Tag Archives: music

Philip Cohen at 50, having been 14 in 1981

This is a sociological reflection about life history. It’s about me because I’m the person I know best, and I have permission to reveal details of my life.

I was born in August 1967, making me 50 years old this month. But life experience is better thought of in cohort terms. Where was I and what was I doing, with whom, at different ages and stages of development? Today I’m thinking of these intersections of biography and history in terms of technology, music, and health.


We had a TV in my household growing up, it just didn’t have a remote control or cable service, or color. We had two phones, they just shared one line and were connected by wires. (After I moved out my parents got an answering machine.) When my mother, a neurobiologist, was working on her dissertation (completed when I was 10) in the study my parents shared, she used a programmable calculator and graph paper to plot the results of her experiments with pencil. My father, a topologist, drew his figures with colored pencils (I can’t describe the sound of his pencils drawing across the hollow wooden door he used for a desktop, but I can still hear it, along with the buzz of his fluorescent lamp). A couple of my friends had personal computers by the time I started high school, in 1981 (one TRS-80 and one Apple II), but I brought a portable electric typewriter to college in 1988. I first got a cell phone in graduate school, after I was married.

The first portable electronic device I had (besides a flashlight) was a Sony Walkman, in about 1983, when I was 16. At the time nothing mattered to me more than music. Music consumed a large part of my imagination and formed the scaffolding of most socializing. The logistics of finding out about, finding, buying, copying, and listening to music played an outsized role in my daily life. From about 1980 to 1984, most of the money I made at my bagel store job went to stereo equipment, concerts, records, blank tapes for making copies, and eventually drums (as well video games). I subscribed to magazines (Rolling Stone, Modern Drummer), hitchhiked across town to visit the record store, pooled money with friends to buy blank tapes, spent hours copying records and labeling tapes with my friends, and made road trips to concerts across upstate New York (clockwise from Ithaca: Geneva, Buffalo, Rochester, Syracuse, Saratoga, Binghamton, New York City, Elmira).

As I’m writing this, I thought, “I haven’t listened to Long Distance Voyager in ages,” tapped it into Apple Music on my phone, and started streaming it on my Sonos player in a matter of seconds, which doesn’t impress you at all – but the sensory memories it invokes are shockingly vivid (like an acid flashback, honestly) – and having the power to evoke that so easily is awesome, in the old sense of that word.

Some of us worked at the Cornell student radio station (I eventually spent a while in the news department), whose album-oriented rock playlist heavily influenced the categories and relative status of the music we listened to. The radio station also determined what music stayed in the rotation – what eventually became known by the then-nonexistent term classic rock – and what would be allowed to slip away; it was history written in real time.

It’s like 1967, in 1981

You could think of the birth cohort of 1967 as the people who entered the world at the time of “race riots,” the Vietnam and Six Day wars, the Summer of Love, the 25th Amendment (you’re welcome!), Monterey Pop, Sgt. Peppper’s, and Loving v. Virginia. Or you could flip through Wikipedia’s list of celebrities born in 1967 to see how impressive (and good looking) we became, people like Benicio del Toro, Kurt Cobain, Paul Giamatti, Nicole Kidman, Pamela Anderson, Will Ferrell, Vin Diesel, Phillip Seymour Hoffman, Matt LeBlanc, Michael Johnson, Liev Schreiber, Julia Roberts, Jimmy Kimmel, Mark Ruffalo, and Jamie Foxx.

But maybe it makes more sense to think of us as the people who were 14 when John Lennon made his great commercial comeback, with an album no one took seriously – only after being murdered. The experiences at age 14, in 1981, define me more than what was happening at the moment of my birth. Those 1981 hits from album-oriented rock mean more to me than the Doors’ debut in 1967. My sense of the world changing in that year was acute – because it was 1981, or because I was 14? In music, old artists like the Moody Blues and the Rolling Stones released albums that seemed like complete departures, and more solo albums – by people like Stevie Nicks and Phil Collins – felt like stakes through the heart of history itself (I liked them, actually, but they were also impostors).

One moment that felt at the time like a historical turning point was the weekend of September 19, 1981. My family went to Washington for the Solidarity Day rally, at which a quarter million people demonstrated against President Reagan and for organized labor, a protest fueled by the new president’s firing of the PATCO air traffic controllers the previous month (and inspired by the Solidarity union in Poland, too). Besides hating Reagan, we also feared a nuclear war that would end humanity – I mean really feared it, real nightmare fear.


