Glorifying poverty?

It's an inevitable part of economic development: as infrastructure is improved, and capital is accumulated, and production processes and markets become more efficient, old ways of doing things disappear. There is a nostalgic Irish song that puts it quite well:

By trade I was a cooper;
lost out to redundancy;
like my house that fell to progress
my trade's a memory

The song played through my mind all the time when we saw what was left of the floating market of Cai Be, in the Mekong delta. Our guide told us the once famous floating market was disappearing rapidly in favour of the land-based market now that infrastructure in the region has been much improved. That is perhaps a shame, but perhaps it's also shameful that we think it's a shame.

Like the wooden barrels and casks they used to make, there are few coopers left in our modern economy. We store our beer in aluminum kegs now: much more durable, much more hygienic, much more efficient. But as tourists, we lament the demise of the old ways, for they make our holiday destinations 'charming' and 'exotic'. We curse progress when floating markets are replaced by supermarkets, when thatched roofs turn into corrugated iron, or when small-scale fishers get a job at an industrial-scale purse seiner.

But much of what we find charming is in fact a sign of poverty. Many people in developing countries prefer a corrugated iron roof over a thatched one, and they will get it as soon as they have the money to buy it. Floating markets emerged due to lack of good road infrastructure. Those men playing Chinese chess in a local Vietnamese coffee shop? You may call it the good life, but you might as well call it unemployment.

Yes, as developing countries get richer they start looking more like us: there will be more shops (more chain stores, also, unfortunately), more cars, fewer street stalls, better infrastructure, cleaner streets. Prosperity is often accompanied by a certain cooling of interpersonal relations as people become busier, more efficient, and more business-like. Some of these changes are to be welcomed (better health care, cleaner streets), some changes may be a necessary price to pay for the welcome changes (more efficiency, less surprises), some changes are to be lamented (loss of traditions, old professions that become folklore at best).

But honestly, should they stay poor so you can make pretty pictures?

The Prisoner's Dilemma in traffic

Witnessing the traffic in Vietnam, whether it was the madhouse on the streets of Hanoi or the exasperating driving style of motorists on the highway, I realized that traffic is very much a Prisoner's Dilemma. The overall motto in Vietnamese traffic is every man for himself. Drives prefer the left lane and adamantly refuse to let others overtake them. Be careful when you cross the street, even at green pedestrian traffic lights: red traffic lights are considered no more than suggestions so you might still be run over by one of the countless motorbikes.

It made me realise that in everyday traffic there are a lot of occasions where you can gain some benefit at the expense of other road users: cross a red light, overtake somebody on his right side, don't let others pass or enter the highway. On the short term you may benefit from this behaviour, but if everybody behaves this way we are all worse off than if we would just show a little courtesy. You can see the result in many of Vietnam's streets: congested roads, traffic accidents, aggressive drivers. A game theorist would say that Vietnamese traffic is somewhere in or near its Nash equilibrium (nobody has a reason to change his or her individual behaviour, given the behaviour of others), but the Nash equilibrium is not Pareto optimal (everybody would be better off if everybody behaved differently): the textbook definition of a Prisoner's Dilemma.

In countries like Norway or the United States, however, the traffic seems much more organised (Dutch drivers, I must confess, are less courteous, but yet more so than Vietnamese drivers). Why don't Norwegian drivers and American drivers obey our microeconomic models by behaving more aggressively? (I don't mean to insult Vietnamese motorists, so let's call it driving assertively.) I believe hefty fines are only part of the explanation. It is also that Norwegian and American motorists consider it bad behaviour: in other words, there are social norms that keep them from driving more assertively.

Another interesting observation is that a Norwegian wouldn't get anywhere in downtown Hanoi unless he drives like a Hanoian. So once this assertive way of driving becomes the norm it will be very difficult to change it to a more courteous norm. Assertive drivers may get somewhere in Oslo, but perhaps their friends would tell them off for their driving style. In any case many Norwegians consider it bad form, and people have a natural tendency to conform to the dominant norm. So there are self-enhancing mechanisms that keep the dominant social norm in place.

