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Ruth Sidel, appreciated

wccc

Some of Victor Sidel’s photos from Women and Child Care in China

I just learned that sociologist Ruth Sidel has died. These are a few scattered notes on the influence of some of her work. I always wanted to meet her but never did. I read her early work on China as a student, and I used her later work on poverty and welfare in the U.S. in my teaching.

She had a great influence on American leftists (and me) initially because of her writing on China, especially Women and Child Care in China, which came from a trip she took in 1971, during the Cultural Revolution, with her husband, Victor Sidel (and one of the founders of Physicians for Social Responsibility). At the time of that trip she was a social worker, having written only a masters thesis, invited in her role as wife, but found their hosts willing to open up their visit (which was supposed to be about medical care) to the issues of women’s liberation and education. She remembered in an oral history interview:

They integrated what I was interested in into every single thing we did. It was just remarkable. … Half way through the trip I said to Vic, “There is a book here.”… He said “How can you have a book after two weeks?” And I said “Trust me, there is a book here.” …and I did and wrote a book called “Women and Childcare in China” which was really like successful. People really wanted to read about it. They wanted to read about mutual aid. They wanted to read about how the communist government was trying to take care of children and women. We went to preschools and how the children were taught to help each other, love each other and take care of each other according to the words of Chairman Mao, literally, I’m quoting. The book went into paperback and sold even more copies and I spoke everywhere. I’d never given a speech before in my life, ever. Terrified but I had to do it because I knew stuff that other people didn’t know and I had to communicate it.

One book led to the next book about neighborhood organization [Families of Fengsheng: Urban Life in China] and I helped Vic finish his book on healthcare. The whole 70s I was writing about China and lecturing about China all over the country and in many parts of Europe. We were invited—I mean it was just unbelievable. It was a total life change and thrilling.

I am awed by that spirit of adventure, the confidence to seize that moment, and the commitment to doing social science for the public interest.

Later she got a PhD in sociology and went on to write on poverty and welfare, the work she was known for after the 1990s (see books listed below).

Although writing books promoting the Cultural Revolution in the early 1970s is not a fast-track ticket to respectability nowadays, if you go back to those books you will also see how close her observations are, and how incisive. The macro-political context of course is important (and she wrote about that), but that was not her primary contribution. In addition to what she learned (or didn’t) from official documents and statements, she did see some things with her own eyes. One of the key insights she brought back from China was the value of deprofessionalization, the role of non-professionals to improving community health and education. This was essential to the dramatic improvements in public health achieved in that period in China (which I wrote about in a remembrance on another China-inspired American feminist, Janet Salaff.) This was a radical-democratic view of public health in particular. From Families of Fengsheng:

Health care, perhaps better than any other single facet of Chinese society, vividly illustrates some of the principles that guide life in China today: a strong belief in mass involvement; recruitment of health workers from among those who live in the community to be served; short periods of training to minimize alienation from the community; a minimum of social distance between the helper and the helped; attempts to demystify as much of medicine as possible; decentralization; and motivating people through altruism rather than through prestige or material incentives.

Wouldn’t that be something!

I hope there will be more comprehensive remembrances from people who knew Ruth Sidel and her work more fully. This note is just to register my own deep appreciation.


Some books by Ruth Sidel:

  • Sidel, Ruth. 1972. Women and Child Care in China; a Firsthand Report. New York: Hill and Wang.
  • Sidel, Ruth. 1974. Families of Fengsheng : Urban Life in China. Baltimore: Penguin Books.
  • Sidel, Ruth. 1978. Urban Survival : The World of Working-Class Women. Boston: Beacon Press.
  • Sidel, Ruth. 1986. Women and Children Last : The Plight of Poor Women in Affluent America. New York: Viking.
  • Sidel, Ruth. 1990. On Her Own : Growing up in the Shadow of the American Dream. New York, N.Y., U.S.A.: Viking.
  • Sidel, Ruth. 1996. Keeping Women and Children Last : America’s War on the Poor. New York, N.Y.: Penguin Books.
  • Sidel, Ruth. 2006. Unsung Heroines : Single Mothers and the American Dream. Berkeley: University of California Press.

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Get your dependency ratio off my lawn

Old people work more than they used to. This is important if you’re worried about what an aging population means for the economy.

When they taught me demography, I learned about the dependency ratio, which was the number of people presumed to be dependents (those ages 0-14 and 65+) relative to those presumed to be working (those ages 15-64). It’s a traditional measure, and a little archaic now that people spend much more time in school. But it’s nice because it sort of assumes that those “working age” adults are being productive whether they have jobs or not – it’s not just counting employed people – so it has an unstated recognition of (mostly women’s) unpaid labor.

