Tag Archives: income

Gender gap, 2011

The good people at the Institute for Women’s Policy Research have a new brief report on the gender gap in pay, based on 2011 data from the Bureau of Labor Statistics.

The gender pay gap reflects both the tendency of women to work in lower-paid occupations, and the tendency of men to earn more than women within occupations. IWPR calculated women’s median weekly earnings as a percentage of men’s, for those working full-time only, for the 20 most common occupations among men and women. Here is my figure from their results, with occupations listed from most to least female-dominated. It shows the extent of segregation in major occupations, and the nearly-universal gender pay gap within them, regardless of gender composition:

A few occupations were on both lists, and some had two few women or men to calculate relative wages, so only 30 are shown here.

Men’s earnings are higher in all but one of these occupations (stock clerks), though the gaps are larger on average in the more-male occupations.*

This report follows a recent appearance by IWPR’s president, Heidi Hartmann, on the Rachel Maddow show. Hartmann has posted this review of their discussion about the gender pay gap.

Recent related posts:


* This is the opposite of the pattern Matt Huffman and I found in our 2003 paper, where the gender gap was greater in female-dominated jobs (with statistical controls and 1990 data). Something to look into.

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Twin peaks of intergenerational mobility

There is a lot of news about economic mobility from recent weeks. Some of it draws from Pew’s Economic Mobility Project. Not as recently, there was an excellent review and analysis by Emily Beller and Michael Hout in the Future of Children a few years ago. In between, I somehow missed a collection of economic analyses in a book titled, Unequal Chances: Family Background and Economic Success, edited by Samuel Bowles, Herbert Gintis, & Melissa Osborne Groves.

The first chapter is posted free, and it includes a good introduction to the statistical and conceptual issues that arise when trying to understand patterns of mobility across generations. It includes a discussion of heritability, genetics, IQ and the like, which is quite approachable to the reader who is ready to think about decomposing correlations.

One good example regarding genetic heritability of traits that determine income: race in South Africa, which is almost entirely inherited (since there’s very little interracial marriage) and has a huge effect on income, but the effect of which is still social/environmental, not “natural.”

Anyway, I like this “twin peaks” figure, which shows the relationship between parent and child family income decile:

Probability of offspring attaining given income decile, by parents’ income deciles, United States. Based on total family income for black and white participants in the Panel Study of Income Dynamics who were born between 1942 and 1972, and their parents. The income of the children was measured when they were aged 26 or older, and was averaged over all such years for which it was observed. The number of years of income data ranged from 1 to 29 with an average of 11.5; the median year of observation was 1991. Parents’ income was averaged over all observed years in which the child lived with the parents. The number of years of income data ranged from 1 to 27 with an average of 11.9; the median year of observation was 1974. The simple age adjusted correlation of parents’ and children’s incomes in the data set represented in the figure is 0.42.

So, 30% of children from the top decile stay there (point D), 32% of children from the bottom decile stay there (C), while the odds of making it from the top to the bottom, or vice versa, are both less than 2% (A and B).

There is a nice symmetry to the figure, but it’s important to know that what’s happening up and down the distribution is highly varied, according to the analyses in the book. For example, at the top there is a lot of transmitted wealth. At the bottom there are a lot of health crises and premature deaths, including from violence. And the bottom is much stickier for Black children than for Whites.

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That giant gobbling sound (is the 1% eating more and more of the cookies)

The Congressional Budget Office has a new report on trends in the income distribution. The big news is the 1%’s blitzkrieg assault on equality.

But it’s not just another rehash of Census numbers. Two adjustments they made seem especially good. First, they used a tricky matching method to combine Current Population Survey numbers (which do better at benefits and low-income households) combined with Internal Revenue Service data (which is better for high-end data). Second, they adjusted for household size and composition, and calculated distributions before and after taxes and transfers, and among different kinds of income.

The headline is the changing share of after-tax-and-transfer household income. Every group except the top 1% had a smaller share of income in 2007 than they did in 1979, or just an equal share in the case of the 81st-99th percentile group. That means the top quintile’s whole gain came in the top 1%.

That is very important. A source of outrage for the hundreds of thousands of Facebook users posting, commenting, or Liking Occupy Wall St. and its related pages.

It would be misleading, however, to view the chart as showing that incomes fell for the other groups. Income growth has been very skewed toward the top, but it is by no means confined to the top 1%. Here is my graph showing the income cutoffs for each quintile, and for the top slices separately. These are the bottom cutoffs in 1979 and 2007 (in inflation-adjusted dollars), with the percentage change in the backgrounded bars.

