Tag Archives: inequality

Review of Relational Inequalities: An Organizational Approach, with audio

cover of Relational Inequalities

I had the privilege of sitting on an author-meets-critics panel for the the book Relational Inequalities: An Organizational Approach, by Donald Tomaskovic-Devey and Dustin Avent-Holt, at the Eastern Sociological Society meetings this weekend. The panel was organized by Steven Vallas, and included Adia Harvey Wingfield. Because two other panelists canceled, I had a lot of time and ended up speaking for 25 minutes. We had a great discussion after the formal remarks, which only deepened my appreciation for the book. I recorded my remarks. Here is audio, with 4 minutes of ums and dead ends edited out:

 

And here is a lightly edited transcript:

I want to thank Steve, as well as Don and Dustin, for organizing and writing, respectively. It’s really been a pleasure. In the same way that once upon a time I used to run faster when I played competitive sports, because someone was yelling at me to run faster, reading a book knowing that I’m going to offer commentary on it to an audience of people whose opinions I respect makes me try harder and pay more attention, and focus more on it. So it’s a privilege to have this be one part of my job. I don’t normally read books all the way through and think about them carefully and sketch out my thoughts, so I really learned a lot doing that.

In the process, you know, it’s 10 months ago whenever we got this invitation, and then finally the book comes, and then I skim through it, then I put it down, and then you know it comes down to the last couple of days in my room reading the book carefully, and it’s been great. And fresh. Very fresh, right through breakfast.

I want to start by talking about my own work. Just kidding.

I have an outline. I start with praise. And then questions about what’s the relationship between organizations and inequality, as far as creating, reflecting, reproducing inequality; discussion of the role of education, as one of the things that it is external to organizations; and then a discussion of inequality within and between organizations, and where this fits in with the path of social change.

Praise

It’s a really really good book. And I look forward to putting it on our comprehensive exam reading list for the inequality reading group, I think it teaches this stuff really well – the literature on organizations and inequality. A great audience for it is people who are designing research projects having to do with inequality, and what is the role of organizations going to be in the work.

One of the things that’s really important, and you have to get to it right away, is the disconnect between the method of most research which is individual observation, and mostly surveys, and the theorized mechanisms about how inequality works, which are largely relational. And so we look at individuals and we say, oh look people with more education have more income, or we say we have racial inequality and we have immigration, and we have all these measures which are usually at the individual level, and then the mechanisms which we think are producing these are schools and segregation and discrimination, and things that are all interactional, or relational, between people within and around organizations. And so that’s just a sociological take that is very important here.

I love the mezo/contextual way of thinking in the analysis, between the individual and the country or the state or something like that, and at the organizational level that complexity and variation – how there is so much difference in the patterns of inequality within organizations. Yes, men make more money than women, but how that works is very different across different organizations and places and times, and the dispersion is different, and the patterns of dispersion change, and all that variation gives us leverage to understand how inequality works, but also where policy and law can intervene. Because if you have a range of practices, and you can see the consequences of the range of practices, that’s where you get something like the idea for a policy – we should do more of this and less of this, and so on. So that variation is key, and having it at the organizational level is important.

They set out a really useful research agenda. They talk a lot about workplace ethnographies and surveys, and various ways that organizational dynamics of inequality have been studied, and the research agenda that emerges has to do with comparative organizational studies, with attention to the role of external influences on organizations. So the gold standard is sort of multi-organizational research where the context is carefully considered between the different organizations and the workings of the relations within the organizations, and hopefully between them.

The relational framework they have here is sort of Charles Tilly’s Durable Inequality plus Cecilia Ridgeway – that’s my background reading on this, which is kind of thin, admittedly. And so it’s categories and the durableness of them within institutions and organizations, and putting people into cognitive categories and how that represents the integration of social structure into personality and interaction and so on. So that’s sort of the frame, which I think is really useful.

And then the moral framework they have is very clear, at the end; and the policies they give us to talk about, both “what about worker cooperatives,” and, “what about a universal basic income” – sort of state level and organizational level policies that address the variety of problems and inequalities that we have.

