Tag Archives: economics

How to illustrate a .61 relationship with a .93 figure: Chetty and Wilcox edition

Yesterday I wondered about the treatment of race in the blockbuster Chetty et al. paper on economic mobility trends and variation. Today, graphics and representation.

If you read Brad Wilcox’s triumphalist Slate post, “Family Matters” (as if he needed “an important new Harvard study” to write that), you saw this figure:

chetty-in-wilcox

David Leonhardt tweeted that figure as “A reminder, via [Wilcox], of how important marriage is for social mobility.” But what does the figure show? Neither said anything more than what is printed on the figure. Of course, the figure is not the analysis. But it is what a lot of people remember about the analysis.

But the analysis on which it is based uses 741 commuting zones (metropolitan or rural areas defined by commuting patterns). So what are those 20 dots lying so perfectly along that line? In fact, that correlation printed on the graph, -.764, is much weaker than what you see plotted on the graph. The relationship you’re looking at is -.93! (thanks Bill Bielby for pointing that out).

In the paper, which presumably few of the people tweeting about it read, the authors explain that these figures are “binned scatter plots.” They broke the commuting zones into equally-sized groups and plotted the means of the x and y variables. They say they did percentiles, which would be 100 dots, but this one only has 20 dots, so let’s call them vigintiles.

In the process of analysis, this might be a reasonable way to eyeball a relationship and look for nonlinearities. But for presentation it’s wrong wrong wrong.* The dots compress the variation, and the line compresses it more. The dots give the misleading impression that you’re displaying the variance around the line. What, are you trying save ink?

Since the data are available, we can look at this for realz. Here is the relationship with all the points, showing a much messier relationship, the actual -.76 (the range of the Chetty et al. figure, which was compressed by the binning, is shown by the blue box):

chetty scattersThat’s 709 dots — one for each of the commuting zones for which they had sufficient data. With today’s powerful computers and high resolution screens, there is no excuse for reducing this down to 20 dots for display purposes.

But wait, there’s more. What about population differences? In the 2000 Census, these 709 commuting zones ranged in population in the 2000 Census from 5,000 (Southwest Jackson, Utah) to 16,000,000 (Los Angeles). Do you want to count Southwest Jackson as much as Los Angeles in your analysis of the relationship between these variables? Chetty et al. do in their figure. But if you weight them by population size, so each person in the population contributes equally to the relationship, that correlation that was -.76 — which they displayed as -.93 — is reduced to -.61. Yikes.

Here is what the plot looks like if you scale the commuting zones according to population size (more or less, not quite sure how Stata does this):

chetty scatters weighted

Now it’s messier, and the slope is much less steep. And you can see that gargantuan outlier — which turns out to be the New York commuting zone, which has 12 million people and with a lot more upward mobility than you would expect based on its family structure composition.

Finally, while we’re at it, we may as well attend to that nonlinearity that has been apparent since the opening figure. We can increase the variance explained from .38 to .42 by adding a quadratic term, to get this:

chetty scatters weighted quad

I hate to go beyond what the data can really tell. But — what the heck — it does appear that after 33% single-mother families, the effect hits its minimum and turns positive. These single mother figures are pretty old (when Chetty et al.’s sample were kids). Now that the country has surpassed 40% unmarried births, I think it’s safe to say we’re out of the woods. But that’s just speculation.**

*OK, OK: “wrong wrong wrong” is going too far. Absolute rules in data visualization are often wrong wrong wrong. Binning 709 groups down to 20 is extreme. Sometimes you have a zillion points. Sometimes the plot obscures the pattern. Sometimes binning is an inherent part of measurement (we usually measure age in years, for example, not seconds). None of that is an excuse in this case. However, Carter Butts sent along an example that makes the point well:

841101_10201299565666336_1527199648_o

On the other hand, the Chetty et al. case is more similar to the following extreme example:

If you were interested in the relationship between age and earnings for a sample of 1,400 full-time, year-round women, you might start with this, which is a little frustrating:

age-wage1

The linear relationship is hard to see, but it’s about +$500 per year of age. However, the correlation is only .13, and the variance explained by linear-age alone is only 1.7%. But if you plotted the mean wage over ages, the correlation jumps to .68:

age-wage2

That’s a different question. It’s not, “how does age affect earnings,” it’s, “how does age affect mean earnings.” And if you binned the women into 10-year age intervals (25-34, 35-44, 45-54), and plotted the mean wage for each group, the correlation is .86.

age-wage3

Chetty et al. didn’t report the final correlation, but they showed it, even adding the regression line, so that Wilcox could call it the “bivariate relationship.”