(Note there is no cutoff for the bottom quintile — the price of entry for that group is always $0).

Two thoughts about this.

1. Even if there were no 1%, if the graph only included the green bars, there would be plenty of increasing inequality for what might then be called “the 80%” to protest. The 81st-99th folks may be lucky to have the popular anger directed at the grotesque opulence of the sliver above them. (I’m not diminishing the 1%’s income gains, but as Matt Taibbi pointed out yesterday, the object of opposition is not just their income, but their influence.)

2. If you look at the families and networks of the top 1%, how many of them have relatives, friends, and even co-”workers” who are only in the top 10%? Would a self-respecting 1% family be appalled if their son married someone from a stable 5%-er family?

What I’m wondering is whether the 1% folks are merely a statistical convenience rather than a socially cohesive group (class?). That’s an empirical question that national income distributions can’t necessarily answer.

The CBO report is here, a summary is here, and the blog post version is here.

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Dependency futures

The demography of dependency doesn’t look good.

NPR has a new report on the personal expectations and economics of retirement, and new poll data. It fits with what I was about to say about the troubling trends in early adulthood incomes. Here are some trends.

The new Census poverty report shows that poverty rates have increased for everyone under age 65 since 1999.

In fact, as the figure shows, compared with previous recessions in particular, the current recession has driven up poverty rates more for people in the ages 18-64.

But this doesn’t show a different aspect of the trend — that 18-64 poor population has been a growing share of the total poor population. They are now 57% of all poor people, up from less than 40% in the 1960s. That’s partly because the Baby Boomers aged into this group, and also because poor people have fewer children than they used to, so there are fewer poor children. When you divide the poor into three age categories, this is how their shares have changed since the 1960s:

Source: Census historical tables.

It’s actually worse than that for the mid-adulthood folks. Not only are they more likely to be poor, but their median incomes have fallen. Among men ages 25-54, the last decade has been brutal.

Source: Census historical income tables.

One of the social problems with having more poverty in the mid-adulthood years is that the social safety net needs their earnings to pay for the old and the young, and they need to be accumulating wealth, too, so that wealth can be seized by the state and redistributed later.

In traditional actuarial models, people in the mid-adulthood ages were expected to support everyone else. That’s why demographers invented the “dependency ratio,” which is the ratio of people not in those prime earning ages per 100 people who are — the lower the number the better — if you want your kids in school and your old people securely retired . By 2030, the ratio rises to a whopping 83 in Census projects:

The unemployment, declining earnings, and growing poverty among middle-aged people is going to reduce their contributions now into pensions, and hurt their savings for later. On the other, if they’re too broke to retire when they want to — as many in the NPR survey report — that would help keep the pension system solvent.

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Little income distribution graph

From the department of unhelpful statistics today I read this:

“Recent estimates indicate that at the current rate it will take more than 800 years for the bottom billion of the world population to achieve 10% of global income.”

Seems like a shockingly slow rate of progress, since anything that takes 800 years is basically not happening. But the problem is with the juxtaposition of a big number (billion) with a small fraction (10%). A billion people isn’t that big a fraction of the population anymore. Actually, if we could ever get to that level of world inequality it would be great.

Since the bottom billion of the world is about 14% of the 7 billion people in the world, getting them 10% of the global income would be a very low level of inequality — they’d only be 4% away from a perfectly egalitarian world. In the United States now, for example, the bottom 14% of families only get about 3% of the income.

Incidentally, here’s that family distribution:

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What it’s all worth, in work-life cash

A Census Bureau research report estimates lifetime earnings by education, race/ethnicity and gender.

The report, by the Bureau’s Tiffany Julian and Robert Kominski, uses national data from the American Community Survey to create “synthetic work-life estimates” of earnings.

The method takes earnings information from one time period — in this case the years 2006-2008 combined (before the recession) — and calculates how much money people would make if they lived through their whole work lives (40 years, from age 25 to 64) during that period. Demographers use the same method to estimate life expectancy. It’s a way of using the most current period to project an image of the future in today’s shape. It’s a better look at the future, for most purposes, than looking back at the lives of people who are wrapping it up today.

Here is a figure they made, using earnings from people working full-time and year round:

That is for people working full-time and year-round at their jobs. That is not reasonable, of course, if people take time out of the labor force, or out of full-time work. So this understates the earnings gaps, especially by gender, since women take more time out of the labor force than men, on average.

