In the wake of MG's essay on the nature and nurture of corruption, I wondered if a hard correlation between consanguinity rates and graft at the national level had been discovered. Searching for as much, the top returns I received were from MG and HBD Chick. Apparently, it hasn't been an area of academic interest, though HBD Chick is deserving of an academic spot for her intellectual curiosity about and indefatigable efforts researching and relaying consanguinity through history and up to the present to any who happen to be interested in as much.
Why should academics and policy makers take note, though, when they've already identified the culprits? They are, of course, bad laws, bad leaders, and bad institutions! Fix these things and any country is capable of resembling Norway. Any day now we'll get the right laws and enforcement mechanisms in place and use them to throw out the crooks and set things straight in Zimbabwe, Zaire, Syria, Sudan, the Congo, Nigeria, Sierra Leone, Somalia, Burma, Iraq, Afghanistan, Papua New Guinea...
As is often the impetus here, not finding what I was looking for meant needing to figure it out. The data aren't perfect by any stretch, but something is better than nothing. Computing simple, unweighted averages for each country for which studies and surveys have been conducted and subsequently recorded on consang.net and then comparing them to Transparency International's 2011 Corruptions Perception Index yields a correlation of .44 (p = 0). In places where extended families are important and family members are more closely related to one another than they are in the West, outsiders are treated with much less even handedness than kin are and nepotism is, if not the rule, at least perfectly acceptable. In these places, if you're not blood, you're going to have to pay to play.
A correlation of .44 is considered fairly strong in the infinitely varied world of the social sciences, but the true relationship between corruption and consanguinity is almost certainly even more vigorous than that. I'm using imperfect and sporadic data. There is nothing available on inbreeding for about half the countries in the world while for India there are 45 studies for which I must, by necessity, compute a simple average from, because even if I wanted to try and weight the sources for geographic and demographic representativeness within India, I'd be utterly unable to do so competently since I know so little about that extremely complicated country of over 1 billion people.
Further, even to the extent that the data are representative, they leave something to be desired, as the chickadee explains:
Why should academics and policy makers take note, though, when they've already identified the culprits? They are, of course, bad laws, bad leaders, and bad institutions! Fix these things and any country is capable of resembling Norway. Any day now we'll get the right laws and enforcement mechanisms in place and use them to throw out the crooks and set things straight in Zimbabwe, Zaire, Syria, Sudan, the Congo, Nigeria, Sierra Leone, Somalia, Burma, Iraq, Afghanistan, Papua New Guinea...
As is often the impetus here, not finding what I was looking for meant needing to figure it out. The data aren't perfect by any stretch, but something is better than nothing. Computing simple, unweighted averages for each country for which studies and surveys have been conducted and subsequently recorded on consang.net and then comparing them to Transparency International's 2011 Corruptions Perception Index yields a correlation of .44 (p = 0). In places where extended families are important and family members are more closely related to one another than they are in the West, outsiders are treated with much less even handedness than kin are and nepotism is, if not the rule, at least perfectly acceptable. In these places, if you're not blood, you're going to have to pay to play.
A correlation of .44 is considered fairly strong in the infinitely varied world of the social sciences, but the true relationship between corruption and consanguinity is almost certainly even more vigorous than that. I'm using imperfect and sporadic data. There is nothing available on inbreeding for about half the countries in the world while for India there are 45 studies for which I must, by necessity, compute a simple average from, because even if I wanted to try and weight the sources for geographic and demographic representativeness within India, I'd be utterly unable to do so competently since I know so little about that extremely complicated country of over 1 billion people.
Further, even to the extent that the data are representative, they leave something to be desired, as the chickadee explains:
what we are talking about here when we discuss inbreeding vs. outbreeding and nepotism and/or corruption are types of altruistic behaviors -- and these behaviors/attitudes have evolved differently in different populations, of course, over time. so you can't just take a population that has been inbreeding for scores of generations, and likely evolved certain altruistic behaviors, and change their behavior patterns via just one or two generations of outbreeding. there is going to be some lag-time.
why do i say this? because the problem with using the consang.net numbers for the kind of analysis you describe is that there is no time depth to them. if you look at the data @consang.net, it appears as though the chinese have similar inbreeding/outbreeding rates to western europe or canada, but that's only in the last generation or so (and even that is debatable). as i've blogged about, the chinese have been inbreeding for literally millennia. any effects that's had on altruistic behaviors are NOT going to be overturned in one or two generations.
what needs to be done is that the histories of inbreeding/outbreeding in different populations need to be quantified (part of my ongoing, neverending project @hbd chick (~_^) ), and then those numbers need to be compared to transparency international's and/or other figures.Yet despite this, we still see a rigorous, statistically significant correlation between corruption and consanguinity. Randomly generated numbers don't correlate with one another. If (when?) much of the remaining randomness in the consanguinity numbers is removed and the appropriate adjustments for time depth are made, the observed correlation will prove to be stronger still.
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