Watercolor regression

I'm in a rush, so I will explain this better later. But Andrew Gelman posted my idea for a type of visually-weighted regression that I jokingly called "watercolor regression" without a picture, so its a little tough to see what I was talking about. Here is the same email but with the pictures to go along with it. The code to do you own watercolor regression is here as the 'SMOOTH' option to vwregress. (Update: I've added a separate cleaner function watercolor_reg.m for folks who want to tinker with the code but don't want to wade through all the other options built into vwregress. Update 2: I've added watercolor regression as a second example in the revised paper here.)

This was the email with figures included:

Two small follow-ups based on the discussion (the second/bigger one is to address your comment about the 95% CI edges).

1. I realized that if we plot the confidence intervals as a solid color that fades (eg. using the "fixed ink" scheme from before) we can make sure the regression line also has heightened visual weight where confidence is high by plotting the line white. This makes the contrast (and thus visual weight) between the regression line and the CI highest when the CI is narrow and dark. As the CI fade near the edges, so does the contrast with the regression line. This is a small adjustment, but I like it because it is so simple and it makes the graph much nicer. 

My posted code has been updated to do this automatically.

2. You and your readers didn't like that the edges of the filled CI were so sharp and arbitrary. But I didn't like that the contrast between the spaghetti lines and the background had so much visual weight.  So to meet in the middle, I smoothed the spaghetti plot to get a nonparametric estimate of the probability that the conditional mean is at a given value:

To do this, after generating the spaghetti through bootstrapping, I estimate a kernel density of the spaghetti in the Y dimension for each value of X.  I set the visual-weighting scheme so it still "preserves ink" along a vertical line-integral, so the distribution dims where it widens since the ink is being "stretched out". To me, it kind of looks like a watercolor painting -- maybe we should call it a "watercolor regression" or something like that.

The watercolor regression turned out to be more of a coding challenge than I expected, because the bandwidth for the kernel smoothing has to adjust to the width of the CI. And since several people seem to like R better than Matlab, I attached 2 figs to show them how I did this. Once you have the bootstrapped spaghetti plot:

I defined a new coordinate system that spanned the range of bootstrapped estimates for each value in X 

The kernel smoothing is then executed along the vertical columns of this new coordinate system.

I've updated the code posted online to include this new option. This Matlab code will generate a similar plot using my vwregress function:

x = randn(100,1);

e = randn(100,1);

y = 2*x+x.^2+4*e;

bins = 200;

color = [.5 0 0];

resamples = 500;

bw = 0.8;

vwregress(x, y, bins, bw, resamples, color, 'SMOOTH');

NOTE TO R USERS: The day after my email to andrew, Felix Schönbrodt posted a nice similar variant with code in R here.

Update: For overlaid regressions, I still prefer the simpler visually-weighted line (last two figs here) since this is what overlaid watercolor regressions look like:

It might look better if the scheme made the blue overlay fade from blue-to-clear rather than blue-to-white, but then it would be mixing (in the color sense) with the red so the overlaid region would then start looking like very dark purple. If someone wants to code that up, I'd like to see it. But I'm predicting it won't look so nice.

High temperatures cause violent crime and implications for climate change

I've posted about high temperature inducing individuals to exhibit more violent behavior when driving,  playing baseball and prowling bars.  These cases are neat anecdotes that let us see the "pure aggression" response in lab-like conditions. But they don't affect most of us too much. But violent crime in the real world affects everyone. Earlier, I posted a paper by Jacob et al. that looked at assault in the USA for about a decade - they found that higher temperatures lead to more assault and that the rise in violent crimes rose more quickly than the analogous rise in non-violent property-crime, an indicator that there is a "pure aggression" component to the rise in violent crime.

A new working paper "Crime, Weather, and Climate Change" by recent Harvard grad Matthew Ranson puts together an impressive data set of all types of crime in USA counties for 50 years. The results tell the aggression story using street-level data very clearly:

Note that all crime increases as temperatures rise from 0 F to about 50 F.  It seems reasonable to hypothesize that a lot of this pattern comes from "logistical constraints", eg. it's hard to steal a car when it's covered in snow. But above 60 F, only the violent crimes continue to go up: murder, rape, and assault.  The comparison between murder and manslaughter is elegantly telling, as manslaughter should be less motivated by malicious intent.

