tag:blogger.com,1999:blog-38137017707084426202024-03-19T04:28:04.043-07:00G-FEEDGlobal Food, Environment and Economic Dynamicssolhttp://www.blogger.com/profile/00936469103707728475noreply@blogger.comBlogger186125tag:blogger.com,1999:blog-3813701770708442620.post-13232573522651516872020-10-19T09:50:00.000-07:002020-10-19T09:50:26.412-07:00The GDP-temperature relationship - some thoughts on Newell, Prest, and Sexton 2018<p>Quantifying the potential effects of climate on aggregate economic outcomes like GDP is a key part of understanding the broader economic impacts of a changing climate. Lots of studies now use modern panel econometrics to study how past fluctuations in temperature have affected GDP around the world, including the landmark <a href="https://scholar.harvard.edu/files/dell/files/aej_temperature.pdf">2012 paper</a> by Dell, Jones, and Olken (henceforth DJO), our <a href="http://web.stanford.edu/~mburke/climate/BurkeHsiangMiguel2015.pdf">2015 paper</a> (henceforth BHM), and a number of more recent papers that either analyze new data on this topic (e.g. subnational GDP or income data, see <a href="https://www.re-source.com/wp-content/uploads/2020/08/Kalkuhl-Wenz-2020-Climate-conditions-and-economic-production.pdf">here</a> or <a href="http://web.stanford.edu/~mburke/papers/BurkeTanutama_NBER_w25779.pdf">here</a> or <a href="https://gspp.berkeley.edu/assets/uploads/research/pdf/Deryugina_Hsiang_w24072.pdf">here</a>) or revisit earlier results. </p><p>Newell, Prest, and Sexton 2018 (draft <a href="https://media.rff.org/documents/RFF20WP-18-17-rev.pdf">here</a>, henceforth NPS) is one recent paper that revisits our earlier work in BHM. Because we've gotten a lot of questions about this paper, Sol and I wanted to share our views of what we think we can learn from NPS. In short, our view is that their approach is not suitable to the question they are trying to ask, and that their conclusions as stated in their abstract (at least in their current draft) are directly contradicted by their results. This is important because their conclusions appear to shed important light on the aggregate economic impacts of unmitigated climate change. Unfortunately we do not believe that this is the case. </p><p>NPS seek to take a data-driven approach to resolving a number of empirical issues that come up in our earlier work. These include: (1) Does temperature affect the level or growth rate of GDP? (2) What is the "right" set of fixed effects or time trends to include as controls in analyzing the effect of temperature on GDP? (3) And what is the "right" functional form to describe the relationship between temperature and GDP changes? These choices -- particularly (1) and (3) -- are certainly influential in the findings of earlier studies. If temperature affects growth rates rather than levels, this can imply huge effects of future temperature increases on economic output, as small impacts on growth aggregate over time. If temperature has a globally nonlinear effect on output, as we argue in BHM, this suggests that both wealthy and poor countries can be affected by changes in temperature -- and not only poor countries, as suggested in earlier analyses. Resolving these questions is key to understanding the impacts of a warming climate, and its great that papers like NPS are taking them on.</p><p>However, we have some serious qualms with the approach that NPS take to answer these questions. NPS wish to use cross-validation to select which set of the above choices performs "best" in describing historical data. Cross-validation is a technique in which available data are split between disjoint training and testing datasets, and candidate models are trained on the training data and then evaluated on the held-out test data using a statistic of interest (typically RMSE or r-squared in these sorts of applications). The model with the lowest RMSE or highest r-squared on test data is then chosen as the preferred model. </p><p><b>Inference, not prediction</b>. This technique works great when your goal is prediction. But what if your goal is causal inference? i.e, in our case, in isolating variation in temperature from other correlated factors that might also affect GDP? It's not clear at all that models that perform the best on a prediction task will also yield the right causal results. For instance, prices for hotel rooms tend to be high when occupancy rates are high, but only a foolish hotel owner would raise prices to increase occupancy (h/t Susan Athey who I stole this example from). A good predictive model can get the causal story wrong. </p><p>This is clearly relevant in the panel studies of temperature on GDP. In existing work, great care is taken to choose controls that account for a broad range of time-invariant and time-varying factors that might be correlated with both temperature and GDP. These typically take the form of unit or time fixed effects and/or time trends. Again the goal in including these is not to better predict GDP but to isolate variation in temperature that is uncorrelated with other factors that could affect GDP, in order to identify the causal effect of temperature on GDP. The chosen set of controls constitute the paper's "identification strategy", and in this fixed effect setup unfortunately there is no clear data-driven approach -- including cross-validation -- for selecting these controls. The test for readers in these papers is not: do the authors predict GDP really well? It is instead: do the authors plausibly isolate the role of temperature from other confounding factors?</p><p><b>Growth vs level effects. </b>The main question NPS are asking is whether the causal effect of a temperature shock has "level effects", where the economy is hurt in one year but catches up in the next year, or "growth effects", where the economy is permanently smaller. This would require an identification strategy, which is what most of the literature has focused on following the method artfully outlined by DJO: isolate the role of temperature from other confounding factors using choices about fixed effects that most plausibly achieve this unconfounding, and then distinguish growth from level effects by looking at the sum of contemporaneous and lagged effects. If the sum is zero, this is evidence of level effects, and if it's not zero, evidence of growth effects. The current manuscript does not have an identification strategy for measuring these lagged effects, and instead is using goodness of fit metrics to draw causal inferences about the magnitude of these effects. The tool they are using is again not commensurate with the question they are asking. </p><p><b>Errors in interpretation.</b> These conceptual issues aside, the authors' conclusions in the abstract and introduction of their paper about the results from their cross validation exercise do not appear consistent with their actual findings as reported in the tables. The authors conclude that "<b>the best performing models</b> have non-linear temperature effects on <b>GDP levels</b>", but the authors demonstrate no statistically distinguishable differences in results between levels and growth models in their main tables (Table 2 and 3, and A1-A4), nor between linear and non-linear models. This directly contradicts the statement in their abstract. </p><p>To be precise, the authors state in the abstract "<span style="font-family: NimbusRomNo9L;">The best-performing
models have non-linear temperature effects on GDP levels." </span>But then on page 27 they clearly state: "<span style="font-family: NimbusRomNo9L;">The MCS </span><span style="font-family: NimbusRomNo9L;">["model confidence sets", or </span><span style="font-family: NimbusRomNo9L;">the set of best performing models whose performance is statistically indistinguishable from one another]</span><span style="font-family: NimbusRomNo9L;">, however, does not discern among temperature
functional forms or growth and level effects." This is in reference to Table 2, reproduced below; models in the MCS are denoted with asterisks, and a range of both growth and levels models have asterisks, meaning their performance cannot be statistically distinguished from one another. </span></p><p></p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhem36uIAhrivTAkoYgNLMfeRJuAHOonEZKwCewSyF9gEm33j5c2Kn60xfZ-l6IvZ16ZJ8Sdfl-nV4CsUVQJ9Pbldam6JRYFpKBpCD02TtCMEaWhZLgXdVWATAe-otkGr54oGbBT5coEJo1/s717/NPS_table2.png" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="636" data-original-width="717" height="355" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhem36uIAhrivTAkoYgNLMfeRJuAHOonEZKwCewSyF9gEm33j5c2Kn60xfZ-l6IvZ16ZJ8Sdfl-nV4CsUVQJ9Pbldam6JRYFpKBpCD02TtCMEaWhZLgXdVWATAe-otkGr54oGbBT5coEJo1/w400-h355/NPS_table2.png" width="400" /></a></div><span style="font-family: NimbusRomNo9L;"><p>So, again, the <i>paper's abstract is not consistent with its own stated results</i>. They do find that the model with quadratic time trends (as used by BHM) is outperformed by models without country time trends -- but again, the purpose of those time trends is to remove potential confounding variation, not to perfectly predict GDP. See <a href="http://www.g-feed.com/2019/08/do-gdp-growth-rates-have-trends.html">here</a> for a simple look at the data on whether individual countries growth rates have differential time trends that might make you worried about time-trending unobservables at a country level [spoiler: yes they do].</p></span><p></p><p><b>Errors in implementation.</b> Even if cross validation was the right tool here, the authors make some non-standard choices in how the CV is implemented which we again believe make the results very hard to interpret. Instead of first splitting the data between disjoint train and test sets, they first transform the data by regressing out the fixed effects, and then split the residualized data into train and test. But the remaining variation in the residualized data will be very different based on what set of fixed effects have been regressed out, and this will directly affect resulting estimates of the RMSE. It is thus not a surprise that models with region-year FE have lower RMSE than models with year FE (in models with no time trends) -- region-year FE's almost mechanically take out more of the variation. But this means you can't meaningfully compare RMSE's across different fixed effects in the way that they are doing -- you are literally looking at 2 different datasets with 2 different outcomes. You can only in principle compare functional forms within a given choice of FE. </p><p>Imagine the following: you have outcome Y, predictor X, and covariates W and Z. Z is a confound: correlated with both X and Y. W is not correlated with Y but X.</p><p>In version 1 you partial Z out of both X and Y, and generate residualized values Y_1 and X_1.</p><p>In version 2 you partial W out of both X and Y, and generate residualized values Y_2 and X_2. </p><p>This is in effect what NPS do, and then they want us to compare Y_1 = f(X_1) versus Y_2 = f(X_2). But this clearly doesn't make sense, because Y_2 and X_2 still have the confounding variation of Z in them, and Y_1 and Y_2 are no longer measuring the same thing. So comparing the predictive power of f(X_1) vs f(X_2) is not meaningful. It is also not how cross validation is supposed to work - instead, we should be comparing predictive performance on the same Y variables. </p><p><b>Further hazards in cross validation</b>. While we don't agree that CV can be used to select the fixed effects in this setting, we then do agree with NPS that cross validation <i>could</i> in principle be used to identify the "right" functional form that relates temperature to GDP (conditional on chosen controls). e.g is the relationship linear? quadratic? something fancier? This works because the causal part has already been taken care of by the fixed effects (or so one hopes), and so what's left is to best describe the functional form of how x relates to y. Cross validation for this purpose has been successfully implemented settings in which temperature explains a lot of the variation in the outcome -- e.g. in studies of agricultural productivity (see Schlenker and Roberts 2009 for an early example in this literature). </p><p>But unfortunately when it comes to GDP, while temperature has a strong and statistically significant relationship to GDP in past work, it does not explain a lot of the overall interannual variation in GDP; GDP growth is noisy and poorly predicted even by the hotshot macro guys. In this low r-squared environment, selecting functional form by cross validation can be difficult and perhaps hazardous. It's too easy to overfit to noise in the training set.</p><p>To see this, consider the following simulation in which we specify a known cubic relationship between y and x and then try to use cross validation to recover the "right" order of polynomial, and study our ability to do so as we crank up the noise in y. We do this a bunch of times, each time training the regression model on 80% of the data and testing on 20%. We calculate RMSE on the test data and compute average % reduction in RMSE relative to a model with only an intercept. As shown in the figure below, we can mostly reject the linear model but have a lot of trouble picking out the cubic model from anything else non-linear, particularly in settings with the overall explained variation in y is small. Given that temperature explains <5% of the variation in GDP growth rates (analogous to the far right grouping of bars in each plot), cross validation is going to really struggle to pick the "right" functional form. </p><p>To be clear, the point we're making in this section is just about functional form, not about growth versus levels. Even for this more narrow task in which cross validation is potentially appropriate, it does not end up being a useful tool because the model overfits to noise in the training set.</p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEh-T4TOiDZ3IFvmizK6YMeez8yzHYNgMldu_Y1MZrN6FDhtum2_xTmPPElUoJN14r-ZCEt75if-q3oV973EHRkVg264V3djhd0x1Hu7G8F5DmLDmhM7hiz840gRp2ruRvBAgrFMYpjJoPEa/s1362/CV_simulation.png" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="1362" data-original-width="1322" height="640" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEh-T4TOiDZ3IFvmizK6YMeez8yzHYNgMldu_Y1MZrN6FDhtum2_xTmPPElUoJN14r-ZCEt75if-q3oV973EHRkVg264V3djhd0x1Hu7G8F5DmLDmhM7hiz840gRp2ruRvBAgrFMYpjJoPEa/w622-h640/CV_simulation.png" width="622" /></a></div><div><br /></div>This exercise does illustrate an important point, however: right now the data are consistent with a bunch of potential functional forms that we can't easily distinguish. We argue in BHM that there is pretty consistent evidence that the temperature/growth relationship is non-linear and roughly quadratic at the global level, but we certainly can't rule out higher order polynomials and we say so in that paper. <br /><p><b>Wrapping up.</b> So where does this leave us? Techniques like cross validation certainly still have utility for other approaches to causal inference questions (e.g. in selecting suitable controls for treated units in synthetic control settings), and there might be opportunities to apply those approaches in this domain. Similarly, we fully agree with NPS's broader point that using data-driven approaches to make key analytical decisions in climate impacts (or any other empirical) paper should be our goal. But in this particular application, we don't think that the specific approach taken by NPS has improved our understanding of climate/economy linkages. We look forward to working with them and others to continue to make progress on these issues. </p>Marshall Burkehttp://www.blogger.com/profile/15436297075698378164noreply@blogger.com1tag:blogger.com,1999:blog-3813701770708442620.post-86127115156565349962020-09-11T14:08:00.001-07:002020-09-11T14:22:27.247-07:00Indirect mortality from recent wildfires in CA<p>[This post is joint with <a href="http://stanford.edu/~samhn/" target="_blank">Sam Heft-Neal</a>]</p><p>Wildfire activity in the US West coast has been unprecedented in the last month, with fires burning larger and faster than ever experienced. Multiple communities in the paths of these fires have been entirely destroyed, and wildfire smoke has blanketed huge swaths of the West Coast for weeks.</p><p>While media coverage on fire impacts has justifiably focused on the lives that have immediately been lost to the fire, the total costs in terms of human lives and health is likely far larger, due to the immense amount of smoke that has been inhaled over the last 3 weeks by the very large number of people living on the West Coast. </p><p>How large might these effects be? We know a lot about how exposure to certain air pollutants -- in particular, PM2.5 -- affects a range of health outcomes, including mortality. Recent wildfire activity has led to a massive increase in PM2.5 above normal levels. As anyone who lives in CA or has watched the news knows, air quality has been terrible, and the monitoring data of course bear this out. Below is a plot of the deviation of daily PM2.5 in 2020 from the previous 5yr average, beginning Aug 1 and going through Sep 10th 2020. This is based on station data from <a href="https://www.epa.gov/outdoor-air-quality-data/download-daily-data" target="_blank">EPA AirNow</a>, and we average over each reporting station in each location on a given day. </p><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><img border="0" data-original-height="2048" data-original-width="1716" height="640" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiHvQtDkSx3_FIykXj1MMA4SKsUZnUqYpV07MqyHIMRINFomflOb3HOL1PN22aD7w1iOX4fU8sgtEPPFHA_dONmzgFuFVJiVFd3n0CxbuLLMvTMuC_j5CvaIYaIBBys7f3qBYkNp08bi_8O/w536-h640/Excess_PM_2020.png" style="margin-left: auto; margin-right: auto;" width="536" /></td></tr><tr><td class="tr-caption" style="text-align: center;"><p class="p1" style="-webkit-text-stroke-color: rgb(0, 0, 0); font-size: 12px; font-stretch: normal; font-variant-east-asian: normal; font-variant-numeric: normal; line-height: normal; margin: 0px; text-align: start;"><span class="s1" style="font-kerning: none;"><i>Difference in PM2.5 on each day, 2020 relative to 2015-19 average. Data are from EPA AirNow, and represent averages over all stations reporting in each area.</i></span></p></td></tr></tbody></table><p><br />For most locations in CA, PM2.5 was 10-50ug/m3 higher than normal on many days, which is a massive increase above average (which in the Bay Area is typically ~ 10ug). For simplicity, we are going to attribute all the excess PM2.5 in Aug/Sept 2020 (relative to 2015-19 average) to wildfires. While this is probably not completely correct, it's not outlandish: the huge observed spikes were definitely due to the wildfires. Before about Aug 15, PM2.5 was running somewhat below recent averages across much of CA; fires kicked up in mid august, and PM2.5 levels shot through the roof. </p><p>What are the health consequences of this change in exposure? The best paper on the short run mortality and morbidity consequences of PM exposure is a <a href="https://secureservercdn.net/166.62.111.84/0zv.ccf.myftpupload.com/wp-content/uploads/2020/01/2019-AER-Costs-of-Air-Pollution.pdf" target="_blank">recent (very impressive) paper by Deryugina et al </a>published in the American Economic Review. Using very detailed Medicare data, they estimate that a +1ug PM2.5 increase on one day increases deaths over the next three days by 0.7 per 1 million people over 65+, and increases ER visits among the elderly by 2.7 per million people. We emphasize that these result is for all PM2.5, not just PM2.5 from smoke; the literature is still undecided whether the latter has differential health effects, so we will just assume here that the effects are similar. </p><p>For simplicity, let's say total PM2.5 values (excess PM2.5 since Aug 1, summed over days) were 300ug/m3 above normal across CA due to the wildfires. This will be a little too high for LA, but will be way too low for most of NorCal and the central valley. </p><p>There are about 6 million people aged 65+ in CA. Applying the Deryugina et al estimates to the change in PM and the exposed population, we arrive at 1200 excess deaths (deaths that would not have happened otherwise) and 4800 additional ER visits among the elderly. If we use the Deryugina et al estimates of how much one day of additional PM2.5 increases mortality over the next month (not just next three days), the estimated number of deaths rises to 3000. This is just in CA alone! And just for people aged 65+. Oregon and Washington are being hit very hard right now too, and non-elderly are also surely affected. So this is likely a substantial lower bound on total health costs. </p><p>There are a number of caveats to this calculation that are important to consider. In most cases they suggest these numbers above could be conservative. Note that we will never know the "true" number because we don't observe the counterfactual -- i.e what would have happened in the absence of the dramatic decline in air quality. But the purpose of the Deryugina et al paper is to get us as close as we could possibly get to that counterfactual. Some caveats:</p><p></p><p></p><p></p><p></p><ul style="text-align: left;"><li>Again, we attribute all the change in PM2.5 in 2020 (relative to 5-yr avg) as due to wildfire. While this is probably not fully correct, the big spikes were definitely from wildfires. Further analysis will help clarify this. </li><li>PM2.5 from wildfire smoke might not have the same effect as overall PM2.5 (only some of which comes from wildfire). The science isn't clear on this yet.</li><li>We are in the midst of the COVID pandemic, and this has as-yet-unknown implications for our understanding of how air pollution affects health outcomes. Early evidence seems to suggest that poor air quality could worsen COVID-related outcomes, and if that's the case then our numbers above could be lower bounds.</li><li>Numbers above only look at elderly mortality and ER visits, and it's likely that other groups are very substantially affected (e.g. the very young, those with pre-existing cardiovascular or respiratory conditions). Again, above numbers likely a dramatic lower bound on overall health consequences for that reason.</li><li>We don't know a ton about health responses at super high exposures. Does it tail off? Does it get way worse? You can find papers telling you both. Right now we're assuming response is linear, which is consistent with a lot of the literature. </li><li>We also don't have a full picture of "displacement" - i.e. if someone passes away due to air pollution exposure, maybe they were already extremely sick and so would have passed away in the near term anyway. Deryugina et al explore this to some degree by looking at 3-day (and longer) responses to a single day of pollution increase. They find effects of 1-day exposure over the next month to be about twice as big as the 3-day estimates we use, so again this could suggest our numbers are a lower bound. </li><li>Effects could also amplify over time, as its probably the case that exposure to 7 days in a row of terrible air is worse that exposure to 7 days spread out. Our numbers do not consider this, nor do we know of good estimates in the literature that would help with this. Please point them out if so!</li></ul><div class="separator" style="clear: both; text-align: center;"><br /></div>These overall effects can be in large part attributable to climate change, which has dramatically increased the likelihood and severity of wildfire. Understanding this pathway of climate impacts is, we think, underappreciated, and something that <a href="http://web.stanford.edu/~mburke/papers/burke_et_al_wildfire_NBER.pdf" target="_blank">we're working on</a> (along with lots of others!).<br /><br />Marshall Burkehttp://www.blogger.com/profile/15436297075698378164noreply@blogger.com0tag:blogger.com,1999:blog-3813701770708442620.post-1332248291978177682020-08-31T06:00:00.001-07:002020-08-31T06:00:03.264-07:00Seminar talk on Spatial First Differences<p><a href="http://www.globalpolicy.science/hannah-druckenmiller">Hannah Druckenmiller</a> and I wrote a <a href="http://www.globalpolicy.science/s/DRUCKENMILLER_HSIANG_w25177.pdf" target="_blank">paper</a> on a new method we developed that allows you to do cross-sectional regressions while eliminating a lot (or all) <a href="https://en.wikipedia.org/wiki/Omitted-variable_bias">omitted variables bias</a>. We are really excited about it and it seems to be performing well in <a href="https://scholar.google.com/scholar?oi=bibs&hl=en&cites=10098127759741634961">a bunch of cases</a>. </p><p>Since people seem to like watching TV more than reading math, here's a recent talk I gave explaining the method to the <a href="https://sites.google.com/view/nz-econ-eseminar-series/home">All New Zealand Economics Series</a>. (Since we don't want to discriminate against the math-reading-folks-who-don't-want-to-read-the-whole-paper, we'll also post a blog post explaining the basics...)</p><p>If you want to try it out, code is <a href="http://www.globalpolicy.science/code" target="_blank">here</a>.</p><div style="text-align: center;"><iframe allowfullscreen="" frameborder="0" height="270" src="https://www.youtube.com/embed/SSbkafo_5aY" width="480"></iframe></div>solhttp://www.blogger.com/profile/00936469103707728475noreply@blogger.com0tag:blogger.com,1999:blog-3813701770708442620.post-2322527859381953342020-06-11T17:28:00.000-07:002020-06-11T17:28:32.232-07:00Public database of non-pharmaceutical interventions for COVID-19This week we published the new paper <a href="https://www.nature.com/articles/s41586-020-2404-8" target="_blank">The effect of large-scale anti-contagion policies on the COVID-19 pandemic</a> which tries to estimate effect of of over 1,700 policies on the day-over-day growth rate of infections. We compiled subnational data across six countries (China, South Korea, Italy, Iran, France, and the USA) and tried to to break down the effects of different overlapping policies.<br />
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One result of the paper is that during the period of study, we estimate that there would be roughly 500 million more COVID-19 infections by early April, across these six countries, in the absence of policy. We get to this number by linking up some reduced-form econometric tools from GDP-growth modeling with some SIR/SIER models from epidemiology. If you don't feel like reading a long paper, you can just watch these GIFs of China and Italy (more are <a href="http://www.globalpolicy.science/covid19" target="_blank">here</a>). Left is what actually happened (area of red circles = cumulative confimred cases), right is a "no policy" simulation:<br />
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Or if you are more serious about research but still don't want to read the paper, we have two videos explaining the paper <a href="http://www.globalpolicy.science/covid19#videos" target="_blank">here</a> -- one is a presentation at the <a href="https://sites.google.com/umn.edu/econhelp-workinggroup/home">HELP Seminar</a>, the other is a 3 min summary that involves a lot of figurative and literal arm-waving.</div>
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<div style="text-align: left;">
The hardest part of the study was actually putting together an original standardized dataset of all 1,717 NPIs. We have posted all the data <a href="http://www.globalpolicy.science/covid19#code-and-data">here</a> (alongside the code). We are hoping that people will use this new data set for other projects and welcome feedback and/or comments, especially if you think we missed something (much of this was collected by hand, so it is a dataset that will certainly improve over time). The dataset is detailed in the <a href="https://static-content.springer.com/esm/art%3A10.1038%2Fs41586-020-2404-8/MediaObjects/41586_2020_2404_MOESM1_ESM.pdf">Supplementary Information</a> of the paper. If you use the data, please just cite the <a href="https://www.nature.com/articles/s41586-020-2404-8">article</a> as the source.</div>
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We constructed the data set to be at the same spatial resolution of the the most finely resolved case data we could obtain, which was the second administrative level for Italy and China (see figs above), and the first admin level for everyone else. The policies were actually deployed at a variety of admin levels, depending on the country and policy. Here's the table from the appendix summarizing the policies in the data set:</div>
