Tag Archives: education

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 IPUMS.org: 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: https://osf.io/ry3zt/ 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.

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Equal-education and wife-more-education married couples don’t have sex less often

In my review of Mark Regnerus’s book, Cheap Sex, I wrote: “The book is an extended rant on the theme, ‘Why buy the cow when you can get the milk for free?’ wrapped in a misogynist theory about sexual exchange masquerading as economics, and motivated by the author’s misogynist religious and political views.”

Someone just reposted an old book-rehash essay of Regnerus’s called, “The Death of Eros.” In it he links to my post documenting the decline in sexual frequency among married couples in the General Social Survey. In marriage, Regnerus writes, “equality is the enemy of eros,” before selectively characterizing some research about the relationship between housework and sex. (Here’s a recent analysis finding egalitarian couples don’t have sex less.)

But I realized I never looked at sexual frequency in married couples by the relative education of the spouses, which is available in the GSS. So here’s a quick take: Married man-woman couples in which the wife has equal or more education don’t have sex less frequently.

I modeled sexual frequency (an interval scale from “not at all” = 0 to “4+ times per week” = 6 as a function of age, age-squared, respondent education, respondent sex, decade, and relative education (wife has lower degree, wife has same degree, wife has higher degree). The result is in this figure. Note the means are between 3 (“2-3 times per month”) and 4 (“weekly”). Stata code for GSS below.

death of eros

OK, that’s it. Here’s the code (I prettied the figure a little by hand afterwards):

*keep married people
keep if marital==1

* with non-missing own and spouse education
keep if spdeg<4 & degree<4
recode age (18/29=18) (30/39=30) (40/49=40) (50/59=50) (60/109=60), gen(agecat)
recode year (1970/1979=1970) (1980/1989=1980) (1990/1999=1990) (2000/2008=2000) (2010/2016=2010), gen(decade)
gen erosdead = spdeg>degree
gen equal=spdeg==degree

gen eros=0
replace eros=1 if spdeg<degree & sex==1
replace eros=2 if spdeg==degree
replace eros=3 if spdeg>degree & sex==1

replace eros=1 if spdeg>degree & sex==2
replace eros=3 if spdeg<degree & sex==2

label define de 1 "wife less"
label define de 2 "equal", add
label define de 3 "wife more", add
label values eros de

reg sexfreq i.sex i.agecat i.decade i.degree i.eros [weight=wtssall]
reg sexfreq i.sex c.age##c.age i.degree i.eros##i.decade [weight=wtssall]
margins i.eros##i.decade
marginsplot, recast(bar) by(decade)

Note: On 25 Dec 2018 I fixed a coding error and replaced the figure; the results are the same.


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How conservatism makes peace with Trump


Jonah Goldberg telling his Howard Zinn story to John Podhoretz on CSPAN.

I  wrote a long essay on Jonah Goldberg’s book, Suicide of the West. Because it has graphs and tables and a lot of references, I made it a paper instead of a blog post, and posted it on SocArXiv, here. If you like it, and you happen to edit some progressive or academic publication that would like to publish it, please let me know! I’m happy (not really, but I will) to shorten it. There, I pitched it. Feedback welcome.

First paragraph:

This essay is a review of Suicide of the West: How the Rebirth of Tribalism, Populism, Nationalism, and Identity Politics is Destroying American Democracy, by Jonah Goldberg (Crown Forum, 2018), with a few data explorations along the way. I read the book to see what I could learn about contemporary conservative thinking, especially anti-Trump conservatism. Opposing Trump and the movement he leads is suddenly the most pressing progressive issue of our time, and it’s important not to be too narrow in mobilizing that opposition. Unfortunately, I found the book to be an extended screed against leftism with but a few pages of anti-Trump material grafted in here and there, which ultimately amounts to blaming leftism and immigration for Trump. And that might sum up the state of the anemic conservative movement. Goldberg’s own weak-kneed position on Trump is not resolved until page 316, when he finally concludes, “As much as I hold Trump in contempt, I am still compelled to admit that, if my vote would have decided the election, I probably would have voted for him” (316). In the end, Goldberg has charted a path toward a détente between his movement and Trump’s.

