tag:blogger.com,1999:blog-37806413215800659162024-03-12T22:04:32.718-04:00Greed, Green & GrainsEconomics, Environment & AgricultureMichael Robertshttp://www.blogger.com/profile/16455035518968529794noreply@blogger.comBlogger417125tag:blogger.com,1999:blog-3780641321580065916.post-18564139653939326012015-12-03T01:47:00.000-05:002015-12-03T01:47:03.401-05:00Renewable energy not as costly as some thinkThe other day Marshall and Sol <a href="http://www.g-feed.com/2015/11/in-cost-benefit-calculation-of-climate.html">took on Bjorn Lomborg</a> for ignoring the benefits of curbing greenhouse gas emissions. Indeed. But Bjorn, among others, is also notorious for exaggerating costs. That fact is that most serious estimates of reducing emissions are fairly low, and there is good reason to believe cost estimates are too high for the simple fact that analysts cannot measure or imagine all ways we might curb emissions. Anything analysts cannot model translates into cost exaggeration. <br />
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Hawai`i is a good case in point. Since moving to Hawai`i I've started digging into energy, in large part because the situation in Hawai`i is so interesting. Here we make electricity mainly from oil, which is super expensive. We are also rich in sun and wind. Add these facts to Federal and state subsidies and it spells a remarkable energy revolution. Actually, renewables are now cost effective even without subsidies.<br />
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In the video below Matthias Fripp, who I'm lucky to be working with now, explains how we can achieve 100% renewable energy by 2030 using current technology at a cost that is roughly comparable to our conventional coal and oil system. In all likelihood, with solar and battery costs continuing to fall, this goal could be achieved for a cost that's far less. And all of this assumes zero subsidies. <br />
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One key ingredient: We need to shift electric loads toward the supply of renewables, and we could probably do this with a combination of smart variable pricing and smart machines that could help us shift loads. More electric cars could help, too. I'm sure some could argue with some of the assumptions, but it's hard to see how this could be wildly unreasonable. <br />
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Michael Robertshttp://www.blogger.com/profile/16455035518968529794noreply@blogger.com0tag:blogger.com,1999:blog-3780641321580065916.post-27810417663863616032015-10-21T21:15:00.001-04:002020-06-30T19:53:52.176-04:00Paul Krugman on Food Economics<div><br /></div><div><br /></div>Paul Krugman doesn't typically write about food, so I was a little surprised to see this. Still, I think he got most things right, at least by my way of thinking. Among the interesting things he discussed.<br />
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1. The importance of behavioral economics in healthy food choices<br />
2. That it's hard to know how many actual farmers are out there, but it's a very <a href="http://greedgreengrains.blogspot.com/2009/02/2007-agricultural-census-and-note-of.html">small number</a>.<br />
3. That we could clean up farming a lot by <a href="http://greedgreengrains.blogspot.com/2013/04/how-farmers-could-benefit-from.html">pricing externalitie</a>s [also <a href="http://greedgreengrains.blogspot.com/2013/11/can-crop-rotations-cure-dead-zones.html">see</a>], or out-right banning of the most heinous practices, but that doesn't mean we're going to go back to the small farms of the pre-industrial era, or anything close to it.<br />
4. Food labels probably don't do all that we might like them to do (see point 1.)<br />
5. How food issues seem to align with Red/Blue politics just a little too much<br />
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There's enough to offend and ingratiate most everyones preconceived ideas in some small way, but mostly on the mark, I think.<br />
Michael Robertshttp://www.blogger.com/profile/16455035518968529794noreply@blogger.com0tag:blogger.com,1999:blog-3780641321580065916.post-60924385929774851952015-10-17T20:28:00.000-04:002015-10-18T17:53:31.634-04:00Angus Deaton and Commodity PricesAngus Deaton just <a href="http://www.nytimes.com/2015/10/13/business/angus-deaton-nobel-economics.html" target="_blank">won the Nobel</a> Prize in economics. He's a brilliant, famous economist who is known for <a href="http://www.nytimes.com/2015/10/13/upshot/why-angus-deaton-deserved-the-economics-nobel-prize.html" target="_blank">many contributions</a>. In graduate school I discovered a bunch of his papers and studied them carefully. He is a clear and meticulous writer which made it easy for me to learn a lot of technical machinery, like stochastic dynamic programming. His care and creativity in statistical matters, and linking data to theory, was especially inspiring. His papers with Christina Paxson inspired me to think long and hard about all the different ways economists might exploit weather as an instrument for identifying important economic phenomena.<br />
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One important set of contributions about which I've seen little mention concerns a body of work on commodity prices that he did in collaboration with Guy Laroque. This is really important research, and I think that many of those who do agricultural economics and climate change might have missed some of its implications, if they are aware of it at all.<br />
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Deaton lays out his work on commodity prices like he does in a lot of his papers: he sets out to test a core theory, insists on using only the most reliable data, and then pushes the data and theory hard to see if they can be reconciled with each other. He ultimately concludes that, while theory can broadly characterize price behavior, there is a critical paradox in the data that the theory cannot reconcile: too much autocorrelation in prices. (ASIDE 1)<br />
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These papers are quite technical and the concluding autocorrelation puzzle is likely to put most economists, and surely all non-economists, into a deep slumber. Who cares? <br />
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Undoubtedly, a lot of the fascination with these papers was about technique. They were written in the generation following discovery of GMM (generalized method of moments) as a way to estimate models centered on rational expectations, models in which iid errors can have an at least somewhat tangible interpretation as unpredictable "expectation errors."<br />
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One thing I always found interesting and useful from these papers was something that Deaton and Larqoue take entirely for granted. They show that the behavior of commodity prices themselves, without any other data, indicate that their supply and demand are extremely inelastic. (ASIDE 2) For, if they weren't, prices would not be as volatile as they are, as autocorrelated as they are, and stored as prevalently as they are. Deaton writes as much in a number of places, but states this as if it's entirely obvious and not of critical concern. (ASIDE 3)<br />
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But here's the thing: the elasticities of supply and demand are really what's critical for thinking about implications of policies, especially those that can affect supply or demand on a large scale, like ethanol mandates and climate change. Anyone who read and digested Deaton and Laroque and knew the stylized facts about corn prices knew that the ethanol subsidies and mandates were going to cause food prices to spike, maybe a whole lot. But no one doing policy analysis in those areas paid any attention. <br />
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Estimated elasticities regularly published in the AJAE for food commodities are typically orders of magnitude larger than are possible given what is plainly clear in price behavior, and the authors typically appear oblivious to the paradox. Also, if you like to think carefully about identification, it's easy to be skeptical of the larger estimated elasticities. Sorry aggies--I'm knocking you pretty hard here and I think it's deserved. I gather there are similar problems in other corners of the literature, say mineral and energy economics.<br />
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And it turns out that the autocorrelation puzzle may not be as large a puzzle as Deaton and Laroque let on. For one, a little refinement of their technique can give rise to greater price autocorrelation, a refinement that also implies even more inelastic supply and demand. Another simple way to reconcile theory and data is to allow for so-called ``convenience yields," which basically amounts to negative storage costs when inventories are low. Negative storage costs don't make sense on their face, but might actually reflect the fact that in any one location---where stores are actually held---prices or the marginal value of commodities can be a lot more volatile than posted prices in a market. Similar puzzles of positive storage when spot prices exceed futures prices can be similarly explained---there might be a lot of uncertain variability in time and space that sit between stored inventories and futures deliveries. <br />
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The graph below, from this recent paper by <a href="http://economix.fr/en/dt/2015.php?id=414" target="_blank">Gouel and Legrand</a>, uses updated techniques to show how inelastic demand needs to be to obtain high price autocorrelation, as we observe in the data (typically well above 0.6).<br />
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Adding bells and whistles to the basic theory easily reconciles the puzzle, but only strengthens the conclusion that demand and supply of commodities are extremely steep. And that basic conclusion should make people a little more thoughtful when it comes to thinking about implications of policies and about the potential impacts of climate change, which could greatly disrupt supply of food commodities.<br />
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ASIDE 1: Papers like Deaton's differ from most of the work that fills up the journals these days. Today we see a lot more empirical work than in the past, but most of this work is nearly atheoretical, at least relative to Deaton's. It most typically follows what <a href="http://davidcard.berkeley.edu/lectures/woytinsky.pdf" target="_blank">David Card describes</a> as ``the design-based approach." I don't think that's bad change. But I think there's a lot of value in the kind of work Deaton did, too.<br />
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ASIDE 2: The canonical model that Deaton and Laroque use, and much of the subsequent literature, has a perfectly inelastic supply curve that shifts randomly with the weather and a fixed demand curve. This is because, using only prices, one cannot identify both demand and supply. Thus, the estimated demand elasticities embody <i>both</i> demand and supply. And since that one elasticity is clearly very inelastic it also implies the sum of the two elasticities is very inelastic.<br />
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ASIDE 3: Lots of people draw conclusions lightly as if they are obvious without really thinking carefully about lies beneath. Not Angus Deaton.<br />
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Michael Robertshttp://www.blogger.com/profile/16455035518968529794noreply@blogger.com0tag:blogger.com,1999:blog-3780641321580065916.post-81635190387408219902015-08-04T21:57:00.001-04:002015-08-05T00:22:54.882-04:00Answering Matthew Kahn's questions about climate adaptation<div class="MsoNormal">
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<i style="mso-bidi-font-style: normal;">Question 1.<span style="mso-spacerun: yes;"> </span>This paper will be published soon by the JPE. Costinot, Arnaud, Dave Donaldson, and Cory B. Smith. Evolving comparative advantage and the impact of climate change in agricultural markets: Evidence from 1.7 million fields around the world. No. w20079. National Bureau of Economic Research, 2014.<span style="mso-spacerun: yes;"> </span>http://www10.iadb.org/intal/intalcdi/PE/2014/14183.pdf<o:p></o:p></i></div>
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<i style="mso-bidi-font-style: normal;">It strongly suggests that adaptation will play a key role protecting us. Which parts of their argument do you reject and why?<o:p></o:p></i></div>
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<b style="mso-bidi-font-weight: normal;"><span style="font-family: Arial;">Answer:<span style="mso-spacerun: yes;"> </span>This looks like a solid paper, much more serious than the average paper I get to review, and I have not yet studied it.<span style="mso-spacerun: yes;"> </span>I’m slow, so it would take me awhile to unpack all the details and study the data and model.<span style="mso-spacerun: yes;"> </span>Although, from a quick look, I think there are a couple points I can make right now.<span style="mso-spacerun: yes;"> </span><o:p></o:p></span></b></div>
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<b style="mso-bidi-font-weight: normal;"><span style="font-family: Arial;">First, and most importantly, I think we need to be clear about the differences between (i) adaptation (ii) price response and trade; (iii) innovation that would happen anyway; (iv) climate-change-induced innovation; and (v) price-induced innovation.<span style="mso-spacerun: yes;"> </span>I’m pretty sure this paper is mainly about (ii), not about adaptation as conventionally defined within literature, although there appears to be some adaptation too.<span style="mso-spacerun: yes;"> </span>I need to study this much more to get a sense of the different magnitudes of elasticities they estimate, and whether I think they are plausible given the data.<o:p></o:p></span></b></div>
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<b style="mso-bidi-font-weight: normal;"><span style="font-family: Arial;">To be clear: I think adaptation, as conventionally defined, pertains to <i style="mso-bidi-font-style: normal;"><u>changing production behavior when changing climate while holding all other factors (like prices, trade, technology, etc.) constant</u></i>.<span style="mso-spacerun: yes;"> </span>My annoyance is chiefly that people are mixing up concepts.<span style="mso-spacerun: yes;"> </span>My second annoyance is that too many are perpetually optimistic--some economists wear it like a badge, and I don’t think evidence or history necessarily backs up that optimism.<o:p></o:p></span></b></div>
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<i style="mso-bidi-font-style: normal;">Question 2. If farmers know that they face uncertain risks due to climate change, what portfolio choices can they engage in to reduce the variability of their earnings? What futures markets exist to allow for hedging? If a risk averse economic agent knows "that he does not know" what ambiguous risks she faces, she will invest in options to protect herself. Does your empirical work capture this medium term investment rational plan? Or do you embrace the Berkeley behavioral view of economic agents as myopic?<o:p></o:p></i></div>
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<b style="mso-bidi-font-weight: normal;"><span style="color: #535353; font-family: Arial;">Some farmers have subsidized crop insurance (nearly all in the U.S. do).<span style="mso-spacerun: yes;"> But I don't think insurance much affects production choices at all. </span>Futures markets seem to “work” pretty well and could be influenced by anticipated climate change.<span style="mso-spacerun: yes;"> </span>We actually use a full-blown rational expectations model to estimate how much they might be affected by anticipated climate change right now: about 2% higher than they otherwise would be.<span style="mso-spacerun: yes;"> </span><o:p></o:p></span></b></div>
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<b style="mso-bidi-font-weight: normal;"><span style="color: #535353; font-family: Arial;">Do I think people are myopic? Very often, yes.<span style="mso-spacerun: yes;"> </span>Do I think markets are myopic?<span style="mso-spacerun: yes;"> </span>By and large, no, but maybe sometimes.<span style="mso-spacerun: yes;"> I believe less in bubbles than Robert Shiller, even though I'm a great admirer of his work. </span>Especially for commodity markets (if not the marcoeconomy) I think rational expectations models are a good baseline for thinking about commodity prices, very much including food commodity prices.<span style="mso-spacerun: yes;"> </span>And I think rational expectations models can have other useful purposes, too.<span style="mso-spacerun: yes;"> </span>I actually do think the Lucas enterprise has created some useful tools, even if I find the RBC center of macro more than a bit delusional.<o:p></o:p></span></b></div>
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<b style="mso-bidi-font-weight: normal;"><span style="color: #535353; font-family: Arial;">I think climate and anticipated climate change will affect output (for good and bad), which will affect prices, and that prices will affect what farmers plant, where they plant it, and trade.</span><span style="color: #535353; font-family: Arial;"> </span><span style="color: #535353; font-family: Arial;">But none of this, I would argue, is what economists conventionally refer to as adaptation. A little more on response to prices below...<o:p></o:p></span></b></div>
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<b style="mso-bidi-font-weight: normal;"><span style="color: #535353; font-family: Arial;">Again, my beef with the field right now is that we are too blase about miracle of adaptation.<span style="mso-spacerun: yes;"> </span>It’s easy to tell horror stories that the data cannot refute.<span style="mso-spacerun: yes;"> </span>Much of economist tribe won’t look there—it feels taboo.<span style="mso-spacerun: yes;"> </span>JPE won’t publish such an article. We have blinders on when uncertainty is our greatest enemy.<o:p></o:p></span></b></div>
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<i style="mso-bidi-font-style: normal;">Question 3. If specific farmers at specific locations suffer, why won't farming move to a new area with a new comparative advantage? How has your work made progress on the "extensive margin" of where we grow our food in the future?<o:p></o:p></i></div>
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<b style="mso-bidi-font-weight: normal;"><span style="font-family: Arial;">The vast majority of arable land is already cropped. <span style="mso-spacerun: yes;"> </span>That which isn’t is in extremely remote and/or politically difficult parts of Africa. <span style="mso-spacerun: yes;"> </span>Yes, there will be substitution and shifting of land.<span style="mso-spacerun: yes;"> </span>But these shifts will come about because of climate-induced changes in productivity.<span style="mso-spacerun: yes;"> </span>In other words, first-order intensive margin effects will drive second-order extensive margin effects.<span style="mso-spacerun: yes;"> </span>The second order effects—some land will move into production, some out--will roughly amount to zero.<span style="mso-spacerun: yes;"> </span>That’s what the envelope theorem says.<span style="mso-spacerun: yes;"> </span>To a first approximation, adaptation in response to climate change will have zero aggregate effect, not just with respect to crop choice, but with respect to other management decisions as well.<span style="mso-spacerun: yes;"> </span>I think Nordhaus himself made this point a long time ago. <o:p></o:p></span></b></div>
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<b style="mso-bidi-font-weight: normal;"><span style="font-family: Arial;">However, there will also be intensive and extensive margin responses to prices.<span style="mso-spacerun: yes;"> </span>Those will be larger than zero.<span style="mso-spacerun: yes;"> </span>But I think the stylized facts about commodity prices ( from the rational expectations commodity price model, plus other evidence ) tell us that supply and demand are <span style="mso-bidi-font-weight: bold;">extremely</span> inelastic. <o:p></o:p></span></b></div>