**This paragraph was a joke that several people missed, so I’m clarifying. I would never draw a conclusion like that from the scraggly tale of a loose correlation like this.

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Where is race in the Chetty et al. mobility paper?

What does race have to do with mobility? The words “race,” “black,” or “African American” don’t appear in David Leonhardt’s report on the new Chetty et al. paper on intergenerational mobility that hit the news yesterday. Or in Jim Tankersley’s report in the Washington Post, which is amazing, because it included this figure: post-race-mobility That’s not exactly a map of Black America, which the Census Bureau has produced, but it’s not that far off: census-black-2010

But even if you don’t look at the map, what if you read the paper? Describing the series of maps of intergenerational mobility, the authors write:

Perhaps the most obvious pattern from the maps in Figure VI is that intergenerational mobility is lower in areas with larger African-American populations, such as the Southeast. … Figure IXa confirms that areas with larger African-American populations do in fact have substantially lower rates of upward mobility. The correlation between upward mobility and fraction black is -0.585. In areas that have small black populations, children born to parents at the 25th percentile can expect to reach the median of the national income distribution on average (y25;c = 50); in areas with
large African-American populations, y25;c is only 35.

Here is that Figure IXa, which plots Black population composition and mobility levels for groups of commuting zones: ixa Yes, race is an important part of the story. In a nice part of the paper, the authors test whether Black population size is related to upward mobility for Whites (or, people in zip codes that are probably White, since race isn’t in their tax records), and find that it is. It’s not just Blacks driving the effect. I’m thinking about the historical patterns of industrial development, land ownership, the backwardness of racist elites in the South, and so on. But they’re not. For some reason, not explained at all, Chetty et al. offer this pivot:

The main lesson of the analysis in this section is that both blacks and whites living in areas with large African-American populations have lower rates of upward income mobility. One potential mechanism for this pattern is the historical legacy of greater segregation in areas with more blacks. Such segregation could potentially affect both low-income whites and blacks, as racial segregation is often associated with income segregation. We turn to the relationship between segregation and upward mobility in the next section.

And that’s it, they don’t discuss Black population size again, instead only focusing on racial segregation. They don’t pursue this “potential mechanism” in the analysis that follows. Instead, they drop percent Black for racial segregation. I have no idea why, especially considering this Table VII, which shows unadjusted (and normalized) correlations (more or less) between each variable and absolute upward mobility (the variable mapped above): tablevii

In these normalized correlations, fraction Black has a stronger relationship to mobility than racial segregation or economic segregation! In fact, it’s just about the strongest relationship on the whole long table (except for single mothers, with which it is of course highly correlated). So why do they not use it in their main models? Maybe someone else can explain this to me. (Full disclosure, my whole dissertation was about this variable.)

This is especially unfortunate because they do an analysis of the association between commuting zone family structure (using macro-level variables) and individual-level mobility, controlling for marital status — but not race — at the individual level. From this they conclude, “Children of married parents also have higher rates of upward mobility if they live in communities with fewer single parents.” I am quite suspicious that this effect is inflated by the omission of race at either level. So they write the following, which goes way beyond what they can find in the data:

Hence, family structure correlates with upward mobility not just at the individual level but also at the community level, perhaps because the stability of the social environment affects children’s outcomes more broadly.

Or maybe, race.

I explored the percent Black versus single mother question in a post a few weeks ago using the Chetty et al. data. I did two very simple OLS regression models using only the 100 largest commuting zones, weighted for population size, the first with just single motherhood, and then a model with proportion Black added: 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. That’s not an analysis, it’s just an argument for keeping percent Black in the more complex models. Substantively, the level of racial segregation is just one part of the complex race story – it measures one kind of inequality in a local area, but not the amount of Black, which matters a lot (I won’t go into it all, but here are three old papers: one, two, three.

The burgeoning elite conversation about economic mobility, poverty, and inequality is good news. It’s avoidance of race is not.

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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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Economics watch, check that basketball evidence edition

Seth Stephens-Davidowitz has done some interesting work, including analyzing Google data to identify anti-Black animus among American voters. In today’s New York Times he has an interesting piece about how NBA players disproportionately come from privileged backgrounds: richer zip codes, non-teenage mothers, married parents.

