Some more thoughts on land grabs and tricky statistics

I suppose you could label this post as my response to Ricardo and Marloes’s response to my post on their recent media brief on the correlation between governance and land grabs. First, I should say that this is all very exciting – it’s nice to have an actual debate about this. NGOs frequently ignore substantive criticism of their analytical work (to be fair, so do a lot of academics), so I must commend Ricardo and Marloes for their enthusiasm and willingness to get in touch and have a reasonable argument about all this.

I think we’ll likely to continue to disagree about when results should be presented (or at least how they should be presented), so I’ll turn my attention to their three main technical points:

 

1) It’s not realistic to assume that investors target poor countries

True, but poor countries themselves might be more likely to put land up for sale. Discerning the difference between targeting and supply-side effects will always be difficult because we only observe actual land deals (in essence, the quantity `consumed’). But this is beside the point – spend fifteen minutes in an economics seminar and you’ll learn that a common way of challenging identifying assumptions is to come up with an equally-credible alternate story. I’ve shown that, at least in this very basic setup, income is a better predictor of a country having a land deal than governance. While my alternate story might be considered implausible (even if it does fit the data better), I really only put it up to point out how equally-flimsy the assumption of investor targeting is.

 

2) My last table is badly specified and then I forget to estimate a hurdle model.

Before delving into the technicalities of this argument, let’s briefly talk about burden of proof. It is Oxfam’s job here to convince us all that investors are targeting countries with poor governance, or at least that there is some consistent correlation between the two. By this very basic metric, I assert that the current analysis falls short, as it doesn’t provide enough evidence to reject the null of no relationship. One doesn’t always need to present and prove an alternate hypothesis, complete with fancy, well-specified econometrics, in order to disprove the one being asserted.

As far as the specification of the first two columns in Table 4 – sure, this is pretty much atheoretic wandering. I’m not going to assert that I’m cleanly identifying any individual channels, but seeing if Oxfam’s relationship stands up (I’m actually trying to help you here guys) once we start controlling for all these things. Multicollinearity doesn’t seem to be preventing some results from shining through. But yes, this is playtime with Stata, although I admit as much up front. See my point about burden of proof here.

Their final point is a technical one – I’m interested in whether, conditional on a country selling any land, governance is correlated with the number of land deals. Technically, this specification is subject to a form of bias due to selection on unobservables: for example, if hotter countries are more likely to sell land, and there is a correlation between temperature and the governance indicators, then estimates in columns (3) and (4) of Table 4 will be biased.  [OK this is not what their point was – see Paul’s comment below.] Ricardo and Marloes would be happier if I estimated a model which took this selection into account.

But the problem is: as I point out at the end of my piece, I don’t really buy the selection equation in the first place, and this factors into their third point:

 

3). They take issue with my worry about “bias” in how land deals are reported. 

I’m worried the Land Matrix is a better measure of “number of reports on land deals” than “number of land deals” and that the measure of “have there been any substantial land deals” in the past ten years is really just a measure of “has anyone bothered to submit a news report to the land matrix on your country in the past ten years.” Ricardo and Marloes make a purely theoretical argument that reporting in the UK should be better than reporting in developing countries. If we were talking about general media reporting, I would be inclined to agree, but I’d be surprised if anyone is scanning British newspapers for land deals and submitting the data to the Land matrix.

Furthermore, consider the  final hurdle a land deal must clear to get into the Land Matrix: “entail the conversion of land from local community use or important ecosystem service provision to commercial production.” This seems like it should only be possible in societies where a significant percentage of the population is involved in agriculture and where large scale commercialisation is yet to happen. Sure, the quality of the British government is one of the reasons there aren’t many dodgy deals going on, but we have to remember that Great Britain has already gone through the long process of moving from smallholder farming to relatively large-scale commercial production. Yet Ricardo and Marloes want to code Great Britain  a zero and include it in the selection equation – I’m just not convinced.

How do we move forward? I’m happy that both Marloes and Ricardo want to continue working on this. This is definitely the best outcome – I can think of several ways that one could try and take it a little bit further

  • Let’s start exploiting the time dimension: we have a panel – let’s use it – although I do have a fear that, as several have pointed out, there won’t be enough meaningful variation in the WDI indicators across time to actually identify anything.
  • Number of deals and size - these are, save for my kitchen sink regressions, currently unexploited. As is information on whether or not deals are international or national.
  • Let’s get more data! I’m hesitant to throw a stake into the ground and say it’s time to make a call, especially when the data is as limited as it is. If we could get our hands on district level data (or, in my wildest dreams, GIS data) on land deals, we could start to say so much  more about what’s going on.

Finally, a word to idle academics out there – I implore you to pay more attention to this stuff. We have a hard enough time encouraging replication of our own studies, but I think the world would be a much better place if we sat down from time to time and just tried to recreate “killer facts” that otherwise dominate the discourse. I didn’t start my analysis with any intention to go after Oxfam’s results, but it only took a little while with the data before I realised that the story was much more complex, and worth a second look.

