Update: Welcome Instapundit readers! Please make sure to follow the very thorough discussion/debate over at Discover Blogs, where this has been cross-posted.

End Update

Over the past few days I’ve been very disturbed…and angry. The reason is that I’ve been reading Misha Angrist and Dr. Daniel MacArthur. First, watch this video:

In the very near future you may be forced to go through a “professional” to get access to your genetic information. Professionals who will be well paid to “interpret” a complex morass of statistical data which they barely comprehend. Let’s be real here: someone who regularly reads this blog (or Dr. Daniel MacArthur or Misha’s blog) knows much more about genomics than 99% of medical doctors. And yet someone reading this blog does not have the guild certification in the eyes of the government to “appropriately” understand their own genetic information. Someone reading this blog will have to pay, either out of pocket, or through insurance, someone else for access to their own information. Let me repeat: the government and professional guilds which exist to defend the financial interests of their members are proposing that they arbitrate what you can know about your genome. A friend with a background in genomics emailed me today: “If they succeed in ramming this through, then you will not be able to access your own damn genome without a doctor standing over your shoulder.” That is my fear. Is it your fear? Do you care?

In the medium term this is all irrelevant. Sequencing will be so cheap that it will be impossible for the government and well-connected self-interested parties to prevent you from gaining access to your own genetic information. Until then, they will slow progress and the potential utility of this business. Additionally, this sector will flee the United States and go offshore, where regulatory regimes are not so strict. BGI should give glowing letters of thanks to Jeffrey Shuren and the A.M.A.! This is a power play where big organizations, the government, corporations, and professional guilds, are attempting to squelch the freedom of the consumer to further their own interests, and also strangle a nascent economic sector of start-ups as a side effect.

You are so much more than your genes. So much more than that 3 billion base pairs. But they are a start, a beginning, and how dare the government question your right to know the basic genetic building blocks of who you are. This is the same government which attempted to construct a database of genetic information on foreign leaders. We know very well then who they think should have access to this data. The Very Serious People with a great deal of Power. People with “clearance,” and “expertise,” have a right to know more about about your own DNA sequence than you do.

What can you do? What can we do? Can we affect change? I don’t know, I can’t predict the future. But this is what I’m going to do.

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It is a bit over a year since Geoffrey Miller wrote this piece foreshadowing a crisis in conscience by human geneticists that would become public knowledge in 2010. The crisis had two parts: that new findings in genetics would reveal less than hoped about disease and that they would reveal more than feared about genetic differences between classes, ethnicities and race.

Now that we are through 2010 with no crisis (that I was aware of – is this crisis still happening in private?), I thought I’d revisit Miller’s suggestion that geneticists would show more than feared about class, ethnic and race differences.

At the time I first read the article, I found it hard to characterise this information as something to fear. As Miller identifies, it would be a consequence of some interesting progress:

Once enough DNA is analysed around the world, science will have a panoramic view of human genetic variation across races, ethnicities and regions. We will start reconstructing a detailed family tree that links all living humans, discovering many surprises about mis-attributed paternity and covert mating between classes, castes, regions and ethnicities.

This sounds good to me. To understand the way genes spread as people migrated and mixed across the world will be to gain an important insight into human history.

Miller then points out that some people may be troubled when researchers start to identify genes that create physical and mental differences between populations and identify when those genes arose. Millers states:

If the shift from GWAS [genome wide association studies] to sequencing studies finds evidence of such politically awkward and morally perplexing facts, we can expect the usual range of ideological reactions, including nationalistic retro-racism from conservatives and outraged denial from blank-slate liberals.

But it is not all bad. He closes with:

The few who really understand the genetics will gain a more enlightened, live-and-let-live recognition of the biodiversity within our extraordinary species—including a clearer view of likely comparative advantages between the world’s different economies.

Reading that last sentence, the title to the article and the first paragraph appear over inflated. People will always misuse information and there will be another body of people who will make great use of it.

Looking back at Miller’s article from the vantage point of 2011, I am not sure much has changed. If anything, there has been a slow trickling of some of these ideas into spaces where they are starting to add value. GWAS studies are filling the journals and the store of population genetic data is increasing quickly. While most blank slaters continue to ignore it and the retro-racists use bits as they see fit, some of us are ploughing through it to learn something new.

Although Miller barely touches on it, the economic idea in that last sentence is interesting. If GWAS and sequencing studies result in different skills and comparative advantages being identified across the world’s populations and economies, research into economic development could be vastly changed. However, I am not convinced that we are particularly close to obtaining that sort of information. As I noted in my last post, it seems that we are some distance from taking the load of genetic information and the associated picture of human evolutionary history and being able to link it to characteristics that matter economically.

**This is a cross-post from my blog Evolving Economics.

The History and Geography of Human Genes has probably influenced the way I think about human evolution more than any other book. Even though it is getting old at a time when masses of population genetic data are being accumulated, a flip through the maps depicting the geographic distribution of genes provides a picture that is available in few other places.

