As you can see in the comments, Christie Boxer, the lead author of the journal article behind the Coontz Opinionator piece has contacted me to let us all know that the article is currently in revise and resubmit phase but will be published in Journal of Family Issues shortly.
The graphic is more legible than the chart from which the data originated. I’m guessing the Journal of Family Issues would not allow such a “fancy” series of graphics in the final published piece so I don’t mean this as a critique of the article’s authors. Just pointing out that there is good reason for journals and other publishers to reconsider their policies about how data can most usefully be presented.
I happen to have created a few graphics in this style myself and tend to favor it over the chart (e.g. this one about agricultural subsidies) in the past and think they work well for displaying changes in attitudes over time.
What needs work
The article from which this news story is drawn clearly provides information on both what women want and what men want in greater detail than what’s seen here. Why did the news story choose to run with less than half the data?
The chart clearly contains information on what men want in a mate AS WELL AS what women want in a mate. I see no reason for going (less than) halfway on this story. In fact, what I find most interesting is the convergence on some things – nobody cares much about chastity in a mate any more – and divergence on other traits – women rank men’s desire for home and children much higher than men rates women’s desire for home and children. That’s a puzzler worthy of thought in a way that a story that reflects only what men want is…well…just not all that interesting. Pair bonding takes two, as I’m sure Coontz knows because she’s been researching marriage for years. It’s unclear if the Times pressured her to come up with a more attention grabbing headline “The M.R.S. and the PhD” or if she chose that on her own or if it was a combination of factors.
I’m glad to see that, at least as far as I can tell from what is available to scholars other than Coontz (who might have an early full-length, unreleased draft of the Boxer, Noonan, Whelan paper), the scholars whose data led to the graphic were not so singly concerned with what men want in a mate. They were looking at how mate selection characteristics have been adjusted over time for both men and women and I hope that their article looks at the consonance and dissonance between the two genders’ mate selection ideals.
I would have preferred more attention paid to the graphic – like, say, the inclusion of what women want or an integrated graphic that displayed the overlaps and distances between what men and women want – and less time put into the accompanying illustrations which I have included to the left. I welcome regular readers of Sociological Images (and others) to comment on the messages coming out of the illustrations.
Boxer, Christie; Noonan, Mary; and Whelan, Christine. (forthcoming) “Measuring Mate Preferences: A Replication and Extension” Journal of Family Issues. [Table drawn from Christine Whelan’s research webpage]
Pew Research has created a tidy series of interactive graphics to describe the demographic characteristics of American generational cohorts from the the Silent Generation (born 1928 – 1945) through the Boomers (born 1946 – 1964), Generation X (1965 – 1980) [this is a disputed age range – a more recent report from Pew suggests that Gen Xers were born from 1965-1976), and the Millennial Generation (born 1981+ [now defined as being born between 1977 and 1992]). The interactive graphics frame the data well. They offer the timeline above as contextual background and a graphic way to offer an impressionistic framework for understanding generational change.
Then users can flip back and forth between comparing each generation to another along a range of variables – labor force participation, education, household income, marital status – while they were in the 18-29 year old age group OR by looking at where each generation is now. The ability to interact makes the presentation extremely illustrative and pedagogically meaningful. It is much easier to understand patterns that are changing over time versus patterns that are life course specific.
For instance, marital trends have been hard to talk about because the age at first marriage moves up over time, so it’s hard to figure out at what age we can expect that people will have gotten married if they are ever going to do so (I tried looking at marriage here).
What I like about the Pew Research graphics is that they show us not only what the generations looked like when they were between 18 and 29 years old (above) but also what they look like now (below). Not only does it become obvious how many millennials are choosing to remain unmarried (either until they are quite a bit older or forever – hard to say because the oldest millennials are still in their 30s), but it also becomes clear that in addition to divorce, widowhood is a major contributor to the end of marriage. Keep that in mind: somewhere around half of all marriages end in divorce so that means the other half ends in death. I would guess that a vanishingly small number of couples die simultaneously which means there are quite a few single older folks who did not choose to be single (of course, even if they didn’t choose to outlive their spouses, they may prefer widowhood to other alternatives, especially if their spouse had a long illness).