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<span style="color: #535353; font-family: 'Trebuchet MS';">Question 4. The key agriculture issue in adapting to climate change appears to be reducing international trade barriers and improving storage and reducing geographic trade costs. Are you pessimistic on each of these margins? Container ships and refrigeration keep getting better, don't they?</span></div>
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<b style="mso-bidi-font-weight: normal;"><span style="font-family: Arial;">I think storage will improve, because almost anyone can do it, and there’s a healthy profit motive.<span style="mso-spacerun: yes;"> </span>It’s a great diversification strategy for deep-pocketed investors. I think many are already into this game and more will get into it soon.<span style="mso-spacerun: yes;"> </span>Greater storage should quell a good share of the greater volatility, but it actually causes average prices to rise, because there will be more spoilage.<span style="mso-spacerun: yes;"> </span>But I’m very “optimistic” if you will, about the storage response. I worry some that the storage response will be too great.<o:p></o:p></span></b></div>
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<b style="mso-bidi-font-weight: normal;"><span style="font-family: Arial;">But I’m pretty agnostic to pessimistic about everything else.<span style="mso-spacerun: yes;"> </span>Look what happened in earlier food price spikes.<span style="mso-spacerun: yes;"> </span>Many countries started banning exports.<span style="mso-spacerun: yes;"> </span>It created chaos and a huge “bubble” (not sure if it was truly rational or not) in rice prices.<span style="mso-spacerun: yes;"> </span>Wheat prices, particularly in the Middle East, shot up much more than world prices because government could no longer retain the subsidized floors. As times get tougher, I worry that politics and conflict could turn crazy.<span style="mso-spacerun: yes;"> </span>It’s the crazy that scares me.<span style="mso-spacerun: yes;"> </span>We’ve had a taste of this, no?<span style="mso-spacerun: yes;"> </span>The Middle East looks much less stable post food price spikes than before. I don’t know how much food prices are too blame, but I think they are a plausible causal factor.<o:p></o:p></span></b></div>
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<span style="color: #535353; font-family: "Trebuchet MS"; mso-bidi-font-family: "Trebuchet MS";">Question 5. With regards to my Climatopolis work, recall that my focus is the urbanized world. The majority of the world already live in cities and cities have a comparative advantage in adapting to climate conditions due to air conditioning, higher income and public goods investments to increase safety.<o:p></o:p></span></div>
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<b style="mso-bidi-font-weight: normal;"><span style="font-family: Arial;">To be fair: I’m probably picking on the wrong straw man.<span style="mso-spacerun: yes;"> </span>What’s bothering me these days has much less to do with your book and more to do with the papers that come across my desk every day.<span style="mso-spacerun: yes;"> </span>I think people are being sloppy and a bit closed minded, and yes, perhaps even tribal.<span style="mso-spacerun: yes;"> </span>I would agree that adaptation in rich countries is easier.<span style="mso-spacerun: yes;"> </span>Max Auffhammer has a nice new working paper looking at air conditioning in California, and how people will use air conditioning more, and people in some areas will install air conditioners that don’t currently have them--that's adaptation.<span style="mso-spacerun: yes;"> </span>This kind of adaptation will surely happen, is surely good for people but bad for energy conservation.<span style="mso-spacerun: yes;"> </span>It’s a really neat study backed by billions of billing records. <span style="mso-spacerun: yes;"> </span>But the adaptation number—an upper bound estimate—is small. <o:p></o:p></span></b></div>
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<b style="mso-bidi-font-weight: normal;"><span style="font-family: Arial;">I thought of you and your book because people at AAEA were making the some of the same arguments you made, and because you’re much bigger fish than most of the folks in my little pond.<span style="mso-spacerun: yes;"> </span>Also, I think your book embodies many economists’ perhaps correct, but perhaps gravely naïve, what-me-worry attitude.</span></b></div>
Michael Robertshttp://www.blogger.com/profile/16455035518968529794noreply@blogger.com0tag:blogger.com,1999:blog-3780641321580065916.post-37984933452425266422015-08-04T11:07:00.000-04:002015-08-04T11:07:54.452-04:00Why Science for Climate Adaptation is Difficult<div class="MsoNormal">
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Matthew Kahn, author of the cheeky book <a href="http://www.amazon.com/Climatopolis-Cities-Thrive-Hotter-Future/dp/0465019269"><i style="mso-bidi-font-style: normal;">Climatopolis: How Our Cities will Thrive in the Hotter Future</i>,</a> likes to compliment our research (<a href="http://www.pnas.org/content/106/37/15594">Schlenker and Roberts, 2009</a>) on potential climate impacts to agriculture by saying it will cause valuable innovation that will prevent its dismal predictions from ever occurring. <o:p></o:p></div>
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Matt has a point, one that has been made many times in other contexts by economists with Chicago School roots.<span style="mso-spacerun: yes;"> </span>Although in Matt’s case (and most all of the others), it feels more like a <a href="http://www.theguardian.com/environment/climate-consensus-97-per-cent/2013/sep/16/climate-change-contrarians-5-stages-denial">third stage of denial</a> than a serious academic argument.<o:p></o:p></div>
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It’s not just Matt.<span style="mso-spacerun: yes;"> </span>Today, the serious climate economist (or <a href="http://krugman.blogs.nytimes.com/?s=%22Very+Serious%22">Serious</a>?) is supposed to write about adaptation.<span style="mso-spacerun: yes;"> </span>It feels taboo to suggest that adaptation is difficult.<span style="mso-spacerun: yes;"> </span>Yet, the conventional wisdom here is almost surely wrong.<span style="mso-spacerun: yes;"> </span>Everyone seems to ignore or miscomprehend basic microeconomic theory: adaptation is a <a href="http://greedgreengrains.blogspot.com/2014/06/adaptation-with-envelope.html">second or higher-order effect</a>, probably as ignorable as it is unpredictable. <o:p></o:p></div>
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While the theory is clear, the evidence needs to be judged on a case-by-case basis. Although it seems to me that much of the research so far is either flawed or doesn’t measure adaptation at all.<span style="mso-spacerun: yes;"> </span>Instead it confounds adaptation—changes in farming and other activities due to changes in climate—with something else, like technological change that would have happened anyway, response to prices, population growth or other factors.<o:p></o:p></div>
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For example, some farmers may be planting earlier or later due to climate change. <span style="mso-spacerun: yes;"> </span>They may also be planting different crops in a few places. But farmers are also changing what, when and where they plant due to innovation of new varieties that would have come about even if Spring weren’t coming a little earlier.<span style="mso-spacerun: yes;"> </span>The effects of climate change on farm practices are actually mixed, and in the big picture, look very small to me, at least so far.<o:p></o:p></div>
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The other week the AAEA meetings in San Francisco, our recent guest blogger Jesse Tack was reminding me of Matt’s optimistic views, and in the course of our ensuing conversations about some of his current research, it occurred to me just why crop science surrounding climate-related factors is so difficult. The reason goes back to struggles of early modern crop science, and the birth of modern statistics and hypothesis testing, all of which probably ushered in the Green Revolution.<o:p></o:p></div>
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How’s all that?<span style="mso-spacerun: yes;"> </span>Well, modern statistical inference and experimental design have some earlier roots, but most of it can be traced to two books, <i style="mso-bidi-font-style: normal;">The Statistical Manual for Research Workers</i>, and <i style="mso-bidi-font-style: normal;">The Arrangement of Field Experiments</i>, both written by Ronald Fisher in the 1920s. Fisher developed his ideas while working at Rothamsted, one of the oldest crop experiment stations in the world.<span style="mso-spacerun: yes;"> </span>In 1919 he was hired to study the vast amount of data collected since the 1840s, and concluded that all the data was essentially useless because all manner of events affecting crop yields (mostly weather) had hopelessly confounded the many experiments, which we unrandomized and uncontrolled. It was impossible to separate signal from noise. To draw scientific inferences and quantify uncertainties, would require <i style="mso-bidi-font-style: normal;">randomized controlled trials</i>, and some new mathematics, which Fisher then developed.<span style="mso-spacerun: yes;"> </span>Fisher’s statistical techniques, combined with his novel experimental designs, literally invented modern science. It’s no surprise then that productivity growth in agriculture accelerated a decade or two later. <o:p></o:p></div>
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So what does this have to do with adaptation?<span style="mso-spacerun: yes;"> </span>Well, the crux of adaptation involves higher-order effects: the interaction of crop varieties, practices and weather.<span style="mso-spacerun: yes;"> </span>It’s not about whether strain X has a typically higher yield than strain Y.<span style="mso-spacerun: yes;"> </span>It’s about the relative performance of strain X and strain Y across a wide range weather conditions. <o:p></o:p></div>
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Much like the early days of modern science, this can be very hard to measure because there’s so much variability in the weather and other factors. Scientists cannot easily intervene to control the temperature and CO2 like they can varieties and crop practices.<span style="mso-spacerun: yes;"> </span>And when they do, other experimental conditions (like soil moisture) are usually carefully controlled such that no water or pest stresses occur.<span style="mso-spacerun: yes;"> </span>Since these other factors are also likely influenced by warming temperatures (like <a href="http://www.nature.com/nclimate/journal/v3/n5/full/nclimate1832.html">VPD-induced drought</a>, also <a href="http://ajae.oxfordjournals.org/content/early/2012/05/22/ajae.aas047.short">here</a>), so it’s not really clear whether these experiments tell us what we need to know about the effects of climate change.<o:p></o:p></div>
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjAnGmoGmYP0ly2U-x6AdWGz8boX9oCVXdO8ulI7LvHqFDF3JrxVe2n5YOjQ8p0Rr15WR-146u2TJrnHYePM-s7JTKGU5aENL0hd0WtpHxLEuIQjSDbWGxlTkQKQC-NJqDHpnZyq81ukes/s1600/co2TempExp.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" height="285" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjAnGmoGmYP0ly2U-x6AdWGz8boX9oCVXdO8ulI7LvHqFDF3JrxVe2n5YOjQ8p0Rr15WR-146u2TJrnHYePM-s7JTKGU5aENL0hd0WtpHxLEuIQjSDbWGxlTkQKQC-NJqDHpnZyq81ukes/s400/co2TempExp.png" width="400" /></a></div>
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(An experiment with controlled temperatures and CO2 concentrations)</div>
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Then, of course, is the curse of dimensionality.<span style="mso-spacerun: yes;"> </span>To measure interactions of practices, temperature and CO2, requires experimentation on a truly grand scale. <span style="mso-spacerun: yes;"> </span>If we constrain ourselves to actual weather events, in most parts of the world we have only one crop per year, so the data accumulate slowly, will be noisy, and discerning cause and effect basically impossible. In the end, it’s not much different from Ronald Fisher trying to discern truth from his pre-1919 experiment station data that lacked randomly assigned treatments and controls.<o:p></o:p></div>
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I would venture to guess that these challenges in the agricultural realm likely apply to other areas as well.<o:p></o:p></div>
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<span style="font-family: Cambria; font-size: 12.0pt; mso-ansi-language: EN-US; mso-ascii-theme-font: minor-latin; mso-bidi-font-family: "Times New Roman"; mso-bidi-language: AR-SA; mso-bidi-theme-font: minor-bidi; mso-fareast-font-family: "MS 明朝"; mso-fareast-language: EN-US; mso-fareast-theme-font: minor-fareast; mso-hansi-theme-font: minor-latin;">So, given the challenges, the high cost, and basic microeconomic prediction that adaptation is a small deal anyway, how much should we actually spend on adaptation versus prevention?</span></div>
Michael Robertshttp://www.blogger.com/profile/16455035518968529794noreply@blogger.com0tag:blogger.com,1999:blog-3780641321580065916.post-80588856060358757012015-04-01T10:51:00.001-04:002015-04-02T02:59:11.270-04:00 Discounting Climate Change Under Secular Stagnation<div class="separator" style="clear: both; text-align: center;">
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Ben Bernanke, recent former Chair of the Federal Reserve, has a <a href="http://www.brookings.edu/blogs/ben-bernanke" target="_blank">new blog</a>. And he's writing about low interest rates and so-called secular stagnation, a pre-WWII phrase recently resurrected by <a href="http://larrysummers.com/" target="_blank">Larry Summers</a>.<br />
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The topic is dismal--hey, they're economists! But for those in the field it's a real hoot to see these titans of economic thought relieved of their official government duties and able to write openly about what they really think.<br />
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These two share many views, but Ben has a less dismal outlook than Larry. Larry thinks we're stuck in a low-growth equilibrium, and low or even negative interest rates are here to stay without large, persistent fiscal stimulus. Ben thinks this situation is temporary, if long lived. He writes:<br />
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<span style="color: blue;"><span style="background-color: white; font-family: Arial, Helvetica, sans-serif; font-size: 14.885858535766602px; line-height: 23.817373275756836px;">I generally agree with the </span><a href="http://econweb.ucsd.edu/~jhamilto/USMPF_2015.pdf" style="font-family: Arial, Helvetica, sans-serif; font-size: 14.885858535766602px; line-height: 23.817373275756836px; text-decoration: none;">recent critique of secular stagnation by Jim Hamilton, Ethan Harris, Jan Hatzius, and Kenneth West</a><span style="background-color: white; font-family: Arial, Helvetica, sans-serif; font-size: 14.885858535766602px; line-height: 23.817373275756836px;">. In particular, they take issue with Larry’s claim that we have never seen full employment during the past several decades without the presence of a financial bubble. They note that the bubble in tech stocks came very late in the boom of the 1990s, and they provide estimates to show that the positive effects of the housing bubble of the 2000’s on consumer demand were largely offset by other special factors, including the negative effects of the sharp increase in world oil prices and the drain on demand created by a trade deficit equal to 6 percent of US output. They argue that recent slow growth is likely due less to secular stagnation than to temporary “headwinds” that are already in the process of dissipating. During my time as Fed chairman I frequently cited the economic headwinds arising from the aftermath of the financial crisis on credit conditions; the slow recovery of housing; and restrictive fiscal policies at both the federal and the state and local levels (for example, see my </span><a href="http://www.federalreserve.gov/newsevents/speech/bernanke20120831a.htm" style="font-family: Arial, Helvetica, sans-serif; font-size: 14.885858535766602px; line-height: 23.817373275756836px; text-decoration: none;">August</a><span style="background-color: white; font-family: Arial, Helvetica, sans-serif; font-size: 14.885858535766602px; line-height: 23.817373275756836px;"> and </span><a href="http://www.federalreserve.gov/newsevents/speech/bernanke20121120a.htm" style="font-family: Arial, Helvetica, sans-serif; font-size: 14.885858535766602px; line-height: 23.817373275756836px; text-decoration: none;">November</a><span style="background-color: white; font-family: Arial, Helvetica, sans-serif; font-size: 14.885858535766602px; line-height: 23.817373275756836px;"> 2012 speeches.)</span></span></blockquote>
These are good points. But then Larry has a <a href="http://larrysummers.com/2015/04/01/on-secular-stagnation-a-response-to-bernanke/" target="_blank">compelling response</a>, too. I particularly agree with Larry about the basic economic plausibility of persistent equilibrium real interest rates that are well below zero. He writes:<br />
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<span style="color: blue;"><span style="border: 0px; font-family: 'Open Sans', Helvetica, sans-serif; font-size: 14.058866500854492px; line-height: 18px; margin: 0px; outline: 0px; padding: 0px; vertical-align: baseline;">Do Real Rates below Zero Make Economic Sen</span><span style="border: 0px; font-family: 'Open Sans', Helvetica, sans-serif; font-size: 14.058866500854492px; line-height: 18px; margin: 0px; outline: 0px; padding: 0px; vertical-align: baseline;">se? </span></span><span style="background-color: white; font-family: 'Open Sans', Helvetica, sans-serif; font-size: 14.058866500854492px; line-height: 18px;"><span style="color: blue;">Ben suggests not– citing my uncle Paul Samuelson’s famous observation that at a permanently zero or subzero real interest rate it would make sense to invest any amount to level a hill for the resulting saving in transportation costs. Ben grudgingly acknowledges that there are many theoretical mechanisms that could give rise to zero rates. To name a few: credit markets do not work perfectly, property rights are not secure over infinite horizons, property taxes that are explicit or implicit, liquidity service yields on debt, and investors with finite horizons.</span></span></blockquote>
Institutional uncertainty seems like a big deal that can't be ignored when thinking about long-run growth and real interest rates (<a href="http://en.wikipedia.org/wiki/Ramsey%E2%80%93Cass%E2%80%93Koopmans_model" target="_blank">these are closely connected</a>). People are pessimistic about growth these days, for seemingly pretty good reasons. Institutional collapse may be unlikely, but far from impossible. Look at history. If we think negative growth is possible, savings are concentrated at the top of the wealth distribution, and people are loss averse, it's not hard to get negative interest rates.<br />
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Still, I kind of think we'd snap out of this if we had a bit more fiscal stimulus throughout the developed world, combined with a slightly higher inflation target--say 3 or 4 percent. But keep in mind I'm just an armchair macro guy.<br />
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The point I want to make is that these low interest rates, and the possibility of secular stagnation, greatly affects the calculus surrounding optimal investments to curb climate change. The titans of environmental economics--Weitzman, Nordhaus and Pindyck--have been arguing about the discount rate we should use to weigh distant future benefits against near-future costs of abating greenhouse gas emissions. They're arguing about this because the right price for emissions is all about the discount rate. Everything else is chump change by comparison. <br />
