Category Archives: Research reports

Divorce recession drop rebound, with the 2012 rate

Note: Technical addendum added.

The Census Bureau’s American Community Survey is the best annual national data source for marital events. The 2012 data came out recently, and I don’t believe anyone else has published a divorce rate for 2012. The refined divorce rate – the number of divorces per 1,000 married people – was 19.0 in 2012. Here is the trend since the ACS starting counting divorces:

divrat08-12

What does this mean? It’s a shame the ACS didn’t start counting marital events till 2008, because it means we can’t put that year’s high rate in context. Was it (a) a spike up, suggesting divorce was a leading indicator for the recession; (b) part of a consistent decline, suggesting the the years since have been a pretty substantial increase from the historical trend; or, (c) a data anomaly.*

To put this in the context of the larger trend doesn’t really help answer the question, since we switched from vital records to a national survey, and had a decade with no national statistics in between:

divrate40-12

So, it’s a mystery. My preferred interpretation is still that the recession caused a decline in divorces because disgruntled people were tied up in other crises, couldn’t sell their houses, or couldn’t afford to move out, followed by a rebound of accumulated divorces to our current level.

I published a working paper suggesting this [now forthcoming in Population Research and Policy Review], in which I use 2008 predictors of divorce and estimate that 4% fewer divorces occurred through 2011 compared to what would have been expected had the determinants of divorce not changed in the subsequent years.

My blog series on divorce includes previous reports on rates, and attempts to predict divorce rates using Google searches.

Technical addendum

To replicate my rates for 2012, you start here at the FactFinder, then get the number of married people by sex (ACS Table B12001) and the number of people who got divorced in the 12 months before the survey (ACS Table S1251) — you can enter the table numbers into the search box. There is a slight problem with this, however. Some people who say they got divorced in the past 12 months also say they are currently married (presumably remarried already). Those people are counted twice in the denominator of the FactFinder-based divorce rate — once as divorced people and once as currently married. If you download the public-use file and count those people only once in the denominator, the divorce rate rises by .02 per 1,000 (or 2 people per 100,000) — but this would not change the figures above at the level of precision reported. However, the public-use files produce slightly different estimates than the FactFinder files anyway, because the latter are based on the Census Bureau’s complete file not a subsample, so I use those even though they produce this tiny under-estimate of the divorce rate.

Secondly, what about the difference in divorce rates between men and women? This is a survey, not a vital records count, and there is no way to verify with the now-missing spouses whether they also consider themselves divorced. Maybe they weren’t legally married, or they didn’t really get legally divorced. So there are several possibilities: (a) lots of lesbian divorces, which is unlikely given the small number of lesbian marriages (but note we don’t know the sex of the spouse who is no longer in the household so we can’t distinguish homogamous from heterogamous divorces); (b) women are more likely to describe a breakup as a divorce for reasons unknown; (c) something funky with the survey weights (unweighted divorce rates from the public-use file also show the disparity, but it’s 20% smaller), or; (d) something funky with the sampling.

Who knows! If you are reading this and considering a new career — or a new direction in your existing career — consider becoming a family demographer and helping us figure it out.

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Sociology citing Becker

Which comes first, the Nobel prize or the citations in sociology journals?

Neal Caren produced a list of the 52 works most cited in sociology journals in 2013, which included two Nobel prize winning economists:

  • Heckman, James J. “Sample selection bias as a specification error.” Econometrica: Journal of the econometric society (1979): 153-161.
  • Gary S. Becker. A Treatise on the Family. Harvard university press, 1981.

I assume those Heckman citations are the result of sadistic journal reviewers or dissertation committee members impressive their colleagues by requiring people to add selection corrections to their regressions.

The Becker citations were applauded by economists. I assumed they were usually cursory mentions in the literature review, representing neoclassical economics in the study of families. And that is basically right. In the 10 most recent citations to Treatise in top-three sociology journals, the book is always mentioned only once. See for yourself. Here are the passages out of context (citations at the end):

  1. A great deal of work in sociological theory addresses the determinants of marriage and the bases of divorce. Some of this work posits marriage as a form of social exchange, whereby internal benefits (sex) and costs (time) are calculated and weighed relative to external costs (money) and benefits (social approval) (Becker 1991).