A piece of radio news copy I wrote and read at WVBR, probably 1983. The slashes are where I’m going to take a breath. “Local AQX” is the name of the tape cartridge with the sound bite (“actuality”) from Alfred Kahn, and “OQ:worse” means that’s the last word coming out of the clip.

On the same day as Solidarity, while we were in D.C., was Simon and Garfunkel’s Concert in Central Park. They were all of 40 (literally my mother’s age), tired old people with a glorious past (I’m sure I ignored the rave reviews). As I look back on these events – Reagan, the Cold War, sell-out music – in the context of what I thought of as my emerging adulthood, they seemed to herald a dark future, in which loss of freedom and individuality, the rise of the machines, and runaway capitalism was reflected in the decline of rock music. (I am now embarrassed to admit that I even hated disco for a while, maybe even while I listened 20 times, dumbstruck, to an Earth, Wind, and Fire album I checked out of the library.)

I don’t want to overdramatize the drama of 1981; I was basically fine. I came out with a penchant for Camus, a taste for art rock, and leftism, which were hardly catastrophic traits. Still, those events, and their timing, probably left a mark of cynicism, sometimes nihilism, which I carry today.


About 1984, with Daniel Besman (who later died) in Ithaca. Photo by Linda Galgani.

Data aside

Maybe one reason 1981 felt like a musical watershed to me is because it really was, because pop music just got worse in the 1980s compared to the 1970s. To test (I mean prove, really) that hypothesis, I fielded a little survey (more like a game) that asked people to rate the same artists in both decades. I chose 58 artists by flipping through album charts from 1975-1984 and finding those that charted in both decades; then I added some suggestions from early respondents. To keep the task from being too onerous, as it required scoring bands twice from 1 (terrible) to 5 (great), once for each period, and some people found it difficult, I set the survey to serve each person just 10 artists at random (a couple of people did it more than once). The participants were 3/4 men, 3/4 over 40, and 3/4 White and US-born; found on Facebook, Twitter, and Reddit. The average artist was rated 11 times in each period (range 5 to 19). (Feel free to play along or share this link; I’ll update it if more come in.)

The results look very bad for the 1980s. The average change was a drop of .59, and only three acts showed noticeable improvement: Pat Benatar, Michael Jackson, and Prince (and maybe Talking Heads and the lowly Bryan Adams). Here is the full set (click to enlarge):

Technology and survival

I don’t think I would have, at age 14, given much weight to the idea that my life would repeatedly be saved by medical technology, but now that seems like business as usual, to me anyway. I guess as long as there’s been technology there have been people who owe their lives to it (and of course we’re more likely to hear from them than from those who didn’t make it). But the details are cohort-specific. These days we’re a diverse club of privileged people, our conditions, or their remnants, often hidden like pebbles wedged under the balls of our aging feet, gnawing reminders of our bodily precarity.

Family lore says I was born with a bad case of jaundice, probably something like Rh incompatibility, and needed a blood transfusion. I don’t know what would have happened without it, but I’m probably better off now for that intervention.

Sometime in my late teens I reported to a doctor that I had periodic episodes of racing heartbeat. After a brief exam I was sent home with no tests, but advised to keep an eye on it; maybe mitral valve prolapse, he said. I usually controlled it by holding my breath and exhaling slowly. We found out later, in 2001 – after several hours in the emergency room at about 200 very irregular beats per minute – that it was actually a potentially much more serious condition called Wolff-Parkinson-White syndrome. The condition is easily diagnosed nowadays, as software can identify the tell-tale “delta wave” on the ECG, and the condition is listed right there in the test report.


Two lucky things combined: (a) I wasn’t diagnosed properly in the 1980s (which might have led to open-heart surgery or a lifetime of unpleasant medication), and; (b) I didn’t drop dead before it was finally diagnosed in 2001. They fixed it with a low-risk radiofrequency ablation, just running a few wires up through my arteries to my heart, where they lit up to burn off the errant nerve ending, all done while I was almost awake, watching the action on an x-ray image and – I believed, anyway – feeling the warmth spread through my chest as the doctor typed commands into his keyboard.

