Now These Are the Names, Pt 2

| Gabriel |

Last time we talked about Social Security names data and how to query particular names. Today I want to talk about the big picture of the variety of names and the question of whether names are getting more diverse over time. Well, this is basically a question of entropy and so let’s do a time-trend of the gini by cohort.

To me, the main thing the graph demonstrates is actually how sensitive these things are to measurement to the extent that I’m unwilling to make substantive claims without better understanding the history of how the data were collected. As you can see, there’s a huge jump in the gini starting with birth cohorts dating to WWI. This predates the enactment of Social Security (which I’ve drawn as a black vertical line) by about twenty years and so my best guess it corresponds to cohorts that were aging into the labor force around the time the act passed. Alternately it could be something about WWI accelerating assimilation in naming practices, but when I see a sharp discontinuity like that my instincts tell me it’s a measurement artifact not a real social change.

Putting that aside, let’s think about the index itself. The Gini coefficient was developed to study social inequality and as such it’s sensitive to both the top and the bottom. Gini is basically a better version of taking the ratio of a high percentile and a low percentile. If you have exactly two people with exactly equal wealth (or exactly two names with equal numbers of babies) then you’d have a very low Gini.

Two names sounds ridiculous but not as much as you’d think. Consider Republican era Rome. We have a pretty good idea of Roman names, at least in the upper classes, because they kept lists called “fasti consulares” of every man who served as consul. I previously did a post showing how a few clans dominated, but for today I want to just use these lists to show how few first names there were. These lists show only 29 male first names, of which only 17 were popular.* (In contrast, the Social Security data lists thousands of male names in circulation in any given year.) The Gini coefficient for praenomen on Republican fasti consulares is .72, which is not that far below the pre-1910 Social Security data. If you’re wondering, the most popular praenomen were Lucius, Gaius, Marcus, and Quintus. Here’s a kernel density plot for praenomen frequency.

As you can see, Roman names follow a count but it’s not ridiculously steep like American names in any arbitrary year (like this graph of 1920). The fact that 29 names following a fairly shallow count could show a comparable gini to thousands of names following an extremely steep count suggests to me that there is something unsatisfying about the metric for our purposes.

Another entropy index we can use is the Herfindahl Hirschman Index (HHI). HHI is meant to measure the potential for monopolies and cartels and as such it’s only really sensitive to the top. HHI is basically a better version of taking the share held by the top-4 (or top-8 or top-k) actors in the system. If you have exactly two people with exactly equal wealth (or exactly two names with equal numbers of babies) then you’d have a very high HHI.

A thought experiment that reveals the difference between Gini and HHI is that if the United States were to suddenly add a few million desperately poor people, for instance by annexing Haiti, this wouldn’t change our income HHI at all but it would drive our income Gini up appreciably. Nonetheless, under a wide range of circumstances the Gini and HHI will be correlated as both measure inequality, they just have different emphases.

In the case of names, HHI will capture the dominance of stock names like “Jake” and “Mary” whereas Gini is better at capturing how common weird names are. So that said, let’s do the time trend again, but this time with HHI.

It’s very interesting that we now don’t see a precipitous change in the late teens but rather a gradual shift leading up to that time. For comparison, the HHI of consular praenomen is 1152, which is off the charts compared to the Social Security data. Finally, let’s note that HHI and Gini agree that girls names show more entropy than boy’s names.

———-
* I say male names because there were no female consuls. Roman women took the feminized form of their father’s clan name. Hence, most of the women of the Julio-Claudian dynasty were (by adoption) descendants of Gaius Julius Caesar and were named “Julia,” which is the feminine form of “Julius.”

