Spring thaw

As long-time readers of the blog may have noticed, posting has been a little light the past 12 months. Sol and I are aiming the rectify that and will start posting more again over the next few weeks. Expect to see updates on some of our work, some resources and code snippets and, of course, coverage of papers and research we've found interesting. We hope you're all well, and look forward to getting the blog running again.

On giving a great applied talk

Jesse Shapiro* has some excellent slides on giving a good applied micro talk that are both specific enough to be of use for students prepping job market talks, as well as general enough to simply provide good fodder for thinking about how one presents one's work to any audience. I highly recommend them. (via Kyle Meng)

*: yet another Stuyvesant High School graduate.

Ecotourism and poverty

This is a hard problem to answer well, but its certainly an interesting question.

Quantifying causal mechanisms to determine how protected areas affect poverty through changes in ecosystem services and infrastructure
Paul J. Ferraroa and Merlin M. Hanauer

Abstract: To develop effective environmental policies, we must understand the mechanisms through which the policies affect social and envi- ronmental outcomes. Unfortunately, empirical evidence about these mechanisms is limited, and little guidance for quantifying them exists. We develop an approach to quantifying the mechanisms through which protected areas affect poverty. We focus on three mechanisms: changes in tourism and recreational services; changes in infrastructure in the form of road networks, health clinics, and schools; and changes in regulating and provisioning ecosystem services and foregone production activities that arise from land- use restrictions. The contributions of ecotourism and other ecosys- tem services to poverty alleviation in the context of a real environ- mental program have not yet been empirically estimated. Nearly two-thirds of the poverty reduction associated with the establish- ment of Costa Rican protected areas is causally attributable to opportunities afforded by tourism. Although protected areas reduced deforestation and increased regrowth, these land cover changes neither reduced nor exacerbated poverty, on average. Protected areas did not, on average, affect our measures of in- frastructure and thus did not contribute to poverty reduction through this mechanism. We attribute the remaining poverty reduction to unobserved dimensions of our mechanisms or to other mecha- nisms. Our study empirically estimates previously unidentified contributions of ecotourism and other ecosystem services to pov- erty alleviation in the context of a real environmental program. We demonstrate that, with existing data and appropriate empiri- cal methods, conservation scientists and policymakers can begin to elucidate the mechanisms through which ecosystem conservation programs affect human welfare.

When evidence does not suffice

Halvard Buhaug and numerous coauthors have released a comment titled “One effect to rule them all? A comment on climate and conflict” which critiques research on climate and human conflict that I published in Science and Climatic Change with my coauthors Marshall Burke and Edward Miguel. 

The comment does not address the actual content of our papers.  Instead it states that our papers say things they do not say (or that our papers do not say thing they actually do say) and then uses those inaccurate claims as evidence that our work is erroneous.

I have posted my reaction to the comment on the G-FEED blog, written as the referee report that I would write if I were asked to referee the comment.

(This is not the first time Buhaug and I have disagreed on what constitutes evidence. Kyle Meng and I recently published a paper in PNAS demonstrating that Buhaug’s 2010 critique of an earlier paper made aggressive claims that the earlier paper was wrong without actually providing evidence to support those claims.)

Weekend Links

1) Ivanpah, the largest solar power plant in the US, is finally online

2) The Weight of Rain, or subtlety in data visualization (via Hackernews)

3) On the perils of P values (again) (Nature)

4) On the uneven spatial distribution of GDP in the US

5) "Principal Component Analysis 4 Dummies"

6) "Syngenta’s public-relations team had drafted a list of four goals. The first was 'discredit Hayes.'” (via Kyle Meng)

7) This past January was the fourth warmest in history, even with the polar vortex (via Johannes Urpelainen)

8) Introducing spritzing, or speed reading for the rest of us

9) "We compare the productivity of Fields medalists (winners of the top mathematics prize) to that of similarly brilliant contenders. The two groups have similar publication rates until the award year, after which the winners’ productivity declines." (NBER)

Ted at TED

Ted Miguel gives a TED talk explaining our work on climate and conflict.



I've been waiting a month to use that title.

