Simulations of direct interference in the large-scale water and energy balances of the atmosphere by humans

A recent NCC and a recent GRL article both use simulations to look at how large scale irrigation and energy consumption might alter large scale circulation patterns of the atmosphere.

If I were a grad student with some statistical skills, I would go out into the world and try to detect these effects with data.

Energy consumption and the unexplained winter warming over northern Asia and North America
Guang J. Zhang, Ming Cai & Aixue Hu

Abstract: The worldwide energy consumption in 2006 was close to 498 exajoules. This is equivalent to an energy convergence of 15.8 TW into the populated regions, where energy is consumed and dissipated into the atmosphere as heat. Although energy consumption is sparsely distributed over the vast Earth surface and is only about 0.3% of the total energy transport to the extratropics by atmospheric and oceanic circulations, this anthropogenic heating could disrupt the normal atmospheric circulation pattern and produce a far-reaching effect on surface air temperature. We identify the plausible climate impacts of energy consumption using a global climate model. The results show that the inclusion of energy use at 86 model grid points where it exceeds 0.4 W m−2 can lead to remote surface temperature changes by as much as 1 K in mid- and high latitudes in winter and autumn over North America and Eurasia. These regions correspond well to areas with large differences in surface temperature trends between observations and global warming simulations forced by all natural and anthropogenic forcings1. We conclude that energy consumption is probably a missing forcing for the additional winter warming trends in observations.

Irrigation in California's Central Valley strengthens the southwestern U.S. water cycle
Min-Hui Lo, James S. Famiglietti

Abstract: Characterizing climatological and hydrological responses to agricultural irrigation continues to be an important challenge to understanding the full impact of water management on the Earth's environment and hydrological cycle. In this study, we use a global climate model, combined with realistic estimates of regional agricultural water use, to simulate the local and remote impacts of irrigation in California's Central Valley. We demonstrate a clear mechanism that the resulting increase in evapotranspiration and water vapor export significantly impacts the atmospheric circulation in the southwestern United States, including strengthening the regional hydrological cycle. We also identify that irrigation in the Central Valley initiates a previously unknown, anthropogenic loop in the regional hydrological cycle, in which summer precipitation is increased by 15%, causing a corresponding increase in Colorado River streamflow of ~30%. Ultimately, some of this additional streamflow is returned to California via managed diversions through the Colorado River aqueduct and the All-American Canal.

Weekend Links

1) Paul Niehaus' non profit, GiveDirectly, lets you directly wire money to the bank accounts of impoverished families in Kenya

2) Empirical estimates suggest most published medical research is true (previously on FE)

3) On quantum smell (PLoS One)

4) On the ecological dead weight loss of cats (via Stephen Hughes)

5) One of the newly announced Tang Prizes' four areas of focus is "sustainable development" (via MR)

6) Thomas Edsall's spectacular piece on whether consumption inequality has been tracking income inequality (also via MR)

7) Gordon and Dahl on consensus in modern economics (NBER)

8) The "Ringelmann effect" and the Statistics of Statisticians

9) Andrew Gelman on the odd spam academic bloggers get (I just got one on "10 Astonishing Examples of Bizarre Parasitic Life Cycles")

10) V. S. Naipual's influence on Robert E. Lucas 

Ethos or Logos?

Last week, Bjorn Lomborg wrote an opinion piece in the WSJ preemptively attacking the climate policies that he speculates Obama will endorse. I don't usually read this kind of thing, but journalists at Climate Science Watch asked what I thought about it since they didn't believe it.  Reading the article, I was surprised the WSJ had published it -- not because the citations were not 100% correct, but because the overall logic of the essay was flawed in obvious ways (regardless of your political stance). This is the kind of thing a copy-editor should have picked up on.  My reply to the CSW mainly focused on this central logical flaw.

Interestingly, when CSW posted its reply to BL (here), which drew on many scientific experts, it was focused entirely on discrediting individual statements that BL had made

Displaying his trademark doublethink, Bjorn Lomborg’s latest op-ed in the Wall Street Journal switches between recognizing the risks of climate change and rejecting the need for meaningful action in the near term. Lomborg incorporates misleading and discredited scientific information to justify dangerous delays in climate action. 

rather than pointing out that the giant "if-then" statement at the core of the article's architecture would obviously return an error if it were fed into any computer capable of boolean logic.

This struck me because the dialogue (in both directions) was focused on discrediting one's opponent, by demonstrating they don't understand science (BL does this to Obama, and CSW does this to BL), rather than finding a logical solution to the problem (or simply having a logical discussion about it).  This seemed unfortunate, since in this particular case, the physical science is pretty irrelevant to the actual policy discussion. The entire discussion should be focused on the economics. I blame BL for inappropriately bringing the science into the discussion, but I wish CSW had pointed out that that was the error, since I think doing so (here and elsewhere) would get us back on track to meaningful discussion rather than escalating the scientific mudslinging.

My fully reply to the CSW is below the fold (I had been in referee-mode at the time, which is probably evident).

Third Interdisciplinary Ph.D. Workshop in Sustainable Development

Third Interdisciplinary Ph.D. Workshop in Sustainable Development
April 12th-13th, 2013: Columbia University in the City of New York, USA

The graduate students in sustainable development at Columbia University are convening the Third Interdisciplinary Ph.D. Workshop in Sustainable Development (IPWSD); scheduled for April 12th-13th, 2013, at Columbia University in New York City.