  2. According to the negotiation framework known as intra-household bargaining (Agarwal 1997), rather than households behaving as cohesive units (Becker 1991), household members’ bargaining and decision making over the allocation of resources (e.g., income, health, education, time use) are conditioned by gender-based power differentials.

  3. In the classic economic and game theoretic models of partner matching and mate selection (Becker 1991; Gale and Shapley 1962), the relative value of every potential mate is assumed to be already known or can easily be determined (Todd and Miller 1999).

  4. Generally used to explain behavior during the waking hours, the time availability perspective suggests that because men spend more time in paid work, they have less time to do caregiving; the related specialization hypothesis suggests that women have the time and incentive to specialize in caregiving and unpaid work (Becker 1991[1981]).

  5. A second means by which household wealth is accrued is by means of family transfers. Economic assets, whether financial or real, are transferred from family members to others, both within and across generations (Becker 1991; Mulligan 1997; Wahl 2002).

  6. The compensating differentials argument suggests that mothers are more willing than non-mothers to trade wages for family-friendly employment. For example, Becker (1991) suggests that mothers may choose jobs that require less energy or that have parent-friendly characteristics, such as flexible hours, few demands for travel or nonstandard shifts, or on-site daycare.

  7. Differences in life course patterns between men and women may reflect the influences of traditional gender roles in the family and corresponding intermittent labor force attachment among women relative to men, particularly during childbearing years (Becker 1991; Bianchi 1995; Mincer and Polachek 1974).

  8. One of the primary ways in which education leads to lower fertility is by changing the calculation of the costs and benefits of childbearing and rearing (Becker 1991).

  9. As has long been recognized in both economics and sociology, an adequate explanation of gender inequality in the labor force therefore requires the researcher to go beyond discrimination and productivity-related attributes (i.e., human capital) and to consider the role of the family (Becker 1973, 1974, 1991; Mincer and Polachek 1974; many others). … First, it is assumed that economic resources are a family-level utility that is shared equally between the spouses (Becker 1973, 1974, 1991; Lundberg and Pollak 1993; Mincer and Polachek 1974).

  10. Fathers’ economic contributions are an important resource for children in all types of families (Becker 1991; Coleman 1988).

I noticed, incidentally, that we may have hit Peak Becker. The Web of Science citation count for his work in journals coded as Sociology peaked in 2011. Maybe the 2012 data just aren’t complete yet.

peak-becker

Out of curiosity, I also checked the citations in major economics journals to the most highly-cited sociology article on the household division of labor known for a theoretical argument, Julie Brines’s 1994 article in the American Journal of Sociology. Just kidding; there aren’t any.

No, that’s not true. The article has been cited once in the top 40 economics journals, in Transportation Research Part A: Policy and Practice:

The higher wage earner enjoys a superior bargaining position, and thus can use that power to demand less household responsibility – a proposition that has been the focus of substantial empirical research among sociologists (Heer, 1963, Brines, 1994, Greenstein, 2000, Bittman et al., 2003, Parkman, 2004 and Gupta, 2007).

