Fertility trends and the myth of Millennials

The other day I showed trends in employment and marriage rates, and made the argument that the generational term “Millennial” and others are not useful: they are imposed before analyzing data and then trends are shoe-horned into the categories. When you look closely you see that the delineation of “generations” is arbitrary and usually wrong.

Here’s another example: fertility patterns. By the definition of “Millennial” used by Pew and others, the generation is supposed to have begun with those born after 1980. When you look at birth rates, however,  you see a dramatic disruption within that group, possibly triggered by the timing of the 2009 recession in their formative years.

I do this by using the American Community Survey, conducted annually from 2001 to 2015, which asks women if they have had a birth in the previous year. The samples are very large, with all the data points shown including at least 8,000 women and most including more than 60,000.

The figure below shows the birth rates by age for women across six five-year birth cohorts. The dots on each line mark the age at which the midpoint of each cohort reached 2009. The oldest three groups are supposed to be “Generation X.” The three youngest groups shown in yellow, blue, and green — those born 1980-84, 1985-89, and 1990-94 — are all Millennials according to the common myth. But look how their experience differs!

cohort birth rates ACS.xlsx

Most of the fertility effect on the recession was felt at young ages, as women postponed births. The oldest Millennial group was in their late twenties when the recession hit, and it appears their fertility was not dramatically affected. The 1985-89 group clearly took a big hit before rebounding. And the youngest group started their childbearing years under the burden of the economic crisis, and if that curve at 25 holds they will not recover. Within this arbitrarily-constructed “generation” is a great divergence of experience driven by the timing of the great recession within their early childbearing years.

You could collapse these these six arbitrary birth cohorts into two arbitrary “generations,” and you would see some of the difference I describe. I did that for you in the next figure, which is made from the same data. And you could make up some story about the character and personality of Millennials versus previous generations to fit that data, but you would be losing a lot of information to do that.

cohort birth rates ACS.xlsx

Of course, any categories reduce information — even single years of age — so that’s OK. The problem is when you treat the boundaries between categories as meaningful before you look at the data — in the absence of evidence that they are real with regard to the question at hand.

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Two examples of why “Millennials” is wrong

When you make up “generation” labels for arbitrary groups based on year of birth, and start attributing personality traits, behaviors, and experiences to them as if they are an actual group, you add more noise than light to our understanding of social trends.

According to generation-guru Pew Research, “millennials” are born during the years 1981-1997. A Pew essay explaining the generations carefully explains that the divisions are arbitrary, and then proceeds to analyze data according to these divisions as if are already real. (In fact, in the one place the essay talks about differences within generations, with regard to political attitudes, it’s clear that there is no political consistency within them, as they have to differentiate between “early” and “late” members of each “generation.”)

Amazingly, despite countless media reports on these “generations,” especially millennials, in a 2015 Pew survey only 40% of people who are supposed to be millennials could pick the name out of a lineup — that is, asked, “These are some commonly used names for generations. Which of these, if any, do you consider yourself to be?”, and then given the generation names (silent, baby boom, X, millennial), 40% of people born after 1980 picked “millennial.”

“What do they know?” You’re saying. “Millennials.

Two examples

The generational labels we’re currently saddled with create false divisions between groups that aren’t really groups, and then obscure important variation within the groups that are arbitrarily lumped together. Here is just one example: the employment experience of young men around the 2009 recession.

In this figure, I’ve taken three birth cohorts: men born four years apart in 1983, 1987, and 1991 — all “millennials” by the Pew definition. Using data from the 2001-2015 American Community Surveys via IPUMS.org, the figure shows their employment rates by age, with 2009 marked for each, coming at age 26, 22, and 18 respectively.


Each group took a big hit, but their recoveries look pretty different, with the earlier cohort not recovered as of 2015, while the youngest 1991 group bounced up to surpass the employment rates of the 1987s by age 24. Timing matters. I reckon the year they hit that great recession matters more in their lives than the arbitrary lumping of them all together compared with some other older “generations.”

Next, marriage rates. Here I use the Current Population Survey and analyze the percentage of young adults married by year of birth for people ages 18-29. This is from a regression that controls for year of age and sex, so it can be interpreted as marriage rates for young adults (click to enlarge).


From the beginning of the Baby Boom generation to those born through 1987 (who turned 29 in 2016, the last year of CPS data), the marriage rate fell from 57% to 21%, or 36 percentage points. Most of that change, 22 points, occurred within the Baby Boom. The marriage experience of the “early” and “late” Baby Boomers is not comparable at all. The subsequent “generations” are also marked by continuously falling marriage rates, with no clear demarcation between the groups. (There is probably some fancy math someone could do to confirm that, with regard to marriage experience, group membership by these arbitrary criteria doesn’t tell you more than any other arbitrary grouping would.)

