Team Sorting

| Gabriel |

Tyler Cowen links to an NBER paper by Hoxby that shows that in recent decades, status sorting has gotten more intense for college. Cowen asks “is this a more general prediction in a superstars model?” The archetypal superstar system is Hollywood, and here’s my quick and dirty stab at answering Tyler’s question for that field. Faulkner and Anderson’s 1987 AJS showed that there is a lot of quality sorting in Hollywood, but they didn’t give a time trend. As shown in my forthcoming ASR with Esparza and Bonacich, there are big team spillovers so this is something we ought to care about.

I’m reusing the dataset from our paper, which is a subset of IMDB for Oscar eligible films (basically, theatrically-released non-porn) from 1936-2005. If I were doing it for publication I’d do it better (i.e., I’d allow the data to have more structure and I’d build confidence intervals from randomness), but for exploratory purposes the simplest way to measure sorting is to see if a given film had at least one prior Oscar nominee writer, director, and actor. From that I can calculate the odds-ratio of having an elite peer in the other occupation.

Overall, a movie that has at least one prior nominee writer is 7.3 times more likely than other films to have a prior nominee director and 4.4 times more likely to have a prior nominee cast. A cast with a prior nominee is 6.5 times more likely to have a prior nominee director. Of course we already knew there was a lot of sorting from Faulker and Anderson, the question suggested by Hoxby/Cowen is what are the effects over time?

This little table shows odds-ratios for cast-director, writer-director, and writer-cast. Big numbers mean more intense sorting.

...+--------------------------------------+
...| decade    cd       wd       wc       |
...|--------------------------------------|
1. | 1936-1945 6.545898 6.452388 4.306554 |
2. | 1946-1955 9.407476 6.425553 5.368151 |
3. | 1956-1965 12.09229 8.741302 6.720059 |
4. | 1966-1975 4.697238 5.399081 4.781106 |
5. | 1976-1985 4.113508 6.984528 4.450109 |
6. | 1986-1995 4.923809 7.599852 3.301461 |
7. | 1996-2005 4.826018 12.35915 3.641975 |
+-----------------------------------------+

The trend is a little complicated. For collaborations between Oscar-nominated casts on the one-hand and either writers or directors, the sorting is most intense in the 1946-1955 decade and especially the 1956-1965 decade. My guess is that this is tied to the decline of the studio system and/or the peak power of MCA. The odds-ratio of good director for nom vs non-nom writers also has a jump around the end of the studio system, but it seems there’s a second jump starting in the 80s. My guess is that this is an artifact of the increasing number of writer-directors (see Baker and Faulkner AJS 1991), but it’s an empirical question.

Putting aside the writer-director thing, it seems that sorting is not growing stronger in Hollywood. My guess is that ever more intense sorting is not a logical necessity of superstar markets, but has to do with contingencies, such as the rise of a national market for elite education in Hoxby’s case or the machinations of Lew Wasserman and Olivia deHavilland in my case.

The Stata code is below. (sorry that wordpress won’t preserve the whitespace). The data consists of film-level data with dummies for having at least one prior nominee for the three occupations.

global parentpath "/Users/rossman/Documents/oscars"

capture program drop makedecade
program define makedecade
gen decade=year
recode decade 1900/1935=. 1936/1945=1 1946/1955=2 1956/1965=3 1966/1975=4 1976/1985=5 1986/1995=6 1996/2005=7
capture lab drop decade
lab def decade 1 "1936-1945" 2 "1946-1955" 3 "1956-1965" 4 "1966-1975" 5 "1976-1985" 6 "1986-1995" 7 "1996-2005"
lab val decade decade
end

