Histories of Numerical Climate Models

Meteorology Project, 

Institute for Advanced Study, Princeton, 1952. 

Left to right: Jule Charney, MANIAC I, Norman Phillips, 

Glenn Lewis, N. Gilbarg, George Platzman.

Courtesy AIP Emilio Segrè Visual Archives.

I was recently doing some background reading on the history of general circulation models (GCMS) for a paper and thought I'd share two of the nice histories I found.  The first is one that I've shared with many colleagues, both for information and for inspiration:

General Circulation Models of Climate

ABSTRACT: The climate system is too complex for the human brain to grasp with simple insight. No scientist managed to devise a page of equations that explained the global atmosphere's operations. With the coming of digital computers in the 1950s, a small American team set out to model the atmosphere as an array of thousands of numbers. The work spread during the 1960s as computer modelers began to make decent short-range predictions of regional weather. Modeling long-term climate change for the entire planet, however, was held back by lack of computer power, ignorance of key processes such as cloud formation, inability to calculate the crucial ocean circulation, and insufficient data on the world's actual climate. By the mid 1970s, enough had been done to overcome these deficiencies so that Syukuro Manabe could make a quite convincing calculation. He reported that the Earth's average temperature should rise a few degrees if the level of carbon dioxide gas in the atmosphere doubled. This was confirmed in the following decade by increasingly realistic models. Skeptics dismissed them all, pointing to dubious technical features and the failure of models to match some kinds of data. By the late 1990s these problems were largely resolved, and most experts found the predictions of overall global warming plausible. Yet modelers could not be sure that the real climate, with features their equations still failed to represent, would not produce some big surprise.

And here is one of my favorite passages about the birth of the enterprise:

In 1922, the British mathematician and physicist Lewis Fry Richardson published a more complete numerical system for weather prediction. His idea was to divide up a territory into a grid of cells, each with its own set of numbers describing its air pressure, temperature, and the like, as measured at a given hour. He would then solve the equations that told how air behaved (using a method that mathematicians called finite difference solutions of differential equations). He could calculate wind speed and direction, for example, from the difference in pressure between two adjacent cells. These techniques were basically what computer modelers would eventually employ. Richardson used simplified versions of Bjerknes's "primitive equations," reducing the necessary arithmetic computations to a level where working out solutions by hand seemed feasible. Even so, "the scheme is complicated," he admitted, "because the atmosphere itself is complicated."  

The number of required computations was so great that Richardson scarcely hoped his idea could lead to practical weather forecasting. Even if someone assembled a "forecast-factory" employing tens of thousands of clerks with mechanical calculators, he doubted they would be able to compute weather faster than it actually happens. But if he could make a model of a typical weather pattern, it could show meteorologists how the weather worked. 

So Richardson attempted to compute how the weather over Western Europe had developed during a single eight-hour period, starting with the data for a day when scientists had coordinated balloon-launchings to measure the atmosphere simultaneously at various levels. The effort cost him six weeks of pencil-work Perhaps never has such a large and significant set of calculations been carried out under more arduous conditions: a convinced pacifist, Richardson had volunteered to serve as an ambulance-driver on the Western Front. He did his arithmetic as a relief from the surroundings of battle chaos and dreadful wounds.

The work ended in complete failure. At the center of Richardson's simulacrum of Europe, the computed barometric pressure climbed far above anything ever observed in the real world. "Perhaps some day in the dim future it will be possible to advance the calculations faster than the weather advances," he wrote wistfully. "But that is a dream." Taking the warning to heart, meteorologists gave up any hope of numerical modeling.

I also found this chapter in a Google-book from a decade ago which had a very nice introduction to some of the early experiments and technical innovations.  It's slightly more technical, but extremely succinct. Less history but maybe more science.

Temperature and aggression

Sometimes we don't take time to think about simple "obvious" things or to seriously consider older bodies of research.  Here's a gem from 1986 that I found recently.

Ambient Temperature and Horn Honking: A Field Study of the Heat/Aggression Relationship
Environment and Behavior, March 1986
Douglas T. Kenrick and Steven W. MacFarlane

Abstract

Using a method developed in previous field studies of aggression, this study examined the influence of ambient temperature on responses to a car stopped at a green light. To investigate alternative models of the effects of high temperature on interpersonal hostility, the study was conducted during the spring and summer in Phoenix, Arizona, and included a range on the temperature humidity discomfort index up to 116 degrees F. Results indicated a direct linear increase in horn honking with increasing temperature. Stronger results were obtained by examining only those subjects who had their windows rolled down (and presumably did not have air conditioners operating).