Anyway, there are lots of fascinating and important ways that birth cohort — or other cohort identifiers — matter in people’s lives. And we could learn more about them if we looked at the data before imposing the categories.


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8 years later: Children of the Deported Wonder, “Who Gets A Family?”

Huh. I published this essay, Children of the Deported Wonder, “Who Gets A Family?”, eight years ago today on Huffington Post. I invite you to draw your own conclusions.


A picture I took of some kids.

Children of the Deported Wonder, “Who Gets A Family?”

Over the 10 years up to 2007, the U.S. deported 108,434 adults whose children were U.S. citizens, according to a Department of Homeland Security report [link updated]. The exact number of citizen children left behind in these deportations is unknown, because no one in the government cared to count them. The homeland security of these citizen children does not seem to have been the paramount concern of the U.S. government. Well, maybe excepting 13 of the removed adults, who were deported for “national security and related grounds.” (Altogether, about half were undocumented immigrants and half were deported for criminal violations.)

Either keeping your parents from being dumped over the border isn’t a right Americans enjoy, or someone in power doesn’t really think these kids are American. Or both.

The New York Times quoted an anti-immigration spokesman as saying, “Should those parents get off the hook just because their kids are put in a difficult position? . . . Children often suffer because of the mistakes of their parents.” As if this is unavoidable.

It is true that children suffer for the mistakes of their parents. They also suffer for the policies of their neighbors’ parents, and for the poverty and discrimination their parents experience. Most children lose out to those whose parents have one advantage or another, but the extent of this intergenerational transfer is something we can affect.

One measure of a society’s meritocracy is the level of advantage – and disadvantage – passed from parents to children. Whatever your own ability and effort, equal opportunity only exists to the extent that your parents’ problems are not your own.

If children get burned by their origins, adults also face unequal opportunities to originate the families they want. Just as deported immigrant workers are denied the right to parent their children, poor parents can’t get Medicaid to cover their infertility treatments – though it might pay for some Viagra. (Even without fertility coverage, economists worry that just providing prenatal care and other services to poor women might increase their tendency to have children. Now that would be a shame.)

Having a family – your family – is not a right of American citizenship, for parents or children. And in a society where intergenerational privilege and disadvantage are deeply entrenched, the denial of that right is a cornerstone of our system of inequality.

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How low is too low for divorce?

I have no idea, but I raised the possibility that there is a too low in this essay for Timeline.

I wrote:

We should ask whether falling divorce rates are always a good thing. Most people getting married would like to think they’ll stay together for the long haul, but what is the right amount of divorce for a society to have?

It seems like an odd question, but divorce really isn’t like crime. Less crime is inarguably good, but we do want some divorces. Otherwise it means people are stuck in bad marriages. If you have no divorce that means even abusive marriages can’t break up. If you have a moderate amount, it means pretty bad marriages can break up but people don’t treat it lightly.

When you put it that way, moderate sounds best. Even as we shouldn’t assume families are always falling apart more than they used to, we should consider the pros and cons of divorce, rather than insisting less is always better.

You can read the whole thing here. In addition to a picture of Donald and Ivana Trump, the piece features my figure:



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Family Demography seminar syllabus


Taipei shopping district / pnc

Here’s my syllabus for Family Demography this semester. Play along at home!

I went for contemporary readings for most subjects, rather than classic readings. I’ll talk about the background myself, and I added an origin/impact analysis assignment, where students dig into the front end of the papers and figure out where they’re coming from – and then follow the citations to see where they went (if they’re not brand new). If I had my stuff together I’d have a better list of background readings as a supplement, but we have comprehensive exam readings lists for that, too. Anyway, we’ll see how that works.

I hope this is useful. Feel free to add your own supplemental readings and suggestions in the comments.


This course is designed to build knowledge on the key theories, empirical patterns, and contemporary debates in the study of family demography, with lesser attention to methodology. (Some students previously took my seminar Families and Modern Social Theory; those who haven’t may find interesting background material in that syllabus: http://www.terpconnect.umd.edu/~pnc/FMST-syllabus.pdf.)

Students are expected to read assigned material and write a response paper each week, and a summary essay or research report at the end of the semester. In addition, each student will do an origin/impact analysis of one of the assigned readings and make a brief presentation to the class. Evaluation will be based on participation, weekly writings, the presentation, and the final paper.