cd $parentpath

capture log close
log using $parentpath/sorting_analysis.log, replace

use sorting, clear
makedecade

*do odds-ratio of working w oscar nom, by own status

capture program drop allstar
program define allstar
preserve
if "`1'"!="" {
keep if decade==`1'
}
tabulate cast director, matcell(CD)
local pooled_cd=(CD[2,2]*CD[1,1])/(CD[1,2]*CD[2,1])
tabulate writers director, matcell(WD)
local pooled_wd=(WD[2,2]*WD[1,1])/(WD[1,2]*WD[2,1])
tabulate writers cast, matcell(WC)
local pooled_wc=(WC[2,2]*WC[1,1])/(WC[1,2]*WC[2,1])
shell echo "`pooled_cd' `pooled_wd' `pooled_wc' `1'" >> sortingresults.txt
restore
end

shell echo "cd wd wc decade" > sortingresults.txt
quietly allstar
forvalues t=1/7 {
quietly allstar `t'
}

insheet using sortingresults.txt, delimiter(" ") names clear
lab val decade decade

 

*have a nice day


Life imitates SPQ

| Gabriel |

Marketing companies are offering services to let advertisers (or really pathetically needy ordinary end users) inflate their apparent popularity. This of course was the gist of the second-half of the Salganik and Watts “Music Lab” experiments, in which the researchers flipped the download count (so popular songs appeared unpopular and vice versa) then watched if anyone could tell the difference.

The big gens

| Gabriel |

I heard that a handful of clans (or “gens” in Latin) dominated the higher offices of the Roman republic and I figured that this would be a good data question. To start, I copied the Fasti Consulares from Wikipedia and limited it to the Republican period, defined as the Rape of Lucretia through the Battle of Actium.

Roman names followed the convention of “personal gens family [honorifics].” So, for instance “Publius Cornelius Scipio Africanus” means “the man Publius from the Scipio branch of the Cornelius clan, the conqueror of Africa.” From the perspective of seeing which clans dominated the Republic, the key bit is the second name so I used the Stata string function “word” to pull the second word out of each of these names.

As can be seen, the distribution of consulships/gens follows a power-law. Since power-laws indicate a cumulative advantage mechanism we can interpret this as meaning that in Rome a family’s power and prestige was endogenous.

The most dominant clans  in the republic were the Furii (41 consulships), the Claudii (45 consulships), the Aemilii (53 consulships), the Fabii (62 consulships), the Valerii (71 consulships), and the Cornelii (106 consulships). This means that a Cornelius was consul about once every six years.

In contrast the Iulii (as in Gaius Iulius Caesar) held the consulship a relatively paltry 29 times, so small wonder that in order to establish the monarchy they had to form a marriage alliance with the Claudii. Likewise, the Pompeii were a politically obscure family but Pompey Magnus became powerful through his patron-client relationships with the Cornelii.

News diffusion

| Gabriel |

The New York Times and Slashdot both have stories on an ambitious paper by a team of computer scientists that studies the diffusion of news stories. The website for the paper and supplementary materials (including the cleaned dataset) is at Memetracker.org.

The most impressive thing about the project is the data collection / cleaning effort. The team scraped basically all of the mainstream media and blogosphere for the last three months of the 2008 election. They then used a fuzzy algorithm to identify temporally-unusual phrases so “palling around with terrorists” would count whereas “in a press release” would not. What’s really impressive is that they not only identify exact phrases (which is pretty easy to code) but paraphrases (which is really hard to code). For instance, they identify about 40 versions of Sarah Palin’s “palling around with terrorists” characterization of Barack Obama’s relationship with Bill Ayers. They then identify and time-stamp every usage of each phrase in their scraped data. The dataset is human-readable and is arranged as a three level hierarchy of time-stamped news items within specific phrasings within broad phrasings. This nesting of paraphrases within general phrases goes a long way towards solving the problem of “reinvention” which might otherwise obscure that several “different” phrases are really only minimally distinct versions of the same phrase. Here’s a sample of the dataset:

