The Connection Between Unemployment and Unmarried Parents

Originally posted at TheAtlantic.com.

The states with more single men without jobs have higher rates of nonmarital births.

“Le berceau” by Berthe Morisot

The Census Bureau has a new report on nonmarital births. Based on the American Community Survey—the largest survey of its kind, and the only one big enough to track all states—the report shows that 35.7 percent of births in 2011 were to unmarried mothers.

Beneath the headline number, two patterns in the data will receive a lot of attention: education and race/ethnicity. I have a brief comment on both patterns.

Education
The education patterns show a very steep dropoff in nonmarital births as women’s education increases. From 57 percent unmarried among those who didn’t finish high school to just nine percent among those who have graduated college.

Given the hardships faced by single mothers (especially in the United States), it looks like women with more education are making the more rational decision to avoid childbearing when they’re not married. And I don’t doubt that’s partly the explanation. But we need to think about marriage, education and childbearing as linked events that unfold over time. The average high-school dropout mother was 26, while the average college-graduate mother was 33. Delaying childbearing and continuing education are decisions that are made together, based on the opportunities people have. And completing more education increases both thelikelihood of marriage and the earning potential of one’s spouse.

So I think you could tell the story like this: Women with better educational opportunities delay childbearing, which increases their marriage prospects, and makes it more likely they will be married and financially better off when they have children in their 30s.

Race/ethnicity
The differences in nonmarital birth rates between race/ethnic groups in the U.S. are shocking, from about two-thirds for black and American Indian women to 29 percent for whites and 11 percent for Asians.

This pattern is related to the education trend, naturally, but that’s not the whole story. One aspect of the story is race/ethnic geography of opportunity in this country. I’ve written before about the shortage of employed men available for women to marry, a particular expression of racial disparity first popularized by sociologist William Julius Wilson a quarter century ago.

Using the new numbers on nonmarital birth rates for each state from the Census report, I compared them to the male non-employment rate—specifically, the percentage of unmarried men ages 22-50 that are not currently employed. Here’s the relationship:

The states with more single men out of work have higher rates of nonmarital births. Single mother, meet jobless man.

My conclusion from these patterns is that unmarried parenthood is primarily a symptom of lack of opportunity, especially for education and employment. Surely that’s not the whole story. Maybe we should be persuading people to marry younger or shaming them into avoiding parenthood. But I think those approaches increase stigma more than they change behavior or improve wellbeing—Pew surveys show that 77 percent of people already say raising a family is easier if you’re married and only 12 percent of single people say they don’t want to marry. So who needs convincing? Meanwhile, if we addressed the problems of education and employment, is there any doubt family security and stability would improve, and with it the wellbeing of children and their parents?

Father care: The more things don’t change, the more they stay the same

The U.S. Census Bureau has released its new report on childcare. This provides a good followup treatment for the hyperventilation induced by fear of fathers taking over (or being relegated to) childcare.*

First, the trend that fits my story of stalled gender progress. Among married fathers with employed wives, how many are providing the “primary care” for their children? That is, among the various childcare arrangements the children are in while their mother is at work, how many are in their fathers’ care more than in any other arrangement? Answer: 10%, which is virtually unchanged from a quarter-century ago:

Source:  U.S. Census Bureau, Who’s Minding the Kids? Child Care Arrangements: Spring 2011. (There was a methodology change in 1997, before which Census asked parents to name their primary arrangement, which they now calculate from the hours in each arrangement.)

Not a lot of change for a quarter century in which we’re told everything has changed.

However, in fairness to the change-is-happening community, here is the trend for the percentage of fathers who say they are providing ANY care to their children while their mothers were at work.

Source: As above.

I don’t give this much weight since it might reflect greater sensitivity to the importance of saying fathers provide care, but there you have it: it’s higher, and it shows some increases up until the early 1990s, which is when gender equality in general stalled on many indicators. Since the mid-1990s: Nothing.

Please note these figures don’t show the total contribution of fathers, but only reflects those married with children, whose wives are employed.

One interesting source of father care is mothers’ shiftwork. As Harriet Presser reported two decades ago, the 24/7 economy stimulates some task sharing among couples. In the current report, the Laughlin writes:

Preschoolers whose mothers worked nights or evenings were more likely to have their father as a child care provider than those with mothers who worked a day shift (42 percent and 23 percent, respectively)

* The report was written by Lynda Laughlin — have you credited a government bureaucrat by name for something valuable they did today?

And the 2011 divorce rate is…

18.9 divorces per 1,000 married people.

You heard it here first.

I don’t have a new analysis, but here’s the trend since 2008, when the American Community Survey started collecting data on “marital events” in the previous 12 months:

These are not recorded legal events, remember, but responses to a giant survey that asks people about their marital status and marital events.

