Wealth data is not easy to get. Still for three years now, Credit Suisse Research Institute has published an annual Global Wealth Databook which attempts to estimate global wealth holdings. The most recent issue includes data covering 2012. According to Credit Suisse, the goal “is to provide the best available estimates of the wealth holdings of households around the world for the period since the year 2000.”
According to the publication, global household wealth was $222.7 trillion in mid-2012, equal to $48,500 for each of the 4.6 billion adults in the world. Wealth is defined as “the marketable value of financial assets plus non-financial assets (principally housing and land) less debts.”
Not surprisingly, as the figure below shows, average global wealth varies considerably across countries and regions.
Also significant are the values of the mean vs the median wealth in each of the countries. Mean or average wealth is calculated by dividing the total wealth of a country by its adult population. Median wealth is the wealth holdings of the adult in the middle of the wealth distribution. The median is generally considered a far more reliable indicator of wealth because it is less sensitive to extremes at the top or bottom of the distribution. The greater the divergence of mean and median wealth, the greater is the wealth inequality.
The table below provides mean and median wealth estimates for those countries with generally reliable data. As you can see, the U.S. ranks high in terms of mean wealth, trailing only 5 countries. Things are quite different when it comes to median wealth; the U.S. trails 26 countries! Not surprisingly, then, the U.S. is No. 1 when it comes to the mean/median wealth ratio, or wealth inequality.
We clearly dominate in the number of millionaires and the upper global wealth categories. Are we a wealthy country? Definitely. Is that wealth concentrated in relatively few hands? Definitely.
Forty years ago Richard Easterlin proposed the paradox that people in wealthier countries were no happier than those in less wealthy countries. Subsequent research on money and happiness brought modifications and variations, notably that within a single country, while for the poor, more money meant fewer problems, for the wealthier people — those with enough or a bit more — enough is enough. Increasing your income from $100,000 to $200,000 isn’t going to make you happier.
It was nice to hear researchers singing the same lyrics we’ll soon be hearing in commencement speeches and that you hear in Sunday sermons and pop songs (“the best things in life are free”; “mo’ money mo’ problems”). But this moral has a sour-grapes taste; it’s a comforting fable we non-wealthy tell ourselves all the while suspecting that it probably isn’t true.
A recent Brookings paper by Betsey Stevenson and Justin Wolfers adds to that suspicion. Looking at comparisons among countries and within countries, they find that when it comes to happiness, you can never be too rich.
Stevenson and Wolfers also find no “satiation point,” some amount where happiness levels off despite increases in income. They provide US data from a 2007 Gallup survey:
The data are pretty convincing. Even as you go from rich to very rich, the proportion of “very satisfied” keeps increasing. (Sample size in the stratosphere might be a problem: only 8 individuals reported annual incomes over $500,000;100% of them, though, were “very happy.”)
Did Biggie and Alexis get it wrong?
Around the time that the Stevenson-Wolfers study was getting attention in the world beyond Brookings, I was having lunch with a friend who sometimes chats with higher ups at places like hedge funds and Goldman Sachs. He hears wheeler dealers complaining about their bonuses. “I only got ten bucks.” Stevenson and Wolfers would predict that this guy’s happiness would be off the charts given the extra $10 million. But he does not sound like a happy master of the universe.
I think that the difference is more than just the clash of anecdotal and systematic evidence. It’s about defining and measuring happiness. The Stevenson-Wolfers paper uses measures of “life satisfaction.” Some surveys ask people to place themselves on a ladder according to “how you feel about your life.” Others ask
All things considered, how satisfied are you with your life as a whole these days?
The GSS uses happy instead of satisfied, but the effect is the same:
Taken all together, how would you say things are these days – would you say that you are very happy, pretty happy, or not too happy?
When people hear these questions, they may think about their lives in a broader context and compare themselves to a wider segment of humanity. I imagine that Goldman trader griping about his “ten bucks” was probably thinking of the guy down the hall who got twelve. But when the survey researcher asks him where he is on that ladder, he may take a more global view and recognize that he has little cause for complaint. Yet moment to moment during the day, he may look anything but happy. There’s a difference between “affect” (the preponderance of momentary emotions) and overall life satisfaction.
Measuring affect is much more difficult — one method requires that people log in several times a day to report how they’re feeling at that moment — but the correlation with income is weaker.
In any case, it’s nice to know that the rich are benefitting from getting richer. We can stop worrying about their being sad even in their wealthy pleasure and turn our attention elsewhere. We got 99 problems, but the rich ain’t one.
