Category Archives: In the news

Black men raping White women: BJS’s Table 42 problem

I’ve been putting off writing this post because I wanted to do more justice both to the history of the Black-men-raping-White-women charge and the survey methods questions. Instead I’m just going to lay this here and hope it helps someone who is more engaged than I am at the moment. I’m sorry this post isn’t higher quality.

Obviously, this post includes extremely racist and misogynist content, which I am showing you to explain why it’s bad.

This is about this very racist meme, which is extremely popular among extreme racists.

tumblr_n2i5w0kygo1qaeo2oo1_500

The modern racist uses statistics, data, and even math. They use citations. And I think it takes actually engaging with this stuff to stop it (this is untested, though, as I have no real evidence that facts help). That means anti-racists need to learn some demography and survey methods, and practice them in public. I was prompted to finally write on this by a David Duke video streamed on Facebook, in which he used exaggerated versions of these numbers, and the good Samaritans arguing with him did not really know how to respond.

For completely inadequate context: For a very long time, Black men raping White women has been White supremacists’ single favorite thing. This was the most common justification for lynching, and for many of the legal executions of Black men throughout the 20th century. From 1930 to 1994 there were 455 people executed for rape in the U.S., and 89% of them were Black (from the 1996 Statistical Abstract):

1996statabs-executions

For some people, this is all they need to know about how bad the problem of Blacks raping Whites is. For better informed people, it’s the basis for a great lesson in how the actions of the justice system are not good measures of the crimes it’s supposed to address.

Good data gone wrong

Which is one reason the government collects the National Crime Victimization Survey (NCVS), a large sample survey of about 90,000 households with 160,000 people. In it they ask about crimes against the people surveyed, and the answers the survey yields are usually pretty different from what’s in the crime report statistics – and even further from the statistics on things like convictions and incarceration. It’s supposed to be a survey of crime as experienced, not as reported or punished.

It’s an important survey that yields a lot of good information. But in this case the Bureau of Justice Statistics is doing a serious disservice in the way they are reporting the results, and they should do something about it. I hope they will consider it.

Like many surveys, the NCVS is weighted to produce estimates that are supposed to reflect the general population. In a nutshell, that means, for example, that they treat each of the 158,000 people (over age 12) covered in 2014 as about 1,700 people. So if one person said, “I was raped,” they would say, “1700 people in the US say they were raped.” This is how sampling works. In fact, they tweak it much more than that, to make the numbers add up according to population distributions of variables like age, sex, race, and region – and non-response, so that if a certain group (say Black women) has a low response rate, their responses get goosed even more. This is reasonable and good, but it requires care in reporting to the general public.

So, how is the Bureau of Justice Statistics’ (BJS) reporting method contributing to the racist meme above? The racists love to cite Table 42 of this report, which last came out for the 2008 survey. This is the source for David Duke’s rant, and the many, many memes about this. The results of Google image search gives you a sense of how many websites are distributing this:

imagesearch

Here is Table 42, with my explanation below:

table42-highlighted

What this shows is that, based on their sample, BJS extrapolates an estimate of 117,640 White women who say they were sexually assaulted, or threatened with sexual assault, in 2008 (in the red box). Of those, 16.4% described their assailant as Black (the blue highlight). That works out to 19,293 White women sexually assaulted or threatened by Black men in one year – White supremacists do math. In the 2005 version of the table these numbers were 111,490 and 33.6%, for 37,460 White women sexually assaulted or threatened by Black men, or:

everyday

Now, go back to the structure of the survey. If each respondent in the survey counts for about 1,700 people, then the survey in 2008 would have found 69 White women who were sexually assaulted or threatened, 11 of whom said their assailant was Black (117,640/1,700). Actually, though, we know it was less than 11, because the asterisk on the table takes you to the footnote below which says it was based on 10 or fewer sample cases. In comparison, the survey may have found 27 Black women who said they were sexually assaulted or threatened (46,580/1,700), none of whom said their attacker was White, which is why the second blue box shows 0.0. However, it actually looks like the weights are bigger for Black women, because the figure for the percentage assaulted or threatened by Black attackers, 74.8%, has the asterisk that indicates 10 or fewer cases. If there were 27 Black women in this category, then 74.8% of them would be 20. So this whole Black women victim sample might be as little as 13, with bigger weights applied (because, say, Black women had a lower response rate). If in fact Black women are just as likely to be attacked or assaulted by White men as the reverse, 16%, you might only expect 2 of those 13 to be White, and so finding a sample 0 is not very surprising. The actual weighting scheme is clearly much more complicated, and I don’t know the unweighted counts, as they are not reported here (and I didn’t analyze the individual-level data).