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That thing where you have a lot of little graphs (single-parent edition)

Yesterday I was on an author-meets-critics panel for The Triple Bind of Single-Parent Families: Resources, Employment, and Policies to Improve Well-Being, a new collection edited by Rense Nieuwenhuis and Laurie Moldonado. The book is excellent — and it’s available free under Creative Commons license.

Most of the chapters are comparative, with data from multiple countries. I like looking at the figures, especially the ones like this, which give a quick general sense and let you see anomalies and outliers. I made a couple, too, which I share below, with code.


Here’s an example, showing the proportion of new births to mothers who aren’t married, by education, for U.S. states.  For this I used the 2012-2016 combined American Community Survey file, which I got from IPUMS.org. I created an sample extract that included only women who reported having a child in the previous year, which gives me about 177,000 cases over the five years. The only other variables are state, education, and marital status. I put the raw data file on the Open Science Framework here. Code below.

My first attempt was bar graphs for each state. This is easiest because Stata lets you do graph means with the bar command (click to enlarge).

marst fertyr educ by state

The code for this is very simple. I made a dummy variable for single, so the mean of that is the proportion single. Edcat is a four-category education variable.

gr bar (mean) single [weight=perwt], over(edcat) bar(1,color(green)) yti(“Proportion not married”) by(state)

The bar graph is easy, and good for scanning the data for weird cases or interesting stories. But maybe it isn’t ideal for presentation, because the bars run from one state to the next. Maybe little lines would be better. This takes another step, because it requires making the graph with twoway, which doesn’t want to calculate means on the fly. So I do a collapse to shrink the dataset down to just means of single by state and edcat.

collapse (mean) single psingle=single [fw=perwt], by(state edcat)

Then I use a scatter graph, with line connectors between the dots. I like this better:

marst fertyr educ by state lines

You can see the overall levels (e.g., high in DC, low in Utah) as well as the different slopes (flatter in New York, steeper in South Dakota), and it’s still clear that the single-mother incidence is lowest in every state for women with BA degrees.

Here’s the code for that graph. Note the weights are now baked into the means so I don’t need them in the graph command. And to add the labels to the scatter plot you have to specify you want that. Still very simple:

gr twoway scatter single edcat , xlab(1 2 3 4, valuelabel) yti(“Proportion not married”) lcolor(green) msymbol(O) connect(l) by(state)

Sadly, I can’t figure out how to put one title and footnote on the graph, rather than a tiny title and footnote on every state graph, so I left titles out of the code and I then added them by hand in the graph editor. Boo.

Here’s the full code:

set more off

quietly infix ///
 byte statefip 1-2 ///
 double perwt 3-12 ///
 byte marst 13-13 ///
 byte fertyr 14-14 ///
 byte educ 15-16 ///
 int educd 17-19 ///
 using "[PATHNAME]\usa_00366.dat"

/* the sample is all women who reported having a child in the previous year, FERTYR==2 */
replace perwt = perwt / 100

format perwt %10.2f

label var statefip "State (FIPS code)"
label var perwt "Person weight"
label var marst "Marital status"
label var educd "Educational attainment [detailed version]"