References

  1. Rose McDermott. and James H. Fowler. and Nicholas A. Christakis. “Breaking Up Is Hard to Do, Unless Everyone Else Is Doing It Too: Social Network Effects on Divorce in a Longitudinal Sample.” Social Forces 92.2 (2013): 491-519.
  2. Greta Friedemann-Sánchez. and Rodrigo Lovatón. “Intimate Partner Violence in Colombia: Who Is at Risk?” Social Forces 91.2 (2012): 663-688
  3. Michael J. Rosenfeld and Reuben J. Thomas. 2012. Searching for a Mate: The Rise of the Internet as a Social Intermediary. American Sociological Review August 2012 77: 523-547. doi:10.1177/0003122412448050
  4. Sarah A. Burgard. “The Needs of Others: Gender and Sleep Interruptions for Caregivers.” Social Forces 89.4 (2011): 1189-1215.
  5. Moshe Semyonov. and Noah Lewin-Epstein. “Wealth Inequality: Ethnic Disparities in Israeli Society.” Social Forces 89.3 (2011): 935-959.
  6. Michelle J. Budig and Melissa J. Hodges. 2010. Differences in Disadvantage: Variation in the Motherhood Penalty across White Women’s Earnings Distribution. American Sociological Review October 2010 75: 705-728, doi:10.1177/0003122410381593.
  7. Jennie E. Brand and Yu Xie. 2010. Who Benefits Most from College?: Evidence for Negative Selection in Heterogeneous Economic Returns to Higher Education. American Sociological Review April 2010 75: 273-302, doi:10.1177/0003122410363567.
  8. Brienna Perelli-Harris. “Family Formation in Post-Soviet Ukraine: Changing Effects of Education in a Period of Rapid Social Change.” Social Forces 87.2 (2008): 767-794.
  9. Emily Greenman. and Yu Xie. “Double Jeopardy?: The Interaction of Gender and Race on Earnings in the United States.” Social Forces 86.3 (2008): 1217-1244.
  10. Daniel N. Hawkins, Paul R. Amato, and Valarie King. 2007. Nonresident Father Involvement and Adolescent Well-Being: Father Effects or Child Effects? American Sociological Review December 72: 990-1010, doi:10.1177/000312240707200607.
  11. Sirui Liu, Pamela Murray-Tuite, Lisa Schweitzer. 2012. Analysis of child pick-up during daily routines and for daytime no-notice evacuations, Transportation Research Part A: Policy and Practice, Volume 46, Issue 1, Pages 48-67.

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Inequality, mobility, single mothers, and race: comment

I have no idea whether inequality increases intergenerational immobility. But I do know that lots of people would like to pin bad social trends on single motherhood, meaning — in their view — the bad decisions of people who already poor. And that has bad implications.

In a blog post by Scott Winship and Donald Schneider at the Manhattan Institute, they argue that the liberal argument that inequality blocks mobility is not well supported. To do that, they show simple bivariate correlations between single motherhood rates and immobility across U.S. labor markets. Their point is that, if you want to use that simple bivariate standard, you can just as well — but better — argue that immobility is caused by single motherhood rather than by income inequality, because the correlation is very strong. For their exercise they use data from the Equality of Opportunity Project, which is freely available here.

In a series of tweets, Winship clarified his point:

point wasn’t to highlight single parenthood—point was to show where low evidentiary standards on left can take you … look, single motherhood may very well be a big problem for mobility. Inequality might too…. but the left has to be held accountable when they make bad arguments skewing policy debates…  I clearly wrote that correlations shouldn’t constitute reason for getting worked up about single moms

I take him at his word on his intentions, but those with well-documented patterns of less scrupulous behavior are not so scrupulous, and so the post was bad. Despite a disclaimer about not reading causation from correlation, they also wrote:

In other words, a [labor market’s] prevalence of single motherhood predicts its relative mobility quite well all by itself. … the relationship between single motherhood and mobility holds up in all of these analyses. … On the basis of these charts, rather than a new Washington Center on Equitable Growth housed at CAP and devoted to discovering the damages that income inequality inflicts, the left should have started a Washington Center on Single Motherhood.

Again, my only dog in the fight is fighting against the easy right-wing causal association of single motherhood with bad outcomes. The Heritage Foundation, Scheider’s employer, is particularly egregious in this, as I’ve occasionally documented (here and here, e.g.)

So here’s a quick debunk on that. A simple glance at the map from the Equal Opportunity Project will tell you that race is involved here, but it didn’t come up in Winship and Schneider’s post:

immobilitymap

So let’s just look at the relationship between immobility, single motherhood and race. (Immobility here is measured by the effect of family income on children’s incomes. Higher scores are bad.)