The IPWSD is a conference open to graduate students working on or interested in issues related to sustainable development.  It is intended to provide a forum to present and discuss research in an informal setting, as well as to meet and interact with similar graduate student researchers from other institutions.  In particular, we hope to facilitate a network among students pursuing in-depth research across a range of disciplines in the social and natural sciences, to generate a larger interdisciplinary discussion concerning sustainable development.  If your research pertains to the field of sustainable development and the linkages between natural and social systems, we encourage you to apply regardless of disciplinary background.

For details, please see the call for papers, or visit our conference website where a detailed list of topics, conference themes and other information is available. 

website: http://blogs.cuit.columbia.edu/sdds/schedule-events/ipwsd_2013/
contact: cu.sdds.ipwsd@gmail.com

Note that the submission deadline has been extended to February 15th.

Implementation capacity matters when scaling up

Paul Niehaus was presenting at USF's seminar yesterday and mentioned the following working paper by Bold, Kimenyi, Mwabu, Ng'ang'a and Sandefur:

Interventions & Institutions: Experimental Evidence on Scaling up Education Reforms in Kenya

Abstract: The recent wave of randomized trials in development economics has provoked criticisms regarding external validity and the neglect of political economy. We investigate these concerns in a randomized trial designed to assess the prospects for scaling-up a contract teacher intervention in Kenya, previously shown to raise test scores for primary students in Western Kenya and various locations in India. The intervention was implemented in parallel in all eight Kenyan provinces by a non-governmental organization (NGO) and the Kenyan government. Institutional di erences had large e ffects on contract teacher performance. We find a signifi cant, positive eff ect of 0.19 standard deviations on math and English scores in schools randomly assigned to NGO implementation, and zero eff ect in schools receiving contract teachers from the Ministry of Education. We discuss political economy factors underlying this disparity, and suggest the need for future work on scaling up proven interventions to work within public sector institutions.

 The paper is openly hosted here, and the Center for Global Development has an interview with Sandefur here. The introduction to the paper gives a particular succinct summary of its motivation:

The recent wave of randomized trials in development economics has catalogued a number of cost-eff ective, small-scale interventions proven to improve learning, health, and other welfare outcomes. This methodology has also provoked a number of criticisms regarding the generalizability of experimental findings, including concerns about external validity, general equilibrium eff ects, and the neglect of political economy in much of the evaluation literature (Acemoglu 2010, Deaton 2010, Heckman 1991, Rodrik 2009). These criticisms are particularly relevant when randomized trials of pilot projects run by well-organized and monitored NGOs are used as the basis for policy prescriptions at the national or global level. As noted by Banerjee and Duflo (2008), what distinguishes possible partners for randomized evaluations is competence and a willingness to implement projects as planned. These may be lost when the project scales up. [. . . ] Not enough e ffort has taken place so far in trying `medium scale' evaluation of programs that have been successful on a small scale, where these implementation issues would become evident." 

In this paper we employ the methodology of randomized trials to assess these substantive concerns about political and institutional constraints and measure precisely how treatment eff ects change when scaling up...

Quickly plotting nonparametric response functions with binned independent variables [in Stata]

Yesterday's post described how we can bin the independent variable in a regression to get a nice non-parametric response function even when we have large data sets, complex standard errors, and many control variables.  Today's post is a function to plot these kinds of results.

After calling bin_parameter.ado to discretize an independent variable (see yesterday's post), run a regression of the outcome variable on the the sequence of generated dummy variables (this command can be as complicated as you like, so feel free to throw your worst semi-hemi-spatially-correlated-auto-regressive-multi-dimensional-cluster-block-bootstrap standard errors at it). Then run plot_response.ado (today's function) to plot the results of that regression (with your fancy standard errors included). It's that easy.

Here's an example. Generate some data where Y is a quadratic function of X and a linear function of Z:

set obs 1000
gen x = 10*runiform()-5
gen z = rnormal()
gen e = rnormal()
gen y = x^2+z+5*e

Then bin the parameter using yesterday's function and run a regression of your choosing, using the the dummy variables output by bin_parameter:

bin_parameter x, s(1) t(4) b(-4) drop(-1.6) pref(_dummy)
reg y _dummy* z

After the regression, call plot_response.ado to plot the results of that regression (only the component related to the binned variables). The arguments describing the bins are the same format as those used by bin_parameter to make this easier:

plot_response, s(1) t(4) b(-4) drop(-1.6) pref(_dummy)

The result is a plot that clearly shows us the correct functional form:

Note: plot_response.ado requires parmest.ado (download from SSC  by typing "net install st0043.pkg" at the command line). It also calls a function parm_bin_center.ado that is included in the download.

Citation note: If you use this suite of functions in publication, please cite: Hsiang, Lobell, Roberts & Schlenker (2012): "Climate and the location of crops."

Help file below the fold.