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Social Structures

| Gabriel |

Shortly before ASA, I finished John Levi Martin’s Social Structures and I loved it, loved it, loved it. (Also see thoughts from Paul DiMaggio, Omar Lizardo, Neil Gross, Fabio Rojas, and Science). I find myself hoping I have to prep contemporary theory just so I can inflict it on unsuspecting undergrads. The book is all about emergence and how fairly minor changes in the nature of social mechanisms can create quite different macro social structures.* It’s just crying out for someone to write a companion suite in NetLogo, chapter by chapter. In addition, JLM knows an enormous amount of history, anthropology, and even animal behavior and uses it all very well to both illustrate his points and show how they work when the friction of reality enters. For instance, he notes that balance theory breaks down to the extent that people have some agency in defining the nature of ties and/or keeping some relations “neutral” rather than the ally versus enemy dichotomy.**

An interesting contrast is Francis Fukuyama’s Origins of Political Order, which I also liked. The two books are broadly similar in scope, giving a sweeping comparative overview of history that starts with animals and attempts to work up to the early modern era. (There are also some similarities in detail, such as their very similar understandings of the “big man” system and that domination is more likely in bounded populations). There is an obvious difference of style in that Fukuyama is easier to read and goes into more extended historical discussions but the more important differences are thematic and theoretical. One such difference is that Fukuyama follows Polybius in seeing the three major socio-political classes as the people, the aristocracy, and the monarch, with the people and the monarch often combining against the aristocracy (as seen in the Roman Revolution and in early modern absolute monarchies). In contrast, JLM’s model tends to see the monarch as just the top aristocrat, though his emphasis on the development of transitivity in command effectively accomplishes some of the same work as the Fukuyama/Polybius model.

The most important difference comes in that  Fukuyama is inspired by Weber whereas JLM uses Simmel, a distinction that becomes especially distinct as they move from small tribal bands to early modern societies. Fukuyama’s book is fundamentally about the tension between kinship and law as the fundamental organizing principle of society. In Fukuyama’s account both have very old roots and modernity represents the triumph of law. In contrast, JLM sees kinship (and analogous structures like patronage) as the fundamental logics of society with modernity being similar in kind but grander in scale. In the last chapter and a half JLM discusses the early modern era and here he sounds a bit more like Fukuyama, but he’s clearly more interested in, for instance, the origins of political parties than in their transformation into modern ideological actors.

In part this is because, as Duncan Watts observed at the “author meets critics” at ASA, JLM is mostly interested in that which can be derived from micro-macro emergence and tends to downplay issues that do not fit into this framework.*** This is seen most clearly in the fact that the book winds down around the year 1800 after noting that (a) institutionalization can partially decouple mature structures from their micro origins and (b) ideology can in effect form a sort of bipartite network structure through which otherwise disconnected factions and patronage structures can be united (usually in order to provide a heuristic through which elites can practice balance theory), as with the formation of America’s original party system of Federalists and Democrats which JLM discusses in detail. Of course as I said in the “critics” Q&A, at the present most politically active Americans have a primarily ideological attachment to their party without things like ward bosses and perhaps more interestingly, a role for ideology as a bridge is not an issue restricted to the transition from early modern to modern. As is known to any reader of Gibbon, there was a similar pattern in late antiquity in how esoteric theological disputes over adoptionist Christology and reconciliation of sinners provided rallying points for core vs periphery political struggles in the late Roman empire. Since this is largely a dispute over emphasis, it’s not surprising that JLM was sympathetic to this but he noted that there are limits to what ideological affinity can accomplish and when it comes to costly action you really need micro structures. (He is of course entirely right about this as seen most clearly in the military importance of unit cohesion, but it’s still interesting that ideology has waxed and patronage waned in party systems of advanced democracies).

There are a few places in the book where JLM seemed to be arguing from end states back to micro-mechanisms and I couldn’t tell whether he meant that the micro-mechanisms necessarily exist (i.e., functionalism) or that such demanding specifications of micro-mechanisms implied that the end state was inherently unstable (i.e., emergence). For instance, in chapter three he discusses exchange of women between patrilineal lineages and notes that if there is not simple reciprocity (usually through cross-cousin marriage) then there must be either be some form of generalized reciprocity or else the bottom-ranked male lineages will go extinct. On reading this I was reminded of this classic exchange:

That is, I think it is entirely possible that powerful male lineages could have asymmetric marital exchange with less powerful male lineages and if the latter are eventually driven into extinction then that sucks for them. (The reason this wouldn’t lead to just a single male lineage clan is because, as Fukuyama notes, large clans can fissure and tracing descent back past the 5th or 6th generation is usually more political than genealogical). This is the sort of thing that can actually be answered empirically by contrasting Y chromosomes with mitochondrial DNA. For instance, a recent much publicized study showed that pretty much all ethnically English men carry the Germanic “Frisian Y” chromosome. The authors’ interpretation of this is that a Saxon mass migration displaced the indigenous Gallo-Roman population but I don’t see how this is at all inconsistent with the older elite transfer model of the Saxon invasion if we assume that the transplanted foreign elite hoarded women, including indigenous women. A testable implication of the elite transfer model is that the English would have the same Y as the Danes and Germans but similar mitochondria as the Irish and Welsh. Similarly, a 2003 study showed that 8% of men in East and Central Asia show descent on the male line from Ghengis Khan but nobody has suggested that this reflects a mass migration. Rather in the 12th and 13th centuries the Mongols used rape and polygamy to impregnate women of many Asian nations and they didn’t really give a damn if this meant extinction of the indigenous male lineages.

A very minor point but one that is important to me as a diffusion guy is that chapter five uses the technical jargon of diffusion in non-standard ways, or to be more neutral about it, he and I use terms differently. That said it’s a good chapter, it just needs to be read carefully to avoid semantic confusion.

This post may read like I’m critical of the book but that’s only because I prefer to react to and puzzle out the book rather than summarize it. What reservations I have are fairly minor and unconfident. My overall assessment is that this is a tremendously important book that should be read carefully by anyone interested in social networks, political sociology, social psychology, or economic sociology. For instance, I wish it had been published before my paper with Esparza and Bonacich as using the chapter on pecking orders would have allowed us to develop more depth to the finding about credit ranking networks. (That and it would have given us a pretext to compare Hollywood celebrities to poultry and small children). Despite the book’s foundation in graph theory, this interest should span qualitative/quantitative — at ASA Randy Collins praised the book enthusiastically and gave a very thoughtful reading and from personal conversation I know that Alice Goffman was also very impressed. I think this is because JLM’s relentless focus on interaction between people is a much thinner but nonetheless similar approach to the kinds of issues that qualitative researchers tend to engage with. Indeed, at a deep level Social Structures has more in common with ethnography than with anything that uses regression to try to describe society as a series of slope-intercept equations.

————-

* Technically, it’s about weak emergence, not strong emergence. At “author meets critics” JLM was very clear that he rejects the idea of sui generis social facts with an independent ontological status rather than just a summary or aggregation of micro structure.

** One of the small delights in the early parts of the book is that he notes how our understanding of network structure is driven in part by the ways we measure and record it. So networks based on observation of proximity are necessarily symmetric whereas networks based on sociometric surveys highlight the contingent nature of reciprocity, networks based on balance theory tend to be positive/negative whereas matrices emphasize presence/absence and are often sparse, etc. I might add to his observations in this line that the extremely common practice of projecting bipartite networks into unipartite space (as with studies of Hollywood, Broadway, corporate boards, and technical consortia) has its own sets of biases, most obviously exaggerating the importance and scalability of cliques. Also, I’ve previously remarked on a similar issue in Saller’s Personal Patronage as to how we need to be careful about directed ties being euphemistically  described as symmetric ties in some of our data.

*** Watts also observed that JLM’s approach is very much a sort of 1960s sociometry and doesn’t use the recent advances in social network analysis driven by the availability of big data about computer-mediated communication (such as Watts’ current work on Twitter). JLM responded with what was essentially a performativity critique of naive reliance on web 2.0 data, noting for instance that Facebook encourages triadic closure, enforces reciprocity, and discourages deletion of old ties.