[As someone who spends the entire day in front of a computer screen, I am jealous of people who do this kind of research:]

[and the results:]

This relatively older literature on temperature and aggression is reviewed here.  It seems to be something that psychologists studied for a while, but hasn't been discussed at all in the highly technical discussions of climate change impacts.  Perhaps it could add something to the recent controversy over the idea that temperature changes can lead to conflict? In my reading of the recent literature, I don't think this mechanism was ever discussed. Somehow we manage to miss simple and "obvious" mechanisms when we think about complex global processes.

(A similar case is the effect of temperature on productivity, which again should be "obvious"...) 

Strategic dissent in the PRC

Yale's Chris Blattman, whose blog on development and violence is one of the more fun academic blogs out there, points to an excellent interview in the New York Review of Books with Yang Jisheng, the author of recent book called Tombstone about China's Great Famine, a.k.a. the three years of hunger and deprivation caused by the Great Leap Forward. The book is noteworthy for the fact that Yang basically went around gathering a variety of records about how horrific the famine was under the pretense of doing agricultural work. If it strikes you as odd that the notoriously secretive Chinese government kept files on things like cannibalism, well:

Ian Johnson: I wondered when reading Tombstone why officials didn’t destroy the files. Why did they preserve all this evidence?

Yang Jisheng: Destroying files isn’t up to one person. As long as a file or document has made it into the archives you can’t so easily destroy it. Before it is in the archives, it can be destroyed, but afterwards, only a directive from a high-ranking official can cause it to be destroyed. I found that on the Great Famine the documentation is basically is intact—how many people died of hunger, cannibalism, the grain situation; all of this was recorded and still exists.

That potentially very embarrassing records don't generally get destroyed once they're in the system (a) makes one wonder what else is in there and (b) gives hope that at some point (hopefully in the not-too-distant future) some bright young researcher will get a nice fat chunk of data out of those records and be able to write some very cool papers.

That said, what I find even more compelling is this: given the notoriously repressive regime, how does Yang operate? He runs a "reform-oriented" journal Annals of the Yellow Emperor and not only keeps it from getting shut down, but manages to publish a very controversial (enough to be subsequently banned) book. How?

Why do you think your magazine seems to enjoy more leeway than other Chinese publications?

Because we know the boundaries. We don’t touch current leaders. And issues that are extremely sensitive, like 6-4 [the June 4th Tiananmen Square massacre], we don’t talk about. The Tibet issue, Xinjiang, we don’t write about them. Current issues related to Hu Jintao, Jiang Zemin and their family members’ corruption, we don’t talk about. If we talk just about the past, the pressure is smaller.

Now, as a total non-sinologist, and probably falling too much into the classic economic style of reasoning, I read this in two ways:

• The first is that there's a reputational cost associated with stifling dissent (of course), and it's one that I suspect is increasing in the apparent harmlessness of the dissenter. For all the noise other governments make about human rights, everyone knows that China censors its internal critics and to a certain real-politik extent accepts it. But if China is seen as being unnecessarily repressive ("why are you going after this guy? He's writing about events that happened two generations ago") then it undermines their censoring practices in general, and they don't want that.
• The second hews more closely to the limited amount of work I've read on how the Chinese elite views change, which is to say that many are in favor of making China less repressive but think it needs to be done piecemeal lest the country fly apart. In this light, and ignoring those in power who are more interested in elite-capture (which may, admittedly, be a dumb thing to do), people like Yang are actually *very* valuable to the ruling class. They allow a slower, more controlled and more co-optable approach towards reform. Looking at how long it takes even democratic governments to admit prior mistakes and wrong doing (Japan and WWII atrocities; the US's treatment of indigenous peoples) this seems to make a lot of sense. For China to up and say "you're right, we shouldn't have cracked down so hard at Tiananmen" would be hugely disruptive and likely terrifying for those in power, but admitting that their forebears made deadly policy mistakes much less so while still moving the country further towards openness and democracy.

