You can never win


“I am Paul Atreides, the Muad’Dib, leader of the Fremen of Arrakis, the *only* source of Spice Melange, which gives me rights as Galactic Emperor! ……. oh I see, you can create it synthetically now. This is awkward.”

It appears that it is now possible to synthetically create artemisinin, used primarily (solely?) as a treatment against malaria. This is the stuff of first-pumping and cheering, especially if the synthetic version turns out to be significantly easy and cheaper to obtain than the natural version. This is a real win for global health.

Not so fast, says Jim Thomas in the Guardian, a newspaper which somehow manages to make us feel guilty about any good development-related news by identifying some poor sod who has lost out (a wonderful example is their silly, excessive worrying over Western consumption of quinoa). According to Thomas, the new synthetic version of the drug will put farmers of sweet wormwood (the plant from which artemisinin is usually derived) out of business:

Now it turns out that artemesia farmers are dismissed as entirely expendable. The rejoinder of “let them plant potatoes” seems dismissive of farmer knowledge: farmers understand markets well and those now growing artemisia annua do so because it helps them bring in income. As for the argument that synbio is necessary to eradicate malaria, the botanical approach was already producing more than enough artemisinin to address malaria.

“There is simply no rationale to have a synthetic product on the market when farmers could produce enough raw material to produce the tablets from pulverised high quality plants,” said Professor Hans Herren, World Food Prize winner, who has worked extensively with east African artemesia farmers.

Now, it remains to be seen how much cheaper the synthetic version will be, but let’s assume that it’s significantly cheaper than the natural one (although it turns out the price of the latter is insanely volatile). How much hand-wringing should we actually do if welfare gains for fighting malaria outweigh the welfare losses of having farmers switch to another type of crop?

If you still feel outraged by all this, would you feel differently if we switched out the words “sweet wormwood” with “biofuel crops”? Would you feel differently if someone had discovered a working vaccine for malaria?

On MPIs and MDGs

Conan, what is best in life? "It is 1/3 crushing your enemies, 1/3 seeing them driven before you, and 1/3 hearing the lamentation of their women."

Conan, what is best in life? “It is 1/3 crushing your enemies, 1/3 seeing them driven before you, and 1/3 hearing the lamentation of their women.”

Sabine Alkire and Andy Sumner have released a short paper suggesting that the Multidimensional Poverty Index (MPI) be used as a `headline indicator’ for the post-2015 Millennium Development Goals (MDGs). If you’re unfamiliar with the MPI, you can read up on it here. Alkire and Sumner are suggesting that whatever indicators emerge out of the inevitable post-2015 intellectual bloodbath be aggregated into a single index using the same method that is used for the current MPI. This has excited some people, including Duncan Green, who thinks it will be useful in inducing governments to take the post-2015 goals seriously:

That in turn would allow the post2015 process to generate more traction on national governments (the lack of which is the subject of my paper) through league tables. Imagine if every year, all countries (including the rich ones) are ranked on a comprehensive human development table that (unlike the Human Development Index and other similar efforts) has buy in and recognition from across the international community. Each annual report would pick out the countries that have risen/fallen relative to the others. Regional tables could compare India and Bangladesh, or Peru and Bolivia, to generate extra public interest and pressure on decision makers.

I’ll go out and say it: I think this is a really bad idea. It combines the two things that make  two things that make me uncomfortable about both the MPI and the MDGs – arbitrary weights on different indicators/goals and an inflexibility to local preferences.

I’ll use a very basic example: let’s say that the next set of MDGs focuses on two things: hunger and access to clean water. After what will bound to be a seriously convoluted process, someone will agree on internationally-agreed weights on these two things. Let’s say the weights are fifty-fifty, that the final index puts just as much weight on a person who is hungry as one who does not have access to clean water.

Now consider a fictional country, Bigmacistan, which has a culture that sees hunger as being the ultimate state of poverty, much more than clean water. If Bigmacistan were allowed to assign its own weights, it would prefer 3/4 of the total weight to go to hunger and 1/4 to clean water. In fact, given limited resources, Bigmacistan will choose to combat poverty in a way that is not only seen as sub-optimal by the post-MDG framework, but would result in a fall in its global rankings, even if every single person in Bigmacistan is in agreement with its national emphasis on hunger. So differences in MPI 2.0 rankings not only reflect aggregate differences in each country’s success in fighting poverty, but differences in the structure of national social welfare functions.