So first, here is the relationship between population percent Black and immobility for the 100 largest metro areas, with the larger ones shown as bigger dots:

pb-immobThat relationship is quite strong: the higher Black population proportions are strongly associated with immobility. But so is the single motherhood relationship, as Winship and Schneider reported. So, we turn to the obvious tool, a multivariate regression. Here are two models, the first with just single motherhood — in effect, the Winship and Schneider result — and then a model with proportion Black added. Both are weighted by population size.

pb-immob-reg

This shows that the association between single motherhood rates and immobility is reduced by two-thirds, and is no longer significant at conventional levels, when percent Black is added to the model. That is: Percent Black statistically explains the relationship between single motherhood and intergenerational immobility across U.S. labor markets.

This is not a rigorous examination of the cause of intergenerational immobility. It is just debunking one bivariate story that is too easily picked up by the forces of bad.

 

 

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Gender devaluation, in one comparison

You can divide the reasons women earn less money than men do, on average, into three categories, in declining order or importance:

  1. Working fewer years, weeks, and hours
  2. Working in different occupations
  3. Being paid less in the same occupations

The first has to do with families and children. That has a large voluntary, or at least kind of voluntary, component (or it reflects hiring discrimination, which is hard to prove, prevent or punish under our legal regime). The third is illegal and sometimes actionable, as in the Lilly Ledbetter situation.

The second — occupational segregation — is a difficult hybrid. Segregation reflects both discrimination in hiring and promotions, and socialization-related choices, including in education. And it is wrapped up with divisions that may even be relatively harmless in a separate-but-equal kind of way — that is, not directly harmful, but contributing to the categorical divisions that make gender inequality more intractable. But the different pay in female- versus male-dominated occupations is a problem, well documented (see here and here) but virtually impossible to address under current law.

nurse-truck

Today’s example: nursing assistants versus light truck drivers

The government’s O*Net job classification system provides detailed descriptions of the qualifications, skills, and conditions of hundreds of occupations. The comparison between nursing assistants (1.5 million workers) and light truck or delivery services drivers (.9 million) is instructive for the question of gender composition. Using the 2009-2011 American Community Survey, I figure nursing assistants are 88% female, compared with 6% female for the light truck drivers. Here are some other facts:

  • The nursing assistants are better educated on average, with only 50% having no education beyond high school, compared with 67% of the light truck drivers.
  • But in terms of job skills, they are both in the O*Net “Job Zone Two,” with 3 months to 1 year of training “required by a typical worker to learn the techniques, acquire the information, and develop the facility needed for average performance in a specific job-worker situation.”
  • The O*Net reported median wage for 2012 was $11.74 for nursing assistants, compared with $14.13 for light truck drivers, so nursing assistants earn 83% of light truck drivers’ hourly earnings.

To make a stricter apples-to-apples comparison, I took those workers from the two occupations who fit these narrow criteria in 2009-2011:

  • Age 20-29
  • High school graduate with no further education
  • Employed 50-52 weeks in the previous year, with usual hours of exactly 40 per week
  • Never married, no children

This gave me 748 light truck drivers and 693 nursing assistants, with median annual earnings of $22,564 and $20,000, respectively — the light truck drivers earn 13% more. Why?

The typical argument for heavy truck drivers’ higher pay is that they spend a lot of time on the road away from home. But that’s not the case with the light truck drivers. They are more likely to work longer hours, but I restricted this comparison to 40-hour workers only. Here are comparisons of the O*Net database scores for abilities and conditions of the two jobs. For each I calculated score differences, so the qualities with bars above zero have higher scores for nursing assistants and those with bars below zero have higher scores for light truck drivers. See what you think (click to enlarge the figures). My comments are below.

abilities

context

You can stare at these lists and see which skills should be rewarded more, or which conditions compensated more. Or you could derive some formula based on the pay of the hundreds of occupations, to see which skills or conditions “the market” values more. But you will not be able to divine a fair market value for these differences that doesn’t have gender composition already baked into it. And “the market” doesn’t make this comparison directly, because nursing assistants and light truck drivers generally don’t work for the same employers or hire from the same labor pools. You might see reasons in these lists for why women choose one occupation and men choose the other, but I don’t see how that fairly leads to a pay difference.