I can’t believe we’re talking about this. The most important bottom line is that the BJS should not report extrapolations to the whole population from samples this small. These population numbers should not be on this table. At best these numbers are estimated with very large standard errors. (Using a standard confident interval calculator, that 16% of White women, based on a sample of 69, yields a confidence interval of +/- 9%.) It’s irresponsible, and it’s inadvertently (I assume) feeding White supremacist propaganda. The Census Bureau generally wouldn’t report numbers like this (I think their cutoff is a sample of 50).

Rape and sexual assault are very disturbingly common, although not as common as they were a few decades ago, by conventional measures. But it’s a big country, and I don’t doubt lots of Black men sexual assault or threaten White women, and that White men sexually assault or threaten Black women a lot, too – certainly more than never. If we knew the true numbers, they would be bad. But we don’t.

A couple more issues to consider. Most sexual assault happens within relationships, and Black women have interracial relationships at very low rates. In round numbers (based on marriages), 2% of White women are with Black men, and 5% of Black women are with White men, which – because of population sizes – means there are more than twice as many couples with Black-man/White-woman than the reverse. At very small sample sizes, this matters a lot. But we would expect there to be more Black-White rape than the reverse based on this pattern alone. Consider further that the NCVS is a household sample, which means that if any Black women are sexually assaulted by White men in prison, it wouldn’t be included. Based on a 2011-2012 survey of prison and jail inmates, 3,500 women per year are the victim of staff sexual misconduct, and Black women inmates were about 50% more likely to report this than White women. So I’m guessing the true number of Black women sexually assaulted by White men is somewhat greater than zero, and that’s just in prisons and jails.

The BJS seems to have stopped releasing this form of the report, with Table 42, maybe because of this kind of problem, which would be great. In that case they just need to put out a statement clarifying and correcting the old reports – which they should still do, because they are out there. (The more recent reports are skimpier, and don’t get into this much detail [e.g., 2014] – and their custom table tool doesn’t allow you to specify the perceived race of the offender).

So, next time you’re arguing with David Duke, the simplest response to this is that the numbers he’s talking about are based on very small samples, and the asterisk means he shouldn’t use the number. The racists won’t take your advice, but it’s good for everyone else to know.

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Teen birth rate low but Bible remains a concern

In 2012 I did a post about teen birth rates, abstinence, and Google searches for Antichrist stuff. The most important point was that abstinence education doesn’t work. In this post I use the percentage of teen (women) having a birth, and see what people are Googling in places with more teen births.

This is an inductive approach that generates ideas and surprises. Out of the billions of things people search for, which searches are most correlated with a demographic or social pattern across states? For example, the relationship between low marriage rates and searches about Kanye West is very strong (even controlling for a bunch of demographics), and state suicide rates are highly correlated with lots of searches about guns. If you think these are random flukes, you may be right — but then look at what searches correlate with racial/ethnic composition of states.

So for teen births, this is easy to get from the American Community Survey via IPUMS (I used the 2010-2014 combined file), which asks of each person if they had a baby in the previous year. Teen birth rate is the percentage of women ages 15-19 who did. Then you surf over to the Google Correlate tool and upload the teen birth rates file. The result is the 100 searches that are most highly correlated with the state file you uploaded. Someone with the keys to Google could get more, but this is what any member of the public can do.

We know that teen births are most common in the Southwest and South, and that broad pattern is really what’s most important: Republican-dominated states, the Bible belt, and places with a lot of poor young people.* Here’s the broad strokes:

db46_fig1

The Google searches is just for thinking about subtler cultural relationships and generating ideas.