Ranson goes on to make projections about the expected effect of climate change:

Between 2010 and 2099, climate change will cause an additional 30,000 murders, 200,000 cases of rape, 1.4 million aggravated assaults, 2.2 million simple assaults, 400,000 robberies, 3.2 million burglaries, 3.0 million cases of larceny, and 1.3 million cases of vehicle theft in the United States.

This is pretty serious stuff. Ranson also shows that these effects haven't changed much over time, so the prospects for adaptation may be low. And there's no reason to believe that this relationship, which is probably neuro-physiological, doesn't hold outside of the USA.

Nonlinearities and exposure to extreme heat: what do we know?

There's been lots of talk about Hanson's work attributing extremes weather events to climate change. For a summary of some of our email discussions about the [ir]relevance of extremes and nonlinearities in measuring climate impacts, check out Marshall Burke's post in the G-FEED blog.

Two percent per degree Celsius

That's the magic number for how worker productivity responds to warm/hot temperatures.

In my 2010 PNAS paper, I found that labor-intensive sectors of national economies decreased output by roughly 2.4% per degree C and argued that this looked suspiously like it came from reductions in worker output. Using a totally different method and dataset, Matt Neidell and Josh Graff Zivin found that labor supply in micro data fell by 1.8% per degree C.  Both responses kicked in at around 26C.

Chris Sheehan just sent me this NYT article on air conditioning, where they mention this neat natural experiment:

[I]n the past year, [Japan] became an unwitting laboratory to study even more extreme air-conditioning abstinence, and the results have not been encouraging. After the Fukushima earthquake and tsunami knocked out a big chunk of the country’s nuclear power, the Japanese government mandated vastly reduced energy consumption. To that end, lights have been dimmed and air-conditioners turned down or off, so that offices comply with the government-prescribed indoor summer temperature of 82.4 degrees (28 Celsius); some offices have tried as high as 86. 

Unfortunately, studies by Shin-ichi Tanabe, a professor of architecture at Waseda University in Tokyo who has long been interested in “thermal comfort,” found that while workers tolerated dimmer light just fine, every degree rise in temperature above 25 Celsius (77 degrees Fahrenheit) resulted in a 2 percent drop in productivity. Over the course of the day that meant they accomplished 30 minutes less work, he said.

I have said before that empirical social science should strive to replicate results and obtain similar parameters. I think we are getting there on this one.

And in case anyone is [still] listening, I [still] think that persistently reduced labor productivity may be one of the largest economic impacts of anthropogenic climate change.

I couldn't locate the Tanabe study (it sounds like it might be in Japanese), but his lab looks really cool (pun intended, but also true): they focus almost exclusively on thermal comfort and productivity. Instead of the Fukushima study, Tanabe sent me this one, which is also relevant and contains the magic number:

Visually-weighted confidence intervals

Following up on my earlier post describing visually weighted regression (paper here) and some suggestions from Andrew Gelman and others, I adjusted my Matlab function (vwregress.m, posted here) and just thought I'd visually document some of the options I've tried.

All of this information is available if you type help vwregress once the program is installed, but I think looking at the pictures helps.

The basic visually weighted regression is just a conditional mean where the visual weight of the line reflects uncertainty.  Personally, I like the simple non-parametric plot overlaid with the OLS regression since its clean and helps us see whether a linear approximation is a reasonable fit or not:

vwregress(x, y, 300, .5,'OLS',[0 0 1])

 

Confidence intervals (CI) can be added, and visually-weighted according to the same scheme as the conditional mean:

vwregress(x, y, 300, .5, 200,[0 0 1]);

Since the CI band is bootstrapped, Gelman suggested that we overlay the spaghetti plot of resampled estimates, I added the option 'SPAG' to do this. If the spaghetti are plotted using a solid color (option 'SOLID'), this ends up looking quasi-visually-weighted:

vwregress(x, y, 300, .5,'SPAG','SOLID',200,[0 0 1]);

But since it gets kind of nasty looking near the edges, where the estimates go little haywire since the observations get thin, we can visually weight the spaghetti too to keep it from getting distracting (just omit the 'SOLID' option).

vwregress(x, y, 300, .5,'SPAG',200,[0 0 1]);

Gelman also suggested that we try approximating the spaghetti by smoothly filling in the CI band, using the original visual-weighting scheme. To do this, I added the 'FILL' option. I like the result quite a bit (even more than the spaghetti, but others may disagree). [Warning: this plotting combination may be very slow, especially with a lot of resamples.]

vwregress(x, y, 300, .5,'FILL',200,[0 0 1]);

If 'SOLID' is combined with 'FILL', only the conditional mean is plotted with solid coloring. (This differs from 'SPAG' and the simple CI bounds).

vwregress(x, y, 300, .5,'FILL','SOLID',200,[0 0 1]);

Finally, I included the 'CI' option which changes the visual-weighting scheme from using weights 1/sqrt(N) to using 1/(CI_max - CI_min), where CI_max is the upper limit (point-wise) of the CI and CI_min is the lower limit. 

