota, 2015. http://doi.org/10.18128/D030.V4.0.

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7 facts about the gender gap, for #EqualPayDay

Well, actually, 7 fact-filled posts culled from the many I’ve written on gender inequality, so just call it lots of facts.

1. The gender gap is just one number.

But when you break it out into hundreds of numbers, it’s variations on a theme. This post shows the gender gap by education, kids, marital status, and hours worked. And then by college major. And then I show the distribution of women across 484 occupations, according to the gender gap within each:

cohen_image5

2. Occupations matter.

But treating occupational differences as “choices” is at least half ridiculous, so controlling for occupation to get the “real” gender gap is at least half wrong. Of course people choose jobs, but they also take what they can get. So why call it “occupational choice”? For example, one of the most common “choices” to make before “choosing” to be a “retail sales supervisor” is “cashier.” Isn’t choice just a big ball of magical idiosynchronicity?

3. When you assume everyone “chose” their jobs, you miss this woman who was fired for being pregnant.

But the “occupational choice” people don’t notice this, because they’re too busy discussing the “professions” women “choose” and the subjects they major in for their advanced degrees.

4. It’s not just occupations, but hours worked.

And men work more hours (for pay). That’s true, and it’s part of why men earn more (see this paper). But I showed here that, in the occupations with the most overwork (people working 50+ hours) men earn more in almost every one — among those working 50+ hours:

5. Nursing assistants earn less than light truck drivers do.

Because gender. Or maybe there’s some other reason, but I couldn’t find it in a long list of job abilities and working conditions. Among those in these two jobs who: are ages 20-29; are high school graduates only; worked exactly 50+ weeks and 40 hours per week last year; and were never married with no children; the light truck drivers earn 13% more.

6. Don’t just compare then and now.

Way too many people compare “then” and “now” without realizing that gender progress (on many indicators) stalled or slowed two decades ago. For example, as I described here, the percentage of Americans who “prefer a male boss” is lower now (33%) than it was in the 1950s (66%). Wow! But it’s barely lower than it was 20 years ago. Here’s the latest figure from Gallup:

ybyrr6idcumt9mqznohada

7. It’s complicated.

And, at the risk of jargoning you: intersectional. White women earn more than Black men. But at each educational level Black men earn more.

 Happy Equal Pay Day!

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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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