In either event, the interaction displays strategic behavior on both sides: Yang recognizes there are certain subjects about which he'd like to write that he ignores because the costs are too great, and the regime recognizes that some types of dissent are either too valuable or too costly to repress. Something to keep in mind when one thinks about authoritarian rule, its limits, and their drivers, especially in places like Russia where allowable dissent seems even more circumscribed than in China.

Collections of papers on climate change economics

Found these recently:

Distributional Aspects of Energy and Climate Policy
Special Issue of The B.E. Journal of Economic Analysis & Policy

The Economics of Climate Change: Adaptations Past and Present
Gary Libecap and Richard H. Steckel, editors
Forthcoming from University of Chicago Press

Catching up on some TED talks

I was recently catching up on TED talks and I found these ones thoughtful.

Hans Rosling (the founder of Gapminder) on infant mortality statistics and the Millenium Development Goals.  [I particularly like his point that Sweden never had declines in its infant mortality rate fast enough to satisfy the MDGs]

Also, David Bismark on verifiable electronic ballots.

The rise of the "global environment" as an idea

Google Books Ngram Viewer was released today and is being heralded as a new tool to peer into our collective social conscious (whatever that is).  Basically, Google has scanned zillions of books and created a database of all the words they contain. This data base allows anyone to search for a specific word or phrase and see how often it comes up (as a fraction of all words written in books).

Below are a few graphs that seemed interesting, where I was looking for terms that are often used in academic discussions of the "global environment".  I'm not sure exactly what these tell us, but I think they're fun to look at.  

First, the "global environment" as a pair of words seems to have taken off in the 70's and 80's and then fallen fast after the turn of the millenium.

Words related to global climate change seem to have exploded in the late 80's. But the "greenhouse effect", which actually describes the scientific concept underlying the issue, seems to have peaked early.  Meanwhile "albedo", which is just the scientific term for the reflectivity of the surface (an important parameter in climate dynamics) was moderately popular long before warming was an issue and hasn't grown in usage with the other terms.

If we look at a few terms that describe human developments, "industrialization" grew early and fast, peaking in the 60's and then declining.  Meanwhile the "green revolution" gained notice as it was occurring in the 70's, but never was widely discussed.  "Globalization" and "sustainable development" grew together at almost identical rates in the 80's and 90's.  I think the original synchrony of those two terms in the "collective consciousness" is something quite meaningful, actually, although they diverge later.

Finally, looking at a few abstract terms, we see that the 90's were the time when things started to take off and we can also see the famous switch in usage between "natural capital" and "ecosystem services".

G-ECON and SAGE data in Google Earth

I was working with William Nordhaus's G-ECON dataset and the SAGE cropland datasets, so I figured I would format them for Google Earth.  (In an earlier post, I explained how to do this with your own data).

Here they are for download, if you'd like to explore them. (Instructions: Download the file, and double click it to open it in google earth.  They all change with time, which you can control with the slider at the upper left of the screen.  The images may take a second to load. You can save the file in google earth by dragging the layer to "my places" so google earth will always open it when it starts up.)

Gross Cell Product (Data from G-Econ)

Log10 Gross cell product (like GDP, but higher resolution)
1990-2005
(Data from William Nordhaus's G-ECON project)

Fraction of land cultivated
1700-1990
(data from SAGE)

Also, since I posted this last time

Maximum tropical cyclone windspeed
1950-2008
(data from LICRICE)

Challenges for interdisciplinary journals

The American Meteorological Society has a new (1 yr old) journal Weather Climate and Society that is trying to integrate research across several disciplines. For people like myself, the birth of journals like this is comforting because it suggests there is a growing community of researchers interested in applying hard physical science to address social issues.  This is a good thing.  But like everyone trying to do this, they are running into real challenges.  Here are portions of an editorial that resonated with some of my own experiences.  

The Costs and Losses of Integrating Social Sciences and Meteorology

Jeffrey K. Lazo

....[A] colleague stopped by to ask my advice on a manuscript concept he was considering submitting to Weather, Climate, and Society. He is an outstanding research meteorologist who has been working with a team on models to improve flood warning systems in developing countries. He was interested in demonstrating the economic benefits of the improved warning system and wanted to apply a cost–loss model. Based on his reading of a number of articles in meteorological journals, the cost–loss model was the method of choice for demonstrating economic value.