It was only a matter of time before some economists grabbed this population genetic data and in particular, this work by L Luca Cavalli-Sforza, Paolo Menozzi and Alberto Piazza, to see whether it could shed any light on economic development. In a paper published (in one of the top economics journals) in 2009, Enrico Spolaore and Romain Wacziarg have taken data on genetic distance from the The History and Geography of Human Genes and asked whether it is correlated with differences in income between countries.

Before examining the data, Spolaore and Wacziarg proposed a model. Take an initial population that branches into two sub-populations each time period, with genetic distance between the two populations being the time since they had a common ancestor. Each sub-population has a transmitted characteristic which is represented by a number. This characteristic mutates either up or down with a 50 per cent probability each generation, so it follows a random walk. As a result, the difference in characteristics (or vertical distance) between two populations is a function of their genetic distance, with the vertical characteristics more likely to have “walked” apart as the time since the shared ancestor increases.

Next, the authors introduce technology. They assume that when a sub-population develops a new technology, other sub-populations’ ability to adopt that technology is a function of their vertical distance from the population at the technological frontier. If technology determines income, then the difference in income between two populations is the size of the relative vertical distance from the population that is at the frontier, which in turn is related to the genetic distance. The core insight from this model is that relative genetic distance and not absolute genetic distance should have a higher correlation with differences in technology.

While I am not sure this model adds much to the initial intuition, it does serve a useful purpose in that it looks to link genetic distance with income differences through differences in vertical characteristics. If genetic distance and income differences had been directly linked (positing that genetic distance is the barrier, as has been proposed within populations by Ashraf and Galor), we would not be left with the interesting question of what these characteristics are.

On the flip-side, Spolaore and Wacziarg have produced a model in which differences in vertical characteristics are a function of random drift, rather than selection. This is a touch unsatisfying, but it is hard to see how the authors could otherwise have produced the model without a theory about what those characteristics are. The model is also agnostic about how one country may develop technology as the authors assume transmitted characteristics do not have any effect on productivity. Introducing a theory of technological development could have been interesting as if certain traits make technological development more likely, there would be two effects creating the income difference – the higher probability of technological progress coupled with the barriers to diffusion.

With model in hand, Spolaore and Wacziarg turned to the population genetic data. Taking data on from 42 world populations, they matched it to countries (for which they have economic data) using information on the ethnic composition of those countries. This formed the basis of determining the genetic distance between countries. They also took a set of European population data (of 26 populations) which would allow them to do a European analysis. The regressions had to depart from the model and test the link between genetic distance and income differences directly as the data does not tell us anything about the vertical characteristics of the population.

The authors completed a mountain of regressions in analysing the data, so here are some of the headline findings. Taking the United States as the world technological frontier in 1995 (a fair assumption), the authors regressed genetic distance against the log of income and, as expected, found that income was negatively correlated with average genetic distance from the United States population. Genetic distance also had reasonably high explanatory power, accounting for 39 per cent of the variation in the sample. The chart below gives the picture. Throwing a range of other explanatory variables into the analysis such as geography and linguistic and religious differences did not materially change this result.

Spolaore and Wacziarg then ploughed deeper into the statistical analysis by creating 9,316 pairs of countries (from 137 countries) for the world sample and 325 pairs (based on 26 countries) for the European sample and assessed the link between genetic distance and income difference. When they use this broader set of pairs, as opposed to the simple comparison with the United States technological frontier, the degree of variation accounted for by genetic distance decreases, although the genetic distance still has a material effect. For example, one standard deviation change in genetic distance accounts for 16.79% of a standard deviation change in income difference when genetic distance alone is entered into the regression.

The authors also examined a range of other factors, such as Jared Diamond’s thesis about differences in geography and domesticable plants and animals. While including these factors in the analysis reduced the explanatory power of the genetic difference measure, the significance remained. The data also allowed some analysis of earlier time periods, which was in fact easier as most countries’ populations were more ethnically uniform in, say, 1500. At for the later dates, the relationship still held.

Given the agnosticism of Spolaore and Wacziarg on what the vertical characteristics driving income differences are, I hope this paper triggers some deeper examination of what is going on. What are the microeconomic mechanisms driving this result? What are the vertical characteristics that are relevant? And going the next step from Spolaore and Wacziarg’s model, how has selection affected these characteristics? Without the characteristics being subject to selection, the change in characteristics would be fairly slow. These slow changes are then hypothesised to create a substantial barrier to technological diffusion even though the populations have been separated a relatively short period. I would suggest that selection is required.

The authors suggest that more research on peaceful and non-peaceful interaction between societies may be useful to tease out the mechanisms that they have proposed. I agree that research may be interesting, but it leaves open the question which the model ignores – how did some countries get that technological lead in the first place. Do these vertical characteristics play a role in that? Asking why others did not follow does not seem as interesting as asking why some countries got the lead in the first place.

**By way of quick introduction, I am a PhD student in Western Australia and blog at Evolving Economics. I’ll be cross-posting the odd piece that might be of interest to gnxp.com readers (of which this is the first).

