How we know what we know about the climate

Physics Today has a nice article this month on the history, development and contributions of the "A-train," a formation of satellites that orbit the planet and help us observe the climate system.  For non-experts, its a gentle and interesting introduction to many of the challenges associated with trying to observe and characterize the state of something as large and dynamic as, well... the entire world.

Without satellites, what we know about climate dynamics would be limited to what we can observe from the ground, balloons and airplanes.  While detailed measurements from the surface are essential to the science, they are expensive to collect and cannot tell us what is happening in locations far away.  Satellites, on the other hand, allow us to observe many locations rapidly at a cost that is lower than it would be if we tried to make all of those measurements from the ground.

The revolution that satellites offered us, in terms of understanding the dynamics of the planet we inhabit is often under-appreciated.  One might think that humans, inhabiting so many locations around the world, would somehow possess a single collective record of the daily temperature and daily rainfall everywhere. After all, how difficult is it to step outside with a thermometer and read it? Or to leave a bucket outside and check every day how much water fell into it?  It isn't terribly hard, but without the right institutional structure to provide funding, incentives to the observers and safe repositories for the data, it doesn't happen consistently.  A quick look at current weather stations in Central Africa compared to the Eastern Unite States compared to the Central Pacific confirms that our ground observations around the world are not of uniform density (or quality).

Here's an example of the challenges we face.  I study how changes in the environment, particularly the atmosphere, influence societies.  One question I've looked at is whether rainfall matters to the economies of the Caribbean and Central America.  To answer this question, what I'd like to do is to compare how economic outcomes (like profits in agriculture) respond to changes in rainfall. To do this, I tried to look at annual average rainfall over different countries over time.  But it turns out that getting this kind of data isn't trivial.

There are thirty-one countries south of Mexico and north of Colombia (each one is given a number on the y-axis in the figure at right).  But only about half of those countries regularly collected any rainfall data between 1950-1980.  In the figure, an open blue circle indicates that the country listed on the y-axis has at least one continuous record of rainfall for that year.  Not too many countries satisfy this criteria.  If I'm willing to work with data that's been processed a bit more, I could access statistically interpolated data that tries to use the existing station data to estimate rainfall in nearby countries.  Countries with complete records of interpolated data are given orange dots in the figure.  Notice that there's more estimated rainfall data, especially before the eighties when enough stations were collecting data to make useful estimates for the countries between stations.  But what happened between 1980 and 2000?  Why did the stations stop collecting data? And even worse, once enough stations stopped recording rainfall, the interpolated estimates become garbage, so the orange dots start to disappear too (note: the orange data stop in 2000 because of the data set, not because of data availability).  It would take a fair amount of detective work to figure out exactly why specific observations were missing, but a lot of social and political changes that might have contributed to the data scarcity were occurring in the 80's and 90's. For example, many of the countries in the region gained independence from European colonizers during that period.  If new young governments were less worried about maintaining unbroken records of rainfall than older colonial governments (which is often the case, and sometimes for good reason) than this could contribute to the breakdown of our station records.

Luckily, the early eighties were also the period when satellites first began taking regular observations of the atmosphere. And while satellite observations are never identical to surface observations, they are (1) often good enough and (2) always better than nothing.  Making the best of what we have,  gauge data, satellite data and numerical simulations can be combined to create longer, continuous and more consistent records of the environment.  The yellow dots above indicate that estimates of rainfall can be constructed using this combination of data sources.

We certainly don't know everything about these planetary scale systems, but sometimes its worth reflecting on the fact that we know anything at all.  Personally, I can't decide whether its more remarkable that someone organizes thousands of scientists around the world so that they all leave buckets outside to measure rainfall or that someone realized thirty years ago that thirty years later we would be so grateful to them for launching machines into space and leaving them there just watching for the weather.

IPCC scholarship to advance climate-change education in vulnerable regions

The IPCC has announced a scholarship program funded by the money awarded to the organization for its 2007 Nobel Peace Prize:

The IPCC Scholarship Programme has been established with the funds received from the 2007 Nobel Peace Prize award to the IPCC. The first funding partner of the Programme is Dr. Gro Harlem Brundtland, Former Norwegian Prime Minister and UN Special Envoy on Climate Change.

The goal of this programme is to help strengthen the scientific and technical capability in developing countries to contribute to climate science and research and to develop and implement climate change policies and measures at the domestic and international level. It should aim to stimulate a sound knowledge base and institutional strengthening in developing countries. Priorities will include research on climate processes or the impacts of climate change in the most vulnerable regions of the world, the potential for adaptation and mitigation, and sustainable development....

The IPCC Scholarship Programme is targeting the most vulnerable regions of the world where the IPCC has identified gaps in knowledge in terms of climate change science and impacts. These comprise developing countries, and in particular Least Developed Countries and Small Island Developing States.

Therefore applications coming from scholars from these areas and addressing issues relevant to them will be given priority. The Scholarship provides for living expenses, and in some cases, for tuition fees as well.

The Programme aims at developing the knowledge, skills and capacity of the scholars in order to address climate change impacts and sustainable development. Priorities include research on the impacts of climate change in the most vulnerable regions of the world and the potential for adaptation, mitigation and sustainable development.