Organizations and inequality

A key question, and a motivating question for them, is what is the role of organizations in the wider system of inequality – that is, are they creating inequality, are they reflecting inequality that comes to them from the outside of the organization, what’s their role in the reproduction of inequality. And so you have the organization – it’s a workplace, which is mostly what they talk about – and there are things coming at it from the outside: cognitive categories and hierarchies, status between groups, privilege groups, esteem groups, minority groups that are less privileged and so on. And then there’s a law and regulatory policy environment that they’re working within, there are market conditions that they’re working within, and then there are the workers that are coming to them with their range of unequal skills and education, their health, their social capital, their histories of incarceration – everything that workers bring to the organization. So you could ignore organizations and say, look we have all this inequality out there, outside the organization, and the organization is basically just sort of applying formulas to this: “Well, men are privileged over women, so we pay them a little bit more, we discriminate against people with criminal records, if you don’t have the skills to do the job you’re out, if you’re health is not good, if you have children, if you can’t show up…” You could think of organizations as just sort of administering the system of inequality, the structures of inequality that they’re in, or you can think of them as implementing or enacting the inequality. So until the organization gets its hands on it, all that inequality is sort of not really operationalized, it’s not really functioning – the status inequality between men and women doesn’t really happen until somebody decides to pay the man more than the woman. That’s sort of their view, not necessarily – [Don: “I agree”] – not necessarily true, but that’s the question, are organizations doing that, or they just sort of receiving that.

And the authors point out – I’ll give you a little taste of this (p. 14): “Most inequalities are generated through the relationships in and around workplaces.” That’s a very strong statement, although “most” is a little bit vague, it’s 51% to 99%. That clearly gives you a strong reason to focus on workplaces, and it’s somewhat debatable.

And they point out in a footnote (p. 58): “Obviously, power can be exercised as violence in addition to discursive claims-making [so it’s not just people debating over rewards within organizations]. Strong-armed robbery and colonial conquest are examples of violent exploitation, genocide, ethnic cleansing, political suppression via arrest of social movements’ claims of dignity and access are the violent faces of closure.” Well, none of that stuff is happening within workplaces. So if you think colonial conquest, genocide, ethnic cleansing, and political suppression are important parts of inequality, and we know that those aren’t happening within workplaces, you know the field is generating a lot of inequality outside workplaces. You have to weigh that up against their, “most of inequality comes from within workplaces,” And to their credit, it’s an empirical question, which they note. It’s hard to quantify and it’s kind of pointless to quantify but the question is where should our focus be?

By the time they’re to their conclusion, they write, “We are not arguing that only organizations matter for inequality,” ok, they are definitely not arguing that – but if you have to say that, it’s obviously relevant, so that’s a question. It really is an organizations manifesto, the book, the importance of organizations, and it makes the case very strongly. It’s extremely useful and valuable and informative. And the fact that they make the claims really strongly helps motivate it and make it clear. And whether I want to argue about whether it’s 51% or 80% of inequality that comes from workplaces, for most uses of it that’s not the point.

Related to the question of what organizations do – whether they’re creating or reflecting – is inequality, unequal what? What are we talking about? Most obviously money, some people have more money than others. But especially when you’re talking about intersectional questions, are race and class and gender just three different ways of deciding who’s going to have how much money? No, it’s much more than money, it’s cultural in terms of who’s valued and esteemed, and who gets to set the discourse, and it’s status in terms of whose opinions get respected, and voice within organizations, and it’s also geographic with segregation, and so on. And so they talk a lot about “organizational resources” being what’s at issue. Whenever I teach inequality I push sociology grad students to get beyond thinking of all these status inequalities as being different ways of deciding how much money we get. And especially, what is the content of the inequality. Unequal amounts of what are we actually talking about? And that’s why I think the feminist discourse over sexuality is so important. Because control over sexuality is sort of orthogonal to the amount of money that you have – it’s obviously related, but it’s a different quality. So that stuff is really important and there’s a lot of food for thought on that here.

