John O’Loughlina, Frank D. W. Witmer, Andrew M. Linke, Arlene Laing, Andrew Gettelman, and Jimy Dudhia
Abstract Recent studies concerning the possible relationship between climate trends and the risks of violent conflict have yielded contradictory results, partly because of choices of conflict measures and modeling design. In this study, we examine climate–conflict relationships using a geographically disaggregated approach. We consider the effects of climate change to be both local and national in character, and we use a conflict database that contains 16,359 individual geolocated violent events for East Africa from 1990 to 2009. Unlike previous studies that relied exclusively on political and economic controls, we analyze the many geographical factors that have been shown to be important in understanding the distribution and causes of violence while also considering yearly and country fixed effects. For our main climate indicators at gridded 1° resolution (∼100 km), wetter deviations from the precipitation norms decrease the risk of violence, whereas drier and normal periods show no effects. The relationship between temperature and conflict shows that much warmer than normal temperatures raise the risk of violence, whereas average and cooler temperatures have no effect. These precipitation and temperature effects are statistically significant but have modest influence in terms of predictive power in a model with political, economic, and physical geographic predictors. Large variations in the climate–conflict relationships are evident between the nine countries of the study region and across time periods.
Here is my full reply:
This is useful paper, and the results are important, but the framing by the authors and the press coverage (which I suppose that the author's guide) is strange.
The authors report that months with hotter and drier conditions have much more violence. And the size of this effect is *large*: rates of violence climb 29.6% when temperatures are 2 degrees higher than normal, and 30.3% when rainfall declines from wet to normal/dry (a 2 standard deviation change). These numbers are really big! To get a sense of scale, shifting from a violent hot/dry anomaly to a more peaceful cool/wet anomaly decreases violence by more than 50%, which is similar to the reduction in crime that NYC felt during Rudy Guiliani's tenure! (see here). Whether or not you think Guiliani was responsible for that decline, it is widely recognized that the reduction in NYC violence on that scale had a large effect on the welfare of New Yorkers.
Now, while these effects are large, they are not new. Our 2011 Nature paper found almost the identical result. And the 2009 PNAS paper by Burke et al. also reported the same sized effects. Burke et al. was at the national scale, and our paper was at the global scale, so I think that the main contribution of O'Loughlin et al is to demonstrate that the findings of those two earlier papers continue to hold up a the local scale.
What I find surprising about the paper's presentation and the press coverage is that it looks like the authors are trying to bury this finding within the paper by suggesting that these effects are small or unimportant. I don't know why (but it certainly has the feel of Nils Petter Gleditsch's attempt to bury similar findings in his 2012 Special Issue of the Journal of Peace Research here). Had I had found this results, I would be putting them front and center in the article rather than reporting them in the text on page 3.
The main way that these authors try to downplay their findings is to argue that temperature and precip anomalies don't have a lot of predictive power compared to "other variables", but this is a red herring. The comparison the authors make is not an apples-to-apples comparison. The statistical model the team uses has two dimensions, time and space. In modeling violence, the team first tries to model *where* violence will occur to get a location-specific baseline. Then, conditional on a location's baseline level of violence, they model *when* violence in a specific location is higher or lower than this baseline. Their main finding has to do with the "when" part of the question, showing that violence within a specific location fluctuates in time, reflecting temperature and precip anomalies. But then they go on to compare whether they can predict the timing or location of violence better, which is not a useful exercise. They conclude that location variables like "population density" or "capital city" are much stronger predictors of violence than timing variables like "temperature" or "presidential election", but spatial variation and temporal variation in violence are completely different in magnitude and dynamics, so it is unclear what this comparison tells us. The authors argue that this tells them that doublings of violence brought on by hot/dry conditions is only a "modest" effect, but this claim doesn't have any statistical foundation and doesn't jibe with common sense.
To see why, consider the NYC/Guiliani example. If we ran the O'Loughlin et al. model for New York State during 1980-2010 with variables describing "population density" and a trend (to capture the decline in the 1990s), we'd see that on average higher population densities would have higher crime (i.e. NYC has more crime than small towns in Upstate New York) and there is a fall in violence by 50% in the 1990s. But if one used the same measures as O'Loughlin to ask whether location or trend was "more important", we'd find that location is a much more "important" predictor of violence because the difference between violence in NYC and the rest of the state is much more dramatic than the 50% decline within NYC during the 1990s. Using this logic, we would conclude that the 50% decline in the 1990's was "modest" or "not important," but anyone who's been living in NYC for the last couple of decades will say that conclusion is nuts. Halving violence is a big deal. One reason this statement doesn't make a lot of sense is because many rural locations have very low crime rates (in part because they don't have many people) so violence could double, triple or increase by a factor of ten and those changes would be trivial (in terms of total violent events) compared to a 50% change in NYC. The fallacy of these kinds of apples-to-oranges comparisons (mixing "where" with "when" variables) is why using "goodness of fit" statistics to assess "importance" doesn't make sense when working with data that covers both time and space. An aside: this mistake also shows up as one of the critical errors make by Buhaug in his 2010 PNAS article that got a lot of press and is widely misunderstood and misinterpreted.
So in short: The paper is important and useful because the authors confirm at the local-scale previously discovered large-scale results linking climatic events and violence. But their conclusion that these effects are "modest" is based on an incorrect interpretation of goodness-of-fit statistics.
In a follow-up email, I sent him a graph that he ended up posting. This is the full explanation:
Marshall Burke and I were looking at the data from this paper and put together this plot (attached). It displays the main result from the paper, but more clearly than any of the figures or language in the paper does (we think). Basically, relatively small changes in temperature lead to really large changes in violence, illustrating my earlier point that this study finds a very large effect of climate on violence in East Africa.
|click to enlarge|
About the plot:
The thin white line is the average response of violence to temperature fluctuations after rainfall, location, season and trend effects are all removed. The red "ink" indicates the probability that the true regression line is at a given location, with more ink reflecting a higher probability (more certainty). There's a fixed amount of "ink" at each temperature value, it just appears lighter if the range of possible values is more spread out (less certain). We estimate the amount of uncertainty in the regression by randomly resampling the data 500 times, re-estimating the regression each time and looking at how much the results change (so-called "bootstrapping"). This "watercolor regression" is a new way of displaying uncertainty in these kinds of results, I describe it in more detail here. A similar and related plot showing the relationship between rape and temperature is here.
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