This seems like a sensible, ethical use of the award and a step in the right direction.  For many years I have argued that the technical knowledge needed to assess the costs or benefits of climate change (and related policies) are in short supply for many of the countries that need the expertise the most.  Colleagues who attended various international climate negotiations also consistently comment that the absence of technical experts, particularly in poorer (probably more vulnerable) countries negatively (and strongly) affects the bargaining power of many countries.  Even in the United States, it seems difficult to obtain the technical know-how necessary to formulate informed, comprehensive climate policies.  Perhaps other wealthy individuals/organizations will follow the lead of the IPCC and support the development of this kind of human capital.

Displaying Matlab data in Google Earth

[ONE UPDATE AT BOTTOM]

It struck me the other day that Google Earth would be good platform for sharing my data with other people and found out that lots of people display their own data on the platform. Google even has a website to distribute Google Earth files that they like (this one, of the world population, was one of my favorites).  

So then I looked around to see if anyone had written code to help people convert Matlab data into Google Earth files.  The Google Earth Toolbox (recommended by my colleague Amir) worked great. It allows you to display data using functions very similar to standard Matlab functions (eg. imagesc()) only the output image is projected onto the planet and can be explored using Google Earth.  The image above is a map of the tropical cyclone climatology that I imported to Google Earth with only two lines of code. 

A = ge_imagesc(LON, LAT, flipud(DATA));
ge_output('FILENAME.kml', A);

[As indicated, the only bug I found was that some of the functions flipped the data vertically, which is easy to fix with one usage of the function flipud().]

It seems to me that one huge advantage of this presentation method is that pretty much anybody can view and explore global data-sets, since the interface is entirely intuitive and requires no manipulation (similar, in many respects to the contribution of Gapminder).

With a little extra work, I made the data-set dynamic in Google Earth, so that users can view data from different dates using a slider (see code below), and made this movie for use in presentations:

Below the fold is the script I used to make a Google Earth data set that changes over time.  Making the movie above required two additional steps.

1. Add a "Tour" in Google Earth (under "Add") which just records your browsing of your data in a movie that plays back in the Google Earth Application.
2. Use a screen capture program to record that movie as it plays back. (My friend Pam recommended this screen capture software, which works nicely and is easy to use). 

UPDATE:  SINCE I'VE REPEATED THIS PROCESS A FEW TIMES NOW, I'VE WRITTEN A FUNCTION IN MATLAB THAT GENERATES THE GOOGLE EARTH FILE.  ITS SIMILAR TO THE SCRIPT BELOW, BUT IS EASIER TO USE FOR AN ARBITRARY DATASET. DOWNLOAD IT HERE.

Nature Climate Change announced

Today, the publishers of Nature announced that they will begin publishing a new journal titled "Nature Climate Change". 

Launching in April 2011, Nature Climate Change will be a monthly journal providing in-depth coverage of the impacts and wider implications of the Earth's changing climate.

A multidisciplinary journal, Nature Climate Change will publish high-quality research across both the natural and social sciences. The journal's mission will be to unify the body of research on the understanding, and impacts, of climate change as well as to place it in a wider social and political context.

This is extremely useful for those of us who are trying to work on climate change related problems but use a variety of methods from different disciplines.

Nature Climate Change will be a focal publication for the research community and for other parties interested in the implications of global and regional climate change, including natural and social scientists, policy makers, economists, governments and all other researchers on the periphery of climate change research....

Nature Climate Change will be a focal publication for the research community and for other parties interested in the implications of global and regional climate change, including natural and social scientists, policy makers, economists, governments and all other researchers on the periphery of climate change research.

Panel finds East Anglia Climate Research Unit "scientific"

Another panel has found that the Climate Research Unit of East Anglia was not un-scientific in its work reconstructing historical surface temperatures.  The story and its background are here.

The new report is the last in a series of investigations of leading British and American climate researchers, prompted by the release of a cache of e-mail messages that cast doubt on their conduct and raised fresh public controversy over the science of global warming.

All five investigations have come down largely on the side of the climate researchers, rejecting a number of criticisms raised by global-warming skeptics. Still, mainstream climate science has not emerged from the turmoil unscathed.

The new report stated that “On the specific allegations made against the behavior of C.R.U. scientists, we find that their rigor and honesty as scientists are not in doubt.”

Quite amazingly, none of the standards of "truth" or "objectiveness" to which these and other climate scientists have been held are in any way applied to the members of the media, pundits or politicians who have made this kind of scientific research even more difficult to undertake.  I think most people would laugh if they imaged this kind of criticism applied to non-scientists, who are actually much more influential in shaping public opinion:

The latest report was by no means a complete vindication. Echoing the findings of an earlier report by a parliamentary committee in London, the reviewers criticized the scientists at the Climatic Research Unit for consistently “failing to display the proper degree of openness” in responding to demands for backup data and other information under Britain’s public-record laws.

As a scientist, I am frequently appalled at the types of things individuals can say on TV, with no accountability for whether their statements are true or not.  Forget about depositing data on public servers, I'd be happy if most people could even cite a single study that supported whatever claim they are making that day.

Even within the CRU drama, the double ethical standards have been upsetting.  Somehow, we've managed to forget that the email exchanges that started the debate were made public by a hacker. When China asks Google for some emails that it would like to use to indict its citizens, the American media explodes in anger.  But when a mysteriously anonymous hacker forcibly extracts emails from a private computer in an effort to discredit an honest scientist, we somehow don't even notice.  Even the NYT article seems to have exhonerated the hacker, carefully stating that

Embarrassing e-mail messages... were purloined from a computer at the university in November and posted to the Internet...

Note there is no use of the word "stolen" or "defamation".