Binning a continuous independent variable for flexible nonparametric models [in Stata]

Sometimes we want to a flexible statistical model to allow for non-linearities (or to test if an observed relationship is actually linear). It's easy to run a model containing a high-degree polynomial (or something similar), but these can become complicated to interpret if the model contains many controls, such as location-specific fixed effects. Fully non-parametric models can be nice, but they require partialling out the data and standard errors can be come awkward if the sample is large or something sophisticated (like accounting for spatial correlation) is required.

An alternative that is easy to interpret, and handles large samples and complex standard errors well, is to convert the independent variable into discrete bins and to regress the outcome variable on dummy variables that represent each bin.

For example, in a paper with Jesse we take the typhoon exposure of Filipino households (a continuous variable) and make dummy variables for each 5 m/s bin of exposure. So there is a 10_to_15_ms dummy variable that is zero for all households except for those households whose exposure was between 10 and 15 m/s, and there is a different dummy for exposure between 15 and 20 m/s, etc.  When we regress our outcomes on all these dummy variables (and controls) at the same time, we recover their respective coefficients -- which together describe the nonlinear response of the outcome. In this case, the response turned out to be basically linear:

The effect of typhoon exposure on Filipino households finances.
From Anttila-Hughes & Hsiang (2011)

This approach coarsens the data somewhat, so there is some efficiency loss and we should be wary of Type 2 error if we compare bins to one another. But as an approach to determine the functional form of a model, this is a great approach so long as you have enough data.

I found myself rewriting Stata code to bin variables like this in many different contexts, so I wrote bin_parameter.ado to do it for me quickly.  Running these models can now be done in two lines of code (one of which is the regression command). bin_parameter allows you to specify a bin width, a top bin, a bottom bin and a dropped bin (for your comparison group). It spits out a bunch of dummy variables that represent all the bins which cover the range of the specified variable. It also has options for naming the dummy variables so you can use the wildcard notation in regression commands. Here's a short example of how it can be used:

set obs 1000
gen x = 10*runiform()-5
gen y = x^2
bin_parameter x, s(1) t(4) b(-4) drop(-1.6) pref(x_dummy)
reg y x_dummy*

Help file below the fold.

Prettier graphs with less headache: use schemes in Stata

I'm picky about graphs looking nice. So for a long time I did silly things that drove me nuts, like typing "ylabel(, angle(horizontal))" for every single Stata graph I made (since some of the default settings in Stata are not pretty). I always knew that you could set a default scheme in stata to manage colors, but I didn't realize that it could do more or be customized. See here to learn more.

After a bit of playing around, I wrote my own scheme. The text file looks pitiful, I know. But it saves me lots of headache and makes each plot look nicer with zero marginal effort.  You can make your own, or if you download mine, put the file scheme-sol.scheme in ado/personal/ (where you put other ado files) and then type

set scheme sol

at the command line. Or set this as the default on your machine permanently with

set scheme sol, perm

It will make your plots that look kind of like this:

Plot polynomial of any degree in Stata (with controls)

FE has been a little sluggish to recover from break. To kick start us back in gear, I'm making good on one resolution by making this FE Week-of-Code. I'll try to post something useful that I've written from the past year each day.

It always bugged me that I could easily plot a linear or quadratic fit in Stata, but if I used a third-order polynomial I could no longer plot the results easily.  Stata has built in functions like lowess, fpfitci and lpolyci that will plot very flexible functions, but those tend to be too flexible for many purposes. Sometimes I just want a function that is flexibly non-linear, but still smooth (so not lowess) and something I can easily write down analytically (so not fpfit or lpoly) and perhaps not symmetric (so not qfit).  We use high-degree polynomial's all the time, but we just don't plot them very often (I think this is because there is no built-in command do it for us).

Here's my function plot_margins.ado that does it. It takes a polynomial that you use in a regression, and plots the response function. Added bonuses: It plots the confidence interval you specify and can handle control variables (which are not plotted)

Reed Walker actually came up with this idea in an email exchange we had last year. So he deserves credit for this. I just wrote it up into an .ado file that you can call easily.

Basically, the idea is that you run any regression using Stata's factor variable notation, where you tell Stata that a variable X is continous and should be interacted with itself, eg

reg y c.x##c.x

is the notation to regress Y on X and X^2. (Check out Stata's documentation on factor variables if this isn't familiar to you.)

Reed's idea was to then use Stata 11's new margins command to evaluate the response of Y to X and X^2 at several points along the support of X, and then to use parmest to plot the result. (To download parmest.do, type "net install st0043.pkg" at the command line in Stata. plot_margins will call parmest, so you need to have it installed to run this function.)

The idea works really well, so long as you have Stata 11 or later (margins was introduced in Stata 11). 