So to understand Elinor Ostrom's work you don't have to go to the irrigated rice fields of rural Bangladesh: just get a driver's license.

 

Fingers crossed now

I presented my research vision to the "Severe Assessment Committee", i.e. the ladies and gentleman who decide on my position at Wageningen University, last Tuesday. Due to the timing of my contract their decision is a bit more complex than it would otherwise be. Usually they decide whether a candidate gets tenure as an associate professor, but in my case they need to decide whether I can become an associate in one year's time. It's a long story.

Anyway, I think the presentation and the discussion went well. You can find my slides here, in case you're interested.

Fingers crossed now.

Greetings from the delta

The last four weeks Monique and I spent our Christmas and New Year's holiday in Vietnam. We made it a holiday combined with a little bit of work: we started in the North, where we met one of my PhD students, Trinh Quang Tu, and visited such tourist destinations as the Mai Chau valley, Tam Coc, and Ha Long Bay. After a few days in the ancient city of Hué and the picturesque merchant town of Hoi An we continued to Ho Chi Minh City, from where we travelled to the Mekong Delta. In the Mekong Delta we had reserved some time to meet people at Can Tho University, visit aquaculture farms in the regions where Tu does his research, and to visit one of the research sites of another PhD student of mine, Phung Thanh Binh. In the following weeks I will post my impressions of my trip.

Vietnam is a magnificent country, with wonderful people, stunning views, delicious food, and a fascinating history. Despite the obvious traces of a millennium of Chinese domination and a century of French domination, the Vietnamese have a strong sense of independence. I think the Dutch and the Vietnamese have something in common: two relatively small countries, surrounded by big powerful neighbours and the deep blue sea. During the Anglo-Dutch wars the English called the Dutch 'frogs' because of our wet and muddy natural habitat (they later later applied the same pejorative to the French, but then for culinary reasons). I like to think there is a similarity between the Dutch frogs in their cold Rhine and Meuse delta, and the Vietnamese tortoise, a mythical specimen of which provided a magic sword to Le Loi, the Vietnamese emperor who kicked the Chinese Ming dynasty out of Vietnam, in the tropical Red River and Mekong deltas.

But Vietnam is also a country in transition from a poor communist economy to a bustling capitalist economy, with all the environmental and social issues associated with that process. You can see beautiful big houses next to crumbling shacks. Small businesses everywhere, Pepsi and Samsung billboards next to Communist propaganda posters. New infrastructure is being built, and shiny new office buildings. Streets and shores are littered with plastic bags and bottles. Both deltas are under threat from upstream dam construction in neighbouring countries, and from climate change. Large areas of mangrove forest have been cut to make room for shrimp farms. And don't forget the motorbikes. You cannot escape the motorbikes, buzzing their way through the streets like angry hornets with vehicle horns.

And no, I am not going to mention the war.

Why (for the time being) I'm sticking with R

I'm a big fan of open source software. OK, I know the Dutch have a reputation for being stingy but let's face it: much of the software we use in economics (Stata, Matlab, Maple) is terribly expensive. So the only time I can use these programs is at the office (which, I admit, should be considered a healthy thing). To be able to work on my laptop when I'm at home (or in a hotel room, or in an airplane, for that matter) I try to work as much as I can with their open source equivalents as much as I can.

One of the programmes I've been using is R (a horrible name to Google for by the way), but in a sort of on-and-off way. It is less user-friendly than Matlab, much slower than Matlab, and contains fewer possibilities for statistical analysis than Stata. So I'm still fiddling around with programming languages like C++ (probably even faster than Matlab, but rabidly user-hostile) and Python (more user-friendly than C++, and perhaps as fast as Matlab) for calculations.

Slowly, however, I'm coming round to R, in my teaching as well as in my research, for a number of reasons:

• Marine biologists use it a lot, and using the same software helps the communication - it also makes it more likely that you can ask a close colleague how this @#%! package works.
  
• By the same token: some of my students, i.e. those who have taken marine ecology modelling courses, know it already.
  
• I can use it in my environmental valuation classes (statistics) as well as in my resource economics classes (modelling), so that again, some students in one course know it from another course I'm teaching.
  