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Nordhaus and Pindyck argue that we should use a higher discount rate and have a low price on greenhouse gas emissions. Basically, they claim that curbing greenhouse gas emissions involves a huge transfer of wealth from current, relatively poor to future supremely rich. And a lot of that conclusion comes from assuming 2%+ baseline growth forever. Weitzman counters that there's a small chance that climate change will be truly devastating, causing losses so great that the future may not be as well off as we expect. Paul Krugman has a <a href="http://www.nytimes.com/2010/04/11/magazine/11Economy-t.html" target="_blank">great summary</a> of this debate.<br />
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Anyway, it always bothered me that Nordhaus and Pindyck had so much optimism built into baseline projections. Today's low interest rates and the secular stagnation hypothesis paint a different picture. Quite aside from climate change, growth and real rates look lower than the 2% baseline many assume, and a lot more uncertain. And that means Weitzman-like discount rates (<a href="https://are.berkeley.edu/courses/ARE263/fall2008/paper/Discounting/weitzman_Why%20the%20far-distant%20future%20should%20be%20discounted%20at%20its%20lowest%20possible%20rate_JEEM98.pdf" target="_blank">near zero</a>) make sense even without fat-tailed uncertainty about climate change impacts.Michael Robertshttp://www.blogger.com/profile/16455035518968529794noreply@blogger.com10tag:blogger.com,1999:blog-3780641321580065916.post-67619058580566830042015-03-16T03:36:00.002-04:002015-03-16T12:14:18.513-04:00The Limits of Econometrics, and Roots of Modern Applied MicroI just stumbled upon <a href="http://www.era.org.tr/makaleler/99.pdf" target="_blank">this article</a> by David Freedman, published post-mortem. A lot of this looks familiar to me: you can find pieces of it in David Freedman's book, <a href="http://www.amazon.com/Statistical-Models-Practice-David-Freedman/dp/0521743850" target="_blank">Statistical Models, Theory and Practice</a>, which I highly recommend.<br />
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Perhaps I've blogged about this before, but I rather suspect that David Freedman's critical writings on use and misuse of regression analysis formed the basis of so-called "applied micro," which grew out of Princeton University, and the work of Ashenfelter, Card, Krueger, Angrist and others. An occasional citation will clue careful readers to this connection, particularly the teaching of natural experiments, and David Freedman's canonical example: <a href="http://www.ph.ucla.edu/epi/snow.html" target="_blank">Snow on Cholera</a>. Some modern references to Snow refer to Freedman; many do not. But I'm pretty sure it was in fact Freedman who <a href="http://www.math.rochester.edu/people/faculty/cmlr/Advice-Files/Freedman-Shoe-Leather.pdf" target="_blank">dug this seminal work out of the dustbin of history</a> and used it to inspire invigorated new empiricism that searches for natural experiments and rigorously tests maintained assumptions in regression models of observational data.<br />
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The one thing about Freedman that can be a little frustrating is his terseness. There's a lot he doesn't say but only implies. Still, he says enough to lay bear the nakedness of the heroic assumptions made in much applied econometrics. The sins remain plentiful, even among his descendants who practice applied micro today. <br />
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Nevertheless, I think empiricism today is much better than it was twenty years ago. I think David Freedman deserves much of the credit for that. And I think he's still worth reading and re-reading, if we want to improve honesty in applied econometrics.<br />
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<br />Michael Robertshttp://www.blogger.com/profile/16455035518968529794noreply@blogger.com0tag:blogger.com,1999:blog-3780641321580065916.post-58649334922033623882015-03-10T15:07:00.001-04:002015-03-19T19:46:38.420-04:00Buying Conservation--For the Right Price<div class="separator" style="clear: both; text-align: center;">
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Erica Goode has an <a href="http://www.nytimes.com/2015/03/10/science/farmers-put-down-the-plow-for-more-productive-soil.html?action=click&contentCollection=Politics&module=MostEmailed&version=Full&region=Marginalia&src=me&pgtype=article" target="_blank">inspiring article</a> about the benefits of conservation tillage, which has been gaining favor among farmers. No-till farming can improve yields, lower costs, and improve the environment. Just the kind of thing we all want to hear--everybody wins!<br />
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One important thing Goode doesn't mention: USDA has been subsidizing conservation tillage, and these subsidies have probably played an important role in spreading adoption.<br />
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Subsidizing conservation practices like no-till can be a little tricky. After all, while this kind of thing has positive externalities, farmers presumably reap rewards too. There are costs involved with undertaking something new. But once the practice is adopted and proven, there would seem to be little need for further subsidies. The problem is that it can be difficult to take subsidies away once they've been established.<br />
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In practice, the costs and benefits of no till and other conservation practices vary. Some of this has to do with the type of land. No-till can be excellent for land in the Midwest with thick topsoil. In the South, where topsoil is thin, maybe not so much. So, for some farmers conservation practices are worthwhile; for others, the hassle may not be worth the illusive future benefits. Ideally, policy would provide subsidies to the later, not the former. But how do policy makers differentiate? In practice, they don't; everybody gets the subsidies.<br />
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Can we do better? Together with some old colleagues at USDA, I've been thinking about this question for a long time, and we recently <a href="http://www.ers.usda.gov/media/1732610/err181.pdf" target="_blank">released a report</a> (PDF) summarizing some of the most essential ideas (<a href="http://www.ers.usda.gov/amber-waves/2015-januaryfebruary/options-for-improving-conservation-programs-insights-from-auction-theory-and-economic-experiments.aspx#.VP88pkL2grg" target="_blank">here's</a> the Amber Waves short take). <br />
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In short, yes, we can do better. The basic idea involves a form of price discrimination, implemented using augmented signup rules. Sign ups for conservation programs operate like an auction: farmers submit offers for enrollment, offers are ranked nationally, and the best offers are selected. The problem is that, when farmers compete on a national scale, farmers happy to do no-till conservation without any subsidies at all are pitted against farmers for whom the private benefits conservation tillage are dubious. A lot of the subsides probably end up going to farmers who would do it anyway.<br />
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Alternatively, signups could impose a degree of local competition, such that the worst offers for any set of observable characteristics--say farms in a crop district with land of similar quality--would be rejected regardless of their national-level standing. This kind of local competition would garner more competitive offers from no-till farmers who would use the practice even without subsidies.<br />
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It's difficult to tell how much more conservation we buy for the tax payer buck using these techniques. We can't really know without testing the mechanisms on real signups. This is where real policy experiments could have a lot of added value. Will USDA give it a try? Only time will tell...<br />
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<br />Michael Robertshttp://www.blogger.com/profile/16455035518968529794noreply@blogger.com0tag:blogger.com,1999:blog-3780641321580065916.post-11551742543320920292015-01-31T02:42:00.000-05:002015-01-31T02:42:29.149-05:00Asymmetric Delusions and Pragmatism<div class="separator" style="clear: both; text-align: center;">
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Sometimes I joke that to conservatives, the solution to every problem is to cut taxes; to liberals, the solution to every problem is to eat local. Of course, purported panaceas on both the left and right are snake oil, even if peddled by true believers.<br />
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So, <a href="http://greedgreengrains.blogspot.com/2015/01/food-waste-delusions.html" target="_blank">the other day </a>I picked on my own tribe: foodies. To be frank, I have a love-hate relationship with the movement. It has thin and oftentimes paranoid underpinnings. But while a lot of things advocated by the movement are illogical or scientifically baseless (like the dangers of GMOs), the movement also strikes me as mostly harmless, and sometimes even beneficial. There are some important and disastrous exceptions, like the movement to kill <a href="http://www.goldenrice.org/" target="_blank">golden rice,</a> which could save the lives of millions and save millions more from blindness. <br />
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But for the most part, the movement to "eat local," and all it's offspring, doesn't strike me as particularly harmful. Here in Hawai'i there are delusional ideas that we ought to stop importing food and go back to the traditional ways of living off taro. Obviously this isn't going to happen. Even though they banned GMOs on some of our islands, exceptions were made for key crops actually grown. Still, the movement does seem to be strong enough to help protect cultural heritage, an important public good. It also cultivates a food culture that breeds great restaurants and fresh local produce. After all, wasn't the whole movement inspired by my former Berkeley neighbor, restaurant extraordinaire, <a href="http://www.chezpanisse.com/about/alice-waters/" target="_blank">Alice Waters</a>? (I never met her, but lived two doors down, in an in-law studio during grad school) The movement isn't going to save the planet or feed the world, but it sure makes my privileged little world a lot nicer and a little healthier.<br />
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What about delusions on the right? Front and center would be climate change denialism. Not far behind would be anti-Keynesianism, or the austerity movement. As Paul Krugman r<a href="http://krugman.blogs.nytimes.com/2014/04/07/asymmetric-stupidity/" target="_blank">eminds us every day</a>, quite persuasively in my view, these delusions have hardly been harmless. And in contrast to foodies, the radical right has a whole lot of power, controlling both houses of Congress and rich backing by Wall Street, the Koch brothers and friends. <br />
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So, while reasoning and herd behavior on both extremes seems equally delusional at times, the delusional right strikes me as more destructive and much more powerful.<br />
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But then, speaking honestly, I identify more with the left than the right. So am I being too soft on the lefties? I don't think so, but feel free to weigh in. I think the lefty culture tends to be a bit more introspective by nature than the right. The right doesn't tend to tease their own quite like I did the other day, not without swift excommunication. And I think lefties, by their nature, are a little bit more susceptible to evidence and persuasion. They also, by nature, possess visceral independence and eschew the party line. Maybe that's what I was doing the other day.<br />
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Given the real political asymmetry here, I do feel a little guilty for picking on foodies. But not too much. I don't really want to go out and do a lot of research on the topic of food waste to prove how silly this stuff is, or to prove that while GMOs can have some undesirable side effects they aren't Frankenfoods. For the most part this is a fight that isn't worth fighting. I figure it's better to focus on the things that really do matter. <br />
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But I figured it was worth a blog post, because I think we all do better when we eschew our innate tendency toward tribalism, try to figure out what's really going on, and find the most pragmatic solutions to real problems.<br />
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Michael Robertshttp://www.blogger.com/profile/16455035518968529794noreply@blogger.com12tag:blogger.com,1999:blog-3780641321580065916.post-68770099334365310202015-01-28T21:18:00.000-05:002015-01-29T00:08:05.398-05:00Food Waste Delusions<div class="separator" style="clear: both; text-align: center;">
<a href="http://www.biocycle.net/wp-content/uploads/2013/01/47.jpg" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" src="http://www.biocycle.net/wp-content/uploads/2013/01/47.jpg" height="301" width="400" /></a></div>
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A couple months ago the New York Times convened a conference "<a href="http://www.nytfoodfortomorrow.com/" target="_blank">Food for Tomorrow: Farm Better. Eat Better. Feed the World</a>." Keynotes predictably included Mark Bittman and Michael Pollan. It featured many food movement activists, famous chefs, and a whole lot of journalists. Folks talked about how we need to farm more sustainably, waste less food, eat more healthfully and get policies in place that stop subsidizing unhealthy food and instead subsidize healthy food like broccoli.<br />
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Sounds good, yes? If you're reading this, I gather you're familiar with the usual refrain of the food movement. They rail against GMOs, large farms, processed foods, horrid conditions in confined livestock operations, and so on. They rally in favor of small local farms who grow food organically, free-range antibiotic free livestock, diversified farms, etc. These are yuppies who, like me, like to shop at Whole Foods and frequent farmers' markets. </div>
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This has been a remarkably successful movement. I love how easy it has become to find healthy good eats, bread with whole grains and less sugar, and the incredible variety and quality of fresh herbs, fruits, vegetables and meat. Whole <strike>Paycheck</strike> Foods Market has proliferated and profited wildly. Even Walmart is getting into the organic business, putting some competitive pressure on Whole Foods. (Shhhh! --<a href="http://www.nytimes.com/2011/12/31/science/earth/questions-about-organic-produce-and-sustainability.html" target="_blank">organic isn't necessarily what people might think it is</a>.)</div>
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This is all great stuff for rich people like us. And, of course, profits. It's good for Bittman's and Pollan's book sales and speaking engagements. But is any of this really helping to change the way food is produced and consumed by the world's 99%? Is it making the world greener or more sustainable? Will any of it help to feed the world in the face of climate change?</div>
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Um, no. </div>
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Sadly, there were few experts in attendance that could shed scientific or pragmatic light on the issues. And not a single economist or true policy wonk in sight. Come on guys, couldn't you have at least invited Ezra Klein or Brad Plummer? These foodie journalists at least have some sense of incentives and policy. Better, of course, would be to have some real agricultural economists who actually know something about large-scale food production and policies around the world. Yeah, I know: BORING!</div>
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About agricultural polices: there are a lot of really bad ones, and replacing them with good policies might help. But a lot less than you might think from listening to foodies. And, um, we do subsidize broccoli and other vegetables, fruits, and nuts. Just look at the water projects in the West. </div>
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Let me briefly take on one issue du jour: food waste. We throw away a <a href="http://www.washingtonpost.com/blogs/wonkblog/wp/2012/08/22/how-food-actually-gets-wasted-in-the-united-states/" target="_blank">heck of a lot of food</a> in this country, even more than in other developed countries. Why? I'd argue that it's because food is incredibly cheap in this country relative to our incomes. We are the world's bread basket. No place can match California productivity in fruit, vegetables and nuts. And no place can match the Midwest's productivity in grains and legumes. All of this comes from remarkable coincidence of climate, geography and soils, combined with sophisticated technology and gigantic (subsidized) canal and irrigation systems in the West. </div>
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Oh, we're fairly rich too. </div>
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Put these two things together and, despite our waste, we consume more while <a href="http://www.ers.usda.gov/data-products/food-expenditures.aspx" target="_blank">spending less on food than any other country</a>. Isn't that a good thing? Europeans presumably waste (a little) less because food is more scarce there, so people are more careful and less picky about what they eat. Maybe it isn't a coincidence that they're skinnier, too.</div>
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What to do? </div>
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First, it's important to realize that there are benefits to food waste. It basically means we get to eat very high quality food and can almost always find what we want where and when we want it. That quality and convenience comes at a cost of waste. That's what people are willing to pay for. </div>
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If anything, the foodism probably accentuates preference for high quality, which in turn probably increases waste. The food I see Mark Bittman prepare is absolutely lovely, and that's what I want. Don't you?</div>
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Second, let's suppose we implemented a policy that would somehow eliminate a large portion of the waste. What would happen? Well, this would increase the supply of food even more. And sinse we have so much already, and demand for food is very inelastic, prices would fall even lower than they are already. And the temptation to substitute toward higher quality--and thus waste more food--would be greater still. </div>
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Could the right policies help? Well, maybe. A little. The important thing here is to have a goal besides simply eliminating waste. Waste itself isn't problem. It's not an externality like pollution. That goal might be providing food for homeless or low income families. Modest incentive payments plus tax breaks might entice more restaurants, grocery stores and others to give food that might be thrown out to people would benefit from it. This kind of thing happens already and it probably could be done on a larger scale. Even so, we're still going to have a lot of waste, and that's not all bad. </div>
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What about correcting the bad policies already in place? Well, water projects in the West are mainly sunk costs. That happened a long time ago, and water rights, as twisted as they may be, are more or less cemented in the complex legal history. Today, traditional commodity program support mostly takes the form of subsidized crop insurance, <a href="http://greedgreengrains.blogspot.com/2013/11/can-crop-rotations-cure-dead-zones.html" target="_blank">which is likely causing some problems</a>. The biggest distortions could likely be corrected with simple, thoughtful policy tweaks, like charging higher insurance premiums to farmers who plant corn after corn instead of corn after soybeans. But mostly it just hands cash (unjustly, perhaps) to farmers and landowners. The odds that politicians will stop handing cash to farmers is about as likely as Senator James Inhofe embracing a huge carbon tax. Not gonna happen.</div>