The only solution I know of to the problem of unequal pay according to gender composition is government wage scales according to a “comparable worth” scheme (the subject of old books by Joan Acker and Paula England, but not high on the current political agenda). Under our current legal regime no one woman, or class of women, can successfully bring a suit to challenge this disparity.* That means occupational integration might be the best way to break this down.

*One exception to this is the public sector in Minnesota, in which local jurisdictions have their pay structures reviewed at regular intervals for evidence of gender bias, based on the required conditions and abilities of their jobs (as reported by me by Patricia Tanji of the Pay Equity Coalition of Minnesota).

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Choose that job?

This is a quick note following up on some posts about the gender gap in pay (like this one on long-hours workers, and this one on the use and abuse of the gender gap statistic).

One of the worst headlines I saw on these subjects was this one from a Time.com post: “The Pay Gap Is Not as Bad as You (and Sheryl Sandberg) Think. [Subhead:] Women don’t make 77 cents to a man’s dollar. They make more like 93 cents, as long as they don’t major in art history.”

I can appreciate a joke, but this just underscores how this debate over job “choice” is going on among the 28% of U.S. adults that have a bachelor’s degree or more. That bias shows up in the telltale use of “profession,” as in Hanna Rosin’s phrase, “Women congregate in different professions than men do, and the largely male professions tend to be higher-paying.” People who are scraping by in dead-end jobs aren’t “congregating in professions.”

In the language of economics, this may be expressed as, “differences in educational attainment, work experience and occupational choice contribute to the gender wage gap.”

Really?

Really?

Many of the critics of my NYTimes op-ed on gender inequality shared the view of “wmdawesrode”:

What about free choice? Nothing holds anyone of whatever gender from pursuing a career in whatever field they prefer.

Occupations

Technically, this comes down to how you handle the issue of occupations in employment data, and transitions between them. In a paper analyzing the job changes of nurses’ aides, Vanesa Ribas, Janet Dill and I found that for 30% of those who left the job, their next job was in an even worse-paid service job.

We looked at nurses’ aides because it is a poorly-paid job, disproportionately female, which employers fret over because of its high turnover rate. But there are hundreds of occupations in the federal statistical system. Some of them reflect career choices made by people with professional options (e.g., “economist” versus “sociologist”). But what about the 3.1 million workers who are “cashiers” versus the 3.1 million workers who are “first-line supervisors/managers of retail sales workers.”? Treating this difference as an occupational choice, rather than as an unequal outcome, is iffy at best.

If you go to the IPUMS archive of Current Population Survey data, you can experiment with this using the “occ” (what is your occupation now?) versus “occly” (what was your occupation last year?) questions. For example, to see what those retail sales supervisors and managers were doing last year, fill out the online analysis window like this:

occoccly

And you will find the major feeder occupations were (in descending order):

For women:

  1. First-line supervisors/managers of non-retail sales workers
  2. Cashiers
  3. Sales representatives, wholesale and manufacturing
  4. Customer service representatives
  5. Food service managers

For men:

  1. First-line supervisors/managers of non-retail sales workers
  2. Sales representatives, wholesale and manufacturing
  3. Marketing and sales managers
  4. Retail salespersons
  5. Driver/sales workers and truck drivers

It looks to me like some of those people were making lateral moves in pursuit of career dreams (e.g., from non-retail to retail sale manager), but for most of them the job is a promotion. (I pooled 10 years of data because the numbers for these are pretty small, since there are so many occupations, and the vast majority of people don’t change jobs each year).

If you look through the list of occupations, many of them reflect hierarchies in vertical career paths. This is empirically observable, but analyzing it systematically requires a creative approach I haven’t figured out (but maybe someone else has).

