Tag Archives: tv

Why aren’t female Charlies killing the name Charles?

Geena Davis as Charly in The Long Kiss Goodnight, 1996

Geena Davis as the best female movie Charly (The Long Kiss Goodnight, 1996)

Charles was a top-10 name for boys in the U.S. into the 1950s, and it has always been more than 99% male. American parents have shown no interest in breaking down that barrier. However, since the early 2000s, they have started naming their daughters Charlie, Charlee, Charleigh, Charli, Charley, and Charly. Last year 4,882 girls got one of those names, which is more than Anna or Samantha (and more than twice as many as were named Mary).

Near the start of that wave, the Disney TV show Good Luck Charlie — about a married, White couple with four children, the last of which was named Charlotte (nick-named Charlie) — debuted in 2010, and peaked in 2012, with 7.5 million viewers on one Sunday.

promo image from Disney show Good Luck Charlie

But Charlie has not become a girls’ name. As a I reported last week, Charlie is now the most common androgynous name (between 40% and 60% female), with 3,556 births split almost equally between boys and girls. The other variations are more female: All versions of Charlie together are 74% female.

So, with girls pouring in, are parents heading for the exits, as we saw with names like Taylor and Kim? Not yet. Charles is much less common than it once was, but it has not slipped appreciably since girls started picking up its nickname. Here are the trends back to 1880:

charlies.xlsx

As girl Charlies have gained ground, in fact, even the spelling Charlie is rising in the rankings for boys, up to 218th last year from 306th a decade ago. Parents are now naming their boys Charlie at twice the rate they did in 1968. This figure zooms in on the Charlie wars for the last 50 years. (For this I combine all the spellings for boys, but 92% of them are Charlies.)

charlies.xlsx

If Charlie follows the path of previous gender battleground names, however (see Tristan Bridges’ two posts on this from last week), we might still see a male crash, or a female crash, or both. Androgyneity has historically been unstable in this system, especially when (from parents’ point of view) femininity contaminates a masculine space.

If the collapse doesn’t come, maybe it will be because both sides have gender unambiguous reinforcements: Charles for boys (99.8% male), and Charlotte for girls (99.9% female). So parents who like the name Charlie, including those who may choose it precisely because of its androgynous image, also know they have a gendered space they or their children can retreat to if necessary.


Data for this analysis are from the Social Security Administration. The data files and my Stata code are available on the OSF, here.

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Do rich people like bad data tweets about poor people? (Bins, slopes, and graphs edition)

Almost 2,000 people retweeted this from Brad Wilcox the other day.

bradpoorstv

Brad shared the graph from Charles Lehman (who noticed later that he had mislabeled the x-axis, but that’s not the point). First, as far as I can tell the values are wrong. I don’t know how they did it, but when I look at the 2016-2018 General Social Survey, I get 4.3 average hours of TV for people in the poorest families, and 1.9 hours for the richest. They report higher highs (looks like 5.3) and lower lows (looks like 1.5). More seriously, I have to object to drawing what purports to be a regression line as if those are evenly-spaced income categories, which makes it look much more linear than it is.

I fixed those errors — the correct values, and the correct spacing on the x-axis — then added some confidence intervals, and what I get is probably not worth thousands of self-congratulatory woots, although of course rich people do watch less TV. Here is my figure, with their line (drawn in by hand) for comparison:

tvfaminc-bradcharles

Charles and Brad’s post got a lot of love from conservatives, I believe, because it confirmed their assumptions about self-destructive behavior among poor people. That is, here is more evidence that poor people have bad habits and it’s just dragging them down. But there are reasons this particular graph worked so well. First, the steep slope, which partly results from getting the data wrong. And second, the tight fit of the regression line. That’s why Brad said, “Whoa.” So, good tweet — bad science. (Surprise.) Here are some critiques.

First, this is the wrong survey to use. Since 1975, GSS has been asking people, “On the average day, about how many hours do you personally watch television?” It’s great to have a continuous series on this, but it’s not a good way to measure time use because people are bad at estimating these things. Also, GSS is not a great survey for measuring income. And it’s a pretty small sample. So if those are the two variables you’re interested in, you should use the American Time Use Survey (available from IPUMS), in which respondents are drawn from the much larger Current Population Survey samples, and asked to fill out a time diary. On the other hand, GSS would be good for analyzing, for example, whether people who believe the Bible is the “the actual word of God and is to be taken literally, word for word” watch TV more than those who believe it is “an ancient book of fables, legends, history, and moral precepts recorded by men” (Yes, they do, about an hour more.) Or looking at all the other social variables GSS is good for.