USF's IDEC Masters Program Now Recruiting

The University of San Francisco's Masters program in International and Development Economics is currently accepting applicants through early March. The program is fairly unique among econ masters programs for having 3 semesters of econometrics (I teach the first class in the sequence), and a mandatory field work portion during the summer between years one and two. It also offers a mix of elective classes in applied micro and international / macro that one would normally be hard pressed to find at the Master's level, including Alessandra Cassar's Experimental Economics class and my survey course in Environment and Development Economics. Graduates complete an original thesis under the advisory oversight of a faculty member, and recent topics have spanned the gamut from in-utero impacts of rainfall shocks in Bangladesh to determinants of gender-differentiated competition in China. If you or someone you know is interested in pursuing graduate education in development and has a bent towards quantitative methods, drop me an email and we can chat.

Ambient carbon dioxide affects human decision-making

Amir Jina and I recently visited William Fisk at LBL who pointed us to his fascinating study:

Is CO2 an Indoor Pollutant? Direct Effects of Low-to-Moderate CO2 Concentrations on Human Decision-Making Performance

Usha Satish, Mark J. Mendell, Krishnamurthy Shekhar, Toshifumi Hotchi, Douglas Sullivan, Siegfried Streufert, and William J. Fisk

Abstract:

Background: Associations of higher indoor carbon dioxide (CO2) concentrations with impaired work performance, increased health symptoms, and poorer perceived air quality have been attributed to correlation of indoor CO2 with concentrations of other indoor air pollutants that are also influenced by rates of outdoor-air ventilation. 

Objectives: We assessed direct effects of increased CO2, within the range of indoor concentrations, on decision making. 

Methods: Twenty-two participants were exposed to CO2 at 600, 1,000, and 2,500 ppm in an office-like chamber, in six groups. Each group was exposed to these conditions in three 2.5-hr sessions, all on 1 day, with exposure order balanced across groups. At 600 ppm, CO2 came from outdoor air and participants’ respiration. Higher concentrations were achieved by injecting ultrapure CO2. Ventilation rate and temperature were constant. Under each condition, participants completed a computer-based test of decision-making performance as well as questionnaires on health symptoms and perceived air quality. Participants and the person administering the decision-making test were blinded to CO2 level. Data were analyzed with analysis of variance models. 

Results: Relative to 600 ppm, at 1,000 ppm CO2, moderate and statistically significant decrements occurred in six of nine scales of decision-making performance. At 2,500 ppm, large and statistically significant reductions occurred in seven scales of decision-making performance (raw score ratios, 0.06–0.56), but performance on the focused activity scale increased. 

Conclusions: Direct adverse effects of CO2 on human performance may be economically important and may limit energy-saving reductions in outdoor air ventilation per person in buildings. Confirmation of these findings is needed.

From the LBL press page

On nine scales of decision-making performance, test subjects showed significant reductions on six of the scales at CO2 levels of 1,000 parts per million (ppm) and large reductions on seven of the scales at 2,500 ppm. The most dramatic declines in performance, in which subjects were rated as “dysfunctional,” were for taking initiative and thinking strategically. “Previous studies have looked at 10,000 ppm, 20,000 ppm; that’s the level at which scientists thought effects started,” said Berkeley Lab scientist Mark Mendell, also a co-author of the study. “That’s why these findings are so startling.” 

The primary source of indoor CO2 is humans. While typical outdoor concentrations are around 380 ppm, indoor concentrations can go up to several thousand ppm. Higher indoor CO2 concentrations relative to outdoors are due to low rates of ventilation, which are often driven by the need to reduce energy consumption. In the real world, CO2 concentrations in office buildings normally don’t exceed 1,000 ppm, except in meeting rooms, when groups of people gather for extended periods of time. 

In classrooms, concentrations frequently exceed 1,000 ppm and occasionally exceed 3,000 ppm. CO2 at these levels has been assumed to indicate poor ventilation, with increased exposure to other indoor pollutants of potential concern, but the CO2 itself at these levels has not been a source of concern. Federal guidelines set a maximum occupational exposure limit at 5,000 ppm as a time-weighted average for an eight-hour workday.