In some economic work (see my paper here for an old review) people assume that non-employed women are being productive. But we don’t usually assume that about old people. That is, non-employed younger adults are assumed to be doing unpaid work, while non-employed old people are assumed to be really retired. I’m sure people are looking at the unpaid work of old people (I just haven’t yet). But their paid work profile has changed a lot, too – especially women’s.

This means the catastrophic view of productivity effects of again needs to be tempered by a better understanding of how much old people work. Here’s what I mean.

First, what the World Bank calls “Dependency Ratio, old,” which is the number of people age 65 and older as a percentage of the population ages 15-64. This is supposed to reflect the burden of age on the the young(er).* Here is it for the USA and the world (click to enlarge):

dependency ratio old

That’s the Baby Boom generation hitting older ages there at the end of the USA trend. As a result, the dependency ratio (old) has increased 30% in the USA since 1980, and the world is following.

But old people work more (or, we don’t label people “old” as early, you might say). Here’s the average annual hours of paid work for people in the USA ages 65 and older. Note this includes all those working no hours in the average, which is what you need to do if you’re interested in the total economic benefit/burden ratio (click to enlarge).

dependency ratio old

Since 1980, women ages 65-74 have increased their hourly employment hours by 138%, and men’s have gone up 44%. For the 75-plus community, the relative increases are even greater: 172% for women and 55% for men.

Now, if you add up those hours, you can calculate how much of a burden old people are relieving from the young by their employment hours. In this figure I calculate the total hours worked for each age-gender group and divide it by the total number of people ages 65 and older. Looking at the bottom blue area, for example, this shows that in 2015, the total population of men ages 65-74 did 166 hours of paid work for each person age 65 and older. Regardless of the size of the old population, then, there is that much less supporting of them to do (click to enlarge).

hours worked per person 65 and older

The per-person contribution of paid work hours from people 65 and older has increased 72% since 1980, from 206 to 354 hours per year. Most of the increase is from women’s employment, and it’s just starting. The oldest Baby Boom women, the women who led the increase in women’s employment over their careers, are still only 69 in 2015. Further, this measurement of paid hours may be an indicator of the unpaid productivity of these groups as well, as their health and activity levels improve.

It may be useful to track the population age composition over time (as in the World Bank data above), but it’s not reasonable to assume a constant level of dependency associated with people of different ages.

*Note: Of course, I use terms like “burden” in the classical demographic sense and tongue-in-cheek. I actually want more old people to live longer and work less, because that burden is what life is all about. But there is the issue of making sure everyone has their needs met.

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81 countries made more progress than the USA on women’s representation

The Inter-Parliamentary Union has a great archive of women’s representation in parliaments in most countries, from 1997 to 2016. I made this figure using the numbers for the lower houses (or single houses, if only one), which in the USA is the House of Representatives.

From 1997 to 2016, women rose from 12% to 19% of House members. During that time, for 163 countries, the average rose from 10% to 21%. When I cut the list down to 137, arbitrarily excluding a lot of very small countries, the USA slipped from 54th place to 84th place. Here’s the breakdown of changes in those countries (click to enlarge):

countries ranked by women's representation in parliament, 1997-2016

At this rate, in just 36 more years the House will get to the level of women’s representation that Hanna Rosin said Congress was at in 2012.


Previous posts:


Note: The code for making this figure in Stata looks like this:

gr twoway scatter rank16 rank97, mlabel(country) mlabposition(0) msymbol(i)

Before tinkering with the appearance and titles in the graph editor.

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On Asian-American earnings

In a previous post I showed that generalizations about Asian-American incomes often are misleading, as some groups have above-average incomes and some have below-average incomes (also, divorce rates) and that inequality within Asian-American groups was large as well. In this post I briefly expand that to show breakdowns in individual earnings by gender and national-origin group.

The point is basically the same: This category is usually not useful for economic statistics, and should usually be dropped for data on specific groups when possible.

Today’s news

What’s new is a Pew report by Eileen Patten showing trends in race and gender wage gaps. The report isn’t focused on Asian-American earnings, but they stand out in their charts. This led Charles Murray, who is fixated on what he believes is the genetic origin of Asian cognitive superiority, to tweet sarcastically, “Oppose Asian male privilege!” Here is one of Pew’s charts:

pewraceearn

The figure, using the Current Population Survey (CPS), shows Asian men earning about 14.5% more per hour than White men, and Asian women earning 11% more than White women. This is not wrong, exactly, but it’s not good information either, as I’ll argue below.

