Climate, conflict, and social stability: what does the evidence say?

The "sister paper" to our recent Science article on climate and conflict has come out in Climatic Change. This new article is a traditional review article that walks readers through individual studies in the literature and discusses some of the debates in less technical terms than the Science article. The sample of studies included is slightly different since the submission timeline for the two studies was different.

Climate, conflict, and social stability: what does the evidence say?
Solomon Hsiang and Marshall Burke

Abstract: Are violent conflict and socio-political stability associated with changes in climatological variables? We examine 50 rigorous quantitative studies on this question and find consistent support for a causal association between climatological changes and various conflict outcomes, at spatial scales ranging from individual buildings to the entire globe and at temporal scales ranging from an anomalous hour to an anomalous millennium. Multiple mechanisms that could explain this association have been proposed and are sometimes supported by findings, but the literature is currently unable to decisively exclude any proposed pathway. Several mechanisms likely contribute to the outcomes that we observe.

Weekend Links

1) Remote sensing evidence of differing institutional history

2) Monasteries and snow leopard conservation

3) RealClimate on the Marcott et al. Holocene temperature reconstruction (previously on FE)

4) Papers from the 1st International Workshop on Econometric Applications in Climatology are up

5) Was Stalin Necessary for Russia's Economic Development? (NBER)

6) Hi-res paleogeography

7) On hours worked vs. productivity (via Yaniz Stopnitzky)

8) Massive groundwater discovery in Kenya (via Emily McPartlon)

9) "[W]e present a new index of the year when the projected mean climate of a given location moves to a state continuously outside the bounds of historical variability" Estimate range: 2047-2069. (Nature, via Sarah Dwyer)

10) Almost half of public school students in the United States are low income (via Dave Pell)

Envirodevonomics

There's a new working paper by Michael Greenstone and Kelsey Jack that's of obvious interest to FE readers:

Envirodevonomics: A Research Agenda for a Young Field
Environmental quality in many developing countries is poor and generates substantial health and productivity costs. However, existing measures of willingness to pay for environmental quality improvements indicate low valuations by affected households. This paper argues that this seeming paradox is the central puzzle at the intersection of environmental and development economics: Given poor environmental quality and high health burdens in developing countries, why is WTP so low? We develop a conceptual framework for understanding this puzzle and propose four potential explanations: (1) due to low income levels, individuals value increases in income more than marginal improvements in environmental quality, (2) the marginal costs of environmental quality improvements are high, (3) political economy factors undermine efficient policy-making, and (4) market failures such as weak property rights and missing capital markets drive a wedge between true and revealed willingness to pay for environmental quality. We review the available literature on each explanation and discuss how the framework also applies to climate change, which is perhaps the most important issue at the intersection of environment and development economics. The paper concludes with a list of promising and unanswered research questions for the emerging sub-field of “envirodevonomics.”

New GIS Data for the Demographic and Health Surveys

USAID's Measure Demographic and Health Surveys (DHS) are an extraordinary (free) data set on maternal and child health from around the world. They've just released their new spatial data repository which, among other things, adds crucial shape files of subnational region borders over time. From the official announcement:

We are pleased to announce the launch of a new open data GIS resource from MEASURE DHS.                                                                          

The Spatial Data Repository provides geographically-linked health and demographic data from the MEASURE Demographic and Health Surveys (DHS) project and the U.S. Census Bureau for mapping in a geographic information system (GIS).

-Boundaries of DHS regions can be explored to visualize change over time.

-Data from DHS indicators and U.S. Census population estimates can be downloaded in GIS format.

Please share with your colleagues and friends.

Go explore!  http://spatialdata.measuredhs.com/

Weekend Links

1) Sol's work on climate and conflict as covered by America's Finest News Source

2) Air pollution elasticity of tourism, China edition (via Sunny Wong)

3) Research idea generation propensity by location (via Alex McQuoid)

4) Weather and consumer behavior (via Kyle Meng)

5) "Twitter mood predicts Hunter College High School start date", if you will (via Luke Stein)

6) "Our undiscounted estimates indicate that the cost of the five main NCDs will total USD 27.8 trillion for China and USD 6.2 trillion for India (in 2010 USD)" (NBER)

