Tag Archives: women’s employment

Now-you-know data graphic series

As I go about my day, revising my textbook, arguing with Trump supporters online, and looking at data, I keep an eye out for easily-told data short stories. I’ve been putting them on Twitter under the label Now You Know, and people seem to appreciate it, so here are some of them. Happy to discuss implications or data issues in the comments.

1. The percentage of women with a child under age 1 rose rapidly to the late 1990s and then stalled out. The difference between these two lines is the percentage of such women who have a job but were not at work the week of the survey, which may mean they are on leave. That gap is also not growing much anymore, which might or might not be good.

2. In the long run both the dramatic rise and complete stall of women’s employment rates are striking. I’m not as agitated about the decline in employment rates for men as some are, but it’s there, too.

3. What looked in 2007 like a big shift among mothers away from paid work as an ideal — greater desire for part-time work among employed mothers, more desire for no work among at-home mothers — hasn’t held up. From a repeated Pew survey. Maybe people have looked this from other sources, too, so we can tell whether these are sample fluctuations or more durable swings.

4. Over age 50 or so divorce is dominated by people who’ve been married more than once, especially in the range 65-74 — Baby Boomers, mostly — where 60% of divorcers have been married more than once.

 

5. People with higher levels of education receive more of the child support they are supposed to get.

 

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Peak women, labor force participation edition

I had a great visit at the University of Pennsylvania the other day, and gave a talk titled, “What Happened to the Gender Revolution?” It was an elaboration of the op-ed I wrote last fall, in which I sketched out the stall in progress toward gender equality (a recurring theme, not my discovery) and offered some ideas about getting it moving again.

One objection I got during the talk (rather belligerently, from Herbert Smith) was that I was making a big deal out of women’s labor force share peaking at just under half the total, which is a natural place to peak and so we shouldn’t expect it to keep going up.

peak-woman

My first response was that the feminism-has-gone-too-far gang (Hanna Rosin, Kay Hymowitz, Christina Hoff Sommers, etc.) complains as if women’s progress has already shot past 50/50. Although it hasn’t on almost all measures, there’s also no reason why women couldn’t become dominant. Judging from history, one gender dominating the labor market is hardly an impossibility. So women’s labor force share tapering off as it approaches 50% shouldn’t be considered a natural phenomenon.

But second, and for this I blame my presentation, women’s share of the labor force isn’t the best measure because it depends also on men’s labor force participation, too, which has been falling since the 1960s. So maybe it’s best to focus on women’s participation rates instead (it is on this measure that the U.S. has slipped behind many other rich countries).

Here are the labor force participation rates for women by age, education, race/ethnicity, and marital status, from 1962 to 2013, from the Current Population Survey, with men for comparison. The dots show the peak year for each trend (click to enlarge).

wlfp

Women’s overall share of the labor force hit 46% in 1994, and has spent the last 20 years within a point of that (as both men’s and women’s rates fell). But if you look at all these groups it’s clear that doesn’t represent the simple slide of women into the home plate of equality. Every line here rose for decades before hitting a peak between 1996 and 2001. And they peaked at different levels: Women with BA degrees peaked at 85%, Black women peaked at 80%, Hispanic women peaked at 68%. Married women peaked at 75%, single women at 82%. And so on.

Maybe all these trends are not being driven by the same underlying forces. But I’m pretty sure it’s not a complete coincidence.

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Fewer children, more employed women: International edition

In the discussion on this post about interpreting historical trends, several people pointed out that the relationship between fertility rates and women’s employment rates is not simple, and has changed, at least in the rich countries. I made some charts using international data about that, which I will show below.

But first a figure from this paper by Rense Nieuwenhuis and colleagues, which he linked from the comments. In that 2012 paper they show that the negative association between motherhood and employment weakened in OECD countries from 1975 to 1999. Still, at the individual level, in almost every country and every year, the odds of being employed are lower for mothers, as this figure shows (dots lower in each box indicate a bigger employment gap between mothers and non-mothers; click to enlarge):

oecd

It’s a very interesting paper I should have recommended earlier.

The fact that mothers are less likely to be employed than women without children doesn’t mean that countries — or time periods — with lower fertility rates necessarily have higher women’s employment rates (see Nieuwenhuis’s comment for a few other papers on this). So it’s good to look at individual as well as macro-level patterns.

Anyway, those are all rich countries. What about poorer countries? Because of the unbelievably good archive of census data (freely available, thank gov) at IPUMS International (74 countries, 238 censuses, 544 million records, and counting), it’s possible to ask questions like this.

Looking for censuses that recorded the number of children ever born to women, as well as their employment status, I sampled 10,000 households each from 89 censuses in 29 countries in Latin America or the Caribbean, Asia, and Africa, ranging in time period from 1960 to 2010. I limited the samples to women ages 25-44, and counted their children up to 7. The countries were:

  • Latin America / Caribbean: Argentina, Bolivia, Brazil, Cambodia, Chile, Colombia, Costa Rica, Ecuador, El Salvador, Haiti, Jamaica, Mexico, Nicaragua, Panama, Peru, Uruguay
  • Africa: Burkina Faso, Ghana, Guinea, Kenya, Malawi, Morocco, Rwanda, Senegal, Sierra Leone, South Africa
  • Asia: China, Indonesia, Vietnam