First a note on data

The CPS data is better for some labor force questions (including wages) than the American Community Survey, which is much larger. However, it’s too small a sample to get into detail on Asian subgroups (notice the Pew report doesn’t mention American Indians, an even smaller group). To do that I will need to activate the ACS, which is better for race/ethnic detail.

As a reminder, this is the “race” question on the 2014 American Community Survey, which I use for this post:

acsrace2014

There is no “Asian” or “Pacific Islander” box to check. So what do you do if you are thinking, “I’m Asian, what do I check?” The question is premised on that assumption that is not what you’re thinking. Instead, you choose from a list of national origins, which the Census Bureau then combines to make “Asian” (the first 7 boxes) and “Pacific Islander” (the last 3) categories. And you can check as many as you like, which is good because there’s a lot of intermarriage among Asians, and between Asians and other groups (mostly Whites). This is a lot like the Hispanic origin question, which also lists national origins — except that question is prefaced by the unifying phrase, “Is Person 1 of Hispanic, Latino, or Spanish origin?” before listing the options, each beginning with “Yes”, as in “Yes, Cuban.”

Although changes have not been announced, it is likely that future questions will combine the race and Hispanic-origin questions, and also preface the Asian categories with the umbrella term. This may mark the progress of getting Asian immigrants to internalize the American racial classification system, so that descendants from groups that in some cases have centuries-old cultural differentiation start to identify and label themselves as from the same racial group (who would have put Pakistanis and Japanese in the same “race” group 100 years ago?). It’s hard to make this progress, naturally, when so many people from these groups are immigrants — in my sample below, for example, 75% of the full-time, year-round workers are foreign-born.

Earnings

The problem with the earnings chart Pew posted, and which Charles Murray loved, is that it lumps all the different Asian-origin groups together. That is not crazy but it’s not really good. Of course every group has diversity within it, so any category masks differences, but in my opinion this Asian grouping is worse in that regard than most. If someone argued that all these groups see themselves as united under a common identity that would push me in the direction of dropping this complaint. In any event, the diversity is interesting even if you don’t object to the Pew/Census grouping.

Here are two breakouts. The first is immigration. As I noted, 75% of the full-time, year-round workers (excluding self-employed people, like Pew does) with an Asian/Pacific Islander (Asian for short) racial identification are foreign born. That ranges from less than 4% for Hawaiians, to around 20% for the White+Asian multiple-race people, to more than 90% for Asian Indian men. It turns out that the wage advantage is mostly concentrated among these immigrants. Here is a replication of the Pew chart using the ACS data (a little different because I had to use FTFY workers), using the same colors. On the left is their chart, on the right is the same data limited to US-born workers.

api1

Among the US-born workers the Asian male advantage is reduced from 14.5% to 4.2% (the women’s advantage is not much changed; as in Pew’s chart, Hispanics are a mutually exclusive category.) There are some very high-earning Asian immigrants, especially Indians. Here are the breakdowns, by gender, comparing each of the larger Asian-American groups to Whites:

api2

Seven groups of men and nine groups of women have hourly earnings higher than Whites’, while nine groups of men and seven groups have women have lower earnings. In fact, among Laotians, Hawaiians, and Hmong, even the men earn less than White women. (Note, in my old post, I showed that Asian household incomes are not as high as they look when they are compared instead with those of their local peers, because they are concentrated in expensive metropolitan markets.)

Sometimes when I have a situation like this I just drop the relatively small, complex group, which leads some people to accuse me of trying to skew results. (For example, I might show a chart that has Blacks in the worst position, even though American Indians have it even worse.)

But generalization has consequences, so we should use it judiciously. In most cases “Asian” doesn’t work well. It may make more sense to group people by regions, such as East-, South-, and Southeast Asia, and/or according to immigrant status.

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Explain to me again how marriage is the problem here

This is one of those things you share with all your friends on social media.

how-marriage-is-the-problem-here

Black married parents are 2.4-times more likely to be in poverty, are 2.1-times more likely to be unemployed, and have one-ninth the median net worth compared with White married parents. So explain to me again how marriage is the problem here.

Why?

The other day I picked on someone’s fact meme, and wondered what makes these things work, without offering a constructive alternative. I can’t answer the question I asked in that post (how old are the fathers of teen mothers’ children?), but I can answer some other questions about families and Black-White inequality. So that’s what I did.

Feel free to take these facts (or any others) and make something better.

How?