I like this because if we combine this with 'FILL', then the confidence band "conserves ink" (which we equate with confidence) in the y-dimension. Imagine that we squirt out ink uniformly to draw the conditional mean and then smear the ink vertically so that it stretches from the lower confidence bound to the upper confidence bound.  In places where the CI band is narrow, this will cause very little spreading of the ink so the CI band will be dark. But in places where the CI band is wide, the ink is smeared a lot so it gets lighter. For any vertical sliver of the CI band (think dx) the amount of ink displayed (integrated along a vertical line) will be constant.

vwregress(x, y, 300, .5,'CI','FILL',200,[0 0 1]);

For Stata users, I have written vwlowess.ado (here), but unfortunately it does not yet have any of these options.

Lucas Leeman has implemented some of these ideas in R (see here), so maybe he'll make that code available.

All of the above plots were made with the random data:

x = randn(200,1); 

e = randn(200,1).*(1+abs(x)).^1.5; 

y = 2*x+x.^2+4*e;

Declining public interest in the drought

David Lobell mentioned that there seemed to be less news coverage of the drought, so I checked Google Trends and David was right. Looking just the USA, interest in the drought peaked about a week ago:

(news report volume looks similar, but Google doesn't give me the raw data). Is interest/news falling because the nation's corn crop has recovered? Probably not.  But a week ago, something else took over the airwaves and peoples' attention:

Is this spurious? It's possible, but this general pattern is well documented. In a 2007 article, David Strömberg linked the quantity of US disaster relief (a proxy for public interest) to "whether the disaster occurs at the same time as other newsworthy events, such as the Olympic Games, which are obviously unrelated to need."  He concludes "that the only plausible explanation of this is that relief decisions are driven by news coverage of disasters and that the other newsworthy material crowds out this news coverage." So it isn't crazy to think that the London Games might soak up some of the public interest that would otherwise go towards our own drought.

In a closely related 2011 paper, Matthew Kahn and Matthew Kotchen showed that "an increase in a state's unemployment rate decreases Google searches for "global warming" and increases searches for "unemployment."" 

Yet, while it seems unlucky for folks in the midwest to get hit by this drought during the Olympics, they are "lucky enough" to get hit just before the presidential race. In their 2007 paper, Thomas Garrett and Russell Sobel "find that presidential and congressional influences affect the rate of disaster declaration and the allocation of FEMA disaster expenditures across states. States politically important to the president have a higher rate of disaster declaration by the president... Election year impacts are also found. Our models predict that nearly half of all disaster relief is motivated politically rather than by need. The findings reject a purely altruistic model of FEMA assistance and question the relative effectiveness of government versus private disaster relief."

(cross posted on G-FEED)

Visually-Weighted Regression

[This is the overdue earth-shattering sequel to this earlier post.]

I recently posted this working paper online. It's very short, so you should probably just read it (I was actually originally going to write it as a blog post), but I'll run through the basic idea here.  

Since I'm proposing a method, I've written functions in Matlab (vwregress.m) and Stata (vwlowess.ado) to accompany the paper. You can download them here, but I expect that other folks can do a much better job implementing this idea.

Visually-Weighted Regression

Solomon M. Hsiang

Abstract: Uncertainty in regression can be efficiently and effectively communicated using the visual properties of regression lines.  Altering the "visual weight" of lines to depict the quality of information represented clearly communicates statistical confidence even when readers are unfamiliar or reckless with the formal and abstract definitions of statical uncertainty. Here, we present an example by decreasing the color-saturation of nonparametric regression lines when the variance of estimates increases. The result is a simple, visually intuitive and graphically compact display of statistical uncertainty. This approach is generalizable to almost all forms of regression.