Here's an example. First generate some false data. Y is the outcome of interest. X is the independent variable of interest. W is a relevant covariate. Z is an irrelevant covariate.

clear 

set obs 1000 

gen year = ceil(_n/100) 

gen x=5*uniform()-2 

gen z=2*uniform() 

gen w=2*uniform() 

gen e = 40*rnormal()  

gen y= w + 3*z + 4*x - x^2 + x^3 + e 

Then run a regression of Y on a polynomial of X (here, it's third degree) along with controls. The standard errors can be computed any fancy way you like. Here, I've done a block-bootstrap by the variable year.

reg y w z c.x##c.x##c.x , vce(bootstrap , reps(500) cluster(year))

Then, right after running the regression, (or when the estimates from the regression are the active in memory) call plot_margins to plot the marginal contribution of X at different values.

plot_margins x

Easy enough? I've added a few features for programming ease. Use the plot_command() option to add labels, etc to the graph

plot_margins x, plotcommand("xtit(X) ytit(Y) tit(Third order polynomial) subtit(plotted with PLOT_MARGINS) note(SE are block-bootstrapped by year and model controls for X and Z)") 

 The result:

 or specify the option "line" to have the CI plotted as lines instead of a shaded area:

There is also an option to save the function generated by parmest. Help file is below the fold. Have fun and don't forget to email Reed and tell him "thank you!"

Update: Reed points us to marginsplot in Stata 12, which basically does the same thing. Funny that the function names are all so unique...

Weekend Links

1) Coursera's next course on data analysis using R starts January 22nd (via Yaniv Stopnitzky)

2) Dropboxifier lets you back up / share folders via Dropbox without relocating them (via The Lazy Economist)

3) Banerjee and Duflo's "The Challenges of Global Poverty" starts Feb. 13th on edX (via Hartmut Fisher)

4) Google Ph.D. Fellowship nominations and applications are due Feb. 1st

5) The first atmospheric carbon dioxide capture plant is planned to open by end of 2014 (via Marion Dumas)

6) Easy ways to alienate yourself from a profession, Forbes lifestyle piece edition (via Ivana Ng)

7) 125 years of National Geographic photos (via Dave Pell)

8) David Enfield's ENSO FAQ is an oldie but a goodie

9) Daniel Aldrich's two pieces on the "soft approach" to countering violent extremism are now out (see under "Countering Violent Extremism", and previously on FE)

10) "Knowledge workers are bad at working." Cal Newport on the value of "deep work"

11) A mysterious patch of light shows up in the North Dakota dark (via Padraic Hughes)

REDD Metrics

Marshall points us to reddmetrics.com, a neat blog/company that FE readers might appreciate:

Founded in early 2011, REDD Metrics, LLC, applies innovations in large-scale spatial data processing to questions in environmental and resource economics.

 The blog is sparse but has several cool posts. They also seem to working on some interesting long term projects:

Large-scale implementation of change detection algorithms
Employing the power of cloud computing to detect changes in earth systems using satellite imagery over wide geographic areas. 

Economic analysis of natural resource use and management
Identifying economic determinants of resource consumption. For example, how do interest rates or agricultural prices affect the rate of forest clearing in Indonesia? 

Predictive modeling of spatial processes
Using massive spatial datasets and cutting-edge spatial modeling techniques to predict the flow of environmental phenomena over space and time. 

Development of mobile apps to disseminate and collect environmental data
Enhancing on-the-ground natural resource management by syncing local field data with indicators from large-scale spatial datasets.

Hottest Day on Record in Australia

From yesterday's Guardian:

"Australia had its hottest day on record on Monday with a nationwide average of 40.33C (104.59 F), narrowly breaking a 1972 record of 40.17C (104.31 F). Tuesday was the third hottest day at 40.11C (104.2F). Four of Australia's hottest 10 days on record have been in 2013."

Tammy Holmes (second from left) and her grandchildren, (from left) Charlotte, Esther, Liam, Matilda and Caleb, take refuge under a jetty. Photograph: Tim Holmes/AP

"The road closed behind me," [Bonnie Walker] told ABC News. "We just waited by the phone. We received a message at 3.30pm to say that mum and dad had evacuated, that they were surrounded by fire, and could we pray. So I braced myself to lose my children and my parents." She described the photo of her family holding on beneath the jetty as upsetting. "It's all of my, our, five children underneath the jetty huddled up to neck-deep seawater, which is cold. We swam the day before and it was cold. So I knew that that would be a challenge, to keep three non-swimmers above water."

Not unrelated, NOAA just announced that 2012 was the hottest year on record for the US.

(via Alkarim Jina)

Pale Blue Dot

My high-school buddy Brett pointed me towards this story, which I was ashamed that I didn't already know. I post it here so that other FE readers can be saved similar embarrassment.

In February of 1990, the unmanned Voyager 1 spacecraft had finished its primary mission of exploring the solar system (it had been launched in 1977) and it was rocketing out of the range of our communication technology at 64,000 km/h (40k mph). 6 billion kilometers away (well past Pluto) it was  further from Earth than anything else we humans had ever built. Realizing a unique opportunity, Carl Sagan requested that NASA turn the camera on the Voyager around and take a snapshot of planet Earth. This is the resulting photograph:

Photo of Planet Earth from 6 billion kilometers away. NASA, February 14, 1990.

Earth is the "Pale Blue Dot" on the right hand side of the photo, sitting in the right-most "beam of sunlight"  (which is actually an artifact of the camera). Click here if you need a hint identifying our planet.