Universal learning

The principle of universal learning means that our classroom and our interactions be as inclusive as possible. Your success in this class is important to me. If there are circumstances that may affect your performance in this class, please let me know as soon as possible so that we can work together to meet both your needs and the requirements of the course. Students with particular needs should contact the UMD Disability Support Service (http://www.counseling.umd.edu/DSS/), which will forward the necessary information to me. Please do it now instead of waiting till late in the semester.


Academic integrity. Students must be familiar with the UMD Code of Academic Integrity (http://president.umd.edu/sites/president.umd.edu/files/documents/policies/III-100A.pdf). In this course there is zero tolerance for academic dishonesty.

Classroom conduct. Students should not come to class late, as this creates a distraction for those who are participating. If your schedule regularly does not permit you to be in class from beginning to end, do not take the course. Students who need to leave early should sit at the back and leave quietly. Students may not use laptops, tablet computers, or mobile phones in class. If you have a need for keeping your phone handy in class notify the professor in advance for an exception.

Discussion. We will discuss course readings and related material, as well as current events, social issues, and politics. Everyone is free to express personal opinions and disagree with others, including the professor – just raise your hand. All discussion must be polite and respectful, and differences of opinion are tolerated. The professor will work to ensure the classroom is a safe space for all of use to participate freely. Please let me know if you have any concerns or suggestions for accomplishing this.


January 31

Theoretical perspectives in demography

Samek, Diana, Bibiana D. Koh, and Martha A. Rueter. 2013. “Overview of Behavioral Genetics Research for Family Researchers.” Journal of Family Theory & Review 5 (3): 214–33. doi:10.1111/jftr.12013.

Ferree, Myra Marx. 2010. “Filling the Glass: Gender Perspectives on Families.” Journal of Marriage and Family 72(3):420-439.

Elder, Glen H., Jr. 1998. “The Life Course as Developmental Theory.” Child Development 69(1):1-12.

February 7

Demographic transition

Kirk, D. 1996. “Demographic Transition Theory.” Population Studies 50 (3): 361-.

Thornton, Arland. 2001. “The Developmental Paradigm, Reading History Sideways, and Family Change.” Demography 38 (4): 449–65. doi:10.2307/3088311

Balbo, Nicoletta, Francesco C. Billari, and Melinda Mills. 2013. “Fertility in Advanced Societies: A Review of Research.” European Journal of Population 29 (1): 1–38. doi:10.1007/s10680-012-9277-y.

Feng, Wang. 2011. “The Future of a Demographic Overachiever: Long-Term Implications of the Demographic Transition in China.” Population and Development Review 37: 173–90.

February 14

Fertility in poor countries

Yount, Kathryn M., Sarah Zureick-Brown, Nafisa Halim, and Kayla LaVilla. 2014. “Fertility Decline, Girls’ Well-Being, and Gender Gaps in Children’s Well-Being in Poor Countries.” Demography 51 (2): 535–61. doi:10.1007/s13524-014-0282-0.

Feng, Wang, Baochang Gu, and Yong Cai. 2016. “The End of China’s One-Child Policy.” Studies in Family Planning 47 (1): 83–86. doi:10.1111/j.1728-4465.2016.00052.x.

Kravdal, Oystein. 2012. “Further Evidence of Community Education Effects on Fertility in Sub-Saharan Africa.” Demographic Research 27 (November): 646–. doi:10.4054/DemRes.2012.27.22.

Bongaarts, John, and Christophe Z. Guilmoto. 2015. “How Many More Missing Women? Excess Female Mortality and Prenatal Sex Selection, 1970–2050.” Population and Development Review 41 (2): 241–69. doi:10.1111/j.1728-4457.2015.00046.x.

February 21

Second demographic transition

Geist, Claudia. 2017. “Marriage Formation in Context: Four Decades in Comparative Perspective.” Social Sciences 6 (1): 9. doi:10.3390/socsci6010009.

Lesthaeghe, Ron. 2010. “The Unfolding Story of the Second Demographic Transition.” Population and Development Review 36 (2): 211-.

Goldscheider, Frances, Eva Bernhardt, and Trude Lappegard. 2015. “The Gender Revolution: A Framework for Understanding Changing Family and Demographic Behavior.” Population and Development Review 41 (2): 207–+. doi:10.1111/j.1728-4457.2015.00045.x.

Cohen, Philip N. 2011. “Homogamy Unmodified.” Journal of Family Theory & Review 3 (1): 47–51.

February 28

U.S. History

Ruggles. Steven. 2015. “Patriarchy, Power, and Pay: The Transformation of American Families, 1800-2015.” Demography 52: 1797-1823. (His lecture version at PAA.)

