papers

WWP: Old fight, new tricks?

One of the most interesting papers I saw at the ASSA meetings in January was Ariel Ortiz-Bobea's new work on the climate and agriculture question. For anyone not in the know, there is a long (read: loooooooong) literature trying to estimate the effects climate change will have on agriculture. Most of this debate has focused on the US, largely for data reasons (and partly because US maize is way sexier than Kenyan maize...amirite?).  

An overly brief summary of this literature is the following:

  • In the Beginning, agronomists created the Crop Model. These models were created using test plots, and used to predict the effects of climate on agriculture.
  • Then, some economists came along, and made the point that the agronomists were selling the farmers short. Crop models ignore the potential for farmer adaptation. And thus the Ricardian model was born: these economists regress land values on average temperatures, plus a bunch of controls, and find mild-to-positive effects of climate change. 
  • But wait! Enter Team ARE. A second set of economists argued that the Ricardian approach, like most cross-sectional regressions, suffered from omitted variable bias. In particular, they note that the presence of irrigation dramatically changes agriculture, and suggested estimating different models for irrigated and non-irrigated regions (if you're keeping score at home, you can also implement this suggestion via an interacted model). When they account for irrigation, climate change looks pretty bad again.
  • A few years later, some other economists arrived on the scene. If you're worried about irrigation, they argued, you should be worried about a whole host of other omitted variables in the cross-section. But do we have the idea for you? These guys used a panel fixed effects model to remove time-invariant omitted variables - also sparking a debate about "weather vs. climate" (using short-run fluctuations rather than long-run variation to estimate the model in question) - and find again that climate change probably isn't so bad.
  • Unnnnnfortunately, our panel-data-wielding heroes had some data problems (brought to light by Team ARE). If you correct them, climate change harms US agriculture to the tune of tens of billions of dollars. Oops.
  • But the weather-vs-climate thing is still unsatisfying! So Team ARE: The Next Generation used a long-difference estimator to show that actually, farmers don't seem to be doing a better job responding to climate change over time - it'll still be bad.

Here's where Ariel's new paper comes along. He notes that (for various reasons glossed over above) we might actually want to run a Ricardian-style model: in essence, weather vs. climate hasn't been fully resolved. At the same time, though, we should be worried about omitted variable bias. But in particular, we should be worried about spatially-dependent omitted variable bias. The argument is pretty simple. Most things that might be left out of an agriculture-climate regression that would bias that regression vary smoothly over space. Conveniently for the econometrician, there are some newer estimators that we can use to understand the magnitude and direction of the bias that might result from these types of omitted variables. Ariel uses these tricks, and finds that (lo and behold) climate change might not be so bad for agriculture in the US after all.

Effects of climate change estimated using OLS: this is the original economist version.

Effects of climate change estimated using OLS: this is the original economist version.

New-fangled effects of climate change using the Spatial Durbin Model. Note the lack of hugely negative effects, especially towards the right of the figure.

New-fangled effects of climate change using the Spatial Durbin Model. Note the lack of hugely negative effects, especially towards the right of the figure.

This paper is full of technical details, makes some fairly strong structural assumptions about exactly how omitted variables vary over space, and ends up with fairly wide confidence intervals, but all in all, it makes a useful contribution to an important debate, and is worth a read. I'll be interested to see where it ends up, and how seriously the literature takes the re-posited suggestion that climate change really isn't that bad for US ag. If nothing else, this paper highlights just how important it is for us to figure out how to measure adaptation!

Bonus: If you've read this far down, you deserve something fun. Go check out my new favorite internet game. h/t Susanna & Paula.

 

Edited to fix links. Thanks to my usual blog-police for pointing this out.

Lies, damn lies, and data visualization

A redundant trifecta? First of all, happy holidays, if you'd like, aka one of the few times of year I feel guilt-free about saying that I'm taking (shock and horror) two full days off! And for whomever of you is out there thinking "wait, but you're blogging...probably about economics"...hush. This blog isn't work (and as pretty much any academic economist will tell you, unless you're Chris Blattman, blogging won't help your career either)...[Lie or damn lie? You decide. I think we can clearly rule out data visualization on this one.]

I went to Edward Tufte's "Presenting Data and Information" one-day course in the city last week. If you're not already familiar with Tufte, you should be! He's one of the best writers out there on how to present data in an honest and compelling way, which is often under-appreciated by academic economists, I think.

The course was largely focused toward a corporate audience, but several of the general principles hold true in academia-land as well:

  • "Know your content" rather than "know your audience". In general, a guiding Tufte principle is that good content speaks for itself. Work to declutter your graphics/presentation so that your data/results are what pop out.
  • Be intellectually honest. I thought this goes without saying, but we've had a few recent egregious examples (see below) of badly misleading figures. Don't be a damn liar (and think about what you're presenting and how. Rules of thumb aren't always good).
  • Don't be afraid to integrate data with words. Good figure labels are essential, and annotative text can often help.
  • Social science is harder than real science [and also potentially has more transparency problems.]
Yikes.

