A Simple, Legal Way to Help Stop Employment Discrimination

Originally posted at TheAtlantic.com.

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

Jacquelyn Martin

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

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

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

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

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

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

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

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

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

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

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

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

Data visualizations: Is U.S. society becoming more diverse?

Trying to summarize a few historical trends for the last half century (because what else is there to do?), I thought of framing them in terms of diversity.

Diversity is often an unsatisfying concept, used to describe hierarchical inequality as mere difference. But inequality is a form of diversity — a kind of difference. And further, not all social diversity is inequality. When people belong to categories and the categories are not ranked hierarchically (or you’re not interested in the ranking for whatever reason), the concept of diversity is useful.

There are various ways of constructing a diversity index, but I use the one sometimes called the Blau index, which is easy to calculate and has a nice interpretation: the probability that two randomly selected individuals are from different groups.

Example: Religion

Take religion. According to the 2001 census of India, this was the religious breakdown of the population:

RELIGION	Number	Proportion
Hindus	827,578,868	.805
Muslims	138,188,240	.134
Christians	24,080,016	.023
Sikhs	19,215,730	.019
Buddhists	7,955,207	.008
Jains	4,225,053	.004
Others	6,639,626	.006
Religion not stated	727,588	.001
Sum of squared proportions		.667
Diversity		.333

Diversity is calculated by summing the squares of the proportions in each category, and subtracting the sum from 1. So in India in 2001, if you picked two people at random, you had a 1/3 chance of getting people with different religions (as measured by the census).

Is .33 a lot of religious diversity? Not really, it turns out. I was surprised to read on the cover of this book by a Harvard professor that the United States is “the world’s most religiously diverse nation.” When I flipped through the book, though, I was disappointed to see it doesn’t actually talk much about other countries, and does not seem to offer the systematic comparison necessary to make such a claim.

With our diversity index, it’s not hard to compare religious diversity across 52 countries using data from World Values Survey, with this result:

The U.S. is quite diverse — .66 — but a number of countries rank higher.

Of course, the categories are important in this endeavor. For example, Turkey and Morocco are both 99% “Muslim.” So is Iraq, but in Iraq that population is divided between people who identify as Muslim, Shia and Sunni, so Iraq is much more diverse. You get the same effect by dividing up the Christians in the U.S., for example.

Increasing U.S. diversity

Anyway, back to describing the last half century in the U.S. On four important measures I’ve got easy-to-identify increasing diversity. What do you think of these (with apologies for the default Microsoft color schemes):

The last one is a little tricky. It’s common to report that the median age at marriage has increased since the 1950s (having fallen before the 1950s). But I realized it’s not just the average increasing, but the dispersion: More people marrying at different ages. So the experience of marriage is not just shifting rightward on the age distribution, but spreading out. Here’s another view of the same data:

These are corrected (5/11/2013) from the first version of this post. I have now calculated these using the this report from the National Center for Health Statistics for 1960, and comparing it with the 2011 American Community Survey for those married in the previous year.

I have complained before that using the 1950s or thereabouts as a benchmark is misleading because it was an unusual period, marked by high conformity, especially with regard to family matters. But it is still the case that since then diversity on a number of important measures has increased. Over the period of several generations, in important ways the people we randomly encounter are more likely to be different from ourselves (and each other).

Incarceration’s contribution to infant mortality

A recent study in the journal Social Problems by sociologist Chistopher Wildeman shows that America’s practice of mass incarceration may be exacerbating both infant mortality in general and stubborn racial inequality in infant mortality in particular.

Drawing on recent literature by himself and others, Wildeman spells out the case for incarceration’s negative effect on family economies, including: lost earnings and financial contributions from fathers, the expensive burden of maintaining the relationship with an incarcerated parent, and the lost value of the incarcerated parent’s unpaid labor. All of those costs may take a toll on mothers’ health, which is the primary cause of infant mortality.

In addition, family members of incarcerated parents may contract infectious diseases, experience significant stress, and lose support networks — all taking an additional health toll.

Sure enough, his analysis of data from the Pregnancy Risk Assessment Monitoring System confirms that children born into families in which a parent has been incarcerated are more likely to die in the first year of life. The association may not be causal, but it holds with a lot of important control variables.