What one could do is let countries set their own weights (I’ve argued that this is the only way the MPI could even be useful for governments in the long run), but this would never appease the technocrats, because once weights start varying across countries, country rankings start making even less sense.

One could argue that, if there are some indicators that we can reach a reasonably broad consensus on, then imposing these preferences on other countries might be defensible. Unfortunately, this still doesn’t adequately justify the use of the MPI, especially if they are used for annual rankings. Imagine the Bigmacistan actually cares as much about clean water as it does about hunger, but realises that, given its own complex context, it needs to deal with its hunger problem before it will have the capacity to deal with its water access problem. It draws up a national plan which ends hunger by 2020 and then improves access to water by 2025. Yet, from 2015 onwards, Bigmacistan is hounded by donors, NGOs and the media for its poor performance on the MPI 2.0 due to its lack of concern for those living without water.

Finally, any time we want to say anything interesting about the MPI 2.0, we’ll still have to unpack it into its composite indicators, a point Claire Melamed makes on Duncan’s blog:

Say the MPI 2.0, or whatever you called it, went up, or down, in a given country. You’d need an extra layer of data analysis – always fatal as that’s the point you lose people’s attention – to know why. It could be that health outcomes got a lot better, but education outcomes got a bit worse, and so the overall MPI score went up a bit. This would neither be helpful for policy makers, nor tell you much about what people think is important, and it would all be much too complicated to generate any campaigning or political energy anyway.

I do think MPI has its uses, but could we please avoid creating another worldwide indicator that doesn’t tell us very much and imposes what will ultimately be imposing fairly arbitrary weights on individual countries?

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).


Response from Oxfam: Governance, land grabs and tricky statistics

by Ricardo Fuentes-Nieva and Marloes Nicholls

It is encouraging to read the post from Aid Thoughts. We appreciate the time he put into Oxfam’s analysis on large scale land deals. Indeed, we were hoping that our blog would spark debate and bring more attention to this topic.

As a quick summary, we took two databases, the Land Matrix and the World Governance Indicators and found that land deals are more likely to occur in countries with lower levels across different governance indicators.  We specified that “This analysis is only the first step towards a more in depth research project. Next steps include a more in depth analysis on the determinants of the number and location of deals

Aid Thoughts seems to take issue with the use of this kind of analysis when they are so preliminary. There are two things to say to this:  Firstly, and as Aid Thoughts acknowledges, there is other evidence in the development literature that points to the fact that land deals are concentrated in poorly governed countries. Our conclusions were not based only on our analysis and we used the best evidence at hand (both internal and external) to generate a better understanding of the problem (which Aid Thoughts actually helped with his critical review). So, we stand by our decision to publish the preliminary results.

Now, there are a couple of things to discuss on the technical front of his critique. Here are some:


1) Investors or governments?

AidThoughts replaces governance indicators with income per capita because they better explain the existence of land deals. This leads him to suggest that “Maybe investors aim for countries who are more willing to sell off land, not because they are poorly governed, but just because they are poor.” This is an interesting idea but if we put aside the regression tables and reflect for a moment, is it sensible to think that land investors are attracted to countries for being poor? Why would investors be attracted to the characteristics of poverty, such as poor infrastructure, limited public services and low levels of education and health? A more interesting hypothesis that AidThoughts raises, and which we think is worth exploring too, is that it might not  be investors who target countries, but bad governments who sell the land of their citizens.


2) Truncated sample bias.

AidThoughts recognizes that running OLS with two control variables, as reported in Table 3, is not serious analysis (and yet he managed to muddle the significance of the estimators in his table). But what’s really puzzling is that, in order to prove his point, he then goes on to throw the entire kitchen sink of governance indicators into the next table (Table 4). These indicators are highly correlated amongst them, and it is difficult to find a sensible explanation to specify the model that way.

He then goes on to say of this table:

“In column (1), prior controlling for income, only one of the relationships we expected to see has returned: countries rated low on the rule of law index are more likely to have land deals. Political stability/violence is also associated with land deals, but unfortunately that wasn’t part of Oxfam’s theoretical model. Now, voice and accountability is positively correlated with land deals! Of course, most of these relationships vanish when we toss in income, although it is worth noting that the rule of law measure keeps its significance and sign. So the relationship between governance and land sales seems to be a lot more complex than the Oxfam brief is suggesting.”

