Monday, September 26, 2016

Errors drive conclusions in World Bank post on ivory trade and elephant poaching: Response to Do, Levchenko, & Ma

(Spoiler alert: if you want to check your own skills in sniffing out statistical errors, skip the summary.) 


In their post on the World Bank blog, Dr. Quy-Toan Do, Dr. Andrei Levchenko, and Dr. Lin Ma (DLM for short) make three claims that they suggest undermine our finding that the 2008 one-time sale of ivory corresponded with a discontinuous 66% increase in elephant poaching globally. First, they claim that the discontinuity vanishes for sites reporting a large number of carcasses—i.e., there was only a sudden increase in poaching in 2008 at sites reporting a small number of total carcasses. Second, they claim that price data for ivory (which they do not make public) from TRAFFIC do not show the increase in 2008 that they argue would be expected if the one-time sale had affected ivory markets. Third, they claim that re-assigning a seemingly small proportion of illegal carcasses counted in 2008 to be legal carcasses makes the finding of a discontinuity in 2008 less than statistically significant, and they speculate that a MIKE (CITES) initiative to improve carcass classification may explain the discontinuous increase in poaching in sites with small numbers of carcasses.

In this post, we systematically demonstrate that none of these concerns are valid. First, as it turns out, the discontinuity does hold for sites reporting a large number of carcasses, so long as one does not commit a coding error that causes a systematic omission of data where poaching was lower before the one-time sale, as did DLM. Furthermore, we show that DLM misreported methods and excluded results that contradicted their narrative, and that they made other smaller coding errors in this part of their analysis. Second, we note that, notwithstanding various concerns we have about the ivory price data, our original paper had already derived why an increase in poaching due to the one-time sale would not have a predictable (and possibly no) effect on black market ivory prices. Finally, we note that (i) DLM provide no evidence that training on carcass identification could have led to a discontinuous change in PIKE (in fact, the provided evidence contradicts that hypothesis), and that (ii) in the contrived reclassification exercise modeled by DLM, the likelihood of surveyors making the seven simultaneous errors DLM state is very likely is in fact, under generous assumptions, actually less than 0.35%— i.e. extraordinarily unlikely.

Overall, while DLM motivated an interesting exercise in which we show that our result is robust to classification of sites based on the number of carcasses found, they provided no valid critiques to our analytical approach or results. The central conclusion, that our results should be dismissed, was the result of a sequence of coding, inferential, and logical errors.

Evidence Should be Used in Global Management of Endangered Species: reply to the CITES Technical Advisory Group

In anticipation of the 17th meeting of the Conference of the Parties of the Convention on the International Trade in Endangered Species (CITES), the CITES Technical Advisory Group (TAG) on elephant poaching and ivory smuggling released a document (numbered CoP17 Inf. 42) in which they directly addressed a working paper released by Nitin Sekar and myself.

The document did not introduce new substantive arguments (see end of post for one exception regarding dates)—in fact, the document’s claims were nearly identical to those made in Fiona Underwood’s prior blog posts about our analysis (here and here). In her second post, Dr. Underwood (one of a handful of members of the TAG) openly states that the TAG asked her to evaluate our analysis, and it seems they have simply adopted her view as the official stance. These arguments provide the basis for the TAG's conclusion:
13) The claims in the working paper by Hsiang and Sekar are fundamentally flawed, both in logic and methodology. The MIKE and ETIS TAG is therefore of the view that the study should not be used to inform CITES policy on elephants.
Given that the arguments have not changed, we have previously responded to almost all the document’s claims in our two prior blogposts, here and here. Below we summarize the relevant points and address new points.

As shown below,
  • numerous statements about our analysis (either in the manuscript or follow-up analysis in these prior posts) made by the CITES white paper are factually incorrect;
  • in supporting its narrative, the white paper misreports results and conclusions of other studies;
  • the white paper makes erroneous statistical arguments;
  • when the approach advocated for in the white paper is correctly implemented, it provides results virtually identical to the original findings in our analysis.
Thus, overall, there is no evidence to support the claims in the CITES white paper and therefore no reason for CITES to refuse to consider these results when developing policy.

Tuesday, September 20, 2016

Normality, Aggregation & Weighting: Response to Underwood’s second critique of our elephant poaching analysis

Nitin Sekar and I recently released a paper where we tried to understand if a 2008 legal ivory sale coordinated by CITES and designed to undercut black markets for ivory increased or decreased elephant poaching. Our results suggested the sale abruptly increased poaching:

Dr. Fiona Underwood, a consultant hired by CITES and serving on the Technical Advisory Group of CITES' MIKE and ETIS programs, had previously analyzed the same data in an earlier paper with coauthors and arrived at different conclusions. Dr. Underwood posted criticisms of our analysis on her blog.  We replied to every point raised by Dr. Underwood and posted our replication code (including an Excel replication) here. We also demonstrated that when we analyzed the data published alongside Dr. Underwood's 2011 study, in an effort to reconcile our findings, we essentially recovered our main result that the sale appeared to increase poaching rates. Dr. Underwood has responded to our post with a second post. This post responds to each point raised in Dr. Underwood's most recent critique.

Dr. Underwood's latest post and associated PDF file makes three arguments/criticisms:

1) Dr. Underwood repeats the criticism that our analysis is inappropriate for poaching data because it assumes normality in residuals.  Dr. Underwood's intuition is that the data do not have this structure, so we should instead rely on generalized linear models she proposes that assume alternative error structures (as she did in her 2011 PlosOne article). Dr. Underwood's preferred approach assumes the number of elephants that die at each site in each year is deterministic, and the fraction that are poached at each site-year (after the total number marked to die is determined) is determined by a weighted coin toss where PIKE reflects the weighting (we discuss our concerns with this model in our last post).

