What Really Happened to Nigeria’s Economy?

You may have heard the news that the size Nigeria’s economy now stands at nearly $500 billion. Taken at face value (as many commenters have seemed all to happy to do) this means that the West African state “overtook” South Africa’s economy, which was roughly $384 billion in 2012. Nigeria’s reported GDP for that year was $262 billion, meaning it roughly doubled in a year.

How did this “growth” happen? As Bloomberg reported:

On paper, the size of the economy expanded by more than three-quarters to an estimated 80 trillion naira ($488 billion) for 2013, Yemi Kale, head of the National Bureau of Statistics, said at a news conference yesterday to release the data in the capital, Abuja….

The NBS recalculated the value of GDP based on production patterns in 2010, increasing the number of industries it measures to 46 from 33 and giving greater weighting to sectors such as telecommunications and financial services.

The actual change appears to be due almost entirely to Nigeria including figures in GDP calculation that had been excluded previously. There is nothing wrong with this, per se, but it makes comparisons completely unrealistic. This would be like measuring your height in bare feet for years, then doing it while wearing platform shoes. Your reported height would look quite different, without any real growth taking place. Similar complications arise when comparing Nigeria’s new figures to other countries’, when the others have not changed their methodology.

Nigeria’s recalculation adds another layer of complexity to the problems plaguing African development statistics. Lack of transparency (not to mention accuracy) in reporting economic activity makes decisions about foreign aid and favorable loans more difficult. For more information on these problems, see this post discussing Morten Jerven’s book Poor NumbersIf you would like to know more about GDP and other economic summaries, and how they shape our world, I would recommend Macroeconomic Patterns and Stories (somewhat technical), The Leading Indicators, and GDP: A Brief but Affectionate History.

“The Impact of Leadership Removal on Mexican Drug Trafficking Organizations”

That’s the title of a new article, now online at the Journal of Quantitative Criminology. Thanks to fellow grad students Cassy Dorff and Shahryar Minhas for their feedback. Thanks also to mentors at the University of Houston (Jim Granato, Ryan Kennedy) and Duke University (Michael D. Ward, Scott de Marchi, Guillermo Trejo) for thoughtful comments. The anonymous reviewers at JQC and elsewhere were also a big help.

Here is the abstract:

Objectives

Has the Mexican government’s policy of removing drug-trafficking organization (DTO) leaders reduced or increased violence? In the first 4 years of the Calderón administration, over 34,000 drug-related murders were committed. In response, the Mexican government captured or killed 25 DTO leaders. This study analyzes changes in violence (drug-related murders) that followed those leadership removals.

Methods

The analysis consists of cross-sectional time-series negative binomial modeling of 49 months of murder counts in 32 Mexican states (including the federal district).

Results

Leadership removals are generally followed by increases in drug-related murders. A DTO’s home state experiences more subsequent violence than the state where the leader was removed. Killing leaders is associated with more violence than capturing them. However, removing leaders for whom a $30m peso bounty was offered is associated with a smaller increase than other removals.

Conclusion

DTO leadership removals in Mexico were associated with an estimated 415 additional deaths during the first 4 years of the Calderón administration. Reforming Mexican law enforcement and improving career prospects for young men are more promising counter-narcotics strategies. Further research is needed to analyze how the rank of leaders mediates the effect of their removal.

I didn’t shell out $3,000 for open access, so the article is behind a paywall. If you’d like a draft of the manuscript just email me.

Mexico Update Following Joaquin Guzmán’s Capture

As you probably know by now, the Sinaloa cartel’s leader Joaquin Guzmán was captured in Mexico last Saturday. How will violence in Mexico shift following Guzman’s removal?

(Alfredo Estrella/AFP/Getty Images)

(Alfredo Estrella/AFP/Getty Images)

I take up this question in an article forthcoming in the Journal of Quantitative Criminology. According to that research (which used negative binomial modeling on a cross-sectional time series of Mexican states from 2006 to 2010), DTO leadership removals in Mexico are generally followed by increased violence. However, capturing leaders is associated with less violence than killing them. The removal of leaders for whom a 30 million peso bounty (the highest in my dataset, which generally identified high-level leaders) been offered is also associated with less violence. The reward for Guzmán’s capture was higher than any other contemporary DTO leader: 87 million pesos. Given that Guzmán was a top-level leader and was arrested rather than killed, I would not expect a significant uptick in violence (in the next 6 months) due to his removal. This follows President Pena Nieto’s goal of reducing DTO violence.