The value of forecasting

Mark Rosenzweig and Chris Udry have a pretty nice new working paper on the value of weather forecasts in India:

Forecasting Profitability
We use newly-available Indian panel data to estimate how the returns to planting-stage investments vary by rainfall realizations. We show that the forecasts significantly affect farmer investment decisions and that these responses account for a substantial fraction of the inter-annual variability in planting-stage investments, that the skill of the forecasts varies across areas of India, and that farmers respond more strongly to the forecast where there is more forecast skill and not at all when there is no skill. We show, using an IV strategy in which the Indian government forecast of monsoon rainfall serves as the main instrument, that the return to agricultural investment depends substantially on the conditions under which it is estimated. Using the full rainfall distribution and our profit function estimates, we find that Indian farmers on average under-invest, by a factor of three, when we compare actual levels of investments to the optimal investment level that maximizes expected profits. Farmers who use skilled forecasts have increased average profit levels but also have more variable profits compared with farmers without access to forecasts. Even modest improvements in forecast skill would substantially increase average profits.

An ungated copy can be found here. 

Weekend Links

1) Time lapse photos of the north pole this July

2) Fungicides further implicated in Colony Collapse Disorder (via Yaniv Stopnitzky)

3) Evidence that a cosmic airburst may have triggered the Younger Dryas (PNAS)

4) Bob Pindyck on "Climate Change Policy: What Do The Models Tell US?" (NBER)

5) "We re-interviewed the study participants 20 years after the intervention. [Psychosocial
] stimulation increased the average earnings of participants by 42 percent." (NBER, and not unrelated on FE)

6) Myopic misery

7) Hackathons and public goods provision (via Emmanuel Letouzé)

Please read our paper on climate and human conflict carefully

Edward Miguel, Marshall Burke and I have a new paper quantifying the link between climate and conflict.

There has already been a lot of public criticism of this paper. Marshall has written detailed replies to many of these comments, explaining the why many of these comments are misguided or simply inaccurate.  His reply is on G-FEED here.

I recommend that researchers and journalists read these replies before they further promote inaccurate statements to the public.

Forward vs. reverse causal questions

Andrew Gelman has a thought-provoking post on asking "Why?" in statistics:

Consider two broad classes of inferential questions: 

1. Forward causal inference. What might happen if we do X? What are the effects of smoking on health, the effects of schooling on knowledge, the effect of campaigns on election outcomes, and so forth? 

2. Reverse causal inference. What causes Y? Why do more attractive people earn more money? Why do many poor people vote for Republicans and rich people vote for Democrats? Why did the economy collapse? [...] 

My question here is: How can we incorporate reverse causal questions into a statistical framework that is centered around forward causal inference. (Even methods such as path analysis or structural modeling, which some feel can be used to determine the direction of causality from data, are still ultimately answering forward casual questions of the sort, What happens to y when we change x?) 

My resolution is as follows: Forward causal inference is about estimation; reverse causal inference is about model checking and hypothesis generation.

Among many gems is this:

A key theme in this discussion is the distinction between causal statements and causal questions. When Rubin dismissed reverse causal reasoning as “cocktail party chatter,” I think it was because you can’t clearly formulate a reverse causal statement. That is, a reverse causal question does not in general have a well-defined answer, even in a setting where all possible data are made available. But I think Rubin made a mistake in his dismissal. The key is that reverse questions are valuable in that they focus on an anomaly—an aspect of the data unlikely to be reproducible by the current (possibly implicit) model—and point toward possible directions of model improvement.

 You can read the rest here.

Pricing the clathrate gun hypothesis

In this week's Nature:

We calculate that the costs of a melting Arctic will be huge, because the region is pivotal to the functioning of Earth systems such as oceans and the climate. The release of methane from thawing permafrost beneath the East Siberian Sea, off northern Russia, alone comes with an average global price tag of $60 trillion in the absence of mitigating action — a figure comparable to the size of the world economy in 2012 (about $70 trillion). The total cost of Arctic change will be much higher. Much of the cost will be borne by developing countries, which will face extreme weather, poorer health and lower agricultural production as Arctic warming affects climate. All nations will be affected, not just those in the far north, and all should be concerned about changes occurring in this region. More modelling is needed to understand which regions and parts of the world economy will be most vulnerable.