Of course, a gender disparity in rates of transitioning from cashier to supervisor isn’t necessarily employer discrimination. Some people, for example, have family obligations (“choices”) that make them less dedicated workers and legitimately less desirable for promotion. The gender system is complicated. If fathers are more likely to move out when their children have disabilities, as suggested by data on living arrangements, then single mothers whose children have disabilities might have a tough time giving 110% to their cashier jobs — to get that promotion at Wal-Mart. And then Hanna Rosin would catch them congregating in the less lucrative professions.

 

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Hell in a handbasket, or the democratization of divorce?

Two ways to look at the results of a new (paywalled) meta-analysis of studies on the educational gradient in divorce rates:

  • Across Europe, most in the liberal welfare states, the privileged access to divorce enjoyed by women with higher education has eroded across the last several decades.
  • Or, led by the welfare state, the liberation of women has progressively destroyed families further and further down the economic food chain.

The study combined many analyses of divorce rates and analyzed them together. The results for the Nordic countries were most pronounced. Here is the relationship between education and divorce in studies over the last 20 years (going down the chart). Dots to the right of the solid line indicate studies in which more educated women had higher odds of divorce, moving to the left means divorce is spreading to women with less education:

divorcemeta

Studies now show a negative gradient, that is, women with less education have higher odds of divorce. The trend was in the same direction in most of Europe, but not as advanced. (In the U.S., incidentally, which was not included in the analysis, we have a curvilinear pattern, with the highest rates among the some-college population, and the lowest among those with advanced degrees. I have a preliminary paper here, and a subsequent version under review.)

Raising the question, how much divorce is the right amount? Some people treat divorce like child abuse — any amount is bad. But can’t we agree there was not enough divorce 50 years ago — meaning people who were in miserable marriages couldn’t get out of them? And, given it was concentrated among more privileged families, wasn’t that evidence of social class privilege? So, what’s the right balance? You might think no education effect is the best, with marriages equally likely to end in divorce regardless of social class. But what if the marriages of poor people have more problems, and they need or want divorce more?

The analysis further showed that the shift in the education gradient was correlated with the overall divorce rate (as divorce increased, it democratized) and with the labor force participation rate for women (the more employed women, the more divorce spread to the lower classes). Divorce laws had no effect.

We shouldn’t assume any increase in divorce is bad. Maybe it’s like living alone: the people who do it are often not happy with their situation, and it often means something has gone wrong for them, but having the option is better than not.

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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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Girls braced for beauty

Sociologists like to say that gender identities are socially constructed. That just means that what it is, and what it means, to be male or female is at least partly the outcome of social interaction between people – visible through the rules, attitudes, media, or ideals in the social world.

And that process sometimes involves constructing people’s bodies physically as well. And in today’s high-intensity parenting, in which gender plays a big part, this includes constructing – or at least tinkering with – the bodies of children.

Today’s example: braces. In my Google image search for “child with braces,” the first 100 images yielded about 75 girls.

google-braces

Why so many girls braced for beauty? More girls than boys want braces, and more parents of girls want their kids to have them, even though girls’ teeth are no more crooked or misplaced than boys’. This is just one manifestation of the greater tendency to value appearance for girls and women more than for boys and men. But because braces are expensive, this is also tied up with social class, so that richer people are more likely to get their kids’ teeth straightened, and as a result richer girls are more likely to meet (and set) beauty standards.

Hard numbers on how many kids get braces are surprisingly hard to come by. However, the government’s medical expenditure survey shows that 17 percent of children ages 11-17 saw an orthodontist in the last year, which means the number getting braces at some point in their lives is higher than that. The numbers are rising, and girls are wearing most of hardware.

A study of Michigan public school students showed that although boys and girls had equal treatment needs (orthodontists have developed sophisticated tools for measuring this need, which everyone agrees is usually aesthetic), girls’ attitudes about their own teeth were quite different:

michigan-braces

Clearly, braces are popular among American kids, with about half in this study saying they want them, but that sentiment is more common among girls, who are twice as likely as boys to say they don’t like their teeth.