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjJpGUo5GmKYB9QGBHkjsrDBezpRt39NujQrYWUY4GaqyMARX9_QbsVsyvzHQ7U7FJCbXAQdc0nJDeGArzelfWfgdSfpD1zqunHzqfFM-hyXPjDxNYMR2d51BjjGRsSDWrF4u5D9nQToGk/s2118/GPL_COVID_Policies.jpg" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="1136" data-original-width="2118" height="344" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjJpGUo5GmKYB9QGBHkjsrDBezpRt39NujQrYWUY4GaqyMARX9_QbsVsyvzHQ7U7FJCbXAQdc0nJDeGArzelfWfgdSfpD1zqunHzqfFM-hyXPjDxNYMR2d51BjjGRsSDWrF4u5D9nQToGk/w640-h344/GPL_COVID_Policies.jpg" width="640" /></a></div>
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<div style="text-align: left;">
We also worked very hard to standardize policy definitions as much as possible across different countries, e.g. we tried to make "business closure" or "no gathering" mean something similar across different countries, but there is substantial nuance and some things could not be standardized, so please make sure to consult the <a href="https://static-content.springer.com/esm/art%3A10.1038%2Fs41586-020-2404-8/MediaObjects/41586_2020_2404_MOESM1_ESM.pdf">documentation</a>.</div>
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Note that there is also variation in the intensity of policies in the data set. This comes from the fact that many policies are encoded as composites (e.g. there are multiple policies that make up a single policy definition) and also because many policies are not deployed uniformly across an entire administrative unit, so we computed the population-weighted "exposure" to each policy (e.g. if only half the population of a US state adopted a policy, since many are set at the county level, this policy got coded as one-half). Here's a plot of policy intensity over time for the US sample (other plots are available <a href="http://www.globalpolicy.science/covid19#image-gallery">here</a>, but weren't in the paper because we ran out of our figure allotment):</div>
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<table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto; text-align: center;"><tbody>
<tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjoSY3uCzVyUC-dsjAxtRb-FG5gflgAQ-dl2E0a0OHalnfW_2pYgrh220f3hkPY3wFwShUF6d3lg5IUEHKZ_SIJG6pr8gegjBTIOf_d0tqEl6xWrTebQ777PiCNMOKAFNHHUpFNaxV64zk/s1600/usa_policy_timelines_GPL.jpg" style="margin-left: auto; margin-right: auto;"><img border="0" data-original-height="488" data-original-width="1600" height="194" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjoSY3uCzVyUC-dsjAxtRb-FG5gflgAQ-dl2E0a0OHalnfW_2pYgrh220f3hkPY3wFwShUF6d3lg5IUEHKZ_SIJG6pr8gegjBTIOf_d0tqEl6xWrTebQ777PiCNMOKAFNHHUpFNaxV64zk/w640-h194/usa_policy_timelines_GPL.jpg" width="640" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;"> non-pharmaceutical interventions over time in the USA (click to enlarge) </td></tr>
</tbody></table>
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<div>
Several folks (including a journal referee and someone at the DOD) asked if we planned to keep collecting this data and making it public. In principle, we would love to do that, but the entire team did the whole project <i>pro bono</i> and have since had to go back to our normal jobs. That said, if someone reading this wants to to support/otherwise enable expansion of this data for the public good, you know where to find me, since I won't be going anywhere for a while...</div>
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Let us know if you use the data, we'll excited to hear if it's useful.</div>
solhttp://www.blogger.com/profile/00936469103707728475noreply@blogger.com0tag:blogger.com,1999:blog-3813701770708442620.post-44225708908670359892020-03-08T23:21:00.000-07:002020-03-17T21:28:26.996-07:00COVID-19 reduces economic activity, which reduces pollution, which saves lives. <div dir="ltr" style="text-align: left;" trbidi="on">
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<br />
COVID-19 is a massive global economic and health challenge, having caused >3500 global deaths as of this writing (Mar 8) and untold economic and social disruption. This disruption is only likely to increase in coming days in regions where the epidemic is just beginning. Strangely, this disruption could also have unexpected health benefits -- and these benefits could be quite large in certain parts of the world. Below I calculate that the reductions in air pollution in China caused by this economic disruption likely saved twenty times more lives in China than have currently been lost directly due to infection with the virus in that country.<br />
<br />
<b>None of my calculations support any idea that pandemics are good for health. The effects I calculate just represent health benefits from the air pollution changes wrought by the economic disruption, and do not account for the many other short- or long-term negative consequences of this disruption on health or other outcomes; these harms likely vastly exceed any health benefits from reduced air pollution. </b>[<i>Edit: added this paragraph 3/16 to emphasize the lessons from my findings, in case readers don't make it further..</i>]<br />
<b><br /></b>
Okay, on to the calculation.<br />
<br />
A few weeks ago, <a href="https://www.earthobservatory.nasa.gov/images/146362/airborne-nitrogen-dioxide-plummets-over-china">NASA published </a>striking satellite images of the massive reduction in air pollution (specifically, NO2) over China resulting from the economic slow-down in that country following it's aggressive response to COVID-19.<br />
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<table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto; text-align: center;"><tbody>
<tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjRRDIGPqbkWX4mDew40cqUyKBSC96BGPZj7HSMtkkhkKxrCXLpcalgyRt-g5cZTAw-yP5AiWT34VZ2mE7AXPMryZe2pph5IFxsgs-3MSThv864yUNhPLGBP8xhoVy6xtwtdrfM1cy9vS9B/s1600/china_trop_2020056.png" imageanchor="1" style="margin-left: auto; margin-right: auto;"><img border="0" data-original-height="545" data-original-width="720" height="302" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjRRDIGPqbkWX4mDew40cqUyKBSC96BGPZj7HSMtkkhkKxrCXLpcalgyRt-g5cZTAw-yP5AiWT34VZ2mE7AXPMryZe2pph5IFxsgs-3MSThv864yUNhPLGBP8xhoVy6xtwtdrfM1cy9vS9B/s400/china_trop_2020056.png" width="400" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;">Source: <a href="https://www.earthobservatory.nasa.gov/images/146362/airborne-nitrogen-dioxide-plummets-over-china">NASA</a></td></tr>
</tbody></table>
<a href="https://energyandcleanair.org/why-does-the-smog-strike-beijing-even-when-the-city-is-closed-down/">Separate analyses</a> indeed found that ground-based concentrations of key pollutants -- namely PM2.5 -- fell substantially across much of the country. These reductions were not uniform. In northern cities such as Beijing, where much of wintertime pollution comes from winter heating, reductions were absent. But in more southern cities such as Shanghai and Wuhan where wintertime pollution is mainly from cars and smaller industry, pollution declines appeared to be dramatic.<br />
<br />
Given the <a href="https://ourworldindata.org/air-pollution">huge amount of evidence</a> that breathing dirty air contributes heavily to premature mortality, a natural -- if admittedly strange -- question is whether the lives saved from this reduction in pollution caused by economic disruption from COVID-19 exceeds the death toll from the virus itself. Even under very conservative assumptions, I think the answer is a clear "yes".<br />
<br />
Here are the ingredients to doing this calculation [<i>Edit: see additional details I added at end of this post, if really wanting to nerd out</i>]. First we need to know <b>how much the economic disruption caused by COVID-19 in China reduced air pollution in the country</b>. The <a href="https://energyandcleanair.org/why-does-the-smog-strike-beijing-even-when-the-city-is-closed-down/">estimates already discussed</a> suggest about a 10ug/m3 reduction in PM across China in Jan-Feb of 2020 relative to the same months in the previous 2 years (I get this from eyeballing the figure mid-way down the <a href="https://energyandcleanair.org/why-does-the-smog-strike-beijing-even-when-the-city-is-closed-down/">blog post</a>). To confirm these results in independent data, I downloaded hourly PM2.5 data from <a href="https://airnow.gov/index.cfm?action=airnow.global_summary">US government sensors</a> in four main cities in China (Beijing, Shanghai, Chengdu, and Guangzhou). I calculated the daily average (and trailing 7-day mean) of PM2.5 since Jan 2016, and then again compared Jan-Feb 2020 to all other Jan-Feb periods in these cities.<br />
<br />
I find results that are highly consistent with the above analysis. Below is a plot of the data in each city. The blue line is the 7-day average in 2020, and the thick red line is the same average over the years 2016-19 (thin red lines are each individual year in that span). On average across these four cities, I find an average daily reduction of 15-17ug/m3 PM2.5 across both Jan-Feb 2020 relative to the average in the previous four years. This average difference is highly statistically significant. As in the above analysis, Beijing appears to be the outlier, with no decline in PM relative to past years when analyzed on its own.<br />
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<table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto; text-align: center;"><tbody>
<tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjNmFYP6g4eFVcZ5ytUtqN7bLajSoG1lKG1JyjyxIbAhUTqNU8dQFEiiNAd5FUmwqar_2zZkBLz76FvXhLfq3fjb8E5M_ceLk1MbP77dqtY_Fo55RrfRtDXK4auz9Q1L8oHesZ0ys6QkWTd/s1600/CityPM25_clean.png" imageanchor="1" style="margin-left: auto; margin-right: auto;"><img border="0" data-original-height="603" data-original-width="855" height="449" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjNmFYP6g4eFVcZ5ytUtqN7bLajSoG1lKG1JyjyxIbAhUTqNU8dQFEiiNAd5FUmwqar_2zZkBLz76FvXhLfq3fjb8E5M_ceLk1MbP77dqtY_Fo55RrfRtDXK4auz9Q1L8oHesZ0ys6QkWTd/s640/CityPM25_clean.png" width="640" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;"><i>PM2.5 concentrations in four Chinese cities in Jan-Feb 2016-2019 (red lines) vs same period in 2020 (blue lines). All cities except Beijing show substantial overall reductions during the period. </i></td></tr>
</tbody></table>
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<br />
To be very conservative, let’s then assume COVID-19 reduced PM2.5 on average by 10ug/m3 for in China, that this effect only lasted 2 months, and that only some urban areas (and no rural areas) experienced this change (see below).<br />
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Next we need to know how these changes in air pollution translated into reductions in premature mortality. Doing this requires knowing three numbers:<br />
<ol style="text-align: left;">
<li><i>The change in the age specific mortality rate per unit change in PM2.5</i>. For this we use estimates from <a href="https://www.sciencedirect.com/science/article/abs/pii/S0095069616300237">He et al 2016</a>, who studied the effects of air quality improvements around the Beijing Olympics on mortality rates -- a quasi-experimental setting on air quality not unlike that induced by COVID-19. He et al found large reductions in mortality for children under five and for adults over 65, with monthly under-5 mortality increasing 2.9% for every 1ug/m3 increase in PM2.5, and about 1.4% for people over 70 years old (note: they measure impacts using PM10, and to convert to PM2.5 we use the common assumption in the literature that PM2.5 = 0.7*PM10 and that all impacts of PM10 are through the PM2.5 component). Estimates from the He et al paper are broadly consistent with other quasi-experimental studies on the effects of air pollution on infant health, including estimates from the US, Turkey, and Africa (see Fig 5b <a href="https://www.nber.org/papers/w26107.pdf">here</a>). As a more conservative approach, we also calculate mortality using 1% increase in mortality per 1ug increase in PM2.5 for both kids and older people. In all cases, we very conservatively assume that no one between the ages of 5 and 70 is affected. </li>
<li><i>The baseline age-specific mortality rates for kids and older people</i>, which we take from <a href="https://vizhub.healthdata.org/gbd-compare/">Global Burden of Disease estimates</a>. I note a large apparent discrepancy between the GBD numbers, which give an under-5 mortality rate of 205 per 100,000 live births in China in the most recent year, and the <a href="https://data.worldbank.org/indicator/SH.DYN.MORT?locations=CN">WB/Unicef estimate</a>, which puts the number at 860 per 100,000 live births (4x higher!). I use the much smaller GBD estimate in my calculations to be conservative, but someone should reconcile these... </li>
<li>The total population affected by the changes in air pollution. For this we take the total Chinese population as <a href="https://population.un.org/wpp/DataQuery/">estimated by the UN</a>, and conservatively assume that only 50% of the population are affected by the change in air quality. Since roughly 60% of Chinese live in urban areas, this is like assuming there is no effect in rural areas and some urban areas remain unaffected. </li>
</ol>
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<div>
Putting these numbers together [see table below for details] yields some very large reductions in premature mortality. Using the He et al 2016 estimates of the impact of changes in PM on mortality, I calculate that having 2 months of 10ug/m3 reductions in PM2.5 likely has saved the lives of 4,000 kids under 5 and 73,000 adults over 70 in China. Using even more conservative estimates of 10% reduction in mortality per 10ug change, I estimate 1400 under-5 lives saved and 51700 over-70 lives saved. Even under these more conservative assumptions, the lives saved due to the pollution reductions are roughly 20x the number of lives that have been directly lost to the virus (based on March 8 estimates of 3100 Chinese COVID-19 deaths, taken from <a href="https://www.worldometers.info/coronavirus/#countries">here</a>). </div>
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What's the lesson here? It seems clearly incorrect and foolhardy to conclude that pandemics are good for health. Again I emphasize that the effects calculated above are just the health benefits of the air pollution changes, and do not account for the many other short- or long-term negative consequences of social and economic disruption on health or other outcomes; these harms could exceed any health benefits from reduced air pollution. But the calculation is perhaps a useful reminder of the often-hidden health consequences of the status quo, i.e. the substantial costs that our current way of doing things exacts on our health and livelihoods.<br />
<br />
Might COVID-19 help us see this more clearly? In my narrow academic world -- in which most conferences and academic meetings have now been cancelled due to COVID-19, and where many folks are cancelling talk invitations and not taking flights -- it might be a nice opportunity to re-think our production function with regard to travel. For most (all?) of us academics, flying on airplanes is by far the most polluting thing we do. Perhaps COVID-19, if we survive it, will help us find less polluting ways to do our jobs. More broadly, the fact that disruption of this magnitude could actually lead to some large (partial) benefits suggests that our normal way of doing things might need disrupting.<br />
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[Thanks to <a href="http://stanford.edu/~samhn/">Sam Heft-Neal</a> for helping me double check these calculations; errors are my own].<br />
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----------------------------------------------------------------------------------------------<br />
<i>Edited 3/9 to add additional details below on calculations and assumptions. </i><br />
<br />
Here are the actual values and math I used, either using He et al 2016 estimates of the PM/mortality relationship or the more conservative 1% change in mortality per 1 ug/m3 PM2.5 estimate. To calculate the % change in mortality rate from the observed change in PM2.5, I multiply (a) by (c). This number is then multiplied by the baseline monthly mortality rate in (d) to get the total change in the mortality rate, which is then multiplied by the total affected population in each age group (e*f).<br />
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<tbody>
<tr>
<td align="left" height="91" valign="bottom"><b><span style="color: black;">age group</span></b></td>
<td align="center" valign="bottom"><b><span style="color: black;">reduction in monthly pm2.5</span></b></td>
<td align="center" valign="bottom"><b><span style="color: black;">% change in monthly mortality rate per 10 ug pm 10 </span></b></td>
<td align="center" valign="bottom"><b><span style="color: black;">% change in mortality per 1 ug PM2.5</span></b></td>
<td align="center" valign="bottom"><b><span style="color: black;">baseline monthly mortality rate per 100,000 for each age group</span></b></td>
<td align="center" valign="bottom"><b><span style="color: black;">population in each age group (100,000s)</span></b></td>
<td align="center" valign="bottom"><b><span style="color: black;">percent of population affected</span></b></td>
<td align="center" valign="bottom"><b><span style="color: black;">two month reduction in mortality</span></b></td>
</tr>
<tr>
<td align="left" height="23" style="border-bottom: 1px solid #000000;" valign="bottom"><span style="color: black;"><br /></span></td>
<td align="center" style="border-bottom: 1px solid #000000;" valign="bottom"><b><span style="color: black;">(a)</span></b></td>
<td align="center" style="border-bottom: 1px solid #000000;" valign="bottom"><b><span style="color: black;">(b)</span></b></td>
<td align="center" style="border-bottom: 1px solid #000000;" valign="bottom"><b><span style="color: black;">(c) </span></b></td>
<td align="center" style="border-bottom: 1px solid #000000;" valign="bottom"><b><span style="color: black;">(d)</span></b></td>
<td align="center" style="border-bottom: 1px solid #000000;" valign="bottom"><b><span style="color: black;">(e)</span></b></td>
<td align="center" style="border-bottom: 1px solid #000000;" valign="bottom"><b><span style="color: black;">(f)</span></b></td>
<td align="center" style="border-bottom: 1px solid #000000;" valign="bottom"><b><span style="color: black;">(g)</span></b></td>
</tr>
<tr>
<td align="left" height="12" valign="bottom"><span style="color: black;"><br /></span></td>
<td align="center" valign="bottom"><b><span style="color: black;"><br /></span></b></td>
<td align="center" valign="bottom"><b><span style="color: black;"><br /></span></b></td>
<td align="center" valign="bottom"><b><span style="color: black;"><br /></span></b></td>
<td align="center" valign="bottom"><b><span style="color: black;"><br /></span></b></td>
<td align="center" valign="bottom"><b><span style="color: black;"><br /></span></b></td>
<td align="center" valign="bottom"><b><span style="color: black;"><br /></span></b></td>
<td align="center" valign="bottom"><b><span style="color: black;"><br /></span></b></td>
</tr>
<tr>
<td align="left" bgcolor="#D9D9D9" colspan="8" height="21" valign="bottom"><i><span style="color: black;">Using He et al 2016 PM10 estimates in (b)</span></i></td>
</tr>
<tr>
<td align="left" height="21" valign="bottom"><span style="color: black;">under 5</span></td>
<td align="center" sdnum="1033;" sdval="-10" valign="bottom"><span style="color: black;">-10</span></td>
<td align="center" sdnum="1033;" sdval="20" valign="bottom"><span style="color: black;">20</span></td>
<td align="center" sdnum="1033;0;0.0" sdval="2.85714285714286" valign="bottom"><span style="color: black;">2.9</span></td>
<td align="center" sdnum="1033;0;0.0" sdval="17.0833333333333" valign="bottom"><span style="color: black;">17.1</span></td>
<td align="center" sdnum="1033;0;0.0" sdval="839.32" valign="bottom"><span style="color: black;">839.3</span></td>
<td align="center" sdnum="1033;" sdval="0.5" valign="bottom"><span style="color: black;">0.5</span></td>
<td align="center" sdnum="1033;0;0" sdval="-4096.68095238095" valign="bottom"><span style="color: black;">-4097</span></td>
</tr>
<tr>
<td align="left" height="21" valign="bottom"><span style="color: black;">over 70</span></td>
<td align="center" sdnum="1033;" sdval="-10" valign="bottom"><span style="color: black;">-10</span></td>
<td align="center" sdnum="1033;" sdval="10" valign="bottom"><span style="color: black;">10</span></td>
<td align="center" sdnum="1033;0;0.0" sdval="1.42857142857143" valign="bottom"><span style="color: black;">1.4</span></td>
<td align="center" sdnum="1033;0;0.0" sdval="523.833333333333" valign="bottom"><span style="color: black;">523.8</span></td>
<td align="center" sdnum="1033;0;0.0" sdval="981.13" valign="bottom"><span style="color: black;">981.1</span></td>
<td align="center" sdnum="1033;" sdval="0.5" valign="bottom"><span style="color: black;">0.5</span></td>
<td align="center" sdnum="1033;0;0" sdval="-73421.2283333333" valign="bottom"><span style="color: black;">-73421</span></td>
</tr>
<tr>
<td align="left" height="21" valign="bottom"><span style="color: black;">total </span></td>
<td align="center" valign="bottom"><span style="color: black;"><br /></span></td>
<td align="center" valign="bottom"><span style="color: black;"><br /></span></td>
<td align="center" valign="bottom"><span style="color: black;"><br /></span></td>
<td align="center" valign="bottom"><span style="color: black;"><br /></span></td>
<td align="center" valign="bottom"><span style="color: black;"><br /></span></td>
<td align="center" valign="bottom"><span style="color: black;"><br /></span></td>
<td align="center" sdnum="1033;0;0" sdval="-77517.9092857143" valign="bottom"><b><span style="color: black;">-77518</span></b></td>
</tr>
<tr>
<td align="left" height="11" valign="bottom"><span style="color: black;"><br /></span></td>
<td align="center" valign="bottom"><span style="color: black;"><br /></span></td>
<td align="center" valign="bottom"><span style="color: black;"><br /></span></td>
<td align="center" valign="bottom"><span style="color: black;"><br /></span></td>
<td align="center" valign="bottom"><span style="color: black;"><br /></span></td>
<td align="center" valign="bottom"><span style="color: black;"><br /></span></td>
<td align="center" valign="bottom"><span style="color: black;"><br /></span></td>
<td align="center" sdnum="1033;0;0" valign="bottom"><b><span style="color: black;"><br /></span></b></td>
</tr>
<tr>
<td align="left" bgcolor="#D9D9D9" colspan="8" height="21" valign="bottom"><i><span style="color: black;">Using 1%/ug PM2.5 estimates in (c)</span></i></td>
</tr>
<tr>
<td align="left" height="21" valign="bottom"><span style="color: black;">under 5</span></td>
<td align="center" sdnum="1033;" sdval="-10" valign="bottom"><span style="color: black;">-10</span></td>
<td align="center" valign="bottom"><span style="color: black;"><br /></span></td>
<td align="center" sdnum="1033;0;0.0" sdval="1" valign="bottom"><span style="color: black;">1.0</span></td>
<td align="center" sdnum="1033;0;0.0" sdval="17.0833333333333" valign="bottom"><span style="color: black;">17.1</span></td>
<td align="center" sdnum="1033;0;0.0" sdval="839.32" valign="bottom"><span style="color: black;">839.3</span></td>
<td align="center" sdnum="1033;" sdval="0.5" valign="bottom"><span style="color: black;">0.5</span></td>
<td align="center" sdnum="1033;0;0" sdval="-1433.83833333333" valign="bottom"><span style="color: black;">-1434</span></td>
</tr>
<tr>
<td align="left" height="21" valign="bottom"><span style="color: black;">over 70</span></td>
<td align="center" sdnum="1033;" sdval="-10" valign="bottom"><span style="color: black;">-10</span></td>
<td align="center" valign="bottom"><span style="color: black;"><br /></span></td>
<td align="center" sdnum="1033;0;0.0" sdval="1" valign="bottom"><span style="color: black;">1.0</span></td>
<td align="center" sdnum="1033;0;0.0" sdval="523.833333333333" valign="bottom"><span style="color: black;">523.8</span></td>
<td align="center" sdnum="1033;0;0.0" sdval="981.13" valign="bottom"><span style="color: black;">981.1</span></td>
<td align="center" sdnum="1033;" sdval="0.5" valign="bottom"><span style="color: black;">0.5</span></td>
<td align="center" sdnum="1033;0;0" sdval="-51394.8598333333" valign="bottom"><span style="color: black;">-51395</span></td>
</tr>
<tr>
<td align="left" height="21" style="border-bottom: 1px solid #000000;" valign="bottom"><span style="color: black;">total</span></td>
<td align="left" style="border-bottom: 1px solid #000000;" valign="bottom"><span style="color: black;"><br /></span></td>
<td align="left" style="border-bottom: 1px solid #000000;" valign="bottom"><span style="color: black;"><br /></span></td>
<td align="left" style="border-bottom: 1px solid #000000;" valign="bottom"><span style="color: black;"><br /></span></td>
<td align="left" style="border-bottom: 1px solid #000000;" valign="bottom"><span style="color: black;"><br /></span></td>
<td align="left" style="border-bottom: 1px solid #000000;" valign="bottom"><span style="color: black;"><br /></span></td>
<td align="left" style="border-bottom: 1px solid #000000;" valign="bottom"><span style="color: black;"><br /></span></td>
<td align="center" sdnum="1033;0;0" sdval="-52828.6981666667" style="border-bottom: 1px solid #000000;" valign="bottom"><b><span style="color: black;">-52829</span></b></td>
</tr>
</tbody></table>
<br />
<div>
<br /></div>
<div>
To get the estimates in (a), I observe daily data for four chinese cities back to Jan 1 2016. I define a COVID-19 treatment dummy equal to one if the year==2020, and then run a panel regression of daily PM2.5 on the treatment dummy plus city-by-day-of-year fixed effects. The sample is all days in Jan-early March. Basically this calculates the daily PM2.5 difference between 2020 and 2016-19, averaged across all sites and days. I can run this on the daily data or on the 7-day running mean, and with city-by-d.o.y fixed effects or city + d.o.y fixed effects and I get answers that are all between 15-18 ug/m3 reductions in PM2.5. We very conservatively round this number down to 10 in column (a). </div>
<div>
<br /></div>
<div>
What are some of the key assumptions in my overall analysis? There are lots:</div>
<div>
<ul style="text-align: left;">
<li>This is only the partial effect of air pollution; it is by no means the overall effect of COVID-19 on mortality. Indeed, the broader disruption caused by COVID-19 could cause many additional deaths that are not directly attributable to being infected with the virus -- e.g. due to declines in households' economic wellbeing, or to the difficulty in accessing health services for non-COVID illnesses. Again, I am absolutely not saying that pandemics are good for health. </li>
<li>The key assumption in using the He et al 2016 estimates (or the 1%/1ug conservative summary estimate from quasi-experimental studies) is that changes in outdoor PM2.5 concentrations are a sufficient statistic for measuring health impacts. One worry is that, instead of being at work, people are staying at home, and that home indoor air pollution is worse than what they would have been exposed to otherwise. While it is true that indoor air pollution can be incredibly high in homes that rely on biomass burning for cooking and heating, existing evidence suggests that even in cold regions, urban Chinese residents probably have better air quality inside their home than outside it. E.g see <a href="https://www.sciencedirect.com/science/article/abs/pii/S1309104217304300">here</a>. So the key question to me is whether other behaviors changed in response to COVID-19 that made individuals exposures look a lot different than they did in the Beijing setting in He et al 2016. Maybe there is a case to be made there but not sure what it is. There's a prima facie case that the Beijing setting looks a lot like what we're worried about here: an acute 2-month event that led to large but temporary changes in air pollution. </li>
<li>Is mortality from COVID-19 interacting with mortality from PM? One possibility is that there are enough COVID-19 infections to actually make people more susceptible to the negative impacts of air pollution. But this would increase rather than decrease deaths from PM! The other possibility is that the people who have very sadly passed away from COVID would have been those most likely to pass away from PM exposure. But even if this were 100% true, it would only account for ~5% of the overall predicted mortality. </li>
<li>We are pinning the entire PM2.5 difference between Jan-Feb 2020 and Jan-Feb 2016-19 on COVID-19. (Note that we estimate a 17ug/m3 difference, and have rounded that down to 10 ug/mg, so in effect are only pinning about 60% of the change on COVID). Without more careful analysis, we can't really know whether this is fair or not. Maybe something else very big was going on at exactly the same time in China? Seems unlikely but my analysis has nothing to say about that. </li>