My paper was in progress for a while, so the data is a few years old. Fortunately Brian Phillips has also taken up this question using additional data and similar methods, and his results largely corroborate mine:

Many governments kill or capture leaders of violent groups, but research on consequences of this strategy shows mixed results. Additionally, most studies have focused on political groups such as terrorists, ignoring criminal organizations – even though they can represent serious threats to security. This paper presents an argument for how criminal groups differ from political groups, and uses the framework to explain how decapitation should affect criminal groups in particular. Decapitation should weaken organizations, producing a short-term decrease in violence in the target’s territory. However, as groups fragment and newer groups emerge to address market demands, violence is likely to increase in the longer term. Hypotheses are tested with original data on Mexican drug-trafficking organizations (DTOs), 2006-2012, and results generally support the argument. The kingpin strategy is associated with a reduction of violence in the short term, but an increase in violence in the longer term. The reduction in violence is only associated with leaders arrested, not those killed.

A draft of the full paper is here.

Visualizing the Indian Buffet Process with Shiny

(This is a somewhat more technical post than usual. If you just want the gist, skip to the visualization.)

N customers enter an Indian buffet restaurant, one after another. It has a seemingly endless array of dishes. The first customer fills her plate with a Poisson(α) number of dishes. Each successive customer i tastes the previously sampled dishes in proportion to their popularity (the number of previous customers who have sampled the kth dish, m_k, divided by i). The ith customer then samples a Poisson(α) number of new dishes.

That’s the basic idea behind the Indian Buffet Process (IBP). On Monday Eli Bingham and I gave a presentation on the IBP in our machine learning seminar at Duke, taught by Katherine Heller. The IBP is used in Bayesian non-parametrics to put a prior on (exchangeability classes of) binary matrices. The matrices usually represent the presence of features (“dishes” above, or the columns of the matrix) in objects (“customers,” or the rows of the matrix). The culinary metaphor is used by analogy to the Chinese Restaurant Process.

Although the visualizations in the main paper summarizing the IBP are good, I thought it would be helpful to have an interactive visualization where you could change α and N to see how what a random matrix with those parameters looks like. For this I used Shiny, although it would also be fun to do in d3.

One realization of the IBP, with α=10.

One realization of the IBP, with α=10.

In the example above, the first customer (top row) sampled seven dishes. The second customer sampled four of those seven dishes, and then four more dishes that the first customer did not try. The process continues for all 10 customers. (Note that this matrix is not sorted into its left-ordered-form. It also sometimes gives an error if α << N, but I wanted users to be able to choose arbitrary values of N so I have not changed this yet.) You can play with the visualization yourself here.

Interactive online visualizations like this can be a helpful teaching tool, and the process of making them can also improve your own understanding of the process. If you would like to make another visualization of the IBP (or another machine learning tool that lends itself to graphical representation) I would be happy to share it here. I plan to add the Chinese restaurant process and a Dirichlet process mixture of Gaussians soon. You can find more about creating Shiny apps here.

Political Forecasting and the Use of Baseline Rates

As Joe Blitzstein likes to say, “Thinking conditionally is a condition for thinking.” Humans are not naturally good at this skill. Consider the following example: Kelly is interested in books and keeping things organized. She loves telling stories and attending book clubs. Is it more likely that Kelly is a bestselling novelist or an accountant?

Many of the “facts” about Kelly in that story might lead you to answer that she is a novelist. Only one–her sense of organization–might have pointed you toward an accountant. But think about the overall probability of each career. Very few bookworms become successful novelists, and there are many more accountants than (successful) authors in the modern workforce. Conditioning on the baseline rate helps make a more accurate decision.

I make a similar point–this time applied to political forecasting–in a recent blog post for the blog of Mike Ward’s lab (of which I am a member):

One piece of advice that Good Judgment forecasters are often reminded of is to use the baseline rate of an event as a starting point for their forecast. For example, insurgencies are a very rare event on the whole. For the period January, 2001 to August, 2013, insurgencies occurred in less than 10 percent of country-months in the ICEWS data set.