That’s a lot of explanation for a badly specified model that includes highly correlated regressors. But that’s not even the most puzzling part of that table. AidThoughts then tries to explain the number of land deals with the same variables but he does not correct for the truncated sample (look how his sample drops from 212 and 183 in the first two columns to just over 50 in the last two). Ignoring the bias in the observed sample is a mistake and something we had identified as a problem, and that’s why we suggested exploring  a double hurdle estimation to understand the issue better.


3) Reported land deals bias.

Aid Thoughts briefly mentions the potential problem of bias in the Land Matrix, but we don’t agree that he identified the right direction of bias. He argues that land deals are more likely to be reported in developing countries by diligent activists than in developed countries like the UK. On the contrary, we argue that land deals are much less likely to be ignored in richer countries with freer press, more access to information and better organized civil societies. Does Aid Thoughts seriously believe that a land deal can be more easily concealed in the UK than in the DRC?

Overall, we are very encouraged by Aid Thoughts’ response. He mentions that he can be convinced of the problem with more data and more, better data is on its way according to conversations we’ve had with the people managing the Land Matrix. So here’s our proposal for Matt: let’s work together – rigorously and objectively – on this issue in the next few months to try to better understand what’s driving the land rush. The problem deserves as much attention as we can give to it.

Land grabbing: whatever you do, don’t mention the G-word.

Large scale land purchases get some more media attention, this time from Jonathan Glennie over at The Guardian:

“A new report on land acquisition by the Munden Project/Rights and Resources Institute brings an important angle to the land “grab” debate. Rather than focusing on the ethics of land grabbing, the report makes the business case for working with local communities, arguing that failure to inform or fairly compensate affected locals heightens the risks to investors. Why? Because affected communities start to make life difficult for abusive or lazy companies, leading to massive unexpected costs or even an eventual full-scale retreat.

What is slightly disconcerting is that Glennie managed to write an entire article on land grabs while only using the world “government” once. NGOs and the media have largely painted the land grabbing story as a situation where evil companies are parachuting in and snatching land away (for example, check out Oxfam’s recent campaigning). In reality, land acquisitions which circumvent local property rights are only possible when governments themselves are incompetent, corrupt or overly-impatient. Of course campaigners realise this, but it’s much easier to set this up as story of evil capitalism than it is of governance, the latter being harder to sell and even harder to treat. I’m not trying to pick on Glennie for leaving out a lengthy discussion of governance in his article, but it would be nice for people to start using the g-word a bit more.

Governance, land grabs and tricky statistics


“I’m a family man- I run a family business. And I heard your town scored low on the World Bank’s governance indicators.”

The last decade has been marked by a sudden increase in large scale land purchases in developing countries, a `land rush’ which has purportedly been driven by concerns over food security, food prices and a growing market for biofuels. The speed, size and lack of transparency over many of these deals, as well as their implications for the welfare and food security of those already living on the land, has led many to dub these large scale purchases as “land grabs.” This is a rather loaded term, but has successfully (and unfortunately) framed the context as one where anonymous, uncaring investors are systematically snatching land away from the poor and needy.

News reports suggest that at least some of this is happening – following the excellent Let’s Talk Land Tanzania for just a few days reveals how problematic some of these purchases have been. Yet, despite the ruckus these deals are creating, we still know precious little about their size and scale, the motivations and expectations of investors, the welfare impact on those in “grabbed” countries and the welfare impact on those in “grabbing” countries.

This lack of knowledge should be alarming rather than disarming, but while this is the perfect time for careful, dispassionate analysis and data collection, many have chosen to instead reinforce the simple “good” vs “evil” story I highlighted above. Take, for instance, this media briefing which Oxfam released last week, based on preliminary research on the relationship between country governance and land deals.