Here's the issue. Statistical uncertainty seems to be important for two different reasons. (1) If you have to make a decision based on data, you want to have a strong understanding of the possible outcomes that might result from your decision, which itself rests on how we interpret the data.  This is the "standard" logic, I think, and it requires a precise, quantitative estimate of uncertainty.  (2) Because there is noise in data, and because sampling is uneven across independent variables, a lot of data analysis techniques generate artifacts that we should mostly just ignore.  We are often unnecessarily focused/intrigued by the wackier results that shows up in analyses, but thinking carefully about statistical uncertainty reminds us to not focus too much on these features. Except when it doesn't.

"Visually-weighted regression" is a method for presenting regression results that tries to address issue (2), taking a normal person's psychological response to graphical displays into account. I had grown a little tired of talks and referee reports where people speculate about the cause of some strange non-linearity at the edge of a regression sample, where there was no reason to believe the non-linear structure was real.  I think this and related behaviors emerge because (i) there seems to be an intellectual predisposition to thinking that "nonlinearity" is inherently more interesting that "linearity" and (ii) the traditional method for presenting uncertainty subconsciously focuses viewers attention on features of the data that are less reliable. I can't solve issue (i) with data visualization, but we can try to fix (ii). 

The goal of visually-weighted regression is to take advantage of viewer's psychological response to images in order to focus their attention on the results that are the most informative.  "Visual weight" is a concept from art and graphical design that is used to to direct a viewer's focus within an image.  Large, dark,  high-contrast, and complex structures tend to "grab" a viewer's attention.  Our brains are constantly looking for visual information and, somewhere along the evolutionary line, detailed/high-contrast structures in our field of view were probably more informative and more useful for survival, so we are programmed to give them more of our attention.  Unfortunately, the traditional approaches to displaying statistical uncertainty give more visual weight to the uncertain portions of the analysis, which is exactly backwards of what we want. Ideally, a viewer will focus more of their attention on the portions of analysis that have some statical confidence and they will mostly ignore the portions of analysis that are so uncertain that they contain little or no information.

[continued below the fold]

G-FEED blog is live!

David Lobell, Michael Roberts, Wolfram Schlenker, Jarrod Welch and I have started a blog on Global Food, Environment and Economic Dynamics (G-FEED).  It's a compliment to Fight Entropy (not a substitute) and will focus primarily on food production around the world and its relationship to the environment and economics.

check it out: www.G-FEED.com

Temperature and infrastructure

Once while presenting this paper on temperature's influence on economic performance, someone in the audience asked whether any of the observed declines in output could be due to stress on infrastructure. I honestly replied that I didn't know, but that it seemed like a possibility.  If high temperatures began to interfere with the structure or integrity of steel, concrete or other materials used in infrastructure, existing systems might begin to slow down or fail.

Apparently, this is mechanisms is beginning to become an issue. One of today's cover stories in the New York Times described various infrastructure failures that are emerging around the country as effects of the persistent and extreme heat. Some highlights:

On a single day this month here, a US Airways regional jet became stuck in asphalt that had softened in 100-degree temperatures, and a subway train derailed after the heat stretched the track so far that it kinked — inserting a sharp angle into a stretch that was supposed to be straight. In East Texas, heat and drought have had a startling effect on the clay-rich soils under highways, which “just shrink like crazy,” leading to “horrendous cracking....” 

Excessive warmth and dryness are threatening other parts of the grid as well. In the Chicago area, a twin-unit nuclear plant had to get special permission to keep operating this month because the pond it uses for cooling water rose to 102 degrees; its license to operate allows it to go only to 100....

When railroads install tracks in cold weather, they heat the metal to a “neutral” temperature so it reaches a moderate length, and will withstand the shrinkage and growth typical for that climate. But if the heat historically seen in the South becomes normal farther north, the rails will be too long for that weather, and will have an increased tendency to kink. 

I don't know of any work on the economic or social impact of these types of failures. And I similarly don't know of any theory explaining how we ought to alter our patterns of infrastructure investment, based on the realization that this will continue into the future. The NYT article describes a few ad hoc adaptive measures that companies are starting to adopt, but since the lifetime of new infrastructure will extend into 2040 (or longer), we would do well to plan. This seems like an area ripe for research.