From this baseline, we can then incorporate information about the specific countries at hand and their recent history… Mozambique has not experienced an insurgency for the entire period of the ICEWS dataset. On the other hand, Chad had an insurgency that ended in December, 2003, and another that extended from November, 2005, to April, 2010. For the duration of the ICEWS data set, Chad has experienced an insurgency 59 percent of the time. This suggests that our predicted probability of insurgency in Chad should be higher than for Mozambique.

I started writing that post before rebels in Mozambique broke their treaty with the government. Maybe I spoke too soon, but the larger point is that baselines are the starting point–not the final product–of any successful forecast.

Having more data is useful, as long as it contributes more signal than noise. That’s what ICEWS aims to do, and I consider it a useful addition to the toolbox of forecasters participating in the Good Judgment Project. For more on this collaboration, as well as a map of insurgency rates around the globe as measured by ICEWS, see the aforementioned post here.

Visualizing Political Unrest in Egypt, Syria, and Turkey

The lab of Michael D. Ward et al now has a blog. The inaugural post describes some of the lab’s ongoing projects that may come up in future entries including modeling of protests, insurgencies, and rebellions, event prediction (such as IED explosions), and machine learning techniques.

The second post compares two event data sets–GDELT and ICEWS–using recent political unrest in the Middle East as a focal point (more here):

We looked at protest events in Egypt and Turkey in 2011 and 2012 for both data sets, and we also looked at fighting in Syria over the same period…. What did we learn from these, limited comparisons?  First, we found out first hand what the GDELT community has been saying: the GDELT data are in BETA and currently have a lot of false positives. This is not optimal for a decision making aid such as ICEWS, in which drill-down to the specific events resulting in new predictions is a requirement. Second, no one has a good ground truth for event data — though we have some ideas on this and are working on a study to implement them. Third, geolocation is a boon. GDELT seems especially good a this, even with a lot of false positives.

The visualization, which I worked on as part of the lab, can be found here.  It relies on CartoDB to serve data from GDELT and ICEWS, with some preprocessing done using MySQL and R. The front-end is Javascript using a combination of d3 for timelines and Torque for maps.

gdelt-icews-static

GDELT (green) and ICEWS (blue) records of protests in Egypt and Turkey and conflict in Syria

If you have questions about the visualizations or the technology behind them, feel free to mention them here or on the lab blog.

Visualizing the BART Labor Dispute

Labor disputes are complicated, and the BART situation is no different. Negotiations resumed this week after the cooling off period called for by the governor of California as a result of the July strikes.

To help get up to speed, check out the data visualizations made by the Bay Area d3 User Group in conjunction with the UC Berkeley VUDLab.  They have a round up of news articles, open data, and open source code, as well as links to all the authors’ Twitter profiles.

The infographics address several key questions relevant to the debate, including how much BART employees earn, who rides BART and where, and the cost of living for BART employees.

bart-salary

bart-ridership

More here.

Organized Crime Roundup

I have been arguing for years that organized crime has an inherently political component. Certainly I am not alone, and researchers far superior to me have made the same point–for example, Charles Tilly and James Buchanan. However, mainstream political reporting seems to have been catching onto this over the past few months. I have rounded up a few of these posts that will be of interest to long-time readers. See also my working paper on violence following targeted leadership removals in Mexico.

Are Mexican Drug Lords the Next ‘Terrorist Targets’?” by Douglas Lucas. Lucas accurately describes the framing of drug lords as terrorists to be a form of “mission creep.”

Peter Andreas responds to Moisés Naim’s essay in “Measuring the Mafia-State Menace.” I was not aware of Andreas’s work until Daniel Solomon recently shared it on Twitter but now I have several of his books (including this one) on my reading list.

Although somewhat sensationalized, Christian Caryl also has a nice overview piece on global organized crime at Foreign Policy: “Mob Rule.” Some of the statistics there seem questionable but the overall point–that students of politics should pay attention to organized crime–is a valid and important one.

Finally, World Politics Review features an interview with Brian Phillips, who argues that targeting DTO leaders in Mexico has not reduced violence. This matches my own research on the topic.