<li><b>My estimates are a prediction of mortality impacts, not a measurement. They are not proof that anything has happened</b>. In a few years, there will likely be enough data to actually try to measure what the overall effect was of the COVID-19 epidemic on health outcomes. You could compare changes in mortality in high-exposure locations to changes in mortality in lower-exposure locations, for instance. But this study is not yet possible, as the epidemic is still underway and the comprehensive all-cause mortality data not yet (to my knowledge) available. </li>
</ul>
</div>
<div>
<br /></div>
<div>
<br /></div>
</div>
Marshall Burkehttp://www.blogger.com/profile/15436297075698378164noreply@blogger.com7tag:blogger.com,1999:blog-3813701770708442620.post-31432838907759491212019-08-27T16:22:00.003-07:002019-08-28T08:25:31.744-07:00On the efficacy of the "sniff test" for understanding climate impacts<div dir="ltr" style="text-align: left;" trbidi="on">
<br />
Economists (and other high-statured people) often pride themselves on having good noses. When encountering a research finding, they can often apply a quick "sniff test" to evaluate whether the results of this finding are likely true or false.<br />
<br />
We encounter the sniff test frequently when we discuss our estimates of the potential impacts of future warming on domestic or global economic output. Economic orthodoxy has has long held that 3-4C of warming will reduce global GDP by a few percentage points over the next century relative to a world were temperature was held fixed. These estimates are based largely on earlier cross-sectional studies relating average output to average temperature, and have provided critical input to the construction of "damage functions" used in benchmark integrated assessment models. Output from these models, in turn, have played a pivotal role in policy-making around the social cost of carbon (e.g. see <a href="https://academic.oup.com/reep/article/7/1/23/1577964">here</a>).<br />
<br />
Newer analysis done by your G-FEED bloggers and others using panel based methods have found much larger potential effects of warming on output (e.g. see <a href="http://web.stanford.edu/~mburke/climate/">here</a>), e.g. ~20% loss of global GDP in a +4C world, relative to a world where temperature had stayed fixed. Because these estimates are so much larger than previous estimates, they have frequently failed the sniff test, both publicly and in referee reports we've received. Some people have told us the results just seem "too big". No way can climate change make us 20% poorer than we would have been without it.<br />
<br />
Rather than adjudicating the methods underlying these newer results, I want to evaluate this sniff test itself by simply looking at some historical data. This is in the spirit of Sol's <a href="http://www.g-feed.com/2019/08/do-gdp-growth-rates-have-trends.html">last post</a>: before we argue so strongly for a particular approach or result based on our priors, how bout we just look at some data. Is there historical evidence for some regions performing 20% better than others over century or shorter time scales?<br />
<br />
Exhibit 1: let's just look at income growth in US states. Below is a plot of real per capita income growth since 1980 in selected US states, with the y-axis normalized to show the % change in income relative to 1980. Even in this relatively short 35-year time span (a third of a century, for those scoring at home), there is huge variation in performance: income in Massachusetts grew >130% over this period, while incomes in Nevada grew only 30%. Even the difference between coastal-elite Massachusetts and California over this 35-year period exceeds 20%. So variation in performance of around 20% tis clearly within our very recent historical experience in this country, on a time scale much shorter than a century.<br />
<table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto; text-align: center;"><tbody>
<tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhKRXxarpb3Fiy9li0XCowYAyGKIG3Lj5rWf9t7IuzxpR26o-AvgDzDH00rUIdMhR9a53q-5w0gDYcXQIUdPKrZY2jjrsdCUKUQ4QaIg1Fbqp8Bxt3Auxm4L225e8tmRAwUqi9VOP9nCVPG/s1600/USStateGDPOverTime_clean.jpg" imageanchor="1" style="margin-left: auto; margin-right: auto;"><img border="0" data-original-height="1352" data-original-width="1444" height="373" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhKRXxarpb3Fiy9li0XCowYAyGKIG3Lj5rWf9t7IuzxpR26o-AvgDzDH00rUIdMhR9a53q-5w0gDYcXQIUdPKrZY2jjrsdCUKUQ4QaIg1Fbqp8Bxt3Auxm4L225e8tmRAwUqi9VOP9nCVPG/s400/USStateGDPOverTime_clean.jpg" width="400" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;">Data: <a href="https://apps.bea.gov/itable/iTable.cfm?ReqID=70&step=1#reqid=70&step=1&isuri=1">Bureau of Labor Statistics</a></td></tr>
</tbody></table>
<div class="separator" style="clear: both; text-align: center;">
</div>
<br />
Ah, you say, but this is just the US, and we know we have rampant inequality in this country, etc etc. This variation can't be representative of the rest of the world, right?<br />
<br />
Okay: below is the plot for a small sample of OECD countries, using real GDP/cap from World Bank WDI. Again, plotting data since only 1980, there's been a ton of variation in observed growth in per capita incomes, with neighboring high-income countries varying by way more than 20% in how much they've grown. The plot is of course <i>way</i> more stark if you include developing countries, with some countries only growing a few percentage points over the period, and others (e.g. China) growing by >1000% (and screwing up the axes of any plot you try to make...). So: massive variation at the country level within just a few decades.<br />
<table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto; text-align: center;"><tbody>
<tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhls3vBSekzzDvRLtodFrNwiXCq80E_J9xsI7h7SUfVHxSNwDVii87PsjX7YdBVyr_wLPyPNkModheJVXJs6xrjztNH0eLWwyJT44pSAPPAWlzsaKjn8N9EWhWl5nooAuVZL1gRBJPwBzJj/s1600/CountryGDPOverTime_clean.jpg" imageanchor="1" style="margin-left: auto; margin-right: auto;"><img border="0" data-original-height="1235" data-original-width="1500" height="328" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhls3vBSekzzDvRLtodFrNwiXCq80E_J9xsI7h7SUfVHxSNwDVii87PsjX7YdBVyr_wLPyPNkModheJVXJs6xrjztNH0eLWwyJT44pSAPPAWlzsaKjn8N9EWhWl5nooAuVZL1gRBJPwBzJj/s400/CountryGDPOverTime_clean.jpg" width="400" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;">Data: World Bank World Development Indicators</td></tr>
</tbody></table>
<br />
Ah, you say, this is just ~35 years of data -- way too short a time scale to look at these long run trends. Can't possibly be true for longer time scales, right? Don't countries converge on longer time scales?<br />
<br />
Okay: below is a plot of real per capita growth rates over multiple centuries, based on the <a href="https://www.rug.nl/ggdc/historicaldevelopment/maddison/releases/maddison-project-database-2018">Maddison data</a>, for a random selection of countries with data back to 1800. At this time scale, you see <i>absolutely gargantuan</i> differences in changes in per capita incomes. German incomes grew nearly 5000% since 1800, Portuguese incomes grew 2000%, while South African incomes grew only about 500%.<br />
<table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto; text-align: center;"><tbody>
<tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEi18-HehJnTatdayYzhy3VPjoqlMHS2Y4E0I87DNkjcyWUIk8KHSYS-uCPu7skEeVAqz50h5QaZnjAmEMCTI3TwKJnUl8MZTcmdUG5npjYmxxRGfb867DXDzIeZ7ibxRcx8bzgczcjMxwYj/s1600/MaddisonGDPOverTime_clean.jpg" imageanchor="1" style="margin-left: auto; margin-right: auto;"><img border="0" data-original-height="1281" data-original-width="1560" height="327" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEi18-HehJnTatdayYzhy3VPjoqlMHS2Y4E0I87DNkjcyWUIk8KHSYS-uCPu7skEeVAqz50h5QaZnjAmEMCTI3TwKJnUl8MZTcmdUG5npjYmxxRGfb867DXDzIeZ7ibxRcx8bzgczcjMxwYj/s400/MaddisonGDPOverTime_clean.jpg" width="400" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;">Data: <a href="https://www.rug.nl/ggdc/historicaldevelopment/maddison/releases/maddison-project-database-2018">Maddison Project</a></td></tr>
</tbody></table>
These century scale differences in income over time are roughly <i>two orders</i> of magnitude larger than what our climate impact estimates would predict to be the difference between unmitigated climate change, and a world where warming didn't happen.<br />
<br />
So in light of these historical data, can we please do away with the vaunted "sniff test"? Our estimates or climate impacts are small relative to the variation in historical performance we've seen, and that's true whether you look within countries over recent decades, or (even more so) across countries over very long time periods. None of this, of course, tells us whether climate change will reduce economic output by 20%; it just tells us that we cannot reject a 20% estimate out of hand for being "way too big".<br />
<br />
But this begs the question: why are our noses so bad? Why is the sniff test so un-efficacious? Perhaps we continue to under-appreciate the power of compounding. A key result from our earlier paper (building on key earlier work by <a href="https://scholar.harvard.edu/files/dell/files/aej_temperature.pdf">Dell, Jones, and Olken</a>, and shown by multiple other groups as well, see <a href="https://www.nber.org/papers/w26167">here</a>, <a href="http://web.stanford.edu/~mburke/papers/BurkeTanutama_NBER_w25779.pdf">here</a>, <a href="https://ideas.repec.org/p/zbw/esprep/178288.html">here</a>), is that changes in temperature can affect the growth rate of GDP. And small effects on the growth rate can have large effects on the level of income over long time scales.<br />
<br />
E.g. consider an economy growing at 2% for a century (the US, roughly). Knocking only a quarter of a percent off the growth rate -- i.e. growing at 1.75% instead of 2% -- leads to an economy that's >20% poorer than it would have been after 100 years. Very small growth effects can lead to large level effects over long times scales.<br />
<br />
Another analogy (h/t to my colleague and co-author <a href="https://climatelab.stanford.edu/noah-diffenbaugh">Noah Diffenbaugh</a>) is the continued success of managed mutual funds, despite the clear evidence that the small management fees charged by these funds leads to huge reductions in your accumulated investment over longer time scales, relative to investing in index funds with no fees. Just as small fees eat away at your total portfolio earnings, small hits to the growth rate cumulate into large level effects down the line. But the mutual fund industry in the US <i>is $18 trillion (!!)</i>.<br />
<br />
So let's continue to argue about methodological issues and about how best to understand the response of aggregate output to warming. More posts from us on that soon. But, please, can we stop applying the sniff test to these results? Our noses work less well than we think.<br />
<br /></div>
Marshall Burkehttp://www.blogger.com/profile/15436297075698378164noreply@blogger.com1tag:blogger.com,1999:blog-3813701770708442620.post-44022049920040746232019-08-26T06:00:00.000-07:002019-08-26T06:00:02.209-07:00Do GDP growth rates have trends? Evidence from GDP growth dataOver the years, there have a been a handful of critiques of some of our prior research on GDP growth centering on the concern that we model country-specific time trends in growth rates. This concern seems to be experiencing a resurgence, due in part to an <a href="https://media.rff.org/documents/RFF20WP-18-17-rev.pdf" target="_blank">RFF working paper by Richard Newell, Brian Prest, and Steven Sexton</a> which claims that including trends in GDP growth regressions is "overfitting" (I'll probably post about this paper in greater depth later). In addition, Richard Rosen recently posted a <a href="https://www.pnas.org/content/116/33/16170" target="_blank">PNAS comment</a> asserting that this approach was obviously flawed, and Richard Tol has been <a href="https://twitter.com/RichardTol/status/1158114076893093889" target="_blank">tweeting</a> something similar.<br />
<br />
So is it obviously wrong to account for trends in growth rates when looking at GDP growth data? One way to settle this would be to look at some GDP growth data.<br />
<br />
Grabbing the <a href="https://purl.stanford.edu/wb587wt4560" target="_blank">GDP growth data from our 2015 paper</a>, I write a single (very boring) command that regresses GDP per capita growth rates for the USA on time and a constant (for the period 1960-2013).<br />
<blockquote class="tr_bq">
<span style="font-family: "courier new" , "courier" , monospace;">reg TotGDPgrowthCap year if iso == "USA"</span></blockquote>
The result is seeing that GDP growth for the USA has been declining by a steady 0.043% per year per year (p-value = 0.018):<br />
<br />
<div class="separator" style="clear: both; text-align: center;">
<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhjtO6A9WPkU3HLKhX67XlfgrcI-s-RwCiZD_v_aWRIDGTHOcbRwDpFKafbtonIY3Kb1UWl3XF6BDd2wXSfpdzCxKmitKAXCiNym2qr2j3qdkS5l0qm-F82kY-7uXHJR3r7hJEsxENvAuY/s1600/USA+copy.jpg" imageanchor="1" style="margin-left: 1em; margin-right: 1em; text-align: center;"><img border="0" data-original-height="1164" data-original-width="1600" height="290" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhjtO6A9WPkU3HLKhX67XlfgrcI-s-RwCiZD_v_aWRIDGTHOcbRwDpFKafbtonIY3Kb1UWl3XF6BDd2wXSfpdzCxKmitKAXCiNym2qr2j3qdkS5l0qm-F82kY-7uXHJR3r7hJEsxENvAuY/s400/USA+copy.jpg" width="400" /></a></div>
<br />
So it certainly seems that growth is unlikely to be constant over time. (Remember that a trend in growth rates means that log(income) over time is not linear, but curved.) Newell et al. advocate for treating the above time series as if it is centered around a constant (not a downward trend), which seems at odds with the data itself, the macro-economic theory of <a href="https://en.wikipedia.org/wiki/Convergence_(economics)" target="_blank">convergence</a>, as well as the findings of the Newell et al analysis itself (more on that in the later post). Nowhere in the Newell et al. paper, the Rosen comment, or the Tol tweets did the authors look at the data and ask if there was statistical evidence of a trend.<br />
<br />
The modeling approach in our previous work goes further than this, however. For example, in <a href="https://www.nature.com/articles/nature15725" target="_blank">our 2015 paper</a> we allow each country to exhibit a growth rate that has a (potentially) quadratic country-specific time trend. This modeling choice was not arbitrary, but actually resulted from looking at our data fairly carefully.<br />
<br />
First, countries sometimes have trends in growth that are not linear. As shown above, the trend in USA growth is extremely linear, so in a quadratic model the coefficient on the quadratic term will basically be zero and have no effect on the model (it's precisely -2.89e-06 if you try it with the USA data). But in other cases, the trend is clearly <i>not linear</i>. Take China for example: economic policies in China have changed dramatically over the last several decades, and the rate of these changes has itself not been steady, so the rate of changes in growth rates might itself change (implying curvature in a trend). This turns out to be reasonable intuition, and the quadratic term in a China-GDP time series regression proves to be nonzero and significant.<br />
<br />
Second, different countries have different trends in growth, so it's important that trends in growth are <i>country-specific</i>. This too is pretty obvious if one looks at GDP growth data. For example, India's growth rate has been increasing over time (0.1% per year per year, p-value < 0.000) while the USA's growth rate has been declining (-0.043% per year per year, p-value = 0.018). A simple F-test strongly rejects the hypothesis that they are the same trend (p-value < 0.000). Newell et al. and others seems to advocate for models that assume trends are the same across all (or many) countries without testing if there is statistical evidence that these trends are different. Halvard Buhaug made a similar error in his <a href="https://www.pnas.org/content/107/38/16477" target="_blank">critique</a> of some of Marshall and David's work on climate and conflict (Kyle Meng and I demonstrated the single-command F-test Buhaug should have used <a href="https://www.pnas.org/content/111/6/2100" target="_blank">in a PNAS paper</a>). Here are plots from four major economies demonstrating the diversity of trends that show up very clearly in the data:<br />
<br />
<div class="separator" style="clear: both; text-align: center;">
<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjhUD7kkayaYWlyKoaa8hgiyJSCS9opgNxpWO7CmF8hL8VC7nHCcwZwZnXthyphenhyphenEjaJputnFGTaQRIBG8Lyl75Ble_1liH4DeVsQKAG5s5xqM4EPY6fnv_zesS4dZjLbUNntD_niIb0-hMUM/s1600/many+copy.jpg" imageanchor="1" style="margin-left: 1em; margin-right: 1em; text-align: center;"><img border="0" data-original-height="1164" data-original-width="1600" height="464" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjhUD7kkayaYWlyKoaa8hgiyJSCS9opgNxpWO7CmF8hL8VC7nHCcwZwZnXthyphenhyphenEjaJputnFGTaQRIBG8Lyl75Ble_1liH4DeVsQKAG5s5xqM4EPY6fnv_zesS4dZjLbUNntD_niIb0-hMUM/s640/many+copy.jpg" width="640" /></a></div>
<br />
<br />
Taken together, the data suggest that there is strong evidence that (1) there are nonzero trends in GDP growth, (2) these trends are not the same across countries, (3) these trends are not linear in many cases.<br />
<br />
The safest way to deal with these facts (at least in the climate-economy contexts that we study) is to allow trends in growth rates to be modeled flexibly so that they can be different across countries and they can be curved if the data suggest they should be. There are multiple ways to do that, and country-specific quadratic trends is one straightforward way to do it. If the trends are actually linear, they will be modeled as basically linear (statistical software may actually drop the quadratic term if trends are linear enough). And if they are are the same across countries, they will be estimated to be the same. But if they are in fact different across countries, or curved over time, the model will not erroneously assume they are homogenous and constant.<br />
<br />
I think the meta-moral of the story is to simply look at your data and listen to what it is telling you. F-tests and the "plot" command are both compelling and often underutilized.solhttp://www.blogger.com/profile/00936469103707728475noreply@blogger.com3tag:blogger.com,1999:blog-3813701770708442620.post-87798481402920101412019-06-18T09:00:00.000-07:002019-06-18T09:51:55.751-07:00Congressional testimony on economic consequences of climate changeSince it's basically a blog post, here's the oral testimony I gave last week to the House Budget Committee during the Hearing on "The Costs of Climate Change: Risks to the U.S. Economy and the Federal Budget." If you have hours to spare, you can <a href="https://budget.house.gov/legislation/hearings/costs-climate-change-risks-us-economy-and-federal-budget" target="_blank">watch the whole hearing</a> or read <a href="https://www.solomonhsiang.com/s/Testimony_Hsiang_6_10_19.pdf" target="_blank">the full (referenced) written testimony</a>. It's not every day that someone asks you summarize a decade of research progress from your field in 800 words, so it seems worth documenting it somewhere.
<br />
<blockquote class="tr_bq" style="line-height: 150%;">
<span style="font-family: inherit;">Thank you Chairman Yarmuth, Ranking
Member Womack, and members of the Committee for inviting me to speak today. </span></blockquote>
<blockquote class="tr_bq" style="line-height: 150%;">
<span style="font-family: inherit;">My name is Solomon Hsiang, and I am
the Chancellor’s Professor of Public Policy</span><br />
<span style="font-family: inherit;">at the University of California,
Berkeley and currently a Visiting Scholar at Stanford. I was trained in both economics
and climate physics at Columbia, MIT, and Princeton. My research focuses on the
use of econometrics to measure the effect of the climate on the economy. </span></blockquote>
<blockquote class="tr_bq" style="line-height: 150%;">
<span style="font-family: inherit;">The last decade has seen dramatic
advances in our understanding of the economic value of the climate. Crucially, we now are able to use real-world
data to quantify how changes in the climate cause changes in the economy. This means that in addition to
being able to project how unmitigated emission of greenhouse gasses will cause the
physical climate to change, we can now also estimate the subsequent effect that
these changes are likely to have on the livelihoods of Americans. </span></blockquote>
<blockquote class="tr_bq" style="line-height: 150%;">
<span style="font-family: inherit;">Although, as with any emerging research
field, there are large uncertainties and much work remains to be done. Nonetheless, I’d like to describe
to you some key insights from this field regarding future risks if past
emissions trends continue unabated. </span></blockquote>
<blockquote class="tr_bq" style="line-height: 150%;">
<span style="font-family: inherit;"><b style="mso-bidi-font-weight: normal;">First, climate change is likely to have substantial negative impact on
the US economy. </b>Expected damages are on the scale of several trillions of
dollars, although there remains uncertainty in these numbers. For example, in a detailed analysis
of county-level productivity, a colleague at University of Illinois and I
estimated that the direct thermal effects alone would likely reduce incomes nation-wide
over the next 80 years, a loss valued at roughly 5-10 trillion dollars in net
present value. In another analysis, a colleague
from University of Chicago and I computed that losses from intensified
hurricanes were valued at around 900 billion dollars. Importantly, these numbers are not
a complete accounting of impacts and other notable studies report larger losses. </span></blockquote>
<blockquote class="tr_bq" style="line-height: 150%;">
<span style="font-family: inherit;"><b style="mso-bidi-font-weight: normal;">Second, extreme weather events are short-lived, but their economic
impact is long-lasting. </b>Hurricanes, floods, droughts, and
fires destroy assets that took communities years to build. Rebuilding then diverts resources
away from new productive investments that would have otherwise supported future
growth. For example, a colleague at Rhodium
Group and I estimated that Hurricane Maria set Puerto Rico back over two
decades of progress; and research from MIT indicates that communities in the
Great Plains have still not fully recovered from the Dustbowl of the 1930s. As climate change makes extreme
events more intense and frequent, we will spend more attention and more money replacing
depreciated assets and repairing communities. </span></blockquote>
<blockquote class="tr_bq" style="line-height: 150%;">
<span style="font-family: inherit;"><b style="mso-bidi-font-weight: normal;">Third, the nature and magnitude of projected costs differs between
locations and industries</b>. For example, extreme heat will impose large
health, energy, and labor costs on the South; sea level rise and hurricanes
will damage the Gulf Coast; and declining crop yields will transform the Plains
and Midwest. </span></blockquote>
<blockquote class="tr_bq" style="line-height: 150%;">
<span style="font-family: inherit;"><b style="mso-bidi-font-weight: normal;">Fourth, because low income regions and individuals tend to be hurt more,
climate change will widen existing economic inequality.</b> For example, in a
national analysis of many sectors, the poorest counties suffered median losses
that were 9 times larger than the richest. </span></blockquote>
<blockquote class="tr_bq" style="line-height: 150%;">
<span style="font-family: inherit;"><b>Fifth, many impacts of climate change will not be felt in the
marketplace, but rather in homes where health, happiness, and freedom from
violence will be affected.</b> There are many examples of this. Mortality due to extreme heat is projected
to rise dramatically. Increasingly humid summers are
projected to degrade happiness<span class="MsoFootnoteReference"> </span>and
sleep quality. Research from Harvard indicates
that warming will likely elevate violent crime nationwide, producing over 180,000
sexual assaults and over 22,000 murders across eight decades. Colleagues at Stanford and I estimate
that warming will generate roughly 14,000 additional suicides in the next
thirty years. Increasing exposure of pregnant
mothers to extreme heat and cyclones will harm fetuses for their lifetime. These impacts do not easily convert
to dollars and cents, but they still merit attention. </span></blockquote>
<blockquote class="tr_bq" style="line-height: 150%;">
<span style="font-family: inherit;"><b>Sixth, populations across the country will try to adapt to climate
change at substantial cost. </b>Some adaptions will transform jobs
and lifestyles, some will require constructing new defensive infrastructure,
and some will involve abandoning communities and industries where opportunities
have deteriorated. In all cases, these adaptations
will come at real cost, since resources expended on coping cannot be invested
elsewhere. </span></blockquote>
<blockquote class="tr_bq" style="line-height: 150%;">
<span style="font-family: inherit;"><b>Lastly, outside of the US, the global consequences of climate change
are projected to be large and destabilizing.</b> Unmitigated warming will likely slow global
growth roughly 0.3 percentage points and reduce political stability throughout
the tropics and subtropics. </span></blockquote>
<blockquote class="tr_bq" style="line-height: 150%;">
<span style="font-family: inherit;">Together, these findings indicate
that our climate is one of the nation’s most important economic assets. We should manage it with the
seriousness and clarity of thought that we would apply to managing any other
asset that also generates trillions of dollars in value for the American
people. </span></blockquote>
<blockquote class="tr_bq" style="line-height: 150%;">
<span style="font-family: inherit;">Thank you.</span></blockquote>
solhttp://www.blogger.com/profile/00936469103707728475noreply@blogger.com0tag:blogger.com,1999:blog-3813701770708442620.post-84281745207251703762018-11-05T06:00:00.000-08:002018-11-05T06:00:00.670-08:00The SHCIT ListJust like George Lucas, I write my literature reviews out of order. But I'm happy to say that after several years of messing around in this field, in collaboration with great coauthors, I've finally finished the tetralogy that I've always wanted to complete. The <a href="https://www.aeaweb.org/articles?id=10.1257/jep.32.4.3">latest installment</a> (Episode I) just came out in the JEP (it's designed to be a soft on-ramp for economists who are unfamiliar with climate change science to get acquainted with the problem).<br />
<br />
<b><u>Sol Hsiang's Climate Impacts Tutorial reading list:</u></b><br />
<br />
<ol>
<li><a href="https://www.aeaweb.org/articles?id=10.1257/jep.32.4.3">An Economist’s Guide to Climate Change Science</a> (<i>what is the physical problem?</i>)</li>
<li><a href="https://academic.oup.com/reep/article/7/2/181/1522753">Using Weather Data and Climate Model Output in Economic Analyses of Climate Change</a> (<i>how do we look at the data for that problem?</i>)</li>
<li><a href="https://www.annualreviews.org/doi/10.1146/annurev-resource-100815-095343">Climate Econometrics</a> (<i>how does one analyze that data to learn about the problem?</i>)</li>
<li><a href="http://science.sciencemag.org/content/353/6304/aad9837">Social and Economic Impacts of Climate</a> (<i>what did we learn when we did that?</i>)</li>
</ol>
<br />
This addition completes the box set that can get any grad student up to speed on the broader climate impacts literature. <br />
<br />
I hope this is helpful. I think I'm going to go and do more research on <a href="http://www.g-feed.com/2016/09/replytocites.html">elephant poaching</a> now...<br />
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<br />solhttp://www.blogger.com/profile/00936469103707728475noreply@blogger.com1tag:blogger.com,1999:blog-3813701770708442620.post-87386671477226855172018-08-23T13:57:00.000-07:002018-09-05T11:11:57.620-07:00Let there be light? Estimating the impact of geoengineering on crop productivity using volcanic eruptions as natural experiments (Guest post by Jonathan Proctor)<i>[This is a guest post by <a href="http://www.globalpolicy.science/jonathan-proctor">Jonathan Proctor</a>, a Doctoral Fellow at the <a href="http://www.globalpolicy.science/">Global Policy Lab</a> and PhD candidate in the <a href="https://are.berkeley.edu/">Ag and Resource Econ department</a> here at Berkeley]</i><br />