I am writing a series of posts on the work of George Price. For the most recent one, with links to the others, see here I was planning next to cover Price’s treatment of group selection, but this raises side issues more conveniently dealt with separately. A previous post here considered what is meant by group selection. In the present post I look at definitions of altruism as used in biology. It has taken me a while to complete, partly because I found there is a lot of recent literature on the subject which I needed to digest. A valuable but difficult recent survey is here.
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Dale and Krueger have responded to Robin Hanson at his blog, which commented on their most recent paper. I’ve also commented on this paper, here.

Most of Dale and Krueger’s comments relate to the stability of estimates that suggest that women earn less after attending high-SAT Colleges. I don’t see particularly compelling evidence here either way, though Hanson is right to note that many of the estimates are consistent in nature. I was surprised by their comment, “The paper is not about gender differences from college selectivity, and we have little reason to suspect that there are such differences.” Well, all three drafts of this paper that are online emphasize the results for attending College on various subgroups — for instance, by race, parental education, and parental income. Surely gender is an equally interesting subgroup.

They do also address the selectivity question — that is, why the Barron’s selectivity measure was large and statistically significant in the working paper, but not used in the published paper. They argue that precise manner in which the Barron’s selectivity measures were coded made a huge difference, and the result was important only for one specification. I’m happy to accept this answer. But as far as the “grand conspiracy” is concerned, I’ll note that even the published paper did make a compelling case that both the identity of the school and tuition paid were hugely important in determining future income. This result, for various reasons, may still have been incomplete. Yet it was the basic message of the published paper, and it’s simply the case that the popular press did not emphasize that result. For the record, I don’t think there was any conspiracy here. But it is awfully easy to trumpet the counter-intuitive but pleasing result — the College you went to doesn’t matter!

Also on the Barron’s measure, Dale and Krueger argue:

“While we did report a 23% return associated with attending the most selective colleges (according to the 1982 Barron’s ranking) in our earliest working paper, these results were from our basic model–which does NOT adjust for student unobserved characteristics.”

Here is the relevant section from Table 7 of their working paper:

If you haven’t seen a regression table, this will be confusing. The dependent variable — what they’re testing the effects for — is a logarithm transformation of wage. They’re testing which of the variables listed on the left matter for that, and each column represents a different specification.

The first three columns select on men. The first one tests to see how these variables impact future wages, without taking into consideration other Colleges you applied to, or where you got in. This is the “basic model,” and the .0234 here next to “Most Competitive” corresponds to the 23% return they mention above (relative to the lowest category of selectivity). But skip over to column 3. This “self-revelation” model is designed to get at student unobserved characteristics. As the authors write:

“The effect of the Barron’s rating is more robust to our attempts to adjust for unobserved school selectivity than the average-school SAT score. Based on the straightforward regression results in column 1, men who attend the most competitive schools earn 23% more than men who attend very competitive colleges, other variables in the equation being equal. In the self-revelation model, the gap is 13 percent… [An] F-test of the null hypothesis that the Barron’s ratings jointly have no effect on earnings is rejected at the .05 level in the matched applicant model for men.”

Now, this was in response to Hanson’s point. Hanson picked up on the 23% number, and Dale and Krueger are right to note that’s a little high (and Hanson is right to concede). But note that the very next sentence reports results from a specification which does adjust for student unobserved characteristics; and it is also quite high.

Finally, I’ll note that while the authors emphasize the significance (or lack of significance) for individual estimates in individual years, my simple calculations suggest that the aggregate, pooled effect of their variables might be quite large in economic importance.

One of the topics I’ve covered here is the all-important issue of whether your choice in College matters in terms of your future earnings. To recap: the best research in the field until a few days ago suggested that the returns to going to a more selective College were quite large; a result which was somehow interpreted by many to suggest the exact opposite claim.

The original result that kicked this off a working paper by Dale and Krueger. They realized that simply comparing students who went to top schools with students who didn’t generates an obvious source of bias: students who go to highly ranked schools tend to earn more than others, but this may be due either to the impact of the school or the personal characteristics that got them in to begin with. To correct for this, the authors compared students who got into top schools, and chose to go; with students who got into those same schools, but decided to matriculate elsewhere. This is also not a perfect comparison, but manages to correct tremendously for this form of bias.

Their results suggested that something about the school was important. In the jargon, they ran a regression of future self-reported income against the identity of all 30 schools in their sample, and found that going to one school instead of another impacted your future income. Then they looked at the particular factors which might explain that, and found that what mattered was the tuition the school charged as well as its level of admissions selectivity as reported by Barron’s; but not the average SAT of the school.