The different rates for men and women are something of a mystery (to me). As long as they rise and fall together, I don’t worry about it too much. Unfortunately, in 2011 they didn’t — it’s up for men and flat for women, resulting in a net uptick. Since I’ve been predicting an uptick following the recession, I figure we should go with that interpretation. (Don’t be tempted attribute the difference to gay men’s divorces — we’re talking about 2.4 million divorces, a tiny tiny slice of which are homogamous.)

To put this in the long-term perspective, here’s the 1940-2011 trend, cobbled together from different sources. Given the long decline after 1979, any uptick feeds suspicion that something is changing or different about the last couple years.

If you want to replicate this, you start here at the FactFinder, then get the number of married people by gender (ACS Table B12001) and the number of people who got divorced in the 12 months before the survey (ACS Table S1251) — you can enter the table numbers into the search box.

For my series on divorce, divorce and the recession, and etc., follow the divorce tag.

 

Single mothers’ hardships

From the article, “Effects of unemployment and underemployment on material hardship in single-mother families,” in Children and Youth Services Review, comes this list of hardships recorded by single mothers on the Survey of Income and Program Participation from the mid-2000s.

For context, you can situate the mid-2000s on this trend-mashup I made:

Sources: Employment from Table FG5 here ; TANF caseloads from these reports; poverty from Census, here.

Employment down (after rising in the 1990s), poverty up, TANF non-responsive; lots of financial, health, food and housing hardship.

 

 

This thing about wives as breadwinnners

Here it is again: Susan Gregory Thomas writing in the Wall Street Journal, starts with:

I’m one of the 40% of American women, according to the Bureau of Labor Statistics, who are the breadwinners for their families—that is, we earn more than our husbands.

Really? No. I don’t know why this keeps going around.

First, let’s set aside that “40% of American women” is not the same thing as 40% of American wives, since about half of women are single. Anyway, the Census Bureau publishes this in a table every year for all married couples (homogamous couples excluded, of course). Here it is, color coded, from 2011:

Source: Table FG3 on this page.

Even if you give half of the “within $5,000″ couples to wives, they would still outearn husbands in only 33% of couples — and I’m not sure that’s a reasonable assumption.

This 40% thing might come from Liza Mundy, who wrote in Time that, in 2009, “nearly 4 in 10 working wives outearned their husbands.” Note: working wives. Lots aren’t. The figure here includes all couples, as Thomas said it in her intro.

I last reported this for the 2009 data. And a Pew report put the number of wives outearning husbands in 2007 at 22%. The facts do change a little now and then, but the details remain only vaguely relevant to some writers and editors.

(No offense to Gregory — I enjoyed the first half of her memoir on divorce, In Spite of Everything, which I just plugged for free.)

Let a hundred churches bloom

For Independence Day, I pause to consider religious freedom and immigration.

I recently made some trips out New Hampshire Ave., in Montgomery County, MD. In a 9-mile stretch of road (including a turn off onto the road where my kids’ summer camp was), here is some of what I saw:

Muslim Community Center

St. Andrew Ukranian Orthodox Cathedral

Iglesia de Dios / Church of God

First Alliance Church

Transfiguration Church Episcopal Anglican

Eun Sam Evangelical Church of Washington

Colesville Baptist Church

Colesville Presbyterian Church

New Hampshire Avenue Gospel Chapel

Our Lady of Vietnam Parish

Kingdom Hall of Jehovah’s Witnesses

Good Shepherd United Methodist Church

Lutheran Church of St. Andrew

Lord’s Prayer Presbyterian Church

St. Thomas Indian Orthodox Church

Unitarian Universalist Church of Silver Spring

New Life Baptist Church

Saints Constantine and Helen Greek Orthodox Church

Heritage Christian Church

Iglesia Adventista

I counted about 30 houses of worship along what some people call the Highway to Heaven. Through some combination of immigration and residential settlement patterns, real estate and zoning conditions, and county tax exemptions, this neighborhood has become an extreme hotbed of religious centers.

I imagine that this kind of eclectic, diverse, religious cacophony is uniquely American, but maybe that’s not true. Anyway, there is something about all this post-modern pre-modernity that I get a kick out of.

Montgomery County, which abuts Washington, D.C., is a major urban suburb, with a million people. Not all those churches are filled with immigrants, but many of them are. In the county, 32% of the population is foreign-born, according to the 2010 American Community Survey (table B05006). Talk about diversity, here are the top 50 – out of 122 – countries of origin for residents of the county, color-coded by region (click to enlarge):

Montgomery County, Maryland, immigrants, by country of origin (top 50 countries, 88% of all immigrants shown).