Diverticulitis is also pretty easily diagnosed nowadays, once they fire up the CT scanner, and usually successfully treated by antibiotics, though sometimes you have to remove some of your colon. Just one of those things people don’t die from as much anymore (though it’s also more common than it used to be, maybe just because we don’t die from other things as much). I didn’t feel like much like surviving when it was happening, but I suppose I might have made it even without the antibiotics. Who knows?

More interesting was the case of follicular lymphoma I discovered at age 40 (I wrote about it here). There is a reasonable chance I’d still be alive today if we had never biopsied the swollen lymph node in my thigh, but that’s hard to say, too. Median survival from diagnosis is supposed to be 10 years, but I had a good case (a rare stage I), and with all the great new treatments coming online the confidence in that estimate is fuzzy. Anyway, since the cancer was never identified anywhere else in my body, the treatment was just removing the lymph node and a little radiation (18 visits to the radiation place, a couple of tattoos for aiming the beams, all in the summer with no work days off). We have no way (with current technology) to tell if I still “have” it or whether it will come “back,” so I can’t yet say technology saved my life from this one (though if I’m lucky enough to die from something else — and only then — feel free to call me a cancer “survivor”).

It turns out that all this life saving also bequeaths a profound uncertainty, which leaves one with an uneasy feeling and a craving for antianxiety medication. I guess you have to learn to love the uncertainty, or die trying. That’s why I cherish this piece of a note from my oncologist, written as he sent me out of the office with instructions never to return: “Your chance for cure is reasonable. ‘Pretest probability’ is low.”


From my oncologist’s farewell note.

Time travel

It’s hard to imagine what I would have thought if someone told my 14-year-old self this story: One day you will, during a Skype call from a hotel room in Hangzhou, where you are vacationing with your wife and two daughters from China, decide to sue President Donald Trump for blocking you on Twitter. On the other hand, I don’t know if it’s possible to know today what it was really like to be me at age 14.

In the classic time travel knot, a visitor from the future changes the future by going back and changing the past. The cool thing about mucking around with your narrative like I’m doing in this essay (as Walidah Imarisha has said) is that it by altering our perception of the past, we do change the future. So time travel is real. Just like it’s funny to think of my 14-year-old self having thoughts about the past, I’m sure my 14-year-old self would have laughed at the idea that my 50-year-old self would think about the future. But I do!


Filed under Me @ work

Here it is, your moment of White

A couple years ago, in a post called “Stuff White People Google,” I showed which Google search patterns were most highly correlated with the representation of different race/ethnic groups in the Census. That was a much better post than this.

This is a moment-of-White followup.

Here are Whites, by county, from this tool:


Here are the searches for “back in black,” from Google Correlate:


Google searches for “back in black”

And here is the correlation between searches for “back in black” and searches for “kitten pictures,” by state:


The scales are normed to a mean of 0 and standard deviation of 1 by Google, I think. I made the graph in Stata with this command (which I’m putting here because I always forget this syntax):

gr twoway scatter backinblack kittenpictures, mlabel(state) mlabposition(0) msymbol(i)

Random question

So, if it is Whites doing the searching for “back in black” and “kitten pictures,” is it possible that the searches are going on in the same households with some kind of gender division?


Don’t let that selectively-chosen picture fool you. According to the Alexa web traffic site, visitors to acdc.com skew only slightly male. And Facebook tells me I can reach a mostly- but not overwhelmingly-male mix of 3 million women versus 4 million men if I target people with an interest in AC/DC for an ad. (However, if people Googling AC/DC are looking for guitar tabs, maybe it’s the intersection of guitar and AC/DC as interests that matter.)

On the other hand, cuteoverload.com, which is loaded with kitten pictures, skews strongly female, and Facebook tells me that “cat pictures” as an interest will attract women more than men at a ratio of 4-to-1 (much more skewed  than the general interest in cats: 1.5-to-1).

Anyway, this might not be the best case. I wonder what other examples there might be of a specific group (e.g., Whites) being divided between men who have a uniquely strong interest in something (AC/DC) and women who have a uniquely strong interest in something else (kitten pictures), with low overlap between the genders. That would be neat – intersectionality seen in Google search patterns.


Anyway, it’s time for another year of graduate student admissions. If you or someone you know like playing with data and making graphs, pursuing hunches about social patterns (more or less important than the ones here), and reading and writing a lot, maybe you or your friend should be in next year’s pile of applications.


Filed under In the news

Marriage is going down, so what does Kanye West have to do with it?