Here are my sources:

Poverty: 2014 American Community Survey from IPUMS.org. It’s Black and White, non-Hispanic, householders who are married and have their own children in the household. The poverty rates were 5% for White married parents and 11.9% for Black married parents. The poverty variable goes from 0 to 501, with 0-99 being below the poverty line, so you specify the recode like this: poverty(r:0-99 “poor”; 100-501 “not poor”). Here’s how you fill out the boxes in the online analysis tool:

povacscode

Unemployment: Again, 2014 American Community Survey from IPUMS.org. It’s Black and White, non-Hispanic, householders who are married and have their own children in the household. For this one you limit it to people in the labor force (empstat(1-2)) to get the unemployment rate. I did it for men and women combined, getting unemployment rates of 3.1% for White married parents and 6.6% for Black married parents. The numbers are higher for women (3.7% versus 7.3%) but the Black/White ratio is a little worse for men (2.6% versus 5.8%). Here’s how:

unempacscode

Median net worth: I used the Survey of Consumer Finances from 2013, available here. These are also non-Hispanic Black and White parents living with children. The median net worths were $150,500 for Whites and $16,000 for Blacks (Hispanics, incidentally, have $18,750, and the rest are just coded “other”). This data set combines married people with those who are “living with partner,” so this comparison includes cohabitors. (I don’t know how that affects the results, but I’m sure there’s still lots of inequality.) I put my STATA code in an Open Science Framework project here, so feel free to play with it yourself.

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The fathers behind teen births (or, statistical memes and motivated blind trust)

When makes people trust statistical memes? I don’t know of any research on this, but it looks like the recipe includes a combination of scientific-sounding specificity, good graphics, a source that looks credible, and – of course – a number that supports what people already believe (and want their Facebook friends to believe, too).

If that’s the problem, and assuming the market can’t figure out how to make journalism work, I have no solution except seizing the Internet and putting it under control of the Minister of Sociology, or, barring that, encouraging social scientists to get engaged, help reporters, and make all their good work available publicly, free, and fast.

Today’s cringe:

13179054_1158267840891715_8960394622916968249_n

The blogger TeenMomNYC takes credit for creating this, and the Facebook version has been shared tens of thousands of times. Its popularity led to this story from Attn: “The Truth About Teenage ‘Baby Mamas’ is Quite Revealing.” (If anyone did want to study this issue, this is a neat case study, because she posted 8 “did you know” graphics on Facebook at the same time, and none of the others took off at all – why?)

I don’t know anything about TeenMomNYC, but I share her desire to stop stigmatizing and shaming young mothers. I wish her work were not necessary, but I applaud the effort. That said, I don’t necessarily think shaming young fathers (even if they’re not quite as young) is a solution to that, but that’s not the point. My point is, what is this statistic?

According to the footnote (thanks!), it comes from this 1995 National Academies report, and (except for changing “29” to “29.7”) it represents it accurately. From p. 205:

These data highlight an additional component of the sexual abuse picture— the evidence that an appreciable portion of the sexual relationships and resulting pregnancies of young adolescent girls are with older males, not peers. For example, using 1988 data from the NSFG and The Alan Guttmacher Institute, Glei (1994) has estimated that among girls who were mothers by the age of 15, 39 percent of the fathers were ages 20–29; for girls who had given birth to a child by age 17, the comparable figure was 53 percent. Although there are no data to measure what portion of such relationships include sexual coercion or violence, the significant age difference suggests an unequal power balance between the parties, which in turn could set the stage for less than voluntary sexual activity. As was recently said at a public meeting on teen pregnancy, “can you really call an unsupervised outing between a 13-year-old girl and a 24-year-old man a ‘date’?”

This is an important point, and was good information in 1995, when it cited a 1994 analysis of 1988 data, which asked women ages 15-44 a retrospective question. In other words, this refers to births that took place as early as 1958, or between 28 and 58 years ago. That is historical, and really shouldn’t be used like this today, given how much has changed regarding teen births.

The analysis is of the 1988 National Survey of Family Growth, a survey that was repeated as recently as 2011-2013. Someone who knows how to use NSFG should figure out the current state of the age gap between young mothers and fathers and let TeenMomNYC know.

Even if I didn’t know the true, current statistic, this would give me pause. Births to women before age 15 are extremely rare. The American Community Survey, which asks millions of women whether they have had a birth in the previous year, does not even ask the question of women younger than 15. The ACS reports there were 179,000 births in the previous year among women who were under 20 when interviewed, of which only 6,500 were to women age 15 at the interview. So that’s 3.7% of teen births, and 3 out of every thousand 15-year-old women. In 1958 this was much more common, and the social environment was much different.