Spatial Data and Analysis

I developed a new course last fall title "Spatial Data and Analysis". Because several people have asked for the material, I've finally posted the syllabus and assignments online here. 

Description of the course:

The recent explosion of spatially explicit data and analytical tools, such as "Geographic Information Systems" (GIS) and spatial econometrics, have aided researchers and decision- makers faced with a variety of challenges. This course introduces students to spatial data and its analysis, as well as the modeling of spatially dependent social processes and policy problems. Students will be introduced to the types, sources, and display of spatial data. Through hands-on analysis, students will learn to extract quantitative information from spatial data for applied research and public policy. Students will be introduced to spatial statistics, spatially dependent simulation, and spatial optimization. Students will learn to think creatively about spatial problems through examples drawn from economics, politics, epidemiology, criminology, agriculture, social networks, and the environment. The goal of the course is to equip advanced masters students and doctoral students with tools that will help them be effective analysts and communicators of spatial information in their future research or policy-related work. Because hands-on analysis plays a central role in the class, students will benefit from prior experience with basic computer programming -- although prior experience is not required. Prerequisites: introductory statistics or equivalent.

The Hidden Impact of Filipino Typhoons at BIDS

Jesse recently posted on our results for the economic and health impact of typhoons in the Philippines.  But in case you're feeling too lazy to read that whole post, here's the results in 4 min of video. It was a "lightning" talk at the launch of the Berkeley Institute for Data Science.

FAQs for "Reconciling disagreement over climate–conflict results in Africa"

[This is a gues blog post by my coauthor Kyle Meng.]

Sol and I just published an article in PNAS in which we reexamine a controversy in the climate-conflict literature. The debate is centered over two previous PNAS articles: the first by Burke et al. (PNAS, 2009) which claims that higher temperature increases conflict risks in sub-Saharan Africa and a second PNAS article by Buhaug (PNAS, 2010) refuting the earlier study.

How did we get here?

First, a bit of background. Whether climate change causes societies to be more violent is a critical question for our understanding of climate impacts. If climate change indeed increases violence, the economic and social costs of climate change may be far greater than what was previously considered, and thus further prompt the need to reduce greenhouse gas emissions. To answer this question, researchers in recent years have turned to data from the past asking whether violence has responded historically to changes in the local climate. Despite the increasing volume of research (summarized by Sol, Marshall Burke, and Ted Miguel in their meta-analysis published in Science and the accompanying review article in Climatic Change) this question remained somewhat controversial in the public eye. Much of this controversy was generated by this pair of PNAS papers.

What did we do?

Our new paper takes a fresh look at these two prior studies by statistically examining whether the evidence provided by Buhaug (2010) overturns the results in Burke et al. (2009). Throughout, we examine the two central claims made by Buhaug:

1) that Burke et al.'s results "do not hold up to closer inspection" and
2) climate change does not cause conflict in sub-Saharan Africa.  

Because these are quantitative papers, Buhaug’s two claims can be answered using statistical methods. What we found was that Buhaug did not run the appropriate statistical procedures needed for the claims made. When we applied the correct statistical tests, we find that:

a) the evidence in Buhaug is not statistically different from that of Burke et al. and
b) Buhaug’s results cannot support the claim that climate does not cause conflict. 

A useful analogy

The statistical reasoning in our paper is a bit technical so an analogy may be helpful here. Burke et al's main result is equivalent to saying "smoking increases lung cancer risks roughly 10%". Buhaug claims above are equivalent to stating that his analysis demonstrates that “smoking does not increase lung cancer risks” and furthermore that “smoking does not affect lung cancer risks at all”.

What we find, after applying the appropriate statistical method, is that the only equivalent claim that can be supported by Buhaug’s analysis is "smoking may increase lung cancer risks by roughly 100% or may decrease them by roughly 100% or may have no effect whatsoever". Notice this is a far different statement than what Buhaug claims he has demonstrated in 1) and 2) above. Basically, the results presented in Buhaug are so uncertain that they do not reject zero effect, but they also do not reject the original work by Burke et al.