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But don't worry too much. If food really does get scarce and prices spike, waste will diminish, because poorer hungry people will be less picky about what they eat.</div>
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Sorry for being so hard on the foodies. While hearts and forks are in the right places, obviously I think most everything they say and write is naive. Still, I think the movement might actually do some good. I like to see people interested in food and paying more attention to agriculture. Of course I like all the good eats. And I think there are some almost reasonable things being said about what's healthy and not (sugar and too much red meat are bad), even if what's healthy has little to do with any coherent strategy for improving environmental quality or feeding the world. </div>
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But perhaps the way to change things is to first get everyones' attention, and I think foodies are doing that better than I ever could.</div>
Michael Robertshttp://www.blogger.com/profile/16455035518968529794noreply@blogger.com18tag:blogger.com,1999:blog-3780641321580065916.post-41885001802183766212015-01-17T18:12:00.003-05:002015-01-17T18:12:34.343-05:00The Hottest Year on Record, But Not in the Corn BeltHere's <a href="http://www.nytimes.com/2015/01/17/science/earth/2014-was-hottest-year-on-record-surpassing-2010.html">Justin Gillis </a>in his usual fine reporting of climate issues, and the map below from NOAA, via the New York Times.<br />
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgsHrj1zm2pt_-hAzxHXiZ2ORzGfwkH0jmTjXOrfoZWnNtyJVHZA6-sMyTQdLw9_XW9Ojnk5eg5_VxtwjXdu3Vvce0FL7-Je6ljCTjrtOL8iQLMqhWHNmeVPtObmI7mDXbJapAxgy9gCYE/s1600/warmestYear2014.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgsHrj1zm2pt_-hAzxHXiZ2ORzGfwkH0jmTjXOrfoZWnNtyJVHZA6-sMyTQdLw9_XW9Ojnk5eg5_VxtwjXdu3Vvce0FL7-Je6ljCTjrtOL8iQLMqhWHNmeVPtObmI7mDXbJapAxgy9gCYE/s1600/warmestYear2014.png" height="612" width="640" /></a></div>
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Note the "warming hole" over the Eastern U.S., especially the upper Midwest, the all important corn belt region. We had a bumper crop this year, and that's because while most of the world was remarkably warm, the corn belt was remarkably cool, especially in summer.<br />
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Should we expect the good fortune to continue? I honestly don't know...Michael Robertshttp://www.blogger.com/profile/16455035518968529794noreply@blogger.com0tag:blogger.com,1999:blog-3780641321580065916.post-48869731609097108502015-01-10T16:08:00.001-05:002015-01-11T17:16:41.872-05:00Searching for critical thresholds in temperature effects<div class="separator" style="clear: both; text-align: left;">
<b>Update 2: </b>Okay, I think it's fixed.</div>
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<b>Update:</b> I just realized the code posted badly. I don't know why. It looks good in the cross-post at <a href="http://www.g-feed.com/2015/01/searching-for-critical-thresholds-in.html" target="_blank">G-FEED</a>. I'll try to fix.</div>
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgmYmR6SpMPQDFXDwo8WS8-3L5UNvqDX88Mx5CNKz3QDZsP1fJ0RYAiS7gWZozjONxEA05d0BMj0mHvIQ68TOP-sKTMj9pdFXb-ydwD9gWyO6EAY4_l22A675lAYuuoEywuf__V1Ur-Sub7/s1600/CornFitAll.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgmYmR6SpMPQDFXDwo8WS8-3L5UNvqDX88Mx5CNKz3QDZsP1fJ0RYAiS7gWZozjONxEA05d0BMj0mHvIQ68TOP-sKTMj9pdFXb-ydwD9gWyO6EAY4_l22A675lAYuuoEywuf__V1Ur-Sub7/s1600/CornFitAll.png" height="316" width="400" /></a></div>
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If google scholar is any guide, my <a href="http://www.pnas.org/content/106/37/15594" target="_blank">2009 paper with Wolfram Schlenker</a> on the nonlinear effects of temperature on crop outcomes has had more impact than anything else I've been involved with.<br />
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A funny thing about that paper: Many reference it, and often claim that they are using techniques that follow that paper. But in the end, as far as I can tell, very few seem to actually have read through the finer details of that paper or try to implement the techniques in other settings. Granted, people have done similar things that seem inspired by that paper, but not quite the same. Either our explication was too ambiguous or people don't have the patience to fully carry out the technique, so they take shortcuts. Here I'm going to try to make it easier for folks to do the real thing.<br />
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So, how does one go about estimating the relationship plotted in the graph above?<br />
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Here's the essential idea: averaging temperatures over time or space can dilute or obscure the effect of extremes. Still, we need to aggregate, because outcomes are not measured continuously over time and space. In agriculture, we have annual yields at the county or larger geographic level. So, there are two essential pieces: (1) estimating the full distribution of temperatures of exposure (crops, people, or whatever) and (2) fitting a curve through the whole distribution.<br />
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The first step involves constructing the distribution of weather. This was most of the hard work in that paper, but it has since become easier, in part because finely gridded daily weather is available (see <a href="http://www.prism.oregonstate.edu/recent/" target="_blank">PRISM</a>) and in part because Wolfram has made some <a href="http://www.wolfram-schlenker.com/dailyData.html" target="_blank">STATA code</a> available. Here I'm going to supplement Wolfram's code with a little bit of R code. Maybe the other G-FEEDers can chime in and explain how to do this stuff more easily.<br />
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<b><i>First step</i></b>: find some daily, gridded weather data. The finer scale the better. But keep in mind that data errors can cause serious attenuation bias. For the lower 48 since 1981, the PRISM data above is very good. Otherwise, you might have to do your own interpolation between weather stations. If you do this, you'll want to take some care in dealing with moving weather stations, elevation and microclimatic variations. Even better, cross-validate interpolation techniques by leaving one weather station out at a time and seeing how well the method works. Knowing the size of the measurement error can also help correcting bias. Almost no one does this, probably because it's very time consuming... Measurement error in weather data creates very serious problems (see <a href="https://www.aeaweb.org/articles.php?doi=10.1257/aer.102.7.3749" target="_blank">here</a> and <a href="https://www.google.com/search?client=safari&rls=en&q=g-feed+schlenker+massetti&ie=UTF-8&oe=UTF-8" target="_blank">here</a>)<br />
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<b><i>Second step</i></b>: estimate the distribution of temperatures over time and space from the gridded daily weather. There are a few ways of doing this. We've typically <a href="http://www.ipm.ucdavis.edu/WEATHER/ddconcepts.html" target="_blank">fit a sine curve between the minimum and maximum temperatures</a> to approximate the time at each degree in each day in each grid, and then aggregate over grids in a county and over all days in the growing season. Here are a couple R functions to help you do this:<br />
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<pre class="r geshifilter-R" style="background-color: #f8f8f8; color: #222222; font-size: 12px; line-height: 15px; overflow: auto; padding: 10px; word-wrap: normal;"><span style="color: #666666; font-style: italic; margin: 0px; padding: 0px;"># This function estimates time (in days) when temperature is</span>
<span style="color: #666666; font-style: italic; margin: 0px; padding: 0px;"># between t0 and t1 using sine curve interpolation. tMin and</span>
<span style="color: #666666; font-style: italic; margin: 0px; padding: 0px;"># tMax are vectors of day minimum and maximum temperatures over</span>
<span style="color: #666666; font-style: italic; margin: 0px; padding: 0px;"># range of interest. The sum of time in the interval is returned.</span>
<span style="color: #666666; font-style: italic; margin: 0px; padding: 0px;"># noGrids is number of grids in area aggregated, each of which </span>
<span style="color: #666666; font-style: italic; margin: 0px; padding: 0px;"># should have exactly the same number of days in tMin and tMax</span>
days.in.range <span style="margin: 0px; padding: 0px;"><- span=""> <a href="http://inside-r.org/r-doc/base/function" style="color: #0077df; margin: 0px; padding: 0px; text-decoration: none;"><span style="color: #003399; font-weight: bold; margin: 0px; padding: 0px;">function</span></a><span style="color: #009900; margin: 0px; padding: 0px;">(</span> t0<span style="color: #339933; margin: 0px; padding: 0px;">,</span> t1 <span style="color: #339933; margin: 0px; padding: 0px;">,</span> tMin<span style="color: #339933; margin: 0px; padding: 0px;">,</span> tMax<span style="color: #339933; margin: 0px; padding: 0px;">,</span> noGrids <span style="color: #009900; margin: 0px; padding: 0px;">)</span> <span style="color: #009900; margin: 0px; padding: 0px;">{</span>
n = <a href="http://inside-r.org/r-doc/base/length" style="color: #0077df; margin: 0px; padding: 0px; text-decoration: none;"><span style="color: #003399; font-weight: bold; margin: 0px; padding: 0px;">length</span></a><span style="color: #009900; margin: 0px; padding: 0px;">(</span>tMin<span style="color: #009900; margin: 0px; padding: 0px;">)</span>
t0 = <a href="http://inside-r.org/r-doc/base/rep" style="color: #0077df; margin: 0px; padding: 0px; text-decoration: none;"><span style="color: #003399; font-weight: bold; margin: 0px; padding: 0px;">rep</span></a><span style="color: #009900; margin: 0px; padding: 0px;">(</span>t0<span style="color: #339933; margin: 0px; padding: 0px;">,</span> n<span style="color: #009900; margin: 0px; padding: 0px;">)</span>
t1 = <a href="http://inside-r.org/r-doc/base/rep" style="color: #0077df; margin: 0px; padding: 0px; text-decoration: none;"><span style="color: #003399; font-weight: bold; margin: 0px; padding: 0px;">rep</span></a><span style="color: #009900; margin: 0px; padding: 0px;">(</span>t1<span style="color: #339933; margin: 0px; padding: 0px;">,</span> n<span style="color: #009900; margin: 0px; padding: 0px;">)</span>
t0<span style="color: #009900; margin: 0px; padding: 0px;">[</span>t0 <span style="margin: 0px; padding: 0px;"><</span> tMin<span style="color: #009900; margin: 0px; padding: 0px;">]</span> = tMin<span style="color: #009900; margin: 0px; padding: 0px;">[</span>t0 <span style="margin: 0px; padding: 0px;"><</span> tMin<span style="color: #009900; margin: 0px; padding: 0px;">]</span>
t1<span style="color: #009900; margin: 0px; padding: 0px;">[</span>t1 <span style="margin: 0px; padding: 0px;">></span> tMax<span style="color: #009900; margin: 0px; padding: 0px;">]</span> = tMax<span style="color: #009900; margin: 0px; padding: 0px;">[</span>t1 <span style="margin: 0px; padding: 0px;">></span> tMax<span style="color: #009900; margin: 0px; padding: 0px;">]</span>
u = <a href="http://inside-r.org/r-doc/base/function" style="color: #0077df; margin: 0px; padding: 0px; text-decoration: none;"><span style="color: #003399; font-weight: bold; margin: 0px; padding: 0px;">function</span></a><span style="color: #009900; margin: 0px; padding: 0px;">(</span>z<span style="color: #339933; margin: 0px; padding: 0px;">,</span> ind<span style="color: #009900; margin: 0px; padding: 0px;">)</span> <span style="color: #009900; margin: 0px; padding: 0px;">(</span>z<span style="color: #009900; margin: 0px; padding: 0px;">[</span>ind<span style="color: #009900; margin: 0px; padding: 0px;">]</span> <span style="margin: 0px; padding: 0px;">-</span> tMin<span style="color: #009900; margin: 0px; padding: 0px;">[</span>ind<span style="color: #009900; margin: 0px; padding: 0px;">]</span><span style="color: #009900; margin: 0px; padding: 0px;">)</span><span style="margin: 0px; padding: 0px;">/</span><span style="color: #009900; margin: 0px; padding: 0px;">(</span>tMax<span style="color: #009900; margin: 0px; padding: 0px;">[</span>ind<span style="color: #009900; margin: 0px; padding: 0px;">]</span> <span style="margin: 0px; padding: 0px;">-</span> tMin<span style="color: #009900; margin: 0px; padding: 0px;">[</span>ind<span style="color: #009900; margin: 0px; padding: 0px;">]</span><span style="color: #009900; margin: 0px; padding: 0px;">)</span>
outside = t0 <span style="margin: 0px; padding: 0px;">></span> tMax <span style="margin: 0px; padding: 0px;">|</span> t1 <span style="margin: 0px; padding: 0px;"><</span> tMin
inside = <span style="margin: 0px; padding: 0px;">!</span>outside
time.at.range = <span style="color: #009900; margin: 0px; padding: 0px;">(</span> <span style="color: #cc66cc; margin: 0px; padding: 0px;">2</span><span style="margin: 0px; padding: 0px;">/</span>pi <span style="color: #009900; margin: 0px; padding: 0px;">)</span><span style="margin: 0px; padding: 0px;">*</span><span style="color: #009900; margin: 0px; padding: 0px;">(</span> <a href="http://inside-r.org/r-doc/base/asin" style="color: #0077df; margin: 0px; padding: 0px; text-decoration: none;"><span style="color: #003399; font-weight: bold; margin: 0px; padding: 0px;">asin</span></a><span style="color: #009900; margin: 0px; padding: 0px;">(</span>u<span style="color: #009900; margin: 0px; padding: 0px;">(</span>t1<span style="color: #339933; margin: 0px; padding: 0px;">,</span>inside<span style="color: #009900; margin: 0px; padding: 0px;">)</span><span style="color: #009900; margin: 0px; padding: 0px;">)</span> <span style="margin: 0px; padding: 0px;">-</span> <a href="http://inside-r.org/r-doc/base/asin" style="color: #0077df; margin: 0px; padding: 0px; text-decoration: none;"><span style="color: #003399; font-weight: bold; margin: 0px; padding: 0px;">asin</span></a><span style="color: #009900; margin: 0px; padding: 0px;">(</span>u<span style="color: #009900; margin: 0px; padding: 0px;">(</span>t0<span style="color: #339933; margin: 0px; padding: 0px;">,</span>inside<span style="color: #009900; margin: 0px; padding: 0px;">)</span><span style="color: #009900; margin: 0px; padding: 0px;">)</span> <span style="color: #009900; margin: 0px; padding: 0px;">)</span>
<a href="http://inside-r.org/r-doc/base/return" style="color: #0077df; margin: 0px; padding: 0px; text-decoration: none;"><span style="color: #003399; font-weight: bold; margin: 0px; padding: 0px;">return</span></a><span style="color: #009900; margin: 0px; padding: 0px;">(</span> <a href="http://inside-r.org/r-doc/base/sum" style="color: #0077df; margin: 0px; padding: 0px; text-decoration: none;"><span style="color: #003399; font-weight: bold; margin: 0px; padding: 0px;">sum</span></a><span style="color: #009900; margin: 0px; padding: 0px;">(</span>time.at.range<span style="color: #009900; margin: 0px; padding: 0px;">)</span><span style="margin: 0px; padding: 0px;">/</span>noGrids <span style="color: #009900; margin: 0px; padding: 0px;">)</span>
<span style="color: #009900; margin: 0px; padding: 0px;">}</span>
<span style="color: #666666; font-style: italic; margin: 0px; padding: 0px;"># This function calculates all 1-degree temperature intervals for </span>
<span style="color: #666666; font-style: italic; margin: 0px; padding: 0px;"># a given row (fips-year combination). Note that nested objects</span>
<span style="color: #666666; font-style: italic; margin: 0px; padding: 0px;"># must be defined in the outer environment.</span>
aFipsYear = <a href="http://inside-r.org/r-doc/base/function" style="color: #0077df; margin: 0px; padding: 0px; text-decoration: none;"><span style="color: #003399; font-weight: bold; margin: 0px; padding: 0px;">function</span></a><span style="color: #009900; margin: 0px; padding: 0px;">(</span>z<span style="color: #009900; margin: 0px; padding: 0px;">)</span><span style="color: #009900; margin: 0px; padding: 0px;">{</span>
afips = Trows<span style="margin: 0px; padding: 0px;">$</span>fips<span style="color: #009900; margin: 0px; padding: 0px;">[</span>z<span style="color: #009900; margin: 0px; padding: 0px;">]</span>
ayear = Trows<span style="margin: 0px; padding: 0px;">$</span>year<span style="color: #009900; margin: 0px; padding: 0px;">[</span>z<span style="color: #009900; margin: 0px; padding: 0px;">]</span>
tempDat = w<span style="color: #009900; margin: 0px; padding: 0px;">[</span> w<span style="margin: 0px; padding: 0px;">$</span>fips <span style="margin: 0px; padding: 0px;">==</span> afips <span style="margin: 0px; padding: 0px;">&</span> w<span style="margin: 0px; padding: 0px;">$</span>year<span style="margin: 0px; padding: 0px;">==</span>ayear<span style="color: #339933; margin: 0px; padding: 0px;">,</span> <span style="color: #009900; margin: 0px; padding: 0px;">]</span>
Tvect = <a href="http://inside-r.org/r-doc/base/c" style="color: #0077df; margin: 0px; padding: 0px; text-decoration: none;"><span style="color: #003399; font-weight: bold; margin: 0px; padding: 0px;">c</span></a><span style="color: #009900; margin: 0px; padding: 0px;">(</span><span style="color: #009900; margin: 0px; padding: 0px;">)</span>
<span style="color: black; font-weight: bold; margin: 0px; padding: 0px;">for</span> <span style="color: #009900; margin: 0px; padding: 0px;">(</span> k <span style="color: black; font-weight: bold; margin: 0px; padding: 0px;">in</span> <span style="color: #cc66cc; margin: 0px; padding: 0px;">1</span><span style="margin: 0px; padding: 0px;">:</span>nT <span style="color: #009900; margin: 0px; padding: 0px;">)</span> Tvect<span style="color: #009900; margin: 0px; padding: 0px;">[</span>k<span style="color: #009900; margin: 0px; padding: 0px;">]</span> = days.in.range<span style="color: #009900; margin: 0px; padding: 0px;">(</span>
t0 = T<span style="color: #009900; margin: 0px; padding: 0px;">[</span>k<span style="color: #009900; margin: 0px; padding: 0px;">]</span><span style="margin: 0px; padding: 0px;">-</span><span style="color: #cc66cc; margin: 0px; padding: 0px;">0.5</span><span style="color: #339933; margin: 0px; padding: 0px;">,</span>
t1 = T<span style="color: #009900; margin: 0px; padding: 0px;">[</span>k<span style="color: #009900; margin: 0px; padding: 0px;">]</span><span style="margin: 0px; padding: 0px;">+</span><span style="color: #cc66cc; margin: 0px; padding: 0px;">0.5</span><span style="color: #339933; margin: 0px; padding: 0px;">,</span>
tMin = tempDat<span style="margin: 0px; padding: 0px;">$</span>tMin<span style="color: #339933; margin: 0px; padding: 0px;">,</span>
tMax = tempDat<span style="margin: 0px; padding: 0px;">$</span>tMax<span style="color: #339933; margin: 0px; padding: 0px;">,</span>
noGrids = <a href="http://inside-r.org/r-doc/base/length" style="color: #0077df; margin: 0px; padding: 0px; text-decoration: none;"><span style="color: #003399; font-weight: bold; margin: 0px; padding: 0px;">length</span></a><span style="color: #009900; margin: 0px; padding: 0px;">(</span> <a href="http://inside-r.org/r-doc/base/unique" style="color: #0077df; margin: 0px; padding: 0px; text-decoration: none;"><span style="color: #003399; font-weight: bold; margin: 0px; padding: 0px;">unique</span></a><span style="color: #009900; margin: 0px; padding: 0px;">(</span>tempDat<span style="margin: 0px; padding: 0px;">$</span>gridNumber<span style="color: #009900; margin: 0px; padding: 0px;">)</span> <span style="color: #009900; margin: 0px; padding: 0px;">)</span>
<span style="color: #009900; margin: 0px; padding: 0px;">)</span>
Tvect
<span style="color: #009900; margin: 0px; padding: 0px;">}</span><!-----><!-----><!-----></-></span></pre>
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The first function estimates time in a temperature interval using the sine curve method. The second function calls the first function, looping through a bunch of 1-degree temperature intervals, defined outside the function. A nice thing about R is that you can be sloppy and write functions like this that use objects defined outside of the environment. A nice thing about writing the function this way is that it's amenable to easy parallel processing (look up 'foreach' and 'doParallel' packages).<br />
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Here are the objects defined outside the second function:</div>