  2  8  we're not commenting on that story i'm afraid   2131865
     3  3  we're not commenting on that    489007
        2008-08-18 14:23:05  1  M  http://business.theage.com.au/business/bb-chief-set-to-walk-plank-20080818-3xp7.html
        2008-11-26 01:27:13  1  B  http://sfweekly.com/2008-11-26/news/buy-line
        2008-11-27 18:55:30  1  B  http://aconstantineblacklist.blogspot.com/2008/11/re-researcher-matt-janovic.html
     5  2  we're not commenting on that story      2131864
        2008-12-08 14:50:18  3  B  http://videogaming247.com/2008/12/08/home-in-10-days-were-not-commenting-on-that-story-says-scee
        2008-12-08 19:35:31  2  B  http://jplaystation.com/2008/12/08/home-in-10-days-were-not-commenting-on-that-story-says-scee

Their analysis was also very good (but nowhere near as amazing as the cleaning effort). Basically their findings were entirely consistent with the diffusion literature. They found that the popularity of different phrases followed a power law and the distribution of new mentions of a phrase followed a bell curve (which is equivalent to saying that the cumulative mentions of a phrase follow an s-curve). Both of these findings are consistent with a cumulative advantage process, and indeed, they model the process as a tension between “imitation” and “recency.”

This “two forces in tension” thing is typical of many endogenous models. It’s actually very easy to figure out a model that results in a stable equilibrium of “everything” or “nothing,” for instance the Schelling segregation model. However, it’s much harder to work out a model that has a more moderate equilibrium. So in cumulative advantage models like this, the trick is to explain why popularity is “only” described by a power-law when it’s easier to see how it could be described by a step function (one target gets all the popularity, everyone else gets absolutely none). Because the memetracker data has a temporal element they use a time decay function. Other similar models have used things like reciprocity (Gould), cost of linking (Podolny), and heterogeneity in taste (Rosen).

In addition to “imitation” and “recency,” they also note that some kind of intrinsic “attractiveness” of the phrase might be an issue, though they bracket this largely because it would require a lot of hard human content analysis. From perusing the most popular stories, my guess is that the ideal phrase, from a newsworthiness perspective, is an ambiguous but potentially inflammatory quote from a prominent person describing another prominent person that should be comprehensible to someone with minimal background knowledge. So something like “lipstick on a pig” (which Barack Obama said about part of the McCain-Palin platform, though it was often interpreted as being a personal insult to Sarah Palin) is just about perfect in all these respects. Slightly less wonderful is the “palling around with terrorists” quote, because this requires some background knowledge about Bill Ayers and the Weather Underground (or, under the dog whistle theory of this quote, comparable familiarity with the “secret Muslim” theory).

The most novel finding was that most of the action for any given phrase occurs within an eight hour window more or less symmetrically centered around the peak popularity, and indeed they describe the process as being qualitatively different (for one thing, it’s more sparse) outside of this window than it is within it. This struck me as an issue where there could be some profit in treating the issue not just as some disembodied complexity science issue, but as the result of a particular social process involving real human beings about whom we know something. One of the things we know about people is that we tend to go to work during the day, usually for eight hours, and this characterizes not just journalists but many of the most important political bloggers. One simple prediction based on this is that the eight hours should similar for most innovations, my guess being that they would be roughly 8am to 4pm east coast time. (Why, you might ask, don’t I just test this myself given that they make their data available? Because the files are too big to open with a text editor and it would take me hours to figure out a way to cull out the aspects of the data that aren’t relevant to this purpose, though someone who was good at “sed” or “awk” could probably write a script to do it in five minutes).

While I think the team made excellent operational decisions, these decisions may nonetheless imply (probably small) biases. The team acknowledges that catch phrases are only somewhat intrinsically interesting but they are mostly using them as a proxy for something even more interesting, what you might call “stories” or “ideas.” This probably has the effect of giving the study a disproportionate emphasis on gotcha and insults like “lipstick on a pig” or “palling around with terrorists” rather than more complex ideas which may be less likely to be consistently described with the same or similar strings of words (though even very complex ideas eventually develop a shorthand, as with “cap and trade”). Similarly, the algorithm itself selects temporally unusual phrases, which may imply a selection for short-run issues rather than perennial debates.