Their publication paper performed virtually the same calculations. They found that the choice of school mattered; the tuition charged mattered; and that the average SAT of the school did not. Bizarrely, they claim that the results of the test for Barron’s selectivity was now no longer important, but they did not report any estimates from that specification (I’m not quite sure how that result could have changed, since the authors did not make any sample or specification changes between the two papers). In any case, even if the Barron’s selectivity measure doesn’t matter, it was clear that something about the choice of school matters, and that tuition charged is a good proxy for figuring out what that something is. In fact, their results suggest that every extra dollar of tuition provides something like a 13-15% internal real rate of return (down from a nominal 20-30% in the working paper). As is covered elsewhere, the results for SAT were highlighted, while the results for tuition were less discussed — even by the authors of the original paper.

Dale and Krueger are back with a new paper, which looks at another age group and also gets income data from government as opposed to self-reported income. Given that the correlation between self-reported income and actual income is .90, you might expect the results to be quite similar. Certainly, this is what David Leonhardt suggests in his writeup. In fact, the results are rather different. The authors now claim that neither the average SAT of the school, the tuition it charges, nor its selectivity influence future income. I have a few quibbles with this paper:

1) Unlike prior versions of their study, in this paper Kruger and Dale don’t run a specification testing whether Colleges matter at all, as opposed to the particular variables of SAT, Selectivity, or tuition. So even if the authors are correct in suggesting that, with the availability of new data and different age groups, none of their chief College selectivity variables predict future income — we don’t know whether some other aspect of College does. It’s possible that your choice of College matters even more than before, but in a different matter — ie, tuition paid could be a worse measure today given widespread tuition inflation; the US News & World report could have changed College rankings, or so forth.

2) In looking at why their results changed for this paper, Krueger and Dale find that their effects already diminish when using the sample of Colleges used for this paper as opposed to the sample from the old paper; and diminish even more when using government income rather than self-reported income. This tells us two things. One, the schools dropped for this paper (Denison, Hamilton, Kenyon, Rice, UNC) may matter a lot for future income, or else the inclusion of two historically black Colleges might affect the results. Second, it’s puzzling to think of why the results would change dramatically depending on the source of income. We know from other studies that individuals systematically under-report income both to surveys and in official government data. It’s not clear that the government data is “better” in the sense of getting a more accurate picture. The authors also exclude income received from capital gains, which doesn’t strike me as a good exclusion. Either students who went to elite schools lie more about their income, or are better at hiding it from the government (or else receive more of it in the form of capital gains). All that we can seriously say is that the conclusion you draw depends enormously on the data source you use for income and set of Colleges.

3) The results for both tuition and selectivity still show sizable effects for the 1976 cohort. Their Table 5 breaks out the effects of tuition and College selectivity by years. While none of these regressions are statistically significant on their own, the net effect is quite large. I applied the estimates on wages to the actual median wages in each time period (interpolating when the authors did not provide actual wage statistics). I estimate that a one percent increase in 1976 tuition (perhaps $100 total over four years) results in roughly a two percent increase in overall compensation through 2007 (assuming that you work for all 24 years), or $43k in non-inflation adjusted dollars. Alternately, a category shift in the Barron’s selectivity criteria (ie, from Highly Competitive to Most Competitive) is associated with $45k more in lifetime income. The effects of both selectivity and tuition grow over time, and are at their highest for wages observed in 2003-2007 (at this point, a one percent increase in tuition paid in 1976 gets you roughly $4k more a year per year. Presumably, this will rise even more by the time this cohort retires.

While the results from any one regression may not be statistically significant, that may simply be due to their sample size. The cumulative effect appears rather large in magnitude for both of the measures that were quite important in earlier drafts of the paper. This does change substantially when looking at the 1989 cohort, and it very possible that College selectivity is less important today (or else that group has not been in the workforce long enough to measure an effect).

4) Robin Hanson has some good commentary as well, focusing on the fact that the estimates for average school SAT on female earnings is negative and statistically significant. He suggests women going to more prestigious schools marry high earners, and so feel less need to make money themselves. I’ll only note that the results do subset among full-time earners, so it’s unlikely that this result is being generated by women withdrawing from the workplace altogether. The tuition/selectivity results above apply to a pooled sample of men and women, and so may result in even higher estimates for male workers.

Anyway, go check out all the papers referenced here. My prior belief on this, created by the first two Krueger and Dale papers, is that the College you go to affects your earnings. This new paper shakes this belief somewhat, and I am now not sure either way. Unfortunately, this data isn’t released publicly, so I can’t check to see if the authors calculations hold up depending on how you cut the data. In any case, you probably shouldn’t be basing your choice in Colleges on the basis of any study, and certainly not from this blog.

The New York Times highlights the issue of hospitals opting not to hire smokers. It’s not clear how many places of employment are really banning smoking (or even how strictly such regulations will be enforced), but certainly there have at least been some high-profile cases (ie, Cleveland Clinic). One question that comes immediately to mind are — who still smokes? The NYT article includes this bit:

But the American Legacy Foundation, an antismoking nonprofit group, has warned that refusing to hire smokers who are otherwise qualified essentially punishes an addiction that is far more likely to afflict a janitor than a surgeon. (Indeed, of the first 14 applicants rejected since the policy went into effect in October at the University Medical Center in El Paso, Tex., one was applying to be a nurse and the rest for support positions.)