TV Party Tonight!

| Gabriel |

A month or so ago bloggingheads had Alyssa Rosenberg and Peter Suderman (mp3 only), my two favorite politically-informed-but-not-hacks culture bloggers. In the course of their conversation they talked about “recapping” culture, which is where a blogger reacts in about 1000 words to each episode of a tv show, usually the day after it airs. I’m sure there were earlier precedents on Usenet forums, but I associate the development of this genre of criticism with Television Without Pity. TWOP recaps are almost Talmudic exegesis that take as long to read as the show itself takes to watch. There are currently many other recaps sites, most notably The Onion’s tv club, and other bloggers do just one or two shows, as Alyssa is currently doing with Breaking Bad and True Blood. It’s a very interesting genre of writing and helps illuminate some theoretical issues with the superstar effect and the demand structure for entertainment.

The superstar effect is of course Sherwin Rosen’s observation that cultural products and cultural workers have a truly ridiculous level of inequality. Rosen first noted that a scope condition is technology for infinite reproducibility and this has held up. However his theoretical mechanism was ordinal selection that was hyper-sensitive to infinitesimmal quality differences and later research has pretty definitively discarded that mechanism. Rather, most everybody now agrees that the superstar effect reflects some kind of cumulative advantage mechanism and the only question is exactly how it works. We know for a fact from Salganik’s music lab work that information cascades are a part of this, but that doesn’t mean that there aren’t also other cumulative advantage mechanisms at work.

Probably the first article to propose a cumulative advantage mechanism for the superstar effect was Moshe Adler’s “Stardom and Talent.” Adler is often cited as synthesizing network externalities and the superstar effect, that is, people read him as articulating a model of “watercooler entertainment” where entertainment is a mixed coordination game (aka, “battle of the sexes“) consumed mostly or entirely for its utility in providing topics of conversation. When you see people citing Adler they are usually arguing that cultural consumption is a means to an end of socializing. For example, imagine that (like any sane human being) you find watching golf on tv to be incredibly tedious but you force yourself to watch it so that you have something to chit chat about with your boss, who is a big golfer.

This is a compelling model, but it’s not actually the model Adler proposed, in part because he’s coming from a theoretical background that emphasizes demand (i.e., micro-economics) rather than a tradition that emphasizes homophily (i.e., sociology). What Adler actually wrote is that chit chat is a means to cultivating taste in entertainment addiction goods. Adler starts from the premise that many art forms function as addiction goods (aka, acquired tastes). However it is often difficult to consume enough of the art to get you into a place where the addiction good has positive expected value and so we use discourse about the art in order to heighten the addiction and thereby increase the utility of arts consumption. That is, I discuss a tv show with you because it helps me develop my relationship with the tv show, not because it helps me develop my relationship with you. We can see this in a formal setting when people take “[wine / opera / painting] appreciation” classes, where (in price theory terms) the class increases your addiction to the good even more so than simply consuming the good.

Adler’s model seems a bit on the aspy side and, like I said, people often get it backwards when they cite it, perhaps because they are forgetting how weird it is and one’s memory’s reconstructs the article’s argument to be more intuitive. Nonetheless, I think that Adler’s original model is also pretty compelling. Notably, there’s no reason why the causation has to go one way. It could be endogenous or it might even be contingent, with “watercooler” for some types of art and “addiction good” for others.

These are subtly different models and provide theoretical implications that are in theory distinguishable (though may be hard to disentangle in practice). In particular, I’m thinking that we can use Omar Lizardo’s argument about the different types of network ties supported by high culture versus pop culture. Omar argues that since pop culture forms a more universal social lubricant it should be (and in fact is) associated more with weak ties whereas high culture is tricky enough that it requires more strong ties.

If we extrapolate this out, we can interpret it as meaning that the “watercooler” network externality effect (ie, the common misreading of Adler) is a mechanism that supports cumulative advantage for shows that are very accessible and not terribly nuanced. That is, you might watch American Idol in order to have a bunch of 2 minute long conversations with acquaintances and strangers whom you normally come into contact with anyway. An important corollary is that you wouldn’t normally seek out fellow fans of crap but just make sure that you’re sufficiently familiar with crap to hold your own in a conversation with random people.