Again, thanks to Ricardo and Marloes for a fun debate (I’ve offered them a second reply if they’d like).

 

Conflict and climate assertions

I always suspected El Niño had a violent past

So Nature has released a study by researchers from Columbia University purporting a link between the El Niño effect and violent conflict in a subset of countries (those that face large shocks in temperature when El Niño comes knocking).

There are tons of reasons to be skeptical of these results – one thing that made me pause was the complete lack of theory in the paper, and thus there’s no real attempt to discern why El Niño might increase the risk of violence. Even stranger – the results get stronger when the authors control for rainfall and temperature in the affected countries, which really doesn’t leave much of a theoretical channel for the resulting outcome. To make matters worse, the structure of the article makes it nearly unapproachable – which is not the fault of the authors, but the journal itself (why isn’t this appearing in a social science journal? I have a theory).

Edward Carr also stepped in and rightly choked-slammed the results. While I agree with him that the paper is a mess and we shouldn’t make too much of the results, I found some of his complaints to be off the mark:

This design makes sense only if you assume that the random back-and-forth shifting did not trigger adaptive livelihoods decisions that, over time, would have served to mitigate the impact of these state shifts (I am being generous here and assuming the authors do not think that changes in rainfall directly cause people to start attacking one another, though they never really make clear the mechanisms linking climate states and human behavior).  The only way to assume non-adaptive livelihoods is to know next to nothing about how people make livelihoods decisions.  Assuming that these livelihoods are somehow optimized for one state or the other such that a state change would create surprising new conditions that introduced new stresses is more or less to assume that the populations affected by these changes were somehow perpetually surprised by the state change (even though it happened fairly frequently).

Carr seems to be suggesting that, by using run-of-the-mill regression analysis, the authors are implicitly assuming that people in `treated’ countries don’t react in ways to offset future El Niño effects. It seems to me that this approach isn’t making that assumption at all, it’s attempting to measure the average impact on the incidence of conflict. We might expect that the impact to be higher under the assumption of non-adaptive livelihoods than under adaptive livelihoods (unless part of that adaptation is picking up an AK-47), but none of this feeds into the average effect.

Now – it is unclear what that average effect is measuring (and this might be what Carr is getting at), as the impact in year 0 is the impact of El Nino on the unadapted, where the impact in year 0 + T might be the impact on El Nino on the semi-adapted. While this might certainly be an issue for the external validity of the findings (if people have been adapting during the period observed, we’re not likely to see similar effects in the future), it doesn’t affect the researchers’ ability to say “here’s the aggregate impact of El Niño on X over this period.” It’s still a valid statement, if a less interesting one.

I should note this isn’t the first time I’ve taken issue with research on the impact of climate on civil conflict, especially with how this stuff gets reported in the news. The Guardian has already managed to mistakenly conflate the result of increased risk of conflict with direct culpability by claiming that the research “shows 50 of 250 conflicts between 1950 and 2004 were triggered by the El Niño cycle.” This is a common mistake, one I discuss in length here.

Women cause earthquakes

I've got three words for you: duck and cover

From the Guardian:

“Many women who do not dress modestly … lead young men astray, corrupt their chastity and spread adultery in society, which increases earthquakes,” Hojatoleslam Kazem Sedighi was quoted as saying by Iranian media.

Perhaps we can use shocks to female gratification/empowerment then to predict earthquakes? Let’s see – there were two earthquakes in 1960 when the pill was introduced… not a particularly strong correlation (then again, the introduction was likely endogenous to female hedonism).

Maybe we should be thinking about completely external shocks: on November 6th, 1983, Cyndi Lauper released “Girls Just Want to Have Fun” and only 10 days later there was an earthquake in Kaoiki, Hawaii! Then again, there were no more earthquakes for five months, but there were two earthquakes (in Turkey and Idaho) just a week before the release of the single.

Wait a minute – what if earthquakes predict female empowerment? Sounds like Sedighi needs to submit his model to peer review.

Another brilliant explanation:

Referring to the violence that followed last June’s disputed presidential election, he said: “The political earthquake that occurred was a reaction to some of the actions [that took place].”

Oh no! Political earthquakes?

The fungible and the furious

New, unpleasant information requires careful analysis, not knee-jerk reactions

Last week the medical journal Lancet released an article suggesting that, on average, governments that receive more  health aid divert tend to shift domestic resources away from health.

The paper made some headlines and upset aid critics and much of the global health community. A part of this has to do with a misunderstanding of what the findings mean – a confusion which isn’t helped by  those that propagate incorrect and sensationalist interpretations of the study.

However, a lot of the anger over the results comes from those that do understand the implications of the study, but are angered by an apparent divergence in priorities between the global health community and recipient governments. Both Ranil and Owen Barder talk about this in more detail, although I’ll go through some similar arguments.

These are my scattered thoughts on the whole issue.

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