• It seems that R finally has a decent package to do conditional logit and probit (or, as others call it, alternative-specific multinomial logit and probit).
  

If only they could make it a lot faster, because it is too slow for value function iteration.

Mapping marine economics (4): Fishers are not alone anymore

One of the major attractions of Scheveningen (if you can pronounce that you've successfully adapted to Dutch culture) is a 360 degrees painting by the Dutch painter Hendrik Willem Mesdag. It depicts the North Sea coast near Scheveningen in the nineteenth century, long before its neighbouring city, The Hague, absorbed this coastal fishing village in one big agglomeration. Mesdag created an illusion that worked surprisingly well: there appears to be depth in the painting and you feel like standing on a dune watching over the beach, or looking down on the village with its neat little houses, or the villas where rich city folk spent their free time. What is also striking is the dominance of fishing, together with transport, in the coastal zone. You see some sunbathers, but they are easily outnumbered by fishers and other workers in the fishery, such as the horsemen towing the bomschuiten (flat-bottomed fishing vessels, a bit like the pink).

How different is it nowadays. International trade has mushroomed. We have largely replaced sails and steam engines by combustion engines running on oil and gas, scattering drilling platforms all over the North Sea to get to the stuff. Wind is making a come-back as wind turbines are forming entire forests in the open sea. Meanwhile, fishing has become something to limit rather than promote: in Mesdag's days the British scientist Thomas Henry Huxley called fishery resources "inexhaustible", but for numerous stocks we have actually found those limits and are now concerned about crossing them. And we're not only concerned for edible species, but also for marine life in general: enter marine protected areas.

So many uses, so many users, so little resource
Like the North Sea, many marine and coastal ecosystems have many different uses, many different users, and many different ways to meet the users' needs. Mangrove forests provide coastal protection, a nursery ground for wild fish, a source of juvenile shrimp for extensive shrimp farming systems, and a fascinating ecosystem to float through for tourists. Likewise, other coastal ecosystems like mudflats and coral reefs provide a variety of goods and services to a variety of users. And none of these biomes are limitless.

Given this variety of uses it is not surprising that policy makers need to make many tradeoffs. How far are we willing to limit fishing for an extra gigawatt of wind energy? How do we trade off port capacity against tourism? Does the income generated by an extra hectare of intensive shrimp aquaculture offset the loss in biodiversity and coastal protection?

All these examples are tradeoffs between uses, but also within one and the same use policy makers have to make difficult choices. What is worse, a small flood every year or a big flood every ten years? How do we rebuild fish stocks if local communities depend so much on fishing that they cannot miss a single year of it?

Note that simply putting a price tag on services may not be enough: the average per hectare value of a mangrove forest may be low when the forest is large, but once we have cut most of it the last few remaining hectares will be much more valuable. Moreover, aggregating monetary values over all stakeholders and over time may give you a single figure (the net present value), but this simplicity obscures problems of poverty and income distribution. So we may need to consider the entire tradeoff.

Tradeoff analyses and bioeconomic modelling
I have done tradeoff analyses of dairy farming and biodiversity conservation in my PhD thesis, and I recently submitted a paper with a former MSc student of ours, Matteo Zavalloni, and fisheries ecologist Paul van Zwieten where we analyze the tradeoff between shrimp aquaculture and mangrove conservation in a coastal area in Viet Nam. Both analyses are spatially explicit, i.e. we analyze not only how much of something can or should be done, but also where. The "where" question is quite important as many uses of marine areas (shipping, fishing, aquaculture) have a spatial dimension.

So this will be one of my major focus points: developing tools to make quantitative tradeoff analyses of coastal and marine ecosystems. I'm very much a bioeconomic modeller. I guess it's the geek in me: I've always been terrible at practical technical stuff (the holes my house's walls and the crappy paint jobs on my window panes bear witness to that), but I enjoy the patient development of a complicated quantitative model, or an insightful analytical model. I also enjoy the interdisciplinary nature of this work: you need to collaborate intensively with other scientists, mainly ecologists, to do it right.