Weekend links

1) Anesthesiologist Fabricates 172 Papers (via Michael Clemens)

2) This much I know: Daniel Kahneman (via MR)

3) Tatyana Deryugina's new paper on local weather fluctuations and attitudes towards climate change (also via MR)

4) A Visual Git Reference

5) Version 3.0 of the Social Conflict in Africa Database (SCAD) is out (via Mona Khalidi)

6) Number sense across the lifespan as revealed by a massive Internet-based sample (PNAS, via Kyle Meng)

7) Development economics and my native borough (via Dave Pell)

8) Optimal TED Talk Structure

9) The IDV User Experience blog has some great (mostly spatial) data visualizations (via Reed Walker)


10) Graphing Every (Wikipedia) Idea in History

11) Convection over Manhattan this past Wednesday

Less is more

There are many things we can do to make our research clearer to readers: make our text well organized and accessible, make clear graphs, consider the psychology of our readers, and use as little math as is necessary to explain our point.  Clarity and elegance trumps formalism and detail.


Intimidated by Equations?
Barbara R. Jasny

Although there is general agreement on the value of a strong tie between theory and data, forging links between theoretical and empirical approaches (and practitioners) is not as straightforward as it should be. New evidence of this disconnect comes from the work of Fawcett and Higginson, who examined the use of mathematical equations in 649 papers dealing with ecology and evolution that were published in 1998. They gathered citation data, excluding instances of self-citation. An increase in the number of equations per page of main text corresponded to a lower rate of citations. Overall, each additional equation in the main text of a paper was associated with a 28% decrease in the citation rate. Burying the equations in an appendix had a salutary effect on citation rate. When the citing papers were divided into theoretical and nontheoretical on the basis of their use of the word "model" in the abstract or title, the authors observed that the negative effect was due to the nontheoretical papers not citing papers with equations. There are caveats to the conclusions—examinations over longer periods of time, analysis of the relative content of the papers, and examination of the effect for online rather than print publication are all warranted. Although the authors conclude that better math education for biologists is the best long-term solution, they suggest that more immediate strategies could include the addition of explanatory text between equations.

The full PNAS article is here.

h/t Marshall Burke

Does climate affect conflict? Evidence from Shakespeare

Mark Cane sends us this:

ROMEO and JULIET         ACT 3, SCENE 1a 

[A street. MERCUTIO, BENVOLIO & Servants] 

BENVOLIO
I pray thee, good Mercutio, let's retire.
The day is hot, the Capulets abroad,
And if we meet we shall not 'scape a brawl,
For now these hot days is the mad blood stirring.

[And later they do meet the Capulets and Tybalt kills Mercutio, then Romeo kills Tybalt and the rest is tragedy.]

For more fun evidence on the psychological effect of heat on aggression, see evidence from road rage and MLB.  Also this.

Using cell phones to track post-disaster population movements in Haiti

Predictability of population displacement after the 2010 Haiti earthquake

Xin Lu, Linus Bengtsson, and Petter Holme

Abstract: Most severe disasters cause large population movements. These movements make it difficult for relief organizations to efficiently reach people in need. Understanding and predicting the locations of affected people during disasters is key to effective humanitarian relief operations and to long-term societal reconstruction. We collaborated with the largest mobile phone operator in Haiti (Digicel) and analyzed the movements of 1.9 million mobile phone users during the period from 42 d before, to 341 d after the devastating Haiti earthquake of January 12, 2010. Nineteen days after the earthquake, population movements had caused the population of the capital Port-au-Prince to decrease by an estimated 23%. Both the travel distances and size of people’s movement trajectories grew after the earthquake. These findings, in combination with the disorder that was present after the disaster, suggest that people’s movements would have become less predictable. Instead, the predictability of people’s trajectories remained high and even increased slightly during the three-month period after the earthquake. Moreover, the destinations of people who left the capital during the first three weeks after the earthquake was highly correlated with their mobility patterns during normal times, and specifically with the locations in which people had significant social bonds. For the people who left Port-au-Prince, the duration of their stay outside the city, as well as the time for their return, all followed a skewed, fat-tailed distribution. The findings suggest that population movements during disasters may be significantly more predictable than previously thought.

h/t Kyle

Early images of earth from space

Everyone knows the famous images of earth rise taken from the moon, but I surprised to run across this earlier amazing 1955 image from space. They pieced it together from a bunch of snapshots automatically shot though a pinhole in the side of a rocket as it rotated at its apex and fell back to earth.

Click to enlarge and read description.

Also cool is this first TV image from the space.