<div>
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On Wednesday I, and some notorious G-FEEDers, published <a href="https://www.nature.com/articles/s41586-018-0417-3">a paper in Nature</a> exploring whether solar geoengineering – a proposed technology cool the Earth by reflecting sunlight back into space—might be able to mitigate climate-change damages to agricultural production. We find that, as intended and <a href="https://www.nature.com/articles/nclimate1373">previously described</a>, the cooling from geoengineering benefits crop yields. We also find, however, that the shading from solar geoengineering makes crops less productive. On net, the damages from reduced sunlight wash out the benefits from cooling, meaning that solar geoengineering is unlikely to be an effective tool to mitigate the damages that climate change poses to global agricultural production and food security. Put another way, if we imagine SRM as an experimental surgery, our findings suggest that the side effects are as bad as the cure.<br />
<br />
Zooming out, solar geoengineering is an <a href="https://www.annualreviews.org/doi/pdf/10.1146/annurev-earth-042711-105548">idea</a> to cool the earth by injecting reflective particles --usually precursors to sulfate aerosols -- into the high atmosphere. The idea is that these particles would bounce sunlight back into space and thus cool the Earth, similarly to how you might cool yourself down by standing in the shade of a tree during a hot day. The idea of such sulfate-based climate engineering was, in part, <a href="https://link.springer.com/article/10.1007%2Fs10584-006-9101-y">inspired</a> by the observation that the Earth tends to cool following massive volcanic eruptions such as that of Pinatubo in 1991, which cooled the earth by about half a degree C in the years following the eruption.<br />
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<table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto; text-align: center;"><tbody>
<tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEi1LCAGRywAQVUKD2foZUlys5sBEIXgQoBWlKpXrdNLU9DBagpVNmu0qF-2q8fWgRuNQ6Q7U11L-4qbJ-dL-kQNfsVNt49y3GpNCXjPhOWNZaAQTs7jVxrgSH_NCH55dW6TxFPiEjNTuuc/s1600/PROCTOR_HSIANG_PINATUBO.gif" imageanchor="1" style="margin-left: auto; margin-right: auto;"><img border="0" data-original-height="741" data-original-width="572" height="400" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEi1LCAGRywAQVUKD2foZUlys5sBEIXgQoBWlKpXrdNLU9DBagpVNmu0qF-2q8fWgRuNQ6Q7U11L-4qbJ-dL-kQNfsVNt49y3GpNCXjPhOWNZaAQTs7jVxrgSH_NCH55dW6TxFPiEjNTuuc/s400/PROCTOR_HSIANG_PINATUBO.gif" width="307" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;">Our visualization of the stratospheric aerosols (blue) that scattered light and shaded the planet after the eruption of Mount Pinatubo in 1991. Each frame is one month of data. Green on the surface indicates global crop lands. (The distance between the aerosol cloud and the surface is much larger than in real life.) </td></tr>
</tbody></table>
<br />
A major challenge in learning the consequences of solar geoengineering is that we can’t do a planetary-scale experiment without actually deploying the technology. (Sol’s questionably-appropriate analogy is that you can’t figure out if you want to be a parent through experimentation.) An innovation here was realizing that we could learn about the impacts of solar geoengineering without incurring the risks of an outdoor experiment by using giant volcanic eruptions as natural experiments. While these eruptions are not perfect proxies for solar geoengineering in every way, they give us the necessary variation we need in high atmosphere aerosol concentrations to study some of the key effects on agriculture. (We expand on how we account for the important differences between the impacts of volcanic eruptions and solar geoengineering on agricultural production later in this post). This approach builds on <a href="https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2007GL030524">previous work</a> in the earth science community which has used the eruptions to study solar geoengineering’s impact on climate. Here’s what we found:<br />
<br />
<h3>
Result 1: Pinatubo dims the lights</h3>
<br />
First, we find that the aerosols from Pinatubo had a profound impact on the global optical environment. By combing remotely sensed data on the eruption’s aerosol cloud with globally-dispersed ground sensors of solar radiation (scraped from a Russian <a href="http://wrdc.mgo.rssi.ru/">website</a> that recommends visitors use Netscape Navigator) we estimate that the Pinatubo eruption temporarily decreased global surface solar radiation (orange) by 2.5%, reduced direct (i.e. unscattered, shown in yellow) insolation by 20% and increased diffuse (i.e. scattered, shown in red) sunlight by 20%.<br />
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<table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto; text-align: center;"><tbody>
<tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEixthYR-aCl56vNkoIhMXsUgGk05LisoV9R_khFrALFQ5IohbiN489SgO4bMSwz3kvq7aHFJxxVp0VbfNvGtPNJOcZCZz4iN5x5JtHRelovvD8JbFgGr5z0DO7rRebuJDQAlZ3-HxvpaBA/s1600/fig1f+copy.jpg" imageanchor="1" style="margin-left: auto; margin-right: auto;"><img border="0" data-original-height="400" data-original-width="1600" height="160" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEixthYR-aCl56vNkoIhMXsUgGk05LisoV9R_khFrALFQ5IohbiN489SgO4bMSwz3kvq7aHFJxxVp0VbfNvGtPNJOcZCZz4iN5x5JtHRelovvD8JbFgGr5z0DO7rRebuJDQAlZ3-HxvpaBA/s640/fig1f+copy.jpg" width="640" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;">Effect of El Chichon (1982) and Mt Pinatubo (1991) on direct (yellow), diffuse (red) and total (orange) insolation for global all-sky conditions.</td></tr>
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These global all-sky results (i.e. the average effect on a given day) generalize previous clear-sky estimates (the effect on a clear day) that have been done at individual stations. Like a <a href="https://www.amazon.com/Neewer-Studio-Umbrella-Continuous-Lighting/dp/B013JV3J1I/ref=sr_1_2_sspa?s=electronics&ie=UTF8&qid=1534786888&sr=1-2-spons&keywords=softbox+photography+lighting&psc=1">softbox</a> or diffusing sheet in photography, this increase in diffuse light reduced shadows on a global scale. The aerosol-scattering also made redder sunsets (sulfate aerosols cause a spectral shift in addition to a diffusion of light), similar to the volcanic sunsets that inspired Edvard Munch’s “<a href="https://en.wikipedia.org/wiki/The_Scream">The Scream</a>.” Portraits and paintings aside, we wanted to know: <b>how did these changes in sunlight impact global agricultural production?</b><br />
<br />
Isolating the effect of changes in sunlight, however, was a challenge. First, the aerosols emitted by the eruption alter not only sunlight, but also other climatic variables such as temperature and precipitation, which impact yield. Second, there just so happened to be El Nino events that coincided with the eruptions of both El Chichon and Pinatubo. This <a href="https://gph.is/2eOICLC">unfortunate coincidence</a> has frustrated the atmospheric science community for decades, leading some to <a href="https://www.nature.com/articles/s41467-017-00755-6">suggest</a> that volcanic eruptions might even cause El Niños, as well as the <a href="http://science.sciencemag.org/content/206/4420/826">reverse</a> (the former theory seems to have more evidence behind it).<br />
<br />
To address the concern that volcanoes affect both light and other climatic conditions, we used a simple “condition on observables” design – by measuring and including potential confounds (such as temperature, precipitation and cloud cover) in the regression we can account for their effects. To address the concurrent El Nino, we do two things. First, we directly condition on the variables though which an El Nino could impact yields – again temperature, precipitation and cloud cover. Second, we condition on the El Nino index itself, which captures any effects that operate outside of these directly modeled channels. Essentially, we isolate the insolation effect by partitioning out the variation due to everything else – like looking for your keys by pulling everything else out of your purse.<br />
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEilmkNJV_hZwmK1RfJbObl917GDXN1_80OFmMvQr6d3hxEAukA5Flz-QYhbe3UgwKLyRjkyafFVsxbchX3p0LFLrwaYlwQ3-1mD53aNYtI4HIcCh5qxV651vnXZpchcroSf-07-aP14OLE/s1600/Derivative_Full.jpg" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="1206" data-original-width="1600" height="481" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEilmkNJV_hZwmK1RfJbObl917GDXN1_80OFmMvQr6d3hxEAukA5Flz-QYhbe3UgwKLyRjkyafFVsxbchX3p0LFLrwaYlwQ3-1mD53aNYtI4HIcCh5qxV651vnXZpchcroSf-07-aP14OLE/s640/Derivative_Full.jpg" width="640" /></a></div>
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<br />
The above figure schematically illustrates our strategy. The total effect (blue) is the sum of optical (red) and climatic components (green). By accounting for the change in yields due to the non-optical factors, we isolate the variation in yields due to stratospheric aerosol-induced changes in sunlight.<br />
<br />
<h3>
Result 2: Dimming the lights decreases yields</h3>
<br />
Our second result, and the main scientific contribution, is the finding that radiative scattering from stratospheric sulfate aerosols decreases yields on net, holding other variables like temperature constant. The magnitude of this impact is substantial – the global average scattering from Pinatubo reduced C4 (maize) yields by 9.3% and C3 (soy, rice and wheat) yields by 4.8%, which is two to three times larger than the change in total sunlight. We reconstruct this effect for each country in the figure below:<br />
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<table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto; text-align: center;"><tbody>
<tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhbTR0Uh_BLwbJn-h64-C_Ns7jcMHY99D0wCYr-CLhfbb-n2F0yB7B2mOSK76Z-3kEECDz6abczO8c22cbEeDkqoalbbQrQs6IcnNhnXiR3flyNnP8D09sd4VQvnys4Btvwt3_2ypL78L8/s1600/Figure3_simp_2.jpg" imageanchor="1" style="margin-left: auto; margin-right: auto;"><img border="0" data-original-height="298" data-original-width="666" height="284" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhbTR0Uh_BLwbJn-h64-C_Ns7jcMHY99D0wCYr-CLhfbb-n2F0yB7B2mOSK76Z-3kEECDz6abczO8c22cbEeDkqoalbbQrQs6IcnNhnXiR3flyNnP8D09sd4VQvnys4Btvwt3_2ypL78L8/s640/Figure3_simp_2.jpg" width="640" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;">Each line represents one crop for one country. These are the reconstructed yield losses due to the estimated direct optical effects of overhead aerosols.</td></tr>
</tbody></table>
<br />
My young cousins dismissed the sign of this effect as obvious – after all plants need sunlight to grow. But, surprising to my young cousins, the prevailing wisdom in the literature tended to be that scattering light should increase crop growth (Sol incessantly points out that David even <a href="https://www.nature.com/articles/nclimate1373">said this once</a>). The argument is that the reduction in yields from loss of total light would be more than offset by gains in yield through an increase in diffuse light. The belief that diffuse light is more useful to plants than direct light stems from both the observation that the biosphere <a href="http://science.sciencemag.org/content/269/5227/1098">breathed in carbon dioxide</a> following the Pinatubo eruption and the accompanying <a href="http://science.sciencemag.org/content/299/5615/2035">theory</a> that diffusing light increases plant growth by redistributing light from the sun-saturated leaves at the top of the canopy to the light-hungry leaves below. Since each leaf has diminishing photosynthetic productivity for each incremental increase in sunlight, the theory argues, a more equal distribution of light should promote growth.<br />
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<table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto; text-align: center;"><tbody>
<tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhumowBIakPdhLorV9YxIHEVdbj5jCVyp2cDBpzj-He5CabmTEAJalPG4TU7HmtUYBUrDqJChm6bdM0fUv6mAvF8zPwVIeT8fMuwlSAqckIw-PNlTAdzfIia5ibYsZAYU9auzNR9EWZXrw/s1600/HSIANG_PROCTOR_GEO.jpg" imageanchor="1" style="margin-left: auto; margin-right: auto;"><img border="0" data-original-height="804" data-original-width="1600" height="320" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhumowBIakPdhLorV9YxIHEVdbj5jCVyp2cDBpzj-He5CabmTEAJalPG4TU7HmtUYBUrDqJChm6bdM0fUv6mAvF8zPwVIeT8fMuwlSAqckIw-PNlTAdzfIia5ibYsZAYU9auzNR9EWZXrw/s640/HSIANG_PROCTOR_GEO.jpg" width="640" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;"><span style="font-size: small; text-align: start;">Aerosols scatter incoming sunlight, which more evenly distributes sunlight across the leaves of the plant. We test whether the loss of total sunlight or the increase in diffuse light from aerosol scattering has a stronger effect on yield.</span></td></tr>
</tbody></table>
<br />
While this “diffuse fertilization” appears to be strong in unmanaged environments, such as the Harvard Forest where the uptake of carbon <a href="http://science.sciencemag.org/content/299/5615/2035">increased</a> following the Pinatubo eruption, our results find that, for agricultural yields, the damages from reduced total sunlight outweigh the benefits from a greater portion of the light being diffuse.<br />
<br />
We cannot tell for sure, but we think that this difference between forest and crop responses could be due to either their differences in geometric structure (which could affect how deeply scattered light might penetrate the canopy):<br />
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgfoJow0i2Z3eWl_NIzP_mtu6LsMCsKr_C0KPHMLvhGubVh8MPlfSG_1K9waig6ng8cCAG5nw3alKPRhlhEH3PHTMzmEeJvRh0iPYmG75hJRKAaOK9con6KNPal25fdPN3GEE3ofRFt250/s1600/geometry_comparison.jpg" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="1153" data-original-width="1600" height="459" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgfoJow0i2Z3eWl_NIzP_mtu6LsMCsKr_C0KPHMLvhGubVh8MPlfSG_1K9waig6ng8cCAG5nw3alKPRhlhEH3PHTMzmEeJvRh0iPYmG75hJRKAaOK9con6KNPal25fdPN3GEE3ofRFt250/s640/geometry_comparison.jpg" width="640" /></a></div>
<br />
Or to a re-optimization towards vegetative growth at the cost of fruit growth in response to the changes in light:<br />
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<table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto; text-align: center;"><tbody>
<tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjO0biNdoeVSIzgEXt_4cE7IXHKge1arCv5ijS3Q9pYRSpibINfHJ2mR-NVuZS4tqxwMXjCCuFBKGnG7oIc0t4TLC0xe1tjmnMxxSNeNKw_ouOhO67mbfALNrKWARDglRzn-5UsbJmmNUM/s1600/NagyLab.jpg" imageanchor="1" style="margin-left: auto; margin-right: auto;"><img border="0" data-original-height="1428" data-original-width="1600" height="285" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjO0biNdoeVSIzgEXt_4cE7IXHKge1arCv5ijS3Q9pYRSpibINfHJ2mR-NVuZS4tqxwMXjCCuFBKGnG7oIc0t4TLC0xe1tjmnMxxSNeNKw_ouOhO67mbfALNrKWARDglRzn-5UsbJmmNUM/s320/NagyLab.jpg" width="320" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;">Two radishes grown in normal (left) and low light (right) conditions. Credit: <a href="http://nagy.bio.ed.ac.uk/">Nagy Lab</a>, University of Edinburgh</td></tr>
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<br />
This latter re-optimization may also explain the relatively large magnitude of the estimated effect on crop yield.<br />
<br />
<h3>
Result 3: Dimming damages neutralize cooling benefits from geo</h3>
<br />
Our final calculation, and the main policy-relevant finding of the paper, is that in a solar geoengineering scenario the damages from reduced sunlight cancel out the benefits from warming. The main challenge here was figuring out how to apply what we learned from volcanic eruptions to solar geoengineering, since the climate impacts (e.g. changes in temperature, precipitation or cloud cover) of a short-term injection of stratospheric aerosols differ from those of more sustained injections (e.g. see <a href="https://link.springer.com/article/10.1007/s10584-013-0777-5">here</a> and <a href="http://www.nap.edu/catalog/18988/climate-intervention-reflecting-sunlight-to-cool-earth">here</a>). To address this, we first used an earth system model to calculate the impact of a long-term injection of aerosols on temperature, precipitation, cloud cover and insolation (measured in terms of aerosol optical depth). We then apply our crop model that we trained on the Pinatubo eruption (which accounts for changes in temperature, rainfall, cloud cover, and insolation independently) to calculate how these geoengineering-induced changes in climate impact crop yields. This two-step process allows us to disentangle the effects of solar geoengineering on climate (which we got from the earth system model) and of climate on crops (which we got from Pinatubo). Thus, we can calculate the change in yields due to a solar geoengineering scenario even though volcanic eruptions and solar geoengineering have different climatic fingerprints. Still, as with any projection to 2050, caveats abound such as the role of adaptation, the possibility of optimized particle design, or the possibility that variables other than sunlight, temperature, rainfall and cloud cover could play a substantial role.<br />
<br />
<table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto; text-align: center;"><tbody>
<tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjMiPD2_ZdvllUVYfZ92-0PADzzeAQqijgVKtmNPgtLeYs-U6BkHTAJbKp1JTChQpfpMAkzlN3v9ErxJpFm7VbBjImhWrUqOoAMZiy9X-RV0SYwXzxJiDhwwOE8uzElMMiv3s-mIuQlOp8/s1600/Figure4_simple.jpg" imageanchor="1" style="margin-left: auto; margin-right: auto;"><img border="0" data-original-height="634" data-original-width="1022" height="397" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjMiPD2_ZdvllUVYfZ92-0PADzzeAQqijgVKtmNPgtLeYs-U6BkHTAJbKp1JTChQpfpMAkzlN3v9ErxJpFm7VbBjImhWrUqOoAMZiy9X-RV0SYwXzxJiDhwwOE8uzElMMiv3s-mIuQlOp8/s640/Figure4_simple.jpg" width="640" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;">Estimated global net effect of a geo-engineering on crop yields through four channels (temperature, insolation, cloud cover, precipitation) for four crops. The total effect is the sum of these four partial effects.</td></tr>
</tbody></table>
<br />
So, what should we do? For agriculture, our findings suggest that sulfate-based solar geoengineering might not work as well as previously thought to limit the damaging effects of climate change. However, there are other sectors of the economy that could potentially benefit substantially from geoengineering (or be substantially damaged, we just don’t know). To continue the metaphor from earlier, just because the first test of an experimental surgery had side effects for a specific part of the human body does not mean that the procedure is always immediately abandoned. There are many illnesses that are so harmful that procedures known to cause side effects are sometimes still worth the risk. Similarly, research into geoengineering should not be entirely abandoned because our analysis demonstrated one adverse side effect, there may remain good reasons to eventually pursue such a strategy despite some known costs. With careful study, humanity will eventually gain a better understanding of this powerful technology. We hope that the methodology developed in this paper might be extended to study the effects of sulfate aerosol injection on ecosystem or human health and would be open to collaborate on future studies. Thanks for reading, and I’m excited to hear any thoughts the community may have.solhttp://www.blogger.com/profile/00936469103707728475noreply@blogger.com0tag:blogger.com,1999:blog-3813701770708442620.post-62057675110185980042018-04-03T08:28:00.000-07:002018-04-03T08:29:24.696-07:00Claims of Bias in Climate-Conflict Research Lack Evidence [Uncut Version]Marshall and I had an <a href="https://www.nature.com/articles/d41586-018-03798-x">extremely brief Correspondence</a> published in Nature last week. We were reacting to an article <a href="http://www.nature.com/articles/s41558-018-0068-2">"Sampling bias in climate–conflict research"</a> by Adams et al. in Nature Climate Change and an <a href="http://www.nature.com/articles/d41586-018-01875-9">Editorial</a> published in Nature discussing and interpreting some of the statements by Adams et al. Below is the full 300-word unedited "director's cut" of our original submission, which was edited down by the journal. Two other short comments in the same issue (actually same page!) provided other perspectives.<br />
<br />
<a href="https://www.nature.com/articles/d41586-018-03795-0">Butler & Kefford</a> pointed out that prior literature describes climate stress as amplifying pre-existing conflict risk, rather than being the "sole cause" as Adams et al. suggest previous studies suggest (yes, this is getting confusing). We agree with this interpretation and the evidence seem to back it up pretty clearly. <a href="https://www.annualreviews.org/doi/abs/10.1146/annurev-economics-080614-115430">Our analyses</a> indicate fairly consistent percentage changes in conflict risk induced by climatic shifts, so places with high initial risk get more of a boost from climatic events.<br />
<br />
<a href="https://www.nature.com/articles/d41586-018-03794-1">Glieck, Lewandowsky & Kelly</a> argued that the earlier articles were an oversimplification of prior research, and that focusing on locations where conflict occurs is important for helping to trace out how climate induces conflict. I would think that this point would resonate with Adams et al, since some of those authors do actual case studies as part of their research portfolio. It does kind of seem to me that case studies would be the most extreme case of "selection on the outcome" as Adams et al define it.<br />
<br />
Finally, here's what we originally wrote to fit on a single MS Word page:<br />
<br />
<blockquote class="tr_bq">
<b>Claims of Bias in Climate-Conflict Research Lack Evidence</b> </blockquote>
<blockquote class="tr_bq">
<b></b><i>Solomon Hsiang and Marshall Burke</i> </blockquote>
<blockquote class="tr_bq">
A recent article by <a href="http://www.nature.com/articles/s41558-018-0068-2">Adams et al.</a> [1] and accompanying <a href="http://www.nature.com/articles/d41586-018-01875-9">editorial</a> [2] criticize the field of research studying links between climate and conflict as systematically biased, sowing doubt in prior findings [3,4]. But the underlying analysis fails to demonstrate any evidence of biased results. </blockquote>
<blockquote class="tr_bq">
Adams et al. claim that because most existing analyses focus on conflict-prone locations, the conclusions of the literature must be biased. This logic is wrong. If it were true, then the field of medicine would be biased because medical researchers spend a disproportionate time studying ill patients rather than studying each of us every day when we are healthy. </blockquote>
<blockquote class="tr_bq">
Adams et al.’s error arises because they confuse sampling observations within a given study based on the dependent variable (a major statistical violation) with the observation that there are more studies in locations where the average of a dependent variable, the conflict rate, is higher (not a violation). Nowhere does Adams et al. provide evidence that any prior analysis contained actual statistical errors. </blockquote>
<blockquote class="tr_bq">
We are also concerned about the argument advanced by Adams et al. and repeated in the editorial that it is “undesirable” to study risk factors for populations at high risk of conflict because it may lead to them being “stigmatized.” Such logic would imply that study of cancer risk factors for high risk patients should not proceed because success of these studies may lead to the patients being stigmatized. We believe that following such recommendations will inhibit scientific research and lead to actual systematic biases in the literature. </blockquote>
<blockquote class="tr_bq">
Research on linkages between climate and conflict is motivated by the desire to identify causes of human suffering so it may be alleviated. We do not believe that shying away from findings in this field is an effective path towards this goal. </blockquote>
<blockquote class="tr_bq">
<i>References</i> </blockquote>
<blockquote class="tr_bq">
1. Adams, Ide, Barnett, Detges. Sampling bias in climate–conflict research. Nature Climate Change (2018).<br />
2. Editorial. Don’t jump to conclusions about climate change and civil conflict. Nature, 555, 275-276 (2018).<br />
3. Hsiang, Burke, Miguel. Science (2013) doi:10.1126/science.1235367<br />
4. Burke, Hsiang, Miguel. Ann. Rev. Econ. (2015) doi:10.1146/annurev-economics-080614-115430</blockquote>
<br />
<br />
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solhttp://www.blogger.com/profile/00936469103707728475noreply@blogger.com0tag:blogger.com,1999:blog-3813701770708442620.post-90063903999702240762017-12-04T09:43:00.002-08:002017-12-04T09:43:55.598-08:00New Damage Functions for the Agricultural Sector – Guest Post by Fran Moore<div class="separator" style="clear: both; text-align: center;">
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Last week I had a <a href="https://www.nature.com/articles/s41467-017-01792-x">new paper</a> come
out with some fantastic co-authors (Delavane Diaz, Tom Hertel, and Uris Baldos)
on new damage functions in the agricultural sector. Since this covers a number
of topics of interest to G-FEED readers (climate damages, agricultural impacts,
SCC etc), I thought I’d dig a bit into what I see as some of the main
contributions.<o:p></o:p></div>
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Firstly what we do. Essentially this is an exercise in
trying to link a damage function directly to the scientific consensus on
climate impacts as represented by findings in the IPCC. We therefore rely on a database
of over 1000 results on the yield response to temperature previously published
by <a href="https://www.nature.com/articles/nclimate2153">Challinor et al.</a>
and used to support conclusions in the food security chapter of Working Group
2. We do a bit of reanalysis (using a multi-variate regression, adding
soybeans, allowing for more geographic heterogeneity in response) to get
yield-temperature response functions that can be extrapolated to a global grid
to give productivity shocks at 1, 2, and 3 degrees of warming (available for
other researchers to use <a href="https://figshare.com/articles/Gridded_Yield_Changes_1-3_Degrees_Global_Warming_with_Uncertainty/5417548">here</a>).
<o:p></o:p></div>
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As readers of G-FEED well know though, getting productivity
shocks is only half the battle because what we really want are welfare changes.
Therefore, we introduce our productivity shocks into the 140-region GTAP CGE
model, which has a particularly rich representation of the agriculture sector
and international trade. This then gives us welfare changes due to agricultural
impacts at different levels of warming, which is what we need for an IAM damage
function. Finally, we take our findings all the way through to the SCC by
replacing the agricultural damages from FUND with our new estimates. Our
headline result is that improving damages <i style="mso-bidi-font-style: normal;">just
in the agricultural sector</i> leads the <i style="mso-bidi-font-style: normal;">overall
</i>SCC to more than double.<o:p></o:p></div>
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There’s lots more fun stuff in the paper and supplementary
information including a comparison with results from AgMIP, some interesting
findings around adaptation effectiveness, and a sensitivity analysis of GTAP
parameters. But here I want to highlight what I see as three main
contributions.<o:p></o:p></div>
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Firstly I think a major contribution is to the literature on
improving the scientific validity of damages underlying IAM results. The importance
of improving the empirical basis of damage functions has been pointed out <a href="http://science.sciencemag.org/content/352/6283/292">by</a> <a href="https://www.nature.com/news/global-warming-improve-economic-models-of-climate-change-1.14991">numerous</a>
<a href="http://science.sciencemag.org/content/353/6304/aad9837/tab-pdf">people</a>.