The marriage rate has fallen almost continuously for more than half a century, from a sky-high 90 per 1,000 unmarried women in 1950 (meaning almost 1 in 10 single women got married that year) to a bare 31 per 1,000 in 2011. Splashdown appears imminent.


Sources: 1940-1960; 1970-2011.

Social scientists understand that there is a combination of demographic, economic, policy, and cultural factors involved. These include the aging population, men’s declining fortunes, the incarceration of millions of poor men, the rise of secular ideology and the sexual revolution.

Often, however, cultural influence is left to what you might call residual interpretation. Proving that culture affects demographic trends is difficult. Instead, people consider how demographic, economic and policy factors play their roles, and then attribute what’s left of the trend to culture.

Recently, the National Center for Family and Marriage Research at Bowling Green University reported the marriage rate for each state and D.C., ranging from 61 marriages per 1,000 unmarried women in Utah down to 19 per 1,000 in Washington, D.C. and 20 in Rhode Island. To explain the pattern using normal demographic practices, I gathered some other data about states from the Census Bureau: The percent of the population over 65, percent female, percent with a BA or higher education, population density, per capita income and race/ethnic composition. With that information – using a regression – I can guess the marriage rate to within 3.1 points on average. This is what the regression looks like, showing what happens when I start with age and sex composition, add income and education, and then add race/ethnicity:


In statistical terms (R2), my simple model explains 73 percent of the variation in marriage rates, which is pretty good. Before I would use the marriage rate as an indicator of something like “culture,” then, I would say most of what’s going on reflects larger demographic and economic patterns that we more or less understand. The differences that remain, however, still might be the result of cultural, religious, or attitudinal factors that are harder to assess. (I stress this is not about low Black marriage rates: note the population percentage Black has no effect once the other factors are controlled.)

Culture, meet big data

What about big data, the billions of bits of information people leave strewn around wherever they go? Marketers and government spying agencies make most of the headlines, but social scientists, too, are scraping up millions of words and turning them into analyzable numbers, so they can tell you things like:

One of the easiest sources to use for this kind of thing is the Google Correlate tool, which finds the search terms whose frequency most closely follows a specified pattern. I entered the marriage rate for each state, shown on the map on the left, with darker green indicating higher marriage rates. Google Correlate tells me which searches track this variation: which searches are most popular in Utah, least popular in D.C., and so on. (I actually trimmed the Utah rate to it wouldn’t be such an outlier, from 61 down to 57, just above the next highest). It turns out the most correlated search is for “rolls recipe,” which is correlated with the marriage rate at .85 on a scale of -1 to 1.


But since my interest is in the decline of marriage, I multiplied the marriage rate by -1 and tried again (so now darker green indicates a lower marriage rate). The answer, overwhelmingly: Kanye West. (Experts at finding any website anywhere will know that he’s a never-married proud father-to-be with co-parent Kim Kardashian.)


That correlation between the inverse of the 2011 marriage rate and “kanye west my beautiful dark twisted fantasy” (his last album) is .81. Further, Google produces the top 100 most correlated searches, and of those, no fewer than 28 were about Kanye West (such as “kanye west new album,” “devil in a new dress lyrics” and “air yeezys”). Another 16 were other hip-hop searches, including some about Jay Z and Lil Wayne. Other apparent themes include mafia-related entertainment (“sopranos episode,” “pacino movies,” “corleone”) Sex and the City, and shopping at Marshalls.

Does this tell us more than the simple demographic analysis I did above? When I put the top Kanye search into my model, it has the strongest effect, and the variance explained jumps to 81 percent. The model now can predict the marriage rate to within 2.5 points on average.  It’s a very good predictor, and it’s not just reflecting simple demographics like age, gender and race. Whether Kanye is in the analysis or not, Black population percentage has no effect on this prediction. Here is the regression, with new parts in red:



So, I dredged all the search data in the world for something correlated with marriage rates, and found something. But what does it mean? Two cautionary stories are revealing. Forecasting guru Nate Silver has a good description of how noise looks like signal. For example, with the tens of thousands of economic statistics available to build a forecasting model, finding a pattern after the fact is deceptively easy. But it usually doesn’t work for predicting future economic trends.