In contrast, we can use the “addiction goods” model (ie, Adler’s actual argument) to explain consumption of less accessible cultural objects of the sort that might sustain an entire dinner’s worth of conversation. The objects might even be so inscrutable that they are difficult to consume without having an interlocutor to help you make sense of them and so you might either seek out strangers who already consume the object or try to convince a close friend to consume the object as well so you can discuss it together. For instance if you read the first paragraph of this post and said “I don’t know or care about this Alyssa person but I’m going to click the link because I’m hoping somebody can help me understand what’s the deal with Hank’s mineral collection” then that would be an illustration of the addiction good model at work. Now if it’s just people who already consume a show finding each other that’s not cumulative advantage but homophily. However there is cumulative advantage if you start watching a show because your favorite blogger is recapping it or if you read a book to participate in a book club or if you buy your best friend a box set of the first season of Battlestar Galactica so you have someone with whom to discuss the downward spiral of Gaius Baltar. In this sense recapping is a complement to the increasing narrative complexity of popular entertainment and one way to see this is that people tend to recap shows with a serial rather than episodic structure.

Life Imitates AJS

| Gabriel |

Compare and contrast:

Postive comments, demonstrations of attention, or expressions of interest reflect approval, thereby influencing opinion, if everyone knows that they are not made lightly; and they will not be made lightly if those making them understand them as forms of deference. It is painful to pay attention to another person if the favor is not repaid.

…

The displeasure of offering unreciprocated gestures of approval keeps such gestures within limits, in turn limiting their impact on other people’s attributions, and so forth. Runaway status hierarchies are thus unlikely to the degree that people are reluctant to make gestures of approval without having the favor returned, at least in part. (Hence the pressure on media celebrities to feign affection for their fans).

— Roger Gould

There is the personal level. I used to call my dear brother [Obama] every two weeks. I said a prayer on the phone for him, especially before a debate. And I never got a call back. And when I ran into him in the state Capitol in South Carolina when I was down there campaigning for him he was very kind. The first thing he told me was, ‘Brother West, I feel so bad. I haven’t called you back. You been calling me so much. You been giving me so much love, so much support and what have you.’ And I said, ‘I know you’re busy.’ But then a month and half later I would run into other people on the campaign and he’s calling them all the time. I said, wow, this is kind of strange. He doesn’t have time, even two seconds, to say thank you or I’m glad you’re pulling for me and praying for me, but he’s calling these other people. I said, this is very interesting. And then as it turns out with the inauguration I couldn’t get a ticket with my mother and my brother. I said this is very strange. We drive into the hotel and the guy who picks up my bags from the hotel has a ticket to the inauguration. My mom says, ‘That’s something that this dear brother can get a ticket and you can’t get one, honey, all the work you did for him from Iowa.’ Beginning in Iowa to Ohio. We had to watch the thing in the hotel.

What it said to me on a personal level, was that brother Barack Obama had no sense of gratitude, no sense of loyalty, no sense of even courtesy, [no] sense of decency, just to say thank you. Is this the kind of manipulative, Machiavellian orientation we ought to get used to? That was on a personal level.

— Cornel West

Misc Links

| Gabriel |

• How to fix GrowlMail after an OS X system update
• Discussion of alpha-centrality (which my co-authors and I used to measure Hollywood stardom).
• Very interesting personal financial history of what it’s like to be an aspiring fashion model (h/t Yglesias). It’s basically a tournament model which means it ain’t fun. A lot of the contractual practices are very similar to the record industry, and to a lesser extent the Hollywood studio system. I may end up assigning this to my undergrads as it makes similar points to Slichter’s So You Wanna Be a Rock N Roll Star (which I already assign).
• How to say “America, fuck yeah!” in dog

Status, Sorting, and Meritocracy

| Gabriel |

Over at OrgTheory, Fabio asked about how much turnover we expect to see in the NRC rankings. In the comments, myself and a few other people discussed the analysis of the rankings in Burris 2004 ASR. Kieran mentioned the interpretation of the data that it could all be sorting.