Wikipedia on the clathrate gun hypothesis here. For scale, Costanza et al. calculated the annual value of the world's ecosystem services in 1997 at $16-54 trillion, or $23-79 trillion in today's dollars.

Seismic externalities

Injection-Induced Earthquakes
William L. Ellsworth

Abstract: Earthquakes in unusual locations have become an important topic of discussion in both North America and Europe, owing to the concern that industrial activity could cause damaging earthquakes. It has long been understood that earthquakes can be induced by impoundment of reservoirs, surface and underground mining, withdrawal of fluids and gas from the subsurface, and injection of fluids into underground formations. Injection-induced earthquakes have, in particular, become a focus of discussion as the application of hydraulic fracturing to tight shale formations is enabling the production of oil and gas from previously unproductive formations. Earthquakes can be induced as part of the process to stimulate the production from tight shale formations, or by disposal of wastewater associated with stimulation and production. Here, I review recent seismic activity that may be associated with industrial activity, with a focus on the disposal of wastewater by injection in deep wells; assess the scientific understanding of induced earthquakes; and discuss the key scientific challenges to be met for assessing this hazard.

Perhaps an enterprising graduate student can figure out an optimal management strategy for this risk.

Weekend Links

1) "Exposure of children to toxic lead, and the subsequent declines in IQ and earning potential, costs the developing world nearly $1 trillion annually" (via Yaniv Stopnitzky)

2) More evidence on the long term health impacts of in-utero radiation exposure

3) On sustainable (or at least resilient...) concrete

4) Global forced displacement / migration is at an 18 year high (via Thomas Dreesen)

5) Deadline to submit to Scientific American's editorial intern program is July 8th (via John Matson)

6) "We suggest that cultivated flowers are rewarding because they have evolved to rapidly induce positive emotion in humans" (via Michael Balter)

7) Gender Identity and Relative Income within Households (NBER via Amir Jina)

8) The Anti-Science Climate Denier Caucus (via Oceans at MIT)

9) Using quant text analysis to advance the "geek" vs. "nerd" debate

Using Weather Data and Climate Model Output in Economic Analyses of Climate Change

After 5 (or 6?) rounds of revisions (a lesson to anyone thinking of writing an interdisciplinary review article...), this is finally published:

Using Weather Data and Climate Model Output in Economic Analyses of Climate ChangeReview of Environmental Economics and PolicyMaximilian Auffhammer, Solomon M. Hsiang, Wolfram Schlenker and Adam Sobel

We tried to write this as a practical and gentle introduction and how-to manual for econometricians and other applied social scientists. I hope it's helpful.

Souped-up Watercolor Regression

I introduced "watercolor regression" here on FE several months ago, after some helpful discussions with Andrew Gelman and our readers. Over the last few months, I've made a few upgrades that I think significantly increase the utility of this approach for people doing work similar to my own.

First, the original paper is now on SSRN and documents the watercolor approach, explaining its relationship to the more general idea of visual-weighting.

Visually-Weighted Regression 

Abstract: Uncertainty in regression can be efficiently and effectively communicated using the visual properties of statistical objects in a regression display. Altering the “visual weight” of lines and shapes to depict the quality of information represented clearly communicates statistical confidence even when readers are unfamiliar with the formal and abstract definitions of statistical uncertainty. Here we present examples where the color-saturation and contrast of regression lines and confidence intervals are parametrized by local measures of an estimate’s variance. The results are simple, visually intuitive and graphically compact displays of statistical uncertainty. This approach is generalizable to almost all forms of regression.

Second, the Matlab code I've posted to do watercolor regression is now parallelized. If you have Matlab running on multiple processors, the code automatically detects this and runs the bootstrap procedure in parallel.  This is helpful because a large number of resamples (>500) is important for getting the distribution of estimates (the watercolored part of the plot) to converge but serial resampling gets very slow for large data sets (eg. >1M obs), especially when block-boostrapping (see below).