Overall a very impressive paper that’s well worth reading for anyone interested in diffusion, news, blogs, or just really high quality database work. Even better is that they provide their data (which as of now they are continuing to update) and a detailed description of how they processed it, thereby providing a platform on which other people can build, perhaps by things like focusing on substantive concerns.

Underneath it all

| Gabriel |

A few years ago I had a friendly argument with Jenn Lena and Pete Peterson about their ASR article on genre trajectories. While I generally love that article, my one minor quibble is their position that there is such a thing as non-genre music, and in particular that “pop” can be considered unmarked, in genre terms. They write “Not all commercial music can be properly considered a genre in our sense of the term.” They exclude Tin Pan Alley (showtunes) and go on to write that, “Much the same argument holds for pop and teen music. At its core, pop music is music found in Billboard magazine’s Hot 100 Singles chart. Songs intended for the pop music market usually have their distinguishing genre characteristics purposely obscured or muted in the interest of gaining wider appeal.”

Myself, I disagree with treating pop as beyond genre. First, the Hot 100 is an aggregate without any real meaning as a categorical marker. I find it interesting that in radio it’s increasingly prevalent to call “Top 40″ as “Contemporary Hits Radio” in recognition of the fact that in the literal sense top 40 hasn’t existed for decades and many bands who are very popular would nonetheless not get played in CHR and many bands (think Britney Spears) only get played in CHR, implying that CHR is itself a genre of what we might call “high pop.” Billboard itself distinguishes between the Hot 100 (whatever is really popular, regardless of genre) and Top 40 Mainstream (CHR).

Second, and more importantly, it is impossible to have non-genre music in the same way that it is impossible to have language-less speech if you take the Howard Becker perspective that genre is about having sufficient shared understandings and expectations so as to allow coordination between actors. Consider the fact that most genres work on the Buddy Holly model of long-lasting bands who write their own songs whereas high pop almost exclusively involves project-based collaborations of songwriters, session musicians, producers, and (most salient to the audience) singers. Since standards are especially important when the collaborations are ephemeral, then coordination through strong shared expectations is more important in high pop than genre music. Likewise, high pop sounds more monotonous than many genre-based music. Furthermore, high pop is not merely the baseline, but involves specialized skills and techniques (e.g., vocal filters) not found in “genres.”

For the most part this issue is orthogonal to the argument they present in the article (which is why I like the article despite this dispute) but I think it potentially creates problems for the IST (Industry-> Scene-> Traditional) trajectory, most of which involves a spin-off of high pop music (as is seen most clearly with the Nashville Sound, which was basically Tin Pan Alley with cowboy hats). In response to this Pete said that there is a distinction between pop and genre in that with pop change is gradual and more Lamarckian than the creative destruction and churn seen with genres. I think this is definition is fair enough, certainly it’s highly relevant to their purposes. So the question of whether it is possible to have non-genre music ultimately comes down to whether you choose to emphasize churn or shared expectations as the defining feature of genre.

Anyway, I was reminded of this discussion a few days ago when my wife and I went to see No Doubt. This band has had 8 singles on the Billboard 100 chart and had multiple singles in four different Billboard format charts (rhythmic, CHR, adult, modern rock) so I think they are a fair candidate for what Jenn and Pete have in mind as “pop.” However the performance I attended made it apparent that at their core they are ultimately still a ska band. Most obviously, during one of Gwen’s costume changes the band did a cover of The Special’s arrangement of “Guns of Navarone” and when she came back she was wearing what can only be described as a two-tone sequined romper and later on she wore a metallic Fred Perry shirt and braces (worn hanging). More generally all of their dancing was based on ska steps, their rhythm section dominates their lead guitar, and they had a horns section and keyboard (tuned as an organ).

