El Niño is coming, make this time different

Kyle Meng and I published an op-ed in the Guardian today trying to raise awareness of the potential socioeconomic impacts, and policy responses, to the emerging El Niño.  Forecasts this year are extraordinary.  In particular, for folks who aren't climate wonks and who live in temperate locations, it is challenging to visualize the scale and scope of what might come down the pipeline this year in the tropics and subtropics. Read the op-ed here.

Countries where the majority of the population experience hotter conditions under El Niño are shown in red. Countries that get cooler under El Niño are shown in blue (reproduced from Hsiang and Meng, AER 2015)


Weekend Links

"Four dozen papers on conflict and fragility in Africa in under 2,000 words"

David Evans' coverage of last month's Annual Bank Conference on Africa is a great overview of some fascinating recent applied research. Highlights:

  • Extreme rain and drought both boost livestock theft in Kenya: raids driven by resource scarcity but also by weather that makes it easy to carry out a raid (Ralston).

  • Drought leads to increased violence against women. When the shock affects income asymmetrically across partners, it is associated with violence for the first time in the marriage (Cools et al.). 

  • Axbard et al. use variation in international mineral prices and within-country time and geographic variation to show that when a mine opens in South Africa, crime doesn’t increase. But you may not want to be around when the mine closes. 

  • “Members of ethnic groups exposed to greater historical missionary activity [in 19th-century Nigeria] express significantly less trust today,” using Afrobarometer trust measures (Okoye).
  • 4.16.2015

    Social welfare and robots

    As long as we're on the joint topics of ways to end an abstract and social welfare:
    "Policies that redistribute income across generations can ensure that a rise in robotic productivity benefits all generations."
    The ungated NBER working paper is here. (h/t Tyler Cowen, who has some thoughts on the general issue)


    Social welfare

    Meanwhile, in excellent ways to end an abstract:
    "[The policy] would also generate a significant welfare gain from the ex-ante standpoint of a newborn under the veil of ignorance."
    The original paper is here


    Data-driven causal inference

    Distinguishing cause from effect using observational data: methods and benchmarks

    From the abstract:
    The discovery of causal relationships from purely observational data is a fundamental problem in science. The most elementary form of such a causal discovery problem is to decide whether X causes Y or, alternatively, Y causes X, given joint observations of two variables X, Y . This was often considered to be impossible. Nevertheless, several approaches for addressing this bivariate causal discovery problem were proposed recently. In this paper, we present the benchmark data set CauseEffectPairs that consists of 88 different "cause-effect pairs" selected from 31 datasets from various domains. We evaluated the performance of several bivariate causal discovery methods on these real-world benchmark data and on artificially simulated data. Our empirical results provide evidence that additive-noise methods are indeed able to distinguish cause from effect using only purely observational data. In addition, we prove consistency of the additive-noise method proposed by Hoyer et al. (2009).
    From the arxiv.org blog (note):
    The basis of the new approach is to assume that the relationship between X and Y is not symmetrical. In particular, they say that in any set of measurements there will always be noise from various cause. The key assumption is that the pattern of noise in the cause will be different to the pattern of noise in the effect. That’s because any noise in X can have an influence on Y but not vice versa.
    There's been a lot of research in stats on "causal discovery" techniques, and the paper in essence is running a horse race between Additive-Noise Methods and Information Geometric Causal Inference, with ANM winning out. Some nice overview slides providing background are here.


    Disasters and religiosity

    Jeanet Sinding Bentzen has a new version of her working paper on disasters (mostly earthquakes) and religiosity:
    Acts of God: Religiosity and Natural Disasters Across Subnational World Districts
    Religiosity affects everything from fertility and labor force participation to health. But why are some societies more religious than others? To answer this question, I test the religious coping theory, which states that many individuals draw on their religious beliefs to understand and deal with adverse life events. Combining subnational district level data on values across the globe from the World Values Survey with spatial data on natural disasters, I find that individuals are more religious when their district was hit recently by an earthquake. And further, that individuals are more religious when living in areas with higher long term earthquake risk. Using data on children of immigrants in Europe, I document that this is mainly due to a long-term effect: high religiosity levels evolving in high earthquake risk areas, is passed on through generations to individuals no longer living in high earthquake risk areas. The impact is global: earthquakes increase religiosity both within Christianity, Islam, and Hinduism, and within all continents. Last, I document that the results are consistent with the literature on religious coping and inconsistent with alternative theories of insurance or selection.
    Selected quote:
    "The estimates indicate that increasing earthquake risk by 30 percentiles from the median increases religiosity by 9 percentiles. The tendency is global: Christians, Muslims, and Hindus all exhibit higher religiosity in response to elevated earthquake risk, and so do inhabitants of every continent."
     via Amir.


    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.)


    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:

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

    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.


    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.