I mentioned genocide and ethnic cleansing, and there are other things which are happening outside organizations that are relevant. Things that happen outside workplaces, that may be in other organizations: welfare, taxation, the education system, residential segregation, incarceration – these are all things that are packaging inequality that arrive at the doorstep of the workplace. So I’ll give two possible policy ideas that are totally outside workplaces: if we had a 90% marginal tax rate on upper incomes, you might say, “who cares about inequality within organizations?” You get rich, and the government takes your money and gives it to poorer people. And so that lowers the stakes. And partly they focus on organizations because in the United States we don’t do that. And so that question of how much empirically are organizations creating of the system of inequality, is partly that number is higher because we don’t have that kind of society. So it’s not a statement about how inequality will always forever work, it’s really driven by the reality that we have now. And the other policy challenge to thinking organizationally is reparations. If the government stepped in and had a big reparations program and orientation, that is totally outside of individual workplaces, what would that do? So those are just things to think about.

Education

Their attitude toward education is interesting. And it’s – what do you call that when it’s not traditional, it’s not “heretic,” it’s very challenging. [The word I was looking for is “heterodox.”] They basically treat education as a proxy for claims-making resources. So the amount of education people have, when they get to the workplace, allows them to essentially bargain for or demand more or less money. Which, if you’ve ever had surgery, from a doctor, you want your surgeon to have gone to medical school. [Don: “You want your surgeon to be a good surgeon.”] Right, exactly. In our system, the proxy for that is that they’ve gone to medical school, and the board certifying and all that. So their issue is how much doctors are paid, not who gets to be a doctor. They’re not talking about inequality in the education system, all the things that create the unequal distribution of medical education.

Consider this also: there are limits to the organizational variation in this. There are no organizations in the United States that let people perform surgery without medical degrees. So that’s something very strong coming from the external reality that workplaces have to deal with. They can only hire people with medical degrees to do surgery, and surgery is very valued, it commands a lot of money in the market. So if they’re going to say “wages and jobs are organizational phenomena,” which they say, and education is this way of making claims on those things, then it’s interesting to push them on this issue of who gets to have the education. They say, sort of grudgingly in my opinion, yes, sometimes educational credentialing has to do with the skills required to do the job, but basically it’s about how much money you can extract from your employer. That’s why I focus on surgery, because lots of other education is just a cruder proxy for particular skills and whatnot.

They review literature on how factories work in Mexico and the U.S., including within the same multinational company, and the gender difference between maquiladoras. But if you think globally, the difference between a doctor in the U.S. and a factory worker in Mexico, and the vast inequality in resources they command, is not determined by the practices of their organizations, right? And an interesting thing about doctors in particular, is we pay a fortune in this country because the government (because of doctors) doesn’t let foreign doctors come practice here. Our doctors get paid ridiculously high amounts (Dean Baker, the economist, has written very compellingly about this). If we allowed foreign doctors to come here, foreign doctors would make a lot more money than they’re making, our doctors would make less money, and we would all pay less for equally good healthcare. So that’s a state policy, and not something that the hospitals can address.

While we’re thinking about the external factors, and I’m pushing them on this, they do a little review of Devah Pager’s work, “the mark of a criminal record” – employers don’t hire people with criminal records – so is that a problem of employer practices or is that a problem of mass incarceration and the distribution of criminal records? It’s both, but you couldn’t understand it by only studying the practices of employers, because that’s not a fixed quantity of a randomly distributed stigma.

So when you get to the intersectional stuff – consider race, class, and gender in our system of inequality. They point out gender and race integration in education “led to a weakening of gender and race based closure” (and that shows up in Don and Kevin’s previous book, and that’s reviewed here). So there’s less job segregation by race and gender than there used to be, and less exclusion, “while leaving unchallenged, or perhaps even strengthening, education based closure.” Well, by one way of thinking, of course, if race and gender are becoming less determinative of workplace outcomes, and education is becoming more determinative, that’s literally the goal of rational modern society, is to stop with the ascriptive criteria, and start using rational educational criteria, for skills and productivity. So they’re all up in arms about this, but it’s interesting to say, well, wait a second isn’t that kind of the point, like meritocracy. “There is an intersectional reality weakening closure on the basis of race and gender even as closure rules around education remain hegemonic.” So it would be worth it to explain, and I guess they do explain, why they think this is not the definition of progress. I’m being provocative. It’s not like education is fairly distributed, so it’s still all about ascriptive inequalities through the education system.