Isn’t Buhaug just showing Burke et al.’s result is “not robust”?

In statistical analyses, we often seek to understand if a result is “robust” by demonstrating that reasonable alterations to the model do not produce dramatically different results. If successful, this type of analysis sometimes convinces us that we have not failed to account for important omitted variables (or other factors) that would alter our estimates substantively.

Importantly, however, the reverse logic is not true and “non-robustness” is not a conclusive (or logical) result. Obtaining different estimates from the application of model alterations alone does not necessarily imply that the original result is wrong since it might be the new estimate that is biased.   Observing unstable results suggests that there are errors in the specification of some (or all) of the models.  It merely means the analyst isn’t working with the right statistical model.

There must exist only one  “true” relationship between climate and conflict, it may be a coefficient of zero or a larger coefficient consistent with Burke et al., but it cannot be all these coefficients at the same time. If models with very different underlying assumptions provide dramatically different estimates, this suggests that all of the models (except perhaps one) is misspecified and should be thrown out.

A central error in Buhaug is his interpretation of his findings.  He removes critical parts of Burke et al.’s model (e.g. those that account for important differences in geography, history and culture) or re-specifies them in other ways and then advocates that the various inconsistent coefficients produced should all be taken seriously. In reality, the varying estimates produced by Buhaug are either due to added model biases or to sampling uncertainty caused by the techniques that he is using. It is incorrect to interpret this variation as evidence that Burke et al.’s estimate is “non-robust”.

So are you saying Burke et al. was right?

No. And this is a very important point. In our article, we carefully state:

“It is important to note that our findings neither confirm nor reject the results of Burke et al.. Our results simply reconcile the apparent contradiction between Burke et al. and Buhaug by demonstrating that Buhaug does not provide evidence that contradicts the results reported in Burke et al. Notably, however, other recent analyses obtain results that largely agree with Burke et al., so we think it is likely that analyses following our approach will reconcile any apparent disagreement between these other studies and Buhaug.”

That is, taking Burke et al’s result as given, we find that the evidence provided in Buhaug does not refute Burke et al. (the central claim of Buhaug). Whether Burke et al. was right about climate causing conflict in sub-Saharan Africa is a different question. We’ve tried to answer that question in other settings (e.g. our joint work published in Nature), but that’s not the contribution of this analysis.

Parting note

Lastly, we urge those interested to read our article carefully. Simply skimming the paper by hunting for statistically significant results would be missing the paper’s point. Our broader hope besides helping to reconcile this prior controversy is that the statistical reasoning underlying our work becomes more common in data-driven analyses.

Fourth Interdisciplinary Ph.D. Workshop in Sustainable Development

The students of the Columbia Sustainable Development Ph.D. program have put out the call for papers for the Fourth Interdisciplinary Ph.D. Workshop in Sustainable Development. It's a great opportunity for Ph.D. students to meet colleagues from a broad array of disciplines, and a bunch of our younger colleagues will be there. Please pass it along.

Fourth Interdisciplinary Ph.D. Workshop in Sustainable Development
April 25^th-26^th, 2014: Columbia University in the City of New York, USA

The graduate students in the Sustainable Development PhD program at Columbia University are convening the Fourth Interdisciplinary Ph.D. Workshop in Sustainable Development (IPWSD); scheduled for April 25th-26th, 2014, at Columbia University in New York City.

The IPWSD is a conference open to graduate students working on or interested in issues related to sustainable development.  It is intended to provide a forum to present and discuss research in an informal setting, as well as to meet and interact with similar graduate student researchers from other institutions.  In particular, we hope to facilitate a network among students pursuing in-depth research across a range of disciplines in the social and natural sciences, to generate a larger interdisciplinary discussion concerning sustainable development.  If your research pertains to the field of sustainable development and the linkages between natural and social systems, we encourage you to apply regardless of disciplinary background.

For details, please see the call for papers, or visit our conference website where a detailed list of topics, conference themes and other information is available.

Please share this information widely with graduate students and other interested parties. We look forward to seeing you in New York City in April!