I had the impression that the remaining prevalence of smoking is strongly stratified by social class, geography, and education; and found this study from the CDC confirms as much:

One of the biggest predictors of smoking is education. Interestingly, the least educated (<8 years) have a lower smoking rate than average, particularly if female. This rises with more education, peaking with GED holders (42%) and falling to a low of 7.2% for graduate holders. This confirms the pattern, seen elsewhere, that credentials matter as well as years of education. ie, even among the group with 12 years of education, there is a large variance between those without a diploma (31%); those with a GED (42%), and those with a diploma only (25%). Similarly, there is a large difference between some college (23%, similar to HS grads) and getting a diploma (12%).

There is also a strong gender disparity among Asians — the smoking rate for Asian men is not much lower than the average (19%), yet among women, being Asian has a comparable effect as having a graduate degree (6.5% v. 6.4%). Asian countries also have these stark gender differences when it comes to smoking rates.

Income is another big factor — 24% smoking rate above the poverty line, 33% below. I checked this out a little more in the GSS. Here, you sometimes see the "inverse U" pattern as with education — the smoking rate for 1991 stays under 33% for the first few thousand dollars, goes up to the 40s-50s for the next few thousand, and then falls to 17% for the $75k+ crowd.

Here's political affiliation:

I’ve seen this pattern a few other places as well — Independents differ on some criteria from both Republicans and Democrats. Their lack of a coherent political ideology is indicative of other traits.

Anyway, it does seem that the class concerns of a smoking ban are somewhat warranted. This is a policy unlikely to affect the doctors, surgeons, or administrators at hospitals — while it will act as a much stronger burden on less educated support staff (who are of course facing substantially higher unemployment rates now anyway).

Just some pointers. Dr. Daniel MacArthur has put up a guest post where I outline my own experience with personal genomics. Cool times that we live in. Also, Zack Ajmal has started posting higher K’s of HAP participants. He’s now in the second batch. My parents will be in the third. Lots of Tamils and Punjabis. The Khan’s are the only Bengalis so far. One individual to represent all of Uttar Pradesh. Here’s a list of participants so far.

Finally, I know 3-D visualization is bad form, but I went for it anyway. Below is a cube which shows the positions of Gujaratis, Chinese, Mexican Americans, and Utah whites and Tuscans from the HapMap, along with a few extra samples from friends and family. Can you tell where my parents are?

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It’s called Razib on Books. I posted the rationale over at Discover Blogs. Basically a way for me to organize past content which new readers are not aware of.

Governments are large or small depending on the level of trust and civic attitudes people have for one another. These attitudes shape peoples’ taste for redistribution and public ownership, and also affect the quality of governance. This position has been advanced by a large literature; most recently in this interesting paper put out by IZA.

Here’s a graph which gets at the central idea:

One key advance in this paper is isolating the non-linear nature of this relationship. Broadly, there are three clusters of countries here — Scandinavian countries (lots of government, high-trust, high-quality); Continental European countries (lots of government, low-trust, low-quality); and Anglo-Saxon countries (low levels of government, medium-trust, medium-quality).

One explanation of this result (provided in the paper) is the following: high levels of government spending can be sustained under two social equilibria. In the low-trust world; you have a chronic levels of mistrust and civic mindedness. Nevertheless, the fact that uncivic minded people benefit from public services, but evade paying taxes, encourages more spending. High levels of corruption and low levels of public trust make the government work poorly. Yet individuals remain attached to the state, as in societies marked with a marked in-group bias it may remain a treasured source of largess and security. Where everyone cheats, as in Greece, it makes sense to demand more for yourself and leave the bill for someone else.

On the other hand, you can also sustain a large and efficient welfare state when everyone is civic minded and people typically do not shirk. High levels of trust allow individuals to coordinate the public provisioning of social insurance. Individuals are less likely to free-ride. I also wonder about thinking about this in light of Amar Bhide’s book, which argues against robotic finance in favor of a more discretionary, case-by-case Hayekian approach. Well, bureaucrats can be trusted with discretionary power in high-trust societies, while they either become corrupt in low-trust societies, or else you have to resort to dumb regulatory rules.

Many Anglo-Saxon countries (and Japan) appear in the middle. They are not so full of shirkers demanding large public provisions; nor are they so trusting that they sustain a Nordic utopia. In the absence of higher levels of trust or pro-social attitudes, it seems plausible that a larger government in these countries would come up somewhere between Sweden and Italy in effectiveness.

It’s also interesting to examine social trust in developing countries, as Ajay Shah and Vijay Kelkar do here:

China comes out as a very high-trust society. One wonders whether its governance successes, if any, ought to be credited to the citizenry of China rather than the wonders of Chinese central planning.

