COVID-19 mortality rates by race/ethnicity and age

Why are there such great disparities in COVID-19 deaths across race/ethnic groups in the U.S.? Here’s a recent review from New York City:

The racial/ethnic disparities in COVID-related mortality may be explained by increased risk of disease because of difficulty engaging in social distancing because of crowding and occupation, and increased disease severity because of reduced access to health care, delay in seeking care, or receipt of care in low-resourced settings. Another explanation may be the higher rates of hypertension, diabetes, obesity, and chronic kidney disease among Black and Hispanic populations, all of which worsen outcomes. The role of comorbidity in explaining racial/ethnic disparities in hospitalization and mortality has been investigated in only 1 study, which did not include Hispanic patients. Although poverty, low educational attainment, and residence in areas with high densities of Black and Hispanic populations are associated with higher hospitalizations and COVID-19–related deaths in NYC, the effect of neighborhood socioeconomic status on likelihood of hospitalization, severity of illness, and death is unknown. COVID-19–related outcomes in Asian patients have also been incompletely explored.

The analysis, interestingly, found that Black and Hispanic patients in New York City, once hospitalized, were less likely to die than White patients were. Lots of complicated issues here, but some combination of exposure through conditions of work, transportation, and residence; existing health conditions; and access to and quality of care. My question is more basic, though: What are the age-specific mortality rates by race/ethnicity?

Start tangent on why age-specific comparisons are important. In demography, breaking things down by age is a basic first-pass statistical control. Age isn’t inherently the most important variable, but (1) so many things are so strongly affected by age, (2) so many groups differ greatly in their age compositions, and (3) age is so straightforward to measure, that it’s often the most reasonable first cut when comparison groups. Very frequently we find that a simple comparison is reversed when age is controlled. Consider a classic example: mortality in a richer country (USA) versus a poorer country (Jordan). People in the USA live four years longer, on average, but Americans are more than twice as likely to die each year (9 per 1,000 versus 4 per 1000). The difference is age: 23% of Americans are over age 60, compared with 6% of Jordanians. More old people means more total deaths, but compare within age groups and Americans are less likely to die. A simple separation by age facilitates more meaningful comparison for most purposes. So that’s how I want to compare COVID-19 mortality across race/ethnic groups in the USA. End tangent.

Age-specific mortality rates

It seems like this should be easier, but I can’t find anyone who is publishing them on an ongoing basis. The Centers for Disease Control posts a weekly data file of COVID-19 deaths by age and race/ethnicity, but they do not include the population denominators that you need to calculate mortality rates. So, for example, it tells you that as of December 5 there have been 2,937 COVID-19 deaths among non-Hispanic Blacks in the age range 30-49, compared with 2,186 deaths among non-Hispanic Whites of the same age. So, a higher count of Black deaths. But it doesn’t tell you there are 4.3-times as many Whites as Blacks in that category. So a much higher mortality rate.

On a different page, they report the percentage of all deaths in each age range that have occurred in each race/ethnic group, don’t include their percentage in the population. So, for example, 36% of the people ages 30-39 who have died from COVID-19 were Hispanic, and 24% were non-Hispanic White, but that’s not enough information to calculate mortality rates either. I have no reason to think this is nefarious, but it’s clearly not adequate.

So I went to the 2019 American Community Survey (ACS) data distributed by to get some denominators. These are a little messy for two main reasons. First, ACS is a survey that asks people what their race and ethnicity are, while death counts are based on death certificates, for which the person who has died is not available to ask. So some people will be identified with a different group when they die than they would if they were surveyed. Second, the ACS and other surveys allow people to specify multiple races (in addition to being Hispanic or not), whereas death certificate data generally does not. So if someone who identifies as Black-and-White on a survey dies, how will the death certificate read? (If you’re very interested, here’s a report on the accuracy of death certificates, and here are the “bridges” they use to try to mash up multiple-race and single-race categories.)