This lines up with other studies that have shown girls want braces more at a given level of need, and they are more likely than boys to get orthodontic treatment after being referred to a specialist. Among those getting braces, there are more girls whose need is low or borderline. A study of 12-19 year-olds getting braces at a university clinic found 56 percent of the girls, compared with 47 percent of the boys, had “little need” for them on the aesthetic scale.

The same pattern is found in Germany, where 38 percent of girls versus 30 percent of boys ages 11-14 have braces, and in Britain – both countries where braces are covered by state health insurance if they are needed, but parents can pay for them if they aren’t.

Among American adults, women are also more likely to get braces, leading the way in the adult orthodontic trend. (Google “mother daughter braces” and you get mothers and daughters getting braces together; “father son braces” brings you to orthodontic practices run by father-son teams.)

anchors-braces

Caption: The teeth of TV anchors Anderson Cooper, Soledad O’Brien, Robin Roberts, Suzanne Malveaux, Don Lemon, George Stephanopolous, David Gregory, Ashley Banfield, and Diane Sawyer.

Teeth and consequences

Today’s rich and famous people – at least the one whose faces we see a lot – usually have straight white teeth, and most people don’t get that way without some intervention. And lots of people get that.

Girls are held to a higher beauty standard and feel the pressure – from media, peers or parents – to get their teeth straightened. They want braces, and for good reason. Unfortunately, this subjects them to needless medical procedures and reinforces the over-valuing of appearance. However, it also shows one way that parents invest more in their girls, perhaps thinking they need to prepare them for successful careers and relationships by spending more on their looks.

When they’re grown up, of course, women get a lot more cosmetic surgery than men do – 87 percent of all surgical procedures, and 94% of Botox-type procedures – and that gap is growing over time.

As is the case with lots of cosmetic procedures, people from wealthier families generally are less likely to need braces but more likely to get them. But add this to the gender pattern, and what emerges is a system in which richer girls (voluntarily or not) and their parents set the standard for beauty – and then reap the rewards (as well as harms) of reaching it.

Note: I didn’t find any sociological studies of this. Why don’t you do one?

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It’s not the one-child policy, repeated correction edition

The Washington Post has a poignant story about elderly parents in China whose lives are disrupted by the deaths of their only children. In a society with low fertility, an inadequate pension system, and a high cultural value on generational legacies, this loss is often devastating. And for those who wanted to have more children, but were prevented from doing so by China’s repressive one-child policy, the suffering is more acute, resulting in anger directed toward the state.

I wish, however, that American media would stop unquestioningly attributing China’s low fertility rate to the one-child policy. The Post‘s William Wan writes:

For more than three decades, debate has raged over China’s one-child policy, imposed in 1979 to rein in runaway population growth. It has reshaped Chinese society — with birthrates plunging from 4.77 children per woman in the early 1970s to 1.64 in 2011, according to estimates by the United Nations — and contributed to the world’s most unbalanced sex ratio at birth, with baby boys far outnumbering girls.

That’s an odd paragraph, because it notes the policy was implemented in 1979 (it was actually 1980), and then compares fertility rates in the “early 1970s” to the present. Isn’t the more reasonable comparison to 1980? The data are available:

Source: World Bank or United Nations.

The drop from 2.6 in 1980 to 1.6 or so today is important (although of course it can’t all be attributed to the policy). But the “plunge” from 4.77 was mostly before the policy took hold.

A recent paper by Wang Feng, Yong Cai, and Baochang Gu considers the common claim that the one-child policy averted 400 million births. They write:

In stating that the one-child policy averted 400 million births, the promoters of the policy first misinterpreted the original results from the study mentioned above. The number of births averted was for the period since 1970, not from 1980, when the one-child policy was formally implemented nationwide. This mistake is crucial because most of China’s fertility transition was completed during the decade of the 1970s—that is, before China’s one-child policy was enacted. Within that decade, China’s total fertility rate dropped by more than half, from 5.8 in 1970 to 2.8 in 1979. Most of the births averted, if any, were due to the rapid fertility decline of that decade, not to the one-child policy that came afterward.