2) Dr. Underwood additionally and specifically advocates for the evaluation of Aggregate PIKE to deal with variation in surveillance and elephant populations (as has been done in many previous reports) rather than average PIKE, as we do. Dr. Underwood argues that this measure better accounts for variation in natural elephant mortality.

3) To account for variation in the number of elephants discovered, Dr. Underwood indicates that we should be running a weighted regression where the weight of each site-year is determined by total mortality, equal to the sum of natural and illegal carcasses discovered in each site-year.

We respond to each of these points individually below. We then point out that these three criticisms are themselves not consistent with one another.  Finally, we note what this debate demonstrates the importance of having multiple approaches to analyzing a data set as critical as PIKE.

Monday, September 12, 2016

Everything we know about the effect of climate on humanity (or "How to tell your parents what you've been doing for the last decade")

Almost exactly ten years, Nick Stern released the his famous global analysis on the economics of climate change.  At the time, I was in my first year of grad school trying to figure out what to do research on and remember fiercely debating all aspects of the study with Ram and Jesse in the way only fresh grad students can.  Almost all the public discussion of the analysis revolved around whether Stern had chosen the correct discount rate, but that philosophical question didn’t seem terribly tractable to us.  We decided instead that the key question research should focus on was the nature of economic damages from climate change, since that was equally poorly known but nobody seemed to really be paying attention to it.  I remember studying this page of the report for days (literally, days) and being baffled that nobody else was concerned about the fact that we knew almost no facts about the actual economic impact of climate—and that it was this core relationship that drove the entire optimal climate management enterprise:

click to enlarge

So we decided to follow in the footsteps of our Sensei Schlenker and to try and figure out effects of climate on different aspects of the global economy using real world data and rigorous econometrics. Together with the other G-FEEDers and a bunch of other folks from around the world, we set out to try and figure out what the climate has been doing and will do to people around the planet. (Regular readers know this.)

Friday, Tamma Carleton and I published a paper in Science trying to bring this last decade of work all together in one place. We have learned a lot, both methodologically and substantively. There is still a massive amount of work to do, but it seemed like a good idea to try and consolidate and synthesize what we’ve learned at least once a decade…

Here’s one of the figures showing some of the things we’ve learned from data across different contexts, it’s kind of like a 2.0 version of the page from Stern above:

click to enlarge

Bringing all of this material together into one place led to a few insights. First, there are pretty clear patterns across sectors where adaptation appears to either be very successful (e.g. heat related mortality or direct losses from cyclones) or surprisingly absent (e.g. temperature losses for maize, GDP-productivity losses to heat). In the latter case, there seem to be “adaptation gaps” that are persistent across time and locations, something that we might not expect if adaptation is costless in the long run (as many people seem to think). We can’t say exactly what’s going on that’s causing these adaptation gaps to persist, for example, it might be that all actors are behaving optimally and this is simply the best we can do with current technology and institutions, or alternatively there might be market failures (such as credit constraints) or other disincentives (like subsidized crop insurance) that prevent individuals from adapting. Figuring out (i) whether current adaptation is efficient, or (ii) if it isn’t, what’s wrong so we can fix it, is a multi-trillion-dollar question and the area where we argue researchers should focus attention.

Eliminating adaptation gaps will have a big payoff today and in the future. To show this, we compute the total economic burden borne by societies today because they are not perfectly adapted today. Even before one accounts for climate change, our baseline climate appears to be a major factor determining human wellbeing around the world.

For example, we compute that on average the current climate

- depresses US maize yields by 48%
- increases US mortality rates 11%
- increases US energy use by 29%
- increases US sexual assault rates 6%
- increases the incidence of civil conflict 29% in Sub-Saharan Africa
- slows global economic growth rates 0.25 percentage points annually

These are all computed by estimating a counterfactual where climate conditions at each location are whatever historically observed values at that location are most ideal.

Our first reaction to some of these numbers were that they were too big. But then we reflected on the numbers more and realized maybe they are pretty reasonable. If we could grow all of US maize in greenhouses where we control the temperature, would our yields really be 48% higher? That’s not actually too crazy if you think about cases where we have insulated living organisms much more from their environment and they do a whole lot better because of it. For example, life expectancy for people has more than double in the last few centuries as we started to protect ourselves from all the little health insults that use to plague people. For similar reasons, if you just look at pet cats in the US, indoor cats live about twice as long as more exposed outdoor cats on average (well, at least according to Petco and our vet). Similarly, lot of little climate insults, each seemingly small, can add up to generate substantial costs—and they apparently do already.

We then compared these numbers (on the current effect of the current climate) to (i) the effects of climate change that has occurred already (a la David and Wolfram) and (ii) projected effects of climate change.

When it comes to effects of climate change to date, these numbers are mostly small. The one exception is that warming should have already increased the integrated incidence of civil conflict  since 1980 by >11%.

When it comes to future climate change, that we haven’t experienced yet, the numbers are generally large and similar-ish in magnitude to the current effect of the current climate. For example, we calculate that future climate change should slow global growth by an additional 0.28 percentage points per year, which is pretty close in magnitude to the 0.25 percentage points per year that temperatures are already slowing things down. For energy demand in the US, current temperatures area actually doing more work today (+29%) than the additional effect of future warming (+11%), whereas for war in Sub-Saharan Africa, current temperatures are doing less (+29%) than near term warming (+54%).

All these numbers are organized in a big table in the paper, since I always love a big table. There's also a bit of history and summary of methods in there as well, for those of you who, like Marshall, don't want to bother slogging through the sister article detailing all the methods.