Between and within organizations

So what about inequality between and within organizations. And here it’s interesting because the world has changed while they were writing this book. In making their case for why organizations are so important, they write, “We are born and die in organizations.” OK, I like that, they obviously think it’s very important. “We spend a great deal of our lives working alongside others in organizations” – and then listen to this list of sort of other things: “We go to one organization to be educated (schools), to another to get income (workplaces), which we then spend in another (stores), in order to bring food and clothing to a fourth (households).” So they’re telling your other organizational fields. What’s interesting is that in schools, stores, and households, there’s more inequality between than within organizations. And so they’re very focused on workplaces, where probably you find more inequality within the organizations. They’re interested in those dynamics: What causes inequality within organizations, why do CEOs make so much, why is there gender segregation in the division of labor, and so on. Interestingly, and the trend over time is probably toward more inequality between. And if you think about families, in the old days, if you had an employed man and three children and a woman who had no income, then you have a tremendous amount of inequality within that organization, within that family. Nowadays if you have two children and the parents both have jobs, you have fewer people with no income and more people with income, and so there’s less within-household inequality, and that’s a trend over time.

In their second-to-last chapter they have a very good discussion about how this is also happening with firms and workplaces in the U.S. So if General Motors outsources their custodial service (I’m just making this up), some big company outsources lower status, or higher status, work, there’s a firm that is less hierarchical somewhere, that’s just all custodians. And there’s a firm that’s just all engineers. And General Motors is like bundling those services. So the inequality is increasingly between organizations there, rather than within. So instead of hierarchy within Amazon being from Bezos to the drivers, the drivers are all contracted, and so on. And Uber, and self-employment, and the gig economy, and all that stuff is sort of like if every Uber driver is an organization the way Uber thinks they are, then the inequality is all between organizations.

And so that’s the direction of social change, and it’s a challenge for their theory. If their theory is focused on inequality within firms, and organizations, then what’s happening in world, and how does their theory address this? And they say, “even if there were no internal inequalities within firms, there still might be considerable inequality between firms, as a function of firm resource inequality.” So they’re sort of already projecting to a world where every company had no inequality within it. We’re not there at all, but their answer to that is maybe more aspirational than empirical, and I think it’s debatable, and it’s worth debating, it’s: “The processes governing inequality between organizations is fundamentally the same as that governing inequality within organizations: relational claims-making, exploitation, and social closure.” OK, that’s a very strong statement. It says we’ve sketched out this whole theory about how inequality works within organizations, we see that the world is moving toward inequality between organizations, and we’re going to apply the concepts that we’ve developed to this new reality also. And that is a challenge for future work in this area. And so I’m not expecting them to have established this empirically before they do it, but that’s their case.

That’s one of the many examples of the great research agenda that comes out of this really interesting and important work. And with that I close. Thank you.

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White children are 2.7-times more likely than Black children to live with a parent who has a PhD

For a reflection Amy Harmon was working on, a followup to her article on the experience of Black mathematicians in American academia, I took a shot at the question: How many children have parents with PhDs?

The result was the highlighted passage (17 words and a link!) in her piece:

[all the racial biases that contribute to Black underrepresentation include] the well-documented racial disparities in public-school resources, the selection of students for gifted programs — and the fact that having a parent with a Ph.D. is helpful to getting one in math, while black children are less than half as likely as white children to live with such a parent.

To get there: I used data from the U.S. Census Bureau via IPUMS.org: The 1990 5% Public Use Microdata Sample (decennial census); and the 2000, 2010, and 2017 American Community Surveys.

I coded race/ethnicity into four mutually-exclusive categories: Single-race White, Black, and Asian/Pacific Islander (API); and Hispanic (including those of any race). I dropped from the analysis non-Hispanic children with multiple races reported, and American Indian / Alaska Natives (for whom about 0.5 percent lived with a PhD parent in 2017).

IPUMS made a tool that attaches values of parents’ variables to children with whom they share a household. I used that to calculate the highest level of education of each child’s coresident parents. In the Census data, children may have up to two parents present (which may be of the same sex in 2010 and 2017). Children living with no parent in the household were not included.

This let me calculate the percentage of children living (at the moment of the survey) with one or more parents who had a PhD. For each of the four groups the percentage of children living with a parent who has a PhD roughly doubled between 1990 and 2017. API children had the highest chance of living with a PhD parent, reaching 6.8 percent in 2017. The percentages for the other groups were: Whites, 2.7 percent; Blacks, 1.0 percent; and Hispanics, 0.7 percent:

pe1

The 2.7% for White children, versus, 1.0% for Black children, is the basis for her statement above.