This is something Delavane and I have worked on previously using <a href="https://www.nature.com/articles/nclimate2481">empirically-estimated
growth-rate damages in DICE</a>, and something that Sol and co-authors have <a href="http://science.sciencemag.org/content/356/6345/1362">done a lot of work
on</a>. I think what’s new here is using the existing biophysical impacts
literature and tracing its implications all the way through to the SCC. There
is an awful lot of scientific work out there on the effects of climate change
relevant to the SCC, but wrangling the results from a large number of dispersed
studies into a form that can be used to support a global damage function is not
straightforward (something Delavane and I discuss in a <a href="https://www.nature.com/articles/nclimate3411?WT.feed_name=subjects_climate-change">recent
review paper</a>). In this sense the agriculture sector is probably one of the
more straightforward to tackle – we definitely relied on previous work from the
lead authors of the IPCC chapter and from the AgMIP team. I do think this paper
provides one template for implementing the recommendation of the <a href="http://sites.nationalacademies.org/dbasse/becs/valuing-climate-damages/index.htm">National
Academy of Sciences SCC report</a> around damage functions – that they be based
on current and peer-reviewed science, that they report and quantify uncertainty
wherever possible, and that calibrations are transparent and well-documented.<o:p></o:p></div>
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Secondly, an important contribution of this paper is on
quantifying the importance of general-equilibrium effects in determining
welfare changes. Since so much climate impacts work estimates local
productivity changes, it raises the question of what these can or can’t tell us
about welfare under global climate change. We address this question directly by
decomposing our welfare changes into three components: the direct productivity
effect (essentially just the local productivity change multiplied by crop value),
the terms-of-trade effect, and the allocative efficiency effect (caused by
interactions with existing market distortions). The last one is generally
pretty small in our work, so regional changes in welfare boil down to a
combination of local productivity changes and the interaction between a
region’s trade position and global price changes. This breakdown for 3 degrees
of warming is shown below.<o:p></o:p></div>
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjBt6nzka1N06hBP0gA8X0LZe9Wl8OeNJg3dW0dzWNWp6LW3aPuDsU9nvxO_phdsWLjxomLhQcEfm-UG69T8tz0XPphUd4LNBmP_0BR-oy88LffBY_nQyl-l_nVXzjVeCHTLXDmAqwoj6x3/s1600/fran_damage_1.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="597" data-original-width="975" height="388" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjBt6nzka1N06hBP0gA8X0LZe9Wl8OeNJg3dW0dzWNWp6LW3aPuDsU9nvxO_phdsWLjxomLhQcEfm-UG69T8tz0XPphUd4LNBmP_0BR-oy88LffBY_nQyl-l_nVXzjVeCHTLXDmAqwoj6x3/s640/fran_damage_1.png" width="640" /></a></div>
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For these productivity shocks, the terms-of-trade (ToT)
effect is regionally pretty important. In a number of cases (notably the US,
Australia, Argentina, and South Africa) it reverses the sign of the welfare
change of the productivity effect. In other words, a number of regions
experience yield losses but welfare gains because the increasing value of
exports more than compensates for the productivity losses (with the opposite in
South Africa). There are a few rules-of-thumb for thinking about how important
the ToT effects are likely to be. Firstly, the ToT effects cancel out at the
global level, so if you are only interested in aggregate global damages, you
don’t have to worry about this effect. By similar logic, the higher the level
of regional aggregation, the less important ToT effects are likely to be, since
larger regions will likely contain both importers and exporters. Secondly, the
importance of ToT effects depends on the magnitude of relative price changes which
in turn depends on the productivity shocks. If the effect of climate change is to
redistribute production around the globe rather than to increase or decrease
aggregate production, then ToT effects will be smaller. We see this in our
AgMIP results, where losses in the tropics are offset to a greater degree by
gains in temperate regions, leading to smaller price changes and
correspondingly smaller ToT effects.<o:p></o:p></div>
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A final point I’d like to highlight is the quantitative importance
of our findings for the SCC. We knew <a href="http://www.worldscientific.com/doi/abs/10.1142/S2010007817500099?src=recsys">from
previous work by Delavane</a> that agriculture is important in driving the SCC
in FUND. Nevertheless, such a large increase in the total SCC from updating just
one sector, in a model that contains 14 different impact sectors, is striking
and begs the question of what would happen with other sectors. Moreover, there
are suggestions that other models might also be underestimating agricultural
impacts. Neither PAGE nor DICE model the agricultural sector explicitly, but
both include agricultural impacts as part of more aggregate damage functions
(market impacts in PAGE09 and non-SLR damages in DICE 2013). By coupling damage
functions from these models to standardized socio-economic and climate modules,
we are able to make an apples-to-apples comparison of our agriculture-sector
damages with these damage functions. The graph below didn’t make it into the
paper, but I think is informative:</div>
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjrM3-2Y1_3rVopkc9z3hyphenhyphenqXAzLARc_Tkgfz-X4_hX0eSefp_p8Z0hOCOJhWpfb7bAmTy6fEwdSpv-UQ0y1K7wPCr75RsbolbmapZpUX68phLZRUP2ZkYQkXj6zuEx7x8SS4ed1lbZ9BXey/s1600/fran_damage_2.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="568" data-original-width="609" height="298" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjrM3-2Y1_3rVopkc9z3hyphenhyphenqXAzLARc_Tkgfz-X4_hX0eSefp_p8Z0hOCOJhWpfb7bAmTy6fEwdSpv-UQ0y1K7wPCr75RsbolbmapZpUX68phLZRUP2ZkYQkXj6zuEx7x8SS4ed1lbZ9BXey/s320/fran_damage_2.png" width="320" /></a></div>
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<span style="font-size: 11pt; text-align: justify;"> </span></div>
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<o:p></o:p></div>
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What you see is that our estimated
agricultural damages (from the meta-analysis of yield estimates) are actually
larger than <i style="mso-bidi-font-style: normal;">all</i> market damages
included in PAGE. This indicates either that there are very small negative impacts
and large off-setting benefits in other market sectors, or that PAGE is
substantially under-estimating market damages. Comparing our results to DICE would
imply that agricultural impacts constitute 45% of non-SLR damages. This seems
high to me, considering this damage function includes both market and non-market
(i.e. mortality) damages. For instance, in their <a href="http://science.sciencemag.org/content/356/6345/1362.full">analysis of US
damages</a>, Sol and co-authors found agricultural impacts to be dwarfed by
costs from increased mortality, and to be smaller than effects on labor
productivity and energy demand, suggesting to me that agricultural damages
might also be currently under-estimated in DICE.<o:p></o:p></div>
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That’s my (excessively-long) take
on the paper. Thank you to the GFEEDers for the opportunity. Do get in touch if
you have any questions or comments on the paper – fmoore at ucdavis dot edu.<o:p></o:p></div>
David Lobellhttp://www.blogger.com/profile/17660903133065588267noreply@blogger.com0tag:blogger.com,1999:blog-3813701770708442620.post-80068288579222532742017-11-07T06:00:00.000-08:002017-11-07T06:00:03.074-08:00[Pretty urgent] Call for papers: Berkeley Climate Economics Workshop<span style="font-family: inherit;">W<span style="color: #222222; font-size: 12.800000190734863px;">e are currently soliciting papers from PhD students and post-docs for a climate economics workshop at UC Berkeley. Proposals are due on </span><span class="aBn" data-term="goog_1627084091" style="border-bottom-color: rgb(204, 204, 204); border-bottom-style: dashed; border-bottom-width: 1px; color: #222222; font-size: 12.800000190734863px; position: relative; top: -2px; z-index: 0;" tabindex="0"><span class="aQJ" style="position: relative; top: 2px; z-index: -1;">November 20, 2017</span></span><span style="color: #222222; font-size: 12.800000190734863px;"> </span><span style="color: #222222; font-size: 12.800000190734863px;">by</span><span style="color: #222222; font-size: 12.800000190734863px;"> </span><span class="aBn" data-term="goog_1627084092" style="border-bottom-color: rgb(204, 204, 204); border-bottom-style: dashed; border-bottom-width: 1px; color: #222222; font-size: 12.800000190734863px; position: relative; top: -2px; z-index: 0;" tabindex="0"><span class="aQJ" style="position: relative; top: 2px; z-index: -1;">8:00am PST</span></span><span style="color: #222222; font-size: 12.800000190734863px;">.</span></span><br />
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<span style="font-family: inherit;">BACKGROUND AND GOALS</span></h4>
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<div style="color: #222222; font-size: 12.800000190734863px;">
<span style="font-family: inherit;">We encourage papers by PhD students and post-docs undertaking research in any area related to the economics of climate change. We encourage papers that use empirical methods, theory or numerical modelling. Papers can be single authored or co-authored. No restrictions apply to co-authors, i.e. coauthors can be senior researchers.</span></div>
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<div style="color: #222222; font-size: 12.800000190734863px;">
<span style="font-family: inherit;">The workshop will explore recent advances in climate economics, with an emphasis on the linkage between empirical and numerical modeling methods. One goal of the workshop is to bring junior and senior researchers together. The final program will combine presentations from invited leading senior researchers and presentations from the most promising junior researchers (PhD students and postdocs).</span></div>
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<span style="font-family: inherit;">HOW TO APPLY</span></h4>
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<span style="font-family: inherit;">Applications should be submitted online <a href="https://tinyurl.com/UCBClimateCFPJan18">here</a>.</span></div>
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<span style="font-family: inherit;"><br /></span></div>
<div style="color: #222222; font-size: 12.800000190734863px;">
<span style="font-family: inherit;">Please include either a full working paper or an extended abstract (1-2 pages). PhD students should include a brief letter of recommendation from their advisor that indicates that the submitted abstract/paper will be ready for a full presentation for the workshop.</span></div>
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<span style="font-family: inherit;">WORKSHOP INFORMATION</span></h4>
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<span style="font-family: inherit;">The workshop will be held at UC Berkeley on <span class="aBn" data-term="goog_1627084093" style="border-bottom-color: rgb(204, 204, 204); border-bottom-style: dashed; border-bottom-width: 1px; position: relative; top: -2px; z-index: 0;" tabindex="0"><span class="aQJ" style="position: relative; top: 2px; z-index: -1;">Fri 1/19 and Sat 1/20</span></span>, 2018. All travel and lodging costs will be covered for presenters.</span></div>
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<span style="font-family: inherit;"><br /></span></div>
<div style="color: #222222; font-size: 12.800000190734863px;">
<span style="font-family: inherit;">The workshop is organized by <a href="http://www.david-anthoff.com/">David Anthoff</a>, <a href="https://www.auffhammer.com/">Max Auffhamer</a>, and <a href="https://www.solomonhsiang.com/">Solomon Hsiang</a>, in collaboration with the <a href="http://matrix.berkeley.edu/">Social Science Matrix at UC Berkeley</a>.</span></div>
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<span style="font-family: inherit;"><br /></span></div>
<div style="color: #222222; font-size: 12.800000190734863px;">
<span style="font-family: inherit;">Any questions regarding the workshop should be directed to <a href="mailto:evaseto@berkeley.edu">Eva Seto</a>.</span></div>
solhttp://www.blogger.com/profile/00936469103707728475noreply@blogger.com1tag:blogger.com,1999:blog-3813701770708442620.post-50616521046592164752017-10-30T06:00:00.000-07:002017-10-30T06:00:21.631-07:00Climate impacts research is getting real<i>A few recent examples of our research getting out of the ivory tower and into the real world. This came up in a recent seminar I teach, so it seemed like others might appreciate the update.</i><br />
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1. The NYT sent some journalists to India to do <a href="https://www.nytimes.com/interactive/2017/10/26/world/middleeast/india-farmers-drought.html">a story</a> following up on Tamma Carleton's <a href="http://www.g-feed.com/2017/08/climate-change-crop-failure-and.html">recent work </a>on climate and economic drivers of suicide in India. This is powerful and humanizing work by the Times making hard-nosed numbers appropriately real and heartbreaking. Some of the striking one-line quotes they obtained from personal interviews:<br />
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<b>"I go without food more days than I eat"</b></blockquote>
<blockquote class="tr_bq">
<b>"If I were to take out any more loans, the interest would grow, and my whole family would be forced to kill themselves."</b></blockquote>
2. Last week the US Government Accountability Office released to congress the report <a href="https://www.gao.gov/assets/690/687466.pdf">Climate Change: Information on Potential Economic Effects Could Help Guide Federal Efforts to Reduce Fiscal Exposure</a>. The report drew heavily form the <a href="http://www.impactlab.org/research/american-climate-prospectus/">American Climate Prospectus</a> that we published a few years ago and our recent <a href="http://science.sciencemag.org/content/356/6345/1362.full">paper</a> on US costs of warming. The GAO summary concludes:<br />
<blockquote class="tr_bq">
<b>Information about the potential economic effects of climate change could inform decision makers about significant potential damages in different U.S. sectors or regions. According to several experts and prior GAO work, this information could help federal decision makers identify significant climate risks as an initial step toward managing such risks. This is consistent with, for example, National Academies leading practices, which call for climate change risk management efforts that focus on where immediate attention is needed. The federal government has not undertaken strategic government-wide planning to manage climate risks by using information on the potential economic effects of climate change to identify significant risks and craft appropriate federal responses. By using such information, the federal government could take an initial step in establishing government-wide priorities to manage such risks.</b></blockquote>
3. Trevor Houser and I recently estimated the potential long-run economic consequences of Hurricane Maria on the economic growth of Puerto Rico and published <a href="https://www.nytimes.com/2017/09/29/opinion/puerto-rico-hurricane-maria.html">an op-ed explaining the issue</a> and putting the event in context. Basically, I ran my LICRICE model to compute wind exposure across the island, which totals 123 mph max wind speeds <i>on average</i> across the entire territory. For a territory of this size, especially in the Atlantic, this is unprecedented in my data.<br />
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Then we applied these numbers to the results of <a href="https://www.princeton.edu/rpds/events_archive/repository/Hsiang030415/Hsiang030415.pdf">work with Amir Jina</a> on the macro-economic effects of these storms. If you were take central estimates from our benchmark model, the picture is a 21% drop in GDP per capita, relative to trend, over the next 15 years. Based on the low pre-Maria growth rate, we estimate that this storm undid roughly 26 years of growth in under 12 hrs. As stated in the op ed </div>
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<b>"Almost nothing on the planet, short of nuclear weaponry, destroys economic value as rapidly as a mega-hurricane."</b></blockquote>
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solhttp://www.blogger.com/profile/00936469103707728475noreply@blogger.com0tag:blogger.com,1999:blog-3813701770708442620.post-54014842759171743982017-08-10T06:00:00.000-07:002017-08-10T06:00:42.401-07:00Climate change, crop failure, and suicides in India (Guest post by Tamma Carleton)<div style="line-height: normal;">
<i>[This is a guest post by <a href="http://www.globalpolicy.science/tamma-carleton">Tamma Carleton</a>, a Doctoral Fellow at the Global Policy Lab and PhD candidate in the Ag and Resource Econ department here at Berkeley]</i></div>
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<span style="font-family: inherit;">Last week, I published a <a href="http://www.pnas.org/content/early/2017/07/25/1701354114.short"><span style="color: #0433ff;">paper</span></a> in <i>PNAS</i> addressing a topic that has captured the attention of media and policymakers around the world for many years – the rising suicide rate in India. As a dedicated student of G-FEED contributors, I focus on the role of climate in this tragic phenomenon. I find that temperatures during India’s main growing season cause substantial increases in the suicide rate, amounting to around 65 additional deaths if all of India gained a degree day. I show that over 59,000 suicides can be attributed to warming trends across the country since 1980. With a range of different approaches I’ll talk about here, I argue that this effect appears to materialize through an agricultural channel in which crops are damaged, households face economic distress, and some cope by taking their own lives. It’s been a pretty disheartening subject to study for the last couple years, and I’m glad to see the findings out in the world, and now here on G-FEED.</span></div>
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<span style="font-family: inherit;">First, a little background on suicides in India. The national suicide rate has approximately doubled since 1980, from around 6 per 100,000 to over 11 per 100,000 (for reference, the rate in the U.S. is about 13 per 100,000). The size of India’s population means this number encompasses many lives – today, about 135,000 are lost to suicide annually. There have been a lot of claims about what contributes to the upward trend, although most focus on increasing risks in agriculture, such as output price volatility, costly hybrid seeds, and crop-damaging climate events like drought and heat (e.g. <a href="http://www.nytimes.com/2006/09/19/world/asia/19india.html"><span style="color: #0433ff;">here</span></a>, <a href="http://www.jstor.org/stable/4412301"><span style="color: #0433ff;">here</span></a>, and <a href="https://www.blogger.com/%22http:/"><span style="color: #0433ff;">here</span></a>). While many academic and non-academic sources have discussed the role of the climate, there was no quantitative evidence of a causal effect. I wanted to see if this relationship was in the data, and I wanted to be able to speak to the ongoing public debate by looking at mechanisms, a notoriously thorny aspect of the climate impacts literature.</span></div>
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<span style="font-family: inherit;">The first finding in my paper is that while growing season temperatures increase the annual suicide rate, also hurting crops (as the G-FEED authors have shown us many times over), these same temperatures have no effect on suicides outside the growing season. While the results are <i>much</i> less certain for rainfall (I’m stuck with state-by-year suicide data throughout the analysis), a similar pattern emerges there, with higher growing season rainfall appearing to cause reductions in suicide: </span></div>
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEj012vEI6Hqnl9TO6WBH2idK94_x5cFpUnFUgp5m9M9qcUDk9Di4d32XHDXet4_NwMdbvw81ncTqnODbKvfJaRndD0AJlOyEQCs2Mv8ZfAkO_8owEcBNCBXaaP9_-sZADCO1Oe_dwCa57I/s1600/fig1.jpg" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="1088" data-original-width="1600" height="434" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEj012vEI6Hqnl9TO6WBH2idK94_x5cFpUnFUgp5m9M9qcUDk9Di4d32XHDXet4_NwMdbvw81ncTqnODbKvfJaRndD0AJlOyEQCs2Mv8ZfAkO_8owEcBNCBXaaP9_-sZADCO1Oe_dwCa57I/s640/fig1.jpg" width="640" /></a></div>
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<span style="font-family: inherit;">These effects seem pretty large to me. As I said above, a degree day of warming throughout the country during the growing season causes about 65 suicides throughout the year, equivalent to a 3.5% increase in the suicide rate per standard deviation increase in growing season degree days. </span></div>
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<span style="font-family: inherit;">The fact that the crop response functions are mirrored by the suicide response functions is consistent with an agricultural mechanism. However, this isn’t really enough evidence. Like in other areas of climate impacts research, it’s difficult to find exogenous variation that turns on or shuts off the hypothesized mechanism here – I don’t have an experiment where I randomly let some households’ farm income be unaffected by temperature, as others’ suffer. Therefore, aspects of life that are different in the growing and non-growing seasons could possibly be driving heterogeneous response functions between temperature and suicide. Because this mechanism is so important to policy, I turn to a couple additional tests.</span></div>
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<span style="font-family: inherit;">I first show that there are substantial lagged effects, which are unlikely to occur if non-economic, direct links between the climate and suicidal behavior were taking place (like the psychological channels linking temperature to violence discussed in <a href="http://www.annualreviews.org/doi/abs/10.1146/annurev-economics-080614-115430"><span style="color: #0433ff;">Sol and Marshall’s work</span></a>). I also estimate spatial heterogeneity in both the suicide response to temperature, as well as the yield response, and find that the locations where suicides are most sensitive to growing season degree days also tend to be the locations where yields are most sensitive: </span></div>
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEj3N43FO-nyPkjDrsErQrVxKTy8Yb1KXKyT0Ku4Js2RCAaohmRyDx7NYKsAJQnbzD4dZ20M5jK3CyfeiS_PiaGLeVRciW1OoIkuFcFwRPAiqIhN9qNpws9WfgUt74Qb9aBGqAasSPTBD3Q/s1600/fig2.jpg" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="424" data-original-width="792" height="340" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEj3N43FO-nyPkjDrsErQrVxKTy8Yb1KXKyT0Ku4Js2RCAaohmRyDx7NYKsAJQnbzD4dZ20M5jK3CyfeiS_PiaGLeVRciW1OoIkuFcFwRPAiqIhN9qNpws9WfgUt74Qb9aBGqAasSPTBD3Q/s640/fig2.jpg" width="640" /></a></div>
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<span style="font-family: inherit;">The fact that higher temperatures mean more suicides is troubling as we think about warming unfolding in the next few decades. However, I’m an economist, so I should expect populations to reallocate resources and re-optimize behaviors to adapt to a gradually warming climate, right? Sadly, after throwing at the data all of the main adaptation tests that I’m aware of from the literature, I find no evidence of adaptation, looking both across space (e.g. are places with hotter <a href="http://www.worldscientific.com/doi/abs/10.1142/S201000781250011X"><span style="color: #0433ff;">average climates</span></a> less sensitive?) and across time (e.g. has the response function <a href="http://www.journals.uchicago.edu/doi/abs/10.1086/684582"><span style="color: #0433ff;">flattened over time</span></a>? What about <a href="https://www.ocf.berkeley.edu/~kemerick/Burke_Emerick_2013.pdf"><span style="color: #0433ff;">long differences</span></a>?):</span></div>
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjFjEugDUSEywOPkgaPuApoOeg7UuAidQpGTML-4OgsDZN7qMpalhzzbvxNvQeofFnOyFIBKMaT_5vJY4wovCDw2NIwQkUNrjrwtMsGfevonuB5_hpWcjPnUGo7ZH53KLUvKv5ZwqHvgUM/s1600/fig3.jpg" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="604" data-original-width="718" height="336" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjFjEugDUSEywOPkgaPuApoOeg7UuAidQpGTML-4OgsDZN7qMpalhzzbvxNvQeofFnOyFIBKMaT_5vJY4wovCDw2NIwQkUNrjrwtMsGfevonuB5_hpWcjPnUGo7ZH53KLUvKv5ZwqHvgUM/s400/fig3.jpg" width="400" /></a></div>
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<span style="font-family: inherit;">Keeping in mind that there is no evidence of adaptation, my last calculation is to estimate the total number of deaths that can be attributed to warming trends observed since 1980. Following the method in David, Wolfram and Justin Costa-Roberts’ <a href="http://science.sciencemag.org/content/333/6042/616"><span style="color: #0433ff;">2011 article in <i>Science</i></span></a>, I find that by end of sample in 2013, over 4,000 suicides per year across the country can be attributed to warming. Integrating from 1980 to today and across all states in India, I estimate that over 59,000 deaths in total can be attributed to warming. With spatially heterogeneous warming trends and population density, these deaths are distributed very differently across space:</span></div>
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjvr00RecWI3AYEYpMGvWAmGlVgoe1i0-qYoBs0mPpunxGXMt_N5LVbXdy48SA9Fomg-tbQsuJFjHpUPimdg83k3QJeCbzKp7EskN-c-yEHBKaeFTXoKDE6k15y4t5egHMfxNZR3tAq8_o/s1600/fig4.jpg" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="1600" data-original-width="1460" height="320" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjvr00RecWI3AYEYpMGvWAmGlVgoe1i0-qYoBs0mPpunxGXMt_N5LVbXdy48SA9Fomg-tbQsuJFjHpUPimdg83k3QJeCbzKp7EskN-c-yEHBKaeFTXoKDE6k15y4t5egHMfxNZR3tAq8_o/s320/fig4.jpg" width="291" /></a></div>
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<span style="font-family: inherit;">While the tools I use are not innovative by any means (thanks to the actual authors of this blog for developing most of them), I think this paper is valuable to our literature for a couple reasons. First, while we talk a lot about integrating our empirical estimates of the mortality effects of climate change into policy-relevant metrics like the SCC, this is a particular type of death I think we should be incredibly concerned about. Suicide indicates extreme distress and hardship, and since we care about welfare, these deaths mean something distinct from the majority of the deaths driving the mortality rate responses that we often study. </span></div>
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<span style="font-family: inherit;">Second, mechanisms really matter. The media response to my paper has been shockingly strong, and everyone wants to talk about the mechanism and what it means for preventative policy. While I have by no means nailed the channel down perfectly here, a focus on testing the agricultural mechanism has made my findings much more tangible for people battling the suicide epidemic on the ground in India. I look forward to trying to find ways to improve the tools at our disposal for identifying mechanisms in this context and in others.</span></div>
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<span style="font-family: inherit;">Finally, as climate change progresses, I think we could learn a lot from applying David, Wolfram, and Justin’s method more broadly. While attribution exercises have their own issues (e.g. we can’t, of course, attribute with certainty the entire temperature trend in any location to anthropogenic warming), I think it’s much easier for many people to engage with damages being felt today, as opposed to those likely to play out in the future. </span></div>
solhttp://www.blogger.com/profile/00936469103707728475noreply@blogger.com2tag:blogger.com,1999:blog-3813701770708442620.post-39380838738458520702017-07-06T15:50:00.000-07:002017-07-06T15:50:40.711-07:00Yesterday's maximum temperature is... today's maximum temperature? (Guest post by Patrick Baylis)<div dir="ltr" style="text-align: left;" trbidi="on">
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[For you climate-data-wrangling nerds out there, today we bring you a guest post by <a href="http://patrickbaylis.com/">Patrick Baylis</a>, current Stanford postdoc and soon-to-be assistant prof at UBC this fall. ]</div>
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<span style="background-color: white;"><span style="font-family: inherit;">You may not know this, but Kahlil Gibran was actually thinking about weather data when he wrote that <em style="box-sizing: inherit;">yesterday is but today’s memory, and tomorrow is today’s dream.</em> (Okay, not really.)</span></span></div>
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<span style="background-color: white;"><span style="font-family: inherit;">Bad literary references aside, readers of this blog know that climate economists project the impacts of climate change is by observing the relationships between historical weather realizations and economic outcomes. Fellow former ARE PhD <a href="http://globalpolicy.science/kendon-bell/" style="background-image: linear-gradient(rgb(64, 114, 175), rgb(64, 114, 175)); background-position: 0px 1.1em; background-repeat: repeat-x; background-size: 1px 1px; box-sizing: inherit; color: #4072af; text-decoration-line: none; text-shadow: rgb(250, 250, 248) 0.04545em 0px 0px, rgb(250, 250, 248) 0.09091em 0px 0px, rgb(250, 250, 248) 0.13636em 0px 0px, rgb(250, 250, 248) -0.04545em 0px 0px, rgb(250, 250, 248) -0.09091em 0px 0px, rgb(250, 250, 248) -0.13636em 0px 0px, rgb(250, 250, 248) 0px 0.04545em 0px, rgb(250, 250, 248) 0px 0.09091em 0px, rgb(250, 250, 248) 0px -0.04545em 0px, rgb(250, 250, 248) 0px -0.09091em 0px; touch-action: manipulation;">Kendon Bell</a> alerted me to an idiosyncrasy in one of the weather datasets we frequently use in our analyses. Since many of us (myself included) rely on high-quality daily weather data to do our work, I investigated. This post is a fairly deep dive into what I learned, so if you happen to not be interested in the minutiae of daily weather data, consider yourself warned.</span></span></div>
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<span style="background-color: white;"><span style="font-family: inherit;">The <a href="http://www.prism.oregonstate.edu/documents/PRISM_datasets.pdf" style="background-image: linear-gradient(rgb(64, 114, 175), rgb(64, 114, 175)); background-position: 0px 1.1em; background-repeat: repeat-x; background-size: 1px 1px; box-sizing: inherit; color: #4072af; text-decoration-line: none; text-shadow: rgb(250, 250, 248) 0.04545em 0px 0px, rgb(250, 250, 248) 0.09091em 0px 0px, rgb(250, 250, 248) 0.13636em 0px 0px, rgb(250, 250, 248) -0.04545em 0px 0px, rgb(250, 250, 248) -0.09091em 0px 0px, rgb(250, 250, 248) -0.13636em 0px 0px, rgb(250, 250, 248) 0px 0.04545em 0px, rgb(250, 250, 248) 0px 0.09091em 0px, rgb(250, 250, 248) 0px -0.04545em 0px, rgb(250, 250, 248) 0px -0.09091em 0px; touch-action: manipulation;">PRISM AN81-d</a> dataset is daily minimum and maximum temperatures, precipitation, and minimum and maximum vapor pressure deficit data for the continental United States from 1981 to present. It is created by the <a href="http://www.prism.oregonstate.edu/" style="background-image: linear-gradient(rgb(64, 114, 175), rgb(64, 114, 175)); background-position: 0px 1.1em; background-repeat: repeat-x; background-size: 1px 1px; box-sizing: inherit; color: #4072af; text-decoration-line: none; text-shadow: rgb(250, 250, 248) 0.04545em 0px 0px, rgb(250, 250, 248) 0.09091em 0px 0px, rgb(250, 250, 248) 0.13636em 0px 0px, rgb(250, 250, 248) -0.04545em 0px 0px, rgb(250, 250, 248) -0.09091em 0px 0px, rgb(250, 250, 248) -0.13636em 0px 0px, rgb(250, 250, 248) 0px 0.04545em 0px, rgb(250, 250, 248) 0px 0.09091em 0px, rgb(250, 250, 248) 0px -0.04545em 0px, rgb(250, 250, 248) 0px -0.09091em 0px; touch-action: manipulation;">PRISM Climate Group</a> at Oregon State, and it is <em style="box-sizing: inherit;">really</em> nice. Why? It’s a gridded data product: it is composed of hundreds of thousands of 4km by 4km grid cells, where the values for each cell are determined by a <a href="http://www.prism.oregonstate.edu/documents/Daly2008_PhysiographicMapping_IntJnlClim.pdf" style="background-image: linear-gradient(rgb(64, 114, 175), rgb(64, 114, 175)); background-position: 0px 1.1em; background-repeat: repeat-x; background-size: 1px 1px; box-sizing: inherit; color: #4072af; text-decoration-line: none; text-shadow: rgb(250, 250, 248) 0.04545em 0px 0px, rgb(250, 250, 248) 0.09091em 0px 0px, rgb(250, 250, 248) 0.13636em 0px 0px, rgb(250, 250, 248) -0.04545em 0px 0px, rgb(250, 250, 248) -0.09091em 0px 0px, rgb(250, 250, 248) -0.13636em 0px 0px, rgb(250, 250, 248) 0px 0.04545em 0px, rgb(250, 250, 248) 0px 0.09091em 0px, rgb(250, 250, 248) 0px -0.04545em 0px, rgb(250, 250, 248) 0px -0.09091em 0px; touch-action: manipulation;">complex interpolation method</a> from weather station data (<a href="https://data.noaa.gov/dataset/global-historical-climatology-network-daily-ghcn-daily-version-3" style="background-image: linear-gradient(rgb(64, 114, 175), rgb(64, 114, 175)); background-position: 0px 1.1em; background-repeat: repeat-x; background-size: 1px 1px; box-sizing: inherit; color: #4072af; text-decoration-line: none; text-shadow: rgb(250, 250, 248) 0.04545em 0px 0px, rgb(250, 250, 248) 0.09091em 0px 0px, rgb(250, 250, 248) 0.13636em 0px 0px, rgb(250, 250, 248) -0.04545em 0px 0px, rgb(250, 250, 248) -0.09091em 0px 0px, rgb(250, 250, 248) -0.13636em 0px 0px, rgb(250, 250, 248) 0px 0.04545em 0px, rgb(250, 250, 248) 0px 0.09091em 0px, rgb(250, 250, 248) 0px -0.04545em 0px, rgb(250, 250, 248) 0px -0.09091em 0px; touch-action: manipulation;">GHCN-D</a>) that accounts for topological factors. Importantly, it’s consistent: there are no discontinuous jumps in the data (see figure below) and it’s a balanced panel: the observations are never missing.</span></span></div>