My initial reaction was largely visceral, because I have a dislike for the cost–loss model. The cost–loss model has been used extensively in the meteorology literature as “the economic model,” but it does not really show up in the economics literature (I should note that my Ph.D. is in economics and, in six years of graduate school, I never once heard of the cost–loss model). A simple search for “cost–loss model” in AMS publications yields 161 hits. A similar search of the economic literature yielded none....

My concern is that the cost–loss model as used in most articles in the meteorology literature does not even begin to capture the full value of economics and build upon the extensive literature in economics on the value of information and decision making under uncertainty. It is simply too simple....

That said, the first issue of Weather, Climate, and Society contained an article based on a theoretical extension of the cost–loss model (Millner 2009; in fact, I recommend reading Millner for an explanation of the cost–loss model). In that article, Millner showed that incorporating a specific behavioral feature in the cost–loss model resulted in net benefit estimates potentially significantly lower than those derived from the basic model. His article demonstrated that behavioral aspects limit the effectiveness of the cost–loss model. I feel this should be read as a demonstration that the meteorological community needs to move beyond the cost–loss model. Building in part on the limitations of the cost–loss model that Millner’s work suggests, I encourage the meteorological community to move beyond the use of that model as the basis for defining economic value....

Weather, Climate, and Society aims to publish “scientific research and analysis on the interactions of weather and climate with society.” This editor’s opinion is that this will largely involve the integration of the social sciences applied to topics of hydrometeorological concern (including weather, water, and climate broadly defined). In light of the prior discussion on cost–loss models, this means we need to better integrate valid economics as economic theory, methods, and practice frame economics with analysis of hydrometeorological issues. More broadly, we need to use appropriate theories and methods from all of the social sciences and not necessarily “accepted” versions of social sciences from the physical sciences perspective.

All of the social sciences have extensive bodies of knowledge they can bring to the study of hydrometeorological issues. Some have a longer history of examining issues related to weather, water, and climate; for instance, sociologists have long studied evacuation decision making during hurricanes. However, for the most part the research and literature is rather thin at the intersection of social and physical sciences relevant to audiences of Weather, Climate, and Society.

Given this landscape, there are definite challenges for the authors, reviewers, and editors for this journal. First, is that most of us (authors, reviewers, and editors) are fairly new to this effort at developing a highly disciplinary but very broadly focused journal combining atmospheric and social sciences. There is a learning curve at this early stage of the journal, because we are all developing and setting standards and expectations. There have been and will continue to be some frustrations as these are clarified. However, as these are clarified and we move forward,Weather, Climate, and Society will be the premier journal for interdisciplinary work “at the interface of weather and/or climate and society.”

There are also difficulties in writing, reviewing, and editing manuscripts for such a highly interdisciplinary journal. For instance, authors, reviewers, and editors for economics journals are almost always economists. I suspect authors, reviewers, and editors for meteorology journals are almost always meteorologists, or at least in general from the “hard” sciences. In addition to meteorologists, authors, reviewers, and editors for Weather, Climate, and Society are from the “harder sciences” such as economists, sociologists, geographers, anthropologists, etc. One challenge for authors will be to maintain high standards for their research while also being able to respond to very diverse (and sometimes divergent) critiques from reviewers with expertise from different disciplines.

For the time being at least, this may make Weather, Climate, and Society the most difficult journal across all American Meteorological Society (AMS) publications to publish in and to be an editor for. However, it may well also make Weather, Climate, and Society the most valuable and dynamic journal in terms of moving the various disciplines forward in new, challenging, and interesting areas of societally relevant research, methods, and applications.

(I hope I cut out enough of it that I'm not infringing on copyrights). 

War statistics to ponder

From Sebastian Junger's War :

Nearly a fifth of the the combat experienced by the 70,000 NATO troops in Afghanistan is being fought by the 150 men of Battle Company. Seventy percent of the bombs dropped in Afghanistan are dropped in and around the Korengal Valley. American soldiers in Iraq who have never been in a firefight start talking about trying to get to Afghanistan so that they can get their combat infantry badges.

The book is great, if a little disorganized. The movie which resulted from the footage Junger shot, Restrepo, is supposed to be phenomenal. More relevantly, the fact that so much of the fighting in Afghanistan was concentrated in this one strategically-not-very-important valley not only makes one wonder about the political economy of the military (that's the first time I've typed that phrase out but it sounds like it should be a field in its own right) but also about every other summary statistic I've heard about the wars in Iraq and Afghanistan.