Now that the validity of the science has been restored, we need to take steps to protect scientists.  It is unfathomably easy get public opion angry about technical scientific issues.  If we allow private individuals to act as intellectual vigilantes without any form of recourse when they are embarressingly wrong, it will become too easy and cheap to defame scientists when their findings are at odds with vested interest groups.  

To prevent such a situation from spiralling out of control, and hindering scientific inquiry, steps should be taken to prosecute those individuals responsible for this unnecessary drama.  I'm far from a legal expert, but I'm fairly certain that hacking, stealing emails and posting them online are sufficient evidence of malicious intent in something that should resemble a libel or defamation suit. While I am certainly angry about the fiasco, I don't think the hacker should be sued out of spite.  It is increadibly important that inaccurate and malicous attempts to discredit science be punished, otherwise there is no reason for people to not do it whenever it helps them to achieve some goal.

Biosphere Influences on Climate and Respect for Complexity

Photo source

Doing academic research involving climate means that every few times I tell someone what I do I get as a response some question to the effect of "So! What do you think about climate change?" I imagine Sol has similar experiences, and I think that responding to those inquiries in a way that manages to be informative, nontechnical, and contextual to what the person's heard before (even if it's counter to what I'm saying) isn't easy. I have a variety of different responses to this, but one thing I always like to emphasize is how complex the climate system is: over-forcing it the way we are now risks engendering potentially huge and baroque interaction effects that we won't anticipate until they are too late to reverse.

Two recent papers that highlight this issue in an interesting way came out recently in Nature and the Proceedings of the Royal Society B. The first, Smith, Elliot and Lyons' "Methane Emissions from Extinct Megafauna,"details an estimate of the net reduction in methane emissions that followed the (widely hypothesized as human-induced) extinction of large mammals in the Americas around 10,000BCE. They find that a large (they give between 12.5 and 100%) portion of the reduction in methane that we observe around that time period (a short period of global cooling known as the Younger Dryas) could be ascribed to the extinction of megafauna during this time period and the concomitant loss of their digestion-related methane emissions. This is important climatologically because methane is a very potent greenhouse gas, despite its relatively short residency time in the atmosphere, and thus big swings in it can be a major driver of short-term climate change.

The second paper, Boyce and Lee's "An exceptional role for flowering plant physiology in the expansion of tropical rainforests and biodiversity," estimates the effect that angiosperm (i.e., flowering) vegetation has on the climate, specifically on precipitation. Angiosperms have dramatically higher transpiration (evaporative loss through the plant, functionally the same as "plant sweat") rates than non-angiosperm plants and thus can contribute quite a bit to local precipitation, especially when heavily concentrated in areas like the Amazon rain forest. Boyce and Lee find that changing the flowering vegetative biomass in the Amazon from angiosperm to entirely non-angiosperm in a climate model would result in a huge decrease in precipitation, implying that such species are a major driver of the Amazon's extraordinarily high precipitation (which in turn drives a variety of other effects in the climate, as well as affecting rainforest biodiversity).

Now, obviously both of these are new papers and the extent to which the hypotheses they propose will stand up to scrutiny is still unknown. My personal interpretation is that the megafauna methane emissions paper, while intellectually pleasing, probably details a relatively minor part of the climate system while the angiosperm paper is pointing out a very interesting and fundamental part of biosphere-climate interactions. But both papers are nonetheless great examples of how complex the climate system is, and how even now we're still discovering fundamental drivers of climate variability. It's this complexity that should give us pause when we think about our current greenhouse gas emissions: if such a relatively minor (at least climatologically speaking) event as the extinction of some of the larger mammal species could potentially have led to or at least contributed to the Younger Dryas, then forcing the climate in so major a fashion as we currently are should definitely give us pause.

*Note: Image copyrighted L. Cunningham 2006.

Haiti in "This American Life"

This American Life has an insightful (albeit depressing) story on some of the troubles facing economic development in Haiti.  Listen to it for free here.  I highly recommend it.

Some of the issues are related to the recent earthquake, some are not.

The statement that struck me the most was along the lines of (I'm paraphrasing):

"Haitian farmer X could get much better prices on her mangoes that she sells to the US if she took steps to prevent them from bruising. However, farmer X doesn't mind bruises on her mangoes, and she cannot even conceptualize an American consumer that is picky about bruises on her mangoes.  So farmer X does not take the simple, cheap steps prevent her mangoes from bruising."

I can't decide if I think this statement is true or not.  The idea that farmer X cannot imagine her consumer is important and interesting, but whether this information should be conveyed through price signals in her market does not seem obvious. Theory would predict that farmers should simply be offered higher prices for those mangoes she has that are less bruised, and thus she would learn that she can make more money if she doesn't let her mangoes bruise.  If this is not happening, why is that?  The inability to conceptualize her consumer does might explain why she is not proactive about preventing mango bruises before she is offered prices, but it doesn't explain why the price mechanism is failing in this instance.  Perhaps someone will do research on this problem so it can be effectively addressed.

Earthscan Sustainable Economics, Business and Policy books

I received this catalog in the mail and was surprised by the number and breadth of academic books on sustainability issues.  Several well known authors and many interesting topics.  I cannot vouch for the quality, but I will certainly browse the catalog and thought it was worth sharing.  The complete list of catalogs is here.  I've added it to the Meta-Resources page.