My solution to this is make denominators more or less the way race/ethnicity was defined before multiple race identification was allowed. I put all Hispanic people, regardless of race, into the Hispanic group. Then I put people who are White, non-Hispanic, and no other race into the White category. And then for the Black, Asian, and American Indian categories, I include people who were multiple race (and not Hispanic). So, for example, a Black-White non-Hispanic person is counted as Black. A Black-Asian non-Hispanic person is counted as both Black and Asian. Note I did also do the calculations for Native Hawaiian and Other Pacific Islanders, but those numbers are very small so I’m not showing them on the graph; they’re on the spreadsheet. Note also I say “American Indian” to include all those who are “non-Hispanic American Indian or Alaska Native.”

This is admittedly crude, but I suggest that you trust me that it’s probably OK. (Probably OK, that is, especially for Whites, Blacks, and Hispanics. American Indians and Asians have higher rates of multiple-race identification among the living, so I expect there would be more slippage there.)

Anyway, here’s the absolutely egregious result:

This figure allows race/ethnicity comparisons within the five age groups (under 30 isn’t shown). It reveals that the greatest age-specific disparities are actually at the younger ages. In the range 30-49, Blacks are 5.6-times more likely to die, and Hispanics are 6.6-times more likely to die, than non-Hispanic Whites are. In the oldest age group, over 85, where death rates for everyone are highest, the disparities are only 1.5- and 1.4-to-1 respectively.

Whatever the cause of these disparities, this is just the bottom line, which matters. Please note how very high these rates are at old ages. These are deaths per 100,000, which means that over age 85, 1.8% of all African Americans have died of COVID-19 this year (and 1.7% for Hispanics and 1.2% for Whites). That is — I keep trying to find words to convey the power of these numbers — one out of every 56 African Americans over age 85.

Please stay home if you can.

A spreadsheet file with the data, calculations, and figure, is here:

Race and racism in America (video)

In my Social Problems class we’re spending the next few weeks on race, racial inequality, and racial politics. Step one is this lecture on race and racism.

After a tangent on racial identity, idealism and its enemies, I address biology and race, describing the classic racist racial categories in relation to vast human diversity in Africa and the world overall, with discussion of biological evolution and the sources of human variation. Then I turn to the US and discuss social definition and self-definition, race versus ethnicity, definitions of racism and discrimination, and how the Census Bureau measures US race and ethnicity, before summarizing current and projected race/ethnic composition. And I used the new Zoom feature where your PowerPoint slides are the virtual background (which is harder than it looks because your image isn’t mirrored while you speak!).

It’s 35 minutes. The slides are here, CC-BY: To see all my videos, visit my YouTube channel.

Race/ethnic intermarriage trends, 2008-2018

Rising, with gender differences.

Since 2008 the American Community Survey has been asking respondents whether they got married in the previous 12 months. Using the race/ethnicity of spouses (when they are living together), you can estimate the proportion of new marriages that cross racial/ethnic lines.

Defining such “intermarriage” is not as simple as it sounds. Some people have multiple racial or ethnic identities. Some people marry across national-origin lines within panethnic groups (e.g., Mexicans marrying Puerto Ricans). Is a Black+White Dominican marrying a White Mexican, or a Black+White person marrying a Black person, “intermarriage”? In these estimates I drop people who are not Hispanic and specified more than one race, then combine Hispanic origin and race into one, mutually exclusive 5-category variable: White, Black, American Indian, Asian/Pacific Islander, Hispanic (of any race). In other words, intrapanethic marriage (Mexicans marrying Puerto Ricans, or Filipinos marrying Koreans) is not intermarriage. I’m not saying this is the best way; it combines conventional categories with convenience. I combine same-sex and different-sex marriages.

To present the results, I separate men and women (you’ll see why), and estimate predicted probabilities of intermarriage at the mean of controls for age and age-squared, using logistic regression with normalized weights. My Stata code is on the Open Science Framework; help yourself. (I previously did something very similar for states and metro areas.)

The results are in figures, with each race/ethnic group presented on its own scale (check the y-axes). I don’t present American Indians because the samples are small (about half the API sample) and the multirace group is large.


Click the images to enlarge

white intermarriageblack intermarriage

api intermarriage

hispanic intermarriage

Health disparities and COVID-19 lecture

Update: I posted a revised version of this lecture, with new facts, here.