Urban ecology doesn't care about your locavore agenda

I'm not sure how well this comes through on the blog, but Sol and I (and, I think it's safe to say, many if not most of the people in our program) have fairly nuanced views on the subjects that people normally associate with "sustainability." Green architecture, recycling, organic foods, hybrid cars, and a host of other topics that spring to mind when someone mentions sustainability tend to be partial solutions to complex problems, and the ways in which they interrelate and sometimes even interfere with each other can be very difficult to disentangle. A really lovely example of this comes from today's NY Times article about urban beekeepers' honey turning red:

Where there should have been a touch of gentle amber showing through the membrane of their honey stomachs was instead a garish bright red. The honeycombs, too, were an alarming shade of Robitussin.

“I thought maybe it was coming from some kind of weird tree, maybe a sumac,” said Ms. Mayo, who tends seven hives for Added Value, an education nonprofit in Red Hook. “We were at a loss.”

An acquaintance, only joking, suggested the unthinkable: Maybe the bees were hitting the juice — maraschino cherry juice, that sweet, sticky stuff sloshing around vats at Dell’s Maraschino Cherries Company over on Dikeman Street in Red Hook.**

I think this a really lovely illustration of how the ways in which we like to conceptualize "doing the right thing" and "acting sustainably" are often based on very tenuous understandings of how science and complex systems actually work. Proponents of eating locally make many claims about its benefits that are often unproven, or difficult to test, or sometimes even known ex-ante to be false. That's not to say that eating locally is not a good thing; it's to say that the answer to that question is complicated and depends on factors that vary with geography, the food in question, what you consider to be 'local,' etc.

Which is why this is such an interesting little article. Urban apiculture has become very popular of late and, I'd say, is probably on bar a pretty good thing; the value from having more pollinators around alone is probably fairly high, and if people are getting good honey out of it all the better. But pursuing local food as a sort of monolithic good is bound to fail, sometimes in predictable ways like the disconnect between local net primary productive potential and local demand, and sometimes in unpredictable ways such as having your honey turn up shades of Red Dye No. 40. Food production is inextricably and definably a part of the local ecology, and when your local ecology is urban that means you're going to end up with different outcomes than out in farmland.

So, if this post were to have a moral (and not to pick on these beekeepers because, like I said, I think urban apiaries are pretty net beneficial), it's this: don't take as received wisdom what those around you claim is "sustainable"; don't claim that the solution to a sustainability problem you've currently settled on is fool-proof or even the right one; and internalize the fact that the world is a complex place and thus anyone who claims they've figured out an answer to a major problem and are "trying to do their part" to advance sustainability should be able to robustly prove that that's true or else humbly say that they don't know.

* Note: Photo copyright New York Times 2010.

** I'd just like to say that, as a native New Yorker, I'm not very surprised that Red Hook was causing trouble.

Complexity and rent-seeking in the non-productive industry

The New Yorker has an Annals of Economics article this week on finance's role in the American economy titled "What good is Wall Street?" Lest you have any doubt about the author's answer to that question, the subheading is "Much of what investment bankers do is socially worthless." Some brief thoughts, preceded by what I think is the necessary admission that I was an investment banker (doing albeit nonstandard work) for two years:

