weekend working papers

WWP: Forests without borders?

As the semester winds to a close, I've been trying to get back into a better habit of reading new papers. Really, I'm just looking for more excuses to hang out at my local Blue Bottle coffee shop - great lighting, tasty snacks, wonderful caffeine, homegrown in Oakland...what more could I ask for? Sometimes doing lots of reading can feel like a slog - but this week, I stumbled across a really cool new working paper that's well worth a look. The paper, by Robin Burgess, Francisco Costa, and Ben Olken, is called "The Power of the State: National Borders and the Deforestation of the Amazon." Deforestation is a pretty big problem in the Amazon, especially when we think that (on top of the idea that we might want to be careful to protect exhaustible natural resources for their own sake, and not pave paradise to build a parking lot) tropical forests have a large role to play in combating climate change, because trees serve as a pretty darn effective carbon sink. Plus they produce oxygen. Win-win! Despite these benefits, trees are also lucrative, and there are also economic benefits to converting forests into farmland. This has led to a spate of deforestation. 

To combat the destruction of the Amazon, Brazil enacted a series of anti-deforestation policies in 2005-06. It's important to understand whether these policies worked, and it's not obvious ex ante that they would: according to evidence from a friend (and badass spatial data guru/data scientist), Dan Hammer, a deforestation moratorium in Indonesia was unsuccessful: if anything, it led to an increased rate of deforestation. Oops. 

The key figure from Dan's paper. The grey bars indicate times when the Indonesian deforestation moratorium was in effect. The teal line is the deforestation rate in Malaysia, and the salmon line is the rate in Indonesia. Not much evidence here that Indonesia's deforestation rate decreased relative to Malayasia during (after) the gray periods.

The key figure from Dan's paper. The grey bars indicate times when the Indonesian deforestation moratorium was in effect. The teal line is the deforestation rate in Malaysia, and the salmon line is the rate in Indonesia. Not much evidence here that Indonesia's deforestation rate decreased relative to Malayasia during (after) the gray periods.

The big challenging with studying deforestation, especially when it's been made illegal, is that it's tough to get data on. Going out and counting trees requires a huge effort, and people don't usually like to self-report illegal activity. Like Dan before them, Burgess, Costa, and Olken (hereafter BCO) turn to satellite imagery. As I've said before, I'm excited about the advances in remote sensing - there's an explosion of data, which can be harnessed to measure all kinds of things where we don't necessarily have good surveys (Marshall Burke, ahead of the curve as usual). BCO use 30 x 30 meter resolution data on forest cover from a paper published in Science - ahh, interdisciplinary (and a win for open data!).

Of course, it's not enough to just have a measurement of forest cover - in order to figure out the causal effect of Brazil's deforestation policies, the authors also need an identification strategy. Maybe this is because I've got regression discontinuities on the brain lately, but I think what BCO do is super cool. They use the border between Brazil and its neighbors in the Amazon to identify the effects of Brazil's policy. The argument is that, other than the deforestation policies in the different countries, parts of the Amazon just to the Brazilian side of the border look just like parts of the Amazon just to the opposite side of the border. This obviously isn't bullet-proof - you might worry that governance, institutions, infrastructure, populations, languages, etc change discontinuously at the border. They do some initial checks to show that this isn't true (including a nice anecdote where the Brazilian president-elect accidentally walked into Boliva for an hour before being stopped by the border patrol), which are decently compelling (though we're always worried about unobservables). Under this assumption, BCO run an RD comparing deforestation rates in Brazil to its neighbors:

The first key figure from BCO: in 2000, well before Brazil's aggressive anti-deforestation policies, the percent of forest cover was much lower on the Brazilian (right-hand) side of the border.

The first key figure from BCO: in 2000, well before Brazil's aggressive anti-deforestation policies, the percent of forest cover was much lower on the Brazilian (right-hand) side of the border.

There's a clear visual difference between deforestation in Brazil and in its neighbors. But here's what I really like about the paper: even if you don't completely buy the static RD identifying assumption here, you have to agree that the following sequence of RD figures is pretty compelling.

This is a little annoying to compare to the first figure, since the y-axis here is different: this time, it's percent of forest cover lost each year - that's why Brazil appears higher in these figures than in the earlier graph. But: it clearly pops out that In 2006, when Brazil's policies went into effect, the discontinuity disappears.

This is a little annoying to compare to the first figure, since the y-axis here is different: this time, it's percent of forest cover lost each year - that's why Brazil appears higher in these figures than in the earlier graph. But: it clearly pops out that In 2006, when Brazil's policies went into effect, the discontinuity disappears.

The cool thing about the data that the authors have, which differentiates it from many spatial RD papers, is that it's not static - they've got multiple years of data. This allows them to look at deforestation over time. Critically, even though Brazil's forest cover is much lower than its neighbors prior to the policy, its annual rate of forest cover loss slows dramatically in 2006, when the deforestation policies came into effect, and appears to remain equal to the neighboring country rate in 2007 and 2008. 

This is pretty strong evidence that the anti-deforestation policies put into place by the Brazilian government worked! You should still be slightly skeptical, and want to see a bunch of robustness checks (many of which are in the paper), but I really like this paper. It combines awesome remote sensing data with a quasi-experimental research design to study the effectiveness of important policies. It's not too often that we can be optimistic about the future of the Amazon - but it looks like we've got some reason to be hopeful here.

 

If you've made it all the way through this post, I'll reward you with John Oliver's new video on science.