For a sense of our progress in extraterrestrial photography, compare these with the incredibly high-res images from the Suomi satellite released earlier this year.

The “Soft Side” Approach to Countering Violent Extremism

This is a guest post by Daniel P. Aldrich, associate professor of public policy at Purdue University. Prof. Aldrich was an American Association for the Advancement of Science (AAAS) fellow at USAID during the 2011-2012 academic year, and a Fulbright research fellow at the University of Tokyo during the 2012-2013 academic year.  He can be reached at daniel.aldrich@gmail.com, and followed at @DanielPAldrich on Twitter.


Violent extremism and terrorism - involving suicide bombings, improvised explosive device and small arms attacks, narco-trafficking, and kidnapping - have taken center stage for many decision makers in the United States and abroad.  The Worldwide Incidents Tracking System (WITS) established by the National Counterterrorism Center has illuminated a rising trend in the number of armed attacks by terror groups over the past decade.  Scholars (using synthetic case control analysis from Spain) have estimated the high economic costs of terrorism, with a loss of 10% in per capita GDP for individuals in areas with high numbers of terrorist attacks (Abadie and Gardeazabal 2003). Policy makers around the world have prioritized their attempts to end, manage, or handle threats from violent extremist organizations (VEOs) such as al-Qaeda in the Islamic Maghreb (AQIM) in northwest Africa, Lashkar-e-Tayyiba in South Asia, and Abu Sayyaf in the Philippines.


Given the broad agreement that violent extremism is a serious issue, what are the best policy responses to violent extremist organizations?  U.S. policymakers have long favored the use of military force, drone strikes, and covert operations as tried-and-true approaches for dealing with extremist groups because they produce clear and immediate results.  These tactics bring with them unintended side effects.  Even the most advanced unmanned drones using the latest in surveillance and tracking technologies have generated civilian casualties and turned host nations partners against the United States.  Such “collateral damage” further drives many local residents to support anti-American groups and bolsters their claims of encirclement and anti-Muslim bias.  National governments and local civilian populations in Pakistan and Yemen provide two unfortunate examples of this phenomenon.
[continued after the break]

Weekend Links

1) On giving (bad) advice to kings (via bb)

2) The epidemiological transition as illustrated by cause of death (NEJM, also via bb)

3) The Cost of Friendship (NBER via, *cough cough*, Sol)

4) A popular press indictment of hurricane risk modeling

5) Estimating the welfare loss of natural disasters in developing countries using willingness to contribute labor (via Amir)

6) Visualizing an infection in real time (via Ram)

7) The New Yorker's top five TED talks (via Dave Pell)

8) Design and Analysis of a Text Mining Experiment (via Suresh)

9) Skill Premia in Commercialized Sex Markets

10) Michael Lewis on idiosyncratic human capital outcomes and public good provision (basically)

Neurological basis for altruism

I don't usually read Nature Neuroscience, but this is an interesting neuro-economics piece.

Dorsolateral and ventromedial prefrontal cortex orchestrate normative choice

Thomas Baumgartner, Daria Knoch, Philine Hotz, Christoph Eisenegger & Ernst Fehr

Abstract: Humans are noted for their capacity to over-ride self-interest in favor of normatively valued goals. We examined the neural circuitry that is causally involved in normative, fairness-related decisions by generating a temporarily diminished capacity for costly normative behavior, a 'deviant' case, through non-invasive brain stimulation (repetitive transcranial magnetic stimulation) and compared normal subjects' functional magnetic resonance imaging signals with those of the deviant subjects. When fairness and economic self-interest were in conflict, normal subjects (who make costly normative decisions at a much higher frequency) displayed significantly higher activity in, and connectivity between, the right dorsolateral prefrontal cortex (DLPFC) and the posterior ventromedial prefrontal cortex (pVMPFC). In contrast, when there was no conflict between fairness and economic self-interest, both types of subjects displayed identical neural patterns and behaved identically. These findings suggest that a parsimonious prefrontal network, the activation of right DLPFC and pVMPFC, and the connectivity between them, facilitates subjects' willingness to incur the cost of normative decisions.