1. I'm glad that the concept of financial work as a fundamentally rent-seeking activity is getting more mainstream. I'd be happier if this were running in USA Today instead of The New Yorker, but still.
2. I strongly suspect that most people who haven't explicitly had to think about it (by which I mean: most people) still don't understand what finance as an industry 'does'. I think the simple model of what banks do (deposits in, loans out, earn the spread) is pretty widely understood, but how the rest of finance operates, or even what makes up the rest of the financial services industry, not so much. That is a bad thing for a lot of reasons.
3. LSE has a Centre for the Study of Capital Market Dysfunctionality? Seriously? I know several people who would probably love to post-doc there.
4. Sol and I were recently talking about Russia since the Soviet collapse (we're required by contract to spend most of our time trying to distract each other from actual productive work) and he mentioned that it was the epitome of elite capture. Every time I hear stats on the perpetually (even during the crisis) increasing gap between top 1% earners and the median, I think about that process.
5. From my own experience and those of some friends I'd say fresh college graduates' decisions to work in finance are driven by two things. The first is the low option value of a year or two of your early twenties versus the salary proffered; how many people do I know took the hipster / slacker / failed artist route for those years and have little to show for it aside from going to a few more parties? The second is the relative attractiveness of job choice coming out of finance. A first job in finance doesn't drastically reduce one's set of possible careers the way a lot of other fields do because it signals that you're at least passably smart (though probably not brilliant) and you're willing to work like a dog. If we're worried about our best and brightest going to work in finance, which I honestly think is a distraction from more serious concerns like under-regulation, we need to do something about the attractiveness of those two drivers. Since salaries won't likely change soon, I suspect that ultimately means changes in social norms and the social acceptability of going to work in what has largely become a parasitic industry.
6. The author touches on but doesn't really delve into what I think is the heart of the problem: finance has enormous returns to complexity. Firms don't generate huge returns by focusing on banking; they get it by being early-actors in markets that haven't become efficient yet. If the hot new derivative your firm has developed is either sufficiently new that other firms aren't familiar with it or sufficiently complex that other firms can't muster the expertise to price and trade them, then you can escape the margin-killing slide towards efficiency, at least for a few years, and make bank. That finance's ability to support million-dollar-a-year salaries derives mostly from the exploitation of market inefficiencies is something that I think is lost in most discussions, especially when there's so much rhetoric claiming finance makes markets more efficient.
7. The complexity rents argument alone is, I think, sufficient to strongly argue in favor of much heavier regulation and reinstating the separation of normal and investment banks. If we add in a political economy / returns to political contributions element to the model it becomes even more pressing.

Official Stata Blog Now Up!

Our colleague Reed Walker points out that there's now an official Stata blog, "Not Elsewhere Classified":

Here we will try to keep you up-to-date about all things related to Stata Statistical Software. That includes not only product announcements from StataCorp and others, but timely tips (and sometimes comments) on other news related to the use of Stata.

Many entries will be signed by members of the StataCorp staff.

If you have any tips or comments for us, email blogteam@stata.com

Fun topics they've covered thus far include which are the most powerful commercially available computers and the advantages of running Stata-MP.

Regression coefficient stability over time (in Stata)

If you're estimating a regression model with time series or panel data, you often would like to know if the coefficient you're interested in is changing over time or if its stable for subsamples of the time series or panel.

Here's my simple but handy script that let's you see if your coefficient is stable or changing with a single command.  You specify your multiple regression model (for OLS, but you can change it easily to run a different estimator), which coefficient to examine.  It does a sequence of regressions for a moving window of specified length and stores the changing coefficient, SE and CI (t-test).

For example, the figure at right shows the coefficient from the model

GDP_it = B * cereal_yields_it + e_it

for a panel of all countries estimated for a moving window that covers 3 years at a time.

The description of the code is just commented out in the script and pasted below the fold.

[7/14/2011: A small but important error in the code was fixed. Thanks to Kyle for catching it.]

AGU Climate Q&A Service for Journalists

Last year the American Geophysical Union (of the superlatively massive AGU Fall Meeting held every December in San Francisco) piloted something they called The Climate Q&A Service for Journalists during their fall meeting. The idea was to set up a website where journalists could submit questions they had about climate science and climate scientists could sign up for slots where they'd monitor questions coming in and answer them.

Apparently it was sufficiently successful that they're doing it again this year, here, except that it's been expanded to run from Oct 24th - Jan 21st. If you're a climate scientist looking to do a good deed and help people understand how the climate works, hop on over and sign up for a time slot. Or if you're a reporter looking to get some questions about climate science asked, go on over and submit a question.

Photoessay on the African Middle Class

I stumbled across a great photo essay on the African middle class over at classesmoyennes-afrique.org. It's in French but there's an English version here.

Nothing terribly deep on the analytical takedown here aside from the fact that it's nice to be reminded that African countries aren't entirely populated by desperately poor farmers and kleptocrat dictators.

Plus the pictures are great.

Prison privatization, political economy, and interest group creation

NPR has a great ongoing series exploring how privatization can have extraordinary externalities, namely the role of private prisons in the drafting of Arizona's immigration law:

Thirty of the 36 co-sponsors received donations over the next six months, from prison lobbyists or prison companies — Corrections Corporation of America, Management and Training Corporation and The Geo Group.