To see how plausible this is I wrote a simulation with 500 grad students, each of whom has a latent amount of talent that can only be observed with some noise. The students are admitted in cohorts of 15 each to 34 PhD granting departments and are strictly sorted so the (apparently) best students go to the best schools. There they work on their dissertations, the quality of which is a function of their talent, luck, and (to represent the possibility that top departments teach you more) a parameter proportional to the inverse root of the department’s rank. There is then a job market, with one job line per PhD granting department, and again, strict sorting (without even an exception for the incest taboo). I then summarize the amount of reproduction as the proportion of top 10 jobs that are taken by grad students from the top ten schools.

So how plausible is the meritocracy explanation? It turns out it’s pretty plausible. This table shows the average closure for the top 10 jobs averaged over 100 runs each for several combinations of assumptions. Each cell shows, on average, what proportion of the top 10 jobs we expect to be taken by students from the top 10 schools if we take as assumptions the row and column parameters. The rows represent different assumptions about how noisy is our observation of talent when we read an application to grad school or a job search. The columns represent a scaling parameter for how much you learn at different ranked schools. For instance, if we assume a learning parameter of “1.5,” a student at the 4th highest-ranked school would learn 1.5/(4^0.5), or .75. It turns out that unless you assume noise to be very high (something like a unit signal:noise ratio or worse), meritocracy is pretty plausible. Furthermore, if you assume that the top schools actually educate grad students better then meritocracy looks very plausible even if there’s a lot of noise.

P of top 10 jobs taken by students from top 10 schools
----------------------------------------
Noisiness |
of        |
Admission |
s and     |
Diss /    |How Much More Do You Learn at
Job       |         Top Schools
Market    |    0    .5     1   1.5     2
----------+-----------------------------
        0 |    1     1     1     1     1
       .1 |    1     1     1     1     1
       .2 |    1     1     1     1     1
       .3 | .999     1     1     1     1
       .4 | .997     1     1     1     1
       .5 | .983  .995  .999     1     1
       .6 | .966   .99  .991  .999  .999
       .7 | .915   .96  .982  .991  .995
       .8 | .867  .932  .963  .975  .986
       .9 | .817  .887  .904  .957  .977
        1 | .788  .853  .873  .919   .95
----------------------------------------

Of course, keep in mind this is all in a world of frictionless planes and perfectly spherical cows. If we assume that lots of people are choosing on other margins, or that there’s not a strict dual queue of positions and occupants (e.g., because searches are focused rather than “open”), then it gets a bit looser. Furthermore, I’m still not sure that the meritocracy model has a good explanation for the fact that academic productivity figures (citation counts, etc) have only a loose correlation with ranking.

Here’s the code, knock yourself out using different metrics of reproduction, inputting different assumptions, etc.

[Update: also see Jim Moody’s much more elaborate/realistic simulation, which gives similar results].

capture program drop socmeritocracy
program define socmeritocracy
	local gre_noise=round(`1',.001) /* size of error term, relative to standard normal, for apparenttalent=f(talent) */
	local diss_noise=round(`2',.001) /* size of error term, relative to standard normal, for dissquality=f(talent) */
	local quality=round(`3',.001) /* scaling parameter for valueadded (by quality grad school) */
	local cohortsize=round(`4',.001) /* size of annual graduate cohort (for each programs) */
	local facultylines=round(`5',.001) /* number of faculty lines (for each program)*/
	local batch `6'