This is Your Brain on Hunger

hungerIn his book Thinking Fast and SlowDaniel Kahneman describes the brain as made up of two systems. System 1 is fast, emotional, and almost automatic–I think of this as “intuition.” System 2 controls more logical, deliberate processes. There are many factors that can influence which system you use to make a decision (anchoring, availability, substitution, loss aversion, framing, etc.) and Kahneman’s book discusses these. But other environmental factors can influence which system takes over. This post discusses how hunger shifts the balance from System 2 to System 1.

First up is a study on Israeli judges’ parole decisions broken up by time of day by Shai DanzigeraJonathan Levavb, and Liora Avnaim-Pessoa (edited for PNAS  by Kahneman). Here’s the abstract:

Are judicial rulings based solely on laws and facts? Legal formalism holds that judges apply legal reasons to the facts of a case in a rational, mechanical, and deliberative manner. In contrast, legal realists argue that the rational application of legal reasons does not sufficiently explain the decisions of judges and that psychological, political, and social factors influence judicial rulings. We test the common caricature of realism that justice is “what the judge ate for breakfast” in sequential parole decisions made by experienced judges. We record the judges’ two daily food breaks, which result in segmenting the deliberations of the day into three distinct “decision sessions.” We find that the percentage of favorable rulings drops gradually from ≈65% to nearly zero within each decision session and returns abruptly to ≈65% after a break. Our findings suggest that judicial rulings can be swayed by extraneous variables that should have no bearing on legal decisions.

The Economist summarized the paper and produced a graphic with the main takeaway:

israeli-parole-board

The second paper, by Ilona Grunwald-Kadow and coauthors, analyzes the neural behavior of fruit flies when deprived of food (via @tylercowen and @IFLscience). Their results are explained in this press release from the Max Planck Institute:

The results show that the innate flight response to carbon dioxide in fruit flies is controlled by two parallel neural circuits, depending on how satiated the animals are. “If the fly is hungry, it will no longer rely on the ‘direct line’ but will use brain centres to gauge internal and external signals and reach a balanced decision,” explains Grunwald-Kadow. “It is fascinating to see the extent to which metabolic processes and hunger affect the processing systems in the brain,” she adds.

Remember this next time you’re trying to decide between working through lunch or grabbing a bite to eat. Do your body and your neighbors a favor by taking a break.

See also: 

Currency and Conflict

According to Lebanon’s Daily Star:

Traders across Syria reported widely fluctuating rates and two currency dealers in Damascus, where the pound appeared to be hit hardest, said it fell below 200 to the dollar for the first time in what one described as panic buying of the U.S. currency.

On Monday evening the pound traded at 205 to the dollar, down 20 percent in four days and 77 percent down since the start of the anti-Assad uprising in March 2011 when it was at 47.

The idea of examining currency prices over the course of a conflict is interesting. There are a number of confounders of course. For instance, the regime can often intervene in certain ways to affect the value of currency. Other incidents besides the conflict itself can also drive currency fluctuations, especially when the conflict is relatively minor.

One nice case (from strictly a research perspective) is the US Civil War, when both the Union and Confederacy issued their own notes. Jeffrey Arnold‘s project, “Pricing the Costly Lottery: Financial Market Reactions to Battlefield Events in the American Civil War,” leverages this fact to see how markets responded to successes and failures of either side. We discussed this project before when it was presented as a poster at PolMeth 2012, and Jeffrey’s website now has his MPSA 2013 slides.

Here’s his abstract, and one of my favorite graphs:

What role does combat play in resolving the disagreement that initiated war? Bargaining theories of war propose two mechanisms, the destruction of capabilities and the revelation of private information. These mechanisms are difficult to analyze quantitatively because the mechanisms are observationally equivalent, the participants’ expectations are unobservable, and there is a lack of data on battles. With new methods and new data on the American Civil War, I address these challenges. I estimate the information revealed by combat with a model of Bayesian learning. I use prices of Union and Cnnnonfederate currencies to measure public expectations of war duration and outcome. Data on battlefield events come from detailed data on the outcomes and casualties of the battles of the American Civil War. The results suggest that battle outcomes rather than casualties or information revelation had the largest influence on the expected duration of the American Civil War.

confederate-union-prices