Highlights from the World Congress of Environment and Resource Economics

This week was the Wold Congress of Environment and Resource Economics (WCERE) in Montreal, an event which happens only once every four years.  With 800 talks, I couldn't see most of them.  But below are titles, abstracts, links to papers and some of my thoughts on those talks that I found most interesting.  There's obviously sampling biases and my comments are based only on the talks, not the papers, so errors are not unlikely. In a future post, I'll include links to papers presented by students in the Columbia PhD program, which is why they are omitted below. To download the full working paper, follow the link to the WCERE site and click on the "download PDF" link on the right

INTER-ANNUAL WEATHER VARIATION AND CROP YIELDS  Wolfram Schlenker, Columbia University  Abstract: While the effects of rising mean temperatures on agricultural output have been studied extensively, there is limited discussion of the impact of inter-annual weather variation on crop yields. This paper estimates the link between weather and crop yields separating the influence of (i) mean weather outcomes (i.e., climate) to which a farmer can adapt from (ii) unpredictable year-to-year weather fluctuations to which a farmer can only partially adapt as crops are planted before the weather shock is realized. We find that corn in extreme climates, both hot and cold, are more sensitive to inter-annual weather variation than the ones in moderate climates. Global warming has two effects on corn yields: First, warming will induce farmers in moderate-temperate climates to plant varieties that are less robust to weather fluctuations, while farmers in cool climates will plant varieties that are more robust to weather fluctuations. Second, the elasticity of reductions in expected corn yields with respect to an increase in the standard deviation of weather fluctuations is -0.4. Since most farmers are eligible for subsidized crop insurance, an increase in weather variation also directly translates into added government payments. Comments: One of this paper's nicest aspects is that it explicitly looks for the "micro" structure underlying aggregate production curves. We often assume there is a sequence of production functions for several crops, each of which has a different peak point along some dimension (here, temperature).  [For an example of this assumed structure, see the discussion in Deschenes and Greenston (AER, 2007); their working paper is here.] The usual assumption is that farmers who adapt to climate changes produce along the upper envelope of several overlapping production curves and are therefore less vulnerable to long term changes in temperature than they are to short term fluctuations.  This paper tries to test this assumption explicitly and finds that it holds up.  The other findings of the paper are nice, but to me, this seems like the most important contribution. EXTREME WEATHER RISKS AND MIGRATION: TORNADOES Trudy Ann Cameron, University of Oregon Eric Duquette, University of Oregon Climate change is expected to alter the spatial pattern, frequency, and severity of extreme weather events. These events will likely catalyze individuals‘ migration decisions as a means to reduce both direct and indirect weather-related risks to economic livelihood, life, and health. We analyze historical patterns of migration among U.S. counties in relation to varying spatial and temporal patterns in tornado activity and as a function of variables intended to capture the evolution of perceptions of these tornado risks. Our results provide an opportunity to understand migration based on extreme weather data that cover a long time-period, have a nationwide spatial extent, and include important economic factors that may constrain or enhance individuals‘ abilities to mitigate economic and health risks via migration. The broader goal of this research is to improve our understanding of how different types of households adapt to changing natural hazards. In particular, we are interested in the distributional consequences of climate change impacts of this type. Comments: The data assimilation involved with this paper is impressive, so I bet they will produce more work using these tornadoes as instruments. The results are clean and somewhat larger than I would have expected.  However, I think the title and interpretation may not be appropriate, since it was not clear that they ever tested for impacts associated with the risk of events occurring, only the events themselves.

GHG TARGETS AS INSURANCE AGAINST CATASTROPHIC CLIMATE DAMAGES

Martin Weitzman, Harvard

Abstract: A critical issue in climate-change economics is the specification of the so-called "damages function" and its interaction with the unknown uncertainty of catastrophic outcomes. This paper asks how much we might be misled by our economic assessment of climate change when we employ a conventional quadratic damages function and/or a thin-tailed probability distribution for extreme temperatures. The paper gives some numerical examples of the indirect value of various GHG concentration targets as insurance against catastrophic climate-change temperatures and damages. These numerical examples suggest that we might be underestimating considerably the welfare losses from uncertainty by using a quadratic damages function and/or a thin-tailed temperature distribution. In these examples, the primary reason for keeping GHG levels down is to insure against high-temperature catastrophic climate risks.

Comments: I think this is the most constructive of Weitzman's string of papers on catastrophic risk, perhaps because the final result (which is actually the absence of a result) doesn't rest on infinite negative losses.  I was most struck by his case that the quadratic loss function assumed by Nordhaus et al. is insufficiently flexible to express large aversions to catastrophic events.  I think this is an example of a seemingly innocuous, esoteric assumption made twenty years ago coming back to bite us later.

THE ECOLOGICAL FOOTPRINT OF POVERTY ALLEVIATION: EVIDENCE FROM MEXICO'S OPORTUNIDADES PROGRAM

Jarrod Welch, University of California, San Diego

Jennifer Alix-Garcia, University of Wisconsin-Madison 

Craig McIntosh, University of California, San Diego 

Katharine Sims, Amherst College

Abstract: We study the consequences of poverty alleviation programs for environmental degradation in Mexico. We exploit the community-level eligibility discontinuity for Oportunidades (a conditional cash transfer program) to identify the impacts of income increases on local deforestation, and use random variation in the initial program phase to explore household responses. We find that additional income significantly increases demand for resource-intensive consumption goods. The corresponding production response increases deforestation but is localized only where communities have poor road infrastructure. The results suggest that better access to markets simply disperses environmental harm; the true impacts are only observable in infrastructure poor areas.