In a sense, I think you can take No Doubt as a vindication of what Jenn and Pete are arguing. Here you have a band that started out within genre music but graduated into commercial success by recording unmarked pop. Note that their return to ska/dancehall with “Rock Steady” didn’t sell nearly as many copies as the mostly pop albums “Tragic Kingdom” and “Return of Saturn”. However there’s also the interesting fact that when Gwen decided to dive headfirst into high pop, she did so as a “solo” act, which in effect meant that she went from collaborating with Tony Kanal to doing so with Dr Dre and the Neptunes. I take Gwen’s solo career as a vindication for my perspective, the idea being that going into high pop involves not just the negative act of losing the markings and skills of genre and becoming generic music (which presumably Kanal could have done), but the positive act of acquiring the markings and skills of high pop (which required soliciting the efforts of high pop specialists like the Neptunes).

Special bonus armchair speculation!

Compare and contrast No Doubt and Dance Hall Crashers. Both are up-tempo California ska bands that started in the late 80s and have girl singers (two of them in the case of DHC). Although this is necessarily disputable, I would submit that c. 1995 (when No Doubt broke), DHC was the more talented band. Likewise, DHC has the better pedigree, being (along with Rancid) the successors to Operation Ivy. So why is it that Gwen Stefani rather than Elyse Rogers or Karina Denike is the one who ultimately became a world class pop star and an entrepreneur of overpriced designer fauxriental baby clothes (as modeled below)?

I have three speculations, listed below in rough order of how much credence I give each of them:

1. Looking for an explanation is futile because cultural markets are radically stochastic. If you have two talented bands it is literally impossible to predict ex ante which will become popular and in some alternate universe DHC are gazillionaires whereas No Doubt is known only to aficionados of California 90s music.
2. Jenn and Pete are right and the issue is that No Doubt was better at transcending genre. Noteworthy in this respect is that basically all of DHC’s music is skacore whereas from their very first recordings No Doubt has always included elements of disco and pop, including AC-friendly Tin-Pan-Alley-esque ballads like “Don’t Speak” that it’s pretty hard to imagine DHC playing.
3. There’s a cluster economy explanation in that No Doubt is from Orange County (which c. 1994 was supposed to be the next Seattle) whereas DHC is from the East Bay.

we can draw the line some other time

| Gabriel |

Lots of people have been talking about the NY Times article on Williamsburg trustafarians suddenly facing reality. A lot of the commentary has been of the “this is the world’s tiniest violin” variety. I have two thoughts.

1. It’s amazing how pretty much all of these people have completely unremunerative creative-sector careers supplemented by service sector work. The article describes a musician, a writer, and a “designer wallpaper” entrepreneur (good luck with that). Of course this actually shouldn’t be surprising to me since I lecture my soc of culture undergrads for about a half hour on the “starving artist” phenomenon. Here’s the elevator version of that lecture.

Lots more people want to be artists than there is demand for and this shift in the supply schedule depresses the price, which is why the median artist makes less than you’d predict from his education. There are two theories as to why (unlike other workers in a similar situation) artists don’t then quit and get jobs that other people are actually willing to pay them for. The self-subsidy theory says that artistic work is really a form of leisure consumption enjoyed by those who can afford it, often by drawing on family resources. The tournament model theory says that the opportunity cost of low wages now is buying entry to a tournament, the winner of which enjoys the kind of decadent lifestyle that could only have been dreamt of by Caligula but is in fact enjoyed by the Rolling Stones even as they fade into vampiric living death. Interestingly, both theories make the (accurate) prediction that this is highly tied to the life course and most (unsuccessful) artists will seek more mainstream employment when they enter prime fertility years.

2. Wow, check out Ms. Calvert’s polyester dress. The elaborate and very ironic backstory for this garment writes itself.

It’s like we finish each others sentences! (is that a good thing?)

| Gabriel |

A meta-analysis just published in the Journal of Applied Psychology shows that teams are more effective when they share non-redundant information but that paradoxically they tend to spend most of their time discussing what is already common knowledge in the group. This is interesting but both findings seem very congruent with findings and theory from sociology and economics.

In one of the most famous sociology articles ever, Granovetter argued that weak ties are powerful precisely because they provide non-redundant info. (In contrast strong ties usually promote triadic closure, and by extension, redundant information).