This simple image immediately changes your view on the human condition. As Sagan writes:

From this distant vantage point, the Earth might not seem of any particular interest. But for us, it's different. Consider again that dot. That's here. That's home. That's us. On it everyone you love, everyone you know, everyone you ever heard of, every human being who ever was, lived out their lives. The aggregate of our joy and suffering, thousands of confident religions, ideologies, and economic doctrines, every hunter and forager, every hero and coward, every creator and destroyer of civilization, every king and peasant, every young couple in love, every mother and father, hopeful child, inventor and explorer, every teacher of morals, every corrupt politician, every "superstar," every "supreme leader," every saint and sinner in the history of our species lived there – on a mote of dust suspended in a sunbeam. 

The Earth is a very small stage in a vast cosmic arena. Think of the rivers of blood spilled by all those generals and emperors so that in glory and triumph they could become the momentary masters of a fraction of a dot. Think of the endless cruelties visited by the inhabitants of one corner of this pixel on the scarcely distinguishable inhabitants of some other corner. How frequent their misunderstandings, how eager they are to kill one another, how fervent their hatreds. Our posturings, our imagined self-importance, the delusion that we have some privileged position in the universe, are challenged by this point of pale light. Our planet is a lonely speck in the great enveloping cosmic dark. In our obscurity – in all this vastness – there is no hint that help will come from elsewhere to save us from ourselves. The Earth is the only world known, so far, to harbor life. There is nowhere else, at least in the near future, to which our species could migrate. Visit, yes. Settle, not yet. Like it or not, for the moment, the Earth is where we make our stand. It has been said that astronomy is a humbling and character-building experience. There is perhaps no better demonstration of the folly of human conceits than this distant image of our tiny world. To me, it underscores our responsibility to deal more kindly with one another and to preserve and cherish the pale blue dot, the only home we've ever known. 

—Carl Sagan, Pale Blue Dot: A Vision of the Human Future in Space, 1997 reprint, pp. xv–xvi

For more (albeit closer) pivotal images of Earth from space, see this earlier post.

Unleash your inner cartographer

In my work, I make a lot of maps. But they're usually just a single image and they're mediocre in terms of color-choice and design.  Mapbox is a product from a DC startup that can help us data-jockeys build svelte and scalable maps that integrate data from lots of sources. From their about page:

MapBox is a platform for designing and publishing fast and beautiful maps. We provide MapBox Streets, a complete customizable world base map, develop the powerful open source map design studio TileMill, make it easy to integrate maps into applications and websites, and support all of these tools on top of scalable, high-performance hosting. We've made MapBox developer friendly with an open API.

The development team has worked on all sorts of projects, from tracking elections to helping document hurricane damage. Their blog is also way cool.

h/t Young

If scientists had a Book of Psalms, it would be this book

While wandering through the Princeton bookstore, I stumbled upon this gem. The Oxford Book of Modern Science Writing by Richard Dawkins will become a treasure of the scientific community. Dawkins gathers 83 choice writing excerpts from the "Greats" of scientific writing (e.g. Pinker, Diamond, Turing, Einstein, Sagan, Penrose, Greene, Hawking, Chandrasekhar, Sacks, Oppenheimer, Wilson, Carson, Dyson, Snow... the whole list is here). The excerpts are each short (a few pages) but masterfully chosen, and Dawkins provides a brief discussion of each writer and their style before presenting the text.  The selected excerpts discuss many of the central philosophical questions/insights of science, as well as many of its key contributions -- so readers are educated about actual science in addition to seeing how to write about it beautifully.

The book is thick, and I haven't finished it myself, but I can't recommend it enough for anyone who considers themselves a scientist.  If science were art, this text would be like a distillation of the best masterworks from the world's best museums into a potent liquor that makes you feel guilty when you read from it because it is so rich and amazing -- representing much of humanity's collective accomplishments -- and undeservingly, you're still just sitting on your couch.

If you're looking for a holiday gift for a scientist, I would recommend this. Or if you're a scientist whose annoyed that your loved ones didn't buy you this book for the holidays, you can read a lot of it for free on google here.

An aside: If I ever get the chance, I hope to lead a seminar/clinic for phd students on scientific communication.  I think this book on writing will round out the curriculum alongside Tufte's book on data display and Baron's book on communicating verbally.

Weekend Links

1) Sachs on Acemoglu & Robinson on Sachs on Acemoglu & Robinson

2) “It is crucial that scientists sound a more realistic, more desperate, note on global warming.” - Jeremy Grantham. Yes, that Jeremy Grantham. (via Paul Armsworth) - update: and Vaclav Smil responds.

3) Applications for next October's DISCCRS VIII Interdisciplinary Climate Change Research Symposium in Colorado are now open

4) Knowing Where and How Criminal Organizations Operate Using Web Content (via Gordon McCord)

5) Google is using drones to help hunt rhino poachers (via Dave Pell)

6) Is Corruption in China 'Out of Control'? A Comparison with the U.S. In Historical Perspective (via Andres Marroquin)

Is it true that "Everyone's a winner?" Dams in China and the challenge of balancing equity and efficiency during rapid industrialization

Jesse and I both come from the Sustainable Development PhD Program at Columbia which has once again turned out a remarkable crop of job market candidates (see outcomes from 2012 and 2011). We both agreed that their job market papers were so innovative, diverse, rigorous and important that we wanted to feature them at FE.  Their results are striking and deserve dissemination (we would probably post them anyway even if the authors weren't on the market), but they also clearly illustrate what the what the Columbia program is all about. (Apply to it here, hire one of these candidates here.) Here is the third and final post.