Here’s what I found. Overall there is not a strong correlation at the country level between mean number of children born per women and employment rates (correlation = -.09):

wlfp1

Closer inspection reveals a pretty strong relationship in the Latin America / Caribbean samples, as well as the three Asian countries, but not the African samples. But this scatter doesn’t show the time trends. If I limit it to the 9 countries that have at least 4 censuses (8 from Latina America, plus Indonesia), they almost all show the pattern I started with: falling fertility and rising women’s employment rates. The arrows track each country’s censuses in chronological order, so moving up and to the left fits the historical pattern:

wlfp2The country-level association is not the same as an individual-level association, because it can’t confirm that women with more children themselves are the ones who aren’t employed. To gauge that I estimate a linear regression within each census, measuring the association between number of children ever born and employment, controlling only for age. These are the results from those 89 regressions. The x-axis is still the mean number of children in each sample, but now the y-axis is the statistical effect of each additional child on the probability of being employed: below 0 indicates that having had more children reduces the probability of employment.

wlfp3In 15 of the 89 samples, each additional child is associated with a greater chance the woman is employed, but in 74 samples the effect is negative*. Furthermore, it appears that countries with lower fertility rates have a stronger negative association between children and employment — each kid reduces the odds of employment more. Consider, though, that a reduction of .11 in the probability of employment for each kid has a lower total effect in a country with two children per mother than a reduction of .05 in a country where people have three kids each**.

If we go back to the 9 countries with at least 4 censuses each, we can compare the trends in fertility to the child effect on employment:

wlfp4Most of these countries (Chile, Colombia, Indonesia, Panama, and Mexico) show the pattern in which the child effect strengthened while the fertility rate fell. Uruguay and Argentina show falling child effects and little fertility change.

Two possible conclusions:

  1. Although it may seem prosaic, this reminds me that the long-run, modern movement of women into the paid labor force is closely associated with the decline in fertility (as well as, incidentally, the decline in marriage). I think of that as indicating that women’s labor is increasingly diffused outward from their own children through market (or otherwise socialized) mechanisms. As the prototype, think of a woman with 2 children teaching 30 children in school (while her own kids are in another classroom) instead of spending the day caring for 6 children at home (while growing food, etc.).
  2. The trend toward a smaller employment gap between mothers and non-mothers is a recent, selective, rich-country phenomenon associated with very low fertility rates and (as the Nieuwenhuis et al. paper nicely shows) state policies designed to encourage mothers’ labor force participation (and, they hope, increase fertility).

Footnotes:

* I didn’t bother with significance tests because these were arbitrarily small subsamples from each census; we could always go test them with the full samples.

** I could test a total motherhood effect, like Nieuwenhuis et al. did, but in almost all of these are samples 80% or 90% of women have children, so the kid/no-kid comparison is not as salient.

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That number you want, it is not precise (women’s labor force edition)

Everyone wants a number. You want to know if the number is different from last year, or 100 years ago. Numbers are great. But the number you’re using is usually a statistic, a number calculated from a sample drawn from a population. You want a good number, you need a good sample. And a big one. And that’s going to cost you.

Who didn’t love the news recently that single British men ages 18-25 change their bedsheets only four times a year? Really? Really. How does anyone know this? Ergoflex, a memory-foam mattress distributor. At least UPI had the decency to report, “No survey details were provided,” although somehow Time found out the sample size was 2,004 (men and women, all ages). Rubbish, I reckon, or bonkers, or whatever. No one can resist a number; methods details don’t make it into the tweet version of the press release.

Here’s a more answerable question: What is the labor force participation rate for married, college graduate women with children, ages 25-54 in the United States? I’d say 76.1% — plus or minus a percentage point — based on the gold standard for labor force data collection, the Current Population Survey, easily analyzable these days for free with the IPUMS online tool.That’s from a sample of 60,000 households with a 90+% response rate, at a cost of umpteen million taxpayer dollars (well spent).

Here’s the trend in that number from 1990 to 2012, with 95% confidence intervals, based on the sample size, as calculated by IPUMS:

cps-error-bars

As more women have gotten college degrees, and the CPS sample has been enlarged, the sample size for this trend has grown and the error bars have shrunk, from a spread of almost 3 points to just less than 2. Still, there are only 8,265 of these women in the sample.

Only! Hold that up to a Gallup or Pew poll and compare confidence intervals when they start dividing and subdividing their samples. (Nothing against them — they give us the information we need to know how much variance there is in the estimates they put out, and then most people [+/- 51%] ignore it.)

There aren’t many one-year changes in this trend that are statistically significant at conventional levels. Of course, with this sample size you could say with confidence the labor force participation rate was higher in the late 1990s than the early 1990s (but check the survey redesign in 1994…), and higher again in the late 2000s than in the early 2000s. But were 2007 and 2002 sample flukes? And if so, what about 2012?

What about if you want a slightly smaller subgroup, say, Black married, college graduate women with children, ages 25-54. That’s a reasonable question. Here’s the trend (note the y-axis scale changed):

cps-error-bars-black

Now the sample size is a couple hundred and the confidence intervals are more than 6 points wide; there isn’t a pair of years in the trend that doesn’t have overlapping confidence intervals. And look at 2007 and 2012 — Black women are blipping in the opposite direction from the larger group in each of those years. Yes, if you put the whole Black trend in the blender with a time trend you have a significant decline of about a fifth of a point per year on average (and a sliver of this change is because of the increasing tendency of college graduates to be in grad school and not working — there are 13 of them in 2012, dragging down the participation rate by 0.6%). But don’t hang a lot on one year.

So, my advice for doing simple description:

  • Eyes on the prize: who cares what the exact number is? Is it a lot or little, going up or going down, higher or lower than some other group? That’s usually what matters.
  • Stick to data with reported methods
  • Know the size of your subsamples, try to get confidence intervals
  • Don’t fixate on (or report) small changes or differences (don’t use that second decimal place if the margin of error is 6%)
  • For trends, pool data from multiple years, or report moving averages
  • Spend tax money on surveys, not war

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