Details (including the whole parents’ education distribution), data, codebook, and code, are available on the Open Science Framework at: https://osf.io/ry3zt/ under CC-BY 4.0 license.

Math bias

Both of Amy’s pieces are important reading for academics in many disciplines, including sociology, to reflect on the experience of Black colleagues in the environments we inherit and reproduce.

With regard to math, Amy points out that Black exclusion is not just about denying economic opportunity, it’s also about denying the public the benefits of all the lost Black math talents — and about denying Black potential mathematicians the joy and satisfaction of a passion for math realized.

As Daniel Zaharopol, the director of a program for mathematically talented low-income middle-school students, put it when I interviewed him for a 2017 article: “Math is beautiful, and being a part of that should not be limited to just some people.”

And Amy makes a good case that math bias and its outcomes contribute directly to racism much more broadly:

Some misguided people claim that there are not many black research mathematicians because African-Americans are not as intelligent as other races. These people, whom I have reported on for other stories in recent months, almost invariably use mathematical accomplishment as their yardstick for intelligence. They note that no individuals of African descent have won the Fields Medal, math’s equivalent of the Nobel Prize. They lack any genetic evidence to explain the gap in average I.Q. scores between white and black Americans that they cite as the basis of their belief, or reason to think that a genetic trait would be impervious to social or educational intervention, or that high I.Q. is key to math ability, which Timothy Gowers, a 1998 Fields medalist, has attributed largely to “the capacity to become obsessed with a math problem.”

But I have been reporting on these topics for several years, and I am acutely aware that math prowess factors heavily into the popular conception of intelligence. There’s a vicious cycle at work: The lack of African-American representation in math can end up feeding pernicious biases, which in turn add to the many obstacles mathematically talented minorities face. Which was one more reason it seemed especially important to hold up to the light all the racial biases that contribute to that underrepresentation.

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No, early marriage is not more common for college graduates

Update: IFS has taken down the report I critiqued here, and put up a revised report. They have added an editor’s note, which doesn’t mention me or link to this post:

Editor’s Note: This post is an update of a post published on March 14, 2018. The original post looked at marriage trends by education among all adults under age 25. It gave the misimpression that college graduates were more likely to be married young nowadays, compared to non-college graduates.


At the Institute for Family Studies, Director of Research Wendy Wang has a post up with the provocative title, “Early Marriage is Now More Common For College Graduates” (linking to the Internet Archive version).

She opens with this:

Getting married at a young age used to be more common among adults who didn’t go to college. But the pattern has reversed in the past decade or so. In 2016, 9.4% of college graduates ages 18 to 24 have ever been married, which is higher than the share among their peers without a college degree (7.9%), according to my analysis of the most recent Census data.

And then the dramatic conclusion:

“What this finding shows is that even at a young age, college-educated adults today are more likely than their peers without a college degree to be married. And this is new.”

That would be new, and surprising, if it were true, but it’s not.

Here’s the figure that supports the conclusion:

figure1wendyupdate-w640

It shows that 9.4% of college graduates in the age range 18-24 have been married, compared with 7.9% of those who did not graduate from college. (The drop has been faster for non-graduates, but I’m setting aside the time trend for now.) Honestly, I guess you could say, based on this, that young college graduates are more likely than non-graduates to “be married,” but not really.

The problem is there are very very few college graduates in the ages 18-19. The American Community Survey, which they used here, reports only about 12,000 in the whole country, compared with 8.7 million people without college degrees ages 18-19 (this is based on the public use files that IPUMS.org uses; which is what I use in the analysis below). Wow! There are lots and lots of non-college graduates below age 20 (including almost everyone who will one day be a college graduate!), and very few of them are married. So it looks like the marriage rate is low for the group 18-24 overall. Here is the breakdown by age and marital status for the two groups: less than BA education, and BA or higher education — on the same population scale, to help illustrate the point:

ifs1ifs2

If you pool all the years together, you get a higher marriage rate for the college graduates, mostly because there are so few college graduates in the younger ages when hardly anyone is married.