Yikes.

Yikes, part 2.

Yikes, part 2.

Maybe none of these points seem super deep to you, but I think they're worth engaging with.

Tufte also spent a long time talking about presenting information in a way that allows the audience to take in your content in a (semi-)unguided manner before beginning to talk. The idea is to present a meeting audience with a handout when you walk in, and letting them read before you start clicking through slides. This helps get everyone on the same page, and also presents them with an opportunity to engage with material at their pace rather than making lots of people wait. Presenting information this way also allows people to dive deeper if they want - and a carefully crafted handout can do a lot in a page. Then when you actually start talking, you can have a more substantive, less hand-holdy discussion. Definitely interesting. Probably not going to be implemented in job talks any time soon, but the point about a carefully-crafted fluff-less written document is appealing in a field where papers are often 35+ pages (scientists manage to publish major work in 2 or 3, so...)

Other (random) things of note:

  • A paper I've been part of (with Catherine, Dave Rapson, Mar Reguant, and Chris Knittel) is being presented at the AEA meetings in January, in the "Evaluating Energy Efficiency Programs" session at (gulp) 8 AM on Jan. 3. Still very preliminary, but I think we're doing some cool things in incorporating machine learning with traditional econometric methods to exploit high-frequency electricity data. If you can suffer the early morning, come check it out!
  • Star Wars: The Force Awakens is awesome. Go see it if you haven't already! And if you have, shut up about the "they remade Episode IV" stuff. Similar plots? Sure. Was it still super awesome? 100%.
  • I basically missed out on all of the COP coverage, but hopefully this agreement represents some steps in the right direction.
  • My new Anova sous vide cooking toy has been a smashing success so far (neither Dana nor I has been harmed yet). 
  • Grist has some compelling maps.
  • The Economist has an infographic advent calendar. 
  • And, finally, on an optimistic note, Quartz's Chart of the Year shows the dramatic decline in people living in poverty over the last 200 years or so. We're doing a lot of things wrong these days, but it's nice to see clear graphical evidence that we're actually doing some stuff right.

That's it - stop reading this and go do something fun/outside/etc!

 

Occasional Workshop Recap 2015

The Occasional Workshop in Environmental and Resource Economics is one of my favorite conferences. No, not only because it's in beautiful Santa Barbara and we got to stick our feet in the warm Southern California ocean on our way back home! This year, there were a few "long" presentations (20' each, with a 10' discussant slot), but most of the papers were "short" presentations - 8' each, with a couple of minutes for questions afterwards. There are no parallel sessions - everyone sees everything - and 8' is a great way to get a quick introduction to a topic. We see only a very limited part of the environmental economics spectrum at Berkeley, for better or for worse, and so getting to see papers on land use and endangered species and fish is really fun. I was also lucky enough to get to present some of my ongoing work on rural electrification in India, and managed to cut Louis' 60-minute, 75-slide presentation of our paper from Berkeley's development lunch earlier in the week down to an 8-minute, 20-slide presentation (which actually fit into 8', in an homage to Meredith's speaking speed). 

A couple of my favorite presentations (the full list can be found here):

  • Robyn Meeks' "Economics of a Light Bulb: Experimental Evidence on CFLs and End-User Behavior" - a cool thought experiment (plus RCT!) on technology adoption in the face of learning and technical externalities
  • The always-excellent Sol Hsiang's "A Global Experiment in Black Market Dynamics: The Effect of Legal Ivory Sales on Illegal Ivory Production" - allowing legal sales of ivory is disastrous for elephant poaching
  • Amanda Faig's "The Economic Gains to Accounting for Fisheries Induced Evolution" - I'm not a fishery person at all, but this was a really cool look at how fishery managers can induce fish evolution - and how, if they don't account for this, they're much worse off
  • Jeff Shrader's "Estimating Adaptation Using Forecasts" - thinking about how to estimate adaptation in a context with skilled forecasters. Application to ENSO (a rash of cool papers using ENSO makes me want to write a paper using ENSO...does that make me a bandwagon researcher?)
  • Corbett Grainger's "Strategic Placement of Ambient Pollution Monitors: How Local Regulators Comply with Federal Rules" - turns out EPA air pollution monitors might not be optimally placed from a social planner's perspective. Cool.

I'm excited about the future of environmental and resource economics!

Obligatory beach photo courtesy of  Becca  (who also gave a great presentation at the Occasional  and  made amazing cookie road trip snacks).

Obligatory beach photo courtesy of Becca (who also gave a great presentation at the Occasional and made amazing cookie road trip snacks).

Finally, two more items of note:

1) T-one week until the wedding!

2) This might be my new favorite energy phenomenon: British grids need to supply additional power during commercial breaks in popular TV shows - because everybody uses them to plug in electric kettles, causing a demand spike. Offfffff course they do.