	clear
	quietly set obs 500 /*create 500 BAs applying to grad school*/
	quietly gen talent=rnormal() /* draw talent from normal */
	quietly gen apparenttalent=talent + rnormal(0,`gre_noise') /*observe talent w error */
	*grad school admissions follows strict dual queue by apparent talent and dept rank
	gsort -apparenttalent
	quietly gen gradschool=1 + floor(([_n]-1)/`cohortsize')
	lab var gradschool "dept rank of grad school"
	*how much more do you actually learn at prestigious schools
	quietly gen valueadded=`quality'*(1/(gradschool^0.5))
	*how good is dissertation, as f(talent, gschool value added, noise)
	quietly gen dissquality=talent+rnormal(0,`diss_noise') + valueadded
	*grad school admissions follows strict dual queue of diss quality and dept rank (no incest taboo/preference)
	gsort -dissquality
	quietly gen placement=1 + floor(([_n]-1)/`facultylines')
	lab var placement "dept rank of 1st job"
	quietly sum gradschool
	quietly replace placement=. if placement>`r(max)' /*those not placed in PhD granting departments do not have research jobs (and may not even have finished PhD)*/
	*recode outcomes in a few ways for convenience of presentation
	quietly gen researchjob=placement
	quietly recode researchjob 0/999=1 .=0
	lab var researchjob "finished PhD and has research job"
	quietly gen gschool_type= gradschool
	quietly recode gschool_type 1/10=1 11/999=2 .=3
	quietly gen job_type= placement
	quietly recode job_type 1/10=1 11/999=2 .=3
	quietly gen job_top10= placement
	quietly recode job_top10 1/10=1 11/999=0
	lab def typology 1 "top 10" 2 "lower ranked" 3 "non-research"
	lab val gschool_type job_type typology
	if "`batch'"=="1" {
		quietly tab gschool_type job_type, matcell(xtab)
		local p_reproduction=xtab[1,1]/(xtab[1,1]+xtab[2,1])
		shell echo "`gre_noise' `diss_noise' `quality' `cohortsize' `facultylines' `p_reproduction'" >> socmeritocracyresults.txt
	}
	else {
		twoway (lowess researchjob gradschool), ytitle(Proportion Placed) xtitle(Grad School Rank)
		tab gschool_type job_type, chi2
	}
end

shell echo "gre_noise diss_noise quality cohortsize facultylines p_reproduction" > socmeritocracyresults.txt

forvalues gnoise=0(.1)1 {
	local dnoise=`gnoise'
	forvalues qualitylearning=0(.5)2 {
		forvalues i=1/100 {
			disp "`gnoise' `dnoise' `qualitylearning' 15 1 1 tick `i'"
			socmeritocracy `gnoise' `dnoise' `qualitylearning' 15 1 1
		}
	}
}

insheet using socmeritocracyresults.txt, clear delim(" ")
lab var gre_noise "Noisiness of Admissions and Diss / Job Market"
lab var quality "How Much More Do You Learn at Top Schools"
table gre_noise quality, c(m p_reproduction)

Life Without Walls

| Gabriel |

So Microsoft now has a page on why you should choose Windows over Mac. What’s interesting to me as an econ soc guy is that most of the things on the list, and certainly most of the things on the list that are actually compelling, rely in one way or another on network externalities, which implies that the advantages of Windows are mostly an issue of path dependence.

• More familiar interface if you’re already used to Windows — network externality
• Easier to share documents — network externality
• Availability of games — mostly a network externality issue, partly that some developers prefer DirectX over OpenGL

Of course they don’t mention that the main disadvantage of Windows is also in large part a network externality issue.

Although the “PC vs Mac” page mostly just finds different ways to say “because they’re popular,” it also lists a few issues that might be interesting even to Robinson Crusoe. Some of these other issues are good points (e.g., Apple’s insistence on bizarre video ports that require you to use dongles and which aren’t even standard within Apples own product line) and others are just stupid or misleading (e.g., that Mac’s don’t have touch).

To avoid giving the impression that I’ve fallen into the reality distortion field, let me provide my own list of advantages of non-network-externality reasons that I see as advantages of PCs over Macs:

• Price
• Two button mice
• A file manager that allows a traditional multi-pane interface
• The availability of tray-loading optical drives that actually work reliably, rather than exclusive use of slot-loading optical drives that often refuse to accept discs, or having accepted them, require you to turn the machine on its side to get it to eject.

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.