On the substantive issue, Gray Kimbrough pointed out that the connection between family income and TV time may be spurious, and is certainly confounded with hours spent at work. When I made a simple regression model of TV time with family income, hours worked, age, sex, race/ethnicity, education, and marital status (which again, should be done better with ATUS), I did find that both hours worked and family income had big effects. Here they are from that model, as predicted values using average marginal effects.

tv work faminc

The banal observation that people who spend more time working spend less time watching TV probably wouldn’t carry the punch. Anyway, neither resolves the question of cause and effect.

Fits and slopes

On the issue of the presentation of slopes, there’s a good lesson here. Data presentation involves trading detail for clarity. And statistics have both have a descriptive and analytical purpose. Sometimes we use statistics to present information in simplified form, which allows better comprehension. We also use statistics to discover relationships we couldn’t otherwise — such as multivariate relationships that you can’t discern visually. The analyst and communicator has to choose wisely what to present. A good propagandist knows what to manipulate for political effect (a bad one just tweets out crap until they get lucky).

Here’s a much less click-worthy presentation of the relationship between family income and TV time. Here I truncate the y-axis at 12 hours (cutting off 1% of the sample), translate the binned income categories into dollar values at the middle of each category, and then jitter the scatterplot so you can see how many points are piled up in each spot. The fitted line is Stata’s median spline, with 9 bands specified (so it’s the median hours at the median income in 9 locations on the x-axis). I guess this means that, at the median, rich people in America watch about an hour of TV per day less than poor people, and the action is mostly under $50,000 per year. Woot.

gss tv income

Finally, a word about binning and the presentation of data (something I’ve written about before, here and here). We make continuous data into categories all the time, starting from measurement. We usually measure age in years, for example, although we could measure it in seconds or decades. Then we use statistics to simplify information further, for example by reporting averages. In the visual presentation of data, there is a particular problem with using averages or data bins to show relationships — you can show slopes that way nicely, but you run the risk of making relationships look more closely correlated than they are. This happens in the public presentation of data when analysts are showing something of their work product — such as a scatterplot with a fitted line — to demonstrate the veracity of their findings. When they bin the data first, this can be very misleading.

Here’s an example. I took about 1000 men from the GSS, and compared their age and income. Between the ages of 25 and 59, older men have higher average incomes, but the fit is curved with a peak around 45. Here is the relationship, again using jittering to show all the individuals, with a linear regression line. The correlation is .23

c1That might be nice to look at but it’s hard to see the underlying relationship. It’s hard to even see how the fitted line relates to the data. So you might reduce it by showing the average income at each age. By pulling the points together vertically into average bins, this shows the relationship much more clearly. However, it also makes the relationship look much stronger. The correlation in this figure is .65. Now the reader might think, “Whoa.”

c2Note this didn’t change the slope much (it still runs from about $30k to $60k), it just put all the dots closer to the line. Finally, here it is pulling the averages together in horizontal bins, grouping the ages in fives (25-29, 30-34 … 55-59). The correlation shown here is .97.

c3

If you’re like me, this is when you figured out that reducing this to two dots would produce a correlation of 1.0 (as long as the dots aren’t exactly level).

To make good data presentation tradeoffs requires experimentation and careful exposition. And, of course, transparency. My code for this post is available on the Open Science Framework here (you gotta get the GSS data first).

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Fox News took my quotes out of context and added wrong information

Following up on Part 1, discussed here, Parts 2 and 3 of the Fox News series on demography and social change also featured quotes from me. Part 2 used a reasonable quote in a reasonable way, but Part 3 did not.

Part 2 is a good teaching lesson in sky-is-fallingism, a Fox News signature. As they’ve done before, they literally start with a 1950s TV show as if it were historical footage, and then proceed to the chaotic now.

“If Tommy suddenly woke up today, he’d be an aging Baby Boomer, receiving benefits from a Social Security trust fund that is more than 2 trillion dollars in debt. He might be tending to his aches and pains with medical marijuana, now legal in 33 states. He might see his childhood friends are legally married [showing gay male weddings] while almost half the mommies in the U.S. are not.”