Does “the abortion culture” cause infanticide? That is, does legalizing the aborting of a fetus in the womb create a cultural, moral climate where people feel free to kill newborn babies?
It’s not a new argument. I recall a 1998 Peggy Noonan op-ed in the Times, “Abortion’s Children,” arguing that kids who grew up in the abortion culture are “confused and morally dulled.”* Earlier this week, USA Today ran an op-ed by Mark Rienzi repeating this argument in connection with the Gosnell murder conviction.
Rienzi argues that the problem is not one depraved doctor. As the subhead says:
The killers are not who you think. They’re moms.
Worse, he warns, infanticide has skyrocketed.
While murder rates for almost every group in society have plummeted in recent decades, there’s one group where murder rates have doubled, according to CDC and National Center for Health Statistics data — babies less than a year old.
Really? The FBI’s Uniform Crime Reports has a different picture.
Many of these victims were not newborns, and Rienzi is talking about day-of-birth homicides — the type killing Dr. Gosnell was convicted of, a substitute for abortion. Most of these, as Rienzi says are committed not by doctors but by mothers. I make the assumption that the method in most of these cases is smothering. These deaths show an even steeper decline since 1998.
Where did Rienzi get his data that rates had doubled? By going back to 1950.
The data on infanticide fit with his idea that legalizing abortion increased rates of infanticide. The rate rises after Roe v. Wade (1973) and continues upward till 2000.
But that hardly settles the issue. Yes, as Rienzi says, “The law can be a potent moral teacher.” But many other factors could have been affecting the increase in infanticide, factors much closer to actual event — the mother’s age, education, economic and family circumstances, blood lead levels, etc.
If Roe changed the culture, then that change should be reflected not just in the very small number of infanticides but in attitudes in the general population. Unfortunately, the GSS did not ask about abortion till 1977, but since that year, attitudes on abortion have changed very little. Nor does this measure of “abortion culture” have any relation to rates of infanticide.
Moreover, if there is a relation between infanticide and general attitudes about abortion, then we would expect to see higher rates of infanticide in areas where attitudes on abortion are more tolerant.
The South and Midwest are most strongly anti-abortion, the West Coast and Northeast the most liberal. So, do these cultural difference affect rates of infanticide?
Well, yes, but it turns out the actual rates of infanticide are precisely the opposite of what the cultural explanation would predict. The data instead support a different explanation of infanticide: Some state laws make it harder for a woman to terminate an unwanted pregnancy. Under those conditions, more women will resort to infanticide. By contrast, where abortion is safe, legal, and available, women will terminate unwanted pregnancies well before parturition.
The absolutist pro-lifers will dismiss the data by insisting that there is really no difference between abortion and infanticide and that infanticide is just a very late-term abortion. As Rienzi puts it:
As a society, we could agree that there really is little difference between killing a being inside and outside the womb.
In fact, very few Americans agree with this proposition. Instead, they do distinguish between a cluster of a few fertilized cells and a newborn baby. I know of no polls that ask about infanticide, but I would guess that a large majority would say that it is wrong under all circumstances. But only perhaps 20% of the population thinks that abortion is wrong under all circumstances.
Whether the acceptance of abortion in a society makes people “confused and morally dulled” depends on how you define and measure those concepts. But the data do strongly suggest that whatever “the abortion culture” might be, it lowers the rate of infanticide rather than increasing it.
* I had trouble finding Noonan’s op-ed at the Times Website. Fortunately, then-Rep. Talent (R-MO) entered it into the Congressional Record.
The New York Public Library posted a page from the first issue (September 1941) of Design for Living: The Magazine for Young Moderns that I thought was sorta neat for bringing some perspective to the increase in the amount and variety of clothing we take as normal today–but also, to my relief, the acceptance of a more casual style of dress. The magazine conducted a poll of women at a number of colleges throughout the U.S. about how many of various articles of clothing they owned. Here’s a visual showing the school where women reported the highest and lowest averages (the top item is a dickey, not a shirt):
Overall the women reported spending an average of $240.33 per year on clothing.
Hats for women were apparently well on their way out of fashion:
Can you imagine a magazine aimed at college women today implying that you might be able to get away with only three or four pairs of shoes, even if that’s what women reported?
At the end of the article they bring readers’ attention to the fact that they used a sample:
I can’t help but find it rather charming that a popular magazine would even bother to clarify anything about their polling methods. So…earnest!
Gwen Sharp is an associate professor of sociology at Nevada State College. You can follow her on Twitter at @gwensharpnv.