Third, the code now has an option to run a block bootstrap. This is important if you have data with serial or spatial autocorrelation (eg. models of crop yields that change in response to weather).  To see this at work, suppose we have some data where there is a weak dependance of Y on X, but all observations within a block (eg. maybe obs within a single year) have a uniform level-shift induced by some unobservable process.

e = randn(1000,1);
block = repmat([1:10]',100,1);
x = 2*randn(1000,1);
y = x+10*block+e;

The scatter of this data looks like:

where each one of stripes of data is block of obs with correlated residuals. Running watercolor_reg without block-bootrapping

 watercolor_reg(x,y,100,1.25,500)

we get an exaggerated sense of precision in the relationship between Y and X:

If we try to account for the fact that residuals within a block are not independent by using the block bootstrap

watercolor_reg(x,y,100,1.25,500,block)

we get a very different result:

Finally, the last addition to the code is a simple option to clip the watercoloring at the edge of a specified confidence interval (default is 95%), an idea suggested by Ted Miguel. This allows us to have a watercolor plot which also allows us to conduct some traditional hypothesis tests visually, without violating the principles of visual weighting. Applying this option to the example above

blue = [0 0 .3]
watercolor_reg(x,y,100,1.25,500,block, blue,'CLIPCI')

we obtain a plot with a clear 95% CI, where the likelihoods within the CI are indicated by watercoloring:

Code is here. Enjoy!

Global Poverty and Practice Postdoctoral Fellows at Berkeley

Blum Center for Developing Economies has a now program for Global Poverty and Practice Post Doctoral Fellows.

(Applications deadline = July 15)

Weather and Climate Data: a Guide for Economists

Now posted as an NBER working paper (it should be out in REEP this summer):

Using Weather Data and Climate Model Output in Economic Analyses of Climate Change
Maximilian Auffhammer, Solomon M. Hsiang, Wolfram Schlenker, Adam Sobel

Abstract: Economists are increasingly using weather data and climate model output in analyses of the economic impacts of climate change. This article introduces weather data sets and climate models that are frequently used, discusses the most common mistakes economists make in using these products, and identifies ways to avoid these pitfalls. We first provide an introduction to weather data, including a summary of the types of datasets available, and then discuss five common pitfalls that empirical researchers should be aware of when using historical weather data as explanatory variables in econometric applications. We then provide a brief overview of climate models and discuss two common and significant errors often made by economists when climate model output is used to simulate the future impacts of climate change on an economic outcome of interest.

Hurricane-induced migration [Plot of the Week]

Impulse:

Hurricane Katrina, as pictured in the Gulf of Mexico at 21:45 UTC on August 28, 2005.

Response:

This map illustrates the national scope of the dispersion of refugees from Hurricane Katrina. It shows the location by zip code of the 800,000 displaced Louisiana residents who requested federal emergency assistance. The evacuees ended up dispersed across the entire nation, illustrating the wide-ranging impacts that can flow from extreme weather events, some of which are projected to increase in frequency and/or intensity as climate continues to change. (Source: Louisiana Geographic Information Center 2005)

Weekend Links

1) FasteR! HigheR! StrongeR! - A Guide to Speeding Up R Code for Busy People (via R-bloggers)

2) African Bus Routes Redrawn Using Cell-Phone Data

3) "[T]he climate that worries me most is the climate of fear, the belief that our current trajectory leads inevitably to total disaster"

4) The glory of replication projects in econometrics (via John Whitehead)

5) Mapping Change in Large Networks

6) Did China really lose over half its rivers over the past 2 decades?

7) Datta and Mullainathan on behavioral design in development policy (via Elizabeth Katz)

8) Fifty years of chaos theory (via Sam Arbesman)

9) "String theory may limit space brain threat" (via Peter Graham)

Climate Change: Recent Discoveries and Future Challenges

A conference at Columbia's Lamont campus this week, if you find yourself in the NYC area.

Climate Change: Recent Discoveries and Future Challenges

Abrupt Climate Change Studies Symposium
Cooperative Institute for Climate Applications and Research

21-23 May 2013
Columbia University Lamont Campus
Monell Building Auditorium

Speakers and agenda here
Register here

Mathematics of Planet Earth

Here's an initiative that will suit FE readers:

More than a hundred scientific societies, universities, research institutes, and organizations all over the world have banded together to dedicate 2013 as a special year for the Mathematics of Planet Earth.

The initiative is multi-pronged, with everything from summer schools, curriculum materials, public lectures and a daily blog (which is quite good).

The idea behind "Mathematics of Planet Earth (MPE) according to MPE: