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Sixteen minutes on The Tumbleweed Society

At the American Sociological Association conference, just concluded, I was on an author-meets-critics panel for Alison Pugh’s book, The Tumbleweed Society: Working and Caring in an Age of Insecurity. The other day I put up a short paper inspired by my reading, on SocArXiv (data and code here).

Here is my talk itself, in an audio file, complete with 6 seconds of music at the beginning and the end, and a lot of the ums and tangents taken out, running 16 minutes. Download it here, or listen below. And below that are the figures I reference in the talk, but you won’t really need them.

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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.

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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.

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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.

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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.)

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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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Race/ethnicity and slacking at work

johnhenry

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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OK, how about the gender gap, within occupations, for people working 50+ hours?

I haven’t had time to write something substantial on this, but I took the time to make this figure so I may as well post it.

Hanna Rosin wrote a blog post in Slate called “The Gender Gap Lie,” boldly proclaiming, “I feel the need to set the record straight,” before summarizing a June 2012 PolitiFact piece on the Obama 2012 ad which said: “President Obama knows that Women being paid 77 cents on the dollar for doing the same work as men isn’t just unfair, it hurts families.”

It turns out, not surprisingly, there are many pieces debunking the misleading use of the gender wage gap statistic, like this one by Kay Hymowitz and this one by Ruth David Konigsberg, with the absurdly offensive sub-head: “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.” Newsflash: most employed women didn’t major in anything because they didn’t go to college (67% don’t have college degrees!), which also speaks to Rosin’s favorite “apples-to-apples comparison,” the study about University of Chicago MBAs.

Just to be clear: the 77 cents on the dollar statistic (and its variations) is based on all people working full time. It is not a measure of pay discrimination “for the same work.” It is a measure of gender inequality. The correct, non-lie way to describe this fact is modeled by the Institute for Women’s Policy Research: “in 2011, female full-time workers made only 77 cents for every dollar earned by men.” Calling that lie is a lie. Not all inequality is discrimination, but that doesn’t mean it’s not a problem.

Occupations are one thing, that is, why “women” insist on majoring in art history before choosing careers as hotel maids instead of CEOs. Another is hours at work, always an issue in wage-gap debunkery. Men work more hours, on average, so they should get paid more, says the anti-lie crowd.

Fair enough, by the rules of our game. To help inform on that issue, I made this figure. It shows the occupations with the most people usually working 50 hours per week or more among those who worked 50 weeks or more in the previous year.

50-plus-hours-occs-earnsSource: My calculations from the 2011 American Community Survey, extracted from IPUMS.

The pink and blue bars show the median annual earnings of workers who put in an average of 50+ hours per week last year, and worked 50 weeks or more. The dots show women’s median as percentage of men’s. You can see that in two occupations — non-retail sales supervisors and human resource workers — women actually earn more than men on average. In some the gender gap is quite large. For example, among doctors working 50+ hours per week, women only earn 54% of men’s median earnings (so the gap doesn’t just result from surgeons working longer hours than pediatricians, I guess). Also, note that the 50-hour crowd are not all in high-status professional jobs where high earnings drive career choices — those women home health aides are making $11 per hour.

Overall, in these 25 occupations, the earnings gap for people working 50+ hours 50+ weeks is 83%. So, the Twitter version: Within occupations, among those working extra-long hours, women earn 83% of what men earn.

Even though these aren’t side-by-side wage gaps (e.g., two janitors working the same shift at the same workplace, with the same performance evaluations and work experience), you could justifiably call this “the same work” if you acknowledge there are different career tracks and working conditions contributing to this gap. That is, surgeons and pediatricians have the same degrees even if they have different specialty training and skills; they are doing varieties of the same work. You could also dispute that, or clarify it (likewise, among truck drivers, people operating different equipment have different skills).

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Education-gender earnings crossover

It has been remarked that, in the olden days, men with only a high school education earned more than women with a college education. That’s true, as I reported to Stephanie Coontz for today’s New York Times column. And the olden days ended about 20 years ago.