By April, the bill was on Gov. Jan Brewer's desk.

Brewer has her own connections to private prison companies. State lobbying records show two of her top advisers — her spokesman Paul Senseman and her campaign manager Chuck Coughlin — are former lobbyists for private prison companies. Brewer signed the bill — with the name of the legislation Pearce, the Corrections Corporation of America and the others in the Hyatt conference room came up with — in four days.

Brewer and her spokesman did not respond to requests for comment.

This is an excellent example of why economics is both an extraordinary tool for analysis but also one that is easily abused. The primary argument for privatization almost always comes down to one of efficiency: the public sector is slow, it's bloated, taxpayers pay $1000 for toilet seat installation, etc. That is not a concern to be belittled, and removing all constraints and checks from public sector workers is clearly a horrible idea.

But the flip side is that there are deep political economy concerns any time you privatize the provision of a public good. The incentives society as a whole faces (i.e., let's *not* incarcerate everyone all the time) are the exact opposite of what a for-profit prison faces (from the article: "They talk [about] how positive this was going to be for the community," Nichols said, "the amount of money that we would realize from each prisoner on a daily rate."). Since politicians respond to incentives, too, and for-profit companies have money to spend on campaign donations, political ads, etc., an already difficult problem is made much more complex (and, I'd argue, welfare decreasing) by following a simple welfare-enhancing efficiency argument.

Now, the flip side of the flip side (since I doubt most people reading a "sustainable development" blog are hungering for privatization) is that the same argument applies for all organized interest groups, not just private ones. This is very similar to the classic argument against unions, for example: New York's widely disliked (even by a lot of teachers) United Federation of Teachers is able to exert a huge amount of political pressure to support policies that are almost certainly harmful to educational outcomes, e.g., making firing bad teachers extraordinarily hard. The problem is the same: once an interest group comes into existence it will do its best to influence policy in its favor.

So what do we do? Legitimately, I think the two solutions are the obvious, difficult ones: transparency in campaign finance and mobilization of counter-interest groups. The first is hard for all of the obvious reasons (including most organized interest groups being against it) and the second is hard because often the counter-interest faces not just asymmetric funding (there's no private interest in keeping people *not* in prisons) but also a fundamental public goods problem: damages tend to be dispersed and costs of abatement concentrated, so anyone joining the counter-interest is either going to be doing it altruistically (e.g., a non-profit), because they were one of the unlucky few who got hit with particularly concentrated damages (e.g., the family of someone unjustly imprisoned), or because they reap some metabenefit (e.g., the NPR reporters covering this story) .

Which is to say, surprise, it's a fairly intractable and complex problem. If there's a lesson to extract I think that it is, as it so often seems to be, to always think about how incentives align, especially before you make large, difficult-to-reverse decisions. In particular, I think it's important to remember that creating a monied interest group is one of the most difficult-to-reverse decisions there is.

Statistical inference isn't easy, either

I was just playing around with the citation management software / website Mendeley (recommended by Amir, and worth checking out for the auto-formatting of citations alone) when I trolled over to their "most read articles in all disciplines" section and saw that the 3rd most read article was a PLoS Medicine piece titled "Why most published research findings are false: author's reply to Goodman and Greenland," by Ioannidis. Ignoring the fact that it's the response and not the original paper (huh?) and led on by the rather provocative title, I poked around and discovered that Ioannidis' work just got written up in The Atlantic and was covered in pretty nice detail by Marginal Revolution back when it came out. So blogging about it does feel a bit like trying to review a restaurant that's already been covered by Frank Bruni and Food and Wine, but I'm going to go ahead and do so anyway since the point is so worthwhile.

The crux of the paper rests on a pretty simple idea: if you're running a huge number of one-off statistical tests (i.e., not testing the same hypothesis over and over) a fraction of your results proportional to the power of your test will be false positives (i.e., type I error). This is pretty straightforward a concept for anyone doing applied work: if you're checking to make sure you've got balance across treated and controlled populations in a randomized trial, for example, having an occasional statistically significant difference between the two populations isn't a huge deal as long as the percentage of variables that turn up that way is proportional to the significance level you're setting. Yes, you should follow through as a good little applied researcher and make sure something's not hiding there, but some portion of your results will always end up that way due to random variation.