Cut to racial minority students in UCLA gear. Etc. The most extreme cut is between the Heritage Foundation person saying, of Democrats, “We’re the party of government, and that way if we have voters attached to government programs they’re going to stick with us,” before, literally, cutting to archival Mao and Stalin footage, with the voice-over:

“That, while the hard lessons of socialism — 70 million dead in China, 20 million dead in the Soviet Union — that happened during Communism, are often neglected in colleges, now focused on social justice curricula.”

Great stuff, good for teaching. Anyway, my quote in the piece is just saying young people nowadays don’t like to be lectured about traditional values. They just frame it like that’s a bad thing. Here it is:

Part 3 is where they misused my quotes, in two places. The episode is about how low fertility leads to immigration, which creates chaos and causes populism. Plenty wrong in here, but I’m just focusing on my beefs. First, on immigration, they say:

“Europe’s accommodation of refugees fleeing ISIS and the civil war in Syria, has proved a bridge too far.”

Philip Cohen: “Immigration poses challenges to the dominant culture. It’s obviously politically fraught.”

Cut to rioting footage. Narrator: “From Greece to Italy, Germany, France, and the Nordic countries, clashes have erupted. Nationalist politicians are forcing a reckoning with multiculturalism.”

According to my own recording of the interview, however, what I said immediate after, “It’s obviously politically fraught,” was this:

“On the other hand, there’s a great pent-up demand for immigration. There are plenty of people who want to come here. The immigrants who come here tend to be the better off, more highly skilled and educated people from the countries that they’re coming from, contrary to some stereotypes, so they end up strengthening the U.S. economy even as they improve their own wellbeing. So if you can get over all the challenges and conflict that sometimes comes along with rapid immigration, what you end up with is an answer to the population [problem].”

Lesson learned. Not surprising they didn’t use my pro-immigration other hand. I should have anticipated that better and made the other hand the only hand in my comment. However, they had invited me to discuss Millennials and marriage, so I wasn’t prepared for immigration.

The piece has distracted tangents into robots in Japan and the one-child policy in China. I also wasn’t prepared for the one-child policy on that day, but I always have a take ready on that. Here’s what I said, according to my recording:

“One thing to know about China is the birthrate had fallen a lot before the one-child policy. So even if you like the idea … [they interrupted to say they had bumped the focus, so I should start my answer again] …One thing that’s important to realize about China is that population growth had already slowed a lot before the one-child policy started, so they really didn’t need the one-child policy to slow down population growth. And it was quite draconian. It went against what most people wanted for their families. The implementation of it was very repressive. It included forced sterilization, and abortion, and very harsh penalties for people who had extra children. So it was really a human rights disaster.”

In the piece, however, they used the part about forced sterilization and the human rights disaster, but didn’t use where I said, “they really didn’t need the one-child policy to slow down population growth” — and replaced it with voice-over that said, “overpopulation compelled the Communist government to force a one-child policy on the populous.” So they took out something true and added something false.

To see how wrong that it, here is the trend in total fertility rate (births per woman) from 1960 to 2016. This shows how much birth rates had come down in China under policies that promoted smaller families along with women’s healthcare, education, and employment, by the time China implemented the one-child policy in 1980:

china-1980-tfr

I put India and Nigeria on the chart to show how successful China already was relative to other large, poor countries with high fertility in the 1960s. There was no demographic justification for the one-child policy, and the fact that it became draconian and repressive is a clue to how out of step it was with the family lives of the Chinese people.

The reason this matters is not particularly important for the Fox News piece, but it’s very important to understand that progress on reducing fertility is better achieved through empowerment and development than through command and repression. Now that we’re seeing countries interested in increasing fertility, this is important historical context. (Here’s a good review article by Wang Feng, Baochang Gu, and Yong Cai [paywalled | bootlegged])

Anyway, regardless of the implications, it just goes against accuracy and honesty to remove true information for false information.

Anyway anyway, here’s Part 3:

 

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TV time at child care

An article in Pediatrics finds that children in center-based child care programs watch less TV than those cared for in home-based programs. And centers with staff who have college degrees show children in their charge fewer hours of television.

At least in North Carolina, staff education is one of the factors that goes into state evaluation of child care centers, which affects the prices they can charge.

Plenty of evidence supports the idea that professional child care is good for children (for example), but the quality of care does matter. Apparently, TV time, which contributes to attention problems in children, is one source of stratification in that quality.

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