This is apparently based on evidence, data from the zip codes in which NBA players grew up and their biographies. But then he describes “the advantages of kids from higher socioeconomic classes” And, astoundingly, comes up with this:

What are these advantages? The first is in developing what economists call noncognitive skills like persistence, self-regulation and trust.

And the evidence for that? A single anecdote (“consider the tragic tale of Doug Wrenn…”). Really?

lawson1

I have an anecdote for you: Ty Lawson, the former UNC point guard who now plays for the NBA’s Denver Nuggets. He seems to fit the profile. He started his career in middle school in Brandywine, Maryland (with a median household income of $108,ooo, twice the national average) before getting into the private-school basketball circuit. I don’t know if his parents were married, but they have the same last name. He is also less than 6 feet tall. So how is he in the NBA? Obviously, he has the incredible physical and mental ability to play at that level. That takes persistence, as well as some probable genetic advantages (his mother ran track). And, of course he is a paragon of self-regulation. Except for all that drunk driving, speeding, not showing up for court appearances, and domestic violence charges.* Throw it all under “non-cognitive skills,” I guess.

Forget the anecdotes. Could we at least consider the possibility that the richer schools of future-NBA stars devote more material resources to cultivating the talents of their most-talented athletes — coaching, facilities, attention? An economist (no offense) does some demographic research, and suddenly pronounces on social psychology without even pretending to offer evidence on what the “first” advantage of a privileged upbringing is? (The “second relative advantage” Stephens-Davidowitz cites is height, since rich kids grow taller.) You would think economists would at least consider the advantage of money itself.

Sheesh.

*This criminal record shows an amazing combination of racial profiling (pulled over for loud music coming from the car, etc.) and basketball-town privilege (made to write a 4-page report on evils of drunk driving; “he’s just a rookie,” etc.). But that’s not the point.

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Are 50% of college graduates unemployed or underemployed?

House Republicans yesterday held a “press conference” (less than 11 minutes) in which I heard two crazy statistics. Each quote is paired with the unidentified Republican Congresswoman who said it:

congresswoman1

Recent college grads still are having a very difficult time finding a job. In fact their unemployment is nearly 25%.

congresswoman2

Today 50% of college graduates can’t find a job or are underemployed, that’s one in every two graduates.

The first one is just completely wrong. The second one may be just completely misleading (except insofar as 50% is one in two).

I doubt it, but it’s possible they were referring to this paper by Thomas Spreen in the February Monthly Labor Review. Spreen used data from the 2007-2011 October CPS surveys. That’s the month the CPS collects education information in detail, and he calculated the unemployment rates for people who had graduated colleges in the same calendar year. The rates were very high, and did at one point – for men only, in 2009 – reach more than 25%, as shown here:

colgradunemp-bls

By 2011 it fell to 16% for men and 11% for women. We should interpret these number cautiously, however, as they are based on quite small samples. According to Spreen, in 2011 the October CPS only included 440 people ages 20-29 who completed their BA degrees that year. Figure about 200 of them were men, and you’re talking about roughly 30 unemployed male recent college graduates. Granted 2009 was the worst year so a spike is plausible, you still need to put a pretty wide confidence interval around that number.

Anyway, because the Republicans used this phrase “or are underemployed,” which is not in the Spreen paper, I suspect the source of these talking points was this 13-month-old AP story, titled “Half of recent college grads underemployed or jobless, analysis says,” or some other version of it. “Underemployed” here means working at a job that doesn’t require a college degree. The number “unemployed” is not given. Those are two pretty different things and should probably never be combined. But Jordan Weissman at The Atlantic, trying to read between the lines, wrote,

Unfortunately, I don’t have all of the data the AP was working with. But their analysis implies that about a quarter of the post-collegiate population is outright unemployed.

That’s not crazy if you were writing about just men for 2009 (and remember most college graduates are women), but Weissman was writing in 2012 about 2011. He might not have all the data the AP had, but he – and you – have what we need to check unemployment rates using the IPUMS CPS data extractor. That will give you March CPS survey data (not the October survey, which identified graduates in the last year, but good for ballparking). It’s pretty easy:

Choose “Analyze data online,” then “Analyze all March samples 1962-2012,” then fill out your table request. Based on the definition given of “recent” college graduates as people under age 25 with a BA, this is what I did:

colgradunemp-codes

That gives me employment status, by sex, for years 1993-2012, among people with BA degree (no more, no less), age 15-24 (hardly any are under 20), who are not currently attending school. Here are the percentages unemployed from that:

colgradunempOkay, so nowhere near 25% unemployed. The worst it was for men was 10.9% in 2012, for women 6.4%. (And note these are based on samples of more than 500 men and 800 women in recent years.) Shockingly high unemployment for college graduates, of course. And it’s interesting that it’s higher for those who graduated within the last few months (what the MLR paper showed) than it is for those who graduated sometime within the last few years (the under-25 grouping I used).

The underemployment thing may be important, but there’s not enough information here to evaluate it.

Listening to the press conference on the radio, I naïvely expected one of the reporters to ask, “Excuse me, did you just say the unemployment rate for recent college graduates is 25%?”

Anyway, thanks for listening.

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That economists’ paper about gender inequality, marriage and divorce

I was planning to write a note about this paper by economists, “Gender identity and relative income within households,” which got a lot of play two weeks ago. But I forgot about it until today, and then noticed that in the New York Times Catherine Rampbell, economics writer, dropped it in her story on the Pew Report about women as breadwinners. In the cautionary part of the article, Rampbell mentioned “A new working paper by economists” that showed:

…perhaps even more tellingly, couples in which the wife earns more report less satisfaction with their marriage and higher rates of divorce.

Maybe reporters like what’s new, or maybe it was just on her radar because she reads Freakonomics, the Economist or the Financial Times, which all uncritically wrote up the paper when it came out. But it’s really a shame in a story about current trends to cite a “new” paper which (for this part of its analysis) used data more than 20 years old. divorce-cartoon
Anyways

Here is a brief critique I was going to give when the paper came out. Just taking two lines from the abstract, I offer a few suggestions:

1. Couple matching

The distribution of the share of household income earned by the wife exhibits a sharp cliff at 0.5, which suggests that a couple is less willing to match if her income exceeds his.

Suggestion: It’s not a good idea to use the relative incomes within couples years after they got married to discuss how relative income affects mate choice decisions. People move, change jobs, have children, etc., in the first few years after they get married. You need to look at income before marriage to study mate selection.

2. Divorce

Couples where the wife earns more than the husband are less satisfied with their marriage and are more likely to divorce.

This part of the analysis uses data from Waves 1 & 2 of the National Survey of Families and Households NSFH), which were collected in 1987-88 and 1992-94. I don’t always insist that everyone use data from this minute, but at some point — around two decades — a study becomes historical. That judgment depends on the context and the question being asked. In this case, relative earnings of spouses (as we just saw in the Pew report) has seen an order-of-magnitude change over this period. And the paper is about norms! That is, the authors speculate that couples with high-earning wives divorce because they are outside the mainstream. So if, 20-25 years later, they’re not outside the mainstream anymore, the paper might not be salient.

Secondly, this is well-worn territory, and the specific hypothesis offered here has been tested and found wanting in several award winning papers using more thorough measures and testing competing hypotheses. (The NSFH, one of the most productive data collection efforts ever, maintained a bibliography up to 2004, which lists 180 papers under the category “union quality and stability.”) For those interested in the fuller story, I recommend these:

…[M]easures of marital commitment and satisfaction are better predictors of marital dissolution than measures of economic independence. This strongly suggests that the independence effect found in prior research, which did not include controls for marital quality, may have been measuring the role of wives’ economic independence in exiting bad marriages, not in exiting all marriages.

We find that when men are not employed, either husbands or wives are more likely to leave. When wives report better than average marital satisfaction, their employment affects neither their nor their husbands’ exits. However, when wives report below average marital satisfaction, their employment makes it more likely that they will leave.

…shifting into full-time employment is more likely for unhappily married than for happily married wives. … [C]ontrary to frequently invoked social and economic theories, wives’ full-time employment is associated with greater marital stability.

This provides a followup to a previous study using the same data which found…

…clear evidence that, at the individual level, women’s employment does not destabilize happy marriages but increases the risk of disruption in unhappy marriages.

The reason these marital satisfaction controls matter so much is that how happy women are within marriage affects their employment, and therefore their earnings. So what looks like an earnings effect is often an unhappy-marriage effect. Careful sequencing of longitudinal data (which these papers do) is required to sort this out.

I only mention the awards because I was shocked (shocked!) to see these major sociology papers in top journals, using the same dataset and asking the same questions, published over a decade, which have been cited hundreds of times in the academic literature, go unnoticed in this economics working paper, which — not-yet published, not-yet peer reviewed — would be quoted all over the place.

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Married women learning that paid work pays

The economist Raquel Fernández has a new paper out called, “Cultural Change as Learning: The Evolution of Female Labor Force Participation over a Century” (published version here, free version here). If I understand it, though, “female” labor force participation really only refers to married women. Correct me if you understand this better than I do and I’m wrong, but I think that’s a problem for the theory.

The basic point is that married women learn from the experience of others, producing a generational change in employment rates as positive experiences transmit to younger cohorts. As it became more culturally acceptable for married women to have jobs, the cultural effect accelerated, but it reached a saturation point resulting in the stalled progress toward higher employment rates among (married) women. Here are the trends she uses:

fernandezThe normative survey question she relies on is about whether it’s OK for a woman to work “if her husband can support her.” The S-shape of labor force participation rates is supposed to be consistent with the cultural transmission theory (rather than being caused by, for example, anemic work-family policy, anti-feminist backlash, or hollow anti-discrimination enforcement).

But I don’t see anything in the paper about increasing non-marriage (now about twice as common as in 1960), or about labor force participation rates for single women. Shouldn’t economists be concerned about that kind of selection issue? In fact, labor force participation rates for single women have stalled, too, as my figure shows:

lfp by marital status 60-11

I don’t think attitudes toward married women’s work — or anything about marriage alone — are going bear the burden of explaining two decades of stalled progress into the labor force for both single and married women. I’m happy to have cultural explanations as part of the mix here, but I don’t think this one will do it.

 

 

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Gender wage gap, 2012 edition

Gender inequality stagnation continues apace.

The Bureau of Labor Statistics has released the wage report for 2012, which shows women’s earnings relative to men’s falling back to the 2005 level. The gender breakdown is available here (the content at that link changes when new data come out), and the historical series from 1979 to 2011 is available here.

The usually-reported number is the median weekly earnings of full-time wage and salary workers. These are the gender ratios (women’s earnings divided by men’s):

gender earnings gap 2012

 

Follow the gender inequality tag for updates and previous posts.

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Quick book review: The Price of Inequality

The Price of Inequality: How Today’s Divided Society Endangers Our Future, by Joseph E. Stiglitz (W. W. Norton, 2012)

My economics training as a sociologist — with a background in American Culture studies — has been spotty and roundabout. I got a healthy dose of Marxist economics in college, and then some feminist economics, a little human capital theory and some dated econometrics in grad school and since.

All that made reading made it interesting, and also frustrating, to read The Price of Inequality, by Joseph Stiglitz – a winner of the Nobel Prize for economics and an “insanely great economist,” according to Paul Krugman.

On the plus side, I am glad to see someone within mainstream economic theory freely discussing all the ways that common assumptions simply do not predominate in the modern economic scene. Especially helpful in this category is his discussion of how “rents” accumulate vast resources at the upper end of the income distribution, with perverse effects on economic development and politics alike. At the very top — in the finance sector especially, but also in energy and big manufacturing — there is nothing like free-market competition. And the beneficiaries of those distortions are the most powerful players in the economy and political system.

It is refreshing to see this concentration of wealth described as waste and distortion, as their vast profits provide little gain to anyone else. In fact, dumping vast wealth on the 1% creates a drag on the macroeconomy while fueling the historic run-up in economic inequality. This is all very timely and takes you right through the financial crisis up to early 2012.

So if you want to understand from an economic perspective how “the market” in America isn’t the way it’s supposed to be, this book may be for you.

Top 1% income shares, including capital gains, for the U.S. and Sweden. From the World Top Incomes Database.

The other good thing about the book for many readers will be its cogent and comprehensive economic rationale for the liberal reforms that many of you probably supported already. Stiglitz makes the case that a suite of reforms – an agenda Rachel Maddow, Elizabeth Warren and Robert Reich probably agree on – would, by (directly or indirectly) increasing taxes (or reducing subsidies) on the wealthy and redistributing wealth downward, reduce the federal debt, increase economic growth, and reduce economic inequality all at the same time.

Round numbers: if the richest 1% earn about 20% of all income, then taxing them another 10% would generate government revenue equivalent to 2% of GDP. (And it wouldn’t hurt anything, since they just hoard or waste their extra cash anyway rather than “creating jobs” with it, and they’re so greedy they wouldn’t be discouraged by the disincentive effect of higher taxes.) That’s an amount of money that could actually be useful for poor people.