Among the top 100 searches most correlated with teen births, American muscle cars stand out: Mustangs (13), Camaros (5), Hummers, Chargers, along with related things like transmissions. Next, however, is Bible stuff. There are 12 searches that correlate with the teen birth rate at .80 or higher on the list:

nephilim
original bible
book of enoch
bible talk
the book of enoch
i believe in god
yeshua
book of enoch pdf
bible names
the truth shall set you free
yhwh
truth shall set you free

Here’s a map showing the ACS teen births rates on the left and searches for “original bible” on the right, correlation .83:

teenbible-map

(A little disturbingly, “what is cinnamon” is also high on the list [correlation .81] — cinnamon is often promoted as a “natural” medicine to cause miscarriage.)

I exported the correlation file from Google and then averaged those 12 searches, producing a bible searches index that correlates with teen births at .87 (all the search correlations come out as z-scores, so the average has mean of 0 and s.d. of .93). Here are the results:**

teen-birth-bible-searchs

I’m no Bible expert, and this could all be a total coincidence, but I think some real research on it might be pretty interesting. Maybe the people who say the Bible is awesome for families and teen births are bad should look into it.***

Followup: Of course, if you only look at the highest correlations out of billions, you find high correlations. So I don’t expect a research award for discovering that. And that fact that these bible searches are from certain niches of Christianity is an interesting tidbit but just as food for thought. The more theory-driven version of this research might start with searches for just the word “bible”and test the hypothesis that it’s correlated with teen births.  That relationship is not as strong (correlation .74), but it’s still plenty to go on:

teen-birth-bible-searchs2

I take from this weaker finding that the stronger pattern above is not just a fluke or an artifact of the method.

  • Follow the Google tag to see the many posts using this stuff.
  • Follow the teen birth tag for more, including the argument that the teen birth rate is a myth, and the racial implications of promoting delayed births.

 


* This survey measure is correlated .89 with the 2008 list of state teen birth rates published by the National Center for Health Statistics. I would have a better sense of which is the right one to use if Google Correlate would say what time period is used for their analysis, but I can’t find that anywhere. When I used the NCHS list instead of my ACS list, it was more dominated by muscle cars and had less Bible stuff, as only “book of enoch” was in the top 100, correlated .87 with teen births.

** Here's the Stata command for making this figure (which I then prettied up a little):
gr twoway (scatter teenbirth biblesearch , mlabel(state) mlabposition(0) msymbol(i)) (lfit teenbirth biblesearch)

*** The 2010-2014 teen birth rates, from the IPUMS release of ACS data are these:

State State Teen birth rate (%)
Alabama AL 2.44
Alaska AK 2.727
Arizona AZ 2.385
Arkansas AR 2.886
California CA 1.901
Colorado CO 1.755
Connecticut CT 0.902
Delaware DE 1.644
District of Columbia DC 2.088
Florida FL 2
Georgia GA 2.578
Hawaii HI 1.991
Idaho ID 2.202
Illinois IL 2.009
Indiana IN 2.69
Iowa IA 1.477
Kansas KS 2.432
Kentucky KY 2.936
Louisiana LA 2.36
Maine ME 0.852
Maryland MD 1.783
Massachusetts MA 0.941
Michigan MI 1.881
Minnesota MN 1.428
Mississippi MS 3.545
Missouri MO 2.756
Montana MT 2.065
Nebraska NE 1.304
Nevada NV 2.449
New Hampshire NH 1.135
New Jersey NJ 1.005
New Mexico NM 3.5
New York NY 1.494
North Carolina NC 2.48
North Dakota ND 2.328
Ohio OH 1.901
Oklahoma OK 3.214
Oregon OR 1.568
Pennsylvania PA 1.928
Rhode Island RI 1.978
South Carolina SC 2.829
South Dakota SD 2.271
Tennessee TN 2.974
Texas TX 3.303
Utah UT 1.666
Vermont VT 1.073
Virginia VA 1.636
Washington WA 1.688
West Virginia WV 2.146
Wisconsin WI 1.305
Wyoming WY 1.6

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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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