Comments: I really like what this paper is trying to do, the data they are willing to integrate and the importance of the findings.  It has all the makings of a great paper.  My only concern is whether the absence of deforestation in communities with high road densities is robust.  In general, its far harder to demonstrate a "null" result than to show that some sort of non-zero relationship exists, since failing to reject a null-hypothesis doesn't tell you much. I think that in order to make the claim described in the last two sentences of the abstract, they'll have to do substantially more work with the NDVI data they are using and/or look at data on trade or other markets.

CLIMATE CHANGE, THE MONSOON, AND RICE YIELD IN INDIA

Jeff Vincent, Duke University

Maximilian Auffhammer, University of California, Berkeley

Abstract: Recent research indicates that monsoon rainfall became less frequent but more intense in India during the latter half of the Twentieth Century, thus increasing the risk of drought and flood damage to the country’s rice crop. Our statistical analysis of state-level Indian data confirms that drought and extreme rainfall negatively affected rice yield during 1966-2002. Using Monte Carlo simulation, we find that yield would have been 2% higher on average if monsoon characteristics, especially drought frequency, had not changed since 1960. Yield would have received an additional boost of nearly 5% if two other meteorological changes (warmer nights, lower surface radiation) had not occurred. Climate change has evidently already negatively affected India’s hundreds of millions of rice producers and consumers.

Comments: I think their Monte Carlo method will become standard in the next few years. Its a nice way at getting at "climate" in comparison to "weather".

SPECIFICATION AND ESTIMATION OF THE TRANSFER FUNCTION IN PALEOCLIMATIC RECONSTRUCTIONS

Maximilian Auffhammer, University of California, Berkeley

Brian Wright, UC Berkeley

Seung-Jick Yoo, Korea Energy Institute

Abstract: We identify two issues with a standard time series approach to reconstruction of past climate fluctuations from paleoclimatic data series, one related to specification of the estimated relationship between climate and the paleoclimatic index, the other to the methodology of estimation. We show that the standard approach provides biased estimates of the reconstructed climate series and underestimates the true variability of historical climate. We demonstrate that inversion of the estimated response function between tree ring growth and climate indicators provides consistent estimates of historical climate. The inversion method results in an overestimation of the variance. We show analytically as well as using Monte Carlo experiments and actual tree ring data, that use of the new specification and reconstruction procedure can be crucial for inferences about the nature of past climate and interpretation of recent climate variations.

Comments: I think this paper is going to have a big impact.  And I think the idea of having statisticians from different fields check one another's work is brilliant.  I hope this is implemented more in the future and I hope economists invite non-economists to check their math, so the exchange is bidirectional.

TEMPERATURE AND THE ALLOCATION OF TIME: IMPLICATIONS FOR CLIMATE CHANGE

Matthew Neidell, Columbia University

Joshua Graff Zivin, UC San Diego

Abstract: In this paper we estimate the impacts of climate change on the allocation of time using econometric models that exploit plausibly exogenous variation in daily temperature over time within counties. We find large reductions in U.S. labor supply in industries with high exposure to climate and similarly large decreases in time allocated to outdoor leisure. We also find suggestive evidence of short-run adaptation through temporal substitutions and acclimatization. Given the industrial composition of the US, the net impacts on total employment are likely to be small, but significant changes in leisure time as well as large scale redistributions of income may be consequential. In developing countries, where the industrial base is more typically concentrated in climate-exposed industries and baseline temperatures are already warmer, employment impacts may be considerably larger.

Comments: Clean, simple and intuitive. I think there will be more work in this direction.  The only surprise is that in the twenty years since Schelling's armchair assessment, nobody thought of this.

Iceland, Freedom of Expression, and Institutional Competition

Iceland's Althing* just passed a resolution that was being heavily pushed by Julian Assange, the founder of Wikileaks (and recently profile-ee of the New Yorker, here) that seeks to make Iceland's protection of freedom of expression, especially over the internet, the strongest in the world.

Now, there are multiple ways of thinking about why this was done (that it's an attempt to bring international accolades to a country that's been rather macroeconomically embarrassed of late doesn't seem out of the realm of possibility...) but what I find most interesting about is that it's yet another example of traditionally noneconomic things getting some very economic treatment. The language that's been used to cover the bill thus far has been quite evocative of another form of "institutional competition," namely tax havens. It's fairly conceptually similar to the way that places like the Caymans Islands have decided to give themselves comparative advantage among investors by setting low tax laws and regulations that encourage the creation and hassle-free maintenance of off shore investment vehicles.

Yes, there's a fundamental information asymmetry difference here in that freedom of expression is, by definition, observable, so unless they do it anonymously dissidents from other countries will only be protected from Iceland's laws, which is probably not what they're worrying about in the first place. That does make it a lot less attractive than the knowledge that I could dump some ill-gotten gains in a numbered account in Lichtenstein and never have it get found, taxed, or linked to my ill-getting, but nonetheless. The decision to institutionally compete is there, and I'm curious to see how it'll pan out and whether it'll have any material effect.

Now all we need is a greater degree of institutional differentiation and a reduction in migration barriers and we can get some megascale Tiebout sorting. Lower taxes for Russian-style restrictions on free speech, anyone?

* I feel like somewhere there's an undergrad viking mythology professor who's very happy I'm linking to the webpage of the oldest parliamentary institution in the world. And no, I can't read Icelandic. But Google Chrome does have Google Translate built in...

Watching soccer and celebrating planetary-scale achievements

I was watching the World Cup live on my laptop during breakfast today when I was struck by the massive achievements underlying this simple activity.