The frustration I feel reading the book is more amorphous. I think there have to be better ways of describing this whole system than using the language of mainstream economics, which ends up painting a picture of an entire system that does not work according to the rules as imagined. Concepts like power, social class, social networks, elites and reification do not figure heavily in this story. In fact, Stiglitz’s apparent ignorance of sociology is sometimes funny as in this passage:

Social sciences like economics differ from the hard sciences in that beliefs affect reality: beliefs about how atoms behave don’t affect how Adams actually behave, but beliefs about how the economic system functions affect how it actually functions. George Soros, the great financier, has referred to this phenomenon has “reflexivity,” and his understanding of it may have contributed to his success.

I guess after what people like me have made of econometrics it’s only fair that economists would attribute the idea of reflexivity to Soros. (The discussion of reflexivity in Anthony Giddens’s book The Consequences of Modernity is very approachable.)

Anyway, the book is easy to read and informative, and has lots of footnotes and references.

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Googling racism, votes for Obama, and population composition

This post contains racially offensive language.

Seth Stephens-Davidowitz, a PhD student in economics at Harvard, has analyzed Google searches for racially offensive terms across metro areas, and tested for a “racial animus” effect on the vote for Obama in 2008.* The results are pretty strong:

 The baseline proxy that I use is the percentage of an area’s total Google searches from 2004-2007 that included the word “nigger” or “niggers.” … A one standard deviation increase in an area’s racially charged search is associated with a 1.5 percentage point decrease in Barack Obama’s vote share, controlling for John Kerry’s vote share. The results imply that, relative to the area with the lowest racial animus, racial animus cost Obama between 3 to 5 percentage points of the national popular vote. … The statistical significance and large magnitude are robust to numerous controls including local unemployment rates; home state candidate preference; Census division fixed effects; changes in House voting over the same period; prior trends in Presidential voting; and a variety of demographics controls.

This is a creative way to measure racism — not perfect, but nothing is. And he did a fair amount of experimenting and tinkering with the measures to make sure it wasn’t fluky. Very nice.

Racism at the population level

Another thing that jumped out at me in the paper, however, was the finding among the control variables that racist searches are more common in markets with higher proportions of Black residents. This raises a potentially difficult issue with the whole Google-search method, since we don’t know who is doing the searching. Does his finding suggest that Blacks are doing racist searches? I don’t think so. I previously looked at state-level correlations between race/ethnic composition and search terms, and it looks to me like the most correlated search terms are indeed being performed by those groups. For example, Americans Indians live in states where people Google “Indian Health Service” and Blacks live in states where people Google stuff about historically Black colleges and universities (and Whites apparently Google AC/DC songs).

But at lower levels of correlation, I would expect the presence of one group to affect the search behavior of others. An obvious example is how Southern states mostly vote Republican in national elections — more Blacks equals more conservative voting, even though the great majority of Black voters vote Democratic. The higher rates of conservatism among Whites in those places outweighs the presence of Democratic-voting Blacks. (The effect on Whites was discovered before Blacks could vote in the South, but remains true.)

We also know from way back that inequality between Blacks and Whites is greater where Blacks are more highly represented in the population, and there’s good evidence at least some of this is due to increased racism by Whites. I’ve found this for earnings for both men and women, for middle and working class workers; and, with Matt Huffman, for occupational segregation and access to managerial positions. Only some of that research has actually measured racial attitudes, however. Google gives us a chance to look from a different angle — at the private behavior, not expressed attitudes, of populations.

Here’s one take, jumping off from Stephens-Davidowitz’s paper: searches for “nigger jokes.” This seems like something Blacks are unlikely to be looking for on Google.** But the searches are more common in states with larger Black populations:

Removing West Virginia, which is an extreme outlier on the jokes variable (more than 3 standard deviations from the mean), the correlation between searches for “nigger jokes” and Black population percentage is .48. Here’s the scatter plot (the non-Southern states have the pink centers).

And here’s the regression numbers for the relationship:

That positive relationship, tapering off, fits the long-standing pattern, as seen for example in this 1998 paper, which tested the percent-Black on common attitude measures in the General Social Survey (the figure estimates are net of a variety of controls):

All adding to the accumulating evidence for search behavior as a valuable research tool.

* Thanks to a tip from Rachel Lovell.

** Some searches seem even better for this purpose, such as “funny nigger jokes,” but fortunately there isn’t enough searching for that to get state-level frequencies, according to Google.

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Filed under Me @ work, Politics