Other countries come out looking much worse — the rest of the “BRICs” for instance, plus Turkey and South Africa. As Arnold Kling and Nick Schulz point out, these countries have built governments much larger as a percentage of their economy than countries like Britain or America had at a comparable level of development. And as their levels of trust suggest, these governments are not particularly effective. Many social democrats expect these countries to build large welfare states as they grow richer, and it will be interesting to see how countries so large and distrusting will handle the challenge. Of course, there is substantial variation here, trust can change over time, the correlations are loose, etc. America for example has a small government even taking its trust into consideration.

So how do countries generate a more cooperative citizenry? One suggestion comes from Garret Jones, economist extraordinaire, who argues that the best way to drive cooperation is to induce patience and perceptivity, which are in turn driven by higher IQs. Jones in fact suggests that one of the ways in which IQ drives growth is through exactly one of the channels by which IQ generates a large “social multiplier.” This multiplier refers to the observation that a two standard deviation increase in IQ increases a person’s wage by 30%; but increases a nation’s wage by 700%.

Zack has finally started posting results from HAP. To the left you see the results generated at K = 5 from his merged data set with the first 10 HAP members. I am HRP002. Zack is HRP001. Paul G., who is an ethnic Assyrian, is HRP010. Some others have already “outed” themselves, so I could proceed via process of elimination for the other bars. There isn’t anything very surprising here. Zack is 1/4 Egyptian, so he has a rather diverse ancestry. Jatts, who are from Northwest India, are known to have more affinity with populations to the west than those of us from the east or south of the subcontinent. With just that knowledge you can make some educated guesses as to what the “ancestral components” inferred from ADMIXTURE might correspond with in a concrete sense. After submitting to Dodecad and the BGA Project I pretty much know what to expect in relation to me. I’m a rather generic South Asian, except, I have an obvious input of “eastern” ancestry.

This is what Dienekes also found. Aggregating various ancestral components together to be analogous to what Zack produced at K = 5, you get the bar plot below from his runs:

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Stanley Engerman and Kenneth Sokoloff famously argued that patterns of growth across the Americas can be traced back to historic levels of inequality. Natural factor endowments in certain areas (for instance, Caribbean islands) encouraged rent-seeking extraction over investments in human capital, and led to the political empowerment of rich landowners. These elites, in turn, created historical institutions that fostered economic coercion rather than entrepreneurship.

A recent paper by Melissa Dell looks into this thesis in more granular detail by examining the role of Peru’s mita system in sparking long-run development. Under the mita, certain local communities were forced to send one seventh their male population to work in Peru’s silver mines; other communities were exempt. Districts under the mita system now have 25% lower household consumption, pointing to a durable, long-run effect of this historical institution.

Dell’s explanation for this difference relies on the role of large-scale hacienda estates that grew up in the areas outside the mita zone. These hacienda owners were able to lobby politically for public goods like roads. They were also able to secure inhabitants from the extractive levys of the state; and under established property rights were able to better make long-term investments. On one level, these results confirm the point of view that “history matters.” But the manner in which history matters here is at odds with the traditional narrative — espoused by Engerman/Sokoloff and Oded Galor, among others — that land inequality is bad. In Peru, wealthy landowners here appear to leave inhabitants better off (at least, relative to other areas subject to extractive labor levies).

Interestingly; the opposite pattern can be found in India. Abhijit Banerjee and Laxmi Iyer highlight the impact of different land tenure systems dating back to the British Raj. Some regions of British India fell under the zamindari system, in which government officials collected revenue directly from landlords. In other parts of India, village communities or individual farmers provided tax revenue.

Fortunately for their analysis, the particular form of land tenure adopted in British India was more dependent on the prevailing political ideology in Britain when the region was occupied rather than local characteristics. For instance, Holt Mackenze implemented the an individual-based raiyatwari system in the Bombay Presidency under the influence of James Mill (father of John Stuart Mill).

Indian areas under landlord-based systems had persistently worse outcomes, especially after the beginning of the Green Revolution dramatically grew agricultural yields. Non-landlord areas had 16% higher agricultural yields and applied 45% more fertilizer. This would reinforce the Engerman/Sokoloff view that inequality of a sort entrenches a rentier class and harms long-run productivity.

Also, compare the above graph (which shows the various British Indian land tenure systems), with the one below, which shows districts in India facing a “naxalite,” or Maoist, insurgency:

This may just be me, but I see some sort of overlap here. This guerilla insurgency is certainly fueled by resource extraction in hilly tribal areas, but as the graph suggests is also strong areas of strong land inequality like Bihar.

The only common theme here is that the relation between inequality and growth is complicated. While an unequal landowner based system was a boom in Peru, it may have hurt in India. Possibly, this is because the British Indian state was better able to protect small landowners from attacks by brigands; while a small landowner lifestyle was simply unsustainable in Peru, where large landholdings provided a second-best solution to the problem of property rights and physical security.