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<div style="text-indent: -4px;">
<pre class="r geshifilter-R" style="background-color: #f8f8f8; color: #222222; font-size: 12px; line-height: 15px; overflow: auto; padding: 10px; text-indent: 0px; word-wrap: normal;">w <span style="color: #666666; font-style: italic; margin: 0px; padding: 0px;"># weather data that includes a "fips" county ID, "gridNumber", "tMin" and "tMax".</span>
<span style="color: #666666; font-style: italic; margin: 0px; padding: 0px;"># rows of w span all days, fips, years and grids being aggregated</span>
tempDat <span style="color: #666666; font-style: italic; margin: 0px; padding: 0px;"># pulls the particular fips/year of w being aggregated.</span>
Trows <span style="color: #666666; font-style: italic; margin: 0px; padding: 0px;"># = expand.grid( fips.index, year.index ), rows span the aggregated data set</span>
T <span style="color: #666666; font-style: italic; margin: 0px; padding: 0px;"># a vector of integer temperatures. I'm approximating the distribution with </span>
<span style="color: #666666; font-style: italic; margin: 0px; padding: 0px;"># the time in each degree in the index T</span></pre>
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<div style="text-indent: 0px;">
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To build a dataset call the second function above for each fips-year in Trows and rbind the results.</div>
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<b style="font-style: italic;">Third step</b>: To estimate a smooth function through the whole distribution of temperatures, you simply need to choose your functional form, linearize it, and then cross-multiply the design matrix with the temperature distribution. For example, suppose you want to fit a cubic polynomial and your temperature bins that run from from 0 to 45 C. The design matrix would be:</div>
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<span style="font-family: CMTT8;">D = [ 0 0 0 </span><br />
<span style="font-family: CMTT8;"> 1 1 1</span><br />
<span style="font-family: CMTT8;"> 2 4 8</span><br />
<span style="font-family: CMTT8;"> ...</span><br />
<span style="font-family: CMTT8;"> 45 2025 91125]</span><br />
<span style="font-family: CMTT8;"><br /></span>
<span style="font-family: CMTT8;">These days, you might want to do something fancier than a basic polynomial, say a spline. It's up to you. I really like restricted cubic splines, although they can over smooth around sharp kinks, which we may have in this case. We have found piecewise linear works best for predicting out of sample (hence all of our references to degree days). If you want something really flexible, just make D and identity matrix, which effectively becomes a dummy variable for each temperature bin (the step function in the figure). Whatever you choose, you will have a (T x K) design matrix, with K being the number of parameters in your functional form and T=46 </span><span style="font-family: CMTT8;">(in this case) </span><span style="font-family: CMTT8;">temperature bins. </span><br />
<span style="font-family: CMTT8;"><br /></span>
<span style="font-family: CMTT8;">To get your covariates for your regression, simply cross multiply D by your frequency distribution. Here's a simple example with restricted cubic splines:</span><br />
<span style="font-family: CMTT8;"><br /></span>
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<pre class="r geshifilter-R" style="background-color: #f8f8f8; color: #222222; font-size: 12px; line-height: 15px; overflow: auto; padding: 10px; word-wrap: normal;"><a href="http://inside-r.org/r-doc/base/library" style="color: #0077df; margin: 0px; padding: 0px; text-decoration: none;"><span style="color: #003399; font-weight: bold; margin: 0px; padding: 0px;">library</span></a><span style="color: #009900; margin: 0px; padding: 0px;">(</span><a href="http://inside-r.org/packages/cran/Hmisc" style="color: #0077df; margin: 0px; padding: 0px; text-decoration: none;">Hmisc</a><span style="color: #009900; margin: 0px; padding: 0px;">)</span>
DMat <span style="margin: 0px; padding: 0px;">= rcspline.eval<span style="color: #009900; margin: 0px; padding: 0px;">(</span><span style="color: #cc66cc; margin: 0px; padding: 0px;">0</span><span style="margin: 0px; padding: 0px;">:</span><span style="color: #cc66cc; margin: 0px; padding: 0px;">45</span><span style="color: #009900; margin: 0px; padding: 0px;">)</span>
XMat <span style="margin: 0px; padding: 0px;">= <a href="http://inside-r.org/r-doc/base/as.matrix" style="color: #0077df; margin: 0px; padding: 0px; text-decoration: none;"><span style="color: #003399; font-weight: bold; margin: 0px; padding: 0px;">as.matrix</span></a><span style="color: #009900; margin: 0px; padding: 0px;">(</span>TemperatureData<span style="color: #009900; margin: 0px; padding: 0px;">[</span><span style="color: #339933; margin: 0px; padding: 0px;">,</span><span style="color: #cc66cc; margin: 0px; padding: 0px;">3</span><span style="margin: 0px; padding: 0px;">:</span><span style="color: #cc66cc; margin: 0px; padding: 0px;">48</span><span style="color: #009900; margin: 0px; padding: 0px;">]</span><span style="color: #009900; margin: 0px; padding: 0px;">)</span><span style="margin: 0px; padding: 0px;">%*%</span>DMat
fit =<span style="margin: 0px; padding: 0px;"> <a href="http://inside-r.org/r-doc/stats/lm" style="color: #0077df; margin: 0px; padding: 0px; text-decoration: none;"><span style="color: #003399; font-weight: bold; margin: 0px; padding: 0px;">lm</span></a><span style="color: #009900; margin: 0px; padding: 0px;">(</span>yield<span style="margin: 0px; padding: 0px;">~</span>XMat<span style="color: #339933; margin: 0px; padding: 0px;">,</span> <a href="http://inside-r.org/r-doc/utils/data" style="color: #0077df; margin: 0px; padding: 0px; text-decoration: none;"><span style="color: #003399; font-weight: bold; margin: 0px; padding: 0px;">data</span></a>=regData<span style="color: #009900; margin: 0px; padding: 0px;">)</span>
<a href="http://inside-r.org/r-doc/base/summary" style="color: #0077df; margin: 0px; padding: 0px; text-decoration: none;"><span style="color: #003399; font-weight: bold; margin: 0px; padding: 0px;">summary</span></a><span style="color: #009900; margin: 0px; padding: 0px;">(</span>fit<span style="color: #009900; margin: 0px; padding: 0px;">)</span><!-----><!-----><!-----><!-----><!-----><!-----><!-----><!-----><!-----><!-----><!-----><!-----><!-----><!-----><!-----></span><!-----><!-----><!-----><!-----><!-----><!-----><!-----><!-----><!-----><!-----><!-----><!-----><!-----><!-----><!-----></span><!-----><!-----><!-----><!-----><!-----><!-----><!-----><!-----><!-----><!-----><!-----><!-----><!-----><!-----><!-----></span></pre>
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Note that regData has the crop outcomes. Also note that we generally include other covariates, like total precipitation during the season, county fixed effects, time trends, etc. All of that is pretty standard. I'm leaving that out to focus on the nonlinear temperature bit. </div>
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Anyway, I think this is a cool and fairly simple technique, even if some of the data management can be cumbersome. I hope more people use it instead of just fitting to shares of days with each maximum or mean temperature, which is what most people following our work tend to do. </div>
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In the end, all of this detail probably doesn't make a huge difference for predictions. But it can make estimates more precise, and confidence intervals stronger. And I think that precision also helps in pinning down mechanisms. For example, I think this precision helped us to figure out that <a href="http://ajae.oxfordjournals.org/content/95/2/236.extract" target="_blank">VPD</a> and <a href="http://www.nature.com/nclimate/journal/v3/n5/full/nclimate1832.html" target="_blank">associated drought</a> was a key factor underlying observed effects of extreme heat.</div>
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<!----->Michael Robertshttp://www.blogger.com/profile/16455035518968529794noreply@blogger.com0tag:blogger.com,1999:blog-3780641321580065916.post-37146531387896731482014-10-04T05:42:00.000-04:002014-10-06T13:04:55.297-04:00Agricultural Economics gets Politico<div class="separator" style="clear: both; text-align: center;">
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<b>Update:</b> For the record, I'm actually not against Federal crop insurance. Like Obamacare, I generally favor it. But the subsidies are surely <a href="http://www.choicesmagazine.org/choices-magazine/theme-articles/3rd-quarter-2014/multiple-peril-crop-insurance" target="_blank">much larger </a>than they need to be for maximum efficiency. And I think premiums could likely be better matched to risk, and that such adjustments would be good for both taxpayers and <a href="http://greedgreengrains.blogspot.com/2013/11/can-crop-rotations-cure-dead-zones.html" target="_blank">the environment</a>.<br />
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgyVtnix3vGsCGJ4HonnEVxNmB265tLYg6yDMO2pdusf8W-WQJZDwNwrQfmILcvY7C1ivov1qXpWxUeb2gVf3FCCItzs3b971XCTMP4gF4Ug20kzwhmPTDMtTS2Q2Mv-g9kNwXxZNAkXWO8/s640/stah_c131022.jpg" imageanchor="1" style="margin-left: 1em; margin-right: 1em; text-align: center;"><img border="0" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgyVtnix3vGsCGJ4HonnEVxNmB265tLYg6yDMO2pdusf8W-WQJZDwNwrQfmILcvY7C1ivov1qXpWxUeb2gVf3FCCItzs3b971XCTMP4gF4Ug20kzwhmPTDMtTS2Q2Mv-g9kNwXxZNAkXWO8/s640/stah_c131022.jpg" height="480" width="640" /></a><br />
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Wow. Frumpy agricultural economics goes <a href="http://www.politico.com/story/2014/10/agriculture-economists-111560.html" target="_blank">Politico</a>!<br />
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Actually, it's kind of strange to see a supposedly scandalous article in Politico in which you know almost every person mentioned. </div>
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At issue is the federal crop insurance program. The program has been around a long time, but its scope and size--the range crops and livestock insurable under the program and the degree to which taxpayers subsidize premiums--have grown tremendously over the last 20 years. And the latest farm bill expands the program and its subsidies to grand new heights.</div>
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Nearly all the agricultural economists I know regard the crop insurance program (aka <a href="http://greedgreengrains.blogspot.com/2012/07/obamacare-for-farmers.html" target="_blank">Obamacare for the corn</a>) as overly subsidized. But the issue here is not the subsides but the huge contracts received by agricultural economists moonlighting as well-paid consultants for USDA's Risk Management Agency (RMA), to help RMA design and run the insurance program.</div>
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For full disclosure: I used to work for USDA in the Economic Research Service and did some research on crop insurance. Although, strangely, ties between ERS and RMA are thin to nonexistent. I've met and spoke to both Joe Glauber (USDA's Chief Economist) and Bruce Babcock (a leading professor of agricultural economics at Iowa State) a few times, and know and respect their work. And I used to work at NC State as a colleague of Barry Goodwin's. I also went to Montana State for a master's degree way back, where I took courses from Myles Watts and Joe Attwood, who are mentioned in the article. I know Vince Smith from that time too.<br />
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Perhaps most importantly, some of my <a href="http://www.sciencemag.org/content/344/6183/516.short" target="_blank">recent research</a> uses some rich data resources that we obtained from RMA. But I have never received any monies from RMA. Believe it or not, my interest is in the science, and despite having no vested financial interest in any of it, I have found myself i<a href="http://greedgreengrains.blogspot.com/2010/06/is-corn-becoming-more-or-less-tolerant.html" target="_blank">n the cross hairs of agricultural interests who didn't seem to like my research findings</a>. Anyway, ag econ is a small, small world...<br />
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Okay, disclosures out of the way: What's the big deal here? So ag economists work for RMA, make some nice cash, and then moonlight for the American Enterprise Institute to <a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgyVtnix3vGsCGJ4HonnEVxNmB265tLYg6yDMO2pdusf8W-WQJZDwNwrQfmILcvY7C1ivov1qXpWxUeb2gVf3FCCItzs3b971XCTMP4gF4Ug20kzwhmPTDMtTS2Q2Mv-g9kNwXxZNAkXWO8/s640/stah_c131022.jpg" target="_blank">bash agricultural subsidies</a>. Yeah, there are are conflicts of interest, but it would seem that there are interests on many sides and the opportunistic ag economists in question seem willing to work for all of them. They'll help RMA design crop insurance programs, but that doesn't mean they advocate for the programs or the level of subsidies farmers, insurance companies and program managers receive under them. We observe the opposite.<br />
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I've got some sense of the people involved and their politics. Most of them are pretty hard-core conservative (Babcock may be an exception, not sure), and my sense is that most are unsupportive of agricultural subsidies in general. But none are going to turn down big pay check to try to make the program as efficient as possible. I don't see a scandal here. Really.<br />
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Except, I do kind of wonder why all this money is going to Illinois, Texas and Montana when folks at <a href="http://www.columbia.edu/~ws2162/" target="_blank">Columbia</a>, <a href="http://www2.hawaii.edu/~mjrobert/main/Home.html" target="_blank">Hawai'i</a>, and <a href="https://pangea.stanford.edu/researchgroups/lobelllab/" target="_blank">Stanford</a> could, almost surely, do a much better job for a fraction of taxpayers' cost. With all due respect (and requisite academic modesty--tongue in cheek), I know these guy's work, and I'm confident folks here at <a href="http://www.g-feed.com/" target="_blank">G-FEED</a> could do a much better job. I personally don't need a penny (okay, twist my arm and I'll take a month of summer salary). Just fund a few graduate students and let us use the data for good science.<br />
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Michael Robertshttp://www.blogger.com/profile/16455035518968529794noreply@blogger.com0tag:blogger.com,1999:blog-3780641321580065916.post-5645316838017984422014-08-31T17:57:00.001-04:002014-08-31T17:57:20.761-04:00Commodity Prices: Financialization or Supply and Demand?<div class="separator" style="clear: both; text-align: center;">
<a href="http://theoptionspecialist.com/wp-content/uploads/2012/06/stock-market-crazy2.jpg" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" src="http://theoptionspecialist.com/wp-content/uploads/2012/06/stock-market-crazy2.jpg" height="358" width="640" /></a></div>
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I've often panned the idea that commodity prices have been greatly influenced by so-called financialization---the emergence of tradable commodity price indices and growing participation by Wall Street in commodity futures trading. No, Goldman Sachs did not cause the food and oil-price spikes in recent years. I've had good company in this view. See, for example, <a href="https://www.imf.org/external/np/seminars/eng/2012/commodity/pdf/kilian.pdf" target="_blank">Killian</a>, K<a href="http://www.nber.org/papers/w18951" target="_blank">nittel and Pindyck</a>, <a href="http://krugman.blogs.nytimes.com/2008/03/19/commodity-prices-wonkish/" target="_blank">Krugman</a> (also <a href="http://krugman.blogs.nytimes.com/2008/04/20/commodities-and-speculation-metallic-evidence/" target="_blank">here</a>), <a href="http://econbrowser.com/archives/2011/08/fundamentals_sp" target="_blank">Hamilton</a>, <a href="http://www.farmdoc.illinois.edu/nccc134/conf_2009/pdf/confp16-09.pdf" target="_blank">Irwin and coauthers</a>, and I expect many others.<br />
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I don't deny that Wall Street has gotten deeper into the commodity game, a trend that many connect to <a href="http://www.nber.org/papers/w10595" target="_blank"> Gorton and Rouwenhorst</a> (and much <a href="http://www.jstor.org/stable/4478342" target="_blank">earlier similar findings</a>). But my sense is that commodity prices derive from more-or-less fundamental factors--supply and demand--and fairly reasonable expectations about future supply and demand. Bubbles can happen in commodities, but mainly when there is poor information about supply, demand, trade and inventories. Consider <a href="http://thumbnails-visually.netdna-ssl.com/rice-prices-through-the-2008-food-price-crisis_50291a9e68c73_w1500.png" target="_blank">rice, circa 2008</a>.<br />
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But most aren't thinking about rice. They're thinking about oil.<br />
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The financialization/speculation meme hasn't gone away, and now bigger guns are entering the fray, with some new theorizing and evidence. <br />
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<a href="http://www.nber.org/reporter/2014number2/xiong.html" target="_blank">Xiong theorize</a>s (also see <a href="http://www.nber.org/papers/w19642" target="_blank">Cheng and Xiong</a> and <a href="http://www.nber.org/papers/w16385" target="_blank">Tang and Xiong</a>) that commodity demand might be upward sloping. A tacit implication is that new speculation of higher prices could feed higher demand, leading to even higher prices, and an upward spiral. A commodity price "bubble" could arise without accumulation of inventories, as many of us have argued. Tang and Xiong don't actually write this, but I think some readers may infer it (incorrectly, in my view).<br />
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It is an interesting and counter-intuitive result. After all, The Law of Demand is the first thing everybody learns in Econ 101: holding all else the same, people buy less as price goes up. Tang and Xiong get around this by considering how market participants learn about future supply and demand. Here it's important to realize that commodity consumers are actually businesses that use commodities as inputs into their production process. Think of refineries, food processors, or, further down the chain, shipping companies and airlines. These businesses are trying to read crystal balls about future demand for their final products. Tang and Xiong suppose that commodity futures tell these businesses something about future demand. Higher commodity futures may indicate stronger future demand for their finished, so they buy more raw commodities, not less.<br />
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There's probably some truth to this view. However, it's not clear whether or when demand curves would actually bend backwards. And more pointedly, even if the theory were true, it doesn't really imply any kind of market failure that regulation might ameliorate. Presumably some traders actually have a sense of the factors causing prices to spike: rapidly growing demand in China and other parts of Asia, a bad drought, an oil prospect that doesn't pan out, conflict in the Middle East that might disrupt future oil exports, and so on. Demand shifting out due to reasonable expectations of higher future demand for finished product is not a market failure or the makings of a bubble. I think Tang and Xiong know this, but the context of their reasoning seems to suggest they've uncovered a real anomaly, and I don't think they have. Yes, it would be good to have more and better information about product supply, demand and disposition. But we already knew that.<br />
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What about the new evidence?<br />
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One piece of evidence is that commodity prices have become more correlated with each other, and with stock prices, with a big spike around 2008, and much more so for indexed commodities than off-index commodities. <br />
