andrew_gelman_stats andrew_gelman_stats-2012 andrew_gelman_stats-2012-1172 knowledge-graph by maker-knowledge-mining
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Introduction: Steve Hsu summarizes the research of economic historian Greg Clark and Neil Cummins : Using rare surnames we track the socio-economic status of descendants of a sample of English rich and poor in 1800, until 2011. We measure social status through wealth, education, occupation, and age at death. Our method allows unbiased estimates of mobility rates. Paradoxically, we find two things. Mobility rates are lower than conventionally estimated. There is considerable persistence of status, even after 200 years. But there is convergence with each generation. The 1800 underclass has already attained mediocrity. And the 1800 upper class will eventually dissolve into the mass of society, though perhaps not for another 300 years, or longer. Read more at Steven’s blog. The idea of rare names to perform this analysis is interesting – and has been recently applied to the study of nepotism in Italy . I haven’t looked into the details of the methodology, but rare events
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1 Steve Hsu summarizes the research of economic historian Greg Clark and Neil Cummins : Using rare surnames we track the socio-economic status of descendants of a sample of English rich and poor in 1800, until 2011. [sent-1, score-1.102]
2 We measure social status through wealth, education, occupation, and age at death. [sent-2, score-0.217]
3 There is considerable persistence of status, even after 200 years. [sent-6, score-0.207]
4 And the 1800 upper class will eventually dissolve into the mass of society, though perhaps not for another 300 years, or longer. [sent-9, score-0.362]
5 The idea of rare names to perform this analysis is interesting – and has been recently applied to the study of nepotism in Italy . [sent-11, score-0.694]
6 I haven’t looked into the details of the methodology, but rare events have their own distributional characteristics, and could benefit from Bayesian modeling in sparse data conditions. [sent-12, score-0.627]
7 Moreover, there seems to be an underlying assumption that rare names are somehow uniformly represented in the population. [sent-13, score-0.822]
8 A hypothetical situation: in feudal days, rare names were good at predicting who’s rich and who’s not – wealth was passed through family by name. [sent-15, score-1.404]
9 But then industrialization perturbed the old feudal order stratified by name into one that’s stratified by skill and no longer identifiable by name. [sent-16, score-1.013]
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Introduction: Steve Hsu summarizes the research of economic historian Greg Clark and Neil Cummins : Using rare surnames we track the socio-economic status of descendants of a sample of English rich and poor in 1800, until 2011. We measure social status through wealth, education, occupation, and age at death. Our method allows unbiased estimates of mobility rates. Paradoxically, we find two things. Mobility rates are lower than conventionally estimated. There is considerable persistence of status, even after 200 years. But there is convergence with each generation. The 1800 underclass has already attained mediocrity. And the 1800 upper class will eventually dissolve into the mass of society, though perhaps not for another 300 years, or longer. Read more at Steven’s blog. The idea of rare names to perform this analysis is interesting – and has been recently applied to the study of nepotism in Italy . I haven’t looked into the details of the methodology, but rare events
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Introduction: Ubs writes: I was looking at baby name data last night and I stumbled upon something curious. I follow the baby names blog occasionally but not regularly, so I’m not sure if it’s been noticed before. Let me present it like this: Take the statement… Of the top 100 boys and top 100 girls names, only ___% contain the letter __. I’m using the SSA baby names page, so that’s U.S. births, and I’m looking at the decade of 2000-2009 (so kids currently aged 4 to 13). Which letters would you expect to have the lowest rate of occurrence? As expected, the lowest score is for Q, which appears zero times. (Jacqueline ranks #104 for girls.) It’s the second lowest that surprised me. (… You can pause and try to guess now. Spoilers to follow.) Of the other big-point Scrabble letters, Z appears in four names (Elizabeth, Zachary, Mackenzie, Zoe) and X in six, of which five are closely related (Alexis, Alexander, Alexandra, Alexa, Alex, Xavier). J is heavily overrepresented, especial
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Introduction: I have no idea what this and this are, but Aleks passed these on, and maybe some of you will find them interesting.