Cherlin, Andrew J. 2004. “The Deinstitutionalization of American Marriage.” Journal of Marriage and Family 66 (4): 848–61.

Ruggles, Steven. 2007. “The Decline of Intergenerational Coresidence in the United States, 1850 to 2000.” American Sociological Review 72 (6): 964–89. doi:10.1177/000312240707200606.

Cohen, Philip N. 2014. The Family: Diversity, Inequality, and Social Change. New York: W. W. Norton & Company. Chapter 2, “History.”

March 7

Marriage and social class

Cherlin, Andrew J. 2014. Labor’s Love Lost: The Rise and Fall of the Working-Class Family in America. New York: Russell Sage Foundation.

Cohen, Philip N. 2014. The Family: Diversity, Inequality, and Social Change. New York: W. W. Norton & Company. Chapter 8, “Marriage and cohabitation.”

March 14

Fatherhood: race, class, and multiple-partner fertility

Edin, Kathryn and Timothy Nelson. 2013. Doing the Best I Can: Fatherhood in the Inner City. University of California Press.

March 21

Spring break

March 28

Transition to adulthood

Crosnoe, Robert, and Monica Kirkpatrick Johnson. 2011. “Research on Adolescence in the Twenty-First Century.” Annual Review of Sociology 37:439–60.

Dow, Dawn Marie. 2016. “The Deadly Challenges of Raising African American Boys: Navigating the Controlling Image of the ‘Thug.’” Gender & Society 30 (2): 161–88. doi:10.1177/0891243216629928.

Billari, Francesco C., and Aart C. Liefbroer. 2010. “Towards a New Pattern of Transition to Adulthood?” Advances in Life Course Research 15 (2–3, SI): 59–75. doi:10.1016/j.alcr.2010.10.003.

Ghimire, D. J., W. G. Axinn, S. T. Yabiku, and A. Thornton. 2006. “Social Change, Premarital Nonfamily Experience, and Spouse Choice in an Arranged Marriage Society.” American Journal of Sociology 111 (4): 1181–1218.

April 11

Economic conditions and family outcomes

Sweeney, Megan M., and R. Kelly Raley. 2014. “Race, Ethnicity, and the Changing Context of Childbearing in the United States.” Annual Review of Sociology 40:539–58.

Currie, Janet, and Hannes Schwandt. 2014. “Short- and Long-Term Effects of Unemployment on Fertility.” Proceedings of the National Academy of Sciences 111 (41): 14734–39. doi:10.1073/pnas.1408975111.

Schneider, Daniel, Kristen Harknett, and Sara McLanahan. 2016. “Intimate Partner Violence in the Great Recession.” Demography 53 (2): 471–505. doi:10.1007/s13524-016-0462-1.

April 18

Policy, race, and nonmarital births

England, Paula. 2016. “Sometimes the Social Becomes Personal: Gender, Class, and Sexualities.” American Sociological Review 81 (1): 4–28.

Cohen, Philip N. 2015. “Maternal Age and Infant Mortality for White, Black, and Mexican Mothers in the United States.” Sociological Science 3 (January): 32–38.

Geronimus, Arline T. 2003. “Damned If You Do: Culture, Identity, Privilege, and Teenage Childbearing in the United States.” Social Science & Medicine 57 (5): 881–93.

Cohen, Philip N. Forthcoming. Enduring Bonds: Families and Modern Inequality, Chapter: “Marriage promotion [Excerpts]” 24pp. [to be provided]

April 25

More U.S. inequality issues

Musick, Kelly, and Robert D. Mare. 2006. “Recent Trends in the Inheritance of Poverty and Family Structure.” Social Science Research 35 (2): 471–99. doi:10.1016/j.ssresearch.2004.11.006.

Western, Bruce, and Christopher Wildeman. 2009. “The Black Family and Mass Incarceration.” Annals of the American Academy of Political and Social Science 621 (1): 221–242.

Two selections from Families in an Era of Increasing Inequality (2015) edited by Paul R. Amato, Alan Booth, Susan M. McHale, and Jennifer Van Hook, 3–23. National Symposium on Family Issues 5. Springer International Publishing.

McLanahan, Sara, and Wade Jacobsen. “Diverging Destinies Revisited.”

Cohen, Philip N. 2015. “Divergent Responses to Family Inequality.”

May 2

Family structure and child wellbeing

Regnerus, Mark. 2012. “How Different Are the Adult Children of Parents Who Have Same-Sex Relationships? Findings from the New Family Structures Study.” Social Science Research 41 (4): 752–70. doi:10.1016/j.ssresearch.2012.03.009.

