Sara P. let us know about a map at National Geographic that shows the distribution of surnames in the U.S.
The names are color coded by region of origin of the name:
A note on methodology: geographers looked at the most common by counting the most common last names in phone books and selecting the most common names in each state. This hides significant diversity in names in large cities that may have had a greater mix of immigrant groups that the state overall; for instance, a map of the most common names just in New York City might look quite a bit different than the most common names in New York state.
Nonetheless, the concentration of last names serves as an echo of immigration and settlement patterns. British-origin names tend to dominate across the U.S., unsurprisingly, particularly Smith, Johnson, and Williams. Because slaves were often given the last names of their owners, a significant proportion of individuals with British last names are African American — for instance, African Americans are about 20% of people named Smith.
Several Irish-origin names stand out in Massachusetts, as well as some French surnames in Maine:
The map of Hawaii reflects the significance of the Asian population there:
Spanish-origin names in the Southwest:
The names common in the Great Lakes/upper plains region reflects the fact that the area was a common destination for immigrations from Germany and Scandinavia:
I looked up the geographers who created the maps (James Cheshire, Paul Longley, and Pablo Mateos at University College London) and that led me to an interesting website sponsored by UCL, the World Names Project. If you type in a surname, it will show where on the globe it is most common. You can also zoom in on individual nations and see the distribution within them. Here’s the global distribution of my last name, Sharp:
You also get some data about the name: its origin, the top 10 regions and individual cities for that name, and the most common first names that go with it (which, in all the names I tried, were overwhelmingly male, so I don’t know what to make of that).
As Sara said of the National Geographic map, many of the results are predictable, but that doesn’t mean it’s not fun to look at them.
UPDATE: Reader Kristina provides an explanation for why male names dominate the most common first names lists:
My explanation for Gwen’s finding that the most common first names are overly represented by male names is that names for boys are less variable than names for girls.
Interesting post on that here, which notes, “it [natural language geocoder] needs 4200 first names for girls to cover 90% of the population, but it only needs 1200 boy’s names to reach a 90% coverage. The reason for this huge difference is mainly found in the top positions. The ten most popular male names reach 23% whereas the ten most popular female names reach a comparatively meager 10%.”
The Guttmacher Institute reports that the decades long fall in the rate of surgical abortions has plateaued:
Decreasing abortion rates is something that most Americans support. Sharon Camp, president and CEO of Guttmacher, suggests that greater availability of cheap effective contraception might help jump start the decrease. That seems like a politically safe recommendation. What say you?
The New York Times has some interactive graphics showing various types of data about social class and class mobility. You can see where you fall in terms of four characteristics often used to measure class status, see the overall class breakdown for various occupations, and so on. This graph shows social class mobility by depicting which social class (divided into quintiles) the U.S. population fell into in 1998 based on the social class they started out in from 1988:
You can hover over a particular group, such as “lower middle,” to see the outcome just for them.
Another graph of social mobility:
This next graph counters the idea that poor families remain poor forever (often explained by some version of the “culture of poverty” thesis) by showing that if you track a poor family over multiple generations, there is a general trend toward upward mobility:
That isn’t to ignore the fact that being poor leads to circumstances (poor schools, etc.) that make upward mobility difficult. But the idea that poor families stay poor for generation after generation, passing on poverty almost like a genetic characteristic, simplifies a more complex story about how families become poor, how long they remain poor, and the importance of looking at structural factors as opposed to a “cycle of poverty” explanation.
First, we can look at a comparison of how much median income earners in the U.S. make compared to other countries (in U.S. dollars). Luxembourg is the standout at the far right, with the U.S. not far behind, showing the fourth highest median income alongside some Scandinavian countries. Mexico, Turkey and some Eastern European countries have the lowest median incomes.
A story starts to emerge, however, if we look at the median income of the bottom 10% of earners. Suddenly the relative position of the U.S. shifts way to the left; the bottom 10% of earners in the U.S. make less than the OECD average. Notice that the relative placements of the other high income and low income states don’t shift very much. This means that while people in the U.S. are doing relatively well overall, the poorest people in the U.S. are doing worse than the poorest in about 2/3rds of the other countries:
Then, if you look at the median income of the top 10%, the relative position of the U.S. moves all the way to the right; that is, the top 10% of U.S. earners make more than the top 10% of earners in any other OECD country. We even beat out Luxembourg:
Most other countries retain their relative position, more or less, with the exception of Sweden, which drops way down. So the richest Swedes are, relatively speaking, not that rich.
The lesson is that income inequality–the difference between the incomes of the high earners and low earners–is significantly more severe in the U.S. than it is in other OECD countries (and that may be an understatement).
I think this may be among the most stunning data I’ve ever posted on SocImages. The figure below contrasts the average U.S. response to various questions measuring perceptions of mobility and inequality with the average response of 27 comparison countries (from the International Social Survey Programme). In other words, how far from the mean are U.S. citizens’ beliefs about life chances and the value of social inequality? The pink triangle is the U.S. and the orange line is everyone else. It’s a bit difficult to read (click to enlarge), so I’ll describe the data below.
- About 62% of Americans think that “people get rewarded for their effort,” compared to about 35% of citizens in our national comparison group.
- About 70% of Americans think that “people get rewarded for their intelligence and skills,” compared to about 40% of citizens in our national comparison group.
- About 19% of Americans think that “coming from a wealthy family is essential/very important to getting ahead,” compared to about 29% of citizens in our national comparison group.
- About 62% of Americans think that “differences in income in their country are too large,” compared to about 87% of citizens in our national comparison group.
- And about 33% of Americans think that “it is the responsibility of the government to reduce the differences in income,” compared to about 69% of citizens in our national comparison group.
Americans, then, are much more likely than the average citizen in our comparison countries to believe that individual characteristics determine success, wide gaps in income are acceptable, and the government should let them be. No wonder Americans tend to vote to cut taxes and services, tolerate unequal educational opportunity, and resist top-down solutions to inequality. They think inequality is good and that individuals will always get what they deserve.