With kind regards,

The Fourth IPWSD Planning Committee

Sustainable Development Doctoral Society, 
Columbia University

Website: http://blogs.cuit.columbia.edu/sdds/schedule-events/ipwsd_2014/

Contact: cu.sdds.ipwsd@gmail.com

Reconciling disagreement over climate–conflict results in Africa

Kyle and I have a paper out in the Early Edition of PNAS this week:

Reconciling disagreement over climate–conflict results in Africa
Solomon M. Hsiang and Kyle C. Meng

Abstract: A recent study by Burke et al. [Burke M, Miguel E, Satyanath S, Dykema J, Lobell D (2009) Proc Natl Acad Sci USA 106(49):20670– 20674] reports statistical evidence that the likelihood of civil wars in African countries was elevated in hotter years. A following study by Buhaug [Buhaug H (2010) Proc Natl Acad Sci USA 107 (38):16477–16482] reports that a reexamination of the evidence overturns Burke et al.’s findings when alternative statistical models and alternative measures of conflict are used. We show that the conclusion by Buhaug is based on absent or incorrect statistical tests, both in model selection and in the comparison of results with Burke et al. When we implement the correct tests, we find there is no evidence presented in Buhaug that rejects the original results of Burke et al. 

Related reconciliation of different results in Kenya.

A brief refresher and discussion of the controversy that we are examining is here.

Climate-conflict research, before the IRB...

Finding old papers on temperature manipulation is turning into a hobby of mine. I actually had to go to the library to dig up this gem. From Rohles, Frederick H. "Environmental psychology: A bucket of worms." Psychology Today 1.2 (1967): 55-63.

Reconciling temperature-conflict results in Kenya

Marshall, Ted and I have a new short working paper out. When we correct the coding of a single variable in a previous study (that uses a new data set), we obtain highly localized temperature-conflict associations in Kenya that are largely in line with the rest of the literature. I think this is a useful example for why we should be careful with how we specify interaction terms.

Reconciling temperature-conflict results in Kenya
Solomon M. Hsiang, Marshall Burke, and Edward Miguel

Abstract: Theisen (JPR, 2012) recently constructed a novel high-resolution data set of intergroup and political conflict in Kenya (1989-2004) and examined whether the risk of conflict onset and incidence responds to annual pixel-level variations in temperature and precipitation.  Thiesen concluded that only extreme precipitation is associated with conflict incidence and that temperature is unrelated to conflict, seemingly at odds with recent studies that found a positive association at the pixel scale (O'laughlin et al., PNAS 2012), at the country scale (Burke et al., PNAS 2009), and at the continental scale (Hsiang et al., Nature 2011) in Africa.  Here we show these findings can be reconciled when we correct the erroneous coding of temperature-squared in Thiesen. In contrast to the original conclusions presented in Theisen, both conflict onset and conflict incidence are significantly and positively associated with local temperature in this new and independently assembled data set.

Landsat at your fingertips

The USGS has put together a slick GUI that let's you browse (as if it were GoogleEarth) and download Landsat data. The interface is described here.

One of my students found this and showed it to me. (Over the next few weeks, hopefully I'll be able to post much of the material and discoveries from my new course "Spatial Data and Analysis".)

What is identification?

There are relatively few non-academic internet resources on identification and causal inference in the social sciences, especially of the sort that can be consumed by a nonspecialist. To remedy that slightly I decided to tidy up and post some slides I've used to give talks on causal inference a few times in the past year. They're aimed at senior undergrad or graduate students with at least some background in statistics or econometrics, and can be found here:

Causal Inference, Identification, and Identification Strategies

Feel free to drop me a line and give me feedback, especially if somethings seems unclear / incorrect. Thanks!