label define statefip_lbl 01 "Alabama"
label define statefip_lbl 02 "Alaska", add
label define statefip_lbl 04 "Arizona", add
label define statefip_lbl 05 "Arkansas", add
label define statefip_lbl 06 "California", add
label define statefip_lbl 08 "Colorado", add
label define statefip_lbl 09 "Connecticut", add
label define statefip_lbl 10 "Delaware", add
label define statefip_lbl 11 "District of Columbia", add
label define statefip_lbl 12 "Florida", add
label define statefip_lbl 13 "Georgia", add
label define statefip_lbl 15 "Hawaii", add
label define statefip_lbl 16 "Idaho", add
label define statefip_lbl 17 "Illinois", add
label define statefip_lbl 18 "Indiana", add
label define statefip_lbl 19 "Iowa", add
label define statefip_lbl 20 "Kansas", add
label define statefip_lbl 21 "Kentucky", add
label define statefip_lbl 22 "Louisiana", add
label define statefip_lbl 23 "Maine", add
label define statefip_lbl 24 "Maryland", add
label define statefip_lbl 25 "Massachusetts", add
label define statefip_lbl 26 "Michigan", add
label define statefip_lbl 27 "Minnesota", add
label define statefip_lbl 28 "Mississippi", add
label define statefip_lbl 29 "Missouri", add
label define statefip_lbl 30 "Montana", add
label define statefip_lbl 31 "Nebraska", add
label define statefip_lbl 32 "Nevada", add
label define statefip_lbl 33 "New Hampshire", add
label define statefip_lbl 34 "New Jersey", add
label define statefip_lbl 35 "New Mexico", add
label define statefip_lbl 36 "New York", add
label define statefip_lbl 37 "North Carolina", add
label define statefip_lbl 38 "North Dakota", add
label define statefip_lbl 39 "Ohio", add
label define statefip_lbl 40 "Oklahoma", add
label define statefip_lbl 41 "Oregon", add
label define statefip_lbl 42 "Pennsylvania", add
label define statefip_lbl 44 "Rhode Island", add
label define statefip_lbl 45 "South Carolina", add
label define statefip_lbl 46 "South Dakota", add
label define statefip_lbl 47 "Tennessee", add
label define statefip_lbl 48 "Texas", add
label define statefip_lbl 49 "Utah", add
label define statefip_lbl 50 "Vermont", add
label define statefip_lbl 51 "Virginia", add
label define statefip_lbl 53 "Washington", add
label define statefip_lbl 54 "West Virginia", add
label define statefip_lbl 55 "Wisconsin", add
label define statefip_lbl 56 "Wyoming", add
label define statefip_lbl 61 "Maine-New Hampshire-Vermont", add
label define statefip_lbl 62 "Massachusetts-Rhode Island", add
label define statefip_lbl 63 "Minnesota-Iowa-Missouri-Kansas-Nebraska-S.Dakota-N.Dakota", add
label define statefip_lbl 64 "Maryland-Delaware", add
label define statefip_lbl 65 "Montana-Idaho-Wyoming", add
label define statefip_lbl 66 "Utah-Nevada", add
label define statefip_lbl 67 "Arizona-New Mexico", add
label define statefip_lbl 68 "Alaska-Hawaii", add
label define statefip_lbl 72 "Puerto Rico", add
label define statefip_lbl 97 "Military/Mil. Reservation", add
label define statefip_lbl 99 "State not identified", add
label values statefip statefip_lbl

label define educd_lbl 000 "N/A or no schooling"
label define educd_lbl 001 "N/A", add
label define educd_lbl 002 "No schooling completed", add
label define educd_lbl 010 "Nursery school to grade 4", add
label define educd_lbl 011 "Nursery school, preschool", add
label define educd_lbl 012 "Kindergarten", add
label define educd_lbl 013 "Grade 1, 2, 3, or 4", add
label define educd_lbl 014 "Grade 1", add
label define educd_lbl 015 "Grade 2", add
label define educd_lbl 016 "Grade 3", add
label define educd_lbl 017 "Grade 4", add
label define educd_lbl 020 "Grade 5, 6, 7, or 8", add
label define educd_lbl 021 "Grade 5 or 6", add
label define educd_lbl 022 "Grade 5", add
label define educd_lbl 023 "Grade 6", add
label define educd_lbl 024 "Grade 7 or 8", add
label define educd_lbl 025 "Grade 7", add
label define educd_lbl 026 "Grade 8", add
label define educd_lbl 030 "Grade 9", add
label define educd_lbl 040 "Grade 10", add
label define educd_lbl 050 "Grade 11", add
label define educd_lbl 060 "Grade 12", add
label define educd_lbl 061 "12th grade, no diploma", add
label define educd_lbl 062 "High school graduate or GED", add
label define educd_lbl 063 "Regular high school diploma", add
label define educd_lbl 064 "GED or alternative credential", add
label define educd_lbl 065 "Some college, but less than 1 year", add
label define educd_lbl 070 "1 year of college", add
label define educd_lbl 071 "1 or more years of college credit, no degree", add
label define educd_lbl 080 "2 years of college", add
label define educd_lbl 081 "Associates degree, type not specified", add
label define educd_lbl 082 "Associates degree, occupational program", add
label define educd_lbl 083 "Associates degree, academic program", add
label define educd_lbl 090 "3 years of college", add
label define educd_lbl 100 "4 years of college", add
label define educd_lbl 101 "Bachelors degree", add
label define educd_lbl 110 "5+ years of college", add
label define educd_lbl 111 "6 years of college (6+ in 1960-1970)", add
label define educd_lbl 112 "7 years of college", add
label define educd_lbl 113 "8+ years of college", add
label define educd_lbl 114 "Masters degree", add
label define educd_lbl 115 "Professional degree beyond a bachelors degree", add
label define educd_lbl 116 "Doctoral degree", add
label define educd_lbl 999 "Missing", add
label values educd educd_lbl