Large infrastructure investments are important for large-scale industrialization and economic development. Investments in power plants, roads, bridges and telecommunications, among others, provide important returns to society and are compliments to many types of private investment. But during rapid industrialization, as leaders focus on growth, there is often concern that questions of equity are cast aside. In the case of large-scale infrastructure investments, there are frequently populations ("losers") that suffer private costs when certain types of infrastructure are built -- for example, people whose homes are in the path of a new highway or who are affected by pollution from a power plant.

In public policy analysis and economics, we try to think objectively of the overall benefits of large investments to an entire society, keeping in mind that there will usually be some "losers" from the new policy in addition to a (hopefully larger) group of "winners."  In the cost-benefit analysis of large projects, we usually say if that a project is worth doing if the gains to the winners outweigh the loses to the losers -- making the implicit assumption that somehow the winners can compensate the losers for their loses and continue to benefit themselves. In cases where the winners compensate the losers enough that their losses are fully offset (i.e. they are no longer net losers), we say that the investment is "Pareto improving" because nobody is made worse off by the project.

A Pareto improving project is probably a good thing to do, since nobody is hurt and probably many people benefit. However, in the case of large infrastructure investments, it is almost guaranteed that some groups will be worse off because of the project's effects, so making sure that everyone benefits from these projects will require that the winners actually compensate the losers. Occasionally this occurs privately, but that tends to be uncommon, so with large-scale projects we often think that a central government authority has a role to play in transferring some of benefits from the project away from the winners and towards the losers.

But do these transfers actually occur? In a smoothly functioning government, one would hope so.  But the governments of rapidly developing countries don't always have the most experienced regulators and often pathologies, like corruption, lead to doubt as to whether large financial transfers will be successful.  Empirically, we have little to no evidence as to whether governments in rapidly industrializing countries (1) accurately monitor the welfare lost by losers in the wake of large projects and (2) have the capacity necessary to compensate these losers for their loses. Thus, establishing whether governments can effectively compensate losers is important for understanding whether large-scale infrastructure investments can be made beneficial (or at least "not harmful") for all members of society.

Xiaojia Bao investigates this question for the famous and controversial example of dams in China. Over the last few decades, a large number of hydroelectric dams have been build throughout China. These dams are an important source of power for China's rapidly growing economy, but they also can lead to inundation upstream, a reduction in water supply downstream, and a slowed flow of water that leads to an accumulation of pollutants both upstream and downstream.

Bao asks whether the individuals who are adversely affected by new dams are compensated for their losses. To do this, she obtains data on dams and municipal-level data on revenue and transfers from the central government.   She uses geospatial analysis to figure out which municipalities are along rivers that are dammed and also which are upstream, downstream or at the dam site.  She then compares how the construction of a new dam alters the distribution of revenues and federal transfers to municipalities along the dammed river, in comparison to adjacent municipalities that are not on the river.

Bao finds that the Chinese government has been remarkably good at compensating those communities who suffer when dams are built.  Municipalities upstream of a dam lose the most revenue both while the dam is being built and after it become operational. But at the same time, the central government increases transfers to those municipalities sufficiently so that these municipalities suffer no net loss in revenue. In contrast, populations just downstream look like they benefit slightly from the dam's operation, increasing their revenue -- and it appears that the central government is also good at reducing transfers to those municipalities so that these gains are effectively "taxed away." The only group that is a clear net winner are the municipalities that host the actual dam itself, as their revenue rises and the central government provides them with additional transfers during a dam's construction.

These findings are important because we often worry that large-scale investment projects may exacerbate existing patterns of inequality, as populations that are already marginalized are saddled with new burdens for the sake of the "greater good." However, in cases where governments can effectively distribute the benefits from large projects so that no group is made worse off, then we should not let this fear prevent us from making the socially-beneficial investments in infrastructure that are essential to long run economic development.

The paper:

Dams and Intergovernmental Transfer: Are Dam Projects Pareto Improving in China?
Xiaojia Bao  

Abstract: Large-scale dams are controversial public infrastructure projects due to the unevenly distributed benefits and losses to local regions. The central government can make redistributive fiscal transfers to attenuate the impacts and reduce the inequality among local governments, but whether large-scale dam projects are Pareto improving is still a question. Using the geographic variation of dam impacts based on distances to the river and distances to dams, this paper adopts a difference-in-difference approach to estimate dam impacts at county level in China from 1996 to 2010. I find that a large-scale dam reduces local revenue in upstream counties significantly by 16%, while increasing local revenue by similar magnitude in dam-site counties. The negative revenue impacts in upstream counties are mitigated by intergovernmental transfers from the central government, with an increase rate around 13% during the dam construction and operation periods. No significant revenue and transfer impacts are found in downstream counties, except counties far downstream. These results suggest that dam-site counties benefit from dam projects the most, and intergovernmental transfers help to balance the negative impacts of dams in upstream counties correspondingly, making large-scale dam projects close to Pareto improving outcomes in China.

In figures...

In China, Bao obtains the location, height, and construction start/stop dates for all dams built before 2010.