Until the early 1990s, men who were high school graduates – but not college graduates – earned more than women who were college graduates. And men who were high school dropouts earned more than women who were high school graduates.

cps-educ-gender-earnings-62-2012Source: My analysis of March Current Population Survey data from IPUMS.

I don’t know what’s going on with the big 1992 drops. It could have to do with CPS survey design changes (I used last year’s earnings for people who worked 35+ hours last week and 50+ weeks last year).

You could describe these crossovers as a modernization of the labor force, with education rising in importance relative to gender. Or not.

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A Simple, Legal Way to Help Stop Employment Discrimination

Originally posted at TheAtlantic.com.

Women and racial minorities are no longer making progress toward equal representation in the workplace. Here’s a way to maybe fix that.

cohen_discrimination_post.jpg
Jacquelyn Martin

Progress toward gender and racial equality in the workplace has basically stalled. One reason for that is the government’s lack of antidiscrimination enforcement. As Donald Tomaskovic-Devey and Kevin Stainback show in their book Documenting Desegregation, ever since the reign of Clarence Thomas as head of the EEOC in the 1980s, the Equal Employment Opportunity Commission (EEOC) has been underfunded, understaffed, and largely ineffective at doing its job. To help get things moving again, under the existing law (more or less), we could use the power of social media and the principle of government transparency to allow workers and consumers themselves to apply pressure on discriminating employers. Would it work? It couldn’t hurt. First a little background.

Anti-discrimination today
Here is the occupational segregation trend from 1966 to 2005, fromDocumenting Desegregation, just comparing white and black men and women. The index of dissimilarity shows what percentage of a group would have to change jobs to have the same representation as white men.

cohen_eeoc.png

The figure shows white women made a lot of progress in the 1970s and 1980s, but less since. Black women have a similar pattern but much slower progress. And black men haven’t budged since 1980. The same pattern holds for representation in managerial jobs.

The burden to fight discrimination today is mostly on workers who have been discriminated against to first discover this fact and second file a complaint and/or lawsuit themselves. The courts have tightened their definition of discrimination to include only deliberate acts proven to have been motivated by discriminatory intent – a very steep burden. And they have reduced workers’ capacity to bring class actions, most notably in the Wal-Mart decision, which makes it hard to get good legal teams. As a result, few cases make it to court, and virtually no one wins. A study of 1,672 employment discrimination cases from 1988 to 2003 found that about half resulted in settlements (with a median value of $30,000), 6 percent went to trial, and one-third of those were victorious (with a median award of $110,000). Although more than 100,000 people file discrimination complaints with the EEOC, most workers lack basic information not only about the law and their options, but about their own employers’ practices (as was painfully revealed when Lilly Ledbetter discovered she had been discriminated against by Goodyear for many years). And people who aren’t hired in the first place have an even smaller chance with the law.

In the 1964, Congress passed the Civil Rights Act, which included in Title VII a mandate to collect information about employment in the private sector. Since 1966, all large employers are required to submit a simple accounting: the number of workers, by race and sex, in each of nine occupational categories. This has produced a treasure-trove of data, which Tomaskovic-Devey and Stainback used to document the trends. But this information could be used more proactively by the government itself, if stopping discrimination were a higher priority.

Anti-discrimination tomorrow
Defining and proving discrimination is difficult. Many employers have no outward motivation to discriminate—they just don’t do enough to stop discrimination by individual supervisors, recruiting practices that produce narrow applicant pools, and malicious co-workers. So not every workplace with an underrepresentation of women or minorities is a case of willful discrimination. But when a workplace has significant underrepresentation in either its management or its overall employee pool, it’s at least worth taking a look to see what’s going on.

Here’s my suggestion, inspired to by Documenting Desegregation. Underrepresentation is very widespread, and easy to detect. Why not label it?