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.

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.

WWP: Choleric intake is bad for housing prices

I have a confession to make: I haven't had a chance to fully read this week's excellent-looking paper from Attila Ambrus, Erica Field, and Robert Gonzalez. But! A quick skim convinced me that it's cool enough that it's absolutely worth mentioning. This paper invokes several of my favorite things:

1) Regression discontinuity design - I'm up to my ears in an RD paper with a classmate, and I'm coming more and more to appreciate the benefits of this design. Yes, only having a LATE around a certain threshold can be frustrating from an external validity perspective - but the gains in terms of identification seem to strongly outweigh the costs in many cases.

2) John Snow's 19th century medical work! I'm a little bit of a data visualization nerd, and John Snow's original map of London's cholera outbreak, which helped provide convincing evidence that the Broad Street water pump was contaminated and was the source of the contagion is a classic. Beautiful figure, and incredibly clever.

3) A surprising result. This paper finds that the areas that were negatively affected by the cholera-bearing pump in the 19th century still have lower house prices today. Not what I would've expected.

One of the money figures of the paper: prices are dramatically lower inside the pump's catchment area.

One of the money figures of the paper: prices are dramatically lower inside the pump's catchment area.

All in all, this seems like a really cool paper that I wish I had more time to actually dig into at the moment. I'll leave you with the authors' abstract:

How do geographically concentrated income shocks influence the long-run spatial distribution of poverty within a city? We examine the impact on housing prices of a cholera epidemic in 19th century London in which one in seven families living in one neighborhood experienced the death of a wage earner. Ten years after the epidemic, housing prices are significantly lower just inside the catchment area of the water pump that transmitted the disease, despite being the same before the epidemic. Moreover, differences in housing prices persist and grow in magnitude over the following century. Census data reveal that price changes coincide with a sharp increase in population density at the border, consistent with anecdotes of impoverished residents taking in subtenants to make ends meet. To illustrate a mechanism through which idiosyncratic shocks to individuals that have no direct effect on infrastructure can have a permanent effect on housing prices, we build a model of a rental market with frictions, with poor tenants exerting a negative externality on their neighbors, in which a locally concentrated negative income shock can permanently change the tenant composition of the affected areas.


What's my current excuse for not reading as much as I should?

Up at Sibley Park before the super blood moon eclipse / fantastic sunset.

Up at Sibley Park before the super blood moon eclipse / fantastic sunset.

Wedding in t-minus 13 (12?) days!

PS: You know you love that pun in the title. You might not admit it out loud, but you do.

WWP: Blowin' in the wind

Berkeley ARE's resident econometrician (of FWER/FDR fame!), Michael Anderson, has a neat new NBER working paper out this (last) week. Titled "As the Wind Blows: The Effects of Long-Term Exposure to Air Pollution on Mortality", this paper tries to get at an important gap in the air pollution and health literature. We know a lot about the short-run effects of air pollution exposure: it's bad for you. But the long run is harder to pin down. Michael gets at this question using one of my all-time favorite things: a clever identification strategy. 

In order to get at the causal effect of long-term exposure to pollution, Michael exploits the Los Angeles (#beatLA) highway system. He performs a battery of tests to demonstrate that people who live upwind of highways look very similar on observables to people who live downwind of highways. He then uses angle to the highway as an instrument for time spent downwind (constructed using high-frequency wind data) to look at the effect of pollution on mortality among the elderly. He further shows that mobility is low in these Census tracts among the population of interest: the median 75-year-old has lived at her current address for 25 years. It's because of this that Michael can really get at the long-run here. 

Living next to this stuff is bad for your (long-run) health. Who knew? Photo stolen from  these guys .

Living next to this stuff is bad for your (long-run) health. Who knew? Photo stolen from these guys.

The cleverness doesn't stop there: there are two other really nice features of this paper. The first is the estimator: In conjunction with the 2SLS strategy, Michael actually uses a modified spatial fixed effects estimator (SFE), such that each observation i is demeaned relative to observations within a radius r along the dimension parallel to the highway - think of this as a highway-segment fixed effect. Instead of a regular fixed effects estimator, the SFE estimator's demeaning group changes for each observation. Running the estimation this way allows for demeaning independently along the highway and in terms of distance away from the highway. Secondly, this paper contains a nice set of falsification tests. Like other papers in this literature, Michael tests for (and finds no evidence of) effects on "placebo" health outcomes. He also shows that highway bearing has no effect on property values, which suggests that people aren't moving away to avoid these negative health consequences.

Michael's abstract describes the paper's results nicely:

There is strong evidence that short-run fluctuations in air pollution negatively impact infant health and contemporaneous adult health, but there is less evidence on the causal link between long-term exposure to air pollution and increased adult mortality. This project estimates the impact of long-term exposure to air pollution on mortality by leveraging quasi- randomvariation inpollution levels generated by wind patterns near major highways. We combine geocoded data on the residence of every decedent in Los Angeles over three years, high-frequency wind data, and Census Short Form data. Using these data, we estimate the effect of downwind exposure to highway-generated pollutants on the age-specific mortality rate byusing bearing to the nearest major highway as an instrument for pollution exposure. We find that doubling the percentage of time spent downwind of a highway increases mortality among individuals 75 and older by 3.6 to 6.8 percent. These estimates are robust and economically significant.

So, next time you're thinking about buying a house, it's probably worth adding yet another thing to the list of deal-breakers: distance to, and direction from, the nearest busy roadway.