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<span style="background-color: white;"><span style="font-family: inherit;"><img alt="PRISM 30 year normals" src="http://patrickbaylis.com/assets/img/PRISM_tmax_30yr_normal_4kmM2_annual.png" style="border-style: none; box-sizing: inherit; max-width: 100%; vertical-align: middle;" /><br style="box-sizing: inherit;" /><em style="box-sizing: inherit;">Source: PRISM Climate Group</em></span></span></div>
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<span style="background-color: white;"><span style="font-family: inherit;">These benefits are well-understood, and as a result many researchers use the PRISM dataset for their statistical models. However, there is a particularity of these data that may be important to researchers making use of the daily variation in the data: most measurements of temperature maximums, and some measurements of temperature minimums, actually refer to the maximum or minimum temperature of the day <em style="box-sizing: inherit;">before</em> the date listed.</span></span></div>
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<span style="background-color: white;"><span style="font-family: inherit;">To understand this, you have to understand that daily climate observations are actually summaries of many within-day observations. The reported maximum and minimum temperature are just the maximum and minimum temperature observations within a given period, like a day. The tricky part is that stations define a “day” as “the 24 hours since I previously recorded the daily summary”, but not all stations record their summaries at the same time. While most U.S. stations record in the morning (i.e, “morning observers”), a hefty proportion of stations are either afternoon or evening observers. PRISM aggregates data from these daily summaries, but in order to ensure consistency tries to only incorporate morning observers. This leads to the definition of a “PRISM day”. The PRISM <a href="http://prism.nacse.org/documents/PRISM_datasets.pdf" style="background-image: linear-gradient(rgb(64, 114, 175), rgb(64, 114, 175)); background-position: 0px 1.1em; background-repeat: repeat-x; background-size: 1px 1px; box-sizing: inherit; color: #4072af; text-decoration-line: none; text-shadow: rgb(250, 250, 248) 0.04545em 0px 0px, rgb(250, 250, 248) 0.09091em 0px 0px, rgb(250, 250, 248) 0.13636em 0px 0px, rgb(250, 250, 248) -0.04545em 0px 0px, rgb(250, 250, 248) -0.09091em 0px 0px, rgb(250, 250, 248) -0.13636em 0px 0px, rgb(250, 250, 248) 0px 0.04545em 0px, rgb(250, 250, 248) 0px 0.09091em 0px, rgb(250, 250, 248) 0px -0.04545em 0px, rgb(250, 250, 248) 0px -0.09091em 0px; touch-action: manipulation;">documentation</a> defines a “PRISM day” as:</span></span></div>
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<span style="background-color: white;"><span style="font-family: inherit;"><i>Station data used in AN81d are screened for adherence to a “PRISM day” criterion. A PRISM day is defined as 1200 UTC-1200 UTC (e.g., 7 AM-7AM EST), which is the same as the [the National Weather Service’s hydrologic day]. Once-per day observation times must fall within +/- 4 hours of the PRISM day to be included in the AN81d tmax and tmin datasets. Stations without reported observation times in the NCEI GHCN-D database are currently assumed to adhere to the PRISM day criterion. The dataset uses a day-ending naming convention, e.g., a day ending at 1200 UTC on 1 January is labeled 1 January.</i></span></span></div>
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<span style="background-color: white;"><span style="font-family: inherit;">This definition means that generally only morning observers should be included in the data. The last sentence is important: because a day runs from 4am-4am PST (or 7am-7am EST) and because days are labeled using the endpoint of that time period, <em style="box-sizing: inherit;">most of the observations from which the daily measures are constructed for a given date are taken from the day prior</em>. A diagram may be helpful here:</span></span></div>
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<span style="background-color: white;"><span style="font-family: inherit;"><img alt="Diagram" height="480" src="http://patrickbaylis.com/assets/img/prism_dates_example.png" style="border-style: none; box-sizing: inherit; max-width: 100%; vertical-align: middle;" width="640" /></span></span></div>
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<span style="background-color: white;"><span style="font-family: inherit;">The above is a plot of temperature over about two days, representing a possible set of within-day monitor data. Let’s say that this station takes a morning reading at 7am PST (10am EST), meaning that this station would be included in the PRISM dataset. The top x-axis is the actual date, while the bottom x axis shows which observations are used under the PRISM day definition. The red lines are actual midnights, the dark green dotted line is the PRISM day definition cutoff and the orange (blue) dots in the diagram are the observations that represent the true maximums (minimums) of that calendar day. Because of the definition of a PRISM day, the maximum temperatures (“tmax”s from here on out) given for Tuesday and Wednesday (in PRISM) are actually observations recorded on Monday and Tuesday, respectively. On the other hand, the minimum temperatures (“tmin”s) given for Tuesday (in PRISM) is actually drawn from Tuesday, but the tmin given for Wednesday (in PRISM) is <em style="box-sizing: inherit;">also</em> from Tuesday.</span></span></div>
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<span style="background-color: white;"><span style="font-family: inherit;">To see this visually, I pulled the GHCN data and plotted a histogram of the average reporting time by station for the stations that report observation time (66% in the United States). The histogram below shows the average observation time by stations for all GHCN-D stations in the continental United States in UTC, colored by whether or not they would be included in PRISM according to the guidelines given above.</span></span></div>
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<span style="background-color: white;"><span style="font-family: inherit;"><img alt="Histogram of observation time" height="266" src="http://patrickbaylis.com/assets/img/obs_time_hist.png" style="border-style: none; box-sizing: inherit; max-width: 100%; vertical-align: middle;" width="400" /></span></span></div>
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<span style="background-color: white;"><span style="font-family: inherit;">This confirms what I asserted above: most, but not all, GHCN-D stations are morning observers, and the PRISM day definition does a good job capturing the bulk of that distribution. On average, stations fulfilling the PRISM criterion report at 7:25am or so.</span></span></div>
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<span style="background-color: white;"><span style="font-family: inherit;">The next step is to look empirically at how many minimum and maximum temperature observations are likely to fall before or after the observation time cutoff. To do that, we need some raw weather station data, which I pulled from <a href="https://www.ncdc.noaa.gov/data-access/land-based-station-data/land-based-datasets/quality-controlled-local-climatological-data-qclcd" style="background-image: linear-gradient(rgb(64, 114, 175), rgb(64, 114, 175)); background-position: 0px 1.1em; background-repeat: repeat-x; background-size: 1px 1px; box-sizing: inherit; color: #4072af; text-decoration-line: none; text-shadow: rgb(250, 250, 248) 0.04545em 0px 0px, rgb(250, 250, 248) 0.09091em 0px 0px, rgb(250, 250, 248) 0.13636em 0px 0px, rgb(250, 250, 248) -0.04545em 0px 0px, rgb(250, 250, 248) -0.09091em 0px 0px, rgb(250, 250, 248) -0.13636em 0px 0px, rgb(250, 250, 248) 0px 0.04545em 0px, rgb(250, 250, 248) 0px 0.09091em 0px, rgb(250, 250, 248) 0px -0.04545em 0px, rgb(250, 250, 248) 0px -0.09091em 0px; touch-action: manipulation;">NOAA’s Quality Controlled Local Climatological Data</a>(QCLCD). To get a sense for which extreme temperatures would be reported as occurring on the actual day they occurred, I assumed that all stations would report at 7:25am, the average observation time in the PRISM dataset. The next two figures show histograms of observed maximum and minimum temperatures.</span></span></div>
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<span style="background-color: white;"><span style="font-family: inherit;"><img alt="Histogram of observed maximum temperatures" src="http://patrickbaylis.com/assets/img/max_temp_hist.png" style="border-style: none; box-sizing: inherit; max-width: 100%; vertical-align: middle;" /> <img alt="Histogram of observed minimum temperatures" src="http://patrickbaylis.com/assets/img/min_temp_hist.png" style="border-style: none; box-sizing: inherit; max-width: 100%; vertical-align: middle;" /></span></span></div>
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<span style="background-color: white;"><span style="font-family: inherit;">I’ve colored the histograms so that all extremes (tmins and tmaxes) after 7:25am are red, indicating that extremes after that time will be reported as occurring during the following day. As expected, the vast majority of tmaxes (>94%) occur after 7:25am. But surprisingly, a good portion (32%) of tmins do as well. If you’re concerned about the large number of minimum temperature observations around midnight, remember that a midnight-to-midnight summary is likely to have this sort of “bump”, since days with a warmer-than-usual morning and a colder-than-usual night will have their lowest temperature at the end of the calendar day.</span></span></div>
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<span style="background-color: white;"><span style="font-family: inherit;">As a more direct check, I compared PRISM leads of tmin and tmax to daily aggregates (that I computed using a local calendar date definition) of the raw QCLCD data described above. The table below shows the pairwise correlations between the PRISM day-of observations, leads (next day), and the QCLCD data for both maximum and minimum daily temperature.</span></span></div>
<table style="border-bottom: 2px solid rgb(51, 51, 51); border-collapse: collapse; border-spacing: 0px; border-top: 2px solid rgb(51, 51, 51); box-sizing: inherit; color: #333333; margin: 0px auto; width: auto;"><thead style="box-sizing: inherit;">
<tr style="box-sizing: inherit;"><th style="border-bottom: 1px solid rgb(115, 115, 115); box-sizing: inherit; font-weight: normal; line-height: 1.71429; padding: 0.65ex 0.5em 0.4ex; text-align: center;"><span style="background-color: white;"><span style="font-family: inherit;">Measure</span></span></th><th style="border-bottom: 1px solid rgb(115, 115, 115); box-sizing: inherit; font-weight: normal; line-height: 1.71429; padding: 0.65ex 0.5em 0.4ex; text-align: center;"><span style="background-color: white;"><span style="font-family: inherit;">PRISM day-of</span></span></th><th style="border-bottom: 1px solid rgb(115, 115, 115); box-sizing: inherit; font-weight: normal; line-height: 1.71429; padding: 0.65ex 0.5em 0.4ex; text-align: center;"><span style="background-color: white;"><span style="font-family: inherit;">PRISM lead</span></span></th></tr>
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<tr style="box-sizing: inherit;"><td style="box-sizing: inherit; line-height: 1.71429; padding-left: 0.5em; padding-right: 0.5em; padding-top: 0.65ex;"><span style="background-color: white;"><span style="font-family: inherit;">tmin (calendar)</span></span></td><td style="box-sizing: inherit; line-height: 1.71429; padding-left: 0.5em; padding-right: 0.5em; padding-top: 0.65ex;"><span style="background-color: white;"><span style="font-family: inherit;">0.962</span></span></td><td style="box-sizing: inherit; line-height: 1.71429; padding-left: 0.5em; padding-right: 0.5em; padding-top: 0.65ex;"><span style="background-color: white;"><span style="font-family: inherit;">0.978</span></span></td></tr>
<tr style="box-sizing: inherit;"><td style="box-sizing: inherit; line-height: 1.71429; padding-left: 0.5em; padding-right: 0.5em;"><span style="background-color: white;"><span style="font-family: inherit;">tmax (calendar)</span></span></td><td style="box-sizing: inherit; line-height: 1.71429; padding-left: 0.5em; padding-right: 0.5em;"><span style="background-color: white;"><span style="font-family: inherit;">0.934</span></span></td><td style="box-sizing: inherit; line-height: 1.71429; padding-left: 0.5em; padding-right: 0.5em;"><span style="background-color: white;"><span style="font-family: inherit;">0.992</span></span></td></tr>
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<span style="background-color: white;"><span style="font-family: inherit;">As you can see, the the PRISM leads, i.e., observations from the next day, correlated more strongly with my aggregated data. The difference was substantial for tmax, as expected. The result for tmin is surprising: it also correlates more strongly with the PRISM tmin lead. I’m not quite sure what to make of this - it may be that the stations who fail to report their observation times and the occasions when the minimum temperature occurs after the station observation time are combining to make the lead of tmin correlate more closely with the local daily summaries I’ve computed. But I’d love to hear other explanations.</span></span></div>
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<span style="background-color: white;"><span style="font-family: inherit;">So who should be concerned about this? Mostly, researchers with econometric models that use daily variation in temperature on the right-hand side, and fairly high frequency variables on the left-hand side. The PRISM group isn’t doing anything wrong, and I’m sure that folks who specialize in working with weather datasets are very familiar with this particular limitation. Their definition matches a widely used definition of how to appropriately summarize daily weather observations, and presumably they’ve thought carefully about the consequences of this definition and of including more data from stations who don’t report their observation times. But researchers who, like me, are not specialists in using meteorological data and who, like me, use PRISM to examine at daily relationships between weather and economics outcomes, should tread carefully.</span></span></div>
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<span style="background-color: white;"><span style="font-family: inherit;">As is, using the PRISM daily tmax data amounts to estimating a model that includes lagged rather than day-of temperature. A quick fix, particularly for models that include only maximum temperature, is to simply use the leads, or the observed weather for the next day, since it will almost always reflect the maximum temperature for the day of interest. A less-quick fix is to make use of the whole distribution using the raw monitor data, but then you would lose the nice gridded quality of the PRISM data. Models with average or minimum temperature should, at the very least, tested for robustness with the lead values. Let’s all do Gibran proud.</span></span></div>
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Marshall Burkehttp://www.blogger.com/profile/15436297075698378164noreply@blogger.com1tag:blogger.com,1999:blog-3813701770708442620.post-61303210309388575692017-06-30T06:00:00.000-07:002017-07-07T16:06:07.888-07:00Building a better damage function<a href="http://www.bobkopp.net/" target="_blank">Bob Kopp</a>, <a href="http://www.amirjina.com/" target="_blank">Amir Jina</a>, <a href="http://www.existencia.org/pro/" target="_blank">James Rising,</a> and our partners at the <a href="http://www.impactlab.org/" target="_blank">Climate Impact Lab</a>, Princeton, and <a href="http://www.rms.com/" target="_blank">RMS</a>, have a <a href="http://science.sciencemag.org/content/356/6345/1362.full">new paper</a> out today. Our goal was to construct a climate damage function for the USA that is "micro-founded," in the sense that it is built up from causal relationships that are empirically measured using real-world data (if you're feeling skeptical, here's are <a href="http://globalpolicy.science/blog/2016/5/24/empirical-climate-damages-episode-ii" target="_blank">two videos</a> where Michael Greenstone and I explain why this matters).<br />
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Until now, the "damage function" has been a theoretical concept. The idea is that there should be some function that links global temperature changes to overall economic costs, and it was floated in the very earliest economic models of climate change, such as the original DICE model by Nordhaus where, in 1992, he <a href="http://cowles.yale.edu/sites/default/files/files/pub/d10/d1009.pdf" target="_blank">described</a> the idea while outlining his model:<br />
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<table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto; text-align: center;"><tbody>
<tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjrL4FQnrAE9uTJLC-sVxCtJmCLmDQUIEXgcIz-5ob_loIiozO49tZWq83mp8i9vWWJ2eoUk0VBVjxT4vi6k9j_Nyne6s9tKTkl4Y-QNctYTSzju-kUxD1NJyeKF4iKFUsCJmABZtOgkgY/s1600/nordhaus_damage92.jpg" imageanchor="1" style="margin-left: auto; margin-right: auto;"><img border="0" height="384" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjrL4FQnrAE9uTJLC-sVxCtJmCLmDQUIEXgcIz-5ob_loIiozO49tZWq83mp8i9vWWJ2eoUk0VBVjxT4vi6k9j_Nyne6s9tKTkl4Y-QNctYTSzju-kUxD1NJyeKF4iKFUsCJmABZtOgkgY/s640/nordhaus_damage92.jpg" width="640" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;">from <a href="http://cowles.yale.edu/sites/default/files/files/pub/d10/d1009.pdf">Nordhaus (1992)</a></td></tr>
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The "extremely elusive" challenge was figuring out what this function should look like, e.g. what should <i><b>theta_1</b></i> and <i><b>theta_2</b></i> be? Should there something steeper than quadratic to capture really catastrophic outcomes? Many strong words have been shared between environmental economists at conferences about the shape and slope of this function, but essentially all discussions have been heuristic or theoretical. We took a different approach, instead setting out to try and use the best available real world empirical results to figure out what the damage function looks like for the USA. Here's what we did.<br />
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We started out by recognizing that a lot of work has already gone into modeling climate projections for the world, by dozens of teams of climate modelers around the world. So we took advantage of all those gazillions of processor-hours that have already been used and simply took all the CMIP5 models off the shelf, and systematically downscaled them to the county level.<br />
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Then we indexed each model against the global mean surface temperature change that it exhibits. Not all models agree on what warming will happen given a certain level of emissions. And even among models that exhibit the global mean temperature change, not all models agree on what will happen for specific locations in the US. So it's important that we keep track of all possible experiences that the US might have in the future for each possible level of global mean temperature change. Here's the array of actual warming patterns in maps, where each map is located on the horizontal axis based on the projected warming under RCP8.5 ("business as usual"). As you can see, the US may experience many different types of outcomes for any specific level of global mean warming.<br />
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjLJsIKLSj54crOB2wYzbgu9P_qz-xXC7LTD4wE_e_x65je45vpZW_ksYW96nt6m0Yb_81cMReJQeZ0Xud-jQE4qJcHmA9ypV-TnVqXjipOMHrNvT-q856kyTcF2LrQcELibZ9vg-wU_Ss/s1600/projection_temperature_global.jpg" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="1000" data-original-width="1295" height="307" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjLJsIKLSj54crOB2wYzbgu9P_qz-xXC7LTD4wE_e_x65je45vpZW_ksYW96nt6m0Yb_81cMReJQeZ0Xud-jQE4qJcHmA9ypV-TnVqXjipOMHrNvT-q856kyTcF2LrQcELibZ9vg-wU_Ss/s400/projection_temperature_global.jpg" width="400" /></a></div>
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Then, for each possible future warming scenario, we build a projection of what impacts will be in a whole bunch of sectors, using studies that meet a high empirical standard (pretty much the same one Marshall and I used in our <a href="http://www.g-feed.com/2013/08/a-climate-for-conflict.html">conflict meta-analysis</a>, plus a few other additional criteria). This was relying on a lot of previous work done by colleagues here, like <a href="http://www.pnas.org/content/106/37/15594.short">Mike/Wolfram</a>/<a href="http://iopscience.iop.org/article/10.1088/1748-9326/8/1/014054/meta">David's</a> crop results and <a href="http://www.pnas.org/content/114/8/1886.abstract">Max's</a> electricity results. We project impacts in agriculture, energy, mortality, labor and crime using empirical response functions. For energy, this got a little fancy because we hooked up the empirical model to <a href="http://en.openei.org/wiki/National_Energy_Modeling_System_(NEMS)">NEMS</a> and ran it as a process model. For coastal damages, we partnered with RMS and restructured their <a href="http://forms2.rms.com/rs/729-DJX-565/images/RMS-North-Atlantic-Hurricane-Models.pdf">coastal cyclone model</a> to take <a href="http://onlinelibrary.wiley.com/doi/10.1002/2014EF000239/full">Bob's</a> probabilistic SLR projections and <a href="http://www.pnas.org/content/110/30/12219.short">Kerry Emanuel's</a> cyclone projections as inputs---their model is pretty cool since it models thousands of coastal flood scenarios, and explicitly models damages for every building along the Atlantic coast. The energy and coastal models were each big lifts, using process models with empirical calibration, as were the reduced form impacts since we resampled daily weather and statistical uncertainty for each impact in each RCP in each climate model; this amounted to tracking 15 impacts across 3,143 counties across 29,000 possible states of the world for every day during 2000-2099. These are maps of the median scenarios for the different impacts:</div>
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiD3y5PQAQu2PnRTKsTaPWZQU2z6cRsJZ5CWGJ0XcfBpoixjiixcrDNwqLWDRx9Jy3pPhU2p9fGMfpEFKBhMH1me75M9SFsMVXZ05WOjvEeVZHp0DLnMbTNFJhPq-L6ccVVYXBEJQrUovE/s1600/damages_everysector_2080_to_2090.jpg" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="1049" data-original-width="1600" height="419" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiD3y5PQAQu2PnRTKsTaPWZQU2z6cRsJZ5CWGJ0XcfBpoixjiixcrDNwqLWDRx9Jy3pPhU2p9fGMfpEFKBhMH1me75M9SFsMVXZ05WOjvEeVZHp0DLnMbTNFJhPq-L6ccVVYXBEJQrUovE/s640/damages_everysector_2080_to_2090.jpg" width="640" /></a></div>
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Then, within each possible state of the world, we added up the costs across these different sectors to get a total cost. Doing this addition first is important because it accounts for any cross-sector correlations that might emerge in the future due to the spatial correlations in economic activity (across sectors) and their joint spatial correlation with the future climate anomaly (a bad realization for energy, ag, and mortality all might happen at the same time). We then take these total costs and plot them against the global mean temperature change that was exhibited by the climate model that generated them. There ended up being 116 climate models that we could use, so there are only 116 different global temperature anomalies, but each model generated a whole distribution of possible outcomes due to weather and econometric uncertainty. Plotting these 116 distributions gives us a sense of the joint distribution between overall economic losses and global temperature changes: </div>
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<table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto; text-align: center;"><tbody>
<tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhOQbgL6MrCopgcH-NPyZT6ax11q5Nlh7XHcBh_YJ5ogScQFhW0e1TYYNl1KF_zGmlozCk39RuYnlBsu1fiQ5WhgDFZ-2PA5_oh_Tr7pyikhFMWZjrJYhChc80m8I9OudXjT3GbtsxwYqA/s1600/hsiang2.jpg" imageanchor="1" style="margin-left: auto; margin-right: auto;"><img border="0" data-original-height="1551" data-original-width="1450" height="400" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhOQbgL6MrCopgcH-NPyZT6ax11q5Nlh7XHcBh_YJ5ogScQFhW0e1TYYNl1KF_zGmlozCk39RuYnlBsu1fiQ5WhgDFZ-2PA5_oh_Tr7pyikhFMWZjrJYhChc80m8I9OudXjT3GbtsxwYqA/s400/hsiang2.jpg" width="373" /></a></td></tr>
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We can then just use normal statistics on these data to describe this joint distribution succinctly, getting out some equations that other folks can plug into their cost calculations or IAMS. Below is the 5-95th intervals for the probability mass, as well as the median. To our knowledge, this is basically the first micro-founded damage function:<br />
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEh9jTKD0ZnwZJ8175BsnxSolsHolkv2QvqYQSah7sVJtUC0nB-hdXEAveZ4XOWcvQOaQ7QXA4gL8g2FIwrQ7qjgi1nbzeuROvPDEAnSb29avZkEyhH8kZABiDXKxhjyDFSPRtrxc5CeoxA/s1600/Hsiang.jpg" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="1551" data-original-width="1450" height="400" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEh9jTKD0ZnwZJ8175BsnxSolsHolkv2QvqYQSah7sVJtUC0nB-hdXEAveZ4XOWcvQOaQ7QXA4gL8g2FIwrQ7qjgi1nbzeuROvPDEAnSb29avZkEyhH8kZABiDXKxhjyDFSPRtrxc5CeoxA/s400/Hsiang.jpg" width="373" /></a></div>
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It turns out that Nordhaus was right about the functional form, it <i>is</i> quadratic. In the paper we try a bunch of other forms, but this thing is definitely quadratic. And if you are happy with the conditional average damage, we can get you the <i><b>thetas</b></i>: </div>
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E[damage | T] = 0.283 x T + 0.146 x T^2</div>
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Now, of course, as we say several times in the paper, this function will change as we learn more about the different parts of the economy that the climate influences (for example, since we submitted the paper, we've learned that <a href="http://advances.sciencemag.org/content/3/5/e1601555">sleep is affected</a> by climate). So for any new empirical study, as long as it meets our basic criteria, we can plug it in and crank out a new and updated damage function.</div>
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Beyond the damage function, there is one other finding which might interest the G-FEED crowd. First, because the South tends to be both hotter, it is disproportionally damaged by nonlinear climate impacts where high temperatures impose higher marginal damages (ag, mortality, energy & labor). Also, along the Gulf and southern Atlantic coast, coastal damages get large. The South also happens to be poorer than the North, which is impacted less heavily (or benefits on net, in many cases). This means that damages are negatively correlated with incomes, so the poor are hit hardest and the rich lose less (or gain). On net, this will increase current patterns of economic inequality (a point the press has emphasized heavily). Here are are whisker plots showing the distribution of total damage for each county, where counties are ordered by their rank in the current income distribution:</div>
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiIbiW9zAjYodwtWrf_v35QxtJ11l_ywiU9gryAZYVdldy_jCD5cfj409WYj4q9fU3Xq0M96lhyEus3sbYwJS95ss5nwBe6pn64w37eIaktaXvE9DHle1zkJ4PM4opVaRrfdltXNkMmAdo/s1600/ineq_scatter_v2_presenting+copy.jpg" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="1427" data-original-width="1600" height="356" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiIbiW9zAjYodwtWrf_v35QxtJ11l_ywiU9gryAZYVdldy_jCD5cfj409WYj4q9fU3Xq0M96lhyEus3sbYwJS95ss5nwBe6pn64w37eIaktaXvE9DHle1zkJ4PM4opVaRrfdltXNkMmAdo/s400/ineq_scatter_v2_presenting+copy.jpg" width="400" /></a></div>
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Note that nothing about this calculation takes into account the possibility that poor counties have fewer resources with which to cope, this is just about interaction of geography and the structure of the dose-response function.</div>
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This widening of inequality probably should matter for all sorts of reasons, including the possibility that it induces strong migration or social conflict (e.g. think about current rural-to-urban migration, the last election, or the Dust Bowl). But it also should matter for thinking about policy design and calculations of the social cost of carbon (SCC). Pretty much all SCC calculations (e.g. DICE, FUND, PAGE) think about climate damages in welfare terms, but they compute damages for a representative agent that either represents the entire word, or enormous regions (e.g. the USA is one region in FUND). This made sense, since most of the models were primarily designed to think about the inter-temporal mitigation-as-investment problem, so collapsing the problem in the spatial dimension made it tractable in the inter-temporal dimension. But it makes it really hard, or impossible, to resolve any inequality of damages among contemporary individuals within a region (in the case of FUND) or on the planet (in the case of DICE). Our analysis shows that there are highly unequal impacts within a single country, and this inequality of damages can be systematically incorporated into the damage function above, which as its shown is simply aggregate losses (treating national welfare as equal to average GDP only). David Anthoff and others have thought about accounting for inequality between the representative agents of different FUND regions, and <a href="http://www.sciencedirect.com/science/article/pii/S0095069610000422?via%3Dihub">shown that it matters a lot</a>. But as far as I know, nobody has accounted for it <i>within </i>a country, and this seems to matter a lot too.</div>
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In the online appendix [Section K] (space is short at Science) we show how we can account for both inequality and risk, capturing both in a welfare-based damage function. Using our data and a welfare function that is additive in CRRA utilities, we compute <i>inequality-neutral certainty-equivelent damage functions.</i> These are the income losses that, if shared equally across the entire US population with certainty, would have the same welfare impact as the uncertain and unequal damages that we cover (i.e. shown in the dot-whisker plot above). Two things to note about this concept. First, this adjustment could theoretically make damages appear smaller if climate changes were sufficiently progressive (i.e. hurting the wealthy and helping the poor). Second, there are two ways to compute this that are not equivelent; one could either compute the (i) inequality in risks borne by different counties or (ii) risks of inequality across counties. We chose to go with the first option, which involves first computing the certainty-equivelent damage for each county, then computing the inequality-neutral equivalent damage for that cross-sectional distribution of risk. (We thought it was a little too difficult for actual people to reasonably imagine all possible unequal states of the future world before integrating out the uncertainty.)</div>
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We compute these adjusted damages for a range of parameters that separately describe risk aversion and inequality aversion, since these are value judgements and we don't have strong priors on what the right number ought to be. Below is a graph of what happens to the damage function as you raise both these parameters above one (values of one just give you back the original damage function, which is the dashed line below). Each colored band is for a single inequality aversion value, where the top edge is for risk aversion = 8 and the lower edge is risk aversion = 2:</div>
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<tr><td class="tr-caption" style="text-align: center;">National inequality-neutral certainty-equivalent loss equal in value to direct damages under different assumptions regarding coefficients of inequality aversion and risk aversion. Shaded regions span results for risk aversion values between 2 and 8 (lower and upper bounds). Dashed line is same as median curve above.</td></tr>
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What we see is that adjustment for inequality starts to matter a lot pretty quickly, more so than risk aversion, but the two actually interact to create huge welfare losses as temperatures start to get high. For a sense of scale, note that in the original DICE model, Nordhaus defined "catastrophic outcomes" as possible events that might lower incomes by 20%.</div>
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Bob, David Anthoff and I have debated a bit what the right values for these parameters are, and I'll be the first to say I don't know what they should be. There are several estimates out there, but I think we really don't talk about inequality aversion much so there's not a ton to draw on. But, just like the discount rate (which has received <i>a lot</i> of attention/thought/debate), these ethical parameters have a huge influence on how we think about these damages. And looking at this figure, my guess is that inequality aversion may be just as influential on the SCC as the discount rate---especially once we start having global estimates with this kind of spatial resolution. I think this is one of the most important directions for research to go: figuring out how we are supposed to value the inequality caused by climate change and accounting for it appropriately in the SCC. </div>
