Tag Archives: labor

Race/ethnicity and slacking at work


From John Henry: An American Legend, by Ezra Jack Keats

I gave some comments to an Economist writer for a story they just published, “New research suggests that effort at work is correlated with race.” They used a snippet of what I said, so I figured I’d dump the rest here (because the piece is not bylined, I’m not using the reporter’s name).

The article is about an NBER working paper (not yet peer reviewed) by, Daniel Hamermesh, Katie Genadek, and Michael Burda. It’s officially here, but I put a copy up in case you don’t have am NBER subscription.) The analysis uses the American Time Use Survey to see whether time at work spent not working varies by race/ethnicity, and they find that it does. The abstract:

Evidence from the American Time Use Survey 2003-12 suggests the existence of small but statistically significant racial/ethnic differences in time spent not working at the workplace. Minorities, especially men, spend a greater fraction of their workdays not working than do white non-Hispanics. These differences are robust to the inclusion of large numbers of demographic, industry, occupation, time and geographic controls. They do not vary by union status, public-private sector attachment, pay method or age; nor do they arise from the effects of equal-employment enforcement or geographic differences in racial/ethnic representation. The findings imply that measures of the adjusted wage disadvantages of minority employees are overstated by about 10 percent.

When the Economist contacted me, I consulted several colleagues for their response. Reeve Vanneman pointed out that minority workers might slack off at work because they are discriminated against, and Liana Sayer pointed out that the activity measures in the ATUS may not be not precise enough to say what if any “non-work” activity is actually contributing to the bottom line – the paper doesn’t detail what these “non-work” activities are. My own critique was that, before we start attributing work behavior to “culture,” we might consider whether work reporting behavior varies by “culture” as well (the ATUS uses self-reported time diaries). The authors did a little monkeying around with the General Social Survey to address that, but I found it unpersuasive.

Anyway, you can read the Economist article yourself. I would have preferred they killed the article, because I don’t think the paper sustains its conclusions, but they did a reasonable job of reporting it. And here are the full comments I sent them:

The analysis in the paper does not support the conclusion that wage disparities between blacks and whites are overstated. There just isn’t enough there to make that claim. As the authors note, the problem of differential reporting is an obvious concern. Their analysis of the “importance of work” questions in the GSS seems immaterial – it’s just not the same question.

This is exacerbated by the problem that they don’t describe the difference between work-related non-work activities and non-work-related non-work activities. We just don’t know enough about what they’re doing to draw the conclusion that the work-related activities are really productivity enhancing while the non-related activities are really not. (Consider trying to parse the effect of eating alone at your desk versus eating with a team-member in the cafeteria. Which is productivity enhancing?) It is always the case that jobs differ between blacks and whites in ways surveys do not capture – that’s the whole question of the wage gap. Controlling for things like industry and occupation helps but it’s the tip of the iceberg. For example, the difference between small and large employers, and between those with formal management procedures and those without, is not captured here.

Finally, consider the possibility of reverse-causality. What if blacks are discriminated against and paid less than whites for the same level of productivity – or treated poorly in other ways – a very reasonable hypothesis? Might that not lead those black workers to be less devoted to their employers, and spend more time on other things when no one is looking? I wouldn’t blame them.

In short, the paper uses a lot of ambiguous information, which is interesting and suggestive, to draw a conclusion that is not warranted. It’s part of a tradition in economics of assuming there must be some rational basis for pay disparities, and looking really hard to find it, rather than treating employer motivations more skeptically and trusting the voluminous evidence of racist bias in the labor market.

In the email exchange, they asked for followup on the evidence of racial bias, so I added this:

The best evidence of discrimination is from audit studies. This is one of the best. That author, Michael  Gaddis at Penn State, can talk much more about it, but the point is that even when you can’t identify an individual act of racism, in the aggregate employer behavior shows a preference for whites — as we can tell by imposing experimental conditions in which the only thing different between resumes is the names. Other approaches include studying disparities in performance evaluation (e.g., this [by Marta Elvira and Robert Town]), or analyzing discrimination case files directly (e.g., this [by Ryan Light, Vincent Roscigno, and Alexandra Kalev]).

That all got reduced to this, in the article: “Worse treatment by managers of minority workers may itself encourage slacking, says Philip Cohen.” (Though they went on to cite evidence that workers work less when their managers are biased against them.)