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhj5HfHdKY21bX-ZAie0WxcLlzMNbImOlLffasK_QY5HCPiT5Ckg9bA70Ao28UtZc_YH9q1byYlE9TPedHMiGGxr5URUp_CB_RgX774n_Oq882j4lInbGIK2NNid0QwDRMBQ-qiZG8W7zO0/s1600/commodityCorrelationTangXiong2012.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhj5HfHdKY21bX-ZAie0WxcLlzMNbImOlLffasK_QY5HCPiT5Ckg9bA70Ao28UtZc_YH9q1byYlE9TPedHMiGGxr5URUp_CB_RgX774n_Oq882j4lInbGIK2NNid0QwDRMBQ-qiZG8W7zO0/s1600/commodityCorrelationTangXiong2012.png" height="340" width="640" /></a></div>
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This spike in correlatedness happens to coincide with the overall spike in commodity prices, especially oil and food commodities. This fact would seem consistent with the idea that aggregate demand growth--real or anticipated--was driving both higher prices and higher correlatedness. This view isn't contrary to Tang and Xiong's theory, or really contrary to any of the other experts I linked to above. And none of this really suggests speculation or financialization has anything to do with it. After all, Wall Street interest in commodities started growing much earlier, between 2004 and 2007, and we don't see much out of the ordinary around that time.<br />
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The observation that common demand factors---mainly China growth pre-2008 and the Great Recession since then---have been driving price fluctuations also helps to explain changing hedging profiles and risk premiums noted by Tang and Xiong and others. When idiosyncratic supply shocks drive commodity price fluctuations (e.g, bad weather), we should expect little correlation with the aggregate economy, and risk premiums should be low, and possibly even negative for critical inputs like oil. But when large demand shocks drive fluctuations, correlatedness becomes positive and so do risk premiums. <br />
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None of this is really contrary to what Tang and Xiong write. But I'm kind of confused about why they see demand growth from China as an alternative explanation for their findings. It all looks the same to me. It all looks like good old fashioned fundamentals.<br />
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Another critical point about correlatedness that Tang and Xiong overlook is the role of ethanol policy. Ethanol started to become serious business around 2007 and going into 2008, making a real if modest contribution to our fuel supply, and drawing a huge share of the all-important US corn crop.<br />
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During this period, even without subsidies, ethanol was competitive with gasoline. Moreover, ethanol concentrations hadn't yet hit 10% blend wall, above which ethanol might damage some standard gasoline engines. So, for a short while, oil and corn were effectively perfect substitutes, and this caused their prices to be highly correlated. Corn prices, in turn, tend to be highly correlated with soybean and wheat prices, since they are substitutes in both production and consumption. <br />
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With ethanol effectively bridging energy and agricultural commodities, we got a big spike in correlatedness. And it had nothing to do with financialization or speculation.<br />
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Note that this link effectively broke shortly thereafter. Once ethanol concentrations hit the blend wall, oil and ethanol went from being nearly perfect substitutes to nearly perfect complements in the production of gasoline. They still shared some aggregate demand shocks, but oil-specific supply shocks and some speculative shocks started to push corn and oil prices in opposite directions.<br />
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Tang and Xiong also present new evidence on the volatility of hedgers positions. Hedgers--presumably commodity sellers who are more invested in commodities and want to their risk onto Wall Street---have highly volatile positions relative to the volatility of actual output. <br />
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These are interesting statistics. But it really seems like a comparison of apples and oranges. Why should we expect hedger's positions to scale with the volatility of output? There are two risks for farmers: quantity and price. For most farmers one is a poor substitute for the other.<br />
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After all, very small changes in quantity can cause huge changes in price due to the steep and maybe possibly backward-bending demand. And it's not just US output that matters. US farmers pay close attention to weather and harvest in Brazil, Australia, Russia, China and other places, too.<br />
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It also depends a little on which farmers we're talking about, since some farmers have a natural hedge if they are in a region with a high concentration of production (Iowa), while others don't (Georgia). And farmers also have an ongoing interest in the value of their land that far exceeds the current crop, which they can partially hedge through commodity markets since prices tend to be highly autocorrelated.<br />
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Also, today's farmers, especially those engaged in futures markets, may be highly diversified into other non-agricultural investments. It's not really clear what their best hedging strategy ought to look like.<br />
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Anyhow, these are nice papers with a bit of good data to ponder, and a very nice review of past literature. But I don't see how any of it sheds new light on the effects of commodity financialization. All of it is easy to reconcile with existing frameworks. I still see no evidence that speculation and Wall Street involvement in commodities is wreaking havoc.<br />
<br />Michael Robertshttp://www.blogger.com/profile/16455035518968529794noreply@blogger.com4tag:blogger.com,1999:blog-3780641321580065916.post-21675662252421891322014-06-05T15:36:00.002-04:002015-08-04T10:50:15.353-04:00Adaptation with an Envelope<div class="separator" style="clear: both; text-align: center;">
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Economists like to emphasize how people and businesses will adapt to climate change. On a geological scale the world is warming very fast. But on a human scale it is warming slowly, so we can easily adjust infrastructure and management decisions to the gradually changing climate. For example, in agriculture farmers can gradually adjust planting times, cultivars, and locations where we grow crops, and so on.<br />
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So how much does adaptation really buy us? As it turns out, probably very little, at least in most contexts. </div>
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Since it is economists who often emphasize this point, sometimes even intimating that otherwise negative impacts could turn positive with adaptation, perhaps we should pause for a moment to consider what basic microeconomic theory says about it. And we have a ready-made tool for the job, called the <a href="http://en.wikipedia.org/wiki/Envelope_theorem" target="_blank">envelope theorem</a> (or <a href="https://www.economics.utoronto.ca/osborne/MathTutorial/MEEF.HTM" target="_blank">here</a>), that provides essential insight.<br />
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I'll try to make this intuitive, but it helps to be a little formal. Suppose agricultural yield is:<br />
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$ y = f(x, r) $<br />
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where $r$ indicates climate and $x$ represents farmers' decisions. I'm just using notation from a generic case in the second link above.<br />
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Farmers' decisions are not random. With time and experience, we should expect farmers to optimize decisions for their climate. Call these optimal decisions $x^*(r)$. So, the outcomes we observe in practice are<br />
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$ y^* = f(x^*(r), r) $<br />
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Now, to obtain a first-order approximation of the effect of climate change on yield, we need to find $\frac{dy^*}{dr}$, which is just a fancy way of saying the marginal change in observed yield for a small change in climate. Multiply this marginal change by the total change in climate (the change in $r$), and we get a first-order approximation to the total impact.<br />
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If you've taken basic calculus, you learned the chain rule, which says that:<br />
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$\frac{dy^*}{dr} = \frac{df}{dx} \frac{dx}{dr} + \frac{df}{dr}$<br />
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If the farmer is optimizing, however, $ \frac{df}{dx} = 0 $. The farmer cannot improve yield outcomes by changing decisions, because s/he's already optimizing. So<br />
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$ \frac{dy^*}{dr} = \frac{df}{dr} $<br />
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And this gives the heart of the envelope theorem: to a first approximation, we don't need to worry about changes in behavior ($\frac{dx}{dr}$, or adaptation) to evaluate the effect of a change in climate on output. The fact that behavior is already optimized means that behavioral adjustments will be second-order.<br />
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Here's an illustration of the math from lifted from the link above. The black and blue curves hold farmers's decisions fixed at different levels of $x$, optimized at each $r$ The $f^*(r)$ ( or $y^*$) we observe is the "upper envelope" of the all the blue and black curves with different, optimized levels of $x$.<br />
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Now, if $f^*(r)$ is highly nonlinear, and we are contemplating a very large change in climate, then adaptation will come into play. But even then it's probably not going to be a primary consideration.<br />
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I don't expect this basic insight, drilled into every economist during their first year of grad school (and even some undergraduates), will stop some economists from over-emphasizing adaptation. But our own basic theory nevertheless indicates it is a small deal. And it seems to me that the <a href="http://www.nature.com/nclimate/journal/v3/n8/full/nclimate1959.html?WT.ec_id=NCLIMATE-201308" target="_blank">evidence</a> <a href="http://www.sciencemag.org/content/344/6183/516.abstract" target="_blank">so far</a> bears this out just as clearly as the theory does.<br />
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Michael Robertshttp://www.blogger.com/profile/16455035518968529794noreply@blogger.com8tag:blogger.com,1999:blog-3780641321580065916.post-60655870968232275232014-04-10T03:18:00.001-04:002014-04-10T03:18:27.003-04:00Devise a better net-metering agreement for residential solar<span style="font-family: Arial,Helvetica,sans-serif;"><span style="font-size: small;">A question for my undergraduate environmental economics students:</span></span><br />
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<span style="font-family: Arial,Helvetica,sans-serif;"><span style="font-size: small;"> Navigate to <a href="http://www.uhero.hawaii.edu/news/view/274">http://www.uhero.hawaii.edu/news/view/274</a> and read about the costs and benefits of installing PV, from a household's perspective and from Hawaiian Electric's. Play around with the interactive calculator. Dick Rosenblum, the CEO of Hawaiian Electric, often complains about net metering agreements because homeowners get retail prices instead of wholesale prices for the energy their panels generate. (Also see <a href="http://energyathaas.wordpress.com/2013/11/12/rate-design-wars-are-the-sound-of-utilities-taking-residential-pv-seriously/" target="_blank">this article</a> by energy economist Severin Borenstein.) </span></span><br />
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<span style="font-family: Arial,Helvetica,sans-serif;"><span style="font-size: small;">As the UHERO blog post points out, a side effect from current net metering agreements is that households over-install solar. As a result, they often pay a zero marginal price for electricity, which discourages conservation. </span></span><br />
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<span style="font-family: Arial,Helvetica,sans-serif;"><span style="font-size: small;">Devise a different model for net metering agreements that can address both Dick Rosenblum's complaints and restore incentives for households with solar to conserve energy.</span></span><br />
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<span style="font-family: Arial,Helvetica,sans-serif;"><span style="font-size: small;">Feel free to help my students out by suggesting answers in the comment section ;-) </span></span></div>
Michael Robertshttp://www.blogger.com/profile/16455035518968529794noreply@blogger.com4tag:blogger.com,1999:blog-3780641321580065916.post-40393512330399538612014-04-03T02:07:00.001-04:002014-04-03T02:17:04.896-04:00Pushing the limit of solar in Hawai'iSorry for the radio silence. Way too much going on.<br />
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However, I am doing a little blogging for UHERO, focusing on Hawai'i's interesting electricity situation. We have the highest electricity prices in the country, about 3.5 times those on the mainland. That fact coupled with tax credits and a lot of sunshine has given us more solar penetration, by far, than anyplace in the country. Most statistics you'll find tend to be a bit dated---there's more penetration here than most people know, and it's pushing the limit of our grid.<br />
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Anyhow, below are links to my first two posts about the situation. More to come soon, I hope.<br />
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<a href="http://www.uhero.hawaii.edu/news/view/271" target="_blank">Is Monopoly a Barrier to Hawai'i's Ascent?</a><br />
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<a href="http://www.uhero.hawaii.edu/news/view/273" target="_blank">Why are Hawai'i's Electricity Prices So High?</a><br />
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<br />Michael Robertshttp://www.blogger.com/profile/16455035518968529794noreply@blogger.com0tag:blogger.com,1999:blog-3780641321580065916.post-69215689174655993162014-01-02T11:05:00.000-05:002014-01-02T14:35:41.648-05:00Not fit to print, but why?A brief follow up to my <a href="http://greedgreengrains.blogspot.com/2013/12/ny-times-attack-by-innuendo-commodity.html" target="_blank">post the other day</a> about the disasterous NY Times article by David Kocieniewski.<br />
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<a href="http://blogs.reuters.com/felix-salmon/2013/12/29/the-non-scandal-of-scott-irwin-and-craig-pirrong-3/" target="_blank">Flex Salmon</a> and <a href="http://jaysonlusk.com/blog/2013/12/31/nyt-on-academics-and-commodity-speculation-research" target="_blank">Jayson Lusk</a>, among others, have written similar and more detailed pieces detailing <span style="background-color: rgba(255, 255, 255, 0);">Kocieniewski</span>'s reporting slight of hand. I really thought this sort of thing was beneath the NY Times. The editors there certainly deserve some of the blame.</div>
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Journalistic malfeasance aside, where is this attack coming from? Why does <span style="background-color: rgba(255, 255, 255, 0);">Kocieniewski</span> and NY Times go to such great lengths to attack two relatively innocuous academic economists? Why do they seem to have a bee in their bonnet about commodity price speculation?</div>
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I don't know the answers to these questions. But I'm wondering if momentum on financial regulatory reform may be a bit less innocent that <a href="http://krugman.blogs.nytimes.com/2013/12/30/bankers-beaten-back-a-bit/" target="_blank">Paul Krugman</a> seems to think. Some might note that the <a href="http://www.newrepublic.com/article/116064/2013-financial-reform-went-way-better-anyone-expected" target="_blank">Mike Konczal article</a> that Krugman references mentions the heavy influence of Gary Gensler, chairman of the Commodity Futures Trading Commission. I wonder if old powerful commodity interests may be an unlikely ally of Elizabeth Warren in the fight to regulate Wall Street banks more tightly. The commodity groups aren't fans of regulation, and I doubt they share Warren's concern for consumers and market stability. But presumably they don't like growing competition from Wall Street. There would seem to be big private interests that favor financial reform.<br />
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Yes, I'm reading tea leaves here. Does anyone have better insight?</div>
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Michael Robertshttp://www.blogger.com/profile/16455035518968529794noreply@blogger.com0tag:blogger.com,1999:blog-3780641321580065916.post-7403846200547251082013-12-28T19:36:00.000-05:002013-12-30T02:20:09.619-05:00NY Times attack by innuendo: Commodity price speculation edition<div>
I've written a few times [e.g., <a href="http://greedgreengrains.blogspot.com/2011/10/on-fallacy-of-speculation-driven-price.html" target="_blank">1</a>, <a href="http://greedgreengrains.blogspot.com/2011/01/commodity-prices-and-speculation-again.html" target="_blank">2</a>, <a href="http://greedgreengrains.blogspot.com/2013/07/commodity-speculation-or-market-power.html" target="_blank">3</a>, ] about commodity price speculation, arguing that speculation hasn't been the cause of volatility in recent years. My views on this haven't changed, but I'm open to new arguments for why I and legions of other economists might be wrong. </div>
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Overall, I haven't found this topic to be a very interesting, because those arguing that Wall Street caused the food price crisis, or caused oil prices to spike, really haven't presented any kind of logical argument. All they do is point to the fact that Wall Street has gotten into the commodity game more than they have in the past. But this isn't news and it falls far short of an explanation for how their trading activities are affecting prices. I haven't seen a serious study claiming a link, only magazine articles and opinion pieces, all thin on substance. Professionally, I don't see this as interesting work. This <a href="http://www-personal.umich.edu/~lkilian/milan030612.pdf" target="_blank">review</a> lays things out pretty clearly.</div>
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I've been waiting for this issue to die. But it seems there are political winds that won't let it. Yesterday the New York Times came out with a <a href="http://www.nytimes.com/2013/12/28/business/academics-who-defend-wall-st-reap-reward.html" target="_blank">new attack on academics</a>, particularly <a href="http://www.bauer.uh.edu/directory/profile.asp?firstname=Craig&lastname=Pirrong" target="_blank">Craig Pirrong</a> and <a href="http://ace.illinois.edu/directory/sirwin" target="_blank">Scott Irwin</a>. These guys have been doing some consulting work, mainly for oil companies and the Chicago Mercantile Exchange. These companies have also been giving money to their universities. The documentary <a href="http://www.sonyclassics.com/insidejob/" target="_blank">Inside Job</a> is referenced (a great movie, by the way). </div>
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There is a lot of innuendo in the story, and I do feel uncomfortable about academic economists getting cushy consulting contracts with big trading companies. Still, the scale of what's mentioned seems tame relative to the kind of consulting gigs that seem commonplace in the economics realm of academia, or even the levels of corporate cash given to universities. </div>
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The story is most notable for what it lacks: how, exactly, are the various companies distorting markets in a way that hurts consumers and or producers? Inside Job describes the shady business of securities backed by stated-income mortgages, how Goldman Sachs was shorting the products it was selling to clients, etc. We can see that there were shady business dealings and how they probably helped to fuel the real estate bubble. So, where's the real underlying story in commodity market trading?</div>