The two Oxfam researchers, Ricardo Fuentes-Nieva and Marloes Nicholls, use data from the World Bank’s Worldwide Governance Indicators (WGI) and the Land Matrix, which gathers data on media reports of land deals, to show that countries that had any land deals between the years 2000 and 2011 had significantly lower WGI scores than those that hadn’t had any. Here is the figure which they use to make their case:


Again, this figure reveals that, across the four governance indicators considered by Fuentes-Neiva and Nicholls, countries with land deals consistently score worse than those without. How does Oxfam interpret these results?

Oxfam believes that investors actively target countries with weak governance in order to maximise profits and minimise red tape. Weak governance might enable this because it helps investors to sidestep costly and time-consuming rules and regulations, which, for example, might require them to consult with affected communities. Furthermore in countries where people are denied a voice, where business regulations are weak or non-existent, or where corruption is out of control it might be easier for investors to design the rules of the game to suit themselves.

So we have a pretty clear story here, right? Well, maybe not. Let me give a bit more structure to the above results by showing them as a series of bivariate regressions of the probability of observing a land deal in any given country between 2000 and 2011 and the average governance indicators for this period (the same data used in the Oxfam briefing).


Each column shows the results from regressing the probability of the country having at least one land deal during this period on each measure separately: voice and accountability, regulatory quality, rule of law and corruption (note that higher is `better’ for each of these measures). So far so good: in isolation, each of these variables is significantly* and negatively correlated with the probability of a land deal (i.e. countries that score poorly on each of these indicators individually are more likely to sell off land).

Yet, it’s a little strange that each of these seems to have about the same magnitude of an effect. We might expect some indicators to matter more. Also, for some reason, the Oxfam brief has left out two other WDI measures: political violence/stability and government effectiveness. Here is what the authors say about this exclusion:

Two of the Worldwide Governance Indicators – political stability and the absence of violence and government effectiveness were excluded from the analysis since there is no evident mechanism that would lead these aspects of governance to improve prospects for  investors.

OK – so we have a somewhat solid theoretical reason for excluding these variables. Presumably, we should not see the same negative correlation between these two WDI measures and land grabs. Table 2 below includes two extra columns in which I re-run the above results, but including both of the excluded indicators (PS and GE).

Continue reading

Bad things and the GDP fallacy

There are many who are (quite rightly) worried about whether or not economic growth is sustainable. This has led to a number of attacks on Gross Domestic Project (GDP) as a measure of economic progress. One of the arguments that is repeatedly made is that things that we, as a society, would consider to be bad, lead to an increase in measured GDP. Consider this video of Oxfam’s Katherine Trebeck (video starts at 3:50 – watch it for a minute):

Trebeck, who is arguing that disasters, both big and small, add to GDP, is quoting economist Mark Anielski:

The ideal economic or GDP hero is a chain-smoking terminal cancer patient going through an expensive divorce whose car is totalled in a 20-car pileup, while munching on fast-take-out-food and chatting on a cell phone. All add to GDP growth. The GDP villain is non-smoking, eats home-cooked wholesome meals and cycles to work.

I would write a long post on why this is a fundamentally misguided reading of GDP, but Alex Tabarrock has already done it more succinctly than I ever could:

Imagine that you are your friends are going to see a jazz concert but on your way to the concert you have a little disaster, a fender bender. Instead of seeing the show, you and your friends have a miserable time waiting for the tow truck to come to have your car fixed. Spending on the tow truck and the auto repair counts as GDP but it does not add to GDP because it is counter-balanced by a decrease in spending on jazz, wine and cheesecake. Nothing Tyler says (see above) about gross substitutability changes this fact.

Consider a bigger disaster, the 9/11 attack. First, the point already mentioned, the resources used in the cleanup count as GDP but don’t add to GDP to the extent that they would have been employed on other projects. Now it is true that some of the workers could work overtime which they otherwise would not – this would tend to increase measured GDP more than real GDP since leisure is not measured in the national income and product accounts. Even this factor, however, must be balanced against the overwhelming fact that the destruction of the twin towers meant that tens of thousands of the most productive people in the United States were forced into unemployment or death. Since GDP can also be measured as the sum of wages, rents, interest etc. the immediate effect of all the unemployed and dead was to reduce GDP. Similarly, Hurricane Katrina has destroyed more jobs in New Orleans than it has added (and not all the added jobs represent real additions) hence the Hurricane reduced measured and real GDP.