To show the whole thing in terms of marriage rates, here is the marital status for the two groups at every age from 15 (when ACS starts asking about marital status) to 54.

ifs3

Ignoring 19-21, where there are a tiny number of college graduates, you see a much more sensible pattern: college graduates delay marriage longer, but then have higher rates at older ages (starting at age 28), for all the reasons we know marriage is ultimately more common among college graduates. In fact, if you used ages 15-24 (why not?), you get an even bigger difference — with 9.4% of college graduates married and just 5.7% of non-college graduates. Why not? In fact, what about ages 0-24? It would make almost as much sense.

Another way to do this is just to look at 24-year-olds. Since we’re talking about the ever-married status, and mortality is low at these ages, this is a case where the history is implied in the cross-sectional data. At age 24, as the figure shows, 19.9% of non-college graduates have been married, compared with 12.9% of college graduates. Early marriage is not more common for college graduates.

In general, I don’t recommend comparing college graduates and non-graduates, at least in cross-sectional data, below age 25. Lots of people finishing college below age 25 (and increasingly after that age as well). There is also an important issue of endogeneity here, which always makes education and age analysis tricky. Some people (mostly women) don’t finish college because they get married and have children).

Anyway, it looks to me like someone working for a pro-marriage organization saw what seemed like a story implying marriage is good (that’s why college graduates do it, after all), and one that also fits with the do-what-I-say-not-what-I-do criticism of liberals, who are supposedly not promoting marriage among poor people while they themselves love to get married (a critique made by Charles Murray, Brad Wilcox, and others). And, before thinking it through, they published it.

Mistakes happen. Fortunately, I dislike the Institute for Family Studies (see the whole series under this tag), and so I read it and pointed out this problem within a couple hours (first on Twitter, less than two hours after Wang tweeted it). It’s a social media post-publication peer review success story! If they correct it.

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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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No Black women are not the “most educated” group in the US

I don’t know where this started, but it doesn’t seem to be stopping. The following headlines are all completely factually wrong, and the organizations that published them should correct them right away:

The Root: Black Women Now the Most Educated Group in US

Upworthy: Black women are now America’s most educated group

SalonBlack women are now the most educated group in the United States

GoodBlack Women Are Now The Most Educated Group In The U.S.

And then the video, by ATTN:, on Facebook, with 6 million views so far. I won’t embed the video here, but it includes these images, with completely wrong facts:

bweduc1

bweduc2

What’s true is that Black women, in the 2009-2010 academic year, received a higher percentage of degrees within their race/ethnic group than did women in any other major group. So, for example, of all the MA degrees awarded to Black students, Black women got 71% of them. In comparison, White women only got 62% of all White MA degrees. Here is the chart, from the data that everyone linked to (which is not new data, by the way, and has nothing to do with 2015):

bwdegchart

For Black women to be the “most educated group,” they would have to have more degrees per person than other groups. In fact, although a greater percentage of Black women have degrees than Black men do, they have less education on average than White women, White men, Asian/Pacific Islander women, and Asian/Pacific Islander men.

Here are the percentages of each group that holds a BA degree or higher (ages 25-54), according to the 2010-2014 American Community Survey, with Black women highlighted:

bwdegchartBA

23% of Black women ages 25-54 have BA degrees or more education, compared with 38% of White women. This does not mean Black women are worse (or that White women are better). It’s just the actual fact. Here are the percentages for PhD degrees:

bwdegchartPhDJPG

Just over half of 1% of Black women have PhDs, compared with just over 1% of White women – and almost 3% of Asian/PI women. White women are almost twice as likely to have a PhD and Black women, Asian/PI women are more than 5-times as likely.

Racism is racism, inequality is inequality, facts are facts. Saying this doesn’t make me racist or not racist, and it doesn’t change the situation of Black women, who are absolutely undervalued in America in all kinds of ways (and one of those ways is that they don’t have the same educational opportunities as other groups). There are some facts in these stories that are true, too. And of course, why Black women (and women in general) are getting more degrees than men are is an important question. But please don’t think it’s my responsibility to research and present all this information correctly before it’s appropriate for me to point out the obvious inaccuracy here. You don’t need this meme to do the good you’re trying to do by sharing these stories.