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I don't think this story measures up to the New York Times' standard. It's sad, because there might actually be a story here, but it would require a lot more legwork by Kocieniewski That story, however, is probably a bit less salacious than what Kocieniewski was shooting for. My guess (I welcome challenges--I'm not sure about this) is that the real story is a battle between old-school commodity groups (ADM, Cargill, and other so-called "majors" that both speculate and store commodities) and new competition from Wall Street, like JP Morgan Chase and Goldman Sachs. I would further guess that some of the strangeness in commodity prices over recent years, like <a href="http://greedgreengrains.blogspot.com/2011/11/on-futures-markets-convergence-to-spot.html" target="_blank">futures prices sometimes not converging to spot prices</a>, is partly a refection of this changing marketplace. Note, however, that the "strangeness" in pricing of which I speak has basically zero relevance to producer and consumer prices. </div>
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My vague sense is that incompleteness in these markets---the fact that future contracts are only for certain months and certain delivery dates---procures a special advantage to the majors who control inventories at delivery points. I'd further guess that Wall Street players are getting into commodities partly for diversification, and partly because they figure they can skim some the rents earned by the commodity players. </div>
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Anyhow, I don't have all the details figured out. It's something I started to pencil out once, but, like I said, I don't see this as especially useful for larger-scale questions about supply and demand, policy, climate change, etc. So I figure I've got better things to do. </div>
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For the record, I've never had a consulting contract with commodity group or Wall Street firm.</div>
Michael Robertshttp://www.blogger.com/profile/16455035518968529794noreply@blogger.com3tag:blogger.com,1999:blog-3780641321580065916.post-70483077192548291782013-11-20T15:41:00.000-05:002013-11-20T15:41:44.741-05:00Fixed Effects InfatuationThe fashionable thing to do in applied econometrics, going on 15 years or so, is to find a gigantic panel data set, come up with a cute question about whether some variable <i>x</i> causes another variable <i>y</i>, and test this hypothesis by running a regression of <i>y</i> on <i>x</i> plus a huge number of fixed effects to control for "unobserved heterogeneity" or deal with "omitted variable bias." I've done a fair amount of work like this myself. The standard model is:<br />
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y_i,t = x_i,t + a_i + b_t + u_i,t<br />
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where a_i are fixed effects that span the cross section, b_t are fixed effects that span the time series, and u_i,t is the model error, which we hope is not associated with the causal variable x_i,t, once a_i<br />
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If you're really clever, you can find geographic or other kinds of groupings of individuals, like counties, and include group-by-year fixed effects:<br />
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y_i,t = x_i,t + a_i + b_g,t + u_i,t<br />
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The generalizable point of my <a href="http://greedgreengrains.blogspot.com/2013/11/weather-storage-and-old-climate-impact.html" target="_blank">lengthy post</a> the other day on storage and agricultural impacts of climate change, was that this approach, while useful in some contexts, can have some big drawbacks. Increasingly, I fear applied econometricians misuse it. They found their hammer and now everything is a nail.</div>
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What's wrong with fixed effects? </div>
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A practical problem with fixed effects gone wild is that they generally purge the data set of most variation. This may be useful if you hope to isolate some interesting localized variation that you can argue is exogenous. But if the most interesting variation derives from a broader phenomenon, then there may be too little variation left over to identify an interesting effect. <br />
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A corollary to this point is that fixed effects tend to exaggerate attenuation bias of measurement errors since they will comprise a much larger share of the overall variation in x after fixed effects have been removed.<br />
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But there is a more fundamental problem. To see this, take a step back and think generically about economics. In economics, almost everything affects everything else, via prices and other kinds of costs and benefits. Micro incentives affect choices, and those choices add up to affect prices, cost and benefits more broadly, and thus help to organize the <a href="http://ideasofeconomists.blogspot.com/2012/01/political-economy-or-economics-is-study.html" target="_blank">ordinary business of life</a>. That's the essence of Adam's Smith's "invisible hand," supply and demand, and equilibrium theory, etc. That insight, a unifying theoretical theme if there is one in economics, implies a fundamental connectedness of human activities over time and space. It's not just that there are unobserved correlated factors; everything literally affects everything else. On some level it's what connects us to ecologists, although some ecologists may be loath to admit an affinity with economics.<br />
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In contrast to the nature of economics, regression with fixed effects is a tool designed for experiments with repeated measures. Heterogeneous observational units get different treatments, and they might be mutually affected by some outside factor, but the observational units don't affect each other. They are, by assumption, siloed, at least with respect to consequences of the treatment (whatever your <i>x</i> is). This design doesn't seem well suited to many kinds of observational data.<br />
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I'll put it another way. Suppose your (hopefully) exogenous variable of choice is <i>x</i>, and <i>x</i> causes <i>z</i>, and then both <i>x</i> and <i>z</i> affect <i>y</i>. Further, suppose the effects of <i>x</i> on <i>z</i> spill outside of the confines of your fixed-effects units. Even if fixed effects don't purge all the variation in <i>x</i>, they may purge much of the path going from x to <i>z</i> and <i>z</i> to <i>y</i>, thereby biasing the reduced form link between <i>x</i> and <i>y</i>. In other words, fixed effects are endogenous.<br />
<br />
None of this is to say that fixed effects, with careful account of correlated unobserved factors, can be very useful in many settings. But the inferences we draw may be very limited. And without care, we may draw conclusions that are very misleading. </div>
Michael Robertshttp://www.blogger.com/profile/16455035518968529794noreply@blogger.com3tag:blogger.com,1999:blog-3780641321580065916.post-19100628966575949282013-11-11T08:25:00.000-05:002014-09-13T12:19:13.288-04:00Can crop rotations cure dead zones?<div class="separator" style="clear: both; text-align: center;">
<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjoa3HS_BvyOae1UtfRNM61so92c_OnedRgliH7F_-khmRyhxxHKF9v7TbjCXaGz5jq3vsuMoXw1oPgXHmATVD_23tPahgXbxmaTFioNVG8MLp_j59NtNzi4qh9J5pbqJTtb8bK9aS0QvVB/s1600/800px-Sediment_in_the_Gulf_of_Mexico_(2).jpg" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjoa3HS_BvyOae1UtfRNM61so92c_OnedRgliH7F_-khmRyhxxHKF9v7TbjCXaGz5jq3vsuMoXw1oPgXHmATVD_23tPahgXbxmaTFioNVG8MLp_j59NtNzi4qh9J5pbqJTtb8bK9aS0QvVB/s640/800px-Sediment_in_the_Gulf_of_Mexico_(2).jpg" height="456" width="640" /></a></div>
It is now fairly well documented that much of the water quality problems leading to the infamous "dead zone" in the Gulf of Mexico (pictured above) come from fertilizer applications on corn. Fertilizer on corn is probably a big part of similar challenges in the Chesapeake Bay and Great Lakes.<br />
<br />
This is a tough problem. The Pigouvian solution---taxing fertilizer runoff, or possibly <a href="http://greedgreengrains.blogspot.com/2013/04/how-farmers-could-benefit-from.html" target="_blank">just fertilizer</a>---would help. But we can't forget that fertilizer is the main source of large crop productivity gains over the last 75 years, gains that have fed the world. It's hard to see how even a large fertilizer tax would much reduce fertilizer applications on any given acre of corn.<br />
<br />
However, one way to boost crop yields and reduce fertilizer applications is to rotate crops. Corn-soybean rotations are most ubiquitous, as soybean fixes nitrogen in the soil which reduces need for applications on subsequent corn plantings. Rotation also reduces pest problems. The yield boost on both crops is remarkable. More rotation would mean less corn, and less fertilizer applied to remaining corn, at least in comparison to planting corn after corn, which still happens a fair amount.<br />
<br />
I've got a <a href="http://www2.hawaii.edu/~mjrobert/main/Working_Papers_files/AJAE_revise.pdf" target="_blank">new paper</a> (actually, an old but newly revised paper), coauthored with Mike Livingston of USDA and Yue Zhang, a graduate student at NCSU, that might provide a useful take on this issue. This paper has taken forever. We've solved a fairly complex stochastic dynamic model that takes the variability of prices, yields and agronomic benefits of rotation into account. It's calibrated using the autoregressive properties of past prices and experimental plot data. All of these stochastic/dynamics can matter for rotations. John Rust once told me that Bellman always thought crop rotations would be a great application for his recursive method of solving dynamic problems.<br />
<br />
Here's the jist of what we found:<br />
<br />
Always rotating, regardless of prices, is close
to optimal, even though economically optimal planting may rotate much less frequently. One implication is
that reduced corn monoculture and fertilizer application rates might be
implemented with modest incentive payments of \$4 per acre or less, and quite possibly less than \$1 per acre.<br />
<br />
In the past I've been skeptical that even a high fertilizer tax could have much influence on fertilizer use. But given low-cost substitutes like rotation, perhaps it wouldn't cost as much as some think to make substantial improvements in water quality.<br />
<br />
Nathan Hendricks and coauthors have a somewhat <a href="http://agecon.ucdavis.edu/people/faculty/aaron-smith/docs/DynamicsofSupply10-28.pdf" target="_blank">different approach</a> on the same issue (also see <a href="http://agecon.ucdavis.edu/people/faculty/aaron-smith/docs/Water_quality.pdf" target="_blank">this paper</a>). It's hard to compare our models, but I gather they are saying roughly similar things. <br />
<br />Michael Robertshttp://www.blogger.com/profile/16455035518968529794noreply@blogger.com8tag:blogger.com,1999:blog-3780641321580065916.post-68617586947701331262013-11-05T01:27:00.001-05:002013-11-07T14:53:21.892-05:00Weather, storage and an old climate impact debateThis somewhat technical post is a belated followup to a <a href="http://www.aeaweb.org/articles.php?doi=10.1257/aer.102.7.3749" target="_blank">comment</a> I wrote with Tony Fisher, Michael Hanemann and Wolfram Schlenker, which was finally published last year in the <i>American Economic Review</i>.
I probably should have done this a long time ago, but I needed to do a
little programming. And I've basically been slammed nonstop.<br />
<br />
First
the back story: The comment re-examines a <a href="http://www.aeaweb.org/articles.php?doi=10.1257/aer.97.1.354" target="_blank">paper</a> by Deschanes and
Greenstone (DG) that supposedly estimates a lower bound on the effects
of climate change by relating county-level farm profits to weather.
They argue that year-to-year variation in weather is random---a fair
proposition---and control for unobserved differences across counties
using <a href="http://en.wikipedia.org/wiki/Fixed_effects_model" target="_blank">fixed effects</a>. This is all pretty standard technique.<br />
<br />
The
overarching argument was that with climate change, farmers could adapt
(adjust their farming practices) in ways they cannot with weather, so
the climate effect on farm profits would be more favorable than their
estimated weather effect.<br />
<br />
Now, bad physical
outcomes in agriculture can actually be good for farmers' profits, since
demand for most agricultural commodities is pretty steep: prices go up
as quantities go down. So, to control for the price effects they
include year fixed effects. And since farmers grow different crops in
different parts of the country and there can be local price anomalies,
they go further and use state-by-year fixed effects so as to squarely
focus on quantity effects in all locations.<br />
<br />
Our comment
pointed out a few problems: (1) there were some data errors like
missing temperature data apparently coded with zeros and much of the
Midwest and most of Iowa dropped from the sample without explanation;
(2) in making climate predictions they applied state-level estimates to
county-level baseline coefficients, in effect making climate predictions
that regress to the state mean (e.g., Death Valley and Mt. Witney have
different baselines but the same future); (3) all those fixed effects
wash out over 99 percent of weather variation, leaving only data errors
for estimation; (4) the standard errors didn't appropriately account for
the panel nature of the spatially correlated errors.<br />
<br />
These
data and econometric issues got the most attention. Correct these
things and the results change a lot. See the comment for details.<br />
<br />
But,
to our minds, there is a deeper problem with the whole approach. Their
measure of profits was really no such thing, at least not in an <a href="https://www.khanacademy.org/economics-finance-domain/microeconomics/firm-economic-profit/economic-profit-tutorial/v/economic-profit-vs-accounting-profit" target="_blank">economic sense</a>:
it was reported sales minus a crude estimate of current expenditures.
The critical thing here is that farmers often do not sell what they
produce. About half the country's grain inventories are held on farm.
Farms also hold inventory in the form of capital and livestock, which
can be held, divested or slaughtered. Thus, effects of weather in one
year may not show up in profits measured in that year. And since
inventories tend to be accumulated in plentiful times and divested in
bad times, these inventory adjustments are going to be correlated with
the weather and cause bias.<br />
<br />
Although DG did not
consider this point originally, they admitted it was a good one, but
argued they had a simple solution: just include the lags of weather in
the regression. When they attempted this, they found lagged weather was
not significant, and thus concluded that this issue was unimportant. This argument is presented in their reply to our comment.<br />
<br />
We
were skeptical about their proposed solution to the storage issue. And
so, one day long ago, I proposed to Michael Greenstone, that we test
his proposed solution. We could solve a competitive storage model,
assume farmers store as a competitive market would, and then simulate
prices and quantities that vary randomly with the weather. Then
we could regress sales (consumption X price) against our constructed
weather and lags of weather plus price controls. If the lags worked in
this instance, where we knew the underlying physical structure, then it
might work in reality.<br />
<br />
Greenstone didn't like this
idea, and we had limited space in the comment, so the storage stuff took a minimalist back seat.
Hence this belated post.<br />
<br />
So I recently coded a toy
storage model in R, which is nice because anyone can download and run
this thing (R is free). Also, this was part of a problem set I gave to
my PhD students, so I had to do it anyway.<br />
<br />
Here's the basic set up: <br />
<br />
y is production which varies randomly (like the weather).<br />
q is consumption, or what's clearly sold in a year.<br />
p is the market price, which varies inversely with q (the demand curve)<br />
z is the amount of the commodity on hand (y plus carryover from last year).<br />
<br />
The
point of the model is to figure out how much production to put in or
take out of storage. This requires numerical analysis (thus, the R
code). Dynamic equilibrium occurs when there is no arbitrage: where
it's impossible to make money by storing more or storing less.<br />
<br />
Once
we've solved the model, which basically gives q, p as a function of z,
we can simulate y with random draws and develop a path of q and p. I
chose a demand curve, interest rate and storage cost that can give rise
to a fair amount of price variability and autocorrelation, which happens
to fit the facts. The code is <a href="https://www.dropbox.com/s/v89bvc3mpsmdsm2/storage.R" target="_blank">here</a>.<br />
<br />
Now, given our simulated y, q and p, we might estimate:<br />
<br />
(1) q_t = a + b0 y_t + b1 y_{t-1} + b2 y_{t-2} + b3 y_{t-3} + ... + error<br />
<br />
(the ... means additional lags, as many as you like. I use five.)<br />
<br />
This
expression makes sense to me, and might have been what DG had in mind:
quantity in any one year is a function of this year's weather and a
reasonable number past years, all of which affect today's output via
storage. For the regression to fully capture the true effect of
weather, the sum of b# coefficients should be one.<br />
<br />
Alternatively we might estimate:<br />
<br />
(2) p_t q_t = a + b0 y_t + b1 y_{t-1} + b2 y_{t-2} + b3 y_{t-3} + ... + error<br />
<br />
This
is almost like DG's profit regression, as costs of production in this
toy model are zero, so "profit" is just total sales. But DG wanted to
control for price effects in order to account for the pure weather
effect on quantity, since the above relationship, the sum of the b#
coefficients is likely negative. So, to do something akin to DG within
the context of this toy model we need to control for price. This might
be something like:<br />
<br />
(3) p_t q_t = a + b0 y_t + b1 y_{t-1} + b2 y_{t-2} + b3 y_{t-3} + ... + c p_t + error <br />
<br />
Or,
if you want to be a little more careful, recognizing there is a
nonlinear relationship, we might have a more flexible control for p_t,
and use a polynomial. Note that we cannot used fixed effects like DG
because this isn't a panel. I'll come back to this later. In any case,
with better controls we get:<br />
<br />
(4) p_t q_t = a + b0 y_t + b1 y_{t-1} + b2 y_{t-2} + b3 y_{t-3} + ... + c1 p_t + c2 p_t^2 + c3 p_t^3 + error <br />
<br />
At
this point you should be worrying about having p_t on both the right
and left side. More on this in a moment. First, let's take a look at
the results:<br />
<br />
Equation 1:<br />
<span style="font-family: "Courier New",Courier,monospace;"> Estimate Std. Error t value Pr(>|t|)<br />(Intercept) 1.68 1.32 1.28 0.20<br />y 0.39 0.03 15.62 0.00<br />l.y 0.23 0.03 9.17 0.00<br />l2.y 0.10 0.03 3.83 0.00<br />l3.y 0.07 0.03 2.66 0.01<br />l4.y 0.07 0.03 2.69 0.01<br />l5.y 0.06 0.03 2.34 0.02</span><br />
<br />
The
sum of the y coefficients is 0.86. I'm sure if you put in enough lags
they would sum to 1. You shouldn't take the Std. Error or t-stats
seriously for this or any of the other regressions, but that doesn't
really matter for the points I want to make. Also, if you run the code,
the exact results will differ because it will take a different random
draw of y's, but the flavor will be the same.<br />
<br />
Equation 2:<br />
<span style="font-family: "Courier New",Courier,monospace;"> Estimate Std. Error t value Pr(>|t|)<br />(Intercept) 4985.23 166.91 29.87 0<br />y -72.15 3.19 -22.63 0<br />l.y -43.67 3.20 -13.64 0<br />l2.y -22.52 3.21 -7.03 0<br />l3.y -15.61 3.21 -4.87 0<br />l4.y -13.58 3.19 -4.26 0<br />l5.y -12.26 3.19 -3.85 0</span><br />
<br />
All
the coefficients are negative. As we expected, good physical outcomes
for y mean bad news for profits, since prices fall through the floor.