(a) Overlay of the pVMPFC cluster that showed a larger change in connectivity after unfair offers (compared with fair offers) with the right DLPFC in the left compared with the right TMS group (yellow, at P < 0.005, cluster extent = 18 voxels42) and the pVMPFC cluster that showed differential activation in the contrast unfair > fair offers in the left compared with the right TMS group (red). Overlapping voxels are displayed in orange. (b) Bar plots based on the functional ROI (red) from a indicate that the differential context-dependent change in connectivity between the left and right TMS group was qualified by a differential change in connectivity during unfair offers (unfair connectivity), but not during fair offers (fair connectivity). The left TMS group therefore only showed an increased connectivity between the right DLPFC and pVMPFC at P < 0.01 during unfair offers, whereas the connectivity between these two brain regions did not change (relative to baseline connectivity) after fair offers. Moreover, after right TMS, the connectivity between right DLPFC and pVMPFC never deviated from the baseline (indicated by the two black bars); that is, these brain regions no longer communicated more after unfair offers. Bar plots depict mean ± s.e.m. [From Nature Neuroscience]

Climate and the [historical] slave trade

This is an interesting data set and a neat reduced-form result, although I think the mechanism is less clear than the authors suggest. I also like that the authors are drawing from a wider body of literature than is usual (and from their references, I can't but help get the creeping feeling that they might be reading FE).

Climate, ecosystem resilience and the slave trade

James Fenske and Namrata Kala

African societies exported more slaves in colder years. Lower temperatures reduced mortality and raised agricultural yields, lowering the cost of supplying slaves. Our results help explain African participation in the slave trade, which is associated with adverse outcomes today. We merge annual data on African temperatures with a panel of port-level slave exports to show that a typical port exported fewer slaves in a year when the local temperature was warmer than normal. This result is strongest where African ecosystems are least resilient to climate change, and is robust to several alternative specifications and robustness checks. We support our interpretation using evidence from the histories of Whydah, Benguela, and Mozambique.

h/t Ted Miguel

Weekend links

1) Ironspread: Scripting Excel using Python

2) Is Chagas Disease the "New HIV/AIDS of the Americas"? (PLoS Neglected Tropical Diseases)

3) Geography, land quality, and the origins of ethnolinguistic diversity (AER)

4) Per capita income correlates with tree density (via bb)

5) Defensive patent licensing pools

6) Humans may have only been partly responsible for the loss of wooly mammoths (Nature)

7) Dani Rodrik on the future of the nation state

8) Germany vs. the Euro area in 7 easy graphs (via cp)

9) "The safe release of genetically-modified [malaria-resistant mosquitos] into the wild will be an issue down the line..." (PNAS)

10) Creating the new development ecosystem (Science, via Hannah)

11) Daniel Aldrich's exhaustive lit review on the politics of natural disasters (via Amir)

12) An economic history of the Sinaloa Cartel (via Dave Pell)

Diarrhoea in Bangladesh: displaying results from fixed effects models

I ran into this 2008 paper doing hurricane work with Jesse. The results are not extremely surprising, but I really liked how they displayed their result.  Many of us use high-dimensional data and multiple regression models to try and account for the many different processes that occur in social data, but it is often difficult to clearly display the effect of just one process while also being clear about all the other controls in the model. I like the approach of this team: they show predictions form the complex model (eg. with week and month fixed effects, socioeconomic controls, etc.) overlaid with the real data.


Factors determining vulnerability to diarrhoea during and after severe floods in Bangladesh
Masahiro Hashizume, Yukiko Wagatsuma, Abu S. G. Faruque, Taiichi Hayashi, Paul R. Hunter, Ben Armstrong and David A. Sack

Abstract: This paper identifies groups vulnerable to the effect of flooding on hospital visits due to diarrhoea during and after a flood event in 1998 in Dhaka, Bangladesh. The number of observed cases of cholera and non-cholera diarrhoea per week was compared to expected normal numbers during the flood and post-flood periods, obtained as the season-specific average over the two preceding and subsequent years using Poisson generalised linear models. The expected number of diarrhoea cases was estimated in separate models for each category of potential modifying factors: sex, age, socio-economic status and hygiene and sanitation practices. During the flood, the number of cholera and non-cholera diarrhoea cases was almost six and two times higher than expected, respectively. In the post-flood period, the risk of non-cholera diarrhoea was significantly higher for those with lower educational level, living in a household with a non- concrete roof, drinking tube-well water (vs. tap water), using a distant water source and unsanitary toilets. The risk for cholera was significantly higher for those drinking tube-well water and those using unsanitary toilets. This study confirms that low socio-economic groups and poor hygiene and sanitation groups were most vulnerable to flood-related diarrhoea.