There’s probably little else that people (including economists and including me) get wrong more than trying to figure out what gets counted as GDP. Tabarrock hasn’t completely diverted worries though – he’s assuming the balance sheets cancel out immediately, when borrowing and saving comes into play, there is a lot of spending which goes towards bad things which, on a year-to-year basis, will look like GDP growth, where the “displacement” effect he discusses is divided over subsequent years. Also, GDP won’t accurately take into account global externalities. Finally, GDP itself will still comprise a lot of bad things at the end of the day (if the car crash is perfectly offset by a drop in spending on jazz and wine, nothing has changed).

Still, the point is that the standard “bad things increase GDP” argument doesn’t always work, once we begin to think the problem through a little more carefully.

Update: a friend replies over Twitter with

@aidthoughts and if the vegan cyclist consumes less expensive leisure and needs less income therefore works less hard and is just as happy?”

I give up, Aid Thoughts will endorse the Happy Planet Index from now on.

Aid as policy


“”The Empire is evil”? Don’t you realise what a silly statement that is? The Empire is actually a complex system of carefully targeted programmes and government departments. You should be asking specific questions like: “Is the Empire’s Death Star project effective at curbing population growth?””

Lee Crawfurd over at The Roving Bandit recently wrote a compelling post about why the question “Does aid work?” is fundamentally flawed.

The question “does policy work” is jarring, because we immediately realise that it makes little sense. Governments have about 20-30 different Ministries, which immediately implies at least 20-30 different areas of policy. Does which one work? We have health and education policy, infrastructure policy (roads, water, energy), trade policy, monetary policy, public financial management, employment policy, disaster response, financial sector policy, climate and environment policy, to name just a few. It makes very little sense to ask if they all collectively “work” or are “effective”. Foreign aid is similar. Aid supports all of these different areas of policy. My colleagues and I at OPM work on aid-financed projects that support most of these different policy areas in different developing countries.

Lee, as he admits himself, is taking his cue from the combined work of Esther Duflo, Abhijeet Banerjee and Dean Karlan. Some policy questions are becoming more and more answerable: does X work in a given context is something that can be tested and applied. Lee is asserting that since aid is just used to fund policies, thus the question of whether aid works just boils down to the complex task of determining whether or not individual policies work.

Yet, when you examine this assertion a little further, it starts to fall apart. Let’s imagine we lived in a world where we, development economists from aid-giving countries, figured out all the good government policies. We knew exactly which actions developing country governments needed to take to save children’s lives, promote income and job creation, reduce hunger and conflict and so on. In the world which Lee has presented to us, policy works, because we knew what all the best policies were. Would aid work?

It certainly wouldn’t be guaranteed to. Aid isn’t just policy – it’s the transfer of financial resources and technical expertise from one country or entity to another. That transfer is inherently non-trivial: it can create huge differences in power, has the potential to distort the recipient’s decision-making, creates opportunities for rent-seeking and often is used for completely political purposes. We cannot, for instance, only judge a US-funded conditional cash-transfer programme in Afghanistan solely on its microeconomic impact – it has to be viewed within the context of the US’s ongoing military intervention in that country, and its likelihood of long-term success. The recent scrap between Rwanda and its donors over the security situation across the border in the DRC again shows that aid is vastly more complicated than simply choosing effective programmes.

Lee acknowledges some of these differences and potential problems, but then dismisses them as things which are hard to gather robust evidence on. This preference to stick to what we know is somewhat admirable and tempting, but ultimately dangerous: it is incredibly difficult to gather widespread, robust evidence on the effects of aid on the macroeconomy or on local political economy. This should make us more, rather than less, wary of possible deleterious effects.

I’m as equally horrified as Lee by the recent attention that right-wing attacks on aid have been getting in the UK, and I completely agree with him that aid cannot always be  judged as a whole. The question, “does aid work?” doesn’t get us very far in life, especially since we have little concept as to what metric we should be using. That said, we have to tread carefully around the argument that small, neat questions are sufficient for success. I agree that aid should be considered more carefully as a bundle of heterogeneous flows and relationships, but I also believe that “aid” is unique in several key ways, and it is only healthy if we continuously question whether the the things that make aid unique also undermine its effectiveness.

