Tag Archives: earnings

Women’s Equality Day earnings data stuff and suffrage note

Tomorrow is Women’s Equality Day, which commemorates the day, in 1920, when U.S. women were granted the right to vote. (Asterisk: White women.)

One historical story

Congress finally passed a Constitutional amendment for women’s suffrage in 1918, after decades of activism. The suffrage movement in the end successfully made a few convincing arguments – and one clarification. The most important may have been that White women had proved their patriotism during the war, and so they finally deserved the vote. I wrote in 1996:

“No one thing connected with the war is of more importance at this time than meeting the reasonable demand of millions of patriotic and Christian women of the Nation that the amendment for woman suffrage be submitted to the states,” declared Representative James Cantrill. And, he added, “Right, justice, liberty and democracy have always been, and will always be, safe in the tender care of American womanhood.”

And you know what he meant by “American womanhood” (an image the mainstream suffrage movement encouraged to various degrees over the years):

American_progress

American Progress, by John Gast (1872)

The important clarification was that women’s suffrage would absolutely not hurt White supremacy in the South. You know how it is when you just need that Southern vote. I went on:

If reluctant congressmen would only believe in the contribution of white women that was waiting to be made, suffrage advocates explained, the political math was irresistible. “There are more white women of voting age in the South to-day than there are negro men and women together,” [Congress’s only woman, Jeannette] Rankin said. Representative Scott Ferris assured them that poll taxes and literacy tests would remain untouched, so that “for every negro woman so enfranchised there will be hundreds and thousands of intelligent white women enfranchised” (Congressional Record 1918, 779). And Representative Thomas Blanton proclaimed, “So far as State rights are concerned, if this amendment sought to take away from any State the right of fixing the qualifications of its voters, I would be against it first, last, and all the time, but such it does not.” Although states should be allowed to set qualifications for voting, he believed, they could not do so at the expense of undermining true republicanism, and, “if you deny the 14,000,000 white women of this country the right to vote, you are interfering with a republican form of government [Applause]” (786). That day, the House passed the amendment with the required two-thirds vote.

Anyway, rights are rights, America is America, history is history (ha ha).

Some pay gap numbers

Back to nowadays. Today’s numbers come from some analysis of the gender earnings gap I did to support the Council on Contemporary Families brief for Women’s Equality Day. One big story is women’s rising education levels, especially BA completion.

In the active labor force as often described (age 25-54, working at least 20 hours per week and 26 weeks in the previous year), women surpassed men in BA completion in 2002:

wed1

That’s very good for women with regard to the earnings gap, because at every level of education men earn more than women. Women’s full-time full-year earnings are between 70% and 80% of men’s at all education levels except the highest, where they diverge: men who are doctors and lawyers earn much more than women, while women PhDs are doing relatively well. Here’s the 2015 breakdown by education:

wed2

With the education trend and differentials in mind, consider these multivariate model results. Going back to the sample of 25-54-year-old people working at least half-time and half the year, here are two results. The first line, in blue, shows the gender earnings ratio when only age is controlled. It shows women gaining on men from 2000 to 2016, from 77% to 83%. This is not much progress for 25 years, but it’s the slow pace we’ve come to expect during that time. The other line shows result from a more complete model, which adds controls for education, race/ethnicity, marital status, and presence of children; it shows even less progress.

wed3

In the full model (orange line) the relative gains for women are not as great. (Note I don’t include occupation in the “full” model although that’s very important; it’s just also an outcome of gender so I let it be in the gender variable for descriptive purposes.)

In the old days, when women had less education than men, controlling for education shrank the gap; now it appears the opposite is true. I haven’t done the whole decomposition to confirm this, but here’s another way to look at it. The next figure shows the same models, but in two separate samples, with and without BA degrees (and no control for education). The figure shows little progress within education groups. This implies it’s the increase in education for women that is driving the progress seen in the previous figure.

wed4

In conclusion: there is a substantial gender earnings gap at every level of education. The limited progress toward equality we’ve seen in the past 25 years may be driven by increases in women’s education.

There is a lot of other research on this — especially about segregation, which I didn’t include here — and a lot more to be done.


This is a little analysis, but if you’d like to do more, or see how I did what I’ve shown here, I posted the Stata code, data from IPUMS.org, codebook, and spreadsheet file on the Open Science Framework site here. You can use any of it for whatever you like, with a citation that looks like this one the OSF generates:

Cohen, P. N. (2017, August 25). Gender wage gap analysis, 1992-2016. Retrieved from osf.io/mhp3z

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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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Race, class, and gender in one chart (just kidding)

You can’t do it (or much of anything) in one chart.

A colleague reports that a college student wrote to her:

The wage gap itself shows white women earn more than more than black men; that is to say my race is a greater determinant of wage than is my gender.

Her link was to this pretty confused post by Derek Thompson, which I’m not going to get into except to show this figure he made:

thompson-earnsThis shows earnings, not taking into account education. Later he shows earnings by education, not taking into account gender. Not wrong, but you can see the confusion it caused for the student quoted above. If she finishes college, she will be in a group whose earnings hierarchy is more by gender than by race, as I show in this figure I made from 2011 American Community Survey data from IPUMS:

earns-race-gen

This shows that Black men and White women — full-time, year-round, 25-54 — have the same median earnings if you don’t take into account education. Within each education group, however, Black men earn more. Who gets to be in the full-time, year-round population (instead of dead, incarcerated, unemployed or underemployed), of course, is a big issue. I can’t show that in one chart.

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