Does this increase racial inequality? Probably, because parental incarceration is so concentrated among Black families, as Wildeman and Bruce Western reported previously (my graph of their numbers):

To make the connection to racial inequality explicit, Wildeman moves to compare states over time, on the suspicion that incarceration could increase infant mortality rates, and racial inequality in infant mortality rates. That could be because concentrated incarceration undermines community support and income, people with felony records often are disenfranchised (so the political system can ignore their needs), and the costs of incarceration crowd out more beneficial spending that could improve community health.

The results of a lot of fancy statistical models comparing states show that:

the imprisonment rate is positively and significantly associated with the total infant mortality rate, the black infant mortality rate, and the black-white gap in the infant mortality rate.

It’s an impressive article on an important subject, one that thankfully is attracting more attention from good scholars.

I previously reported on Wildeman’s work on how the drug war affect families, here.

The disparate lives of fifth graders

A new study of about 5,000 fifth-grade students in the three public school districts shows wide disparities by race/ethnicity in a number of important health practices and outcome measures. The study, published in the New England Journal of Medicine, showed unadjusted disparities and then attempted to account for them statistically with common control variables, such as family socioeconomic status and school characteristics.

Here is a breakdown of some of the indicators (my graph):

On all but alcohol consumption (remember these are fifth graders), the white students showed advantages over Black and Latino students. In the subsequent analysis, the authors showed what amount of the disparity was accounted for by the different control variables. Here is their graph illustrating the findings:

It shows, for example, that about 10 points out of the 20-point difference between Latinos and Whites on the frequency of reporting fair or poor health is accounted for by their control variables. For Black children, about four points out of the eight point difference is accounted for. (These gaps would likely be larger if private school students were included.)

Determining the causal story behind these disparities is interesting and important, however it is most important to realize that at the descriptive level these represent major disparities in the lived experience of young children who are blameless.

It is interesting to note that some of these practices and outcomes speak to parenting practices, which has been the subject of a growing literature in recent years. However, after Annette Lareau reported that parenting practices in her study differed more by social class than they did by race, class has been the focus of much of this research. For example, although I did not see it, a study by Jessica McCrory Calarco at Indiana University, presented at the annual meeting of the American Sociological Association last week, looks very interesting. She used observation and interviews and found stark differences between middle-class and working-class parent-child interactions. From the press release:

Working-class parents, she found, coached their children on how to avoid problems, often through finding a solution on their own and by being polite and deferential to authority figures. Middle-class parents, on the other hand, were more likely to encourage their kids to ask questions or ask for help.

Do Asians in the U.S. have high incomes?

The Pew Research Center last week released a lengthy research report on Asians in the U.S., titled “The Rise of Asian Americans.” It combines information from the Census and government sources with the results of Pew’s own national survey of attitudes and opinions.

The report has lots of good information, but there are some thorny problems here. I’ll describe a few problems, then offer one data exercise to help clarify. This gets technical and it’s long, so I will give you the substantive conclusion at the top:

1. Because Asians are a diverse category made up of groups with very different profiles, and their household composition and geographic distribution vary by national origin group, generalizations are often unhelpful.
2. Among the 10 largest Asian groups, five (Japanese, Indian, Chinese, Filipino, Korean) are above average in income and five (Vietnamese, Pakistani, Laotian, Cambodian, Hmong) are below. But all 10 Asian groups are doing better compared to the national average than they are compared to the average incomes in the places they live — they are richer nationally than they are locally.
3. The amount of income inequality within Asian groups varies as well. Pakistanis,  Chinese, Koreans and Indians have the highest levels of inequality, while Filipinos and Laotians have low levels of inequality.

Details follow.

But first: Who is Asian? On the Census questionnaire, Asian is not exactly a category – rather, the category is created from all the responses of people who specify Asian national origins in the race question. To refresh, this is the question:

So “Asian” is all the people who specify Asian Indian, Chinese, Filipino, Japanese, Korean, Vietnamese or “Other Asian.” (The right-hand column is for Pacific Islanders.) Yes, in the U.S., Hispanic/Latino national origins are “ethnicities,” but Asian national origins are “races.” Go figure.