In 2009, 470,000 15-year-olds in 65 developed nations took a science test. Boys in the U.S. outperformed girls by 14 points: 509 to 495. How does the U.S. compare to other countries?
The figure below — from the New York Times — features Western and Northern Europe and the Americas (in turquoise), Asia and the Pacific Islands (in pink), and the Middle East and Eastern and Southern Europe (in yellow). The line down the middle separates societies in which boys scored higher than girls (left) and vice versa (right).
Notice that the countries in which boys outscore girls are overwhelmingly Western and Northern Europe and the Americas.
This data tells a similar story to the data on gender and math aptitude. Boys used to outperform girls in math in the U.S., but no longer. And if you look transnationally, cultural variation swamps gender differences. Analyses have shown that boys outperforming girls in math is strongly correlated with the degree of inequality in any given society.
One lesson to take is this: any given society is just one data point and can’t be counted on to tell the whole story.
Six years ago, I wrote that the Pittsburgh Steelers had become “America’s Team,” a title once claimed, perhaps legitimately, by the Dallas Cowboys.
Now Ben Blatt at The Harvard College Sports Analysis Collective concludes that it’s still the Cowboys:
…based on their huge fan base and ability to remain the most popular team coast-to-coast, I think the Dallas Cowboys have earned the right to use the nickname ‘America’s Team’.
To get data, Blatt posed as an advertiser and euchred Facebook into giving him some data from 155 million Facebook users, about half of the US population. Blatt counted the “likes” for each NFL team:
It’s Superbowls X, XIII, and XXX all over again – Steelers vs. Cowboys. And the Cowboys have a slight edge. But does that make them “America’s Team”? It should be easy to get more likes when you play to a metro area like Dallas that has twice as many people as Pittsburgh. If the question is about “America’s Team,” we’re not interested in local support. Just the opposite: if you want to know who America’s team is, you should find out how many fans it has outside its local area.
Unfortunately, Blatt doesn’t provide that information. So for a rough estimate, I took the number of Facebook likes and subtracted the metro area population. Most teams came out on the negative side. The Patriots, for example, had 2.5 million likes. but they are in a media market of over 4 million people. The Cowboys too wound up in the red 3.7 million likes in a metro area of 5.4 million people.
Likes outnumbered population for only five teams. The clear winner was the Steelers.
Advanced quantitative analysis often controls for variables that aren’t of central interest. But what does it mean to “control for” a variable? XKCD offers a fun example:
So, do subscribers to Martha Stewart Living live alongside furries? Probably not. In any case, these maps don’t offer any evidence in favor of this conclusion. This is because of a variable that hasn’t been controlled for: population density. XKCD captions the cartoon:
To control for population, one would have to divide the number of subscribers/furries by the total population. This would give us the percentage of the population that is described by both proclivities, instead of the sheer number of devotees. Then the maps would actually show variance in the proportion of the population instead of variance in the population itself.
In other words, we would have controlled for population in order to get a closer look at what we’re really interested in: furries, of course.
Lisa Wade is a professor of sociology at Occidental College and the author of Gender: Ideas, Interactions, Institutions, with Myra Marx Ferree. You can follow her on Twitter and Facebook.
Many expected that the severity of the Great Recession, a recognition that prior expansion was largely based on unsustainable “bubbles,” and an anemic post-crisis recovery, would lead to serious discussion about the need to transform our economy. Yet, it hasn’t happened.
One important reason is that not everyone has experienced the Great Recession and its aftermath the same. Jordan Weissmann, writing in the Atlantic, published a figure from the work of Edward Wolff. The charts shows the rise and fall of median and mean net worth among Americans: how much one owns (e.g., savings, investments, and property) minus how much one owes (e.g., credit card debt and outstanding loans).
Both the mean and the median are interesting because, while they’re both measures of central tendency, one is more sensitive to extremes than the other. The mean is the statistical average (literally, all the numbers added up and divided by the number of numbers), so it is influenced by very low and very high numbers. The median, in contrast is, literally, the number in the middle of the sample of numbers. So, if there are very high or low numbers, their status as outliers doesn’t shape the measure.
Back to the figure: as of 2010, median household net worth (dark purple) had fallen back to levels last seen in the early 1960s. In contrast, mean household net worth (light purple) had only retreated to the 2000s. This shows that a small number of outliers — the very, very rich — have weathered the Great Recession much better than the rest of us.
The great disparity between median and mean wealth declines is a reflection of the ability of those at the top of the wealth distribution to maintain most of their past gains. And the lack of discussion about the need for change in our economic system is largely a reflection of the ability of those very same people to influence our political leaders and shape our policy choices.