Labor force participation
Here’s another set of “when they were young” vs. “where they are now” comparisons, this time on labor force participation. It appears that the recession has walloped the youngest, least experienced workers the hardest. They have the highest unemployment rate AND the highest rate of educational attainment (and school loan debt), which leaves them much worse off as they start out than their parents were in the Boomer Generation. Even if their parents were in Generation X, they were still better off than today’s 20-something Millennials.
What needs work – Are generations meaningful?
My first minor complaint is that the graphic does not make clear *exactly* what “when they were young” means. If we look at the first graphic in the series, the timeline, it appears that “when they were young” was measured when each generation was between 18 and 29 years old. I hope that is the case. I might have had an asterisk somewhere explaining that “when they were young = when they were 18-29 years old”.
The concept of generations, in my opinion, is a head-scratcher. The idea that I had to come update this blog because the definition Pew was using to define Millennials and GenXers changed (without explanation that I could find) adds to my initial skepticism about the analytical purchase of generational categories. What is the analytical purchase of looking at generations – strictly birth-year delimited groups that supposedly share a greater internal coherence than other affinal or ascribed statuses we might imagine? If we believe that social, technological, and most all kinds of change happen over time, of course there are going to be measurable differences between one generation and the next. I imagine, though I have never seen the comparison, that if social scientists split people into 10- or 20-year pools based on their birth years they would end up with the same sorts of results. So why not think of generations as even units? And is it clear that the meaningful changes are happening in 20-year cycles? Or would 10-year age cohorts also work?
The real trickiness comes in when we think about individuals. Say someone is like myself, born in a year on the border between one generation and the next. Am I going to be just as much like a person born firmly in the middle of my cohort as a person on the far end of it? Or will people like me have about as much in common with the people about 8 years above and below us, but less in common with the people 15 years older than us who are considered to be in the same generation, and thus to have many similar tendencies/life chances/characteristics?
A better way to measure the cohort effect would seem to be to consider each individual’s age distance from each other individual in the sample – the closer we are in age, the more similar we could be expected to be with respect to things like labor force participation and educational attainment. Large structural realities like recessions are going to hit us all when we have roughly similar amounts of work force experience, impacting us similarly (though someone 10 years older and still officially in the same generation will probably fare much better). Since it is computationally possible to run models that can take the actual age distances of individuals in the same into account, I don’t understand the analytical purchase of the concept of generations.
We see that the current rise in never married Americans still doesn’t match the numbers of unmarried Americans back at the turn of the century.
We see that what is changing now isn’t so much the overall number of never married Americans (which has been hovering at around 30% for the past three decades) but the number of relatively older Americans who have never been married. I couldn’t find consistent numbers for people any older than the 30-34 year old category, nor could I find numbers for the 30-34 year olds available online from before 1960. I am still working on extending that portion of the graphic back to 1900.
What needs work
I need more numbers! I can’t understand the overall trend – which is the increase in never married Americans – without getting more historical context. I need that 30-34 year old category to extend all the way back. I also need to know what the deal is with slightly older cohorts, like 40-44 year olds. If all this graph tells us is that people are getting married later that is a very different story than the one that sounds like: “Americans aren’t getting married at all”. Marrying late and never marrying are two different scenarios. I cannot yet tell from the numbers I’ve got, just what is going on. And the problem with the aggregate data is that it is not granular enough to help understand current trends. Pooling 30 year olds with their parents and grandparents does not help me understand the 30 year olds (or the 20 year olds). And I really want to know what is going to happen in the near future, not what happened in the relatively distant past.
Other people have complained that the ’15 and older’ marital status category is crazy. Who gets married at 15?? But the problem is that we have to keep looking at that category or we cannot follow trends over time. That was the way the category was established back in the beginning, so in order to look across time, we have to keep the boundaries of the category the same. Now, to get around that problem, I included the 30-34 year olds, but that data slice doesn’t go all the way back.
Tricky census data.
And it’s black and white for easy printing. Otherwise I would have gone color.
About Graphic Sociology
Analyzing the visual presentation of social data. Each post, Laura Norén takes a chart, table, interactive graphic or other display of sociologically relevant data and evaluates the success of the graphic. Read more…