The players in Capetown are 7,800 miles (12,600 km) away from my NYC apartment (as the albatross flies, following the curved surface of the planet: see Google Earth image). Only 200 years ago on a fast clipper ship sailing 16.7 mph, it would have taken 19.5 days for information to travel that distance.  Driving a similar distance non-stop at 60 mph would take 5.4 days.  But now I can watch a goal being scored in almost real time (it only takes light, which is slightly faster than the transmitted signals, 0.042 seconds to travel that distance in a vacuum) while eating a bowl of oatmeal.

Not only have we developed the technology to send these signals around the world, but we have built the infrastructure to carry them, we have developed the legal and institutional machinery necessary to support and organize such massive infrastructure investments, and we have implemented an economic system that rewards individuals for doing all of the above.  Moreover, the information isn't just traveling from Capetown to my breakfast table, but to a few billion breakfast/lunch/dinner tables around the world.

It's easy to get down on humanity when you're focused on our mistakes and shortcomings, but sometimes its worth it to step back and celebrate our achievements.  We've come a long way from where we once were.

Risk Denialism and the Costs of Prevention

Sol's posts about the BP oil spill (here and here) got me thinking a little bit about the interplay between denialism and risk and how deeply related that is to the sprawling mess of concepts we put under the umbrella of "sustainable development."

One of the major themes in the coverage of the spill has been how poorly equipped BP was to deal with a spill of this magnitude. Now, regardless of your opinion of BP as a company, ex-post this is a bad thing. BP would much rather, right now, be known for its quick, competent and effective response to a major catastrophe than be roundly (and for that matter, rightly) villainized. They've lost about half their market cap since the spill happened, and now they have to set up a $20 billion clean up escrow account. Why weren't they better prepared?

There are a few potential answers to that. The spill's magnitude may simply have been completely unforeseeable, a "Black Swan" -style event that BP can be forgiven for not anticipating the same way New Yorkers can be forgiven for not buying tornado insurance. Or perhaps, net of prior cost-benefit analysis, the probability of a spill this big was so low compared to the cost of maintaining intervention equipment that BP decided to skimp on it, akin to how most New Yorkers spurn flood or wind insurance despite the fact that hurricanes intermittently hammer the city. But the answer that now seems most likely is that that there was a fundamental disconnect between what the rig workers told upper management (internal BP documents refer to it pre-spill as a "nightmare") and what upper management told them to do. This is akin to New Yorkers not buying renter's insurance after they've been told the burglary rate in their neighborhood is quite high.

What I find interesting about this is how closely the framework of this story jibes with so many of the other narratives in environment and development. A group of technically trained experts warns of a potentially catastrophic risk (climate change, overfishing, pandemic flu) only to have their warnings discarded by cost-bearing decision makers (politicians, corporate executives, voters) who deny that the risk is as great or even extant. In these scenarios, it's not that the decision makers wouldn't face massive costs should they turn out to be wrong, it's that there's a big difference between what their behavior indicates they think the probability of facing those costs is and what they're being told by technical advisors.

Why? Well, it seems that cost-bearing seems to have a very strong influence on how someone interprets difficult-to-verify information about risks, especially social / shared risks. In psychology this is known as the defensive denial hypothesis and there's a fair bit of empirical evidence to support it. The BP managers tasked with running the platform knew the immediate costs of reducing flow, or stopping drilling, or increasing safeguards, and it seems highly likely that this influenced how they interpreted warnings from the rig workers. The same sort of phenomenon seems to occur in a lot of other areas: fishermen are more sanguine about the risks of overfishing, oil executives downplay the risk of climate change, and derivatives traders claim that their activities are nowhere near dangerous enough to warrant regulation. Now, many other factors are clearly at stake in all of these, from discounting to strategic maneuvering to cheap talk, but given the genuineness with which deniers of, say, climate change argue their case, it seems difficult to say that they are not at least somewhat personally convinced that their interpretation of the evidence is correct.

Now that on its own isn't terribly revelatory, but when you combine it with the notion that perceptions of costs can be subject to manipulation as well, you get an interesting result. The more (or less) salient a risk's mitigation cost is made, the lower (or higher) people come to view the probability of that risk. Witness, for example, the repeated attempts to link efforts to combat climate change to personal tax burden by those who think (or claim to think) that it's a bogus risk. This may not even necessarily be an actual cost: the Jennifer McCarthy- led trend in parents refusing to vaccinate their children (leading to the actual risk of polio, measles, etc.) seems to be almost entirely a function of parents being led to believe that there is a potential cost to vaccination (possible autism) that is not supported by scientific evidence. BP's managers faced the immediate and salient costs of risk mitigation steps that they'd need to justify to highers-up and behaved in a way that seems to indicate that they didn't think the risk of a major industrial accident was worth fretting over.

So what? I think the lesson here is that while risk denial is often depicted as stemming from short-sightedness, or ignorance, or political zealotry, it's actually pretty common human behavior. People have preferences and like to align their behavior appropriately, and if that means that they have to subconsciously alter their assessments of how dangerous some far off activity may be, they'll do so. If we are concerned about arresting climate change, or preserving biodiversity, or managing natural resources, then it's important to keep in mind that the way people perceive the incidence of the cost of mitigation will not only affect their preferences in terms of raw cost-benefit analysis, but also legitimately move their perception of the riskiness of their behavior. If we want political support for efforts to deal with these sort of risks, it thus seems similarly important to find and then emphasize ways in which the costs can be made low and painless as it is to stress the potential for future damages.