Another issue is the age range of the fathers, 20-29, which is very wide when dealing with such young mothers. Look at the next phrase from the 1995 report: “girls who had given birth to a child by age 17, the comparable figure was 53 percent.” Realize that the great majority of girls who had a birth “by age 17” were 17 when they did, and the great majority of those men were probably close to 20. I’m not very positive about 20-year-old men having children with 17-year-old women, but it’s pretty different from 29-versus-13.

I can’t find the original source for this, but this report from the Resource Center for Adolescent Pregnancy Protection attributes this table to the California Center for Health Statistics in 2002, which shows that the father was age 20 or older  for 23% of women who had a birth before age 15. And of those, 93% were 20-24 (rather than 25+).

cateen

Anyway, this is a good case of a well-intentioned but under-resourced effort to sway people with true information, picked up by click-bait media and repeated because people think it will help them win arguments, not because they have any real reason to believe it’s true (or not true).

So I really hope someone with the resources, skills, and training to answer this question will produce the real numbers regarding father’s age for teen births, and post them, with accompanying non-technical language, along with their code, on the Open Science Framework (or other open-access repository).

Fixing the media and its economy is a tall order, but academics can do better if we put our energy into this work, reward it, and restructure our own system so that good information gets out better, faster and more reliably.

Related posts:

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Black women really do have high college enrollment rates (at age 25+)

The other day I reported on the completely incorrect meme that Black women are the “most educated group” in the U.S. That was a simple misreading of a percentage term on an old table of degree attainment, which was picked up by dozens of news-repeater websites. Too many writers/copiers and editors/selectors don’t know how to read or interpret social statistics, so this kind of thing happens when the story is just too good to pass up.

I ignored another part of those stories, which was the claim that Black women have the highest college enrollment rates, too. This is more complicated, and the repeated misrepresentation is more understandable.

Asha Parker in Salon wrote:

By both race and gender there is a higher percentage of black women (9.7 percent) enrolled in college than any other group including Asian women (8.7 percent), white women (7.1 percent) and white men (6.1 percent), according to the 2011 U.S. Census Bureau.

You know the rewrite journalists are playing telephone when they all cite the same out-of-date statistics. (That Census report comes out every year — here’s the 2014 version; pro-tip: with government reports, try changing the year in the URL as a shortcut to the latest version.)

But is that true? Sort of. Here I have to blame the Census Bureau a little, because on that table they do show those numbers, but what they don’t say is that 9.7% (in the case of Black women) is the percentage of all Black “women” age 3 or older who are attending college. On that same table you can see that about 2% of Black “women” are attending nursery school or kindergarten; more relevant, probably, is the attendance rate for those ages 3-4, which is 59%.

So it’s sort of true. Particularly odd on that table is the low overall college attendance rate of Asian women, who are far and away the most likely to go to college at the “traditional” college ages of 18-24. That’s because they are disproportionately over age 25 (partly because many have immigrated as adults). But, if you just limit the population to those ages 18-54, Black women still have the highest enrollment rates: 15.5%, compared with 14.6% for Asians, 12.6% for Hispanics, and 12.4% for Whites. Asians are just the most likely to be over 25 and not attending college, most of them having graduated college already.

This does not diminish the importance of high enrollment rates for Black women, which are real — after age 25; the pattern is interesting and important. Here it is:

womcolen

Under age 25, Black women are the least likely to be in college, over 25 they’re the most likely. This really may say something about Black women’s resilience and determination, but it is not a feel-good story of barriers overcome and opportunity achieved. And, despite her presence in the videos and stories illustrating this meme, it is not the story of Michelle Obama, who had a law degree from Harvard at age 24.

This is part of a pattern in which family events are arrayed differently across the life course for different race/ethnic groups, and the White standard is often mistaken as universal. I have noted this before with regard to marriage (with more Black women marrying at later ages) and infant mortality (which Black women facing the lowest risk of infant death when they have children young). It’s worth looking at more systematically.

ADDENDUM 6/29/2016: Cumulative projected years of higher education

If you take the proportion of women enrolled in each age group, multiply it by the years if the age group (so, for example, 18-19 is two years), and sum up those products, you can get a projected total years in college (including graduate school) for each group of women. It looks like this:

bweducaddend

Note this makes the unreasonable assumption that everyone who says they are enrolled in college in an October survey attends college for a full year. So, for example, Asian women are projected to spend 6.2 years in college on average between ages 18 and 54. What’s interesting here is that Black women are projected to spend more years in higher education than White women (5.5 versus 4.9). But we know they are much less likely than White women to end up with a bachelor’s degree (currently 23% versus 33%). This has to be some combination of Black women not spending full years in college, not going to school full time, or not completing bachelor’s degrees after however many years in school. Attendance may be an indicator of resilience or determination, but it’s not as good an indicator of success.

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