The Stapel affair: it is worse than we thought

After Diederik Stapel was caught cooking the scientific books, three committees investigated the extent of the fraud in their universities (Amsterdam, Groningen, Tilburg), and how it was possible that Stapel committed his fraud on such a massive scale. The report came out last week, and I find its content no less than shocking. And then I'm not just referring to what they found Stapel did, or how the universities where he did it never suspected anything. What shocked me most was the conduct of the other researchers. Worse even, many admitted to these practices without the slightest notion they were doing something wrong.

Repeat the experiments until you get the results you want
Suppose your hypothesis says that X leads to Y. You divide your test subjects into two groups: a group that gets the X treatment and a control group that gets no treatment. If your hypothesis is correct the treatment group should show Y more often than the control group. But how can you be sure the difference is not a coincidence? The problem is that you can never be certain of that, so the difference should be so large that a coincidence is very unlikely. Statisticians express this through the 'P-value': if your hypothesis is not true, the probability that you get these results is estimated by the P-value. In general scientists are satisfied if this P-value is lower than 5%. Note that this means that if the hypothesis were not true, you still have a 1 in 20 chance of getting results that suggest it is!

So here is the problem. Some of the interviewees in the Stapel investigation argued it is perfectly normal to do several experiments until you find an effect large enough for a P-value lower than 5%. Once you have found such a result, you report the experiment that gave you this result and ignore the other experiments. The problem here is that any difference you find can be due to coincidence. If you do two experiments, you have a chance of about 1 in 10 that at least one of them gives a P-value lower than 5% if the hypothesis is not true; if you do three experiments, the chance is about 1 in 7. This strategy must have given a lot of false positives.

Select the control group you want
No significant difference between the treatment group and the control group in this experiment? No sweat, you still have data on the control group in an experiment you did last year. After all, they are all random groups, aren't they? So you simply select the control group that gives the difference you were looking for. Another recipe for false positives.

Keep mum about what you did not find
Another variety is that you had three hypotheses you wanted to test, but only two are confirmed (ok, technically hypotheses are not confirmed - you merely reject their negation). So what do you do? You simply pretend that you wanted to test these two all along and ignore the third one.

Select your outliers strategically
Suppose one of your test subjects scores extremely low or high on a variable: this person could be an exception who cannot be compared to the rest of your sample. For instance, somebody scores very high on some performance test, and when you check who it is it turns out that this person has done the test before. This is a good reason to remove this observation from your dataset because you are comparing this person to people who do the test for the first time. However, two things are important here: (1) you should explain that you excluded this observation, and why; and (2) you should do this regardless of its effect on the significance of your results. It turned out that many interviewees (1) did not report such exclusions in their publications; and (2) would only exclude an observation if doing so would make their results 'confirm' their hypothesis.

And all this seemed perfectly normal to some
But as I said earlier, the most troubling observation is that the interviewees had no idea that they were doing anything wrong. They said that these practices are perfectly normal in their field - in fact, in one occasion even the anonymous reviewer of an article requested that some results be removed from the article because they did not confirm their hypothesis!

The overall picture emerges of a culture where research is done not to test hypotheses, but to confirm them. Roos Vonk, a Dutch professor who, just before the whole fraud came out, had announced 'results' from an experiment with Stapel 'showing' that people who eat meat are more likely to show antisocial behaviour, argued on Dutch television that an experiment has "failed" if it does not confirm your hypothesis. It all reeks of a culture where the open-minded view of the curious researcher is traded for narrow-minded tunnel vision.

Don't get me wrong here: the committee emphasizes (as any scientist should) that their sample was too small and too selective to draw any conclusions about the field of social psychology as a whole. Nevertheless, the fact that the committee observed this among several interviewees is troubling.

But the journals are also to blame, and there we come to a problem which I am sure is present in many fields, including economics. Have a sexy hypothesis? If your research confirms it the reviewers and the editor will crawl purring at your feet. If your research does not confirm it they will call your hypothesis far-fetched, the experimental set-up flawed, and the results boring. It's the confirmed result that gets all the attention - and that makes for a huge bias in the overall scientific literature.