They also reported the projected lifetime earnings for all workers — including those working only part-time or part of the year. The figure above showed a ratio of 4.7-to-1 from top to bottom, whereas the all-worker data has a ratio of 5.6-to-1 from White male professional-degree holders to Latina high school graduates.

I turned their all-worker table into this graph with men and women color coded:

This is not a real prediction, just a projection of the present into the future. But the scale is good for the imagination — the gap from top to bottom is 3.65 million dollars in 2008 terms.

Note that in addition to employer discrimination, these gaps reflects the full range of influences on people’s earnings, including sorting into occupations, part-time work, lost tenure and experience from time out of the labor force, and regional variation (which is one reason Asian workers show up high – many live in expensive cities like San Francisco and Honolulu).

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Income gradient for children’s mental health

Lining them up (by income) and knocking them down.

I didn’t realize how strong the income gradient is for children’s emotional and behavioral problems. This new graph from the CDC combines data from 6 years of the National Health Interview Survey, and shows a steep relationship at all ages:

Percentage of Children with Serious Emotional or Behavioral Difficulties, by Age Group and Family: U.S., 2004-2009

The question asked was, “Overall, do you think that [child] has any difficulties in one or more of the following areas: emotions, concentration, behavior, or being able to get along with other people?” Children are included here if the parent said “yes, definite difficulties” or, “yes, severe difficulties.”

As background: I’ve posted before on the income gradient for asthma, overall health, diagnosis timing, mammographypregnancy, and women generally. That makes me curious, but not an expert. That is probably a good description for the authors of this recent review article, Janet Currie and Wanchuan Lin, who conclude:

Low-income children are in worse health than other children are. This paper explores the extent to which insults to health and activity limitations are responsible. In the most recent National Health Interview Survey (NHIS) data, low-income children are more likely than other children to have virtually every measured chronic or acute condition and are more likely to be limited by these conditions. Mental health conditions are particularly common and limiting. But the higher incidence of measured conditions and limits does not explain all of the relationships between income and overall health status, which suggests that unmeasured illnesses and injuries are also involved.

And finally, this reminds me of a good research tip. To get started on your subject, find a review article that’s a few years old or older, and then see which articles cite it — that should help bring you up to date. In this case, you could get these, which look highly relevant:

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Pay gaps you can see

How many multiples can you see at your workplace?

I’ve got four multiples of at least four outside my window — within one organization. Is this a good idea? Is it fair? Etc. To ask these questions it’s good to make it as concrete as possible. The infographic below is here to help.

I can ask two different questions about the costs and benefits of unequal pay — and I like to try to keep them separate. Most people believe pay differentials are important for motivating people to try harder, invest in education for scarce skills, and reward talent. So, one question is whether that’s true.

If you believe that’s true, you should also ask the second question: How much inequality do you need to accomplish that?

Last fall we had some news about housekeepers at UNC — the people who clean our buildings — being suspended for taking unauthorized breaks. One of them was Odessa Davis:

Without getting into the pay of individuals like her, I’m happy to say that state salaries are publicly available in North Carolina, made accessible by search tool from the News & Observer. A quick search on position title shows housekeepers usually make in the mid-$20,000s. From there, multiply that by 4 to get to a typical Full Professor (one without a special title or high-profile administrative job). Multiply that by four and you’re almost to what the University Chancellor makes.

Finally, we’ve got at least one more multiple of 4 to get to the Men’s Basketball Coach. His official state salary in the database is only $334,000, but with funds from sports promoters he’s paid a “retention bonus” that has been estimated to put him around $2 million per year.

Any of these people could be making extra money I’m not including — like a second job for the housekeepers, patent or grant income for the professor, who knows what for the Chancellor, and book royalties or endorsements for the basketball coach.

And now for the infographic: One dollar-sign equals $1,000 of annual income:

HOUSEKEEPER: $25,000
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FULL PROFESSOR: $100,000
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CHANCELLOR: $420,000
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MEN’S BASKETBALL COACH: $2,000,000
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Gender gaps by education and age

I ran these numbers for a colleague, and figured others might find them handy.

Gender wage ratios for full-time year-round workers range from .68 for older advanced-degree holders to .85 for young high school dropouts.

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Working wives, upward

In the category of more wives make more, the Carsey Institute has a new factsheet updating the trend in income contribution for working wives, by parental status, through 2009. Exactly how this is happening for different subgroups — husbands losing jobs and earning less, wives taking/keeping jobs and/or earning more — remains to be seen.

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