For Social Problems, an introductory level sociological course, I gave a lecture that combines an introduction to health disparities and some issues of disparate impacts of the COVID-19 pandemic. It’s 23 minutes. Some slides and links below.

The first half describes the theory of fundamental causes (as I understand it), and has some basic health disparities examples. Here are some graphs:

Then I apply some of the ideas to what we know about COVID-19 impacts, and likely problem areas. Here is some of that:

The PowerPoint slides, with references in the notes, is up here:

White children are 2.7-times more likely than Black children to live with a parent who has a PhD

For a reflection Amy Harmon was working on, a followup to her article on the experience of Black mathematicians in American academia, I took a shot at the question: How many children have parents with PhDs?

The result was the highlighted passage (17 words and a link!) in her piece:

[all the racial biases that contribute to Black underrepresentation include] the well-documented racial disparities in public-school resources, the selection of students for gifted programs — and the fact that having a parent with a Ph.D. is helpful to getting one in math, while black children are less than half as likely as white children to live with such a parent.

To get there: I used data from the U.S. Census Bureau via The 1990 5% Public Use Microdata Sample (decennial census); and the 2000, 2010, and 2017 American Community Surveys.

I coded race/ethnicity into four mutually-exclusive categories: Single-race White, Black, and Asian/Pacific Islander (API); and Hispanic (including those of any race). I dropped from the analysis non-Hispanic children with multiple races reported, and American Indian / Alaska Natives (for whom about 0.5 percent lived with a PhD parent in 2017).

IPUMS made a tool that attaches values of parents’ variables to children with whom they share a household. I used that to calculate the highest level of education of each child’s coresident parents. In the Census data, children may have up to two parents present (which may be of the same sex in 2010 and 2017). Children living with no parent in the household were not included.

This let me calculate the percentage of children living (at the moment of the survey) with one or more parents who had a PhD. For each of the four groups the percentage of children living with a parent who has a PhD roughly doubled between 1990 and 2017. API children had the highest chance of living with a PhD parent, reaching 6.8 percent in 2017. The percentages for the other groups were: Whites, 2.7 percent; Blacks, 1.0 percent; and Hispanics, 0.7 percent:


The 2.7% for White children, versus, 1.0% for Black children, is the basis for her statement above.

Details (including the whole parents’ education distribution), data, codebook, and code, are available on the Open Science Framework at: under CC-BY 4.0 license.

Math bias

Both of Amy’s pieces are important reading for academics in many disciplines, including sociology, to reflect on the experience of Black colleagues in the environments we inherit and reproduce.

With regard to math, Amy points out that Black exclusion is not just about denying economic opportunity, it’s also about denying the public the benefits of all the lost Black math talents — and about denying Black potential mathematicians the joy and satisfaction of a passion for math realized.

As Daniel Zaharopol, the director of a program for mathematically talented low-income middle-school students, put it when I interviewed him for a 2017 article: “Math is beautiful, and being a part of that should not be limited to just some people.”

And Amy makes a good case that math bias and its outcomes contribute directly to racism much more broadly:

Some misguided people claim that there are not many black research mathematicians because African-Americans are not as intelligent as other races. These people, whom I have reported on for other stories in recent months, almost invariably use mathematical accomplishment as their yardstick for intelligence. They note that no individuals of African descent have won the Fields Medal, math’s equivalent of the Nobel Prize. They lack any genetic evidence to explain the gap in average I.Q. scores between white and black Americans that they cite as the basis of their belief, or reason to think that a genetic trait would be impervious to social or educational intervention, or that high I.Q. is key to math ability, which Timothy Gowers, a 1998 Fields medalist, has attributed largely to “the capacity to become obsessed with a math problem.”

But I have been reporting on these topics for several years, and I am acutely aware that math prowess factors heavily into the popular conception of intelligence. There’s a vicious cycle at work: The lack of African-American representation in math can end up feeding pernicious biases, which in turn add to the many obstacles mathematically talented minorities face. Which was one more reason it seemed especially important to hold up to the light all the racial biases that contribute to that underrepresentation.

Survey says 23% of Whites think Whites are more intelligent than Blacks

In response to a request from New York Times reporter Amy Harmon, I used the General Social Survey (GSS) to address the question: “How Many Whites think Whites are more intelligent than Blacks?”

She made the request as part of her research for this story about how White supremacists are selectively manipulating genetics research, under the banner of “race realism,” to spread their ideas. This analysis didn’t end up in her story, but I put the tables and a brief write-up, with the code, here:

GSS asks about both Whites and Blacks: “Do people in these groups tend to be unintelligent or tend to be intelligent?” The responses were coded on a seven-point scale from “unintelligent” to “intelligent.” Without asking people to make a comparison, then, the survey allows us to identify people who rate White intelligence higher than Black intelligence.

For a contemporary estimate, I pooled three surveys (2012, 2014, and 2016). You can see how Whites rated the intelligence of Blacks and Whites in this table. Cells on the diagonal show Whites who rated Blacks and Whites equally. Cells below the diagonal show the percentages of Whites who rated White intelligence higher.


The table shows that 23 percent of Whites assess the intelligence of Whites as greater than the intelligence of Blacks, according to the General Social Survey, compared with 8 percent who said the reverse. This 23 percent is down from more than 50 percent in 1990, but only a few points lower than it was a decade ago. The assessment that Whites are more intelligent than Blacks is more common among male, older, less formally educated, and conservative Whites, and (in multivariate models only) among Democrats compared to Republicans. Here are the marginal results from a linear regression model predicting whether Whites think Whites are more intelligent than Blacks.


This is just one slice of racist beliefs as told to survey takers. In a previous analysis, Sean McElwee and I showed that the tendency of Whites to describe Blacks as violent and lazy was more common among Trump supporters, and Republicans generally, but a substantial minority of Democrats expressed those views as well. Racism, in its structural as well as interpersonal forms, is a lot bigger and more complicated than expressed beliefs on a survey, but I think it’s useful to analyze patterns like this as well.

Unequal marriage markets for Black and White women

Joanna Pepin and I have posted a new paper titled, “Unequal marriage markets: Sex ratios and first marriage among Black and White women.” In the paper, we find that the marriage markets of Black and White women are very different, with Black women living in metropolitan areas that have many fewer single men than White women do. And, in a regression model with other important predictors of marriage, this unmarried sex ratio is strongly associated with the odds of marrying.

We count this as evidence on the side of “structure” over “culture” in the debates over the decline in marriage. Here’s the main result, showing Black and White women in 172 metro areas (scaled for size), and the difference in sex ratios (the horizontal spread), the difference in marriage rates (the vertical spread), and the statistical effect of sex ratios on marriage (the slopes).


In a nutshell: As you move from left to right, there are more men, and higher odds of marriage. And almost all the White women are up and to the right compared with the Black women. One implication is that this could be one reason why marriage promotion programs in the welfare system aren’t working.

There are a couple of noteworthy innovations here. First, we used the American Community Survey marital events data, which is marriage happening (did you get married in the last year?) rather than just existing (are you married?). This is a better way to assess what might influence marriage. Second, young people, especially single young people who might be getting married, move around a lot. So what is their marriage market? It’s impossible to say exactly, but we define it as the metro area where they lived one year earlier, rather than just where they live now. (This is especially important because the people who move may move because they just got married.)

The paper is on SocArXiv, where if you follow the links you get to the project page, where we put most of the data and code. The paper is under review now, and we’d love to know if you find any mistakes or have any suggestions.

(This began with a blog post four years ago in which I critiqued a NYT Magazine piece by Anne Lowrie about using marriage to cure poverty. Then we presented a first pass at the American Sociological Association in 2014, and I put some of the descriptive statistics in my textbook, and we made a short video out of it, in which I said, “So, larger social forces — the economy, job discrimination, incarceration policies, and health disparities — all impinge on the ability of individuals to shape their own family lives.” Along the way, I presented some about it here and there, while thinking of new ways to measure marriage inequalities.)

Teaching Black family history in sociology, student resistance edition

There is an amazing story from a family sociology class at the University of Tennessee. I don’t know the whole chronology of the reports, but I read pieces from As It Happens, BET, the local news. The gist of it is that there was an ambiguous quiz question about Black slave families, and when a Black student named Kayla Renee Parker complained, it led to her making a rebuttal presentation to the class, and then the White instructor, Judy Morelock, going on an abusive, racist social media rant and getting fired.

Before the details, my conclusions:

  • Good test questions are important, and as a teacher it’s OK to admit you’re wrong or there is ambiguity.
  • Two things are true: Black families were devastated by slavery and as a generalization most Black children under slavery lived with both parents.
  • There is a line, but not a straight line, between Black families under slavery and those under today’s system of racial domination.
  • Students who do research, honestly engage the material, and bring passionate or political arguments to class should have their courage and commitment encouraged, not punished.
  • Some White people who say they are against racism, and maybe even are against racism, are also racist and hate students.
  • Social media is public, so expect consequences.

The story, and then my approach, follows.

The quiz

Here is the question at issue:

Historical research on African-American families during slavery shows that:

A) Family ties weren’t important in African cultures where the slaves ancestors originated; consequently, family bonds were never strong among slaves.

B) Two-parent families were extremely rare during the slave period.

C) Black family bonds were destroyed by the abuses of slave owners, who regularly sold off family members to other slave owners.

D) Most slave families were headed by two parents.

Parker chose C, but Morelock said the correct answer is D. In a back and forth that Parker put on her Facebook page, she pointed out that the textbook talked about “disruption of families through sale of family members,” and Morelock countered that “bonds were maintained among family members who were geographically separated” referring to people passing information between plantations. These are long-running and unsettled issues in the historical scholarship. If you revise answer C to read “bonds were often destroyed” then it is obviously true. If you take a legalistic approach you could say, “family bonds were destroyed” means all bonds, so C is incorrect. This is not a good argument for a teacher to have. Correct the ambiguity, figure out how to handle the points, take it as a teaching opportunity, and move on.

In fact, there appears to have been one good outcome, which was Parker making a very good presentation to the class (video in the As It Happens story). If that was the end of it, we never would have heard. Maybe it’s good that it wasn’t the end of it, though, because when Morelock’s Facebook posts came out we might agree it’s just as well that the incident led to her being fired. The posts are in the BET story, and include Morelock calling Parker (thought not naming her), “ignorant simple-minded,” and threatening to ruin her reputation after the end of the semester, specifically saying, “I will post her name, her picture, and her bio on Facebook, Twitter, Instagram, and Linkedin. Count on it.” Wow. (She also says Parker was spreading “venomous rumors” about her, which I don’t see reported.)

Many teachers complain about their students on Facebook. If you have reasonable complaints, don’t compromise their identities, don’t reveal or advocate unprofessional or vindictive behavior, and don’t be really racist, I think this is ethically defensible. It’s like a teaching workshop, or talking about your job in the staff lounge. But it’s risky and if you screw up you can get fired (which might or might not be a good thing).

The key thing is always, “If there was a hidden camera here or someone hacked my account, would I be able to defend my behavior?” If the answer is yes, you might still be taking a risk to talk about students, but at least you can live with yourself.

Anyway, as far as what I see in the classroom video and Facebook post of her email exchange, I have nothing but kudos for Parker although I might argue with her a little, too. If she did bad things elsewhere, she shouldn’t have.

Classroom exchange

In Parker’s presentation, she quotes Frederick Douglass saying it was “common custom” where he was born “to part children from their mothers from a very early age.” This is good evidence in favor of Answer C. Obviously experiences varied dramatically across the slave system and over time. Throwing down over a generalization like “most” is not really worth it.

She added, “We continue to see those impacts today and that’s why I believe that family bonds were destroyed.” She says Morelock told her she can’t teach by anecdotes, and she countered that we have to pay attention to the stories of real people affected. This is a really good argument to have, in theory.

Parker recommends The New Jim Crow, and Slavery by Another Name, and she says of the present “it’s by a different name, it’s still slavery in itself. … Slavery is still continuing to destroy the Black family” because of the “prison industrial complex.” She cites an article by Rose Brewer, “Black Families Imperiled by Growth of Nation’s Prisons Industrial Complex.”

Finally Parker says Morelock recommended some books, one of which was a 1998 edition of Minority Families in the United States, by Ronald Taylor, which she said was good but should be more current.

It’s really an excellent presentation. If you care about educating students, this would make you happy (again, not knowing what else may have happened off camera). At the end Parker takes questions, and Morelock pipes up, saying in part (my transcript):

I don’t have a lot of recent books, because the publishers just don’t send us books the way they used to. And I’ve been using [Andrew] Cherlin [Public and Private Families] for many, many years, the book you have in this course. He says the same thing, and that book is in its seventh edition. If there had been additional sociological research since he wrote that book I would think that it would appear in it, but it doesn’t. So I have to go by what my discipline shows, and I understand no matter how much I revere and respect a historical figure like Frederick Douglass, who was absolutely one of the bravest, most articulate persons of his generation, and highly respected, I still have to go with what has been done systematically, the kind of systematic methods that did not exist at that time, when sociology was still in its infancy. So, in the 70s, you know, the research that was done, with historical documents, on Black families demonstrated that people forged bonds, this is written by sociologist Ronald Taylor, he also happens to be African American, I don’t think he would try to minimize the effects of slavery, which I never ever ever would myself, and he talks about studies here [she quotes Taylor on the strong bonds in Black families, and how they maintained them even when they were separated] … Nonetheless, as I said, no one has to accept the sociological point of view. All students in my class, as is always the case, are free to make up their own minds, in fact I encourage it, and I always encourage you to do as Kayla did, do more research, find out more information about a topic, and come to your own conclusions.

Aside from the giant red flag of calling Frederick Douglass “articulate,” this is a reasonable argument. Although it’s sad that Morelock doesn’t keep up with the literature, and her reliance on authority rather than reason and analysis is bad, the truth is her facts are pretty current. Even though she’s racist, it’s not her take on the history that makes her racist. The prison industrial complex is important but it’s not the same thing as slavery breaking up families, it’s a different but related thing. (Incidentally, Cherlin has a good newer book about working class families that addresses some of this; my review is here.)

It’s not surprising we’ve been arguing about this for a century or so. It’s complicated. Here is the trend, back to 1880, in the proportion of Black children ages 0-14 living with married parents. There are issues with the data and measurement, but this basic pattern holds: the share of Black children living with two married parents increased after the end of slavery, and fell a lot more later:

black children married parents 1880-2015

Of course, some students would also get mad if you said, “slavery destroyed all Black families,” which isn’t true either. I don’t agree with the first part of the BET headline, “Professor Denies Slavery Destroyed Black Families And Threatens Student Who Called Her Out,” but because the second part is true I have no interest in defending her.

My version

Anyone who teaches this material should wrestle with this. Here’s what I have in the first edition of my book, in the history chapter (there is much more current material in the subsequent chapter on race and ethnicity). I would be happy to hear your response to this:

Families Enslaved

African families had gone through their own transitions, of course, of a particularly devastating nature. From the arrival of the first slaves in Jamestown in 1619 until the mid-1800s, Africans were forcibly removed from their homelands in western and central Africa and subjected to the unspeakable horrors of the Middle Passage aboard slave ships, slave auctions, and ultimately the hardships of plantation labor in the American South (as well as in the Caribbean and South America). Because they were thrown together from diverse backgrounds, and because their own languages and customs were suppressed by slavery, we do not know how much of slave family life was a reflection of African traditions and how much was an adaptation to their conditions and treatment in America (Taylor 2000).

But there is no doubt that family life was one of the victims of the slave system. The histories that have come down to us feature heart-wrenching stories of family separation, including diaries that tell of children literally ripped from their mothers’ arms by slave traders, mothers taking poison to prevent themselves from being sold, and parents enduring barbaric whippings as punishment for trying to keep their families together (Lerner 1973). In fact, most slaves only had a given name with no family name, which made the formation and recognition of family lineages difficult or impossible (Frazier 1930). Slave marriage and parenthood were not legally recognized by the states, and separation was a constant threat. Any joy in having children was tempered by the recognition that those children were the property of the slave owner and could be sold or transferred away forever.

Nevertheless, most slaves lived in families for some or all of their lives. Most married (if not legally) and had children in young adulthood, and most children lived with both parents. This was especially the case on larger plantations rather than small farms, because slaves could carve out some protection for community life if they were in larger groups, and husbands and wives were more likely to remain together (Coles 2006). Even if they had families, however, African Americans for the most part were excluded from the emerging modern family practices described in the next section until after slavery ended.

Relevant references:

Coles, Roberta L. Race and Family: A Structural Approach. 2006. Thousand Oaks, CA: Sage.

Frazier, E. Franklin. 1930. “The Negro Slave Family.” The Journal of Negro History 15(2):198–259.

Lerner, Gerda. 1973. Black Women in White America: A Documentary History. New York: Vintage Books.

Taylor, Ronald L. 2000. “Diversity within African American Families.” In Handbook of Family Diversity, edited by David H. Demo, Katherine R. Allen, and Mark A. Fine, pp. 232–251. New York: Oxford University Press.

And in our teaching materials, we address it this way, with a multiple choice question:

Most African American slave children lived with: A. grandparents. B. unrelated adults.  C. one parent. D. both parents [D is correct].

And an essay question:

Describe the impact of slavery on the family structure of African Americans throughout U.S. history.

Answer guide: Students should address the lost customs and languages of diverse Africans brought as slaves. Social scientists are often unsure which of the resulting cultural features of African American family life are held over from African traditions and which are adaptations to slavery. Family lineage was difficult or impossible to trace. Separation of parents and children was common. After the Civil War, African American families were legally recognized, and some were reunited. Emerging African American families were more egalitarian in gender roles and had strong extended family and kinship networks.

This story has good lessons about a number of things that scare people who teach family sociology (and lots of other people, too): being wrong, being called racist, and getting fired for saying something on Facebook. Good chance to reflect on teaching, which is hard, but also great.

Intermarriage rates relative to diversity

Addendum: Metro-area analysis added at the end.

The Pew Research Center has a new report out on race/ethnic intermarriage, which I recommend, by Gretchen Livingston and Anna Brown. This is mostly a methodological note, which also nods at some other issues.

How do you judge the amount of intermarriage? For example, in the U.S., smaller groups — Asians and American Indians — marry exogamously at higher rates. Is that because they have fewer same-race people to choose from? Or is it because Whites shun them less than they do Blacks, which are also a larger group. To answer this, you can look at the intermarriage rates relative to group size in various ways.

The Pew report gives some detail about different groups marrying each other, but the topline number is the total intermarriage rate:

In 2015, 17% of all U.S. newlyweds had a spouse of a different race or ethnicity, marking more than a fivefold increase since 1967, when 3% of newlyweds were intermarried, according to a new Pew Research Center analysis of U.S. Census Bureau data.

Here’s one way to assess that topline number, which I’ll do by state just to illustrate the variation in the U.S. (and then I repeat this by metro area below, by popular request).*

The American Community Survey (which I download from identified people who married within the previous 12 months, whom I’ll call newlyweds. I use the 2011-2015 combined data file to increase the sample size in small states. I define intermarriage a little differently than Pew does (for convenience, not because it’s better). I call a couple intermarried if they don’t match each other in a five-category scheme: White, Black, Asian/Pacific Islander, American Indian, Hispanic. I discard those newlyweds (about 2%) who are are multiracial or specified other race and not Hispanic. I only include different-sex couples.

The Herfindahl index is used by economists to measure market concentration. It looks like this:

H =\sum_{i=1}^N s_i^2

where si is the market share of firm i in the market, and N is the number of firms. It’s the sum of the squared proportions held by each firm (or race/ethnicity). The higher the score, the greater the concentration. In race/ethnic terms, if you subtract the Herfindahl index from 1, you get the probability that two randomly selected people are in a different race/ethnic group, which I call diversity.