Our current information economy rewards speed and clickability. Journalists who know what they’re doing are more expensive and slower. Making good graphics and funny GIFs is a good skill, but it’s a different skill than interpreting and presenting information. We can each help a little by pausing before we share. And those of us with the skills and training to track these things down should all pitch in and do some debunking once in a while. For academics, there is little extra reward in this (as evidenced by my most recent, sup-par departmental “merit” review), beyond the rewards we already get for our cushy jobs, but it should be part of our mission.

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Why I snarked on a 538 blog post (and I’m sorry)

Gaza. What does inequality have to do with it? (Photo by gloucester2gaza)

Gaza. What does inequality have to do with it? (Photo by gloucester2gaza)

The first thing that bugged me about this blog post by Jay Ulfelder at Five Thirty Eight was not the most important thing. The first thing I reacted to was that Ulfelder opened by asking whether “economic inequality causes political turmoil,” and then chastising, “Just because a belief is widely held, however, does not make it true,” before offering only evidence from economics studies. So I tweeted this obnoxious thing:

It was obnoxious, and I apologize. That response was part of my routine, defensive, complaining about how complex sociological work is neglected in favor of glib economics (e.g., here, here, here). But I do substantively object to the piece. If I had taken the time to figure out what really bugged me about it I could have sent a more constructive Tweet. Oh well, you never get a second chance to make a first snarky response.

What really bugged me is that the piece reduced this question of world-historic importance to a matter of microdata quality and measurement:

In fact, it’s still hard to establish with confidence whether and how economic inequality shapes political turmoil around the world. That’s largely because of the difficulty in measuring inequality…

Despite the slipperiness of “whether and how,”* Ulfelder’s point is definitely that we are “not there yet” on the question of “the belief that inequality causes political crises.” Still, maybe this is a case of trying to sell a narrow empirical piece as something bigger than it is — in which case it’s also a lesson in how people overreact when you do that.

I have to examine my own motives here, because this is one of those times when someone’s empirical claims threaten something that I don’t routinely subject to empirical testing. If there is an actual article of faith in my sociological worldview — and I would not really use the word faith to describe it, it’s more like a foundational understanding — it’s that inequality causes conflict, which causes social change. Ulfelder notes this is attributed partly to Marx, which is one reason why I and so many other sociologists hold it dear, but it’s also because it’s actually true. But that depends on what you mean by true, and here I think I disagree with Ulfelder, who writes:

With such incomplete and blurry information about the crucial quantities, why are so many of us so sure that economic inequality is a principal cause of political turmoil? Careful observation is one answer. Aristotle and Marx drew inferences about the destabilizing effects of inequality from their deep knowledge of the societies around them.

He never explains why this isn’t good enough, instead wandering into a critique of contemporary activist claims, based in part on an argument that “the seminal economic study” on the question is methodologically flawed (I’m sure it is).

This reducing of the question is too reductionist. I would be very interested to know whether within-country economic inequality, measured at the national level, if accurately measured, could help predict which countries would experience political turmoil, if that could be measured with a single indicator. But that’s not answering the question of whether inequality causes political turmoil — it’s one very narrow slice of that giant historical question, for which we have many sources of data and many affirmative answers.

Use a little of Marx’s “deep knowledge of societies” to consider, for example, the anti-colonial revolutions in many countries after 1945. Do you need to test a within-country economic inequality measure to know that such “turmoil” was one consequence of inequality? Of course, the timing and nature of those revolts is an interesting question to be addressed through research, but is such research asking whether inequality causes conflict?

What about slave revolts? What if someone found that harsher slavery regimes were not more likely to explode in revolt than those in which the slaves had enough food and water — would that tell you that inequality does not “cause” conflict? (Inequality causes conflict; that’s why they’re called slave revolts.)

Even, what about the civil rights movement, women’s movement, gay rights movement, or Black Lives Matter?

Does inequality cause conflict? Yes. Of course the relationship is not necessarily linear or simplistically univariate, which is the subject of lots of great sociology (and probably some minor work in other disciplines). But this is the kind of complex issue that data journalism nowadays loves to turn into yes-or-no, show-me-the-scatterplot short blog posts. I’ve done some of that myself, of course — and if I do it with something that’s a vital part of your analytical worldview, feel free to send me a snarky tweet about it.