Dear American news media: Please make a note of a it.

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Fundamentally opposed to science?

Conservative religious fundamentalists really don’t trust the scientific establishment.

In the discussion of academia’s liberalism, we should also consider the public’s mistrust of science, especially the conservative and fundamentalist public. Why would people who don’t trust science become scientists?

Last year Gordon Gauchat reported in American Sociological Review that Americans’ trust in the scientific community was holding steady except for political conservatives and those who attend church regularly, and that the trend was not explained by the lower education levels of conservatives or religious people (in fact, educated conservatives expressed the lowest levels of trust in science). His conclusion was that the trend showed the politicization of science, which is not the way modernity is supposed to go.

In response, Darren Sherkat blogged that Gauchat underestimated the importance of religion in explaining conservatives’ opposition to science because he only used the General Social Survey’s measure of the frequency of religious attendance instead of a measure of beliefs. And he provided a chart from the GSS showing that religious fundamentalists had lower trust in science whether they were Republicans or not. Sherkat wrote:

Any social scientist who studies politics, religion, and science should know that the reason why Republicans are at war against science is to court the vote of fundamentalist Christian simpletons who are opposed to science and reason. … What drives Republican opposition to science is that more Republicans are fundamentalists who believe that the Bible is the literal word of god.

You got your fundamentalism in my conservatism

As I look at it, conservatism and fundamentalism are both at fault. My take on the trends shows that, in addition to the growing divide between politically conservative fundamentalists and politically liberal non-fundamentalists, liberal fundamentalists have grown more trusting of science, while conservative non-fundamentalists have grown less trusting.

I used the GSS from 1974 through the latest 2012 survey. To highlight the polarization I show only those who are “extremely liberal,” “liberal,” “conservative,” or “extremely conservative,” leaving out those who are “slightly” liberal or conservative, or moderate. So this is not the whole population (I’ll return to that below).

The question was:

I am going to name some institutions in this country. As far as the people running these institutions are concerned, would you say you have a great deal of confidence, only some confidence, or hardly any confidence at all in them? … Scientific community.

It’s as close as we get to a question about science itself. For fundamentalism, GSS asked whether the respondent’s religion was fundamentalist, moderate, or liberal. I dichotomized it to fundamentalists versus everyone else (including people with no religion).*

These are the people expressing a great deal of confidence in the scientific community:

confidence-in-scienceThese trends are heavily smoothed (down to four decades), because the numbers bounce around a lot from year to year, as the samples are only between 60 and 220 in each cell in the individual years. To do a simple test of the trends, I ran a regression using time and interactions between time and politics-fundamentlism dummy variables, with controls for age and sex (old people and men hate science more than regular people, net of religion and politics).

The regression confirms what the graph shows: significant declines in trust among conservatives whether fundamentalist or not, and an increase in trust among liberal fundamentalists. The trend for liberal non-fundamentalists was flat. (Details on request.)

I left out of that analysis the people who were slightly conservative, moderate, or slightly liberal. That’s a shrinking majority of the population, which breaks down like this from the 1970s to the last decade (click to enlarge):

confidence-in-science-popsSo the bad news for science is that the increasingly anti-science groups are increasing in the population: conservative fundamentalists and non-fundamentalists. The big green majority is not growing more or less anti-science (even when you break it down by fundamentalism), but it’s also shrinking. The liberal fundamentalists are getting more into science, but also vanishing.

Just wait till they find out (some) sociology is part of the “scientific community.”

Note: This is a blog-post, not peer-reviewed research. I might be wrong.

* Skerkat instead uses a question about how to interpret the Bible instead of the fundamentalism question (literal word of God, inspired word of God, book of fables). 95% of the people who described themselves as having a “fundamentalist” describe the Bible as either the literal or the inspired word of God.

 

 

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