If you know a little about the history of agriculture, this seems about
right. So, let's "control" for price.<br />
<br />
Equation 3:<br />
<span style="font-family: "Courier New",Courier,monospace;"> Estimate Std. Error t value Pr(>|t|)<br />(Intercept) 2373.15 167.51 14.17 0<br />y -28.12 2.91 -9.66 0<br />l.y -17.72 2.10 -8.43 0<br />l2.y -11.67 1.63 -7.17 0<br />l3.y -8.07 1.57 -5.16 0<br />l4.y -5.99 1.56 -3.84 0<br />l5.y -5.68 1.54 -3.68 0<br />p 7.84 0.44 17.65 0</span><br />
<br />
Oh,
good, the coefficients are less negative. But we still seem to have a
problem. So, let's improve our control for price by making it a 3rd
order polynomial:<br />
<br />
Equation 4:<br />
<span style="font-family: "Courier New",Courier,monospace;"> Estimate Std. Error t value Pr(>|t|)<br />(Intercept) 1405.32 0 1.204123e+15 0.00<br />y 0.00 0 2.000000e-02 0.98<br />l.y 0.00 0 3.000000e-02 0.98<br />l2.y 0.00 0 6.200000e-01 0.53<br />l3.y 0.00 0 -3.200000e-01 0.75<br />l4.y 0.00 0 -9.500000e-01 0.34<br />l5.y 0.00 0 -2.410000e+00 0.02<br />poly(p, 3)1 2914.65 0 3.588634e+15 0.00<br />poly(p, 3)2 -716.53 0 -1.795882e+15 0.00<br />poly(p, 3)3 0.00 0 1.640000e+00 0.10</span><br />
<br />
The y coefficients are now almost precisely zero. <br />
<br />
By
DG's interpretation, we say that weather has no effect on profit
outcomes and thus climate change is likely to have little influence on
US agriculture. Except in this simulation we know that in the
underlying physical reality is that one unit of y ultimately has a one
unit effect on the output. DG's interpretation is clearly wrong.<br />
<br />
What's going on here? <br />
<br />
The problem comes from an attempt to "control" for price. Price, after all, is a key (<i>the</i>
key?) consequence of the weather. Because storage theory predicts that prices incorporate all past production
shocks, whether they are caused by weather or something else, in controlling for price, we
remove all weather effects on quantities. So, DG are ultimately mixing up cause
and effect, in their case by using a zillion fixed effects. One should take care in
adding "controls" that might actually be an effect, especially when you
supposedly have a random source of variation. <a href="http://www.stat.berkeley.edu/~freedman/" target="_blank">David Freedman</a>, the late statistician who famously critiqued regression analysis in the social sciences and provided inspiration to the modern empirical revolution in economics, often emphasized this point.<br />
<br />
Now, some might argue that the above analysis is just a single crop, that it doesn't apply to DG's panel data. I'd argue that if you can't make it work in a simpler case, it's unlikely to work in a case that's more complicated. More pointedly, this angle poses a catch 22 for the identification strategy: If inclusion of state-by-year fixed effects does not absorb all historic weather shocks, then it implies that the weather shocks must have been crop- or substate-specific, in which case there is bias due to endogenous price movements even after the inclusion of these fixed effects. On the other hand, if enough fixed effects are included to account for all endogenous price movements, then lagged weather by definition does not add any additional information and should not be significant in the regression. Prices are a sufficient statistic for all past and current shocks.<br />
<br />
All of this
is to show that the whole DG approach has problems. However, I think
the idea of using lagged weather is a good one if combined with a
somewhat different approach. We might, for example, relate all manner
of endogenous outcomes (prices, quantities, and whatever else) to current and past
weather. This is the correct "reduced form." From these relationships, combined with some minimalist
economic structure, we might learn all kinds of interesting and useful
things, and not just about climate change. This observation, in my
view, is the over-arching contribution of my <a href="http://www.aeaweb.org/articles.php?doi=10.1257/aer.103.6.2265" target="_blank">new article</a> with Wolfram Schlenker in the <i>AER</i><br />
<br />
I
think there is a deeper lesson in this whole episode that gets at a
broader conversation in the discipline about data-driven applied
microeconomics over the last 20 years. Following Angrist, Ashenfelter,
Card and Krueger, among others, everyone's doing experiments and natural
experiments. A lot of this stuff has led to some interesting and useful
discoveries. And it's helped to weed out some applied econometric
silliness.<br />
<br />
Unfortunately, somewhere along the way, some
folks lost sight of basic theory. In many contexts we do need to
attach our reduced forms to some theoretical structure in order to
interpret them. For example, bad weather causing profits to go up in agriculture
actually makes sense, and indicates something bad for consumers and for
society as a whole.<br />
<br />
And in some contexts a little theory might help us
remember what is and isn't exogenous.Michael Robertshttp://www.blogger.com/profile/16455035518968529794noreply@blogger.com6tag:blogger.com,1999:blog-3780641321580065916.post-23121998410850610842013-10-23T17:51:00.000-04:002013-10-24T02:20:33.064-04:00What is the value of symbolic action?<span style="font-family: Arial, Helvetica, sans-serif;">Robert Stavins <a href="http://www.robertstavinsblog.org/2013/10/22/climate-change-public-policy-and-the-university/" target="_blank">argues</a> that largely symbolic actions do not help and may ultimately hurt the cause for action on climate change (HT <a href="http://economistsview.typepad.com/economistsview/2013/10/climate-change-public-policy-and-the-university.html" target="_blank">Mark Thoma</a>):</span><br />
<blockquote class="tr_bq">
<span style="font-family: Arial, Helvetica, sans-serif;"><span style="background-color: white; color: blue; line-height: 19px;">Over the past year or more, across the United States, there has been a groundswell of student activism pressing colleges and universities to divest their holdings in fossil fuel companies from their investment portfolios. On October 3, 2013, after many months of </span><a href="http://issuu.com/huce/docs/newslettervolume5" style="background-color: white; line-height: 19px;">assessment, discussion, and debate</a><span style="background-color: white; color: blue; line-height: 19px;">, the President of Harvard University, </span><a href="http://www.harvard.edu/president/biography" style="background-color: white; line-height: 19px;">Drew Faust</a><span style="background-color: white; color: blue; line-height: 19px;">, issued a long, well-reasoned, and – in my view – ultimately sensible</span><a href="http://www.harvard.edu/president/fossil-fuels" style="background-color: white; line-height: 19px;">statement on “fossil fuel divestment,”</a><span style="background-color: white; color: blue; line-height: 19px;"> in which she explained why she and the Corporation (Harvard’s governing board) do not believe that “university divestment from the fossil fuel industry is warranted or wise.” I urge you to read </span><a href="http://www.harvard.edu/president/fossil-fuels" style="background-color: white; line-height: 19px;">her statement</a><span style="background-color: white; color: blue; line-height: 19px;">, and decide for yourself how compelling you find it, and whether and how it may apply to your institution, as well.</span> </span></blockquote>
<blockquote class="tr_bq">
<span style="font-family: Arial, Helvetica, sans-serif;"><span style="background-color: white; color: blue; line-height: 19px;">About 10 days later, </span><a href="http://www.thenation.com/blog/176648/time-divest-response-harvard-president-drew-faust" style="background-color: white; line-height: 19px;">two leaders of the student movement at Harvard responded to President Faust in The Nation</a><span style="background-color: white; color: blue; line-height: 19px;">. </span><a href="http://topics.nytimes.com/top/reference/timestopics/people/r/andrew_c_revkin/" style="background-color: white; line-height: 19px;">Andrew Revkin</a><span style="background-color: white; color: blue; line-height: 19px;">, writing at the </span><a href="http://www.nytimes.com/" style="background-color: white; line-height: 19px;">New York Times</a><span style="background-color: white; color: blue; line-height: 19px;"> </span><a href="http://dotearth.blogs.nytimes.com/2013/10/04/a-closer-look-at-harvards-choice-on-fossil-fuel-divestment/" style="background-color: white; line-height: 19px;">Dot Earth blog</a><span style="background-color: white; color: blue; line-height: 19px;">, highlighted the fact that the students responded in part by saying, “We do not expect divestment to have a financial impact on fossil fuel companies … Divestment is a moral and political strategy to expose the reckless business model of the fossil fuel industry that puts our world at risk.</span> </span></blockquote>
<blockquote class="tr_bq">
<span style="font-family: Arial, Helvetica, sans-serif;"><span style="background-color: white; color: blue; line-height: 19px;">I agree with these students that fossil-fuel divestment by the University would not have financial impacts on the industry, and I also agree with their implication that it would be (potentially) of symbolic value only. However, it is precisely because of this that I believe President Faust made the right decision. Let me explain.</span></span></blockquote>
<div>
<div>
<span style="font-family: Arial, Helvetica, sans-serif;">Some may feel exasperated. If students cannot even make a symbolic or moral point, what can they do? </span><span style="font-family: Arial, Helvetica, sans-serif;">If your initial reaction is skepticism, I encourage you to click through and read the whole thing, </span><span style="font-family: Arial, Helvetica, sans-serif;">including an earlier post where he addresses what individuals and small institutions can do to curb global warming. His bottom line: </span></div>
<blockquote class="tr_bq">
<span style="color: blue; font-family: Arial, Helvetica, sans-serif;"><span style="background-color: white; line-height: 24px;">Try to focus on actions that can make a </span><em style="background-color: white; border: 0px; line-height: 24px; margin: 0px; padding: 0px; vertical-align: baseline;">real </em><span style="background-color: white; line-height: 24px;">difference, as opposed to actions that </span><em style="background-color: white; border: 0px; line-height: 24px; margin: 0px; padding: 0px; vertical-align: baseline;">may feel good</em><span style="background-color: white; line-height: 24px;"> or </span><em style="background-color: white; border: 0px; line-height: 24px; margin: 0px; padding: 0px; vertical-align: baseline;">look good</em><span style="background-color: white; line-height: 24px;"> but have relatively </span><em style="background-color: white; border: 0px; line-height: 24px; margin: 0px; padding: 0px; vertical-align: baseline;">little</em><span style="background-color: white; line-height: 24px;"> real-world impact, particularly when those feel-good/look-good actions have opportunity costs, that is, divert us from focusing on actions that </span><em style="background-color: white; border: 0px; line-height: 24px; margin: 0px; padding: 0px; vertical-align: baseline;">would</em><span style="background-color: white; line-height: 24px;"> make a significant difference. Climate change is a </span><em style="background-color: white; border: 0px; line-height: 24px; margin: 0px; padding: 0px; vertical-align: baseline;">real and pressing problem</em><span style="background-color: white; line-height: 24px;">. Strong government actions will be required, as well as enlightened political leadership at the national and international levels.</span></span></blockquote>
<span style="font-family: Arial, Helvetica, sans-serif;">Stavins also describes some reasons why symbolic activities might be counterproductive. </span><br />
<span style="font-family: Arial, Helvetica, sans-serif;"><br /></span>
<span style="font-family: Arial, Helvetica, sans-serif;">I've got one small quibble. Stavins is right that the over-arching actions required to curb global warming require national and international government. This may seem too remote for many individuals and institutions who want to actively engage.</span><br />
<span style="font-family: Arial, Helvetica, sans-serif;"><br /></span>
<span style="font-family: Arial, Helvetica, sans-serif;">But with CO2 concentrations already reaching over 400ppm, there is also a fairly large amount of warming already baked into our future. Different locations will be affected in different ways, and state and local communities and governments need contingency plans and strategies for adaptation. Building codes and land use regulations need to be revised. There is also plenty of waste and inefficiency in current state and local policies.</span><br />
<span style="font-family: Arial, Helvetica, sans-serif;"><br /></span>
<span style="font-family: Arial, Helvetica, sans-serif;">So, i</span><span style="font-family: Arial, Helvetica, sans-serif;">f you want to act locally instead of nationally or globally, try to find practical ways for local governments and institutions to improve their regulatory systems. Here in Hawai'i, we could probably manage our local public resources: energy, water and coastal ecosystems much better, regardless of climate change. And given warming and sea level rise already anticipated, we need to develop sensible policies for adaptation.</span><br />
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<span style="font-family: Arial, Helvetica, sans-serif;">The first step is to thoroughly educate yourself on the local challenges and policy tradeoffs. Doing that is probably more work than you think. As Stavins intimates, it seems people often focus on simple (but ultimately useless) symbolic actions because they're easy to do. Perhaps we make ourselves feel better by talking gravely about the problems and in making great moral pronouncements about what other people should be doing. Nevermind that all of this accomplishes precisely nothing.</span></div>
Michael Robertshttp://www.blogger.com/profile/16455035518968529794noreply@blogger.com0tag:blogger.com,1999:blog-3780641321580065916.post-49646906944190046392013-09-30T10:40:00.000-04:002013-10-02T15:52:46.353-04:00Desperate times bring desperate measures<b>Update</b>: <a href="http://www.nytimes.com/2013/10/02/business/economy/why-the-health-care-law-scares-the-gop.html" target="_blank">Edwardo Porter</a> makes the same point, only he does a much better job of it.<br />
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A bit off topic, but the impending government shutdown has me thinking in simple game theoretic terms.<br />
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Some on the left (and right) seem to think that Congressional actions are "crazy" as government shutdown is likely to hurt the Republican party. After all, that's what happened the last time when Newt Gingrich shut down the government in 1995, which led to his demise and helped Clinton win reelection against Dole in 1996.<br />
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It's probably fair to guess that, while this time is different (isn't every time, at least a little?), the shutdown will likely hurt the Republican party. So why are they doing it? Are they really crazy? Has the radical fringe taken over and leading us over the cliff to disaster?<br />
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Well maybe. But maybe their actions, even if potentially disastrous, are rational and not surprising given the circumstances. It seems to me the Republican party is in a desperate situation, and desperate times rationally bring about desperate actions. It's possible, though probably unlikely, that Obama and the Democrats will cave and give Republicans something in exchange, like partial repeal of the health care law, for not blowing up the economy. It also seems possible, though unlikely, that shutdown and/or default will hurt Democrats as much or more than Republicans. Even if these are unlikely propositions, they have more than zero probability.<br />
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The alternative is that Republicans do nothing and let Obamacare be implemented, the economy continues to recover, and the nation's demographics steadily change, all of which basically ensures death of the modern Republican party. So, do they go for the Hail Mary pass or just give up? It seems to me that a rational party goes for the Hail Mary pass, which is what they're doing.<br />
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So, the good news is that the Republican party, Tea Partiers included, probably isn't crazy. The bad news is that it's hard to see how this whole thing plays out without the country, and possibly much of the world, being badly hurt.Michael Robertshttp://www.blogger.com/profile/16455035518968529794noreply@blogger.com1tag:blogger.com,1999:blog-3780641321580065916.post-11463350141614162322013-09-30T10:26:00.001-04:002013-10-02T03:30:17.968-04:00Climate Change and Resource Rents<br />
With the next IPCC report coming out, there's been more reporting on climate change issues. Brad Plumer over a Wonkblog has <a href="http://www.washingtonpost.com/blogs/wonkblog/wp/2013/09/27/the-worlds-top-climate-scientists-explain-how-to-avoid-drastic-global-warming-its-not-easy/" target="_blank">nice summary</a> that helps to illustrate how much climate change is already "baked in" so to speak.<br />
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I'd like to comment one point. Brad writes "Humans can only burn about one-sixth of their fossil fuel reserves if they want to keep global warming below <span style="background-color: white; border: 0px; color: #2c2c2c; font-family: georgia, serif; font-size: 16px; line-height: 26px; margin: 0px; outline: 0px; padding: 0px; text-align: left; vertical-align: baseline;">2ºC</span>."</div>
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I'd guess some might quibble with the measurement a bit, since viable reserves depends on price and technology, plus many unknowns about much fossil fuel there really is down there. But this is probably in the ballpark, and possibly conservative.</div>
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Now imagine you own a lot of oil, coal and/or natural gas, you're reading Brad Plumber, and wondering what might happen to climate policy in the coming years. Maybe not next year or even in the next five or ten years, but you might expect that eventually governments will start doing a lot more to curb fossil fuel use. You might then want to sell your fossil fuels now or very soon, while you can. If many resource owners feel this way, fossil fuel prices could fall and CO2 emissions would increase. </div>
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This observation amounts to the so-called "<a href="http://en.wikipedia.org/wiki/Green_paradox">green paradox</a>." Related arguments suggest that taxing carbon may have little influence on use, and subsidizing renewable fuels and alternative technologies, without taxing or otherwise limiting carbon-based fuels, might make global warming worse, since it could push emissions toward the present.</div>
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Research on these ideas, mostly theoretical, is pretty hot in environmental economics right now. It seems like half the submissions I manage at JEEM touch on the green paradox in one way or another. </div>
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All of it has me thinking about a point my advisor Peter Berck often made when I was in grad school. At the time, we were puzzling over different reasons why prices for non-renewable resources--mostly metals and fossil fuels--were not trending up like <a href="http://en.wikipedia.org/wiki/Hotelling's_rule" target="_blank">Hotelling's rule</a> says they should. Peter suggested that we may never use the resources up, because if we did, we'd choke on all the pollution. Resource use would effectively be banned before all of it could be used. If resource owners recognized this, they'd have no incentive to hold or store natural resources and the resource rent (basically the intrinsic value based on its finite supply) would be zero, which could help explain non-increasing resource prices.</div>
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For all practical purposes, Peter understood the green paradox some 15-20 years ago. Now the literature is finally playing catch up. </div>
Michael Robertshttp://www.blogger.com/profile/16455035518968529794noreply@blogger.com0