Job market talks for 2012 have concluded at many schools, and therefore this is my last post on a job candidate paper. This is also the only paper I didn’t have a change to see presented live, and for good reason: Melissa Dell is clearly this year’s superstar, and I think it’s safe to assume she can have any job she wants, and at a salary she names. I have previously discussed another paper of hers – the Mining Mita paper – which would also have been a mindblowing job market paper; essentially, she gives a cleanly identified and historically important example of long-run effects of institutions a la Acemoglu and Robinson, but the effect she finds is that “bad” institutions in the colonial era led to “good” outcomes today. The mechanism by which historical institutions persist is not obvious and must be examined on a case-by-case basis.
Today’s paper is about another critical issue: the Mexican drug war. Over 40,000 people have been killed in drug-related violence in Mexico in the past half-decade, and that murder rate has been increasing over time. Nearly all of Mexico’s domestic drug production, principally pot and heroin, is destined for the US. There have been suggestions, quite controversial, that the increase in violence is a result of Mexican government policies aimed at shutting down drug gangs. Roughly, some have claimed that when a city arrests leaders of a powerful gang, the power vacuum leads to a violent contest among new gangs attempting to move into that city; in terms of the most economics-laden gang drama, removing relatively non-violent Barksdale only makes it easier for violent Marlo.
But is this true? And if so, when is it true? How ought Mexico deploy scarce drugfighting resources? Dell answers all three questions. First, she notes that the Partido Acción Nacional is, for a number of reasons, associated with greater crackdowns on drug trafficking in local areas. She then runs a regression discontinuity on municipal elections – which vary nicely over time in Mexico – where PAN barely wins versus barely loses. These samples appear balanced according to a huge range of regressors, including the probability that PAN has won elections in the area previously, a control for potential corruption at the local level favoring PAN candidates. In a given municipality-month, the probability of a drug-related homicide rises from 6 percent to 15 percent following a PAN inauguration after such a close election. There does not appear to be any effect during the lame duck period before PAN takes office, so the violence appears correlated to anti-trafficking policies that occur after PAN takes control. There is also no such increase in cases where PAN barely loses. The effect is greatest in municipalities on the border of two large drug gang territories. The effect is also greatest in municipalities where detouring around that city on the Mexican road network heading toward the US is particularly arduous.
These estimates are interesting, and do suggest that Mexican government policy is casually related to increasing drug violence, but the more intriguing question is what we should do about this. Here, the work is particularly fascinating. Dell constructs a graph where the Mexican road network forms edges and municipalities form vertices. She identifies regions which are historical sources of pot and poppyseed production, and identifies ports and border checkpoints. Two models on this graph are considered. In the first model, drug traffickers seek to reach a US port according to the shortest possible route. When PAN wins a close election, that municipality is assumed closed to drug traffic and gangs reoptimize routes. We can then identify which cities are likely to receive diverted drug traffic. Using data on drug possession arrests above $1000 – traffickers, basically – she finds that drug confiscations in the cities expected by the model to get traffic post-elections indeed rises 18 to 25 percent, depending on your measure. This is true even when the predicted new trafficking routes do not have a change in local government party: the change in drug confiscation is not simply PAN arresting more people, but actually does seem like more traffic along the route.
A second model is even nicer. She considers the equilibrium where traffickers try to avoid congestion. That is, if all gangs go to the same US port of entry, trafficking is very expensive. She estimates a cost function using pre-election trafficking data that is fairly robust to differing assumptions about the nature of the cost of congestion, and solves for the Waldrop equilibrium, a concept allowing for relatively straightforward computational solutions to congestion games on a network. The model in the pre-election period for which parameters on the costs are estimated very closely matches actual data on known drug trafficking at that time – congestion at US ports appears to be really important, whereas congestion on internal Mexican roads doesn’t matter too much. Now again, she considers the period after close PAN elections, assuming that these close PAN victories increase the cost of trafficking by some amount (results are robust to the exact amount), and resolves the congestion game from the perspective of the gangs. As in the simpler model, drug trafficking rises by 20 percent or so in municipalities that gain a drug trafficking route after the elections. Probability of drug-related homicides similarly increases. A really nice sensitivity check is performed by checking cocaine interdictions in the same city: they do not increase at all, as expected by the model, since the model maps trafficking routes from pot and poppy production sites to the US, and cocaine is only transshipped to Mexico via ports unknown to the researcher.