Using the same EEOC data, my colleague Matt Huffman and I identified workplaces in which there were fewer African-American managers than would be expected by chance, using a test common in employment litigation. With a wide statistical margin—95 percent confidence—we found, for example, that 7 percent of black private-sector workers in the D.C. metropolitan area worked for employers with easily identified underrepresentation of black managers. That is, they had fewer black managers, compared with other firms in their same industry in their same town, than would have occurred by chance. Maybe they aren’t discriminating on purpose, but they’re probably doing something wrong. As a customer, client, business partner or job applicant at that firm, wouldn’t you like to know that? (Of course, as researchers we are prohibited from revealing information about individual employers.)

So why doesn’t the EEOC generate a simple certificate, like the one I have mocked up here, to notify the employer, the employees, and the public, about such cases? (This would only apply to those with 50 workers or more.)

This hypothetical firm has an overrepresentation of white men in management compared with the local industry (for example, a department store with 60 percent white male managers when the local industry average is 30 percent). They have underrepresentation of black women across the board, and Latina women compared with the rest of the industry locally. Representation of the other groups isn’t outside the range of the 95 percent test, or there aren’t enough cases to judge. The test accounts for sample size—if you only have two managers at your business, and one is a white man, you’re not going to fail.

cohen_checklist.png

It could be like the health department certificate posted on a restaurant wall (and online). Then, maybe someone who worked there would get up the courage to file a complaint. Maybe customers wouldn’t shop there. Maybe politicians running for office would promise to improve the local statistics. Maybe concerned managers would honestly consider their hiring practices to look for ways to do better.

This doesn’t reveal any trade secrets. It doesn’t increase the reporting burden on employers, since they’re already required to submit the forms. It wouldn’t cost much. But it gives the public a little more leverage and increases the accountability for employers. It wouldn’t solve everything either. But if equal opportunity employment were a major priority, a small step like this would seem pretty reasonable.

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Do people working work in working families?

It’s not that “working families” don’t exist, it’s just the way most people use this term it doesn’t mean anything.

Search Google images for “working families,” and you’ll find images like this:

4f4a9a28-ff28-4bc7-88e5-f0df4522b2dbAnd that’s pretty much the way the term is used: every family is a working family.

To hear the White House talk, you have to wonder whether there are people who aren’t in families. I’ve complained about this before, Obama’s tendency to say things like, “This reform is good for families; it’s good for businesses; it’s good for the entire economy.” As if “families” covers all people.

Specifically, if you Google search the White House website‘s press office directory, which is where the speeches live, like this, you get 457 results, such as this transcript of remarks by Michelle Obama at a “Corporate Voices for Working Families” event. The equivalent search for “working people” yields a paltry 108 hits (many of them Obama speeches at campaign events, which include false-positives, like him making the ridiculous claim that Americans are the “hardest working people on Earth.”) If you search the entire Googleverse for “working families” you get about 318 million hits, versus just just 7 million for “working people” (less than the 10 million that turns up for “Kardashians,” whatever that means.)

You would never know that 33 million Americans live alone – comprising 27% of all households. And 50 million people, or one out of every 6 people, lives in what the Census Bureau defines as a “non-family household,” or a household in which the householder has no relatives (some of those people may be cohabitors, however). The rise of this phenomenon was ably described by Eric Klinenberg in Going Solo: The Extraordinary Rise and Surprising Appeal of Living Alone.

This is partly a complaint about cheap rhetoric, but it’s also about the assumption that families are primary social units when it comes to things like policy and economics, and about the false universality of “middle class” (which is made up of “working families”) in reference to anyone (in a family with anyone) with a job.

Here’s one visualization, from a Google ngrams search of millions of books. The blue line is use of the phrase “working people” as a fraction of references to “people,” while the red line is use of the phrase “working families” as a fraction of references to “families.” It shows, I think, that “working” is coming to define families, not people.

CaptureThis isn’t all bad. Families matter, and part of the attention to “working families” (or Families That Work) is driven by important problems of work-family conflict, unequal care work burdens, and so on. But ultimately these are problems because they affect people (some of whom are in families). When we treat families as the primary unit of analysis, we mask the divisions within families – the conflicts of interest and exploitation, the violence and abuse, and the ephemeral nature of many family relationships and commitments – and we contribute to the marginalization of people who aren’t in, or don’t have, families.  And those members of the No Family community need our attention, too.

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