recode educd (0/61=1) (62/64=2) (65/90=3) (101/116=4), gen(edcat)

label define edlbl 1 "<HS"
label define edlbl 2 "HS", add
label define edlbl 3 "SC", add
label define edlbl 4 "BA+", add
label values edcat edlbl

label define marst_lbl 1 "Married, spouse present"
label define marst_lbl 2 "Married, spouse absent", add
label define marst_lbl 3 "Separated", add
label define marst_lbl 4 "Divorced", add
label define marst_lbl 5 "Widowed", add
label define marst_lbl 6 "Never married/single", add
label values marst marst_lbl

gen married = marst==1 /* this is married spouse present */
gen single=marst>3 /* this is divorced, widowed, and never married */

gr bar (mean) single [weight=perwt], over(edcat) bar(1,color(green)) yti("Proportion not married") by(state)

collapse (mean) single psingle=single [fw=perwt], by(state edcat)

gr twoway scatter single edcat , xlab(1 2 3 4, valuelabel) yti("Proportion not married") lcolor(green) msymbol(O) connect(l) by(state)




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Theology majors marry each other a lot, but business majors don’t (and other tales of BAs and marriage)

The American Community Survey collects data on the college majors of people who’ve graduated college. This excellent data has lots of untapped potential for family research, because it tells us something about people’s character and experience that we don’t have from any other variables in this massive annual dataset. (It even asks about a second major, but I’m not getting into that.)

To illustrate this, I did two data exercises that combine college major with marital events, in this case marriage. Looking at people who just married in the previous year, and college major, I ask: Which majors are most and least likely to marry each other, and which majors are most likely to marry people who aren’t college graduates?

I combined eight years of the ACS (2009-2016), which gave me a sample of 27,806 college graduates who got married in the year before they were surveyed (to someone of the other sex). Then I cross-tabbed the major of wife and major of husband, and produced a table of frequencies. To see how majors marry each other, I calculated a ratio of observed to expected frequencies in each cell on the table.

Example: With weights (rounding here), there were a total of 2,737,000 BA-BA marriages. I got 168,00 business majors marrying each other, out of 614,000 male and 462,000 female business majors marrying altogether. So I figured the expected number of business-business pairs was the proportion of all marrying men that were business majors (.22) times the number of women that were business majors (461,904), for an expected number of 103,677 pairs. Because there were 168,163 business-business pairs, the ratio is 1.6.  (When I got the same answer flipping the genders, I figured it was probably right, but if you’ve got a different or better way of doing it, I wouldn’t be surprised!)