Likewise, in the first article to provide a positive feedback explanation for the superstar effect in cultural markets, Moshe Adler said that fandom is a coordination problem in that fandom is more fun if its a shared experience where the fans can discuss the nuances of the artwork or artist. (All the jargon about addiction goods makes the article kind of alien to sociologists, but that’s the gist of it).

So Granovetter told us 36 years ago that non-redundant information is really important and Adler told us 24 years ago that people like to talk about their shared interests and experiences. Both articles have a lot of similar findings following from them but this is the first thing I can think of that highlights the irony of taking their conclusions together. I think the reason it’s ironic can be shown by other research, mostly in the sociology of science but also in the sociology of culture that shows that creative breakthroughs tend to come from circles rather than isolated intellectuals. The reason is that like so many things, creativity is a Goldilocks problem, if you’re too redundant you get stale but if you’re not redundant enough it’s too hard to coordinate. If so this suggests that the meta-analysis make be restricting it’s analysis to a certain part of the range (or a certain class of problems) and that a broader focus might show the values of some redundancy for promoting innovation.

(btw, the article doesn’t cite Granovetter, Adler, or anything similar like “birds of a feather,” the parallels I’m drawing are my own).

h/t Marginal Revolution

Bootstrapping superstars

| Gabriel |

Most cultural products and cultural workers follow a scale-free or power-law distribution for success with a tiny handful of ludicrously successful products/workers, a fairly small number with a modicum of success, and a truly ginormous number that are absolute failures. Ever since Sherwin Rosen described this phenomena in an influential American Economic Review theory piece this phenomena has been nicknamed “the superstar effect.” For a review of the major theories as to why there is a superstar effect, check out this lecture from my undergrad course (mp3 of the lecture and pdf of the slides).

One methodological problem this creates is that if you are interested in describing the overall market share of abstract categories the measure is highly sensitive to flukes. For instance, say you were interested in drawing trend lines for the sales volumes of different book genres and you noticed that in 2002 there was a decent-sized jump in sales of the genre “Christian.” One interpretation of this would be that this is a real trend, for instance you could make up some post hoc explanation that after the 9/11 terrorist attacks people turned to God for comfort. Another interpretation would be that there was no trend and all this reflects is that one book, The Purpose Driven Life, was a surprise hit. Distinguishing statistically between these concepts is surprisingly hard because it’s very hard (at least for me) to figure out how to model the standard error of a ratio based on an underlying count.

Fortunately you don’t have to because when in doubt about error structure you can just bootstrap it. My solution is to bootstrap on titles then calculate the ratio variable (e.g., genre market share) based on the bootstrapped sample of titles. You can then use the standard deviation of the bootstrapped distribution of ratios as a standard error. To return to our example of book genres, we could bootstrap book titles in 2001 and 2002 and calculate a bootstrapped distribution of estimates of Christian market share for book sales. You then do a t-test of means to see whether 2002 was statistically different from 2001 or whether any apparent difference is just the result of a few fluke hits. In other words, was 2002 a good year for Christian books as a genre, or just a good year for Rick Warren (whose book happened to be in that genre).

Here’s some Stata code to create bootstrapped samples of titles, weight them by sales, and record the proportion of sales with the relevant trait:

clear
set obs 1
gen x=.
save results.dta, replace
use salesdatset, clear
*var desc
*  title  -- a title
*  sales  -- some measure of success
*  trait  -- dummy for some trait of interest for title (eg genre, author gender, etc)
*  period -- period of the obs (eg, year)
compress
forvalues i=1/1000 {
 preserve
 bsample, strata (period)
 gen traitsales = trait*sales
 ren sales salestotal
 collapse (sum) traitsales salestotal, by (period)
 gen traitshare=traitsales/salestotal
 drop traitsales salestotal
 gen bs=`i' /*records the run of the bstrap */
 reshape wide traitshare, i(bs) j(period)
 append using results
 save results.dta, replace
 restore
}
use results, clear
sum
*for each "traitshare*" variable, the sd can be interpreted as bstrapped standard error