Standard error adjustment (OLS) for spatial correlation and serial correlation in panel data in (Stata and Matlab)

I've been doing statistical work on climate impacts (see a typhoon climatology of the Philippines to the right) and have been having trouble finding code that will properly account for spatial correlation and serial correlation when estimating linear regression models (OLS) with panel (longitudinal) data.  So I wrote my own scripts for Matlab and Stata.  They implement GMM estimates similar to Newey-West (see Conley, 2008). You can download them here.

They are "beta versions" at this point, they may contain errors.  If you find one, please let me know.

In the future, I may try to document what I've learned about the different estimators out there. But for the time being, some of the advantages of what I've written are:

• The weighting kernels are isotropic in space
• The spatial weighting kernel can be uniform (as Conley, 2008 recommends) or decay linearly (a la Bartlett).
• Serial correlation at a specific location is accounted for non-parametrically
• Locations are specified with Lat-Lon, but kernel cutoffs are specified in kilometers; the code accounts for the curvature of the earth (to first order) so relatively large spatial domains can be accounted for in a single sample
• An option allows you to examine the impact of adjustments for spatial correlation and spatial + serial correlation on your standard errors
• The Stata version follows the format of all Stata estimates, so it should be compatible with post-estimation commands (eg. you can output your results using "outreg2").

[If you use this code, please cite: Hsiang (PNAS 2010)]


STATA VERSION 2 UPDATE 2013: Thanks to my field-testing team (Gordon McCord and Kyle Meng), several bugs in the code and additional options have been added.  Most useful changes: the code now correctly accepts the wildcard "*" when specifying variables and the option "dropvar" can be used to drop variables that Stata regards as 'too collinear' (contributed by KM).

STATA VERSION 3 UPDATE 2018: Thanks to some careful bug-chasing by Mathias Thoenig and Jordan Adamson, I've fixed a single line of code that led to a miscalculation in the weights for autocorrelation across within-unit panel observations. This error did not affect the Matlab implementation. This error did not affect calculations for the influence of spatial autocorrelation within contemporary observations or adjustments for heteroskedasticity. The previous version (v2) sometimes over-inflated or under-estimated the standard error estimate adjustment due to auto-correlation, but the magnitude of this effect depends on both the serial-correlation structure in the data and the maximum lag length parameter (lagcutoff) determined by the user. For very long lag lengths (e.g. infinity) the standard error estimates are too small, but for shorter lag lengths the bias may be of either sign.

My (currently ad-hoc) help file for the Stata script is below the fold. The Matlab code has an associated help-file embedded.

Spatial data analysis resources

A nice site at Carleton College lists many resources for spatial data analysis (books, programs, data, etc).  Be sure to explore the menu bar on the left, since not all the links are visible initially.

It's been added to the page of Meta-resources.

Free online JPAL lectures about conducting randomized field trials

Real experiments have set a new standard for establishing causality in development economics.  In an evaluation process that looks very similar to clinical trials in medicine, social policies in a randomly assigned "treatment group" are evaluated against outcomes for other individuals in a "control group." The argument for this kind of work is that (1) the effect of social policies must be measured carefully if we want to understand whether they are worth implementing at a national level and (2) historical analysis of policies is often insufficient because there is no "control group" against which outcomes can be compared.  For a good example of this kind of research, read this Tech Review article on Ben Olken's work on democracy in Indonesia.

Not all modern work in development is of this type since not all interventions or questions are suitable for the method (eg. my own work studying the impact of hurricanes on development cannot be), but there is a trend toward relying on it more and more. 


The Abdul Latif Jameel Poverty Action Lab (JPAL) {co-founded by Ester Duflo, the subject of an earlier post by Jesse} is a group of researchers that do randomized field trials of various policy interventions for poor communities. While many people do this kind of work, the folks at the (JPAL) have honed the method and mastered the logistics (which are tremendously complex).  There is course taught by several people at JPAL for executives about how to conduct a randomized trial (and why) which has been recorded and is free online.  To access it:

1. open up iTunes (which you can download for free here) 
2. go to the iTunes Store (there is a tab on the left for it)
3. search "poverty action lab executive training" 

All the lectures should show up and are free to download.

How wise is a cellulosic ethanol mandate?

The Tech Review describes recent "slow progress" on the development of commercial cellulosic ethanol.  Apparently, some companies are moving ahead with strategies to scale up:

ZeaChem, based in Lakewood, CO, has begun construction of a 250,000-gallon-per-year demonstration plant in Boardman, OR, that will produce chemicals from sugar and eventually ethanol from wood and other cellulosic materials...

The company's strategy for making the business a financial success and attracting investment for commercial scale plants is to start by producing ethyl acetate, which "takes about half the equipment and sells for twice the price of ethanol, so it's an ideal starter product," he says. Other biofuels companies are taking a similar approach--looking for high value products to offset high costs, at least initially. ZeaChem plans to incorporate the technology into an existing corn ethanol plant for commercial production of ethyl acetate. "If all goes well, that plant could be in operation by the end of next year," he says. A stand-alone commercial cellulosic ethanol plant would follow. It could switch between selling acetic acid, ethyl acetate, or ethanol, depending on the market.

