remote sensing

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: Ocean risks

Maybe I have holdover inspiration from my weekend in the beautiful Pacific Northwest, but this week's WWP features two papers that are forthcoming in the AEJ: Applied (but not out yet - so I can still count them as working papers, right? Okay, maybe not, but at the very least, they're new work!) with oceanic themes. 

The first is a really cool new paper by Sebastian Axbard, a PhD candidate at Uppsala University in Sweden (turns out he was also a visiting scholar here at Berkeley a few years ago. I swear I didn't know until after I found the paper!). Axbard combines two of my favorite topics: piracy (or crime) and environmental (climate) economics. His paper uses some neat new remote sensed data on sea surface temperature and chlorophyll-a concentrations to construct a measure of fishing conditions, which he then combines with fish market price data, labor outcomes from one of Indonesia's (many great) datasets, SAKERNAS, and finally, geocoded piracy data. He uses exogenous variation in fishing to show that piracy responds to local incomes, and then goes on to show that a military exercise targeted at piracy (which is called, I kid you not, Operation Octopus !!!!!) did reduce attacks. He finds that the operation had a strong effect on piracy in locations with bad fishing conditions, but that the effects persist in time more effectively when the military operation is accompanied by good fishing conditions (and therefore, we surmise, a good outside option for these potential pirates). This paper is super cool - here's Axbard's abstract:

The effect of climatic variation on conflict and crime is well established, but less is known about the mechanism through which this effect operates. This study contributes to the literature by exploiting a new source of exogenous variation in climate to study the effect of fishermen’s income opportunities on sea piracy. Using satellite data to construct a monthly measure of local fishing conditions it is found that better income opportunities reduce piracy. A wide range of approaches are employed to ensure that these effects are driven by income opportunities rather than other mechanisms through which climate could affect piracy.

The most recent ungated copy of the paper I could find is here

Cool display of the attacks (red dots) and fishing conditions (blue squares) from Axbard's paper. From Figure 5. 

Cool display of the attacks (red dots) and fishing conditions (blue squares) from Axbard's paper. From Figure 5. 


This week's second paper is also about the ocean - sort of (okay, this is a little bit of a stretch - but typhoons come from the ocean, so I'm going to claim that this post is cohesive). André Gröger at Goethe Universitaet Frankfurt and Yanos Zylberberg at Bristol also use remote sensed data, this time to look at the effect of a huge typhoon in Vietnam on migration. These guys use satellite information on coastal inundation (I had to look this up - NOAA's definition is "Water covering normally dry land is a condition known as inundation"), which they construct from MODIS images, which they match to another great Vietnamese panel dataset. They find that the typhoon caused a large decrease in incomes in affected areas, and households respond by sending migrants out of the home, or for homes that already have a migrant, seeing increased remittances from their migrant. The body of work so far on migration as a response to (climatic) shocks is small, but rapidly growing - this is a really cool new addition. The authors themselves write:

We analyze how internal labor migration facilitates shock coping in rural economies. Employing high precision satellite data, we identify objective variations in the inundations generated by a catastrophic typhoon in Vietnam and match them with household panel data before and after the shock. We find that, following a massive drop in income, households cope mainly through labor migration to urban areas. Households with settled migrants ex-ante receive more remittances. Nonmigrant households react by sending new members away who then remit similar amounts than established migrants. This mechanism is most effective with long-distance migration, while local networks fail to provide insurance.

 I again found an ungated copy here

Again, more awesome satellite data. Figure 2 from the Gröger and Zylberberg paper.

Again, more awesome satellite data. Figure 2 from the Gröger and Zylberberg paper.

Really excited to see remote sensing data being used in (soon-to-be) published papers! I consider myself lucky to be an economist now in our current era of amazing data availability - and it gets better every day. I hope that in a few years I'll look back at this blog post and laugh at what I used to think was data abundance.