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Introduction: In our Cliff thread from yesterday, sociologist Philip Cohen pointed to his discussions in the decline in the popularity of the name Mary. One thing that came up was the traditional trendiness of girls’ names. So I thought I’d share my thoughts from a couple of years ago, as reported by David Leonhardt: Andrew Gelman, a statistics professor at Columbia and an amateur name-ologist, argues that many parents want their boys to seem mature and so pick classic names. William, David, Joseph and James, all longtime stalwarts, remain in the Top 20. With girls, Gelman says, parents are attracted to names that convey youth even into adulthood and choose names that seem to be on the upswing. By the 1990s, of course, not many girls from the 1880s were still around, and that era’s names could seem fresh again. This search for youthfulness makes girls’ names more volatile — and increasingly so, as more statistics about names become available and parents grow more willing to experiment
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Introduction: I was trying to make some new graphs using 5-year-old R code and I got all these problems because I was reading in files with variable names such as “co.fipsid” and now R is automatically changing them to “co_fipsid”. Or maybe the names had underbars all along, and the old R had changed them into dots. Whatever. I understand that backward compatibility can be hard to maintain, but this is just annoying.
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same-blog 1 0.95649201 1172 andrew gelman stats-2012-02-17-Rare name analysis and wealth convergence
Introduction: Steve Hsu summarizes the research of economic historian Greg Clark and Neil Cummins : Using rare surnames we track the socio-economic status of descendants of a sample of English rich and poor in 1800, until 2011. We measure social status through wealth, education, occupation, and age at death. Our method allows unbiased estimates of mobility rates. Paradoxically, we find two things. Mobility rates are lower than conventionally estimated. There is considerable persistence of status, even after 200 years. But there is convergence with each generation. The 1800 underclass has already attained mediocrity. And the 1800 upper class will eventually dissolve into the mass of society, though perhaps not for another 300 years, or longer. Read more at Steven’s blog. The idea of rare names to perform this analysis is interesting – and has been recently applied to the study of nepotism in Italy . I haven’t looked into the details of the methodology, but rare events
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Introduction: Aleks pointed me to this recent article by Pablo Mateos, Paul Longley, and David O’Sullivan on one of my favorite topics. The authors produced a potentially cool naming network of the city of Auckland New Zealand . I say “potentially cool” because I have such difficulty reading the article–I speak English, statistics, and a bit of political science and economics, but this one is written in heavy sociologese–that I can’t quite be sure what they’re doing. However, despite my (perhaps unfair) disdain for the particulars of their method, it’s probably good that they’re jumping in with this analysis. Others can take their data (and similar datasets from elsewhere) and do better. Ya gotta start somewhere, and the basic idea (to cluster first names that are associated with the same last names, and to cluster last names that are associated with the same first names) seems good. I have to admit, though, that I was amused by the following line, which, amazingly, led off the paper:
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Introduction: Aleks points me to this attractive visualization by David Sparks of U.S. voting. On the plus side, the pictures and associated movie (showing an oddly horizontally-stretched-out United States) are pretty and seem to have gotten a bit of attention–the maps have received 31 comments, which is more than we get on almost all our blog entries here. On the minus side, the movie is misleading. In many years it shows the whole U.S. as a single color, even when candidates from both parties won some votes. The text has errors too, for example the false claim that the South favored a Democratic candidate in 1980. The southern states that Jimmy Carter carried in 1980 were Georgia and . . . that’s it. But, as Aleks says, once this tool is out there, maybe people can use it to do better. It’s in that spirit that I’m linking. Ya gotta start somewhere. Also, this is a good example of a general principle: When you make a graph, look at it carefully to see if it makes sense!
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Introduction: From Alexandr Grigoryev: “Америка: «красная», «синяя» и «пурпурная».” Apparently my name is Эндрю Гелман. I had no idea that the Voice of America even existed anymore!