On the other hand

As I think about it more, there is another important angle on this, which goes back to Reeve’s comment, and also something in the conclusion to the Economist article:

Within hours of publication, Mr Hamermesh received vitriolic messages and was labelled a racist in an online forum popular among economists. Mr Hamermesh, an avowed progressive, who refers to Donald Trump only by amusing nicknames and resigned from a post at the University of Texas over a state law permitting the open carrying of firearms, finds this unfair. He notes that Americans work too much. His preferred solution would not be for some groups to work more, but for others to work less.

There is an understandable anti-racist tendency to want to avoid a story of minority workers as lazy and shiftless – which is a character flaw. But there is a resistance story to tell as well, and the liberal anti-racist approach papers it over. For this, we need historian Robin D. G. Kelley, who wrote a brilliant paper called, “‘We Are Not What We Seem’: Rethinking Black Working-Class Opposition in the Jim Crow South” (free copy here). Here’s a relevant excerpt, in which he cites W. E. B. Du Bois:

Part of the reason [labor historians have not written more about workplace theft and sabotage by Southern Blacks], I think, lies in southern labor historians’ noble quest to redeem the black working class from racist stereotypes. In addition, company personnel records, police reports, mainstream white newspaper accounts, and correspondence have left us with a somewhat serene portrait of black folks who only occasionally deviate from what I like to call the “cult of true Sambohood.” The safety and ideological security of the South required that pilfering, slowdowns, absenteeism, tool breaking, and other acts of black working-class resistance be turned into ineptitude, laziness, shiftlessness, and immorality. But rather than reinterpret these descriptions of black working-class behavior, sympathetic labor historians are often too quick to invert the images, remaking the black proletariat into the hardest working, thriftiest, most efficient labor force around. Historians too readily naturalize the Protestant work ethic and project onto black working people as a whole the ideologies of middle-class and prominent working-class blacks. But if we regard most work as alienating, especially work done amid racist and sexist oppression, then a crucial aspect of black working-class struggle is to minimize labor with as little economic loss as possible. Let us recall one of Du Bois’s many beautiful passages from Black Reconstruction: “All observers spoke of the fact that the slaves were slow and churlish; that they wasted material and malingered at their work. Of course they did. This was not racial but economic. It was the answer of any group of laborers forced down to the last ditch. They might be made to work continuously but no power could make them work well.”

Working hard for the man’s benefit is not the only way to build character.


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What, my workers? (Hershey park edition)

The previous post was about interracial civility at Hershey Park.. This is about something else I noticed there.

The “free” “chocolate” “tour” at Hershey Park is probably not best enjoyed on a Saturday in August while the roller coasters are closed due to inclement weather. Unless what you enjoy is people watching — which, although an odd omission from the tour itself, you will have plenty of time to do in line.


The tour promises “NEW features,” including:

  • Immerse yourself in the cocoa farms of West Africa and the dairy farms in Central Pennsylvania
  • Hear and see the story of chocolate making through new technological effects
  • See the new, state-of-the-art animated figures, including our famous barnyard cows
  • Sing along to the sweet, catchy, and new theme song
  • Experience the social media-enhanced finale, featuring Hershey fans from around the world

When you finally get to the little train car that will take you on the tour, you ride past a series of big video screens showing machines, some big machines simulating chocolate-making, and some fake cows:


The voices you hear belong to a woman who says she’s a quality control expert, and some animated or mechanical pieces of candy. There are literally no humans visible on the immersive tour of chocolate making. Hershey of course does have many people working to make chocolate for them, in Mexico and Brazil and Pennsylvania, among other places. But the tour designers who figured out how to pump chocolate smell into the confined, warmed, darkened, orange-glowing oven your car creeps through to simulate roasting, decided not to include any reference to those workers.


At the very end, a high-school aged temp worker hands you a free sample, though!


Maybe before Trump and Clinton bring back “our” manufacturing jobs, they can start by bringing back some pictures of our manufacturing jobs.


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Filed under Me @ work, Uncategorized

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.


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.



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).


Filed under Research reports

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.”



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.


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:


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.



Filed under Research reports

All work and no pay

To Nicholas Kristoff, “One of the puzzles of the developing world is that women frequently do some of the hardest physical labor.”

Photo by Gretchen Draper

This is not such a puzzle in light of the general principle that the harder people work (as a group), the less money they make — and the less “productive” they are in economic terms (meaning what they produce sells for less).

I like these pictures to illustrate the principle with farmers:

Women’s backbreaking work shows the extension of the principle to include work for no pay — the hardest work of all.


Filed under In the news, Me @ work