So we know now that, particularly when a territory is on a predicted trafficking route near the boundary of multiple gang territories, violence will likely increase after a crackdown. And we can use the network model to estimate what will happen to trafficking costs if we set checkpoints to make some roads harder to use. Now, given that the government has resources to set checkpoints on N roads, with the goal of increasing trafficking costs and decreasing violence, where ought checkpoints be set? Exact solutions turn out to be impossible – this “vital edges” problem in NP-hard and the number of edges is in the tens of thousands – but approximate algorithms can be used, and Dell shows which areas will benefit most from greater police presence. The same model, as long as data is good enough, can be applied to many other countries. Choosing trafficking routes is a problem played often enough by gangs that if you buy the 1980s arguments about how learning converges to Nash play, then you may believe (I do!) that the problem of selecting where to spend government counter-drug money is amenable to game theory using the techniques Dell describes. Great stuff. Now, between the lines, and understand this is my reading and not Dell’s claim, I get the feeling that she also thinks that the violence spillovers of interdiction are so large that the Mexican government may want to consider giving up altogether on fighting drug gangs.
http://econ-www.mit.edu/files/7484 (Nov 2011 Working Paper. I should note that this year is another example of strong female presence at the top of the economics job market. The lack of gender diversity in economics is problematic for a number of reasons, but it does appear things are getting better: Heidi Williams, Alessandra Voena, Melissa Dell, and Aislinn Bohren, among others, have done great work. The lack of socioeconomic diversity continues to be worrying, however; the field does much worse than fellow social sciences at developing researchers hailing from the developing world, or from blue-collar family backgrounds. Perhaps next year.)
just curious, how do you know the family socioeconomic background of job market candidates?
No idea about American job candidates in general, but socioeconomic background of classmates is something I’ve discussed with friends in my own program and elsewhere. More broadly, though, it’s obvious to anyone who looks at the job market that there are very few students at top programs who are both a) from developing countries, and b) not from the elite (Robert College, St. Stephen’s, etc.) of that country.
Yes but being from an elite college does not mean you’re from the socio-economic elite. It may be true from developing countries. I come from a working class family and attended a top10 economics program . The lack of socioeconomic diversity is not a problem that concerns econ graduate programs, but a more general problem of american academic elite.
I’m with you – my father is a machinist. At one point, we figured out that he was the only working class parent among any of the American students in my cohort. I think the problem is more pronounced in economics than in other social sciences, though. I do some work with sociologists, and (unsurprisingly) there appears to be a much more visible attempt to recruit top students into graduate programs who come from non-elite undergraduate institutions or from underrepresented parts of the world. My sense is that economists don’t particularly care about this issue.
“The same model, as long as data is good enough, can be applied to many other countries.”
How? Mexican drug violence, mexican politics…are mexican.
Look back at the first paragraph…”The mechanism by which historical institutions persist is not obvious and must be examined on a case-by-case basis.”
Why ignore this later then? (And quite frankly, the above quote is not so revolutionary, although the study is well done, if that’s the lesson it’s not a major one).
The model is just a method for solving the difficult statistical problem of how to optimally limit trafficking when I know a country’s road network. There are results in the paper that are Mexico specific, but that model seems general enough to me. It may be that in Mexico, it’s best to do searches on major highways near the US border, whereas in Afghanistan, diverting traffic from, say, Iran, would simply shift trafficking to a route toward Pakistan.
As for the quote about the Mita paper, I agree with you: it’s not revolutionary. But the long-run persistence of institutions literature features a lot of sloppy logic about how bad past institutions (say, slavery) lead to bad outcomes today. A convincing example where bad institutions (slavery again) persist and lead to a good outcome (greater infrastructure and hence growth) is certainly non-obvious.
…or an example of sloppy logic again.
they used slaves to build the great wall of china, this probably helped a bit, wouldn’t you think. seems pretty obvious to me.
slavery may be a “good” institution if you’re wondering about infrastructure.
have you heard of McCloskey’s A-prime C-prime theorem?
if I assume A, then my theory proves B. if A’ then I prove B’.
end scientific gain: 0.
(you may argue people will try to figure out what is going wrong in the theory. but that almost never happens, apart from the odd Ronald Coase here and there).
I think this is all very cool. I don’t understand how someone can know so much such different time periods (colonial Peru and modern day Mexico) and have so many technical tools. Amazing.