It’s tempting to infer from these cases some general rule that can apply to today’s sky high inequality. Yet the lesson from at least these two studies on Peru and India may provide some reasons for reassurance. In Peru, elite landowner dominance actually led to better outcomes. It did not in India, but the problem there inequality caused by differences in endowments, not differences in earned income. When inequality fosters a rentier class that grows its status through economic coercion; inequality might be bad. But it’s not obvious that this is the case now. Many of today’s rich are “working rich”; and have made their income through entrepreneurial activities. Many plan on donating large proceeds of their income. To the extent we worry about such issues, ideal policies might target, say, copyright or other monopolies, as opposed to income inequality itself.

It’s also not clear how much about inequality we can learn from the gini coefficient. It’s true that high-inequality Latin America had a high gini coefficient, but so did comparable European societies without coercive economic institutions. The problem, according to one team of scholars, is that the total feasible inequality varies from society to society, given the fact that people have certain substance needs. They argue that the relevant comparison is not income inequality by itself; but rather the overall share of surplus extracted by elites. Taking this into account, they find that Latin America historically has had a higher level of elite extraction than Europe.

America, too, comes of looking better in their measure. While it’s gini coefficient measured as of 2000 is reasonably high by international standards, its inequality extraction ratio lies substantially lower than a number of developing countries like Brazil or South Africa.

A comment below inquired about “good books” on American history. Unfortunately I don’t know as much about American history as I do about Roman or Chinese history. But over the years there have been several books which I find to have been very value-add in terms of understanding where we are now. In other words, these are works which operate with a broader theoretical framework, and aren’t just a telescope putting a spotlight on a sequence of facts.

- Albion’s Seed. I read this in 2004, and it was a page turner.

- The Cousins’ Wars. I had thought of Kevin Phillips as a political writer, but this was a very engaging and deep cultural history. My prejudice resulted in my not reading this until 2009.

- What Hath God Wrought. This book focuses on the resistance of the Whigs and Greater New England to the cultural ascendancy of the Democrats and their “big-tent” coalition which included most of the South, the Mid-Atlantic, and much of the “Lower North” (e.g., the “butternut” regions of the Midwest settled from the Border South).

- The Rise of American Democracy. This is a good compliment to the previous book, in that it takes the “other side,” that of the Democrats. In many ways this is the heir to Arthur Schlesinger’s Age of Jackson.

- Throes of Democracy. A somewhat “chattier” book than the previous ones, it is still an informative read. It covers a period of history with the Civil War as its hinge, and so gives one the tail end of the Age of Sectionalism.

- Freedom Just Around the Corner. By the same author, but covering a period of history overlapping more with Albion’s Seed.

- The Age of Lincoln. This is not a “Civil War book.” It is of broader scope, though since the the war is right in the middle of the period which the book covers it gets some treatment. I’d judge this the “easiest” read so far of the list.

- Replenishing the Earth. This is about the Anglo world more generally, but it is nice to plug in America into a more general framework. North America is not sui generis.

- The English Civil War. This is obviously not focused on America, but it is a nice complement to Albion’s Seed, as it shows the very deep roots of the division between two of America’s folkways. The Cousins’ Wars serves as a bridge between the two, shifting as it does between both shores of the Atlantic.

I’m game for recommendations! I had a relatively traditional education in American history, and did very well in my advanced courses, but I knew very little before I read books like this.

1936 presidential election, blue = F.D.R.

Walter Russell Mead has a fascinating blog post up, The Birth of the Blues. In it, he traces the roots of modern American “Blue-state” liberalism back to the Puritans, the Yankees of New England. This is a plausible argument. I believe that many social-political coalitions and configurations in contemporary America do have deep historical roots. But assertions and models must be tested. It is for example absolutely correct that early New England was the redoubt of American statism. First the Federalists, and then later to a lesser extent the Whigs, took refuge in New England during the long phase of anti-government Democratic ascendancy which led up to the presidency of Abraham Lincoln. But New England statism has its limits; the map above shows that it is in Greater New England that resistence to FDR seems to have been deepest. I don’t necessarily chalk this up to “flinty Yankee” anti-government sentiment. Rather, I think we need to consider that the ideological content of social-political coalitions and configurations sometimes matter less than long persistent affinities across cultural networks and domains.

Very few Americans for example are aware today that in 1800 New England was the region with the strongest adherence in the United States to orthodox Protestant Christianity. In contrast, Deism was firmly rooted among the Southern planter aristocracy. As late as 1850, even after the Second Great Awakening transformed the religious landscape of the South, the conservative Carolina aristocrat John C. Calhoun remained a Unitarian. And it was in the South than support for Revolutionary France ran strongest, while New England favored the United Kingdom and its allies. I suspect most modern Americans would be taken aback by such affinities simply based on the substance of what New England and the American South represent in terms of ideology at any given moment.

Until a few years ago I was very ignorant of American history. And therefore I was totally innocent of many important patterns which span the generations in our nation. Scholars such as Walter Russell Mead would have impressed me with their erudition, but I didn’t have the data base to evaluate the plausibility of their claims. In everyday discourse we often bandy about history learned when we were teenagers as if they can serve as robust frames for the sorts of inferences we make. Alas, they can not. There is no substitute for genuine knowledge. Albion’s Seed is a good start, but many accessible books which cover the first period of American sectionalism are filled with much relevant insight.