Rosenfeld, Michael J. 2015. “Revisiting the Data from the New Family Structure Study: Taking Family Instability into Account.” Sociological Science 2 (September): 478–501. doi:10.15195/v2.a23.

Cohen, Philip N. Forthcoming. Enduring Bonds: Families and Modern Inequality, Chapter: “Marriage equality in social science and the courts.” 19pp. [to be provided]

Gates, Gary J. 2015. “Marriage and Family: LGBT Individuals and Same-Sex Couples.” Future of Children 25(2):67-87.

May 9

Divorce, Remarriage and Stepfamilies

Amato, Paul R. 2010. “Research on Divorce: Continuing Trends and New Developments.” Journal of Marriage and Family 72(3):650-666.

Kennedy, Sheela, and Steven Ruggles. 2014. “Breaking Up Is Hard to Count: The Rise of Divorce in the United States, 1980–2010.” Demography 51 (2): 587–98. doi:10.1007/s13524-013-0270-9.

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

Anderson, Lydia R. 2016. “Divorce Rate in the U.S.: Geographic Variation, 2015.” National Center for Marriage and Family Research. http://www.bgsu.edu/ncfmr/resources/data/family-profiles/anderson-divorce-rate-us-geo-2015-fp-16-21.html.

Cohen, Philip N. 2016. “Life Table Says Divorce Rate Is 52.7%.” Family Inequality. June 8. https://familyinequality.wordpress.com/2016/06/08/life-table-says-divorce-rate-is-52-7/.

Bennett, Neil G. 2017. “A Reflection on the Changing Dynamics of Union Formation and Dissolution.” Demographic Research 36 (12): 371–90. doi:10.4054/DemRes.2017.36.12.



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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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Breaking: Trump has terrible judgment

Say what you want about his decrepit values, noxious personality, and authoritarian political views, but we should all be able to agree Trump has terrible judgment. Also that he doesn’t care about little people. And a lot of people who like him are deplorable.

This time, the story behind the story.

On Monday night Trump pulled himself away from MLK reverie long enough to notice that CNN was doing a show about his daughter, Ivanka. He saw someone praising her on Twitter and copied his message. He wrote:


The Daily News captured the original tweet, by Lawrence Goodstein (drgoodspine) revealing that Trump had lowercased “Great” and added a comma after it, but failed to notice that the good Dr. Goodstein got Ivanka’s Twitter handle wrong.


Hilarity ensued, and the story focused on how the real @Ivanka responded by telling him to pay attention to climate change.

I haven’t seen any media focus on Goodstein, apparently because he deleted his account right away. But I happened to notice it in time, and screen-grabbed a few tweets. The point of my showing them is: Trump has no idea what he’s doing or how it affects real (little) people, and doesn’t care anyway. Secondarily, the guy is awful and any reasonable public figure would want nothing to do with him – at least as he represented himself on Twitter – and certainly wouldn’t give him a platform of millions on Twitter. (I didn’t notice how many followers Goodstein had, but I remember thinking it wasn’t many.)

I didn’t save all of his tweets, but I got a few that I considered representative, because it was immediately apparent that a lot of what he did on Twitter was call people assholes, including President Obama, “Norm” Chomsky, and a lot of journalists, often by juxtaposing their face with a picture of an asshole. Take a look (click to view individual images):


Who cares? I don’t care about Goodstein. He claims to have spent a year treating 9/11 victims in New York, and for all I know he’s a good chiropractor. So he loves Trump – not surprising given what an unpleasant person he seems to be. The point is about Trump’s bad behavior. Some Trump fans live for a retweet from the great Tiny Hands. Maybe Goodstein did, too. But he apparently wasn’t really prepared for that big of a spotlight to shine on his nasty asshole-screaming habit (or maybe he was fine with it and it was a Trump goon squad that made him shut it down to prevent embarrassment – to Trump.)

And who shouts to millions of people without the slightest consideration of the context and content of what they’re shouting? Trump has had worse tweets, and done many much worse things, but his platform is actually still growing, and the power he has is increasing. He should not treat individuals like this. Before he turns someone’s life inside out, someone should check it out. Can the person handle it? Do they want to? Obama has had some wonderful moments with random citizens, but I don’t think they started with him landing Marine 1 on their lawn with the press pool and no advance people.

Finally, there are potential security implications, obviously, when a president acts so impulsively. One thing to notice is that Goodstein’s handle, @drgoodspine, was snapped up by someone, and they now have a potentially damaging platform as well, as Trump’s tweet is still out there.

Anyway, I just wrote this to help keep the record of bad judgment complete, seeing that no one was reporting it.

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