solhttp://www.blogger.com/profile/00936469103707728475noreply@blogger.com2tag:blogger.com,1999:blog-3813701770708442620.post-73934816499569740232017-06-22T15:23:00.001-07:002017-06-29T14:21:56.128-07:00Drink more coffee and run faster! Get fit in only 9 seconds! A rant on NYTimes exercise coverage.<div dir="ltr" style="text-align: left;" trbidi="on">
As a fairly half-assed exerciser, I am always on the lookout for quick/easy/delicious ways to get in shape without having to really do anything. Thus a lot of the exercise articles on NYTimes's "<a href="https://www.nytimes.com/section/well">Well</a>" blog are irresistible clickbait for me -- as they are for the presumably millions of other NYT-reading half-assed exercisers who ensure that these articles consistently top of the NYT most-read lists. <a href="https://www.nytimes.com/2017/05/31/well/move/boost-your-workouts-with-caffeine-even-if-you-chug-coffee-daily.html">Drink more caffeine and run faster</a>! Take a <a href="https://www.nytimes.com/2017/06/14/well/move/hot-weather-workout-try-a-hot-bath-beforehand.html">hot bath and run faster</a>! Get all the exercise you need in only <a href="https://well.blogs.nytimes.com/2013/05/09/the-scientific-7-minute-workout/">seven minutes</a>! Scratch that -- <a href="https://well.blogs.nytimes.com/2013/06/19/the-4-minute-workout/">only four</a>!! Scratch that -- <a href="https://well.blogs.nytimes.com/2016/04/27/1-minute-of-all-out-exercise-may-equal-45-minutes-of-moderate-exertion/">only one</a>!!!<br />
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<tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEg8_cKvGMe5og-XkAjK1heRv8PlYIkBUoJirIFTzDNuHs_1dW-AcgU9u-0EsB_rBT9z97f0qFD-2Ud4djWah7-V77UIQaAAmZamQr5DRT8esjj-DeIBJ_5lpU8QlGNYAtNYZUG2mcG9PCwa/s1600/wellrun34-tmagArticle.jpg" imageanchor="1" style="margin-left: auto; margin-right: auto;"><img border="0" data-original-height="395" data-original-width="592" height="213" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEg8_cKvGMe5og-XkAjK1heRv8PlYIkBUoJirIFTzDNuHs_1dW-AcgU9u-0EsB_rBT9z97f0qFD-2Ud4djWah7-V77UIQaAAmZamQr5DRT8esjj-DeIBJ_5lpU8QlGNYAtNYZUG2mcG9PCwa/s320/wellrun34-tmagArticle.jpg" width="320" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;">image stolen from <a href="https://well.blogs.nytimes.com/2016/04/27/1-minute-of-all-out-exercise-may-equal-45-minutes-of-moderate-exertion/?action=click&contentCollection=Well&module=RelatedCoverage&region=Marginalia&pgtype=article">NYT article</a></td></tr>
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Here's one from this week: <a href="https://www.nytimes.com/2017/06/14/well/move/hot-weather-workout-try-a-hot-bath-beforehand.html?">Hot weather workout? Try a hot bath beforehand</a>. Triple clickbait for me, as this week is really hot in California, I was hoping to get in some runs, and I certainly enjoy hot baths (+long walks on the beach, white wine, etc). Original study <a href="https://insights.ovid.com/pubmed?pmid=28486332">here</a> (can't tell if that's paywalled), published in the Journal of Strength and Conditioning Research. This study takes n=9 (!) people, 8 dudes and 1 woman, and first has them run 5k on a treadmill in a lab where they cranked the temperature to 90F. Then all subjects underwent about a week of heat acclimation, where they pedaled a stationary bike in the hot lab for five 90-minute sessions. Then, finally, they ran the 5k again in the hot lab. Low and behold, times had improved! The now-heat acclimated participants shaved an average of about a minute and a half off their 5k times (~ 6% reduction) when running in hot conditions for the second time. <br />
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But wait a sec. You took some mildly fit runners (5k time of 25min is not exactly <a href="https://en.wikipedia.org/wiki/5000_metres_world_record_progression">East African pace</a>), had them put in almost 8 hours of training, and then tested to see whether they got faster?? As a lazy, mildly-fit runner myself, 8 hours of training constitutes a good month for me, so I would be PISSED if I <i>didn't</i> get faster with that much training. Now, you might say, they were pedaling a bike and not running, and this is indeed what the paper tries to argue in some hard-to-understand phrasing ("Cycling training controlled for performance that could arise from increased training volume were participants to acclimate through running"). But to me it seems pretty unlikely that a lot of biking is going to give you no running benefit at all, and some quick googling found about 1000 blog posts by runner/bikers that claim that it does (and also 1000 that claim that is doesn't, go figure). In any case, clearly this is not anywhere close to the optimal research design you'd want for figuring out the causal effect of training-when-hot on performing-when-hot. Strangely, the "hot bath" part does not appear in the paper at all, but just in an off-hand comment by a research quoted by the NYT. So that's weird too. <br />
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Or take the one-minute-of-exercise-is-all-you-need study (<a href="http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0154075">original study</a>; <a href="https://well.blogs.nytimes.com/2016/04/27/1-minute-of-all-out-exercise-may-equal-45-minutes-of-moderate-exertion">NYT clickbait</a>). This study takes n=25 "sedentary" men and divides them into three groups: a control group (n=6), a group that does moderate-intensity stationary-bike pedaling for 45min at a time (n=10), and a group that does high-intensity ("all out") stationary-bike pedaling for 3x 20 seconds (n=9). Only 60 total seconds! This happens three times a week for 12 weeks, after which researchers compare various measures of fitness, including how much oxygen you can take up at your peak (i.e. VO2-max). Both the moderate (VO2 = 3.2) and intense groups (VO2=3.0) had improved significantly upon the control group (VO2 = 2.5), but the post-training VO2max levels in the moderate and intense groups were not statistically different from each other. Hence the paper's exciting title: "Twelve Weeks of Sprint Interval Training Improves Indices of Cardiometabolic Health Similar to Traditional Endurance Training despite a Five-Fold Lower Exercise Volume and Time Commitment", and the NYT clickbait translation: "1 Minute of All-Out Exercise May Have Benefits of 45 Minutes of Moderate Exertion".<br />
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But wait a sec. Sample size is n=19 in these treatment groups combined! A quick calculation suggests that, at this sample size, the study is DRAMATICALLY underpowered to find an effect. That is, the study has a high chance of failing to find an effect when there actually is one. I calculate that to detect a significant difference in means of 0.2 between these two groups (on a control group standard deviation of 0.7, given in the table), the study would have needed 388 participants, or about 20x what they had! (This assumes the researchers would want an 80% chance of correctly rejecting the null that the two groups had the same mean; in Stata, if I did it right: <i>power twomeans 3.2 3.0, sd(0.7)</i>). Even reliably detecting a 20% increase in VO2 max between the two treated groups would have needed 46 participants, more than twice the number they had. Put more simply: with sample sizes this small, you are very unlikely to find significant differences between two groups, even when differences actually exist. So maybe the moderate exercise group actually did do better, or maybe they didn't, but either way this study can't tell us. The same thing appears to be true with the <a href="https://well.blogs.nytimes.com/2013/06/19/the-4-minute-workout/">4-min-exercise paper</a> (n=26) -- it's way underpowered. And I haven't looked systematically, but my guess is this is true of a lot of the studies they cover that find no effect. <a href="http://andrewgelman.com/">Andrew Gelman</a> is always grumping about studies with large effects, but we should probably be just as cautious believing small-N studies that find no effect.<br />
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So should the NYT stop covering these underpowered or poorly designed studies? There's not a lot at stake here I guess, so one reasonable reaction is, "who gives a sh*t"? But surely this sort of coverage crowds out coverage of other higher-quality science, such as <a href="http://rspa.royalsocietypublishing.org/content/473/2202/20170076">why your roller-bag wheels wobble</a> so much when you're running to catch a flight, or why <a href="http://vis.sciencemag.org/eggs/">eggs are egg-shaped</a>. And cutting down on this crappy exercise coverage will save me the roughly 20min/week of self-loathing I feel when I click on yet another too-good-to-be-true NYT exercise article, look up the article it actually references, and find my self not very convinced. Twenty minutes I could have spent exercising!! That's like 20 1-minute workouts...<br />
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Marshall Burkehttp://www.blogger.com/profile/15436297075698378164noreply@blogger.com6tag:blogger.com,1999:blog-3813701770708442620.post-37294127001487684202017-06-07T17:15:00.000-07:002017-06-07T17:16:01.548-07:00Trump's climate gift to Russia<div dir="ltr" style="text-align: left;" trbidi="on">
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Trump's recent announcement that the US would withdraw from the Paris Accords was hailed as a monumental political, environmental, and <a href="http://www.g-feed.com/2017/06/the-cost-of-paris-withdrawal.html">economic</a> mistake. But given all the theater surrounding the announcement, <a href="http://freebeacon.com/issues/morning-joe-trumps-climate-distraction-russia-investigation/">others</a> also saw it as an effort to distract the public from the ongoing investigation of the Administration's ties to Russia.<br />
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It's hard to see how this latter claim could actually be evaluated. But it got me thinking: what are the benefits to Russia of the US withdrawing from the Paris accords? Was the US withdrawal a climate gift to Russia? <br />
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Now, I'm guessing Trump has not read <a href="http://www.g-feed.com/2015/10/climate-change-and-global-economy.html">our paper</a> showing that warming temperatures will have unequal economic effects around the world (<a href="http://science.sciencemag.org/content/early/2017/01/06/science.aam6284.full">unlike Obama</a>, to repeat my shameless self promotion from last week). In that paper, and consistent with a <a href="http://www.g-feed.com/2016/09/everything-we-know-about-effect-of.html">huge microeconomic literature</a>, we see clear evidence in the historical data that cold high-latitude countries tend to experience higher GDP growth when temperatures warm, with the reverse being true in most of the rest of the world where average temperatures are already warmer (the US included). Basically, if you're currently cold, you do better when it warms up; if you're already warm, additional warming hurts you. Pretty intuitive, and also shows up very clearly in the half century of data we have on economic growth from around the world.<br />
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Here's the original plot from our paper, with the figure on the left showing the historical relationship between temperature and GDP growth for all countries in the world. If you're average temperature is below about 13C, historically your economy grows faster when annual temperatures warm. If your at or above 13C, growth slows as temperatures warm. The US has a population-weighted annual average temperature of just over 13C. Russia has a population-weighted average temperature of just under 5C. Russia is cold! <br />
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<tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEj5I5SjbYSlGZ7891lFpeZLhWDbOfs9z1ARaj1o4JJR7eFQXrUi7fLh1WIlPE-vWM3KtwFAshPsvzsz4u_I0Co6DKJ0J4fU-E2yk66q8pX7o0JkLbUONcjx6Mzi7aHIsADdlKFeV3M3UuSD/s1600/Figure2.jpg" imageanchor="1" style="margin-left: auto; margin-right: auto;"><img border="0" data-original-height="812" data-original-width="1600" height="202" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEj5I5SjbYSlGZ7891lFpeZLhWDbOfs9z1ARaj1o4JJR7eFQXrUi7fLh1WIlPE-vWM3KtwFAshPsvzsz4u_I0Co6DKJ0J4fU-E2yk66q8pX7o0JkLbUONcjx6Mzi7aHIsADdlKFeV3M3UuSD/s400/Figure2.jpg" width="400" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;">Figure 2 from Burke, Hsiang, Miguel 2015 <i>Nature</i>. Effect of annual average temperature on economic production. <b style="background-color: white; color: #666666; font-family: "Trebuchet MS", Trebuchet, Verdana, sans-serif; font-size: 10.56px;">a</b><span style="background-color: white; color: #666666; font-family: "trebuchet ms" , "trebuchet" , "verdana" , sans-serif; font-size: 10.56px;">, Global non-linear relationship between annual average temperature and change in log gross domestic product (GDP) per capita (thick black line, relative to optimum) during 1960–2010 with 90% confidence interval (blue, clustered by country, N= 6,584). Model includes country fixed effects, flexible trends, and precipitation controls. Vertical lines indicate average temperature for selected countries. Histograms show global distribution of temperature exposure (red), population (grey), and income (black).</span><b style="background-color: white; color: #666666; font-family: "Trebuchet MS", Trebuchet, Verdana, sans-serif; font-size: 10.56px;"> b</b><span style="background-color: white; color: #666666; font-family: "trebuchet ms" , "trebuchet" , "verdana" , sans-serif; font-size: 10.56px;">, Comparing rich (above median, red) and poor (below median, blue) countries. Blue shaded region is 90% confidence interval for poor countries. Histograms show distribution of country–year observations. </span><b style="background-color: white; color: #666666; font-family: "Trebuchet MS", Trebuchet, Verdana, sans-serif; font-size: 10.56px;">c</b><span style="background-color: white; color: #666666; font-family: "trebuchet ms" , "trebuchet" , "verdana" , sans-serif; font-size: 10.56px;">, Same as b but for early (1960– 1989) and late (1990–2010) subsamples (all countries). </span><b style="background-color: white; color: #666666; font-family: "Trebuchet MS", Trebuchet, Verdana, sans-serif; font-size: 10.56px;">d</b><span style="background-color: white; color: #666666; font-family: "trebuchet ms" , "trebuchet" , "verdana" , sans-serif; font-size: 10.56px;">, Same as b but for agricultural income. </span><b style="background-color: white; color: #666666; font-family: "Trebuchet MS", Trebuchet, Verdana, sans-serif; font-size: 10.56px;">e</b><span style="background-color: white; color: #666666; font-family: "trebuchet ms" , "trebuchet" , "verdana" , sans-serif; font-size: 10.56px;">, Same as b but for non-agricultural income.</span></td></tr>
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Last week we <a href="http://www.g-feed.com/2017/06/the-cost-of-paris-withdrawal.html">calculated the potential harm</a> done to the economy of withdrawing from Paris. The idea was this: withdrawing from the Paris accords would make global temperatures rise relative to what they would have been if the US had met its Paris obligations (an additional +0.3C by 2100, according to <a href="https://www.climateinteractive.org/analysis/us-role-in-paris/">these guys</a>). For reasons already stated, warming temperatures are bad for overall economic output in the US. So we can then calculate, what's the difference in output between now and 2100 that would occur in a withdrawal versus a non-withdrawal world? For the US, the effects were pretty big: I calculated that, in present value (i.e. discounting future losses at 3%), the US economy would lose about $8 trillion between now and 2100 due to the extra temperature increase induced by withdrawing from Paris.<br />
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What about Russia? Again, Russia is cold, so extra warming is likely to help the Russian economy, all else equal. You can actually see this really clearly in the Russian data. Below is the plot of Russian GDP growth rates versus Russian temperatures, using data 1990-2010 (1990 being the first post-Soviet-collapse year that "Russia" shows up in the national accounts data). Specifically, these are growth deviations from trend versus temperature deviations from trend; we are detrending the data since you don't want to conflate trends in temperature that could be correlated with other trending factors that also affect growth. <br />
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiHotguUMBqH4a_ko7oRD-t5wH-MzEzkE2h5Mte5CWSGU-N3CBxuOb1xP1_vyp-YiiIwsvRtVO9QHUmVpArY59dVbgbxYt3MftVPfgkuRXT_p00cw8dcC_CfgKYquzrY11uJObS-IW7Ewib/s1600/RussianGrowth.jpg" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="600" data-original-width="825" height="232" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiHotguUMBqH4a_ko7oRD-t5wH-MzEzkE2h5Mte5CWSGU-N3CBxuOb1xP1_vyp-YiiIwsvRtVO9QHUmVpArY59dVbgbxYt3MftVPfgkuRXT_p00cw8dcC_CfgKYquzrY11uJObS-IW7Ewib/s320/RussianGrowth.jpg" width="320" /></a></div>
This is just 20 data points, but the estimated effects are HUGE. Basically, Russian GDP growth is multiple percentage points higher when temperatures warm by a degree C. And the Russia-specific estimate is even higher than what we would predict the effect would be for Russia using the global response function pictured in blue above. <br />
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Anyway... Basically what I did is to re-do <a href="http://www.g-feed.com/2017/06/the-cost-of-paris-withdrawal.html">the same calculation we did last week</a> for the US, but now focusing on effects on the Russian economy and calculating what happens to Russian GDP in the scenario where the US withdraws from Paris versus the scenario where the US stays in. To be conservative, I use estimates from the global response function, not the Russia-specific mega-response just noted.<br />
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Here's the main finding: <b>Trump's decision to withdraw the US from Paris is a $2.2 trillion dollar gift to Russia </b>(paid out over the next 85 years). Below is the figure showing what happens to Russian GDP under a withdrawal versus a no-withdrawal scenario (left), and the annual gains in GDP in each year (to 2100). By 2100, Russia is ~10% richer than it would have been otherwise, and the (discounted) sum of these GDP gains is about $2.2 trillion dollars.<br />
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgRR2KwNfefLIHNc-fU16dvh1cqk9xofReym_DZ5RoCddbHYa1D5kiATmVxuVUkk3w7wgzAl7COIEvYtXn6eFiIwtumLx1vFcxaFSXdAnl2xyGzcCLHngeEiZr0ZI5rW6BABcR27nbRIkZc/s1600/RussiaGDP_ParisWithdrawal_final.jpg" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="654" data-original-width="1600" height="260" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgRR2KwNfefLIHNc-fU16dvh1cqk9xofReym_DZ5RoCddbHYa1D5kiATmVxuVUkk3w7wgzAl7COIEvYtXn6eFiIwtumLx1vFcxaFSXdAnl2xyGzcCLHngeEiZr0ZI5rW6BABcR27nbRIkZc/s640/RussiaGDP_ParisWithdrawal_final.jpg" width="640" /></a></div>
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Given that there's no evidence that Trump has read our paper, I don't think we can claim that this climatic gift to Russia was purposeful. But it's sadly ironic that an announcement that might have been meant to distract us from Russian meddling was simultaneously a monumental economic gift to that country.</div>
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Marshall Burkehttp://www.blogger.com/profile/15436297075698378164noreply@blogger.com1tag:blogger.com,1999:blog-3813701770708442620.post-17520373142496823972017-06-01T00:34:00.000-07:002017-06-01T00:42:05.006-07:00The cost of Paris withdrawal<div dir="ltr" style="text-align: left;" trbidi="on">
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Lots of discussion today about the potential ramifications of the US withdrawing from the Paris Accords. Folks have already done some nice calculations looking at the climate consequences of US withdrawal, but there's a lot of interest in the potential economic consequences and I hadn't seen anyone take a heroic stab at that yet. So.....<br />
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The clearest picture I (read: google) could find on the climate implications was this nice website <a href="https://www.climateinteractive.org/analysis/us-role-in-paris/">here</a> from ClimateInteractive.org, where they find that a Paris-minus-US world and a Paris-including US world is the difference between +3.6C of warming and +3.3C of warming by 2100. There are clearly a lot of assumptions that go into this calculation (e.g., what the hell happens after 2030 when the INDCs run out, what happens if Trump's successor (Kamala Harris? Zuck? Steph Curry?) re-signs us up, etc etc), but let's take this calculation as God's truth. Withdrawal gives the world +0.3C of additional warming. <br />
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So I wanted to figure out: what is the cost to the US in terms of additional damages that are wrought by this extra warming that withdrawal would bring about? A difference of +0.3C might not sound like much, but we've got <a href="http://www.g-feed.com/2015/10/climate-change-and-global-economy.html">this paper</a> (<a href="http://science.sciencemag.org/content/early/2017/01/06/science.aam6284.full">cited by Obama</a>, so it must be right) that suggests that changes in temperature can affect the growth rate of GDP in rich and poor countries alike. So the current administration <i>might</i> be right that meeting the US's Paris commitments would have economic costs, but these need to be weight against the benefits of the reduced warming that we would get. So what are those benefits?<br />
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So I took the basic setup we had in that paper, and ran the world forward to 2100 under +3.3C warming versus +3.6C warming, and I looked at what our results in that paper said would happen to US GDP in those different worlds. See <a href="http://web.stanford.edu/~mburke/climate/">here</a> for more info on how we do these sorts of calculations in this framework. Basically, we have a function that tells us how growth rates change as temperatures change, derived from historical data. Then we walk countries along this function as you crank up the temperature to your desired level. To get GDP in levels, you apply these changes in the growth rate to some baseline growth scenario, which we take off-the-shelf from the Shared Socioeconomic Pathways (SSPs, see <a href="https://tntcat.iiasa.ac.at/SspDb/dsd?Action=htmlpage&page=about">here</a>). We also need population numbers, and take those from the SSPs as well.<br />
<br />
Below is what I get when I run the US forward under +3.3C warming versus +3.6C warming. Under SSP5 (the baseline scenario we use), the US clips along at an average per capita growth rate of above 2%/year. Once you crank up the temperature by 3.5C or so, historical data tells us that we should shave between about 0.5-1 percentage point off of annual US growth. So this means by 2100, instead of growing at 2%/year under a no warming scenario, the US would be growing at less than 1.5%/year in this much warmer world. The effects earlier in the century are smaller, of course.<br />
<br />
Our comparison is between +3.3C and +3.6C, and the effects on the growth rate are of course smaller. But even small effects on the growth rate can add up to big cumulative effects on GDP over time. The left plot below compares total US GDP in the "no withdrawal" world versus the "withdrawal" world, and the right plot gives you the amount of GDP that's lost in each year from withdrawing. To be crystal clear, we're again only thinking about the differences brought about by the change in temperature between the two scenarios -- we're not thinking about what it would cost the US to meet its Paris commitments.<br />
<br />
<div class="separator" style="clear: both; text-align: center;">
<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgVpHDJow1Id3SnXEmtQZMGCns_kM5KJAh2HX53EeJBsyb1DaucFjqPiScIzIXKdFo6FxHAvABV6s2ZR0vsifPfmMEzKKloHiHPn98TQ8eQj1iQ4IzKTE3QPEXRWAuF3jANip2YyoAeKAMY/s1600/USGDP_ParisWithdrawal_final.jpg" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="538" data-original-width="1600" height="214" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgVpHDJow1Id3SnXEmtQZMGCns_kM5KJAh2HX53EeJBsyb1DaucFjqPiScIzIXKdFo6FxHAvABV6s2ZR0vsifPfmMEzKKloHiHPn98TQ8eQj1iQ4IzKTE3QPEXRWAuF3jANip2YyoAeKAMY/s640/USGDP_ParisWithdrawal_final.jpg" width="640" /></a></div>
<br />
<br />
If my calculations are right, the numbers are large. By 2100, withdrawing from Paris makes us (i.e. people in the US) about 5% poorer than we would have been otherwise. The cumulative US GDP losses over time from withdrawal, discounted back to 2010 at 3%, are also impressive - I calculate them to be $8.2 trillion dollars (right plot above). That is, withdrawing from the Paris agreement costs the US economy $8.2 trillion dollars in present discounted value. That is a large pile of money. Even if I'm off by a factor of 5, we're still talking low trillions. <br />
<br />
And to be clear, my calculations do not take into account many other near-term benefits of reducing our own emissions, such as the 'co-benefits' of better health outcomes from cleaner air. These could be <a href="http://www.nature.com/nclimate/journal/v6/n5/full/nclimate2935.html">quite big</a> as well.<br />
<div>
<br /></div>
The key policy question for the Trump administration is: do we think the costs of meeting our obligations under Paris are going to run more than $8 trillion? Put another way, are they going to amount to almost half of current US GDP? <br />
<br />
To generate $8 trillion in costs between now and 2030 (after discounting at 3%), annual costs would have to be somewhere around $750 billion. The compliance cost estimates for the Clean Power Plan that I've seen are about <a href="https://www3.epa.gov/ttnecas1/docs/ria/utilities_ria_final-clean-power-plan-existing-units_2015-08.pdf">two orders of magnitude smaller than that</a>, so even if the CPP only got us 10% of the way to our Paris commitment (which is very conservative), these costs do not come close to the overall benefits -- even if other reductions are many times as expensive as the CPP. (Hopefully somebody can correct me if I'm way off on these cost numbers -- definitely not my specialty).<br />
<br />
With benefits this big, withdrawal seems like bad policy. </div>
Marshall Burkehttp://www.blogger.com/profile/15436297075698378164noreply@blogger.com1tag:blogger.com,1999:blog-3813701770708442620.post-53844580201375280242017-05-08T06:00:00.000-07:002017-05-08T06:00:07.375-07:00Chat with Nature editor Michael White about publishing, interdisciplinary research, and the state of climate economicsI recently had a long discussion with <a href="https://www.researchgate.net/profile/Michael_White26/publications">Dr. Michael White</a> (the editor at Nature who handles climate science and economics) about a whole bunch of issues that probably interest g-feeders: working in an interdisciplinary space, deciding when to publish in econ vs. general interest journals, what we're all doing in climate econ these days, and the things he thought were funny (as a non-economist) about the AEA meetings. (There's also a few stories about my oddball childhood in there for Marshall to laugh at.)<br />
<br />
You can listen <a href="http://media.blubrry.com/forecast/p/content.blubrry.com/forecast/episode_44_sol_hsiang.mp3" target="_blank">here</a> (1 hr).<br />
<br />
He thought he was interviewing me for his podcast, but really I was interviewing him for our blog;) The session was an episode of Michael's podcast <a href="http://forecastpod.org/index.php/2017/05/02/climate-economics-sol-hsiang/" target="_blank">Forecast</a>, which he produces to cover climate science. Michael has been a regular attendee at our <a href="http://ccelunch.berkeley.edu/" target="_blank">climate economics lunch</a> and it's been helpful to get his take on the recent developments in our field.solhttp://www.blogger.com/profile/00936469103707728475noreply@blogger.com1tag:blogger.com,1999:blog-3813701770708442620.post-71316043021450045562017-05-06T06:50:00.001-07:002017-05-06T06:50:13.111-07:00Are the curious trends in despair and diets related?
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<span style="font-size: 12pt;">There’s a new </span><a href="https://www.brookings.edu/bpea-articles/mortality-and-morbidity-in-the-21st-century/" style="font-size: 12pt;">working
paper</a><span style="font-size: 12pt;"> out by Anne Case and Angus Deaton on one of the most curious (and
saddest) trends in America – mortality rates for whites have been rising. There
have been various stories about these trends, such as </span><a href="https://www.nytimes.com/2015/11/09/opinion/despair-american-style.html" style="font-size: 12pt;">this
one</a><span style="font-size: 12pt;"> that first turned me onto it. It’s clear that the proximate causes are
an increase in “deaths of despair,” namely suicides, drugs, and alcohol. It’s
also clear that self-reported mental health has been declining in this group.
But why?</span></div>
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<o:p></o:p></div>
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Any explanation has to account for the fact that the same
trends aren’t seen in other racial groups, even though many of them have lower
incomes (see figure below from Case and Deaton): <o:p></o:p></div>
<div class="MsoNormal">
<br /></div>
<div class="separator" style="clear: both; text-align: center;">
<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEghU-jFEn6zuqM9S67_oHBFLPOXW187ysC5l4c2FCeXVcus_ilj4BGBu993tUAbzo4sqDm5x7hUQW0O7G-3zNZkL1ewAcVuJ4autQpSttbrZqF53An8RmdGWlgPEmQ-e6o_uf4N0JEsdQZO/s1600/case.Picture2.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" height="234" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEghU-jFEn6zuqM9S67_oHBFLPOXW187ysC5l4c2FCeXVcus_ilj4BGBu993tUAbzo4sqDm5x7hUQW0O7G-3zNZkL1ewAcVuJ4autQpSttbrZqF53An8RmdGWlgPEmQ-e6o_uf4N0JEsdQZO/s320/case.Picture2.png" width="320" /></a></div>
<br />
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It should also account for the fact that the same trend isn’t
seen in other predominantly white countries:<o:p></o:p></div>
<div class="separator" style="clear: both; text-align: center;">
<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhu9M9axobnbhdg4458f_Nb9SWjBE2NgztaTThxcPgygRVp8K2uYiBLGOIq5K7bRdoW03YrkFpto1FOigv-m1rnYI6hsOevWVYf-ts77P8D31oQfFxtMG4acV8Qoi7k9zYvX93p7m8dO0fE/s1600/case.Picture1.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" height="320" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhu9M9axobnbhdg4458f_Nb9SWjBE2NgztaTThxcPgygRVp8K2uYiBLGOIq5K7bRdoW03YrkFpto1FOigv-m1rnYI6hsOevWVYf-ts77P8D31oQfFxtMG4acV8Qoi7k9zYvX93p7m8dO0fE/s320/case.Picture1.png" width="277" /></a></div>
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One explanation that seems to have gained most traction is
that whites <span style="background: white; color: #333333; mso-bidi-font-family: "Times New Roman"; mso-fareast-font-family: "Times New Roman";">“lost the narrative of their
lives.” That is, maybe rising economic inequality and other economic trends have
affected all groups, but whites expected more. To me this seems plausible but
not really convincing. </span><o:p></o:p></div>
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Ok, so let me offer another
theory. I’ll first say that I think it’s a bit off the wall, but I don’t think I’m
going crazy (despite Marshall’s frequent hinting that I am). The idea basically
stems from another body of literature that I’ve recently been exploring, mainly
because I was interested in allergies. Yeah, allergies. Specifically, a bunch
of people I know have sworn that their allergies were fixed by eliminating
certain foods, and given that some people in my family have bad seasonal allergies,
I decided to look into it. <o:p></o:p></div>
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It turns out that wheat is
thought by many to trigger inflammation and allergies. But what’s relevant here
is that it’s also thought to affect mental health. More than that, there are
actually clinical studies like <a href="http://onlinelibrary.wiley.com/doi/10.1111/apt.12730/full">this one</a> showing
that depression increases with gluten intake. There are only 22 subjects in that
study, which seems low to me but obviously I don’t do that sort of work. A
good summary of scientific and plenty of non-scientific views on the topic can
be found in <a href="http://www.everydayhealth.com/columns/therese-borchard-sanity-break/gluten-depression-and-anxiety-gut-brain-link/">this
article</a>. Incredibly, there was even <a href="http://ajcn.nutrition.org/content/18/1/7.extract">a study</a> in the
1960s showing how hospital admission rates for schizophrenia varied up and down
with gluten grain rations during World War 2. <o:p></o:p></div>
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So what’s the connection to the
trends in deaths of despair? Well the striking thing to me is that wheat
effects are generally only seen in white non-hispanics. Celiac disease, for instance,
is much lower in other racial groups. Second, it’s apparently known that celiac
has been rising over time, which is thought to indicate increased exposure (of all people) to gluten early in life. And the trends are most apparent in whites, such as
seen in the figure below from <a href="https://www.nature.com/ajg/journal/v110/n3/pdf/ajg20158a.pdf">this paper</a>.