It turns out business majors, which are the most numerous of all majors (sigh), have the lowest tendency to marry each other of any major pair. The most homophilous major is theology, where the ratio is a whopping 31. (You have to watch out for the very small cells though; I didn’t calculate confidence intervals.) You can compare them with the rest of the pairs along the diagonal in this heat map (generated with conditional formatting in Excel):

spouse major matching

Of course, not all people with college degrees marry others with college degrees. In the old days it was more common for a man with higher education to marry a woman without than the reverse. Now that more women have BAs, I find in this sample that 35% of the women with BAs married men without BAs, compared to just 22% of BA-wielding men who married “down.” But the rates of down-marriage vary a lot depending on what kind of BA people have. So I made the next figure, which shows the proportion of male and female BAs, by major, marrying people without BAs (with markers scaled to the size of each major). At the extreme, almost 60% of the female criminal justice majors who married ended up with a man without a BA (quite a bit higher than the proportion of male crim majors who did the same). On the other hand, engineering had the lowest overall rate of down-marriage. Is that a good thing about engineering? Something people should look at!

spouse matching which BAs marry down

We could do a lot with this, right? If you’re interested in this data, and the code I used, I put up data and Stata code zips for each of these analyses (including the spreadsheet): BA matching, BA’s down-marrying. Free to use!


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No, early marriage is not more common for college graduates

Update: IFS has taken down the report I critiqued here, and put up a revised report. They have added an editor’s note, which doesn’t mention me or link to this post:

Editor’s Note: This post is an update of a post published on March 14, 2018. The original post looked at marriage trends by education among all adults under age 25. It gave the misimpression that college graduates were more likely to be married young nowadays, compared to non-college graduates.

At the Institute for Family Studies, Director of Research Wendy Wang has a post up with the provocative title, “Early Marriage is Now More Common For College Graduates” (linking to the Internet Archive version).

She opens with this:

Getting married at a young age used to be more common among adults who didn’t go to college. But the pattern has reversed in the past decade or so. In 2016, 9.4% of college graduates ages 18 to 24 have ever been married, which is higher than the share among their peers without a college degree (7.9%), according to my analysis of the most recent Census data.

And then the dramatic conclusion:

“What this finding shows is that even at a young age, college-educated adults today are more likely than their peers without a college degree to be married. And this is new.”

That would be new, and surprising, if it were true, but it’s not.

Here’s the figure that supports the conclusion:


It shows that 9.4% of college graduates in the age range 18-24 have been married, compared with 7.9% of those who did not graduate from college. (The drop has been faster for non-graduates, but I’m setting aside the time trend for now.) Honestly, I guess you could say, based on this, that young college graduates are more likely than non-graduates to “be married,” but not really.

The problem is there are very very few college graduates in the ages 18-19. The American Community Survey, which they used here, reports only about 12,000 in the whole country, compared with 8.7 million people without college degrees ages 18-19 (this is based on the public use files that IPUMS.org uses; which is what I use in the analysis below). Wow! There are lots and lots of non-college graduates below age 20 (including almost everyone who will one day be a college graduate!), and very few of them are married. So it looks like the marriage rate is low for the group 18-24 overall. Here is the breakdown by age and marital status for the two groups: less than BA education, and BA or higher education — on the same population scale, to help illustrate the point:


If you pool all the years together, you get a higher marriage rate for the college graduates, mostly because there are so few college graduates in the younger ages when hardly anyone is married.

To show the whole thing in terms of marriage rates, here is the marital status for the two groups at every age from 15 (when ACS starts asking about marital status) to 54.


Ignoring 19-21, where there are a tiny number of college graduates, you see a much more sensible pattern: college graduates delay marriage longer, but then have higher rates at older ages (starting at age 28), for all the reasons we know marriage is ultimately more common among college graduates. In fact, if you used ages 15-24 (why not?), you get an even bigger difference — with 9.4% of college graduates married and just 5.7% of non-college graduates. Why not? In fact, what about ages 0-24? It would make almost as much sense.

Another way to do this is just to look at 24-year-olds. Since we’re talking about the ever-married status, and mortality is low at these ages, this is a case where the history is implied in the cross-sectional data. At age 24, as the figure shows, 19.9% of non-college graduates have been married, compared with 12.9% of college graduates. Early marriage is not more common for college graduates.

In general, I don’t recommend comparing college graduates and non-graduates, at least in cross-sectional data, below age 25. Lots of people finishing college below age 25 (and increasingly after that age as well). There is also an important issue of endogeneity here, which always makes education and age analysis tricky. Some people (mostly women) don’t finish college because they get married and have children).