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For every dam, Bao follows the corresponding river and calculates which municipalities are "upstream" and which are "downstream." She then computes finds comparison "control" municipalities that are adjacent to these "treatment" municipalities (to account for regional trends). Here is an example for a single dam:

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Bao estimates the average effect of dam construction (top) and operation(bottom) on municipal revenues as a function of distance upstream (left) or downstream (right).  Locations just upstream lose revenue, perhaps from losing land (inundation) or pollution. Locations at the dam gain revenue, perhaps because of spillovers from dam-related activity (eg. consumer spending). During operation, downstream locations benefit slightly, perhaps from flood control.

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Government transfers during construction/operation upstream/downstream. Upstream locations receive large positive transfers. Municipalities at the dam receive transfers during construction. Downstream locations lose some transfers (taxed away).

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Transfers (y-axis) vs. revenue (x-axis) for locations upstream/downstream and at the dam site, during dam construction. Locations are net "winners" if they are northeast of the grey triangle. Upstream municipalities are more than compensated for their lost revenue through transfers.   Municipalities at the dam site benefit through revenue increases and transfers.

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Same, but for dam operation (after construction is completed). Upstream locations are compensated for losses. Benefits to downstream locations are taxed away. Dam-site locations are net "winners".

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Urban bus pollution and infant health: how New York City's smog reduction program generates millions of dollars in benefits

Jesse and I both come from the Sustainable Development PhD Program at Columbia which has once again turned out a remarkable crop of job market candidates (see outcomes from 2012 and 2011). We both agreed that their job market papers were so innovative, diverse, rigorous and important that we wanted to feature them at FE.  Their results are striking and deserve dissemination (we would probably post them anyway even if the authors weren't on the market), but they also clearly illustrate what the what the Columbia program is all about. (Apply to it here, hire one of these candidates here.) Here is the second post.

Around the world, diesel-powered vehicles play a major role in moving people and goods. In particular, buses are heavily utilized in densely populated cities where large numbers of people are exposed to their exhaust. If bus exhaust has an impact on human health, then urban policy-makers would want to know this since it will affect whether or not it's worth it to invest in cleaner bus technologies. Upgrading the quality of public transport systems is usually expensive, but upgrading could have potentially large benefits since so many people live in dense urban centers and are exposed to their pollution. Deciding whether or not to invest in cleaner bus technologies is an important policy decision made by city officials, since buses aren't replaced very often and poor choices can affect city infrastructure for decades -- so its important that policy-makers know what the trade offs are when they make these decisions.

Unfortunately, to date, it has been extremely difficult to know if there are any effects of bus pollution on human health because cities are complex and bustling environments where people are constantly exposed to all sorts of rapidly changing environmental conditions. As one might imagine, looking at a city of ten-million people, each of whom is engaged daily in dozens of interacting activities, and trying to disentangle the web of factors that affect human health to isolate the effect of bus pollution is a daunting task. To tackle this problem, we would need to assemble a lot of data and conduct a careful analysis. This is exactly what Nicole Ngo has done.

Between 1990 and 2010,  New York City made major investments that transformed the city's bus fleet, reducing its emissions dramatically. To study the impact of this policy on human health, Ngo assembled a new massive data set that details exactly which bus drove on which route at what time every single day. Because the city's transition from dirty buses to clean buses occurred gradually over time, and because the dispatcher at the bus depot randomly assigns buses to different routes at different times, the people who live along bus routes were sometimes exposed to exhaust from dirtier buses and sometimes exposed to exhaust from clean buses.  By comparing health outcomes in households that are randomly exposed to the dirtier bus pollution with comparable households randomly exposed to cleaner bus pollution, Ngo can isolate the effect of the bus pollution on health.

In this paper, Ngo focuses on infant health (although I expect she will use this unique data set to study many more outcomes in the future) and measures the effect of a mother's exposure to bus pollution during pregnancy on a child's health at birth.  This is hard problem, since its impossible to know exactly all the different things that a mother does while she's pregnant and because Ngo has to use pollution data collected from air-quality monitors to model how pollution spreads from bus routes to nearby residences.  Despite these challenges, Ngo is able to detect the effect of in utero exposure to bus pollution on an infant's health at birth.  Fetuses that are exposed to higher levels of bus-generated Nitrous-Oxides (NOx) during their second and third trimester have a lower birthweight on average and fetuses exposed to more bus-generated particulate matter (PM) during those trimesters have a lower Apgar 5 score (a doctors subjective evaluation of newborn health).

The size of the effects that Ngo measures are relatively small for any individual child (so if you are pregnant and living near a bus route, you shouldn't panic).  But the aggregate effect of New York City's investment in clean buses is large, since there are many pregnant mothers who live near bus routes and who were exposed to less dangerous emissions because of these policies. Since its easiest to think about city-wide impacts using monetized measures, and because previous studies have demonstrated that higher birth weight causes an infants future income to be higher, Ngo aggregates these small impacts across many babies and estimates that the city's effort to upgrade buses increase total future earnings of these children by $66 million. Considering that the city upgraded roughly 4500 buses, this implies that each bus that was upgraded generated about $1,460 in value just through its influence on infant health and future earnings. Importantly however, Ngo notes:

This [benefit] is likely a lower bound since I do not consider increased hospitalizations costs from lower birth weights as discussed in Almond et al. (2005), nor could I find short-run or long-run costs associated with lower Apgar 5 scores.

and I expect that Ngo will uncover additional health benefits of New York City's bus program, which will likely increase estimates for the program's total benefits. Furthermore, I suspect that these estimates for the value of pollution control can be extrapolated to diesel trucks, although Ngo is appropriately cautious about doing so in her formal analysis.