<o:p></o:p></div>
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<o:p><br /></o:p></div>
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Just to be clear, I realize this
is mostly speculation. Not only is this not my area of expertise, but I don’t
have any data on the regional trends in gluten or wheat intake in the U.S. to
compare to the regional trends in death. I’m not even sure that such data
exist. It seems that studies like <a href="http://www.bmj.com/content/357/bmj.j1892">this one</a> looking at trends
in gluten consumption just assume the gluten content of foods is fixed, but it also
seems a lot of products now have gluten added to make them rise quicker and
better. (Some blame the obsession with whole grain foods, which don't rise as quickly.) If anyone knows of good data on trends in consumption, let me know. It
would also be interesting to know if they add less gluten in other countries,
where mortality rates haven’t risen. <o:p></o:p></div>
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<br /></div>
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(As an aside: there’s also a <a href="http://www.aimspress.com/fileOther/PDF/aimsph/publichealth-03-00313.pdf">recent
study</a> looking at wheat and obesity in cross-section. Apparently country obesity
rates are related to wheat availability, but not much else.)<o:p></o:p></div>
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<br /></div>
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Also to be clear, I still like
wheat. Maybe having spent most of my career studying wheat producing systems
has made me sympathetic. Or maybe it’s the fact that it has sustained
civilization since the dawn of agriculture. But I think it’s possible
that we recently have gone overboard in how much is eaten or, more
specifically, in how much gluten is added to processed food in this country. And even if there’s
only a small chance it’s partly behind the trends of despair (which aren’t just
causing mortality, but all sorts of other damage), it’s worth looking into. <o:p></o:p></div>
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</style>David Lobellhttp://www.blogger.com/profile/17660903133065588267noreply@blogger.com0tag:blogger.com,1999:blog-3813701770708442620.post-81630135902578061162017-04-26T11:13:00.000-07:002017-04-26T11:13:15.006-07:00Better than the real thing<div>
Why read a stack of research papers when you can watch a 3 min cartoon?</div>
<div>
<br /></div>
<iframe allowscriptaccess="always" frameborder="0" src="https://www.bloomberg.com/api/embed/iframe?id=18554df9-ab3c-4832-b331-9263a8e2f7be"></iframe><br />
<br />
Thanks to <a href="https://www.bloomberg.com/news/videos/2017-04-19/how-rising-temperatures-can-fry-the-economy-video">Eric Roston of Bloomberg</a> for putting this together.solhttp://www.blogger.com/profile/00936469103707728475noreply@blogger.com0tag:blogger.com,1999:blog-3813701770708442620.post-55537088622745809272017-02-18T08:59:00.000-08:002017-02-18T09:05:43.453-08:00Targeting poverty with satellites<div dir="ltr" style="text-align: left;" trbidi="on">
<span id="docs-internal-guid-6ab31268-4e9e-086a-42bd-55c0dcf4e1c9"></span><br />
<div dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt;">
<span style="background-color: transparent; color: black; font-style: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;"><span style="font-family: "times" , "times new roman" , serif;">[This post is co-authored with <b>Matt Davis</b>, co-author and RA extraordinaire...]</span></span></div>
<br />
About six months ago, our Stanford <a href="http://sustain.stanford.edu/">SustainLab</a> crew had <a href="http://web.stanford.edu/~mburke/papers/JeanBurkeEtAl2016.pdf">a paper in Science</a> showing that you can make pretty good predictions about local-level economic wellbeing in Africa <a href="http://www.g-feed.com/2016/08/economics-from-space.html">by combining satellite imagery with fancy tools from machine learning</a>. To us this was (and is) a promising finding, as it suggests a way to address the fundamental lack of data on economic outcomes in much of the developing world. As has been widely acknowledged, these data gaps inhibit our ability to both evaluate what interventions reduce poverty and to target assistance to those who need it most.<br />
<br />
A natural question that comes up (e.g. <a href="https://www.cgdev.org/blog/can-we-measure-poverty-outer-space">here</a>) is: are these satellite-based estimates good enough to actually be useful for either evaluation or targeting? Our original paper didn't really answer that question. We're in the process of putting together a follow-up paper that looks at this question on an expanded country set and with an improved machine learning pipeline, but in the meantime we [by which I mean "we", meaning <a href="http://sustain.stanford.edu/team/">Matt and Neal</a>] wanted to use some of the data from our original paper to more quantitatively explore this question.<br />
<br />
Folks have been thinking for decades about how whether using geographic information to inform the targeting of anti-poverty programs could improve their efficiency. The standard thought experiment goes like this. Imagine you're a policymaker who has a fixed budget F that she can distribute as cash transfers to anyone in the country (this sort of cash transfer program happens all the time these days, it turns out). Lets say in particular that the poverty metric that this policymakers cares about is the squared poverty gap (SPG), a common poverty measure that takes into account the distance of individuals from the poverty line. [If you're having trouble sleeping at night: For a given poverty line P, an individual with income Y<P has a poverty gap of P-Y and an SPG of (P-Y)^2. The SPG in a region is the average over all individuals in that region, where anyone with Y>=P has a SPG==0. So this measure gives a lot of weight to people far below the poverty line]. If you're goal is to reduce the SPG, you do best by giving money to the poorest person you can find until they're equal to the next poorest, giving them both enough money until they're equal to the third poorest, and so-on. <br />
<br />
So how should the policymaker distribute the cash? If she knows nothing about where poor people are, a naive approach would be to distribute money uniformly -- i.e. to just give each of n constituents F/n dollars. Clearly this could be pretty inefficient, since people already above the poverty line will get money and this won't reduce the SPG.<br />
<br />
An alternate approach, now a few decades old in the economics literature, has been to construct "small area estimates" (SAE) of poverty by combining a detailed household survey with a less detailed but more geographically-comprehensive census. The idea is that while only the household survey measures your outcome of interest (typically consumption expenditure), there are a small set of questions common to both the detailed household survey and the census (call these <b>X</b>). These are typically questions about respondent age, gender, education, and perhaps a few basic questions on assets. So using the household survey you can fit a model Y = f(<b>X</b>), which tells you how the <b>X</b>s map into consumption expenditure (your outcome of interest), and then using the same <b>X</b>s in the census and your model f(<b>X</b>) to predict consumption expenditure for everyone in the census. Then you can aggregate these to any level of interest (e.g. village or district), and use them to potentially inform your cash transfers. This has been explored in a number of papers, e.g. <a href="https://are.berkeley.edu/~ligon/Teaching/ARE251/elbers-etal03.pdf">here</a> and <a href="http://www.sciencedirect.com/science/article/pii/S0304387806000150">here</a>, and apparently has been <a href="http://siteresources.worldbank.org/INTPGI/Resources/342674-1092157888460/493860-1192739384563/More_Than_a_Pretty_Picture_ebook.pdf">used to inform policy</a> in a number of settings.<br />
<br />
Our purpose here is to compare a targeting approach that uses our satellite-based estimates to either the naive (uniform) transfer or a transfer that's informed by SAE estimates. To actually evaluate these approaches against each other, we are going to just use the household survey data, aggregated to the cluster (village) level. In particular, we estimate both the SAE and the satellite-based model on a subset of our household survey data in each country, make predictions for the remainder of the data that the models have not seen, and in this holdout sample, evaluate for a fixed budget the reduction in the SPG you'd get if you allocated using either the naive, SAE, or satellite-based model. The allocation rule for SAE and satellites is the one described above: giving money to the poorest village until equal to the next poorest, giving them both enough money until they're equal to the third poorest, and so-on.<br />
<br />
Below is what we get, with the figure showing how much reduction in SPG you get from each targeting scheme under increasing program budgets. The table summarizes the results, showing the cross-validated R2 for the SAE and satellite-features models (the goodness-of-fits from the models that we then use to make predictions that are then used in targeting), and the amount of money each approach saves relative to the uniform transfer to achieve a 50% decline in SPG.<style type="text/css"><!--td {border: 1px solid #ccc;}br {mso-data-placement:same-cell;}--></style><br />
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhosjkEzkalfZHFcPvWhlsCOVQ-CntZxdgWFyQFO7EUnl4BRdb6ZcrtMfjvz2AIabQeKxyN5TWtToYyERI53cCWVKMtbdo7-tLwQmJkMyq9C6GYOYUKveM9hjUejzxtxTSmJ67Qtz_AEPkw/s1600/SPG+Reduction+vs+Budget+%2528%25241%2529+%25281%2529.jpg" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" height="160" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhosjkEzkalfZHFcPvWhlsCOVQ-CntZxdgWFyQFO7EUnl4BRdb6ZcrtMfjvz2AIabQeKxyN5TWtToYyERI53cCWVKMtbdo7-tLwQmJkMyq9C6GYOYUKveM9hjUejzxtxTSmJ67Qtz_AEPkw/s640/SPG+Reduction+vs+Budget+%2528%25241%2529+%25281%2529.jpg" width="640" /></a></div>
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<table border="1" cellpadding="0" cellspacing="0" style="border-collapse: collapse; border: none; font-family: arial, sans, sans-serif; font-size: 13px; table-layout: fixed; text-align: left;"><colgroup><col width="87"></col><col width="82"></col><col width="63"></col><col width="78"></col><col width="79"></col></colgroup><tbody>
<tr style="height: 21px;"><td style="padding: 2px 3px 2px 3px; vertical-align: bottom;"></td><td colspan="2" data-sheets-value="{"1":2,"2":"R-squared"}" rowspan="1" style="background-color: #fce5cd; font-family: Calibri; font-weight: bold; padding: 2px 3px; text-align: center; vertical-align: middle; white-space: nowrap;">R-squared</td><td colspan="2" data-sheets-value="{"1":2,"2":"% Reduction in budget to achieve 50% decline in SPG"}" rowspan="1" style="background-color: #cfe2f3; font-family: Calibri; font-weight: bold; padding: 2px 3px; text-align: center; vertical-align: middle; word-wrap: break-word;">% Reduction in budget to achieve 50% decline in SPG</td></tr>
<tr style="height: 21px;"><td data-sheets-value="{"1":2,"2":"Country"}" style="font-family: Calibri; font-style: italic; padding: 2px 3px; vertical-align: bottom; white-space: nowrap;">Country</td><td data-sheets-value="{"1":2,"2":"SAE"}" style="background-color: #fce5cd; font-family: Calibri; font-style: italic; padding: 2px 3px; text-align: center; text-decoration: underline; vertical-align: bottom; white-space: nowrap;">SAE</td><td data-sheets-value="{"1":2,"2":"Features"}" style="background-color: #fce5cd; font-family: Calibri; font-style: italic; padding: 2px 3px; text-align: center; text-decoration: underline; vertical-align: bottom; white-space: nowrap;">Features</td><td data-sheets-value="{"1":2,"2":"SAE"}" style="background-color: #cfe2f3; font-family: Calibri; font-style: italic; padding: 2px 3px; text-align: center; text-decoration: underline; vertical-align: bottom; white-space: nowrap;">SAE</td><td data-sheets-value="{"1":2,"2":"Features"}" style="background-color: #cfe2f3; font-family: Calibri; font-style: italic; padding: 2px 3px; text-align: center; text-decoration: underline; vertical-align: bottom; white-space: nowrap;">Features</td></tr>
<tr style="height: 21px;"><td data-sheets-value="{"1":2,"2":"Malawi"}" style="font-family: Calibri; padding: 2px 3px; vertical-align: bottom; white-space: nowrap;">Malawi</td><td data-sheets-value="{"1":3,"3":0.45}" style="background-color: #fce5cd; font-family: Calibri; padding: 2px 3px; text-align: center; vertical-align: bottom; white-space: nowrap;">0.45</td><td data-sheets-value="{"1":3,"3":0.44}" style="background-color: #fce5cd; font-family: Calibri; padding: 2px 3px; text-align: center; vertical-align: bottom; white-space: nowrap;">0.44</td><td data-sheets-value="{"1":3,"3":6.8}" style="background-color: #cfe2f3; font-family: Calibri; padding: 2px 3px; text-align: center; vertical-align: bottom; white-space: nowrap;">6.8</td><td data-sheets-value="{"1":3,"3":8}" style="background-color: #cfe2f3; font-family: Calibri; padding: 2px 3px; text-align: center; vertical-align: bottom; white-space: nowrap;">8</td></tr>
<tr style="height: 21px;"><td data-sheets-value="{"1":2,"2":"Nigeria"}" style="font-family: Calibri; padding: 2px 3px; vertical-align: bottom; white-space: nowrap;">Nigeria</td><td data-sheets-value="{"1":3,"3":0.38}" style="background-color: #fce5cd; font-family: Calibri; padding: 2px 3px; text-align: center; vertical-align: bottom; white-space: nowrap;">0.38</td><td data-sheets-value="{"1":3,"3":0.4}" style="background-color: #fce5cd; font-family: Calibri; padding: 2px 3px; text-align: center; vertical-align: bottom; white-space: nowrap;">0.4</td><td data-sheets-value="{"1":3,"3":30.7}" style="background-color: #cfe2f3; font-family: Calibri; padding: 2px 3px; text-align: center; vertical-align: bottom; white-space: nowrap;">30.7</td><td data-sheets-value="{"1":3,"3":25.9}" style="background-color: #cfe2f3; font-family: Calibri; padding: 2px 3px; text-align: center; vertical-align: bottom; white-space: nowrap;">25.9</td></tr>
<tr style="height: 21px;"><td data-sheets-value="{"1":2,"2":"Tanzania"}" style="font-family: Calibri; padding: 2px 3px; vertical-align: bottom; white-space: nowrap;">Tanzania</td><td data-sheets-value="{"1":3,"3":0.62}" style="background-color: #fce5cd; font-family: Calibri; padding: 2px 3px; text-align: center; vertical-align: bottom; white-space: nowrap;">0.62</td><td data-sheets-value="{"1":3,"3":0.54}" style="background-color: #fce5cd; font-family: Calibri; padding: 2px 3px; text-align: center; vertical-align: bottom; white-space: nowrap;">0.54</td><td data-sheets-value="{"1":3,"3":36}" style="background-color: #cfe2f3; font-family: Calibri; padding: 2px 3px; text-align: center; vertical-align: bottom; white-space: nowrap;">36</td><td data-sheets-value="{"1":3,"3":18.3}" style="background-color: #cfe2f3; font-family: Calibri; padding: 2px 3px; text-align: center; vertical-align: bottom; white-space: nowrap;">18.3</td></tr>
<tr style="height: 21px;"><td data-sheets-value="{"1":2,"2":"Uganda"}" style="font-family: Calibri; padding: 2px 3px; vertical-align: bottom; white-space: nowrap;">Uganda</td><td data-sheets-value="{"1":3,"3":0.64}" style="background-color: #fce5cd; font-family: Calibri; padding: 2px 3px; text-align: center; vertical-align: bottom; white-space: nowrap;">0.64</td><td data-sheets-value="{"1":3,"3":0.44}" style="background-color: #fce5cd; font-family: Calibri; padding: 2px 3px; text-align: center; vertical-align: bottom; white-space: nowrap;">0.44</td><td data-sheets-value="{"1":3,"3":39.1}" style="background-color: #cfe2f3; font-family: Calibri; padding: 2px 3px; text-align: center; vertical-align: bottom; white-space: nowrap;">39.1</td><td data-sheets-value="{"1":3,"3":20.7}" style="background-color: #cfe2f3; font-family: Calibri; padding: 2px 3px; text-align: center; vertical-align: bottom; white-space: nowrap;">20.7</td></tr>
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<span id="goog_1631886705"></span><span id="goog_1631886706"></span>What do we learn from these simulations? First, geographical targeting appears to save you money relative to the naive transfer. You achieve a 50% reduction in SPG for 7-40% less budget than a naive transfer when you use either SAE or satellites to target. Second, and not surprisingly, when the satellite model and the SAE model fit the data roughly equally well (e.g. Malawi, Nigeria), they deliver similar savings relative to a uniform transfer. But the amount of budget that you save by using SAE or satellites to target transfers can differ even for similar R2. Compare Malawi to Nigeria: the targeted approaches help a lot more in Nigeria than in Malawi, which is consistent with Malawi having poor people all over the place (e.g. see <a href="https://static1.squarespace.com/static/57a8ec72c534a5448c606796/t/57dc8d49bebafbc0a13659e7/1474071900269/?format=1500w">the map</a>s we produced for Malawi and Nigeria) including in somewhat better-off vilages, which in turn makes targeting on the village mean not as helpful. Third, SAE leads to more efficient targeting in the two countries where the SAE model has more predictive power -- Tanzania and Uganda. <br />
<br />
We're somewhat biased of course, but this to us is fairly promising from a satellite perspective. First, these SAE estimates are probably and upper bound on actual SAE performance, since it's very rarely the case that you have a household survey and a census in the same year, and we've been generous in the variables we included to calculate the SAE (some of which are not be available in many censuses). Second, since many countries lack either a census or a household survey, it's not clear whether we can use SAE in these countries, whereas in our Science paper we showed decent out-of-country fits for the satellite-based approach. Third, we're working on improvements to the satellite-based estimates and anticipate meaningfully higher R2 relative to these benchmarks. And finally, and perhaps most importantly, the satellite-based approach is going to be incredibly cheap to implement relative to SAE in areas where surveys don't already exist. So you might be willing to trade off some loss in targeting performance given the low expense of developing the targeting tool. </div>
<div>
<br />
So our tentative conclusion is that satellites might have something to offer here. They're probably going to be even more useful when combined with other approaches and data -- something that we are exploring in ongoing work. </div>
</div>
Marshall Burkehttp://www.blogger.com/profile/15436297075698378164noreply@blogger.com0tag:blogger.com,1999:blog-3813701770708442620.post-77203892462815715172017-02-15T13:47:00.001-08:002017-02-15T17:53:24.258-08:00Some scintillating satellite studies<div class="MsoNormal">
We’ve had a couple of papers come out that might be of
interest to readers of the blog. Both relate to using satellites to measure
crop yields for individual fields. This type of data is very hard to come by
from ground-based sources. In many places, especially poor ones, farmers simply
don’t keep records on production on a field by field basis. In other places,
like the U.S., individual farmers often have data, and government agencies or
private companies often have data for lots of individuals, but it’s typically
not made available to public researchers.<o:p></o:p></div>
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All of this is bad for science. The less data you have on
yields, the less we can understand how to improve yields or reduce
environmental impacts of crop production. We now know exactly how <a href="https://fivethirtyeight.com/features/buster-poseys-pitch-framing-makes-him-a-potential-mvp/">much a catcher’s glove moves</a> each time they frame a pitch, exactly <a href="http://grantland.com/features/kirk-goldsberry-introduces-new-way-understand-nba-best-scorers/">how well every NBA player shoots from every spot on the floor</a>, and exactly how fast LeBron James
flops to ground when he is touched by an opposing player. And all of this knowledge
has helped improve team strategies and player performance. <o:p></o:p></div>
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But for agriculture, with all the talk of “big data”, we are
still often shooting in the dark. And in some cases it’s getting worse, such as
this <a href="http://farmdocdaily.illinois.edu/2017/01/falling-response-rates-to-usda-crop-surveys.html">recent
post</a> at farmdoc daily about how the farmer response rate to area and
production surveys is fading faster than my hairline (but not quite as fast as
Sol’s):<o:p></o:p></div>
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgxT4g9qGKnhorp4K382zVbONVBjbnQ-o75kbeY414nm1eILWfA9c0sSYCItHwUWTJV7IO7jlQtEzJtJ1NvdEwl1r0phX8mR6PW_htwnfgcPbi2QC6_bBDwPA-VMOa5iy-fJBxRvVYN1Atr/s1600/fdd190117_fig1.jpg" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" height="290" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgxT4g9qGKnhorp4K382zVbONVBjbnQ-o75kbeY414nm1eILWfA9c0sSYCItHwUWTJV7IO7jlQtEzJtJ1NvdEwl1r0phX8mR6PW_htwnfgcPbi2QC6_bBDwPA-VMOa5iy-fJBxRvVYN1Atr/s400/fdd190117_fig1.jpg" width="400" /></a></div>
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As we’ve discussed before on this blog (e.g., <a href="http://www.g-feed.com/2015/02/measuring-yields-from-space.html">here</a> and
<a href="http://www.g-feed.com/2015/05/introducing-scym.html">here</a>),
satellites offer a potential workaround. They won’t be perfect, but anyone who
has done phone or field surveys, or even crop cuts within fields, knows that
they have problems too. Plus, satellites are getting cheaper and better, and
it’s also becoming easier to work with past satellite data thanks to platforms
like Google Earth Engine. <o:p></o:p></div>
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Now to the studies. Both employ what we are calling <a href="http://www.g-feed.com/2015/05/introducing-scym.html">SCYM</a>,
which stands either for Scalable Crop Yield Mapper or Steph Curry’s Your MVP, depending
on the context. In the first context, the basic idea is to eliminate any
reliance on ground calibration by using simulations to calibrate regression
models. <o:p></o:p></div>
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The first study (joint with George Azzari) applied SCYM to
Landsat data in the Corn Belt in the U.S. for 2000-2015, and then analyzed the
resulting patterns and trends for maize and soybean yields. Here’s the <a href="http://iopscience.iop.org/article/10.1088/1748-9326/aa5371/meta">link to
the paper</a>, and a brief <a href="https://www.youtube.com/watch?v=XDG3r7JyGc4&t=20s">animation </a>we
had made to accompany the story. Also, we’ve made the maps of average yields
available for people to visualize and inspect <a href="http://www.fsedata.stanford.edu/scym-landsat-mean-maize-usa">here</a>.
Below is a figure that summarizes what I thought was the most interesting
finding: that maize yield distributions are getting wider over time. <o:p></o:p></div>
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<o:p><br /></o:p></div>
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjIGw5_-ChaPpmRnAKRE55DH5iM1-CkRKAMwWd0XHZKRzU2yR16Re4fr1Jhez6rOMF1PiNYNtgb5-Ue0i8TK8dUu5AlRpacUjrekltQINXht8vfaSqZ0kcYm7TPWV5eQqIyySP47nv5bHfU/s1600/maize_avg_viz_pyv1f_labels_east_bar_distr_titles_small.jpg" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" height="382" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjIGw5_-ChaPpmRnAKRE55DH5iM1-CkRKAMwWd0XHZKRzU2yR16Re4fr1Jhez6rOMF1PiNYNtgb5-Ue0i8TK8dUu5AlRpacUjrekltQINXht8vfaSqZ0kcYm7TPWV5eQqIyySP47nv5bHfU/s640/maize_avg_viz_pyv1f_labels_east_bar_distr_titles_small.jpg" width="640" /></a></div>
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Overall maize yields are still increasing, but mainly this
is driven by growth in high yielding areas. This is true at the scale of
counties, and also within individual fields. The paper discusses reasons why (e.g.
uptake of precision ag) and possible implications. Maybe I’ll return to it in a
future post. A short side note is that since that paper was written we’ve
expanded the dataset to a nine state area, and made some useful tweaks to SCYM to
improve performance.<o:p></o:p></div>
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The second <a href="http://www.pnas.org/content/early/2017/02/14/1616919114">paper </a>(joint
with Marshall) looks at yield mapping with some of the newer high-res sensors
in smallholder systems. In this case, we looked at two years of data in Western
Kenya, using detailed surveys with hundreds of farmers to test our yield
estimates, which were derived using 1m resolution imagery from Terra Bella. The
results were pretty good, especially when you consider only fields above half
an acre, where problems with self-reported yields are not as big. Just like in the US, the satellites reveal a lot of yield heterogeneity both within and between fields (the top image shows the true-color image, the bottom shows the derived yield estimates for maize fields):</div>
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjcKpC31t6vAdl07bSBmFcX1AE_6N4fQZ7fZhEPgnka7rPwHvsDHtyXUs9rjCT0odou6xxkHe3EynWjSyWVYtkJb69ChV_xdqEFIANyxayGXE3D-rSvB8yoDh_Pao6p55wKAz66OcUR5ssL/s1600/kenya.2015.map.jpg" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" height="452" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjcKpC31t6vAdl07bSBmFcX1AE_6N4fQZ7fZhEPgnka7rPwHvsDHtyXUs9rjCT0odou6xxkHe3EynWjSyWVYtkJb69ChV_xdqEFIANyxayGXE3D-rSvB8yoDh_Pao6p55wKAz66OcUR5ssL/s640/kenya.2015.map.jpg" width="640" /></a></div>
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One
interesting result is that in many ways it looks like the satellite estimates
are no less accurate than the (expensive) field surveys. For example, when we correlate
yields with inputs that we think should affect yields, like fertilizer or seed
density, we see roughly equal correlations when using satellite or self-report
yields. <o:p></o:p></div>
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<div class="separator" style="clear: both; text-align: center;">
<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiy611PMr3sA_0mFiUdJMgtx4d8Ed0uh-Gp-TkR-CiW29LPddxWsrts7pjlkuwCC9HoDJkRYNNZNwJ7iB34X-0M_isN5Q_O5F7K_iuOCCUSEm91zQZ3SGtXFnYlcf3800cK3YHmUaBlIUTn/s1600/kenya.pnas.fig4..gif" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" height="320" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiy611PMr3sA_0mFiUdJMgtx4d8Ed0uh-Gp-TkR-CiW29LPddxWsrts7pjlkuwCC9HoDJkRYNNZNwJ7iB34X-0M_isN5Q_O5F7K_iuOCCUSEm91zQZ3SGtXFnYlcf3800cK3YHmUaBlIUTn/s320/kenya.pnas.fig4..gif" width="308" /></a></div>
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None of this is to say that satellite-based yields are
perfect. But that was never the goal. The goal is to get insights into what
factors are (or aren’t) important for yields in different settings. Both
studies show how satellites can provide insights that match or even go beyond
what is possible with traditional yield measures. We hope to continue to test and
apply these approaches in new settings, and plan to make the datasets available
to researchers, probably <a href="http://www.fsedata.stanford.edu/">at this site</a>. <o:p></o:p></div>
David Lobellhttp://www.blogger.com/profile/17660903133065588267noreply@blogger.com2