Anyway, it looks to me like someone working for a pro-marriage organization saw what seemed like a story implying marriage is good (that’s why college graduates do it, after all), and one that also fits with the do-what-I-say-not-what-I-do criticism of liberals, who are supposedly not promoting marriage among poor people while they themselves love to get married (a critique made by Charles Murray, Brad Wilcox, and others). And, before thinking it through, they published it.

Mistakes happen. Fortunately, I dislike the Institute for Family Studies (see the whole series under this tag), and so I read it and pointed out this problem within a couple hours (first on Twitter, less than two hours after Wang tweeted it). It’s a social media post-publication peer review success story! If they correct it.


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Black women really do have high college enrollment rates (at age 25+)

The other day I reported on the completely incorrect meme that Black women are the “most educated group” in the U.S. That was a simple misreading of a percentage term on an old table of degree attainment, which was picked up by dozens of news-repeater websites. Too many writers/copiers and editors/selectors don’t know how to read or interpret social statistics, so this kind of thing happens when the story is just too good to pass up.

I ignored another part of those stories, which was the claim that Black women have the highest college enrollment rates, too. This is more complicated, and the repeated misrepresentation is more understandable.

Asha Parker in Salon wrote:

By both race and gender there is a higher percentage of black women (9.7 percent) enrolled in college than any other group including Asian women (8.7 percent), white women (7.1 percent) and white men (6.1 percent), according to the 2011 U.S. Census Bureau.

You know the rewrite journalists are playing telephone when they all cite the same out-of-date statistics. (That Census report comes out every year — here’s the 2014 version; pro-tip: with government reports, try changing the year in the URL as a shortcut to the latest version.)

But is that true? Sort of. Here I have to blame the Census Bureau a little, because on that table they do show those numbers, but what they don’t say is that 9.7% (in the case of Black women) is the percentage of all Black “women” age 3 or older who are attending college. On that same table you can see that about 2% of Black “women” are attending nursery school or kindergarten; more relevant, probably, is the attendance rate for those ages 3-4, which is 59%.

So it’s sort of true. Particularly odd on that table is the low overall college attendance rate of Asian women, who are far and away the most likely to go to college at the “traditional” college ages of 18-24. That’s because they are disproportionately over age 25 (partly because many have immigrated as adults). But, if you just limit the population to those ages 18-54, Black women still have the highest enrollment rates: 15.5%, compared with 14.6% for Asians, 12.6% for Hispanics, and 12.4% for Whites. Asians are just the most likely to be over 25 and not attending college, most of them having graduated college already.

This does not diminish the importance of high enrollment rates for Black women, which are real — after age 25; the pattern is interesting and important. Here it is:


Under age 25, Black women are the least likely to be in college, over 25 they’re the most likely. This really may say something about Black women’s resilience and determination, but it is not a feel-good story of barriers overcome and opportunity achieved. And, despite her presence in the videos and stories illustrating this meme, it is not the story of Michelle Obama, who had a law degree from Harvard at age 24.

This is part of a pattern in which family events are arrayed differently across the life course for different race/ethnic groups, and the White standard is often mistaken as universal. I have noted this before with regard to marriage (with more Black women marrying at later ages) and infant mortality (which Black women facing the lowest risk of infant death when they have children young). It’s worth looking at more systematically.

ADDENDUM 6/29/2016: Cumulative projected years of higher education

If you take the proportion of women enrolled in each age group, multiply it by the years if the age group (so, for example, 18-19 is two years), and sum up those products, you can get a projected total years in college (including graduate school) for each group of women. It looks like this:


Note this makes the unreasonable assumption that everyone who says they are enrolled in college in an October survey attends college for a full year. So, for example, Asian women are projected to spend 6.2 years in college on average between ages 18 and 54. What’s interesting here is that Black women are projected to spend more years in higher education than White women (5.5 versus 4.9). But we know they are much less likely than White women to end up with a bachelor’s degree (currently 23% versus 33%). This has to be some combination of Black women not spending full years in college, not going to school full time, or not completing bachelor’s degrees after however many years in school. Attendance may be an indicator of resilience or determination, but it’s not as good an indicator of success.


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