That lack of a common definition is compounded by two factors: First, there is so much diversity among Asians that the using a single category is as challenging statistically as it is politically. And second, Asians – as the Pew report shows – have a high rate of intermarriage with Whites, as well as (among some groups) across Asian national-origin lines. As a result, some Asian groups have high rates of “multiple-race identification” — especially those whose immigration was generations ago.

The controversy over the Pew report is summarized in this Color Lines story and this response from the Asian American / Pacific Islander Policy Research Consortium. The gist of it is that the report was too rosy in its description of Asian advantages and too homogenizing in its treatment of Asian diversity – as a result repeating the “divisive trope” of the “model minority.” Here’s part of the summary from the New York Times:

Drawing on Census Bureau and other government data as well as telephone surveys from Jan. 3 to March 27 of more than 3,500 people of Asian descent, the 214-page study found that Asians are the highest-earning and best-educated racial group in the country.

Among Asians 25 or older, 49 percent hold a college degree, compared with 28 percent of all people in that age range in the United States. Median annual household income among Asians is $66,000 versus $49,800 among the general population.

In the survey, Asians are also distinguished by their emphasis on traditional family mores. About 54 percent of the respondents, compared with 34 percent of all adults in the country, said having a successful marriage was one of the most important goals in life; another was being a good parent, according to 67 percent of Asian adults, compared with about half of all adults in the general population.

Asians also place greater importance on career and material success, the study reported, values reflected in child-rearing styles. About 62 percent of Asians in the United States believe that most American parents do not put enough pressure on their children to do well in school.

Did Pew homogenize or glorify too much? I don’t know. Here’s a graph from the report, which shows that Asian groups differ, but they all have higher-than-average household incomes:

The Color Lines story quotes Deepa Iyer, head of the National Council of Asian Pacific Americans and executive director of South Asian Americans Leading Together:

The danger in framing the study the way Pew did, and the way the media picked up on it, is that folks who are in the general public and institutional stakeholders and policy makers might get the impression that they don’t necessarily need to dig deep into our communities to understand any sort of disparities that exist.

The problem of homogenizing Asians is longstanding in American sociology. In most data analyses, the Asian sample is small to begin with, so they are often collapsed into one category (which I’ve done) or dropped from the story (which I’ve also done, angering some readers). Here is a typical passage, from a 2001 article by Leslie McCall:

That didn’t stop her (or lots of other people) from extensively analyzing Asians as a combined group, and offering speculation on her results.

There are other examples. In my experience, Jen’nan Read and I broke out six Asian groups for a study of women’s employment with the 2000 Decennial Census data — which reinforced my conviction that disaggregating is best. (This 2010 Census report gives some detail on more than 20 national-origin groups.)

Some new numbers

Anyway, I’ve got four specific issues to address with Pew’s comparison of household incomes (some of which they acknowledge in the report): a) Household composition differs between groups (more or fewer kids, grandparents); b) Asians disproportionately live in parts of the U.S. with high costs of living (like Hawaii and California, and urban areas generally); c) different members of a household might have different “race” identities (so, a Korean man married to a Chinese woman might define their child is either or both); and d), levels of inequality differ between groups, so central tendency comparisons don’t capture the whole story.

In this exercise I address these problems. I adjust for household size and composition, count individuals’ own “race” rather than imposing a single identity on the household, compare incomes to the average in the local metropolitan area as well as the national average, and compare levels of within-group inequality.

All in one blog post! Someone might want to work this up into a real paper (and maybe someone else already has? The last time I really read about this was more than 10 years ago.) So I’m just offering this approach as a suggestion, and making my code available if anyone wants to pursue it (see below).

I use the 2006-2010 combined American Community Survey, from IPUMS, for maximum recent sample size. This is about 15 million people, and the Asian samples range from about 160,000 Chinese to 7,500 Laotians. I identify individuals according to their individual “race.”

I calculate their incomes as per capita household income, adjusted for economies of scale. To do that, I count adults as 1 person, kids under 18 as .7 of a person, and divide the total household income by that count to the power of .65 for economies of scale (see here for details). Then I take the natural log of all that to pull in the right tail of the distribution (so the mean isn’t pulled up by the ~1%). When I’m done, everyone in the household has the same income, and the distribution is pretty normal. Nice!

To see what this does: The mean household income for individuals in the country in 2006-2010 is $79,174, and the natural log of the composition-and-scale adjusted per capita income is 10.26 (see figure), which works out to $28,439. In comparison, the logged incomes for Asians range from 10.6 (~$40,000) for Indians and Japanese, down to 9.7 (~$16,000) for Hmong.

To deal with the issue of living in expensive areas, I take the mean of that logged income in each metropolitan area, and compare each person’s own per capita income to that. So a score of 0 means you have the average income in your area — more than 0 means richer than average, less than zero is poorer.

There is not one correct answer about how to do this: Having an average income in a rich area still means you can buy more stuff on Amazon than someone with a lower absolute income. But it might also mean having a smaller house, or not being considered rich by your neighbors. On the third hand, if a rich family moves to a rich area, we shouldn’t feel sorry for them for not being above average in their neighborhood. For your consideration, I show the incomes compared with the national average and with the local metro mean, for the 10 largest Asian groups (click for higher resolution):

To interpret the figure, you can see that Japanese and Indians are about 0.36 higher in log dollars than the national average but only 0.26 higher than their metro-area averages. On the downside, Hmong individuals have adjusted per capita incomes of 0.58 less than the national average, but 0.63 less than their local average.

Higher-than-average-income Japanese, Indians, Filipinos and Chinese are about 73% of the total; Koreans are about average, and the lower-than-average groups are 17% of the total. By this method, then, a big majority of Asians in the U.S. belong to above-local-average income groups, but a substantial fraction are well below average. And they are all doing worse relative to their metro area neighbors than they are to the national average.

Notice how it’s different from the Pew figure. In that, Vietnamese households had higher incomes than Koreans, and both were above the national average. Here Koreans are doing substantially better, mostly as a result of the household size adjustments. Also, the smaller groups I show – the ones Pew did not detail in that figure – are the poorer ones. And they are also doing worse locally relative to their national position.

Finally, consider the inequality within groups. Without doing a full-blown analysis of this, I can show the importance of the question with a simple box-and-whisker plot. This shows the distribution of income — adjusted as described above for household composition and size — for each group, including non-Asians for comparison.

The graph shows a lot of information in a small space:

• The line through the middle of each box is the median, or mid point, of each income distribution.
• The blue + sign is the mean. The further the mean is above the median, the more rich people there are pulling the mean up.
• The top and bottom of the boxes are the 75th and 25th percentiles. The further apart they are, the greater the income gap between top and bottom.

(The top whiskers, which can be used to show the highest point in each distribution, aren’t shown here, because they’re so far away it would make the graph unreadable.)

As I mentioned at the top, the graph shows that Pakistanis and Chinese, and to a lesser extent Koreans and Indians, have high levels of inequality — their + signs are far from their median lines, and their 75/25 spreads are large. On the other hand, Filipinos, Laotians and Hmong have much narrower spreads.

Practically speaking, all this means that some groups are misrepresented by measures of the overall status of “Asians,” especially the smaller, poorer groups. And further, that generalizing will represent some groups worse than others because of their internal diversity. For example, the average Chinese American is quite a bit richer than the average non-Asian American, but the poorest 25% of Chinese are not much better off than the poorest 25% of the population at large.

Like I said, just an idea, with a few examples.

Take it away

Feel free to do it more, and/or better, yourself. Here’s my SAS code. Please credit me if it works, but don’t blame me if it’s wrong. This has not been peer-reviewed – it’s rough work product. Send any corrections written on the back of a $20-bill. (Everyone else: You can stop reading now!)

Just get these variables from IPUMS:

SERIAL
 METAREA
 HHINCOME
 PERWT
 AGE
 RACED

And then do this to them:

/* exclude households with no income */
if hhincome>0;
/* this codes folks into this scheme, with Asians from richest to poorest:
0="Not Asian"
1= "Japanese" 
2= "Indian" 
3= "Filipino" 
4= "Chinese" 
5= "Korean" 
6= "Vietnam" 
7= "Pakistani"
8= "Laotian"
9= "Cambodian"
10= "Hmong"
11= "OtherA"
12= "twoplusA" 
*/
/* these codes refer to RACED, the detailed race variable on IPUMS */
/* Count asians as those who are asian alone, multiple asian, asian and white, asian and PI, or white-asian-PI */
asian=0;
if raced in (400 410 420 811 861 911) then asian=4;
if raced in (610 814) then asian=2;
if raced in (600 813 864 865 914) then asian=3;
if raced in (640 816) then asian=6;
if raced in (620 815) then asian=5;
if raced in (500 812) then asian=1;
if raced in (660) then asian=9;
if raced in (661) then asian=10;
if raced in (662) then asian=8;
if raced in (669) then asian=7;
if raced in ( 663 664 665 666 667 668 670 671 672 810 817 818 860 867 868 910 915) then asian = 11;
if raced in ( 673 674 675 676 677 678 679 819 869) then asian = 12;
/* so the variable labels display in output */
format
 METAREA METAREA_f.
 ASIAN asian.
;
/* add the decimal to the weight variable */
format PERWT 11.2;
run;
/* this counts up the number of kids and adults in each household */
proc sort data=temp; by serial; run;
data hh;
set temp (keep=serial age);
by serial;
if first.serial then do;
kids=0;
adults=0;
end;
retain kids adults;
if age le 18 then do; kids=kids+1; end;
if age gt 18 then do; adults=adults+1; end;
keep serial kids adults;
if last.serial;
run;
proc sort data=hh; by serial; run;
/* this merges in those people counts, and then calculates the household income variable */
data people;
merge temp hh; by serial;
equiv = hhincome/((adults+(.7*kids))**.65);
lnequiv = log(hhincome/((adults+(.7*kids))**.65));
run;
/* this outputs the mean logged household equivalent income for each metro area (with non-metro folks as 0 */
proc means noprint data=people;
var lnequiv;
class metarea;
weight perwt;
output out=msa mean=msaequiv;
run;
proc sort data=msa; by metarea; run;
proc sort data=people; by metarea; run;
/* this merges in the metro area variable and calculates the income-difference variable */
data merged;
merge people (in=a) msa;
by metarea;
if a;
relhhinc = lnequiv-msaequiv;
run;
/* Distribution of the logged income variable */
proc univariate data=merged; var lnequiv; run;
proc univariate data=merged; var lnequiv; class asian; run;
/* Boxplots */
proc sort data=merged; by asian; run;
title 'Income distributions, household composition- and scale-adjusted';
proc boxplot data=merged;
 plot equiv*asian / clipfactor = 1.5 grid;
where asian le 10;
run;
title;
/* National income means */
proc means mean data=merged;
var lnequiv;
weight perwt;
run;
/* National asian income means by group */
proc means mean missing data=merged;
var lnequiv; class asian; weight perwt;
run;
/* Relative income for each Asian group, for metro people only */
proc means mean;
var relhhinc; class asian; weight perwt;
where asian >0 and metarea>0;
run;

Tripping on tipping points

There you have it: since 2007, the number non-Hispanic White women having babies has fallen faster than the number of babies born to women from all the other race/ethnic groups in the U.S. combined — enough so that the Census Bureau determined that births from the second group outnumbered those in the first for the first time ever in 2011.

If that’s not an exciting enough lead sentence for you, how’s this?

TIPPING POINT!!!

If you followed my Twitter feed yesterday, I’m sorry. At first, my reaction to the New York Times story was disturbed…

Again @NYtimes says 50% is a “tipping point.” Agh! They even published my letter objecting to this & now ignore it.

The too-detailed history of my objecting is here, but it involves the same demographer, William Frey, repeatedly pitching 50% as a “tipping point” to the news media in reference to a series of demographic events. I don’t have anything against the term tipping point, I just don’t like to see it used to hype trends. Also, the figure is mislabeled, since the “Non-white” population there specifically includes White Hispanics. So the dark line should be labeled, “non-[non-Hispanic White],” and the light line should be labeled “non-Hispanic White.”

You’re probably beginning to see why these reporters rarely call me back, and attributing my tirades to sour grapes (which really turned soured after my slogan, “We are the 75%!” failed to catch on).

Anyway, after I settled down, I realized that the reporter, Sabrine Tavernise, had really written a very good third paragraph that attempted to capture the historic moment:

Such a turn has been long expected, but no one was certain when the moment would arrive — signaling a milestone for a nation whose government was founded by white Europeans and has wrestled mightily with issues of race, from the days of slavery, through a civil war, bitter civil rights battles and, most recently, highly charged debates over efforts to restrict immigration.

But then when I got to the Washington Post editorial, my tweeting devolved back to deranged…

America at a tipping point: http://wapo.st/JVflcF <– tipping point in headline, AND “milestone” in lede?! Now they’re gaslighting me.

In fact, that Post editorial also used the term watershed, which they possibly picked up from Andrew Cherlin, who was quoted using it: ”This is a watershed moment. It shows us how multicultural we’ve become.”

What is the right way to say it?

The Post was really just using terms randomly. But there must be a way to describe things. I think this event was definitely a milestone. In the Oxford English Dictionary, that is,

A significant stage or event in the progress or development of a society, a career, an individual’s physical and mental growth, etc.; a measure of progress or change.

I would additionally stress the socially-constructed nature of a milestone, since its namesake is a marker placed by humans at arbitrary intervals along a continuous path.

Cherlin may be right that this news will turn out to be a watershed, sometimes called a “watershed divide,” or the point at which water has to choose which way to flow:

Watershed can refer to an important point of division in time as well as geography, as in this from 1878:

Midnight! the outpost of advancing day!‥ The watershed of Time, from which the streams of Yesterday and To-morrow take their way.

All the media attention to this trend may in fact have made it a watershed moment in public discourse. But that is quite different from making it a tipping point, defined now nicely by the OED:

tipping point n. the prevalence of a social phenomenon sufficient to set in motion a process of rapid change; the moment when such a change begins to occur.

It’s very hard to announce the arrival of either watersheds or tipping points when they happen — which is one reason milestones are so useful for marking distance. Looking at the trends in births above, and projections of future demographic change, there is no reason to think this moment is a demographic tipping point. Here is the population projection to 2050, based on the Census Bureau’s current calculations (and using mutually-exclusive race/ethnicity categories):

Based on the 5-year intervals they use, I don’t see anything too non-linear here, suggesting an actual tipping point.

Finally, for some longer-range perspective:

Black is not a color

When I saw this magazine cover, I did a double-take:

At a glance I didn’t think that was Black Hair. Seems like a good time to bring up the old schoolyard debate point: Black is not a color.

In many quarters, such as the those administered under the rules of the Chicago Manual of Style, black is a color, which means it’s not capitalized:

8.39 Color. Common designations of ethnic groups by color are usually lowercased unless a particular publisher or author prefers otherwise… (black people; blacks; people of color; white people; whites)

That rule, from the 16th edition, is progress from the 15th, which said “capitalization may be appropriate if the writer strongly prefers it” (8.43, emphasis added). Under that older provision in 1996, the journal Signs required that I add a footnote in my first journal publication, which read, “I … capitalize Black to signify its reference to a people rather than a color or a ‘race.’”

Most media do not capitalize Black or White. The Associated Press Stylebook reads:

black Acceptable for a person of the black race. African-American is acceptable for an American black person of African descent. (Use Negro only in names of organizations or in quotations.) Do not use colored as a synonym.

So, for example:

Trayvon Martin was shot and killed by a crime watch volunteer in a gated community in Sanford, Fla., in February 2012. The death of the unarmed black teenager and the decision of the local police not to bring charges against the volunteer, George Zimmerman, 28, set off a national outcry…

Sociology journals are inconsistent. For example, the American Sociological Review goes both ways (e.g., this 2010 presidential address used uncapitalized black, while our 2007 article’s capitalization sailed through without objection). On the other hand, some sociology journals follow the more progressive APA Style, in which Black is capitalized (as is White).

In the wider American world – at least as measured by Google Books ngrams – the uncapitalized version is leading by about 3-to-1.

(Black by itself wouldn’t work, so I added “people.” The pattern is the same if you use “community” instead.)

The Census Bureau capitalizes, as in this report on the 2010 Census:

“Black or African American” refers to a person having origins in any of the Black racial groups of Africa. It includes people who indicated their race(s) as “Black, African Am., or Negro” or reported entries such as African American, Kenyan, Nigerian, or Haitian.

That usage differs from the Office of Management and Budget directive, from which that language is drawn: “…any of the black racial groups of Africa,” without capitalization. That Census practice of capitalizing seems to have started between 1990 and 1995. (Others, like the Department of Education, have their own rules, which specify that racial designations should be capitalized.)

Finally, African American is not going to get us out of this. It is not appropriate when the subject really is race rather than ethnicity. I feel for this poor research subject in a Census cognitive interview:

She is an immigrant to the US from Africa. However, roughly six generations ago her ancestors were from India. She lived in an Indian community in Africa prior to immigrating to the United States. She answered “no” to … “Black or African American” because she was from an African country, but of Indian origin. She answered “yes” to the Asian question and “yes” to Asian Indian. She also reported ‘some other race’ by saying “African, not African American, African from Africa, Asian African.”

Anyway, Black and White are racial terms. They are a social construction and not a biological classification. We use them socially. Whether or not that’s OK, I think it’s better to capitalize them at least.

P.S. If your organization or publication has its own way – or I’ve misrepresented a practice you know better than I do – please let us know.

Maryland’s marriage rights bottleneck

Last year I wrote that Black Christian leaders in Prince George’s County, Maryland, were the political force that blocked the state’s marriage-rights legislation from passing. According to the Washington Post, despite the “state’s reputation as one of the nation’s most liberal states,” the percentage of people here who support gay and lesbian (homogamous) marriage rights is about the same as the national average. That’s because of a large population of Christian African Americans who oppose the law, it appears.

Here is the breakdown of the Post‘s latest poll:

Among Democrats, the gay/lesbian marriage divide has got to be one of the sharpest between Blacks (41% support) and Whites (71% support). Evidence from the General Social Survey (reported here) attributes the race difference to the denominational and religiosity differences between Blacks and Whites. (Of course, 41% Black support is not negligible.)

Those on the wrong side of history appear to be swimming against an insurmountable demographic tide (or whatever). Barring a dramatic turn of events, all the evidence points toward popular support for marriage rights becoming a solid majority in the next few years. That shows in the trend over time, as well as the age split, in the Post poll. Sooner or later, I think, either the churches will decide to change or the population will swim out from under them, dunking them in the drink of political history.

Unfreedom update: 2010 incarceration stats

I can’t teach my course on family sociology without these graphs, which show the rise of the unfree population, and the incredible race/ethnic and gender disparities behind them.

The Bureau of Justice Statistics has released Correctional Population in the United States, 2010, which updates my standard figures. First, the total trend toward unfreedom in the population — from less than 2 million in 1980 to more than 7 million 30 years later:

And second, to understand the disparate impact of this change on Black men in young adulthood primarily — and secondarily, Latino men — here are the rates of incarceration for men by age and race/ethnicity (Blacks here exclude Latinos; Asians and American Indians are not included in the statistics):

Just to make sure you read the scale right, that incarceration rate for Black men in their early 30s is 9,892 per 100,000, or 9.9%, or one-in-ten — more than five-times the rate for White men.

I come at this largely from its effects on families. In a nutshell: The overall trend is largely a consequence of how the U.S. has waged its drug war over this period; these policies fit into a web of practices that deny families to millions of people in the U.S. (only a minority of whom have been convicted of crimes), including by simply removing men from communities and increasing the number of single-parent families.

All that said, you may notice the little decline at the end of that long upward trend in the first figure. In fact, for the first time since 1980, there has been a decline in the incarcerated population for two years running. There has been a long-term decline in crime, but I don’t know whether that is more important than the budget crises facing so many states, or the diminished lust for locking people up. In New York, for example, seven incarceration facilities were closed in the last year, after the number of prisoners dropped about one-fifth in the past decade:

The inmate decline followed a 25 percent statewide drop in crime over the past decade and revisions in sentencing laws that allowed earlier releases and alternative programs for nonviolent drug offenders. The number of prisoners in medium-security prisons declined almost 20 percent from 2001 to 2010 while those in minimum-security facilities dropped 57 percent.

The numbers on the charts are still off the charts, meanwhile — and remember these are just those in the system now. Many more people (and their families) live lives permanently hampered by criminal records and the experience of imprisonment.

Divorce by race/ethnicity and education, 2010

Earlier this month I calculated that the divorce rate per 1,000 married people rose slightly from 2009 to 2010, but is still lower than it was in 2008. Now we have more information for 2010 from the National Center for Family and Marriage Research at Bowling Green State University.

NCFMR’s new release (in PDF) shows the divorce rate among women married for the first time, by race/ethnicity and education.

Just a quick update.

For my previous posts on divorce, follow the divorce tag.