The nice step that Ioannidis takes is to look at the entire field of medical research and apply the same logic, effectively viewing the suite of randomized trials as a game where we keep picking new potential tests for the same problems over and over again, some subset of which are guaranteed to be incorrectly not-rejected. To quote Alex Tabarrok's pithy wording of it in the Marginal Revolution post:

Want to avoid colon cancer? Let's see if an apple a day keeps the doctor away. No? What about a serving of bananas? Let's try vitamin C and don't forget red wine.

Moreover, since the number of things that actually, say, help avoid colon cancer is likely small, and the number of tests being run to find things which do is large, Ioannidis concludes that a large portion ("most") results are in fast false positives and thus meaningless. It's a pretty simple premise which leads to a pretty deep statement about how we think about learning about the world.

So the solutions to this are, of course, pretty intuitive: don't trust small sample size studies; insist on retesting hypotheses; be skeptical of results in any field where a large number of researchers are pursuing solutions to the same problem. In short, demand robustness checks on everything, and make sure that what's being shown is not just an artifact of your specific data set. Good lessons that all applied researchers should have tattooed across their proverbial chests already, but nonetheless a nice thing to be reminded of.

The structure of human knowledge

Following up on Jesse's post:

After dinner today I told Brenda that I wanted a network map of all papers ever written so we could see where the biggest gaps in human knowledge were. In moments she had us browsing the site well-formed.eigenfactor.org looking at a coarser approximation of my dream (see picture).

I highly recommend any academic or casual intellectual browse the highly interactive site, it is simply too interesting, beautiful and [maybe] important to ignore.

Perhaps the two most striking observations one can make from simple visual inspection are that (1) biologists write a lot of papers and (2) social sciences/mathematics/computer science are extremely insular (observe the big "hole" in the network picture).

I'll let the data speak for itself (please please look at the site); but the only thing I'll say is that if anyone wanted to create a new field, bridging the social and physical sciences looks like a conspicuously good place to start.

Interdisciplinarity isn't easy

I'm currently wrapping up edits on a paper on interdisciplinarity and research success and came across a pretty cool paper for my lit review with a couple of choice quotes:

"[T]he young scientist, who grows up in the midst of a competition between university departments and amidst competition within his department, who inherits the individualistic research tradition and graduates without having had an opportunity to develop skills in cooperative thinking and collaborative study, is poorly prepared to participate in the activities of a committee or a research team.

"Over and above this pressure from the outside, there are important scientific grounds why interdisciplinary (and interdepartmental) research should become a greater concern of the universities. The assertion that institutes of an interdisciplinary character will be associated more often with industrial enterprises than with universities may be correct in the statistical sense, but it should not imply that cooperative research is an industrial prerogative.

"For the research worker who has grown up in the traditional departmentalized university and who is anxious to take part in interdisciplinary work, the first step is to get a bird's-eye view of the neighboring fields and to obtain familiarity with the problems which are currently the foci of interest. However, text-book acquaintance is not enough; some contact with actual work methods is essential. "

I think these are all reasonably fair points. The problem is that this article is from the December 8th, 1944 issue of Science. Reading through it and noting how little change there's been in the language around "interdisciplinary research" is fairly shocking, and makes me appreciate not only how difficult working outside of one discipline is, but also the extent to which the road towards doing quality work combining the social and natural sciences (which is probably the best way to describe the specific flavor of interdisciplinarity that Sol and I are in) has been a long and arduous one.

Not that there hasn't been any progress, mind you. The flip side of interdisciplinary work is field creation. Climatology, neuropsychology, behavioral economics, and an untold number of additional academic disciplines were all, at one point, "inter-discipline." It's just nice to be reminded of the fact that establishing those fields isn't easy.

Cleanup cost from Haiti earthquake

I was struck by this NYT article stating that just the cost of debris removal in Haiti is estimated at $1B

By late summer, however, the need to tackle the earthquake damage directly became so glaring that some initial steps were taken. The government tendered its very first cleanup contract to Mr. Perkins’s Haiti Recovery Group. Worth $7.5 million to $13.5 million — nobody would be more precise — the contract represented a minuscule piece of a debris removal operation expected to cost $1.2 billion.

This is an incredible sum, when you consider that income for the entire country is about $7B



The cost of cleanup is just what is paid for the removal of damaged property and excludes the value of the lost assets and lost revenue due to the destruction of assets.