Consider Maine. In my analysis of newlyweds in 2011-2015, 4.55% were intermarried as defined above. The diversity calculation for Maine looks like this (ignore the scale):


So in Maine two newlyweds have a 5.2% chance of being intermarried if you scramble up the marriage applications, compared with 4.6% who are actually intermarried. (A very important decision here is to use the newlywed population to calculate diversity, instead of the single population or the total population; it’s easy to change that.) Taking the ratio of these, I calculate that Maine is operating at 87% of its intermarriage potential (4.55 / 5.23). Maybe call it a diversity-adjusted intermarriage propensity. So here are all the states (and D.C.), showing diversity and intermarriage. (The diagonal line shows what you’d get if people married at random; the two illegible clusters are DC+NY and WA+KS; click to enlarge.)

State intermarriage

How far each state is off the line is the diversity-adjusted intermarriage propensity (intermarriage divided by diversity). Here is is in map form (using maptile):


And here are the same calculations for the top 50 metro areas (in terms of number of newlyweds in the sample). I chose the top 50 by sample size of newlyweds, by which the smallest is Tucson, with a sample of 478. First, the figure (click to enlarge):

State intermarriage

And here’s the list of metro areas, sorted by diversity-adjusted intermarriage propensity:

Diversity-adjusted intermarriage propensity
Birmingham-Hoover, AL .083
Memphis, TN-MS-AR .127
Richmond, VA .133
Atlanta-Sandy Springs-Roswell, GA .147
Detroit-Warren-Dearborn, MI .155
Philadelphia-Camden-Wilmington, PA-NJ-D .157
Louisville/Jefferson County, KY-IN .170
Columbus, OH .188
Baltimore-Columbia-Towson, MD .197
St. Louis, MO-IL .204
Nashville-Davidson–Murfreesboro–Frank .206
Cleveland-Elyria, OH .213
Pittsburgh, PA .215
Dallas-Fort Worth-Arlington, TX .219
New York-Newark-Jersey City, NY-NJ-PA .220
Virginia Beach-Norfolk-Newport News, VA .224
Washington-Arlington-Alexandria, DC-VA- .224
New Orleans-Metairie, LA .229
Jacksonville, FL .234
Houston-The Woodlands-Sugar Land, TX .235
Los Angeles-Long Beach-Anaheim, CA .239
Indianapolis-Carmel-Anderson, IN .246
Chicago-Naperville-Elgin, IL-IN-WI .249
Charlotte-Concord-Gastonia, NC-SC .253
Raleigh, NC .264
Cincinnati, OH-KY-IN .266
Providence-Warwick, RI-MA .278
Milwaukee-Waukesha-West Allis, WI .284
Tampa-St. Petersburg-Clearwater, FL .286
San Francisco-Oakland-Hayward, CA .287
Orlando-Kissimmee-Sanford, FL .295
Boston-Cambridge-Newton, MA-NH .305
Buffalo-Cheektowaga-Niagara Falls, NY .305
Riverside-San Bernardino-Ontario, CA .311
Miami-Fort Lauderdale-West Palm Beach, .312
San Jose-Sunnyvale-Santa Clara, CA .316
Austin-Round Rock, TX .318
Kansas City, MO-KS .342
San Diego-Carlsbad, CA .343
Sacramento–Roseville–Arden-Arcade, CA .345
Minneapolis-St. Paul-Bloomington, MN-WI .345
Seattle-Tacoma-Bellevue, WA .346
Phoenix-Mesa-Scottsdale, AZ .362
Tucson, AZ .363
Portland-Vancouver-Hillsboro, OR-WA .378
San Antonio-New Braunfels, TX .388
Denver-Aurora-Lakewood, CO .396
Las Vegas-Henderson-Paradise, NV .406
Provo-Orem, UT .421
Salt Lake City, UT .473

At a glance no big surprises compared to the state list. Feel free to draw your own conclusions in the comments.

* I put the data, codebook, code, and spreadsheet files on the Open Science Framework here, for both states and metro areas.

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.