If you’re a regular reader, you may have noticed some changes. Since I moved to Discover blogs I’ve been posting less and less here. Additionally, I’ve been putting some of my shorter less science oriented stuff at Brown Pundits and Secular Right. And I suspect twitter has cannibalized some of the link aggregation function of blogging in general.

So where does this leave this website? The archives are obviously active and useful for many people. Even without any front page content this blog serves 1-2,000 pages per day just as a function of search engines sending traffic to old posts. That’s important. GNXP could turn into an archive site, as I always imagined it would at some point, and still play a vital role in the information ecology.

But I’m not ready to turn this into a hibernating site yet. Kevin and David are still posting obviously. And, because of the traffic and the old links that come to this domain GNXP has good PageRank. My main interest then is to promote science bloggers whose content should “get out there.” So I’ve been soliciting contributions from people now and then with the promise that cross-posting will boost the PageRank of their site and give them some publicity. If you have a weblog with content that I think would fit the front page of this weblog, and are interested in cross-posting, feel free to email me at contactgnxp -at- gmail.com with a link. I’ll add it to my RSS and see if it’s a good fit. If you seem a good candidate for front page privs, I’ll shoot you an email with the details about your login, etc.

Additionally, I’ve modified the column format some. At the top of the sidebar now are a set of articles which come from an aggregation site where I curated various weblog RSS feeds (as well as some google searches). And, there’s always my pinboard and Jason’s delicious. There’s also a footer column now where you can find archives, books, etc.

I’ll probably be tweaking with the format and what not every now and then. All things must change.

Speaking of using PageRank, the Harappa Ancestry Project now has its own domain, http://www.harappadna.org. If you’re South Asian, Iranian, Tibetan, or Burmese, please check it out.

Had to reinstall WordPress because of security problem over the last couple of days (iframe injection). I’ll slowly be getting the site back to normal look & feel wise.

A few weeks ago I hinted at a South Asian equivalent to Dodecad & Eurogenes BGA. It is now public and in the data collection phase. You can read the whole thing here:

http://www.zackvision.com/weblog/2011/01/harappa-ancestry-project

This is the feed:

http://www.zackvision.com/feed/

If your ancestry is from these nations:

• Afghanistan
• Bangladesh
• Bhutan
• Burma
• India
• Iran
• Maldives
• Nepal
• Pakistan
• Sri Lanka
• Tibet

Read on! If not, “for entertainment purposes only”….

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I have a new post over on the Scientific American Mind Matters website. It describes new research which suggests that tune deafness and face blindness – two examples of conditions known as agnosias, both of which can be genetic – are caused not by a failure of the brain to recognise previously seen faces or detect incongruous musical notes, but a failure to communicate these events to frontal brain regions where conscious awareness is triggered. In essence, your brain knows something but can’t tell you. Read more…

I am writing a series of posts on the work of George Price. For the most recent one, with links to the others, see here. I was planning next to cover Price’s treatment of group selection, but this raises side issues more conveniently dealt with separately. This post considers what is meant by ‘group selection’. I have tried to establish what various key authors meant by the term (or similar expressions) up to the mid-1970s, when Price’s own work began to be influential.

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If some guy spilt your beer by accident, would you punch him in the face? If he was unapologetic, you might at least consider it – you might in fact feel a pretty strong urge to do it. What stops you? Or, if you’re the type who acts on those urges, what doesn’t stop you? New research has found a mutation in one gene that may contribute to these differences in temperament.

Self-control is the ability to inhibit an immediate course of action in the pursuit of a longer-term goal or to consciously override a base urge. Some people show far more inhibitory control than others. This trait is very stable – indeed, inhibitory control in children, which can be assessed using the famous “marshmallow test”, is predictive of their score on scales of impulsivity as adults. (The marshmallow test must go down as one of the cruellest experiments in psychology – it involves asking four-year olds not to eat a lovely yummy marshmallow for five minutes, after which they will be given another one to go with it if they have resisted. The videos of these poor kids as they struggle to resist this urge are priceless). Impulsivity is also partly heritable – that is, more closely related people are more similar in this trait.

This is generally true of all personality traits, suggesting they are influenced by genetic variation. However, the specific genes involved are almost entirely unknown. Indeed, a recent study that failed to find any such genes was interpreted by many (e.g., 1, 2) as evidence that either personality was not really genetic or that measures of personality traits were effectively meaningless. In fact, this was a gross misinterpretation of the results of this study. What these researchers did was look for common genetic variants that were associated with differences in personality traits, across a sample of over 5,000 people. Common variants are ancient differences at specific positions in the DNA code, where some proportion of the population carries one base, say a “C”, and the rest carry another base, say an “A”. There are millions of such variable positions across the human genome. Most of them do not do anything – they do not affect the sequence of a protein or how much of it is made. And, it seems, none of them affects personality significantly.

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