How policies end: not with a bang, but a whimper

Masimba Tafirenyika describes how dire the food security situation in Malawi has become:

Once again Malawi finds itself in a tight spot. A food crisis set off by erratic rains, rising food prices and economic hardships is slowly unfolding. For the first time in several years, the country’s ability to feed its citizens is at risk. Sadly and unexpectedly, Malawi has lost its hard-earned status as an agricultural success story — it used to produce enough maize for its people to eat and still provide a surplus to neighbours. Many are now wondering what went wrong and whether there could be lessons for other African countries.

More than 1.63 million people, or 11 per cent of the population, are facing severe food shortages, according to the World Food Programme, a UN relief agency. Malawi needed $30 million to the end of 2012 to cover the shortfall.

As Tafirenyika hints, this stands in stark contrast to reporting on Malawi over the past few years, where it was heralded as a shining example of how to tackle food security. Five years, ago Celia Dugger wrote in the NYT how the country’s president, Bingu wa Mutharika, despite the protests of many, “ignored the experts” and subsequently dealt with the country’s hunger problems by drastically scaling up its fertiliser subsidy programme. Malawi subsequently enjoyed a spate of bumper harvests and many were quick to tout the large-scale subsidisation as being both a success and worth of replication in other countries. Most notable was the support of Jeffrey Sachs, who’s incessant belief that the fertiliser subsidisation was a policy holy grail led him to write an oddly-appreciative obituary for Mutharika, who died at the end of a thuggish, repressive and disastrous second term in office.

Meanwhile, hunger returns to Malawi, but we have not yet established a convincing narrative. Many economists (including a few on this blog) have pointed out, time and time again, that the fertiliser subsidy programme was fraught with pitfalls, both political and practical. While the recent crisis is probably too complex to fully substantiate these concerns, now would be an appropriate time for the fertiliser advocates to turn their attention to the food situation in Malawi, and begin to ask why. Otherwise, we risk touting a policy that might actually have been a complete failure, or at the very least lacked the sort of robustness that anti-hunger policies desperately need.

Ask not what your country can do for you


“Sorry Bilbo, I was going to take you on this *amazing adventure*, but then I checked your expenses from last year, and you seem to be spending your entire budget on food, not travelling.”

Economists can sometimes be a little sceptical of asking people what they want. If we’re trying to provide and finance a public good, for instance, we might be worried that beneficiaries will understate their value of that good to try and get away with paying less for it. Others – often those in the behavioural science camp – can be wary that people may not be reasonably informed as to what is good for them, or might let cognitive quirks and biases undermine their prioritisation.

Over at the African Can End Poverty blog, this scepticism seems to have been extended to Tanzanian businessmen, as Jacques Morisset argues that we should pay less attention to what local firms claim are policy priorities:

Allow me to illustrate. According to the entrepreneurs operating In Tanzania, electricity is their major constraint (85 per cent) followed by access to finance (52 per cent), taxes (37 per cent), and administrative red tape (25 per cent). Source: World Bank. Investment Climate Assessment, 2009. Surprisingly, labor and transports costs are only at the bottom of their concerns (less than 10 per cent). According to this ranking, the priority should be therefore given to reducing electricity costs, increasing access to finance and reducing taxation.

A closer look at the firms’ financial balance sheets provides a different picture. In reality, electricity counts for a marginal share of firms’ operating costs in Tanzania (see Figure). For example, it is equivalent to only 3 per cent for a standard firm operating in the apparel sector. In other words, a decline, say, of 50 per cent in electricity prices would only reduce its costs by 1.5 per cent – hardly a high number for such a big effort. By contrast, transport and labor costs are equivalent to 41 per cent and 38 per cent of its total operating costs. This means that reducing transport costs by only 4 per cent would achieve the same gains for the enterprise than cutting by half its energy costs.

I’m not entirely convinced by Morissets argument: he only presents data on the current breakdown of firm’s operating costs, but no evidence on how firm electricity usage might change if prices did come down. This is a little like arguing that that since poor, stunted children in a rural village only appear to consume maize, there’s little point in subsidising the cost of protein-rich foods.

Morisset admits that electricity access might be an issue, but then goes on to make his argumet based on the static view: that we should target inputs which are currently the most costly for Tanzanian firms. Perhaps this it the right course, but a difficult argument to make without more information on how firms change their behaviour when relative prices change.