Another caution comes from genomic studies. In a study of, say, cancer genetics, statisticians may conduct millions of tests for the association between any genetic variant and the occurrence of cancer. With the typical definition of “statistical significance” – which tolerates a 5 percent random chance of being wrong – that means they’d find hundreds of thousands of bogus “significant” associations. So good scientists set their significance threshold for such studies much tighter, more like.00005 percent than 5 percent. That way they are sure to only blow the whistle on genes if the chances of being wrong are vanishingly small.

So, this is a suggestive game of Big-Data Craps, not real research. It’s meant to provoke a little. I hope we’ll think creatively about new kinds of data we can use. Also, I want to generate ideas about cultural explanations for demographic trends. It should be at least as useful as some pundit simply declaring, for example, that gay marriage is killing real marriage. (“As the cause of gay marriage has pressed forward,” wrote Ross Douthat, “the social link between marriage and childbearing has indeed weakened faster than before.” That theory has about as much going for it as one linking the decline of marriage to the rise of high fructose corn syrup or the explosion of red cards in World Cup soccer.)


Kanye’s fantasy

With those caveats, here are three possible explanations for the finding:

  1. Google, by trawling through millions of search term patterns, has come up with a random bit of noise that just happened to catch my attention. There’s nothing there, really.
  2. The hip-hop Google search is capturing a more finely-grained demographic pattern than I did with my simple Census numbers. So what matters for marriage is not just things like the percentage female, education levels and racial composition of the population, but the presence of particular combinations of these demographic groups. Hip hop’s audience is notoriously difficult to define — it’s featured on top-five radio stations in markets such as San Francisco and Los Angeles as well as Detroit and Atlanta — but it’s certainly not as simple as age, gender, and race
  3. Hip-hop actually is weakening marriage in America. People who listen to Kanye West and other hip-hop music are taken in by the music’s consumerist individualism and shun marriage, with its staid image of tradition, conformity and restraint. As a result, they are less likely to get married than the people Googling “rolls recipe.”

I lean toward explanation #2. Explanation #3 might have something to it. As the philosopher xkcd wrote, “correlation does not imply causation, but it does waggle its eyebrows suggestively and gesture furtively while mouthing ‘look over there.’” But I wouldn’t draw that conclusion without a lot more evidence, including doing some comparisons to other cultural factors, like other kinds of music or religious patterns. Since I have no expertise in hip hop (post 1989), I would be glad to hear from people who know about it for realz.

Addendum: Here’s a scattergram showing the correlations between some of the variables in the regression. In each cell there’s a dot for every state plus DC. The Kanye variable is scaled (by Google) to have a mean of 0 and standard deviation of 1 (click to enlarge).



Filed under Uncategorized

Hit song, hit name

In the comments of my Atlantic post about the name Mary, and in a number of personal emails, people have suggested pop culture triggers for changes in name direction – like the turnaround in Emma and its eventual peak at #1 in 2002. In that year, Emma jumped 9 places, from 13th to 4th among U.S. girls. There are other examples, but I prefer a few that date back to a golden era of pop songs with women’s names in the titles.

Some hits in the 1960s did not produce name bounces (Sharona, Sherry). But in the 1970s three #1 songs produced name bounces: Maggie (from “Maggie May,” by Rod Stewart in 1971), Brandy (Looking Glass, 1972), and Angie (Rolling Stones, 1973). I added a personal favorite, Rhiannon (Fleetwood Mac, 1975).


Each of these tells a good story.

  • Rhiannon and Brandy both debuted in the top 1,000 names the year they made the charts, with Brandy making a credible run for the top before collapsing in the 1990s. Rhiannon hung on as a niche name until the 2000s.
  • Maggie changed direction the year of the Rod Stewart hit, reversing a long slide.
  • Angie, which gained popularity in the 1950s (Angie Dickinson?) seemed to get a boost from the Stones song, but it was not long lasting (it is a breakup song, after all). I don’t know why Angie came back in the 1990s (1994 movie with Geena Davis?).

My series of posts on names is here.

The names database is here.


Filed under Me @ work

Where did we go wrong, or did we? (love and sex edition)

For ever – or at least for 70 years – make love was many times more common than have sex, at least in the Google Ngrams database of millions of books in American English. And, then — well, you can guess what happened then:

The results are the same with “making” and “having” (you can play with the search here).

Why? What happened? Could it be “the culture”? Zooming in on the period since 1950, preliminary evidence is mixed:

I’m open to hypotheses.




Filed under Me @ work