Year of Reviews in Review: The New Environment and Development Literature

Amir Jina and I were recently discussing the multiple literature reviews that have come out on environment and development topics lately, and realized that there were so many we were starting to lose track. To that end, and as a service to those of you who aren't constantly trawling the working paper and journal lists, here's a quick rundown of the over ten (and counting) recent literature reviews that have come out in the newly emerging environment and development literature:

• Dell, Jones, and Olken's "What Do We Learn from the Weather? The New Climate-Economy Literature". Reviews the growing number of papers focusing on climate and weather impacts, and provides conceptual guidelines for using these results in IAMs and similar models.
• Greenstone and Jack's "Envirodevonomics". Explores low valuation of environmental quality in developing contexts, proposes four basic mechanisms through which this might occur, and reviews the environment and development literature in search of relevant evidence.
• Zivin and Neidell's "Environment, Health, and Human Capital". Highlights economists' main contributions to our understanding of the relationship between the environment and human health, and reviews the applied micro literature on pollution exposure.
• Kousky's "Informing climate adaptation: A review of the economic costs of natural disasters" (ungated RFF working paper here). Reviews the empirical literature on disaster impacts with an eye towards informing the adaptation literature.
• Hsiang, Burke, and Miguel's "Quantifying the Influence of Climate on Human Conflict" (ungated version here). Metanalysis of the climate and conflict literature documenting a general relationship between climate fluctuations and conflict across both spatial and temporal scales. Accompanying review article is Hsiang and Burke's "Climate, Conflict and Social Stability: what does the evidence say?"
• Currie, Zivin, Mullins, and Neidell's "What Do We Know About Short and Long Term Effects of Early Life Exposure to Pollution?". Provides a conceptual model and reviews the empirical literature on the effects of early life pollution exposure.
• Auffhammer and Mansur's "Measuring Climatic Impacts on Energy Expenditures: A Review of the Empirical Literature". Overview of the empirical literature on climate fluctuations as a driver of energy demand.
• Auffhammer and Schlenker's "Empirical Studies on Agricultural Impacts and Adaptation" (forthcoming). Reviews the growing ag-climate literature and the empirical adaptation literature that has come from it.
• Deschênes' "Temperature, Human Health, and Adaptation: A Review of the Empirical Literature". Outlines conceptual issues related to deducing empirical relationships between temperature and health and overviews relevant literature with an eye towards informing IAMs.
• Almond and Currie's "Killing Me Softly: The Fetal Origins Hypothesis". Provides an overview of the explosion of literature on fetal origins / in-utero impacts on latter life outcomes that has emerged in the last decade. 
• Auffhammer, Hsiang, Schlenker and Sobel's "Using Weather Data and Climate Model Output in Economic Analyses of Climate Change"provides a review of methods and issues associated with researching climate and its impact on social systems.
• Pattanyak and Pfaff's "Behavior, Environment, and Health in Developing Countries: Evaluation and  Valuation" surveys the literature on the interaction between household behavior and environmental health problems and comes up with four generalizable policy pathways

Weekend Links

1) A general audience-accessible explanation of why was Typhoon Haiyan so damaging

2) Statistically derived contributions of diverse human influences to twentieth-century temperature changes

3) Dani Rodrik on ideas in political economy models (NBER)

4) Andrew Gelman and Guido Imbens defend the search for "causes of effects" (NBER, and previously on FE)

5) ODI has a new report on "The geography of poverty, disasters and climate extremes in 2030"

6) Guy Grossman and Walker Hanlon's paper on leadership quality and monitoring is out in AJPS

7) "The Climate Corporation sells weather insurance, but it is an insurance company the way Google is an encyclopedia company"

8) "Climate models" (via Colin Schultz)

9) Maternal health externalities of youtube

10) "Learning how to die in the Anthropocene"

Destruction, Disinvestment, and Death: Economic and Human Losses Following Environmental Disaster

Typhoon Haiyan as seen from space, Copyright 2013 JMA/EUMETSAT

Last spring Sol and I finished up the working paper version of our paper "Destruction, Disinvestment, and Death: Economic and Human Losses Following Environmental Disaster." Since the paper is long and fairly technical, we decided it would be worthwhile to do a shorter, more general-audience-appropriate piece for the blog, something that seems especially relevant given Typhoon Haiyan's devastating landfall this past weekend. If you'd like to take a look at the paper itself, you can find a copy of it here on SSRN; a copy of the supplemental appendix can be found here.

The motivation for "Destruction, Disinvestment, and Death" stems from the fact that we actually know surprisingly little about how people fare in the wake of natural disasters.