Immigrants tip the county’s numbers toward the “traditional” side of the ledger in terms of marriage and family structure: 60% of immigrants here are married, compared with 49% of the U.S.-born adults (B06008); and 51% of immigrant kids live with both parents, compared with 46% of the natives (B05009). But their religious and cultural diversity push the county toward the less-traditional future, ethnicity-wise.

Good site for some research, I reckon.

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;

Male nursing explodes: from 9% to 10%

Lots of buzz over a New York Times article about men moving into female-dominated occupations, which reported that “more and more men are starting to see the many benefits of jobs long-dominated by women.”

The Times produced this table, which shows the fastest growing occupations for (for some reason) college-educated White men, ages 25-39.

The ones with the pink dots are 70% female or more. The increase of young college educated White men in these occupations over 10 years appears striking, but the numbers are small. For example, compare that increase of (round numbers) 10,000 young White male registered nurses to the 1,900,000 full-time year-round nurses there were in 2010.

Moreover, consider that increase of 10,000 nurses in light of the overall growth of registered nurses from 2000 to 2010: about 500,000. Overall, the representation of men among full-time year-round registered nurses increased from 9.4% to 10.3% during the decade.

The Times article attempts to describe a broad trend of men moving into “pink-collar” jobs:

The trend began well before the crash, and appears to be driven by a variety of factors, including financial concerns, quality-of-life issues and a gradual erosion of gender stereotypes. An analysis of census data by The New York Times shows that from 2000 to 2010, occupations that are more than 70 percent female accounted for almost a third of all job growth for men, double the share of the previous decade.

Bold claims. But check the next sentence: “That does not mean that men are displacing women — those same occupations accounted for almost two-thirds of women’s job growth.” So, lots more men are in these jobs, but even more women are? How does that reflect an “erosion of gender stereotypes”? It seems like it reflects an increase in the size of female-dominated occupations.

In fact, as I reported briefly before, occupational gender segregation dropped barely a hair in the 2000s, from 51 to 50 on a scale of 0 to 100, compared with drops of 5 or 6 points in the decades before 1990. That is a lost decade for integration.

And if you look specifically at the category the Times chose — occupations that are 70% female or more — the percentage of men in those occupations increased, but only from 5.0% to 6.1%. And nurses? In 2010, 0.4% of all full-time year-round working men were nurses, up from 0.3% in 2000. Women are still 11-times more likely to be nurses than men.

Now that’s what you call a “gradual erosion of gender stereotypes.”

Sources: U.S. Census tables for 2000 and 2010 (table B24121).

Coming soon

To get the latest on trends in gender segregation and what they mean, look for the session titled, “Gendered Work: Occupational Segregation and Differential Representation,” at the American Sociological Associations meeting in Denver this summer. Among the papers there will be “Still Stalled? Occupational Gender Desegregation, 1950-2010,” by David Cotter, Joan Hermsen and Reeve Vanneman. Their abstract:

This paper examines trends and patterns in occupational gender segregation over six decades, from the 1950s to the 2000s. It identifies two distinct periods: first a period from 1960 to 1990 of relatively rapid integration of occupations and a period after 1990 of diminishing declines in segregation. In short, while the level of occupational gender segregation fell steadily in the 1960s, 1970s and 1980s, it declined much more slowly in 1990s and little if any in the 2000s. While most of the desegregation in the early period can be attributed to changing sex composition of occupations, in the later period most of the desegregation comes from shifts in occupational structure. These diminishing declines are observed regardless of the measure of segregation or occupational classifications, and broadly across race, class, and cohorts.

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:

Marriage, since when? (New Pew report)

The Pew Research Center has a new report on marriage trends that’s worth reading. But the comparison with 1960 is unfortunate.

First, though, the original part is their own survey data, where we learn, for example, that most people who aren’t married still want to get married:

For the historical comparison, the report uses 1960 to represent “then” and 2010 to represent “now.” That’s convenient from a data perspective, and half a century is a good round number to cover. But it misses the opportunity to show how anomalous the 1950s were in U.S. history.

Here is their chart on the increasing age at first marriage:

Here is the same data trend stretched back to 1890:

Source: U.S. Census Bureau (spreadsheet).

The change since 1960 is big and important. But the 1950s doesn’t represent the “traditional” family.

UPDATE: The Heritage Foundation has taken the opportunity of the Pew Report to have more fun with charts that offer misleading start dates. Here’s their version:

None of this is to deny the importance of the decline of marriage rates in the last half century. It’s just to say the trend is not linear from traditional-then to hell-in-a-handbasket-now.

Previous posts:

• Married, more and less
• Visualizing marriage decline