* Nothing against this expression in general, it’s just slippery in this case because it might or might not be moving the goalposts from the opening question. 

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Sex ratios as if not everyone is a college graduate

Quick: What percentage of 22-to-29-year-old, never-married Americans are college graduates? Not sure? Just look around at your friends and colleagues.

Actually, unlike among your friends and colleagues, the figure is only 27.5% (as of 2010). Yep, barely more than a quarter of singles in their 20s have finished college. Or, as the headlines for the last few days would have it: basically everyone.

The tweeted version of this Washington Post Wonkblog story was, “Why dating in America is completely unfair,” and the figure was titled “Best U.S. cities for dating” (subtitle: “based on college graduates ages 22-29”). This local news version listed “best U.S. cities for dating,” but never even said they were talking about college graduates only. The empirical point is simple: there are more women than men among young college graduates, so those women have a small pool to choose from, so we presume it’s hard for them to date.* (Also, in these stories everyone is straight.) In his Washington Post excerpt the author behind this, Jon Birger, talks all about college women. The headline is, “Hookup culture isn’t the real problem facing singles today. It’s math.” You have to get to the sixth paragraph before you find out that singles means college and post-college women.

In his Post interview the subject of less educated people did come up briefly — if they’re men:

Q: Some of these descriptions make it sound like the social progress and education that women have obtained has been a lose-lose situation: In the past women weren’t able to get college educations, today they can, but now they’re losing in this other realm [dating]. Is it implying that less educated men are still winning – they don’t go to college but they still get the pick of all these educated, more promiscuous women?

A: Actually, it’s the opposite. Less educated men are actually facing as challenging a dating and marriage market as the educated women. So for example, among non-college educated men in the U.S. age 22 to 29, there are 9.4 million single men versus 7.1 million single women. So the lesser-educated men face an extremely challenging data market. They do not have it easy at all.

It’s almost as if the non-college-educated woman is inconceivable. She’s certainly invisible. The people having trouble finding dates are college-educated women and non-college-educated men. By this simple sex-ratio logic, it should be raining men for the non-college women. Too bad no one thought to think of them.

Yes, the education-specific sex ratio is much better for women who haven’t been to college. That is, they are outnumbered by non-college men. But it’s not working out that well for them in mating-market terms.

I can’t show dating patterns with Census data (and neither can Birger), but I can show first-marriage rates — that is, the rate at which never-married people get married. Here are the education-specific sex ratios, and first-marriage rates, for 18-34-year-old never-married women in 279 metropolitan areas, from the 2009-2011 American Community Survey.** Blue circles for women with high school education or less, orange for BA-holders (click to enlarge):

educ-marpool

Note that for both groups marriage rates are lower for women when there are more of them relative to men — the downward sloping lines (which are weighted by population size). Fewer men for women to choose from, plus men eschew marriage when they’re surrounded by desperate women, so lower marriage rates for women. But wait: the sex ratios are so much better for non-college women — they are outnumbered by male peers in almost every market, and usually by a lot. Yet their marriage rates are still much lower than the college graduates’. Who cares?

I don’t have time to get into the reasons for this pattern; this post is media commentary more than social analysis. But let’s just agree to remember that non-college-educated women exist, and acknowledge that the marriage market is even more unfair for them. Imagine that.***


* I once argued that this could help explain why gender segregation has dropped so much faster for college graduates.

** It was 296 metro areas but I dropped the extreme ones: over 70% female and marriage rates over 0.3.

*** Remember, if we want to use marriage to solver poverty for poor single mothers, we have enough rich single men to go around, as I showed.

A little code:

I generated the figure using Stata. I got the data through a series of clunky Windows steps that aren’t easily shared, but here at least is the code for making a graph with two sets of weighted circles, each with its own weighted linear fit line, in case it helps you:

twoway (scatter Y1 X1 [w=count1], mc(none) mlc(blue) mlwidth(vthin)) ///

(scatter Y2 X2 [w=count2], mc(none) mlc(orange_red) mlwidth(vthin)) ///

(lfit Y1 X1 [w=count1], lc(blue)) ///

(lfit Y2 X2 [w=count2], lc(orange_red)) , ///

xlabel(30(10)70) ylabel(0(.1).3)

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