During a compelling class on criminal organizations taught by Guillermo Trejo, at Duke University, I was struck by the complex consequences of criminal–and political–violence on civilian life. At the same time, I was enrolled in a course on social networks with Jim Moody, a wonderfully talented sociologist who convincingly situates network dynamics at the center of the human experience. By the end of the semester I was left with the question: how do networks moderate the effects of violence on civilian life? This question eventually led me to co-organize a national survey in Mexico in July 2012, with my colleague Sandra Ley Gutierrez, focusing on the consequences of criminal victimization. In this survey, I collected original data on 1,000 kinship networks as a way to capture social networks at the individual level.

Studies on victimization have repeatedly reported that victimization is associated with an increase in political participation, but we don’t really understand why. I find that for self-identified victims, kinship connectiveness increases probability of participation in political party meetings by 5%, all else constant (when the other covariate values from my model are set at their mean or median). The size of this result is consistent with other studies on political participation which typically find effects under the 10% range. These predicted probabilities, of course, are contingent on the selected covariate values. Thus, let’s also review specific “real world” scenarios.

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Journal Article , 2013


在冲突研究的领域中,虽然预测分析的重要性不言可喻,但是却一直没有受到足够的重视。我们认为,预测不仅具有实质公共政策参考的能力,另一方面也能用来检证既有理论模型、避免统计上过度配适(overfitting)且降低确认误差(confirmation bias),藉以建构出更可靠的冲突预测。在本篇文章中,我们回顾了学界在冲突预测研究中有哪些进展,发现由于这五十年来学科在资料搜集和运算能力的进步下,研究者得以从事过去所难以企及的预测研究工作,尤其在自动化的编码程序辅助下,快速的搜集数字化的新闻讯息成为可能,冲突研究得以应用以每日、每周、每月为单位的事件解析数据(disaggregated event data)来进行国家层次以下,有关政府与反抗团体的个体活动资料进行及时性的冲突预测工作。

为了呈现冲突研究在过去几年的重大进展,本文重新检视Fearon and Laitin (2003)这份奠定冲突研究基础的文献,从而比较和凸显预测分析在近几年的进展。结果发现,虽然Fearon and Laitin的研究中有很多的解释变量具有统计上的显著性,但是模型对于样本外事件的预测精确度却不高,这因为利用观察型的资料建构出具有统计上显著变量的模型,并无法回答像是何时、何处会发生内战这种决策者所关注的预测问题。

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Mining Texts to Generate Fuzzy Measures of Political Regime Type at Low Cost.  Reposted from Dart Throwing Chimp, by Jay Ulfelder.

Political scientists use the term “regime type” to refer to the formal and informal structure of a country’s government. Of course, “government” entails a lot of things, so discussions of regime type focus more specifically on how rulers are selected and how their authority is organized and exercised. The chief distinction in contemporary work on regime type is between democracies and non-democracies, but there’s some really good work on variations of non-democracy as well (see here and here, for example).

Unfortunately, measuring regime type is hard, and conventional measures of regime type suffer from one or two crucial drawbacks.

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This post was written by Jay Ulfelder and originally appeared on Dart-Throwing Chimp. The work it describes is part of the NSF-funded MADCOW project to automate the coding of common political science datasets.

Guess what? Text mining isn’t push-button, data-making magic, either. As Phil Schrodt likes to say, there is no Data Fairy.

I’m quickly learning this point from my first real foray into text mining. Under a grant from the National Science Foundation, I’m working with Phil Schrodt and Mike Ward to use these techniques to develop new measures of several things, including national political regime type.

I wish I could say that I’m doing the programming for this task, but I’m not there yet. For the regime-data project, the heavy lifting is being done by Shahryar Minhas, a sharp and able Ph.D. student in political science at Duke University, where Mike leads the WardLab. Shahryar and I are scheduled to present preliminary results from this project at the upcoming Annual Meeting of the American Political Science Association in Washington, DC (see here for details).

When we started work on the project, I imagined a relatively simple and mostly automatic process running from location and ingestion of the relevant texts to data extraction, model training, and, finally, data production. Now that we’re actually doing it, though, I’m finding that, as always, the devil is in the details. Here are just a few of the difficulties and decision points we’ve had to confront so far.

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Improvised explosive devices, or IEDs, were extensively used during the US wars in Iraq and Afghanistan, causing half of all US and coalition casualties despite increasingly sophisticated countermeasures. Although both of these wars have come to a close, it is unlikely that the threat of IEDs will disappear. If anything, their success implies that US and European forces are more likely to face them in similar future conflicts. As a result there is value in understanding the process by which they are employed, and being able to predict where and when they will be used. This is a goal we have been working on for some time now as part of a project funded by the Office of Naval Research, using SIGACT event data on IEDs and other forms of violence in Afghanistan.