5 0.58565658 845 andrew gelman stats-2011-08-08-How adoption speed affects the abandonment of cultural tastes
Introduction: Interesting article by Jonah Berger and Gael Le Mens: Products, styles, and social movements often catch on and become popular, but little is known about why such identity-relevant cultural tastes and practices die out. We demonstrate that the velocity of adoption may affect abandonment: Analysis of over 100 years of data on first-name adoption in both France and the United States illustrates that cultural tastes that have been adopted quickly die faster (i.e., are less likely to persist). Mirroring this aggregate pattern, at the individual level, expecting parents are more hesitant to adopt names that recently experienced sharper increases in adoption. Further analysis indicate that these effects are driven by concerns about symbolic value: Fads are perceived negatively, so people avoid identity-relevant items with sharply increasing popularity because they believe that they will be short lived. Ancillary analyses also indicate that, in contrast to conventional wisdom, identity-r
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simIndex simValue blogId blogTitle
1 0.95756519 469 andrew gelman stats-2010-12-16-2500 people living in a park in Chicago?
Introduction: Frank Hansen writes: Columbus Park is on Chicago’s west side, in the Austin neighborhood. The park is a big green area which includes a golf course. Here is the google satellite view. Here is the nytimes page. Go to Chicago, and zoom over to the census tract 2521, which is just north of the horizontal gray line (Eisenhower Expressway, aka I290) and just east of Oak Park. The park is labeled on the nytimes map. The census data have around 50 dots (they say 50 people per dot) in the park which has no residential buildings. Congressional district is Danny Davis, IL7. Here’s a map of the district. So, how do we explain the map showing ~50 dots worth of people living in the park. What’s up with the algorithm to place the dots? I dunno. I leave this one to you, the readers.
2 0.95746315 1515 andrew gelman stats-2012-09-29-Jost Haidt
Introduction: Research psychologist John Jost reviews the recent book, “The Righteous Mind,” by research psychologist Jonathan Haidt. Some of my thoughts on Haidt’s book are here . And here’s some of Jost’s review: Haidt’s book is creative, interesting, and provocative. . . . The book shines a new light on moral psychology and presents a bold, confrontational message. From a scientific perspective, however, I worry that his theory raises more questions than it answers. Why do some individuals feel that it is morally good (or necessary) to obey authority, favor the ingroup, and maintain purity, whereas others are skeptical? (Perhaps parenting style is relevant after all.) Why do some people think that it is morally acceptable to judge or even mistreat others such as gay or lesbian couples or, only a generation ago, interracial couples because they dislike or feel disgusted by them, whereas others do not? Why does the present generation “care about violence toward many more classes of victims
3 0.95505321 845 andrew gelman stats-2011-08-08-How adoption speed affects the abandonment of cultural tastes
Introduction: Interesting article by Jonah Berger and Gael Le Mens: Products, styles, and social movements often catch on and become popular, but little is known about why such identity-relevant cultural tastes and practices die out. We demonstrate that the velocity of adoption may affect abandonment: Analysis of over 100 years of data on first-name adoption in both France and the United States illustrates that cultural tastes that have been adopted quickly die faster (i.e., are less likely to persist). Mirroring this aggregate pattern, at the individual level, expecting parents are more hesitant to adopt names that recently experienced sharper increases in adoption. Further analysis indicate that these effects are driven by concerns about symbolic value: Fads are perceived negatively, so people avoid identity-relevant items with sharply increasing popularity because they believe that they will be short lived. Ancillary analyses also indicate that, in contrast to conventional wisdom, identity-r
4 0.95326853 1379 andrew gelman stats-2012-06-14-Cool-ass signal processing using Gaussian processes (birthdays again)
Introduction: Aki writes: Here’s my version of the birthday frequency graph . I used Gaussian process with two slowly varying components and periodic component with decay, so that periodic form can change in time. I used Student’s t-distribution as observation model to allow exceptional dates to be outliers. I guess that periodic component due to week effect is still in the data because there is data only from twenty years. Naturally it would be better to model the whole timeseries, but it was easier to just use the cvs by Mulligan. ALl I can say is . . . wow. Bayes wins again. Maybe Aki can supply the R or Matlab code? P.S. And let’s not forget how great the simple and clear time series plots are, compared to various fancy visualizations that people might try. P.P.S. More here .
5 0.9525516 1538 andrew gelman stats-2012-10-17-Rust
Introduction: I happened to be referring to the path sampling paper today and took a look at Appendix A.2: I’m sure I could reconstruct all of this if I had to, but I certainly can’t read this sort of thing cold anymore.
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