If you're at all confused about why this happening, when we don't know how to make cellulosic ethanol without net energy expenditure, its driven by government support:

A renewable fuel standard signed into law in late 2007 requires the use of 100 million gallons of cellulosic ethanol in the United States this year and will ramp up to 16 billion gallons by 2022. But so far no commercial plants are operating, according to the Biotechnology Industry Organization (BIO), a leading trade group representing biofuel companies. The U.S. Environmental Protection Agency announced in February that it was scaling back the mandates to just 6.5 million gallons, which could be supplied by existing small-scale demonstration plants and new plants expected to open this year. That's up from approximately 3.5 million gallons produced in 2009.

Is it wise for our government to be driving this kind of investment?   One concern is that biofuels, in general, lead to the production of crops for energy which necessarily will increase the prices of food crops that are displaced.  In a recent working paper, Wolfram Schlenker and Michael Roberts try to identify (using weather shocks) the effect of the biofuel mandate on world food prices.  They predict that the biofuel mandate, as it stood at the time of writing, would lead to an increase of world food prices by 20-30%.  Further, they argue that since agricultural production will expand to meet this demand, and expansion of cultivated land releases CO2 in net, the policy may not even reduce GHG emissions.

This second point reminds me of a blog post I wrote two years ago, where I argued that innovations in the technology for the conversion of cellulose into fuel may have dramatic externalities.  If biomass that is usually considered "useless" suddenly has a shadow price, the strategic incentives to harvest entire ecosystems may be dangerously strong.  

The government should almost certainly not be subsidizing the development of this technology; and one can argue (depending on how risk-averse you are) that they should be taxing it for the risk we all are bearing should it succeed.  

1980

This is surreal to me. This is an excerpt from an article in the New York Times on April 12, 1980.

I repeat. 1980. Thirty years ago. The internet was still science fiction then.

It is about the oil spill caused by the Ixtoc I oil well in the Gulf of Mexico, which spewed 140 million gallons over several months in 1980:

History's largest oil spill has been a fiasco form the beginning to end. Human errors and ineffctive safety equipment caused the blowout, and none of the "advanced" techniques for plugging the well or recapturing the oil worked satisfactorily thereafter. The gusher ran wild for nearly ten months....

The enduring question is whether a devastating blowout could occur in our own offshore waters....

A second question: Could a blowout in American waters be quickly capped and cleaned up? Ixtoc I shows that control technology is still quite primitive.  Attempts were made to jam the pipe back into the hole; a large cone was lowered over the well to capture oil and gas, and steel and lead ball were dropped down the well to plug it. Nothing worked. Relief wells to pump in mud failed for months to reach their target... The mop-up techniques did not function effectively either....

Most Americans would accept risking such blowouts to find oilfields as rich as Mexico's.  But the lessons of Ixtoc I can help reduce the risks.

If you don't know why this is darkly funny, look over the list of strategies BP has employed to stop the current spill.  There is obviously something wrong with the incentives to innovate emergency/cleanup technology.  In 1980, the techniques we are still using today were being joked about as "advanced."

The sheer volume of innovation that has occurred in the last 30 years across an uncountable number of research fields is astonishing and a tremendous feat of human ingenuity.  The fact that effectively zero innovation has occurred in oil-drilling-catastrophe-management suggests that nobody believed there was a sufficient payout to warrant such investments.  Since these catastrophes are massive public-bads, and the cost of the externality is almost certainly not internalized by the oil-companies, then standard economic theory would suggest the government needs to create incentives to invest in these technologies.  The problem is doubly difficult because we often think that research in profitable industries is under-supplied, since researchers cannot capture the full value of their work.  So policies to reduce our risk must counteract both a public-bad problem and an innovation problem.

The obvious tendency is to heap blame on BP.  But the current situation is a result of 30 years (at least) of improper policy.  Whether the US government had sufficient information in 1980 to realize its policies motivating innovation [in these technologies] was too weak, I cannot say.  But this article seems to suggest that perhaps they did.

(Oil spill & windmill) x (technology & management)

The BP spill is tragic, but it's gotten me learning about oil drilling technology and history. Some nice sites by the NYTimes, which I think are generally much more interesting then their coverage of the political drama:

A graphical explanation of how the well was supposed to be plugged.

A reference describing the different attempts at stopping the leak.

A timeline of historical oil spills, with actual newspaper clippings from the events and descriptions of the legislation that followed each.

Also, quite depressing, is this innovative way of communicating to people exactly how large of an area is affected by the slick.

After all of this, I think (or at least hope) that we're all more educated about the technology underlying our massive energy infrastructure.

Trying to be a pragmatist, I keep looking at these diagrams (especially the ones that show how far underwater, and underground, the leaking well is) and thinking to myself, "is this really easier than windmills, seriously?" It's understandable (at least from a micro-economists perspective) that power companies with massive amounts of sunk capital would misrepresent the costs of a transition to cleaner technology, even if it would have large positive externalities. Interestingly, on this point, a recent NREL study suggests that the costs of stabilizing energy supply using wind and solar power have been exaggerated.

A major argument against more rapid investment in renewables provided by many energy experts has been that it is difficult to ensure a steady supply of energy when weather and daylight fluctuate. To me, this always seemed dubious, since the US is enormous and there are large anti-correlations in wind and cloud-cover across the country. It seemed that if panels and farms were distributed intelligently across space, we could take advantage of these known structures to provide a smoother power-supply to the country. All it would take is some planning, knowledge of meteorology and some cooperation among utilities. This is exactly the finding of the NREL study. They say that 35% of energy could be supplied by renewables without installing substantial backup capacity. All it would take is a little coordination.