Explosive hazards, which include IEDs, for our SIGACT data.

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Thailand’s Army chief General Prayuth announces the coup on television on 22 May 2014. Source: SCMP

This morning (May 22nd, 2014, East Coast time), the Thai military staged a coup against the caretaker government that had been in power for the past several weeks, after months of protests and political turmoil directed at the government of Yingluck Shinawatra, who herself had been ordered to resign on 7 May by the judiciary. This follows a military coup in 2006, and more than a dozen successful or attempted coups before then.

We predicted this event last month, in a report commissioned by the CIA-funded Political Instability Task Force (which we can’t quite share yet). In the report, we forecast irregular regime changes, which include coups but also successful protest campaigns and armed rebellions, for 168 countries around the world for the 6-month period from April to September 2014. Thailand was number 4 on our list, shown below alongside our top 20 forecasts. It was number 10 on Jay Ulfelder’s 2014 coup forecasts. So much for our inability to forecast (very rare) political events, and the irrelevance of what we do.

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Recently, Syrian rebels (under EU embargo until mid-2013) have relied on weapons smuggled from neighboring states including Iraq, Lebanon, and Turkey (source).

Recently, Syrian rebels (under EU embargo until mid-2013) have relied on weapons smuggled from neighboring states including Iraq, Lebanon, and Turkey (source).  Image from commons.wikimedia.org.

Why do arms embargoes fail? Despite their frequent use by international organizations like the United Nations and the European Union, arms embargoes suffer from a poor record of success. For half a century now, multilateral arms embargoes have been the primary tool used to fight the proliferation of small arms and light weapons (SALW) to conflict zones and perpetrators of mass violence. These agreements between countries prohibit the sale of weapons to a particular target country (or sometimes a target organization). However, official reviews and academic studies alike tend to conclude that small arms are still making their way to embargoed actors.

Black markets are often cited as a source of this failure. Still, no large-n studies have presented evidence of increased black market activity in the presence of embargoes. To remedy this, I look for evidence of black market activity in records of legal arms trades. The data reveal that arms embargoes are associated with a substantial increase in the value of arms imports into nearby states. Given previous research on the nature of black market arms trade, this seems likely to result from an incentive for neighboring states to import more weapons that will then be transferred illegally to the embargoed state.

Black market arms transfers are difficult to study. Most of what we know about illicit arms transfers comes from those cases where somebody has made a mistake and the illicit activity has been uncovered. Apart from those few select cases, reliable data on actual illegal arms transfers is unavailable. Nonetheless, the illicit arms trade is big business, measuring roughly one billion USD per year.

Embargoed states and their neighbors.

Embargoed states and their neighbors. Embargoes based on data from Erickson (2013), Journal of Peace Research.

Black markets are of particular concern in situations where the legal supply of weapons is low but the demand is high. These circumstances often apply to criminal organizations, rebel groups, and embargoed states. While these illicit trades are difficult to collect data on systematically, most of the weapons involved begin as legally-traded arms. They are traded legally and then diverted from their authorized recipients. Arms embargoes provide an interesting case for the study of illicit arms. Those countries that border embargoed states can take advantage of their shared border to traffic illegal arms to the embargoed neighbor without fear of discovery by a third party. Therefore, if embargoed countries circumvent those embargoes by purchasing arms illicitly, we should expect to see an increase in the arms imported to their neighbors.

I have used data on multilateral arms embargos and legal arms transfers to test this proposition. Statistical models reveal that arms embargoes are indeed associated with greater levels of weapons imports in nearby countries. In fact, the predicted increase is substantial: those countries that border embargoed countries are estimated to import 38% more arms than they would have had they not been neighboring an embargoed country (measured in value, constant 2000 USD). This can translate into hundreds of thousands or even millions of dollars worth of additional weapons. Furthermore, this result takes into account both domestic and international conflict as well as other predictors of arms imports like the overall level of arms imports to the region, government type, and GDP per capita. On the other hand, this analysis indicates that arms embargoes are indeed effective at stemming the flow of legal arms into embargoed countries. Countries targeted by an embargo are predicted to import, on average, 63% fewer arms than they would otherwise.

Predicted levels of arms imports for a hypothetical median state bordering an embargoed state and not bordering an embargoed state.  Fixed effect uncertainty included.  Based on 100,000 simulations.

Predicted levels of arms imports for a hypothetical median state bordering an embargoed state and not bordering an embargoed state. Fixed effect uncertainty included. Based on 100,000 simulations.

Arms embargoes appear to effectively decrease the legal, or recorded, sale of arms to target states. However, this effect is accompanied by a significant increase in the level of arms imported to the surrounding region. Absent other possible explanations, it seems likely that many of these arms are destined for the embargoed country. Effective arms control measures must account for the regional conditions that may undermine nonproliferation efforts.


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