These results are important for urban planners and policy-makers in cities around the world who must decide whether or not it is worth it to invest in cleaner public transit systems.  In addition, they are an excellent example of how great data and careful analysis can help us understand important human-environment relationships in complex urban systems.

The paper:

Transit buses and fetal health: An evaluation of bus pollution policies in New York City 

Nicole Ngo

Abstract The U.S. Environmental Protection Agency (EPA) reduced emission standards for transit buses by 98% between 1988 and 2010. I exploit the variation caused by these policy changes to evaluate the impacts of transit bus pollution policies on fetal health in New York City (NYC) by using bus vintage as a proxy for street-level bus emissions. I construct a novel panel data set for the NYC Transit bus fleet to assign maternal exposure to bus pollution at the census block level. Results show a 10% reduction in emission standards for particulate matter (PM) and nitrogen oxides (NOx) during pregnancy increased infant Apgar 5 scores by 0.003 points and birth weight by 6.6 grams. While the impacts on fetal health are modest, the sensitivity of later-life outcomes to prenatal conditions suggests improved emission standards between 1990 and 2009 have increased total earnings for the 2009 birth cohort who live near bus routes in NYC by at least $65.7 million.

In figures...

Bus routes in New York City, which Ngo links to residential exposure through geospatial analysis:

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Buses are upgraded throughout the two decades, with several large and abrupt changes in the fleet's composition:

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When dirtier buses are randomly assigned to travel a route, Ngo can detect this using air-monitoring stations near that route:

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Using her mathematical model of bus pollution (and its spatial diffusion) Ngo computes how New York City's investment in buses lead to a dramatic reduction in exposure to bus-generated pollutants:

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Exposure to bus-generated NOx during the second and third trimesters lowers birthweight, and exposure to bus-generated PM lowers Apgar5 scores:

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Probably the most important class I took at MIT

"Solving complex problems" (aka "12.000" or "Mission") is an innovated class for MIT freshman that was just awarded a Science Prize for Inquiry-Based Instruction.  

The course is a component of the larger Terrascope program (of which I am a proud member of the first cohort) designed to teach leadership and teamwork skills to students as they work on unbearably large and complex problems related to global environmental management (usually) or a coupling between human and environmental systems more broadly. The class is expertly designed and run, played a major role in my own personal development, and would be the one class that I would unconditionally recommend to any incoming MIT freshman.  Furthermore, if any faculty FE-reader is trying to build a program in "sustainable development" at the undergraduate level, I would strongly recommend that they try to develop a similar course.

The course is described by Kip Hodges (my first research supervisor, now at ASU) in a Science article this week (read it here for free):

Students are presented in the first class with a challenge that can be stated simply, but that is deceptively complex and has no straightforward answer. Over the course of the semester, it is their job collectively to “imagineer” a proposed solution, to articulate their solution, and to explain how they arrived at it.

(To be clear about how the class works, on the first day of my first semester at MIT, Kip walked into the room and put up on the board 

"Develop a way to characterize and monitor the well-being of one of the last true frontiers on Earth – the Amazon Basin rainforest – and devise a set of practical strategies to ensure its preservation."

and said "go". I'm not kidding.)

The instructor’s role in this class is primarily to create an environment conducive to self-directed learning. There are no lectures, although the students are exposed in a casual way to a series of case studies that are germane to their problem.... 

In the early years of offering this subject, we passed on to the students a list of people who had been recruited by the instructional staff and had volunteered to participate in such discussions. However, we soon found that such recruiting efforts were unnecessary; many at all levels of the academic community are open to such informal interactions when they are precipitated by students asking questions that begin with a phrase like: “What is your take on ….” These casual conversations are especially valuable because they impart an appreciation for practical integration of acquired knowledge

The course is currently run by Sam Bowring and Ari Epstein and it's material is up on MIT's Open CourseWare.  This year's cohort recently gave their final presentation (watch it here). My cohort's website is still up here (search long enough, and you can even find my freshman photo).

h/t Kip

Weekend links

1) Two papers (one each in Science and Nature) have come out in the past month indicating that onset of human tool use was much earlier than we thought, at multiple points of development.

2) Stata's gift shop is remarkably charming

3) England has maintained a military presence in all but 22 countries around the world (via Artem Peplov)

4) Sam Arbesman on the Kolmogorov compexity of holidays (via MR)

5) Someone needs to come up with a good instrument for high fructose corn syrup consumption

6) Nature's editorial board weighs in on the PNAS gender bias paper and their take on how best to respond (previously on FE)

7) Long Run Impacts of Childhood Access to the Safety Net (NBER)

8) Attention as a scarce resource, experimental development econ edition (Science, previously on FE)

9) Suicide and drought in New South Wales, Australia, 1970–2007 (PNAS)

10) There will be an open mic night at this year's AGU Annual Meeting in San Francisco